Ventilation during COVID-19 in a school for students with intellectual and developmental disabilities (IDD).

BACKGROUND: This study examined the correlation of classroom ventilation (air exchanges per hour (ACH)) and exposure to CO2 ≥1,000 ppm with the incidence of SARS-CoV-2 over a 20-month period in a specialized school for students with intellectual and developmental disabilities (IDD). These students were at a higher risk of respiratory infection from SARS-CoV-2 due to challenges in tolerating mitigation measures (e.g. masking). One in-school measure proposed to help mitigate the risk of SARS-CoV-2 infection in schools is increased ventilation. METHODS: We established a community-engaged research partnership between the University of Rochester and the Mary Cariola Center school for students with IDD. Ambient CO2 levels were measured in 100 school rooms, and air changes per hour (ACH) were calculated. The number of SARS-CoV-2 cases for each room was collected over 20 months. RESULTS: 97% of rooms had an estimated ACH ≤4.0, with 7% having CO2 levels ≥2,000 ppm for up to 3 hours per school day. A statistically significant correlation was found between the time that a room had CO2 levels ≥1,000 ppm and SARS-CoV-2 PCR tests normalized to room occupancy, accounting for 43% of the variance. No statistically significant correlation was found for room ACH and per-room SARS-CoV-2 cases. Rooms with ventilation systems using MERV-13 filters had lower SARS-CoV-2-positive PCR counts. These findings led to ongoing efforts to upgrade the ventilation systems in this community-engaged research project. CONCLUSIONS: There was a statistically significant correlation between the total time of room CO2 concentrations ≥1,000 and SARS-CoV-2 cases in an IDD school. Merv-13 filters appear to decrease the incidence of SARS-CoV-2 infection. This research partnership identified areas for improving in-school ventilation.

Investigating Ethical Tradeoffs in Crisis Standards of Care through Simulation of Ventilator Allocation Protocols.

Introduction: Arguments over the appropriate Crisis Standards of Care (CSC) for public health emergencies often assume that there is a tradeoff between saving the most lives, saving the most life-years, and preventing racial disparities. However, these assumptions have rarely been explored empirically. To quantitatively characterize possible ethical tradeoffs, we aimed to simulate the implementation of five proposed CSC protocols for rationing ventilators in the context of the COVID-19 pandemic. Methods: A Monte Carlo simulation was used to estimate the number of lives saved and life-years saved by implementing clinical acuity-, comorbidity- and age-based CSC protocols under different shortage conditions. This model was populated with patient data from 3707 adult admissions requiring ventilator support in a New York hospital system between April 2020 and May 2021. To estimate lives and life-years saved by each protocol, we determined survival to discharge and estimated remaining life expectancy for each admission. Results: The simulation demonstrated stronger performance for age- and comorbidity-sensitive protocols. For a capacity of 1 bed per 2 patients, ranking by age bands saves approximately 28.7 lives and 3408 life-years per thousand patients, while ranking by Sequential Organ Failure Assessment (SOFA) bands saved the fewest lives (13.2) and life-years (416). For all protocols, we observed a positive correlation between lives saved and life-years saved. For all protocols except lottery and the banded SOFA, significant disparities in lives saved and life-years saved were noted between White non-Hispanic, Black non-Hispanic, and Hispanic sub-populations. Conclusion: While there is significant variance in the number of lives saved and life-years saved, we did not find a tradeoff between saving the most lives and saving the most life-years. Moreover, concerns about racial discrimination in triage protocols require thinking carefully about the tradeoff between enforcing equality of survival rates and maximizing the lives saved in each sub-population.

Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells.

