High-affinity, neutralizing antibodies to SARS-CoV-2 can be made without T follicular helper cells.

T follicular helper (TFH) cells are the conventional drivers of protective, germinal center (GC)­based antiviral antibody responses. However, loss of TFH cells and GCs has been observed in patients with severe COVID-19. As T cell­B cell interactions and immunoglobulin class switching still occur in these patients, noncanonical pathways of antibody production may be operative during SARS-CoV-2 infection. We found that both TFH-dependent and -independent antibodies were induced against SARS-CoV-2 infection, SARS-CoV-2 vaccination, and influenza A virus infection. Although TFH-independent antibodies to SARS-CoV-2 had evidence of reduced somatic hypermutation, they were still high affinity, durable, and reactive against diverse spike-derived epitopes and were capable of neutralizing both homologous SARS-CoV-2 and the B.1.351 (beta) variant of concern. We found by epitope mapping and B cell receptor sequencing that TFH cells focused the B cell response, and therefore, in the absence of TFH cells, a more diverse clonal repertoire was maintained. These data support an alternative pathway for the induction of B cell responses during viral infection that enables effective, neutralizing antibody production to complement traditional GC-derived antibodies that might compensate for GCs damaged by viral inflammation.

High-resolution epitope mapping and characterization of SARS-CoV-2 antibodies in large cohorts of subjects with COVID-19.

As Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to spread, characterization of its antibody epitopes, emerging strains, related coronaviruses, and even the human proteome in naturally infected patients can guide the development of effective vaccines and therapies. Since traditional epitope identification tools are dependent upon pre-defined peptide sequences, they are not readily adaptable to diverse viral proteomes. The Serum Epitope Repertoire Analysis (SERA) platform leverages a high diversity random bacterial display library to identify proteome-independent epitope binding specificities which are then analyzed in the context of organisms of interest. When evaluating immune response in the context of SARS-CoV-2, we identify dominant epitope regions and motifs which demonstrate potential to classify mild from severe disease and relate to neutralization activity. We highlight SARS-CoV-2 epitopes that are cross-reactive with other coronaviruses and demonstrate decreased epitope signal for mutant SARS-CoV-2 strains. Collectively, the evolution of SARS-CoV-2 mutants towards reduced antibody response highlight the importance of data-driven development of the vaccines and therapies to treat COVID-19.

Immunogenic amino acid motifs and linear epitopes of COVID-19 mRNA vaccines.

Reverse vaccinology is an evolving approach for improving vaccine effectiveness and minimizing adverse responses by limiting immunizations to critical epitopes. Towards this goal, we sought to identify immunogenic amino acid motifs and linear epitopes of the SARS-CoV-2 spike protein that elicit IgG in COVID-19 mRNA vaccine recipients. Paired pre/post vaccination samples from N = 20 healthy adults, and post-vaccine samples from an additional N = 13 individuals were used to immunoprecipitate IgG targets expressed by a bacterial display random peptide library, and preferentially recognized peptides were mapped to the spike primary sequence. The data identify several distinct amino acid motifs recognized by vaccine-induced IgG, a subset of those targeted by IgG from natural infection, which may mimic 3-dimensional conformation (mimotopes). Dominant linear epitopes were identified in the C-terminal domains of the S1 and S2 subunits (aa 558-569, 627-638, and 1148-1159) which have been previously associated with SARS-CoV-2 neutralization in vitro and demonstrate identity to bat coronavirus and SARS-CoV, but limited homology to non-pathogenic human coronavirus. The identified COVID-19 mRNA vaccine epitopes should be considered in the context of variants, immune escape and vaccine and therapy design moving forward.

Cytokine release syndrome in a patient with colorectal cancer after vaccination with BNT162b2.

