Abstract
In this paper, we discuss approaches for integrating biological
information reflecting diverse physiologic levels. In particular, we
explore statistical and model-based methods for integrating
transcriptomic, proteomic and metabolomics data. Our case studies
reflect responses to a systemic inflammatory stimulus and in response
to an anti-inflammatory treatment. Our paper serves partly as a review
of existing methods and partly as a means to demonstrate, using case
studies related to human endotoxemia and response to methylprednisolone
(MPL) treatment, how specific questions may require specific methods,
thus emphasizing the non-uniqueness of the approaches. Finally, we
explore novel ways for integrating -omics information with PKPD models,
toward the development of more integrated pharmacology models.
Keywords: transcriptomics, proteomics, metabolomics, pharmacogenomics,
inflammation, glucocorticoids, LPS
Introduction
Life is complex at all scales. From a single cell to the whole body,
there are myriad intricate mechanisms that control every aspect of this
complexity, Figure [39]1. The ultimate aim of biomedical sciences is to
establish a thorough understanding of how these control mechanisms
function when in a healthy state and how the control is lost (or
shifted to a new mode) when symptoms of a disease are displayed, in
order to explain the observed phenotypic changes with the known
paradigms at the molecular level. Our ability to collect information
about molecular events in our bodies has tremendously increased with
great advancements in technology. However, we still have a long way to
go finding the best ways to fully utilize this information.
FIGURE 1.
FIGURE 1
[40]Open in a new tab
Multiscale nature of inflammatory response.
Numerous -omics tools are available, each of which makes it possible to
observe the physiologic responses at their complementary level. They
enable the examination of a broad array of cellular or systemic
elements and functions through the use of vast amounts of quantitative
or semi-quantitative data from various levels of biological
organizations ([41]Richards et al., 2010). Systems biology rises on
these new technologies and its most current and significant challenge
is developing methods to integrate the vast amount of information into
a conceptual framework that is holistic, quantitative and predictive
([42]Kritikou et al., 2006). The intent is to reach a thorough
understanding of the biological mechanisms driving different processes
in our bodies and ascertain insights how to manipulate these processes
for our benefit.
Inflammation is one of those core processes through which we produce
responses against various stressors, such as pathogens or trauma. It is
a complex and multiscale biological phenomenon that needs to be
orchestrated under tight regulation ([43]Laroux, 2004). Factors
inducing physiological stress are sensed and translated into biological
cues that transmit signals throughout organs and down to the cellular
level. These incoming signals are then recognized and processed to
produce a response in a dynamic and highly regulated manner. Collective
cell responses change the dynamic of biological metrics, manifesting
change at the individual organ level and ultimately throughout the
entire body. Under normal conditions, the outcome of inflammation is
the mounting of required immune response for pathogen elimination or
regeneration following injury; however, in any instance of
dysregulation of this complex process, the uncontrolled response can
induce further damage or lead to a non-sustainable systemic disease
state ([44]Bone, 1996), Figure [45]2. Considering its critical role in
our survival, inherent complexity and intricate relationships with
other essential physiologic processes; inflammation and inflammatory
diseases are among the research fields that can benefit from adapting
the systems approach. In this respect, the emerging -omics tools are
very promising, since they offer the advantage of observing the
inflammatory response at a much broader level together with the ability
to analyze multiple variables simultaneously which empowers the
application of systems analysis for rationalizing and modeling the
course of physiologic events.
FIGURE 2.
FIGURE 2
[46]Open in a new tab
Inflammatory response is a complex process, which has to be tightly
regulated in order to balance the defense mechanisms of the host with
the severity of infection/tissue damage. Loss of this control in favor
of either side may have fatal consequences.
Comprehending the continuum of physiologic responses to pathologic
stimuli is essential for making sense of how molecular changes develop
to induce observable symptoms of a particular disease. Drugs, i.e.,
pharmacologic stimuli, are intended to reverse this disease progression
and reduce the symptoms. For most cases, physiologic effects of drugs
are also complex and include re-directing physiologic responses to
alleviate symptoms of a particular pathologic condition as well as
inducing adverse-effects associated with off-target reactions. Analyses
of the physiologic effects of drugs by monitoring a handful of markers
for the targeted effects has been used for building models of drug
action for many years. Extensive -omics analyses done at multiple
physiologic levels, however, also impacted this research area
tremendously and initiated a shift from classic
pharmacokinetic/pharmacodynamic modeling (PK/PD) toward a systems
approach defined as quantitative systems pharmacology (QSP)
([47]Iyengar et al., 2012; [48]Jusko, 2013; [49]Androulakis, 2015,
[50]2016). The ultimate direction for this field is the realization of
personalized and precision medicine by building progressively more
accurate drug action models. However, the first steps toward these
goals involve devising methods to fully utilize the wealth of
information produced by the extensive analyses of pharmacologic
responses.
This paper is centered on integrating information from multiple
physiologic levels. We focused on how critical relationships are shaped
over time during the development of the response to a systemic
inflammatory stimulus and in response to an anti-inflammatory
treatment. The systems approach allowed us to track the continuum of
physiologic responses through their evolution and in relation to
multiple dynamics running in harmony. We extracted the coherent dynamic
responses represented in the -omics analyses at multiple physiologic
levels and integrated them using multiple approaches. The analyses
described in the sections that follow include metabolic and
transcriptional responses to endotoxemia, an experimental model in
humans that recapitulates the dynamics of systemic inflammatory
response. We then switch to an anti-inflammatory therapy and focus on
the effects of a commonly used synthetic glucocorticoid in liver. This
analysis represents a more direct integration approach, in which we
evaluate the concordance of the hepatic response to the drug treatment
at gene and protein expression levels. Finally, we discuss a
network-based approach that integrates -omics information with existing
pharmacokinetic/pharmacodynamic models.
Integration of -Omics Data: Why and How?
“Why” and “how” are, likely, the most critical questions that need to
be addressed as we aggressively enter the -omics era. The answer to
“why” is straightforward: life is a complex organization of functional
entities each manifesting the actions and activities of appropriate
levels through corresponding markers at a suitable level, broadly
described as -omics data. Therefore, the -omics data provide a snapshot
of the system across multiple levels of organization. The suffix “ome”
is used to identify groups of objects sharing common characteristics,
either descriptive or functional. At an elementary level we routinely
think of genomic (sequence of the genome), transcriptomic (expression
of the genome), proteomic (expression of the proteome), metabolomics
(expression of the metabolome) and the list is continuously expanded to
include the epigenome ([51]Koch, 2016), interactome ([52]Karagoz et
al., 2016), regulome ([53]Cheng et al., 2015), microbiome ([54]Althani
et al., 2016) to name only a few.
Clearly, one of the key challenges in the -omics era is “integration”
and most precisely how to integrate. It is important to realize that
although the various -omics components at some elementary level augment
the number of descriptors, the augmentation is not passive, i.e., it is
not simply increasing the dimensionality of the space. Rather, it
introduces additional layers of knowledge which are not independent of
other layers, that is the various -omics data are functionally related,
most likely implicitly among themselves. Seemingly simple, yet
profound, challenges emerge ([55]Gomez-Cabrero et al., 2014) while a
wide range of methods have been proposed and extensively reviewed
([56]Rogers, 2011; [57]Meng et al., 2014). Broadly speaking we will
argue that integration can be accomplished either (a) via statistical
means considering the computational challenges associated with
simultaneous analysis of disparate data sources ([58]Meng et al., 2014;
[59]Gligorijevic et al., 2016) or (b) realizing that the -omics
information comes together, eventually, in the form of a yet to be
determined, dynamics model expressing interactions, cross-functionality
and constraints ([60]Chen et al., 1999; [61]Palsson, 2002; [62]Joyce
and Palsson, 2006; [63]Hyduke et al., 2013). However, the purpose of
the present discussion is not to provide a detailed account of the
field and/or the approaches, but rather articulate, through the use of
specific examples: (1) how the nature of the data can guide, and to
some extent constrain, the type of question that can be asked; and (2)
how questions can guide the approach that need to be developed and
pursued. Both of these topics will be discussed in the context of
specific applications related to inflammation and anti-inflammatory
drugs. Each of the sections below is primarily concerned with
addressing one particular questions and outlining one, of the likely
many approaches that exist for addressing it.
