1
|
Karapetian M, Alimbarashvili E, Vishnepolsky B, Gabrielian A, Rosenthal A, Hurt DE, Tartakovsky M, Mchedlishvili M, Arsenadze D, Pirtskhalava M, Zaalishvili G. Evaluation of the synergistic potential and mechanisms of action for de novo designed cationic antimicrobial peptides. Heliyon 2024; 10:e27852. [PMID: 38560672 PMCID: PMC10979160 DOI: 10.1016/j.heliyon.2024.e27852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 03/01/2024] [Accepted: 03/07/2024] [Indexed: 04/04/2024] Open
Abstract
Antimicrobial peptides (AMPs) have emerged as promising candidates in combating antimicrobial resistance - a growing issue in healthcare. However, to develop AMPs into effective therapeutics, a thorough analysis and extensive investigations are essential. In this study, we employed an in silico approach to design cationic AMPs de novo, followed by their experimental testing. The antibacterial potential of de novo designed cationic AMPs, along with their synergistic properties in combination with conventional antibiotics was examined. Furthermore, the effects of bacterial inoculum density and metabolic state on the antibacterial activity of AMPs were evaluated. Finally, the impact of several potent AMPs on E. coli cell envelope and genomic DNA integrity was determined. Collectively, this comprehensive analysis provides insights into the unique characteristics of cationic AMPs.
Collapse
Affiliation(s)
- Margarita Karapetian
- Laboratory of Chromatin Biology, Institute of Cellular and Molecular Biology, Agricultural University of Georgia, 240 David Aghmashenebeli Alley, 0159, Tbilisi, Georgia
| | - Evgenia Alimbarashvili
- Laboratory of Chromatin Biology, Institute of Cellular and Molecular Biology, Agricultural University of Georgia, 240 David Aghmashenebeli Alley, 0159, Tbilisi, Georgia
- Ivane Beritashvili Center of Experimental Biomedicine, 0160, Tbilisi, Georgia
| | - Boris Vishnepolsky
- Ivane Beritashvili Center of Experimental Biomedicine, 0160, Tbilisi, Georgia
| | - Andrei Gabrielian
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Darrell E. Hurt
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Mariam Mchedlishvili
- Laboratory of Chromatin Biology, Institute of Cellular and Molecular Biology, Agricultural University of Georgia, 240 David Aghmashenebeli Alley, 0159, Tbilisi, Georgia
| | - Davit Arsenadze
- Laboratory of Chromatin Biology, Institute of Cellular and Molecular Biology, Agricultural University of Georgia, 240 David Aghmashenebeli Alley, 0159, Tbilisi, Georgia
| | - Malak Pirtskhalava
- Ivane Beritashvili Center of Experimental Biomedicine, 0160, Tbilisi, Georgia
| | - Giorgi Zaalishvili
- Laboratory of Chromatin Biology, Institute of Cellular and Molecular Biology, Agricultural University of Georgia, 240 David Aghmashenebeli Alley, 0159, Tbilisi, Georgia
- Ivane Beritashvili Center of Experimental Biomedicine, 0160, Tbilisi, Georgia
| |
Collapse
|
2
|
Fang WJ, Tang SN, Liang RY, Zheng QT, Yao DQ, Hu JX, Song M, Zheng GP, Rosenthal A, Tartakovsky M, Lu PX, Wáng YXJ. Differences in pulmonary nodular consolidation and pulmonary cavity among drug-sensitive, rifampicin-resistant and multi-drug resistant tuberculosis patients: the Guangzhou computerized tomography study. Quant Imaging Med Surg 2024; 14:1010-1021. [PMID: 38223080 PMCID: PMC10783999 DOI: 10.21037/qims-23-694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 10/28/2023] [Indexed: 01/16/2024]
Abstract
Background Pulmonary nodular consolidation (PN) and pulmonary cavity (PC) may represent the two most promising imaging signs in differentiating multidrug-resistant (MDR)-pulmonary tuberculosis (PTB) from drug-sensitive (DS)-PTB. However, there have been concerns that literature described radiological feature differences between DS-PTB and MDR-PTB were confounded by that MDR-PTB cases tend to have a longer history. This study seeks to further clarify this point. Methods All cases were from the Guangzhou Chest Hospital, Guangzhou, China. We retrieved data of consecutive new MDR cases [n=46, inclusive of rifampicin-resistant (RR) cases] treated during the period of July 2020 and December 2021, and according to the electronic case archiving system records, the main PTB-related symptoms/signs history was ≤3 months till the first computed tomography (CT) scan in Guangzhou Chest Hospital was taken. To pair the MDR-PTB cases with assumed equal disease history length, we additionally retrieved data of 46 cases of DS-PTB patients. Twenty-two of the DS patients and 30 of the MDR patients were from rural communities. The first CT in Guangzhou Chest Hospital was analysed in this study. When the CT was taken, most cases had anti-TB drug treatment for less than 2 weeks, and none had been treated for more than 3 weeks. Results Apparent CT signs associated with chronicity were noted in 10 cases in the DS group (10/46) and 9 cases in the MDR group (10/46). Thus, the overall disease history would have been longer than the assumed <3 months. Still, the history length difference between DS patients and MDR patients in the current study might not be substantial. The lung volume involvement was 11.3%±8.3% for DS cases and 8.4%±6.6% for MDR cases (P=0.022). There was no statistical difference between DS cases and MDR cases both in PN prevalence and in PC prevalence. For positive cases, MDR cases had more PN number (mean of positive cases: 2.63 vs. 2.28, P=0.38) and PC number (mean of positive cases: 2.14 vs. 1.38, P=0.001) than DS cases. Receiver operating characteristic curve analysis shows, PN ≥4 and PC ≥3 had a specificity of 86% (sensitivity 25%) and 93% (sensitivity 36%), respectively, in suggesting the patient being a MDR cases. Conclusions A combination of PN and PC features allows statistical separation of DS and MDR cases.
