Differences between Dorsal Root and Trigeminal Ganglion Nociceptors in Mice Revealed by Translational Profiling

Abstract

Nociceptors located in the trigeminal ganglion (TG) and DRG are the primary sensors of damaging or potentially damaging stimuli for the head and body, respectively, and are key drivers of chronic pain states. While nociceptors in these two tissues show a high degree of functional similarity, there are important differences in their development lineages, their functional connections to the CNS, and recent genome-wide analyses of gene expression suggest that they possess some unique genomic signatures. Here, we used translating ribosome affinity purification to comprehensively characterize and compare mRNA translation in Scn10a-positive nociceptors in the TG and DRG of male and female mice. This unbiased method independently confirms several findings of differences between TG and DRG nociceptors described in the literature but also suggests preferential utilization of key signaling pathways. Most prominently, we provide evidence that translational efficiency in mechanistic target of rapamycin (mTOR)-related genes is higher in the TG compared with DRG, whereas several genes associated with the negative regulator of mTOR, AMP-activated protein kinase, have higher translational efficiency in DRG nociceptors. Using capsaicin as a sensitizing stimulus, we show that behavioral responses are greater in the TG region and this effect is completely reversible with mTOR inhibition. These findings have implications for the relative capacity of these nociceptors to be sensitized upon injury. Together, our data provide a comprehensive, comparative view of transcriptome and translatome activity in TG and DRG nociceptors that enhances our understanding of nociceptor biology.

SIGNIFICANCE STATEMENT The DRG and trigeminal ganglion (TG) provide sensory information from the body and head, respectively. Nociceptors in these tissues are critical first neurons in the pain pathway. Injury to peripheral neurons in these tissues can cause chronic pain. Interestingly, clinical and preclinical findings support the conclusion that injury to TG neurons is more likely to cause chronic pain and chronic pain in the TG area is more intense and more difficult to treat. We used translating ribosome affinity purification technology to gain new insight into potential differences in the translatomes of DRG and TG neurons. Our findings demonstrate previously unrecognized differences between TG and DRG nociceptors that provide new insight into how injury may differentially drive plasticity states in nociceptors in these two tissues.

Introduction

Mechanical, thermal, and chemical peripheral stimuli are detected by the pseudo-unipolar sensory neurons of the DRG and the trigeminal ganglion (TG) (Devor, 1999; Woolf and Ma, 2007; Dubin and Patapoutian, 2010). Neurons in the DRG transmit signals from the limbs and body, including much of the viscera, to the CNS through the dorsal horn of the spinal cord. TG neurons relay sensory information from the head and face through a region of the dorsal brainstem known as the trigeminal nucleus caudalis. Although TG and DRG neurons express similar markers and are often considered as very similar, there are differences in their cellular populations (Price and Flores, 2007). The tissues also have distinct embryonic origins with important functional consequences (Durham and Garrett, 2010). Finally, neurons in these ganglia innervate distinct targets in the periphery (e.g., the teeth and dura mater for the TG) and in the CNS. An excellent example of this differential innervation in the CNS is the discovery of a subset of TG nociceptors that bypass the traditional second-order relay in the nucleus caudalis projecting directly to the parabrachial nucleus (Rodriguez et al., 2017). These findings suggest distinct molecular signatures of DRG and TG neurons that may be important for understanding sensory neurobiology from these different regions of an organism.

Advances in next-generation sequencing have allowed the characterization of DRG and TG tissues at the genome-wide level using RNA sequencing (RNA-seq) (Manteniotis et al., 2013; Reynders et al., 2015; Gong et al., 2016; Hu et al., 2016; Kogelman et al., 2017). These studies provide significant insight into genes that are differentially expressed between these tissues, including differences between species (Manteniotis et al., 2013; Flegel et al., 2015; Kogelman et al., 2017). However, these studies lack cell-type specificity and fail to capture translational efficiency. Cell specificity is a key advantage for single-cell transcriptomic methods (Usoskin et al., 2015; Hu et al., 2016) and other cellular enrichment protocols (Isensee et al., 2014; Thakur et al., 2014; Lopes et al., 2017) that have now been applied to the DRG and/or TG. However, only one direct comparison has thus far been made between TG and DRG transcriptomes using neuronal enrichment followed by RNA-seq (Lopes et al., 2017). Examining ribosome-bound RNA is advantageous because there is strong evidence that transcriptional and translational efficiencies are decoupled in most cells (Fortelny et al., 2017). Methods that sequence ribosome-bound RNAs give more accurate predictions of cellular proteomes (Heiman et al., 2008; Ingolia, 2016). Two techniques have emerged in this area. The first, ribosome footprint profiling, comprehensively and quantitatively provides a snapshot of translation activity at single codon resolution through deep sequencing of ribosome-protected mRNA fragments from cells or tissues (Ingolia, 2016). This technique, which has recently been applied to the DRG (Uttam et al., 2018), does not allow insight into cell-type-specific translational profiling. A second technique is translating ribosome affinity purification (TRAP), which relies on genetic tagging of ribosomal proteins for cell-specific pulldown of translating ribosomes bound to mRNAs for RNA-seq (Doyle et al., 2008; Heiman et al., 2008, 2014). This technique lacks the single codon resolution of ribosome footprint profiling but allows for precise assessment of cellular translatomes in vitro and in vivo.

Here we used the TRAP technology using the Nav1.8^Cre mouse (Stirling et al., 2005) to achieve sensory neuron-specific ribosome tagging with enrichment in the nociceptor population. We then compared TG and DRG nociceptor translatomes and quantified mRNAs that are differentially expressed at the transcriptional and/or translational level. Interestingly, we found that translational activity of mechanistic target of rapamycin (mTOR)-related genes is higher in the TG compared with DRG. Given the key role that this signaling pathway plays in rapid sensitization of nociceptors (Khoutorsky and Price, 2018), this result is intriguing because activation of nociceptors in the facial region produces greater sensitization and perceived pain in human subjects (Schmidt et al., 2015, 2016), an effect that our experiments also demonstrate in mice. Therefore, our work pinpoints important signaling differences between DRG and TG nociceptors that have direct functional consequences on the susceptibility of these nociceptors to rapid sensitization.

Materials and Methods

Transgenic animals: Nav1.8^cre/Rosa26^fsTRAP mice.

All animal procedures were approved by the Institutional Animal Care and Use Committee of University of Texas at Dallas.

Rosa26^fsTRAP mice were purchased from The Jackson Laboratory (stock #022367). Transgenic mice expressing Cre recombinase under the control of the Scn10a (Nav1.8) promoter were obtained initially from Professor John Wood (University College London) but are commercially available from Infrafrontier (EMMA ID: 04582). The initial characterization of these mice demonstrated that the introduction of the Cre recombinase in heterozygous animals does not affect pain behavior, and their DRG neurons have normal electrophysiological properties (Stirling et al., 2005). Nav1.8^cre mice on a C57BL/6J genetic background were maintained and bred at the University of Texas at Dallas. Upon arrival, Rosa26^fsTRAP mice were crossed to Nav1.8^cre to generate the Nav1.8-TRAP mice that express a fused EGFP-L10a protein in Nav1.8-expressing neurons. All experiments were performed using male and female littermates 8–12 weeks old. Mice were group housed (4 maximum) in nonenvironmentally enriched cages with food and water ad libitum on a 12 h light-dark cycle. Room temperature was maintained at 21 ± 2°C.

TRAP.

Nav1.8-TRAP male and female mice were decapitated and DRG and TG rapidly dissected in ice-cold dissection buffer (1× HBSS; Invitrogen, 14065006), 2.5 mm HEPES, 35 mm glucose, 4 mm NaHCO₃, 100 μg/ml cycloheximide, 0.001V 2 mg/ml emetine). DRGs or TGs were transferred to ice-cold polysome buffer (20 mm HEPES, 12 mm MgCl₂, 150 mm KCl, 0.5 mm DTT, 100 μg/ml cycloheximide, 20 μg/ml emetine, 40 U/ml SUPERase IN, Promega, 1 μl DNase, and protease inhibitor) and homogenized using a Dounce homogenizer. Samples were centrifuged at 3000 × g for 10 min to prepare postnuclear fraction (S1). Then, 1% NP-40 and 30 mm 1,2-dihexanoyl-sn-glycero-3-phosphocholine were added to the S1 fraction and then centrifuged at 15,000 × g for 15 min to generate a postmitochondrial fraction (S20). A 200 μl sample of S20 was removed for use as Input, and 800 μl of S20 was incubated with protein G-coated Dynabeads (Invitrogen) bound to 50 μg of anti-GFP antibodies (HtzGFP-19F7 and HtzGFP-19C8, Memorial Sloan Kettering Centre) for 3 h at 4°C with end-over-end mixing. Anti-GFP beads were washed with high salt buffer (20 mm HEPES, 5 mm MgCl₂, 350 mm KCl, 1% NP-40, 0.5 mm DTT, and 100 μg/ml cycloheximide), and RNA was eluted from all samples using a Direct-zol kit (Zymo Research) according to the manufacturer's instructions. RNA yield was quantified using a Nanodrop system (Thermo Fisher Scientific), and RNA quality was determined by fragment analyzer (Advanced Analytical Technologies).

Library generation and sequencing.

Libraries were generated from 100 ng to 1 μg of total RNA using Quantseq 3′ mRNA-Seq library kit (Lexogen) with RiboCop rRNA depletion kit (Lexogen) treatment according to the manufacturer's protocols. The endpoint PCR amplification cycle number for each sample was determined by qPCR assay with PCR Add-on kit for Illumina (Lexogen). The cycle number was selected when the fluorescence value reached 33% of the maximum for each sample. Purified libraries were quantified by Qubit (Invitrogen), and the average size was determined by fragment analyzer (Advanced Analytical Technologies) with high-sensitivity next-generation sequencing fragment analysis kit. Libraries were then sequenced on an Illumina NextSeq500 Sequencer using 50 bp single-end reads.

Sequence files generated by the Illumina NextSeq500 Sequencer were downloaded from BaseSpace. An initial quality check using FastQC 0.11.5 (Babraham Bioinformatics) was done on the sequencing files, and then trimming was performed on the server with the FASTQ Toolkit. Sequences were trimmed with optimized parameters (13 bases from 3′ end, 17 bases from 5′ end, and any poly-adenine longer than 2 bases from the 3′ side). Trimming parameters were optimized based on FastQC results and mapping rate, as well as manually checking high reads or abundant chromosomal regions with IGV 2.3.80. The trimmed sequencing samples were then processed using TopHat 2.1.1 (with Bowtie 2.2.9) and mapped to the mouse reference genome (NCBI reference assembly GRCm38.p4) and reference transcriptome (Gencode vM10) generating files in .bam format. Processed .bam files were then quantified for each gene using Cufflinks 2.2.1 with gencode.vM10 genome annotation. Because reads only mapped to the 3′UTR of the gene, read counts were not normalized by length by using the Cufflinks option − no-length-correction. Relative abundance for the i^th gene was determined by calculating TPM (transcripts per million) values as follows:

where a_j is the Cufflinks reported relative abundance. Finally, TPM values were normalized to upper decile for each biological replicate, and udTPM (upper decile TPM) were used for analysis (Glusman et al., 2013). This was done to provide uniform processing for samples with varying sequencing depth and because of varying number of genes in the transcriptome and translatome samples.

Behavioral procedures.

Female C57BL/6J mice were injected subdermally with capsaicin (0.1 μm) into either cheek or hindpaw in a volume of 10 μl with Hamilton syringe and 30G needle. For cheek injections, mice cheeks were shaved 3 d before injections. AZD8055 (mTORC1 inhibitor) or vehicle was administered intraperitoneally (10 mg/kg) 2 h before capsaicin injections into the cheek. AZD8055 was dissolved in DMSO (50 mg/ml) and further diluted in 30% (w/v) cyclodextrin to make up the correct dose for each animal. Vehicle consisted of 10% DMSO and 30% w/v cyclodextrin. Baseline videos were recorded for 15 min for each mouse. After cheek or hindpaw injections, experimental videos were recorded for 60 min. The recording setup consisted of one camera in front and one in the back. The sum of facially directed behaviors with the forepaws following injection of capsaicin into the whisker pad as well as the number of hindpaw directed behaviors for the hindpaw were scored and classified as nocifensive behaviors.

The Mouse Grimace Scale was used to quantify affective aspects of pain in mice (Langford et al., 2010). We scored the changes in the facial expressions (using the facial action coding system) at baseline and then 15 and 30 min after intraplantar or facial injection of capsaicin.

qRT-PCR.

Lumbar DRGs and TGs were isolated from 4 male mice per genotype and flash-frozen on dry ice and stored at −80°C until ready to be processed. Tissues were homogenized using a pestle, and total RNA was extracted using RNAqueous Total RNA Isolation kits (Thermo Fisher Scientific). RNA was subsequently treated with TURBO DNase (Thermo Fisher Scientific) according to the manufacturer's instructions. RNA concentration was measured on a NanoDrop 2000 (Thermo Fisher Scientific). cDNA was synthesized using iScript Reverse Transcriptase (Bio-Rad). qRT-PCR was done using a Applied Biosystems Lightcycler 7500 Real-Time PCR system using iTaq Universal SYBR Green Supermix (Bio-Rad) according to the manufacturer's instructions with 3 technical replicates per biological replicate (averages of the technical replicates per biological replicate are reported) using primers pairs: Gapdh forward 5′-GACAACTTTGGCATTGTGGA-3′ and Gapdh reverse 5′-CATCATACTTGGCAGGTTTCTC-3′, Rraga forward 5′-ACGTCCGATTCTTGGGGAAC-3′ and Rraga reverse 5′-TACGGAAGATGTTGTCCCGC-3′, Fth forward 5′-GCACTGCACTTGGAAAAGAGT-3′ and Fth reverse 5′-ACGTGGTCACCCAGTTCTTT-3′. Primers were made by Integrated DNA Technologies.

