Epstein-Barr virus (EBV) encodes a G protein-coupled receptor (GPCR) termed BILF1 that is essential for EBV-mediated immunosuppression and oncogenesis. BILF1 couples with inhibitory G protein (Gi), the major intracellular signaling effector for human chemokine receptors, and exhibits constitutive signaling activity; the ligand(s) for BILF1 are unknown. We studied the origins of BILF1's constitutive activity through structure determination of BILF1 bound to the inhibitory G protein (Gi) heterotrimer. The 3.2-Å resolution cryo-electron microscopy structure revealed an extracellular loop within BILF1 that blocked the typical chemokine binding site, suggesting ligand-autonomous receptor activation. Rather, amino acid substitutions within BILF1 transmembrane regions at hallmark ligand-activated class A GPCR "microswitches" stabilized a constitutively active BILF1 conformation for Gi coupling in a ligand-independent fashion. Thus, the constitutive activity of BILF1 promotes immunosuppression and virulence independent of ligand availability, with implications for the function of GPCRs encoded by related viruses and for therapeutic targeting of EBV.

B cells are capable of a wide range of effector functions including antibody secretion, antigen presentation, cytokine production, and generation of immunological memory. A consistent strategy for classifying human B cells by using surface molecules is essential to harness this functional diversity for clinical translation. We developed a highly multiplexed screen to quantify the co-expression of 351 surface molecules on millions of human B cells. We identified differentially expressed molecules and aligned their variance with isotype usage, VDJ sequence, metabolic profile, biosynthesis activity, and signaling response. Based on these analyses, we propose a classification scheme to segregate B cells from four lymphoid tissues into twelve unique subsets, including a CD45RB+CD27- early memory population, a class-switched CD39+ tonsil-resident population, and a CD19hiCD11c+ memory population that potently responds to immune activation. This classification framework and underlying datasets provide a resource for further investigations of human B cell identity and function.

Given the limited efficacy of clinical approaches that rely on ex vivo generated dendritic cells (DCs), it is imperative to design strategies that harness specialized DC subsets in situ. This requires delineating the expression of surface markers by DC subsets among individuals and tissues. Here, we performed a multiparametric phenotypic characterization and unbiased analysis of human DC subsets in blood, tonsil, spleen, and skin. We uncovered previously unreported phenotypic heterogeneity of human cDC2s among individuals, including variable expression of functional receptors such as CD172a. We found marked differences in DC subsets localized in blood and lymphoid tissues versus skin, and a striking absence of the newly discovered Axl+ DCs in the skin. Finally, we evaluated the capacity of anti-receptor monoclonal antibodies to deliver vaccine components to skin DC subsets. These results offer a promising path for developing DC subset-specific immunotherapies that cannot be provided by transcriptomic analysis alone.

To identify disease-relevant T cell receptors (TCRs) with shared antigen specificity, we analyzed 778,938 TCRβ chain sequences from 178 non-small cell lung cancer patients using the GLIPH2 (grouping of lymphocyte interactions with paratope hotspots 2) algorithm. We identified over 66,000 shared specificity groups, of which 435 were clonally expanded and enriched in tumors compared to adjacent lung. The antigenic epitopes of one such tumor-enriched specificity group were identified using a yeast peptide-HLA A∗02:01 display library. These included a peptide from the epithelial protein TMEM161A, which is overexpressed in tumors and cross-reactive epitopes from Epstein-Barr virus and E. coli. Our findings suggest that this cross-reactivity may underlie the presence of virus-specific T cells in tumor infiltrates and that pathogen cross-reactivity may be a feature of multiple cancers. The approach and analytical pipelines generated in this work, as well as the specificity groups defined here, present a resource for understanding the T cell response in cancer.