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Upregulation of TLR4-Dependent ATP Production Is Critical for Glaesserella parasuis LPS-Mediated Inflammation. Cells 2023; 12:cells12050751. [PMID: 36899887 PMCID: PMC10001010 DOI: 10.3390/cells12050751] [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: 01/01/2023] [Revised: 02/15/2023] [Accepted: 02/24/2023] [Indexed: 03/02/2023] Open
Abstract
Glaesserella parasuis (G. parasuis), an important pathogenic bacterium, cause Glässer's disease, and has resulted in tremendous economic losses to the global swine industry. G. parasuis infection causes typical acute systemic inflammation. However, the molecular details of how the host modulates the acute inflammatory response induced by G. parasuis are largely unknown. In this study, we found that G. parasuis LZ and LPS both enhanced the mortality of PAM cells, and at the same time, the level of ATP was enhanced. LPS treatment significantly increased the expressions of IL-1β, P2X7R, NLRP3, NF-κB, p-NF-κB, and GSDMD, leading to pyroptosis. Furthermore, these proteins' expression was enhanced following extracellular ATP further stimulation. When reduced the production of P2X7R, NF-κB-NLRP3-GSDMS inflammasome signaling pathway was inhibited, and the mortality of cells was reduced. MCC950 treatment repressed the formation of inflammasome and reduced mortality. Further exploration found that the knockdown of TLR4 significantly reduced ATP content and cell mortality, and inhibited the expression of p-NF-κB and NLRP3. These findings suggested upregulation of TLR4-dependent ATP production is critical for G. parasuis LPS-mediated inflammation, provided new insights into the molecular pathways underlying the inflammatory response induced by G. parasuis, and offered a fresh perspective on therapeutic strategies.
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Rajput A, Tsunemoto H, Sastry AV, Szubin R, Rychel K, Sugie J, Pogliano J, Palsson BO. Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators. Nucleic Acids Res 2022; 50:3658-3672. [PMID: 35357493 PMCID: PMC9023270 DOI: 10.1093/nar/gkac187] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/28/2022] [Accepted: 03/29/2022] [Indexed: 12/16/2022] Open
Abstract
The transcriptional regulatory network (TRN) of Pseudomonas aeruginosa coordinates cellular processes in response to stimuli. We used 364 transcriptomes (281 publicly available + 83 in-house generated) to reconstruct the TRN of P. aeruginosa using independent component analysis. We identified 104 independently modulated sets of genes (iModulons) among which 81 reflect the effects of known transcriptional regulators. We identified iModulons that (i) play an important role in defining the genomic boundaries of biosynthetic gene clusters (BGCs), (ii) show increased expression of the BGCs and associated secretion systems in nutrient conditions that are important in cystic fibrosis, (iii) show the presence of a novel ribosomally synthesized and post-translationally modified peptide (RiPP) BGC which might have a role in P. aeruginosa virulence, (iv) exhibit interplay of amino acid metabolism regulation and central metabolism across different carbon sources and (v) clustered according to their activity changes to define iron and sulfur stimulons. Finally, we compared the identified iModulons of P. aeruginosa with those previously described in Escherichia coli to observe conserved regulons across two Gram-negative species. This comprehensive TRN framework encompasses the majority of the transcriptional regulatory machinery in P. aeruginosa, and thus should prove foundational for future research into its physiological functions.
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Affiliation(s)
- Akanksha Rajput
- Department of Bioengineering, University of California, San Diego, La Jolla, USA
| | - Hannah Tsunemoto
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Anand V Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, USA
| | - Richard Szubin
- Department of Bioengineering, University of California, San Diego, La Jolla, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, USA
| | - Joseph Sugie
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Joe Pogliano
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, USA.,Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,Center for Microbiome Innovation, 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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3
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Mohammad FK, Palukuri MV, Shivakumar S, Rengaswamy R, Sahoo S. A Computational Framework for Studying Gut-Brain Axis in Autism Spectrum Disorder. Front Physiol 2022; 13:760753. [PMID: 35330929 PMCID: PMC8940246 DOI: 10.3389/fphys.2022.760753] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 01/17/2022] [Indexed: 12/28/2022] Open
Abstract
Introduction The integrity of the intestinal epithelium is crucial for human health and is harmed in autism spectrum disorder (ASD). An aberrant gut microbial composition resulting in gut-derived metabolic toxins was found to damage the intestinal epithelium, jeopardizing tissue integrity. These toxins further reach the brain via the gut-brain axis, disrupting the normal function of the brain. A mechanistic understanding of metabolic disturbances in the brain and gut is essential to design effective therapeutics and early intervention to block disease progression. Herein, we present a novel computational framework integrating constraint based tissue specific metabolic (CBM) model and whole-body physiological pharmacokinetics (PBPK) modeling for ASD. Furthermore, the role of gut microbiota, diet, and oxidative stress is analyzed in ASD. Methods A representative gut model capturing host-bacteria and bacteria-bacteria interaction was developed using CBM techniques and patient data. Simultaneously, a PBPK model of toxin metabolism was assembled, incorporating multi-scale metabolic information. Furthermore, dynamic flux balance analysis was performed to integrate CBM and PBPK. The effectiveness of a probiotic and dietary intervention to improve autism symptoms was tested on the integrated model. Results The model accurately highlighted critical metabolic pathways of the gut and brain that are associated with ASD. These include central carbon, nucleotide, and vitamin metabolism in the host gut, and mitochondrial energy and amino acid metabolisms in the brain. The proposed dietary intervention revealed that a high-fiber diet is more effective than a western diet in reducing toxins produced inside the gut. The addition of probiotic bacteria Lactobacillus acidophilus, Bifidobacterium longum longum, Akkermansia muciniphila, and Prevotella ruminicola to the diet restores gut microbiota balance, thereby lowering oxidative stress in the gut and brain. Conclusion The proposed computational framework is novel in its applicability, as demonstrated by the determination of the whole-body distribution of ROS toxins and metabolic association in ASD. In addition, it emphasized the potential for developing novel therapeutic strategies to alleviate autism symptoms. Notably, the presented integrated model validates the importance of combining PBPK modeling with COBRA -specific tissue details for understanding disease pathogenesis.
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Affiliation(s)
- Faiz Khan Mohammad
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Meghana Venkata Palukuri
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Shruti Shivakumar
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Raghunathan Rengaswamy
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Swagatika Sahoo
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
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4
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Medeiros Filho F, do Nascimento APB, Costa MDOCE, Merigueti TC, de Menezes MA, Nicolás MF, Dos Santos MT, Carvalho-Assef APD, da Silva FAB. A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models. Front Mol Biosci 2021; 8:728129. [PMID: 34616771 PMCID: PMC8488468 DOI: 10.3389/fmolb.2021.728129] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/31/2021] [Indexed: 12/26/2022] Open
Abstract
Pseudomonas aeruginosa is an opportunistic human pathogen that has been a constant global health problem due to its ability to cause infection at different body sites and its resistance to a broad spectrum of clinically available antibiotics. The World Health Organization classified multidrug-resistant Pseudomonas aeruginosa among the top-ranked organisms that require urgent research and development of effective therapeutic options. Several approaches have been taken to achieve these goals, but they all depend on discovering potential drug targets. The large amount of data obtained from sequencing technologies has been used to create computational models of organisms, which provide a powerful tool for better understanding their biological behavior. In the present work, we applied a method to integrate transcriptome data with genome-scale metabolic networks of Pseudomonas aeruginosa. We submitted both metabolic and integrated models to dynamic simulations and compared their performance with published in vitro growth curves. In addition, we used these models to identify potential therapeutic targets and compared the results to analyze the assumption that computational models enriched with biological measurements can provide more selective and (or) specific predictions. Our results demonstrate that dynamic simulations from integrated models result in more accurate growth curves and flux distribution more coherent with biological observations. Moreover, identifying drug targets from integrated models is more selective as the predicted genes were a subset of those found in the metabolic models. Our analysis resulted in the identification of 26 non-host homologous targets. Among them, we highlighted five top-ranked genes based on lesser conservation with the human microbiome. Overall, some of the genes identified in this work have already been proposed by different approaches and (or) are already investigated as targets to antimicrobial compounds, reinforcing the benefit of using integrated models as a starting point to selecting biologically relevant therapeutic targets.
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5
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Gil-Gil T, Ochoa-Sánchez LE, Baquero F, Martínez JL. Antibiotic resistance: Time of synthesis in a post-genomic age. Comput Struct Biotechnol J 2021; 19:3110-3124. [PMID: 34141134 PMCID: PMC8181582 DOI: 10.1016/j.csbj.2021.05.034] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/13/2021] [Accepted: 05/20/2021] [Indexed: 12/20/2022] Open
Abstract
Antibiotic resistance has been highlighted by international organizations, including World Health Organization, World Bank and United Nations, as one of the most relevant global health problems. Classical approaches to study this problem have focused in infected humans, mainly at hospitals. Nevertheless, antibiotic resistance can expand through different ecosystems and geographical allocations, hence constituting a One-Health, Global-Health problem, requiring specific integrative analytic tools. Antibiotic resistance evolution and transmission are multilayer, hierarchically organized processes with several elements (from genes to the whole microbiome) involved. However, their study has been traditionally gene-centric, each element independently studied. The development of robust-economically affordable whole genome sequencing approaches, as well as other -omic techniques as transcriptomics and proteomics, is changing this panorama. These technologies allow the description of a system, either a cell or a microbiome as a whole, overcoming the problems associated with gene-centric approaches. We are currently at the time of combining the information derived from -omic studies to have a more holistic view of the evolution and spread of antibiotic resistance. This synthesis process requires the accurate integration of -omic information into computational models that serve to analyse the causes and the consequences of acquiring AR, fed by curated databases capable of identifying the elements involved in the acquisition of resistance. In this review, we analyse the capacities and drawbacks of the tools that are currently in use for the global analysis of AR, aiming to identify the more useful targets for effective corrective interventions.
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Affiliation(s)
- Teresa Gil-Gil
- Centro Nacional de Biotecnología, CSIC, Darwin 3, 28049 Madrid, Spain
| | | | - Fernando Baquero
- Department of Microbiology, Hospital Universitario Ramón y Cajal (IRYCIS), Madrid, Spain
- CIBER en Epidemiología y Salud Pública (CIBER-ESP), Madrid, Spain
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6
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Lewis JE, Forshaw TE, Boothman DA, Furdui CM, Kemp ML. Personalized Genome-Scale Metabolic Models Identify Targets of Redox Metabolism in Radiation-Resistant Tumors. Cell Syst 2021; 12:68-81.e11. [PMID: 33476554 PMCID: PMC7905848 DOI: 10.1016/j.cels.2020.12.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/04/2020] [Accepted: 12/16/2020] [Indexed: 12/14/2022]
Abstract
Redox cofactor production is integral toward antioxidant generation, clearance of reactive oxygen species, and overall tumor response to ionizing radiation treatment. To identify systems-level alterations in redox metabolism that confer resistance to radiation therapy, we developed a bioinformatics pipeline for integrating multi-omics data into personalized genome-scale flux balance analysis models of 716 radiation-sensitive and 199 radiation-resistant tumors. These models collectively predicted that radiation-resistant tumors reroute metabolic flux to increase mitochondrial NADPH stores and reactive oxygen species (ROS) scavenging. Simulated genome-wide knockout screens agreed with experimental siRNA gene knockdowns in matched radiation-sensitive and radiation-resistant cancer cell lines, revealing gene targets involved in mitochondrial NADPH production, central carbon metabolism, and folate metabolism that allow for selective inhibition of glutathione production and H2O2 clearance in radiation-resistant cancers. This systems approach represents a significant advancement in developing quantitative genome-scale models of redox metabolism and identifying personalized metabolic targets for improving radiation sensitivity in individual cancer patients.
