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D’Orso I, Forst CV. Mathematical Models of HIV-1 Dynamics, Transcription, and Latency. Viruses 2023; 15:2119. [PMID: 37896896 PMCID: PMC10612035 DOI: 10.3390/v15102119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
HIV-1 latency is a major barrier to curing infections with antiretroviral therapy and, consequently, to eliminating the disease globally. The establishment, maintenance, and potential clearance of latent infection are complex dynamic processes and can be best described with the help of mathematical models followed by experimental validation. Here, we review the use of viral dynamics models for HIV-1, with a focus on applications to the latent reservoir. Such models have been used to explain the multi-phasic decay of viral load during antiretroviral therapy, the early seeding of the latent reservoir during acute infection and the limited inflow during treatment, the dynamics of viral blips, and the phenomenon of post-treatment control. Finally, we discuss that mathematical models have been used to predict the efficacy of potential HIV-1 cure strategies, such as latency-reversing agents, early treatment initiation, or gene therapies, and to provide guidance for designing trials of these novel interventions.
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Affiliation(s)
- Iván D’Orso
- Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;
| | - Christian V. Forst
- Department of Genetics and Genomic Sciences, Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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2
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Forst CV, Zeng L, Wang Q, Zhou X, Vatansever S, Xu P, Song W, Tu Z, Zhang B. Multiscale network analysis identifies potential receptors for SARS-CoV-2 and reveals their tissue-specific and age-dependent expression. FEBS Lett 2023; 597:1384-1402. [PMID: 36951513 PMCID: PMC10294276 DOI: 10.1002/1873-3468.14613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 02/13/2023] [Accepted: 02/27/2023] [Indexed: 03/24/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has affected tens of millions of individuals and caused hundreds of thousands of deaths worldwide. Here, we present a comprehensive, multiscale network analysis of the transcriptional response to the virus. In particular, we focused on key regulators, cell receptors, and host processes that were hijacked by the virus for its advantage. ACE2-controlled processes involved CD300e (a TYROBP receptor) as a key regulator and the activation of IL-2 pro-inflammatory cytokine signaling. We further investigated the age dependency of such receptors in different tissues. In summary, this study provides novel insights into the gene regulatory organization during the SARS-CoV-2 infection and the tissue-specific, age-dependent expression of the cell receptors involved in COVID-19.
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Affiliation(s)
- Christian V. Forst
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of MicrobiologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Lu Zeng
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Qian Wang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Xianxiao Zhou
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Peng Xu
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Won‐Min Song
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Zhidong Tu
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
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Forst CV, Chung M, Hockman M, Lashua L, Adney E, Hickey A, Carlock M, Ross T, Ghedin E, Gresham D. Vaccination History, Body Mass Index, Age, and Baseline Gene Expression Predict Influenza Vaccination Outcomes. Viruses 2022; 14:2446. [PMID: 36366544 PMCID: PMC9697051 DOI: 10.3390/v14112446] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/24/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Seasonal influenza is a primary public health burden in the USA and globally. Annual vaccination programs are designed on the basis of circulating influenza viral strains. However, the effectiveness of the seasonal influenza vaccine is highly variable between seasons and among individuals. A number of factors are known to influence vaccination effectiveness including age, sex, and comorbidities. Here, we sought to determine whether whole blood gene expression profiling prior to vaccination is informative about pre-existing immunological status and the immunological response to vaccine. We performed whole transcriptome analysis using RNA sequencing (RNAseq) of whole blood samples obtained prior to vaccination from 275 participants enrolled in an annual influenza vaccine trial. Serological status prior to vaccination and 28 days following vaccination was assessed using the hemagglutination inhibition assay (HAI) to define baseline immune status and the response to vaccination. We find evidence that genes with immunological functions are increased in expression in individuals with higher pre-existing immunity and in those individuals who mount a greater response to vaccination. Using a random forest model, we find that this set of genes can be used to predict vaccine response with a performance similar to a model that incorporates physiological and prior vaccination status alone. A model using both gene expression and physiological factors has the greatest predictive power demonstrating the potential utility of molecular profiling for enhancing prediction of vaccine response. Moreover, expression of genes that are associated with enhanced vaccination response may point to additional biological pathways that contribute to mounting a robust immunological response to the seasonal influenza vaccine.
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Affiliation(s)
- Christian V. Forst
- Department of Genetics and Genomic Sciences, Department of Microbiology, Icahn School of Medicine at Mt Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029-6574, USA
| | - Matthew Chung
- Systems Genomics Section, Laboratory of Parasitic Diseases, NIAID, NIH, Bethesda, MD 20894, USA
| | - Megan Hockman
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| | - Lauren Lashua
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| | - Emily Adney
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| | - Angela Hickey
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| | - Michael Carlock
- Center for Vaccines and Immunology, University of Georgia, Athens, GA 30602, USA
- Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Ted Ross
- Center for Vaccines and Immunology, University of Georgia, Athens, GA 30602, USA
- Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Elodie Ghedin
- Systems Genomics Section, Laboratory of Parasitic Diseases, NIAID, NIH, Bethesda, MD 20894, USA
| | - David Gresham
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
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Forst CV, Martin-Sancho L, Tripathi S, Wang G, Dos Anjos Borges LG, Wang M, Geber A, Lashua L, Ding T, Zhou X, Carter CE, Metreveli G, Rodriguez-Frandsen A, Urbanowski MD, White KM, Stein DA, Moulton H, Chanda SK, Pache L, Shaw ML, Ross TM, Ghedin E, García-Sastre A, Zhang B. Common and species-specific molecular signatures, networks, and regulators of influenza virus infection in mice, ferrets, and humans. Sci Adv 2022; 8:eabm5859. [PMID: 36197970 PMCID: PMC9534503 DOI: 10.1126/sciadv.abm5859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 08/11/2022] [Indexed: 05/04/2023]
Abstract
Molecular responses to influenza A virus (IAV) infections vary between mammalian species. To identify conserved and species-specific molecular responses, we perform a comparative study of transcriptomic data derived from blood cells, primary epithelial cells, and lung tissues collected from IAV-infected humans, ferrets, and mice. The molecular responses in the human host have unique functions such as antigen processing that are not observed in mice or ferrets. Highly conserved gene coexpression modules across the three species are enriched for IAV infection-induced pathways including cell cycle and interferon (IFN) signaling. TDRD7 is predicted as an IFN-inducible host factor that is up-regulated upon IAV infection in the three species. TDRD7 is required for antiviral IFN response, potentially modulating IFN signaling via the JAK/STAT/IRF9 pathway. Identification of the common and species-specific molecular signatures, networks, and regulators of IAV infection provides insights into host-defense mechanisms and will facilitate the development of novel therapeutic interventions against IAV infection.
