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Identifying Novel Drug Targets for Pain Using Machine Learning Approaches

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Pain Neurobiology Identifying novel drug targets for Pain Research Group Michael Zhang using Machine Learning approaches Laboratory 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 Results KCNV2KCNV2 0.990.99 KCNV20.9950.9950.99 0.995 1 í 110 2 íí í 0 2 00 22 íí 00 22 Abstract Methods SCN5ASCN5A SCN5A OR51E1 OR51E1 KCNV2KCNV2 OR51E1 0.99 0.995 1 í 0 2 í 0 2 Cloud computing JPH2JPH2 SCN5ASCN5A JPH2 TACR2TACR2 OR51E1OR51E1 TACR2 Clustering of BDKRB2BDKRB2 JPH2JPH2 BDKRB2 Chronic neuropathic pain is characterized as a dysfunction of the nervous system due to nerve injury or PTGER4PTGER4 TACR2TACR2 PTGER4 tissue-specic human MST1RMST1R BDKRB2BDKRB2 MST1R ANO9ANO9 PTGER4PTGER4 ANO9 GPRC5AGPRC5A MST1RMST1R GPRC5A genes with log tissue damage. It has become an increasingly more common pathological state in humans, with a GPR160GPR160 ANO9ANO9 GPR160 P2RX1 1010 GPRC5AGPRC5A 10 10 10 10 1010 P2RX1 GPR160GPR160 P2RX1 transformed [relative 1 F2RL1F2RL1 P2RX1P2RX1 F2RL110 10 10 prevalence of 30% in the general population (up to 7% being attributed to neuropathy) . Such damage NMUR1NMUR1 F2RL1F2RL1 NMUR1 CCR9CCR9 NMUR1NMUR1 CCR9 abundance] FPKM GPR31GPR31 CCR9CCR9 GPR31 TRPM5TRPM5 GPR31GPR31 TRPM5 heatmap and often causes nerves to send incorrect pain signals to the Central Nervous System resulting in unrelenting Exon Intron KCNE3KCNE3 TRPM5TRPM5 KCNE3 KCNK16KCNK16 KCNE3KCNE3 KCNK16 Reference Genome EPHB2EPHB2 KCNK16KCNK16 EPHB2 corresponding orthologs 2020 EPHB2EPHB2 20 20 20 2020 GRM5GRM5 GRM520 20 20 chronic pain and a severely lessened quality of life. Neuropathic pain is hard to treat, with only 11 - 14% of Read mapping GLRA2GLRA2 GRM5GRM5 GLRA2 20 in mouse GABRA1GABRA1 GLRA2GLRA2 GABRA1 GRM3 GABRA1GABARA1 2 RNA Sequencing to reference GRM3 GRM3GRM3 GRM3 those aected achieving partial relief through treatment . We aim to characterize the molecular biology of GPM6AGPM6A GPM6AGPM6A GPM6A results transcriptome / TUBB3TUBB3 TUBB3TUBB3 TUBB3 HTR3AHTR3A HTR3AHTR3A HTR3A 5,9 genome CACNB3CACNB3 CACNB3CACNB3 CACNB3 chronic neuropathic pain by performing high-throughput RNA sequencing for proling gene expression KCNA6KCNA6 KCNA6KCNA6 KCNA6 GRIK1GRIK1 3030 GRIK1GRIK1 GRIK13030 30 30 30 30 303030 3 RETRET RETRET RET TMEM63CTMEM63C TMEM63CTMEM63C TMEM63C in human pain-sensing tissues (specically Dorsal Root Ganglia / DRG). Using statistical, entropy-based GPR158GPR158 GPR158GPR158 GPR158 GABRA5 GABRA5GABARA5 GABRA5 KCNC2KCNC2 GABRA5 KCNC2KCNC2 SLC24A2SLC24A2 KCNC2 measures and hierarchical clustering, we identify DRG-specic pain receptors by contrasting DRG gene SLC24A2SLC24A2 CACNG8CACNG8 SLC24A2 CACNG8CACNG8 GPR26GPR26 CACNG8 GPR26GPR26 GRIA2GRIA2 GPR2640 40 40 Known DRG-Specic Receptors 