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Adriana Tomic

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Preprint content
Published: 15 June 2021
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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is normally controlled by effective host immunity including innate, humoral and cellular responses. However, the trajectories and correlates of acquired immunity, and the capacity of memory responses months after infection to neutralise variants of concern - which has important public health implications - is not fully understood. To address this, we studied a cohort of 78 UK healthcare workers who presented in April to June 2020 with symptomatic PCR-confirmed infection or who tested positive during an asymptomatic screening programme and tracked virus-specific B and T cell responses longitudinally at 5-6 time points each over 6 months, prior to vaccination. We observed a highly variable range of responses, some of which - T cell interferon-gamma (IFN-γ) ELISpot, N-specific antibody waned over time across the cohort, while others (spike-specific antibody, B cell memory ELISpot) were stable. In such cohorts, antiviral antibody has been linked to protection against re-infection. We used integrative analysis and a machine-learning approach (SIMON - Sequential Iterative Modeling Over Night) to explore this heterogeneity and to identify predictors of sustained immune responses. Hierarchical clustering defined a group of high and low antibody responders, which showed stability over time regardless of clinical presentation. These antibody responses correlated with IFN-γ ELISpot measures of T cell immunity and represent a subgroup of patients with a robust trajectory for longer term immunity. Importantly, this immune-phenotype associates with higher levels of neutralising antibodies not only against the infecting (Victoria) strain but also against variants B.1.1.7 (alpha) and B.1.351 (beta). Overall memory responses to SARS-CoV-2 show distinct trajectories following early priming, that may define subsequent protection against infection and severe disease from novel variants.

ACS Style

Adriana Tomic; Donal T. Skelly; Ane Ogbe; Daniel O'Connor; Matthew Pace; Emily Adland; Frances Alexander; Mohammad Ali; Kirk Allott; M. Azim Ansari; Sandra Belij-Rammerstorfer; Sagida Bibi; Luke Blackwell; Anthony Brown; Helen Brown; Breeze Cavell; Elizabeth A. Clutterbuck; Thushan I de Silva; David Eyre; Amy Flaxman; James Grist; Carl-Philipp Hackstein; Rachel Halkerston; Adam C. Harding; Jennifer Hill; Tim James; Cecilia Jay; Síle A. Johnson; Barbara Kronsteiner; Yolanda Lie; Aline Linder; Stephanie Longet; Spyridoula Marinou; Philippa C. Matthews; Jack Mellors; Christos Petropoulos; Patpong Rongkard; Cynthia Sedik; Laura Silva-Reyes; Holly Smith; Lisa Stockdale; Stephen Taylor; Stephen Thomas; Timothy Tipoe; Lance Turtle; Vinicius Adriano Vieira; Terri Wrin; OPTIC Clinical Group; PITCH Study Group; C-MORE Group; Andrew J. Pollard; Teresa Lambe; Christopher P. Conlon; Katie Jeffery; Simon Travis; Philip J. Goulder; John Frater; Alexander J. Mentzer; Lizzie Stafford; Miles W. Carroll; William S. James; Paul Klenerman#; Eleanor Barnes#; Christina Dold#; Susanna J. Dunachie#. Divergent trajectories of antiviral memory after SARS-Cov-2 infection. 2021, 1 .

AMA Style

Adriana Tomic, Donal T. Skelly, Ane Ogbe, Daniel O'Connor, Matthew Pace, Emily Adland, Frances Alexander, Mohammad Ali, Kirk Allott, M. Azim Ansari, Sandra Belij-Rammerstorfer, Sagida Bibi, Luke Blackwell, Anthony Brown, Helen Brown, Breeze Cavell, Elizabeth A. Clutterbuck, Thushan I de Silva, David Eyre, Amy Flaxman, James Grist, Carl-Philipp Hackstein, Rachel Halkerston, Adam C. Harding, Jennifer Hill, Tim James, Cecilia Jay, Síle A. Johnson, Barbara Kronsteiner, Yolanda Lie, Aline Linder, Stephanie Longet, Spyridoula Marinou, Philippa C. Matthews, Jack Mellors, Christos Petropoulos, Patpong Rongkard, Cynthia Sedik, Laura Silva-Reyes, Holly Smith, Lisa Stockdale, Stephen Taylor, Stephen Thomas, Timothy Tipoe, Lance Turtle, Vinicius Adriano Vieira, Terri Wrin, OPTIC Clinical Group, PITCH Study Group, C-MORE Group, Andrew J. Pollard, Teresa Lambe, Christopher P. Conlon, Katie Jeffery, Simon Travis, Philip J. Goulder, John Frater, Alexander J. Mentzer, Lizzie Stafford, Miles W. Carroll, William S. James, Paul Klenerman#, Eleanor Barnes#, Christina Dold#, Susanna J. Dunachie#. Divergent trajectories of antiviral memory after SARS-Cov-2 infection. . 2021; ():1.

Chicago/Turabian Style

Adriana Tomic; Donal T. Skelly; Ane Ogbe; Daniel O'Connor; Matthew Pace; Emily Adland; Frances Alexander; Mohammad Ali; Kirk Allott; M. Azim Ansari; Sandra Belij-Rammerstorfer; Sagida Bibi; Luke Blackwell; Anthony Brown; Helen Brown; Breeze Cavell; Elizabeth A. Clutterbuck; Thushan I de Silva; David Eyre; Amy Flaxman; James Grist; Carl-Philipp Hackstein; Rachel Halkerston; Adam C. Harding; Jennifer Hill; Tim James; Cecilia Jay; Síle A. Johnson; Barbara Kronsteiner; Yolanda Lie; Aline Linder; Stephanie Longet; Spyridoula Marinou; Philippa C. Matthews; Jack Mellors; Christos Petropoulos; Patpong Rongkard; Cynthia Sedik; Laura Silva-Reyes; Holly Smith; Lisa Stockdale; Stephen Taylor; Stephen Thomas; Timothy Tipoe; Lance Turtle; Vinicius Adriano Vieira; Terri Wrin; OPTIC Clinical Group; PITCH Study Group; C-MORE Group; Andrew J. Pollard; Teresa Lambe; Christopher P. Conlon; Katie Jeffery; Simon Travis; Philip J. Goulder; John Frater; Alexander J. Mentzer; Lizzie Stafford; Miles W. Carroll; William S. James; Paul Klenerman#; Eleanor Barnes#; Christina Dold#; Susanna J. Dunachie#. 2021. "Divergent trajectories of antiviral memory after SARS-Cov-2 infection." , no. : 1.