While animal model studies have extensively defined the mechanisms controlling cell diversity in the developing mammalian lung, there exists a significant knowledge gap with regards to late-stage human lung development. The NHLBI Molecular Atlas of Lung Development Program (LungMAP) seeks to fill this gap by creating a structural, cellular and molecular atlas of the human and mouse lung. Transcriptomic profiling at the single-cell level created a cellular atlas of newborn human lungs. Frozen single-cell isolates obtained from two newborn human lungs from the LungMAP Human Tissue Core Biorepository, were captured, and library preparation was completed on the Chromium 10X system. Data was analyzed in Seurat, and cellular annotation was performed using the ToppGene functional analysis tool. Transcriptional interrogation of 5500 newborn human lung cells identified distinct clusters representing multiple populations of epithelial, endothelial, fibroblasts, pericytes, smooth muscle, immune cells and their gene signatures. Computational integration of data from newborn human cells and with 32,000 cells from postnatal days 1 through 10 mouse lungs generated by the LungMAP Cincinnati Research Center facilitated the identification of distinct cellular lineages among all the major cell types. Integration of the newborn human and mouse cellular transcriptomes also demonstrated cell type-specific differences in maturation states of newborn human lung cells. Specifically, newborn human lung matrix fibroblasts could be separated into those representative of younger cells (n = 393), or older cells (n = 158). Cells with each molecular profile were spatially resolved within newborn human lung tissue. This is the first comprehensive molecular map of the cellular landscape of neonatal human lung, including biomarkers for cells at distinct states of maturity.

RocHealthData.org: Development and usage of a publicly available, geographic source of social determinants of health data.

Access to local, population specific, and timely data is vital in understanding factors that impact population health. The impact of place (neighborhood, census tract, and city) is particularly important in understanding the Social Determinants of Health. The University of Rochester Medical Center's Clinical and Translational Science Institute created the web-based tool RocHealthData.org to provide access to thousands of geographically displayed publicly available health-related datasets. The site has also hosted a variety of locally curated datasets (eg., COVID-19 vaccination rates and community-derived health indicators), helping set community priorities and impacting outcomes. Usage statistics (available through Google Analytics) show returning visitors with a lower bounce rate (leaving a site after a single page access) and spent longer at the site than new visitors. Of the currently registered 1033 users, 51.7% were from within our host university, 20.1% were from another educational institution, and 28.2% identified as community members. Our assessments indicate that these data are useful and valued across a variety of domains. Continuing site improvement depends on new sources of locally relevant data, as well as increased usage of data beyond our local region.

Ventilation during COVID-19 in a school for students with intellectual and developmental disabilities (IDD).

Background: This study examined the correlation of classroom ventilation (air exchanges per hour (ACH)) and exposure to CO2 ≥1,000 ppm with the incidence of SARS-CoV-2 over a 20-month period in a specialized school for students with intellectual and developmental disabilities (IDD). These students were at a higher risk of respiratory infection from SARS-CoV-2 due to challenges in tolerating mitigation measures (e.g. masking). One in-school measure proposed to help mitigate the risk of SARS-CoV-2 infection in schools is increased ventilation. Methods: We established a community-engaged research partnership between the University of Rochester and the Mary Cariola Center school for students with IDD. Ambient CO2 levels were measured in 100 school rooms, and air changes per hour (ACH) were calculated. The number of SARS-CoV-2 cases for each room was collected over 20 months. Results: 97% of rooms had an estimated ACH ≤4.0, with 7% having CO2 levels ≥2,000 ppm for up to 3 hours per school day. A statistically significant correlation was found between the time that a room had CO2 levels ≥1,000 ppm and SARS-CoV-2 PCR tests normalized to room occupancy, accounting for 43% of the variance. No statistically significant correlation was found for room ACH and per-room SARS-CoV-2 cases. Rooms with ventilation systems using MERV-13 filters had lower SARS-CoV-2-positive PCR counts. These findings led to ongoing efforts to upgrade the ventilation systems in this community-engaged research project. Conclusions: There was a statistically significant correlation between the total time of room CO2 concentrations ≥1,000 and SARS-CoV-2 cases in an IDD school. Merv-13 filters appear to decrease the incidence of SARS-CoV-2 infection. This research partnership identified areas for improving in-school ventilation.

Common Data Element Collection in Underserved School Communities: Challenges and Recommendations.

OBJECTIVES: To provide recommendations for future common data element (CDE) development and collection that increases community partnership, harmonizes data interpretation, and continues to reduce barriers of mistrust between researchers and underserved communities. METHODS: We conducted a cross-sectional qualitative and quantitative evaluation of mandatory CDE collection among Rapid Acceleration of Diagnostics-Underserved Populations Return to School project teams with various priority populations and geographic locations in the United States to: (1) compare racial and ethnic representativeness of participants completing CDE questions relative to participants enrolled in project-level testing initiatives and (2) identify the amount of missing CDE data by CDE domain. Additionally, we conducted analyses stratified by aim-level variables characterizing CDE collection strategies. RESULTS: There were 15 study aims reported across the 13 participating Return to School projects, of which 7 (47%) were structured so that CDEs were fully uncoupled from the testing initiative, 4 (27%) were fully coupled, and 4 (27%) were partially coupled. In 9 (60%) study aims, participant incentives were provided in the form of monetary compensation. Most project teams modified CDE questions (8/13; 62%) to fit their population. Across all 13 projects, there was minimal variation in the racial and ethnic distribution of CDE survey participants from those who participated in testing; however, fully uncoupling CDE questions from testing increased the proportion of Black and Hispanic individuals participating in both initiatives. CONCLUSIONS: Collaboration with underrepresented populations from the early study design process may improve interest and participation in CDE collection efforts.

