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White Paper: Current and Future Development and Use of Molecular Subtyping by 

USDA-FSIS

USDA-FSIS Subtyping Work Group: Nate Bauer1

, Peter Evans2, Beth Leopold3, 

Jeoffrey Levine1, and Patricia White1

Executive Summary

This White Paper describes current Food Safety and Inspection Service (FSIS) 

subtyping procedures and provides examples of how the data are used. The document 

also discusses subtyping methods under development (one to four years to 

implementation), and ideas for the future (five or more years to potential 

implementation). It was created to inform FSIS program offices and stakeholders about 

the availability, uses, strengths, and limitations of current and future methods from the 

perspective of the laboratorians and epidemiologists, and to provide Agency decisionmakers with information necessary to make informed, science-based decisions related 

to routine monitoring programs and illness investigations, and to evaluate and build 

FSIS capabilities.

FSIS is the public health agency of the U.S. Department of Agriculture (USDA) 

responsible for ensuring that the nation’s commercial supply of meat, poultry, and 

processed egg products is safe, wholesome, and correctly labeled and packaged. FSIS 

conducts microbiological sampling programs to verify that establishments are controlling 

foodborne hazards. All FSIS microbial testing procedures include cultural isolation and 

isolate characterization to the species level. In addition, one or more isolates from each 

sample are subtyped. 

FSIS currently uses Pulsed-field Gel Electrophoresis (PFGE) and Multi-Locus Variable 

Number of Tandem Repeat (VNTR) Analysis (MLVA) to identify outbreaks, trace 

contamination within an establishment, and investigate potential harborage sites. 

 

1 Office of Public Health Science (OPHS) 

2 FDA Center for Food Safety and Applied Nutrition (CFSAN)

3 FSIS Office of Investigation, Enforcement and Audit (OIEA) 
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Currently, PFGE is considered the best method available for public health laboratories 

to detect and track outbreaks. MLVA may help to further differentiate subtypes during 

outbreak investigations. PFGE and MLVA data must be used in conjunction with 

epidemiological evidence, environmental assessments, trace-back activities, and other 

information collected during outbreak investigations.

Currently the Outbreak Section of the Eastern Laboratory (OSEL) is responsible for 

performing all FSIS subtyping analyses. OSEL communicates PFGE pattern information 

to the national PFGE database maintained by the Centers for Disease Control and 

Prevention (CDC), where they are compared to patterns from clinical and food isolates 

provided by local, state, and federal public health and agricultural agencies and by 

academia. PulseNet is an inter-laboratory network consisting of CDC, state, and local 

health departments, and food regulatory agencies (FDA and FSIS) which share PFGE 

pattern data for foodborne pathogens. PulseNet scientists continuously evaluate the 

pattern databases, and review, oversee quality assurance, and develop new subtyping 

methods. 

Most PFGE pattern-related information communicated within FSIS is related to outbreak 

investigations, but PFGE pattern information also is communicated to Office of Field 

Operations (OFO) district offices when there is evidence of recurring subtypes or 

serotypes of concern in an establishment. The Agency plans to provide this information 

routinely to federally regulated establishments. FSIS has implemented a Public Health 

Information System (PHIS), with the capability to detect trends in subtype data.

 

FSIS has a unique authority to regulate establishments engaged in the production and 

distribution of meat, poultry, and egg products. Subtyping data can allow FSIS to make 

scientifically-informed decisions and allocate resources. In general, subtyping 

information may be used to: 

1. Detect and track outbreak clusters or harborage within an establishment;

2. Assess sample-specific risk of illness associated with a product, a process, or an 

establishment;

3. Provide highly specific data for risk assessments and attribution studies;
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4. Define a regulatory adulterant with greater accuracy; and

5. Improve the accuracy and efficiency of current subtyping methods. 

FSIS collaborates with USDA Agriculture Research Service (ARS), CDC, and FDA to 

develop new subtyping and detection assays. FSIS worked with ARS groups to develop 

PCR-based screening assays for virulence genes of non-O157 STEC and a single 

nucleotide polymorphism (SNP)-based typing assay for Listeria monocytogenes (Lm). 

The Agency implemented a molecular serotyping method for Salmonella developed by 

CDC. FSIS participates in meetings designed to bring public health agencies together to 

discuss the development and implementation of subtyping methods, including next 

generation methods which may yield useful data from mixed cultures such as 

enrichment broths. These meetings foster inter-governmental collaboration and 

coordination.
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Section Page

Executive Summary 1 

Abbreviations 6 

1 Introduction 8 

1.1 Purpose 8 

1.2 Scope 8 

1.3 Target Audience 9 

2 Background 10 

2.1 Subtyping Definition 10

2.2 Subtyping Methods for Bacteria 10

2.3 Validating the Performance of Subtyping Methods 13

2.4 The Relationship of Subtypes to Virulence 15

2.5 Using Subtyping Data to Determine Familial Relationships 

Among Bacteria 16

2.6 Laboratory Networks for Sharing Subtype Data: PulseNet 

and VetNet 19

2.7 FSIS’ Sampling and Subtyping Programs 20

2.7.1 Antimicrobial Resistance Analysis (ARA) for Salmonella 22

2.8 FSIS Current and Potential Use of Subtyping Data 22

3 Current Use of Subtyping Methods by FSIS 26

 3.1 Pulsed-Field Gel Electrophoresis (PFGE) 26

3.1.1 PFGE Laboratory Method and Pattern Normalization 26

3.1.2 FSIS PFGE Data Analysis and Data Monitoring 29

 3.1.3 PFGE on FSIS PR/HACCP Raw Product Salmonella Isolates 32

3.1.4 Distinguishing PFGE Patterns--Linking Clinical and Product Isolates 33

3.1.5 Using PFGE in Outbreak Investigations 37

3.1.6 A Case Study Using PFGE FSIS Salmonella Multiple-drug 

 Resistant (MDR) Outbreak Investigation 39

3.1.7 Using PFGE to Detect Lm Harborage within Establishments 40

3.1.8 Using PFGE to Refine Attribution Models 41

3.1.9 Using PFGE in FSIS’ Salmonella Verification Testing Program 42

3.1.10 Campylobacter PFGE 43

3.2 Multiple Locus Variable Number Tandem Repeat Analysis (MLVA) 45

3.2.1 Using MLVA in Outbreak Investigations 47

3.2.2 A Case Study Using MLVA: FSIS E. coli O157:H7 

 Outbreak Investigation 49

3.2.3 Using MLVA in Salmonella Enteritidis (SE) Outbreaks 52

 3.2.4 Caution Needed to Interpret MLVA and PFGE Data 52

3.3 Subtyping Methods for Non-O157 STEC 53

3.4 Molecular Confirmation of E. coli O157:H7 54

3.5 Molecular Serotyping of Salmonella 54

4 Subtyping Technologies Being Developed or Under Evaluation 

(Short Term, 1 - 4 Years) 55

4.1 MLVA for Non-O157 STEC, Lm, S. Newport, and other Salmonella

Serotypes 56 

4.2 Public Health Information System (PHIS) 57 

4.3 Targeted Multilocus Genotyping (TMLGT) for Lm 57
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5 Subtyping Technologies of the Future (Five or More Years to Implementation) 58

5.1 High-Throughput Sequencing of Bacterial Genomes 59

5.2 STEC 63

5.3 Campylobacter 67

5.4 Listeria monocytogenes (Lm) 69

5.5 Salmonella 72

5.5.1 Use of Subtyping to Track On-Farm and Illness Related Clusters 73

5.5.2 Use of Subtyping to Support Salmonellosis Attribution Estimates 73

5.5.3 Use of Subtyping to Differentiate Pathogenic Subtypes of 

 Salmonella 75

6 Conclusions 77

7 Acknowledgements 81

8 Appendices 83

A: PFGE Theory 83

B: PFGE Laboratory Method: From Isolate to Gel Image 88

C: PFGE Data Workflow and Cluster Investigation 89

D: PFGE Pattern Record Metadata in OSEL Database, PulseNet, and 

VetNet 90

E: Confirmed and Unconfirmed PFGE Patterns and Matches 91

F: Descriptive Terms for PFGE Pattern Frequency 92

G: MLVA Theory and Laboratory Method 95

H: MLVA Protocol: From FSIS Isolate to CDC Analysis 97

I: Molecular Serotyping of E. coli O157:H7 98

J: Molecular Serotyping of Salmonella 99

K. Salmonella Serotypes Reportable by Molecular Detection 100

9 References 101
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Abbreviations 

AES Applied Epidemiology Staff

AFLP Amplified Fragment Length Polymorphism 

APHIS Animal and Plant Health Inspection Service, USDA 

AMS Agricultural Marketing Service, USDA

ARA antimicrobial resistance analysis 

ARS Agricultural Research Service, USDA

AST antimicrobial susceptibility testing 

CDC Centers for Disease Control and Prevention

DNA deoxyribonucleic acid 

EHEC enterohemorrhagic E. coli

ELISA enzyme-linked immunosorbent assay

ERIC Enterobacterial Repetitive Intergenic Consensus

FDA-CFSAN Food and Drug Administration - Center for Food Safety and Applied 

Nutrition

FDA-CVM Food and Drug Administration - Center for Veterinary Medicine

FSIS Food Safety and Inspection Service, USDA 

HUS hemolytic uremic syndrome

ID identification

Lm Listeria monocytogenes

MALDI-TOF matrix-assisted laser desorption/ionization time of flight 

MDR multiple-drug resistant

MLG Microbiology Laboratory Guidebook

MLGT multi-locus genotyping

MLST multi-locus sequence typing 

MLVA multi-locus VNTR analysis

NACMCF National Advisory Committee on Microbiological Criteria for Foods 

NARMS National Antibiotic Resistance Monitoring System 

NCBI National Center for Biotechnology Information 

NM non-motile

NRTE not ready-to-eat

NVSL National Veterinary Services Laboratories (APHIS lab in Ames, Iowa)

ODIFP Office of Data Integration and Food Protection, FSIS 

OFO Office of Field Operations, FSIS 

OIEA Office of Investigation, Enforcement and Audit, FSIS 

OPHS Office of Public Health Science, FSIS 

OPPD Office of Policy and Program Development, FSIS 

OSEL Outbreaks Section of the Eastern Lab, OPHS 

PCR polymerase chain reaction 

PFGE pulsed-field gel electrophoresis 

PHIS Public Health Information System 

PHLIS Public Health Laboratory Information System 
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PR/HACCP Pathogen Reduction/Hazard Analysis and Critical Control Point 

RTE ready-to-eat 

SE Salmonella Enteritidis

SNP single nucleotide polymorphism 

SPI Salmonella pathogenicity island 

ST sequence type

STEC Shiga toxin-producing E. coli

TMLGT targeted multilocus genotyping

VNTR variable number tandem repeat

WGS whole genome sequencing

WHO World Health Organization

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1 Introduction

1.1 Purpose

This White Paper provides detailed information about the biology and technology 

associated with subtyping procedures, the interpretation of subtyping data during 

outbreak investigations, and the potential uses of subtyping data generated by Food 

Safety and Inspection Service (FSIS) laboratories. FSIS managers use the information 

to enhance FSIS’ food safety mission and to support the adoption of new procedures. 

The paper combines and updates two FSIS internal documents:

1. “Proposed Protocol for the Use of Pulsed-Field Gel Electrophoresis (PFGE) 

Data from FSIS Isolates” was a technical document created in 2006 which 

described how PFGE patterns are analyzed, how matches are identified and 

investigated, and how FSIS investigators interpreted data.

2. “Analysis of Molecular Subtyping Methods for FSIS Regulatory Testing: The 

Present and Future of FSIS Regulatory Subtyping” was created in 2008. It 

provided information about current subtyping procedures, how the data were used, 

methods in the immediate pipeline (projected implementation 2009-2013), and 

proposals for new methods for future work (potential implementation 2014 or later). 

In addition, Withee and Dearfield discussed the potential advantages and challenges 

posed by genomic technologies to FSIS functional activities including outbreak 

investigation, microbial risk assessment studies and attribution studies (Withee and 

Dearfield 2007). The National Advisory Committee for the Microbiological Criteria of 

Food (NACMCF) also have discussed FSIS potential use of subtyping technologies

(NACMCF 2010). 

1.2 Scope

This White Paper focuses on subtyping procedures4 for bacterial pathogens of primary 

concern to FSIS. Pathogens of concern include those specifically mentioned in FSIS 

 

4 Also referred to as methods and assays in this document.
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regulations as well as those subject to testing, which cause foodborne illness from 

meat, poultry, or egg products. Recent regulatory changes include FSIS Pathogen 

Reduction-Hazard Analysis and Critical Control Point (PR/HACCP) verification testing 

for Campylobacter and Campylobacter performance standards (FSIS 2010) and 

verification procedures, including sampling and testing of beef trim and other raw 

ground beef products components for non-O157 STEC (FSIS 2011d). Sections 2 and 5 

of this document provide a generic overview of subtyping methods and how subtyping 

data could be used by a regulatory agency. Sections 3 and 4 focus on the development 

of subtyping methods and the use of subtyping data to advance FSIS’ mission.

1.3 Target Audience

There are three target audiences. The first is internal to FSIS. Staff and managers from 

specific program areas (Office of Public Health Science (OPHS), Office of Field 

Operations (OFO), Office of Data Integration and Food Protection ODIFP), Office of 

Policy and Program Development (OPPD), and Office of Investigation, Enforcement and 

Audit (OIEA)), as well as the Office of the Undersecretary for Food Safety, may wish to 

learn more about the biology and technology associated with various subtyping 

procedures, the interpretation of subtyping data during outbreak investigations, and the 

potential uses of subtyping data generated by FSIS laboratories. Another audience is 

FSIS partner federal agencies, including FDA, Centers for Disease Control and 

Prevention (CDC), and the Agricultural Research Service (ARS), which develop 

subtyping methods and may be interested in learning more about FSIS’ data and 

technology needs. A third audience is FSIS stakeholders, including industry and 

consumer groups, who may be interested in learning about FSIS’ present and future 

ability to generate, interpret, and respond to subtype data. This paper strives to present 

the information in a clear, concise, and non-technical language for all audiences. This 

version will be edited for submission to a peer-reviewed scientific journal.

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2 Background

This section provides a working definition for subtyping, and provides information about 

subtyping methods (including methods not used currently by FSIS) and procedures to 

evaluate subtyping method performance. This section also describes how subtype data 

is used in epidemiological investigations, and how it may be used to classify strains with 

virulence factors and to determine the familial relationships of bacteria. Lastly, it 

describes FSIS sampling programs and how FSIS generates, uses, and shares subtype 

data from its sampling programs. 

2.1 Subtyping Definition

A bacterial subtype is a group of organisms with the same attributes (characteristics) 

within a larger type5

. Subtyping procedures identify common attributes which assign it to 

the larger type and different attributes to distinguish it from other subtypes. Typically, 

the term is used to define groups below the level of bacterial species. Bacteria 

reproduce asexually when a parent cell divides to produce two daughter cells. The 

descendants of each daughter cell are considered a lineage, which means they are all 

descendants from a common ancestor. Mutations are errors in the deoxyribonucleic 

acid (DNA) sequence that accumulate in each lineage independently, which is referred 

to as vertical transfer. In bacteria, genetic material, with its associated mutations, also 

can be exchanged between lineages, a phenomenon referred to as lateral or horizontal 

transfer. Mutations distinguish individuals within and among lineages. “Subtype” refers 

to distinguishable entities within and between lineages. 

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2.2 Subtyping Methods for Bacteria

A large number of procedures, spanning the history of classical and molecular 

microbiology, have been used for subtyping bacteria. Biochemical, metabolic, and 

serological attributes are measured using traditional methods, many of which are simple 

 

5 Subtype is also referred to as a group or clade in this paper.
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to perform and very powerful. Growth assays are used to measure bacterial growth in 

the presence of a variety of compounds that stimulate or inhibit bacterial growth. These 

assays can be used to develop a comprehensive growth phenotype to identify and 

characterize bacterial isolates. For example, antimicrobial resistance analysis (ARA), 

also referred to as antimicrobial susceptibility testing (AST), is a specialized type of 

growth assay to evaluate resistance to specific compounds, including antibiotics. 

Another growth assay, based on the failure to rapidly ferment (digest) a specific sugar 

called sorbitol, is used to distinguish a pathogenic subtype of E. coli, O157:H7 from 

other pathogenic subtypes. Additional typing of E. coli subtype O157:H7 is based on its 

inability to ferment glucoronides. A variety of solid plating media were developed to 

identify strains that slowly ferment sorbitol and glucoronide. Phenotype microarrays 

(developed by Biolog) are used to screen bacteria simultaneously for growth in the 

presence of hundreds of compounds. 

Serology is another commonly used method to type bacteria. An extensive set of 

antisera developed against somatic (O), flagellar (H), and virulence (K, Vi) antigens of 

E. coli and/or Salmonella allowed investigators to distinguish thousands of E. coli and 

Salmonella serotypes6

. Serological methods still are used, and latex-coupled antisera 

are very useful for identifying the major serotypes of concern. However, typing for the 

lesser known serotypes is both time and labor intensive and maintaining a large panel 

of typing antisera is costly and not readily available to many labs. 

Separation methods also are used to develop new bacterial subtyping methods. For 

instance, early evolutionary studies of bacteria lineages relied on separation of cellular 

enzymes using electric fields, a procedure called multilocus enzyme electrophoresis 

(MLEE). Newer technologies, such as mass spectrometry (developed by Abbott Labs 

and Sequenom), which can distinguish molecular fragments and estimate nucleic acid 

sequence, and Raman spectroscopy (developed by River Diagnostics), which is based 

on the scattering of monochromatic light (e.g., a laser) when it encounters cellular 

 

6

A serogroup is defined by the O-antigen, and a serotype is defined by a combination of O- and Hantigens
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biomass (nucleic acids, carbohydrates, fatty acids, and proteins), are used to identify 

and subtype bacteria.

The growing importance in molecular biology in the 1970s and the invention of the 

polymerase chain reaction (PCR) in the 1980s led to the large number of molecular 

subtyping methods available today. These can be distinguished as fragment-based and 

sequence-based methods. 

Fragment-based methods rely on the separation of PCR-amplified nucleic acid 

fragments by molecular size7, such as amplified fragment length polymorphism (AFLP), 

Enterobacterial Repetitive Intergenic Consensus (ERIC)-PCR, allele-specific PCR, and 

multi-locus variable number tandem repeat (VNTR) analysis (MLVA). Other methods 

rely on enzymatic restriction of DNA fragments. These include PFGE, ribotyping, and 

optical mapping. Optical mapping of bacterial genomes (developed by Opgen) can 

detect large scale changes like inversions, insertions, deletions, and duplications, which 

are not detected with assays based on short sequences. Genome Sequencing 

Scanning (developed by PathoGenetix) is a rapid method based on DNA fragment size 

and the presence and location of short sequence repeats, which can detect subtypes 

directly from complex biological samples without the need for culture or primer design. 

Sequence-based typing methods are based on the differences in short sequences 

(multi-locus sequence typing (MLST), SNP, and microarray) or entire genomes. 

Analysis of sequence data typically focuses on specific informative positions, which are 

a subset of all differences (Section 2.5). Solid phase or liquid suspension microarrays 

can simultaneously assay samples for presence or absence or relative expression level 

(e.g., the quantity of RNA in a cell) for hundreds to millions of genes or sequences, and 

to detect SNPs. Nucleic acid sequences are determined using a variety of chemical 

analyses, many of which have been developed in the past few years. Synthetic beads 

(microspheres) and novel chemistries are used to create nucleic acid libraries consisting 

 

7 Under the influence of an electric field, nucleic acid molecules, which carry a net negative charge, 

elongate and migrate through a sieve-like matrix at a rate that is inversely proportional to their molecular 

size. Larger fragments migrate through the field at a slower pace than smaller fragments.
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of beads that contain 1,000,000 or more of an identical DNA fragment. DNA from 100 to 

100,000 beads from these libraries can be sequenced or scored for SNP using flow 

cytometry (e.g., Luminex xMAP ) or a series of charge-coupled device camera images 

(e.g., Roche 454 GS FLX). This process provides extremely robust data for sequencebased studies. Next generation sequencing methods are discussed in section 5.1.

Currently available subtyping methods require a culturally-pure isolate because the 

presence of multiple subtypes in the sample would create a confusing pattern or signal. 

Cultural isolation is time- and resource-consuming, and since a small number of 

available pure colonies are picked for further analysis, this step may preclude subtyping 

of slow-growing lineages, which can provide misleading data for foodborne pathogen 

surveillance programs (Singer et al. 2009). Importantly, manufacturers of diagnostic 

tests for infectious agents are developing formats that do not require cultural isolation 

therefore making it impossible to subtype strains from positive samples. This trend in 

diagnostic test design will have an impact on public health surveillance systems for 

bacterial enteric pathogens (Cronquist et al. 2012). New technologies, including 

metagenomics (Section 5.1), may lead to the development of methods capable of 

providing useful subtype data without the need for a cultural isolation step.

