Pan-vaccine analysis reveals innate immune endotypes predictive of antibody responses to vaccination.

Several studies have shown that the pre-vaccination immune state is associated with the antibody response to vaccination. However, the generalizability and mechanisms that underlie this association remain poorly defined. Here, we sought to identify a common pre-vaccination signature and mechanisms that could predict the immune response across 13 different vaccines. Analysis of blood transcriptional profiles across studies revealed three distinct pre-vaccination endotypes, characterized by the differential expression of genes associated with a pro-inflammatory response, cell proliferation, and metabolism alterations. Importantly, individuals whose pre-vaccination endotype was enriched in pro-inflammatory response genes known to be downstream of nuclear factor-kappa B showed significantly higher serum antibody responses 1 month after vaccination. This pro-inflammatory pre-vaccination endotype showed gene expression characteristic of the innate activation state triggered by Toll-like receptor ligands or adjuvants. These results demonstrate that wide variations in the transcriptional state of the immune system in humans can be a key determinant of responsiveness to vaccination.

Transcriptional atlas of the human immune response to 13 vaccines reveals a common predictor of vaccine-induced antibody responses.

Systems vaccinology has defined molecular signatures and mechanisms of immunity to vaccination. However, comparative analysis of immunity to different vaccines is lacking. We integrated transcriptional data of over 3,000 samples, from 820 adults across 28 studies of 13 vaccines and analyzed vaccination-induced signatures of antibody responses. Most vaccines induced signatures of innate immunity and plasmablasts at days 1 and 7, respectively, after vaccination. However, the yellow fever vaccine induced an early transient signature of T and B cell activation at day 1, followed by delayed antiviral/interferon and plasmablast signatures that peaked at days 7 and 14-21, respectively. Thus, there was no evidence for a 'universal signature' that predicted antibody response to all vaccines. However, accounting for the asynchronous nature of responses, we defined a time-adjusted signature that predicted antibody responses across vaccines. These results provide a transcriptional atlas of immunity to vaccination and define a common, time-adjusted signature of antibody responses.

Deep resolution of clinical, cellular, and transcriptomic inflammatory markers during 52 weeks of IL-17A inhibition by secukinumab.

BACKGROUND: Secukinumab, an anti-IL-17A monoclonal antibody, induces histological and molecular resolution of psoriatic plaques by 12 weeks. However, the long-term effects of secukinumab on molecular resolution of psoriatic inflammation remain unknown. OBJECTIVE: To investigate the molecular resolution of psoriasis following 52-weeks of secukinumab treatment. METHODS: NCT01537432 was a two-part Phase 2, randomised, double-blinded, placebo-controlled, 52-week study of patients with moderate to severe psoriasis receiving secukinumab 300 mg. Psoriatic lesional and non-lesional skin biopsies were obtained at baseline, Week 12, and Week 52, and the composition of the residual disease genomic profile (RDGP, i.e., "molecular scar") of biopsies from secukinumab-responders was analysed. RESULTS: After 52 weeks of treatment, 14/24 enrolled patients were considered clinical responders (≥75% improvement in Psoriasis Area and Severity Index [PASI]; PASI75), 4/24 were considered non-responders (

Epitope-Specific IgE at 1 Year of Age Can Predict Peanut Allergy Status at 5 Years.

