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March 13, 2007
External Review Draft
U.S. Environmental Protection Agency
DRAFT
Interim Guidance
for Microarray-Based Assays:
Data Submission, Quality, Analysis,
Management, and Training Considerations
Prepared for the U.S. Environmental Protection Agency
by Members of the Genomics Workgroup,
a Group Tasked by EPA's Science Policy Council
Science Policy Council
U.S. Environmental Protection Agency
Washington, DC 20460
NOTICE
This document is an External Review draft. It has not been formally released by the U.S.
Environmental Protection Agency and should not at this stage be construed to represent
Agency position
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DISCLAIMER
This draft interim guidance, when finalized, will represent EPA's current thinking on this topic.
It does not create or confer any legal rights for or on any person or operate to bind the public.
The use of any mandatory language in this document is intended to describe laws of nature,
scientific principles, or technical requirements and is not intended to impose any legally
enforceable rights or obligations. Alternative approaches may be used if the approach satisfies
the requirements of the applicable statutes and regulations. If you would like to discuss an
alternative approach (you are not required to do so), you may contact the EPA staff responsible
for implementing this guidance. Mention of trade names or commercial products does not
constitute endorsement of recommendation for use.
Note: This is an external review draft, and is not approved for final publication.
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Genomics Microarray Workgroup
Co-Chairs
William H. Benson Kathryn Gallagher J. Thomas McClintock
Office of Research and Office of the Office of Pollution
Development Science Advisor Prevention and Toxics
Kerry Dearfield
Office of the Science Advisor
(2004 - June 2005)
Science Policy Council Staff
Jeremy Johnson
(2004)
Subgroup Co-Chairs
Performance Approach to
Quality Assessment
David Lattier, ORD
Susan Lundquist, OEI
Data Management
Susan Hester, ORD
Joseph Retzer, OEI
Data Submission
Greg Miller, OPEI
Doug Wolf, ORD
Training
Bobbye Smith, Region 9 RSL
Julian Preston, ORD
Data Analysis
David Dix, ORD
Brenda Groskinsky, Region 7 RSL
Microbial Source Tracking
Jorge Santo Domingo, ORD
Ron Landy Region 3 RSL
Additional Coordinating Committee Members
Wafa Harrouk, US FDA
Lee Hofmann, OSWER
Robert Kavlock, ORD
Rita Schoeny, OW
Genomics Workgroup Lead for the Science Policy Council
Larry Reiter
Office of Research and Development
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Genomics Microarray Workgroup Members
Gregory Akerman, OPPTS
Wenjun Bao, ORD
David Bencic, ORD
Lynn Bradley, OEI
Kevin Cavanaugh, ORD
Barbara Collins, ORD
Brion Cook, OPPTS
Don Delker, ORD
Michelle Embry, OPPTS
Robin Gonzalez, OEI
Susan Griffin, Region 8
Stephanie Harris, Region 10
Belinda Hawkins, ORD
Kenneth Haymes, OPPTS
Michael Hemmer, ORD
Todd Holdermann, OPPTS
Gene Hsu, ORD
Margo Hunt, OEI
Sid Hunter, ORD
Channa Keshava, ORD
Steven Kueberuwa, OW
Mitch Kostich, ORD
Richard Leukroth, OPPTS
Nancy McCarroll, OPPTS
Jesse Meiller, OPPTS
Elizabeth Mendez, OPPTS
Ann Miracle, ORD
Ines Pagan, ORD
Santhini Ramasamy, OPPTS
Ann Richard, ORD
Mitch Rosen, ORD
Phil Sayre, OPPTS
Judy Schmid, ORD
John Sykes, ORD
Freshteh Toghrol, OPPTS
Mark Townsend, OPPTS
Nancy Wentworth, OEI
Lori White, ORD
Witold Winnik, ORD
Steve Young, OEI
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Additional Genomics Resources
Genomics Training Workgroup Members
Barbara Abbott, ORD
Gilberto Alvarez, Region 5
Michele Burgess, OSWER
Michelle Embry, OPPTS
Audrey Galizia, ORD
Karen Hamernik, OPPTS
Steven Kueberuwa, OW
David Lattier, ORD
David Lee, ORD
Roseanne Lorenzana, Region 10
Marian 01 sen, Region 2
Jennifer Seed, OPPTS
Microbial Source Tracking Workgroup Members
Bobbye Smith, Region 9
Jafrul Hasan, OW
James Goodrich, ORD
Rita Schoeny, OW
Robin Oshiro, OW
Roland Hemmett, Region 2
Sally Gutierrez, ORD
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Table of Contents
ACRONYMS VIII
EXECUTIVE SUMMARY 1
1.0 INTRODUCTION 5
1.1 Background 5
1.2 Overview of Genomic Science 6
1.3 Emerging Impacts of Genomics Technologies 8
1.4 Purpose and Intent of this Document 11
2.0 THE PERFORMANCE APPROACH TO QUALITY ASSURANCE FOR MICROARRAYS 12
3.0 DATA SUBMISSION GUIDANCE 14
3.1 Introduction 14
3.2 Abstract 14
3.3 Experimental Design 15
3.4 Array Design 16
3.5 Biomaterials 16
3.6 Hybridization 17
3.7 Measurements 17
4.0 DATA ANALYSIS GUIDANCE 19
4.1 Introduction 19
4.2 Data Analysis 20
4.3 Data Evaluation 23
4.4 Data Analysis Conclusions 24
5.0 DATA MANAGEMENT 25
6.0 RECOMMENDATIONS 28
6.1 Training Needs and Recommendations 28
6.2 Collaborative Development of Genomic Tools for Data Analysis and Data Management ....31
6.3 Applying this Interim Guidance for Microarray-Based Assays to Case Studies 32
6.4 Updating Genomics Guidance as Needed 32
REFERENCES 33
APPENDIX A: EPA QUALITY SYSTEM AND THE PERFORMANCE APPROACH TO QUALITY
MEASUREMENT SYSTEMS 35
APPENDIX B: MIAME-BASED DATA SUBMISSION TABLES 51
Table B.l Abstract 51
Table B.2 Experimental Design 51
Table B.3 Array Design 53
Table B.4 Biomaterials 56
Table B. 5 Hybridization 61
Table B. 6 Measurements 62
APPENDIX C: GENOMICS DATA EVALUATION RECORD (GDER) TEMPLATE 64
APPENDIX D: GENOMICS DATA EVALUATION RECORD (GDER) FOR ALACHLOR (SAMPLE) ....67
APPENDIX E: MIAME GLOSSARY 77
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APPENDIX F: ADDITIONAL GLOSSARY FROM GENOMICS WHITE PAPER 82
APPENDIX G: CONTENT AND INSTRUCTIONAL GOALS FOR THE THREE LEVELS OF
GENOMICS TECHNICAL TRAINING: 88
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ACRONYMS
CBI
Confidential Business Information
cDNA
Complementary Deoxyribonucleic Acid
CEBS
Chemical Effects in Biological Systems knowledgebase
cRNA
Complementary Ribonucleic Acid
CWA
Clean Water Act
DER
Data Evaluation Record
DNA
Deoxyribonucleic Acid
DQO
Data Quality Objective
EPA
Environmental Protection Agency
FACS
Fluorescence Activated Cell Sorter
FDA
Food and Drug Administration
FNR
False Negative Rate
FPR
False Positive Rate
gDER
Genomics Data Evaluation Record
HPV
High Production Volume
IRB
Institutional Review Board
IVT
In Vitro Transcription
JPEG
Joint Photographic Experts Group
MAGE
Microarray And Gene Expression
MAGE-OM
Microarray And Gene Expression - Object Model
MGED
Microarray Gene Expression Data
MIAME
Minimal Information About Microarray Experiments
MOA
Mode of Action
MOPS-EDTA
[MOPS] 3-(N-Morpholino) propanesulfonic acid],
[EDTA] ethylenediaminetetraacetic acid
MPSS
Massively Parallel Signature Sequencing
mRNA
Messenger RNA
MQO
Measurement Quality Objective
MST
Microbial Source Tracking
NHEERL
National Health and Environmental Effects Research Laboratory
NIEHS
National Institute of Environmental Health Sciences
NPDES
National Pollutant Discharge Elimination System
OEI
Office of Environmental Information
OPPTS
Office of Prevention, Pesticides and Toxic Substances
ORD
Office of Research and Development
OSWER
Office of Solid Waste and Emergency Response
OW
Office of Water
PCR
Polymerase Chain Reaction
PMN
Pre-Manufacture Notification
PMT
Photomultiplier Tube
QA
Quality Assurance
QAARWP
Quality Assurance Annual Report and Work Plan
QC
Quality Control
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QMP
Quality Management Plan
qPCR
Quantitative Polymerase Chain Reaction
qRT-PCR
Quantitative Reverse Transcriptase PCR
RFU
Relative Fluorescent Unit
RNA
Ribonucleic Acid
RNase
Ribonuclease
RTP
Research Triangle Park
RT-PCR
Reverse-Transcription Polymerase Chain Reaction
SAGE
Serial Analysis of Gene Expression
SNP
Single Nucleotide Polymorphism
SOPs
Standard Operating Procedures
SPC
Science Policy Council
TIFF
Tagged Image File Format
TMDL
Total Maximum Daily Load
U.S. EPA
U.S. Environmental Protection Agency
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EXECUTIVE SUMMARY
The mapping of diverse animal, plant, and microbial species genomes using molecular
technologies has significantly affected research across all areas of the life sciences. The current
understanding of biological systems is rapidly changing in ways previously unimagined and
novel applications of this technology have already been commercialized. These advances in
genomics will have significant implications for risk assessment policies and regulatory decision
making. In 2002, the U.S. Environmental Protection Agency (EPA or "the Agency") issued its
Interim Policy on Genomics (U.S. EPA, 2002a) that communicated the Agency's initial
approach to using genomics information in risk assessment and decision making. The Interim
Policy described genomics as the study of all the genes of a cell or tissue, at the DNA
(genotype), mRNA (transcriptome), or protein (proteome) level. While noting that the
understanding of genomics is far from established, the Agency stated that such data may be
considered in the decision making process, but that these data alone are insufficient as a basis for
decisions.
Following the release of the Interim Policy, the Science Policy Council (SPC) created a
cross-EPA Genomics Task Force and charged it with examining the broader implications
genomics is likely to have on Agency programs and policies. The Genomics Task Force
developed a Genomics White Paper entitled "Potential Implications of Genomics for Regulatory
and Risk Assessment Applications at EPA" (U.S. EPA, 2004). That document identified four
areas likely to be influenced by the generation of genomics information within EPA and the
submission of such information to EPA: 1) prioritization of contaminants and contaminated sites,
2) monitoring, 3) reporting provisions; and 4) risk assessment. One critical need in the area of
technical development was identified: the need to establish a framework for analysis and
acceptance criteria for genomics information for scientific and regulatory purposes. The Task
Force recommended that the Agency charge a workgroup to establish such a framework and in
doing so consider the performance of assays across genomic platforms (e.g., reproducibility,
sensitivity, pathway analysis tools) and the criteria for accepting genomics data for use in a risk
assessment (e.g., assay validity, biologically meaningful response).
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In 2004, the Genomics Technical Framework and Training Workgroup was formed with
the responsibility to ensure that the technical framework and training activities build upon the
Agency's Interim Policy on Genomics while continuing to engage other interested parties.
Information developed by these workgroups will be used by EPA program offices and regions to
determine the applicability of specific genomics information to the evaluation of risks under
various statutes.
To this end, the Genomics Technical Workgroup considered all of the "omics"
technologies and applications and decided that an interim guidance document on the use of data
generated by DNA microarray technology would be most beneficial to the Agency and regulated
community at this time. Consequently, this document provides recommendations regarding: 1)
data that should be considered for submission to the Agency for microarray studies, 2) the use of
a performance approach to microarray quality assessment parameters, 3) data analysis
approaches for microarrays, and 4) data management and storage issues for microarray data
submitted to or used by the Agency. The guidance applies to both human health and ecological
DNA microarray data.
With respect to experimental performance considerations, the Genomics Workgroup
concluded that quality issues are critical considerations in the application of new technologies
such as genomics. The Genomics Workgroup recommends that the Agency not prescribe
specific methods to be used in microarray experiments at this time, but instead provide general
guidance on the recommended performance of microarray experiments in order to obtain data of
the quality required for a specific use; this guidance is provided herein. Investigators submitting
data to the Agency in support of regulatory decision making, methods development, and
technical transfer, may want to consider, in addition to compliance with MIAME (Minimal
Information About Microarray Experiments) Workgroup standards
(http://vvvvvv.mued.oru/Workuroups/MlAME/miame.html). the performance-related experimental
and system factors outlined in this document (Appendix A). Further activities on the part of
investigators to address experimental performance issues will serve to strengthen scientific
arguments and experimental claims.
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This document also provides information regarding submission of microarray data to
EPA to ensure appropriate review and consistent evaluation of data from multiple sources. In
accordance with accepted practice, it is recommended that submissions include sufficient
information to allow an independent reviewer to reconstruct how the data were collected and
analyzed. This approach allows reviewers to judge the quality of the data and the strength of any
conclusions. Many scientific journal editors grappling with these issues have adopted the
MIAME guidelines as a standard for submission of microarray data as part of a submitted
publication. A slightly modified version of MIAME is proposed as the microarray data
submission template for EPA; this submission template will be subject to change as the
technology evolves.
With regard to data analysis, the Genomics Workgroup concluded that a systematic
approach for genomics data evaluation is necessary for the further use of such data in risk
assessments. A genomics Data Evaluation Record template is provided herein as a way to
present and organize data from genomics studies in order to derive information necessary for a
regulatory application (see Appendix C for the Genomics Data Evaluation Record [DER]). A
completed sample DER is also provided in Appendix D to facilitate the use of the template. An
overview of issues to be considered in analyzing microarray data is also provided. The transfer
of these evaluations, and the underlying genomics data, into searchable, electronic databases will
be essential to making the data useful in risk assessments. Furthermore, development of
databases containing gene expression profiles for a wide variety of chemicals should facilitate
creation of statistical/computational methods that will help predict the toxic potential of a
chemical.
Due to potentially large volumes of genomic and associated toxicological data, it is
essential that the Agency consider the development of a complete data management solution.
The functional needs of a solution of this magnitude would minimally include items listed in the
section on data management. In addition, this Agency data management solution should address
needs unique to scientifically-based risk assessments, confidential and proprietary data security,
public access, and other aspects of regulatory application. It should be noted that consistency,
scientific and operational robustness, common access, and availability in a scalable environment
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are data management needs for an Agency data management solution. While the Agency has
begun to utilize bioinformatics research approaches, both intramurally (e.g., the National Center
for Computational Toxicology in EPA's Office of Research and Development [ORD]) and
extramurally (Environmental Bioinformatics Centers in North Carolina and New Jersey funded
by EPA's Science to Achieve Results (STAR) Program), an Agency-wide data management
solution integrating genomics, toxicological, and other key data required for regulatory
applications is now necessary.
The document concludes with the Genomics Workgroup's recommendations to the
Agency for follow-up activities to this interim guidance including: 1) further development of the
outlined training materials and modules, to be offered throughout the Agency to risk assessors
and decision makers who will be faced with the challenge of interpreting and applying genomics
information, 2) continued collaboration of EPA personnel with staff from other federal agencies
and stakeholders in the development of tools for the analysis of genomics data, 3) application of
this guidance to a series of case studies to evaluate its utility in risk assessment and regulatory
applications; and 4) the updating of this guidance as needed as the technology evolves.
This document is intended to provide information to the regulated community and other
interested parties regarding submitting microarray data to the Agency and to provide guidance
for EPA reviewers in evaluating such data and/or information. This interim guidance can be
used by EPA program offices to determine the applicability of specific genomics information to
the evaluation of chemical risks.
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1.0 Introduction
1.1 Background
The mapping of diverse animal, plant, and microbial species genomes using molecular
technologies has significantly affected research across all areas of the life sciences. The current
understanding of biological systems is rapidly changing in ways previously unimagined and
novel applications of this technology have already been commercialized. These scientific and
technological advances have spurred many federal agencies to consider the far-reaching
implications for policy, regulation, and society as a whole.
In 2002, EPA released the Interim Policy on Genomics (U.S. EPA, 2002a)
communicating its initial approach to using genomics information in risk assessment and
decision making (http://www.epa.gov/osa/spc/genomics.htm). This policy describes genomics as
the study of all the genes of a cell or tissue, at the DNA (genotype), mRNA (transcriptome), or
protein (proteome) level. The Interim Policy notes that while genomics offers the opportunity to
understand how an organism responds at the gene expression level to stressors in the
environment, understanding such molecular events with respect to adverse ecological and/or
human health outcomes is far from established. This policy states that while genomics data may
be considered in the decision making process at this time, these data alone are insufficient as a
basis for decisions. Consequently, currently EPA will only consider genomics information for
assessment purposes on a case-by-case basis.
Following the release of the Interim Policy, the Science Policy Council (SPC) created a
cross-EPA Genomics Task Force and charged it with examining the broader implications
genomics is likely to have on Agency programs and policies. To that end, the Genomics Task
Force developed a Genomics White Paper entitled "Potential Implications of Genomics for
Regulatory and Risk Assessment Applications at EPA" (USEPA, 2004,
www.epa.gov/osa/genomics.htm). The Task Force identified scenarios to describe various
circumstances under which EPA might receive these data. Four areas were identified as those
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likely to be influenced by the generation of genomics information within EPA and the
submission of such information to EPA: 1) prioritization of contaminants and contaminated sites,
2) monitoring, 3) reporting provisions; and 4) risk assessment. The Task Force also identified
several challenges and/or critical needs that included research, technical development, and
capacity {i.e., strategic hiring practices and training).
The Genomics Task Force recommended that the Agency charge a workgroup with
developing a technical framework for analysis and acceptance criteria for genomics information
for scientific and regulatory purposes. The Genomics White Paper identified issues that need to
be considered in developing such a framework including the performance of assays across
genomic platforms {e.g., reproducibility, sensitivity, pathway analysis tools) and the criteria for
accepting genomics data for use in a risk assessment (e.g., assay validity, biologically
meaningful response).
In June, 2004, the Genomics Technical Framework and Training Workgroup was
established with representatives from ORD, numerous program offices (OPPTS, OSWER, OW,
OEI, OPEI) and regional offices (2, 3, 5, 7, 8, and 9). The Genomics Workgroup was comprised
of a Coordinating Committee, several technical genomics guidance workgroups (Performance
Approach Quality Assurance Workgroup, Data Submission Workgroup, Data Analysis
Workgroup, and a Data Management and Storage Workgroup), a Training Workgroup, and a
Microbial Source Tracking Workgroup. The Genomics Workgroup's responsibility was to
ensure that the technical framework and training activities build upon the Agency's Interim
Policy on Genomics while continuing to engage other interested parties. This document will be
used by EPA program offices and regions to determine the applicability of specific genomics
information to the evaluation of risks under various statutes.
1.2 Overview of Genomic Science
As a means of introduction to genomics and its potential impact on regulatory decision
making, it is important to understand the basic principles behind genomic technology. Only
about 1-2% of the human DNA actually codes for RNA that can be translated into proteins. This
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1-2% is considered to be the theoretical functional genome. Any particular cell type {i.e., from
various organs or species) will have its own practical functional genome, which is a subset of the
entire functional genome that encodes for functional proteins in that cell. The functional genome
for any cell type can be assessed by determining the messenger RNA (mRNA) profile of the cell,
tissue, or organ. The mRNA copies the necessary portion of the cell's DNA code and transports
this information to the ribosomes where protein synthesis occurs. Thus, the assessment of
mRNA profiles is called functional genomics. Such profiles are constructed using microarrays
that contain all (or a sampling) of a cell's functional genome. Hybridization of a DNA copy
(cDNA) of the mRNA that is being actively produced by the cell to these microarrays
demonstrates which genes are currently active in that cell. Within the 98-99% of DNA not
coding for RNA message is information that affects the activity of the functional genome by
influencing where and when genes are active in an organism. Thus both coding and noncoding
DNA are important in organismal function and response to perturbations.