Data-Driven Integration of Transcriptomic and Proteomic Data at the Same
Level of Physiological Organization
The first case study refers to a scenario where the -omics data reflect
different processes, principally within the same cell type, reflecting
a sequence of events. Likely the most characteristic example of this
would be transcriptomic and proteomic information. The fundamental
question arising here is how to upgrade the longitudinal information
quantifying gene and protein expression simultaneously, for a
particular cell type in response to an external perturbation or
environmental condition. In a simplistic way, one can view this problem
as an extension of the so-called central dogma. We demonstrate the
alternatives by focusing on the analysis of transcriptomic and
proteomic data obtained from the in vivo response of a rodent model
following bolus administration of synthetic glucocorticoids. Studies
focusing on understanding the relationship between global mRNA
transcription and protein translation have produced mixed results, many
of which concluded that the transcriptomic and proteomic data is far
from being easily described as complementary ([64]Greenbaum et al.,
2003; [65]Hegde et al., 2003; [66]Nicholson et al., 2004; [67]Waters et
al., 2006; [68]Haider and Pal, 2013; [69]Motta and Pappalardo, 2013;
[70]Castiglione et al., 2014). Nevertheless, both data types reflect
the dynamics of the cellular response to any given perturbation, thus
capture critical information reflecting different facets of the
response. Without considering any type of functional relation between
transcriptomic and proteomic data, each can be considered independently
or in tandem. In any “data driven” query, the approach undertaken
largely reflects the “biases” imposed by the type of question one
wishes to address ([71]Androulakis et al., 2007).
In the sections that follow, we will address a basic question: assuming
that the response to drug elicits changes at both the transcriptional
and translational level, is it reasonable to assume that one would
focus on gene transcripts and corresponding protein abundance both
exhibiting substantial temporal changes relative to base? (Note: in the
analysis that follows, we assume that the base line expression levels,
i.e., in the absence of the drug, are represented by mRNA and protein
abundance levels prior to drug administration). In a number of previous
publications, we have illustrated the analysis of longitudinal data
with a time-varying base line ([72]Androulakis et al., 2007; [73]Yang
et al., 2007b, [74]2008a, [75]2009b, [76]2012a,[77]b; [78]Yang E.H. et
al., 2009; [79]Almon et al., 2008a,[80]b; [81]Nguyen et al., 2009,
[82]2010a,[83]b, [84]2011, [85]2014a; [86]Ovacik et al., 2010;
[87]Scheff et al., 2010a; [88]Swiss et al., 2011). However, temporal
relations among time-varying quantities are known to be non-trivial,
extending far beyond the classic view of correlation ([89]Qian et al.,
2001).
Synthetic Glucocorticoids
Synthetic corticosteroids (CS), such as MPL, are widely used
anti-inflammatory and immunosuppressive agents for the treatment of
many inflammatory and auto-immune conditions including organ
transplantation, rheumatoid arthritis, lupus erythematosus, asthma and
allergic rhinitis ([90]Swartz and Dluhy, 1978; [91]Barnes, 1998). The
mechanism of action of CS drugs is basically magnifying the
physiological actions of the endogenous glucocorticoid hormones, which
have anti-inflammatory properties depending on their secretion level
and the time at which they are secreted. These hormones also have
diverse effects on a variety of physiological processes including
carbohydrate, lipid and protein metabolism, immune-regulation, bone
homeostasis and developmental processes ([92]Barnes, 1998;
[93]Vegiopoulos and Herzig, 2007). The well-established molecular
mechanism of action for CS includes the passive diffusion of the highly
lipophilic CS molecule through the cell membrane and binding to the
cytosolic glucocorticoid receptor, which is held inactive through the
association with heat shock proteins ([94]Schaaf and Cidlowski, 2002).
Binding of the drug to the receptor causes conformational changes,
phosphorylation and activation of receptor, resulting in the formation
of a homodimer of the drug receptor complex ([95]Schaaf and Cidlowski,
2002; [96]Oakley and Cidlowski, 2011). This activated complex
translocates into the nucleus and binds to regulator sites,
glucocorticoid regulatory elements (GREs) in the DNA, resulting in the
regulation of transcription rate. In addition to direct binding, the
activated complex can regulate gene expression by other mechanisms
including tethering and composite binding to other transcription
factors, activators, or repressors ([97]Barnes, 1998; [98]Schaaf and
Cidlowski, 2002). Studies have shown that CS can regulate pathways by
signaling through receptors in a transcription-independent manner,
although the exact mechanisms for the non-genomic effects are still
unclear ([99]Schaaf and Cidlowski, 2002). While short-term use of CSs
is beneficial for reducing the inflammation, long-term use is
associated with serious side-effects – including hyperglycemia,
negative nitrogen balance, and fat redistribution, leading to
complications including diabetes, muscle wasting, osteoporosis,
hypertension, cataracts, peptic ulcers, and reduced resistance to
infection and adrenal insufficiency following withdrawal of therapy
([100]Andrews and Walker, 1999; [101]Morand and Leech, 1999). The
undesirable metabolic effects of CS cannot usually be separated from
their favorable anti-inflammatory effects since most actions manifest
via the same glucocorticoid system.
The analysis of the diverse physiological effects of synthetic
glucocorticoids has been the subject of many pharmacokinetic and
pharmacodynamic modeling efforts. A series of models has been developed
to explain the dynamics of receptor regulation and enzyme induction
following MPL administration ([102]Sun et al., 1998; [103]Ramakrishnan
et al., 2002), Figure [104]3 (top). The models were progressively
enhanced to capture the effects of the drug under several doses and
dosing regimens. However, these models were based on the data generated
by traditional message quantification methods that only allow
measurements of single end points. Because of the diverse effects of CS
and different molecular mechanisms potentially involved in these
actions, a high-throughput transcriptomic, i.e., microarray, approach
was effective in gaining better understanding of the temporal and
tissue-specific effects of CS on different pathways and functions
([105]Almon et al., 2007a,[106]b). The diversity of the available
models describing gene induction by MPL have been expanded to several
pharmacogenomics models that explain the response of numerous genes
with various dynamic patterns ([107]Almon et al., 2003, [108]2005,
[109]2007a,[110]b; [111]Jin et al., 2003; [112]Nguyen et al., 2014a).
Our earlier studies in collaboration with Prof. Jusko, characterized
global dynamics of the systems that are regulated by CS at the
transcriptional level across multiple tissues in adrenalectomized (ADX)
and intact male rats, following single and chronic dosing of MPL
enabling us to: (a) develop transcription-level understanding of MPL
(acute vs. chronic) effects; (b) elaborate on tissue-specific
transcriptional differences; (c) assess MPL-induced dose- and
tissue-specific transcriptional regulation; and (d) assess circadian
dynamics and regulation of intact and MPL-dosed animals ([113]Yang et
al., 2007b, [114]2008a; [115]Yang E.H. et al., 2009; [116]Almon et al.,
2008a; [117]Nguyen et al., 2010a,[118]b, [119]2014a; [120]Ovacik et
al., 2010; [121]Scheff et al., 2010a, [122]2011).
FIGURE 3.
FIGURE 3
[123]Open in a new tab
(Top) Schematic representation of the network-based indirect response
model to MPL (D). Once the drug-receptor complex translocates into the
nucleus (DR[n]), it induces its effects on the transcription of its
target genes (mRNA[1], mRNA[2]) either by stimulation (open rectangle)
or by inhibiting it (closed rectangle). (Bottom) When transcribed
messages are translated to the active proteins (Protein[1],
Protein[2]), they can also have effects, either stimulatory (green) or
inhibitory (red), on the transcription of target genes affected by MPL.
All these effects are considered as indirect as there might be
additional biological processes in between.
