Genome-wide identification of natural RNA aptamers in prokaryotes and eukaryotes

Abstract

RNAs are well-suited to act as cellular sensors that detect and respond to metabolite changes in the environment, due to their ability to fold into complex structures. Here, we introduce a genome-wide strategy called PARCEL that experimentally identifies RNA aptamers in vitro, in a high-throughput manner. By applying PARCEL to a collection of prokaryotic and eukaryotic organisms, we have revealed 58 new RNA aptamers to three key metabolites, greatly expanding the list of natural RNA aptamers. The newly identified RNA aptamers exhibit significant sequence conservation, are highly structured and show an unexpected prevalence in coding regions. We identified a prokaryotic precursor tmRNA that binds vitamin B2 (FMN) to facilitate its maturation, as well as eukaryotic mRNAs that bind and respond to FMN, suggesting FMN as the second RNA-binding ligand to affect eukaryotic expression. PARCEL results show that RNA-based sensing and gene regulation is more widespread than previously appreciated in different organisms.

Full text

ARTICLE
Genome-wide identification of natural RNA
aptamers in prokaryotes and eukaryotes
Sidika Tapsin1, Miao Sun2, Yang Shen2, Huibin Zhang3, Xin Ni Lim1, Teodorus Theo Susanto1, Siwy Ling Yang1,
Gui Sheng Zeng4, Jasmine Lee4, Alexander Lezhava5, Ee Lui Ang3, Lian Hui Zhang4, Yue Wang 4,
Huimin Zhao 3,6, Niranjan Nagarajan2& Yue Wan1
RNAs are well-suited to act as cellular sensors that detect and respond to metabolite changes
in the environment, due to their ability to fold into complex structures. Here, we introduce a
genome-wide strategy called PARCEL that experimentally identifies RNA aptamers in vitro, in
a high-throughput manner. By applying PARCEL to a collection of prokaryotic and eukaryotic
organisms, we have revealed 58 new RNA aptamers to three key metabolites, greatly
expanding the list of natural RNA aptamers. The newly identified RNA aptamers exhibit
significant sequence conservation, are highly structured and show an unexpected prevalence
in coding regions. We identified a prokaryotic precursor tmRNA that binds vitamin B2 (FMN)
to facilitate its maturation, as well as eukaryotic mRNAs that bind and respond to FMN,
suggesting FMN as the second RNA-binding ligand to affect eukaryotic expression. PARCEL
results show that RNA-based sensing and gene regulation is more widespread than pre-
viously appreciated in different organisms.
DOI: 10.1038/s41467-018-03675-1 OPEN
1Stem Cell and Development Biology, Genome Institute of Singapore, Singapore 138672, Singapore. 2Computational and Systems Biology, Genome Institute
of Singapore, Singapore 138672, Singapore. 3Metabolic Engineering Research Laboratory (MERL), Science and Engineering Institutes, Agency for Science,
Technology, and Research (A*STAR), 31 Biopolis Way, Nanos #01-01, Singapore 138669, Singapore. 4Institute of Molecular and Cell Biology, Proteos, 61
Biopolis Drive, Singapore 138673, Singapore. 5Translational research group, Genome Institute of Singapore, Singapore 138672, Singapore. 6Department of
Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States. These authors contributed equally:
Sidika Tapsin, Miao Sun, Yang Shen, Huibin Zhang. Correspondence and requests for materials should be addressed to
N.N. (email: nagarajann@gis.a-star.edu.sg) or to Y.W. (email: wany@gis.a-star.edu.sg)
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Microorganisms are constantly sensing their environment
for changes in temperature, pH, metabolites, and
nutrients so as to regulate their gene expression pro-
grams to best adapt to different signals for growth, survival, and
virulence1. As such, comprehensive mapping of regulatory net-
works in microbes under different environmental conditions is
crucial to understanding their biology. While many of these
regulators have been extensively studied at the protein and DNA
levels, RNA’s role as direct sensors and responders remains
relatively under-explored. One class of cellular RNA sensors,
known as riboswitches, can recognize and respond to specific
metabolites by altering gene expression2. Upon binding to their
ligands, riboswitches undergo conformational changes that result
in the regulation of gene expression through diverse means, such
as changes in transcription, translation and decay2, informing the
host of its environmental conditions. The modularity of ribos-
witches also allows them to be transplantable to different systems,
broadening their use as biological sensors.
While in vitro selection methods, such as SELEX, have been
applied with variable success to generate new synthetic RNA
aptamers3, the ability to comprehensively identify natural RNA
aptamers from transcriptomes would expand our toolbox for
synthetic biology and deepen our understanding of RNA -based
gene regulation in vivo. Currently, potential natural RNA apta-
mers are mostly identified through computational determination
of sequence and structural homology to known riboswitches4.
However, as RNA can adopt different folds for binding to the
same ligand and organisms can diverge greatly in sequence
content, computational means of searching for riboswitches
through sequence homology have limited scope5, and strategies
that allow direct experimental detection are highly desirable. One
recent strategy, Term-seq, utilizes high-throughput sequencing to
detect differential transcription termination events in bacteria
under different conditions6. However, complementary strategies
for detecting riboswitches that act through other mechanisms
(such as translation inhibition), as well as riboswitches that bind
to metabolites whose intracellular concentrations are not easily
altered, need to be developed.
Here, we report an in vitro method for experimentally identi-
fying RNA aptamers in transcriptomes by detecting ligand-
induced RNA structural changes using high-throughput sequen-
cing (Fig. 1a). This method allows us to rapidly screen through
transcriptomes to identify natural RNA aptamers toward almost
any ligand of choice. Specifically, we extract total RNA from
organisms grown under different conditions and probe their
structures in the presence or absence of different metabolites by
using a double-stranded nuclease, RNase V1, which recognizes
and cleaves at base-paired regions in RNAs. The different cleavage
sites, in the presence and absence of metabolites, are cloned into
cDNA libraries for deep sequencing. Subtle differences between
these two libraries point to the few true ligand-specific structure
changing RNA elements in the genome and we developed a
sensitive and robust computational analysis pipeline to identify
these (Methods). This experimental and computational approach,
termed Parallel Analysis of RNA Conformations Exposed to
Ligand binding (PARCEL), revealed the breadth of RNA-ligand
interactions in prokaryotic and eukaryotic transcriptomes, iden-
tifying many new natural RNA aptamers in the process.
