PulseNet USA:

The National

Molecular

Subtyping Network

for Foodborne Disease Surveillance

Jennifer A. Kincaid

Centers for Disease Control and Prevention

May 30, 2008

What is PulseNet USA?

„ Established in 1996, The National Molecular Subtyping Network for Foodborne Disease Surveillance

„ National network of state and local public health/food regulatory agency laboratories (USDA, FDA) coordinated by CDC and APHL „ Perform standardized molecular typing of foodborne disease-causing bacteria by Pulsed-field gel electrophoresis (PFGE) Dynamic databases of

DNA “fingerprints” at CDC—available on-demand to participants

The National Molecular Subtyping Network for Foodborne Disease Surveillance

USDA-APHIS

New York Ag. Lab

NEW HAMPSHIRE Milwaukee MAINE FDA-ORA

VERMONT WASHINGTON MI State

MONTANA NORTH DAKOTA MINNESOTA Vet Lab MASSACHUSETTS

Philadelphia

OREGON WISCONSIN Ohio Ag. Lab

NEW YORK RHODE ISLAND

CONNECTICUT IDAHO SOUTH DAKOTA

FDA-ORA WYOMING

IOWA New York City

NEW JERSEY OHIO

NEBRASKA FDA-CVM NEVADA DELAWARE

ILLINOIS FDA-CFSAN

UTAH FDA-ORA MARYLAND

FDA-ORA COLORADO Washington D.C. Las KANSAS VIRGINIA

Vegas KENTUCKY Santa Clara CALIFORNIA MISSOURI County

USDA-AMS

TENNESSEE

OKLAHOMA ARKANSAS SOUTH FDA-ORA ARIZONA CAROLINA

Los Angeles NEW MEXICO

County USDA- FDA-ORA ARS/FSIS/FERN

ALABAMA Orange County

GEORGIA FDA-ORA Area Laboratories

San Diego TEXAS

County LOUISIANA

Florida Ag. Lab PulseNet Headquarters

ALASKA State/County/City/

Tarrant County Dallas County Veterinary/ Agricultural

Laboratories Tampa

Houston PUERTO USDA Laboratories

RICO

HAWAII

FDA Laboratories

West Mountain South Central North Central Midwest Mid-Atlantic Southeast Northeast

PulseNet Objectives

„ To detect foodborne diseases case clusters that may be widespread outbreaks

„ Assist epidemiologists in investigating outbreaks

„ Separate outbreak-associated cases from other sporadic cases (case definition)

„ Assist in rapidly identifying the source of outbreaks (culture confirmation)

„ Act as a rapid and effective means of communication between public health laboratories (rapid alert system) „ Attribution analyses

PulseNet: Communication and QA/QC

„On-line databases „Standardized protocols

„CDC Team postings and molecular size

„Cluster detection standards „Outbreak investigations „QA/QC Manual „Technical support

„Standardized software „Quarterly/Annual Reports

and nomenclature from CDC

„Training workshops (lab “PulseNet News” Newsletter & software) „Tri-annual publication

„PulseNet Website „Certification and

(www.cdc.gov/pulsenet) proficiency testing „Annual update meetings

The Three Basic Elements of PulseNet

2.Data analysis 1.Data acquisition

3. Data exchange

Pulsed Field Gel Electrophoresis

„ PFGE highly discriminatory

„ Universal, relatively simple technique that can be used in most laboratories „ Definitive subtyping method, if highly standardized Current “Gold

Standard” for molecular subtyping

PFGE Process

Bacterial Suspension

Mix with Agarose

Plug Mold

Chemical Lysis and Washing

DNA in Plugs

Restriction Enzymes

Electrophoresis (PFGE)

Documentation (capture gel image)

Data Analysis (BioNumerics)

PFGE Patterns of E. coli O157:H7

Fragment * * * Sizes (in kilobases) 1135 Kb

452.

7 Kb

216.

9 Kb 76.8

Kb

33.3

Kb

DNA “fingerprints”

*Global Reference Standard

PulseNet Laboratory Network

Participating Labs PFGE Patterns & PulseNet National

Demographic Data Databases (CDC)

TAT from receipt to upload: ~4 working days

D base

Management

Local Reports Databases

Local vs. National Databases

Local National

„ Smaller, results from a few „ Larger, results from many

people different labs

„ Smaller representation of „ Larger representation of

patterns patterns

„ Tends to be less diverse „ More diverse

„ Sometimes different Different “common” patterns

“common” patterns than seen than seen locally

nationally or in other regions „ See representation from all

„ Can only see local over, different regions

representation, no national perspective

PulseNet Activity* *as of May 15, 2008

Over 325,000 PFGE patterns or DNA “fingerprints” submitted to PulseNet databases since 1996 Database Entries Patterns submitted

