Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants

Immunomodulatory markers and therapies for the management of infant respiratory syncytial virus infection

INTRODUCTION

The human respiratory syncytial virus (hRSV) is one of childhood diseases' most common respiratory pathogens and is associated with lower respiratory tract infections. The peak in disease that this virus can elicit during outbreaks is often a significant burden for healthcare systems worldwide. Despite theapproval of treatments against hRSV, this pathogen remains one the most common causative agent of infant mortality around the world.

AREAS COVERED

This review focuses on the key prognostic and immunomodulatory biomarkers associated with hRSV infection, as well as prophylactic monoclonal antibodies and vaccines. The goal is to catalyze a paradigm shift within the scientific community toward the discovery of novel targets to predict the clinical outcome of infected patients, as well as the development of novel antiviral agents targeting hRSV. The most pertinent research on this topic was systematically searched and analyzed using PubMed ISI Thomson Scientific databases.

EXPERT OPINION

Despite advances in approved therapies against hRSV, it is crucial to continue researching to develop new therapies and to find specific biomarkers to predict the severity of infection. Along these lines, the use of multi-omics data, artificial intelligence and natural-derived compounds with antiviral activity could be evaluated to fight hRSV and develop methods for rapid diagnosis of severity.

Host Subcellular Organelles: Targets of Viral Manipulation

Viruses have evolved sophisticated mechanisms to manipulate host cell processes and utilize intracellular organelles to facilitate their replication. These complex interactions between viruses and cellular organelles allow them to hijack the cellular machinery and impair homeostasis. Moreover, viral infection alters the cell membrane's structure and composition and induces vesicle formation to facilitate intracellular trafficking of viral components. However, the research focus has predominantly been on the immune response elicited by viruses, often overlooking the significant alterations that viruses induce in cellular organelles. Gaining a deeper understanding of these virus-induced cellular changes is crucial for elucidating the full life cycle of viruses and developing potent antiviral therapies. Exploring virus-induced cellular changes could substantially improve our understanding of viral infection mechanisms.

Gene Expression Risk Scores for COVID-19 Illness Severity

BACKGROUND

The correlates of coronavirus disease 2019 (COVID-19) illness severity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are incompletely understood.

METHODS

We 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 nonsevere COVID-19.

RESULTS

Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus nonsevere illness, we identified >4000 genes differentially expressed (false discovery rate < 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 receiver operating characteristic-area under the curve [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.

CONCLUSIONS

These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

Cilia-related gene signature in the nasal mucosa correlates with disease severity and outcomes in critical respiratory syncytial virus bronchiolitis

Background

Respiratory syncytial virus (RSV) can cause life-threatening respiratory failure in infants. We sought to characterize the local host response to RSV infection in the nasal mucosa of infants with critical bronchiolitis and to identify early admission gene signatures associated with clinical outcomes.

Methods

Nasal scrape biopsies were obtained from 33 infants admitted to the pediatric intensive care unit (PICU) with critical RSV bronchiolitis requiring non-invasive respiratory support (NIS) or invasive mechanical ventilation (IMV), and RNA sequencing (RNA-seq) was performed. Gene expression in participants who required shortened NIS (</= 3 days), prolonged NIS (> 3 days), and IMV was compared.

Findings

Increased expression of ciliated cell genes and estimated ciliated cell abundance, but not immune cell abundance, positively correlated with duration of hospitalization in infants with critical bronchiolitis. A ciliated cell signature characterized infants who required NIS for > 3 days while a basal cell signature was present in infants who required NIS for </= 3 days, despite both groups requiring an equal degree of respiratory support at the time of sampling. Infants who required invasive mechanical ventilation had increased expression of genes involved in neutrophil activation and cell death.

Interpretation

Increased expression of cilia-related genes in clinically indistinguishable infants with critical RSV may differentiate between infants who will require prolonged hospitalization and infants who will recover quickly. Validation of these findings in a larger cohort is needed to determine whether a cilia-related gene signature can predict duration of illness in infants with critical bronchiolitis. The ability to identify which infants with critical RSV bronchiolitis may require prolonged hospitalization using non-invasive nasal samples would provide invaluable prognostic information to parents and medical providers.

A systems genomics approach uncovers molecular associates of RSV severity

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to 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. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.

Gene Expression Risk Scores for COVID-19 Illness Severity

BACKGROUND

The correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood.

METHODS

We 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.

RESULTS

Gene 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.

CONCLUSION

These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.