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Podkowik M, Perault AI, Putzel G, Pountain A, Kim J, DuMont AL, Zwack EE, Ulrich RJ, Karagounis TK, Zhou C, Haag AF, Shenderovich J, Wasserman GA, Kwon J, Chen J, Richardson AR, Weiser JN, Nowosad CR, Lun DS, Parker D, Pironti A, Zhao X, Drlica K, Yanai I, Torres VJ, Shopsin B. Quorum-sensing agr system of Staphylococcus aureus primes gene expression for protection from lethal oxidative stress. eLife 2024; 12:RP89098. [PMID: 38687677 PMCID: PMC11060713 DOI: 10.7554/elife.89098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024] Open
Abstract
The agr quorum-sensing system links Staphylococcus aureus metabolism to virulence, in part by increasing bacterial survival during exposure to lethal concentrations of H2O2, a crucial host defense against S. aureus. We now report that protection by agr surprisingly extends beyond post-exponential growth to the exit from stationary phase when the agr system is no longer turned on. Thus, agr can be considered a constitutive protective factor. Deletion of agr resulted in decreased ATP levels and growth, despite increased rates of respiration or fermentation at appropriate oxygen tensions, suggesting that Δagr cells undergo a shift towards a hyperactive metabolic state in response to diminished metabolic efficiency. As expected from increased respiratory gene expression, reactive oxygen species (ROS) accumulated more in the agr mutant than in wild-type cells, thereby explaining elevated susceptibility of Δagr strains to lethal H2O2 doses. Increased survival of wild-type agr cells during H2O2 exposure required sodA, which detoxifies superoxide. Additionally, pretreatment of S. aureus with respiration-reducing menadione protected Δagr cells from killing by H2O2. Thus, genetic deletion and pharmacologic experiments indicate that agr helps control endogenous ROS, thereby providing resilience against exogenous ROS. The long-lived 'memory' of agr-mediated protection, which is uncoupled from agr activation kinetics, increased hematogenous dissemination to certain tissues during sepsis in ROS-producing, wild-type mice but not ROS-deficient (Cybb-/-) mice. These results demonstrate the importance of protection that anticipates impending ROS-mediated immune attack. The ubiquity of quorum sensing suggests that it protects many bacterial species from oxidative damage.
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Affiliation(s)
- Magdalena Podkowik
- Department of Medicine, Division of Infectious Diseases, NYU Grossman School of MedicineNew YorkUnited States
- Antimicrobial-Resistant Pathogens Program, New York University School of MedicineNew YorkUnited States
| | - Andrew I Perault
- Antimicrobial-Resistant Pathogens Program, New York University School of MedicineNew YorkUnited States
- Department of Microbiology, NYU Grossman School of MedicineNew YorkUnited States
| | - Gregory Putzel
- Antimicrobial-Resistant Pathogens Program, New York University School of MedicineNew YorkUnited States
- Department of Microbiology, NYU Grossman School of MedicineNew YorkUnited States
- Microbial Computational Genomic Core Lab, NYU Grossman School of MedicineNew YorkUnited States
| | - Andrew Pountain
- Institute for Systems Genetics; NYU Grossman School of MedicineNew YorkUnited States
| | - Jisun Kim
- Department of Pathology, Immunology and Laboratory Medicine, Center for Immunity and Inflammation, Rutgers New Jersey Medical SchoolNewarkUnited States
| | - Ashley L DuMont
- Department of Medicine, Division of Infectious Diseases, NYU Grossman School of MedicineNew YorkUnited States
| | - Erin E Zwack
- Department of Microbiology, NYU Grossman School of MedicineNew YorkUnited States
| | - Robert J Ulrich
- Department of Medicine, Division of Infectious Diseases, NYU Grossman School of MedicineNew YorkUnited States
| | - Theodora K Karagounis
- Antimicrobial-Resistant Pathogens Program, New York University School of MedicineNew YorkUnited States
- Ronald O. Perelman Department of Dermatology; NYU Grossman School of MedicineNew YorkUnited States
| | - Chunyi Zhou
- Department of Medicine, Division of Infectious Diseases, NYU Grossman School of MedicineNew YorkUnited States
- Antimicrobial-Resistant Pathogens Program, New York University School of MedicineNew YorkUnited States
| | - Andreas F Haag
- School of Medicine, University of St AndrewsSt AndrewsUnited Kingdom
| | - Julia Shenderovich
- Antimicrobial-Resistant Pathogens Program, New York University School of MedicineNew YorkUnited States
- Department of Microbiology, NYU Grossman School of MedicineNew YorkUnited States
| | - Gregory A Wasserman
