Download miRNA-seq of mouse brain regions qg

miRNA‐seq of mouse brain regions
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Iiris Hovatta, PhD
University of Helsinki
Research Program of Molecular Neurology and Department of Medical Genetics,
F
Faculty
lt off Medicine
M di 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, 1984
1
Anxiety disorders share increased anxiety but have distinct symptoms as well
• Panic disorder P i di d
• Obsessive‐compulsive disorder
• Post‐traumatic stress disorder
• Social phobia
• Specific phobias
• Generalized anxiety disorder
G
li d
i t di d
Treated with SSRI, MAOI, benzodiazepines, beta-blockers
2
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
Ci
Cingulate
l t cortex
t
Hippocampus
Hypothalamus
Periaqueductal gray
Pituitary gland
Functional studies
Hovatta et al. Nature 2005
Mechanisms that regulate susceptibility genes?
miRNA
From www.uta.edu/.../henry/classnotes/2457/index.htm.
3
micro‐RNAs are abundant regulators of gene expression
• Small ~22 nt long single stranded non‐coding RNA molecules g g
g
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
– miRNAs involved in many neurobiological diseases
miRNAs 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)
4
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!
• Discovery and quantitation
without prior genome annotation
• Can gget information about
isomiRs as well
Challenges
• Large quantity of starting
material (µg amount of 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
y
q
y y
y
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
Illumina library preparation workflow
• Modification to the Illumina protocol:
•Addition of indexes to the 5’ adapter sequence to increase throughput
•Otherwise follow the Illumina protocol
Barcoding for miRNA‐Seq
Without Index:
3’TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAG-miRNA-AGCATACGGCAGAAGACGAAC’5
5’AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATC-miRNA-TCGTATGCCGTCTTCTGCTTG’3
Sequencing primer
Index 3 TTAGGC:
3’TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAGAATCCG-miRNA-AGCATACGGCAGAAGACGAAC’5
5’AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATCTTAGGC-miRNA-TCGTATGCCGTCTTCTGCTTG’3
index
Sequencing primer
Index 7 CAGATC:
3’TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAGGTCTAG-miRNA-AGCATACGGCAGAAGACGAAC’5
5’AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATCCAGATC-miRNA-TCGTATGCCGTCTTCTGCTTG’3
i d
index
Sequencing primer
Index 11 GGCTAC:
3’TTACTATGCCGCTGGTGGCTGTCCAAGTCTCAAGATGTCAGGCTGCTAGGTCTAG-miRNA-AGCATACGGCAGAAGACGAAC’5
5’AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGACGATCGGCTAC-miRNA-TCGTATGCCGTCTTCTGCTTG’3
index
Sequencing primer
6
Sequencing
• 10 µl of 10nM miRNA
Library is needed for cluster generation (1 day).
day)
•3 samples / lane
• Sequencing by Solexa
Genome Analyzer II (3 days)
• Read lenght 35bp or
50bp (piRNAs ~30nt)
Lane 1
Lane 2
FC
Ind3
FC
Ind7
Lane 3
FC
Ind11
Lane 4
Lane 5
Lane 6
FC
Pool
HI
Ind3
HI
Ind7
Lane 7
HI
Ind11
Lane8
HI
Pool
• Sample Prep Kit 1.0
• 3 indexes
• 36 bp read length
C57BL/6J (CR)
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Bioinformatics pipeline
1. Pre‐filtered reads after base calling; •
Total # of reads (avrg of 6 repl.)
FC: 6,223,794
HP: 5,652,753
FC : 5,554153 (89%)
HP: 5,246,449 (93%)
2. Data sorting according to the indexes;
2. Data sorting
to the indexes;
•
# of reads with indexes
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
# of aligned reads
FC: 630,435
# of known miRNAs (out of 579 M. musculus) FC: 372
HP: 2,276,631
HP: 410
4. Alignment miRBase v. 14.0 (Bowtie)
•
•
5. Alignment against mouse genome mmu 8 (Bowtie)
•
•
•
# of uniquely mapped
# of reads mapping to multiple locations
# reads not matching the genome
FC: 427,526 (50%)
FC: 353,551 (42%)
FC: 66,874 (8%)
HP: 1,235,866 (47%)
HP: 1,317963 (50%)
HP: 73,272 (3%)
6. Counting of the reads = digital expression profiling, normalization, analysis of differential expression
Different approaches have been used
for normalization between samples
1 To
1.
