Abstract
Opioids are widely used for treating different types of pains, but
overuse and abuse of prescription opioids have led to opioid epidemic
in the United States. Besides analgesic effects, chronic use of opioid
can also cause tolerance, dependence, and even addiction. Effective
treatment of opioid addiction remains a big challenge today. Studies on
addictive effects of opioids focus on striatum, a main component in the
brain responsible for drug dependence and addiction. Some transcription
regulators have been associated with opioid addiction, but relationship
between analgesic effects of opioids and dependence behaviors mediated
by them at the molecular level has not been thoroughly investigated. In
this paper, we developed a new computational strategy that identifies
novel targets and potential therapeutic molecular compounds for opioid
dependence and addiction. We employed several statistical and machine
learning techniques and identified differentially expressed genes over
time which were associated with dependence-related behaviors after
exposure to either morphine or heroin, as well as potential
transcription regulators that regulate these genes, using time course
gene expression data from mouse striatum. Moreover, our findings
revealed that some of these dependence-associated genes and
transcription regulators are known to play key roles in opioid-mediated
analgesia and tolerance, suggesting that an intricate relationship
between opioid-induce pain-related pathways and dependence may develop
at an early stage during opioid exposure. Finally, we determined small
compounds that can potentially target the dependence-associated genes
and transcription regulators. These compounds may facilitate
development of effective therapy for opioid dependence and addiction.
We also built a database ([36]http://daportals.org) for all
opioid-induced dependence-associated genes and transcription regulators
that we discovered, as well as the small compounds that target those
genes and transcription regulators.
Introduction
Opioids such as morphine have long been used as mainstay therapy for
treating different types of chronic severe pains such as cancer pain,
noncancer-related pain, and neuropathic pain [[37]1]. As a result of
relaxation on restriction of prescription opioids for treating chronic
noncancer pain and promotion of opioids in treatment by pharmaceutical
industry, practitioners and many organizations, non-medical use and
abuse of prescription opioids has been increasing rapidly in the United
States [[38]2], which in turn leads to opioid epidemic [[39]3].
Despite that opioids have beneficial analgesic effects of alleviating
acute and chronic pain, chronic opioid use can lead to adverse side
effects including tolerance, hyperalgesia, withdrawal reactions, and
even dependence [[40]2, [41]4, [42]5]. In order to achieve optimal pain
management, many studies have been carried out to elucidate mechanisms
underlying both the beneficial as well as the adverse effects of
opioids. Plenty of evidence has shown that crosstalks between neuronal
signaling, immune responses and chemokines play significant roles in
the pain pathways responsive to opioids such as morphine, and these
signaling networks can lead to both behavioral and structural changes
in the brain [[43]6, [44]7].
Studies on effects of opioids to relieve pain mainly focus on brain
regions such as periaqueductal gray, rostral ventromedial medulla, and
dorsal root ganglia [[45]6], while investigations of addictive effects
of opioids mostly focus on striatum, since striatum is a main component
of the reward system responsible for drug dependence and addiction.
Striatum receives several types of neuronal inputs from prefrontal
cortex (PFC), ventral tegmental area (VTA), and other areas of the
brain [[46]8], and over time, drug dependence and addiction can be
reinforced [[47]9]. At the molecular level, many transcription factors
(TFs) have been associated with behaviors related to drug abuse and
addiction. Some TFs known to play key roles in drug addiction include
ΔFosB, cyclic AMP-responsive element binding protein (CREB), NF-κB, and
MEF2 [[48]7]. For instance, opioids can reduce Fos expression in the
direct pathway striatal neurons [[49]10]. Moreover, drugs of abuse can
also alter gene transcription and induce addiction by epigenetic
mechanisms [[50]7].
Despite that different drugs of abuse often elicit similar behavioral
responses in animals and humans, molecular mechanisms underlying
addiction induced by different drugs can be distinctly different
[[51]10]. Opioids such as morphine and heroin increase dopamine level
in nucleus accumbens, the main component of the ventral striatum,
through activation of dopaminergic neurons in VTA [[52]10]. It is
popularly believed opioids inhibition of GABAergic neurons in VTA is
another contributing factor of disinhibition of dopamine in VTA and
increased rewarding effects in nucleus accumbens [[53]11–[54]16].
Opioids can also increase glutamate release in the nucleus accumbens,
which results in changes in synaptic plasticity such as decreased
dendritic branching and spine density [[55]17].
Despite all these efforts, however, much remains to be known about
molecular connections between the genes and pathways activated by
opioids in the pain-related processes and those involved in opioid
dependence and addiction. Elucidating such connections can not only
shed light on the mechanisms which contribute to the opioid epidemic,
but may also allow people to identify better candidate targets for
therapeutic interventions to prevent opioid dependence and addiction
during pain management.
In order to treat opioid addiction, opioid antagonists such as
naltrexone have been used to treat opioid addiction for several
decades. However, they have shown limited efficacy in relapse
prevention [[56]18, [57]19]. Recently, alternative approaches such as
receptor-based therapeutic strategies have been proposed which aim to
target receptors including G protein-coupled receptors (GPCRs) such as
μ-, δ-, and κ-opioid receptors, chemokine receptors, as well as
neuroimmune receptors such as Toll-like receptors 4 (TLR4)
[[58]20–[59]22]. However, despite all the efforts and advances in
understanding the mechanism of addiction in the past decades, they have
not led to development of effective new anti-addiction agents [[60]23].
Therefore, finding novel targets and strategies is needed for treating
opioid dependence and addiction.
In this work, we developed a new computational strategy for
identification of novel genome-wide targets and potential therapeutic
treatments for opioid dependence. In particular, this strategy involves
first detecting genes and pathways induced by either morphine or heroin
which are associated with dependence, then identification of small
compounds which can target the dependence-associated genes responsive
to the opioids. We analyzed a data set generated from a previous study
by Piechota et al [[61]24], in which, gene expression responses in
mouse striatum were investigated after mice were exposed to each of the
six drugs of abuse (i.e., morphine, heroin, cocaine, methamphetamine,
ethanol, or nicotine). Using the strategy we developed, we identified
morphine and heroin-induced genes and pathways associated with
dependence-related behaviors such as physical dependence and
psychological dependence, as well as transcription regulators which can
potentially regulate these genes. Finally, we identified small
compounds which can potentially target some of the dependence-related
genes and transcription factors. A database for all the dependence
associated genes and transcription regulators, along with small
compounds for targeting those genes and transcription regulators, is
available at [62]http://daportals.org. Our findings can facilitate
identification of novel candidate gene targets as well as potential
therapeutic interventions for treating opioid dependence and addiction.
