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R E S E A R C H A R T I C L E Open Access
RNA secondary structure profiling in
zebrafish reveals unique regulatory features
Kriti Kaushik
1,4
, Ambily Sivadas
2,4
, Shamsudheen Karuthedath Vellarikkal
1,4
, Ankit Verma
1
, Rijith Jayarajan
1
,
Satyaprakash Pandey
1,4
, Tavprithesh Sethi
3
, Souvik Maiti
1,4
, Vinod Scaria
2,4*
and Sridhar Sivasubbu
1,4*
Abstract
Background: RNA is known to play diverse roles in gene regulation. The clues for this regulatory function of RNA
are embedded in its ability to fold into intricate secondary and tertiary structure.
Results: We report the transcriptome-wide RNA secondary structure in zebrafish at single nucleotide resolution using
Parallel Analysis of RNA Structure (PARS). This study provides the secondary structure map of zebrafish coding
and non-coding RNAs. The single nucleotide pairing probabilities of 54,083 distinct transcripts in the zebrafish
genome were documented. We identified RNA secondary structural features embedded in functional units of
zebrafish mRNAs. Translation start and stop sites were demarcated by weak structural signals. The coding regions
were characterized by the three-nucleotide periodicity of secondary structure and display a codon base specific
structural constrain. The splice sites of transcripts were also delineated by distinct signature signals. Relatively
higher structural signals were observed at 3’Untranslated Regions (UTRs) compared to Coding DNA Sequence
(CDS) and 5’UTRs. The 3′ends of transcripts were also marked by unique structure signals. Secondary structural
signals in long non-coding RNAs were also explored to better understand their molecular function.
Conclusions: Our study presents the first PARS-enabled transcriptome-wide secondary structure map of zebrafish,
which documents pairing probability of RNA at single nucleotide precision. Our findings open avenues for exploring
structural features in zebrafish RNAs and their influence on gene expression.
Keywords: PARS, Zebrafish, Transcriptome, Gene regulation, RNA secondary structure
Background
RNA is a multitasking biomolecule, which not only acts as
a messenger molecule to transfer genetic information
from DNA to proteins, but also plays a vital role in regula-
tion and catalysis of major biological reactions like tran-
scription [1], post-transcriptional processing [2,3]
including splicing events, editing, degradation [4]and
translation. In order to perform these processes, RNA
adapts specific conformations owing to its ability to fold
into secondary and tertiary structures [5]. The nucleotide
sequence of RNA is primarily responsible for the second-
ary structure, formed by Watson Crick base pairing within
the polynucleotide backbone. Subsequently, the tertiary
structure is governed by the secondary structure and sev-
eral other interactions with biomolecules [6]. The second-
ary structure of RNA is relatively stable and is present all
throughout the length of mRNAs including CDS and
UTRs [7]. A large number of diverse secondary structural
motifs in mRNAs have been studied extensively including
riboswitches, IRES [7], AU-rich, localisation elements [8]
and structures that enhance transcription, alternative
splicing and translation [2].
In addition to mRNAs, non-coding RNAs also display
secondary structural features. The secondary structural el-
ements in non-coding RNAs have been shown to regulate
gene expression [9–12] and orchestrate the process of
protein production. Small non-coding RNAs such as
microRNAs [13] are known to fold in a pre-defined stem
with loop structure that aids in binding to protein com-
plexes and small molecules. Furthermore, long non-coding
RNAs (lncRNAs) represent a class of regulatory RNAs that
* Correspondence: vinods@igib.res.in;s.sivasubbu@igib.res.in;sridhar@igib.in
2
G.N. Ramachandran Knowledge Centre for Genome Informatics, CSIR
Institute of Genomics and Integrative Biology, Sukhdev Vihar, Mathura Road,
New Delhi 110025, India
1
Genomics and Molecular Medicine, CSIR Institute of Genomics and
Integrative Biology, Sukhdev Vihar, Mathura Road, New Delhi 110025, India
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Kaushik et al. BMC Genomics (2018) 19:147
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are abundantly present in eukaryotic transcriptome [14,
15]. LncRNAs display less nucleotide sequence conserva-
tion across species [16]; however, the secondary structural
core of lncRNAs are conserved by reciprocal base pair mu-
tations [17–22]. It is a well-known fact that structure and
synteny conservation preserves the function of protein-
coding mRNAs in species [23–27] separated by large evo-
lutionary distances and this may also apply to non-coding
RNAs. Therefore, understanding the secondary structure
would be important for predicting the function of non--
coding RNAs in general and lncRNAs in particular.
