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. Author manuscript; available in PMC: 2018 Aug 1.
Published in final edited form as: Psychopharmacology (Berl). 2017 Jun 26;234(15):2259–2275. doi: 10.1007/s00213-017-4657-y

Alterations of expression of inflammation/immune-related genes in the dorsal and ventral striatum of adult C57BL/6J mice following chronic oxycodone self-administration: A RNA sequencing study

Yong Zhang 1,*, Yupu Liang 2,*, Orna Levran 1, Matthew Randesi 1, Vadim Yuferov 1, Connie Zhao 3, Mary Jeanne Kreek 1
PMCID: PMC5826641  NIHMSID: NIHMS888326  PMID: 28653080

Abstract

Non-medical use of prescription opioids such as the mu opioid receptor (MOP-r) agonist oxycodone is a growing problem in the United States and elsewhere. There is limited information about oxycodone's impact on diverse gene systems in the brain.

Objectives

The current study was designed to examine how chronic oxycodone self administration (SA) affects gene expression in the terminal areas of the nigrostriatal and mesolimbic dopaminergic pathways in mice.

Method

Adult male C57BL/6J mice underwent a 14-day oxycodone self-administration procedure (4 hours/day, 0.25mg/kg/infusion, FR1), and were euthanized 1 hour after the last session. The dorsal and ventral striatum were dissected and total RNAs were extracted. Gene expressions were examined using RNA sequencing.

Result

We found that oxycodone self-administration exposure led to alterations of expression in numerous genes related to inflammation/immune functions: in the dorsal striatum (54 upregulated genes, 1 downregulated gene), ventral striatum (126 upregulated genes and 15 downregulated genes), with 38 upregulated genes identified in both brain regions.

Conclusion

This study reveals novel neurobiological mechanisms underlying some of the effects of a commonly abused prescription opioid. We propose that inflammation/immune gene systems may undergo a major change during chronic self-administration of oxycodone.

Keywords: Oxycodone self-administration, RNA sequencing, Inflammation and immune related genes, C57BL/6J mice

Introduction

Over the past 15 years, there has been a dramatic increase in the medical and non-medical use of prescription opioids (short-acting MOP-r agonists). Oxycodone is one of the most commonly abused prescription opioids, which accounts for a substantial percentage of opioid-related deaths in the United States (Paulozzi et al. 2015). The effects of oxycodone are predominantly mediated initially through MOP-r (Beardsley et al. 2004). There is limited information about oxycodone's impact on the brain and the underlying neurobiological mechanisms involved in its abuse.

An early study (using a microarray technique) reported that repeated experimenter-administered oxycodone in rats (15 mg/kg, twice a day for 8 days) led to upregulation of 31 genes and downregulation of 25 genes, in the whole brain (Hassan et al. 2007). We extended this study by examining the effects of oxycodone in a mouse model of 14-day oxycodone self-administration on expression of genes in specific areas of the brain. We identified 61 genes that were downregulated and 18 genes that were upregulated in several brain regions in male adult and adolescent mice. Those studies focused on genes involved in neurotransmission or synaptic plasticity (Mayer-Blackwell et al. 2014; Zhang et al. 2015; Zhang et al. 2014).

Although microarrays have been widely used in quantitative transcriptomics, these techniques have several limitations: reliance on prior knowledge about genes for probes or PCR primer design, and monitoring only some portions of the known genes. RNA sequencing (RNA-seq) has been recently introduced for transcriptome profiling based on massively parallel, unbiased sequencing. RNA-seq offers several important advantages over other gene expression technologies: it provides more accurate, sensitive and reliable gene expression data, in addition to the detection of gene variant and alternative splicing (e.g., Goldman and Domschke 2014; Wang et al. 2014). However, RNA-seq technology is still in a developmental stage, and there is little consensus regarding optimal methods for quantification and downstream analysis, especially for brain tissues. In the current study, RNA-seq was used for the first time to identify changes in the genome-wide expression of transcripts in the mouse dorsal and ventral striatum following 14-day extended access (4-hour) oxycodone self-administration. The dorsal and ventral striatum (terminal regions of the nigrostriatal and mesolimbic dopaminergic pathways, respectively) are thought to mediate different aspects of reward and drug-seeking (e.g., Koob and Volkow 2010; Lobo and Nestler 2011). Numerous studies have shown that the dorsal striatum is gradually engaged in habitual and compulsive-like drug-seeking (e.g., Barker and Taylor 2014; Belin et al. 2009; Everitt 2014; Everitt and Robbins 2013; Porrino et al. 2007). The ventral striatum plays a crucial role in processing rewarding stimuli, as well as stimulations which are both rewarding and reinforcing (e.g., Olsen 2011).

Methods

Subjects

Male adult (11 weeks old on arrival) C57BL/6J mice (Jackson Laboratory, Bar Harbor, ME) were housed in groups of four, with free access to food and water in a light-(12:12 hour light/dark cycle, light on at 7:00 pm and off at 7:00 am) and temperature-(25 °C) controlled room. Animal care and experimental procedures were conducted according to the Guide for the Care and Use of Laboratory Animals (Institute of Laboratory Animal Resources Commission on Life Sciences 2016). The experimental protocols used were approved by the Institutional Animal Care and Use Committee of The Rockefeller University. In the current study, we examined gene expression using RNA-seq in the male C57BL/6J mice because we had carried out numerous studies in this strain of male mice in the past (e.g., Mayer-Blackwell et al. 2014; Zhang et al. 2015; Zhang et al. 2014). Comparative studies of female mice would be of great interest for follow-up studies.

Self-Administration Procedure

Catheter Implantation and Intravenous Self-administration Chambers

Following acclimation for 7 days, the mice were anesthetized with a combination of xylazine (8.0 mg/kg, i.p.) and ketamine (80 mg/kg, i.p.). For details of surgical procedure and description of operant conditioning chambers, see Zhang et al. (Mayer-Blackwell et al. 2014; Zhang et al. 2014; Zhang et al. 2009). Immediately after surgery, the surgical sutures and incision areas of all the mice studied were covered with antibiotic ointment. No further antibiotic was used.

Oxycodone Self Administration

Study 1

A 4-hour self-administration session was conducted once a day for 14 consecutive days. Each day, mice were weighed and heparinized saline (0.01 ml of 30 IU/ml solution) was used to flush the catheter to maintain patency. During self-administration sessions, mice were placed in the self-administration chambers and a nose poke through the active hole led to an infusion of oxycodone (0.25 mg/kg/infusion; Sigma, St. Louis, MO) under a FR1 schedule. Drug dose was adjusted on a daily basis by a computer program which took into account the changes in an animal’s body weight. Mice in the saline control groups received yoked saline infusions during all sessions (saline was infused in the control mouse whenever the oxycodone mouse self-administered oxycodone). At the end of the experiment, only data from mice that passed a catheter patency test, defined as loss of muscle tone within a few seconds after administration of 30 ul ketamine (5mg/ml) (Fort Dodge, IA), were included in the analysis of data.

