Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2018 Jan 1.
Published in final edited form as: Biochim Biophys Acta. 2016 Oct 14;1863(1):274–283. doi: 10.1016/j.bbadis.2016.10.007

Altered expression of glial markers, chemokines, and opioid receptors in the spinal cord of type 2 diabetic monkeys

Norikazu Kiguchi a,d,1, Huiping Ding a,1, Christopher M Peters b, Nancy D Kock c, Shiroh Kishioka d, J Mark Cline c, Janice D Wagner c, Mei-Chuan Ko a,*
PMCID: PMC5154845  NIHMSID: NIHMS823585  PMID: 27751964

Abstract

Neuroinflammation is a pathological condition that underlies diabetes and affects sensory processing. Given the high prevalence of pain in diabetic patients and crosstalk between chemokines and opioids, it is pivotal to know whether neuroinflammation-associated mediators are dysregulated in the central nervous system of diabetic primates. Therefore, the aim of this study was to investigate whether mRNA expression levels of glial markers, chemokines, and opioid receptors are altered in the spinal cord and thalamus of naturally occurring type 2 diabetic monkeys (n=7) compared with age-matched non-diabetic monkeys (n=6). By using RT-qPCR, we found that mRNA expression levels of both GFAP and IBA1 were up-regulated in the spinal dorsal horn (SDH) of diabetic monkeys compared with non-diabetic monkeys. Among all chemokines, expression levels of three chemokine ligand-receptor systems, i.e., CCL2-CCR2, CCL3-CCR1/5, and CCL4-CCR5, were up-regulated in the SDH of diabetic monkeys. Moreover, in the SDH, seven additional chemokine receptors, i.e., CCR4, CCR6, CCR8, CCR10, CXCR3, CXCR5, and CXCR6, were also up-regulated in diabetic monkeys. In contrast, expression levels of MOP, KOP, and DOP, but not NOP receptors, were down-regulated in the SDH of diabetic monkeys, and the thalamus had fewer changes in the glial markers, chemokines and opioids. These findings indicate that neuroinflammation, manifested as glial activation and simultaneous up-regulation of multiple chemokine ligands and receptors, seems to be permanent in type 2 diabetic monkeys. As chemokines and opioids are important pain modulators, this first-in-primate study provides a translational bridge for determining the functional efficacy of spinal drugs targeting their signaling cascades.

Keywords: Astrocytes, Microglia, Chemokines, Opioids, Neuroinflammation, Chronic pain, Spinal cord, Thalamus, Diabetes, Macaques

1. Introduction

Type 2 diabetes mellitus is a chronic degenerative metabolic disease that comprises the vast majority (90%) of people with diabetes; an estimated 422 million adults were living with diabetes globally in 2014 [1]. Diabetes results in long-term complications affecting the eyes, kidneys, and nervous system and causes severe morbidity and mortality [2, 3]. Adults with type 2 diabetes suffer from incredibly high rates (50–60%) of chronic pain, at levels similar to patients living with cancer [4–6]. Across the disease course, diabetic patients develop neuropathy and experience reduced analgesic efficacy of opioids [7–9], which has been challenging for symptomatic treatment [3]. Knowing the neuroplastic changes of ligands and receptors for sensory processing in the central nervous system (CNS) of diabetic primates may lead to the refinement of current therapeutic strategies as well as the development of novel medications.

Glial cells, the most abundant cell type in the CNS, are critical participants in multiple physiological functions [10]. Although astrocytes and microglia play fundamental roles in the maintenance of homeostasis, they can switch to reactive phenotypes depending on the cellular environment and result in neurophysiological pathology [11]. Mounting evidence indicates that reactive astrocytes and microglia in the spinal dorsal horn (SDH) lead to sensitization of nociceptive neurons via release of pro-inflammatory mediators, and glial activation is implicated in the development and maintenance of various chronic pain conditions [12–14]. Despite substantial evidence from rodent studies, few studies document the reaction of glial cells in primates under chronic pain or disease [15, 16].

Chemokines are small chemotactic cytokines that regulate the migration of leukocytes and act as pro-inflammatory mediators [17, 18]. To date, the chemokine system comprises approximately 50 ligands and 20 receptors in humans, and they are classified into four subfamilies, i.e., CC, CXC, XC, and CX3C chemokines [19, 20]. Chemokines and their cognate receptors are differentially expressed in neurons and glial cells, and they promote neuron-glia signaling [18, 21]. More importantly, several chemokine ligand-receptor systems are indicated as emerging targets for treating neuroinflammation-driven chronic pain [12, 22, 23]. In particular, CC chemokine ligand 2 (CCL2, MCP-1)-CCR2 and CX3C chemokine ligand 1 (CX3CL1, fractalkine)-CX3CR1 have been well characterized for the induction and maintenance of neuropathic pain [12, 22, 23]. Recent studies also demonstrate that other chemokines such as CC chemokine ligand 3 (CCL3, MIP-1α) and CXC chemokine ligand 13 (CXCL13) contribute to long-lasting neuropathic pain [22, 24, 25]. Although nerve injury causes spinal up-regulation of chemokine ligands and receptors in rodents [22, 26, 27], it is unclear which chemokine ligand-receptor systems are simultaneously dysregulated in primates with neuroinflammation-related chronic pain and/or disease.

Recent studies show astrocytic activation and an elevated mRNA level of CCL2 in the SDH of mice with type 2 diabetes [28, 29]. However, whether glial cells are activated and chemokine receptors are up-regulated in diabetic primates remains unknown. Given that neuroinflammation is a pathological condition underlying diabetes and affects sensory processing [22, 30, 31], it is pivotal to investigate if glial activation persists and if dysregulation occurs in the chemokine system (i.e., up-regulation) versus the opioid receptors (i.e., down-regulation) in the SDH of diabetic primates.

Old World monkeys represent an appropriate animal model to study the pathogenesis of diabetes, risk factors associated with diabetes, and its disease progression [32, 33]. It is known that monkeys with type 2 diabetes have abnormalities in cutaneous innervation which are similar to chronic pain and sensory deficits experienced by patients with diabetic neuropathy [34, 35]. We recently studied a small group of naturally occurring type 2 diabetic monkeys. This rare opportunity allows us to take the first step in examining the potential changes of neuroinflammation-associated mediators in the CNS of diabetic primates, namely to determine whether the mRNA expression levels of glial markers, chemokines, and opioid receptors are altered in the SDH and thalamus (i.e., the spinothalamic tract) of type 2 diabetic monkeys, as compared to age-matched non-diabetic monkeys.

2. Materials and methods

Animals

All animal care and experimental procedures were conducted in accordance with the Guide for the Care and Use of Laboratory Animals as adopted and promulgated by the US National Institutes of Health (Bethesda, MD, USA) and approved by the Institutional Animal Care and Use Committee of Wake Forest University School of Medicine (Winston-Salem, NC, USA). All care and procedures were as humane as possible. Seven cynomolgus macaques (Macaca fascicularis) used in this study were diagnosed with 3–6 years of spontaneously occurring type 2 diabetes by continuous documentation of fasting hyperglycemia [33] as defined by the American Diabetes Association. Six age-matched non-diabetic cynomolgus macaques were used as a control group. All monkeys were housed at Wake Forest University Primate Center and they were fed with regular chow (LabDiet 5038, St. Louis, MO, USA), fresh fruit and water ad libitum. Profiles of all subjects are shown in Table 1.

Table 1.