The study of a cell's protein composition is called proteomics. Currently, it is possible to
analyze only a fraction of a cell's proteins, but rapid advances in this field will allow more
complete profiling in the near future. Another discipline of biology analyzes biofluids and
tissues to determine the profiles of endogenous metabolites present under normal conditions or
when the organism has been affected by factors such as exposure to environmental chemicals.
This type of whole cell analysis is called metabolomics (or metabolic profiling). In order to
understand how a cell functions under normal or stressed circumstances, it is necessary to
characterize the proteins that are manufactured by the cell, as well as endogenous metabolites.
This facilitates an understanding of global metabolism and how proteins interact along
biochemical pathways. This approach describes the area of systems biology, in which the cell,
tissue, or organism is considered as a complete, albeit complex, system.
Broadly defined, genomics tools provide the means to examine changes in gene
expression, protein, and metabolite profiles within the cells and tissues, in contrast to current risk
assessment methods which are restricted to whole organism effects or changes in single
biochemical pathways. Genomics tools have the potential to provide detailed data about the
underlying biochemical mechanisms of disease or toxicity {i.e., disease etiology, biochemical
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pathways), sensitive measures of exposures to chemicals, new approaches to detecting effects of
such exposures, and methods for predicting genetic predispositions that may possibly lead to
disease or higher sensitivity to particular stressors in the environment.
Another type of application is chemical identification. By utilizing genomic expression
profiles it is possible to identify and classify environmental contaminants. For example,
Hamadeh et al. (2002a,b) found chemical-specific gene expression profiles in liver tissue of
exposed rats. The authors demonstrated that 24-hour exposure to compounds from the same
chemical class (peroxisome proliferators) resulted in gene expression profiles that were unique
but more similar to each other than to patterns corresponding to exposure to a chemical of a
different class (enzyme inducers). These gene expression profiles were associated with
differences in histopathology between the different chemical classes following longer durations
These and other published works indicate the utility of genomic approaches in chemical
identification and in investigations of mode-of-action of chemical hazards.
1.3 Emerging Impacts of Genomics Technologies
Toxicology has been moving from observation of changes in tissue histology,
physiology, and chemistry to a mechanistic understanding through assessment of large scale
changes of gene activity within those tissues. Identification of changes in gene expression using
microarrays is becoming an important tool for informing our understanding of toxicological
processes as well as informing the hazard identification process and mode of action analysis as
part of safety and risk assessment. As the price of conducting microarray experiments declines
and an appreciation of their value increases, their use for basic research and as part of the
environmental regulatory process is likely to increase.
The use of data generated by microarray technology in peer reviewed scientific
publications has grown exponentially over the last few years. Microarray technology allows
monitoring of changes in gene expression across thousands of genes, or even entire genomes or
proteomes in response to experimentally manipulated or natural conditions. We are now
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beginning to understand several important toxicological processes in terms of changes in the
activity of single genes or ensembles of genes acting in concert. The identification of these
changes is increasingly the product of the use of microarray technology. As a result of these
research trends, EPA anticipates receiving increasing volumes of microarray data from
environmental researchers, and as a part of the regulatory process. In order to ensure optimal
utilization of these data, EPA has developed this guidance to address the quality, submission,
analysis, and storage of microarray data.
While many new genomic technologies do exist, most are not as yet ready for application
in risk/safety assessment and decision making. Therefore, it is important for the Agency to
consider how these genomic technologies might be incorporated into existing programs. It
should be noted that genomics will not fundamentally alter the risk assessment process, but is
expected to serve as a powerful tool for evaluating the exposure to and effects of environmental
stressors and will offer a means to simultaneously examine a number of response pathways.
EPA and other regulatory agencies are beginning to address the use of genomics data for various
risk assessment applications, including the need to establish a link between genomic alterations
and adverse outcomes of regulatory concern. Given the rapidly evolving nature of genomics
technologies, care should be taken to develop an acceptable scheme to simplify and refine the
risk-related information and to distinguish it from the large amount of complex scientific and
statistical data available. This strategy should remain dynamic and fluid in anticipation of
continuing technical evolution at the molecular levels (e.g., DNA, RNA, and protein levels).
Furthermore, bioinformatic approaches for data acquisition and analysis, including technologies
designed to store and analyze the profusion of data generated from microarray analyses, should
be considered in parallel with the data generating methods. Finally, many scientific, policy,
ethical, and legal concerns developing along with the emergence of this science will need to be
addressed.
The Interim Policy on Genomics provides guidance concerning how and when genomics
information should be used to assess the risks of environmental contaminants under the various
regulatory programs implemented by the Agency at the present time. The standardization of
experimental design, the selection of informative biomarkers, and data analysis for genomics is
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important for the utility of genomics information in future risk assessment and regulatory
decisions. Such standardization will enhance the reproducibility of results obtained and the
reliability of conclusions drawn from microarray data. Furthermore, EPA is considering the
development of data quality standards based on performance of microarrays, as well as other
genomics technologies (e.g., functional genomics). This in turn will help to ensure the integrity
of EPA's approach to assessing the genomics information submitted to the Agency.
Genomics issues have already arisen in environmental decision-making. For example, a
pesticide registrant has cited a published genomic article (Genter et al., 2002) as part of the data
package submission for product registration to EPA's Office of Pesticide Programs. The data
were submitted in support of an alternative mode of action that would affect human health
assessment conclusions. Similar submissions are quite likely to be made by other pesticide
registrants.
Although this document focuses on the use of microarrays for toxicological studies as
they pertain to macroorganisms, it should be noted that the impact of microarray technologies
goes beyond the exploration of toxicological effects in eukaryotic systems. For example, the use
of microarray techniques in environmental and clinical microbiology has increased significantly
in the last few years. Microarrays can also be used to screen for host specific markers that can be
used in microbial source tracking (MST). As an example of the application of genomics to MST,
a research consortium including State of California regulatory agencies, public utilities, and EPA
recently participated in a study comparing the performance of various genomics-based methods
designed to identify the source of fecal material in ambient waters in an MST approach (Griffith
et al., 2003). Moreover, genomics methods are being evaluated to assist dischargers in
complying with Clean Water Act (CWA) requirements to develop Total Maximum Daily Loads
(TMDLs) for water bodies that are listed as impaired due to the presence of fecal coliforms. This
MST work will also address the issue of beach closures; current microbial methods require
several days to complete and do not distinguish between bacteria from humans and other sources
such as sea gulls or seals. Further details on these MST efforts are described in Microbial
Source Tracking Guide Document (available at:
http://www.epa.gov/ORD/NRMRL/pubs/600r05064/600r05Q64.htm ; U.S. EPA, 2005).
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These examples indicate the need to make proactive policy decisions and to develop
processes to address how genomics data will be used in Agency decision-making.
1.4 Purpose and Intent of this Document
As a result of research trends, EPA anticipates receiving increasing volumes of
microarray data from environmental researchers, and as a part of the regulatory process. The
Genomics Technical Workgroup considered all of the "omics" technologies and applications and
decided that a guidance document on the use of data generated by DNA microarray analysis
would be most beneficial to the Agency and regulated community at this time. This guidance
applies to microarray data relevant to human health and ecological risk assessment and decision
making. This guidance is provided in order to facilitate appropriate submission, consistent
review, and optimal utilization of these data. Consequently, this document provides
recommendations regarding: 1) data that should be considered for submission to the Agency for
microarray studies, 2) the use of a performance approach to microarray quality assessment
parameters, 3) data analysis approaches for microarrays, and 4) data management and storage
issues for microarray data submitted to or used by the Agency.
The purpose of this document is to provide information to the regulated community and
other interested parties regarding submitting microarray data to the Agency and to provide
guidance for reviewers in evaluating and utilizing such data and/or information. This interim
guidance can be used by EPA program offices to determine the applicability of specific
genomics information to the evaluation of chemical risks. It is important to note that microarray
technology is rapidly changing, such that methodologies for generating such data and ensuring
its quality will likely change; however the need to ensure consistency and quality in generating,
analyzing and using the data will not. As the state of the science develops, EPA plans to revisit
the guidance as necessary.
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2.0 The Performance Approach to Quality Assurance for
Microarrays
Quality issues are critical considerations in the application of new technologies or
approaches, such as genomics. The Workgroup recommends that the Agency not prescribe
specific methods to be used in microarray experiments at this time. This section instead provides
general guidance on the recommended performance of microarray experiments in order to obtain
data of the quality needed for a specific use.
The Agency acknowledges that continued advancement of tools and platforms for
describing biological phenomena will be pivotal in supporting claims for regulatory decision
making. It is also noted that at this time there exist numerous approaches, investigator fabricated
and commercially available platforms, hardware and other peripheral equipment by which to
measure biologic trends and changes at the level of tissues and cells. The following technical
statements relate primarily to "expression" measurements (up- and down-regulation of
macromolecules) and certain other multiplex technologies used to generate and collect
quantitative and qualitative data about changing biologic conditions. This guidance is also
relevant to the evolving nature of "expression" measures, particularly as recommendations for
standardization in experimental performance put forth by the combined efforts of academic,
industry and government scientists, become universally accepted and applied.
Although there are currently numerous means by which to observe and acquire biological
expression measurements, such as Massively Parallel Signature Sequencing (MPSS) and Serial
Analysis of Gene Expression (SAGE), the most frequently used experimental approach to
collecting expression data is microarray-based studies. This technology, which has expanded
well beyond the sphere of human health, is exploited to describe changing transcriptional profiles
in genes of countless species that are important to numerous areas of biological sciences.
Unfortunately, many of these investigations are undertaken without the benefit of explicit
consensus for quality assurance and quality control and there has yet to be firmly established
criteria for intra-experimental and cross-platform performance evaluation.
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Investigators submitting data to the Agency in support of regulatory decision-making,
methods development, and technical transfer, should also consider at a minimum the
performance-related experimental and system factors outlined in Appendix A, in addition to
compliance with MIAME (Minimal Information About Microarray Experiments) Workgroup
standards (http://vvvvvv.mued.oru/Workuroups/MlAME/miame.html) discussed in Section 3
below. Further activities on the part of investigators to address experimental performance issues
will serve to strengthen scientific arguments and experimental claims.
Each EPA program, regional, or research and development office's Quality System
should be defined and documented in their Quality Management Plan (QMP). A summary of
their individual office's Quality System activities is detailed in a Quality Assurance Annual
Report and Work Plan (QAARWP), which also includes information on their annual internal
assessment of their Quality System.
Additional detailed discussion of the EPA Quality System and the performance approach
to quality assurance for microarrays is provided in Appendix A.
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3.0 Data Submission Guidance
3.1 Introduction
EPA developed the following information regarding submission of microarray data to
facilitate appropriate review and consistent evaluation of data from multiple sources. The text
that follows was written as a preliminary template guiding the submission of microarray data to
the EPA. As the state of the science develops, EPA plans to revisit this submission format as
necessary. In accordance with accepted practice, it is useful if submissions include sufficient
information to allow an independent reviewer to reconstruct how the data were collected and
analyzed. This approach allows reviewers to judge the quality of the data and the strength of any
conclusions. It is also useful if the submission includes enough information in a format that
facilitates comparison or integration with similar data from other experiments.
Microarray technology is rapidly evolving with many competing platforms, native data
formats, and analysis tools. As a result, a data submission standard should not be so specific as
to stifle flexibility or innovation. Similarly, standards should not be burdensome, discouraging
submission or slowing scientific progress. Many scientific journal editors grappling with these
issues have adopted the Minimal Information About Microarray Experiments (MIAME)
guidelines as a standard for submission of microarray data as part of a submitted publication
(http://www.mged.org/Workgroups/MIAME/miame.html). A slightly modified version of
MIAME, described below in Sections 3.2 through 3.7 and Appendix B, is proposed as the
recommended microarray data submission template for EPA, which will be subject to change as
the technology evolves. As genomics science and the associated technologies evolve, it can be
expected that the MIAME guidance will concomitantly evolve. If the MIAME guidance in this
document conflicts with the most recent changes to the MIAME guidance, the reader is directed
to consider the MIAME guidance as the most recent, correct version.
3.2 Abstract
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An abstract or executive summary of the source and type of data as well as the type of
data evaluation and its final interpretation would provide a useful introduction to the data
submission. Such a summary would not need to be exhaustive but would optimally provide the
key highlights so that the reader will know the source of the data and how it was interpreted.
The abstract might be written in a similar manner as for the submission to a scientific meeting or
a journal article. It is advantageous if the reader is able to extract the important features of the
submission and its interpretation from the abstract, although it is understood that a thorough
evaluation of the substance of the data will involve a review of all the submitted material.
3.3 Experimental Design
It would be beneficial if voluntary submissions of genomics data to EPA included a
sufficient description of the experimental design necessary to understand the source and nature
of the data as well as the materials used to conduct the research. The following discussion is not
an exhaustive listing or meant to be complete but indicates the spectrum of information on the
experimental design that might be submitted for review. The submitter should consider
providing the standard information one would include in the materials and methods section of
any scientific article including a list of all the endpoints examined in the study. Such
information would include information about the biological model system, treatment methods
and doses, husbandry of animals, and cell culture information for in vitro systems. If whole
animal models were employed, then submission of information regarding the exposure system,
exposure doses, time points, details on euthanasia, length of time between harvesting of tissues
and freezing or other processing, numbers of samples utilized for DNA array analysis, methods
of RNA processing, and RNA quantification should be considered. The submitter should
consider providing information on the methods employed for hybridization and incorporation of
label and the numbers of hybridizations. When relevant, the submission of additional
information necessary for interpretation of the data should be considered. Such information
might include reference sample information, sample amplification, or any additional information
unique to the study. The submitter should also consider providing information regarding any
problems that arose during the study that could have an impact on interpretation.
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3.4 Array Design
The inclusion of a complete description the platform used for transcriptional expression
analysis such that the reviewer can assess the appropriateness of the analysis should be
considered. The platform might be a commercially available platform (e.g., Affymetrix, Agilent,
Clontech) such that reference may be made to the specific type of chip used and the locations
(weblink) of the source of the proprietary information so that the reviewer may access this
information to aid in the review of the data analysis. If the transcriptional expression analysis
was derived from a custom array designed for or by the submitter, then a inclusion of complete
description of the production of the array would be useful. This information would likely
include but certainly not be limited to the source of the nucleotide sequences used on the array,
how the arrays were prepared, equipment used to prepare the arrays, description of the slides or
membranes on which the arrays were spotted, gene lists, and any supportive data which confirms
the specificity of the sequences used. A more complete listing of the types of data that would be
useful in supporting the submission of custom arrays can be found in Appendix B.
3.5 Biomaterials
It is advantageous if the submitted data package presents the physical characteristics of
the studied biomaterials as these will likely vary between experiments. Such characteristics
might include age, sex, cell type/line, and/or genetic variation. When applicable, this
information would address the biological material from which nucleic acids (or proteins) have
been extracted for subsequent labelling and hybridization. It is also recommended that submitted
information on biomaterials detail the source properties, treatment, extract preparation, and
labelling of the sample. Any pertinent information about sample controls would also be useful in
analyzing submitted data.
The exposure conditions applied to each test organism or tissue are important parameters
influencing the experimental response. As a result, it is useful to document the incubation and
treatment conditions applied to the studied biomaterial. Other key submission information might
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include the method of chemical or physical exposure using the appropriate dosing units.
Furthermore, any processing of samples taking place after exposure would be of interest.
Information on the hybridization extract preparation protocol might include such details
as the nucleic acid type and amplification method used. It would also be useful to record and
submit the labeling materials and technique used in the experiment. Finally, the data submitter
should consider outlining the type and position on the array of any external controls that may
have been added to the hybridization extract(s). Please see Table B.4 in Appendix B for further
information.
3.6 Hybridization
It would be usedul to submit a concise description of the procedures adopted for each
hybridization. If a commercially available platform is utilized, reference may be made to the
specific type of hybridization procedures and parameters adopted in the experiment. Web or
literature citations describing the source of the hybridization protocol and materials are useful.
Furthermore, information regarding the relationship between the labelled sample extracts and
their corresponding arrays (design, batch and serial number) would be useful for understanding
the experiment. Documentation of the steps taken in the hybridization including information
regarding the solution, blocking agent and concentration used, wash procedure, quantity of
labelled target used, time, concentration, volume, temperature, and a description of the
hybridization instruments is encouraged.
3.7 Measurements
The submitter should consider completely describing the methods used to acquire the
image of the array, the nature of the image (e.g., TIFF), the nature of the extraction of image data
into quantified image data, and the nature of the spreadsheets used to house the quantified data.
Submission of the original TIFF images is encouraged as is the submission of the initial
quantization matrix. The description of the spreadsheet normalization of the TIFF data and any
subsequent data analysis is also of value in a submission. In addition, features of the data used
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1 for analysis such as background correction, normalization methods, methods used to test
2 usability of the raw data, and types of analytical approaches would be useful information for the
3 reviewer. Analytical approaches might include statistical models, graphical models, image based
4 displays of data, and various analytical software packages. Information about the software may
5 include weblink, proprietary information from instruction manual, or specific description of
6 custom analytic methods. More complete description of information that should be considered
7 for a submission for review may be found in Appendix B.
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4.0 Data Analysis Guidance
This section provides information that will assist in regulatory and risk assessment efforts
when considering the use of genomics data. Genomics data can be used to aid in reducing the
level of uncertainty in the decision making process and provide a means to further evaluate
exposure and effects. This guidance effort is also an attempt to highlight the need for developing
genomics data analysis tool criteria, and the standardization of methods for the use of these tools.
4.1 Introduction
Evaluation of qualified genomics data, which have been properly analyzed and submitted
(see Sections 2.0 and 3.0), has the potential to dramatically improve the mechanistic
understanding of toxicities and their relevance to human health and ecological hazard
identification and risk assessments. For example, DNA microarrays may be used to identify
gene expression profiles associated with exposure to particular compounds, or characteristic of
certain modes of action or mechanisms of toxicity. When a correlation has been established
between a gene expression profile and a toxic mechanism, then these genomic data provide
supportive evidence for that mechanism. Even when the mechanism for a particular compound
is unknown, genomic data can help identify plausible toxicity pathways that may be involved in
the biological process under study (Crosby et al., 2000) for the purposes of prioritization or
screening.
Genomic technologies generate vast amounts of data (gigabytes) quickly (during a single
analytical session), especially when using DNA microarrays for gene expression profiling. This
wealth of data increases the importance of careful documentation of experimental and analytical
methods while working towards data interpretation and evaluation. The Minimal Information for
the Analysis of Microarray Experiments (MIAME) guidelines have helped to standardize DNA
microarray experiment documentation. Extension of the MIAME guidelines into
toxicogenomics has provided even more applicable prerequisites for analysis
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(http://www.mged.org/MIAMEl. l-DenverDraft.DOC; Fostel at al., 2005). Also critical to
analysis of genomics, and particularly microarray data, is access to the raw data from published
or submitted experiments, and accompanying documentation of experimental and analysis
details. Establishment of public genomic databases such as the Gene Expression Omnibus
(GEO, http://www.ncbi.nlm.nih.gov/geo/) provides limited access to microarray data, but these
are not compatible with all monitoring or regulatory applications.