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2.3 Validating the Performance of Subtyping Methods

Validation is the process of evaluating the performance of methods, including subtyping 

methods. Guidelines for bacterial subtyping methods evaluation and validation have 

been published by the European Society for Clinical Microbiology and Infectious 

Diseases (van Belkum A. et al. 2007). The authors suggested the following performance 

criteria for evaluating subtyping methods:

1. Stability: the attributes targeted by the subtyping method should not change during 

lab culture

2. Typeability: the method should assign a subtype to all isolates

3. Discriminatory power: the method should distinguish (assign a different subtype to) 

isolates from different lineages
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4. Epidemiological concordance (relevance): the method should group isolates 

associated with an outbreak or epidemic while excluding unrelated lineages, such as 

concurrent, sporadic case-patients

5. Reproducibility: the method should yield the same results when tested on multiple, 

independent occasions, preferably in different laboratories or by different operators

These criteria should be viewed as guidance, especially when evaluating the 

performance of a new subtyping method against an established method like PFGE. In 

addition to these criteria, practical aspects, such as cost of equipment and reagents, the 

level of technical expertise needed to operate the test, time to result, and compatibility 

with older data systems, should be considered before investing in a specific subtyping 

capability. The relative importance of each criterion should be weighed against specific 

informational needs and public health goals. For example, Salmonella serotyping would 

be intended to place strains into categories that indicate relative risk, whereas PFGE or 

MLVA would be intended to distinguish outbreak and non-outbreak case-patients. 

Therefore, PFGE and MLVA would be expected to have maximum discriminatory power 

and epidemiological concordance, but Salmonella serotyping would not. Some high 

frequency genetic mutations would be expected to affect some subtyping methods and 

not others. For example, the loss or gain of horizontally-transferrable DNA (e.g., 

bacteriophage, insertion elements, and plasmids) in humans or animals, or during 

culture, can result in changes to a PFGE pattern, but may not affect a MLVA or SNP 

subtype.

Analysts performing subtyping to support epidemiological investigations should 

demonstrate proficiency with the methods through an externally and routinely 

administered program. For example, CDC PulseNet administers a proficiency program 

for PFGE and MLVA. Each analyst at Outbreaks Section of the Eastern Lab (OSEL) 

completes proficiency sample testing to be certified for the analyses that he or she 

performs.
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Pathogen isolate panels are needed to assist in developing and validating subtyping 

assays. FSIS shared L. monocytogenes (Lm) and E. coli O157:H7 isolate panels with 

ARS researchers through an ARS-FSIS material transfer agreement, and FSIS 

Salmonella isolates were shared with Food and Drug Administration - Center for Food 

Safety and Applied Nutrition (FDA-CFSAN) through an ARS-FDA-CFSAN material 

transfer agreement. These collections are housed at the ARS facilities in Peoria, IL, and 

Clay Center, NE, and at a CFSAN facility in College Park, MD. The isolates could be 

shared with CDC and FDA researchers (and possibly other parties) for the purpose of 

developing and validating methods.

Organizations using subtyping data to support regulatory decisions should consider the 

standards for admissibility of scientific evidence. Under the current Federal standard, 

trial judges are responsible for monitoring the quality and relevance of scientific 

evidence. Judges should consider (1) testability, (2) peer-review, (3) error rate, and (4) 

general acceptance of a method or technique before finding that an opinion is more 

likely than not to be valid and reliable (Wilson et al. 2013). 

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2.4 The Relationship of Subtypes to Virulence

Virulence is defined as the severity of the disease after infection, as measured by the 

portion of case-patients developing severe disease or dying after exposure. Subtyping 

can be used to estimate the potential virulence of a bacterial isolate from a food sample. 

If the subtype pattern of a bacterial isolate is indistinguishable (or very similar) to the 

pattern commonly isolated from ill people, it would be reasonable to conclude that the 

bacterial lineage represented by the pattern can cause disease and may be virulent. 

The subtype may contain specific attributes (which may be genes related to the cellular 

mechanisms responsible for virulence) associated with virulent lineages. For example, 

E. coli isolates containing the Shiga toxin gene may be considered more virulent than 

those without the gene since the Shiga toxin plays a central role in destroying cells 

lining the gastrointestinal tract and in the kidneys, resulting in disease and death in 
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susceptible individuals. In some strains, multiple virulence factor genes are linked 

together in the genome as “pathogenicity islands.” Salmonella serotypes, which are 

based on the determination of surface molecules (O- and H-antigens), are not believed 

to be directly responsible for virulence. However, certain Salmonella serotypes are 

isolated more commonly from ill persons than other serotypes. According to CDC Public 

Health Laboratory Information System (PHLIS), the top 20 Salmonella serotypes from 

human sources accounted for 72.8% of all cases in 2009, while all other serotyped 

isolates accounted for 15.5% (CDC 2012a). Other DNA sequences, not directly linked to 

serotype or virulence genes, may similarly be used to identify virulent lineages of 

bacteria.

In some situations, pathogens with indistinguishable sequences or subtypes have been 

associated with multiple unrelated outbreaks over months and years, and across 

multiple jurisdictions and countries. These outbreaks, which are related by a common 

pathogen lineage, have been called epidemic or pandemic clones (Chen et al. 

2007;Kathariou et al. 2006; Lomonaco et al., 2012; Pang et al. 2007;Prager et al. 

1999;Ridley and Threlfall 1998) and could represent subtypes containing genes that act 

together to confer enhanced survival, transmissibility, or virulence. Careful study of 

these clones may identify individual genes associated with traits that greatly influence 

risk of illness or outbreaks, referred to as public health risk. Regulatory agencies may 

wish to perform enhanced surveillance for epidemic or pandemic clones, which may 

exhibit greater virulence and resistance to antimicrobials, or which may pose a higher 

risk of contaminating foods or causing illnesses or outbreaks.

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2.5 Using Subtyping Data to Determine Familial Relationships among Bacteria

Subtyping data refers to scientific data that is capable of distinguishing groups by 

directly or indirectly detecting mutations. Subtyping data can provide information about 

the underlying familial (evolutionary) relationship between clades. A clade is defined as 

a group of bacteria that have a common ancestor and therefore share mutations. By 

studying these mutations, we can not only visualize the clades, but also estimate the 
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familial relationships between clades, analogous to distinguishing first, second and third 

cousins. In a general sense, clades distinguished by a larger number of mutations are 

more distantly related. However, the association of mutations and familial relationships 

is complicated by a number of factors: 1) differences in the rate that mutations are 

created and incorporated into the population8

, 2) passage of mutations between clades 

(horizontally-transmitted mutations), and 3) very large mutations, called insertion and 

deletion mutations (indels), which are created by unpredictable events such as the gain 

or loss of horizontally-transferred DNA (e.g., bacteriophage, insertion elements, and 

plasmids).

Phylogenetic analysis is the study of familial relationships based on shared and 

distinguishable data. Phylogenetic analysis can be performed using any type of 

attribute, provided it is inherited and affected by mutation. To perform phylogenetic 

analysis, first the data must be properly aligned so that characters (e.g., sequences or 

PFGE fragment bands) from one lineage are properly compared to the equivalent, codescending characters from the other lineages. Next, distance, maximum likelihood, or 

parsimony methods are applied to the aligned data to produce a similarity matrix which

summarizes the occurrences of matching and non-matching characters among the 

compared isolates. A tree-like diagram (dendrogram) is created to visually represent the 

familial relationships among these data. A dendrogram is similar to a family tree. 

However, unlike a family tree, data from the most recent generation is used to estimate 

familial relationships among the older generations. Each point where the dendrogram 

splits is called a node and represents a hypothesized common ancestor for the clade. 

Members of a clade share more mutations than non-members and are thus believed to 

be more related to each other than to non-members. The cohesiveness of a clade can 

be tested by repeating the phylogenetic analysis hundreds or thousands of times 

(referred to as bootstrap analysis). The proportion of analyses leading to the formation 

of the clade is directly related to clade cohesiveness. The rarity and significance of the 

data can be evaluated using databases containing appropriate and comparable data 

 

8 Incorporation of cells with mutations into the population influences whether these mutations are 

“observed” among isolates characterized by scientists.
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from the larger population of related organisms (e.g., the same species or serotype). 

Analysts would consider the total number of samples that fall within the clade compared 

with the total number that are excluded from the clade (Wilson et al. 2013). 

PFGE and MLVA are not very useful for assigning familial relationships among bacterial 

clades. PFGE should not be used to determine strain relatedness. PFGE indirectly 

detects mutations by visualizing restriction enzyme fragments from the genome. Most 

observable differences in fragment size are caused by indels, many of which are 

horizontally transmitted (Kudva et al. 2002). Digestion with two or more enzymes 

enhances the discriminatory power of PFGE by increasing the number of fragments, but 

does not make the data more amenable to analysis of familial relationships (Zheng et al. 

2011). Similarly, MLVA detects indel mutations in specific areas of the genome 

containing tandem repeats, which are subject to different mutation rates than the rest of 

the genome and therefore do not accurately represent the overall mutation rate of the 

genome. For this reason, PFGE and MLVA analyses are used to identify clades with 

identical or highly similar patterns. Using PFGE and MLVA together enhances the ability 

to discriminate clades (Broschat et al. 2010). Methods based on direct sequence 

analysis (MLST, multi-locus genotyping (MLGT), SNP, and whole genome sequencing

(WGS)) are more accurate for measuring familial relationships because the mutations 

are more readily observed and predictable. However, like PFGE and MLVA, the 

accuracy of DNA or RNA sequence may be compromised by indels and horizontallytransferred mutations. MLST, MLGT, or SNP analysis typically relies on a limited 

number of mutations culled from all available mutations from the genomes to estimate 

familial relationships. Therefore, accuracy depends on whether the mutation rates in the 

chosen genetic regions truly represent the familial relationships among the clades. The 

same issues could arise when comparing whole genome sequences and therefore 

programs are designed to find mutations that accurately represent familial relationships 

between clades.

OSEL and other PulseNet labs use BioNumerics software to create dendrograms from 

subtype data. For PFGE data, the Unweighted Pair Group Method with Arithmetic Mean 
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(UPGMA) method is used to create a similarity matrix from the fragment size data. The 

dendrogram provides an estimate of "similarity,” which can range from 0% (all different) 

to 100% (all the same). PulseNet considers PFGE or MLVA subtypes with 100% 

similarity to be indistinguishable, and subtypes assigned to the same pattern 

identification (ID) are referred to as a match. However, they may not be identical 

because these methods do not measure every potential difference among the matching 

isolates. Because foodborne illness investigations typically occur over a period of days 

or weeks, patterns related to a foodborne outbreak should be indistinguishable. In 

practice, this means that an outbreak clade may not vary by more than one or two 

PFGE bands, or have only a limited number of variations at MLVA repeat locations. This 

implies that a limited number of mutations would be likely within an outbreakresponsible clade. However, if the outbreak occurred over a long period of time or if the 

measured patterns had a high mutation rate, the investigator may consider less similar 

matches (Barrett et al. 2006)9

. The degree of acceptable differences during an outbreak 

should be determined by observation for each organism and subtyping method. The 

degree of acceptable differences for Salmonella, E. coli O157:H7 and Lm PFGE 

patterns have been determined through experience over many years and hundreds of 

outbreak investigations. Expected differences in MLVA patterns are being determined 

because these methods are relatively new and mutation rates for the tandem repeat loci 

are different than PFGE patterns. Similarly, next generation methods such as MLST, 

SNP, and WGS will require similar determinations to be of maximum use during 

foodborne outbreak investigations (see Section 5.1). 

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2.6 Laboratory Networks for Sharing Subtype Data: PulseNet and VetNet

In the 1990s, CDC organized PulseNet10, the first inter-laboratory network for sharing 

PFGE pattern data for foodborne pathogens. The initial participants included CDC, 

several state health departments, and the major food regulatory agencies, including 

FSIS, FDA-CFSAN, and FDA-Center for Veterinary Medicine (CVM). PulseNet now 

 

9 An example is Lm that may persist in a production facility over a period of time that allows for the 

generation of distinct lineages, any of which may contaminate product leading to illness.

10 See CDC Web site for more information: http://www.cdc.gov/pulsenet/pathogens.htm 
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includes all 50 state health departments and a number of local health departments. 

CDC PulseNet staff maintains databases of patterns submitted by member laboratories, 

administers a quality assurance program, and develop and validate new protocols and 

methods. PulseNet networks have been established in Canada, Europe, the Asia 

Pacific region, and Latin America (Gerner-Smidt et al. 2006;Swaminathan et al. 2006). 

PulseNet PFGE procedures and databases have been developed for E. coli O157:H7,

non-O157 Shiga toxin-producing E. coli (STEC) serogroups, Salmonella serotypes, Lm, 

Campylobacter, and other bacterial pathogens of public health importance. PulseNet 

member laboratories perform PFGE analyses, process the raw data in BioNumerics, a 

common software platform that allows for pairwise comparison of patterns generated in 

different laboratories (Section 3.1.1). CDC staff manages the PulseNet databases and 

provides identifications for each pattern uploaded by a member lab. 

VetNet was developed by ARS to house separate PFGE and antimicrobial resistance 

(NARMS) databases for Salmonella and Campylobacter isolated from diagnostic animal 

specimens, healthy farm animals, carcasses of food-producing animals at slaughter, as 

well as ground beef, chicken, and turkey products (Jackson et al. 2007). PulseNet and 

VetNet databases can be searched remotely by CDC, ARS, and FSIS staff. By the end 

of the 2013 calendar year, OSEL assumed several subtyping duties currently/formerly 

performed by VetNet, including Salmonella and Campylobacter PR/HACCP PFGE 

analysis. 

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2.7 FSIS’ Sampling and Subtyping Programs

FSIS conducts microbiological sampling to assess the effectiveness of industry process 

controls; provide critical feedback to industry; monitor compliance with performance 

standards and zero tolerance policies; monitor industry-wide trends; serve as an 

incentive to reduce the occurrence of pathogens in product; identify the origins of 

contaminated products that are of public health concern; and capture pathogen PFGE 

patterns for evaluation in PulseNet. FSIS collects samples at federal establishments, 
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U.S. ports of entry, in-commerce facilities (e.g., warehouses, distributors, centers, retail 

firms), and the homes of consumers. The sampling rate at federal establishments is 

random, volume-adjusted, risk-adjusted, or directed as a follow-up to a positive sample. 

Sampling of imported products is performance based11 or as a follow up to a positive 

sample (FSIS 2011c). FSIS also samples meat, poultry, and egg products associated 

with outbreak investigations and consumer complaints when product is available. In 

addition, FSIS receives isolates of Salmonella, Lm, and E. coli O157:H7 referred by 

state meat and poultry inspection programs and the National School Lunch Program 

commodity-testing program administered by the Agricultural Marketing Service (AMS). 

In fiscal year 2010 (Oct 1, 2009 to Sept 30, 2010), FSIS collected 90,678 samples 

which were tested for either E. coli O157:H7, Lm, or Salmonella12. Of these, 1,298 

(1.43%) samples were positive13. 

All FSIS testing procedures include cultural isolation and characterization steps, and the 

isolates are identified to the species level. In addition, one or more isolates from each 

sample are subtyped.

• All Lm isolates are subtyped using two-enzyme PFGE. 

• All E. coli O157:H7 isolates are subtyped using two-enzyme PFGE and MLVA. 

Under certain circumstances, E. coli O157:H7 are PCR-typed for genes associated 

with H7 (fliCH7) as well as for the presence of Shiga toxin (Stx) and intimin (eae) 

genes. 

• All Salmonella isolates are subtyped using serology, and one- or two-enzyme PFGE 

(for raw and ready-to-eat (RTE) isolates, respectively). ARA is performed on some 

Salmonella isolates, while some S. Typhimurium isolates are subtyped using phage 

typing14 or MLVA. 

 

11 Performance based means that countries with a poor food safety performance record are sampled 

more frequently than countries with strong food safety performance records.

12 17,453 samples (59 positive) were tested for E. coli O157:H7, 25,600 (225 positive) samples were 

tested for Lm, and 47,625 samples (1,014 positive) were tested for Salmonella

13 Source provided from the FSIS Data Warehouse on Feb 2, 2011.

14 Phage-typing is a procedure for characterizng and detecting bacterial strains by their reaction 

(susceptibility or resistance) to various known strains of phages. Bacteriophages are specific in action.
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In recent years, FSIS has added subtyping capabilities, such that MLVA is performed on 

all E. coli O157:H7 isolates and on S. Typhimurium by special request during 

investigations. CDC performs MLVA on S. Enteritidis by request from FSIS program 

staff (requests are typically made through OPHS headquarters). Since July, 2012 OSEL 

has performed molecular serotyping on Salmonella isolates following the CDC method 

as a replacement for the traditional serotyping method (FSIS 2013a) (Section 3.5).

Several subtyping duties previously performed by VetNet and NARMS-Animal Arm are 

currently being conducted by OSEL. These include PFGE and ARA laboratory and data 

analysis for all FSIS Salmonella and Campylobacter positive samples obtained through 

the PR/HACCP sampling program. 

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2.7.1 Antimicrobial Resistance Analysis (ARA) for Salmonella 

Through 2013 the NARMS-Animal Arm of ARS performed antimicrobial susceptibility 

testing on all FSIS Salmonella isolates and maintained a database of the resistance 

information. The FSIS Eastern Laboratory has assumed these duties. The panel of 

antimicrobials tested is representative of common antimicrobials used in both humans 

and animals.15 Testing is done using a semi-automated system (Sensitire®, Trek 

Diagnostic Systems, Inc., Cleveland, Ohio). 

2.8 FSIS’ Current and Potential Use of Subtyping Data

Subtype data may be used to:

1. Detect and track outbreak clusters or harborage within an establishment;

2. Assess the sample-specific risk of illnesses or outbreaks associated with a 

product, process, or establishment ;

 

Susceptibility to lysis by a particular phage may be the only apparent phenotypic difference between two 

bacterial strains.

15 Breakpoints Used for Susceptibility Testing of Salmonella 1997-2012.

http://www.ars.usda.gov/Main/docs.htm?docid=6750&page=3).
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3. Provide highly specific data for risk assessments and attribution studies;

4. Define a regulatory adulterant with greater accuracy; or

5. Improve the accuracy and efficiency of current subtyping methods. 

Detect outbreak clusters: Isolates sharing the same subtype form a cluster. 

If the isolates are from case patients, the subtype cluster could indicate an outbreak, 

especially if one or more of the case patients were related, lived in the same area, ate in 

the same restaurant, or shared any type of activity, which is referred to as a common

exposure. Outbreak clusters may be identified through subtype surveillance before a 

large number of illnesses are identified, especially important when case-patients are 

located in different cities or states. 

 

Outbreak investigators often use subtype cluster data to create two groups of people: 

case-patients who are ill and share the cluster subtype and those who are healthy or

who are ill and do not share the cluster subtype. Subtyping data allows investigators to 

exclude case-patients that do not fit in the cluster. Such sporadic, non-outbreak cases 

could weaken the association of the illness with an exposure. 

Networks such as PulseNet have proven invaluable for identifying widely disseminated 

clusters with no known evidence of common exposure, actively finding new casepatients during an investigation, and detecting the specific pathogen in environmental 

samples including foods, surface swabs, water and air. In this way, subtype data can 

promote timely outbreak investigations and interventions to prevent further illness. 

Detect harborage within an establishment: Subtype clusters which include food and 

other environmental isolates assist food regulatory agencies by providing support for a 

specific transmission route, or the persistence of a foodborne pathogen within an FSIS 

regulated establishment. For example, a subtype cluster detected from samples 

collected over days, weeks or months from samples collected at a single establishment 

could support the existence of a contaminated product contact surface that is not being 

properly cleaned and sanitized, or which contains a biofilm that is resistant to the 
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establishment’s cleaning and sanitizing procedures. If the cluster subtype is detected 

from raw (e.g., uncooked) and processed (e.g., cooked) product samples, the data 

could indicate the transfer of the pathogen from raw to RTE operation areas, suggesting 

improper separation of these operations. If the cluster subtype is detected in the 

finished product and individual ingredients, the data suggest that the ingredient supplier 

is unable to control the pathogen. Together, these observations, obtained through 

careful study of subtype data, suggest modifications to operating procedures, such as 

HACCP plans, prerequisite programs, or Sanitary SOPs

Assess public health risk: Subtyping methods can provide information on the relative 

probability of a subtype to cause illness, which could be a component of an overall 

estimate of public health risk. Typically, this is not a rigorously calculated ratio, but 

rather an assessment of the relative frequency by which the subtype is recovered from 

outbreak and non-outbreak settings, or finding the subtype associated with multiple 

independent outbreaks. FSIS currently assesses the relative public health risk of 

establishment production practices and products based on the use of interventions, 

production volume, and testing history. FSIS uses Salmonella serotype to help 

determine the Salmonella verification testing set sampling frame for raw meat and 

poultry products and to schedule food safety assessments. FSIS is actively working to 

include PFGE and antimicrobial susceptibility profiles in its “end of set” letters sent to 

establishments at the end of a Salmonella verification set. However, the Agency has not 

established a regulatory standard for Salmonella subtypes, or formally defined public 

health risk with respect to pathogen subtypes. 

