Front. Pharmacol.Frontiers in PharmacologyFront. Pharmacol.1663-9812Frontiers Media S.A.10.3389/fphar.2018.01008PharmacologyOriginal ResearchDevelopment of an AmpliSeqTM Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting PainKringelDario1KaunistoMari A.2LippmannCatharina3KalsoEija4LötschJörn13*1Institute of Clinical Pharmacology, Goethe-University, Frankfurt, Germany2Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland3Fraunhofer Institute for Molecular Biology and Applied Ecology – Project Group Translational Medicine and Pharmacology, Frankfurt, Germany4Division of Pain Medicine, Department of Anesthesiology, Intensive Care and Pain Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
Edited by: Ulrich M. Zanger, Dr. Margarete Fischer-Bosch-Institut für Klinische Pharmakologie (IKP), Germany
Reviewed by: Theodora Katsila, University of Patras, Greece; Cheryl D. Cropp, Samford University, United States
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Background: Many gene variants modulate the individual perception of pain and possibly also its persistence. The limited selection of single functional variants is increasingly being replaced by analyses of the full coding and regulatory sequences of pain-relevant genes accessible by means of next generation sequencing (NGS).
Methods: An NGS panel was created for a set of 77 human genes selected following different lines of evidence supporting their role in persisting pain. To address the role of these candidate genes, we established a sequencing assay based on a custom AmpliSeqTM panel to assess the exomic sequences in 72 subjects of Caucasian ethnicity. To identify the systems biology of the genes, the biological functions associated with these genes were assessed by means of a computational over-representation analysis.
Results: Sequencing generated a median of 2.85 ⋅ 106 reads per run with a mean depth close to 200 reads, mean read length of 205 called bases and an average chip loading of 71%. A total of 3,185 genetic variants were called. A computational functional genomics analysis indicated that the proposed NGS gene panel covers biological processes identified previously as characterizing the functional genomics of persisting pain.
Conclusion: Results of the NGS assay suggested that the produced nucleotide sequences are comparable to those earned with the classical Sanger sequencing technique. The assay is applicable for small to large-scale experimental setups to target the accessing of information about any nucleotide within the addressed genes in a study cohort.
Persisting pain has been proposed to result from a gene environment interaction where nerve injuries or inflammatory processes act as triggers while the clinical symptoms develop only in a minority of subjects (Lee and Tracey, 2013). A role of the genetic background in pain is supported by evidence of many variants modulating the individual perception of pain and the development of its persistence (Diatchenko et al., 2005; Lötsch et al., 2009b; Mogil, 2012). Genetic variants have been reported to confer protection against pain such as the rs1799971 variant in the μ-opioid receptor gene (OPRM1) (Lötsch et al., 2006), or to increase the risk for persisting pain such as the rs12584920 variant of the 5-hydroxytryptamine receptor 2A gene (HTR2A) (Nicholl et al., 2011) or the rs734784 polymorphism in the voltage-gated potassium ion channel modifier, subfamily S member 1, gene (KCNS1) (Costigan et al., 2010). Nevertheless, the genetic background of persisting pain is still incompletely understood (Mogil, 2009; Lötsch and Geisslinger, 2010) and under intense discussion.
Until recently, research focused on the role of selected functional genetic variants as protective or risk factors of persisting pain. This has changed with the broader availability of next generation sequencing (NGS) (Metzker, 2010). To make use of these technical advancements, we developed a custom AmpliSeqTM library and sequencing assay for efficient detection of genetic variants possibly associated with persisting pain. We propose an assay of a set of 77 genes supported by evidence of an involvement in pain and its development toward persistence. The set size fully uses the technical specifications of the AmpliSeqTM gene sequencing library technique.
Materials and MethodsSelection of Genes Relevant for Persisting Pain
A set of candidate genes with shown or biologically plausible relevance to persisting pain was created by applying a combination of criteria, which provided three different genetic subsets. Subset 1 was chosen exclusively on the basis of computational functional genomics based on a recently published analysis of persisting pain regarded as displaying systemic features of learning and neuronal plasticity (Mansour et al., 2014). As discussed previously (Ultsch et al., 2016), the view of chronic pain as a dysregulation in biological processes of learning and neuronal plasticity (Alvarado et al., 2013) seems to be captured by the controlled vocabulary (Camon et al., 2004) of the Gene Ontology (GO) knowledge base by the GO terms “learning or memory” (GO:0007611)1 and “nervous system development” (GO:0007399)2. An intersection of the genes annotated to these GO terms with a set of 539 “pain genes” identified empirically as relevant to pain provided the first subset of 34 genes described in detail previously (Ultsch et al., 2016). Briefly, the intersecting set of so-called “pain genes” consists of a combination of (i) genes listed in the PainGenes database (Lacroix-Fralish et al., 2007)3, (ii) genes causally involved in human hereditary diseases associated with extreme pain phenotypes, (iii) genes found to be associated with chronic pain in at least three human studies, and (iv) genes coding for targets of novel analgesics under clinical development (Lötsch et al., 2013).
Subset 2 consisted of genes that were reported to carry variants modulating the risk or the phenotypic symptoms in at least two different clinical settings of persisting pain. They were obtained using (i) a PubMed database search for the string “(chronic OR persisting OR neuropathic OR back OR inflammatory OR musculoskeletal OR visceral OR widespread OR idiopathic OR fibromyalgia) AND pain AND (polymorphism OR variant) NOT review,” to which genes highlighted in overviews on pain genetics (e.g., Edwards, 2006) were added. The intersection of the queried genes with the set of 539 “pain genes” (see above) provided a subset of 13 genes (Table 1).
Genes included in the proposed NGS panel of persisting pain, combined from three subsets included on different bases.