Patients with cancer are currently prioritized in coronavirus disease 2019 (COVID-19) vaccination programs globally, which includes administration of mRNA vaccines. Cytokine release syndrome (CRS) has not been reported with mRNA vaccines and is an extremely rare immune-related adverse event of immune checkpoint inhibitors. We present a case of CRS that occurred 5 d after vaccination with BTN162b2 (tozinameran)-the Pfizer-BioNTech mRNA COVID-19 vaccine-in a patient with colorectal cancer on long-standing anti-PD-1 monotherapy. The CRS was evidenced by raised inflammatory markers, thrombocytopenia, elevated cytokine levels (IFN-γ/IL-2R/IL-18/IL-16/IL-10) and steroid responsiveness. The close temporal association of vaccination and diagnosis of CRS in this case suggests that CRS was a vaccine-related adverse event; with anti-PD1 blockade as a potential contributor. Overall, further prospective pharmacovigillence data are needed in patients with cancer, but the benefit-risk profile remains strongly in favor of COVID-19 vaccination in this population.

Protein-Based Immunome Wide Association Studies (PIWAS) for the Discovery of Significant Disease-Associated Antigens.

Identification of the antigens associated with antibodies is vital to understanding immune responses in the context of infection, autoimmunity, and cancer. Discovering antigens at a proteome scale could enable broader identification of antigens that are responsible for generating an immune response or driving a disease state. Although targeted tests for known antigens can be straightforward, discovering antigens at a proteome scale using protein and peptide arrays is time consuming and expensive. We leverage Serum Epitope Repertoire Analysis (SERA), an assay based on a random bacterial display peptide library coupled with next generation sequencing (NGS), to power the development of Protein-based Immunome Wide Association Study (PIWAS). PIWAS uses proteome-based signals to discover candidate antibody-antigen epitopes that are significantly elevated in a subset of cases compared to controls. After demonstrating statistical power relative to the magnitude and prevalence of effect in synthetic data, we apply PIWAS to systemic lupus erythematosus (SLE, n=31) and observe known autoantigens, Smith and Ribosomal protein P, within the 22 highest scoring candidate protein antigens across the entire human proteome. We validate the magnitude and location of the SLE specific signal against the Smith family of proteins using a cohort of patients who are positive by predicate anti-Sm tests. To test the generalizability of the method in an additional autoimmune disease, we identified and validated autoantigenic signals to SSB, CENPA, and keratin proteins in a cohort of individuals with Sjogren's syndrome (n=91). Collectively, these results suggest that PIWAS provides a powerful new tool to discover disease-associated serological antigens within any known proteome.

Serum and Cervicovaginal Fluid Antibody Profiling in Herpes Simplex Virus-Seronegative Recipients of the HSV529 Vaccine.

Previous herpes simplex virus type 2 (HSV-2) vaccines have not prevented genital herpes. Concerns have been raised about the choice of antigen, the type of antibody induced by the vaccine, and whether antibody is present in the genital tract where infection occurs. We reported results of a trial of an HSV-2 replication-defective vaccine, HSV529, that induced serum neutralizing antibody responses in 78% of HSV-1-/HSV-2- vaccine recipients. Here we show that HSV-1-/HSV-2- vaccine recipients developed antibodies to epitopes of several viral proteins; however, fewer antibody epitopes were detected in vaccine recipients compared with naturally infected persons. HSV529 induced antibodies that mediated HSV-2-specific natural killer (NK) cell activation. Depletion of glycoprotein D (gD)-binding antibody from sera reduced neutralizing titers by 62% and NK cell activation by 81%. HSV-2 gD antibody was detected in cervicovaginal fluid at about one-third the level of that in serum. A vaccine that induces potent serum antibodies transported to the genital tract might reduce HSV genital infection.

Autoantibody Landscape in Patients with Advanced Prostate Cancer.