Of a total 31 mice that started in this study, 22 mice finished the 14-day self-administration sessions and passed the catheter patency test. Of these 22 mice, the brain tissues of 12 mice (6 oxycodone and 6 yoked saline controls) were randomly chosen for RNA-seq study. These mice were studied as part of a recent publication and detailed behavioral data are available in that publication (Zhang et al., 2014).

Study 2

In order to have sufficient RNA for confirmation of RNA-seq data by qPCR, separate groups of mice underwent an identical 4-hour self-administration session once a day for 14 consecutive days. Of a total 26 mice that started in the second study, 16 mice completed the 14-day SA study and passed the catheter patency test. The brain tissues from these mice (9 oxycodone and 7 yoked saline controls) were used in the qPCR confirmation study.

RNA Extraction

Mice were sacrificed within 1 hr after the last self-administration session by decapitation after brief exposure to CO2; the dorsal and ventral striatum from each mouse were dissected from the brain and homogenized in Qiazol (Qiagen, Valencia, CA). Total RNA was isolated from the dorsal and ventral striatum using the miRNeasy kit (Qiagen, Valencia, CA). The quality and quantity of RNA from each sample was determined using an Agilent 2100 Bioanalyzer.

RNA-seq library preparation and sequencing

The integrity of RNA samples was evaluated again by Agilent 6000 RNA Nano Chips. RNA-seq library preparation and sequencing of samples isolated from dorsal striatum was performed by LC Sciences (Houston, TX) whereas RNA-seq library preparation and sequencing of RNA isolated from ventral striatum was performed by the Genomic Resource Center at the Rockefeller University. Both RNA-seq libraries were prepared using Illumina’s TruSeq® Stranded Total RNA LT kit (with Ribo-Zero™) following manufacturer’s protocol. Briefly, starting with 100 ng total RNA, cytoplasmic ribosomal RNA was removed, and the remaining RNA was fragmented by incubating at 94°C for 8 minutes with divalent cations. The cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase and random primers. This was followed by second strand cDNA synthesis using DNA Polymerase I and RNaseH. The double stranded cDNA fragments then had the addition of a single 'A' nucleotide to prevent self-ligation during the subsequent addition of the indexing adapters. PCR was then used to enrich only those DNA fragments that had adapter molecules on both ends to amplify the amount of DNA in the library. Libraries were validated using Agilent Tape Station High Sensitivity DNA kits and normalized. Libraries were multiplexed, four samples per lane and sequenced. Illumina HiSeq 2500 was used to obtain 100 bp single-end reads for samples from the ventral striatum. Illumina HiSeq 2000 was used to obtain 100 bp paired-end reads for samples from the dorsal striatum.

RNA-seq data quality assessment and differential gene expression (DE) Analysis

The fastq files were generated by configuring BclToFastq.pl from CASAVA v1.8.2 with the following parameters: --ignore-missing-stats, --ignore-missing-bcl, --ignore-missing-control, --positions-format, clocs, --fastq-cluster-count 350000000. They were then examined using FASTQC (Andrews, 2010). The reads were aligned to the mouse reference genome (version mm10) using STAR v2.3 (Dobin et al. 2013) aligner with default parameters. The alignment results were then evaluated through RNA-SeQC v1.17 (DeLuca et al. 2012) to ensure that all the samples had a consistent alignment rate, and no obvious 5’ or 3’ bias. Aligned reads were summarized through feature Counts (Liao et al. 2014) with the gene model from Ensemble (Mus_musculus.GRCm38.75.gtf) at gene level: specifically, the uniquely mapped reads (NH ‘tag’ in bam file) that overlapped with an exon (feature) by at least 1bp were counted and then the counts of all exons annotated to an Ensemble gene (meta features) were summed into a single number. Only protein coding genes were used for the downstream analysis of this study. To eliminate the between-sample differences in read counts, the summarized counts matrix was then normalized through a set of housekeeping genes Ppia, Gusb and Gapdh (The sums of the counts are normalized to a fixed number for all samples). Principal Component Analysis (PCA) was then applied to the normalized count of all the samples from the two brain regions to detect outliers (see Figure 1). DESeq2 (Love et al. 2014) was applied to the normalized counts to estimate the fold change between the samples from mice that had self-administered oxycodone and those from yoked saline controls, using negative binomial distribution. An adjusted p-value of less than 0.05 (padj <0.05) was used to select genes that have a significant expression change.

Figure 1.

Figure 1

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Principal Component Analysis on normalized count in dorsal striatum (A) and ventral striatum (B).

Gene expression study with quantitative polymerase chain reaction (qPCR)

Samples from the replicate SA study (Study 2) were used to confirm expression differences between oxycodone and yoked saline control mice of a subset of randomly-selected genes with high and low expression level in the dorsal and ventral striatum, as identified by RNA-seq. We examined genes showing alteration in the expression levels in both the dorsal and ventral striatum, and also studied genes that were altered in response to oxycodone SA in only one of those brain regions. cDNA was synthesized from 1 µg of total RNA using the Super Script III first strand synthesis kit (Invitrogen, Carlsbad, CA). The reaction was performed in an ABI Prism 7900 HT Sequence Detection System (Applied Biosystems, Foster City, CA), applying the following program: 2 minutes at 50°C, 10 minutes at 95°C and 40 cycles of 15 seconds at 95°C and 1 minute at 60°C. Sequence Detection Software (Applied Biosystems, Foster City, CA) was used to calculate the cycle threshold (Ct) value for each well. Any sample with Ct greater than 35 was not included in the analysis according to manufacturer’s recommendations. The relative expression of each gene was calculated by subtracting the Ct value of the housekeeping gene Gapdh from the Ct value of the target gene to determine the ΔCt. The final measure of relative expression level of a gene was calculated as 2−ΔCt (details see Zhang et al. 2015; Zhang et al. 2014).

Statistical Analysis on qPCR data

In the qPCR study, the significance of the difference between oxycodone and yoked saline control mice in the expression levels of each gene was evaluated by one tailed t test. A p value of < 0.05 was accepted as indicating a significant difference. Adjustment for multiple comparisons (false discovery rate) was performed for the qPCR data.

Gene Ontology analysis

For functional enrichment analyses, DAVID (Ashburner et al., 2000) with default parameters and all mouse genes as background model, was used to test biological functional enrichment of genes with significant expression changes (padj <0.05). The analysis was done through Gene Ontology (GO) (Kanehisa and Goto 2000) and KEGG pathways (Ashburner et al. 2000). Briefly, DAVID tests the statistical significance of whether the identified genes are over-represented in a specific term (such as GO or KEGG).