Provide data for risk assessments and attribution studies: Some risk assessments 

deal with strain (subtype) variation by pooling data from multiple strains, or modeling a 

worst-case strain, with the intention of providing conservative risk estimates. However, 

models that ignore potential strain effects could provide a misleading representation of 

both the public health implications and the confidence that certain risk management 

strategies would be effective in reducing risk (Coleman et al. 2004). Biological variation 

among pathogen species of concern to risk assessments and attribution studies include 
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virulence, dose response, environmental reservoir, growth and inactivation kinetics in 

foods, and resistance to various food processing procedures. Subtyping methods can 

distinguish existing biological variation within a pathogen species, either by detecting 

genes directly controlling such variation or detecting marker sequences that are strongly 

associated with subpopulations with different properties. When information is available 

about the degree of between-strain differences, then adjustments can be made to 

increase the predictive accuracy of risk assessments (Juneja et al. 2003). Subtyping 

methods support some attribution studies by characterizing the distribution of specific 

subtypes isolated from different sources (e.g., animals, foods, processing environments) 

and comparing those subtypes with those isolated from humans. Subtypes that are 

exclusively, or almost exclusively, associated with a single source are referred to as 

“indicator subtypes,” and are used to assign human illnesses to specific sources (Pires 

et al. 2009). This approach was taken with Salmonella serotypes (Guo et al. 2011;Pires 

and Hald 2010). Subtyping methods with greater discriminatory power could be more 

likely to detect indicator subtypes, and therefore improve the accuracy of certain

attribution studies.

Define a legal adulterant: The FSIS Federal Meat Inspection Act (FMIA) and Poultry 

Products Inspection Act (PPIA) define a food as adulterated if it bears or contains a 

poisonous or deleterious substance that may render a food injurious to health. If the 

substance is a natural constituent of the food, the food is not considered adulterated if 

the quantity of the substance does not ordinarily render the food injurious to health, 

(Title 21 U.S.C Chap. 12, Sec. 601 (m) (1) and 21 U.S.C. Chap. 10, Sec. 453 (g) (1)). 

FSIS can use data from subtyping assays to interpret the meaning of the acts and 

establish science-based policies. FSIS may use subtype data to determine that certain 

subtypes with higher virulence are injurious to health, or to determine that subtypes with 

lower effective doses are “ordinarily injurious” even when present as a natural 

constituent of a food. For example, in 1994, FSIS announced its intention to declare raw 

ground beef containing a single E. coli subtype (serotype O157:H7) to be adulterated, 

and in 2012, expanded this restriction to include additional subtypes of non-O157 

STEC. FSIS laboratory methods use traditional serology, biochemical methods (sorbitol 
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fermentation), and virulence gene detection using real time PCR to define these 

subtypes from other E. coli subtypes. In the future, FSIS may use additional subtyping 

methods, including molecular methods, to define subtypes of Campylobacter, E. coli, 

Lm, or Salmonella as adulterants. For example, FSIS is considering a petition to declare 

certain antibiotic resistant serotypes of Salmonella to be adulterants (CSPI 2011)].

Improve accuracy and efficiency of current subtyping methods. FSIS labs use 

PCR-based tests to identify serotypes of E. coli O157:H7, non-O157 STEC, and 

Salmonella. These molecular methods reduce FSIS’ reliance on antibody reagents, 

which are expensive to maintain and which are not effective unless the target antigen is 

produced and located on the surface of the pathogen. With Salmonella, molecular 

serotyping assays reduce time to result from weeks to days, providing actionable data 

to the field workforce.

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3 Current Use of Subtyping Methods by FSIS 

This section describes the molecular subtyping methods currently used by FSIS and the 

ways in which the Agency communicates and interprets the data. 

3.1 Pulsed-Field Gel Electrophoresis (PFGE)

PFGE is a DNA-based subtyping method for characterizing bacterial isolates. It is the 

primary method used by all PulseNet laboratories. Within FSIS, OSEL is responsible for 

performing PFGE. PFGE is easily applied to different species and generally yields a 

high amount of pattern diversity that provides good discriminatory power and other 

measures of subtyping method validity (Section 2.3). PulseNet laboratories use 

standardized PFGE protocols and computer-assisted pattern normalization techniques, 

which result in high levels of reproducibility within and between laboratories (Section 

2.6).

Isolates with indistinguishable PFGE patterns are more likely to be genetically related, 

but a PFGE pattern is not as specific as a fingerprint. Relatedness of patterns should be 
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used as a guide and not a true measure of relatedness. PFGE is one piece of data that 

should be used in conjunction with epidemiologic evidence or other information 

collected during investigations (Section 3.1.5). 

3.1.1 PFGE Laboratory Method and Pattern Normalization

The PFGE theory and the laboratory method are described in Appendix A and Appendix 

B respectively. The characteristic of DNA that makes PFGE possible is the restriction 

site. Restriction sites are DNA sequences that are recognized by one or more restriction 

enzymes that cut the DNA molecule only at that particular target sequence. Restriction 

enzymes used for PFGE cut the bacterial genome at multiple sites. Digestion of 

bacterial DNA with a restriction enzyme cuts all restriction sites, producing DNA 

fragments of varying sizes. The digestion step is done in a solution called a “restriction 

digest” that contains genomic DNA, buffer, and the restriction enzyme. The resulting 

fragment sizes depend on the spacing of the restriction sites along the DNA molecule. 

The restriction digest containing the fragments from one to 12 isolates are loaded onto 

an agarose gel along with two to four restriction digests from known strains (referred to 

as size standards) which contain DNA fragments producing an expected array of 

fragment sizes. The gel is exposed to an electric field that is constantly changing 

directions and polarity. Under the electric field, the DNA fragments become arranged in 

a characteristic pattern, with the large fragments located on the top of the gel and 

smaller fragments at the bottom. The fragments are stained to produce “bands” on the 

gel. The PFGE pattern is a characteristic of the subtype. Different restriction enzymes 

recognize different restriction sites, and therefore produce a different array of fragments

when applied to the same isolate. 

PFGE data workflow is described in Appendix C. A digital image of the gel is saved to a 

local computer and analyzed using BioNumerics, a computer program that normalizes 

and saves PFGE images and transfers images to and from the national PulseNet 

database at CDC. The normalization step adjusts the image to a standard PulseNet 

format allowing it to be compared to images from other laboratories. During 
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normalization, the analyst defines the portion of the gel image area occupied by each 

isolate (referred to as a lane). The bands are marked in the lanes occupied by the 

standards, which links them to PulseNet-defined band sizes saved in the BioNumerics 

program. BioNumerics can perform initial band marking automatically, but this often 

results in errors, so an OSEL analyst verifies the results for accuracy. The analyst then 

marks and saves the position of the isolate bands in a file, links names to each lane, 

and adds additional information (metadata) about the sample as described in Appendix 

D. Certain metadata are saved by FSIS and not shared with PulseNet or VetNet. Other 

metadata that reasonably could not be expected to identify a specific establishment are 

uploaded to PulseNet or VetNet along with the gel image, so that the pattern can be 

analyzed by CDC-PulseNet database managers and can receive a PulseNet name, 

which is referred to as pattern confirmation. Processed PFGE images also are available 

for immediate use within FSIS, while still unconfirmed (unnamed) by CDC-PulseNet. 

OSEL typically analyzes unconfirmed patterns because there could be some delay in 

the process of pattern confirmation. To avoid confusion, it is important to distinguish 

confirmed and unconfirmed patterns in FSIS communications. FSIS’s use of confirmed 

and unconfirmed PFGE patterns and matches is described in Appendix E 

To facilitate comparisons of PFGE patterns between laboratories, CDC developed a 

standardized naming system for designating patterns in PulseNet. Each unique pattern 

in the database is represented by a 10-character code (e.g., XXXYYY.0000). The first 

three characters in the code represent the bacterial pathogen, the next three characters 

denote the enzyme used for DNA restriction, and the last four characters represent the 

pattern designation. For example, in the pattern designation EXHX01.0026, EXH 

represents E. coli O157:H7, X01 represents restriction endonuclease XbaI, and 0026 is 

the pattern number. Because the pattern numbers are assigned sequentially to unique 

patterns, no similarity should be implied from the order of pattern numbers. The CDC 

PulseNet laboratory confirms the pattern ID to ensure consistency. Because pattern 

confirmation is subjective, pattern names may be changed occasionally by the CDC 

PulseNet laboratory without informing other PulseNet member laboratories, including 
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FSIS. Some PulseNet laboratories, and the VetNet laboratory, assign a lab-specific 

name to their patterns to serve more local needs. 

Although PFGE is a powerful tool, there are a variety of considerations involved with its 

use. Data interpretation is not entirely objective. Analysis is labor-intensive and takes 

about two or three days after bacterial isolation to complete. Occasionally, isolates are 

not typed easily and may require multiple attempts to produce a usable pattern. 

Rigorous standardization of the protocols is necessary to enable inter-laboratory 

comparison of the PFGE patterns. Even with the aid of image analysis software, the 

analysis becomes increasingly cumbersome, subjective, and difficult as more profiles 

are entered into PFGE databases (Hyytia-Trees et al. 2007). 

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3.1.2 FSIS PFGE Data Analysis and Data Monitoring 

FSIS and local, state, and other federal public health agencies contribute PFGE pattern 

images from human clinical, food, and environmental isolates to the national PulseNet 

database located at CDC. These entities assist the CDC in monitoring the database for 

matching patterns. CDC maintains separate databases for E. coli O157:H7, non-O157 

STEC, Lm, Salmonella, and other microbial pathogens. The Salmonella pattern 

database is the largest within PulseNet (the number of profiles exceeds 110,000 and 

contains profiles of approximately 500 serotypes) (Gerner-Schmidt, 2006). Therefore, it 

is managed slightly differently. Isolates within the Salmonella database are classified 

first by serotype and then pattern ID. Occasionally a PulseNet database manager may 

confirm a Salmonella isolate before the serotype is determined.

Each isolate uploaded by OSEL is digested separately with a primary and secondary 

enzyme, so two patterns (a primary and secondary pattern) are associated with each 

isolate. While all PulseNet laboratories submit a primary pattern for each isolate, some 

may not submit a secondary pattern for all organisms. For E. coli O157:H7 and Lm, 

most laboratories immediately submit a primary and secondary pattern. Due to the large 

number of Salmonella isolates, most laboratories submit a primary PFGE pattern for the 
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top five to 10 serotypes in their state and both a primary and secondary PFGE pattern 

for isolates associated with outbreak investigations. It is not feasible for most 

laboratories to PFGE all received Salmonella isolates. Within PulseNet, database 

managers confirm each PFGE image. Isolates with a matching pattern are given the 

same pattern name. During multistate outbreak investigations, PulseNet may request a 

secondary pattern from a laboratory if one was not initially submitted. When both 

patterns are available, isolates are considered to match only if the confirmed pattern 

names for both the primary and secondary patterns are identical. 

In PulseNet, PFGE patterns are compared visually and with the use of BioNumerics 

software, which is especially helpful for comparing large numbers of patterns. A 

similarity matrix is used to create a dendrogram to visually depict the relative strengths 

of all pair-wise pattern comparisons. The dendrogram also provides an estimate of 

pattern "similarity,” which can range from 0% (all different) to 100% (all the same). 

PulseNet considers PFGE patterns with 100% similarity to be indistinguishable and 

assigns the same pattern ID. These patterns are considered to match.

A variety of database searches can be performed before and after the pattern ID is 

confirmed by CDC-PulseNet, including “hotlist search,” “search to server,” “plant 

comparison,” and “cluster search,” as described below. Database search results and 

metadata are stored on FSIS servers, which are accessible to OPHS investigators.

Hotlist Searches compare an FSIS pattern (referred to as the query) to patterns 

recently uploaded to PulseNet (referred to as the Hotlist). OSEL usually will perform a 

hotlist search before CDC PulseNet confirms the pattern ID. The BioNumerics program 

is used to determine the relatedness of the query and hotlist patterns, which is 

visualized as a dendrogram and does not rely on CDC PulseNet pattern confirmation. 

OSEL determines the PulseNet upload dates in the hotlist and the dates range from 30 

to 90 days, with most searches performed against a 60-day hotlist. 
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Search to Server Searches compare a query pattern to the most closely matching 

patterns in the entire PulseNet database (i.e., not limited to recent isolates like the 

hotlist search). If the query matches a confirmed pattern, a combined pattern search is 

performed to provide a list of the most likely recent matches.

Plant Comparisons compare all patterns derived from the same FSIS-sampled 

establishment. The BioNumerics program determines the relatedness of the patterns. 

The output of this type of search is a dendrogram and is accompanied by a spreadsheet 

that contains all demographic information that OSEL has on the isolates.

Cluster Searches are specific comparisons of an isolate to an active cluster (a group of 

isolates with a similar pattern that has elicited interest at the CDC). CDC-PulseNet 

assigns PulseNet cluster codes to these groups of isolates that are of heightened 

interest due to a number of factors (e.g., increase in frequency above baseline, 

specificity to a location, epidemiologic link, and recently uploaded food isolate).

While the searches detailed above can be enhanced by PulseNet confirmation of an 

isolate’s pattern, certain searches can be performed only after PulseNet confirms the 

pattern. These searches include combined pattern search, frequency determination, 

and Salmonella pattern and serotype ranking within PulseNet.

 

Combined Pattern Searches provide a list of isolates that have the same confirmed 

pattern combination as the query. These searches can be limited to a specific time or 

include all matching isolates in the PulseNet database.

Frequency Determination provides the frequency of a confirmed primary and/or 

secondary pattern ID and/or the combination of primary and secondary patterns. 

Because of the size and complexity of the PulseNet Salmonella database, this type of 

search is difficult and less informative for Salmonella isolates, which is why a 

Salmonella pattern and serotype ranking within PulseNet often is requested.
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Salmonella Serotype and Pattern Ranking within PulseNet is based on the PulseNet 

database managers’ lists of the 30 most common serotypes and the 5 most common 

patterns within each Salmonella serotype in the PulseNet database. After pattern 

confirmation, OSEL can report the serotype and pattern rank Salmonella if present on 

the list.

Additional searches may be performed for patterns associated with specific text in the 

PulseNet database, for example, all E. coli O157:H7 patterns submitted by the Ohio 

PulseNet lab and uploaded to the database in 2010. 

Under OSEL’s current practice, either a hotlist search or search to server is performed 

before the pattern is confirmed. A plant comparison always is performed, and a 

combined pattern search is performed on patterns from all Lm, E. coli O157:H7, and 

non-O157 STEC isolates. Salmonella isolates are monitored using cluster searches. 

OPHS investigators and the FSIS liaison to CDC receive routine search results for Lm, 

E. coli O157:H7, and non-O157 STEC, or when a Salmonella isolate matches a recent 

PulseNet cluster. If matches to clinical isolates are revealed, investigators in OPHS and 

other program areas discuss further actions (Section 3.1.4). If a potential match is 

supported by additional epidemiologic data, OPHS may request that trace forward 

and/or trace back investigations be initiated to determine whether case-patients were 

exposed to the product. OPHS investigators may request additional and repeated 

searches (Section 3.1.4).

Relative pattern frequency is calculated for all primary, secondary, and combined PFGE 

patterns uploaded to the PulseNet database. Pattern frequency is used to judge the 

significance of matches. The combined pattern frequency of Lm and E. coli O157:H7 

isolates can be assigned to one of five descriptive terms: common, less common, rare, 

very rare, and new to the PulseNet database, as described in Appendix F

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3.1.3 PFGE on FSIS PR/HACCP Raw Product Salmonella Isolates

Prior to 2014 Salmonella isolates collected from raw products through the PR/HACCP

Salmonella Verification Testing Program were not uploaded to the PulseNet database. 

The ARS Bacterial Epidemiology and Antibiotic Resistance laboratory (co-located with 

the FSIS Eastern Lab in Athens, GA) typed these isolates by PFGE and uploaded each 

pattern to the VetNet database. OSEL assumed these duties before the conclusion of 

the 2013 calendar year; this change has resulted in the inclusion of these isolates into 

the PulseNet database.

Prior to 2011, VetNet only routinely performed PFGE on Salmonella isolates using a 

single enzyme (XbaI). Since 2011, every isolate uploaded to the VetNet database was

digested separately with two enzymes, XbaI and BlnI. The PulseNet and VetNet 

databases are distinct. However, database managers for each database and OSEL staff 

have access to both databases, so there is a high level of cooperation and 

communication. All VetNet isolates are assigned a VetNet pattern ID and the equivalent 

PulseNet ID, if there is a matching pattern in the PulseNet database, so a list of VetNet 

isolates matching a PulseNet pattern of interest can be downloaded using a pattern 

search.

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3.1.4 Distinguishing PFGE Patterns—Linking Clinical and Product Isolates

PFGE patterns from strains originating from the same source are more likely to be 

identical or highly similar compared with strains that do not originate from the same 

source. If CDC has determined that two isolates share the same PFGE pattern, the 

isolates are said to ‘match.’ A group of such matches is referred to as a cluster. By 

continuously monitoring PFGE patterns from foodborne pathogens isolated from 

humans, it is possible to detect clusters of indistinguishable subtypes that, upon

epidemiological investigation may turn out to be outbreaks (illnesses linked to a 

common source) (Barrett et al. 2006;Hyytia-Trees et al. 2007). 
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Detection of PFGE pattern matches between clinical and product isolates is typically 

insufficient to link an illness to a specific product. In many situations, such matches are 

detected first, followed by corroborating epidemiologic, food history, and trace back 

data, gathered by FSIS and public health partners. When PFGE pattern clusters 

containing FSIS-regulated product isolates and clinical isolates are detected, FSIS must 

decide whether to initiate an investigation to collect additional information necessary to 

determine if an association exists. This process is described in Appendix C. Clusters 

that include recent clinical isolate uploads (indicating an ongoing occurrence of 

illnesses), especially when there is an appropriate temporal (time) and spatial 

relationship between the product and clinical isolates, are of greatest concern.

Several considerations are necessary in making the decision to investigate clusters 

involving both FSIS-regulated product and clinical isolates with no known epidemiologic 

association (Figure 1).

Figure 1. Considerations made by FSIS before Investigating a Cluster 

1. CDC designation of a PFGE pattern cluster as an outbreak cluster or knowledge of an 

ongoing investigation being conducted by local, state, or federal public health officials

a. Number of clinical isolates

b. Continued occurrence of illnesses

c. Severity of disease outcomes

2. Pathogen of concern

3. PFGE pattern frequency

4. Type of FSIS-regulated product (e.g., RTE, not ready-to-eat (NRTE), raw)

a. Whether product represented by the sample was held or distributed into 

commerce

b. Temporal sequence and spatial relationship between the FSIS-regulated product 

and case-patient isolates

5. Interest or concerns expressed by the FSIS Management Team and public health 

officials
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Pattern frequency is used to help judge the significance of pattern matches. It is 

important for FSIS staff to consider pattern frequency when deciding whether to commit 

FSIS resources to an investigation of a pattern match between FSIS regulated products 

and clinical isolates. Other information should be considered as well, such as evaluating 

the recent history of the pattern, because a large outbreak can significantly alter 

frequency statistics in subsequent years. For example, if a specific pattern was 

observed 100 times during a large outbreak in 2005, but not seen at all in following 

years, its cumulative frequency still would be high even though the pattern was 

infrequent in the subsequent years. In addition, a match involving a common pattern 

should not be disregarded if it has not been seen in the PulseNet database recently, if 

there is a spike in pattern frequency, or if public health partners are investigating the 

cluster. Surveillance of patterns for less common serotypes is important to identify the 

emergence of new pathogens of concern.

After identifying a Salmonella illness cluster In PulseNet that may be associated with 

meat or poultry products, OSEL will provide OPHS investigators with information on 

FSIS isolates with indistinguishable PFGE patterns. This information may assist the 

epidemiologists in identifying possible sources of illness in those investigations.

Investigation of specific PFGE matches between clinical Salmonella isolates and raw 

product isolates is usually reserved for instances where the isolate pattern is part of a 

CDC active cluster of human illness with a designated PulseNet cluster code16 (Section 

3.1.2). The Applied Epidemiology Staff (AES) Surveillance Team also follows up on

combined pattern searches conducted by OSEL when the PFGE pattern is novel or rare 

or the serotype is not ranked in the top 5 in humans (as determined annually by the 

CDC). Investigations of Salmonella clusters may also be initiated when the isolates are 

found to exhibit antibiotic resistance, especially to an important drug class (e.g.,

cephalosporins or fluroquinolones) used for treating salmonellosis in the United States. 

 

16 A group of isolates with indistinguishable PFGE pattern that are of heightened interest due to a recent increase 

in frequency, specificity to a location, epidemiologic link or recent upload of a food isolate are often assigned a 

distinct PFGE cluster code by CDC/PulseNet staff
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Antibiotic resistance may result in treatment failures and increased morbidity and 

mortality. 

When there is limited evidence that FSIS-regulated product is (or is not) the source of a 

cluster of illnesses, AES monitors the PFGE cluster. Once there is evidence of possible 

association of an FSIS-regulated product with human illness, a ‘watch’ is opened and 

the AES Investigation Team actively collects epidemiologic data from public health 

partners in order to investigate potential linkage between case-patients and the FSISregulated products. According to the FSIS foodborne illness directive 8080.3 (FSIS 

2008a), when case-patient food histories indicate the likely involvement of FSISregulated product, an official foodborne disease investigation is initiated, an Incident 

Report (IR) is opened in FSIS’s Foodborne Incident Management System (FIMS), and 

other FSIS program areas, such as OFO and OIEA, become actively involved in the 

investigation. OPHS continues to collect and analyze epidemiological data, such as

information about the exposure of case-patients to specific FSIS-regulated products, 

and microbiological data. OFO and OIEA conduct trace forward and trace back 

investigations based upon documented case-patient exposures (e.g., product 

type/brand, store location, date of purchase) to assist in the identification of the source

(e.g. food or other exposure) of illness. During foodborne disease investigations, FSIS 

requests combined pattern searches or cluster searches to detect newly-uploaded 

isolates at least weekly, or as needed, until the investigation is closed.

