1
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Chokkakula S, Oh S, Choi WS, Kim CI, Jeong JH, Kim BK, Park JH, Min SC, Kim EG, Baek YH, Choi YK, Song MS. Mammalian adaptation risk in HPAI H5N8: a comprehensive model bridging experimental data with mathematical insights. Emerg Microbes Infect 2024; 13:2339949. [PMID: 38572657 PMCID: PMC11022924 DOI: 10.1080/22221751.2024.2339949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 04/03/2024] [Indexed: 04/05/2024]
Abstract
Understanding the mammalian pathogenesis and interspecies transmission of HPAI H5N8 virus hinges on mapping its adaptive markers. We used deep sequencing to track these markers over five passages in murine lung tissue. Subsequently, we evaluated the growth, selection, and RNA load of eight recombinant viruses with mammalian adaptive markers. By leveraging an integrated non-linear regression model, we quantitatively determined the influence of these markers on growth, adaptation, and RNA expression in mammalian hosts. Furthermore, our findings revealed that the interplay of these markers can lead to synergistic, additive, or antagonistic effects when combined. The elucidation distance method then transformed these results into distinct values, facilitating the derivation of a risk score for each marker. In vivo tests affirmed the accuracy of scores. As more mutations were incorporated, the overall risk score of virus heightened, and the optimal interplay between markers became essential for risk augmentation. Our study provides a robust model to assess risk from adaptive markers of HPAI H5N8, guiding strategies against future influenza threats.
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Affiliation(s)
- Santosh Chokkakula
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Sol Oh
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Won-Suk Choi
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Chang Il Kim
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Ju Hwan Jeong
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Beom Kyu Kim
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Ji-Hyun Park
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Seong Cheol Min
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Eung-Gook Kim
- Department of Biochemistry, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Yun Hee Baek
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Young Ki Choi
- Center for Study of Emerging and Re-emerging Viruses, Korea Virus Research Institute, Institute for Basic Science (IBS), Daejeon, Republic of Korea
| | - Min-Suk Song
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
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2
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Smith AM. Decoding immune kinetics: unveiling secrets using custom-built mathematical models. Nat Methods 2024; 21:744-747. [PMID: 38710785 DOI: 10.1038/s41592-024-02265-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Affiliation(s)
- Amber M Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA.
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3
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Zitzmann C, Ke R, Ribeiro RM, Perelson AS. How robust are estimates of key parameters in standard viral dynamic models? PLoS Comput Biol 2024; 20:e1011437. [PMID: 38626190 PMCID: PMC11051641 DOI: 10.1371/journal.pcbi.1011437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 04/26/2024] [Accepted: 04/01/2024] [Indexed: 04/18/2024] Open
Abstract
Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.
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Affiliation(s)
- Carolin Zitzmann
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Ruian Ke
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Ruy M. Ribeiro
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Alan S. Perelson
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico
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4
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Shabman RS, Craig M, Laubenbacher R, Reeves D, Brown LL. NIAID/SMB Workshop on Multiscale Modeling of Infectious and Immune-Mediated Diseases. Bull Math Biol 2024; 86:44. [PMID: 38512541 PMCID: PMC10957590 DOI: 10.1007/s11538-024-01276-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/23/2024]
Abstract
On July 19th, 2023, the National Institute of Allergy and Infectious Diseases co-organized a workshop with the Society of Mathematical Biology, with the authors of this paper as the organizing committee. The workshop, "Bridging multiscale modeling and practical clinical applications in infectious diseases" sought to create an environment for mathematical modelers, statisticians, and infectious disease researchers and clinicians to exchange ideas and perspectives.
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Affiliation(s)
- Reed S Shabman
- National Institute of Allergy and Infectious Diseases, Rockville, MD, 20852, USA.
| | - Morgan Craig
- Department of Mathematics and Statistics, Sainte-Justine University Hospital Research Centre, Université de Montréal, Montreal, Canada
| | | | - Daniel Reeves
- Department of Global Health, University of Washington, Seattle, WA, 98195, USA
| | - Liliana L Brown
- National Institute of Allergy and Infectious Diseases, Rockville, MD, 20852, USA.
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5
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Gubbins S. Quantifying the relationship between within-host dynamics and transmission for viral diseases of livestock. J R Soc Interface 2024; 21:20230445. [PMID: 38379412 PMCID: PMC10879856 DOI: 10.1098/rsif.2023.0445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 01/18/2024] [Indexed: 02/22/2024] Open
Abstract
Understanding the population dynamics of an infectious disease requires linking within-host dynamics and between-host transmission in a quantitative manner, but this is seldom done in practice. Here a simple phenomenological model for viral dynamics within a host is linked to between-host transmission by assuming that the probability of transmission is related to log viral titre. Data from transmission experiments for two viral diseases of livestock, foot-and-mouth disease virus in cattle and swine influenza virus in pigs, are used to parametrize the model and, importantly, test the underlying assumptions. The model allows the relationship between within-host parameters and transmission to be determined explicitly through their influence on the reproduction number and generation time. Furthermore, these critical within-host parameters (time and level of peak titre, viral growth and clearance rates) can be computed from more complex within-host models, raising the possibility of assessing the impact of within-host processes on between-host transmission in a more detailed quantitative manner.
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Affiliation(s)
- Simon Gubbins
- The Pirbright Institute, Ash Road, Pirbright, Surrey GU24 0NF, UK
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6
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Feng Y, Wen S, Xue S, Hou M, Jin Y. Potential co-infection of influenza A, influenza B, respiratory syncytial virus, and Chlamydia pneumoniae: a case report with literature review. Front Med (Lausanne) 2024; 10:1325482. [PMID: 38259842 PMCID: PMC10800736 DOI: 10.3389/fmed.2023.1325482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/15/2023] [Indexed: 01/24/2024] Open
Abstract
The occurrence of a co-infection involving four distinct respiratory pathogens could be underestimated. Here, we report the case of a 72-year-old woman who presented to a community hospital with a cough productive of sputum as her main clinical manifestation. Antibody detection of common respiratory pathogens revealed potential co-infection with influenza A, influenza B, respiratory syncytial virus, and Chlamydia pneumoniae. We treated her with 75 mg oseltamivir phosphate administered orally twice daily for 5 days, 0.5 g azithromycin administered orally for 5 days, and 0.3 g acetylcysteine aerosol inhaled twice daily for 3 days. The patient showed a favorable outcome on the eighth day after early diagnosis and treatment. Since co-infection with these four pathogens is rare, we performed an extensive PubMed search of similar cases and carried out a systematic review to analyze the epidemiology, clinical manifestations, transmission route, susceptible population, and outcomes of these four different pathogens. Our report highlights the importance for general practitioners to be vigilant about the possibility of mixed infections when a patient presents with respiratory symptoms. Although these symptoms may be mild, early diagnosis and timely treatment could improve outcomes. Additionally, further research is warranted to explore the potential influence of SARS-CoV-2 infection on the co-occurrence of multiple respiratory pathogens.
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Affiliation(s)
| | | | | | | | - Ying Jin
- Huangpu District Dapuqiao Community Health Center, Shanghai, China
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7
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Deng X, Gantner P, Forestell J, Pagliuzza A, Brunet‐Ratnasingham E, Durand M, Kaufmann DE, Chomont N, Craig M. Plasma SARS-CoV-2 RNA elimination and RAGE kinetics distinguish COVID-19 severity. Clin Transl Immunology 2023; 12:e1468. [PMID: 38020729 PMCID: PMC10666810 DOI: 10.1002/cti2.1468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 09/01/2023] [Accepted: 09/08/2023] [Indexed: 12/01/2023] Open
Abstract
Objectives Identifying biomarkers causing differential SARS-CoV-2 infection kinetics associated with severe COVID-19 is fundamental for effective diagnostics and therapeutic planning. Methods In this work, we applied mathematical modelling to investigate the relationships between patient characteristics, plasma SARS-CoV-2 RNA dynamics and COVID-19 severity. Using a straightforward mathematical model of within-host viral kinetics, we estimated key model parameters from serial plasma viral RNA (vRNA) samples from 256 hospitalised COVID-19+ patients. Results Our model predicted that clearance rates distinguish key differences in plasma vRNA kinetics and severe COVID-19. Moreover, our analyses revealed a strong correlation between plasma vRNA kinetics and plasma receptor for advanced glycation end products (RAGE) concentrations (a plasma biomarker of lung damage), collected in parallel to plasma vRNA from patients in our cohort, suggesting that RAGE can substitute for viral plasma shedding dynamics to prospectively classify seriously ill patients. Conclusion Overall, our study identifies factors of COVID-19 severity, supports interventions to accelerate viral clearance and underlines the importance of mathematical modelling to better understand COVID-19.
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Affiliation(s)
- Xiaoyan Deng
- Research Centre of the Centre Hospitalier Universitaire Sainte‐JustineMontréalQCCanada
- Département de mathématiques et de statistiqueUniversité de MontréalMontréalQCCanada
| | - Pierre Gantner
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
- Département de Microbiologie, Infectiologie et ImmunologieUniversité de MontréalMontréalQCCanada
| | - Julia Forestell
- Research Centre of the Centre Hospitalier Universitaire Sainte‐JustineMontréalQCCanada
| | - Amélie Pagliuzza
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
- Département de Microbiologie, Infectiologie et ImmunologieUniversité de MontréalMontréalQCCanada
| | - Elsa Brunet‐Ratnasingham
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
- Département de Microbiologie, Infectiologie et ImmunologieUniversité de MontréalMontréalQCCanada
| | - Madeleine Durand
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
| | - Daniel E Kaufmann
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
- Centre hospitalier de l'Université de Montréal (CHUM)MontréalQCCanada
- Département de MédecineUniversité de MontréalMontréalQCCanada
- Division of Infectious Diseases, Department of MedicineUniversity Hospital and University of LausanneLausanneSwitzerland
| | - Nicolas Chomont
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
- Département de Microbiologie, Infectiologie et ImmunologieUniversité de MontréalMontréalQCCanada
| | - Morgan Craig
- Research Centre of the Centre Hospitalier Universitaire Sainte‐JustineMontréalQCCanada
- Département de mathématiques et de statistiqueUniversité de MontréalMontréalQCCanada
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8
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Pinky L, DeAguero JR, Remien CH, Smith AM. How Interactions during Viral-Viral Coinfection Can Shape Infection Kinetics. Viruses 2023; 15:1303. [PMID: 37376603 DOI: 10.3390/v15061303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/29/2023] Open
Abstract
Respiratory viral infections are a leading global cause of disease with multiple viruses detected in 20-30% of cases, and several viruses simultaneously circulating. Some infections with unique viral copathogens result in reduced pathogenicity, while other viral pairings can worsen disease. The mechanisms driving these dichotomous outcomes are likely variable and have only begun to be examined in the laboratory and clinic. To better understand viral-viral coinfections and predict potential mechanisms that result in distinct disease outcomes, we first systematically fit mathematical models to viral load data from ferrets infected with respiratory syncytial virus (RSV), followed by influenza A virus (IAV) after 3 days. The results suggest that IAV reduced the rate of RSV production, while RSV reduced the rate of IAV infected cell clearance. We then explored the realm of possible dynamics for scenarios that had not been examined experimentally, including a different infection order, coinfection timing, interaction mechanisms, and viral pairings. IAV coinfection with rhinovirus (RV) or SARS-CoV-2 (CoV2) was examined by using human viral load data from single infections together with murine weight-loss data from IAV-RV, RV-IAV, and IAV-CoV2 coinfections to guide the interpretation of the model results. Similar to the results with RSV-IAV coinfection, this analysis shows that the increased disease severity observed during murine IAV-RV or IAV-CoV2 coinfection was likely due to the slower clearance of IAV-infected cells by the other viruses. The improved outcome when IAV followed RV, on the other hand, could be replicated when the rate of RV infected cell clearance was reduced by IAV. Simulating viral-viral coinfections in this way provides new insights about how viral-viral interactions can regulate disease severity during coinfection and yields testable hypotheses ripe for experimental evaluation.