Submitted

1st Enzyme 2nd Enzyme

Campylobacter 5,453 5,400 1,834

E. coli 31,634 30,262 16,674

Listeria 9,226 9,007 8,002

Salmonella 190,298 188,241 28,998

Shigella 32,922 32,222 2,156

V. cholerae 303 282 272

V. parahaemolyticus 37 37 37

Y. pestis 1,976 1,975 33

PulseNet Activity, 1996-2007

PFGE patterns submitted to PulseNet Databases

70000

600

00

500

00

400

00

300

00

200

00

100

00

0

1996 1997 1998 1999 2000 2001* 2002 2003 2004 2005 2006 2007

PulseNet is a cluster detection tool, not an outbreak detection system „ A PulseNet CLUSTER is a group of patterns that are found indistinguishable by PFGE

„ CLUSTERS of cases identified by PulseNet are investigated by epidemiologists „ If epidemiologic links are found between cases, the cluster is classified as an OUTBREAK

PulseNet Data Analysis: The Cluster Search •Patterns submitted

electronically

• 60- or 120-day cluster search performed

•Visually compare indistinguishable

•Patterns and clusters are named by CDC

•Clusters communicated to the foodborne epidemiologists

Cluster of indistinguishable patterns

Cluster Detection in PulseNet: PulseNet Workspace on CDC Team Subject:0802LACJPX-1c (JPXX01.0088)_LAC_Typhimurium

LAC has a cluster of 3 Typhimurium isolates that are indistinguisable by XbaI and BlnI. The collection dates are from 12/10/2007 to 1/10/2008. Epi under investigation. The isolate numbers are: LAC_Z19766 2 year old boy, no travel LAC_Z19999, 43 year old female, no travel LAC_Z20072, 60 year old female, no travel We have seen this pattern before in LAC. Our epi contact is (name and phone)

LAC08008PN.BDL

Posted: 05 March 2008 09:52 AM

Cluster Detection

„ Local „ National

„ Perform cluster search „ Perform cluster search

within local database within national database or

respond to local CDC „ Compare to national Team posting database

„ Verify isolate information „ Post message to CDC

„ Name patterns Team „ Check band markings

„ Begin epi investigation „ Assign cluster code „ Look at pattern frequencies/trends

A large outbreak in one place may be obvious

Detected and investigated locally

A dispersed outbreak in many places may be difficult to detect, unless…

„ Detect outbreaks centrally (or locally) through surveillance (widely dispersed, organism too common to notice small increase, identify related cases) „ Investigation coordinated centrally „ Distinguish from concurrent sporadic cases „ Provide microbiological evidence of sources of outbreaks

Common CDC Epi Requests

„ Past trends or past association of cluster/outbreak pattern „ Pattern Frequency graphs „ PulseNet Cluster Log „ Cluster/Outbreak codes „ Updated line lists

„ Often daily for very active or high profile outbreaks „ Additional information for rumor logs and conference calls

CDC Epi Requests: Additional Info

„ Second enzyme results

„ VetNet matches

„ USDA-ARS database

„ Connection to VetNet network established in 2007

„ All cluster patterns identified by PulseNet are routinely checked against VetNet

„ International matches

„ Connection to PulseNet Canada databases established in 2007

„ All cluster patterns identified by PulseNet are routinely checked against PulseNet Canada databases

„ PulseNet participants abroad (PulseNet Latin America, Europe, Asia Pacific, and Middle East)

„ Email alerts to PulseNet International

Interpreting Patterns within Outbreaks

„ For surveillance (cluster detection), allow no differences for possible matches „ One-band differences are so

common in large databases that virtually all submissions would be part of a cluster „ For outbreaks (case definition), differences may be acceptable

„ May be necessary for shigellosis infections (person to person)

„ If the isolates with different patterns have epidemiologic links, assign different pattern names but include in reports (give same outbreak code)

Additional Restriction Enzymes

„ Adds to the discrimination of all organisms when using PulseNet protocols „ makes cluster detection

more precise „ Can limit the need for epi follow-up of likely unrelated cases „ Although it adds to the cost

of PulseNet surveillance, running fingerprint profiles of both enzymes on the initial gel is often cost-effective and saves time

Additional Restriction Enzymes

Example

Salmonella Montevideo

Indistinguishable 1st enzyme patterns: need 2nd enzyme results to distinguish clusters

Examples of PulseNet Involvement in Foodborne Outbreaks

E. Coli O157:H7 Outbreak in Colorado, July 2002 „ Outbreak pattern was rare

„ Closely related to a very common pattern „ 34 cases in 11 states „ Outbreak strain found in

ground beef from meat processing plant by USDA/FSIS „ Outbreak stopped after recall of 18.4 million pounds of ground beef products from a plant