Primer efficiency curves were determined by diluting total RNA of DRG and TG samples with 6 points of 1:5 serial dilutions. RNA dilutions were then converted to cDNA, and standard curves were determined for DRG and TG with each primer set separately. Concentrations resulting in multiple products or incorrect product size via melt-curve analysis (derivative reporter vs temperature) were omitted. Efficiencies for each primer set for DRGs and TGs were calculated using the Applied Biosystems 7500 software version 2.3. Total RNA (115 ng) used in experiments fell within primer standard curves with efficiencies between 85% and 110%. Data were analyzed as 2^−ΔΔCt and normalized as shown in Results.

Antibodies.

The peripherin antibody used for immunohistochemistry were obtained from Sigma-Aldrich. Isolectin B₄ (IB₄) conjugated to AlexaFluor-568 and secondary AlexaFluor antibodies were purchased from Invitrogen. Calcitonin gene-related peptide (CGRP) antibody was purchased from Peninsula Laboratories. RagA and Akt1s1 (also known as PRAS40) antibodies were from Cell Signaling Technology. Antibodies for TRAP (HtzGFP-19F7 and HtzGFP-19C8) were obtained from Sloan Memorial Kettering Centre, after establishing Material Transfer Agreements with the laboratory of Prof. Nathaniel Heintz (Rockefeller University).

Immunohistochemistry.

Animals were anesthetized with isoflurane (4%) and killed by decapitation, and tissues were flash-frozen in OCT on dry ice. Sections of TG (20 μm) were mounted onto SuperFrost Plus slides (Thermo Fisher Scientific) and fixed in ice-cold 10% formalin in 1× PBS for 45 min, and then subsequently washed 3 times for 5 min each in 1× PBS. Slides were then transferred to a solution for permeabilization made of 1× PBS with 0.2% Triton X-100 (Sigma-Aldrich). After 30 min, slides were washed 3 times for 5 min each in 1× PBS. Tissues were blocked for at least 2 h in 1× PBS and 10% heat-inactivated normal goat serum. TG or DRG slices were stained with peripherin, CGRP, and IB₄ conjugated to AlexaFluor-568. Immunoreactivity was visualized following 1 h incubation with goat anti-rabbit, goat anti-mouse, and goat anti-guinea pig AlexaFluor antibodies at room temperature. All immunohistochemical images are representations of samples taken from 3 animals per genotype. Images were taken using an Olympus FluoView 1200 confocal microscope. Analysis of images was done using ImageJ Version 1.48 for Apple OSX (National Institutes of Health).

Western blotting.

Male and female mice were used for all Western blotting experiments and were killed by decapitation while under anesthesia and tissues (DRG or TG) were flash frozen on dry ice. Frozen tissues were homogenized in lysis buffer (50 mm Tris, pH 7.4, 150 mm NaCl, 1 mm EDTA, pH 8.0, and 1% Triton X-100) containing protease and phosphatase inhibitors (Sigma-Aldrich), and homogenized using a pestle. A total of 15 μg of protein was boiled for 5 min in loading dye and then loaded into each well and separated by a 10%–12% SDS-PAGE gel. Proteins were transferred to a 0.45 μm PVDF membrane (Millipore) at 25 V overnight at 4°C. Subsequently, membranes were blocked with 5% nonfat dry milk in 1× Tris buffer solution containing Tween 20 (TTBS) for 3 h. Membranes were washed in 1× TTBS 3 times for 5 min each, then incubated with primary antibody overnight at 4°C. The following day, membranes were washed 3 times in 1× TTBS for 5 min each, then incubated with the corresponding secondary antibody at room temperature for 1 h. Membranes were then washed with 1× TTBS 5 times for 5 min each. Signals were detected using Immobilon Western Chemiluminescent HRP substrate (Millipore). Bands were visualized using film (Kodak) or with a Bio-Rad ChemiDoc Touch. Membranes were stripped using Restore Western Blot Stripping buffer (Thermo Fisher Scientific) and reprobed with another antibody. Analysis was performed using Image Lab (Bio-Rad).

Statistics.

All data are presented as mean ± SEM. All analysis was done using GraphPad Prism 6 version 6.0 for Mac OS X. Single comparisons were performed using Student's t test or one-way ANOVA if multiple groups were compared. For behavioral experiments, two-way ANOVA (time × treatment) was used to measure effects across time between different groups. If significant effects were found by ANOVA, post hoc analyses were performed. Multiple comparisons between groups/within groups were performed using Sidak's correction. Statistical results can be found in the figure legends.

Statistics for RNA sequencing.

Differential expression analysis was performed using MATLAB scripts. TPM values were normalized to their 90th percentile to generate udTPMs, and the probability density function of the udTPM was used to set the threshold value for further analysis. Genes showing consistent expression above the set threshold across biological replicates were then used to generate lists of differentially expressed genes. Standard t test was first performed assuming unequal variances between experimental groups generating p values for each gene as follows. A q value for the i^th test was then calculated using Benjamini–Hochberg correction for multiple comparisons as follows:

where N is the number of tests.

Finally, the cumulative density function of the fold change was plotted and used to set the fold change for the input and TRAP fraction for both DRG and TG datasets. Gene set enrichment analyses were performed with Enrichr (Kuleshov et al., 2016) using the Gene Ontology molecular function 2015 term, the biological process 2015 term, and the Reactome 2015 libraries.

For motif finding, 5′-UTR sequences of corresponding genes were obtained from gencode.vM10 (mouse genome assembly GRCm38), with all transcript isoforms kept for analysis. As most 5′-UTRs of different isoforms from the same gene share partial/whole sequences with each other, when a 5′-UTR sequence was fully shared by another longer 5′-UTR isoform of the same gene, the shorter version was removed to prevent genes with a large amount of isoforms being overrepresented in the motif analysis. All 5′-UTR sequences remaining after filtering were then passed through MEME Suite 5.0.2 for motif discovery, with the following parameters: all motifs are within 10–20 bp length range, only found on the provided strand, and appear in at least 10% of the genes provided. Motifs appearing in >30% of the genes with significant E value are shown in the text.

Results

To generate nociceptor-TRAP mice, Nav1.8^cre animals were crossed with Rosa26^fs-TRAP (Zhou et al., 2013) to express the eGFP fused to the ribosomal L10a protein in Nav1.8⁺ neurons. This approach generates Nav1.8-TRAP neurons in both the DRG and TG. While the specificity of our approach was recently shown in the DRG (Megat et al., 2019), we characterized expression of the transgene in the TG (Fig. 1A). We found that eGFP-L10a-positive neurons primarily colocalized with small-diameter peripherin-positive neurons and that extensive overlap was found with both CGRP immunoreactivity and with IB₄ staining (Fig. 1B). These findings demonstrate that this technique labels an equivalent subset of neurons in the DRG and TG of mice.

Figure 1.

TRAP-seq strategy and expression in TG. A, Schematic representation of TRAP-seq approach showing isolation of translating ribosomes with immunoprecipitation using anti-GFP-coated beads. B, Immunostaining of CGRP, IB₄, and peripherin (Prph) on TG sections from Nav1.8-TRAP mice (GFP). Scale bar, 100 μm.

Having confirmed that the Nav1.8-TRAP approach yields robust expression in nociceptors in the TG, we set out to conduct TRAP sequencing to compare nociceptor translatomes in the DRG and TG. To successfully isolate ribosome-associated mRNAs from Nav1.8-TRAP cells, we determined that TGs from 4 animals were required for a single biological replicate. This number matches the number of DRGs needed for TRAP sequencing. To make comparisons between the TG and DRG, we generated TRAP sequencing from the TG that was then compared with our previously generated DRG dataset (GSE 113941). We sequenced the total mRNA input from all biological replicates and mRNAs associated with translating ribosomes in the Nav1.8 subset of TG neurons, equivalently to what was done from DRG (Megat et al., 2019). This approach allowed us to make comparisons between the whole tissue transcriptional and Nav1.8⁺ neuron translational landscapes between DRG and TG.

The first dimension of the clustering analysis identified clear differences between TG and DRG as well as distinctions within each subcluster comprised of the input (transcriptome) and TRAP (translatome) RNA sequencing (Fig. 2A). We observed strong correlation coefficients between biological replicates demonstrating low variability in the experimental protocol (Fig. 2B). Gene expression values (TPMs) were normalized to the 90th percentile for each biological replicate, and the empirical probability density function of the normalized expression level (upper decile (ud)TPM) was plotted for the input and TRAP fractions (Fig. 2C). The probability density function identified 2 peaks, and the inflection point was used to set the threshold expression values according to the sequencing depth (Fig. 2C). After further filtering, based on consistent expression among biological replicates, we included a total of 7358 genes in the final analysis to make comparisons between the DRG and TG transcriptomes and Nav1.8-TRAP translatomes. Finally, we plotted the cumulative frequency distribution as a function of the log twofold change for each of these 7358 genes in TG and DRG biological replicates, and the 95th percentile was used to set the threshold fold change values for the input and TRAP fractions (Fig. 2D). Principal component (PC) analysis indicated that PC1 distinguished between TG and DRG, whereas PC2 detected a difference between input and Nav1.8-TRAP, suggesting a clear transcriptional and translational signature for both of these tissues (Fig. 3A). Detailed analysis of the variances for each PC clearly showed that the first 2 PCs (PC1 = difference between DRG and TG transcriptomes; and PC2 = difference between the DRG and TG Nav1.8-TRAP translatomes) explained the majority of the variance seen in the dataset (Fig. 3B). Further clustering analysis confirmed the findings of the PC analysis (Fig. 3C).

Figure 2.

DRG and TG TRAP-seq shows high correlation between biological replicates and similar sequencing depth. A, Heatmap of the correlation coefficient and cluster analysis showing clear separation between DRG and TG as well as in between TRAP-seq and bulk RNA-seq from each tissue. B, Scatter plot of input and TRAP-seq shows high correlation between biological replicates for each approach. C, Empirical probability density function (PDF) of the TPM for all genes in analysis shows a similar distribution between replicates, which are each shown as a different color, for TRAP-seq and input. D, Cumulative distribution of the fold change (FC) in input and TRAP-seq shows higher FCs in TRAP-seq samples. Kolgomorov-Smirnov test, ***p < 0.001.

Figure 3.

PC analysis shows a clear difference between transcriptomes and translatomes in TG and DRG. A, PC analysis shows that differences between TG and DRGs whole tissue transcriptomes represent the first PC, whereas differences between transcriptome and translatome are the second PC. B, Absolute variances for each PC show that PC1 and PC2 provide the majority of variation in the entire datasets. C, Heatmap of the absolute PC distances showing 4 distinct clusters, each of which is defined by whole transcriptome (input) versus TRAP-seq and the tissue.

Analysis of the input transcriptome data between TG and DRG revealed that 379 genes were significantly enriched in the TG and 315 in the DRG (Fig. 4A; Tables 1, 2). Among these 315 genes in the DRG, we observed enrichment of the Hox family transcription factors (Fig. 4A). These genes are well-known regulators of rostral to caudal segmental development, so enrichment in DRG is expected given the rostral–caudal extent of the DRG (Kammermeier and Reichert, 2001). Among the 379 genes enriched in the TG, we found particularly high expression and enrichment of Fth1 and Pak1 (Fig. 4A). Analysis of the Nav1.8-TRAP dataset revealed 372 genes enriched in the TG and 348 in the DRG (Fig. 4A; Tables 3, 4). Consistent with the transcriptome results, the Hox genes showed a highly enriched translational profile in the DRG (Fig. 4A). Among the top mRNAs highly associated with ribosomes in the TG Nav1.8-TRAP dataset, we found Nme3, Il1rl2, and Edf1(Fig. 4A). None of these 3 genes has been associated with a specific TG function previously, although the Il1rl2 gene encodes a receptor for interleukin 1β (IL1β), which activates TG nociceptors through a mechanism that has previously been attributed to IL1β Type 1 receptors (Takeda et al., 2008). GO term analysis of the differentially expressed genes in the Nav1.8-TRAP datasets revealed an enrichment in specific pathways, including VEGFR, FGFR, as well as the PI3K-mTOR pathway (Fig. 4B). Interestingly, we observed an enrichment in AMP-activated protein kinase (AMPK)-related genes in the DRG-TRAP dataset (Fig. 4B). This finding is intriguing because the AMPK pathway is a negative regulator of PI3K-mTOR signaling (Hardie, 2014, 2015) and suggests shifting in the balance between these two signaling pathways between the DRG and TG.

Figure 4.

Transcriptomic and translatomic differences between the TG and DRG of mice. A, B, Volcano plots showing genes that are enriched in the DRG or TG in the whole tissue transcriptome (input) or in the TRAP-seq sample (Nav1.8-TRAP) with genes highlighted in the text labeled (yellow dots). C, GO term analysis of the TRAP-seq-enriched mRNAs in DRG or TG using EnrichR (adjusted p value < 0.05) shows an enrichment in AMPK-related genes in the DRGs, whereas mTOR-related genes are highly translated in the TG. D, Heatmaps showing the expression level of enriched mRNAs (input) and enriched translated mRNAs (Nav1.8 TRAP) in both tissues showing discordance between the transcriptome and translatome mRNA levels.

Table 1.

