Dimensions and Interactions of Large T-Cell Surface Proteins

Apoptosis-mediated ADAM10 activation removes a mucin barrier promoting T cell efferocytosis

Efferocytic clearance of apoptotic cells in general, and T cells in particular, is required for tissue and immune homeostasis. Transmembrane mucins are extended glycoproteins highly expressed in the cell glycocalyx that function as a barrier to phagocytosis. Whether and how mucins may be regulated during cell death to facilitate efferocytic corpse clearance is not well understood. Here we show that normal and transformed human T cells express a subset of mucins which are rapidly and selectively removed from the cell surface during apoptosis. This process is mediated by the ADAM10 sheddase, the activity of which is associated with XKR8-catalyzed flipping of phosphatidylserine to the outer leaflet of the plasma membrane. Mucin clearance enhances uptake of apoptotic T cells by macrophages, confirming mucins as an enzymatically-modulatable barrier to efferocytosis. Together these findings demonstrate a glycocalyx regulatory pathway with implications for therapeutic intervention in the clearance of normal and transformed apoptotic T cells.

Enforced Dimerization of CD45 by the Adenovirus E3/49K Protein Inhibits T Cell Receptor Signaling

Human adenoviruses (HAdVs) are widespread pathogens that generally cause mild infections in immunocompetent individuals but severe or even fatal diseases in immunocompromised patients. In order to counteract the host immune defenses, HAdVs encode various immunomodulatory proteins in the early transcription unit 3 (E3). The E3/49K protein is a highly glycosylated type I transmembrane protein uniquely expressed by species D HAdVs. Its N-terminal ectodomain sec49K is released by metalloprotease-mediated shedding at the cell surface and binds to the receptor-like protein tyrosine phosphatase CD45, a critical regulator of leukocyte activation and functions. It remained elusive which domains of CD45 and E3/49K are involved in the interaction and whether such an interaction can also occur on the cell surface with membrane-anchored full-length E3/49K. Here, we show that the two extracellular domains R1 and R2 of E3/49K bind to the same site in the domain d3 of CD45. This interaction enforces the dimerization of CD45, causing the inhibition of T cell receptor signaling. Intriguingly, the membrane-anchored E3/49K appears to be designed like a "molecular fishing rod" using an extended disordered region of E3/49K as a "fishing line" to bridge the distance between the plasma membrane of infected cells and the CD45 binding site on T cells to effectively position the domains R1 and R2 as baits for CD45 binding. This design strongly suggests that both secreted sec49K as well as membrane-anchored full-length E3/49K have immunomodulatory functions. The forced dimerization of CD45 may be applied as a therapeutic strategy in chronic inflammatory disorders and cancer. IMPORTANCE The battle between viruses and their hosts is an ongoing arms race. Whereas the host tries to detect and eliminate the virus, the latter counteracts such antiviral measures to replicate and spread. Adenoviruses have evolved various mechanisms to evade the human immune response. The E3/49K protein of species D adenoviruses mediates the inhibition of immune cell function via binding to the protein tyrosine phosphatase CD45. Here, we show that E3/49K triggers the dimerization of CD45 and thereby inhibits its phosphatase activity. Intriguingly, the membrane-anchored E3/49K seems to be designed like a "molecular fishing rod" with the two CD45 binding domains of E3/49K as baits positioned at the end of an extended disordered region reminiscent of a fishing line. The adenoviral strategy to inhibit CD45 activity by forced dimerization may be used for therapeutic intervention in autoimmune diseases or to prevent graft rejection after transplantation.

Identification of prognosis-related gene features in low-grade glioma based on ssGSEA

Low-grade gliomas (LGG) are commonly seen in clinical practice, and the prognosis is often poor. Therefore, the determination of immune-related risk scores and immune-related targets for predicting prognoses in patients with LGG is crucial. A single-sample gene set enrichment analysis (ssGSEA) was performed on 22 immune gene sets to calculate immune-based prognostic scores. The prognostic value of the 22 immune cells for predicting overall survival (OS) was assessed using the least absolute shrinkage and selection operator (LASSO) and univariate and multivariate Cox analyses. Subsequently, we constructed a validated effector T-cell risk score (TCRS) to identify the immune subtypes and inflammatory immune features of LGG patients. We divided an LGG patient into a high-risk-score group and a low-risk-score group based on the optimal cutoff value. Kaplan-Meier survival curve showed that patients in the low-risk-score group had higher OS. We then identified the differentially expressed genes (DEGs) between the high-risk-score group and low-risk-score group and obtained 799 upregulated genes and 348 downregulated genes. The analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) show that DEGs were mainly concentrated in immune-related processes. In order to further explore the immune-related genes related to prognosis, we constructed a protein-protein interaction (PPI) network using Cytoscape and then identified the 50 most crucial genes. Subsequently, nine DEGs were found to be significantly associated with OS based on univariate and multivariate Cox analyses. It was further confirmed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were independent prognostic factors for LGG through batch survival analysis and a nomogram prediction model. In addition, we used an RT-qPCR assay to validate the bioinformatics results. The results showed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were highly expressed in LGG. Our study can provide a reference value for the prediction of prognosis in LGG patients and may help in the clinical development of effective therapeutic agents.