Collapse
Affiliation(s)
- Wei-Jun Fang
- Department of Radiology, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China
| | - Sheng-Nan Tang
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Rui-Yun Liang
- Department of Radiology, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China
| | - Qiu-Ting Zheng
- Department of Medical Imaging, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Dian-Qi Yao
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Jin-Xing Hu
- Department of Tuberculosis, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China
| | - Min Song
- Department of Radiology, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China
| | - Guang-Ping Zheng
- Department of Radiology, The Third People’s Hospital of Shenzhen, Shenzhen, China
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Pu-Xuan Lu
- Department of Medical Imaging, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Yì Xiáng J. Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| |
Collapse
|
3
|
Giovanni MY, Whalen C, Hurt DE, Ware-Allen L, Noble K, McCarthy M, Quinones M, Cruz P, Jjingo D, Wele M, Seydou D, Tartakovsky M. African Centers of Excellence in Bioinformatics and Data Intensive Science: Building Capacity for Enhancing Data Intensive Infectious Diseases Research in Africa. J Infect Dis Microbiol 2023; 1:006. [PMID: 37987019 PMCID: PMC10658664 DOI: 10.37191/mapsci-jidm-1(2)-006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Africa faces both a disproportionate burden of infectious diseases coupled with unmet needs in bioinformatics and data science capabilities which impacts the ability of African biomedical researchers to vigorously pursue research and partner with institutions in other countries. The African Centers of Excellence in Bioinformatics and Data Intensive Science are collaborating with African academic institutions, industry partners, the Foundation for the National Institutes of Health (FNIH) and the National Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes of Health (NIH) in a public-private partnership to address these challenges through enhancing computational infrastructure, fostering the development of advanced bioinformatics and data science skills among local researchers and students and providing innovative emerging technologies for infectious diseases research.
Collapse
Affiliation(s)
- Maria Y Giovanni
- Office of Data Science and Emerging Technologies and Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Christopher Whalen
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Darrell E Hurt
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Latrice Ware-Allen
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Karlynn Noble
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Meghan McCarthy
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Mariam Quinones
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Phillip Cruz
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Daudi Jjingo
- Department of Computer Science, College of Computing and Information Sciences, and The African Center of Excellence in Bioinformatics and Data-Intensive Science, Infectious Disease Institute, Makerere University, Kampala, Uganda
| | - Mamadou Wele
- Institute of Applied Sciences, University of Sciences, Techniques and Technologies of Bamako, and The African Center of Excellence in Bioinformatics and Data-Intensive Science, Bamako
| | - Doumbia Seydou
- Department of Public Health, Faculty of Medicine and Odontostomatology, University of Sciences, Techniques, and Technologies of Bamako, Bamako
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| |
Collapse
|
4
|
Song QS, Zheng CJ, Wang KP, Huang XL, Tartakovsky M, Wáng YXJ. Differences in pulmonary nodular consolidation and pulmonary cavity among drug-sensitive, rifampicin-resistant and multi-drug resistant tuberculosis patients: a computerized tomography study with history length matched cases. J Thorac Dis 2022; 14:2522-2531. [PMID: 35928612 PMCID: PMC9344412 DOI: 10.21037/jtd-22-145] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/29/2022] [Indexed: 11/25/2022]
Abstract
Background There have been concerns that literature described radiological feature differences between drug-sensitive pulmonary tuberculosis (DS-PTB) and multidrug-resistant (MDR)-PTB were confounded by that MDR-PTB cases tend to have a longer history. Using history length matched DS-PTB and MDR-PTB cases from a well-defined urban region in Dalian, we retrospectively analysed the CT feature differences of these paired cases with a focus on pulmonary nodular (PN) consolidation and pulmonary cavity (PC). Methods There were 33 consecutive MDR-PTB cases [inclusive of rifampicin-resistant (RR) cases, 27 males and 6 females, mean age: 49.2 years], with 19 cases had a history of <1 month and 8 and 6 cases had a history of 1–6 and >6 months respectively. To pair the MDR-PTB cases with history length, matched 33 cases of DS-PTB patients (21 males and 12 females, mean age: 56.5 years) were included. All patients were new PTB without HIV infection. The first CT exams prior to treatment were analysed. Results Compared with DS cases, MDR cases had a much higher prevalence of PN (75.76% vs. 45.45%) and a higher number of PN per positive case for PN (6.2 vs.1.53). For the cases >1 month history, MDR-PTB had a higher number of PC per positive case than that of DS-PTB cases (7.18 vs. 2.36). To differentiate DS-PTB from MDR-PTB, receiver operating characteristic (ROC) analysis showed a cutoff PN number of ≥3 had 48.5% sensitivity and 93.9% specificity, and a cutoff PC number of ≥4 had 39.4% sensitivity and 84.9% specificity. The lung field distribution of all lesions tended to be wider for MDR-PTB cases. MDR-PTB cases appeared to be associated with a faster progression in the absence of treatment. Conclusions MDR-TB is likely intrinsically more invasive than DS-TB. Multiple PN and Multiple PC are promising signs for the suspicion of MDR-PTB on chest imaging.
Collapse
Affiliation(s)
- Qi-Sheng Song
- Department of Internal Medicine, Dalian Public Health Clinical Center, Dalian, China
| | - Cun-Jing Zheng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Kun-Peng Wang
- Department of Radiology, Dalian Public Health Clinical Center, Dalian, China
| | - Xi-Ling Huang
- Department of Ultrasonic Medicine, West China Second University Hospital of Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Yì Xiáng J. Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| |
Collapse
|
5
|
Vishnepolsky B, Grigolava M, Managadze G, Gabrielian A, Rosenthal A, Hurt DE, Tartakovsky M, Pirtskhalava M. Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction. Brief Bioinform 2022; 23:6611915. [PMID: 35724561 PMCID: PMC9294419 DOI: 10.1093/bib/bbac233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 12/29/2022] Open
Abstract
The evolution of drug-resistant pathogenic microbial species is a major global health concern. Naturally occurring, antimicrobial peptides (AMPs) are considered promising candidates to address antibiotic resistance problems. A variety of computational methods have been developed to accurately predict AMPs. The majority of such methods are not microbial strain specific (MSS): they can predict whether a given peptide is active against some microbe, but cannot accurately calculate whether such peptide would be active against a particular MS. Due to insufficient data on most MS, only a few MSS predictive models have been developed so far. To overcome this problem, we developed a novel approach that allows to improve MSS predictive models (MSSPM), based on properties, computed for AMP sequences and characteristics of genomes, computed for target MS. New models can perform predictions of AMPs for MS that do not have data on peptides tested on them. We tested various types of feature engineering as well as different machine learning (ML) algorithms to compare the predictive abilities of resulting models. Among the ML algorithms, Random Forest and AdaBoost performed best. By using genome characteristics as additional features, the performance for all models increased relative to models relying on AMP sequence-based properties only. Our novel MSS AMP predictor is freely accessible as part of DBAASP database resource at http://dbaasp.org/prediction/genome.
