Understanding West Nile Virus Infection

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1 Understanding West Nile Virus Infection The QIAGEN Bioinformatics Solution: Biomedical Genomics Workbench (BXWB) + Ingenuity Pathway Analysis (IPA) Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 1

2 : Integration of applications, technologies, and processes RNA DNA BXWB BXWB RNA-Seq Variants Variants IPA VA Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 2

3 West Nile Virus (WNV), a member of Flaviviridae WNV is a neurotropic virus (same family as yellow fever and dengue viruses) Mosquito-transmitted virus ( ArBovirus e.g. ARthropod-BOrne) The most common cause of epidemic viral encephalitis in North America Worldwide public health concern Viral pathogenesis is incompletely understood Genome: Positive sense, ~10 kb ss-stranded RNA; encodes a single polyprotein, post-translationally cleaved into 3 structural (C, prm/m, E) and 7 non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, NS5). Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 3

4 WNV infection: No approved therapy for use in humans In 2012: highest number of human WNV cases in the U.S. since 2003 (CDC). ü 51% reported to be neuroinvasive (highest number in U.S. history) Neuroinvasive forms of WNV infection: ü ü characterized both by neuronal death and infiltration of mononuclear effector cells whose activities promote both viral clearance and neuronal injury Infection in healthy humans is asymptomatic, but can be severe in elderly and immunocompromised ü ü Life-threatening neurological disease (meningitis and encephalitis) Ocular diseases: chorioretinitis, uveitis, occlusive retinal vasculitis, or optic neuritis Immune Response: ü ü Induction of type-1 IFNs and humoral immunity provide front-line defense against WNV pathogenesis, Cellular immunity (γδ T cells, CD4+, and CD8+ αβ T cells) for host recovery from WNV infection. In mice: ü ü WNV induces systemic infection and then crosses the blood-brain barrier, leading to encephalitis and death Mice depleted of macrophages, neutrophils, or lacking key components of innate immunity exhibit higher and extended viremia and increased mortality to infection with WNV Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 4

5 RNA-Seq data analyzed using QIAGEN Bioinformatics GSE40718: Study: Infection of primary macrophages from healthy human donors (10 individuals, MOI = 1, 24 hrs) For our case study, data was processed with BXWB using Ensembl gene models. 976 genes with fold change >2 and p-value <0.01 Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 5

6 Biomedical Genomics Workbench Fast and Easy Analysis u Accurate and trustworthy results Whole Genome Sequencing, Whole Exome Sequencing, Targeted or Whole Transcriptome Sequencing, ChIP-Seq data u Intuitive and easy-in-use Comprehensive end-to-end analysis workflows for single samples or cohort studies One-click analysis of QIAGEN GeneRead DNASeq Amplicon Panels Streamlined integration with Ingenuity Pathway Analysis (IPA) & Ingenuity Variant Analysis u Flexible & customizable Ready-to-use workflows can be customized Build your own workflows Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 6

7 Biomedical Genomics Workbench (BXWB) to IPA for RNA-Seq MAIN STEPS: Selection of Dataset (GSE 40718) Download from SRA* using SRA tool kit, the FASTQ files (single for single end, 2 for paired ends) on your laptop OR Download the FASTQ files from European Nucleotide Archive Import the FASTQ files in BXWB Set up the RNA-seq Analysis in BXWB - Select Reference Genome (human Ensembl) - Select Mapping options - Select Expression Level Options Set Up the Experiment at Gene Level (GE) or Transcripts Level (TE): Sample vs. control Send dataset to IPA using Plugin IPA from BXWB (choice of parameters Fold Change, p-value, FDR p-value) Analyze the processed dataset in IPA - Dataset: 3285 isoforms with >10 RPKM in either mock or infected, fold change >1, p< Analysis: 976 genes with fold change >2 and p<0.01 Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 7

8 IPA: Understanding and Interpreting Biology Some of the Questions you may ask using IPA in Transcriptomics: What are the signaling or metabolic pathways present, are they activated, are they inhibited? (Canonical Pathways) What are the underlying transcriptional programs? (Upstream Analysis) What biological processes are involved and in what way? (Diseases & Functions) What are the connections between all these differentially expressed molecules? Are there any splicing variants of interest and how regulated are they? (Isoform View) What hypotheses can be drawn further (Mechanistic Network, Causal Network, Regulator Effects) What are the molecules associated with a specific disease or a particular biological process (and vice-versa) (BioProfiler)? Can I incorporate my proprietary data and be the only one to use/see it? (My Findings) Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 8

9 Which pathway is activated or inhibited? Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 9

10 Interferon Signaling is predicted to be activated Overlay expression from the dataset Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 10

11 Interferon Signaling with added Functions Adding statistically and functionally relevant biological processes Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 11

12 Interferon Signaling with added Functions Simulation of effects on neighboring molecules / processes: MAP tool in IPA Antiviral Response Replication of Flaviviridae Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 12

13 Downstream Effects Analysis: Diseases & Functions Strong Immune response Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 13

14 Recruitment of white blood cells to drive an antiviral response IFITM3 Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 14

15 Upstream Regulator Analysis Top cytokines predicted to be activated Top Transcription Regulators predicted to be inhibited Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 15

16 Mechanistic Network Driven by IFNG and some targets Interconnected Regulators (13) network plausibly drive the expression of 352 Targets downstream Downstream Targets Regulators Upstream Regulator Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 16

17 Regulator Effects: How Regulators May Impact Function Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 17

18 Splicing Variants of IFITM3 Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 18

19 Causal Network driven by CLEC7A CLEC7A has been described as Host Susceptibility Factor required by WNV. Encephalitis Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 19

20 Molecule Activity Predictor (MAP) Hypothesis: Simulating Inhibition or Down-regulation of CLEC7A would decrease Encephalitis associated with WNV infection. Encephalitis Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 20

21 Conclusions: Using QIAGEN Bioinformatics Using Biomedical Genomics Workbench, we have been able to: ü Upload RNA-seq data (FASTQ files from SRA). ü Align to the genome of interest (human Ensembl) ü Quantitate and obtain differential expression between the samples ü Seamlessly send data directly into IPA for biological interpretation Using IPA, we have been able to: ü Visualize the differentially expressed genes in virus-infected macrophages vs. noninfected ü Understand which signaling pathways are involved in immune response against virus ü Discover which potential transcriptional program(s) is induced or repressed (IFNG or TRIM24) ü Visualize differentially expressed splicing variants (view of IFITM3, involved in antiviral defense) ü Discover specific biological processes that participate in the antiviral response ü Highlight new hypotheses (ready to be tested and need to be validated) that could explain how encephalitis is increased Functional Genomics & Predictive Medicine, May 21-22, 2015, Boston, MA, Stuart Tugendreich, PhD & Jean-Noel Billaud, PhD 21

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