If FSIS-regulated product is implicated and recalled, the PFGE pattern is monitored 

using hot list or combined pattern searches to assure the adequacy of the recall and to 

expand the scope of the recall as necessary. Additional clinical isolate uploads to 

PulseNet with the same PFGE pattern as the recalled product may indicate that the 

product is linked to those illnesses necessitating further investigation. The AES 

Surveillance Team, with the assistance of OSEL, also monitors FSIS recalls triggered 

by positive FSIS product samples without reported human illness. This is referred to as 

a ‘recall watch.’ During the recall watch, AES requests follow-up pattern searches for E. 

coli O157:H7 positive isolates approximately every two weeks for at least 60 days 
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following the recall date. Lm follow up searches are requested approximately every two 

weeks for at least 90 days following the recall date to ensure that illness incubation 

periods are addressed appropriately. If a new clinical isolate upload to PulseNet 

matches the cluster pattern, OPHS investigators typically will discuss further actions. 

Under most circumstances, cluster investigations involve product isolates where the 

product yielding the positive isolate with an indistinguishable pattern was produced prior 

to the clinical isolate collection date. However, this expected temporal sequence may 

not apply if a portion of common source material (e.g., intact boneless beef) was 

distributed and used as ingredients in other products (e.g., ground beef) which were 

subsequently sampled, or if there is evidence of harborage at a producing facility over 

an extended period of time. To account for long-term harborage Lm in production 

facilities, Lm PFGE clusters should be investigated even in situations where the positive 

product has been held and where clinical isolates were identified prior to the date that 

the Lm-positive product was manufactured. Investigators should assess the trends in 

PFGE pattern frequency prior to the product isolation date. Lm clusters with identified 

product or environmental isolates should be followed 90 days before and 90 days after 

the isolation date. Such follow-ups may suggest harborage issues, especially when an 

outbreak was previously associated with the same PFGE pattern. 

<back

3.1.5 Using PFGE in Outbreak Investigations 

PFGE is one of several important tools used by FSIS to identify links between FSISregulated products and foodborne illnesses and outbreaks. PFGE data may be used in 

conjunction with microbiological and epidemiologic data, environmental assessments, 

trace back activities, and other information collected by FSIS and its public health 

partners during foodborne disease investigations. The success of PFGE cluster 

investigations relies on timely surveillance, detection of clusters, and the ability to collect 

adequate information for trace back activities. PFGE analysis is especially valuable in 

identifying outbreaks in which the illnesses are not clustered geographically, and, as 

such, would often be considered sporadic cases. PFGE data combined with 
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epidemiologic data, food consumption history, trace back information, and point of 

preparation data are used to establish an association between ill individuals and 

contaminated product.

PFGE patterns generated from FSIS samples and uploaded to the PulseNet or VetNet 

databases are compared with other patterns, with the goal of detecting illness clusters

that may be associated with FSIS-regulated products. When clinical isolates of 

foodborne pathogens with the same pattern combination are found to increase in 

frequency within a specific time-frame, referred to as a temporal cluster, an 

epidemiological investigation can determine whether such a cluster represents an 

outbreak. If a pattern from an FSIS isolate matches a recently uploaded clinical isolate 

or recently identified cluster, OPHS collaborates with state and/or local public health 

partners to determine whether the match represents an outbreak of foodborne illness

from a single exposure (e.g., exposure to a specific production lot of a product), often 

referred to as a point source outbreak. 

Epidemiologists use PFGE data to focus their working hypotheses during the course of 

the outbreak investigation. Investigators expect subtypes from clinical isolates 

recovered from a single source outbreak to be indistinguishable (high similarity) and 

subtypes of clinical isolates recovered from different outbreaks should be 

distinguishable (low similarity). In the absence of epidemiologic information, analysts 

may conclude that isolates sharing one of the less common patterns are more likely to 

have come from a common source than isolates sharing a predominant pattern. 

The interpretation of PFGE patterns in the context of foodborne disease epidemiology, 

however, is not simple, and guidelines for interpreting this data are needed. Method 

reproducibility with a particular organism, the quality of the PFGE gel, the variability 

(also referred to as the clonality) of the organism being subtyped, and the pattern 

prevalence in question must be considered. Over-reliance on conclusions from 

subtyping data in the absence of (or despite) epidemiological data may result in a type I 

error (false negative conclusion), leading to the premature termination of an 
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investigation or regulatory action. It also may lead to a type II error (false positive 

conclusion), leading to an unnecessary expenditure of public health resources (i.e., a 

“wild goose chase”).

Only the indistinguishable pattern should be included in the initial case-patient definition 

in an outbreak investigation. The shorter the duration of an outbreak, the less time the 

outbreak strain has to undergo mutations that change the PFGE pattern (Barrett et al. 

2006;Tenover et al. 1995). More variability (patterns differing from each other in two to 

three band positions) may be acceptable if the outbreak has been going on for some 

time or if person-to-person spread is a prominent feature. If epidemiological information 

is sufficiently strong, case-patients with isolates exhibiting differing PFGE patterns may 

be included as part of an outbreak. 

OPHS investigators prioritize and may follow up on FSIS-regulated product isolates and 

clinical isolates with indistinguishable PFGE patterns even if these product isolates are 

not currently associated with a designated outbreak cluster investigation. Efforts are 

made to establish any associations between positive FSIS-regulated products and 

foodborne illness. Priority is given to product isolates collected from recalled products, 

isolates exhibiting uncommon or new PFGE patterns, and isolates exhibiting increased 

virulence (e.g., hemolytic uremic syndrome (HUS) in E. coli O157:H7 infections). 

OSEL’s VetNet search to determine the frequency of the specific isolate pattern from 

animal sources and FSIS-regulated products is important. A PFGE pattern may be rare 

in PulseNet and not a significant cause of human illness, but may be a common pattern 

in VetNet. 

Once clusters are recognized, epidemiological investigations help determine whether a 

cluster actually represents an outbreak and help identify the clinical isolates resulting 

from a common source (Besser et al. 2008;Gerner-Smidt et al. 2006). Supporting data, 

such as case-patient report forms, food history questionnaires, analytical 

epidemiological study results, and purchase and point of preparation data, are collected 

from state and local public health departments. When information indicates exposure to 
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FSIS-regulated product, other FSIS program areas, such as OFO or OIEA, become 

involved, per FSIS Directive 8080.3 (FSIS 2008a). Epidemiological evidence combined 

with trace forward and trace back activities (establishment and product information) and 

laboratory sampling information help determine whether an FSIS-regulated product is 

associated with foodborne disease. 

<back

3.1.6 A Case Study Using PFGE: FSIS Salmonella Multiple-drug Resistant (MDR) 

Outbreak Investigation

In June, 2009, the Colorado Department of Health and Environment conducted an 

investigation into a cluster of Salmonella Newport MDR infections. The XbaI PFGE 

pattern was not one of the top 10 most common PulseNet patterns of S. Newport. 

Ground beef isolates with patterns matching the case-patient isolates were identified in 

both PulseNet and VetNet. The ground beef isolates were recovered from an FSIS 

sample collected from a California processing plant (Establishment A) on May 21 and 

from a USDA-AMS sample collected from the same plant on June 1, 2009. Twenty of 22 

Colorado case-patients reported consumption of ground beef during the week prior to 

illness onset; 18 of 20 reported purchasing ground beef from the same large 

supermarket chain (Chain A). The Colorado and Wyoming health departments provided 

FSIS case-patient ground beef purchase information via Chain A’s shopper loyalty 

cards. FSIS investigators visited 11 Chain A supermarket locations to review ground 

beef production records. The FSIS review of retail production records in Colorado and 

Wyoming identified the suppliers of ground beef source materials to Chain A. 

Establishment A supplied coarse ground beef to Chain A supermarkets that 

manufactured various ground beef products purchased by case-patients. The first recall 

of raw ground beef due to Salmonella contamination was made possible due to detailed 

exposure information, adequate retail production records, and laboratory subtyping 

data, all of which successfully linked ground beef products to illnesses. This 

investigation illustrates the enhanced surveillance and hypothesis generation during 

foodborne illness investigations by cross referencing PulseNet and VetNet data.

<back
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3.1.7 Using PFGE to Detect Lm Harborage within Establishments

Lm can establish a niche in damp environments and form biofilms in the processing 

environment, creating harborage sites that are difficult to eliminate with routine 

sanitation activities. Continued product contamination may occur until an establishment 

conducts sufficient corrective actions to eliminate the source of harborage or 

contamination. Inadequate corrective actions may lead to the organism’s increased 

resistance to sanitizers and food preservation strategies, which, in turn, increases the 

risk of product adulteration. PFGE is an important tool to trace contamination and 

investigate potential bacterial harborage sites within an establishment and to evaluate 

the effectiveness of an establishment’s corrective actions. OSEL performs a plant 

comparison on each Lm pattern. When the same PFGE pattern is detected over time or 

at multiple locations within an establishment, harborage is indicated, but not proven, 

because repeated reintroduction of the same organism also is possible. During a threeyear period ending on September 30, 2011, 65 of 387 (17%) sampling events resulted 

in the detection of a PFGE pattern previously identified in the establishment indicating 

the potential for Lm harborage. In 118 of 387 (30%) sampling events, a PFGE pattern 

appeared to match a recent clinical isolate illustrating the potential public health risk of 

these harborage events. This information was communicated within FSIS and was 

presented to the establishment as evidence for harborage and of its lack of control over 

Lm in its post-lethality production environment17. 

<back

3.1.8 Using PFGE to Refine Attribution Models 

Comparing the distribution of different pathogen subtypes in humans, food, animals, 

and the environment is useful in evaluating the burden of sporadic illness attributable to 

FSIS-regulated products. FSIS constituents routinely request more data, as well as 

more accurate attribution data on food/pathogen combinations to implement effective 

interventions. Pathogen subtyping is a critical component of foodborne disease 

attribution, by which FSIS should be able to get a better picture of the extent to which

 

17 Source: FSIS Quarterly Reports prepared by OPHS and OPPD
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FSIS-regulated products contribute to human illness. By combining human surveillance 

data on laboratory-confirmed infections from CDC’s National Salmonella Surveillance 

System with food data from FSIS regulatory testing programs, FSIS and CDC, working 

with the Foodborne Diseases Active Surveillance Network (FoodNet) adapted the 

Bayesian attribution model methodology developed by Hald and colleagues (Hald et al. 

2004) for food source attribution for Salmonella at the point-of-processing (Guo et al. 

2011). The principle of the Danish Hald model18 is to compare the number of human 

salmonellosis cases caused by different Salmonella subtypes with the prevalence of the 

subtypes isolated from different animal reservoirs or food sources, weighted by the 

amount of each food source consumed. The U.S. model uses food data collected 

primarily at processing establishments while the original model from Denmark was 

based on an integrated surveillance system with data collected from farm to table. In 

addition, Denmark has a smaller number of Salmonella serotypes of public health 

concern while in the U.S. there are over 100 serotypes of public health concern in food 

products, though the focus is on the CDC-designated top 20 serotypes (CDC 2012a). 

FSIS used the model to estimate the number of human infections resulting from 

Salmonella contamination of raw meat, poultry, and egg products in the United States. 

The model looks at the attribution by individual Salmonella serotypes as well as by all 

Salmonella. Next steps in model refinement may include: 1) incorporating other sources 

of food data, such as prevalence estimates for Salmonella contamination of FDAregulated commodities; 2) use of PFGE and other molecular subtyping data; and 3) 

adding other foodborne pathogens.

<back

 

18 A method was developed in Denmark (Hald, T. et al, A Bayesian approach to quantify the contribution 

of animal-food sources to human salmonellosis) that uses information about food contamination, 

consumption patterns of the population, and human foodborne illnesses to estimate the number of 

illnesses associated with specific food reservoirs. This method has been adapted in several countries for 

foodborne illness source attribution of several pathogens. 
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3.1.9 Using PFGE in FSIS’ Salmonella Verification Testing Program

FSIS obtains serotype and PFGE results from Salmonella-positive samples as part of 

the Agency’s Salmonella Verification Testing Program for raw products. FSIS strives to 

share Salmonella serotype data (provided by OSEL and the Animal and Plant Health 

Inspection Service (APHIS) National Veterinary Services Laboratory (NVSL)) with 

establishments within two weeks after a sample has been reported as positive. In 2011, 

FSIS revised establishment end-of-set letters (EOSL), which are sent to establishments 

following a pathogen reduction-hazard analysis and critical control point (PR/HACCP) 

sample set, to include the Salmonella serotype for each positive sample. The letters 

also indicate whether the serotypes detected during the set are on the CDC’s top 20 list 

of most common serotypes from human sources. FSIS schedules Salmonella sets 

according to criteria outlined in a sampling algorithm (FSIS 2011a), which includes 

whether serotypes of human health concern (defined as serotypes on the CDC’s top 20 

list for the most recent calendar year) were detected during the establishment’s last set. 

FSIS is actively working to report PFGE pattern and ARA data from PR/HACCP sets to 

establishments in a separate letter or in a further revision to the EOSL format. This 

information is intended to assist an establishment to evaluate and improve its 

Salmonella process control19. 

The FSIS Salmonella initiative concentrates resources on establishments with higher 

levels of Salmonella and with serotypes of human health concern. FSIS takes follow-up 

action, which may include scheduling another sample set or assessing the design and 

execution of the establishment’s food safety system. Food safety assessments are 

conducted at an establishment failing a Salmonella verification sample set, at an 

establishment with repetitive Salmonella serotypes of public health concern on its 

products, and at an establishment with Salmonella PFGE patterns associated with 

foodborne illness in its most recent sample set (FSIS 2008b;FSIS 2009). 

<back

 

19 FSIS has published a compliance guideline for controlling Salmonella and Campylobacter in poultry 

[http://origin-www.fsis.usda.gov/PDF/Compliance_Guideline_Controlling_Salmonella_Poultry.pdf].
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3.1.10 Campylobacter PFGE

In May 2010, FSIS announced new Campylobacter performance standards for young 

chicken and turkeys at slaughter establishments (FSIS 2010). One year later, FSIS 

initiated a verification sampling program. ARS-VetNet performed PFGE on all FSIS 

Campylobacter isolates using the primary restriction enzyme, SmaI, and by request, the 

secondary enzyme, KpnI until June, 2013 when OSEL assumed these duties. Current 

patterns are uploaded to the PulseNet database; the older isolates are included in the 

VetNet database. VetNet Campylobacter PFGE patterns are assigned unique pattern 

names that are not equivalent to PulseNet pattern names. The VetNet Campylobacter

database is currently not available online and is not directly accessible by OSEL or 

CDC-PulseNet. 

The PulseNet protocol for preparing Campylobacter DNA for PFGE is different from the 

protocols used for E. coli, Salmonella, and Lm. The Campylobacter protocol and the 

interpretation of Campylobacter PFGE data is technically challenging. It is often difficult 

to culturally isolate Campylobacter, which is necessary to avoid getting a mixture of 

different patterns. In addition, Campylobacter patterns are often complex, with 

numerous bands, which makes it challenging to distinguish clones with different 

patterns based on size alone.

The PulseNet Campylobacter database is much smaller than the Salmonella, E. coli, or 

Lm databases, but it is growing. Some submitting laboratories often ‘batch” many 

months of data before uploading to the PulseNet database. Much of the database is 

represented by single enzyme digest patterns. While PulseNet does maintain a national 

Campylobacter database, it is not actively used to detect clusters. 

Routine PFGE subtyping is of limited value because of the high genetic diversity within

the major Campylobacter species: C. jejuni and C. coli. However, when Campylobacter

is identified as a pathogen in an active outbreak, PFGE may be a useful investigative 

tool. PulseNet recommends only confirmatory subtyping of the strains when outbreaks 
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are detected by other means (Gerner-Smidt, 2005). PFGE data generated from FSIS 

samples may be used for purposes other than outbreak cluster detection. Other groups 

have used PFGE to determine Campylobacter cross-contamination at broiler slaughter 

(Ellerbroek et al. 2010); to demonstrate a change in genotype during the course of 

antimicrobial treatment of pigs (Juntunen et al. 2010); to demonstrate the role of poultry 

products in human campylobacteriosis in Finland (Lyhs et al. 2010); and to characterize 

samples collected from broilers (Griekspoor et al. 2010;Miller et al. 2010;Wilson et al. 

2009). Additional typing methods for Campylobacter are discussed in Section 5.3.

<back

3.2. Multiple Locus Variable Number Tandem Repeat Analysis (MLVA)

MLVA takes advantage of tandem repeat sequences that occur in discrete locations of 

bacterial genomes. Each tandem repeat location contains different numbers of simple 

sequence units. MLVA is performed as a two-step process.

1. The repeat locations are copied using PCR. Primers specific for areas outside of the 

repeat locations are used to make these copies. 

2. Once copied, the sizes of the PCR-copied fragments are measured by a DNA 

sequencer or another instrument that can make very accurate estimates of DNA 

fragment size. The repeat number is determined after accounting for the extra 

sequence copied through the PCR process and the size of the simple sequence unit.

The process is described in greater detail in Appendix G and Appendix H. A MLVA 

assay consists of multiple (about eight to 20) assays, each designed to determine the 

number of units at a specific location. Bacteria from the same source are expected to 

contain the same number of tandem repeats at a specific location (Example).

By experimentation, the CDC and other groups identified eight specific locations of the 

E. coli O157:H7 genome that contains tandem repeats. Each repeat tends to be the By 

Example 

The DNA sequence “ACG” in an E. coli strain may be repeated five times at a specific 

location in its genome (ACG-ACG-ACG-ACG-ACG). Bacterial isolates from the same 

source are expected to contain five “ACG” repeats in the same site while isolates from an 

unrelated source would most likely contain a differing number of ACG repeats at the same 

locus. 
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By experimentation, the CDC and other groups identified eight specific locations of the 

E. coli O157:H7 genome that cntains tandem repeats. Each repeat ends to be the 

same in bacterial isolates generated from the same source and different in bacterial 

isolates from different sources. The same eight repeat loci are measured for every E. 

coli O157:H7 isolate. Because these loci tend to vary, MLVA assays must be designed 

or validated for each major lineage, such as serotype or serogroup. In some instances, 

this involves finding and testing candidate repeat loci for epidemiological concordance, 

designing and testing primers, and designing algorithms to calculate repeat numbers. A 

large number of Salmonella, Lm, and E. coli genome sequencing projects are providing 

raw data that can be used to discover candidate repeat loci. In other instances, an 

existing MLVA assay can be applied to a related lineage on an experimental basis. For 

example, the S. Typhimurium MLVA assay has been used during recent high-profile S.

Heidelberg and S. Montevideo outbreaks associated with FSIS-regulated foods. 

MLVA and PFGE visualize different kinds of mutations. Large indel mutations that would 

be readily apparent using PFGE may not be detected with MLVA because they occur 

outside of the tandem repeat loci specified by the assay. Conversely, MLVA detects 

small mutations at specified loci, which are likely to be invisible to PFGE. During 

validation of the E. coli O157:H7 MLVA protocol by CDC, MLVA was slightly less 

discriminating than PFGE with two enzymes. However, MLVA provides a useful 

complement to PFGE because MLVA can subtype further some of the most common 

PFGE patterns. Epidemiological concordance of MLVA data was found to be better than 

that of PFGE (Hyytia-Trees et al. 2006). The discriminatory power of MLVA depends on 

the assay design, including the choice of repeat loci and the level of genetic variation 

within the lineage to be subtyped. In general, MLVA assays developed by PulseNet 

have not proven to be more discriminatory than PFGE. However, in some cases, a 

MLVA assay discriminated strains within a lineage that was indistinguishable by PFGE. 

MLVA is helpful during outbreaks involving a common PFGE pattern or outbreaks 

involving organisms that have very limited PFGE pattern diversity. In these situations, 

MLVA is used to identify case-patients who were not associated with an outbreak. 
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PulseNet laboratories tend to use MLVA to complement PFGE during certain 

investigations. The CDC does not expect that MLVA will replace PFGE. 

CDC PulseNet has invested significant resources to develop MLVA to a point where it is 

now useful for discriminating strains within E. coli O157:H7 and some Salmonella

serotypes. Currently, state and federal laboratories submit select isolates to MLVAcertified PulseNet Laboratories for laboratory analysis. CDC personnel perform data 

analysis using BioNumerics, the same software used to analyze PFGE data. Currently, 

PulseNet has validated MLVA assays for E. coli O157:H7, S. Typhimurium and S. 

Enteritidis (Hyytia-Trees et al. 2006;Hyytia-Trees et al. 2010). OSEL analysts are 

PulseNet-certified for the E. coli O157:H7 and S. Typhimurium MLVA assays and are 

planning to acquire certification for the S. Enteritidis assay. OSEL performs MLVA on all 

E. coli O157:H7 and on S. Typhimurium isolates upon request by FSIS program staff 

(requests are typically made through OPHS at headquarters). 

In FSIS’ experience, start-up costs and training for MLVA are more significant than for 

PFGE. MLVA is substantially easier to perform than PFGE. MLVA data are somewhat

easier to interpret than PFGE patterns. The data analysis procedures for MLVA can be 

automated and therefore require less analyst time. MLVA has a greater reproducibility 

and comparability between analysts than PFGE. As the PulseNet MLVA database 

grows, the significance of the differences in MLVA profiles will become more apparent.