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Affiliation(s)
- Lubna Pinky
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Joseph R DeAguero
- Bioinformatics and Computational Biology Program, University of Idaho, Moscow, ID 83844, USA
| | - Christopher H Remien
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID 83844, USA
| | - Amber M Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
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9
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Sachak-Patwa R, Lafferty EI, Schmit CJ, Thompson RN, Byrne HM. A target-cell limited model can reproduce influenza infection dynamics in hosts with differing immune responses. J Theor Biol 2023; 567:111491. [PMID: 37044357 DOI: 10.1016/j.jtbi.2023.111491] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 03/02/2023] [Accepted: 04/05/2023] [Indexed: 04/14/2023]
Abstract
We consider a hierarchy of ordinary differential equation models that describe the within-host viral kinetics of influenza infections: the IR model explicitly accounts for an immune response to the virus, while the simpler, target-cell limited TEIV and TV models do not. We show that when the IR model is fitted to pooled experimental murine data of the viral load, fraction of dead cells, and immune response levels, its parameters values can be determined. However, if, as is common, only viral load data are available, we can estimate parameters of the TEIV and TV models but not the IR model. These results are substantiated by a structural and practical identifiability analysis. We then use the IR model to generate synthetic data representing infections in hosts whose immune responses differ. We fit the TV model to these synthetic datasets and show that it can reproduce the characteristic exponential increase and decay of viral load generated by the IR model. Furthermore, the values of the fitted parameters of the TV model can be mapped from the immune response parameters in the IR model. We conclude that, if only viral load data are available, a simple target-cell limited model can reproduce influenza infection dynamics and distinguish between hosts with differing immune responses.
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Affiliation(s)
- Rahil Sachak-Patwa
- Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
| | - Erin I Lafferty
- Biosensors Beyond Borders Limited, 9 Bedford Square, London, WC1B 3RE, UK
| | - Claude J Schmit
- Biosensors Beyond Borders Limited, 9 Bedford Square, London, WC1B 3RE, UK
| | - Robin N Thompson
- Mathematics Institute, University of Warwick, Zeeman Building, Coventry, CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, CV4 7AL, UK
| | - Helen M Byrne
- Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK
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10
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Pinky L, DeAguero JR, Remien CH, Smith AM. How Interactions During Viral-Viral Coinfection Can Shape Infection Kinetics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.05.535744. [PMID: 37066297 PMCID: PMC10104040 DOI: 10.1101/2023.04.05.535744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Respiratory virus infections are a leading cause of disease worldwide with multiple viruses detected in 20-30% of cases and several viruses simultaneously circulating. Some infections with viral copathogens have been shown to result in reduced pathogenicity while other virus pairings can worsen disease. The mechanisms driving these dichotomous outcomes are likely variable and have only begun to be examined in the laboratory and clinic. To better understand viral-viral coinfections and predict potential mechanisms that result in distinct disease outcomes, we first systematically fit mathematical models to viral load data from ferrets infected with respiratory syncytial virus (RSV) followed by influenza A virus (IAV) after 3 days. The results suggested that IAV reduced the rate of RSV production while RSV reduced the rate of IAV infected cell clearance. We then explored the realm of possible dynamics for scenarios not examined experimentally, including different infection order, coinfection timing, interaction mechanisms, and viral pairings. IAV coinfection with rhinovirus (RV) or SARS-CoV-2 (CoV2) was examined by using human viral load data from single infections together with murine weight loss data from IAV-RV, RV-IAV, and IAV-CoV2 coinfections to guide the interpretation of the model results. Similar to the results with RSV-IAV coinfection, this analysis showed that the increased disease severity observed during murine IAV-RV or IAV-CoV2 coinfection was likely due to slower clearance of IAV infected cells by the other viruses. On the contrary, the improved outcome when IAV followed RV could be replicated when the rate of RV infected cell clearance was reduced by IAV. Simulating viral-viral coinfections in this way provides new insights about how viral-viral interactions can regulate disease severity during coinfection and yields testable hypotheses ripe for experimental evaluation.
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11
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Lv YW, Du Y, Ma SS, Shi YC, Xu HC, Deng L, Chen XY. Proanthocyanidins attenuates ferroptosis against influenza-induced acute lung injury in mice by reducing IFN-γ. Life Sci 2023; 314:121279. [PMID: 36526043 DOI: 10.1016/j.lfs.2022.121279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/24/2022] [Accepted: 12/07/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Acute lung injury (ALI) is associated with high morbidity and mortality and is partly driven promoted by ferroptosis. Proanthocyanidins (PAs) is a natural bioactive flavonoid with anti-inflammatory and antioxidant activities. PAs can also significantly protect against acute lung inflammation and ferroptosis in alveolar epithelial cells. However, it is unclear whether PAs can alleviate ALI by reducing ferroptosis. This study aimed to evaluate the protective effects of PAs and the potential mechanisms against Influenza A virus (IAV)-induced ALI. METHODS Mice were inoculated nasally with IAV to induce ALI. IAV-induced pulmonary inflammation and ferroptosis was tested by measuring the levels of malondialdehyde (MDA), glutathione (GSH), glutathione peroxidase 4 (GPX4), solute carrier family 7 member 11 (SLC7A11) and acyl-CoA synthetase long-chain family member (ACSL4) in lung tissue. The potential targets that PAs protect against IAV-induced ALI were determined via a systemic pharmacological analysis. The molecular mechanism of PAs in ALI treatment was investigated by assessing the level of inflammation and ferroptosis markers using Western Blot and quantitative real-time PCR. RESULTS Systemic pharmacological analysis suggested that PAs protect against IAV-induced pneumonia thorough TGF-β1 and its relative signaling pathway. PAs effectively alleviated histopathological lung injury, reduced inflammatory cytokines and chemokines secretion, which were increased in IAV-infected mice. Meanwhile, PAs further prevented mouse airway inflammation in ALI, concomitant with the decreased expression TGF-β1, smad2/3, p-Smad2, p-Smad3 and ferroptosis mediator IFN-γ. Furthermore,IFN-γ promotes cell lipid peroxidation and ferroptosis,PAs significantly reduced MDA and ACSL4 levels and upregulated GSH, GPX4, and SLC7A11. CONCLUSION Overall, PAs can attenuate ferroptosis against IAV-induced ALI via the TGF-β1/Smad2/3 pathway and is a promising novel therapeutic candidate for ALI.
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Affiliation(s)
- Yi-Wen Lv
- College of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Yang Du
- College of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Sheng-Suo Ma
- College of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Yu-Cong Shi
- College of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Hua-Chong Xu
- College of Traditional Chinese Medicine, Jinan University, Guangzhou, China.
| | - Li Deng
- College of Traditional Chinese Medicine, Jinan University, Guangzhou, China; State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China; Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Guangzhou, China.
| | - Xiao-Yin Chen
- College of Traditional Chinese Medicine, Jinan University, Guangzhou, China; State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China; Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Guangzhou, China.
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12
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Jhutty SS, Boehme JD, Jeron A, Volckmar J, Schultz K, Schreiber J, Schughart K, Zhou K, Steinheimer J, Stöcker H, Stegemann-Koniszewski S, Bruder D, Hernandez-Vargas EA. Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning. mSystems 2022; 7:e0045922. [PMID: 36346236 PMCID: PMC9765554 DOI: 10.1128/msystems.00459-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
The tracking of pathogen burden and host responses with minimally invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e., cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing of the models. We show here that lung viral load, neutrophil counts, cytokines (such as gamma interferon [IFN-γ] and interleukin 6 [IL-6]), and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models showed that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path toward improved tracking and monitoring of influenza virus infections and possibly other respiratory infections based on minimally invasively obtained hematological parameters. IMPORTANCE During the course of respiratory infections such as influenza, we do have a very limited view of immunological indicators to objectively and quantitatively evaluate the outcome of a host. Methods for monitoring immunological markers in a host's lungs are invasive and expensive, and some of them are not feasible to perform. Using machine learning algorithms, we show for the first time that minimally invasively acquired hematological parameters can be used to infer lung viral burden, leukocytes, and cytokines following influenza virus infection in mice. The potential of the framework proposed here consists of a new qualitative vision of the disease processes in the lung compartment as a noninvasive tool.
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Affiliation(s)
- Suneet Singh Jhutty
- Frankfurt Institute for Advanced Studiesgrid.417999.b, Frankfurt am Main, Germany
- Faculty of Biological Sciences, Goethe University, Frankfurt am Main, Germany
| | - Julia D. Boehme
- Immune Regulation Group, Helmholtz Centre for Infection Researchgrid.7490.a, Braunschweig, Germany
- Infection Immunology Group, Institute of Medical Microbiology, Infection Control and Prevention, Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Andreas Jeron
- Immune Regulation Group, Helmholtz Centre for Infection Researchgrid.7490.a, Braunschweig, Germany
- Infection Immunology Group, Institute of Medical Microbiology, Infection Control and Prevention, Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Julia Volckmar
- Immune Regulation Group, Helmholtz Centre for Infection Researchgrid.7490.a, Braunschweig, Germany
- Infection Immunology Group, Institute of Medical Microbiology, Infection Control and Prevention, Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Kristin Schultz
- Immune Regulation Group, Helmholtz Centre for Infection Researchgrid.7490.a, Braunschweig, Germany
- Department of Infection Genetics, Helmholtz Centre for Infection Researchgrid.7490.a, Braunschweig, Germany
| | - Jens Schreiber
- Department of Pneumology, Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke University Magdeburggrid.5807.a, Magdeburg, Germany
| | - Klaus Schughart
- Department of Infection Genetics, Helmholtz Centre for Infection Researchgrid.7490.a, Braunschweig, Germany
- Department of Microbiology, Immunology, and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee, USA
- University of Veterinary Medicine Hannover, Hannover, Germany
| | - Kai Zhou
- Frankfurt Institute for Advanced Studiesgrid.417999.b, Frankfurt am Main, Germany
| | - Jan Steinheimer
- Frankfurt Institute for Advanced Studiesgrid.417999.b, Frankfurt am Main, Germany
| | - Horst Stöcker
- Frankfurt Institute for Advanced Studiesgrid.417999.b, Frankfurt am Main, Germany
- Institut für Theoretische Physik, Goethe Universität Frankfurt, Frankfurt am Main, Germany
- GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany
| | - Sabine Stegemann-Koniszewski
- Department of Pneumology, Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke University Magdeburggrid.5807.a, Magdeburg, Germany
| | - Dunja Bruder
- Immune Regulation Group, Helmholtz Centre for Infection Researchgrid.7490.a, Braunschweig, Germany
- Infection Immunology Group, Institute of Medical Microbiology, Infection Control and Prevention, Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Esteban A. Hernandez-Vargas
- Frankfurt Institute for Advanced Studiesgrid.417999.b, Frankfurt am Main, Germany
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, Idaho, USA
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, USA
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13
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Desikan R, Linderman SL, Davis C, Zarnitsyna VI, Ahmed H, Antia R. Vaccine models predict rules for updating vaccines against evolving pathogens such as SARS-CoV-2 and influenza in the context of pre-existing immunity. Front Immunol 2022; 13:985478. [PMID: 36263031 PMCID: PMC9574365 DOI: 10.3389/fimmu.2022.985478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 09/16/2022] [Indexed: 11/13/2022] Open
Abstract
Currently, vaccines for SARS-CoV-2 and influenza viruses are updated if the new vaccine induces higher antibody-titers to circulating variants than current vaccines. This approach does not account for complex dynamics of how prior immunity skews recall responses to the updated vaccine. We: (i) use computational models to mechanistically dissect how prior immunity influences recall responses; (ii) explore how this affects the rules for evaluating and deploying updated vaccines; and (iii) apply this to SARS-CoV-2. Our analysis of existing data suggests that there is a strong benefit to updating the current SARS-CoV-2 vaccines to match the currently circulating variants. We propose a general two-dose strategy for determining if vaccines need updating as well as for vaccinating high-risk individuals. Finally, we directly validate our model by reanalysis of earlier human H5N1 influenza vaccine studies.
