Circular RNAs: Characterization, cellular roles, and applications.

Most circular RNAs are produced from the back-splicing of exons of precursor mRNAs. Recent technological advances have in part overcome problems with their circular conformation and sequence overlap with linear cognate mRNAs, allowing a better understanding of their cellular roles. Depending on their localization and specific interactions with DNA, RNA, and proteins, circular RNAs can modulate transcription and splicing, regulate stability and translation of cytoplasmic mRNAs, interfere with signaling pathways, and serve as templates for translation in different biological and pathophysiological contexts. Emerging applications of RNA circles to interfere with cellular processes, modulate immune responses, and direct translation into proteins shed new light on biomedical research. In this review, we discuss approaches used in circular RNA studies and the current understanding of their regulatory roles and potential applications.

Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition.

Checkpoint inhibitors (CPIs) augment adaptive immunity. Systematic pan-tumor analyses may reveal the relative importance of tumor-cell-intrinsic and microenvironmental features underpinning CPI sensitization. Here, we collated whole-exome and transcriptomic data for >1,000 CPI-treated patients across seven tumor types, utilizing standardized bioinformatics workflows and clinical outcome criteria to validate multivariable predictors of CPI sensitization. Clonal tumor mutation burden (TMB) was the strongest predictor of CPI response, followed by total TMB and CXCL9 expression. Subclonal TMB, somatic copy alteration burden, and histocompatibility leukocyte antigen (HLA) evolutionary divergence failed to attain pan-cancer significance. Dinucleotide variants were identified as a source of immunogenic epitopes associated with radical amino acid substitutions and enhanced peptide hydrophobicity/immunogenicity. Copy-number analysis revealed two additional determinants of CPI outcome supported by prior functional evidence: 9q34 (TRAF2) loss associated with response and CCND1 amplification associated with resistance. Finally, single-cell RNA sequencing (RNA-seq) of clonal neoantigen-reactive CD8 tumor-infiltrating lymphocytes (TILs), combined with bulk RNA-seq analysis of CPI-responding tumors, identified CCR5 and CXCL13 as T-cell-intrinsic markers of CPI sensitivity.

Profiling SARS-CoV-2 HLA-I peptidome reveals T cell epitopes from out-of-frame ORFs.

T cell-mediated immunity plays an important role in controlling SARS-CoV-2 infection, but the repertoire of naturally processed and presented viral epitopes on class I human leukocyte antigen (HLA-I) remains uncharacterized. Here, we report the first HLA-I immunopeptidome of SARS-CoV-2 in two cell lines at different times post infection using mass spectrometry. We found HLA-I peptides derived not only from canonical open reading frames (ORFs) but also from internal out-of-frame ORFs in spike and nucleocapsid not captured by current vaccines. Some peptides from out-of-frame ORFs elicited T cell responses in a humanized mouse model and individuals with COVID-19 that exceeded responses to canonical peptides, including some of the strongest epitopes reported to date. Whole-proteome analysis of infected cells revealed that early expressed viral proteins contribute more to HLA-I presentation and immunogenicity. These biological insights, as well as the discovery of out-of-frame ORF epitopes, will facilitate selection of peptides for immune monitoring and vaccine development.

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction.

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.

Structural and Functional Basis of SARS-CoV-2 Entry by Using Human ACE2.

The recent emergence of a novel coronavirus (SARS-CoV-2) in China has caused significant public health concerns. Recently, ACE2 was reported as an entry receptor for SARS-CoV-2. In this study, we present the crystal structure of the C-terminal domain of SARS-CoV-2 (SARS-CoV-2-CTD) spike (S) protein in complex with human ACE2 (hACE2), which reveals a hACE2-binding mode similar overall to that observed for SARS-CoV. However, atomic details at the binding interface demonstrate that key residue substitutions in SARS-CoV-2-CTD slightly strengthen the interaction and lead to higher affinity for receptor binding than SARS-RBD. Additionally, a panel of murine monoclonal antibodies (mAbs) and polyclonal antibodies (pAbs) against SARS-CoV-S1/receptor-binding domain (RBD) were unable to interact with the SARS-CoV-2 S protein, indicating notable differences in antigenicity between SARS-CoV and SARS-CoV-2. These findings shed light on the viral pathogenesis and provide important structural information regarding development of therapeutic countermeasures against the emerging virus.