Genes upregulated in the TG input

Genes	Log2 fold change	p	q	Genes	Log2 fold change	p	q
1700037C18Rik	1.847	0.006	0.058	Mrpl36	1.645	0.024	0.100
6430548M08Rik	1.249	0.000	0.026	Mrpl44	1.331	0.020	0.093
9130401M01Rik	1.884	0.023	0.098	Mrpl46	1.476	0.005	0.052
Aard	2.059	0.001	0.030	Mrps11	1.718	0.003	0.042
Abca2	1.917	0.000	0.026	Mrps14	1.614	0.004	0.050
Abhd6	1.493	0.001	0.031	Mrps23	1.332	0.001	0.031
Adarb1	1.426	0.011	0.072	Mrps36	1.262	0.002	0.034
Adck2	1.969	0.004	0.049	Mt2	3.017	0.023	0.097
Ak5	1.564	0.004	0.050	Mt3	2.988	0.011	0.071
Alkbh3	2.585	0.002	0.039	Mtap	1.600	0.017	0.086
Amdhd2	1.234	0.001	0.030	Mtfp1	1.528	0.001	0.034
Anapc13	1.952	0.003	0.046	Mtmr4	1.402	0.005	0.052
Ap4s1	1.506	0.006	0.057	Mxd3	1.297	0.020	0.092
Apbb1	3.191	0.005	0.054	Mxra8	2.100	0.004	0.051
Apip	1.677	0.023	0.099	Myl12a	2.141	0.000	0.027
Apmap	1.356	0.007	0.061	Mylk	1.285	0.019	0.090
Apod	3.523	0.001	0.029	Naa38	3.315	0.019	0.090
Apoe	1.621	0.018	0.088	Nap1l2	1.759	0.002	0.035
Arfip2	1.328	0.000	0.026	Ndp	1.418	0.005	0.053
Arhgef3	1.405	0.014	0.079	Ndufa12	1.436	0.012	0.073
Arhgef4	1.255	0.005	0.055	Ndufa13	1.921	0.024	0.100
Arih2	1.638	0.008	0.066	Ndufb2	3.404	0.005	0.053
Armc5	1.203	0.020	0.093	Necab3	1.663	0.007	0.062
Arpc5l	1.248	0.021	0.095	Nefh	1.820	0.001	0.031
Atp5c1	2.858	0.012	0.074	Nif3l1	1.999	0.018	0.087
Atp5d	2.923	0.004	0.052	Nme3	3.589	0.008	0.063
Atp5j	2.373	0.011	0.070	Nnat	1.368	0.023	0.099
Atp5sl	1.335	0.005	0.053	Nr2f6	1.334	0.016	0.084
Atxn7l3	1.365	0.015	0.080	Nsg1	2.505	0.003	0.041
Avpi1	1.810	0.015	0.082	Nsmaf	1.511	0.002	0.037
B930041F14Rik	2.887	0.004	0.051	Nubp2	2.798	0.000	0.026
Bad	1.797	0.005	0.052	Nudt1	1.395	0.017	0.086
Bet1l	1.486	0.014	0.079	Nudt13	1.358	0.007	0.062
Bod1	1.529	0.013	0.077	Odc1	2.129	0.005	0.053
Cacng5	2.058	0.008	0.063	Otud3	2.021	0.002	0.037
Calb2	3.253	0.005	0.053	P2rx6	1.581	0.009	0.068
Calu	1.213	0.001	0.031	Pacs2	1.388	0.000	0.029
Camkk1	1.514	0.013	0.076	Pak1	3.982	0.001	0.030
Casp3	1.506	0.002	0.035	Pard6a	1.806	0.011	0.072
Cbx7	1.324	0.015	0.081	Pced1a	1.460	0.018	0.088
Ccdc12	1.344	0.018	0.087	Pcp4l1	1.512	0.000	0.020
Ccdc124	1.505	0.015	0.082	Pdia4	1.410	0.002	0.037
Ccdc63	3.251	0.018	0.088	Pdlim2	2.857	0.001	0.030
Cd81	1.413	0.005	0.055	Pex11b	2.281	0.017	0.086
Cda	1.665	0.003	0.044	Pgbd5	1.688	0.001	0.031
Cdc37	1.442	0.014	0.079	Pin1	1.948	0.013	0.076
Cdk5r1	1.482	0.005	0.055	Pkdcc	1.610	0.003	0.045
Cdpf1	1.216	0.014	0.079	Pkm	1.408	0.014	0.080
Cdr2l	1.629	0.015	0.082	Pla2g16	2.793	0.000	0.022
Cela1	1.982	0.014	0.080	Plcd4	1.204	0.015	0.082
Cenpf	3.061	0.020	0.091	Plekha4	1.487	0.001	0.030
Cep19	1.321	0.005	0.053	Plk5	2.167	0.007	0.063
Cgrrf1	2.813	0.002	0.035	Pllp	1.905	0.002	0.040
Chchd1	2.920	0.001	0.029	Plpp1	2.148	0.003	0.046
Chchd3	1.514	0.018	0.088	Plxdc1	1.659	0.003	0.046
Chga	1.707	0.007	0.063	Pnpla2	1.296	0.013	0.078
Chgb	1.656	0.002	0.039	Polr2b	1.215	0.021	0.095
Chmp6	2.654	0.003	0.045	Polr2l	4.483	0.010	0.070
Chpf2	1.312	0.015	0.082	Pon2	1.207	0.002	0.040
Chrac1	1.481	0.002	0.035	Ppa2	1.552	0.005	0.052
Ckmt1	1.257	0.020	0.093	Ppdpf	1.387	0.023	0.097
Clcn7	1.490	0.005	0.053	Ppfia4	2.311	0.001	0.030
Clec2l	2.448	0.002	0.036	Ppm1f	1.386	0.011	0.071
Clu	1.283	0.000	0.029	Ppp1r16b	1.798	0.023	0.097
Clybl	1.665	0.015	0.081	Ppp2r4	1.920	0.003	0.044
Cnnm4	1.420	0.022	0.097	Prorsd1	1.630	0.018	0.088
Cnp	2.467	0.000	0.024	Prpsap1	1.397	0.001	0.031
Cnpy3	1.844	0.008	0.063	Prss12	2.286	0.000	0.029
Cnst	1.991	0.017	0.085	Prx	1.489	0.001	0.033
Cops3	1.363	0.008	0.064	Psmb11	2.692	0.021	0.094
Coq10a	2.084	0.010	0.068	Psmb7	2.699	0.003	0.045
Cotl1	1.523	0.002	0.035	Ptcd2	1.699	0.016	0.085
Cox6b1	1.564	0.018	0.088	Ptgds	1.888	0.008	0.063
Cox7a2l	1.430	0.005	0.053	Rab11fip5	1.791	0.008	0.063
Cplx1	1.869	0.008	0.065	Rab35	1.527	0.008	0.063
Crip1	1.529	0.014	0.079	Rab3ip	1.739	0.005	0.053
Crispld2	1.532	0.006	0.056	Rad54l	1.412	0.018	0.088
Cspg5	1.427	0.010	0.069	Rarres1	1.432	0.001	0.031
Csrp2	2.150	0.001	0.030	Rcor2	1.946	0.006	0.056
Cst3	1.798	0.004	0.048	Rep15	3.803	0.006	0.059
Ctif	1.444	0.023	0.099	Rhbdd2	1.301	0.002	0.035
Ctnnbl1	1.636	0.019	0.089	Rhot2	1.367	0.013	0.076
Ctsf	1.591	0.000	0.029	Rimklb	1.410	0.021	0.094
Cyb5a	1.289	0.009	0.068	Rnaseh2c	2.276	0.010	0.069
Cyc1	3.399	0.002	0.034	Rnf114	1.743	0.001	0.029
Dbi	1.474	0.015	0.081	Rnf121	1.390	0.005	0.053
Dexi	2.255	0.000	0.026	Rnf157	1.611	0.005	0.055
Dffa	1.337	0.011	0.072	Rom1	2.221	0.000	0.032
Dhdh	4.211	0.008	0.064	Rpl10a	2.066	0.008	0.063
Dlg2	1.409	0.004	0.048	Rprm	1.661	0.004	0.048
Dnajb9	1.633	0.001	0.030	Rps27	2.271	0.023	0.099
Dnajc11	1.379	0.004	0.050	S100a4	1.233	0.003	0.045
Dnal4	1.470	0.019	0.090	Sac3d1	3.758	0.002	0.037
Dpm3	2.497	0.012	0.074	Sap18	1.448	0.024	0.100
Dpp9	1.459	0.007	0.061	Sat1	1.390	0.005	0.055
Eaf1	1.246	0.002	0.035	Scg5	1.738	0.011	0.072
Edf1	2.374	0.017	0.086	Scn4b	1.595	0.005	0.052
Efcc1	4.477	0.009	0.068	Scrn1	1.936	0.001	0.030
Egln2	1.685	0.001	0.031	Scx	2.374	0.004	0.049
Eif2b2	2.535	0.001	0.031	Scyl3	3.634	0.010	0.068
Eif3l	2.072	0.012	0.075	Sec13	2.615	0.017	0.086
Elp3	1.598	0.001	0.031	Selm	2.482	0.009	0.067
Eme1	1.587	0.009	0.068	Sepp1	2.699	0.002	0.035
Eme2	1.501	0.006	0.056	Sfxn5	1.714	0.003	0.046
Endod1	1.402	0.002	0.034	Sh3bgr	1.391	0.024	0.100
Enho	1.447	0.001	0.031	Sh3gl2	1.278	0.010	0.069
Eno2	1.548	0.020	0.092	Sh3rf1	1.571	0.002	0.038
Eny2	1.328	0.009	0.067	Shd	1.737	0.002	0.038
Epn3	1.200	0.014	0.079	Sirt2	1.287	0.004	0.050
Esrrg	1.705	0.007	0.063	Slc22a17	4.451	0.005	0.054
Etl4	1.537	0.005	0.052	Slc25a25	1.508	0.015	0.082
Fabp3	1.275	0.022	0.096	Slc25a43	3.074	0.021	0.095
Fabp7	3.165	0.002	0.038	Slc25a5	3.786	0.002	0.036
Faim2	1.328	0.002	0.034	Slc38a10	1.399	0.017	0.087
Fam160b2	1.237	0.015	0.083	Slc4a2	1.322	0.014	0.079
Fam162a	2.032	0.005	0.054	Slc6a8	1.844	0.008	0.063
Fam19a5	2.083	0.016	0.084	Slc9a3r1	2.002	0.004	0.051
Fam57b	2.035	0.005	0.054	Slco2b1	2.443	0.004	0.051
Fars2	1.969	0.002	0.038	Smim1	1.230	0.009	0.066
Fbxl12	1.201	0.006	0.057	Smim4	1.838	0.002	0.035
Fbxo27	2.483	0.001	0.031	Smoc2	1.302	0.004	0.049
Fbxo44	1.807	0.004	0.048	Smox	2.826	0.001	0.030
Fchsd1	1.429	0.005	0.053	Smpx	1.427	0.001	0.034
Fdx1l	1.385	0.018	0.088	Sncb	1.711	0.007	0.060
Fhdc1	1.306	0.000	0.026	Snn	2.308	0.012	0.074
Fkbp2	1.980	0.015	0.083	Snx22	2.722	0.009	0.068
Fkbp4	1.407	0.005	0.055	Sphkap	2.242	0.001	0.030
Fth1	4.168	0.000	0.022	Sptb	1.567	0.020	0.093
Fuca1	1.308	0.013	0.076	Srm	1.623	0.012	0.075
Gatb	1.370	0.008	0.064	Stard3	1.798	0.002	0.039
Glb1l2	2.686	0.001	0.031	Stk32c	1.467	0.022	0.096
Gle1	1.391	0.020	0.091	Stmn4	2.074	0.000	0.028
Glyr1	1.263	0.014	0.079	Stxbp6	1.486	0.024	0.100
Gps1	2.939	0.008	0.065	Suclg1	2.146	0.008	0.064
Gpx1	1.719	0.023	0.097	Supt4a	3.247	0.001	0.031
Grk6	1.513	0.007	0.061	Suv420h1	2.426	0.019	0.090
Gtf2h4	4.152	0.003	0.045	Syn2	1.312	0.001	0.030
Gtf2i	1.710	0.003	0.045	Syne4	2.723	0.002	0.037
Gtf2ird1	2.143	0.010	0.069	Sys1	1.933	0.003	0.046
Haghl	1.514	0.008	0.063	Taf6l	1.539	0.006	0.056
Hapln4	1.571	0.012	0.075	Tango2	1.243	0.008	0.065
Harbi1	1.772	0.011	0.070	Tecr	1.980	0.024	0.100
Haus8	3.145	0.010	0.069	Tfb1m	1.304	0.012	0.073
Hax1	1.463	0.007	0.063	Thap11	1.324	0.004	0.049
Hebp2	1.567	0.001	0.033	Tifab	1.602	0.018	0.088
Hhatl	2.106	0.003	0.045	Timm9	1.715	0.009	0.066
Hid1	1.773	0.013	0.077	Tmco1	1.240	0.014	0.079
Hist3h2ba	1.307	0.005	0.052	Tmem101	1.649	0.020	0.092
Hlcs	1.772	0.012	0.075	Tmem126a	1.893	0.001	0.030
Homer3	2.804	0.001	0.031	Tmem132c	2.205	0.007	0.059
Hpca	1.469	0.003	0.046	Tmem14c	1.461	0.022	0.096
Hs3st1	1.520	0.012	0.075	Tmem18	1.588	0.002	0.040
Hsdl2	1.375	0.005	0.053	Tmem201	1.336	0.001	0.030
Htra1	2.177	0.007	0.063	Tmem203	1.637	0.018	0.087
Hunk	3.047	0.007	0.061	Tmem229b	1.572	0.002	0.038
Iba57	1.357	0.014	0.080	Tmem242	1.386	0.018	0.088
Id3	2.405	0.005	0.053	Tmem25	2.039	0.001	0.030
Idh2	1.732	0.002	0.036	Tmem258	3.156	0.013	0.076
Idh3b	1.848	0.007	0.062	Tmem60	1.268	0.001	0.031
Imp3	2.481	0.007	0.060	Tnfrsf1a	1.468	0.005	0.055
Impdh2	1.694	0.004	0.051	Tpbgl	2.734	0.008	0.064
Inpp5j	2.890	0.006	0.058	Trak1	1.435	0.005	0.053
Itih5	1.235	0.006	0.058	Trappc3	2.247	0.020	0.093
Itm2c	1.551	0.002	0.035	Trf	2.565	0.002	0.037
Jam3	2.138	0.000	0.030	Trp53rka	1.316	0.001	0.030
Kat2a	1.756	0.009	0.067	Tspan3	1.262	0.012	0.074
Kcnq4	2.581	0.001	0.030	Ttc9b	1.870	0.011	0.071
Kctd15	2.542	0.016	0.084	Txnl4b	1.574	0.003	0.044
Krt10	1.758	0.008	0.064	Tyr	2.829	0.023	0.097
Lancl1	1.907	0.000	0.029	Tyro3	2.928	0.019	0.089
Laptm4b	1.441	0.001	0.031	U2af1l4	1.981	0.002	0.035
Ldhb	1.497	0.014	0.080	Ube2v1	1.615	0.006	0.058
Letm1	1.305	0.015	0.081	Ubl5	1.577	0.021	0.093
Lgi3	1.359	0.006	0.055	Ufsp1	2.295	0.001	0.034
Limd1	1.389	0.000	0.028	Ulk1	1.439	0.008	0.064
Lrp1	1.242	0.001	0.033	Uqcc2	2.331	0.006	0.058
Lyz2	3.211	0.002	0.040	Uqcc3	1.376	0.005	0.054
Lztr1	1.525	0.018	0.088	Uqcrh	1.253	0.016	0.084
Maged2	1.849	0.012	0.074	Vasp	1.204	0.024	0.100
Map1lc3b	1.800	0.001	0.030	Vim	1.384	0.007	0.060
Mark4	2.680	0.002	0.040	Vwa7	1.728	0.001	0.033
Mars	1.517	0.003	0.046	Wbp1	1.987	0.009	0.068
Mat2a	1.570	0.001	0.030	Wfs1	2.149	0.001	0.031
Meis2	1.776	0.004	0.051	Wwox	1.523	0.023	0.098
Mgat5	1.417	0.001	0.032	Yif1a	1.236	0.006	0.059
Mgst3	3.432	0.001	0.035	Zfand2b	2.929	0.005	0.054
Mief1	1.479	0.013	0.078	Zfp180	1.595	0.014	0.080
Mmd2	2.595	0.003	0.044	Zfp335	1.645	0.017	0.085
Mobp	1.996	0.015	0.082	Zfp771	1.604	0.023	0.097
Mpc2	1.877	0.004	0.052

