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Translation and translational control in eukaryotes I Winterterm 2015/2016 Ernst Müllner, MFPL Department of Medical Biochemistry [email protected] Helmut Dolznig Institute of Medical Genetics [email protected] TransCon I 2015 / 2016 (Stand 2015-12) – 'Structure' * Bedeutung der TransCon als regulatorisches Prinzip * Die Translations-Maschinerie (Erinnererung und darüber hinaus) * Signaltransduktion und Translation (H. Dolznig) * regulatorische Elemente in den 5' UTRs (TOPs, 5'uORFs, …) * IRESs in viralen und zellulären Systemen / Apoptose TransCon II 2016 (Stand 2015-12) – 'Function' * Onkogenese durch ribosomale Proteine, miRs, Initiationsfaktoren… Dolznig * spezifisch RNA-bindende und regulatorisch wirksame Proteine * mRNA Stabilität (NMD, ARE-Elemente, IRE/IRP System) * mRNA Export aus dem Zellkern, ‘pioneer round of translation’ * zelluläre Verteilung von mRNAs in der Entwicklung und in differenzierten Zellen * TransCon und Stress (heat shock, amino acid starvation, frühe Apoptose, PERK TRansCon I und II sind voneinander unabhängig, ‘I’ vor ‘II’ macht natürlich Sinn. Kein Thema: small and large (nuclear) RNAs (lncRNAs, miR, shRNA, snoRNAs etc), splicing, Viren (nur punktuell), Bakterien, Pflanzen und unizelluläre Eukaryoten ^-^ Our work: signal transduction in red blood cells Participation of erythrocytes in blood clotting Current literature: only platelets involved, red blood cells ‘trapped‘ in fibrin network; we have good data to the contrary. Aberrant thrombus formation via proteins secreted by tumor cells (ovarial carcinoma) Our work – signal transduction in red blood cells Participation of erythrocytes in blood clotting (bottom) genetic diseases related to aberrant signaling in RBCs medicalschool.tumblr.com/post/18256087351/r ed-blood-cells-erythrocytes-trapped-by-fibrin Why Translational Control? Ideology of lectures Why study translational control ? Obvious – to cure cancer like everybody else REVIEW Targeting the translational machinery as a novel treatment strategy for hematologic malignancies Patrick R. Hagner, Abraham Schneider, and Ronald B. Gartenhaus Blood, 14 January 2010 10.1182/blood-2009-09-220020 http://bloodjournal.hematologylibrary.org/cg i/content/abstract/blood-2009-09220020v1?papetoc Why study translational control ? Asslaber et al. Treatment free survival of B-CLL* patients with or without alterations in the p53 axis (A) p53 del/mut (B) p53attenuated (p53 del/mut, SNP309 G/G, del11q) (C) low or high miR-34a expression and (D) correlation of miR-34a expression with lymphocyte doubling time. Similar figure in other ref but not so nice BOTH miR 34 + 29 function as tumor suppressors connection miR TransCon onco-miRs in tumorigenesis vs. miRs as tumor suppressors * B-CLL = B-cell chronic lymphatic leukemia (NonHodgkin B cell lymphoma) Post-transcriptional regulatory elements affecting gene expression sometimes, the older figure is the better one but look to the title of the review, next slide Why study translational control ? Dolznig TransCon II Müllner TransCon I + II Why study translational control ? Relationships between mRNA and protein abundances, as observed in large-scale proteome- and transcriptomeprofiling experiments. mRNA transcript abundances only partially correlate with protein abundances, typically explaining approximately one- to two-thirds of the variance in steady-state protein levels, depending on the organism. This trend is evident in data from NIH3T3 mouse fibroblast cells. Vogel & Marcotte, Nat Rev Genet 13(4), 227-232, (2012) Weak correlation between mRNA and protein levels I transcripts from various S. cerevisiae genes Apparently, the transcriptome does not faithfully represent the proteome. This as a serious problem e.g. in expression profiling since the proteome and not the transcriptome determines cell phenotype ! TIBS 26, 225-229, (2001) Weak correlation between mRNA and protein levels I transcripts from various S. cerevisiae genes Figure legend: The