Results
Identification of genes and patterns induced by either morphine or heroin
In order to identify genes induced by either morphine or heroin over
time, we applied a local regression method to gene expression
microarray data collected from mice treated by each opioid at different
time points [[63]24]. Using this approach, we found that 423 genes were
differentially expressed (DE) after morphine administration in mice,
while 608 genes were differentially induced by heroin. Using k-means
clustering, we identified 6 expression patterns among genes induced by
each opioid (Figs [64]1 and [65]2), with genes upregulated and
downregulated at 3 phases: Immediate-Early (IE), Middle (M), and Late
(L), respectively. Compared to morphine, heroin induced twice as many
genes in the IE and L phases, respectively (Figs [66]1 and [67]2),
indicating that heroin elicits neurobiological responses in mice not
only faster but also longer lasting than morphine.
Fig 1. Genes differentially expressed after morphine exposure in mouse
striatum.
[68]Fig 1
[69]Open in a new tab
(A) Patterns of differentially expressed genes induced by morphine.
(B-C) These plots show six genes upregulated (B) and downregulated (C)
by morphine in the IE, M, and L phase, respectively.
Fig 2. Genes differentially expressed after heroin exposure in mouse
striatum.
[70]Fig 2
[71]Open in a new tab
(A) Patterns of differentially expressed genes induced by heroin. (B-C)
These plots show six genes upregulated (B) and downregulated (C) by
heroin in the IE, M, and L phase, respectively.
Enriched Gene Oncology (GO) and KEGG terms among differentially expressed
genes (DEGs)
Our GO and KEGG analyses revealed that many DEGs induced by either
morphine or heroin in the mouse striatum were involved in the immune
and neuronal processes and pathways (Tables [72]1 and [73]2, [74]S1 and
[75]S2 Tables). Many of these biological processes are previously known
to play important roles in opioid-mediated pain pathways in brain
regions such as dorsal root ganglia [[76]6]. However, despite that the
neuroimmune signaling processes induced by either morphine or heroin in
mouse striatum share some similarities, it is also apparent that the
two opioids elicit distinct neurobiological responses in the animals
which we will detail below:
Table 1. Significantly enriched biological processes and pathways induced by
morphine which were involved in opioid-mediated pain pathways and
corresponding literature support.
In the “Phase” column, Up-IE, Up-M, and Up-L represent upregulated in
the IE, M, and L phase, respectively, while that Down-IE, Down-M, and
Down-L represent downregulated in the IE, M, and L phase, respectively.
GO/KEGG term Phase Effect (Literature support) DEGs Associated with
Dependence and Other Harmful Effects
Immune system Pattern recognition receptor signaling pathway Up-M
Proinflammatory responses, tolerance [[77]6, [78]22] Irak1: phys dep;
Ptafr: phys dep.
Activation of innate immune response Proinflammatory responses,
tolerance [[79]6, [80]22] Irak1: phys dep; Ptafr: phys dep.
Toll-like receptor signaling pathway Allodynia and hyperalgesia
[[81]25, [82]26];
NF-κB activation [[83]27–[84]29] Irak1: phys dep.
Response to xenobiotic stimulus Up-L Tolerance [[85]30, [86]31]
Negative regulation of NF-κB TF activity Analgesia
NF-κB activation [[87]6]
MAPK signaling pathway Down-M Anti-inflammatory responses Analgesia
[[88]32]
Humoral immune response
Induction of positive chemotaxis Down-L Nociceptive pathways [[89]22]
Neuronal signaling Cell projection morphogenesis Up-L Structural
plasticity [[90]33] Numb: acute, dep, HCC, phys dep, phys harm
Calcium signaling pathway Down-IE Analgesia [[91]22]
Synaptic transmission, glutamatergic Down-M
Analgesia [[92]34–[93]36] Grin1: phys dep, pleasure
Ensheathment of neurons Nociceptive pathways [[94]22] Cldn5 (HM*): phys
dep.
Synaptic transmission, GABAergic Proinflammatory; tolerance [[95]6,
[96]37]
Glutamate receptor signaling pathway Analgesia [[97]6] Cacng7: dep,
phys dep, pleasure
Sensory organ development Analgesia [[98]38, [99]39] Anp32b: phys dep.
Negative regulation of neuron apoptotic process Neuronal apoptosis
[[100]6] Grin1: phys dep, pleasure
Other key processes Protein dephosphorylation Up-IE
Alleviating inflammatory hyperalgesia [[101]6, [102]40] Dusp12 (HM):
phys dep.
positive regulation of autophagy Production of proinflammatory
cytokines, tolerance [[103]6] Plekhf1: psycho dep.
Regulation of programmed cell death Production of proinflammatory
cytokines, tolerance [[104]6] Dapk1 (HM): psycho dep; Plekhf1 (HM):
psycho dep; Pim3 (HM): phys dep.
[105]Open in a new tab
Note: the full names of the behaviors can be found in [106]S3 Table.
HM* indicates that the DEG is induced by both heroin and morphine.
Table 2. Significantly enriched biological processes and pathways induced by
heroin which were involved in opioid-mediated pain pathways and corresponding
literature support.
All of the abbreviations used in this table can be found in the legend
of [107]Table 1.