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Affiliation(s)
- Christian V. Forst
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
| | - Laura Martin-Sancho
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Shashank Tripathi
- Centre for Infectious Disease Research, Department of Microbiology and Cell Biology, Indian Institute of Science, Bengaluru 560012, India
| | - Guojun Wang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, People’s Republic of China
| | | | - Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Adam Geber
- Center for Genomics and Systems Biology, Department of Biology, New York University, 12 Waverly Place, New York, NY 10003, USA
| | - Lauren Lashua
- Center for Genomics and Systems Biology, Department of Biology, New York University, 12 Waverly Place, New York, NY 10003, USA
| | - Tao Ding
- Center for Genomics and Systems Biology, Department of Biology, New York University, 12 Waverly Place, New York, NY 10003, USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Chalise E. Carter
- Department of Infectious Diseases, Center for Vaccines and Immunology, University of Georgia, Athens, GA 30602, USA
| | - Giorgi Metreveli
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
| | - Ariel Rodriguez-Frandsen
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Matthew D. Urbanowski
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
| | - Kris M. White
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
| | - David A. Stein
- Department of Biomedical Sciences, College of Veterinary Medicine, Oregon State University, Corvallis, OR 97331, USA
| | - Hong Moulton
- Department of Biomedical Sciences, College of Veterinary Medicine, Oregon State University, Corvallis, OR 97331, USA
| | - Sumit K. Chanda
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Lars Pache
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Megan L. Shaw
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
| | - Ted M. Ross
- Department of Infectious Diseases, Center for Vaccines and Immunology, University of Georgia, Athens, GA 30602, USA
| | - Elodie Ghedin
- Center for Genomics and Systems Biology, Department of Biology, New York University, 12 Waverly Place, New York, NY 10003, USA
- Systems Genomics Section, Laboratory of Parasitic Diseases, NIAID, NIH, Bethesda, MD 20892, USA
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
- Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
- The Tisch Cancer Center, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
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5
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Yasutomi M, Christiaansen AF, Imai N, Martin-Orozco N, Forst CV, Chen G, Ueno H. CD226 and TIGIT Cooperate in the Differentiation and Maturation of Human Tfh Cells. Front Immunol 2022; 13:840457. [PMID: 35273617 PMCID: PMC8902812 DOI: 10.3389/fimmu.2022.840457] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 01/28/2022] [Indexed: 11/29/2022] Open
Abstract
Costimulation pathways play an essential role in T cell activation, differentiation, and regulation. CD155 expressed on antigen-presenting cells (APCs) interacts with TIGIT, an inhibitory costimulatory molecule, and CD226, an activating costimulatory molecule, on T cells. TIGIT and CD226 are expressed at varying levels depending on the T cell subset and activation state. T follicular helper cells in germinal centers (GC-Tfh) in human tonsils express high TIGIT and low CD226. However, the biological role of the CD155/TIGIT/CD226 axis in human Tfh cell biology has not been elucidated. To address this, we analyzed tonsillar CD4+ T cell subsets cultured with artificial APCs constitutively expressing CD155. Here we show that CD226 signals promote the early phase of Tfh cell differentiation in humans. CD155 signals promoted the proliferation of naïve CD4+ T cells and Tfh precursors (pre-Tfh) isolated from human tonsils and upregulated multiple Tfh molecules and decreased IL-2, a cytokine detrimental for Tfh cell differentiation. Blocking CD226 potently inhibited their proliferation and expression of Tfh markers. By contrast, while CD155 signals promoted the proliferation of tonsillar GC-Tfh cells, their proliferation required only weak CD226 signals. Furthermore, attenuating CD226 signals rather increased the expression of CXCR5, ICOS, and IL-21 by CD155-stimulated GC-Tfh cells. Thus, the importance of CD226 signals changes according to the differentiation stage of human Tfh cells and wanes in mature GC-Tfh cells. High TIGIT expression on GC-Tfh may play a role in attenuating the detrimental CD226 signals post GC-Tfh cell maturation.
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Affiliation(s)
- Motoko Yasutomi
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Allison F Christiaansen
- EMD Serono Research and Development Institute Inc. (The Healthcare Business of Merck KGaA, Darmstadt, Germany), Billerica, MA, United States
| | - Naoko Imai
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Natalia Martin-Orozco
- EMD Serono Research and Development Institute Inc. (The Healthcare Business of Merck KGaA, Darmstadt, Germany), Billerica, MA, United States
| | - Christian V Forst
- Department of Genetics and Genomic Sciences, Department of Microbiology, The Icahn Institute for Data Science and Genomic Technology, New York, NY, United States
| | - Gang Chen
- EMD Serono Research and Development Institute Inc. (The Healthcare Business of Merck KGaA, Darmstadt, Germany), Billerica, MA, United States
| | - Hideki Ueno
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,ASHBi Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
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6
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Wang C, Lashua LP, Carter CE, Johnson SK, Wang M, Ross TM, Ghedin E, Zhang B, Forst CV. Sex disparities in influenza: A multiscale network analysis. iScience 2022; 25:104192. [PMID: 35479404 PMCID: PMC9036134 DOI: 10.1016/j.isci.2022.104192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 12/05/2021] [Accepted: 03/30/2022] [Indexed: 12/12/2022] Open
Abstract
Sex differences in the pathogenesis of infectious diseases because of differential immune responses between females and males have been well-documented for multiple pathogens. However, the molecular mechanism underlying the observed sex differences in influenza virus infection remains poorly understood. In this study, we used a network-based approach to characterize the blood transcriptome collected over the course of infection with influenza A virus from female and male ferrets to dissect sex-biased gene expression. We identified significant differences in the temporal dynamics and regulation of immune responses between females and males. Our results elucidate sex-differentiated pathways involved in the unfolded protein response (UPR), lipid metabolism, and inflammatory responses, including a female-biased IRE1/XBP1 activation and male-biased crosstalk between metabolic reprogramming and IL-1 and AP-1 pathways. Overall, our study provides molecular insights into sex differences in transcriptional regulation of immune responses and contributes to a better understanding of sex biases in influenza pathogenesis. Regulation of immune responses between females and males is significantly different Rapid activation of UPR in females triggers potent immune and inflammatory responses Male-specific regulatory pattern in the AP1 pathway indicate a bias in immune response
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Affiliation(s)
- Chang Wang
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA
| | - Lauren P. Lashua
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA
| | - Chalise E. Carter
- Center for Vaccines and Immunology, University of Georgia, Athens, GA 30602, USA
| | - Scott K. Johnson
- Center for Vaccines and Immunology, University of Georgia, Athens, GA 30602, USA
| | - Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029-6574, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029-6501, USA
| | - Ted M. Ross
- Center for Vaccines and Immunology, University of Georgia, Athens, GA 30602, USA
- Department of Infectious Diseases, Center for Vaccines and Immunology, University of Georgia, Athens, GA 30602, USA
| | - Elodie Ghedin
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA
- Systems Genomics Section, Laboratory of Parasitic Diseases, NIAID, NIH, Bethesda, MD, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029-6574, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029-6501, USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1677, New York, NY 10029-6574, USA
| | - Christian V. Forst
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029-6574, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029-6501, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029-6574
- Corresponding author
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7
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Horiuchi S, Wu H, Liu WC, Schmitt N, Provot J, Liu Y, Bentebibel SE, Albrecht RA, Schotsaert M, Forst CV, Zhang B, Ueno H. Tox2 is required for the maintenance of GC T FH cells and the generation of memory T FH cells. Sci Adv 2021; 7:eabj1249. [PMID: 34623911 PMCID: PMC8500513 DOI: 10.1126/sciadv.abj1249] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/19/2021] [Indexed: 06/13/2023]
Abstract
Memory T follicular helper (TFH) cells play an essential role to induce secondary antibody response by providing help to memory and naïve B cells. Here, we show that the transcription factor Tox2 is vital for the maintenance of TFH cells in germinal centers (GCs) and the generation of memory TFH cells. High Tox2 expression was almost exclusive to GC TFH cells among human tonsillar and blood CD4+ T cell subsets. Tox2 overexpression maintained the expression of TFH-associated genes in T cell receptor–stimulated human GC TFH cells and inhibited their spontaneous conversion into TH1-like cells. Tox2-deficient mice displayed impaired secondary TFH cell expansion upon reimmunization with an antigen and upon secondary infection with a heterologous influenza virus. Collectively, our study shows that Tox2 is highly integrated into establishment of durable GC TFH cell responses and development of memory TFH cells in mice and humans.