Gene symbol
NCBI
Gene description
Reference
Subset #1
ADCY1
107
Adenylate cyclase 1
Vadakkan et al., 2006
BDNF
627
Brain-derived neurotrophic factor
Obata and Noguchi, 2006
CDK5
1020
Cyclin-dependent kinase 5
Yang et al., 2014
CHRNB2
1141
Cholinergic receptor, nicotinic, beta 2
Dineley et al., 2015
CNR1
1268
Cannabinoid receptor 1 (brain)
Smith et al., 1998
DLG4
1742
Disks, large homolog 4 (Drosophila)
Florio et al., 2009
DRD1
1812
Dopamine receptor D1
Onojjighofia et al., 2014
DRD2
1813
Dopamine receptor D2
Onojjighofia et al., 2014
DRD3
1814
Dopamine receptor D3
Potvin et al., 2009
EGR1
1958
Early growth response 1
Ko et al., 2005
FOS
2353
Cellular oncogene FOS
Abbadie et al., 1994
FYN
2534
Src family tyrosine kinase
Liu et al., 2014
GABRA5
2558
GABA A receptor, alpha 5
Bravo-Hernández et al., 2016
GALR2
8811
Galanin receptor 2
Hulse et al., 2012
GRIN1
2902
Glutamate receptor, NMDA 1
Petrenko et al., 2003
GRIN2A
2903
Glutamate receptor, NMDA 2A
Petrenko et al., 2003
GRIN2B
2904
Glutamate receptor, NMDA 2B
Petrenko et al., 2003
GRM5
2915
Glutamate receptor, metabotropic 5
Walker et al., 2001
HRH3
11255
Histamine receptor H3
Huang et al., 2007
KIT
3815
Tyrosine kinase KIT
Sun et al., 2009
NF1
4763
Neurofibromin 1
Wolters et al., 2015
NGF
4803
Nerve growth factor
Kumar and Mahal, 2012
NTF4
4909
Neurotrophin 4
Kumar and Mahal, 2012
NTRK1
4914
Neurotrophic tyrosine kinase 1
Kumar and Mahal, 2012
OXT
5020
Oxytocin prepropeptide
Goodin et al., 2015
PLCB1
23236
Phospholipase C, beta 1
Shi T.-J.S. et al., 2008
PRKCG
5582
Protein kinase C, gamma
Sluka and Audette, 2006
PRNP
5621
Prion protein
Gadotti and Zamponi, 2011
PTN
5764
Pleiotrophin
Gramage and Herradon, 2010
PTPRZ1
5803
Protein tyrosine phosphatase Z 1
Ultsch et al., 2016
RELN
5649
Reelin
Buchheit et al., 2012
S100B
6285
S100 calcium binding protein B
Zanette et al., 2014
SLC6A4
6532
Serotonin transporter
Offenbaecher et al., 1999
TH
7054
Tyrosine hydroxylase
Bravo et al., 2014
Subset #2
ADRB2
154
Adrenoceptor beta 2
Hocking et al., 2010
COMT
1312
Catechol-O-methyltransferase
Feng et al., 2013
ESR1
2099
Extrogen Receptor 1
Ribeiro-Dasilva et al., 2009
GCH1
2643
GTP cyclohydrolase 1
Tegeder et al., 2006
IL1B
3553
Interleukin 1B
Loncar et al., 2013
IL4
3565
Interleukin 4
Sugaya et al., 2002
IL6
3569
Interleukin 6
Shoskes et al., 2002
IL10
3586
Interleukin 10
Stephens et al., 2014
P2RX7
5027
Purinergic Receptor P2X7
Sorge et al., 2012
SCN9A
6335
Sodium voltage-gated alpha subunit 9
Reimann et al., 2010
SOD2
6648
Superoxide dismutase 2
Schwartz et al., 2009
TNF
7124
Tumor necrosis factor
Leung and Cahill, 2010
TRPV1
7442
Transient receptor potential cation channel, subfamily V, member 1
Major histocompatibility complex, class II, DQ beta 1
Dominguez et al., 2013
HLA-DRB1
3123
Major histocompatibility complex, class II, DR beta 1
Dominguez et al., 2013
HTR1A
3350
5-hydroxytryptamine (serotonin) receptor 1A
Lindstedt et al., 2012
HTR2A
3356
5-hydroxytryptamine (serotonin) receptor 2A
Nicholl et al., 2011
IL1R2
7850
Interleukin 1 receptor type 2
Stephens et al., 2014
KCNS1
3787
Potassium voltage-gated channel, modifier subfamily S, member 1
Costigan et al., 2010
LTB4R
1241
Leukotriene b4 receptor
Zinn et al., 2017
LTB4R2
56413
Leukotriene b4 receptor 2
Zinn et al., 2017
OPRD1
4985
Opioid receptor delta 1
Law et al., 2013
OPRK1
4986
Opioid receptor kappa 1
Guerrero et al., 2010
OPRM1
4988
Opioid receptor mu 1
Lötsch and Geisslinger, 2005
RET
5979
RET receptor tyrosine kinase
Snider and McMahon, 1998
RUNX1
861
Runt related transcription factor 1
Chen et al., 2006
TLR4
7099
Toll like Receptor 4
Hutchinson et al., 2010
TRPA1
8989
Transient receptor potential cation channel, subfamily A, member 1
Bourinet et al., 2014
TRPM8
79054
Transient receptor potential cation channel, subfamily M, member 8
Bourinet et al., 2014
TRPV4
59341
Transient receptor potential cation channel, subfamily V, member 4
Bourinet et al., 2014
TSPO
706
Translocator protein
Loggia et al., 2015
Subset #1 comprises d = 34 genes that had resulted from a computational functional genomics analysis (Ultsch et al., 2016) pursuing the hypothesis that persisting pain displays systemic features of learning and neuronal plasticity (Mansour et al., 2014). Hence, from a set of genes identified empirically as relevant to pain and listed in the PainGenes database (http://www.jbldesign.com/jmogil/enter.html, Lacroix-Fralish et al., 2007), those were selected that are annotated to the Gene Ontology (Ashburner et al., 2000) terms “learning or memory” and “nervous system development.” The references are those found to provide evidence for an association with pain, except for PTPRZ1 that was a novel finding in (Ultsch et al., 2016). Subset #2 comprises d = 13 genes identified empirically as relevant to pain and listed in the PainGenes database (http://www.jbldesign.com/jmogil/enter.html, Lacroix-Fralish et al., 2007) and reported to carry variants that modulated the risk or the symptomatology in at least two different clinical settings of persisting paint. Subset #3 comprises d = 30 genes repeatedly shown during the last several years to play a role in the human genetics of persisting pain or recently reported as novel players.
Finally, subset 3 comprised genes that have consistently been included in human pain research projects over the last several years. One of them is the OPRM1 gene that codes for the human μ-opioid receptor and which has been shown to modulate the time course of persisting cancer pain by delaying the necessity of opioid treatment (Lötsch et al., 2010). However, further genes were added such as the GDNF gene coding for the glial cell derived neurotrophic factor, which has been shown to be involved in a glia-dependent mechanism of neuropathic pain (Wang et al., 2014) although no modulating human genetic variants have been reported so far. Following expert counseling within the EU-funded “glial-opioid interface in chronic pain, GLORIA” research consortium (Kringel and Lötsch, 2015)4, a subset of 30 genes (Table 1) was identified. Thus, the complete set as the union of the three subsets comprised 43 + 13 + 30 = 77 genes that are proposed to be included in an NGS panel of human persisting pain.
DNA Sample Origin
Due to the costs of assay development (for details, see second paragraph of the Discussion), the AmpliseqTM panel was established in a limited number of n = 72 DNA samples. This corresponds to the number of samples used in comparable recent studies for NGS assay establishment and validation (Bruera et al., 2018; De Luca et al., 2018; Mustafa et al., 2018; Shah et al., 2018). To further limit the project costs, the AmpliseqTM panel was established in a subset of samples originating from a clinical cohort of 1,000 women who had undergone breast cancer surgery (Kaunisto et al., 2013; Lötsch et al., 2018). The study followed the Declaration of Helsinki and was approved by the Coordinating Ethics Committee of the Helsinki University Hospital. Each participating subject had provided a written informed consent including genetic studies.
Specifically, for the presently reported method establishment, a subsample of 72 women (age 58.4 ± 8 years, mean ± standard deviation, weight 69.3 ± 11 kg), was drawn from the clinical subgroup not having developed persisting pain during the observation period. This was believed to come closer to a random sample than a mixture of patients with persisting and without persisting pain. This limitation of the sample selection has probably affected which and how many variants were identified. However, it is unlikely to have jeopardized the general applicability of the gene selection heuristics, assay establishment and validation, and of the functional analysis of the selected subset of genes.
DNA Template Preparation and Amplification
A multiplex PCR amplification strategy for the coding gene sequences was accomplished online (Ion AmpliseqTM Designer)5 to amplify the target region specified above (for primer sequences, see Supplementary Table 1) with 25 base pair exon padding. After a comparison of several primer design options, the design providing the maximum target sequence coverage was chosen. The ordered 1,953 amplicons covered approximately 97.5% of the target sequence (Supplementary Table 2). A total of 10 ng DNA per sample was used for the target enrichment by a multiplex PCR and each DNA pool was amplified with the Ion AmpliseqTM Library Kit in conjunction with the Ion AmpliseqTM “custom Primer Pool”-protocols according to the manufacturer’s procedures (Life Technologies, Darmstadt, Germany).
