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14 results on '"Halfmann, Peter J."'

1. Replicative capacity of SARS-CoV-2 omicron variants BA.5 and BQ.1.1 at elevated temperatures

Author

Muramoto, Yukiko, Takahashi, Senye, Halfmann, Peter J, Gotoh, Shimpei, Noda, Takeshi, and Kawaoka, Yoshihiro

Subjects
Microbiology (medical), Infectious Diseases, Virology, Microbiology
Abstract

新型コロナウイルス・オミクロン株のBA.5系統およびBQ.1.1系統が、高温で増殖しづらいことを解明. 京都大学プレスリリース. 2023-04-27.

Published
2023

2. Defining the risk of SARS-CoV-2 variants on immune protection

Author

DeGrace, Marciela M, Ghedin, Elodie, Frieman, Matthew B, Krammer, Florian, Grifoni, Alba, Alisoltani, Arghavan, Alter, Galit, Amara, Rama R, Baric, Ralph S, Barouch, Dan H, Bloom, Jesse D, Bloyet, Louis-Marie, Bonenfant, Gaston, Boon, Adrianus CM, Boritz, Eli A, Bratt, Debbie L, Bricker, Traci L, Brown, Liliana, Buchser, William J, Carreño, Juan Manuel, Cohen-Lavi, Liel, Darling, Tamarand L, Davis-Gardner, Meredith E, Dearlove, Bethany L, Di, Han, Dittmann, Meike, Doria-Rose, Nicole A, Douek, Daniel C, Drosten, Christian, Edara, Venkata-Viswanadh, Ellebedy, Ali, Fabrizio, Thomas P, Ferrari, Guido, Fischer, Will M, Florence, William C, Fouchier, Ron AM, Franks, John, García-Sastre, Adolfo, Godzik, Adam, Gonzalez-Reiche, Ana Silvia, Gordon, Aubree, Haagmans, Bart L, Halfmann, Peter J, Ho, David D, Holbrook, Michael R, Huang, Yaoxing, James, Sarah L, Jaroszewski, Lukasz, Jeevan, Trushar, Johnson, Robert M, Jones, Terry C, Joshi, Astha, Kawaoka, Yoshihiro, Kercher, Lisa, Koopmans, Marion PG, Korber, Bette, Koren, Eilay, Koup, Richard A, LeGresley, Eric B, Lemieux, Jacob E, Liebeskind, Mariel J, Liu, Zhuoming, Livingston, Brandi, Logue, James P, Luo, Yang, McDermott, Adrian B, McElrath, Margaret J, Meliopoulos, Victoria A, Menachery, Vineet D, Montefiori, David C, Mühlemann, Barbara, Munster, Vincent J, Munt, Jenny E, Nair, Manoj S, Netzl, Antonia, Niewiadomska, Anna M, O'Dell, Sijy, Pekosz, Andrew, Perlman, Stanley, Pontelli, Marjorie C, Rockx, Barry, Rolland, Morgane, Rothlauf, Paul W, Sacharen, Sinai, Scheuermann, Richard H, Schmidt, Stephen D, Schotsaert, Michael, Schultz-Cherry, Stacey, Seder, Robert A, Sedova, Mayya, Sette, Alessandro, Shabman, Reed S, Shen, Xiaoying, Shi, Pei-Yong, Shukla, Maulik, Simon, Viviana, Stumpf, Spencer, Sullivan, Nancy J, Thackray, Larissa B, and Theiler, James

Subjects
and promotion of well-being, COVID-19 Vaccines, Pharmacogenomic Variants, General Science & Technology, Vaccine Related, National Institute of Allergy and Infectious Diseases (U.S.), Biodefense, Animals, Humans, 2.1 Biological and endogenous factors, Aetiology, Pandemics, Lung, Virulence, SARS-CoV-2, Prevention, COVID-19, Pneumonia, Prevention of disease and conditions, Biological Evolution, United States, Infectious Diseases, Emerging Infectious Diseases, Good Health and Well Being, 3.4 Vaccines, Pneumonia & Influenza, Immunization, Infection, Biotechnology
Abstract

