identifying novel drug targets for pain pain neurobiology ......[8] tillu, dipti v., et al....
TRANSCRIPT
Other Transmembrane Domain7TM_1 Transmembrane Domain (Rhodopsin-like)
Low Complexity Region
1 40 203 312 1 37 265 310
Expression Pathway of Proteins (GPCR)
Transcription
Translation
DNA(Exons & Introns)
RNA (Exons)
GPCR Gene Coding Region
G-Protein Coupled Receptor (GPCR)
DNA from Reference Genome (Human or
Mouse)
Extracted RNA from Tissues or Cell Lines(Dorsal Root Ganglion)
GPCR Gene Coding Region
Sequencing RNA Back to Genome DNA
RNA-Sequencing Pathway
Andrew Torck1, Ji-Young Kim3, Theodore Price1, Pradipta Ray2
1.School of Behavioral and Brain Sciences, The University of Texas at Dallas 2. School of Natural Sciences and Mathematics, The University of Texas at Dallas 3. Department of Pharmacology, University of Arizona
Methods
.
Results
S
Identifying novel drug targets for Pain using Machine Learning approaches
Pain Neurobiology Research Group
Michael Zhang Laboratory
AbstractChronic neuropathic pain is characterized as a dysfunction of the nervous system due to nerve injury or tissue damage. It has become an increasingly more common pathological state in humans, with a prevalence of 30% in the general population (up to 7% being attributed to neuropathy)1. Such damage often causes nerves to send incorrect pain signals to the Central Nervous System resulting in unrelenting chronic pain and a severely lessened quality of life. Neuropathic pain is hard to treat, with only 11 - 14% of those a�ected achieving partial relief through treatment2. We aim to characterize the molecular biology of chronic neuropathic pain by performing high-throughput RNA sequencing5,9 for pro�ling gene expression in human pain-sensing tissues3 (speci�cally Dorsal Root Ganglia / DRG). Using statistical, entropy-based measures and hierarchical clustering, we identify DRG-speci�c pain receptors by contrasting DRG gene expression against other excitable tissues. A similar pro�ling for Mus musculus4,7, combined with sophisticated evolutionary analysis6,10, then allows us to pinpoint drug targets compatible with mouse models. Functional analytic8 studies can then be performed with the aim of taking the �rst chronic pain drug to market.
Citations
Acknowledgements I would like to thank Dr. Michael Q. Zhang at the UT Dallas Center for Systems Biology for allowing me to collaborate with his lab, Paul Miller at AnaBios for the sourcing of human DRG samples, and to Beth Keithly who represents the UT Dallas O�ce of Undergraduate Research and the University of Texas STARS Program for funding this research.
[7] Stamatoyannopoulos, John A., et al. "An encyclopedia of mouse DNA elements (Mouse ENCODE)." Genome biology 13.8 (2012): 418.
[8] Tillu, Dipti V., et al. "Resveratrol engages AMPK to attenuate ERK and mTOR signaling in sensory neurons and inhibits incision-induced acute and chronic pain." Mol Pain 8.5 (2012): 8069-8.
[9] Trapnell, Cole, et al. "Di�erential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cu�inks." Nature protocols 7.3 (2012): 562-578.
[10] Xie, Wei, et al. "Epigenomic analysis of multilineage di�erentiation of human embryonic stem cells." Cell 153.5 (2013): 1134-1148.
SRAToolkit (http://www.ncbi.nlm.nih.gov/books/NBK158900/) & SamTools (http://samtools.sourceforge.net/) were used for NGS data processing
SVG artwork under attribution from eng.wikipedia.org & www.�aticon.com [CC-BY-3.0, �aticon basic, and �aticon select licenses, attribution required]Poster shared under license CC-BY-3.0 [https://creativecommons.org/licenses/by/3.0/us/]
[1] Bouhassira, Didier, et al. "Prevalence of chronic pain with neuropathic characteristics in the general population." Pain 136.3 (2008): 380-387.