After each pool had undergone 18 PCR cycles, the PCR primers were removed with FuPa Reagent and the amplicons were ligated to the sequencing adaptors with short stretches of index sequences (barcodes) that enabled sample multiplexing for subsequent steps (Ion XpressTM Barcode Adapters Kit; Life Technologies). After purification with AMPure XP beads (Beckman Coulter, Krefeld, Germany), the barcoded libraries were quantified with a Qubit® 2.0 Fluorimeter (Life Technologies, Darmstadt, Germany) and normalized for DNA concentration to a final concentration of 20 pmol/l using the Ion Library EqualizerTM Kit (Life Technologies, Darmstadt, Germany). Equalized barcoded libraries from seven to eight samples at a time were pooled. To clonally amplify the library DNA onto the Ion Sphere Particles (ISPs; Life Technologies, Darmstadt, Germany), the library pool was subjected to emulsion PCR by using an Ion PGM HI-Q View Template Kit on an PGM OneTouch system (Life Technologies, Darmstadt, Germany) following the manufacturer’s protocol.
Sequencing
Enriched ISPs which carried many copies of the same DNA fragment were subjected to sequencing on an Ion 318 Chip to sequence pooled libraries with seven to eight samples. During this process, bases are inferred from light intensity signals, a process commonly referred to as base-calling (Ledergerber and Dessimoz, 2011). The number of combined libraries that can be accommodated in a single sequencing run depends on the size of the chip, the balance of barcoded library concentration, and the coverage required. The high-capacity 318 chip was chosen (instead of the low-capacity 314 or the medium-capacity 316 chip) to obtain a high sequencing depth of coverage for a genomic DNA library with >95% of bases at 30x. Sequencing was performed using the sequencing kit (Ion PGM Hi-Q Sequencing Kit; Life Technologies, Darmstadt, Germany) as per the manufacturer’s instructions with the 200 bp single-end run configuration. This kit contained the most advanced sequencing chemistry available to users of the Ion PGM System (Life Technologies, Darmstadt, Germany).
Data AnalysisBioinformatics Generation of Sequence Information
The raw data (unmapped BAM-files) from the sequencing runs were processed using Torrent Suite Software (Version 5.2.2, Life Technologies, Darmstadt, Germany) to generate read alignments which were filtered by the software into mapped BAM-files using the reference genomic sequence (hg19) of target genes. Variant calling was performed with the Torrent Variant Caller Plugin using as key parameters: minimum allele frequency = 0.15, minimum quality = 10, minimum coverage = 20 and minimum coverage on either strand = 3.
The annotation of called variants was done using the Ion Reporter Software (Version 4.4; Life Technologies, Darmstadt, Germany) for the VCF files that contained the nucleotide reads and the GenomeBrowse® software (Version 2.0.4, Golden Helix, Bozeman, MT, United States) to map the sequences to the reference sequences GRCh37 hg19 (dated February 2009). The SNP and Variation Suite software (Version 8.4.4; Golden Helix, Bozeman, MT, United States) was used for the analysis of sequence quality, coverage and for variant identification.
Based on the observed allelic frequency, the expected number of homozygous and heterozygous carriers of the respective SNP (single nucleotide polymorphism) was calculated using the Hardy-Weinberg equation. Only variants within the Hardy-Weinberg equilibrium as assessed using Fisher’s exact test (Emigh, 1980) were retained. The SNP and Variation Suite software (Version 8.4.4; Golden Helix, Bozeman, MT, United States) was used for the analysis of sequence quality, coverage and for variant identification.
Assay Validation
Method validation was accomplished by means of Sanger sequencing (Sanger and Coulson, 1975; Sanger et al., 1977) in an independent external laboratory (Eurofins Genomics, Ebersberg, Germany). As performed previously with different AmpliSeqTM panels (Kringel et al., 2017) and other genotyping assays (Skarke et al., 2004, 2005), four DNA samples have been chosen randomly from an independent cohort of healthy subjects and sequenced with the current NGS panel. For the detected variant type, single nucleotide polymorphisms from five different genomic regions for which clinical associations have been reported (Table 2), i.e., rs324420 (FAAH), rs333970 (CSF1), rs4986790 (TLR4), rs4633 (COMT), and rs17151558 (RELN) were chosen for external sequencing. Amplification of the respective DNA segments was done using PCR primer pairs (forward, reverse) of (i) 5′-TTTCTTAAAAAGGCCAGCCTCCT-3′ and 5′-AATGACCCAAGATGCAGAGCA-3′ (ii) 5′-GCCTTCAACCCCGGGATGG-3′ and 5′-CTCCGATCCCTGGTGCTCCTC-3′ (iii) 5′-TTTATTGCACAGACTTGCGGGTTC-3′ and 5′-AGCCTTTTGAGAGATTTGAGTTTCA-3′ (iv) 5′-CCTTATCGGCTGGAACGAGTT-3′ and 5′-GTAAGGGCTTTGATGCCTGGT-3′ (v) 5′-GTTATTCCTCTGTAAGCAGCTGCCT-3′ and 5′-TGTTTGTTTTAGATTGTGGTGGGTT-3′. Results of Sanger sequencing were aligned with the genomic sequence and analyzed using Chromas Lite® (Version 2.1.1, Technelysium Pty Ltd, South Brisbane, QLD, Australia) and the GenomeBrowse® (Version 2.0.4, Golden Helix, Bozeman, MT, United States) was used to compare the sequences obtained with NGS or Sanger techniques.
A list of coding human variants in the 77 putative chronic pain genes, found in the present random sample of 72 subjects of Caucasian ethnicity, for which clinical associations have been reported.