4,7 GRIA2GRIA2 4040 KCNS1KCNS1 GRIA2 40 40 40 40 4040 expression against other excitable tissues. A similar proling for Mus musculus , combined with KCNS1KCNS1 JPH3JPH3 KCNS1 JPH3JPH3 CACNG7CACNG7 JPH3 CACNG7CACNG7 FXYD7FXYD7 CACNG7 6,10 FXYD7FXYD7 FKBP1BFKBP1B FXYD7 sophisticated evolutionary analysis , then allows us to pinpoint drug targets compatible with mouse FKBP1BFKBP1B ASIC3ASIC3 FKBP1B ASIC3ASIC3 P2RX3P2RX3 ASIC3 8 P2RX3P2RX3 TRPV2TRPV2 P2RX3 TRPV2 NTRK1NTRK1 50 50 models. Functional analytic studies can then be performed with the aim of taking the rst chronic pain TRPV2 TTYH1TTYH1 TRPV2 50 NTRK1 NTRK1 NTRK1 5050 PTGIRPTGIR 50 50 50 50 5050 M TTYH1TTYH1 KCNIP3KCNIP3 TTYH1 PTGIRPTGIR SCN4BSCN4B PTGIR M K M KCNIP3KCNIP3 KCNAB2KCNAB2 KCNIP3 drug to market. M SCN4B SCN10ASCN10A SCN4B K SCN4B P K KCNAB2KCNAB2 MRGPREMRGPRE KCNAB2 K F Candidate Receptor P SCN10ASCN10A P2RX6P2RX6 SCN10A P MRGPRE KCNT1KCNT1 P MRGPRE F MRGPRE 60 60 60 Dorsal Root Ganglion F P2RX6P2RX6 HCN2HCN2 P2RX6 F SCN1B KCNT1KCNT1 SCN1B KCNT1 Primary Aerent Neurons Gene HCN2 6060 KCNC4KCNC4 60 60 60 60 6060 HCN2 SCN11ASCN11A HCN2 Posterior Grey Horn SCN1BSCN1B GPR149GPR149 SCN1B Gene Gene KCNC4KCNC4 KCNK4KCNK4 KCNC4 Gene SCN11ASCN11A KCNG4KCNG4 SCN11A GPR149GPR149 CHRNA6CHRNA6 GPR149 KCNK4KCNK4 PROKR1PROKR1 KCNK4 KCNG4KCNG4 KCNK18KCNK18 KCNG470 70 70 Transcript CHRNA6CHRNA6 HTR3BHTR3B CHRNA6 Transcriptome PROKR1PROKR1 OR6B3OR6B3 PROKR1 quantification in DRG KCNK18KCNK18 7070 OR13J1OR13J1 KCNK18 70 70 70 70 7070 profiling in sensitive HTR3BHTR3B GRM4GRM4 HTR3B Log[FPKM] estimation and KCNA10KCNA10 OR6B3OR6B3 OR6B3 0.5 1 1.5 1 2 3 4 5 tissues most prone to OR13J1 SLC17A3SLC17A3 1 2 3 4 5 normalization across OR13J1 HPNHPN OR13J1 GRM4GRM4 SUCNR1SUCNR1 GRM4 drug side effects and KCNA10KCNA10 KCNA10 genes & expts 0.5 1 1.5 1 2 3 4 5 neural tissue SLC17A3SLC17A31 0.5 0.50 1 SLC17A31.50.5 1 1.5 1 2 3114 5 221 2 3 334 5 44 55 1 2 3KCNV2 0.994 0.9955 1 í 0 2 í 0 2 SAMPLE SOURCING HPNHPN Pearson’sHPN Correlation DRG Heart WhBr Liver Smll Int DRG Heart WhBrSCN5A Liver Smll Int SUCNR1SUCNR1 SUCNR1 OR51E1 JPH2 11 0.50.5 00 1 0.5 0 TACR2 & EXTRACTION BDKRB2 PTGER4 KCNV2 0.99 0.995 1 í 0 2 í 0 2 BY Anabios, Inc SCN5A MST1R Primary Eerent Neurons OR51E1 ANO9 JPH2 GPRC5A Anterior Grey Horn Transmembrane Segments GPR160 Protein Sequence Alignment TACR2 10 10 10 BDKRB2 P2RX1 PTGER4 F2RL1 MST1R NMUR1 ANO9 CCR9 RNA preserved QEDGPR31VAFNLIILSLTEGLGLGGLLGNGAVLWLLSSNVYRNPFAIYLLDVACADLIFLGCHM MRGPRE GPRC5A DRG Tissue Sample GPR160 QGEMAFNLTTRPM5 ILSLTELLSLGGLLGNGVALWLLNQNVYRNPFSIYLLDVACADLIFLCCHM in RNA Later P2RX1 10 10 10 KCNE3 F2RL1 * KCNK16 **** ****** * ******** **** ******* ************** *** Donor EPHB2 NMUR1 20 20 CCR9 GRM5 20 GPR31 VAIGLRA2VPDLLQGRLDFPGFVQTSLATLRFFCYIVGLSLLAAVSVEQCLAALFPAWYSCRRPR TRPM5 GABRA1 KCNE3 1 40 203 312 1 37 265 310 VAIIPELLGRM3 QDQLNFPEFVHISLTMLRFFCYIVGLSLLAAISTEQCLATLFPAWYLCRRPR