Document ID: EPA-HQ-ORD-2007-0212-0002
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2007-03-15T04:00Z

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

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.

Genomics Microarray WorkgroupCo-Chairs

  

Science Policy Council Staff

Jeremy Johnson

(2004)

Subgroup Co-Chairs

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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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 Olsen, 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



Table of Contents

  TOC \o "1-2" \h \z \u    HYPERLINK \l "_Toc131395364"  ACRONYMS	 
PAGEREF _Toc131395364 \h  viii  

  HYPERLINK \l "_Toc131395365"  EXECUTIVE SUMMARY	  PAGEREF
_Toc131395365 \h  1  

  HYPERLINK \l "_Toc131395366"  1.0	Introduction	  PAGEREF _Toc131395366
\h  5  

  HYPERLINK \l "_Toc131395367"  1.1	Background	  PAGEREF _Toc131395367
\h  5  

  HYPERLINK \l "_Toc131395368"  1.2	Overview of Genomic Science	 
PAGEREF _Toc131395368 \h  6  

  HYPERLINK \l "_Toc131395369"  1.3	Emerging Impacts of Genomics
Technologies	  PAGEREF _Toc131395369 \h  8  

  HYPERLINK \l "_Toc131395370"  1.4	Purpose and Intent of this Document	
 PAGEREF _Toc131395370 \h  11  

  HYPERLINK \l "_Toc131395371"  2.0	The Performance Approach to Quality
Assurance for Microarrays	  PAGEREF _Toc131395371 \h  12  

  HYPERLINK \l "_Toc131395372"  3.0	Data Submission Guidance	  PAGEREF
_Toc131395372 \h  14  

  HYPERLINK \l "_Toc131395373"  3.1	Introduction	  PAGEREF _Toc131395373
\h  14  

  HYPERLINK \l "_Toc131395374"  3.2	Abstract	  PAGEREF _Toc131395374 \h 
14  

  HYPERLINK \l "_Toc131395375"  3.3	Experimental Design	  PAGEREF
_Toc131395375 \h  15  

  HYPERLINK \l "_Toc131395376"  3.4	Array Design	  PAGEREF _Toc131395376
\h  16  

  HYPERLINK \l "_Toc131395377"  3.5	Biomaterials	  PAGEREF _Toc131395377
\h  16  

  HYPERLINK \l "_Toc131395378"  3.6	Hybridization	  PAGEREF
_Toc131395378 \h  17  

  HYPERLINK \l "_Toc131395379"  3.7	Measurements	  PAGEREF _Toc131395379
\h  17  

  HYPERLINK \l "_Toc131395380"  4.0	Data Analysis Guidance	  PAGEREF
_Toc131395380 \h  19  

  HYPERLINK \l "_Toc131395381"  4.1	Introduction	  PAGEREF _Toc131395381
\h  19  

  HYPERLINK \l "_Toc131395382"  4.2	Data Analysis	  PAGEREF
_Toc131395382 \h  20  

  HYPERLINK \l "_Toc131395383"  4.3	Data Evaluation	  PAGEREF
_Toc131395383 \h  23  

  HYPERLINK \l "_Toc131395384"  4.4	Data Analysis Conclusions	  PAGEREF
_Toc131395384 \h  24  

  HYPERLINK \l "_Toc131395385"  5.0	Data Management	  PAGEREF
_Toc131395385 \h  25  

  HYPERLINK \l "_Toc131395386"  6.0  Recommendations	  PAGEREF
_Toc131395386 \h  28  

  HYPERLINK \l "_Toc131395387"  6.1	Training Needs and Recommendations	 
PAGEREF _Toc131395387 \h  28  

  HYPERLINK \l "_Toc131395388"  6.2	Collaborative Development of Genomic
Tools for Data Analysis and Data Management	  PAGEREF _Toc131395388 \h 
31  

  HYPERLINK \l "_Toc131395389"  6.3	Applying this Interim Guidance for
Microarray-Based Assays to Case Studies	  PAGEREF _Toc131395389 \h  32  

