Persistent T cell unresponsiveness associated with chronic visceral leishmaniasis in HIV-coinfected patients.

A large proportion of HIV-coinfected visceral leishmaniasis (VL-HIV) patients exhibit chronic disease with frequent VL recurrence. However, knowledge on immunological determinants underlying the disease course is scarce. We longitudinally profiled the circulatory cellular immunity of an Ethiopian HIV cohort that included VL developers. We show that chronic VL-HIV patients exhibit high and persistent levels of TIGIT and PD-1 on CD8+/CD8- T cells, in addition to a lower frequency of IFN-γ+ TIGIT- CD8+/CD8- T cells, suggestive of impaired T cell functionality. At single T cell transcriptome and clonal resolution, the patients show CD4+ T cell anergy, characterised by a lack of T cell activation and lymphoproliferative response. These findings suggest that PD-1 and TIGIT play a pivotal role in VL-HIV chronicity, and may be further explored for patient risk stratification. Our findings provide a strong rationale for adjunctive immunotherapy for the treatment of chronic VL-HIV patients to break the recurrent disease cycle.

Identification of TCR repertoire patterns linked with anti-cancer immunotherapy.

The highly diverse T cell receptor (TCR) repertoire is a crucial component of the adaptive immune system that aids in the protection against a wide variety of pathogens. This TCR repertoire, comprising the collection of all TCRs in an individual, is a valuable source of information on both recent and ongoing T cell activation. Cancer cells, like pathogens, have the ability to trigger an adaptive immune response. However, because cancer cells use a variety of strategies to escape immune responses, this is often insufficient to completely eradicate them. As a result, immunotherapy is a promising treatment option for cancer patients. This treatment is expected to increase T cell activation and subsequently alter the TCR repertoire composition in these patients. Monitoring TCR repertoires before and after immunotherapy can therefore provide additional insight into T cell responses and might identify cancer-associated TCR sequences. Here we present a computational strategy to identify those changes in the TCR repertoire that occur after treatment with immunotherapy. Since this method allows the identification of TCR patterns that might be treatment-associated, it can help future research by revealing those patterns that are related with response. This TCR analysis workflow is illustrated using public data from three different cancer patients who received anti-PD-1 treatment.

Training of epitope-TCR prediction models with healthy donor-derived cancer-specific T cells.

Discovery of epitope-specific T-cell receptors (TCRs) for cancer therapies is a time consuming and expensive procedure that usually requires a large amount of patient cells. To maximize information from and minimize the need of precious samples in cancer research, prediction models have been developed to identify in silico epitope-specific TCRs. In this chapter, we provide a step-by-step protocol to train a prediction model using the user-friendly TCRex webtool for the nearly universal tumor-associated antigen Wilms' tumor 1 (WT1)-specific TCR repertoire. WT1 is a self-antigen overexpressed in numerous solid and hematological malignancies with a high clinical relevance. Training of computational models starts from a list of known epitope-specific TCRs which is often not available for new cancer epitopes. Therefore, we describe a workflow to assemble a training data set consisting of TCR sequences obtained from WT137-45-reactive CD8 T cell clones expanded and sorted from healthy donor peripheral blood mononuclear cells.

Lack of strong innate immune reactivity renders macrophages alone unable to control productive Varicella-Zoster Virus infection in an isogenic human iPSC-derived neuronal co-culture model.

With Varicella-Zoster Virus (VZV) being an exclusive human pathogen, human induced pluripotent stem cell (hiPSC)-derived neural cell culture models are an emerging tool to investigate VZV neuro-immune interactions. Using a compartmentalized hiPSC-derived neuronal model allowing axonal VZV infection, we previously demonstrated that paracrine interferon (IFN)-α2 signalling is required to activate a broad spectrum of interferon-stimulated genes able to counteract a productive VZV infection in hiPSC-neurons. In this new study, we now investigated whether innate immune signalling by VZV-challenged macrophages was able to orchestrate an antiviral immune response in VZV-infected hiPSC-neurons. In order to establish an isogenic hiPSC-neuron/hiPSC-macrophage co-culture model, hiPSC-macrophages were generated and characterised for phenotype, gene expression, cytokine production and phagocytic capacity. Even though immunological competence of hiPSC-macrophages was shown following stimulation with the poly(dA:dT) or treatment with IFN-α2, hiPSC-macrophages in co-culture with VZV-infected hiPSC-neurons were unable to mount an antiviral immune response capable of suppressing a productive neuronal VZV infection. Subsequently, a comprehensive RNA-Seq analysis confirmed the lack of strong immune responsiveness by hiPSC-neurons and hiPSC-macrophages upon, respectively, VZV infection or challenge. This may suggest the need of other cell types, like T-cells or other innate immune cells, to (co-)orchestrate an efficient antiviral immune response against VZV-infected neurons.