<back

3.2.1 Using MLVA in Outbreak Investigations

When the PFGE pattern is common, foodborne disease investigators may use MLVA to 

separate sporadic case-patients from outbreak-related case-patients to help establish 

the extent of an outbreak. FSIS consults with CDC and the PulseNet/Enteric Diseases 

Laboratory Branch laboratorians to determine if MLVA is likely to result in a substantial 

increase in specificity or discriminatory power to identify a cluster of human cases. CDC 

and state epidemiologists then may include both MLVA and PFGE patterns in casepatient definitions to guide case-patient ascertainment, refine the case count and 
Page 48 of 113

 

conserve the use of investigative resources. A request is made for MLVA analysis of all 

isolates in a cluster when MLVA has been included in the epidemiologic case definition 

to help determine the scope of the outbreak.

When a foodborne disease investigation is in the hypothesis-generating phase and 

MLVA data are available, CDC recommends that investigators focus on collecting food 

exposure information from case-patients with an indistinguishable MLVA pattern. If an 

active epidemiologic investigation is ongoing, PulseNet-MLVA staff may request isolates 

from all interviewed people to restrict the analysis of exposure information to outbreakrelated case-patients. 

During outbreak investigations, MLVA may differentiate isolates that are 

indistinguishable by PFGE but have no common epidemiological source (Noller et al. 

2003). Torpdahl (Torpdahl et al. 2006) used MLVA characterization for source 

identification in a S. Typhimurium DT12 outbreak in Denmark. MLVA correlated more 

closely than PFGE with epidemiological data in this outbreak of a highly clonal strain of 

S. Typhimurium. MLVA also is useful to assess the relatedness of closely related PFGE 

patterns (e.g., variant PFGE Blnl patterns) in temporally related illness clusters.

Highly variable VNTR loci are preferred for short-term cluster detection because of their 

ability to discriminate between closely related strains. However, this high variability may 

cause instability in some of these loci during outbreaks. Each VNTR site may have 

different mutation rates, so variation at some loci would be expected within an outbreak 

cluster, while variation at other loci would not be expected. This may be determined 

through experience for each assay. CDC developed rules in this manner to deal with 

variation in E. coli O157:H7 MLVA patterns during an outbreak. From past outbreaks, 

CDC recognizes that a difference of up to three repeats at a single locus (except at 

locus 34) or one repeat at two loci (excluding locus 34) can occur during an E. coli 

outbreak.20 No difference should occur in the number of repeats at VNTR locus 34. In 

order to develop data interpretation guidelines, thorough mutation studies and an 

 

20 Dr. Eja Hyytiä-Trees, Personal communication, March 17, 2009
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understanding of the factors affecting mutation rates are needed to distinguish VNTR 

loci that are useful during outbreak investigations (Hyytia-Trees et al. 2007). 

Isolate selection for MLVA typing includes: 1) isolates from large multi-state clusters (as 

prioritized by the CDC PulseNet Laboratory) with a common PFGE pattern and a wide 

geographical and temporal distribution; 2) isolates from states with multiple PFGE 

matches; 3) isolates recovered from animal or other food sources with indistinguishable 

PFGE patterns; 4) PFGE patterns associated with prior large outbreaks (e.g., a more 

virulent strain); or 5) outbreaks with skewed demographics (e.g., a large number of 

children, large proportion of one gender). 

During foodborne disease investigations involving E. coli O157:H7, trace back of ground 

beef is often complex and may include analyzing data from multiple establishments, 

evaluating grinding records, and assessing clean-up between production lots of different 

source material. Sporadic illnesses that might not be associated with a common source 

could have the potential to confound trace back information. FSIS increasingly uses 

MLVA to focus trace back activities when investigating PFGE subtype clusters. Trace 

back should focus on the case-patients who have indistinguishable MLVA patterns. 

Once the source of the outbreak has been identified, epidemiologic evidence may 

indicate that close variants to the outbreak primary MLVA pattern should be included in 

the case definition. Case-patients with similar or indistinguishable MLVA patterns should 

be included in the case count only if a similar exposure/source of infection has been 

identified. 

<back

3.2.2 A Case Study Using MLVA: FSIS E. coli O157:H7 Outbreak Investigation

Two investigations illustrate the utility of MLVA data.

In August 2009, a Utah cluster of E. coli O157:H7 infections was reported and 

investigated by a multi-agency team. Fourteen primary case-patients, with onset dates 

ranging from July 11, 2009, through August 27, 2009, were reported with an 
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indistinguishable PFGE pattern combination common in PulseNet, but relatively 

uncommon in Utah. Because this pattern was uncommon in Utah, CDC initially did not 

recommend conducting MLVA. Investigators suspected a common source outbreak in 

Utah due to the temporality and geographic cluster of the cases. Upon case-patient 

interviews, it was determined that a high proportion of case-patients reported 

consuming ground beef (13 or 93%) and attending rodeos (12 or 86%). Because casepatients reported attending four different rodeos, ground beef exposure was an initial 

hypothesis. FSIS trace back investigations of ground beef did not converge on a single 

supplier, but rather identified multiple establishments. One establishment was identified 

as a possible common source supplier accounting for a number of the illnesses, but 

some infections could not be linked to that establishment’s products. Samples of ground 

beef products were collected from case-patient homes and retail locations, and all 

samples tested negative. MLVA was used in an attempt to discover if sporadic E. coli

O157:H7 infections were confounding the trace back investigation of an underlying subcluster. CDC performed MLVA on 14 primary cases and two secondary cases. Four 

different MLVA patterns (A1-A4) were identified —10 isolates were indistinguishable 

(MLVA pattern A1), five had slight variations at one locus, and one had slight variations 

at two loci. The case-patients with the outbreak PFGE pattern could be considered part 

of the same outbreak based upon CDC-PulseNet’s interpretation of the variation in E. 

coli O157:H7 MLVA patterns. If the outbreak definition was tightened to only those 

case-patients with MLVA pattern A1, some case-patients with this MLVA pattern were 

not linked to the suspect establishment’s ground beef products. MLVA did not provide 

much resolution for the PFGE pattern cluster and did not strengthen the association of 

the cluster with a particular ground beef source. The Utah Department of Health further 

investigated the possibility that case-patients were linked through rodeo attendance. 

Since case-patients did not consume common food items at the rodeos, animal contact 

was investigated as a possible source of infection. It was determined that the same 

stock contractor supplied cattle at all four rodeos. An environmental (soil) sample taken 

from one of the rodeo grounds tested positive for E. coli O157:H7 with an isolate that 

had an indistinguishable PFGE pattern combination to the outbreak strain and with 

MLVA pattern A1. The evidence supported environmental (cattle) exposure at the 
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rodeos, not ground beef consumption, as the source of the outbreak. Case-patients 

whose isolates were the three variant MLVA patterns were possibly sporadic cases with 

differing exposures; however most of these also reported rodeo attendance. Because of 

the investigation, the Utah Department of Health alerted the public and rodeo/fair 

organizers of the risks of E. coli O157:H7 exposures at live animal venues and provided 

guidelines focusing on reducing exposures at such events (Lanier et al. 2011). 

In another example, in October 2009, CDC PulseNet staff assigned an outbreak code to 

a PFGE cluster of E. coli O157:H7 isolates due to an increase in uploads. Thirty-one 

case-patients were reported from eight states with an indistinguishable PFGE pattern 

combination that is common in PulseNet. Onset dates ranged from August 18, 2009, 

through October 7, 2009. Among case-patients with exposure information available, 20 

of 24 (83%) reported exposure to ground beef. Leftover ground beef samples were 

collected from case-patient homes and submitted for testing. The leftover samples 

tested positive for E. coli O157:H7 with an indistinguishable PFGE pattern combination 

from the outbreak strain. CDC performed MLVA on isolates from 31 clinical and 3 

ground beef samples and all were indistinguishable. FSIS Investigators, in collaboration 

with public health partners, performed trace back investigations of ground beef from two 

grocery store chains, which converged on a single supplying establishment. Because 

the PFGE pattern combination was common, MLVA provided additional evidence, 

supported the conclusion, and validated the trace back results. On October 31, 2009, 

FSIS announced a recall of approximately 545,699 pounds of fresh ground beef 

products.

In summary, MLVA has the potential to provide powerful insight for ground beef trace 

back investigations, particularly with common PFGE patterns. However, as with any 

single point of evidence, it is critical that results be compared with additional data 

including other laboratory results, epidemiologic information, and results from 

environmental assessments. MLVA may help to identify epidemiologically linked 

isolates correctly as being genetically related, to differentiate epidemiologically linked 

isolates from background sporadic isolates, and to differentiate between outbreaks not 
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due to a common source. When the PFGE pattern combination is common, MLVA 

provides additional evidence and validation of product sampling and trace back results.

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3.2.3 Using MLVA in Salmonella Enteritidis (SE) Outbreaks

SE is one of the most genetically homogenous serotypes of Salmonella and is poorly 

differentiated by most commonly used subtyping methods (Allard et al. 2013;Pang et al. 

2007;Saeed et al. 2006;Zheng et al. 2011). Two PFGE patterns make up nearly 48% of 

the SE isolates in PulseNet. Therefore, attributing SE to foodborne disease outbreaks is 

difficult. MLVA provides improved discriminatory power for subtyping. One study 

showed that, overall, MLVA typing of SE had enhanced resolution, good reproducibility, 

and good epidemiological concordance (Boxrud et al. 2007). 

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3.2.4 Caution Needed to Interpret MLVA and PFGE Data 

Caution is needed for interpreting a three of more enzyme digests during PFGE 

analysis, or when adding MLVA to PFGE results. Even when a food isolate and a 

patient isolate do come from the same source, it is doubtful that they are genetically 

identical. Therefore, the purpose of genetic subtyping methods is not to determine if 

two isolates have genetic differences, but to determine if two isolates are sufficiently 

similar to each other, and distinguishable from other isolates from non-epidemiologically 

related sources. The effectiveness of the two-enzyme protocol used in 

differentiation/categorization of PulseNet isolates has been proven empirically; that is, 

the hundreds of thousands of isolates in PulseNet have been categorized using one or 

two of the enzymes. The two-enzyme PulseNet PFGE method is sensitive enough to 

assign a reliable pattern to hundreds, and for some organisms, thousands of categories 

(pattern names), while at the same time has detected many clusters representing 

common source outbreaks. Empirical evidence does not support performing PFGE with 

additional enzymes not routinely used by PulseNet labs. Using other enzymes may be 

helpful in some cases, but improper use may be misleading. Additional enzyme digests 

are therefore performed only when requested by PulseNet. 
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CDC PulseNet has invested significant resources to develop MLVA to a point where it is 

now useful for differentiating E. coli O157:H7 and some Salmonella serotypes. The 

significance of the information collected through PulseNet is under constant evaluation 

and review even for organisms that have an effective protocol. Currently, only PulseNet

analysts located at the CDC have access to the PulseNet MLVA database, unlike the 

PFGE databases, which are accessible by all PulseNet laboratories.

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3.3 Subtyping Methods for Non-O157 STEC

 

FSIS and ARS developed screening and isolation procedures for six non-O157 STEC 

serogroups: O26, O45, O103, O111, O121, and O145. These serogroups accounted for 

more than 70% of the clinical isolates reported to the CDC during the period from 1983 

through 2002 (Brooks et al. 2005). The method was added to the FSIS Microbiology 

Laboratory Guidebook (MLG) in October 2010. FSIS initiated verification testing of raw 

beef manufacturing trimmings for these STEC serogroups in June 2012. Food 

enrichment broths are screened for the presence of the stx1 and stx2 genes encoding 

Shiga toxins; the virulence gene eae that encodes the intimin protein, which allows 

close adherence of STEC to human intestinal cells; and the wzx or wbdI genes 

associated with the biosynthesis of O-antigens for the six serogroups. If the enrichment 

broths screen positive for stx, eae, and one or more of the top six O-antigen associated 

genes, cultural isolation is initiated and the isolates obtained are confirmed for virulence 

and serogroup genes using PCR. 

The PFGE procedure for non-O157 STEC uses the same standards, enzymes, and 

equipment as the E. coli O157:H7 procedure. Lab analysts who are certified by 

PulseNet for E. coli O157:H7 also are certified for non-O157 STEC, but a separate 

certification for the analysis of non-O157 STEC gels is required. Data collection 

procedures also are similar with the following exceptions: OSEL and PulseNet maintain 

separate databases and separate pattern name format for non-O157 STEC and E. coli

O157:H7. Currently, data analysis procedures are similar, but this may change as 
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PulseNet receives more non-O157 STEC isolates. A MLVA test is not available for nonO157 STEC.

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3.4 Molecular Confirmation of E. coli O157:H7

Routine confirmation of E. coli O157:H7 is comprised of three parts: (1) serology for the 

O-group (H group serology is performed, but is not necessary for confirmation); (2) an 

enzyme-linked immunosorbent assay (ELISA) method for the Shiga toxin 

(enterohemorrhagic E. coli (EHEC) test kit); and (3) an automated biochemical test 

(VITEK). As an alternative to the traditional serological method, which uses antisera 

prepared against the H7 antigen, FSIS laboratories may perform a molecular serotyping 

method, which is illustrated in Appendix I. OSEL performs PCR-based molecular 

serotyping of E. coli O157:H7 when routine confirmation methods yield indeterminate 

results (e.g., the isolate is O157 positive by serology and Shiga toxin negative by 

ELISA). The molecular serotyping PCR test is run on the SmartCycler (an instrument 

used to perform a real-time PCR) and is designed to simultaneously detect the intimin 

gene (eae), the H7 flagella antigen gene (fliCH7), and the Shiga toxin gene. The assay 

does not differentiate between the major Shiga toxin genes, stx1 and stx2, and only 

serves to confirm the presence of at least one of these genes. A positive O157 serology 

result, combined with a positive PCR test for the flagella antigen gene fliCH7 or stx, is 

sufficient to confirm the isolate as E. coli O157:H7 subtype (FSIS 2012b). The eae

result is not used for confirmation of E. coli O157:H7 because certain eae types are 

found in non-O157 STEC strains as well and are therefore not sufficiently specific to E. 

coli O157:H7. 

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3.5 Molecular Serotyping of Salmonella 

Traditional serotyping of Salmonella uses specific antibodies to type certain cell surface 

antigens. The method is very specialized, time consuming, and expensive. Laboratories 

performing traditional serotyping must maintain a large library of antisera to detect all 

Salmonella H- and O- antigens. For this reason, prior to July 2012, FSIS sent all of its 
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Salmonella isolates to APHIS’ NVSL for traditional serotyping resulting in significant 

delays in reporting serotype results. This makes it difficult to report the results within the 

Agency and to stakeholders in a useful timeframe. CDC developed a molecular 

serotyping method for Salmonella (Fitzgerald et al. 2007;McQuiston et al. 2011), which 

has been verified by FSIS. The molecular method is applied to purified isolates and can 

be divided into two steps.

1. Regions of the Salmonella genome responsible for producing the flagellar and 

somatic antigens (H- and O-antigens encoded by the fliB, fliC, and rfb genes) are 

amplified using PCR. 

2. Specific probes complementary to sequences specific for the fliB, fliC, and rfb genes 

of major Salmonella serotypes are combined with the PCR-amplified DNA. If the 

probes bind their target sequence, a signal can be detected using the Bioplex 

platform.

This process is illustrated in Appendix J. The CDC method is quick, straightforward, and 

economical when compared to traditional serotyping. 

In June 2012, FSIS began to use the CDC's molecular serotyping method to identify 

some Salmonella serotypes. As of January 2, 2013, the OSEL laboratory is responsible 

for the routine identification of twenty-five Salmonella serotypes by the molecular 

method (listed in Appendix K), with all other serotypes confirmed by the NVSL using

traditional serotyping methodology (FSIS 2013a). 

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4 Subtyping Technologies Being Developed or Under Evaluation 

(Short Term, One to Four Years)

This section describes subtyping methods that FSIS is currently exploring with possible 

implementation dates in the next one to four years. This includes new subtyping 

methodologies for Campylobacter, non-O157 STEC, Lm, and Salmonella spp. In 

addition, FSIS is in the process of implementing a Public Health Information System 
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(PHIS) that will provide direct access to PFGE pattern information within the Agency 

and to other federal and state government stakeholders.

 

4.1 MLVA for Non-O157 STEC, Lm, S. Newport, and other Salmonella serotypes

In general, VNTR regions targeted by MLVA assays are specific to serologicallydistinguishable groups of bacteria. Thus, MLVA assays developed for one group are not 

expected to be valid for others. For example, MLVA assays validated for E. coli

serotype O157:H7 and Salmonella serotypes Typhimurium and Enteritidis are not 

expected to be applicable to other non-O157 STEC serogroups or to important 

Salmonella serotypes such as Heidelberg, Montevideo, and Newport. Therefore, 

additional MLVA assays are under development or being validated for use with 

additional bacterial groups. For non-O157 STEC groups, PFGE has proved to be 

sufficiently discriminatory, and therefore, development of MLVA for other organisms is 

currently a higher priority for CDC. However, this may change as more strains from food 

products are isolated, typed, and added to the PulseNet database. A MLVA protocol for 

Lm is currently undergoing internal validation at CDC’s Enteric Diseases Laboratory 

Branch, and CDC developed a MLVA protocol for S. Newport, which has been internally 

validated and is undergoing external validation. CDC has evaluated additional MLVA 

assays for S. Heidelberg and S. Montevideo following large foodborne investigations 

involving FSIS-regulated products. These assays have been used under an 

experimental basis and have not been validated for use by PulseNet labs. In 2014 the 

ultility of whole genome sequence comparison for outbreak surveillance/detection is 

being pursued by CDC in lieu of new MLVA techniques for foodborne pathogens.

To discover new VNTR regions suitable for MLVA assay development, researchers 

must first study many isolates from a particular group. Whole genome sequencing 

initiatives, such as the 100K Genome Project (Section 5.1), will help researchers 

discover VNTR and understand their variation within a specific group, such as a 

serotype. 

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4.2 Public Health Information System (PHIS) 

FSIS implemented PHIS, an information technology system that collects, consolidates, 

and analyzes data. It replaced many of FSIS' other data systems, such as the 

Performance Based Inspection System (PBIS) and the Automated Import Information 

System (AIIS). Field and headquarters staff access PHIS through a web-based 

application. The system has four components: domestic inspection, import activities, 

export activities, and predictive analytics. The predictive analytics component will allow 

FSIS staff to perform advanced data analyses to detect temporal and spatial trends. 

The system will be capable of integrating other data streams generated by ARS, AMS, 

and CDC. The PHIS will be capable of expanding to accept other data sources and 

types and new subtyping methods as adapted by FSIS. FSIS is working with CDC 

PulseNet and ARS to transfer PFGE pattern names, serotype data, and associated 

metadata from the FSIS Laboratory Information Management System (LIMS), as well as 

the PulseNet and VetNet databases into the PHIS, which will allow authorized users to 

access and generate reports on specific isolates and to perform comprehensive studies 

within the system. Due to the real-time nature of the data transfers, OSEL will still 

handle time-sensitive needs, such as requests related to ongoing outbreak 

investigations.

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4.3 Targeted Multilocus Genotyping (TMLGT) for Lm

ARS, in collaboration with FSIS, developed and validated an SNP-based assay, 

TMLGT, to facilitate rapid strain characterization and the integration of subtype data into 

risk-based inspection programs. Where TMLGT has poor discrimination for specific 

outbreaks, PFGE has strong discrimination. Where PFGE is poorly able to assign Lm to 

lineage, serogroup, and EC clades, TMLGT is able to assign. TMLGT was designed to 

use Luminex-xMAP technology and the Bioplex platform (also used for molecular 

serotyping of Salmonella, Section 3.5). This technology uses flow cytometry to detect 

specific nucleic acid sequences. However, the SNP used in the TMLGT methodology 

could be determined by other means, such as by direct sequencing (Section 5.1). 

TMLGT was validated in a study that included 906 isolates that had been independently 
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subtyped using MLGT. Nine hundred three isolates were correctly typed by the method 

(99.7% typability) (Ward et al. 2010b). 

FSIS may consider using more detailed information about Lm subtype, which provides 

information about key differences in public health risk and ecology, to improve existing 

risk-based sampling programs or inspection policies. In 2005, FSIS implemented a 

sampling program that incorporates specific data related to the risk of Lm contamination 

(e.g., production volume, outgrowth potential in the product, steps taken to prevent postlethality contamination, and FSIS sampling history) to create a risk-based sampling 

framework (FSIS 2007). Through this program, FSIS collects samples from 

establishments with higher risk more frequently than establishments with lower risk. 

Along with sampling, FSIS assigns additional inspection to an establishment when there 

is evidence that it is manufacturing hazardous products, such as when an 

establishment’s products are found to be adulterated with Lm or are determined to be 

associated with an Lm outbreak. Data from TMLGT subtyping of FSIS’ Lm isolates 

could be used to extend the risk-based sampling framework to include estimates of 

public health risk (Section 2.8) 

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5 Subtyping Technologies of the Future (Five or More Years to 

Implementation)

This section examines examples of how new technologies, including high-throughput 

sequencing, can be used to provide informative subtype data on bacterial pathogens of 

concern to FSIS (e.g., STEC, Lm, Salmonella, and Campylobacter). FSIS is not 

currently evaluating these methods, and Agency implementation may take five or more 

years. Other U.S. agencies, including CDC, FDA, and ARS, as well as organizations in 

Canada and the European Union, are developing methods and exploring the use of 

these data. In recent years, FSIS has participated in meetings designed to bring federal 

public health agencies together to discuss the development and implementation of 

subtyping methods. These include the Annual PulseNet meetings organized by CDC, 

the New Frontiers of Molecular Epidemiology (NFME) meeting organized by FDA and 
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CDC in 2009 and 2011, the Systems for Food Examination (SAFE) meetings organized 

by the Department of Defense’s Advance Research Projects Agency (DARPA) in 2009, 

an American Society for Microbiology (ASM) Symposium on the Development and Use 

of Subtyping Data by Regulatory Agencies organized by FDA and FSIS in 2010 and the 

Global Microbial Identifier meetings that were initiated in 2011. These meetings provide 

a forum where FSIS can share ideas and recommendations on subtyping method 

development with federal partners. The meetings encourage ongoing and two-way 

communication to ensure that all organizations understand each other’s goals and 

timelines.

