The endosomal escape of lipid nanoparticles (LNPs) is crucial for efficient mRNA-based therapeutics. Here, we present a cationic polymeric micelle (cPM) as a safe and potent co-delivery system with enhanced endosomal escape capabilities. Methods: We synthesized a cationic and ampholytic di-block copolymer, poly (poly (ethylene glycol)4-5 methacrylatea-co-hexyl methacrylateb)X-b-poly(butyl methacrylatec-co-dimethylaminoethyl methacrylated-co-propyl acrylatee)Y (p(PEG4-5MAa-co-HMAb)X-b-p(BMAc-co-DMAEMAd-co-PAAe)Y), via reversible addition–fragmentation chain transfer polymerization.

Target selection of the personalized cancer neoantigen vaccine, which is highly dependent on computational prediction algorithms, is crucial for its clinical efficacy. Due to the limited number of experimentally validated immunogenic neoepitopes as well as the complexity of neoantigens in eliciting T cell response, the accuracy of neoepitope immunogenicity prediction methods requires persistent efforts for improvement. We present a deep learning framework for neoepitope immunogenicity prediction – SIGANEO by integrating GAN-like network with similarity network to address issues of missing values and limited data concerning neoantigen prediction.

Gasdermins, a family of five pore-forming proteins (GSDMA–GSDME) in humans expressed predominantly in the skin, mucosa and immune sentinel cells, are key executioners of inflammatory cell death (pyroptosis), which recruits immune cells to infection sites and promotes protective immunity. Pore formation is triggered by gasdermin cleavage. Although the proteases that activate GSDMB, C, D and E have been identified, how GSDMA—the dominant gasdermin in the skin—is activated, remains unknown. Streptococcus pyogenes, also known as group A Streptococcus (GAS), is a major skin pathogen that causes substantial morbidity and mortality worldwide.

Background: Accurate prediction of binding between class I human leukocyte antigen (HLA) and neoepitope is critical for target identification within personalized T-cell based immunotherapy.
Results: We present a pan-allele HLA-peptide binding prediction framework-MATHLA which integrates bi-directional long short-term memory network and multiple head attention mechanism.
Conclusion: Our method demonstrates the necessity of further development of deep learning algorithm in improving and interpreting HLA-peptide binding prediction in parallel to increasing the amount of high-quality HLA ligandome data.

Host cells initiate cell death programs to limit pathogen infection. Inhibition of transforming growth factor–β–activated kinase 1 (TAK1) by pathogenic Yersinia in macrophages triggers receptor-interacting serine-threonine protein kinase 1 (RIPK1)–dependent caspase-8 cleavage of gasdermin D (GSDMD) and inflammatory cell death (pyroptosis). A genome-wide CRISPR screen to uncover mediators of caspase-8–dependent pyroptosis identified an unexpected role of the lysosomal folliculin (FLCN)–folliculin-interacting protein 2 (FNIP2)–Rag-Ragulator supercomplex, which regulates metabolic signaling and the mechanistic target of rapamycin complex 1 (mTORC1).