TY - JOUR
T1 - Mature neutrophils and a NF-κB-to-IFN transition determine the unifying disease recovery dynamics in COVID-19
AU - Frishberg, Amit
AU - Kooistra, Emma
AU - Nuesch-Germano, Melanie
AU - Pecht, Tal
AU - Milman, Neta
AU - Reusch, Nico
AU - Warnat-Herresthal, Stefanie
AU - Bruse, Niklas
AU - Händler, Kristian
AU - Theis, Heidi
AU - Kraut, Michael
AU - van Rijssen, Esther
AU - van Cranenbroek, Bram
AU - Koenen, Hans JPM
AU - Heesakkers, Hidde
AU - van den Boogaard, Mark
AU - Zegers, Marieke
AU - Pickkers, Peter
AU - Becker, Matthias
AU - Aschenbrenner, Anna C.
AU - Ulas, Thomas
AU - Theis, Fabian J.
AU - Shen-Orr, Shai S.
AU - Schultze, Joachim L.
AU - Kox, Matthijs
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2022/6/21
Y1 - 2022/6/21
N2 - Disease recovery dynamics are often difficult to assess, as patients display heterogeneous recovery courses. To model recovery dynamics, exemplified by severe COVID-19, we apply a computational scheme on longitudinally sampled blood transcriptomes, generating recovery states, which we then link to cellular and molecular mechanisms, presenting a framework for studying the kinetics of recovery compared with non-recovery over time and long-term effects of the disease. Specifically, a decrease in mature neutrophils is the strongest cellular effect during recovery, with direct implications on disease outcome. Furthermore, we present strong indications for global regulatory changes in gene programs, decoupled from cell compositional changes, including an early rise in T cell activation and differentiation, resulting in immune rebalancing between interferon and NF-κB activity and restoration of cell homeostasis. Overall, we present a clinically relevant computational framework for modeling disease recovery, paving the way for future studies of the recovery dynamics in other diseases and tissues.
AB - Disease recovery dynamics are often difficult to assess, as patients display heterogeneous recovery courses. To model recovery dynamics, exemplified by severe COVID-19, we apply a computational scheme on longitudinally sampled blood transcriptomes, generating recovery states, which we then link to cellular and molecular mechanisms, presenting a framework for studying the kinetics of recovery compared with non-recovery over time and long-term effects of the disease. Specifically, a decrease in mature neutrophils is the strongest cellular effect during recovery, with direct implications on disease outcome. Furthermore, we present strong indications for global regulatory changes in gene programs, decoupled from cell compositional changes, including an early rise in T cell activation and differentiation, resulting in immune rebalancing between interferon and NF-κB activity and restoration of cell homeostasis. Overall, we present a clinically relevant computational framework for modeling disease recovery, paving the way for future studies of the recovery dynamics in other diseases and tissues.
KW - COVID-19
KW - cell deconvolution
KW - disease modeling
KW - disease recovery
KW - gene regulation
KW - immunology
KW - medicine
KW - systems biology
KW - viral infection
UR - http://www.scopus.com/inward/record.url?scp=85131811413&partnerID=8YFLogxK
U2 - 10.1016/j.xcrm.2022.100652
DO - 10.1016/j.xcrm.2022.100652
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C2 - 35675822
AN - SCOPUS:85131811413
SN - 2666-3791
VL - 3
JO - Cell Reports Medicine
JF - Cell Reports Medicine
IS - 6
M1 - 100652
ER -