Biology subjects

Chu, C.-Y.

Publications and source records attributed to Chu, C.-Y..

6 recordsLinked to original sources

bioRxiv2026

Transcriptional regulation of the main olfactory epithelium by environmental olfactory exposures

The nasal epithelium is a complex tissue composed of both respiratory and olfactory tissue, and is constantly exposed to environmental insults, including toxins and pathogens. The main olfactory epithelium (MOE) serves as the critical site for olfaction, or sense of smell. Dysfunction at this critical barrier tissue can result in partial or total loss of olfactory function, resulting in significant impact to quality of life. The MOE is heterogeneous, comprised of many cell types including olfactory sensory neurons, support cells, and immune cells. It is not well understood how these diverse cell types in the MOE interact to regulate this tissue during homeostasis, and during times of injury and inflammation. We investigated how environmental olfactory exposures impact cell type specific transcriptional responses in the mouse MOE. We performed single-cell RNA sequencing (scRNA-seq) of the MOE following controlled environmental exposure to both well-known odorants and allergens. We identified major cell types and subtypes within the MOE, and identified transcriptional changes in response to the olfactory exposures. We identified transcriptional changes in OSNs, sustentacular cells, and resident immune cells to each condition. This indicated that environmental olfactory exposures drive changes to multiple cell types in the MOE. To our knowledge, this is the first study to identify effects of environmental olfactory exposures on cell-type specific transcription at homeostasis. These findings highlight the potential importance of multi-cellular interactions and communication in regulation of the olfactory epithelium.

neuroscience↗

bioRxiv2024

Single-cell analysis of lung epithelial cells reveals age and cell population-specific responses to SARS-CoV-2 infection in ciliated cells

The ability of SARS-CoV-2 to evade antiviral immune signaling in the airway contributes to the severity of COVID-19 disease. Additionally, COVID-19 is influenced by age and has more severe presentations in older individuals. This raises questions about innate immune signaling as a function of lung development and age. Therefore, we investigated the transcriptome of different cell populations of the airway epithelium using pediatric and adult lung tissue samples from the LungMAP Human Tissue Core Biorepository. Specifically, lung lobes were digested and cultured into a biomimetic model of the airway epithelium on an air-liquid interface. Cells were then infected with SARS-CoV-2 and subjected to single-cell RNA sequencing. Transcriptional profiling and differential expression analysis were carried out using Seurat. The clustering analysis identified several cell populations: club cells, proliferating epithelial cells, multiciliated precursor cells, ionocytes, and two biologically distinct clusters of ciliated cells (FOXJ1high and FOXJ1low). Interestingly, the two ciliated cell clusters showed different infection rates and enrichment of processes involved in ciliary biogenesis and function; we observed a cell-type-specific suppression of innate immunity in infected cells from the FOXJ1low subset. We also identified a significant number of genes that were differentially expressed in lung cells derived from children as compared to adults, suggesting the differential pathogenesis of SARS-CoV-2 infection in children versus adults. We discuss how this work can be used to identify drug targets to modulate molecular signaling cascades that mediate an innate immune response and begin to understand differences in COVID-19 outcomes for pediatric vs. adult populations. ImportanceViral innate immune evasion leads to uncontrolled viral spread in infected tissues and increased pathogenicity in COVID-19. Understanding the dynamic of the antiviral signaling in lung tissues may help us to understand which molecular signals lead to more severe disease in different populations, particularly considering the enhanced vulnerability of older populations. This study provides foundational insight into the age-related differences in innate immune responses to SARS-CoV-2, identifying distinct patterns of infection and molecular signaling in different cell populations of airway epithelial cells from pediatric and adult lung tissues. The findings provide a deeper understanding of age-related differences in COVID-19 pathology and pave the way for developing targeted therapies.