The global emergence of many severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants jeopardizes the protective antiviral immunity induced after infection or vaccination. To address the public health threat caused by the increasing SARS-CoV-2 genomic diversity, the National Institute of Allergy and Infectious Diseases within the National Institutes of Health established the SARS-CoV-2 Assessment of Viral Evolution (SAVE) programme. This effort was designed to provide a real-time risk assessment of SARS-CoV-2 variants that could potentially affect the transmission, virulence, and resistance to infection- and vaccine-induced immunity. The SAVE programme is a critical data-generating component of the US Government SARS-CoV-2 Interagency Group to assess implications of SARS-CoV-2 variants on diagnostics, vaccines and therapeutics, and for communicating public health risk. Here we describe the coordinated approach used to identify and curate data about emerging variants, their impact on immunity and effects on vaccine protection using animal models. We report the development of reagents, methodologies, models and notable findings facilitated by this collaborative approach and identify future challenges. This programme is a template for the response to rapidly evolving pathogens with pandemic potential by monitoring viral evolution in the human population to identify variants that could reduce the effectiveness of countermeasures.

Published
2022

3. Additional file 2 of Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Feng, Song, Heath, Emily, Jefferson, Brett, Joslyn, Cliff, Kvinge, Henry, Mitchell, Hugh D., Praggastis, Brenda, Eisfeld, Amie J., Sims, Amy C., Thackray, Larissa B., Fan, Shufang, Walters, Kevin B., Halfmann, Peter J., Westhoff-Smith, Danielle, Tan, Qing, Menachery, Vineet D., Sheahan, Timothy P., Cockrell, Adam S., Kocher, Jacob F., Stratton, Kelly G., Heller, Natalie C., Bramer, Lisa M., Diamond, Michael S., Baric, Ralph S., Waters, Katrina M., Kawaoka, Yoshihiro, McDermott, Jason E., and Purvine, Emilie

Abstract

Additional file 2. These figure is analogous to Figure 4 for additional z-score threshold z.

Published
2021
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4. Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Kawaoka, Yoshihiro, Stratton, Kelly G., Purvine, Emilie, Cockrell, Adam S., Feng, Song, Bramer, Lisa M., Baric, Ralph S., Menachery, Vineet D., Praggastis, Brenda, Waters, Katrina M., Joslyn, Cliff, Sheahan, Timothy P., Kvinge, Henry, Tan, Qing, Diamond, Michael S., Fan, Shufang, Thackray, Larissa B., Heath, Emily, Walters, Kevin B., Eisfeld, Amie J., McDermott, Jason E., Heller, Natalie C., Jefferson, Brett, Mitchell, Hugh D., Kocher, Jacob F., Sims, Amy C., Halfmann, Peter J., and Westhoff‑Smith, Danielle

Abstract

Background: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions: Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.

Published
2021
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5. Additional file 4 of Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Feng, Song, Heath, Emily, Jefferson, Brett, Joslyn, Cliff, Kvinge, Henry, Mitchell, Hugh D., Praggastis, Brenda, Eisfeld, Amie J., Sims, Amy C., Thackray, Larissa B., Fan, Shufang, Walters, Kevin B., Halfmann, Peter J., Westhoff-Smith, Danielle, Tan, Qing, Menachery, Vineet D., Sheahan, Timothy P., Cockrell, Adam S., Kocher, Jacob F., Stratton, Kelly G., Heller, Natalie C., Bramer, Lisa M., Diamond, Michael S., Baric, Ralph S., Waters, Katrina M., Kawaoka, Yoshihiro, McDermott, Jason E., and Purvine, Emilie

Abstract

Additional file 4. This figure is analogous to Figure 4 for additional z-score threshold z.