Aberrant newborn T cell and microbiota developmental trajectories predict respiratory compromise during infancy.

Neonatal immune-microbiota co-development is poorly understood, yet age-appropriate recognition of - and response to - pathogens and commensal microbiota is critical to health. In this longitudinal study of 148 preterm and 119 full-term infants from birth through one year of age, we found that postmenstrual age or weeks from conception is a central factor influencing T cell and mucosal microbiota development. Numerous features of the T cell and microbiota functional development remain unexplained; however, by either age metric and are instead shaped by discrete perinatal and postnatal events. Most strikingly, we establish that prenatal antibiotics or infection disrupt the normal T cell population developmental trajectory, influencing subsequent respiratory microbial colonization and predicting respiratory morbidity. In this way, early exposures predict the postnatal immune-microbiota axis trajectory, placing infants at later risk for respiratory morbidity in early childhood.

A systems genomics approach uncovers molecular associates of RSV severity.

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.

Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants.

BACKGROUND: A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness. METHOD: We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2). RESULTS: NGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ = 0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%. CONCLUSION: Airway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.

Temporal Dysbiosis of Infant Nasal Microbiota Relative to Respiratory Syncytial Virus Infection.

BACKGROUND: Respiratory syncytial virus (RSV) is a leading cause of infant respiratory disease. Infant airway microbiota has been associated with respiratory disease risk and severity. The extent to which interactions between RSV and microbiota occur in the airway, and their impact on respiratory disease susceptibility and severity, are unknown. METHODS: We carried out 16S rRNA microbiota profiling of infants in the first year of life from (1) a cross-sectional cohort of 89 RSV-infected infants sampled during illness and 102 matched healthy controls, and (2) a matched longitudinal cohort of 12 infants who developed RSV infection and 12 who did not, sampled before, during, and after infection. RESULTS: We identified 12 taxa significantly associated with RSV infection. All 12 taxa were differentially abundant during infection, with 8 associated with disease severity. Nasal microbiota composition was more discriminative of healthy vs infected than of disease severity. CONCLUSIONS: Our findings elucidate the chronology of nasal microbiota dysbiosis and suggest an altered developmental trajectory associated with RSV infection. Microbial temporal dynamics reveal indicators of disease risk, correlates of illness and severity, and impact of RSV infection on microbiota composition.

Airway Gene Expression Correlates of Respiratory Syncytial Virus Disease Severity and Microbiome Composition in Infants.

BACKGROUND: Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. The causes and correlates of severe illness in the majority of infants are poorly defined. METHODS: We recruited a cohort of RSV-infected infants and simultaneously assayed the molecular status of their airways and the presence of airway microbiota. We used rigorous statistical approaches to identify gene expression patterns associated with disease severity and microbiota composition, separately and in combination. RESULTS: We measured comprehensive airway gene expression patterns in 106 infants with primary RSV infection. We identified an airway gene expression signature of severe illness dominated by excessive chemokine expression. We also found an association between Haemophilus influenzae, disease severity, and airway lymphocyte accumulation. Exploring the time of onset of clinical symptoms revealed acute activation of interferon signaling following RSV infection in infants with mild or moderate illness, which was absent in subjects with severe illness. CONCLUSIONS: Our data reveal that airway gene expression patterns distinguish mild/moderate from severe illness. Furthermore, our data identify biomarkers that may be therapeutic targets or useful for measuring efficacy of intervention responses.

Characterizing Emerging Canine H3 Influenza Viruses.