  HYPERLINK \l "_Toc131395390"  6.4	Updating Genomics Guidance as Needed
  PAGEREF _Toc131395390 \h  32  

  HYPERLINK \l "_Toc131395391"  References	  PAGEREF _Toc131395391 \h 
33  

  HYPERLINK \l "_Toc131395392"  Appendix A:  EPA Quality System and the
Performance Approach to Quality Measurement Systems	  PAGEREF
_Toc131395392 \h  35  

  HYPERLINK \l "_Toc131395393"  APPENDIX B:  MIAME-Based Data Submission
Tables	  PAGEREF _Toc131395393 \h  51  

  HYPERLINK \l "_Toc131395394"  Table B.1  Abstract	  PAGEREF
_Toc131395394 \h  51  

  HYPERLINK \l "_Toc131395395"  Table B.2  Experimental Design	  PAGEREF
_Toc131395395 \h  51  

  HYPERLINK \l "_Toc131395396"  Table B.3  Array Design	  PAGEREF
_Toc131395396 \h  53  

  HYPERLINK \l "_Toc131395397"  Table B.4  Biomaterials	  PAGEREF
_Toc131395397 \h  56  

  HYPERLINK \l "_Toc131395398"  Table B.5  Hybridization	  PAGEREF
_Toc131395398 \h  61  

  HYPERLINK \l "_Toc131395399"  Table B.6  Measurements	  PAGEREF
_Toc131395399 \h  62  

  HYPERLINK \l "_Toc131395400"  Appendix C:  Genomics Data Evaluation
Record (gDER) Template	  PAGEREF _Toc131395400 \h  64  

  HYPERLINK \l "_Toc131395401"  Appendix D:  Genomics Data Evaluation
Record (gDER) for Alachlor (Sample)	  PAGEREF _Toc131395401 \h  67  

  HYPERLINK \l "_Toc131395402"  Appendix E: MIAME Glossary	  PAGEREF
_Toc131395402 \h  77  

  HYPERLINK \l "_Toc131395403"  Appendix F:  Additional Glossary from
Genomics White Paper	  PAGEREF _Toc131395403 \h  82  

  HYPERLINK \l "_Toc131395404"  Appendix G:  Content and Instructional
Goals for the Three Levels of Genomics Technical  Training:	  PAGEREF
_Toc131395404 \h  88  

 

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 

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

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.

	  SEQ CHAPTER \h \r 1 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).

	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 (  HYPERLINK
"http://www.mged.org/Workgroups/MIAME/miame.html" 
http://www.mged.org/Workgroups/MIAME/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.

	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 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.

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 (  HYPERLINK
"http://www.epa.gov/osa/spc/genomics.htm" 
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.  

	  SEQ CHAPTER \h \r 1 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,   HYPERLINK
"http://www.epa.gov/osa/genomics.htm"  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 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 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 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 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 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   SEQ CHAPTER \h \r 1 (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:   HYPERLINK
"http://www.epa.gov/ORD/NRMRL/pubs/600r05064/600r05064.htm%20" 
http://www.epa.gov/ORD/NRMRL/pubs/600r05064/600r05064.htm  ; U.S. EPA,
2005) .

	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.  

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.

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 (  HYPERLINK "http://www.mged.org/Workgroups/MIAME/miame.html"
 http://www.mged.org/Workgroups/MIAME/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.

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 (  HYPERLINK
"http://www.mged.org/Workgroups/MIAME/miame.html" 
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

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.

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 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 for analysis such as background correction, normalization
methods, methods used to test usability of the raw data, and types of
analytical approaches would be useful information for the reviewer. 
Analytical approaches might include statistical models, graphical
models, image based displays of data, and various analytical software
packages.  Information about the software may include weblink,
proprietary information from instruction manual, or specific description
of custom analytic methods.  More complete description of information
that should be considered for a submission for review may be found in
Appendix B.



