Metabolomics analysis of bronchoalveolar lavage fluid predicts unique features of the lower airway in pediatric cystic fibrosis.

BACKGROUND: Progressive, obstructive lung disease resulting from chronic infection and inflammation is the leading cause of morbidity and mortality in persons with cystic fibrosis (PWCF). Metabolomics and next -generation sequencing (NGS) of airway secretions can allow for better understanding of cystic fibrosis (CF) pathophysiology. In this study, global metabolomic profiling on bronchoalveolar lavage fluid (BALF) obtained from pediatric PWCF and disease controls (DCs) was performed and compared to lower airway microbiota, inflammation, and lung function. METHODS: BALF was collected from children undergoing flexible bronchoscopies for clinical indications. Metabolomic profiling was performed using a platform developed by Metabolon Inc. Total bacterial load (TBL) was measured using quantitative polymerase chain reaction (qPCR), and bacterial communities were characterized using 16S ribosomal RNA (rRNA) sequencing. Random Forest Analysis (RFA), principal component analysis (PCA), and hierarchical clustering analysis (HCA) were performed. RESULTS: One hundred ninety-five BALF samples were analyzed, 142 (73 %) from PWCF. Most metabolites (425/665) and summed categories (7/9) were significantly increased in PWCF. PCA of the metabolomic data revealed CF BALF exhibited more dispersed clustering compared to DC BALF. Higher metabolite concentrations correlated with increased inflammation, increased abundance of Staphylococcus, and decreased lung function. CONCLUSIONS: The lower airway metabolome of PWCF was defined by a complex expansion of metabolomic activity. These findings could be attributed to heightened inflammation in PWCF and aspects of the CF airway polymicrobial ecology. CF-specific metabolomic features are associated with the unique underlying biology of the CF airway.

An integrated metaproteomics workflow for studying host-microbe dynamics in bronchoalveolar lavage samples applied to cystic fibrosis disease.

Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our understanding of host-microbe interactions, we have developed an integrated analytical and bioinformatic mass spectrometry (MS)-based metaproteomics workflow to analyze clinical bronchoalveolar lavage (BAL) samples from people with airway disease. Proteins from BAL cellular pellets were processed and pooled together in groups categorized by disease status (CF vs. non-CF) and bacterial diversity, based on previously performed small subunit rRNA sequencing data. Proteins from each pooled sample group were digested and subjected to liquid chromatography tandem mass spectrometry (MS/MS). MS/MS spectra were matched to human and bacterial peptide sequences leveraging a bioinformatic workflow using a metagenomics-guided protein sequence database and rigorous evaluation. Label-free quantification revealed differentially abundant human peptides from proteins with known roles in CF, like neutrophil elastase and collagenase, and proteins with lesser-known roles in CF, including apolipoproteins. Differentially abundant bacterial peptides were identified from known CF pathogens (e.g., Pseudomonas), as well as other taxa with potentially novel roles in CF. We used this host-microbe peptide panel for targeted parallel-reaction monitoring validation, demonstrating for the first time an MS-based assay effective for quantifying host-microbe protein dynamics within BAL cells from individual CF patients. Our integrated bioinformatic and analytical workflow combining discovery, verification, and validation should prove useful for diverse studies to characterize microbial contributors in airway diseases. Furthermore, we describe a promising preliminary panel of differentially abundant microbe and host peptide sequences for further study as potential markers of host-microbe relationships in CF disease pathogenesis.IMPORTANCEIdentifying microbial pathogenic contributors and dysregulated human responses in airway disease, such as CF, is critical to understanding disease progression and developing more effective treatments. To this end, characterizing the proteins expressed from bacterial microbes and human host cells during disease progression can provide valuable new insights. We describe here a new method to confidently detect and monitor abundance changes of both microbe and host proteins from challenging BAL samples commonly collected from CF patients. Our method uses both state-of-the art mass spectrometry-based instrumentation to detect proteins present in these samples and customized bioinformatic software tools to analyze the data and characterize detected proteins and their association with CF. We demonstrate the use of this method to characterize microbe and host proteins from individual BAL samples, paving the way for a new approach to understand molecular contributors to CF and other diseases of the airway.

Host DNA depletion on frozen human respiratory samples enables successful metagenomic sequencing for microbiome studies.