Results
Development of PARCEL. To establish PARCEL, we system-
atically tested different structure-probing strategies to determine
the approach that best captures ligand-induced structural changes
genome-wide, allowing for a simplified, cost-effective workflow
without multiple probing assays7. Strategies using double-strand
or single-strand specific nucleases (RNase V1 and S1 nuclease,
respectively), as well as in-line probing which probes nucleotide
flexibility8, were tested for their abilities to detect structural
changes using high-throughput sequencing (Fig. 1b, Supple-
mentary Fig. 1,2,3). The known thiamine pyrophosphate (TPP)
and S-adenosylmethionine (SAM) riboswitches were used as
positive controls and other RNA sequences not known to bind
TPP or SAM were used as negative controls in this experi-
ment9,10. As expected, the known riboswitches showed large
structural changes upon ligand binding (Fig. 1b, Supplementary
Fig. 1,2), while the negative controls did not (Supplementary
Fig. 3), indicating that the structure changes captured by nuclease
digestion followed by sequencing are highly specific. These
structural differences can be less pronounced in libraries prepared
using in-line probing (Fig. 1b, Supplementary Fig. 2). This is
likely due to the noise introduced by the additional 5′phos-
phorylation step that is used in in-line probing library prepara-
tion. As degraded cellular RNAs are also phosphorylated and
cloned into the library, it is difficult to distinguish in-line probed
fragments from degradation fragments. Among the nucleases, we
observed a higher degree of correlation between two biological
replicates of RNase V1 (R=0.99) versus S1 nuclease libraries (R
=0.66, Supplementary Fig. 4a), a key feature for differential
analysis. The structural changes observed using the nucleases
could also be reproduced using low-throughput RNA footprint-
ing and mapped to the secondary structure of the TPP riboswitch
(Supplementary Fig. 1b-d). Correspondingly, we selected RNase
V1 as the probing strategy of choice in all PARCEL experiments
to identify natural RNA aptamers genome-wide.
As Bacillus subtilis and Pseudomonas aeruginosa are bacteria for
which many riboswitches are known, we performed structure
probing in the presence and absence of key metabolites known to
interact with RNAs in their transcriptomes. To maximize our
chances of finding RNA aptamers which may only be expressed
under specific conditions, we grew the bacteria in rich or minimal
media to exponential or stationary phases (Methods). We then
extracted total RNA from the pooled bacteria, performed ribosomal
depletion to enrich for mRNAs, and did structure probing, followed
by deep sequencing. We had two biological replicates for each
experiment and obtained more than 7 million reads per replicate
(Supplementary Table 1). RNA aptamers that bind specifically to
one ligand should not recognize other unrelated ligands. As such,
we developed a novel computational pipeline to identify contiguous
positions of structural change (to increase signal-to-noise ratio) that
show statistically different numbers of reads that indicate base
pairing in one metabolite condition but not in others (Supplemen-
tary Fig. 4b, Methods). This approach aggregates signals of variation
in each base pair across conditions to define ligand-responsive
regions using dynamic programming, and combines this with the
computation of a BLAST-like E-value to identify RNA aptamers
with statistical confidence (Methods).
PARCEL finds known riboswitches in B.subtilis and P.aeru-
ginosa. To evaluate PARCEL, we first determined if we could
identify known riboswitches that have been previously reported
in the literature. We identified 17 out of 20 known riboswitches
that interact with key metabolites (TPP, FMN, and SAM) in B.
subtilis and P.aeruginosa, including 4/5 known TPP riboswitches,
2/2 FMN riboswitches, 9/11 SAM riboswitches in B.subtilis,as
well as 1/1 TPP and 1/1 FMN riboswitches in P.aeruginosa11,
highlighting the high sensitivity of the method (85%; Fig. 1c,
Supplementary Fig. 5a-d). Furthermore, pair-wise analysis of
control libraries that were generated using the same metabolite
did not identify any candidate RNA aptamers, indicating that the
approach is highly specific to the presence of metabolites
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(Supplementary Fig. 5e). We noted that undetected known
riboswitches have low numbers of RNase V1 and S1 nuclease
reads along their regulons, indicating that they are either poorly
expressed or present in nuclease-inaccessible regions (Supple-
mentary Fig. 5f, g).
Besides detecting structural changes upon ligand binding, we
also determined whether PARCEL can detect RNA-ligand
interactions quantitatively. As TPP riboswitches bind most
strongly to TPP, followed by thiamine and oxythiamine, we
tested the sensitivity of PARCEL in detecting RNA structural
changes due to differences in ligand binding affinities9. Indeed,
we observed the strongest structural change in TPP riboswitches
upon binding to TPP, followed by thiamine and then oxythia-
mine (Fig. 1d, Supplementary Fig. 6a). We also treated B.subtilis
TPP FMN SAM
No. of known riboswitches
Identified
Missed
0
2
4
6
8
10
12
Water control
100 μM TPP
50
100
150
200
250
0
0
50
100
150
200
250
50
100
150
200
250
0
Normalized reads
Bases to start codon
Water control
100 μM thiamine
Water control
100 μM oxythiamine
0
4
8
12
16
20
19 24 29 34 39 44 49 54 59 64 69 74 79 84
0
4
8
12
16
20
0
4
8
12
16
20
Bases
TPP
No TPP
S1 nuclease
In-line probing
Normalized sequencing reads
–218 –208 –198 –188 –178 –168 –158 –148 –138 –128
thiM riboswitch
RNase V1
10
70
20 30
40
50
60
Library construction
Deep sequencing
Mapping to transcriptome
Library construction
Deep sequencing
Mapping to transcriptome
Ligand
TPP
FMN
V1
V1
Double
stranded signal
Double
stranded signal
10
5′70
20 30
40 10
5′70
20 30
40
50
60
10 7020 30 40 50 60
110 7020 30 40 50 601
0
100
200
300
62 67 72 77 82 87 92 97 102 107 112 117 122
Base
Normalized V1 reads
0.16 μM TPP H2O
0.8 μM TPP H2O
4 μM TPP H2O
20 μM TPP H2O
100 μM TPP H2O
500 μM TPP H2O
127
0
100
200
300
0
100
200
300
0
100
200
300
0
100
200
300
0
100
200
300
a
bc
de
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ribosomal RNA-depleted pool with different concentrations of
TPP to determine the approximate binding affinity of known TPP
riboswitches (Fig. 1e). PARCEL data on the known thiC
riboswitch shows a graded change in RNA structure under
different ligand concentrations, and an approximate K
D
of 110
nM in the most ligand sensitive regions, similar to the previously
reported K
D
of 100nM9(Supplementary Fig. 6b). These data
collectively demonstrate that PARCEL is quantitative and can be
used to approximate relative binding affinities of RNA-ligand
interactions.
PARCEL identifies new RNA aptamers in B.subtilis and P.
aeruginosa. Beyond known riboswitches, we also identified new
aptamers; 17 TPP and 12 FMN RNA aptamers in the B.subtilis
and P.aeruginosa transcriptomes, as well as 6 SAM RNA apta-
mers in the B.subtilis transcriptome (Fig. 2a, Supplementary
Table 2). RNA footprinting validated four out of six ligand-
induced structure changes identified by PARCEL (Supplementary
Fig. 7a-d). While known riboswitches are mostly located
upstream of their operons, we observed that 28% and 47% of the
newly identified B.subtilis and P.aeruginosa RNA aptamers are
found in coding regions, respectively (Fig. 2b, Supplementary
Fig. 8). Two of our validated RNA aptamers fall in coding regions
(Supplementary Fig. 7a, c), indicating that these coding regions
exhibit real structural changes in the presence of TPP.