PURPOSE: Autoantibody responses in cancer are of great interest, as they may be concordant with T-cell responses to cancer antigens or predictive of response to cancer immunotherapies. Thus, we sought to characterize the antibody landscape of metastatic castration-resistant prostate cancer (mCRPC). EXPERIMENTAL DESIGN: Serum antibody epitope repertoire analysis (SERA) was performed on patient serum to identify tumor-specific neoepitopes. Somatic mutation-specific neoepitopes were investigated by associating serum epitope enrichment scores with whole-genome sequencing results from paired solid tumor metastasis biopsies and germline blood samples. A protein-based immunome-wide association study (PIWAS) was performed to identify significantly enriched epitopes, and candidate serum antibodies enriched in select patients were validated by ELISA profiling. A distinct cohort of patients with melanoma was evaluated to validate the top cancer-specific epitopes. RESULTS: SERA was performed on 1,229 serum samples obtained from 72 men with mCRPC and 1,157 healthy control patients. Twenty-nine of 6,636 somatic mutations (0.44%) were associated with an antibody response specific to the mutated peptide. PIWAS analyses identified motifs in 11 proteins, including NY-ESO-1 and HERVK-113, as immunogenic in mCRPC, and ELISA confirmed serum antibody enrichment in candidate patients. Confirmatory PIWAS, Identifying Motifs Using Next-generation sequencing Experiments (IMUNE), and ELISA analyses performed on serum samples from 106 patients with melanoma similarly revealed enriched cancer-specific antibody responses to NY-ESO-1. CONCLUSIONS: We present the first large-scale profiling of autoantibodies in advanced prostate cancer, utilizing a new antibody profiling approach to reveal novel cancer-specific antigens and epitopes. Our study recovers antigens of known importance and identifies novel tumor-specific epitopes of translational interest.

Integrated, multicohort analysis reveals unified signature of systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that follows an unpredictable disease course and affects multiple organs and tissues. We performed an integrated, multicohort analysis of 7,471 transcriptomic profiles from 40 independent studies to identify robust gene expression changes associated with SLE. We identified a 93-gene signature (SLE MetaSignature) that is differentially expressed in the blood of patients with SLE compared with healthy volunteers; distinguishes SLE from other autoimmune, inflammatory, and infectious diseases; and persists across diverse tissues and cell types. The SLE MetaSignature correlated significantly with disease activity and other clinical measures of inflammation. We prospectively validated the SLE MetaSignature in an independent cohort of pediatric patients with SLE using a microfluidic quantitative PCR (qPCR) array. We found that 14 of the 93 genes in the SLE MetaSignature were independent of IFN-induced and neutrophil-related transcriptional profiles that have previously been associated with SLE. Pathway analysis revealed dysregulation associated with nucleic acid biosynthesis and immunometabolism in SLE. We further refined a neutropoiesis signature and identified underappreciated transcripts related to immune cells and oxidative stress. In our multicohort, transcriptomic analysis has uncovered underappreciated genes and pathways associated with SLE pathogenesis, with the potential to advance clinical diagnosis, biomarker development, and targeted therapeutics for SLE.

Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters.

OBJECTIVES: To find and validate generalizable sepsis subtypes using data-driven clustering. DESIGN: We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700). SETTING: Retrospective analysis. SUBJECTS: Persons admitted to the hospital with bacterial sepsis. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A unified clustering analysis across 14 discovery datasets revealed three subtypes, which, based on functional analysis, we termed "Inflammopathic, Adaptive, and Coagulopathic." We then validated these subtypes in nine independent datasets from five different countries (n = 600). In both discovery and validation data, the Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. Further, these clusters are statistically associated with clusters derived by others in independent single sepsis cohorts. CONCLUSIONS: The three sepsis subtypes may represent a unifying framework for understanding the molecular heterogeneity of the sepsis syndrome. Further study could potentially enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.

Gene annotation bias impedes biomedical research.

We found tremendous inequality across gene and protein annotation resources. We observed that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate that researchers reduce these biases by pursuing data-driven hypotheses.

Methods to increase reproducibility in differential gene expression via meta-analysis.

Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a 'silver standard' of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini-Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size.

EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY.

