Patients with multiple sclerosis who develop immunogenicity to interferon-beta have distinct transcriptomic and proteomic signatures prior to treatment which are associated with disease severity.

Anti-drug antibodies (ADA) reduce the efficacy of immunotherapies in multiple sclerosis (MS) and are associated with increased disease progression risk. Blood biomarkers predicting immunogenicity to biopharmaceuticals represent an unmet clinical need. Patients with relapsing remitting (RR)MS were recruited before (baseline), three, and 12 (M12) months after commencing interferon-beta treatment. Neutralising ADA-status was determined at M12, and patients were stratified at baseline according to their M12 ADA-status (ADA-positive/ADA-negative). Patients stratified as ADA-positive were characterised by an early dampened response to interferon-beta (prior to serum ADA detection) and distinct proinflammatory transcriptomic/proteomic peripheral blood signatures enriched for 'immune response activation' including phosphoinositide 3-kinase-γ and NFκB-signalling pathways both at baseline and throughout the treatment course, compared to ADA-negative patients. These immunogenicity-associated proinflammatory signatures significantly overlapped with signatures of MS disease severity. Thus, whole blood molecular profiling is a promising tool for prediction of ADA-development in RRMS and could provide insight into mechanisms of immunogenicity.

Robust humoral and cellular immune responses and low risk for reinfection at least 8 months following asymptomatic to mild COVID-19.

BACKGROUND: Emerging data support detectable immune responses for months after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and vaccination, but it is not yet established to what degree and for how long protection against reinfection lasts. METHODS: We investigated SARS-CoV-2-specific humoral and cellular immune responses more than 8 months post-asymptomatic, mild and severe infection in a cohort of 1884 healthcare workers (HCW) and 51 hospitalized COVID-19 patients. Possible protection against SARS-CoV-2 reinfection was analyzed by a weekly 3-month polymerase chain reaction (PCR) screening of 252 HCW that had seroconverted 7 months prior to start of screening and 48 HCW that had remained seronegative at multiple time points. RESULTS: All COVID-19 patients and 96% (355/370) of HCW who were anti-spike IgG positive at inclusion remained anti-spike IgG positive at the 8-month follow-up. Circulating SARS-CoV-2-specific memory T cell responses were detected in 88% (45/51) of COVID-19 patients and in 63% (233/370) of seropositive HCW. The cumulative incidence of PCR-confirmed SARS-CoV-2 infection was 1% (3/252) among anti-spike IgG positive HCW (0.13 cases per 100 weeks at risk) compared to 23% (11/48) among anti-spike IgG negative HCW (2.78 cases per 100 weeks at risk), resulting in a protective effect of 95.2% (95% CI 81.9%-99.1%). CONCLUSIONS: The vast majority of anti-spike IgG positive individuals remain anti-spike IgG positive for at least 8 months regardless of initial COVID-19 disease severity. The presence of anti-spike IgG antibodies is associated with a substantially reduced risk of reinfection up to 9 months following asymptomatic to mild COVID-19.

Serum Metabolomic Signatures Can Predict Subclinical Atherosclerosis in Patients With Systemic Lupus Erythematosus.

[Figure: see text].

Transcriptional sex and regional differences in paired human atrial and ventricular cardiac biopsies collected in vivo.

Transcriptional studies of the human heart provide insight into physiological and pathophysiological mechanisms, essential for understanding the fundamental mechanisms of normal cardiac function and how they are altered by disease. To improve the understanding of why men and women may respond differently to the same therapeutic treatment it is crucial to learn more about sex-specific transcriptional differences. In this study the transcriptome of right atrium and left ventricle was compared across sex and regional location. Paired biopsies from five male and five female patients undergoing aortic valve replacement or coronary artery bypass grafting were included. Gene expression analysis identified 620 differentially expressed transcripts in atrial and ventricular tissue in men and 471 differentially expressed transcripts in women. In total 339 of these transcripts overlapped across sex but notably, 281 were unique in the male tissue and 162 in the female tissue, displaying marked sex differences in the transcriptional machinery. The transcriptional activity was significantly higher in atrias than in ventricles as 70% of the differentially expressed genes were upregulated in the atrial tissue. Furthermore, pathway- and functional annotation analyses performed on the differentially expressed genes showed enrichment for a more heterogeneous composition of biological processes in atrial compared with the ventricular tissue, and a dominance of differentially expressed genes associated with infection disease was observed. The results reported here provide increased insights about transcriptional differences between the cardiac atrium and ventricle but also reveal transcriptional differences in the human heart that can be attributed to sex.

