Social support of virtual characters reduces pain perception

BACKGROUND

Psychosocial factors, such as social support, can reduce pain. Virtual reality (VR) is a powerful tool to decrease pain, but social factors in VR-based pain analgesia have rarely been studied. Specifically, it is unclear whether social support by virtual characters can reduce pain and whether the perceived control behind virtual characters (agency) and varying degrees of social cues impact pain perception.

METHODS

Healthy participants (N = 97) received heat pain stimulation while undergoing four within-subject conditions in immersive VR: (1) virtual character with a low number of social cues (virtual figure) provided verbal support, (2) virtual character with a high number of social cues (virtual human) provided verbal support, (3) no social support (hearing neutral words), (4) no social support. Perceived agency of the virtual characters served as between-subjects factor. Participants in the avatar group were led to believe that another participant controlled the virtual characters. Participants in the agent group were told they interacted with a computer. However, in both conditions, virtual characters were computer-controlled. Pain ratings, psychophysiological measurements and presence ratings were recorded.

RESULTS

Virtual social support decreased pain intensity and pain unpleasantness ratings but had no impact on electrodermal activity nor heart rate. A virtual character with a high number of social cues led to lower pain unpleasantness and higher feelings of presence. Agency had no significant impact.

CONCLUSIONS

Virtual characters providing social support can reduce pain independent of perceived agency. A more human visual appearance can have beneficial effects on social pain modulation by virtual characters.

SIGNIFICANCE

Social influences are important factors in pain modulation. The current study demonstrated analgesic effects through verbal support provided by virtual characters and investigated modulating factors. A more human appearance of a virtual character resulted in a higher reduction of pain unpleasantness. Importantly, agency of the virtual characters had no impact. Given the increasing use of digital health interventions, the findings suggest a positive impact of virtual characters for digital pain treatments.

Semi-supervised integration of single-cell transcriptomics data

Batch effects in single-cell RNA-seq data pose a significant challenge for comparative analyses across samples, individuals, and conditions. Although batch effect correction methods are routinely applied, data integration often leads to overcorrection and can result in the loss of biological variability. In this work we present STACAS, a batch correction method for scRNA-seq that leverages prior knowledge on cell types to preserve biological variability upon integration. Through an open-source benchmark, we show that semi-supervised STACAS outperforms state-of-the-art unsupervised methods, as well as supervised methods such as scANVI and scGen. STACAS scales well to large datasets and is robust to incomplete and imprecise input cell type labels, which are commonly encountered in real-life integration tasks. We argue that the incorporation of prior cell type information should be a common practice in single-cell data integration, and we provide a flexible framework for semi-supervised batch effect correction.

IL-10-expressing CAR T cells resist dysfunction and mediate durable clearance of solid tumors and metastases

The success of chimeric antigen receptor (CAR) T cell therapy in treating several hematopoietic malignancies has been difficult to replicate in solid tumors, in part because of T cell exhaustion and eventually dysfunction. To counter T cell dysfunction in the tumor microenvironment, we metabolically armored CAR T cells by engineering them to secrete interleukin-10 (IL-10). We show that IL-10 CAR T cells preserve intact mitochondrial structure and function in the tumor microenvironment and increase oxidative phosphorylation in a mitochondrial pyruvate carrier-dependent manner. IL-10 secretion promoted proliferation and effector function of CAR T cells, leading to complete regression of established solid tumors and metastatic cancers across several cancer types in syngeneic and xenograft mouse models, including colon cancer, breast cancer, melanoma and pancreatic cancer. IL-10 CAR T cells also induced stem cell-like memory responses in lymphoid organs that imparted durable protection against tumor rechallenge. Our results establish a generalizable approach to counter CAR T cell dysfunction through metabolic armoring, leading to solid tumor eradication and long-lasting immune protection.

Activation of the transcription factor NFAT5 in the tumor microenvironment enforces CD8+ T cell exhaustion

Persistent exposure to antigen during chronic infection or cancer renders T cells dysfunctional. The molecular mechanisms regulating this state of exhaustion are thought to be common in infection and cancer, despite obvious differences in their microenvironments. Here we found that NFAT5, an NFAT family transcription factor that lacks an AP-1 docking site, was highly expressed in exhausted CD8+ T cells in the context of chronic infections and tumors but was selectively required in tumor-induced CD8+ T cell exhaustion. Overexpression of NFAT5 in CD8+ T cells reduced tumor control, while deletion of NFAT5 improved tumor control by promoting the accumulation of tumor-specific CD8+ T cells that had reduced expression of the exhaustion-associated proteins TOX and PD-1 and produced more cytokines, such as IFNɣ and TNF, than cells with wild-type levels of NFAT5, specifically in the precursor exhausted PD-1+TCF1+TIM-3-CD8+ T cell population. NFAT5 did not promote T cell exhaustion during chronic infection with clone 13 of lymphocytic choriomeningitis virus. Expression of NFAT5 was induced by TCR triggering, but its transcriptional activity was specific to the tumor microenvironment and required hyperosmolarity. Thus, NFAT5 promoted the exhaustion of CD8+ T cells in a tumor-selective fashion.