BACKGROUND: Currently, there is no laboratory test that can accurately identify children at risk of developing peanut allergy. Utilizing a subset of children randomized to the peanut avoidance arm of the LEAP trial, we monitored the development of epitope-specific (ses-)IgE and ses-IgG4 from 4-11 months to 5 years of age. OBJECTIVE: The aim of the study was to evaluate the prognostic ability of epitope-specific antibodies to predict the result of an oral food challenge (OFC) at 5 years. METHODS: A Bead-Based Epitope Assay was used to quantitate IgE and IgG4 to 64 sequential (linear) epitopes from Ara h 1-3 proteins at 4-11 months, 1 and 2.5 years of age in 74 subjects (38 of them with a positive OFC at 5 years). Specific IgE (sIgE) to peanut and component proteins was measured using ImmunoCAP. Machine learning methods were used to identify the earliest time point to predict 5-year outcome, developing prognostic algorithms based only on 4-11 month samples, 1-year or 2.5-year, and a combination of them. Data from 74 children were iteratively split 3:1 into training and validation sets, and machine learning models were developed to predict the 5-year outcome. A test set (n = 90) from an independent cohort was used for final evaluation. RESULTS: Elastic-Net algorithm combining ses-IgE and IgE to Ara h 1, 2, 3, and 9 proteins could predict the 5-year peanut allergy status of LEAP participants with an average validation accuracy of 64% at baseline. Samples taken at 1 year accurately predicted a 5-year OFC outcome with 83% accuracy. This performance remained consistent when evaluated on an independent CoFAR2 cohort with an accuracy of 78% for the 1-year model. CONCLUSION: IgE antibody profiles at 1 year of age are predictive of peanut OFC at 5 years in children avoiding peanuts. If further confirmed, this model may enable early identification of infants who may benefit from early immunotherapeutic interventions.

Development of a prediction model of postpartum hospital use using an equity-focused approach.

BACKGROUND: Racial inequities in maternal morbidity and mortality persist into the postpartum period, leading to a higher rate of postpartum hospital use among Black and Hispanic people. Delivery hospitalizations provide an opportunity to screen and identify people at high risk to prevent adverse postpartum outcomes. Current models do not adequately incorporate social and structural determinants of health, and some include race, which may result in biased risk stratification. OBJECTIVE: This study aimed to develop a risk prediction model of postpartum hospital use while incorporating social and structural determinants of health and using an equity approach. STUDY DESIGN: We conducted a retrospective cohort study using 2016-2018 linked birth certificate and hospital discharge data for live-born infants in New York City. We included deliveries from 2016 to 2017 in model development, randomly assigning 70%/30% of deliveries as training/test data. We used deliveries in 2018 for temporal model validation. We defined "Composite postpartum hospital use" as at least 1 readmission or emergency department visit within 30 days of the delivery discharge. We categorized diagnosis at first hospital use into 14 categories based on International Classification of Diseases-Tenth Revision diagnosis codes. We tested 72 candidate variables, including social determinants of health, demographics, comorbidities, obstetrical complications, and severe maternal morbidity. Structural determinants of health were the Index of Concentration at the Extremes, which is an indicator of racial-economic segregation at the zip code level, and publicly available indices of the neighborhood built/natural and social/economic environment of the Child Opportunity Index. We used 4 statistical and machine learning algorithms to predict "Composite postpartum hospital use", and an ensemble approach to predict "Cause-specific postpartum hospital use". We simulated the impact of each risk stratification method paired with an effective intervention on race-ethnic equity in postpartum hospital use. RESULTS: The overall incidence of postpartum hospital use was 5.7%; the incidences among Black, Hispanic, and White people were 8.8%, 7.4%, and 3.3%, respectively. The most common diagnoses for hospital use were general perinatal complications (17.5%), hypertension/eclampsia (12.0%), nongynecologic infections (10.7%), and wound infections (8.4%). Logistic regression with least absolute shrinkage and selection operator selection retained 22 predictor variables and achieved an area under the receiver operating curve of 0.69 in the training, 0.69 in test, and 0.69 in validation data. Other machine learning algorithms performed similarly. Selected social and structural determinants of health features included the Index of Concentration at the Extremes, insurance payor, depressive symptoms, and trimester entering prenatal care. The "Cause-specific postpartum hospital use" model selected 6 of the 14 outcome diagnoses (acute cardiovascular disease, gastrointestinal disease, hypertension/eclampsia, psychiatric disease, sepsis, and wound infection), achieving an area under the receiver operating curve of 0.75 in training, 0.77 in test, and 0.75 in validation data using a cross-validation approach. Models had slightly lower performance in Black and Hispanic subgroups. When simulating use of the risk stratification models with a postpartum intervention, identifying high-risk individuals with the "Composite postpartum hospital use" model resulted in the greatest reduction in racial-ethnic disparities in postpartum hospital use, compared with the "Cause-specific postpartum hospital use" model or a standard approach to identifying high-risk individuals with common pregnancy complications. CONCLUSION: The "Composite postpartum hospital use" prediction model incorporating social and structural determinants of health can be used at delivery discharge to identify persons at risk for postpartum hospital use.

