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Appendix C
The Rapidly Expanding Field of
"Omics" Technologies
Technologic advances in "-omics" technologies--especially in the genom-
ics, proteomics, metabolomics, bioinformatics, and related fields of the molecu-
lar sciences (referred to here collectively as panomics)--have transformed the
understanding of biologic processes at the molecular level and should eventually
allow detailed characterization of molecular pathways that underlie the biologic
responses of humans and other organisms to environmental perturbations. The
following sections discuss recent advances in omics technologies and ap-
proaches. They also discuss some of the implications of omics technologies for
the US Environmental Protection Agency (EPA), areas in which EPA is at the
leading edge of applying the technologies to address environmental problems,
and the areas in which EPA could benefit from more extensive engagement.
GENOMICS
Beginning in the late 1990s, the Human Genome Project (DOE 2011) ush-
ered in an unprecedented leap in technologies that allow scientists to discern the
fundamental sequences of genes of entire genomes--not only the human ge-
nome but a plethora of model organisms, such as plants, microorganisms, inver-
tebrates, vertebrates, and even the long-extinct woolly mammoth (Miller et al.
2008; NHGRI 2012). The ability to derive, quickly and relatively inexpensively,
the entire sequence of an organism's genome provides unprecedented opportuni-
ties in biologic and ecologic sciences, including the opportunity to understand
how environmental factors influence biology at the molecular level.
The Human Genome Project fueled the development of faster and less ex-
pensive DNA sequencing. So called first-generation sequencing technologies,
originally described by Sanger and Coulson (1975), have served as the primary
technology for DNA sequencing for the last several decades, with estimated
costs of $3 billion to sequence the human genome (NHGRI 2010; Woollard et
al. 2011). Large-scale sequencing projects based on several next-generation se-
215
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216 Science For Environmental Protection: The Road Ahead
quencing technologies can now be conducted faster and less expensively than
was possible with previous generations of technologies. Next-generation se-
quencing technologies are substantially different from those based on the origi-
nal Sanger method (Box C-1) and promise remarkable increases in sequencing
capabilities.
Next-generation sequencing instruments have made it possible to sequence
huge amounts of DNA quickly, thoroughly, and affordably and have opened
opportunities to study a wide array of biologic questions, from the metagenom-
ics of water, to characterization of the genetic basis of species differences in
response to environmental insults, to human variability in susceptibility to envi-
ronmentally related diseases. Third-generation sequencing promises to provide
full genome sequencing of individuals (humans or other organisms) for less than
$1,000 per genome by the end of 2013 (Valigra 2012), and at least one company
already offers such services at about $5,000 per genome (Knome 2012).
TRANSCRIPTOMICS
The sequencing of the human genome, and of the genomes of hundreds of
other model organisms of great importance for human and environmental health
constitutes an enormous step forward in understanding genetic origins of dis-
ease, genetic variability, evolutionary biology, and many other subjects of scien-
tific relevance to EPA. However, from a biologic perspective, it is the expres-
sion of the genes in specific cells and tissues that ultimately defines an organism
and how it responds to its environment. Thus, measuring the extent of gene ex-
pression at a given time in a particular cell or tissue is potentially even more
informative of biologic mechanisms. The universe of small RNA molecules that
are transcribed from DNA and that are present in a cell or tissue at any given
time is referred to as the transcriptome. In the last 2 decades, new tools have
been developed that allow one to analyze the entire transcriptome in a cell or
tissue and to study changes in gene expression that might be created by changes
in the environment, such as exposure to a chemical. There are now microarray
methods that allow for the analysis of virtually all mRNA molecules that are
transcribed from active genes. Typically, these arrays contain hundreds of thou-
sands of unique features that quantitatively identify the amount of a particular
mRNA transcript in the sample. Having multiple features that can use the array
to look at different parts of a single gene, such as different exons or exonintron
boundaries (potential splice sites), provides a remarkable snapshot of what genes
are functioning in a cell at a particular time.
