Microbiota-derived inosine programs protective CD8+ T cell responses against influenza in newborns.

The immunological defects causing susceptibility to severe viral respiratory infections due to early-life dysbiosis remain ill-defined. Here, we show that influenza virus susceptibility in dysbiotic infant mice is caused by CD8+ T cell hyporesponsiveness and diminished persistence as tissue-resident memory cells. We describe a previously unknown role for nuclear factor interleukin 3 (NFIL3) in repression of memory differentiation of CD8+ T cells in dysbiotic mice involving epigenetic regulation of T cell factor 1 (TCF 1) expression. Pulmonary CD8+ T cells from dysbiotic human infants share these transcriptional signatures and functional phenotypes. Mechanistically, intestinal inosine was reduced in dysbiotic human infants and newborn mice, and inosine replacement reversed epigenetic dysregulation of Tcf7 and increased memory differentiation and responsiveness of pulmonary CD8+ T cells. Our data unveils new developmental layers controlling immune cell activation and identifies microbial metabolites that may be used therapeutically in the future to protect at-risk newborns.

Blood Cholesterol and Triglycerides Associate with Right Ventricular Function in Pulmonary Hypertension.

Background: Blood lipids are dysregulated in pulmonary hypertension (PH). Lower high-density lipoproteins cholesterol (HDL-C) and low-density lipoproteins cholesterol (LDL-C) are associated with disease severity and death in PH. Right ventricle (RV) dysfunction and failure are the major determinants of morbidity and mortality in PH. This study aims to test the hypothesis that dyslipidemia is associated with RV dysfunction in PH. Methods: We enrolled healthy control subjects (n=12) and individuals with PH (n=30) (age: 18-65 years old). Clinical characteristics, echocardiogram, 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (PET) scan, blood lipids, including total cholesterol (TC), triglycerides (TG), lipoproteins (LDL-C and HDL-C), and N-terminal pro-B type Natriuretic Peptide (NT-proBNP) were determined. Results: Individuals with PH had lower HDL-C [PH, 41±12; control, 56±16 mg/dL, p<0.01] and higher TG to HDL-C ratio [PH, 3.6±3.1; control, 2.2±2.2, p<0.01] as compared to controls. TC, TG, and LDL-C were similar between PH and controls. Lower TC and TG were associated with worse RV function measured by RV strain (R=-0.43, p=0.02 and R=-0.37, p=0.05 respectively), RV fractional area change (R=0.51, p<0.01 and R=0.48, p<0.01 respectively), RV end-systolic area (R=-0.63, p<0.001 and R=-0.48, p<0.01 respectively), RV end-diastolic area: R=-0.58, p<0.001 and R=-0.41, p=0.03 respectively), and RV glucose uptake by PET (R=-0.46, p=0.01 and R=-0.30, p=0.10 respectively). NT-proBNP was negatively correlated with TC (R=-0.61, p=0.01) and TG (R=-0.62, p<0.02) in PH. Conclusion: These findings confirm dyslipidemia is associated with worse right ventricular function in PH.

The role of ACT score in mepolizumab discontinuation.

BACKGROUND: Mepolizumab is a therapy for severe asthma. We have little knowledge of the characteristics of people in the US that discontinue mepolizumab in clinical care. OBJECTIVE: To investigate the real-world efficacy and time to clinical discontinuation of mepolizumab, we evaluated individuals with asthma started on mepolizumab at the Cleveland Clinic. We hypothesized that individuals that discontinue mepolizumab have more severe and uncontrolled asthma at baseline. METHODS: Between 2016 and 2022, patients who started on mepolizumab consented to be assessed over 18 months. At baseline, a questionnaire including demographic and medical history was collected. Laboratory findings such as ACT score, FENO (Fractional Excretion of Nitric Oxide), and spirometry were recorded. At the conclusion of the observation period, the participants were divided into two categories: Group A and Group B. RESULTS: Group B [N = 28] discontinued mepolizumab (p < 0.05) at an average of 5.8 months (SD 4.2 months). Group A [N = 129] stayed on the therapy for at least 1 year. A participant with an ACT score less than 13 has an odds ratio of 6.64 (95% CI, 2.1 - 26.0) of discontinuing mepolizumab therapy. For a male, the odds of discontinuing mepolizumab therapy is 3.39 (95% CI, 1.1-11.2). CONCLUSION: In this real-world study, we find that high eosinophil count may not be adequate in screening which individuals will benefit from mepolizumab. Up to 17% of patients fail therapy within 6 months, with male sex and low ACT score increasing risk of mepolizumab discontinuation at Cleveland Clinic.

