Innate lymphoid cells integrate stromal and immunological signals to enhance antibody production by splenic marginal zone B cells.

Innate lymphoid cells (ILCs) regulate stromal cells, epithelial cells and cells of the immune system, but their effect on B cells remains unclear. Here we identified RORγt(+) ILCs near the marginal zone (MZ), a splenic compartment that contains innate-like B cells highly responsive to circulating T cell-independent (TI) antigens. Splenic ILCs established bidirectional crosstalk with MAdCAM-1(+) marginal reticular cells by providing tumor-necrosis factor (TNF) and lymphotoxin, and they stimulated MZ B cells via B cell-activation factor (BAFF), the ligand of the costimulatory receptor CD40 (CD40L) and the Notch ligand Delta-like 1 (DLL1). Splenic ILCs further helped MZ B cells and their plasma-cell progeny by coopting neutrophils through release of the cytokine GM-CSF. Consequently, depletion of ILCs impaired both pre- and post-immune TI antibody responses. Thus, ILCs integrate stromal and myeloid signals to orchestrate innate-like antibody production at the interface between the immune system and circulatory system.

B cell-helper neutrophils stimulate the diversification and production of immunoglobulin in the marginal zone of the spleen.

Neutrophils use immunoglobulins to clear antigen, but their role in immunoglobulin production is unknown. Here we identified neutrophils around the marginal zone (MZ) of the spleen, a B cell area specialized in T cell-independent immunoglobulin responses to circulating antigen. Neutrophils colonized peri-MZ areas after postnatal mucosal colonization by microbes and enhanced their B cell-helper function after receiving reprogramming signals, including interleukin 10 (IL-10), from splenic sinusoidal endothelial cells. Splenic neutrophils induced immunoglobulin class switching, somatic hypermutation and antibody production by activating MZ B cells through a mechanism that involved the cytokines BAFF, APRIL and IL-21. Neutropenic patients had fewer and hypomutated MZ B cells and a lower abundance of preimmune immunoglobulins to T cell-independent antigens, which indicates that neutrophils generate an innate layer of antimicrobial immunoglobulin defense by interacting with MZ B cells.

The interdependence of machine learning and LC-MS approaches for an unbiased understanding of the cellular immunopeptidome.

INTRODUCTION: The comprehensive collection of peptides presented by major histocompatibility complex (MHC) molecules on the cell surface is collectively known as the immunopeptidome. The analysis and interpretation of such data sets holds great promise for furthering our understanding of basic immunology and adaptive immune activation and regulation, and for direct rational discovery of T cell antigens and the design of T-cell-based therapeutics and vaccines. These applications are, however, challenged by the complex nature of immunopeptidome data. AREAS COVERED: Here, we describe the benefits and shortcomings of applying liquid chromatography-tandem mass spectrometry (MS) to obtain large-scale immunopeptidome data sets and illustrate how the accurate analysis and optimal interpretation of such data is reliant on the availability of refined and highly optimized machine learning approaches. EXPERT OPINION: Further, we demonstrate how the accuracy of immunoinformatics prediction methods within the field of MHC antigen presentation has benefited greatly from the availability of MS-immunopeptidomics data, and exemplify how optimal antigen discovery is best performed in a synergistic combination of MS experiments and such in silico models trained on large-scale immunopeptidomics data.

Improved prediction of HLA antigen presentation hotspots: Applications for immunogenicity risk assessment of therapeutic proteins.

