Airway epithelium respiratory illnesses and allergy (AERIAL) birth cohort: study protocol.

Introduction: Recurrent wheezing disorders including asthma are complex and heterogeneous diseases that affect up to 30% of all children, contributing to a major burden on children, their families, and global healthcare systems. It is now recognized that a dysfunctional airway epithelium plays a central role in the pathogenesis of recurrent wheeze, although the underlying mechanisms are still not fully understood. This prospective birth cohort aims to bridge this knowledge gap by investigating the influence of intrinsic epithelial dysfunction on the risk for developing respiratory disorders and the modulation of this risk by maternal morbidities, in utero exposures, and respiratory exposures in the first year of life. Methods: The Airway Epithelium Respiratory Illnesses and Allergy (AERIAL) study is nested within the ORIGINS Project and will monitor 400 infants from birth to 5 years. The primary outcome of the AERIAL study will be the identification of epithelial endotypes and exposure variables that influence the development of recurrent wheezing, asthma, and allergic sensitisation. Nasal respiratory epithelium at birth to 6 weeks, 1, 3, and 5 years will be analysed by bulk RNA-seq and DNA methylation sequencing. Maternal morbidities and in utero exposures will be identified on maternal history and their effects measured through transcriptomic and epigenetic analyses of the amnion and newborn epithelium. Exposures within the first year of life will be identified based on infant medical history as well as on background and symptomatic nasal sampling for viral PCR and microbiome analysis. Daily temperatures and symptoms recorded in a study-specific Smartphone App will be used to identify symptomatic respiratory illnesses. Discussion: The AERIAL study will provide a comprehensive longitudinal assessment of factors influencing the association between epithelial dysfunction and respiratory morbidity in early life, and hopefully identify novel targets for diagnosis and early intervention.

A phase I clinical trial assessing the safety, tolerability, and pharmacokinetics of inhaled ethanol in humans as a potential treatment for respiratory tract infections.

Background: Current treatments for respiratory infections are severely limited. Ethanol's unique properties including antimicrobial, immunomodulatory, and surfactant-like activity make it a promising candidate treatment for respiratory infections if it can be delivered safely to the airway by inhalation. Here, we explore the safety, tolerability, and pharmacokinetics of inhaled ethanol in a phase I clinical trial. Methods: The study was conducted as a single-centre, open-label clinical trial in 18 healthy adult volunteers, six with no significant medical comorbidities, four with stable asthma, four with stable cystic fibrosis, and four active smokers. A dose-escalating design was used, with participants receiving three dosing cycles of 40, 60%, and then 80% ethanol v/v in water, 2 h apart, in a single visit. Ethanol was nebulised using a standard jet nebuliser, delivered through a novel closed-circuit reservoir system, and inhaled nasally for 10 min, then orally for 30 min. Safety assessments included adverse events and vital sign monitoring, blood alcohol concentrations, clinical examination, spirometry, electrocardiogram, and blood tests. Results: No serious adverse events were recorded. The maximum blood alcohol concentration observed was 0.011% immediately following 80% ethanol dosing. Breath alcohol concentrations were high (median 0.26%) following dosing suggesting high tissue levels were achieved. Small transient increases in heart rate, blood pressure, and blood neutrophil levels were observed, with these normalising after dosing, with no other significant safety concerns. Of 18 participants, 15 completed all dosing cycles with three not completing all cycles due to tolerability. The closed-circuit reservoir system significantly reduced fugitive aerosol loss during dosing. Conclusion: These data support the safety of inhaled ethanol at concentrations up to 80%, supporting its further investigation as a treatment for respiratory infections.Clinical trial registration: identifier ACTRN12621000067875.

Thoracic electrical impedance tomography identifies heterogeneity in lungs associated with respiratory disease in cattle. A pilot study.