A global genomic analysis of Salmonella Concord reveals lineages with high antimicrobial resistance in Ethiopia.

Antimicrobial resistant Salmonella enterica serovar Concord (S. Concord) is known to cause severe gastrointestinal and bloodstream infections in patients from Ethiopia and Ethiopian adoptees, and occasional records exist of S. Concord linked to other countries. The evolution and geographical distribution of S. Concord remained unclear. Here, we provide a genomic overview of the population structure and antimicrobial resistance (AMR) of S. Concord by analysing genomes from 284 historical and contemporary isolates obtained between 1944 and 2022 across the globe. We demonstrate that S. Concord is a polyphyletic serovar distributed among three Salmonella super-lineages. Super-lineage A is composed of eight S. Concord lineages, of which four are associated with multiple countries and low levels of AMR. Other lineages are restricted to Ethiopia and horizontally acquired resistance to most antimicrobials used for treating invasive Salmonella infections in low- and middle-income countries. By reconstructing complete genomes for 10 representative strains, we demonstrate the presence of AMR markers integrated in structurally diverse IncHI2 and IncA/C2 plasmids, and/or the chromosome. Molecular surveillance of pathogens such as S. Concord supports the understanding of AMR and the multi-sector response to the global AMR threat. This study provides a comprehensive baseline data set essential for future molecular surveillance.

Viral Evolution and Immunology of SARS-CoV-2 in a Persistent Infection after Treatment with Rituximab.

BACKGROUND: Prolonged shedding of SARS-CoV-2 in immunocompromised patients has been described. Furthermore, an accumulation of mutations of the SARS-CoV-2 genome in these patients has been observed. METHODS: We describe the viral evolution, immunologic response and clinical course of a patient with a lymphoma in complete remission who had received therapy with rituximab and remained SARS-CoV-2 RT-qPCR positive for 161 days. RESULTS: The patient remained hospitalised for 10 days, after which he fully recovered and remained asymptomatic. A progressive increase in Ct-value, coinciding with a progressive rise in lymphocyte count, was seen from day 137 onward. Culture of a nasopharyngeal swab on day 67 showed growth of SARS-CoV-2. Whole genome sequencing (WGS) demonstrated that the virus belonged to the wildtype SARS-CoV-2 clade 20B/GR, but rapidly accumulated a high number of mutations as well as deletions in the N-terminal domain of its spike protein. CONCLUSION: SARS-CoV-2 persistence in immunocompromised individuals has important clinical implications, but halting immunosuppressive therapy might result in a favourable clinical course. The long-term shedding of viable virus necessitates customized infection prevention measures in these individuals. The observed accelerated accumulation of mutations of the SARS-CoV-2 genome in these patients might facilitate the origin of new VOCs that might subsequently spread in the general community.

Identification of Epitope-Specific T Cells in T-Cell Receptor Repertoires.

Recognition of cancer epitopes by T cells is fundamental for the activation of targeted antitumor responses. As such, the identification and study of epitope-specific T cells has been instrumental in our understanding of cancer immunology and the development of personalized immunotherapies. To facilitate the study of T-cell epitope specificity, we developed a prediction tool, TCRex, that can identify epitope-specific T-cell receptors (TCRs) directly from TCR repertoire data and perform epitope-specificity enrichment analyses. This chapter details the use of the TCRex web tool.

Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires.

High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual's TCR repertoire and directly queries their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining.be.

Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing.

