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Pereira JM, Xu S, Leong JM, Sousa S. The Yin and Yang of Pneumolysin During Pneumococcal Infection. Front Immunol 2022; 13:878244. [PMID: 35529870 PMCID: PMC9074694 DOI: 10.3389/fimmu.2022.878244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/23/2022] [Indexed: 12/15/2022] Open
Abstract
Pneumolysin (PLY) is a pore-forming toxin produced by the human pathobiont Streptococcus pneumoniae, the major cause of pneumonia worldwide. PLY, a key pneumococcal virulence factor, can form transmembrane pores in host cells, disrupting plasma membrane integrity and deregulating cellular homeostasis. At lytic concentrations, PLY causes cell death. At sub-lytic concentrations, PLY triggers host cell survival pathways that cooperate to reseal the damaged plasma membrane and restore cell homeostasis. While PLY is generally considered a pivotal factor promoting S. pneumoniae colonization and survival, it is also a powerful trigger of the innate and adaptive host immune response against bacterial infection. The dichotomy of PLY as both a key bacterial virulence factor and a trigger for host immune modulation allows the toxin to display both "Yin" and "Yang" properties during infection, promoting disease by membrane perforation and activating inflammatory pathways, while also mitigating damage by triggering host cell repair and initiating anti-inflammatory responses. Due to its cytolytic activity and diverse immunomodulatory properties, PLY is integral to every stage of S. pneumoniae pathogenesis and may tip the balance towards either the pathogen or the host depending on the context of infection.
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Affiliation(s)
- Joana M. Pereira
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
- Instituto de Biologia Molecular e Celular, Universidade do Porto, Porto, Portugal
- Molecular and Cellular (MC) Biology PhD Program, ICBAS - Instituto de Ciência Biomédicas Abel Salazar, University of Porto, Porto, Portugal
| | - Shuying Xu
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, United States
- Graduate Program in Immunology, Tufts Graduate School of Biomedical Sciences, Boston, MA, United States
| | - John M. Leong
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, United States
| | - Sandra Sousa
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
- Instituto de Biologia Molecular e Celular, Universidade do Porto, Porto, Portugal
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Clarithromycin Inhibits Pneumolysin Production via Downregulation of ply Gene Transcription despite Autolysis Activation. Microbiol Spectr 2021; 9:e0031821. [PMID: 34468195 PMCID: PMC8557819 DOI: 10.1128/spectrum.00318-21] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Streptococcus pneumoniae, the most common cause of community-acquired pneumonia, causes severe invasive infections, including meningitis and bacteremia. The widespread use of macrolides has been reported to increase the prevalence of macrolide-resistant S. pneumoniae (MRSP), thereby leading to treatment failure in patients with pneumococcal pneumonia. However, previous studies have demonstrated that several macrolides and lincosamides have beneficial effects on MRSP infection since they inhibit the production and release of pneumolysin, a pneumococcal pore-forming toxin released during autolysis. In this regard, we previously demonstrated that the mechanisms underlying the inhibition of pneumolysin release by erythromycin involved both the transcriptional downregulation of the gene encoding pneumolysin and the impairment of autolysis in MRSP. Here, using a cell supernatant of the culture, we have shown that clarithromycin inhibits pneumolysin release in MRSP. However, contrary to previous observations in erythromycin-treated MRSP, clarithromycin upregulated the transcription of the pneumococcal autolysis-related lytA gene and enhanced autolysis, leading to the leakage of pneumococcal DNA. On the other hand, compared to erythromycin, clarithromycin significantly downregulated the gene encoding pneumolysin. In a mouse model of MRSP pneumonia, the administration of both clarithromycin and erythromycin significantly decreased the pneumolysin protein level in bronchoalveolar lavage fluid and improved lung injury and arterial oxygen saturation without affecting bacterial load. Collectively, these in vitro and in vivo data reinforce the benefits of macrolides on the clinical outcomes of patients with pneumococcal pneumonia. IMPORTANCE Pneumolysin is a potent intracellular toxin possessing multiple functions that augment pneumococcal virulence. For over 10 years, sub-MICs of macrolides, including clarithromycin, have been recognized to decrease pneumolysin production and release from pneumococcal cells. However, this study indicates that macrolides significantly slowed pneumococcal growth, which may be related to decreased pneumolysin release recorded by previous studies. In this study, we demonstrated that clarithromycin decreases pneumolysin production through downregulation of ply gene transcription, regardless of its inhibitory activity against bacterial growth. Additionally, administration of clarithromycin resulted in the amelioration of lung injury in a mouse model of pneumonia induced by macrolide-resistant pneumococci. Therefore, therapeutic targeting of pneumolysin offers a good strategy to treat pneumococcal pneumonia.