systems biology↗

bioRxiv2023

The genetic evolution of acral melanoma

Acral melanoma is an aggressive type of melanoma with unknown origins, arising on the sole, palm, or nail apparatus. It is the most common type of melanoma in individuals with dark skin and is notoriously challenging to treat. Our study examined exome sequencing data from 139 tissue samples, spanning different progression stages, collected from 37 patients. We found that 78.4% of the melanomas displayed one or more clustered copy number transitions with focal amplifications, recurring predominantly on chromosomes 5, 11, 12, and 22. These genomic "hailstorms" were typically shared across all progression stages within individual patients. Genetic alterations known to activate TERT also arose early. By contrast, mutations in the MAP- kinase pathway appeared later during progression, often leading to different tumor areas harboring non-overlapping driver mutations. We conclude that the evolutionary trajectories of acral melanomas substantially diverge from those of melanomas on sun-exposed skin, where MAP-kinase pathway activation initiates the neoplastic cascade followed by immortalization later. The punctuated formation of hailstorms, paired with early TERT activation, suggests a unique mutational mechanism underlying the origins of acral melanoma. Our findings highlight an essential role for telomerase, likely in re-stabilizing tumor genomes after hailstorms have initiated the tumors. The marked genetic heterogeneity, in particular of MAP-kinase pathway drivers, may partly explain the limited success of targeted and other therapies in treating this melanoma subtype.

cancer biology↗

bioRxiv2020

A systems genomics approach uncovers molecular associates of RSV severity

Globally, RSV infection results in millions of hospitalizations and thousands of deaths each year. Specific factors that contribute to disease severity, such as premature birth and certain comorbidities, are well established. Additionally, genetic variants resulting in alterations in the adaptive and innate immune response appear to be associated with RSV severity. While previous studies focused on a single aspect of the disease, we jointly modeled the association of disparate data modalities with RSV severity. To investigate the host response to respiratory syncytial virus (RSV) infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. Specifically, we developed and employed a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation. Our novel approach enabled us to identify cell type specific and shared cellular pathways associated with RSV severity. Of particular interest was the association between RSV severity, activation of pathways controlling Th17 and acute phase response signaling, and inhibition of B cell receptor signaling, which were present in both airway and immune cells. These data identify specific aspects of dysregulation between the humoral and mucosal response to RSV that may play a critical role in determining illness severity.

genomics↗

bioRxiv2019

Insufficiency in airway interferon activation defines clinical severity to infant RSV infection

Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. Other than age at the time of infection, the causes and correlates of severe illness in infants lacking known risk factors are poorly defined. We recruited a cohort of confirmed RSV-infected infants and simultaneously assayed the presence of resident airway microbiota and the molecular status of their airways using a novel method. Rigorous statistical analyses identified a molecular airway gene expression signature of severe illness dominated by excessive chemokine expression. Global 16S rRNA sequencing confirmed an association between H. influenzae and clinical severity. Interestingly, adjusting for H. influenzae in our gene expression analysis revealed an association between severity and airway lymphocyte accumulation. Exploring the relationship between airway gene expression and the time of onset of clinical symptoms revealed a robust, acute activation of interferon (IFN) signaling, which was absent in subjects with severe illness. Finally, we explored the relationship between IFN activity, airway gene expression and productive RSV infection using a novel in vitro model of bona fide pediatric human airway epithelial cells. Interestingly, blocking IFN signaling, but not IFN ligand production, in these cells leads to increased viral infection. Our data reveal that acute airway interferon responses are physiologically relevant in the context of infant RSV infection and may be a target for therapeutic intervention. Additionally, the airway gene expression signature we define may be useful as a biomarker for efficacy of intervention responses.

genomics↗

bioRxiv2019

Airway Gene-Expression Classifiers for Respiratory Syncytial Virus (RSV) Disease Severity in Infants

BackgroundA substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness. MethodWe defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2). ResultsNGSS1 is strongly correlated with the disease severity, demonstrating a naive correlation ({rho}) of {rho}=0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%. ConclusionAirway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.

immunology↗