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 (  HYPERLINK "http://www.mged.org/MIAME1.1-DenverDraft.DOC" 
http://www.mged.org/MIAME1.1-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,   HYPERLINK "http://www.ncbi.nlm.nih.gov/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 (   HYPERLINK
"http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/" 
http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ ) and
National Institute of Environmental Health Sciences (NIEHS) on the
Chemical Effects in Biological Systems knowledgebase (  HYPERLINK
"http://cebs.niehs.nih.gov/"  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

	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 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

hierarchical clustering, and κ-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. 

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)

4.4	Data Analysis Conclusions

	The above considerations demonstrate that a systematic approach for
genomics data evaluation is necessary for further use of genomic data in
risk assessment efforts.  Documentation methods, like those in the
proposed genomics DER (Appendix C) can help capture some requisite
information, but 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 predict the toxic potential of a chemical.  

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:

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.

Provision of connectivity to external public biological databases such
as Affymetrix, Agilent, and GenBank

A quality control mechanism to ensure the fidelity of entered data

Capability for importing and exporting data by means of automatic
routines

Inclusion of a wide range of data analysis and visualization tools such
as filtering, clustering, and statistical analysis

Sufficient scalability to address large data submissions, many users,
and later addition of metabonomics and proteomics data at times in the
future

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.

Automatic data back up and recovery system

For security and management purposes, additional key needs include:

Database hosting, administration and management. This includes managing
data submission, database access and privileges, software and hardware
updates, back-up and storage.

 Physical and electronic security, including user authentication,
firewalls, and virus protection.

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:

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.

Capability of partitioning the database to secure Confidential Business
Information (CBI) or other non-public information, if this is part of a
regulatory submission.

Workflow enabled, so that reviewer can address data in systematic steps
needed for response to submission.

For public access purposes, key needs are:

Database is Web enabled, with easy routine for export of data.

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 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:

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.

Data Access.  All users in the Agency would have access to all Agency
genomic data (except CBI data), greatly enhancing our risk assessment
capabilities. 

Quality Control and Consistency.  A quality control mechanism would
ensure that all Agency data passes a consistency test.

Availability of a Common Set of Analysis Tools.  As new tools are
developed, they would become available to all users. 

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.

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.

	Presented below is a draft outline that describes a modular training
course in molecular techniques, in general, and genomics data
interpretation, in particular.  The Genomics Training would consist of
three levels of training targeted to specific audiences, each consisting
of a series of modules devoted to a particular group of concepts and/or
techniques.  Each training level is outlined below in Table 1, with
descriptions of the content and instructional goals.  More detail on the
proposed training is provided in Appendix G.  



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.



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 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.

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., et al. 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., et al. 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., et al. 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

  SEQ CHAPTER \h \r 1 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.   HYPERLINK
"http://www.epa.gov/ORD/NRMRL/pubs/600r05064/600r05064.htm" 
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:   HYPERLINK
"www.epa.gov/osa/genomics.htm"  www.epa.gov/osa/genomics.htm 

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. 

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 5360.1 A2, Policy and Program Requirements for the
Mandatory Agency-Wide Quality System, the EPA Quality Manual for
Environmental Programs, EPA Manual 5360 A1, the Contracts Management
Manual, and the Agency’s Website (  HYPERLINK
"http://www.epa.gov/quality"  www.epa.gov/quality ).  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).

   

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  
HYPERLINK "http://www.EPA.gov/QARus/QMPexample" 
www.epa.gov/quality/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   HYPERLINK
"http://www.epa.gov/fedrgstr/EPA-WASTE/1997/October/Day-06/f26443.htm" 
http://www.epa.gov/fedrgstr/EPA-WASTE/1997/October/Day-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.

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. 

  

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:

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. 

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. 

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 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, S1 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.

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 whatsoever,’ since 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 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 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.

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.

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 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.	

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 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

	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 et al., 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 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 

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.  