Investigation of plaque psoriasis relapse after secukinumab withdrawal in patients from two Phase 3 studies.

BACKGROUND: Secukinumab is effective against a range of psoriatic manifestations. Investigating psoriasis (PsO) relapse following secukinumab discontinuation could provide insights into long-term PsO remission. OBJECTIVE: To examine PsO relapse rates upon treatment discontinuation following one year of secukinumab treatment. METHODS: This study (NCT01544595) is an extension of the Phase 3 ERASURE/FIXTURE studies in patients with moderate-to-severe plaque PsO. After one year of secukinumab 300 mg or 150 mg treatment, Week 52 PASI75 responders were randomly assigned to receive placebo. Upon relapse, patients receiving placebo were switched to their previous secukinumab dose. The study primary outcome was non-relapse rate after secukinumab withdrawal. RESULTS: Following the last dose of secukinumab 300 mg, 21% and 10% of patients who switched to placebo did not relapse at one and two years after discontinuation, respectively. Patients who received secukinumab 150 mg for one year showed a lower proportion of non-relapse following treatment discontinuation (14% and 6%) at one and two years, respectively). Non-relapsing patients maintained low mean PASI (2.8) at one year drug-free versus baseline (20.9); 1.7 at two years drug-free versus baseline (19.2). Disease duration (P=0.017) and severity (P=0.022) were significantly associated with time-to-relapse in patients initially treated with secukinumab 300 mg; patients with shorter disease duration and lower baseline PASI remained relapse-free for longer. CONCLUSIONS: Following discontinuation of secukinumab, a proportion of patients stayed relapse-free. Further, patients with shorter disease duration remained relapse-free for longer, suggesting that earlier treatment with secukinumab may result in long-term clinical control of moderate-to-severe PsO.

cosinoRmixedeffects: an R package for mixed-effects cosinor models.

BACKGROUND: Wearable devices enable monitoring and measurement of physiological parameters over a 24-h period, and some of which exhibit circadian rhythm characteristics. However, the currently available R package cosinor could only analyze daily cross-sectional data and compare the parameters between groups with two levels. To evaluate longitudinal changes in the circadian patterns, we need to extend the model to a mixed-effect model framework, allowing for random effects and interaction between COSINOR parameters and time-varying covariates. RESULTS: We developed the cosinoRmixedeffects R package for modelling longitudinal periodic data using mixed-effects cosinor models. The model allows for covariates and interactions with the non-linear parameters MESOR, amplitude, and acrophase. To facilitate ease of use, the package utilizes the syntax and functions of the widely used emmeans package to obtain estimated marginal means and contrasts. Estimation and hypothesis testing involving the non-linear circadian parameters are carried out using bootstrapping. We illustrate the package functionality by modelling daily measurements of heart rate variability (HRV) collected among health care workers over several months. Differences in circadian patterns of HRV between genders, BMI, and during infection with SARS-CoV2 are evaluated to illustrate how to perform hypothesis testing. CONCLUSION: cosinoRmixedeffects package provides the model fitting, estimation and hypothesis testing for the mixed-effects COSINOR model, for the linear and non-linear circadian parameters MESOR, amplitude and acrophase. The model accommodates factors with any number of categories, as well as complex interactions with circadian parameters and categorical factors.

Factors Associated With Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data.