T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps

T cells are endowed with T-cell antigen receptors (TCR) that give them the capacity to recognize specific antigens and mount antigen-specific adaptive immune responses. Because TCR sequences are distinct in each naïve T cell, they serve as molecular barcodes to track T cells with clonal relatedness and shared antigen specificity through proliferation, differentiation, and migration. Single-cell RNA sequencing provides coupled information of TCR sequence and transcriptional state in individual cells, enabling T-cell clonotype-specific analyses. In this protocol, we outline a computational workflow to perform T-cell states and clonal analysis from scRNA-seq data based on the R packages Seurat, ProjecTILs, and scRepertoire. Given a scRNA-seq T-cell dataset with TCR sequence information, cell states are automatically annotated by reference projection using the ProjecTILs method. TCR information is used to track individual clonotypes, assess their clonal expansion, proliferation rates, bias towards specific differentiation states, and the clonal overlap between T-cell subtypes. We provide fully reproducible R code to conduct these analyses and generate useful visualizations that can be adapted for the needs of the protocol user. Key features Computational analysis of paired scRNA-seq and scTCR-seq data Characterizing T-cell functional state by reference-based analysis using ProjecTILs Exploring T-cell clonal structure using scRepertoire Linking T-cell clonality to transcriptomic state to study relationships between clonal expansion and functional phenotype Graphical overview.

A CSF-1R-blocking antibody/IL-10 fusion protein increases anti-tumor immunity by effectuating tumor-resident CD8+ T cells

Strategies to increase intratumoral concentrations of an anticancer agent are desirable to optimize its therapeutic potential when said agent is efficacious primarily within a tumor but also have significant systemic side effects. Here, we generate a bifunctional protein by fusing interleukin-10 (IL-10) to a colony-stimulating factor-1 receptor (CSF-1R)-blocking antibody. The fusion protein demonstrates significant antitumor activity in multiple cancer models, especially head and neck cancer. Moreover, this bifunctional protein not only leads to the anticipated reduction in tumor-associated macrophages but also triggers proliferation, activation, and metabolic reprogramming of CD8+ T cells. Furthermore, it extends the clonotype diversity of tumor-infiltrated T cells and shifts the tumor microenvironment (TME) to an immune-active state. This study suggests an efficient strategy for designing immunotherapeutic agents by fusing a potent immunostimulatory molecule to an antibody targeting TME-enriched factors.

Orthogonal cytokine engineering enables novel synthetic effector states escaping canonical exhaustion in tumor-rejecting CD8+ T cells

To date, no immunotherapy approaches have managed to fully overcome T-cell exhaustion, which remains a mandatory fate for chronically activated effector cells and a major therapeutic challenge. Understanding how to reprogram CD8+ tumor-infiltrating lymphocytes away from exhausted effector states remains an elusive goal. Our work provides evidence that orthogonal gene engineering of T cells to secrete an interleukin (IL)-2 variant binding the IL-2Rβγ receptor and the alarmin IL-33 reprogrammed adoptively transferred T cells to acquire a novel, synthetic effector state, which deviated from canonical exhaustion and displayed superior effector functions. These cells successfully overcame homeostatic barriers in the host and led-in the absence of lymphodepletion or exogenous cytokine support-to high levels of engraftment and tumor regression. Our work unlocks a new opportunity of rationally engineering synthetic CD8+ T-cell states endowed with the ability to avoid exhaustion and control advanced solid tumors.

A CD4+ T cell reference map delineates subtype-specific adaptation during acute and chronic viral infections

CD4⁺ T cells are critical orchestrators of immune responses against a large variety of pathogens, including viruses. While multiple CD4⁺ T cell subtypes and their key transcriptional regulators have been identified, there is a lack of consistent definition for CD4⁺ T cell transcriptional states. In addition, the progressive changes affecting CD4⁺ T cell subtypes during and after immune responses remain poorly defined. Using single-cell transcriptomics, we characterized the diversity of CD4⁺ T cells responding to self-resolving and chronic viral infections in mice. We built a comprehensive map of virus-specific CD4⁺ T cells and their evolution over time, and identified six major cell states consistently observed in acute and chronic infections. During the course of acute infections, T cell composition progressively changed from effector to memory states, with subtype-specific gene modules and kinetics. Conversely, in persistent infections T cells acquired distinct, chronicity-associated programs. By single-cell T cell receptor (TCR) analysis, we characterized the clonal structure of virus-specific CD4⁺ T cells across individuals. Virus-specific CD4⁺ T cell responses were essentially private across individuals and most T cells differentiated into both Tfh and Th1 subtypes irrespective of their TCR. Finally, we showed that our CD4⁺ T cell map can be used as a reference to accurately interpret cell states in external single-cell datasets across tissues and disease models. Overall, this study describes a previously unappreciated level of adaptation of the transcriptional states of CD4⁺ T cells responding to viruses and provides a new computational resource for CD4⁺ T cell analysis.

