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.

Influenza vaccination reveals sex dimorphic imprints of prior mild COVID-19

Acute viral infections can have durable functional impacts on the immune system long after recovery, but how they affect homeostatic immune states and responses to future perturbations remain poorly understood1-4. Here we use systems immunology approaches, including longitudinal multimodal single-cell analysis (surface proteins, transcriptome and V(D)J sequences) to comparatively assess baseline immune statuses and responses to influenza vaccination in 33 healthy individuals after recovery from mild, non-hospitalized COVID-19 (mean, 151 days after diagnosis) and 40 age- and sex-matched control individuals who had never had COVID-19. At the baseline and independent of time after COVID-19, recoverees had elevated T cell activation signatures and lower expression of innate immune genes including Toll-like receptors in monocytes. Male individuals who had recovered from COVID-19 had coordinately higher innate, influenza-specific plasmablast, and antibody responses after vaccination compared with healthy male individuals and female individuals who had recovered from COVID-19, in part because male recoverees had monocytes with higher IL-15 responses early after vaccination coupled with elevated prevaccination frequencies of 'virtual memory'-like CD8+ T cells poised to produce more IFNγ after IL-15 stimulation. Moreover, the expression of the repressed innate immune genes in monocytes increased by day 1 to day 28 after vaccination in recoverees, therefore moving towards the prevaccination baseline of the healthy control individuals. By contrast, these genes decreased on day 1 and returned to the baseline by day 28 in the control individuals. Our study reveals sex-dimorphic effects of previous mild COVID-19 and suggests that viral infections in humans can establish new immunological set-points that affect future immune responses in an antigen-agnostic manner.

Influenza vaccination and single cell multiomics reveal sex dimorphic immune imprints of prior mild COVID-19

Viral infections can have profound and durable functional impacts on the immune system. There is an urgent need to characterize the long-term immune effects of SARS-CoV-2 infection given the persistence of symptoms in some individuals and the continued threat of novel variants. Here we use systems immunology, including longitudinal multimodal single cell analysis (surface proteins, transcriptome, and V(D)J sequences) from 33 previously healthy individuals after recovery from mild, non-hospitalized COVID-19 and 40 age- and sex-matched healthy controls with no history of COVID-19 to comparatively assess the post-infection immune status (mean: 151 days after diagnosis) and subsequent innate and adaptive responses to seasonal influenza vaccination. Identification of both sex-specific and -independent temporally stable changes, including signatures of T-cell activation and repression of innate defense/immune receptor genes (e.g., Toll-like receptors) in monocytes, suggest that mild COVID-19 can establish new post-recovery immunological set-points. COVID-19-recovered males had higher innate, influenza-specific plasmablast, and antibody responses after vaccination compared to healthy males and COVID-19-recovered females, partly attributable to elevated pre-vaccination frequencies of a GPR56 expressing CD8+ T-cell subset in male recoverees that are "poised" to produce higher levels of IFNγ upon inflammatory stimulation. Intriguingly, by day 1 post-vaccination in COVID-19-recovered subjects, the expression of the repressed genes in monocytes increased and moved towards the pre-vaccination baseline of healthy controls, suggesting that the acute inflammation induced by vaccination could partly reset the immune states established by mild COVID-19. Our study reveals sex-dimorphic immune imprints and in vivo functional impacts of mild COVID-19 in humans, suggesting that prior COVID-19, and possibly respiratory viral infections in general, could change future responses to vaccination and in turn, vaccines could help reset the immune system after COVID-19, both in an antigen-agnostic manner.

OMiCC: An expanded and enhanced platform for meta-analysis of public gene expression data

OMiCC (OMics Compendia Commons) is a biologist-friendly web platform that facilitates data reuse and integration. Users can search over 40,000 publicly available gene expression studies, annotate and curate samples, and perform meta-analysis. Since the initial publication, we have incorporated RNA-seq datasets, compendia sharing, RESTful API support, and an additional meta-analysis method based on random effects. Here, we provide a step-by-step guide for using OMiCC.

For complete details on the use and execution of this protocol, please refer to Shah et al. (2016).

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OMiCC (OMics Compendia Commons) is a free web-based tool for gene expression data reuse

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Search publicly available studies to perform sample group comparisons to explore a disease

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In meta-analysis, multiple studies are combined to identify coherent signals

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OMiCC supports crowd-sharing and users can share their own analyses with the community

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Pre-vaccination and early B cell signatures predict antibody response to SARS-CoV-2 mRNA vaccine

SARS-CoV-2 mRNA vaccines are highly effective, although weak antibody responses are seen in some individuals with correlates of immunity that remain poorly understood. Here we longitudinally dissected antibody, plasmablast, and memory B cell (MBC) responses to the two-dose Moderna mRNA vaccine in SARS-CoV-2-uninfected adults. Robust, coordinated IgA and IgG antibody responses were preceded by bursts of spike-specific plasmablasts after both doses, but earlier and more intensely after dose two. Distinct antigen-specific MBC populations also emerged post-vaccination with varying kinetics. We identified antigen non-specific pre-vaccination MBC and post-vaccination plasmablasts after dose one and their spike-specific counterparts early after dose two that correlated with subsequent antibody levels. These baseline and response signatures can thus provide early indicators of serological efficacy and explain response variability in the population.