Review
Published: 20 May 2021 in Viruses
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Understanding protective influenza immunity and identifying immune correlates of protection poses a major challenge and requires an appreciation of the immune system in all of its complexity. While adaptive immune responses such as neutralizing antibodies and influenza-specific T lymphocytes are contributing to the control of influenza virus, key factors of long-term protection are not well defined. Using systems immunology, an approach that combines experimental and computational methods, we can capture the systems-level state of protective immunity and reveal the essential pathways that are involved. New approaches and technological developments in systems immunology offer an opportunity to examine roles and interrelationships of clinical, biological, and genetic factors in the control of influenza infection and have the potential to lead to novel discoveries about influenza immunity that are essential for the development of more effective vaccines to prevent future pandemics. Here, we review recent developments in systems immunology that help to reveal key factors mediating protective immunity.

ACS Style

Adriana Tomic; Andrew Pollard; Mark Davis. Systems Immunology: Revealing Influenza Immunological Imprint. Viruses 2021, 13, 948 .

AMA Style

Adriana Tomic, Andrew Pollard, Mark Davis. Systems Immunology: Revealing Influenza Immunological Imprint. Viruses. 2021; 13 (5):948.

Chicago/Turabian Style

Adriana Tomic; Andrew Pollard; Mark Davis. 2021. "Systems Immunology: Revealing Influenza Immunological Imprint." Viruses 13, no. 5: 948.

Preprint content
Published: 11 May 2021
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Summary Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete understanding of potentially druggable immune mediators of disease. To advance this, we present a comprehensive multi-omic blood atlas in patients with varying COVID-19 severity and compare with influenza, sepsis and healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity revealed cells, their inflammatory mediators and networks as potential therapeutic targets, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Tensor and matrix decomposition of the overall dataset revealed feature groupings linked with disease severity and specificity. Our systems-based integrative approach and blood atlas will inform future drug development, clinical trial design and personalised medicine approaches for COVID-19.

ACS Style

COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium; David J Ahern; Zhichao Ai; Mark Ainsworth; Chris Allan; Alice Allcock; Azim Ansari; Carolina V Arancibia-Carcamo; Dominik Aschenbrenner; Moustafa Attar; J. Kenneth Baillie; Eleanor Barnes; Rachael Bashford-Rogers; Archana Bashyal; Sally Beer; Georgina Berridge; Amy Beveridge; Sagida Bibi; Tihana Bicanic; Luke Blackwell; Paul Bowness; Andrew Brent; Andrew Brown; John Broxholme; David Buck; Katie L Burnham; Helen Byrne; Susana Camara; Ivan Candido Ferreira; Philip Charles; Wentao Chen; Yi-Ling Chen; Amanda Chong; Elizabeth Clutterbuck; Mark Coles; Christopher P Conlon; Richard Cornall; Adam P Cribbs; Fabiola Curion; Emma E Davenport; Neil Davidson; Simon Davis; Calliope Dendrou; Julie Dequaire; Lea Dib; James Docker; Christina Dold; Tao Dong; Damien Downes; Alexander Drakesmith; Susanna J Dunachie; David A Duncan; Chris Eijsbouts; Robert Esnouf; Alexis Espinosa; Rachel Etherington; Benjamin Fairfax; Rory Fairhead; Hai Fang; Shayan Fassih; Sally Felle; Maria Fernandez Mendoza; Ricardo Ferreira; Roman Fischer; Thomas Foord; Aden Forrow; John Frater; Anastasia Fries; Veronica Gallardo Sanchez; Lucy Garner; Clementine Geeves; Dominique Georgiou; Leila Godfrey; Tanya Golubchik; Maria Gomez Vazquez; Angie Green; Hong Harper; Heather A Harrington; Raphael Heilig; Svenja Hester; Jennifer Hill; Charles Hinds; Clare Hird; Ling-Pei Ho; Renee Hoekzema; Benjamin Hollis; Jim Hughes; Paula Hutton; Matthew Jackson; Ashwin Jainarayanan; Anna James-Bott; Kathrin Jansen; Katie Jeffery; Elizabeth Jones; Luke Jostins; Georgina Kerr; David Kim; Paul Klenerman; Julian C Knight; Vinod Kumar; Piyush Kumar Sharma; Prathiba Kurupati; Andrew Kwok; Angela Lee; Aline Linder; Teresa Lockett; Lorne Lonie; Maria Lopopolo; Martyna Lukoseviciute; Jian Luo; Spyridoula Marinou; Brian Marsden; Jose Martinez; Philippa Matthews; Michalina Mazurczyk; Simon McGowan; Stuart McKechnie; Adam Mead; Alexander J Mentzer; Yuxin Mi; Claudia Monaco; Ruddy Montadon; Giorgio Napolitani; Isar Nassiri; Alex Novak; Darragh O'Brien; Daniel O'Connor; Denise O'Donnell; Graham Ogg; Lauren Overend; Inhye Park; Ian Pavord; Yanchun Peng; Frank Penkava; Mariana Pereira Pinho; Elena Perez; Andrew J Pollard; Fiona Powrie; Bethan Psaila; T. Phuong Quan; Emmanouela Repapi; Santiago Revale; Laura Silva-Reyes; Jean-Baptiste Richard; Charlotte Rich-Griffin; Thomas Ritter; Christine S Rollier; Matthew Rowland; Fabian Ruehle; Mariolina Salio; Stephen N Sansom; Alberto Santos Delgado; Tatjana Sauka-Spengler; Ron Schwessinger; Giuseppe Scozzafava; Gavin Screaton; Anna Seigal; Malcolm G Semple; Martin Sergeant; Christina Simoglou Karali; David Sims; Donal Skelly; Hubert Slawinski; Alberto Sobrinodiaz; Nikolaos Sousos; Lizzie Stafford; Lisa Stockdale; Marie Strickland; Otto Sumray; Bo Sun; Chelsea Taylor; Stephen Taylor; Adan Taylor; Supat Thongjuea; Hannah Thraves; John A Todd; Adriana Tomic; Orion Tong; Amy Trebes; Dominik Trzupek; Felicia A Tucci; Lance Turtle; Irina Udalova; Holm Uhlig; Erinke van Grinsven; Iolanda Vendrell; Marije Verheul; Alexandru Voda; Guanlin Wang; Lihui Wang; Dapeng Wang; Peter Watkinson; Robert Watson; Michael Weinberger; Justin Whalley; Lorna Witty; Katherine Wray; Luzheng Xue; Hing Yuen Yeung; Zixi Yin; Rebecca K Young; Jonathan Youngs; Ping Zhang; Yasemin-Xiomara Zurke. A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. 2021, 1 .