Although transcriptional information is useful and highly relevant,
direct profiling of the protein expression changes and integration of
the information from proteomic data will provide deeper insights into
CS actions ([124]Nouri-Nigjeh et al., 2014). Recently, high-throughput,
ion current-based liquid chromatography/mass spectrometry (LC/MS),
allowed comprehensive and accurate profiling of the tissue proteome
([125]Tu et al., 2012). Using this methodology, the temporal changes in
the expression of almost a 1000 proteins in rat liver following MPL
administration were characterized ([126]Nouri-Nigjeh et al., 2014). The
analysis of the combined transcriptomic and proteomic data confirmed
that significant indirect regulation by MPL was evident, notably
indicated by significant changes in mRNA and protein levels in the
absence of glucocorticoid responsive promoter elements. With this new
information from the protein expression level, it was possible to
evaluate complementarities between transcription and translation of the
target genes and elaborate on the interplay between gene and protein
expression in liver toward a more complete understanding of the
indirect mechanisms of action of MPL ([127]Kamisoglu et al., 2014).
This data confirmed that MPL effects are propagated across a network of
interacting genes and proteins, Figure [128]3 (bottom). Without
considering any type of functional relation between transcriptomic and
proteomic data, each can be considered independently ([129]Androulakis
et al., 2007) and using statistical methods one can analyze temporal
trends at each level. This was the approach undertaken recently in our
lab and an extensive discussion of the methods and results was recently
presented ([130]Kamisoglu et al., 2014).
Animal Model, Proteomic and Transcriptomic Data
Proteomic Studies
Sixty adrenalectomized (ADX) Wistar rats were injected with 50 mg/kg
methylprednisolone (MPL) intramuscularly and sacrificed at 12 different
time points between 0.5 and 66 h post-dosing (five animals/time point).
Five animals, injected with saline and sacrificed at random time points
in the same time window, served as controls. In order to remove the
high concentrations of blood protein, it was necessary to use perfused
tissue for proteomic analyses, which precluded the use of the same
tissues employed for transcriptomics (below). Proteins from perfused
and flash frozen livers were extracted, digested and analyzed using a
nano-LC/LTQ/Orbitrap instrument. The Nano Flow Ultra-high Pressure LC
system (nano-UPLC) consisted of a Spark Endurance autosampler (Emmen,
Holland) and an ultra-high pressure Eksigent (Dublin, CA, USA) Nano-2D
Ultra capillary/nano-LC system, with a LTQ Orbitrap mass spectrometer
(Thermo Fisher Scientific, San Jose, CA, USA) used for detection.
Protein quantification was based on the area under the curve (AUC) of
the ion-current-peaks. A more extensive description of the experimental
setup and the analytical methodology can be found in the previously
published study ([131]Nouri-Nigjeh et al., 2014).
Transcriptomic Studies
Forty-three ADX Wistar rats were given a bolus dose of 50 mg/kg MPL
intravenously. Animals were sacrificed at 16 different time points
between 0.25 and 72 h post-dosing. Four untreated animals sacrificed at
0 h served as controls. The mRNA expression profiles of the liver were
arrayed via Affymetrix GeneChips Rat Genome U34A (Affymetrix, Inc.),
which contained 8800 full-length sequences and approximately 1000
expressed sequence tag clusters ([132]Jin et al., 2003). This dataset
was previously submitted to the GEO ([133]GSE490). We have previously
presented extensive analyses and studies of the transcription response
to acute and chronic administration of MPL on multiple tissues
([134]Almon et al., 2008a,[135]b; [136]Yang et al., 2008a; [137]Nguyen
et al., 2010a, [138]2014a; [139]Ovacik et al., 2010).
All animal experiments were performed at the University of Buffalo and
protocols adhered to “Principles of Laboratory Animal Care” (NIH
publication 85-23, revised in 1985) and were approved by the University
at Buffalo IACUC committee.
Integration and Analysis of Transcriptomic/Proteomic Data
Assessment of the problem greatly reflects the approach(es) taken
([140]Androulakis et al., 2007) whereas in ([141]Kamisoglu et al.,
2015b) we described multiple approaches to integrate this temporal
information from the proteome level with the corresponding dynamics in
the transcriptomic level through data-driven approaches. In the
analysis that follows we addressed the question in two different ways.
First, we identified transcripts and proteins which are over-expressed
post MPL administration. The intersection of the two sets indicates
genes from which both transcription and translation is significantly
altered. The simultaneous consideration enables us to consider the
following question: what is the dynamic correlation of the subset of
MPL-regulated transcripts and proteins? As mentioned earlier, and shown
later, the relation is far from trivial. However, such analysis, only
depicts part of the picture. It is highly likely that transcriptional
events do not manifest themselves at the protein levels and also
post-transcriptional alterations may not include transcriptional
alterations. For such an analysis, the two -omics data need to be
separated and analyses performed. Therefore, in the former case the
mining of the data is concurrent but the functional interpretation
separate, whereas in the latter the mining of the data is separate but
the functional analysis concurrent. The case study discussed in the
following section demonstrates both approaches. Once again, it is
important to realize that we only discuss two of the many questions
which could be posed and addressed and the presentation is by no means
exhaustive or all-encompassing.
Augmenting the Feature Vector: Clustering of Concatenated Dataset
This analysis will help us identify the genes which were differentially
expressed both at the transcriptional and translational levels. Data
analysis for both proteomic and transcriptomic datasets started first
by filtering for differential expression over time. Proteins and
transcripts with differential temporal profiles were determined by
using EDGE ([142]Leek et al., 2006). We employed within-class
differential expression to extract profiles that have a differential
expression over time ([143]Storey et al., 2005, [144]2007; [145]Leek et
al., 2006). Integration of these two datasets for any further analysis
required matching the object identifiers which was achieved through
running a comparison between two filtered datasets in Ingenuity Pathway
Analysis (IPA, Ingenuity^® Systems)^[146]1.
In order to find potential co-regulatory relationships at these two
levels, hierarchical clustering was used for first-pass analysis. For
this purpose, temporal transcriptomic and proteomic data for the common
genes were first concatenated and then clustered using the clustergram
function in the Bioinformatics toolbox of MATLAB (Mathworks, Natick,
MA, USA). The two clusters obtained by using correlation as the
distance metric. The workflow is illustrated in Figure [147]4 (top).
FIGURE 4.
FIGURE 4
[148]Open in a new tab
(Top) Clustering of concatenated transcriptomic-proteomic MPL data
workflow; (Bottom) Sequential transcriptomic and proteomic – forward
and reverse – clustering analysis of MPL data workflow.
This study aimed to compare and contrast the transcriptional and
translational changes in liver induced by the exposure to a synthetic
CS at a pharmacological dose. Although high-throughput -omics analyses
have been obtained from samples collected from two independent studies;
the strain of experimental animals, dose and type of pharmacologic
agent, sampled tissue, sampling procedures, and most of the time points
for sample collection were the same for these studies. These conditions
allowed us to assume that the experiments are similar enough to conduct
individual and integrated bioinformatics analyses.
The preprocessing before performing the first-pass analysis involved
identifying the significant genes whose both transcripts and proteins
existed in the individual datasets. Differential expression analysis
through EDGE identified that 475 out of 959 proteins and 1624 out of
around 8800 transcripts had temporal profiles that significantly varied
over time (meeting p-value < 0.05 and q-value < 0.01 cut-offs). After
this filtering step, both datasets were fed into IPA in order to match
distinct identifiers used (Swiss-Prot IDs for proteins and Affymetrix
IDs for transcripts). A comparison between two datasets indicated that
163 genes were commonly found in both transcriptomic and proteomic
datasets; i.e., both mRNAs and proteins corresponding to these genes
were differentially expressed over time.
