1. ………………..…………………………………………………………………………………………………………………………………….. Gene Profiling: Clinical Application in Infectious Diseases Octavio Ramilo

2. OR April 2007 1.Instead of traditional pathogen based diagnosis 2.Analysis of host response ALTERNATIVE TO TRADITIONAL MICROBIOLOGIC DIAGNOSIS

3. Microbe CMicrobe AMicrobe B Immune Response A Pattern Recognition Receptors Immune Response B Immune Response C DIFFERENT PATHOGENS STIMULATE DISTINCT HOST IMMUNE RESPONSES DC

4. TRANSCRIPTIONAL PROFILES IN DISEASE PATHOGENESIS Expression Profiles (mRNA) Clinical Disease Environment Host Factors Other unknown factors MICROBE

5. 1. S. aureus infections 2. Febrile infants 3. Respiratory infections GENE PROFILING CLINICAL APPLICATIONS

6. Staphylococcus aureus Gram-positive spherical bacteria Skin / Nose Commensal Causes a range of illnesses – Skin Abscesses – Bacteremia – Osteoarticular infections – Pneumonia – Death Caused >18,000 deaths in the U.S. in 2005; Cost $14 billion to hospitals in extended length of stay

7. Study Design Tempus Tubes DC B PC T M NK Er N E B RNA Extraction Globin Reduction Amplification and cRNA Synthesis Hybridization and Scan 99 patients vs. 44 healthy controls split into independent training and test sets Age range: 7 years (0.06 – 17) Average draw day: 5 days (1 – 35) Treatment: antibiotics, no steroids No co-infection

8. Patient Demographics and Lab Characteristics

9. Clinical Presentation Classification

10. Characterization of 63 Cultured Isolates

11. Toxin Profiling Reveals High Homogeneity Among Bacterial Isolates

12. 1,458 Transcripts Differentiate Patients with S. aureus Infection from Healthy Controls Student T-Test, p<0.01, Benjamini-Hochberg Correction, 1.25 fold change Hierarchical clustering (Spearman correlation)

13. Increased Inflammatory Response and Decreased Adaptive Immunity in Patients with S. aureus Infection Myeloid Lineage Neutrophils Inflammation Coagulation Hematopoiesis T Cells B Cells Cytotoxicity / NK Cells Protein Synthesis

14. Increased Numbers of Circulating Inflammatory Cells and APCs during S. aureus Infection From Hospital WBCFrom Flow Cytometry on PBMC 13 Healthy Controls 23 Patients Healthy ControlsS. aureus patients * * * *

15. Group Signature vs. Individual Signature S. aureus patient cohort signature Individual Signature Hospitalization Stage Bacterial Strain Disease Severity Clinical Presentation Treatment

16. Correlating Clinical Heterogeneity with the Molecular Signature Signature  Clinic Molecular signatures derived for each patient Patients are clustered based on signature X clusters are identified Distribution of clinical observations is studied for each cluster Group patients based on clinical observations Distribution of signatures studied for each group Clinic  Signature

17. The Draw Index as a Measure of Progression to Recovery 16 32 25 26 AdmissionDraw Discharge Hospitalization Duration Time to Draw Draw Index = Time to Draw Hospitalization Duration 0 <= Draw Index <= 1 99 Patients

18. Can we measure disease activity at the molecular level ? Molecular Distance to Health (MDTH): Metric that summarizes in a single score all the information derived from whole genome transcriptional analysis in a way that can be applied in the clinical context

19. The Transcriptional Signature of S. aureus Infection is Heterogeneous 99 Patients

20. Cluster C1 Displays Increased Inflammation Clinically

21. Clinical Presentations Vary Between Clusters + no correlation between clusters and clinical isolate characteristics

22. MDTH Positively Correlates with Inflammation Markers

23. Correlating Clinical Heterogeneity with the Molecular Signature Signature  Clinic Molecular signatures derived for each patient Patients are clustered based on signature X clusters are identified Distribution of clinical observations is studied for each cluster Group patients based on clinical observations Distribution of signatures studied for each group Clinic  Signature