KCNK16 GPM6A EPHB2 ***TUBB3 * *** * ** ** ** *************** * ***** ****** ***** GRM5 20 20 20 HTR3A Expression Pathway of Proteins (GPCR) RNA-Sequencing Pathway GLRA2 CACNB3 GABRA1 KCNA6 GRM3 HLTTGRIK1CVCALTWALCLLLH30 30LLLSGACTQFFGEPSRHLCRT30 LWLVAAVLLALLCCTMCGASL GPM6A YLTTCVCALIWVLCLLLDLLLRET SGACTQFFGAPSYHLCDMLWLVVAVLLAALCCTMCVTSL TUBB3 TMEM63C GPCR Gene Coding Region GPCR Gene Coding Region HTR3A ********GPR158 * ***** ************ ** *** **** ***** ****** ** CACNB3 Other Transmembrane Domain GABRA5 KCNA6 KCNC2 DNA from Reference GRIK1 30 30 30 MLLLRVERGPQRPPPRGFPGSLC24A2 LILLTVLLFLFCGLPFGIYWLSRNLLWYIPHYFYHFSFLM DNA RET 7TM_1 Transmembrane CACNG8 Genome (Human or Modbase TMEM63C LLGPR26LLRVERGPERHQPRGFPTLVLLAVLLFLFCGLPFGIFWLSKNLSWHIPLYFYHFSFFM (Exons & Introns) Mouse) GPR158 Domain (Rhodopsin-like) GRIA2 40 40 40 GABRA5 *********KCNS1 * ***** * ** ************* *** ** * ** ******* * KCNC2 JPH3 Transcription SLC24A2 CACNG7 Sequencing RNA Back to CACNG8 FXYD7 Genome DNA GPR26 AAVHCFKBP1BAAKPVVYFCLGSAQGRRL--PLRLVLQRALGDEAELGAVRETSRRGLVDI Low Complexity Region ASIC3 Extracted RNA from GRIA2 40 40 40 ASVHSAAKPAIYFFLGSTPGQRFREPLRLVLQRALGDEAELGAGREASQGGLVDM KCNS1 P2RX3 RNA Tissues or Cell Lines JPH3 * TRPV2** **** ** *** * * ****************** ** * **** (Exons) (Dorsal Root Ganglion) CACNG7 NTRK1 50 50 50 RNA-seq FXYD7 TTYH1 FKBP1B PTGIR Identify DRG Differential expression ASIC3 KCNIP3 PERFORMED Translation P2RX3 SCN4B TRPV2 KCNAB2 & gene clustering using SCN10A specific genes NTRK1 50 50 BY Active Motif, TTYH1 50 MRGPRE PTGIR P2RX6 enriched in the information theoretic KCNIP3 KCNT1 HCN2 60 60 60 Inc G-Protein Coupled SCN4B KCNAB2 SCN1B Receptor (GPCR) disease state means : DRG specific SCN10A KCNC4 MRGPRE SCN11A transcript identified P2RX6 Heart GPR149 KCNT1 KCNK4 HCN2 60 60 60 KCNG4 SCN1B CHRNA6 PROKR1 KCNC4 70 70 DRG SCN11A DRG KCNK18 70 GPR149 HTR3B KCNK4 OR6B3 KCNG4 Cerebellum OR13J1 CHRNA6 GRM4 KCNA10 PROKR1 0.5 1 1.5 1 2 3 4 5 KCNK18 70 70 70 SLC17A3 1 2 3 4 5 HTR3B Heart HPN Heart OR6B3 SUCNR1 OR13J1 GRM4 1 0.5 0 KCNA10 SLC17A3 0.5 1 1.5 1 2 3 4 5 1 2 3 4 5 DRG S HPN Liver SUCNR1 Liver 1 0.5 0 Gene Gene Gene RNA extraction, Creation of purification, library complementary DNA RNA Sequencing selection of Poly A+ from RNA results RNA chr11:3,246,181-3,256,476 10,296 bp Citations ? 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    A Dominant-Negative Mutation in the TRESK Potassium Channel Is Linked to Familial Migraine with Aura

