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smRNA and RNA Silencing

smRNA and RNA Silencing. Xiaole Shirley Liu STAT115, STAT215, BIO298, BIST520. Outline. RNAi and siRNA perfect match, degrade mRNA Used as gene knockout for function annotation Need to design efficient siRNA miRNA discovery and prediction

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smRNA and RNA Silencing

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  1. smRNA and RNA Silencing Xiaole Shirley Liu STAT115, STAT215, BIO298, BIST520

  2. Outline • RNAi and siRNA • perfect match, degrade mRNA • Used as gene knockout for function annotation • Need to design efficient siRNA • miRNA discovery and prediction • miRNA target predict: near perfect match, “repress translation” mRNA stability • Effect on mRNA and evolution • New strategies: smRNA-seq, RIP-seq • Competing endogenous RNA (ceRNA) on miRNA

  3. Post Transcriptional Gene Silencing in Nematodes • Guo and Kemphues (1995) tried to use antisense RNA to silence a gene in C. elegans to assess the gene’s function • Injection of anti-sense RNA worked, and injection of sense RNA also worked?!?!?! • Fire and Mello (1998) injected double stranded RNA, and it worked much better • Previous sense RNA has tiny antisense contamination • Only a few dsRNA molecules / cell are enough to completely silence the gene expression mRNA antisense

  4. RNAi Mechanism • Baulcombe & Hamilton (1999) found ~25 nt RNAs (sense + antisense) in silenced plant • Zamore et al (2000) found dsRNA added to fly was processed to 21-23 nt. • Initiation step: • Long double-stranded RNA • Dicer cleaves dsRNA to ~21 bp small interfering RNA (siRNA), with 2nt 3’ overhangs • Effector step: • Form RNA-induced silencing complexes (RISC) • siRNA guide RISC to target mRNA • RISC degrades target mRNA

  5. RNAi and Functional Genomics • Long dsRNA does not work in mammals due to antiviral response, but siRNA works • RNAi becomes a popular gene knockout method • Quickly and easily create loss-of-function phenotype, understand gene function! • Over 200 companies spawn off from siRNA technology • Problem: siRNA does not always work!!

  6. Design siRNA • 21-23 nt dsRNA, GC% slightly < 50% • Perfect complimentary to, and only to, target mRNA • Check both sense and antisense for specificity • Targeting 3’ UTR works better than 5’ UTR • Target mRNA region is open in 2nd structure • Have 1-2 nt 3’ U (T) overhang (mRNA target starting with AA) --------------------UU UU-------------------- • Add multiple (4) siRNA together

  7. siRNA Strand Bias • Compare functional with non-functional RNAi local RNA stability • Strand whose 5’ is less stable preferentially incorporate with RISC • Check local RNA 2nd structure energy AS 5’ less stable • Antisense 5’ lower GC%, 3’ higher GC%

  8. microRNA Discovery • V. Ambros found that a small RNA (lin-4) mutation in C. elegans caused increased translation of lin-14 gene (1999) • Precursor ~70 bp fold to hairpin • Diced to 21-24 nt dsRNA by Dicer! • Complimentary to few places in lin-14 3’ UTR • A second small RNA let-7 was cloned (G. Ruvkun, 2000) in C. elegans, with fly and human homolog • In less than one year, many miRNA were cloned • 20 in fly, 30 in human, and 60 in C. elegans

  9. Conserved across species Some appear in introns Possibly transcribed by pol II Some clustered and co-transcribed in one transcript Expressed in particular cell types Some abundant (50K molecules / cell) Maturation similar to siRNA Hairpin cleaved by dicer, ~22nt dsRNA Helicase unwind dsRNA ssRNA incorporate with RISC Repress target gene translation Seem to regulate (fine tune) developmental process and transcription factors miRNA Genes

  10. Computational miRNA Prediction • Genome project and bioinformatics came to play • miRNA exist (and cloned) in other organism • miRNA genes can be predicted • Earlier approaches • Find known miRNA orthologs in other organisms • Find hairpin loops near known miRNA (cluster)

  11. Systematic miRNA Prediction • MiRscan, Lim et al 2003 (elegans, human) • MiRseeker, Lai et al 2003 (fly) • Find many candidates in non-coding (for protein) sequences • Conserved: C. elegans/C. briggsae, human/mouse • Can form good hairpin structures, Vienna RNA package (RNA 2nd structure, energy) • ~40K in C. elegans • Score candidate conserved hairpins based on known miRNAs

  12. MiRscan Features • Base pairing of hairpin loop • Sequence conservation in 5’ or 3’ half of miRNA • Sequence bias in the first 5 nt • Symmetric internal loops/bulges • Extension of base-pairing • Distance to the loop

  13. MiRscan Scores • Feature score • fi(xi) frequency of known miRNA • gi(xi) frequency in all 40K candidates • Hairpin score • Relative contribution

  14. Scores Distribution • Pick cutoff to include most known miRNA • Many predicted ones were verified

  15. TargetScan: miRNA Target Prediction in Human • Align miRNA to 3’ UTR (Vienna RNA package) • 7-mer perfect match towards the miRNA 5’ • Extends with good pairing (2nd structure) • Target region conserved (Hs, Mm, Rn) • In plants, high % of TF targets; when perfect match degrade mRNA

  16. miRNA Target Prediction in Human • Some miRNA have multiple pairing with same mRNA 3’ UTR • Paired region conserved • Using miRNA combination predicts targets better • Similar to transcription factor module regulation, many miRNAs can regulate the same mRNA • miRNA registry at Sanger Institute

  17. miRNA Effect on mRNA • Expression profiling after siRNA found off-target transcripts to be down-regulated • Many have 3’ UTR sequence similarity to siRNA, especially 7 bp on siRNA 5’ side • The siRNA is like an miRNA to the off-target mRNA • Introduce miRNA to HeLa cell (cervical cancer) • miR124 (expressed in brain): shift HeLa towards brain • miR1 (expressed in muscle): shift HeLa towards muscle • miRNA can also cleave mRNA although weaker, and regulate tissue-specific gene expression

  18. miRNA Effect on mRNA • miRNA effect on mRNA • Seed on 7bp perfect match • If the rest match well, cleaves mRNA: strength ~ match • Recent studies suggest miRNA’s main effect is mRNA degradation instead of translational repression • Faster degrading transcripts (e.g. TFs) often have longer 3’ UTR and more miRNA target sites • miRNA effect on evolution • Genes that express at the same time / tissue with miRNA evolve to avoid the 7bp seed in their 3’UTR mRNA miRNA

  19. smRNA-Seq • Relative abundance of: • Length • First nucleotide • Mapping location • Strand bias • Processing • Conservation • 2nd structure energy

  20. RIP-Seq • ChIP RNA-binding protein, then sequence out the RNA, find out what protein works for which smRNA type

  21. ceRNA Effect on miRNA • miRNA sponge: expression of one gene might influence the expression of another through miRNA • Endogenous mRNA • Expressed pseudogenes • Expressed non-coding RNA miRNA RNA1 RNA2

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