Collapse
Affiliation(s)
- Boris Vishnepolsky
- Corresponding authors: B. Vishnepolsky, Laboratory of Bioinformatics, Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi, Georgia. Tel: +995595771363; E-mail: ; M. Pirtskhalava, Laboratory of Bioinformatics, Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi, Georgia. Tel: +995574162397; E-mail:
| | - Maya Grigolava
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Grigol Managadze
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Andrei Gabrielian
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Darrell E Hurt
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Malak Pirtskhalava
- Corresponding authors: B. Vishnepolsky, Laboratory of Bioinformatics, Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi, Georgia. Tel: +995595771363; E-mail: ; M. Pirtskhalava, Laboratory of Bioinformatics, Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi, Georgia. Tel: +995574162397; E-mail:
| |
Collapse
|
6
|
Kasirye R, Hume HA, Bloch EM, Lubega I, Kyeyune D, Shrestha R, Ddungu H, Musana HW, Dhabangi A, Ouma J, Eroju P, de Lange T, Tartakovsky M, White JL, Kakura C, Fowler MG, Musoke P, Nolan M, Grabowski MK, Moulton LH, Stramer SL, Whitby D, Zimmerman PA, Wabwire D, Kajja I, McCullough J, Goodrich R, Quinn TC, Cortes R, Ness PM, Tobian AAR. The Mirasol Evaluation of Reduction in Infections Trial (MERIT): study protocol for a randomized controlled clinical trial. Trials 2022; 23:257. [PMID: 35379302 PMCID: PMC8978156 DOI: 10.1186/s13063-022-06137-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/02/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Transfusion-transmitted infections (TTIs) are a global health challenge. One new approach to reduce TTIs is the use of pathogen reduction technology (PRT). In vitro, Mirasol PRT reduces the infectious load in whole blood (WB) by at least 99%. However, there are limited in vivo data on the safety and efficacy of Mirasol PRT. The objective of the Mirasol Evaluation of Reduction in Infections Trial (MERIT) is to investigate whether Mirasol PRT of WB can prevent seven targeted TTIs (malaria, bacteria, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, hepatitis E virus, and human herpesvirus 8). METHODS MERIT is a randomized, double-blinded, controlled clinical trial. Recruitment started in November 2019 and is expected to end in 2024. Consenting participants who require transfusion as medically indicated at three hospitals in Kampala, Uganda, will be randomized to receive either Mirasol-treated WB (n = 1000) or standard WB (n = 1000). TTI testing will be performed on donor units and recipients (pre-transfusion and day 2, day 7, week 4, and week 10 after transfusion). The primary endpoint is the cumulative incidence of one or more targeted TTIs from the Mirasol-treated WB vs. standard WB in a previously negative recipient for the specific TTI that is also detected in the donor unit. Log-binomial regression models will be used to estimate the relative risk reduction of a TTI by 10 weeks associated with Mirasol PRT. The clinical effectiveness of Mirasol WB compared to standard WB products in recipients will also be evaluated. DISCUSSION Screening infrastructure for TTIs in low-resource settings has gaps, even for major TTIs. PRT presents a fast, potentially cost-effective, and easy-to-use technology to improve blood safety. MERIT is the largest clinical trial designed to evaluate the use of Mirasol PRT for WB. In addition, this trial will provide data on TTIs in Uganda. TRIAL REGISTRATION Mirasol Evaluation of Reduction in Infections Trial (MERIT) NCT03737669 . Registered on 9 November 2018.
Collapse
Affiliation(s)
- Ronnie Kasirye
- grid.421981.7MUJHU Research Collaboration, Kampala, Uganda
| | - Heather A. Hume
- grid.14848.310000 0001 2292 3357Department of Pediatrics, University of Montreal, Montréal, QC Canada
| | - Evan M. Bloch
- grid.21107.350000 0001 2171 9311Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD USA
| | - Irene Lubega
- grid.421981.7MUJHU Research Collaboration, Kampala, Uganda
| | | | - Ruchee Shrestha
- grid.21107.350000 0001 2171 9311Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD USA
| | - Henry Ddungu
- grid.512320.70000 0004 6015 3252Uganda Cancer Institute, Kampala, Uganda
| | | | - Aggrey Dhabangi
- grid.11194.3c0000 0004 0620 0548Child Health and Development Centre, Makerere University College of Health Sciences, Kampala, Uganda
| | - Joseph Ouma
- grid.421981.7MUJHU Research Collaboration, Kampala, Uganda
| | | | - Telsa de Lange
- grid.419681.30000 0001 2164 9667National Institute of Allergy and Infectious Diseases Office of Cyber Infrastructure and Computational Biology, Bethesda, MD USA
| | - Michael Tartakovsky
- grid.419681.30000 0001 2164 9667National Institute of Allergy and Infectious Diseases Office of Cyber Infrastructure and Computational Biology, Bethesda, MD USA
| | - Jodie L. White
- grid.21107.350000 0001 2171 9311Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD USA
| | - Ceasar Kakura
- grid.421981.7MUJHU Research Collaboration, Kampala, Uganda
| | - Mary Glenn Fowler
- grid.21107.350000 0001 2171 9311Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD USA
| | - Philippa Musoke
- grid.11194.3c0000 0004 0620 0548Makerere University, Kampala, Uganda
| | - Monica Nolan
- grid.421981.7MUJHU Research Collaboration, Kampala, Uganda
| | - M. Kate Grabowski
- grid.21107.350000 0001 2171 9311Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD USA
| | - Lawrence H. Moulton
- grid.21107.350000 0001 2171 9311Department of International Health, School of Public Health, Johns Hopkins University, Baltimore, MD USA
| | - Susan L. Stramer
- grid.281926.60000 0001 2214 8581Department of Scientific Affairs, American Red Cross, Gaithersburg, MD USA
| | - Denise Whitby
- grid.418021.e0000 0004 0535 8394Leidos Biomedical Research, AIDS and Cancer Virus Program, Frederick National Laboratory for Cancer Research, Frederick, MD USA
| | - Peter A. Zimmerman
- grid.67105.350000 0001 2164 3847The Center for Global Health & Diseases, Pathology Department, Case Western Reserve University, Cleveland, OH USA
| | - Deo Wabwire
- grid.421981.7MUJHU Research Collaboration, Kampala, Uganda
| | - Isaac Kajja
- grid.11194.3c0000 0004 0620 0548Department of Orthopaedics, Makerere University College of Health Sciences, Kampala, Uganda
| | - Jeffrey McCullough
