Human Immune System Variation during 1 Year

Seasonal influences on the efficacy of anti-programmed cell death (ligand) 1 inhibitors in lung cancer

BACKGROUND

Seasonal variations in systemic immunity have been reported. This study aimed to evaluate whether seasonality affects the efficacy of anticancer immunotherapy.

METHODS

A total of 604 patients with lung cancer receiving single anti-programmed cell death (ligand) 1 (anti-PD-[L]1) inhibitors from two prospective observational cohorts were screened. Primary outcomes were progression-free survival (PFS) and overall survival (OS). Patients were classified into two groups according to the season when the treatment started: winter (November-February) and other seasons (March-October). Kaplan-Meier analysis and Cox proportional hazards models were fitted to evaluate the impact of seasonality on survival. For validation, propensity score matching was performed.

RESULTS

A total of 484 patients with advanced non-small cell lung cancer were included. In an unmatched population, multivariable analysis demonstrated that the winter group (n = 173) had a significantly lower risk of progression or death from immunotherapy than the other group (n = 311) (PFS: hazard ratio [HR], 0.77 [95% confidence interval (CI), 0.62-0.96]; p = .018; OS: HR, 0.77 [95% CI, 0.1-0.98]; p = .032). In a propensity score-matched population, the winter group (n = 162) showed significantly longer median PFS (2.8 months [95% CI, 1.9-4.1 months] vs. 2.0 months [95% CI, 1.4-2.7 months]; p = .009) than the other group (n = 162). The winter group's median OS was also significantly longer than that of the other group (13.4 months [95% CI, 10.2-18.0 months] vs. 8.0 months [95% CI, 3.6-8.7 months]; p = .012). The trend toward longer survival in the winter group continued in subgroup analyses.

CONCLUSIONS

Starting an anti-PD-(L)1 inhibitor in winter was associated with better treatment outcomes in patients with lung cancer compared to other seasons.

Restricting datasets to classifiable samples augments discovery of immune disease biomarkers

Immunological diseases are typically heterogeneous in clinical presentation, severity and response to therapy. Biomarkers of immune diseases often reflect this variability, especially compared to their regulated behaviour in health. This leads to a common difficulty that frustrates biomarker discovery and interpretation - namely, unequal dispersion of immune disease biomarker expression between patient classes necessarily limits a biomarker's informative range. To solve this problem, we introduce dataset restriction, a procedure that splits datasets into classifiable and unclassifiable samples. Applied to synthetic flow cytometry data, restriction identifies biomarkers that are otherwise disregarded. In advanced melanoma, restriction finds biomarkers of immune-related adverse event risk after immunotherapy and enables us to build multivariate models that accurately predict immunotherapy-related hepatitis. Hence, dataset restriction augments discovery of immune disease biomarkers, increases predictive certainty for classifiable samples and improves multivariate models incorporating biomarkers with a limited informative range. This principle can be directly extended to any classification task.

Peripheral immunophenotyping reveals lymphocyte stimulation in healthy women living with hereditary breast and ovarian cancer syndrome

Germline pathogenic variants in BRCA1 and BRCA2 (gpath(BRCA1/2)) represent genetic susceptibility for hereditary breast and ovarian cancer syndrome. Tumor-immune interactions are key contributors to breast cancer pathogenesis. Although earlier studies confirmed pro-tumorigenic immunological alterations in breast cancer patients, data are lacking in healthy carriers of gpath(BRCA1/2). Peripheral blood mononuclear cells of 66 women with or without germline predisposition or breast cancer were studied with a mass cytometry panel that identified 4 immune subpopulations of altered frequencies between healthy controls and healthy gpath(BRCA1) carriers, while no difference was observed in healthy gpath(BRCA2) carriers compared to controls. Moreover, 3 (one IgD-CD27+CD95+ B cell subpopulation and two CD45RA-CCR7+CD38+ CD4+ T cell subpopulations) out of these 4 subpopulations were also elevated in triple-negative breast cancer patients compared to controls. Our results reveal an activated peripheral immune phenotype in healthy carriers of gpath(BRCA1) that needs to be further elucidated to be leveraged in risk-reducing strategies.