Gene
Variant
dbSNP# accession number
Known clinical association
Reference
Pain context
FAAH
1:46870761-SNV
rs324420
Effect of endocannabinoid degradation on pain
Cajanus et al., 2016
FAAH
1:46870761-SNV
rs324420
Cold and heat pain sensitivity
Kim et al., 2006b
CSF1
1:110466338-SNV
rs333970
Rheumatoid arthritis
Solus et al., 2015
NGF
1:115829313-SNV
rs6330
Procedural pain
Ersig et al., 2017
NGF
1:115829313-SNV
rs6330
Susceptibility to migraine
Coskun et al., 2016
IL1B
2:113590966-SNV
rs1143634
Adverse effects in postoperative pain
Somogyi et al., 2016
IL1B
2:113590966-SNV
rs1143634
Low back pain
Feng et al., 2016
SCN9A
2:167099158-SNV
rs6746030
Pain susceptibility in Parkinson disease
Greenbaum et al., 2012
SCN9A
2:167099158-SNV
rs6746030
Congenital insensitivity to pain
Klein et al., 2013
SCN9A
2:167099158-SNV
rs6746030
Basal Pain Sensitivity
Duan et al., 2015
SCN9A
2:167145122-SNV
rs188798505
Altered pain perception
Reimann et al., 2010
DRD3
3:113890815-SNV
rs6280
Acute pain in sickle cell disease
Jhun et al., 2014
DRD3
3:113890815-SNV
rs6280
Higher prevalence of migraine
Hu et al., 2014
ADRB2
5:148206646-SNV
rs1042717
Musculoskeletal pain
Diatchenko et al., 2006
ADRB2
5:148206885-SNV
rs1800888
Migraine
Schurks et al., 2009
ESR1
6:152129077-SNV
rs2077647
Migraine
Schürks et al., 2010
ESR1
6:152129077-SNV
rs2077647
Musculoskeletal pain
Wise et al., 2009
OPRM1
6:154360797-SNV
rs1799971
Pain of various origins
Lötsch et al., 2009c
SOD2
6:160113872-SNV
rs4880
Migraine
Palmirotta et al., 2015
IL6
7:22771039-SNV
rs13306435
Low back pain
Eskola et al., 2010
OPRK1
8:54142157-SNV
rs702764
Neuropathic pain
Garassino et al., 2013
TLR4
9:120475302-SNV
rs4986790
Musculoskeletal pain
Gbbbebura et al., 2017
TH
11:2188238-SNV
rs6357
Widespread Pain
Jhun et al., 2015
TH
11:2190951-SNV
rs6356
Migraine
Corominas et al., 2009
BDNF
11:27679916-SNV
rs6265
Widespread Pain
Ersig et al., 2017
DRD2
11:113283459-SNV
rs6277
Post-surgical pain
Kim et al., 2006a
DRD2
11:113283477-SNV
rs6275
Migraine
Onaya et al., 2013
P2RX7
12:121600253-SNV
rs208294
Cold pain sensitivity
Ide et al., 2014
P2RX7
12:121605355-SNV
rs7958311
Neuropathic pain
Ursu et al., 2014
HTR2A
13:47409034-SNV
rs6314
Migraine susceptibility
Yücel et al., 2016
TRPV1
17:3480447-SNV
rs8065080
Neuropathic pain
Doehring et al., 2011
KCNS1
20:43723627-SNV
rs734784
Neuropathic pain
Doehring et al., 2011
COMT
22:19950235-SNV
rs4633
Postoperative pain
Khalil et al., 2017
COMT
22:19950263-SNV
rs6267
Widespread Pain
Lin et al., 2017
COMT
22:19951271-SNV
rs4680
Altered pain perception
Wang et al., 2015
Other context
CSF1
1:110466466-SNV
rs1058885
Periodontitis
Chen et al., 2014
CSF1
1:110466555-SNV
rs2229165
Carcinogenesis/breast cancer
Savas et al., 2006
NTRK1
1:156846233-SNV
rs6334
Nephropathy
Hahn et al., 2011
NTRK1
1:156848946-SNV
rs6339
Acute myeloid leukemia
Schweinhardt et al., 2008
SCN9A
2:167143050-SNV
rs41268673
Erythromelalgia
Klein et al., 2013
TRPM8
2:234854550-SNV
rs11562975
Hyperresponsiveness in bronchial asthma
Naumov et al., 2015
TRPM8
2:234905078-SNV
rs11563208
Anthropometric parameters
Potapova et al., 2014
DRD3
3:113890789-SNV
rs3732783
Phenotypic traits relevant to anorexia nervosa
Root et al., 2011
KIT
4:55593464-SNV
rs3822214
Cancer risk
Pelletier and Weidhaas, 2010
KIT
4:55602765-SNV
rs3733542
Glandular odontogenic cyst
Siqueira et al., 2017
HTR1A
5:63257483-SNV
rs1799921
Bipolar disorders
Goodyer et al., 2010
ADRB2
5:148206646-SNV
rs1042717
Cognitive dysfunction in opioid-treated patients with cancer
Kurita et al., 2016
DRD1
5:174868840-SNV
rs155417
Alcohol dependence
Hack et al., 2011
HLA-DQB1
6:32629920-SNV
rs41544112
Ulcerative colitis
Achkar et al., 2012
FKBP5
6:35544942-SNV
rs34866878
Clinical response in pediatric acute myeloid leukemia
Mitra et al., 2011
CNR1
6:88853635-SNV
rs1049353
Bone mineral density
Woo et al., 2015
CNR1
6:88853635-SNV
rs1049353
Alcohol dependence
Marcos et al., 2012
CNR1
6:88853635-SNV
rs1049353
Nicotine dependence
Chen et al., 2008
CNR1
6:88853635-SNV
rs1049353
Obesity
Schleinitz et al., 2010
CNR1
6:88853635-SNV
rs1049353
Psychiatric disorders
Hillard et al., 2012
ESR1
6:152129077-SNV
rs2077647
Breast cancer susceptibility
Li et al., 2016
ESR1
6:152129077-SNV
rs2077647
Prostate cancer development
Jurečeková et al., 2015
ESR1
6:152129077-SNV
rs2077647
Osteoporosis
Sonoda et al., 2012
ESR1
6:152129308-SNV
rs746432
Mood disorders
Mill et al., 2008
ESR1
6:152201875-SNV
rs4986934
Endometrial cancer risk
Wedrén et al., 2008
OPRM1
6:154360508-SNV
rs6912029
Irritable bowel syndrome
Camilleri et al., 2014
OPRM1
6:154360797-SNV
rs1799971
Schizophrenia
Serý et al., 2010
OPRM1
6:154414573-SNV
rs562859
Depressive disorder
Garriock et al., 2010
OPRM1
6:154414563-SNV
rs675026
Treatment response for opiate dependence
Al-Eitan et al., 2012
SOD2
6:160113872-SNV
rs4880
Development of type 2 diabetes mellitus
Li et al., 2015
SOD2
6:160113872-SNV
rs4880
Breast cancer susceptibility
Rodrigues et al., 2014
SOD2
6:160113872-SNV
rs4880
Asthma
Yucesoy et al., 2012
ADCY1
7:45703971-SNV
rs1042009
Bipolar disorder
Shi J. et al., 2008
RELN
7:103124207-SNV
rs1062831
Attention deficit hyperactivity disorder
Kwon et al., 2016
RELN
7:103251161-SNV
rs362691
Childhood epilepsy
Dutta et al., 2011
OPRK1
8:54142154-SNV
rs16918875
Susceptibility to addiction
Kumar et al., 2012
TRPV1
8:72948588-SNV
rs13280644
Perception olfactory stimuli
Schütz et al., 2014
TLR4
9:120475602-SNV
rs4986791
Breast cancer susceptibility
Milne et al., 2014
GRIN1
9:140051238-SNV
rs6293
Schizophrenia
Georgi et al., 2007
RET
10:43610119-SNV
rs1799939
Hirschsprung’s disease
Vaclavikova et al., 2014
RET
10:43615094-SNV
rs1800862
Medullary thyroid carcinoma
Ceolin et al., 2012
GFRA1
10:117884950-SNV
rs2245020
Age-related macular degeneration
Schmidt et al., 2006
DRD4
11:637537-Del
rs587776842
Acousticous neurinoma
Nöthen et al., 1994
BDNF
11:27720937-SNV
rs66866077
Irritable bowel syndrome-diarrhea
Camilleri et al., 2014
DRD2
11:113283484-SNV
rs1801028
Neurologic disorders
Doehring et al., 2009
GRIN2B
12:13717508-SNV
rs1806201
Alzheimer’s disease
Andreoli et al., 2014
TRPV4
12:110252547-SNV
rs3742030
Hyponatremia
Tian et al., 2009
P2RX7
12:121592689-SNV
rs17525809
Multiple sclerosis
Oyanguren-Desez et al., 2011
HTR2A
13:47466622-SNV
rs6305
Susceptibility to substance abuse
Herman and Balogh, 2012
LTB4R
14:24785092-SNV
rs34645221
Asthma susceptibility
Tulah et al., 2012
GABRA5
15:27182357-SNV
rs140682
Autism-spectrum disorders
Hogart et al., 2007
GRIN2A
16:9943666-SNV
rs2229193
Hyperactivity disorder
Kim et al., 2017
DLG4
17:7099811-SNV
rs17203281
Schizophrenia
Tsai et al., 2007
SLC6A4
17:28530193-SNV
rs6352
Autism-spectrum disorders
Prasad et al., 2009
NF1
17:29553485-SNV
rs2285892
Neurofibromatosis
Maertens et al., 2007
HCN2
19:607984-SNV
rs3752158
Risk of depression
McIntosh et al., 2012
PRKCG
19:54394965-SNV
rs3745396
Osteosarcoma susceptibility
Lu et al., 2015
PRNP
20:4680251-SNV
rs1799990
Creutzfeldt-Jakob disease
Mead et al., 2009
HRH3
20:60791422-SNV
rs3787430
Risk of chronic heart failure
He et al., 2016
S100B
21:48022230-SNV
rs1051169
Schizophrenia
Liu et al., 2005
The selection is restricted to one or two publications per variant, and it focuses on a pain context corresponding to the main aim of the present NGS gene panel; however, functional variants highlighted in another clinical context are additionally provided in the lower part of the table. #Database of Single Nucleotide Polymorphisms (dbSNP). Bethesda (MD, United States): National Center for Biotechnology Information, National Library of Medicine. Available from: http://www.ncbi.nlm.nih.gov/SNP/ (Sherry et al., 2001).Results
The NGS assay of the proposed set of 77 human genes relevant to persisting pain was established in 72 genomic DNA samples. As applied previously (Kringel et al., 2017), only exons including 25 bases of padding around all targeted coding regions for which the realized read-depths for each nucleotide was higher than 20 were contemplated as successfully analyzed. With this acceptance criterion the whole or almost whole coverage of the relevant sequences was obtained (Table 1; for details on missing variants, see Supplementary Table 3). The NGS sequencing process of the whole patient cohort required ten separate runs, each with samples of n = 7 or n = 8 patients. Coverage statistics were analogous between all runs and matched the scope of accepted quality levels [20–22]. A median of 2.85 ⋅ 106 reads per run was produced. The mean depth was close to 200 reads, the mean read length of called bases resulted in 205 bases and average chip loading was 71% (Figure 1A). To establish a sequencing output with a high density of ISPs on a sequencing chip, the chip loading value should exceed 60% (Life Technologies, Carlsbad, United States). The generated results of all NGS runs matched with the results obtained with Sanger sequencing of random samples (Figure 1B), meaning the accordance of nucleotide sequences between NGS and Sanger sequencing was 100% in all validated samples.