Stimuli-responsive nanodelivery systems for amplifying immunogenic cell death in cancer immunotherapy.

Immunogenic cell death (ICD) is a special pattern of tumor cell death, enabling to elicit tumor-specific immune response via the release of damage-associated molecular patterns and tumor-associated antigens in the tumor microenvironment. ICD-induced immunotherapy holds the promise for completely eliminating tumors and long-term protective antitumor immune response. Increasing ICD inducers have been discovered for boosting antitumor immunity via evoking ICD. Nonetheless, the utilization of ICD inducers remains insufficient owing to serious toxic reactions, low localization efficiency within the tumor microenvironmental niche, etc. For overcoming such limitations, stimuli-responsive multifunctional nanoparticles or nanocomposites with ICD inducers have been developed for improving immunotherapeutic efficiency via lowering toxicity, which represent a prospective scheme for fostering the utilization of ICD inducers in immunotherapy. This review outlines the advances in near-infrared (NIR)-, pH-, redox-, pH- and redox-, or NIR- and tumor microenvironment-responsive nanodelivery systems for ICD induction. Furthermore, we discuss their clinical translational potential. The progress of stimuli-responsive nanoparticles in clinical settings depends upon the development of biologically safer drugs tailored to patient needs. Moreover, an in-depth comprehending of ICD biomarkers, immunosuppressive microenvironment, and ICD inducers may accelerate the advance in smarter multifunctional nanodelivery systems to further amplify ICD.

DNA damage repair kinase DNA-PK and cGAS synergize to induce cancer-related inflammation in glioblastoma.

Cytosolic DNA promotes inflammatory responses upon detection by the cyclic GMP-AMP (cGAMP) synthase (cGAS). It has been suggested that cGAS downregulation is an immune escape strategy harnessed by tumor cells. Here, we used glioblastoma cells that show undetectable cGAS levels to address if alternative DNA detection pathways can promote pro-inflammatory signaling. We show that the DNA-PK DNA repair complex (i) drives cGAS-independent IRF3-mediated type I Interferon responses and (ii) that its catalytic activity is required for cGAS-dependent cGAMP production and optimal downstream signaling. We further show that the cooperation between DNA-PK and cGAS favors the expression of chemokines that promote macrophage recruitment in the tumor microenvironment in a glioblastoma model, a process that impairs early tumorigenesis but correlates with poor outcome in glioblastoma patients. Thus, our study supports that cGAS-dependent signaling is acquired during tumorigenesis and that cGAS and DNA-PK activities should be analyzed concertedly to predict the impact of strategies aiming to boost tumor immunogenicity.

RNA Circularization Diminishes Immunogenicity and Can Extend Translation Duration In Vivo.

Circular RNAs (circRNAs) are a class of single-stranded RNAs with a contiguous structure that have enhanced stability and a lack of end motifs necessary for interaction with various cellular proteins. Here, we show that unmodified exogenous circRNA is able to bypass cellular RNA sensors and thereby avoid provoking an immune response in RIG-I and Toll-like receptor (TLR) competent cells and in mice. The immunogenicity and protein expression stability of circRNA preparations are found to be dependent on purity, with small amounts of contaminating linear RNA leading to robust cellular immune responses. Unmodified circRNA is less immunogenic than unmodified linear mRNA in vitro, in part due to the evasion of TLR sensing. Finally, we provide the first demonstration to our knowledge of exogenous circRNA delivery and translation in vivo, and we show that circRNA translation is extended in adipose tissue in comparison to unmodified and uridine-modified linear mRNAs.

A journey to your self: The vague definition of immune self and its practical implications.