Integrating population and single-cell variations in vaccine responses identifies a naturally adjuvanted human immune setpoint

Multimodal single-cell profiling methods can capture immune cell variations unfolding over time at the molecular, cellular, and population levels. Transforming these data into biological insights remains challenging. Here, we introduce a framework to integrate variations at the human population and single-cell levels in vaccination responses. Comparing responses following AS03-adjuvanted versus unadjuvanted influenza vaccines with CITE-seq revealed AS03-specific early (day 1) response phenotypes, including a B cell signature of elevated germinal center competition. A correlated network of cell-type-specific transcriptional states defined the baseline immune status associated with high antibody responders to the unadjuvanted vaccine. Certain innate subsets in the network appeared "naturally adjuvanted," with transcriptional states resembling those induced uniquely by AS03-adjuvanted vaccination. Consistently, CD14+ monocytes from high responders at baseline had elevated phospho-signaling responses to lipopolysaccharide stimulation. Our findings link baseline immune setpoints to early vaccine responses, with positive implications for adjuvant development and immune response engineering.

Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population

As we learned during the COVID-19 pandemic, vaccines are one of the most important tools in infectious disease control. To date, an unprecedentedly large volume of high-quality data on COVID-19 vaccinations have been accumulated. For preparedness in future pandemics beyond COVID-19, these valuable datasets should be analyzed to best shape an effective vaccination strategy. We are collecting longitudinal data from a community-based cohort in Fukushima, Japan, that consists of 2,407 individuals who underwent serum sampling two or three times after a two-dose vaccination with either BNT162b2 or mRNA-1273. Using the individually reconstructed time courses of the vaccine-elicited antibody response based on mathematical modeling, we first identified basic demographic and health information that contributed to the main features of the antibody dynamics, i.e., the peak, the duration, and the area under the curve. We showed that these three features of antibody dynamics were partially explained by underlying medical conditions, adverse reactions to vaccinations, and medications, consistent with the findings of previous studies. We then applied to these factors a recently proposed computational method to optimally fit an "antibody score", which resulted in an integer-based score that can be used as a basis for identifying individuals with higher or lower antibody titers from basic demographic and health information. The score can be easily calculated by individuals themselves or by medical practitioners. Although the sensitivity of this score is currently not very high, in the future, as more data become available, it has the potential to identify vulnerable populations and encourage them to get booster vaccinations. Our mathematical model can be extended to any kind of vaccination and therefore can form a basis for policy decisions regarding the distribution of booster vaccines to strengthen immunity in future pandemics.

Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes

BACKGROUND

While 'immuno-competence' is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated.

MATERIALS AND METHODS

Longitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested.

RESULTS

While non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly.

DISCUSSION

The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.

The Impact of Immune System Aging on Infectious Diseases

Immune system aging is becoming a field of increasing public health interest because of prolonged life expectancy, which is not paralleled by an increase in health expectancy. As age progresses, innate and adaptive immune systems undergo changes, which are defined, respectively, as inflammaging and immune senescence. A wealth of available data demonstrates that these two conditions are closely linked, leading to a greater vulnerability of elderly subjects to viral, bacterial, and opportunistic infections as well as lower post-vaccination protection. To face this novel scenario, an in-depth assessment of the immune players involved in this changing epidemiology is demanded regarding the individual and concerted involvement of immune cells and mediators within endogenous and exogenous factors and co-morbidities. This review provides an overall updated description of the changes affecting the aging immune system, which may be of help in understanding the underlying mechanisms associated with the main age-associated infectious diseases.

In vitro evaluation of the immunomodulatory activity of the nutraceutical formulation AminoDefence

Immune system (IS) functionality is warranted by inter-dependent processes that balance body defences without exceeding in inflammation. An ideal nutraceutical approach should sustain the protective IS activity while controlling inflammation. The potential immunomodulatory activity of the food supplement (FS) AminoDefence was studied in resting macrophages RAW264.7 and following stimulation of bacterial- and viral-associated inflammation trough LPS and PolyI:C treatments, respectively. In unstimulated macrophages, the formulation exerted a dose-dependent immunostimulant activity by up-regulating NO, IL-6, TNF-α and MCP-1 release, while it dampened the aberrant release of these factors induced by pro-inflammatory stimuli. Exploring the contribution of single components Echinacea purpurea (E. purpurea) extract and quercetin, used at proportional concentrations than in whole formulation, a more pronounced immunostimulant effect was observed for E. purpurea, and an anti-inflammatory activity for quercetin. Hence, AminoDefence exerts an immunomodulatory activity in macrophages by effectively stimulating a protective inflammatory response and limiting it in cases of excessive inflammation.

