Surveying Gene Expression in Whole Blood: Host Response and Classification of Infection

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1 Surveying Gene Expression in Whole Blood: Host Response and Classification of Infection Stephen Popper ScD Dept. of Microbiology and Immunololgy Stanford University School of Medicine

2 Syndromes of suspected* microbial origin: success in achieving microbiological diagnosis Sepsis: ~10% Pneumonia:~50-70% Encephalitis:~20% Acute Diarrhea:~30-50% * suspected on basis of response to antibiotics, among other observations

3 Gene Expression Profiling and Infection Microarrays Relatively unbiased approach to examining host response Potential for high resolution, high specificity Host response Finite target set: always present Insight into immunity, immune-mediated pathogenesis Whole blood Accessible, commonly used, minimizes need for processing Commonly collected Minimizes need for processing in clinical settings

4 Gene Expression Profiling: Lessons from Cancer 1800 Articles (PubMed) Cancer Infectious Diseases Year

5 Hierarchical Clustering and Biological Function: DLCL Alizadeh, A.A. et al Nature 403: , 2000

6 DLCL cont d Alizadeh, A.A. et al Nature 403: , 2000

7 Microarrays: Host Response to Infection Diagnostic Markers Febrile Illness Nepal Emergency Rooms (US) Kawasaki Disease Prognostic Markers (Time) Kawasaki Disease Treatment Response Aneurysm Formation Pathophysiology Dengue Shock Syndrome

8 Gene Expression Profiling in Whole Blood Determine the extent and source of variation in infected individuals Relative to variation in the absence of infection? How much is due to the presence of a specific etiologic agent? Can we dissect variation in a complex tissue? Identify gene expression profiles characteristic of classes of infection Potential markers for diagnostic and clinical management Insight into molecular mechanisms that characterize the immune response during infection Classes corresponding to specific pathogens, or?

9 Human cdna microarray: Lymphochip LC30 37,632 spots ~18,000 genes ~10,250 named

10 cdna Microarray Scheme Reference Sample Using Confocal Laser Microscopy

11 Image analysis and visualization Image analysis Data filtering Normalization Cy3 Cy5 Cy5 Cy3 log Cy5 2 Cy Transformation to log ratios Genes R/G ratio represents relative abundance of transcripts Experiments

12 Inter-individual variation in whole blood gene expression Objectives: Identify primary sources of variation Baseline variation in health for future studies of disease Paxgene samples from 77 healthy individuals Stabilize Whole Blood total RNA at time of draw 41 male and 36 female; med age = 31.5 CBC, time of day recorded A. Whitney et al, PNAS 2003

13 Inter-individual variation in whole blood gene expression Correlation Coefficient Lymph Neut RDW Age Time Reticulocyte cluster Lymphocyte cluster Neutrophil cluster Fold induced >2.5X Unchanged >2.5X Fold repressed Immunoglobulins Time-correlated Interferon-regulated

14 Inter-individual variation in gene expression X 3.0X 4.0X 8.0X Percent of genes changing Normal WB CLL (Purified B- Cells) Minimum fold change from mean in at least 3 of 45 samples All analyses performed on 3826 genes DLCL (Bulk LN Bx)

15 Febrile Illness: Nepal Active surveillance for consecutive cases of febrile illness in adult non-surgical patients Axillary temp 38.0 C 14 years Informed consent Wet season: July - August Patan Hospital, Kathmandu 251 beds 250,000 outpatients 30,000 ER visits Murdoch et al; AJTMH 70(6):

16 Samples for Host Gene Expression: 2.5ml blood in PAXgene tubes RNA isolated < 5 days post-collection Diagnostic Tests: culture, IgM serology, Urine Ag, confirmation off-site (PCR) Linear amplification for microarray analysis Universal Human Reference LymphoChip

17 Microbiological Diagnoses Single-agent, well-established Dx, on arrays: n=76 Also 7 healthy local donors Etiologic Agent n Age Temp (F) WBC Sxs (days) (Median) (Median) (Median) (Median) E. coli Leptospira O. tsutsugamushi R. typhi S. aureus S. pneumoniae S. enterica* * typhi (22), paratyphi (18)