GO/KEGG term Phase Effect
(Literature support) DEGs Associated with Dependence and Other Harmful
Effects
Immune system Response to biotic stimulus
Up-IE Nociceptive pathways [[108]6] Ace: dep, phys dep; Baiap2:
pleasure, psycho dep; Stab1 (HM): phys dep
MyD88-dependent toll-like receptor signaling pathway Down-L
Anti-inflammatory effect; analgesia [[109]30, [110]41]
T-helper 1 type immune response (Il4, Tlr6, Il27)
Positive regulation of I-κB kinase/NF-κB signaling
Inflammatory response
Regulation of MAP kinase activity
Microglial cell activation
Anti-inflammatory responses; analgesia [[111]6]
Regulation of granulocyte chemotaxis Nociceptive pathways [[112]22]
Neuronal signaling Cell morphogenesis involved in neuron
differentiation Up-IE
Nociceptive pathways [[113]22] Baiap2: pleasure, psycho dep
Regulation of nervous system development Nociceptive pathways [[114]22]
Ace: dep, phys dep; Ncs1: dep; Baiap2: pleasure, psycho dep
Regulation of excitatory postsynaptic membrane potential Tolerance and
hyperalgesia [[115]22] Sez6: dep
Potassium ion transport Up-M Proanalgesic effect [[116]22]
Cation transmembrane transport Tolerance and hyperalgesia [[117]32]
Slc25a42: dep
Sodium ion transmembrane transport Up-L Allodynia and hyperalgesia
[[118]22]
Regulation of synapse organization Analgesia [[119]22]
Regulation of neuron death Down-IE Linked to anti-inflammatory
response; analgesia [[120]42] Bag1: dep, pleasure; Tfap2d: dep,
pleasure, psycho dep
Other key processes Protein autophosphorylation Up-IE Hyperalgesia
[[121]22] Dapk1 (HM): psycho dep; Pim3 (HM): phys dep
Negative regulation of receptor recycling Up-L Tolerance [[122]22]
Pcsk9: pleasure, psycho dep
Response to cAMP Down-M Analgesia [[123]6]
[124]Open in a new tab
Immune and neuronal responses to morphine in mouse striatum
Immune responses
Most genes involved in the immune system were induced by morphine in
the M phase. As shown in [125]Table 1, some genes participating in
anti-inflammatory processes were responsive to morphine, e.g., genes
involved in negative regulation of NF-κB transcription factor activity
were upregulated, and those involved in MAPK pathway, humoral immune
response, and induction of positive chemotaxis were downregulated.
Since anti-inflammatory pathways are known to play key roles in
opioid-induced pain relief [[126]6], these results are consistent with
the fact that morphine induces analgesia effect in animals.
On the other hand, some genes participating in proinflammatory
responses were also upregulated by morphine, including, e.g., genes
involved in Toll-like receptor signaling pathway, and activation of
innate immune response. Notably, previous evidence showed that
proinflammatory responses play central roles which contribute to
tolerance during chronic opioid exposure [[127]7]. Genes involved in
response to xenobiotic stimulus were also upregulated in the L phase,
in line with the fact that these genes are essential in sensing that
the cells are under the ‘insult’ of the drug.
Together, these results suggest that crosstalks between genes involved
in proinflammatory and anti-inflammatory pathways have already
initiated in mouse striatum during short-term morphine administration.
Neuronal responses
Our results also showed that genes participating in neuronal responses
were induced by morphine ([128]Table 1), which is not surprising, given
known neurological effects of morphine in the brain. For example,
positive regulation of autophagy and programmed cell death were
upregulated in the IE phase, and also genes involved in glutamatergic
synaptic transmission, glutamate receptor signaling pathway, and
sensory organ development were all downregulated in the M phase.
Notably, all these neuronal signaling events have been implicated in
the analgesia induced by morphine ([129]Table 1), again supporting the
notion that morphine can induce analgesia in the treated mice. Also,
genes involved in cell projection morphogenesis were upregulated in the
L phase, in line with the evidence that morphine can induce structural
changes in mice during chronic exposure [[130]33].
Together, our results suggest that even during short-term morphine
exposure, complex crosstalks between genes involved in proinflammatory
and anti-inflammatory pathways, neuronal signaling, and the chemokine
system have already initiated in mouse striatum, which can contribute
to analgesia and/or tolerance effects if exposure of the drug lasts
longer.
Immune and neuronal responses to heroin in mouse striatum
Immune responses
Our results showed that distinct from morphine, many immune genes
induced by heroin were downregulated in the L phase ([131]Table 2),
which included MyD88-dependent Toll-like receptor signaling pathway,
microglial cell activation, T-helper 1 type immune response (Il4, Tlr6,
Il27), positive regulation of I-κB kinase/NF-κB signaling, regulation
of granulocyte chemotaxis, inflammatory response, and regulation of MAP
kinase activity. Notably, all these pathways are known to be active in
proinflammatory responses during chronic opioid exposure [[132]6].
Therefore, downregulation of these pathways indicates that heroin
induces strong anti-inflammatory response and thus elicits strong
analgesic effects in the mice.
Neuronal responses
Genes involved in neuronal activities, such as regulation of nervous
system development and excitatory postsynaptic membrane potential (IE
phase) and cation transmembrane transport (M phase) were upregulated by
heroin, whereas genes involved in regulation of neuron death were
downregulated (IE phase) ([133]Table 2). These results also agree with
previous findings that neuronal responses play active roles in
opioid-related pain process [[134]6].
Also, we noticed that genes involved in cell morphogenesis involved in
neuron differentiation, positive regulation of axonogenesis, and
regulation of synapse organization were upregulated among IE and L
phases after heroin exposure. These results are supported by the
previous evidence that drugs of abuse can induce changes in structural
plasticity in animals during chronic exposure [[135]43].
Other key biological responses
Our results also showed that heroin induced genes participating in
other key biological processes involved in pain-related pathways, e.g.,
genes involved in protein autophosphorylation are upregulated in the IE
phase. Since protein phosphorylation is known to play key roles in
desensitization and implicated in opioid-induced hyperalgesia
[[136]22], our results suggest that these genes contribute to
dependence induced by chronic use of heroin; this speculation is
confirmed by our association analysis described below.
Association of morphine- and heroin-induced DEGs with harmful effects of
drugs of abuse
In order to find out whether DEGs induced by either morphine or heroin
are associated with various harmful effects linked to drugs of abuse,
we conducted the association analysis between expression levels of
morphine- or heroin-induced DEGs and twelve DA-related harmful effects
including dependence, physical dependence, psychological dependence,
pleasure, physical harm, social harm, health care cost, and conditioned
place preference (See [137]S3 Table and [138]Methods for details).
Using this approach, we detected 44 morphine-induced DEGs and 61
heroin-induced DEGs significantly associated with dependence-related
behaviors at the nominal level of significance (p < 0.05) ([139]S4 and
[140]S5 Tables). Among these dependence-associated DEGs, 9 were induced
by both morphine and heroin, of which 6 were induced in the IE phase.
These results can be searched in our database at
[141]http://daportals.org.