To study the structural properties of these RNA aptamers, we
utilized the program Alifoldz12 to calculate folding energies
across their RNA orthologs in different bacterial species
(Methods). The newly identified elements exhibit lower folding
energies than their dinucleotide shuffled controls, indicating that
they are more structured (Fig. 2c). As functionally important
RNAs frequently show evolutionary constraints in their
sequences, we calculated the nucleotide substitution rate of our
new RNA aptamers, as compared to other synonymous regions
and UTRs. Similar to known riboswitches, the novel RNA
aptamers show a significant reduction in nucleotide substitution
rate (Fig. 2d, e), suggesting that they are evolutionarily conserved
and likely to be functional.
We observed that one of our prokaryotic metabolite-sensitive
regions encodes a small non-coding RNA, specifically a tmRNA
(Fig. 3a). The B.subtilis tmRNA is transcribed from two
promoters to produce both long and short precursor RNAs,
which are then processed into the mature tmRNA. To understand
the functional role of metabolite sensing in tmRNA, we cloned
and in vitro transcribed all three isoforms of tmRNA, then
performed structure probing in the presence and absence of FMN
in the dark, to avoid FMN-induced photocleavage of RNAs.
Interestingly, we observed FMN-induced structure changes in
both precursor forms of tmRNA, but not in the mature form
(Fig. 3b, c, d, Supplementary Fig. 7d), highlighting that it is the
precursor tmRNA that responds to FMN. To determine whether
FMN binding influences the processing of precursor tmRNAs, we
grew B.subtilis in minimal media with and without the FMN
precursor, riboflavin. Addition of riboflavin resulted in a decrease
in precursor tmRNA levels and a two-fold increase in mature
tmRNA levels (Fig. 3e), supporting the hypothesis that FMN
regulates RNA maturation by binding and altering precursor
tmRNA structures.
PARCEL finds new eukaryotic FMN aptamers in Candida
albicans. To date, only riboswitches that bind to TPP have been
found in eukaryotes, and they regulate splicing and 3′UTR
usage13,14. Identifying new eukaryotic riboswitches is important
for broadening our understanding of eukaryotic gene regulation.
To maximize our chances of finding eukaryotic riboswitches, we
screened the fungal pathogen, C.albicans, using a pool of meta-
bolites that correspond to highly abundant classes of riboswitches
in bacteria, including FMN, SAM, glycine, lysine, and vitamin
B12 (Adocbl). PARCEL identified 23 new RNA aptamers that
exhibited structural changes in the presence of the metabolite
pool (Supplementary Table 3). 87% of the new C.albicans RNA
aptamers reside in coding regions (Fig. 4a, Supplementary Fig. 8),
in contrast to known riboswitches and new prokaryotic aptamers,
indicating that they may have different functions from classical
riboswitches. We validated seven out of nine PARCEL-identified
structural changes by performing in-line probing of these novel
RNA aptamers in the presence of the pooled metabolites (Sup-
plementary Fig. 9–11), all of which fall in the coding regions of
these genes, confirming that the PARCEL-detected structural
changes are real. Similar to their prokaryotic counterparts, the
eukaryotic RNA aptamers were found to be significantly more
structured compared to dinucleotide shuffled controls, suggesting
that structure is likely to be important for their function (Fig. 4b).
As many of the new eukaryotic RNA aptamers are located in
highly conserved coding regions, we observed an expected
increase in conservation of these elements as compared to UTRs,
but not a further reduced nucleotide substitution rate compared
to other coding sequences (Fig. 4c).
Eukaryotic RNA aptamers undergo gene expression changes
with FMN. To better understand the cellular roles of the new
eukaryotic RNA aptamers, we performed structure probing in the
presence of each individual compound in the metabolite pool on
two RNA aptamers identified in the coding regions of the genes
RPS31 and ATP1. Interestingly, structure probing of these apta-
mers revealed that they respond specifically to FMN, and not to
other metabolites in the solution (Supplementary Fig. 10a, 11a).
Detailed structure probing, in the dark, along the length of these
two RNAs identified several regions that changed structure in the
presence of FMN (Fig. 4d, Supplementary Fig. 10b), suggesting
that FMN binding results in structural remodeling of these RNAs.
To determine whether changes in the intracellular concentration
Fig. 1 Measuring RNA-ligand binding by structure probing and deep sequencing. aRNA undergoes structure changes upon ligand binding. This structural
change is detected by the double-strand specific nuclease, RNase V1, which cuts at different double-stranded places along the RNA in the presence and
absence of the ligand. The cleavage sites are then captured and cloned into a cDNA library for deep sequencing. After mapping the reads to the
transcriptome, we can identify which bases have undergone changes in structuredness upon ligand binding (highlighted in beige boxes). bDeep
sequencing reveals structure changes of a known TPP riboswitch, thiM, using RNase V1 (top), S1 nuclease (middle), and in-line probing (bottom). The red
and black lines indicate the structure profiles of thiM treated with and without 100 µM TPP, respectively. The beige regions highlight regions of structural
changes upon ligand binding. cPARCEL identified 85% of known TPP, FMN, and SAM riboswitches in B.subtilis and P.aeruginosa. The black and the white
bars indicate the number of known riboswitches that were captured and missed in our study, respectively. dPARCEL sequencing data for the B.subtilis TPP
riboswitch, thiT, in the presence and absence of 100 µM TPP (top), 100 µM thiamine (middle), and 100 µM oxythiamine (bottom). PARCEL detected
strongest structural change in thiT in the presence of TPP, followed by thiamine and then oxythiamine, which corresponds to the binding affinities of TPP
riboswitches for these metabolites9.eThe plots show normalized V1 read counts of the thiC TPP riboswitch under increasing concentrations of TPP.