- grid.215654.10000 0001 2151 2636College of Health Solutions, Arizona State University, Phoenix, AZ USA
| | - Raymond Goodrich
- grid.47894.360000 0004 1936 8083Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO USA
| | - Thomas C. Quinn
- grid.21107.350000 0001 2171 9311Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD USA ,grid.21107.350000 0001 2171 9311Department of International Health, School of Public Health, Johns Hopkins University, Baltimore, MD USA ,grid.94365.3d0000 0001 2297 5165Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | | | - Paul M. Ness
- grid.21107.350000 0001 2171 9311Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD USA
| | - Aaron A. R. Tobian
- grid.21107.350000 0001 2171 9311Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD USA ,grid.11194.3c0000 0004 0620 0548Department of Paediatrics and Child Health, College of Health Sciences, Makerere University, Kampala, Uganda
| |
Collapse
|
7
|
Pirtskhalava M, Amstrong AA, Grigolava M, Chubinidze M, Alimbarashvili E, Vishnepolsky B, Gabrielian A, Rosenthal A, Hurt DE, Tartakovsky M. DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Res 2021; 49:D288-D297. [PMID: 33151284 PMCID: PMC7778994 DOI: 10.1093/nar/gkaa991] [Citation(s) in RCA: 182] [Impact Index Per Article: 60.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/09/2020] [Accepted: 10/14/2020] [Indexed: 12/30/2022] Open
Abstract
The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is an open-access, comprehensive database containing information on amino acid sequences, chemical modifications, 3D structures, bioactivities and toxicities of peptides that possess antimicrobial properties. DBAASP is updated continuously, and at present, version 3.0 (DBAASP v3) contains >15 700 entries (8000 more than the previous version), including >14 500 monomers and nearly 400 homo- and hetero-multimers. Of the monomeric antimicrobial peptides (AMPs), >12 000 are synthetic, about 2700 are ribosomally synthesized, and about 170 are non-ribosomally synthesized. Approximately 3/4 of the entries were added after the initial release of the database in 2014 reflecting the recent sharp increase in interest in AMPs. Despite the increased interest, adoption of peptide antimicrobials in clinical practice is still limited as a consequence of several factors including side effects, problems with bioavailability and high production costs. To assist in developing and optimizing de novo peptides with desired biological activities, DBAASP offers several tools including a sophisticated multifactor analysis of relevant physicochemical properties. Furthermore, DBAASP has implemented a structure modelling pipeline that automates the setup, execution and upload of molecular dynamics (MD) simulations of database peptides. At present, >3200 peptides have been populated with MD trajectories and related analyses that are both viewable within the web browser and available for download. More than 400 DBAASP entries also have links to experimentally determined structures in the Protein Data Bank. DBAASP v3 is freely accessible at http://dbaasp.org.
Collapse
Affiliation(s)
- Malak Pirtskhalava
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Anthony A Amstrong
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Maia Grigolava
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Mindia Chubinidze
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | | | - Boris Vishnepolsky
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Andrei Gabrielian
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Darrell E Hurt
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| |
Collapse
|
8
|
Wollenberg K, Harris M, Gabrielian A, Ciobanu N, Chesov D, Long A, Taaffe J, Hurt D, Rosenthal A, Tartakovsky M, Crudu V. A retrospective genomic analysis of drug-resistant strains of M. tuberculosis in a high-burden setting, with an emphasis on comparative diagnostics and reactivation and reinfection status. BMC Infect Dis 2020; 20:17. [PMID: 31910804 PMCID: PMC6947865 DOI: 10.1186/s12879-019-4739-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 12/27/2019] [Indexed: 12/01/2022] Open
Abstract
Background Recurrence of drug-resistant tuberculosis (DR-TB) after treatment occurs through relapse of the initial infection or reinfection by a new drug-resistant strain. Outbreaks of DR-TB in high burden regions present unique challenges in determining recurrence status for effective disease management and treatment. In the Republic of Moldova the burden of DR-TB is exceptionally high, with many cases presenting as recurrent. Methods We performed a retrospective analysis of Mycobacterium tuberculosis from Moldova to better understand the genomic basis of drug resistance and its effect on the determination of recurrence status in a high DR-burden environment. To do this we analyzed genomes from 278 isolates collected from 189 patients, including 87 patients with longitudinal samples. These pathogen genomes were sequenced using Illumina technology, and SNP panels were generated for each sample for use in phylogenetic and network analysis. Discordance between genomic resistance profiles and clinical drug-resistance test results was examined in detail to assess the possibility of mixed infection. Results There were clusters of multiple patients with 10 or fewer differences among DR-TB samples, which is evidence of person-to-person transmission of DR-TB. Analysis of longitudinally collected isolates revealed that many infections exhibited little change over time, though 35 patients demonstrated reinfection by divergent (number of differences > 10) lineages. Additionally, several same-lineage sample pairs were found to be more divergent than expected for a relapsed infection. Network analysis of the H3/4.2.1 clade found very close relationships among 61 of these samples, making differentiation of reactivation and reinfection difficult. There was discordance between genomic profile and clinical drug sensitivity test results in twelve samples, and four of these had low level (but not statistically significant) variation at DR SNPs suggesting low-level mixed infections. Conclusions Whole-genome sequencing provided a detailed view of the genealogical structure of the DR-TB epidemic in Moldova, showing that reinfection may be more prevalent than currently recognized. We also found increased evidence of mixed infection, which could be more robustly characterized with deeper levels of genomic sequencing.