24. The MDTH Decreases as Patients Get Closer to Discharge

25. MDTH Increases With Infection Dissemination

26. MDTH Varies With Clinical Presentation

27. Patients With Osteoarticular Infection Display Increased Expression of 14 Modules

28. Patients With Osteoarticular Infection Display Increased Coagulation and Erythropoiesis Signatures

29. Question: Can we differentiate between patients presenting with acute febrile syndromes?

30. MODULAR ANALYSIS DIAGNOSIS: DISEASE FINGERPRINTS Chaussabel, et al Immunity 2008 29(1): 150-64; Pankla R et al Genome Biol 2009 10(11), Ardura, et al. Plos One 2009; 4(5), O’Garra 2010 Nature 2010; 466: 973-7

31. Biosignatures for Diagnosis of Febrile Infants Pediatric Emergency Care and Research Network (PECARN)

32. SBI+ SBI- WHOLE BLOOD MODULAR ANALYSIS

33. OR April 2007 Question: Can we differentiate between patients presenting with similar clinical findings?

34. IMPACT OF RESPIRATORY INFECTIONS IN CHILDHOOD  First cause of children morbidity & mortality in the world  Viral respiratory infections are responsible for a large number of visits to the pediatrician, to the ER and hospital admissions  First cause of asthma attacks  Important morbidity in immunocompromised patients and children with chronic illnesses (i.e., BPD, congenital heart disease)

35. OR April 2007 ANALYSIS OF PNEUMONIA ( LOWER RESPIRATORY TRACT INFECTION)  Genes used to classify different patient groups (n=137)  All patients who presented with pneumonia (n=30)  Healthy controls (n=8)  Cluster analysis

36. OR April 2007 * CLUSTER ANALYSIS IN PATIENTS WITH PNEUMONIA Interferon Genes Neutrophil Genes Mixed Signature S. pneumoniae S. aureus Influenza A Healthy

37. Can we apply this technology to patients with respiratory viral infections? And what about children…. Can we apply this technology to patients with respiratory viral infections?

38. 193 samples 16,469 genes HEALTHY (n=40)RSV (n=91)Influenza (n=32)HRV (n=30) VIRAL RESPIRATORY SIGNATURE IN CHILDREN UNSUPERVISED ANALYSIS QC: PAL2_2xUDAL10%: 16, 469

39. Can we measure disease activity in pathogens that do not cause blood stream infections? Molecular Distance to Health (MDTH):

40. HEALTHY (n=40) 193 samples 16,469 genes RSV (n=91)Influenza (n=32)HRV (n=30) VIRAL RESPIRATORY SIGNATURE IN CHILDREN Ctrl (n=40)RSV (n=91) Flu (n=32)RV (n=30) Weighted MDTH Scores QC: PAL2_2xUDAL10%: 16, 469

41. Disease Severity in Children with RSV vs RV Bronchiolitis Kruskal-Wallis (median 10-90 percentile) Garcia C,….Mejias A. IDSA 2010 p<0.01 Disease Severity Score* % Sp O 2 Respiratory rate Retractions Wheezing General Condition Disease Severity Score n=128n=108n=26 * Wang et al (modified). Am Rev Respir Dis 1992;145:106 RV RSV Co-infx

42. MDTH Scores Correlates with RSV Disease Severity Spearman Correlation r = 0.5 p = 0.002 Clinical Disease Severity Score* MDTH Scores Length of Hospitalization r = 0.6 p < 0.01 Disease Severity Score: % Sp O 2 ; respiratory rate; IVF; retractions; auscultation

43. OR April 2007 1.Pathogens induce distinct transcriptional profiles 2.Profiles can be used to identify common features and also differences between patients 3.Modular analysis: disease fingerprints useful for differential diagnosis 4.New perspective on disease pathogenesis 5.New tool for assessing disease severity SUMMARY

44. Acknowledgements Asuncion Mejías Monica Ardura Carla Garcia Susana Chavez-Bueno Ana Gomez Evelyn Torres Juanita Lozano Alejandro Jordan Juan P. Torres Buddy Creech (VUMC) Prashant Mahajan Romain Banchereau Damien Chaussabel Blerta Dimo Hasan Jafri Michael Chang Jacques Banchereau Derek Blankership Casey Glaser Phuong Nguyen Nate Kupperman Pablo Sanchez NIH (NIAID), Medimmune, PECARN, HRSA EMSC, Dana Foundation UT Southwestern Medical Center Baylor Institute for Immunology Research