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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. - PowerPoint PPT Presentation
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………………..……………………………………………………………………………………………………………………………………..
Gene Profiling: Clinical Application in Infectious Diseases
Octavio Ramilo
OR April 2007
1. Instead of traditional pathogen based diagnosis
2. Analysis of host response
ALTERNATIVE TO TRADITIONAL MICROBIOLOGIC DIAGNOSIS
Microbe CMicrobe A Microbe B
Immune Response A
Pattern RecognitionReceptors
Immune Response B
Immune Response C
DIFFERENT PATHOGENS STIMULATE DISTINCT HOST IMMUNE RESPONSES
DC DC DC
TRANSCRIPTIONAL PROFILES IN DISEASE PATHOGENESIS
Patient Genotype(DNA)
Expression Profiles(mRNA)
Clinical Disease
Environment
HostFactors
Other unknownfactors
MICROBE
1. S. aureus infections
2. Febrile infants
3. Respiratory infections
GENE PROFILING CLINICAL APPLICATIONS
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
Study Design
Tempus Tubes
DC
B PC
TM
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
Patient Demographics and Lab Characteristics
Clinical Presentation Classification
Characterization of 63 Cultured Isolates
Toxin Profiling Reveals High Homogeneity Among Bacterial Isolates
1,458 Transcripts Differentiate Patients with S. aureus Infection from Healthy Controls
Student T-Test, p<0.01, Benjamini-Hochberg Correction, 1.25 fold changeHierarchical clustering (Spearman correlation)
Increased Inflammatory Response and Decreased Adaptive Immunity in Patients with S. aureus Infection
Myeloid LineageNeutrophilsInflammationCoagulationHematopoiesis
T CellsB CellsCytotoxicity / NK CellsProtein Synthesis
Increased Numbers of Circulating Inflammatory Cells and APCs during S. aureus Infection
From Hospital WBC From Flow Cytometry on PBMC
13 Healthy Controls23 PatientsHealthy Controls S. aureus patients
*
*
*
*
Group Signature vs. Individual Signature
S. aureus patient cohort signature
Individual Signature
Hospitalization StageBacterial Strain
Disease SeverityClinical Presentation
Treatment
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
The Draw Index as a Measure of Progression to Recovery
16
3225
26
Admission Draw Discharge
Hospitalization Duration
Time to Draw
Draw Index =Time to Draw
Hospitalization Duration
0 <= Draw Index <= 1
99 Patients
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
The Transcriptional Signature of S. aureus Infection is Heterogeneous
99 Patients
Cluster C1 Displays Increased Inflammation Clinically
Clinical Presentations Vary Between Clusters
+ no correlation between clusters and clinical isolate characteristics
MDTH Positively Correlates with Inflammation Markers
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
The MDTH Decreases as Patients Get Closer to Discharge
MDTH Increases With Infection Dissemination
MDTH Varies With Clinical Presentation
Patients With Osteoarticular Infection Display Increased Expression of 14 Modules
Patients With Osteoarticular Infection Display Increased Coagulation and Erythropoiesis Signatures
Question:
Can we differentiate between patients presenting with acute febrile syndromes?
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
Biosignatures for Diagnosis of Febrile Infants
Pediatric Emergency Care and Research Network (PECARN)
SBI+
SBI-
WHOLE BLOOD MODULAR ANALYSIS
OR April 2007
Question:
Can we differentiate between patients presenting with similar clinical findings?
IMPACT OF RESPIRATORY INFECTIONS IN IMPACT OF RESPIRATORY INFECTIONS IN CHILDHOODCHILDHOOD
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)
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
OR April 2007
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.2
1.5
2.0
2.5
3.0
4.0
5.04.0
3.0
2.5
2.0
1.5
1.2
1.0
0.8
0.7
0.6
0.5
0.4
0.3
S.pneumoniae
S.aureus
Influenza A
Healthy
38 Samples
13
7 G
en
es
Selected Gene Tree: Respiratory INF_FLU_Staph_Strep_38 (137 Classification Genes)Selected Condition Tree:Respiratory INF_FLU_Staph_Strep_38 (137 Classification Genes)Branch color parameter::
Colored by: Respiratory INF_FLU_Staph_Strep_38 (Default Interpretation)Gene List: Classification Genes 137 (137)
Respiratory INF_FLU_St...
:
*
CLUSTER ANALYSIS IN PATIENTS WITH PNEUMONIA
CLUSTER ANALYSIS IN PATIENTS WITH PNEUMONIA
Interferon Genes Neutrophil
Genes
Mixed Signature
S. pneumoniaeS. aureusInfluenza AHealthy
And what about children….Can we apply this technology to patients with Can we apply this technology to patients with
respiratory viral infections?respiratory viral infections?
And what about children….Can we apply this technology to patients with Can we apply this technology to patients with
respiratory viral infections?respiratory viral infections?
193 samples
16,4
69 g
enes
HEALTHY (n=40) RSV (n=91) Influenza (n=32) HRV (n=30)
VIRAL RESPIRATORY SIGNATURE IN CHILDRENUNSUPERVISED ANALYSIS
QC: PAL2_2xUDAL10%: 16, 469
Can we measure disease activity in pathogens that do not cause blood
stream infections?
Molecular Distance to Health (MDTH):
HEALTHY (n=40)
193 samples
16,4
69 g
enes
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)
Wei
ghte
d M
DTH
Sco
res
QC: PAL2_2xUDAL10%: 16, 469
Disease Severity in Children with RSV vs RV Bronchiolitis
Kruskal-Wallis (median 10-90 percentile)Garcia C,….Mejias A. IDSA 2010
p<0.01Disease Severity Score* •% Sp O2
•Respiratory rate•Retractions•Wheezing•General Condition
Dis
ease
Severi
ty S
core
n=128 n=108 n=26
* Wang et al (modified). Am Rev Respir Dis 1992;145:106
RV RSV Co-infx
MDTH Scores Correlates with RSV Disease Severity
Spearman Correlation
r = 0.5p = 0.002
Clinical Disease Severity Score*
MD
TH S
core
s
Length of Hospitalization
r = 0.6p < 0.01
Disease Severity Score: % Sp O2; respiratory rate; IVF; retractions; auscultation
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
Acknowledgements
Asuncion MejíasMonica ArduraCarla GarciaSusana Chavez-BuenoAna GomezEvelyn TorresJuanita LozanoAlejandro Jordan Juan P. TorresBuddy Creech (VUMC)Prashant Mahajan
Romain BanchereauDamien ChaussabelBlerta DimoHasan JafriMichael ChangJacques BanchereauDerek BlankershipCasey GlaserPhuong NguyenNate Kupperman Pablo Sanchez
NIH (NIAID), Medimmune, PECARN, HRSA EMSC, Dana Foundation
UT Southwestern Medical Center Baylor Institute for Immunology Research