avoidance of stochastic rna interactions can be harnessed to control protein expression levels in...
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Avoidance of stochastic RNA interactions can beharnessed to control protein expression levels in bacteria
and archaea
Paul Gardner
University of CanterburyChristchurchNew Zealand
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These slides are available at: http://www.slideshare.net/ppgardne/presentations
The hard work of Sinan Ugur Umu
http://dx.doi.org/10.7554/eLife.13479
http://dx.doi.org/10.7554/eLife.20686
mRNA levels are imperfectly correlated with protein levels
Lu et al. (2007) Nature biotechnology.
Two general models describe variation in translation rate
I Codon usage (Ikemura, 1981)
I mRNA structure (Pelletier & Sonenberg, 1987)
I We think we have found a third general modelFigures from: Tuller & Zur (2015) Nucl. Acids Res.
Non-coding RNAs are abundant
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01
23
45
log 1
0(M
ean
Rea
d D
epth
)
Core ncRNA genesCore protein coding genes
Lindgreen, Umu et al. (2014) PLOS Computational Biology.
Checking for mRNA:ncRNA interactions
I Looking for regulatory interactions which are specific and small innumber, off-targets are non-specific and large in number
I Compare 5′ ends of CDS & ncRNAsI Looking for a bump on the left...
−15 −10 −5 0
0.00
0.05
0.10
0.15
0.20
0.25
Binding Energy (kcal/mol)
Den
sity
Checking for mRNA:ncRNA interactions
−15 −10 −5 0
0.00
0.05
0.10
0.15
0.20
0.25
Binding Energy (kcal/mol)
NativeShuffled (P = 7.69−52)
Checking for mRNA:ncRNA interactions
−15 −10 −5 0
0.00
0.05
0.10
0.15
0.20
0.25
Binding Energy (kcal/mol)
NativeShuffled (P = 7.69−52)Different phylum (P = 0 )Downstream (P = 2.66−124)Rev. complement (P = 6.51−57)Intergenic (P = 6.16−93)
Do ubiquitous and abundant RNAs influence translation?
I Given that ncRNAs are among the most abundant RNAs in the cell([ncRNA] >> [mRNA])
I AND that RNAs frequently hybridiseI THEN maybe stochastic interactions with mRNAs inhibit translation
Corley & Laederach (2016) Bioinformatics: Selecting against accidental RNA interactions. eLife.
How can this hypothesis be tested?
I We predict that:1. There is selection against mRNA:ncRNA interactions2. That stochastic mRNA:ncRNA interactions influence [protein]:[mRNA]
ratiosI For consistency: focus on 6 ncRNA families & 114 mRNAs/proteins
that are highly conserved & expressed; And first 21 nts of CDS.I Tested 1,582 bacterial & 118 archaeal genomes
Avoidance(mRNAi ) =∑j
∆G (mRNAi : ncRNAj)
AGCU
UUGC G
CA
G
UGGCAGUAUCGUAGCCAAUGAGGUU
AA U
C CG A
G G C G C G A UUA U U G C U AAUUGAAAACUUUUCCCAAUACCCCG C C A U G
A C G A C UU G A A
AUAUAGUCG
GCAUUGGCAAUUU
UUGA
CAGUCUC
UAC
GGA
GA
GU
GC
UCG
CUUC
G GC
AG
CA
CAUAUACUAA
AA
UU
GG
AA
CGAU A C
AGA G
AA
GAUU AG
CA U
GG
C C CC
UG C G
CAA
GGAUGAC
ACG
CA
AAUU
CGU
GA
AGCG
UU
CC
AUA
UU
UUU
+ =
ΔG = -39.70 kcal/mol ΔG = -32.60 kcal/mol ΔG = -73.80 + 13.10 + 19.10 = -41.6 kcal/mol
Gallus U4 snRNA Gallus U6 snRNA U4/U6 snRNAcomplex
5`
5`
5`
5`
3`
3`3`
3`U4 U6
Are mRNA:ncRNA interactions selected against?
−15 −10 −5 0
−0.0
10−0
.005
0.00
00.
005
0.01
00.
015
Binding Energy (kcal/mol)
Den
sity
Diff
eren
ceActinobacteria (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
Nat
ive in
tera
ctio
nsSh
uffle
d in
tera
ctio
ns
Act
Bac
Chl
Cya Fi
rPr
oSp
iAr
c
010
2030
40
−log
10P
Do mRNA:ncRNA interactions influence proteinexpression?
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2.0
2.5
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−300 −250 −200 −150
Rs=0.65
log 1
0(flu
ores
cenc
e)
Avoidance (kcal/mol)I Avoidance vs synonymous GFP mRNAs (n = 154)
Do mRNA:ncRNA interactions influence proteinexpression?
I Testing the relationship between protein abundance estimates andavoidance, mRNA secondary structure, codon usage and mRNAabundance
mRNA ab.Codon
Sec. St.Avoid.
GFP reporter(n = 52(13))
GFP reporter(n = 154)
sfGFP−mCherry(n = 14234)
Microarray−MS(n = 389)
Microarray−AP(MS)(n = 3301)
Microarray−MS(n = 5479)
Microarray−MS(n = 1148)
* * * * * * ** * * * ** * * * ** * * * * * *
P. aeruginosaP. aeruginosaE. coliE. coliE. coliE. coliE. coli
*P < 0.05Correlation Coefficient
−0.20.00.20.40.6
Testing the extremes of expression
0.1
0.5
0.8
1.2
1.6
1.9
2.3
2.6 3
3.3
3.7
4.1
4.4
4.8
Freq
0
20
40
60
80
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120
A
log10([Protein]/[mRNA])
Freq
uenc
y
low expression (n=10)high expression (n=10)
B
Avoi
danc
e
Cod
on
Sec.
Str.
Nul
l
Sec.
Str.
Cod
on
Avoi
danc
e
−2
−1
0
1
2
*
*
Z sc
ore
low expression (n=10)high expression (n=10)
I E. coli genes (n = 389)
Designing mRNAs
I 239aa GFP can be encoded by 7.62x10111 synonymous mRNAs
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4.2
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0.60 0.65 0.70 0.75 0.80 0.85CAI
log 1
0(flu
ores
cenc
e)
Rs=0.29
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−15 −10 −5 0Folding Energy (kcal/mol)
Rs=0.34
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−350 −300 −250 −200 −150 −100Binding Energy (kcal/mol)
Rs=0.56
hi low●
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AvoidFoldCodonOptimal●
Avoidance in 3D
I There is less protein binding to regions with high avoidance (blue) thanthose without (green): P = 9.3x10 − 15, Fishers exact test
Further Work
I Further work:I Do mRNA:ncRNA interactions influence eukaryotic gene expression?
I Number of possible interactions increases quadratically with number ofgenes. May require spatial & temporal separation of genes
I Does avoidance drive compartmentalisation and increases in nucleotidebinding proteins?
I Do mRNA:ncRNA interactions influence viral infection, hybridisation,HGT & transformation expts?
I Are protein, DNA and protein:nucleotide interactions also avoided?
Thanks
I Sinan Ugur Umu, Anthony Poole & Renwick Dobson
Umu, Poole, Dobson & Gardner (2016) Avoidance of stochastic RNAinteractions can be harnessed to control protein expression levels in bacteriaand archaea. eLife.
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