Genomic profiling of influenza infections to identify biomarkers of disease severity

National Institute Of Allergy And Infectious Diseases

Investigators

Linked publications, trials & patents

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 obesity affects (a) the evolution and transmission of influenza virus, and (b) specific host 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. This pipeline works across multiple virus types and datasets, including viral mRNA, genomic RNA, and genomic DNA data. We applied the first iteration of this pipeline on Zika virus in a collaboration with Dr. Kenny Stapleford (NYU Langone) Johnson et al. 2020, Virus Evol. Through a collaboration with Dr. Schultz-Cherry who performs animal infections with influenza, we are determining the 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. Results suggest DI-mediated protection against lethal infection is independent of type I and III IFN signaling, while disease progression in wild-type virus-only infection is impacted by the presence or absence of type I and III IFN signaling. By integrating transcriptional and post-transcriptional regulatory data we identified unique host signatures in response to DI co-treatment. We are also analyzing the host systemic response to infection and vaccination by transcriptomic profiling using whole blood. With human cohorts we are focusing on response to vaccination by integrating the transcriptomic data with metabolomic, proteomic, and glycomic data from our collaborators who analyzed the same samples. In our animal studies, we perform multiscale analyses across data types and are comparing the key drivers of disease severity in ferrets and humans, and integrate data across both systems (human and ferret) to determine if other drivers are identified. In two collaborative studies with Prof. Lara Mahal (U. Alberta), we explored how host glycosylation plays a role in influenza biology. We profiled the glycomic host response to influenza as a function of severity using a ferret model and a lectin microarray. We identified the glycan epitope high mannose as a marker of influenza pathogenesis and severity of outcome. We showed that induction of high mannose is dependent upon the unfolded protein response (UPR) pathway. We also found that mannan-binding lectin (MBL2), an innate immune lectin that negatively impacts influenza outcomes, recognizes influenza-infected cells in a high mannose dependent manner. Together, our data argue that the high mannose motif is an infection-associated molecular pattern on host cells that may guide immune responses leading to the concomitant damage associated with severity Heindel et al 2020, PNAS; Chen et al. 2020, J Proteome Res. 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 are currently finishing the analysis of the metagenomes and metatranscriptomes from >50 household index and contact influenza cases at multiple timepoints over the course of infection to profile the functional microbiome and its effect on the dynamics of ARG transmission. There are technical difficulties in doing so, which we have partly resolved by characterizing CRISPR arrays from the metagenomic data and using the unique combination of repeats as barcodes to track commensals and pathobionts within and across households. While we did observe more shared CRISPR arrays between individuals within households, we did not see any correlation with influenza infections. We did however observe functional changes in influenza-infected individuals versus no influenza when analyzing the metatranscriptome, with overrepresentation of certain taxa in each group. These results indicate the microbiome data obtained from the respiratory tract has enough resolution to be used in quantifying host factor effects on disease severity. 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 published a proof-of-concept paper early last year Yeh et al. 2020, PNAS. We also contributed to the development of the first comprehensive mobile genome analysis application, with capabilities to align reads, call variants, and visualize the results entirely on an iOS device Palatnick et al. 2020, Gigascience. This open-source software, called iGenomics, was a collaboration with Dr. Michael Schatz (Johns Hopkins). The goal of this type of effort is to help in field applications to accelerate characterization of viruses in real-time.