mirna seq of mouse brain regions

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1 mirna seq of mouse brain regions Iiris Hovatta, PhD University of Helsinki Research Program of Molecular Neurology and Department of Medical Genetics, Faculty of Medicine i National Network of Molecular Medicine, Institute of Molecular Medicine Finland FIMM National Institute for Health and Welfare Department of Mental Health and Substance Abuse Services Edvard Munch: Anxiety,

2 Anxiety disorders share increased anxiety but have distinct symptoms as well Panic disorder d Obsessive compulsive disorder Post traumatic stress disorder Social phobia Specific phobias Generalized anxiety it disorderd Treated with SSRI, MAOI, benzodiazepines, beta-blockers 2

3 Identification of candidate genes by gene expression profiling in inbred mouse strains Behavioral testing + Gene expression profiling with microarrays Genes with expression levels that correlate with behavior 6 mouse strains: 129S6/SvEvTac A/J C3H/HeJ C57BL/6J DBA/2J FVB/NJ 7 brain regions: Amygdala Bed nucleus of the stria terminalis Cingulate cortex Hippocampus Hypothalamus Periaqueductal gray Pituitary gland Functional studies Hovatta et al. Nature 2005 Mechanisms that regulate susceptibility genes? mirna From 3

4 micro RNAs are abundant regulators of gene expression Small ~22 nt long single stranded non coding RNA molecules that bind to mrna Comprise 1 5% of all genes Regulate ~40 50% of mammalian genes 1 mirna can have hundreds of target mrnas SNPs within mirna target sequences can alter the binding of the mirna and thereby affect the regulation of its target gene mirnasinvolved involved in many neurobiological diseases mirnas in neuropsychiatric disease Tourette s syndrome a polymorphism in the 3 UTR of SLITRK1 alters the binding site for mir 189 leading to a more stringent repression of SLITRK1 expression (Abelson & al., Science 2005) Autism spectrum disorders 23 mirnas differentially expressed in the cerebellum of patients vs controls (Abu Elneel & al., Neurogenet 2008) Schizophrenia 16 mirnas differentially expressed in the PFC of patients vs controls (Perkins & al., Genome Biol 2007) Upregulation of mir 181b in cortical gray matter in patients vs controls (Beveridge & al. Hum Mol Genet 2008) A genetic association to SNPs close to mir 206 and mir198 in patients vs controls (DNA) (Hansen & al. PlosOne 2008) Alcoholism Alcohol leads to upregulation of mir 9 and reorganization of BK channel mrna pool > change is BK channel pool > development of tolerance (Pietrzykowski & al. Neuron 2008) 4

5 Why mirna seq? Advantages Compared to microarrays, no need to worry about SNPs under probe sequences Quantitative over 6 orders of magnitude; basically no background! Detects everything that is expressed Can get information about isomirs as well Challenges Large quantity of starting material (10 µg totrna) Methods still under development lab: lots of PAGE gels, adapter dimers bioinfo: reads that map to several locations, normalization Data storage Price High quality RNA for Illumina library preparation Starting material is total RNA that contains all RNAs (1 10 µg) RNA extraction methods suitable for mirna extraction Trizol based methods High yield & purity Specific kits for small RNAs (max 100 mg tissue = 100 µg RNA): mirvana (AB) mirneasy (Qiagen) Check your RNA quality by BioAnalyzer Nano kit Small RNA kit Two sample prep kits from Illumina: v. 1.0 wet lab time ~4 day v. 1.5 wet lab time ~1 day 5

6 Illumina library preparation workflow Modification to the Illumina protocol: Addition of indexes to the 5 adapter sequence to increase throughput Otherwise follow with Illumina protocol with own reagents (cost reduction) Addition of synthetic spike in control oligos from step 1 to help in normalization Without Index: Barcoding for mirna Seq Barcoding and Illumina GAII (24 samples instead of 8) 3 TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAG-miRNA-AGCATACGGCAGAAGACGAAC 5 5 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATC-miRNA-TCGTATGCCGTCTTCTGCTTG 3 Index 3 TTAGGC: Sequencing primer 3 TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAGAATCCG-miRNA-AGCATACGGCAGAAGACGAAC 5 5 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATCTTAGGC-miRNA-TCGTATGCCGTCTTCTGCTTG 3 index Index 7 CAGATC: Sequencing primer 3 TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAGGTCTAG-miRNA-AGCATACGGCAGAAGACGAAC 5 5 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATCCAGATC-miRNA-TCGTATGCCGTCTTCTGCTTG 3 index Sequencing primer Index 11 GGCTAC: 3 TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAGGTCTAG-miRNA-AGCATACGGCAGAAGACGAAC 5 5 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATCGGCTAC-miRNA-TCGTATGCCGTCTTCTGCTTG 3 Sequencing primer index 6

