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miRNA‐seq of mouse brain regionsq g

Iiris Hovatta, PhD

University of HelsinkiResearch Program of Molecular Neurology and Department of Medical Genetics,

F lt f M di iFaculty of Medicine National Network of Molecular Medicine,

Institute of Molecular Medicine Finland FIMM

National Institute for Health and WelfareDepartment of Mental Health and Substance Abuse Services

Edvard Munch: Anxiety, 1984

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Anxiety disorders share increased anxiety but have distinct symptoms as well

P i di d• Panic disorder 

• Obsessive‐compulsive disorder

• Post‐traumatic stress disorder

• Social phobia

• Specific phobias

G li d i t di d• Generalized anxiety disorder

Treated with SSRI, MAOI, benzodiazepines, beta-blockers

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Identification of candidate genes by gene expression profiling in inbred mouse strains

Behavioral testing+

6 mouse strains:129S6/SvEvTac

Gene expression profiling with microarrays

Genes with expression

A/JC3H/HeJC57BL/6JDBA/2JFVB/NJ

7 brain regions:Amygdala

Bed nucleus of the stria terminalisCi l t tlevels that correlate

with behavior

Functional studiesHovatta et al. Nature 2005

Cingulate cortexHippocampusHypothalamus

Periaqueductal grayPituitary gland

Mechanisms that regulate susceptibility genes?

miRNA

From www.uta.edu/.../henry/classnotes/2457/index.htm.

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micro‐RNAs are abundant regulators of gene expression

• Small ~22 nt long single stranded non‐coding RNA molecules g g gthat 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– miRNAs involved in many neurobiological diseasesmiRNAs 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)g p p ( )

• 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)

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Why miRNA‐seq?

Advantages• Compared to microarrays, no 

Challenges• Large quantity of starting 

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 

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

gisomiRs as well

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

• Two sample prep kits from Illumina: • v. 1.0 wet lab time ~4 day• v. 1.5 wet lab time ~1 day

y q y y yNano‐kitSmall RNA‐kit

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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

3’TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAG-miRNA-AGCATACGGCAGAAGACGAAC’55’AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATC-miRNA-TCGTATGCCGTCTTCTGCTTG’3

Without Index:

Barcoding for miRNA‐Seq• Barcoding and Illumina GAII (24 samples instead of 8)

Sequencing primer

3’TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAGAATCCG-miRNA-AGCATACGGCAGAAGACGAAC’55’AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATCTTAGGC-miRNA-TCGTATGCCGTCTTCTGCTTG’3

Sequencing primer

Index 3 TTAGGC:

3’TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAGGTCTAG-miRNA-AGCATACGGCAGAAGACGAAC’5

5’AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATCCAGATC-miRNA-TCGTATGCCGTCTTCTGCTTG’3

Index 7 CAGATC:

index

i dSequencing primer

3’TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAGGTCTAG-miRNA-AGCATACGGCAGAAGACGAAC’55’AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATCGGCTAC-miRNA-TCGTATGCCGTCTTCTGCTTG’3

Sequencing primer

Index 11 GGCTAC:

index

index

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Sequencing• 10 µl of 10nM miRNA Library is needed for cluster generation (1 day)day).

• Sequencing by Solexa Genome Analyzer II (3 days)

• Read lenght 35bp or 50b ( iRNA 30 t)50bp (piRNAs ~30nt)

• 8 samples/Flow Cell (Indexing 24).

Lane 1 Lane 7Lane 6Lane 2 Lane 3 Lane 4 Lane 5 Lane8

FCInd3

FCPool

FCInd11

FCInd7

HIInd3

HIPool

HIInd11

HIInd7

Pilot 3

C57BL/6J (CR)

• Sample Prep Kit 1.0• Indexing• 36 bp read length

Pilot 3

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Bioinformatics pipeline1. Pre‐filtered reads after base calling; 

• Total # of reads (avrg of 6 repl.) FC: 6,223,794 HP: 5,652,753

2. Data sorting according to the indexes;2. Data sorting according to the indexes;• # of reads with indexes FC : 5,554153 (89%) HP: 5,246,449 (93%)

3. Data trimming by removing the index sequence, 3’ adapter sequence , mitochondrial and ribosomal RNAs

• # of trimmed reads FC: 847,951 HP: 2,627,101

4. Alignment miRBase v. 14.0 (Bowtie)• # of aligned reads FC: 630,435 HP: 2,276,631• # of known miRNAs (out of 579 M. musculus) FC: 372 HP: 410

5. 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: 1,317963 (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 miRNAin FC and HP