The continual emergence of novel influenza A strains from non-human hosts requires constant vigilance and the need for ongoing research to identify strains that may pose a human public health risk. Since 1999, canine H3 influenza A viruses (CIVs) have caused many thousands or millions of respiratory infections in dogs in the United States. While no human infections with CIVs have been reported to date, these viruses could pose a zoonotic risk. In these studies, the National Institutes of Allergy and Infectious Diseases (NIAID) Centers of Excellence for Influenza Research and Surveillance (CEIRS) network collaboratively demonstrated that CIVs replicated in some primary human cells and transmitted effectively in mammalian models. While people born after 1970 had little or no pre-existing humoral immunity against CIVs, the viruses were sensitive to existing antivirals and we identified a panel of H3 cross-reactive human monoclonal antibodies (hmAbs) that could have prophylactic and/or therapeutic value. Our data predict these CIVs posed a low risk to humans. Importantly, we showed that the CEIRS network could work together to provide basic research information important for characterizing emerging influenza viruses, although there were valuable lessons learned.

Eight practices for data management to enable team data science.

INTRODUCTION: In clinical and translational research, data science is often and fortuitously integrated with data collection. This contrasts to the typical position of data scientists in other settings, where they are isolated from data collectors. Because of this, effective use of data science techniques to resolve translational questions requires innovation in the organization and management of these data. METHODS: We propose an operational framework that respects this important difference in how research teams are organized. To maximize the accuracy and speed of the clinical and translational data science enterprise under this framework, we define a set of eight best practices for data management. RESULTS: In our own work at the University of Rochester, we have strived to utilize these practices in a customized version of the open source LabKey platform for integrated data management and collaboration. We have applied this platform to cohorts that longitudinally track multidomain data from over 3000 subjects. CONCLUSIONS: We argue that this has made analytical datasets more readily available and lowered the bar to interdisciplinary collaboration, enabling a team-based data science that is unique to the clinical and translational setting.

Microbiome-Transcriptome Interactions Related to Severity of Respiratory Syncytial Virus Infection.

Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infections and hospital visits during infancy and childhood. Although risk factors for RSV infection have been identified, the role of microbial species in the respiratory tract is only partially known. We aimed to understand the impact of interactions between the nasal microbiome and host transcriptome on the severity and clinical outcomes of RSV infection. We used 16 S rRNA sequencing to characterize the nasal microbiome of infants with RSV infection. We used RNA sequencing to interrogate the transcriptome of CD4+ T cells obtained from the same set of infants. After dimension reduction through principal component (PC) analysis, we performed an integrative analysis to identify significant co-variation between microbial clade and gene expression PCs. We then employed LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) to estimate the clade-gene association patterns for each infant. Our network-based integrative analysis identified several clade-gene associations significantly related to the severity of RSV infection. The microbial taxa with the highest loadings in the implicated clade PCs included Moraxella, Corynebacterium, Streptococcus, Haemophilus influenzae, and Staphylococcus. Interestingly, many of the genes with the highest loadings in the implicated gene PCs are encoded in mitochondrial DNA, while others are involved in the host immune response. This study on microbiome-transcriptome interactions provides insights into how the host immune system mounts a response against RSV and specific infectious agents in nasal microbiota.

Come from away: Best practices in mini-sabbaticals for the development of young investigators: a White Paper by the SEQUIN (mini-Sabbatical Evaluation and QUality ImprovemeNt) Group.

Mini-sabbaticals are formal short-term training and educational experiences away from an investigator's home research unit. These may include rotations with other research units and externships at government research or regulatory agencies, industry and non-profit programs, and training and/or intensive educational programs. The National Institutes of Health have been encouraging training institutions to consider offering mini-sabbaticals, but given the newness of the concept, limited data are available to guide the implementation of mini-sabbatical programs. In this paper, we review the history of sabbaticals and mini-sabbaticals, report the results of surveys we performed to ascertain the use of mini-sabbaticals at Clinical and Translational Science Award hubs, and consider best practice recommendations for institutions seeking to establish formal mini-sabbatical programs.

Measuring the Severity of Respiratory Illness in the First 2 Years of Life in Preterm and Term Infants.

