Does RNA avoidance dictate protein expression level?

Does RNA avoidance dictate protein expression level?
Paul Gardner
Department of Biochemistry
University of Otago
Dunedin
New Zealand

The hard work of Sinan Umu, Ant Poole, Ren Dobson &
recently Chun Shen Lim

“Mycoplasma mycoides” watercolour by Prof. David S. Goodsell

mRNA levels are imperfectly correlated with protein levels
Lu et al. (2007) Nature biotechnology.

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.

Two general models describe variation in translation rate
1. Codon usage (Ikemura, 1981)
Figure from: Tuller & Zur (2015) Nucl. Acids Res.

Two general models describe variation in translation rate
2. mRNA structure (Pelletier & Sonenberg, 1987)
Figure from: Tuller & Zur (2015) Nucl. Acids Res.

We think we have a third general model...
http://dx.doi.org/10.7554/eLife.13479
http://dx.doi.org/10.7554/eLife.20686

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.

Core bacterial non-coding RNAs
Figure by Bethany Jose

Bacterial regulatory non-coding RNAs
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

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

Checking for mRNA:ncRNA interactions
−15 −10 −5 0
0.000.050.100.150.200.25
Native
Shuffled (P = 7.69−52
)

Checking negative controls!
−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
ﬁrst 21 nts of CDS
Tested 1,582 bacterial & 118 archaeal genomes
Use a nearest-neighbour model for predicting hybridisation and
unfolding energy

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

“Intrinsic” avoidance
0.0 0.2 0.4 0.6 0.8 1.0
0246810
Density
G+C content
ncRNA
mRNA
High
Low
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0 10 20 30 40 50
0510152025
−log10P(Avoidance)
−log10P(Intrinsic)
Signiﬁcant signals of intrinsic or extrinsic avoidance found in 97% of
bacteria and archaea

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)
Expression data from: Kudla et al. (2009) Science.

Do mRNA:ncRNA interactions inﬂuence protein
expression?
Testing the relationship between protein abundance estimates and
avoidance, mRNA secondary structure, codon usage and mRNA
abundance
GFP datasets Mass-Spec datasets
E.coli
(n=52)
GFP/qPCR
E.coli
(n=154)
GFP/Northern
E.coli
(n=14,234)
mCherry/RNAseq
E.coli
(n=389)
MS/microarray
E.coli
(n=3,301)
MS/microarray
P.aeruginosa
(n=5,479)
MS/microarray
P.aeruginosa
(n=1,148)
MS/microarray
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*P < 0.05
0.0 0.60.2 0.4-0.2
Correlation Coeﬃcient
Avoidance
Secondary
Structure
Codon
[mRNA]

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
Extremes of avoidance have a stronger eﬀect than codon usage or
secondary structure
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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 on the ribosome
Protein binds to regions with low avoidance (green) while exposed
regions are high avoidance (blue): P = 9.3x10−15, Fisher’s exact test

Further Questions
Further work:
Write a nice software package for mRNA design! –funded by MBIE
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 for larger genomes?
Check organelles & endosymbionts
Do mRNA:ncRNA interactions inﬂuence viral infection, hybridisation,
HGT & transformation expts?
Are protein, DNA and protein:nucleotide interactions also avoided?

My other project: What and who?
A new approach for estimating functionally signiﬁcant genetic variation
Application to genome associations with bacterial invasiveness

How can I analyse my genome variation?
Genome-wide association studies (GWAS) with
SNPs
Gene gain/losses
Tajima’s D ≈ diﬀerence between an observed
and expected measure of genetic diversity
Compare non-synonymous and synonymous
mutation rates: dN
dS
Should every variant/SNP really be treated equally?
Is there an approach that works for any timescale and any genome variant?

Proﬁle HMMs: a powerful homology search tool
Image provided by Sean Eddy.