    LETTERS A dominant-negative mutation in the TRESK potassium channel is linked to familial migraine with aura Ronald G Lafrenière1,2,13, M Zameel Cader3,4,13, Jean-François Poulin2, Isabelle Andres-Enguix5, Maryse Simoneau2, Namrata Gupta2, Karine Boisvert2, François Lafrenière2, Shannon McLaughlan2, Marie-Pierre Dubé6, Martin M Marcinkiewicz7, Sreeram Ramagopalan8, Olaf Ansorge9, Bernard Brais1, Jorge Sequeiros10, Jose Maria Pereira-Monteiro11, Lyn R Griffiths12, Stephen J Tucker5, George Ebers8 & Guy A Rouleau1,2 Migraine with aura is a common, debilitating, recurrent of the trigeminal ganglion afferents5 and may lead to central sensiti- headache disorder associated with transient and reversible focal zation. Considerable insights into the pathogenesis of migraine have neurological symptoms1. A role has been suggested for the come from the investigation of the rare autosomal dominant subtype two-pore domain (K2P) potassium channel, TWIK-related spinal of migraine with aura, familial hemiplegic migraine. Three suscepti- cord potassium channel (TRESK, encoded by KCNK18), in pain bility genes (CACNA1A, ATP1A2 and SCNA1), which encode either pathways and general anaesthesia2. We therefore examined ion channels or ion transport proteins, have so far been identified6–8, whether TRESK is involved in migraine by screening the and it is likely that mutations in these genes reduce the threshold for KCNK18 gene in subjects diagnosed with migraine. Here we CSD9,10. However, such mutations are not found in typical migraine report a frameshift mutation, F139WfsX24, which segregates with aura, suggesting that other ion channels may be involved. perfectly with typical migraine with aura in a large pedigree. K2P channels are expressed throughout the central nervous system We also identified prominent TRESK expression in migraine- and have a key role in controlling neuronal resting membrane potential salient areas such as the trigeminal ganglion.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
  • Distinguishing Pleiotropy from Linked QTL Between Milk Production Traits

    Distinguishing Pleiotropy from Linked QTL Between Milk Production Traits

    Cai et al. Genet Sel Evol (2020) 52:19 https://doi.org/10.1186/s12711-020-00538-6 Genetics Selection Evolution RESEARCH ARTICLE Open Access Distinguishing pleiotropy from linked QTL between milk production traits and mastitis resistance in Nordic Holstein cattle Zexi Cai1*†, Magdalena Dusza2†, Bernt Guldbrandtsen1, Mogens Sandø Lund1 and Goutam Sahana1 Abstract Background: Production and health traits are central in cattle breeding. Advances in next-generation sequencing technologies and genotype imputation have increased the resolution of gene mapping based on genome-wide association studies (GWAS). Thus, numerous candidate genes that afect milk yield, milk composition, and mastitis resistance in dairy cattle are reported in the literature. Efect-bearing variants often afect multiple traits. Because the detection of overlapping quantitative trait loci (QTL) regions from single-trait GWAS is too inaccurate and subjective, multi-trait analysis is a better approach to detect pleiotropic efects of variants in candidate genes. However, large sample sizes are required to achieve sufcient power. Multi-trait meta-analysis is one