General characteristics of non-diabetic and type 2 diabetic monkeys

Subject Age Gender Body weight Duration of diabetes
Plasma glucose
(Years)		 (kg) (Years) (mg/dL)
Non-diabetic
1 14.7 F 6.3 - 75
2 14.7 F 7.2 - 69
3 15.1 M 11.5 - 55
4 18.9 F 6.3 - 126
5 19.2 F 3.3 - 84
6 19.9 F 4.0 - 87
Diabetic
1 15.3 F 4.8 3.1 325
2 18.2 F 5.5 3.3 319
3 24.5 M 10.5 3.9 301
4 16.6 F 5.6 4.9 592
5 21.9 F 6.3 5.9 385
6 21.9 F 3.5 6.6 398
7 19.4 F 6.6 6.8 318
Open in a new tab

Tissue collection

Monkeys were sedated with ketamine (10 mg/kg, intramuscular) and were deeply anesthetized by administration of sodium pentobarbital (30 mg/kg, intravenous). An incision was made on the abdomen and thorax, and the vascular system was flushed with chilled saline using a cardiac cannula and incision of the caudal vena cava. Then, the skull and spinal column were opened up to expose brain and spinal cord. The whole brain was cut into several coronal sections on a cold brain matrix, and the thalamus was dissected using a razor blade. The lumbar spinal cord was cut into five segments (i.e., L1–L5) and each segment was cut into three equally sized blocks. The caudal two blocks were separated into the dorsal horn and ventral horn using a razor blade. For quantitative real-time PCR (RT-qPCR), dissected ventral posterior thalamus [36] and L4 spinal dorsal horn tissues were put in RNase free cryogenic vials and were snap frozen in dry ice and kept at −80°C until use. For immunohistochemistry, one block of L4 spinal cord segment was post-fixed in 4% paraformaldehyde for 2 days, cryoprotected in a 30% sucrose in 0.1M PBS solution at 4°C for a week, and embedded in optimal cutting temperature compound (Sakura Finetek USA, Torrance, CA).

Reverse transcription and quantitative real-time PCR (RT-qPCR)

The TRIzol® Plus RNA Purification Kit (Thermo Fisher Scientific, Waltham, MA) was used for the isolation of total RNA from the tissues following manufacturer's instructions. Briefly, tissues were placed in a 1.5 mL RNase free tube and homogenized with TRIzol reagent. Chloroform was added to each sample, and then centrifuged for 15 min at 4°C. The aqueous phase containing RNA was transferred to a fresh tube and RNA was isolated by purification column. One microgram of total RNA extract was used for the synthesis of cDNA by reverse transcription as follows. Total RNA was incubated with Random Primers (Promega, Madison, WI) at 70°C for 5 min and then it was cooled on ice. Samples were converted to cDNA by incubation with M-MLV Reverse Transcriptase (Promega) and dNTP Mix (Thermo Fisher Scientific) at 37 °C for 60 min. qPCR was performed using iCycler and iQ Real-Time PCR systems (Bio-Rad, Hercules, CA) by using the cDNA as the template, primers for each gene (Sigma-Aldrich, St. Louis, MO) and iTaq™ Universal SYBR® Green Supermix containing dNTPs, DNA Polymerase, MgCl2, SYBR® Green I (Bio-Rad). The primer sequences are listed in Table 2. Reactions were performed under the following conditions: 10 min at 95 °C, followed by 50 cycles of two steps, 20 sec at 95 °C and 40 sec at 60 or 62 °C, depending on the Tm value of primers, and the fluorescence intensities were recorded. The melting curve analysis was incorporated at the end of the qPCR by heating from 55 to 95°C in 0.5 °C increments/30 sec with fluorescence recorded at each increment.

Table 2.

Primer sequences for RT-qPCR

Gene Forward (5’ to 3’) Reverse (5’ to 3’)
ACTB TCTTCCAACCTTCCTTCCTG TGTGTTGGCGTACAGGTCTT
GFAP ATCAACTCACCGCCAACAGT CAGCCTCAGGTTGGTTTCAT
IBA1 GGATCTGCTCTCCAAACTGG GTTTCTCCAGCATTCGCTTC
MAP2 CTGGCATTGACCTCCCTAAA TGGAGCAACTAAACCCCACT
CCL1 GAGCATGCAGGTACCCTTCT GCAGCTGGAGCTGGTATTTC
CCL2 GAATCACCAGCAGCAAGTGT TGGAATCCTGAACCCACTTC
CCL3 GCAACATTTGCTGCTGACAC CACTGGCTGTTGGTCTCAAA
CCL4 TTCTGCTCTCCAGCACTCTC CTACCACAAAGTTGCGAGGA
CCL5 CTGCTGCTTTGCCTACATTG TTTCGGGTGACAAAGACGAC
CCL7 AGAAAATCCCGAAGCAGAGG AGCACAGATCTCCTTGTCCA
CCL8 ACTTTCAGCCCACAGGGACT TGCAGCCTCTGGATAGGAAT
CCL13 AGCTATCGGATCACCACCAG CTGGACCCACTTCTCCTTTG
CCL17 AGCCATTCCCCTTAGAAAGC ACAGATGGCCTTGTTCTGGA
CCL20 TATTGTGGGCTTCACACAGC CCCAGGTCTGCTTTGGATTT
CCL21 TGCCTCAAGTACAGCCAAAG GGGCAAGAACAGGATAGCTG
CCL27 GCTGATGGGGACTGTCATCT GGTGCTCAAACCACTGTGAC
CXCL1 CACCCCAAGAATATCCAAAG AGACAAGCTTTCTGCCCATT
CXCL2 ACGGAATTCACCCCAAGAAC GACAAGCTTTCTGCCCATTC
CXCL9 TCCTCTTGGGCATCATCTTC TGGATAGTCCCTTGGTTGGT
CXCL10 GTCCACATGTTGAGATCATTGC CCTTTCCTTGCTAACTGCTTTC
CXCL12 AGAGCCAACGTCAAGCATCT CTTCGGGTCAATGCACACTT
CXCL13 TCTCTCCAGTCCAAGGTGTT TTCGATCAATGAAGCGTCTG
CXCL16 CCTGTCCATCTTGCAGTCAA AGTGGGAATGGTGGTTTCAC
XCL1 GAGGTCCTCAAAGAGCCTGA GTGAGACAGCAGATGCCAAG
CX3CL1 TCTTGGAGACGAGACAGCAC CATTTCGAGTCAGAGCAGCA
CCR1 CCTGGTCCTTGTGCAATACA AGGAAGGGCAGTGTGAACAG
CCR2 GATACCTGGCAATCGTCCAT TGGGACAGAAGCAAACACAG
CCR3 GGACCTGCTCTTCCTCTTCA AAAACCCCGAGAGGACCTTA
CCR4 TGACGTGTACCTGCTCAACC ACCTAGCCCAAAAACCCACT
CCR5 ACTGCAAAAGGCTGAAAAGC AGCATAGTGAGCCCAGAAGG
CCR6 GCCATTGCAGACATCCTCTT GCCTTTCATCAACTTGCACA
CCR7 CGGCATGCTCCTACTTCTTT AGCTTGCTGATGAGGAGGAC
CCR8 CATCCTGGTCCTTGTGGTCT AGGTCTGAAAGGGGAAGGAG
CCR9 TGGTTTACCCTAGCGACGAG GTAACAGCAAGCCATGACCA
CCR10 TCTGTCATTGTGTGGCTGCT CTCGGGGAAAATGAGACGAC
CXCR1 ATGCCACCTACGGATGAAGA CAGGCTCAGCAGGAACACTA
CXCR2 TATTCCTGCTGAGCTTGCTG CATGGCCAGGTTCAGTAGGT
CXCR3 ACGAGAGTGACTCGTGCTGT CAGCAGAAAGAGGAGGCTGT
CXCR4 ATCTTCCTGCCCACCATCTA TTGTCCGTCATGCTTCTCAG
CXCR5 ATCCTGGTGACGAGCATCTT TAGCTCGCAGGTATTGTCCA
CXCR6 CTCGCCATGATTGTCTGCTA GAACACAGCCATCACAAGGA
CXCR7 CTCTTCGGCAGCATCTTCTT ACACGGCGTACCATCTTCTT
XCR1 TTGTTGCCTGTGTGGATCTC GATGCTGCTGTAGAGGCTGA
CX3CR1 GGGACTGTGTTCCTGTCCAT CTTGGGCTTCTTGCTATTGG
MOP TCTTCAGCCATTGGTCTTCC TTCCCAGTACCAGCTTGGAT
DOP AAGACGGCCACCAACATCTA GGCCACGTCTTCATCAGGTA
KOP TGTGATCCCTGTCCTCATCA AGGTTGCGATCTTTCTCTCG
NOP GGTCGAGGATGAAGAGATCG GGGACGATGAAGGAGAAGAG
Open in a new tab