Open modification searching (OMS) is a powerful search strategy that identifies peptides carrying any type of modification by allowing a modified spectrum to match against its unmodified variant by using a very wide precursor mass window. A drawback of this strategy, however, is that it leads to a large increase in search time. Although performing an open search can be done using existing spectral library search engines by simply setting a wide precursor mass window, none of these tools have been optimized for OMS, leading to excessive runtimes and suboptimal identification results. We present the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. This approach is combined with a cascade search strategy to maximize the number of identified unmodified and modified spectra while strictly controlling the false discovery rate as well as a shifted dot product score to sensitively match modified spectra to their unmodified counterparts. ANN-SoLo achieves state-of-the-art performance in terms of speed and the number of identifications. On a previously published human cell line data set, ANN-SoLo confidently identifies more spectra than SpectraST or MSFragger and achieves a speedup of an order of magnitude compared with SpectraST. ANN-SoLo is implemented in Python and C++. It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo .

On the feasibility of mining CD8+ T cell receptor patterns underlying immunogenic peptide recognition.

Current T cell epitope prediction tools are a valuable resource in designing targeted immunogenicity experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, recognition of the peptide-MHC complex by a T cell receptor (TCR) is often not included in these tools. We developed a classification approach based on random forest classifiers to predict recognition of a peptide by a T cell receptor and discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) distinguishing between two sets of TCRs that each bind to a known peptide and (2) retrieving TCRs that bind to a given peptide from a large pool of TCRs. Evaluation of the models on two HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can determine peptide immunogenicity. These results are of particular importance as they show that prediction of T cell epitope and T cell epitope recognition based on sequence data is a feasible approach. In addition, the validity of our models not only serves as a proof of concept for the prediction of immunogenic T cell epitopes but also paves the way for more general and high-performing models.

Increased herpes zoster risk associated with poor HLA-A immediate early 62 protein (IE62) affinity.

Around 30% of individuals will develop herpes zoster (HZ), caused by the varicella zoster virus (VZV), during their life. While several risk factors for HZ, such as immunosuppressive therapy, are well known, the genetic and molecular components that determine the risk of otherwise healthy individuals to develop HZ are still poorly understood. We created a computational model for the Human Leukocyte Antigen (HLA-A, -B, and -C) presentation capacity of peptides derived from the VZV Immediate Early 62 (IE62) protein. This model could then be applied to a HZ cohort with known HLA molecules. We found that HLA-A molecules with poor VZV IE62 presentation capabilities were more common in a cohort of 50 individuals with a history of HZ compared to a nationwide control group, which equated to a HZ risk increase of 60%. This tendency was most pronounced for cases of HZ at a young age, where other risk factors are less prevalent. These findings provide new molecular insights into the development of HZ and reveal a genetic predisposition in those individuals most at risk to develop HZ.

Immunological evasion of immediate-early varicella zoster virus proteins.

The varicella zoster virus (VZV) causes the childhood disease commonly known as chickenpox and can later in life reactivate as herpes zoster. The adaptive immune system is known to play an important role in suppressing VZV reactivation. A central aspect of this system is the presentation of VZV-derived peptides by the major histocompatibility complex (MHC) proteins. Here, we investigate if key VZV proteins have evolved their amino acid sequence to avoid presentation by MHC based on predictive models of MHC-peptide affinity. This study shows that the immediate-early proteins of all characterized VZV strains are profoundly depleted for high-affinity MHC-I-restricted epitopes. The same depletion can be found in its closest animal analog, the simian varicella virus. Further orthology analysis towards other herpes viruses suggests that the protein amino acid frequency is one of the primary drivers of targeted epitope depletion.

Unsupervised Quality Assessment of Mass Spectrometry Proteomics Experiments by Multivariate Quality Control Metrics.

Despite many technological and computational advances, the results of a mass spectrometry proteomics experiment are still subject to a large variability. For the understanding and evaluation of how technical variability affects the results of an experiment, several computationally derived quality control metrics have been introduced. However, despite the availability of these metrics, a systematic approach to quality control is often still lacking because the metrics are not fully understood and are hard to interpret. Here, we present a toolkit of powerful techniques to analyze and interpret multivariate quality control metrics to assess the quality of mass spectrometry proteomics experiments. We show how unsupervised techniques applied to these quality control metrics can provide an initial discrimination between low-quality experiments and high-quality experiments prior to manual investigation. Furthermore, we provide a technique to obtain detailed information on the quality control metrics that are related to the decreased performance, which can be used as actionable information to improve the experimental setup. Our toolkit is released as open-source and can be downloaded from https://bitbucket.org/proteinspector/qc_analysis/ .