Results

Extended access oxycodone self-administration

Self-administration behaviors of oxycodone and yoked saline control mice from Study 1 (for RNA-seq study, also see Zhang at al., 2014) and Study 2 (for qPCR study) were similar; we therefore combined the data from the two studies. The nose poking of mice from both SA studies is shown in Figure 2. A two-way ANOVA, for Drug Condition (oxycodone or saline) × Session (Days 1–14) showed a significant main effect of Drug Condition [F(1,252)=672.6, p < 0.0001], and a significant main effect of Session [F(13,252)=2.359, p < 0.01] with a significant interaction of Drug Condition × Session [F(13,252)=21.12, p < 0.0001]. Mice in the oxycodone group nose poked significantly more at the active hole than at the inactive hole. In contrast, the yoked saline control group did not differ in nose poking between the “active” and inactive hole. The daily oxycodone intake increased significantly across sessions.

Figure 2.

Figure 2

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Oxycodone self-administration in 4 hour sessions across the 14 days is shown. Data represent mean daily number of nose pokes (±SEM). Mice in the oxycodone group nose poked significantly more at the active hole than at the inactive hole. Mice in the yoked saline group did not differ in nose poking between the "active" and inactive hole. See also Zhang et al., 2014.

Extended oxycodone self-administration led to transcriptional changes related to inflammation and immune function in the dorsal and ventral striatum

Dorsal striatum

Significant changes were observed in the expression of 55 genes related to inflammation/immune function in the dorsal striatum (Table 1), between mice that had self-administered oxycodone and the yoked saline controls. Fifty-four genes in the dorsal striatum showed significant upregulation, whereas one gene showed significant downregulation.

Table 1. Altered inflammation /immune related genes following oxycodone SA in the dorsal striatum.

Genes that were upregulated following 14-day oxycodone self-administration in the dorsal striatum. Genes labeled with * were upregulated in both the dorsal and ventral striatum.

Gene
Symbol		 Gene Name Direction Fold
change		 pvalue padj
Fcgr4* Fc receptor, IgG, low affinity IV ↑ 3.02 2.26E-08 6.76E-05
Treml4* triggering receptor expressed on myeloid cells-like 4 ↑ 3.01 1.27E-07 1.90E-04
Plac8* placenta-specific 8 ↑ 2.89 6.10E-08 1.33E-04
Lilrb4* leukocyte immunoglobulin-like receptor, subfamily B, member 4B ↑ 2.85 8.04E-08 1.33E-04
Tgtp1* T cell specific GTPase 1 ↑ 2.72 4.84E-09 3.61E-05
Fpr2* formyl peptide receptor 2 ↑ 2.65 2.54E-06 1.65E-03
Il1b* interleukin 1 beta ↑ 2.65 1.19E-05 5.21E-03
Spn* sialophorin ↑ 2.64 1.14E-06 1.00E-03
Cybb* cytochrome b-245, beta polypeptide ↑ 2.59 4.24E-07 4.22E-04
Gbp4 guanylate binding protein 4 ↑ 2.55 1.32E-06 1.03E-03
Iigp1 interferon inducible GTPase 1 ↑ 2.53 1.33E-05 5.68E-03
Itgal* integrin alpha L ↑ 2.53 3.95E-07 4.21E-04
C3* complement component 3 ↑ 2.52 4.08E-06 2.48E-03
Ly6c2 lymphocyte antigen 6 complex, locus C2 ↑ 2.51 3.51E-05 1.12E-02
Casp4* caspase 4, apoptosis-related cysteine peptidase ↑ 2.42 4.09E-05 1.22E-02
Gbp5* guanylate binding protein 5 ↑ 2.39 2.23E-05 8.31E-03
Tgtp2* T cell specific GTPase 2 ↑ 2.39 1.83E-05 7.01E-03
Lcn2* lipocalin 2 ↑ 2.35 1.38E-05 5.70E-03
Gvin1 GTPase, very large interferon inducible 1 ↑ 2.34 2.41E-05 8.66E-03
Stx11 syntaxin 11 ↑ 2.34 1.33E-04 2.96E-02
Irf1* interferon regulatory factor 1 ↑ 2.33 9.61E-06 4.78E-03
Nlrc5* NLR family, CARD domain containing 5 ↑ 2.30 5.04E-06 2.79E-03
Socs3* suppressor of cytokine signaling 3 ↑ 2.29 1.13E-05 5.10E-03
Lyz2* lysozyme 2 ↑ 2.29 4.16E-06 2.48E-03
Hck* hemopoietic cell kinase ↑ 2.27 7.61E-08 1.33E-04
Csf2rb* colony stimulating factor 2 receptor, beta, low-affinity (granulocyte-macrophage) ↑ 2.27 1.01E-04 2.44E-02
Irgm2* immunity-related GTPase family M member 2 ↑ 2.21 7.61E-08 1.33E-04
Gbp9* guanylate-binding protein 9 ↑ 2.21 6.82E-07 6.37E-04
Pirb* paired Ig-like receptor B ↑ 2.14 1.51E-04 3.14E-02
Psmb9 proteasome (prosome, macropain) subunit, beta type 9 (large multifunctional peptidase 2) ↑ 2.14 1.45E-05 5.70E-03
C1ra complement component 1, r subcomponent A ↑ 2.11 1.09E-04 2.58E-02
Bst1* bone marrow stromal cell antigen 1 ↑ 2.10 2.18E-04 4.23E-02
Irgm1* immunity-related GTPase family M member 1 ↑ 2.09 7.16E-05 1.91E-02
Tap1 transporter 1, ATP-binding cassette, sub-family B (MDR/TAP) ↑ 2.08 5.75E-06 3.07E-03
Il4ra* interleukin 4 receptor, alpha ↑ 2.08 2.45E-07 2.82E-04
Hp* haptoglobin ↑ 2.08 1.50E-04 3.14E-02
Ctla2a* cytotoxic T lymphocyte-associated protein 2 alpha ↑ 1.95 3.11E-05 1.03E-02
Ly6a lymphocyte antigen 6 complex, locus A ↑ 1.91 1.80E-08 6.70E-05
Ptprc* protein tyrosine phosphatase, receptor type, C ↑ 1.91 1.28E-06 1.03E-03
Myo1g* myosin IG ↑ 1.89 1.38E-04 2.98E-02
Ifitm3 interferon induced transmembrane protein 3 ↑ 1.88 8.07E-05 2.08E-02
Itgb2* integrin beta 2 ↑ 1.88 2.75E-05 9.33E-03
Pglyrp1* peptidoglycan recognition protein 1 ↑ 1.87 1.83E-04 3.64E-02
Anxa2 annexin A2 ↑ 1.81 2.20E-06 1.50E-03
Sla src-like adaptor ↑ 1.80 1.81E-04 3.64E-02
Tgm2 transglutaminase 2, C polypeptide ↑ 1.80 1.44E-07 1.95E-04
Slc11a1* solute carrier family 11 (proton-coupled divalent metal ion transporters), member 1 ↑ 1.72
Zbtb16* zinc finger and BTB domain containing 16 ↑ 1.69 1.04E-05 5.01E-03
Osmr* oncostatin M receptor ↑ 1.65 1.30E-04 2.95E-02
Abcc9* ATP-binding cassette, sub-family C (CFTR/MRP), member 9 ↑ 1.55 1.44E-05 5.70E-03
Samhd1 SAM domain and HD domain, 1 ↑ 1.52 2.37E-04 4.47E-02
Ly6c1 lymphocyte antigen 6 complex, locus C1 ↑ 1.50 1.13E-05 5.10E-03
Slc2a1* solute carrier family 2 (facilitated glucose transporter), member 1 ↑ 1.44 1.67E-07 2.08E-04
Pdzd2 PDZ domain containing 2 ↑ 1.28 1.52E-04 3.14E-02
Itga9 integrin alpha 9 ↓ 0.72 3.82E-05 1.17E-02
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Ventral Striatum

In the ventral striatum, significant changes in the expression of 141 inflammation/immune-related genes were found between mice that had self-administered oxycodone and the yoked saline controls (Table 2). Two samples of ventral striatum were not included in the analysis due to their outlier status detected by PCA analysis (see Figure 1). Among the 141 inflammation/immune-related genes, 126 showed significant upregulation after oxycodone SA, whereas 15 genes showed significant downregulation.

