A unified metric of human immune health.

Immunological health has been challenging to characterize but could be defined as the absence of immune pathology. While shared features of some immune diseases and the concept of immunologic resilience based on age-independent adaptation to antigenic stimulation have been developed, general metrics of immune health and its utility for assessing clinically healthy individuals remain ill defined. Here we integrated transcriptomics, serum protein, peripheral immune cell frequency and clinical data from 228 patients with 22 monogenic conditions impacting key immunological pathways together with 42 age- and sex-matched healthy controls. Despite the high penetrance of monogenic lesions, differences between individuals in diverse immune parameters tended to dominate over those attributable to disease conditions or medication use. Unsupervised or supervised machine learning independently identified a score that distinguished healthy participants from patients with monogenic diseases, thus suggesting a quantitative immune health metric (IHM). In ten independent datasets, the IHM discriminated healthy from polygenic autoimmune and inflammatory disease states, marked aging in clinically healthy individuals, tracked disease activities and treatment responses in both immunological and nonimmunological diseases, and predicted age-dependent antibody responses to immunizations with different vaccines. This discriminatory power goes beyond that of the classical inflammatory biomarkers C-reactive protein and interleukin-6. Thus, deviations from health in diverse conditions, including aging, have shared systemic immune consequences, and we provide a web platform for calculating the IHM for other datasets, which could empower precision medicine.

Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data.

Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.

Multiomics integration of 22 immune-mediated monogenic diseases reveals an emergent axis of human immune health.

Monogenic diseases are often studied in isolation due to their rarity. Here we utilize multiomics to assess 22 monogenic immune-mediated conditions with age- and sex-matched healthy controls. Despite clearly detectable disease-specific and "pan-disease" signatures, individuals possess stable personal immune states over time. Temporally stable differences among subjects tend to dominate over differences attributable to disease conditions or medication use. Unsupervised principal variation analysis of personal immune states and machine learning classification distinguishing between healthy controls and patients converge to a metric of immune health (IHM). The IHM discriminates healthy from multiple polygenic autoimmune and inflammatory disease states in independent cohorts, marks healthy aging, and is a pre-vaccination predictor of antibody responses to influenza vaccination in the elderly. We identified easy-to-measure circulating protein biomarker surrogates of the IHM that capture immune health variations beyond age. Our work provides a conceptual framework and biomarkers for defining and measuring human immune health.

Platelet response to influenza vaccination reflects effects of aging.

Platelets are uniquely positioned as mediators of not only hemostasis but also innate immunity. However, how age and geriatric conditions such as frailty influence platelet function during an immune response remains unclear. We assessed the platelet transcriptome at baseline and following influenza vaccination in Younger (age 21-35) and Older (age ≥65) adults (including community-dwelling individuals who were largely non-frail and skilled nursing facility (SNF)-resident adults who nearly all met criteria for frailty). Prior to vaccination, we observed an age-associated increase in the expression of platelet activation and mitochondrial RNAs and decrease in RNAs encoding proteins mediating translation. Age-associated differences were also identified in post-vaccination response trajectories over 28 days. Using tensor decomposition analysis, we found increasing RNA expression of genes in platelet activation pathways in young participants, but decreasing levels in (SNF)-resident adults. Translation RNA trajectories were inversely correlated with these activation pathways. Enhanced platelet activation was found in community-dwelling older adults at the protein level, compared to young individuals both prior to and post-vaccination; whereas SNF residents showed decreased platelet activation compared to community-dwelling older adults that could reflect the influence of decreased translation RNA expression. Our results reveal alterations in the platelet transcriptome and activation responses that may contribute to age-associated chronic inflammation and the increased incidence of thrombotic and pro-inflammatory diseases in older adults.

Benchmarking transcriptional host response signatures for infection diagnosis.

Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness and cross-reactivity. Applied to 30 published signatures of infection, the analysis showed that signatures were generally robust in detecting viral and bacterial infections in independent data. Asymptomatic and chronic infections were also detectable, albeit with decreased performance. However, many signatures were cross-reactive with unintended infections and aging. In general, we found robustness and cross-reactivity to be conflicting objectives, and we identified signature properties associated with this trade-off. The data compendium and evaluation framework developed here provide a foundation for the development of signatures for clinical application. A record of this paper's transparent peer review process is included in the supplemental information.

Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature.

The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

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.

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.

A curated collection of human vaccination response signatures.