Immunogenicity risk assessment is a critical element in protein drug development. Currently, the risk assessment is most often performed using MHC-associated peptide proteomics (MAPPs) and/or T-cell activation assays. However, this is a highly costly procedure that encompasses limited sensitivity imposed by sample sizes, the MHC repertoire of the tested donor cohort and the experimental procedures applied. Recent work has suggested that these techniques could be complemented by accurate, high-throughput and cost-effective prediction of in silico models. However, this work covered a very limited set of therapeutic proteins and eluted ligand (EL) data. Here, we resolved these limitations by showcasing, in a broader setting, the versatility of in silico models for assessment of protein drug immunogenicity. A method for prediction of MHC class II antigen presentation was developed on the hereto largest available mass spectrometry (MS) HLA-DR EL data set. Using independent test sets, the performance of the method for prediction of HLA-DR antigen presentation hotspots was benchmarked. In particular, the method was showcased on a set of protein sequences including four therapeutic proteins and demonstrated to accurately predict the experimental MS hotspot regions at a significantly lower false-positive rate compared with other methods. This gain in performance was particularly pronounced when compared to the NetMHCIIpan-3.2 method trained on binding affinity data. These results suggest that in silico methods trained on MS HLA EL data can effectively and accurately be used to complement MAPPs assays for the risk assessment of protein drugs.

NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions.

The set of peptides presented on a cell's surface by MHC molecules is known as the immunopeptidome. Current mass spectrometry technologies allow for identification of large peptidomes, and studies have proven these data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. Immunopeptidomes are usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in the system under investigation. Motif deconvolution -the process of associating each ligand to its presenting MHC molecule(s)- is therefore a critical and challenging step in the analysis of MS-eluted MHC ligand data. Here, we describe NNAlign_MA, a computational method designed to address this challenge and fully benefit from large, poly-specific data sets of MS-eluted ligands. NNAlign_MA simultaneously performs the tasks of (1) clustering peptides into individual specificities; (2) automatic annotation of each cluster to an MHC molecule; and (3) training of a prediction model covering all MHCs present in the training set. NNAlign_MA was benchmarked on large and diverse data sets, covering class I and class II data. In all cases, the method was demonstrated to outperform state-of-the-art methods, effectively expanding the coverage of alleles for which accurate predictions can be made, resulting in improved identification of both eluted ligands and T-cell epitopes. Given its high flexibility and ease of use, we expect NNAlign_MA to serve as an effective tool to increase our understanding of the rules of MHC antigen presentation and guide the development of novel T-cell-based therapeutics.

Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data.

Major histocompatibility complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyze and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Furthermore, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0 demonstrated improved performance beyond current state-of-the-art predictors for ligand and CD4+ T cell epitope prediction. These results suggest that NNAlign_MA and NetMHCIIpan-4.0 are powerful tools for analysis of immunopeptidome MS data, prediction of T cell epitopes, and development of personalized immunotherapies.

Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes.

Recent advances in proteomics and mass-spectrometry have widely expanded the detectable peptide repertoire presented by major histocompatibility complex (MHC) molecules on the cell surface, collectively known as the immunopeptidome. Finely characterizing the immunopeptidome brings about important basic insights into the mechanisms of antigen presentation, but can also reveal promising targets for vaccine development and cancer immunotherapy. This report describes a number of practical and efficient approaches to analyze immunopeptidomics data, discussing the identification of meaningful sequence motifs in various scenarios and considering current limitations. Guidelines are provided for the filtering of false hits and contaminants, and to address the problem of motif deconvolution in cell lines expressing multiple MHC alleles, both for the MHC class I and class II systems. Finally, it is demonstrated how machine learning can be readily employed by non-expert users to generate accurate prediction models directly from mass-spectrometry eluted ligand data sets.

Experimental and DFT Study on the Compounds [PdCl2L2] (L = 4-methylpyrazole, 4-iodopyrazole).

Theoretical molecular structures of the complexes [PdCl(2)(HmPz)(2)] (1) and [PdCl(2)(HIPz)(2)] (2) (HmPz = 4-methylpyrazole; HIPz = 4-iodopyrazole) were studied using B3LYP/DFT method. The new complex 2 and the complex 1 were synthesized and characterized by elemental analysis and IR spectroscopy. The calculated bond distances and angles showed that both compounds exhibited a slightly distorted square planar coordination environment around the palladium center. The theoretical IR spectra of C(s) symmetry (electronic state 1A') of the complexes agree well with the experimental data.