Refactoring proﬁle HMMs
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FL Y
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V LM
IQ
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LRS
RH
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PAS
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bitscore = log2
P(seq|model)
P(seq|null)

A new scoring sequence for genome variation
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Species 1
Species 2
∆bitscore = bitscoreseq1 − bitscoreseq2
= log2
P(seqseq1|model)
P(seqseq2|model)
Databases of HMMs: Pfam, eggNOG, SMART, InterPro, ...

A new scoring sequence for genome variation
Wheeler, Barquist, Kingsley, Gardner (2016) A proﬁle-based method for identifying functional divergence of orthologous genes in
bacterial genomes. Bioinformatics.

Bacterial pathogen evolution
Gene gain, adaptive mutation
help to invade a new niche
Subsequent adaptation to may
allow gene loss or inactivation
Merhej, Georgiades, Raoult (2013) Postgenomic analysis of bacterial pathogens repertoire reveals genome reduction rather than
virulence factors. Brief. Funct. Genomics.

Random Forests
Training genome set
Random forest
Variation scoring
with DeltaBS
Each node identifies the
most informative gene,
and selects an optimal
gene-wide DeltaBS value
to split the data
Decision treepredicted impact of
mutation
DeltaBS
DeltaBS incorporates the predicted impacts
of all mutations in a gene
Wheeler, Gardner, Barquist (2018) Machine learning identiﬁes signatures of host adaptation in the bacterial pathogen Salmonella
enterica. To appear in PLoS Genetics.

Salmonella enterica
Feasey et al. (2012) The Lancet
Nuccio & Bäumler (2014) Comparative analysis of Salmonella genomes
identifies a metabolic network for escalating growth in the inflamed gut. MBio

Reproducible signatures of gene degradation/diversifying
selection
Out-of-bag votes: lineages are only voted on using decision trees that
weren’t trained on them
Each model iteration uses a smaller subset of genes that are most
informative of phenotype
enterica. To appear in PLoS Genetics. https://doi.org/10.1101/204669

Which genes are most informative of pathogenicity?

Global and African isolates
Invasiveness
ranking
Clade
300
A
B
ProportionofisolatescarryingHDCs
Afican Enteritidisr
Global Enteritidis
−1.0
−0.5
0.0
0.5
1.0
tcuR
ybiU
rnfC
STM4519
STM1499
pocR
STM2529
priA
ydeE
phrB
STM1630
STM2245
STM2532
nrdF
rcnA
pqaA
STM1940
acrB
pepT
STM0019
pps
pgtA
yjeF
rna
pgl
mglA
STM3125
bcsA
slsA
STM0018
bcfC
STM0042
citF2
carB
caiC
Gene
Clade
Global epidemic
Central/Eastern African
Outlier
Other
West Africa
C
0.20
0.24
0.28
Africa Global
Region
Invasivenessindex
Invasiveness
ranking
Low
High
enterica. To appear in PLoS Genetics.

Within-patient evolution
Chronic infection of an
immunocompromised patient
Constantly recolonised with a
hypermutator S. Enteritidis.
Klemm et al (2016) Emergence of host-adapted Salmonella Enteritidis through rapid evolution in an immunocompromised host.
Nat. Microbiol.

Summary
We have developed a simple & scalable approach for determining the
significance of variation
Working well in “the field” (applications to Salmonella, Campylobacter,
PSA, Ratites, ...)
Adapted to evaluate ncRNAs and conserved DNA elements, and
include more phylogeny (ASR, phylo-regression?)
Wheeler, Barquist, Kingsley & Gardner (2016) A profile-based method for identifying functional divergence of orthologous genes in
bacterial genomes. Bioinformatics.
Sackton et al. (2018) Convergent regulatory evolution and the origin of flightlessness in palaeognathous birds
https://doi.org/10.1101/262584

Thanks!
Avoidance: Sinan Umu, Anthony Poole & Renwick Dobson
Nicole Wheeler, Lars Barquist, Alexandra Gavryushkina

Does RNA avoidance dictate protein expression level?

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