AMA Style

COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium, David J Ahern, Zhichao Ai, Mark Ainsworth, Chris Allan, Alice Allcock, Azim Ansari, Carolina V Arancibia-Carcamo, Dominik Aschenbrenner, Moustafa Attar, J. Kenneth Baillie, Eleanor Barnes, Rachael Bashford-Rogers, Archana Bashyal, Sally Beer, Georgina Berridge, Amy Beveridge, Sagida Bibi, Tihana Bicanic, Luke Blackwell, Paul Bowness, Andrew Brent, Andrew Brown, John Broxholme, David Buck, Katie L Burnham, Helen Byrne, Susana Camara, Ivan Candido Ferreira, Philip Charles, Wentao Chen, Yi-Ling Chen, Amanda Chong, Elizabeth Clutterbuck, Mark Coles, Christopher P Conlon, Richard Cornall, Adam P Cribbs, Fabiola Curion, Emma E Davenport, Neil Davidson, Simon Davis, Calliope Dendrou, Julie Dequaire, Lea Dib, James Docker, Christina Dold, Tao Dong, Damien Downes, Alexander Drakesmith, Susanna J Dunachie, David A Duncan, Chris Eijsbouts, Robert Esnouf, Alexis Espinosa, Rachel Etherington, Benjamin Fairfax, Rory Fairhead, Hai Fang, Shayan Fassih, Sally Felle, Maria Fernandez Mendoza, Ricardo Ferreira, Roman Fischer, Thomas Foord, Aden Forrow, John Frater, Anastasia Fries, Veronica Gallardo Sanchez, Lucy Garner, Clementine Geeves, Dominique Georgiou, Leila Godfrey, Tanya Golubchik, Maria Gomez Vazquez, Angie Green, Hong Harper, Heather A Harrington, Raphael Heilig, Svenja Hester, Jennifer Hill, Charles Hinds, Clare Hird, Ling-Pei Ho, Renee Hoekzema, Benjamin Hollis, Jim Hughes, Paula Hutton, Matthew Jackson, Ashwin Jainarayanan, Anna James-Bott, Kathrin Jansen, Katie Jeffery, Elizabeth Jones, Luke Jostins, Georgina Kerr, David Kim, Paul Klenerman, Julian C Knight, Vinod Kumar, Piyush Kumar Sharma, Prathiba Kurupati, Andrew Kwok, Angela Lee, Aline Linder, Teresa Lockett, Lorne Lonie, Maria Lopopolo, Martyna Lukoseviciute, Jian Luo, Spyridoula Marinou, Brian Marsden, Jose Martinez, Philippa Matthews, Michalina Mazurczyk, Simon McGowan, Stuart McKechnie, Adam Mead, Alexander J Mentzer, Yuxin Mi, Claudia Monaco, Ruddy Montadon, Giorgio Napolitani, Isar Nassiri, Alex Novak, Darragh O'Brien, Daniel O'Connor, Denise O'Donnell, Graham Ogg, Lauren Overend, Inhye Park, Ian Pavord, Yanchun Peng, Frank Penkava, Mariana Pereira Pinho, Elena Perez, Andrew J Pollard, Fiona Powrie, Bethan Psaila, T. Phuong Quan, Emmanouela Repapi, Santiago Revale, Laura Silva-Reyes, Jean-Baptiste Richard, Charlotte Rich-Griffin, Thomas Ritter, Christine S Rollier, Matthew Rowland, Fabian Ruehle, Mariolina Salio, Stephen N Sansom, Alberto Santos Delgado, Tatjana Sauka-Spengler, Ron Schwessinger, Giuseppe Scozzafava, Gavin Screaton, Anna Seigal, Malcolm G Semple, Martin Sergeant, Christina Simoglou Karali, David Sims, Donal Skelly, Hubert Slawinski, Alberto Sobrinodiaz, Nikolaos Sousos, Lizzie Stafford, Lisa Stockdale, Marie Strickland, Otto Sumray, Bo Sun, Chelsea Taylor, Stephen Taylor, Adan Taylor, Supat Thongjuea, Hannah Thraves, John A Todd, Adriana Tomic, Orion Tong, Amy Trebes, Dominik Trzupek, Felicia A Tucci, Lance Turtle, Irina Udalova, Holm Uhlig, Erinke van Grinsven, Iolanda Vendrell, Marije Verheul, Alexandru Voda, Guanlin Wang, Lihui Wang, Dapeng Wang, Peter Watkinson, Robert Watson, Michael Weinberger, Justin Whalley, Lorna Witty, Katherine Wray, Luzheng Xue, Hing Yuen Yeung, Zixi Yin, Rebecca K Young, Jonathan Youngs, Ping Zhang, Yasemin-Xiomara Zurke. A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. . 2021; ():1.

