Lack of functional TCR-epitope interaction is associated with herpes zoster through reduced downstream T cell activation.

The role of T cell receptor (TCR) diversity in infectious disease susceptibility is not well understood. We use a systems immunology approach on three cohorts of herpes zoster (HZ) patients and controls to investigate whether TCR diversity against varicella-zoster virus (VZV) influences the risk of HZ. We show that CD4+ T cell TCR diversity against VZV glycoprotein E (gE) and immediate early 63 protein (IE63) after 1-week culture is more restricted in HZ patients. Single-cell RNA and TCR sequencing of VZV-specific T cells shows that T cell activation pathways are significantly decreased after stimulation with VZV peptides in convalescent HZ patients. TCR clustering indicates that TCRs from HZ patients co-cluster more often together than TCRs from controls. Collectively, our results suggest that not only lower VZV-specific TCR diversity but also reduced functional TCR affinity for VZV-specific proteins in HZ patients leads to lower T cell activation and consequently affects the susceptibility for viral reactivation.

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.

Unravelling the immune signature of herpes zoster: Insights into pathophysiology and the HLA risk profile.

The varicella-zoster virus (VZV) infects over 95% of the population. VZV reactivation causes herpes zoster (HZ), known as shingles, primarily affecting the elderly and immunocompromised individuals. However, HZ can also occur in otherwise healthy individuals. We analyzed the immune signature and risk profile in HZ patients using a genome-wide association study across different UK Biobank HZ cohorts. Additionally, we conducted one of the largest HZ HLA association studies to date, coupled with transcriptomic analysis of pathways underlying HZ susceptibility. Our findings highlight the significance of the MHC locus for HZ development, identifying five protective and four risk HLA alleles. This demonstrates that HZ susceptibility is largely governed by variations in the MHC. Furthermore, functional analyses revealed the upregulation of type I interferon and adaptive immune responses. These findings provide fresh molecular insights into the pathophysiology and the activation of innate and adaptive immune responses triggered by symptomatic VZV reactivation.

Diagnosing Viral Infections Through T-Cell Receptor Sequencing of Activated CD8+ T Cells.

T-cell-based diagnostic tools identify pathogen exposure but lack differentiation between recent and historical exposures in acute infectious diseases. Here, T-cell receptor (TCR) RNA sequencing was performed on HLA-DR+/CD38+CD8+ T-cell subsets of hospitalized coronavirus disease 2019 (COVID-19) patients (n = 30) and healthy controls (n = 30; 10 of whom had previously been exposed to severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]). CDR3α and CDR3ß TCR regions were clustered separately before epitope specificity annotation using a database of SARS-CoV-2-associated CDR3α and CDR3ß sequences corresponding to >1000 SARS-CoV-2 epitopes. The depth of the SARS-CoV-2-associated CDR3α/ß sequences differentiated COVID-19 patients from the healthy controls with a receiver operating characteristic area under the curve of 0.84 ± 0.10. Hence, annotating TCR sequences of activated CD8+ T cells can be used to diagnose an acute viral infection and discriminate it from historical exposure. In essence, this work presents a new paradigm for applying the T-cell repertoire to accomplish TCR-based diagnostics.

Clinical Decision Support for Improved Neonatal Care: The Development of a Machine Learning Model for the Prediction of Late-onset Sepsis and Necrotizing Enterocolitis.

OBJECTIVE: To develop an artificial intelligence-based software system for predicting late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) in infants admitted to the neonatal intensive care unit (NICU). STUDY DESIGN: Single-center, retrospective cohort study, conducted in the NICU of the Antwerp University Hospital. Continuous monitoring data of 865 preterm infants born at <32 weeks gestational age, admitted to the NICU in the first week of life, were used to train an XGBoost machine learning (ML) algorithm for LOS and NEC prediction in a cross-validated setup. Afterward, the model's performance was assessed on an independent test set of 148 patients (internal validation). RESULTS: The ML model delivered hourly risk predictions with an overall sensitivity of 69% (142/206) for all LOS/NEC episodes and 81% (67/83) for severe LOS/NEC episodes. The model showed a median time gain of ≤10 hours (IQR, 3.1-21.0 hours), compared with historical clinical diagnosis. On the complete retrospective dataset, the ML model made 721 069 predictions, of which 9805 (1.3%) depicted a LOS/NEC probability of ≥0.15, resulting in a total alarm rate of <1 patient alarm-day per week. The model reached a similar performance on the internal validation set. CONCLUSIONS: Artificial intelligence technology can assist clinicians in the early detection of LOS and NEC in the NICU, which potentially can result in clinical and socioeconomic benefits. Additional studies are required to quantify further the effect of combining artificial and human intelligence on patient outcomes in the NICU.

Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response.

The immune system acts as an intricate apparatus that is dedicated to mounting a defense and ensures host survival from microbial threats. To engage this faceted immune response and provide protection against infectious diseases, vaccinations are a critical tool to be developed. However, vaccine responses are governed by levels that, when interrogated, separately only explain a fraction of the immune reaction. To address this knowledge gap, we conducted a feasibility study to determine if multi-view modeling could aid in gaining actionable insights on response markers shared across populations, capture the immune system's diversity, and disentangle confounders. We thus sought to assess this multi-view modeling capacity on the responsiveness to the Hepatitis B virus (HBV) vaccination. Seroconversion to vaccine-induced antibodies against the HBV surface antigen (anti-HBs) in early converters (n = 21; <2 months) and late converters (n = 9; <6 months) and was defined based on the anti-HBs titers (>10IU/L). The multi-view data encompassed bulk RNA-seq, CD4+ T-cell parameters (including T-cell receptor data), flow cytometry data, and clinical metadata (including age and gender). The modeling included testing single-view and multi-view joint dimensionality reductions. Multi-view joint dimensionality reduction outperformed single-view methods in terms of the area under the curve and balanced accuracy, confirming the increase in predictive power to be gained. The interpretation of these findings showed that age, gender, inflammation-related gene sets, and pre-existing vaccine-specific T-cells could be associated with vaccination responsiveness. This multi-view dimensionality reduction approach complements clinical seroconversion and all single modalities. Importantly, this modeling could identify what features could predict HBV vaccine response. This methodology could be extended to other vaccination trials to identify the key features regulating responsiveness.

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.

Leveraging T-cell receptor - epitope recognition models to disentangle unique and cross-reactive T-cell response to SARS-CoV-2 during COVID-19 progression/resolution.

Despite the general agreement on the significance of T cells during SARS-CoV-2 infection, the clinical impact of specific and cross-reactive T-cell responses remains uncertain. Understanding this aspect could provide insights for adjusting vaccines and maintaining robust long-term protection against continuously emerging variants. To characterize CD8+ T-cell response to SARS-CoV-2 epitopes unique to the virus (SC2-unique) or shared with other coronaviruses (CoV-common), we trained a large number of T-cell receptor (TCR) - epitope recognition models for MHC-I-presented SARS-CoV-2 epitopes from publicly available data. These models were then applied to longitudinal CD8+ TCR repertoires from critical and non-critical COVID-19 patients. In spite of comparable initial CoV-common TCR repertoire depth and CD8+ T-cell depletion, the temporal dynamics of SC2-unique TCRs differed depending on the disease severity. Specifically, while non-critical patients demonstrated a large and diverse SC2-unique TCR repertoire by the second week of the disease, critical patients did not. Furthermore, only non-critical patients exhibited redundancy in the CD8+ T-cell response to both groups of epitopes, SC2-unique and CoV-common. These findings indicate a valuable contribution of the SC2-unique CD8+ TCR repertoires. Therefore, a combination of specific and cross-reactive CD8+ T-cell responses may offer a stronger clinical advantage. Besides tracking the specific and cross-reactive SARS-CoV-2 CD8+ T cells in any TCR repertoire, our analytical framework can be expanded to more epitopes and assist in the assessment and monitoring of CD8+ T-cell response to other infections.

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.

Clustering and Annotation of T Cell Receptor Repertoires.

Immunological protection against a wide variety of pathogens is largely mediated by the diverse and dynamic T cell receptor (TCR) repertoire, a crucial component of the adaptive immune system. An encounter with infectious agents stimulates specific T cells to initiate a direct immune response to combat intruders. Hence, the TCR repertoire may conceal crucial information regarding current and past infections and might assist in the development and monitoring of vaccines. To unlock its knowledge, we describe a computational workflow involving both supervised and unsupervised machine learning techniques to analyze and annotate full TCR repertoire data. The method is explained using data from a published yellow fever virus (YFV) vaccination study in healthy individuals. The TCR repertoire of one individual is studied before and 2 weeks after vaccination, using an efficient clustering method and identification of YFV-specific TCRs.

Kynurenine pathway abnormalities are state-specific but not diagnosis-specific in schizophrenia and bipolar disorder.