Nanoconfinement of microvilli alters gene expression and boosts T cell activation

T cells sense and respond to their local environment at the nanoscale by forming small actin-rich protrusions, called microvilli, which play critical roles in signaling and antigen recognition, particularly at the interface with the antigen presenting cells. However, the mechanism by which microvilli contribute to cell signaling and activation is largely unknown. Here, we present a tunable engineered system that promotes microvilli formation and T cell signaling via physical stimuli. We discovered that nanoporous surfaces favored microvilli formation and markedly altered gene expression in T cells and promoted their activation. Mechanistically, confinement of microvilli inside of nanopores leads to size-dependent sorting of membrane-anchored proteins, specifically segregating CD45 phosphatases and T cell receptors (TCR) from the tip of the protrusions when microvilli are confined in 200-nm pores but not in 400-nm pores. Consequently, formation of TCR nanoclustered hotspots within 200-nm pores allows sustained and augmented signaling that prompts T cell activation even in the absence of TCR agonists. The synergistic combination of mechanical and biochemical signals on porous surfaces presents a straightforward strategy to investigate the role of microvilli in T cell signaling as well as to boost T cell activation and expansion for application in the growing field of adoptive immunotherapy.

Motifs of Three HLA-DQ Amino Acid Residues (α44, β57, β135) Capture Full Association With the Risk of Type 1 Diabetes in DQ2 and DQ8 Children

HLA-DQA1 and -DQB1 are strongly associated with type 1 diabetes (T1D), and DQ8.1 and DQ2.5 are major risk haplotypes. Next-generation targeted sequencing of HLA-DQA1 and -DQB1 in Swedish newly diagnosed 1- to 18 year-old patients (n = 962) and control subjects (n = 636) was used to construct abbreviated DQ haplotypes, converted into amino acid (AA) residues, and assessed for their associations with T1D. A hierarchically organized haplotype (HOH) association analysis allowed 45 unique DQ haplotypes to be categorized into seven clusters. The DQ8/9 cluster included two DQ8.1 risk and the DQ9 resistant haplotypes, and the DQ2 cluster included the DQ2.5 risk and DQ2.2 resistant haplotypes. Within each cluster, HOH found residues α44Q (odds ratio [OR] 3.29, P = 2.38 * 10^-85) and β57A (OR 3.44, P = 3.80 * 10^-84) to be associated with T1D in the DQ8/9 cluster representing all ten residues (α22, α23, α44, α49, α51, α53, α54, α73, α184, β57) due to complete linkage disequilibrium (LD) of α44 with eight such residues. Within the DQ2 cluster and due to LD, HOH analysis found α44C and β135D to share the risk for T1D (OR 2.10, P = 1.96 * 10^-20). The motif "QAD" of α44, β57, and β135 captured the T1D risk association of DQ8.1 (OR 3.44, P = 3.80 * 10^-84), and the corresponding motif "CAD" captured the risk association of DQ2.5 (OR 2.10, P = 1.96 * 10^-20). Two risk associations were related to GAD65 autoantibody (GADA) and IA-2 autoantibody (IA-2A) but in opposite directions. CAD was positively associated with GADA (OR 1.56, P = 6.35 * 10^-8) but negatively with IA-2A (OR 0.59, P = 6.55 * 10^-11). QAD was negatively associated with GADA (OR 0.88; P = 3.70 * 10^-3) but positively with IA-2A (OR 1.64; P = 2.40 * 10^-14), despite a single difference at α44. The residues are found in and around anchor pockets 1 and 9, as potential T-cell receptor contacts, in the areas for CD4 binding and putative homodimer formation. The identification of three HLA-DQ AAs (α44, β57, β135) conferring T1D risk should sharpen functional and translational studies.

Identification of Potential Key Genes and Pathways for Inflammatory Breast Cancer Based on GEO and TCGA Databases

Introduction

Inflammatory breast cancer (IBC) is a rare type of breast cancer with poor prognosis, and the pathogenesis of this life-threatening disease is yet to be fully elucidated. This study aims to identify key genes of IBC, which could be potential diagnostic or therapeutic targets.

Methods

Four datasets GSE5847, GSE22597, GSE23720, and GSE45581 were downloaded from the Gene Expression Omnibus (GEO) and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to understand the potential bio-functions of the differentially expressed genes (DEGs). Protein-protein interaction (PPI) network was constructed for functional modules analysis and hub genes identification, and TCGA survival analysis and qRT-PCR of clinical samples were used to further explore and validate the effect of hub genes on IBC.