To total
total number of reads
of reads / reads
/ reads that map to the to the
genome / reads that map against known miRNAs
2. Spike‐in controls
3. Use of Bayesian methods for differential expression
8
Let‐7c is the most abundant miRNA in FCx and HP in miRNA‐seq
miR‐137
1 %
miR‐
miR‐
let‐ 26a
125b‐
7g 1 %
5p
1 %
1 %
Rest
14 %
miR‐132
1 %
let 7d
let‐7d
2 %
miR‐9
3 %
let‐7e
3 %
miR‐124
3 %
let‐7f
4 %
miR‐125b*
1 %
Frontal Cortex; miRNA‐Seq
let‐7c
21 %
Hippocampus; miRNA‐Seq
miR‐
miR‐ 99b
26a 1 %
miR‐
2 %
137
2 %
miR‐
let‐7g
124
2 %
2 %
let‐7c
27 %
Rest
13 %
let‐7d
2 %
miR‐128
20 %
let‐7a
7 % miR‐29a let‐7b
9 %
9 %
let‐7e
3 %
miR‐9
4 %
let‐7f
7 %
let‐7a
9 %
let‐7b
miR‐29a
8 % miR‐128 9 %
8 %
How does miRNA‐seq compare to miRNA microarrays?
• Frontal cortex and hippocampus
and hippocampus samples (3 (3
replicates) hybridized on Affymetrix miRNA
microarrays
• Criterion for ”expressed”: ≥ ⅔ of samples display probe as "present“
• 245 miRNAs
245 miRNAs detected in FCx
detected in FCx and 238 in Hp
and 238 in Hp
9
miR‐709 is the most abundant miRNA in FCx and HP using Affymetrix microarrays
Frontal Cortex; Affymetrix
miR‐709
11 %
Rest
40 %
Hippocampus; Affymetrix
miR‐124
8 %
let‐7c
6 %
miR‐
125a‐
5p let‐
2 % 7a miR‐128
miR‐23b
2 % 2 %2 %
miR‐
138
3 %
miR‐709
11 %
Rest
37 %
let‐7b
8%
8 %
let‐7b
5 %
miR‐26a
4 %
miR‐ let‐7d
125b‐ 3 %
let‐
7e miR‐1325p miR‐24
3 % 3 % 3 % 3 %
let‐7c
8 %
miR‐124
7 %
miR‐222
2 %
miR‐107
2 % miR‐
miR‐23b 127 miR‐24
2 % 2 %
2 %
miR‐
103
2 %
miR‐26a
4 %
miR‐ let‐ let‐
125b‐5p 7e 7d
miR‐138
3 %3 % 3 % 4 %
Overlap between miRNA‐seq and microarrays is considerable
90
205
33
51
163
82
10
Differentially expressed miRNAs between
frontal cortex and hippocampus
miRNA‐seq
Frontal cortex
Hippocampus
Total number of miRNAs
214
295
Upregulated with FC>2
26
13
miRNA microarrays
Frontal cortex
Hippocampus
Total number of miRNAs
245
238
Upregulated with FC>2
17
10
miRNA-seq
miRNA
seq
miRNA-seq
iRNA
18
8
9
10
3
7
array
array
Frontal cortex
Hippocampus
Target prediction of identified miRNAs
• miRWalk tool (http://www.ma.uni‐heidelberg.de/apps/zmf/mirwalk/)
– Poisson p‐value < 0.05 (calculated by miRWalk)
P i
l < 0 05 ( l l t d b iRW lk)
– At least 2 hits out of 5 target prediction softwares
Frontal cortex
Hippocampus
Number of targets
1446
624
Number of targets expressed in that brain region
758
447
Pathway analysis with DAVID and Ingenuity Pathways Analysis
11
Erk5 Signaling
(FCx pathway)
Predicted target
g
genes:
RRAS2
RPS6KB1
YWHAZ
FOXO3
CREB1
MEF2C
Predicted regulating
miRNAS:
iRNAS
mir-182
mir-132/mir-212
mir-200a
mir-153
mir-141
mir-206