Zebrafish has been extensively used to study spatiotempo-
ral expression profiles of genes including protein-coding
genes [28,29] and non-coding RNAs [30–32]. In recent
years, several groups, including ours have documented
spatiotemporal expression profiles of lncRNAs across early
developmental stages [16,33] and tissues in adult zebrafish
[34,35]. Amongst the lncRNAs discovered, only small
fraction display nucleotide sequence conservation across
different species. Majority of the lncRNAs do not display se-
quence conservation across evolutionary distances and this
poses a significant hurdle for understanding their functional
relevance. It is widely envisaged that the conserved struc-
tural features in non-coding RNAs especially lncRNAs may
provide cues to conserved function across species [19,22].
In this study, we probe the zebrafish transcriptome
using Parallel Analysis of RNA Structure (PARS) [36,37]
to reveal the landscape of pairing probability at single
nucleotide resolution. We undertook enzyme based
probing of one day old zebrafish transcriptome using
RNase V1 and S1 Nuclease to discover paired and un-
paired nucleotide respectively. The enzyme cleaved frag-
ments were subjected to next generation sequencing to
yield pairing probability at single nucleotide resolution.
Results
Sequence data generation and mapping
About, 400 million reads were generated in total, with
approximately 200 million reads in RNase V1 and S1
Nuclease cleaved samples respectively (Table 1).
The total sequencing reads generated from RNase V1
and S1 Nuclease cleaved fragments mapping to zebrafish
transcriptome (n= 169 million) were aligned to 54,083
transcripts. Load score for all the transcripts were evalu-
ated to check their abundance in the data. All the
transcripts (n= 54,083) displayed a load ≥1 (Table 1).
To verify if the data obtained from the two datasets
were unbiased, ratio score for each of the position was
determined in both the samples (see Methods). The read
counts for every position in the transcriptome were esti-
mated in both RNase V1 (n= 8,700,581) and S1 nuclease
(n= 13,151,051) cleaved samples (Fig. 1a). In total
18,375,999 unique positions were covered by both the
enzyme cleaved samples in the transcriptome, of which
3,475,633 positions were jointly covered in both (RNase
V1 and S1 Nuclease) datasets. There were 2,409,350 peaks
(ratio score > 1) in RNase V1 dataset and 2,434,014 peaks
in S1 Nuclease dataset. While, 186,306 positions had over-
lapped peaks (Fig. 1a) in both datasets i.e. 4% of the peaks
exhibited ambiguous pairing probability, which suggested
minimum biases in the data or impartial enzymatic cleav-
age and the two enzymes cleaved independent positions in
the in-vitro folded zebrafish transcriptome.
PARS scores for all the respective positions (n=
18,375,999) cleaved by RNase V1 and S1 Nuclease were de-
termined as per the formula described in Methods section.
Normalisation constants K
v
=1.17And K
s
=0.88wereused
to normalise the read counts for every position.
The composite unique positions as obtained from
RNase V1 and S1 Nuclease cleavage (n= 18,375,999)
were aligned to 54,083 transcripts in zebrafish genome
assembly (v79/Zv9). Amongst these transcripts, a total of
25,158 transcripts had positions represented by overlap-
ping peaks in both the datasets with ratio score greater
than one and 11,450 transcripts were termed as multi-
conformation transcripts as they constituted at least five
positions with overlapping peaks (Fig. 1b).