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Affiliation(s)
- Joshua E. Lewis
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Tom E. Forshaw
- Department of Internal Medicine, Section on Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - David A. Boothman
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Cristina M. Furdui
- Department of Internal Medicine, Section on Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Melissa L. Kemp
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA,Corresponding Author: Correspondence:
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7
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McGill SL, Yung Y, Hunt KA, Henson MA, Hanley L, Carlson RP. Pseudomonas aeruginosa reverse diauxie is a multidimensional, optimized, resource utilization strategy. Sci Rep 2021; 11:1457. [PMID: 33446818 PMCID: PMC7809481 DOI: 10.1038/s41598-020-80522-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/17/2020] [Indexed: 12/19/2022] Open
Abstract
Pseudomonas aeruginosa is a globally-distributed bacterium often found in medical infections. The opportunistic pathogen uses a different, carbon catabolite repression (CCR) strategy than many, model microorganisms. It does not utilize a classic diauxie phenotype, nor does it follow common systems biology assumptions including preferential consumption of glucose with an 'overflow' metabolism. Despite these contradictions, P. aeruginosa is competitive in many, disparate environments underscoring knowledge gaps in microbial ecology and systems biology. Physiological, omics, and in silico analyses were used to quantify the P. aeruginosa CCR strategy known as 'reverse diauxie'. An ecological basis of reverse diauxie was identified using a genome-scale, metabolic model interrogated with in vitro omics data. Reverse diauxie preference for lower energy, nonfermentable carbon sources, such as acetate or succinate over glucose, was predicted using a multidimensional strategy which minimized resource investment into central metabolism while completely oxidizing substrates. Application of a common, in silico optimization criterion, which maximizes growth rate, did not predict the reverse diauxie phenotypes. This study quantifies P. aeruginosa metabolic strategies foundational to its wide distribution and virulence including its potentially, mutualistic interactions with microorganisms found commonly in the environment and in medical infections.
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Affiliation(s)
- S Lee McGill
- Department of Chemical and Biological Engineering, Center for Biofilm Engineering, Montana State University, Bozeman, MT, 59717, USA.,Department of Microbiology and Immunology, Montana State University, Bozeman, MT, 59717, USA
| | - Yeni Yung
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - Kristopher A Hunt
- Department of Chemical and Biological Engineering, Center for Biofilm Engineering, Montana State University, Bozeman, MT, 59717, USA.,Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, 98115, USA
| | - Michael A Henson
- Department of Chemical Engineering, Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA, 01003, USA
| | - Luke Hanley
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - Ross P Carlson
- Department of Chemical and Biological Engineering, Center for Biofilm Engineering, Montana State University, Bozeman, MT, 59717, USA. .,Department of Microbiology and Immunology, Montana State University, Bozeman, MT, 59717, USA.
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8
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Zardini Buzatto A, Abdel Jabar M, Nizami I, Dasouki M, Li L, Abdel Rahman AM. Lipidome Alterations Induced by Cystic Fibrosis, CFTR Mutation, and Lung Function. J Proteome Res 2020; 20:549-564. [PMID: 33089695 DOI: 10.1021/acs.jproteome.0c00556] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Cystic fibrosis is a genetic pathology characterized by abnormal accumulation of mucus in the respiratory, gastrointestinal, and reproductive tracts, caused by mutations in the CFTR gene. Although the classical presentation of the condition is well known, there is still a need for a better characterization of metabolic alterations related to cystic fibrosis and different genotypic mutations. We employed untargeted, comprehensive lipidomics of blood serum samples to investigate alterations in the lipid metabolism related to the pathology, mutation classes, and lung function decline. Six unique biomarker candidates were able to independently differentiate diseased individuals from healthy controls with excellent performance. Cystic fibrosis patients showed dyslipidemia for most lipid subclasses, with significantly elevated odd-chain and polyunsaturated fatty acyl lipids. Phosphatidic acids and diacylglycerols were particularly affected by different genotypic mutation classes. We selected a biomarker panel composed of four lipids, including two ceramides, one sphingomyelin, and one fatty acid, which correctly classified all validation samples from classes III and IV. A biomarker panel of five oxidized lipids was further selected to differentiate patients with reduced lung function, measured as predicted FEV1%. Our results indicate that cystic fibrosis is deeply related to lipid metabolism and provide new clues for the investigation of the disease mechanisms and therapeutic targets.
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Affiliation(s)
| | - Mai Abdel Jabar
- Department of Genetics, King Faisal Specialist Hospital and Research Centre (KFSHRC), Zahrawi Street, Al Maather, Riyadh 11211, Saudi Arabia
| | - Imran Nizami
- Lung Transplant Section, Organ Transplant Center, King Faisal Specialist Hospital and Research Center, Zahrawi Street, Al Maather, Riyadh 11211, Saudi Arabia
| | - Majed Dasouki
- Department of Genetics, King Faisal Specialist Hospital and Research Centre (KFSHRC), Zahrawi Street, Al Maather, Riyadh 11211, Saudi Arabia
| | - Liang Li
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Anas M Abdel Rahman
- Department of Genetics, King Faisal Specialist Hospital and Research Centre (KFSHRC), Zahrawi Street, Al Maather, Riyadh 11211, Saudi Arabia.,Department of Biochemistry and Molecular Medicine, College of Medicine, Al Faisal University, Riyadh 11533, Saudi Arabia.,Department of Chemistry, Memorial University of Newfoundland, St. John's, NL A1B 3X7, Canada
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9
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Leonard-Duke J, Evans S, Hannan RT, Barker TH, Bates JHT, Bonham CA, Moore BB, Kirschner DE, Peirce SM. Multi-scale models of lung fibrosis. Matrix Biol 2020; 91-92:35-50. [PMID: 32438056 DOI: 10.1016/j.matbio.2020.04.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/13/2020] [Accepted: 04/15/2020] [Indexed: 02/08/2023]
Abstract
The architectural complexity of the lung is crucial to its ability to function as an organ of gas exchange; the branching tree structure of the airways transforms the tracheal cross-section of only a few square centimeters to a blood-gas barrier with a surface area of tens of square meters and a thickness on the order of a micron or less. Connective tissue comprised largely of collagen and elastic fibers provides structural integrity for this intricate and delicate system. Homeostatic maintenance of this connective tissue, via a balance between catabolic and anabolic enzyme-driven processes, is crucial to life. Accordingly, when homeostasis is disrupted by the excessive production of connective tissue, lung function deteriorates rapidly with grave consequences leading to chronic lung conditions such as pulmonary fibrosis. Understanding how pulmonary fibrosis develops and alters the link between lung structure and function is crucial for diagnosis, prognosis, and therapy. Further information gained could help elaborate how the healing process breaks down leading to chronic disease. Our understanding of fibrotic disease is greatly aided by the intersection of wet lab studies and mathematical and computational modeling. In the present review we will discuss how multi-scale modeling has facilitated our understanding of pulmonary fibrotic disease as well as identified opportunities that remain open and have produced techniques that can be incorporated into this field by borrowing approaches from multi-scale models of fibrosis beyond the lung.
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Affiliation(s)
- Julie Leonard-Duke
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Stephanie Evans
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Riley T Hannan
- Department of Pathology, University of Virginia, Charlottesville, VA 22908, USA
| | - Thomas H Barker
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Jason H T Bates
- Department of Medicine, Vermont Lung Center, University of Vermont College of Medicine, Burlington, VT 05405, USA
| | - Catherine A Bonham
- Division of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville VA 22908, USA
| | - Bethany B Moore
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, and Department of Microbiology and Immunology, University of Michigan Medical Center, Ann Arbor, MI, 48109, USA
| | - Denise E Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Shayn M Peirce
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA 22908, USA.
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10
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Sundararaghavan A, Mukherjee A, Sahoo S, Suraishkumar GK. Mechanism of the oxidative stress‐mediated increase in lipid accumulation by the bacterium,R. opacusPD630: Experimental analysis and genome‐scale metabolic modeling. Biotechnol Bioeng 2020; 117:1779-1788. [DOI: 10.1002/bit.27330] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 02/22/2020] [Accepted: 03/09/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Archanaa Sundararaghavan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences buildingIndian Institute of Technology Madras Chennai India
| | | | - Swagatika Sahoo
- Department of Chemical Engineering and Initiative for Biological Systems EngineeringIndian Institute of Technology Madras Chennai India
| | - G. K. Suraishkumar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences buildingIndian Institute of Technology Madras Chennai India
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11
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Park H, McGill SL, Arnold AD, Carlson RP. Pseudomonad reverse carbon catabolite repression, interspecies metabolite exchange, and consortial division of labor. Cell Mol Life Sci 2020; 77:395-413. [PMID: 31768608 PMCID: PMC7015805 DOI: 10.1007/s00018-019-03377-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 11/04/2019] [Accepted: 11/12/2019] [Indexed: 10/25/2022]
Abstract
Microorganisms acquire energy and nutrients from dynamic environments, where substrates vary in both type and abundance. The regulatory system responsible for prioritizing preferred substrates is known as carbon catabolite repression (CCR). Two broad classes of CCR have been documented in the literature. The best described CCR strategy, referred to here as classic CCR (cCCR), has been experimentally and theoretically studied using model organisms such as Escherichia coli. cCCR phenotypes are often used to generalize universal strategies for fitness, sometimes incorrectly. For instance, extremely competitive microorganisms, such as Pseudomonads, which arguably have broader global distributions than E. coli, have achieved their success using metabolic strategies that are nearly opposite of cCCR. These organisms utilize a CCR strategy termed 'reverse CCR' (rCCR), because the order of preferred substrates is nearly reverse that of cCCR. rCCR phenotypes prefer organic acids over glucose, may or may not select preferred substrates to optimize growth rates, and do not allocate intracellular resources in a manner that produces an overflow metabolism. cCCR and rCCR have traditionally been interpreted from the perspective of monocultures, even though most microorganisms live in consortia. Here, we review the basic tenets of the two CCR strategies and consider these phenotypes from the perspective of resource acquisition in consortia, a scenario that surely influenced the evolution of cCCR and rCCR. For instance, cCCR and rCCR metabolism are near mirror images of each other; when considered from a consortium basis, the complementary properties of the two strategies can mitigate direct competition for energy and nutrients and instead establish cooperative division of labor.