1993 Western States E. coli O157 Outbreak 70

60 outbreak detected 1993 726 ill, 4 deaths

5

0

4

0

3

0 20

39 d

10

0 1 8 15 22 29 36 43 50 57 64 71

Day of Outbreak

2002 Colorado E. coli O157 Outbreak 70

60 50 40 outbreak detected 2002 44 ill, no deaths

30 20 10 18 d

0 1 8 15 22 29 36 43 50 57 64 71

Day of Outbreak

S. Tennessee outbreak numbers (April 11, 2007) „ 567 cases

„ 47 states involved „ No international cases „ Median age 51 years (<1- 95 years) „ 73 % females „ No deaths, 20% hospitalized „ Among females: 1/3 UTI

Salmonella Tennessee Cases by State, (N=563)

1-9 cases 10-19 cases 20+ cases

*Slide courtesy of EIS officer Anandi Sheth

Peanut Butter Samples

„ 34 positive samples of peanut butter

„ Production dates from contaminated jars: 7/27/06 - 1/29/07

„ 2 of the 3 outbreak patterns were found in the peanut butter

Notable Outbreaks

„ Salmonella Wandsworth and Typhimurium: Veggie Booty

„ Salmonella Heidelberg: Hummus „ Salmonella I 4, 5, 12:i:- : Pot pies „ Salmonella Enteritidis: Chicken Cordon Bleu „ E. coli O157: ground beef „ E. coli O157: frozen pepperoni pizza „ Listeria monocytogenes: milk

Examples of Other Database Uses

Database Uses: Pattern Frequencies

Long-term surveillance to aide in active

surveillance—is this a true increase?

Database Uses: Pattern Trends

Long-term surveillance to see if a

pattern is still seen as commonly in the database

Database Uses: Attribution Analysis

Long-term surveillance to see

if certain patterns may be associated with specific foods

Legend

CDS 1

CDS 2

CDS 3

CDS 4

CDS 5

PulseNet S. Newport Attribution Analysis

Figure 13: Proportional Distribution of Illnesses by food

commodities using three attribution analysis methods

30.0%

25.0% 20.0%

Method 1

15.0% Method 2

Method 3 10.0%

5.0% 0.0%

Food Commodities

Method 1: Considering three major clusters Method 2: Considering major clusters and sub-clusters Method 3: Considering sub-clusters with no non-human isolates of unknown

PulseNet International…

A Family of Networks

Why are we going international with PulseNet?

„ We live in a global community

„ Foods produced on one continent may be consumed and cause disease in another - a global issue

„ Need an effective global early warning system „ Global networking to utilize scarce public health resources effectively „ Respond to other emerging

infectious diseases or acts of bioterrorism

PulseNet International Collaborations

„ Outbreak investigations

„ Laboratory and Analysis Troubleshooting „ Protocol development and validation

„ Vibrio cholerae

„ Vibrio parahaemolyticus

„ Development, Evaluation and Validation of Next Generation Typing Methods „ Canada, China, Hong Kong,

Japan and U.K. MLVA subtyping protocol for E. coli O157

PulseNet Challenges and Future Improvements „ Enhance support for outbreak investigations

„ New faster methods

„ Reduce the time it takes for isolates to go from clinical lab to the state/local public health lab „ Reduce the time for PFGE testing of isolates

„ Expectation: ~4 days from receipt of isolate to upload to the national database(s)

„ Achieve real-time detection of clusters

„ Improve communication between laboratorians and epidemiologists at the state and national levels

PulseNet Challenges and Future Improvements „ Strengthen collaborations

with food industry „ VetNet (USDA-ARS)

„ Currently collecting Salmonella and Campylobacter NARMS isolates

„ Attribution analysis „ Protocols

„ Vibrio parahaemolyticus

„ Protocol has been developed and database scripts were included in the latest version of the MasterScripts (March 2007) The number of

submissions to PulseNet continues to rise…..

New Subtyping Methods

„ PFGE data is complex

„ Need for simple, non-image based as discriminatory supplementary methods „ Sequencing based methods

„ Multi Locus Variable number of tandem repeats Analysis (MLVA)

„ SNP analysis

MLVA Analysis

„ Sequence-based subtyping

„ Can further discriminate common PFGE patterns through highly variable target sequences „ Data may be epidemiologically more relevant than PFGE data

„ Results more straightforward

Don’t have the same interpretation issues as with PFGE band marking

Thank you for your attention

The findings and conclusions in this presentation are those of the author and do not necessarily represent the views of the Centers for Disease Control and Prevention.