Collapse
Affiliation(s)
- Kurt Wollenberg
- Office of Cyber Infrastructure & Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
| | - Michael Harris
- Office of Cyber Infrastructure & Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Andrei Gabrielian
- Office of Cyber Infrastructure & Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Nelly Ciobanu
- Microbiology and Morphology Laboratory, Institute of Phthisiopneumology, Chisnau, Moldova
| | - Dumitru Chesov
- Department of Pneumology and Allergology, Nicolae Testemitanu State University of Medicine and Pharmacy, Chisinau, Moldova.,Division of Clinical Infectious Diseases, Research Center Borstel, Leibniz Lung Center, Borstel, Germany
| | - Alyssa Long
- Office of Cyber Infrastructure & Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Jessica Taaffe
- Office of Cyber Infrastructure & Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Darrell Hurt
- Office of Cyber Infrastructure & Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Alex Rosenthal
- Office of Cyber Infrastructure & Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure & Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Valeriu Crudu
- Microbiology and Morphology Laboratory, Institute of Phthisiopneumology, Chisnau, Moldova
| |
Collapse
|
9
|
Sergeev RS, Kavaliou IS, Sataneuski UV, Gabrielian A, Rosenthal A, Tartakovsky M, Tuzikov AV. Genome-Wide Analysis of MDR and XDR Tuberculosis from Belarus: Machine-Learning Approach. IEEE/ACM Trans Comput Biol Bioinform 2019; 16:1398-1408. [PMID: 28678713 DOI: 10.1109/tcbb.2017.2720669] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Emergence of drug-resistant microorganisms has been recognized as a serious threat to public health worldwide. This problem is extensively discussed in the context of tuberculosis treatment. Alterations in pathogen genomes are among the main mechanisms by which microorganisms exhibit drug resistance. Analysis of 144 M. tuberculosis strains of different phenotypes including drug susceptible, MDR, and XDR isolated in Belarus was fulfilled in this paper. A wide range of machine learning methods that can discover SNPs related to drug-resistance in the whole bacteria genomes was investigated. Besides single-SNP testing approaches, methods that allow detecting joint effects from interacting SNPs were considered. We proposed a framework for automated selection of the best performing statistical model in terms of recall, precision, and accuracy to identify drug resistance-associated mutations. Analysis of whole-genome sequences often leads to situations where the number of treated features exceeds the number of available observations. For this reason, special attention is paid to fair evaluation of the model prediction quality and minimizing the risk of overfitting while estimating the underlying parameters. Results of our experiments aimed at identifying top-scoring resistance mutations to the major first-line and second-line anti-TB drugs are presented.
Collapse
|
10
|
Yu WY, Lu PX, Assadi M, Huang XL, Skrahin A, Rosenthal A, Gabrielian A, Tartakovsky M, Wáng YXJ. Updates on 18F-FDG-PET/CT as a clinical tool for tuberculosis evaluation and therapeutic monitoring. Quant Imaging Med Surg 2019; 9:1132-1146. [PMID: 31367568 DOI: 10.21037/qims.2019.05.24] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Tuberculosis (TB) is currently the world's leading cause of infectious mortality. The complex immune response of the human body to Mycobacterium tuberculosis (M.tb) results in a wide array of clinical manifestations, thus the clinical and radiological diagnosis can be challenging. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) scan with/without computed tomography (CT) component images the whole body and provides a metabolic map of the infection, enabling clinicians to assess the disease burden. 18F-FDG-PET/CT scan is particularly useful in detecting the disease in previously unknown sites, and allows the most appropriate site of biopsy to be selected. 18F-FDG-PET/CT is also very valuable in assessing early disease response to therapy, and plays an important role in cases where conventional microbiological methods are unavailable and for monitoring response to therapy in cases of multidrug-resistant TB or extrapulmonary TB. 18F-FDG-PET/CT cannot reliably differentiate active TB lesion from malignant lesions and false positives can also be due to other infective or inflammatory conditions. 18F-FDG PET is also unable to distinguish tuberculous lymphadenitis from metastatic lymph node involvement. The lack of specificity is a limitation for 18F-FDG-PET/CT in TB management.
Collapse
Affiliation(s)
- Wei-Ye Yu
- Shenzhen Center for Chronic Disease Control, Shenzhen 518055, China
| | - Pu-Xuan Lu
- Shenzhen Center for Chronic Disease Control, Shenzhen 518055, China
| | - Majid Assadi
- The Persian Gulf Nuclear Medicine Research Center, Bushehr University Of Medical Sciences, Bushehr, Iran
| | - Xi-Ling Huang
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Aliaksandr Skrahin
- Republican Scientific and Practical Centre of Pulmonology and Tuberculosis, Ministry of Health, Minsk, Belarus.,Belarus State Medical University, Minsk, Belarus
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
| | - Andrei Gabrielian
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
| | - Yì Xiáng J Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| |
Collapse
|
11
|
Vishnepolsky B, Gabrielian A, Rosenthal A, Hurt DE, Tartakovsky M, Managadze G, Grigolava M, Makhatadze GI, Pirtskhalava M. Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria. J Chem Inf Model 2018; 58:1141-1151. [DOI: 10.1021/acs.jcim.8b00118] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Boris Vishnepolsky
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Andrei Gabrielian
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Darrell E. Hurt
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Grigol Managadze
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Maya Grigolava
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | | | - Malak Pirtskhalava
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| |
Collapse
|
12
|
Wáng YXJ, Chung MJ, Skrahin A, Rosenthal A, Gabrielian A, Tartakovsky M. Radiological signs associated with pulmonary multi-drug resistant tuberculosis: an analysis of published evidences. Quant Imaging Med Surg 2018; 8:161-173. [PMID: 29675357 DOI: 10.21037/qims.2018.03.06] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Background Despite that confirmative diagnosis of pulmonary drug-sensitive tuberculosis (DS-TB) and multidrug resistant tuberculosis (MDR-TB) is determined by microbiological testing, early suspicions of MDR-TB by chest imaging are highly desirable in order to guide diagnostic process. We aim to perform an analysis of currently available literatures on radiological signs associated with pulmonary MDR-TB. Methods A literature search was performed using PubMed on January 29, 2018. The search words combination was "((extensive* drug resistant tuberculosis) OR (multidrug-resistant tuberculosis)) AND (CT or radiograph or imaging or X-ray or computed tomography)". We analyzed English language articles reported sufficient information of radiological signs of DS-TB vs. MDR-TB. Results Seventeen articles were found to be sufficiently relevant and included for analysis. The reported pulmonary MDR-TB cases were grouped into four categories: (I) previously treated (or 'secondary', or 'acquired') MDR-TB in HIV negative (-) adults; (II) new (or 'primary') MDR-TB in HIV(-) adults; (III) MDR-TB in HIV positive (+) adults; and (IV) MDR-TB in child patients. The common radiological findings of pulmonary MDR-TB included centrilobular small nodules, branching linear and nodular opacities (tree-in-bud sign), patchy or lobular areas of consolidation, cavitation, and bronchiectasis. While overall MDR-TB cases tended to have more extensive disease, more likely to be bilateral, to have pleural involvement, to have bronchiectasis, and to have lung volume loss; these signs alone were not sufficient for differential diagnosis of MDR-TB. Current literatures suggest that the radiological sign which may offer good specificity for pulmonary MDR-TB diagnosis, though maybe at the cost of low sensitivity, would be thick-walled multiple cavities, particularly if the cavity number is ≥3. For adult HIV(-) patients, new MDR-TB appear to show similar prevalence of cavity lesion, which was estimated to be around 70%, compared with previously treated MDR-TB. Conclusions Thick-walled multiple cavity lesions present the most promising radiological sign for MDR-TB diagnosis. For future studies cavity lesion characteristics should be quantified in details.