7 Sequencing 10 µl of 10nM mirna Library is needed for cluster generation (1 day). Sequencing by Solexa Genome Analyzer II (3 days) Read lenght 35bp or 50bp (pirnas ~30nt) 8 samples/flow Cell (Indexing 24). Lane 1 Lane 2 Lane 3 Lane 4 Lane 5 Lane 6 Lane 7 Lane8 FC Ind3 FC Ind7 FC Ind11 FC Pool HI Ind3 HI Ind7 HI Ind11 HI Pool Pilot 3 C57BL/6J (CR) Sample Prep Kit 1.0 Indexing 36 bp read length 7

8 Bioinformatics pipeline 1. Pre filtered reads after base calling; Total # of reads (avrg of 6 repl.) FC: 6,223,794 HP: 5,652, Data sorting according to the indexes; # of reads with indexes FC : 5, (89%) HP: 5,246,449 (93%) 3. Data trimming by removing the index sequence, 3 adapter sequence, mitochondrial and ribosomalrnas # of trimmed reads FC: 847,951 HP: 2,627, Alignment mirbase v (Bowtie) # of aligned reads FC: 630,435 HP: 2,276,631 # of known mirnas (out of 579 M. musculus) FC: 372 HP: Alignment against mouse genome mmu 8 (Bowtie) # of uniquely mapped FC: 427,526 (50%) HP: 1,235,866 (47%) # of reads mapping to multiple locations FC: 353,551 (42%) HP: 1, (50%) # reads not matching the genome FC: 66,874 (8%) HP: 73,272 (3%) 6. Counting of the reads = digital expression profiling, normalization, analysis of differential expression Let 7c is the most abundant mirna in FC and HP Expressed mirnas normalized to a total number of trimmed reads mmumir 137 7g mmu letmmumir 132 Rest 26a mmumir b 5p 9 mmu let 7d mmu let 7e mmu let 7f mmu let 7a mmumir 29a Frontal cortex mmu let 7b mmu let 7c b 5p 26a let 7g mmumir 137 mmu let 7d 9 mmu let 7e 99a mmu let 7f Hippocampus Rest 128 mmumir 29a mmu let 7a mmu let 7c mmu let 7b 8

9 Let 7 family constitutes about half of expressed mirnas in hp and fc Frontal Cortex: Let7 Family Hippocampus: Let7 Family mmu let 7a mmu let 7b Rest mmu let 7a mmu let 7b Rest mmu let 7c mmu let 7c mmu let 7i mmu let 7f mmu let 7d mmu let 7e mmu let 7i mmu let 7g mmulet 7g mmu let 7f mmu let 7e mmu let 7d Differentially expressed mirnas smirnadb as part of the mirzdatabase: Bayesian model Posterior probability that the frequency of a mirna in the total mirna population differs between two sets of samples Log-likelihood ratio log(p same /P diff ) of two models Hausser et al. 2009: MirZ: an integrated microrna expression atlas and target prediction resource. Nucleic Acid Research: 37. Comparison of relative mirna expression patterns between frontal cortex and hippocampus: 421 mirna with log values varying from to mirnas with negative log value 9

10 Differentially expressed mirnas Let7/miR-98 family up in hippocampus (except 7d and 7e) mir-200 family (a, b, c, mir-141 and mir-429) up in frontal cortex Frontal Cortex Hippocampus Number mirna Name Direction Pool1 Pool1 Pool2 Pool2 count frequency count frequency Log(Psame/Pdiff): mmu let 7f mmu let 7c a a mmu let 7g a mmu let 7a mmu let 7i d b mmu let 7d a mmu let 7b c Let 7 and mir 200 families studied extensively in cancer and stem cells ME Peter Cell Cycle