PARCEL was performed on the B.subtilis transcriptome
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3′UTR5′UTR CDS
15
B. subtilis
Known riboswitches
B. subtilis
New RNA aptamers
1
8
8
P. aeruginosa
New RNA aptamers
B. subtilis
Z-score (obtained from Alifoldz)
Shuffled
−8
−6
−4
−2
0
p = 6.25×10−6
P. aeruginosa
Z-score (obtained from Alifoldz)
−10
−8
−6
−4
−2
0
p = 0.006
No. of RNA aptamers
B. subtilis P. aeruginosa
TPP FMN SAM TPP FMN
0
2
4
6
8
10
12
14
16
18
1
12
5
Known riboswitches
New RNA aptamers
KrCDS KrUTR Ks
0.1
0.2
0.3
0.4
0.5
1.1
0
1.2
K5′UTR
K3′UTR Ka
Nucleotide substitution rate
Krknown
0.1
0.3
0.5
1.1
0
1.2
0.4
0.2
Nucleotide substitution rate
KrCDS KrUTR KsK5′UTR
K3′UTR Ka
p = 0.001
B.spi vs. B.sub
PAO1 vs. PA7
p = 0.002
Observed ShuffledObserved
ab
dc
e
Fig. 2 PARCEL identifies new RNA aptamers in bacterial species. aPARCEL identifies a total of 52 RNA aptamers in B.subtilis and P.aeruginosa. Black and
white bars indicate the numbers of known riboswitches and novel aptamers that are identified in our study, respectively. bDistribution of known
riboswitches and new RNA aptamers along the 5′UTR, CDS, and 3′UTR regions for B.subtilis and P.aeruginosa, showing that a substantial proportion of
RNA aptamers are located in the 3′UTR and CDS regions. cComparison of score distribution of Alifoldz12 for RNA aptamers vs. shuffled counterparts. The
upper, middle, and lower bounds of the boxplot represent the 75, 50, and 25th percentile of the values, respectively. A negative score indicates a stable,
conserved consensus structure. p-value was obtained using the non-parametric Kolmogorov–Smirnov test. d, e Comparison of the nucleotide substitution
rate (number of substitutions per base-pair) for new RNA aptamers in coding region (Kr
CDS
), new RNA aptamers in UTR (Kr
UTR
), 3′UTR (K
3UTR
), 5′UTR
(K
5UTR
), synonymous sites (Ks), and non-synonymous sites (Ka). The upper, middle, and lower bounds of the boxplot represent the 75, 50, and 25th
percentile of the values, respectively. To calculate nucleotide substitutions, B.subtilis 168 was compared to B.subtilis subsp.spizizenii W23 (d), and P.
aeruginosa PAO1 was compared to P.aeruginosa PA7 (e). Note that Kr
known
denotes the substitution rate of known riboswitches in B.subtilis (15 in total) as
annotated in the RegPrecise database11.Kr
known
was not calculated in P.aeruginosa as there are too few known TPP and FMN riboswitches. p-values were
calculated using the non-parametric Kolmogorov–Smirnov test
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of FMN could alter gene expression changes of RPS31 or ATP1
in vivo, we integrated FLAG-tagged C.albicans RPS31 or ATP1 in
aS.cerevisae FMN1 synthase deletion mutant (fmn1Δ)asS.
cerevisae is known to take up exogenous FMN, unlike C.albi-
cans15. Both transcript and protein levels of FLAG-tagged C.
albicans RPS31 and ATP1 were measured, following growth of
the integrated strains under varying FMN concentrations. We
found that while transcript levels of FLAG-tagged C.albicans
RPS31 and ATP1 in fmn1Δdid not change with increasing FMN
concentrations (Supplementary Fig. 10c, 11b), RPS31 and ATP1
protein levels were decreased and increased, respectively (Fig. 4e,
Supplementary Fig. 10d-f,11c), suggesting that FMN sensing
could have a regulatory effect on gene expression in vivo, and that
3,450,000 3,450,500 3,451,000 3,451,500
BSU33590 BSU33600
tmRNA
Short precursor
Long precursor
Short precursor tmRNALong precursor tmRNA Mature tmRNA
B. subtilis
A NAI
FMN (μM) 0 1000
Long precursor
0
0.5
1
1.5
2
2.5
3
Veg FMN
riboswitch
Precursor
tmRNA
tmRNA
Fold change +/– riboflavin
p = 0.04
p = 0.02
U
0
175A
168A
162A
154A
158A
150A
148A
143A
139A
ACGTTACGGATTCGACAGGGATGGATCGAGCTTGAGCTGCG
0
0.5
1
1.5
2
2.5
3
3.5
ACGTTACGGATTCGACAGGGATGGATCGAGCTTGAGCTGCG
ACGT TACGGAT TCGAC AGGGATGGATCGAGCT TGAGCT GCG
Water
FMN
Long precursor
tmRNA
Short precursor
Mature tmRNA
Base
Relative NAI probing intensity
0
0.5
1
1.5
2
2.5
3
3.5
0
0.5
1
1.5
2
2.5
3
3.5
**
*** *
A
C
G
A
T
C
A
G
A
T
C
A
C
G
A
C
G
C
C
A
T
T
C
A
T
T
T
GA
A
G
G
A
T
T
T
GAC
A
A
TT
G
A
A
A
A
GAG
C
C
G
T
G
A
T
C
A
T
G
T
T
A
T
A
A
T
A
A
G
ACTATAGC
C
A
G
T
TT
G
A
G
T
G
A
G
A
G
C
T
T
G
A
TATTTTCTC
C
C
G
T
A
T
T
T
C
C
C
T
T
A
T
A
C
CAAGGGGA
CGTTACGGATTC
G
A
C
A
G
G
G
A
T
G
G
A
T
C
G
A
G
C
T
T
G
A
G
C
T
G
C
G
A
G
C
C
G
A
G
A
G
G
CGA
TCTC
G
T
A
A
A
C
A
C
G
C
A
C
T
TAAATATAACTGGC
A
A
A
A
C
T
A
AC
A
G
TTTTA
A
C
CA
A
A
A
C
G
T
A
G
C
A
T
T
A
G
C
T
G
C
C
T
A
ATA
A
G
C
G
C
A
G
C
GA
G
C
T
C
T
T
C
C
TG
A
C
A
T
T
GCCT
A
T
G
T
G
T
CT
G
T
G
A
A
G
A
G
C
A
C
A
T
C
C
A
A
G
T
A
G
G
C
T
A
C
GCTTGCGTTC
C
CGTC
T
G
A
G
A
A
C
G
T
A
A
G
AAGAGATGAACAGACTAGCTCTCG
G
A
A
G
G
C
C
C
G
C
C
C
G
C
A
G
G
C
A
A
G
A
A
G
ATGAGTG
A
A
A
C
C
A
TAAAT
A
T
G
C
A
G
G
C
T
A
C
G
C
T
C
G
T
A
G
A
C
G
C
T
T
A
A
G
T
A
A
T
C
G
A
T
G
T
T
T
C
T
G
GACGTGGGTT
C
G
A
C
T
C
C
C
A
C
C
G
T
C
T
C
C
A
T
A
C
A
T
A
C
T
GA
C
A
A
T
A
A
A
G
C
A
G
A
A
C
C
T
CT
T
A
A
G
A
G
G
T
T
C
T
G
C
T
T
T
A
T
T
T
T
T
T
1
140
150
abc
de
Fig. 3 Precursor tmRNA can act as an RNA sensor for FMN. aSchematic of the B.subtilis tmRNA genomic locus (top) and predicted secondary structures
of the long precursor tmRNA, short precursor tmRNA, and mature tmRNA, using the RNAfold program20 (bottom). bRNA footprinting analysis of the long
precursor tmRNA, using a SHAPE-like chemical (NAI), in the presence (lane 4) and absence (lane 3) of 100 µM FMN. Also shown are A ladder (lane 1) and
unmodified RNA (lane 2). The red bar indicates bases that become more single-stranded in the presence of FMN. cPredicted secondary structure of the
tmRNA long precursor using the RNAfold program20. The red bases correspond to the positions marked by the red bar in b.dAverage footprinting analysis
(n=3, SAFA) of mature (top), short precursor (middle), and long precursor tmRNA (bottom), in the presence (red) and absence (black) of 100 µM FMN,
