Translation: what is it good for?
Featuring a review of “Reuveni, S. et.al (2011). Genome-Scale Analysis of
Translation Elongation with a Ribosome Flow Model. PLoS Computational Biology,
7(9), e1002127”.
cellular
cellular
dynamics
dynamics
Bradly Alicea
http://www.msu.edu/~aliceabr/
Introduction
Understanding translation as a complex process is an important
aspect of health and evolution:
Molecular Systems Biology:
* viral adaptation to host -- analyzed proteome to look at codon
usage and amino acid preferences (Vol. 5, 311).
* dependence on GroEL – affects codon usage, supports
protein misfolding (Vol. 6, 340).
Cell:
* mistranslation leads to protein misfolding -- constraint on
coding sequence evolution (Vol. 134, 341-352)
* efficiency of protein translation is evolutionarily conserved
(Vol. 141, 344-354).
INSETS: IEEE Spectrum,
March 2011, 38-43
Introduction
Translation Measurement techniques:
tRNA distribution (inferential)
Proteomic (inferential)
RNA seperation (empirical)
Ribosome profiling (empirical)
Direct Measures of Translation (other than sequence):
1) tRNA adaptation index (tAI): mean adaptation of codons to tRNA pool.
2) codon adaptation index (CAI): tAI + each codon weighted based on
frequency in set of highly-expressed genes.
3) tRNA pool: total collection of tRNAs that contribute to construction of
peptide chains.
SLLTISSA
UUC
UAA
tRNA pool
selection
AEDIVSRE
UUCGCUAAUAUCCGCG
rRNA/mRNA affinity
Peptide Sequences
Goal of Reuveni et.al Paper
Focus on elongation: an iterative process, each codon are recognized by a
specific tRNA, which add an amino acid to growing peptide chain.
Contributions:
1) physically-plausible computational model solely based on coding sequence.
2) new way to study translational elongation (e.g. capture effect of codon order
on translation rates). Translation is rate-limiting (first-come, first-serve).
3) uncover several central and uncharacterized processes (e.g. stochastic nature of
translation, interaction between ribosomes).
Protein and ribosome footprinting datasets:
* E.coli (bacteria), S. pombe and S. cerevisiae (yeast).
* compare what is flowing through ribosome at any given time with massspectrometry derived protein abundance (produces a correlative relationship that
can be compared with gene expression).
Introduction to Model Components
Dynamical (TASEP) model:
*
exponentially-distributed
translation
time
(vi
is
nonlinear).
* ribosomes have volume, can
interfere with each other.
mRNA compete for spots on ribosome. Speed
of translation = greater protein abundance
Subset of all
“docked”mRNA
More abstracted
from biology than
TASEP (fewer
parameters)
Dynamical (RFM) model:
two free parameters:
* initiation rate (λ), transition
rate (λn) – related to coadaptation between codons and
tRNA pool.
* number of codons (C) at a
site (ribosome) – related to
ribosome density (pn).
Agreement between RFM and TASEP
Stack that describes conditions for
parameter values 1, 1 < i < n, and n.
Differential equations that describe
RFM dynamics.
Comparison of RFM and TASEP
models for a range of transition
rates.
* tight correlation between the two,
with less tight correlation at higher
values.
* transition rate similar to a
mutation rate (μ - stochastic
parameter).
Elongation rate capacity,
comparisons with protein abundance
Elongation rate capacity:
* each gene = different translation elongation capacity.
* capacity = maximal translation rate of gene.
Protein abundance (PA) predictor:
* tAI central feature (does incorporate mRNA levels and evolutionary rates – but
not codon order or ribosome jamming).
PA vs. RFM/tAI for bacteria and yeast
PA vs. RFM
PA vs. tAI
E.coli
R = 0.54, p < 10-16
R = 0.43, p < 10-16
S. pombe
R = 0.63, p < 10-16
R = 0.56, p < 10-16
For S. cervisiae, tAI performs better than RFM only for most highly expressed
genes (more robust to permutations in codon order).
Steady State Model Function
RNA molecules: sites C codons in length (e.g. size of footprint).
C = {3, 4,…, 25}
* where Cmin = 3 and best predictor of protein abundance = 25.
RNA molecules arrive at ribosomes with initiation rate λ, and can only bind if
site is not occupied.