To study complex and common diseases that may be influenced by envi-
ronmental factors (such as cardiovascular disease and cancer), human studies
typically require high-quality DNA from thousands of patients, often from small
quantities of tissues or blood. Several common commercial microarrays for
RNA applications in studies of this sort have been available for more than a dec-
ade and measure the expression of individual genes. However, understanding the
human transcriptome is much more complex than simply measuring the com-
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Appendix C 217
plement of mRNAs from the genome because alternative splicing1 is common
and contributes largely to protein and functional diversity in humans and other
higher organisms (Xu et al. 2011). Technologies for measuring mRNA tran-
scripts in all their varieties, including alternatively spliced transcripts and copy-
number variants, have grown rapidly in the last few years. For example, a new
approach called the Glue Grant Human Transcriptome Array completes a com-
prehensive analysis of the human transcriptome using a 6.9 millionfeature oli-
gonucleotide array. The array assesses gene-level and exon-level expression by
using high-density tiling of probes that cover a large collection of transcriptome.
It can also detect alternative splicing and can analyze noncoding transcripts and
common variants (such as single nucleotide polymorphisms) of genes (so called
cSNPs) (Xu et al. 2011). This technology was recently used in a multicenter
clinical program that produced high-quality reproducible data (Xu et al. 2011). It
is an example of the rapid change in technologies in the -omics world and will
increasingly provide new approaches to understanding how environmental fac-
tors influence the development of common diseases. Such technologies will also
have many applications in the fields of microbial genomics, evolutionary biol-
ogy, and other areas of interest to EPA.
BOX C-1 Comparison of Sanger and Next-Generation Sequencing (NGS)
The initial preparation of the DNA sample is more labor intensive for
NGS than for Sanger, but the amount of sequence data obtained per sample
is substantially more.
The number of sequencing reads from a single instrument per run is of
the order of thousands with Sanger, but millions to billions with NGS; for ex-
ample, a bacterial genome can be sequenced in a single run in days using
NGS, versus months using Sanger sequencing.
Read lengths from Sanger sequencing are up to 900 [base pairs], but
in NGS vary from 30 to 500 [base pairs] depending on the platform.
DNA sequencing costs have been driven down by NGS and base pair
per dollar costs show a consistent 19-months doubling time reduction for
Sanger sequencing. For NGS, the equivalent figure is approximately 5-
months doubling time cost reduction.
NGS can detect somatic mutations at [less than or equal to] 1%,
whereas Sanger sequencing has significantly less sensitivity.
The greater versatility of NGS is illustrated in generating whole-
genome datasets, such as miRNA and ChIP-Seq; Sanger sequencing lacks
this capability.
Abbreviations: ChIP-Seq, chromatin immunoprecipitation sequencing; miRNA,
micro RNA; NGS, next-generation sequencing. Source: Woollard et al. 2011.
1
Alternative splicing "the process by which individual exons of pre-mRNAs are
spliced to produce different isoforms of mRNA transcripts from the same gene" (Xu et al.
2011).
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218 Science For Environmental Protection: The Road Ahead
PROTEOMICS
Proteomics is the study of the entire complement of proteins in a cell or
tissue--the proteome. The proteome is much more complicated than the genome
because the proteome differs from cell to cell and from time to time, whereas the
genome of an organism is largely unchanged between cells and over time. Fur-
thermore, most proteins in a cell undergo posttranslational modifications (for
example, phosphorylation, glycosylation, methylation, and ubiquination), which
can result in several functional forms of the same protein. The proteome is po-
tentially far more informative than the genome with respect to environmental
response. Measuring and understanding changes in the proteome after environ-
mental perturbations are therefore increasingly important in many fields of envi-
ronmental science and engineering. Proteomic technologies and approaches will
have an increasingly important role in environmental monitoring and health risk
assessment of relevance to EPA. For example, proteome-based biomarkers may
be useful in deciphering the associations between pesticide exposure and cancer
and will perhaps lead to potential predictive biomarkers of pesticide-induced
carcinogenesis (George and Shukla 2011).
Proteomics has been used to explore "a multitude of bacterial processes,
ranging from the analysis of environmental communities [and the] identification
of virulence factors to the proteome-guided optimization of production strains"
(Chao and Hansmeier 2012). Proteomics has become a valuable tool for the
global analysis of bacterial physiology and pathogenicity, although many chal-
lenges remain, especially in the accurate prediction of phenotypic consequences
based on a given proteome composition (Chao and Heinsmeyer 2012). Lemos et
al. (2010) have discussed the advantages of and challenges to using proteomics
in ecosystems research.