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

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

The balance between protective and pathogenic immune responses to pneumonia in the neonatal lung is enforced by gut microbiota.

Although modern clinical practices such as cesarean sections and perinatal antibiotics have improved infant survival, treatment with broad-spectrum antibiotics alters intestinal microbiota and causes dysbiosis. Infants exposed to perinatal antibiotics have an increased likelihood of life-threatening infections, including pneumonia. Here, we investigated how the gut microbiota sculpt pulmonary immune responses, promoting recovery and resolution of infection in newborn rhesus macaques. Early-life antibiotic exposure interrupted the maturation of intestinal commensal bacteria and disrupted the developmental trajectory of the pulmonary immune system, as assessed by single-cell proteomic and transcriptomic analyses. Early-life antibiotic exposure rendered newborn macaques more susceptible to bacterial pneumonia, concurrent with increases in neutrophil senescence and hyperinflammation, broad inflammatory cytokine signaling, and macrophage dysfunction. This pathogenic reprogramming of pulmonary immunity was further reflected by a hyperinflammatory signature in all pulmonary immune cell subsets coupled with a global loss of tissue-protective, homeostatic pathways in the lungs of dysbiotic newborns. Fecal microbiota transfer was associated with partial correction of the broad immune maladaptations and protection against severe pneumonia. These data demonstrate the importance of intestinal microbiota in programming pulmonary immunity and support the idea that gut microbiota promote the balance between pathways driving tissue repair and inflammatory responses associated with clinical recovery from infection in infants. Our results highlight a potential role for microbial transfer for immune support in these at-risk infants.

Telepractice Treatment of Residual Rhotic Errors Using App-Based Biofeedback: A Pilot Study.

PURPOSE: Although mobile apps are used extensively by speech-language pathologists, evidence for app-based treatments remains limited in quantity and quality. This study investigated the efficacy of app-based visual-acoustic biofeedback relative to nonbiofeedback treatment using a single-case randomization design. Because of COVID-19, all intervention was delivered via telepractice. METHOD: Participants were four children aged 9-10 years with residual errors affecting American English /ɹ/. Using a randomization design, individual sessions were randomly assigned to feature practice with or without biofeedback, all delivered using the speech app Speech Therapist's App for /r/ Treatment. Progress was assessed using blinded listener ratings of word probes administered at baseline, posttreatment, and immediately before and after each treatment session. RESULTS: All participants showed a clinically significant response to the overall treatment package, with effect sizes ranging from moderate to very large. One participant showed a significant advantage for biofeedback over nonbiofeedback treatment, although the order of treatment delivery poses a potential confound for interpretation in this case. CONCLUSIONS: While larger scale studies are needed, these results suggest that app-based treatment for residual errors can be effective when delivered via telepractice. These results are compatible with previous findings in the motor learning literature regarding the importance of treatment dose and the timing of feedback conditions. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.18461576.

Perinatal infection, inflammation, preterm birth, and brain injury: A review with proposals for future investigations.

Preterm newborns are exposed to several risk factors for developing brain injury. Clinical studies have suggested that the presence of intrauterine infection is a consistent risk factor for preterm birth and white matter injury. Animal models have confirmed these associations by identifying inflammatory cascades originating at the maternofetal interface that penetrate the fetal blood-brain barrier and result in brain injury. Acquired diseases of prematurity further potentiate the risk for cerebral injury. Systems biology approaches incorporating ante- and post-natal risk factors and analyzing omic and multiomic data using machine learning are promising methodologies for further elucidating biologic mechanisms of fetal and neonatal brain injury.

Iterative development and pilot testing of an intervention fidelity monitoring plan for the enhanced, electronic health record-facilitated pragmatic clinical trial: Implications for training and protocol integrity.