In addition to data submission and management activities, computational tools for
genomics data analysis are another critical need for routine application of genomics data.
Although evaluation of many of the currently available computational tools for genomics data
analysis is underway through multiple internal and external Agency research efforts, these tools
have not been examined by the Agency in sufficient detail that would allow for specific final
recommendations to be made. Furthermore, while the variability and complexity of microarray
experiments make prescribing a common, all-encompassing protocol functionally problematic,
general components for the successful analysis and interpretation of all microarray approaches
are discussed. The Agency is currently participating in several projects designed to develop
appropriate protocols and methods for microarray data analysis. These include collaborative
efforts with Food and Drug Administration (FDA) on the Microarray Quality Control project (
http://vvvvvv.fda.gov/nctr/science/centers/toxicoinformatics/macic/) and National Institute of
Environmental Health Sciences (NIEHS) on the Chemical Effects in Biological Systems
knowledgebase (http://cebs.niehs.nih.gov/). As an interim solution a genomics Data Evaluation
Record (DER) template (Appendix C) is proposed as a means to outline a framework for
genomics data analysis and documentation.
4.2 Data Analysis
A few general features of genomic data analysis areas are described below with the intent
to provide a basic but broad overview.
4.2.1 Data Processing and Filtering
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Data processing covers the steps from scanning the array, to obtaining reliable estimates
for the relative abundance of each gene transcript in all of the samples. Generally, these steps
are classified as image analysis, quality control filtering, background correction, transformation
and normalization. Each hybridized array has an associated and unique image file from which
individual values (pixel intensities) can be collected. Data can be filtered to exclude signals that
fail quality criteria. The specifics of data filtering and the threshold levels chosen are dependent
upon the details and goals of the experiment. Standardization of processing and filtering criteria
will be a critical step toward intra- and inter-laboratory agreement. The final output of the initial
processing will be data that can be analyzed further to identify differentially-expressed genes.
4.2.2 Statistics
A standard, or common, statistical approach, that would be appropriate for all microarray
experiments, cannot be specified because of unique experimental variables such as differences in
microarray platforms, experimental design (reference versus matched), levels of replication
(technical versus biological), as well as within experiment sources of variation (spot to spot, slide
to slide, etc.). Therefore, the types of methods and tools used for statistical analyses of
microarray results often differ not only from more traditional experimental approaches, but also
from one microarray experiment to another. Sample size strongly affects the statistical method
chosen for analysis. For example, while a relative balance may exist between the number of
samples and data points measured in a standard non-genomic experiment, microarrays, as well as
proteomic and metabonomic technologies, generate hundreds and often thousands of data points
from each sample. Furthermore, a variety of formulae exist to calculate appropriate microarray
sample sizes, depending on experimental design. Nevertheless, the cost of conducting such
experiments prohibits large scale studies with multiple sample sizes. Another constraint is
sample pooling, at times a necessity due to the complex nature and paucity of biological material
(i.e., tissues and/or RNA quantities). It is, nonetheless, important to recognize that sample
pooling may impact microarray experiments at multiple levels, including experimental design
and subsequent analyses. Finally, data replication should be considered. It is important to
distinguish the two types of replication that exist in biological experiments, including
microarrays: technical (repeats of the same sample) and biological (starting material from unique
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sources, such as different animals in a test group). For scientifically sound reasons, the latter
assumes greater significance in most biological assays including microarray experiments.
4.2.3 Interpretation
Numerous approaches can be used as a secondary level of analysis to interpret
differentially expressed genes detected using microarray experiments. For example, genes can
be sorted by ontology (gene ontology, GO) and subsequent cluster analyses (principal
component analysis, hierarchical clustering, and K-means clustering) can be used to better
organize the data and help identify patterns of gene expression.
Various bioinformatics (mathematical and statistical) algorithms can be used to integrate
these patterns of expression with common biological pathways and networks of co-regulated
genes. Linking these functional and pathway analyses to concurrent and previously identified
phenotypic characteristics will significantly advance the understanding of the biological
processes involved along the source-to-outcome continuum.
4.2.4 Inference
Integration of these various data analyses and interpretation tools can be used to infer
cause and effect relationships from these genomic data (Freeman, 2005). Biological inference
may lead to biomarker development as well as descriptions of dose-response relationships,
mechanisms of action, and predictive toxicity. Biomarkers are recognized as providing data
linking exposure to internal dose and effect. The application of biomarkers to the risk assessment
process that is linked to toxic processes or mechanisms may provide additional information for
risk assessors. Additionally, data generated from microarray studies on model test organisms
could be 1) applied to the identification of susceptible subpopulations, 2) used to develop
surrogate species for toxicity testing, and 3) extrapolated to additional species, once the
biomarkers and mechanism(s) of action are identified.
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4.3 Data Evaluation
The goals of the evaluation of genomics data are directed toward risk assessment for
regulatory applications. Currently, however, decisions cannot be made based solely upon gene
expression pattern recognition, according to EPA's Interim Genomics Policy; this technology has
not yet come to set precedence on its own. Currently, confirmatory studies are useful for
potential risk assessment and regulatory use. If the data generated from microarray assays are
confirmed using other techniques {i.e., real-time quantitative PCR, functional enzyme assays,
protein and metabolite profiles and/or linked to bioassay results), these data will help support
links between gene expression, exposure and the resulting adverse effects in organisms.
Furthermore, interpretation of microarray data with respect to existing toxicity profiles and
endpoints of other perhaps higher level tests (clinical chemistry, immunochemistry,
histopathology, and reproductive endpoints) should significantly increase the diagnostic and
predictive applications of these technologies in the future.
A genomics Data Evaluation Record is used here as a way to present and organize-data
from genomics studies in order to derive information necessary for a regulatory application (see
Appendix C for the Genomics Data Evaluation Record (DER) Template). For monitoring
applications such information and standardization is recommended. The sections of the DER
include the general information about a study and a brief executive summary as well as the
materials and methods used. The test performance section includes: treatment and sampling
times, tissues and cells examined, details of tissue harvest and storage, sample preparation, data
analysis, evaluation criteria and statistical analysis. The results, discussions and conclusions are
also components of the DER. Sections of the DER are included to provide example information
to the risk assessor as a means to document the incorporation of genomics information in the risk
assessment process. Genomic data used to support the more conventional data {e.g., limited
clastogenesis in vitro associated with cytotoxicity, DNA strand breaks, lipid peroxidation) are
presented in an example DER for rats exposed to alachlor (see Appendix D: Draft Genomics
Data Evaluation Record for Alachlor)
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1 4.4 Data Analysis Conclusions
2
3 The above considerations demonstrate that a systematic approach for genomics data
4 evaluation is necessary for further use of genomic data in risk assessment efforts.
5 Documentation methods, like those in the proposed genomics DER (Appendix C) can help
6 capture some requisite information, but the transfer of these evaluations, and the underlying
7 genomics data, into searchable, electronic databases will be essential to making the data useful in
8 risk assessments. Furthermore, development of databases containing gene expression profiles
9 for a wide variety of chemicals should facilitate creation of statistical/computational methods
10 that predict the toxic potential of a chemical.
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5.0 Data Management
The goal of this section is to outline recommendations to EPA for an approach to
managing genomic data submitted to the Agency or developed internally by EPA scientists. This
includes the need to consider an Agency-wide warehouse for storage, retrieval and analysis of
information submitted for regulatory or risk assessment purposes.
There are several major types of needs to consider in addressing the issue of an EPA-
wide database: broad scientific needs for risk assessment purposes, program-specific regulatory
needs, Agency Information Technology (IT) security needs, and public access needs. Although
there is an overlap of issues for each of these purposes, it is useful to think of each additional
purpose adding another layer of needs.
For scientific risk assessment purposes the key needs include the following items:
1) Standardization of data inputs as identified by the Data Submission Workgroup. This
includes both microarray data and experiment parameters associated with the
toxicogenomics study. It also provides for electronic submission of data.
2) Provision of connectivity to external public biological databases such as Affymetrix,
Agilent, and GenBank
3) A quality control mechanism to ensure the fidelity of entered data
4) Capability for importing and exporting data by means of automatic routines
5) Inclusion of a wide range of data analysis and visualization tools such as filtering,
clustering, and statistical analysis
6) Sufficient scalability to address large data submissions, many users, and later addition of
metabonomics and proteomics data at times in the future
7) Audit trail capability. This would provide a time line and information on who added,
changed or deleted specific data. It would also provide versions prior to deletions and
changes.
8) Automatic data back up and recovery system
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For security and management purposes, additional key needs include:
1) Database hosting, administration and management. This includes managing data
submission, database access and privileges, software and hardware updates, back-up and
storage.
2) Physical and electronic security, including user authentication, firewalls, and virus
protection.
3) Governance structure to provide policies and procedures for submissions, access,
security, cost sharing, and priority for development of new features.
For regulatory purposes, additional considerations may be necessary:
1) Electronic signature or other formal identity management capability. If the data are
submitted electronically as part of a regulatory submission, the system needs to ensure
that the submission is linked to the submitter.
2) Capability of partitioning the database to secure Confidential Business Information (CBI)
or other non-public information, if this is part of a regulatory submission.
3) Workflow enabled, so that reviewer can address data in systematic steps needed for
response to submission.
For public access purposes, key needs are:
1) Database is Web enabled, with easy routine for export of data.
2) Clear policies governing the management of the public database as opposed to an internal
or staging database.
There may also be staging considerations in building or adopting an Agency-wide
genomic database. The first phase might include genomic data only, and have limited analytic
capability. Eventually the database should provide quality assessment tools, extensive analytical
capability, gene-centric queries, and encompass proteomic, metabonomic, and conventional
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toxicology assay results. Integrating these diverse types of experimental data will support data
mining as well as the development of predictive toxicology systems.
Currently, there is no single database at EPA for managing genomics data each program
or lab is developing its own approach. As the needs are currently identified above, there are
several advantages to creating and maintaining an EPA-wide genomics database:
1) Cost. All of the scientific and management/security needs identified above should be
addressed by any genomic database used at EPA. Addressing these items once in a
uniform way would avoid duplication of these costs.
2) Data Access. All users in the Agency would have access to all Agency genomic data
(except CBI data), greatly enhancing our risk assessment capabilities.
3) Quality Control and Consistency. A quality control mechanism would ensure that all
Agency data passes a consistency test.
4) Availability of a Common Set of Analysis Tools. As new tools are developed, they
would become available to all users.
5) Scaleable. While lab or program specific databases may focus on a narrow range of data
or analysis, an EPA database would be built to include a wider range of "omics" data and
a full portfolio of analytical tools enabling Agency scientists to pursue a wider range of
data mining and biological systems-oriented studies.
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6.0 Additional Recommendations
The Genomics Workgroup recommends that the Agency undertake a number of follow-
up activities to this interim guidance including: 1) further development of the training materials
and modules outlined below, to be offered throughout the Agency to risk assessors and decision
makers who will be faced with the challenge of interpreting and applying genomics information,
2) continued collaboration of EPA personnel with staff from other federal agencies and
stakeholders in the development of tools for the analysis of genomics data, 3) application of this
guidance to a series of case studies to evaluate its utility in risk assessment and regulatory
applications; and 4) the updating of this guidance as needed as the technology evolves.
6.1 Training Needs and Recommendations
The charge to the Genomics Task Force Training Workgroup was to develop an approach
and appropriate delivery mechanisms for training Agency risk assessors and managers to
understand and interpret genomics data in the context of risk assessment. The need for a better
understanding of molecular biology concepts, and ultimately how genomics, proteomics, and
other "omics" data may be used to support decision making, is the primary driver for the
development of such training for staff and managers.
In designing training genomics, the Training Workgroup considered several issues: 1) the
need to develop a modular approach that could build on basic information and change as new
information becomes available, 2) the need to vary the level of complexity based on the needs of
a particular audience, 3) the importance of considering the target audience, based on the
recognition that different staff and managers will have different needs, 4) the need to develop a
schedule for production of training materials, recognizing that, by taking advantage of existing
public sector resources to build the initial version of the Genomics Training, time and resources
may be saved; and 5) identifying internal capacity to provide training, such as ORD scientists
and risk assessors to save time and resources.
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1 Presented below is a draft outline that describes a modular training course in molecular
2 techniques, in general, and genomics data interpretation, in particular. The Genomics Training
3 would consist of three levels of training targeted to specific audiences, each consisting of a series
4 of modules devoted to a particular group of concepts and/or techniques. Each training level is
5 outlined below in Table 1, with descriptions of the content and instructional goals. More detail
6 on the proposed training is provided in Appendix G.
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Table 1. Overview of Genomics Training Plan (see Appendix G for more detail)
Training Level
Number
of Module
Target Audiences
Content
Goal
Level I:
Introductory
Modules-
8
Non-scientists and/or
technical staff without
training in biological
sciences.
Molecular Biology concepts:
cell structure and function, DNA,
RNA, proteins, gene arrays, risk
assessment concepts, regulatory and
risk assessment communication,
EPA's current genomics policy.
Provide basic information
necessary for understanding
assessments of cellular
functions at the molecular
level and how genomics
data may affect risk
assessments.
Level II:
Intermediate
Modules
3
Scientists and/or those
likely to use genomics:
Intended for staff who need
more in-depth
understanding of genomics
data generation, but do not
necessarily generate data.
Background on molecular techniques
such as microarrays, DNA
amplification techniques, DNA
fingerprinting, protein analysis, etc.
Modules to be targeted for specific
applications, (e.g., microbial source
tracking, homeland security, field
inspectors, etc.)
Provide a general
understanding of various
applications that may be
currently considered by
programs throughout EPA.
Intended to support human
health and ecological risk
assessors.
Level III
Advanced
Modules
Dependent
on specific
technical
needs.
Scientists and those likely
to use genomics data to
generate risk assessments.
Modules would include statistical,
computational and bioinformatics
approaches to analyze genomic data,
the use of molecular biology in mode-
of-action determinations, and using
genomics data in hazard/risk
assessments. Flexible to account for
changes in the field and to meet needs
of the different EPA programs. As
new technologies/ applications appear,
additional modules developed,
enhanced and/or revised.
Provide advanced-level
knowledge on specific
technical needs that
scientists performing
research or developing
hazard/risk assessments
associated with chemical
registrations and other
regulatory activities may
face.
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6.2 Collaborative Development of Genomic Tools for Data Analysis and Data
Management
The Agency, in concert with other federal agencies, has begun to investigate and evaluate
the currently available computational tools for genomic data analysis. EPA has been testing the
toxicogenomic data management and analysis features of the NIEHS Chemical Effects in
Biological Systems (CEBS) knowledgebase and FDA National Center for Toxicological
Research's ArrayTrack database. EPA has also been collaborating with FDA, National Institutes
of Health (NIH), National Institute of Standards and Technology (NIST), and other stakeholders
on the microarray quality control (MAQC) project to establish protocols for genomic data
analysis. Further, EPA has participated in National Academy of Sciences (NAS) workshops and
International Life Sciences Institute (ILSI) projects on the application of genomics to toxicology
and risk assessment. Building on these prior efforts, recommendations on the use of genomics
tools should be identified recognizing that the goal is the appropriate application of genomic data
in risk assessments and regulatory decision making. The Agency should also consider and
identify limitations of the currently available tools. Ultimately, the Agency is looking for
quantitative and predictive modeling tools, which will likely call for the development of new
algorithms and models. These tools will need to provide reliable and repeatable data analyses,
and the consistent and necessary information for EPA decision making processes. The scientific,
mathematical, and statistical methods that are used for these models and analyses will need to be
validated and standardized.
Due to the potentially large volumes of genomic and associated toxicological data, it is
essential that the Agency consider the development of a complete data management solution.
The functional needs of a solution of this magnitude should minimally include items listed in
Section 5.0 Data Management. In addition, this data management solution should address needs
unique to scientifically-based risk assessments, confidential and proprietary data security, public
access, and other aspects of regulatory application. It should be noted that consistency, scientific
and operational robustness, common access, and availability in a scalable environment are
important data management needs. While the Agency has begun to develop bioinformatics
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research efforts, both intramurally (e.g., ORD's National Center for Computational Toxicology)
and extramurally (the STAR funded Environmental Bioinformatics Center in NC and NJ), an
Agency-wide data management solution integrating genomics, toxicological, and other key data
for regulatory applications is now needed.
6.3 Applying this Interim Guidance for Microarray-Based Assays to Case Studies to
Verify its Utility in Risk Assessment and Regulatory Applications
The EPA's Risk Assessment Forum and other appropriate groups should apply this
interim guidance to several case studies to evaluate its utility in risk assessment and regulatory
applications and to identify potential areas for improvement.
6.4 Updating Genomics Guidance as Needed
This interim guidance should be revised and updated as indicated through its application
to case studies (see section 6.3 above), and as genomics technologies evolve. Additional
genomics guidances (e.g., proteomics, metabonomics) should be developed as needed to ensure
the Agency is prepared to receive and apply such data as the need develops.
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References
American Public Health Association, American Water Works Association, & Water
Environment Federation. Standard Methods for the Examination of Water and Wastewater.
Revision in process.
Brooks, A.N., Pennie, W.D. 2001. Transcript profiling of the response to environmental hormone
mimics. Comments Toxicol 7:303-315.
Burczynski, M.E., McMillian, M., Ciervo, J., Li, L., Parker, J.B., Dunn, R,T., Hicken, S., etal.
2000. Toxicogenomics-based discrimination of toxic mechanism in HepG2 human hepatoma
cells. Toxicol Sci 58:399-415.
Crosby, L.M., Hyder, K.S., DeAngelo, A.R., Kepler, T.B., Gaskill, R., Benavides, G.R., etal.
2000. Morphologic analysis correlates with gene expression changes in cultured F344 rat
mesothelial cells. Toxicol Appl Pharmacol 189:205-222.
Fostel, J., Choi, D., Zwick, C., Morrison, N., Rashid, A., Hasan, A., Bao, W., etal. 2005.
Chemical Effects in Biological Systems—Data Dictionary (CEBS-DD): A Compendium of
Terms for the Capture and Integration of Biological Study Design Description, Conventional
Phenotypes, and 'Omics Data. Toxicol Sci 88:585-601.
Freeman, M.R., Cinar, B., and Lu, M.L. 2005. Membrane rafts as potential sites of nongenomic
hormonal signaling in prostate cancer. Trends Endocrinol Metab 16(6):273-9
(Freeman, 2005
Genter, M.B., Burman, D.M., Soundarapandian, V., Ebert, C.L., Aronow, B.J. 2002. Genomic
analysis of alachlor-induced oncogenesis in rat olfactory mucosa. Physiol. Genomics 12:35-45.
Hamadeh, H.K, Bushel, P.R., Jayadev, S., Martin K., DiSorbo 0., Sieber S., et. al. 2002. Gene
expression analysis reveals chemical-specific profiles. Toxicol Sci 67:219-231.
Moreau, Y., Aerts, S., De Moor, B., De Strooper, B., Dabrowski, M. 2003. Comparison and
meta-analysis of microarray data: from the bench to the computer desk. Trends Genetics
19:570-577.
U.S. Environmental Protection Agency. 2005. Microbial Source Tracking Guide Document.
Office of Research and Development, Washington, DC EPA-600/R-05/064. 131 pp.
http://www.epa.gOv/ORD/NRMRL/pubs/600r05064/600r05064.htm
U.S. Environmental Protection Agency, Science Policy Council. 2004. Potential Implications of
Genomics for Regulatory and Risk Assessment Applications at EPA. EPA 100/B-04/002.
available at: www.epa.gov/osa/genomics.htm
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U.S. Environmental Protection Agency, Science Policy Council. 2002a. Interim Policy on
Genomics
U.S. Environmental Protection Agency. 2002b. NELAC Constitution, Bylaws and Standards
EPA/600/R-03/049.