* initiation rate (λ) is a function of physical constraints (number of free
ribosomes, folding energies, base pairing between RNA and rRNA).
Probability that ith site occupied at time t, or pi(t), represents ribosome flow (e.g.
translation rate, or R).
* each gene has a different elongation capacity. Maximal translation rate (Rmax)
of a gene occurs for infinitely large initiation rate (λ).
* RNA degradation was taken into account in previous work, but could not
improve the performance of this model.
Translation Rate
Experiment
Prediction
Left: genomic profiling of R vs. λ for 10 typical genes (gene expression), red curve
is the mean.
* very small values of λ show little to no activity (artifact).
Right: predicted profile for top 25% (blue) bottom 25% (red) of genes (w.r.t.
expression).
* characteristic level of gene expression is asymptotic to characteristic value for R.
Comparisons to Protein Abundance
Figure 4, A) coarse-graining parameter (C, xaxis) vs. correlation between protein
abundance and translation rate (y-axis).
Coarse-graining parameter = number of codons
considered at one time (representative of ribosomal
footprint, or maximal ribosomal RNA fragment
size using this approach).
Heterologous gene expression (produce mRNA in one species using gene libraries
from another species):
1) Welch et.al (PLoS One, 4, e7002 -- 2009), genes for Bacillus phage proteins). All genes
encode same AA, each has different codon composition.
* correlation between RFM predictions and protein abundance (p = .0004), only for tiny initiation rates (ratelimiting in this context).
2) Burgess-Brown et.al (Protein Experimental Purification, 59, 94-102 -- 2008), optimized
codons from 31 human genes, expressed in E.coli.
* in 18/31 cases (58%), protein abundance improved post-optimization. Correlation with fold-change
upregulation higher (0.45) for RFM model than tAI model (0.34).
Speed, Optimality and Variation
Figure 6, A. Mean genomic translation rate
against initiation rate.
* dotted lines = saturation points on x- and y-axes
(~90% of maximal rate, or "working point" that
varies by species) for 0 hours (dark blue
function), 9 hours (light blue function).
Figure 6, B. Variety of human tissues, cell
types (left-hand side = brain regions, righthand side = tissues such as kidney, skin,
lung, liver, and heart).
* correlations between known mRNA expression
levels and model predictions. Blue bars = tAI.
Reddish brown = RFM. Inset is the improvement
in correlation with mRNA for RFM model > tAI
model.
Effects of tRNA pool
Specialization versus adaptation: two strategies employed by cyanophages to enhance
their translation efficiencies. Nucleic Acids Research, 2011, 1-13:
* specialization vs. adaptation in viruses w.r.t. translation efficiencies.
Modify translation
efficiency during
infection cycle
In both cases, virus must use host’s replication
machinery:
Specialization: virus sequence evolves to match tRNA pool
management, CG context of a specific taxon.
Adaptation: virus sequence highly evolvable, allows virus
to adapt to a number of taxonomic targets.
Bias tRNA pool towards
CG content of host,
negligible effect on host
Higgs, P.G. and Ran, W. (2008). Molecular Biology
and Evolution, 25(11), 2279-2291:
Enhancement of fitness
for virus, commensalism
* coevolutionary dynamics: tRNA gene content bias codons
used in translation towards those most rapidly translated.
Copy number can evolve to optimize codon usage.
* tRNA genes provide a new force of evolution (bias protein
production).
Ribosome footprinting
Ingolia et.al (2009). Genome-wide analysis in vivo of translation with nucleotide
resolution using ribosome profiling. 324, 218-223.
Ribosomal profiling: use deep sequencing (sequence data) to uncover protected RNA
fragments. Quantify RNA abundance.
Ribosomal occupancy of RNA: short sequences that allow us to
detect different phases of translation.
* short sequences (footprint) = number of codons on ribosome.
GCN4: highly upregulated in translatome (starvation response),
less so using standard polysome harvest techniques.
Relation between CRL technique
and ribosome footprinting
CRL METHOD (e.g. TRAP, buffer-based extraction)
RNA that has a looser association (e.g. moieties) with ribosome
(larger fragments, represent effects of post-transcriptional
modifications, transcriptome-like quantification).
RIBOSOME FOOTPRINTING (RF)
RNA that is feeding through ribosome at time t, explicit
association with ribosome (smaller fragments, directly
correlate to protein abundance).