METABOLOMICS
Substantial improvements in instrumentation, especially nuclear magnetic
resonance spectroscopy (Serkova and Niemann 2006) and mass spectrometry
(Dettmer et al. 2007), provide increasingly sensitive approaches to measuring
hundreds or even thousands of small molecules in a cell in a matter of minutes.
The new technologies have given rise to a promising new -omics technology
referred to as metabolomics--the "systematic study of the unique chemical fin-
gerprints that specific cellular processes leave behind" (Bennett 2005) or, more
specifically, the study of their small-molecule metabolite profiles. "In analogy to
the genome, which is used as synonym for the entirety of all genetic informa-
tion, the metabolome represents the entirety of the metabolites within a biologi-
cal system" (Oldiges et al. 2007). The total number of metabolites in a single
cell, tissue, or organism is, of course, highly variable and depends on the bio-
logic system investigated. Hundreds of distinct metabolites have been identified
in microorganisms. For example, the Escherichia coli database EcoCYC con-
tains over 2,000 metabolite entries (Keseler et al. 2011), and the metabolome of
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Appendix C 219
the common baker's yeast, Saccharomyces cerevisiae, has about 600 metabo-
lites, the major ones having molecular weight below 300 g/mol (reviewed in
Oldiges et al. 2007). It has been projected that plants have more than 200,000
primary and secondary metabolites (Mungur et al. 2005).
Although far less mature than transcriptomics and proteomics, me-
tabolomics offers great promise for the development of early biomarkers of dis-
ease (Hollywood et al. 2006) and other uses of relevance to EPA. Because me-
tabolomics in many ways is the final integration of genomics, transcriptomics,
and proteomics, it is likely that future developments in this area will become
essential for understanding the functions of the genomes of organisms of interest
to EPA, ranging from pathogenic bacteria in drinking water to humans. Indeed,
EPA scientists are applying metabolomics approaches to aquatic toxicology
(Ekman et al. 2011), in vitro assessments for developmental toxicology (Klein-
streuer et al. 2011), and carcinogenic risk assessment (Wilson et al. 2012 in
press), to name a few.
EPIGENETICS
As noted by Rothstein et al. (2009), "epigenetics is one of the most scien-
tifically important, and legally and ethically significant, cutting-edge subjects of
scientific discovery." Epigenetic changes are the chemical alterations or chemi-
cal modifications of DNA that do not involve changes in the nucleotide se-
quence in the DNA. Those alterations play a critical role in how and when a
particular gene is expressed. It is clear that environmental factors, including diet,
can influence how epigenetic regulation of gene expression occurs. It is espe-
cially important during periods of cell and tissue growth, such as embryonic and
fetal development. Epigenetic changes can be triggered by environmental fac-
tors. For example, exposure to metals, persistent organic pollutants, and some
endocrine disruptors modulate epigenetic markers in mammalian cells and in
other environmentally relevant species and have the potential to cause disease
(Vandegehuchte and Janssen 2011; Guerrero-Bosagna and Skinner 2012). Some
studies have demonstrated that epigenetic changes can sometimes be transferred
to later generations, even in the absence of the external factors that induced the
epigenetic changes (Skinner 2011).
EPA scientists in the National Health and Environmental Effects Research
Laboratory (NHEERL) are aware of the growing importance of epigenetics in
environmental health assessment. A seminal review of the application of epige-
netic mechanisms to carcinogenic risk assessment was published by NHEERL's
scientist Julian Preston (2007). Since then, relatively few publications from
NHEERL or other EPA laboratories have addressed epigenetics. A PubMed
search identified five publications by EPA scientists in the last 5 years. A recent
review by Jardim (2011) discussed the implications of microRNAs (a form of
epigenetic regulation of gene expression) for air-pollution research, and Lau et
al. (2011) reviewed fetal programming of adult disease (also thought to be an
epigenetic phenomenon) and its implications for prenatal care. Hsu et al. (2007)
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220 Science For Environmental Protection: The Road Ahead
addressed the implications of epigenetics in the carcinogenic mode of action of
nitrobenzene, but only two original research publications that provided experi-
mental data from EPA have directly assessed epigenetic mechanisms. One study
(Grace et al. 2011) evaluated the role of maternal influences on epigenetic pro-
gramming in the in utero development of endocrine signaling in the brain. The
second (DeAngelo et al. 2008) provided dose-response data on the development
of hepatocellular neoplasia in male mice exposed over a lifetime to trichloroace-
tic acid, a putative carcinogenic product of trichloroethylene solvent breakdown
and a chlorination disinfection byproduct. Although they did not assess epige-
netic changes experimentally, they suggested that epigenetic mechanisms might
explain the observed tumors inasmuch as the compound was not genotoxic. EPA
has not published many original papers on epigenetics, but the EPA grants data-
base lists 36 extramural research grants to universities across the country that are
exploring the role of epigenetics in environmental response (EPA 2012). Given
the relevance of this emerging field, it is important that EPA scientists and regu-
lators become more active in the accumulation of epigenetic knowledge and its
application to human and environmental health risk assessment. Although much
remains to be learned about epigenetic phenomena, it is likely to be a critical
contributor to many diseases that have both a genetic and environmental com-
ponent, and will be especially important in understanding how exposures early
in life might contribute to disease onset later in life.