A major contributor to the scientific reproducibility crisis has been that the results from homogeneous, single-center studies do not generalize to heterogeneous, real world populations. Multi-cohort gene expression analysis has helped to increase reproducibility by aggregating data from diverse populations into a single analysis. To make the multi-cohort analysis process more feasible, we have assembled an analysis pipeline which implements rigorously studied meta-analysis best practices. We have compiled and made publicly available the results of our own multi-cohort gene expression analysis of 103 diseases, spanning 615 studies and 36,915 samples, through a novel and interactive web application. As a result, we have made both the process of and the results from multi-cohort gene expression analysis more approachable for non-technical users.

Development of Th17-Associated Interstitial Kidney Inflammation in Lupus-Prone Mice Lacking the Gene Encoding STAT-1.

OBJECTIVE: Type I interferon (IFN) signaling is a central pathogenic pathway in systemic lupus erythematosus (SLE), and therapeutics targeting type I IFN signaling are in development. Multiple proteins with overlapping functions play a role in IFN signaling, but the signaling events downstream of receptor engagement are unclear. This study was undertaken to investigate the roles of the type I and type II IFN signaling components IFN-α/ß/ω receptor 2 (IFNAR-2), IFN regulatory factor 9 (IRF-9), and STAT-1 in a mouse model of SLE. METHODS: We used immunohistochemical staining and highly multiplexed assays to characterize pathologic changes in histology, autoantibody production, cytokine/chemokine profiles, and STAT phosphorylation in order to investigate the individual roles of IFNAR-2, IRF-9, and STAT-1 in MRL/lpr mice. RESULTS: We found that STAT-1(-/-) mice, but not IRF-9(-/-) or IFNAR-2(-/-) mice, developed interstitial nephritis characterized by infiltration with retinoic acid receptor-related orphan nuclear receptor γt-positive lymphocytes, macrophages, and eosinophils. Despite pronounced interstitial kidney disease and abnormal kidney function, STAT-1(-/-) mice had decreased proteinuria, glomerulonephritis, and autoantibody production. Phosphospecific flow cytometry revealed shunting of STAT phosphorylation from STAT-1 to STAT-3/4. CONCLUSION: We describe unique contributions of STAT-1 to pathology in different kidney compartments in a mouse model, and provide potentially novel insight into tubulointerstitial nephritis, a poorly understood complication that predicts end-stage kidney disease in SLE patients.

Mining Twitter Data to Improve Detection of Schizophrenia.

Individuals who suffer from schizophrenia comprise I percent of the United States population and are four times more likely to die of suicide than the general US population. Identification of at-risk individuals with schizophrenia is challenging when they do not seek treatment. Microblogging platforms allow users to share their thoughts and emotions with the world in short snippets of text. In this work, we leveraged the large corpus of Twitter posts and machine-learning methodologies to detect individuals with schizophrenia. Using features from tweets such as emoticon use, posting time of day, and dictionary terms, we trained, built, and validated several machine learning models. Our support vector machine model achieved the best performance with 92% precision and 71% recall on the held-out test set. Additionally, we built a web application that dynamically displays summary statistics between cohorts. This enables outreach to undiagnosed individuals, improved physician diagnoses, and destigmatization of schizophrenia.

Differential expression analysis for pathways.

Life science technologies generate a deluge of data that hold the keys to unlocking the secrets of important biological functions and disease mechanisms. We present DEAP, Differential Expression Analysis for Pathways, which capitalizes on information about biological pathways to identify important regulatory patterns from differential expression data. DEAP makes significant improvements over existing approaches by including information about pathway structure and discovering the most differentially expressed portion of the pathway. On simulated data, DEAP significantly outperformed traditional methods: with high differential expression, DEAP increased power by two orders of magnitude; with very low differential expression, DEAP doubled the power. DEAP performance was illustrated on two different gene and protein expression studies. DEAP discovered fourteen important pathways related to chronic obstructive pulmonary disease and interferon treatment that existing approaches omitted. On the interferon study, DEAP guided focus towards a four protein path within the 26 protein Notch signalling pathway.