The Journal of Toxicological Sciences
Online ISSN : 1880-3989
Print ISSN : 0388-1350
ISSN-L : 0388-1350
Original Article
Gene expression profiles in the dorsal root ganglia of methylmercury-exposed rats
Yo ShinodaSatoshi TatsumiEiko YoshidaTsutomu TakahashiKomyo EtoToshiyuki KajiYasuyuki Fujiwara
Author information

2019 Volume 44 Issue 8 Pages 549-558


Methylmercury (MeHg) exposure is known to induce neurodegeneration in both the central nervous system (CNS) and peripheral nervous system (PNS). Molecular mechanisms of MeHg-induced neurotoxicity have been well investigated in the CNS, however, it remains unclear in the PNS. In the present study, comprehensive gene expression analysis was performed by analyzing MeHg-exposed adult rat dorsal root ganglion (DRG) by DNA microarray. Methylmercuric chloride (6.7 mg/kg/day) was administered to nine-week-old male Wistar rats for five days, followed by two days without administration; this cycle was repeated once. Rats were anesthetized at 7 or 14 days after commencement of MeHg exposure, and their DRGs were removed and homogenized to make total RNA samples. DNA microarray data from Day 7 samples identified 100 out of 18,513 detected genes as annotated genes with more than two-fold upregulated or downregulated expression compared with controls. Database for Annotation, Visualization, and Integrated Discovery (DAVID) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses suggested strong involvement of immune activation and inflammation pathways in rat DRG exposed to MeHg, and some genes overlapped with previously reported genes affected by MeHg exposure in the cerebellum. The present results suggest that MeHg-induced neurotoxicity is associated with immune activation and inflammatory responses in rat DRG.


Methylmercury (MeHg) is a well-known pollutant that causes severe neuropathological changes in the cerebral cortex, cerebellum, and dorsal root ganglion (DRG) (McAlpine and Araki, 1958; Eto and Takeuchi, 1978; Takeuchi et al., 1978; Eto, 1997). As the principal neuronal and behavioral impairments elicited by MeHg exposure are caused by neural degeneration in some parts of the brain, most efforts to clarify the mechanisms underlying MeHg neurotoxicity have been performed in the central nervous system (CNS) of rodents and primates (Eto et al., 2002; Costa et al., 2004; Farina et al., 2011; Patel and Reynolds, 2013; Antunes Dos Santos et al., 2016; Unoki et al., 2018). Therefore, despite observations of peripheral nerve impairment in humans (Eto and Takeuchi, 1978; Takeuchi et al., 1978; Eto, 1997), mechanisms underlying MeHg-induced neurotoxicity in the peripheral nervous system (PNS) have not been thoroughly investigated.

One of the effective approaches to investigate molecular mechanisms of tissue and cellular responses to MeHg is comprehensive gene expression analysis using DNA microarray or next-generation sequencing. Indeed, comprehensive analyses of changes in gene expression influenced by MeHg exposure were previously performed in mouse cerebellum (Hwang et al., 2011), zebrafish brain (Richter et al., 2011), largemouth bass brain (Richter et al., 2014), and human neuroblastoma cells such as SH-SY5Y and IMR-32 in a culture system (Hwang and Naganuma, 2006; Toyama et al., 2011). These data provided a lot of information about the molecular mechanisms underlying MeHg-induced neurotoxicity; however, comprehensive gene expression analysis of MeHg-exposed PNS tissue, such as DRG, has not been performed. A comparison of results from the CNS and PNS would be both informative and valuable.

In the present study, we performed DNA microarray analysis of adult rat DRG after exposure to MeHg to examine potential molecular mechanisms underlying MeHg-induced neurotoxicity in rat DRG.


Animals and MeHg administration

MeHg administration was performed as previously reported (Shinoda et al., 2019). Briefly, nine-week-old male Wistar rats (Tokyo Laboratory Animals Science, Tokyo, Japan) were housed in cages under a 12/12-hr light-dark cycle, with ad libitum access to water and food. Methylmercuric chloride (Sigma-Aldrich, Tokyo, Japan) was dissolved in MilliQ water to a concentration of 2 mg/mL. The MeHg solution was administered orally using a gastric tube as described previously (Qu et al., 2019) at a daily dosage of 6.7 mg/kg for 5 days, followed by no administration for 2 days (to obtain Day 7 samples). This cycle was repeated once to obtain Day 14 samples. Age-matched rats not administered treatment were used as controls. We prepared four rats for each group (control, Day 7, and Day 14). All experimental protocols were evaluated and approved by the Regulations for Animal Research at Tokyo University of Pharmacy and Life Sciences. All efforts were made to minimize the number of animals used and their suffering.