qPCR data analysis by comparative CT method

qPCR data was analyzed by the comparative CT method [37]. The threshold cycle (CT) value of each reaction was defined as the PCR cycle at which the relative fluorescent unit (RFU) crossed a threshold line (100 RFU) in the exponential phase of amplification as determined by the software. PCR amplification efficiency for each primer was determined by the slope of standard curve generated from tenfold serial dilutions (0.03–30 ng) of the cDNA mixture from six control subjects. Ten nanograms of synthesized cDNA were used for quantification of mRNA expression levels. Three replicates were performed for each cDNA-primer pair combination and the average CT was calculated. The mRNA expression level of each gene was quantified based on the CT value and was normalized to the housekeeping gene (β-actin; ACTB) using the formula 2−(CT target gene − CT ACTB) (2−ΔCT) [37]. For relative comparison of 2−ΔCT values between two groups, mRNA expression levels in diabetic monkeys were shown as a fold change against the mean of the non-diabetic control monkeys which was set to 1. All data are presented as mean ± SEM calculated from individual monkeys and analyzed using unpaired t-tests. The criterion for significance was set at P<0.05.

Chromogenic immunohistochemistry

Frozen tissues were cut at 30 µm with a cryostat (Leica CM3050S; Leica Biosystems GmbH, Wetzlar, Germany), and washed in PBS 3 times. The sections were treated with 0.3% H2O2 for 15 min, washed with PBS containing 0.3% Triton X-100 (PBST) for 30 min and then blocked with 3% normal donkey serum, 0.3% Triton X-100 in PBS at room temperature for 30 min. The sections were incubated with primary antibodies against IBA1 (rabbit polyclonal, 1:1000; Wako, Osaka, Japan), GFAP (rabbit polyclonal, 1:1000; EMD Millipore, Billerica, MA) or CCR2 (rabbit polyclonal, 1:1000; LifeSpan BioSciences, Seattle, WA) at 4 °C overnight. All antibodies were diluted in 1% normal donkey serum, 0.1% Triton-X 100 in PBS. The following day, sections were rinsed in PBST, and incubated with biotinylated secondary antibodies (1:200; Jackson ImmunoResearch Laboratories, West Grove, PA) at room temperature for 1 hour. Sections were rinsed in PBST, and incubated with Vectastain ABC kit (PK-6100; Vector Laboratories, Burlingame, CA) for 30 min. Subsequently, sections were rinsed in PBST, and then stained using a DAB (3,3’-diaminobenzidine) substrate kit (SK-4100; Vector Laboratories) for 2 min following manufacturer's instructions. Finally, sections were mounted on glass slides, air-dried for 30 min, dehydrated through a graded series of ethanol and xylene, and coverslipped with mounting medium DPX (Sigma-Aldrich, St. Louis, MO, USA).

Image analysis

Digital images of spinal cord sections were captured at 10× magnification using a Nikon Eclipse Ni fluorescent and brightfield microscope system (Nikon, Tokyo, Japan). Image analysis software (Image J; National Institutes of Health, Bethesda, MD) was used to quantify IBA1, GFAP and CCR2 immunostaining in a minimum of 4 randomly selected lumbar spinal cord sections [38] from each animal. For analysis of IBA1 and GFAP immunostaining, a square with a fixed area (200 × 200 mm2) was positioned in the medial, central, and lateral third of the dorsal horn. The number of pixels occupied by immunostaining within a defined threshold was measured. The upper and lower threshold optical densities were adjusted to match positive immunoreactivity and the determined thresholds were applied uniformly to all sections. Results from the three sampled regions of the dorsal spinal cord were summed for each section and expressed as total number of pixels (# pixels) in the area. For CCR2, sampling area was confined to superficial laminae (I–II) of the spinal cord [38]. The data were presented as fractional area reflecting the percent of the sampled area that fell within a defined immunodensity threshold. The individuals who quantified the images were blind to the experimental conditions.

3. Results

In order to determine whether astrocytes and microglia were activated in the SDH and thalamus of diabetic monkeys, mRNA expression levels of glial fibrillary acidic protein (GFAP, astrocytic marker) and ionized calcium binding adaptor molecule 1(IBA1, microglial marker) [12], were evaluated by RT-qPCR. GFAP, IBA1, microtubule-associated protein 2 (MAP2, neuronal marker), and ACTB (β-actin) cDNAs were amplified and crossed threshold line in the SDH of non-diabetic monkeys (Figure 1A). The single narrow peak in the melting curve indicated high specificity of each qPCR reaction (Figure 1B). The slopes of the standard curves for each gene were almost identical (GFAP, −3.68; IBA1, −3.68; MAP2, −3.62; ACTB, −3.63), which indicated that the PCR amplification efficiencies were similar (Figure 1C). In the SDH of non-diabetic monkeys, basal mRNA expression level of GFAP (relative to ACTB) was higher than that of IBA1 and MAP2, whereas IBA1 showed the lowest expression (Figure 1D). Interestingly, the mRNA expression levels of both GFAP and IBA1 were up-regulated in the SDH of diabetic monkeys compared with non-diabetic monkeys (Figure 1E). In the thalamus, basal mRNA expression patterns of GFAP, IBA1 and MAP2 were similar to those in the SDH (Figure 1F). However, none of these three genes showed significant differences in their mRNA expressions in the thalamus of diabetic and non-diabetic monkeys (Figure 1G). To further confirm the up-regulation of IBA1 and GFAP in the SDH of diabetic monkeys, protein levels of IBA1 and GFAP were evaluated by immunohistochemistry. IBA1- and GFAP-immunoreactivity and hypertrophied microglia and astrocytes were significantly increased in diabetic monkeys compared with non-diabetic monkeys (Figure 2A–F). Together, these findings demonstrate glial activation in the spinal cord of non-human primates with type 2 diabetes.

Figure 1. Up-regulation of astrocytic and microglial markers in the spinal dorsal horn of type 2 diabetic monkeys.

Figure 1

Open in a new tab

The mRNA expression levels of GFAP, IBA1, and MAP2 in the spinal dorsal horn and thalamus were evaluated by RT-qPCR using the comparative CT method. Top panels present amplification plot (A), melting curve (B), and standard curve (C) in the spinal dorsal horn of non-diabetic monkeys. Bottom panels present relative mRNA expression levels (2−ΔCT) in the spinal dorsal horn (D, E) or thalamus (F, G). Basal mRNA expression levels relative to ACTB in the non-diabetic group are shown in D and F. Fold changes of mRNA expression levels in the type 2 diabetic group as compared with the non-diabetic control group are shown in E and G. Each value represents mean ± SEM. (n=6–7). *P<0.05 vs. non-diabetic.

Figure 2. Activation of microglia and astrocytes in the spinal dorsal horn of type 2 diabetic monkeys.