Chicago/Turabian Style

COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium; David J Ahern; Zhichao Ai; Mark Ainsworth; Chris Allan; Alice Allcock; Azim Ansari; Carolina V Arancibia-Carcamo; Dominik Aschenbrenner; Moustafa Attar; J. Kenneth Baillie; Eleanor Barnes; Rachael Bashford-Rogers; Archana Bashyal; Sally Beer; Georgina Berridge; Amy Beveridge; Sagida Bibi; Tihana Bicanic; Luke Blackwell; Paul Bowness; Andrew Brent; Andrew Brown; John Broxholme; David Buck; Katie L Burnham; Helen Byrne; Susana Camara; Ivan Candido Ferreira; Philip Charles; Wentao Chen; Yi-Ling Chen; Amanda Chong; Elizabeth Clutterbuck; Mark Coles; Christopher P Conlon; Richard Cornall; Adam P Cribbs; Fabiola Curion; Emma E Davenport; Neil Davidson; Simon Davis; Calliope Dendrou; Julie Dequaire; Lea Dib; James Docker; Christina Dold; Tao Dong; Damien Downes; Alexander Drakesmith; Susanna J Dunachie; David A Duncan; Chris Eijsbouts; Robert Esnouf; Alexis Espinosa; Rachel Etherington; Benjamin Fairfax; Rory Fairhead; Hai Fang; Shayan Fassih; Sally Felle; Maria Fernandez Mendoza; Ricardo Ferreira; Roman Fischer; Thomas Foord; Aden Forrow; John Frater; Anastasia Fries; Veronica Gallardo Sanchez; Lucy Garner; Clementine Geeves; Dominique Georgiou; Leila Godfrey; Tanya Golubchik; Maria Gomez Vazquez; Angie Green; Hong Harper; Heather A Harrington; Raphael Heilig; Svenja Hester; Jennifer Hill; Charles Hinds; Clare Hird; Ling-Pei Ho; Renee Hoekzema; Benjamin Hollis; Jim Hughes; Paula Hutton; Matthew Jackson; Ashwin Jainarayanan; Anna James-Bott; Kathrin Jansen; Katie Jeffery; Elizabeth Jones; Luke Jostins; Georgina Kerr; David Kim; Paul Klenerman; Julian C Knight; Vinod Kumar; Piyush Kumar Sharma; Prathiba Kurupati; Andrew Kwok; Angela Lee; Aline Linder; Teresa Lockett; Lorne Lonie; Maria Lopopolo; Martyna Lukoseviciute; Jian Luo; Spyridoula Marinou; Brian Marsden; Jose Martinez; Philippa Matthews; Michalina Mazurczyk; Simon McGowan; Stuart McKechnie; Adam Mead; Alexander J Mentzer; Yuxin Mi; Claudia Monaco; Ruddy Montadon; Giorgio Napolitani; Isar Nassiri; Alex Novak; Darragh O'Brien; Daniel O'Connor; Denise O'Donnell; Graham Ogg; Lauren Overend; Inhye Park; Ian Pavord; Yanchun Peng; Frank Penkava; Mariana Pereira Pinho; Elena Perez; Andrew J Pollard; Fiona Powrie; Bethan Psaila; T. Phuong Quan; Emmanouela Repapi; Santiago Revale; Laura Silva-Reyes; Jean-Baptiste Richard; Charlotte Rich-Griffin; Thomas Ritter; Christine S Rollier; Matthew Rowland; Fabian Ruehle; Mariolina Salio; Stephen N Sansom; Alberto Santos Delgado; Tatjana Sauka-Spengler; Ron Schwessinger; Giuseppe Scozzafava; Gavin Screaton; Anna Seigal; Malcolm G Semple; Martin Sergeant; Christina Simoglou Karali; David Sims; Donal Skelly; Hubert Slawinski; Alberto Sobrinodiaz; Nikolaos Sousos; Lizzie Stafford; Lisa Stockdale; Marie Strickland; Otto Sumray; Bo Sun; Chelsea Taylor; Stephen Taylor; Adan Taylor; Supat Thongjuea; Hannah Thraves; John A Todd; Adriana Tomic; Orion Tong; Amy Trebes; Dominik Trzupek; Felicia A Tucci; Lance Turtle; Irina Udalova; Holm Uhlig; Erinke van Grinsven; Iolanda Vendrell; Marije Verheul; Alexandru Voda; Guanlin Wang; Lihui Wang; Dapeng Wang; Peter Watkinson; Robert Watson; Michael Weinberger; Justin Whalley; Lorna Witty; Katherine Wray; Luzheng Xue; Hing Yuen Yeung; Zixi Yin; Rebecca K Young; Jonathan Youngs; Ping Zhang; Yasemin-Xiomara Zurke. 2021. "A blood atlas of COVID-19 defines hallmarks of disease severity and specificity." , no. : 1.

Journal article
Published: 01 January 2021 in Gene Expression Patterns
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Summary Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of biological datasets, but the necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software, SIMON, to facilitate the application of 180+ state-of-the-art machine-learning algorithms to high-dimensional biomedical data. With an easy-to-use graphical user interface, standardized pipelines, and automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

ACS Style

Adriana Tomic; Ivan Tomic; Levi Waldron; Ludwig Geistlinger; Max Kuhn; Rachel L. Spreng; Lindsay C. Dahora; Kelly E. Seaton; Georgia Tomaras; Jennifer Hill; Niharika A. Duggal; Ross D. Pollock; Norman R. Lazarus; Stephen D.R. Harridge; Janet M. Lord; Purvesh Khatri; Andrew J. Pollard; Mark M. Davis. SIMON: Open-Source Knowledge Discovery Platform. Gene Expression Patterns 2021, 2, 100178 .