RNA isolation

RNA isolation was performed as previously reported with some modifications (Shinoda et al., 2016). The lumbar spinal cord of deeply anesthetized control, Day 7, and Day 14 rats (four per group) were quickly excised and DRGs were removed in cold PBS. At least 6 DRGs were collected from each individual and mixed as an individual sample. Total RNA was extracted from DRGs using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions. RNA quantity and quality were determined using a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific) and Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), as recommended.

RNA amplification, labeling, and hybridization

RNA amplification, labeling, and subsequent hybridization methods were performed as described previously with some modifications (Yoshikawa et al., 2010). Total RNA was amplified and labeled with Cyanine 3 (Cy3) using a Low-Input Quick Amp Labeling Kit, One-Color (Agilent Technologies) according to the manufacturer’s instructions. Briefly, total RNA was reversed transcribed into double-strand cDNA using a poly dT-T7 promoter primer. Primer, template RNA, and quality-control transcripts of known concentration and quality were first denatured at 65°C for 10 min and then incubated for 2 hr at 40°C with 5 × first strand buffer containing 0.1 M DTT, 10 mM dNTP mix, and AffinityScript RNase Block Mix. The AffinityScript enzyme was inactivated at 70°C for 15 min. cDNA products were then used as templates for in vitro transcription to generate fluorescent cRNA. cDNA products were mixed with a transcription master mix in the presence of T7 RNA polymerase and Cy3 labeled-CTP, and incubated at 40°C for 2 hr. Labeled cRNAs were purified using Qiagen’s RNeasy mini spin columns and eluted in 30 μL of nuclease-free water. After amplification and labeling, cRNA quantity and Cy3 incorporation were determined using a Nanodrop ND-1000 spectrophotometer and Agilent Bioanalyzer. For each hybridization, 0.60 μg of Cy3-labeled cRNA were fragmented and hybridized at 65°C for 17 hr to an Agilent SurePrint G3 Rat Gene Expression v2 8 × 60K Microarray (Design ID: 074036). After washing, microarrays were scanned using an Agilent DNA microarray scanner.

Microarray analysis

DNA microarray analysis was performed as previously reported (Furuichi et al., 2011; Watanabe et al., 2017). Intensity values of each scanned feature were quantified using Agilent Feature Extraction software version, which performs background subtractions. We only used features that were flagged as no errors (“detected”) and excluded features that were not positive, not significant, not uniform, not above background, saturated, or population outliers (“not detected” or “compromised”). Normalization was performed using Agilent GeneSpring software version 14.8 (per chip:normalization to 75 percentile shift). There are a total of 45,598 probes on the Agilent SurePrint G3 Rat Gene Expression v2 8×60K Microarray (Design ID: 074036) without control probes. Four individual samples from each group (control, Day 7, and Day 14; 12 samples total) were analyzed, and statistical significance was detected using one-way ANOVA with post hoc Tukey Honest Significant Difference test. The Benjamini Hochberg False Discovery Rate was used for the correction of multiple comparisons.

Functional and pathway analyses

Functional and pathway analyses were performed as previously described (Sadakata et al., 2017). We used The Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 ( and Kyoto Encyclopedia of Genes and Genomes (KEGG) ( to analyze DNA microarray data. The list of differentially expressed genes with fold change > 2 and p < 0.05 was analyzed for common functions of altered genes using KEGG pathway and gene ontology (GO) terms. Enrichment analysis was performed using Fisher’s exact test. For GO term analysis, % scores (analyzed genes involved in each GO term divided by total genes analyzed) were calculated; GO terms with a score of more than 5% are shown in each table.


Using total RNA samples extracted from DRGs in control and MeHg exposed rats, we identified 18,513 genes on the microarray having raw signals with the flag value “detected” in at least three samples from each group. Cluster analysis of differentially regulated genes (p < 0.05) showed a specific pattern of gene expression attributable to the treatment (Fig. 1). Compared with controls, a total of 982 and 9,706 genes were identified as significantly different in Day 7 and Day 14 samples, respectively. In the present study, genes from Day 7 samples that were differentially expressed compared with the control were used for further analysis, as there was no observable neuronal cell death in DRG at this stage (Shinoda et al., 2019). Therefore, genes differentially expressed in this stage must be related to the subsequent initiation of neuronal death.