BACKGROUND: The COVID-19 pandemic has resulted in a high degree of psychological distress among health care workers (HCWs). There is a need to characterize which HCWs are at an increased risk of developing psychological effects from the pandemic. Given the differences in the response of individuals to stress, an analysis of both the perceived and physiological consequences of stressors can provide a comprehensive evaluation of its impact. OBJECTIVE: This study aimed to determine characteristics associated with longitudinal perceived stress in HCWs and to assess whether changes in heart rate variability (HRV), a marker of autonomic nervous system function, are associated with features protective against longitudinal stress. METHODS: HCWs across 7 hospitals in New York City, NY, were prospectively followed in an ongoing observational digital study using the custom Warrior Watch Study app. Participants wore an Apple Watch for the duration of the study to measure HRV throughout the follow-up period. Surveys measuring perceived stress, resilience, emotional support, quality of life, and optimism were collected at baseline and longitudinally. RESULTS: A total of 361 participants (mean age 36.8, SD 10.1 years; female: n=246, 69.3%) were enrolled. Multivariate analysis found New York City's COVID-19 case count to be associated with increased longitudinal stress (P=.008). Baseline emotional support, quality of life, and resilience were associated with decreased longitudinal stress (P<.001). A significant reduction in stress during the 4-week period after COVID-19 diagnosis was observed in the highest tertial of emotional support (P=.03) and resilience (P=.006). Participants in the highest tertial of baseline emotional support and resilience had a significantly different circadian pattern of longitudinally collected HRV compared to subjects in the low or medium tertial. CONCLUSIONS: High resilience, emotional support, and quality of life place HCWs at reduced risk of longitudinal perceived stress and have a distinct physiological stress profile. Our findings support the use of these characteristics to identify HCWs at risk of the psychological and physiological stress effects of the pandemic.

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.

BACKGROUND: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. OBJECTIVE: We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. METHODS: Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. RESULTS: Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19-related symptom compared to all other symptom-free days (P=.01). CONCLUSIONS: Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19-related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Short-term transcriptional response to IL-17 receptor-A antagonism in the treatment of psoriasis.

BACKGROUND: IL-17 antagonists induce impressive clinical benefits in psoriasis, but it is unknown to what extent cellular and molecular psoriasis characteristics are suppressed by a clinically relevant dose/schedule of any IL-17-receptor antagonist. OBJECTIVE: We sought to examine the effects of the IL-17 receptor-A antagonist brodalumab, on clinical and molecular psoriasis features over a 12-week period. METHODS: A subset of patients (n = 116) enrolled in 3 phase-3 randomized clinical trials (AMAGINE -1 [Efficacy, Safety, and Withdrawal and Retreatment With Brodalumab in Moderate to Severe Plaque Psoriasis Subjects], -2 [P3 Study Brodalumab in Treatment of Moderate to Severe Plaque Psoriasis], and -3 [Efficacy and Safety of Brodalumab Compared With Placebo and Ustekinumab in Moderate to Severe Plaque Psoriasis in Subjects]) participated in a mechanistic substudy where punch biopsies were collected (lesional and nonlesional skin) between baseline and 12 weeks. This cohort included moderate-to-severe psoriasis patients treated with 140 mg (n = 46), 210 mg (n = 41) brodalumab, or placebo (n = 29). Key epidermal psoriatic features, including T-cell and dendritic cell subsets, were examined using immunohistochemistry. Treatment-induced changes in lesional skin gene expression profiles were evaluated using Affymetrix arrays. RESULTS: IL-17 receptor-A antagonism caused extensive improvements in clinical, histologic, and transcriptomic features of psoriasis. Cellular infiltrates (CD3+, CD8+, CD11c+, CD163+), markers of keratinocyte proliferation (Ki67+, KRT16), and inflammatory cytokines (IL-17A/C/F, IL-23A, IL-12B) decreased progressively, reaching close to nonlesional levels, paralleled by decreases in epidermal thickness. Psoriasis transcriptome gene expression improved ∼85% to 95% in responders whose psoriasis area severity index improved by 75% from baseline by week 12 (n = 63), compared with ∼30% to 65% in nonresponders (n = 12), while the residual disease genomic profile was 10% of the psoriasis transcriptome, which is less than for earlier generation drugs. IL-17-dependent gene expression, including keratinocyte genes, improved earlier and more extensively following brodalumab treatment compared with ustekinumab treatment (anti-IL-23/-IL-12). CONCLUSIONS: The clinically approved dose and schedule for brodalumab leads to nearly complete resolution of clinical, histologic, and transcriptomic features of psoriasis. Evidently, IL-17-induced release of keratinocyte-derived inflammatory mediators is a key driver of psoriasis pathogenesis.

