TIGER * Biosensor for Emerging Infectious Disease Surveillance
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Transcript of TIGER * Biosensor for Emerging Infectious Disease Surveillance
TIGER* Biosensor for Emerging Infectious Disease Surveillance
*Triangulation Identification for Genetic Evaluation of Risks
Ranga SampathDavid Ecker
Ibis Therapeutics
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)
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Problems Addressed by TIGER
Animal Reservoirs of Infectious
Agents
Animal Reservoirs of Infectious
Agents
Environmental Surveillance of Public Places
Environmental Surveillance of Public Places
Clinical Diagnostics/
Biosurveillance
Clinical Diagnostics/
Biosurveillance
Agricultural Diagnostics/
Biosurveillance
Agricultural Diagnostics/
Biosurveillance
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TIGER Process Part 1: Sample Preparation and Broad Range PCR
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TIGER Process Part 2: Post PCR Spray and Analysis
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Triangulation Using Multiple Primer Pairs
Correlated information from multiple primer pairs add redundancyand resolving power
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“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
Chart 8
RNA Virus Families
Chart 9
Detection and Classification of
Coronavirus Species
RNA Virus Families
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Coronavirus Phylogenetic Tree
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
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Primer Target Site in Polymerase
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Ibis Chemically Modified Oligonucleotides
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ESI-FTICR Two Strands of a PCR Product
1008.2 1008.6 1009.0m/z
(M-27H+)27-
27- 27-
25-25-
23-23-
29- 29-
31-31-
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A27 G19 C14 T28
A22 G22 C14 T30
A25 G24 C11 T28
Base Compositions from Mass Spectra
27100 27300 2750026900MW (Da)
SARSCoV
27125.542 27298.508
HCoVOC4327098.562 27328.473
HCoV229E
27450.50626975.512
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Triangulation Identification Using Base Compositions
GroupCoronavirus Species
DB Predicted Base
Compositions
Experimentally Observed Base Compositions
DB Predicted Base
Compositions
Experimentally Observed Base Compositions
CCoV A24 G24 C8 T32 A24 G24 C8 T32 No Data A33 G31 C19 T54
CCoV A24 G24 C8 T32 A24 G24 C9 T31 No Data A34 G30 C18 T55FCoV A23 G25 C10 T30 A24 G24 C9 T31 No Data A33 G31 C18 T55FCoV A23 G25 C10 T30 A23 G25 C10 T30 No Data A33 G30 C19 T55HCoV229E A25 G24 C11 T28 A25 G24 C11 T28 A36 G30 C20 T51 A36 G30 C20 T51HCoV229E A25 G24 C11 T28 A25 G24 C11 T28 A36 G30 C20 T51 A36 G30 C20 T51BCoV A22 G22 C12 T32 A22 G22 C12 T32 A38 G32 C15 T52 A38 G32 C15 T52HCoV OC43 A22 G22 C14 T30 A22 G22 C14 T30 No Data A38 G31 C15 T53MHV A21 G23 C14 T30 A21 G23 C14 T30 A37 G33 C18 T49 A37 G34 C18 T48MHV A21 G23 C14 T30 A21 G23 C14 T30 A34 G34 C21 T48 A34 G34 C21 T48MHV A21 G23 C14 T30 A21 G23 C14 T30 A34 G35 C18 T50 A34 G35 C18 T50RtCoV A21 G23 C14 T30 A21 G23 C14 T30 No Data A35 G33 C19 T50
3 IBV
A24 G24 C14 T26 (Beaudette)
A26 G23 C12 T27 (LX4)
A24 G24 C14 T26
A33 G32 C17 T55 (Beaudette)
A36 G31 C17 T53 (LX4)
A33 G32 C17 T55
4 SCoV A27 G19 C14 T28 A27 G19 C14 T28 A34 G33 C20 T50 A34 G33 C20 T50
RdRp nsp11
1
2
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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
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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
CG
Base Compositions as Virus Classifier -Resolution within a viral group
•RNA viruses mutate–Multiple isolates could vary in sequence and composition
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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
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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
[0 0 0 0]
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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
12141618202224262830
16
18
20
22
24
26
28
30
32
3418
14106
SARS
HCoV 229E
A
C
HCoV OC43G
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SARS Classification Probabilities
Both Primers
Virus Predicted Base Compositions
Changes from SARS
Misclassification probability
Predicted Base Compositions
Changes from SARS
Misclassification probability
Joint Probability
SARS CoV A27 G19 C14 T28 [0 0 0 0 ] Matched A34 G33 C20 T50 [0 0 0 0 ] Matched MatchedMHV A21 G23 C14 T30 [-6 +4 0 +2] <1.00E-10 A34 G34 C21 T48 [0 +1 +1 -2] 8.01E-06 0.00E+00HCoV 229E A25 G24 C11 T28 [-2 +5 -3 0] <1.00E-10 A36 G30 C20 T51 [+2 -3 0 +1] 6.99E-06 0.00E+00
PEDV A23 G23 C12 T30 [-4 +4 -2 +2] <1.00E-10 A29 G33 C21 T54 [-5 0 +1 +4] 6.99E-06 0.00E+00
HCoV OC43 A22 G22 C14 T30 [-5 -2 +3 +2] <1.00E-10 A38 G31 C15 T53 [+4 -2 -5 +3] <1.00E-10 0.00E+00FCoV A23 G25 C10 T30 [-4 +6 -4 +2] <1.00E-10 A33 G30 C19 T55 [-1 -3 -1 +5] <1.00E-10 0.00E+00IBV A24 G24 C14 T26 [-3 +5 0 -2] <1.00E-10 A33 G32 C17 T55 [-1 -1 -3 +5] <1.00E-10 0.00E+00CCoV A24 G24 C8 T32 [-3 +5 -6 +4] <1.00E-10 A34 G30 C18 T55 [0 -3 -2 +5] <1.00E-10 0.00E+00
BCoV A22 G22 C12 T32 [-5 +3 -2 +4] <1.00E-10 A38 G32 C15 T52 [+4 -1 -5 +2] <1.00E-10 0.00E+00
RdRp nsp11
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Mixture sample of SARS, HCoV OC43, HCoV 229E
SARS CoVHCoV OC43HCoV 229E
27298.514MIX
27450.518
27328.483
27125.54427098.56526975.529
27100 27300 27500MW (Da)
26900
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TIGER SensitivityMaximum Achievable by PCR
0.01
0.1
1
0.01 0.1 1 10 100
Genome Copies
Pro
bab
ilit
y o
f d
etec
tio
n
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Current Semi-Automated Process
Collect Sample
Suspend and/or
Concentrate Lyse
DNA IsolationPCR Preparation
Mass. Spec.
Q ui ckTi me™ and a TI FF (Uncompressed) decompressor are needed to see thi s pi cture.
PCR
QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture.
Analyze Results
Signal Processing
Drill-down
Cleanup
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Future Automated Process
Collect Sample
Suspend and/or
Concentrate Lyse
AutoSignal
Processing
Drill-down
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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
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Exact Mass Measurements Facilitate Unambiguous Base Composition Determination
ppm0-2550100250500
# comp pairs1
1366
3781447
AWGXCYTZ
TWCXGYAZ