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 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. As part of this project we tackle different types of studies that include: (a) Virus evolution and genetic diversity within infected hosts (b) Systems biology of infection and vaccination (c) Respiratory tract microbiome profiles in lung diseases (viral and non-viral) (d) Virus capture and detection for novel diagnostic platforms (a) Virus evolution and genetic diversity within infected hosts: We continue collaborations on the molecular epidemiology of influenza in different parts of the world, with a study published in April on the emergence of antigenic variants in Brazil (Pillai et al Virus Evol, April 2023). But a main aspect of our studies is to model the evolution of the virus over the course of the infection, looking at intra-host and inter-host virus genetic diversity. In June we published a study on the best practices to delineate minority variants from errors introduced during amplification and sequencing of RNA virus genomes (influenza and SARS-CoV-2), providing extensive guidance for future studies of viral diversity and evolution (Roder et al mBio, June 2023). The genetic diversity we identify in our studies includes defective virus genomes (DVGs). 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 work is still ongoing (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. This work was published in June (Wang et al J. Virology, June 2023). We have a study under review on the analysis of nasal wash samples from 60 ferrets (lean and obese) infected with influenza to determine the mutational spectrum and the dynamics of the mutations at different timepoints. An ongoing study is on defective variant genomes and their dynamics in the lean and obese hosts. (b) Systems biology of infection and vaccination This past year, as part of the Collaborative Influenza Vaccine Innovation Centers (CIVIC) program we have collaborated on the analysis of the host systemic response to infection and vaccination using transcriptomic profiling of whole blood. In a first study with human cohorts, published in November 2022, 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 (Forst et al Viruses, Nov. 2022). (c) Respiratory tract microbiome profiles in lung diseases (viral and non-viral) 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 bacteria and ARGs. We did an extensive characterization of the microbial ecology of the upper airways by metagenomic analysis using nasopharyngeal swabs collected from households with and without influenza. We demonstrate that the microbiome compositional potential is altered in influenza infection. In this study, we determined whether metagenomic-type analyses of the microbiome provide the resolution necessary to track transmission of airway bacteria. We identified CRISPR spacers detected in the metagenomic sequence reads and used these 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. We published this work in June and are pursuing further studies on the metatranscriptome to further probe the functional potential of the respiratory tract (Zhang et al Microbiome, June 2023). We have an on-going collaborative study with Dr. Leo Segal (NYU Langone) where we are probing the dysbiosis of the lower airways in early COPD. A manuscript was accepted in August (Sulaiman et al American Journal of Respiratory and Critical Care Medicine). (d) Virus capture and detection for novel diagnostic platforms In collaboration with Dr. Mauricio Terrones group (PSU) and Dr. Steven Jacobson (NINDS) we continue our work on the development of a microfluidics platform made of built-in carbon nanotube cartridges for the capture and enrichment 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.