Table 2.

Genes upregulated in the DRG input

Genes	Log2 fold change	p	q	Genes	Log2 fold change	p	q
1700019D03Rik	−1.367	0.001	0.030	Mpped2	−2.004	0.000	0.026
9330159F19Rik	−1.337	0.001	0.031	Mpv17l2	−1.509	0.006	0.055
9330182L06Rik	−1.668	0.006	0.056	Mt-Co3	−1.553	0.007	0.063
Abca5	−1.456	0.008	0.063	Myc	−1.925	0.001	0.030
Acacb	−1.267	0.010	0.070	Myh1	−4.915	0.002	0.037
Acbd5	−1.220	0.018	0.088	Myl1	−7.949	0.000	0.022
Acpp	−1.662	0.000	0.022	Myo1b	−1.242	0.004	0.049
Acsl4	−1.505	0.000	0.025	Myom1	−2.336	0.002	0.038
Acta1	−6.161	0.000	0.027	Myt1l	−1.439	0.005	0.055
Actn1	−1.467	0.002	0.036	Nectin1	−1.609	0.010	0.069
Adcyap1	−1.814	0.003	0.045	Nedd4l	−1.349	0.001	0.029
Adgrd1	−1.531	0.006	0.057	Nek1	−1.475	0.017	0.086
Adgrf5	−1.678	0.004	0.049	Nfya	−1.752	0.018	0.087
Adk	−2.250	0.000	0.026	Nhs	−1.233	0.001	0.031
Agtr1a	−2.244	0.002	0.038	Nktr	−1.403	0.008	0.063
Ammecr1	−1.266	0.012	0.074	Noct	−1.573	0.015	0.082
Ank3	−1.585	0.000	0.029	Nptx2	−1.297	0.002	0.040
Ankrd6	−1.453	0.003	0.043	Nptxr	−1.481	0.001	0.031
Ano3	−1.205	0.003	0.044	Npy2r	−2.372	0.007	0.061
Arfgef2	−1.305	0.000	0.026	Nras	−1.633	0.012	0.075
Arhgap23	−1.415	0.012	0.074	Nrip1	−1.304	0.000	0.027
Arhgap26	−2.827	0.000	0.026	Nrxn3	−1.701	0.008	0.064
Arrdc3	−1.213	0.023	0.099	Nt5e	−2.145	0.001	0.034
Ass1	−1.757	0.002	0.040	Nup88	−1.244	0.023	0.098
Astn1	−1.500	0.007	0.061	Ocrl	−1.219	0.004	0.049
Atp2b4	−2.126	0.001	0.030	Ormdl1	−1.558	0.004	0.048
Auts2	−1.836	0.000	0.027	Osbpl3	−1.696	0.000	0.026
B630005N14Rik	−1.380	0.017	0.086	Pabpc1	−1.654	0.020	0.093
Bdnf	−1.450	0.003	0.042	Pabpn1	−1.450	0.009	0.067
Bnc2	−1.401	0.004	0.050	Palm2	−2.056	0.005	0.055
Brms1l	−1.357	0.012	0.073	Palmd	−1.212	0.000	0.026
Cacna2d1	−1.978	0.000	0.039	Pam	−1.491	0.006	0.057
Camk2a	−1.912	0.001	0.031	Panx1	−1.364	0.002	0.040
Camk2d	−1.608	0.011	0.071	Paqr3	−1.230	0.006	0.058
Camta1	−1.640	0.013	0.077	Pde10a	−1.289	0.001	0.030
Capn1	−1.429	0.003	0.042	Pde11a	−1.938	0.000	0.021
Car8	−1.627	0.011	0.072	Pdlim1	−1.524	0.005	0.052
Casz1	−1.381	0.004	0.048	Pfkp	−1.296	0.004	0.052
Ccdc141	−1.908	0.002	0.035	Pfn1	−2.156	0.001	0.030
Cct8	−1.364	0.009	0.068	Pgm2l1	−1.494	0.000	0.029
Cd274	−1.797	0.005	0.052	Phip	−1.266	0.004	0.050
Cd2ap	−1.457	0.012	0.074	Pitpnc1	−1.209	0.000	0.026
Cd44	−1.559	0.001	0.030	Pitpnm2	−1.297	0.015	0.080
Cd47	−1.588	0.000	0.020	Pkia	−1.817	0.003	0.042
Cd55	−2.309	0.001	0.031	Plcb3	−1.560	0.000	0.025
Cdc14b	−1.372	0.000	0.025	Plekha6	−1.243	0.008	0.064
Celf4	−1.240	0.005	0.052	Plvap	−1.298	0.018	0.087
Celf6	−1.775	0.013	0.077	Plxnc1	−1.740	0.005	0.053
Cep170	−1.468	0.006	0.058	Plxnd1	−1.345	0.002	0.035
Cfap157	−1.378	0.002	0.040	Polr2a	−1.357	0.001	0.031
Chml	−1.782	0.012	0.074	Pou1f1	−2.038	0.019	0.089
Chpt1	−1.408	0.010	0.068	Ppef1	−1.257	0.004	0.049
Ciapin1	−1.263	0.020	0.093	Ppp1r12a	−1.564	0.001	0.030
Ckm	−7.336	0.001	0.031	Ppp3ca	−1.498	0.000	0.021
Clgn	−1.231	0.009	0.067	Ppp6c	−1.218	0.010	0.068
Clip2	−2.335	0.003	0.046	Prdm8	−2.355	0.001	0.030
Cmip	−1.580	0.002	0.037	Prg2	−3.717	0.001	0.030
Cnot1	−1.720	0.000	0.025	Prkag2	−1.331	0.022	0.097
Cnot4	−1.280	0.004	0.048	Prkar2b	−1.651	0.000	0.025
Cntrl	−1.361	0.004	0.050	Prkca	−2.159	0.000	0.025
Cpeb1	−1.200	0.005	0.053	Ptgdr	−1.577	0.002	0.035
Cpne2	−1.790	0.018	0.087	Ptger1	−1.387	0.008	0.063
Cpsf7	−1.260	0.010	0.069	Ptms	−1.523	0.000	0.026
Csrnp3	−1.215	0.000	0.025	Ptprt	−2.355	0.001	0.029
Ctsl	−1.450	0.002	0.034	Ptrf	−1.688	0.002	0.040
Ddx3x	−1.330	0.001	0.030	Pum1	−1.405	0.008	0.065
Deptor	−1.652	0.001	0.033	Pura	−1.301	0.006	0.056
Dgkh	−1.341	0.005	0.055	Purb	−1.209	0.021	0.095
Dgkz	−1.775	0.000	0.024	Pygl	−1.338	0.001	0.031
Disp2	−1.423	0.010	0.069	Rab27b	−1.526	0.016	0.083
Dpp10	−1.486	0.000	0.021	Rab39b	−1.515	0.003	0.045
Dpp6	−1.293	0.002	0.039	Rab3c	−1.439	0.013	0.075
Ebf3	−1.919	0.002	0.035	Rabgap1l	−1.317	0.003	0.044
Eif4e3	−1.421	0.001	0.032	Raph1	−1.327	0.007	0.060
Etnk1	−1.896	0.001	0.030	Rasgrp1	−1.547	0.001	0.031
F2rl2	−1.621	0.012	0.075	Rbms1	−1.558	0.002	0.034
Fabp4	−5.383	0.000	0.026	Reps2	−1.310	0.003	0.041
Fam102b	−1.588	0.000	0.025	Rgmb	−1.207	0.015	0.082
Fam122b	−2.137	0.004	0.050	Rgs17	−2.168	0.000	0.026
Fam179b	−1.540	0.003	0.041	Rnf144a	−1.423	0.000	0.029
Fam214b	−1.405	0.001	0.031	Robo2	−1.321	0.001	0.030
Fam222b	−1.668	0.007	0.062	Rps11	−1.411	0.005	0.055
Filip1	−1.223	0.001	0.030	Rspo2	−1.809	0.000	0.026
Fsd2	−2.160	0.012	0.073	Runx1	−1.204	0.006	0.055
Gal	−2.045	0.002	0.035	Ryr1	−3.459	0.010	0.069
Ghr	−2.439	0.004	0.049	S100a11	−2.366	0.007	0.063
Gm17305	−1.767	0.003	0.046	S100a8	−3.282	0.001	0.029
Gm42417	−4.801	0.000	0.029	S100a9	−4.220	0.000	0.021
Gmfb	−1.259	0.004	0.049	Safb	−1.574	0.011	0.070
Gna14	−1.468	0.021	0.095	Samsn1	−2.440	0.001	0.030
Gnai3	−1.748	0.011	0.072	Scd1	−1.809	0.001	0.034
Gnao1	−1.462	0.001	0.032	Scg2	−1.809	0.009	0.066
Gnaq	−1.531	0.002	0.034	Scg3	−1.743	0.002	0.038
Gnb4	−1.236	0.008	0.064	Scn9a	−1.579	0.000	0.027
Gp1bb	−1.271	0.020	0.093	Scyl2	−1.375	0.001	0.031
Grip1	−1.423	0.013	0.076	Sdcbp	−1.233	0.001	0.030
Grm7	−1.784	0.001	0.033	Sema4b	−1.437	0.002	0.039
H2-K1	−1.611	0.019	0.091	Sepw1	−1.253	0.013	0.077
Hace1	−1.481	0.008	0.065	Slc16a3	−2.036	0.001	0.029
Hba-a2	−1.762	0.004	0.052	Slc27a3	−1.610	0.008	0.065
Hbb-bt	−1.668	0.004	0.052	Slc35a5	−1.304	0.000	0.038
Hcn3	−1.929	0.003	0.045	Slc37a1	−1.336	0.016	0.085
Hgf	−1.762	0.003	0.044	Slc39a6	−1.502	0.003	0.041
Hmbox1	−1.450	0.011	0.072	Slc51a	−1.622	0.002	0.035
Hmgcl	−1.314	0.013	0.077	Slc5a3	−1.415	0.014	0.080
Hoxa10	−7.016	0.000	0.020	Slc9a6	−1.278	0.002	0.039
Hoxa7	−7.044	0.000	0.045	Smim10l1	−1.212	0.002	0.036
Hoxa9	−6.931	0.000	0.027	Smim5	−1.524	0.015	0.080
Hoxb2	−5.272	0.001	0.029	Socs2	−1.430	0.001	0.031
Hoxb4	−6.121	0.000	0.026	Sorl1	−1.403	0.002	0.034
Hoxb5	−6.619	0.000	0.020	Spred2	−1.203	0.008	0.064
Hoxb9	−9.266	0.000	0.024	Spryd7	−1.357	0.010	0.068
Hoxc6	−11.922	0.000	0.024	Srek1	−1.309	0.009	0.067
Hoxd10	−11.238	0.000	0.023	Ssbp3	−1.358	0.008	0.064
Hoxd4	−2.142	0.003	0.046	St8sia3	−1.270	0.007	0.060
Hoxd8	−1.603	0.004	0.048	Stag2	−1.202	0.005	0.053
Hoxd9	−3.929	0.001	0.030	Sycp3	−1.430	0.019	0.089
Hs6st2	−2.118	0.004	0.050	Synpr	−1.887	0.001	0.029
Hsp90ab1	−1.392	0.006	0.056	Syt1	−1.331	0.000	0.021
Idh1	−2.275	0.000	0.047	Syt4	−2.148	0.004	0.052
Idi1	−1.751	0.022	0.097	Syt7	−1.673	0.001	0.029
Ids	−1.571	0.009	0.067	Syt9	−1.899	0.000	0.026
Il10rb	−1.208	0.020	0.093	Tac1	−1.726	0.000	0.028
Il6st	−1.838	0.001	0.030	Taf1	−1.263	0.024	0.100
Impad1	−1.490	0.002	0.035	Taok1	−1.691	0.005	0.055
Ina	−2.171	0.000	0.028	Tlx3	−1.349	0.001	0.030
Irf2	−1.226	0.000	0.029	Tmem158	−1.431	0.003	0.045
Izumo4	−1.366	0.009	0.068	Tmem164	−1.228	0.008	0.063
Kcna4	−1.408	0.010	0.069	Tmem185b	−1.358	0.019	0.088
Kcnab1	−1.296	0.003	0.046	Tmem200a	−1.785	0.003	0.041
Kcnb2	−2.196	0.001	0.032	Tmem233	−1.233	0.019	0.089
Kcnt1	−1.215	0.017	0.086	Tmem255a	−1.367	0.001	0.033
Kdelc2	−1.279	0.010	0.070	Tmem56	−1.466	0.009	0.068
Kdm7a	−1.460	0.009	0.068	Tmtc2	−1.914	0.015	0.082
Kif5b	−1.301	0.008	0.064	Tmx3	−1.230	0.008	0.064
Klf7	−1.346	0.000	0.026	Tnnc2	−6.667	0.001	0.030
Larp1	−1.374	0.006	0.056	Tnnt3	−7.079	0.000	0.028
Lbh	−1.629	0.003	0.042	Top2a	−1.930	0.008	0.064
Lcor	−1.431	0.008	0.063	Tra2a	−1.451	0.015	0.081
Ldb2	−1.834	0.001	0.031	Trp53bp1	−1.257	0.004	0.049
Ldlr	−1.708	0.002	0.034	Trpc3	−1.616	0.012	0.073
Lifr	−1.742	0.005	0.052	Trpv1	−2.016	0.008	0.065
Lonrf1	−1.453	0.002	0.035	Ubb	−1.257	0.022	0.097
Lox	−2.537	0.018	0.087	Ubqln2	−1.407	0.004	0.050
Lpar3	−1.721	0.003	0.046	Ugcg	−1.398	0.007	0.060
Lrfn1	−1.409	0.000	0.022	Unc13c	−1.455	0.002	0.037
Lrrc8b	−1.402	0.001	0.030	Usp17la	−1.946	0.011	0.072
Lrrtm2	−1.389	0.018	0.088	Usp9x	−1.500	0.011	0.072
Ly86	−2.557	0.009	0.068	Vwa5a	−1.352	0.002	0.040
Magi3	−1.665	0.001	0.030	Wasf1	−1.501	0.000	0.020
Mal2	−2.994	0.000	0.029	Wfdc2	−1.246	0.019	0.090
Mb	−6.746	0.000	0.027	Xirp2	−8.656	0.000	0.026
Mbnl1	−1.392	0.018	0.088	Yod1	−1.273	0.009	0.067
Mbnl2	−1.795	0.000	0.029	Zbtb44	−1.315	0.015	0.080
Mcoln1	−1.305	0.010	0.070	Zdhhc13	−1.497	0.000	0.028
Mfsd7b	−1.521	0.007	0.062	Zeb2	−1.453	0.011	0.070
Mon1a	−1.310	0.000	0.029	Zfhx2	−1.542	0.016	0.084
				Zfhx3	−1.317	0.007	0.060