Published
2021
Full Text
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6. Additional file 3 of Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Feng, Song, Heath, Emily, Jefferson, Brett, Joslyn, Cliff, Kvinge, Henry, Mitchell, Hugh D., Praggastis, Brenda, Eisfeld, Amie J., Sims, Amy C., Thackray, Larissa B., Fan, Shufang, Walters, Kevin B., Halfmann, Peter J., Westhoff-Smith, Danielle, Tan, Qing, Menachery, Vineet D., Sheahan, Timothy P., Cockrell, Adam S., Kocher, Jacob F., Stratton, Kelly G., Heller, Natalie C., Bramer, Lisa M., Diamond, Michael S., Baric, Ralph S., Waters, Katrina M., Kawaoka, Yoshihiro, McDermott, Jason E., and Purvine, Emilie

Abstract

Additional file 3. These figure is analogous to Figure 4 for additional z-score threshold z.

Published
2021
Full Text
View/download PDF

7. Additional file 7 of Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Feng, Song, Heath, Emily, Jefferson, Brett, Joslyn, Cliff, Kvinge, Henry, Mitchell, Hugh D., Praggastis, Brenda, Eisfeld, Amie J., Sims, Amy C., Thackray, Larissa B., Fan, Shufang, Walters, Kevin B., Halfmann, Peter J., Westhoff-Smith, Danielle, Tan, Qing, Menachery, Vineet D., Sheahan, Timothy P., Cockrell, Adam S., Kocher, Jacob F., Stratton, Kelly G., Heller, Natalie C., Bramer, Lisa M., Diamond, Michael S., Baric, Ralph S., Waters, Katrina M., Kawaoka, Yoshihiro, McDermott, Jason E., and Purvine, Emilie

Subjects
3. Good health
Abstract

Additional file 7. This figure shows the p-values for the GSEA enrichment scores (shown in Figure S2) for z-score theshold z.

8. Additional file 5 of Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Feng, Song, Heath, Emily, Jefferson, Brett, Joslyn, Cliff, Kvinge, Henry, Mitchell, Hugh D., Praggastis, Brenda, Eisfeld, Amie J., Sims, Amy C., Thackray, Larissa B., Fan, Shufang, Walters, Kevin B., Halfmann, Peter J., Westhoff-Smith, Danielle, Tan, Qing, Menachery, Vineet D., Sheahan, Timothy P., Cockrell, Adam S., Kocher, Jacob F., Stratton, Kelly G., Heller, Natalie C., Bramer, Lisa M., Diamond, Michael S., Baric, Ralph S., Waters, Katrina M., Kawaoka, Yoshihiro, McDermott, Jason E., and Purvine, Emilie

Subjects
3. Good health
Abstract

Additional file 5. This figure shows the p-values for the GSEA enrichment scores (shown in Figure 4) for z-score theshold z.

9. Additional file 8 of Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Feng, Song, Heath, Emily, Jefferson, Brett, Joslyn, Cliff, Kvinge, Henry, Mitchell, Hugh D., Praggastis, Brenda, Eisfeld, Amie J., Sims, Amy C., Thackray, Larissa B., Fan, Shufang, Walters, Kevin B., Halfmann, Peter J., Westhoff-Smith, Danielle, Tan, Qing, Menachery, Vineet D., Sheahan, Timothy P., Cockrell, Adam S., Kocher, Jacob F., Stratton, Kelly G., Heller, Natalie C., Bramer, Lisa M., Diamond, Michael S., Baric, Ralph S., Waters, Katrina M., Kawaoka, Yoshihiro, McDermott, Jason E., and Purvine, Emilie

Subjects
3. Good health
Abstract

Additional file 8. This figure shows the p-values for the GSEA enrichment scores (shown in Figure S3) for z-score theshold z.

10. Additional file 7 of Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Feng, Song, Heath, Emily, Jefferson, Brett, Joslyn, Cliff, Kvinge, Henry, Mitchell, Hugh D., Praggastis, Brenda, Eisfeld, Amie J., Sims, Amy C., Thackray, Larissa B., Fan, Shufang, Walters, Kevin B., Halfmann, Peter J., Westhoff-Smith, Danielle, Tan, Qing, Menachery, Vineet D., Sheahan, Timothy P., Cockrell, Adam S., Kocher, Jacob F., Stratton, Kelly G., Heller, Natalie C., Bramer, Lisa M., Diamond, Michael S., Baric, Ralph S., Waters, Katrina M., Kawaoka, Yoshihiro, McDermott, Jason E., and Purvine, Emilie

Subjects
3. Good health
Abstract

Additional file 7. This figure shows the p-values for the GSEA enrichment scores (shown in Figure S2) for z-score theshold z.