Background: Most respiratory microbiome studies have focused on amplicon rather than metagenomics sequencing due to high host DNA content. We evaluated efficacy of five host DNA depletion methods on previously frozen human bronchoalveolar lavage (BAL), nasal swabs, and sputum prior to metagenomic sequencing. Results: Median sequencing depth was 76.4 million reads per sample. Untreated nasal, sputum and BAL samples had 94.1%, 99.2%, and 99.7% host-reads. The effect of host depletion differed by sample type. Most treatment methods increased microbial reads, species richness and predicted functional richness; the increase in species and predicted functional richness was mediated by higher effective sequencing depth. For BAL and nasal samples, most methods did not change Morisita-Horn dissimilarity suggesting limited bias introduced by host depletion. Conclusions: Metagenomics sequencing without host depletion will underestimate microbial diversity of most respiratory samples due to shallow effective sequencing depth and is not recommended. Optimal host depletion methods vary by sample type.

Limited effects of azithromycin on the oropharyngeal microbiome in children with CF and early pseudomonas infection.

BACKGROUND: Tobramycin inhalation solution (TIS) and chronic azithromycin (AZ) have known clinical benefits for children with CF, likely due to antimicrobial and anti-inflammatory activity. The effects of chronic AZ in combination with TIS on the airway microbiome have not been extensively investigated. Oropharyngeal swab samples were collected in the OPTIMIZE multicenter, randomized, placebo-controlled trial examining the addition of AZ to TIS in 198 children with CF and early P. aeruginosa infection. Bacterial small subunit rRNA gene community profiles were determined. The effects of TIS and AZ were assessed on oropharyngeal microbial diversity and composition to uncover whether effects on the bacterial community may be a mechanism of action related to the observed changes in clinical outcomes. RESULTS: Substantial changes in bacterial communities (total bacterial load, diversity and relative abundance of specific taxa) were observed by week 3 of TIS treatment for both the AZ and placebo groups. On average, these shifts were due to changes in non-traditional CF taxa that were not sustained at the later study visits (weeks 13 and 26). Bacterial community measures did not differ between the AZ and placebo groups. CONCLUSIONS: This study provides further evidence that the mechanism for AZ's effect on clinical outcomes is not due solely to action on airway microbial composition.

Upper airway microbiota development in infants with cystic fibrosis diagnosed by newborn screen.

BACKGROUND: Changes in upper airway microbiota may impact early disease manifestations in infants with cystic fibrosis (CF). To investigate early airway microbiota, the microbiota present in the oropharynx of CF infants over the first year of life was assessed along with the relationships between microbiota and growth, antibiotic use and other clinical variables. METHODS: Oropharyngeal (OP) swabs were collected longitudinally between 1 and 12 months of age from infants diagnosed with CF by newborn screen and enrolled in the Baby Observational and Nutrition Study (BONUS). DNA extraction was performed after enzymatic digestion of OP swabs. Total bacterial load was determined by qPCR and community composition assessed using 16S rRNA gene analysis (V1/V2 region). Changes in diversity with age were evaluated using mixed models with cubic B-splines. Associations between clinical variables and bacterial taxa were determined using a canonical correlation analysis. RESULTS: 1,052 OP swabs collected from 205 infants with CF were analyzed. Most infants (77%) received at least one course of antibiotics during the study and 131 OP swabs were collected while the infant was prescribed an antibiotic. Alpha diversity increased with age and was only marginally impacted by antibiotic use. Community composition was most highly correlated with age and was only moderately correlated with antibiotic exposure, feeding method and weight z-scores. Relative abundance of Streptococcus decreased while Neisseria and other taxa increased over the first year. CONCLUSIONS: Age was more influential on the oropharyngeal microbiota of infants with CF than clinical variables including antibiotics in the first year of life.

Detection and identification of fungi in the lower airway of children with and without cystic fibrosis.