The fission yeast Schizosaccharomyces pombe as a model to understand how peroxiredoxins influence cell responses to hydrogen peroxide.

As a more selectively reactive oxygen species, H2O2 (hydrogen peroxide) has been co-opted as a signalling molecule, but high levels can still lead to lethal amounts of cell damage. 2-Cys Prxs (peroxiredoxins) are ubiquitous thioredoxin peroxidases which utilize reversibly oxidized catalytic cysteine residues to reduce peroxides. As such, Prxs potentially make an important contribution to the repertoire of cell defences against oxidative damage. Although the abundance of eukaryotic 2-Cys Prxs suggests an important role in maintaining cell redox, the surprising sensitivity of their thioredoxin peroxidase activity to inactivation by H2O2 has raised questions as to their role as an oxidative stress defence. Indeed, work in model yeast has led the way in revealing that Prxs do much more than simply remove peroxides and have even uncovered circumstances where their thioredoxin peroxidase activity is detrimental. In the present paper, we focus on what we have learned from studies in the fission yeast Schizosaccharomyces pombe about the different roles of 2-Cys Prxs in responses to H2O2 and discuss the general implications of these findings for other systems.

Quantifying redox transcription factor dynamics as a tool to investigate redox signalling.

A critical feature of the cellular antioxidant response is the induction of gene expression by redox-sensitive transcription factors. In many cells, activating these transcription factors is a dynamic process involving multiple redox steps, but it is unclear how these dynamics should be measured. Here, we show how the dynamic profile of the Schizosaccharomyces pombe Pap1 transcription factor is quantifiable by three parameters: signal amplitude, signal time and signal duration. In response to increasing hydrogen peroxide concentrations, the Pap1 amplitude decreased while the signal time and duration showed saturable increases. In co-response plots, these parameters showed a complex, non-linear relationship to the mRNA levels of four Pap1-regulated genes. We also demonstrate that hydrogen peroxide and tert-butyl hydroperoxide trigger quantifiably distinct Pap1 activation profiles and transcriptional responses. Based on these findings, we propose that different oxidants and oxidant concentrations modulate the Pap1 dynamic profile, leading to specific transcriptional responses. We further show how the effect of combination and pre-exposure stresses on Pap1 activation dynamics can be quantified using this approach. This method is therefore a valuable addition to the redox signalling toolbox that may illuminate the role of dynamics in determining appropriate responses to oxidative stress.

Infarct-related structural disconnection and delirium in surgical aortic valve replacement patients.