AMA Style

Adriana Tomic, Ivan Tomic, Levi Waldron, Ludwig Geistlinger, Max Kuhn, Rachel L. Spreng, Lindsay C. Dahora, Kelly E. Seaton, Georgia Tomaras, Jennifer Hill, Niharika A. Duggal, Ross D. Pollock, Norman R. Lazarus, Stephen D.R. Harridge, Janet M. Lord, Purvesh Khatri, Andrew J. Pollard, Mark M. Davis. SIMON: Open-Source Knowledge Discovery Platform. Gene Expression Patterns. 2021; 2 (1):100178.

Chicago/Turabian Style

Adriana Tomic; Ivan Tomic; Levi Waldron; Ludwig Geistlinger; Max Kuhn; Rachel L. Spreng; Lindsay C. Dahora; Kelly E. Seaton; Georgia Tomaras; Jennifer Hill; Niharika A. Duggal; Ross D. Pollock; Norman R. Lazarus; Stephen D.R. Harridge; Janet M. Lord; Purvesh Khatri; Andrew J. Pollard; Mark M. Davis. 2021. "SIMON: Open-Source Knowledge Discovery Platform." Gene Expression Patterns 2, no. 1: 100178.

Preprint content
Published: 17 August 2020
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Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of the biological datasets, but necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software SIMON to facilitate the application of 180+ state-of-the-art machine learning algorithms to high-dimensional biomedical data. With an easy to use graphical user interface, standardized pipelines, automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

ACS Style

Adriana Tomic; Ivan Tomic; Levi Waldron; Ludwig Geistlinger; Max Kuhn; Rachel L. Spreng; Lindsay C. Dahora; Kelly E. Seaton; Georgia Tomaras; Jennifer Hill; Niharika A. Duggal; Ross D. Pollock; Norman R. Lazarus; Stephen D.R. Harridge; Janet M. Lord; Purvesh Khatri; Andrew J. Pollard; Mark M. Davis. SIMON: open-source knowledge discovery platform. 2020, 1 .

AMA Style

Adriana Tomic, Ivan Tomic, Levi Waldron, Ludwig Geistlinger, Max Kuhn, Rachel L. Spreng, Lindsay C. Dahora, Kelly E. Seaton, Georgia Tomaras, Jennifer Hill, Niharika A. Duggal, Ross D. Pollock, Norman R. Lazarus, Stephen D.R. Harridge, Janet M. Lord, Purvesh Khatri, Andrew J. Pollard, Mark M. Davis. SIMON: open-source knowledge discovery platform. . 2020; ():1.

Chicago/Turabian Style

Adriana Tomic; Ivan Tomic; Levi Waldron; Ludwig Geistlinger; Max Kuhn; Rachel L. Spreng; Lindsay C. Dahora; Kelly E. Seaton; Georgia Tomaras; Jennifer Hill; Niharika A. Duggal; Ross D. Pollock; Norman R. Lazarus; Stephen D.R. Harridge; Janet M. Lord; Purvesh Khatri; Andrew J. Pollard; Mark M. Davis. 2020. "SIMON: open-source knowledge discovery platform." , no. : 1.

Data descriptor
Published: 21 October 2019 in Scientific Data
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Machine learning has the potential to identify novel biological factors underlying successful antibody responses to influenza vaccines. The first attempts have revealed a high level of complexity in establishing influenza immunity, and many different cellular and molecular components are involved. Of note is that the previously identified correlates of protection fail to account for the majority of individual responses across different age groups and influenza seasons. Challenges remain from the small sample sizes in most studies and from often limited data sets, such as transcriptomic data. Here we report the creation of a unified database, FluPRINT, to enable large-scale studies exploring the cellular and molecular underpinnings of successful antibody responses to influenza vaccines. Over 3,000 parameters were considered, including serological responses to influenza strains, serum cytokines, cell phenotypes, and cytokine stimulations. FluPRINT, facilitates the application of machine learning algorithms for data mining. The data are publicly available and represent a resource to uncover new markers and mechanisms that are important for influenza vaccine immunogenicity.

ACS Style

Adriana Tomic; Ivan Tomic; Cornelia L. Dekker; Holden T. Maecker; Mark M. Davis. The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system. Scientific Data 2019, 6, 1 -10.

AMA Style

Adriana Tomic, Ivan Tomic, Cornelia L. Dekker, Holden T. Maecker, Mark M. Davis. The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system. Scientific Data. 2019; 6 (1):1-10.

Chicago/Turabian Style

Adriana Tomic; Ivan Tomic; Cornelia L. Dekker; Holden T. Maecker; Mark M. Davis. 2019. "The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system." Scientific Data 6, no. 1: 1-10.

Preprint
Published: 28 February 2019
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Recent advances in machine learning have allowed identification of molecular and cellular factors that underly successful antibody responses to influenza vaccines. Results of these studies have revealed the high level of complexity necessary to establish influenza immunity, and many different cellular and molecular components involved. However, identified correlates of protection fail to account for the majority of vaccinated cases across ages, cohorts, and influenza seasons. Major challenges arise from small sample sizes and from analysis of only one aspect of the biology such by using transcriptome data. The objective of the current study is to create a unified database, entitled FluPRINT, to enable a large-scale study exploring novel cellular and molecular underpinnings of successful immunity to influenza vaccines. Over 3,000 parameters were considered, including serological responses to influenza strains, serum cytokines, cell subset phenotypes, and cytokine stimulations. FluPRINT, thus facilitates application of machine learning algorithms for data mining. The data are publicly available and represent a resource to uncover new markers and mechanisms that drive successful influenza vaccination.