Structural and functional characterization of a novel nonglycosidic type I NKT agonist with immunomodulatory properties.

Activation of type I NKT (iNKT) cells by CD1d-presented agonists is a potent immunotherapeutic tool. α-Galactosylceramide (α-GalCer) is the prototypic agonist, but its excessive potency with simultaneous production of both pro- and anti-inflammatory cytokines hampers its potential therapeutic use. In search for novel agonists, we have analyzed the structure and function of HS44, a synthetic aminocyclitolic ceramide analog designed to avoid unrestrained iNKT cell activation. HS44 is a weaker agonist compared with α-GalCer in vitro, although in vivo it induces robust IFN-γ production, and highly reduced but still functional Th2 response. The characteristic cytokine storm produced upon α-GalCer activation was not induced. Consequently, HS44 induced a very efficient iNKT cell-dependent antitumoral response in B16 animal model. In addition, intranasal administration showed the capacity to induce lung inflammation and airway hyperreactivity, a cardinal asthma feature. Thus, HS44 is able to elicit functional Th1 or Th2 responses. Structural studies show that HS44 binds to CD1d with the same conformation as α-GalCer. The TCR binds to HS44 similarly as α-GalCer, but forms less contacts, thus explaining its weaker TCR affinity and, consequently, its weaker recognition by iNKT cells. The ability of this compound to activate an efficient, but not massive, tailored functional immune response makes it an attractive reagent for immune manipulation.

Stromal endothelial cells establish a bidirectional crosstalk with chronic lymphocytic leukemia cells through the TNF-related factors BAFF, APRIL, and CD40L.

Chronic lymphocytic leukemia (CLL) is a clonal B cell disorder of unknown origin. Accessory signals from the microenvironment are critical for the survival, expansion, and progression of malignant B cells. We found that the CLL stroma included microvascular endothelial cells (MVECs) expressing BAFF and APRIL, two TNF family members related to the T cell-associated B cell-stimulating molecule CD40L. Constitutive release of soluble BAFF and APRIL increased upon engagement of CD40 on MVECs by CD40L aberrantly expressed on CLL cells. In addition to enhancing MVEC expression of CD40, leukemic CD40L induced cleavases that elicited intracellular processing of pro-BAFF and pro-APRIL proteins in MVECs. The resulting soluble BAFF and APRIL proteins delivered survival, activation, Ig gene remodeling, and differentiation signals by stimulating CLL cells through TACI, BAFF-R, and BCMA receptors. BAFF and APRIL further amplified CLL cell survival by upregulating the expression of leukemic CD40L. Inhibition of TACI, BCMA, and BAFF-R expression on CLL cells; abrogation of CD40 expression in MVECs; or suppression of BAFF and APRIL cleavases in MVECs reduced the survival and diversification of malignant B cells. These data indicate that BAFF, APRIL, and CD40L form a CLL-enhancing bidirectional signaling network linking neoplastic B cells with the microvascular stroma.

New light on the HLA-DR immunopeptidomic landscape.

The set of peptides processed and presented by major histocompatibility complex class II molecules defines the immunopeptidome, and its characterization holds keys to understanding essential properties of the immune system. High-throughput mass spectrometry (MS) techniques enable interrogation of the diversity and complexity of the immunopeptidome at an unprecedented scale. Here, we analyzed a large set of MS immunopeptidomics data from 40 donors, 221 samples, covering 30 unique HLA-DR molecules. We identified likely co-immunoprecipitated HLA-DR irrelevant contaminants using state-of-the-art prediction methods and unveiled novel light on the properties of HLA antigen processing and presentation. The ligandome (HLA binders) was enriched in 15-mer peptides, and the contaminome (nonbinders) in longer peptides. Classification of singletons and nested sets showed that the first were enriched in contaminants. Investigating the source protein location of ligands revealed that only contaminants shared a positional bias. Regarding subcellular localization, nested peptides were found to be predominantly of endolysosomal origin, whereas singletons shared an equal distribution between the cytosolic and endolysosomal origin. According to antigen-processing signatures, no significant differences were observed between the cytosolic and endolysosomal ligands. Further, the sensitivity of MS immunopeptidomics was investigated by analyzing overlap and saturation between biological MS replicas, concluding that at least 5 replicas are needed to identify 80% of the immunopeptidome. Moreover, the overlap in immunopeptidome between donors was found to be very low both in terms of peptides and source proteins, the latter indicating a critical HLA bias in the antigen sampling in the HLA antigen presentation. Finally, the complementarity between MS and in silico approaches for comprehensively sampling the immunopeptidome was demonstrated.