Introduction: Airway infection and inflammation lead to the progression of obstructive lung disease in persons with cystic fibrosis (PWCF). However, cystic fibrosis (CF) fungal communities, known drivers of CF pathophysiology, remain poorly understood due to the shortcomings of traditional fungal culture. Our objective was to apply a novel small subunit rRNA gene (SSU-rRNA) sequencing approach to characterize the lower airway mycobiome in children with and without CF. Methods: Bronchoalveolar lavage fluid (BALF) samples and relevant clinical data were collected from pediatric PWCF and disease control (DC) subjects. Total fungal load (TFL) was measured using quantitative PCR, and SSU-rRNA sequencing was used for mycobiome characterization. Results were compared across groups, and Morisita-Horn clustering was performed. Results: 161 (84%) of the BALF samples collected had sufficient load for SSU-rRNA sequencing, with amplification being more common in PWCF. BALF from PWCF had increased TFL and increased neutrophilic inflammation compared to DC subjects. PWCF exhibited increased abundance of Aspergillus and Candida, while Malassezia, Cladosporium, and Pleosporales were prevalent in both groups. CF and DC samples showed no clear differences in clustering when compared to each other or to negative controls. SSU-rRNA sequencing was used to profile the mycobiome in pediatric PWCF and DC subjects. Notable differences were observed between the groups, including the abundance of Aspergillus and Candida. Discussion: Fungal DNA detected in the airway could represent a combination of pathogenic fungi and environmental exposure (e.g., dust) to fungus indicative of a common background signature. Next steps will require comparisons to airway bacterial communities.

Mucosal Microbiota Associated With Eosinophilic Esophagitis and Eosinophilic Gastritis.

OBJECTIVE: The aim of the study was to determine the mucosal microbiota associated with eosinophilic esophagitis (EoE) and eosinophilic gastritis (EoG) in a geographically diverse cohort of patients compared to controls. METHODS: We conducted a prospective study of individuals with eosinophilic gastrointestinal disease (EGID) in the Consortium of Eosinophilic Gastrointestinal Disease Researchers, including pediatric and adult tertiary care centers. Eligible individuals had clinical data, mucosal biopsies, and stool collected. Total bacterial load was determined from mucosal biopsy samples by quantitative polymerase chain reaction (PCR). Community composition was determined by small subunit rRNA gene amplicons. RESULTS: One hundred thirty-nine mucosal biopsies were evaluated corresponding to 93 EoE, 17 EoG, and 29 control specimens (18 esophageal) from 10 sites across the United States. Dominant community members across disease activity differed significantly. When comparing EoE and EoG with controls, the dominant taxa in individuals with EGIDs was increased ( Streptococcus in esophagus; Prevotella in stomach). Specific taxa were associated with active disease for both EoE ( Streptococcus , Gemella ) and EoG ( Leptotrichia ), although highly individualized communities likely impacted statistical testing. Alpha diversity metrics were similar across groups, but with high variability among individuals. Stool analyses did not correlate with bacterial communities found in mucosal biopsy samples and was similar in patients and controls. CONCLUSIONS: Dominant community members ( Streptococcus for EoE, Prevotella for EoG) were different in the mucosal biopsies but not stool of individuals with EGIDs compared to controls; taxa associated with EGIDs were highly variable across individuals. Further study is needed to determine if therapeutic interventions contribute to the observed community differences.

Integrating airway microbiome and blood proteomics data to identify multi-omic networks associated with response to pulmonary infection.

Host response to airway infections can vary widely. Cystic fibrosis (CF) pulmonary exacerbations provide an opportunity to better understand the interplay between respiratory microbes and the host. This study aimed to investigate the observed heterogeneity in airway infection recovery by analyzing microbiome and host response (i.e., blood proteome) data collected during the onset of 33 pulmonary infection events. We used sparse multiple canonical correlation network (SmCCNet) analysis to integrate these two types of -omics data along with a clinical measure of recovery. Four microbe-protein SmCCNet subnetworks at infection onset were identified that strongly correlate with recovery. Our findings support existing knowledge regarding CF airway infections. Additionally, we discovered novel microbe-protein subnetworks that are associated with recovery and merit further investigation.

Lower respiratory tract infections in children requiring mechanical ventilation: a multicentre prospective surveillance study incorporating airway metagenomics.

