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MIRNAS ARRAYS: DATABASES AND PLATFORMS. U B io Training Courses. Gonzalo Gómez//[email protected] miRNAs. Firstly detected in C. elegans (V. Ambros, 1993) Mapping to non-coding regions (introns) P ri-miRNA processed by Drosha DICER removes the structural loop

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MIRNAS ARRAYS: DATABASES AND PLATFORMS

UBio Training Courses

Gonzalo Gómez//[email protected]


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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.


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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)


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miRNAs and cancer

OncomiRs

Oncogene

miRNA

Tumor

formation

Tumor suppressor

miRNA

Upregulation

Proliferation

Invasion

Angiogenesis

Cell death

  • Proliferation

  • Invasion

  • Angiogenesis

  • Cell death

Downregulation

Esquela-Kerscher & Slack. Nature Reviews Cancer. 2006.


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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.


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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

Other prediction algorithms…

Bartel D. Cell 2009.

More target prediction tools:

http://en.wikipedia.org/wiki/List_of_RNA_structure_prediction_software#Inter_molecular_interactions:_MicroRNA:UTR


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miRNAs

Databases

miRBase

Version Date miRNAs

1.0 12/02 218

1.1 01/03 262

1.2 04/03 295

1.3 05/03 332

1.4 07/03 345

2.0 07/03 506

2.1 09/03 558

2.2 11/03 593

3.0 01/04 719

3.1 04/04 899

4.0 07/04 1185

5.0 09/04 1345

5.1 12/04 1420

6.0 04/05 1650

7.0 06/05 2909

7.1 10/05 3424

8.0 02/06 3518

8.1 05/06 3963

8.2 07/06 4039

9.0 10/06 4361

9.1 02/07 4449

9.2 05/07 4584

10.0 08/07 5071

10.1 12/07 5395

11.0 04/08 6396

12.0 09/08 8619

13.0 03/09 9539

14.0 09/09 10833

http://www.mirbase.org/

H. Sapiens ~695 miRNAs

M. musculus: ~488 miRNAs


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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*


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miRNAs

Databases

  • TarBase

  • http://microrna.gr/tarbase

Contains only those miRNA-target relationships experimentally validated

The database of experimentally supported targets: a functional update of TarBase.

Papadopoulos GL, Reczko M, Simossis VA, Sethupathy P, Hatzigeorgiou AG.,

Nucleic Acids Res. 2009 Jan;37(Database issue):D155-8.


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miRNAs and disease

Databases

http://cmbi.bjmu.edu.cn/hmdd

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).


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miRNAs

Detection Methods


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miRNA microarrays

Commercial platforms

Human, rat, mouse

Human, rat, mouse,

dog, chimpanzee, etc

Human, rat, mouse

Human, multispecie


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miRNA microarrays

Commercial platforms

  • Agilent recommendations.

  • No normalization

  • Normalization 75th percentile

  • (75th value = 1)

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


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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)

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.


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T test, SAM,

limma

METHOD

FWER

FDR

Pvalue

adjustment

miRNA microarrays

Differential expression analysis

20 normalized arrays

600 miRNAs

2 classes (healthy y tumor)

GEPAS

Asterias

SAM tools…

pvalue

OK!

Differentially expressed miRNAs between classes

FWER: Type I Family Wise Error Rate

FDR: False Discovery Rate


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THANKS!

Visit UBio web !

http://bioinfo.cnio.es/


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