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Affiliation(s)
- Heejoon Park
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, USA
- Center for Biofilm Engineering, Montana State University, Bozeman, USA
| | - S Lee McGill
- Department of Microbiology and Immunology, Montana State University, Bozeman, USA
- Center for Biofilm Engineering, Montana State University, Bozeman, USA
| | - Adrienne D Arnold
- Department of Microbiology and Immunology, Montana State University, Bozeman, USA
- Center for Biofilm Engineering, Montana State University, Bozeman, USA
| | - Ross P Carlson
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, USA.
- Department of Microbiology and Immunology, Montana State University, Bozeman, USA.
- Center for Biofilm Engineering, Montana State University, Bozeman, USA.
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12
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Cesur MF, Siraj B, Uddin R, Durmuş S, Çakır T. Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale. Front Cell Infect Microbiol 2020; 9:447. [PMID: 31993376 PMCID: PMC6970976 DOI: 10.3389/fcimb.2019.00447] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 12/12/2019] [Indexed: 01/28/2023] Open
Abstract
Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of the infections by healthcare-associated K. pneumoniae isolates. Thus, despite intensive efforts, the current treatment strategies remain insufficient to eradicate such infections. Failure of the conventional infection-prevention and treatment efforts explicitly indicates the requirement of new therapeutic approaches. This prompted us to systematically analyze the K. pneumoniae metabolism to investigate drug targets. Genome-scale metabolic networks (GMNs) facilitating the systematic analysis of the metabolism are promising platforms. Thus, we used a GMN of K. pneumoniae MGH 78578 to determine putative targets through gene- and metabolite-centric approaches. To develop more realistic infection models, we performed the bacterial growth simulations within different host-mimicking media, using an improved biomass formation reaction. We selected more suitable targets based on several property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. In addition, the metabolite-centric approach extended the drug target list based on synthetic lethality. This revealed the importance of combined metabolic analyses for a better understanding of the metabolism. To our knowledge, this is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through the constraint-based analysis of its GMN in conjunction with several bioinformatic approaches. This study can guide the researchers for the future drug designs by providing initial findings regarding crucial components of the Klebsiella metabolism.
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Affiliation(s)
- Müberra Fatma Cesur
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Turkey
| | - Bushra Siraj
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Reaz Uddin
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Saliha Durmuş
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Turkey
| | - Tunahan Çakır
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Turkey
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Merigueti TC, Carneiro MW, Carvalho-Assef APD, Silva-Jr FP, da Silva FAB. FindTargetsWEB: A User-Friendly Tool for Identification of Potential Therapeutic Targets in Metabolic Networks of Bacteria. Front Genet 2019; 10:633. [PMID: 31333719 PMCID: PMC6620235 DOI: 10.3389/fgene.2019.00633] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 06/17/2019] [Indexed: 11/13/2022] Open
Abstract
Background: Healthcare-associated infections (HAIs) are a serious public health problem. They can be associated with morbidity and mortality and are responsible for the increase in patient hospitalization. Antimicrobial resistance among pathogens causing HAI has increased at alarming levels. In this paper, a robust method for analyzing genome-scale metabolic networks of bacteria is proposed in order to identify potential therapeutic targets, along with its corresponding web implementation, dubbed FindTargetsWEB. The proposed method assumes that every metabolic network presents fragile genes whose blockade will impair one or more metabolic functions, such as biomass accumulation. FindTargetsWEB automates the process of identification of such fragile genes using flux balance analysis (FBA), flux variability analysis (FVA), extended Systems Biology Markup Language (SBML) file parsing, and queries to three public repositories, i.e., KEGG, UniProt, and DrugBank. The web application was developed in Python using COBRApy and Django. Results: The proposed method was demonstrated to be robust enough to process even non-curated, incomplete, or imprecise metabolic networks, in addition to integrated host-pathogen models. A list of potential therapeutic targets and their putative inhibitors was generated as a result of the analysis of Pseudomonas aeruginosa metabolic networks available in the literature and a curated version of the metabolic network of a multidrug-resistant P. aeruginosa strain belonging to a clone endemic in Brazil (P. aeruginosa ST277). Genome-scale metabolic networks of other gram-positive and gram-negative bacteria, such as Staphylococcus aureus, Klebsiella pneumoniae, and Haemophilus influenzae, were also analyzed using FindTargetsWEB. Multiple potential targets have been found using the proposed method in all metabolic networks, including some overlapping between two or more pathogens. Among the potential targets, several have been previously reported in the literature as targets for antimicrobial development, and many targets have approved drugs. Despite similarities in the metabolic network structure for closely related bacteria, we show that the method is able to selectively identify targets in pathogenic versus non-pathogenic organisms. Conclusions: This new computational system can give insights into the identification of new candidate therapeutic targets for pathogenic bacteria and discovery of new antimicrobial drugs through genome-scale metabolic network analysis and heterogeneous data integration, even for non-curated or incomplete networks.
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Affiliation(s)
| | - Marcia Weber Carneiro
- Graduate Program in Biotechnology for Health and Investigative Medicine-Oswaldo Cruz Foundation (FIOCRUZ), Bahia, Brazil
| | - Ana Paula D'A Carvalho-Assef
- Research Laboratory in Hospital Infection (LAPIH), Oswaldo Cruz Institute-Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | - Floriano Paes Silva-Jr
- Laboratory of Experimental and Computational Biochemistry of Drugs (LaBECFar), Oswaldo Cruz Institute-Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
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14
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Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance. mSystems 2019; 4:mSystems00026-19. [PMID: 31020043 PMCID: PMC6478966 DOI: 10.1128/msystems.00026-19] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 03/29/2019] [Indexed: 01/08/2023] Open
Abstract
Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies. Cystic fibrosis (CF) is a fatal genetic disease characterized by chronic lung infections due to aberrant mucus production and the inability to clear invading pathogens. The traditional view that CF infections are caused by a single pathogen has been replaced by the realization that the CF lung usually is colonized by a complex community of bacteria, fungi, and viruses. To help unravel the complex interplay between the CF lung environment and the infecting microbial community, we developed a community metabolic model comprised of the 17 most abundant bacterial taxa, which account for >95% of reads across samples, from three published studies in which 75 sputum samples from 46 adult CF patients were analyzed by 16S rRNA gene sequencing. The community model was able to correctly predict high abundances of the “rare” pathogens Enterobacteriaceae, Burkholderia, and Achromobacter in three patients whose polymicrobial infections were dominated by these pathogens. With these three pathogens removed, the model correctly predicted that the remaining 43 patients would be dominated by Pseudomonas and/or Streptococcus. This dominance was predicted to be driven by relatively high monoculture growth rates of Pseudomonas and Streptococcus as well as their ability to efficiently consume amino acids, organic acids, and alcohols secreted by other community members. Sample-by-sample heterogeneity of community composition could be qualitatively captured through random variation of the simulated metabolic environment, suggesting that experimental studies directly linking CF lung metabolomics and 16S sequencing could provide important insights into disease progression and treatment efficacy. IMPORTANCE Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.
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Wojciech J, Kamila M, Wojciech B. Investigation of the population dynamics within a Pseudomonas aeruginosa biofilm using a flow based biofilm model system and flow cytometric evaluation of cellular physiology. BIOFOULING 2018; 34:835-850. [PMID: 30332894 DOI: 10.1080/08927014.2018.1508569] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2018] [Revised: 07/16/2018] [Accepted: 07/25/2018] [Indexed: 06/08/2023]
Abstract
In this study a flow based biofilm model system was used to simulate the formation of Pseudomonas aeruginosa biofilms on a stainless steel surface. To investigate the complexity of biofilm-associated P. aeruginosa populations a combination of microscopic observations and flow cytometric analysis (FCM) was adopted. Biofilm-associated P. aeruginosa cells were evaluated (1) under optimal vs reduced nutrient-availability at the initial adhesion stage, and (2) irrespective of nutrient-availability within a mature biofilm. Microscopic estimation of the extent of attachment revealed more effective colonization upon optimal vs starvation conditions. FCM allowed an in situ evaluation of P. aeruginosa vitality, using cellular redox potential measurements to discriminate active, mid-active and non-active sub-populations. Samples from recently attached cells and mature biofilms showed significant differences in the percentages of bacterial cells from the defined sub-populations. The approach demonstrated that distribution of individual P. aeruginosa sub-populations was influenced by the stage of the biofilm life-cycle and nutrient availability.
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Affiliation(s)
- Juzwa Wojciech
- a Department of Biotechnology and Food Microbiology , Poznan University of Life Sciences , Poznan , Poland
| | - Myszka Kamila
- a Department of Biotechnology and Food Microbiology , Poznan University of Life Sciences , Poznan , Poland
| | - Białas Wojciech
- a Department of Biotechnology and Food Microbiology , Poznan University of Life Sciences , Poznan , Poland
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16
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Zhu Y, Czauderna T, Zhao J, Klapperstueck M, Maifiah MHM, Han ML, Lu J, Sommer B, Velkov T, Lithgow T, Song J, Schreiber F, Li J. Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa. Gigascience 2018; 7:4931736. [PMID: 29688451 PMCID: PMC6333913 DOI: 10.1093/gigascience/giy021] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 01/19/2018] [Accepted: 02/22/2018] [Indexed: 01/06/2023] Open
Abstract
Background Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level. Findings A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1%, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9%. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover. Conclusions Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.
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Affiliation(s)
- Yan Zhu
- Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne 3800, Australia
| | - Tobias Czauderna
- Faculty of Information Technology, Monash University, Melbourne 3800, Australia
| | - Jinxin Zhao
- Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne 3800, Australia
| | | | | | - Mei-Ling Han
- Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne 3052, Australia
| | - Jing Lu
- Monash Institute of Cognitive and Clinical Neurosciences, Department of Anatomy and development biology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne 3800, Australia
| | - Björn Sommer
- Department of Computer and Information Science, University of Konstanz, Konstanz 78457, Germany
| | - Tony Velkov
- Department of Pharmacology and Therapeutics, University of Melbourne, Melbourne 3010, Australia
| | - Trevor Lithgow
- Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne 3800, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne 3800, Australia
| | - Falk Schreiber
- Faculty of Information Technology, Monash University, Melbourne 3800, Australia
- Department of Computer and Information Science, University of Konstanz, Konstanz 78457, Germany
| | - Jian Li
- Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne 3800, Australia
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Cesur MF, Abdik E, Güven-Gülhan Ü, Durmuş S, Çakır T. Computational Systems Biology of Metabolism in Infection. EXPERIENTIA SUPPLEMENTUM (2012) 2018; 109:235-282. [PMID: 30535602 DOI: 10.1007/978-3-319-74932-7_6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.