Table 2. Altered inflammation /immune related genes following oxycodone SA in the ventral striatum.

Genes that were up- or down-regulated following 14-day oxycodone self-administration in the ventral striatum. Genes labeled with * were upregulated in both the dorsal and ventral striatum.
Gene Symbol Gene Name Direction Fold
change		 pvalue padj
Sele selectin, endothelial cell ↑ 3.84 3.10E-15 4.59E-11
Lcn2* lipocalin 2 ↑ 3.26 2.95E-14 2.19E-10
C3* complement component 3 ↑ 3.19 6.42E-12 2.38E-08
Spn* sialophorin ↑ 3.17 1.12E-10 2.37E-07
Plac8* placenta-specific 8 ↑ 3.05 7.98E-10 1.18E-06
Fpr2* formyl peptide receptor 2 ↑ 2.88 7.69E-09 9.50E-06
Hp* haptoglobin ↑ 2.87 4.56E-10 7.51E-07
Selp selectin, platelet ↑ 2.81 1.79E-08 1.91E-05
Fgr FGR proto-oncogene, Src family tyrosine kinase ↑ 2.79 1.80E-08 1.91E-05
Ncf4 neutrophil cytosolic factor 4 ↑ 2.66 2.14E-08 2.09E-05
Il1b* interleukin 1 beta ↑ 2.65 1.09E-07 8.48E-05
Itgal* integrin alpha L ↑ 2.54 3.43E-08 2.99E-05
Treml4* triggering receptor expressed on myeloid cells-like 4 ↑ 2.53 4.15E-07 0.000228194
Prl prolactin ↑ 2.45 4.04E-07 0.000228194
Cybb* cytochrome b-245, beta polypeptide ↑ 2.42 8.88E-07 0.000356587
Myo1g* myosin IG ↑ 2.41 2.26E-08 2.09E-05
Icam1 intercellular adhesion molecule 1 ↑ 2.37 1.07E-06 0.000416759
Cd14 CD14 antigen ↑ 2.35 1.20E-07 8.94E-05
Hspa1b heat shock protein 1B ↑ 2.32 8.90E-07 0.000356587
Ly6i lymphocyte antigen 6 complex, locus I ↑ 2.29 6.04E-06 0.00149208
Bst1* bone marrow stromal cell antigen 1 ↑ 2.26 6.60E-06 0.001604811
Pilra paired immunoglobin-like type 2 receptor alpha ↑ 2.25 5.78E-06 0.00149208
Socs3* suppressor of cytokine signaling 3 ↑ 2.22 2.96E-06 0.000944922
Mefv Mediterranean fever ↑ 2.19 1.17E-05 0.0024599
Csf2rb* colony stimulating factor 2 receptor, beta, low-affinity (granulocyte-macrophage) ↑ 2.14 1.32E-05 0.002573582
Pirb* paired Ig-like receptor B ↑ 2.13 1.29E-05 0.002559713
Runx1 runt related transcription factor 1 ↑ 2.12 3.35E-06 0.001034761
Casp4* caspase 4, apoptosis-related cysteine peptidase ↑ 2.11 2.66E-05 0.004083134
Cytip cytohesin 1 interacting protein ↑ 2.08 4.26E-05 0.005519554
Slfn2 schlafen 2 ↑ 2.08 4.23E-05 0.005519554
Fcgr4* Fc receptor, IgG, low affinity IV ↑ 2.07 7.40E-05 0.008131867
Tnfaip2 tumor necrosis factor, alpha-induced protein 2 ↑ 2.03 1.16E-05 0.0024599
Irf1* interferon regulatory factor 1 ↑ 2.03 2.14E-05 0.003533595
Ptprc* protein tyrosine phosphatase, receptor type, C ↑ 2.02 8.71E-07 0.000356587
Hck* hemopoietic cell kinase ↑ 2.01 7.52E-07 0.000338049
C5ar1 complement component 5a receptor 1 ↑ 2.00 0.000108393 0.01086204
Gbp5* guanylate binding protein 5 ↑ 1.98 0.000176751 0.015696982
Il4ra* interleukin 4 receptor, alpha ↑ 1.98 6.97E-07 0.000333552
Ifi47 interferon gamma inducible protein 47 ↑ 1.97 0.000200308 0.0171499
Rnf125 ring finger protein 125 ↑ 1.97 5.62E-05 0.006671
Ccl12 chemokine (C-C motif) ligand 12 ↑ 1.97 0.000213169 0.0171499
Lilrb4a* (replaced Lilrb4) leukocyte immunoglobulin-like receptor, subfamily B, member 4A ↑ 1.94 0.000273844 0.019157455
Irgm2* immunity-related GTPase family M member 2 ↑ 1.93 6.03E-05 0.006982474
Lyz2* lysozyme 2 ↑ 1.91 0.000341027 0.022680563
Fas Fas (TNF receptor superfamily member 6) ↑ 1.90 2.36E-05 0.003801418
Fcgr2b Fc receptor, IgG, low affinity IIb ↑ 1.90 5.22E-05 0.00634768
Batf2 basic leucine zipper transcription factor, ATF-like 2 ↑ 1.89 0.000391349 0.025125934
Tnfsf10 tumor necrosis factor (ligand) superfamily, member 10 ↑ 1.89 0.000246682 0.0180525
Adamts1 a disintegrin-like and metallopeptidase (reprolysin type) with thrombospondin type 1 motif, 1 ↑ 1.89 8.15E-06 0.00191844
Robo3 roundabout homolog 3 (Drosophila) ↑ 1.87 0.000548487 0.030104231
S100a9 S100 calcium binding protein A9 (calgranulin B) ↑ 1.87 0.000663427 0.033596569
Fyb FYN binding protein ↑ 1.86 1.81E-07 0.000127701
Il2rg interleukin 2 receptor, gamma chain ↑ 1.86 0.000603152 0.031721065
Isg20 interferon-stimulated protein ↑ 1.85 0.000428365 0.025934276
Birc3 baculoviral IAP repeat-containing 3 ↑ 1.85 0.000249888 0.0180785
Plaur plasminogen activator, urokinase receptor ↑ 1.84 0.000372616 0.024027265
Adm adrenomedullin ↑ 1.83 0.000412153 0.025791711
C5ar2 complement component 5a receptor 2 ↑ 1.82 0.000168154 0.015299921
Tgtp1* T cell specific GTPase 1 ↑ 1.81 0.00115296 0.048440665
Mlkl mixed lineage kinase domain-like ↑ 1.81 0.000657386 0.033596569
Tgtp2* T cell specific GTPase 2 ↑ 1.81 0.001178572 0.048961902
Cd300lf CD300 antigen like family member F ↑ 1.81 0.001183609 0.049033829
Cd52 CD52 antigen ↑ 1.79 0.000534611 0.029807573
Csf2rb2 colony stimulating factor 2 receptor, beta 2, low-affinity (granulocyte-macrophage) ↑ 1.79 0.000923022 0.042121064
Fcgr3 Fc receptor, IgG, low affinity III ↑ 1.78 5.03E-07 0.000257089
Ptgs2 prostaglandin-endoperoxide synthase 2 ↑ 1.77 2.95E-05 0.004345051
Ctla2a* cytotoxic T lymphocyte-associated protein 2 alpha ↑ 1.76 0.000291616 0.020116107
Gbp9* guanylate-binding protein 9 ↑ 1.76 0.000878369 0.040709675
Hspa1a heat shock protein 1A ↑ 1.76 0.000242993 0.0180525
Map3k8 mitogen-activated protein kinase kinase kinase 8 ↑ 1.76 4.32E-05 0.005519554
Bcl3 B cell leukemia/lymphoma 3 ↑ 1.76 0.000766635 0.037035702
Apobec3 apolipoprotein B mRNA editing enzyme, catalytic polypeptide 3 ↑ 1.76 0.000266281 0.018805747
Nlrc5* NLR family, CARD domain containing 5 ↑ 1.76 0.001064565 0.045763941