U.S. Environmental Protection Agency. 1997. "Performance Based Measurement System," 62
Federal Register 52098 - 52100, October 6, 1997.
Waring, J.F., Ciurlionis, R., Jolly, R.A., Heindel, M., Ulrich, R.G. 2001. Microarray analysis of
hepatotoxins in vitro reveals a correlation between gone expression profiles and mechanisms of
toxicity. Toxicol Lett 120:359-368.
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Appendix A: EPA Quality System and the Performance Approach
to Quality Measurement Systems
The best quality data may not be technically available, affordable, or even applicable to
the exact problem at hand. To address a variety of circumstances, EPA has developed a Quality
System by which reasonable quality assurance (QA) guidelines or policies are offered for
assuring, documenting, and assessing data quality. EPA's Quality System is defined in EPA
Order 5360.1A2, Policy and Program Requirements for the Mandatory Agency-Wide Quality
System, the EPA Quality Manual for Environmental Programs, EPA Manual 5360 Al, the
Contracts Management Manual, and the Agency's Website (www.epa.gov/qualitv). The
requirements for EPA-funded organizations and organizations submitting data to EPA under
applicable statutes and regulations are also found in the Code of Federal Regulations (48 CFR
Part 46), also available through www.epa.gov/quality. Parties submitting data under applicable
statutes and regulations are expected to document the quality of the data submitted as well as
how it was achieved. Quality System parameters apply to environmental data operations and
measurements or information that describe: (1) environmental processes, (2) location or
conditions, (3) ecological or health effects and consequences; and (4) performance of
environmental technology
What is a Quality System?
As illustrated in Figure 1, a Quality System is viewed as a tiered organizational approach
for its work processes because it defines how the work is conducted, and provides a scientific
and technical basis for EPA's decision making process. The Quality System is a documented
management structure to ensure the quality of an organization's work processes, products and
services. Adhering to the Quality System helps to ensure that all operations, no matter where
they are performed, occur in a consistent manner and that the processes and outputs in the system
are effective, stable, and consistently followed. Key components in a Quality System are: (1)
Quality management, (2) Quality assurance (QA), and (3) Quality control (QC).
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Quality Control
Quality Assurance
Quality
Management
Quality System
Figure 1. A Generic Quality System
What Documentation is Needed for Organizations Submitting "Genomics Data??
An organization documents its Quality System in a Quality Management Plan while a
laboratory may document its implementation of specific quality policies and practices in a
document entitled a Quality Manual or Quality Assurance Plan. However named, the document
details the efforts to produce data that are adequate for their intended use and for assuring
conformity with regulations and customer requirements for data quality. Examples of a Quality
Management Plan are available at www.epa.gov/qualitv/qmps.html.
What is a Performance Approach?
A Performance Approach conveys "what" needs to be accomplished, but not
prescriptively "how" to do it. EPA defines the performance approach as a set of processes
wherein the data needs, mandates, or limitations of a program or project are specified, and serve
as criteria for selecting appropriate methods to meet those needs in a cost-effective manner. The
criteria may be published in regulations, technical guidance documents, permits, work plans, or
enforcement orders. Under a performance approach, EPA would specify the questions to be
answered, the decisions to be supported by the data, the level of uncertainty acceptable for
making decisions, and the documentation to be generated to support this approach (see
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http://www.epa.gov/fedrgstr/EPA-WASTE/1997/October/Dav-06/f26443.htm. or 62 FR 52098
for more details about Agency policy regarding the performance approach)
Performance approaches can be defined as either: (1) measurement data that are of
specified quality when demonstrating compliance (measurement quality objective (MQO)
approach), or (2) a demonstration of compliance that achieve specified statistical confidence (the
data quality objective (DQO) approach). Any appropriate measurement technology and
sampling frequency/thoroughness may be used as long as MQO or DQO is documented and met.
Key components that need to be considered in a performance approach are:
a) Sampling procedures and sample acceptance criteria, describing procedures for
collecting, handling (e.g., time and temperature), accepting, and tracking submitted
samples, and procedures for chain-of-custody.
b) Analytical methods, listing the laboratory's scope for testing and denoting
accreditation/certification status for individual methods, for non-standard methods or new
methods, the laboratory's validation procedures.
c) Analytical quality control measures, stating the laboratory's requirements for
measurement assurance, e.g., method verification and documentation, error prevention,
and analytical checks such as duplicate analyses, blanks, positive and negative culture
controls, sterility checks, and verification tests.
d) Documentation control and record keeping specifications, identifying recordkeeping
procedures to ensure data review, acquisition, traceability; accountability noting
procedures to ensure customer confidentiality; and other parameters such as control,
security, storage, retention, and disposal of laboratory records.
e) Assessments, describing the laboratory's processes to monitor the effectiveness of its
QA program.
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1) Internal audits of laboratory operations, performed on a routine basis,
minimally annually, by the QA officer and supervisor. For a small laboratory, an
outside expert may be needed.
2) On-site evaluations by outside experts to ensure that the laboratory and its
personnel are following an acceptable QA program.
3) Proficiency test studies, in which the laboratory participates. These
collaborative studies confirm the abilities of a laboratory to generate acceptable
data comparable to those of other laboratories and to identify potential problems.
f) Correction and preventative activities, identifying procedures used to determine the
causes of identified problems and to record, correct, and prevent their re-occurrence.
Systematic Project or Experimental Planning
In general, systematic project planning is essential before any activity begins, whether it's
sampling or analysis. For any project, the scientist needs to develop the experimental study
design by first identifying and documenting what the problem is, why the new information is
needed, and the objectives for the experiment or series of experiments.
Once the study objectives are defined, the hypothesis is then developed. In
systematically planning a project, the team or researcher then needs to determine the study
parameters or test variables, both critical and the secondary (if any). The data quality objectives
or performance criteria {i.e., how good the data should be for the intended purpose) should be
defined before the experiment starts along with all the appropriate quality control activities. For
example, how types and numbers of replicates will be followed in the experiment, how is the
specificity/selectivity of the analytical method to the target determined, how will the precision be
determined in terms of repeatability and reproducibility. In the process of determining all these
quality control activities, the experimental design can be optimized and documented.
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Parameters of Microarray Platform Performance
Most microarray gene expression experiments fall into three broad classes, depending on
outcome, that should have distinct QC reporting needs:
1. The first class of microarray experiments is that for which the investigator concludes
that a treatment/exposure causes a biological effect. This is the most common
conclusion from published microarray experiments, and the simplest from a QC point
of view.
2. The next category of experiments is one wherein the investigator concludes that the
treatment has no observable effect. These results are rarely reported in the literature,
but might be common in regulatory submissions. These are somewhat more
complicated from a QC perspective.
3. The third group offers claims about the magnitude of changes in transcription.
Examples of this last class of experiments are rare, and are the most difficult and
expensive on which to perform adequate QC. Currently, a cost effective microarray
platform on which to perform this last class of experiments is not available. While the
minimum QC of the experiment may be unchanged, the extent of documentation needed
to verify that an ensuing experimental report is acceptable, may vary based on
accompanying results.
Although negative and positive controls are part of experimental designs and
investigative approaches, the requisite controls for "expression" studies - particularly microarray
experiments - are not always obvious. Investigators are encouraged to consider not only the
biological system under scrutiny, but also the nature of the assertions about the system. The
need to have adequate controls should be considered in an experimental scheme, in order to
demonstrate that measurements are accurate enough to support scientific claims and assertions.
Needs for several straightforward situations are listed below, and can be applied as a guide to
more complex scenarios. It is useful if control samples are constructed in a way that ensures the
control and experimental samples are as similar to one another as possible (e.g., with regard to
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biologic composition and complexity of RNA) except in such cases where control sample
characteristics are unambiguously presumed to differ.
In cases where the investigator proposes that a biologic effect is present (the first case
noted above), the primary QC issues are precision and specificity, and the use of a negative
control is encouraged demonstrate that the measurement system is not likely to produce false
positives. Accuracy is rarely a concern, since claims are not being made regarding the
magnitude of differential expression between experimental and control groups, but only whether
a difference exists. Sensitivity is also not relevant, since no difference or effect would be
observed if sensitivity were too low. It is useful if the negative control and experimental groups
include sufficient replicates, relative to the magnitude of effect and experimental variability, in
order to show that the claimed effect, and no effect cases, can be statistically distinguished with
desired confidence. RNA from untreated samples is usually as adequate and readily available as
a control in this case. Precision is accounted for by statistical procedures (e.g., t-test, chi squared
test, and respective non-parametric analogs) routinely used to determine whether the
experimental and control groups differ significantly. Additional consideration should be given to
demonstrating the specificity of measurement(s) for the effect of interest. Demonstrating that a
variety of probes exhibit binding affinity for discrete regions of a given transcript, can provide
congruent results and is often a sound way to address the issue of platform specificity. The
ability of probes to distinguish similar transcripts, including splice variants, is also useful to
address. The issue of specificity is best addressed by using complementary "expression"
measurement technologies (e.g., quantitative real-time PCR, Northern blot analysis, RNase
protection assays, SI nuclease protection) to confirm microarray results. This will control for
technique-specific effects, and by using distinct set(s) of amplification primers, help control for
non-specific or unintended hybridization to microarray probes. Alternatively, a different
microarray platform could be used to confirm specificity if the second microarray platform uses
distinct probe sequences for detecting the transcripts of interest. A useful way to control
systematic error is to ensure randomization of both processing order and acquisition of
measurements for control and experimental samples. Using blind samples can be a useful
approach to avoid operator bias.
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In cases where the investigator maintains that there is no biologic effect (class two,
above), a positive control is useful to show that the measurement system is capable of detecting
the smallest effect sizes for which the claim is being made. In scenarios such as this, the
additional QC factor of sensitivity comes into play, while specificity becomes less important. It
is advantageous if the positive control and experimental groups both contain a sufficient number
of replicates to show that the two groups can be statistically distinguished with the desired
confidence level. It is useful to avoid absolute claims to the effect of 'there is no effect
whatsoeversince only effects equal to or larger than those readily observable in the positive
control can realistically be ruled out. Instead, conclusions might take the form of 'there is no
effect larger than X, where X represents the smallest magnitude of effect readily detectable in
positive controls. Some validated positive controls, such as samples subjected to a treatment
widely recognized to produce the desired effect, are considered the preferred source for positive
controls. However, there are cases for which no adequate model exists for the effect being
studied. Then, it is useful to construct a positive control using methods such as spiking complex
RNA samples with purified and quantified RNA of interest. Alternatively, investigators might
use mixtures of complex RNA samples in which the RNA of interest is present in varying known
concentrations (see also section on System Linearity and Calibration). These controls are
useful for demonstrating that the measurement system can readily detect the effect sizes for
which the negative claim is being asserted.
As always, it is beneficial to randomize the order of processing and measurement relative
to the sample group, and using blind samples should be considered. The same statistics, as
applied to the ' there is an effect case, are generally used to control for inconsistencies in
precision, but in this case acceptable performance means that the positive and negative controls
may be reliably distinguished from one another, while the experimental sample is statistically
indistinguishable from (appears to come from the same population as) the negative control.
When an investigator submits a claim regarding the magnitude of an effect, and not only
the presence or absence of effects, a more complex system of control (i.e., calibration curve)
should be considered (see also section on System Linearity and Calibration). In cases such as
this, where quantification of differential expression is critical (e.g., when stating that
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transcription of gene X increases 1.8 fold after exposure/treatment), accuracy becomes the
foremost QC factor, and more complex positive controls and statistics should be considered. A
calibration curve typically demonstrates the accuracy of the measurement system across the
range of concentrations being considered. Researchers should consider assembling appropriate
materials for constructing calibration curves in cases where standard reference RNA is not
available. In many cases, investigators may consider methods such as spiking a complex RNA
sample with a known series of concentrations of the RNA species of interest, or using a mixture
series of two complex samples where the concentration of the RNA species of interest differs by
a known amount. In the latter case, combining the two RNA 'targets' at different ratios produces
a series of known concentrations of the RNA species of interest. It is useful to adjust the range
and spacing of concentrations on the calibration curve (e.g., log linear scale) and the number of
replicates per concentration based on the level of precision desired and amount of experimental
variability observed. Specificity of signals of interest might be confirmed by showing
congruence with signals produced by probes that hybridize to a different portion of the same
transcript. It is beneficial if conclusions on the magnitude of an effect include confidence
intervals that reflect the performance of the measurement system during calibration curve
construction, as well as variability seen in the experimental samples.
Overview of Array Technology - The Physical Platform
The current method for fabricating DNA microarrays (DNA chips) is to use either cDNA
or oligonucleotides as probes that represent specific genes in the organism of interest, attached to
a suitable solid substrate such as a glass microscope slide. It is acknowledged that the specificity
of these probes is limited by the current understanding of gene sequence, among other things. It
is useful if all sequences are periodically reevaluated based on the newest gene sequence
information to ensure valid assessments.
Microarrays populated with cDNA probes are created by 'spotting' amplified cDNA
fragments in a desired density pattern onto a solid medium such as a glass slide. Arrays using
oligonucleotide probes are either mechanically 'spotted' or assembled by chemically
synthesizing short, unique oligonucleotide probes directly onto a glass or silicon surface using
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covalent chemistry or photolithographic technologies. It has been well established that
numerous possibilities exist for errors to become 'fixed' during the manufacture of the arrays;
therefore, the fidelity of the DNA fragments immobilized to microarray surfaces may be
compromised by several different kinds of experimental and manufacturing inconsistencies.
Given the QA/QC challenges in manufacturing gene arrays, a trend has emerged in recent years
towards the use of gene arrays from several large vendors rather than arrays from smaller scale
manufacturers or those prepared "in-house." While this may limit choice, it may also offer an
advantage to the array community when addressing issues of cross-platform compatibility.
There are a number of sources of technical error which can adversely impact data quality of a
gene array experiment. These include, but are not limited to, poorly functioning probes or probe
sets, cross hybridization of related genomic sequences, scanner settings and function, and
atmospheric ozone. Unfortunately, a set of performance standards by which individual
laboratories may be evaluated are not currently in place, although it is anticipated that such
standards may be developed in the near future.
In many array-based studies, the investigators report microarray data for which there is
no corroborating validation for the observed transcriptional measures. For profile data observed
on array platforms regarding novel findings that are not readily supported in the peer-reviewed
scientific literature, it is useful to include supporting data generated by traditional methods of
evaluating gene expression, such as PCR, Northern blot hybridization analysis and RNase
protection assays. In addition, the quality of probe sequences selected for particular transcribed
regions incorporated onto the array is also a critically important consideration. For example, if
probes are selected primarily from the 3' end of given genes, splice variants of those genes can
evade identification, if the alternative splicing events occur 5' of a probe region. Additionally,
by microarray analysis, it is very difficult to distinguish between two expressed genes that share
a high degree of sequence homology. Variation in probe specificity is also a commonly
encountered problem in oligonucleotide arrays. This problem frequently arises in instances
where nucleic acid sequences are practically identical between two coding regions and the
oligonucleotide probes are synthesized from 3'ends of the genes.
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Isolation of Nucleic Acid 'Targets"
Since this biological analyte comprises the molecular species that will be measured, it is
beneficial to ensure efficient isolation as well as post-isolation stability and structural integrity.
Total RNA is generally used for gene expression analysis, although, mRNA is also used. RNA
isolation techniques often involve homogenization of either fresh or frozen samples at high
concentrations of guanidine isothiocyanate followed by phenol extraction and alcohol
precipitation, although other methods can produce RNA of high quality.
Methods for determining purity {i.e., absence of contaminating reagents) include nucleic
acid analysis by spectrophotometry at absorbance ratios of 260/280 nm, with expected values
between 1.90 and 2.10 at pH 7.5. Another conventional method for determining structural
integrity is through the use of MOPS-EDTA formaldehyde (or glyoxal) agarose gel
electrophoresis, during which either the integrity of ribosomal RNA or the relative size
distribution of mRNA can be evaluated. Recent advances in microfluidics and analytical
equipment, {e.g., 2100 Bioanalyzer , Agilent, Inc.), allow investigators to evaluate the integrity
of nucleic acids with greater speed and accuracy than possible with agarose gel electrophoresis.
It is anticipated that this technology will soon replace the more frequently used methods.
Experimental Design
The importance of pre-planning the experimental design cannot be overemphasized. Since
the critical outputs from biological "expression" analyses are largely dependent upon
experimental design investigators should consider devoting extensive attention to performing
experiments with the appropriate design parameters. It is advantageous if the chosen
experimental design provides sufficient statistical power to unambiguously test the biologic
argument. The level of analytical power needed to allow for the detection of differentially
expressed transcripts at a ratio greater or equal to 'X-fold' should be considered. In addition, it is
useful if such analyses take into account the percentage of false positives that the researcher is
willing to accept. The false positive rate (FPR) and the false negative rate (FNR) are necessarily
dependent upon each other i.e., a decrease in one results in an increase in the other.
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When designing an experiment, adequate consideration should be given to sample sizes,
the use of controls, the use of sample randomization, and blind sample procedures. Specific
needs will depend on a number of factors, including the nature of the conclusion being presented,
the manner by which samples are compared with one another, the range of measured effects, and
experimental precision. To ensure adequate statistical power will be realized to support
scientific arguments and conclusions, it is advantageous to consult a statistician during the
experimental design phase. In order to estimate the projected magnitude of effect and
experimental precision conducting a small scale pilot experiment in advance of the definitive
experiment might be considered. Alternatively, one technology (e.g., DNA microarrays) might
be used as an exploratory tool for hypothesis generation, followed by the use of a secondary
technology (e.g., quantitative RT-PCR, qPCR) to generate adequate numbers of experimental
and control replicates to fulfill hypothesis testing. Some general issues that should be considered
are listed below, but their specific applicability will vary among experiments. Careful
consideration of these issues should provide sufficient information for a reliable estimate of
overall experimental performance, and the statistical strength of conclusions put forth.
It is expected that technical variation will be introduced at each critical laboratory step
during expression analysis. In addition, unique sources of variation are likely to be associated
with individual laboratories and/or technicians. It is important, therefore, that this variation be
considered during study design and statistical analysis in order to avoid confounding of these
sources of variation with treatment effects. For those experiments in which data are collected
from array-based studies, there are three design schemes typically used; these are briefly
described below. Although these are certainly not all inclusive, identification of the acceptable
system is left to individual research teams. The three fundamental design alternatives typically
used are 1) the flexible universal reference design, which is used for analysis of many
experimental factors of equal importance, or those that will be integral to future meta-analysis, 2)
the efficient balanced block design, for use in looking for genes that are upregulated or
downregulated between two samples, and 3) the more integral loop design, which when
comparing samples of equal interest and high quality results in half the variance per estimate,
because each sample is included two times, rather than once, at the minimal expense of one
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additional chip. There is, however, a rather large experimental cost of this latter design, because
it relies on not even one chip failing to reach the highest quality level.
The use of universal reference RNA has appeal when conducting experiments using gene
arrays in a two-color hybridization approach. In such experiments, both the control and treated
samples are labeled separately with a single sulfonated indocyanine fluorescent dye (Cy ™; e.g.,
Cy3 or Cy5) and are compared to a reference RNA sample which is labeled with the other of the
two Cy ™ dyes. Not only does this approach help minimize the potential for dye bias, which is a
significant concern when using the two-dye hybridization approach, but this also allows for
comparison of data across studies that use the same reference RNA. One practical approach may
be to take advantage of commercially available universal reference RNAs for gene expression
profiling which, at this time, are offered for use with arrays representing a limited number of
organisms (human, mouse, rat). Another experimental design often used to address dye bias is
the 'dye swap' or 'dye flip'. In this method a second experiment is conducted by exchanging
labeling reactions such that the treated and control samples are conversely labeled with the
respective Cy ™ dyes. The approach entails the use of additional arrays; however, because dye
bias has been observed by numerous investigators and noted in the literature, such a scheme
should be considered when designing experiments using two-color array systems.
