Spectrum of KV 2.1 Dysfunction in KCNB1-Associated Neurodevelopmental Disorders.

OBJECTIVE: Pathogenic variants in KCNB1, encoding the voltage-gated potassium channel KV 2.1, are associated with developmental and epileptic encephalopathy (DEE). Previous functional studies on a limited number of KCNB1 variants indicated a range of molecular mechanisms by which variants affect channel function, including loss of voltage sensitivity, loss of ion selectivity, and reduced cell-surface expression. METHODS: We evaluated a series of 17 KCNB1 variants associated with DEE or other neurodevelopmental disorders (NDDs) to rapidly ascertain channel dysfunction using high-throughput functional assays. Specifically, we investigated the biophysical properties and cell-surface expression of variant KV 2.1 channels expressed in heterologous cells using high-throughput automated electrophysiology and immunocytochemistry-flow cytometry. RESULTS: Pathogenic variants exhibited diverse functional defects, including altered current density and shifts in the voltage dependence of activation and/or inactivation, as homotetramers or when coexpressed with wild-type KV 2.1. Quantification of protein expression also identified variants with reduced total KV 2.1 expression or deficient cell-surface expression. INTERPRETATION: Our study establishes a platform for rapid screening of KV 2.1 functional defects caused by KCNB1 variants associated with DEE and other NDDs. This will aid in establishing KCNB1 variant pathogenicity and the mechanism of dysfunction, which will enable targeted strategies for therapeutic intervention based on molecular phenotype. ANN NEUROL 2019;86:899-912.

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.

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.

Network Analysis to Identify Multi-Omic Correlations in the Lower Airways of Children With Cystic Fibrosis.

The leading cause of morbidity and mortality in cystic fibrosis (CF) is progressive lung disease secondary to chronic airway infection and inflammation; however, what drives CF airway infection and inflammation is not well understood. By providing a physiological snapshot of the airway, metabolomics can provide insight into these processes. Linking metabolomic data with microbiome data and phenotypic measures can reveal complex relationships between metabolites, lower airway bacterial communities, and disease outcomes. In this study, we characterize the airway metabolome in bronchoalveolar lavage fluid (BALF) samples from persons with CF (PWCF) and disease control (DC) subjects and use multi-omic network analysis to identify correlations with the airway microbiome. The Biocrates targeted liquid chromatography mass spectrometry (LC-MS) platform was used to measure 409 metabolomic features in BALF obtained during clinically indicated bronchoscopy. Total bacterial load (TBL) was measured using quantitative polymerase chain reaction (qPCR). The Qiagen EZ1 Advanced automated extraction platform was used to extract DNA, and bacterial profiling was performed using 16S sequencing. Differences in metabolomic features across disease groups were assessed univariately using Wilcoxon rank sum tests, and Random forest (RF) was used to identify features that discriminated across the groups. Features were compared to TBL and markers of inflammation, including white blood cell count (WBC) and percent neutrophils. Sparse supervised canonical correlation network analysis (SsCCNet) was used to assess multi-omic correlations. The CF metabolome was characterized by increased amino acids and decreased acylcarnitines. Amino acids and acylcarnitines were also among the features most strongly correlated with inflammation and bacterial burden. RF identified strong metabolomic predictors of CF status, including L-methionine-S-oxide. SsCCNet identified correlations between the metabolome and the microbiome, including correlations between a traditional CF pathogen, Staphylococcus, a group of nontraditional taxa, including Prevotella, and a subnetwork of specific metabolomic markers. In conclusion, our work identified metabolomic characteristics unique to the CF airway and uncovered multi-omic correlations that merit additional study.

Divergence of bacterial communities in the lower airways of CF patients in early childhood.

RATIONALE: Chronic airway infection and inflammation resulting in progressive, obstructive lung disease is the leading cause of morbidity and mortality in cystic fibrosis. Understanding the lower airway microbiota across the ages can provide valuable insight and potential therapeutic targets. OBJECTIVES: To characterize and compare the lower airway microbiota in cystic fibrosis and disease control subjects across the pediatric age spectrum. METHODS: Bronchoalveolar lavage fluid samples from 191 subjects (63 with cystic fibrosis) aged 0 to 21 years were collected along with relevant clinical data. We measured total bacterial load using quantitative polymerase chain reaction and performed 16S rRNA gene sequencing to characterize bacterial communities with species-level sensitivity for select genera. Clinical comparisons were investigated. MEASUREMENTS AND MAIN RESULTS: Cystic fibrosis samples had higher total bacterial load and lower microbial diversity, with a divergence from disease controls around 2-5 years of age, as well as higher neutrophilic inflammation relative to bacterial burden. Cystic fibrosis samples had increased abundance of traditional cystic fibrosis pathogens and decreased abundance of the Streptococcus mitis species group in older subjects. Interestingly, increased diversity in the heterogeneous disease controls was independent of diagnosis and indication. Sequencing was more sensitive than culture, and antibiotic exposure was more common in disease controls, which showed a negative relationship with load and neutrophilic inflammation. CONCLUSIONS: Analysis of lower airway samples from people with cystic fibrosis and disease controls across the ages revealed key differences in airway microbiota and inflammation. The divergence in subjects during early childhood may represent a window of opportunity for intervention and additional study.