Position coverage i.e. positions with read starts across
a transcript (generating from RNase V1/S1 Nuclease
cleavages) was estimated. Out of 54,083 transcripts, 544
transcripts had more than 85% position represented by
Table 1 RNA-seq data production and alignment results for zebrafish poly (A) RNA reads
S1 data
(in millions)
V1 data
(in millions)
Total
(in millions)
Total Reads 213.05 204.96 418
Trimmed Reads 180.77 161.14 341.91
Total Mapped reads 169.67 (93.8%) 140.16 (86.9%) 309.84 (90.6%)
Uniquely mapped reads to genome 139.17 (76.9%) 104.85 (65.06%) 244.02 (71.37%)
Mapped reads to transcriptome 109.03 (60.32%) 59.73 (37.06%) 168.77 (49.36%)
Transcripts with load > 1 54,083
The total number of sequence reads obtained from enzymatically probing (S1 Nuclease and RNase V1) the poly (A) RNA using RNA sequencing is mentioned.
Mapped reads are aligned back to zebrafish genome (zv9)
Kaushik et al. BMC Genomics (2018) 19:147 Page 2 of 17
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enzyme cleavage across the length of the transcript.
While 7366 transcripts showed coverage from 40 to
85%, but a majority of transcripts 46,173 had coverage
less than 40% (Fig. 1c). The biological distribution of
54,083 transcripts was determined. Majority of the tran-
scripts (77%) were protein-coding, 16% were lncRNAs,
3% rRNAs, 1% NMD and rest 3% were labelled miscel-
laneous transcripts (Fig. 1d). Of these transcripts, those
with more than 85% positions represented by enzyme
cleavages were considered for further analysis. Further,
the biotype of these transcripts is displayed in Fig. 1d
with 90% of transcripts as protein coding, 7% lncRNAs,
3% NMD and less than 1% was rRNA.
PARS-enabled pairing probability at single nucleotide
resolution reveals structural conservation of protein
coding genes across species
The homologs of rpl35 in zebrafish (NCBI Gene ID:
192,299) and human (NCBI Gene ID: 11,224) have 81%
sequence homology in the CDS region encompassing 372
nucleotides (Fig. 2a). The investigation was restricted to
CDS as UTRs have no sequence homology and are of
varied lengths. Pairing probability for zebrafish rpl35 was
determined and plotted (Fig. 2b). Out of the 371 positions
investigated, 233 are paired and 138 are unpaired positions.
PARS scores of human RPL35 were obtained from pub-
lished PARS data of humans [37]. The comparison of PARS
scores reveals 71% conservation in RNA secondary struc-
ture of CDS (Fig. 2c). Out of 360 positions, 71 positions are
not conserved by sequence. However, of these 71 positions,
72% (n= 51) positions are structurally conserved.
Comparison of PARS derived structure with enzymatic
footprinting
Prior to analysing PARS-enabled pairing probability in
zebrafish we wanted to investigate the validity of PARS
using an orthogonal technology. We chose in vitro en-
zymatic footprinting to validate pairing probability of
ubiquitin c (NCBI Gene ID: 777,766), a candidate
protein-coding gene. Ubiquitin c was chosen as it had
97% nucleotide positions represented by enzyme cleavage
across the length of the transcript. Since UTRs are known
to play important role in gene regulation owing to the sec-
ondary structural features [38], we chose the 3’UTR of
Fig. 1 Overview of the transcriptome generated by PARS. a. Venn diagram representing paired and unpaired positions at 24 hpf zebrafish transcriptome
obtained from PARS data. Approximately, 3.4 million positions are jointly obtained in both V1 and S1 cleaved samples. Blue (V1) and the green (S1)
ellipse display positions with ratio score > 1, termed as peaks. Of these, 186,306 positions are overlapped peaks showing ambiguous positions. b.Atotal
of 54,083 transcripts were assembled, from which 25,158 transcripts have positions with overlapping peaks in both V1 and S1 dataset. Amongst these
11,450 transcripts had more than 5 positions overlapping and are categorised as multi-conformation transcripts. c. Abundance of transcripts based on per
base structure coverage. Coverage of the total transcripts (54,083) was estimated by the number of reads relative to the positions covered and the length
of the transcript. Most of the transcripts (46,173) were less than 40% covered, 7366 transcripts were 40–85% covered, while 544 transcripts were > 85%
covered. d. Bar plot representing the biotype of 54,083 transcripts. The biotype of the transcripts is represented. The inset pie shows distribution of
transcripts with > 85% covered based on the function. PC: protein coding, lncRNA: long non-coding RNA, NMD: non-sense mediated decay, rRNA:
ribosomal RNA, Misc: miscellaneous
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ubc for validation. In addition, short length of 105 bases
was considered favourable for enzymatic footprinting.