Dissecting the treatment-naive ecosystem of human melanoma brain metastasis

Melanoma brain metastasis (MBM) frequently occurs in patients with advanced melanoma; yet, our understanding of the underlying salient biology is rudimentary. Here, we performed single-cell/nucleus RNA-seq in 22 treatment-naive MBMs and 10 extracranial melanoma metastases (ECMs) and matched spatial single-cell transcriptomics and T cell receptor (TCR)-seq. Cancer cells from MBM were more chromosomally unstable, adopted a neuronal-like cell state, and enriched for spatially variably expressed metabolic pathways. Key observations were validated in independent patient cohorts, patient-derived MBM/ECM xenograft models, RNA/ATAC-seq, proteomics, and multiplexed imaging. Integrated spatial analyses revealed distinct geography of putative cancer immune evasion and evidence for more abundant intra-tumoral B to plasma cell differentiation in lymphoid aggregates in MBM. MBM harbored larger fractions of monocyte-derived macrophages and dysfunctional TOX+CD8+ T cells with distinct expression of immune checkpoints. This work provides comprehensive insights into MBM biology and serves as a foundational resource for further discovery and therapeutic exploration.

Abstract 984: Dissecting the ecosystem of treatment-naïve melanoma brain metastasis using multi-modal single-cell analysis

Brain metastases are the most frequent malignancies in the brain and are associated with significant morbidity and mortality. Melanoma brain metastases (MBM) occur in most patients with advanced melanoma and are challenging to treat. Our understanding of the treatment-naïve landscape of MBM is still rudimentary, and there are no site-specific molecular therapies available. To gain comprehensive insights into the niche-specific biology of MBM, we performed multi-modal profiling of fresh and frozen samples using single-cell RNA-seq, single-cell TCR-seq, single-nuclei RNA-seq, and spatial transcriptional profiling. We evolved single-nucleus RNA-seq processing methods to enable profiling of minute amounts of archival, frozen specimens and compared data quality and structure between matched fresh and frozen MBM. We curated a treatment-naïve single-transcriptome atlas of MBM, collected either fresh samples over 1 year or profiled frozen samples dating back more than 15 years, and compared these samples to extracranial melanoma metastases (ECMM). In total, we profiled 25 samples with more than 114,000 transcriptomes. We identified more than 20 different cell types, including diverse tumor-infiltrating T-cell subsets and rare dendritic cell types, and tissue-specific cell types, such as activated microglia. Tumor cells in MBM showed an increase in copy number alterations (CNAs) compared to ECMM, which we validated using an external dataset of whole exome sequencing (WES) data including both MBM and ECMM. MBM-derived tumor cells show enrichment of genes involved in neuronal development and function, and site-specific metabolic programs (e.g., oxidative phosphorylation). Comparison with an external bulk RNA-seq dataset validated enriched key genes in MBM and ECMM as putative dependencies. We recovered cell-cell interactions between tumor and brain-resident cells involved in brain development, homeostasis, and disease. Similar to ECMM, the tumor microenvironment of MBM contained CD8+ T cells across a spectrum of differentiation, exhaustion and expansion, which was associated with loss of TCF7 expression and adoption of a TOX+ cell state. CD4+ T cells included T regulatory, T helper and T follicular-helper-like expression profiles. Plasma cells showed spatially localized expansion and limited heterogeneity. Myeloid cells largely adopted pro-tumorigenic cell states, including microglia, the brain-resident myeloid cells, which showed an activation trajectory characterized by expression of SPP1 (osteopontin). Spatial transcriptional analysis revealed restricted expression of antigen presentation genes with only a subset of these locations showing a type I interferon response. In summary, this work presents a multi-modal single-cell approach to dissect and compare the landscape of treatment-naïve MBM and ECMM.