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Affiliation(s)
- Shu Horiuchi
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, TX 75204, USA
| | - Hanchih Wu
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Wen-Chun Liu
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Biomedical Translation Research Center, Academia Sinica, Taipei 11571, Taiwan
| | - Nathalie Schmitt
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, TX 75204, USA
- ImmunoConcEpT, CNRS UMR 5164, Bordeaux University, Bordeaux 33076, France
| | - Jonathan Provot
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, TX 75204, USA
| | - Yang Liu
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, TX 75204, USA
| | | | - Randy A. Albrecht
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Schotsaert
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christian V. Forst
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bin Zhang
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Hideki Ueno
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, TX 75204, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Immunology, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
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8
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Sulaiman I, Chung M, Angel L, Tsay JCJ, Wu BG, Yeung ST, Krolikowski K, Li Y, Duerr R, Schluger R, Thannickal SA, Koide A, Rafeq S, Barnett C, Postelnicu R, Wang C, Banakis S, Pérez-Pérez L, Shen G, Jour G, Meyn P, Carpenito J, Liu X, Ji K, Collazo D, Labarbiera A, Amoroso N, Brosnahan S, Mukherjee V, Kaufman D, Bakker J, Lubinsky A, Pradhan D, Sterman DH, Weiden M, Heguy A, Evans L, Uyeki TM, Clemente JC, de Wit E, Schmidt AM, Shopsin B, Desvignes L, Wang C, Li H, Zhang B, Forst CV, Koide S, Stapleford KA, Khanna KM, Ghedin E, Segal LN. Microbial signatures in the lower airways of mechanically ventilated COVID-19 patients associated with poor clinical outcome. Nat Microbiol 2021; 6:1245-1258. [PMID: 34465900 PMCID: PMC8484067 DOI: 10.1038/s41564-021-00961-5] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023]
Abstract
Respiratory failure is associated with increased mortality in COVID-19 patients. There are no validated lower airway biomarkers to predict clinical outcome. We investigated whether bacterial respiratory infections were associated with poor clinical outcome of COVID-19 in a prospective, observational cohort of 589 critically ill adults, all of whom required mechanical ventilation. For a subset of 142 patients who underwent bronchoscopy, we quantified SARS-CoV-2 viral load, analysed the lower respiratory tract microbiome using metagenomics and metatranscriptomics and profiled the host immune response. Acquisition of a hospital-acquired respiratory pathogen was not associated with fatal outcome. Poor clinical outcome was associated with lower airway enrichment with an oral commensal (Mycoplasma salivarium). Increased SARS-CoV-2 abundance, low anti-SARS-CoV-2 antibody response and a distinct host transcriptome profile of the lower airways were most predictive of mortality. Our data provide evidence that secondary respiratory infections do not drive mortality in COVID-19 and clinical management strategies should prioritize reducing viral replication and maximizing host responses to SARS-CoV-2.
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Affiliation(s)
- Imran Sulaiman
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Matthew Chung
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Luis Angel
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Jun-Chieh J Tsay
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Division of Pulmonary and Critical Care Medicine, VA New York Harbor Healthcare System, New York, NY, USA
| | - Benjamin G Wu
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Division of Pulmonary and Critical Care Medicine, VA New York Harbor Healthcare System, New York, NY, USA
| | - Stephen T Yeung
- Department of Microbiology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Kelsey Krolikowski
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Yonghua Li
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Ralf Duerr
- Department of Microbiology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Rosemary Schluger
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Sara A Thannickal
- Department of Microbiology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Akiko Koide
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Laura and Isaac Perlmutter Cancer Center, New York University School of Medicine, NYU Langone Health, New York, NY, USA
| | - Samaan Rafeq
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Clea Barnett
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Radu Postelnicu
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Chang Wang
- Center for Genomics & Systems Biology, Department of Biology, New York University, New York, NY, USA
| | - Stephanie Banakis
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Lizzette Pérez-Pérez
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health Rocky Mountain Laboratories, Hamilton, MT, USA
| | - Guomiao Shen
- Department of Pathology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - George Jour
- Department of Pathology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Peter Meyn
- Division of Pediatrics, Longhua Hospital affiliated to Shanghai University of Chinese Medicine, Shanghai, China
| | - Joseph Carpenito
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Xiuxiu Liu
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Division of Pediatrics, Longhua Hospital affiliated to Shanghai University of Chinese Medicine, Shanghai, China
| | - Kun Ji
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Dongfang Hospital affiliated to Beijing University of Traditional Chinese Medicine, Beijing, China
| | - Destiny Collazo
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Anthony Labarbiera
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Nancy Amoroso
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Shari Brosnahan
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Vikramjit Mukherjee
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - David Kaufman
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Jan Bakker
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Anthony Lubinsky
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Deepak Pradhan
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Daniel H Sterman
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Michael Weiden
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Adriana Heguy
- Department of Pathology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- NYU Langone Genome Technology Center, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Laura Evans
- Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, USA
| | - Timothy M Uyeki
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jose C Clemente
- Department of Genetics and Genomic Sciences and Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Emmie de Wit
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health Rocky Mountain Laboratories, Hamilton, MT, USA
| | - Ann Marie Schmidt
- Diabetes Research Program, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Bo Shopsin
- Division of Infectious Diseases, Department of Medicine, New York University School of Medicine, NYU Langone Health, New York, NY, USA
| | - Ludovic Desvignes
- Department of Microbiology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Chan Wang
- Department of Population Health, New York University School of Medicine, NYU Langone Health, New York, NY, USA
| | - Huilin Li
- Department of Population Health, New York University School of Medicine, NYU Langone Health, New York, NY, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Christian V Forst
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shohei Koide
- Laura and Isaac Perlmutter Cancer Center, New York University School of Medicine, NYU Langone Health, New York, NY, USA
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Kenneth A Stapleford
- Department of Microbiology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Kamal M Khanna
- Department of Microbiology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA
- Laura and Isaac Perlmutter Cancer Center, New York University School of Medicine, NYU Langone Health, New York, NY, USA
| | - Elodie Ghedin
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
- Center for Genomics & Systems Biology, Department of Biology, New York University, New York, NY, USA.
| | - Leopoldo N Segal
- Division of Pulmonary and Critical Care Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA.
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, USA.
- Laura and Isaac Perlmutter Cancer Center, New York University School of Medicine, NYU Langone Health, New York, NY, USA.