Figure 2

Open in a new tab

Relative protein levels of IBA1 and GFAP in the spinal dorsal horn of type 2 diabetic and age-matched non-diabetic monkeys were evaluated by chromogenic immunohistochemistry. Representative micrographs of IBA1 (A) and GFAP (D) immunostaining in non-diabetic and diabetic monkeys. Higher magnification of square regions in A and D indicate hypertrophy of microglia (B) and astrocytes (E), respectively, in the diabetic group. Quantification of IBA1 (C) and GFAP (F) immunostaining in the dorsal spinal cord is expressed as number of pixels (# pixels) of positive immunostaining per sampled region. Scale bars, 200 µm (A, D) and 50 µm (B, E). Each value represents mean ± SEM. (n=6–7). *P<0.05 vs. non-diabetic.

To investigate whether the chemokine systems were changed in the SDH of diabetic monkeys, mRNA expression levels of chemokine ligands and receptors were evaluated by RT-qPCR. Figure 3 shows the mRNA expressions of chemokine ligands in the SDH of monkeys. In the SDH of non-diabetic monkeys, basal mRNA expression level of chemokine ligand CX3CL1 was the highest followed by CXC-chemokine ligand 12 (CXCL12), CCL2, CXCL10, and CCL4 (Figure 3A). The mRNA expression levels of CCL2, CCL3, and CCL4 were up-regulated, whereas XC-chemokine ligand 1 (XCL1) was down-regulated in the SDH of diabetic monkeys compared with non-diabetic monkeys (Figure 3B). Figure 4 shows the mRNA expressions of chemokine receptors in the SDH of monkeys. In the SDH of non-diabetic monkeys, basal mRNA expression level of CX3C-chemokine receptor 1 (CX3CR1) was the highest followed by CC-chemokine receptor 1 (CCR1), CXC-chemokine receptor 4 (CXCR4), CXCR6, CXCR7, and CCR5 (Figure 4A). The mRNA expression levels of CCR1, CCR2, CCR4, CCR5, CCR6, CCR8, CCR10, CXCR3, CXCR5, and CXCR6 were up-regulated in the SDH of diabetic monkeys compared with non-diabetic monkeys (Figure 4B). To further confirm the up-regulation of CCR2 in the SDH of diabetic monkeys, protein levels of CCR2 were evaluated by immunohistochemistry. In line with qPCR data, CCR2-immunoreactivity was significantly increased in diabetic monkeys compared with non-diabetic monkeys (Figure 5A–C).

Figure 3. Up-regulation of chemokine ligands in the spinal dorsal horn of type 2 diabetic monkeys.

Figure 3

Open in a new tab

The mRNA expression levels of CC-, CXC-, XC-, and CX3C-chemokine ligands in the spinal dorsal horn were evaluated by RT-qPCR using the comparative CT method (2−ΔCT). Basal mRNA expression levels relative to ACTB in the non-diabetic group are shown in A. Fold changes of mRNA expression levels in the type 2 diabetic group as compared with the non-diabetic control group are shown in B. Each value represents mean ± SEM. (n=6–7). *P<0.05 vs. non-diabetic.

Figure 4. Up-regulation of chemokine receptors in the spinal dorsal horn of type 2 diabetic monkeys.

Figure 4

Open in a new tab

The mRNA expression levels of CC-, CXC-, XC-, and CX3C-chemokine receptors in the spinal dorsal horn were evaluated by RT-qPCR using the comparative CT method (2−ΔCT). Basal mRNA expression levels relative to ACTB in the non-diabetic group are shown in A. Fold changes of mRNA expression levels in the type 2 diabetic group as compared with the non-diabetic control group are shown in B. Each value represents mean ± SEM. (n=6–7). *P<0.05 vs. non-diabetic.

Figure 5. Up-regulation of CCR2 immunoreactivity in the spinal dorsal horn of type 2 diabetic monkeys.

Figure 5

Open in a new tab

Relative protein levels of CCR2 in the spinal dorsal horn of type 2 diabetic and age-matched non-diabetic monkeys were evaluated by chromogenic immunohistochemistry. Representative micrographs in non-diabetic and diabetic monkeys (A). Higher magnification of region in A indicates dense immunostaining of fibers and cell soma in the superficial laminae (I–II) particularly in the diabetic group (B). Quantification of CCR2 immunostaining was expressed as percent of fractional area (% Fractional area) or the percent of the sampled area that was within a defined intensity threshold range (C). Scale bars, 200 µm (A) and 50 µm (B). Each value represents mean ± SEM. (n=6–7). *P<0.05 vs. non-diabetic.

Next, we examined whether the expressions of chemokine systems were also altered in the thalamus of diabetic monkeys. Figure 6 shows the mRNA expressions of chemokine ligands in the thalamus of monkeys. Basal mRNA expression patterns of chemokine ligands in the thalamus were also similar to those of the SDH (Figure 6A). In the thalamus of diabetic monkeys, chemokine ligands were not significantly changed except that CXCL12 was up-regulated and CCL21 was down-regulated (Figure 6B). Figure 7 shows the mRNA expression of chemokine receptors in the thalamus of monkeys. Basal mRNA expression patterns of chemokine receptors in the thalamus were similar to those of the SDH (Figure 7A). In contrast to the SDH, only three receptors (CCR8, CXCR4, and CXCR6), but not others, were up-regulated in the thalamus of diabetic monkeys (Figure 7B). Collectively, these data suggest that in diabetic monkeys, CCL2-CCR2, CCL3-CCR1/5, and CCL4-CCR5 systems were up-regulated in the spinal cord and CXCL12-CXCR4 system was up-regulated in the thalamus.

Figure 6. Expressions of chemokine ligands in the thalamus of type 2 diabetic monkeys.

Figure 6

Open in a new tab

The mRNA expression levels of CC-, CXC-, XC-, and CX3C-chemokine ligands in the thalamus were evaluated by RT-qPCR using the comparative CT method (2−ΔCT). Basal mRNA expression levels relative to ACTB in the non-diabetic group are shown in A. Fold changes of mRNA expression levels in the type 2 diabetic group as compared with the non-diabetic control group are shown in B. Each value represents mean ± SEM. (n=6–7). *P<0.05 vs. non-diabetic.

Figure 7. Expressions of chemokine receptors in the thalamus of type 2 diabetic monkeys.

Figure 7

Open in a new tab

The mRNA expression levels of CC-, CXC-, XC-, and CX3C-chemokine receptors in the thalamus were evaluated by RT-qPCR using the comparative CT method (2−ΔCT). Basal mRNA expression levels relative to ACTB in the non-diabetic group are shown in A. Fold changes of mRNA expression levels in the type 2 diabetic group as compared with the non-diabetic control group are shown in B. Each value represents mean ± SEM. (n=6–7). *P<0.05 vs. non-diabetic.

Finally, we assessed whether the expressions of opioid receptors were altered in the SDH and thalamus of diabetic monkeys by evaluating mRNA expression levels of mu-, delta-, kappa-opioid receptors (MOP, DOP, and KOP receptors, respectively) and nociceptin/orphanin FQ peptide (NOP) receptor using RT-qPCR. In the SDH of non-diabetic monkeys, basal mRNA expression level of NOP was the highest followed by MOP and then KOP and DOP (Figure 8A). Interestingly, mRNA expressions of the three classical opioid receptors (i.e., MOP, KOP, and DOP), but not NOP receptors, were down-regulated in the SDH of diabetic monkeys compared with non-diabetic monkeys (Figure 8B). On the other hand, in the thalamus, basal mRNA expressions of MOP, KOP and NOP were comparable and more abundant than DOP, suggesting a different expression patterns from the SDH (Figure 8C). Unlike in the SDH, none of the opioid receptors showed significant changes in the thalamus of diabetic monkeys compared with non-diabetic monkeys (Figure 8D).

Figure 8. Down-regulation of opioid receptors in the spinal dorsal horn of diabetic monkeys.