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Domon H, Terao Y. The Role of Neutrophils and Neutrophil Elastase in Pneumococcal Pneumonia. Front Cell Infect Microbiol 2021; 11:615959. [PMID: 33796475 PMCID: PMC8008068 DOI: 10.3389/fcimb.2021.615959] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 03/01/2021] [Indexed: 12/11/2022] Open
Abstract
Streptococcus pneumoniae, also known as pneumococcus, is a Gram-positive diplococcus and a major human pathogen. This bacterium is a leading cause of bacterial pneumonia, otitis media, meningitis, and septicemia, and is a major cause of morbidity and mortality worldwide. To date, studies on S. pneumoniae have mainly focused on the role of its virulence factors including toxins, cell surface proteins, and capsules. However, accumulating evidence indicates that in addition to these studies, knowledge of host factors and host-pathogen interactions is essential for understanding the pathogenesis of pneumococcal diseases. Recent studies have demonstrated that neutrophil accumulation, which is generally considered to play a critical role in host defense during bacterial infections, can significantly contribute to lung injury and immune subversion, leading to pneumococcal invasion of the bloodstream. Here, we review bacterial and host factors, focusing on the role of neutrophils and their elastase, which contribute to the progression of pneumococcal pneumonia.
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Affiliation(s)
- Hisanori Domon
- Division of Microbiology and Infectious Diseases, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.,Research Center for Advanced Oral Science, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yutaka Terao
- Division of Microbiology and Infectious Diseases, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.,Research Center for Advanced Oral Science, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
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Abstract
Bacterial proteases and peptidases are integral to cell physiology and stability, and their necessity in Streptococcus pneumoniae is no exception. Protein cleavage and processing mechanisms within the bacterial cell serve to ensure that the cell lives and functions in its commensal habitat and can respond to new environments presenting stressful conditions. For S. pneumoniae, the human nasopharynx is its natural habitat. In the context of virulence, movement of S. pneumoniae to the lungs, blood, or other sites can instigate responses by the bacteria that result in their proteases serving dual roles of self-protein processors and virulence factors of host protein targets.