APPENDIX B:  MIAME-Based Data Submission Tables

Table B.1  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 type(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

Experimental factor(s)	Parameter(s) or condition(s) tested in the
experiment.	Always	Experimental factor(s) consistent with Experiment
Type(s)	Time,

dose,

 compound,

temperature,

 extraction, hybridization, labelling, scanning

Number of hybridization replicates	Number of hybridization replicates 
Always	Consistent with Experiment Type(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 (may use more than once)	Any further
information about the experiment .	Additional useful information 

Qualifier= name 

Value= value 

Source= database entry or ontology entry

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.

	Array design 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 ver1.1)

Platform type	Technology to place biological sequence on array.	Array is
novel and cannot refer to manufacturer

in situ synthesized, spotted cDNA,

etc.

Surface and coating specification	Surface coating type and name	Array is
novel and cannot refer to manufacturer	Consistent with Platform Type
SurfaceType:

glass, membrane, coating type 

Array dimensions	Dimensions of the array support slide.	Array is novel
and cannot refer to manufacturer

width, length

Number of features on the array	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

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 type	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.

Single or double stranded	Reporter sequences are single or double
stranded.	Array is novel and cannot refer to manufacturer	Consistent
with Platform Type	Single, double

B.3.2.2 For each reporter

Reporter sequence 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 Type and clone
Sequence annotation,

accession number,

PCR primer pair

Reporter approximate length	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 generation protocol	A description of how the reporters were
generated.	Array  is novel and cannot refer to manufacturer

Protocol

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 sequence 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

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 type	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 B.4  Biomaterials

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.

	Organism	The genus and species (and subspecies) of the organism from
which the biomaterial is derived.	Always

Genus, species,

subspecies from NCBI taxonomy

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 type	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 stage	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)

Organism 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)

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 genetic 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

Targeted cell type	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

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) under study .
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 type

	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

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 type	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
extract(s).

	Spiking 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

Spike type and qualifier	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

Qualifier, value, source (may 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 image file	The image file including header	Always

TIFF,

JPEG

Scanning 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.

Image analysis output	The complete image analysis output for each image.
Always.

Spreadsheet or tab-delimited file

Image 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 processing 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 gene 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 (may 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



Appendix C:  Genomics Data Evaluation Record (gDER) Template

Genomics

DATA EVALUATION RECORD

STUDY TYPE: 

PC CODE:	DP BARCODE:            

	SUBMISSION NO.:            

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: 

	[Structure]

2. Control Materials:			

	Negative control (if not vehicle) :	

Final Volume:  	

Route: 

	Vehicle:		

				

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:

Histology:   

Immunochemistry:

Western blot analysis:  

Array analysis: 

Characteristics of the arrays: 

	

Methods:   

Data Analysis:

Evaluation Criteria/Statistical Analysis

II. REPORTED RESULTS 

A. IMMUNOCHEMISTRY: 

B. WESTERN BLOT ANALYSIS: 

C. MICROARRAY ANALYSIS: 

III. DISCUSSION and CONCLUSIONS

A. INVESTIGATORS’ CONCLUSIONS:  

B. REVIEWER COMMENTS:  	.

C. STUDY DEFICIENCIES: 

REFERENCES 

ATTACHMENTS and TABLES

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:

-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. 

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 (2-fold increase over untreated
control) of genes correlated with the following steps in the
carcinogenic process: oxidative stress and damage to DNA  (heme
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 -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:	 Alachlor

	Description:	Not provided

	Lot/Batch #:	Not reported

	Purity:	        % a.i. Not reported

	CAS # of TGAI: 

	[Structure]

2. Control Materials:			

	Negative control

 	(if not vehicle) :	Final Volume: NA	Route: NA

	Vehicle:		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

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. 

-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 g 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 	(  HYPERLINK
"(http://www.ncbi.nlm.gov/BLAST)"  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 (  HYPERLINK
"(http://www.geneontology.org)"  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 U343A 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.  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.”  

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 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 -catenin was also
confirmed using immunochemical staining.  It was stated that
alachlor-induced polyps and early adenomas did not exhibit nuclear
localization of -catenin but more advanced adenocarcinomas displayed
abundant cytoplasmic and nuclear -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. 