- Department of Surgery, Northwell Health Lenox Hill HospitalNew YorkUnited States
| | - Junbeom Kwon
- Department of Medicine, Division of Infectious Diseases, NYU Grossman School of MedicineNew YorkUnited States
| | - John Chen
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of SingaporeSingaporeSingapore
| | - Anthony R Richardson
- Department of Microbiology and Molecular Genetics, University of PittsburghPittsburghUnited States
| | - Jeffrey N Weiser
- Department of Microbiology, NYU Grossman School of MedicineNew YorkUnited States
| | - Carla R Nowosad
- Department of Pathology, NYU Grossman School of MedicineNew YorkUnited States
| | - Desmond S Lun
- Center for Computational and Integrative Biology and Department of Computer Science, Rutgers UniversityCamdenUnited States
| | - Dane Parker
- Department of Pathology, Immunology and Laboratory Medicine, Center for Immunity and Inflammation, Rutgers New Jersey Medical SchoolNewarkUnited States
| | - Alejandro Pironti
- Antimicrobial-Resistant Pathogens Program, New York University School of MedicineNew YorkUnited States
- Department of Microbiology, NYU Grossman School of MedicineNew YorkUnited States
- Microbial Computational Genomic Core Lab, NYU Grossman School of MedicineNew YorkUnited States
| | - Xilin Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen UniversityXiamenChina
| | - Karl Drlica
- Public Health Research Institute, New Jersey Medical School, Rutgers UniversityNew YprkUnited States
- Department of Microbiology, Biochemistry & Molecular Genetics, New Jersey Medical School, Rutgers UniversityNewarkUnited States
| | - Itai Yanai
- Institute for Systems Genetics; NYU Grossman School of MedicineNew YorkUnited States
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of MedicineNew YorkUnited States
| | - Victor J Torres
- Antimicrobial-Resistant Pathogens Program, New York University School of MedicineNew YorkUnited States
- Department of Microbiology, NYU Grossman School of MedicineNew YorkUnited States
| | - Bo Shopsin
- Department of Medicine, Division of Infectious Diseases, NYU Grossman School of MedicineNew YorkUnited States
- Antimicrobial-Resistant Pathogens Program, New York University School of MedicineNew YorkUnited States
- Department of Microbiology, NYU Grossman School of MedicineNew YorkUnited States
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Podkowik M, Perault AI, Putzel G, Pountain A, Kim J, Dumont A, Zwack E, Ulrich RJ, Karagounis TK, Zhou C, Haag AF, Shenderovich J, Wasserman GA, Kwon J, Chen J, Richardson AR, Weiser JN, Nowosad CR, Lun DS, Parker D, Pironti A, Zhao X, Drlica K, Yanai I, Torres VJ, Shopsin B. Quorum-sensing agr system of Staphylococcus aureus primes gene expression for protection from lethal oxidative stress. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.08.544038. [PMID: 37333372 PMCID: PMC10274873 DOI: 10.1101/2023.06.08.544038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The agr quorum-sensing system links Staphylococcus aureus metabolism to virulence, in part by increasing bacterial survival during exposure to lethal concentrations of H2O2, a crucial host defense against S. aureus. We now report that protection by agr surprisingly extends beyond post-exponential growth to the exit from stationary phase when the agr system is no longer turned on. Thus, agr can be considered a constitutive protective factor. Deletion of agr increased both respiration and fermentation but decreased ATP levels and growth, suggesting that Δagr cells assume a hyperactive metabolic state in response to reduced metabolic efficiency. As expected from increased respiratory gene expression, reactive oxygen species (ROS) accumulated more in the agr mutant than in wild-type cells, thereby explaining elevated susceptibility of Δagr strains to lethal H2O2 doses. Increased survival of wild-type agr cells during H2O2 exposure required sodA, which detoxifies superoxide. Additionally, pretreatment of S. aureus with respiration-reducing menadione protected Δagr cells from killing by H2O2. Thus, genetic deletion and pharmacologic experiments indicate that agr helps control endogenous ROS, thereby providing resilience against exogenous ROS. The long-lived "memory" of agr-mediated protection, which is uncoupled from agr activation kinetics, increased hematogenous dissemination to certain tissues during sepsis in ROS-producing, wild-type mice but not ROS-deficient (Nox2-/-) mice. These results demonstrate the importance of protection that anticipates impending ROS-mediated immune attack. The ubiquity of quorum sensing suggests that it protects many bacterial species from oxidative damage.