Barrier Properties and Transcriptome Expression in Human iPSC-Derived Models of the Blood-Brain Barrier.

Cell-based models of the blood-brain barrier (BBB) are important for increasing the knowledge of BBB formation, degradation and brain exposure of drug substances. Human models are preferred over animal models because of interspecies differences in BBB structure and function. However, access to human primary BBB tissue is limited and has shown degeneration of BBB functions in vitro. Human induced pluripotent stem cells (iPSCs) can be used to generate relevant cell types to model the BBB with human tissue. We generated a human iPSC-derived model of the BBB that includes endothelial cells in coculture with pericytes, astrocytes and neurons. Evaluation of barrier properties showed that the endothelial cells in our coculture model have high transendothelial electrical resistance, functional efflux and ability to discriminate between CNS permeable and non-permeable substances. Whole genome expression profiling revealed transcriptional changes that occur in coculture, including upregulation of tight junction proteins, such as claudins and neurotransmitter transporters. Pathway analysis implicated changes in the WNT, TNF, and PI3K-Akt pathways upon coculture. Our data suggest that coculture of iPSC-derived endothelial cells promotes barrier formation on a functional and transcriptional level. The information about gene expression changes in coculture can be used to further improve iPSC-derived BBB models through selective pathway manipulation. Stem Cells 2018;36:1816-12.

Population-specific design of de-immunized protein biotherapeutics.

Immunogenicity is a major problem during the development of biotherapeutics since it can lead to rapid clearance of the drug and adverse reactions. The challenge for biotherapeutic design is therefore to identify mutants of the protein sequence that minimize immunogenicity in a target population whilst retaining pharmaceutical activity and protein function. Current approaches are moderately successful in designing sequences with reduced immunogenicity, but do not account for the varying frequencies of different human leucocyte antigen alleles in a specific population and in addition, since many designs are non-functional, require costly experimental post-screening. Here, we report a new method for de-immunization design using multi-objective combinatorial optimization. The method simultaneously optimizes the likelihood of a functional protein sequence at the same time as minimizing its immunogenicity tailored to a target population. We bypass the need for three-dimensional protein structure or molecular simulations to identify functional designs by automatically generating sequences using probabilistic models that have been used previously for mutation effect prediction and structure prediction. As proof-of-principle we designed sequences of the C2 domain of Factor VIII and tested them experimentally, resulting in a good correlation with the predicted immunogenicity of our model.

Methotrexate and BAFF interaction prevents immunization against TNF inhibitors.

OBJECTIVES: TNF inhibitors (TNFi) can induce anti-drug antibodies (ADA) in patients with autoimmune diseases (AID) leading to clinical resistance. We explored a new way of using methotrexate (MTX) to decrease this risk of immunisation. METHODS: We treated BAFF transgenic (BAFFtg) mice, a model of AID in which immunisation against biologic drugs is high, with different TNFi. We investigated the effect of a single course of MTX during the first exposure to TNFi. Wild-type (WT) and BAFFtg mice were compared for B-Cell surface markers involved in MTX-related purinergic metabolism, adenosine production and regulatory B-cells (Bregs).We translated the study to macaques and patients with rheumatoid arthritis from the ABIRISK cohort to determine if there was an interaction between serum BAFF levels and MTX that prevented immuniation. RESULTS: In BAFFtg but not in WT mice or macaques, a single course of MTX prevented immunisation against TNFi and maintained drug concentration for over 52 weeks. BAFFtg mice B-cells expressed more CD73 and CD39 compared to WT mice. MTX induced adenosine release from B cells and increased Bregs and precursors. Use of CD73 blocking antibodies reversed MTX-induced tolerance. In patients from the ABIRISK cohort treated with TNFi for chronic inflammatory diseases, high BAFF serum level correlated with absence of ADA to TNFi only in patients cotreated with MTX but not in patients on TNFi monotherapy. CONCLUSION: MTX and BAFF interact in mice where CD73, adenosine and regulatory B cells were identified as key actors in this phenomenon. MTX and BAFF also interact in patients to prevent ADA formation.