Fig. 1

Cluster analysis of gene expression in control (Ctrl), Day 7, and Day 14 samples (calculated from the beginning of MeHg exposure) of dorsal root ganglia in MeHg-exposed rats. Red and green represent upregulated and downregulated genes, respectively.

All genes that showed more than two-fold upregulation or downregulation compared with the control are listed in Table 1. A total of 117 genes containing 100 annotated and 17 non-annotated genes were identified. The number of downregulated genes was markedly small compared with upregulated genes. Previously, gene expression profiling of MeHg-exposed mouse cerebellum was analyzed adult C57BL/6 mice subcutaneously injected with 10 mg/kg/day MeHg for 7 days using DNA microarray (Hwang et al., 2011). Their results indicated that the expression of 21 genes was increased, while that of 11 genes was decreased in MeHg-exposed mice compared with controls. Compared with our present data, some of the same genes such as Gfap, Ccl12, Fcgr2b, Ccl7, Timp1, and Cd14 were increased in both ours and the previous one (Hwang et al., 2011). In addition, expression of similar families of genes were also increased in both studies, such as Fcgr3a, Ms4a6a, Bcl3, Clec4a3 and Chi3l1 in our study and Fcgr2b, Ms4a6d, Bcl2a1b, Clec7a and Chi3l in the previous study, respectively. Therefore, there may be similar mechanisms for MeHg-induced neuronal death in both the cerebellum and DRG. In contrast, gene profiling of MeHg-exposed human neuroblastoma, SH-SY5Y, and IMR-32 cells (Hwang and Naganuma, 2006; Toyama et al., 2011), showed no cross-correlation with the present study. Therefore, MeHg-induced neural toxicity might employ different mechanisms in a heterogeneous in vivo environment and homogeneous in vitro environment. Conceivably, mechanisms underlying MeHg neurotoxicity may be different between species, tissues, cell types, experimental conditions, or other factors.

