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)

Chart 3

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

Chart 4

TIGER Process Part 1: Sample Preparation and Broad Range PCR

Chart 5

TIGER Process Part 2: Post PCR Spray and Analysis

Chart 6

Triangulation Using Multiple Primer Pairs

Correlated information from multiple primer pairs add redundancyand resolving power

Chart 7

“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

Chart 10

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

Chart 12

Primer Target Site in Polymerase

Chart 13

Ibis Chemically Modified Oligonucleotides

Chart 14

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-

Chart 15

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

Chart 16

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

Chart 17

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

Chart 18

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

Chart 19

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

Chart 20

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]

Chart 21

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

Chart 22

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

Chart 23

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

Chart 24

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

Chart 25

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

Chart 26

Future Automated Process

Collect Sample

Suspend and/or

Concentrate Lyse

AutoSignal

Processing

Drill-down

Chart 27

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

Chart 28

Exact Mass Measurements Facilitate Unambiguous Base Composition Determination

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# comp pairs1

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