Respiratory disease in cattle is a significant global concern, yet current diagnostic methods are limited, and there is a lack of crush-side tests for detecting active disease. To address this gap, we propose utilizing electrical impedance tomography (EIT), a non-invasive imaging technique that provides real-time visualization of lung ventilation dynamics. The study included adult cattle from farms in Western Australia. The cattle were restrained in a crush. A standardized respiratory scoring system, which combined visual, auscultation, and clinical scores, was conducted by two non-conferring clinicians for each animal. The scores were blinded and averaged. During assessment, an EIT electrode belt was placed around the thorax. EIT recordings of ten suitable breaths were taken for analysis before the cattle were released back to the herd. Based on the combined examination scoring, the cattle were categorized as having healthy or diseased lungs. To allow visual interpretation of each breath and enable the creation of the quartile ventilation ratio (VQR), Flow/Tidal Impedance Variation curves (F/TIV) were generated for each breath. The analysis focused on two EIT variables: The novel VQR over time during inhalation and exhalation and global expiratory impedance (TIVEXP) adjusted by breath length. A mixed effects model was used to compare these variables between healthy and diseased cattle. Ten adult cattle of mixed ages were used in the current analysis. Five cattle were scored as healthy and five as diseased. There was a significant difference in the examination scores between the healthy and diseased group (P = 0.03). A significant difference in VQR during inhalation (P = 0.03) was observed between the healthy and diseased groups. No difference was seen in VQR over time during exhalation (P = 0.3). The TIVEXP was not different between groups (P = 0.36). In this study, EIT was able to detect differences in inhalation mechanics when comparing healthy and diseased cattle as defined via clinical examination, highlighting the clinical utility of EIT.

Lipopolysaccharide-induced interferon response networks at birth are predictive of severe viral lower respiratory infections in the first year of life.

Appropriate innate immune function is essential to limit pathogenesis and severity of severe lower respiratory infections (sLRI) during infancy, a leading cause of hospitalization and risk factor for subsequent asthma in this age group. Employing a systems biology approach to analysis of multi-omic profiles generated from a high-risk cohort (n=50), we found that the intensity of activation of an LPS-induced interferon gene network at birth was predictive of sLRI risk in infancy (AUC=0.724). Connectivity patterns within this network were stronger among susceptible individuals, and a systems biology approach identified IRF1 as a putative master regulator of this response. These findings were specific to the LPS-induced interferon response and were not observed following activation of viral nucleic acid sensing pathways. Comparison of responses at birth versus age 5 demonstrated that LPS-induced interferon responses but not responses triggered by viral nucleic acid sensing pathways may be subject to strong developmental regulation. These data suggest that the risk of sLRI in early life is in part already determined at birth, and additionally that the developmental status of LPS-induced interferon responses may be a key determinant of susceptibility. Our findings provide a rationale for the identification of at-risk infants for early intervention aimed at sLRI prevention and identifies targets which may be relevant for drug development.

AI-Driven Cell Tracking to Enable High-Throughput Drug Screening Targeting Airway Epithelial Repair for Children with Asthma.

The airway epithelium of children with asthma is characterized by aberrant repair that may be therapeutically modifiable. The development of epithelial-targeting therapeutics that enhance airway repair could provide a novel treatment avenue for childhood asthma. Drug discovery efforts utilizing high-throughput live cell imaging of patient-derived airway epithelial culture-based wound repair assays can be used to identify compounds that modulate airway repair in childhood asthma. Manual cell tracking has been used to determine cell trajectories and wound closure rates, but is time consuming, subject to bias, and infeasible for high-throughput experiments. We therefore developed software, EPIC, that automatically tracks low-resolution low-framerate cells using artificial intelligence, analyzes high-throughput drug screening experiments and produces multiple wound repair metrics and publication-ready figures. Additionally, unlike available cell trackers that perform cell segmentation, EPIC tracks cells using bounding boxes and thus has simpler and faster training data generation requirements for researchers working with other cell types. EPIC outperformed publicly available software in our wound repair datasets by achieving human-level cell tracking accuracy in a fraction of the time. We also showed that EPIC is not limited to airway epithelial repair for children with asthma but can be applied in other cellular contexts by outperforming the same software in the Cell Tracking with Mitosis Detection Challenge (CTMC) dataset. The CTMC is the only established cell tracking benchmark dataset that is designed for cell trackers utilizing bounding boxes. We expect our open-source and easy-to-use software to enable high-throughput drug screening targeting airway epithelial repair for children with asthma.

Personal Network Inference Unveils Heterogeneous Immune Response Patterns to Viral Infection in Children with Acute Wheezing.