Assay establishment and validation. (A) Pseudo-color image of the Ion 318TM v2 Chip plate showing percent loading across the physical surface. This sequencing run had a 76% loading, which ensures a high Ion Sphere Particles (ISP) density. Every 318 chip contains 11 million wells and the color scale on the right side conduces as a loading indicator. Deep red coloration stays for a 100% loading, which means that every well in this area contains an ISP (templated and non-templated) whereas deep blue coloration implies that the wells in this area are empty. (B) Alignment of a segment of the ion torrent sequence of the COMT gene as a Golden Helix Genome Browse® readout versus the same sequence according to an externally predicted Sanger electropherogram. Highlighted is the COMT variant rs4633 (COMT c.186C>T → p.His62 =) as a heterozygous mutation and a non-mutated wild type. The SNP is part of the functional COMT haplotype comprising rs4633, rs4818 and rs4680, which showed >11-fold difference in expressed enzyme activity and was reported to be associated with different phenotypes of pain sensitivity (Diatchenko et al., 2005).
Following elimination of nucleotides agreeing with the standard human genome sequence GRCh37 g1k (dated February 2009), the result of the NGS consisted of a vector of nucleotide information about the d = 77 genes for each individual DNA sample (Figure 2). This vector had a length equaling the set union of the number of chromosomal positions in which a non-reference nucleotide had been found in any probe of the actual cohort. Specifically, a total of 3,185 genetic variants was found, of which 659 were located in coding parts of the genes, 1,241 were located in introns and 1,285 in the 3′-UTR, 5′-UTR, upstream or downstream regions. The coding variants for which a clinical or phenotypic association have been reported are listed in Table 2 together with an example of each variant. Most of the observed variants were single nucleotide polymorphisms (d = 571) whereas mixed polymorphisms (d = 26), nucleotide insertions (d = 18) or nucleotide deletions (d = 44) were more rarely found.
Mosaic plot representing a contingency table of the types of genetic variants detected by means of the present AmpliSeqTM panel versus the genes included in the assay. The vertical size of the cells is proportional to the number of variants of a particular type; the horizontal size of the cells is proportional to the number of variants found in the respective gene. The location of the variants is indicated at the left of the mosaic plot in letters colored similarly to the respective bars in the mosaic plot. Variants were not found at all possible locations of each gene, which causes the reduction of several bars to dashed lines drawn as placeholders and indicating that at the particular location no variant has been found in the respective gene. The figure has been created using the R software package (version 3.4.2 for Linux; http://CRAN.R-project.org/, R Development Core Team, 2008). UTR: untranslated region. NCExonic: Non-coding exonic.
Discussion
In this report, development and validation of a novel AmpliseqTM NGS assay for the coding regions and boundary parts of d = 77 genes qualifying as candidate modulators of persisting pain is described. The NGS assay produced nucleotide sequences that corresponded, with respect to the selected validation probes, to the results of classical Sanger sequencing. However, the NGS assay substantially reduced the laboratory effort to obtain the genetic information and provides the perquisites to be used in high throughput environments. In particular, the presented NGS assay is convenient for small up to large-scale setups. As mentioned in the methods section, a limitation of the present results applies to the identified genetic variants as only samples from Caucasian women were included. By contrast, the validity of gene selection and assay establishment is unlikely to be reduced by this selection chosen to remain within the financial limits of the present project.
Specifically, as observed previously (Kringel et al., 2017), the comprehensive genetic information and the high throughput are reflected in the assay costs. Specifically, sequencing of the 77 genes in 72 DNA samples required approximately € 18,000 for the AmpliSeqTM custom panel, € 5,500 for library preparation, € 700 for template preparation and € 700 for sequencing. Ten 318 sequencing chips cost around € 7,000 and in addition and basic consumables and laboratory supplies issued approximately € 800. With 7–8 barcoded samples loaded on ten chips, the expense to analyses the gene sequence for a single patient were around € 325. While NGS costs are likely to decrease in the near future (Lohmann and Klein, 2014), present assay establishment was therefore applied in DNA samples planned for future genotype versus phenotype association analysis, which required using DNA from patients of a pain-relevant cohort instead from a true random sample of healthy subjects.
As a result of the present assay development, a set of d = 77 genes was chosen as potentially relevant to persisting pain. The chosen set of genes differs from alternative proposals aiming at similar phenotypes (Mogil, 2012; Zorina-Lichtenwalter et al., 2016). However, when analyzing these alternatives for mutual agreement, only limited overlap could be observed (Figure 3). This emphasizes that the genetic architecture of persisting pain is incompletely understood, and several independent lines of research can be pursued. Of note, the present set showed the largest agreement with a set of d = 539 genes identified empirically as relevant to pain and listed in the PainGenes database (Lacroix-Fralish et al., 2007)6 or recognized as causing human hereditary diseases associated with extreme pain phenotypes (Lötsch et al., 2013; Ultsch et al., 2016). Combining all proposals into a large panel was not an option due to the technical limitations of the IonTorrent restricting the panel size to 500 kb (pipeline version 5.6.2); therefore, further genes would need to be addressed in separate panels.
Venn diagram (Venn, 1880) visualizing the intersections between the presently proposed set of human genes involved in modulating the risk or the clinical course of persisting pain (“Current set,” green frame), and two alternative proposals [“Mogil” (Mogil, 2012), blue frame and “Zorina-Lichtenwalter” (Zorina-Lichtenwalter et al., 2016), violet frame]. In addition, a set of d = 539 genes identified empirically as relevant to pain and either listed in the PainGenes database (http://www.jbldesign.com/jmogil/enter.html, Lacroix-Fralish et al., 2007) or added because recognized as causing human hereditary diseases associated with extreme pain phenotypes, found to be regulated in chronic pain in at least three studies including human association studies, or being targets of novel analgesics. The number of shared genes between data sets is numerically shown in the respective intersections of the Venn diagram. The figure has been created using the R software package (version 3.4.2 for Linux; http://CRAN.R-project.org/, R Development Core Team, 2008) with the particular package “Vennerable” (Swinton J., https://r-forge.r-project.org/R/?group_id=474).