Rac2 RAS-related C3 botulinum substrate 2 ↑ 1.75 0.000258464 0.018518239
Ccr5 chemokine (C-C motif) receptor 5 ↑ 1.74 8.70E-06 0.002014954
Osmr* oncostatin M receptor ↑ 1.74 5.31E-06 0.001431624
Tlr7 toll-like receptor 7 ↑ 1.73 2.70E-05 0.004083134
Pomc pro-opiomelanocortin-alpha ↑ 1.73 0.001001447 0.044601992
Hspb1 heat shock protein 1 ↑ 1.71 0.000623632 0.032567179
Spi1 spleen focus forming virus (SFFV) proviral integration oncogene ↑ 1.70 4.14E-05 0.005519554
Themis2 thymocyte selection associated family member 2 ↑ 1.70 0.000262915 0.018675116
Irgm1* immunity-related GTPase family M member 1 ↑ 1.69 0.001159624 0.048583023
Itgb2* integrin beta 2 ↑ 1.69 6.15E-05 0.007074965
Lcp1 lymphocyte cytosolic protein 1 ↑ 1.69 4.17E-05 0.005519554
Syk spleen tyrosine kinase ↑ 1.66 1.37E-05 0.002622998
Pglyrp1* peptidoglycan recognition protein 1 ↑ 1.66 1.51E-06 0.000561269
Kcnj8 potassium inwardly-rectifying channel, subfamily J, member 8 ↑ 1.65 7.87E-06 0.001882344
Ifitm2 interferon induced transmembrane protein 2 ↑ 1.63 0.000505586 0.028619616
Ikzf1 IKAROS family zinc finger 1 ↑ 1.61 0.001076374 0.046004904
Zbtb16* zinc finger and BTB domain containing 16 ↑ 1.61 9.69E-06 0.002176541
Nod2 nucleotide-binding oligomerization domain containing 2 ↑ 1.61 3.44E-05 0.004849278
Nostrin nitric oxide synthase trafficker ↑ 1.60 1.71E-05 0.003088282
Bcl6b B cell CLL/lymphoma 6, member B ↑ 1.60 0.000268213 0.018852472
Clec5a C-type lectin domain family 5, member a ↑ 1.60 0.001024552 0.04495601
Lcp2 lymphocyte cytosolic protein 2 ↑ 1.60 0.000207807 0.0171499
Clec2d C-type lectin domain family 2, member d ↑ 1.60 6.74E-05 0.007687459
Tnfrsf1b tumor necrosis factor receptor superfamily, member 1b ↑ 1.59 1.87E-06 0.000661649
Kremen1 kringle containing transmembrane protein 1 ↑ 1.58 5.03E-05 0.006168173
Zfp36 zinc finger protein 36 ↑ 1.58 0.000576625 0.030852116
Vcam1 vascular cell adhesion molecule 1 ↑ 1.58 0.000232666 0.017803663
Slc11a1* solute carrier family 11 (proton-coupled divalent metal ion transporters), member 1 ↑ 1.57 0.000955187 0.043322241
Nod1 nucleotide-binding oligomerization domain containing 1 ↑ 1.55 0.000420586 0.025934276
Myo1f myosin IF ↑ 1.54 0.000464259 0.027107957
Serpinh1 serine (or cysteine) peptidase inhibitor, clade H, member 1 ↑ 1.53 0.000471669 0.027325452
Slc19a3 solute carrier family 19, member 3 ↑ 1.53 1.78E-05 0.003110677
Ackr1 atypical chemokine receptor 1 (Duffy blood group) ↑ 1.53 1.29E-05 0.002559713
Abcc9* ATP-binding cassette, sub-family C (CFTR/MRP), member 9 ↑ 1.52 2.56E-07 0.000157988
Ncf1 neutrophil cytosolic factor 1 ↑ 1.51 0.00054815 0.030104231
Hspa5 heat shock protein 5 ↑ 1.51 7.29E-07 0.00033795
Ier3 immediate early response 3 ↑ 1.50 0.000188989 0.016487626
Itpr1 inositol 1,4,5-trisphosphate receptor 1 ↑ 1.45 0.000191707 0.016530279
Nfkbiz nuclear factor of kappa light polypeptide gene enhancer in B cells inhibitor, zeta ↑ 1.45 0.000747804 0.03648252
Xbp1 X-box binding protein 1 ↑ 1.45 3.48E-06 0.001054445
Nfe2l2 nuclear factor, erythroid derived 2, like 2 ↑ 1.45 0.000248905 0.0180785
Slc2a1* solute carrier family 2 (facilitated glucose transporter), member 1 ↑ 1.44 2.53E-05 0.003945444
Il17ra interleukin 17 receptor A ↑ 1.43 0.000532623 0.029807573
Htr2a 5-hydroxytryptamine (serotonin) receptor 2A ↑ 1.43 0.000915986 0.041928973
Ezr ezrin ↑ 1.41 1.78E-06 0.000644428
C1qc complement component 1, q subcomponent, C chain ↑ 1.32 0.001169468 0.048720184
Kazn kazrin, periplakin interacting protein ↑ 1.27 0.000771733 0.037160936
Adcy9 adenylate cyclase 9 ↑ 1.25 0.000548252 0.030104231
Dapk1 death associated protein kinase 1 ↑ 1.25 3.80E-06 0.001127402
Cd163 CD163 antigen ↑ 1.23 0.000848315 0.040068045
Dnajc3 DnaJ heat shock protein family (Hsp40) member C3 ↑ 1.21 0.000481134 0.027657747
Mlf2 myeloid leukemia factor 2 ↑ 1.21 2.34E-05 0.003801418
Lzts1 leucine zipper, putative tumor suppressor 1 ↑ 1.18 0.000666227 0.033596569
Ddx3x DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 3, X-linked ↓ 0.88 1.60E-05 0.002936785
Rfx5 regulatory factor X, 5 (influences HLA class II expression) ↓ 0.79 3.47E-05 0.004849278
Hmgb3 high mobility group box 3 ↓ 0.77 0.000422766 0.025934276
Podxl2 podocalyxin-like 2 ↓ 0.74 0.000578095 0.030852116
Ecel1 endothelin converting enzyme-like 1 ↓ 0.71 0.000598238 0.031574611
Adcy8 adenylate cyclase 8 ↓ 0.71 0.000829787 0.039444134
Tspan6 tetraspanin 6 ↓ 0.69 0.000216119 0.0171499
Isl1 ISL1 transcription factor, LIM/homeodomain ↓ 0.68 0.000216799 0.0171499
Grik1 glutamate receptor, ionotropic, kainate 1 ↓ 0.65 0.000708151 0.034662015
Mc3r melanocortin 3 receptor ↓ 0.61 0.000297137 0.020308023
Gpc3 glypican 3 ↓ 0.60 0.000232885 0.017803663
Glra1 glycine receptor, alpha 1 subunit ↓ 0.54 0.000491955 0.028062222
Cd83 CD83 antigen ↓ 0.51 3.20E-11 7.91E-08
Igsf1 immunoglobulin superfamily, member 1 ↓ 0.47 4.84E-06 0.00132888
Ngfr nerve growth factor receptor (TNFR superfamily, member 16) ↓ 0.42 8.84E-07 0.000356587
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Thirty-eight genes (labeled with *) in Tables 1 and 2 were found to be upregulated in both the dorsal and ventral striatum, following 14-day extended-access oxycodone SA, compared to yoked saline control. The heat map in Figure 3 shows normalized expression of all the differentially expressed inflammation and immune related genes (p< 0.05) from the dorsal and ventral striatum in the oxycodone and yoked saline control mice.