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9
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Sulaiman I, Chung M, Angel L, Tsay JCJ, Wu BG, Yeung ST, Krolikowski K, Li Y, Duerr R, Schluger R, Thannickal SA, Koide A, Rafeq S, Barnett C, Postelnicu R, Wang C, Banakis S, Perez-Perez L, Jour G, Shen G, Meyn P, Carpenito J, Liu X, Ji K, Collazo D, Labarbiera A, Amoroso N, Brosnahan S, Mukherjee V, Kaufman D, Bakker J, Lubinsky A, Pradhan D, Sterman DH, Weiden M, Hegu A, Evans L, Uyeki TM, Clemente JC, De Wit E, Schmidt AM, Shopsin B, Desvignes L, Wang C, Li H, Zhang B, Forst CV, Koide S, Stapleford KA, Khanna KM, Ghedin E, Segal LN. Microbial signatures in the lower airways of mechanically ventilated COVID19 patients associated with poor clinical outcome. medRxiv 2021:2021.02.23.21252221. [PMID: 33655261 PMCID: PMC7924286 DOI: 10.1101/2021.02.23.21252221] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Mortality among patients with COVID-19 and respiratory failure is high and there are no known lower airway biomarkers that predict clinical outcome. We investigated whether bacterial respiratory infections and viral load were associated with poor clinical outcome and host immune tone. We obtained bacterial and fungal culture data from 589 critically ill subjects with COVID-19 requiring mechanical ventilation. On a subset of the subjects that underwent bronchoscopy, we also quantified SARS-CoV-2 viral load, analyzed the microbiome of the lower airways by metagenome and metatranscriptome analyses and profiled the host immune response. We found that isolation of a hospital-acquired respiratory pathogen was not associated with fatal outcome. However, poor clinical outcome was associated with enrichment of the lower airway microbiota with an oral commensal ( Mycoplasma salivarium ), while high SARS-CoV-2 viral burden, poor anti-SARS-CoV-2 antibody response, together with a unique host transcriptome profile of the lower airways were most predictive of mortality. Collectively, these data support the hypothesis that 1) the extent of viral infectivity drives mortality in severe COVID-19, and therefore 2) clinical management strategies targeting viral replication and host responses to SARS-CoV-2 should be prioritized.
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10
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Muñoz-Moreno R, Martínez-Romero C, Blanco-Melo D, Forst CV, Nachbagauer R, Benitez AA, Mena I, Aslam S, Balasubramaniam V, Lee I, Panis M, Ayllón J, Sachs D, Park MS, Krammer F, tenOever BR, García-Sastre A. Viral Fitness Landscapes in Diverse Host Species Reveal Multiple Evolutionary Lines for the NS1 Gene of Influenza A Viruses. Cell Rep 2020; 29:3997-4009.e5. [PMID: 31851929 PMCID: PMC7010214 DOI: 10.1016/j.celrep.2019.11.070] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 07/03/2019] [Accepted: 11/15/2019] [Indexed: 12/23/2022] Open
Abstract
Influenza A viruses (IAVs) have a remarkable tropism in their ability to
circulate in both mammalian and avian species. The IAV NS1 protein is a
multifunctional virulence factor that inhibits the type I interferon host
response through a myriad of mechanisms. How NS1 has evolved to enable this
remarkable property across species and its specific impact in the overall
replication, pathogenicity, and host preference remain unknown. Here we analyze
the NS1 evolutionary landscape and host tropism using a barcoded library of
recombinant IAVs. Results show a surprisingly great variety of NS1 phenotypes
according to their ability to replicate in different hosts. The IAV NS1 genes
appear to have taken diverse and random evolutionary pathways within their
multiple phylogenetic lineages. In summary, the high evolutionary plasticity of
this viral protein underscores the ability of IAVs to adapt to multiple hosts
and aids in our understanding of its global prevalence. Muñoz-Moreno et al. report that influenza A virus NS1 undergoes
diverse and unpredictable evolutionary pathways based on its different
phylogenetic lineages. A high-throughput approach using a barcoded library is
used to test the interactions between NS1-recombinant viruses and to study their
preference for specific or multiple hosts.
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Affiliation(s)
- Raquel Muñoz-Moreno
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Carles Martínez-Romero
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Daniel Blanco-Melo
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christian V Forst
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Raffael Nachbagauer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Asiel Arturo Benitez
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ignacio Mena
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sadaf Aslam
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Vinod Balasubramaniam
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, 47500 Bandar Sunway, Malaysia
| | - Ilseob Lee
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Maryline Panis
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Juan Ayllón
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - David Sachs
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Man-Seong Park
- Department of Microbiology, Institute for Viral Diseases, College of Medicine, Korea University, Seoul 02841, Republic of Korea
| | - Florian Krammer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Benjamin R tenOever
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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11
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Zhang L, Forst CV, Gordon A, Gussin G, Geber AB, Fernandez PJ, Ding T, Lashua L, Wang M, Balmaseda A, Bonneau R, Zhang B, Ghedin E. Characterization of antibiotic resistance and host-microbiome interactions in the human upper respiratory tract during influenza infection. Microbiome 2020; 8:39. [PMID: 32178738 PMCID: PMC7076942 DOI: 10.1186/s40168-020-00803-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 02/10/2020] [Indexed: 05/06/2023]
Abstract
BACKGROUND The abundance and diversity of antibiotic resistance genes (ARGs) in the human respiratory microbiome remain poorly characterized. In the context of influenza virus infection, interactions between the virus, the host, and resident bacteria with pathogenic potential are known to complicate and worsen disease, resulting in coinfection and increased morbidity and mortality of infected individuals. When pathogenic bacteria acquire antibiotic resistance, they are more difficult to treat and of global health concern. Characterization of ARG expression in the upper respiratory tract could help better understand the role antibiotic resistance plays in the pathogenesis of influenza-associated bacterial secondary infection. RESULTS Thirty-seven individuals participating in the Household Influenza Transmission Study (HITS) in Managua, Nicaragua, were selected for this study. We performed metatranscriptomics and 16S rRNA gene sequencing analyses on nasal and throat swab samples, and host transcriptome profiling on blood samples. Individuals clustered into two groups based on their microbial gene expression profiles, with several microbial pathways enriched with genes differentially expressed between groups. We also analyzed antibiotic resistance gene expression and determined that approximately 25% of the sequence reads that corresponded to antibiotic resistance genes mapped to Streptococcus pneumoniae and Staphylococcus aureus. Following construction of an integrated network of ARG expression with host gene co-expression, we identified several host key regulators involved in the host response to influenza virus and bacterial infections, and host gene pathways associated with specific antibiotic resistance genes. CONCLUSIONS This study indicates the host response to influenza infection could indirectly affect antibiotic resistance gene expression in the respiratory tract by impacting the microbial community structure and overall microbial gene expression. Interactions between the host systemic responses to influenza infection and antibiotic resistance gene expression highlight the importance of viral-bacterial co-infection in acute respiratory infections like influenza. Video abstract.
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Affiliation(s)
- Lingdi Zhang
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA
| | - Christian V Forst
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Aubree Gordon
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Gabrielle Gussin
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA
| | - Adam B Geber
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA
| | - Porfirio J Fernandez
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA
| | - Tao Ding
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA
| | - Lauren Lashua
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA
| | - Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Angel Balmaseda
- National Virology Laboratory, Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, Nicaragua
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Richard Bonneau
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Elodie Ghedin
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA.
- Department of Epidemiology, School of Global Public Health, New York University, New York, NY, 10003, USA.
