Non-messenger RNAs. Karin Lagesen

Transcription

1 Non-messenger RNAs Karin Lagesen

2 Outline RNA is not DNA Kinds of non-messenger RNAs sirna, mirna trnas, rrnas ncrnas: Finding non-messenger RNA trnascan-se, RNammer RFAM

3 RNA basepairing

4 RNA is not DNA RNA DNA

5 Different structures

6 Structure consequences RNA double helices more stable than DNA, allows for more diverse structures RNA more prone to hydrolysis than DNA, OH group involved in auto-hydrolytic cleavage Sugar pucker gives tilted helix > not efficient packing Groove widths hinder protein interactions DNA sequence specific interactions > major groove RNA sequence specific interactions in bulges, loops, hairpin loops

7 Many names General ncrna: non-coding RNA frna: functional RNA, another name for ncrnas Eukaryotes mirna: microrna, form a putative translational regulatory gene family strna: small temporal RNA, some are also called mirnas sirna: short dsrna which silence genes by base pairing to their mrnas, halting transcription ->RNA interference snmrna: small non-mrna, mostly the same as small RNAs snorna: small nucleolar RNAs, involved in rrna modification snrna: small nuclear RNA, includes eukaryote spliceosomal RNAs Bacteria srna: small RNAs

8 2006 Nobel prize in medicine Andrew Fire Craig C Mello "for their discovery of RNA interference - gene silencing by double-stranded RNA"

9 sirna and mirna mechanism

10 RNA interference (RNAi) Result: loss of function Function: immune system for the genome Can be used to do knock-down induced silencing of genes Easy delivery sometimes Microinjections Soaking cells in solution Transgenic organisms

11 sirna nts long precursor molecule: 500 nts long, double-stranded Allows no mismatches in basepairing exogenous origin degradation of target mrna

12 sirna example Silencing of white gene Results in loss of pigmentation of the eye Done by microinjections in embryos Normal Silenced Carthew lab

13 mirna nts long, single-stranded Pri-miRNA long transcripts, cleaved by Drosha enszyme into ~ 70 nts long, pre-mirna Allows some mismatches, upto 3 Endogenous origin Transcript regulation

14 C. elegans lin-4 mutation 22 nts long Basepairs with 3 end of developmental mrnas Causes the worm to repeat the L1 developmental stage

15 Central Dogma

16 Ribosomal RNA Large subunit: Peptidyl transferase centre 5S (120nt), 23S (2900nt) rrnas more convoluted than 16S Small subunit: Decoding centre 16S (1500nt) 16S clear domain structure

17 23S

18 16S

19 Ribosome

20 RNA functional sites Yusupov et a, Science 2001

21 trna

22 srnas in E.coli 81 known 25 with presumed known function 62 with approx known position 35 of 55 on left replicore No leading/lagging strand skew in those with known direction

23 Functions Gottesman, Annu. Rev. Microbiol 2004

24 Mechanisms Antisense: Basepairs with target, helps or hinders transcription

25 RpoS regulation Gottesman, Annu. Rev. Microbiol 2004

26 rpos ribosome binding site Repoila et al Mol Micro

27 srna regulation OxyS: induced by oxidative stress, depresses fhla, activator for formate metabolism, represses rpos, stationary phase sigma factor DsrA: induced by low temperature, de-represses rpos RprA: induced by cell density, de-represses rpos GvcB: induced by high glycine, de-represses oppa, dppa, genes in glycine breakdown, products used for purines and more. DicF: represses ftsz, cell division gene MicF: induced by high osmolarity, represses ompf, membrane permeability MicC: induced by low temperature, represses ompc, membrane permeability GadY: induced in stationary phase, represses GadX, increased expression of acid resistance genes Spot42: induced by glucose, negative galetkm, galactose metabolism RyhB: repressed by Fur, de-repressed by absence of Fe 2+, degrades sodb and other iron needing genes Storz et al Curr Opin Microbiol Gottesman 2004 Annu Rev Microbiol w/refs

28 Mechanisms Antisense: Basepairs with target, helps or hinders transcription Ribozyme Catalytic activity

29 RNAse P RNA RNAse P cleaves 5 ends of trnas to produce mature trnas RNAse P 1 protein, 1 RNA RNA subunit catalyses reaction alone, but more efficient with protein Essensial in all organisms RNAse P RNA structure Wassarman et al Trends Microbiol

30 Mechanisms Antisense: Basepairs with target, helps or hinders transcription Ribozyme Catalytic activity Protein activity modification Modulates protein availability

