1. MIRNAS ARRAYS: DATABASES AND PLATFORMS
UBio Training Courses
Gonzalo

2. miRNAs Firstly detected in C. elegans (V. Ambros, 1993)
Mapping to non-coding regions (introns)
Pri-miRNA processed by Drosha
DICER removes the structural loop
mature miRNA: ssRNA, 22 nucleotides
miRNA-RISK complex: mRNAs post-transcriptional inhibition
33% of human genes are supposed to be regulated by miRNAs.

3. miRNAs Biological role Brain development (miR-430)
• Nervous system development(miR-273)
• Pancreatic Langerhans islands development (miR-375)
• Adipocytes development (miR-143)
• Heart development(miR-1)
Inmune response (miR-223, cluster miR-17~92, miR-146a,miR-155…)
• Apoptosis (miR-14)
Maduracion linea mieloide y linfoide

4. miRNAs and cancer OncomiRs  Proliferation  Invasion Oncogene
 Angiogenesis
 Cell death
Oncogene
miRNA
Proliferation
Invasion
Angiogenesis
 Cell death
Tumor suppressor
Upregulation
Tumor
formation
Downregulation
miR-148a y miR-34b/c parecen inhibir la diseminacion tumoral regulando genes relacionados con la metastasis. Cuando estan los genes de los miRs estan hipermetilados se upregulan los genes de metástasis genes favoreciendo la diseminacion.
Esquela-Kerscher & Slack. Nature Reviews Cancer

5. miRNAs Target sites miRNAs seed region: 5’, nucleotides 2-7(8)
Most gene targets of a given miRNA have only a
single 7 nt matching to that miRNA seed region.
7-8 nt hundreds of target predictions for each
miRNA family
(~300 conserved targets per miRNA family in vertebrates)
High rate of false positives in predictions.
Bartel D. Cell 2009.

6. Target Prediction Algorithms
miRNAs
Target Prediction Algorithms
PREDICTION CRITERIA
miR seed-target complete base-pairing
Interspecies conservation
Number of binding sites for a given 3’UTR in a particular gene
Free energy for the miR-target duplex
Binding site accessibility
miRNA- target secondary structure
Overlapping entre los metodos (targetSacn, PicTAR, EMBL) es bastante alto aunque no al 100% . Sources of these differences could be: alignment artifacts, use of slightly different UTR DBs, use of different miRNAs sequences…
Other prediction algorithms…
Bartel D. Cell 2009.
More target prediction tools:

7. miRBase miRNAs Databases H. Sapiens ~695 miRNAs
Version Date miRNAs / / / / / / / / / / / / / / / / / / / / / / / / / / / /
H. Sapiens ~695 miRNAs
M. musculus: ~488 miRNAs

8. miRNA nomenclature (miRBase)
miRNAs
miRNA nomenclature (miRBase)
mir: immature sequence (hairpin). E.g. hsa-mir-203
b) miR: mature miRNA sequence. E.g. hsa-miR-203
- a/b: paralog miRs, difer in 1-2 nucleotides.
E.g. hsa-miR-9a, hsa-miR-9b
- 1-2: Identical miRs, different hairpin.
Ej. hsa-miR-19b-1, hsa-miR-19b-2
- 5p-3p: mature miR generated from precursor 5´ (or 3) sequence.
E.g. hsa-miR-17-5p
- *: Minor transcript complementary to mature miR.
E.g. hsa-miR-33a*
Paralogos: origen por duplicacion intraespecie

9. TarBase miRNAs Databases http://microrna.gr/tarbase
Contains only those miRNA-target relationships experimentally validated
TarBase: Contiene solo miRNAs-targets verificados experimentalmente. Logicamente tiene menios información.
The database of experimentally supported targets: a functional update of TarBase.
Papadopoulos GL, Reczko M, Simossis VA, Sethupathy P, Hatzigeorgiou AG.,
Nucleic Acids Res Jan;37(Database issue):D155-8.

10. miRNAs and disease Databases http://www.mir2disease.org/
Cancer (Calin and Croce 2006; Ura et al 2008; Stamatopoulos et al 2009…)
Cardiovascular disease (Latronico et al. 2007; van Rooij and Olson 2007)
Schizophrenia (Hansen, et al. 2007; Perkins et al. 2007)
Renal misfunction (Williams 2007)
Tourette syndrome (Esau and Monia 2007)
Psoriasis (Sonkoly et al. 2007)
Muscle disorders (Eisenberg et al. 2007),
X fragile syndrome (Fiore and Schratt 2007)
Policitemia vera (Bruchova et al. 2007)
Diabetes (Williams 2007)
Chronic hepatitis (Murakami et al. 2006)
AIDS (Hariharan etal. 2005)
Obesity (Weiler et al. 2006, Lovis et al. 2008, Xie et al. 2009).

11. miRNAs
Detection Methods

12. miRNA microarrays Commercial platforms Human, rat, mouse
dog, chimpanzee, etc
EXIQON
LNA = Locked Nucleic Acid
LNA es una modificacion quimica que consiste en: ribose ring is "locked" with a methylene bridge connecting the 2’-O atom and the 4’-C atom.
LNA hace mas estable el par DNA-miR para perfect match y 1mismatch.
INVITROGEN:
Human, rat, mouse
Human, multispecie

13. Agilent recommendations. No normalization
miRNA microarrays
Commercial platforms
Agilent recommendations.
No normalization
Normalization 75th percentile
(75th value = 1)
Abutting: colindando. Algunos autores (Pedro Lopez-Romero) proponen RMA with no background correction.
Additional G-C pair in the probe-target interaction region stabilizes targeted miRNAs relative to homologous RNAs. Additionally, all probes contain a 5' hairpin (blue), abutting the probe-target region, to increase target and size miRNA specificity

14. miRNA microarrays Commercial platforms
LNA = Locked Nucleic Acid
Preprocessing scripts provided by Exiqon
Background correction = normexp
Normalization = quantiles
RG_normexp <- backgroundCorrect(RGfilt_0, method = "normexp", offset=50)
MA_norm <- normalizeBetweenArrays(RG_normexp$G, method="quantile")
MA_lognorm<-log2(MA_norm)
EXIQON
LNA = Locked Nucleic Acid
LNA es una modificacion quimica que consiste en: ribose ring is "locked" with a methylene bridge connecting the 2’-O atom and the 4’-C atom.
LNA hace mas estable el par DNA-miR para perfect match y 1mismatch.
LNA is chemical modification.
Ribose ring is "locked" with a methylene bridge
connecting the 2’-O atom and the 4’-C atom.
LNA makes DNA-miR pairs more stable (higher Tm)
when perfect match and 1mismatch hybridizations occurs.

15. OK! 20 normalized arrays 600 miRNAs 2 classes (healthy y tumor)
miRNA microarrays
Differential expression analysis
20 normalized arrays
600 miRNAs
2 classes (healthy y tumor)
T test, SAM,
limma
FWER
FDR
METHOD
Pvalue
adjustment
GEPAS
Asterias
SAM tools…
pvalue
OK!
Differentially expressed miRNAs between classes
FWER: Type I Family Wise Error Rate
FDR: False Discovery Rate

16. THANKS!
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