Figure 8

Open in a new tab

The mRNA expression levels of MOP, DOP, KOP, and NOP receptors in the spinal dorsal horn (A, B) and thalamus (C, D) were evaluated by RT-qPCR using the comparative CT method (2−ΔCT). Basal mRNA expression levels relative to ACTB in the non-diabetic group are shown in A and C. Fold changes of mRNA expression levels in the type 2 diabetic group as compared with the non-diabetic control group are shown in B and D. Each value represents mean ± SEM. (n=6–7). *P<0.05 vs. non-diabetic.

4. Discussion

The present study provides the first evidence for an up-regulation of GFAP and IBA1 mRNA and protein levels, indicating reactive gliosis in the SDH of diabetic monkeys (Figures 1 & 2). We found that basal expression level of GFAP, a common astrocyte marker, was much higher than that of the microglial marker IBA1 and neuronal marker MAP2 in the SDH and thalamus of monkeys (Figure 1). Human studies suggest that glial activation may be a key mechanism underlying the pathophysiology of chronic pain [12, 13]. For instance, activated astrocytes and/or microglia in post-mortem spinal cord samples have been reported in a patient with complex regional pain syndrome and in human immunodeficiency virus-infected patients with chronic pain [15, 16]. Ample evidence demonstrates that pharmacological or genetic inhibition of spinal glia attenuates different chronic pain states in animal studies [13, 39]. In diabetic monkeys, activation of microglia and astrocytes may be involved in neuroinflammation in the SDH, which contributes to chronic pain through the secretion of diverse pro-inflammatory mediators. It would be interesting to determine whether blockage of microglial and astrocytic reactions can ameliorate symptoms associated with neuroinflammation in diabetic patients.

Among all chemokines examined herein, we found that mRNA expression levels of three chemokine ligand-receptor systems, i.e., CCL2-CCR2, CCL3-CCR1/5, and CCL4-CCR5, were up-regulated in the SDH of diabetic monkeys (Figures 3 & 4). The up-regulation of these chemokine ligands (e.g., central CCL2 and CCL3, peripheral CCL4) has been separately shown in rodents with chronic pain [25, 40–42]. The present study is the first to document that these three chemokine systems are simultaneously up-regulated in diabetic primates and that neuroinflammation appears to be permanent in primates with chronic disease. In order to determine if an up-regulation of CCR2 gene expression levels corresponds with its changes at protein levels, we used immunohistochemistry to further confirm an up-regulation of CCR2 protein levels in the SDH of diabetic monkeys (Figure 5). Although microglial expression of CCR2 was reported in mice [43], neuronal expression of CCR2 in the mouse spinal cord has been clearly demonstrated [41]. Future studies using double immunostaining will elucidate if the CCR2 is expressed in neurons or microglia of the monkey SDH.

On the other hand, intrathecal delivery of CCL2 or CCL3 elicits pain-like behaviors in rodents for 4 or 7 days, which can be blocked by CCR2 and CCR5 antagonists, respectively [27, 40]. Patients with chronic pain also display increased CCL2 and CCL3 levels in the cerebrospinal fluid or plasma [44, 45]. Chemokine-mediated neuron-glia interactions are bidirectional. For example, CCL2 released from primary sensory neurons activates CCR2 in microglia, leading to the release of proinflammatory cytokines and growth factors and subsequent central sensitization. CCL2 produced from astrocytes can directly activates CCR2 in spinal cord neurons which enhances neuropathic pain states [21, 22]. These CCL2-CCR2-mediated neuron-microglia and astrocyte-neuron interactions are involved in the induction and maintenance of chronic pain [21, 22]. Given functional species differences in the neuroimmune responses and sensory neurons between rodents and primates [46–48], it is essential to define the functional profile of chemokines and determine the efficacy of chemokine receptor antagonists for modulating neuroinflammation and/or pain in primates.

CX3CL1-CX3CR1 is another well-characterized chemokine system for neuron-glia interactions and regulation of chronic pain [22, 23, 49, 50]. Basal mRNA expression levels of CX3CL1 and CX3CR1 are the most abundant among all chemokine ligands and receptors, whereas their expression levels do not change in the SDH and thalamus of diabetic monkeys (Figures 3–7). These findings may indicate that central CX3CL1-CX3CR1 is not involved in neuroinflammation of diabetic monkeys and its functional role needs to be investigated in primates with different etiologies. Importantly, the expression levels of seven additional chemokine receptors, i.e., CCR4, CCR6, CCR8, CCR10, CXCR3, CXCR5, and CXCR6, were up-regulated in the SDH of diabetic monkeys (Figure 4). Although CCR8, CXCR3, and CXCR5 have been identified as pain mediators in rodents [24, 51, 52], the functional significance of spinal CCR4, CCR6, CCR10, and CXCR6 for modulating neuroinflammation and sensory processing in rodents and primates remain to be elucidated. Compared to the SDH, the thalamus has fewer changes in the expression levels of chemokines, i.e., up-regulation of CXCL12-CXCR4, CCR8, and CXCR6 in the thalamus of diabetic monkeys (Figures 6 & 7). As we have recently established a method of intrathecal/intracisternal catheterization in monkeys [53], this experimental setting can be utilized to further investigate if these chemokine ligands and receptors in the supraspinal versus spinal region differentially regulate nociceptive responses in primates. More importantly, the present study documents that multiple chemokine ligands and receptors are simultaneously up-regulated in primates with chronic disease. Future studies are warranted to determine if a pharmacological agent that targets several chemokine receptors is more effective than a single chemokine receptor inhibitor for ameliorating neuroinflammation-associated symptoms.

The opioid receptor family comprises 4 receptor subtypes, i.e., MOP, KOP, DOP, and NOP receptors. We found that mRNA expression levels of MOP, KOP, and DOP receptors were down-regulated in the SDH of diabetic monkeys (Figure 8). Previous studies showed that the expression of MOP receptors is down-regulated in the spinal cord of rodents with either nerve injury or type 1 diabetes [54, 55]. These findings may partially explain the reduced analgesic efficacy of opioids in patients with chronic pain and/or neuroinflammatory diseases [7, 9, 56]. Interestingly, emerging evidence shows that pro-inflammatory chemokines such as CCL3-CCR5 signaling is capable of desensitizing MOP receptors, and CCR5 and MOP receptors may engage in crosstalk through dimerization [57, 58]. Following intrathecal administration, the CCR5 antagonist maraviroc not only attenuated development of neuropathic pain symptoms, but also increased morphine’s analgesic effects in rats [59]. As bivalent ligands with MOP agonist and CCR5 antagonist activities are being developed [60], it is crucial to assess the efficacy of such novel ligands in chronic pain states with varying etiologies. On the other hand, there is no change of NOP receptor expression in the SDH of diabetic monkeys (Figure 8). Spinal NOP receptor agonists exhibit functional plasticity, especially with enhanced analgesic efficacy in rodents under chronic pain such as nerve injury and diabetic neuropathy [61, 62]. Given that the expression of NOP receptors is not affected by diabetes and NOP-related ligands have a wider therapeutic window than MOP agonists in primates [63, 64], it is worth investigating the therapeutic potential of NOP-related ligands as analgesics in diabetic individuals.

In the present study, diabetic monkeys had the duration of diabetes ranging from 3.1 to 6.8 years, which is approximately equivalent to 10 to 20 years in humans. Although no assay was available to measure pain or peripheral neuropathy in this group of monkeys, previous studies have shown that diabetic monkeys develop reduced motor nerve conduction velocities as similar to diabetic patients [32, 34]. These neurological defects can be detected as early as 2 years after the onset of hyperglycemia in macaques [34]. Additionally, monkeys with a similar duration of type 2 diabetes have severe changes of innervation in the epidermis and upper dermis of the glabrous hand skin and such changes are consistent with chronic pain and sensory deficits experienced by humans diagnosed with diabetic neuropathy [35]. Clearly, Old World monkeys are a valuable model to study the etiology and progression of diabetes and to develop therapies to treat diabetes-driven symptoms [32, 33, 35]. However, monkeys are known to be adept at hiding pain and diabetic monkeys do not display overt neurological symptoms [32, 35]. Future functional studies in monkeys are needed to establish outcome measures for mechanical and thermal hypersensitivity. Such assays will allow non-human primate researchers to further define the functional roles of chemokines and opioids for pain modulation in diabetic monkeys.