Fidelity monitoring is the degree to which a clinical trial intervention is implemented as intended by a research protocol. Consistent implementation of research protocols supported with extant fidelity monitoring plans contribute rigor and validity of study results. Fidelity monitoring plans should be comprehensive yet practical to accommodate the realities of conducting research, particularly a pragmatic clinical trial, in dynamic settings with heterogeneous patient populations. The purposes of this paper are to describe the (1) iterative development and implementation of protocols for intervention fidelity monitoring, (2) pilot testing of the fidelity monitoring plan, (3) the identification of interventionist training deficiencies, and (4) opportunities to enhance protocol rigor for a cancer symptom management intervention delivered through the electronic health record patient portal and telephone as part of a complex, multi-component pragmatic clinical trial to uncover training deficits and bolster protocol integrity. The intervention focuses on prominent symptoms reported among medical oncology patients including sleep disturbance, pain, anxiety, depression, low energy (fatigue) and physical function. In this pragmatic trial, the role of interventionist is a registered nurse symptom care manager (RN SCM). A three-part fidelity monitoring plan with checklists audit: Part-1 RN SCM role training activities in research components, clinical training components, and protocol simulation training; Part-2 RN SCM adherence to the intervention core components delivered over the telephone; and Part-3 maintenance of adherence to core intervention components. The goal is ≥ 80% adherence to components of each of the three checklists. An initial pilot test of the fidelity monitoring plan was conducted to evaluate the checklists and the RN SCM adherence to core protocol components. RN SCM skills and training deficits were identified during the pilot phase, as were opportunities to improve protocol integrity. Overall, approximately 50% of the audited RN SCM telephone calls had ≥80% fidelity to the core components. There remains on-going need for RN SCM training and skill building in action planning. The content presented in this paper is intended to begin to fill the gap of fidelity monitoring plans for complex interventions tested in pragmatic clinical trials and delivered remotely in an effort to strengthen protocol integrity.

Metabolic endophenotype associated with right ventricular glucose uptake in pulmonary hypertension.

Alterations in metabolism and bioenergetics are hypothesized in the mechanisms leading to pulmonary vascular remodeling and heart failure in pulmonary hypertension (PH). To test this, we performed metabolomic analyses on 30 PH individuals and 12 controls. Furthermore, using 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography, we dichotomized PH patients into metabolic phenotypes of high and low right ventricle (RV) glucose uptake and followed them longitudinally. In support of metabolic alterations in PH and its progression, the high RV glucose group had higher RV systolic pressure (p < 0.001), worse RV function as measured by RV fractional area change and peak global longitudinal strain (both p < 0.05) and may be associated with poorer outcomes (33% death or transplantation in the high glucose RV uptake group compared to 7% in the low RV glucose uptake group at five years follow-up, log-ranked p = 0.07). Pathway enrichment analysis identified key metabolic pathways including fructose catabolism, arginine-nitric oxide metabolism, tricarboxylic acid cycle, and ketones metabolism. Integrative human protein-protein interactome network analysis of metabolomic and transcriptomic data identified key pathobiological pathways: arginine biosynthesis, tricarboxylic acid cycle, purine metabolism, hypoxia-inducible factor 1, and apelin signaling. These findings identify a PH metabolomic endophenotype, and for the first time link this to disease severity and outcomes.

A Peripheral Immune Signature of Labor Induction.

Approximately 1 in 4 pregnant women in the United States undergo labor induction. The onset and establishment of labor, particularly induced labor, is a complex and dynamic process influenced by multiple endocrine, inflammatory, and mechanical factors as well as obstetric and pharmacological interventions. The duration from labor induction to the onset of active labor remains unpredictable. Moreover, prolonged labor is associated with severe complications for the mother and her offspring, most importantly chorioamnionitis, uterine atony, and postpartum hemorrhage. While maternal immune system adaptations that are critical for the maintenance of a healthy pregnancy have been previously characterized, the role of the immune system during the establishment of labor is poorly understood. Understanding maternal immune adaptations during labor initiation can have important ramifications for predicting successful labor induction and labor complications in both induced and spontaneous types of labor. The aim of this study was to characterize labor-associated maternal immune system dynamics from labor induction to the start of active labor. Serial blood samples from fifteen participants were collected immediately prior to labor induction (baseline) and during the latent phase until the start of active labor. Using high-dimensional mass cytometry, a total of 1,059 single-cell immune features were extracted from each sample. A multivariate machine-learning method was employed to characterize the dynamic changes of the maternal immune system after labor induction until the establishment of active labor. A cross-validated linear sparse regression model (least absolute shrinkage and selection operator, LASSO) predicted the minutes since induction of labor with high accuracy (R = 0.86, p = 6.7e-15, RMSE = 277 min). Immune features most informative for the model included STAT5 signaling in central memory CD8+ T cells and pro-inflammatory STAT3 signaling responses across multiple adaptive and innate immune cell subsets. Our study reports a peripheral immune signature of labor induction, and provides important insights into biological mechanisms that may ultimately predict labor induction success as well as complications, thereby facilitating clinical decision-making to improve maternal and fetal well-being.

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/NFκB signaling in antigen presenting cells; increasing responsiveness of cytotoxic lymphocytes to interferon-α; and decreasing frequency of regulatory and invariant T cells, including NKT-like cells and CD8+CD161+ T 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.

Women's Wellness Through Equity and Leadership (WEL): A Program Evaluation.

