10/31/2014

1

Leading Technologies in Microbial Identifications:

Genotypic and Proteotypic

Maximizing Accuracy and Efficiency

Christine Farrance, PhD Director of Research and Development Endotoxin and Microbial Detection | Charles River christine.farrance@crl.com | www.criver.com

• Risk Management and Environmental Monitoring of Microorganisms

• Analyzing your State of Control - Tracking and Trending

• Review of Microbial Identification Technologies

• Factors Impacting Accuracy and Reproducibility

• Maximizing Efficiencies and Decreasing Process Variability

2

Presentation Agenda

Question: Why Do You ID Microbes?

3

10/31/2014

2

MANAGING RISK Close scrutiny by the FDA and other regulatory agencies keeps industry focused on effectively managing risk

Because…

Preventing contamination is critical to consumer safety and product efficacy

4

Managing Risk

• Manufacturing processes are a series of steps which are interlinked and independent and all processes have variability

• Variability increases risk and lowers quality

• The goal is to understand, control and reduce variability

• Quality risk management is an important part of science-based decision making which is essential to manufacturing

5

The Importance of Accurate Identifications

The Isolate of Concern

• Risk management – Evaluate level of risk

• Permits determination of the potential origin of contamination

• Is the isolate objectionable? – Adverse effect on the product

or the consumer

• CAPA planning to mitigate risk

The Environmental Monitoring Program

• Measures the state of control of the manufacturing facility

• Provides reliable and comprehensive information on the quality of the environment

• Acts as a surveillance, early warning, system

6

Document consistent quality management and control

10/31/2014

3

Environmental Monitoring

Establishing an EM program is one of the most important components of an effective manufacturing production and process control system

7 7

• The program should provide accurate information to measure the state of control of the manufacturing facility through the specific detection, identification and quantification of microorganisms

• The program should identify areas for continual improvement, and help understand and reduce process variability

• Apply risk management principles to the program to design processes to prevent contamination, investigate ways to correct contamination events and assess the potential impact of contamination events

Environmental Monitoring

8

• Provides a baseline profile of a manufacturing environment

• Acts as an early warning system to detect possible environmental contaminants that are out of limits and that may impact the product

• Promptly identifies the sites at risk of contaminating the product

• Documents consistent quality and control

The value of viable microbial monitoring is realized when the data are used to identify and correct an unacceptable work practice.

Monitoring the State of Control Tracking and Trending

• The data gathered in a well-designed and executed EM program provides critical information for tracking and trending (T&T) on a routine basis

• Accurate and consistent species-level identifications will result in more confidence in the T&T information

• Any significant change in microbial flora can be considered in a review of the ongoing monitoring data and used in investigations of the excursion, affect the mitigation process and aid in root cause determination

• T&T provides a detailed analysis regarding the state of environmental control within the manufacturing area

– Detect the emergence of indicator organisms and – Allow excursions from normal operating conditions to be identified

quickly and reliably

9 9

10/31/2014

4

Trending Bacteria by Gram Reaction and Morphology

10

11

Trending Species by Frequency of Occurrence Over Specified Time Period

0

5

10

15

20

25

30

35

40

302 IDs from April – June 2014

Trending Specific Species Over Time

12

10/31/2014

5

Importance of Microbial Identities

13

• Need to be able to accurately identify an organism to the species level, and many times, to the strain level

• Know the flora of your environment

• Know what is around and in your ingredients, components or product

• Establish limits on those types of contaminants that may adulterate your finished product

• Know if you have detected an objectionable organism

Microbiological identification platforms are available that can provide the analytical tools necessary to accomplish these tasks

with accuracy and repeatibility

The Evolution of Microbial Identification

14

The identification of microorganisms can be done through different processes, each with its own level of accuracy

and reproducibility

Phenotypic ID – Biochemical Analysis

• Differentiate between organisms based on the results of biochemical tests such as sugar fermentation or physiological properties such as salt or pH tolerance, manual or automated systems

• Differentiate based on patterns of cellular fatty acids that are extracted, methylated and separated by gas chromatography to generate a fingerprint

Systems have been in use for decades

15

10/31/2014

6

Phenotypic Characterization

• These methods have a role in a microbial quality program, but need a prior accurate identification to utilize the information

• The results from phenotypic reactions can help determine the biochemical activity of an organism on the product and the resulting stability of the product in the presence of that organism