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Affiliation(s)
- Müberra Fatma Cesur
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Ecehan Abdik
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Ünzile Güven-Gülhan
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Saliha Durmuş
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Tunahan Çakır
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
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18
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Chua SL, Ding Y, Liu Y, Cai Z, Zhou J, Swarup S, Drautz-Moses DI, Schuster SC, Kjelleberg S, Givskov M, Yang L. Reactive oxygen species drive evolution of pro-biofilm variants in pathogens by modulating cyclic-di-GMP levels. Open Biol 2017; 6:rsob.160162. [PMID: 27881736 PMCID: PMC5133437 DOI: 10.1098/rsob.160162] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 10/23/2016] [Indexed: 12/14/2022] Open
Abstract
The host immune system offers a hostile environment with antimicrobials and reactive oxygen species (ROS) that are detrimental to bacterial pathogens, forcing them to adapt and evolve for survival. However, the contribution of oxidative stress to pathogen evolution remains elusive. Using an experimental evolution strategy, we show that exposure of the opportunistic pathogen Pseudomonas aeruginosa to sub-lethal hydrogen peroxide (H2O2) levels over 120 generations led to the emergence of pro-biofilm rough small colony variants (RSCVs), which could be abrogated by l-glutathione antioxidants. Comparative genomic analysis of the RSCVs revealed that mutations in the wspF gene, which encodes for a repressor of WspR diguanylate cyclase (DGC), were responsible for increased intracellular cyclic-di-GMP content and production of Psl exopolysaccharide. Psl provides the first line of defence against ROS and macrophages, ensuring the survival fitness of RSCVs over wild-type P. aeruginosa Our study demonstrated that ROS is an essential driving force for the selection of pro-biofilm forming pathogenic variants. Understanding the fundamental mechanism of these genotypic and phenotypic adaptations will improve treatment strategies for combating chronic infections.
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Affiliation(s)
- Song Lin Chua
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 639798 .,Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore 637551
| | - Yichen Ding
- Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore 637551.,Interdisciplinary Graduate School, Nanyang Technological University, Singapore 637551
| | - Yang Liu
- Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore 637551
| | - Zhao Cai
- Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore 637551.,Interdisciplinary Graduate School, Nanyang Technological University, Singapore 637551
| | - Jianuan Zhou
- Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore 637551.,Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou 510642, People's Republic of China
| | - Sanjay Swarup
- Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore 637551.,Department of Biological Sciences, National University of Singapore, Singapore 117543.,NUS Environmental Research Institute, National University of Singapore, Singapore
| | - Daniela I Drautz-Moses
- Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore 637551
| | - Stephan Christoph Schuster
- School of Biological Sciences, Nanyang Technological University, Singapore 639798.,Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore 637551
| | - Staffan Kjelleberg
- Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore 637551.,Center for Marine Bio-Innovation and School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney 2052, Australia
| | - Michael Givskov
- Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore 637551.,Costerton Biofilm Center, Department of Immunology and Microbiology, University of Copenhagen, 2200 København N, Denmark
| | - Liang Yang
- School of Biological Sciences, Nanyang Technological University, Singapore 639798 .,Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore 637551
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19
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Wang X, Li W, Zhang Y, Feng Y, Zhao X, He Y, Zhang J, Chen L. Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information. PLoS One 2017; 12:e0184299. [PMID: 28873096 PMCID: PMC5584748 DOI: 10.1371/journal.pone.0184299] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 08/21/2017] [Indexed: 02/07/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.
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Affiliation(s)
- Xinyan Wang
- Department of Respiratory, the Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yihua Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yuyan Feng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xilei Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yuehan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Jun Zhang
- Department of pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, Heilongjiang, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
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20
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Otero-Muras I, Banga JR. Automated Design Framework for Synthetic Biology Exploiting Pareto Optimality. ACS Synth Biol 2017; 6:1180-1193. [PMID: 28350462 DOI: 10.1021/acssynbio.6b00306] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC,
Spanish National Research Council, Vigo, 36208, Spain
| | - Julio R. Banga
- BioProcess Engineering Group, IIM-CSIC,
Spanish National Research Council, Vigo, 36208, Spain
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21
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Carey MA, Papin JA, Guler JL. Novel Plasmodium falciparum metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance. BMC Genomics 2017; 18:543. [PMID: 28724354 PMCID: PMC5518114 DOI: 10.1186/s12864-017-3905-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 06/27/2017] [Indexed: 02/06/2023] Open
Abstract
Background Malaria remains a major public health burden and resistance has emerged to every antimalarial on the market, including the frontline drug, artemisinin. Our limited understanding of Plasmodium biology hinders the elucidation of resistance mechanisms. In this regard, systems biology approaches can facilitate the integration of existing experimental knowledge and further understanding of these mechanisms. Results Here, we developed a novel genome-scale metabolic network reconstruction, iPfal17, of the asexual blood-stage P. falciparum parasite to expand our understanding of metabolic changes that support resistance. We identified 11 metabolic tasks to evaluate iPfal17 performance. Flux balance analysis and simulation of gene knockouts and enzyme inhibition predict candidate drug targets unique to resistant parasites. Moreover, integration of clinical parasite transcriptomes into the iPfal17 reconstruction reveals patterns associated with antimalarial resistance. These results predict that artemisinin sensitive and resistant parasites differentially utilize scavenging and biosynthetic pathways for multiple essential metabolites, including folate and polyamines. Our findings are consistent with experimental literature, while generating novel hypotheses about artemisinin resistance and parasite biology. We detect evidence that resistant parasites maintain greater metabolic flexibility, perhaps representing an incomplete transition to the metabolic state most appropriate for nutrient-rich blood. Conclusion Using this systems biology approach, we identify metabolic shifts that arise with or in support of the resistant phenotype. This perspective allows us to more productively analyze and interpret clinical expression data for the identification of candidate drug targets for the treatment of resistant parasites. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3905-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Maureen A Carey
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, School of Medicine, Charlottesville, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, USA.
| | - Jennifer L Guler
- Department of Biology, University of Virginia, Charlottesville, USA. .,Division of Infectious Diseases and International Health, University of Virginia, School of Medicine, Charlottesville, USA.
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22
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Robinson JL, Jaslove JM, Murawski AM, Fazen CH, Brynildsen MP. An integrated network analysis reveals that nitric oxide reductase prevents metabolic cycling of nitric oxide by Pseudomonas aeruginosa. Metab Eng 2017; 41:67-81. [PMID: 28363762 DOI: 10.1016/j.ymben.2017.03.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 12/21/2016] [Accepted: 03/27/2017] [Indexed: 01/08/2023]
Abstract
Nitric oxide (NO) is a chemical weapon within the arsenal of immune cells, but is also generated endogenously by different bacteria. Pseudomonas aeruginosa are pathogens that contain an NO-generating nitrite (NO2-) reductase (NirS), and NO has been shown to influence their virulence. Interestingly, P. aeruginosa also contain NO dioxygenase (Fhp) and nitrate (NO3-) reductases, which together with NirS provide the potential for NO to be metabolically cycled (NO→NO3-→NO2-→NO). Deeper understanding of NO metabolism in P. aeruginosa will increase knowledge of its pathogenesis, and computational models have proven to be useful tools for the quantitative dissection of NO biochemical networks. Here we developed such a model for P. aeruginosa and confirmed its predictive accuracy with measurements of NO, O2, NO2-, and NO3- in mutant cultures devoid of Fhp or NorCB (NO reductase) activity. Using the model, we assessed whether NO was metabolically cycled in aerobic P. aeruginosa cultures. Calculated fluxes indicated a bottleneck at NO3-, which was relieved upon O2 depletion. As cell growth depleted dissolved O2 levels, NO3- was converted to NO2- at near-stoichiometric levels, whereas NO2- consumption did not coincide with NO or NO3- accumulation. Assimilatory NO2- reductase (NirBD) or NorCB activity could have prevented NO cycling, and experiments with ΔnirB, ΔnirS, and ΔnorC showed that NorCB was responsible for loss of flux from the cycle. Collectively, this work provides a computational tool to analyze NO metabolism in P. aeruginosa, and establishes that P. aeruginosa use NorCB to prevent metabolic cycling of NO.
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Affiliation(s)
- Jonathan L Robinson
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
| | - Jacob M Jaslove
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA; Rutgers Robert Wood Johnson Medical School, Piscataway, NJ 08854, USA
| | - Allison M Murawski
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA; Rutgers Robert Wood Johnson Medical School, Piscataway, NJ 08854, USA
| | - Christopher H Fazen
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
| | - Mark P Brynildsen
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA; Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
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23
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Biggs MB, Papin JA. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA. PLoS Comput Biol 2017; 13:e1005413. [PMID: 28263984 PMCID: PMC5358886 DOI: 10.1371/journal.pcbi.1005413] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 03/20/2017] [Accepted: 02/15/2017] [Indexed: 11/19/2022] Open
Abstract
Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository. Metabolism is the driving force behind all biological activity. Genome-scale metabolic network reconstructions (GENREs) are representations of metabolic systems that can be analyzed mathematically to make predictions about how a system will behave, as well as to design systems with new properties. GENREs have traditionally been reconstructed manually, which can require extensive time and effort. Recent software solutions automate the process (drastically reducing the required effort) but the resulting GENREs are of lower quality and produce less reliable predictions than the manually-curated versions. We present a novel method (“EnsembleFBA”) which accounts for uncertainties involved in automated reconstruction by pooling many different draft GENREs together into an ensemble. We tested EnsembleFBA by predicting the growth and essential genes of the common pathogen Pseudomonas aeruginosa. We found that when predicting growth or essential genes, ensembles of GENREs achieved much better precision or captured many more essential genes than any of the individual GENREs within the ensemble. By improving the predictions that can be made with automatically-generated GENREs, this approach enables the modeling of biochemical systems which would otherwise be infeasible.
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Affiliation(s)
- Matthew B. Biggs
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America
- * E-mail:
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24
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Jinawath N, Bunbanjerdsuk S, Chayanupatkul M, Ngamphaiboon N, Asavapanumas N, Svasti J, Charoensawan V. Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research. J Transl Med 2016; 14:324. [PMID: 27876057 PMCID: PMC5120462 DOI: 10.1186/s12967-016-1078-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 11/08/2016] [Indexed: 01/22/2023] Open
Abstract
With the wealth of data accumulated from completely sequenced genomes and other high-throughput experiments, global studies of biological systems, by simultaneously investigating multiple biological entities (e.g. genes, transcripts, proteins), has become a routine. Network representation is frequently used to capture the presence of these molecules as well as their relationship. Network biology has been widely used in molecular biology and genetics, where several network properties have been shown to be functionally important. Here, we discuss how such methodology can be useful to translational biomedical research, where scientists traditionally focus on one or a small set of genes, diseases, and drug candidates at any one time. We first give an overview of network representation frequently used in biology: what nodes and edges represent, and review its application in preclinical research to date. Using cancer as an example, we review how network biology can facilitate system-wide approaches to identify targeted small molecule inhibitors. These types of inhibitors have the potential to be more specific, resulting in high efficacy treatments with less side effects, compared to the conventional treatments such as chemotherapy. Global analysis may provide better insight into the overall picture of human diseases, as well as identify previously overlooked problems, leading to rapid advances in medicine. From the clinicians’ point of view, it is necessary to bridge the gap between theoretical network biology and practical biomedical research, in order to improve the diagnosis, prevention, and treatment of the world’s major diseases.