31 6S Represses use of σ 70 in stationary phase σ 70 still abundant in stationary phase 6S higher affinity for core enzyme than σ S Presents σ 70 binding motif to RNAP, binds to σ 70 and β/β subunits RNA Polymerase 6S structure Wassarman et al Trends Microbiol

32 CsrB and CsrC Both binds CsrA, and is induced by it CsrA regulator of carbon metabolism, helps cell survive in stationary phase Repeated motif in loops: GAGGXXG - CsrA binding motif Sequesters CsrA away CsrB structure Wassarman et al Trends Microbiol

33 Mechanisms Antisense: Basepairs with target, helps or hinders transcription Ribozyme Catalytic activity Protein activity modification Modulates protein activity Structural component With or without activity

34 tmrna Both mrna and trna activity, trans-translation tmrna Ala enters A site of stalled ribosome, adds 10 aa tag to protein, original mrna released Tag signals proteolytic degradation tmrna structure Wassarman et al Trends Microbiol

35 4.5S Part of signal recognition particle SRP Essensial for cell growth SRP binding to nascent protein requires 4.5S 4.5S structure Wassarman et al Trends Microbiol

36 Predicting RNAs Sequence conservation Blast Hidden Markov Models Transcription start and stop sites Secondary structure conservation Stochastic context free grammars

37 Why rrna prediction? BLAST does not work Annotation of rrnas in genomes is: Sometimes lacking Appears to be on wrong strand Too long? rrnas often used for functional studies wrong data give wrong results

38 Predicting rrnas RNAmmer Uses Hidden Markov Models, HMMs to predict rrnas HMM: probabilistic description, a model, of an alignment Q: how well does the new sequence fit the model, i.e. the alignment?

39 Conservation of rrnas bac.ssu: Conserved Regions (n = 743) bac.tsu: Conserved Regions (n = 360) Information content bac.lsu: Conserved Regions (n = 127) Position in Alignment Information content Information content Position in Alignment Position in Alignment

40 HMM Sequence: TAC: 0.5*0.7 * 0.4*0.7 * 0.4*0.9 = 0.04 Sequence: AGT: 0.1*0.7 * 0.1*0.7 * 0.1*0.7 =

41 RNAmmer Predicts 5S, ssu RNA, lsu RNA in archaea, bacteria, eukaryotes Uses two HMMs for each RNA/kingdom Screener HMM finds core of rrna Complete HMM region around core rrna > result: very fast Very accurate for bacteria and archaea Lagesen et al, in preparation

42 Available on the web

43 Predicting trnas trnascan-se defacto standard Combines three programs into one: trnascan promoter elements and cloverleaf EufindtRNA promoter elements, distance between them Cove probabilistic model for trna secondary structure Result: false positive rate < 1 per 15 billion nt Standalone program/online predictions Lowe and Eddy, NAR 1997

44 Stocastic context free grammars Way of dealing with secondary structure RNA sequence as sentence in a language Q: is the sentence grammatically correct? Reformat: can the grammar produce this sentence?

45 SCFGs con t Make rules that describe the language Check if the sequence can be created by using these rules Every rule assigned probability stochastic RNA language rules: S -> asu usa csg gsc S -> as cs gs us S -> Sa Sc Sg Su S -> SS S ->

46 Secondary structure comparison A set of rules are made based on the secondary structure Matching is a process of seeing whether the new RNA can fit the secondary structure Q: GUCGCAGCGAGCGAC x xxxx xxxxxx x A: AUGAGUGAACCUCAU

47 SCFG example Q A B Structural homologs Not homolog Q: GUCGCAGCGAGCGAC x xxxx xxxxxx x A: AUGAGUGAACCUCAU Q: GUCGCAGCGAGCGAC x xxxx xxxxxx x B: AUUCGUGAGCGACAG

48 srna prediction Two different problems: Finding already known srnas Finding novel srnas Known srnas Secondary structure conservation RFAM, using the Infernal program Novel srnas Transcription signals, secondary structure rules, sequence conservation ++

49 RFAM Griffiths-Jones et al, NAR 2005

50 RFAM con t Database made with SCFG based program Infernal Seed alignments used to make SCFG, then search genomes with model Version 7: 503 different models

51 Novel RNAs srnapredict: Promoters, terminators, conserved sequence GMMI HMMs of sequence patterns RNAZ, EVOFOLD, QRNA Multiple alignment of conserved sequence Find conserved secondary structure in alignment RNAGENiE Searches for structural elements such as bulges

52 "Biologists should not deceive themselves with the thought that some new class of biological molecules, of comparable importance to proteins, remains to be discovered. This seems highly unlikely." Francis Crick, 1958