1. Gene Search by use of MySQL Background – myself NsGene – DTU satellite Parkinson Disease (Affymetrix GeneChip) Analysis of fetal brain tissue Gene Discovery Use of MySQL Search for a new Growth Factor family

2. Background Thomas Nordahl Petersen Chemist, Ph.D protein Crystallography, University of Copenhagen Computational Scientist, SBI-AT Prediction of protein structure, secondary structure, fold recognition, homology modelling Bioinformatics - Gene discovery, NsGene Devolop novel cell and gene based products for the treatment of neurological diseases.

4. Growth of cells in a capsule matrix The therapeutic protein be released directly in the relevant brain area Safe delivery across the blood-brain-barrier ECT Products ECT for Parkinson’s Disease Michael J. Fox foundation granted US $3 million to support a clinical “proof-of-concept” (May 2004)

5. Identification of novel genes by use of bioinformatics Neublastin (GDNF family – potent neuroprotective effects) Factor Products Scanning the human genome or assembled protein sets for different features of interest

6. Two case studies Parkinson related gene(s) Affymetrix GeneChip experiments Novel factor products Scanning an assembled protein set A new growth factor family

7. Parkinson Disease Degenerative central nervous system (CNS) disorder

8. Parkinson Disease Loss of dopamine producing brain cells

9. Parkinson’s Disease Dopamine from Substantia nigra activates neurons in Striatum/Basal ganglia Important for initiation of movement

10. Cure for Parkinson’s Disease ? Parkinson disease may be cured provided that new dopamine producing cells replace the dead ones. Dopamin producing brain cells from aborted foetuses have been operated into the brain of parkinson patients and ín some cases cured the disease. Brain tissue from approx 6 foetuses were needed. Major ethical problems ! Search for a protein drug is the only valid option

11. Parkinson Disease Dopamine producing cells Dopaminergic neurons can be found in the ventral part of the mesencephalon (VM) from approximately 6 weeks No dopaminergic neurons can be found in the neighbouring dorsal part (DM). Dopaminergic differentiation by use of GeneChips to compare the expression profiles of VM and DM

12. Fetal brain tissue Midbrain mesencephalon VmDm + Dopamine producing cells - Dopamine producing cells Aborted feotus brain tissue – Karolinska hospital Feotus of age 6-10 weeks, 2 cases

13. Midbrain mesencephalon Vm Dm + Dopamine producing cells - Dopamine producing cells RNA purification + amplification Affymetrix genechip analysis Isolate the two samples (Vm/Dm) Dopamine producing cells at the interface ?

14. GenePublisher (program by Steen Knudsen) Scale, normalize the Affymetrix GeneChip experiments A1A2A2B1B2B2P-value 319 315 314 44 48 38 1.26e-07 314 334 327 443 434 444 6.55e-05 1980 1974 1973 1801 1785 1763 6.77e-05 123 123 126 87 88 93 8.01e-05 103 101 104 77 78 73 0.000112 107 107 111 79 77 82 0.000124 128 123 117 189 184 1960.000142 179 179 186 145 147 149 0.000191 78 77 79 86 87 87 0.000202 96 90 93 136 129 138 0.000215

15. Vulcano plot P-value Log2 Fold change

16. Assigning Affymetrix GeneChip probes to a protein sequence ~20.000 probes on each of the A/B Affymetrix chips. The probes are normally not a part of a protein sequence. Affymetrix probe Blast IPI protein sequence Blast inferred Unigene sequence (cDNA) 5’3’

17. Internal database

18. Signal Peptide prediction

19. Conclusion – so far The most up-regulated genes include several ‘known’ genes like dopamine transporter (good positive control) The most interesting genes are the ‘unknowns’ that were up-regulated in Vm. Futher analysis is ongoing.

20. A new growth factor family Criteria ‘Unknown’ family of protein sequences Growth factor like (Cys-Cys, SigP) Data source Assembled protein set/genomic data Search criteria are dynamic Use of MySQL

21. MySQL – a relational database language Data are stored in tables as a ’black box’ Data physically separated from user Language is easy to read and understand Complex search queries Combine data in different tables/databases Result can be obtained in seconds Search criteria can be changed

