Systems Biology in Toxicology and Environmental Health

273 Pages·2015·15.747 MB·English

by  Rebecca Fry MS PhD

Systems Biology in
TOXICOLOGY AND
ENVIRONMENTAL HEALTH
Edited by
REBECCA C. FRY
University of North Carolina at Chapel Hill, Department of Environmental
Sciences and Engineering, Gillings School of Global Public Health,
Chapel Hill, NC, USA
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CONTRIBUTORS
Kathryn A. Bailey
University of North Carolina at Chapel Hill, Department of Environmental Sciences and
Engineering, Gillings School of Global Public Health, Chapel Hill, NC, USA
Robert Clark
Discovery Sciences, RTI International, Research Triangle Park, NC, USA
Suraj Dhungana
Discovery Sciences, RTI International, Research Triangle Park, NC, USA
Rebecca C. Fry
University of North Carolina at Chapel Hill, Department of Environmental Sciences and
Engineering, Gillings School of Global Public Health, Chapel Hill, NC, USA
William K. Kaufmann
University of North Carolina at Chapel Hill, Department of Pathology and Laboratory
Medicine, Chapel Hill, NC, USA
Susan McRitchie
Discovery Sciences, RTI International, Research Triangle Park, NC, USA
Michele Meisner
North Carolina State University, Department of Biological Sciences, Raleigh, NC, USA
Susan K. Murphy
Duke University Medical Center, Department of Obstetrics and Gynecology, Durham, NC,
USA
Wimal Pathmasiri
Discovery Sciences, RTI International, Research Triangle Park, NC, USA
Julia E. Rager
University of North Carolina at Chapel Hill, Department of Environmental Sciences and
Engineering, Gillings School of Global Public Health, Chapel hill, NC, USA
Paul D. Ray
University of North Carolina at Chapel Hill, Department of Environmental Sciences and
Engineering, Gillings School of Global Public Health, Chapel Hill, NC, USA
David M. Reif
North Carolina State University, Department of Biological Sciences, Raleigh, NC, USA
James Sollome
University of North Carolina at Chapel Hill, Department of Environmental Sciences and
Engineering, Gillings School of Global Public Health, Chapel Hill, NC, USA
Delisha Stewart
Discovery Sciences, RTI International, Research Triangle Park, NC, USA
ix
x Contributors
Susan Sumner
Discovery Sciences, RTI International, Research Triangle Park, NC, USA
Michele M. Taylor
Duke University Medical Center, Department of Obstetrics and Gynecology, Durham, NC,
USA
Sloane K. Tilley
University of North Carolina at Chapel Hill, Department of Environmental Sciences and
Engineering, Gillings School of Global Public Health, Chapel Hill, NC, USA
Andrew E. Yosim
University of North Carolina at Chapel Hill, Department of Environmental Sciences and
Engineering, Gillings School of Global Public Health, Chapel Hill, NC, USA
PREFACE
This textbook, ‘Systems Biology in Toxicology and Environmental Health’ is the first of
its kind. The human genome was sequenced in 2003. With the genome sequenced there
was the possibility for the development of the sophisticated suite of systems-level tools
and technologies that are currently available. As an investigator who worked with these
tools early in their development, it is notable how far we have come from the very early
days aimed at standardizing methodologies and ensuring data quality. Because of the
significant advances in their reproducibility and applicability, systems-level tools and the
accompanying sophisticated computational analytics are now used actively across vari-
ous scientific disciplines. The use of systems-level tools across scientific arenas is facilitat-
ing a greater understanding of the biological basis of many diseases. There is no doubt
that systems biology has truly revolutionized science, increasing the understanding of
the role of genome, transcriptome, proteome, metabolome, and epigenome as etiologic
factors for disease. Moving forward, it is clear that systems biology will continue to revo-
lutionize the sciences, opening windows into the complex cellular responses to toxic
agents.
This textbook places an emphasis on systems biology as it informs environmental
health sciences and toxicology highlighting toxic substances in the environment and
their known associations with disease. It details systems biology-based approaches and
technologies that have been employed for their study. Chapter 1 serves as an introduc-
tion to the field of systems biology and details potential applications for understanding
responses to toxic substances in the environment and association with disease. Chapter 2
introduces the biology of the cell as the details of eukaryotic cell structure and mecha-
nisms for cellular signaling are critical to understanding biological system perturbations
discussed later in the textbook. Chapter 3 highlights key components of the epigenome
and their roles in mediating gene function. These components are proving to be targets
of environmental toxicants with the potential for enormous impact on cellular function.