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Affiliation(s)
- Rajat Desikan
- Clinical Pharmacology Modeling & Simulation, GlaxoSmithKline (GSK), Stevenage, Hertfordshire, United Kingdom
- *Correspondence: Rajat Desikan, ; Rustom Antia,
| | - Susanne L. Linderman
- Department of Microbiology and Immunology, Emory University, Atlanta, GA, United States
| | - Carl Davis
- Department of Microbiology and Immunology, Emory University, Atlanta, GA, United States
| | | | - Hasan Ahmed
- Department of Biology, Emory University, Atlanta, GA, United States
| | - Rustom Antia
- Department of Biology, Emory University, Atlanta, GA, United States
- *Correspondence: Rajat Desikan, ; Rustom Antia,
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14
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Hofflich B, Lunardhi A, Sunku N, Tsujimoto J, Cauwenberghs G. Modeling the Viral Kinetics of Influenza A During Infection in Humans. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2244-2247. [PMID: 36086157 DOI: 10.1109/embc48229.2022.9871075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This study explores the natural control system in the body for responding to exposure to the Influenza A virus. More specifically, it delves into the development of a model to simulate the responses of target uninfected cell counts, infected cell counts, and viral titers. There are two particular models of interest: a delayed model that incorporates the brief inactive period for newly infected cells, and a non-delayed model reflecting only infected cells without delay after initial infection. Both models are commonly used in the literature and the benefits of each model are studied and explained. We generate Simulink models for both the delayed and non-delayed sets of ordinary differential equations (ODEs) to simulate responses to different viral titer impulses. Additionally, this study aims to extrapolate these models to the case for a vaccinated individual. To do this, we modify the viral clearance rate and infected cell death rate of our initial model to account for the improved immune response generated by vaccines.
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15
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Liu W, Qiu S, Zhang L, Wu H, Tian X, Li X, Xu D, Dai J, Gu S, Liu Q, Chen D, Zhou R. Analysis of severe human adenovirus infection outbreak in Guangdong Province, southern China in 2019. Virol Sin 2022; 37:331-340. [PMID: 35307598 PMCID: PMC9243629 DOI: 10.1016/j.virs.2022.01.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 12/06/2021] [Indexed: 12/24/2022] Open
Abstract
During 2018-2019, a severe human adenovirus (HAdV) infection outbreak occurred in southern China. Here, we screened 18 respiratory pathogens in 1704 children (≤ 14 years old) hospitalized with acute respiratory illness in Guangzhou, China, in 2019. In total, 151 patients had positive HAdV test results; 34.4% (52/151) of them exhibited severe illness. HAdV infection occurred throughout the year, with a peak in summer. The median patient age was 3.0 (interquartile range: 1.1-5.0) years. Patients with severe HAdV infection exhibited increases in 12 clinical indexes (P ≤ 0.019) and decreases in four indexes (P ≤ 0.007), compared with patients exhibiting non-severe infection. No significant differences were found in age or sex distribution according to HAdV infection severity (P > 0.05); however, the distributions of comorbid disease and HAdV co-infection differed according to HAdV infection severity (P < 0.05). The main epidemic types were HAdV-3 (47.0%, 71/151) and HAdV-7 (46.4%, 70/151). However, the severe illness rate was significantly higher in patients with HAdV-7 (51.4%) than in patients with HAdV-3 (19.7%) and other types of HAdV (20%) (P < 0.001). Sequencing analysis of genomes/capsid genes of 13 HAdV-7 isolates revealed high similarity to previous Chinese isolates. A representative HAdV-7 isolate exhibited a similar proliferation curve to the curve described for the epidemic HAdV-3 strain Guangzhou01 (accession no. DQ099432) (P > 0.05); the HAdV-7 isolate exhibited stronger virulence and infectivity, compared with HAdV-3 (P < 0.001). Overall, comorbid disease, HAdV co-infection, and high virulence and infectivity of HAdV-7 were critical risk factors for severe HAdV infection; these data can facilitate treatment, control, and prevention of HAdV infection.
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Affiliation(s)
- Wenkuan Liu
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou Medical University, Guangzhou, 510040, China
| | - Shuyan Qiu
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou Medical University, Guangzhou, 510040, China
| | - Li Zhang
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou Medical University, Guangzhou, 510040, China
| | - Hongkai Wu
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou Medical University, Guangzhou, 510040, China
| | - Xingui Tian
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou Medical University, Guangzhou, 510040, China
| | - Xiao Li
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou Medical University, Guangzhou, 510040, China
| | - Duo Xu
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou Medical University, Guangzhou, 510040, China
| | - Jing Dai
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou Medical University, Guangzhou, 510040, China
| | - Shujun Gu
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou Medical University, Guangzhou, 510040, China
| | - Qian Liu
- Scientific Research Center, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, 510062, China.
| | - Dehui Chen
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou Medical University, Guangzhou, 510040, China.
| | - Rong Zhou
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Disease, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou Medical University, Guangzhou, 510040, China; Bioland Laboratory, Guangzhou Laboratory, Guangzhou, 510320, China.
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16
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Desikan R, Linderman SL, Davis C, Zarnitsyna V, Ahmed H, Antia R. Modeling suggests that multiple immunizations or infections will reveal the benefits of updating SARS-CoV-2 vaccines. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.05.21.492928. [PMID: 35665010 PMCID: PMC9164442 DOI: 10.1101/2022.05.21.492928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
When should vaccines to evolving pathogens such as SARS-CoV-2 be updated? Our computational models address this focusing on updating SARS-CoV-2 vaccines to the currently circulating Omicron variant. Current studies typically compare the antibody titers to the new variant following a single dose of the original-vaccine versus the updated-vaccine in previously immunized individuals. These studies find that the updated-vaccine does not induce higher titers to the vaccine-variant compared with the original-vaccine, suggesting that updating may not be needed. Our models recapitulate this observation but suggest that vaccination with the updated-vaccine generates qualitatively different humoral immunity, a small fraction of which is specific for unique epitopes to the new variant. Our simulations suggest that these new variant-specific responses could dominate following subsequent vaccination or infection with either the currently circulating or future variants. We suggest a two-dose strategy for determining if the vaccine needs updating and for vaccinating high-risk individuals.
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Affiliation(s)
- Rajat Desikan
- Clinical Pharmacology Modeling & Simulation, GlaxoSmithKline (GSK), Gunnels Wood Rd, Stevenage, Hertfordshire, SG1 2NY, United Kingdom
- These authors contributed equally
| | - Susanne L. Linderman
- Department of Microbiology and Immunology, Emory University, Atlanta, GA 30322, USA
| | - Carl Davis
- Department of Microbiology and Immunology, Emory University, Atlanta, GA 30322, USA
| | - Veronika Zarnitsyna
- Department of Microbiology and Immunology, Emory University, Atlanta, GA 30322, USA
| | - Hasan Ahmed
- Department of Biology, Emory University, Atlanta, GA 30322, USA
| | - Rustom Antia
- Department of Biology, Emory University, Atlanta, GA 30322, USA
- These authors contributed equally
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17
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The Contribution of Viral Proteins to the Synergy of Influenza and Bacterial Co-Infection. Viruses 2022; 14:v14051064. [PMID: 35632805 PMCID: PMC9143653 DOI: 10.3390/v14051064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/12/2022] [Accepted: 05/12/2022] [Indexed: 02/04/2023] Open
Abstract
A severe course of acute respiratory disease caused by influenza A virus (IAV) infection is often linked with subsequent bacterial superinfection, which is difficult to cure. Thus, synergistic influenza-bacterial co-infection represents a serious medical problem. The pathogenic changes in the infected host are accelerated as a consequence of IAV infection, reflecting its impact on the host immune response. IAV infection triggers a complex process linked with the blocking of innate and adaptive immune mechanisms required for effective antiviral defense. Such disbalance of the immune system allows for easier initiation of bacterial superinfection. Therefore, many new studies have emerged that aim to explain why viral-bacterial co-infection can lead to severe respiratory disease with possible fatal outcomes. In this review, we discuss the key role of several IAV proteins-namely, PB1-F2, hemagglutinin (HA), neuraminidase (NA), and NS1-known to play a role in modulating the immune defense of the host, which consequently escalates the development of secondary bacterial infection, most often caused by Streptococcus pneumoniae. Understanding the mechanisms leading to pathological disorders caused by bacterial superinfection after the previous viral infection is important for the development of more effective means of prevention; for example, by vaccination or through therapy using antiviral drugs targeted at critical viral proteins.
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18
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Smith AP, Williams EP, Plunkett TR, Selvaraj M, Lane LC, Zalduondo L, Xue Y, Vogel P, Channappanavar R, Jonsson CB, Smith AM. Time-Dependent Increase in Susceptibility and Severity of Secondary Bacterial Infections During SARS-CoV-2. Front Immunol 2022; 13:894534. [PMID: 35634338 PMCID: PMC9134015 DOI: 10.3389/fimmu.2022.894534] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 04/11/2022] [Indexed: 12/20/2022] Open
Abstract
Secondary bacterial infections can exacerbate SARS-CoV-2 infection, but their prevalence and impact remain poorly understood. Here, we established that a mild to moderate infection with the SARS-CoV-2 USA-WA1/2020 strain increased the risk of pneumococcal (type 2 strain D39) coinfection in a time-dependent, but sex-independent, manner in the transgenic K18-hACE2 mouse model of COVID-19. Bacterial coinfection increased lethality when the bacteria was initiated at 5 or 7 d post-virus infection (pvi) but not at 3 d pvi. Bacterial outgrowth was accompanied by neutrophilia in the groups coinfected at 7 d pvi and reductions in B cells, T cells, IL-6, IL-15, IL-18, and LIF were present in groups coinfected at 5 d pvi. However, viral burden, lung pathology, cytokines, chemokines, and immune cell activation were largely unchanged after bacterial coinfection. Examining surviving animals more than a week after infection resolution suggested that immune cell activation remained high and was exacerbated in the lungs of coinfected animals compared with SARS-CoV-2 infection alone. These data suggest that SARS-CoV-2 increases susceptibility and pathogenicity to bacterial coinfection, and further studies are needed to understand and combat disease associated with bacterial pneumonia in COVID-19 patients.