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Affiliation(s)
- Natini Jinawath
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom, Thailand.,Program in Translational Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Sacarin Bunbanjerdsuk
- Program in Translational Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Maneerat Chayanupatkul
- Department of Physiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.,Division of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Nuttapong Ngamphaiboon
- Medical Oncology Unit, Department of Medicine Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Nithi Asavapanumas
- Department of Physiology, Faculty of Science, Mahidol University, Bangkok, Thailand
| | - Jisnuson Svasti
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom, Thailand.,Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand.,Laboratory of Biochemistry, Chulabhorn Research Institute, Bangkok, Thailand
| | - Varodom Charoensawan
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom, Thailand. .,Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand. .,Systems Biology of Diseases Research Unit, Faculty of Science, Mahidol University, Bangkok, Thailand.
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25
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Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates. Metab Eng 2016; 38:251-263. [PMID: 27637318 DOI: 10.1016/j.ymben.2016.09.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Revised: 06/07/2016] [Accepted: 09/11/2016] [Indexed: 01/22/2023]
Abstract
Pseudomonas aeruginosa is a metabolically versatile wide-ranging opportunistic pathogen. In humans P. aeruginosa causes infections of the skin, urinary tract, blood, and the lungs of Cystic Fibrosis patients. In addition, P. aeruginosa's broad environmental distribution, relatedness to biotechnologically useful species, and ability to form biofilms have made it the focus of considerable interest. We used 13C metabolic flux analysis (MFA) and flux balance analysis to understand energy and redox production and consumption and to explore the metabolic phenotypes of one reference strain and five strains isolated from the lungs of cystic fibrosis patients. Our results highlight the importance of the oxidative pentose phosphate and Entner-Doudoroff pathways in P. aeruginosa growth. Among clinical strains we report two divergent metabolic strategies and identify changes between genetically related strains that have emerged during a chronic infection of the same patient. MFA revealed that the magnitude of fluxes through the glyoxylate cycle correlates with growth rates.
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26
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Maldonado RF, Sá-Correia I, Valvano MA. Lipopolysaccharide modification in Gram-negative bacteria during chronic infection. FEMS Microbiol Rev 2016; 40:480-93. [PMID: 27075488 PMCID: PMC4931227 DOI: 10.1093/femsre/fuw007] [Citation(s) in RCA: 410] [Impact Index Per Article: 45.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 08/23/2015] [Accepted: 03/10/2016] [Indexed: 12/16/2022] Open
Abstract
The Gram-negative bacterial lipopolysaccharide (LPS) is a major component of the outer membrane that plays a key role in host-pathogen interactions with the innate immune system. During infection, bacteria are exposed to a host environment that is typically dominated by inflammatory cells and soluble factors, including antibiotics, which provide cues about regulation of gene expression. Bacterial adaptive changes including modulation of LPS synthesis and structure are a conserved theme in infections, irrespective of the type or bacteria or the site of infection. In general, these changes result in immune system evasion, persisting inflammation and increased antimicrobial resistance. Here, we review the modifications of LPS structure and biosynthetic pathways that occur upon adaptation of model opportunistic pathogens (Pseudomonas aeruginosa, Burkholderia cepacia complex bacteria, Helicobacter pylori and Salmonella enterica) to chronic infection in respiratory and gastrointestinal sites. We also discuss the molecular mechanisms of these variations and their role in the host-pathogen interaction.
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Affiliation(s)
- Rita F. Maldonado
- Institute for Bioengineering and Biosciences, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon 1049-001, Portugal
| | - Isabel Sá-Correia
- Institute for Bioengineering and Biosciences, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon 1049-001, Portugal
| | - Miguel A. Valvano
- Department of Microbiology and Immunology, University of Western Ontario, London, ON N6A 5C1, Canada
- Centre for Infection and Immunity, Queen's University Belfast, Belfast BT9 7BL, UK
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27
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Varga JJ, Barbier M, Mulet X, Bielecki P, Bartell JA, Owings JP, Martinez-Ramos I, Hittle LE, Davis MR, Damron FH, Liechti GW, Puchałka J, dos Santos VAPM, Ernst RK, Papin JA, Albertí S, Oliver A, Goldberg JB. Genotypic and phenotypic analyses of a Pseudomonas aeruginosa chronic bronchiectasis isolate reveal differences from cystic fibrosis and laboratory strains. BMC Genomics 2015; 16:883. [PMID: 26519161 PMCID: PMC4628258 DOI: 10.1186/s12864-015-2069-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Accepted: 10/03/2015] [Indexed: 01/24/2023] Open
Abstract
Background Pseudomonas aeruginosa is an environmentally ubiquitous Gram-negative bacterium and important opportunistic human pathogen, causing severe chronic respiratory infections in patients with underlying conditions such as cystic fibrosis (CF) or bronchiectasis. In order to identify mechanisms responsible for adaptation during bronchiectasis infections, a bronchiectasis isolate, PAHM4, was phenotypically and genotypically characterized. Results This strain displays phenotypes that have been associated with chronic respiratory infections in CF including alginate over-production, rough lipopolysaccharide, quorum-sensing deficiency, loss of motility, decreased protease secretion, and hypermutation. Hypermutation is a key adaptation of this bacterium during the course of chronic respiratory infections and analysis indicates that PAHM4 encodes a mutated mutS gene responsible for a ~1,000-fold increase in mutation rate compared to wild-type laboratory strain P. aeruginosa PAO1. Antibiotic resistance profiles and sequence data indicate that this strain acquired numerous mutations associated with increased resistance levels to β-lactams, aminoglycosides, and fluoroquinolones when compared to PAO1. Sequencing of PAHM4 revealed a 6.38 Mbp genome, 5.9 % of which were unrecognized in previously reported P. aeruginosa genome sequences. Transcriptome analysis suggests a general down-regulation of virulence factors, while metabolism of amino acids and lipids is up-regulated when compared to PAO1 and metabolic modeling identified further potential differences between PAO1 and PAHM4. Conclusions This work provides insights into the potential differential adaptation of this bacterium to the lung of patients with bronchiectasis compared to other clinical settings such as cystic fibrosis, findings that should aid the development of disease-appropriate treatment strategies for P. aeruginosa infections. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2069-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- John J Varga
- Department of Pediatrics, Division of Pulmonology, Allergy/Immunology, Cystic Fibrosis and Sleep, Children's Healthcare of Atlanta, Atlanta, GA, USA. .,Emory + Children's Center for Cystic Fibrosis Research, Emory University and Children's Healthcare of Atlanta, Atlanta, GA, USA. .,Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA.
| | - Mariette Barbier
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA. .,Department of Microbiology, Immunology and Cell Biology, West Virginia University School of Medicine, Morgantown, WV, USA.
| | - Xavier Mulet
- Servicio de Microbiología and Unidad de Investigación, Hospital Son Espases, Instituto de Investigación Sanitaria de Palma (IdISPa), Palma, de Mallorca, Spain.
| | - Piotr Bielecki
- Synthetic and Systems Biology Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany. .,Present address: Immunobiology Department, Yale University, School of Medicine, New Haven, CT, 06511, USA.
| | - Jennifer A Bartell
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Joshua P Owings
- Department of Pediatrics, Division of Pulmonology, Allergy/Immunology, Cystic Fibrosis and Sleep, Children's Healthcare of Atlanta, Atlanta, GA, USA. .,Emory + Children's Center for Cystic Fibrosis Research, Emory University and Children's Healthcare of Atlanta, Atlanta, GA, USA. .,Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA.
| | | | - Lauren E Hittle
- Department of Microbial Pathogenesis, University of Maryland School of Dentistry, University of Maryland, Baltimore, MD, USA.
| | - Michael R Davis
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA.
| | - F Heath Damron
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA. .,Department of Microbiology, Immunology and Cell Biology, West Virginia University School of Medicine, Morgantown, WV, USA.
| | - George W Liechti
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA.
| | - Jacek Puchałka
- Servicio de Microbiología and Unidad de Investigación, Hospital Son Espases, Instituto de Investigación Sanitaria de Palma (IdISPa), Palma, de Mallorca, Spain. .,Present address: Dr. von Hauner Children's Hospital, Ludwig Maximilians University, Munich, Germany.
| | - Vitor A P Martins dos Santos
- Systems and Synthetic Biology, Wageningen University, Wageningen, Netherlands. .,Present address: Chair of Systems and Synthetic Biology, Wageningen University, Wageningen, The Netherlands. .,Present address: LifeGlimmer GmbH, Berlin, Germany.
| | - Robert K Ernst
- Department of Microbial Pathogenesis, University of Maryland School of Dentistry, University of Maryland, Baltimore, MD, USA.
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Sebastian Albertí
- IUNICS, University of the Balearic Islands, Palma, de Mallorca, Spain.
| | - Antonio Oliver
- Servicio de Microbiología and Unidad de Investigación, Hospital Son Espases, Instituto de Investigación Sanitaria de Palma (IdISPa), Palma, de Mallorca, Spain.
| | - Joanna B Goldberg
- Department of Pediatrics, Division of Pulmonology, Allergy/Immunology, Cystic Fibrosis and Sleep, Children's Healthcare of Atlanta, Atlanta, GA, USA. .,Emory + Children's Center for Cystic Fibrosis Research, Emory University and Children's Healthcare of Atlanta, Atlanta, GA, USA. .,Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA.
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28
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Vital-Lopez FG, Reifman J, Wallqvist A. Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism. PLoS Comput Biol 2015; 11:e1004452. [PMID: 26431398 PMCID: PMC4592021 DOI: 10.1371/journal.pcbi.1004452] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Accepted: 07/14/2015] [Indexed: 12/28/2022] Open
Abstract
A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm-based infections that are difficult to eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic cells. Developing treatments against biofilms requires an understanding of bacterial biofilm-specific physiological traits. Research efforts have started to elucidate the intricate mechanisms underlying biofilm development. However, many aspects of these mechanisms are still poorly understood. Here, we addressed questions regarding biofilm metabolism using a genome-scale kinetic model of the P. aeruginosa metabolic network and gene expression profiles. Specifically, we computed metabolite concentration differences between known mutants with altered biofilm formation and the wild-type strain to predict drug targets against P. aeruginosa biofilms. We also simulated the altered metabolism driven by gene expression changes between biofilm and stationary growth-phase planktonic cultures. Our analysis suggests that the synthesis of important biofilm-related molecules, such as the quorum-sensing molecule Pseudomonas quinolone signal and the exopolysaccharide Psl, is regulated not only through the expression of genes in their own synthesis pathway, but also through the biofilm-specific expression of genes in pathways competing for precursors to these molecules. Finally, we investigated why mutants defective in anthranilate degradation have an impaired ability to form biofilms. Alternative to a previous hypothesis that this biofilm reduction is caused by a decrease in energy production, we proposed that the dysregulation of the synthesis of secondary metabolites derived from anthranilate and chorismate is what impaired the biofilms of these mutants. Notably, these insights generated through our kinetic model-based approach are not accessible from previous constraint-based model analyses of P. aeruginosa biofilm metabolism. Our simulation results showed that plausible, non-intuitive explanations of difficult-to-interpret experimental observations could be generated by integrating genome-scale kinetic models with gene expression profiles.