12
isomiRs are frequent
Counts
Sequence
39550 TTATTGCTTAAGAATACGCGTAG
33699 TTATTGCTTAAGAATACGCGT
30289 TTATTGCTTAAGAATACGCGTA
5668 TTATTGCTTAAGAATACGCGTAA
2156 TTATTGCTTAAGAATACGCGTAC
1532 TTATTGCTTAAGAATACGCGTAT
1050 TTATTGCTTAAGAATACGCGTAGT
631 TTATTGCTTAAGAATACGCGTAGA
528 TTATTGCTTAAGAATGCGCGTA
454 TTATTGATTAAGAATACGCGTAG
433 TTATTGATTAAGAATACGCGT
384 TTATTGCTTAAGAATACGCGGAG
339 TTATTGATTAAGAATACGCGTA
319 TTATTGCTTAAGAATGCGCGT
249 TTATTGCTTAAGAATACGCGGA
216 TTATTGCTTAAGAATATGCGTAG
208 TTATTGCTTAAGAATACGCGA
178 TTATTGCTTAAGAATACGCGTT
155 TTATTGCTTAAGAATACGTGTAG
149 TTATTGCTTAGGAATACGCGTAG
140 TTATTGCTTAAGAATATGCGTA
137 TTATTGCTTAAGAATGCGCGTAG
130 TTATTGCTTAGGAATACGCGTA
129 TTATTGCTTAAGAATACGCGTCG
114 TTATTGCTTAGGAATACGCGT
106 TTATCGCTTAAGAATACGCGTAG
104 TTATCGCTTAAGAATACGCGT
mmu‐miR‐137 TTATTGCTTAAGAATACGCGTAG
Chr
Position Strand
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
3 118136818 +
Mismatch
• Variability
22:G>A
22:G>C
22:G>T
23:T>A
15:A>G
6:C>A
6:C>A
20:T>G
6:C>A
15:A>G
20:T>G
16:C>T
20:T>A
21:A>T
18:C>T
10:A>G
16:C>T
15:A>G
10:A>G
21:A>C
10:A>G
4:T>C
4:T>C
in Dicer and Drosha
cleavage or RNA editing
• 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
g bindingg
• Biological function?
Identification of miRNAs that regulate anxiety
frontal cortex
hippocampus
hypothalamus
anxiety
•
•
Identification of expression differences of known miRNAs & correlation to anxiety phenotype
Identification of novel miRNAs and isomiRs & correlation to anxiety phenotype
13
Conclusions
• miRNAs are abundant regulators of gene expression
• miRNAs might
g regulate
g
anxiety‐associated
y
ggenes 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))
• Concomitant analysis of miRNA and mRNA patterns may lead to a network‐oriented view of disease
Acknowledgments
University of Helsinki
Research Program of Molecular Neurology
Hovatta Lab
Jonas Donner
Petri Hyytiä
Katherine Icay
Juuso Juhila
Laura Kananen
Helena Kilpinen
Kaisa Manninen
Sirli Raud
Mari Rossi
Tessa Sipilä
Institute for Molecular Medicine, FIMM
Pekka Ellonen
Sari Hannula
Daniel Nicorici
Funding:
Academy of Finland (NEURO program and academy research fellowship)
Biocentrum Helsinki
University of Helsinki / FIMM
g
Foundation
Sigrid Jusélius
Jalmari and Rauha Ahokas Foundation
Yrjö Jahnsson Foundation
L'Oréal Finland – UNESCO Women in Science Program
Yrjö and Tuulikki Ilvonen Foundation
Signe & Ane Gyllenberg Foundation
Peter and Patricia Gruber Foundation
(Rosalind Franklin Young Investigator
Award)
Helsinki Biomedical Graduate School
Helsinki University Central Hospital
Dario Greco
14