Out of the 105 positions investigated in the ubc
3’UTR, PARS scores were obtained for the first 87 posi-
tions. PARS scores could not be obtained for the last few
positions, due to low quality reads obtained from en-
zyme cleaved fragments at the ends of the transcripts.
PARS signals for ubc 3’UTR are displayed in Fig. 3a for
the 87 nucleotide positions of which 59 are paired, 27
are unpaired and one has no PARS score.
Enzymatic probing followed by gel based footprinting
of the ubc 3’UTR revealed differentially cleaved nucleo-
tide positions (Fig. 3b). Only 40 positions were resolved
in one gel. Higher molecular weight positions up to 68th
nucleotide positions were resolved in separate gels.
Relative structure signals for every nucleotide position as
procured from enzymatic footprinting, PARS and the
consensus between the two methods are represented as
a heatmap (Fig. 3c). Out of the 68 positions resolved in
footprinting gel, 28 positions (41%) match the pairing
and unpairing possibilities as covered by PARS scores. In
summary, conservative estimates of pairing probabilities
as determined by PARS and enzymatic footprinting
displayed modest concordance with each other.
Attributes of RNA secondary structure as determined
by PARS based pairing probability across functional
units of mRNAs
The nature and extent of RNA secondary structure along
different regions of spliced mRNAs, namely coding region
Fig. 2 RNA secondary structure of rpl35 in zebrafish and human. a. Sequence conservation within CDS of rpl35 across human and zebrafish. Rpl35
homologs in zebrafish and humans show 81% sequence homology. b. Bar graph representing PARS scores from CDS of rpl35 in zebrafish. Red
bars with negative PARS scores are unpaired, while positive scores in green are paired positions. c. Heatmap showing comparison of the paired
and unpaired positions in rpl35 of zebrafish and human. PARS based secondary structure signals reveal 71% structure homology. Red represents
unpaired, green represents paired positions while yellow represents no consensus between the two homolog structures
Kaushik et al. BMC Genomics (2018) 19:147 Page 4 of 17
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(CDS), transcription start and stop sites, splice sites, 5′-
untranslated region (5’-UTR), 3′-untranslated region (3’-
UTR) and poly-A sites were compared. In order to study
this, the protein coding transcripts with at least 85% read
start positions across the transcript length were priori-
tised. Further, amongst these transcripts, only those with
well annotated translation start and stop signals were se-
lected. Nucleotide position-wise average PARS scores for
each region were calculated and results were plotted
(Fig. 4). Amongst the transcripts with at least 85% read
start positions (n= 544), there were 451 transcripts with
well-defined translation start and stop sites. Average PARS
score was 0.33 in CDS, 0.26 in 5’UTR and 0.46 in 3’UTR.
A sharp decrease in pairing probabilities of nucleotide po-
sitions was seen at the translational start (p-value = 1.83 ×
10
−7
) and stop sites (p-value = 8.44 × 10
−57
)(Fig.4a). A
sharp increase in the pairing probability was followed after
the translation start sites (p-value = 5.37 × 10
−36
). How-
ever, the 3’-UTR was highly structured followed by CDS
and 5’-UTR. The structure signals at 5’UTR were
positively correlated with GC content (r=0.32), but
showed negative correlation at CDS (r=−0.4) and no cor-
relation at 3’UTR (r=−0.003). A periodic pattern of
pairing probability was also observed in CDS, but absent
in UTRs.
We also explored the pairing probability across splice
sites of highly expressed mRNAs. The splice junctions
(n= 2538) across the 451 transcripts were aligned. Aver-
age pairing probabilities of 25 nucleotide positions flank-
ing the splice junctions were calculated (Fig. 4b). It was
observed that the pairing probability of terminal di-
nucleotide at the 5′exon are different relative to the rest
of the positions of the transcript (p-value = 5.5 × 10
−28
).