Next, we investigated whether the dependence-associated DEGs induced by
either morphine or heroin were involved in the pain-related neuroimmune
pathways mediated by opioids. As shown in Tables [142]1 and [143]2, we
found that a significant number of the dependence-associated DEGs
induced by the opioids were involved in the pain-related neuroimmune
pathways. Moreover, some of these genes could be induced by both
morphine and heroin, e.g., Dapk1, Plekhf1, Pim3, and Dusp12 were
upregulated by both morphine and heroin in the IE phase, and were
associated with psychological dependence and physical dependence,
respectively.
Detection of potential transcription regulators that regulate the
opioid-induced dependence-associated DEGs
In order to find out whether the dependence-associated DEGs induced by
each opioid were co-regulated by any TFs or epigenetic factors, we
performed the TF and epigenetic factor binding site enrichment test
using the ENCODE ChIP-Seq significance tool [[144]44]. Our analysis
showed that 17 transcription regulators potentially modulated
morphine-responsive dependence-associated DEGs ([145]S6 Table), while
that 12 transcription regulators modulated heroin-responsive
dependence-associated DEGs ([146]S7 Table). More details about our
results are described below.
Transcription regulators detected after morphine exposure
Known TFs associated with dependence and addiction
More than half of the TFs we detected regulating dependence-associated
DEGs after morphine exposure are previously known to play important
roles in drug dependence and addiction. For example, we found that
MEF2A upregulated four DEGs which were associated with physical
dependence in the M phase, while that MEF2C upregulated one DEG
associated with dependence in the IE phase after morphine exposure,
agreeing with the evidence that MEF2 is crucial in inducing behavioral
changes after exposure to drugs of abuse [[147]7].
Novel TFs induced by morphine
Our results also showed a few novel TFs activated by morphine. In order
to quantify the magnitude of the effects the detected transcription
regulators on the dependence-related behaviors induced by the opioids,
we developed a scoring metric, called dependence score, based on the
total fold changes of the dependence-associated DEGs co-regulated by
each regulator (see details in [148]Methods). Using this approach, we
found that E2f6, which potentially regulated 13 DEGs associated with
physical dependence after morphine exposure, had the highest dependence
score of 17.93. Also, we detected ZBTB33 and ZKSCAN1 associated with
physical dependence having high dependence scores (11 and 6.8,
respectively) after morphine exposure. In particular, ZBTB33 encodes a
transcriptional regulator Kaiso which can promote histone deacetylation
and decrease expression levels of its target genes [[149]45],
consistent with our results showing that ZBTB33 downregulates 8 genes
in the M phase. Also, Zkscan1 encodes a member of the Kruppel C2H2-type
zinc-finger family of proteins, which has been implicated in regulating
the expression of GABA type-A receptors in the brain [[150]46].
Epigenetic factors
Our results showed that about half (8 out of 17) of the detected
transcription regulators after morphine exposure were epigenetic
factors ([151]S6 Table). Literature search ([152]S8 Table) suggests
that these factors including HDAC8 (encoding histone deacetylase 8) and
HDAC6 (encoding histone deacetylase 6) play important roles in histone
acetylation, histone methylation, DNA methylation, and chromatin
remodeling [[153]45, [154]47–[155]52], in line with the previous
evidence that all these epigenetic events have been implicated in the
neurobiological responses to drugs of abuse in the brain [[156]7].
Notably, among all the epigenetic factors, SAP30 which encodes a
component of the histone deacetylase complex had the highest dependence
score of 15.49 and can potentially co-regulate 11 morphine-responsive
DEGs associated with physical dependence.
Transcription regulators detected after heroin exposure
Known TFs associated with dependence and addiction
DEGs upregulated by EGR1 and CREB1 were associated with psychological
dependence in the IE phase after administration of heroin, consistent
with the fact that both EGR1 and CREB are key TFs which regulate genes
involved in dependence-related behavioral responses during exposure of
opioids as well as other drugs of abuse [[157]5, [158]7]. Moreover, our
results showed that Polr2a (encoding the largest subunit of RNA
polymerase II), EGR1, and CREB1 were associated with social and
physical harm with the highest scores (> 20), suggesting that these TFs
play key roles in the biological mechanism that underlies the higher
social and physical harm caused by heroin compared to other drugs of
abuse [[159]4].
Novel TFs induced by heroin
We found that E2F6 (which showed the highest dependence score in
morphine) also had the highest dependence score of 6.52, potentially
regulating four DEGs associated with psychological dependence after
exposure to heroin.
Epigenetic regulators
Similar to morphine, more than 40% of the detected transcription
regulators were epigenetic factors after heroin exposure ([160]S7
Table). All these factors have been known to play major roles in
histone and DNA methylation, and chromatin remodeling methylation
[[161]50–[162]53]. Notably, 3 epigenetic factors CTCF, EZH2, and SUZ12
were identified during both morphine and heroin exposure.
Taken together, our results suggest that distinct transcriptional
regulatory mechanisms are responsive to exposure of morphine and heroin
in mouse striatum, and that epigenetic regulation plays major roles
after exposure to both opioids. Further investigation is needed to
elucidate the roles of the novel TFs (e.g., E2F6) with high dependence
scores after exposure of either morphine or heroin.
Finding small compounds which can target the DEGs and the
dependence-associated transcription regulators induced by the opioids
To facilitate development of therapeutic interventions for treating
morphine or heroin dependence, we developed a strategy which allowed us
to identify small compounds that can target the opioid-induced
dependence-associated DEGs and their potential transcription regulators
(see details in [163]Methods). Our results are shown in [164]S9 and
[165]S10 Tables for morphine and heroin, respectively. Among all the
small compounds we identified, we found that Calmidazolium could target
8 morphine-induced dependence-associated DEGs ([166]S9A Table), and
that Securinine could target E2F6 which had the highest dependence
score after morphine exposure ([167]Table 3). Also, we identified that
Phenacetin and Buspirone could target 11 and 4 heroin-induced
dependence-associated DEGs, respectively ([168]Table 4, [169]S1 Fig),
and that Meclofenoxate could target E2F6 which had the highest
dependence score also after heroin exposure ([170]S10B Table).
Table 3. Small compounds negatively correlated with the potential
transcription regulators after morphine administration.
All of the abbreviations used in this table can be found in the legend
of [171]Table 1.