BACKGROUND AND OBJECTIVES: The Women's Wellness through Equity and Leadership (WEL) program was developed as a collaboration between 6 major medical associations in the United States. The goal was to contribute to the creation of equitable work environments for women physicians. The purpose of the current study was to evaluate the pilot implementation of WEL. METHODS: Participants included a diverse group of 18 early career to midcareer women physicians from across medical specialties, 3 from each partner organization. WEL was developed as an 18-month program with 3 series focused on wellness, equity, and leadership and included monthly virtual and in-person meetings. After institutional board review approval, a mixed-methods evaluation design was incorporated, which included postseries and postprogram surveys and in-depth telephone interviews. RESULTS: Participants delineated several drivers of program success, including peer support and/or networks; interconnectedness between the topics of wellness, equity, and leadership; and diversity of participants and faculty. Areas for improvement included more opportunities to connect with peers and share progress and more structured mentorship. Regarding program impact, participants reported increased knowledge and behavior change because of their participation. CONCLUSIONS: This longitudinal, cohort initiative resulted from a successful collaboration between 6 medical associations. Evaluation findings suggest that providing opportunities for women physicians to connect with and support each other while building knowledge and skills can be an effective way to advance wellness, equity, and leadership for women in medicine.

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.

Resting Energy Expenditure Is Elevated in Asthma.

Background: Asthma physiology affects respiratory function and inflammation, factors that may contribute to elevated resting energy expenditure (REE) and altered body composition. Objective: We hypothesized that asthma would present with elevated REE compared to weight-matched healthy controls. Methods: Adults with asthma (n = 41) and healthy controls (n = 20) underwent indirect calorimetry to measure REE, dual-energy X-ray absorptiometry (DEXA) to measure body composition, and 3-day diet records. Clinical assessments included spirometry, fractional exhaled nitric oxide (FENO), and a complete blood count. Results: Asthmatics had greater REE than controls amounting to an increase of ~100 kcals/day, even though body mass index (BMI) and body composition were similar between groups. Inclusion of asthma status and FENO in validated REE prediction equations led to improved estimates. Further, asthmatics had higher white blood cell (control vs. asthma (mean ± SD): 4.7 ± 1.1 vs. 5.9 ± 1.6, p < 0.01) and neutrophil (2.8 ± 0.9 vs. 3.6 ± 1.4, p = 0.02) counts that correlated with REE (both p < 0.01). Interestingly, despite higher REE, asthmatics reported consuming fewer calories (25.1 ± 7.5 vs. 20.3 ± 6.0 kcals/kg/day, p < 0.01) and carbohydrates than controls. Conclusion: REE is elevated in adults with mild asthma, suggesting there is an association between REE and the pathophysiology of asthma.

Understanding how biologic and social determinants affect disparities in preterm birth and outcomes of preterm infants in the NICU.

To understand the disparities in spontaneous preterm birth (sPTB) and/or its outcomes, biologic and social determinants as well as healthcare practice (such as those in neonatal intensive care units) should be considered. Disparities in sPTB have been largely intractable and remain obscure in most cases, despite a myriad of identified risk factors for and causes of sPTB. We still do not know how they lead to the different outcomes at different gestational ages and if they are independent of NICU practices. Here we describe an integrated approach to study the interplay between the genome and exposome, which may drive biochemistry and physiology and lead to health disparities.

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.

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions.

The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

VoPo leverages cellular heterogeneity for predictive modeling of single-cell data.

High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

Implementing Common Metrics across the NIH Clinical and Translational Science Awards (CTSA) consortium.

The Clinical and Translational Science Award (CTSA) Consortium and the National Center for Advancing Translational Science (NCATS) undertook a Common Metrics Initiative to improve research processes across the national CTSA Consortium. This was implemented by Tufts Clinical and Translational Science Institute at the 64 CTSA academic medical centers. Three metrics were collaboratively developed by NCATS staff, CTSA Consortium teams, and outside consultants for Institutional Review Board Review Duration, Careers in Clinical and Translational Research, and Pilot Award Publications and Subsequent Funding. The implementation program included training on the metric operational guidelines, data collection, data reporting system, and performance improvement framework. The implementation team provided small-group coaching and technical assistance. Collaborative learning sessions, driver diagrams, and change packages were used to disseminate best and promising practices. After 14 weeks, 84% of hubs had produced a value for one metric and about half had produced an initial improvement plan. Overall, hubs reported that the implementation activities facilitated their Common Metrics performance improvement process. Experiences implementing the first three metrics can inform future directions of the Common Metrics Initiative and other research groups implementing standardized metrics and performance improvement processes, potentially including other National Institutes of Health institutes and centers.