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12
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Morton EL, Forst CV, Zheng Y, DePaula-Silva AB, Ramirez NGP, Planelles V, D'Orso I. Transcriptional Circuit Fragility Influences HIV Proviral Fate. Cell Rep 2019; 27:154-171.e9. [PMID: 30943398 PMCID: PMC6461408 DOI: 10.1016/j.celrep.2019.03.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 11/14/2018] [Accepted: 02/28/2019] [Indexed: 01/12/2023] Open
Abstract
Transcriptional circuit architectures in several organisms have been evolutionarily selected to dictate precise given responses. Unlike these cellular systems, HIV is regulated through a complex circuit composed of two successive phases (host and viral), which create a positive feedback loop facilitating viral replication. However, it has long remained unclear whether both phases operate identically and to what extent the host phase influences the entire circuit. Here, we report that, although the host phase is regulated by a checkpoint whereby KAP1 mediates transcription activation, the virus evolved a minimalist system bypassing KAP1. Given the complex circuit's architecture, cell-to-cell KAP1 fluctuations impart heterogeneity in the host transcriptional responses, thus affecting the feedback loop. Mathematical modeling of a complete circuit reveals how these oscillations ultimately influence homogeneous reactivation potential of a latent virus. Thus, although HIV drives molecular innovation to fuel robust gene activation, it experiences transcriptional fragility, thereby influencing viral fate and cure efforts.
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Affiliation(s)
- Emily L Morton
- Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Christian V Forst
- Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yue Zheng
- Division of Microbiology and Immunology, Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Ana B DePaula-Silva
- Division of Microbiology and Immunology, Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Nora-Guadalupe P Ramirez
- Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Vicente Planelles
- Division of Microbiology and Immunology, Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Iván D'Orso
- Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
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13
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Zhao Y, Forst CV, Sayegh CE, Wang IM, Yang X, Zhang B. Molecular and genetic inflammation networks in major human diseases. Mol Biosyst 2017; 12:2318-41. [PMID: 27303926 DOI: 10.1039/c6mb00240d] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervention.
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Affiliation(s)
- Yongzhong Zhao
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA. and Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA
| | - Christian V Forst
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA. and Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA
| | - Camil E Sayegh
- Vertex Pharmaceuticals (Canada) Incorporated, 275 Armand-Frappier, Laval, Quebec H7V 4A7, Canada
| | - I-Ming Wang
- Informatics and Analysis, Merck Research Laboratories, Merck & Co., Inc., 770 Sumneytown Pike, West Point, PA 19486, USA.
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA 90025, USA.
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA. and Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA
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Reeder JE, Kwak YT, McNamara RP, Forst CV, D'Orso I. HIV Tat controls RNA Polymerase II and the epigenetic landscape to transcriptionally reprogram target immune cells. eLife 2015; 4. [PMID: 26488441 PMCID: PMC4733046 DOI: 10.7554/elife.08955] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Accepted: 10/20/2015] [Indexed: 12/22/2022] Open
Abstract
HIV encodes Tat, a small protein that facilitates viral transcription by binding an RNA structure (trans-activating RNA [TAR]) formed on nascent viral pre-messenger RNAs. Besides this well-characterized mechanism, Tat appears to modulate cellular transcription, but the target genes and molecular mechanisms remain poorly understood. We report here that Tat uses unexpected regulatory mechanisms to reprogram target immune cells to promote viral replication and rewire pathways beneficial for the virus. Tat functions through master transcriptional regulators bound at promoters and enhancers, rather than through cellular ‘TAR-like’ motifs, to both activate and repress gene sets sharing common functional annotations. Despite the complexity of transcriptional regulatory mechanisms in the cell, Tat precisely controls RNA polymerase II recruitment and pause release to fine-tune the initiation and elongation steps in target genes. We propose that a virus with a limited coding capacity has optimized its genome by evolving a small but ‘multitasking’ protein to simultaneously control viral and cellular transcription. DOI:http://dx.doi.org/10.7554/eLife.08955.001 The human immunodeficiency virus (HIV) reproduces and spreads throughout the body by hijacking human immune cells and causing them to copy the virus’s genetic information. As the virus multiplies, it also causes the death of the immune system cells that help the human body recognize and eliminate viruses. This allows the virus to multiply unchecked. Studies of the genetic material of HIV – which is in the form of single-stranded RNA molecules and contains only a handful of genes – have begun to reveal how the virus can wreak such havoc to the human immune system. A small protein encoded by the virus, called Tat, boosts the expression of HIV genes in infected immune cells by binding to a structure that forms on newly synthesized viral RNAs. Recent evidence suggests that HIV also changes the expression of human genes to make immune cells more hospitable to the virus. However, it was not known exactly which specific genes are targeted, or how the virus alters their expression. Now, Reeder, Kwak et al. reveal how the Tat protein alters the expression of more than 400 human genes. Rather than bind to the same structure seen in newly forming HIV RNAs, Tat turns on or off the expression of its human target genes by interacting with proteins that regulate human gene expression. In doing so, Tat is able to precisely control the activity of an enzyme called RNA Polymerase II that is necessary for the early steps of gene expression. Tat’s multitasking ability – boosting HIV gene expression at the same time as reprogramming human gene expression – helps explain how a virus with so little genetic material of its own can perform such a wide range of activities in infected cells. The work of Reeder, Kwak et al. suggests that Tat reshapes the human genome to position target genes in ways that allow them to be efficiently turned on or off. Future studies will further reveal how Tat accomplishes this genome remodeling during different stages of infection. In addition, further research is also necessary to look closely into the sets of genes targeted by Tat to find patterns of genes that work together to alter cell behavior, and investigate how these new behaviors allow HIV to thrive. DOI:http://dx.doi.org/10.7554/eLife.08955.002
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Affiliation(s)
- Jonathan E Reeder
- Department of Biological Sciences, University of Texas at Dallas, Richardson, United States.,Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, United States
| | - Youn-Tae Kwak
- Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, United States
| | - Ryan P McNamara
- Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, United States
| | - Christian V Forst
- Department of Genetics and Genomic Sciences, Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Iván D'Orso
- Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, United States
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Abstract
Seasonal flu affects 5–20% of the human population each year. Although mortality rates are typically <0.1% and the pandemic 2009 H1N1 influenza strain has been well contained by vaccination and strict hygiene, particularly virulent pandemic forms have emerged three times in the last century, resulting in millions of deaths. Current vaccine and therapeutic strategies are limited by the ability of the virus to generate variants that evade vaccine-induced immune responses and resist the therapeutic effects of antiviral drugs. Host genetic variations affect immune responses and may induce adverse effects during drug treatment or against vaccines. To develop new, first-in-class therapeutics, new antiviral targets and new chemical entities must be identified in the context of the immunogenomic repertoire of the patient. Since influenza and so many other viruses need to escape innate immunity to become pathogenic, the viral proteins responsible for this, as well as the host cell molecular pathways that lead to the antiviral response, are an excellent potential source of new therapeutic targets within a systems approach against influenza infections.