Compound Transcription Regulators Fold Change Phase Associated Harmful
Effects
Benserazide TAF1 2.9 Up-IE Dep, pleasure
MEF2A 5.34 Up-M Phys dep
ZKSCAN1 -6.81 Down-M Phys dep
Piperacetazine SUZ12 1.2 Up-IE Dep
ZKSCAN1 -6.81 Down-M Phys dep
Securinine MEF2C 1.7 Up-IE Dep
BRF2 1.39 Up-M Phys dep
E2F6 -17.93 Down-M Phys dep
Isocorydine MEF2A 5.34 Up-M Phys dep
Gabapentin CTCF 2.97 Up-IE Chronic, dep, phys harm
MEF2A 5.34 Up-M Phys dep
HDAC6 -4.84 Down-M Acute, phys dep
[172]Open in a new tab
Table 4. Small compounds negatively correlated with dependence-associated
DEGs after heroin administration.
All of the abbreviations used in this table can be found in the legend
of [173]Table 1.
Compound DEGs Fold Change Phase Harmful Effects Associated with DEGs
Prilocaine DAPK1 1.63 Up-IE Acute, intox, psycho dep
ACE 1.34 Up-IE Acute, dep, HCC, phys dep, phys harm, soc
CEP350 1.29 Up-IE Dep, HCC, soc harm
FAAH -1.42 Down-IE Acute, dep, phys harm, pleasure, soc harm, soc
MPDU1 -1.39 Down-L Dep, HCC, phys dep, phys harm, psycho dep
Phenacetin PLEKHF1 1.64 Up-IE Psycho dep
RPL7L1 1.56 Up-IE Dep, HCC, phys dep, phys harm, soc harm, soc
EXTL1 1.24 Up-IE Dep, soc harm, soc
SPON2 1.23 Up-M Acute, dep, HCC, phys dep
TMTC4 1.19 Up-M Dep, HCC, phys dep, phys harm
PCSK9 1.52 Up-L Chronic, CPP, pleasure, psycho dep
FBRSL1 -1.17 Down-IE Psycho dep
ACSL4 -1.34 Down-M CPP, dep, HCC, phys dep
FEM1C -1.32 Down-M Hcc, phys dep, soc harm
TOR3A -1.21 Down-M Acute, dep, HCC, phys dep, phys harm, soc harm, soc
Procyclidine KLF2 2.31 Up-IE CPP, dep, HCC, phys dep, phys harm
FEM1C -1.32 Down-M Hcc, phys dep, soc harm
Spiradoline PLEKHF1 1.64 Up-IE Psycho dep
KIF23 1.36 Up-IE Dep, HCC, phys harm, soc harm, soc
CEP350 1.29 Up-IE Dep, HCC, soc harm
PTBP3 -1.19 Down-L Chronic, CPP, intox, phys dep, phys harm, psycho dep
Buspirone NCS1 1.34 Up-IE Dep, HCC, soc harm
TBC1D2B 1.31 Up-IE Dep, HCC, phys dep, phys harm, soc harm, soc
SLC35D1 -1.29 Down-M Acute, phys dep
TOR3A -1.21 Down-M Acute, dep, HCC, phys dep, phys harm, soc harm, soc
[174]Open in a new tab
Comparison of our approach and findings with those in a previous work by
Piechota et al. [[175]24]
Since the data set we analyzed in this work was generated from a
previous study in [[176]24], it is worthwhile to compare our approach
and results with those reported in [[177]24]. There are two main
differences between the two studies. First, Piechota et al. employed
two-way ANOVA to identify genes differentially expressed in mouse
striatum responsive to some of the six drugs of abuse. The limitation
of using two-way ANOVA to analyze time-course data is that time is
considered as a factor in the ANOVA analysis and thus the trends of
expression levels of genes (over time) are ignored. We, instead,
employed a local regression smoothing technique to identify
differentially expressed genes responsive to the drug exposure, which
was able to capture the trends of the expression levels of the genes
over time. Second, Piechota et al. focused on identification and
characterization of gene expression patterns induced by multiple drugs
of abuse, whereas we were interested in the genes and patterns induced
by either morphine or heroin, and particularly when the genes were
involved in pain pathways and associated with dependence.
Using our approach, we identified 423 and 608 DE genes induced by
morphine and heroin, respectively, in mouse striatum (Figs [178]1 and
[179]2). Since only the two-way ANOVA was used to identify the 42 DE
genes in [[180]24], it is unknown which of the six drug(s) induced the
genes. Regardless, we compared the DE genes in the two studies, and
found that 11 morphine-induced genes and 17 heroin-induced genes in our
work overlapped with the previous study. We also compared the enriched
functional groups of genes identified by the two studies, and found
that the majority of enriched GO groups identified in the previous
study were also discovered in our work (see [181]S11 Table), e.g.,
protein phosphatase activity, apoptosis, anatomical structure
morphogenesis, calcium ion binding, GTPase mediated signal
transduction, and transmembrane transporter activity. However, as shown
in [182]S1 and [183]S2 Tables, we also identified many more
significantly enriched GO and KEGG groups of genes than those in
[[184]24], such as those involved in the neuroimmune signaling
processes (Tables [185]1 and [186]2).
Discussion
Many studies have been conducted which intend to delineate pain-related
pathways induced by opioids. However, much remains to be known about
the molecular connection between these opioid-mediated pain pathways
and those playing key roles in drug dependence and addiction.
Dissecting these pathways can facilitate identification of candidate
targets for developing effective therapeutic interventions which
ideally can target opioid tolerance and dependence while preserving
opioid analgesic effect.
In this study, we developed a computational strategy to identify
candidate dependence-associated DEGs induced by either morphine or
heroin, as well as to find small compounds which could target these
genes for treating dependence of the opioids. Using this strategy, we
analyzed a time-course gene expression microarray data set generated
previously to investigate gene expression patterns responsive to
various drugs of abuse in mouse striatum [[187]24]. In particular, we
first employed a local regression technique to detect genes
differentially expressed over 8 hours of time in mouse striatum after
either morphine or heroin exposure. Then, we performed correlation
analysis to identify morphine or heroin-induced DEGs which were
associated with twelve harmful effects including dependence commonly
linked to drugs of abuse. Furthermore, we detected potential
transcription regulators including TFs and epigenetic factors that
regulated the dependence-associated DEGs using an ENCODE enrichment
tool. Finally, to facilitate the identification of candidate targets
and development of effective therapy for morphine and heroin-induced
dependence, we identified small compounds which could potentially
target against some of the detected dependence-associated DEGs and
transcription regulators.
