Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Su, Yapeng; Chen, Daniel; Lausted, Christopher; Yuan, Dan; Choi, Jongchan; Dai, Cheng; Voillet, Valentin; Scherler, Kelsey; Troisch, Pamela; Duvvuri, Venkata; Baloni, Priyanka; Qin, Guangrong; Smith, Brett; Kornilov, Sergey A.; Rostomily, Clifford; Xu, Alex Fan; Li, Jing; Dong, Shen; Rothchild, Alissa C.; Murray, Kim M.; Edmark, Rick; Hong, Steven; Jones, Lesley C.; Zhou, Yong; Roper, Ryan; Mackay, Sean; O’Mahony, D. Shane; Dale, Christopher; Wallick, Julie A.; Algren, Heather; Magis, Andrew T.; Wei, Wei; Price, Nathan D.; Huang, Sui; Subramanian, Naeha; Wang, Kai; Hadlock, Jennifer; Hood, Leroy; Aderem, Alan; Bluestone, Jeffrey A.; Lanier, Lewis L.; Greenberg, Phil; Gottardo, Raphaël; Davis, Mark M.; Goldman, Jason D.; Heath, James R.

doi:10.1101/2020.07.27.224063

Cited by 15 publications

(12 citation statements)

References 70 publications

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“…Multiple groups have generated mechanistic (66)(67)(68) and datadriven (17,(69)(70)(71) computational models of SARS-CoV-2 infection and COVID-19 progression. In our study, we extend this prior work by treating severe COVID-19 disease as a form of critical illness (72).…”

Section: Discussionmentioning

confidence: 99%

Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

et al. 2021

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COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed “patient archetypes”, whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.

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Section: Discussionmentioning

confidence: 99%

Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

et al. 2021

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“…While this may not necessarily imply absolute changes in cell numbers, we observed good overall agreement between changes in proportions and absolute counts when comparing severe and mild disease status ( Supplementary Figure 6 and Supplementary Table 7). This highlights the importance of studies employing orthogonal modalities such as cytokine profiling 51 , single-cell RNA sequencing [52][53][54][55][56] , and their integration 57,58 . Nevertheless, even without orthogonal studies, our machine learning approach for predicting disease severity demonstrates predictive potential, although it should be tested in a validation cohort before use in a clinical setting.…”

Section: Discussionmentioning

confidence: 99%

Longitudinal immune profiling of mild and severe COVID-19 reveals innate and adaptive immune dysfunction and provides an early prediction tool for clinical progression

Rendeiro

Casano

Vorkas

et al. 2020

Preprint

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With a rising incidence of COVID-19-associated morbidity and mortality worldwide, it is critical to elucidate the innate and adaptive immune responses that drive disease severity. We performed longitudinal immune profiling of peripheral blood mononuclear cells from 45 patients and healthy donors. We observed a dynamic immune landscape of innate and adaptive immune cells in disease progression and absolute changes of lymphocyte and myeloid cells in severe versus mild cases or healthy controls. Intubation and death were coupled with selected natural killer cell KIR receptor usage and IgM+ B cells and associated with profound CD4 and CD8 T cell exhaustion. Pseudo-temporal reconstruction of the hierarchy of disease progression revealed dynamic time changes in the global population recapitulating individual patients and the development of an eight-marker classifier of disease severity. Estimating the effect of clinical progression on the immune response and early assessment of disease progression risks may allow implementation of tailored therapies.

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“…A very recent example is the analysis of COVID19 patient gene expression samples to identify a single constraint, a gene module, that strongly aligns with disease severity. 36 In the present work we develop an approach that allows to extract the dominant constraints for different scenarios of the coherent quantum dynamics on multiple electronic states. We show that such an approximate but compact treatment of the density provides good accuracy for both electronic and vibrational population and coherence dynamics.…”

Section: Discussionmentioning

confidence: 99%

Surprisal of a quantum state: Dynamics, compact representation, and coherence effects

2020

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Progress towards quantum technologies continues to provide essential new insights on the microscopic dynamics of systems in phase space. This highlights coherence effects whether these are due to ultrafast lasers whose energy width spans several states all the way to the output of quantum computing. Surprisal analysis has provided seminal insights on the probability distributions of quantum systems from elementary particle and also nuclear physics, through molecular reaction dynamics to system biology. It is therefore necessary to extend surprisal analysis to the full quantum regime where it characterizes not only the probabilities of states but also their coherence. In principle this can be done by the maximal entropy formalism but in the full quantum regime its application is far from trivial [E.g., S. Dagan and Y. Dothan, Phys Rev D 26, 248 1982] because an exponential function of not commuting operators is not easily accommodated. Starting from an exact dynamical approach we develop a description of the dynamics where the quantum mechanical surprisal, a linear combination of operators, plays a central role. We provide an explicit route to the Lagrange multipliers of the system and identify those operators that act as the dominant constraints.

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Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Cited by 15 publications

References 70 publications

Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

Longitudinal immune profiling of mild and severe COVID-19 reveals innate and adaptive immune dysfunction and provides an early prediction tool for clinical progression

Surprisal of a quantum state: Dynamics, compact representation, and coherence effects

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