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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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

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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

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sity

Checking for mRNA:ncRNA interactions

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Binding Energy (kcal/mol)

NativeShuffled (P = 7.69−52)

Checking for mRNA:ncRNA interactions

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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?

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00.

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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

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Bac

Chl

Cya Fi

rPr

oSp

iAr

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010

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10P

Do mRNA:ncRNA interactions influence proteinexpression?

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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

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log10([Protein]/[mRNA])

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low expression (n=10)high expression (n=10)

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I E. coli genes (n = 389)

Designing mRNAs

I 239aa GFP can be encoded by 7.62x10111 synonymous mRNAs

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0.60 0.65 0.70 0.75 0.80 0.85CAI

log 1

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ores

cenc

e)

Rs=0.29

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−350 −300 −250 −200 −150 −100Binding Energy (kcal/mol)

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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.