5.1 High-Throughput Sequencing of Bacterial Genomes

In 2013, most subtyping methods used by public health and regulatory authorities were 

based on the interpretation of DNA or RNA fragments or the differential presence or 

absence of genetic markers associated with virulent strains (Section 2). At present, 

nucleic acid sequence is used in limited capacity, and includes SNP, MLST, and whole 

genome sequence comparison.

Instrumentation, technology, and chemistry needed to perform nucleic acid sequencing 

have progressed enormously in the past decade and further advances are expected in 

the next five years. High-throughput sequencing usually involves collecting tens of 

thousands of partially overlapping sequence fragments. By a process called “assembly,” 

the fragments are arranged into their correct order by matching their overlapping 

sequences, so that large contiguous sequences, and even entire genomes, are 

reconstructed. In the past, these instruments and capabilities were only available at 

large facilities. With new, more economical technology, large scale sequencing is 

beginning to be available to individual laboratories, and large facilities have increased 

their capabilities dramatically, resulting in an impressive increase in the availability of 

whole genome sequences deposited into public databases such as the National Center 

for Biotechnology Information (NCBI). The cost of high-throughput sequencing has 

decreased in the past decade due to increased availability of instruments and reagents. 

Next-generation sequencing platforms, such as the 454 GS FLX (Roche Life Sciences), 
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Miseq and Hiseq (Illumina), PacBio RS (Pacific Biosystems), and the Ion Torrent (Life 

Technologies) sequencers, have become readily available to smaller laboratories

including public health labs. These platforms allow multiple genome sequences to be 

determined in a single run, which will further drive down prices. The availability of 

genome sequence is driving the need for computational software for assembly and 

scientists who are trained to perform sequence assembly and annotation (i.e., 

describing the features of the assembled sequence). Assembly, annotation, and 

interpretation eventually may be automated. This is being pursued actively by NCBI. 

Programs eventually may be designed to extract useful information on MLVA, MLST, 

and SNP subtype from a genome assembly. For FSIS, these trends mean that highthroughput sequence technology and data will be routinely available at a substantially 

lower cost. CDC, FDA, the Public Health Agency of Canada, New York State 

Department of Health, and other organizations are beginning to produce and analyze 

high-throughput and WGS data during the course of an outbreak investigation, and in 

2013, CDC initiated a Lm real time surveillance project using WGS. WGS data likely will 

be available to industry as well. The 100K Pathogen Genome project is a consortium 

initiated by FDA, University of California Davis, and Agilent Technologies, to develop a 

database of bacterial pathogen genomes accessible through the NCBI. The project 

seeks to improve the understanding of pathogen genetic variation, and the development 

of new subtyping assays. FSIS has stated its intention to contribute strains from its 

regulatory testing program to this project 

FSIS will need to consider how to respond to trends in the availability of large scale 

sequencing capability. For example, should FSIS continue to support the use of these 

data by contributing strains to the 100K Pathogen Genome Project or other 

organizations like ARS, CDC, and FDA, and should the Agency acquire the technology 

to collect these data in real time? Some of the issues raised by the availability of large 

scale sequencing technology are presented below. 

WGS data potentially provides one of the most complete and definitive assessments of 

subtype. This data is portable (i.e., it can be easily shared between researchers through 
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the internet, as PFGE and MLVA pattern data currently is shared) and the data are 

compatible with previous fragment-based, virulence marker, and limited sequencedbased typing schemes like MLVA and SNP analyses. For example, provided the 

assembly is sufficiently correct, WGS data could be used to find restriction enzyme sites 

and calculate the size of restriction fragments without the need for PFGE. WGS data 

can detect large genetic changes, such as inversions, insertions, deletions, and 

duplications that would not be detected by shorter sequences obtained with MLVA, 

MLST, SNP, or microarray based typing assays. The data can be used to define new 

sequences or genes that distinguish and identify hyper-virulent lineages for further 

research. In summary, WGS provides a wealth of genetic information about a strain, 

some of which would be relevant during outbreak investigations, but also would drive 

the development of new subtyping assays to detect lineages or virulent clones of public 

health concern.

An important issue is data specificity. WGS will detect much greater detail about strains

than other subtyping methods, and in many situations will distinguish strains that would 

be conclusively indistinguishable by PFGE, MLVA, SNP and other methods. Some of 

these differences are meaningful and distinguish the outbreak lineage from sporadic 

cases. Other differences may be found both within and outside the outbreak lineage and 

therefore should be considered inconsequential to the interpretation of the outbreak 

data. Like any other assay, the proper balance between sensitivity and specificity (i.e., 

to correctly detect and act to limit the spread of true outbreaks) will need to be 

determined from practical observation and experience. It is possible that Industry will 

use WGS data to “exonerate” their products or otherwise limit regulatory action. 

Interpretative guidelines on the collection, analysis, and interpretation of WGS data are 

needed to provide standards for this rapidly growing area of investigation.

FDA-CFSAN researchers have used WGS to evaluate S. Montevideo from a multistate 

outbreak of salmonellosis associated with pepper-coated pork salami products that 

resulted in multiple FSIS and FDA-initiated product recalls. The researchers evaluated 

complete genomes from patients, boxes of black and crushed red pepper, and an 
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environmental isolate recovered from a drain at the facility. The density of data provided 

by the WGS allowed investigators to exclude the involvement of pistachio nuts, while 

validating the association with multiple production lots of black and crushed red pepper 

used as an ingredient in the salami products (Lienau et al. 2011). Interestingly, the FDA 

group concluded that the S. Montevideo strain came from a domestic source, which 

conflicts with the international origin of the spices (China and India). This could indicate 

that the spices were cross-contaminated following export to the United States, perhaps 

at a spice grinding facility. Another group used WGS data (SNP typing assay of 112 

isolates) from the same sources to suggest that a majority of the case-patients were 

sporadic, and not outbreak associated as originally assumed by CDC and FDA (Bakker 

et al. 2011). 

Other outbreak investigations using WGS have been reported, including Lm outbreaks 

in the United States and Canada (Gilmour et al. 2010;Orsi et al. 2008), a large clonal S. 

Enteritidis outbreak associated with eggs (Allard et al. 2013), and STEC outbreaks 

associated with spinach and sprouts in the U.S. and Europe (Brzuszkiewicz et al. 

2011;Kotewicz et al. 2008;Mellmann et al. 2011;Rasko et al. 2011). Researchers will 

continue to perform WGS from bacteria in strain collections, including FSIS positive 

product samples, which will help to interpret the degree of variation seen in genomes 

from outbreak isolates. This information will be extremely useful to define and test 

whether certain sequences can be used reliably to distinguish unrelated strains and, 

thus, could be used prospectively for outbreak cluster detection. 

The availability of low-cost genome sequence data should benefit public health and 

regulatory agencies. However, WGS data generally would be more expensive to collect 

than existing subtyping strategies, require more computer database storage, and will 

produce replicate datasets that are identical in all but a small number of sequence 

differences. The per-genome cost could make WGS prohibitive for surveillance 

samples. Surveillance samples, including isolates from “sporadic” illnesses and routine 

food and environmental samples may be an important application for subtyping. A great 

deal of information may be gained from sequencing surveillance samples, such as 
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detecting virulence genes, characterizing emerging subtypes, or discovering new SNP 

that could be used to distinguish future outbreak clades. The cost of high-throughput 

sequencing technologies could be offset by using the data for multiple purposes. Such 

purposes include: obtaining MLVA, SNP, PFGE subtype from the WGS data, and 

finding important genes responsible for virulence, persistence, and resistance to 

antibiotics or sanitizers; developing new assays for detecting future outbreak cluster 

detection and routine surveillance; and using virulence gene or marker discovery to 

study mechanisms of pathogenicity for future risk assessments and attribution studies. 

Metagenomics is the genetic study of organism communities (Frank and Pace 

2008;Nakamura et al. 2008;Nakamura et al. 2011). Molecular methods, including highthroughput sequencing, have been applied to study the relative abundance of bacterial 

species and subtypes within these communities without the need for cultural isolation. 

This information could be useful to FSIS. For example, studies could characterize 

bacterial communities in feces, meat, rinsates, and the processing environment, or 

studies could characterize the potential for enrichment bias associated with commonly 

used laboratory methods, such as those initiated by FDA-CFSAN (Pettengill et al. 

2012). Although FSIS procedures require a cultured isolate before the Agency takes 

regulatory action based on the “M1” or “P1” definitions of adulteration (9 CFR 301.1, 

381.1, and 590.5), metagenomic data could be used to support other decision-making 

by the Agency as described in Section 2.8.

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5.2 STEC

More than 160 serologically distinct STEC have been identified, but only a subset of 

these have been definitively implicated with illness (Bettelheim 2007). Thus, the ability 

to produce Shiga toxin does not by itself render E. coli pathogenic; the presence and 

expression of additional virulence factor genes are required to cause human illness 

(Wickham et al. 2006). In general, STEC serotype O157:H7 is associated with large 

foodborne outbreaks and with severe outcomes (including death). Additional STEC 

serogroups (defined by their O-antigen) more commonly are recovered from clinical 
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cases in the United States (Brooks et al. 2005). Non-O157 STEC serogroups have been 

associated with less severe outcomes. However, some infections with non-O157 STEC 

have led to severe complications, including HUS and death; these strains have been 

responsible for outbreaks with severity comparable to E. coli O157:H7 outbreaks, 

including a STECO111:NM21 outbreak associated with an Oklahoma restaurant 

(Bradley et al. 2012) and a STEC O104:H4 outbreak in Europe associated with sprout 

consumption (Frank et al. 2011;Mellmann et al. 2011). Seventy to 80% of U.S. clinical 

non-O157 isolates are serogroups O26, O45, O103, O111, O121, and O145 and form 

the basis of the current FSIS method for detecting non-O157 STEC (Fratamico et al. 

2011;FSIS 2012a;FSIS 2012c). STEC strains not belonging to the top six serogroups or 

not containing the eae (intimin) gene have been isolated from cattle and beef products 

(Galli et al. 2010;Hussein 2007). This type of strain was responsible for the 2011 

European sprout outbreak, but is not identifiable by using the current FSIS method 

(FSIS 2012a). Other regulatory authorities and industry eventually may rely on more 

discriminatory methods that seek to distinguish strains with higher or lower 

pathogenicity, rather than take the approach of FSIS, which is more inclusive. For FSIS, 

it may become important to identify genes or sequences that are preferentially 

associated with pathogenic STEC (pSTEC) or enterohemorhagic E. coli (EHEC) to 

facilitate future diagnostic method development.

Adherence of STEC to cells lining the intestinal tract is an important step in the disease 

process. Strains of E. coli O157:H7 and certain non-O157 STEC form attaching and 

effacing (A/E) lesions, which reshape the human cells, recruit immune cells, and allow 

Shiga toxin to be transferred to the circulatory and organ systems, resulting in severe 

outcomes including HUS. Coordinated expression of genes (including but not limited to 

intimin) found in a specific region in E. coli O157:H7 and other STEC strains and 

referred to as the “locus of enterocyte effacement (LEE) pathogenicity island” are 

necessary to form an A/E lesion. In LEE-negative STEC strains, other genes substitute 

for the absence of LEE. Genetic analyses have identified adhesion genes in STEC 

serogroups O104, O113, and O174 that appear to allow for attachment in the absence 

 

21 NM indicates the organism is non-motile.
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of intimin, and these serotypes have been associated with sporadic and outbreak 

associated illness in Europe and Australia (Bielaszewska et al. 2011;Paton et al. 

1999;Tarr et al. 2008). These additional virulence genes could be used to expand the 

scope of the pathogenic STEC detected by FSIS tests. Other genes have been 

associated with pathogenic STEC strains include alpha-hemolysin (hyl), serine protease 

(esp), catalase peroxidase (kat), immunomodulator (lif), subtilase cytotoxin (sub), 

cytolethal distending toxin (cdt), and EHEC enterohemolysin (ehx or E-hly)(FSIS 

2012c). In addition, O-islands (OI), which are chromosomal DNA segments first 

discovered in the whole genome sequence of E. coli O157:H7 but absent in the whole 

genome sequence of a non-pathogenic E. coli strain (Perna et al. 2001), could be used 

to distinguish pathogenic and non-pathogenic strains. 

Karmali (Karmali et al. 2003) first proposed the concept of sero-pathotype categories 

based on reported frequencies of serogroups in human illness of known associations 

with outbreaks and with severe disease, such as HUS and hemorrhagic colitis. Five 

sero-pathotype categories were proposed. Category A, which includes E. coli O157:H7 

and E. coli O157:NM, occur at relatively high incidence, are commonly associated with 

outbreaks, and are associated with severe disease. Category B, which include some 

but not all non-O157 strains detected by the FSIS method, occur at moderate incidence, 

are uncommonly involved with outbreaks, and are associated with severe illness. 

Infections with the category C sero-pathotype occur at low incidence, are rarely involved 

with outbreaks, but may be associated with severe illness. Category D occur at low 

incidence, are rarely involved with outbreaks, and do not cause severe illness. Lastly, 

category E does not cause human illness. A number of virulence genes or other 

markers could be used to place STEC into these types of categories and therefore 

distinguish pathogenic STEC (also called EHEC) from non-pathogenic STEC (Bugarel 

et al. 2011;Coombes et al. 2008a). In particular, specific OI genes, such as the nle (nonLEE encoded effector) genes, seem to be associated with the Karmali sero-pathotype 

categories (Bugarel et al. 2010a;Coombes et al. 2008b;Delannoy et al. 2012;Imamovic 

et al. 2010;Konczy et al. 2008). A number of methods have been developed to detect 

pSTEC or EHEC, or to distinguish STEC subtypes including E. coli O157:H7. 
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FSIS is aware of STEC subtyping methods that are under development for commercial 

applications. Pall has developed PCR assay platform (GeneDisk) for a variety of STEC 

virulence factors and OI genes. This assay performs multiplex real-time PCR in a plastic 

tray engraved with reaction chambers preloaded with dried reagents (Beutin et al. 

2009). The specific genetic targets can be changed to meet research or regulatory 

testing needs. Currently, ARS and the French Food Safety Regulatory Agency have 

evaluated the GeneDisk STEC assay (Beutin et al. 2009;Bugarel et al. 2010b;Fratamico 

and Bagi 2012) to detect the presence of bacteria that carry stx genes in foods. A 

recently described GeneDisk assay targets genes and probes for 12 O-groups 

(including the top six strains discussed earlier), 7 H- types, and 15 virulence genes, 

including stx (1 and 2), eae, plasmid- and chromosomally-associated genes, and nle

genes associated with O157, O71, and O122 (Bugarel et al. 2010b). Neogen is 

developing a mass-spectroscopy-based test, called Neoseek, which could detect more 

than 70 STEC gene targets, including O-antigen, H-antigen, stx, eae, virulence factor 

genes, and OI genes simultaneously from cultures and isolates22. Roka Bioscience is 

developing a commercial assay that claims to distinguish pathogenic and nonpathogenic STEC strains.

The GeneDisk and Neogen assays initially were designed to confirm isolated colonies 

of STEC, but also have been used on food enrichment broths with good results23. FSIS 

may consider these or other assays for additional screening of enrichment broths as a 

way to monitor associations and trends in the occurrence of virulence markers. One 

drawback of using multiplex (many targets) assay to screen food enrichment that 

contains a large population of mixed bacteria is the inability to determine whether all of 

the detected markers originated from a single bacterium or from various bacterial cells. 

From FSIS’ perspective, it is critical that the organism is isolated and shown to carry all 

the relevant virulence and trait markers. To address this problem, immunomagnetic 

separation (IMS) beads that use various O-specific antibodies can be used to capture 

 

22 Dr. Eden Hosking, Senior Research Scientist, R&D, Molecular Biology, Neogen Corporation, May 6, 2012 23 Dr. Pina Fratimico, Personal communication, October 27, 2010
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organisms of specific serogroups. PCR then can be performed on the separated fraction 

containing the serogroup to confirm that they carry trait virulence genes. Although 

useful, IMS is not a perfect solution as each serogroup is complex and comprised of 

both known pathogenic and non-pathogenic serotypes, and some non-specific binding 

will occur. Currently, the only reliable confirmation method is to culturally isolate the 

organism and confirm the presence of Shiga toxin and a certain combination of 

virulence factor genes.

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5.3 Campylobacter

Campylobacter is a leading cause of foodborne illness in the U.S. Infection can lead to 

clinical outcomes ranging from diarrhea to debilitating arthritis. Most cases of human 

campylobacteriosis are sporadic (Altekruse et al. 1999). Outbreaks have been 

associated with ingestion of unpasteurized milk, water from a contaminated municipal 

source, and undercooked poultry (Blaser 1997). 

Campylobacter species are identified and differentiated using phenotypic, biochemical, 

and PCR methods (FDA 2001;FSIS 2011b). FSIS uses direct microscopy and a 

commercially-available latex agglutination assay to identify isolates belonging to species 

coli, jejuni, or lari (FSIS 2011b). A method for serotyping C. jejuni is based on the 

detection of heat-stable antigens (Moran and Penner 1999). Genotypic methods include 

AFLP, MLST, PFGE, ribotyping, SNP profiling, random amplification of polymorphic 

DNA (RAPD), matrix-assisted laser desorption/ionization time of flight with mass 

spectroscopy (MALDI-TOF-MS), confocal micro-raman spectroscopy, high-resolution 

melting (HRM) analysis, PCR–denaturing gradient gel electrophoresis (PCR-DGGE),

and PCR–single strand conformation polymorphism (PCR-SSCP), (Ahmed et al. 

2012;Eberle and Kiess 2012;Lu et al. 2012). 

Although PFGE has been referred to as the “gold standard” for the molecular typing 

method for Campylobacter (Foley et al. 2009a;Nielsen et al. 2000;Nielsen et al. 

2010;Sails et al. 2003), a NACMCF panel countered, “...while a number of subtyping 
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methods have been used with Campylobacter species (serotyping, antibiotic resistance, 

MLST, PFGE, flagellin (flaA) sequencing, etc.), none have yet been sufficiently 

discriminatory to be generally applicable as a gold standard” (NACMCF 2007). Routine 

PFGE subtyping of sporadic case-patient isolates is of limited value because of the high 

genetic diversity and non-clonal population structure of C. jejuni and C. coli (GernerSmidt et al. 2006;Wilson et al. 2009). 

MLST is used to provide greater discriminatory power. One MLST method (Dingle et al. 

2001) includes the sequences of seven housekeeping genes24. However, this method is 

insufficient for differentiating Campylobacter strains, especially among Campylobacter

coli25. Another group (Zautner et al. 2012) distinguished six C. jejuni-groups by 

combining MLST with 14 additional markers to distinguish a group with higher virulence 

for humans (higher prevalence, bloody diarrhea, and hospitalization) from another group 

that was more prevalent in animal hosts and associated with less severe 

campylobacteriosis.

Campylobacter subtyping methods are needed to identify and trace the source of 

foodborne outbreaks, to more fully understand the ecology of this pathogen (e.g.,

transmission routes and vectors in the food chain, the emergence of novel, including 

antibiotic resistant, strains); and to understand how strain variation contributes to 

disease progression and development of debilitating clinical outcomes, including 

Guillain-Barré (GBS), Miller-Fisher, and Reiter’s syndromes (Altekruse et al. 1999). 

At present, the contribution of Campylobacter strain variation to disease manifestation 

and ecology is not clear (Ahmed et al. 2012), although there are interesting findings. 

Proteins responsible for colonization, adherence and invasion, induction of host cell 

death, and one toxin, cytolethal distending toxin, were detected in the genome of C. 

jejuni (Dasti et al. 2010). Whole genome sequence analysis of multiple strains indicated 

a correlation between C. jejuni genomic content, particularly in surface-coding regions, 

 

24 Seven housekeeping genes: aspA (aspartase A), glnA (glutamine synthetase), gltA (citrate synthase), 

glyA (serine hydroxymethyltransferase), pgm (phosphoglucomutase), tkt (transketolase), and uncA (ATP 

synthase α subunit)

25 Dr. William Miller, personal communication, October 7, 2010 
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and its capacity for environmental survival (On et al. 2006). In addition, strains with 

sialic acid modified lipooligosaccharide (LOS) mimic host gangliosides, induces the 

production of autoantibodies and leading to the development of GBS (Godschalk et al. 

2007). Subtypes from FSIS surveillance samples containing genes, or otherwise 

strongly associated with disease progression could be of greater concern to the agency. 

Subtype data could be incorporated into risk-based sampling algorithms, or follow up 

inspection programs. 

