sep 24 08 fwf seminar eda final

9/28/2008

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New diagnostic methods for infectious diseases

Shigetoshi Eda, PhD

Research Assistant ProfessorCenter for Wildlife Health

Department of Forestry, Wildlife, and Fisheries

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Fig. 1. (A) Lab-on-a-Chip developed at the Oak Ridge National Laboratory (ORNL) and (B) a hand-held device developed based on the Chip at the Sandia National Laboratories, Courtesy of Dr. Robert Foote at ORNL..

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Wednesday, September 24, 2008 - 160 Plant Biotech

New diagnostic test for Johne’s disease in cattle

I. Past - present

Topics

Point-of-care diagnostic device for infectious diseases

II. Present -

Part 1

New diagnostic test for Johne’s disease in cattle

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Pathogen or Etiologic agent ---

Bacteria, Mycobacterium avium subsp. paratuberculosis (MAP)

Host --- Primarily ruminants in livestock and wildlife. Many other animals can be carriers or sporadically infected.

Signs --- Diarrhea and weigh loss. Often cows will show no signs of the disease for first 3-5 years of infection.

Pathology --- The primary site of infection is small intestine.

Transmission --- Oral-fecal transmission

Why studyJohne’s disease?

Highly prevalent worldwide; in the US, Africa, Australia, New Zealand and Europe the prevalence of infected cattle herds varies from 10-60%.

Annual loss of more than $200 million to the US dairy industry. These loss are mainly due to reduced milk production and early culling.

The causative bacteria of Johne’s disease is suspected to cause or worsen a human disease, called Crohn’s disease

Obstacles in Johne’s disease control

Antibiotics are expensive and require a long course of treatment: not a practical option for the livestock industry.

Vaccines have had limited success.

So, current management is based on: Diagnosis and culling of infected animals.

However …

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Problems in Johne’s disease diagnosis

Detection of bacteria in fecesa. Culture

Time consuming -- up to 4 months (!) Expensive -- $20/sample

b. DNA detectionLabor intensive – requires a skilled examinerExpensive -- $25/sample

Detection of antibodies in serumRapid -- Half a dayCheap -- $5/sampleEasy -- but very low sensitivity!

Antibody detection VERY LOW sensitivity!

Antibody detection (ELISA)

Author Year Sensitivity (%) Specificity

Reichel 1999 31.1 97.9

Stable 2002 25 94

McKenna 2005 16.6 97.113.9 95.9

Sweeney 2006 13.5 98.1

Wells 2006 27.5 95.327.8 99.7

Sensitivity at30% means

Only 1 in 3 diseased cattle can be detected and removed.

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The remaining cattle will spread the disease to the healthy cattle in the herd.

Help me…

So, we urgently dneed a more

sensitive test!

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What’s wrong with current antibody-detection tests?

This is Volkswagen Golf.

Not hard to recognize.

Also, an emblem and badge are available on its surface that allow for provide easy identification of the car.

What’s wrong with current antibody-detection tests?

But this is what current tests use as antigens.

These internal parts are shared with other types of Volkswagen cars, and interfere with our car identification.

Our idea ….

Extract just the surface antigens

from the bacteria without destroying the organisms and use them for antibody detection.

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Approach

Since the surface of the MAP bacterium is oily, we tested

Lipids

Surface

is oily, we tested several organic solutions for gentle extraction of MAP surface antigens.

Internal space

MethodBacilli of MAP

Mixed with buffers or organic solutions

Gentle agitation

MAP

solvent

MAPCentrifuged

Immobilized the antigens in the supernatant

Reacted with serum from JD-negative or JD-positive cow

MAP

JD-positive serumJD-negative serum

Detection of antibody binding by ELISA

Extraction of surface antigens

Before

After

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IgG binding to MAP surface antigens extracted with various organic solutions

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MeOH: methanol, EtOH: ethanol, ProOH: iso-propanol, ACN, acetonitrile, DCM, dichloromethane, CHCl3: chloroform

: JD-positive serum: JD-negative serum: No serum

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MeOH: methanol, EtOH: ethanol, ProOH: iso-propanol, ACN, acetonitrile, DCM, dichloromethane, CHCl3: chloroform

: JD-positive serum: JD-negative serum: No serum : JD-positive serum: JD-negative serum: No serum

Effects of various ethanol concentrations on IgG binding to MAP or MAA antigens

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Diagnostic specificity and sensitivity of EVELISA EVELISA= Ethanol-Vortex ELISA

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Diagnostic specificity and sensitivity of EVELISA

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100% sensitivity !!

The novel test improved the sensitivity of Johne’s disease diagnosis from 30% to 100%.

Part 1 Summary

Currently being evaluated by a veterinary diagnostic company for future (~two years) marketing.

Once in market, the test would have a positive impact on Johne’s disease control efforts.

Part 1 Summary

Angus Journal

Funding

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Part 2

Point-of-care diagnostic device for infectious diseases

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Main idea

Acquire and analyze Multiplex data for diagnosis of infectious diseases.