Human rhinovirus (RV)-induced exacerbations of asthma and wheeze are a major cause of emergency room presentations and hospital admissions among children. Previous studies have shown that immune response patterns during these exacerbations are heterogeneous and are characterized by the presence or absence of robust interferon responses. Molecular phenotypes of asthma are usually identified by cluster analysis of gene expression levels. This approach however is limited, since genes do not exist in isolation, but rather work together in networks. Here, we employed personal network inference to characterize exacerbation response patterns and unveil molecular phenotypes based on variations in network structure. We found that personal gene network patterns were dominated by two major network structures, consisting of interferon-response versus FCER1G-associated networks. Cluster analysis of these structures divided children into subgroups, differing in the prevalence of atopy but not RV species. These network structures were also observed in an independent cohort of children with virus-induced asthma exacerbations sampled over a time course, where we showed that the FCER1G-associated networks were mainly observed at late time points (days four-six) during the acute illness. The ratio of interferon- and FCER1G-associated gene network responses was able to predict recurrence, with low interferon being associated with increased risk of readmission. These findings demonstrate the applicability of personal network inference for biomarker discovery and therapeutic target identification in the context of acute asthma which focuses on variations in network structure.

Aberrant cell migration contributes to defective airway epithelial repair in childhood wheeze.

Abnormal wound repair has been observed in the airway epithelium of patients with chronic respiratory diseases, including asthma. Therapies focusing on repairing vulnerable airways, particularly in early life, present a potentially novel treatment strategy. We report defective lower airway epithelial cell repair to strongly associate with common pre-school-aged and school-aged wheezing phenotypes, characterized by aberrant migration patterns and reduced integrin α5ß1 expression. Next generation sequencing identified the PI3K/Akt pathway as the top upstream transcriptional regulator of integrin α5ß1, where Akt activation enhanced repair and integrin α5ß1 expression in primary cultures from children with wheeze. Conversely, inhibition of PI3K/Akt signaling in primary cultures from children without wheeze reduced α5ß1 expression and attenuated repair. Importantly, the FDA-approved drug celecoxib - and its non-COX2-inhibiting analogue, dimethyl-celecoxib - stimulated the PI3K/Akt-integrin α5ß1 axis and restored airway epithelial repair in cells from children with wheeze. When compared with published clinical data sets, the identified transcriptomic signature was also associated with viral-induced wheeze exacerbations highlighting the clinical potential of such therapy. Collectively, these results identify airway epithelial restitution via targeting the PI3K-integrin α5ß1 axis as a potentially novel therapeutic avenue for childhood wheeze and asthma. We propose that the next step in the therapeutic development process should be a proof-of-concept clinical trial, since relevant animal models to test the crucial underlying premise are unavailable.

Aspergillus Infections and Progression of Structural Lung Disease in Children with Cystic Fibrosis.

Rationale: Recent data show that Aspergillus species are prevalent respiratory infections in children with cystic fibrosis (CF). The biological significance of these infections is unknown.Objectives: We aimed to evaluate longitudinal associations between Aspergillus infections and lung disease in young children with CF.Methods: Longitudinal data on 330 children participating in the Australian Respiratory Early Surveillance Team for Cystic Fibrosis surveillance program between 2000 and 2018 who underwent annual chest computed tomography (CT) imaging and BAL were used to determine the association between Aspergillus infections and the progression of structural lung disease. Results were adjusted for the effects of other common infections, associated variables, and repeated visits. Secondary outcomes included inflammatory markers in BAL, respiratory symptoms, and admissions for exacerbations.Measurements and Main Results:Haemophilus influenzae, Staphylococcus aureus, Pseudomonas aeruginosa, and Aspergillus infections were all associated with worse CT scores in the same year (Poverall < 0.05). Only P. aeruginosa and Aspergillus were associated with progression in CT scores in the year after an infection and worse CT scores at the end of the observation period. P. aeruginosa was most significantly associated with development of bronchiectasis (difference, 0.9; 95% confidence interval, 0.3-1.6; P = 0.003) and Aspergillus with trapped air (difference, 3.2; 95% confidence interval, 1.0-5.4; P = 0.004). Aspergillus infections were also associated with markers of neutrophilic inflammation (P < 0.001) and respiratory admissions risk (P = 0.008).Conclusions: Lower respiratory Aspergillus infections are associated with the progression of structural lung disease in young children with CF. This study highlights the need to further evaluate early Aspergillus species infections and the feasibility, risk, and benefit of eradication regimens.