Pre-vaccination immunotypes reveal weak and robust antibody responders to influenza vaccination

Effective vaccine-induced immune responses are particularly essential in older adults who face an increased risk of immunosenescence. However, the complexity and variability of the human immune system make predicting vaccine responsiveness challenging. To address this knowledge gap, our study aimed to characterize immune profiles that are predictive of vaccine responsiveness using "immunotypes" as an innovative approach. We analyzed an extensive set of innate and adaptive immune cell subsets in the whole blood of 307 individuals (aged 25-92) pre- and post-influenza vaccination which we associated with day 28 hemagglutination inhibition (HI) antibody titers. Building on our previous work that stratified individuals into nine immunotypes based on immune cell subsets, we identified two pre-vaccination immunotypes associated with weak and one showing robust day 28 antibody response. Notably, the weak responders demonstrated HLA-DR+ T-cell signatures, while the robust responders displayed a high naïve-to-memory T-cell ratio and percentage of nonclassical monocytes. These specific signatures deepen our understanding of the relationship between the baseline of the immune system and its functional potential. This approach could enhance our ability to identify individuals at risk of immunosenescence. Our findings highlight the potential of pre-vaccination immunotypes as an innovative tool for informing personalized vaccination strategies and improving health outcomes, particularly for aging populations.

Variation in the basal immune state and implications for disease

Analysis of pre-existing immunity and its effects on acute infection often focus on memory responses associated with a prior infectious exposure. However, memory responses occur in the context of the overall immune state and leukocytes must interact with their microenvironment and other immune cells. Thus, it is important to also consider non-antigen-specific factors which shape the composite basal state and functional capacity of the immune system, termed here as I0 ('I naught'). In this review, we discuss the determinants of I0. Utilizing influenza virus as a model, we then consider the effect of I0 on susceptibility to infection and disease severity. Lastly, we outline a mathematical framework and demonstrate how researchers can build and tailor models to specific needs. Understanding how diverse factors uniquely and collectively impact immune competence will provide valuable insights into mechanisms of immune variation, aid in screening for high-risk populations, and promote the development of broadly applicable prophylactic and therapeutic treatments.

Baseline innate and T cell populations are correlates of protection against symptomatic influenza virus infection independent of serology

Evidence suggests that innate and adaptive cellular responses mediate resistance to the influenza virus and confer protection after vaccination. However, few studies have resolved the contribution of cellular responses within the context of preexisting antibody titers. Here, we measured the peripheral immune profiles of 206 vaccinated or unvaccinated adults to determine how baseline variations in the cellular and humoral immune compartments contribute independently or synergistically to the risk of developing symptomatic influenza. Protection correlated with diverse and polyfunctional CD4+ and CD8+ T, circulating T follicular helper, T helper type 17, myeloid dendritic and CD16+ natural killer (NK) cell subsets. Conversely, increased susceptibility was predominantly attributed to nonspecific inflammatory populations, including γδ T cells and activated CD16- NK cells, as well as TNFα+ single-cytokine-producing CD8+ T cells. Multivariate and predictive modeling indicated that cellular subsets (1) work synergistically with humoral immunity to confer protection, (2) improve model performance over demographic and serologic factors alone and (3) comprise the most important predictive covariates. Together, these results demonstrate that preinfection peripheral cell composition improves the prediction of symptomatic influenza susceptibility over vaccination, demographics or serology alone.

Immunodiagnosis - the promise of personalized immunotherapy

Immunotherapy showed remarkable efficacy in several cancer types. However, the majority of patients do not benefit from immunotherapy. Evaluating tumor heterogeneity and immune status before treatment is key to identifying patients that are more likely to respond to immunotherapy. Demographic characteristics (such as sex, age, and race), immune status, and specific biomarkers all contribute to response to immunotherapy. A comprehensive immunodiagnostic model integrating all these three dimensions by artificial intelligence would provide valuable information for predicting treatment response. Here, we coined the term "immunodiagnosis" to describe the blueprint of the immunodiagnostic model. We illustrated the features that should be included in immunodiagnostic model and the strategy of constructing the immunodiagnostic model. Lastly, we discussed the incorporation of this immunodiagnosis model in clinical practice in hopes of improving the prognosis of tumor immunotherapy.

