Porcine deltacoronavirus nsp5 antagonizes type I interferon signaling by cleaving IFIT3.

Porcine deltacoronavirus (PDCoV) has caused enormous economic losses to the global pig industry. However, the immune escape mechanism of PDCoV remains to be fully clarified. Transcriptomic analysis revealed a high abundance of interferon (IFN)-induced protein with tetratricopeptide repeats 3 (IFIT3) transcripts after PDCoV infection, which initially implied a correlation between IFIT3 and PDCoV. Further studies showed that PDCoV nsp5 could antagonize the host type I interferon signaling pathway by cleaving IFIT3. We demonstrated that PDCoV nsp5 cleaved porcine IFIT3 (pIFIT3) at Gln-406. Similar cleavage of endogenous IFIT3 has also been observed in PDCoV-infected cells. The pIFIT3-Q406A mutant was resistant to nsp5-mediated cleavage and exhibited a greater ability to inhibit PDCoV infection than wild-type pIFIT3. Furthermore, we found that cleavage of IFIT3 is a common characteristic of nsp5 proteins of human coronaviruses, albeit not alphacoronavirus. This finding suggests that the cleavage of IFIT3 is an important mechanism by which PDCoV nsp5 antagonizes IFN signaling. Our study provides new insights into the mechanisms by which PDCoV antagonizes the host innate immune response.IMPORTANCEPorcine deltacoronavirus (PDCoV) is a potential emerging zoonotic pathogen, and studies on the prevalence and pathogenesis of PDCoV are ongoing. The main protease (nsp5) of PDCoV provides an excellent target for antivirals due to its essential and conserved function in the viral replication cycle. Previous studies have revealed that nsp5 of PDCoV antagonizes type I interferon (IFN) production by targeting the interferon-stimulated genes. Here, we provide the first demonstration that nsp5 of PDCoV antagonizes IFN signaling by cleaving IFIT3, which affects the IFN response after PDCoV infection. Our findings reveal that PDCoV nsp5 is an important interferon antagonist and enhance the understanding of immune evasion by deltacoronaviruses.

Assessing the causal relationship between 731 immunophenotypes and the risk of lung cancer: a bidirectional mendelian randomization study.

BACKGROUND: Previous studies have observed a link between immunophenotypes and lung cancer, both of which are closely associated with genetic factors. However, the causal relationship between them remains unclear. METHODS: Bidirectional Mendelian randomization (MR) was performed on publicly available genome-wide association study (GWAS) summary statistics to analyze the causal relationships between 731 immunophenotypes and lung cancer. Sensitivity analyses were conducted to verify the robustness, heterogeneity, and potential horizontal pleiotropy of our findings. RESULTS: Following Bonferroni adjustment, CD14- CD16+ monocyte (OR = 0.930, 95%CI 0.900-0.960, P = 8.648 × 10- 6, PBonferroni = 0.006) and CD27 on CD24+ CD27+ B cells (OR = 1.036, 95%CI 1.020-1.053, P = 1.595 × 10 - 5, PBonferroni = 0.012) were identified as having a causal role in lung cancer via the inverse variance weighted (IVW) method. At a more relaxed threshold, CD27 on IgD+ CD24+ B cell (OR = 1.035, 95%CI 1.017-1.053, P = 8.666 × 10- 5, PBonferroni = 0.063) and CD27 on switched memory B cell (OR = 1.037, 95%CI 1.018-1.056, P = 1.154 × 10- 4, PBonferroni = 0.084) were further identified. No statistically significant effects of lung cancer on immunophenotypes were found. CONCLUSIONS: The elevated level of CD14- CD16+ monocytes was a protective factor against lung cancer. Conversely, CD27 on CD24+ CD27+ B cell was a risk factor. CD27 on class-switched memory B cells and IgD+ CD24+ B cells were potential risk factors for lung cancer. This research enhanced our comprehension of the interplay between immune responses and lung cancer risk. Additionally, these findings offer valuable perspectives for the development of immunologically oriented therapeutic strategies.

Topology Controlled All-(Meth)acrylic Thermoplastic Elastomers by Multi-Functional Lewis Pairs-Mediated Polymerization.

It remains challenging to synthesize all-(meth)acrylic triblock thermoplastic elastomers (TPEs), due to the drastically different reactivities between the acrylates and methacrylates and inevitable occurrence of side reactions during polymerization of acrylates. By taking advantage of the easy structural modulation features of N-heterocyclic olefins (NHOs), we design and synthesize strong nucleophilic tetraphenylethylene-based NHOs varying in the number (i.e. mono-, dual- and tetra-) of initiating functional groups. Its combination with bulky organoaluminum [iBuAl(BHT)2] (BHT=bis(2,6-di-tBu-4-methylphenoxy)) constructs Lewis pair (LP) to realize the living polymerization of both acrylates and methacrylates, furnishing polyacrylates with ultrahigh molecular weight (Mn up to 2174 kg ⋅ mol-1) within 4 min. Moreover, these NHO-based LPs enable us to not only realize the control over the polymers' topology (i.e. linear and star), but also achieve triblock star copolymers in one-step manner. Mechanical studies reveal that the star triblock TPEs exhibit better mechanical properties (elongation at break up to 1863 % and tensile strength up to 19.1 MPa) in comparison with the linear analogs. Moreover, the presence of tetraphenylethylene group in the NHOs entitled the triblock TPEs with excellent AIE properties in both solution and solid state.

Resolving the dynamic properties of entangled linear polymers in non-equilibrium coarse grain simulation with a priori scaling factors.

The molecular weight of polymers can influence the material properties, but the molecular weight at the experiment level sometimes can be a huge burden for property prediction with full-atomic simulations. The traditional bottom-up coarse grain (CG) simulation can reduce the computation cost. However, the dynamic properties predicted by the CG simulation can deviate from the full-atomic simulation result. Usually, in CG simulations, the diffusion is faster and the viscosity and modulus are much lower. The fast dynamics in CG are usually solved by a posteriori scaling on time, temperature, or potential modifications, which usually have poor transferability to other non-fitted physical properties because of a lack of fundamental physics. In this work, a priori scaling factors were calculated by the loss of degrees of freedom and implemented in the iterative Boltzmann inversion. According to the simulation results on 3 different CG levels at different temperatures and loading rates, such a priori scaling factors can help in reproducing some dynamic properties of polycaprolactone in CG simulation more accurately, such as heat capacity, Young's modulus, and viscosity, while maintaining the accuracy in the structural distribution prediction. The transferability of entropy-enthalpy compensation and a dissipative particle dynamics thermostat is also presented for comparison. The proposed method reveals the huge potential for developing customized CG thermostats and offers a simple way to rebuild multiphysics CG models for polymers with good transferability.