OBJECTIVE: Although acute brain infarcts are common after surgical aortic valve replacement (SAVR), they are often unassociated with clinical stroke symptoms. The relationship between clinically "silent" infarcts and in-hospital delirium remains uncertain; obscured, in part, by how infarcts have been traditionally summarized as global metrics, independent of location or structural consequence. We sought to determine if infarct location and related structural connectivity changes were associated with postoperative delirium after SAVR. METHODS: A secondary analysis of a randomized multicenter SAVR trial of embolic protection devices (NCT02389894) was conducted, excluding participants with clinical stroke or incomplete neuroimaging (N = 298; 39% female, 7% non-White, 74 ± 7 years). Delirium during in-hospital recovery was serially screened using the Confusion Assessment Method. Parcellation and tractography atlas-based neuroimaging methods were used to determine infarct locations and cortical connectivity effects. Mixed-effect, zero-inflated gaussian modeling analyses, accounting for brain region-specific infarct characteristics, were conducted to examine for differences within and between groups by delirium status and perioperative neuroprotection device strategy. RESULTS: 23.5% participants experienced postoperative delirium. Delirium was associated with significantly increased lesion volumes in the right cerebellum and temporal lobe white matter, while diffusion weighted imaging infarct-related structural disconnection (DWI-ISD) was observed in frontal and temporal lobe regions (p-FDR < 0.05). Fewer brain regions demonstrated DWI-ISD loss in the suction-based neuroprotection device group, relative to filtration-based device or standard aortic cannula. INTERPRETATION: Structural disconnection from acute infarcts was greater in patients who experienced postoperative delirium, suggesting that the impact from covert perioperative infarcts may not be as clinically "silent" as commonly assumed.

The innate immune response following multivalent dengue vaccination and implications for protection against dengue challenge.

Understanding the immune response to dengue virus (DENV) is essential for developing a dengue vaccine that is protective against all 4 DENV serotypes. We evaluated the immune response after vaccination (live attenuated tetravalent dengue vaccine TV005 or trivalent admixture) and after challenge with DEN2Δ30 (Tonga/74) to better understand the importance of homotypic immunity in vaccine protection. Significant increases in IP-10 expression were observed following receipt of either the trivalent or tetravalent vaccine. After challenge, a large increase in IP-10 expression was observed in the placebo and trivalent admixture groups but not in the tetravalent vaccine group. MCP-1, IL-1RA, and MIP-1ß exhibited a similar pattern as IP-10. These results demonstrate protective effects of trivalent and tetravalent vaccines against DENV and suggest that the tetravalent vaccine has a better protective effect compared with the trivalent admixture. We also explored the postvaccination and postchallenge immune response differences between Black and White participants. White participants responded to vaccine differently than Black participants; Black participants receiving trivalent and tetravalent vaccines responded strongly and White participants responded only transiently in trivalent group. In response to challenge, White participants elicited a stronger response than Black participants. These results may explain why White participants may have a more vigorous DENV immune response than Black participants, as reported in literature.

Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers.

Objective: To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices. Materials and Methods: Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app. Results: We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±âˆ¼4%) and specificity of 77% (CI ±âˆ¼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age. Discussion: We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection. Conclusion: Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.

The Immune Signatures data resource, a compendium of systems vaccinology datasets.

Vaccines are among the most cost-effective public health interventions for preventing infection-induced morbidity and mortality, yet much remains to be learned regarding the mechanisms by which vaccines protect. Systems immunology combines traditional immunology with modern 'omic profiling techniques and computational modeling to promote rapid and transformative advances in vaccinology and vaccine discovery. The NIH/NIAID Human Immunology Project Consortium (HIPC) has leveraged systems immunology approaches to identify molecular signatures associated with the immunogenicity of many vaccines. However, comparative analyses have been limited by the distributed nature of some data, potential batch effects across studies, and the absence of multiple relevant studies from non-HIPC groups in ImmPort. To support comparative analyses across different vaccines, we have created the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets. This data resource is available through ImmuneSpace, along with code to reproduce the processing and batch normalization starting from the underlying study data in ImmPort and the Gene Expression Omnibus (GEO). The current release comprises 1405 participants from 53 cohorts profiling the response to 24 different vaccines. This novel systems vaccinology data release represents a valuable resource for comparative and meta-analyses that will accelerate our understanding of mechanisms underlying vaccine responses.

Role of the Complement Pathway in Inflammatory Skin Diseases: A Focus on Hidradenitis Suppurativa.