ACS Style

Adriana Tomic; Ivan Tomic; Cornelia L. Dekker; Holden T. Maecker; Mark M. Davis. The FluPRINT dataset: A multidimensional analysis of the influenza vaccine imprint on the immune system. 2019, 564062 .

AMA Style

Adriana Tomic, Ivan Tomic, Cornelia L. Dekker, Holden T. Maecker, Mark M. Davis. The FluPRINT dataset: A multidimensional analysis of the influenza vaccine imprint on the immune system. . 2019; ():564062.

Chicago/Turabian Style

Adriana Tomic; Ivan Tomic; Cornelia L. Dekker; Holden T. Maecker; Mark M. Davis. 2019. "The FluPRINT dataset: A multidimensional analysis of the influenza vaccine imprint on the immune system." , no. : 564062.

Preprint content
Published: 10 February 2019
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Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. Here, we developed Sequential Iterative Modelling “OverNight” or SIMON, an automated machine learning system that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4+ and CD8+ T cell subsets strongly associated with a robust antibody response to influenza antigens. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available.

ACS Style

Adriana Tomic; Ivan Tomic; Yael Rosenberg-Hasson; Cornelia L. Dekker; Holden T. Maecker; Mark M. Davis. SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses. 2019, 545186 .

AMA Style

Adriana Tomic, Ivan Tomic, Yael Rosenberg-Hasson, Cornelia L. Dekker, Holden T. Maecker, Mark M. Davis. SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses. . 2019; ():545186.

Chicago/Turabian Style

Adriana Tomic; Ivan Tomic; Yael Rosenberg-Hasson; Cornelia L. Dekker; Holden T. Maecker; Mark M. Davis. 2019. "SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses." , no. : 545186.

Journal article
Published: 09 April 2018 in Nature Immunology
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Natural killer (NK) cells are innate lymphocytes that lack antigen-specific rearranged receptors, a hallmark of adaptive lymphocytes. In some people infected with human cytomegalovirus (HCMV), an NK cell subset expressing the activating receptor NKG2C undergoes clonal-like expansion that partially resembles anti-viral adaptive responses. However, the viral ligand that drives the activation and differentiation of adaptive NKG2C+ NK cells has remained unclear. Here we found that adaptive NKG2C+ NK cells differentially recognized distinct HCMV strains encoding variable UL40 peptides that, in combination with pro-inflammatory signals, controlled the population expansion and differentiation of adaptive NKG2C+ NK cells. Thus, we propose that polymorphic HCMV peptides contribute to shaping of the heterogeneity of adaptive NKG2C+ NK cell populations among HCMV-seropositive people.

ACS Style

Quirin Hammer; Timo Rückert; Eva Maria Borst; Josefine Dunst; André Haubner; Pawel Durek; Frederik Heinrich; Gilles Gasparoni; Marina Babic; Adriana Tomic; Gabriella Pietra; Mikalai Nienen; Igor Wolfgang Blau; Jörg Hofmann; Il-Kang Na; Immo Prinz; Christian Koenecke; Philipp Hemmati; Nina Babel; Renate Arnold; Jörn Walter; Kevin Thurley; Mir-Farzin Mashreghi; Martin Messerle; Chiara Romagnani. Peptide-specific recognition of human cytomegalovirus strains controls adaptive natural killer cells. Nature Immunology 2018, 19, 453 -463.

AMA Style

Quirin Hammer, Timo Rückert, Eva Maria Borst, Josefine Dunst, André Haubner, Pawel Durek, Frederik Heinrich, Gilles Gasparoni, Marina Babic, Adriana Tomic, Gabriella Pietra, Mikalai Nienen, Igor Wolfgang Blau, Jörg Hofmann, Il-Kang Na, Immo Prinz, Christian Koenecke, Philipp Hemmati, Nina Babel, Renate Arnold, Jörn Walter, Kevin Thurley, Mir-Farzin Mashreghi, Martin Messerle, Chiara Romagnani. Peptide-specific recognition of human cytomegalovirus strains controls adaptive natural killer cells. Nature Immunology. 2018; 19 (5):453-463.

Chicago/Turabian Style

Quirin Hammer; Timo Rückert; Eva Maria Borst; Josefine Dunst; André Haubner; Pawel Durek; Frederik Heinrich; Gilles Gasparoni; Marina Babic; Adriana Tomic; Gabriella Pietra; Mikalai Nienen; Igor Wolfgang Blau; Jörg Hofmann; Il-Kang Na; Immo Prinz; Christian Koenecke; Philipp Hemmati; Nina Babel; Renate Arnold; Jörn Walter; Kevin Thurley; Mir-Farzin Mashreghi; Martin Messerle; Chiara Romagnani. 2018. "Peptide-specific recognition of human cytomegalovirus strains controls adaptive natural killer cells." Nature Immunology 19, no. 5: 453-463.

Research article
Published: 01 December 2016 in PLOS Pathogens
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Development of an effective vaccine against human cytomegalovirus (HCMV) is a need of utmost medical importance. Generally, it is believed that a live attenuated vaccine would best provide protective immunity against this tenacious pathogen. Here, we propose a strategy for an HCMV vaccine that aims at the simultaneous activation of innate and adaptive immune responses. An HCMV strain expressing the host ligand ULBP2 for the NKG2D receptor was found to be susceptible to control by natural killer (NK) cells, and preserved the ability to stimulate HCMV-specific T cells. Infection with the ULBP2-expressing HCMV strain caused diminished cell surface levels of MHC class I molecules. While expression of the NKG2D ligand increased the cytolytic activity of NK cells, NKG2D engagement in CD8+ T cells provided co-stimulation and compensated for lower MHC class I expression. Altogether, our data indicate that triggering of both arms of the immune system is a promising approach applicable to the generation of a live attenuated HCMV vaccine. Human cytomegalovirus (CMV) is a major cause of morbidity and mortality in congenitally infected newborns and immunocompromised individuals, indicating an utmost need for a vaccine to protect these vulnerable groups. Recent experimental studies in animal models, including non-human primates, have shown that attenuated CMVs trigger a potent immune response and are attractive vaccine candidates. However, an effective CMV vaccine is still not available. Here, we demonstrate that rational engineering of a live attenuated human CMV vaccine candidate is feasible. We equipped a CMV strain with an immunostimulatory molecule that is a ligand for an activating receptor present on both Natural Killer cells and CD8+ T cells. Moreover, we deleted several immunoevasins involved in downregulation of MHC class I molecules and of a ligand for Natural Killer cells in order to elicit stronger immune responses. In vitro assays using human immune cells and a first assessment in a humanized mouse model in vivo suggest that the generated CMV strain is attenuated and has the ability to induce a virus-specific immune response. Our study proposes this novel approach for the development of a rationally engineered CMV vaccine.

