Real time monitoring of respiratory viral infections in cohort studies using a smartphone app.

Cohort studies investigating respiratory disease pathogenesis aim to pair mechanistic investigations with longitudinal virus detection but are limited by the burden of methods tracking illness over time. In this study, we explored the utility of a purpose-built AERIAL TempTracker smartphone app to assess real-time data collection and adherence monitoring and overall burden to participants, while identifying symptomatic respiratory illnesses in two birth cohort studies. We observed strong adherence with daily app usage over the six-month study period, with positive feedback from participant families.

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, exposures, and respiratory exposures in the first year of life.

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.

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.

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).

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.

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.

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 α5β1 expression.

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

Recent data show that species are prevalent respiratory infections in children with cystic fibrosis (CF). The biological significance of these infections is unknown. We aimed to evaluate longitudinal associations between infections and lung disease in young children with CF.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF++.

Many mass spectrometry-based studies, as well as other biological experiments produce cluster-correlated data. Failure to account for correlation among observations may result in a classification algorithm overfitting the training data and producing overoptimistic estimated error rates and may make subsequent classifications unreliable. Current common practice for dealing with replicated data is to average each subject replicate sample set, reducing the dataset size and incurring loss of information.

Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition.

Motivation: LC-MS allows for the identification and quantification of proteins from biological samples. As with any high-throughput technology, systematic biases are often observed in LC-MS data, making normalization an important preprocessing step. Normalization models need to be flexible enough to capture biases of arbitrary complexity, while avoiding overfitting that would invalidate downstream statistical inference.

A statistical framework for protein quantitation in bottom-up MS-based proteomics.

Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level.