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Affiliation(s)
- Magdalena Podkowik
- Department of Medicine, Division of Infectious Diseases, NYU Grossman School of Medicine, New York, NY, USA
- Antimicrobial-Resistant Pathogens Program, New York University School of Medicine, New York, NY, USA
| | - Andrew I. Perault
- Antimicrobial-Resistant Pathogens Program, New York University School of Medicine, New York, NY, USA
- Department of Microbiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Gregory Putzel
- Antimicrobial-Resistant Pathogens Program, New York University School of Medicine, New York, NY, USA
- Department of Microbiology, NYU Grossman School of Medicine, New York, NY, USA
- Microbial Computational Genomic Core Lab, NYU Grossman School of Medicine, New York, NY, USA
| | - Andrew Pountain
- Institute for Systems Genetics; NYU Grossman School of Medicine, New York, NY, USA
| | - Jisun Kim
- Department of Pathology, Immunology and Laboratory Medicine, Center for Immunity and Inflammation, Rutgers New Jersey Medical School Cancer Center, Newark, NJ, USA
| | - Ashley Dumont
- Department of Microbiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Erin Zwack
- Department of Microbiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Robert J. Ulrich
- Department of Medicine, Division of Infectious Diseases, NYU Grossman School of Medicine, New York, NY, USA
| | - Theodora K. Karagounis
- Antimicrobial-Resistant Pathogens Program, New York University School of Medicine, New York, NY, USA
- Ronald O. Perelman Department of Dermatology; NYU Grossman School of Medicine, New York, NY, USA
| | - Chunyi Zhou
- Department of Medicine, Division of Infectious Diseases, NYU Grossman School of Medicine, New York, NY, USA
- Antimicrobial-Resistant Pathogens Program, New York University School of Medicine, New York, NY, USA
| | - Andreas F. Haag
- School of Medicine, University of St Andrews, St Andrews, UK
| | - Julia Shenderovich
- Antimicrobial-Resistant Pathogens Program, New York University School of Medicine, New York, NY, USA
- Department of Microbiology, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Junbeom Kwon
- Department of Medicine, Division of Infectious Diseases, NYU Grossman School of Medicine, New York, NY, USA
| | - John Chen
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Anthony R. Richardson
- Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jeffrey N. Weiser
- Department of Microbiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Carla R. Nowosad
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Desmond S. Lun
- Center for Computational and Integrative Biology and Department of Computer Science, Rutgers University, Camden, NJ, USA
| | - Dane Parker
- Department of Pathology, Immunology and Laboratory Medicine, Center for Immunity and Inflammation, Rutgers New Jersey Medical School Cancer Center, Newark, NJ, USA
| | - Alejandro Pironti
- Antimicrobial-Resistant Pathogens Program, New York University School of Medicine, New York, NY, USA
- Department of Microbiology, NYU Grossman School of Medicine, New York, NY, USA
- Microbial Computational Genomic Core Lab, NYU Grossman School of Medicine, New York, NY, USA
| | - Xilin Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian Province, China
| | - Karl Drlica
- Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA
- Department of Microbiology, Biochemistry & Molecular Genetics, New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | - Itai Yanai
- Institute for Systems Genetics; NYU Grossman School of Medicine, New York, NY, USA
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, USA
| | - Victor J. Torres
- Antimicrobial-Resistant Pathogens Program, New York University School of Medicine, New York, NY, USA
- Department of Microbiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Bo Shopsin
- Department of Medicine, Division of Infectious Diseases, NYU Grossman School of Medicine, New York, NY, USA
- Antimicrobial-Resistant Pathogens Program, New York University School of Medicine, New York, NY, USA
- Department of Microbiology, NYU Grossman School of Medicine, New York, NY, USA