correlation between the expression levels of 136 Saccharomyces cerevisiae mRNAs and their corresponding proteins is depicted as protein / mRNA ratio (arbitrary units) against a running index. Numbers of transcripts per cell for each mRNA were deduced from SAGE (serial analysis of gene expression) data; protein levels were determined after separation of samples in 2D gels, followed by quantification and identification of the spots. The data reveal a poor correlation between mRNA and protein abundance (r = 0.57). Indeed, 72% of the genes analysed fall outside a confidence interval, being twofold higher or lower than the average ratio. Protein abundance can vary more than 20-fold for a given expression level of a particular mRNA. Conversely, for a given amount of protein, the expression of the corresponding transcript can differ by up to 30-fold. Gygi, S.P. et al. (1999) Correlation between protein and mRNA abundance in yeast Mol. Cell. Biol. 19, 1720–1730 Futcher, B. et al. (1999) A sampling of the yeast proteome Mol. Cell. Biol. 19, 7357–7368 TIBS 26, 225-229, (2001) Weak correlation between mRNA and protein levels II same mammalian mRNA in diverse cell types and tissues raw data from: Anderson, N.L. and Anderson, N.G. (1998) Proteome and proteomics: new technologies, new concepts, and new words. Electrophoresis 19, 1853– 1861 Tew, K.D. et al. (1996) Glutathione-associated enzymes in the human cell lines of the National Cancer Institute Drug Screening Program. Mol. Pharmacol. 50, 149–159 Legend: The correlation between mRNA and protein abundance for glutathione-S-transferase in 57 human cell lines derived from nine different tissues represented as in (a). Here, the correlation is even lower (r = 0.43). There is a greater than 40-fold variation with respect to the average protein:mRNA ratio (aqua line). Apparently, protein levels cannot be reliably predicted from mRNA abundance. TIBS 26, 225-229, (2001) Weak correlation between mRNA and protein abundance due to widely different ribosome-loading of individual mRNAs as determined in POLYSOME GRADIENTS linear sucrose density gradients, 15-40% saturation Assembled polysomes sediment fastest, followed by monosomes, ribosomal subunits and free mRNPs. Inherent problem in expression profiling TIBS 26, 225-229, (2001) LEGEND to previous figure Different mRNAs are translated with widely different efficiencies Cytoplasmic extracts from primary human activated T cells were fractionated in sucrose gradients, and the RNA from each fraction analysed by northern blotting. The ribosomal RNA distribution profile (28S, 18S and 5S rRNA; indicated by arrowheads in the top panel) enables the distinction of fractions comprising mRNPs and mRNAs engaged in pre-translational complexes (fractions 1–10; lightblue area, designated as ‘free’), and those containing the polysome-bound mRNAs (fractions 11–20; dark-blue area, labelled ‘bound’). Hybridization with probes specific for the mRNAs encoding p38MAPK (red), GADD153 (green) and NT-4 (purple) revealed extensive variations in mRNA distribution in sucrose gradients. In the corresponding distribution profiles (bottom), intensities for each fraction are plotted as a percentage of the total signal on the filter for a given mRNA to facilitate comparison. The differential distribution of these mRNAs implies different translation efficiencies. This phenomenon appears to be much more common than previously anticipated, and is not restricted to particular transcripts, cell types or species. Abbreviations: GADD153, growth arrest and DNA damage; MAPK, mitogen-activated protein kinase; NT-4, neurotrophin 4. TIBS 26, 225-229, (2001) Conventional mRNA expression profiling only detects transcriptionally regulated genes In standard experiments, profiling total mRNA from cell pairs identifies genes being either transcriptionally induced (green) or repressed (red) during the transition. However, mRNAs potentially regulated by translational control (grey circles / blue arrows) are not detected. TIBS 26, 225-229, (2001) Polysome-bound mRNA expression profiling detects both, transcriptionally and translationally regulated genes II Fractionating total mRNA into polysome-bound and ribosome-free mRNA populations (blue arrows) enables detection of translational changes (bottom half). mRNAs redistributing (blue squares) from ribosome-free towards polysome-bound pool (translational activation), or vice versa (translational repression), will display different hybridization signals. Note that hybridization signals for transcriptionally regulated mRNAs (red, sucrose gradient green) will be uncovered as in fractionation, profiles of standard profiling. 4 compartments Validation of Method I Translational control during differentiation of erythroid cells Joosten, M., Blazquez-Domingo, M., Lindeboom, F., Boulme, F., Van Hoven-Beijen, A., Habermann, B., Lowenberg, B., Beug, H., Müllner, E. W., Delwel, R., and Von Lindern, M.; Translational Control of Putative Protooncogene Nm23-M2 by Cytokines via Phosphoinositide 3-Kinase Signaling; J Biol Chem 279, 38169-38176, (2004) Validation of Method II Translational regulation during activation of T lymphocytes (A) Staining of the filter indicating the distribution of 28S, 18S, and 5S RNA. (B–E) Sequential hybridisation of the filters with [alpha-32P]dCTP-labeled probes specific for ATF-4, transducinbeta2, Gadd 153, and p38 MAP kinase cDNAs, respectively. F) Signals were quantified by laser densitometry. To facilitate comparison, total RNA content summarised over all fractions was set to 100%. Empty circles, distribution of mRNA in resting T cells (R); filled circles, distribution of mRNA in activated T cells (A). Mikulits, W., Pradet-Balade, B., Habermann, B., Beug, H., Garcia-Sanz, J.A., and Müllner, E.W. Isolation of translationally controlled mRNAs by differential screening FASEB J. 14, 1641-1652, (2000) Protein abundance, not mRNA abundance is conserved Considering orthologs across highly divergent species, abundances of proteins are more conserved than abundances of the corresponding mRNAs, suggesting that protein abundances may be evolutionarily favored. (Numbers indicate Spearman rank correlation coefficients [?] between molecular abundances.) Data such as these support an important role for regulatory mechanisms occurring downstream from the setting of mRNA levels. Vogel & Marcotte, Nat Rev Genet 13(4), 227-232, (2012) Key concepts for analysis of protein abundance I The ‘Western blot-problem’ Vogel & Marcotte, Nat Rev Genet 13(4), 227-232, (2012) ‘Western blot-problem’: How to compare abundance of two different proteins? Calibration curve for each one with pure (isolated or synthesized) protein Key concepts for analysis of protein abundance II !! Western flow cytometry Vogel & Marcotte, Nat Rev Genet 13(4), 227-232, (2012) Sidestep / old story – miR binding sites discovered before miRs Systematic discovery of regulatory motifs in human promoters and 3' UTRs by comparison of several mammals X. Xie, J. Lu, E. J. Kulbokas, T. R. Golub, V. Mootha, K. Lindblad-Toh, E. S. Lander and M. Kellis Comprehensive identification of all functional elements encoded in the human genome is a fundamental need in biomedical research. Here, we present a comparative analysis of the human, mouse, rat and dog genomes to create a systematic catalogue of common regulatory motifs in promoters and 3' untranslated regions (3' UTRs). The promoter analysis yields 174 candidate motifs, including most previously known transcription-factor binding sites and 105 new motifs. The 3'-UTR analysis yields 106 motifs likely to be involved in post-transcriptional regulation. Nearly one-half are associated with microRNAs (miRNAs), leading to the discovery of many new miRNA genes and their likely target genes. Our results suggest that previous estimates of the number of human miRNA genes were low, and that miRNAs regulate at least 20% of human genes. The overall results provide a systematic view of gene regulation in the human, which will be refined as additional mammalian genomes become available. Xie et al., Nature 434(7031), 338-345, 2005 Ernst Müllner, 2011-01 Conservation properties of Err-α promoter regions Conservation in GABPA promoter region reveals functional Err-α motif. Asterisks denote conserved bases. The yellow box marks the experimentally validated Err-α-binding site. Xie et al., Nature 434 (#7031), 338345, (2005) Ernst Müllner, 2011-01 Conservation properties in human promoter regions and 3’UTRs The comparative analysis can be illustrated by considering a known regulatory motif. The 8-mer TGACCTTG is known to be a binding site of the Err-α protein and to occur in the promoters of many genes induced during mitochondrial biogenesis. The promoter of the GABPA gene contains a well-studied Err-α-binding site, which is conserved across all four species, and stands out from the non-conserved flanking sequence (Fig. 1b, see next slide). More generally, the Err-α motif occurs 434 times in human promoter regions and 162 of these occurrences are conserved across all four species; the conservation rate is thus 37%. By contrast, a random 8-mer motif shows a markedly lower conservation rate in promoters (6.8%). Moreover, the high conservation of the Err-α motif is specific to promoter regions; it shows a much lower conservation rate in introns (6.2%). Extent of excess conservation was quantified by using a motif conservation score (MCS), which essentially represents the number of standard deviations (s.d.) by which the observed conservation rate of a motif exceeds the expected conservation rate for comparable random motifs. Figure c–e, Excess conservation in promoter and 3’UTR regions reveals short sequences under evolutionary selection. Motif conservation score (MCS) distribution is shown for all 6-mer motifs in aligned promoters (c), 3’UTR regions (d) and introns (e). The dashed curve shows fit to gaussian distribution. Excess conservation relative to this distribution is shown in red. Xie et al., Nature 434(7031), 338-345, 2005 Properties of discovered 3’UTR motifs and corresponding miRNA genes a, Directionality of 30 3’UTR motifs revealed by comparing conservation on forward and reverse strands. Strand preference is also seen for splice signals, but conservation of promoter motifs is largely symmetric. hsa-let-7a, Homo sapiens let-7a. Xie et al., Nature 434 (#7031), 338-345, (2005) Ernst Müllner, 2011-01 from this and much other work more than 50 % of all mRNAs are subject to (individual ! ) translational regulation or differential mRNA stability or localization or … Remainder of lecture: How ? Basics I – Molecules Level ~ reminder 1st year plus extension Eukaryotic messenger RNA (mRNA) ’Insult-Variant’ Das primäre Transkript erhält durch Übertragung von GTP und Methylierung von Guanin in Position 7 eine Cap-Struktur am 5'-Ende und einen poly-A-Schwanz am 3'-Ende. Die so modifizierte RNA nennt man hnRNA (heterogenous nuclear RNA) oder "pre-messengerRNA". Diese reift im danach im Kern durch Splicing zur eigentlichen mRNA, bei welcher die nicht-kodierenden Abschnitte (Introns) entfernt sind. Die mRNA passiert die Kernporen und gelangt so ins Cytoplasma. Dort erfüllt sie ihren eigentlichen Zweck, sie wird übersetzt ("translatiert"). Die mRNA ist Übermittler (Bote) der genetischen Information zwischen Kern und Cytoplasma: sie dient als Matrize für die Übersetzung von Nukleotidsequenzen in Aminosäurensequenzen. Translatiert wird aber nur derjenige Abschnitt der mRNA, welcher vom Startcodon AUG bis zu einem Stopcodon (UAA, UAG oder UGA) reicht. Initiation bei Prokaryoten ’Insult-Variant’ Prokaryoten können mehrere Proteine auf einer einzigen mRNA codieren und somit gleichzeitig verschiedene Proteine von derselben mRNA synthetisieren. Solche polycistronische mRNAs benötigen deshalb auch dementsprechend viele Shine-Dalgarno-Sequenzen (S/D; 5'-AGGAGGU-3’) und ebensoviele Start- und Stop-Codons: Der Translationsvorgang selbst beginnt, sobald eine Initiator-tRNA mit dem Start-Codon, das durch die 30S Untereinheit ausfindig gemacht