Figure 3.

Figure 3

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A heat map shows normalized expression of all differentially expressed inflammation and immune related genes (p< 0.05) of the dorsal and ventral striatum samples in the oxycodone and yoked saline control groups.

The overlap of inflammation and immune-related genes from the dorsal and ventral striatum is shown in the union map (see Figure 4).

Figure 4.

Figure 4

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A union map shows the overlap between differentially expressed genes from the dorsal and ventral striatum. The genes expressed identically in the two brain regions are indicated by the overlap between the two circles.

Examination of gene expression changes in the dorsal and ventral striatum with qPCR

qPCR was used to examine the relative mRNA levels of a subset of genes identified by RNA-seq in the dorsal striatum (Table 3A), and three genes were found to increase in expression levels in the oxycodone group compared with yoked saline group as identified by RNA-seq: cytochrome b-245, beta polypeptide (Cybb), proteasome (prosome, macropain) subunit, beta type 9 (large multifunctional peptidase 2) (Psmb9) and solute carrier family 2 (facilitated glucose transporter), member 1 (Slc2a1). Figure 5 shows the expression levels of these three genes as measured by qPCR.

Table 3.

A: List of inflammation /immune related genes examined with qPCR in the dorsal striatum.

B: List of inflammation /immune related genes examined with qPCR in the ventral striatum.

A. Confirmation of significant changes in selected inflammation/immune related genes in dorsal striatum using qPCR;
6 genes studied of the 55 found to be changed by RNA-seq
Gene
Symbol		 Gene Name p-value,
qPCR		 qPCR
confirmation* 		Direction of
change ±
Cybb cytochrome b-245, beta polypeptide 0.0199 Yes ↑
Psmb9 proteasome (prosome, macropain) subunit, beta type 9 (large multifunctional peptidase 2) 0.0348 No ↑
Irgm1 immunity-related GTPase family M member 1 0.0996 Yes ↑
Tap1 transporter 1, ATP-binding cassette, sub-family B (MDR/TAP) 0.2662 No ↑
Slc2a1 solute carrier family 2 (facilitated glucose transporter), member 1 0.3444 Yes ↑
Socs3 suppressor of cytokine signaling 3 0.3444 No ↑
B. Confirmation of significant changes in selected inflammation/immune related genes in ventral striatum using
qPCR; 11 genes studied of the 141 found to be changed by RNA-seq
Gene
Symbol		 Gene Name p-value, qPCR qPCR
confirmation* 		Direction of
change ±
Ccr5 chemokine (C-C motif) receptor 5 0.0140 Yes ↑
Itpr1 inositol 1,4,5-trisphosphate receptor 1 0.0175 Yes ↑
Icam1 intercellular adhesion molecule 1 0.0175 Yes ↑
Cybb cytochrome b-245, beta polypeptide 0.0406 Yes ↑
Socs3 suppressor of cytokine signaling 3 0.0678 No ↑
Ngfr nerve growth factor receptor (TNFR superfamily, member 16) 0.1578 No ↓
Cd83 CD83 antigen 0.2068 No ↓
Robo3 roundabout homolog 3 (Drosophila) 0.2426 No ↑
Slc2a1 solute carrier family 2 (facilitated glucose transporter), member 1 0.3955 No ↑
C3 complement component 3 0.8558 No ↑
Tlr7 toll-like receptor 7 0.9455 No ↑
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Genes are listed alphabetically by gene symbol

*

“Yes” indicates that the qPCR replication result was statistically significant and in the same direction. “No” indicates that the qPCR replication result was not statistically significant.

±

“Direction of change” from RNA-seq results.

Figure 5.

Figure 5

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Confirmation of mRNA expression changes in the dorsal striatum. qPCR confirmed three upregulated transcripts. The value for each individual sample was determined and from these individual values, the means and SEMs were calculated. The mean (+SEM) levels of Cybb (A), Psmb9 (B) and Slc2al (C) mRNA levels in the dorsal striatum in mice that had self-administered oxycodone was higher than that of yoked saline controls (n=7–9/group).

qPCR was used to examine the relative mRNA levels of a subset of genes identified by RNA-seq in the ventral striatum (Table 3B), and four genes were found to increase in expression levels in the oxycodone group compared with the yoked saline group as identified by RNA-seq: Cytochrome b-245, beta polypeptide (Cybb); Chemokine (C-C motif) receptor 5 (Ccr5); Intercellular adhesion molecule 1(Icam) and Inositol 1,4,5-trisphosphate receptor 1 (Itpr1). Figure 6 shows the expression levels of these four genes measured by qPCR.