• Understanding the nutrient requirements of an organism can reduce risk by providing insight into controlling or eliminating the organism in the environment or preserving the product

16

17

Genotypic DNA Sequence-Based

Proteotypic Protein Fingerprint-Based

Identifications are based on information contained

in the Ribosome

Ribosomal Structure

18

Small Subunit Large Subunit

70S

50S 30S

16S rRNA 21 proteins

5S and 23S rRNAs 34 proteins

Bacterial Genome organization Bacterial Ribosome

SSU LSU

16S 5S 23S

ITS1 ITS2

SSU LSU

18S 5.8S 28S

ITS1 ITS2 D2

ETS

Fungal Genome organization

ITS2 (Internal Transcribed Spacer) is used by taxonomists for the ID of fungal species as it is more variable than other regions, providing more species level differentiation

10/31/2014

7

19

Genotypic Identifications

DNA Sequence-Based

• Genotypic identification methods involve sequencing different regions of the microorganisms’ ribosomal RNA genes (16S or ITS2), resulting in an identification to the species or occasionally the subspecies level

• Universal system for all microorganisms since rRNA ubiquitous in all organisms and functionally conserved as part of the ribosomal complex

• The use of ribosomal DNA sequences for the purposes of bacterial and fungal taxonomic classification has been in practice for many decades

Target for Genotypic Identification: Ribosomal RNA

• Highly conserved regions of sequence

• Highly variable regions

• Allowing for species level resolution

• Bacterial identification

• Taxonomy from ~1500 bases

• Identification from ~500 bases

• Information content is high

• Complexity is in the data

• High level of species discrimination

20

1 GATGAACGCT GGCGGCGTGC TTAACACATG CAAGTCGAAC GATGAAGCCC AGCTTGCTGG

61 GTGGATTAGT GGCGAACGGG TGAGTAACAC GTGAGTAACC TGCCCTTAAC TCTGGGATAA

121 GCCTGGGAAA CTGGTCTAAT ACCGGATAGG AGCGTCCACC GCATGGTGGG TGTTGGAAAG

181 ATTTATCGGT TTTGGATGGA CTCGCGGCCT ATCAGCTTGT TGGTGAGGTA ATGGCTCACC

241 AAGGCGACGA CGGGTAGCCG GCCTGAGAGG GTGACCGGCC ACACTGGGAC TGAGACACGG

301 CCCAGACTCC TACGGGAGGC AGCAGTGGGG AATATTGCAC AATGGGCGCA AGCCTGATGC

361 AGCGACGCCG CGTGAGGGAT GACGGCCTTT CGGGTTGTAA ACCTCTTTCA GTAGGGAAGA

421 AGCGAAAGTG ACGGTACCTG CAGAAGAAGC ACCGGCTAAC TACGTGCCAG CAGCCGCGGT

481 AATACGTAGG GTGCGAGCGT TATCCGGAAT TATTGGGCGT AAAGAGCTCG TAGGCGGTTT

541 GTCGCGTCTG TCGTGAAAGT CCGGGGCTTA ACCCCGGATC TGCGGTGGGT ACGGGCAGAC

Sequence Data

Isolate DNA from a single, pure colony

PCR amplify target sequences and subject to cycle sequencing

Raw Sequence Electropherogram

21

Genotypic Identification Methodology

Analyze, assemble and interpret the data utilizing the semi-automated reference method for the highest accuracy

Compare full length sequence to a comprehensive library for ID

10/31/2014

8

22

Genotypic Identification Methodology

When assigning taxonomic designation

• The genetic distance measure (% difference or similarity) and

• The branching order of the phylogenetic tree which indicates how all the organisms relate to one another

Are equally important in making the final identification and confidence determination

Proteotypic Identifications

Protein Fingerprinting-Based ID

• Identification method for utilizing matrix-assisted laser desorption/ionization – time of flight (MALDI-TOF) mass spectrometry

• High level of species discrimination

• High informational content in the assay (mass peaks)

• Rapid, accurate and reproducible

• A paradigm shift in routine identifications for EM programs

23

MALDI-TOF Theory

24

Matrix molecule Analyte molecules

laser

Theory and Instrument • MALDI-TOF MS

– Matrix-assisted laser desorption/ionization Time-of-Flight Mass Spectrometry

• Ion Source (MALDI)