In Silico Tools for Predicting Novel Epitopes.

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.

MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer.

Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue.

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition.

Background: Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition. Method: To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy. Results: We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity. Conclusion: Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.

NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction.

Motivation: Allergy is a pathological immune reaction towards innocuous protein antigens. Although only a narrow fraction of plant or animal proteins induce allergy, atopic disorders affect millions of children and adults and cost billions in healthcare systems worldwide. In silico predictors can aid in the development of more innocuous food sources. Previous allergenicity predictors used sequence similarity, common structural domains, and amino acid physicochemical features. However, these predictors strongly rely on sequence similarity to known allergens and fail to predict protein allergenicity accurately when similarity diminishes. Results: To overcome these limitations, we collected allergens from AllergenOnline, a curated database of IgE-inducing allergens, carefully removed allergen redundancy with a novel protein partitioning pipeline, and developed a new allergen prediction method, introducing MHC presentation propensity as a novel feature. NetAllergen outperformed a sequence similarity-based BLAST baseline approach, and previous allergenicity predictor AlgPred 2 when similarity to known allergens is limited. Availability and implementation: The web service NetAllergen and the datasets are available at https://services.healthtech.dtu.dk/services/NetAllergen-1.0/.

Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome.

Human leukocyte antigen (HLA) class II antigen presentation is key for controlling and triggering T cell immune responses. HLA-DQ molecules, which are believed to play a major role in autoimmune diseases, are heterodimers that can be formed as both cis and trans variants depending on whether the α- and ß-chains are encoded on the same (cis) or opposite (trans) chromosomes. So far, limited progress has been made for predicting HLA-DQ antigen presentation. In addition, the contribution of trans-only variants (i.e. variants not observed in the population as cis) in shaping the HLA-DQ immunopeptidome remains largely unresolved. Here, we seek to address these issues by integrating state-of-the-art immunoinformatics data mining models with large volumes of high-quality HLA-DQ specific mass spectrometry immunopeptidomics data. The analysis demonstrates highly improved predictive power and molecular coverage for models trained including these novel HLA-DQ data. More importantly, investigating the role of trans-only HLA-DQ variants reveals a limited to no contribution to the overall HLA-DQ immunopeptidome. In conclusion, this study furthers our understanding of HLA-DQ specificities and casts light on the relative role of cis versus trans-only HLA-DQ variants in the HLA class II antigen presentation space. The developed method, NetMHCIIpan-4.2, is available at https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.2 .

Neoepitope load, T cell signatures and PD-L2 as combined biomarker strategy for response to checkpoint inhibition immunotherapy.

Immune checkpoint inhibition for the treatment of cancer has provided a breakthrough in oncology, and several new checkpoint inhibition pathways are currently being investigated regarding their potential to provide additional clinical benefit. However, only a fraction of patients respond to such treatment modalities, and there is an urgent need to identify biomarkers to rationally select patients that will benefit from treatment. In this study, we explore different tumor associated characteristics for their association with favorable clinical outcome in a diverse cohort of cancer patients treated with checkpoint inhibitors. We studied 29 patients in a basket trial comprising 12 different tumor types, treated with 10 different checkpoint inhibition regimens. Our analysis revealed that even across this diverse cohort, patients achieving clinical benefit had significantly higher neoepitope load, higher expression of T cell signatures, and higher PD-L2 expression, which also correlated with improved progression-free and overall survival. Importantly, the combination of biomarkers serves as a better predictor than each of the biomarkers alone. Basket trials are frequently used in modern immunotherapy trial design, and here we identify a set of biomarkers of potential relevance across multiple cancer types, allowing for the selection of patients that most likely will benefit from immune checkpoint inhibition.