HOME: a histogram based machine learning approach for effective identification of differentially methylated regions.

BACKGROUND: The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate. RESULTS: We present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organism's dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME . CONCLUSION: HOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation.

Extensive transcriptomic and epigenomic remodelling occurs during Arabidopsis thaliana germination.

BACKGROUND: Seed germination involves progression from complete metabolic dormancy to a highly active, growing seedling. Many factors regulate germination and these interact extensively, forming a complex network of inputs that control the seed-to-seedling transition. Our understanding of the direct regulation of gene expression and the dynamic changes in the epigenome and small RNAs during germination is limited. The interactions between genome, transcriptome and epigenome must be revealed in order to identify the regulatory mechanisms that control seed germination. RESULTS: We present an integrated analysis of high-resolution RNA sequencing, small RNA sequencing and MethylC sequencing over ten developmental time points in Arabidopsis thaliana seeds, finding extensive transcriptomic and epigenomic transformations associated with seed germination. We identify previously unannotated loci from which messenger RNAs are expressed transiently during germination and find widespread alternative splicing and divergent isoform abundance of genes involved in RNA processing and splicing. We generate the first dynamic transcription factor network model of germination, identifying known and novel regulatory factors. Expression of both microRNA and short interfering RNA loci changes significantly during germination, particularly between the seed and the post-germinative seedling. These are associated with changes in gene expression and large-scale demethylation observed towards the end of germination, as the epigenome transitions from an embryo-like to a vegetative seedling state. CONCLUSIONS: This study reveals the complex dynamics and interactions of the transcriptome and epigenome during seed germination, including the extensive remodelling of the seed DNA methylome from an embryo-like to vegetative-like state during the seed-to-seedling transition. Data are available for exploration in a user-friendly browser at https://jbrowse.latrobe.edu.au/germination_epigenome .

Population scale mapping of transposable element diversity reveals links to gene regulation and epigenomic variation.

Variation in the presence or absence of transposable elements (TEs) is a major source of genetic variation between individuals. Here, we identified 23,095 TE presence/absence variants between 216 Arabidopsis accessions. Most TE variants were rare, and we find these rare variants associated with local extremes of gene expression and DNA methylation levels within the population. Of the common alleles identified, two thirds were not in linkage disequilibrium with nearby SNPs, implicating these variants as a source of novel genetic diversity. Many common TE variants were associated with significantly altered expression of nearby genes, and a major fraction of inter-accession DNA methylation differences were associated with nearby TE insertions. Overall, this demonstrates that TE variants are a rich source of genetic diversity that likely plays an important role in facilitating epigenomic and transcriptional differences between individuals, and indicates a strong genetic basis for epigenetic variation.

Novel Elements of the Chondrocyte Stress Response Identified Using an in Vitro Model of Mouse Cartilage Degradation.

The destruction of articular cartilage in osteoarthritis involves chondrocyte dysfunction and imbalanced extracellular matrix (ECM) homeostasis. Pro-inflammatory cytokines such as interleukin-1α (IL-1α) contribute to osteoarthritis pathophysiology, but the effects of IL-1α on chondrocytes within their tissue microenvironment have not been fully evaluated. To redress this we used label-free quantitative proteomics to analyze the chondrocyte response to IL-1α within a native cartilage ECM. Mouse femoral heads were cultured with and without IL-1α, and both the tissue proteome and proteins released into the media were analyzed. New elements of the chondrocyte response to IL-1α related to cellular stress included markers for protein misfolding (Armet, Creld2, and Hyou1), enzymes involved in glutathione biosynthesis and regeneration (Gstp1, Gsto1, and Gsr), and oxidative stress proteins (Prdx2, Txn, Atox1, Hmox1, and Vnn1). Other proteins previously not associated with the IL-1α response in cartilage included ECM components (Smoc2, Kera, and Crispld1) and cysteine proteases (cathepsin Z and legumain), while chondroadherin and cartilage-derived C-type lectin (Clec3a) were identified as novel products of IL-1α-induced cartilage degradation. This first proteome-level view of the cartilage IL-1α response identified candidate biomarkers of cartilage destruction and novel targets for therapeutic intervention in osteoarthritis.