BACKGROUND: Lower respiratory tract infections (LRTI) are a leading cause of critical illness and mortality in mechanically ventilated children; however, the pathogenic microbes frequently remain unknown. We combined traditional diagnostics with metagenomic next generation sequencing (mNGS) to evaluate the cause of LRTI in critically ill children. METHODS: We conducted a prospective, multicentre cohort study of critically ill children aged 31 days to 17 years with respiratory failure requiring mechanical ventilation (>72 h) in the USA. By combining bacterial culture and upper respiratory viral PCR testing with mNGS of tracheal aspirate collected from all patients within 24 h of intubation, we determined the prevalence, age distribution, and seasonal variation of viral and bacterial respiratory pathogens detected by either method in children with or without LRTI. FINDINGS: Between Feb 26, 2015, and Dec 31, 2017, of the 514 enrolled patients, 397 were eligible and included in the study (276 children with LRTI and 121 with no evidence of LRTI). A presumptive microbiological cause was identified in 255 (92%) children with LRTI, with respiratory syncytial virus (127 [46%]), Haemophilus influenzae (70 [25%]), and Moraxella catarrhalis (65 [24%]) being most prevalent. mNGS identified uncommon pathogens including Ureaplasma parvum and Bocavirus. Co-detection of viral and bacterial pathogens occurred in 144 (52%) patients. Incidental carriage of potentially pathogenic microbes occurred in 82 (68%) children without LRTI, with rhinovirus (30 [25%]) being most prevalent. Respiratory syncytial virus (p<0·0001), H influenzae (p=0·0006), and M catarrhalis (p=0·0002) were most common in children younger than 5 years. Viral and bacterial LRTI occurred predominantly during winter months. INTERPRETATION: These findings demonstrate that respiratory syncytial virus, H influenzae, and M catarrhalis contribute disproportionately to severe paediatric LRTI, co-infections are common, and incidental carriage of potentially pathogenic microbes occurs frequently. Further, we provide a framework for future epidemiological and emerging pathogen surveillance studies, highlighting the potential for metagenomics to enhance clinical diagnosis. FUNDING: US National Institutes of Health and CZ Biohub.

Modified PCR protocol to increase sensitivity for determination of bacterial community composition.

BACKGROUND: The objective of this project was to increase the sensitivity of sequence-based bacterial community determination without impacting community composition or interfering with cluster formation during sequencing. Two PCR protocols (standard and modified) were examined in airway samples where we observed a large range in bacterial load (3.1-6.2 log10 16S rRNA gene copies/reaction). Tracheal aspirate (TA) samples (n = 99) were collected from sixteen children requiring mechanical ventilation at a single center. DNA was extracted, and total bacterial load (TBL) was assessed using qPCR. Amplification of 16S rRNA was attempted with both protocols in all samples. RESULTS: PCR product was observed using both protocols in 52 samples and in 24 additional samples only with the modified protocol. TBL, diversity metrics, and prominent taxa were compared for samples in three groups based on success of the two protocols (successful with both, success with modified only, unsuccessful for both). TBL differed significantly across the three groups (p<0.001). Specifically, the modified protocol allowed amplification from samples with intermediate TBL. Shannon diversity was similar between the two protocols, and Morisita-Horn beta diversity index showed high agreement between the two protocols within samples (median value 0.9997, range 0.9947 to 1). We show that both protocols identify similar communities, and the technical variability of both protocols was very low. The use of limited PCR cycles was a key feature to limit impact of background by exclusion of 24% of samples with no evidence of bacterial DNA present in the sample. CONCLUSION: The modified amplification protocol represents a viable approach that increased sensitivity of bacterial community analysis, which is important for study of the human airway microbiome where bacterial load is highly variable. Video abstract.

Influence of Acid Blockade on the Aerodigestive Tract Microbiome in Children With Cystic Fibrosis.

BACKGROUND: Acid blockade is commonly prescribed in patients with cystic fibrosis (CF). Growing concerns, however, exist about its possible role in the pathophysiology of pulmonary infections. We aimed to investigate if acid blockade alters esophageal and respiratory microbiota leading to dysbiosis and inflammation. METHODS: We performed a cross sectional study of children with CF who were either prescribed acid blockade or not. Samples from the gastrointestinal and respiratory tracts were obtained and microbiome analyzed. Mixed effect models were used to compare outcomes between cohorts and across sampling sites. A random subject intercept was included to account for the multiple sampling sites per individual. RESULTS: A cohort of 25 individuals, 44% girls with median age of 13.8 years [IQR 11.2--14.8] were enrolled. Alpha diversity, total bacterial load, and beta diversity were similar across anatomic compartments, across the upper gastrointestinal tract, and in respiratory samples. Similar alpha diversity, total bacterial load, and beta diversity results were also observed when comparing individuals on versus those off acid blockade. IL-8 was elevated in the distal versus proximal esophagus in the whole cohort (P < 0.01). IL-8 concentrations were similar in the distal esophagus in patients on and off acid blockade, but significantly greater in the proximal esophagus of subjects on treatment (P < 0.01). CONCLUSIONS: On the basis of these data, acid blockade use does not appear to influence the microbiome of the aerodigestive tract in children with cystic fibrosis suggesting a complex interplay between these medications and the bacterial composition of the esophagus and lung.