Using the approach described above, we found that a significant number
of the DEGs responsive to either morphine or heroin in mouse striatum
were involved in the neuroimmune signaling pathways, which are
typically activated in the pain-related pathways during chronic opioid
use previously identified in other brain areas including periaqueductal
gray, rostral ventromedial medulla, and dorsal root ganglia [[188]6].
Using correlation analysis, we found that a considerable portion of the
pain pathway-related DEGs, previously known to play active roles in
opioid analgesia, tolerance, hyperalgesia, and allodynia, were
associated with the harmful effects (such as dependence) linked to
morphine and heroin as well as many other drugs of abuse, e.g., Irak1
(encoding interleukin-1 receptor-associated kinase 1) in the enriched
Toll-like receptor signaling pathway ([189]Table 1) induced by morphine
was correlated with physical dependence at a nominal level of
significance (p < 0.05). Toll-like receptor signaling pathway has been
known to play crucial roles in proinflammatory signaling and tolerance
to opioid analgesia. We also noticed that some dependence-associated
DEGs could be induced by both morphine and heroin, e.g., among DEGs
upregulated in the IE phase, Dapk1 and Plekhf1 were correlated with
psychological dependence, while Dusp12 and Pim3 were associated with
physical dependence after exposure to both morphine and heroin. It is
unclear what roles these genes (induced by both opioids) play when mice
were first exposed to morphine, then switched to heroin later on, a
scenario commonly seen among human drug abusers.
Despite the similarities in gene expression responses induced by both
morphine and heroin in mouse striatum, differences between the two
opioids are also obvious. For example, a large number of the DEGs
involved in immune signaling were downregulated in the L phase after
heroin exposure, as opposed to morphine exposure, suggesting that
heroin elicited strong anti-inflammatory responses in the L phase and
thus induced acute analgesic effects in the mice. Considering that
heroin is diamorphine which is rapidly converted into several
psychoactive metabolites including 6-mono-acetylmorphine (6MAM) and
morphine in humans and mice [[190]54, [191]55], it is not surprising
that morphine and heroin elicit similar responses in vivo. However,
several factors may account for the differences between the two
opioids. First, evidence suggests that heroin and its metabolite 6MAM
can elicit different responses in mice than morphine, e.g., the former,
but not the latter, can elicit acute toxic effect on locomotor
activity, particularly at high doses [[192]55], suggesting that 6MAM
may contribute to the differential responses in mice induced by heroin
and morphine. Second, since both heroin and 6MAM have much higher
degrees of lipophilicity than morphine, after intraperitoneal (i.p.)
injection as done in [[193]24], heroin and 6MAM can cross the
blood-brain barrier much faster than morphine [[194]56] and thus some
of the late gene expression response to heroin (but not morphine)
exposure could be captured by the gene expression profiling which
measured gene expression levels at no later than 8 hours after the
opioid injection. Finally, since different dosages of morphine (20
mg/kg) and heroin (10 mg/kg) were used for i.p. injection of the mice,
it is difficult to assess to what extend this dosage effect affects the
gene expression responses in mouse striatum induced by morphine and
heroin.
Among detected transcription regulators that potentially regulate the
dependence-associated DEGs, MEF2A induced by morphine as well as EGR1
and CREB1 by heroin are known to play crucial roles in drug addiction.
We also found that more than 40% of the detected transcription
regulators are epigenetic factors after both morphine and heroin
exposure, including HDAC8 and HDAC6 activated by morphine, which
supports the previous notion that epigenetic factors are important for
addiction. Furthermore, using the dependence score, a metric we
developed for measuring the extent the detected transcription
regulators affect the dependence-associated DEGs, we found that E2F6
has the highest dependence scores after exposure of both morphine and
heroin.
In summary, our work here intent to elucidate molecular connections
between the analgesic and tolerance-related pain pathways and harmful
side effects of opioid use during pain treatment. Despite the general
belief that morphine is safe for managing patients with pain, our
results suggest that morphine may induce tolerance to analgesia and
dependence on the drug in the patients in the very early stage, which
may increase the possibility of the same patients to abuse heroin
thereafter, since heroin may further induce acute analgesic effects as
suggested by our results. Moreover, because heroin can cause both
structural and behavioral changes among patients, abusing heroin after
morphine may lead to more potent dependence on the drugs among the
patients.
Furthermore, we found several small compounds which could potentially
target some of the dependence-associated DEGs and the detected
transcription regulators induced by the opioids. In particular, we
identified Securinine and Meclofenoxate which could target E2F6 in
humans after exposure to morphine and heroin, respectively. These
compounds can facilitate future development of effective therapeutic
interventions which can target the adverse side effects of morphine and
heroin, while preserving their analgesic effects.
We also compared the approach and findings from this work with those
from the previous study by Piechota et al. [[195]24] from which the
data set was generated. The approaches employed by the two studies were
different but complement with each other. Piechota et al. aimed to
identify and characterize genes differentially induced by any of the
six drugs using the two-way ANOVA analysis, while we focused on finding
and investigating genes induced by either morphine or heroin (using a
local regression technique) which were also associated with dependence
and previously known to affect pain pathways. Comparison of the
enriched functional (GO and KEGG) groups of genes identified in the two
studies suggest that the majority of the GO groups discovered in the
previous study were also identified in our work and that our results
revealed many more biologically meaningful functional groups of genes
involved in neuroimmune signaling pathways.
The limitations of our work include the following. The gene expression
microarray data we analyzed spanned only 8 hours after administration
of morphine and heroin, which limited our ability to discover chronic
effects of the drugs. In the future study, we intend to employ the same
strategy to investigate long-term effects of morphine and heroin, and
to compare them with the acute effects we discovered in this work.
Also, our results in this work were generated based on analyzing a
single gene expression profiling data set, further biological
validation of some of the genes differentially induced over time in
mouse striatum by morphine and heroin should be conducted to verify our
findings here. Despite these limitations, we found that our results
agree well with the previously known evidence about drug abuse and
addiction, suggesting our findings are valid and worth further in-depth
investigation. Moreover, our work provides insight into the molecular
connections between the opioid-induced pain-related pathways and the
adverse harmful effects associated with morphine and heroin.
Understanding such connections may facilitate development of effective
therapies which allow people to target dependence-associated genes and
transcription regulators at an early stage of opioid use while
preserving analgesic effects of opioids.