Accurate MHC Motif Deconvolution of Immunopeptidomics Data Reveals a Significant Contribution of DRB3, 4 and 5 to the Total DR Immunopeptidome.

Mass spectrometry (MS) based immunopeptidomics is used in several biomedical applications including neo-epitope discovery in oncology, next-generation vaccine development and protein-drug immunogenicity assessment. Immunopeptidome data are highly complex given the expression of multiple HLA alleles on the cell membrane and presence of co-immunoprecipitated contaminants. The absence of tools that deal with these challenges effectively and guide the analysis and interpretation of this complex type of data is currently a major bottleneck for the large-scale application of this technique. To resolve this, we here present the MHCMotifDecon that benefits from state-of-the-art HLA class-I and class-II predictions to accurately deconvolute immunopeptidome datasets and assign individual ligands to the most likely HLA molecule, allowing to identify and characterize HLA binding motifs while discarding co-purified contaminants. We have benchmarked the tool against other state-of-the-art methods and illustrated its application on experimental datasets for HLA-DR demonstrating a previously underappreciated role for HLA-DRB3/4/5 molecules in defining HLA class II immune repertoires. With its ease of use, MHCMotifDecon can efficiently guide interpretation of immunopeptidome datasets, serving the discovery of novel T cell targets. MHCMotifDecon is available at https://services.healthtech.dtu.dk/service.php?MHCMotifDecon-1.0.

Galacto-configured aminocyclitol phytoceramides are potent in vivo invariant natural killer T cell stimulators.

A new class of α-galactosylceramide (αGC) nonglycosidic analogues bearing galacto-configured aminocyclitols as sugar surrogates have been obtained. The aminocyclohexane having a hydroxyl substitution pattern similar to an α-galactoside is efficiently obtained by a sequence involving Evans aldol reaction and ring-closing metathesis with a Grubbs catalyst to give a key intermediate cyclohexene, which has been converted in galacto-aminocyclohexanes that are linked through a secondary amine to a phytoceramide lipid having a cerotyl N-acyl group. Natural Killer T (NKT) cellular assays have resulted in the identification of an active compound, HS161, which has been found to promote NKT cell expansion in vitro in a similar fashion but more weakly than αGC. This compound stimulates the release of Interferon-γ (IFNγ) and Interleukin-4 (IL-4) in iNKT cell culture but with lower potency than αGC. The activation of Invariant Natural Killer T (iNKT) cells by this compound has been confirmed in flow cytometry experiments. Remarkably, when tested in mice, HS161 selectively induces a very strong production of IFN-γ indicative of a potent Th1 cytokine profile. Overall, these data confirm the agonist activity of αGC lipid analogues having charged amino-substituted polar heads and their capacity to modulate the response arising from iNKT cell activation in vivo.

Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction.

Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adverse effects, including allergic reactions and compromised therapeutic efficacy. Production of ADAs is most often associated with activation of CD4 T cell responses resulting from proteolysis of the biotherapeutic and loading of drug-specific peptides into major histocompatibility complex (MHC) class II on professional antigen-presenting cells. Recently, readouts from MHC-associated peptide proteomics (MAPPs) assays have been shown to correlate with the presence of CD4 T cell epitopes. However, the limited sensitivity of MAPPs challenges its use as an immunogenicity biomarker. In this work, MAPPs data was used to construct an artificial neural network (ANN) model for MHC class II antigen presentation. Using Infliximab and Rituximab as showcase stories, the model demonstrated an unprecedented performance for predicting MAPPs and CD4 T cell epitopes in the context of protein-drug immunogenicity, complementing results from MAPPs assays and outperforming conventional prediction models trained on binding affinity data.