The identification of immunogenic peptides has become essential in an increasing number of fields in immunology, ranging from tumor immunotherapy to vaccine development. The nature of the adaptive immune response is shaped by the similarity between foreign and self-protein sequences, a concept extensively applied in numerous studies. Can we precisely define the degree of similarity to self? Furthermore, do we accurately define immune self? In the current work, we aim to unravel the conceptual and mechanistic vagueness hindering the assessment of self-similarity. Accordingly, we demonstrate the remarkably low consistency among commonly employed measures and highlight potential avenues for future research.

Development of porcine skeletal muscle extracellular matrix-derived hydrogels with improved properties and low immunogenicity.

Hydrogels derived from decellularized extracellular matrices (ECM) of animal origin show immense potential for regenerative applications due to their excellent cytocompatibility and biomimetic properties. Despite these benefits, the impact of decellularization protocols on the properties and immunogenicity of these hydrogels remains relatively unexplored. In this study, porcine skeletal muscle ECM (smECM) underwent decellularization using mechanical disruption (MD) and two commonly employed decellularization detergents, sodium deoxycholate (SDC) or Triton X-100. To mitigate immunogenicity associated with animal-derived ECM, all decellularized tissues were enzymatically treated with α-galactosidase to cleave the primary xenoantigen-the α-Gal antigen. Subsequently, the impact of the different decellularization protocols on the resultant hydrogels was thoroughly investigated. All methods significantly reduced total DNA content in hydrogels. Moreover, α-galactosidase treatment was crucial for cleaving α-Gal antigens, suggesting that conventional decellularization methods alone are insufficient. MD preserved total protein, collagen, sulfated glycosaminoglycan, laminin, fibronectin, and growth factors more efficiently than other protocols. The decellularization method impacted hydrogel gelation kinetics and ultrastructure, as confirmed by turbidimetric and scanning electron microscopy analyses. MD hydrogels demonstrated high cytocompatibility, supporting satellite stem cell recruitment, growth, and differentiation into multinucleated myofibers. In contrast, the SDC and Triton X-100 protocols exhibited cytotoxicity. Comprehensive in vivo immunogenicity assessments in a subcutaneous xenotransplantation model revealed MD hydrogels' biocompatibility and low immunogenicity. These findings highlight the significant influence of the decellularization protocol on hydrogel properties. Our results suggest that combining MD with α-galactosidase treatment is an efficient method for preparing low-immunogenic smECM-derived hydrogels with enhanced properties for skeletal muscle regenerative engineering and clinical applications.

Subcellular location of source proteins improves prediction of neoantigens for immunotherapy.

Antigen presentation via the major histocompatibility complex (MHC) is essential for anti-tumor immunity. However, the rules that determine which tumor-derived peptides will be immunogenic are still incompletely understood. Here, we investigated whether constraints on peptide accessibility to the MHC due to protein subcellular location are associated with peptide immunogenicity potential. Analyzing over 380,000 peptides from studies of MHC presentation and peptide immunogenicity, we find clear spatial biases in both eluted and immunogenic peptides. We find that including parent protein location improves the prediction of peptide immunogenicity in multiple datasets. In human immunotherapy cohorts, the location was associated with a neoantigen vaccination response, and immune checkpoint blockade responders generally had a higher burden of neopeptides from accessible locations. We conclude that protein subcellular location adds important information for optimizing cancer immunotherapies.

Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.

Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

Obesity triggers tumoral senescence and renders poorly immunogenic malignancies amenable to senolysis.

Obesity is a major risk factor for cancer. Conventional thought suggests that elevated adiposity predisposes to heightened inflammatory stress and potentiates tumor growth, yet underlying mechanisms remain ill-defined. Here, we show that tumors from patients with a body mass index >35 carry a high burden of senescent cells. In mouse syngeneic tumor models, we correlated a pronounced accretion of senescent cancer cells with poorly immunogenic tumors when mice were subjected to diet-induced obesity (DIO). Highly immunogenic tumors showed lesser senescence burden suggesting immune-mediated elimination of senescent cancer cells, likely targeted as a consequence of their senescence-associated secretory phenotype. Treatment with the senolytic BH3 mimetic small molecule inhibitor ABT-263 selectively stalled tumor growth in mice with DIO to rates comparable to regular diet-fed mice. Thus, consideration of body adiposity in the selection of cancer therapy may be a critical determinant for disease outcome in poorly immunogenic malignancies.