Collapse
Affiliation(s)
- Yì Xiáng J Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Myung Jin Chung
- Department of Radiology and Center for Imaging Science; Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Aliaksandr Skrahin
- Republican Scientific and Practical Centre of Pulmonology and Tuberculosis, Ministry of Health, Minsk, Belarus.,Belarus State Medical University, Minsk, Belarus
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
| | - Andrei Gabrielian
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
| |
Collapse
|
13
|
Chen RY, Via LE, Dodd LE, Walzl G, Malherbe ST, Loxton AG, Dawson R, Wilkinson RJ, Thienemann F, Tameris M, Hatherill M, Diacon AH, Liu X, Xing J, Jin X, Ma Z, Pan S, Zhang G, Gao Q, Jiang Q, Zhu H, Liang L, Duan H, Song T, Alland D, Tartakovsky M, Rosenthal A, Whalen C, Duvenhage M, Cai Y, Goldfeder LC, Arora K, Smith B, Winter J, Barry Iii CE. Using biomarkers to predict TB treatment duration (Predict TB): a prospective, randomized, noninferiority, treatment shortening clinical trial. Gates Open Res 2017. [PMID: 29528048 PMCID: PMC5841574 DOI: 10.12688/gatesopenres.12750.1] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: By the early 1980s, tuberculosis treatment was shortened from 24 to 6 months, maintaining relapse rates of 1-2%. Subsequent trials attempting shorter durations have failed, with 4-month arms consistently having relapse rates of 15-20%. One trial shortened treatment only among those without baseline cavity on chest x-ray and whose month 2 sputum culture converted to negative. The 4-month arm relapse rate decreased to 7% but was still significantly worse than the 6-month arm (1.6%, P<0.01). We hypothesize that PET/CT characteristics at baseline, PET/CT changes at one month, and markers of residual bacterial load will identify patients with tuberculosis who can be cured with 4 months (16 weeks) of standard treatment. Methods: This is a prospective, multicenter, randomized, phase 2b, noninferiority clinical trial of pulmonary tuberculosis participants. Those eligible start standard of care treatment. PET/CT scans are done at weeks 0, 4, and 16 or 24. Participants who do not meet early treatment completion criteria (baseline radiologic severity, radiologic response at one month, and GeneXpert-detectable bacilli at four months) are placed in Arm A (24 weeks of standard therapy). Those who meet the early treatment completion criteria are randomized at week 16 to continue treatment to week 24 (Arm B) or complete treatment at week 16 (Arm C). The primary endpoint compares the treatment success rate at 18 months between Arms B and C. Discussion: Multiple biomarkers have been assessed to predict TB treatment outcomes. This study uses PET/CT scans and GeneXpert (Xpert) cycle threshold to risk stratify participants. PET/CT scans are not applicable to global public health but could be used in clinical trials to stratify participants and possibly become a surrogate endpoint. If the Predict TB trial is successful, other immunological biomarkers or transcriptional signatures that correlate with treatment outcome may be identified. Trial Registration: NCT02821832
Collapse
Affiliation(s)
- Ray Y Chen
- Tuberculosis Research Section, Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Laura E Via
- Tuberculosis Research Section, Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA.,Wellcome Centre for Infectious Diseases Research in Africa,Institute of Infectious Disease and Molecular Medicine, University of Cape Town (UCT), Cape Town, South Africa
| | - Lori E Dodd
- Biostatistics Research Branch, Division of Clinical Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Gerhard Walzl
- South Africa Department of Science and Technology - National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Stephanus T Malherbe
- South Africa Department of Science and Technology - National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - André G Loxton
- South Africa Department of Science and Technology - National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Rodney Dawson
- Division of Pulmonology, Department of Medicine, University Of Cape Town Lung Institute, University of Cape Town (UCT), Cape Town, South Africa
| | - Robert J Wilkinson
- Wellcome Centre for Infectious Diseases Research in Africa,Institute of Infectious Disease and Molecular Medicine, University of Cape Town (UCT), Cape Town, South Africa.,Francis Crick Institute, London, NW1 2AT, UK.,Department of Medicine, Imperial College London, London, W2 1PG, UK
| | - Friedrich Thienemann
- Wellcome Centre for Infectious Diseases Research in Africa,Institute of Infectious Disease and Molecular Medicine, University of Cape Town (UCT), Cape Town, South Africa.,Department of Internal Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Michele Tameris
- South African Tuberculosis Vaccine Initiative, University of Cape Town (UCT), Cape Town, South Africa
| | - Mark Hatherill
- South African Tuberculosis Vaccine Initiative, University of Cape Town (UCT), Cape Town, South Africa
| | - Andreas H Diacon
- TASK Applied Science and Stellenbosch University, Cape Town, South Africa
| | - Xin Liu
- Henan Provincial Chest Hospital, Zhengzhou, Henan, China
| | - Jin Xing
- Henan Provincial Institute of Tuberculosis and Prevention, Henan Center for Disease Control, Zhengzhou, Henan, China
| | - Xiaowei Jin
- Xinmi City Institute of Tuberculosis Prevention and Control, Xinmi, Henan, China
| | - Zhenya Ma
- Kaifeng City Institute of Tuberculosis Prevention and Control, Kaifeng, Henan, China
| | - Shouguo Pan
- Zhongmu County Health and Epidemic Prevention Station, Zhongmu, Henan, China
| | - Guolong Zhang
- Henan Provincial Institute of Tuberculosis and Prevention, Henan Center for Disease Control, Zhengzhou, Henan, China
| | - Qian Gao
- Fudan University, Shanghai, China
| | - Qi Jiang
- Fudan University, Shanghai, China
| | - Hong Zhu
- Sino-US Tuberculosis Collaborative Research Program, Zhengzhou, Henan, China
| | - Lili Liang
- TASK Applied Science and Stellenbosch University, Cape Town, South Africa
| | | | - Taeksun Song
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town (UCT), Cape Town, South Africa
| | - David Alland
- Division of Infectious Diseases, Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Christopher Whalen
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Michael Duvenhage
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Ying Cai
- Tuberculosis Research Section, Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Lisa C Goldfeder
- Tuberculosis Research Section, Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Kriti Arora
- Tuberculosis Research Section, Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Bronwyn Smith