The analysis is described in greater in detail ([149]Kamisoglu et al.,
2015b). However, we briefly report here that this analysis identified
two dominant patterns: one with corresponding mRNA and protein
expression profiles essentially parallel in direction, and another
where the directionality was reversed. The early response appears to be
the most critical time period during which mRNA and protein expression
profiles change direction. Genes in Cluster 1 display up-regulation for
both mRNA and protein expression profiles in the first 8 h, followed by
down-regulation, most markedly in the transcriptional profiles. In the
second cluster of response profiles, down-regulation dominates for
transcriptional profiles; however, corresponding protein expression
profiles are not parallel. While down-regulation is observed in these
transcripts most notably in the first 8 h; expression of the same
proteins seems to be up-regulated in the same time frame. After the 8th
hour; both transcriptional and protein expression profiles approach
basal levels, though from opposite directions; elevated mRNA levels
start to be down-regulated and reduced protein levels start to be
up-regulated. Functional annotation of the genes in these two clusters,
indicated that the first cluster contains a number of genes coding for
heat shock proteins, which take part in the negative regulation of CS
signaling through direct protein-protein interaction with
glucocorticoid receptor to prevent its translocation to nucleus
([150]Chrousos and Kino, 2005). Complementary transcriptional and
proteomic profiles of these genes indicated that this is a negative
feedback control induced by MPL delivery, which is regulated at the
transcriptional level. Proteins functioning in the regulation of
protein degradation and translation machinery were also among the genes
in the first cluster, implying that these processes are also controlled
at the transcriptional level after CS exposure. In contrast, functions
enriched by the genes in the second cluster appear to be regulated at
post-transcriptional levels, likely through control of mRNA processing,
initiation of protein translation or protein stability, since the
transcriptional profiles are not emulated by protein expression,
([151]Waters et al., 2006). Among these functions, most notable are the
modulation of oxidative stress, lipid metabolism and bile acid
biosynthesis. Concurrent analysis of promoter region identified
upstream regulators that can explain the observed changes in
gene/protein expression based on the prior knowledge of expected
effects between the upstream regulators and target genes/proteins in
the dataset.
Two-Way Sequential Clustering of Individual Proteomic and Transcriptomic
Datasets
While the hierarchical clustering analysis described above identifies
the potential co-regulatory schemes for the genes in the intersection
of transcriptomic and proteomic datasets; it fails to capture the
dynamics in the rest of the genes which may also show differences in
expression over time, although they may not co-exist in both datasets.
In order to evaluate the overall dynamic patterns and extract the most
useful information integrating these two datasets, a consensus
clustering ([152]Nguyen et al., 2009) method was applied to these two
datasets separately. First, proteins with differential temporal
profiles were clustered using p-values of 0.05 for significant clusters
and an agreement level of 0.70 for the genes in each cluster. Then,
probe sets corresponding to the proteins in each cluster were
identified through the comparison function in IPA as before. Temporal
profiles of these probe sets corresponding to the proteins were
compiled and separately sub-clustered through the less stringent
hierarchical clustering method, again using clustergram function in
MATLAB. The reverse of the same procedure was also performed – starting
from transcriptional analysis and continuing with the corresponding
proteomic analysis. Here, differential transcriptional profiles were
first determined and then clustered using the same procedures described
above. As with the previous analysis, proteins that correspond to the
probe sets within each of these clusters were then identified and
sub-clustered. The workflow is illustrated in Figure [153]4 (bottom).
Functional annotations of proteins and transcripts at each level of
analysis were conducted in IPA by running a core analysis for each
cluster and evaluating the enriched canonical pathways (at p-value
threshold of 0.05) and predicted upstream regulators obtained in IPA.
Summarizing the observations described in great detail in
([154]Kamisoglu et al., 2015b) we note that EDGE identified 475 out of
959 while the ensuing consensus clustering revealed five coherent
temporal profiles containing 217 of the 475 regulated proteins. Of the
217 clustered proteins, 158 showed regulation of at the mRNA level as
well. This analysis was repeated in the reverse direction; i.e.,
starting from the transcriptomic dataset and progressing to the
proteomic dataset. 1624 of the probe sets were differentially
expressed, 1132 of those were in five clusters obtained by consensus
clustering. Only 217 of these 1132 probe sets had corresponding
proteins in the proteomic dataset. Compared to the first part of
sequential clustering analysis, considerably fewer proteins actually
correlate with the transcriptional profile of their respective
clusters. Considering that protein expression is a more reliable
predictor of function, the annotation analysis was based on the
proteomic data in this part of the analysis. Elaboration on the results
enabled a more complete characterization of the functional implications
and relations among gene and proteins ([155]Kamisoglu et al., 2015b).
Data-Driven Integration -Omics Data Across Multiple Levels of Physiological
Organization
The second study addresses a question of increasing importance. Blood
sampling is the most widely utilized way of probing dynamic responses,
and it has tremendous translational potential as it is the most readily
accessible sample in humans. Metabolomics in particular is becoming a
readily available tool given the ability of the serum metabolome to
capture biological responses at a higher level ([156]Minami et al.,
2009; [157]Fitzpatrick and Young, 2013; [158]Kosmides et al., 2013;
[159]Kaddurah-Daouk et al., 2014; [160]Kell and Goodacre, 2014;
[161]Carroll et al., 2016). Likely one of the most celebrated examples
was the Inflammation and the Host Response to Injury, so-called, “Glue
Grant,” aiming at providing a blue-print of the host response to injury
and trauma based on sophisticated analysis of blood samples
([162]Nathens et al., 2005; [163]Cuenca et al., 2011; [164]Tompkins,
2015). However, despite the fact that the majority of studies
attempting to integrate various types of -omics information, analyses
based on blood measurement add an extra level of complexity. Namely,
circulating metabolites originate from a wide variety of tissues and
organs and eventually accumulate in systemic circulation. Therefore,
although the various data structures provide actual information
describing the host response, the fact that the origin of each type of
information is not unique, as well as non-specific, significantly
complicates the analysis. One such example, to be discussed shortly,
relates to combining transcriptomic and metabolomics information from
blood samples. In this direction, the question we pursued was based on
the hypothesis that the drastic changes in the immediate environment of
blood leukocytes might have an adaptive effect on shaping their
transcriptional response in the regulation of metabolism in conjunction
with the initial inflammatory stimuli. The coupling of leukocyte
transcriptomic and systemic metabolomics information may enable us to
provide a more complete picture of the drivers of the response of
immune cells under inflammatory conditions.
Human Endotoxemia
Elective administration of bacterial endotoxin (lipopolysaccharide;
LPS) to healthy human subjects has been used as a reproducible
experimental procedure providing mechanistic insights into how cells,
tissues and organs respond to systemic inflammation. Low doses of LPS
transiently alter many physiologic and metabolic processes in a
qualitatively similar manner to those observed after acute injury and
systemic inflammation ([165]Lowry, 2005; [166]Calvano and Coyle, 2012),
thus allowing the analysis of the responses to infectious stress at
multiple physiologic levels. This model has been extensively employed
for the development and assessment of rational clinical therapies to
prevent or attenuate systemic inflammatory response syndrome (SIRS)
([167]Calvano and Coyle, 2012).
Response to endotoxemia is closely associated with alterations in
metabolism. Inflammatory processes change the direction of the
substrate flow from the periphery toward splanchnic organs while also
triggering the release of catabolic signals in order to meet increased
energy and substrate demands ([168]Fong et al., 1990; [169]Khovidhunkit
et al., 2004); and hence, considerably alter the levels of plasma
metabolites. Individual changes in the major metabolites, such as some
lipids, amino acids, and glucose, are previously documented for the
case of human endotoxemia ([170]Fong et al., 1990). However, an
untargeted bioinformatics-empowered approach to elucidate the effects
of endotoxemia on plasma metabolite levels is lacking.