in the dark. The beige box indicate the region of increased flexibility in the precursor tmRNAs in the presence of FMN. The stars indicate bases that show
statistically significant changes with FMN (p≤0.05, Student t-test). eqPCR analysis of the mRNA expression level of precursor tmRNA and mature
tmRNA, across six biological replicates, after addition of 100 µM of riboflavin to the growth media of B.subtilis. Fold changes are normalized to the negative
control Veg gene. The known B.subtilis FMN riboswitch is used as the positive control. p-values were calculated by Student’st-test, the error bars indicate
standard deviation of the replicates
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-03675-1
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3′ UTR 5′ UTR CDS
C. albicans
New RNA aptamers
C. albicans
−3
−2
−1
0
Z-score (obtained from Alifoldz)
p = 0.016
Ka Kr Ks
0.1
0.2
0.3
0.4
0.5
1.1
0
1.2
Nucleotide substitution rate
1
2
20
Shuffled
FMN FAD RiboflavinA
20 100 500 20 100 500 20 100 500
(μM)
RPS31
Loading
2.5 10.05.0
FMN (mM)
WT fmn1Δ
2.5 10.05.0
Wildtype RPS31 protein levels
RPS31
Loading
2.5 10.05.0FMN (mM)
WT fmn1Δ
2.5 10.05.0
Codon-optimized RPS31 protein levels
U
H2O
359
337337
317
282
240
348
+–A
In-line probing
+–A
RNase V1
359
348
337337
317
282
240
FMN (100 μM) FMN
p = 3.4×10–12
0
1
2
3
4
5
6
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
WT RPS31
Codon-optimized RPS31
Base
Water
100 μM FMN
0
1
2
3
4
5
6
C.alb vs. C.dub
Observed K3′UTR K5′UTR
Normalized in-line
probing intensity
abc
e
d
f
gh
Fig. 4 PARCEL identifies new RNA aptamers in Candida albicans.aPie chart of the number of C.albicans RNA aptamers that are located in 5′UTR, CDS, and
3′UTR. The majority of C.albicans RNA aptamers are found in CDSs. bComparison of the distribution of Alifoldz scores for RNA aptamers vs. shuffled
counterpart. The upper, middle, and lower bounds of the boxplot represent the 75, 50, and 25th percentile of the values, respectively. A negative score
indicates a stable, conserved consensus structure. P-value was obtained using the non-parametric Kolmogorov–Smirnov test. cNucleotide substitution
rates, calculated as the number of substitutions per base-pair, for RNA aptamers (Kr), 3′UTR (K
3UTR
), 5′UTR(K
5UTR
), synonymous sites (Ks), and non-
synonymous sites (Ka). The upper, middle, and lower bounds of the boxplot represent the 75, 50, and 25th percentile of the values, respectively. C.albicans
SC5314 was compared to Candida dubliniensis for the calculation. p-value was obtained using the non-parametric Kolmogorov–Smirnov test. dGel analysis
of RPS31 mRNA using in-line probing (left) and RNase V1 (right) in the presence (lane 3) and absence (lane 2) of 100 µM FMN. The A ladder (A, lane 1) is
also shown. The black arrows indicate positions along the RNA that changed in the presence of FMN. eA representative Western blot showing RPS31::
FLAG (top) and loading (bottom) protein levels in RPS31::FLAG knock-in strains with WT (left) and fmn1Δ(right) backgrounds cultured at different FMN
concentrations (mM). Using t-test (n=8), significant p-values of 0.009 and 0.01 (for 2.5 and 5.0 mM against 10.0 mM, respectively) were determined for
fmn1Δ, but not WT (p-values of 0.2 and 0.5). fGel analysis of RPS31 mRNA using in-line probing in the presence of 20, 100, or 500 µM of FMN, FAD or
riboflavin. In-line probing of RNA in the absence of metabolite (H
2
O, lane 2) and A ladder (A, lane 1) are also shown. gSAFA analysis of WT RPS31 (top)
and codon-optimized RPS31 (bottom) in the presence (red line) and absence (black line) of 100 µM FMN. The beige box indicates the region of structural
change in WT RPS31 when it interacts with FMN. This structural change is absent in the codon-optimized RPS31. hA representative Western blot showing
codon-optimized RPS31::FLAG (top) and loading (bottom) protein levels in codon-optimized RPS31::FLAG knock-in strains with WT (left) and fmn1Δ(right)
backgrounds cultured at different FMN concentrations (mM). Using t-test (n=3), calculated p-values for 2.5 and 5.0 mM were insignificant for both fmn1Δ
(both 0.7) and WT (0.9 and 0.09)
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these genes could represent the first known eukaryotic FMN
riboswitches.
To further understand ligand binding specificities and affinities
of this putative FMN riboswitch, we performed detailed structural
studies on the RPS31 transcript. We observed that RPS31 RNA
binds specifically to FMN and does not respond to structurally
similaranalogs,suchasriboflavin and FAD (Fig. 4f, Supplementary
Fig. 11d). Integrating double-stranded (RNase V1), single-stranded
(S1 nuclease) structure probing and in-line probing information
along the length of the RPS31 transcript into the RNAfold
prediction software showed that RPS31 RNA consists of seven
stems around a central loop, and that FMN binding results in
extensive structural rearrangements (Supplementary Fig. 12a). The
FMN-bound RPS31 aptamer consists of six stems around the
central FMN bound loop, and appears to resemble the prokaryotic
FMN riboswitch structure16. This attests to the structural plasticity
of RNA molecules, whereby different sequences can be utilized to
form similar structures for cellular function.
To further test whether the FMN-induced change in RPS31
protein levels is mediated post-transcriptionally, we designed a
codon-optimized version of C.albicans RPS31 (by changing
nucleotides of synonymous bases) to disrupt RPS31 RNA structure
without altering its protein sequence. As expected, codon-optimized
RPS31 mRNA is structurally different from wildtype RPS31 mRNA,
and does not show structure changes in the presence and absence of
FMN (Fig. 4g). Codon-optimized RPS31 maintained similar mRNA
and protein levels at varying concentrations of FMN (Fig. 4h,
Supplementary Fig.12b-d), supporting the hypothesis that the
binding of FMN to native C.albicans RPS31 RNA results in post-
transcriptional regulation of RPS31 protein levels.