11 isomirs are frequent Counts Sequence Chr Position Strand Mismatch 39550TTATTGCTTAAGAATACGCGTAG TTATTGCTTAAGAATACGCGT TTATTGCTTAAGAATACGCGTA TTATTGCTTAAGAATACGCGTAA :G>A 2156TTATTGCTTAAGAATACGCGTAC :G>C 1532TTATTGCTTAAGAATACGCGTAT :G>T 1050TTATTGCTTAAGAATACGCGTAGT TTATTGCTTAAGAATACGCGTAGA :T>A 528TTATTGCTTAAGAATGCGCGTA :A>G 454TTATTGATTAAGAATACGCGTAG :C>A 433TTATTGATTAAGAATACGCGT :C>A 384TTATTGCTTAAGAATACGCGGAG :T>G 339TTATTGATTAAGAATACGCGTA :C>A 319TTATTGCTTAAGAATGCGCGT :A>G 249TTATTGCTTAAGAATACGCGGA :T>G 216TTATTGCTTAAGAATATGCGTAG :C>T 208TTATTGCTTAAGAATACGCGA :T>A 178TTATTGCTTAAGAATACGCGTT :A>T 155TTATTGCTTAAGAATACGTGTAG :C>T 149TTATTGCTTAGGAATACGCGTAG :A>G 140TTATTGCTTAAGAATATGCGTA :C>T 137TTATTGCTTAAGAATGCGCGTAG :A>G 130TTATTGCTTAGGAATACGCGTA :A>G 129TTATTGCTTAAGAATACGCGTCG :A>C 114TTATTGCTTAGGAATACGCGT :A>G 106TTATCGCTTAAGAATACGCGTAG :T>C 104TTATCGCTTAAGAATACGCGT :T>C 137TTATTGCTTAAGAATACGCGTAG Not sequencing errors (see also Morin 2008 Genome Res.) Variability in Dicer and Drosha cleavage Not available in mirbase Often modifications in 3 end (21, 22 and 23) Also modifications in seed area (5 end) that is important for target binding Biological function? Next steps & challenges frontal cortex hippocampus anxiety hypothalamus Identification of expression differences of known mirnas & correlation to anxiety phenotype Identification of novel mirnas and isomirs & correlation to anxiety phenotype Functional verification of candidate mirnas in vivo by lentivirus mediated gene transfer (overexpression & silencing by an overexpression of an antagomir) followed by behavioral testing Modeling of mrna / mirna / SNP interactions 11

12 Conclusions mirnas are abundant regulators of gene expression mirnas might regulate anxiety associated genes and have been shown to be involved in the etiology of many neuropsychiatric diseases mirna seq allows digital expression profiling and identification of novel mirnas Indexing increases throughput and reduces costs of mirna seq Remaining challenges of mirna seq include quantity of starting material and bioinformatic data analysis (normalization and modelling of mirna/mrna interaction) Several mirnas are differentially expressed between mouse frontal cortex and hippocampus (let 7 and mir 200 families) Concomitant analysis of mirna and mrna patterns may lead to a network oriented view of disease University of Helsinki Research Program of Molecular Neurology Jonas Donner Juuso Juhila Laura Kananen Helena Kilpinen Kaisa Manninen Pia Rasinkangas Tessa Sipilä Institute for Molecular Medicine, FIMM Pekka Ellonen Sari Hannula Daniel Nicorici Institute of Biotechnology Petri Auvinen Dario Greco Lars Paulin Acknowledgments Funding: Academy of Finland (NEURO program and academy research fellowship) Biocentrum Helsinki University of Helsinki / FIMM Sigrid Jusélius Foundation Jalmari and Rauha Ahokas Foundation Yrjö Jahnsson Foundation L'Oréal Finland UNESCO Women in Science Program Yrjö and Tuulikki Ilvonen Foundation Peter and Patricia Gruber Foundation (Rosalind Franklin Young Investigator Award) Helsinki Biomedical Graduate School 12