Analysis of the complete metabolic response to systemic inflammation is
of special interest since metabolic composition of a tissue is uniquely
altered in response to stimuli due to collective effects of the
regulations at various levels of cellular processes including
transcription, translation and signal transduction. Concentrations of
metabolites in a sample at a given time, i.e., the “metabolome”
([171]Nicholson and Lindon, 2008), can be thought of as the metabolic
fingerprint representative of the state of body at that time and
provide information on the dominant regulatory mechanisms. The emerging
field of metabonomics, combines this unique metabolic information with
bioinformatics approaches to provide an integrated temporal picture of
the interactions in the system ([172]Nicholson, 2006; [173]Holmes et
al., 2008). Since the metabolic phenotype is determined by eventual
production of metabolites through the complex cellular processes
trickling down from transcription, translation and signal transduction,
this field offers promise in advancing the knowledge in many clinical
conditions. For endotoxemia, understanding the alterations in plasma
metabolome is critical; since, metabolite levels impacts the regulation
of anti-inflammatory defenses, in turn, through steering critical
cellular processes in immune cells ([174]Pearce and Pearce, 2013).
Global transcriptomic studies of circulating leukocytes in experimental
human endotoxemia previously elucidated the intricate regulatory
schemes governing the inflammatory response ([175]Calvano et al., 2005;
[176]Nguyen et al., 2011). However, inflammatory response is also
closely associated with alterations in metabolism. In [177]Kamisoglu et
al. (2013) we discussed the drastic effect of a mild inflammatory
stimulus on the homeostasis of the whole-body metabolism. This single
level analysis uncovered the temporal patterns in the host metabolism
reflecting collective impacts of regulations at various organs and at
multiple levels of cellular processes including transcription,
translation and signal transduction. For endotoxemia, understanding the
alterations in plasma metabolome is critical, since metabolite levels
impact the regulation of anti-inflammatory defenses, in turn, by
directing critical cellular processes in immune cells ([178]Pearce and
Pearce, 2013).
Building on this knowledge; we integrated the transcriptional response
of leukocytes with systemic metabolic response to understand how
inflammation-induced changes in the composition of plasma, in turn,
affecting the transcriptional processes in the leukocytes.
Metabolomic Studies
Archived blood plasma samples, which had been flash frozen, were used
in this proof-of-principle study. These samples were collected from 19
healthy subjects, ages 18–40. Fifteen of the subjects (11 males and 4
females; mean age of 22.7) were administered National Institutes of
Health (NIH) Clinical Center Reference Endotoxin, at a bolus dose of 2
ng/kg body weight as previously described [179]Alvarez et al. (2007);
[180]Jan et al. (2009) and [181]Jan et al. (2010). Four control
subjects (three males and one female; mean age of 22.2) were
administered placebo (saline). During the protocol, subjects received a
solution of 5% dextrose and 0.45% saline crystalloid. Blood draws were
conducted sequentially at t = 1, 2, 6, and 24 h from both groups,
samples were inventoried and stored at -80°C until the analysis.
Metabolomic analysis was performed by Metabolon (Durham, NC, USA)
according to previously published methods ([182]Evans et al., 2009).
The resulting extracts were subjected to either liquid chromatography
(LC) or gas chromatography (GC) followed by mass spectroscopy (MS)
analysis. Identification of known chemical entities was based on
comparison to metabolomic library entries of purified standards.
Complete details of the profiling of plasma metabolome are previously
described ([183]Kamisoglu et al., 2013, [184]2015a).
Transcriptomic Studies
For the transcriptomic study, four subjects (one female and three male)
received LPS at a bolus dose of 2 ng/kg body weight and four subjects
(one female and three male) received saline. Blood samples were
collected before (t = 0 h) and 2, 4, 6, 9, and 24 h after LPS
administration. Leukocytes were recovered by centrifugation; total
cellular RNA was isolated from the leukocyte pellets and hybridized
onto Hu133A and Hu133B oligonucleotide arrays (Affymetrix). Further
details about the experimental design are presented in the original
analysis ([185]Calvano et al., 2005). The transcriptional analysis
generated expression measurement data of over 44000 probe sets in
total, which is also publicly available through the GEO Omnibus
Database^[186]2 under the Accession No: [187]GSE3284. Integration and
analysis of transcriptomic/metabolomic data from different tissues. We
presented extensive data analysis and modeling associated with the
transcriptional response to endotoxin in humans ([188]Foteinou et al.,
2009a,[189]b,[190]c, [191]2010, [192]2011; [193]Dong et al., 2010;
[194]Nguyen et al., 2011, [195]2013; [196]Yang et al., 2011b;
[197]Scheff et al., 2013).
Integration and Analysis of Transcriptomic/Metabolomic Data
Data analysis for both transcriptomic and metabolomic datasets started
first by filtering for differential expression over time. Transcripts
and metabolites with differential temporal profiles were determined
using EDGE software ([198]Leek et al., 2006). The significance cut-off
for the transcriptomic dataset were p < 0.05 at 0.10 false discovery
rate. To determine the potential co-regulatory relationships,
differentially expressed transcripts and metabolites with differential
temporal profiles were hierarchically clustered using clustergram
function in the Bioinformatics toolbox of MATLAB (Mathworks, Natick,
MA, USA). The two clusters were obtained by using correlation as the
distance metric.
Pathway enrichment analysis of genes in the clusters were completed in
Enrichr ([199]Chen et al., 2013) using the gene-set libraries of Kyoto
Encyclopedia of Genes and Genomes (KEGG) ([200]Kanehisa and Goto,
2000). Three types of enrichment scores are calculated by Enrichr to
assess the significance of overlap between the input list and the gene
sets in each gene-set library for ranking a term’s relevance to the
input list. These are Fisher exact test, z-score of the deviation from
the expected rank by the Fisher exact test, and a combined score that
multiplies the log of the p-value computed with the Fisher exact test
by the z-score. The pathways which have a combined score higher than
1.0 were called significant. The combined score was devised because
Fisher exact test had a slight bias that affects the ranking of terms
solely based on the length of the gene sets in each gene-set library
([201]Chen et al., 2013).
The goal in the current analysis was to reveal transcriptional
regulation of leukocyte metabolic processes, specifically, then to
assess if these regulatory patterns might have been affected by
concurrent fluctuations of metabolite levels in the surrounding plasma
along with the initial stimuli. For this purpose, we opted to focus the
transcriptional analysis to the genes that are associated with
metabolic processes only. Therefore, any differential transcripts which
code for genes that are not associated with any of the metabolic
pathways were filtered out. Gene set libraries and pathway
classifications in KEGG database were used as reference at this
filtering process. Then, clustering analysis was repeated for the
remaining transcripts. Clustered metabolism-associated genes were
functionally annotated through Enrichr similar to the analysis of the
complete transcriptome described above. A detailed account of the
results is described in length in ([202]Kamisoglu et al., 2013,
[203]2014, [204]2015a).
The study aimed at defining the impact of altered plasma composition on
the transcriptional response of leukocytes during an inflammatory
challenge. Earlier transcriptional studies ([205]Calvano et al., 2005)
highlighted components of pro- and anti-inflammatory processes, whereas
the integrative analysis focused on metabolic processes controlled at
the transcriptional level and enabled the development of guiding
principles driving the impact of the immediate leukocyte environment.
One of the key observations indicated that leukocytes tune the activity
of lipid and protein associated processes at the transcriptional level
in accordance with the fluctuations in metabolite compositions of
surrounding plasma. A closer look into the transcriptional control of
metabolic pathways uncovered alterations in bioenergetics and defenses
against oxidative stress closely associated with mitochondrial
dysfunction and shifts in energy production observed during
inflammatory processes. We observed that in parallel with the peaking
lipid and plunging amino acid levels in plasma, lipid associated
metabolic pathways were activated while protein translation machinery
slowed. We hypothesize that drastic changes in the immediate
environment of the leukocytes might have an adaptive effect in this
response in conjunction with the initial stimuli. Furthermore, focusing
only on metabolism associated transcripts uncovered alterations in
bioenergetics and defenses against oxidative stress that can shed light
into the mechanisms underlying mitochondrial dysfunction and shifts in
energy production observed during inflammatory processes. Besides
describing the metabolic response of human body to a basic inflammatory
cue at the systemic level together with affected immune mechanisms,
this study can inspire future translational studies as the -omics
analyses becomes more routine in clinical practice. Blood is one of the
most rapid and least invasive biological samples collected from
patients, yielding useful information about the state of the body.