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Affiliation(s)
- Amanda P. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Evan P. Williams
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Taylor R. Plunkett
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Muneeswaran Selvaraj
- Department of Acute and Tertiary Care, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Lindey C. Lane
- College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Lillian Zalduondo
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Yi Xue
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Peter Vogel
- Animal Resources Center and Veterinary Pathology Core, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Rudragouda Channappanavar
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, United States
- Department of Acute and Tertiary Care, University of Tennessee Health Science Center, Memphis, TN, United States
- Institute for the Study of Host-Pathogen Systems, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Colleen B. Jonsson
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, United States
- Institute for the Study of Host-Pathogen Systems, University of Tennessee Health Science Center, Memphis, TN, United States
- *Correspondence: Amber M. Smith, ; Colleen B. Jonsson,
| | - Amber M. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, United States
- Institute for the Study of Host-Pathogen Systems, University of Tennessee Health Science Center, Memphis, TN, United States
- *Correspondence: Amber M. Smith, ; Colleen B. Jonsson,
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19
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Smith AP, Williams EP, Plunkett TR, Selvaraj M, Lane LC, Zalduondo L, Xue Y, Vogel P, Channappanavar R, Jonsson CB, Smith AM. Time-Dependent Increase in Susceptibility and Severity of Secondary Bacterial Infection during SARS-CoV-2 Infection.. [PMID: 35262077 PMCID: PMC8902874 DOI: 10.1101/2022.02.28.482305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Secondary bacterial infections can exacerbate SARS-CoV-2 infection, but their prevalence and impact remain poorly understood. Here, we established that a mild to moderate SARS-CoV-2 infection increased the risk of pneumococcal coinfection in a time-dependent, but sex-independent, manner in the transgenic K18-hACE mouse model of COVID-19. Bacterial coinfection was not established at 3 d post-virus, but increased lethality was observed when the bacteria was initiated at 5 or 7 d post-virus infection (pvi). Bacterial outgrowth was accompanied by neutrophilia in the groups coinfected at 7 d pvi and reductions in B cells, T cells, IL-6, IL-15, IL-18, and LIF were present in groups coinfected at 5 d pvi. However, viral burden, lung pathology, cytokines, chemokines, and immune cell activation were largely unchanged after bacterial coinfection. Examining surviving animals more than a week after infection resolution suggested that immune cell activation remained high and was exacerbated in the lungs of coinfected animals compared with SARS-CoV-2 infection alone. These data suggest that SARS-CoV-2 increases susceptibility and pathogenicity to bacterial coinfection, and further studies are needed to understand and combat disease associated with bacterial pneumonia in COVID-19 patients.
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20
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Understanding Rhinovirus Circulation and Impact on Illness. Viruses 2022; 14:v14010141. [PMID: 35062345 PMCID: PMC8778310 DOI: 10.3390/v14010141] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/08/2022] [Accepted: 01/10/2022] [Indexed: 01/27/2023] Open
Abstract
Rhinoviruses (RVs) have been reported as one of the main viral causes for severe respiratory illnesses that may require hospitalization, competing with the burden of other respiratory viruses such as influenza and RSV in terms of severity, economic cost, and resource utilization. With three species and 169 subtypes, RV presents the greatest diversity within the Enterovirus genus, and despite the efforts of the research community to identify clinically relevant subtypes to target therapeutic strategies, the role of species and subtype in the clinical outcomes of RV infection remains unclear. This review aims to collect and organize data relevant to RV illness in order to find patterns and links with species and/or subtype, with a specific focus on species and subtype diversity in clinical studies typing of respiratory samples.
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21
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Getz M, Wang Y, An G, Asthana M, Becker A, Cockrell C, Collier N, Craig M, Davis CL, Faeder JR, Ford Versypt AN, Mapder T, Gianlupi JF, Glazier JA, Hamis S, Heiland R, Hillen T, Hou D, Islam MA, Jenner AL, Kurtoglu F, Larkin CI, Liu B, Macfarlane F, Maygrundter P, Morel PA, Narayanan A, Ozik J, Pienaar E, Rangamani P, Saglam AS, Shoemaker JE, Smith AM, Weaver JJA, Macklin P. Iterative community-driven development of a SARS-CoV-2 tissue simulator. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2020.04.02.019075. [PMID: 32511322 PMCID: PMC7239052 DOI: 10.1101/2020.04.02.019075] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.
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22
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Contreras C, Newby JM, Hillen T. Personalized Virus Load Curves for Acute Viral Infections. Viruses 2021; 13:1815. [PMID: 34578396 PMCID: PMC8472998 DOI: 10.3390/v13091815] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 07/09/2021] [Accepted: 09/03/2021] [Indexed: 12/11/2022] Open
Abstract
We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis of acute viral infections without solving a full virus load dynamic model. We validate our model on data from mice influenza A, human rhinovirus data, human influenza A data, and monkey and human SARS-CoV-2 data. We find wide distributions for the model parameters, reflecting large variability in the disease outcomes between individuals. Further, we compare the virus load function to an established target model of virus dynamics, and we provide a new way to estimate the exponential growth rates of the corresponding infection phases. The virus load function, the target model, and the exponential approximations show excellent fits for the data considered. Our virus-load function offers a new way to analyze patient-specific virus load data, and it can be used as input for higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.
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Affiliation(s)
- Carlos Contreras
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Jay M. Newby
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Thomas Hillen
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
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23
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Kong D, Zheng Y, Hu L, Chen J, Wu H, Teng Z, Zhou Y, Qiu Q, Lu Y, Pan H. Epidemiological and co-infection characteristics of common human coronaviruses in Shanghai, 2015-2020: a retrospective observational study. Emerg Microbes Infect 2021; 10:1660-1668. [PMID: 34350810 PMCID: PMC8381891 DOI: 10.1080/22221751.2021.1965498] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The coronavirus disease (COVID-19) pandemic is a major challenge worldwide. However, the epidemic potential of common human coronaviruses (HCoVs) remains unclear. This study aimed to determine the epidemiological and co-infection characteristics of common HCoVs in individuals with influenza-like illness (ILI) and severe acute respiratory infection (SARI). This retrospective, observational, multicentre study used data collected from patients admitted to nine sentinel hospitals with ILI and SARI from January 2015 through December 2020 in Shanghai, China. We prospectively tested patients for a total of 22 respiratory pathogens using multi-real-time polymerase chain reaction. Of the 4541 patients tested, 40.37% (1833/4541) tested positive for respiratory pathogens and 3.59% (163/4541) tested positive for common HCoVs. HCoV infection was more common in the non-endemic season for respiratory pathogens (odds ratio: 2.33, 95% confidence interval: 1.64–3.31). HCoV-OC43 (41.72%, 68/163) was the most common type of HCoV detected. The co-infection rate was 31.29% (51/163) among 163 HCoV-positive cases, with HCoV-229E (53.13%, 17/32), the HCoV type that was most frequently associated with co-infection. Respiratory pathogens responsible for co-infections with HCoVs included parainfluenza virus, rhinovirus/enterovirus, influenza A virus, and adenovirus. Furthermore, we identified one patient co-infected with HCoV-OC43 and HCoV-NL63/HKU1. The prevalence of common HCoVs remains low in ILI/SARI cases, in Shanghai. However, the seasonal pattern of HCoVs may be opposite to that of other respiratory pathogens. Moreover, HCoVs are likely to co-exist with specific respiratory pathogens. The potential role of co-infections with HCoVs and other pathogenic microorganisms in infection and pathogenesis of ILI and SARI warrants further study.
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Affiliation(s)
- Dechuan Kong
- Department of Acute Communicable Diseases Control and Prevention, Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, People's Republic of China
| | - Yaxu Zheng
- Department of Acute Communicable Diseases Control and Prevention, Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, People's Republic of China.,Department of Epidemiology, Ministry of Education Key Laboratory of Public Health Safety, Fudan School of Public Health, Shanghai, People's Republic of China
| | - Linjie Hu
- Department of Epidemiology, Ministry of Education Key Laboratory of Public Health Safety, Fudan School of Public Health, Shanghai, People's Republic of China
| | - Jian Chen
- Department of Logistics Support, Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, People's Republic of China
| | - Huanyu Wu
- Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, People's Republic of China
| | - Zheng Teng
- Institute of Pathogen Examination, Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, People's Republic of China
| | - Yanqiu Zhou
- Institute of Pathogen Examination, Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, People's Republic of China
| | - Qi Qiu
- Department of Acute Communicable Diseases Control and Prevention, Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, People's Republic of China
| | - Yihan Lu
- Department of Epidemiology, Ministry of Education Key Laboratory of Public Health Safety, Fudan School of Public Health, Shanghai, People's Republic of China
| | - Hao Pan
- Institute of Communicable Diseases Control and Prevention, Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, People's Republic of China
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24
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Quantifying dose-, strain-, and tissue-specific kinetics of parainfluenza virus infection. PLoS Comput Biol 2021; 17:e1009299. [PMID: 34383757 PMCID: PMC8384156 DOI: 10.1371/journal.pcbi.1009299] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 08/24/2021] [Accepted: 07/23/2021] [Indexed: 11/25/2022] Open
Abstract
Human parainfluenza viruses (HPIVs) are a leading cause of acute respiratory infection hospitalization in children, yet little is known about how dose, strain, tissue tropism, and individual heterogeneity affects the processes driving growth and clearance kinetics. Longitudinal measurements are possible by using reporter Sendai viruses, the murine counterpart of HPIV 1, that express luciferase, where the insertion location yields a wild-type (rSeV-luc(M-F*)) or attenuated (rSeV-luc(P-M)) phenotype. Bioluminescence from individual animals suggests that there is a rapid increase in expression followed by a peak, biphasic clearance, and resolution. However, these kinetics vary between individuals and with dose, strain, and whether the infection was initiated in the upper and/or lower respiratory tract. To quantify the differences, we translated the bioluminescence measurements from the nasopharynx, trachea, and lung into viral loads and used a mathematical model together a nonlinear mixed effects approach to define the mechanisms distinguishing each scenario. The results confirmed a higher rate of virus production with the rSeV-luc(M-F*) virus compared to its attenuated counterpart, and suggested that low doses result in disproportionately fewer infected cells. The analyses indicated faster infectivity and infected cell clearance rates in the lung and that higher viral doses, and concomitantly higher infected cell numbers, resulted in more rapid clearance. This parameter was also highly variable amongst individuals, which was particularly evident during infection in the lung. These critical differences provide important insight into distinct HPIV dynamics, and show how bioluminescence data can be combined with quantitative analyses to dissect host-, virus-, and dose-dependent effects. Human parainfluenza viruses (HPIVs) cause acute respiratory infections and can lead to the hospitalization of children. HPIV infection severity may vary due to dose, strain, patient, and whether the infection initiates within the upper or lower respiratory tract. There is a need to determine how the rates of virus spread and clearance change in different infection scenarios in order to better understand varying clinical manifestations. The significance of our research is in identifying the dominant mechanisms driving strain-, dose-, and tissue-specific HPIV infection kinetics, and in pairing bioluminescence data with quantitative analyses to determine how the same virus can yield patient-specific outcomes. This work enhances our understanding of HPIV infection and broadens our knowledge viral dynamics in the upper and lower respiratory tracts.
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25
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Myers MA, Smith AP, Lane LC, Moquin DJ, Aogo R, Woolard S, Thomas P, Vogel P, Smith AM. Dynamically linking influenza virus infection kinetics, lung injury, inflammation, and disease severity. eLife 2021; 10:68864. [PMID: 34282728 PMCID: PMC8370774 DOI: 10.7554/elife.68864] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/14/2021] [Indexed: 12/12/2022] Open
Abstract
Influenza viruses cause a significant amount of morbidity and mortality. Understanding host immune control efficacy and how different factors influence lung injury and disease severity are critical. We established and validated dynamical connections between viral loads, infected cells, CD8+ T cells, lung injury, inflammation, and disease severity using an integrative mathematical model-experiment exchange. Our results showed that the dynamics of inflammation and virus-inflicted lung injury are distinct and nonlinearly related to disease severity, and that these two pathologic measurements can be independently predicted using the model-derived infected cell dynamics. Our findings further indicated that the relative CD8+ T cell dynamics paralleled the percent of the lung that had resolved with the rate of CD8+ T cell-mediated clearance rapidly accelerating by over 48,000 times in 2 days. This complimented our analyses showing a negative correlation between the efficacy of innate and adaptive immune-mediated infected cell clearance, and that infection duration was driven by CD8+ T cell magnitude rather than efficacy and could be significantly prolonged if the ratio of CD8+ T cells to infected cells was sufficiently low. These links between important pathogen kinetics and host pathology enhance our ability to forecast disease progression, potential complications, and therapeutic efficacy.