Table 3.

Genes upregulated in the TG Nav1.8-TRAP dataset

Genes	Log2 fold change	p	q	Genes	Log2 fold change	p	q
0610009B22Rik	3.295	0.001	0.031	Nap1l5	2.348	0.001	0.029
1110032A03Rik	2.542	0.007	0.059	Nbea	2.525	0.001	0.028
1110065P20Rik	5.685	0.001	0.032	Nbl1	2.738	0.015	0.085
1700037C18Rik	2.913	0.010	0.072	Ndel1	3.132	0.000	0.010
2010107E04Rik	2.008	0.000	0.016	Ndufa1	2.495	0.002	0.034
2210013O21Rik	2.570	0.006	0.056	Ndufb2	3.065	0.002	0.034
2700094K13Rik	3.894	0.004	0.049	Ndufb5	2.214	0.001	0.027
9430016H08Rik	3.239	0.016	0.089	Ndufb9	2.915	0.000	0.017
Aard	7.736	0.005	0.050	Ndufs2	2.315	0.003	0.043
Acp1	2.522	0.004	0.049	Ngfr	2.166	0.011	0.075
Adam9	4.085	0.004	0.045	Nme3	11.466	0.000	0.020
Adra2c	5.673	0.001	0.025	Nrn1l	4.040	0.009	0.068
Akirin1	2.298	0.009	0.068	Nsun3	3.157	0.018	0.093
Akt1	3.554	0.006	0.058	Nt5m	3.252	0.017	0.090
Alkbh3	7.497	0.002	0.035	Nubp2	5.238	0.007	0.063
Amacr	5.927	0.013	0.082	Nudt10	4.615	0.010	0.072
Anapc13	3.962	0.015	0.087	Nudt11	3.346	0.018	0.093
Ankrd24	2.576	0.012	0.078	Nudt7	4.780	0.002	0.037
Anxa5	2.142	0.004	0.047	Nup37	4.108	0.000	0.017
Apip	7.421	0.003	0.042	Ost4	2.585	0.004	0.050
Apln	4.200	0.009	0.069	Ostf1	2.376	0.003	0.042
Arl5a	2.178	0.002	0.038	Pacsin1	2.217	0.016	0.089
Arl6	4.831	0.003	0.042	Pak1	5.331	0.006	0.056
Arl8a	2.045	0.008	0.066	Pak3	3.228	0.009	0.069
Armc1	2.080	0.016	0.089	Parp3	2.863	0.010	0.072
Arpc1b	3.965	0.000	0.020	Pcbd2	3.529	0.002	0.037
Arpc5l	2.223	0.002	0.039	Pcolce2	3.567	0.003	0.042
Asna1	3.806	0.010	0.073	Pcp4l1	3.151	0.004	0.045
Atg4c	4.484	0.001	0.028	Pcsk1n	2.681	0.010	0.072
Atox1	2.116	0.010	0.072	Pcsk7	4.142	0.005	0.050
Atp5c1	2.674	0.003	0.040	Pdcd6	2.770	0.009	0.067
Atp5d	2.552	0.006	0.058	Pde6d	5.181	0.004	0.050
Atp5g1	2.613	0.003	0.040	Pdlim2	3.067	0.001	0.027
Atp5s	6.255	0.000	0.018	Pdzd9	2.834	0.005	0.050
Atp6v0d1	3.072	0.000	0.018	Pex11b	7.187	0.000	0.003
Avpi1	2.049	0.005	0.053	Pfdn1	2.368	0.013	0.082
Banf1	2.411	0.001	0.028	Pfkm	2.196	0.011	0.074
Bbs9	4.103	0.021	0.099	Phospho2	2.332	0.013	0.082
Bloc1s1	2.178	0.015	0.085	Phpt1	2.748	0.007	0.063
Bloc1s3	4.164	0.003	0.042	Pigh	2.609	0.011	0.075
Btbd2	2.239	0.002	0.036	Pin1	3.518	0.001	0.031
C77080	3.683	0.000	0.016	Pla2g16	2.041	0.017	0.091
Cacng7	2.455	0.005	0.054	Plekhb1	3.646	0.014	0.083
Calca	2.290	0.001	0.027	Plpp1	4.280	0.017	0.091
Calm2	2.036	0.014	0.085	Pole3	3.127	0.020	0.097
Camk2g	2.396	0.003	0.044	Polr2d	6.787	0.000	0.004
Cbln1	6.228	0.004	0.047	Polr2j	6.438	0.001	0.027
Ccdc88a	2.029	0.007	0.059	Polr2l	2.072	0.011	0.076
Cdc123	2.146	0.020	0.098	Polr2m	2.330	0.005	0.050
Cdk2ap1	4.137	0.003	0.040	Pomgnt1	3.461	0.020	0.099
Cdkn1b	2.713	0.001	0.024	Ppm1j	2.163	0.009	0.067
Cdr2l	2.794	0.007	0.061	Ppp2r5c	2.205	0.014	0.084
Cebpzos	3.122	0.004	0.049	Praf2	2.266	0.019	0.096
Cela1	4.351	0.004	0.049	Prkcd	2.054	0.003	0.040
Cenpq	4.971	0.002	0.037	Prkcdbp	6.444	0.002	0.035
Cfap69	3.913	0.012	0.077	Prkrir	2.325	0.008	0.064
Cfl1	2.008	0.001	0.032	Prorsd1	5.144	0.008	0.065
Chchd1	3.792	0.000	0.017	Psma1	2.561	0.017	0.091
Chchd10	3.927	0.011	0.074	Psmb11	2.717	0.000	0.021
Chchd4	2.146	0.003	0.040	Psmc3ip	6.150	0.007	0.061
Chd3os	2.704	0.003	0.040	Qars	2.763	0.012	0.079
Chmp6	2.299	0.021	0.099	Rab10	2.106	0.010	0.071
Chodl	5.030	0.016	0.089	Rab15	3.719	0.013	0.080
Chp1	4.425	0.000	0.017	Rab1a	4.034	0.008	0.064
Clmp	2.253	0.019	0.094	Rab28	2.269	0.001	0.032
Cnih2	4.508	0.007	0.061	Rab33a	3.270	0.015	0.085
Cnrip1	2.211	0.008	0.066	Rab35	2.067	0.012	0.078
Commd1	3.042	0.003	0.045	Rab4b	2.707	0.008	0.065
Commd4	2.980	0.019	0.094	Rad54l	5.057	0.001	0.026
Coq3	3.088	0.011	0.075	Rad9b	2.382	0.012	0.079
Cox6b1	2.708	0.000	0.023	Rho	4.096	0.003	0.040
Cox7a2	2.299	0.006	0.056	Rhog	4.929	0.012	0.078
Cox7b	4.295	0.000	0.017	Rnase4	2.128	0.018	0.092
Cox7c	2.255	0.001	0.028	Rnf114	3.644	0.013	0.082
Cox8a	2.576	0.000	0.021	Rnf215	4.125	0.006	0.058
Crlf2	3.905	0.011	0.075	Rnf7	2.072	0.013	0.081
Crtc2	5.091	0.001	0.023	Romo1	2.456	0.000	0.021
Crtc3	3.703	0.018	0.092	Rpl10	3.076	0.013	0.080
Ctxn3	2.686	0.000	0.023	Rpl28	2.167	0.019	0.095
Cyb5a	2.330	0.012	0.079	Rpl29	5.333	0.021	0.099
Cyc1	3.989	0.013	0.080	Rpl35	3.039	0.003	0.040
Cystm1	3.446	0.002	0.038	Rpl37	2.967	0.003	0.042
Dad1	4.088	0.006	0.056	Rpl39	3.241	0.001	0.027
Dalrd3	7.942	0.002	0.036	Rps23	2.219	0.020	0.097
Dda1	2.393	0.017	0.091	Rps29	2.912	0.002	0.037
Dlg2	4.233	0.010	0.072	Rpusd1	3.617	0.008	0.064
Dnajc12	3.352	0.016	0.088	Rraga	2.131	0.000	0.023
Dnal4	4.100	0.012	0.076	Rtn4r	2.805	0.001	0.028
Dpep2	4.510	0.001	0.026	Rxrg	4.463	0.004	0.049
Dpm3	3.461	0.019	0.095	Sac3d1	4.162	0.009	0.070
Dzank1	3.857	0.011	0.074	Sap18	3.653	0.019	0.095
Edf1	2.907	0.000	0.002	Sdhb	4.145	0.013	0.082
Eef1a1	2.126	0.016	0.088	Sdhd	2.350	0.008	0.066
Efcc1	5.725	0.001	0.032	Sec13	5.366	0.002	0.037
Eif4a3	2.748	0.008	0.066	Sec23ip	2.817	0.014	0.083
Emb	2.251	0.000	0.017	Sep15	2.455	0.009	0.068
Enox1	3.300	0.017	0.090	Sepp1	2.412	0.013	0.081
Epm2a	2.756	0.003	0.041	Serp2	3.709	0.004	0.046
Esyt1	4.806	0.004	0.045	Serping1	4.031	0.007	0.060
Exd2	5.752	0.000	0.016	Sh2d3c	3.339	0.019	0.095
Exosc6	4.476	0.005	0.053	Sh3bgrl	2.659	0.004	0.049
Fabp7	3.901	0.001	0.024	Sh3bgrl3	2.229	0.001	0.032
Fam105a	3.610	0.013	0.082	Shd	8.734	0.007	0.061
Fam188a	2.065	0.011	0.075	Shisa5	2.629	0.011	0.074
Fam58b	3.355	0.002	0.036	Sirt2	3.344	0.020	0.098
Fam89a	3.170	0.010	0.072	Sirt3	3.145	0.017	0.090
Far2	3.913	0.006	0.056	Sla2	2.598	0.002	0.037
Farp1	6.925	0.000	0.016	Slc22a17	3.985	0.018	0.092
Fbxl16	2.675	0.007	0.060	Slc24a2	2.209	0.001	0.028
Fbxo2	3.859	0.007	0.063	Slc25a3	3.404	0.006	0.058
Fbxo27	8.071	0.000	0.023	Slc25a43	2.557	0.019	0.095
Fgf9	4.057	0.007	0.063	Slc35d2	6.734	0.000	0.018
Fgfr2	6.744	0.003	0.043	Slc3a2	3.247	0.001	0.025
Fhl1	2.487	0.001	0.024	Slc45a4	2.344	0.009	0.068
Fkbp2	2.064	0.008	0.065	Slc46a3	5.451	0.003	0.040
Fsd1	2.330	0.010	0.072	Slc6a15	3.041	0.014	0.084
Fth1	3.259	0.001	0.023	Slco2b1	5.287	0.004	0.047
Fxyd6	2.042	0.006	0.056	Slitrk1	3.031	0.018	0.094
Gabrg1	3.806	0.000	0.018	Smdt1	2.724	0.000	0.019
Galnt18	2.412	0.013	0.080	Smim12	2.038	0.000	0.021
Gatad1	2.512	0.004	0.047	Smim8	2.292	0.003	0.042
Gipc1	3.901	0.018	0.093	Snapc5	2.360	0.016	0.088
Glb1l2	4.152	0.008	0.066	Snn	3.703	0.015	0.085
Gm15440	5.964	0.012	0.079	Snrpb	2.974	0.002	0.037
Gm5113	6.649	0.001	0.032	Snrpn	3.974	0.002	0.037
Gmnn	4.878	0.001	0.026	Snx3	2.491	0.002	0.037
Gng5	4.458	0.000	0.018	Spcs1	2.441	0.021	0.099
Gng8	4.700	0.006	0.057	Spon1	3.949	0.021	0.099
Golga1	3.883	0.016	0.089	Srp14	2.998	0.000	0.021
Gpr35	3.338	0.017	0.091	Ssr4	3.347	0.011	0.075
Gramd1b	2.251	0.000	0.020	Stau2	2.158	0.008	0.066
Grpel2	3.678	0.018	0.092	Strada	5.449	0.008	0.064
Gtf2i	3.014	0.011	0.075	Stx2	3.170	0.000	0.017
H2-T23	4.183	0.010	0.070	Suclg1	2.738	0.010	0.073
H2afz	2.207	0.003	0.042	Supt4a	3.527	0.001	0.032
Hist3h2ba	2.569	0.016	0.089	Tagln3	3.422	0.004	0.047
Hsbp1	2.142	0.016	0.089	Tbc1d14	2.192	0.009	0.070
Idh3b	3.457	0.001	0.025	Tfb1m	3.745	0.013	0.082
Ift122	3.334	0.011	0.074	Thoc7	2.488	0.008	0.066
Ift20	2.144	0.021	0.099	Tifab	5.602	0.004	0.045
Ift43	6.053	0.018	0.094	Timm8b	4.092	0.007	0.061
Igsf21	4.754	0.014	0.084	Tjap1	3.096	0.021	0.099
Il1rl2	8.506	0.000	0.016	Tmem106b	2.653	0.011	0.076
Imp3	2.343	0.001	0.032	Tmem199	4.071	0.014	0.084
Inpp5j	4.481	0.008	0.064	Tmem216	8.212	0.001	0.026
Isca2	2.096	0.011	0.074	Tmem230	2.058	0.006	0.058
Iscu	3.142	0.006	0.058	Tmem258	5.441	0.000	0.017
Kcnip1	5.122	0.001	0.032	Tmem53	3.911	0.009	0.069
Kcnip4	2.177	0.002	0.038	Tmem62	4.157	0.014	0.083
Kctd8	2.176	0.006	0.055	Tnfrsf1a	5.108	0.008	0.065
Klf8	4.565	0.004	0.047	Tnfsfm13	5.814	0.015	0.085
Lamtor5	2.577	0.005	0.055	Tomm7	2.169	0.000	0.017
Lcmt1	2.326	0.006	0.056	Tpd52l1	4.373	0.009	0.068
Lgals3bp	6.428	0.008	0.064	Tpgs2	3.087	0.004	0.049
Limk1	2.887	0.002	0.034	Tppp3	2.673	0.000	0.015
Lipa	2.672	0.005	0.054	Trappc1	3.056	0.019	0.095
Lix1	2.853	0.002	0.035	Trdmt1	5.930	0.000	0.021
Lrrc8a	2.077	0.000	0.018	Triap1	2.123	0.008	0.064
Ltbr	3.603	0.007	0.063	Trim12a	2.989	0.011	0.075
Lxn	2.922	0.001	0.026	Trim9	3.505	0.014	0.084
Lyrm2	4.541	0.000	0.023	Trnp1	2.255	0.003	0.040
Lyrm5	2.559	0.019	0.095	Tspan17	5.060	0.008	0.066
M6pr	2.171	0.007	0.062	Tspan3	2.352	0.003	0.040
Manbal	2.620	0.009	0.069	Tspan7	2.270	0.001	0.027
Map1lc3b	2.978	0.001	0.028	Tub	6.954	0.000	0.016
Mapkap1	2.645	0.010	0.070	Tubb4b	2.426	0.002	0.037
Mapkapk5	2.476	0.004	0.045	Tyro3	5.176	0.000	0.023
Mast2	4.666	0.004	0.049	Ubald1	3.325	0.003	0.040
Mbd2	3.693	0.012	0.078	Ubl5	2.981	0.006	0.058
Mblac2	3.198	0.007	0.063	Ulk1	2.369	0.009	0.067
Mboat7	4.459	0.005	0.055	Uqcrb	3.669	0.001	0.025
Mgst3	5.093	0.004	0.046	Uqcrfs1	2.994	0.005	0.052
Mid2	6.101	0.001	0.028	Usmg5	3.313	0.001	0.027
Minpp1	4.786	0.004	0.047	Vkorc1l1	3.000	0.010	0.072
Mipol1	3.394	0.015	0.086	Vta1	2.955	0.012	0.076
Mlf2	2.377	0.000	0.018	Vwc2l	3.597	0.010	0.071
Mob3b	3.803	0.020	0.099	Wbp1	4.041	0.019	0.095
Mrpl18	2.783	0.000	0.023	Wbp2	2.195	0.000	0.002
Mrpl27	2.877	0.013	0.081	Wdr59	3.560	0.004	0.049
Mrps14	4.314	0.004	0.049	Wfs1	4.090	0.009	0.067
Mrps36	4.605	0.006	0.058	Wisp1	5.770	0.008	0.064
Msra	3.657	0.013	0.082	Ypel3	2.511	0.003	0.042
mt-Nd2	2.268	0.007	0.063	Ywhaq	2.191	0.001	0.026
Mxd3	2.495	0.007	0.060	Zdhhc6	4.565	0.008	0.064
Mzt1	2.130	0.000	0.021	Zfp932	3.625	0.020	0.097
Naa38	4.005	0.018	0.093	Zfp944	6.108	0.006	0.056