22. Parsing Blast files (Preparing data for MySQL) # Qname Dname Mlen Alen Qlen % a_id % q_ide-value Qfrom Qto Dlen DfromDto IPI00000001.1 STAU_HUMAN 577 577 577 100.0 100.00.0 1 577 577 1577 IPI00000005.1 RASN_HUMAN 189 189 189 100.0 100.0e-106 1 189 189 1189 IPI00000006.1 RASH_HUMAN 189 189 189 100.0 100.0e-106 1 189 189 1189 IPI00000009.1 RASK_HUMAN 189 189 189 100.0 100.0e-106 1 189 189 1189 IPI00000010.1 RASL_HUMAN 188 188 188 100.0 100.0e-105 1 188 188 1188 IPI00000012.3 ZNT1_MOUSE 86 261 240 33.0 35.81e-32 1 230 503 248500 IPI00000013.1 CSL2_HUMAN 334 334 334 100.0 100.00.0 1 334 334 1334 IPI00000015.2 SFR4_HUMAN 494 494 494 100.0 100.00.0 1 494 494 1494 IPI00000016.1 LMA3_MOUSE 114 145 145 78.6 78.69e-62 1 145 3333 15211665

23. Storing data from blast alignments FieldType query_dbenum('hs_2_18','hs_2_23','affym','mm_1_11','affym_mouse') query_accvarchar(20) target_dbenum('swissp','mm_1_11','sid','sid_mouse’) target_accvarchar(20) align_lensmallint(6) match_lensmallint(6) query_lensmallint(6) perc_align_lenfloat(5,1) perc_query_lenfloat(5,1) minus_ln_efloat(6,2) query_fromsmallint(6) query_tosmallint(6) target_fromsmallint(6) target_tosmallint(6) target_lenint(11)

24. MySQl example SELECT a.query_db, a.query_acc, a.target_db, a.target_acc, a.perc_align_len, a.minus_ln_e, b.target_db, b.target_acc, c.cleavage_site FROM blastdb AS a, blastdb AS b, signalp AS c WHERE a.query_db='hs_2_23' AND a.target_db = 'mm_1_11' AND a.target_acc != 'NULL' AND b.target_db='swissp' AND a.query_acc=b.query_acc AND b.target_acc='NULL' AND c.query_db='hs_2_23' AND c.query_acc = a.query_acc AND c.cleavage_site >= 15 AND c.cleavage_site<=45;

25. Output from MySQL query_dbquery_acctarget_dbtarget_accperc_align_lenminus_ln_etarget_dbtarget_acccleavage_site hs_2_23IPI00000111mm_1_11IPI00223686 48.6999.00swisspNULL35 hs_2_23IPI00000183mm_1_11IPI00108107 74.0999.00swisspNULL26 hs_2_23IPI00000381mm_1_11IPI00128682 78.5206.13swisspNULL21 hs_2_23IPI00001001mm_1_11IPI00221700 91.7173.39swisspNULL45 hs_2_23IPI00001443mm_1_11IPI00221913 60.017.73swisspNULL30 hs_2_23IPI00001578mm_1_11IPI00122466 88.8207.93swisspNULL38 hs_2_23IPI00001719mm_1_11IPI00120961 83.152.27swisspNULL44 hs_2_23IPI00001952mm_1_11IPI00225921 76.0999.00swisspNULL44 hs_2_23IPI00002173mm_1_11IPI00112960 85.4999.00swisspNULL42

26. 47306

27. Clustering of protein sequences Tribe-mcl 47306 sequences 13130 clusters Store in MySQL 1) Cluster size ACPGICSKSCCPF LTPALCSRTCCPY 2) Cys-Cys 230 2 16 (3)

28. Conserved Cys-Cys Many growth factor families have their own specific Cys- pattern,TGF-  family. Transforming growth factor- is a multifunctional peptide that controls proliferation, differentiation and other functions in many cell types. Search for Cys-pattern without any a priori knowledge

29. Search criteria Family cluster size > 1 No SwissProt homologues Cys count > 4 Signal Peptide Mouse homologue/orthologue 48 Families Manual inspection of alignments (- isoforms) Upload remaining sequences to internal database

30. Internal database

32. Outcome from Gene Search Family including 5 sequences Predicted as growth factors/hormones Some of them expressed in the brain

33. Outcome from Gene Search Family including 5 sequences Predicted as growth factors/hormones Some of them expressed in the brain Family including 2 sequences Labwork at NsGene

34. Tissue-specific expression 100 bp ladder Universal ref Whole brain Heart Kidney Liver Lung Placenta Prostate Salivary gland Skeletal muscle Spleen Testis 100 bp ladder Thymus Thyroid gland Trachea Uterus Colon Small Intestine Spinal Cord Fetal Liver Fetal brain Pancreas Neurosphere ctrl dH2O 100 bp ladder

35. Patent applications Existing patent claims ? Product claim Medical use Futher function analysis Knock-out mouse experiments Clinical tests

36. Gene discovery