The Src-ZNRF1 axis controls TLR3 trafficking and interferon responses to limit lung barrier damage.

Type I interferons are important antiviral cytokines, but prolonged interferon production is detrimental to the host. The TLR3-driven immune response is crucial for mammalian antiviral immunity, and its intracellular localization determines induction of type I interferons; however, the mechanism terminating TLR3 signaling remains obscure. Here, we show that the E3 ubiquitin ligase ZNRF1 controls TLR3 sorting into multivesicular bodies/lysosomes to terminate signaling and type I interferon production. Mechanistically, c-Src kinase activated by TLR3 engagement phosphorylates ZNRF1 at tyrosine 103, which mediates K63-linked ubiquitination of TLR3 at lysine 813 and promotes TLR3 lysosomal trafficking and degradation. ZNRF1-deficient mice and cells are resistant to infection by encephalomyocarditis virus and SARS-CoV-2 because of enhanced type I interferon production. However, Znrf1-/- mice have exacerbated lung barrier damage triggered by antiviral immunity, leading to enhanced susceptibility to respiratory bacterial superinfections. Our study highlights the c-Src-ZNRF1 axis as a negative feedback mechanism controlling TLR3 trafficking and the termination of TLR3 signaling.

An Adaptive-to-Model Test for Parametric Functional Single-Index Model

Model checking methods based on non-parametric estimation are widely used because of their tractable limiting null distributions and being sensitive to high-frequency oscillation alternative models. However, this kind of test suffers from the curse of dimensionality, resulting in slow convergence, especially for functional data with infinite dimensional features. In this paper, we propose an adaptive-to-model test for a parametric functional single-index model by using the orthogonality of residual and its conditional expectation. The test achieves model adaptation by sufficient dimension reduction which utilizes functional sliced inverse regression. This test procedure can be easily extended to other non-parametric test methods. Under certain conditions, we prove the asymptotic properties of the test statistic under the null hypothesis, fixed alternative hypothesis and local alternative hypothesis. Simulations show that our test has better performance than the method that does not use functional sufficient dimension reduction. An analysis of COVID-19 data verifies our conclusion.