WIPI proteins: essential PtdIns3P effectors at the nascent autophagosome.

Autophagy is a pivotal cytoprotective process that secures cellular homeostasis, fulfills essential roles in development, immunity and defence against pathogens, and determines the lifespan of eukaryotic organisms. However, autophagy also crucially contributes to the development of age-related human pathologies, including cancer and neurodegeneration. Macroautophagy (hereafter referred to as autophagy) clears the cytoplasm by stochastic or specific cargo recognition and destruction, and is initiated and executed by autophagy related (ATG) proteins functioning in dynamical hierarchies to form autophagosomes. Autophagosomes sequester cytoplasmic cargo material, including proteins, lipids and organelles, and acquire acidic hydrolases from the lysosomal compartment for cargo degradation. Prerequisite and essential for autophagosome formation is the production of phosphatidylinositol 3-phosphate (PtdIns3P) by phosphatidylinositol 3-kinase class III (PI3KC3, also known as PIK3C3) in complex with beclin 1, p150 (also known as PIK3R4; Vps15 in yeast) and ATG14L. Members of the human WD-repeat protein interacting with phosphoinositides (WIPI) family play an important role in recognizing and decoding the PtdIns3P signal at the nascent autophagosome, and hence function as autophagy-specific PtdIns3P-binding effectors, similar to their ancestral yeast Atg18 homolog. The PtdIns3P effector function of human WIPI proteins appears to be compromised in cancer and neurodegeneration, and WIPI genes and proteins might present novel targets for rational therapies. Here, we summarize the current knowledge on the roles of the four human WIPI proteins, WIPI1-4, in autophagy. This article is part of a Focus on Autophagosome biogenesis. For further reading, please see related articles: 'ERES: sites for autophagosome biogenesis and maturation?' by Jana Sanchez-Wandelmer et al. (J. Cell Sci. 128, 185-192) and 'Membrane dynamics in autophagosome biogenesis' by Sven R. Carlsson and Anne Simonsen (J. Cell Sci. 128, 193-205).

Fluorescence-based imaging of autophagy progression by human WIPI protein detection.

Central to the process of macroautophagy (hereafter autophagy) is the formation of autophagosomes, double-membrane vesicles that sequester cytoplasmic cargo, including proteins, lipids and organelles, for lysosomal degradation and macromolecule recycling. Tight regulation of both autophagic activity and capacity is crucial to secure cellular homeostasis and aberrant autophagy is tightly linked to the development of many human diseases. Hence it is of great importance to accurately measure autophagy progression in health and disease. Members of the human WIPI ß-propeller proteins associate with autophagosomal membranes due to specific phosphatidylinositol 3-phosphate (PtdIns3P) binding at the onset of autophagy. The specific autophagosomal localization of both WIPI1 and WIPI2 (refered to as WIPI puncta) has been employed to assess autophagy using fluorescence microscopy methods, such as confocal and live-cell video microscopy and was extended for automated high-throughput image acquisition and analyses procedures. We here provide an overview on the employment of human WIPI members for the assessment of autophagy in higher eukaryotic cells, suitable for systems biology approaches such as mathematical modelling.

Atherosclerosis Progression in the APPLE Trial Can Be Predicted in Young People With Juvenile-Onset Systemic Lupus Erythematosus Using a Novel Lipid Metabolomic Signature.

