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

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

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

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

10

20

30

40

50

60

70

0 2

1 2 3 4 5

10

20

30

40

50

60

70

0 2KCNV2SCN5AOR51E1JPH2TACR2BDKRB2PTGER4MST1RANO9GPRC5AGPR160P2RX1F2RL1NMUR1CCR9GPR31TRPM5KCNE3KCNK16EPHB2GRM5GLRA2GABARA1GRM3GPM6ATUBB3HTR3ACACNB3KCNA6GRIK1RETTMEM63CGPR158GABARA5KCNC2SLC24A2CACNG8GPR26GRIA2KCNS1JPH3CACNG7FXYD7FKBP1BASIC3P2RX3TRPV2NTRK1TTYH1PTGIRKCNIP3SCN4BKCNAB2SCN10AMRGPREP2RX6KCNT1HCN2SCN1BKCNC4SCN11AGPR149KCNK4KCNG4CHRNA6PROKR1KCNK18HTR3BOR6B3OR13J1GRM4KCNA10SLC17A3HPNSUCNR1

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

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50

60

70

0 2

Pearson’s Correlation

00.51

SUCNR1HPNSLC17A3KCNA10GRM4OR13J1OR6B3HTR3BKCNK18PROKR1CHRNA6KCNG4KCNK4GPR149SCN11AKCNC4SCN1BHCN2KCNT1P2RX6MRGPRESCN10AKCNAB2SCN4BKCNIP3PTGIRTTYH1NTRK1TRPV2P2RX3ASIC3FKBP1BFXYD7CACNG7JPH3KCNS1GRIA2GPR26CACNG8SLC24A2KCNC2GABRA5GPR158TMEM63CRETGRIK1KCNA6CACNB3HTR3ATUBB3GPM6AGRM3GABRA1GLRA2GRM5EPHB2KCNK16KCNE3TRPM5GPR31CCR9NMUR1F2RL1P2RX1GPR160GPRC5AANO9MST1RPTGER4BDKRB2TACR2JPH2OR51E1SCN5AKCNV2

0.5 1 1.5

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30

40

50

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70

0.99 0.995 1

1 2 3 4 5

10

20

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50

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0 2

1 2 3 4 5

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50

60

70

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