Making human immune systems more interpretable through systems immunology

The human immune system is a distributed system of specialized cell populations with unique functions that collectively give rise to immune responses to infections and during immune-mediated diseases. Cell composition, plasma proteins, and functional responses vary among individuals, making the system difficult to interpret, but this variation is nonrandom. With careful analyses using novel experimental and computational tools, human immune system composition and function carry interpretable information. Here, we propose that systems-level analyses offer an opportunity to make human immune responses more interpretable in the future, and we discuss herein important considerations and lessons learned to this end. Predictable human immunology holds implications for better diagnostic and curative precision in patients with infectious and immune-associated diseases.

Integrated Drivers of Basal and Acute Immunity in Diverse Human Populations

Prior studies have identified genetic, infectious, and biological associations with immune competence and disease severity; however, there have been few integrative analyses of these factors and study populations are often limited in demographic diversity. Utilizing samples from 1,705 individuals in 5 countries, we examined putative determinants of immunity, including: single nucleotide polymorphisms, ancestry informative markers, herpesvirus status, age, and sex. In healthy subjects, we found significant differences in cytokine levels, leukocyte phenotypes, and gene expression. Transcriptional responses also varied by cohort, and the most significant determinant was ancestry. In influenza infected subjects, we found two disease severity immunophenotypes, largely driven by age. Additionally, cytokine regression models show each determinant differentially contributes to acute immune variation, with unique and interactive, location-specific herpesvirus effects. These results provide novel insight into the scope of immune heterogeneity across diverse populations, the integrative effects of factors which drive it, and the consequences for illness outcomes.

Multiscale integration of human and single-cell variations reveals unadjuvanted vaccine high responders are naturally adjuvanted

Advances in multimodal single cell analysis can empower high-resolution dissection of human vaccination responses. The resulting data capture multiple layers of biological variations, including molecular and cellular states, vaccine formulations, inter- and intra-subject differences, and responses unfolding over time. Transforming such data into biological insight remains a major challenge. Here we present a systematic framework applied to multimodal single cell data obtained before and after influenza vaccination without adjuvants or pandemic H5N1 vaccination with the AS03 adjuvant. Our approach pinpoints responses shared across or unique to specific cell types and identifies adjuvant specific signatures, including pro-survival transcriptional states in B lymphocytes that emerged one day after vaccination. We also reveal that high antibody responders to the unadjuvanted vaccine have a distinct baseline involving a rewired network of cell type specific transcriptional states. Remarkably, the status of certain innate immune cells in this network in high responders of the unadjuvanted vaccine appear "naturally adjuvanted": they resemble phenotypes induced early in the same cells only by vaccination with AS03. Furthermore, these cell subsets have elevated frequency in the blood at baseline and increased cell-intrinsic phospho-signaling responses after LPS stimulation ex vivo in high compared to low responders. Our findings identify how variation in the status of multiple immune cell types at baseline may drive robust differences in innate and adaptive responses to vaccination and thus open new avenues for vaccine development and immune response engineering in humans.

Inter-Individual Variations: A Challenge for the Standardisation of Complement Activation Assays

Introduction

The role of the human immune system in pathologic responses to chemicals including nanomaterials was identified as a gap in current hazard assessments. However, the complexity of the human immune system as well as interspecies variations make the development of predictive toxicity tests challenging. In the present study, we have analysed to what extent fluctuations of the complement system of different individuals will have an impact on the standardisation of immunological tests.

Methods

We treated commercially available pooled sera (PS) from healthy males, individual sera from healthy donors and from patients suffering from cancer, immunodeficiency and allergies with small molecules and liposomes. Changes of iC3b protein levels measured in enzyme-linked immunosorbent assays served as biomarker for complement activation.

Results

The level of complement activation in PS differed significantly from responses of individual donors (p < 0.01). Only seven out of 32 investigated sera from healthy donors responded similarly to the pooled serum. This variability was even more remarkable when investigating the effect of liposomes on the complement activation in sera from donors with pre-existing pathologies. Neither the 26 sera of donors with allergies nor sera of 16 donors with immunodeficiency responded similar to the PS of healthy donors. Allergy sufferers showed an increase in iC3b levels of 4.16-fold changes when compared to PS treated with liposomes.