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Affiliation(s)
- Francisco G. Vital-Lopez
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Jaques Reifman
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
- * E-mail:
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
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29
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Kim M, Yi JS, Lakshmanan M, Lee DY, Kim BG. Transcriptomics-based strain optimization tool for designing secondary metabolite overproducing strains of Streptomyces coelicolor. Biotechnol Bioeng 2015; 113:651-60. [PMID: 26369755 DOI: 10.1002/bit.25830] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Revised: 08/26/2015] [Accepted: 09/07/2015] [Indexed: 12/23/2022]
Abstract
In silico model-driven analysis using genome-scale model of metabolism (GEM) has been recognized as a promising method for microbial strain improvement. However, most of the current GEM-based strain design algorithms based on flux balance analysis (FBA) heavily rely on the steady-state and optimality assumptions without considering any regulatory information. Thus, their practical usage is quite limited, especially in its application to secondary metabolites overproduction. In this study, we developed a transcriptomics-based strain optimization tool (tSOT) in order to overcome such limitations by integrating transcriptomic data into GEM. Initially, we evaluated existing algorithms for integrating transcriptomic data into GEM using Streptomyces coelicolor dataset, and identified iMAT algorithm as the only and the best algorithm for characterizing the secondary metabolism of S. coelicolor. Subsequently, we developed tSOT platform where iMAT is adopted to predict the reaction states, and successfully demonstrated its applicability to secondary metabolites overproduction by designing actinorhodin (ACT), a polyketide antibiotic, overproducing strain of S. coelicolor. Mutants overexpressing tSOT targets such as ribulose 5-phosphate 3-epimerase and NADP-dependent malic enzyme showed 2 and 1.8-fold increase in ACT production, thereby validating the tSOT prediction. It is expected that tSOT can be used for solving other metabolic engineering problems which could not be addressed by current strain design algorithms, especially for the secondary metabolite overproductions.
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Affiliation(s)
- Minsuk Kim
- School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea.,Bioengineering Institute, Seoul National University, Seoul, Republic of Korea
| | - Jeong Sang Yi
- School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea.,Bioengineering Institute, Seoul National University, Seoul, Republic of Korea
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, A*STAR (Agency for Science, Technology and Research), Centros, Singapore
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, A*STAR (Agency for Science, Technology and Research), Centros, Singapore. .,Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore. .,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore.
| | - Byung-Gee Kim
- School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea. .,Bioengineering Institute, Seoul National University, Seoul, Republic of Korea. .,Interdisciplinary Program for Biochemical Engineering and Biotechnology, Seoul National University, Seoul, Republic of Korea.
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30
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An Integrated Modeling and Experimental Approach to Study the Influence of Environmental Nutrients on Biofilm Formation of Pseudomonas aeruginosa. BIOMED RESEARCH INTERNATIONAL 2015; 2015:506782. [PMID: 25954752 PMCID: PMC4411446 DOI: 10.1155/2015/506782] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 02/09/2015] [Indexed: 12/18/2022]
Abstract
The availability of nutrient components in the environment was identified as a critical regulator of virulence and biofilm formation in Pseudomonas aeruginosa. This work proposes the first systems-biology approach to quantify microbial biofilm formation upon the change of nutrient availability in the environment. Specifically, the change of fluxes of metabolic reactions that were positively associated with P. aeruginosa biofilm formation was used to monitor the trend for P. aeruginosa to form a biofilm. The uptake rates of nutrient components were changed according to the change of the nutrient availability. We found that adding each of the eleven amino acids (Arg, Tyr, Phe, His, Iso, Orn, Pro, Glu, Leu, Val, and Asp) to minimal medium promoted P. aeruginosa biofilm formation. Both modeling and experimental approaches were further developed to quantify P. aeruginosa biofilm formation for four different availability levels for each of the three ions that include ferrous ions, sulfate, and phosphate. The developed modeling approach correctly predicted the amount of biofilm formation. By comparing reaction flux change upon the change of nutrient concentrations, metabolic reactions used by P. aeruginosa to regulate its biofilm formation are mainly involved in arginine metabolism, glutamate production, magnesium transport, acetate metabolism, and the TCA cycle.
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31
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Bacterial Adaptation during Chronic Respiratory Infections. Pathogens 2015; 4:66-89. [PMID: 25738646 PMCID: PMC4384073 DOI: 10.3390/pathogens4010066] [Citation(s) in RCA: 117] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Revised: 02/15/2015] [Accepted: 02/25/2015] [Indexed: 01/22/2023] Open
Abstract
Chronic lung infections are associated with increased morbidity and mortality for individuals with underlying respiratory conditions such as cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD). The process of chronic colonisation allows pathogens to adapt over time to cope with changing selection pressures, co-infecting species and antimicrobial therapies. These adaptations can occur due to environmental pressures in the lung such as inflammatory responses, hypoxia, nutrient deficiency, osmolarity, low pH and antibiotic therapies. Phenotypic adaptations in bacterial pathogens from acute to chronic infection include, but are not limited to, antibiotic resistance, exopolysaccharide production (mucoidy), loss in motility, formation of small colony variants, increased mutation rate, quorum sensing and altered production of virulence factors associated with chronic infection. The evolution of Pseudomonas aeruginosa during chronic lung infection has been widely studied. More recently, the adaptations that other chronically colonising respiratory pathogens, including Staphylococcus aureus, Burkholderia cepacia complex and Haemophilus influenzae undergo during chronic infection have also been investigated. This review aims to examine the adaptations utilised by different bacterial pathogens to aid in their evolution from acute to chronic pathogens of the immunocompromised lung including CF and COPD.
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32
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Shi C, Yin J, Liu Z, Wu JX, Zhao Q, Ren J, Yao N. A systematic simulation of the effect of salicylic acid on sphingolipid metabolism. FRONTIERS IN PLANT SCIENCE 2015; 6:186. [PMID: 25859253 PMCID: PMC4373270 DOI: 10.3389/fpls.2015.00186] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2014] [Accepted: 03/08/2015] [Indexed: 05/04/2023]
Abstract
The phytohormone salicylic acid (SA) affects plant development and defense responses. Recent studies revealed that SA also participates in the regulation of sphingolipid metabolism, but the details of this regulation remain to beexplored. Here, we use in silico Flux Balance Analysis (FBA) with published microarray data to construct a whole-cell simulation model, including 23 pathways, 259 reactions, and 172 metabolites, to predict the alterations in flux of major sphingolipid species after treatment with exogenous SA. This model predicts significant changes in fluxes of certain sphingolipid species after SA treatment, changes that likely trigger downstream physiological and phenotypic effects. To validate the simulation, we used (15)N-labeled metabolic turnover analysis to measure sphingolipid contents and turnover rate in Arabidopsis thaliana seedlings treated with SA or the SA analog benzothiadiazole (BTH). The results show that both SA and BTH affect sphingolipid metabolism, altering the concentrations of certain species and also changing the optimal flux distribution and turnover rate of sphingolipids. Our strategy allows us to estimate sphingolipid fluxes on a short time scale and gives us a systemic view of the effect of SA on sphingolipid homeostasis.
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Affiliation(s)
| | | | | | | | | | | | - Nan Yao
- *Correspondence: Nan Yao, State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources, Department of Biological Science and Technology, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
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33
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Hosseinkhan N, Zarrineh P, Masoudi-Nejad A. Analysis of Genome-scale Expression Network in Four Major Bacterial Residents of Cystic Fibrosis Lung. Curr Genomics 2014; 15:408-18. [PMID: 25435803 PMCID: PMC4245700 DOI: 10.2174/1389202915666140818205444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2013] [Revised: 07/06/2014] [Accepted: 08/15/2014] [Indexed: 11/22/2022] Open
Abstract
In polymicrobial communities where several species co-exist in a certain niche and consequently the possibility of interactions among species is very high, gene expression data sources can give better insights in to underlying adaptation mechanisms assumed by bacteria. Furthermore, several possible synergistic or antagonistic interactions among species can be investigated through gene expression comparisons. Lung is one of the habitats harboring several distinct pathogens during severe pulmonary disorders such as chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). Expression data analysis of these lung residents can help to gain a better understanding on how these species interact with each other within the host cells. The first part of this paper deals with introducing available data sources for the major bacteria responsible for causing lung diseases and their genomic relations. In the second part, the main focus is on the studies concerning gene expression analyses of these species.
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Affiliation(s)
- Nazanin Hosseinkhan
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Peyman Zarrineh
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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34
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Diez D, Agustí A, Wheelock CE. Network Analysis in the Investigation of Chronic Respiratory Diseases. From Basics to Application. Am J Respir Crit Care Med 2014; 190:981-8. [DOI: 10.1164/rccm.201403-0421pp] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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35
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Line L, Alhede M, Kolpen M, Kühl M, Ciofu O, Bjarnsholt T, Moser C, Toyofuku M, Nomura N, Høiby N, Jensen PØ. Physiological levels of nitrate support anoxic growth by denitrification of Pseudomonas aeruginosa at growth rates reported in cystic fibrosis lungs and sputum. Front Microbiol 2014; 5:554. [PMID: 25386171 PMCID: PMC4208399 DOI: 10.3389/fmicb.2014.00554] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Accepted: 10/03/2014] [Indexed: 12/13/2022] Open
Abstract
Chronic Pseudomonas aeruginosa lung infection is the most severe complication in patients with cystic fibrosis (CF). The infection is characterized by the formation of biofilm surrounded by numerous polymorphonuclear leukocytes (PMNs) and strong O2 depletion in the endobronchial mucus. We have reported that O2 is mainly consumed by the activated PMNs, while O2 consumption by aerobic respiration is diminutive and nitrous oxide (N2O) is produced in infected CF sputum. This suggests that the reported growth rates of P. aeruginosa in lungs and sputum may result from anaerobic respiration using denitrification. The growth rate of P. aeruginosa achieved by denitrification at physiological levels (~400 μM) of nitrate (NO(-) 3) is however, not known. Therefore, we have measured growth rates of anoxic cultures of PAO1 and clinical isolates (n = 12) in LB media supplemented with NO(-) 3 and found a significant increase of growth when supplementing PAO1 and clinical isolates with ≥150 μM NO(-) 3 and 100 μM NO(-) 3, respectively. An essential contribution to growth by denitrification was demonstrated by the inability to establish a significantly increased growth rate by a denitrification deficient ΔnirS-N mutant at <1 mM of NO(-) 3. Activation of denitrification could be achieved by supplementation with as little as 62.5 μM of NO(-) 3 according to the significant production of N2O by the nitrous oxide reductase deficient ΔnosZ mutant. Studies of the promoter activity, gene transcripts, and enzyme activity of the four N-oxide reductases in PAO1 (Nar, Nir, Nor, Nos) further verified the engagement of denitrification, showing a transient increase in activation and expression and rapid consumption of NO(-) 3 followed by a transient increase of NO(-) 2. Growth rates obtained by denitrification in this study were comparable to our reported growth rates in the majority of P. aeruginosa cells in CF lungs and sputum. Thus, we have demonstrated that denitrification is required for P. aeruginosa growth in infected endobronchial CF mucus.