Although the role of immune dysregulation in hidradenitis suppurativa (HS) has yet to be elucidated, recent studies identified several complement abnormalities in patients with HS. The complement system serves a critical role in the modulation of immune response and regulation of cutaneous commensal bacteria. Complement is implicated in several inflammatory skin diseases including systemic lupus erythematosus, angioedema, pemphigus, bullous pemphigoid, and HS. A model of HS pathogenesis is proposed, integrating the role of commensal bacteria, cutaneous immune responses, and complement dysregulation. The role of complement in disease pathogenesis has led to the development of novel anticomplement agents and clinical trials investigating the efficacy of such treatments in HS.

Early Quantification of Systemic Inflammatory Proteins Predicts Long-Term Treatment Response to Tofacitinib and Etanercept.

The application of machine learning to longitudinal gene-expression profiles has demonstrated potential to decrease the assessment gap, between biochemical determination and clinical manifestation, of a patient's response to treatment. Although psoriasis is a proven testing ground for treatment-response prediction using transcriptomic data from clinically accessible skin biopsies, these biopsies are expensive, invasive, and challenging to obtain from certain body areas. Response prediction from blood biochemical measurements could be a cheaper, less invasive predictive platform. Longitudinal profiles for 92 inflammatory and 65 cardiovascular disease proteins were measured from the blood of psoriasis patients at baseline, and 4-weeks, following tofacitinib (janus kinase-signal transducer and activator of transcription-inhibitor) or etanercept (tumor necrosis factor-inhibitor) treatment, and predictive models were developed by applying machine-learning techniques such as bagging and ensembles. This data driven approach developed predictive models able to accurately predict the 12-week clinical endpoint for psoriasis following tofacitinib (area under the receiver operating characteristic curve [auROC] = 78%), or etanercept (auROC = 71%) treatment in a validation dataset, revealing a robust predictive protein signature including well-established psoriasis markers such as IL-17A and IL-17C, highlighting potential for biologically meaningful and clinically useful response predictions using blood protein data. Although most blood classifiers were outperformed by simple models trained using Psoriasis Area Severity Index scores, performance might be enhanced in future studies by measuring a wider variety of proteins.

Hyperoxidation of Peroxiredoxins: Gain or Loss of Function?

SIGNIFICANCE: In 2003, structural studies revealed that eukaryotic 2-Cys peroxiredoxins (Prx) have evolved to be sensitive to inactivation of their thioredoxin peroxidase activity by hyperoxidation (sulfinylation) of their peroxide-reacting catalytic cysteine. This was accompanied by the unexpected discovery, that the sulfinylation of this cysteine was reversible in vivo and the identification of a new enzyme, sulfiredoxin, that had apparently co-evolved specifically to reduce hyperoxidized 2-Cys Prx, restoring their peroxidase activity. Together, these findings have provided the impetus for multiple studies investigating the purpose of this reversible, Prx hyperoxidation. Recent Advances: It has been suggested that inhibition of the thioredoxin peroxidase activity by hyperoxidation can both promote and inhibit peroxide signal transduction, depending on the context. Prx hyperoxidation has also been proposed to protect cells against reactive oxygen species (ROS)-induced damage, by preserving reduced thioredoxin and/or by increasing non-peroxidase chaperone or signaling activities of Prx. CRITICAL ISSUES: Here, we will review the evidence in support of each of these proposed functions, in view of the in vivo contexts in which Prx hyperoxidation occurs, and the role of sulfiredoxin. Thus, we will attempt to explain the basis for seemingly contradictory roles for Prx hyperoxidation in redox signaling. FUTURE DIRECTIONS: We provide a rationale, based on modeling and experimental studies, for why Prx hyperoxidation should be considered a suitable, early biomarker for damaging levels of ROS. We discuss the implications that this has for the role of Prx in aging and the detection of hyperoxidized Prx as a conserved feature of circadian rhythms. Antioxid. Redox Signal. 28, 574-590.