1. STEPHANIE I. FRALEY, PH.D. ASSISTANT PROFESSOR BIOENGINEERING, UC SAN DIEGO ENABLING ACCESSIBLE AND SENSITIVE SEQUENCE PROFILING FOR SYSTEMS MEDICINE Disclosure: The technology presented is patented, and I am an inventor on the patents.

2. SYSTEMS MEDICINE Major goals: Make blood a diagnostic window of health & disease Provide deep insights into disease mechanisms in patient-specific contexts Mandates: Develop technologies to explore patient’s data space in new dimensions Democratize data-generation & analysis tools Leroy Hood & Mauricio Flores, New Biotechnology 29, 2012.

3. TECHNOLOGY DEVELOPMENT Need to measure rapidly and often in humans Reduced cost, effort, & time to profile Portability for broad access Time-course readouts to understand disease processes Low level regulators critical to biological meta-stability & contribute to diversity 1,2 Higher sensitivity and quantitative power than sequencing & microarrays Many applications have no need to re-sequence As de-novo sequencing discovers & catalogs all sequence possibilities, profiling can play a larger and larger role 1. M.B. Elowitz, et.al., Science 297, 2002. 2. J.L. Spudich & D.E. Koshland. Nature 262, 1976.

4. BACTERIAL INFECTIONS & SEPSIS Hourly mortality risk increase Polymicrobial risk higher Incorrect treatment Antibiotic resistance Spread Biothreat Host-Pathogen system- specific responses

5. IDEAL PROFILING TECHNOLOGY FOR DYNAMIC SYSTEMS MEDICINE Broad-based Identify each individual sequence present sensitively & specifically Absolute quantitative power Rapid User-independent identification Today’s sequencing Sensitivity-specificity trade-off Speed Other tech’s have similar limitations

6. TYPICAL CLINICAL SCENARIO Patient admitted Patient was discharged Antibiotic Therapy Blood culture: Gram (-) bacilli 12304561112 days Salmonella enteritidis confirmed via Sequence ID Technology Salmonella enteritidis confirmed via serotyping Salmonella genus identified

7. OBJECTIVE: PATHOGEN SEQUENCE IDENTIFICATION In the context of bacterial blood stream infections… Broad-based = detect all bacterial pathogens Specific = which pathogens  which drugs Sensitive = single cell as dictated by sample limitations Unaffected by contamination Resolve heterogeneity = resolve polymicrobial infections Fast User independent automated analysis

8. Fluorescence Universal Bacterial PCR High Resolution Melt METHODS

9. ADVANCING HIGH RESOLUTION MELT Traditional Bulk HRMUniversal Digital HRM Fluorescence Temperature population average Fluorescence Temperature Individual sequences S.I. Fraley, et.al. Nucleic Acids Research, 2013

10. Small volume = less reagents, less $$ = greater partitioning of molecules = higher sensitivity Integrated, automated, disposable – DNA extraction – PCR – Readout Portable Rabid heating/cooling MICROFLUIDIC DIGITIZATION ~$15 per test

11. Experimental Variation Temperature Buffer Extraction carry-over Machine Learning Generate training data—known species Algorithm compares test to training Automatic ID Training Data Testing Data ? B. anthracis E. coli AUTOMATE & IMPROVE IDENTIFICATION WITH MACHINE LEARNING

12. LONG AMPLICON “READS” Long sequences, ~1000 bp 1-339 nt difference 100% accuracy inclusivity testing S.I. Fraley, et.al. in review, 2015

13. SUPERIORITY OF MACHINE LEARNING ALGORITHM Reaction Chemistry Variations Mimicking User-to-User Differences Accuracy Athamanolap P, et al., PLoS ONE, 2014

14. POLYMICROBIAL DETECTION S.I. Fraley, et.al. Nucleic Acids Research, 2013

15. OTHER APPLICATIONS Cancer Research & Diagnostics 100% accuracy in genotyping 6 methylation states of RASSF1 Epidemiology Research 98.9% accuracy in serotyping 92 S. pneumoniae strains Athamanolap P, et al., PLoS ONE, 2014

16. Lethal-7 MicroRNA Family S.I. Fraley, et.al. Nucleic Acids Research, 2013 OTHER APPLICATIONS

17. Automated, scalable, rapid, quantitative, inexpensive sequence identification Universal PCR Nucleic Acid Melting Machine Learning Microfluidics Tunable for diverse applications Bacterial pathogens MicroRNA biomarkers Cancer gene methylation signatures Future Goals Portability Integrating automated sample preparation Expand database, 200 clinically relevant bacterial pathogens Sequence melt prediction for training in silico and emerging pathogens SUMMARY

18. Contact Information:  Samuel Yang, Emergency Medicine, Johns Hopkins, now at Stanford  Jeff Wang, Biomedical Engineering, Johns Hopkins  Richard Rothman, Emergency Medicine, Johns Hopkins  Charlotte Gaydos, Emergency Medicine, Johns Hopkins  Karen Carroll, Pathology, Johns Hopkins Sifraley@ucsd.edu  Pornpat Apthamanolap, Johns Hopkins  Justin Hardick, Johns Hopkins  Billie Maseck, Johns Hopkins  Helena Zec, Johns Hopkins Fraley Lab  Daniel Ortiz-Velez, UC San Diego  Sinead Hawker, UC SanDiego ACKNOWLEDGEMENTS