Presented at the NZSBMB/NZMS Conference in Christchurch 2016
CustomScience Award
A core assumption of gene expression analysis is that mRNA abundances broadly correlate with protein abundance, but these two can be imperfectly correlated. Some of the discrepancy can be accounted for by two important mRNA features: codon usage and mRNA secondary structure. We present a new global factor, called mRNA:ncRNA avoidance, and provide evidence that avoidance increases translational efficiency. We demonstrate a strong selection for the avoidance of stochastic mRNA:ncRNA interactions across prokaryotes, and that these have a greater impact on protein abundance than mRNA structure or codon usage. By generating synonymously variant green fluorescent protein (GFP) mRNAs with different potential for mRNA:ncRNA interactions, we demonstrate that GFP levels correlate well with interaction avoidance. Therefore, taking stochastic mRNA:ncRNA interactions into account enables precise modulation of protein abundance.
4. mRNA levels are imperfectly correlated with protein levels
Lu et al. (2007) Nature biotechnology.
5. Determinants of protein concentration
Protein concentration depends on mRNA concentration, translation and
degradation rates
DNA
[D]
RNA
[R]
Protein
[P]
ktranscription ktranslation
kmRNA degradation kprotein degradation
0 1
A
T GGC
TA
A
GGGGCA
A
T
C
T
T
TA
C
A A
G
AT
CC
G
T
T
C
C
T
G
A
AC
G
C
AC
T G
C
G
T C
G
G
G
A
A
C
G
T
G
T
T C
CAGTTTCTATTTATT
T
G G T G A A T G GTATTA A G C T GC
AA
G
G G
C
AA
A
T
C
G
A
G
T
C
T
TT
T
G
A
T
C
AG
T
T
C
G
T
G
A
T
C
C
T
G
T
T
G
A A
A
A
A
C
A
C
G
G
T
C
A GC
C
A
G
A
T
G
G
T TT
A
C
A
A
GC
A
C
G
C
G
A
T
T
T C T A
C
T
G
T
T G T C C CG
T CT
C
G C C C G G T T T C
T
C
AT
CA
CA
GTAA
CAACGCCG
GT
GGC
G
G
T
A
C
C
A
G
C
A
G
T
A
A
C T A C C A T
C
A
TGGTAGCAGCG
C
G
C A
G
A A
T
AC
T
T
CC
G
C
G
C
A
ACAGG
A
C
A
G
C
G
A
A
GAAACCG
A
A
TAA
de Sousa Abreu, Penalva, Marcotte & Vogel (2009) Global signatures of protein and mRNA expression levels. Molecular
BioSystems.
6. Two general models describe variation in translation rate
1. Codon usage (Ikemura, 1981)
Figure from: Tuller & Zur (2015) Nucl. Acids Res.
7. Two general models describe variation in translation rate
2. mRNA structure (Pelletier & Sonenberg, 1987)
Figure from: Tuller & Zur (2015) Nucl. Acids Res.
8. We think we have a third general model...
http://dx.doi.org/10.7554/eLife.13479
http://dx.doi.org/10.7554/eLife.20686
9. Non-coding RNAs are abundant
q
q
q
q
q
q
q
q
012345
log10(MeanReadDepth)
Core ncRNA genes
Core protein coding genes
Lindgreen, Umu et al. (2014) PLOS Computational Biology.
10. Bacterial non-coding RNA function
Hfq
AUG
SD
X
Ribosome
sRNA
AUG
RNase E
recruitment
AUG
SD
Ribosome
Anti-antisense mechanism
Selective mRNA stabilisation
AUG
RNase E
Shine-Dalgarno
sequence
Sequestration of ribosome binding site
Induction of mRNA decay
SD =
Figure by Bethany Jose
11. Checking for mRNA:ncRNA interactions
Looking for regulatory interactions which are specific and small in
number, off-targets are non-specific and large in number
Compare 5 ends of CDS & ncRNAs
Looking for a bump on the left...
−15 −10 −5 0
0.000.050.100.150.200.25
Binding Energy (kcal/mol)
Density
12. Checking for mRNA:ncRNA interactions
−15 −10 −5 0
0.000.050.100.150.200.25
Binding Energy (kcal/mol)
Native
Shuffled (P = 7.69−52
)
14. Do ubiquitous and abundant RNAs influence translation?
Given that ncRNAs are among the most abundant RNAs in the cell
([ncRNA] >> [mRNA])
AND that RNAs frequently hybridise
THEN maybe stochastic interactions with mRNAs inhibit translation
Corley & Laederach (2016) Bioinformatics: Selecting against accidental RNA interactions. eLife.