3-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 2-fold following
acute exposure included multiple genes which encode proteins associated
with oxidative stress; these included heme oxygenase, glutathione
synthase and metallothionein (MT)-1 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 2-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
-catenin late in the carcinogenesis process.  Primary normalized
data, gene lists and K-means groups can be obtained from   HYPERLINK
"http://genet.chmcc.org"  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.” 

2-fold increase) of genes consistent with a mutagenic response
possibly as a result of oxidative damage to DNA (GADD 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.

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
ß-catenin genes, which is critical for tumor formation in other organs
and is associated with mutations in the wnt pathway.

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.” 

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”. 

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.

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.

Appendix E: MIAME Glossary

For the most recent version of the MIAME glossary, please see:  
HYPERLINK "http://www.mged.org/Workgroups/MIAME/miame_glossary.html" 
http://www.mged.org/Workgroups/MIAME/miame_glossary.html 

  

Age:  The time period elapsed since an identifiable point in the life
cycle of an organism. (If a developmental stage is specified, the
identifiable point would be the beginning of that stage. Otherwise the
identifiable point must be specified such as planting) [MGED Ontology
Definition]

Amount of nucleic acid labeled:  The amount of nucleic acid labeled

Amplification method:  The method used to amplify the nucleic acid
extracted

Array design:  The layout or conceptual description of array that can be
implemented as one or more physical arrays. The array design
specification consists of the description of the common features of the
array as the whole, and the description of each array design elements
(e.g., each spot). MIAME distinguishes between three levels of array
design elements: feature (the location on the array), reporter (the
nucleotide sequence present in a particular location on the array), and
composite sequence (a set of reporters used collectively to measure an
expression of a particular gene)

Array design name:  Given name for the array design, that helps to
identify a design between others (e.g., EMBL yeast 12K ver1.1)

Array dimensions:  The physical dimension of the array support (e.g. of
slide)

Array related information:  Description of the array as the whole

Attachment:  How the element (reporter) sequences are physically
attached to the array (e.g. covalent, ionic)

Author, laboratory, and contact:  Person(s) and organization (s) names
and details (address, phone, FAX, email, URL)

Biomaterial manipulation:  Information on the treatment applied to the
biomaterial

Bio-source properties:  Information on the source of the sample

Cell line:  The identifier for the immortalized cell line if one was
used to derive the BioMaterial [MGED Ontology Definition]

Cell type:  Cell type used in the experiment if non mixed. If mixed the
targeted cell type should be used [MGED Ontology Definition]

Clone information:  For each reporter, the identity of the clone along
with information on the clone provider, the date obtained, and
availability

Common reference:  A hybridization to which all the other hybridizations
have been compared

Composite sequence information:  The set of reporters contained in the
composite sequence. The nucleotide sequence information for each
composite element: number of oligonucleotides, oligonucleotide sequences
(if given), and the reference sequence accession number (from relevant
databases)

Composite sequence related information:  Information on the set of
reporters used collectively to measure an expression of a particular
gene

Compound:  A drug, solvent, chemical, etc., that can be measured [MGED
Ontology Definition]

Contact details for sample:  The resource (e.g., company, hospital,
geographical location) used to obtain or purchase the BioMaterial and
the type of specimen [MGED Ontology Definition]

Control elements position:  The position of the control features on the
array

Control elements related information:  Array elements that have an
expected value and/or are used for normalization

Control type:  The type of control used for the normalization and their
qualifier

Data processing protocol:  Documentation of the set of steps taken to
process the data, including: the normalization strategy and the
algorithm used to allow comparison of all data

Developmental stage:  The developmental stage of the organism's life
cycle during which the BioMaterial was extracted [MGED Ontology
Definition]

Disease state:  The name of the pathology diagnosed in the organism from
which the BioMaterial was derived. The disease state is normal if no
disease has been diagnosed [MGED Ontology Definition]

Element dimensions:  The physical dimensions of each features

Experiment description:  Free text description of the experiment and
link to an electronic publication in a peer-reviewed journal

