Anti-TIFA monoclonal antibody Made to order

Immune recognition of pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors often activates proinflammatory NF-kappa B signalling(1). Recent studies indicate that the bacterial metabolite D-glycero-beta-D-manno-heptose 1,7-bisphosphate (HBP) can activate NF-kappa B signalling in host cytosol(2-4), but it is unclear whether HBP is a genuine PAMP and the cognate pattern recognition receptor has not been identified. Here we combined a transposon screen in Yersinia pseudotuberculosis with biochemical analyses and identified ADP-beta-D-manno-heptose (ADP-Hep), which mediates type III secretion system-dependent NF-kappa B activation and cytokine expression. ADP-Hep, but not other heptose metabolites, could enter host cytosol to activate NF-kappa B. A CRISPR-Cas9 screen showed that activation of NF-kappa B by ADP-Hep involves an ALPK1 (alpha-kinase 1)-TIFA (TRAF-interacting protein with forkhead-associated domain) axis. ADP-Hep directly binds the N-terminal domain of ALPK1, stimulating its kinase domain to phosphorylate and activate TIFA. The crystal structure of the N-terminal domain of ALPK1 and ADP-Hep in complex revealed the atomic mechanism of this ligand-receptor recognition process. HBP was transformed by host adenylyltransferases into ADP-heptose 7-P, which could activate ALPK1 to a lesser extent than ADP-Hep. ADP-Hep (but not HBP) alone or during bacterial infection induced Alpk1-dependent inflammation in mice. Our findings identify ALPK1 and ADP-Hep as a pattern recognition receptor and an effective immunomodulator, respectively.

Accidents involving large trucks very often end up with deadly consequences. Innocent people getting killed are acknowledged globally as one of the traffic safety greatest problems and challenges. While risk factors on truck-related accidents have been researched extensively, the impact on fatalities has received little or no attention, especially considering rural and urban areas, respectively. In this study, the generalized ordered logit model was used in Stata 11.0 to explore the complex mechanism of truck-related accidents in different areas. Data were obtained from The Trucks in Fatal Accidents database (TIFA). The Akaike Information Criterion (AIC) indicates that the model used in this paper is superior to traditional ordered logit model. The results showed that 9 variables affect the vehicle damage level in a fatal crash in both areas but with different directions. Furthermore, 23 indicators significantly affect the disabling damage in the same manner. Also, there are factors that are significant solely in one area and not in the other: 12 in rural and 2 in urban areas.