[2] Dworkin, Robert H., et al. "Pharmacologic management of neuropathic pain: evidence-based recommendations." Pain 132.3 (2007): 237-251.
[3] ENCODE Project Consortium. "An integrated encyclopedia of DNA elements in the human genome." Nature 489.7414 (2012): 57-74.
[4] Gerhold, Kristin A., et al. "The star-nosed mole reveals clues to the molecular basis of mammalian touch." PloS one 8.1 (2013): e55001.
[5] Harrow, Jennifer, et al. "GENCODE: producing a reference annotation for ENCODE." Genome Biol 7.Suppl 1 (2006): S4.
[6] Pruitt, Kim D., et al. "The consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the human and mouse genomes." Genome research 19.7 (2009): 1316-1323.
Modbase
MRGPREProtein Sequence AlignmentTransmembrane Segments
chr11:3,246,181-3,256,47610,296 bp
ChimpGorillaMouse
RatElephant
Tasmanian_devilRock_pigeon
Zebra�sh
RNA Sequencingresults
Read mappingto reference
transcriptome /genome
Gene
FPK
M
Gene
FPK
M
Gene
FPK
M
Gene
FPK
M
Transcriptomeprofiling in sensitive
tissues most prone todrug side effects and
neural tissue
Transcriptquantification in DRG
estimation andnormalization across
genes & expts
Cloud computing
Differential expression& gene clustering using
information theoreticmeans : DRG specifictranscript identified
Identify DRGspecific genesenriched in thedisease state
?
Translational studiesand functional
analysis inmouse/other models
Evolutionary studiesto identify appropiate
model species fordisease state in
humans
Primary A�erent NeuronsPosterior Grey Horn
Anterior Grey HornPrimary E�erent Neurons
Dorsal Root Ganglion
RNA extraction,purification, libraryselection of Poly A+
RNA
Creation ofcomplementary DNA
from RNA
DRG Tissue SampleDonor
RNA Sequencingresults
RNA preservedin RNA Later
RNA-seqPERFORMED
BY Active Motif,Inc
SAMPLE SOURCING& EXTRACTIONBY Anabios, Inc
IntronExonReference Genome
Heart
Cerebellum
DRG
Gene
Clustering of tissue-speci�c human
genes with log transformed [relative
abundance] FPKM heatmap and
corresponding orthologs in mouse
Candidate Receptor