In the present study sample, selected with a certain bias by using, as explained above for cost saving, clinical samples from only women and only Caucasians, a total of 659 genetic coding variants were found. Regardless of the sample preselection, 105 clinical associations (Table 2) could be queried for the observed variants from openly obtainable data sources comprising (i) the Online Mendelian Inheritance in Man (OMIM®) database7, (ii) the NCBI gene index database8, the GeneCards database9 [27] and the “1000 Genomes Browser”10 (all accessed in December 2017). The observation of functional variants in the present cohort preselected for the absence of pain persistence is plausible as (i) variants can exert protective effects against chronic pain and (ii) most genetic variants identified so far exert only small effects on pain and the individual result of their functional modulations depends on their combined effects or from the sum of positive and negative effects on pain perception (Lötsch et al., 2009a).
The selection of genes (Table 1) relied on empirical evidence of their involvement in pain. For subset #1 (d = 34), this had been shown for 33 genes in the original paper (Ultsch et al., 2016). As the hypothesis that persisting pain displays systemic features of learning and of neuronal plasticity (Mansour et al., 2014) could be substantiated at a computational functional genomics level, the further gene (PTPRZ1, protein tyrosine phosphatase Z 1) can also be regarded as supported by prior knowledge to be included in the present set. The subset comprised, for example, genes associated with the mesolimbic dopaminergic system, i.e., DRD1, DRD2, DRD3, which code for dopamine receptors, and TH, which is the coding gene for the tyrosine hydroxylase, a metabolic restricting enzyme in dopaminergic pathways, which have been implicated in promoting chronic back pain (Hagelberg et al., 2003, 2004; Jaaskelainen et al., 2014; Martikainen et al., 2015). Further 14 genes were involved in the circadian rhythm recognized as a modulatory factor in various pain conditions such as arthritis (Haus et al., 2012; Gibbs and Ray, 2013) and neuropathic pain (Gilron and Ghasemlou, 2014). The subset further included three NMDA receptor genes (GRIN1, GRIN2A, and GRIN2B) known to be major players in a number of essential physiological functions including neuroplasticity (Coyle and Tsai, 2004). In addition, metabotropic glutamate receptors (mGluR) have been implemented in several chronic pain conditions. One subtype, mGluR5, coded by GRM5, is of particular interest in the context of pain conditions as recent studies showed a pro-nociceptive role of mGluR5 in models of chronic pain (Walker et al., 2001; Crock et al., 2012). Furthermore, genes associated with histaminergic signaling such as HRH3 have been implicated in pain transmission (Hough and Rice, 2011) and analgesia (Huang et al., 2007).
The second subset of genes relied on a new PubMed search rather than on a previously published and hypothesis-based selection of candidate genes. A computational functional genomics analysis of this subset (details not shown) suggested its involvement in (i) immune processes and (ii) nitric oxide signaling. The genes annotated to the GO term “immune system process” included interleukin (IL1B, IL4, IL6, IL10) (Dinarello, 1994; Choi and Reiser, 1998; Mocellin et al., 2004; Nemeth et al., 2004) and histocompatibility complex related (HLA-B) genes (Dupont and Ceppellini, 1989), which have been shown to be involved in immunological mechanisms of pain (Sato et al., 2002; de Rooij et al., 2009). This is also supported by published evidence for the further genes in this list, such as, TNF (Vassalli, 1992; Franchimont et al., 1999), GCH1 (Schott et al., 1993) and P2RX7 (Chen and Brosnan, 2006). The second major process group emerging from the functional genomics analysis of the key evidence for genetic modulation of clinical chronic pain was nitric oxide signaling, in particular metabolic processes, summarized in this context under the GO term “reactive oxygen species metabolic process” which includes the genes IL6 (Deakin et al., 1995), TNF (Deakin et al., 1995; Katusic et al., 1998), ESR1 (Clapauch et al., 2014), IL10 (Cattaruzza et al., 2003), GCH1 (Katusic et al., 1998; Zhang et al., 2007), IL1B (Katusic et al., 1998), IL4 (Coccia et al., 2000), P2RX7 (Gendron et al., 2003), SOD2 (Fridovich, 1978). Furthermore, catecholamines including noradrenaline, adrenaline and dopamine have multiple functions in the brain and spinal cord including pain perception and processing (D’Mello and Dickenson, 2008). Catechol-O-methyltransferase, encoded by the COMT gene, is one of several enzymes that degrade dopamine, noradrenaline and adrenaline and has become one of the most frequently addressed genes in pain research (Nackley et al., 2006).
Finally, subset #3 (d = 30) consists of genes repeatedly shown to play a role in the genetic modulation of persisting pain in humans or, by contrast, included a few novel items only recently published in the context of pain. This included members of the transient receptor potential (TRP) family (TRPA1, TRPM8, TRPV4) that are expressed at nociceptors and which are well established players in the perception of pain via their excitation by chemical, thermal or mechanical stimuli (Clapham, 2003). This similarly applies to the opioidergic system represented by the inclusion of the genes coding for the major opioid receptors (OPRM1, OPRK1 OPRD1), which have been associated with variations in pain or opioid response in various settings (Lötsch and Geisslinger, 2005). The most important of this group, the μ-opioid receptor encoded by the OPRM1 gene, carriers several variants of which the 118 A>G (rs1799971) has been studied most extensively since the early description of its association with a functional phenotype in humans (Lötsch et al., 2002).
Almost half of the present sets of genes were chosen based on a computational functional genomics analysis that attributed persisting pain to GO processes of “learning or memory” and “nervous system development” (Ultsch et al., 2016) as likely to reflect systemic features of persisting pain. This implied a functional bias and therefore, the present set of d = 77 genes (Figure 4) was analyzed whether this bias prevailed when comparing it with the alternative sets of human genes proposed to modulate persisting pain (Mogil, 2012; Zorina-Lichtenwalter et al., 2016). As applied previously (Lippmann et al., 2018), the biological roles of the set of d = 77 genes were queried from the Gene Ontology knowledgebase (GO)11 (Ashburner et al., 2000) where the knowledge about the biological processes, the molecular functions and the cellular components of genes is formulated using a controlled and clearly defined vocabulary of GO terms. Particular biological roles of the set of d = 77 genes, among all human genes, were analyzed by means of over-representation analysis (ORA). This compared the occurrence of the particular GO terms associated with the present set of genes with their expected occurrence by chance (Backes et al., 2007). In contrast to enrichment analysis, any quantitative criteria such as gene expression values are disregarded (Backes et al., 2007). The analyses were performed using our R library “dbtORA” (Lippmann et al., 2018)12 on the R software environment (version 3.4.2 for Linux; R Development Core Team, 2008)13.
Top–down representation of the annotations (GO terms) representing the taxonomy of the functional differences between the set of d = 77 genes included in the proposed NGS panel of persisting pain and two alternative proposals of genes modulating persisting pain in humans (Mogil, 2012; Zorina-Lichtenwalter et al., 2016). The figure represents the results of an over-representation analysis of the present set of d = 77 genes against the reference comprising the set intersection of the alternative gene lists. A p-value threshold of 0.01 and Bonferroni α-correction were applied. Significant terms are shown as colored circles with the number of member genes, the number of expected genes by change and the significance of the deviation of the observed from the expected number of genes indicated (yellow = headline, red = significant term, blue = significant term located as a leave at the end of a taxonomy in the polyhierarchy). The graphical representation follows the standard of the GO knowledgebase, where GO terms are related to each other by “is-a,” “part-of,” and “regulates” relationships forming a polyhierarchy organized in a directed acyclic graph (DAG, Thulasiraman and Swamy, 1992). The figure has been created using our R library “dbtORA” (https://github.com/IME-TMP-FFM/dbtORA, Lippmann et al., 2018) on the R software package (version 3.4.2 for Linux; http://CRAN.R-project.org/, R Development Core Team, 2008) and the freely available graph visualization software GraphViz (http://www.graphviz.org, Gansner and North, 2000).