Discussion

Our studies demonstrate that the use of pooled serum can lead to an over- or under-estimation of immunological response in particular for individuals with pre-existing pathologies. This is of high relevance when developing medical products based on nanomaterials and asks for a review of the current practice to use PS from healthy donors for the prediction of immunological effects of drugs in patients. A better understanding of individual toxicological responses to xenobiotics should be an essential part in safety assessments.

Identification of aging-associated immunotypes and immune stability as indicators of post-vaccination immune activation

Immunosenescence describes immune dysfunction observed in older individuals. To identify individuals at-risk for immune dysfunction, it is crucial to understand the diverse immune phenotypes and their intrinsic functional capabilities. We investigated immune cell subsets and variation in the aging population. We observed that inter-individual immune variation was associated with age and cytomegalovirus seropositivity. Based on the similarities of immune subset composition among individuals, we identified nine immunotypes that displayed different aging-associated immune signatures, which explained inter-individual variation better than age. Additionally, we correlated the immune subset composition of individuals over approximately a year as a measure of stability of immune parameters. Immune stability was significantly lower in immunotypes that contained aging-associated immune subsets and correlated with a circulating CD38 + CD4+ T follicular helper cell increase 7 days after influenza vaccination. In conclusion, immune stability is a feature of immunotypes and could be a potential indicator of post-vaccination cellular kinetics.

The MaDo real-life study of dose adjustment of allergen immunotherapy liquid formulations in an indication of respiratory allergic disease: Reasons, practices, and outcomes

Sublingual allergen immunotherapy (SLIT) is a safe, effective, disease-modifying treatment for moderate-to-severe respiratory allergies. The function and responsiveness of the immune system components underlying the effects of allergen immunotherapy may vary from one patient to another. Furthermore, the severity of the symptoms of allergic disease can fluctuate over time, due to changes in environmental allergen exposure, effector cell responsiveness, and cell signaling. Hence, the allergen dose provided through SLIT can be fine-tuned to establish an optimal balance between effectiveness and tolerability. The objective of the MaDo study was to describe and understand dose adjustments of SLIT liquid formulations in France. We performed a retrospective, observational, cross-sectional, real-life study of allergists and other specialist physicians. Physicians described their patients via an anonymous case report form (CRF). The main patient inclusion criteria were age 5 years or over, at least one physician-confirmed IgE-driven respiratory allergy, and treatment for at least 2 years with one or more SLIT liquid preparations. A nationally representative sample of 33 specialist physicians participated in the study. The physicians' main stated reasons for dose adjustment were adverse events (according to 90.9% of the physicians), treatment effectiveness (60.6%), sensitivity to the allergen (42.4%) and other characteristics (30.3%: mainly symptom severity, type of allergen, and asthma). 392 CRFs (mean ± standard deviation patient age: 27.8 ± 17.5; under-18s: 42.1%; polyallergy: 30.9%) were analyzed. Respectively 53.6%, 25.8%, 15.3%, and 8.7% of the patients received house dust mite, grass pollen, birch pollen and cypress pollen SLIT. Dose adjustments were noted in 258 (65.8%) patients (at the start of the maintenance phase for 101 patients (39.2%) and later for 247 (95.7%)). Dose adjustment was not linked to sex, age, or the number of allergens administered. All measures of disease severity (including symptom severity noted on a 0-to-10 visual analogue scale by the physician) decreased significantly during SLIT. Notably, the mean AR symptom severity score decreased to a clinically relevant extent from 7.6 at SLIT initiation to 2.4 at last follow-up, and the mean asthma symptom severity score decreased from 5.0 to 1.3. The few differences in effectiveness between patients with vs. without dose adjustment were not major. For about one patient in five, a specialist physician decided to reduce or increase the SLIT liquid dose at the start of maintenance treatment and/or during maintenance treatment. This decision was influenced by a broad range of patient and treatment factors, mainly to improve tolerability to treatment and/or enhance effectiveness. In France, dose adjustment of SLIT liquid preparations as a function of the patient profile and/or treatment response is anchored in clinical practice. Precision dosing might optimize the overall benefit-risk profile of AIT for individual patients throughout their entire treatment course, enabling them to achieve both short- and long-term treatment goals, whilst maximizing the safety and tolerability.