Chapter 4 details current state-of-the art tools and technologies used in systems biology-
based research. Chapter 5 explores computational tools used for analysis of the complex
data derived from the systems-level analyses discussed in the earlier chapters. Chapter 6
is an introduction to environmental contaminants highlighted in the textbook. Most of
the substances that were selected for inclusion in the textbook belong to the class of the
highest ranking contaminants according to the Agency of Toxic Substances and Disease
Registry which ranks harmful substances based on their toxicity, potential for exposure,
and presence at national priorities list sites. Chapters 7–10 focus on a few key biological
pathways that respond to environmental contaminants known to be involved in disease
xi
xii Preface
states. These include inflammation/immune pathways, apoptosis-associated pathways,
DNA damage response pathways, and hormone response pathways. Finally, Chapter 11
discusses the importance of timing of exposure to environmental perturbants and the
developmental origins of disease hypothesis. A current research area of great interest is
pinpointing critical developmental windows of susceptibility to environmental contami-
nants. My intention is that this textbook will be of interest to investigators from a range
of fields including environmental health sciences, toxicology, genetics, epidemiology,
chemistry, medicine, and molecular biology. It can be used by teachers for both under-
graduate and graduate level courses.
There are many individuals who made this textbook a reality. The completion of
this textbook would not have been possible without the efforts of numerous contribu-
tors. First, I would like to thank Rhys Griffiths and Molly McLaughlin at Elsevier for
their strong support from the inception to completion of this book. I would also like to
acknowledge my coauthors for their efforts. They have my sincere gratitude for their
time in developing their contributions that together result in a high-quality and useful
resource for the research community. My passion for research focused on children’s envi-
ronmental health was fueled during my post-doctoral research. Thus, to my mentor,
Dr. Leona Samson, I will always be grateful. Finally, to Dr. William Kaufmann who sup-
ported and encouraged me during the writing of this textbook-you have my gratitude.
This book is dedicated to my children, Luke, Jacob and Catherine who bring joy and
light into my life every day.
Rebecca C. Fry
CHAPTER 1
Systems Biology in Toxicology and
Environmental Health
Andrew E. Yosim, Rebecca C. Fry
Contents
Systems Biology Defined 1
Technologies Utilized in Systems Biology 3
Applications of Systems Biology in Environmental Health 5
Applications of Systems Biology in Toxicology 6
Applications of Systems Biology in the Clinic 7
Summary 9
References 9
SYSTEMS BIOLOGY DEFINED
Systems biology is the integrated study of the properties and interactions of the com-
ponents of the cell. It represents a holistic approach to studying complex biological
phenomena [1]. Such a research framework has been enabled by technological advances
and the development of high-throughput tools and technologies and their accompany-
ing bioinformatics approaches. Together, systems biology provides a novel framework
and methodology for collecting and analyzing data in order to understand complex
biological structures and their interactions.
Historically, the biological sciences have been largely driven by a reductionist
approach focusing on individual genes, molecules (proteins, lipids, etc.), and pathways
to determine their function, role, and activity [2]. Differing from this reductionist
approach, a systems biology view seeks to understand how the constituent parts of the
system interact. Fundamental to the power of the systems biology approach is the inte-
gration of two separate modes of conducting science: discovery science and hypothe-
sis-driven science [1]. Discovery science seeks to assess and enumerate all the
components and interactions within a biological system. Hypothesis-driven science is
the approach by which scientists make predictions about how a biological system works
or might respond to change and then test their hypothesis via experimentation. By
integrating these two research frameworks, a systems biology-based approach is an
iterative process in which system-level data are collected to produce an accurate model
Systems Biology in Toxicology and Environmental Health Copyright © 2015 Elsevier Inc.
http://dx.doi.org/10.1016/B978-0-12-801564-3.00001-8 All rights reserved. 1
2 Systems Biology in Toxicology and Environmental Health
that can be used to design experiments to test new hypotheses or to fill in gaps in the
model (Figure 1). There are four main steps involved in conducting research using a
systems biology approach: (1) formation of a model of the system and all constituent
parts; (2) systematic perturbation of the system and discovery; (3) assessment of pertur-
bation data within the framework of the original model; and (4) formulation of new
experiments based on hypotheses derived from step 3 [3]. The formation of a model
requires that the researcher designates the components, structures, and molecules to be
studied within the target system. These data can then be used to develop a mathemati-
cal or computational model detailing how the components interact with each other
and quantifying how the expression or activity of one component is influenced by or
related to changes in the other components. Once this initial model is formulated,
systematic perturbations can begin. Using exogenous agents or experimental condi-
tions, the homeostasis of the system can be perturbed, allowing for the collection of
data detailing how each of the components responds to such perturbations. Following
this, large-scale data sets can be assessed to determine whether the perturbations are in
line with predictions made by the model. The last step, and arguably the most critical,
is to use the observations from the assessment of the perturbation studies to design
further studies testing the newly revised model created by reconciling the data from the
previous perturbation studies.
Create a working
model of the
system and all its
cons(cid:24)tuent parts
Model Refinement Experimenta(cid:24)on
Formulate new
Systema(cid:24)cally
experiments to
perturb the
test new
system
hypotheses
Hypothesis Formula(cid:24)on Data Collec(cid:24)on
Assess the
results of
systema(cid:24)c
perturba(cid:24)on
Figure 1 Steps involved in conducting research using a systems biology-based approach.
Introduction to Systems Biology 3
TECHNOLOGIES UTILIZED IN SYSTEMS BIOLOGY
The human genome was first sequenced in 2003. The project lasted more than 13 years
and required more than 3 billion US dollars to complete [4]. Today, genome-wide
deoxyribonucleic acid (DNA) sequencing can be performed in less than a day for less
than 1000 US dollars. This unbelievable pace of technological improvement has far out-
paced Moore’s law [5] and has largely driven many of the research advancements in
systems biology. As a result of these technological advancements, researchers are now
able to query ever increasingly large collections of genomic, transcriptomic, proteomic,
metabolomic, and epigenomic data sets.
The study of these “-omics” data sets can be organized based on the central dogma
of biology, which states that molecular information flows in a hierarchical manner:
DNA → RNA → protein (Figure 2). An organism’s genome, or genetic material, consists
of all the genes as well as noncoding sequences of DNA. The transcriptome includes
the messenger ribonucleic acids (mRNAs) which are transcribed from DNA by RNA
polymerases which encode proteins as well as other RNAs such as noncoding RNAs.
The proteome refers to the entire set of proteins within cells that are produced by the
translation of mRNAs into amino acids. These amino acids are combined to form a
diverse number of proteins. The metabolome represents all the small molecule chemi-
cals (such as metabolites). Metabolites are highly dynamic, and measurements of their
levels at a particular point in time can be used to produce a metabolic “signature,”
providing a snapshot of cellular function or insight into an individual’s metabolic
response to exogenous/endogenous substances or cellular processes.
The ability to generate multicomponent data sets informs our understanding of bio-
logical systems and network interactions. However, the sheer magnitude of data can pres-
ent a practical problem that complicates the research. For example, the accumulation of
systems-level data resulted in data sets composed of millions or billions of discrete data
Figure 2 The Central Dogma. Flow of molecular information from DNA to RNA to protein.
4 Systems Biology in Toxicology and Environmental Health
points (the human genome, for example, contains approximately 3.2 billion base pairs).
Very few researchers could afford the storage capabilities and computing power necessary
to analyze the gigabytes or terabytes of data. Over time the pace of technological advance-
ment simultaneously reduced the expense associated with such large datasets and intro-
duced novel technologies and applications to collect, store, and analyze such datasets.
In addition to advances in processing power and data storage, other commonly utilized
technologies for assessing biological systems have improved, most notably relating to high-
throughput technologies. Building on the work of the Human Genome Project, genome-
and epigenome-wide association studies (GWAS and EWAS, respectively) enable researchers
to investigate the entire genome for single nucleotide polymorphisms (SNPs) or epigenetic
marks that may be associated with disease [6,7]. Unlike study designs that investigate a par-
ticular health condition or phenotype tied to a single polymorphism or epigenetic mark,
GWAS and EWAS can be utilized to assess the entire genome to pinpoint which SNPs or
epigenetic alterations may be tied to a particular disease or may affect susceptibility to that
disease. The high-throughput nature of such technologies has uncovered thousands of poly-
morphisms and alterations linked to discrete health outcomes [8,9], and is a cost-effective
means of assessing specific health conditions for possible therapeutic interventions.
Another high-throughput technology driving systems biology, the microarray, allows
researchers to assess thousands of genes, proteins, or other analytes through a variety of means
including direct hybridization [10]. For example, DNA microarrays enable the assessment of
genome-wide gene expression [11], while various DNA methylation arrays currently allow
the assessment of hundreds of thousands of methylation probes located throughout the
genome [12]. It is predicted that as microarray technology continues to develop, the number
of other epigenetic modifications, proteins, and small molecules that can be queried simulta-
neously will continue to increase. In addition to microarray technology, recent technological
advancements have dramatically reduced the cost of next-generation sequencing (NGS).
NGS, which allows the sequencing of nucleic acids in millions of parallel reactions, is rela-
tively inexpensive, scalable, and can quickly sequence billions or trillions of bases. The rise of
NGS has increased the high-throughput capabilities of a number of analyses, such as DNA/
RNA–protein interactions (chromatin immunoprecipitation sequencing) and gene expres-
sion (RNA sequencing). Additionally, there have been advancements in other technologies
including nuclear magnetic resonance spectroscopy, gas chromatography–mass spectrometry,
and liquid chromatography–mass spectrometry, enabling researchers to quickly collect
system-level data in order to understand complex biological and cellular processes and
interactions.
In addition to the technologies described above, systems biology relies on a variety
of in silico tools in order to assess and ultimately model biological networks or pathways
[13]. One of the central tenets of systems biology is that observations or data enable the
creation of system-level networks which can then be utilized to inform the next set of
hypothesis-driven experiments. As this approach has gained in popularity, so too have
the numbers and diversity of computational tools which are used to interpret such