Experiment design:  Experiment is a set of one or more hybridizations
that are in some way related (e.g., related to the same publication
MIAME distinguishes between: the experiment design (the design, purpose
common to all hybridizations performed in the experiment), the sample
used (sample characteristics, the extract preparation and the labeling),
the hybridization (procedures and parameters) and the data (measurements
and specifications)

Experiment type (s):  A controlled vocabulary that classify an
experiment

Experimental design:  Design and purpose common to all hybridizations
performed in the experiment

Experimental factor (s):  Parameter (s) or condition (s) tested in the
experiment

Extraction method:  The protocol used to extract nucleic acids from the
sample

Features related information:  Information on the location of the
reporters on the array

Final gene expression table (s):  Derived measurement value summarizing
related elements and replicates, providing the type of reliability
indicator used

Gene name:  The gene represented at each composite sequence: name and
links to appropriate databases (e.g. SWISS-PTOR or organism specific
database)

Genetic variation:  The genetic modification introduced into the
organism from which the BioMaterial was derived. Examples of genetic
variation include specification of a transgene or the gene knocked-out
[MGED Ontology Definition]

Growth conditions:  A description of the isolated environment used to
grow organisms or parts of the organism [MGED Ontology Definition]

Hybridization protocol:  Documentation of the set of steps taken in the
hybridization, including: solution (e.g. concentration of solutes);
blocking agent and concentration used; wash procedure; quantity of
labelled target used; time; concentration; volume, temperature, and
description of the hybridization instruments

Hybridization extract preparation:  Information on the extract
preparation for each extract prepared from the sample

Hybridizations:  Procedures and parameters for each hybridization

Image analysis and quantitation:  Each image has a corresponding image
quantitation table, where a row represents an array design element and a
column to a different quantitation types (e.g. mean or median pixel
intensity)

Image analysis output:  The complete image analysis output for each
image

Image analysis protocol:  Documentation of the set of steps taken to
quantify the image including: the image analysis software, the algorithm
and all the parameters used

In vitro treatment:  The manipulation of the cell culture condition for
the purposes of generating one of the variables under study and the
documentation of the set of steps taken in the treatment

In vivo treatment:  The manipulation of the organism for the purposes of
generating one of the variables under study and the documentation of the
set of steps taken in the treatment

Individual genetic characteristics:  The genotype of the individual
organism from which the BioMaterial was derived [MGED Ontology
Definition]

Individual number:  Identifier or number of the individual organism from
which the BioMaterial was derived. For patients, the identifier must be
approved by Institutional Review Boards (IRB, review and monitor
biomedical research involving human subjects) or appropriate body [MGED
Ontology Definition]

Label incorporation method:  The label incorporation method used

Label used:  The name of the label used

Measurements:  MIAME distinguishes between three levels of data
processing: image (raw data), image analysis and quantitation, gene
expression data matrix (normalized and summarized data)

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

Nucleic acid type:  The type of nucleic acid extracted (e.g. total RNA,
mRNA)

Number of elements on the array:  The number of features on the array

Number of hybridizations:  Number of hybridizations performed in the
experiment

Organism:  The genus and species (and subspecies) of the organism from
which the BioMaterial is derived [MGED Ontology Definition]

Organism part:  The part or tissue of the organism's anatomy from which
the BioMaterial was derived MGED Ontology Definition]

Platform type:  The technology type used to place the biological
sequence on the array

Production protocol:  A description of how the array was manufactured

Provider:  The primary contact (manufacturer) for the information on the
array design

Qualifier, value, source (may use more than once):  Describe any further
information about the array in a structured manner

Quality control steps:  Measures taken to ensure or measure quality:
replicates (number and description), dye swap (for two channel
platforms) or others (unspecific binding, low complexity regions, polyA
tails)

Raw data:  Each hybridization has at least one image

Relationship between samples and arrays:  Relationship between the
labelled extract (related to which sample which extract) and arrays
(design, batch and serial number) in the experiment

Reporter and location:  The arrangement and the system used to specify
the location of each features on the array (e.g. grid, row, column,
zone)