Human Variation in DNA Repair, Immune Function, and Cancer Risk

DNA damage constantly threatens genome integrity, and DNA repair deficiency is associated with increased cancer risk. An intuitive and widely accepted explanation for this relationship is that unrepaired DNA damage leads to carcinogenesis due to the accumulation of mutations in somatic cells. But DNA repair also plays key roles in the function of immune cells, and immunodeficiency is an important risk factor for many cancers. Thus, it is possible that emerging links between inter-individual variation in DNA repair capacity and cancer risk are driven, at least in part, by variation in immune function, but this idea is underexplored. In this review we present an overview of the current understanding of the links between cancer risk and both inter-individual variation in DNA repair capacity and inter-individual variation in immune function. We discuss factors that play a role in both types of variability, including age, lifestyle, and environmental exposures. In conclusion, we propose a research paradigm that incorporates functional studies of both genome integrity and the immune system to predict cancer risk and lay the groundwork for personalized prevention.

Leveraging Systems Immunology to Optimize Diagnosis and Treatment of Inborn Errors of Immunity

Inborn errors of immunity (IEI) are monogenic disorders that can cause diverse symptoms, including recurrent infections, autoimmunity and malignancy. While many factors have contributed, the increased availability of next-generation sequencing has been central in the remarkable increase in identification of novel monogenic IEI over the past years. Throughout this phase of disease discovery, it has also become evident that a given gene variant does not always yield a consistent phenotype, while variants in seemingly disparate genes can lead to similar clinical presentations. Thus, it is increasingly clear that the clinical phenotype of an IEI patient is not defined by genetics alone, but is also impacted by a myriad of factors. Accordingly, we need methods to amplify our current diagnostic algorithms to better understand mechanisms underlying the variability in our patients and to optimize treatment. In this review, we will explore how systems immunology can contribute to optimizing both diagnosis and treatment of IEI patients by focusing on identifying and quantifying key dysregulated pathways. To improve mechanistic understanding in IEI we must deeply evaluate our rare IEI patients using multimodal strategies, allowing both the quantification of altered immune cell subsets and their functional evaluation. By studying representative controls and patients, we can identify causative pathways underlying immune cell dysfunction and move towards functional diagnosis. Attaining this deeper understanding of IEI will require a stepwise strategy. First, we need to broadly apply these methods to IEI patients to identify patterns of dysfunction. Next, using multimodal data analysis, we can identify key dysregulated pathways. Then, we must develop a core group of simple, effective functional tests that target those pathways to increase efficiency of initial diagnostic investigations, provide evidence for therapeutic selection and contribute to the mechanistic evaluation of genetic results. This core group of simple, effective functional tests, targeting key pathways, can then be equitably provided to our rare patients. Systems biology is thus poised to reframe IEI diagnosis and therapy, fostering research today that will provide streamlined diagnosis and treatment choices for our rare and complex patients in the future, as well as providing a better understanding of basic immunology.

Analysis of the Seasonal Fluctuation of γδ T Cells and Its Potential Relation with Vitamin D3

In addition to its role in bone metabolism, vitamin D3 exerts immunomodulatory effects and has been proposed to contribute to seasonal variation of immune cells. This might be linked to higher vitamin D3 levels in summer than in winter due to differential sun exposure. γδ T cells comprise a numerically small subset of T cells in the blood, which contribute to anti-infective and antitumor immunity. We studied the seasonal fluctuation of γδ T cells, the possible influence of vitamin D3, and the effect of the active metabolite 1α,25(OH)2D3 on the in vitro activation of human γδ T cells. In a retrospective analysis with 2625 samples of random blood donors, we observed higher proportions of γδ T cells in winter when compared with summer. In a prospective study over one year with a small cohort of healthy adults who did or did not take oral vitamin D3 supplementation, higher proportions of γδ T cells were present in donors without oral vitamin D3 uptake, particularly in spring. However, γδ T cell frequency in blood did not directly correlate with serum levels of 25(OH)D3. The active metabolite 1α,25(OH)2D3 inhibited the in vitro activation of γδ T cells at the level of proliferation, cytotoxicity, and interferon-γ production. Our study reveals novel insights into the seasonal fluctuation of γδ T cells and the immunomodulatory effects of vitamin D3.