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Affiliation(s)
- Margaret A Myers
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, United States
| | - Amanda P Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, United States
| | - Lindey C Lane
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, United States
| | - David J Moquin
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, United States
| | - Rosemary Aogo
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, United States
| | - Stacie Woolard
- Flow Cytometry Core, St. Jude Children's Research Hospital, Memphis, United States
| | - Paul Thomas
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, United States
| | - Peter Vogel
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, United States
| | - Amber M Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, United States
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26
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Jenner AL, Aogo RA, Alfonso S, Crowe V, Deng X, Smith AP, Morel PA, Davis CL, Smith AM, Craig M. COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes. PLoS Pathog 2021; 17:e1009753. [PMID: 34260666 PMCID: PMC8312984 DOI: 10.1371/journal.ppat.1009753] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 07/26/2021] [Accepted: 06/24/2021] [Indexed: 12/11/2022] Open
Abstract
To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.
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Affiliation(s)
- Adrianne L. Jenner
- Sainte-Justine University Hospital Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
| | - Rosemary A. Aogo
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Sofia Alfonso
- Department of Physiology, McGill University, Montréal, Québec, Canada
| | - Vivienne Crowe
- Department of Mathematics and Statistics, Concordia University, Montréal, Québec, Canada
| | - Xiaoyan Deng
- Sainte-Justine University Hospital Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
| | - Amanda P. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Penelope A. Morel
- Department of Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Courtney L. Davis
- Natural Science Division, Pepperdine University, Malibu, California, United States of America
| | - Amber M. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Morgan Craig
- Sainte-Justine University Hospital Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
- Department of Physiology, McGill University, Montréal, Québec, Canada
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27
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Baabdulla AA, Now H, Park JA, Kim WJ, Jung S, Yoo JY, Hillen T. Homogenization of a reaction diffusion equation can explain influenza A virus load data. J Theor Biol 2021; 527:110816. [PMID: 34161792 DOI: 10.1016/j.jtbi.2021.110816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 06/14/2021] [Accepted: 06/15/2021] [Indexed: 11/18/2022]
Abstract
We study the influence of spatial heterogeneity on the antiviral activity of mouse embryonic fibroblasts (MEF) infected with influenza A. MEF of type Ube1L-/- are composed of two distinct sub-populations, the strong type that sustains a strong viral infection and the weak type, sustaining a weak viral load. We present new data on the virus load infection of Ube1L-/-, which have been micro-printed in a checker board pattern of different sizes of the inner squares. Surprisingly, the total viral load at one day after inoculation significantly depends on the sizes of the inner squares. We explain this observation by using a reaction diffusion model and we show that mathematical homogenization can explain the observed inhomogeneities. If the individual patches are large, then the growth rate and the carrying capacity will be the arithmetic means of the patches. For finer and finer patches the average growth rate is still the arithmetic mean, however, the carrying capacity uses the harmonic mean. While fitting the PDE to the experimental data, we also predict that a discrepancy in virus load would be unobservable after only half a day. Furthermore, we predict the viral load in different inner squares that had not been measured in our experiment and the travelling distance the virions can reach after one day.
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Affiliation(s)
- Arwa Abdulla Baabdulla
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada.
| | - Hesung Now
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Ju An Park
- Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Woo-Jong Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Sungjune Jung
- Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea; School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Joo-Yeon Yoo
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Thomas Hillen
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada.
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Dynamic Pneumococcal Genetic Adaptations Support Bacterial Growth and Inflammation during Coinfection with Influenza. Infect Immun 2021; 89:e0002321. [PMID: 33875471 PMCID: PMC8208518 DOI: 10.1128/iai.00023-21] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Streptococcus pneumoniae (pneumococcus) is one of the primary bacterial pathogens that complicates influenza virus infections. These bacterial coinfections increase influenza-associated morbidity and mortality through a number of immunological and viral-mediated mechanisms, but the specific bacterial genes that contribute to postinfluenza pathogenicity are not known. Here, we used genome-wide transposon mutagenesis (Tn-Seq) to reveal bacterial genes that confer improved fitness in influenza virus-infected hosts. The majority of the 32 genes identified are involved in bacterial metabolism, including nucleotide biosynthesis, amino acid biosynthesis, protein translation, and membrane transport. We generated mutants with single-gene deletions (SGD) of five of the genes identified, SPD1414, SPD2047 (cbiO1), SPD0058 (purD), SPD1098, and SPD0822 (proB), to investigate their effects on in vivo fitness, disease severity, and host immune responses. The growth of the SGD mutants was slightly attenuated in vitro and in vivo, but each still grew to high titers in the lungs of mock- and influenza virus-infected hosts. Despite high bacterial loads, mortality was significantly reduced or delayed with all SGD mutants. Time-dependent reductions in pulmonary neutrophils, inflammatory macrophages, and select proinflammatory cytokines and chemokines were also observed. Immunohistochemical staining further revealed altered neutrophil distribution with reduced degeneration in the lungs of influenza virus-SGD mutant-coinfected animals. These studies demonstrate a critical role for specific bacterial genes and for bacterial metabolism in driving virulence and modulating immune function during influenza-associated bacterial pneumonia.
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29
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Reeves DB, Rolland M, Dearlove BL, Li Y, Robb ML, Schiffer JT, Gilbert P, Cardozo-Ojeda EF, Mayer BT. Timing HIV infection with a simple and accurate population viral dynamics model. J R Soc Interface 2021; 18:20210314. [PMID: 34186015 PMCID: PMC8241492 DOI: 10.1098/rsif.2021.0314] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 06/03/2021] [Indexed: 12/18/2022] Open
Abstract
Clinical trials for HIV prevention can require knowledge of infection times to subsequently determine protective drug levels. Yet, infection timing is difficult when study visits are sparse. Using population nonlinear mixed-effects (pNLME) statistical inference and viral loads from 46 RV217 study participants, we developed a relatively simple HIV primary infection model that achieved an excellent fit to all data. We also discovered that Aptima assay values from the study strongly correlated with viral loads, enabling imputation of very early viral loads for 28/46 participants. Estimated times between infecting exposures and first positives were generally longer than prior estimates (average of two weeks) and were robust to missing viral upslope data. On simulated data, we found that tighter sampling before diagnosis improved estimation more than tighter sampling after diagnosis. Sampling weekly before and monthly after diagnosis was a pragmatic design for good timing accuracy. Our pNLME timing approach is widely applicable to other infections with existing mathematical models. The present model could be used to simulate future HIV trials and may help estimate protective thresholds from the recently completed antibody-mediated prevention trials.
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Affiliation(s)
- Daniel B. Reeves
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Morgane Rolland
- US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Bethany L. Dearlove
- US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Yifan Li
- US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Merlin L. Robb
- US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Joshua T. Schiffer
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Peter Gilbert
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Statistics, University of Washington, Seattle, WA, USA
| | - E. Fabian Cardozo-Ojeda
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Bryan T. Mayer
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
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30
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Nakahira Y, Liu Q, Sejnowski TJ, Doyle JC. Diversity-enabled sweet spots in layered architectures and speed-accuracy trade-offs in sensorimotor control. Proc Natl Acad Sci U S A 2021; 118:e1916367118. [PMID: 34050009 PMCID: PMC8179159 DOI: 10.1073/pnas.1916367118] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Nervous systems sense, communicate, compute, and actuate movement using distributed components with severe trade-offs in speed, accuracy, sparsity, noise, and saturation. Nevertheless, brains achieve remarkably fast, accurate, and robust control performance due to a highly effective layered control architecture. Here, we introduce a driving task to study how a mountain biker mitigates the immediate disturbance of trail bumps and responds to changes in trail direction. We manipulated the time delays and accuracy of the control input from the wheel as a surrogate for manipulating the characteristics of neurons in the control loop. The observed speed-accuracy trade-offs motivated a theoretical framework consisting of two layers of control loops-a fast, but inaccurate, reflexive layer that corrects for bumps and a slow, but accurate, planning layer that computes the trajectory to follow-each with components having diverse speeds and accuracies within each physical level, such as nerve bundles containing axons with a wide range of sizes. Our model explains why the errors from two control loops are additive and shows how the errors in each control loop can be decomposed into the errors caused by the limited speeds and accuracies of the components. These results demonstrate that an appropriate diversity in the properties of neurons across layers helps to create "diversity-enabled sweet spots," so that both fast and accurate control is achieved using slow or inaccurate components.
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Affiliation(s)
- Yorie Nakahira
- Electrical and Computer Engineering, College of Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
- Control and Dynamical Systems, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125
| | - Quanying Liu
- Control and Dynamical Systems, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Terrence J Sejnowski
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037;
- Neurobiology Section, Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093
| | - John C Doyle
- Control and Dynamical Systems, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125;
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31
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Mathematical modeling of ventilator-induced lung inflammation. J Theor Biol 2021; 526:110738. [PMID: 33930440 DOI: 10.1016/j.jtbi.2021.110738] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/20/2021] [Accepted: 04/22/2021] [Indexed: 12/13/2022]
Abstract
Despite the benefits of mechanical ventilators, prolonged or misuse of ventilators may lead to ventilation-associated/ventilation-induced lung injury (VILI). Lung insults, such as respiratory infections and lung injuries, can damage the pulmonary epithelium, with the most severe cases needing mechanical ventilation for effective breathing and survival. Damaged epithelial cells within the alveoli trigger a local immune response. A key immune cell is the macrophage, which can differentiate into a spectrum of phenotypes ranging from pro- to anti-inflammatory. To gain a greater understanding of the mechanisms of the immune response to VILI and post-ventilation outcomes, we developed a mathematical model of interactions between the immune system and site of damage while accounting for macrophage phenotype. Through Latin hypercube sampling we generated a collection of parameter sets that are associated with a numerical steady state. We then simulated ventilation-induced damage using these steady state values as the initial conditions in order to evaluate how baseline immune state and lung health affect outcomes. We used a variety of methods to analyze the resulting parameter sets, transients, and outcomes, including a random forest decision tree algorithm and parameter sensitivity with eFAST. Analysis shows that parameters and properties of transients related to epithelial repair and M1 activation are important factors. Using the results of this analysis, we hypothesized interventions and used these treatment strategies to modulate the response to ventilation for particular parameters sets.
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32
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Néant N, Lingas G, Le Hingrat Q, Ghosn J, Engelmann I, Lepiller Q, Gaymard A, Ferré V, Hartard C, Plantier JC, Thibault V, Marlet J, Montes B, Bouiller K, Lescure FX, Timsit JF, Faure E, Poissy J, Chidiac C, Raffi F, Kimmoun A, Etienne M, Richard JC, Tattevin P, Garot D, Le Moing V, Bachelet D, Tardivon C, Duval X, Yazdanpanah Y, Mentré F, Laouénan C, Visseaux B, Guedj J. Modeling SARS-CoV-2 viral kinetics and association with mortality in hospitalized patients from the French COVID cohort. Proc Natl Acad Sci U S A 2021; 118:e2017962118. [PMID: 33536313 PMCID: PMC7929555 DOI: 10.1073/pnas.2017962118] [Citation(s) in RCA: 142] [Impact Index Per Article: 47.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral kinetics in hospitalized patients and its association with mortality is unknown. We analyzed death and nasopharyngeal viral kinetics in 655 hospitalized patients from the prospective French COVID cohort. The model predicted a median peak viral load that coincided with symptom onset. Patients with age ≥65 y had a smaller loss rate of infected cells, leading to a delayed median time to viral clearance occurring 16 d after symptom onset as compared to 13 d in younger patients (P < 10-4). In multivariate analysis, the risk factors associated with mortality were age ≥65 y, male gender, and presence of chronic pulmonary disease (hazard ratio [HR] > 2.0). Using a joint model, viral dynamics after hospital admission was an independent predictor of mortality (HR = 1.31, P < 10-3). Finally, we used our model to simulate the effects of effective pharmacological interventions on time to viral clearance and mortality. A treatment able to reduce viral production by 90% upon hospital admission would shorten the time to viral clearance by 2.0 and 2.9 d in patients of age <65 y and ≥65 y, respectively. Assuming that the association between viral dynamics and mortality would remain similar to that observed in our population, this could translate into a reduction of mortality from 19 to 14% in patients of age ≥65 y with risk factors. Our results show that viral dynamics is associated with mortality in hospitalized patients. Strategies aiming to reduce viral load could have an effect on mortality rate in this population.