approach to deal with this prob- lem. Thus, we performed two multi-trait meta-analyses, one for three milk production traits (milk yield, protein yield and fat yield), and one for milk yield and mastitis resistance. Results: For highly correlated traits, the power to detect pleiotropy was increased by multi-trait meta-analysis com- pared with the subjective assessment of overlapping of single-trait QTL confdence intervals. Pleiotropic efects of lead single nucleotide polymorphisms (SNPs) that were detected from the multi-trait meta-analysis were confrmed by bivariate association analysis. The previously reported pleiotropic efects of variants within the DGAT1 and MGST1 genes on three milk production traits, and pleiotropic efects of variants in GHR on milk yield and fat yield were con- frmed.
  • Glycine Receptors Control the Generation of Projection Neurons in the Developing Cerebral Cortex

    Glycine Receptors Control the Generation of Projection Neurons in the Developing Cerebral Cortex

    Cell Death and Differentiation (2014) 21, 1696–1708 & 2014 Macmillan Publishers Limited All rights reserved 1350-9047/14 www.nature.com/cdd Glycine receptors control the generation of projection neurons in the developing cerebral cortex A Avila*,1,2,3, PM Vidal1, S Tielens2,3, G Morelli1,2,3, S Laguesse2,3, RJ Harvey5, J-M Rigo*,1,6 and L Nguyen*,2,3,4,6 The development of the cerebral cortex requires coordinated regulation of proliferation, specification, migration and differentiation of cortical progenitors into functionally integrated neurons. The completion of the neurogenic program requires a dynamic interplay between cell intrinsic regulators and extrinsic cues, such as growth factor and neurotransmitters. We previously demonstrated a role for extrasynaptic glycine receptors (GlyRs) containing the a2 subunit in cerebral cortical neurogenesis, revealing that endogenous GlyR activation promotes interneuron migration in the developing cortical wall. The proliferative compartment of the cortex comprises apical progenitors that give birth to neurons directly or indirectly through the generation of basal progenitors, which serve as amplification step to generate the bulk of cortical neurons. The present work shows that genetic inactivation of Glra2, the gene coding the a2 subunit of GlyRs, disrupts dorsal cortical progenitor homeostasis with an impaired capability of apical progenitors to generate basal progenitors. This defect results in an overall reduction of projection neurons that settle in upper or deep layers of the cerebral cortex. Overall, the depletion of cortical neurons observed in Glra2-knockout embryos leads to moderate microcephaly in newborn Glra2-knockout mice. Taken together, our findings support a contribution of GlyR a2 to early processes in cerebral cortical neurogenesis that are required later for the proper development of cortical circuits.