Citation Format: Johannes C. Melms, Jana Biermann, Amit Dipak Amin, Yiping Wang, Somnath Tagore, Massimo Andreatta, Ajay Nair, Meri Rogava, Patricia Ho, Lindsay A. Caprio, Zachary H. Walsh, Shivem Shah, Daniel H. Vacarro, Blake Caldwell, Adrienne M. Luoma, Joseph Driver, Matthew Ingham, Suthee Rapisuwon, Jennifer Wargo, Craig L. Slinguff, Evan Z. Macosco, Fei Chen, Richard Carvajal, Michael B. Atkins, Michael A. Davies, Elham Azizi, Santiago J. Carmona, Hanina Hibshoosh, Peter D. Canoll, Jeffrey N. Bruce, Wenya L. Bi, Gary K. Schwartz, Benjamin Izar. Dissecting the ecosystem of treatment-naïve melanoma brain metastasis using multi-modal single-cell analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 984.

scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets

Summary

A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. Here, we present scGate, an algorithm that automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. scGate purifies a cell population of interest using a set of markers organized in a hierarchical structure, akin to gating strategies employed in flow cytometry. scGate outperforms state-of-the-art single-cell classifiers and it can be applied to multiple modalities of single-cell data (e.g. RNA-seq, ATAC-seq, CITE-seq). scGate is implemented as an R package and integrated with the Seurat framework, providing an intuitive tool to isolate cell populations of interest from heterogeneous single-cell datasets.

Availability and implementation

scGate is available as an R package at https://github.com/carmonalab/scGate (https://doi.org/10.5281/zenodo.6202614). Several reproducible workflows describing the main functions and usage of the package on different single-cell modalities, as well as the code to reproduce the benchmark, can be found at https://github.com/carmonalab/scGate.demo (https://doi.org/10.5281/zenodo.6202585) and https://github.com/carmonalab/scGate.benchmark. Test data are available at https://doi.org/10.6084/m9.figshare.16826071.

Supplementary information

Supplementary data are available at Bioinformatics online.

Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy

Developing strategies to inflame tumors is critical for increasing response to immunotherapy. Here, we report that low-dose radiotherapy (LDRT) of murine tumors promotes T-cell infiltration and enables responsiveness to combinatorial immunotherapy in an IFN-dependent manner. Treatment efficacy relied upon mobilizing both adaptive and innate immunity and depended on both cytotoxic CD4⁺ and CD8⁺ T cells. LDRT elicited predominantly CD4⁺ cells with features of exhausted effector cytotoxic cells, with a subset expressing NKG2D and exhibiting proliferative capacity, as well as a unique subset of activated dendritic cells expressing the NKG2D ligand RAE1. We translated these findings to a phase I clinical trial administering LDRT, low-dose cyclophosphamide, and immune checkpoint blockade to patients with immune-desert tumors. In responsive patients, the combinatorial treatment triggered T-cell infiltration, predominantly of CD4⁺ cells with Th1 signatures. Our data support the rational combination of LDRT with immunotherapy for effectively treating low T cell-infiltrated tumors. SIGNIFICANCE: Low-dose radiation reprogrammed the tumor microenvironment of tumors with scarce immune infiltration and together with immunotherapy induced simultaneous mobilization of innate and adaptive immunity, predominantly CD4⁺ effector T cells, to achieve tumor control dependent on NKG2D. The combination induced important responses in patients with metastatic immune-cold tumors.This article is highlighted in the In This Issue feature, p. 1.

SPICA: Swiss portal for immune cell analysis

Single-cell transcriptomics allows the study of immune cell heterogeneity at an unprecedented level of resolution. The Swiss portal for immune cell analysis (SPICA) is a web resource dedicated to the exploration and analysis of single-cell RNA-seq data of immune cells. In contrast to other single-cell databases, SPICA hosts curated, cell type-specific reference atlases that describe immune cell states at high resolution, and published single-cell datasets analysed in the context of these atlases. Additionally, users can privately analyse their own data in the context of existing atlases and contribute to the SPICA database. SPICA is available at https://spica.unil.ch.

UCell: Robust and scalable single-cell gene signature scoring

UCell is an R package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with Seurat objects. The UCell package and documentation are available on GitHub at https://github.com/carmonalab/UCell.

STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

Summary

STACAS is a computational method for the identification of integration anchors in the Seurat environment, optimized for the integration of single-cell (sc) RNA-seq datasets that share only a subset of cell types. We demonstrate that by (i) correcting batch effects while preserving relevant biological variability across datasets, (ii) filtering aberrant integration anchors with a quantitative distance measure and (iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations.

Availability and implementation

Source code and R package available at https://github.com/carmonalab/STACAS; Docker image available at https://hub.docker.com/repository/docker/mandrea1/stacas_demo.