Experimental Replication
It is not possible to analyze expression data without an estimate of variance. Since
experimental variance has both technical and biological components, replication could be
incorporated at several levels. In the case of a gene array experiment, technical replication could
be in the form of multiple spots per gene on the same array or, perhaps, multiple arrays for a
given sample. While including technical replicates will improve data analysis it is not an
absolute necessity. On the other hand, biological replication is an important consideration.
While it is generally accepted that in a gene array experiment an absolute minimum of three
biological replicates is needed, additional replication is often needed to detect a treatment effect
when less than robust changes in gene expression are observed. Pilot studies could be conducted
to estimate variance and give insight as to what level of replication may be useful. Although not
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comprehensive, additional considerations for determination of replicate numbers are the relative
quality and integrity of samples, the range of expected effects and the method of raw data
analysis. The optimal replicate number is affected other factors such as the type of array
technology and platform (single or dual channel RFU capture), array platform linearity
(precision), feature density (number of representative gene probes), and the selected percentage
value of FPR. Since replication is an asymptotic process, even a small number of replicates will
strengthen any conclusions that can be drawn from the data, irrespective of the technological
approach used to collect these data.
Pooling of Samples
From a theoretical perspective, most biological material used in expression studies arises
from 'pooled' sources because most tissues used in such investigations contain many distinct cell
types. Pooling of samples is primarily encouraged in those cases where the quantity of nucleic
acid 'target' (total RNA) is limiting to the point that this represents the only means by which to
obtain the requisite mass. It is recognized that, in certain studies, pooling of samples across
individuals is a logical approach in order to limit study size. In fact, pooling of samples can help
to minimize biological variation. However, it should be recognized that pooling will not be as
effective in controlling biological variation as increasing the number of biological replicates in a
study. Theoretically, most total RNA samples are pooled, since they are isolated from cells of
related or different types after having been amplified from the original source to produce the test
product. Combining samples does have the advantage of decreasing noise in the system. If
biological variability is not a major concern, a pooled sample could be considered the same as a
single individual when applying an experimental design. If biological variability is important to
the interpretation of the data, and RNA from pooled sources is used in determination of
expression measurements, it is useful to include more than one independent pool of samples for
the purpose of estimating biological variability. Biological replicates are generally regarded as
more critical than are technical replicates to measures of expression in biologic systems unless
otherwise indicated.
Specificity and Sensitivity
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Specificity and sensitivity of assays are affected by sequence-dependent (length and
inclusive base composition) and sequence-independent (relative concentrations of probes and
targets, hybridization time, temperature, etc.) factors. The specificity and sensitivity of assays
have been the subject of numerous cross platform comparison studies recently cited in scientific
literature (Venkatasubbarao, 2004; Enders, 2004; de Longueville etal., 2004). The term
specificity refers to the ability of an expression platform to discriminate or select between
distinct members of the same gene family, whereas sensitivity is the potential to discriminate
transcripts expressed at low level in a complex background. In recent years there has been a
trend in microarray design towards oligonucleotide probe sets to improve the specificity of gene
targeting. Oligonucleotide microarrays (25 to 70 bp) have some advantages over arrays on
which cDNA probes have been affixed. Oligonucleotide probes are designed to be identical with
respect to the number of bases (length) and concentration, with comparable annealing
temperatures of hybridization. These considerations account for enhanced uniformity over the
entire platform. Oligonucleotides are also designed to reduce inadvertent target cross-
hybridization, thereby increasing specificity during hybridization reactions. These combined
properties increase the stability and reproducibility of hybridization signal on each feature on the
array.
In addition to the quality of the probe sequences, the specific region of a gene that is
selected as a probe to be incorporated onto the array is also critically important. For example, if
probes are selected primarily from the 3' end of given genes, as is often the case, there is a
distinct possibility that splice variants of those genes will evade identification if the alternative
splicing events occur 5' of a probe region. Additionally, it is very difficult to distinguish
between two expressed genes that share a high degree of sequence homology by microarray
analysis. Irregularity in probe specificity is also a frequently encountered problem in
oligonucleotide arrays. This problem frequently arises in instances where nucleic acid sequences
are practically identical between two coding regions and the oligonucleotide probes are
synthesized from 3' ends of the genes.
Decrease in specificity on microarray platforms generally results from the technical
limitations inherent in enzymatic labeling of the RNA target. One of the most widely used
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methods for enzymatic modification of total RNA, for microarray analysis of gene expression,
uses T7 viral RNA polymerase in vitro transcription (IVT) to produce complementary RNA
(cRNA) that can be hybridized to gene-specific probes affixed to arrays. Multiple rounds of
amplification are used to label a limited mass of RNA by this IVT method, which has been
shown to inadvertently introduce errors. Because cRNA-DNA sequence mismatches are more
thermo stable than comparable cDNA-DNA mismatches, intensity artifacts have been observed
due to increased non-specific hybridization.
System Linearity and Calibration
Linearity of signal responses and other measurable output are perhaps among the most
significant aspects of obtaining reliable gene expression data. Regardless of technological
modes (e.g., microarray-based studies, semi-quantitative gel based PCR, 'real-time' PCR, or
densitometric scanning of pixel density), usable data collected within the linear region of the
output curve for any chosen system is essential. Given the increased number and overall density
of gene-specific probes present on microarrays, it is particularly useful to demonstrate linearity
of relative fluorescence units (RFUs) for the greatest number of discrete features represented on
the chip. Recent observations from microarray workgroups suggest that specific reference RNA
is the most efficient means by which to accomplish this. In an attempt to measure precision (B.
Aronow, personal communication), it was determined that the greatest coverage of features was
attained by hybridizing 4-5 different ratio mixtures, on as many chips, of species-specific RNA
obtained from different sources. For instance, the study in question mixed RNA prepared from
the colons of 8-week old C57BL/6 8 mice and post partum day one C57BL/6 whole animals in
different proportions. The relative fluorescent unit (RFU) value changes for every gene probe
that yielded a response to the mixture of mouse RNA, were statistically analyzed using least-
square linear regression. This suggested approach permits investigators to ascertain a global
perspective regarding the degree of linear response in a chosen system.
Randomization of Samples
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Technical variations or differences in "expression" measurements can be introduced at
several junctures in the experimental process including, but not limited to, methods of RNA
labeling, the choice of microarray platform, capture methods for RFU intensity and signal
quantitation, ozone-mediated fluorescent signal degradation, humidity and temperature, and
moreover, those individuals charged with performing the experiments.
Many experimental designs suggest that blind randomization of samples is integral to the
analyses. This approach offers the promise of 'flattening' both internal and external
experimental sources of variation. Sample randomization should be considered wherever
practical. Numerous confounding variables have been identified that can distort microarray
results. Some of these sources of variation are well known (e.g., RNA degradation during tissue
extraction), and others have been more recently identified (e.g., ozone-mediated bleaching of
some florescent dyes), and some causative factors have yet to be characterized. If adequate care
is taken to randomize the order in which samples are processed, and operators are unaware of the
nature of each sample, known and unanticipated sources of variability are not likely to bias the
outcome of the experiments. However, such sources of variability can nevertheless exert
influence on the observed precision of the system. For instance, if all the experimental group
samples are run on a given day, and all the negative control samples are run on the following
day, it is possible that experimental features can differ on the two days (e.g., operator identity,
photomultiplier drift, and/or ozone concentration). Such differences could systematically bias
results for the experimental samples relative to the control samples, creating the false impression
of a real difference between the two groups. On the other hand, if samples for the two groups are
randomized, with half the samples run on day one, and the other half on day two, factors that
differ between the two days will decrease the precision observed in both groups (a readily
detected and addressed occurrence), without creating the false impression of systematic
differences between the groups in question.
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APPENDIX B: MIAME-Based Data Submission Tables
Table B.l Abstract
MIAME
Description
When applicable
Notes
Values
B.1 Abstract
Brief summary of the
purpose and findings of
the experiment.
Always
Table B.2 Experimental Design
MIAME
Description
When applicable
Notes
Values
B.2 Experiment design
Design and purpose
common to all
hybridizations
Always
Related hybridizations
interpreted as a single
experiment.
Author, laboratory, and
contact
Person(s), organization(s),
names and contacts
(address, phone, FAX,
email, URL).
Always
Contact details
Experiment tvoe(s)
A controlled vocabulary
that classifies an
experiment.
Always
Experimental Factor(s).
Time course,
dose response,
comparison
(disease vs normal,
treated vs untreated),
temperature shock,
gene knock out,
gene knock in
(transgenic), etc.
Experiment Description
Description of the
experiment and relevant
electronic peer-reviewed
journal publication(s)
When additional
information is available
and an electronic
publication exists.
Consistent with
experimental design.
Text description, citation,
URL. Database entry
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Table B.2 Experimental Design
MIAME
Description
When applicable
Notes
Values
Experimental factor(s)
Para mete r(s) or
condition(s) tested in the
experiment.
Always
Experimental factor(s)
consistent with
Experiment Tvoe(s)
Time,
dose,
compound,
temperature,
extraction, hybridization,
labelling, scanning
Number of hybridization
replicates
Number of hybridization
replicates
Always
Consistent with
Experiment Tvpe(s)
Single,
multiple
Common reference
A hybridization to which all
the other hybridizations
have been compared.
Always
Yes, no
Quality control steps
Measures to ensure
quality: replicates (number
and description), dye
swap (for two channel
platforms) or other
When appropriate
Text description.
biological,
technical
Qualifier, value, source
(mav use more than once)
Any further information
about the experiment.
Additional useful
information
Qualifier name
Value= value
Source= database entry or
ontology entry
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Table B.3 Array Design
MIAME
Description
When applicable
Notes
Values
B.3 Array design
Array layout. Description
of the common features of
the array and each array
element.
When an array design is
novel and cannot refer to
manufacturer
Array design should be
provided by the array
manufacturer.
B.3.1. Array related
information
Overall description of the
array.
Arrav desian name
Unique name, that
identifies a specific design
Array is novel and cannot
refer to manufacturer
Consistent with the design
name given for the array.
Design name,
number of features,
version (e.g.: EMBL yeast
12K verl .1)
Platform tvoe
Technology to place
biological sequence on
array.
Array is novel and cannot
refer to manufacturer
in situ synthesized,
spotted cDNA,
etc.
Surface and coatina
specification
Surface coatina tvoe and
name
Array is novel and cannot
refer to manufacturer
Consistent with Platform
Type
SurfaceType:
glass, membrane, coating
type
Arrav dimensions
Dimensions of the array
support slide.
Array is novel and cannot
refer to manufacturer
width, length
Number of features on the
arrav
The number of features on
the array.
Array is novel and cannot
refer to manufacturer
number of features
Production protocol
A description of how the
array was manufactured.
Array is novel and cannot
refer to manufacturer
Protocol
description,
printing hardware,
printing software
Provider
The primary contact
(manufacturer) for the
information on the array
design.
Always
Contact details of
manufacturer
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Table B.3 Array Design
MIAME
Description
When applicable
Notes
Values
B.3.2 Reporter related
information
Information on the
nucleotide sequence
present in a particular
location on the array.
B.3.2.1 For each reporter
type
Reporter tvpe
Physical nature of the
reporter (e.g. PCR
product, synthesized
oligonucleotide).
Array is novel and cannot
refer to manufacturer
Consistent with Platform
Type
Types: empty, PCR,
synthesized
oligonucleotide,
plasmid, colony,
etc.
Sinale or double stranded
Reporter sequences are
single or double stranded.
Array is novel and cannot
refer to manufacturer
Consistent with Platform
Tvoe
Single, double
B.3.2.2 For each reporter
Reporter seauence
information
Nucleotide sequence for
each reporter: accession
number (from
DDBJ/EMBL/GenBank),
the sequence itself or
reference sequences and
primers pair information
Array is novel and cannot
refer to manufacturer
Consistent with Platform
Tvpe and clone
Sequence annotation,
accession number,
PCR primer pair
Reporter approximate
lenath
The approximate length of
the reporter sequence.
When the exact reporter
sequence is NOT known
Number of bases
Clone information
For each reporter, identity
of the clone, clone
provider, date obtained,
and availability.
When elements are from
clones When an array
design is novel and cannot
refer to manufacturer
Consistent with Platform
Type
Clone ID,
provider,
date obtained,
availability
Reporter aeneration
protocol
A description of how the
reporters were generated.
Array is novel and cannot
refer to manufacturer
Protocol
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Table B.3 Array Design
MIAME
Description
When applicable
Notes
Values
B.3.3 Features related
information
Information on the location
of the reporters on the
array
B.3.3.1 For each feature
type
Feature dimensions
Dimensions of each
feature.
Array is novel and cannot
refer to manufacturer
Consistent with array
dimensions and number of
features
Width, length, height,
diameter
Attachment
How the elements
(reporters) are physically
attached to the array.
Array is novel and cannot
refer to manufacturer
Consistent with element
generation protocol
Covalent,
ionic, hydrophobic,
etc.
B.3.3.2 For each feature
Reporter and location
Arrangement and system
used to specify location of
each feature
Array is novel and cannot
refer to manufacturer
Consistent with array
dimensions and number of
features
Row, column,
x microns,
y microns,
zone
B.3.4 Composite
sequence related
information
Information on the set of
reporters used collectively
to measure an expression
of a particular gene.
B.3.4.1 For each
composite sequence
Composite seauence
information
The set of reporters
contained in the
composite sequence.
When elements are
composite array is novel
cannot refer to
manufacturer
Consistent with element
type
Oligonucleotide
sequences,
number of
oligonucleotides,
reference sequence
Gene name
The gene represented at
each composite sequence
Array is novel and cannot
refer to manufacturer
Consistent with clone and
composite sequence
information
Gene name,
accession number,
annotation
Qualifier, value, source
(may use more than once')
Describe any further
information about the
array in a structured
manner.
When additional
information is available
that would be useful to
base queries on
Qualifier name
Value= value
Source= database entry or
ontology entry
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Table B.3 Array Design
MIAME
Description
When applicable
Notes
Values
B.3.5 Control
elements related
information
Array elements that have
an expected value and/or
are used for normalization.
Control element position
The position of the control
features on the array.
When any elements on
the array were used as
controls
Consistent with
Quality Control
Description
Row, column,
x microns,
y microns,
zone
Control tvoe
The type of control used
for the normalization and
their qualifier.
When any elements on
the array were used as
controls
Consistent with
Quality Control
Description
Control type (spiking,
negative, positive),
control qualifier
(endogenous, exogenous)
Table li.4 liiomalerials
MIAME
Description
When applicable
Notes
Values
B.4 Biomaterials
The biological material
from which the nucleic
acids have been extracted
for subsequent labelling
and hybridization.
Always
B.4.1 Biosource
properties
Information on the source
of the sample.
Oraanism
The genus and species
(and subspecies) of the
organism from which the
biomaterial is derived.
Always
Genus, species,
subspecies from NCBI
taxonomy
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Table IJ.4 liiomalerials
MIAME
Description
When applicable
Notes
Values
Sample Contact details
The resource used to
obtain the biomaterial
When biomaterial was
prepared or grown outside
of the laboratory listed for
the author
Biosource provider
Type of specimen (tumor
biopsy,
paraffin section,
stool sample}
Cell tvoe
Cell type(s) or organs
used in the experiment.
Always
Consistent with organism
and targeted cell type
Name of organ tissue cell
type (ATCC #) and
source
Sex
Term applied to any
organism able to undergo
sexual reproduction in
order to differentiate the
individuals or type
involved.
When applicable
Consistent with organism
Mating type alpha, F+, F",
Hfr,
Mating type a,
Mixed sex,
Unknown sex
Age
The time period elapsed
since an identifiable point
in the life cycle of an
organism.
When applicable
Consistent with organism
Age =
combination of real
number (measurement)
and initial time point e.g.:
coitus, birth, planting,
beginning of stage
Developmental staae
The developmental stage
of the organism's life cycle
during which the
biomaterial was extracted.
For multicellular species
Consistent with organism
Developmental stage (i.e.,
embryo, fetus, adult)
Oraanism part
The part or tissue of the
organism's anatomy from
which the biomaterial was
derived.
For multicellular species
Consistent with organism
Organism part
term)
Strain or line
Animals or plants that
have an ancestral
breeding.
When known
Consistent with organism
Strain or line (e.g.:
Jax mouse strains,
Cultivar,NCBI taxonomy)
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Table IJ.4 liiomalerials
MIAME
Description
When applicable
Notes
Values
Genetic variation
The genetic modification
introduced into the
organism from which the
biomaterial was derived.
When the source
organism is genetically
modified
Consistent with organism
Examples of genetic
variation include
specification of a
transgene or the gene
knocked-out.
Individual number
Identifier or number of the
individual organism from
which the biomaterial was
derived.
When the organism can
be distinguished on an
individual basis with a
unique ID
Consistent with organism
Individual ID. For patients,
the identifier should be
approved by
Institutional Review
Boards (IRB, review and
monitor biomedical
research involving human
subjects) or appropriate
body.
Individual aenetic
characteristics
The genotype of the
individual organism from
which the biomaterial was
derived.
When applicable
Consistent with organism
Allele,
genotype,
haplotype,
polymorphisms.
Disease state
The name of the
pathology diagnosed in
the organism from which
the biomaterial was
derived.
When applicable
Consistent with organism
"Normal" or.
disease state
description
Taraeted cell tvoe
Cell of primary interest.
Biomaterial is a mixed
population of cells
Consistent with organism
and cell type
Biomaterial may be
derived from a mixed
population of cells
although only one cell type
is of interest.
Targeted cell type=
term,
(Mouse Anatomical
Dictionary,
FlyBase,
CBIL vocabulary)
Cell line
Identifier for the cell line
Biomaterial is derived from
an immortalized cell line
Consistent with organism
and cell type
Cell line term, source of
term (ATCC # ),e.g.,Hela,
Caco-2
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Table IJ.4 liiomalerials
MIAME
Description
When applicable
Notes
Values
B.4.2 Biomaterial
manipulation
Information on the
treatment applied to the
biomaterial
Growth conditions
Description of
environment used to grow
organisms
Culture condition details
In vivo treatment
Manipulation to generate
variable(s) understudy.
When sample has been
treated or manipulated for
the study
Consistent with
Experiment Type and
Experimental Factors
Documentation of the set
of steps taken in the
treatment
In vitro
treatment
Manipulation of cell culture
condition for generating
variables under study.
When the sample has
been treated or
manipulated in vitro for the
study purpose
Should be consistent
(where appropriate) with
Experiment Type,
Experimental
Factors
Documentation of the set
of steps taken in the
treatment
Treatment tvoe
Manipulation for
generating variables under
study.
When sample has been
treated or manipulated for
the study
Consistent with
experiment type,
experimental factors and
treatment
Description of treatment
(behavioral stimulus,
compound based
treatment,
infection,
modification (genetic,
somatic)),
Compound
Drug, solvent, chemical,
etc., that can be
measured.
When sample has been
treated or manipulated
with a compound
Consistent with treatment
type
Description of compound's
physical and chemical
characteristics
Separation technique
Technique to separate
tissues or cells.