OBJECTIVE: Patients with juvenile-onset systemic lupus erythematosus (JSLE) have increased atherosclerosis risk. This study investigated novel atherosclerosis progression biomarkers in the Atherosclerosis Prevention in Pediatric Lupus Erythematosus (APPLE) trial, the largest investigator-led randomized control trial of atorvastatin versus placebo for atherosclerosis progression in JSLE, using carotid intima-media thickness (CIMT) as the primary outcome. METHODS: Unsupervised clustering of baseline CIMT and CIMT progression over 36 months was used to stratify patients with JSLE. Disease characteristics, cardiovascular risk scores, and baseline serum metabolome were investigated in CIMT-stratified patients. Machine learning techniques were used to identify and validate a serum metabolomic signature of CIMT progression. RESULTS: Baseline CIMT stratified patients with JSLE (N = 151) into three groups with distinct high, intermediate, and low CIMT trajectories irrespective of treatment allocation, despite most patients having low cardiovascular disease risk based on recommended assessment criteria. In the placebo group (n = 60), patients with high versus low CIMT progression had higher total (P = 0.001) and low-density lipoprotein (LDL) (P = 0.002) cholesterol levels, although within the reference range. Furthermore, a robust baseline metabolomic signature predictive of high CIMT progression was identified in the placebo arm (area under the curve, 80.7%). Patients treated with atorvastatin (n = 61) had reduced LDL cholesterol levels after 36 months, as expected; however, despite this, 36% still had high atherosclerosis progression, which was not predicted by metabolomic biomarkers, suggesting nonlipid drivers of atherosclerosis in JSLE with management implications for this subset of patients. CONCLUSION: Significant baseline heterogeneity and distinct subclinical atherosclerosis progression trajectories exist in JSLE. Metabolomic signatures can predict atherosclerosis progression in some patients with JSLE with relevance for clinical trial stratification.

Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity.

There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.

The Combination of Vascular Endothelial Growth Factor A (VEGF-A) and Fibroblast Growth Factor 1 (FGF1) Modified mRNA Improves Wound Healing in Diabetic Mice: An Ex Vivo and In Vivo Investigation.

BACKGROUND: Diabetic foot ulcers (DFU) pose a significant health risk in diabetic patients, with insufficient revascularization during wound healing being the primary cause. This study aimed to assess microvessel sprouting and wound healing capabilities using vascular endothelial growth factor (VEGF-A) and a modified fibroblast growth factor (FGF1). METHODS: An ex vivo aortic ring rodent model and an in vivo wound healing model in diabetic mice were employed to evaluate the microvessel sprouting and wound healing capabilities of VEGF-A and a modified FGF1 both as monotherapies and in combination. RESULTS: The combination of VEGF-A and FGF1 demonstrated increased vascular sprouting in the ex vivo mouse aortic ring model, and topical administration of a combination of VEGF-A and FGF1 mRNAs formulated in lipid nanoparticles (LNPs) in mouse skin wounds promoted faster wound closure and increased neovascularization seven days post-surgical wound creation. RNA-sequencing analysis of skin samples at day three post-wound creation revealed a strong transcriptional response of the wound healing process, with the combined treatment showing significant enrichment of genes linked to skin growth. CONCLUSION: f-LNPs encapsulating VEGF-A and FGF1 mRNAs present a promising approach to improving the scarring process in DFU.

Enhancing Maturation and Translatability of Human Pluripotent Stem Cell-Derived Cardiomyocytes through a Novel Medium Containing Acetyl-CoA Carboxylase 2 Inhibitor.

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) constitute an appealing tool for drug discovery, disease modeling, and cardiotoxicity screening. However, their physiological immaturity, resembling CMs in the late fetal stage, limits their utility. Herein, we have developed a novel, scalable cell culture medium designed to enhance the maturation of hPSC-CMs. This medium facilitates a metabolic shift towards fatty acid utilization and augments mitochondrial function by targeting Acetyl-CoA carboxylase 2 (ACC2) with a specific small molecule inhibitor. Our findings demonstrate that this maturation protocol significantly advances the metabolic, structural, molecular and functional maturity of hPSC-CMs at various stages of differentiation. Furthermore, it enables the creation of cardiac microtissues with superior structural integrity and contractile properties. Notably, hPSC-CMs cultured in this optimized maturation medium display increased accuracy in modeling a hypertrophic cardiac phenotype following acute endothelin-1 induction and show a strong correlation between in vitro and in vivo target engagement in drug screening efforts. This approach holds promise for improving the utility and translatability of hPSC-CMs in cardiac disease modeling and drug discovery.

Longitudinal analysis of anti-drug antibody development in multiple sclerosis patients treated with interferon beta-1a (Rebif™) using B cell receptor repertoire analysis.

