A patient-centric modeling framework captures recovery from SARS-CoV-2 infection

Hélène Ruffieux; Aimee L. Hanson; Samantha Lodge; Nathan G. Lawler; Luke Whiley; Nicola Gray; Tui H. Nolan; Laura Bergamaschi; Federica Mescia; Lorinda Turner; Aloka de Sa; Victoria S. Pelly; Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID BioResource Collaboration; Prasanti Kotagiri; Nathalie Kingston; John R. Bradley; Elaine Holmes; Julien Wist; Jeremy K. Nicholson; Paul A. Lyons; Kenneth G.C. Smith; Sylvia Richardson; Glenn R. Bantug; Christoph Hess

doi:10.1038/s41590-022-01380-2

A patient-centric modeling framework captures recovery from SARS-CoV-2 infection

Hélène Ruffieux, Aimee L. Hanson, Samantha Lodge, Nathan G. Lawler, Luke Whiley, Nicola Gray, Tui H. Nolan, Laura Bergamaschi, Federica Mescia, Lorinda Turner, Aloka de Sa, Victoria S. Pelly, Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID BioResource Collaboration, Prasanti Kotagiri, Nathalie Kingston, John R. Bradley, Elaine Holmes, Julien Wist, Jeremy K. Nicholson, Paul A. LyonsKenneth G.C. Smith, Sylvia Richardson, Glenn R. Bantug, Christoph Hess

Research output: Contribution to journal › Article › Research › peer-review

25 Citations (Scopus)

Abstract

The biology driving individual patient responses to severe acute respiratory syndrome coronavirus 2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data and covering a year after disease onset, from 215 infected individuals with differing disease severities. Our analyses revealed distinct ‘systemic recovery’ profiles, with specific progression and resolution of the inflammatory, immune cell, metabolic and clinical responses. In particular, we found a strong inter-patient and intra-patient temporal covariation of innate immune cell numbers, kynurenine metabolites and lipid metabolites, which highlighted candidate immunologic and metabolic pathways influencing the restoration of homeostasis, the risk of death and that of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool http://shiny.mrc-bsu.cam.ac.uk/apps/covid-19-systemic-recovery-prediction-app, designed to test our findings prospectively.

Original language	English
Pages (from-to)	349-358
Number of pages	10
Journal	Nature Immunology
Volume	24
Issue number	2
DOIs	https://doi.org/10.1038/s41590-022-01380-2
Publication status	Published - Feb 2023
Externally published	Yes

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Ruffieux, H., Hanson, A. L., Lodge, S., Lawler, N. G., Whiley, L., Gray, N., Nolan, T. H., Bergamaschi, L., Mescia, F., Turner, L., de Sa, A., Pelly, V. S., Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID BioResource Collaboration, Kotagiri, P., Kingston, N., Bradley, J. R., Holmes, E., Wist, J., Nicholson, J. K., ... Hess, C. (2023). A patient-centric modeling framework captures recovery from SARS-CoV-2 infection. Nature Immunology, 24(2), 349-358. https://doi.org/10.1038/s41590-022-01380-2

@article{a1afe9f7a7da445fb71c0100afe18c21,

title = "A patient-centric modeling framework captures recovery from SARS-CoV-2 infection",

abstract = "The biology driving individual patient responses to severe acute respiratory syndrome coronavirus 2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data and covering a year after disease onset, from 215 infected individuals with differing disease severities. Our analyses revealed distinct {\textquoteleft}systemic recovery{\textquoteright} profiles, with specific progression and resolution of the inflammatory, immune cell, metabolic and clinical responses. In particular, we found a strong inter-patient and intra-patient temporal covariation of innate immune cell numbers, kynurenine metabolites and lipid metabolites, which highlighted candidate immunologic and metabolic pathways influencing the restoration of homeostasis, the risk of death and that of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool http://shiny.mrc-bsu.cam.ac.uk/apps/covid-19-systemic-recovery-prediction-app, designed to test our findings prospectively.",