When the cells or tissue
are separated from a
heterogenous sample
Protocol
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Table IJ.4 liiomalerials
MIAME
Description
When applicable
Notes
Values
B.4.3 Hybridization
extract preparation
Information on the extract
preparation for each
extract prepared from the
sample
Extraction method
The protocol used to
extract nucleic acids from
the sample.
Always
Protocol
Nucleic acid tvoe
The type of nucleic acid
extracted (e.g. total RNA,
mRNA).
Always
Polymer type
(total RNA,
mRNA,
DNA)
Amplification method
The method used to
amplify the nucleic acid
extracted.
When applicable
Protocol
B.4.4 Sample labelling
Information on the
labelling preparation for
each labelled extract.
Amount of nucleic acid
labelled
Amount of nucleic acid
labelled.
Protocol
Label used
Label used.
Always
Label
(Cy3,
Cy5,
etc.)
Label incorporation
method
Label incorporation
method
Always
Protocol
B.4.5 Spiking control
External controls added to
the hybridization
extracts).
Soikina control feature
Position of the feature(s)
on the array expected to
hybridize to the spiking
control.
When applicable
Consistent with
quality control description
row, column,
x microns,
y microns,
zone
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Table IJ.4 liiomalerials
MIAME
Description
When applicable
Notes
Values
Spike type and aualifier
Type of spike used and
its qualifier
When applicable
Consistent with
quality control description
Oligonucleotide, plasmid
DNA, transcript,,
concentration, expected
ratio, labelling methods
Qualifier, value, source
(may use more than once)
Describe any further
information about the
sample in a structured
manner.
When additional
information is available
that would be useful to
base queries on
Table B.5 Hybridization
MIAME
Description
When applicable
Notes
Values
Included in DER?
B.5 Hybridization
Procedures and
parameters for each
hybridization.
Always
Relationship between
samples and arrays
Relationship between
the labelled extract
Always
Consistent with
technology quality
control
Which sample, which
extract "array design,
batch and serial
number, during which
hybridization
Yes
Hybridization protocol
Set of steps taken in
the hybridization:
(solution blocking
agent, concentration,
wash procedure);
quantity of labelled
target used;
time;
concentration;
volume,
temperature.
Always
Description of the
hybridization
instruments
Yes
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Table B.5 Hybridization
MIAME
Description
When applicable
Notes
Values
Included in DER?
Qualifier, value,
source (mav use
more than once')
Describe any further
information about the
hybridization in a
structured manner.
When additional
information is
available that would
be useful to base
queries on
Non-specific
Table B.6 Measurements
(MIAME distinguishes between three levels of data processing: image (raw data), image analysis and quantitation, gene expression data matrix
(normalized and summarized data).
MIAME
Description
When applicable
Notes
Values
B.6.1 Raw data
Each hybridization has at
least one image.
Scanner imaae file
The image file including
header
Always
TIFF,
JPEG
Scannina protocol
Steps taken for scanning
array and generating an
image
Always
Description of the
scanning instruments and
the parameter settings.
B.6.2 Image analysis
and quantitation
Each image has a
corresponding image
quantitation table, where a
row represents an array
design element and a
column represents
different quantitation types
Mean or median pixel
intensity.
Imaae analysis output
The complete image
analysis output for each
image.
Always.
Spreadsheet or tab-
delimited file
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Table B.6 Measurements
(MIAME distinguishes between three levels of data processing: image (raw data), image analysis and quantitation, gene expression data matrix
(normalized and summarized data).
MIAME
Description
When applicable
Notes
Values
Imaae analysis protocol
Documentation of the set
of steps taken to quantify
the image
Always.
Image analysis software,
the algorithm and all the
parameters used
B.6.3 Normalized and
summarized data
Several quantitation tables
are combined using data
processing metrics to
obtain the 'final' gene
expression measurement
table (gene expression
data matrix) associated
with the experiment.
Data orocessina protocol
Documentation of the set
of steps taken to process
the data.
When normalization has
been performed
Normalization strategy
and the algorithm used to
allow comparison of all
data.
Final aene expression
table (s)
Derived measurement
value summarizing related
elements and replicates,
providing the type of
reliability indicator used.
When a value used for a
reliability indicator has
been generated
Should be consistent with
quality control description
and replicate description
Replicates of the elements
on the same or different
arrays or hybridizations,
as well as different
elements related to the
same entity (e.g., gene).
Reliability indicator for
each data point (e.g.,
standard deviation)
Qualifier, value, source
(mav use more than once)
Describe any further
information about the
measurements in a
structured manner
When additional
information is available
that would be useful to
base queries on
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Appendix C: Genomics Data Evaluation Record (gDER) Template
Genomics
DATA EVALUATION RECORD
STUDY TYPE:
PC CODE:
TEST MATERIAL (PURITY):
SYNONYMS:
CITATION:
SPONSOR:
EXECUTIVE SUMMARY:
COMPLIANCE:
I. MATERIALS AND METHODS
A. MATERIALS:
1. Test Material:
Description:
Lot/Batch #:
Purity:
CAS # of TGAI:
[StructureJ
DP BARCODE:
SUBMISSION NO.:
2. Control Materials:
Negative control (if not vehicle):
Final Volume:
Route:
Vehicle:
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3. Test animals:
Species:
Strain:
Age/weight at study initiation:
Source:
No. animals used per dose per duration:
Properly Maintained?
4. Compound administration:
a. Test material |
Dose levels:
Route:
Method:
b. Vehicle control:
c. Positive control:
B. TEST PERFORMANCE
1. Treatment and Sampling Times:
Duration of dosing:
Frequency of dosing:
Total number of doses:
Timing and frequency of sampling:
Time elapsed between dosing and sampling:
2. Tissues and Cells Examined:
3. Details of tissue harvest:
4. Detail of tissue storage:
5. Sample Preparation:
a RNA Isolation, Labelling, Amplification:
b. Histology:
c. Immunochemistry:
d. Western blot analysis:
e. Array analysis:
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Characteristics of the arrays:
Methods:
5. Data Analysis:
6. Evaluation Criteria/Statistical Analysis
II. REPORTED RESULTS
A. TMMTTNOCHEMISTRY:
B. WESTERN BLOT ANALYSIS:
C. MICROARRAY ANALYSIS:
III. DISCUSSION and CONCLUSIONS
A. INVESTIGATORS' CONCLUSIONS:
B. REVIEWER COMMENTS:
C. STUDY DEFICIENCIES:
REFERENCES
ATTACHMENTS and TABLES
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Appendix D: Genomics Data Evaluation Record (gDER) for
Alachlor (Sample)
Genomics
DATA EVALUATION RECORD
STUDY TYPE: Mode of Action In vivo Genomic Analysis in Rat Olfactory Mucosa
PC CODE: DP BARCODE:
SUBMISSION NO.:
TEST MATERIAL (PURITY): Alachlor (Purity not listed)
SYNONYMS:
CITATION: Genter, M.B., Burman, D.M., Vijayakumar, S., Ebert, C.L., Aronow, B.J. (2002).
Genomic analysis of alachlor-induced oncogenesis in rat olfactory mucosa.
Physiol. Genomics 12:35-45.
SPONSOR:
EXECUTIVE SUMMARY:
In an in vivo genomic analysis, groups of male Long-Evans rats (1-2 rats/ group) were
administered dietary preparations of an established tumorgenic dose of Alachlor (126
mg/kg/day) or untreated feed 1 day to 18 months. Ethmoid turbinates were removed and frozen
in liquid N2. Animals were sacrificed at 3, 4 or 5 months and two separate olfactory mucosal
RNA samples were isolated. Other RNA samples were harvested from single rats treated with
alachlor for 1 or 4 days. After 18 months of treatment, single RNA samples derived from
alachlor-induced tumors were also isolated. Total RNA was extracted from frozen tissue
homogenates by precipitation with ethanol/sodium acetate, screened for quality, labeled with
biotin and hybridized. Histological examinations were performed on additional rats dosed for
the same treatment duration. For the determination of ebrenin (a gene related to the human
tumor suppressor gene, DMBT1) or 3-catenin (gene/product associated with the wnt signaling
pathway), immunochemistry was also performed on sections prepared for histology using an
anti-hensin antibody or a commercial antibody; intestinal sections served as the positive control
for antibody staining. CYP2A3 levels were assessed by Western blot analysis. For the array
analysis, total RNA was reverse transcribed followed by second-strand cDNA synthesis. The
resulting cRNA was biotinylated and hybridized to the Affymetrix GeneChip Rat U34A.
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Based on an independent review of qualitative data only (presented as graphs or photographic
copies of tissue sections), it was concluded that alachlor induces olfactory nasal carcinomas
through a nongenotoxic mode of action {i.e., oxidative stress). Support for this conclusion comes
from data showing upregulation (32-fold increase over untreated control) of genes correlated
with the following steps in the carcinogenic process: oxidative stress and damage to DNA
(8heme oxygenase, glutathione and metalothionein, GADD 45, apurinic/apyramidinic
endonuclease);progression of adenomas to malignant adenocarcinomas (activation of the wnt
signaling pathway), and transformation to adenocarcinomas (activation of nuclear 3-catenin
genes, also associated with the wnt signaling pathway).
This study is classified as acceptable (non-guideline) but does not satisfy current regulatory data
requirements for pesticides. Although guidelines do not exist for genomic data, the results
presented in this published article provided critical information that enhances the understanding
of the nongenotoxic mode of action for olfactory mucosal tumors induced by alachlor.
COMPLIANCE: Not applicable; the publication, however, comes from a reputable, peer-
reviewed scientific journal.
I. MATERIALS AND METHODS
A. MATERIALS:
1. Test Material:
Description:
Lot/Batch #:
Purity:
CAS # of TGAI:
Alachlor
Not provided
Not reported
% a.i. Not reported
[Structure]
2. Control Materials:
Negative control
(if not vehicle):
Vehicle:
Final Volume: NA Route: NA
Harlan powder diet
3. Test animals:
Species: Rat
Strain: Long-Evans
Age/weight at study initiation: Not specified
Source: Harlan, Indianapolis, IN
No. animals used per dose per duration 1-2 males; 0 females
Properly Maintained? Not specified
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4. Compound administration:
a. Test material |
Dose levels: 126 mg/kg/day Route Oral - Feeding
Preliminary: Not performed, referred to a citation (Genter et al., (2000).
Main Study: Dietary administration of 0 or 126 mg/kg/day (tumorigenic dose as per
EPA 1985)
b. Vehicle control: |
Untreated Harlan powdered feed
c. Positive control: |
See Immunochemistry
B. TEST PERFORMANCE
1. Treatment and Sampling Times: Male rats were fed dietary preparations of 0 or 126
mg/kg/day for 1 day to 126 mg/kg.
Sampling (after last dose): 1 and 4 days, 3, 4 and 5 month. Tumors were harvested from
rats treated for 3, 4, 5, 11 or 18 months.
2. Tissues and Cells Examined: Ethmoid turbinates and/or oflactory mucosal tumors
3. Details of tissue harvest: Rats were sacrificed by a pentobarbitol overdose and decapitated.
Ethmoid turbinates were rapidly removed and frozen in liquid N2 until use. RNA samples of
olfactory mucosal tumors derived from two different rats were also harvested.
4. Detail of tissue storage: Stored in liquid N2 until use.
5. Sample Preparation:
a RNA Isolation: Selected frozen tissues were homogenized and total RNA was
precipitated with ethanol/sodium acetate, resuspended in DEPC-treated water and
screened for RNA quality using an Agilent Bioanalyzer. Acceptable samples had
cut-off ratios of 1.8 for the 28S:18S ribosomal subunits. Duplicate samples were
prepared for 2 rats/group at each sampling time (3, 4, 5 months) and one sample
was used for single rats at days 1 and 4.
b Histology: Progression of the olfactory mucosa tumors was followed by
harvesting tissue at 3, 4, 5, 11, and 18 months from dosed rats doses. Tissue was
prepared for histological examinations as previously described (Genter et al.,
2000)1 with the exception that decalcified after fixation in multiple changes of
cold 0.3 M EDTA prior to embedding in paraffin.
Center, MB, Burman, DM, Dingeldein, MW, Clough, I, Bolon, B (2000). Characterization of cell proliferation and
immunochemical markers of alachlor-induced olfactory mucosal tumors in Long-Evans rats. Toxicol Pathol 28:770-781.
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c Immunochemistry: Sections (5-Om) prepared for histology were stained for
detection of ebnerin (a gene related to the putative human tumor suppressor gene,
DMBT1) using immunochemistry techniques. Ebnerin was localized in the nasal
cavity sections and in tumors with anti-hensin antibody. Localized ebnerin was
reacted with anti-guinea pig horseradish peroxidase (HRP)-conjugated secondary
antibody (1:100) with tyramide signal amplification. Since this antibody also
detects the intestinal crp-ductin, intestinal sections were taken from the rats,
stained and served as the positive control. 3-catenin (associated with the
activation of the wnt signaling pathway)was localized with antibody from BD-
Transduction Laboratories, Lexington, KY and visualized using an HRP-
conjugated anti-mouse secondary antibody and TSA amplification as described.
d Western blot analysis: Gene expression changes in the olfactory specific
cytochrome P-450 enzyme (CYP2A3), were assessed by Western blot analysis of
5 Og of olfactory mucosal microsomal protein per lane. Visualization was
achieved with HRP-conjugated secondary antibody, enhanced chemiluminescence
and exposure to X-ray film.
e Array analysis: Characteristics of the arrays: The total number of probe sets
(genes or expressed sequence tags, ESTs) interrogated was not reported.
However, the study authors provided the following information: ESTs represented
on the U34A GeneChip were derived from the Rat Unigene Build No. 34
assembly. All clones represented on the chip were ESTs on gene lists of interest
and were subjected to re-annotation by use of Unigene and execution of the
National Center for Biotechnology Information (BLASTN) searches
(http://www.ncbi.nlm.gov/BLAST) against non-redundant nucleotide databases
during February-April, 2002. Gene category information was based on all
publically available gene ontology information from the Gene and Ontology
Consortium (http://www.geneontology.org) as harvested from SWISS-PROT,
GeneCards, Compugen. LocusLinks, and GeneBank as well as exhaustive
Medline literature searches.
Methods: Total DNA was reverse transcribed with an oligo-dT primer and
second-strand cDNA was synthesized. The resulting T7 RNA polymerase-
mediated cRNA was biotin-labeled and hybridized on the Affymetrix GeneChip
Rat U343 A using the recommended protocol provided by Affymetrix
Data Analysis:
The study authors provided the following information. "MicroArray Suite 5.0 software
was used to scan and quantitate the GeneChip data using a default scan setting; intensity
data were scaled to target intensity of 1,500, and results were analyzed using the
MicroArray Suite 5.0 and GenSpring 4.1.5 software. Data values used for filtering and
clustering were "signal", signal confidence", "absolute call" (absent or present), and
"change" (increased, decreased, unchanged) as implemented in MicroArray Suite 5.0.
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Data were normalized as follows: the 50th percentile of all measurements was used as the
positive control for each array. Each measurement for each gene was divided by this
synthetic positive control, assuming that this was at least 10. The bottom 10th percentile
signal level was used as a test for correct background subtraction. The measurement of
each gene in each sample was divided by the corresponding value in untreated samples,
assuming that the value was at least 0.01. Genes regulated across the experimental study
were identified by data filtering for those over-or under- expressed in at least two samples
whose signal strength was greater than 500 in two samples, and were also called
"present" in at least two samples. An additional approach combined those with genes
that could predict length of alachlor exposure or histological responses using Kruskal-
Wallis ANOVA at p<0.001 and a Benjamini-Hochberg multiple testing correction as
implemented in GeneSpring. K-means analysis were similarly executed in GeneSpring
to organize genes into clusters based on similar expression across the treatment time
course."
8. Evaluation Criteria/Statistical Analysis
Initial Filter Criteria: The study authors indicated that 4,777 probe set elements (a pool
of genes that fulfill a series of initial filter criteria) were called "present" by the
Affymetrix algorithm on at least one of 26 chips, 998 probe set elements were
overexpressed by 1.8X or more in at least 2 samples, whereas 584 were underexpressed
0.5 X in at least 2 samples. Additionally, significant gene regulation was detected using a
Welch t-test with a cutoff of p<0.001 (without correction for false discovery rate error).
Using this approach, alachlor-exposed samples could be distinguished from untreated
controls based on differential expression of 644 genes.
Cluster analysis: 1392 probe sets elements were provided for cluster analysis by
combining the under and over expressed genes along with the alachlor-regulated genes
and then restricting these to only genes that met the 'present' criteria. Using the K-means
algorithm, 16 set were determined to serve as excellent representations of the prominent
patterns in the data set. Clusters with "highly chaotic" patterns were eliminated from
further analysis. Accordingly, 1,265 genes whose variance was well represented by 16
K-means sets were found. These K-means sets were grouped into the following behavior
patterns:
Sets that were upregulated acutely
Sets that were upregulated only in alachlor-induced tumors
Sets that were downregulated following alachlor treatment
Sets that were downregulated in alachlor-induced tumors
Sets that were persistently upregulated across the treatment intervals.
II. REPORTED RESULTS
A. IMMUNOCHEMISTRY: The study authors stated that ebnerin was highly expressed after
4 months of treatment with 126 mg/kg/day alachlor in nasal respiratory mucosa. Tissue sections
of olfactory mucosal tumors induced by alachlor and control nasal mucosa were provided to
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support the study authors' claim that this gene product was detected in vehicle control nasal
respiratory tissue but was absent in the control olfactory mucosa. Increased gene expression of
this protein was also noted in the progression of alachlor-induced tumors. In the olfactory
tumors, ebnerin was displayed on the surface and in the ductal lumens of the tumor. In addition,
nuclear localization of 3-catenin was also confirmed using immunochemical staining. It was
stated that alachlor-induced polyps and early adenomas did not exhibit nuclear localization of 3-
catenin but more advanced adenocarcinomas displayed abundant cytoplasmic and nuclear 3-
catenin; a tissue section of olfactory mucosal tumors induced by alachlor was presented to
support this claim.
B. WESTERN BLOT ANALYSIS: K-mean analysis indicated that after exposure of the rats
to alachlor for 2 or 4 days or 1 month, 137 genes were downregulated; included among these
genes was the olfactory predominant cytochrome P-450 enzyme, CYP2A3 and CYP2F1, an
olfactory marker protein. Western blot analysis, depicted in graphs and accompanied by
histological alterations, also indicated that these genes/products returned to background levels in
the presence of foci of respiratory metaplasia (3- and 4- month samples), in the presence of more
pronounced epithelial atypia and small neoplasms present in -25% of the animals (5-month
samples). CYP2A3 and CYP2F1 were downregulated in the presence of numerous tumors, some
of which were invasive. These results were supported by the composite graphs of the 16 K-
means sets presented in the publication.
C. MICRO ARRAY ANALYSIS: One-hundred and forty-eight genes and ESTs that were
upregulated (i.e., 33-fold increase in the normalized intensity value of 1.0) with acute (1 day to 1
month) exposure to alachlor were identified. These include genes associated with the control of
extracellular matrix such as matrix metalloproteinase-9 (MMP-9, upregulated 9-fold),
carboxypeptidase Z (upregulated 7-fold) and tissue inhibitor of metalloproteinase-1 (upregulated
3-fold); immune system functions; cell proliferation/ cell cycle regulation, including apoptosis-
related genes; calcium homeostasis/signaling; olfactory-related; nervous system-related;
oncogene-related; transporters; and structural machinery. These genes, subgrouped according to
the key functional categories listed above are presented in Table 1 of the article (see Attachment
1). Other genes mentioned by the study authors as being upregulated 32-fold following acute
exposure included multiple genes which encode proteins associated with oxidative stress; these
included heme oxygenase, glutathione synthase and metallothionein (MT)-l and MT-2.