Surprisingly, the results of this analysis indicated that the functional bias of the present gene set toward “learning or memory” (GO:0007611) and “nervous system development” (GO:0007399) was not maintained against the alternative gene sets. Instead, a few more general GO terms such as “behavior” (“single organism behavior,” GO:0044708), or “response to organic cyclic compound” (GO:0014070) and response to alkaloid (GO:0043279), which could be identified as morphine and cocaine when repeating the analysis with a less conservative α-correction (further details not shown), were overrepresented, as well as the pain specific term “sensory perception of pain” (GO:0019233). A possible explanation that the selection bias of the present gene set was not maintained when comparing it with alternative proposals is that the two biological processes, “learning or memory” and “nervous system development,” reflect indeed an important biological function of persisting pain and even when choosing candidate genes without having these processes in mind as for the alternative gene sets, they are nevertheless included. This may be regarded as support for the present gene set as suitable candidates for future association studies with persisting pain phenotypes.
Although the present gene set has been assembled with a focus of a relevance to pain, many of its members have pharmacological implications. Specifically, 58 of the 77 genes (75%) have been chosen as targets of analgesics, approved or under current clinical development (Table 3). Moreover, several of the genes in the present NGS panel have been implicated in pharmacogenetic modulations of drug effects (Table 4). Possibly the most widely studied gene in analgesic research is OPRM1 because coding for the primary target of opioids (Peiro et al., 2016). Several polymorphisms have been described in OPRM1, among which the best characterized may be rs1799971 (OPRM1 118A>G) that leads to an asparagine to aspartate substitution at the extracellular terminal of the receptor protein (Bond et al., 1998). May studies have addressed this variant (for reviews, see Walter et al., 2013; Somogyi et al., 2015). Summarizing its effects, the variant is associated with decreased receptor expression and signaling efficiency (Oertel et al., 2012) which leads to reproducibly reduced pharmacodynamic effects in human experimental settings while the effect size seems insufficient to be a major factor of opioid response in clinical settings, despite several reports of modulations of opioid demands or side effects. For example, subjects carrying the 118A>G variant were found to have a reduced response to morphine treatment (Hwang et al., 2014), reduced analgesic response to alfentanil (Oertel et al., 2006) and demanded higher doses of morphine for pain relief (Klepstad et al., 2004; Hwang et al., 2014). However, the importance of this variant seems to be comparatively high in patients with an Asian ethnic background, which might be related to the higher allelic frequency as compared to other ethnicities. COMT is a key modulator of dopaminergic neurotransmission and in the signaling response to opioids The Val158Met polymorphism (rs4680) causes an amino acid substitution in the enzyme, which reduced the enzyme active to a forth (Peiro et al., 2016). Carriers of the homozygous Met/Met variant had lower morphine requirements than those with a the wild type COMT (Rakvag et al., 2005). Furthermore, a modulation of the effects of TRPV1 targeting analgesics is supported by observations that intronic TRPV1 variants were associated with insensitivity to capsaicin (Park et al., 2007) while the coding TRPV1 variant rs8065080 was associated with altered responses to experimentally induced pain(Kim et al., 2004). Moreover, gain-of-function mutations in TRPV1 have been associated with increased pain sensitivity (Boukalova et al., 2014), for which TRPV1 antagonists would enable a specific pharmacogenetics-based personalized cure.
Current targeting of the genes included in the proposed NGS panel of persisting pain by novel drugs that are currently under active clinical development and include analgesia as the main clinical target or at least as one of the intended clinical indication.
Gene
Status
Drug
Action
Company
ABHD12
–
–
–
–
ABHD16A
–
–
–
–
ABHD6
Preclinical
Benzylpiperidin methanone
Acylamino-Acid-Releasing Enzyme
Scripps Research Institute
ADCY1
Under Active Development
NB-001
Adenylate Cyclase Inhibitors
Forever Cheer International
ADRB2
Phase II/III
Gencaro
Signal Transduction Modulators
ARCA
BDNF
Phase I
CXB-909
Nerve Growth Factor (NGF) Enhancers
Krenitsky
CACNG2
Preclinical
Hanfangchin
Calcium Channel Blockers
Millenia Hope Kaken
CDK5
Biological Testing
Litvinolin
CDK5/p25 Inhibitors
Hong Kong University
CHRNB2
Biological Testing
Epiboxidine
Nicotinic alpha4beta2 Receptor Agonists
Pfizer
CNR1
Registered
Epidiolex
Cannabinoid Receptor Agonists
InSys Therapeutics
COMT
Clinical
Nitecapone
Catechol-O-Methyl Transferase (COMT) Inhibitors
Orion
CSF1
–
–
–
–
DLG4
Preclinical
AB-125
Protein Inhibitors
Lundbeck University of Copenhagen
DRD1
Phase II/III
Ecopipam
Dopamine D1 Receptor (DRD1) Antagonists
Merck & Co.