5. Conclusions

This is the first study to show that neuroinflammation, manifested as glial activation and simultaneous up-regulation of multiple chemokine ligands and receptors, is permanent in the spinal cord of diabetic monkeys. Neuroinflammation in the spinal cord drives chronic pain via neuron-glia interactions, and it may also compromise opioid analgesia due to crosstalk between chemokine and opioid signaling systems [22, 57]. These ligand-receptor systems as pain modulators in the spinal cord are more susceptible than those in the thalamus to changes leading to neuroplasticity in diabetic monkeys. As chemokines and opioids are important regulators of neuronal, immune, and inflammatory responses, diabetic monkeys provide a translational bridge for determining the functional efficacy of spinal drugs targeting their signaling cascades. This primate disease model not only opens new avenues to study the functional roles of chemokines and their cognate receptor antagonists, but also offers a valuable resource for future studies to explore novel mediators involved in neuroinflammation.

Highlights.

  • Astrocytes and microglia are activated and chemokine ligand-receptor systems, CCL2-CCR2, CCL3-CCR1/5, and CCL4-CCR5, are up-regulated in the spinal dorsal horn of diabetic monkeys.

  • Opioid receptors, MOP, DOP, KOP, but not NOP receptors, are down-regulated in the spinal dorsal horn of diabetic monkeys.

  • Ligand-receptor systems as pain modulators in the spinal cord are more susceptible than in the thalamus to changes, leading to neuroplasticity in diabetic monkeys.

  • Neuroinflammation, manifested as glial activation and simultaneous up-regulation of multiple chemokine ligands and receptors, is permanent in type 2 diabetic monkeys.