The Human Salivary Proteome Wiki: A Community-Driven Research Platform

Saliva has become an attractive body fluid for on-site, remote, and real-time monitoring of oral and systemic health. At the same time, the scientific community needs a saliva-centered information platform that keeps pace with the rapid accumulation of new data and knowledge by annotating, refining, and updating the salivary proteome catalog. We developed the Human Salivary Proteome (HSP) Wiki as a public data platform for researching and retrieving custom-curated data and knowledge on the saliva proteome. The HSP Wiki is dynamically compiled and updated based on published saliva proteome studies and up-to-date protein reference records. It integrates a wide range of available information by funneling in data from established external protein, genome, transcriptome, and glycome databases. In addition, the HSP Wiki incorporates data from human disease-related studies. Users can explore the proteome of saliva simply by browsing the database, querying the available data, performing comparisons of data sets, and annotating existing protein entries using a simple, intuitive interface. The annotation process includes both user feedback and curator committee review to ensure the quality and validity of each entry. Here, we present the first overview of features and functions the HSP Wiki offers. As a saliva proteome-centric, publicly accessible database, the HSP Wiki will advance the knowledge of saliva composition and function in health and disease for users across a wide range of disciplines. As a community-based data- and knowledgebase, the HSP Wiki will serve as a worldwide platform to exchange salivary proteome information, inspire novel research ideas, and foster cross-discipline collaborations. The HSP Wiki will pave the way for harnessing the full potential of the salivary proteome for diagnosis, risk prediction, therapy of oral and systemic diseases, and preparedness for emerging infectious diseases.Database URL: https://salivaryproteome.nidcr.nih.gov/.

Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19

COVID-19 exhibits extensive patient-to-patient heterogeneity. To link immune response variation to disease severity and outcome over time, we longitudinally assessed circulating proteins as well as 188 surface protein markers, transcriptome, and T cell receptor sequence simultaneously in single peripheral immune cells from COVID-19 patients. Conditional-independence network analysis revealed primary correlates of disease severity, including gene expression signatures of apoptosis in plasmacytoid dendritic cells and attenuated inflammation but increased fatty acid metabolism in CD56^dimCD16^hi NK cells linked positively to circulating interleukin (IL)-15. CD8⁺ T cell activation was apparent without signs of exhaustion. Although cellular inflammation was depressed in severe patients early after hospitalization, it became elevated by days 17–23 post symptom onset, suggestive of a late wave of inflammatory responses. Furthermore, circulating protein trajectories at this time were divergent between and predictive of recovery versus fatal outcomes. Our findings stress the importance of timing in the analysis, clinical monitoring, and therapeutic intervention of COVID-19.

Mass mining: a crowdsourcing approach for meta-analyzing gene expression signatures of autoimmunity using large-scale public data sets

The volume and diversity of large-scale biological data available in the public domain continues to grow. This data has the potential to be reused to answer questions beyond those envisioned when the data was generated; however, few immunologists have sufficient bioinformatics expertise to do so. We used OMiCC, a free online platform that enables programming-free meta-analysis of public gene expression data and facilitates “crowdsharing” the work of annotating and constructing data compendia. We organized an “OMiCC Jamboree” to evaluate if biologists without bioinformatics training could use OMiCC to identify and annotate public gene expression datasets and design proper disease versus control comparisons for meta-analysis. Twenty-nine volunteer NIH biologists gathered to search and annotate public microarray data of human autoimmune conditions and the corresponding mouse models. Meta-analyses across studies explored 1) gene expression signatures for each disease, 2) pan-disease signatures, and 3) cross-species signatures. A large number of differentially expressed genes and enriched pathways were identified for each disease, with substantial overlap among diseases both within and between species, including pan-disease and pan-species signatures such as those associated with interferon.

Supported by the Intramural Research Programs of NIAID and CIT, NIH.

Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene Network

Cell-to-cell variation in gene expression and the propagation of such variation (PoV or "noise propagation") from one gene to another in the gene network, as reflected by gene-gene correlation across single cells, are commonly observed in single-cell transcriptomic studies and can shape the phenotypic diversity of cell populations. While gene network "rewiring" is known to accompany cellular adaptation to different environments, how PoV changes between environments and its underlying regulatory mechanisms are less understood. Here, we systematically explored context-dependent PoV among genes in human macrophages, utilizing different cytokines as natural perturbations of multiple molecular parameters that may influence PoV. Our single-cell, epigenomic, computational, and stochastic simulation analyses reveal that environmental adaptation can tune PoV to potentially shape cellular heterogeneity by changing parameters such as the degree of phosphorylation and transcription factor-chromatin interactions. This quantitative tuning of PoV may be a widespread, yet underexplored, property of cellular adaptation to distinct environments.