DRD2
Phase II/III
Sarizotan hydrochloride
Dopamine D2 Receptor (DRD2) Antagonists
Newron
DRD3
Phase II
Brilaroxazine
D3 Receptor (DRD3) Agonists
Reviva Pharmaceuticals
DRD4
Biological Testing
Mesulergine hydrochloride
Dopamine Receptor Agonists
Novartis
EGR1
Phase II
Brivoligide
EGR1 Expression Inhibitors
Adynxx
ESR1
Phase II
Zindoxifene
Selective Estrogen Receptor Modulators
Evonik
FAAH
Phase I/II
Minerval
Fatty Acid Amide Hydrolase (FAAH) Inhibitors
Scripps Research Institute
FKBP5
Phase II
Barusiban
Oxytocin Receptor Antagonist
Ferring
FOS
Registered
Macrilen
FOS Expression Enhancers
Strongbridge Biopharma
FYN
Phase II
Bafetinib
Fyn Kinase Inhibitors
Nippon Shinyaku
GABRA5
Phase III
Ganaxolone
GABA(A) Receptor Modulators
Marinus Pharmaceuticals
GALR2
Preclinical
NAX-810-2
GAL2 Receptor Ligands
NeuroAdjuvants
GCH1
–
–
–
–
GDNF
Phase II
Edonerpic maleate
Signal Transduction Modulators
Toyama
GFRA1
–
–
–
–
GPR132
–
–
–
–
GRIN1
Phase II
Dimiracetam
Signal Transduction Modulators
Metys Pharmaceuticals
GRIN2A
Phase I
Dexanabinol
NMDA Receptor Antagonists
e-Therapeutics Pharmos
GRIN2B
Phase I
Gacyclidine
NMDA Receptor Antagonists
INSERM
GRM5
Phase II
Mavoglurant
Signal Transduction Modulators
Novartis
HCN2
Clinical
Ivabradine
Adrenoceptor Antagonists
Servier
HLA-DQB1
–
–
–
–
HLA-DRB1
–
–
–
–
HRH3
Phase I
Immethridine
Histalean
Abbott
HTR1A
Phase II
Eltoprazine hydrochloride
5-HT1A Receptor Agonists
Elto Pharma
HTR2A
Phase II
Midomafetamine
5-HT2 Receptor Agonists
Assoc
IL10
Phase II
BT-063
Signal Transduction Modulators Anti-IL-10
Biotest AG
IL1B
Phase III
Resunab
IL-1beta Inhibitors
Corbus
IL1R2
–
–
–
–
IL4
–
–
–
–
IL6
Preclinical
Azintrel
Signal Transduction Modulators Anti-IL-6
Jazz Pharmaceuticals
KCNS1
Preclinical
Crotamine
Voltage-Gated K(V) Channel Blockers
Celtic Biotech
KIT
Phase II
Vatalanib succinate
KIT (C-KIT) Inhibitors
Novartis
LTB4R
Phase II
Coversin
Signal Transduction Modulators
Akari Therapeutics
LTB4R2
Phase II
Coversin
Signal Transduction Modulators
Akari Therapeutics
NF1
–
–
–
–
NGF
Phase III
Tanezumab
Anti-Nerve Growth Factor (NGF)
Pfizer
NTF4
–
–
–
–
NTRK1
Phase II
Danusertib
NTRK1 Inhibitors
Pfizer
OPRD1
Preclinical
Metenkephalin
Delta-Opioid Receptor Agonists
TNI Pharmaceuticals
OPRK1
Phase III
Morphine glucuronide
Opioid Receptor Agonists
PAION
OPRM1
Registered
Naltrexone
mu-Opioid Receptor Antagonists
Pfizer
OXT
Phase II
Barusiban
Oxytocin Receptor Antagonist
Ferring
P2RX7
Preclinical
BIL-06v
Anti-P2RX7
Biosceptre International
PLCB1
Biological Testing
Vinaxanthone
Signal Transduction Modulators
Roche
PRKCG
Phase III
Rydapt
Protein Kinase C (PKC) Inhibitors
Yeda
PRNP
–
–
–
–
PTN
–
–
–
–
PTPRZ1
–
–
–
–
RELN
Preclinical
IAIPs
Serine Protease Inhibitors
ProThera Biologics
RET
Phase II
Danusertib
Ret (RET) Inhibitors
Pfizer
RUNX1
–
–
–
–
S100B
–
–
–
–
SCN9A
Phase III
Priralfinamide
Voltage-Gated Sodium Channel Blockers
Newron
SLC6A4
Phase II
Litoxetine
Signal Transduction Modulators
Sanofi
SOD2
Phase II
Avasopasem manganese
Superoxide Dismutase (SOD) Mimetics
MetaPhore
TH
–
–
–
–
TLR4
Phase II
Eritoran tetrasodium
Toll-Like Receptor 4 (TLR4) Antagonists
Eisai
TNF
Phase III
Givinostat hydrochloride
TNF-alpha Release Inhibitors
Italfarmaco
TRPA1
Phase II
Cannabidivarin
TRPA1 Agonists
GW Pharmaceuticals
TRPM8
Phase II
Cannabidivarin
TRPM8 Antagonists
GW Pharmaceuticals
TRPV1
Phase I/II
Resiniferatoxin
TRPV1 (Vanilloid VR1 Receptor) Agonists
Icos
TRPV4
Phase II
GSK-2798745
TRPV4 Antagonists
GlaxoSmithKline
TSPO
Clinical
[11C]CB-184
Translocator Protein (TSPO) Ligands
Tokyo Metrop Geriatr Hosp Inst Gerontol
The information was queried from the Thomson Reuters Integrity database at https://integrity.thomson-pharma.com on July 11, 2018.
Summary of variants in genes included in the proposed NGS panel of persisting pain, that have been implicated in a pharmacogenetic context to modulate the effects of drugs administered for the treatment of pain or as disease modifying therapeutics in painful disease.
Modulated process
Gene
Variant
Affected drug
Findings
Reference
G protein coupled signaling
COMT
rs4680 (Val158Met)
Morphine
Carriers of val/val and val/met genotype required higher morphine dose compared to carriers of met/met genotype
Reyes-Gibby et al., 2007
DRD2
rs6275
Heroine
Polymorphism is associated with decreased likelihood of headache disorders
Cargnin et al., 2014
DRD4
rs1800955
Heroine
Polymorphism had lower pain threshold versus CC/CT controls
Ho et al., 2008
OPRM1
rs1799971 (A118G)
Various opioids
Tendency toward increased pain in dose-dependent manner with the μ-opioid receptor variant 118G
Lötsch et al., 2009c
OPRK1
rs1051660
Morphine
Patients with the polymorphism and cancer-related pain may require a reduced dose escalation of morphine
Chatti et al., 2017
Neurotransmitters
BDNF
rs6265
Various opioids
Polymorphism is associated with decreased likelihood of headache disorders
Cargnin et al., 2014
HTR2A
rs12584920
Various opioids
Increased likelihood of having chronic widespread pain
Nicholl et al., 2011
Ion Channels
TRPV1
7 intronic SNPs
Capsaicin
TRPV1 polymorphisms had only 50% of the mRNA and protein expression levels of normally sensing subjects
Park et al., 2007
Proinflammatory Cytokines
IL6
rs1800795
Etanercept
Polymorphism is associated with increased response to adalimumab, etanercept or infliximab in people with painful Arthritis
Davila-Fajardo et al., 2014
Other
ESR1
rs2234693
Leflunomide
Polymorphism is associated with increased response to leflunomide in women with painful Arthritis
Dziedziejko et al., 2011
FAAH
rs2295632
Various opioids
Polymorphism is associated with increased risk of Respiratory Insufficiency
Biesiada et al., 2014
TLR4
rs4986790
Methotrexate
Polymorphism associated with increased risk of adverse drug events when treated with folic acid and methotrexate in people with Arthritis
Kooloos et al., 2010
TNF
rs361525
Infliximab
Polymorphism is associated with increased response to infliximab in people with painful Arthritis
Maxwell et al., 2008
The information was derived by literature search and by querying the Pharmacogenetics Research Network/Knowledge base at http://www.pharmgkb.org (accessed in July 2018). Only key or example references are given.Conclusion
The breakthrough in mapping the whole human genome (Lander et al., 2001; Venter et al., 2001) along with genome wide association studies (GWAS) has led to rapid advances in the knowledge of the genetic bases of human diseases (Wellcome Trust Case Control and Consortium, 2007). Genetic research in pain medicine has directed to the recognition of genes in which variants influence pain behavior, post-operative drug requirements, and the temporal developments of pain toward persistence (James, 2013). While many candidate gene association studies have identified multiple genes relevant for pain phenotypes (Fillingim et al., 2008), pain related genetic studies have so far been owned by investigations of a limited number of genes. Roughly ten genes or gene complexes account for over half of the extant findings and several of these candidate gene associations have held up in replication (Mogil, 2012). The selection of variants has been limited and they have been addressed in most studies repeatedly, leading to the perception that genetic research in pain produces often unsatisfactory results (Mogil, 2009). However, this may soon change with the arise of new technologies. In this manuscript, we present a validated NGS assay for a set of 77 genes supported by empirical evidence and computational functional genomics analyses as relevant factors modulating the risk for persisting pain or its clinical picture.
Author Contributions
JL, DK, and EK conceived and designed the experiments. DK performed the experiments. JL and DK analyzed the data and wrote the paper. CL provided methodological expertise and bioinformatical tools. DK and JL interpreted the results. EK and MK provided DNA samples.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding. This work has been funded by the European Union Seventh Framework Programme (FP7/2007 – 2013) under grant agreement no. 602919 (“GLORIA”, EK and JL) and the LandesOffensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz (LOEWE), LOEWE-Zentrum für Translationale Medizin und Pharmakologie (JL). These public funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2018.01008/full#supplementary-material
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