Microbial and Biogeochemical Indicators of Methane in Groundwater Aquifers of the Denver Basin, Colorado.

The presence of methane and other hydrocarbons in domestic-use groundwater aquifers poses significant environmental and human health concerns. Isotopic measurements are often relied upon as indicators of groundwater aquifer contamination with methane. While these parameters are used to infer microbial metabolisms, there is growing evidence that isotopes present an incomplete picture of subsurface microbial processes. This study examined the relationships between microbiology and chemistry in groundwater wells located in the Denver-Julesburg Basin of Colorado, a rapidly urbanizing area with active oil and gas development. A primary goal was to determine if microbial data can reliably indicate the quantities and sources of groundwater methane. Comprehensive chemical and molecular analyses were performed on 39 groundwater well samples from five aquifers. Elevated methane concentrations were found in only one aquifer, and both isotopic and microbial data support a microbial origin. Microbial parameters had similar explanatory power as chemical parameters for predicting sample methane concentrations. Furthermore, a subset of samples with unique microbiology corresponded with unique chemical signatures that may be useful indicators of methane gas migration, potentially from nearby coal seams interacting with the aquifer. Microbial data may allow for more accurate determination of groundwater contamination and improved long-term water quality monitoring compared solely to isotopic and chemical data in areas with microbial methane.

Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis.

Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies. Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses. Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively. Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis.

Temporal airway microbiome changes related to ventilator-associated pneumonia in children.

We sought to determine whether temporal changes in the lower airway microbiome are associated with ventilator-associated pneumonia (VAP) in children.Using a multicentre prospective study of children aged 31 days to 18 years requiring mechanical ventilation support for >72 h, daily tracheal aspirates were collected and analysed by sequencing of the 16S rRNA gene. VAP was assessed using 2008 Centers for Disease Control and Prevention paediatric criteria. The association between microbial factors and VAP was evaluated using joint longitudinal time-to-event modelling, matched case-control comparisons and unsupervised clustering.Out of 366 eligible subjects, 66 (15%) developed VAP at a median of 5 (interquartile range 3-5) days post intubation. At intubation, there was no difference in total bacterial load (TBL), but Shannon diversity and the relative abundance of Streptococcus, Lactobacillales and Prevotella were lower for VAP subjects versus non-VAP subjects. However, higher TBL on each sequential day was associated with a lower hazard (hazard ratio 0.39, 95% CI 0.23-0.64) for developing VAP, but sequential values of diversity were not associated with VAP. Similar findings were observed from the matched analysis and unsupervised clustering. The most common dominant VAP pathogens included Prevotella species (19%), Pseudomonas aeruginosa (14%) and Streptococcus mitis/pneumoniae (10%). Mycoplasma and Ureaplasma were also identified as dominant organisms in several subjects.In mechanically ventilated children, changes over time in microbial factors were marginally associated with VAP risk, although these changes were not suitable for predicting VAP in individual patients. These findings suggest that focusing exclusively on pathogen burden may not adequately inform VAP diagnosis.

Nanodiagnostics to monitor biofilm oxygen metabolism for antibiotic susceptibility testing.

In clinical environments, many serious antibiotic-resistant infections are caused by biofilm-forming species. This presents issues when attempting to determine antimicrobial dosing as traditional antibiotic susceptibility tests (ASTs) are typically designed around planktonic bacteria and thus offer information that is not relevant to the biofilm phenotype present in the patient. Even the popular Calgary biofilm device may provide inaccurate minimum biofilm inhibitory concentrations (MBICs) and can be time- and material-intensive. In this work, we present a method utilizing oxygen-sensitive nanosensor technology to monitor the oxygen consumption dynamics of living biofilms as they are exposed to antibiotics. We incorporated our nanosensors into biofilms grown from P. aeruginosa strains of varying sensitivity to traditional classes of antibiotics. Through measuring nanosensor response under antibiotic administration we determined the concentrations able to cease biofilm metabolism. This method provides information on the MBIC as well as kinetic response information in a manner that requires fewer materials and is more reflective of biofilm behavior than a traditional AST.