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Affiliation(s)
- Christian V Forst
- University of Texas Southwestern Medical Center, Department of Clinical Sciences, 5323 Harry Hines Boulevard, Dallas, TX 75390-9066, USA
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Ward SE, Kim HS, Komurov K, Mendiratta S, Tsai PL, Schmolke M, Satterly N, Manicassamy B, Forst CV, Roth MG, García-Sastre A, Blazewska KM, McKenna CE, Fontoura BM, White MA. Host modulators of H1N1 cytopathogenicity. PLoS One 2012; 7:e39284. [PMID: 22876275 PMCID: PMC3410888 DOI: 10.1371/journal.pone.0039284] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2012] [Accepted: 05/17/2012] [Indexed: 11/28/2022] Open
Abstract
Influenza A virus infects 5–20% of the population annually, resulting in ∼35,000 deaths and significant morbidity. Current treatments include vaccines and drugs that target viral proteins. However, both of these approaches have limitations, as vaccines require yearly development and the rapid evolution of viral proteins gives rise to drug resistance. In consequence additional intervention strategies, that target host factors required for the viral life cycle, are under investigation. Here we employed arrayed whole-genome siRNA screening strategies to identify cell-autonomous molecular components that are subverted to support H1N1 influenza A virus infection of human bronchial epithelial cells. Integration across relevant public data sets exposed druggable gene products required for epithelial cell infection or required for viral proteins to deflect host cell suicide checkpoint activation. Pharmacological inhibition of representative targets, RGGT and CHEK1, resulted in significant protection against infection of human epithelial cells by the A/WS/33 virus. In addition, chemical inhibition of RGGT partially protected against H5N1 and the 2009 H1N1 pandemic strain. The observations reported here thus contribute to an expanding body of studies directed at decoding vulnerabilities in the command and control networks specified by influenza virulence factors.
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Affiliation(s)
- Samuel E. Ward
- Department of Cell Biology, University of Texas Southwestern Medical School, Dallas, Texas, United States of America
| | - Hyun Seok Kim
- Department of Cell Biology, University of Texas Southwestern Medical School, Dallas, Texas, United States of America
| | - Kakajan Komurov
- Divisions of Experimental Hematology and Cancer Biology, Human Genetics and Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Saurabh Mendiratta
- Department of Cell Biology, University of Texas Southwestern Medical School, Dallas, Texas, United States of America
| | - Pei-Ling Tsai
- Department of Cell Biology, University of Texas Southwestern Medical School, Dallas, Texas, United States of America
| | - Mirco Schmolke
- Department of Microbiology, Mount Sinai School of Medicine, New York, New York, United States of America
| | - Neal Satterly
- Department of Cell Biology, University of Texas Southwestern Medical School, Dallas, Texas, United States of America
| | - Balaji Manicassamy
- Department of Microbiology, Mount Sinai School of Medicine, New York, New York, United States of America
| | - Christian V. Forst
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Michael G. Roth
- Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Adolfo García-Sastre
- Department of Microbiology, Mount Sinai School of Medicine, New York, New York, United States of America
- Department of Medicine, Division of Infectious Diseases, Mount Sinai School of Medicine, New York, New York, United States of America
- Global Health and Emerging Pathogens Institute, Mount Sinai School of Medicine, New York, New York, United States of America
| | - Katarzyna M. Blazewska
- Institute of Organic Chemistry, Technical University of Łódź, Łódź, Poland
- Department of Chemistry, University of Southern California, Los Angeles, California, United States of America
| | - Charles E. McKenna
- Department of Chemistry, University of Southern California, Los Angeles, California, United States of America
| | - Beatriz M. Fontoura
- Department of Cell Biology, University of Texas Southwestern Medical School, Dallas, Texas, United States of America
| | - Michael A. White
- Department of Cell Biology, University of Texas Southwestern Medical School, Dallas, Texas, United States of America
- * E-mail:
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Zhang L, Das P, Schmolke M, Manicassamy B, Wang Y, Deng X, Cai L, Tu BP, Forst CV, Roth MG, Levy DE, García-Sastre A, de Brabander J, Phillips MA, Fontoura BMA. Inhibition of pyrimidine synthesis reverses viral virulence factor-mediated block of mRNA nuclear export. ACTA ACUST UNITED AC 2012; 196:315-26. [PMID: 22312003 PMCID: PMC3275370 DOI: 10.1083/jcb.201107058] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The NS1 protein of influenza virus is a major virulence factor essential for virus replication, as it redirects the host cell to promote viral protein expression. NS1 inhibits cellular messenger ribonucleic acid (mRNA) processing and export, down-regulating host gene expression and enhancing viral gene expression. We report in this paper the identification of a nontoxic quinoline carboxylic acid that reverts the inhibition of mRNA nuclear export by NS1, in the absence or presence of the virus. This quinoline carboxylic acid directly inhibited dihydroorotate dehydrogenase (DHODH), a host enzyme required for de novo pyrimidine biosynthesis, and partially reduced pyrimidine levels. This effect induced NXF1 expression, which promoted mRNA nuclear export in the presence of NS1. The release of NS1-mediated mRNA export block by DHODH inhibition also occurred in the presence of vesicular stomatitis virus M (matrix) protein, another viral inhibitor of mRNA export. This reversal of mRNA export block allowed expression of antiviral factors. Thus, pyrimidines play a necessary role in the inhibition of mRNA nuclear export by virulence factors.
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Affiliation(s)
- Liang Zhang
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Tatebe K, Zeytun A, Ribeiro RM, Hoffmann R, Harrod KS, Forst CV. Response network analysis of differential gene expression in human epithelial lung cells during avian influenza infections. BMC Bioinformatics 2010; 11:170. [PMID: 20370926 PMCID: PMC2868837 DOI: 10.1186/1471-2105-11-170] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2009] [Accepted: 04/06/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The recent emergence of the H5N1 influenza virus from avian reservoirs has raised concern about future influenza strains of high virulence emerging that could easily infect humans. We analyzed differential gene expression of lung epithelial cells to compare the response to H5N1 infection with a more benign infection with Respiratory Syncytial Virus (RSV). These gene expression data are then used as seeds to find important nodes by using a novel combination of the Gene Ontology database and the Human Network of gene interactions. Additional analysis of the data is conducted by training support vector machines (SVM) with the data and examining the orientations of the optimal hyperplanes generated. RESULTS Analysis of gene clustering in the Gene Ontology shows no significant clustering of genes unique to H5N1 response at 8 hours post infection. At 24 hours post infection, however, a number of significant gene clusters are found for nodes representing "immune response" and "response to virus" terms. There were no significant clusters of genes in the Gene Ontology for the control (Mock) or RSV experiments that were unique relative to the H5N1 response. The genes found to be most important in distinguishing H5N1 infected cells from the controls using SVM showed a large degree of overlap with the list of significantly regulated genes. However, though none of these genes were members of the GO clusters found to be significant. CONCLUSIONS Characteristics of H5N1 infection compared to RSV infection show several immune response factors that are specific for each of these infections. These include faster timescales within the cell as well as a more focused activation of immunity factors. Many of the genes that are found to be significantly expressed in H5N1 response relative to the control experiments are not found to cluster significantly in the Gene Ontology. These genes are, however, often closely linked to the clustered genes through the Human Network. This may suggest the need for more diverse annotations of these genes and verification of their action in immune response.