Discussion
In summary, we have developed a new strategy named PARCEL
that experimentally identifies RNA aptamers transcriptome-wide
by detecting ligand-induced structure changes. As PARCEL
allows us to rapidly screen through transcriptomes to identify
RNA aptamers, we identified a total of 58 novel candidate RNA
aptamers in two prokaryotic and one eukaryotic species, includ-
ing a second class of putative eukaryotic riboswitches. Unlike
known riboswitches described in the literature, the newly iden-
tified aptamers reside in both UTRs and coding sequences, and
are not necessarily linked to the biosynthetic pathways of their
respective ligands. Further characterization of three new RNA
aptamers showed that they could be riboswitches as FMN-sensing
induces RNA structural changes and regulates transcript levels of
tmRNA in B.subtilis, and protein levels of RPS31 and ATP1 in C.
albicans. As PARCEL can be readily applied to any transcriptome
and ligand, we believe that further application of PARCEL to
diverse organisms will result in the identification of many novel
natural RNA aptamers in the near future, providing new building
blocks for biological sensing and deepening our understanding of
RNA-based gene regulation in vivo.
Methods
Bacterial and yeast cultures.P.aeruginosa PAO1 and B.subtilis 168, were grown
in LB or minimal media to log (OD
600
=0.6–0.8) or stationary phases (OD
600
> 2).
Total RNA from P.aeruginosa was extracted using Trizol reagent (Thermo Fisher
Scientific). Total RNA from B.subtilis was extracted by first incubating B.subtilis in
4 mg per mL of lysozyme for 15 min before using Trizol LS reagent. Ribosomal
depleted RNA, Ribo(−) RNA, was obtained by using Ribo-Zero rRNA Removal Kit
(Epicenter) according to manufacturer’s instructions. S.cerevisiae S288C was
grown in YPD to exponential phase (OD
600
=0.6–0.8). C.albicans strain SC5314
was grown in YPD or GMM (yeast nitrogen base without amino acids and with 2%
glucose) to exponential (OD
600
=0.6–0.8) or stationary phases (OD
600
> 2). Total
RNA from S.cerevisiae or C.albicans was extracted using a slightly modified
protocol that uses hot acid phenol17. Poly(A) +RNA was obtained by using the
Poly(A) Purist MAG kit (Thermo Fisher Scientific) according to manufacturer’s
instructions. Poly(A)+or Ribo(−) RNA were then structure probed in the pre-
sence and absence of metabolites.
fmn1Δmutant was created by replacement of FMN1 in BY4741 strain with
KanMX using homologous recombination18. RPS31 from C.albicans, with a C-
terminal FLAG-tag (GATTACAAGGACGACGATGACAAG), was integrated
together with URA3 at the ura3Δsite to generate the RPS31::FLAG knock-in
strains. ATP1 from C.albicans, with a N-terminal FLAG-tag, was integrated
together with URA3 at the ura3Δsite to generate the ATP1::FLAG knock-in
strains.
RNA structure probing. Briefly, 250 ng of Poly(A)+or Ribo(−) RNA was heated
to 90 °C for 2 min and cooled on ice for 2min before adding 10X RNA structure
buffer (500 mM Tris pH 7.4, 1.5 M NaCl, 100 mM MgCl
2
) and metabolites to the
RNA. The RNA pool was slowly brought to 37 °C for 30 min and structure probed
using RNase V1 (1:2000 dilution, AM2275 Life Technologies) or S1 nuclease (1:500
dilution, Fermentas) at 37 °C for 15 min. The nuclease reactions were inactivated
using phenol chloroform extraction and ethanol precipitated. In-line probing
reactions were performed in 50 mM Tris-HCl (pH 8.3), 20 mM MgCl
2
, and 100
mM KCl at 25 °C for 40 hours8. The in-line probed RNA was phosphorylated using
T4 polynucleotide kinase (PNK) in T4 PNK buffer and 1 mM ATP to capture the
cleavage sites.
Library preparation. Structure probed RNA was fragmented at 95 °C for 3.5 min
in alkaline hydrolysis buffer (Ambion). As fragmentation results in 5′OH, and is
hence ligation incompatible, it does not interfere with the downstream library
preparation process. Fragmented RNA was then purified using RiboMinus con-
centration module (Life Technologies), using the modified protocol for RNAs that
are <200 bases. The RNA was eluted in 12 µl of nuclease free water and con-
centrated to 2 µl using a vacuum centrifuge. The RNA was then ligated to 5′
adapter from NEBNext Multiplex Small RNA Library Prep Set for Illumina using
T4 RNA ligase1 (T4 RNA ligase buffer, 1 mM ATP, 10% PEG, 10% DMSO) at 16 °
C overnight. The 5′adapter ligated RNAs were then purified through a 6% TBE
urea PAGE gel and size selected for 50–200 bases. The RNA was then ligated to 3′
adapter, reverse transcribed, and PCR amplified using the NEBNext Multiplex
Small RNA Library Prep Set (New England Biolabs) for Illumina using manu-
facturer’s instructions. Eighteen cycles of PCR amplification were typically per-
formed for each library.
RNA footprinting analysis. Cleavage and modification sites along structure pro-
bed RNA were identified using primer extension. Briefly, a primer located ~30–50
bases downstream of the structure probed region was labeled with ɣP32 ATP using
T4 PNK kinase. The labeled primer was then purified using a 15% TBE urea PAGE
gel. The labeled primer was incubated with the RNA at 65 °C for 5 min, followed by
35 °C for 5 min, and then cooled at 4 °C. To detect the structure probed sites, we
add 3 µl of enzyme mix (4:1:1 of first-strand buffer: DTT: NTP) to the reaction,
incubated at 52 °C for 1 min, and Superscript III was added to the reaction at 52 °C
for 10 min. To generate a sequencing ladder for the RNA, we added 1 µl of ddNTP
(5 mM) to the reaction after the enzyme mix, and before adding Superscript III. 4
M sodium hydroxide was added to the reaction to denature the RNA before the
samples were loaded onto a 7 M TBE-Urea PAGE sequencing gel. Gel images were
quantified using the software Semi-automated footprinting analysis (SAFA)19.
RNA structure models. RNA secondary structure predictions were generated
using RNA footprinting data with RNase V1, S1 nuclease, and NAI as constraints,
using the program RNAfold20 with default parameters.
qPCR and Western blotting for wildtype and codon-optimized RPS31. The
RPS31::FLAG and ATP1::FLAG strains were inoculated from single colonies into 2
mL SC-ura media and grown overnight at 30 °C, with shaking. Strains with the
fmn1Δmutation were supplemented with 10 mM FMN and 200 µg per mL G418 in
the cultures. The overnight cultures (1:100 dilution of OD
600
2.0) were used to seed
50 mL YPD cultures supplemented with 2.5, 5.0 or 10.0 mM FMN. Cells were
harvested after 4–6 h of growth at 30 °C, with shakin g (when OD
600
reaches 0.4).