Interpretation of T cell states from single-cell transcriptomics data using reference atlases

Single-cell RNA sequencing (scRNA-seq) has revealed an unprecedented degree of immune cell diversity. However, consistent definition of cell subtypes and cell states across studies and diseases remains a major challenge. Here we generate reference T cell atlases for cancer and viral infection by multi-study integration, and develop ProjecTILs, an algorithm for reference atlas projection. In contrast to other methods, ProjecTILs allows not only accurate embedding of new scRNA-seq data into a reference without altering its structure, but also characterizing previously unknown cell states that "deviate" from the reference. ProjecTILs accurately predicts the effects of cell perturbations and identifies gene programs that are altered in different conditions and tissues. A meta-analysis of tumor-infiltrating T cells from several cohorts reveals a strong conservation of T cell subtypes between human and mouse, providing a consistent basis to describe T cell heterogeneity across studies, diseases, and species.

Immunoinformatics: Predicting Peptide-MHC Binding

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.

IEDB-AR: immune epitope database-analysis resource in 2019

The Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis.

The Influence of Methylphenidate on Hyperactivity and Attention Deficits in Children With ADHD: A Virtual Classroom Test

Objective: This study compares the performance in a continuous performance test within a virtual reality classroom (CPT-VRC) between medicated children with ADHD, unmedicated children with ADHD, and healthy children. Method:N = 94 children with ADHD (n = 26 of them received methylphenidate and n = 68 were unmedicated) and n = 34 healthy children performed the CPT-VRC. Omission errors, reaction time/variability, commission errors, and body movements were assessed. Furthermore, ADHD questionnaires were administered and compared with the CPT-VRC measures. Results: The unmedicated ADHD group exhibited more omission errors and showed slower reaction times than the healthy group. Reaction time variability was higher in the unmedicated ADHD group compared with both the healthy and the medicated ADHD group. Omission errors and reaction time variability were associated with inattentiveness ratings of experimenters. Head movements were correlated with hyperactivity ratings of parents and experimenters. Conclusion: Virtual reality is a promising technology to assess ADHD symptoms in an ecologically valid environment.

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.

NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions

Peptides are extensively used to characterize functional or (linear) structural aspects of receptor–ligand interactions in biological systems, e.g. SH2, SH3, PDZ peptide-recognition domains, the MHC membrane receptors and enzymes such as kinases and phosphatases. NNAlign is a method for the identification of such linear motifs in biological sequences. The algorithm aligns the amino acid or nucleotide sequences provided as training set, and generates a model of the sequence motif detected in the data. The webserver allows setting up cross-validation experiments to estimate the performance of the model, as well as evaluations on independent data. Many features of the training sequences can be encoded as input, and the network architecture is highly customizable. The results returned by the server include a graphical representation of the motif identified by the method, performance values and a downloadable model that can be applied to scan protein sequences for occurrence of the motif. While its performance for the characterization of peptide–MHC interactions is widely documented, we extended NNAlign to be applicable to other receptor–ligand systems as well. Version 2.0 supports alignments with insertions and deletions, encoding of receptor pseudo-sequences, and custom alphabets for the training sequences. The server is available at http://www.cbs.dtu.dk/services/NNAlign-2.0.

GibbsCluster: unsupervised clustering and alignment of peptide sequences

Receptor interactions with short linear peptide fragments (ligands) are at the base of many biological signaling processes. Conserved and information-rich amino acid patterns, commonly called sequence motifs, shape and regulate these interactions. Because of the properties of a receptor-ligand system or of the assay used to interrogate it, experimental data often contain multiple sequence motifs. GibbsCluster is a powerful tool for unsupervised motif discovery because it can simultaneously cluster and align peptide data. The GibbsCluster 2.0 presented here is an improved version incorporating insertion and deletions accounting for variations in motif length in the peptide input. In basic terms, the program takes as input a set of peptide sequences and clusters them into meaningful groups. It returns the optimal number of clusters it identified, together with the sequence alignment and sequence motif characterizing each cluster. Several parameters are available to customize cluster analysis, including adjustable penalties for small clusters and overlapping groups and a trash cluster to remove outliers. As an example application, we used the server to deconvolute multiple specificities in large-scale peptidome data generated by mass spectrometry. The server is available at http://www.cbs.dtu.dk/services/GibbsCluster-2.0.

A pan-allele Class I MHC prediction tool in mice enables tumor immunology studies

