The pattern recognition receptor RIG I is essential for the recognition of viral dsRNA and the activation of a cell autonomous antiviral response. Upon stimulation, RIG I triggers a signaling cascade leading to the expression of cytokines, most prominently type I and III interferons (IFNs). IFNs are secreted and signal in an auto and paracrine manner to trigger the expression of a large variety of IFN stimulated genes, which in concert establish an antiviral state of the cell. While the topology of this pathway has been studied quite intensively, the dynamics, particularly of the RIG I mediated IFN induction, is much less understood. Here, we employed electroporation based transfection to synchronously activate the RIG I signaling pathway, enabling us to characterize the kinetics and dynamics of cell intrinsic innate immune signaling to virus infections. By employing an A549 IFNAR1/IFNLR deficient cell line, we could analyze the difference between the primary RIG I signaling phase and the secondary signaling phase downstream of the IFN receptors. We further used our quantitative data to set up and calibrate a comprehensive dynamic mathematical model of the RIG I and IFN signaling pathways. This model accurately predicts the kinetics of signaling events downstream of dsRNA recognition by RIG I as well as the feedback and signal amplification by secreted IFN and JAK/STAT signaling. We have furthermore investigated the impact of various viral immune antagonists on the signaling dynamics experimentally, and we utilized the here described modelling approach to simulate and in silico study these critical virus-host interactions. Our work provides a comprehensive insight into the signaling events occurring early upon virus infection and opens up new avenues to study and disentangle the complexity of the host-virus interface.
RIG-I recognizes viral dsRNA and activates a cell-autonomous antiviral response. Upon stimulation, it triggers a signaling cascade leading to the production of type I and III IFNs. IFNs are secreted and signal to elicit the expression of IFN-stimulated genes, establishing an antiviral state of the cell. The topology of this pathway has been studied intensively, however, its exact dynamics are less understood. Here, we employed electroporation to synchronously activate RIG-I, enabling us to characterize cell-intrinsic innate immune signaling at a high temporal resolution. Employing IFNAR1/IFNLR-deficient cells, we could differentiate primary RIG-I signaling from secondary signaling downstream of the IFN receptors. Based on these data, we developed a comprehensive mathematical model capable of simulating signaling downstream of dsRNA recognition by RIG-I and the feedback and signal amplification by IFN. We further investigated the impact of viral antagonists on signaling dynamics. Our work provides a comprehensive insight into the signaling events that occur early upon virus infection and opens new avenues to study and disentangle the complexity of the host–virus interface.
Objective Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. Materials and methods Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. Results The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. Discussion Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. Conclusion This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.
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