ACS Style

Adriana Tomic; Pavankumar Varanasi; Mijo Golemac; Suzana Malić; Peggy Riese; Eva M. Borst; Eva Mischak-Weissinger; Carlos A. Guzmán; Astrid Krmpotic; Stipan Jonjić; Martin Messerle. Activation of Innate and Adaptive Immunity by a Recombinant Human Cytomegalovirus Strain Expressing an NKG2D Ligand. PLOS Pathogens 2016, 12, e1006015 .

AMA Style

Adriana Tomic, Pavankumar Varanasi, Mijo Golemac, Suzana Malić, Peggy Riese, Eva M. Borst, Eva Mischak-Weissinger, Carlos A. Guzmán, Astrid Krmpotic, Stipan Jonjić, Martin Messerle. Activation of Innate and Adaptive Immunity by a Recombinant Human Cytomegalovirus Strain Expressing an NKG2D Ligand. PLOS Pathogens. 2016; 12 (12):e1006015.

Chicago/Turabian Style

Adriana Tomic; Pavankumar Varanasi; Mijo Golemac; Suzana Malić; Peggy Riese; Eva M. Borst; Eva Mischak-Weissinger; Carlos A. Guzmán; Astrid Krmpotic; Stipan Jonjić; Martin Messerle. 2016. "Activation of Innate and Adaptive Immunity by a Recombinant Human Cytomegalovirus Strain Expressing an NKG2D Ligand." PLOS Pathogens 12, no. 12: e1006015.

Journal article
Published: 08 August 2016 in Journal of Experimental Medicine
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The poliovirus receptor (PVR) is a ubiquitously expressed glycoprotein involved in cellular adhesion and immune response. It engages the activating receptor DNAX accessory molecule (DNAM)-1, the inhibitory receptor TIGIT, and the CD96 receptor with both activating and inhibitory functions. Human cytomegalovirus (HCMV) down-regulates PVR expression, but the significance of this viral function in vivo remains unknown. Here, we demonstrate that mouse CMV (MCMV) also down-regulates the surface PVR. The m20.1 protein of MCMV retains PVR in the endoplasmic reticulum and promotes its degradation. A MCMV mutant lacking the PVR inhibitor was attenuated in normal mice but not in mice lacking DNAM-1. This attenuation was partially reversed by NK cell depletion, whereas the simultaneous depletion of mononuclear phagocytes abolished the virus control. This effect was associated with the increased expression of DNAM-1, whereas TIGIT and CD96 were absent on these cells. An increased level of proinflammatory cytokines in sera of mice infected with the virus lacking the m20.1 and an increased production of iNOS by inflammatory monocytes was observed. Blocking of CCL2 or the inhibition of iNOS significantly increased titer of the virus lacking m20.1. In this study, we have demonstrated that inflammatory monocytes, together with NK cells, are essential in the early control of CMV through the DNAM-1–PVR pathway.

ACS Style

Tihana Lenac Rovis; Paola Kucan Brlic; Noa Kaynan; Vanda Juranic Lisnic; Ilija Brizic; Stefan Jordan; Adriana Tomic; Daria Kvestak; Marina Babic; Pinchas Tsukerman; Marco Colonna; Ulrich Koszinowski; Martin Messerle; Ofer Mandelboim; Astrid Krmpotic; Stipan Jonjic. Inflammatory monocytes and NK cells play a crucial role in DNAM-1–dependent control of cytomegalovirus infection. Journal of Experimental Medicine 2016, 213, 1835 -1850.

AMA Style

Tihana Lenac Rovis, Paola Kucan Brlic, Noa Kaynan, Vanda Juranic Lisnic, Ilija Brizic, Stefan Jordan, Adriana Tomic, Daria Kvestak, Marina Babic, Pinchas Tsukerman, Marco Colonna, Ulrich Koszinowski, Martin Messerle, Ofer Mandelboim, Astrid Krmpotic, Stipan Jonjic. Inflammatory monocytes and NK cells play a crucial role in DNAM-1–dependent control of cytomegalovirus infection. Journal of Experimental Medicine. 2016; 213 (9):1835-1850.

Chicago/Turabian Style

Tihana Lenac Rovis; Paola Kucan Brlic; Noa Kaynan; Vanda Juranic Lisnic; Ilija Brizic; Stefan Jordan; Adriana Tomic; Daria Kvestak; Marina Babic; Pinchas Tsukerman; Marco Colonna; Ulrich Koszinowski; Martin Messerle; Ofer Mandelboim; Astrid Krmpotic; Stipan Jonjic. 2016. "Inflammatory monocytes and NK cells play a crucial role in DNAM-1–dependent control of cytomegalovirus infection." Journal of Experimental Medicine 213, no. 9: 1835-1850.