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19 Scrub typhus cluster GENE ONTOLOGY: Molecular function Biological process Cellular compartment Metabolism, Intracellular (p<10-5) Ribonucleoprotein Complex Structural Components, RNA splicing Hydrogen ion transport ATP synthases, Cytochrome c oxidase subunits

20 Scrub typhus: Which are the active cells? CD8+ CTL

21 MHCII loci IFN-stimulated genes

22 % total cdna elements Inter-individual variation in leukocyte g expression patterns X 47.6% % 3.0X 4.0X 8.0X 0.3% healthy WB 8.7% 0.8% 0.1% typhoid 15.0% 1.9% 0.5% infected 29.7% 5.9% 4.2% 2.7% 0.5% 0.8% CLL (B cells) 11.2% DLCL (bulk LN Bx) min fold change in expression level from mean in 3 of 45 specimens of each type (3826 elements)

23 Sources of variation: Clinical parameters Temp Length illness Age Hematocrit Lymphocyte % Neutrophil % Eosinophil %

24 Variation and microbial species How much of the variation in gene expression is due to the presence of different pathogens? Multivariate linear regression Quantify relative contribution of different parameters Examine effect of non-continuous variables Classes of pathogens Simultaneously adjust for multiple parameters Control for differences in composition of sampled cell populations

25 Test case: Linear regression to adjust for neutrophil % PBMC Whole Blood PBMC Whole Blood 11 matched samples (9 individuals) - remaining variation largely associated with intrinsic genes

26 Significant Sources of Variation Gender Age Microbial Diagnosis Microbial Diagnosis WBC Cell populations: Lymphocytes Neutrophils Monocytes Eosinophils WBC+Lymphs+Neuts Temperature Hematocrit Length of illness

27 Variation due to Microbial Diagnosis: After Adjusting for Cell Populations After adjusting for other significant sources of variation: p<0.05

28 Conclusions Microbial species is an important source of variation in gene expression Person-person differences do not dominate patterns of expression Even after adjusting for differences in cellular composition Driven by scrub typhus in this dataset Can identify pathogen-specific signatures in whole blood Scrub typhus (unsupervised), Salmonella (supervised) Potential utility as clinical markers Identity of the signatures suggests critical aspects of host response Possible to dissect sources of variation in whole blood gene expression in vivo Complex tissue, dynamic process

29 Expression Profiling: Issues in Clinical Studies Statistical approaches Strength in patterns; weakness in (individual) numbers Biological Insights Need Reference Experiments: Cell subsets; Type I & Type II Interferon Bioinformatics Throughput and technical standardization 2000: 30ug total RNA 2002: 3ug RNA, 5 weeks/100 samples 2004: 300ng RNA, 2.5 weeks 2006:?, 1 week MEEBO/HEEBO arrays Exon-evidence Based Oligonucleotide Arrays Alternate splicing Sensitivity/specificity Low cost (for the oligos) Stanford, UCSF, Stowers Institute, Rockefeller Institute, University of Basel alizadehlab.stanford.edu

30 Acknowledgements Oxford Clinical Research Unit Jeremy Farrar, Vietnam Cameron Simmons Christiane Dolocek Tran Nguyen Bich Chau Nguyen Thi Phuong Dung Dang Minh Hoang Truong Hoang Long Nguyen Vinh Chau Tran Tinh Hien Le Thi Thu Thao Stanford David Relman Pat Brown Adeline Whitney Kate Rubins Judy Yen Mike Griffiths Simon Waddell Febrile Illness: Nepal Barth Reller, Duke Chris Woods Marc Fisher, CDC Peter Dull Lennox Archibald David Murdoch, NZ Mark Zimmerman, Nepal Buddha Basyat Bishwa Shrestha Robert Scott Kawasaki Disease Jane Burns, UCSD Chisato Shimizu Hiroko Shize John Kanegaye Jane Newburger, Boston Children s

31 MEEBO & HEEBO arrays Exonic-evidence Based Oligonucleotide arrays Both Comparative Genomic Hybridizations and Expression Studies Alternative splicing Sensitivity/Specificity Modeling & controls Low cost (for oligos) ~$5/array alizadehlab.stanford.edu Stanford, UCSF, Stowers Institute, Rockefeller, University of Basel