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Mitigating the Impact of Antibacterial Drug Resistance through Host-Directed Therapies: Current Progress, Outlook, and Challenges. mBio 2018; 9:mBio.01932-17. [PMID: 29382729 PMCID: PMC5790911 DOI: 10.1128/mbio.01932-17] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Increasing incidences of multidrug resistance in pathogenic bacteria threaten our ability to treat and manage bacterial infection. The development and FDA approval of novel antibiotics have slowed over the past decade; therefore, the adoption and improvement of alternative therapeutic strategies are critical for addressing the threat posed by multidrug-resistant bacteria. Host-directed therapies utilize small-molecule drugs and proteins to alter the host response to pathogen infection. Here, we highlight strategies for modulating the host inflammatory response to enhance bacterial clearance, small-molecule potentiation of innate immunity, and targeting of host factors that are exploited by pathogen virulence factors. Application of state-of-the-art "omic" technologies, including proteomics, transcriptomics, and image-omics (image-based high-throughput phenotypic screening), combined with powerful bioinformatics tools will enable the modeling of key signaling pathways in the host-pathogen interplay and aid in the identification of host proteins for therapeutic targeting and the discovery of host-directed small molecules that will regulate bacterial infection. We conclude with an outlook on research needed to overcome the challenges associated with transitioning host-directed therapies into a clinical setting.
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2
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Deeter A, Dalman M, Haddad J, Duan ZH. Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks. PLoS One 2017; 12:e0186004. [PMID: 29049295 PMCID: PMC5648141 DOI: 10.1371/journal.pone.0186004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 09/22/2017] [Indexed: 11/25/2022] Open
Abstract
The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.
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Affiliation(s)
- Anthony Deeter
- Integrated Bioscience, University of Akron, Akron, Ohio, United States of America
- Department of Computer Science, University of Akron, Akron, Ohio, United States of America
- * E-mail:
| | - Mark Dalman
- College of Public Health, Department of Biostatistics, Environmental Health Sciences and Epidemiology, Kent State University, Kent, Ohio, United States of America
- College of Podiatric Medicine, Department of Preclinical Sciences, Kent State University, Kent, Ohio, United States of America
| | - Joseph Haddad
- Department of Computer Science, University of Akron, Akron, Ohio, United States of America
| | - Zhong-Hui Duan
- Integrated Bioscience, University of Akron, Akron, Ohio, United States of America
- Department of Computer Science, University of Akron, Akron, Ohio, United States of America
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3
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Culibrk L, Croft CA, Tebbutt SJ. Systems Biology Approaches for Host-Fungal Interactions: An Expanding Multi-Omics Frontier. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2016; 20:127-38. [PMID: 26885725 PMCID: PMC4799697 DOI: 10.1089/omi.2015.0185] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Opportunistic fungal infections are an increasing threat for global health, and for immunocompromised patients in particular. These infections are characterized by interaction between fungal pathogen and host cells. The exact mechanisms and the attendant variability in host and fungal pathogen interaction remain to be fully elucidated. The field of systems biology aims to characterize a biological system, and utilize this knowledge to predict the system's response to stimuli such as fungal exposures. A multi-omics approach, for example, combining data from genomics, proteomics, metabolomics, would allow a more comprehensive and pan-optic "two systems" biology of both the host and the fungal pathogen. In this review and literature analysis, we present highly specialized and nascent methods for analysis of multiple -omes of biological systems, in addition to emerging single-molecule visualization techniques that may assist in determining biological relevance of multi-omics data. We provide an overview of computational methods for modeling of gene regulatory networks, including some that have been applied towards the study of an interacting host and pathogen. In sum, comprehensive characterizations of host-fungal pathogen systems are now possible, and utilization of these cutting-edge multi-omics strategies may yield advances in better understanding of both host biology and fungal pathogens at a systems scale.
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Affiliation(s)
- Luka Culibrk
- Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - Carys A. Croft
- Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - Scott J. Tebbutt
- Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
- Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, British Columbia, Canada
- Department of Medicine, Division of Respiratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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4
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Davidsen PK, Turan N, Egginton S, Falciani F. Multilevel functional genomics data integration as a tool for understanding physiology: a network biology perspective. J Appl Physiol (1985) 2016; 120:297-309. [DOI: 10.1152/japplphysiol.01110.2014] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Accepted: 11/04/2015] [Indexed: 01/30/2023] Open
Abstract
The overall aim of physiological research is to understand how living systems function in an integrative manner. Consequently, the discipline of physiology has since its infancy attempted to link multiple levels of biological organization. Increasingly this has involved mathematical and computational approaches, typically to model a small number of components spanning several levels of biological organization. With the advent of “omics” technologies, which can characterize the molecular state of a cell or tissue (intended as the level of expression and/or activity of its molecular components), the number of molecular components we can quantify has increased exponentially. Paradoxically, the unprecedented amount of experimental data has made it more difficult to derive conceptual models underlying essential mechanisms regulating mammalian physiology. We present an overview of state-of-the-art methods currently used to identifying biological networks underlying genomewide responses. These are based on a data-driven approach that relies on advanced computational methods designed to “learn” biology from observational data. In this review, we illustrate an application of these computational methodologies using a case study integrating an in vivo model representing the transcriptional state of hypoxic skeletal muscle with a clinical study representing muscle wasting in chronic obstructive pulmonary disease patients. The broader application of these approaches to modeling multiple levels of biological data in the context of modern physiology is discussed.