Additionally, the GADD 45 gene (associated with mutagenesis possibly caused by oxidative
damage to DNA) was listed as one of the most highly regulated genes by alachlor.
An additional, 417 genes and ESTs were identified, based on a 32-fold upregulated
expression, in alachlor-induced tumors as compared to the untreated mucosa. These genes
included several immune response genes (i.e., neutrophil defensin, mast cell proteases, squamous
cell carcinoma antigens and major histocompatible complex antigens and genes associated with
cell proliferation (e.g., nucleolin, the major nucleolar protein in exponentially-growing
eukaryotic cells). Another set of highly expressed gene were axin2 and frizzled. The study
authors claim that the increased expression of these genes is suggestive of activation of the wnt
signaling pathway. This pattern is consistent with the results of immunochemical staining
confirming nuclear localization of 3-catenin late in the carcinogenesis process. Primary
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normalized data, gene lists and K-means groups can be obtained from http://genet.chmcc.org in
the U34A folder listed under Genter et al., 2002.
III. DISCUSSION and CONCLUSIONS
A. INVESTIGATORS' CONCLUSIONS: Based on these analyses, the study authors
concluded that "initiation and progression of alachlor-induced olfactory mucosal tumors is
associated with alterations in extracellular matrix components, induction of oxidative stress,
upregulation of ebnerin, and final transformation to a malignant state by wnt pathway
activation."
B. REVIEWER COMMENTS: Based on an independent analysis of the genomic data
presented by the study authors, Agency reviewers conclude the following with respect to the
proposed steps in the alachlor-mediated carcinogenesis model:
Initial progression from histologically normal olfactory mucosa to foci of abnormal
mucosa
This step, which is regulated by genes in the acute phase of exposure, is accompanied by
"upregulation" (32-fold increase) of genes consistent with a mutagenic response possibly
as a result of oxidative damage to DNA (8GADD 45, apurinic/apyramidinic
endonuclease). While the exact role of GADD (growth arrest and DNA-damage
inducible) gene products is not known, this gene group is upregulated in response to
stress to allow cells time to repair macromolecular damage or to lead cells into apoptosis
so that a genetic defect is not propagated. Types of environmental stress that induce
GADD genes include UV irradiation, alkylating agents and glucose starvation (Takahashi
et al., 2001; Jackman et al., 1994). Stokes et al. (2002) also demonstrated that GADD 45
gene induction occurs in response to reactive oxygen species (ROS) and quinones and is
abolished in the presence of the antioxidant, ascorbic acid. It is of note that quinones,
which are operationally non-genotoxic (Clayson et al., 1994), are highly redox active
molecules which can redox cycle with their semiquinone radicals, leading to formation of
ROS, including superoxide, hydrogen peroxide, and ultimately the hydroxyl radical.
Production of ROS can cause severe oxidative stress within cells through the formation
of oxidized cellular macromolecules, including lipids, proteins and DNA (Bolton et al.,
2000). Supporting the hypothesis of oxidative stress, Genter et al., also observed
upregulation of other genes associated with oxidative stress,[ i.e., heme oxygenase
(Otterbein et al., 2000), glutathione synthase and metallothionein (Andrews 2000)].
Progression from histologically altered olfactory mucosa to the development of
adenomas
The study authors stated that this step was accompanied by expression of genes
indicating inhibition of apoptosis [Bid3(AI102299)] and enhancement of cell
proliferation (zyxin). However, no data were provided to support this claim.
Nevertheless, it is of note that Sarafian and Bredesen (1994) state that ROS can serve as
common mediators of apoptosis.
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Progression to a malignant adenocarcinoma phenotype
This phase was indicated by induction of genes {i.e., axin2 and frizzled) related to
activation of the wnt signaling pathway, which are generally upregulated late in the
carcinogenesis process.
Transformation to adenocarcinomas
In the late stages of tumor progression, the activation of nuclear B-catenin genes, which
is critical for tumor formation in other organs and is associated with mutations in the wnt
pathway.
Several other studies support a role for oxidative stress in Alachlor-induced toxicity.
Burman etal. (2003) show that dietary exposure of Long-Evans rats to 126 mg/kg/day for 1 day
caused an -20% depletion of the olfactory mucosa antioxidant, GHS followed by a significantly
(p<0.001) increased expression of genes associated with increased GHS production after 2 and 4
days of treatment. A return to control values was seen by 10 days of treatment. A pattern
somewhat similar to GSH was observed for ascorbate in the olfactory tissue of 126-mg/kg/day
male rats {i.e., initially, a significant decrease 1 day post-treatment, followed by significant
increases 2 and 4 days after dosing). In contrast to the GSH data, there was a reduction in
ascorbate at 10 days. We noted, however, that the response with either antioxidant was not dose
related. From these results, the investigators concluded that, "Despite the fact that GHS levels
recovered, acute antioxidant perturbations may have been sufficient to trigger other steps in the
carcinogenic process. Therefore, acute depletion of GSH and ascorbate may trigger more
sustained events involved in both the initiation and promotion of the carcinogenic process."
There is also evidence of the ability of alachlor to induce oxidative stress in other tissues.
Bagchi etal.{ 1995) evaluated the potential of alachlor to induce oxidative stress and oxidative
tissue damage, as measured by production of lipid peroxidation and DNA-single strand breaks
(SSB), in the liver and brain of Sprague-Dawley rats administered two equal oral doses (at 0 and
21 hours) of 300 mg/kg. As noted by Clay son et al. (1994), SSB are considered by to be a good
indicator of oxygen damage to DNA. Results from the study of Bagchi et al. (2003) show that
alachlor induced moderate lipid peroxidation in liver and brain tissues and SSB in brain but not
liver DNA in samples harvested 24 hours after exposure to the first dose. The same authors also
conducted in vitro studies of chemiluminescence on liver and brain homogenates, and found that
Inmol/mL alachlor induced 3-fold increases in chemiluminescence in both tissues further
suggesting that alachlor induced ROS. Finally, the results from in vitro studies with cultured
PC-12 neuroactive cells exposed to 100 nM alachlor illustrate the sequence of early events
postulated for this MOA (generation of ROS =DNA damage ^tissue damage) with a 2-fold
increase in DNA-SSB and a 3-fold increase in LDH leakage. Although olfactory nasal tissue
was not examined in this series of assays, the ability of alachlor to generate ROS with
subsequent DNA damage and tissue damage both in vivo and in vitro has been established.
Finally, Bagchi et al. cite the work of Akubue and Stohs (1991) showing that the oral
administration of 800 mg/kg alachlor to rats caused the increased urinary excretion of the
"oxidative lipid metabolites, malondialdehyde, formaldehyde, acetaldehyde and acetone".
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Based on the above considerations, the postulated MO A (generation of ROS =DNA
damage ^tissue damage = cell proliferation^olfactory nasal tumors) in rats is plausible and
coherent. An additional factor favoring this MOA is the evidence of weak and sporadic
mutagenic effects, generally seen only at concentration near or at cytotoxic concentrations.
C. STUDY DEFICIENCIES:
The independent review of the data presented in this publication was limited to the
analysis of qualitative results presented in graphs or photographs copies of tissue sections.
Attempts to access the link for raw data provided in the article failed. Additionally, there were
no data to support the study authors' claim of upregulation of genes associated with apoptosis or
cell proliferation. These data would complete the sequence of key events in the carcinogenic
process for alachlor. Access to the primary microarray data through a functioning, public website
would have been preferable.
Based on an independent review of qualitative genomic data (presented as graphs or
photographic copies of tissue sections) in conjunction with the conventional data, it was
concluded that alachlor induces olfactory nasal carcinomas through a nongenotoxic mode of
action {i.e., cytotoxicity manifested through oxidative stress). Partial support for this conclusion
comes from data showing upregulation (2-fold increase over untreated control) of genes
correlated with the following steps in the carcinogenic process: oxidative stress and damage to
DNA progression of adenomas to malignant adenocarcinomas, and transformation to
adenocarcinomas. Although guidelines do not yet exist for genomic data, the results presented in
this DER provided critical information that enhanced the understanding of the nongenotoxic
mode of action for olfactory mucosal tumors induced by alachlor in the rat.
REFERENCES
Andrews, G.K., .2000. Regulation of metallathionein gene expression by oxidative stress and
metal ions. Biochem. Pharm. 59: 95-104.
Bagchi, D., Bagchi, M., Hassoun, E.A., Stohs, S.J. 1995.. In vitro and in vivo generation of
ROS, DNA damage and lactate dehydrogenase leakage by selected pesticides. Toxico. 104: 129-
140.
Bolton, J.L., Trush, M.A., Penning, T.M., Dryhurst, G. Monks, TJ. 2000. Role of quinones in
toxicology. Chem. Res. Toxicol. 13:135-160.
Burman, D.M., Shertzer, H.G., Senft, A.P., Dalton, T., Genter, M.B. 2003. Antioxidant
perturbations in the olfactory mucosa of alachlor-treated rats. Biochem Pharm 66:1707-1715.
Clayson, D.B., Mehta, R., Iverson, F. 1994. Oxidative DNA damage - The effect of certain
genotoxic and operationally non-genotoxic carcinogens. Mutat. Res.317: 25-42.
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Kasai, H.1997. Analysis of a form of oxidative DNA damage, 8-hydroxy-2'-deoxyguanosine, as
a marker of cellular oxidative stress during carcinogenesis. Mutat. Res. 387:147-163.
Jackman, J., Alamo I.Jr.,, Forance, A.J. Jr. 1994. Genotoxic stress confers preferential and
coordinate messenger RNA stability on the five gadd genes. Cancer Res. 54:5656-5662.
Otterbein, L.E., Augustine, M.K.C. 2000. Heme oxygenase: colors of defense against cellular
stress. Am. J. Physiol. Lung Cell. Mol. Physiol. 279: 1029-1037.
Sarafian, T.A. and Bredesen, D.E. 1994. Is apoptosis mediated by ROS? Free Rad. Res. 21:1-8.
Stokes, A.H., Freeman, W.M., Mitchell, S.G., Burnette, T.A., Hellman, G.M., Vrana, K.E. 2002.
Induction of GADD 45 and GADD153 in Neuroblastoma Cells by Dopamine-Induced Toxicity.
Neuro.Toxicol. 23:675-684.
Takahashi, S., Saito, S., Ohtani, N., Sakai, T. 2001. Involvement of the Oct-1 regulatory
Element of the gadd45 Promoter in the p53-independent Response to Ultraviloet Irradiation.
Cancer Res. 61:1187-1195.
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1 Appendix E: MIAME Glossary
2
3 For the most recent version of the MIAME glossary, please see:
4 http://www.mged.org/Workgroups/MIAME/miame glossarv.html
5
6
7
8 Age: The time period elapsed since an identifiable point in the life cycle of an organism. (If a
9 developmental stage is specified, the identifiable point would be the beginning of that stage.
10 Otherwise the identifiable point must be specified such as planting) [MGED Ontology
11 Definition]
12 Amount of nucleic acid labeled: The amount of nucleic acid labeled
13 Amplification method: The method used to amplify the nucleic acid extracted
14 Array design: The layout or conceptual description of array that can be implemented as one or
15 more physical arrays. The array design specification consists of the description of the common
16 features of the array as the whole, and the description of each array design elements (e.g., each
17 spot). MIAME distinguishes between three levels of array design elements: feature (the location
18 on the array), reporter (the nucleotide sequence present in a particular location on the array), and
19 composite sequence (a set of reporters used collectively to measure an expression of a particular
20 gene)
21 Array design name: Given name for the array design, that helps to identify a design between
22 others (e.g., EMBL yeast 12K verl. 1)
23 Array dimensions: The physical dimension of the array support (e.g. of slide)
24 Array related information: Description of the array as the whole
25 Attachment: How the element (reporter) sequences are physically attached to the array (e.g.
26 covalent, ionic)
27 Author, laboratory, and contact: Person(s) and organization (s) names and details (address,
28 phone, FAX, email, URL)
29 Biomaterial manipulation: Information on the treatment applied to the biomaterial
30 Bio-source properties: Information on the source of the sample
31 Cell line: The identifier for the immortalized cell line if one was used to derive the BioMaterial
32 [MGED Ontology Definition]
33 Cell type: Cell type used in the experiment if non mixed. If mixed the targeted cell type should
34 be used [MGED Ontology Definition]
35 Clone information: For each reporter, the identity of the clone along with information on the
36 clone provider, the date obtained, and availability
37 Common reference: A hybridization to which all the other hybridizations have been compared
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1 Composite sequence information: The set of reporters contained in the composite sequence.
2 The nucleotide sequence information for each composite element: number of oligonucleotides,
3 oligonucleotide sequences (if given), and the reference sequence accession number (from
4 relevant databases)
5 Composite sequence related information: Information on the set of reporters used collectively
6 to measure an expression of a particular gene
7 Compound: A drug, solvent, chemical, etc., that can be measured [MGED Ontology Definition]
8 Contact details for sample: The resource (e.g., company, hospital, geographical location) used
9 to obtain or purchase the BioMaterial and the type of specimen [MGED Ontology Definition]
10 Control elements position: The position of the control features on the array
11 Control elements related information: Array elements that have an expected value and/or are
12 used for normalization
13 Control type: The type of control used for the normalization and their qualifier
14 Data processing protocol: Documentation of the set of steps taken to process the data,
15 including: the normalization strategy and the algorithm used to allow comparison of all data
16 Developmental stage: The developmental stage of the organism's life cycle during which the
17 BioMaterial was extracted [MGED Ontology Definition]
18 Disease state: The name of the pathology diagnosed in the organism from which the
19 BioMaterial was derived. The disease state is normal if no disease has been diagnosed [MGED
20 Ontology Definition]
21 Element dimensions: The physical dimensions of each features
22 Experiment description: Free text description of the experiment and link to an electronic
23 publication in a peer-reviewed j ournal
24 Experiment design: Experiment is a set of one or more hybridizations that are in some way
25 related (e.g., related to the same publication MIAME distinguishes between: the experiment
26 design (the design, purpose common to all hybridizations performed in the experiment), the
27 sample used (sample characteristics, the extract preparation and the labeling), the hybridization
28 (procedures and parameters) and the data (measurements and specifications)
29 Experiment type (s): A controlled vocabulary that classify an experiment
30 Experimental design: Design and purpose common to all hybridizations performed in the
31 experiment
32 Experimental factor (s): Parameter (s) or condition (s) tested in the experiment
33 Extraction method: The protocol used to extract nucleic acids from the sample
34 Features related information: Information on the location of the reporters on the array
35 Final gene expression table (s): Derived measurement value summarizing related elements and
36 replicates, providing the type of reliability indicator used
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1 Gene name: The gene represented at each composite sequence: name and links to appropriate
2 databases (e.g. SWISS-PTOR or organism specific database)
3 Genetic variation: The genetic modification introduced into the organism from which the
4 BioMaterial was derived. Examples of genetic variation include specification of a transgene or
5 the gene knocked-out [MGED Ontology Definition]
6 Growth conditions: A description of the isolated environment used to grow organisms or parts
7 of the organism [MGED Ontology Definition]
8 Hybridization protocol: Documentation of the set of steps taken in the hybridization,
9 including: solution (e.g. concentration of solutes); blocking agent and concentration used; wash
10 procedure; quantity of labelled target used; time; concentration; volume, temperature, and
11 description of the hybridization instruments
12 Hybridization extract preparation: Information on the extract preparation for each extract
13 prepared from the sample
14 Hybridizations: Procedures and parameters for each hybridization
15 Image analysis and quantitation: Each image has a corresponding image quantitation table,
16 where a row represents an array design element and a column to a different quantitation types
17 (e.g. mean or median pixel intensity)
18 Image analysis output: The complete image analysis output for each image
19 Image analysis protocol: Documentation of the set of steps taken to quantify the image
20 including: the image analysis software, the algorithm and all the parameters used
21 In vitro treatment: The manipulation of the cell culture condition for the purposes of
22 generating one of the variables under study and the documentation of the set of steps taken in the
23 treatment
24 In vivo treatment: The manipulation of the organism for the purposes of generating one of the
25 variables under study and the documentation of the set of steps taken in the treatment
26 Individual genetic characteristics: The genotype of the individual organism from which the
27 BioMaterial was derived [MGED Ontology Definition]
28 Individual number: Identifier or number of the individual organism from which the
29 BioMaterial was derived. For patients, the identifier must be approved by Institutional Review
30 Boards (IRB, review and monitor biomedical research involving human subjects) or appropriate
31 body [MGED Ontology Definition]
32 Label incorporation method: The label incorporation method used
33 Label used: The name of the label used
34 Measurements: MIAME distinguishes between three levels of data processing: image (raw
35 data), image analysis and quantitation, gene expression data matrix (normalized and summarized
36 data)
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1 Normalized and summarized data: Several quantitation tables are combined using data
2 processing metrics to obtain the 'final' gene expression measurement table (gene expression data
3 matrix) associated with the experiment
4 Nucleic acid type: The type of nucleic acid extracted (e.g. total RNA, mRNA)
5 Number of elements on the array: The number of features on the array
6 Number of hybridizations: Number of hybridizations performed in the experiment
7 Organism: The genus and species (and subspecies) of the organism from which the BioMaterial
8 is derived [MGED Ontology Definition]
9 Organism part: The part or tissue of the organism's anatomy from which the BioMaterial was
10 derived MGED Ontology Definition]
11 Platform type: The technology type used to place the biological sequence on the array
12 Production protocol: A description of how the array was manufactured
13 Provider: The primary contact (manufacturer) for the information on the array design
14 Qualifier, value, source (may use more than once): Describe any further information about
15 the array in a structured manner
16 Quality control steps: Measures taken to ensure or measure quality: replicates (number and
17 description), dye swap (for two channel platforms) or others (unspecific binding, low complexity
18 regions, polyA tails)
19 Raw data: Each hybridization has at least one image
20 Relationship between samples and arrays: Relationship between the labelled extract (related
21 to which sample which extract) and arrays (design, batch and serial number) in the experiment
22 Reporter and location: The arrangement and the system used to specify the location of each
23 features on the array (e.g. grid, row, column, zone)
24 Reporter approximate length: The approximate length of the reporter's sequence
25 Reporter generation protocol: A description of how the reporters were generated
26 Reporter related information: Information on the nucleotide sequence present in a particular
27 location on the array
28 Reporter sequence information: The nucleotide sequence information for reporter: sequence
29 accession number (from DDBJ/EMBL/GenBank), the sequence itself (if known) or a reference
30 sequences (e.g. for oligonucleotides) and PCR primers pair information (if relevant)
31 Reporter type: Physical nature of the reporter (e.g. PCR product, synthesized oligonucleotide)
32 Sample: The biological material from which the nucleic acids have been extracted for
33 subsequent labelling and hybridization. MIAME distinguishes between: source of the sample
34 (bio-source), its treatment, the extract preparation, and its labeling
35 Sample labeling: Information on the labeling preparation for each labelled extract
36 Scanner image file: The TIFF file including header
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1 Scanning protocol: Documentation of the set of steps taken for scanning the array and
2 generating an image including: description of the scanning instruments and the parameter
3 settings
4 Separation technique: Technique to separate tissues or cells from a heterogenous sample (e.g.
5 trimming, microdissection, FACS)
6 Sex: Term applied to any organism able to undergo sexual reproduction in order to differentiate
7 the individuals or type involved. Sexual reproduction is defined as the ability to exchange
8 genetic material with the potential of recombinant progeny [MGED Ontology Definition]
9 Single or double stranded: Whether the reporter sequences are single or double stranded
10 Spike type and qualifier: The type of spike used (e.g. oligonucleotide, plasmid DNA,
11 transcript) and its qualifier (e.g. concentration, expected ratio, labeling methods)
12 Spiking control: External controls added to the hybridization extract (s)
13 Spiking control feature: Position of the feature (s) on the array expected to hybridize to the
14 spiking control
15 Strain or line: Animals or plants that have a single ancestral breeding pair or parent as a result
16 of brother x sister or parent x offspring matings [MGED Ontology Definition]
17 Surface and coating specification: Type of surface and name for the type of coating used
18 Targeted cell type: The targeted cell type is the cell of primary interest. The BioMaterial may
19 be derived from a mixed population of cells although only one cell type is of interest [MGED
20 Ontology Definition]
21 Treatment type: The type of manipulation applied to the BioMaterial for the purposes of
22 generating one of the variables under study [MGED Ontology Definition]
23
24
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Appendix F: Additional Glossary from Genomics White Paper
Allele: An alternative form of a gene or any other segment of a chromosome
Bioinformatics: The analysis of biological information using computers and statistical
techniques; the science of developing and utilizing computer databases and algorithms to
accelerate and enhance biological research.