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Affiliation(s)
- Ken Tatebe
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ahmet Zeytun
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Ruy M Ribeiro
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Robert Hoffmann
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kevin S Harrod
- Lovelace Respiratory Research Institute, Albuquerque, NM, USA
| | - Christian V Forst
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
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19
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Abstract
The precise elucidation of the gene concept has become the subject of intense discussion in light of results from several, large high-throughput surveys of transcriptomes and proteomes. In previous work, we proposed an approach for constructing gene concepts that combines genomic heritability with elements of function. Here, we introduce a definition of the gene within a computational framework of cellular interactions. The definition seeks to satisfy the practical requirements imposed by annotation, capture logical aspects of regulation, and encompass the evolutionary property of homology.
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Affiliation(s)
- Peter F. Stadler
- Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, 04107 Leipzig, Germany
- Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 04103 Leipzig, Germany
- Fraunhofer Institut für Zelltherapie und Immunologie, IZI Perlickstraße 1, 04103 Leipzig, Germany
- Department of Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090 Vienna, Austria
- Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501 USA
| | - Sonja J. Prohaska
- Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, 04107 Leipzig, Germany
| | - Christian V. Forst
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390-9066 USA
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20
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Gebert J, Motameny S, Faigle U, Forst CV, Schrader R. Identifying Genes of Gene Regulatory Networks Using Formal Concept Analysis. J Comput Biol 2008; 15:185-94. [DOI: 10.1089/cmb.2007.0107] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Jutta Gebert
- Center for Applied Computer Science, University of Cologne, Cologne, Germany
| | - Susanne Motameny
- Center for Applied Computer Science, University of Cologne, Cologne, Germany
| | - Ulrich Faigle
- Center for Applied Computer Science, University of Cologne, Cologne, Germany
| | | | - Rainer Schrader
- Center for Applied Computer Science, University of Cologne, Cologne, Germany
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Abstract
MOTIVATION A quantitative description of interactions between cell components is a major challenge in Computational Biology. As a method of choice, differential equations are used for this purpose, because they provide a detailed insight into the dynamic behavior of the system. In most cases, the number of time points of experimental time series is usually too small to estimate the parameters of a model of a whole gene regulatory network based on differential equations, such that one needs to focus on subnetworks consisting of only a few components. For most approaches, the set of components of the subsystem is given in advance and only the structure has to be estimated. However, the set of components that influence the system significantly are not always known in advance, making a method desirable that determines both, the components that are included into the model and the parameters. RESULTS We have developed a method that uses gene expression data as well as interaction data between cell components to define a set of genes that we use for our modeling. In a subsequent step, we estimate the parameters of our model of piecewise linear differential equations and evaluate the results simulating the behavior of the system with our model. We have applied our method to the DNA repair system of Mycobacterium tuberculosis. Our analysis predicts that the gene Rv2719c plays an important role in this system.
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Affiliation(s)
- Nicole Radde
- Center for Applied Computer Science, University of Cologne Weyertal 80, 50931 Cologne, Germany. {radde.gebert}@zpr.uni-koeln.de
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22
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Abstract
Unlike traditional biological research that focuses on a small set of components, systems biology studies the complex interactions between a large number of genes, proteins and other elements of biological networks and systems. Host-pathogen systems biology examines the interactions between the components of two distinct organisms, either a microbial or viral pathogen and its animal host or two different microbial species in a community. With the availability of complete genomic sequences of various hosts and pathogens, together with breakthroughs in proteomics, metabolomics and other experimental areas, the investigation of host-pathogen systems on a multitude of levels of detail has come within reach.
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Affiliation(s)
- Christian V Forst
- Bioscience Division, Los Alamos National Laboratory, Mailstop M888, P.O. Box 1663, Los Alamos, NM 87545, USA.
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23
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Forst CV, Flamm C, Hofacker IL, Stadler PF. Algebraic comparison of metabolic networks, phylogenetic inference, and metabolic innovation. BMC Bioinformatics 2006; 7:67. [PMID: 16478540 PMCID: PMC1475643 DOI: 10.1186/1471-2105-7-67] [Citation(s) in RCA: 32] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2005] [Accepted: 02/14/2006] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Comparison of metabolic networks is typically performed based on the organisms' enzyme contents. This approach disregards functional replacements as well as orthologies that are misannotated. Direct comparison of the structure of metabolic networks can circumvent these problems. RESULTS Metabolic networks are naturally represented as directed hypergraphs in such a way that metabolites are nodes and enzyme-catalyzed reactions form (hyper)edges. The familiar operations from set algebra (union, intersection, and difference) form a natural basis for both the pairwise comparison of networks and identification of distinct metabolic features of a set of algorithms. We report here on an implementation of this approach and its application to the procaryotes. CONCLUSION We demonstrate that metabolic networks contain valuable phylogenetic information by comparing phylogenies obtained from network comparisons with 16S RNA phylogenies. The algebraic approach to metabolic networks is suitable to study metabolic innovations in two sets of organisms, free living microbes and Pyrococci, as well as obligate intracellular pathogens.
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Affiliation(s)
- Christian V Forst
- Bioscience Division, Los Alamos National Laboratory, Mailstop M888, P.O. Box 1663, Los Alamos, NM 87545, USA
| | - Christoph Flamm
- Department of Theoretical Chemistry, University of Vienna, Währingerstraβe 17, A-1090 Wien, Austria
| | - Ivo L Hofacker
- Department of Theoretical Chemistry, University of Vienna, Währingerstraβe 17, A-1090 Wien, Austria
| | - Peter F Stadler
- Department of Theoretical Chemistry, University of Vienna, Währingerstraβe 17, A-1090 Wien, Austria
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraβe 16-18, D-04107 Leipzig, Germany
- Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA
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Abstract
MOTIVATION The application of microarray chip technology has led to an explosion of data concerning the expression levels of the genes in an organism under a plethora of conditions. One of the major challenges of systems biology today is to devise generally applicable methods of interpreting this data in a way that will shed light on the complex relationships between multiple genes and their products. The importance of such information is clear, not only as an aid to areas of research like drug design, but also as a contribution to our understanding of the mechanisms behind an organism's ability to react to its environment. RESULTS We detail one computational approach for using gene expression data to identify response networks in an organism. The method is based on the construction of biological networks given different sets of interaction information and the reduction of the said networks to important response sub-networks via the integration of the gene expression data. As an application, the expression data of known stress responders and DNA repair genes in Mycobacterium tuberculosis is used to construct a generic stress response sub-network. This is compared to similar networks constructed from data obtained from subjecting M.tuberculosis to various drugs; we are thus able to distinguish between generic stress response and specific drug response. We anticipate that this approach will be able to accelerate target identification and drug development for tuberculosis in the future. CONTACT chris@lanl.gov SUPPLEMENTARY INFORMATION Supplementary Figures 1 through 6 on drug response networks and differential network analyses on cerulenin, chlorpromazine, ethionamide, ofloxacin, thiolactomycin and triclosan. Supplementary Tables 1 to 3 on predicted protein interactions. http://www.santafe.edu/~chris/DifferentialNW.