An understanding of tumor-presented Class I MHC peptides is a prerequisite to effective cancer immunotherapy, and more tools are needed to elucidate immune interactions in breast cancer. One tool, the mouse mammary tumor virus (MMTV)-Polyoma Middle T Antigen (PyMT) mouse, models the development of spontaneous metastatic breast cancer in vivo. Surprisingly, little data exists for the MHCI of MMTV-PyMT tumors, which express the H2-q haplotype. To develop the MMTV-PyMT tumor model for tumor immunology, we present an open-access Class I MHC peptide prediction tool for multiple murine haplotypes (including H2-q): NetH2pan (http://www.cbs.dtu.dk/services//NetH2pan/). H2-d/k/b peptides were obtained from the IEDB, while H2-q peptides were eluted from MHC: HeLa and 721.221 cell lines transfected with PyMT and soluble H2-Kq/Dq. Cells secreting these MHC I were expanded, peptide-bearing MHC I affinity purified, and presented peptides analyzed by mass spectrometry. The identified peptide sequence data were used to develop a predictive algorithm to enable in silico candidate peptide searches. Validation of the algorithm using peptides eluted from tumors demonstrated a positive predictive value (34%) improved from other MHC I prediction tools. For a candidate antigen PyMT, the 5 PyMT-peptides eluted from tumors were in the top 25 predicted. Three of 5 peptides were immunogenic as determined by IFNγ ELISpot and peptide:MHCI tetramers in vaccinated mice, and correlated with tumor prevention. These data show that NetH2pan can predict immunogenic epitopes with high-fidelity and that these peptides can be utilized for vaccine development. These studies enable the MMTV-PyMT model for mechanistic studies involving tumor-T cell crosstalk.

Improved methods for predicting peptide binding affinity to MHC class II molecules

Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.

Predicting HLA CD4 Immunogenicity in Human Populations

Background

Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides.

Methods

Sets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an “immunogenicity score.” We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level.

Results

The immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore).

Conclusion

The new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification.

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.

An automated benchmarking platform for MHC class II binding prediction methods

Motivation

Computational methods for the prediction of peptide-MHC binding have become an integral and essential component for candidate selection in experimental T cell epitope discovery studies. The sheer amount of published prediction methods-and often discordant reports on their performance-poses a considerable quandary to the experimentalist who needs to choose the best tool for their research.

Results

With the goal to provide an unbiased, transparent evaluation of the state-of-the-art in the field, we created an automated platform to benchmark peptide-MHC class II binding prediction tools. The platform evaluates the absolute and relative predictive performance of all participating tools on data newly entered into the Immune Epitope Database (IEDB) before they are made public, thereby providing a frequent, unbiased assessment of available prediction tools. The benchmark runs on a weekly basis, is fully automated, and displays up-to-date results on a publicly accessible website. The initial benchmark described here included six commonly used prediction servers, but other tools are encouraged to join with a simple sign-up procedure. Performance evaluation on 59 data sets composed of over 10 000 binding affinity measurements suggested that NetMHCIIpan is currently the most accurate tool, followed by NN-align and the IEDB consensus method.

Availability and implementation

Weekly reports on the participating methods can be found online at: http://tools.iedb.org/auto_bench/mhcii/weekly/.

Contact

mniel@bioinformatics.dtu.dk.

Supplementary information

Supplementary data are available at Bioinformatics online.

NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine Tumors

With the advancement of personalized cancer immunotherapies, new tools are needed to identify tumor antigens and evaluate T-cell responses in model systems, specifically those that exhibit clinically relevant tumor progression. Key transgenic mouse models of breast cancer are generated and maintained on the FVB genetic background, and one such model is the mouse mammary tumor virus-polyomavirus middle T antigen (MMTV-PyMT) mouse-an immunocompetent transgenic mouse that exhibits spontaneous mammary tumor development and metastasis with high penetrance. Backcrossing the MMTV-PyMT mouse from the FVB strain onto a C57BL/6 genetic background, in order to leverage well-developed C57BL/6 immunologic tools, results in delayed tumor development and variable metastatic phenotypes. Therefore, we initiated characterization of the FVB MHC class I H-2^q haplotype to establish useful immunologic tools for evaluating antigen specificity in the murine FVB strain. Our study provides the first detailed molecular and immunoproteomic characterization of the FVB H-2^q MHC class I alleles, including >8,500 unique peptide ligands, a multiallele murine MHC peptide prediction tool, and in vivo validation of these data using MMTV-PyMT primary tumors. This work allows researchers to rapidly predict H-2 peptide ligands for immune testing, including, but not limited to, the MMTV-PyMT model for metastatic breast cancer. Cancer Immunol Res; 6(6); 636-44. ©2018 AACR.

NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.

Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules

MHC class II molecules play a fundamental role in the cellular immune system: they load short peptide fragments derived from extracellular proteins and present them on the cell surface. It is currently thought that the peptide binds lying more or less flat in the MHC groove, with a fixed distance of nine amino acids between the first and last residue in contact with the MHCII. While confirming that the great majority of peptides bind to the MHC using this canonical mode, we report evidence for an alternative, less common mode of interaction. A fraction of observed ligands were shown to have an unconventional spacing of the anchor residues that directly interact with the MHC, which could only be accommodated to the canonical MHC motif either by imposing a more stretched out peptide backbone (an 8mer core) or by the peptide bulging out of the MHC groove (a 10mer core). We estimated that on average 2% of peptides bind with a core deletion, and 0·45% with a core insertion, but the frequency of such non-canonical cores was as high as 10% for certain MHCII molecules. A mutational analysis and experimental validation of a number of these anomalous ligands demonstrated that they could only fit to their MHC binding motif with a non-canonical binding core of length different from nine. This previously undescribed mode of peptide binding to MHCII molecules gives a more complete picture of peptide presentation by MHCII and allows us to model more accurately this event.