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Affiliation(s)
- Nadège Néant
- Université de Paris, INSERM, IAME, F-75018 Paris, France;
| | | | - Quentin Le Hingrat
- Université de Paris, INSERM, IAME, F-75018 Paris, France
- AP-HP, Hôpital Bichat, Laboratoire de Virologie, F-75018 Paris, France
| | - Jade Ghosn
- Université de Paris, INSERM, IAME, F-75018 Paris, France
- AP-HP, Hopital Bichat, Service de Maladies Infectieuses et Tropicales, F-75018 Paris, France
| | - Ilka Engelmann
- Univ. Lille, Virology Laboratory, EA3610, Institute of Microbiology, Centre Hospitalier-Universitaire de Lille, F-59037 Lille Cedex, France
| | - Quentin Lepiller
- Laboratoire de Virologie, Centre Hospitalier-Universitaire de Besançon, F-25000 Besançon, France
| | - Alexandre Gaymard
- Laboratoire de Virologie, Institut des Agents Infectieux, Hospices Civils de Lyon, Groupement Hospitalier Nord, F-69004 Lyon, France
- Centre National de Référence des Virus Respiratoires, Hospices Civils de Lyon, Groupement Hospitalier Nord, F-69004 Lyon, France
| | - Virginie Ferré
- Service de Virologie, Centre Hospitalier-Universitaire de Nantes, F-44093 Nantes, France
| | - Cédric Hartard
- Laboratoire de Microbiologie, Centre Hospitalier-Universitaire de Nancy, F-54000 Nancy, France
- Université de Lorraine, CNRS, Laboratoire de Chimie Physique et Microbiologie pour les Matériaux et l'Environnement, F-54000 Nancy, France
| | - Jean-Christophe Plantier
- Normandie University, UNIROUEN Rouen, EA2656, Virology, Rouen University Hospital, F-76000 Rouen, France
| | - Vincent Thibault
- Virology, Pontchaillou University Hospital, F-35033 Rennes cedex, France
| | - Julien Marlet
- Laboratoire de Virologie, Centre Hospitalier-Universitaire de Bretonneau, F-37044 Tours, France
- INSERM UMR 1259, Université de Tours, F-37044 Tours, France
| | - Brigitte Montes
- Laboratoire de Virologie, Centre Hospitalier-Universitaire de Montpellier, F-34295 Montpellier, France
| | - Kevin Bouiller
- Infectious and Tropical Disease Department, Besancon University Hospital, F-25000 Besancon, France
- UMR CNRS 6249, Chrono Environnement, University of Bourgogne Franche-Comté, F-25000 Besancon, France
| | - François-Xavier Lescure
- AP-HP, Hopital Bichat, Service de Maladies Infectieuses et Tropicales, F-75018 Paris, France
| | - Jean-François Timsit
- Université de Paris, INSERM, IAME, F-75018 Paris, France
- AP-HP, Hôpital Bichat, Service de Réanimation Médicale et Infectieuse, F-75018 Paris, France
| | - Emmanuel Faure
- Centre Hospitalier-Universitaire de Lille, Univ. Lille, Infectious Disease Department, CNRS, Inserm, U1019-UMR9017-CIIL, F-59000 Lille, France
| | - Julien Poissy
- Université de Lille, INSERM U1285, Centre Hospitalier-Universitaire de Lille, Pôle de réanimation, CNRS, UMR 8576-UGSF-Unité de Glycobiologie Structurale et Fonctionnelle, F-59000 Lille, France
| | - Christian Chidiac
- Infectious and Tropical Disease Department, Croix-Rousse Hospital, University Hospital of Lyon, F-69004 Lyon, France
| | - François Raffi
- Service de Maladies Infectieuses et Tropicales, Centre Hospitalier-Universitaire de Nantes, F-44093 Nantes, France
- Centre d'Investigation Clinique Unité d'Investigation Clinique 1413 INSERM, Centre Hospitalier-Universitaire de Nantes, F-44093 Nantes, France
| | - Antoine Kimmoun
- Université de Lorraine, Centre Hospitalier Régional Universitaire de Nancy, INSERM U1116, F-CRIN INICRCT, Service de Médecine Intensive et Réanimation Brabois, F-54000 Nancy, France
| | - Manuel Etienne
- Infectious Diseases Department, Rouen University Hospital, F-76000 Rouen, France
| | - Jean-Christophe Richard
- Lyon University, CREATIS, CNRS UMR5220, INSERM U1044, INSA, F-69000 Lyon, France
- Intensive Care Unit, Hospices Civils de Lyon, F-69002 Lyon, France
| | - Pierre Tattevin
- Infectious Diseases and Intensive Care Unit, Pontchaillou University Hospital, F-35000 Rennes, France
| | - Denis Garot
- Centre Hospitalier Régional Universitaire de Tours, Service de Médecine Intensive Réanimation, F-37044 Tours Cedex 9, France
| | - Vincent Le Moing
- Tropical and Infectious Diseases, Saint Eloi Hospital, Université de Montpellier, Medical School, Montpellier University Hospital, F-34295 Montpellier Cedex 5, France
| | - Delphine Bachelet
- AP-HP, Hôpital Bichat, Department of Epidemiology Biostatistics and Clinical Research, F-75018 Paris, France
| | - Coralie Tardivon
- AP-HP, Hôpital Bichat, Department of Epidemiology Biostatistics and Clinical Research, F-75018 Paris, France
| | - Xavier Duval
- Université de Paris, INSERM, IAME, F-75018 Paris, France
- AP-HP, Hôpital Bichat, Centre d'Investigation Clinique, INSERM CIC-1425, F-75018 Paris, France
| | - Yazdan Yazdanpanah
- Université de Paris, INSERM, IAME, F-75018 Paris, France
- AP-HP, Hopital Bichat, Service de Maladies Infectieuses et Tropicales, F-75018 Paris, France
| | - France Mentré
- Université de Paris, INSERM, IAME, F-75018 Paris, France
- AP-HP, Hôpital Bichat, Department of Epidemiology Biostatistics and Clinical Research, F-75018 Paris, France
| | - Cédric Laouénan
- Université de Paris, INSERM, IAME, F-75018 Paris, France
- AP-HP, Hôpital Bichat, Department of Epidemiology Biostatistics and Clinical Research, F-75018 Paris, France
| | - Benoit Visseaux
- Université de Paris, INSERM, IAME, F-75018 Paris, France
- AP-HP, Hôpital Bichat, Laboratoire de Virologie, F-75018 Paris, France
| | - Jérémie Guedj
- Université de Paris, INSERM, IAME, F-75018 Paris, France
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Jenner AL, Aogo RA, Alfonso S, Crowe V, Smith AP, Morel PA, Davis CL, Smith AM, Craig M. COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.01.05.425420. [PMID: 33442689 PMCID: PMC7805446 DOI: 10.1101/2021.01.05.425420] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results indicate that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8 + T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation. AUTHOR SUMMARY Understanding of how the immune system responds to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results identify key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational approach to studying novel pathogens using intra-host models.
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Affiliation(s)
- Adrianne L. Jenner
- CHU Sainte-Justine Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
| | - Rosemary A. Aogo
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Sofia Alfonso
- Department of Physiology, McGill University, Montréal, Québec, Canada
| | - Vivienne Crowe
- Department of Mathematics and Statistics, Concordia University, Montréal, Québec, Canada
| | - Amanda P. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Penelope A. Morel
- Department of Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Courtney L. Davis
- Natural Science Division, Pepperdine University, Malibu, California, USA
| | - Amber M. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Morgan Craig
- CHU Sainte-Justine Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
- Department of Physiology, McGill University, Montréal, Québec, Canada
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34
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Hernandez-Vargas EA. Modeling Viral Infections. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11620-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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35
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Nissly RH, Zaman N, Ibrahim PAS, McDaniel K, Lim L, Kiser JN, Bird I, Chothe SK, Bhushan GL, Vandegrift K, Neibergs HL, Kuchipudi SV. Influenza C and D viral load in cattle correlates with bovine respiratory disease (BRD): Emerging role of orthomyxoviruses in the pathogenesis of BRD. Virology 2020; 551:10-15. [PMID: 33010670 PMCID: PMC7519714 DOI: 10.1016/j.virol.2020.08.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/18/2020] [Accepted: 08/25/2020] [Indexed: 11/29/2022]
Abstract
Bovine respiratory disease (BRD) is the costliest disease affecting the cattle industry globally. Orthomyxoviruses, influenza C virus (ICV) and influenza D virus (IDV) have recently been implicated to play a role in BRD. However, there are contradicting reports about the association of IDV and ICV to BRD. Using the largest cohort study (cattle, n = 599) to date we investigated the association of influenza viruses in cattle with BRD. Cattle were scored for respiratory symptoms and pooled nasal and pharyngeal swabs were tested for bovine viral diarrhea virus, bovine herpesvirus 1, bovine respiratory syncytial virus, bovine coronavirus, ICV and IDV by real-time PCR. Cattle that have higher viral loads of IDV and ICV also have greater numbers of co-infecting viruses than controls. More strikingly, 2 logs higher IDV viral RNA in BRD-symptomatic cattle that are co-infected animals than those infected with IDV alone. Our results strongly suggest that ICV and IDV may be significant contributors to BRD.
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Affiliation(s)
- Ruth H Nissly
- Penn State Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Noriza Zaman
- Penn State Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Puteri Ainaa S Ibrahim
- Penn State Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Kaitlin McDaniel
- Penn State Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Levina Lim
- Penn State Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Jennifer N Kiser
- Department of Animal Sciences and Center for Reproductive Biology, Washington State University, Pullman, WA, USA
| | - Ian Bird
- Penn State Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Shubhada K Chothe
- Penn State Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Gitanjali L Bhushan
- Penn State Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Kurt Vandegrift
- The Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Holly L Neibergs
- Department of Animal Sciences and Center for Reproductive Biology, Washington State University, Pullman, WA, USA
| | - Suresh V Kuchipudi
- Penn State Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, USA; The Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, 16802, USA.
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36
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Leveraging Computational Modeling to Understand Infectious Diseases. CURRENT PATHOBIOLOGY REPORTS 2020; 8:149-161. [PMID: 32989410 PMCID: PMC7511257 DOI: 10.1007/s40139-020-00213-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 09/16/2020] [Indexed: 02/06/2023]
Abstract
Purpose of Review Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation. Recent Findings Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps. Summary Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.