OBJECTIVE: To develop a valid research tool to measure infant respiratory illness severity using parent-reported symptoms. STUDY DESIGN: Nose and throat swabs were collected monthly for 1 year and during respiratory illnesses for 2 years in a prospective study of term and preterm infants in the Prematurity, Respiratory Outcomes, Immune System and Microbiome study. Viral pathogens were detected using Taqman Array Cards. Parents recorded symptoms during respiratory illnesses using a Childhood Origins of Asthma (COAST) scorecard. The COAST score was validated using linear mixed effects regression modeling to evaluate associations with hospitalization and specific infections. A data-driven method was also used to compute symptom weights and derive a new score, the Infant Research Respiratory Infection Severity Score (IRRISS). Linear mixed effects regression modeling was repeated with the IRRISS illness data. RESULTS: From April 2013 to April 2017, 50 term, 40 late preterm, and 28 extremely low gestational age (<29 weeks of gestation) infants had 303 respiratory illness visits with viral testing and parent-reported symptoms. A range of illness severity was described with 39% of illness scores suggestive of severe disease. Both the COAST score and IRRISS were associated with respiratory syncytial virus infection and hospitalization. Gestational age and human rhinovirus infection were inversely associated with both scoring systems. The IRRISS and COAST scores were highly correlated (r = 0.93; P < .0001). CONCLUSIONS: Using parent-reported symptoms, we validated the COAST score as a measure of respiratory illness severity in infants. The new IRRISS score performed as well as the COAST score.

Aims, Study Design, and Enrollment Results From the Assessing Predictors of Infant Respiratory Syncytial Virus Effects and Severity Study.

BACKGROUND: The majority of infants hospitalized with primary respiratory syncytial virus (RSV) infection have no obvious risk factors for severe disease. OBJECTIVE: The aim of this study (Assessing Predictors of Infant RSV Effects and Severity, AsPIRES) was to identify factors associated with severe disease in full-term healthy infants younger than 10 months with primary RSV infection. METHODS: RSV infected infants were enrolled from 3 cohorts during consecutive winters from August 2012 to April 2016 in Rochester, New York. A birth cohort was prospectively enrolled and followed through their first winter for development of RSV infection. An outpatient supplemental cohort was enrolled in the emergency department or pediatric offices, and a hospital cohort was enrolled on admission with RSV infection. RSV was diagnosed by reverse transcriptase-polymerase chain reaction. Demographic and clinical data were recorded and samples collected for assays: buccal swab (cytomegalovirus polymerase chain reaction, PCR), nasal swab (RSV qualitative PCR, complete viral gene sequence, 16S ribosomal ribonucleic acid [RNA] amplicon microbiota analysis), nasal wash (chemokine and cytokine assays), nasal brush (nasal respiratory epithelial cell gene expression using RNA sequencing [RNAseq]), and 2 to 3 ml of heparinized blood (flow cytometry, RNAseq analysis of purified cluster of differentiation [CD]4+, CD8+, B cells and natural killer cells, and RSV-specific antibody). Cord blood (RSV-specific antibody) was also collected for the birth cohort. Univariate and multivariate logistic regression will be used for analysis of data using a continuous Global Respiratory Severity Score (GRSS) as the outcome variable. Novel statistical methods will be developed for integration of the large complex datasets. RESULTS: A total of 453 infants were enrolled into the 3 cohorts; 226 in the birth cohort, 60 in the supplemental cohort, and 78 in the hospital cohort. A total of 126 birth cohort infants remained in the study and were evaluated for 150 respiratory illnesses. Of the 60 RSV positive infants in the supplemental cohort, 42 completed the study, whereas all 78 of the RSV positive hospital cohort infants completed the study. A GRSS was calculated for each RSV-infected infant and is being used to analyze each of the complex datasets by correlation with disease severity in univariate and multivariate methods. CONCLUSIONS: The AsPIRES study will provide insights into the complex pathogenesis of RSV infection in healthy full-term infants with primary RSV infection. The analysis will allow assessment of multiple factors potentially influencing the severity of RSV infection including the level of RSV specific antibodies, the innate immune response of nasal epithelial cells, the adaptive response by various lymphocyte subsets, the resident airway microbiota, and viral factors. Results of this study will inform disease interventions such as vaccines and antiviral therapies.

Influenza Virus Vaccination Elicits Poorly Adapted B Cell Responses in Elderly Individuals.