Immunogenicity and efficacy of Ad26.COV2.S: An adenoviral vector-based COVID-19 vaccine.

Since its emergence in late 2019, the coronavirus disease 2019 (COVID-19) pandemic has caused substantial morbidity and mortality. Despite the availability of efficacious vaccines, new variants with reduced sensitivity to vaccine-induced protection are a troubling new reality. The Ad26.COV2.S vaccine is a recombinant, replication-incompetent human adenovirus type 26 vector encoding a full-length, membrane-bound severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein in a prefusion-stabilized conformation. This review discusses the immunogenicity and efficacy of Ad26.COV2.S as a single-dose primary vaccination and as a homologous or heterologous booster vaccination. Ad26.COV2.S elicits broad humoral and cellular immune responses, which are associated with protective efficacy/effectiveness against SARS-CoV-2 infection, moderate to severe/critical COVID-19, and COVID-19-related hospitalization and death, including against emerging SARS-CoV-2 variants. The humoral immune responses elicited by Ad26.COV2.S vaccination are durable, continue to increase for at least 2-3 months postvaccination, and involve a range of functional antibodies. Ad26.COV2.S given as a heterologous booster to mRNA vaccine-primed individuals markedly increases humoral and cellular immune responses. The use of Ad26.COV2.S as primary vaccination and as part of booster regimens is supporting the ongoing efforts to control and mitigate the COVID-19 pandemic.

Antibodies internalization mechanisms by dendritic cells and their role in therapeutic antibody immunogenicity.

Internalization and processing by antigen-presenting cells such as dendritic cells (DCs) are critical steps for initiating a T-cell response to therapeutic antibodies. Consequences are the production of neutralizing antidrug antibodies altering the clinical response, the presence of immune complexes, and, in some rare cases, hypersensitivity reactions. In recent years, significant progress has been made in the knowledge of cellular uptake mechanisms of antibodies in DCs. The uptake of antibodies could be directly related to their immunogenicity by regulating the quantity of materials entering the DCs in relation to antibody structure. Here, we summarize the latest insights into cellular uptake mechanisms and pathways in DCs. We highlight the approaches to study endocytosis, the impact of endocytosis routes on T-cell response, and discuss the link between how DCs internalize therapeutic antibodies and the potential mechanisms that could give rise to immunogenicity. Understanding these processes could help in developing assays to evaluate the immunogenicity potential of biotherapeutics.

Flt3 agonist enhances immunogenicity of arenavirus vector-based simian immunodeficiency virus vaccine in macaques.