Table 4.

Genes upregulated in the DRG Nav1.8-TRAP dataset

Genes	Log2 fold change	p	q	Genes	Log2 fold change	p	q
2810417H13Rik	−7.030	0.002	0.036	Mif	−2.343	0.012	0.078
A430078G23Rik	−4.575	0.005	0.055	Mmp15	−2.137	0.005	0.055
Abca6	−10.621	0.000	0.017	Mon1a	−2.894	0.002	0.037
Abhd17c	−2.949	0.000	0.017	Mrpl14	−3.067	0.020	0.099
Abt1	−2.637	0.008	0.065	Mrpl37	−2.040	0.003	0.044
Acaca	−2.654	0.002	0.037	Mrto4	−3.186	0.006	0.056
Acacb	−3.534	0.005	0.055	Msn	−2.617	0.003	0.040
Acot6	−7.991	0.001	0.029	Mt-Co3	−6.804	0.001	0.026
Acta1	−5.881	0.006	0.057	Mterf4	−2.725	0.009	0.068
Actb	−2.404	0.000	0.016	Mvd	−2.888	0.002	0.035
Adap1	−2.420	0.010	0.073	Myc	−6.466	0.001	0.030
Adgrb3	−4.063	0.019	0.095	Myh1	−10.133	0.002	0.035
Adra2a	−8.313	0.000	0.017	Myh9	−2.359	0.004	0.046
Akap5	−8.506	0.000	0.017	Myo10	−4.730	0.006	0.056
Akr7a5	−2.071	0.013	0.081	Myoc	−3.234	0.015	0.085
Akt1s1	−2.002	0.004	0.050	Nat9	−3.387	0.012	0.078
Anapc5	−2.274	0.001	0.023	Ndfip1	−4.237	0.010	0.071
Ankrd13d	−2.118	0.006	0.058	Ndst3	−4.583	0.007	0.062
Anp32b	−2.272	0.003	0.040	Ndufa3	−2.237	0.017	0.090
Ap1s1	−2.586	0.003	0.040	Ndufs5	−2.374	0.018	0.092
Ap2s1	−2.371	0.012	0.078	Ndufs6	−3.439	0.000	0.018
Aqr	−2.439	0.019	0.094	Nes	−5.213	0.001	0.030
Arhgap26	−3.451	0.000	0.017	Neurl4	−2.326	0.005	0.052
Arhgap39	−2.113	0.006	0.058	Nfkbil1	−4.943	0.001	0.027
Arhgef11	−2.432	0.016	0.088	Nfya	−4.346	0.018	0.093
Ascc2	−10.550	0.001	0.024	Noct	−2.815	0.013	0.081
Astn2	−8.207	0.001	0.027	Nol10	−2.001	0.001	0.024
Atf6b	−2.528	0.010	0.072	Nop16	−2.605	0.001	0.032
Atg2a	−2.559	0.001	0.028	Nptx1	−2.018	0.005	0.055
Atm	−2.175	0.017	0.090	Npy1r	−3.647	0.007	0.061
Atp2b4	−2.641	0.000	0.017	Nsun2	−2.052	0.002	0.037
Atp6v0a1	−3.155	0.001	0.032	Nts	−6.880	0.003	0.040
B3gnt8	−2.129	0.015	0.085	Nuak1	−2.011	0.003	0.040
Bach1	−4.987	0.002	0.035	Nup155	−5.434	0.000	0.021
Baz1b	−2.451	0.016	0.089	Nup88	−2.178	0.005	0.050
C130074G19Rik	−6.277	0.001	0.027	Nyap1	−2.210	0.003	0.045
Cadm4	−2.525	0.005	0.050	Obfc1	−5.064	0.003	0.041
Capn1	−2.714	0.001	0.028	Obox3	−3.209	0.002	0.035
Cast	−2.300	0.000	0.017	Ogfr	−3.157	0.004	0.047
Ccdc130	−5.043	0.003	0.040	P2rx3	−2.594	0.015	0.086
Ccdc3	−2.703	0.021	0.099	Pabpn1	−2.231	0.008	0.065
Cct5	−2.433	0.002	0.037	Palm3	−2.239	0.010	0.072
Cct7	−2.291	0.004	0.045	Panx1	−2.446	0.006	0.056
Cct8	−4.490	0.002	0.034	Pcbp2	−5.332	0.003	0.041
Cdc26	−2.164	0.002	0.039	Pcdh11x	−7.446	0.000	0.003
Cdh1	−2.230	0.004	0.049	Pex6	−2.364	0.005	0.055
Cdk11b	−3.476	0.004	0.047	Pfdn2	−2.988	0.004	0.045
Cep85l	−9.647	0.001	0.026	Pfkp	−2.031	0.001	0.032
Cetn2	−2.177	0.010	0.072	Pfn1	−2.932	0.000	0.020
Ckb	−2.670	0.002	0.035	Pgls	−2.444	0.009	0.068
Ckm	−8.225	0.006	0.056	Phldb2	−8.699	0.004	0.046
Clgn	−2.261	0.000	0.017	Pih1d1	−2.509	0.002	0.035
Clint1	−2.048	0.009	0.067	Plcb3	−3.864	0.000	0.018
Clip2	−4.567	0.000	0.015	Plec	−3.238	0.000	0.017
Cln6	−3.765	0.001	0.025	Plekhm1	−4.182	0.005	0.053
Col1a1	−2.067	0.011	0.074	Pnpo	−3.077	0.008	0.066
Col5a2	−7.121	0.021	0.100	Poll	−4.050	0.002	0.035
Col5a3	−5.712	0.005	0.050	Polr2 h	−3.280	0.011	0.075
Col8a1	−7.657	0.002	0.037	Pop1	−7.207	0.000	0.001
Cops6	−2.017	0.002	0.037	Prcc	−2.257	0.002	0.033
Coq7	−2.086	0.015	0.085	Prg2	−10.331	0.000	0.020
Cpe	−2.053	0.002	0.034	Prkag2	−2.591	0.000	0.019
Cpt1c	−2.190	0.006	0.059	Prrx1	−5.132	0.004	0.047
Cpxm1	−3.325	0.005	0.052	Psmb3	−2.915	0.002	0.036
Csgalnact1	−9.636	0.001	0.028	Psmb5	−2.213	0.002	0.036
Csnk2a2	−2.122	0.006	0.059	Psmc1	−2.405	0.000	0.016
Csrnp1	−10.669	0.001	0.030	Psmd13	−2.670	0.003	0.044
Ctu2	−3.003	0.014	0.084	Psmd2	−2.182	0.001	0.027
Cul7	−3.621	0.007	0.062	Psmd4	−2.809	0.003	0.044
Cwc25	−2.930	0.004	0.045	Ptger1	−3.477	0.018	0.094
Dapk2	−2.224	0.014	0.084	Ptms	−3.871	0.002	0.037
Ddx56	−2.158	0.004	0.045	Ptpn23	−7.273	0.002	0.036
Dgkz	−2.686	0.003	0.040	Ptprb	−7.624	0.000	0.004
Disp1	−2.321	0.015	0.085	Ptrf	−4.042	0.005	0.053
Dlg3	−3.824	0.008	0.064	Pycr2	−2.066	0.015	0.085
Dnaja2	−2.167	0.001	0.028	Pygl	−2.181	0.014	0.083
Dnm1	−2.469	0.009	0.068	Pygo2	−2.586	0.007	0.063
Dph2	−6.419	0.000	0.017	Qsox1	−2.054	0.003	0.040
Dph7	−6.003	0.000	0.014	R3hdm4	−2.054	0.013	0.082
Dpp7	−2.645	0.013	0.080	Rack1	−2.006	0.005	0.055
Dpp8	−2.446	0.003	0.042	Rae1	−2.346	0.014	0.084
Dpy19l4	−6.024	0.001	0.028	Rara	−4.005	0.005	0.053
Dpysl5	−2.386	0.016	0.088	Rbm14	−6.125	0.001	0.032
Drg2	−2.986	0.011	0.076	Rbm6	−2.191	0.007	0.061
Ebpl	−2.024	0.006	0.056	Rcc2	−2.150	0.002	0.037
Edc4	−3.342	0.003	0.040	Reps1	−4.535	0.000	0.022
Ehmt2	−2.944	0.008	0.066	Rexo4	−3.169	0.015	0.087
Eif2b4	−2.226	0.009	0.068	Rfc2	−2.711	0.005	0.054
Eif2b5	−2.424	0.002	0.033	Riok1	−2.211	0.017	0.090
Eif3j1	−2.155	0.018	0.092	Rnf122	−2.237	0.000	0.018
Eif3m	−2.731	0.006	0.057	Rnf2	−2.259	0.015	0.086
Eif5b	−2.048	0.008	0.066	Rpia	−2.151	0.017	0.090
Emc1	−2.455	0.004	0.047	Rpl24	−3.600	0.003	0.040
Eps8	−7.159	0.003	0.044	Rplp2	−2.166	0.003	0.040
Fabp4	−7.365	0.000	0.021	Rrp1	−2.684	0.001	0.025
Fam195b	−2.538	0.003	0.043	Rrp7a	−2.173	0.010	0.070
Fam21	−2.569	0.001	0.024	Rsph9	−3.818	0.009	0.067
Fam65b	−7.543	0.003	0.044	Rsrc1	−2.223	0.005	0.054
Fbn1	−5.976	0.004	0.046	Ryr1	−9.068	0.021	0.099
Fh1	−2.129	0.006	0.056	S100a8	−7.163	0.001	0.025
Fhl3	−3.273	0.014	0.084	S100a9	−6.621	0.002	0.033
Fkbp10	−8.824	0.001	0.027	Sae1	−2.758	0.008	0.064
Fkbp14	−2.261	0.005	0.053	Sart3	−2.174	0.001	0.024
Fnbp4	−4.246	0.012	0.078	Sass6	−2.773	0.015	0.087
Frg1	−2.094	0.001	0.030	Scn2a1	−4.227	0.008	0.066
Ftsj3	−2.299	0.004	0.048	Sdad1	−2.183	0.014	0.084
Gab2	−2.044	0.004	0.047	Sdk2	−4.549	0.013	0.081
Gdap1l1	−2.493	0.011	0.074	Sdsl	−4.243	0.017	0.092
Gfod2	−2.657	0.010	0.071	Selenbp1	−8.748	0.001	0.027
Gga1	−2.391	0.004	0.049	Senp1	−5.212	0.018	0.092
Gm21967	−3.888	0.010	0.073	Sept6	−2.159	0.006	0.058
Gm42417	−9.870	0.005	0.053	Serpina11	−3.644	0.014	0.085
Golga2	−2.272	0.003	0.045	Sertad1	−2.157	0.006	0.059
Golga7b	−2.577	0.004	0.047	Sfrp5	−2.379	0.001	0.028
Gp1bb	−2.047	0.001	0.030	Sin3b	−2.401	0.002	0.032
Gpr179	−8.139	0.001	0.028	Slc16a3	−10.302	0.005	0.050
Gpx4	−2.608	0.009	0.069	Slc25a24	−8.902	0.000	0.017
Grcc10	−2.915	0.003	0.040	Slc27a4	−2.479	0.020	0.097
Gsn	−2.133	0.015	0.085	Slc39a6	−2.296	0.002	0.036
Gtf2f2	−9.634	0.003	0.042	Slc43a1	−4.289	0.009	0.068
Gys1	−2.353	0.000	0.016	Slc51a	−6.814	0.000	0.015
H2afy2	−3.646	0.001	0.026	Slc7a3	−2.557	0.018	0.093
Hba-a2	−3.246	0.010	0.072	Slc7a5	−3.490	0.000	0.020
Hddc2	−2.355	0.005	0.055	Slfn2	−8.252	0.014	0.084
Hdgfrp2	−2.558	0.001	0.028	Smarcd2	−3.411	0.018	0.092
Hgf	−3.196	0.008	0.065	Smarce1	−2.804	0.016	0.089
Hmgcl	−2.512	0.006	0.058	Snf8	−2.088	0.006	0.056
Hnrnpf	−2.067	0.005	0.053	Snrnp200	−2.004	0.018	0.093
Hnrnpk	−2.677	0.000	0.016	Snw1	−2.689	0.000	0.016
Hnrnpm	−2.317	0.006	0.057	Snx9	−7.202	0.001	0.029
Hoxa10	−7.037	0.000	0.018	Sox8	−8.131	0.000	0.012
Hoxa7	−6.596	0.000	0.018	Sppl3	−2.328	0.012	0.079
Hoxa9	−6.957	0.000	0.023	Spred3	−4.439	0.003	0.040
Hoxb2	−5.943	0.000	0.019	Srcap	−3.676	0.007	0.060
Hoxb4	−8.161	0.000	0.017	Ssb	−2.140	0.005	0.053
Hoxb5	−7.221	0.000	0.017	Supt16	−2.360	0.000	0.018
Hoxb9	−9.174	0.000	0.016	Syp	−2.076	0.015	0.087
Hoxc6	−9.853	0.000	0.002	Taf4b	−10.301	0.002	0.037
Hoxd10	−7.982	0.001	0.030	Tango6	−10.199	0.002	0.037
Hoxd4	−4.810	0.001	0.026	Tarbp2	−3.521	0.003	0.040
Hoxd8	−5.752	0.000	0.020	Tbc1d10b	−2.056	0.011	0.074
Hoxd9	−7.597	0.005	0.053	Thap4	−6.562	0.000	0.017
Hps4	−5.021	0.003	0.042	Timm50	−2.701	0.011	0.074
Hsp90ab1	−2.714	0.000	0.018	Tkt	−3.256	0.010	0.072
Hspa9	−2.531	0.000	0.023	Tlx3	−2.618	0.002	0.035
Ier5l	−5.059	0.006	0.057	Tmem101	−2.205	0.015	0.087
Il11ra1	−2.334	0.012	0.078	Tmem201	−5.403	0.000	0.020
Ino80	−4.042	0.006	0.057	Tmem205	−4.532	0.001	0.026
Irf5	−8.535	0.000	0.004	Tnnc2	−9.833	0.001	0.030
Irgq	−2.836	0.010	0.072	Tnnt3	−7.545	0.002	0.038
Kdm1a	−2.263	0.006	0.058	Tnpo2	−2.492	0.007	0.063