Influenza is a leading cause of death in the elderly, and the vaccine protects only a fraction of this population. A key aspect of antibody-mediated anti-influenza virus immunity is adaptation to antigenically distinct epitopes on emerging strains. We examined factors contributing to reduced influenza vaccine efficacy in the elderly and uncovered a dramatic reduction in the accumulation of de novo immunoglobulin gene somatic mutations upon vaccination. This reduction is associated with a significant decrease in the capacity of antibodies to target the viral glycoprotein, hemagglutinin (HA), and critical protective epitopes surrounding the HA receptor-binding domain. Immune escape by antigenic drift, in which viruses generate mutations in key antigenic epitopes, becomes highly exaggerated. Because of this reduced adaptability, most B cells activated in the elderly cohort target highly conserved but less potent epitopes. Given these findings, vaccines driving immunoglobulin gene somatic hypermutation should be a priority to protect elderly individuals.

Neonatal gut and respiratory microbiota: coordinated development through time and space.

BACKGROUND: Postnatal development of early life microbiota influences immunity, metabolism, neurodevelopment, and infant health. Microbiome development occurs at multiple body sites, with distinct community compositions and functions. Associations between microbiota at multiple sites represent an unexplored influence on the infant microbiome. Here, we examined co-occurrence patterns of gut and respiratory microbiota in pre- and full-term infants over the first year of life, a period critical to neonatal development. RESULTS: Gut and respiratory microbiota collected as longitudinal rectal, throat, and nasal samples from 38 pre-term and 44 full-term infants were first clustered into community state types (CSTs) on the basis of their compositional profiles. Multiple methods were used to relate the occurrence of CSTs to temporal microbiota development and measures of infant maturity, including gestational age (GA) at birth, week of life (WOL), and post-menstrual age (PMA). Manifestation of CSTs followed one of three patterns with respect to infant maturity: (1) chronological, with CST occurrence frequency solely a function of post-natal age (WOL), (2) idiosyncratic to maturity at birth, with the interval of CST occurrence dependent on infant post-natal age but the frequency of occurrence dependent on GA at birth, and (3) convergent, in which CSTs appear first in infants of greater maturity at birth, with occurrence frequency in pre-terms converging after a post-natal interval proportional to pre-maturity. The composition of CSTs was highly dissimilar between different body sites, but the CST of any one body site was highly predictive of the CSTs at other body sites. There were significant associations between the abundance of individual taxa at each body site and the CSTs of the other body sites, which persisted after stringent control for the non-linear effects of infant maturity. Canonical correlations exist between the microbiota composition at each pair of body sites, with the strongest correlations between proximal locations. CONCLUSION: These findings suggest that early microbiota is shaped by neonatal innate and adaptive developmental responses. Temporal progression of CST occurrence is influenced by infant maturity at birth and post-natal age. Significant associations of microbiota across body sites reveal distal connections and coordinated development of the infant microbial ecosystem.

Dissociation, cellular isolation, and initial molecular characterization of neonatal and pediatric human lung tissues.

Human lung morphogenesis begins by embryonic life and continues after birth into early childhood to form a complex organ with numerous morphologically and functionally distinct cell types. Pulmonary organogenesis involves dynamic changes in cell proliferation, differentiation, and migration of specialized cells derived from diverse embryonic lineages. Studying the molecular and cellular processes underlying formation of the fully functional lung requires isolating distinct pulmonary cell populations during development. We now report novel methods to isolate four major pulmonary cell populations from pediatric human lung simultaneously. Cells were dissociated by protease digestion of neonatal and pediatric lung and isolated on the basis of unique cell membrane protein expression patterns. Epithelial, endothelial, nonendothelial mesenchymal, and immune cells were enriched by fluorescence-activated cell sorting. Dead cells and erythrocytes were excluded by 7-aminoactinomycin D uptake and glycophorin-A (CD235a) expression, respectively. Leukocytes were identified by membrane CD45 (protein tyrosine phosphatase, receptor type C), endothelial cells by platelet endothelial cell adhesion molecule-1 (CD31) and vascular endothelial cadherin (CD144), and both were isolated. Thereafter, epithelial cell adhesion molecule (CD326)-expressing cells were isolated from the endothelial- and immune cell-depleted population to enrich epithelial cells. Cells lacking these membrane markers were collected as "nonendothelial mesenchymal" cells. Quantitative RT-PCR and RNA sequencing analyses of population specific transcriptomes demonstrate the purity of the subpopulations of isolated cells. The method efficiently isolates major human lung cell populations that we announce are now available through the National Heart, Lung, and Blood Institute Lung Molecular Atlas Program (LungMAP) for their further study.