Unconventional Peptide Presentation by Major Histocompatibility Complex (MHC) Class I Allele HLA-A*02:01: BREAKING CONFINEMENT

Peptide antigen presentation by major histocompatibility complex (MHC) class I proteins initiates CD8⁺ T cell-mediated immunity against pathogens and cancers. MHC I molecules typically bind peptides with 9 amino acids in length with both ends tucked inside the major A and F binding pockets. It has been known for a while that longer peptides can also bind by either bulging out of the groove in the middle of the peptide or by binding in a zigzag fashion inside the groove. In a recent study, we identified an alternative binding conformation of naturally occurring peptides from Toxoplasma gondii bound by HLA-A*02:01. These peptides were extended at the C terminus (PΩ) and contained charged amino acids not more than 3 residues after the anchor amino acid at PΩ, which enabled them to open the F pocket and expose their C-terminal extension into the solvent. Here, we show that the mechanism of F pocket opening is dictated by the charge of the first charged amino acid found within the extension. Although positively charged amino acids result in the Tyr-84 swing, amino acids that are negatively charged induce a not previously described Lys-146 lift. Furthermore, we demonstrate that the peptides with alternative binding modes have properties that fit very poorly to the conventional MHC class I pathway and suggest they are presented via alternative means, potentially including cross-presentation via the MHC class II pathway.

NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets

Background

Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells.

Results

Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space.

Conclusions

We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0288-x) contains supplementary material, which is available to authorized users.

Gapped sequence alignment using artificial neural networks: application to the MHC class I system

MOTIVATION

Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment.

RESULTS

We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm.

AVAILABILITY AND IMPLEMENTATION

The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped sequence alignment is publicly available at: http://www.cbs.dtu.dk/services/NetMHC-4.0.

Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification

A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

Quantifying Significance of MHC II Residues

The major histocompatibility complex (MHC), a cell-surface protein mediating immune recognition, plays important roles in the immune response system of all higher vertebrates. MHC molecules are highly polymorphic and they are grouped into serotypes according to the specificity of the response. It is a common belief that a protein sequence determines its three dimensional structure and function. Hence, the protein sequence determines the serotype. Residues play different levels of importance. In this paper, we quantify the residue significance with the available serotype information. Knowing the significance of the residues will deepen our understanding of the MHC molecules and yield us a concise representation of the molecules. In this paper we propose a linear programming-based approach to find significant residue positions as well as quantifying their significance in MHC II DR molecules. Among all the residues in MHC II DR molecules, 18 positions are of particular significance, which is consistent with the literature on MHC binding sites, and succinct pseudo-sequences appear to be adequate to capture the whole sequence features. When the result is used for classification of MHC molecules with serotype assigned by WHO, a 98.4 percent prediction performance is achieved. The methods have been implemented in java (http://code.google.com/p/quassi/).

Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach

MOTIVATION

Proteins recognizing short peptide fragments play a central role in cellular signaling. As a result of high-throughput technologies, peptide-binding protein specificities can be studied using large peptide libraries at dramatically lower cost and time. Interpretation of such large peptide datasets, however, is a complex task, especially when the data contain multiple receptor binding motifs, and/or the motifs are found at different locations within distinct peptides.

RESULTS

The algorithm presented in this article, based on Gibbs sampling, identifies multiple specificities in peptide data by performing two essential tasks simultaneously: alignment and clustering of peptide data. We apply the method to de-convolute binding motifs in a panel of peptide datasets with different degrees of complexity spanning from the simplest case of pre-aligned fixed-length peptides to cases of unaligned peptide datasets of variable length. Example applications described in this article include mixtures of binders to different MHC class I and class II alleles, distinct classes of ligands for SH3 domains and sub-specificities of the HLA-A*02:01 molecule.

AVAILABILITY

The Gibbs clustering method is available online as a web server at http://www.cbs.dtu.dk/services/GibbsCluster.