• Expressed miRNAs normalized to a total number of trimmed reads

mmu‐let‐7c

mmu‐miR‐9

mmu‐miR‐125b‐5p

mmu‐miR‐124

mmu‐miR‐26a

mmu‐miR‐132

mmu‐miR‐137

mmu‐let‐7g

Rest

Frontal cortex

mmu‐let‐7c

mmu‐let‐7d

mmu‐miR‐137

mmu‐let‐7g

mmu‐miR‐26a

mmu‐miR‐125b‐5p

mmu‐miR‐103

mmu‐miR‐99a

Rest

Hippocampus

mmu‐miR‐128

mmu‐let‐7b

mmu‐miR‐29a

mmu‐let‐7a

mmu‐let‐7f

mmu‐let‐7e

mmu‐let‐7d9

mmu‐let‐7b

mmu‐let‐7a

mmu‐miR‐29a

mmu‐miR‐128

mmu‐let‐7fmmu‐let‐7e

mmu‐miR‐9

mmu‐let‐7d

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Let‐7 family constitutes about half of expressed miRNAs in hp and fc

mmu‐let‐7a

mmu‐let‐7b

Rest

Frontal Cortex: Let7‐Family

mmu‐let‐7a

mmu‐let‐7bRest

Hippocampus: Let7‐Family

mmu‐let‐7c

mmu‐let‐7d

mmu‐let‐7emmu‐let‐7f

mmu‐let‐7g

mmu‐let‐7i

mmu‐let‐7c

mmu‐let‐7dmmu‐let‐7e

mmu‐let‐7fmmu‐let‐7g

mmu‐let‐7i

Differentially expressed miRNAs

• smiRNAdb as part of the miRZDatabase: http://www.mirz.unibas.ch/cloningprofiles/

• 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(Psame/Pdiff) 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 iRNA ith l l i f 6 73119 t 16584 7– 421 miRNA with log values varying from 6.73119 to -16584.7– 85 miRNAs with negative log value

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Differentially expressed miRNAs

Number miRNA Name DirectionPool1count

Pool1frequency

Pool2count

Pool2frequency

Log(Psame/Pdiff):

1 mmu‐miR‐128 ↑ 69995 0.181 151656 0.079 ‐16584.72 mmu‐let‐7f ↓ 15551 0.041 140629 0.073 ‐3104.123 mmu‐miR‐1961 ↑ 1215 0.0040 16 0.0010 ‐2086.13

Frontal Cortex Hippocampus

•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

↑4 mmu‐let‐7c ↓ 95097 0.246 566092 0.294 ‐1866.455 mmu‐miR‐200a ↑ 834 0.0030 58 0.0010 ‐1280.876 mmu‐miR‐137 ↓ 2055 0.0060 26669 0.014 ‐1162.217 mmu‐miR‐132 ↑ 3502 0.01 6292 0.0040 ‐1026.838 mmu‐miR‐99a ↓ 2693 0.0070 27539 0.015 ‐777.1449 mmu‐miR‐429 ↑ 516 0.0020 57 0.0010 ‐739.26710 mmu‐let‐7g ↓ 3938 0.011 33743 0.018 ‐601.98411 mmu‐miR‐26a ↓ 4075 0.011 33176 0.018 ‐499.80112 mmu‐miR‐124 ↑ 5329 0.014 15825 0.0090 ‐484.17413 mmu‐let‐7a ↓ 26371 0.069 159328 0.083 ‐474.79214 mmu‐miR‐9 ↓ 7639 0.02 52890 0.028 ‐397.54415 mmu‐miR‐383 ↑ 616 0.0020 714 0.0010 ‐307.74616 mmu‐let‐7i ↓ 2293 0.0060 19068 0.01 ‐303.91217 mmu‐miR‐30d ↑ 1993 0.0060 4989 0.0030 ‐295.8618 mmu‐miR‐200b ↑ 228 0.0010 44 0.0010 ‐289.11219 mmu‐let‐7d ↑ 11064 0.029 43468 0.023 ‐234.93420 mmu‐miR‐30a ↑ 2364 0.0070 6952 0.0040 ‐217.13421 mmu‐miR‐218 ↓ 314 0.0010 4349 0.0030 ‐200.82422 mmu‐let‐7b ↑ 42256 0.11 189541 0.099 ‐197.60423 mmu‐miR‐1928 ↑ 129 0.0010 17 0.0010 ‐174.07724 mmu‐miR‐103 ↓ 3798 0.01 25891 0.014 ‐172.42625 mmu‐miR‐1224 ↑ 137 0.0010 74 0.0010 ‐114.95526 mmu‐miR‐1971 ↑ 114 0.0010 48 0.0010 ‐108.04327 mmu‐miR‐98 ↓ 501 0.0020 4769 0.0030 ‐107.23428 mmu‐miR‐200c ↑ 68 0.0010 6 0.0010 ‐94.269329 mmu‐miR‐674 ↑ 238 0.0010 364 0.0010 ‐81.275730 mmu‐miR‐191 ↓ 145 0.0010 1895 0.0010 ‐76.6774