Table 1. MeHg responsive genes (fold change > 2.0).
Gene Symbol Gene Name Fold Change p value Genbank
Cxcl14 chemokine (C-X-C motif) ligand 14 20.2 2.75E-04 NM_001013137
Gfap glial fibrillary acidic protein 7.98 3.66E-04 NM_017009
H19 H19, imprinted maternally expressed transcript (non-protein coding) 6.76 2.50E-04 NR_027324
Cxcl10 chemokine (C-X-C motif) ligand 10 6.34 1.53E-02 NM_139089
Slpi secretory leukocyte peptidase inhibitor 5.91 1.11E-02 NM_053372
Slpil3 antileukoproteinase-like 3 5.18 1.32E-02 NM_001008873
Fcgr3a Fc fragment of IgG, low affinity IIIa, receptor 5.09 2.86E-03 NM_207603
Cxcl13 chemokine (C-X-C motif) ligand 13 4.75 4.64E-03 NM_001017496
Fcnb ficolin B 4.72 3.11E-03 NM_053634
Ccl12 chemokine (C-C motif) ligand 12 4.68 1.28E-02 NM_001105822
Mt2A metallothionein 2A 4.39 1.02E-03 NM_001137564
Defa5 defensin, alpha 5, Paneth cell-specific 4.35 1.11E-03 NM_173329
Socs3 suppressor of cytokine signaling 3 4.34 2.39E-04 XM_008768398
4.21 1.13E-03 U50353
Np4 defensin NP-4 precursor 3.95 1.08E-03 NM_173299
Fcgr2b Fc fragment of IgG, low affinity IIb, receptor 3.91 3.40E-03 NM_175756
Il1rn interleukin 1 receptor antagonist 3.86 2.77E-03 NM_022194
RatNP-3b defensin RatNP-3 precursor 3.66 2.64E-03 NM_001079898
Fgf2 fibroblast growth factor 2 3.43 4.05E-03 M22427
Siglec1 sialic acid binding Ig-like lectin 1, sialoadhesin 3.38 1.23E-02 NM_001107777
Col7a1 collagen, type VII, alpha 1 3.37 1.04E-02 NM_001106858
C3 complement component 3 3.28 1.65E-02 NM_016994
Gbp2 guanylate binding protein 2, interferon-inducible 3.18 9.18E-03 NM_133624
Ccl7 chemokine (C-C motif) ligand 7 3.13 2.38E-04 NM_001007612
3.11 4.59E-03 XM_006250150
C4a complement component 4A (Rodgers blood group) 3.09 2.45E-03 NM_031504
Plac8 placenta-specific 8 3.01 1.30E-02 NM_001108353
Inmt indolethylamine N-methyltransferase 3.01 5.46E-04 NM_001109022
2.99 4.81E-03 XM_006250150
Adamts4 ADAM metallopeptidase with thrombospondin type 1 motif, 4 2.96 2.37E-04 NM_023959
Ms4a6a membrane-spanning 4-domains, subfamily A, member 6A 2.95 2.42E-02 XM_001075502
Ucn2 urocortin 2 2.94 4.82E-02 NM_133385
Defa10 defensin alpha 10 2.94 4.25E-03 NM_001033074
LOC103691468 uncharacterized LOC103691468 2.91 2.15E-02 XR_590927
Gpr84 G protein-coupled receptor 84 2.89 3.19E-03 NM_001109509
A2m alpha-2-macroglobulin 2.89 5.75E-03 NM_012488
C4b complement component 4B (Chido blood group) 2.88 1.96E-03 NM_001002805
Napsa napsin A aspartic peptidase 2.85 5.96E-03 NM_031670
Clcf1 cardiotrophin-like cytokine factor 1 2.83 2.47E-03 NM_207615
Ccl11 chemokine (C-C motif) ligand 11 2.79 7.57E-03 NM_019205
Ifitm3 interferon induced transmembrane protein 3 2.79 2.24E-03 NM_001136124
S100a8 S100 calcium binding protein A8 2.79 1.69E-03 NM_053822
Atf3 activating transcription factor 3 2.76 1.06E-03 NM_012912
Sbsn suprabasin 2.75 5.91E-03 NM_001044231
Timp1 TIMP metallopeptidase inhibitor 1 2.75 2.15E-03 NM_053819
Gpr171 G protein-coupled receptor 171 2.73 8.11E-04 NM_001109510
2.72 2.36E-03 XM_223483
Upp1 uridine phosphorylase 1 2.67 2.83E-03 NM_001030025
2.62 2.01E-02 XM_006225974
Cebpd CCAAT/enhancer binding protein (C/EBP), delta 2.61 2.50E-04 NM_013154
Tlr12 toll-like receptor 12 2.58 2.75E-02 XM_008764148
2.57 1.07E-02 XM_221091
2.55 3.14E-03 XM_001054983
S100a9 S100 calcium binding protein A9 2.53 3.11E-03 NM_053587
2.52 4.48E-03 XM_008774527
LOC102550593 uncharacterized LOC102550593 2.51 7.77E-04 XR_591063
Myo1g myosin IG 2.51 5.23E-03 NM_001134843
Bcl3 B-cell CLL/lymphoma 3 2.5 2.15E-04 NM_001109422
Icam1 intercellular adhesion molecule 1 2.48 4.86E-04 NM_012967
Batf3 basic leucine zipper transcription factor, ATF-like 3 2.48 3.76E-03 XM_008769874
Camp cathelicidin antimicrobial peptide 2.47 8.74E-03 NM_001100724
Akr1cl aldo-keto reductase family 1, member C-like 2.47 6.56E-03 NM_001109900
LOC684871 similar to Protein C8orf4 (Thyroid cancer protein 1) (TC-1) 2.43 8.64E-04 NM_001115043
Lst1 leukocyte specific transcript 1 2.43 1.76E-03 NM_022634
2.43 4.47E-02 XM_006237321
Dmbt1 deleted in malignant brain tumors 1 2.38 2.44E-03 NM_022849
2.38 8.09E-03 XM_006241766
Rac2 ras-related C3 botulinum toxin substrate 2 2.37 2.57E-03 NM_001008384
Runx2 runt-related transcription factor 2 2.37 2.26E-03 NM_001278483
Slco2a1 solute carrier organic anion transporter family, member 2a1 2.35 2.23E-03 NM_022667
Fos FBJ osteosarcoma oncogene 2.34 4.24E-03 NM_022197
Cd14 CD14 molecule 2.32 2.01E-02 NM_021744
Thbs2 thrombospondin 2 2.31 1.91E-04 NM_001169138
Junb jun B proto-oncogene 2.3 6.88E-04 NM_021836
2.3 1.84E-02 XM_008760706
Scube1 signal peptide, CUB domain, EGF-like 1 2.29 1.55E-03 NM_001134884
2.29 2.24E-02 XM_006247739
LOC103690369 leukocyte immunoglobulin-like receptor subfamily B member 4 2.28 8.73E-03 XM_008774415
Rgs16 regulator of G-protein signaling 16 2.27 3.01E-04 XM_006250012
Crlf1 cytokine receptor-like factor 1 2.23 2.93E-03 NM_001106074
Ripk3 receptor-interacting serine-threonine kinase 3 2.23 4.95E-03 NM_139342
Lsp1 lymphocyte-specific protein 1 2.23 2.96E-03 NM_001025420
Cd68 Cd68 molecule 2.22 8.85E-03 NM_001031638
Fcer1g Fc fragment of IgE, high affinity I, receptor for; gamma polypeptide 2.21 5.29E-03 NM_001131001
Clec4a3 C-type lectin domain family 4, member A3 2.21 1.25E-02 NM_001005891
Il1b interleukin 1 beta 2.2 3.33E-03 NM_031512
Gbp5 guanylate binding protein 5 2.2 1.70E-02 NM_001108569
Mmp19 matrix metallopeptidase 19 2.2 4.88E-03 NM_001107159
2.19 3.60E-03 FQ220235
Cd3g CD3 molecule, gamma 2.15 3.41E-04 NM_001077646
LOC100911413 deleted in malignant brain tumors 1 protein-like 2.15 7.98E-03 XM_006223692
Cldn14 claudin 14 2.15 3.41E-02 NM_001013429
Chi3l1 chitinase 3-like 1 (cartilage glycoprotein-39) 2.15 1.30E-02 NM_053560
2.14 3.73E-02 XM_006224276
Col16a1 collagen, type XVI, alpha 1 2.1 4.11E-04 NM_001302967
Igtp interferon gamma induced GTPase 2.1 7.63E-03 NM_001008765
Emp1 epithelial membrane protein 1 2.08 2.04E-02 NM_012843
Mt1a metallothionein 1a 2.07 4.96E-03 NM_138826
Egr2 early growth response 2 2.06 2.57E-03 NM_053633
Arid5a AT rich interactive domain 5A (Mrf1 like) 2.05 1.09E-03 NM_001034934
Slamf9 SLAM family member 9 2.04 1.17E-02 NM_001105971
Phf11b PHD finger protein 11B 2.04 1.21E-02 NM_001014235
Gas7 growth arrest specific 7 2.04 3.46E-04 NM_053484
C3ar1 complement component 3a receptor 1 2.04 4.65E-03 NM_032060
Fam150b family with sequence similarity 150, member B 2.03 3.89E-03 NM_001109372
2.03 4.01E-02 FQ217794
2.03 2.17E-03 FQ217918
Glipr1 GLI pathogenesis-related 1 2.02 1.22E-02 NM_001011987
Cyth4 cytohesin 4 2.01 2.90E-03 NM_001130577
Lyz2 lysozyme 2 2 1.82E-02 NM_012771
Igsf10 immunoglobulin superfamily, member 10 2 9.23E-04 NM_198768
Maff v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog F 2 7.28E-04 NM_001130573
Ptpn7 protein tyrosine phosphatase, non-receptor type 7 2 3.46E-04 NM_145683
LOC102552721 uncharacterized LOC102552721 -2.02 1.37E-02 XR_345109
-2.15 8.23E-03 DY472854
LOC102556699 uncharacterized LOC102556699 -2.21 2.14E-02 XM_008774562
Atp2b3 ATPase, Ca++ transporting, plasma membrane 3 -2.57 7.76E-03 NM_133288