Biomarker: A molecular indicator of a specific biological property; a biochemical feature or
facet that can be used to measure the progress of disease or the effects of treatment.
Complementary DNA (cDNA): DNA made from a messenger RNA (mRNA) template. The
single-stranded form of cDNA is often used as a probe in physical mapping.
Biotechnology: Set of biological techniques developed through basic research and now applied
to research and product development. In particular, biotechnology refers to the use by industry of
recombinant DNA, cell fusion, and new bioprocessing techniques.
Computational Toxicology (Comp Tox): Word used first in EPA's Interim Policy on Genomics
- "Computational Toxicology is defined as the application of models from computational and
mathematical biology and computational chemistry for prediction and understanding
mechanisms" - Computational Toxicology Framework Document, ORD, April 2003.
DER: Data Evaluation Record
Deoxyribonucleic acid (DNA): Nucleic acid that constitute the genetic material of all cellular
organisms and DNA viruses. The genetic information is used in the synthesis of ribonucleic
acids (RNAs) from DNA templates (transcription), and in the synthesis of proteins from
messenger RNA (mRNA) templates (translation).
DNA Microarray:. Microarray is a tool used to sift through and analyze the information
contained within a genome. A microarray consists of different deoxyribonucleic acid (DNA)
probes that are chemically attached to a substrate, which can be a microchip, a glass slide or a
microsphere-sized bead.
Expressed sequence tag: A unique stretch of DNA within a coding region of a gene that is
useful for identifying full-length genes and serves as a landmark for mapping.
FACS: Fluorescence Activated Cell Sorter
Gene: The fundamental physical and functional unit of heredity. A gene is an ordered sequence
of nucleotides located in a particular position on a particular chromosome that encodes a specific
functional product {i.e., a protein or RNA molecule).
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Gene chip technology: Development of cDNA microarrays from a large number of genes. Used
to monitor and measure changes in gene expression for each gene represented on the chip.
Gene expression: Process by which a gene's coded information is converted into the structures
present and operating in the cell. Expressed genes include those that are transcribed into mRNA
and then translated into protein and those that are transcribed into RNA but not translated into
protein (e.g., transfer and ribosomal RNAs).
Genetics: Study of inheritance patterns of specific traits.
Genetic testing: Analyzing an individual's genetic material to determine predisposition to a
particular health condition or to confirm a diagnosis of genetic disease.
Genomics: Comprehensive study of whole sets of genes, gene products and their interaction.
Genome: All the genetic material in the chromosomes of a particular organism; its size is
generally given as its total number of base pairs.
Genotype: The genetic composition of an organism or a group of organisms; a group or class of
organisms having the same genetic constitution.
Hazard Assessment: The process of determining whether exposure to an agent can cause an
increase in the incidence of a particular adverse health effect (e.g., cancer, birth defect) and
whether the adverse health effect is likely to occur in humans.
Hazard Characterization: A description of the potential adverse health effects attributable to a
specific environmental agent, the mechanisms by which agents exert their toxic effects, and the
associated dose, route, duration, and timing of exposure.
Hazard identification: The process of determining whether it is scientifically correct to infer
that toxic effects observed in one setting will occur in other settings (e.g., whether substances
found to be carcinogenic or teratogenic in experimental animals are likely to have the same
results in humans).
In Vitro: A biological study is one which is performed in isolation from a living organism (in
contrast to In Vivo studies).
In Vivo: A biological study is one which is performed within a living biological organism (as
opposed to an In Vitro study).
Knockout: Inactivation of specific genes. Knockouts are often created in laboratory organisms
such as yeast or mice so that scientists can study the knockout or null organism as a model for a
particular disease.
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MAGE: MicroArray and Gene Expression; the group aims to provide a standard for the
representation of microarray expression data that would facilitate the exchange of microarray
information between different data systems.
MAGE-OM: Microarray Gene Expression: Object Model
MGED: The Microarray Gene Expression Data (MGED) Society is an international
organization of biologists, computer scientists, and data analysts that aims to facilitate the
sharing of microarray data generated by functional genomics and proteomics experiments.
Mapping: Charting the location of genes on chromosomes.
Mass spectrometry: A method used to determine the masses of atoms or molecules in which an
electrical charge is placed on the molecule and the resulting ions are separated by their mass to
charge ratio.
Metabolome: Entire complement of all the small molecular weight metabolites inside a cell
suspension (or other sample) of interest (Aberystwyth, University of Wales Web site-
http://dbk.ch.umist.ac.uk/metabol.htm). This profile is a product of the genome of the organism,
the expression of that genome, and the operation of the metabolism is a particular part of the
organism, in a particular environment.
Metabolomics: Involves the systematic estimation of metabolomes from a range of organisms,
followed by statistical analyses and other investigations of that large quantity of data.
Metabonomics: Study of the endogenous composition of biofluids and tissues of an organism in
order to probe the metabolic state in homeostasis, and when under interventional stress. Hector
Keun (Biological Chemistry and Biological Sciences, Imperial College, London); Metabolic
Profiling: Application to Toxicology and Risk Reduction. International Conference, May 14-15,
2003, NIEHS, Research Triangle Park, North Carolina.
MIAME: Minimum Information About a Microarray Experiment that is needed to enable the
interpretation of the results of the experiment unambiguously and potentially to reproduce the
experiment
Microarray: A tool used to sift through and analyze the information contained within a
genome. A microarray consists of different nucleic acid probes that are chemically attached to a
substrate, which can be a microchip, a glass slide or a microsphere-sized bead.
Mode of Action: Key events and processes, starting with the interaction of an agent with a cell,
through functional and anatomical changes observed on the progression to toxicity
MOPS-EDTA: [MOPS] 3-(N-Morpholino) propanesulfonic acid], [EDTA]
ethylenediaminetetraacetic acid
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Northern blot: A technique used to separate and identify RNA.
Nucleotide: A subunit of DNA or RNA. To form a DNA or RNA molecule, thousands of
nucleotides are joined in a long chain.
"Omics": Term including genomics, proteomics, metabonomics (some differentiate this term
from metabolomics), transcriptomics, and associated bioinformatics (Environmental Health
Perspectives, 110: 2002, 1047-1050; Meeting Report: Use of Genomics in Toxicology and
Epidemiology: Findings and recommendations of a workshop). Carol J. Henry and Vanessa Vu,
first and last authors, respectively.
Omics Technologies: A quote often cited describes this phrase"... are based on comprehensive
biochemical and molecular characterizations of an organism, tissue or cell type" Sumner et. al.
2003.
Phenotype: The observable physical or biochemical traits of an organism, as determined by
genetics and the environment; the expression of a given trait based on phenotype; an individual
or group of organisms with a particular phenotype.
PMT: Photomultiplier tube; used in the capture of raw data
Polymorphism: The quality or character of genes occurring in several different forms.
Proteome: All of the proteins produced by a given species, just as the genome is the totality of
the genetic information possessed by that species.
Proteomics: Study of the function of all expressed proteins (Nature, 422: 2003, 193-197).
Quality policy statement: Describing the specific objectives and commitment of the laboratory
and its management to quality and data integrity. An ethics statement may be included at this
point.
RNA: Nucleic acid found in all living cells that plays a role in the transfer of information from
DNA to the protein-forming system of the cell. The base sequence of an RNA is specified by the
base sequence of a section of the DNA (a Gene) which is used as the template for RNA synthesis
(transcription). (Dorland's Medical Dictionary)
Risk Assessment (in the context of human health): The evaluation of scientific information on
the hazardous properties of environmental agents (hazard characterization), the dose-response
relationship (dose-response assessment), and the extent of human exposure to those agents
(exposure assessment). The product of the risk assessment is a statement regarding the
probability that populations or individuals so exposed will be harmed and to what degree (risk
characterization).
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Signal transduction pathway: The course by which a signal from outside a cell is converted to
a functional change within the cell.
Single nucleotide polymorphism (SNP): A change in which a single base in the DNA differs
from the usual base at that position.
Standard operating procedures (SOPs): listing all routine laboratory operations documented
and signed by management which are available to clients upon request and readily accessible to
staff. Also known as laboratory operating procedures and protocols.
Susceptibility: Increased likelihood of an adverse effect, often discussed in terms of relationship
to a factor that can be used to describe a human subpopulation (e.g. life stage, demographic
feature, or genetic characteristic).
Susceptible Subgroups: May refer to life stages, for example, children or the elderly, or to
other segments of the population, for example, asthmatics or the immune-compromised, but are
likely to be somewhat chemical-specific and may not be consistently defined in all cases.
Systems Biology: A holistic approach to the study of biology with the objective of
simultaneously monitor all biological processes operating as an integrated system. Sumner et.
al., 2003.
Systems Toxicology: ".. .involves the study of perturbation of organisms by chemicals and
stressors, monitoring changes in molecular expression and conventional toxicological
parameters, and iteratively integrating biological response data to describe the functioning
organism".
Throughput: Output or production, as of a computer program, over a period of time.
Toxicity: Deleterious or adverse biological effects elicited by a chemical, physical, or biological
agent.
Toxicology: The study of harmful interactions between chemical, physical, or biological agents
and biological systems.
Toxicogenomics: The collection, interpretation, and storage of information about gene and
protein activity in order to identify toxic substances in the environment, and to help treat people
at the greatest risk of diseases caused by environmental pollutants or toxicants. Study of the
roles that genes play in the biological responses to environmental toxicants and stressors
(Environmental Health Perspective Toxicogenomics (NIEHS).
Transgenic: Having genetic material (DNA) from another species. This term can be applied to
an organism that has genes from another organism.
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Web-based Glossary Sources
1- National Center for Toxicogenomics (NCT, NIEHS) Glossary
2- Human Genome Project Information Web Glossary
3- Cambridge Healthtech Institute
4- The Physical and Theoretical Chemistry Laboratory, Oxford University Chemical and Other
Safety Information
5- NIH Glossary
6- Integrated Risk Information Systems (IRIS, EPA) Glossary
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Appendix G: Content and Instructional Goals for the Three Levels
of EPA Genomics Technical Training:
Level I: Introductory Modules - Molecular Biology Concepts
Modules 1-8
Goal: Provide the basic information necessary for understanding the more intricate
assessments of cellular functions at the molecular level. Introduce gene arrays and discuss how
genomics data may affect risk assessments in the future - this module will tie into EPA's current
Genomics Policy. Issues relating to how to communicate genomics information to risk managers
and the public will be addressed.
Target Audience: Non-scientists and/or technical staff without training in biological sciences, such as:
Managers from Office of Research and Development, Regional and Program Offices
Regional Risk Managers (e.g., Remedial Project Managers, On Scene Coordinators)
Attorneys
Staff from Regional Office Programs (e.g., Air, Water, Waste, Pesticides, Community
Involvement, Tribal Program)
Staff from States and Tribes
Components: Cell structure and function
DNA structure and replication
RNA - Types, functions, transcription (gene expression)
Proteins - General features, formation (translation)
Gene Arrays - General principles and types
Risk Assessment Concepts - Cancer and non-cancer risk, how genomics data may
affect risk assessments in the future
Regulatory Framework and Risk communication (different regulatory
applications)
Level II: Intermediate Level Modules - Techniques in Molecular Biology
Modules 9-12
Goal: Provide a general understanding of all of the various applications that may be currently
considered by programs throughout EPA and is intended to support human health and ecological
risk assessors. Specific modules for individual program applications are considered separately
(see Level II Modules - Specific Applications for Molecular Tools)
Target Audience: Scientists and/or those likely to use genomics data generated by risk assessors
are the audience. Modules are intended for staff who need a more in-depth understanding of how
genomics data is generated, but do not necessarily need to generate that data to support decision-
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making. Modules for specific applications will be developed (e.g., microbial source tracking,
homeland security, field inspectors). Examples include:
Laboratory Staff
Regional Laboratories
Office of Research and Development
Enforcement/Compliance Staff (e.g., Water programs, TMDLs, FIFRA)
Risk Assessors - Human Health and Ecological
Regional Offices
Office of Research and Development
Program Offices
Components: Background on molecular techniques, such as:
Microarrays
DNA amplification using PCR and RT-PCR
Isolation kits
Restriction enzymes
Electrophoresis
DNA fingerprinting
Protein Analysis
Laboratory exercises using various molecular techniques (see above)
Techniques for specific applications, such as:
Microbial source tracking
Homeland security
Field inspection
Molecular Biology Approaches in Quantitative Risk Assessment
Level II: Intermediate Level Modules - Specific Applications for Molecular Tools
Module 13: Homeland Security
Module 14: Microbial and/or Bacterial Source Tracking
Module 15: Molecular Techniques to Assess Exposure in Environmental Media
Module 16: Molecular Techniques for Genetically Modified Crop Plant Inspectors
Goal: Reinforce information and techniques learned in the Level II Modules - Techniques in Molecular
Biology, and to provide more in-depth knowledge and skills in the performance of molecular techniques.
Each of these modules is focused on a separate and specific application of the molecular tools
(introduced in modules 8-11) to support different programs and needs of the Agency and its staff. Each
module is intended to provide technical training to staff to increase the breadth of scientific
understanding that will assist in improving job competencies with respect to science in their particular
program area.
Target Audience: Same as Level II Modules - Techniques in Molecular Biology.
Components: Technical training in particular program areas, focusing on research and tools currently
under development by or through ORD. For example, Module 13: Microbial and/or Bacterial Source
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Tracking will use a newly developed Guide on Tools for Microbial Source Tracking (Jorge
Santodomingo, in preparation), which compares a number of molecular (RT-PCR, DNA finger printing)
and non-molecular (antibiotic resistance) techniques for identifying pathogenic bacteria from in water.
This information may be supplemented by laboratory exercises.
Level III: Advanced Modules
Module 17: Data Analysis (1) - Statistical Analysis
Module 18: Data Analysis (2) - Bioinformatics Approaches, Computational Toxicology
Module 19: Use of Molecular Biology in Mode-Of-Action Determinations
Module 20: Using Genomics Data in Chemical Hazard/Risk Assessment
Overall Goal: Provide advanced-level knowledge on specific technical needs that scientists
performing research or developing hazard/risk assessments associated with chemical
registrations and other regulatory activities may face. Due to the novel and continually evolving
nature of the genomics field, the advanced training modules will be flexible to account for these
potential dynamic changes. As new technologies and applications appear, additional or existing
training modules will be developed, enhanced and/or revised. Modules will also be flexible to
meet the needs of the different EPA programs.
Target Audience: Scientists and those likely to use genomics data to generate Risk Assessments,
such as:
ORD Researchers
Program Office Risk Assessors
Modules 17 & 18 (Data Analysis 1 & 2)
Goal: Provide information to research scientists and program office risk assessors on
computational toxicology, bioinformatics and statistics. The modules will focus on how to
identify and interpret patterns within the large volumes of genomics data and assess data
significance and accuracy, offering insight into the critical evaluation, including pros, cons and
limitations of possible approaches.
Components - Module 17:
Statistical approaches to microarray data analysis including, but not limited to:
Bayesian statistics
Correlation
Clustering
Principle component analysis
Components - Module 18:
Computational toxicology and bioinformatic approaches and tools used to analyze genomics
data.
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Models and molecular biological applications used to predict effects and understand the cascade
of events leading to an effect and how statistical analyses fits together with other information to
form a bigger picture.
Bioinformatics tools (algorithms and statistics) that will be used to discriminate unique signature
and families of signatures indicative of stressors and groups of stressors.
Data access (i.e. data mining) and management of data
Module 19: Use of Molecular Biology in Mode-Of-Action Determinations
Goal: Introduce the approaches for and limitations of data interpretation. This module will
provide a link between molecular biology methods and information and the risk assessment
process.
Components:
The module will present the general concept that an understanding of the key events
associated with the production of adverse health outcomes at the molecular level could enhance
our ability to predict these outcomes in a qualitative and quantitative sense. In addition,
variability and other uncertainties (e.g., adaptive responses and homeostatic compensation)
surrounding the analysis and interpretation of microarray data for making quantitative
conclusions about effect/response levels will be discussed. The concept of mode-of-action
(MOA including key events) will also be introduced. The different classes of MO A will be
discussed: these will include genotoxicity, mutagenicity, receptor-mediated, cell killing
regenerative cell proliferation, and mitogenic responses. Each of these will be discussed in terms
of the current understanding at the molecular level. For example, what is cell signaling and how
do changes affect cell function; what is apoptosis and how is it induced; what controls the cell
cycle and how can it be abrogated; what is the mechanism for the induction of mutations and
chromosome changes and the role of DNA repair and replication? These molecular
underpinnings will allow for examples of key event pathways to be discussed and how chemicals
might potentially impact the various pathways.
Module 20: Using Genomics Data in Chemical Hazard/Risk Assessment
Goal: Provide guidance on the incorporation of genomics (microarray) data in a weight-of-
evidence approach for hazard assessment. Present principles and pitfalls using simple case
studies. Case studies will be flexible to meet the needs of the programs and offices, for example,
case studies may focus on homeland security and microbial source tracking applications.
Components:
Case studies such as:
Examples where microarray data quality is high
Examples that demonstrate data concerns which could lead to erroneous conclusions
Case Studies should be developed to support the need of the programs and Regional offices, e.g.,
homeland security, microbial source tracking, ambient water quality monitoring, etc. to support
the use of microarray data or for other molecular-biology-based or "omics" approaches.
Examples include, but are not limited to:
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• Demonstration of purported evidence that a particular chemical belongs to
a particular class of hepatotoxins
• Demonstration of purported evidence that chemical has characteristics of a
certain class of hormonally active substances
These Case Studies should include the following elements:
• Purpose
• Overall (microarray or other "omics" approach) study design
• Purported mode of action and details of how data support proposal, including purported
rationale for utility of microarray data; arguments to support conclusions
• Conventional mechanistic support: histopathology, clinical chemistry, metabolic profile,
time course to appearance of critical elements, dose-response information, special
studies, etc.
• Microarray data: summary gene expression profile data presentation and necessary
supporting raw data, proposed up and down regulated and constituent gene identification,
rationale for platform and chip design, demonstration of reproducibility, analysis of
variability, positive and negative controls, dose response/temporal elements analysis,
statistical analysis; RNA stability
• Correlation and comparison: between conventional and microarray data to support
argument; phenotypic anchoring
• Other Evidence: Structure-Activity Relationship, etc.
• Any perceived data gaps
• Potential relevance to humans
• Weight-Of-Evidence Conclusion
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