Achieve this using:

1. a ‘Microfluidic’ Flow Cytometry;

2. Machine Learning analysis of the data.

Why Multiplex analysis?y p y

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Why multiplex analysis?

antigen A

antigen B

antigen CAntibody production against

30% of cases

50% of cases

40% of cases

antigen D

antigen E

against

antigen F

early stage

late stage

antigen Gvirulent strain infection

avirulent strain infection

Antigen A Antigen B Antigen C Total


30% 50% 40% 100%

Improved accuracy of diagnostic tests


antigen A

antigen B

antigen CAntibody production against

30% of cases

50% of cases

40% of cases

Additional information for effective medical treatments

antigen D

antigen E

against

antigen F

early stage

late stage

antigen Gvirulent strain infection

avirulent strain infection

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How we identify candidate antigens?Phage Display Library (PDL)

• Library of genetically modified phage• Each phage clone express a specific sequence of peptide• The diversity of peptides in the PDL is higher than one billion

Peptide

• Quick, easy, and high-throughput screening method• Unbiased --- No preceding knowledge required• Can be used for any infectious disease applications

Library of billions of different peptides

Phage

Advantage

PDL ~ Screening method ~

Antibodies in diseased individuals



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• Antibody binding assay• Read peptide sequence

Preliminary data

Eda et al. 2004 “Selection of peptides recognized by human antibodies against the surface of Plasmodium falciparum (malaria)-infected erythrocytes“

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OK, so Multiplex analysis maybe important.

How can we do such assay?

Microfluidic Flow CytometryMicrofluidic Flow Cytometry is a portable, rapid method that is capable of conducting Multiplex assays in a single run.

A BA BBApplications of flow cytometry:

• Counting and identification of microorganisms



• Host leukocyte counting

• Biomarker detection

• Antibody detection High-throughput analysis is also possible.

Microfluidic Flow Cytometry (contd.)Typical method:

Different size of beads

Dif ferent ant igens on dif ferent size of beads

Mix the beads (* one test tube for mult i ant igen analysis)

Dif ferent size of beads

Dif ferent ant igens on dif ferent size of beads

Mix the beads (* one test tube for mult i ant igen analysis)Mix the beads (* one test tube for mult i- ant igen analysis)

React with blood samples

React then with f luorescing- secondary ant ibody

Flow cytometric analysis of ant ibody binding

Mix the beads (* one test tube for mult i- ant igen analysis)

React with blood samples

React then with f luorescing- secondary ant ibody

Flow cytometric analysis of ant ibody binding

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Microfluidic Flow Cytometry (contd.)

Expected result:

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Antigen BAntigen E

Antigen FAntigen C

Size of beads

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Antigen AAntigen G

Antigen D

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Preliminary data

Fig. 1. Binding of IgG in bovine sera to MAP-antigen-coated magnetic beads.

A. Magnetic beads coated with MAP antigen were treated with no serum (1), JD-negative serum (2), orJD-positive serum (3). IgG molecules bound to the beads were labeled with FITC-labeled secondaryantibody and detected by using a flow cytometer.

B. Magnetic beads coated with MAP antigen were treated with 30 JD-negative sera (1), or 30 JD-positive serum (2). IgG molecules bound to the beads were labeled with FITC-labeled secondaryantibody and detected by using a flow cytometer.

C. Log transformation data of Fig. 3B. Dotted line: JD-negative sera, Solid line: JD-positive sera

D. ROC analysis of Fig. 3B, AUC=0.991 with standard error = 0.012

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Complicated data × number of samples

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OK, so Flow-Cytometric Multiplexanalysis seems doable.

But how can we analyze such complicated data?

Machine learningSome of these methods have been used for classification of complicated data…

Artificial Neural Network(ANN)

Support Vector Machine(SVM)

Ensemble LearningRandom Forest, Logistic boost, Model Tree etc.

Machine learningcan convert complicated data into simple classifications

Complicated Simple

Hidden layerOutput layer

Input layer

Man

Woman

Output layer

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Preliminary dataClinical data from Crohn’s disease (CD) patients and healthy individuals (n=73)

Each sample was tested for antibody binding to Antigens A-F using a flow cytometer.

Randomly selected 20 data were used for training of machine learning algorithms and the remaining data (n=53) were used for accuracy evaluation.

Antigen A Antigens A-F

Machine learning - SVM ANN

Ensemble learningRandomForest LogiBoost ModelTree

t test 0.057 0.009 0.029 - - -

Accuracy (%) 138.6 141.1 147 151.4 152.3 156.3

Part 2 Summary

Encouraging data in each step: high-throughput screening, microfluidics, and machine learning methods.

This approach can be used for development of a diagnostic test for any infectious disease.

A ‘point-of-care’ (hand-held) diagnostic device is desirable for diagnosis, particularly of wild animals.

Oak Ridge National LaboratoryRobert Foote

AcknowledgementsUniversity of Tennessee Knoxville

Cathy ScottGraham HicklingStacey PattersonSteve OliverDilip Patel

University of PennsylvaniaRobert H. Whitlock

The Scripps Research InstituteIrwin Sherman

Supported by:

Robert FooteRobert ShawEdward Uberbacher #2007-3504-18462

United States Department of AgricultureJohn P. BannantineW. Ray Waters

Centers for Disease Control and Prevention

Patricia Wilkins

sep 24 08 fwf seminar eda final

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