All genes in the Day 7 sample that exhibited more than two-fold upregulation or downregulation compared with the control were applied to DAVID pathway analysis and confirmed by KEGG pathway analysis. In Tables 2-4, the number of counts represents the number of genes included in the related term. KEGG-pathway analysis indicated that 19 significant (p < 0.05) pathways responded to MeHg (Table 2). This result contains seven terms related to infection (tuberculosis, staphylococcus aureus infection, malaria, pertussis, leishmaniasis, Chagas disease, and legionellosis), four terms related to immune response (cytokine-cytokine receptor interaction, chemokine signaling pathway, toll-like receptor signaling pathway, and natural killer cell-mediated cytotoxicity), and two terms related to inflammatory response (tumor necrosis factor signaling pathway and rheumatoid arthritis). These results suggest that infection-associated-like immune and inflammatory responses were induced in MeHg-exposed DRG. A similar tendency was also shown for GO terms of biological processes (Table 3). Fourteen out of 26 GO terms identified for the response to MeHg were related to immune and inflammatory responses (Fig. 2; inflammatory response, response to lipopolysaccharide, defense response to bacterium, innate immune response, cellular response to lipopolysaccharide, neutrophil chemotaxis, cellular response to interferon-gamma, immune response, defense response to Gram-positive bacterium, cellular response to interleukin-1, cellular response to tumor necrosis factor, chemokine-mediated signaling pathway, positive regulation of inflammatory response and chemotaxis). Indeed, most of the MeHg-responsive genes that overlapped between the present study and previous studies, such as Ccl12, Ccl7, Timp1, and Cd14 (Hwang et al., 2011), were highly associated with inflammation. In addition, inflammatory responses induced by MeHg have been reported in several species, tissues, and cell types both in vivo (Cambier et al., 2012; da Silva et al., 2012; Nøstbakken et al., 2012) and in vitro (David et al., 2017; Yamamoto et al., 2017; Crowe et al., 2018).

