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.

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).

Single-cell RNA sequencing (scRNA-seq) promises to provide higher resolution of cellular differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq has been routinely conducted to reveal cell heterogeneity and diversity. However, clustering analysis of scRNA-seq data remains a statistical and computational challenge, due to the pervasive dropout events obscuring the data matrix with prevailing ‘false’ zero count observations.

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.

The global liquid biopsy industry is expected to exceed $US5 billion by 2023. One application of liquid biopsy technology is the diagnosis of disease using biomarkers found in blood, urine, stool, saliva, and other biological samples from patients. These biomarkers could be DNA, RNA, protein, or even a cell. More recently, the use of cell-free DNA from plasma is emerging as an important minimally invasive tool for clinical diagnosis. The development of technology has increased the diversity of its application. Here, we discuss how liquid biopsies have been used in the clinic, and how personalized medicine are likely to use liquid biopsies in the near future.