System Complexity in Influenza Infection and Vaccination: Effects upon Excess Winter Mortality

Unexpected outcomes are usually associated with interventions in complex systems. Excess winter mortality (EWM) is a measure of the net effect of all competing forces operating each winter, including influenza(s) and non-influenza pathogens. In this study over 2400 data points from 97 countries are used to look at the net effect of influenza vaccination rates in the elderly aged 65+ against excess winter mortality (EWM) each year from the winter of 1980/81 through to 2019/20. The observed international net effect of influenza vaccination ranges from a 7.8% reduction in EWM estimated at 100% elderly vaccination for the winter of 1989/90 down to a 9.3% increase in EWM for the winter of 2018/19. The average was only a 0.3% reduction in EWM for a 100% vaccinated elderly population. Such outcomes do not contradict the known protective effect of influenza vaccination against influenza mortality per se—they merely indicate that multiple complex interactions lie behind the observed net effect against all-causes (including all pathogen causes) of winter mortality. This range from net benefit to net disbenefit is proposed to arise from system complexity which includes environmental conditions (weather, solar cycles), the antigenic distance between constantly emerging circulating influenza clades and the influenza vaccine makeup, vaccination timing, pathogen interference, and human immune diversity (including individual history of host-virus, host-antigen interactions and immunosenescence) all interacting to give the observed outcomes each year. We propose that a narrow focus on influenza vaccine effectiveness misses the far wider complexity of winter mortality. Influenza vaccines may need to be formulated in different ways, and perhaps administered over a shorter timeframe to avoid the unanticipated adverse net outcomes seen in around 40% of years.

Estrogen, the Peripheral Immune System and Major Depression – A Reproductive Lifespan Perspective

Major depression is a significant medical issue impacting millions of individuals worldwide. Identifying factors contributing to its manifestation has been a subject of intense investigation for decades and several targets have emerged including sex hormones and the immune system. Indeed, an extensive body of literature has demonstrated that sex hormones play a critical role in modulating brain function and impacting mental health, especially among female organisms. Emerging findings also indicate an inflammatory etiology of major depression, revealing new opportunities to supplement, or even supersede, currently available pharmacological interventions in some patient populations. Given the established sex differences in immunity and the profound impact of fluctuations of sex hormone levels on the immune system within the female, interrogating how the endocrine, nervous, and immune systems converge to impact women’s mental health is warranted. Here, we review the impacts of endogenous estrogens as well as exogenously administered estrogen-containing therapies on affect and immunity and discuss these observations in the context of distinct reproductive milestones across the female lifespan. A theoretical framework and important considerations for additional study in regards to mental health and major depression are provided.

Intensive single-cell analysis reveals immune-cell diversity among healthy individuals

Immune responses are different between individuals and personal health histories and unique environmental conditions should collectively determine the present state of immune cells. However, the molecular systems underlying such heterogeneity remain elusive. Here, we conducted a systematic time-lapse single-cell analysis, using 171 single-cell libraries and 30 mass cytometry datasets intensively for seven healthy individuals. We found substantial diversity in immune-cell profiles between different individuals. These patterns showed daily fluctuations even within the same individual. Similar diversities were also observed for the T-cell and B-cell receptor repertoires. Detailed immune-cell profiles at healthy statuses should give essential background information to understand their immune responses, when the individual is exposed to various environmental conditions. To demonstrate this idea, we conducted the similar analysis for the same individuals on the vaccination of influenza and SARS-CoV-2. In fact, we detected distinct responses to vaccines between individuals, although key responses are common. Single-cell immune-cell profile data should make fundamental data resource to understand variable immune responses, which are unique to each individual.