Figure 6.

Figure 6

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Confirmation of mRNA expression changes in the ventral striatum. qPCR confirmed upregulated transcripts of Ccr5 (A), Cybb (B), Icam (C) and Itpr1 (D) in mice that had self-administered oxycodone compared to yoked saline controls (n=7–9/group).

The expression levels of three genes in the dorsal striatum and seven genes in the ventral striatum were found to be not significantly different between the oxycodone and yoked saline groups, as measured by qPCR. Figure 7 shows the expression levels of these genes.

Figure 7.

Figure 7

Figure 7

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The expression levels of genes, that were found to be not significantly different between oxycodone and yoked saline control groups as measured by qPCR, were shown in A for the dorsal striatum and B for the ventral striatum.

After adjustment for multiple comparisons of these qPCR data, none of the genes retained an experimental-wise significance.

Enriched function-related gene groups

Thirty Gene Ontology (GO) terms were found to be significantly enriched (Table 4). Among these processes are immune response (GO: 0006955), defense response (GO: 0006952), immune effector process (GO: 0002252) and inflammatory response (GO: 0006954), suggesting that the expression of inflammatory/immune-related genes found in this study was altered as a result of chronic oxycodone self-administration. We also found that the genes listed in Tables 1 and 2 were enriched in several immune functions including response to bacterium (GO: 0009617) and defense response to bacterium (GO:0042742).

Table 3. Enriched molecular function categories in GO analysis.

Biological processes (groups) enriched with genes with significant differences in expression between mice that had self-administered oxycodone compared to yoked saline controls (Tables 1 and 2).

GO ID Groups Count p-value Bonferroni Benjamini FDR
GO:0006955 Immune response 38 2.41E-13 3.97E-10 3.97E-10 4.05E-10
GO:0006952 Defense response 36 1.34E-12 2.21E-09 1.11E-09 2.26E-09
GO:0002252 Immune effector process 16 1.84E-08 3.03E-05 1.01E-05 3.09E-05
GO:0050830 Defense response to Gram-positive bacterium 8 1.68E-07 2.76E-04 6.91E-05 2.82E-04
GO:0009617 Response to bacterium 16 3.49E-07 5.76E-04 1.15E-04 5.87E-04
GO:0042742 Defense response to bacterium 13 1.07E-06 1.75E-03 2.93E-04 1.79E-03
GO:0007159 Leukocyte adhesion 6 4.37E-06 7.18E-03 1.03E-03 7.35E-03
GO:0002443 Leukocyte mediated immunity 11 7.71E-06 1.26E-02 1.59E-03 1.30E-02
GO:0006954 Inflammatory response 16 2.99E-05 4.81E-02 5.46E-03 5.02E-02
GO:0001817 Regulation of cytokine production 12 7.60E-05 1.18E-01 1.24E-02 1.28E-01
GO:0002819 Regulation of adaptive immune response 8 9.24E-05 1.41E-01 1.38E-02 1.55E-01
GO:0002822 Regulation of adaptive immune response based on Somatic recombination of immune receptors built from immunoglobulin superfamily domains 8 9.24E-05 1.41E-01 1.38E-02 1.55E-01
GO:0002449 Lymphocyte mediated immunity 9 1.02E-04 1.54E-01 1.38E-02 1.71E-01
GO:0051241 Negative regulation of multicellular organismal process 10 1.29E-04 1.91E-01 1.62E-02 2.16E-01
GO:0009611 Response to wounding 19 1.33E-04 1.96E-01 1.55E-02 2.23E-01
GO:0032680 Regulation of tumor necrosis factor production 6 1.44E-04 2.11E-01 1.57E-02 2.41E-01
GO:0051607 Defense response to virus 5 1.54E-04 2.24E-01 1.57E-02 2.58E-01
GO:0032101 Regulation of response to external stimulus 10 1.61E-04 2.34E-01 1.55E-02 2.71E-01
GO:0001819 Positive regulation of cytokine production 8 1.78E-04 2.54E-01 1.61E-02 2.98E-01
GO:0002250 Adaptive immune response 9 2.06E-04 2.88E-01 1.77E-02 3.45E-01
GO:0002460 Adaptive immune response based on somatic Recombination of immune receptors built from immunoglobulin superfamily domains 9 2.06E-04 2.88E-01 1.77E-02 3.45E-01
GO:0048585 Negative regulation of response to stimulus 8 2.40E-04 3.26E-01 1.96E-02 4.02E-01
GO:0042330 Taxis 10 2.48E-04 3.36E-01 1.93E-02 4.16E-01
GO:0006935 Chemotaxis 10 2.48E-04 3.36E-01 1.93E-02 4.16E-01
GO:0045321 Leukocyte activation 14 3.20E-04 4.10E-01 2.37E-02 5.36E-01
GO:0045807 Positive regulation of endocytosis 6 4.44E-04 5.19E-01 3.13E-02 7.43E-01
GO:0002274 Myeloid leukocyte activation 6 5.10E-04 5.68E-01 3.44E-02 8.53E-01
GO:0002683 Negative regulation of immune system process 8 6.29E-04 6.46E-01 4.06E-02 1.05E+00
GO:0002684 Positive regulation of immune system process 13 6.39E-04 6.51E-01 3.97E-02 1.07E+00
GO:0050727 Regulation of inflammatory response 7 7.57E-04 7.13E-01 4.52E-02 1.27E+00
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Discussion

RNA-seq was used to examine differential gene expression in the dorsal and ventral striatum of adult male mice that had undergone 14-day extended-access self-administration of oxycodone, compared with yoked saline controls. This regimen resulted in substantial daily intake of oxycodone and escalation across sessions. We found here that the mRNA levels of numerous genes related to the inflammation and immune functions changed as a result of oxycodone self-administration, in the dorsal and ventral striatum.

Early studies revealed that chronic morphine and oxycodone can modulate both adaptive and innate immune systems, as well as activate neuroinflammation (e.g., Cui et al. 2014; Wang et al. 2012). Acute and chronic morphine administrations showed inhibitory effects on humoral and cellular immune responses (Fecho et al. 1996; Manfredi et al. 1993; Novick et al. 1989; West et al. 1998). In contrast, one study found that hydromorphone and oxycodone were not immunosuppressive, and may act as immune stimulants (e.g., Sacerdote et al. 1997). In our prior hypothesis-based qPCR studies, we did not examine whether oxycodone SA affected the expression of genes related to inflammation and immune functions (Mayer-Blackwell et al. 2014; Zhang et al. 2015; Zhang et al. 2014). One study had examined the effect of chronic high-dose experimenter-administered oxycodone on gene expression in whole rat brain, and found that this led to changes in the expression of the following immune function-related genes: Hla-dra, CD163, Lims2, Dsipi, Cklfsf6, Tnfrsf11b (Hassan et al. 2007). However, it is unknown which brain region(s) were involved in these effects, and whether these or other changes would be observed in animals self-administering oxycodone.

