Genomic profiling of influenza infections to identify biomarkers of disease severity

National Institute Of Allergy And Infectious Diseases

Investigators

Linked publications & trials

Abstract

Host response to influenza infection is a complex trait that involves entire host-pathogen interaction networks of RNA transcripts, proteins, and metabolites that have an impact on cellular, tissue, and whole-organism behaviors, which ultimately define both the risk and severity of infection. This project could substantially broaden our understanding of severe influenza infection and help us make accurate predictions of influenza severity. As part of this project, we use an integrative systems-level approach to discover how host factors affect the evolution and transmission of influenza virus, and whether specific host factors could be leveraged as predictive markers of the responses to infection and to vaccination. We also set out to determine how disease severity is impacted by microbial communities in the respiratory tract, to reveal key signatures that could be targeted in novel therapeutics. We model the evolution of the virus over the course of the infection, looking at intra-host and inter-host virus genetic diversity. For this part of the project our focus is currently on defective virus genomes (DVGs) as certain defective genomes can have interfering functions on wild-type viruses, and they are thought to modulate disease severity and pathogenicity of the influenza infection. DVGs are identified across many different viruses and play essential roles in virus-host and virus-virus dynamics during an infection. To better understand the dynamics of DVGs, we created a computationally efficient pipeline to identify DVGs within next generation sequencing data. The first iteration of this pipeline was originally tested on Zika virus in a collaboration with Dr. Kenny Stapleford (NYU Langone), but has been further developed for analysis of influenza and SARS-CoV-2 (Johnson et al, in preparation). In a collaboration with Dr. Schultz-Cherry (St. Jude) and Dr. Mirella Salvatore (Weil Cornell) who performed mouse infections with influenza, we identified a potential mechanism underlying defective-interfering-mediated protection by DVGs independent of interferons. We analyzed host response data using mice lacking functional type I IFN receptor (IFNAR-/-), or type III IFN receptor (IFNLR-/-) to determine the mechanism of how DVGs are modulating pathogenesis in acute influenza infections. We determined that DVGs stimulate multiciliogenesis, potentially contributing to decreased pathogenicity during infection. This study (https://biorxiv.org/cgi/content/short/2022.01.25.477719v1) is currently under review at the Journal of Virology but to address reviewer comments we are doing immunofluorescent assays of mouse lungs to detect increased expression of multicilin protein in the lung tissue of DVG-treated mice. In collaboration with a team at Mt. Sinai, we have been modeling the molecular mechanisms of host response and sex disparity to influenza virus infection. Sex differences in the pathogenesis of infectious diseases due to differential immune responses between females and males have been well documented for multiple pathogens. However, the molecular mechanism underlying the observed sex differences in influenza virus infection remains poorly understood. We identified significant differences in the temporal dynamics and regulation of immune responses between females and males. Our study revealed an emerging picture in which genetic factors mediating sex differences could manifest in the temporal dynamics and regulatory relationship of commonly induced immune and inflammatory responses, the activation of the IRE1/XBP1 pathway during the UPR, and the modulation of lipid metabolism and inflammatory response involving the IL-1 and AP-1 pathways. This study was published in iScience earlier this year (Wang et al, 2022). We are also analyzing the host systemic response to infection and vaccination by transcriptomic profiling using whole blood. In a first study with human cohorts, we identified genes whose expression prior to vaccination are predictive of the vaccine response. We used these genes in a machine learning model to show that it is possible to predict vaccine response on the basis of gene expression with accuracy similar to that using detailed physiological information. These findings have important implications for understanding the biology underlying the extensive variation in interindividual effectiveness of the seasonal influenza vaccine and potential practical applications for identifying those individuals who are likely to mount a strong immunological response to vaccination. MedRxiv (https://www.medrxiv.org/content/10.1101/2022.06.15.22276462v1. We are currently integrating these data with metabolomic, proteomic, and glycomic data from our collaborators who analyzed the same samples. A factor that can also impact disease severity in respiratory infections is the microbiome. Our analyses on the microbiome of the upper airways in influenza infection show that the respiratory tract is a potentially important reservoir of antibiotic resistance genes in humans and should be further characterized, especially regarding inter-host transmission of ARGs during influenza epidemics. We did an extensive characterization of the microbial ecology of the upper airways by metagenomic and metatranscriptomic analysis using nasopharyngeal swabs collected from households with and without influenza. While we demonstrate that the microbiome compositional and functional potentials are altered in influenza infection, we observed that in both flu positive and control individuals commensal bacteria and potential pathobionts were readily transmitted within and across households. Using CRISPR arrays characterized in the metagenomic sequence reads as barcodes, we detected a clear sharing of bacteria commensals and pathobionts, within and between households, indicating community transmission of these microbes. Determining the transmission of airway commensals, which can carry antibiotic resistance genes that could in turn be transferred to bacterial pathogens, is of public health interest but can be difficult to do when relying solely on single nucleotide polymorphisms to identify shared microbes. Because of their unique structure and sequential accumulation of repeat elements from phage genomes, CRISPR arrays provide the level of resolution necessary for microbial tracking to quantify the level of transmission that can occur within households and across a community. This manuscript is currently under review in the journal Microbiome and available on MedRXiv as a preprint (MEDRXIV/2022/278625). In collaboration with Dr. Mauricio Terrones group (PSU), we have been working on the development of a microfluidics platform made of built-in carbon nanotube cartridges for the capture of virus particles from clinical samples for rapid detection and characterization. The enriched viruses trapped within the carbon nanotube cartridges can be identified quickly by Raman spectrometry and can be used for subsequent genomic analysis. We have followed up these early proof-of-concept studies with the better development of machine learning analyses methods to identify viruses detected by Raman on the platform. We are also testing different viruses, such as the JC virus in collaboration with Dr. Jacobson (NINDS) under a CIT funding initiative. The goal of this type of effort is to help in field applications to accelerate characterization of viruses in real-time.