QEDVAFNLIILSLTEGLGLGGLLGNGAVLWLLSSNVYRNPFAIYLLDVACADLIFLGCHM QGEMAFNLTILSLTELLSLGGLLGNGVALWLLNQNVYRNPFSIYLLDVACADLIFLCCHM * **** ****** * ******** **** ******* ************** *** VAIVPDLLQGRLDFPGFVQTSLATLRFFCYIVGLSLLAAVSVEQCLAALFPAWYSCRRPR VAIIPELLQDQLNFPEFVHISLTMLRFFCYIVGLSLLAAISTEQCLATLFPAWYLCRRPR *** * *** * ** ** ** *************** * ***** ****** ***** HLTTCVCALTWALCLLLHLLLSGACTQFFGEPSRHLCRTLWLVAAVLLALLCCTMCGASL YLTTCVCALIWVLCLLLDLLLSGACTQFFGAPSYHLCDMLWLVVAVLLAALCCTMCVTSL ******** * ***** ************ ** *** **** ***** ****** ** MLLLRVERGPQRPPPRGFPGLILLTVLLFLFCGLPFGIYWLSRNLLWYIPHYFYHFSFLM LLLLRVERGPERHQPRGFPTLVLLAVLLFLFCGLPFGIFWLSKNLSWHIPLYFYHFSFFM ********* * ***** * ** ************* *** ** * ** ******* * AAVHCAAKPVVYFCLGSAQGRRL--PLRLVLQRALGDEAELGAVRETSRRGLVDI ASVHSAAKPAIYFFLGSTPGQRFREPLRLVLQRALGDEAELGAGREASQGGLVDM * ** **** ** *** * * ****************** ** * ****
00.51
SUCNR1HPNSLC17A3KCNA10GRM4OR13J1OR6B3HTR3BKCNK18PROKR1CHRNA6KCNG4KCNK4GPR149SCN11AKCNC4SCN1BHCN2KCNT1P2RX6MRGPRESCN10AKCNAB2SCN4BKCNIP3PTGIRTTYH1NTRK1TRPV2P2RX3ASIC3FKBP1BFXYD7CACNG7JPH3KCNS1GRIA2GPR26CACNG8SLC24A2KCNC2GABRA5GPR158TMEM63CRETGRIK1KCNA6CACNB3HTR3ATUBB3GPM6AGRM3GABRA1GLRA2GRM5EPHB2KCNK16KCNE3TRPM5GPR31CCR9NMUR1F2RL1P2RX1GPR160GPRC5AANO9MST1RPTGER4BDKRB2TACR2JPH2OR51E1SCN5AKCNV2
0.5 1 1.5
10
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70
0.99 0.995 1
1 2 3 4 5
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00.51
SUCNR1HPNSLC17A3KCNA10GRM4OR13J1OR6B3HTR3BKCNK18PROKR1CHRNA6KCNG4KCNK4GPR149SCN11AKCNC4SCN1BHCN2KCNT1P2RX6MRGPRESCN10AKCNAB2SCN4BKCNIP3PTGIRTTYH1NTRK1TRPV2P2RX3ASIC3FKBP1BFXYD7CACNG7JPH3KCNS1GRIA2GPR26CACNG8SLC24A2KCNC2GABRA5GPR158TMEM63CRETGRIK1KCNA6CACNB3HTR3ATUBB3GPM6AGRM3GABRA1GLRA2GRM5EPHB2KCNK16KCNE3TRPM5GPR31CCR9NMUR1F2RL1P2RX1GPR160GPRC5AANO9MST1RPTGER4BDKRB2TACR2JPH2OR51E1SCN5AKCNV2
0.5 1 1.5
10
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60
70
0.99 0.995 1
1 2 3 4 5
10
20
30
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50
60
70
0 2
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0 2
00.51
SUCNR1HPNSLC17A3KCNA10GRM4OR13J1OR6B3HTR3BKCNK18PROKR1CHRNA6KCNG4KCNK4GPR149SCN11AKCNC4SCN1BHCN2KCNT1P2RX6MRGPRESCN10AKCNAB2SCN4BKCNIP3PTGIRTTYH1NTRK1TRPV2P2RX3ASIC3FKBP1BFXYD7CACNG7JPH3KCNS1GRIA2GPR26CACNG8SLC24A2KCNC2GABRA5GPR158TMEM63CRETGRIK1KCNA6CACNB3HTR3ATUBB3GPM6AGRM3GABRA1GLRA2GRM5EPHB2KCNK16KCNE3TRPM5GPR31CCR9NMUR1F2RL1P2RX1GPR160GPRC5AANO9MST1RPTGER4BDKRB2TACR2JPH2OR51E1SCN5AKCNV2
0.5 1 1.5
10
20
30
40
50
60
70
0.99 0.995 1
1 2 3 4 5
10
20
30
40
50
60
70