BIOINFORMATICS
Rapid advances in biotechnology have resulted in an explosion in -omics
data and in information on biochemical and physiologic processes in complex
biologic systems. The advent of the internet, new technologies, and high-
throughput sequencing has spurred further growth of -omics data and has made
it possible to disseminate data globally (Attwood et al. 2011). Since the 1990s,
the field of bioinformatics has seen growth in response to the need for the gen-
eration, storage, retrieval, processing, analysis, and interpretation of -omics data.
It draws on the principles, theories, and methods of the biologic sciences, com-
puter science and engineering, mathematics, and statistics, and it has always
been at the core of understanding of biologic processes and disease pathways
(Attwood et al. 2011). As the -omics revolution continues, bioinformatics will
continue to evolve, and EPA will continue to require inhouse expertise and
state-of-the-science capacity in the field.
Analysis of biologic data has evolved from comparisons of various kinds
of sequence data (Needleman and Wunsch 1970; Smith and Waterman 1981;
Lipman and Pearson 1985) to algorithms that can search various sequence data-
bases. Methods and tools have also been developed for the analysis of sequence,
annotation, and expression data in support of a wide variety of applications, such
as pattern recognition, protein and RNA structure prediction, micro data analysis
(Attwood et al. 2011), and biomarker discovery (Baumgartner et al. 2011; Roy
et al. 2011). There is an increasing emphasis on understanding biologic systems
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Appendix C 221
through modeling of biologic, physiologic, and biochemical processes (Deville
et al. 2003; Ng et al. 2006; Viswanathan et al. 2008;), including genegene and
proteinprotein interactions (Tong et al. 2004; Rual et al. 2005); pathway analy-
sis (Schilling et al. 2000; Wishart 2007; Viswanathan et al. 2008); and network
mapping (Lee and Tzou 2009.).
An integrative approach is needed to use different types of databases to
identify distinct system components (organized in modules and subnetworks)
and to understand their relationships and thereby reduce the complexity of a
biologic system as a whole (Lee and Tzou 2009). There are outstanding chal-
lenges to the integrative modeling of biologic systems, some of which are sum-
marized in a recent report from the SYSGENET Bioinformatics Working Group
(Durrant et al. 2011). Because integrative systems modeling requires synthesiz-
ing and harmonizing the analyses of transcriptome, proteome, interactome, me-
tabolome, and phenome data, which are likely to be held in numerous heteroge-
neous databases, it is critical to improve the interoperability, compatibility, and
exchange of software modules that are the foundation of data-processing plat-
forms (such as TIQS and xQTL), database platforms (such as GeneNetwork and
XGAP), and data-analysis toolboxes (such as HAPPY and R/QTL). A standard
computer language for software development and cloud sourcing would facili-
tate efficient software dissemination to the bioinformatics community. In addi-
tion, further development of public repositories for data models and software
source code would promote the use of common data structures and file formats.
To stay at the cutting edge of bioinformatics and take full advantage of its
rapid advance, EPA will need a highly skilled bioinformatics workforce that can
closely follow the development of trends in bioinformatics tools and software
closely. As discussed in Chapter 3, EPA already has a leadership role in bioin-
formatics as applied to toxicity assessment and is well positioned to contribute
to standardization and harmonization processes in the field.
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