In the current study, we observed changes in the expression levels of numerous genes related to inflammation and immune functions following oxycodone SA. For example, a significant increase in mRNA for C-C chemokine receptor type 5 (Ccr5) was found in this study. Ccr5 are seven-transmembrane G-protein-coupled receptors that associate with Gi/Go (Raport et al. 1996). Ccr5 regulates chemotaxis and cell activation via interactions with several chemokine ligands (Allen et al. 2007). Ccr5 is expressed by several cell types including lymphocytes, microglia, and astrocytes, as well as neurons in the brain (Sorce et al. 2011; Westmoreland et al. 2002). Several in vitro experiments have shown an increase in Ccr5 expression following 24–48 hours of morphine exposure in primary normal human astrocytes (Mahajan et al. 2005; Mahajan et al. 2002) and human lymphocytic CEMx174 cells (Miyagi et al. 2000; Suzuki et al. 2002a; Suzuki et al. 2002b). Co-immunoprecipitation studies have shown that Ccr5 heterodimerizes with MOP-r opioid receptors (Chen et al. 2004). Further, heterologous cross-deactivation of the MOP-r and Ccr5 has been shown in both in vivo and in vitro experiments (Szabo et al. 2003).

Ccr5 plays an important role in HIV infection in humans. Individuals with the Ccr5 variant, 32 bp deletion, thus without functional Ccr5 protein, are highly resistant to HIV infection and show delayed progression to AIDS (Alkhatib and Berger 2007; Kaslow et al. 2005). Genetic modification in Ccr5 gene results in HIV resistance in T cells (Romano Ibarra et al. 2016). Pharmacological blockade of Ccr5 inhibits the entry of HIV viruses into target cells (e.g., Romano Ibarra et al. 2016). The Ccr5 antagonists maraviroc and cenicriviroc are used for the treatment of HIV infection, but also for improving impairment in HIV- related cognitive function (e.g., Gates et al. 2016; Kim et al. 2016; Kramer et al. 2014; Ndhlovu et al. 2014).

The observed increase in Ccr5 mRNA following 14-day oxycodone self-administration extends the in vitro findings of protracted exposure to MOP-r agonists resulting in increased Ccr5 expression. Further experiments are needed to examine the mechanism by which oxycodone self-administration results in increased Ccr5 mRNA expression, and whether activity at Ccr5 influences escalation of oxycodone self-administration.

It is postulated that the gene expression changes in the brains of mice that had self-administered oxycodone were initiated by microglia. Microglia represent 10–15% of all cells found within the brain (Lawson et al. 1992). Microglia are extremely sensitive to small changes in the micro-environment, and are activated by various factors such as proinflammatory cytokines, necrosis factors, and changes in extracellular potassium (e.g., Aloisi 2001; Brown et al. 1998; Kust et al. 1997; Tooyama et al. 2002). Recent neuroimaging studies have detected microglial activation in the brains of rats exposed to repeated morphine administration (Auvity et al. 2016). We therefore hypothesize that in response to persistent MOP-r mediated activity caused by oxycodone, microglia may proliferate and become activated. The involvement of microglia in the activation of immune-related genes in the brain following chronic oxycodone self-exposure could be of interest in future studies.

Like macrophages in the periphery, microglia secrete inflammatory mediators to orchestrate the immune response in the brain. Using RNA-seq, we have found upregulation of the expression of genes encoding for interleukin 1 beta (Il1b), monocyte chemotactic protein-5 (Ccl12), tumor necrosis factors (Tnfaip2) and interferon gamma-induced GTPase (Igtp), which may play important roles in proinflammatory functions.

The immune systems modulate a variety of brain functions and affect behaviors (Boulanger 2009; Miller et al. 2013).

Prescription opioids such as oxycodone are thought to initiate their reward-related effects by acting as MOP-r agonists, and also by subsequently increasing dopamine in the mesolimbic and nigrostriatal dopaminergic pathways (Di Chiara and Imperato 1988a; Di Chiara and Imperato 1988b). However, recent studies suggest that innate immune signaling in the brain also plays an important part in the rewarding properties of opioids. For example, suppression of glial proinflammatory responses by AV411, a glia inhibitor, significantly reduced morphine or oxycodone withdrawal, while improving analgesia (Hutchinson et al. 2009) and blocking morphine-induced increases of dopamine in the nucleus accumbens (Bland et al. 2009). Thus, whether inflammation and immune responses are involved in escalation of oxycodone self-administration and development of vulnerability to oxycodone addiction will be an important area of future study.

We identified expression changes in a large number of inflammation/immune-related genes in this study after chronic oxycodone SA, in the absence of provoked bacterial and viral infections. These altered inflammation/immune systems were found in the terminal regions of the nigrostriatal and mesolimbic dopaminergic pathways (e.g., Lindvall et al. 1984; Skagerberg et al. 1984). Future studies could determine whether alterations in inflammation/immune genes and gene products are involved in the rewarding properties of oxycodone, or escalation of oxycodone self-administration. The potential impact of prescription opioid exposure on these inflammation/immune gene targets in the context of clinical analgesia is also a potential area for further study.

Since gene expression changes were examined immediately after 14 consecutive days of chronic oxycodone self-administration, it is unknown whether such alterations occurred immediately after one session of oxycodone self-administration, or resulted from chronic oxycodone self-administration exposure, ort from escalation of intake. It is also not clear whether the changes in inflammation/immune gene expression are long-lasting, which may play an important role in the development of sensitivity to oxycodone upon re-exposure (“relapse”), or during withdrawal. Further studies could also determine whether similar changes also occur in other brain regions, or in the peripheral nervous system.

Conclusion

We found here, using an unbiased RNA-seq approach, that chronic oxycodone SA resulted in alterations in multiple inflammatory and immune genes in the dorsal and ventral striatum. Some human studies also report changes in various immune and inflammatory mediators, in brain of persons with chronic heroin exposure (Neri et al. 2013).

The role of neuroinflammatory processes and immune response in the effects of opioids (either to heroin or prescription opioids such as oxycodone) is not well understood. Overall, the present study on the transcriptome provides an initial set of targets and gene systems to examine how the brain inflammatory and immune gene systems are affected by chronic self-administration of the widely abused prescription opioid oxycodone.

Acknowledgments

This work was supported by NIH 1R01DA029147 (YZ) and the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (MJK). Y. Liang is supported by the NIH Center for Clinical and Translational Science Award (CTSA) and National Center for Advancing Sciences (NCATS).

We thank Dr. Yuval Itan from Dr. Jean-Laurent Casanova’s laboratory at the Rockefeller University for his help with the functional enrichment analyses.

We thank Dr. Tom Rogers for his interpretation of the data and suggestions for this manuscript.

We thank Drs. Ann Ho, Eduardo Butelman and Susan Russo for proofreading the manuscript.

Footnotes

Conflict of interest

The authors declare that, except for income received from our primary employer, no financial support or compensation has been received from any individual or corporate entity over the past three years for research or professional service and there are no personal financial holdings that could be perceived as constituting a potential conflict of interest.

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