11. Additional file 6 of Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Feng, Song, Heath, Emily, Jefferson, Brett, Joslyn, Cliff, Kvinge, Henry, Mitchell, Hugh D., Praggastis, Brenda, Eisfeld, Amie J., Sims, Amy C., Thackray, Larissa B., Fan, Shufang, Walters, Kevin B., Halfmann, Peter J., Westhoff-Smith, Danielle, Tan, Qing, Menachery, Vineet D., Sheahan, Timothy P., Cockrell, Adam S., Kocher, Jacob F., Stratton, Kelly G., Heller, Natalie C., Bramer, Lisa M., Diamond, Michael S., Baric, Ralph S., Waters, Katrina M., Kawaoka, Yoshihiro, McDermott, Jason E., and Purvine, Emilie

Subjects
3. Good health
Abstract

Additional file 6. This figure shows the p-values for the GSEA enrichment scores (shown in Figure S1) for z-score theshold z.

12. Additional file 5 of Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Feng, Song, Heath, Emily, Jefferson, Brett, Joslyn, Cliff, Kvinge, Henry, Mitchell, Hugh D., Praggastis, Brenda, Eisfeld, Amie J., Sims, Amy C., Thackray, Larissa B., Fan, Shufang, Walters, Kevin B., Halfmann, Peter J., Westhoff-Smith, Danielle, Tan, Qing, Menachery, Vineet D., Sheahan, Timothy P., Cockrell, Adam S., Kocher, Jacob F., Stratton, Kelly G., Heller, Natalie C., Bramer, Lisa M., Diamond, Michael S., Baric, Ralph S., Waters, Katrina M., Kawaoka, Yoshihiro, McDermott, Jason E., and Purvine, Emilie

Subjects
3. Good health
Abstract

Additional file 5. This figure shows the p-values for the GSEA enrichment scores (shown in Figure 4) for z-score theshold z.

13. Additional file 6 of Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Feng, Song, Heath, Emily, Jefferson, Brett, Joslyn, Cliff, Kvinge, Henry, Mitchell, Hugh D., Praggastis, Brenda, Eisfeld, Amie J., Sims, Amy C., Thackray, Larissa B., Fan, Shufang, Walters, Kevin B., Halfmann, Peter J., Westhoff-Smith, Danielle, Tan, Qing, Menachery, Vineet D., Sheahan, Timothy P., Cockrell, Adam S., Kocher, Jacob F., Stratton, Kelly G., Heller, Natalie C., Bramer, Lisa M., Diamond, Michael S., Baric, Ralph S., Waters, Katrina M., Kawaoka, Yoshihiro, McDermott, Jason E., and Purvine, Emilie

Subjects
3. Good health
Abstract

Additional file 6. This figure shows the p-values for the GSEA enrichment scores (shown in Figure S1) for z-score theshold z.

14. Additional file 8 of Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Author

Feng, Song, Heath, Emily, Jefferson, Brett, Joslyn, Cliff, Kvinge, Henry, Mitchell, Hugh D., Praggastis, Brenda, Eisfeld, Amie J., Sims, Amy C., Thackray, Larissa B., Fan, Shufang, Walters, Kevin B., Halfmann, Peter J., Westhoff-Smith, Danielle, Tan, Qing, Menachery, Vineet D., Sheahan, Timothy P., Cockrell, Adam S., Kocher, Jacob F., Stratton, Kelly G., Heller, Natalie C., Bramer, Lisa M., Diamond, Michael S., Baric, Ralph S., Waters, Katrina M., Kawaoka, Yoshihiro, McDermott, Jason E., and Purvine, Emilie

Subjects
3. Good health
Abstract

Additional file 8. This figure shows the p-values for the GSEA enrichment scores (shown in Figure S3) for z-score theshold z.

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