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Abstract Background Rhinovirus infections commonly evoke asthma exacerbations in children and adults. Recurrent asthma exacerbations are associated with injury-repair responses in the airways that collectively contribute to airway remodeling. The physiological consequences of airway remodeling can manifest as irreversible airway obstruction and diminished responsiveness to bronchodilators. Structural cells of the airway, including epithelial cells, smooth muscle, fibroblasts, myofibroblasts, and adjacent lung vascular endothelial cells represent an understudied and emerging source of cellular and extracellular soluble mediators and matrix components that contribute to airway remodeling in a rhinovirus-evoked inflammatory environment. Main body While mechanistic pathways associated with rhinovirus-induced airway remodeling are still not fully characterized, infected airway epithelial cells robustly produce type 2 cytokines and chemokines, as well as pro-angiogenic and fibroblast activating factors that act in a paracrine manner on neighboring airway cells to stimulate remodeling responses. Morphological transformation of structural cells in response to rhinovirus promotes remodeling phenotypes including induction of mucus hypersecretion, epithelial-to-mesenchymal transition, and fibroblast-to-myofibroblast transdifferentiation. Rhinovirus exposure elicits airway hyperresponsiveness contributing to irreversible airway obstruction. This obstruction can occur as a consequence of sub-epithelial thickening mediated by smooth muscle migration and myofibroblast activity, or through independent mechanisms mediated by modulation of the β2 agonist receptor activation and its responsiveness to bronchodilators. Differential cellular responses emerge in response to rhinovirus infection that predispose asthmatic individuals to persistent signatures of airway remodeling, including exaggerated type 2 inflammation, enhanced extracellular matrix deposition, and robust production of pro-angiogenic mediators. Conclusions Few therapies address symptoms of rhinovirus-induced airway remodeling, though understanding the contribution of structural cells to these processes may elucidate future translational targets to alleviate symptoms of rhinovirus-induced exacerbations.

Accounting for genetic variation is an essential consideration during human immunodeficiency virus type 1 (HIV-1) investigation. Nanopore sequencing preserves proviral integrity by passing long genomic fragments through ionic channels, allowing reads that span the entire genome of different viral quasispecies (vQS). However, this sequencing method has suffered from high error rates, limiting its utility. This was the inspiration behind HIV-Quasipore: an HIV-1-specific Nanopore basecaller suite designed to overcome these error rates through training with gold-standard data. It comprises three deep learning-based R9.4.1 basecallers: fast, high accuracy (HAC), super accuracy (SUP), and two R10.3 deep learning-based basecallers: HAC and SUP. This was accomplished by sequencing the HIV-1 J-Lat 10.6 cell line using Nanopore and high-quality Sanger techniques. Training significantly reduced basecaller error rates across all models (Student’s one-sided t-test; p = 0.0) where median error rates were 0.0189, 0.0018, 0.0008, for R9.4.1 HIV-Quasipore-fast, HAC, SUP, and 0.0007, 0.0011 for R10.3 HIV-Quasipore-HAC, and SUP, respectively. This improved quality reduces the resolution needed to accurately detect a vQS from 22.4 to 2.6% of total positional coverage for R9.4.1 HIV-Quasipore-fast, 6.9 to 0.5% for R9.4.1 HIV-Quasipore-HAC, 4.5 to 0.3% for R9.4.1 HIV-Quasipore-SUP, 8.0 to 0.3% for R10.3 HIV-Quasipore-HAC, and 5.4 to 0.3% for R10.3 HIV-Quasipore-SUP. This was consistently observed across the entire J-Lat 10.6 genome and maintained across longer reads. Reads with greater than 8,000 nucleotides display a median nucleotide identity of 0.9819, 0.9982, and 0.9991, for R9.4.1 HIV-Quasipore-fast, HAC, SUP, and 0.9993, 0.9988 for R10.3 HIV-Quasipore-HAC, and SUP, respectively. To evaluate the robustness of this tool against unseen data, HIV-Quasipore and their corresponding pretrained basecallers were used to sequence the J-Lat 9.2 cell line and a clinical isolate acquired from the Drexel Medicine CARES cohort. When sample reads were compared against their corresponding consensus sequence, all HIV-Quasipore basecallers displayed higher median alignment accuracies than their pretrained counterparts for both the J-Lat 9.2 cell line and clinical isolate. Using Nanopore sequencing can allow investigators to explore topics, such as vQS profile detection, HIV-1 integration site analysis, whole genome amplification, gene coevolution, and CRISPR-induced indel detection, among others. HIV-Quasipore basecallers can be acquired here: https://github.com/DamLabResources/HIV-Quasipore-basecallers.