Arenaviral vaccine vectors encoding simian immunodeficiency virus (SIV) immunogens are capable of inducing efficacious humoral and cellular immune responses in nonhuman primates. Several studies have evaluated the use of immune modulators to further enhance vaccine-induced T-cell responses. The hematopoietic growth factor Flt3L drives the expansion of various bone marrow progenitor populations, and administration of Flt3L was shown to promote expansion of dendritic cell populations in spleen and blood, which are targets of arenaviral vectors. Therefore, we evaluated the potential of Flt3 signaling to enhance the immunogenicity of arenaviral vaccines encoding SIV immunogens (SIVSME543 Gag, Env, and Pol) in rhesus macaques, with a rhesus-specific engineered Flt3L-Fc fusion protein. In healthy animals, administration of Flt3L-Fc led to a 10- to 100-fold increase in type 1 dendritic cells 7 days after dosing, with no antidrug antibody (ADA) generation after repeated dosing. We observed that administration of Flt3L-Fc fusion protein 7 days before arenaviral vaccine increased the frequency and activation of innate immune cells and enhanced T-cell activation with no treatment-related adverse events. Flt3L-Fc administration induced early innate immune activation, leading to a significant enhancement in magnitude, breadth, and polyfunctionality of vaccine-induced T-cell responses. The Flt3L-Fc enhancement in vaccine immunogenicity was comparable to a combination with αCTLA-4 and supports the use of safe and effective variants of Flt3L to augment therapeutic vaccine-induced T-cell responses.IMPORTANCEInduction of a robust human immunodeficiency virus (HIV)-specific CD4+ and CD8+ T-cell response through therapeutic vaccination is considered essential for HIV cure. Arenaviral vaccine vectors encoding simian immunodeficiency virus (SIV) immunogens have demonstrated strong immunogenicity and efficacy in nonhuman primates. Here, we demonstrate that the immunogenicity of arenaviral vectors encoding SIV immunogens can be enhanced by administration of Flt3L-Fc fusion protein 7 days before vaccination. Flt3L-Fc-mediated increase in dendritic cells led to robust improvements in vaccine-induced T- and B-cell responses compared with vaccine alone, and Flt3L-Fc dosing was not associated with any treatment-related adverse events. Importantly, immune modulation by either Flt3L-Fc or αCTLA-4 led to comparable enhancement in vaccine response. These results indicate that the addition of Flt3L-Fc fusion protein before vaccine administration can significantly enhance vaccine immunogenicity. Thus, safe and effective Flt3L variants could be utilized as part of a combination therapy for HIV cure.

IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity.

CD8+ T cells can recognize peptides presented by class I human leukocyte antigen (HLA-I) of nucleated cells. Exploring this immune mechanism is essential for identifying T-cell vaccine targets in cancer immunotherapy. Over the past decade, the wealth of data generated by experiments has spawned many computational approaches for predicting HLA-I binding, antigen presentation and T-cell immune responses. Nevertheless, existing HLA-I binding and antigen presentation prediction approaches suffer from low precision due to the absence of T-cell receptor (TCR) recognition. Direct modeling of T-cell immune responses is less effective as TCR recognition's mechanism still remains underexplored. Therefore, directly applying these existing methods to screen cancer neoantigens is still challenging. Here, we propose a novel immune epitope prediction method termed IEPAPI by effectively incorporating antigen presentation and immunogenicity. First, IEPAPI employs a transformer-based feature extraction block to acquire representations of peptides and HLA-I proteins. Second, IEPAPI integrates the prediction of antigen presentation prediction into the input of immunogenicity prediction branch to simulate the connection between the biological processes in the T-cell immune response. Quantitative comparison results on an independent antigen presentation test dataset exhibit that IEPAPI outperformed the current state-of-the-art approaches NetMHCpan4.1 and mhcflurry2.0 on 100 (25/25) and 76% (19/25) of the HLA subtypes, respectively. Furthermore, IEPAPI demonstrates the best precision on two independent neoantigen datasets when compared with existing approaches, suggesting that IEPAPI provides a vital tool for T-cell vaccine design.

TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning.

Major histocompatibility complex (MHC) class II molecules play a pivotal role in antigen presentation and CD4+ T cell response. Accurate prediction of the immunogenicity of MHC class II-associated antigens is critical for vaccine design and cancer immunotherapies. However, current computational methods are limited by insufficient training data and algorithmic constraints, and the rules that govern which peptides are truly recognized by existing T cell receptors remain poorly understood. Here, we build a transfer learning-based, long short-term memory model named 'TLimmuno2' to predict whether epitope-MHC class II complex can elicit T cell response. Through leveraging binding affinity data, TLimmuno2 shows superior performance compared with existing models on independent validation datasets. TLimmuno2 can find real immunogenic neoantigen in real-world cancer immunotherapy data. The identification of significant MHC class II neoantigen-mediated immunoediting signal in the cancer genome atlas pan-cancer dataset further suggests the robustness of TLimmuno2 in identifying really immunogenic neoantigens that are undergoing negative selection during cancer evolution. Overall, TLimmuno2 is a powerful tool for the immunogenicity prediction of MHC class II presented epitopes and could promote the development of personalized immunotherapies.

Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.