Changes in Airway Microbiome and Inflammation with Ivacaftor Treatment in Patients with Cystic Fibrosis and the G551D Mutation.

Rationale: Modulation of the cystic fibrosis (CF) transmembrane conductance regulator (CFTR) protein improves clinical outcomes in patients with CF and specific CFTR genetic mutations. It remains unclear how improving CFTR function modifies existing airway infection and inflammation.Objectives: To compare sputum microbiome and markers of inflammation before and after 6 months of ivacaftor treatment.Methods: The study included 31 people with CF, ages 10 years and older, with at least one G551D CFTR allele and an forced expiratory volume in 1 second (FEV1) of 40% predicted or greater who were enrolled in the GOAL (G551D Observational) study. Sputum samples were collected either by induction (n = 14) or by spontaneous expectoration (n = 17) before and 6 months after initiation of ivacaftor. Changes in bacterial community indices by sequencing of 16S rRNA amplicons, total and specific bacterial load, and a panel of proteases, antiproteases, and inflammatory cytokines were determined.Results: The cohort that spontaneously expectorated sputum had a lower FEV1, a higher proportion with Pseudomonas aeruginosa infection, and higher concentrations of sputum inflammatory markers compared with the cohort that provided sputum by induction. Although the overall cohort experienced significant improvements in FEV1 and reductions in sweat chloride, no significant changes in bacterial diversity, specific bacterial pathogens, or markers of inflammation were observed in these subjects. Neither total bacterial load nor presence of Pseudomonas changed significantly between paired samples with ivacaftor treatment. Younger patients experienced more shifts in their microbial communities than older patients.Conclusions: In this multicenter cohort, 6 months of ivacaftor treatment were not associated with significant changes in airway microbial communities or measures of inflammation. These data suggest that concomitant antimicrobial and antiinflammatory treatments will still be needed to manage airway disease in patients with CF treated with highly effective CFTR modulator therapy, especially in older patients with more advanced disease.

Pulmonary Aptamer Signatures in Children's Interstitial and Diffuse Lung Disease.

Rationale: Biomarker signatures are needed in children with children's interstitial and diffuse lung disease (chILD) to improve diagnostic approaches, increase our understanding of disease pathogenesis, monitor disease progression, and develop new treatment strategies. Proteomic technology using SOMAmer (Slow Off-rate Modified Aptamer) nucleic acid-based protein-binding reagents allows for biomarker discovery.Objectives: We hypothesized that proteins and protein pathways in BAL fluid (BALF) would distinguish children with neuroendocrine cell hyperplasia of infancy (NEHI), surfactant dysfunction mutations, and other chILD diagnoses and control subjects.Methods: BALF was collected for clinical indications and banked in patients with chILD and disease control subjects using standardized protocols over 10 years. BALF supernatant was analyzed using an aptamer assay to measure 1,129 protein levels. Protein levels were compared between groups using an ANOVA and adjusted for multiple comparisons using false discovery rate. Proteins were classified into pathways. Hierarchical clustering was used to define endotypes in the group of children with NEHI.Measurements and Main Results: After correcting for multiple testing, children with NEHI (n = 22) had 202 aptamers that were significantly different (P < 0.05) in BALF compared with control subjects (n = 9). Children with surfactant mutation (n = 8) had 51 aptamers significantly different (P < 0.05) in BALF compared with control subjects (n = 9). Proteins associated with pulmonary fibrosis and inflammation were associated with the surfactant dysfunction group but not the NEHI group. Using hierarchical clustering analysis, two distinct NEHI endotypes were identified.Conclusions: Distinct proteins and protein pathways can be determined from BALF of children with chILD, and these hold promise to further our understanding of chILD.

Prenatal complications are associated with the postnatal airway host response and microbiota in intubated preterm infants.