Acknowledgments

We thank Dr. Li Zhang, Jean Gardin, Christina Long, Diana Swaim, Melissa Ayers, Renee Parker, Jade Lackey, Jillian Odom, and Emily Whitaker for their technical assistance on tissue collection and data analysis. Research reported in this publication was supported by the U.S. National Institutes of Health, NIDA (R01-DA032568 and R21-DA040104) and NIAMS (R01-AR059193 and R21-AR064456). The content is solely the responsibility of the authors and does not necessarily represent the official views of the U.S. federal agencies.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  • 1.WHO. World Health Organization: Global Report on Diabetes. 2016 http://www.who.int/diabetes/global-report/en/, ISBN: 978 992 974 156525 156527.
  • 2.Laiteerapong N, Karter AJ, Liu JY, Moffet HH, Sudore R, Schillinger D, John PM, Huang ES. Correlates of quality of life in older adults with diabetes: the diabetes & aging study. Diabetes care. 2011;34:1749–1753. doi: 10.2337/dc10-2424. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Nathan DM. Diabetes: Advances in Diagnosis and Treatment. Jama. 2015;314:1052–1062. doi: 10.1001/jama.2015.9536. [DOI] [PubMed] [Google Scholar]
  • 4.Bair MJ, Brizendine EJ, Ackermann RT, Shen C, Kroenke K, Marrero DG. Prevalence of pain and association with quality of life, depression and glycaemic control in patients with diabetes. Diabetic medicine : a journal of the British Diabetic Association. 2010;27:578–584. doi: 10.1111/j.1464-5491.2010.02971.x. [DOI] [PubMed] [Google Scholar]
  • 5.Krein SL, Heisler M, Piette JD, Makki F, Kerr EA. The effect of chronic pain on diabetes patients' self-management. Diabetes care. 2005;28:65–70. doi: 10.2337/diacare.28.1.65. [DOI] [PubMed] [Google Scholar]
  • 6.Sudore RL, Karter AJ, Huang ES, Moffet HH, Laiteerapong N, Schenker Y, Adams A, Whitmer RA, Liu JY, Miao Y, John PM, Schillinger D. Symptom burden of adults with type 2 diabetes across the disease course: diabetes & aging study. Journal of general internal medicine. 2012;27:1674–1681. doi: 10.1007/s11606-012-2132-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Arner S, Meyerson BA. Lack of analgesic effect of opioids on neuropathic and idiopathic forms of pain. Pain. 1988;33:11–23. doi: 10.1016/0304-3959(88)90198-4. [DOI] [PubMed] [Google Scholar]
  • 8.Kamenov ZA, Parapunova RA, Georgieva RT. Earlier development of diabetic neuropathy in men than in women with type 2 diabetes mellitus. Gender medicine. 2010;7:600–615. doi: 10.1016/j.genm.2010.11.001. [DOI] [PubMed] [Google Scholar]
  • 9.Veves A, Backonja M, Malik RA. Painful diabetic neuropathy: epidemiology, natural history, early diagnosis, and treatment options. Pain Med. 2008;9:660–674. doi: 10.1111/j.1526-4637.2007.00347.x. [DOI] [PubMed] [Google Scholar]
  • 10.Allen NJ, Barres BA. Neuroscience: Glia - more than just brain glue. Nature. 2009;457:675–677. doi: 10.1038/457675a. [DOI] [PubMed] [Google Scholar]
  • 11.Glass CK, Saijo K, Winner B, Marchetto MC, Gage FH. Mechanisms underlying inflammation in neurodegeneration. Cell. 2010;140:918–934. doi: 10.1016/j.cell.2010.02.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Grace PM, Hutchinson MR, Maier SF, Watkins LR. Pathological pain and the neuroimmune interface. Nature reviews. Immunology. 2014;14:217–231. doi: 10.1038/nri3621. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Ji RR, Berta T, Nedergaard M. Glia and pain: is chronic pain a gliopathy? Pain. 2013;154(Suppl 1):S10–S28. doi: 10.1016/j.pain.2013.06.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Xanthos DN, Sandkuhler J. Neurogenic neuroinflammation: inflammatory CNS reactions in response to neuronal activity. Nat Rev Neurosci. 2014;15:43–53. doi: 10.1038/nrn3617. [DOI] [PubMed] [Google Scholar]
  • 15.Del Valle L, Schwartzman RJ, Alexander G. Spinal cord histopathological alterations in a patient with longstanding complex regional pain syndrome. Brain, behavior, and immunity. 2009;23:85–91. doi: 10.1016/j.bbi.2008.08.004. [DOI] [PubMed] [Google Scholar]
  • 16.Shi Y, Gelman BB, Lisinicchia JG, Tang SJ. Chronic-pain-associated astrocytic reaction in the spinal cord dorsal horn of human immunodeficiency virus-infected patients. J Neurosci. 2012;32:10833–10840. doi: 10.1523/JNEUROSCI.5628-11.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Baggiolini M. Chemokines and leukocyte traffic. Nature. 1998;392:565–568. doi: 10.1038/33340. [DOI] [PubMed] [Google Scholar]
  • 18.Ransohoff RM. Chemokines and chemokine receptors: standing at the crossroads of immunobiology and neurobiology. Immunity. 2009;31:711–721. doi: 10.1016/j.immuni.2009.09.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Blanchet X, Langer M, Weber C, Koenen RR, von Hundelshausen P. Touch of chemokines. Frontiers in immunology. 2012;3:175. doi: 10.3389/fimmu.2012.00175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Proudfoot AE, Bonvin P, Power CA. Targeting chemokines: Pathogens can, why can't we? Cytokine. 2015;74:259–267. doi: 10.1016/j.cyto.2015.02.011. [DOI] [PubMed] [Google Scholar]
  • 21.Gao YJ, Ji RR. Chemokines, neuronal-glial interactions, and central processing of neuropathic pain. Pharmacology & therapeutics. 2010;126:56–68. doi: 10.1016/j.pharmthera.2010.01.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Ji RR, Xu ZZ, Gao YJ. Emerging targets in neuroinflammation-driven chronic pain. Nature reviews. Drug discovery. 2014;13:533–548. doi: 10.1038/nrd4334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Kiguchi N, Kobayashi Y, Kishioka S. Chemokines and cytokines in neuroinflammation leading to neuropathic pain. Current opinion in pharmacology. 2012;12:55–61. doi: 10.1016/j.coph.2011.10.007. [DOI] [PubMed] [Google Scholar]
  • 24.Jiang BC, Cao DL, Zhang X, Zhang ZJ, He LN, Li CH, Zhang WW, Wu XB, Berta T, Ji RR, Gao YJ. CXCL13 drives spinal astrocyte activation and neuropathic pain via CXCR5. J Clin Invest. 2016;126:745–761. doi: 10.1172/JCI81950. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Kiguchi N, Kobayashi Y, Maeda T, Saika F, Kishioka S. CC-chemokine MIP-1alpha in the spinal cord contributes to nerve injury-induced neuropathic pain. Neuroscience letters. 2010;484:17–21. doi: 10.1016/j.neulet.2010.07.085. [DOI] [PubMed] [Google Scholar]
  • 26.Imai S, Ikegami D, Yamashita A, Shimizu T, Narita M, Niikura K, Furuya M, Kobayashi Y, Miyashita K, Okutsu D, Kato A, Nakamura A, Araki A, Omi K, Nakamura M, James Okano H, Okano H, Ando T, Takeshima H, Ushijima T, Kuzumaki N, Suzuki T, Narita M. Epigenetic transcriptional activation of monocyte chemotactic protein 3 contributes to long-lasting neuropathic pain. Brain : a journal of neurology. 2013;136:828–843. doi: 10.1093/brain/aws330. [DOI] [PubMed] [Google Scholar]
  • 27.Matsushita K, Tozaki-Saitoh H, Kojima C, Masuda T, Tsuda M, Inoue K, Hoka S. Chemokine (C-C motif) receptor 5 is an important pathological regulator in the development and maintenance of neuropathic pain. Anesthesiology. 2014;120:1491–1503. doi: 10.1097/ALN.0000000000000190. [DOI] [PubMed] [Google Scholar]
  • 28.Dauch JR, Yanik BM, Hsieh W, Oh SS, Cheng HT. Neuron-astrocyte signaling network in spinal cord dorsal horn mediates painful neuropathy of type 2 diabetes. Glia. 2012;60:1301–1315. doi: 10.1002/glia.22349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Zhang YP, Song CY, Yuan Y, Eber A, Rodriguez Y, Levitt RC, Takacs P, Yang Z, Goldberg R, Candiotti KA. Diabetic neuropathic pain development in type 2 diabetic mouse model and the prophylactic and therapeutic effects of coenzyme Q10. Neurobiology of disease. 2013;58:169–178. doi: 10.1016/j.nbd.2013.05.003. [DOI] [PubMed] [Google Scholar]
  • 30.Cai D. Neuroinflammation and neurodegeneration in overnutrition-induced diseases. Trends in endocrinology and metabolism: TEM. 2013;24:40–47. doi: 10.1016/j.tem.2012.11.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.De Felice FG, Ferreira ST. Inflammation, defective insulin signaling, and mitochondrial dysfunction as common molecular denominators connecting type 2 diabetes to Alzheimer disease. Diabetes. 2014;63:2262–2272. doi: 10.2337/db13-1954. [DOI] [PubMed] [Google Scholar]
  • 32.Gong L, Zeng W, Yang Z, Chen Z, Cheng A, Shen Y, Zeng L, Luo Q, Yang Y. Comparison of the clinical manifestations of type 2 diabetes mellitus between rhesus monkey (Macaca mulatta lasiotis) and human being. Pancreas. 2013;42:537–542. doi: 10.1097/MPA.0b013e3182732501. [DOI] [PubMed] [Google Scholar]
  • 33.Wagner JD, Kavanagh K, Ward GM, Auerbach BJ, Harwood HJ, Jr, Kaplan JR. Old world nonhuman primate models of type 2 diabetes mellitus, ILAR journal / National Research Council. Institute of Laboratory Animal Resources. 2006;47:259–271. doi: 10.1093/ilar.47.3.259. [DOI] [PubMed] [Google Scholar]
  • 34.Cornblath DR, Hillman MA, Striffler JS, Herman CN, Hansen BC. Peripheral neuropathy in diabetic monkeys. Diabetes. 1989;38:1365–1370. doi: 10.2337/diab.38.11.1365. [DOI] [PubMed] [Google Scholar]