Recent advances in high-throughput experiments and systems biology approaches have resulted in hundreds of publications identifying "immune signatures". Unfortunately, these are often described within text, figures, or tables in a format not amenable to computational processing, thus severely hampering our ability to fully exploit this information. Here we present a data model to represent immune signatures, along with the Human Immunology Project Consortium (HIPC) Dashboard ( www.hipc-dashboard.org ), a web-enabled application to facilitate signature access and querying. The data model captures the biological response components (e.g., genes, proteins, cell types or metabolites) and metadata describing the context under which the signature was identified using standardized terms from established resources (e.g., HGNC, Protein Ontology, Cell Ontology). We have manually curated a collection of >600 immune signatures from >60 published studies profiling human vaccination responses for the current release. The system will aid in building a broader understanding of the human immune response to stimuli by enabling researchers to easily access and interrogate published immune signatures.

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.

Sex-Biased Aging Effects on Ig Somatic Hypermutation Targeting.

Aged individuals, particularly males, display an impaired level of Ab response compared with their younger counterparts, yet the molecular mechanisms responsible for the discrepancy are not well understood. We hypothesize that some of this difference may be linked to B cell somatic hypermutation (SHM) targeting, including error-prone DNA repair activities that are crucial to Ab diversification. To examine the effects of aging on SHM targeting, we analyzed B cell Ig repertoire sequences from 27 healthy male and female human subjects aged 20-89. By studying mutation patterns based on 985,069 mutations obtained from 123,415 sequences, we found that the SHM mutability hierarchies on microsequence motifs (i.e., SHM hot/cold spots) are mostly consistent between different age and sex groups. However, we observed a lower frequency in mutations involving Phase II SHM DNA repair activities in older males, but not in females. We also observed, from a separate study, a decreased expression level of DNA mismatch repair genes involved in SHM in older individuals compared with younger individuals, with larger fold changes in males than in females. Finally, we showed that the balance between Phase I versus Phase II SHM activities impacts the resulting Ig phenotypes. Our results showed that the SHM process is altered in some older individuals, providing insights into observed clinical differences in immunologic responses between different age and sex groups.

Seasonal Variability and Shared Molecular Signatures of Inactivated Influenza Vaccination in Young and Older Adults.

The seasonal influenza vaccine is an important public health tool but is only effective in a subset of individuals. The identification of molecular signatures provides a mechanism to understand the drivers of vaccine-induced immunity. Most previously reported molecular signatures of human influenza vaccination were derived from a single age group or season, ignoring the effects of immunosenescence or vaccine composition. Thus, it remains unclear how immune signatures of vaccine response change with age across multiple seasons. In this study we profile the transcriptional landscape of young and older adults over five consecutive vaccination seasons to identify shared signatures of vaccine response as well as marked seasonal differences. Along with substantial variability in vaccine-induced signatures across seasons, we uncovered a common transcriptional signature 28 days postvaccination in both young and older adults. However, gene expression patterns associated with vaccine-induced Ab responses were distinct in young and older adults; for example, increased expression of killer cell lectin-like receptor B1 (KLRB1; CD161) 28 days postvaccination positively and negatively predicted vaccine-induced Ab responses in young and older adults, respectively. These findings contribute new insights for developing more effective influenza vaccines, particularly in older adults.

Caenorhabditis elegans glutamylating enzymes function redundantly in male mating.

Microtubule glutamylation is an important modulator of microtubule function and has been implicated in the regulation of centriole stability, neuronal outgrowth and cilia motility. Glutamylation of the microtubules is catalyzed by a family of tubulin tyrosine ligase-like (TTLL) enzymes. Analysis of individual TTLL enzymes has led to an understanding of their specific functions, but how activities of the TTLL enzymes are coordinated to spatially and temporally regulate glutamylation remains relatively unexplored. We have undertaken an analysis of the glutamylating TTLL enzymes in C. elegans We find that although all five TTLL enzymes are expressed in the embryo and adult worm, loss of individual enzymes does not perturb microtubule function in embryonic cell divisions. Moreover, normal dye-filling, osmotic avoidance and male mating behavior indicate the presence of functional amphid cilia and male-specific neurons. A ttll-4(tm3310); ttll-11(tm4059); ttll-5(tm3360) triple mutant, however, shows reduced male mating efficiency due to a defect in the response step, suggesting that these three enzymes function redundantly, and that glutamylation is required for proper function of the male-specific neurons.