Meta-analysis of crowdsourced data compendia suggests pan-disease transcriptional signatures of autoimmunity

Background: The proliferation of publicly accessible large-scale biological data together with increasing availability of bioinformatics tools have the potential to transform biomedical research. Here we report a crowdsourcing Jamboree that explored whether a team of volunteer biologists without formal bioinformatics training could use OMiCC, a crowdsourcing web platform that facilitates the reuse and (meta-) analysis of public gene expression data, to compile and annotate gene expression data, and design comparisons between disease and control sample groups.

Methods: The Jamboree focused on several common human autoimmune diseases, including systemic lupus erythematosus (SLE), multiple sclerosis (MS), type I diabetes (DM1), and rheumatoid arthritis (RA), and the corresponding mouse models. Meta-analyses were performed in OMiCC using comparisons constructed by the participants to identify 1) gene expression signatures for each disease (disease versus healthy controls at the gene expression and biological pathway levels), 2) conserved signatures across all diseases within each species (pan-disease signatures), and 3) conserved signatures between species for each disease and across all diseases (cross-species signatures).

Results: A large number of differentially expressed genes were identified for each disease based on meta-analysis, with observed overlap among diseases both within and across species. Gene set/pathway enrichment of upregulated genes suggested conserved signatures (e.g., interferon) across all human and mouse conditions.

Conclusions: Our Jamboree exercise provides evidence that when enabled by appropriate tools, a "crowd" of biologists can work together to accelerate the pace by which the increasingly large amounts of public data can be reused and meta-analyzed for generating and testing hypotheses. Our encouraging experience suggests that a similar crowdsourcing approach can be used to explore other biological questions.

Humoral Fingerprinting of Immune Responses: 'Super-Resolution', High-Dimensional Serology

In a recent study, Chung et al. report the development of a high-dimensional approach to assess humoral responses to immune perturbation that goes beyond antibody neutralization and titers. This approach enables the identification of potentially novel correlates and mechanisms of protective immunity to HIV vaccination, thus offering a glimpse of how dense phenotyping of serological responses coupled with bioinformatics analysis could lead to much-sought-after markers of protective vaccination responses.

Three-Dimensional Spot Detection in Ratiometric Fluorescence Imaging For Measurement of Subcellular Organelles

Lysosomes are subcellular organelles playing a vital role in the endocytosis process of the cell. Lysosomal acidity is an important factor in assuring proper functioning of the enzymes within the organelle, and can be assessed by labeling the lysosomes with pH-sensitive fluorescence probes. To enhance our understanding of the acidification mechanisms, the goal of this work is to develop a method that can accurately detect and characterize the acidity of each lysosome captured in ratiometric fluorescence images. We present an algorithm that utilizes the h-dome transformation and reconciles spots detected independently from two wavelength channels. We evaluated our algorithm using simulated images for which the exact locations were known. The h-dome algorithm achieved an f-score as high as 0.890. We also computed the fluorescence ratios from lysosomes in live HeLa cell images with known lysosomal pHs. Using leave-one-out cross-validation, we demonstrated that the new algorithm was able to achieve much better pH prediction accuracy than the conventional method.

Overview of BioCreative II gene normalization

Background:

The goal of the gene normalization task is to link genes or gene products mentioned in the literature to biological databases. This is a key step in an accurate search of the biological literature. It is a challenging task, even for the human expert; genes are often described rather than referred to by gene symbol and, confusingly, one gene name may refer to different genes (often from different organisms). For BioCreative II, the task was to list the Entrez Gene identifiers for human genes or gene products mentioned in PubMed/MEDLINE abstracts. We selected abstracts associated with articles previously curated for human genes. We provided 281 expert-annotated abstracts containing 684 gene identifiers for training, and a blind test set of 262 documents containing 785 identifiers, with a gold standard created by expert annotators. Inter-annotator agreement was measured at over 90%.

Results:

Twenty groups submitted one to three runs each, for a total of 54 runs. Three systems achieved F-measures (balanced precision and recall) between 0.80 and 0.81. Combining the system outputs using simple voting schemes and classifiers obtained improved results; the best composite system achieved an F-measure of 0.92 with 10-fold cross-validation. A 'maximum recall' system based on the pooled responses of all participants gave a recall of 0.97 (with precision 0.23), identifying 763 out of 785 identifiers.