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5.4 Listeria monocytogenes (Lm)

Lm strains have substantial genetic diversity which influences traits such as 

environmental persistence, resistance to sanitizers, as well as human and animal 

virulence. Using subtyping methods, research groups have discovered subtype clades 

which correlate with differences in ecology and virulence. These clades include four 

lineages, four major serogroups, and seven epidemic clones (EC). Of the four Lm

lineages (called lineage I, II, III, and IV), only lineages I and II are commonly isolated 

from foods and cases of human listeriosis. Lineage III is commonly isolated from 

animals, but is not a significant contaminant of foods, nor does it appear to cause 

illness. The ecology and virulence of lineage IV are poorly understood because it is very 

rarely isolated. The four major serotype groups (4b, 1/2a, 1/2b, and 1/2c) account for 

the majority of food contaminants, but three (4b, 1/2a, and 1/2b) are significantly 

associated with human listeriosis. Serogroup 4b is responsible for many outbreaks and 

for about 46-49% of sporadic listeriosis cases reported to CDC (CDC 2013). Recently, 

major serogroup 1/2a has been associated with large outbreaks, including outbreaks 

linked to FSIS-regulated foods (CDC 2011a;CDC 2011c;Gaul et al. 2012;Gilmour et al. 

2010). Lastly, seven EC have each been associated with multiple outbreaks even 

though they are rare contaminants of food. EC types I, II, and IV belong to lineage I and 

major serogroup 4b. EC types III, V, and VI belong to lineage II and major serogroups 

1/2a and 1/2b. Investigators believe EC possess hyper-virulent properties because 

these clades have been isolated from multiple outbreaks. Of particular concern to FSIS, 

EC type II has been associated with two large multistate outbreaks involving FSIS 
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regulated foods (1998-1999 outbreak linked to contaminated hot dogs, and a 2002 

outbreak linked to contaminated turkey deli meats). EC type III is thought to have 

persisted in an FSIS-regulated establishment over a 12 year period. ECIII serotype 1/2a 

isolates have caused outbreaks linked to contaminated hot dogs (U.S. 1989) and turkey 

deli meats (U.S.2000) manufactured in the same food processing plant. Six of the seven 

EC types have been identified in poultry processing facilities. (Chen et al. 2005;Chen et 

al. 2007;Kathariou et al. 2006;Lomonaco et al. 2013;Orsi et al. 2008;Verghese et al. 

2011). 

Lm has a complicated life history owing to its ability to survive in diverse locations (on 

non-living surfaces, on vegetation and soil, and in animals and humans) and its ability to 

invade and cause disease in animals and humans. In any given subtype, genes evolve 

together and influence each other. The outcome may not be predictable by the 

presence or absence of any single gene but by specific mutations that influence the 

ecology or pathogenicity within the subtype. Certain genes influence the ability of Lm to 

survive outside and inside of a host animal or human. A regulator of gene expression, 

prfA, plays an important role that allows Lm to switch between life outside and inside a 

host in response to environmental stimuli, including temperature and the availability of 

certain nutrients (Freitag et al. 2009). Other genes have been studied for their roles in 

Lm ecology and virulence. The internalin (inl) genes are responsible for allowing Lm to 

bind and invade animal or human cells, an important step in developing listeriosis. 

Certain mutations in inlA greatly influence virulence as demonstrated by the small 

number of these mutations in strains from clinical cases and the abundance in strains 

from food isolates (Jacquet et al. 2004;Tamburro et al. 2010;Van et al. 2010). The 

importance of inlA in Lm virulence also has been demonstrated in human cell lines and 

animal models (Chen et al. 2011;Nightingale et al. 2005;Van Stelten et al. 2011). InlA

mutations do not render Lm completely avirulent, but they are important markers for 

less virulent subtypes. Notably, inlA mutations cannot explain completely why certain 

serotypes are responsible for most sporadic and outbreak associated listeriosis. 
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Based on an analysis of subtype diversity among 501 Lm isolates from FSIS’ RTE 

sampling program, 34 (6.8%) isolates were from the 4b complex, which includes 

serotype 4b. Epidemic clones from previously documented outbreaks were identified in 

38 (7.6%) isolates, and InlA mutations were identified in 243 (48.5%) isolates, which is 

consistent with previous surveys (Ward et al. 2010a). This survey indicated the range of 

subtypes with important differences present in FSIS-regulated products for host, 

ecology, and virulence properties. Information on Lm subtypes present in FSISregulated foods can be used to prioritize or target additional resources to 

establishments where subtypes that are more likely to be associated with sporadic or 

outbreak-associated illnesses have been identified (Section 4.3). 

The distribution of Lm subtypes in foods is likely to have an important role in risk 

assessments, including estimates of dose-response and risk associated with specific 

foods. Older risk assessments sponsored by the World Health Organization (WHO), 

FDA, and FSIS employed a mouse animal model with a single Lm serotype 4b strain to 

generate the dose response curve and used additional strains to estimate variability 

(FAO/WHO 2004;FDA/USDA. 2003). The dose response relationship was multiplied by 

seven orders of magnitude to account for levels of Lm likely to be consumed by people 

during listeriosis outbreaks. Food surveys indicate different relative subtype frequencies 

in different foods and in clinical patients (Chen et al. 2006;Van et al. 2010;Ward et al. 

2010a), possibly due to different ecological and survival characteristics among Lm

subtypes. Using food survey and epidemiological data, Chen provided subtype-specific 

dose response estimates for different lineages and ribotypes, which differed by three or 

four orders of magnitude (Chen et al. 2006). Newer dose-response models have 

employed pregnant non-human primate, guinea pig, and hamster animal models, along 

with different Lm subtypes with inlA mutants. These models, along with improvements in 

the choice of animal model, disease endpoint, experimental set-up, or substitution of 

epidemiological data for animal data, may lead to considerable improvements to Lm

dose-response modeling (Hoelzer et al. 2013). 

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5.5 Salmonella

Traditionally, Salmonella diversity has been defined by serotyping and phage typing. 

Salmonella serotypes are distinguished using antisera to four highly variable molecules 

on the surface of the Salmonella bacterium: O (lipopolysaccharide); two H (flagella) 

variants, H1 and H2; and a virulence antigen, Vi. More than 2,500 unique combinations 

of O, H1, H2, and Vi types have been discovered (WHO 2007). Phage typing is 

performed by determining the resistance or sensitivity to infection by an established set 

of 34 bacterial viruses (phages) (Anderson et al. 1977). There are 209 definitive phage 

types, referred to as DT (Lan et al. 2009a). 

Because Salmonella serotypes or phage types are based on a limited number of 

markers (e.g., O, H, and Vi antigens, as well as phage resistance/sensitivity genes), 

these methods are imperfect indications of Salmonella diversity. Sequence diversity 

within a serotype or phage type could be due to genetic divergence (i.e., a common 

ancestor acquired the serotype or phage type) or the independent acquisition of 

antigens and genes by diverse clones (i.e., distinct clonal groups independently acquire 

the serotype or phage type). Both of these mechanisms have been demonstrated. 

Multiple sequence types (STs) have been observed within the same serotype and 

phage type. For example, genetic studies indicate that the S. Typhimurium phage type 

DT104 is composed of multiple independent clones; although multidrug resistant 

DT104, which appeared in the United Kingdom in the 1980s and acquired resistance to 

antimicrobial compounds ampicillin, chloramphenicol, streptomycin, sulfonamides, and 

tetracycline, likely has a single origin (Cooke et al. 2008;Lan et al. 2009a). Conversely, 

a single ST may include two closely related serotypes. For example, serotypes S. I 

4,[5],12:i- and S. Typhimurium share a common sequence type ST6 (Alcaine et al. 

2006). Newer sequence-based methods (virulence gene profiling, expression profiling, 

SNP, and WGS) can provide a more specific and scientifically-accurate definition of 

diversity within Salmonella. Measures of genetic diversity within Salmonella could be 

used to detect and track on-farm and illness or outbreak related clusters to support 
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illness attribution and define pathogenic subtypes with greater precision, as described 

below. 

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5.5.1 Use of Subtyping to Track On-Farm and Illness Related Clusters

As described in Section 3.1.1, PFGE, the current gold-standard technique for detection 

of Salmonella clusters, is laborious, and the process of PFGE pattern comparison 

requires precise standardization and subjective interpretation of band matches. In 

addition, Salmonella PFGE is of limited effectiveness for detecting on-farm or illness 

clusters with certain serotypes, including Enteritidis and Hadar, which are dominated by 

a limited number of PFGE pattern subtypes (Pang et al. 2007;Zheng et al. 2011). 

Sequence-based subtyping methods can increase the discrimination of certain 

serotypes by distinguishing clonal lineages within each serotype. By improving 

discrimination, these methods will improve the epidemiological relevance, which is the 

ability of a method to discriminate outbreak-associated from sporadic case-patients that 

occur over the same timeframe or geographic location, or to reliably detect clusters 

associated with a single farm or processing facility In one study, PFGE and MLVA 

developed for S. Enteritidis each distinguished 34 isolates into 13 subtypes; the 

discriminatory ability, judged by Simpson’s and Shannon’s Discriminatory Index, was 

higher for MLVA because the groups were more evenly distributed (Cho et al. 2007). 

Combinations of methods, such as phage typing and MLVA (Cho et al. 2010), PFGE 

and MLVA (Broschat et al. 2010), or the use of multiple restriction enzymes (Zheng et 

al. 2011) may be useful for increasing discrimination of serotypes, especially Enteritidis 

and Hadar. However, increasing the number of assays to determine subtype may 

increase the overall probability of an incorrect conclusion due to compounding errors 

from each assay (Call et al. 2008). 

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5.5.2 Use of Subtyping to Support Salmonellosis Attribution Estimates

There are considerable differences in the distribution of Salmonella serotypes isolated 

from clinically ill humans and farm animals (Alcaine et al. 2006;Foley et al. 2006;Sarwari 
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et al. 2001), which indicates non-farm animal origins for some pathogenic Salmonellae. 

Attribution of human Salmonella illnesses to general food classes can be accomplished 

by analyzing data from foodborne disease outbreaks. Such analysis has demonstrated 

substantial variability in the share of illnesses attributed to foods in the aquatic animal, 

land animal, and plant classes for six Salmonella serotypes (Painter et al. 2013). For 

example, outbreaks suggest that 80% of serotype Enteritidis illnesses are attributed to 

foods in the land animal class (predominantly eggs and poultry). Outbreaks suggest that 

over 75% of serotype Javiana illnesses are attributed to foods in the plant class. There 

is considerable overlap in serotype occurrence among human and farm animal isolates.

The 10 most common Salmonella serotypes accounted for a majority (73%) of 

serotyped isolates from human infections in 10 FoodNet sites in 2009 (CDC 2011b). 

Nine of these serotypes26 were among the more commonly identified serotypes in meat 

and poultry products. As indicated earlier, some serotypes and phage types appear to 

consist of multiple clones with different origins as determined by genetic sequence 

analysis, and some serotypes contain more genetic diversity than others. In one 

analysis performed on publicly-available MLST data, S. Newport was the most divergent 

serotype, followed by S. Typhimurium. SE had three ST. A majority of isolates belonged 

to a single sequence type, ST11, which is closely related to Salmonella serotypes 

associated with poultry (Lan et al. 2009b). The other STs were unrelated to the poultry 

serotypes indicating a possible non-poultry origin. A study of Salmonella isolates from 

clinically ill cattle and humans indicated disparities in ST that were not apparent by 

traditional serotyping (Alcaine et al. 2006). For example, ST6, which includes 

Salmonella serotypes I 4,[5],12:i:- and Typhimurium, was one of the most common ST 

among the human and cattle isolates. Serotype 4,[5],12:i:- is considered an emerging 

serotype that originated from a S. Typhimurium ancestor. The clonal population 

represented by ST6, which includes S. Typhimurium, may actually be the emerging 

subtype that presents a public health threat, at least with respect to contact with cattle 

and beef. The same study indicated that S. Newport isolates consist of two major 

lineages, type A and type B, which represent cow- and bird-derived lineages, 

 

26 Enteritidis, Typhimurium, Newport, Montevideo, I 4,[5],12:i:-, Heidelberg, Muenchen, Saintpaul, 

Oranienburg. Note: Javiana is common in clinically ill humans but uncommon in farm animals.
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respectively. Further, MDR S. Newport was statistically associated with the cow lineage 

(Alcaine et al. 2006). These examples indicate that sequence-based subtyping of 

Salmonella strains would provide more specific information that would be useful for 

illness attribution estimates, as well as defining and tracking lineages that are linked 

more specifically to serious public health risks.

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5.5.3 Use of Subtyping to Differentiate Pathogenic Subtypes of Salmonella

The Salmonella genus is very diverse, including two species (bongori and enterica) and 

six sub-species within S. enterica; 99% of salmonellosis cases are caused by a single 

subspecies, Salmonella enterica enterica, and about 70% of human illnesses are 

caused by 20 serotypes within this subspecies (Brenner et al. 2000;CDC 2012a;CDC 

2012b). Illness outcomes, in terms of invasive disease, hospitalization, and death, differ 

significantly by serotype (Jones et al. 2008).The intensity of Salmonella illness depends 

on factors related to the host, pathogen, and environment. Under some circumstances, 

there are no recognizable symptoms. For example, humans are known to chronically 

carry and shed typhoid and paratyphoid Salmonella, sometimes in the complete 

absence of symptoms. Likewise, many types of non-typhoid Salmonella are carried and 

shed by otherwise healthy animals. In humans, non-typhoidal Salmonella infection 

typically results in gastroenteritis. About 5% of these cases develop bacteremia (blood 

infection) and focal infection of tissues and organs, resulting in a variety of severe 

clinical outcomes (Hohmann 2001). Higher rates of bacteremia and focal infection are 

seen in immunocompromised patients. Although outcomes, including death, depend on 

underlying comorbidities of bacteremic patients (Hohmann 2001), Dhanoa found higher 

probability of bacteremia for group D Salmonella, including S. Enteritidis, among 

patients at a hospital in Malaysia (Dhanoa and Fatt 2009). 

Salmonella virulence factors are encoded by a variety of genetic elements, including 

plasmids, phages, and Salmonella Pathogenicity Islands (SPI). Fourteen SPI have been 

described. SPI-1 encodes type 3 secretion system 1 (T3SS-1), which causes secretion 

and translocation of a range of bacterial proteins to the host cell. SPI-2 encodes T3SS-2 
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that allows intracellular survival and replication (Foley et al. 2009b;Foley and Lynne 

2008). Unique combinations of virulence factors may be responsible for certain 

characteristics associated with specific serotypes, such as the capacity for invasive 

disease, resistance to antimicrobials, and invasion of hen reproductive organs resulting 

in internal egg contamination. 

It is difficult to determine the exact combination of genes necessary to acquire a given 

characteristic in Salmonella. In some situations, variation may result from differential 

expression of virulence genes and not the presence or absence of specific genes. For 

example, a study examining internal egg contamination by SE used RAPD, plasmid 

profiling, phage typing, PCR detection of 30 virulence genes, and relative expression 

levels for two genes (agfA and fimA), and was unable to distinguish SE strains of egg 

and non-egg related origin (Botteldoorn et al. 2010), but did find a limited role for one 

gene (type I fimbrae encoded by the fim operon27) (Botteldoorn et al. 2010). On the 

other hand, experimental deletion of an SE gene (SPI-2 regulator, coded by the gene 

ssrA) that controls the expression28 of other genes drastically reduced SE colonization 

of the reproductive tract but not the gut (Bohez et al. 2008), indicating a potential role for

gene expression as a factor determining whether SE strains are capable of invading the 

internal contents of eggs. Similarly, ARS researchers determined that the cyaA gene, 

which encodes adenylate cyclase that produces cyclic AMP and pyrophosphate from 

ATP, is variable in SE and separates highly-related PT4 strains into subtypes that either 

invade or do not invade the internal contents of eggs (Morales et al. 2007). 

If many genetic factors are involved, a summary profile may be necessary to distinguish 

virulent subtypes within a serotype. To date, such a profile has not been identified. A 

recent study using microarray technology demonstrates this difficulty (Litrup et al. 2010). 

 

27 Fimbrae (plural of fimbria) are proteinaceous structures on the surface of certain bacteria that allow the 

bacteria to adhere to other bacteria, animal cells, or non-living surfaces.

28 Bacterial genes are expressed through a process that involves copying the gene to messenger RNA, 

and in some cases, translating the genetic code on the messenger RNA to create a protein with a 

structure or activity that contributes to the properties of the bacterium. 
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In this study, the authors attempted to link the molecular data obtained by microarray 

analysis of S. Typhimurium strains to detailed epidemiological and clinical patient data 

of the patients previously infected with those strains. The strains were selected from 

patients with mild infections and from patients with severe infections; clinical data 

allowed the authors to correct for known underlying diseases and patient age. Strains 

were analyzed for the presence or absence of 281 genes covering marker groups 

related to pathogenicity, phages, antimicrobial resistance, fimbriae, mobility, serotype, 

and metabolism. The authors showed that S. Typhimurium strains causing very different 

symptoms in patients had little genomic variation, and the observed variation did not 

correlate to the severity of disease. The Litrup study, unlike a previous study (Fierer et 

al. 1992), also failed to show an association between the presence of the Salmonella

virulence plasmid, pSLT, and strains causing severe illness, including systemic illness. 

In summary, a variety of sequence-based subtyping methods are available or are under 

development to discriminate and provide information on Salmonella isolates that can be 

used for cluster detection, illness attribution, and defining pathogenic lineages. At this 

point, a reliable virulence gene screening profile for Salmonella is unavailable. A genetic 

definition of pathogenic Salmonella may depend on specific virulence genes or SNP 

responsible for specific characteristics associated with virulent strains. These 

characteristics include the capability for systemic infection, reproductive tract 

colonization, and antibiotic resistance. Serotyping (and phage typing) is currently used

as a surrogate to provide information on the potential for an isolate to cause human 

illness. Although these methods are not tightly linked to virulence factors (Section 2.4), 

they are a reasonable first cut for differentiating isolates of human health significance 

(Jones et al. 2008). 

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6 Conclusions

FSIS’ microbiological sampling and testing programs are designed to verify that 

establishments maintain control of their production processes and to ensure that they 

adhere to FSIS regulations, policies, and performance standards ((FSIS 2011c). FSIS’ 
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laboratories have implemented subtyping procedures, including PFGE, MLVA, and 

molecular serotyping of selected STEC serogroups and Salmonella serotypes. OSEL is 

the primary unit within FSIS that performs subtyping of FSIS isolates, and evaluates 

new methods developed in collaboration with PulseNet (such as MLVA for additional 

Salmonella serotypes) and ARS (such as TMLGT for Lm and non-O157 STEC 

screening assays). FSIS subtyping methods should be consistent with existing 

regulatory policy, and the Agency has worked closely with PulseNet to share data and 

to develop and validate methods. 

In 2013, PFGE subtyping is applied to all Lm, Salmonella, STEC, and Campylobacter

isolates. MLVA methods for E. coli O157:H7 and Salmonella serotypes supplement, but 

do not replace, PFGE. New MLVA methods for Salmonella serotypes, STEC 

serogroups, and Lm are being developed or validated, but these provide data that are 

complementary to PFGE and therefore would not be appropriate replacements for 

PFGE. PCR methods are used to screen samples for declared STEC adulterants. 

Traditional and molecular serotyping assays are used to determine Salmonella

sampling frames. In the future, SNP based methods, such as TMLGT, may be 

employed to improve the Agency’s risk-based sampling algorithms.

PFGE and MLVA are designed to detect clusters, and to accurately measure statistical 

association of illnesses with a variety of exposures allowing public health agencies to 

investigate and identify exposures of concern, including foodborne exposures. PFGE 

clusters obtained from FSIS’ surveillance samples are used to support evidence for 

pathogen transmission and persistence in regulated establishments. These 

observations suggest the need for additional pathogen control through the 

establishment’s existing food safety systems. FSIS collaborates with other Federal, 

state, and local public health agencies through PulseNet to share PFGE and MLVA 

data. Since its inception in the late 1990s, the PulseNet network has detected many 

multi-state or multi-jurisdictional clusters, often associated with the consumption of 

foods under Federal inspection. Identification of PulseNet clusters lead to outbreak and 
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trace back investigations and ultimately to the implementation of effective public health 

actions, including product recalls. 

This paper describes FSIS’ current procedures for performing PFGE and MLVA and for 

communicating these data throughout the Agency. FSIS will continue to improve its 

ability to respond to clusters detected in the PulseNet system by developing guidelines 

for interpreting subtype data based on its own experience with outbreak and trace back 

investigations. This White Paper provides current guidance for interpreting PFGE and 

MLVA data. The lessons learned during future investigations should be used to 

continually update and refine this guidance. 

FSIS’ use of subtyping data extends beyond outbreak cluster detection. FSIS also uses 

a traditional serotyping procedure and a multiplex PCR-based method (Bioplex) to 

detect Salmonella serotypes in samples collected in its PR/HACCP verification program. 

The Bioplex method was implemented to reduce the time-to-result for certain serotypes 

(FSIS 2013a). The Agency’s risk-based algorithm for determining the monthly 

PR/HACCP sampling frame considers the number of human health serotype isolates 

identified in samples from the establishment’s most recent set (FSIS 2013b). Human 

health serotypes are defined as the top 20 serotypes identified most recently among 

clinical isolates by the PHLIS (CDC 2011b). As FSIS has not established a regulatory 

performance standard for Salmonella serotypes, this information is used only to 

schedule future sets, not for determining whether the establishment has passed or 

failed the set.