Results

A total of 114 DEGs were identified from the GEO datasets. GO and KEGG analyses showed that the DEGs were mainly enriched in oncogenesis and cell adhesion. From the PPI network, we screened out five hub genes, including PTPRC, IL6, SELL, CD40, and SPN. Survival analysis and expression validation verified the robustness of the hub genes.

Discussion

The present study provides new insight into the understanding of IBC pathogenesis and the identified hub genes may serve as potential targets for diagnosis and treatment.

Phagocytic Integrins: Activation and Signaling

Phagocytic integrins are endowed with the ability to engulf and dispose of particles of different natures. Evolutionarily conserved from worms to humans, they are involved in pathogen elimination and apoptotic and tumoral cell clearance. Research in the field of integrin-mediated phagocytosis has shed light on the molecular events controlling integrin activation and their effector functions. However, there are still some aspects of the regulation of the phagocytic process that need to be clarified. Here, we have revised the molecular events controlling phagocytic integrin activation and the downstream signaling driving particle engulfment, and we have focused particularly on α_Mβ₂/CR3, α_Xβ₂/CR4, and a brief mention of α_Vβ₅/α_Vβ₃integrins.

T Cell Reprogramming Against Cancer

Advances in academic and clinical studies during the last several years have resulted in practical outcomes in adoptive immune therapy of cancer. Immune cells can be programmed with molecular modules that increase their therapeutic potency and specificity. It has become obvious that successful immunotherapy must take into account the full complexity of the immune system and, when possible, include the use of multifactor cell reprogramming that allows fast adjustment during the treatment. Today, practically all immune cells can be stably or transiently reprogrammed against cancer. Here, we review works related to T cell reprogramming, as the most developed field in immunotherapy. We discuss factors that determine the specific roles of αβ and γδ T cells in the immune system and the structure and function of T cell receptors in relation to other structures involved in T cell target recognition and immune response. We also discuss the aspects of T cell engineering, specifically the construction of synthetic T cell receptors (synTCRs) and chimeric antigen receptors (CARs) and the use of engineered T cells in integrative multifactor therapy of cancer.

Synergistic factors control kinase-phosphatase organization in B-cells engaged with supported bilayers

B-cells become activated by ligands with varying valency and mode of presentation to the B-cell receptor (BCR). We previously demonstrated that clustering the immunoglobulin M (IgM) isotype of BCR with an artificial soluble cross-linker stabilized an ordered phase-like domain that enriched kinases and depleted phosphatases to promote receptor tyrosine phosphorylation. BCR is also activated by ligands presented at surfaces, and here we activate B-cells via supported bilayers of phosphatidylcholine lipids, a natural ligand for the IgM BCR expressed in the CH27 cells used. Using superresolution fluorescence localization microscopy, along with a quantitative cross-correlation analysis, we find that BRCs engaged with bilayers sort minimal peptide markers of liquid-ordered and liquid-disordered phases, indicating that ordered-domain stabilization is a general feature of BCR clustering. The phosphatase CD45 is more strongly excluded from bilayer-engaged BRCs than a transmembrane peptide, indicating that mechanisms other than domain partitioning contribute to its organization. Experimental observations are assembled into a minimal model of receptor activation that incorporates both ordered domains and direct phosphatase exclusion mechanisms to produce a more sensitive response.

A cell topography-based mechanism for ligand discrimination by the T cell receptor

The T cell receptor (TCR) initiates the elimination of pathogens and tumors by T cells. To avoid damage to the host, the receptor must be capable of discriminating between wild-type and mutated self and nonself peptide ligands presented by host cells. Exactly how the TCR does this is unknown. In resting T cells, the TCR is largely unphosphorylated due to the dominance of phosphatases over the kinases expressed at the cell surface. However, when agonist peptides are presented to the TCR by major histocompatibility complex proteins expressed by antigen-presenting cells (APCs), very fast receptor triggering, i.e., TCR phosphorylation, occurs. Recent work suggests that this depends on the local exclusion of the phosphatases from regions of contact of the T cells with the APCs. Here, we developed and tested a quantitative treatment of receptor triggering reliant only on TCR dwell time in phosphatase-depleted cell contacts constrained in area by cell topography. Using the model and experimentally derived parameters, we found that ligand discrimination likely depends crucially on individual contacts being ∼200 nm in radius, matching the dimensions of the surface protrusions used by T cells to interrogate their targets. The model not only correctly predicted the relative signaling potencies of known agonists and nonagonists but also achieved this in the absence of kinetic proofreading. Our work provides a simple, quantitative, and predictive molecular framework for understanding why TCR triggering is so selective and fast and reveals that, for some receptors, cell topography likely influences signaling outcomes.