Schizophrenia (SCZ) and bipolar disorder (BD) are associated with immunological dysfunctions that have been hypothesized to lead to clinical symptomatology in particular through kynurenine pathway abnormalities. The aim of this study was thus to investigate the impact of serum kynurenine metabolite levels on diagnosis, clinical state, symptom severity and clinical course in a large French transdiagnostic cohort of SCZ and BD patients. Four patient groups (total n = 507) were included in a cross-sectional observational study: 1) hospitalized acute bipolar patients (n = 205); 2) stable bipolar outpatients (n = 116); 3) hospitalized acute schizophrenia patients (n = 111) and 4) stable schizophrenia outpatients (n = 75), in addition to healthy controls (HC) (n = 185). The quantitative determination of serum kynurenine metabolites was performed using liquid chromatography-tandem mass spectrometry. Kynurenine levels were lower in all patients combined compared to HC while ANCOVA analyses did not reveal inter-diagnostic difference between SCZ and BD. Interestingly, hospitalized patients of both diagnostic groups combined displayed significantly lower kynurenine levels than stabilized outpatients. Psychotic symptoms were associated with lower quinaldic acid (F = 9.18, p=<.001), which is KAT-driven, whereas a longer duration of illness contributed to abnormalities in tryptophan (F = 5.41, p = .023), kynurenine (F = 16.93, p=<.001), xanthurenic acid (F = 9.34, p = .002), quinolinic acid (F = 9.18, p = .003) and picolinic acid (F = 4.15, p = .043), metabolized through the KMO-branch. These data confirm illness state rather than diagnosis to drive KP alterations in SCZ and BD. Lower levels of KP metabolites can thus be viewed as a transdiagnostic feature of SCZ and BD, independently associated with acute symptomatology and a longer duration of illness. Quinaldic acid has seldomly been investigated by previous studies and appears an important state marker in SCZ and BD. As serum samples are used in this study, it is not possible to extrapolate these findings to the brain.

T cell immunity in HSV-1- and VZV-infected neural ganglia.

Herpesviruses hijack the MHC class I (MHC I) and class II (MHC II) antigen-presentation pathways to manipulate immune recognition by T cells. First, we illustrate herpes simplex virus-1 (HSV-1) and varicella-zoster virus (VZV) MHC immune evasion strategies. Next, we describe MHC-T cell interactions in HSV-1- and VZV- infected neural ganglia. Although studies on the topic are scarce, and use different models, most reports indicate that neuronal HSV-1 infection is mainly controlled by CD8+ T cells through noncytolytic mechanisms, whereas VZV seems to be largely controlled through CD4+ T cell-specific immune responses. Autologous human stem-cell-derived in vitro models could substantially aid in elucidating these neuroimmune interactions and are fit for studies on both herpesviruses.

Current annotation strategies for T cell phenotyping of single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) has become a popular technique for interrogating the diversity and dynamic nature of cellular gene expression and has numerous advantages in immunology. For example, scRNA-seq, in contrast to bulk RNA sequencing, can discern cellular subtypes within a population, which is important for heterogenous populations such as T cells. Moreover, recent advancements in the technology allow the parallel capturing of the highly diverse T-cell receptor (TCR) sequence with the gene expression. However, the field of single-cell RNA sequencing data analysis is still hampered by a lack of gold-standard cell phenotype annotation. This problem is particularly evident in the case of T cells due to the heterogeneity in both their gene expression and their TCR. While current cell phenotype annotation tools can differentiate major cell populations from each other, labelling T-cell subtypes remains problematic. In this review, we identify the common automated strategy for annotating T cells and their subpopulations, and also describe what crucial information is still missing from these tools.

Activation of Interferon-Stimulated Genes following Varicella-Zoster Virus Infection in a Human iPSC-Derived Neuronal In Vitro Model Depends on Exogenous Interferon-α.

Varicella-zoster virus (VZV) infection of neuronal cells and the activation of cell-intrinsic antiviral responses upon infection are still poorly understood mainly due to the scarcity of suitable human in vitro models that are available to study VZV. We developed a compartmentalized human-induced pluripotent stem cell (hiPSC)-derived neuronal culture model that allows axonal VZV infection of the neurons, thereby mimicking the natural route of infection. Using this model, we showed that hiPSC-neurons do not mount an effective interferon-mediated antiviral response following VZV infection. Indeed, in contrast to infection with Sendai virus, VZV infection of the hiPSC-neurons does not result in the upregulation of interferon-stimulated genes (ISGs) that have direct antiviral functions. Furthermore, the hiPSC-neurons do not produce interferon-α (IFNα), a major cytokine that is involved in the innate antiviral response, even upon its stimulation with strong synthetic inducers. In contrast, we showed that exogenous IFNα effectively limits VZV spread in the neuronal cell body compartment and demonstrated that ISGs are efficiently upregulated in these VZV-infected neuronal cultures that are treated with IFNα. Thus, whereas the cultured hiPSC neurons seem to be poor IFNα producers, they are good IFNα responders. This could suggest an important role for other cells such as satellite glial cells or macrophages to produce IFNα for VZV infection control.

Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study.