  • 35.Pare M, Albrecht PJ, Noto CJ, Bodkin NL, Pittenger GL, Schreyer DJ, Tigno XT, Hansen BC, Rice FL. Differential hypertrophy and atrophy among all types of cutaneous innervation in the glabrous skin of the monkey hand during aging and naturally occurring type 2 diabetes. J Comp Neurol. 2007;501:543–567. doi: 10.1002/cne.21262. [DOI] [PubMed] [Google Scholar]
  • 36.Saleem KS, Logothetis NK. A Combined MRI and Histology Atlas of the Rhesus Monkey Brain in Stereotaxic Coordinates. second. Place Published: Academic Press; 2012. [Google Scholar]
  • 37.Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative C(T) method. Nature protocols. 2008;3:1101–1108. doi: 10.1038/nprot.2008.73. [DOI] [PubMed] [Google Scholar]
  • 38.Sengul G, Watson C, Tanaka I, Paxinos G. Atlas of the Spinal Cord of the Rat, Mouse, Marmoset, Rhesus, and Human. Place Published: Academic Press; 2013. [Google Scholar]
  • 39.Watkins LR, Hutchinson MR, Rice KC, Maier SF. The "toll" of opioid-induced glial activation: improving the clinical efficacy of opioids by targeting glia. Trends in pharmacological sciences. 2009;30:581–591. doi: 10.1016/j.tips.2009.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Dansereau MA, Gosselin RD, Pohl M, Pommier B, Mechighel P, Mauborgne A, Rostene W, Kitabgi P, Beaudet N, Sarret P, Melik-Parsadaniantz S. Spinal CCL2 pronociceptive action is no longer effective in CCR2 receptor antagonist-treated rats. Journal of neurochemistry. 2008;106:757–769. doi: 10.1111/j.1471-4159.2008.05429.x. [DOI] [PubMed] [Google Scholar]
  • 41.Gao YJ, Zhang L, Samad OA, Suter MR, Yasuhiko K, Xu ZZ, Park JY, Lind AL, Ma Q, Ji RR. JNK-induced MCP-1 production in spinal cord astrocytes contributes to central sensitization and neuropathic pain. J Neurosci. 2009;29:4096–4108. doi: 10.1523/JNEUROSCI.3623-08.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Saika F, Kiguchi N, Kobayashi Y, Fukazawa Y, Kishioka S. CC-chemokine ligand 4/macrophage inflammatory protein-1beta participates in the induction of neuropathic pain after peripheral nerve injury. European journal of pain (London, England) 2012;16:1271–1280. doi: 10.1002/j.1532-2149.2012.00146.x. [DOI] [PubMed] [Google Scholar]
  • 43.Zhang J, Shi XQ, Echeverry S, Mogil JS, De Koninck Y, Rivest S. Expression of CCR2 in both resident and bone marrow-derived microglia plays a critical role in neuropathic pain. J Neurosci. 2007;27:12396–12406. doi: 10.1523/JNEUROSCI.3016-07.2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Makimura C, Arao T, Matsuoka H, Takeda M, Kiyota H, Tsurutani J, Fujita Y, Matsumoto K, Kimura H, Otsuka M, Koyama A, Imamura CK, Yamanaka T, Tanaka K, Nishio K, Nakagawa K. Prospective study evaluating the plasma concentrations of twenty-six cytokines and response to morphine treatment in cancer patients. Anticancer research. 2011;31:4561–4568. [PubMed] [Google Scholar]
  • 45.McCarthy KF, Connor TJ, McCrory C. Cerebrospinal fluid levels of vascular endothelial growth factor correlate with reported pain and are reduced by spinal cord stimulation in patients with failed back surgery syndrome. Neuromodulation : journal of the International Neuromodulation Society. 2013;16:519–522. doi: 10.1111/j.1525-1403.2012.00527.x. discussion 522. [DOI] [PubMed] [Google Scholar]
  • 46.Liu Z, Miner JJ, Yago T, Yao L, Lupu F, Xia L, McEver RP. Differential regulation of human and murine P-selectin expression and function in vivo. The Journal of experimental medicine. 2010;207:2975–2987. doi: 10.1084/jem.20101545. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Vulchanova L, Riedl MS, Shuster SJ, Buell G, Surprenant A, North RA, Elde R. Immunohistochemical study of the P2X2 and P2X3 receptor subunits in rat and monkey sensory neurons and their central terminals. Neuropharmacology. 1997;36:1229–1242. doi: 10.1016/s0028-3908(97)00126-3. [DOI] [PubMed] [Google Scholar]
  • 48.Zhang X, Verge V, Wiesenfeld-Hallin Z, Ju G, Bredt D, Synder SH, Hokfelt T. Nitric oxide synthase-like immunoreactivity in lumbar dorsal root ganglia and spinal cord of rat and monkey and effect of peripheral axotomy. J Comp Neurol. 1993;335:563–575. doi: 10.1002/cne.903350408. [DOI] [PubMed] [Google Scholar]
  • 49.Milligan ED, Zapata V, Chacur M, Schoeniger D, Biedenkapp J, O'Connor KA, Verge GM, Chapman G, Green P, Foster AC, Naeve GS, Maier SF, Watkins LR. Evidence that exogenous and endogenous fractalkine can induce spinal nociceptive facilitation in rats. The European journal of neuroscience. 2004;20:2294–2302. doi: 10.1111/j.1460-9568.2004.03709.x. [DOI] [PubMed] [Google Scholar]
  • 50.Zhuang ZY, Kawasaki Y, Tan PH, Wen YR, Huang J, Ji RR. Role of the CX3CR1/p38 MAPK pathway in spinal microglia for the development of neuropathic pain following nerve injury-induced cleavage of fractalkine. Brain, behavior, and immunity. 2007;21:642–651. doi: 10.1016/j.bbi.2006.11.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Akimoto N, Honda K, Uta D, Beppu K, Ushijima Y, Matsuzaki Y, Nakashima S, Kido MA, Imoto K, Takano Y, Noda M. CCL-1 in the spinal cord contributes to neuropathic pain induced by nerve injury. Cell death & disease. 2013;4:e679. doi: 10.1038/cddis.2013.198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Bu H, Shu B, Gao F, Liu C, Guan X, Ke C, Cao F, Hinton AO, Jr, Xiang H, Yang H, Tian X, Tian Y. Spinal IFN-gamma-induced protein-10 (CXCL10) mediates metastatic breast cancer-induced bone pain by activation of microglia in rat models. Breast cancer research and treatment. 2014;143:255–263. doi: 10.1007/s10549-013-2807-4. [DOI] [PubMed] [Google Scholar]
  • 53.Ding H, Hayashida K, Suto T, Sukhtankar DD, Kimura M, Mendenhall V, Ko MC. Supraspinal actions of nociceptin/orphanin FQ, morphine and substance P in regulating pain and itch in non-human primates. Br J Pharmacol. 2015;172:3302–3312. doi: 10.1111/bph.13124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Kohno T, Ji RR, Ito N, Allchorne AJ, Befort K, Karchewski LA, Woolf CJ. Peripheral axonal injury results in reduced mu opioid receptor pre- and post-synaptic action in the spinal cord. Pain. 2005;117:77–87. doi: 10.1016/j.pain.2005.05.035. [DOI] [PubMed] [Google Scholar]
  • 55.Shaqura M, Khalefa BI, Shakibaei M, Winkler J, Al-Khrasani M, Furst S, Mousa SA, Schafer M. Reduced number, G protein coupling, and antinociceptive efficacy of spinal mu-opioid receptors in diabetic rats are reversed by nerve growth factor. J Pain. 2013;14:720–730. doi: 10.1016/j.jpain.2013.01.776. [DOI] [PubMed] [Google Scholar]
  • 56.Karci A, Tasdogen A, Erkin Y, Aktas G, Elar Z. The analgesic effect of morphine on postoperative pain in diabetic patients. Acta anaesthesiologica Scandinavica. 2004;48:619–624. doi: 10.1111/j.1399-6576.2004.00387.x. [DOI] [PubMed] [Google Scholar]
  • 57.Melik Parsadaniantz S, Rivat C, Rostene W, Reaux-Le Goazigo A. Opioid and chemokine receptor crosstalk: a promising target for pain therapy? Nat Rev Neurosci. 2015;16:69–78. doi: 10.1038/nrn3858. [DOI] [PubMed] [Google Scholar]
  • 58.Zhang N, Rogers TJ, Caterina M, Oppenheim JJ. Proinflammatory chemokines, such as C-C chemokine ligand 3, desensitize mu-opioid receptors on dorsal root ganglia neurons. Journal of immunology (Baltimore, Md. : 1950) 2004;173:594–599. doi: 10.4049/jimmunol.173.1.594. [DOI] [PubMed] [Google Scholar]
  • 59.Kwiatkowski K, Piotrowska A, Rojewska E, Makuch W, Jurga A, Slusarczyk J, Trojan E, Basta-Kaim A, Mika J. Beneficial properties of maraviroc on neuropathic pain development and opioid effectiveness in rats. Progress in neuropsychopharmacology & biological psychiatry. 2016;64:68–78. doi: 10.1016/j.pnpbp.2015.07.005. [DOI] [PubMed] [Google Scholar]
  • 60.Akgun E, Javed MI, Lunzer MM, Powers MD, Sham YY, Watanabe Y, Portoghese PS. Inhibition of Inflammatory and Neuropathic Pain by Targeting a Mu Opioid Receptor/Chemokine Receptor5 Heteromer (MOR-CCR5) Journal of medicinal chemistry. 2015;58:8647–8657. doi: 10.1021/acs.jmedchem.5b01245. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Kiguchi N, Ding H, Ko MC. Central N/OFQ-NOP Receptor System in Pain Modulation. Advances in pharmacology (San Diego, Calif.) 2016;75:217–243. doi: 10.1016/bs.apha.2015.10.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Schroder W, Lambert DG, Ko MC, Koch T. Functional plasticity of the N/OFQ-NOP receptor system determines analgesic properties of NOP receptor agonists. Br J Pharmacol. 2014;171:3777–3800. doi: 10.1111/bph.12744. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Lin AP, Ko MC. The therapeutic potential of nociceptin/orphanin FQ receptor agonists as analgesics without abuse liability. ACS Chem Neurosci. 2013;4:214–224. doi: 10.1021/cn300124f. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Ding H, Czoty PW, Kiguchi N, Cami-Kobeci G, Sukhtankar DD, Nader MA, Husbands SM, Ko MC. A novel orvinol analog, BU08028, as a safe opioid analgesic without abuse liability in primates. Proc Natl Acad Sci U S A. 2016;113:E5511–E5518. doi: 10.1073/pnas.1605295113. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES