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Regulation of transcript stability and post-transcriptional processes – from yeast to human

Regulation of transcript stability and post-transcriptional processes – from yeast to human. Reut Shalgi Weizmann Institute of Science, Israel RSMD workshop Uppsala 11/2006. The central Dogma. Translation. Transcription. DNA. Protein. mRNA. The central Dogma. miRNA (ncRNAs).

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Regulation of transcript stability and post-transcriptional processes – from yeast to human

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  1. Regulation of transcript stability and post-transcriptional processes– from yeast to human Reut Shalgi Weizmann Institute of Science, Israel RSMD workshop Uppsala 11/2006

  2. The central Dogma Translation Transcription DNA Protein mRNA

  3. The central Dogma miRNA (ncRNAs) Transcription Translation Transcription DNA Protein mRNA Degradation Degradation

  4. miRNA Transcription Translation Transcription mRNA Protein DNA Degradation Degradation Post transcriptional control • Functional sequence motifs in 3’ UTRs stability associated motifs • (Shalgi et al. Genome Biology2005) • miRNA regulation (Xi et al. Clin Cancer Res. 2006)

  5. A catalog of stability-associated sequence elements in 3' UTRs ofyeast mRNAs Shalgi R, Lapidot M, Shamir R and Pilpel Y. Genome Biology 2005

  6. The cell transcriptome gene expression profile Expression level AAATCGGAATTGGAGGTATCGGATCTTGTTGAATATCCACCAATGTCTTACCCCTGTATTTTA… time promoter 5’ UTR Protein coding region 3’ UTR AAAAAAAAAAA… TGTATAAT Balance between transcription activation and transcript degradation

  7. mRNA transcription and degradation –both determine the cell transcriptome conditions Promoter sequence 3’ UTR sequence AAATCGGAATTGGAGGTATCGGATCTTGTTGAATATCCACCAATGTCTTACCCCTGTATTTTAACAAGAGTTTACGGAATACTGTTATATGGTTAAAGGTGTGGACGCCTTGAAGGTTTACCTTACCGAATGACACCTGAATATTACAATAGTCAGATCGAATAACGTTCTGGAATATGGCGTTATCCAAAGTTAGCGCAGTTTTCCGATGGTCCAATGTAATCATTAGAAATAGTAAAAACTGTGTAATGGTAAAGATTGTGTCACTGGAAAAAAACTGCTACAAATAATAAATAAATAAAAAAATACGAAAGCACAGTACTACGGGTGCCTCCACAAATAGATAAGAAACCAAGCGGAGACATGCGTTTAGACTACGGTGAGGATATAAATTATTTATACAACCAGACCTACGGTATATAAAAGAGCATCTAGTTTACCTGTTATGATGAATGGACATTCGCTACATCTACGGATCTTACTCTCTATTTGTTAAAAAAAATTACAAAGAGAACTACTGCATATATAAATAACATACCTACGGAATAACAT ACCAATCACATCGGTCGCGGAAGCCGTCTGTGTTTCAGCATGATTGAATCTTGAAATTGAAGAGGTGACTACTGTTTTCGTCTCAGCAGCTCCAGTACTGGTAGTTGTCTCAGCAGCTCCAGTATTGGTTGTTGTCTCACTGGTAGCACTGTTCATTTTAGAGCTGACAGACTCTTCATTCGTAGTCTGTGGCCTCCATGTTGGATAGACCGTAACAACATCATTCACAGTAGCCGTGGCCGTCGAAACAATGGCAGGTGAAGCAGTTTCGGAACACACACCAGATTCGCAGGAAGTAACAGTAACTAGCGTAGTTTGTTGCCTCGATTCTGTGGTGGAAATAGGACACCATGTCGTGTATTCTGTGGTAACGCCGTTAATAGTAGCAGTGCTTATAGATACAATGACCAATCACATCGGTCGCGGAAGCCGTCTGTGTTTCAGCATGATTGAATCTTGAAATTGAAGAGGTGACTACTGTTTTCGTCTCAGCAGCTCCAGTACTGGTAGTTGTCTCAGCAGCTCCAGT ? genes

  8. Functional sequence motifs in 3’ UTRs Finding sequence elements associated with transcript stability derived from 3’ UTRs of yeast mRNAs ? Why 3’ UTRs • 3’ UTRs were previously inferred to be involved in controlling: • Transcript Stability • Sub-cellular localization • (Keursten & Goodwin, Nature Reviews Gen. 2003)

  9. Expression level Time (min) Discovery of stability-associated motifs in yeast 3’ UTRsThe data Yeast mRNA half lives (Calculated from mRNA Decay profiles) 3’ UTR sequence ACCAATCACATCGGTCGCGGAAGCCGTCTGTGTTTCAGCATGATTGAATCTTGAAATTGAAGAGGTGACTACTGTTTTCGTCTCAGCAGCTCCAGTACTGGTAGTTGTCTCAGCAGCTCCAGTATTGGTTGTTGTCTCACTGGTAGCACTGTTCATTTTAGAGCTGACAGACTCTTCATTCGTAGTCTGTGGCCTCCATGTTGGATAGACCGTAACAACATCATTCACAGTAGCCGTGGCCGTCGAAACAATGGCAGGTGAAGCAGTTTCGGAACACACACCAGATTCGCAGGAAGTAACAGTAACTAGCGTAGTTTGTTGCCTCGATTCTGTGGTGGAAATAGGACACCATGTCGTGTATTCTGTGGGGAGTTGTCTCAGCAGCTCCAGT the “virtual northern” Data by Hurowitz & Brown, Genome Biol., 2003 Taken from Wang , PNAS 2002

  10. AAGCTTCC TCATTGAAAGCTTCCCTTATCCCTTCCA…TCTCCTACAACGCCTGAGGAGGACCAGA…GCACCATCCCTCCTACAACTAACTACCAG…TGAGCTCATTAAGCTTCCCAGCACAACT… CCTACAAC Discovery of stability-associated motifs in yeast 3’ UTRs AAGCTTCC gene1 AAGCTTCC gene2 AAGCTTCC gene22 AAGCTTCC … AAGCTTCC … AAGCTTCC … AAGCTTCC … # CCTACAAC gene5 CCTACAAC gene9 CCTACAAC … CCTACAAC … CCTACAAC … CCTACAAC … # 1. Exhaustive kmer enumeration (8<=k<=12) • A List of all kmers in the 3’ UTRs for each kmer, a list of the gene that contain it in their 3’ UTR:

  11. Functional sequence motifs in 3’ UTRs Finding sequence elements associated with transcript stability derived from 3’ UTRs of yeast mRNAs Genome average half life = 26 min CCTACAAC AAGCTTCC Average half life = 9 min Average half life = 38 min Expression level Time (min)

  12. Motif mean ½ life #genes p-value 0.3 26 min 200 AAAAAAAA 30 CCTACAAC 8 min 10-6 46 min AAGCTTCC 10-11 80 … Discovery of stability-associated motifs in yeast 3’ UTRs 2. Kmer Stability p-value calculation: Mean transcript half-life is 26.3 minutes. Do the genes that contain the kmer in their 3’ UTR have a significantly lower/higher mean half-life? 3. Controlling for multiple hypotheses: using theFDR - False Discovery Rate A list of significant kmers

  13. Discovery of stability-associated motifs in yeast 3’ UTRs 4. Creating motifs from kmers by clustering: AAGGCCT TTCCATCC GGCGCCT GGCGCCTT AAGGGCTT AAGGGCTA AGGCCTT TTCCATCT AGGGCTT GCCCCTT TTCCTTC GGCCCCTT TTCCATC GCACCTT AAGGGCTC AAGGCCTC GGCACCTT TCCTTCC TTCCTTCC

  14. A catalog of stability-associated motifs Motif M11 Motif M1 Expression level time • Mean half life: 46.5 min. • Number of genes: 89 • P-value: 1*10-300 • Mean half life: 16 min. • Number of genes: 640 • P-value: 1.2*10-50

  15. A catalog of stability-associated motifs Motif M11 Motif M1 • Mean half life: 46.5 min. • Number of genes: 89 • P-value: 1*10-300 • Mean half life: 16 min. • Number of genes: 640 • P-value: 1.2*10-50

  16. A catalog of stability-associated motifs • For the first time, a catalog of stability-associated • motifs was assembled • 53 motifs: • 40 de-stabilizing • 13 stabilizing • For comparison, the current promoter motif • catalog (Harbison et al.) contains 102 motifs. • ~1700 genes contain a stability-associated motif • Out of those, 850 contain both a stability motif, • and a promoter motif

  17. S. cerevisiae S. paradoxus S. kudriavzevii Many stability motifs are evolutionary conserved In other yeasts 16 were found to be significantly conserved M24 220 genes 105 56 Highly Conserved P-value=0.009 47

  18. HUMAN YEAST Evolutionary conservation remains all the way to human Comparing to mammalian 3’ UTR motifs (Xie et al. Nature, 2005) 11 were significantly similar to a mammalian conserved motif

  19. Functional enrichment Processp-value M1 cell growth and/or maintenance 7.32*10-5 cell organization and biogenesis 2.52*10-5 protein biosynthesis 4.22*10-5 nucleic acid metabolism 7.27*10-6 transcription from Pol II promoter 6.47*10-4 protein modification 4.69*10-4 M24 ribosome biogenesis and assembly 3.87*10-7 rRNA processing 3.88*10-6 protein biosynthesis 3.03*10-5 nucleic acid metabolism 1.42*10-4 RNA processing 1.32*10-6 transcription from Pol I promoter 1.86*10-7

  20. stop stop stop Stability affecting Motifs are complementary to promoter motifs • Integrating Harbison et al.’s data on promoter motifs • Three potential modes of regulation: M24

  21. Stability affecting Motifs are complementary to promoter motifs Processp-value M24 ribosome biogenesis and assembly 3.87*10-7 rRNA processing 3.88*10-6 protein biosynthesis 3.03*10-5 nucleic acid metabolism 1.42*10-4 RNA processing 1.32*10-6 transcription from Pol I promoter 1.86*10-7 Rap1

  22. X X √ √ Rap1 √ X √ X M24 #genes 10 82 282 21 1 0 -1 2 4 6 2 4 6 2 2 6 6 4 4 Stability affecting Motifs are complementary to promoter motifs All protein biosynthesis related genes Checked their steady-state expression profiles in a set of 40 conditions Normalized expression Time points

  23. SCC=0.5 SCC=0.05 Cellular component Cellular component mitochondria Mitochondria inner membrane 3’ UTR motifs associated with sub-cellular localization • Sub cellular clustering score & p-value • Uses GO annotation (cellular component) • And a similarity measure by (Lord, Bioinformatics, 2003) The SCC (Sub-Cellular Clustering) score

  24. Example – a putative mitochondrial zipcode The 3’ UTR yeast motif SCC score: 0.289 SCC p-value: < 10-6 Associated with 610 genes Out of which 260 genes are known to be Localized to the Mitochondria 3’ UTR motifs associated with sub-cellular localization Sub Cellular Clustering score:

  25. M1 0.289 <1E-6 610 YES (p-val<1E-3) mitochondrion mitochondrial intermembrane space mitochondrial matrix mitochondrial ribosome mitochondrial membrane mitochondrial inner & outer membrane mitochondrial inner & outer membrane translocase complex M22 0.11 1E-06 72 YES (p-val<1E-3) endoplasmic reticulum M13 0.43 3.9E-05 8 NO endomembrane system A catalog of 23 motifs associated with sub-cellular localization name logo SCC score SCC p-value #targets conservation enriched terms

  26. Support from the literature • Very few experimentally verified motifs: • M1: CYC1 (Russo, Mol Cell Biol, 93) • M24: was suggested to be the • binding site for Puf4p (Gerber, PLOS, 2004) • Foat B, PNAS, Dec. 2005 • Mitochondrial Motif: • was suggested to be the binding site for Puf3p (Gerber, PLOS, 2004, Gerber, PNAS, 2006) • the co-translational import model of mitochondrial genes (Kaltimbacher V, RNA, Jul. 2006)

  27. Summary – part 1 • A first large scale catalog of 3’ UTR motifs that are directly associated with effects on transcript stability (and sub-cellular localization) in yeast. • 53 motifs: 40 de-stabilizers, 13 stabilizers • many of them are conserved in other yeasts • 11 are significantly similar to recently published mammalian conserved 3’ UTR motifs • intricate relationship with promoter motifs • http://longitude.weizmann.ac.il/3UTRMotifs/ A first step towards filling the gap of transcript level regulation

  28. miRNA Transcription Translation Transcription mRNA Protein DNA Degradation Degradation Post transcriptional control • Functional sequence motifs in 3’ UTRs stability associated motifs • (Shalgi et al. Genome Biology2005) • miRNA regulation (Xi et al. Clin Cancer Res. 2006)

  29. Differentially Regulated Micro-RNAs by Tumor Suppressor p53 in Colon Cancer Xi Y, Shalgi R, Fodstad O, Pilpel Y, Ju J. Clin Cancer Res. 2006

  30. Background – p53 • p53 is a tumor suppressor. • regulates DNA repair, cell senescence, appoptosis, and more. • Is a critical inhibitor of tumour development • is the most frequently mutated gene in human cancers • p53 is a TF.

  31. Background – microRNAs (miRs) • Small (~21 nt) RNAs • Post-transcriptional silencing: • Regulate mRNA degradation • and translation inhibition • Through RISC (RNA induced silencing complex)

  32. miRNAs that are transcriptionally regulated by p53 • Identification of miRNAs regulated by p53: • Cancer cells: HCT-116 +/+ and p53- cells show differential miRNA expression • using miRNA microarray: • 43 miRs were downregulated • 11 were upregulated in the wt (vs. the mutant) p53+ p53-

  33. 0- 3 miRNAs that are transcriptionally regulated by p53 • Looking for p53 binding sites in miRNA promoters • p53 binding site search: (Wei CL, Cell, Jan 2006)

  34. miRNAs that are transcriptionally regulated by p53 • List of p53 binding sites in promoters of the 10 most highly variable miRNAs miRNA pos. gap len. Site Score hsa-let-7b 828 0 AGCCATGTCT..CTTCTTGTCT 87.56 • 9 out of the 10 had a site in their promoter

  35. Global analysis of p53 sites in miRNAs promoters • Looking at all known miRNAs in the database (326): • 130 (~40%) have a putative p53 binding site in their promoter • control: 1000 sets of reshuffled promoters: • p-value < 0.001

  36. p53 regulation of miRNAs • A highly significant enrichment for p53 binding sites in miRNA upstream regions • Is there a specific tendency for p53 to regulate miRNAs? • p53 as a hub in the signaling network • another mechanism of p53 global control of cellular processes under stress

  37. S. cereviciae de-adenylation dependant degradation Stabilizing/de-stabilizing RNA binding proteins. No Dicer & RISC 3’ UTR motifs (perhaps also 5’ UTR) Higher organisms Both de-adenylation dependant deg. And miRNA Dicer & RISC 3’ UTR motifs miRNA DNA mRNA Protein Summary – transcript stability mechanisms

  38. Thanks ! • Tzachi Pilpel • Moshe Oren • Ron Shamir • Michal Lapidot • Ophir Shalem • and all the other Pilpel lab members Collaborators: P53 miR project: Ju group Cancer Research Institute, Mobile, Alabama

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