PURPOSE: To prospectively examine the relationship between prenatal events, postnatal airway host response and microbiota, and clinical outcomes. MATERIALS AND METHODS: Tracheal aspirates collected at seven days of age from 71 mechanically ventilated infants (median gestational age (GA), 25 weeks [range 23-28]) were simultaneously processed for a 12-plex protein assay and bacterial identification by 16S rRNA sequencing. Phenotypes were determined by unsupervised clustering of the protein analytes. Subject characteristics, microbial communities and clinical factors and outcomes were compared across the phenotype groups. RESULTS: Three clusters were identified: 1 (high protein levels), 2 (high proinflammatory proteins and low anti-inflammatory proteins), and 3 (low protein levels), respectively. Antenatal hemorrhage was most common in cluster 1, while chorioamnionitis characterized cluster 2 and preeclampsia was most prevalent in cluster 3, which was characterized by a predominance of Staphylococcus and relative absence of Ureaplasma. There were higher rates of adverse clinical outcomes in cluster 1. CONCLUSIONS: Airway protein profiles in seven days old mechanically ventilated preterm infants are associated with important antenatal events and unique airway microbial communities. These relationships may reveal new mechanisms by which antenatal events impact the course and outcomes of preterm infants.

Succession of toxicity and microbiota in hydraulic fracturing flowback and produced water in the Denver-Julesburg Basin.

Hydraulic fracturing flowback and produced water (FPW) samples were analyzed for toxicity and microbiome characterization over 220 days for a horizontally drilled well in the Denver-Julesberg (DJ) Basin in Colorado. Cytotoxicity, mutagenicity, and estrogenicity of FPW were measured via the BioLuminescence Inhibition Assay (BLIA), Ames II mutagenicity assay (AMES), and Yeast Estrogen Screen (YES). Raw FPW stimulated bacteria in BLIA, but were cytotoxic to yeast in YES. Filtered FPW stimulated cell growth in both BLIA and YES. Concentrating 25× by solid phase extraction (SPE) revealed significant toxicity throughout well production by BLIA, toxicity during the first 55 days of flowback by YES, and mutagenicity by AMES. The selective pressures of fracturing conditions (including toxicity) affected bacterial and archaeal communities, which were characterized by 16S rRNA gene V4V5 region sequencing. Conditions selected for thermophilic, anaerobic, halophilic bacteria and methanogenic archaea from the groundwater used for fracturing fluid, and from the native shale community. Trends in toxicity echoed the microbial community, which indicated distinct stages of early flowback water, a transition stage, and produced water. Biota in another sampled DJ Basin horizontal well resembled similarly aged samples from this well. However, microbial signatures were unique compared to samples from DJ Basin vertical wells, and wells from other basins. These data can inform treatability, reuse, and management decisions specific to the DJ Basin to minimize adverse environmental health and well production outcomes.

On the Use of Diversity Measures in Longitudinal Sequencing Studies of Microbial Communities.

Identification of the majority of organisms present in human-associated microbial communities is feasible with the advent of high throughput sequencing technology. As substantial variability in microbiota communities is seen across subjects, the use of longitudinal study designs is important to better understand variation of the microbiome within individual subjects. Complex study designs with longitudinal sample collection require analytic approaches to account for this additional source of variability. A common approach to assessing community changes is to evaluate the change in alpha diversity (the variety and abundance of organisms in a community) over time. However, there are several commonly used alpha diversity measures and the use of different measures can result in different estimates of magnitude of change and different inferences. It has recently been proposed that diversity profile curves are useful for clarifying these differences, and may provide a more complete picture of the community structure. However, it is unclear how to utilize these curves when interest is in evaluating changes in community structure over time. We propose the use of a bi-exponential function in a longitudinal model that accounts for repeated measures on each subject to compare diversity profiles over time. Furthermore, it is possible that no change in alpha diversity (single community/sample) may be observed despite the presence of a highly divergent community composition. Thus, it is also important to use a beta diversity measure (similarity between multiple communities/samples) that captures changes in community composition. Ecological methods developed to evaluate temporal turnover have currently only been applied to investigate changes of a single community over time. We illustrate the extension of this approach to multiple communities of interest (i.e., subjects) by modeling the beta diversity measure over time. With this approach, a rate of change in community composition is estimated. There is a need for the extension and development of analytic methods for longitudinal microbiota studies. In this paper, we discuss different approaches to model alpha and beta diversity indices in longitudinal microbiota studies and provide both a review of current approaches and a proposal for new methods.