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Affiliation(s)
- Laura Line
- Department of Clinical Microbiology Rigshospitalet, Copenhagen, Denmark ; Department of International Health, Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen Copenhagen, Denmark
| | - Morten Alhede
- Department of Clinical Microbiology Rigshospitalet, Copenhagen, Denmark ; Department of International Health, Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen Copenhagen, Denmark
| | - Mette Kolpen
- Department of Clinical Microbiology Rigshospitalet, Copenhagen, Denmark ; Department of International Health, Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen Copenhagen, Denmark
| | - Michael Kühl
- Marine Biological Section, Department of Biology, University of Copenhagen Copenhagen, Denmark ; Plant Functional Biology and Climate Change Cluster, University of Technology Sydney Sydney, NSW, Australia ; Singapore Centre on Environmental Life Sciences Engineering, Nanyang Technological University Singapore, Singapore
| | - Oana Ciofu
- Department of International Health, Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen Copenhagen, Denmark
| | - Thomas Bjarnsholt
- Department of Clinical Microbiology Rigshospitalet, Copenhagen, Denmark ; Department of International Health, Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen Copenhagen, Denmark
| | - Claus Moser
- Department of Clinical Microbiology Rigshospitalet, Copenhagen, Denmark
| | - Masanori Toyofuku
- Graduate School of Life and Environmental Sciences, University of Tsukuba Tsukuba, Japan
| | - Nobuhiko Nomura
- Graduate School of Life and Environmental Sciences, University of Tsukuba Tsukuba, Japan
| | - Niels Høiby
- Department of Clinical Microbiology Rigshospitalet, Copenhagen, Denmark ; Department of International Health, Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen Copenhagen, Denmark
| | - Peter Ø Jensen
- Department of Clinical Microbiology Rigshospitalet, Copenhagen, Denmark
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Wu X, Chen L, Wang X. Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases. Clin Transl Med 2014; 3:16. [PMID: 24995123 PMCID: PMC4072888 DOI: 10.1186/2001-1326-3-16] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2014] [Accepted: 06/12/2014] [Indexed: 11/17/2022] Open
Abstract
Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease-specific biomarkers, which are functionally changed during disease development, progression or treatment. The present review headlined the definition, significance, research and potential application for network biomarkers, interaction networks and dynamical network biomarkers (DNB). Disease-specific interaction networks, network biomarkers, or DNB have great significance in the understanding of molecular pathogenesis, risk assessment, disease classification and monitoring, or evaluations of therapeutic responses and toxicities. Protein-based DNB will provide more information to define the differences between the normal and pre-disease stages, which might point to early diagnosis for patients. Clinical bioinformatics should be a key approach to the identification and validation of disease-specific biomarkers.
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Affiliation(s)
- Xiaodan Wu
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China ; Shanghai Respiratory Research Institute, Shanghai, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, SIBS-Novo Nordisk PreDiabetes Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xiangdong Wang
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China ; Shanghai Respiratory Research Institute, Shanghai, China ; Biomedical Research Center, Zhongshan Hospital, Fudan University, Shanghai 200032, China
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Abstract
Motivation: Genome-scale metabolic reconstructions summarize current knowledge about a target organism in a structured manner and as such highlight missing information. Such gaps can be filled algorithmically. Scalability limitations of available algorithms for gap filling hinder their application to compartmentalized reconstructions. Results: We present fastGapFill, a computationally efficient tractable extension to the COBRA toolbox that permits the identification of candidate missing knowledge from a universal biochemical reaction database (e.g. Kyoto Encyclopedia of Genes and Genomes) for a given (compartmentalized) metabolic reconstruction. The stoichiometric consistency of the universal reaction database and of the metabolic reconstruction can be tested for permitting the computation of biologically more relevant solutions. We demonstrate the efficiency and scalability of fastGapFill on a range of metabolic reconstructions. Availability and implementation: fastGapFill is freely available from http://thielelab.eu. Contact:ines.thiele@uni.lu Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg, L-4362
| | - Nikos Vlassis
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg, L-4362
| | - Ronan M T Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg, L-4362
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Qin X. Chronic pulmonary pseudomonal infection in patients with cystic fibrosis: A model for early phase symbiotic evolution. Crit Rev Microbiol 2014; 42:144-57. [PMID: 24766052 DOI: 10.3109/1040841x.2014.907235] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Gain of "antimicrobial resistance" and "adaptive virulence" has been the dominant view of Pseudomonas aeruginosa (Pa) in cystic fibrosis (CF) in the progressively damaged host airway over the course of this chronic infection. However, the pathogenic effects of CF airway-adapted Pa strains are notably reduced. We propose that CF Pa and other bacterial cohabitants undergo host adaptation which resembles the changes found in bacterial symbionts in animal hosts. Development of clonally selected and intraspecific isogenic Pa strains which display divergent colony morphology, growth rate, auxotrophy, and antibiotic susceptibility in vitro suggests an adaptive sequence of infective exploitation-parasitism-symbiotic evolution driven by host defenses. Most importantly, the emergence of CF pseudomonal auxotrophy is frequently associated with a few specific amino acids. The selective retention or loss of specific amino acid biosynthesis in CF-adapted Pa reflects bacterium-host symbiosis and coevolution during chronic infection, not nutrient availability. This principle also argues against the long-standing concept of dietary availability leading to evolution of essential amino acid requirements in humans. A novel model of pseudomonal adaptation through multicellular bacterial syntrophy is proposed to explain early events in bacterial gene decay and decreased (not increased) virulence due to symbiotic response to host defense.
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Affiliation(s)
- Xuan Qin
- a Microbiology Laboratory, Seattle Children's Hospital , and.,b Department of Laboratory Medicine , University of Washington , School of Medicine Seattle , Washington , USA
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39
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Masoudi-Nejad A, Asgari Y. Metabolic cancer biology: structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment. Semin Cancer Biol 2014; 30:21-9. [PMID: 24495661 DOI: 10.1016/j.semcancer.2014.01.007] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2013] [Revised: 01/15/2014] [Accepted: 01/18/2014] [Indexed: 12/21/2022]
Abstract
The cancer cell metabolism or the Warburg effect discovery goes back to 1924 when, for the first time Otto Warburg observed, in contrast to the normal cells, cancer cells have different metabolism. With the initiation of high throughput technologies and computational systems biology, cancer cell metabolism renaissances and many attempts were performed to revise the Warburg effect. The development of experimental and analytical tools which generate high-throughput biological data including lots of information could lead to application of computational models in biological discovery and clinical medicine especially for cancer. Due to the recent availability of tissue-specific reconstructed models, new opportunities in studying metabolic alteration in various kinds of cancers open up. Structural approaches at genome-scale levels seem to be suitable for developing diagnostic and prognostic molecular signatures, as well as in identifying new drug targets. In this review, we have considered these recent advances in structural-based analysis of cancer as a metabolic disease view. Two different structural approaches have been described here: topological and constraint-based methods. The ultimate goal of this type of systems analysis is not only the discovery of novel drug targets but also the development of new systems-based therapy strategies.
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Affiliation(s)
- Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Yazdan Asgari
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Madeira A, dos Santos SC, Santos PM, Coutinho CP, Tyrrell J, McClean S, Callaghan M, Sá-Correia I. Proteomic profiling of Burkholderia cenocepacia clonal isolates with different virulence potential retrieved from a cystic fibrosis patient during chronic lung infection. PLoS One 2013; 8:e83065. [PMID: 24349432 PMCID: PMC3862766 DOI: 10.1371/journal.pone.0083065] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2013] [Accepted: 11/07/2013] [Indexed: 11/18/2022] Open
Abstract
Respiratory infections with Burkholderia cepacia complex (Bcc) bacteria in cystic fibrosis (CF) are associated with a worse prognosis and increased risk of death. In this work, we assessed the virulence potential of three B. cenocepacia clonal isolates obtained from a CF patient between the onset of infection (isolate IST439) and before death with cepacia syndrome 3.5 years later (isolate IST4113 followed by IST4134), based on their ability to invade epithelial cells and compromise epithelial monolayer integrity. The two clonal isolates retrieved during late-stage disease were significantly more virulent than IST439. Proteomic profiling by 2-D DIGE of the last isolate recovered before the patient’s death, IST4134, and clonal isolate IST439, was performed and compared with a prior analysis of IST4113 vs. IST439. The cytoplasmic and membrane-associated enriched fractions were examined and 52 proteins were found to be similarly altered in the two last isolates compared with IST439. These proteins are involved in metabolic functions, nucleotide synthesis, translation and protein folding, cell envelope biogenesis and iron homeostasis. Results are suggestive of the important role played by metabolic reprogramming in the virulence potential and persistence of B. cenocepacia, in particular regarding bacterial adaptation to microaerophilic conditions. Also, the content of the virulence determinant AidA was higher in the last 2 isolates. Significant levels of siderophores were found to be secreted by the three clonal isolates in an iron-depleted environment, but the two late isolates were more tolerant to low iron concentrations than IST439, consistent with the relative abundance of proteins involved in iron uptake.
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Affiliation(s)
- Andreia Madeira
- Institute for Biotechnology and Bioengineering, Centre for Biological and Chemical Engineering, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Sandra C. dos Santos
- Institute for Biotechnology and Bioengineering, Centre for Biological and Chemical Engineering, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Pedro M. Santos
- Institute for Biotechnology and Bioengineering, Centre for Biological and Chemical Engineering, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Carla P. Coutinho
- Institute for Biotechnology and Bioengineering, Centre for Biological and Chemical Engineering, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Jean Tyrrell
- Centre of Microbial Host Interactions, Department of Science, ITT-Dublin, Dublin, Ireland
| | - Siobhán McClean
- Centre of Microbial Host Interactions, Department of Science, ITT-Dublin, Dublin, Ireland
| | - Máire Callaghan
- Centre of Microbial Host Interactions, Department of Science, ITT-Dublin, Dublin, Ireland
| | - Isabel Sá-Correia
- Institute for Biotechnology and Bioengineering, Centre for Biological and Chemical Engineering, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- * E-mail:
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Abstract
Burkholderia cenocepacia and Burkholderia multivorans are opportunistic drug-resistant pathogens that account for the majority of Burkholderia cepacia complex infections in cystic fibrosis patients and also infect other immunocompromised individuals. While they share similar genetic compositions, B. cenocepacia and B. multivorans exhibit important differences in pathogenesis. We have developed reconciled genome-scale metabolic network reconstructions of B. cenocepacia J2315 and B. multivorans ATCC 17616 in parallel (designated iPY1537 and iJB1411, respectively) to compare metabolic abilities and contextualize genetic differences between species. The reconstructions capture the metabolic functions of the two species and give insight into similarities and differences in their virulence and growth capabilities. The two reconstructions have 1,437 reactions in common, and iPY1537 and iJB1411 have 67 and 36 metabolic reactions unique to each, respectively. After curating the extensive reservoir of metabolic genes in Burkholderia, we identified 6 genes essential to growth that are unique to iPY1513 and 13 genes uniquely essential to iJB1411. The reconstructions were refined and validated by comparing in silico growth predictions to in vitro growth capabilities of B. cenocepacia J2315, B. cenocepacia K56-2, and B. multivorans ATCC 17616 on 104 carbon sources. Overall, we identified functional pathways that indicate B. cenocepacia can produce a wider array of virulence factors compared to B. multivorans, which supports the clinical observation that B. cenocepacia is more virulent than B. multivorans. The reconciled reconstructions provide a framework for generating and testing hypotheses on the metabolic and virulence capabilities of these two related emerging pathogens.