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Motawi TK, Shahin NN, Awad K, Maghraby AS, Abd-Elshafy DN, Bahgat MM. Glycolytic and immunological alterations in human U937 monocytes in response to H1N1 infection. IUBMB Life 2020; 72:2481-2498. [PMID: 32941696 DOI: 10.1002/iub.2378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/07/2020] [Accepted: 08/14/2020] [Indexed: 11/06/2022]
Abstract
We monitored changes that took place in glycolytic enzymes, the pyruvate end product of glycolysis, tumor necrosis factor α (TNFα), and toll-like receptors (TLRs) both at the transcriptional and translational levels upon direct interaction between PR8-H1N1 and the human monocytes U937 in vitro system. U937 were first treated with H1N1 infectious viral particles or phorbol-12-myristate-13-acetate (PMA) or left untreated and later infected with the H1N1 virus. Levels of phosphofructokinase 1 (PFK1) and pyruvate were biochemically quantified. In addition, levels of TNFα, TLR3, and TLR7 were measured by ELISA. The transcriptional profiles of PFKs, inflammatory cytokines, TLR3 and TLR7 were relatively quantified by qRT-PCR. The results generally revealed significant changes in both the transcriptional and translational profiles of the studied biochemical and immunological parameters upon influenza infection in a time-dependent manner. In conclusion, H1N1 infection triggers transcriptional and translational changes in immortalized human monocytes, which might serve as markers for infection subject for further validation for their specificities.
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Affiliation(s)
- Tarek Kamal Motawi
- Biochemistry Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Nancy Nabil Shahin
- Biochemistry Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Kareem Awad
- Research Group Immune- and Bio-markers for Infection, the Center of Excellence for Advanced Sciences, the National Research Center, Cairo, Egypt.,Department of Therapeutic Chemistry, Division of Pharmaceutical and Drug Industries Research, the National Research Center, Cairo, Egypt
| | - Amany Sayed Maghraby
- Research Group Immune- and Bio-markers for Infection, the Center of Excellence for Advanced Sciences, the National Research Center, Cairo, Egypt.,Department of Therapeutic Chemistry, Division of Pharmaceutical and Drug Industries Research, the National Research Center, Cairo, Egypt
| | - Dina Nadeem Abd-Elshafy
- Research Group Immune- and Bio-markers for Infection, the Center of Excellence for Advanced Sciences, the National Research Center, Cairo, Egypt.,Department of Water Pollution Research, Division of Environmental Research, the National Research Center, Cairo, Egypt
| | - Mahmoud Mohamed Bahgat
- Research Group Immune- and Bio-markers for Infection, the Center of Excellence for Advanced Sciences, the National Research Center, Cairo, Egypt.,Department of Therapeutic Chemistry, Division of Pharmaceutical and Drug Industries Research, the National Research Center, Cairo, Egypt
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38
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Gonçalves A, Bertrand J, Ke R, Comets E, de Lamballerie X, Malvy D, Pizzorno A, Terrier O, Rosa Calatrava M, Mentré F, Smith P, Perelson AS, Guedj J. Timing of Antiviral Treatment Initiation is Critical to Reduce SARS-CoV-2 Viral Load. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:509-514. [PMID: 32558354 PMCID: PMC7323384 DOI: 10.1002/psp4.12543] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 06/12/2020] [Indexed: 12/11/2022]
Abstract
We modeled the viral dynamics of 13 untreated patients infected with severe acute respiratory syndrome‐coronavirus 2 to infer viral growth parameters and predict the effects of antiviral treatments. In order to reduce peak viral load by more than two logs, drug efficacy needs to be > 90% if treatment is administered after symptom onset; an efficacy of 60% could be sufficient if treatment is initiated before symptom onset. Given their pharmacokinetic/pharmacodynamic properties, current investigated drugs may be in a range of 6–87% efficacy. They may help control virus if administered very early, but may not have a major effect in severely ill patients.
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Affiliation(s)
| | | | - Ruian Ke
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | | | - Xavier de Lamballerie
- Institut Hospitalo-Universitaire Méditerranée Infection, UMR "Emergence des Pathologies Virales" (EPV: Aix-Marseille University - IRD 190 - Inserm 1207 - EHESP), Marseille, France
| | - Denis Malvy
- Inserm, UMR 1219, Université de Bordeaux, Bordeaux, France.,Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
| | - Andrés Pizzorno
- CIRI, Centre International de Recherche en Infectiologie, (Team VirPath), Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Lyon, France
| | - Olivier Terrier
- CIRI, Centre International de Recherche en Infectiologie, (Team VirPath), Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Lyon, France
| | - Manuel Rosa Calatrava
- CIRI, Centre International de Recherche en Infectiologie, (Team VirPath), Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Lyon, France
| | | | - Patrick Smith
- Certara, Integrated Drug Development, Princeton, New Jersey, USA
| | - Alan S Perelson
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
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Rippee-Brooks MD, Marcinczyk RN, Lupfer CR. What came first, the virus or the egg: Innate immunity during viral coinfections. Immunol Rev 2020; 297:194-206. [PMID: 32761626 DOI: 10.1111/imr.12911] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/09/2020] [Accepted: 07/10/2020] [Indexed: 12/13/2022]
Abstract
Infections with any pathogen can be severe and present with numerous complications caused by the pathogen or the host immune response to the invading microbe. However, coinfections, also called polymicrobial infections or secondary infections, can further exacerbate disease. Coinfections are more common than is often appreciated. In this review, we focus specifically on coinfections between viruses and other viruses, bacteria, parasites, or fungi. Importantly, innate immune signaling and innate immune cells that facilitate clearance of the initial viral infection can affect host susceptibility to coinfections. Understanding these immune imbalances may facilitate better diagnosis, prevention, and treatment of such coinfections.
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40
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Belser JA, Pulit-Penaloza JA, Maines TR. Ferreting Out Influenza Virus Pathogenicity and Transmissibility: Past and Future Risk Assessments in the Ferret Model. Cold Spring Harb Perspect Med 2020; 10:cshperspect.a038323. [PMID: 31871233 DOI: 10.1101/cshperspect.a038323] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
As influenza A viruses continue to jump species barriers, data generated in the ferret model to assess influenza virus pathogenicity, transmissibility, and tropism of these novel strains continues to inform an increasing scope of public health-based applications. This review presents the suitability of ferrets as a small mammalian model for influenza viruses and describes the breadth of pathogenicity and transmissibility profiles possible in this species following inoculation with a diverse range of viruses. Adaptation of aerobiology-based techniques and analyses have furthered our understanding of data obtained from this model and provide insight into the capacity of novel and emerging influenza viruses to cause human infection and disease.
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Affiliation(s)
- Jessica A Belser
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia 30329, USA
| | - Joanna A Pulit-Penaloza
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia 30329, USA
| | - Taronna R Maines
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia 30329, USA
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41
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Gonçalves A, Bertrand J, Ke R, Comets E, de Lamballerie X, Malvy D, Pizzorno A, Terrier O, Calatrava MR, Mentré F, Smith P, Perelson AS, Guedj J. Timing of antiviral treatment initiation is critical to reduce SARS-CoV-2 viral load. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020. [PMID: 32511641 DOI: 10.1101/2020.04.04.20047886] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
We modeled the viral dynamics of 13 untreated patients infected with SARS-CoV-2 to infer viral growth parameters and predict the effects of antiviral treatments. In order to reduce peak viral load by more than 2 logs, drug efficacy needs to be greater than 80% if treatment is administered after symptom onset; an efficacy of 50% could be sufficient if treatment is initiated before symptom onset. Given their pharmacokinetic/pharmacodynamic properties, current investigated drugs may be in a range of 20-70% efficacy. They may help control virus if administered very early, but may not have a major effect in severe patients.
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42
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Sasaki K, Bruder D, Hernandez-Vargas EA. Topological data analysis to model the shape of immune responses during co-infections. COMMUNICATIONS IN NONLINEAR SCIENCE & NUMERICAL SIMULATION 2020; 85:105228. [PMID: 32288422 PMCID: PMC7129978 DOI: 10.1016/j.cnsns.2020.105228] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 01/17/2020] [Accepted: 02/11/2020] [Indexed: 05/23/2023]
Abstract
Co-infections by multiple pathogens have important implications in many aspects of health, epidemiology and evolution. However, how to disentangle the non-linear dynamics of the immune response when two infections take place at the same time is largely unexplored. Using data sets of the immune response during influenza-pneumococcal co-infection in mice, we employ here topological data analysis to simplify and visualise high dimensional data sets. We identified persistent shapes of the simplicial complexes of the data in the three infection scenarios: single viral infection, single bacterial infection, and co-infection. The immune response was found to be distinct for each of the infection scenarios and we uncovered that the immune response during the co-infection has three phases and two transition points. During the first phase, its dynamics is inherited from its response to the primary (viral) infection. The immune response has an early shift (few hours post co-infection) and then modulates its response to react against the secondary (bacterial) infection. Between 18 and 26 h post co-infection the nature of the immune response changes again and does no longer resembles either of the single infection scenarios.
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Affiliation(s)
- Karin Sasaki
- Frankfurt Institute for Advanced Studies, Frankfurt am Main 60438, Germany
| | - Dunja Bruder
- Infection Immunology Group, Institute of Medical Microbiology, Infection Prevention and Control, Health Campus Immunology, Infectiology and Inflammation Otto-von-Guericke University Magdeburg, Germany
- Immune Regulation Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Esteban A Hernandez-Vargas
- Frankfurt Institute for Advanced Studies, Frankfurt am Main 60438, Germany
- Instituto de Matematicas, UNAM, Unidad Juriquilla, Blvd. Juriquilla 3001, Queretaro C.P. 76230, Mexico
- Xidian-FIAS Joint Research Center, Germany-China
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43
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Moore JR, Ahmed H, Manicassamy B, Garcia-Sastre A, Handel A, Antia R. Varying Inoculum Dose to Assess the Roles of the Immune Response and Target Cell Depletion by the Pathogen in Control of Acute Viral Infections. Bull Math Biol 2020; 82:35. [PMID: 32125535 DOI: 10.1007/s11538-020-00711-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 02/19/2020] [Indexed: 02/05/2023]
Abstract
It is difficult to determine whether an immune response or target cell depletion by the infectious agent is most responsible for the control of acute primary infection. Both mechanisms can explain the basic dynamics of an acute infection-exponential growth of the pathogen followed by control and clearance-and can also be represented by many different differential equation models. Consequently, traditional model comparison techniques using time series data can be ambiguous or inconclusive. We propose that varying the inoculum dose and measuring the subsequent infectious load can rule out target cell depletion by the pathogen as the main control mechanism. Infectious load can be any measure that is proportional to the number of infected cells, such as viraemia. We show that a twofold or greater change in infectious load is unlikely when target cell depletion controls infection, regardless of the model details. Analyzing previously published data from mice infected with influenza, we find the proportion of lung epithelial cells infected was 21-fold greater (95% confidence interval 14-32) in the highest dose group than in the lowest. This provides evidence in favor of an alternative to target cell depletion, such as innate immunity, in controlling influenza infections in this experimental system. Data from other experimental animal models of acute primary infection have a similar pattern.