Human immune diversity: from evolution to modernity

The extreme diversity of the human immune system, forged and maintained throughout evolutionary history, provides a potent defense against opportunistic pathogens. At the same time, this immune variation is the substrate upon which a plethora of immune-associated diseases develop. Genetic analysis suggests that thousands of individually weak loci together drive up to half of the observed immune variation. Intense selection maintains this genetic diversity, even selecting for the introgressed Neanderthal or Denisovan alleles that have reintroduced variation lost during the out-of-Africa migration. Variations in age, sex, diet, environmental exposure, and microbiome each potentially explain the residual variation, with proof-of-concept studies demonstrating both plausible mechanisms and correlative associations. The confounding interaction of many of these variables currently makes it difficult to assign definitive contributions. Here, we review the current state of play in the field, identify the key unknowns in the causality of immune variation, and identify the multidisciplinary pathways toward an improved understanding.

Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health

We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.

The immune landscape of SARS-CoV-2-associated Multisystem Inflammatory Syndrome in Children (MIS-C) from acute disease to recovery

Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening disease occurring several weeks after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Deep immune profiling showed acute MIS-C patients had highly activated neutrophils, classical monocytes and memory CD8+ T-cells, with increased frequencies of B-cell plasmablasts and double-negative B-cells. Post treatment samples from the same patients, taken during symptom resolution, identified recovery-associated immune features including increased monocyte CD163 levels, emergence of a new population of immature neutrophils and, in some patients, transiently increased plasma arginase. Plasma profiling identified multiple features shared by MIS-C, Kawasaki Disease and COVID-19 and that therapeutic inhibition of IL-6 may be preferable to IL-1 or TNF-α. We identified several potential mechanisms of action for IVIG, the most commonly used drug to treat MIS-C. Finally, we showed systemic complement activation with high plasma C5b-9 levels is common in MIS-C suggesting complement inhibitors could be used to treat the disease.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

Next generation plasma proteome profiling to monitor health and disease

The need for precision medicine approaches to monitor health and disease makes it important to develop sensitive and accurate assays for proteome profiles in blood. Here, we describe an approach for plasma profiling based on proximity extension assay combined with next generation sequencing. First, we analyze the variability of plasma profiles between and within healthy individuals in a longitudinal wellness study, including the influence of genetic variations on plasma levels. Second, we follow patients newly diagnosed with type 2 diabetes before and during therapeutic intervention using plasma proteome profiling. The studies show that healthy individuals have a unique and stable proteome profile and indicate that a panel of proteins could potentially be used for early diagnosis of diabetes, including stratification of patients with regards to response to metformin treatment. Although validation in larger cohorts is needed, the analysis demonstrates the usefulness of comprehensive plasma profiling for precision medicine efforts.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

Integration of molecular profiles in a longitudinal wellness profiling cohort

An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, we sample a longitudinal wellness cohort with 100 healthy individuals and analyze blood molecular profiles including proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies and immune cell profiling, complemented with gut microbiota composition and routine clinical chemistry. Overall, our results show high variation between individuals across different molecular readouts, while the intra-individual baseline variation is low. The analyses show that each individual has a unique and stable plasma protein profile throughout the study period and that many individuals also show distinct profiles with regards to the other omics datasets, with strong underlying connections between the blood proteome and the clinical chemistry parameters. In conclusion, the results support an individual-based definition of health and show that comprehensive omics profiling in a longitudinal manner is a path forward for precision medicine.

An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, the authors sample a longitudinal wellness cohort and analyse blood molecular profiles as well as gut microbiota composition.

Systems-Level Immunomonitoring from Acute to Recovery Phase of Severe COVID-19

Severe disease of SARS-CoV-2 is characterized by vigorous inflammatory responses in the lung, often with a sudden onset after 5–7 days of stable disease. Efforts to modulate this hyperinflammation and the associated acute respiratory distress syndrome rely on the unraveling of the immune cell interactions and cytokines that drive such responses. Given that every patient is captured at different stages of infection, longitudinal monitoring of the immune response is critical and systems-level analyses are required to capture cellular interactions. Here, we report on a systems-level blood immunomonitoring study of 37 adult patients diagnosed with COVID-19 and followed with up to 14 blood samples from acute to recovery phases of the disease. We describe an IFNγ-eosinophil axis activated before lung hyperinflammation and changes in cell-cell co-regulation during different stages of the disease. We also map an immune trajectory during recovery that is shared among patients with severe COVID-19.

Immunomonitoring from acute to recovery phase COVID-19

An IFNγ-eosinophil axis precedes lung hyperinflammation

Basophils modulate SARS-CoV-2 IgG responses

A shared trajectory of immunological recovery in COVID-19