Benchmarking the metabolic state of the system and transcriptional
state of the immune cells by a single biological sample may expedite
clinical decision making and help reduce mortality in critical cases.
The studies of ([206]Langley et al., 2013, [207]2014), which formed the
basis for our comparative analysis between endotoxemia and sepsis
([208]Kamisoglu et al., 2015a), specifically aimed at benchmarking the
metabolic state and identifying critical biomarkers. The analysis
presented earlier, based on ([209]Kamisoglu et al., 2015a), points to
directions where control studies (human endotoxemia) with established
links to clinical cases (sepsis) can be used to elaborate on likely
markers and/or expected dynamics enabling easier translation of
clinical data.
The integration of multiple temporal data of diverse nature, raises
several issues: the temporal resolution of sampling is often assumed.
The hypothesis is that if one is interested in deciphering the dynamic
interactions between an input and its output, the sampling frequency
and timing of the sampling need to be similar. However, data
acquisition in living systems reflects a balance between what is
physiologically appropriate and what is realistically feasible,
accounting not only for cost, but for other practical and ethical
constraints. In human studies for example, sampling frequency is
limited due to ethical and other constraints. In animal studies,
practicality of experiments and their cost often limit sampling,
especially if/when it involves sacrificing the animal, in addition to
ethical considerations evaluated by the appropriate IRB. Therefore,
multiple confounding factors exist, including pre-existing conditions,
multiple drugs, age, sex, ethnicity etc. It is the hope that large
scale studies will provide some stratification and generate somewhat
more coherent data. Furthermore, deciphering evolution of dynamics
following an external perturbation needs to take into consideration
that the “homeostatic” dynamics may in fact demonstrate a baseline
dynamics response – most likely in the form of circadian variation
([210]Almon et al., 2008a,[211]b; [212]Nguyen et al., 2010b,
[213]2014b; [214]Ovacik et al., 2010; [215]Rao et al., 2016b).
Therefore, deviations from homeostasis in response to a pharmacological
agent need to consider the homeostatic dynamics as well. This is
evident not only in terms of data, but also in the context of model
development and its implications ([216]Scheff et al., 2010b;
[217]Mavroudis et al., 2013, [218]2015; [219]Pierre et al., 2016;
[220]Rao et al., 2016a).
Model-Based Integration of -Omics Data
Modeling the responses of the body to a drug is a fundamental process
in the drug development and it helps us quantitatively reflect the
time-course of the effects of drug on the body. Building and successful
utilization of these models allow quantification of drug-system
interactions and prediction of both therapeutic and adverse effects
([221]Mager et al., 2003; [222]Felmlee et al., 2012). Diverse
physiological effects of synthetic glucocorticoids are the subject of
many of these pharmacokinetic and pharmacodynamic modeling efforts. A
series of models were developed to explain the dynamics of receptor
regulation and enzyme induction following MPL administration ([223]Sun
et al., 1998; [224]Ramakrishnan et al., 2002). The models progressively
enhanced to capture the effects of the drug under several doses and
dosing regimens. However, these models were based on the data generated
by traditional message quantification methods that only allow
measurements of single end points. Together with the analysis of MPL
effects on various tissues via high-throughput technologies such as
gene microarrays ([225]Almon et al., 2007a,[226]b), the diversity of
the available models increased. The fifth-generation model that
described the simple gene induction by MPL was expanded to several
pharmacogenomics models that may explain the response of all the
hepatic genes with various dynamic patterns ([227]Jin et al., 2003).
Earlier modeling analyses have primarily concentrated on the effects
observed at the gene expression level ([228]Almon et al., 2003,
[229]2005, [230]2007a,[231]b; [232]Jin et al., 2003; [233]Nguyen et
al., 2010a, [234]2014a). The next quest in the development of more
comprehensive models is the incorporation of information at the protein
expression level. This information, made available by a novel
high-throughput and reproducible method, allows the temporal profiling
of tissue proteome ([235]Nouri-Nigjeh et al., 2014). A future direction
we envision is to achieve integration of this information from
complementary studies with a model-driven approach. With this, the
current PK/PD models of MPL response could be augmented to reflect the
physiological response observed at the protein expression level.
As our results demonstrated earlier, transcriptional and proteomic
expression patterns roughly correlate for some of the genes, yet for
others, the dynamics are more unexpected. One way to work with the
existing PK/PD models would be teasing out the protein counterparts of
the transcriptional clusters that are described by the observed
dynamics and examining the potential mechanisms that could explain the
observed protein expression profiles corresponding to the same genes.
Another approach is considering the physiologic response as systems
response composed of dynamics of individual elements. However, studies
focusing on understanding the relationship between global mRNA
transcription and protein translation have produced mixed results,
often concluding that the transcriptomic and proteomic data is far from
being easily described as complementary ([236]Greenbaum et al., 2003;
[237]Hegde et al., 2003; [238]Nicholson et al., 2004; [239]Waters et
al., 2006; [240]Haider and Pal, 2013). Nevertheless, both data types
reflect the dynamics of the cellular response, thus capture critical
information reflecting different facets of the response. The key
challenge is realizing that although the various -omics (genomic,
transcriptomic and proteomic in this case) components at some
elementary level augment the number of descriptors, the augmentation is
not passive, i.e., it is not simply increasing the dimensionality of
the space.
We will present a preliminary study applying this second approach. In
our study, the driver of the response is the drug-receptor complex in
the nucleus; transcripts and proteins, the nodes of the network, are
the individual elements with diverse dynamics. The observed phenotype
reflects the systems response arising from the dynamics of these
individual elements, and these elements include genes and proteins,
directly and indirectly, affected by the MPL. A number of target genes
to be included in the network depends on the available biological data,
literature information about the interaction of the nodes, as well as
the desired complexity level. As more nodes are added into the network,
the direct and indirect interactions between the elements of the
network, as well as the number of parameters to be estimated,
increases.
Building the Response Network
A functional approach was undertaken to construct a proof-of-concept
initial network. Genes included in this network are selected from the
most informative transcripts that are both differentially expressed at
the transcriptomic and proteomic levels, Figure [241]5 (left). The list
was further reduced by focusing on genes functionally related to the
major metabolic effects of MPL on metabolism, including hyperglycemia,
dyslipidemia and muscle wasting ([242]Schäcke et al., 2002). Genes
functioning in glutathione metabolism and redox regulation, associated
with oxidative stress, are situated at the major cross-roads critical
for the regulation of both inflammation and metabolism ([243]Pearce and
Pearce, 2013). These nodes seem to be closely connected to one of the
core genes in pyruvate metabolism, also coding for enzymes catalyzing
the rate limiting step of fatty acid synthesis. Another critical
response to inflammation, regulation of cytoskeleton, although mostly
epithelial in origin, is also represented in this network in
collaboration with other genes functioning in metabolic pathways.
FIGURE 5.
FIGURE 5
[244]Open in a new tab
(Left) Concurrent analysis of temporal mRNA and protein liver-specific
data post MPL dosing revealed two dominant patterns. The first
populated with genes whose corresponding mRNA and protein expression
profiles were parallel in direction; while in the second directionality
was reversed. Heat shock proteins and proteins regulating protein
degradation and translation were in the first cluster. The second
pattern was dominated by proteins involved in the modulation of
oxidative stress, lipid metabolism and bile-acid biosynthesis. Such
analyses likely point to transcriptional or post-translational
regulatory events. (Right) Liver-specific differentially expressed
proteins and transcripts following a dose of MP were determined using
EDGE and common functionally enriched pathways using Enrichr. Direct
and indirect regulatory relations identified using four databases
(Biocarta, KEGG, NCI, and Reactome) established a global network
subsequently reduced (6 nodes, 16 edges) using a variation of
Dijkstra’s algorithm. Network includes: oxidative stress genes in
glutathione metabolism and redox regulation critical for the regulation
of inflammation and metabolism; core genes in pyruvate metabolism and
fatty acid synthesis; regulation of cytoskeleton as well as metabolic
genes.