The cultures were split for RNA extraction and Western blotting, pelleted, and
washed once with PBS. The resultant cell pellets were frozen and stored at −80 °C.
RNA extraction and qPCR. RNA was extracted from frozen yeast pellets using the
hot acidic phenol method and treated with TURBOTM DNase (ThermoFisher
Scientific)17. We made cDNA using the Transcriptor First Strand cDNA Synthesis
Kit (Roche) and qPCR was performed using SYBR Green Master Mix (Roche) on a
Light Cycler 96 instrument (Roche). Primers used are listed as below. The RPS31
and ATP1 primers are specific for the knock-in C.albicans RPS31 and ATP1, and
do not amplify the endogenous S.cerevisiae RPS31 and ATP1. Normalized fold
changes were calculated by normalizing against actin (ACT1) and the respective
strain cultured at 10 mM FMN.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-03675-1
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Forward Reverse
RPS31 set 1 (HZ_pri072) TCCACC
AGACCAAC
AAAGATTG
(HZ_pri073) ACCAAGTG
CAAGGTGGATTC
RPS31 set 2 (HZ_pri023) GAATCCA
CCTTGCACTTGGTC
(HZ_pri024) GCCAACT
TGTGTTTTCTGTGC
RPS31 set 3 (HZ_pri015) GCACAGAA
AACACAAGTTGGC
(HZ_pri016) CCATGAAA
ATACCGGCACCAC
ACT1 (HZ_pri051) ATGGATTC
TGAGGTTGCTGC
(HZ_pri052) TGGTCT
ACCGACGATAGATGG
RPS31_codon-
optimized set 1
(HZ_pri118) TGGAGGTCG
AGTCATCAGATAC
(HZ_pri119) CGTCTTA
TTTTCGCAGGGAAGC
RPS31_codon-
optimized set 2
(HZ_pri122) TTTCGCA
GGGAAGCAGTTAG
(HZ_pri123) TTTTTCCC
CCACCCCTTAACC
RPS31_codon-
optimized set 3
(HZ_pri120) TAGAGAG
GTTGAGGCGTGAATG
(HZ_pri121) GGCACTT
ACCGCAATATTGACG
ATP1 set 1 (HZ_pri064) TTACGTA
CTGCTGCTCGTACAG
(HZ_pri065) GGCAGAG
GCAAATCTTTGAGC
ATP1 set 2 (HZ_pri058) AAGTCG
GGGTTGTGTTGTTC
(HZ_pri059) TTCTGGA
CCAATTGGAACGG
ATP1 set 3 (HZ_pri062) TCGCTGG
TGTTAACGGTTTC
(HZ_pri063) CACCCTT
GGTCTTAATAGCATCC
Western blotting. The frozen cell pellet was resuspended in 80 µL of lysis buffer
(50 mM Tris pH 7.4, 4% SDS) with proteinase inhibitor (one tablet of completeTM,
Mini, EDTA-free [Roche] in 2.5 mL of lysis buffer). An equal volume of glass beads
(425–600 µm, Sigma-Aldrich) was added and cells were lysed in a Mini-
Beadbeater-96 (Biospec Products) for two cycles of 15 s with a two minute interval.
Cell lysates were centrifuged and supernatants were run on 4–20% Mini-Protean
TGX Stain-Free protein gels (Bio-Rad). To determine the relative levels of total
protein (loading), the gels were first imaged on a ChemiDoc MP System (Bio-Rad)
using the stain-free technology21. Following wet transfer and blocking with 5%
milk in PBST, RPS31::FLAG and ATP1::FLAG were detected using mouse anti-
FLAG M2 primary antibody (1:4000 in PBS at 4 °C overnight, Sigma-Aldrich,
F1804) and sheep anti-mouse IgG, HRP-linked secondary antibodies (1:20000 in
1% milk at 25 °C for an hour, GE Healthcare, NA931). All images were taken using
the ChemiDoc MP System (Bio-Rad) and analyzed with ImageJ22. Corresponding
uncropped images of blots (in main figures) can be found in supplementary figures.
Read mapping. Short reads from PARCEL libraries (Illumina HiSeq, 50 bp, single-
end) in P.aeruginosa PAO1, B.subtilis 168, and E.coli K12 were aligned to their
corresponding reference genomes downloaded from NCBI using the short-read
aligner bowtie2 (parameters: −k1–local)23. In the case of S.cerevisiae S288C and C.
albicans SC5314, we extracted the UTR annotation from Bruno et al.24,25 and
integrated them into their corresponding transcriptomes before alignment by bow-
tie2. In both cases, only uniquely mapped reads were used for subsequent analysis.
Identification of RNA aptamers. For each position along the genome or tran-
scriptome, we counted the number of reads whose first mapped base was one base
downstream of the inspected position. Higher counts suggest greater accessibility to
V1 nuclease, and are more likely to be associated with a double-stranded con-
formation. In all expressed transcripts, positions with zero count could either be
associated with a single-stranded conformation, or come from a heavily folded
region that is inaccessible to V1 nuclease.
Since RNA structural changes should typically span across multiple bases, we
looked for regions that exhibit differential V1 counts to increase the sensitivity/
specificity of detecting RNA aptamers. We first evaluated the significance of
differential V1 counts at each nucleotide position using the edgeR package26, where
we compared samples treated by one specific metabolite (e.g., TPP) to samples
from all other conditions. We focused on positions that were generally accessible to
the V1 nuclease by applying a minimum abundance threshold (average counts per
sample per position, a> 1) and then computed a score s
i
for each passed position i
based on edgeR-generated p-values (pval
i
): s
i
=ln(0.1) −ln(pval
i
) (in effect, giving
negative scores for p-values > 0.1).
The higher the score, s
i
, the more likely that differential V1 cutting was observed
at that specific position. Accordingly, positions that failed to pass the abundance
threshold were assigned a penalty score of −10. We then looked for segments of
contiguous positions (e.g., a segment from position m to n) with the highest
aggregate score S¼P
n
i¼m
s
i
, by applying the Kadane algorithm (maximal subarray
problem). We then determined the significance of these high-scoring segments
based on Karlin–Altschul statistics, similar to the approach used in BLAST27.