wurde, eine Basenpaarung eingeht. Diese Initiator-tRNA ist bei Bakterien mit NFormylmethionin (fMet) beladen. Den Komplex aus mRNA, 30S Untereinheit und Formylmethionin-tRNA nennt man Initiationskomplex, und seine Bildung markiert das Ende der Initiationsphase der Translation. this will remain the only slide on prokaryotes Initiation bei Eukaryoten ’Insult-Variant’ Der wesentliche Unterschied zu Prokaryoten betrifft die Art, wie die kleine Untereinheit des Ribosoms (40S bei Eukaryoten) an die mRNA bindet und das AUG-Codon, an dem die Translation beginnen soll, ausfindig macht. Außerdem sind für die Initiation mindestens elf Initiationsfaktoren erforderlich, im Gegensatz zu E. coli, wo drei solche Proteine ausreichen. Bei Eukaryoten bindet einer dieser Initiationsfaktoren, das cap-bindende Protein eIF4E, an die 5'Cap-Struktur der mRNA und fördert die Bildung eines Komplexes zwischen der mRNA und der 40S Untereinheit des Ribosoms. Die 40S Untereinheit hat bereits Initiator-Methionyl-tRNA gebunden. Die 40S Untereinheit wandert dann der mRNA entlang, bis sie ein Start-Codon erreicht. Dort wird die 60S Untereinheit des Ribosoms angefügt, und es resultiert ein 80S-Initiationskomplexes, der die Initiator-Met-tRNAMet sowie mRNA enthält und für die Elongation bereit ist. System components – characterization of the transcriptome RNA sub-classes in a typical mammalian cell Sidestep – intron versus exon length Alberts, Molecular Biology of the Cell 5/e © 2008 Wiley-VCH, Fig 6.32 functions for intron RNAs ? long non-coding (lnc)RNAs System components – ribosomal RNAs Alberts, Molecular Biology of the Cell 5/e © 2008 Wiley-VCH, Fig 6.42+43 System components – ribosomal RNAs Alberts, Molecular Biology of the Cell 5/e © 2008 Wiley-VCH, Fig 6.44+45 System components – ribosomes Alberts, Molecular Biology of the Cell 5/e © 2008 Wiley-VCH, Fig 6.63 System components – ribosomes Atomic structure of the 30S Subunit from Thermus thermophilus. Proteins in blue, the single RNA chain in orange. en.wikipedia.org/wiki/Ribosome contains animated GIFs of the structures Atomic structure of the 50S Subunit from Haloarcula marismortui (Archebacterium). Proteins in blue, the two RNA chains in orange and yellow. The small patch of green in the center of the subunit is the active site. System components – tRNAs Alberts, Molecular Biology of the Cell 5/e © 2008 Wiley-VCH, Fig 6.52 System components – tRNAs + aminoacyl-tRNAs activation of amino acids by individual specific aminoacyl-tRNA-synthetases in consequence, RNA double strands contain G=U as well as A=U base pairs! Alberts, Molecular Biology of the Cell 5/e © 2008 Wiley-VCH, Fig 6.53+56 System components – mRNAs G. Pesole et al., Gene 276, 73–81, (2001) poly A-tail + 3' end-formation is missing in the present Transcon I files (is in context of CPE In TransCon II) put this or similar slide here together with another one on cap-structure System components – polysomes The length of a eukaryotic mRNA (2000 b on average), permits translation by several ribosomes simultaneously. Such ribosome-mRNA (better: mRNP) complexes are called either poly-ribosomes or polysomes. This allows for rapid and efficient protein synthesis. Polysomes can be visualized by electron microscopy. To put the scale bar below into perspective: As can be seen, 4 ribosomes next to each other 0.1µm = 100 nm 1 ribosome = 25 nm. Average diameter of a cell is ~ 15 µm = 15.000 nm a chain of 600 ribosomes reaches from end of a cell to the other. what about the size of mitochondria? Alberts, Molecular Biology of the Cell 5/e © 2008 Wiley-VCH, Fig 6.76 System components – bringing it all together into a RING Alberts, Molecular Biology of the Cell 5/e © 2008 Wiley-VCH, Fig 6.76 Complexes formed on capped, polyadenylated double-stranded RNA in the presence of eIF4G, poly(A)-binding protein (PABP) and eIF4E but in the absence of ribosomes as visualized by atomic force microscopy. Nature Rev Mol Cell Biol 2, 521-529, (2001) youtube.com/watch?v=bJmml5KLJO8 youtube.com/watch?v=ReogkhkoArw