author = "H{\'e}l{\`e}ne Ruffieux and Hanson, {Aimee L.} and Samantha Lodge and Lawler, {Nathan G.} and Luke Whiley and Nicola Gray and Nolan, {Tui H.} and Laura Bergamaschi and Federica Mescia and Lorinda Turner and {de Sa}, Aloka and Pelly, {Victoria S.} and {Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID BioResource Collaboration} and Prasanti Kotagiri and Nathalie Kingston and Bradley, {John R.} and Elaine Holmes and Julien Wist and Nicholson, {Jeremy K.} and Lyons, {Paul A.} and Smith, {Kenneth G.C.} and Sylvia Richardson and Bantug, {Glenn R.} and Christoph Hess",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s).",

year = "2023",

month = feb,

doi = "10.1038/s41590-022-01380-2",

language = "English",

volume = "24",

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Ruffieux, H, Hanson, AL, Lodge, S, Lawler, NG, Whiley, L, Gray, N, Nolan, TH, Bergamaschi, L, Mescia, F, Turner, L, de Sa, A, Pelly, VS, Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID BioResource Collaboration, Kotagiri, P, Kingston, N, Bradley, JR, Holmes, E, Wist, J, Nicholson, JK, Lyons, PA, Smith, KGC, Richardson, S, Bantug, GR & Hess, C 2023, 'A patient-centric modeling framework captures recovery from SARS-CoV-2 infection', Nature Immunology, vol. 24, no. 2, pp. 349-358. https://doi.org/10.1038/s41590-022-01380-2

TY - JOUR

T1 - A patient-centric modeling framework captures recovery from SARS-CoV-2 infection

AU - Ruffieux, Hélène

AU - Hanson, Aimee L.

AU - Lodge, Samantha

AU - Lawler, Nathan G.

AU - Whiley, Luke

AU - Gray, Nicola

AU - Nolan, Tui H.

AU - Bergamaschi, Laura

AU - Mescia, Federica

AU - Turner, Lorinda

AU - de Sa, Aloka

AU - Pelly, Victoria S.

AU - Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID BioResource Collaboration

AU - Kotagiri, Prasanti

AU - Kingston, Nathalie

AU - Bradley, John R.

AU - Holmes, Elaine

AU - Wist, Julien

AU - Nicholson, Jeremy K.

AU - Lyons, Paul A.

AU - Smith, Kenneth G.C.

AU - Richardson, Sylvia

AU - Bantug, Glenn R.

AU - Hess, Christoph

PY - 2023/2

Y1 - 2023/2

N2 - The biology driving individual patient responses to severe acute respiratory syndrome coronavirus 2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data and covering a year after disease onset, from 215 infected individuals with differing disease severities. Our analyses revealed distinct ‘systemic recovery’ profiles, with specific progression and resolution of the inflammatory, immune cell, metabolic and clinical responses. In particular, we found a strong inter-patient and intra-patient temporal covariation of innate immune cell numbers, kynurenine metabolites and lipid metabolites, which highlighted candidate immunologic and metabolic pathways influencing the restoration of homeostasis, the risk of death and that of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool http://shiny.mrc-bsu.cam.ac.uk/apps/covid-19-systemic-recovery-prediction-app, designed to test our findings prospectively.

AB - The biology driving individual patient responses to severe acute respiratory syndrome coronavirus 2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data and covering a year after disease onset, from 215 infected individuals with differing disease severities. Our analyses revealed distinct ‘systemic recovery’ profiles, with specific progression and resolution of the inflammatory, immune cell, metabolic and clinical responses. In particular, we found a strong inter-patient and intra-patient temporal covariation of innate immune cell numbers, kynurenine metabolites and lipid metabolites, which highlighted candidate immunologic and metabolic pathways influencing the restoration of homeostasis, the risk of death and that of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool http://shiny.mrc-bsu.cam.ac.uk/apps/covid-19-systemic-recovery-prediction-app, designed to test our findings prospectively.

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U2 - 10.1038/s41590-022-01380-2

DO - 10.1038/s41590-022-01380-2

M3 - Article

C2 - 36717723

AN - SCOPUS:85147236962

SN - 1529-2908

VL - 24

SP - 349

EP - 358

JO - Nature Immunology

JF - Nature Immunology

IS - 2

ER -

A patient-centric modeling framework captures recovery from SARS-CoV-2 infection

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