1. TIGER * Biosensor for Emerging Infectious Disease Surveillance *Triangulation Identification for Genetic Evaluation of Risks Ranga Sampath David Ecker Ibis Therapeutics

2. Chart 2 Infectious Disease Detection Today Culture techniques –Detects a subset of all pathogens Nucleic Acid Tests (NAT’s) –One test at a time (HIV, HCV, tuberculosis, etc.) –Need too many tests –Fail to detect newly emergent pathogens There is currently no good method to detect organisms that have never been seen before Nucleic acid tests (NAT’s)

3. Chart 3 Problems Addressed by TIGER Animal Reservoirs of Infectious Agents Environmental Surveillance of Public Places Clinical Diagnostics/ Biosurveillance Agricultural Diagnostics/ Biosurveillance

4. Chart 4 TIGER Process Part 1: Sample Preparation and Broad Range PCR

5. Chart 5 TIGER Process Part 2: Post PCR Spray and Analysis

6. Chart 6 Triangulation Using Multiple Primer Pairs Correlated information from multiple primer pairs add redundancy and resolving power

7. Chart 7 “Back-Ends” to PCR 1- 4 analyses per well Hybridization-based detection based upon selected probes Thousands of analyses in parallel Hybridization-based detection with selected probes Thousands of analyses in parallel Base composition detection without having to select probes Information rich results Taqman probes Chips

8. Chart 8 RNA Virus Families

9. Chart 9 Detection and Classification of Coronavirus Species RNA Virus Families

10. Chart 10 Coronavirus Phylogenetic Tree

11. Chart 11 Coronavirus Broad-range Primers RdRp Primer nsp11 Primer Multiple primers selected based on alignment of all available sequences in Genbank in March 2003 Primers target all known CoV species Specificity verified using electronic PCR

12. Chart 12 Primer Target Site in Polymerase

13. Chart 13 Ibis Chemically Modified Oligonucleotides

14. Chart 14 ESI-FTICR Two Strands of a PCR Product 1008.21008.61009.0m/z (M-27H + ) 27- 27 - 25 - 23 - 29 - 31 -

15. Chart 15 A27 G19 C14 T28 A22 G22 C14 T30 A25 G24 C11 T28 Base Compositions from Mass Spectra 27100273002750026900 MW (Da) SARS CoV 27125.54227298.508 HCoV OC43 27098.562 27328.473 HCoV 229E 27450.50626975.512

16. Chart 16 Triangulation Identification Using Base Compositions

17. Chart 17 Base Compositions as Virus Classifier - Resolution across viral groups Base compositions are remarkably rich in information content –Corresponding regions from different viral families occupy distinct base composition space

18. Chart 18 SARS CoV [A27 G19 C14 T28] HCoV 229E [A25 G24 C11 T28] HCoV OC43 [A22 G22 C14 T30]  = [-2A, +5G, -3C, 0T]  = [-5A, +3G, 0C, +2T]  = [-3A, -2G, +3C, +2T] Rotate by T 28 30 A C G Base Compositions as Virus Classifier - Resolution within a viral group RNA viruses mutate –Multiple isolates could vary in sequence and composition

19. Chart 19 Base Compositions as Virus Classifier - Resolution within a viral group HCV-1b (50 sequences X 6 regions) Training Set (40 sequences) Test Set (10 sequences) Threshold @ 95% sensitivity Estimate pairwise sequence variation Average “Cloud” Non HCV-1b (50 sequences) Derive probabilities for [A G C T] changes Species variations modeled on HCV sequences –>100 complete genomes; multiple subtypes –Multiple TIGER-like primer regions analyzed –Derived classification probabilities based on observed changes

20. Chart 20 Base Compositions as Virus Classifier - Resolution within a viral group RNA viruses mutate –Most of these variations are constrained and not random Species variations modeled on HCV sequences –>100 complete genomes; multiple subtypes –Multiple TIGER-like primer regions analyzed –Derived classification probabilities based on observed changes A C G [0 0 0 0] A C G

21. Chart 21 The probability of mis- assigning an unknown 229E or OC43 variant as SARS is nearly zero Distribution of probabilities for HCoV 229E or OC43 variants 12 14 16 18 20 22 24 26 28 30 16 18 20 22 24 26 28 30 32 34 18 14 10 6 SARS HCoV 229E A C HCoV OC43 G

22. Chart 22 SARS Classification Probabilities

23. Chart 23 Mixture sample of SARS, HCoV OC43, HCoV 229E SARS CoVHCoV OC43HCoV 229E 27298.514 MIX 27450.518 27328.483 27125.544 27098.565 26975.529 271002730027500 MW (Da) 26900

24. Chart 24 TIGER Sensitivity Maximum Achievable by PCR Genome Copies Probability of detection

25. Chart 25 Current Semi-Automated Process Collect Sample Suspend and/or Concentrate Lyse DNA Isolation PCR Preparation Mass. Spec. PCR Analyze Results Signal Processing Drill-down Cleanup

26. Chart 26 Future Automated Process Collect Sample Suspend and/or Concentrate Lyse Auto Signal Processing Drill-down

27. Chart 27 Conclusions TIGER is a new paradigm for broad detection of infectious disease causative agents Can detect and identify emerging infectious organisms Detections are broad yet highly information rich Sensitive to theoretical limit of PCR High throughput (1800 samples/day/instrument) Applications –Diagnosis of infectious agents in humans –Identification of animal reservoirs –Environmental surveillance of infectious agents

28. Chart 28 Exact Mass Measurements Facilitate Unambiguous Base Composition Determination ppm 0-25 50 100 250 500 # comp pairs 1 13 66 378 1447 AWGXCYTZAWGXCYTZ TWCXGYAZTWCXGYAZ