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The non-responders already showed an activated state of the immune system before vaccination. Furthermore, after vaccination, they exhibited a delayed and partial immune response in comparison to the responders. Our data may indicate that the baseline and untriggered immune system can influence the response upon hepatitis B vaccination.

Auranofin (AF) is an FDA-approved antirheumatic drug with anticancer properties that acts as a thioredoxin reductase 1 (TrxR) inhibitor. The exact mechanisms through which AF targets cancer cells remain elusive. To shed light on the mode of action, this study provides an in-depth analysis on the molecular mechanisms and immunogenicity of AF-mediated cytotoxicity in the non-small cell lung cancer (NSCLC) cell line NCI–H1299 (p53 Null) and its two isogenic derivates with mutant p53 R175H or R273H accumulation. TrxR is highly expressed in a panel of 72 NSCLC patients, making it a valid druggable target in NSCLC for AF. The presence of mutant p53 overexpression was identified as an important sensitizer for AF in (isogenic) NSCLC cells as it was correlated with reduced thioredoxin (Trx) levels in vitro . Transcriptome analysis revealed dysregulation of genes involved in oxidative stress response, DNA damage, granzyme A (GZMA) signaling and ferroptosis. Although functionally AF appeared a potent inhibitor of GPX4 in all NCI–H1299 cell lines, the induction of lipid peroxidation and consequently ferroptosis was limited to the p53 R273H expressing cells. In the p53 R175H cells, AF mainly induced large-scale DNA damage and replication stress, leading to the induction of apoptotic cell death rather than ferroptosis. Importantly, all cell death types were immunogenic since the release of danger signals (ecto-calreticulin, ATP and HMGB1) and dendritic cell maturation occurred irrespective of (mutant) p53 expression. Finally, we show that AF sensitized cancer cells to caspase-independent natural killer cell-mediated killing by downregulation of several key targets of GZMA. Our data provides novel insights on AF as a potent, clinically available, off-patent cancer drug by targeting mutant p53 cancer cells through distinct cell death mechanisms (apoptosis and ferroptosis). In addition, AF improves the innate immune response at both cytostatic (natural killer cell-mediated killing) and cytotoxic concentrations (dendritic cell maturation).

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Pathogens of past and current infections have been identified directly by means of PCR or indirectly by measuring a specific immune response (e.g., antibody titration). Using a novel approach, Emerson and colleagues showed that the cytomegalovirus serostatus can also be accurately determined by using a T cell receptor repertoire data mining approach. In this study, we have sequenced the CD4 memory T cell receptor repertoire of a Belgian cohort with known cytomegalovirus serostatus. A random forest classifier was trained on the CMV specific T cell receptor repertoire signature and used to classify individuals in the Belgian cohort. This study shows that the novel approach can be reliably replicated with an equivalent performance as that reported by Emerson and colleagues. Additionally, it provides evidence that the T cell receptor repertoire signature is to a large extent present in the CD4 memory repertoire.

Background Meningitis can be caused by several viruses and bacteria. Identifying the causative pathogen as quickly as possible is crucial to initiate the most optimal therapy, as acute bacterial meningitis is associated with a significant morbidity and mortality. Bacterial meningitis requires antibiotics, as opposed to enteroviral meningitis, which only requires supportive therapy. Clinical presentation is usually not sufficient to differentiate between viral and bacterial meningitis, thereby necessitating cerebrospinal fluid (CSF) analysis by PCR and/or time-consuming bacterial cultures. However, collecting CSF in children is not always feasible and a rather invasive procedure. Methods In 12 Belgian hospitals, we obtained acute blood samples from children with signs of meningitis (49 viral and 7 bacterial cases) (aged between 3 months and 16 years). After pathogen confirmation on CSF, the patient was asked to give a convalescent sample after recovery. 3′ mRNA sequencing was performed to determine differentially expressed genes (DEGs) to create a host transcriptomic profile. Results Enteroviral meningitis cases displayed the largest upregulated fold change enrichment in type I interferon production, response and signaling pathways. Patients with bacterial meningitis showed a significant upregulation of genes related to macrophage and neutrophil activation. We found several significantly DEGs between enteroviral and bacterial meningitis. Random forest classification showed that we were able to differentiate enteroviral from bacterial meningitis with an AUC of 0.982 on held-out samples. Conclusions Enteroviral meningitis has an innate immunity signature with type 1 interferons as key players. Our classifier, based on blood host transcriptomic profiles of different meningitis cases, is a possible strong alternative for diagnosing enteroviral meningitis.

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Antigen recognition through the T cell receptor (TCR) αβ 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 TCRβ 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 TCRβ 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.