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Affiliation(s)
- Peter K. Davidsen
- Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Nil Turan
- School of Biosciences, University of Birmingham, Birmingham, United Kingdom; and
| | - Stuart Egginton
- School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom
| | - Francesco Falciani
- Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom
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5
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Bahnassawy L, Perumal TM, Gonzalez-Cano L, Hillje AL, Taher L, Makalowski W, Suzuki Y, Fuellen G, del Sol A, Schwamborn JC. TRIM32 modulates pluripotency entry and exit by directly regulating Oct4 stability. Sci Rep 2015; 5:13456. [PMID: 26307407 PMCID: PMC4642535 DOI: 10.1038/srep13456] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 07/17/2015] [Indexed: 12/27/2022] Open
Abstract
Induced pluripotent stem cells (iPSCs) have revolutionized the world of regenerative medicine; nevertheless, the exact molecular mechanisms underlying their generation and differentiation remain elusive. Here, we investigated the role of the cell fate determinant TRIM32 in modulating such processes. TRIM32 is essential for the induction of neuronal differentiation of neural stem cells by poly-ubiquitinating cMyc to target it for degradation resulting in inhibition of cell proliferation. To elucidate the role of TRIM32 in regulating somatic cell reprogramming we analysed the capacity of TRIM32-knock-out mouse embryonic fibroblasts (MEFs) in generating iPSC colonies. TRIM32 knock-out MEFs produced a higher number of iPSC colonies indicating a role for TRIM32 in inhibiting this cellular transition. Further characterization of the generated iPSCs indicated that the TRIM32 knock-out iPSCs show perturbed differentiation kinetics. Additionally, mathematical modelling of global gene expression data revealed that during differentiation an Oct4 centred network in the wild-type cells is replaced by an E2F1 centred network in the TRIM32 deficient cells. We show here that this might be caused by a TRIM32-dependent downregulation of Oct4. In summary, the data presented here reveal that TRIM32 directly regulates at least two of the four Yamanaka Factors (cMyc and Oct4), to modulate cell fate transitions.
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Affiliation(s)
- Lamia'a Bahnassawy
- Westfälische Wilhelms-Universität Münster, ZMBE, Institute of Cell Biology, Stem Cell Biology and Regeneration Group, Von-Esmarch-Str. 56, 48149 Münster, Germany.,Luxembourg Centre for Systems Biomedicine (LCSB), Developmental and Cellular Biology, University of Luxembourg, 7 avenue des Hauts-Fourneaux, 4362 Esch-Belval, Luxembourg
| | - Thanneer M Perumal
- Luxembourg Centre for Systems Biomedicine (LCSB), Computational Biology, University of Luxembourg, 7 avenue des Hauts-Fourneaux, 4362 Esch-Belval, Luxembourg
| | - Laura Gonzalez-Cano
- Luxembourg Centre for Systems Biomedicine (LCSB), Developmental and Cellular Biology, University of Luxembourg, 7 avenue des Hauts-Fourneaux, 4362 Esch-Belval, Luxembourg
| | - Anna-Lena Hillje
- Luxembourg Centre for Systems Biomedicine (LCSB), Developmental and Cellular Biology, University of Luxembourg, 7 avenue des Hauts-Fourneaux, 4362 Esch-Belval, Luxembourg
| | - Leila Taher
- Institute for Biostatistics and Informatics in Medicine und Ageing Research, Rostock University Medical Centre, Ernst-Heydemann-Str. 8, 18057 Rostock, Germany
| | - Wojciech Makalowski
- Westfälische Wilhelms-Universität Münster, Institute of Bioinformatics, Niels-Stensen-Straße 14, 48149 Münster, Germany
| | - Yutaka Suzuki
- Department of Medical Genome Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba-ken 227-8561, Japan
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine und Ageing Research, Rostock University Medical Centre, Ernst-Heydemann-Str. 8, 18057 Rostock, Germany
| | - Antonio del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), Computational Biology, University of Luxembourg, 7 avenue des Hauts-Fourneaux, 4362 Esch-Belval, Luxembourg
| | - Jens Christian Schwamborn
- Westfälische Wilhelms-Universität Münster, ZMBE, Institute of Cell Biology, Stem Cell Biology and Regeneration Group, Von-Esmarch-Str. 56, 48149 Münster, Germany.,Luxembourg Centre for Systems Biomedicine (LCSB), Developmental and Cellular Biology, University of Luxembourg, 7 avenue des Hauts-Fourneaux, 4362 Esch-Belval, Luxembourg
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Darnell CL, Schmid AK. Systems biology approaches to defining transcription regulatory networks in halophilic archaea. Methods 2015; 86:102-14. [PMID: 25976837 DOI: 10.1016/j.ymeth.2015.04.034] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Revised: 04/27/2015] [Accepted: 04/28/2015] [Indexed: 12/31/2022] Open
Abstract