In order to establish regulatory connections between the sub-set of
genes and proteins, we explored several databases, including Ingenuity
Pathway Analysis ([245]Calvano et al., 2005), KEGG ([246]Lissauer et
al., 2009), Biocarta ([247]Amirian et al., 2011) and PSTIING ([248]Ng
et al., 2011), which provide network data. The network was constructed
by combining the transcriptomic and proteomic data along with
functionally annotated biological information and literature-based
functional associations, with emphasis on regulatory relations.
Utilizing the regulatory relations between genes/proteins from the
databases, first a “global network” in which regulatory links are well
known was established. The analysis produced a small, yet complex
network (6 nodes, 16 connections) providing an avenue to explore both
therapeutic and adverse effects of MPL, Figure [249]5 (right).
Refinement and further extension of the network through modeling will
yield an accurate representation of the complete effects of the drug.
Once the sub-set of genes and proteins of interest has been identified,
we then need to establish a network structure expressing putative
regulatory relations. A variety of computational methodologies for
further refining regulatory network structures are discussed elsewhere
([250]Yang et al., 2007b; [251]Foteinou et al., 2009d; [252]Nguyen and
Androulakis, 2009; [253]Nguyen et al., 2011; [254]Saharidis et al.,
2011).
Integrating the Network With an Existing PK/PD Model for MPL
Inference (regression) methods ([255]Fujii et al., 2017) correlate
inputs and outputs without prior knowledge of the underlying network
structure. Instead we pursued a “reaction-based” approach ([256]Newman
et al., 2013) which is an a priori biological knowledge enabling the
quantification of the dynamics ([257]Androulakis et al., 2007,
[258]2013) exploring the principles of mass action ([259]Aldridge et
al., 2006) and indirect response modeling ([260]Krzyzanski and Jusko,
1998a,[261]b) for expressing the rates of synthesis and degradation of
mRNA and protein. The general structure of the model is composed of
three sub-units: (a) the PK of MPL describes a bolus injection which
has already been described using a two-compartment model and is
maintained throughout ([262]Jin et al., 2003); (b) the PD module
describes the transduction cascade leading to the formation of the
active, nuclear, complex binding to the GRE ([263]Jin et al., 2003);
and (c) the module describing the transcription/translation dynamics.
For the latter, we model the transcription/translation process as a
dynamic system where the basal rate of transcription of mRNA is
regulated by the activity of regulating proteins, whereas the
translation is proportional to the amount of mRNA. The basic formalism
Inline graphic = k[s,m] f[i](P) - k[d,m] mRNA[i]; Inline graphic =
k[s,P] mRNA[i] - k[d,P]P[i] reflects the fundamental dynamics of
transcription and translation processes through the use of ordinary
differential equations ([264]Meister et al., 2013). The function f(P)
reflects the regulatory action of the various proteins on the
transcription mRNA. The f(P) formally reflects the likelihood of the
regulatory events and its functional form will reflect mechanistic
interpretations. In its most general form, and based on thermodynamic
arguments, it includes regulatory complexes as well as activation or
repression of transcription ([265]Bintu et al., 2005a,[266]b):
[MATH: fi(P)=a0(1+DRn)+∑j=1mλi<
/mi>j∏k
∈Sij
Pk(
1+∑j=1mμi<
/mi>j∏k
∈Sij
Pk)
. :MATH]
The set S[ij] defines a regulatory complex and the coefficients λ[ij],
μ[ij] reflect activation and repression constants respectively.
Discrete optimization formalisms ([267]Yang et al., 2008b,[268]c;
[269]Foteinou et al., 2009d; [270]Saharidis et al., 2011), could also
be used in order to augment the network connectivity. In all cases, the
basic structure of the network will be as identified via the network
analysis, while allowing for the possibility of minor adjustments based
on the estimation results. The estimation of the model parameters take
appropriate measures of identifiability and uncertainty into
consideration in order to achieve robust parameter estimation and is
discussed elsewhere ([271]Yang et al., 2011a,[272]b; [273]Wu et al.,
2015).
The network structure for Figure [274]5 along with the corresponding
data of the same figure was used to develop a simple model (Equations
1–9).
[MATH:
D=C1·e−λ1·t<
/mrow>+C2·e−λ2·t :MATH]
(1)
[MATH:
dRmdt=k
syn_Rm·(1−DRnIC50_Rm+DRn)−kdg
r_Rm·<
mi>Rm :MATH]
(2)
[MATH:
dRdt=ksyn_R·R
m+Rf·kre·D
Rn−ko<
/mi>n·D·<
mo>R-kdgr_R·R :MATH]
(3)
[MATH:
dDRdt=kon·D·R-k<
/mo>T·DR; :MATH]
(4)
[MATH:
dDRndt=k<
/mi>T·DR−
kre·DRn :MATH]
(5)
[MATH:
dmRNAdt=f−
kdgr_
m·mRNA<
/mrow> :MATH]
(6)
[MATH:
f=kP·(1+DRn+∑j=1Nai<
/mi>j·P<
mi>j) :MATH]
(7)
[MATH:
aij={1,
if prot
ein j r<
/mi>egulates gene i0,
otherwi
se <
/mo> :MATH]
(8)
[MATH:
dPdt=ksyn_P·mR
NA−kdgr_P·P :MATH]
(9)
In this model, the PK and PD of MPL, i.e., the equations for D, R[m],
R, DR and DR[n], and their corresponding kinetic parameters, are as
established earlier in [275]Sukumaran et al. (2011). The kinetics for
mRNA follow standard mass action kinetics with a 0th order
transcription and 1st order degradation. The transcription, however, is
conditionally regulated by MPL and/or other proteins, depending on the
network structure expressed through the matrix a[ij]. The dynamics of
protein synthesis was assumed in its simplest form (1st order
translation and degradation). The normalized data associated with the
mRNA and protein levels of the network in Figure [276]5 were used for
estimating the parameters involved in the mRNA and protein dynamics
only (the remaining were fixed based on prior deconvolution of the
PK/PD model). MPL plasma concentration (D) exhibit a biexponential
decline. Following the binding of the drug to the glucocorticoid
receptor (DR), this complex translocates into the nucleus [DR(N)] and
acts as the driving force for MPL-induced response patterns. Firstly,
this effect is observed as inhibition of mRNA expression for the
glucocorticoid receptor (R[m]), and consecutively the receptor protein
(R). DR(N) is also introduced as a stimulatory factor to all of the
nodes in the network. Level of mRNA expression is modeled to be
controlled by the presence of drug-receptor complex in the nucleus
together with indirect interactions between the proteins of the
network. Degradation of mRNA, protein translation from mRNA, and
protein degradation are all modeled as linear processes.
The initial, motivating, results, Figure [277]6, are only beginning to
scratch the surface and point to directions for improvement, as they
should with any iterative model development process. In general, for
most network elements early dynamics seem to be represented well
compared to later fluctuations in the response. However, significant
improvements and refinements are expected. We anticipate the need to
develop complex representations, likely requiring precursor and
receptor-mediated indirect responses ([278]Sharma et al., 1998;
[279]Sharma and Jusko, 1998; [280]Almon et al., 2002; [281]Hazra et
al., 2007). It is important to realize that model structure adjustments
have the potential to lead testable hypotheses in the laboratory to
further elaborate on the actual PK/PD. Given prior experience with both
the modeling principles ([282]Wang et al., 2012; [283]Stamatelos et
al., 2013; [284]Mavroudis et al., 2014; [285]Mavroudis et al., 2015) as
well as the associated parameter estimation of such pharmacology models
in terms of structure ([286]Yang et al., 2007a, [287]2008b,[288]c,
[289]2009a; [290]Foteinou et al., 2009d; [291]Saharidis et al., 2011)
and parameter identification ([292]Yang et al., 2011a,[293]b; [294]Wang
et al., 2012; [295]Stamatelos et al., 2013; [296]Wu et al., 2015), a
reasonable estimation problem can be formulated. Some of these efforts
might include introducing time delays to the elements which demonstrate
more pronounced dynamics in the later phase of the response. The
synthesis of the network mRNAs currently involves linear relationships.