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Affiliation(s)
- James R Moore
- Division of Vaccines and Infectious Diseases, Fred Hutchinson Cancer Research Center, Seattle, USA.
| | - Hasan Ahmed
- Department of Biology, Emory University, Atlanta, USA
| | - Balaji Manicassamy
- Department of Microbiology and Immunology, University of Iowa School College of Medicine, Iowa City, USA
| | | | - Andreas Handel
- Epidemiology and Biostatistics, University of Georgia, Athens, USA
| | - Rustom Antia
- Department of Biology, Emory University, Atlanta, USA
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44
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Villeret B, Solhonne B, Straube M, Lemaire F, Cazes A, Garcia-Verdugo I, Sallenave JM. Influenza A Virus Pre-Infection Exacerbates Pseudomonas aeruginosa-Mediated Lung Damage Through Increased MMP-9 Expression, Decreased Elafin Production and Tissue Resilience. Front Immunol 2020; 11:117. [PMID: 32117268 PMCID: PMC7031978 DOI: 10.3389/fimmu.2020.00117] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 01/16/2020] [Indexed: 12/13/2022] Open
Abstract
Individuals with impaired immune responses, such as ventilated and cystic fibrosis patients are often infected with Pseudomonas aeruginosa (P.a) bacteria, and a co-infection with the Influenza virus (IAV) is often present. It has been known for many years that infection with IAV predisposes the host to secondary bacterial infections (such as Streptococcus pneumoniae or Staphylococcus aureus), and there is an abundance of mechanistic studies, including those studying the role of desensitization of TLR signaling, type I IFN- mediated impairment of neutrophil chemokines and antimicrobial production, attenuation of IL1β production etc., showing this. However, little is known about the mechanistic events underlying the potential deleterious synergy between Influenza and P.a co-infections. We demonstrate here in vitro in epithelial cells and in vivo in three independent models (two involving mice given IAV +/– P.a, and one involving mice given IAV +/– IL-1β) that IAV promotes secondary P.a-mediated lung disease or augmented IL-1β-mediated inflammation. We show that IAV-P.a-mediated deleterious responses includes increased matrix metalloprotease (MMP) activity, and MMP-9 in particular, and that the use of the MMP inhibitor improves lung resilience. Furthermore, we show that IAV post-transcriptionally inhibits the antimicrobial/anti-protease molecule elafin/trappin-2, which we have shown previously to be anti-inflammatory and to protect the host against maladaptive neutrophilic inflammation in P.a infections. Our work highlights the capacity of IAV to promote further P.a-mediated lung damage, not necessarily through its interference with host resistance to the bacterium, but by down-regulating tissue resilience to lung inflammation instead. Our study therefore suggests that restoring tissue resilience in clinical settings where IAV/P.a co-exists could prove a fruitful strategy.
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Affiliation(s)
- Berengère Villeret
- Inserm, UMR1152, Laboratoire d'Excellence Inflamex, Département Hospitalo-Universtaire FIRE (Fibrosis, Inflammation and Remodeling), Université de Paris, Paris, France
| | - Brigitte Solhonne
- Inserm, UMR1152, Laboratoire d'Excellence Inflamex, Département Hospitalo-Universtaire FIRE (Fibrosis, Inflammation and Remodeling), Université de Paris, Paris, France
| | - Marjolène Straube
- Inserm, UMR1152, Laboratoire d'Excellence Inflamex, Département Hospitalo-Universtaire FIRE (Fibrosis, Inflammation and Remodeling), Université de Paris, Paris, France
| | - Flora Lemaire
- Inserm, UMR1152, Laboratoire d'Excellence Inflamex, Département Hospitalo-Universtaire FIRE (Fibrosis, Inflammation and Remodeling), Université de Paris, Paris, France
| | - Aurélie Cazes
- Inserm, UMR1152, Laboratoire d'Excellence Inflamex, Département Hospitalo-Universtaire FIRE (Fibrosis, Inflammation and Remodeling), Université de Paris, Paris, France.,Assistance Publique-Hôpitaux de Paris (APHP), Hôpital Bichat, Service de Pneumologie A, Paris, France
| | - Ignacio Garcia-Verdugo
- Inserm, UMR1152, Laboratoire d'Excellence Inflamex, Département Hospitalo-Universtaire FIRE (Fibrosis, Inflammation and Remodeling), Université de Paris, Paris, France
| | - Jean-Michel Sallenave
- Inserm, UMR1152, Laboratoire d'Excellence Inflamex, Département Hospitalo-Universtaire FIRE (Fibrosis, Inflammation and Remodeling), Université de Paris, Paris, France
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Abstract
The immune system is inordinately complex with many interacting components determining overall outcomes. Mathematical and computational modelling provides a useful way in which the various contributions of different immunological components can be probed in an integrated manner. Here, we provide an introductory overview and review of mechanistic simulation models. We start out by briefly defining these types of models and contrasting them to other model types that are relevant to the field of immunology. We follow with a few specific examples and then review the different ways one can use such models to answer immunological questions. While our examples focus on immune responses to infection, the overall ideas and descriptions of model uses can be applied to any area of immunology.
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46
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Chung M, Binois M, Gramacy RB, Bardsley JM, Moquin DJ, Smith AP, Smith AM. PARAMETER AND UNCERTAINTY ESTIMATION FOR DYNAMICAL SYSTEMS USING SURROGATE STOCHASTIC PROCESSES. SIAM JOURNAL ON SCIENTIFIC COMPUTING : A PUBLICATION OF THE SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS 2019; 41:A2212-A2238. [PMID: 31749599 PMCID: PMC6867882 DOI: 10.1137/18m1213403] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Inference on unknown quantities in dynamical systems via observational data is essential for providing meaningful insight, furnishing accurate predictions, enabling robust control, and establishing appropriate designs for future experiments. Merging mathematical theory with empirical measurements in a statistically coherent way is critical and challenges abound, e.g., ill-posedness of the parameter estimation problem, proper regularization and incorporation of prior knowledge, and computational limitations. To address these issues, we propose a new method for learning parameterized dynamical systems from data. We first customize and fit a surrogate stochastic process directly to observational data, front-loading with statistical learning to respect prior knowledge (e.g., smoothness), cope with challenging data features like heteroskedasticity, heavy tails, and censoring. Then, samples of the stochastic process are used as "surrogate data" and point estimates are computed via ordinary point estimation methods in a modular fashion. Attractive features of this two-step approach include modularity and trivial parallelizability. We demonstrate its advantages on a predator-prey simulation study and on a real-world application involving within-host influenza virus infection data paired with a viral kinetic model, with comparisons to a more conventional Markov chain Monte Carlo (MCMC) based Bayesian approach.
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Affiliation(s)
- Matthias Chung
- Department of Mathematics, Computational Modeling and Data Analytics Division, Academy of Integrated Science, Virginia Tech, Blacksburg, VA 24061
| | - Mickaël Binois
- Booth School of Business, University of Chicago, Chicago, IL 60637
| | | | | | - David J Moquin
- Department of Internal Medicine, University of Tennessee Health Science Center, Memphis, TN 38103
| | - Amanda P Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103
| | - Amber M Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103
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Pinky L, Gonzalez-Parra G, Dobrovolny HM. Effect of stochasticity on coinfection dynamics of respiratory viruses. BMC Bioinformatics 2019; 20:191. [PMID: 30991939 PMCID: PMC6469119 DOI: 10.1186/s12859-019-2793-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 04/03/2019] [Indexed: 12/17/2022] Open
Abstract
Background Respiratory viral infections are a leading cause of mortality worldwide. As many as 40% of patients hospitalized with influenza-like illness are reported to be infected with more than one type of virus. However, it is not clear whether these infections are more severe than single viral infections. Mathematical models can be used to help us understand the dynamics of respiratory viral coinfections and their impact on the severity of the illness. Most models of viral infections use ordinary differential equations (ODE) that reproduce the average behavior of the infection, however, they might be inaccurate in predicting certain events because of the stochastic nature of viral replication cycle. Stochastic simulations of single virus infections have shown that there is an extinction probability that depends on the size of the initial viral inoculum and parameters that describe virus-cell interactions. Thus the coinfection dynamics predicted by the ODE might be difficult to observe in reality. Results In this work, a continuous-time Markov chain (CTMC) model is formulated to investigate probabilistic outcomes of coinfections. This CTMC model is based on our previous coinfection model, expressed in terms of a system of ordinary differential equations. Using the Gillespie method for stochastic simulation, we examine whether stochastic effects early in the infection can alter which virus dominates the infection. Conclusions We derive extinction probabilities for each virus individually as well as for the infection as a whole. We find that unlike the prediction of the ODE model, for similar initial growth rates stochasticity allows for a slower growing virus to out-compete a faster growing virus. Electronic supplementary material The online version of this article (10.1186/s12859-019-2793-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lubna Pinky
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.,Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA
| | | | - Hana M Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.
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Gallagher ME, Brooke CB, Ke R, Koelle K. Causes and Consequences of Spatial Within-Host Viral Spread. Viruses 2018; 10:E627. [PMID: 30428545 PMCID: PMC6267451 DOI: 10.3390/v10110627] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 11/08/2018] [Accepted: 11/10/2018] [Indexed: 02/07/2023] Open
Abstract
The spread of viral pathogens both between and within hosts is inherently a spatial process. While the spatial aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread within infected hosts are still understudied. Here, with a focus on influenza A viruses (IAVs), we first review experimental studies that have shed light on the mechanisms and spatial dynamics of viral spread within hosts. These studies provide strong empirical evidence for highly localized IAV spread within hosts. Since mathematical and computational within-host models have been increasingly used to gain a quantitative understanding of observed viral dynamic patterns, we then review the (relatively few) computational modeling studies that have shed light on possible factors that structure the dynamics of spatial within-host IAV spread. These factors include the dispersal distance of virions, the localization of the immune response, and heterogeneity in host cell phenotypes across the respiratory tract. While informative, we find in these studies a striking absence of theoretical expectations of how spatial dynamics may impact the dynamics of viral populations. To mitigate this, we turn to the extensive ecological and evolutionary literature on range expansions to provide informed theoretical expectations. We find that factors such as the type of density dependence, the frequency of long-distance dispersal, specific life history characteristics, and the extent of spatial heterogeneity are critical factors affecting the speed of population spread and the genetic composition of spatially expanding populations. For each factor that we identified in the theoretical literature, we draw parallels to its analog in viral populations. We end by discussing current knowledge gaps related to the spatial component of within-host IAV spread and the potential for within-host spatial considerations to inform the development of disease control strategies.
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Affiliation(s)
| | - Christopher B Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
| | - Ruian Ke
- T-6, Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Katia Koelle
- Department of Biology, Emory University, Atlanta, GA 30322, USA.
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Abstract
Viruses are a main cause of disease worldwide and many are without effective therapeutics or vaccines. A lack of understanding about how host responses work to control viral spread is one factor limiting effective management. How different immune components regulate infection dynamics is beginning to be better understood with the help of mathematical models. These models have been key in discriminating between hypotheses and in identifying rates of virus growth and clearance, dynamical control by different host factors and antivirals, and synergistic interactions during multi-pathogen infections. A recent focus in evaluating model predictions in the laboratory and clinic has illuminate the accuracy of models for a variety of viruses and highlighted the critical nature of theoretical approaches in virology. Here, I discuss recent model-driven exploration of host-pathogen interactions that have illustrated the importance of model validation in establishing the model's predictive capability and in defining new biology.
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Abstract
Influenza virus infections are a leading cause of morbidity and mortality worldwide. This is due in part to the continual emergence of new viral variants and to synergistic interactions with other viruses and bacteria. There is a lack of understanding about how host responses work to control the infection and how other pathogens capitalize on the altered immune state. The complexity of multi-pathogen infections makes dissecting contributing mechanisms, which may be non-linear and occur on different time scales, challenging. Fortunately, mathematical models have been able to uncover infection control mechanisms, establish regulatory feedbacks, connect mechanisms across time scales, and determine the processes that dictate different disease outcomes. These models have tested existing hypotheses and generated new hypotheses, some of which have been subsequently tested and validated in the laboratory. They have been particularly a key in studying influenza-bacteria coinfections and will be undoubtedly be useful in examining the interplay between influenza virus and other viruses. Here, I review recent advances in modeling influenza-related infections, the novel biological insight that has been gained through modeling, the importance of model-driven experimental design, and future directions of the field.
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Affiliation(s)
- Amber M Smith
- University of Tennessee Health Science CenterMemphisTNUSA
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