Let‐7 and miR‐200 families• studied extensively in cancer and stem cells

ME Peter Cell Cycle 2009

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isomiRs are frequentCounts Sequence Chr Position Strand Mismatch39550TTATTGCTTAAGAATACGCGTAG 3 118136818+33699TTATTGCTTAAGAATACGCGT 3 118136818+30289TTATTGCTTAAGAATACGCGTA 3 118136818+5668TTATTGCTTAAGAATACGCGTAA 3 118136818+ 22:G>A2156TTATTGCTTAAGAATACGCGTAC 3 118136818+ 22:G>C1532TTATTGCTTAAGAATACGCGTAT 3 118136818+ 22:G>T

• Not sequencing errors(see also Morin 2008 Genome Res.)

•Variability in Dicer and Drosha1050TTATTGCTTAAGAATACGCGTAGT 3 118136818+631TTATTGCTTAAGAATACGCGTAGA 3 118136818+ 23:T>A528TTATTGCTTAAGAATGCGCGTA 3 118136818+ 15:A>G454TTATTGATTAAGAATACGCGTAG 3 118136818+ 6:C>A433TTATTGATTAAGAATACGCGT 3 118136818+ 6:C>A384TTATTGCTTAAGAATACGCGGAG 3 118136818+ 20:T>G339TTATTGATTAAGAATACGCGTA 3 118136818+ 6:C>A319TTATTGCTTAAGAATGCGCGT 3 118136818+ 15:A>G249TTATTGCTTAAGAATACGCGGA 3 118136818+ 20:T>G216TTATTGCTTAAGAATATGCGTAG 3 118136818+ 16:C>T208TTATTGCTTAAGAATACGCGA 3 118136818+ 20:T>A178TTATTGCTTAAGAATACGCGTT 3 118136818+ 21:A>T155TTATTGCTTAAGAATACGTGTAG 3 118136818+ 18:C>T

•Variability in Dicer and Drosha cleavage

• Not available in miRbase

• Often modifications in 3’ end (21, 22 and 23)

149TTATTGCTTAGGAATACGCGTAG 3 118136818+ 10:A>G140TTATTGCTTAAGAATATGCGTA 3 118136818+ 16:C>T137TTATTGCTTAAGAATGCGCGTAG 3 118136818+ 15:A>G130TTATTGCTTAGGAATACGCGTA 3 118136818+ 10:A>G129TTATTGCTTAAGAATACGCGTCG 3 118136818+ 21:A>C114TTATTGCTTAGGAATACGCGT 3 118136818+ 10:A>G106TTATCGCTTAAGAATACGCGTAG 3 118136818+ 4:T>C104TTATCGCTTAAGAATACGCGT 3 118136818+ 4:T>C

mmu‐miR‐137TTATTGCTTAAGAATACGCGTAG 3 118136818+

• Also modifications in seed area (5’ end) that is important for target binding

• Biological function?

Next steps & challengesfrontal cortex

hippocampus

• Identification of expression differences of known miRNAs & correlation to anxiety phenotype

anxiety

hypothalamus

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

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Conclusions

• miRNAs are abundant regulators of gene expression

• miRNAs might regulate anxiety‐associated genes and have been shown to g g y gbe 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

Acknowledgments

Funding:Academy of Finland (NEURO program and 

academy research fellowship)Biocentrum HelsinkiUniversity of Helsinki / FIMMSigrid Jusélius Foundation

University of HelsinkiResearch Program of Molecular NeurologyJonas DonnerJuuso JuhilaLaura KananenHelena Kilpinen g

Jalmari and Rauha Ahokas FoundationYrjö Jahnsson FoundationL'Oréal Finland – UNESCO 

Women in Science ProgramYrjö and Tuulikki Ilvonen FoundationPeter and Patricia Gruber Foundation 

(Rosalind Franklin Young Investigator Award)

Helsinki Biomedical Graduate School

Kaisa ManninenPia RasinkangasTessa Sipilä

Institute for Molecular Medicine, FIMMPekka EllonenSari HannulaDaniel Nicorici

Institute of BiotechnologyPetri AuvinenDario GrecoLars Paulin