Research article
Published: 13 March 2014 in PLOS Pathogens
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Receptors of the signalling lymphocyte-activation molecules (SLAM) family are involved in the functional regulation of a variety of immune cells upon engagement through homotypic or heterotypic interactions amongst them. Here we show that murine cytomegalovirus (MCMV) dampens the surface expression of several SLAM receptors during the course of the infection of macrophages. By screening a panel of MCMV deletion mutants, we identified m154 as an immunoevasin that effectively reduces the cell-surface expression of the SLAM family member CD48, a high-affinity ligand for natural killer (NK) and cytotoxic T cell receptor CD244. m154 is a mucin-like protein, expressed with early kinetics, which can be found at the cell surface of the infected cell. During infection, m154 leads to proteolytic degradation of CD48. This viral protein interferes with the NK cell cytotoxicity triggered by MCMV-infected macrophages. In addition, we demonstrate that an MCMV mutant virus lacking m154 expression results in an attenuated phenotype in vivo, which can be substantially restored after NK cell depletion in mice. This is the first description of a viral gene capable of downregulating CD48. Our novel findings define m154 as an important player in MCMV innate immune regulation. Cytomegalovirus (CMV) has developed diverse tactics to elude the host immune response and guarantee its survival. The signalling lymphocyte-activation molecules (SLAM) family of receptors encompasses a number of adhesion molecules expressed on the surface of leukocytes that play critical roles in both innate and adaptive immunity. In this study, we report that murine CMV drastically reduces the expression of several SLAM family receptors at the cell surface of infected macrophages, most likely as part of its immunoevasion mechanisms. We have identified a murine CMV gene product (m154) that downregulates CD48, a SLAM family member that functions as a ligand of CD244, a molecule involved in the regulation of natural killer (NK) and cytotoxic T cell functions. We show that during infection, m154 targets CD48 for degradation. Moreover, this viral protein contributes to increased MCMV growth during acute infection in the mouse by protecting against NK cell mediated surveillance. These findings are important for better understanding CMV pathogenesis, and provide a novel example of host innate immune subversion by CMV.

ACS Style

Angela Zarama; Natàlia Pérez-Carmona; Domenec Farre; Adriana Tomic; Eva Maria Borst; Martin Messerle; Stipan Jonjic; Pablo Engel; Ana Angulo. Cytomegalovirus m154 Hinders CD48 Cell-Surface Expression and Promotes Viral Escape from Host Natural Killer Cell Control. PLOS Pathogens 2014, 10, e1004000 .

AMA Style

Angela Zarama, Natàlia Pérez-Carmona, Domenec Farre, Adriana Tomic, Eva Maria Borst, Martin Messerle, Stipan Jonjic, Pablo Engel, Ana Angulo. Cytomegalovirus m154 Hinders CD48 Cell-Surface Expression and Promotes Viral Escape from Host Natural Killer Cell Control. PLOS Pathogens. 2014; 10 (3):e1004000.

Chicago/Turabian Style

Angela Zarama; Natàlia Pérez-Carmona; Domenec Farre; Adriana Tomic; Eva Maria Borst; Martin Messerle; Stipan Jonjic; Pablo Engel; Ana Angulo. 2014. "Cytomegalovirus m154 Hinders CD48 Cell-Surface Expression and Promotes Viral Escape from Host Natural Killer Cell Control." PLOS Pathogens 10, no. 3: e1004000.

Journal article
Published: 19 September 2013 in Proceedings of the National Academy of Sciences
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Due to a unique pattern of CD8 T-cell response induced by cytomegaloviruses (CMVs), live attenuated CMVs are attractive candidates for vaccine vectors for a number of clinically relevant infections and tumors. NKG2D is one of the most important activating NK cell receptors that plays a role in costimulation of CD8 T cells. Here we demonstrate that the expression of CD8 T-cell epitope of Listeria monocytogenes by a recombinant mouse CMV (MCMV) expressing the NKG2D ligand retinoic acid early-inducible protein 1-gamma (RAE-1γ) dramatically enhanced the effectiveness and longevity of epitope-specific CD8 T-cell response and conferred protection against a subsequent challenge infection with Listeria monocytogenes. Unexpectedly, the attenuated growth in vivo of the CMV vector expressing RAE-1γ and its capacity to enhance specific CD8 T-cell response were preserved even in mice lacking NKG2D, implying additional immune function for RAE-1γ beyond engagement of NKG2D. Thus, vectors expressing RAE-1γ represent a promising approach in the development of CD8 T-cell-based vaccines

ACS Style

Tihana Trsan; A. Busche; Maja Abram; Felix Wensveen; Niels Lemmermann; M. Arapovic; M. Babic; Adriana Tomic; M. Golemac; M. M. Brinkmann; W. Jager; A. Oxenius; Bojan Polic; Astrid Krmpotic; M. Messerle; S. Jonjic. Superior induction and maintenance of protective CD8 T cells in mice infected with mouse cytomegalovirus vector expressing RAE-1. Proceedings of the National Academy of Sciences 2013, 110, 16550 -16555.

AMA Style

Tihana Trsan, A. Busche, Maja Abram, Felix Wensveen, Niels Lemmermann, M. Arapovic, M. Babic, Adriana Tomic, M. Golemac, M. M. Brinkmann, W. Jager, A. Oxenius, Bojan Polic, Astrid Krmpotic, M. Messerle, S. Jonjic. Superior induction and maintenance of protective CD8 T cells in mice infected with mouse cytomegalovirus vector expressing RAE-1. Proceedings of the National Academy of Sciences. 2013; 110 (41):16550-16555.

Chicago/Turabian Style

Tihana Trsan; A. Busche; Maja Abram; Felix Wensveen; Niels Lemmermann; M. Arapovic; M. Babic; Adriana Tomic; M. Golemac; M. M. Brinkmann; W. Jager; A. Oxenius; Bojan Polic; Astrid Krmpotic; M. Messerle; S. Jonjic. 2013. "Superior induction and maintenance of protective CD8 T cells in mice infected with mouse cytomegalovirus vector expressing RAE-1." Proceedings of the National Academy of Sciences 110, no. 41: 16550-16555.