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Affiliation(s)
- Lawrence Cabusora
- Los Alamos National Laboratory, PO Box 1663, Mailstop M888, Los Alamos, NM 87545, USA
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25
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Mawuenyega KG, Forst CV, Dobos KM, Belisle JT, Chen J, Bradbury EM, Bradbury ARM, Chen X. Mycobacterium tuberculosis functional network analysis by global subcellular protein profiling. Mol Biol Cell 2005; 16:396-404. [PMID: 15525680 PMCID: PMC539182 DOI: 10.1091/mbc.e04-04-0329] [Citation(s) in RCA: 171] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2004] [Revised: 09/13/2004] [Accepted: 10/17/2004] [Indexed: 11/11/2022] Open
Abstract
Trends in increased tuberculosis infection and a fatality rate of approximately 23% have necessitated the search for alternative biomarkers using newly developed postgenomic approaches. Here we provide a systematic analysis of Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and computational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane, and cytosol) of Mtb. We report the identifications of 1044 proteins and their corresponding localizations in these compartments. Genome-based computational and metabolic pathways analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.
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Affiliation(s)
- Kwasi G Mawuenyega
- Cell Biology, Structural Biology, and Flow Cytometry, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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27
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Abstract
Network Genomics studies genomics and proteomics foundations of cellular networks in biological systems. It complements systems biology in providing information on elements, their interaction and their functional interplay in cellular networks. The relationship between genomic and proteomic high-throughput technologies and computational methods are described, as well as several examples of specific network genomic application are presented.
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Affiliation(s)
- Christian V Forst
- Bioscience Division, Mailstop M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
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29
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Abstract
Global properties of the genotype-phenotype map induced by RNA secondary structures can be described by random graph theory. The success of this approach depends on details of the respective secondary structure. A small selection of these dependencies, such as stem length, free energy and well-definedness, are analyzed in this paper. In addition, we present an algorithm, which, given a network that complies to a random graph model, provides estimates whether the network is connected or not. The algorithm is linear in time and in the sequence length, local connectivity provided, which is a graph property of the random graph model.
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30
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Abstract
The information provided by completely sequenced genomes can yield insights into the multi-level organization of organisms and their evolution. At the lowest level of molecular organization individual enzymes are formed, often through assembly of multiple polypeptides. At a higher level, sets of enzymes group into metabolic networks. Much has been learned about the relationship of species from phylogenetic trees comparing individual enzymes. In this article we extend conventional phylogenetic analysis of individual enzymes in different organisms to the organisms' metabolic networks. For this purpose we suggest a method that combines sequence information with information about the underlying reaction networks. A distance between pathways is defined as incorporating distances between substrates and distances between corresponding enzymes. The new analysis is applied to electron-transfer and amino acid biosynthesis networks yielding a more comprehensive understanding of similarities and differences between organisms.
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Affiliation(s)
- C V Forst
- Theoretical Biophysics Group, University of Illinois at Urbana-Champaign, Beckman Institute, MC-251, 405 North Mathews Avenue, Urbana, IL 61801, USA.
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31
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Abstract
Folding of RNA sequences into secondary structures is viewed as a map that assigns a uniquely defined base pairing pattern to every sequence. The mapping is non-invertible since many sequences fold into the same minimum free energy (secondary) structure or shape. The pre-images of this map, called neutral networks, are uniquely associated with the shapes and vice versa. Random graph theory is used to construct networks in sequence space which are suitable models for neutral networks. The theory of molecular quasispecies has been applied to replication and mutation on single-peak fitness landscapes. This concept is extended by considering evolution on degenerate multi-peak landscapes which originate from neutral networks by assuming that one particular shape is fitter than all the others. On such a single-shape landscape the superior fitness value is assigned to all sequences belonging to the master shape. All other shapes are lumped together and their fitness values are averaged in a way that is reminiscent of mean field theory. Replication and mutation on neutral networks are modeled by phenomenological rate equations as well as by a stochastic birth-and-death model. In analogy to the error threshold in sequence space the phenotypic error threshold separates two scenarios: (i) a stationary (fittest) master shape surrounded by closely related shapes and (ii) populations drifting through shape space by a diffusion-like process. The error classes of the quasispecies model are replaced by distance classes between the master shape and the other structures. Analytical results are derived for single-shape landscapes, in particular, simple expressions are obtained for the mean fraction of master shapes in a population and for phenotypic error thresholds. The analytical results are complemented by data obtained from computer simulation of the underlying stochastic processes. The predictions of the phenomenological approach on the single-shape landscape are very well reproduced by replication and mutation kinetics of tRNA(phe). Simulation of the stochastic process at a resolution of individual distance classes yields data which are in excellent agreement with the results derived from the birth-and-death model.
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Affiliation(s)
- C Reidys
- Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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32
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Abstract
In this paper, we consider the evolutionary dynamics of catalytically active species with a distinct genotype-phenotype relationship. Folding landscapes of RNA molecules serve as a paradigm for this relationship with essential neutral properties. The landscape itself is partitioned by phenotypes (realized as RNA secondary structures). To each genotype (represented as a sequence) a structure is assigned in a unique way. The set of all sequences which map into a particular structure is modeled as a random graph in sequence space (the so-called neutral network). A catalytic network is realized as a random digraph with maximal out-degree two and secondary structures as vertex sets. A population of catalytic RNA molecules shows significantly different behavior compared to a deterministic description: hypercycles are able to co-exist and out-compete a parasite with superior catalytic support. A "switching" between different dynamic organizations of the network can be observed, dynamical stability of hypercyclic organizations against errors and the existence of an error-threshold of catalysis can be reported.
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33
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Abstract
The abundance of information provided by completely sequenced genomes defines a starting point for new insights in the multilevel organization of organisms and their evolution. At the lowest level enzymes and other protein complexes are formed by aggregating multiple polypeptides. At a higher level enzymes group conceptually into metabolic pathways as part of a dynamic information-processing system, and substrates are processed by enzymes yielding other substrates. A method based on a combination of sequence information with graph topology of the underlying pathway is presented. With this approach pathways of different organisms are related to each other by phylogenetic analysis, extending conventional phylogenetic analysis of individual enzymes. The new method is applied to pathways related to electron transfer and to the Krebs citric acid cycle. In addition to providing a more comprehensive understanding of similarities and differences between organisms, this method indicates different evolutionary rates between substrates and enzymes.
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Affiliation(s)
- C V Forst
- Theoretical Biophysics, Beckman Institute, University of Illinois, Urbana-Champaign, Urbana 61801, USA.
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34
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35
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Abstract
A general system of high-order differential equations describing complex dynamics of replicating biomolecules is given. Symmetry relations and coordinate transformations of general replication systems leading to topologically equivalent systems are derived. Three chaotic attractors observed in Lotka-Volterra equations of dimension n = 3 are shown to represent three cross-sections of one and the same chaotic regime. Also a fractal torus in a generalized three-dimensional Lotka-Volterra Model has been linked to one of the chaotic attractors. The strange attractors are studied in the equivalent four-dimensional catalytic replicator network. The fractal torus has been examined in adapted Lotka-Volterra equations. Analytic expressions are derived for the Lyapunov exponents of the flow in the replicator system. Lyapunov spectra for different pathways into chaos has been calculated. In the generalized Lotka-Volterra system a second inner rest point--coexisting with (quasi)-periodic orbits--can be observed; with an abundance of different bifurcations. Pathways from chaotic tori, via quasi-periodic tori, via limit cycles, via multi-periodic orbits--emerging out of periodic doubling bifurcations--to "simple" chaotic attractors can be found.
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Affiliation(s)
- C V Forst
- Institut für Molekulare Biotechnologie, Beutenbergstrasse 11, PF 100 813, D-07708 Jena, Germany
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