To survive complex and changing environmental conditions, microorganisms use gene regulatory networks (GRNs) composed of interacting regulatory transcription factors (TFs) to control the timing and magnitude of gene expression. Genome-wide datasets; such as transcriptomics and protein-DNA interactions; and experiments such as high throughput growth curves; facilitate the construction of GRNs and provide insight into TF interactions occurring under stress. Systems biology approaches integrate these datasets into models of GRN architecture as well as statistical and/or dynamical models to understand the function of networks occurring in cells. Previously, these types of studies have focused on traditional model organisms (e.g. Escherichia coli, yeast). However, recent advances in archaeal genetics and other tools have enabled a systems approach to understanding GRNs in these relatively less studied archaeal model organisms. In this report, we outline a systems biology workflow for generating and integrating data focusing on the TF regulator. We discuss experimental design, outline the process of data collection, and provide the tools required to produce high confidence regulons for the TFs of interest. We provide a case study as an example of this workflow, describing the construction of a GRN centered on multi-TF coordinate control of gene expression governing the oxidative stress response in the hypersaline-adapted archaeon Halobacterium salinarum.
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Affiliation(s)
| | - Amy K Schmid
- Biology Department, Duke University, Durham, NC 27708, USA; Center for Systems Biology, Duke University, Durham, NC 27708, USA.
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7
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Zhu F, Shi L, Engel JD, Guan Y. Regulatory network inferred using expression data of small sample size: application and validation in erythroid system. Bioinformatics 2015; 31:2537-44. [PMID: 25840044 DOI: 10.1093/bioinformatics/btv186] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 03/27/2015] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Modeling regulatory networks using expression data observed in a differentiation process may help identify context-specific interactions. The outcome of the current algorithms highly depends on the quality and quantity of a single time-course dataset, and the performance may be compromised for datasets with a limited number of samples. RESULTS In this work, we report a multi-layer graphical model that is capable of leveraging many publicly available time-course datasets, as well as a cell lineage-specific data with small sample size, to model regulatory networks specific to a differentiation process. First, a collection of network inference methods are used to predict the regulatory relationships in individual public datasets. Then, the inferred directional relationships are weighted and integrated together by evaluating against the cell lineage-specific dataset. To test the accuracy of this algorithm, we collected a time-course RNA-Seq dataset during human erythropoiesis to infer regulatory relationships specific to this differentiation process. The resulting erythroid-specific regulatory network reveals novel regulatory relationships activated in erythropoiesis, which were further validated by genome-wide TR4 binding studies using ChIP-seq. These erythropoiesis-specific regulatory relationships were not identifiable by single dataset-based methods or context-independent integrations. Analysis of the predicted targets reveals that they are all closely associated with hematopoietic lineage differentiation.
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Affiliation(s)
- Fan Zhu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lihong Shi
- State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China
| | | | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Internal Medicine, and Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
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8
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Guitart-Pla O, Kustagi M, Rügheimer F, Califano A, Schwikowski B. The Cyni framework for network inference in Cytoscape. Bioinformatics 2014; 31:1499-501. [PMID: 25527096 DOI: 10.1093/bioinformatics/btu812] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2014] [Accepted: 12/04/2014] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Research on methods for the inference of networks from biological data is making significant advances, but the adoption of network inference in biomedical research practice is lagging behind. Here, we present Cyni, an open-source 'fill-in-the-algorithm' framework that provides common network inference functionality and user interface elements. Cyni allows the rapid transformation of Java-based network inference prototypes into apps of the popular open-source Cytoscape network analysis and visualization ecosystem. Merely placing the resulting app in the Cytoscape App Store makes the method accessible to a worldwide community of biomedical researchers by mouse click. In a case study, we illustrate the transformation of an ARACNE implementation into a Cytoscape app. AVAILABILITY AND IMPLEMENTATION Cyni, its apps, user guides, documentation and sample code are available from the Cytoscape App Store http://apps.cytoscape.org/apps/cynitoolbox CONTACT benno.schwikowski@pasteur.fr.