Reporter approximate length:  The approximate length of the reporter’s
sequence

Reporter generation protocol:  A description of how the reporters were
generated

Reporter related information:  Information on the nucleotide sequence
present in a particular location on the array

Reporter sequence information:  The nucleotide sequence information for
reporter: sequence accession number (from DDBJ/EMBL/GenBank), the
sequence itself (if known) or a reference sequences (e.g. for
oligonucleotides) and PCR primers pair information (if relevant)

Reporter type:  Physical nature of the reporter (e.g. PCR product,
synthesized oligonucleotide)

Sample:  The biological material from which the nucleic acids have been
extracted for subsequent labelling and hybridization. MIAME
distinguishes between: source of the sample (bio-source), its treatment,
the extract preparation, and its labeling

Sample labeling:  Information on the labeling preparation for each
labelled extract

Scanner image file:  The TIFF file including header

Scanning protocol:  Documentation of the set of steps taken for scanning
the array and generating an image including: description of the scanning
instruments and the parameter settings

Separation technique:  Technique to separate tissues or cells from a
heterogenous sample (e.g. trimming, microdissection, FACS)

Sex:  Term applied to any organism able to undergo sexual reproduction
in order to differentiate the individuals or type involved. Sexual
reproduction is defined as the ability to exchange genetic material with
the potential of recombinant progeny [MGED Ontology Definition]

Single or double stranded:  Whether the reporter sequences are single or
double stranded

Spike type and qualifier:  The type of spike used (e.g. oligonucleotide,
plasmid DNA, transcript) and its qualifier (e.g. concentration, expected
ratio, labeling methods)

Spiking control:  External controls added to the hybridization extract
(s)

Spiking control feature:  Position of the feature (s) on the array
expected to hybridize to the spiking control

Strain or line:  Animals or plants that have a single ancestral breeding
pair or parent as a result of brother x sister or parent x offspring
matings [MGED Ontology Definition]

Surface and coating specification: Type of surface and name for the type
of coating used

Targeted cell type:  The targeted cell type is the cell of primary
interest. The BioMaterial may be derived from a mixed population of
cells although only one cell type is of interest [MGED Ontology
Definition]

Treatment type:  The type of manipulation applied to the BioMaterial for
the purposes of generating one of the variables under study [MGED
Ontology Definition]

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).

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.

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

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).

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.

Web-based Glossary Sources

1- National Center for Toxicogenomics (NCT, NIEHS) Glossary
<http://www.niehs.nih.gov/nct/glossary.htm>

2- Human Genome Project Information Web Glossary
<http://www.ornl.gov/sci/techresources/Human_Genome/glossary/>

3- Cambridge Healthtech Institute
<http://www.genomicglossaries.com/CONTENT/omes.asp>

4- The Physical and Theoretical Chemistry Laboratory, Oxford University
Chemical and Other Safety Information <http://ptcl.chem.ox.ac.uk/MSDS/>

5- NIH Glossary
<http://www.accessexcellence.org/AE/AEPC/NIH/gene27.html>

6- Integrated Risk Information Systems (IRIS, EPA) Glossary
<http://www.epa.gov/iris/gloss8.htm>

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-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 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
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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.   

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 MOA 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:

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

Genter, 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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External Review Draft of the Interim Guidance for Microarray-Based
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Performance Approach to 

Quality Assessment

David Lattier, ORD

Susan Lundquist, OEI

Data Submission

Greg Miller, OPEI

Doug Wolf, ORD

Data Analysis

David Dix, ORD

Brenda Groskinsky, Region 7 RSL

 

Data Management

Susan Hester, ORD

Joseph Retzer, OEI

Training

Bobbye Smith, Region 9 RSL

Julian Preston, ORD

Microbial Source Tracking

Jorge Santo Domingo, ORD

Ron Landy Region 3 RSL

William H. Benson

Office of Research and Development

Kathryn Gallagher

Office of the 

Science Advisor

J. Thomas McClintock

Office of Pollution Prevention and Toxics

Kerry Dearfield

Office of the Science Advisor

(2004 - June 2005)