Methods
Dataset
The gene expression microarray data set we analyzed in this work was
obtained from the NCBI Gene Expression Omnibus (GEO) database under the
accession number [GEO:[196]GSE15774]. This data set was generated from
a previous work described in [[197]24], in which, gene expression
alterations in mouse striatum were investigated after the mice were
treated by various drugs of abuse, including morphine, heroin,
methamphetamine, cocaine, nicotine and alcohol. Detailed description of
the data set can be found in [[198]24]. Briefly, after a single dose of
drug administration, gene expression was obtained from the mouse
striatum at 1, 2, 4, 8 hours afterwards. Meanwhile, samples from
saline- and naïve-treated control group were collected at 0, 1, 2, 4, 8
hours as controls. There were three biological replicates for each drug
group and each time point.
Identification of genes differentially expressed over time in mouse striatum
after exposure of either morphine or heroin
In order to identify genes differentially expressed over time in mouse
striatum after administration of either morphine or heroin, we employed
a local regression smoothing technique [[199]57] to estimate the
smoothed time course gene expression data for each opioid. The detailed
description of the strategy can be found in [[200]58]. For each opioid,
expression values of each gene (i.e., transcript) were available for 1,
2, 4, and 8 hours, and expression values for time point 0 for the
corresponding genes from the naïve group were used to represent the
control time point (i.e., 0 hour) for each opioid. In particular,
expression values for each gene over different time points were first
fitted using a local polynomial quadratic (degree = 2) model with the
bandwidth optimally estimated using a leave-one-out cross validation
procedure [[201]57]. To determine whether a gene is differentially
expressed over time with respect to the control time point, we
calculated the simultaneous 95% confidence intervals for the fitted (or
expected) intensity values using a method due to Sun and Loader
[[202]59]. The p-values were adjusted using the Bonferroni correction
to account for multiple hypothesis testing. We determined a gene as
differentially expressed if its expression value at any time point T
relative to the control time point satisfied: 1) adjusted p-value <
0.05, and 2) fold change ≥ 1.2.
Identification of temporal patterns for DEGs induced by either morphine or
heroin using cluster analysis
In order to identify temporal patterns for DEGs responsive to either
morphine or heroin exposure, we applied a k-means clustering algorithm
proposed by Hartigan and Wong [[203]60] to the temporal expression
values of the DEGs. The Euclidean distance was used to measure
dissimilarities between different genes. A thousand iterations were
performed to find an optimal partition of K clusters where K is
pre-assigned. To determine an optimal number of the clusters for the
DEGs, we employed the average silhouette width (ASW) as described in
[[204]61], and when K = 6, ASW is the largest for the DEGs induced by
both morphine and heroin.
GO and KEGG pathway enrichment analysis
We performed GO and KEGG pathway enrichment analysis to identify
biological processes and pathways that were overrepresented among DEGs
in each cluster after exposure of either morphine or heroin. We
performed the enrichment analysis with the GOstats R software package
[[205]62], which finds enriched functional groups using the
hypergeometric test with the aid of the functional terms in the GO and
KEGG databases. GO terms and KEGG pathways were considered as
significantly enriched if their p-values < 0.05.
Association of morphine- and heroin-induced DEGs with harmful effects of
drugs of abuse
Our association analysis aimed to determine whether morphine- and
heroin-induced DEGs were associated with any harmful effects of drugs
of abuse. The scores which assessed the magnitudes of the twelve
harmful effects of various drugs of abuse were taken from Nutt et. al.
[[206]4] and can be found in [207]S3 Table. In particular, the scores
for the harmful effects encompassing three categories, including
physical harm (overall, acute, chronic), dependence (pleasure,
psychological, physical), social harm (overall, health-care costs), and
conditioned place preference for the drugs including morphine, heroin,
cocaine, methamphetamine, ethanol, and nicotine were used to calculate
the association of each harmful effect and the DEGs induced by either
morphine or heroin. Specifically, let S[i] denote a vector of the
scores for harmful effect i corresponding to drugs D, where D =
[morphine, heroin, cocaine, methamphetamine, ethanol, and nicotine].
Let G[j] denote a vector of expression values of gene G corresponding
to drugs D at time point j; G[j] is a DEG induced by either morphine or
heroin at time point j, but the gene is not required to be
differentially induced by the other drugs at the same time point. The
expression values of gene G for the drugs including cocaine,
methamphetamine, ethanol, and nicotine were estimated for each drug by
using the same local regression smoothing techniques as described above
for morphine and heroin. Finally, we calculated the correlation between
G[j] and S[i] using both the Pearson correlation and a quadratic
polynomial regression; if the resulting p-value from any of the methods
was less than 0.05, G[j] was considered as significantly associated
with S[i].
Identification of human transcription and epigenetic factors that potentially
regulate the dependence-associated DEGs induced by each opioid
To identify potential transcription and epigenetic factors that
regulate dependence-associated DEGs responsive to each opioid in mouse
striatum, we employed the ENCODE ChIP-Seq significance tool [[208]44]
to identify human TFs and epigenetic factors whose binding sites were
significantly enriched among the DEGs associated with dependence (i.e.,
dependence, psychological, and/or physical dependence) in each of the
six identified clusters after either morphine or heroin exposure. The
ENCODE ChIP-Seq significance tool calculates enrichment scores of the
transcription regulators using the hypergeometric test, and the
resulting p-values were corrected by an FDR procedure to account for
multiple hypothesis testing. A 1000-base pair (bp) window upstream of
the transcription start site (TSS) and downstream of the transcription
termination site (TTS) were considered for each DEG. A TF or an
epigenetic factor was considered as significantly enriched if its FDR
p-value < 0.05.
Furthermore, to facilitate ranking the significantly enriched TFs and
epigenetic factors in terms of their impact on dependence, we developed
a scoring metric called the ‘dependence score’ as follows. For each
transcription regulator R activated in a certain phase P, we assume
that R regulates a number N of morphine or heroin-induced DEGs
associated with a dependence-related harmful effect (such as
dependence, psychological, or physical dependence) within phase P. As
shown in Figs [209]1 and [210]2, the IE, M, and L phases correspond to
0–2 hours, 2–4 hours, and 4–8 hours after exposure to either morphine
or heroin. The ‘dependence score’ for R in phase P was then defined as
[MATH:
∑i=1NFCi :MATH]
, where FC[i] represents the maximum absolute fold change of the
expression values of a DEG G[i] within phase P, relative to that of the
control time point, and G[i] is regulated by R. The higher the
dependence score, the more impact the transcription regulator R can
have on dependence.
