Download mRNA and protein abundance for glutathione-S

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)
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