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42
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Novel multiscale modeling tool applied to Pseudomonas aeruginosa biofilm formation. PLoS One 2013; 8:e78011. [PMID: 24147108 PMCID: PMC3798466 DOI: 10.1371/journal.pone.0078011] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Accepted: 09/12/2013] [Indexed: 01/05/2023] Open
Abstract
Multiscale modeling is used to represent biological systems with increasing frequency and success. Multiscale models are often hybrids of different modeling frameworks and programming languages. We present the MATLAB-NetLogo extension (MatNet) as a novel tool for multiscale modeling. We demonstrate the utility of the tool with a multiscale model of Pseudomonas aeruginosa biofilm formation that incorporates both an agent-based model (ABM) and constraint-based metabolic modeling. The hybrid model correctly recapitulates oxygen-limited biofilm metabolic activity and predicts increased growth rate via anaerobic respiration with the addition of nitrate to the growth media. In addition, a genome-wide survey of metabolic mutants and biofilm formation exemplifies the powerful analyses that are enabled by this computational modeling tool.
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43
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Oberhardt MA, Yizhak K, Ruppin E. Metabolically re-modeling the drug pipeline. Curr Opin Pharmacol 2013; 13:778-85. [DOI: 10.1016/j.coph.2013.05.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2013] [Revised: 05/04/2013] [Accepted: 05/06/2013] [Indexed: 02/07/2023]
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Schmidt BJ, Ebrahim A, Metz TO, Adkins JN, Palsson BØ, Hyduke DR. GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data. ACTA ACUST UNITED AC 2013; 29:2900-8. [PMID: 23975765 DOI: 10.1093/bioinformatics/btt493] [Citation(s) in RCA: 85] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
MOTIVATION Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed. RESULTS GIM(3)E (Gene Inactivation Moderated by Metabolism, Metabolomics and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics and cellular metabolomics data. GIM(3)E establishes metabolite use requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions and provides calculations of the turnover (production/consumption) flux of metabolites. GIM(3)E was used to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. AVAILABILITY GIM(3)E has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/ CONTACTS brianjamesschmidt@gmail.com
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Affiliation(s)
- Brian J Schmidt
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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45
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Vital-Lopez FG, Wallqvist A, Reifman J. Bridging the gap between gene expression and metabolic phenotype via kinetic models. BMC SYSTEMS BIOLOGY 2013; 7:63. [PMID: 23875723 PMCID: PMC3733621 DOI: 10.1186/1752-0509-7-63] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2012] [Accepted: 06/27/2013] [Indexed: 01/22/2023]
Abstract
Background Despite the close association between gene expression and metabolism, experimental evidence shows that gene expression levels alone cannot predict metabolic phenotypes, indicating a knowledge gap in our understanding of how these processes are connected. Here, we present a method that integrates transcriptome, fluxome, and metabolome data using kinetic models to create a mechanistic link between gene expression and metabolism. Results We developed a modeling framework to construct kinetic models that connect the transcriptional and metabolic responses of a cell to exogenous perturbations. The framework allowed us to avoid extensive experimental characterization, literature mining, and optimization problems by estimating most model parameters directly from fluxome and transcriptome data. We applied the framework to investigate how gene expression changes led to observed phenotypic alterations of Saccharomyces cerevisiae treated with weak organic acids (i.e., acetate, benzoate, propionate, or sorbate) and the histidine synthesis inhibitor 3-aminotriazole under steady-state conditions. We found that the transcriptional response led to alterations in yeast metabolism that mimicked measured metabolic fluxes and concentration changes. Further analyses generated mechanistic insights of how S. cerevisiae responds to these stresses. In particular, these results suggest that S. cerevisiae uses different regulation strategies for responding to these insults: regulation of two reactions accounted for most of the tolerance to the four weak organic acids, whereas the response to 3-aminotriazole was distributed among multiple reactions. Moreover, we observed that the magnitude of the gene expression changes was not directly correlated with their effect on the ability of S. cerevisiae to grow under these treatments. In addition, we identified another potential mechanism of action of 3-aminotriazole associated with the depletion of tetrahydrofolate. Conclusions Our simulation results show that the modeling framework provided an accurate mechanistic link between gene expression and cellular metabolism. The proposed method allowed us to integrate transcriptome, fluxome, and metabolome data to determine and interpret important features of the physiological response of yeast to stresses. Importantly, given its flexibility and robustness, our approach can be applied to investigate the transcriptional-metabolic response in other cellular systems of medical and industrial relevance.
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Affiliation(s)
- Francisco G Vital-Lopez
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advance Technology Research Center, U.S. Army Medical Research and Materiel Command, Ft. Detrick, MD 21702, USA
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46
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Manos J, Hu H, Rose BR, Wainwright CE, Zablotska IB, Cheney J, Turnbull L, Whitchurch CB, Grimwood K, Harmer C, Anuj SN, Harbour C. Virulence factor expression patterns in Pseudomonas aeruginosa strains from infants with cystic fibrosis. Eur J Clin Microbiol Infect Dis 2013; 32:1583-92. [DOI: 10.1007/s10096-013-1916-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2013] [Accepted: 06/17/2013] [Indexed: 12/16/2022]
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47
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 512] [Impact Index Per Article: 42.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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Kim YM, Schmidt BJ, Kidwai AS, Jones MB, Deatherage Kaiser BL, Brewer HM, Mitchell HD, Palsson BO, McDermott JE, Heffron F, Smith RD, Peterson SN, Ansong C, Hyduke DR, Metz TO, Adkins JN. Salmonella modulates metabolism during growth under conditions that induce expression of virulence genes. MOLECULAR BIOSYSTEMS 2013; 9:1522-34. [PMID: 23559334 PMCID: PMC3665296 DOI: 10.1039/c3mb25598k] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes to virulence in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome-scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome-scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Furthermore, analyses of omics data in the context of the metabolic model indicated rewiring of the metabolic network to support pathways associated with virulence. For example, cellular concentrations of polyamines were perturbed, as well as the predicted capacity for secretion and uptake.
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Affiliation(s)
- Young-Mo Kim
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Brian J. Schmidt
- Department of Bioengineering, University of California at San Diego, San Diego, CA 92093
| | - Afshan S. Kidwai
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR 97239
| | | | - Brooke L. Deatherage Kaiser
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Heather M. Brewer
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Hugh D. Mitchell
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California at San Diego, San Diego, CA 92093
| | - Jason E. McDermott
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Fred Heffron
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR 97239
| | - Richard D. Smith
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352
| | | | - Charles Ansong
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Daniel R. Hyduke
- Department of Bioengineering, University of California at San Diego, San Diego, CA 92093
| | - Thomas O. Metz
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Joshua N. Adkins
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352
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49
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Xu Z, Fang X, Wood TK, Huang ZJ. A systems-level approach for investigating Pseudomonas aeruginosa biofilm formation. PLoS One 2013; 8:e57050. [PMID: 23451140 PMCID: PMC3579789 DOI: 10.1371/journal.pone.0057050] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2012] [Accepted: 01/16/2013] [Indexed: 12/14/2022] Open
Abstract
Prevention of the initiation of biofilm formation is the most important step for combating biofilm-associated pathogens, as the ability of pathogens to resist antibiotics is enhanced 10 to 1000 times once biofilms are formed. Genes essential to bacterial growth in the planktonic state are potential targets to treat biofilm-associated pathogens. However, the biofilm formation capability of strains with mutations in these essential genes must be evaluated, since the pathogen might form a biofilm before it is eliminated. In order to address this issue, this work proposes a systems-level approach to quantifying the biofilm formation capability of mutants to determine target genes that are essential for bacterial metabolism in the planktonic state but do not induce biofilm formation in their mutants. The changes of fluxes through the reactions associated with the genes positively related to biofilm formation are used as soft sensors in the flux balance analysis to quantify the trend of biofilm formation upon the mutation of an essential gene. The essential genes whose mutants are predicted not to induce biofilm formation are regarded as gene targets. The proposed approach was applied to identify target genes to treat Pseudomonas aeruginosa infections. It is interesting to find that most essential gene mutants exhibit high potential to induce the biofilm formation while most non-essential gene mutants do not. Critically, we identified four essential genes, lysC, cysH, adk, and galU, that constitute gene targets to treat P. aeruginosa. They have been suggested by existing experimental data as potential drug targets for their crucial role in the survival or virulence of P. aeruginosa. It is also interesting to find that P. aeruginosa tends to survive the essential-gene mutation treatment by mainly enhancing fluxes through 8 metabolic reactions that regulate acetate metabolism, arginine metabolism, and glutamate metabolism.
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Affiliation(s)
- Zhaobin Xu
- Department of Chemical Engineering, Villanova University, Villanova, Pennsylvania, United States of America
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50
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Filiatrault MJ, Tombline G, Wagner VE, Van Alst N, Rumbaugh K, Sokol P, Schwingel J, Iglewski BH. Pseudomonas aeruginosa PA1006, which plays a role in molybdenum homeostasis, is required for nitrate utilization, biofilm formation, and virulence. PLoS One 2013; 8:e55594. [PMID: 23409004 PMCID: PMC3568122 DOI: 10.1371/journal.pone.0055594] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2012] [Accepted: 12/27/2012] [Indexed: 12/31/2022] Open
Abstract
Pseudomonas aeruginosa (Pae) is a clinically important opportunistic pathogen. Herein, we demonstrate that the PA1006 protein is critical for all nitrate reductase activities, growth as a biofilm in a continuous flow system, as well as virulence in mouse burn and rat lung model systems. Microarray analysis revealed that ΔPA1006 cells displayed extensive alterations in gene expression including nitrate-responsive, quorum sensing (including PQS production), and iron-regulated genes, as well as molybdenum cofactor and Fe-S cluster biosynthesis factors, members of the TCA cycle, and Type VI Secretion System components. Phenotype Microarray™ profiles of ΔPA1006 aerobic cultures using Biolog plates also revealed a reduced ability to utilize a number of TCA cycle intermediates as well as a failure to utilize xanthine as a sole source of nitrogen. As a whole, these data indicate that the loss of PA1006 confers extensive changes in Pae metabolism. Based upon homology of PA1006 to the E. coli YhhP protein and data from the accompanying study, loss of PA1006 persulfuration and/or molybdenum homeostasis are likely the cause of extensive metabolic alterations that impact biofilm development and virulence in the ΔPA1006 mutant.
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Affiliation(s)
- Melanie J. Filiatrault
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
| | - Gregory Tombline
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
| | - Victoria E. Wagner
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
| | - Nadine Van Alst
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
| | - Kendra Rumbaugh
- Department of Surgery, Texas Tech University Health Sciences Center, Lubbock, Texas, United States of America
| | - Pam Sokol
- Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, Alberta, Canada
| | - Johanna Schwingel
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
| | - Barbara H. Iglewski
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
- * E-mail:
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