- South Africa Department of Science and Technology - National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Jill Winter
- Catalysis Foundation for Health, Emeryville, CA, USA
| | - Clifton E Barry Iii
- Tuberculosis Research Section, Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA.,Wellcome Centre for Infectious Diseases Research in Africa,Institute of Infectious Disease and Molecular Medicine, University of Cape Town (UCT), Cape Town, South Africa
| | | |
Collapse
|
14
|
Pirtskhalava M, Gabrielian A, Cruz P, Griggs HL, Squires RB, Hurt DE, Grigolava M, Chubinidze M, Gogoladze G, Vishnepolsky B, Alekseyev V, Rosenthal A, Tartakovsky M. DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides. Nucleic Acids Res 2016; 44:6503. [PMID: 27060142 PMCID: PMC4994862 DOI: 10.1093/nar/gkw243] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Malak Pirtskhalava
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Andrei Gabrielian
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Phillip Cruz
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hannah L Griggs
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - R Burke Squires
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Darrell E Hurt
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Maia Grigolava
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Mindia Chubinidze
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - George Gogoladze
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Boris Vishnepolsky
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Vsevolod Alekseyev
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| |
Collapse
|
15
|
Pirtskhalava M, Gabrielian A, Cruz P, Griggs HL, Squires RB, Hurt DE, Grigolava M, Chubinidze M, Gogoladze G, Vishnepolsky B, Alekseyev V, Rosenthal A, Tartakovsky M. DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides. Nucleic Acids Res 2015; 44:D1104-12. [PMID: 26578581 PMCID: PMC4702840 DOI: 10.1093/nar/gkv1174] [Citation(s) in RCA: 124] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 10/22/2015] [Indexed: 01/26/2023] Open
Abstract
Antimicrobial peptides (AMPs) are anti-infectives that may represent a novel and untapped class of biotherapeutics. Increasing interest in AMPs means that new peptides (natural and synthetic) are discovered faster than ever before. We describe herein a new version of the Database of Antimicrobial Activity and Structure of Peptides (DBAASPv.2, which is freely accessible at http://dbaasp.org). This iteration of the database reports chemical structures and empirically-determined activities (MICs, IC50, etc.) against more than 4200 specific target microbes for more than 2000 ribosomal, 80 non-ribosomal and 5700 synthetic peptides. Of these, the vast majority are monomeric, but nearly 200 of these peptides are found as homo- or heterodimers. More than 6100 of the peptides are linear, but about 515 are cyclic and more than 1300 have other intra-chain covalent bonds. More than half of the entries in the database were added after the resource was initially described, which reflects the recent sharp uptick of interest in AMPs. New features of DBAASPv.2 include: (i) user-friendly utilities and reporting functions, (ii) a ‘Ranking Search’ function to query the database by target species and return a ranked list of peptides with activity against that target and (iii) structural descriptions of the peptides derived from empirical data or calculated by molecular dynamics (MD) simulations. The three-dimensional structural data are critical components for understanding structure–activity relationships and for design of new antimicrobial drugs. We created more than 300 high-throughput MD simulations specifically for inclusion in DBAASP. The resulting structures are described in the database by novel trajectory analysis plots and movies. Another 200+ DBAASP entries have links to the Protein DataBank. All of the structures are easily visualized directly in the web browser.
Collapse
Affiliation(s)
- Malak Pirtskhalava
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Andrei Gabrielian
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Phillip Cruz
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hannah L Griggs
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - R Burke Squires
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Darrell E Hurt
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Maia Grigolava
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Mindia Chubinidze
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - George Gogoladze
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Boris Vishnepolsky
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia
| | - Vsevolod Alekseyev
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| |
Collapse
|
16
|
Chen RY, Dodd LE, Lee M, Paripati P, Hammoud DA, Mountz JM, Jeon D, Zia N, Zahiri H, Coleman MT, Carroll MW, Lee JD, Jeong YJ, Herscovitch P, Lahouar S, Tartakovsky M, Rosenthal A, Somaiyya S, Lee S, Goldfeder LC, Cai Y, Via LE, Park SK, Cho SN, Barry CE. PET/CT imaging correlates with treatment outcome in patients with multidrug-resistant tuberculosis. Sci Transl Med 2015; 6:265ra166. [PMID: 25473034 DOI: 10.1126/scitranslmed.3009501] [Citation(s) in RCA: 104] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Definitive clinical trials of new chemotherapies for treating tuberculosis (TB) require following subjects until at least 6 months after treatment discontinuation to assess for durable cure, making these trials expensive and lengthy. Surrogate endpoints relating to treatment failure and relapse are currently limited to sputum microbiology, which has limited sensitivity and specificity. We prospectively assessed radiographic changes using 2-deoxy-2-[(18)F]-fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) at 2 and 6 months (CT only) in a cohort of subjects with multidrug-resistant TB, who were treated with second-line TB therapy for 2 years and then followed for an additional 6 months. CT scans were read semiquantitatively by radiologists and were computationally evaluated using custom software to provide volumetric assessment of TB-associated abnormalities. CT scans at 6 months (but not 2 months) assessed by radiologist readers were predictive of outcomes, and changes in computed abnormal volumes were predictive of drug response at both time points. Quantitative changes in FDG uptake 2 months after starting treatment were associated with long-term outcomes. In this cohort, some radiologic markers were more sensitive than conventional sputum microbiology in distinguishing successful from unsuccessful treatment. These results support the potential of imaging scans as possible surrogate endpoints in clinical trials of new TB drug regimens. Larger cohorts confirming these results are needed.