As described by Karlin and Altschul27, the expected value (E-value) of high-
scoring segments with an aggregate score of at least Sis given by the formula:
Ev¼KeλS
:ð1Þ
Therefore, we examined the extreme value distribution of the aggregate score S
to estimate the two parameters required i.e., Kand λ. Specifically, λcan be
calculated from the formula: Ppieλsi¼1, where p
i
is the corresponding
probability of the scores
i
.Ass
i
=ln(0.1) −ln(pval
i
) and pval
i
approximately
follows a uniform distribution (U(0,1)) due to the assumption that the majority of
nucleotide positions do not undergo any structural changes, the equation
Ppieλsi¼1can be translated to:
1
pvali¼0eλln 0:1ðÞln pvali
ðÞðÞ
¼1:
This can be solved to 0:1λ
1λ¼1, and λ=0.862871. The parameter Kis bounded
between K¼Cλδ
eλδ1

and Kþ¼Cλδ
1eλδ

. Since δis the smallest span of s
i
,K
is bounded between limδ!0Kand lim
δ!0Kþand C*isdefined by the formula:
C¼e2P1
k¼1
1
kðEe
λSk;Sk<0
ðÞ
þProb Sk0ðÞÞ
λEðS1eλS1Þ:
Here, S
k
is the random variable representing the sum of kindependently chosen
s
i
i.e., Sk¼Psi¼kIn 0:1ðÞðÞþ
Pln pvali

. Since pvali
U0;1ðÞ
Pln pvali

approximately follows the gamma distribution i.e.,
Γk¼k;δ¼1ðÞ:Let Xk¼Pk
i¼1ln pvali

, it can then be derived that:
Ee
λSk;Sk<0

¼0:1λkZkln0:1
Xk¼0
Xk1
keλ1ðÞXk
k1ðÞ!;
ProbðSk0Þ¼1Rkln0:1
t¼0tk1et
k1ðÞ!;
and
λES
1eλS1

¼λ0:1λ1λ1ðÞln0:1
λ1
ðÞ
2:
Taken together, C* can be solved to take the value of 0.0809635, and the upper
and lower bounds of K,K−, and K+, both equal C*, i.e., K=0.0809635. We then
calculated E
v
for high-scoring segments by applying equation (1). Segments that
pass the E
v
threshold of 10 were considered as candidate RNA aptamers that
undergo metabolite-responsive conformational changes. Under all conditions, we
report candidate regions that have positions with absolute fold-change f>2,
relative abundance greater than median +standard deviation for the transcript
(abundance-filter; to avoid segments with lower accessibility) and low bonferroni-
corrected p-value (<10; to avoid segments with no strongly changing position).
Distribution of RNA aptamers across operons and transcripts. We evaluated
the distribution of RNA aptamers across operons in bacteria, and along transcripts
in fungi (including a 500 bp window on either side when UTRs were not specified).
We plotted the histogram of all RNA aptamer positions, with operons in bacteria
and coding regions in fungi being scaled to 1 kbp. There are cases where the same
position can be considered as belonging to multiple classes and in such cases, we
preferentially assigned positions to the 5′UTR, then to the operon or CDS, and
lastly to the 3′UTR.
Sequence conservation of RNA aptamers. We estimated the sequence con-
servation of identified RNA aptamers by measuring nucleotide substitution rate of
these regions to their blastn-identified orthologous sequences. If the identified
aptamer regions were shorter than 200 bases in length, we extended them on both
sides to a maximum of 200 bases. As highly divergent and highly similar sequences
would result in an unreliable estimate of nucleotide substitution rate28, we chose
fairly divergent, and yet not too divergent species (median Ks ranges from 0.1 to
0.4) for this analysis. The orthologous riboswitches, 3′UTR, 5′UTR, and protein
coding regions were identified using blastn, for non-coding, or genblastG29, for
coding sequence, in other species, respectively. To identify orthologous noncoding
sequences in other organisms with high sensitivity, we changed the default blastn
parameters as follows: “-e 1e-5 -word_size 7 –gapopen 2 –gapextend 1”30.We
aligned the noncoding sequences using MUSCLE31, and the coding sequences
using MACSE32, to construct the multiple sequence alignment. The nucleotide
substitution rate of riboswitches, 3′UTR and 5′UTR were calculated using
Kimura’s 2-parameter method33. The synonymous and non-synonymous sub-
stitution rates was calculated using Kakscalculator34 with the LPB method.
Calculating the degree of pairedness for RNA aptamers and controls.We
searched for orthologous sequences of RNA aptamers identified in B.subtilis,P.
aeruginosa, and C.albicans across the Bacillus,Pseudomonas, and Candida genus
using blastn (with parameters: “-e 1e-5 -word_size 7 –gapopen 2 –gapextend 1”).
The species that were used in each genus are: B.subtilis XF-1, B.subtilis BSn5, B.
malacitensis CR-95, B.natto BEST195, B.licheniformis DSM13, B.subtilis subsp.
NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-03675-1 ARTICLE
NATURE COMMUNICATIONS | (2018) 9:1289 |DOI: 10.1038/s41467-018-03675-1 |www.nature.com/naturecommunications 9
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spizizenii W23, and B.cereus ATCC-14579 for Bacillus;P.aeruginosa PAO1, P.
aeruginosa PA7, P.mendocina 1267_PMEN, P.knackmussii B13, P.oryzihabitans
USDA-ARS-USMARC-56511, P.pseudoalcaligenes KF707, P.stutzeri A1501, P.
stutzeri A1501, P.mendocina ymp, P.entomophila L48, P.putida F1, P.fluorescens
SBW25, and P.syringae pv.tomato str. DC3000 for Pseudomonas;C.albicans
SC5314,C.albicans WO-1, and C.dubliniensis for Candida. We then built multiple
species alignments for each RNA aptamer region using MUSCLE31. We used the
program Alifoldz12 to calculate the energy and structural stability of the consensus
structure. For each RNA aptamer alignment, a shuffled alignment was generated as
a control using the “shuffle.pl”script from the Alifoldz package.
Data availability. All relevant data are available from the authors upon request.
Data has been deposited under GEO accession number GSE106133.
Received: 21 December 2017 Accepted: 5 March 2018
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Acknowledgements
We thank members of the Wan lab, Nagarajan lab, S. Chen, W.F. Burkholder, A. Sim, H.
H. Ng, and B. Lim for discussions. B.subtilis 168 was obtained from the Bacillus Genetic
Stock Center. N. Nagarajan is supported by funding from A*STAR. Y. Wan is supported
by funding from A*STAR, Society in Science - Branco Weiss Fellowship, and EMBO
Young Investigatorship.
Author contributions
Y.W. conceived the project, developed the protocol, and designed the experiments.
N.N. and M.S. designed the computational pipeline. Y.W., S.T., X.N.L., T.T.S., G.S.Z., J.L.,
Y.W., L.H.Z., E.L.A., H.Z. and H.Z. planned and performed all the experiments. N.N.,
S.L.Y., and M.S. planned and conducted the data analysis. A.L. helped with the
sequencing. Y.W. organized and wrote the paper with contributions from H.Z., S.L.Y.,
N.N. and all other authors.
Additional information
Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467-
018-03675-1.
Competing interests: The authors declare no competing interests.
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ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-03675-1
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