15. How can this hypothesis be tested?
We predict that:
1. There is selection against mRNA:ncRNA interactions
2. That stochastic mRNA:ncRNA interactions influence [protein]:[mRNA]
ratios
For consistency: focus on 6 ncRNA families & 114 mRNAs/proteins
that are highly conserved & expressed; And first 21 nts of CDS.
Tested 1,582 bacterial & 118 archaeal genomes
16. Are mRNA:ncRNA interactions selected against?
−15 −10 −5 0
−0.010−0.0050.0000.0050.0100.015
Binding Energy (kcal/mol)
DensityDifference Actinobacteria (n:163) P = 9.8x10−69
Bacteroidetes (n:60) P = 8.7x10−148
Chlamydiae (n:38) P = 1.4x10−193
Cyanobacteria (n:40) P = 3.8x10−11
Firmicutes (n:378) P = 0
Proteobacteria (n:756) P = 0
Spirochaetes (n:38) P = 1.6x10−98
Archaea (n:118) P = 4.2x10−177
Background (n:100)
More stable interactions
NativeinteractionsShuffledinteractions
Act
Bac
Chl
Cya
Fir
Pro
Spi
Arc
010203040
−log10P
21. Avoidance in 3D on the ribosome
Protein binds to regions with low avoidance (green) while exposed
regions are high avoidance (blue): P = 9.3x10−15, Fishers exact test
22. Further Work
Further work:
Testing adaptation with experimental evolution experiments
Do mRNA:ncRNA interactions influence eukaryotic gene expression?
Number of possible interactions increases quadratically with number of
genes. May require spatial & temporal separation of genes
Does avoidance drive compartmentalisation and increases in nucleotide
binding proteins?
Do mRNA:ncRNA interactions influence viral infection, hybridisation,
HGT & transformation expts?
Are protein, DNA and protein:nucleotide interactions also avoided?
28. Is there really a relationship between software speed &
accuracy?
Can we run a meta-analysis of bioinformatic benchmarks?
If speed isn’t related to accuracy, then what is?
Some possibilities:
Software age
Journal “impact” (IF & GoogleScholar H5)
Number of citations
Corresponding author’s H-index & M-index
30. Nothing is correlated with accuracy!
R
el.age
Year
AccuracySpeed
JH
5
JIF
C
ites
R
el.citesH
−index
M
−index
R
el.age
Year
Accuracy
Speed
JH
5
JIF
C
ites
R
el.cites
H
−index
M
−index
R
el.age
Year
Speed
JH
5
JIF
C
ites
R
el.cites
H
−index
M
−index
X X X X X X
X X X X X X X
X
X
X X X X
X X X X X X X
X X X X X X X
X X X X X X X X
X X X X X X
X X X X X
Correlates with accuracy rank
Spearman'srho
−0.2
−0.1
0.0
0.1
0.2
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-1 0 1
Spearman's rho
A B
31. -3 30
Z-score
Speed
Accuracy
Freq.
0 6 12
0
1000
2000
Freq.
0 6 12
0
1000
2000
Freq.
0 20
0
5000
10000
10
Freq.
0 6 12
0
1000
2000
Freq.
0 6 12
0
1000
2000
X
X X
X X
X X
X X X X X
X X
X X
X X X
X X
32. Conclusions
Speed is NOT reflective of accuracy
Neither is author/journal reputation, software age & # citations
The only reasonable way to select software is by benchmarking
Publication bias is influencing software accuracy
It doesn’t matter how famous you are, you can still write great software!
33. Thanks!
Avoidance: Sinan Umu, Anthony Poole & Renwick Dobson
Meta-benchmark: James Paterson, Fatemeh Ashari Ghomi, Sinan Umu,
Stephanie McGimpsey, Aleksandra Pawlik
Umu, Poole, Dobson & Gardner (2016) Avoidance of stochastic RNA interactions can be harnessed to control protein expression
levels in bacteria and archaea. eLife.
Gardner et al. (2017) A meta-analysis of bioinformatics software benchmarks reveals that publication-bias influences software
accuracy. In preparation.
These slides are available at: http://www.slideshare.net/ppgardne/presentations