In both cases, sequencing is possible:
* requires library construction, which is the main distinction
between the two approaches.
* matter of subsampling (RF is a subsample of polysome
method, not necessarily more precise or with better resolution).
Sequenced derived
from fragments
between subunits
Effects of translation and phenotype
Wilson, M.A., Meaux, S., Parker, R., and van Hoof, A. (2005). Genetic interactions
between [PSI] and nonstop mRNA decay affect phenotypic variation. PNAS,
102(29), 10244-10249.
Yeast strains can reversibly interconvert
between [PSI+] and [psi-] states:
* [PSI+] = prion form the translation termination factor
eRF3.
* causes read-through at stop codons, can lead to
phenotypic variation.
Nonstop mRNA decay triggered
ribosome reaches 3' end of transcript:
when
* interaction between [PSI+]-induced phenotypic
variation, defects in nonstop mRNA decay.
* some phenotypic effects of [PSI+] may be due to
read-through of normal stop codons (produces extended
proteins, modulates phenotype).
* periodic sampling of 3' UTR = rapid divergence (for
novel and beneficial protein extensions).
The “big picture”
Foss, E.J. et.al (2011). Genetic variation shapes protein networks mainly through
non-transcriptional mechanisms. PLoS Biology, 9(9), e1001144.
Beyond the transcriptome: what controls protein variation? PLoS Biology, 9(9),
e1001146:
* previous studies in yeast -- demonstrated correlation between protein abundance
and transcript abundance.
* does not imply that this correlation will exist for the same gene across different
individuals.
* only 27% of genes exhibit correlation. Vast majority of highly expressed genes
determine either transcript or protein levels (but not both).
* what are post-transcriptional mechanisms? Gray area/lots of nuance between
transcriptional RNA and peptide sequence/protein structure.
RNA decay and Regulatory Control
What’s going on here: physiological “control”. Based on RNA kinetics (decay,
transcription/translation rate, ½ life). Initial model:
Feedforward scenario
Mechanism for differences observed
between TLT, TST within passage,
condition.
Feedback with saturation scenario
Mechanism for differences
observed across TLT, TST or
between passage, condition.
(+)
(+)
Stimulus
(+)
(+)
Presence of
mRNA
Rein control:
* two sources (TLT, TST) that
are independently regulating
(controlling)
a
common
process (cellular state).
(+)
Production at
ribosome
Stimulus
(+)
Presence of
mRNA
Production at
ribosome
If above threshold, (-)
If below threshold, (+)
Decay rate
(1/d)
Decay rate
(1/d)
INSETS: IEEE Spectrum, March 2011, 38-43
Control Model Based on Decay
C
A1
TST
B
D
TLT
A2
Feedback with saturation model using drug
treatments (stripped-down version of RNA
regulation and decay in cell).
Theoretically:
* Actinomycin D disallows A.
* Mitomycin C disallows A, allows C and D.
FB
FF
TST
D
* Saporin disallows B, C, and D.
Control strategy: rein control (FB, FF drive state of TST over time)
with brake (saturation, characterized by decay).
Decay off example
TST
TLT
FB, FF off example
TST
TLT
Control Model Based on Decay
Transition rules:
3) if TST > TLT, then x > 0.
1) for t1, difference between input and TST
2) for tn > 1, difference between TSTtn
and TSTtn+1 or TLTtn and TLTtn+1
4) if Bt-1> Bt, then FB is x > 0.
Example model run using data from COL qPCR in L10A fibroblasts under
Actinomycin D treatment, 3d.
0
FB
20.0104/
28.4165
TST
FF
D
0d
0
TLT
D
21.25073
1.24
1d
3.34
2d
2.811
12.799
0
0
24.06
31.235
0
27.89
0
26.46
3d
5.74
30.21
2.313
Conclusions
Translation is a relatively unexplored area:
Sequence compression:
Translation (mRNA)
DNA-RNA,
RNA-RNA’,
Transcription (mRNA)
Post-transcriptional
modifications
Translation-associated RNA provides several new pieces
of information about cellular biocomplexity:
1) what goes into protein production?
2) what is the speed of translation? Aggregation rates of
RNA?
3) new computational models for cellular function?
RNA-PROTEIN
Protein (peptide)
Translation (tRNA
conversion)