Avoidance of stochastic RNA interactions can be harnessed to control protein expression levels in bacteria and archaea

Avoidance of stochastic RNA interactions can be
harnessed to control protein expression levels in bacteria
and archaea
Paul Gardner
University of Canterbury
Christchurch
New Zealand

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These slides are available at: http://www.slideshare.net/ppgardne/presentations

The hard work of Sinan U˘gur 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
Codon usage (Ikemura, 1981)
mRNA structure (Pelletier & Sonenberg, 1987)
We think we have found a third general model
Figures from: Tuller & Zur (2015) Nucl. Acids Res.

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.

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

−15 −10 −5 0
0.000.050.100.150.200.25
Native
Shuffled (P = 7.69−52
)

−15 −10 −5 0
0.000.050.100.150.200.25
Native
Shuffled (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?
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.

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
Avoidance(mRNAi ) =
j
∆G(mRNAi : ncRNAj )
AG
C
U
UU
G
C
G
C
A
G
UGGCAGUAUCGUAGCC
AAUG
A
GG
UU
A
A U
C C
G A
G G C G C G A UUA U U G C U A
A
U
U
G
A
AAACUUUUCCCA
AUAC
C
C
C
G C C A U G
A C G A C U
U
G A
A
A
U
AU
AGUCG
GCAUUGGC
A
A
U
U
U
U
U
GACAGU
C
U
C
U
AC
G
G
A
G
A
G
U
G
C
U
CG
C
U
U
C
G G
C
A
G
C
A
C
A
U
A
UACUA
A
A
A
U
U
G
G
A
A
C
G
A
U
A C
A
G
AG
A
A
G
A
UU AG
C
A U
G
G
C
C C
C
U
G
C G
C
AA
G
G
A
U
GAC
A
CG
C
A
A
AU
U
C
GU
G
A
A
GC
G
U
U
C
C
A
UA
U
U
U
U
U
+ =
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 snRNA
complex
5`
5`
5`
5`
3`
3`
3`
3Ù4 U6

Are mRNA:ncRNA interactions selected against?
−15 −10 −5 0
−0.010−0.0050.0000.0050.0100.015
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

Do mRNA:ncRNA interactions inﬂuence protein
expression?
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2.02.53.03.54.0
−300 −250 −200 −150
Rs=0.65
log10(fluorescence)
Avoidance (kcal/mol)
Avoidance vs synonymous GFP mRNAs (n = 154)

Do mRNA:ncRNA interactions inﬂuence protein
expression?
Testing the relationship between protein abundance estimates and
avoidance, mRNA secondary structure, codon usage and mRNA
abundance
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 < 0.05
Correlation Coefficient
−0.2
0.0
0.2
0.4
0.6

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
100
120
A
log10([Protein]/[mRNA])
Frequency
low expression (n=10)
high expression (n=10)
B
Avoidance
Codon
Sec.Str.
Null
Sec.Str.
Codon
Avoidance
−2
−1
0
1
2
*
*
Zscore
low expression (n=10)
high expression (n=10)
E. coli genes (n = 389)

Designing mRNAs
239aa GFP can be encoded by 7.62x10111 synonymous mRNAs
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4.24.34.44.54.64.7
0.60 0.65 0.70 0.75 0.80 0.85
CAI
log10(fluorescence)
Rs=0.29
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4.24.34.44.54.64.7
−15 −10 −5 0
Folding Energy (kcal/mol)
Rs=0.34
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4.24.34.44.54.64.7
−350 −300 −250 −200 −150 −100
Rs=0.56
hi low
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Avoid
Fold
Codon
Optimal●

Avoidance in 3D
There is less protein binding to regions with high avoidance (blue) than
those without (green): P = 9.3x10 − 15, Fishers exact test

Further Work
Further work:
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?

Thanks
Sinan U˘gur Umu, Anthony Poole & Renwick Dobson
Umu, Poole, Dobson & Gardner (2016) Avoidance of stochastic RNA
interactions can be harnessed to control protein expression levels in bacteria
and archaea. eLife.

Avoidance of stochastic RNA interactions can be harnessed to control protein expression levels in bacteria and archaea

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