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Affiliation(s)
- Oriol Guitart-Pla
- Systems Biology Lab, Institut Pasteur, 75724 Paris Cedex 15, France and Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Manjunath Kustagi
- Systems Biology Lab, Institut Pasteur, 75724 Paris Cedex 15, France and Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Frank Rügheimer
- Systems Biology Lab, Institut Pasteur, 75724 Paris Cedex 15, France and Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Andrea Califano
- Systems Biology Lab, Institut Pasteur, 75724 Paris Cedex 15, France and Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Benno Schwikowski
- Systems Biology Lab, Institut Pasteur, 75724 Paris Cedex 15, France and Department of Systems Biology, Columbia University, New York, NY 10032, USA
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9
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Abstract
Systems-level analysis of biological processes strives to comprehensively and quantitatively evaluate the interactions between the relevant molecular components over time, thereby enabling development of models that can be employed to ultimately predict behavior. Rapid development in measurement technologies (omics), when combined with the accessible nature of the cellular constituents themselves, is allowing the field of innate immunity to take significant strides toward this lofty goal. In this review, we survey exciting results derived from systems biology analyses of the immune system, ranging from gene regulatory networks to influenza pathogenesis and systems vaccinology.
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10
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Grennan KS, Chen C, Gershon ES, Liu C. Molecular network analysis enhances understanding of the biology of mental disorders. Bioessays 2014; 36:606-16. [PMID: 24733456 DOI: 10.1002/bies.201300147] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We provide an introduction to network theory, evidence to support a connection between molecular network structure and neuropsychiatric disease, and examples of how network approaches can expand our knowledge of the molecular bases of these diseases. Without systematic methods to derive their biological meanings and inter-relatedness, the many molecular changes associated with neuropsychiatric disease, including genetic variants, gene expression changes, and protein differences, present an impenetrably complex set of findings. Network approaches can potentially help integrate and reconcile these findings, as well as provide new insights into the molecular architecture of neuropsychiatric diseases. Network approaches to neuropsychiatric disease are still in their infancy, and we discuss what might be done to improve their prospects.
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Affiliation(s)
- Kay S Grennan
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
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11
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Turkarslan S, Wurtmann EJ, Wu WJ, Jiang N, Bare JC, Foley K, Reiss DJ, Novichkov P, Baliga NS. Network portal: a database for storage, analysis and visualization of biological networks. Nucleic Acids Res 2013; 42:D184-90. [PMID: 24271392 PMCID: PMC3964938 DOI: 10.1093/nar/gkt1190] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The ease of generating high-throughput data has enabled investigations into organismal complexity at the systems level through the inference of networks of interactions among the various cellular components (genes, RNAs, proteins and metabolites). The wider scientific community, however, currently has limited access to tools for network inference, visualization and analysis because these tasks often require advanced computational knowledge and expensive computing resources. We have designed the network portal (http://networks.systemsbiology.net) to serve as a modular database for the integration of user uploaded and public data, with inference algorithms and tools for the storage, visualization and analysis of biological networks. The portal is fully integrated into the Gaggle framework to seamlessly exchange data with desktop and web applications and to allow the user to create, save and modify workspaces, and it includes social networking capabilities for collaborative projects. While the current release of the database contains networks for 13 prokaryotic organisms from diverse phylogenetic clades (4678 co-regulated gene modules, 3466 regulators and 9291 cis-regulatory motifs), it will be rapidly populated with prokaryotic and eukaryotic organisms as relevant data become available in public repositories and through user input. The modular architecture, simple data formats and open API support community development of the portal.
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Affiliation(s)
- Serdar Turkarslan
- Institute for Systems Biology, Seattle, WA 98109, USA and Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Castelhano Santos N, Pereira MO, Lourenço A. Pathogenicity phenomena in three model systems: from network mining to emerging system-level properties. Brief Bioinform 2013; 16:169-82. [PMID: 24106130 DOI: 10.1093/bib/bbt071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Understanding the interconnections of microbial pathogenicity phenomena, such as biofilm formation, quorum sensing and antimicrobial resistance, is a tremendous open challenge for biomedical research. Progress made by wet-lab researchers and bioinformaticians in understanding the underlying regulatory phenomena has been significant, with converging evidence from multiple high-throughput technologies. Notably, network reconstructions are already of considerable size and quality, tackling both intracellular regulation and signal mediation in microbial infection. Therefore, it stands to reason that in silico investigations would play a more active part in this research. Drug target identification and drug repurposing could take much advantage of the ability to simulate pathogen regulatory systems, host-pathogen interactions and pathogen cross-talking. Here, we review the bioinformatics resources and tools available for the study of the gram-negative bacterium Pseudomonas aeruginosa, the gram-positive bacterium Staphylococcus aureus and the fungal species Candida albicans. The choice of these three microorganisms fits the rationale of the review converging into pathogens of great clinical importance, which thrive in biofilm consortia and manifest growing antimicrobial resistance.
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