Serum metabolic profile predicts adverse central haemodynamics in patients with type 2 diabetes mellitus.

AIMS: People with type 2 diabetes mellitus (T2DM) have abnormal peripheral and central haemodynamics at rest and during exercise, probably due to metabolic perturbations, but mechanisms are unknown. We used untargeted metabolomics to determine the relationships between metabolic perturbations and haemodynamics (peripheral and central) measured at rest and during exercise. METHODS: Serum samples from 39 participants with T2DM (62 ± 9 years; 46 % male) and 39 controls (52 ± 10 years; 51 % male) were analysed by liquid chromatography-mass spectrometry, nuclear magnetic resonance spectroscopy and principal component analysis. Scores on principal components (PC) were used to assess relationships with haemodynamics including peripheral and central BP, central augmentation index (AIx) and central augmentation pressure (AP). RESULTS: Participants with T2DM had higher resting and exercise haemodynamics (peripheral and central BP, central AIx and central AP) compared to controls (p < 0.05). PC that comprised of a signature metabolic pattern of T2DM was independently associated with resting and exercise central AIx and central AP (p < 0.05). CONCLUSIONS: Serum metabolic profile was associated with central, but not peripheral, haemodynamics in T2DM participants, suggesting that metabolic irregularities may explain abnormal central haemodynamics in T2DM patients.

Metabolomics data normalization with EigenMS.

Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05) as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data.

Normalization and missing value imputation for label-free LC-MS analysis.

Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.

DanteR: an extensible R-based tool for quantitative analysis of -omics data.

MOTIVATION: The size and complex nature of mass spectrometry-based proteomics datasets motivate development of specialized software for statistical data analysis and exploration. We present DanteR, a graphical R package that features extensive statistical and diagnostic functions for quantitative proteomics data analysis, including normalization, imputation, hypothesis testing, interactive visualization and peptide-to-protein rollup. More importantly, users can easily extend the existing functionality by including their own algorithms under the Add-On tab. AVAILABILITY: DanteR and its associated user guide are available for download free of charge at http://omics.pnl.gov/software/. We have an updated binary source for the DanteR package up on our website together with a vignettes document. For Windows, a single click automatically installs DanteR along with the R programming environment. For Linux and Mac OS X, users must install R and then follow instructions on the DanteR website for package installation. CONTACT: rds@pnnl.gov.

Label-free quantitative LC-MS proteomics of Alzheimer's disease and normally aged human brains.

Quantitative proteomics analysis of cortical samples of 10 Alzheimer's disease (AD) brains versus 10 normally aged brains was performed by following the accurate mass and time tag (AMT) approach with the high resolution LTQ Orbitrap mass spectrometer. More than 1400 proteins were identified and quantitated. A conservative approach of selecting only the consensus results of four normalization methods was suggested and used. A total of 197 proteins were shown to be significantly differentially abundant (p-values <0.05, corrected for multiplicity of testing) in AD versus control brain samples. Thirty-seven of these proteins were reported as differentially abundant or modified in AD in previous proteomics and transcriptomics publications. The rest to the best of our knowledge are new. Mapping of the discovered proteins with bioinformatic tools revealed significant enrichment with differentially abundant proteins of pathways and processes known to be important in AD, including signal transduction, regulation of protein phosphorylation, immune response, cytoskeleton organization, lipid metabolism, energy production, and cell death.

A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics.

Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, because this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referenced as Statistical Tools for AMT Tag Confidence (STAC). STAC additionally provides a uniqueness probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download, as both a command line and a Windows graphical application.

Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects.

Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer. The two fundamental challenges in the analysis of bottom-up MS-based proteomics are: (1) Identifying the proteins that are present in a sample, and (2) Quantifying the abundance levels of the identified proteins. Both of these challenges require knowledge of the biological and technological context that gives rise to observed data, as well as the application of sound statistical principles for estimation and inference. We present an overview of bottom-up proteomics and outline the key statistical issues that arise in protein identification and quantification.