Kdm4b	−8.283	0.010	0.073	Tnrc18	−2.271	0.005	0.053
Kif3a	−2.029	0.014	0.084	Top2a	−7.193	0.019	0.095
Kif3c	−4.039	0.002	0.035	Ttbk1	−2.591	0.007	0.061
Klf2	−6.811	0.001	0.025	Tubb3	−2.963	0.000	0.015
Lcmt2	−4.928	0.003	0.040	Txnrd2	−2.946	0.008	0.066
Ldlr	−2.419	0.001	0.024	Ubc	−2.316	0.002	0.037
Leo1	−2.433	0.008	0.065	Ube2s	−2.379	0.009	0.069
Lig1	−2.253	0.002	0.034	Uchl1	−3.022	0.009	0.069
Loxl1	−6.618	0.001	0.030	Uck2	−2.829	0.006	0.056
Lrrc17	−3.531	0.008	0.064	Upf3b	−3.615	0.007	0.060
Lrrfip1	−3.487	0.005	0.050	Urgcp	−4.220	0.001	0.029
Lta4 h	−2.258	0.003	0.042	Usp17la	−7.689	0.000	0.016
Ltbp3	−2.457	0.007	0.062	Utp18	−4.338	0.009	0.068
Man2b2	−8.690	0.000	0.018	Vezt	−2.143	0.015	0.087
Map1a	−2.487	0.002	0.035	Vps33a	−2.623	0.008	0.066
Mb	−9.449	0.002	0.035	Vps8	−3.249	0.000	0.018
Mboat1	−5.624	0.003	0.041	Xirp2	−8.750	0.002	0.038
Mcoln1	−2.973	0.012	0.079	Yipf1	−2.497	0.001	0.025
Mdfic	−10.177	0.001	0.032	Zfp212	−2.281	0.013	0.081
Med16	−2.428	0.018	0.092	Zfp292	−2.238	0.015	0.086
Med29	−2.799	0.008	0.064	Zfp30	−6.842	0.002	0.035
Mfap4	−4.773	0.003	0.045	Zfp384	−4.615	0.004	0.049
Mgat4c	−5.486	0.002	0.035	Zfp428	−2.044	0.003	0.045

Next, we evaluated correlation between differentially transcribed and translated mRNAs between the TG and DRG. To do this, we plotted the 379 mRNAs with higher transcript levels in TG and 315 with higher levels in the DRG. We plotted these against TPMs from the Nav1.8-TRAP datasets from both tissues. We did the same thing for the 372 Nav1.8-TRAP enriched mRNAs from TG and 348 from DRG and compared these with TPMs from input RNA sequencing (Fig. 4C). We observed that only 144 genes were shared between these datasets, suggesting that transcriptional and translational regulation is decoupled in these tissues, at least for the most highly enriched genes. This finding is consistent with genome-wide experiments showing that transcription and translation are decoupled for many, if not most, mRNAs (Liu et al., 2016).

We then sought to validate some specific findings from whole transcriptome or Nav1.8-TRAP sequencing data obtained from the comparison between TG and DRG. Analysis of the differentially expressed genes between TG and DRG showed that Fth1 is highly enriched in the TG (Fig. 5A). We used qRT-PCR on mRNA prepared from both tissues to validate that there is a significant enrichment of Fth1 mRNA in the TG by this method (Fig. 5B). Comparisons of the TG and DRG transcriptome showed that multiple genes among the AMPK pathway were enriched in the DRG, such as Prkag2, Acacb, Akt1s1, and Gys (Fig. 6A). Interestingly, these same mRNAs were among the 144 that were regulated at the translational level as well (Fig. 6A), but there were also a number of additional mRNAs involved in the AMPK pathway that were only found in the translatome dataset, including Cpt1c and Acaca. In stark contrast, we observed an enrichment of mRNAs in the translatome in the TG that are associated with the PI3K-mTORC1 pathway, including Strada, Lamtor5, Akt1, and Rraga (Fig. 6A,B). As mentioned previously, this predicts a higher level of mTOR activity in TG than in the DRG nociceptors. To begin to address this prediction, we examined steady-state protein levels for selected targets between DRG and TG. We chose to focus on RragA, which encodes the RagA protein, because it is a critical activator of mTORC1 activity that links mTORC1 to amino acid and glucose signaling at the interface with lysosomes (Efeyan et al., 2013, 2014). Consistent with transcriptome data, we observed no differences in the level of Rraga mRNA between TG and DRG, but we did detect a significant increase in protein level in the TG versus the DRG (Fig. 6C). We have previously shown that Rraga mRNA translation is finely controlled by the activity of Mnk1 and correlated with the level of eIF4E phosphorylation. Here, we also detected a higher level of eIF4E phosphorylation in the TG compared with DRG (Fig. 6D), suggesting that TG nociceptors may display higher translational activity via this pathway than their DRG counterparts (Megat et al., 2019). We also focused on Akt1s1, which encodes the PRAS40 protein, because this is a negative regulator of mTORC1 activity with actions that are inversely related to RagA (Wiza et al., 2012; Chong, 2016). In the DRG, we observed that the level of the ribosome-associated Akt1s1 mRNAs was higher in the DRG compared with TG, and this was validated by increased PRAS40 protein in DRG (Fig. 6E).

Figure 5.

RNA-seq analysis reveals Fth1 as differentially expressed in the TG and validated through qRT-PCR. A, Volcano plot shows Fth1 (yellow dot) as being significantly enriched in TG versus DRGs. B, qRT-PCR shows a 50% increase in Fth1 mRNA expression in TG. (paired t test, t = 4,15; df = 6; **p = 0.0048).

Figure 6.

TRAP-seq analysis reveals that AMPK- and mTORC1-related genes are differential expressed and/or translated in the DRG and TG, respectively. A, Volcano plot showing an enrichment in AMPK-related genes in the input DRG sample, including Prkag2, Akt1s1, Gys1, Acacb, as well as in TRAP-seq (including Prkag2, Akt1s1, Gys1, Acacb, Acaca, and Cpt1c). In converse, mTORC1-related genes are enriched in TG, such as Strada, Rraga, Akt, and Lamtor5. B, Heatmap shows increase translation of AMPK and mTORC1 genes in the DRGs and TG, respectively. C, Immunoblotting shows an upregulation of RagA protein inTG (RagA: DRG = 100 ± 8.39,T = 149.8 ± 8.03, *p = 0.0003, n = 11), whereas Rraga mRNA measured by qRT-PCR was not different between DRGs and TG (Rraga: DRG = 1.120 ± 0.075, TG = 1.01 ± 0.024, p = 0.152, n = 4). D, Immunoblotting shows a lower level of eIF4E phosphorylation in the DRG compared with TG (p-eIF4E: TG = 100.5 ± 4.28, DRG = 79.98 ± 7.13, *p = 0.0404). E, The negative regulator of mTORC1, PRAS40 (Akt1s1) mRNA, and TE was significantly increased in DRGs and confirmed by an increase in protein level (Akt1s1: DRG = 100 ± 5.28, TG = 52.62 ± 5.48, ***p = 0.008, n = 4). F, Nocifensive behavior and grimace score after injection of capsaicin (0.1 μm) into the whisker pad or the hindpaw. Capsaicin induces a more intense affective response when injected into the whisker pad compared with the hindpaw as shown by the mouse grimace score at 15 and 30 min (two-way ANOVA: F_(2,24) = 22.98, ****p < 0.0001, post hoc Sidak ****p < 0.0001 at 15 and 30 min). Likewise, nocifensive behavior is more pronounced when capsaicin was injected into the whisker pad compared with the hindpaw (F_(1,12) = 11.62, **p < 0.0052, post hoc Sidak ***p = 0.002 at 60 min after capsaicin). G, Pretreatment with an mTORC1 inhibitor (AZD8055, 10 mg/kg) blocked capsaicin-induced nocifensive behavior in the whisker pad (F_(2,24) = 13.93, ****p < 0.0001, post hoc Sidak ***p = 0.002 at 60 min after capsaicin) and affective pain (F_(2,24) = 21.62, ****p < 0.0001, post hoc Sidak ****p < 0.0001 at 15 and 30 min). H, Intraperitoneal injection of AZD8055 (10 mg/kg) decreased the level of p-4EBP1 at 2 h (one-way ANOVA: F_(2,6) = 19.15, **p = 0.0025, post hoc Dunnett: Veh vs 2 h, *p = 0.027) in the TG. I, AZD8055 inhibited capsaicin-induced grimace at 30 min (F_(2,27) = 4.52, *p = 0.02, post hoc Sidak **p = 0.0034) and nocifensive behavior (F_(1,9) = 17.45, **p < 0.0024, post hoc Sidak ***p < 0.001 at 60 min after capsaicin) when injected into the hindpaw. J, For each group of animals, the difference between the vehicle- and AZD8055-treated values was calculated and plotted for the nocifensive behavior and mouse grimace score. We observed a significantly larger effect size of AZD8055 in nocifensive behavior (unpaired t test, t = 3.52, df = 11, **p = 0.0048) and grimacing (unpaired t test, t = 5.54, df = 11, ***p = 0.0002) when capsaicin was injected in the whisker pad. ns, not significant.