FSIS has used molecular subtyping methods for detecting adulterants in food samples. 

In 1994, FSIS defined E. coli serotype O157:H7 as an “unusual and urgent food safety 

problem” and declared that ground beef containing this serotype was adulterated within 

the meaning of 9 CFR 301.2(1), meaning that the detection of this serotype in ground 

beef is interpreted as an added poisonous or deleterious substance (FSIS 2012c). In 

subsequent years, FSIS expanded this definition to include beef cuts (used for 

preparing ground beef) and other “non-intact” beef products (e.g., mechanically 
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tenderized beef). In 2011, FSIS expanded the definition to include six additional nonO157 STEC serogroups. The FSIS laboratory method for detecting E. coli O157:H7 and 

non-O157 STEC serogroups includes real time PCR assays. The E. coli O157:H7 

screen is a commercially available assay for DNA sequences closely associated with 

the E. coli O157:H7 clade, but not with many other E. coli groups29. The non-O157 

STEC screen tests are specific for O-antigen and Shiga toxin genes. These assays 

allow FSIS to focus on samples most likely to contain adulterants.

FSIS is working with CDC and FDA to generate preliminary foodborne illness source 

attribution estimates of Salmonella serotypes for specific FDA- and FSIS-regulated 

commodities 30(IFSAC 2012). Subtype data is useful for understanding the source of 

biological variation that improves risk assessments and attribution studies including 

variation in virulence, dose response, environmental reservoir, growth and inactivation 

kinetics in foods, and resistance to various food processing procedures (Coleman et al. 

2004). 

Over the past decade, there has been explosive growth in the development and 

availability of sequence based methods for subtyping bacterial pathogens. Researchers 

with ARS, CDC, FDA, and PulseNet are developing and validating a wide portfolio of 

subtyping methods for Salmonella, Lm, STEC, Campylobacter, and other pathogens. 

The 100K Pathogen Genome Project will make 100,000 whole genome sequences for a 

variety of pathogens, including Campylobacter, Lm, Salmonella, and STEC, available 

through a publically-accessible database at the NCBI. These data will improve the 

understanding of variation among pathogen genomes and stimulate the identification of 

new subtyping tests. FSIS will contribute isolates from its regulatory testing program for 

sequencing in the 100K Pathogen Genome Project, and looks forward to the benefits 

that the database could provide federal, state, and local public health agencies.

 

29 Dupont technical bulletin, Development of the BAX® system multiplex (MP) assay for detecting E. coli 

O157:H7

30 IFSAC draft strategic plan 2012

http://www.fsis.usda.gov/wps/wcm/connect/18beaa4a-1105-4a78-91b5-

6fc9d7fc5a37/IFSAC_Draft_Strategic_Plan_Attribution.pdf?MOD=AJPERES
Page 81 of 113

 

Some subtyping methods are ideal for detecting clusters and may, in some situations, 

be preferable to the gold standard, PFGE. Other methods are optimized for measuring 

familial relationships or genetic distance between isolates, or they may be useful for 

distinguishing characteristics important to FSIS. Data from subtyping methods capable 

of detecting or distinguishing subtype clades with enhanced expression of virulence, 

historical association with outbreaks, expression of resistance against important 

antimicrobials, or different ecological characteristics, such as biofilm formation and egg 

invasion, could be incorporated into FSIS’ existing risk-based sampling algorithms. This 

would allow establishments identified as having a higher risk of producing contaminated 

products to be sampled more frequently. FSIS may use such data to develop new 

screening assays specific for adulterants. The Agency will need to calibrate a proper 

response to these data, keeping in mind prior findings and declarations, especially with 

respect to existing “zero tolerance” pathogens.

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7 Acknowledgements

The members of the FSIS Subtyping Workgroup gratefully thank the following 

individuals for their timely information and personal communications.

FSIS

William Cray, OPHS Eastern Laboratory 

Adrienne Dunham, ODIFP contractor

Eric Ebel, OPHS Risk Assessment and Analytics Staff

Neal Golden, OPHS Risk Assessment and Analytics Staff

Neelam Narang, OPHS Eastern Laboratory

Chuanfa Guo, OPHS Risk Assessment and Analytics Staff

ARS

Jim Bono, Meat Animal Research Center, Clay Center, NE
Page 82 of 113

 

Mark Englen, Richard B. Russell Research Center, Athens, GA

Jonathan Frye, Richard B. Russell Research Center, Athens, GA

Jean Guard, Richard B. Russell Research Center, Athens, GA

Dayna Harhay, Meat Animal Research Center, Clay Center, NE

Bill Miller, Western Regional Research Center, Albany, CA

Pina Fratamico, Eastern Regional Research Center, Wyndmoor, PA

Todd Ward, National Center for Agricultural Utilization Research, Peoria, IL

CDC

Eja Hyytia-Trees, Foodborne and Diarrheal Diseases Branch, Atlanta, GA

FDA

Eric Brown, Center for Food Safety and Applied Nutrition, College Park, MD

Peter Feng, Center for Food Safety and Applied Nutrition, College Park, MD

Steven Foley, National Center for Toxicological Research, Jefferson, AR

Other

Patrice Arbault, BioAdvantage Consulting, France

Erin Dreyling, Roka Biosciences, Warren, NJ

Eden Hosking, Neogen Corportation, Lansing, MI

Mansour Samadpour, IEH Inc., Lake Forest Park, WA
Page 83 of 113

 

8 Appendices

Appendix A: PFGE Theory

Specialized enzymes, called restriction enzymes, are used to cleave bacterial DNA at 

specific sites to create a collection of well-defined fragments that can be separated by 

molecular size to create a diagnostic pattern. A variety of restriction enzymes are known 

to cut DNA. Each enzyme recognizes and cuts DNA at an enzyme-specified sequence. 

The spacing of these sequences varies within bacteria, creating differences in PFGE 

patterns. (Note: Bacterial DNA is double stranded, and restriction enzyme cut double 

stranded DNA. For the sake of simplicity, only a single strand of DNA is shown in the 

diagrams below.)

. 

Figure A1. Site-specific digestion of bacterial DNA using restriction enzymes. The 

restriction enzyme XbaI is used for illustration, but the general idea is the same for all 

restriction enzymes.
Page 84 of 113

 

Figure A2. Between-strain differences in the spacing of restriction sites creates 

differences in PFGE patterns derived from these strains. 
Page 85 of 113

 

Figure A3. The number of restriction fragments is related to the number of 

restriction sites. Regions that contain more restriction sites will yield a higher number 

of DNA segments and more bands.
Page 86 of 113

 

Figure A4. Large DNA fragments derived from restriction digestion of bacterial 

DNA are visualized by pulsed field gel electrophoresis. A gel image is shown below. 

Each DNA fragment is visualized as a band on the gel. Smaller fragments travel faster 

than larger fragments in the electric field, so bands at the top of the gel represent the 

larger fragments and bands at the bottom of the gel represent the smaller fragments.

Figure A5: PFGE Pattern Comparison. Five PFGE pattern images labeled A, B, C, D, 

and E are shown below. The dendrogram on the left side of each figure groups 

patterns according to their similarity. A percent similarity scale is shown on the top lefthand corner. 

One enzyme dendrogram. PFGE patterns A, B, and C are matches and could be 

considered as an outbreak cluster. Patterns D and E do not match the ABC cluster. D 

is a closer match than E, but these samples (from case-patients or foods) typically 

would not be included in the outbreak investigation.

 

Two enzyme dendrogram. When two or more enzymes are used, there is greater 

discrimination. The secondary pattern for C does not match A and B. Therefore, C, as 

well as D and E, would not be included in cluster AB. In this case, the dendrogram was 
Page 87 of 113

 

calculated using the primary, but not the secondary, enzyme pattern. Distinctions 

among the secondary patterns can be detected visually, or another dendrogram may be 

calculated. 

Primary and secondary enzymes. The following primary and secondary enzymes are 

recommended for use by PulseNet laboratories.

Organism Primary enzyme Secondary enzyme

Listeria monocytogenes AscI ApaI

E. coli O157:H7, non-O157 

STEC, and most 

Salmonella serotypes

XbaI BlnI 

Campylobacter SmaI KpnI

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Page 88 of 113

 

Appendix B: PFGE Laboratory Method: From Isolate to Gel Image

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Appendix C: PFGE Data Workflow and Cluster Investigation 

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Page 90 of 113

 

Appendix D: PFGE Pattern Record Metadata in OSEL Database, PulseNet, and 

VetNet

Each PFGE pattern in the OSEL BioNumerics database has certain data electronically linked to it 

within the database, called metadata. When FSIS patterns are uploaded to the national PulseNet 

database, only a specific subset of these data are uploaded. Any participating PulseNet 

laboratory can view information in the PulseNet database.

FSIS metadata available in the OSEL BioNumerics database is listed below. Metadata available 

to the national CDC-PulseNet database is designated with an asterisk (*).

• OSEL LIMS number (2011-forward)*

• OB (outbreaks) number (discontinued in 2011, was replaced by LIMS number)*

• Species name * 

• Serotype *

• Links to routine queries with dates 

• Establishment number 

• Primary PFGE pattern name (imported from PulseNet) *

• Number of isolates with matching primary PFGE pattern name in PulseNet *

• Secondary PFGE pattern name (imported from PulseNet) *

• Number of isolates with matching Secondary PFGE pattern name in PulseNet *

• Occurrences of isolates with matching primary and secondary PFGE pattern names in 

PulseNet*

• Outbreak code (imported from PulseNet) *

• Lab MX# (internal laboratory number from Eastern, Midwestern, and Western laboratories)

• Form number

• Establishment state *

• Establishment city

• Establishment country *

• Date sample taken at establishment *

• Date sample received at OSEL *

• Source site (ground beef, workers’ gloves, etc. NO BRAND NAMES) *

• Source type (food, environmental, swab) *

• Foreign establishment number

• OSEL gel name

• Lab name *

• Upload date *

Metadata for human clinical isolates that is uploaded to the PulseNet

In addition to the metadata listed above, State and local health departments often provide 

additional data including patient information linked to the isolates that they upload to the national 

CDC-PulseNet database. This information is available when OSEL downloads clinical sample 

PFGE patterns from the national database. This information may be useful during 

epidemiological investigations.

• Source city

• Patient age

• Patient sex

• Toxin

• Type details (often from notes compiled by epidemiologists)

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Page 91 of 113

 

Appendix E: Confirmed and Unconfirmed PFGE Patterns and Matches

PulseNet database managers confirm PFGE patterns when the pattern is assigned a 

pattern name (pattern number). Pattern matches may be “confirmed” or “unconfirmed.”

Confirmed pattern means that the CDC-PulseNet database manager has analyzed the 

isolate’s PFGE pattern image, assigned it a specific PulseNet pattern name, and 

considered it a final result. PulseNet database managers rarely reverse confirmed 

pattern names and FSIS should rely on the names. A confirmed pattern can be referred 

to as a confirmed pattern name, confirmed isolate, or confirmed PFGE pattern.

Unconfirmed pattern means a pattern that has not been assigned a final name by a 

CDC-PulseNet database manager. This could indicate that either no pattern name has 

been assigned, or an automatically assigned unconfirmed pattern has been assigned by 

the PulseNet computer. PulseNet database managers frequently reverse the temporary 

unconfirmed pattern names and therefore FSIS should not rely on them. 

Unconfirmed match means the CDC-PulseNet database manager has not confirmed a 

match among isolates. Because PFGE patterns often are not confirmed immediately 

following upload, OSEL performs its own comparisons. This enables FSIS to pursue 

epidemiologic leads earlier, alerts OSEL to isolates that should be followed closely, and 

encourages OSEL to double-check the PulseNet database manager decisions. OSEL’s 

unconfirmed comparisons can be visual (from the subjective judgment of the OSEL 

PFGE analyst), based on the results of the OSEL BioNumerics program, or a 

combination of both. If OSEL believes that two unconfirmed isolates match, it uses the 

description “appears to match.” “Appears to match” usually coincides with “confirmed 

match.” Other commonly used terms are “very similar to” and “similar to,” which are 

meant to imply that the isolates may be related, but are not exact matches, and are not 

expected to have the same confirmed pattern name. Unless noted otherwise, 

unconfirmed matches are two restriction enzymes; Salmonella matches are for one 

enzyme only (the primary enzyme).

PulseNet database managers use two kinds of computer generated pattern names 

when referring to unconfirmed matches. The first group is for patterns the computer is 

able to successfully group with existing pattern names. The second group is for patterns 

that the computer is unable to type, which likely may result in the creating a new pattern 

name.

Confirmed match means a match among isolates that have the same confirmed 

pattern. These isolates usually have not been compared directly to each other, but 

instead, independently have been given the same pattern name by the CDC-PulseNet 

database manager. Rarely, OPHS requests that PulseNet staff re-evaluate confirmed 

patterns. These instances have included high-profile FSIS investigations or situations 

where FSIS believes that the results of a “pattern confirmation” are questionable. 

Unless noted otherwise, E. coli O157:H7, STEC, and Lm matches are for two restriction 

enzymes and Salmonella matches are for one enzyme (the primary enzyme)

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Appendix F: Descriptive Terms for PFGE Pattern Frequency

OSEL calculates the frequency for all E. coli O157:H7 and Lm PFGE patterns. Pattern 

frequency is calculated as the percentage of isolates in the PulseNet database that 

have the same primary and secondary PFGE patterns. PulseNet uses some descriptive 

terms to describe the frequency of individual pattern names. However, these names are 

somewhat subjective and are applied at the database manager’s discretion, which may 

vary by pathogen. Until PulseNet develops objective terms for describing PFGE pattern 

frequency, FSIS will use the following descriptive terms for communicating pattern 

frequency information within the Agency (Tables F1 and F2). These descriptive terms 

are based on the observed combined pattern frequency for FSIS isolates uploaded to 

PulseNet, and the size of a typical 60-day Hotlist search (number of isolates submitted 

to PulseNet in a 60-day period). 

For Lm, descriptive terms are based on data from 261 pattern combinations calculated 

during routine PulseNet searches for frequency information for the years 2004 through 

March 2007. During that time, FSIS uploaded about 20% of the PulseNet Lm isolates, 

so the data is representative of the PulseNet database (Table F1). For E. coli O157:H7, 

descriptive terms were based on routine searches for all isolates uploaded to PulseNet 

during two 30-day periods (May 2007 and November 2006) (Table F2). Pattern 

frequency should be considered in context of other information, such as the recent 

history of the pattern, which can be observed in a Combined Pattern Search.
Page 93 of 113

 

Table F1: Proposed FSIS Descriptive Terms for Lm PFGE Combined Pattern 

Frequency

Proposed 

Descriptive 

Name

Frequency1 

Number of 

total hits in 

PN 

database2 

Expected 

number of PN 

database hits 

per search 

period3

Number of 

pattern

combinations

found in 

OSEL data 

set N=2614

Percentage 

of isolates 

in OSEL 

data set

N=5845

Common > 0.62% greater than 

40

1 Hit per 60 or 

less days 12 22%

Less 

common

≤ 0.62% 

and

> 0.31%

21 to 40 1 Hit per 61 to 

120 days 7 4%

Rare

≤ 0.31%

and

> 0.16%

11 to 20 1 Hit per 121 

to 240 days 25 16%

Very rare

≤ 0.16%

and

≥2 entries in 

PN 

database

2 to 10

1 Hit per 241 

or more days 

to two 

observations 

in PN 

database

116 41%

New to the 

PN 

database

1 entry in 

PN 

database

1 

No prior 

observations 

of pattern in 

PN database

101 17%

1 Frequency — frequency of pattern combination in PulseNet database 2 Number of total hits in PulseNet database — total number of isolates with a specific pattern combination 

in PulseNet database

3 Expected number of PulseNet database hits per search period — total number of isolates with a specific 

pattern combination in PulseNet database expected within the defined time period

4 Number of unique patterns found in OSEL data set (Lm) — total number of unique pattern combinations 

in data set that falls in category specified

5 Number of isolates found in OSEL data set (Lm) — total number of isolates in data set that falls in 

category specified (includes multiples of same pattern combination)
Page 94 of 113

 

Table F2: Proposed FSIS Descriptive Terms for E. coli O157:H7 PFGE Combined 

Pattern Frequency

6 Frequency — frequency of pattern combination in PulseNet database 7 Number of total hits in PulseNet database — total number of isolates with a specific pattern combination 

in PulseNet database

8 Expected number of PulseNet database hits per search period — total number of isolates with a specific 

pattern combination in PulseNet database expected within the defined time period

9 Number of unique patterns found in 60-day data set (E. coli O157:H7) — total number of unique pattern 

combinations in data set that falls in category specified

10 Number of isolates found in 60-day data set (E. coli O157:H7) — total number of isolates in data set 

that falls in category specified (includes multiples of same pattern combination)

<back

Proposed 

Descriptive 

Name

Frequency6

Number of 

total hits in 

PN 

database7

Expected 

number of PN 

database hits 

per search 

period8

Number of 

pattern

combinations

found in 60 

day data set 

N=1989

Percentage 

of isolates

in 60 day 

data set.

N=43610

Common > 0.31% greater than 

39

1 Hits per 60 

or less days 16 36%

Less 

common

≤ 0.31%

and

> 0.16%

20 to 38 1 Hit per 61 to 

120 days 

13 15%

Rare

≤ 0. 16%

and

> 0.08%

10 to 19 1 Hit per 121 

to 240 days 10 7%

Very rare

≤ 0.08%

and

≥2 entries in 

PN 

database

2 to 9

1 Hit per 241 

or more days 

to two 

observations 

in PN 

database

57 19%

New to the 

PN 

database

1 entry in 

PN 

database

1 

No prior 

observations 

of pattern in 

PN database

102 23%
Page 95 of 113

 

Appendix G: MLVA Theory and Laboratory Method

The current PulseNet E. coli O157:H7 MLVA method used by FSIS analyzes eight DNA loci 

(specific regions) within the genome. Very similar methods are used for other MLVA assays 

specific for Lm, and Salmonella serotypes Enteritidis and Typhimurium.

Figures G1 through G4 show how PCR is used to amplify a specific MLVA locus. Although 

these figures describe the process for a single locus, the amplification process is the same for 

all eight loci. Figure G4 shows the end product amplified “forward strand” DNA that is tagged 

so that it can be detected by a DNA sequencer. The sequencer determines the length of the 

amplified sections, which effectively determines the number of repeats in the specific locus. 

By generating repeat data for eight loci, a bacterial isolate is characterized such that it can be 

compared reliably to other isolates. Isolates that do not have the same repeat number for 

each of the eight loci are unlikely to have been recently generated from a common “ancestor.” 

Figure G1. MLVA targets eight DNA loci within the E. coli genome. Bacterial DNA is 

double stranded, which means it is made of two single strands of complementary DNA that

are stuck together. One strand is called the “forward strand” and the other is called the 

“reverse strand.” Other MLVA assays target DNA loci in the genomes of Lm, and Salmonella

serotypes Enteritidis and Typhimurium.

Figure G2. Forward and reverse strands of E. coli DNA. During the first step of MLVA, 

double stranded DNA at the individual loci are copied many times using PCR. In the diagram 

below, the forward strand of DNA is black with white letters and the reverse strand is white with 

black letters. The grey area represents the rest of the genome that is not copied by PCR.

Forward and reverse strands of DNA are highlighted as “Repeat region 6” in Figure G1. “x” 

marks additional nucleotides in the forward strand. “y” marks additional nucleotides in the

reverse strand.
Page 96 of 113

 

Figure G3. Primers stick to the complementary DNA strand. Heat is used to separate the 

two DNA strands, and two primers are added to the mixture. The mixture is cooled, which allows 

the forward primer, which is labeled, to bind to the reverse strand, and the reverse primer, which 

is unlabeled, to bind to the forward strand. A DNA-producing enzyme, called DNA polymerase, 

copies the rest of the region of interest using the other strand as a guide. The temperature of 

the mixture is raised and lowered repeatedly using a thermocycler to produce thousands of 

copies of the labeled forward strands and unlabeled reverse strands.

Figure G4. The number of DNA bases in the forward strand is determined. The mixture of 

amplified DNA is put into a sequencer, which is used to determine the number of bases in the 

labeled forward strand. The number of bases in the repeat region is calculated by subtracting 

the number of bases in the forward and reverse primers.

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Page 97 of 113

 

Appendix H: MLVA Protocol: From FSIS Isolate to CDC Analysis

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Appendix I: Molecular Subtyping of E. coli O157:H7

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Page 99 of 113

 

Appendix J: Molecular Serotyping of Salmonella

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Page 100 of 113

Appendix K: Salmonella Serotypes Reportable by Molecular Detection 

(Laboratory Guidebook, effective date 1/02/13)

Agona 

Albany 

Altona 

Anatum 

Berta 

Blockley 

Bredeney 

Dublin 

Enteritidis

Give 

Havana 

Heidelberg 

Infantis 

Kentucky 

Litchfield 

Montevideo 

Muenchen 

Newport 

Ouakam

Panama 

Saintpaul 

Schwarzengrund 

Thompson 

Typhimurium* 

4,[5],12:i:-**

*“Typhimurium” includes variant 5

** Molecular result of serotype B:i:-is reported as 4,[5],12:i:-to 

maintain consistency with existing nomenclature as per CDC 

recommendation.

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Page 101 of 113

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