0 2
1 2 3 4 5
10
20
30
40
50
60
70
0 2
00.51
SUCNR1HPNSLC17A3KCNA10GRM4OR13J1OR6B3HTR3BKCNK18PROKR1CHRNA6KCNG4KCNK4GPR149SCN11AKCNC4SCN1BHCN2KCNT1P2RX6MRGPRESCN10AKCNAB2SCN4BKCNIP3PTGIRTTYH1NTRK1TRPV2P2RX3ASIC3FKBP1BFXYD7CACNG7JPH3KCNS1GRIA2GPR26CACNG8SLC24A2KCNC2GABRA5GPR158TMEM63CRETGRIK1KCNA6CACNB3HTR3ATUBB3GPM6AGRM3GABRA1GLRA2GRM5EPHB2KCNK16KCNE3TRPM5GPR31CCR9NMUR1F2RL1P2RX1GPR160GPRC5AANO9MST1RPTGER4BDKRB2TACR2JPH2OR51E1SCN5AKCNV2
0.5 1 1.5
10
20
30
40
50
60
70
0.99 0.995 1
1 2 3 4 5
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20
30
40
50
60
70
0 2
1 2 3 4 5
10
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0 2KCNV2SCN5AOR51E1JPH2TACR2BDKRB2PTGER4MST1RANO9GPRC5AGPR160P2RX1F2RL1NMUR1CCR9GPR31TRPM5KCNE3KCNK16EPHB2GRM5GLRA2GABARA1GRM3GPM6ATUBB3HTR3ACACNB3KCNA6GRIK1RETTMEM63CGPR158GABARA5KCNC2SLC24A2CACNG8GPR26GRIA2KCNS1JPH3CACNG7FXYD7FKBP1BASIC3P2RX3TRPV2NTRK1TTYH1PTGIRKCNIP3SCN4BKCNAB2SCN10AMRGPREP2RX6KCNT1HCN2SCN1BKCNC4SCN11AGPR149KCNK4KCNG4CHRNA6PROKR1KCNK18HTR3BOR6B3OR13J1GRM4KCNA10SLC17A3HPNSUCNR1
00.51
SUCNR1HPNSLC17A3KCNA10GRM4OR13J1OR6B3HTR3BKCNK18PROKR1CHRNA6KCNG4KCNK4GPR149SCN11AKCNC4SCN1BHCN2KCNT1P2RX6MRGPRESCN10AKCNAB2SCN4BKCNIP3PTGIRTTYH1NTRK1TRPV2P2RX3ASIC3FKBP1BFXYD7CACNG7JPH3KCNS1GRIA2GPR26CACNG8SLC24A2KCNC2GABRA5GPR158TMEM63CRETGRIK1KCNA6CACNB3HTR3ATUBB3GPM6AGRM3GABRA1GLRA2GRM5EPHB2KCNK16KCNE3TRPM5GPR31CCR9NMUR1F2RL1P2RX1GPR160GPRC5AANO9MST1RPTGER4BDKRB2TACR2JPH2OR51E1SCN5AKCNV2
0.5 1 1.5
10
20
30
40
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70
0.99 0.995 1
1 2 3 4 5
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Pearson’s Correlation
00.51
SUCNR1HPNSLC17A3KCNA10GRM4OR13J1OR6B3HTR3BKCNK18PROKR1CHRNA6KCNG4KCNK4GPR149SCN11AKCNC4SCN1BHCN2KCNT1P2RX6MRGPRESCN10AKCNAB2SCN4BKCNIP3PTGIRTTYH1NTRK1TRPV2P2RX3ASIC3FKBP1BFXYD7CACNG7JPH3KCNS1GRIA2GPR26CACNG8SLC24A2KCNC2GABRA5GPR158TMEM63CRETGRIK1KCNA6CACNB3HTR3ATUBB3GPM6AGRM3GABRA1GLRA2GRM5EPHB2KCNK16KCNE3TRPM5GPR31CCR9NMUR1F2RL1P2RX1GPR160GPRC5AANO9MST1RPTGER4BDKRB2TACR2JPH2OR51E1SCN5AKCNV2
0.5 1 1.5
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0.99 0.995 1
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0 2
Log[FPKM]
Known DRG-Speci�c Receptors
DRG DRGHeart HeartWhBr WhBrLiver LiverSmll Int Smll Int
Gene
Heart
DRG
Liver
Gene
Heart
DRG
Liver