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Gene Profiling: Clinical Application in Infectious Diseases

Gene Profiling: Clinical Application in Infectious Diseases. Octavio Ramilo. ALTERNATIVE TO TRADITIONAL MICROBIOLOGIC DIAGNOSIS. Instead of traditional pathogen based diagnosis Analysis of host response. DIFFERENT PATHOGENS STIMULATE DISTINCT HOST IMMUNE RESPONSES. Microbe A. Microbe B.

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Gene Profiling: Clinical Application in Infectious Diseases

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

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

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

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

  5. GENE PROFILING CLINICAL APPLICATIONS S. aureusinfections Febrile infants Respiratory infections

  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 RNA Extraction T Globin Reduction Tempus Tubes Amplification and cRNA Synthesis 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 E N B Hybridization and Scan PC B NK M DC Er

  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 WBC From Flow Cytometry on PBMC * * * * 13 Healthy Controls 23 Patients Healthy Controls S. aureus patients

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

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

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

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

  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. WHOLE BLOOD MODULAR ANALYSIS SBI+ SBI-

  33. 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. 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. Mixed Signature Interferon Genes Neutrophil Genes * CLUSTER ANALYSIS IN PATIENTS WITH PNEUMONIA S. pneumoniae S. aureus Influenza A Healthy

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

  38. VIRAL RESPIRATORY SIGNATURE IN CHILDREN UNSUPERVISED ANALYSIS HEALTHY (n=40) RSV (n=91) Influenza (n=32) HRV (n=30) 16,469 genes 193 samples 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. VIRAL RESPIRATORY SIGNATURE IN CHILDREN HEALTHY (n=40) RSV (n=91) Influenza (n=32) HRV (n=30) 16,469 genes 193 samples 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 Disease Severity Score* • % Sp O2 • Respiratory rate • Retractions • Wheezing • General Condition p<0.01 Disease Severity Score * Wang et al (modified). Am Rev Respir Dis 1992;145:106 RV RSV Co-infx n=128 n=108 n=26 Kruskal-Wallis (median 10-90 percentile) Garcia C,….Mejias A. IDSA 2010

  42. MDTH Scores Correlates with RSV Disease Severity r = 0.6 p < 0.01 r = 0.5 p = 0.002 MDTH Scores Length of Hospitalization Clinical Disease Severity Score* Disease Severity Score: % Sp O2; respiratory rate; IVF; retractions; auscultation Spearman Correlation

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

  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 UT Southwestern Medical Center Baylor Institute for Immunology Research NIH (NIAID), Medimmune, PECARN, HRSA EMSC, Dana Foundation

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