• Mass Analyzer (TOF)

• Detector (SEM) • Secondary Electron Multiplier

• Analysis (Mass Spectra)

Process • Sample preparation – co-crystallization

• Ionization

• Electrode Acceleration

• Ionic Separation

• Detection

• Analysis

10/31/2014

9

25

MALDI-TOF Methodology

Escherichia coli

Enterobacter aerogenes

Bacillus niacini

Spectra Unknown (primarily ribosomal proteins) Sample Preparation

Sample Spotting

Spotted Target Plate MALDI-TOF analysis

Library Comparison

Identification

Normalized logarithmic value of: The number of peaks in the known library entry that match to peaks detected in the unknown sample. The number of peaks in the unknown sample that match to peaks in the known library entry. The symmetry of the intensity of peaks that match between the known library entry and unknown library entry.

Library Comparison – Peak Lists and Match Factors

Match Factor = 2.84

26

MALDI-TOF and Microbial IDs

Match Factor = 1.56

Library Reference Entry: Brevundimonas diminuta

Library Reference Entry: Enterobacter aerogenes

Unknown Sample

• Visually see the Match Factor Score of the identified organism and its 10 next closest matches (green=species confidence level)

• Top Match Score is the identification

Identification Report

27

10/31/2014

10

28

Level of Resolution for ID Methods • Different technologies have varying degrees of resolution

• What level of resolution do you use? When? Why?

• “Genus-level is good enough”?

• “Species-level is overkill” for routine?

Family Genus Species Subspecies Strain

Protein Coding or Housekeeping Gene Sequencing

Ribosomal DNA Sequencing (16S, ITS2)

Ribotyping

Proteotypic Mass Spectrometry ID

Strain Typing

Phenotypic Biochemical

If the underlying causes of variation are understood and

controlled, there is a good shot at a long term, defect-free process

29

What Are The Factors That Contribute To Process Variability?

• Information content to speciate unknown organisms

• Reference libraries and error rates – Outdated databases

– Limited coverage

– Clinical focus

• Method Variation – Phenotypic

– Genotypic

– Proteotypic

• Process Controls

30

10/31/2014

11

Ability of a System to Discriminate is Based on the Potential Information Content

Genotypic methods • 16S rDNA Sequencing, 460 bp informative, 4 bases…………..4+460 = 8 x 10+276

• Fragment technology 64 Fragment sizes, 10 levels .………….10+64 = 1 x 10+64

Proteotypic methods • MALDI-TOF, 700 Mass frag. sizes, yes/no……………………….…2+700 = 5 x 10+210

Phenotypic methods • Fatty Acids, 128 FAs, 4 quantitative levels…………………........4+128 = 1 x 10+77

• Carbon utilization, 95 tests, yes/no……….……………………….…2+95 = 4 x 10+28

• Biochemical, 40 tests, yes/no...………………………………………...2+40 = 1 x 10+12

31

16S rDNA sequencing and mass spectrometry easily discriminate thousands of species

What Are The Factors That Contribute To Process Variability?

• Information content to speciate unknown organisms

• Reference libraries and error rates – Outdated databases

– Limited coverage

– Clinical focus

• Method Variation – Phenotypic

– Genotypic

– Proteotypic

• Process Controls

32

Impact of the Reference Library

• Accuracy of an identification is dependent on the library against which you compare your data

• Relevant coverage - library should complement the source environment

• Manufacturing EM programs, as well as clinical and industrial isolates

• Insufficient library coverage impacts the number of reportable results

33

10/31/2014

12

1

10

100

1,000

10,000

1 501 1001 1501 2001 2501

34

Library Comparison – 16S FOO

90% 95% 99% 100%

n = 987

55

45

Represented in the OEM Library

Not Represented in the OEM Library

55% of most frequently identified bacteria are contained in the OEM Library

n = 85,557 Species Count

Log

(Fre

qu

ency

of

Occ

urr

ence

)

What Diversity is Needed?

16

84

Represented in the OEM Library

Not Represented in the OEM Library1

10

100

1,000

10,000

1 251 501 751 1001

90% 95% 99% 100%

Library Comparison – ITS FOO

16% of the most frequently identified fungi are contained in the OEM Library

n = 25,930 Species Count

n = 446

Log

(Fre

qu

ency

of

Occ

urr

ence

)

35

What Diversity is Needed?