BACKGROUND: Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models. METHODS: RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted. RESULTS: Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups. CONCLUSION: Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice.

Four layer multi-omics reveals molecular responses to aneuploidy in Leishmania.

Aneuploidy causes system-wide disruptions in the stochiometric balances of transcripts, proteins, and metabolites, often resulting in detrimental effects for the organism. The protozoan parasite Leishmania has an unusually high tolerance for aneuploidy, but the molecular and functional consequences for the pathogen remain poorly understood. Here, we addressed this question in vitro and present the first integrated analysis of the genome, transcriptome, proteome, and metabolome of highly aneuploid Leishmania donovani strains. Our analyses unambiguously establish that aneuploidy in Leishmania proportionally impacts the average transcript- and protein abundance levels of affected chromosomes, ultimately correlating with the degree of metabolic differences between closely related aneuploid strains. This proportionality was present in both proliferative and non-proliferative in vitro promastigotes. However, as in other Eukaryotes, we observed attenuation of dosage effects for protein complex subunits and in addition, non-cytoplasmic proteins. Differentially expressed transcripts and proteins between aneuploid Leishmania strains also originated from non-aneuploid chromosomes. At protein level, these were enriched for proteins involved in protein metabolism, such as chaperones and chaperonins, peptidases, and heat-shock proteins. In conclusion, our results further support the view that aneuploidy in Leishmania can be adaptive. Additionally, we believe that the high karyotype diversity in vitro and absence of classical transcriptional regulation make Leishmania an attractive model to study processes of protein homeostasis in the context of aneuploidy and beyond.

Predicting compound-protein interaction using hierarchical graph convolutional networks.

MOTIVATION: Convolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a computational method based on convolutional neural networks to tackle a fundamental question in drug discovery and development, i.e. the prediction of compound-protein interactions based on compound structure and protein sequence. We propose a hierarchical graph convolutional network (HGCN) to encode small molecules. The HGCN aggregates a molecule embedding from substructure embeddings, which are synthesized from atom embeddings. As small molecules usually share substructures, computing a molecule embedding from those common substructures allows us to learn better generic models. We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction. RESULTS: Experiments using different datasets show the improvement of our model compared to other GCN-based methods and a sequence based method, DeepDTA, in predicting compound-protein interactions. Each prediction made by the model is also explainable and can be used to identify critical residues mediating the interaction.

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.

Preexisting memory CD4 T cells in naïve individuals confer robust immunity upon hepatitis B vaccination.

Antigen recognition through the T cell receptor (TCR) αß heterodimer is one of the primary determinants of the adaptive immune response. Vaccines activate naïve T cells with high specificity to expand and differentiate into memory T cells. However, antigen-specific memory CD4 T cells exist in unexposed antigen-naïve hosts. In this study, we use high-throughput sequencing of memory CD4 TCRß repertoire and machine learning to show that individuals with preexisting vaccine-reactive memory CD4 T cell clonotypes elicited earlier and higher antibody titers and mounted a more robust CD4 T cell response to hepatitis B vaccine. In addition, integration of TCRß sequence patterns into a hepatitis B epitope-specific annotation model can predict which individuals will have an early and more vigorous vaccine-elicited immunity. Thus, the presence of preexisting memory T cell clonotypes has a significant impact on immunity and can be used to predict immune responses to vaccination.

Immune cells called CD4 T cells help the body build immunity to infections caused by bacteria and viruses, or after vaccination. Receptor proteins on the outside of the cells recognize pathogens, foreign molecules called antigens, or vaccine antigens. Vaccine antigens are usually inactivated bacteria or viruses, or fragments of these pathogens. After recognizing an antigen, CD4 T cells develop into memory CD4 T cells ready to defend against future infections with the pathogen. People who have never been exposed to a pathogen, or have never been vaccinated against it, may nevertheless have preexisting memory cells ready to defend against it. This happens because CD4 T cells can recognize multiple targets, which enables the immune system to be ready to defend against both new and familiar pathogens. Elias, Meysman, Bartholomeus et al. wanted to find out whether having preexisting memory CD4 T cells confers an advantage for vaccine-induced immunity. Thirty-four people who were never exposed to hepatitis B or vaccinated against it participated in the study. These individuals provided blood samples before vaccination, with 2 doses of the hepatitis B vaccine, and at 3 time points afterward. Using next generation immune sequencing and machine learning techniques, Elias et al. analyzed the individuals' memory CD4 T cells before and after vaccination. The experiments showed that preexisting memory CD4 T cells may determine vaccination outcomes, and people with more preexisting memory cells develop quicker and stronger immunity after vaccination against hepatitis B. This information may help scientists to better understand how people develop immunity to pathogens. It may guide them develop better vaccines or predict who will develop immunity after vaccination.