Table 2. KEGG-pathways related to MeHg responsive genes.
Term Count % p value Fold Enrichment
TNF signaling pathway 8 8.6 3.06E-06 12.1
Tuberculosis 8 8.6 8.67E-05 7.25
Cytokine-cytokine receptor interaction 8 8.6 1.52E-04 6.63
Staphylococcus aureus infection 7 7.53 6.51E-07 21.37
Chemokine signaling pathway 7 7.53 5.72E-04 6.52
Osteoclast differentiation 6 6.45 9.38E-04 7.67
MAPK signaling pathway 6 6.45 1.75E-02 3.85
Malaria 5 5.38 3.90E-04 13.97
Pertussis 5 5.38 8.77E-04 11.29
Phagosome 5 5.38 2.70E-02 4.27
Leishmaniasis 4 4.3 8.77E-03 9.16
Complement and coagulation cascades 4 4.3 8.77E-03 9.16
Salivary secretion 4 4.3 1.02E-02 8.68
Rheumatoid arthritis 4 4.3 1.60E-02 7.33
Toll-like receptor signaling pathway 4 4.3 1.96E-02 6.8
Natural killer cell mediated cytotoxicity 4 4.3 2.01E-02 6.73
Chagas disease (American trypanosomiasis) 4 4.3 2.53E-02 6.16
Cytosolic DNA-sensing pathway 3 3.23 4.34E-02 8.83
Legionellosis 3 3.23 4.48E-02 8.68
Table 3. GO term (Biological Process) related to MeHg responsive genes.
Term Count % p value Fold Enrichment
inflammatory response 14 15.05 1.33E-09 9.84
response to lipopolysaccharide 12 12.9 8.25E-08 8.95
positive regulation of transcription from RNA polymerase II promoter 12 12.9 8.71E-03 2.45
defense response to bacterium 11 11.83 6.81E-10 17.27
innate immune response 10 10.75 3.93E-06 8
positive regulation of cell proliferation 10 10.75 6.12E-04 4.15
cellular response to lipopolysaccharide 9 9.68 1.23E-06 11.25
positive regulation of ERK1 and ERK2 cascade 9 9.68 2.76E-06 10.1
positive regulation of angiogenesis 8 8.6 1.34E-06 14.27
neutrophil chemotaxis 7 7.53 3.11E-07 25.19
cellular response to interferon-gamma 7 7.53 4.22E-07 23.95
negative regulation of endopeptidase activity 7 7.53 7.22E-05 9.87
immune response 7 7.53 1.67E-03 5.47
response to drug 7 7.53 3.90E-02 2.77
defense response to Gram-positive bacterium 6 6.45 7.49E-05 13.61
cellular response to interleukin-1 6 6.45 1.01E-04 12.78
cellular response to tumor necrosis factor 6 6.45 4.21E-04 9.42
response to organic cyclic compound 6 6.45 9.41E-03 4.6
transcription from RNA polymerase II promoter 6 6.45 4.05E-02 3.15
chemokine-mediated signaling pathway 5 5.38 1.52E-04 18.31
positive regulation of inflammatory response 5 5.38 2.25E-04 16.57
chemotaxis 5 5.38 2.39E-04 16.31
wound healing 5 5.38 3.02E-03 8.28
regulation of cell shape 5 5.38 3.02E-03 8.28
response to glucocorticoid 5 5.38 3.66E-03 7.85
response to ethanol 5 5.38 1.33E-02 5.41
Table 4. GO term (Molecular Function) related to MeHg responsive genes.
Term Count % p value Fold Enrichment
protein heterodimerization activity 9 9.68 3.20E-03 3.59
cytokine activity 7 7.53 6.97E-05 9.9
endopeptidase inhibitor activity 6 6.45 1.98E-07 44.4
chemokine activity 6 6.45 5.12E-07 37
heparin binding 6 6.45 4.80E-04 9.12
transcription regulatory region DNA binding 6 6.45 4.07E-03 5.62
RNA polymerase II core promoter proximal region sequence-specific DNA binding 6 6.45 1.89E-02 3.85
carbohydrate binding 5 5.38 5.99E-03 6.81
GTPase activity 5 5.38 1.17E-02 5.61
transcriptional activator activity, RNA polymerase II core promoter proximal region sequence-specific binding 5 5.38 2.65E-02 4.37
Fig. 2