0

20

40

60

80

100

Yeast MoldMushroom

N/A

Pe

rcen

t

0

20

40

60

80

100

Yeast MoldMushroom

N/A

Pe

rcen

t

ITS OEM Library

Species and type of fungi contained in the OEM library are not representative of the most frequently encountered fungi observed

36

n = 446

What Distribution is Needed?

1

10

100

1,000

10,000

1 251 501 751 1001

90% 95% 99% 100%

Library Comparison – ITS FOO

n = 25,930 Species Count

Log

(Fre

qu

ency

of

Occ

urr

ence

)

10/31/2014

13

What Are The Factors That Contribute To Process Variability?

• Information content to speciate unknown organisms

• Reference libraries and error rates – Outdated databases

– Limited coverage

– Clinical focus

• Method Variation – Phenotypic

– Genotypic

– Proteotypic

• Process Controls

37

Process Variability - Phenotypic

• Requires live, healthy organism

• Manual systems - labor intensive

• Automated systems have limited process control checks

• May or may not need to be cultured on specific media and at specific temperatures

• May or may not require ancillary testing such as Gram stain to achieve an identification

• Limited system complexity with limited databases

• Can have subjective interpretation of the test results and thus a higher rate of inaccurate or inconsistent identifications

• Microorganisms isolated from manufacturing environments will likely be physiologically stressed and may not fully express their phenotypic or biochemical characteristics resulting in erroneous ID or no ID

38

• Genotypic Methods

– DNA extraction, amplification and sequencing reactions (multiple sample preparation steps) • DNA concentrations, reagents, clean-ups

– Instrumentation (thermocycler, sequencer, pipettes)

– Data/sequence quality, polymorphic positions

– Transcription/technician errors

– May require additional tests for an ID

– Limited and non-relevant reference libraries to make the ID

Process Variability - Genotypic

39

10/31/2014

14

• MALDI-TOF Methods

– All require a live, healthy organism

– Temperature exposure and age of culture

– Sample preparation

– Transcription/technician errors

– May require additional tests for an ID

– Limited and non-relevant reference libraries to make the ID

Process Variability - Proteotypic

40

What Are The Factors That Contribute To Process Variability?

• Information content to speciate unknown organisms

• Reference libraries and error rates – Outdated databases

– Limited coverage

– Clinical focus

• Method Variation – Phenotypic

– Genotypic

– Proteotypic

• Process Controls

41

Minimizing Process Variability through Six Sigma and Theory of Constraints

42

• Six Sigma focuses on variation and design

• TOC has a focus on process flow and efficiency

• Utilizing these principles, and others, it is possible to optimize processes to increase robustness, throughput, and quality

10/31/2014

15

Process Controls

43

• Site-wide education on TOC/Lean/5S

• Unidirectional, sequential process flow

• Small batch sizes

• Point of use storage

• Visual indicators

• Reduction of non-value added steps

• Samples barcoding

• LIMS

Theory of Constraint measures also reduce process variability as they allow employees to be focused on the task at hand and minimize the chance of error

Sequencing Process Controls

• Utilize data analysis programs to track and trend key assay performance indicators

– Signal/noise ratios of each batch – Percent difference to reference(s) – Length of sequence read – Peak uniformity

• Strict QC system for reagent mixes and lot acceptance

• Optimize personnel and workflow to minimize bottlenecks and meet turnaround times

• Rapidly identify and address potential problems

44

Points to Consider • Can apply principals of risk management to the microbial

identification process and your Environmental Monitoring program

• Not all microbial identification technologies are created equal, but by using the more “modern” technologies you can have unparalleled performance and high confidence in the data generated

• The data gathered in your EM and Tracking and Trending programs can be used to document the state of control in your manufacturing facility

45

10/31/2014

16

• Monitoring key parameters during the microbial identification process maximizes throughput while maintaining performance

• Assures quality and provides confidence

• The benefits of improving your microbial identification process are increased accuracy, repeatability and reliability

– Increase operational efficiency

– Positive impact on decision making

– Improved compliance position

46

Maximize Efficiency and Decrease Process Variability

Thank You!

47

Christine Farrance, PhD Director of Research and Development Endotoxin and Microbial Detection | Charles River christine.farrance@crl.com | www.criver.com