Michaelis–Menten kinetics can be introduced to allow self-limiting
responses. Finally, the network structure can also be reshaped by
eliminating the nodes that are not insightful as well as introducing
new nodes that carry important regulatory information about the
existing parts of the network. Once the model is matured to fully
capture the experimental data, it can be utilized to make predictions
about long-term effects, or different delivery kinetics.
FIGURE 6.
FIGURE 6
[297]Open in a new tab
Preliminary results indicative of the potential to capture the complex
dynamics of the interplay between the PK dynamics of MPL and the
putative mRNA/protein cross-regulation in the form of networked
interactions. Despite the simplicity of the model equations, key
characteristics are captured. The simple form of the added model of
regulation clearly needs to be augmented. Furthermore, the preliminary
calculations did not take into account the error in measurements. Open
symbol/dashed line: data, solid line: model.
Another layer of information that can be incorporated into this model
in the future is the metabolome layer. As we have previously pointed
out, gene expression signatures give information about the lowest level
of organization, shedding light on the origin of a specific phenotype.
Proteomics provide information about the abundance of proteins,
elucidating the next level up from gene expression data. The
integration of these fields provides a unified picture of
cellular-level responses from transcription through translation.
However, there are multiple other levels of processes that control the
sequence of events from the translation of a protein until it becomes a
fully functional piece of the organism that can shape processes
affecting the metabolic phenotype. Metabolomics complements these more
traditional -omics techniques by allowing the investigation of
properties that cannot be directly assessed through gene and protein
expression. Integration of metabolomics with transcriptomics and
proteomics can help make the relationship between the levels of
information produced by each technique clearer. Changes in gene
expression levels and protein concentrations can be linked to
physiologic changes and interpreted in the biological context.
Future metabolomic studies in the same animal model can elicit the
metabolic shifts occurring in response to MPL administration that
ultimately cause the development of the adverse effects. Careful
assessment of the connections of these shifts with the defined
alterations in hepatic gene and protein expression levels can help
identify the critical nodes that control the metabolism-associated
adverse effects of the drug. Importantly, the indirect effects of the
drug on whole body metabolism through altering the microbiome would
have to be considered here as well, since the symbiotic organisms might
have tremendous influence on shaping the metabolic response to the
drug. Nevertheless, integration of information from the whole-body
metabolism with existing information on the hepatic response to MPL can
be useful in multiple ways. Firstly, alterations in the critical nodes
that are linked to long-term adverse effects can be identified and
adjunct therapies that can alleviate these alterations can be devised.
Secondly, patient populations which would be more susceptible to
experiencing those adverse effects, or who have better drug response,
can be pre-determined based on their genomic profiles. Thirdly, more
realistic models of drug response can be designed integrating
information from this ultimate phenotypic level and be used to evaluate
different scenarios, helping in the design and development of better
therapies.
Conclusion
The purpose of this review article is to provide a perspective on the
opportunities and challenges associated with the integration of
disparate -omics data sources. Without a doubt the next frontier in
PK/PD modeling will take advantage of our increasing abilities to
incorporate biological information across multiple and diverse layers
of physiologic organization. Although the richness of the data is
impressive, rationalizing the content in a systematic and coherent
manner remains a challenge. Given the overall difficulty of the
problem, upgrading the information content of the data is a major
challenge.
In this review, we have attempted to discuss three topics, based on our
own experience. We discussed challenges associated with integrating
-omics information within and between tissues. Interestingly, the
nature of the data, to some extent, drives the type of question and the
corresponding methods needed for answering the question. Finally, we
provided a brief summary of what we feel is a promising new frontier:
integrating -omics information in a model-based manner. The
expectations are twofold: (1) we may achieve a better rationalization
of the information; and (2) perhaps more importantly, we may be able to
further advance the frontiers of PK/PD modeling which could have
significant impact. This preliminary work introduces an approach for
bridging the classic PK/PD modeling efforts with the multi-level
systems response. This allows us to explore the paths of utilizing the
vast amount of information made available by new -omic profiling tools.
These tools make it possible to evaluate the response as a whole at a
certain biological level over time. The model-based integration
approach discussed here ultimately aims to connect this valuable
information coming from multiple layers in a useful framework which
reflects the continuity of biological events in response to
pharmacological stimuli. Achieving such integration will allow the
development of model-based approaches for rationalizing the genomic,
transcriptomic and proteomic data in the context of integrated dynamic
regulatory network models, critical for the development of MPL
PK/PD/pharmacogenomics models, enabling us to move beyond using -omics
as a complex descriptor toward the development of pharmacologically
relevant and predictive computational models.
Without a doubt, we are only scratching the surface. Numerous
challenges and open questions remain, charting an exciting future. To
name a few, our case studies present a modeler’s idealized scenario:
relatively homogenous cohorts, whether a single animal strain in our
MPL studies or a population of relatively healthy humans in our human
endotoxemia studies; single dosing with a single pharmaceutical agent.
Hidden within is reasonable biological variability; however the main
focus of these designs was to tease the agent’s primary effects. As we
consider moving integrative -omics to the next level, we must expect a
number of challenges: (1) as we hinted in the comparison of
metabolomics profiles between a controlled human endotoxemia study and
the clinical cases, -omics data from clinical studies and/or patient
population will, unavoidably, express and capture many confounding
factors beyond responses elicited by the agent under study; (2)
circulating, i.e., systemic, markers pose, as discussed in the
manuscript, additional challenges since the tissue-specificity is lost,
complicating further the interpretation of the observed responses in a
cause-and-effect sense; (3) the temporal granularity of the data will
remain a key challenge. The disparities in temporal resolution of the
responses at different physiologic levels will further complicate any
data driven-approach. In such cases, it is likely that methods aiming
at features and/or models – as discussed in the paper – will prove more
beneficial; (4) patient history, including medication, will constantly
nuance the data obtained; (5) despite the ability to probe an ever
increasing number of likely biological descriptors and mediators
(genome, transcriptome, proteome, metabolome, epigenome, fluxome, etc.)
leading to an increase in the dimensionality of the “input” space, the
actual “output” space, that is the number of subjects, volunteers
and/or patients, being sampled will always lack, especially if
appropriate population stratifications are implemented. This is a
classic problem in machine learning often referred to as
classification/feature selection in “almost empty spaces” ([298]Duin,
2000) Therefore, key challenges will remain, primarily focusing on the
many aspects of the heterogeneous nature of the data. However, as our
ability to collect, archive, annotate and query new challenges and
opportunities emerges ([299]Toga et al., 2015; [300]Dinov et al.,
2016), large cohort studies are already emerging ([301]Hood and Price,
2015).
Finally, one of the key themes of this presentation is that integration
can occur at two levels: (a) the level of the features of the data, or
(b) the level of the features of the models that could describe the
dynamics of the data. Each method offers distinct advantages and
challenges. Considering features of the data, characteristics of the
responses and likely important biomarkers are able to describe
intricacies of the response. Features of the models underlying the
dynamics of the data, on the other hand, enable a likely quantification
of cause-and-effect relations, as well as the likelihood of expressing,
and predicting, complex dynamics and emergent behaviors, not
necessarily obvious while studying the features themselves. However, it
is important to realize that the approaches are complimentary and are
often combined to improve the overall effectiveness of the analysis. To
some extent, effectiveness is also an emergent property of the
concurrent and multi-prong analysis of information-rich -omics data.
Author Contributions
KK, AA, and TN: performed calculations, conducted analyses and edited
the manuscript. RA, SC, SC, DD, and WJ: edited the manuscript. IA:
conceived the studies, and developed the manuscript.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Funding. KK, TN, RA, SC, SC, DD, WJ, and IA acknowledge funding from
[302]GM082974, GM34695 and GM24211. AA acknowledges support from the
National Institute of General Medical Sciences of the National
Institutes of Health (T32 [303]GM008339).
^1
[304]www.ingenuity.com
^2
[305]http://www.ncbi.nlm.nih.gov/geo/
References