Biology subjects

Wheelock, A. M.

Publications and source records attributed to Wheelock, A. M..

3 recordsLinked to original sources

bioRxiv2024

microRNA and non-targeted proteome analysis of liquid biopsies from the distal lung collected by Particles in Exhaled Air (PExA) reveals presence from extracellular vesicles

Background. Early detection and longitudinal follow-up are essential for timely diagnosis and treatment for lung diseases. Currently, invasive methods are often required to examine the distal parts of the lungs. The growing need to explore the bio-molecular mechanisms in various lung diseases highlights the importance of non-invasive methods. The use of particles in exhaled air (PExA), a non-invasive technique for sampling of epithelial lining fluid from distal airways, is gaining attention. MicroRNAs (miRNAs) are crucial in modulating protein expression both intracellularly and intercellularly, often transported via extracellular vesicles. Dysregulated miRNAs have been linked to many pulmonary diseases, and their relative stability, especially when encapsulated in EVs, makes them promising biomarkers. Here we report for the first time multi-modal analysis of miRNAs and proteins in PExA, offering an opportunity to study the role of miRNAs in the pathophysiology of respiratory diseases in a non-invasive manner. Methods. Exhaled particles were collected from healthy subjects using the PExA 2.0 instrument utilizing the optimized PExA breathing maneuver. PExA samples collected on different types of impaction membranes were analyzed using a non-targeted mass spectrometry-based proteomics workflow optimized for single-cell detection, and a miRNAseq workflow optimized for low input starting material. Technical validation of a subset of the detected miRNAs was performed using custom-designed miRCURY LNA miRNA PCR assays. Pathway enrichment analyses for the detected proteins were performed using STRING. Results. Proteomic analysis consistently identified over 50 proteins across multiple types of impaction membranes, sample dilution series, and individuals down to a single PEx sPOT (24ng starting material). We observed a significant enrichment of proteins associated with extracellular vesicles, including blood microparticles, and secretory granules. miRNA-seq revealed 39 mature miRNAs, the majority of which have been previously reported to be detected in the airways. Some were also reported to be secreted by primary human airway epithelial cells via extracellular vesicles. miRNA-125b and the members of the let-7 family were among the most abundant miRNAs detected. Fluorometric assays showed significant RNase activity in both PExA and other lung-related samples, such as bronchoalveolar lavage fluid, suggesting that this activity originates from the airways and is independent of the sampling techniques used. The workflow for extraction and processing of the PExA collection membrane, tested with abundant synthetic miRNAs and analyzed using the miRCURY LNA miRNA PCR assay, yielded results comparable to control samples, indicating that the membrane material does not interfere with the assay. Conclusions. Using PExA, we identified several miRNAs reported to be dysregulated in pulmonary disorders. The enrichment of extracellular secretory components in the core protein list, along with the elevated RNAse activity in the respiratory tract, suggest that the detected miRNAs may be encapsulated within extracellular vesicles. These miRNAs are of particular interest due to their potential role in intercellular communication. Our findings suggest that PExA holds a potential as a non-invasive tool for studying extracellular vesicle-mediated miRNA cargo in the small airways.

physiology↗

bioRxiv2024

Permutation analysis prior to variable selection greatly enhances robustness of OPLS analysis in small cohorts

The R-workflow ropls-ViPerSNet (R orthogonal projections of latent structures with Variable Permutation Selection and Elastic Net) facilitates variable selection, model optimization and significance testing using permutations of OPLS-DA models, with the scaled loadings (p[corr]) as the main metric of significance cutoff. Permutations including (over) the variable selection procedure, prior to (pre-), as well as post variable selection are performed. The resulting p-values for the correlation of the model (R2) and the cross-validated correlation of the model (Q2) pre-, post- and over-variable selection are provided as additional model statistics. These model statistics are useful for determining the true significance level of OPLS models, which otherwise have proven difficult to assess particularly for small sample sizes. Furthermore, a means for estimating the background noise level based on permuted false positive rates of R2 and Q2 is proposed. This novel metric is then used to calculate an adjusted Q2 value. Using a publicly available metabolomics dataset, the advantage of performing permutations over variable selection was demonstrated for small sample sizes. Iteratively reducing the sample sizes resulted in overinflated models with increasing R2 and Q2 and permutations post variable selection indicated falsely significant models. In contrast, the adjusted Q2 was marginally affected by sample size, and represents a robust estimate of model predictability, and permutations over variable selection showed true significance of the models. An additional Elastic Net variable selection option is included in the workflow for variable selection by coefficient value penalization using an iterative approach to reduce noise while avoiding overfitting.

bioinformatics↗

bioRxiv2024

CORACLE (COVID-19 liteRAture CompiLEr): A platform for efficient tracking and extraction of SARS-CoV-2 and COVID-19 literature, with examples from post-COVID with respiratory involvement

BackgroundDuring the COVID-19 pandemic there emerged a need to efficiently monitor and process large volumes of scientific literature on the subject. Currently, as the pandemic is winding down, the clinicians encountered a novel syndrome - Post-acute Sequelae of COVID- 19 (PASC) - that affects over 10% of those who contract SARS-CoV-2 and presents a significant and growing challenge in the medical field. The continuous influx of new research publications underscores a critical need for efficient tools for navigating the literature. ObjectivesWe aimed to develop an application which will allow monitoring and categorizing COVID-19-related literature through building publication networks and medical subject headings (MeSH) maps to be able to quickly identify key publications and publication networks. MethodsWe introduce CORACLE (COVID-19 liteRAture CompiLEr), an innovative web application designed for the analysis of COVID-19-related scientific articles and the identification of research trends. CORACLE features three primary interfaces: The "Search" interface, which displays research trends and citation links; the "Citation Map" interface, allowing users to create tailored citation networks from PubMed Identifiers (PMIDs) to uncover common references among selected articles; and the "MeSH" interface, highlighting current MeSH trends and associations between MeSH terms. ResultsOur web application, CORACLE, leverages regularly updated PubMed data to aggregate and categorize the extensive literature on COVID-19 and PASC, aiding in the identification of relevant research publication hubs. Using lung function in PASC patients as a search example, we demonstrate how to identify and visualize the interactions between the relevant publications. ConclusionCORACLE proves to be an effective tool for the extraction and analysis of literature. Its functionalities, including the MeSH trends and customizable citation mapping, facilitate the discovery of relevant information and emerging trends in COVID-19 and PASC research.

bioinformatics↗