Collapse
Affiliation(s)
- Ray Y Chen
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD 20892, USA
| | - Lori E Dodd
- Biostatistics Research Branch, NIAID, NIH, Bethesda, MD 20892, USA
| | - Myungsun Lee
- International Tuberculosis Research Center, Changwon 631-710, South Korea
| | - Praveen Paripati
- NET Esolutions Corporation (NETE), NETE-FGI Imaging Team, McLean, VA 22102, USA
| | - Dima A Hammoud
- Division of Diagnostic Radiology, Clinical Center, NIH, Bethesda, MD 20892, USA
| | - James M Mountz
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Doosoo Jeon
- National Masan Hospital, Changwon 631-710, South Korea
| | - Nadeem Zia
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Homeira Zahiri
- Division of Diagnostic Radiology, Clinical Center, NIH, Bethesda, MD 20892, USA
| | - M Teresa Coleman
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Matthew W Carroll
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD 20892, USA
| | - Jong Doo Lee
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul 120-752, South Korea
| | - Yeon Joo Jeong
- Department of Diagnostic Radiology, Pusan National University School of Medicine, Busan 609-735, South Korea
| | | | - Saher Lahouar
- NET Esolutions Corporation (NETE), NETE-FGI Imaging Team, McLean, VA 22102, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, NIAID, NIH, Bethesda, MD 20892, USA
| | - Alexander Rosenthal
- Office of Cyber Infrastructure and Computational Biology, NIAID, NIH, Bethesda, MD 20892, USA
| | - Sandeep Somaiyya
- NET Esolutions Corporation (NETE), NETE-FGI Imaging Team, McLean, VA 22102, USA
| | - Soyoung Lee
- International Tuberculosis Research Center, Changwon 631-710, South Korea
| | - Lisa C Goldfeder
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD 20892, USA
| | - Ying Cai
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD 20892, USA
| | - Laura E Via
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD 20892, USA
| | | | - Sang-Nae Cho
- International Tuberculosis Research Center, Changwon 631-710, South Korea. Department of Microbiology and Institute of Immunology and Immunological Disease, Yonsei University College of Medicine, Seoul 120-752, South Korea
| | - Clifton E Barry
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD 20892, USA. Institute of Infectious Disease and Molecular Medicine, and the Department of Clinical Laboratory Sciences, Faculty of Health Sciences, University of Cape Town, Rondebosch 7701, South Africa.
| |
Collapse
|
17
|
Via LE, Weiner DM, Schimel D, Lin PL, Dayao E, Tankersley SL, Cai Y, Coleman MT, Tomko J, Paripati P, Orandle M, Kastenmayer RJ, Tartakovsky M, Rosenthal A, Portevin D, Eum SY, Lahouar S, Gagneux S, Young DB, Flynn JL, Barry CE. Differential virulence and disease progression following Mycobacterium tuberculosis complex infection of the common marmoset (Callithrix jacchus). Infect Immun 2013; 81:2909-19. [PMID: 23716617 PMCID: PMC3719573 DOI: 10.1128/iai.00632-13] [Citation(s) in RCA: 82] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Accepted: 05/23/2013] [Indexed: 11/20/2022] Open
Abstract
Existing small-animal models of tuberculosis (TB) rarely develop cavitary disease, limiting their value for assessing the biology and dynamics of this highly important feature of human disease. To develop a smaller primate model with pathology similar to that seen in humans, we experimentally infected the common marmoset (Callithrix jacchus) with diverse strains of Mycobacterium tuberculosis of various pathogenic potentials. These included recent isolates of the modern Beijing lineage, the Euro-American X lineage, and M. africanum. All three strains produced fulminant disease in this animal with a spectrum of progression rates and clinical sequelae that could be monitored in real time using 2-deoxy-2-[(18)F]fluoro-d-glucose (FDG) positron emission tomography (PET)/computed tomography (CT). Lesion pathology at sacrifice revealed the entire spectrum of lesions observed in human TB patients. The three strains produced different rates of progression to disease, various extents of extrapulmonary dissemination, and various degrees of cavitation. The majority of live births in this species are twins, and comparison of results from siblings with different infecting strains allowed us to establish that the infection was highly reproducible and that the differential virulence of strains was not simply host variation. Quantitative assessment of disease burden by FDG-PET/CT provided an accurate reflection of the pathology findings at necropsy. These results suggest that the marmoset offers an attractive small-animal model of human disease that recapitulates both the complex pathology and spectrum of disease observed in humans infected with various M. tuberculosis strain clades.
Collapse
Affiliation(s)
- Laura E. Via
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute for Allergy and Infectious Disease, National Institutes of Health, Bethesda, Maryland, USA
| | - Danielle M. Weiner
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute for Allergy and Infectious Disease, National Institutes of Health, Bethesda, Maryland, USA
| | - Daniel Schimel
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute for Allergy and Infectious Disease, National Institutes of Health, Bethesda, Maryland, USA
| | - Philana Ling Lin
- Children's Hospital of Pittsburgh of UPMC, Pittsburgh, Pennsylvania, USA
| | - Emmanuel Dayao
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute for Allergy and Infectious Disease, National Institutes of Health, Bethesda, Maryland, USA
| | - Sarah L. Tankersley
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute for Allergy and Infectious Disease, National Institutes of Health, Bethesda, Maryland, USA
| | - Ying Cai
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute for Allergy and Infectious Disease, National Institutes of Health, Bethesda, Maryland, USA
| | - M. Teresa Coleman
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Jaime Tomko
- University of Pittsburgh School of Medicine, Department of Microbiology and Molecular Genetics, Pittsburgh, Pennsylvania, USA
| | | | | | | | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Disease, National Institutes of Health, Bethesda, Maryland, USA
| | - Alexander Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Disease, National Institutes of Health, Bethesda, Maryland, USA
| | - Damien Portevin
- MRC National Institute for Medical Research, London, United Kingdom
| | - Seok Yong Eum
- International Tuberculosis Research Center, Changwon, South Korea
| | | | - Sebastien Gagneux
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Douglas B. Young
- MRC National Institute for Medical Research, London, United Kingdom
| | - JoAnne L. Flynn
- University of Pittsburgh School of Medicine, Department of Microbiology and Molecular Genetics, Pittsburgh, Pennsylvania, USA
| | - Clifton E. Barry
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute for Allergy and Infectious Disease, National Institutes of Health, Bethesda, Maryland, USA
| |
Collapse
|
18
|
Sazbon L, Najenson T, Tartakovsky M, Becker E, Grosswasser Z. Widespread periarticular new-bone formation in long-term comatose patients. J Bone Joint Surg Br 1981; 63-B:120-5. [PMID: 7204466 DOI: 10.1302/0301-620x.63b1.7204466] [Citation(s) in RCA: 57] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Widespread periarticular new-bone formation (myositis ossificans) was studied in 45 patients with brain damage who were in long-term coma. Thirty-six of these patients displayed myositis ossificans around at least one major joint. The development of myositis ossificans was shown to be independent of the sex and age of the patient and also of the aetiology, duration and outcome of the coma. Radiographic evidence first appeared between one and two months after the onset of coma. The maximal spread of myositis ossificans was reached in the first five months after the onset of coma. Progression of the disease was not observed after 14 months. Myositis ossificans was defined as a progressive self-limiting disease found in comatose patients.
Collapse
|