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Affiliation(s)
- Mary E Marquart
- Department of Microbiology and Immunology, University of Mississippi Medical Center, Jackson, Mississippi USA
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Macesic N, Bear Don't Walk OJ, Pe'er I, Tatonetti NP, Peleg AY, Uhlemann AC. Predicting Phenotypic Polymyxin Resistance in Klebsiella pneumoniae through Machine Learning Analysis of Genomic Data. mSystems 2020; 5:e00656-19. [PMID: 32457240 PMCID: PMC7253370 DOI: 10.1128/msystems.00656-19] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 05/01/2020] [Indexed: 02/06/2023] Open
Abstract
Polymyxins are used as treatments of last resort for Gram-negative bacterial infections. Their increased use has led to concerns about emerging polymyxin resistance (PR). Phenotypic polymyxin susceptibility testing is resource intensive and difficult to perform accurately. The complex polygenic nature of PR and our incomplete understanding of its genetic basis make it difficult to predict PR using detection of resistance determinants. We therefore applied machine learning (ML) to whole-genome sequencing data from >600 Klebsiella pneumoniae clonal group 258 (CG258) genomes to predict phenotypic PR. Using a reference-based representation of genomic data with ML outperformed a rule-based approach that detected variants in known PR genes (area under receiver-operator curve [AUROC], 0.894 versus 0.791, P = 0.006). We noted modest increases in performance by using a bacterial genome-wide association study to filter relevant genomic features and by integrating clinical data in the form of prior polymyxin exposure. Conversely, reference-free representation of genomic data as k-mers was associated with decreased performance (AUROC, 0.692 versus 0.894, P = 0.015). When ML models were interpreted to extract genomic features, six of seven known PR genes were correctly identified by models without prior programming and several genes involved in stress responses and maintenance of the cell membrane were identified as potential novel determinants of PR. These findings are a proof of concept that whole-genome sequencing data can accurately predict PR in K. pneumoniae CG258 and may be applicable to other forms of complex antimicrobial resistance.IMPORTANCE Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection of antibiotic resistance, but this has been difficult to apply to polymyxin resistance because of its complex polygenic nature. The significance of our research is that we successfully applied machine learning methods to predict polymyxin resistance in Klebsiella pneumoniae clonal group 258, a common health care-associated and multidrug-resistant pathogen. Our findings highlight that machine learning can be successfully applied even in complex forms of antibiotic resistance and represent a significant contribution to the literature that could be used to predict resistance in other bacteria and to other antibiotics.
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Affiliation(s)
- Nenad Macesic
- Division of Infectious Diseases, Columbia University Irving Medical Center, New York, New York, USA
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, Australia
| | | | - Itsik Pe'er
- Department of Computer Science, Columbia University, New York, New York, USA
| | - Nicholas P Tatonetti
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Anton Y Peleg
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, Australia
- Infection and Immunity Program, Monash Biomedicine Discovery Institute, Department of Microbiology, Monash University, Clayton, Victoria, Australia
| | - Anne-Catrin Uhlemann
- Division of Infectious Diseases, Columbia University Irving Medical Center, New York, New York, USA
- Microbiome & Pathogen Genomics Core, Columbia University Irving Medical Center, New York, New York, USA
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Wang L, Zhang X, Wu G, Qi Y, Zhang J, Yang J, Wang H, Xu W. Streptococcus pneumoniae aminopeptidase N contributes to bacterial virulence and elicits a strong innate immune response through MAPK and PI3K/AKT signaling. J Microbiol 2020; 58:330-339. [PMID: 32103444 DOI: 10.1007/s12275-020-9538-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 12/26/2019] [Accepted: 01/20/2020] [Indexed: 10/25/2022]
Abstract
Streptococcus pneumoniae is a Gram-positive pathogen with high morbidity and mortality globally but some of its pathogenesis remains unknown. Previous research has provided evidence that aminopeptidase N (PepN) is most likely a virulence factor of S. pneumoniae. However, its role in S. pneumoniae virulence and its interaction with the host remains to be confirmed. We generated a pepN gene deficient mutant strain and found that its virulence for mice was significantly attenuated as were in vitro adhesion and invasion of host cells. The PepN protein could induce a strong innate immune response in vivo and in vitro and induced secretion of IL-6 and TNF-α by primary peritoneal macrophages via the rapid phosphorylation of MAPK and PI3K/AKT signaling pathways and this was confirmed using specific pathway inhibitors. In conclusion, PepN is a novel virulence factor that is essential for the virulence of S. pneumoniae and induces host innate immunity via MAPK and PI3K/AKT signaling.
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Affiliation(s)
- Ling Wang
- Key Laboratory of Clinical Laboratory Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine, Chongqing Medical University, Chongqing, P. R. China
| | - Xuemei Zhang
- Key Laboratory of Clinical Laboratory Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine, Chongqing Medical University, Chongqing, P. R. China
| | - Guangying Wu
- Key Laboratory of Clinical Laboratory Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine, Chongqing Medical University, Chongqing, P. R. China
| | - Yuhong Qi
- Key Laboratory of Clinical Laboratory Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine, Chongqing Medical University, Chongqing, P. R. China
| | - Jinghui Zhang
- Key Laboratory of Clinical Laboratory Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine, Chongqing Medical University, Chongqing, P. R. China
| | - Jing Yang
- Key Laboratory of Clinical Laboratory Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine, Chongqing Medical University, Chongqing, P. R. China
| | - Hong Wang
- Key Laboratory of Clinical Laboratory Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine, Chongqing Medical University, Chongqing, P. R. China
| | - Wenchun Xu
- Key Laboratory of Clinical Laboratory Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine, Chongqing Medical University, Chongqing, P. R. China.