Conclusion:

Major advances for the BioCreative II gene normalization task include broader participation (20 versus 8 teams) and a pooled system performance comparable to human experts, at over 90% agreement. These results show promise as tools to link the literature with biological databases.

Rule-based human gene normalization in biomedical text with confidence estimation

The ability to identify gene mentions in text and normalize them to the proper unique identifiers is crucial for "down-stream" text mining applications in bioinformatics. We have developed a rule-based algorithm that divides the normalization task into two steps. The first step includes pattern matching for gene symbols and an approximate term searching technique for gene names. Next, the algorithm measures several features based on morphological, statistical, and contextual information to estimate the level of confidence that the correct identifier is selected for a potential mention. Uniqueness, inverse distance, and coverage are three novel features we quantified. The algorithm was evaluated against the BioCreAtIvE datasets. The feature weights were tuned by the Nealder-Mead simplex method. An F-score of .7622 and an AUC (area under the recall-precision curve) of .7461 were achieved on the test data using the set of weights optimized to the training data.

Is dietary counselling effective in increasing dietary calcium, protein and energy intake in patients with osteoporotic fractures? A randomized controlled clinical trial

To determine the feasibility of increasing the calcium, protein and calorie intake of osteoporotic fracture patients by repeated dietary counselling delivered by a dietitian, a randomized controlled trial was conducted. Among 189 patients presenting with osteoporotic fractures to an Orthopaedics and Traumatology Department of a large regional hospital, 98 patients were randomized to the intervention group and 91 were randomized to the control group (with usual care). Intervention group received three sessions of dietary counselling with tailored made recommendations over a period of 4 months, while the control group only received dietary assessment and pamphlets on the prevention of osteoporosis. Almost all subjects in both intervention and control groups had calcium intake below the recommended level of 1000 mg at baseline. Half and 60% of subjects in both groups had total energy and protein intake below recommended levels respectively. The mean weights of control and intervention groups at baseline were 51.5 and 50.9 kg respectively, while the body mass index (BMI) were 22.6 (kg m(-2)) and 22.6 (kg m(-2)) respectively. After dietary intervention, significant increase of intake was seen in calcium intake (P = 0.0095 by t-test) in the intervention group. No significant increase was seen in protein or calorie intake. No significant change was observed in the body weight or BMI although there was a positive trend in the intervention group for all these parameters. We concluded that there was general malnutrition in Chinese elderly who presented with osteoporotic fractures. Dietary calcium could be increased by repeated professional dietary counselling. Future studies with longer duration and more objective clinical outcomes will be helpful to further demonstrate the long-term effects of dietary intervention on osteoporosis and other chronic diseases.

Decreasing influenza impact in lodges: 1997-2000 Calgary Regional Health Authority

Influenza causes high morbidity and hospitalization rates in residents of seniors lodges, I causing increased pressure on emergency departments and hospital beds every winter. This quasi-experimental study assessed the prevention of influenza outbreaks and their consequences in Calgary lodges. A multidisciplinary team worked to improve communication between health professionals, increase resident and staff immunization coverage, obtain weights and creatinines prior to influenza season, and facilitate amantadine prophylaxis during influenza A outbreaks. We had an increase in standing orders for amantadine and up to 56% of residents from one lodge had documented creatinine levels. Amantadine was administered to residents within two days of outbreak notification. Influenza morbidity in lodge outbreaks decreased from a rate of 37% to 9% over the three years and hospitalization rates decreased from 9% to 1%. We recommend that other regions consider a similar approach to decreasing influenza morbidity and hospitalization in lodge residents.

A genetic study of signaling processes for repression of PHO5 transcription in Saccharomyces cerevisiae

In the yeast Saccharomyces cerevisiae, transcription of a secreted acid phosphatase, PHO5, is repressed in response to high concentrations of extracellular inorganic phosphate. To investigate the signal transduction pathway leading to transcriptional regulation of PHO5, we carried out a genetic selection for mutants that express PHO5 constitutively. We then screened for mutants whose phenotypes are also dependent on the function of PHO81, which encodes an inhibitor of the Pho80p-Pho85p cyclin/cyclin-dependent kinase complex. These mutations are therefore likely to impair upstream functions in the signaling pathway, and they define five complementation groups. Mutations were found in a gene encoding a plasma membrane ATPase (PMA1), in genes required for the in vivo function of the phosphate transport system (PHO84 and PHO86), in a gene involved in the fatty acid synthesis pathway (ACC1), and in a novel, nonessential gene (PHO23). These mutants can be classified into two groups: pho84, pho86, and pma1 are defective in high-affinity phosphate uptake, whereas acc1 and pho23 are not, indicating that the two groups of mutations cause constitutive expression of PHO5 by distinct mechanisms. Our observations suggest that these gene products affect different aspects of the signal transduction pathway for PHO5 repression.