Characterizing the binding motifs of 11 common human HLA-DP and HLA-DQ molecules using NNAlign

Compared with HLA-DR molecules, the specificities of HLA-DP and HLA-DQ molecules have only been studied to a limited extent. The description of the binding motifs has been mostly anecdotal and does not provide a quantitative measure of the importance of each position in the binding core and the relative weight of different amino acids at a given position. The recent publication of larger data sets of peptide-binding to DP and DQ molecules opens the possibility of using data-driven bioinformatics methods to accurately define the binding motifs of these molecules. Using the neural network-based method NNAlign, we characterized the binding specificities of five HLA-DP and six HLA-DQ among the most frequent in the human population. The identified binding motifs showed an overall concurrence with earlier studies but revealed subtle differences. The DP molecules revealed a large overlap in the pattern of amino acid preferences at core positions, with conserved hydrophobic/aromatic anchors at P1 and P6, and an additional hydrophobic anchor at P9 in some variants. These results confirm the existence of a previously hypothesized supertype encompassing the most common DP alleles. Conversely, the binding motifs for DQ molecules appear more divergent, displaying unconventional anchor positions and in some cases rather unspecific amino acid preferences.

NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data

Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new “omics”-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points.

NNAlign is available online at http://www.cbs.dtu.dk/services/NNAlign.

Using electronic patient records to discover disease correlations and stratify patient cohorts

Electronic patient records remain a rather unexplored, but potentially rich data source for discovering correlations between diseases. We describe a general approach for gathering phenotypic descriptions of patients from medical records in a systematic and non-cohort dependent manner. By extracting phenotype information from the free-text in such records we demonstrate that we can extend the information contained in the structured record data, and use it for producing fine-grained patient stratification and disease co-occurrence statistics. The approach uses a dictionary based on the International Classification of Disease ontology and is therefore in principle language independent. As a use case we show how records from a Danish psychiatric hospital lead to the identification of disease correlations, which subsequently can be mapped to systems biology frameworks.

Text mining and information extraction can be seen as the challenge of converting information hidden in text into manageable data. We have used text mining to automatically extract clinically relevant terms from 5543 psychiatric patient records and map these to disease codes in the International Classification of Disease ontology (ICD10). Mined codes were supplemented by existing coded data. For each patient we constructed a phenotypic profile of associated ICD10 codes. This allowed us to cluster patients together based on the similarity of their profiles. The result is a patient stratification based on more complete profiles than the primary diagnosis, which is typically used. Similarly we investigated comorbidities by looking for pairs of disease codes cooccuring in patients more often than expected. Our high ranking pairs were manually curated by a medical doctor who flagged 93 candidates as interesting. For a number of these we were able to find genes/proteins known to be associated with the diseases using the OMIM database. The disease-associated proteins allowed us to construct protein networks suspected to be involved in each of the phenotypes. Shared proteins between two associated diseases might provide insight to the disease comorbidity.

In silico prediction of human pathogenicity in the γ-proteobacteria

BACKGROUND

Although the majority of bacteria are innocuous or even beneficial for their host, others are highly infectious pathogens that can cause widespread and deadly diseases. When investigating the relationships between bacteria and other living organisms, it is therefore essential to be able to separate pathogenic organisms from non-pathogenic ones. Using traditional experimental methods for this purpose can be very costly and time-consuming, and also uncertain since animal models are not always good predictors for pathogenicity in humans. Bioinformatics-based methods are therefore strongly needed to mine the fast growing number of genome sequences and assess in a rapid and reliable way the pathogenicity of novel bacteria.

METHODOLOGY/PRINCIPAL FINDINGS

We describe a new in silico method for the prediction of bacterial pathogenicity, based on the identification in microbial genomes of features that appear to correlate with virulence. The method does not rely on identifying genes known to be involved in pathogenicity (for instance virulence factors), but rather it inherently builds families of proteins that, irrespective of their function, are consistently present in only one of the two kinds of organisms, pathogens or non-pathogens. Whether a new bacterium carries proteins contained in these families determines its prediction as pathogenic or non-pathogenic. The application of the method on a set of known genomes correctly classified the virulence potential of 86% of the organisms tested. An additional validation on an independent test-set assigned correctly 22 out of 24 bacteria.

CONCLUSIONS

The proposed approach was demonstrated to go beyond the species bias imposed by evolutionary relatedness, and performs better than predictors based solely on taxonomy or sequence similarity. A set of protein families that differentiate pathogenic and non-pathogenic strains were identified, including families of yet uncharacterized proteins that are suggested to be involved in bacterial pathogenicity.

[Evaluation of bone age in patients with kwashiorkor]

The present work is the result of a multicentric study performed at Hoima and Kitgum Hospitals in Uganda on a group of 20 children with Kwashiorkor. Bone age was evaluated on the X-ray film of the children's left hand and wrist, according to Tanner and to De Roo, by 2 different evaluators. The final results were compared. Eighty-five % of the patients presented delayed bone maturation: such a result confirms the importance of malnutrition on skeletal development. The data reported by other authors are also discussed.