1,25-Dihydroxyvitamin D-3 and its analog TX527 promote a stable regulatory T cell phenotype in T cells from type 1 diabetes patients

The emergence of regulatory T cells (Tregs) as central mediators of peripheral tolerance in the immune system has led to an important area of clinical investigation to target these cells for the treatment of autoimmune diseases such as type 1 diabetes. We have demonstrated earlier that in vitro treatment of T cells from healthy individuals with TX527, a low-calcemic analog of bioactive vitamin D, can promote a CD4(+)CD25(high)CD127(low) regulatory profile and imprint a migratory signature specific for homing to sites of inflammation. Towards clinical application of vitamin D-induced Tregs in autologous adoptive immunotherapy for type 1 diabetes, we show here that 1,25-dihydroxyvitamin D-3 [1,25(OH)(2)D-3] and TX527 similarly imprint T cells from type 1 diabetes patients with a CD4(+)CD25(high)CD127(low) regulatory profile, modulate surface expression of skin- and inflammation-homing receptors, and increase expression of CTLA-4 and OX-40. Also, 1,25(OH)(2)D-3 and TX527 treatment inhibit the production of effector cytokines IFN-gamma, IL-9, and IL-17. Importantly, 1,25(OH)(2)D-3 and TX527 promote the induction of IL-10-producing CD4(+)CD25(high)CD127(low) T cells with a stable phenotype and the functional capacity to suppress proliferation of autologous responder T cells in vitro. These findings warrant additional validation of vitamin D-induced Tregs in view of future autologous adoptive immunotherapy in type 1 diabetes

A combination of immune cell types identified through ensemble machine learning strategy detects altered profile in recurrent pregnancy loss: a pilot study

OBJECTIVE: To compare the immunologic profiles of peripheral and menstrual blood (MB) of women who experience recurrent pregnancy loss and women without pregnancy complications. DESIGN: Explorative case-control study. Cross-sectional assessment of flow cytometry-derived immunologic profiles. SETTING: Academic medical center. PATIENT(S): Women who experienced more than 2 consecutive miscarriages. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Flow cytometry-based immune profiles of uterine and systemic immunity (recurrent pregnancy loss, n = 18; control, n = 14) assessed by machine learning classifiers in an ensemble strategy, followed by recursive feature selection. RESULT(S): In peripheral blood, the combination of 4 cell types (nonswitched memory B cells, CD8+ T cells, CD56bright CD16- natural killer [NKbright] cells, and CD4+ effector T cells) classified samples correctly to their respective cohort. The identified classifying cell types in peripheral blood differed from the results observed in MB, where a combination of 6 cell types (Ki67+CD8+ T cells, (Human leukocyte antigen-DR+) regulatory T cells, CD27+ B cells, NKbright cells, regulatory T cells, and CD24HiCD38Hi B cells) plus age allowed for assigning samples correctly to their respective cohort. Based on the combination of these features, the average area under the curve of a receiver operating characteristic curve and the associated accuracy were >0.8 for both sample sources. CONCLUSION(S): A combination of immune subsets for cohort classification allows for robust identification of immune parameters with possible diagnostic value. The noninvasive source of MB holds several opportunities to assess and monitor reproductive health

Single-cell Analysis of the Neonatal Immune System Across the Gestational Age Continuum

Although most causes of death and morbidity in premature infants are related to immune maladaptation, the premature immune system remains poorly understood. We provide a comprehensive single-cell depiction of the neonatal immune system at birth across the spectrum of viable gestational age (GA), ranging from 25 weeks to term. A mass cytometry immunoassay interrogated all major immune cell subsets, including signaling activity and responsiveness to stimulation. An elastic net model described the relationship between GA and immunome (R=0.85, p=8.75e-14), and unsupervised clustering highlighted previously unrecognized GA-dependent immune dynamics, including decreasing basal MAP-kinase/NFkB signaling in antigen presenting cells; increasing responsiveness of cytotoxic lymphocytes to interferon-a; and decreasing frequency of regulatory and invariant T cells, including NKT cells and MAIT cells. Knowledge gained from the analysis of the neonatal immune landscape across GA provides a mechanistic framework to understand the unique susceptibility of preterm infants to both hyper-inflammatory diseases and infections

Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset

Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 × 10−40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 × 10−7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies

Multiomics Longitudinal Modeling of Preeclamptic Pregnancies

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women\u27s physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology, can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI):[0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC= 0.87, 95% CI:[0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC= 0.90, 95% CI:[0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.ObjectivesThe primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.Materials and MethodsIn a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).ResultsJointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.ConclusionsElucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics

1,25-Dihydroxyvitamin D-3 and Its Analog TX527 Promote a Stable Regulatory T Cell Phenotype in T Cells from Type 1 Diabetes Patients

The emergence of regulatory T cells (Tregs) as central mediators of peripheral tolerance in the immune system has led to an important area of clinical investigation to target these cells for the treatment of autoimmune diseases such as type 1 diabetes. We have demonstrated earlier that in vitro treatment of T cells from healthy individuals with TX527, a low-calcemic analog of bioactive vitamin D, can promote a CD4+ CD25high CD127low regulatory profile and imprint a migratory signature specific for homing to sites of inflammation. Towards clinical application of vitamin D-induced Tregs in autologous adoptive immunotherapy for type 1 diabetes, we show here that 1,25-dihydroxyvitamin D3 [1,25(OH)2D3] and TX527 similarly imprint T cells from type 1 diabetes patients with a CD4+ CD25high CD127low regulatory profile, modulate surface expression of skin- and inflammation-homing receptors, and increase expression of CTLA-4 and OX-40. Also, 1,25(OH)2D3 and TX527 treatment inhibit the production of effector cytokines IFN-γ, IL-9, and IL-17. Importantly, 1,25(OH)2D3 and TX527 promote the induction of IL-10-producing CD4+ CD25high CD127low T cells with a stable phenotype and the functional capacity to suppress proliferation of autologous responder T cells in vitro. These findings warrant additional validation of vitamin D-induced Tregs in view of future autologous adoptive immunotherapy in type 1 diabetes.status: publishe

Respiratory syncytial virus (RSV) infects primary neonatal and adult natural killer cells and affects their anti-viral effector function.

Respiratory syncytial virus (RSV) is a major cause of severe acute lower respiratory tract infections in infants. Natural killer (NK) cells are important anti-viral effector cells that likely encounter RSV in the presence of virus-specific (maternal) antibodies. Since NK cells potentially contribute to immunopathology, we investigated whether RSV affects their anti-viral effector functions

Towards multiomic analysis of oral mucosal pathologies

Oral mucosal pathologies comprise an array of diseases with worldwide prevalence and medical relevance. Affecting a confined space with crucial physiological and social functions, oral pathologies can be mutilating and drastically reduce quality of life. Despite their relevance, treatment for these diseases is often far from curative and remains vastly understudied. While multiple factors are involved in the pathogenesis of oral mucosal pathologies, the host's immune system plays a major role in the development, maintenance, and resolution of these diseases. Consequently, a precise understanding of immunological mechanisms implicated in oral mucosal pathologies is critical (1) to identify accurate, mechanistic biomarkers of clinical outcomes; (2) to develop targeted immunotherapeutic strategies; and (3) to individualize prevention and treatment approaches. Here, we review key elements of the immune system's role in oral mucosal pathologies that hold promise to overcome limitations in current diagnostic and therapeutic approaches. We emphasize recent and ongoing multiomic and single-cell approaches that enable an integrative view of these pathophysiological processes and thereby provide unifying and clinically relevant biological signatures