Schematic of mechanisms of MeHg-induced neuronal cell death in rat DRG. GO terms for biological processes related to immune and inflammatory responses are depicted.

GO terms (molecular function) were also investigated by DAVID analysis (Table 4). The results indicated that the higher order in the table contains cytokine and chemokine activities. Detailed gene components of identified terms are shown in Table 5. Several principal cytokine and chemokine ligands were induced by MeHg exposure. Cytokines and chemokines are well known for their roles in immune responses and activation of inflammation (Turner et al., 2014). In fact, microglial activation and/or macrophage invasion induced by MeHg exposure were previously reported in rat cerebellum, common marmoset, and mouse cerebral cortex (Sakamoto et al., 2008; Fujimura et al., 2009; Yamamoto et al., 2012). Therefore, we hypothesized that MeHg-induced cytokines and chemokines in rat DRG attract activation/invasion of microglia and macrophages. This hypothesis is strongly supported by our previous results showing activation and increased numbers of microglia and macrophages in DRG after MeHg exposure (Shinoda et al., 2019).

Table 5. Genes included in cytokine and chemokine activities.
Gene Name Genbank
Cytokine activity
C-X-C motif chemokine ligand 10 (Cxcl10) NM_139089
TIMP metallopeptidase inhibitor 1 (Timp1) NM_053819
cardiotrophin-like cytokine factor 1 (Clcf1) NM_207615
cytokine receptor-like factor 1 (Crlf1) NM_001106074
fibroblast growth factor 2 (Fgf2) NM_019305
interleukin 1 beta (Il1b) NM_031512
interleukin 1 receptor antagonist (Il1rn) NM_022194
Chemokine activity
C-C motif chemokine ligand 11 (Ccl11) NM_019205
C-C motif chemokine ligand 7 (Ccl7) NM_001007612
C-X-C motif chemokine ligand 10 (Cxcl10) NM_139089
C-X-C motif chemokine ligand 13 (Cxcl13) NM_001017496
C-X-C motif chemokine ligand 14 (Cxcl14) NM_001013137
C-C motif chemokine ligand 12 (Ccl12) NM_001105822

In the present study, we investigated gene expression responses to MeHg exposure using rat DRG. Many genes detected in our experimental conditions showed significant association with immune activation and induction of inflammation. However, further investigations are required to clarify the origin of involved cytokines and chemokines, the cause of microglial activation and macrophage invasion (whether the cause or result of neural cell death in MeHg-exposed rat DRG), and related molecular mechanisms of MeHg-induced neurotoxicity.


This work was supported by the Study of the Health Effects of Heavy Metals organized by the Ministry of the Environment, Japan. We thank Edanz Group ( for editing a draft of this manuscript.

Conflict of interest

The authors declare that there is no conflict of interest.

© 2019 The Japanese Society of Toxicology