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Zhang X, Guan C, Hang Y, Liu F, Sun J, Yu H, Gan L, Zeng H, Zhu Y, Chen Z, Song H, Cheng C. An M29 Aminopeptidase from Listeria Monocytogenes Contributes to In Vitro Bacterial Growth but not to Intracellular Infection. Microorganisms 2020; 8:microorganisms8010110. [PMID: 31941013 PMCID: PMC7023490 DOI: 10.3390/microorganisms8010110] [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: 12/16/2019] [Revised: 01/06/2020] [Accepted: 01/10/2020] [Indexed: 12/30/2022] Open
Abstract
Aminopeptidases that catalyze the removal of N-terminal residues from polypeptides or proteins are crucial for physiological processes. Here, we explore the biological functions of an M29 family aminopeptidase II from Listeria monocytogenes (LmAmpII). We show that LmAmpII contains a conserved catalytic motif (EEHYHD) that is essential for its enzymatic activity and LmAmpII has a substrate preference for arginine and leucine. Studies on biological roles indicate that LmAmpII is required for in vitro growth in a chemically defined medium for optimal growth of L. monocytogenes but is not required for bacterial intracellular infection in epithelial cells and macrophages, as well as cell-to-cell spreading in fibroblasts. Moreover, LmAmpII is found as dispensable for bacterial pathogenicity in mice. Taken together, we conclude that LmAmpII, an M29 family aminopeptidase, can efficiently hydrolyze a wide range of substrates and is required for in vitro bacterial growth, which lays a foundation for in-depth investigations of aminopeptidases as potential targets to defend Listeria infection.
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Affiliation(s)
- Xian Zhang
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, China-Australian Joint Laboratory for Animal Health Big Data Analytics, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China; (X.Z.); (J.S.)
| | - Chiyu Guan
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, China-Australian Joint Laboratory for Animal Health Big Data Analytics, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China; (X.Z.); (J.S.)
| | - Yi Hang
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, China-Australian Joint Laboratory for Animal Health Big Data Analytics, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China; (X.Z.); (J.S.)
| | - Fengdan Liu
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, China-Australian Joint Laboratory for Animal Health Big Data Analytics, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China; (X.Z.); (J.S.)
| | - Jing Sun
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, China-Australian Joint Laboratory for Animal Health Big Data Analytics, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China; (X.Z.); (J.S.)
| | - Huifei Yu
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, China-Australian Joint Laboratory for Animal Health Big Data Analytics, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China; (X.Z.); (J.S.)
| | - Li Gan
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, China-Australian Joint Laboratory for Animal Health Big Data Analytics, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China; (X.Z.); (J.S.)
| | - Huan Zeng
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, China-Australian Joint Laboratory for Animal Health Big Data Analytics, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China; (X.Z.); (J.S.)
| | - Yiran Zhu
- Jixian Honors College of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China;
| | - Zhongwei Chen
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, China-Australian Joint Laboratory for Animal Health Big Data Analytics, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China; (X.Z.); (J.S.)
| | - Houhui Song
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, China-Australian Joint Laboratory for Animal Health Big Data Analytics, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China; (X.Z.); (J.S.)
- Correspondence: (H.S.); (C.C.)
| | - Changyong Cheng
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, China-Australian Joint Laboratory for Animal Health Big Data Analytics, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Zhejiang A&F University, Lin’an 311300, China; (X.Z.); (J.S.)
- Correspondence: (H.S.); (C.C.)
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