A significant proportion of multiple sclerosis (MS) patients treated with interferon beta-1a (Rebif™) develop anti-drug antibodies (ADA) with a negative impact on treatment efficacy. We hypothesized that high-throughput B-cell receptor (BCR) repertoire analysis could be used to predict and monitor ADA development. To study this we analyzed 228 peripheral blood samples from 68 longitudinally followed patients starting on interferon beta-1a. Our results show that whole blood BCR analysis does not reflect, and does not predict ADA development in MS patients treated with interferon beta-1a. We propose that BCR analysis of phenotypically selected cell subsets or tissues might be more informative.

Long-term SARS-CoV-2-specific and cross-reactive cellular immune responses correlate with humoral responses, disease severity, and symptomatology.

BACKGROUND: Cellular immune memory responses post coronavirus disease 2019 (COVID-19) have been difficult to assess due to the risks of contaminating the immune response readout with memory responses stemming from previous exposure to endemic coronaviruses. The work herein presents a large-scale long-term follow-up study investigating the correlation between symptomology and cellular immune responses four to five months post seroconversion based on a unique severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific peptide pool that contains no overlapping peptides with endemic human coronaviruses. METHODS: Peptide stimulated memory T cell responses were assessed with dual interferon-gamma (IFNγ) and interleukin (IL)-2 Fluorospot. Serological analyses were performed using a multiplex antigen bead array. RESULTS: Our work demonstrates that long-term SARS-CoV-2-specific memory T cell responses feature dual IFNγ and IL-2 responses, whereas cross-reactive memory T cell responses primarily generate IFNγ in response to SARS-CoV-2 peptide stimulation. T cell responses correlated to long-term humoral immune responses. Disease severity as well as specific COVID-19 symptoms correlated with the magnitude of the SARS-CoV-2-specific memory T cell response four to five months post seroconversion. CONCLUSION: Using a large cohort and a SARS-CoV-2-specific peptide pool we were able to substantiate that initial disease severity and symptoms correlate with the magnitude of the SARS-CoV-2-specific memory T cell responses.

Plasma profiling reveals human fibulin-1 as candidate marker for renal impairment.

There is a need for reliable and sensitive biomarkers for renal impairments to detect early signs of kidney toxicity and to monitor progression of disease. Here, antibody suspension bead arrays were applied to profile plasma samples from patients with four types of kidney disorders: glomerulonephritis, diabetic nephropathy, obstructive uropathy, and analgesic abuse. In total, 200 clinical renal-associated cases and control plasma samples from different cohorts were profiled. Parallel plasma protein profiles were obtained using biotinylated and nonfractionated samples and a selected set of 94 proteins targeted by 129 antigen-purified polyclonal antibodies. Out of the analyzed target proteins, human fibulin-1 was detected at significantly higher levels in the glomerulonephritis patient group compared to the controls and with elevated levels in patient samples for all other renal disorders investigated. Two polyclonal antibodies and one monoclonal antibody directed toward separate, nonoverlapping epitopes showed the same trend in the discovery cohorts. A technical verification using Western blot analysis of selected patient plasma confirmed the trends toward higher abundance of the target protein in disease samples. Furthermore, a verification study was carried out in the context of glomerulonephritis using an independent case and control cohort, and this confirmed the results from the discovery cohort, suggesting that plasma levels of fibulin-1 could serve as a potential indicator to monitor kidney malfunction or kidney damage.

Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges.

In the past decade, the emergence of machine learning (ML) applications has led to significant advances towards implementation of personalised medicine approaches for improved health care, due to the exceptional performance of ML models when utilising complex big data. The immune-mediated chronic inflammatory diseases are a group of complex disorders associated with dysregulated immune responses resulting in inflammation affecting various organs and systems. The heterogeneous nature of these diseases poses great challenges for tailored disease management and addressing unmet patient needs. Applying novel ML techniques to the clinical study of chronic inflammatory diseases shows promising results and great potential for precision medicine applications in clinical research and practice. In this review, we highlight the clinical applications of various ML techniques for prediction, diagnosis and prognosis of autoimmune rheumatic diseases, inflammatory bowel disease, autoimmune chronic kidney disease, and multiple sclerosis, as well as ML applications for patient stratification and treatment selection. We highlight the use of ML in drug development, including target identification, validation and drug repurposing, as well as challenges related to data interpretation and validation, and ethical concerns related to the use of artificial intelligence in clinical research.