Using a similar concept as the dependence score, we also assigned
association scores to transcription regulator R if the DEGs it
regulates were also associated with other harmful effects of drugs (as
shown in [211]S3 Table). Specifically, an association score between a
transcription regulator R and a harmful effect H was defined as the sum
of the maximum absolute fold change of the DEGs regulated by R in phase
P that were associated with H.
Finding small molecular compounds to target the opioid-induced
dependence-associated DEGs and the transcription regulators
Gene expression patterns in cells can change during treatment by
small-molecule drugs or compounds [[212]63]. If a small compound has an
opposite effect on transcription than opioids, the small compound has
potential to reverse the gene signature induced by opioids and hence
the subsequent harmful effects caused by opioids. With the availability
of gene expression profiles of small molecular compounds, we were able
to compare them with those of morphine and heroin, and identify small
compounds with the potential for treating dependence and addiction
induced by each opioid.
Specifically, we employed the following two-step strategy to find the
small compounds:
Finding DEGs induced by small compounds
First, a commercial Illumina BaseSpace (former Nextbio™) software
(Santa Clara, CA, USA, [213]http://www.nextbio.com) were used to obtain
the DEGs induced by small compounds in cells. In BaseSpace, most of the
raw gene expression datasets involving perturbations by small compounds
were obtained from the Gene Expression Omnibus (GEO) database
([214]http://www.ncbi.nlm.nih.gov/geo/). Only genes with the p-values <
0.05 and absolute fold changes >1.2 were considered as DEGs induced by
small compounds. To identify top compounds that have gene expression
profiles most correlated with the dependence-associated DEG or the
transcription regulators induced by morphine or heroin, we searched the
gene expression profiles of small compounds stored in BaseSpace through
BaseSpace integrated Pharmaco Atlas search. Then, the correlation
between the DEGs induced by small compounds and the opioid-induced DEGs
or transcription regulators was calculated as described below.
Calculation of the correlation between the DEGs induced by small compounds
and the opioid-induced DEGs or transcription regulators
For each opioid-induced dependence-associated DEG, we used its maximum
absolute fold change over the measured time points (i.e., 1, 2, 4, and
8 hours) (which was defined as the ratio of the highest absolute
expression value of the gene relative to that at the control time
point) to represent its fold change. For each transcription regulator,
we used its dependence score to represent its fold change value.
The correlation between the DEGs induced by each small compound and the
opioid-induced dependence-associated DEGs or transcription regulators
was calculated using the BaseSpace software. This software provided a
modified form of the rank-based enrichment statistics to compare the
two sets of the DEGs [[215]64, [216]65]. BaseSpace pre-processed gene
expression data with biomedical ontologies to enable comparison among
heterogeneous datasets from different species. It also used
meta-analyses to provide consistent predictions from multiple instances
of similar perturbations, e.g., genes expression profiles from
different cell lines induced by the same compounds [[217]66]. All
analyses using the BaseSpace software were performed with the default
parameters.
[218]S1 Fig shows an example of the significant negative correlation
between the (61) heroin-induced dependence-associated DEGs and the
buspirone-induced DEGs (p-value = 0.0277). Four genes were regulated by
both heroin and buspirone, but in opposite directions.
Supporting information
S1 Fig. Significant negative correlation between the 61 heroin-induced
dependence-associated DEGs and the buspirone-induced DEGs (p-value =
0.0277).
Four genes were regulated by both heroin and buspirone, but in opposite
directions.
(TIF)
[219]Click here for additional data file.^ (662.5KB, tif)
S1 Table. Significantly enriched GO terms and KEGG pathways induced by
morphine in different phases and their association with harmful effects
of drugs.
(A) Significantly enriched GO terms induced by morphine. (B)
Significantly enriched KEGG pathways induced by morphine.
(XLSX)
[220]Click here for additional data file.^ (28.7KB, xlsx)
S2 Table. Significantly enriched GO terms and KEGG pathways induced by
heroin in different phases and their association with harmful effects
of drugs.
(A) Significantly enriched GO terms induced by heroin. (B)
Significantly enriched KEGG pathways induced by heroin.
(XLSX)
[221]Click here for additional data file.^ (25.6KB, xlsx)
S3 Table. The scores of the harmful effects associated with drugs of
abuse (adapted from Nutt, et. al., Lancet 2007; 369: 1047–53).
(XLSX)
[222]Click here for additional data file.^ (11.3KB, xlsx)
S4 Table. 44 dependence-associated DEGs induced after morphine
exposure.
(XLSX)
[223]Click here for additional data file.^ (13.2KB, xlsx)
S5 Table. 61 dependence-associated DEGs induced after heroin exposure.
(XLSX)
[224]Click here for additional data file.^ (12.5KB, xlsx)
S6 Table. Significantly enriched transcription regulators and
associated harmful effects after morphine exposure.
(DOCX)
[225]Click here for additional data file.^ (35.7KB, docx)
S7 Table. Significantly enriched transcription regulators and
associated harmful effects after heroin exposure.
(DOCX)
[226]Click here for additional data file.^ (23.5KB, docx)
S8 Table. Supporting evidence of involvement of transcription
regulators in drug dependence and addiction from literature.
(DOCX)
[227]Click here for additional data file.^ (27.9KB, docx)
S9 Table. Small compounds negatively correlated with
dependence-associated DEGs and the potential transcription regulators
after morphine administration.
(A) Small compounds negatively correlated with dependence-associated
DEGs induced by morphine. (B) Small compounds negatively correlated
with the transcription regulators induced by morphine.
(XLSX)
[228]Click here for additional data file.^ (14.5KB, xlsx)
S10 Table. Small compounds negatively correlated with
dependence-associated DEGs and the potential transcription regulators
after heroin administration.
(A) Small compounds negatively correlated with dependence-associated
DEGs induced by heroin. (B) Small compounds negatively correlated with
the transcription regulators induced by heroin.
(XLSX)
[229]Click here for additional data file.^ (13KB, xlsx)
S11 Table. Comparison of the enriched functional terms in this work
with those in Piechota, et al. (2011).
(XLSX)
[230]Click here for additional data file.^ (10.8KB, xlsx)
Data Availability
All relevant data are within the paper and its Supporting Information
files.
Funding Statement
This study was conducted with the support of grants from the National
Institutes of Health P30DA035778 and R01GM114311.
References