Collectively, the results described above suggest that the balance of mTORC1 signaling through the lysosome is shifted toward activation in the TG compared with the DRG, which could influence nociceptive responses in the facial area compared with areas innervated by the DRG. To test this hypothesis, we gave injections of a low dose of capsaicin (0.1 μm), a TRPV1 agonist, into the hindpaw and the whisker pad (facial area). We observed a significantly more pronounced spontaneous pain response following facial capsaicin compared with the hindpaw (Fig. 6F). Also, the intensity/number of the nocifensive behavior was significantly larger following injection of capsaicin into the cheek, again suggesting that nociceptive stimuli trigger larger behavioral responses when administered in the facial area (Fig. 6F). We next sought to investigate whether capsaicin-induced nocifensive behavior was dependent on mTORC1 activity in the TG region. We treated animals with an mTORC1 inhibitor (AZD8055, 10 mg/kg) 2 h before injection of capsaicin into the whisker pad. We observed that the mTORC1 inhibitor significantly attenuated grimace responses and nocifensive behaviors (Fig. 6G), and this behavioral change correlated with a significant decrease in the level of p-4EBP1 (Fig. 6H), a downstream target of mTORC1. While we also observed that mTORC1 inhibition significantly attenuated grimace responses and nocifensive behavior induced by a plantar injection of capsaicin (Fig. 6I), the effect size was significantly smaller compared with capsaicin into the whisker pad (Fig. 6J). Previous clinical findings reported that repetitive noxious heat stimulation, which also acts via TRPV1, creates greater sensitization in the TG region in people (Schmidt et al., 2015). Our findings parallel these observations and support a model wherein enhanced mTORC1 signaling in TG nociceptors is a cause of this enhanced sensitization.

Combining the datasets described above with single-cell RNA sequencing from existing data sources (Usoskin et al., 2015; Hu et al., 2016) allowed us to infer translation efficiencies (TEs) for all mRNAs translated in Nav1.8 neurons. First, we used the most discriminative genes in each cell type cluster (Hu et al., 2016) and calculated the correlation coefficients with all the protein coding genes in our Nav.8-TRAP sequencing datasets. Then, we plotted the heatmap of the correlation coefficient, and we observed a clear cluster of genes highly correlated with Scn10a (Fig. 7A). The Scn10a cluster (2594 genes) was compared with our TRAP-filtered dataset (7358 genes), which generated a list of 854 Scn10a-enriched genes (Fig. 7A). We then looked at the expression level of the Scn10a-enriched genes and calculated the TEs (the ratio of the TRAP and Input values) for each gene in TG and DRG datasets. Cluster 1 (C1) identified the Scn10a-enriched genes showing high TEs in the DRG (Fig. 7B; Fig. 7-1). Among them, we again found Acaca that codes for the protein ACC (acetyl-CoA carboxylase 1), a downstream target of AMPK (Hardie, 2014). Cluster 2 (C2) identified genes showing high TEs in the TG, such as Lamtor5, Rraga, and Fkbp1a, all important regulators of the mTORC1 pathway (Fig. 7B; Fig. 7-1). This cluster also identified the CGRPβ mRNA Calcb and the MrgprD receptor mRNA. Cluster (C3) contained genes with low TEs in both TG and DRG, and cluster 4 (C4) identifies genes with high TE in TG and DRG (Fig. 7-1). Finally, we examined functional gene families (e.g., ion channels, GPCRs and kinases) for any systematic differences in TEs for mRNAs expressed in Nav1.8⁺ nociceptors in the TG. Interestingly, we observed that ion channels and GPCRs tend to show higher TEs compared with other gene families, such as kinases or transcription factors (Fig. 7C; Fig. 7-2), a finding that is consistent with observations in DRG Nav1.8-expressing neurons (Megat et al., 2019).

Figure 7.

TE analysis for Scn10a-enriched genes in TG- and DRG-TRAP-seq shows differential TEs between tissues. A, Heatmap showing the correlation coefficient of the protein coding genes with the most discriminative expression between cell populations based on the DRG single-cell dataset published previously (Usoskin et al., 2015; Hu et al., 2016) with Nav1.8 (Scn10a) highlighted. A cluster of 2547 genes was identified as highly enriched in the Scn10a-positive neuronal population. Those 2547 genes were then merged to the TRAP-seq filtered dataset (∼8000 genes) to identify a group of 854 mRNAs that were highly enriched in the single-cell population that also expressed Scn10a and not found in other cell populations. B, Heatmap of the TE for the 854 mRNAs shows 4 separate clusters. C1 identifies mRNAs with high TEs in the DRG but lower in TG. C2 shows genes with high TE in the TG and low TEs in the DRG. C3 identifies mRNAs with low TEs in both tissues. C4 identifies mRNAs with high TEs in both tissues. C, Calculation of TE efficiencies for gene families in the TG shows higher TEs for mRNAs coding for ion channels and GPCRs compared with splicing and transcription factors. Figure 7-1 shows estimated TEs for all genes shown in clusters in Figure 7A. Figure 7-2 shows estimated TEs by gene family.

Figure 7-1

Estimated TE for all genes in clusters shown in Fig 7A. The Table shows estimated TEs in the DRG and TG for each cluster shown in Fig 7A. Download Figure 7-1, DOCX file ^{(106.5KB, docx)}

Figure 7-2

Estimated TE for all genes by gene family. The Table shows estimated TEs in the DRG and TG for each of the gene families mentioned in the text. Download Figure 7-2, DOCX file ^{(41.1KB, docx)}

Finally, we used MEME Suite (Bailey et al., 2015) to search for motifs in the 5′ UTRs of mRNAs in clusters 1–4 described above. We only considered motifs that were found in >30% of genes in each of the clusters. In C1, we did not identify any enriched motifs; however, in C2, we identified 2 motifs in mRNAs of 5′-UTRs for genes with increased TE in the TG versus the DRG (Fig. 8). One of these was a GC-rich motif found in 82 of 307 mRNAs, and another was a terminal oligo pyrimidine tract motif found in 57 of 307 mRNAs. The latter motif is interesting because it is consistent with the finding that mTORC1 genes are more translated in the TG because terminal oligo pyrimidine tract element containing mRNAs show increased TE when mTORC1 activity is high (Thoreen et al., 2012). In the C3 cluster, which contains mRNAs with low TEs in both TG and DRG, we found a G quadruplex motif (57 of 193 mRNAs) (Fig. 8) that is likely a target for eIF4A-mediated translation control (Wolfe et al., 2014), suggesting that eIF4A activity might be low under normal conditions in TG and DRG neurons. We did not find any enriched motifs in C4.

Figure 8.

mRNA motifs enriched in 5′-UTRs from clusters of genes that show altered TEs between TG and DRG. Two motifs were found in cluster C2 (higher TE in TG than in DRG) and 1 motif was found in cluster C3 (low TE in both TG and DRG). Genes with motifs found in their 5′-UTRs are shown to the right of the corresponding motifs.

Discussion

Our work uses the TRAP technology to highlight differences in the translatomes of Nav1.8⁺ neurons in the DRG and TG. Although there are many consistencies between these tissues, as would be expected by the similar function of Nav1.8⁺ neurons in the DRG and TG, there are some striking differences that may have important functional implications. Prominent among these are higher levels of protein synthesis for many regulators of the mTORC1 pathway in the TG and higher protein synthesis for members of the AMPK signaling pathway in the DRG. mTORC1 is a well-known downstream target of the AMPK kinase (Alers et al., 2012). It has been documented that, under energy-low conditions, increases in AMPK activity inhibit mTORC1, resulting in decreased overall protein synthesis and promotion of autophagy mechanisms (Schmidt et al., 2016). Because these signaling pathways regulate one another, this suggests that mRNAs that are regulated by the mTORC1 pathway are likely to have higher translational efficiencies in the TG than in the DRG. Previous psychophysical studies in humans have shown that painful stimulation of the TG area causes greater sensitization than stimulation of DRG-innervated regions (Schmidt et al., 2015, 2016). These studies have also demonstrated a lack of habitation in the TG region with repeated painful thermal stimulation (Schmidt et al., 2015). It is now well established that the mTORC1 signaling pathway plays a key role in controlling nociceptor excitability and sensitization (Melemedjian et al., 2010; Moy et al., 2017; Khoutorsky and Price, 2018), and this sensitization is strongly attenuated by activation of the AMPK pathway (Melemedjian et al., 2011; Burton et al., 2017). Our findings are in line with somatotopic differences in response to painful stimulation and a higher propensity to sensitization in TG nociceptors. While this might be explained by the biological relevance of the head and facial area for vital functions, our data show that differences in basal mTORC1 activity between TG and DRG nociceptors could drive differences in magnitude of sensitization following injury. However, it is also important to note that recently discovered anatomical differences between central projections of DRG and TG neurons may also mediate these differences (Rodriguez et al., 2017), in particular as they relate to enhanced fear and anxiety from painful stimulation of the TG region (Schmidt et al., 2016).

A recent paper examined differences in mRNA expression on FACS-sorted TG and DRG neurons from mice, demonstrating that >99% of mRNA showed consistent expression between TG and DRG neurons (Lopes et al., 2017). These authors only identified 24 mRNAs with differential expression, but these included Hox genes, as we also found, and an arginine vasopressin receptor (Lopes et al., 2017). Many other differentially expressed genes they attributed to non-neuronal cell types. We found >300 differentially expressed genes in the whole tissue transcriptome of the DRG versus the TG, and many of these mRNAs may be attributable to non-neuronal cells because we did not sort cells for whole transcriptome. This likely explains the major discrepancies between transcriptomes in these two papers. However, major differences in translatome findings cannot be attributable to non-neuronal cells because the approach we use is specific to Nav1.8-expressing neurons, most of which are nociceptors. Our work also identifies potential differences in translation regulation signaling between the DRG and TG that provides a plausible explanation for these difference in the translatome. This is especially important considering that the mTOR (Patursky-Polischuk et al., 2009; Thoreen et al., 2012) and AMPK (Dowling et al., 2007) pathways have distinct effects on TE of specific subsets of mRNAs.

There are limitations to our approach. Primary among these is that our TE estimates could only be applied to a subset of genes that have been identified as highly enriched in the Nav1.8⁺ population of neurons by single-cell RNA sequencing. Future efforts may use cell-sorting techniques (Thakur et al., 2014; Lopes et al., 2017) for transcriptome generation in combination with TRAP sequencing to make estimates of the TE across the active genome of Nav1.8⁺ population of cells. A technical shortcoming of this potential approach is that tissue homogenization and cellular dissociation protocols that are needed to sort cells for transcriptomic analysis cause induction of classes of genes, including molecular chaperones and immediate early genes that can bias transcriptomes and distort TE estimates (van den Brink et al., 2017). A second limitation is that, while our data are suggestive of important differences in mTORC1 and AMPK signaling between these two tissues that may regulate susceptibility to nociceptor sensitization, we have not shown this directly with behavioral or electrophysiological evidence. However, the notion of that TG nociceptors are more intensely sensitized by noxious stimuli is supported by preclinical models and human psychophysical data (Schmidt et al., 2015, 2016). For example, it has recently been demonstrated that injury to TG nerves induces a grimacing effect in both rats and mice (Akintola et al., 2017). This is in stark contrast to effects of injury to the sciatic nerve where grimacing effects are not observed (Langford et al., 2010). These findings suggest that injury to TG nerves induces a stronger ongoing pain phenotype in both of these rodent species. Additional work is needed to clarify whether this is driven by the mTOR signaling axis in the TG.

The results presented here add to a growing body of literature that there are important differences between the DRG and TG that are likely relevant for understanding pain disorders that originate from these regions. These include differential developmental origins (Zou et al., 2004), differential expression of neuronal subtype markers (Price and Flores, 2007), and altered response to injury, such as sympathetic sprouting into the DRG in response to injury (Chung et al., 1996; Chien et al., 2005; Xie et al., 2007, 2015), which does not occur in the TG (Bongenhielm et al., 1999). Our use of the TRAP technique to define the translatomes of Nav1.8⁺ neurons in DRG and TG points to a host of newly discovered differences between these two tissues and generates a new resource that can be mined to gain addition insight.