Biology subjects

Walsh, E. E.

Publications and source records attributed to Walsh, E. E..

6 recordsLinked to original sources

bioRxiv2026

ARMH3 acts as a central scaffold at the Golgi/TGN through interactions with Arl5, GBF1, and PI4KB

The armadillo repeat protein ARMH3 regulates the activity and localisation of the Golgi resident lipid kinase phosphatidylinositol 4 kinase III{beta} (PI4KB) and the Golgi-specific brefeldin A-resistance guanine nucleotide exchange factor 1 (GBF1) that activates Arf1. ARMH3 localises to the trans Golgi network (TGN) via the GTPase Arl5. We used hydrogen deuterium exchange mass spectrometry (HDX-MS) and AI-enabled modeling to define the interfaces of ARMH3 with its binding partners Arl5, PI4KB, and GBF1. The ARMH3-Arl5 interface was determined to consist of the N and C termini of ARMH3, with Arl5 binding causing conformational changes in ARMH3 located at a shared PI4KB/GBF1 interface. Both PI4KB and GBF1 form mutually exclusive complexes with ARMH3, with GBF1 binding to ARMH3 through a disordered loop we have named the ARMH3 binding region (ABR). The ARMH3 interfaces in PI4KB and GBF1 contain phosphosites, with the phosphomimetic mutation of GBF1 blocking complex formation. These findings provide new insights into the role of ARMH3 as a master coordinator of GTPase and phosphoinositide signaling at the Golgi/TGN.

biochemistry↗

bioRxiv2025

Activity and Dynamics of p110α are not Differentially Modulated by Regulatory Subunit Isoforms

Class IA phosophoinositide kinases (PI3Ks) are master regulators of growth, metabolism, and immunity. The class IA PI3Ks are a heterodimer composed of a p110 catalytic subunit and one of five possible regulatory subunits (p85, p85{beta}, p55{gamma}, p55, p50). The regulatory subunit plays critical roles in stability, inhibition, and activation of the p110 catalytic subunit. The p110 catalytic subunit frequently contains activating mutations in human cancer, with many of these mutations altering the interaction between catalytic and regulatory subunits. It has been found that different regulatory subunits play unique roles in human disease, but it is unknown how these different subunits regulate p110. Here, using a synergy of biochemical assays and hydrogen deuterium exchange mass spectrometry (HDX-MS) we examined how the five different regulatory subunits inhibit, activate, and interact with the p110 catalytic subunit. We find that there are no significant differences in lipid kinase activity or in membrane recruitment between the different heterodimer complexes. HDX-MS in the presence and absence of an activating phosphopeptide also showed only minor conformational differences between different regulatory subunit complexes. Overall, our work reveals that the different regulatory subunits interact with and inhibit p110 in a similar fashion at a molecular level.

biochemistry↗

bioRxiv2021

Gene Expression Risk Scores for COVID-19 Illness Severity

BackgroundThe correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood. MethodsWe assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID. ResultsGene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort. ConclusionThese data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

genomics↗

bioRxiv2020

Temporal Dysbiosis of Infant Nasal Microbiota Relative to Respiratory Syncytial Virus Infection

RationaleRespiratory Syncytial Virus (RSV) infection is a leading cause of infant respiratory disease and hospitalization. Infant airway microbiota occupying the nasopharynx have been associated with respiratory disease risk and severity. The extent to which interactions between RSV and microbiota occur in the airway, and their impact on respiratory disease severity and infection susceptibility, are not well understood. ObjectivesTo characterize associations between the nasal microbiota and RSV infection before, during, and after infants first respiratory illness. MethodsNasal 16S rRNA microbial community profiling of two cohorts of infants in the first year of life: 1) a cross-sectional cohort of 89 RSV infected infants sampled during illness and 102 population matched healthy controls, and 2) an individually matched longitudinal cohort of 12 infants who developed RSV infection and 12 who did not, sampled at time points before, during, and after infection. Measurements and Main ResultsWe identified 12 taxa significantly associated with RSV infection. All 12 were differentially abundant during infection, with seven differentially abundant prior to infection, and eight differentially abundant after infection. Eight of these taxa were associated with disease severity. Nasal microbiota composition was more discriminative of healthy vs. infected than of disease severity. ConclusionsOur findings elucidate the chronology of nasal microbiota dysbiosis and suggest an altered developmental trajectory associated with first-time RSV infection. Microbial temporal dynamics reveal indicators of disease risk, correlates of illness and severity, and the impact of RSV infection on microbiota composition. Identified taxa represent appealing targets for additional translationally-oriented research.

microbiology↗

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↗