Protein interaction, monocyte toxicity and immunogenic properties of cerium oxide crystals with 5% or 14% gadolinium, cobalt oxide and iron oxide nanoparticles - an interdisciplinary approach.

Metal oxide nanoparticles are widely used in both consumer products and medical applications, but the knowledge regarding exposure-related health effects is limited. However, it is challenging to investigate nanoparticle interaction processes with biological systems. The overall aim of this project was to improve the possibility to predict exposure-related health effects of metal oxide nanoparticles through interdisciplinary collaboration by combining workflows from the pharmaceutical industry, nanomaterial sciences, and occupational medicine. Specific aims were to investigate nanoparticle-protein interactions and possible adverse immune reactions. Four different metal oxide nanoparticles; CeOx nanocrystals with 5% or 14% Gd, Co3O4, and Fe2O3, were characterized by dynamic light scattering and high-resolution transmission electron microscopy. Nanoparticle-binding proteins were identified and screened for HLA-binding peptides in silico. Monocyte interaction with nanoparticle-protein complexes was assessed in vitro. Herein, for the first time, immunogenic properties of nanoparticle-binding proteins have been characterized. The present study indicates that especially Co3O4-protein complexes can induce both 'danger signals', verified by the production of inflammatory cytokines and simultaneously bind autologous proteins, which can be presented as immunogenic epitopes by MHC class II. The clinical relevance of these findings should be further evaluated to investigate the role of metal oxide nanoparticles in the development of autoimmune disease. The general workflow identified experimental difficulties, such as nanoparticle aggregate formation and a lack of protein-free buffers suitable for particle characterization, protein analyses, as well as for cell studies. This confirms the importance of future interdisciplinary collaborations.

An evaluation of a FluoroSpot assay as a diagnostic tool to determine SARS-CoV-2 specific T cell responses.

Numerous assays evaluating serological and cellular responses have been developed to characterize immune responses against SARS-CoV-2. Serological assays are both cost- and time-effective compared to cellular assays, but cellular immune responses may provide a diagnostic value to determine previous SARS-CoV-2 infection in seronegative individuals. However, potential cross-reactive T cell responses stemming from prior encounters with human coronaviruses (HCoVs) may affect assay specificity. In this study, we evaluated the specificity and sensitivity of a SARS-CoV-2 IFN-γ Release Assay (IGRA) based on the FluoroSpot method employing commercially available SARS-CoV-2-specific peptide pools, as well as an in-house designed SARS-CoV-2 peptide pool restricted to 5 amino acid stretches or less aligning with endemic HCoVs. Blood samples were obtained from healthcare workers (HCW) 5-6 months post SARS-CoV-2 spike (S) IgG and nucleocapsid (N) IgG dual seroconversion (n = 187) and HCW who had been S IgG and N IgG dual seronegative at repeated occasions, including the current sampling time point (n = 102). In addition, samples were obtained 4 to 5 months post infection from 55 polymerase chain reaction (PCR)-confirmed COVID-19 patients. Assay specificity and sensitivity were calculated with serology as a reference standard for HCW. The in-house generated peptide pool displayed a specificity of 96.1%, while the commercially available peptide pools displayed specificities of 80.4% and 85.3%, respectively. Sensitivity was higher in a cohort of previously hospitalized COVID-19 patients (96.4% and 84.0% for the commercially available peptide pools and 92.7% for the in-house generated peptide pool) compared to the HCW cohort (92.0% and 66.8% for the commercially available peptide pools and 76.0% for the in-house generated peptide pool). Based on these findings, the individual diagnostic value of T cell immune responses against SARS-CoV-2 currently appears to be limited but remain an important research tool ahead.

FRED--a framework for T-cell epitope detection.

UNLABELLED: Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods. AVAILABILITY: FRED is freely available for download at http://www-bs.informatik.uni-tuebingen.de/Software/FRED.