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González-Arrué N, Inostroza I, Conejeros R, Rivas-Astroza M. Phenotype-specific estimation of metabolic fluxes using gene expression data. iScience 2023; 26:106201. [PMID: 36915687 PMCID: PMC10006673 DOI: 10.1016/j.isci.2023.106201] [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: 09/22/2022] [Revised: 11/30/2022] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
A cell's genome influences its metabolism via the expression of enzyme-related genes, but transcriptome and fluxome are not perfectly correlated as post-transcriptional mechanisms also regulate reaction's kinetics. Here, we addressed the question: given a transcriptome, how unobserved mechanisms of reaction kinetics should be systematically accounted for when inferring the fluxome? To infer the most likely and least biased fluxome, we present Pheflux, a constraint-based model maximizing Shannon's entropy of fluxes per mRNA. Benchmarked against 13C fluxes of yeast and bacteria, Pheflux accurately estimates the carbon core metabolism. We applied Pheflux to thousands of normal and tumor cell transcriptomes obtained from The Cancer Genome Atlas. Pheflux showed statistically significantly higher glucose yields on lactate in breast, kidney, and bronchus-lung tumoral cells than their normal counterparts. Results are consistent with the Warburg effect, a hallmark of cancer metabolism, suggesting that Pheflux can be efficiently used to study the metabolism of eukaryotic cells.
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Affiliation(s)
- Nicolás González-Arrué
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Isidora Inostroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Raúl Conejeros
- Pontificia Universidad Católica de Valparaíso, Escuela de Ingeniería Bioquímica, Valparaíso, 2362803, Chile
| | - Marcelo Rivas-Astroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
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Lee SM, Lee G, Kim HU. Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models. Comput Struct Biotechnol J 2022; 20:3041-3052. [PMID: 35782748 PMCID: PMC9218235 DOI: 10.1016/j.csbj.2022.06.027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/11/2022] [Accepted: 06/12/2022] [Indexed: 11/30/2022] Open
Abstract
Genome-scale metabolic model (GEM) has been established as an important tool to study cellular metabolism at a systems level by predicting intracellular fluxes. With the advent of generic human GEMs, they have been increasingly applied to a range of diseases, often for the objective of predicting effective metabolic drug targets. Cancer is a representative disease where the use of GEMs has proved to be effective, partly due to the massive availability of patient-specific RNA-seq data. When using a human GEM, so-called context-specific GEM needs to be developed first by using cell-specific RNA-seq data. Biological validity of a context-specific GEM highly depends on both model extraction method (MEM) and model simulation method (MSM). However, while MEMs have been thoroughly examined, MSMs have not been systematically examined, especially, when studying cancer metabolism. In this study, the effects of pairwise combinations of three MEMs and five MSMs were evaluated by examining biological features of the resulting cancer patient-specific GEMs. For this, a total of 1,562 patient-specific GEMs were reconstructed, and subjected to machine learning-guided and biological evaluations to draw robust conclusions. Noteworthy observations were made from the evaluation, including the high performance of two MEMs, namely rank-based ‘task-driven Integrative Network Inference for Tissues’ (tINIT) or ‘Gene Inactivity Moderated by Metabolism and Expression’ (GIMME), paired with least absolute deviation (LAD) as a MSM, and relatively poorer performance of flux balance analysis (FBA) and parsimonious FBA (pFBA). Insights from this study can be considered as a reference when studying cancer metabolism using patient-specific GEMs.
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