Similarly, the pairing probability of the first dinucleotide
at 3′exon are different relative to the rest of the posi-
tions of the transcript (p-value = 1 × 10
−3
). However, a
comparison of the dinucleotides at the splice junctions
displayed that terminal dinucleotides at 5′exon are
structurally flexible than first dinucleotides at 3′exon
(p-value = 2.5 × 10
−5
). This pattern of secondary
Fig. 3 Comparison of RNA structures of ubc 3’UTR as determined by PARS based pairing probability and enzymatic footprinting using RNase V1
and S1 Nuclease. a. Bar plot represents PARS scores of 3’UTR region of ubiquitin c (ubc). Out of 105 positions, 87 positions are captured by PARS.
b. Enzymatic footprinting of ubc 3’UTR probed by S1 Nuclease and RNase V1. Nucleotide positions are correlated with alkaline hydrolysis (AH)
ladder and RNase T1 (G) ladder. Positions with similar structural pattern with PARS scores are highlighted. Red dots indicate unpaired positions;
green indicates paired positions while yellow represents ambiguous regions. c. Heatmap representing secondary structure of 68 positions of ubc
3’UTR as determined by PARS and enzymatic footprinting (FP). Top panel represents PARS pairing probability; bottom panel indicates enzymatic
footprinting pairing probability; middle panel represents the consensus between the two (PARS: FP). Red represents unpaired, green represents
paired and yellow represents ambiguous regions
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Fig. 4 PARS reveals distinct RNA secondary structural signatures in functional units of transcripts. a. PARS scores across the 5’UTR, the coding region
(CDS), and the 3’UTR of Zebrafish mRNAs are represented. PARS scores averaged across 451 transcripts with load > 1 and position coverage > 85%,
aligned by the translational start and stop sites are represented. Averaged PARS scores and GC% are reported for regions are shaded in grey. b. Line
graph representing average PARS scores and GC% across 25 nucleotides flanking the splice junctions of 451 transcripts are represented. c. Line graph
displaying average PARS scores for last 50 nucleotides of the 3’UTRs (n= 451) are represented. d. Line graph representing amplitude vs frequency of
the Discrete Fourier Transform analysis of the average PARS scores of CDS, 3’UTR and 5’UTR corresponds to 451 transcripts. The highest frequency
peak is obtained at 0.33 in CDS, showing a periodicity of 3 bases. e. Boxplot for average PARS scores for every codon position for first 100 CDS
positions in 451 transcripts. The pairing probability of every position in a codon follows 1 > 2 > 3 (pvalue = 1.9e-07). Every position significantly differs
from the other position by a pvalue = 1.702e-08 (ANOVA). f. Region-wise pattern of RNA secondary structures within enriched molecular function GO
categories. The heatmap represents the region-wise (5’UTR, CDS and 3’UTR) significant p-values obtained from Wilcoxon rank sum test performed using
the average PARS scores calculated for transcripts belonging to each enriched GO category. Red color suggests that genes belonging to the specific
GO category shows under-structuring or lower PARS scores than the expected average PARS score for the region, where as shades of green depict
over-structuring of genes belonging to the specific GO category in the respective regions. The asterisk * indicates that no significant conclusion can be
drawn for a small number of genes (n= 2) in rRNA binding category
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structure signal at the splice junctions is inversely corre-
lated with GC content.
Additionally, we investigated the pairing probability
across poly-A sites at 3’UTR across mRNAs. The
transcripts (n=451) were aligned at ends of 3’UTRs of
transcripts and average pairing probability till 50 positions
upstream were calculated (Fig. 4c). Positions from −10 to
−30 showed low structure signals (p-value = 4.5 × 10
−18
)
relative to upstream region from −30 to −50.
The periodic structural pattern in CDS as observed
in Fig. 4a was further investigated. Therefore, to
decipher if the primary sequence codes for a struc-
tural pattern, periodicity of pairing probability in the
CDS was tested. The periodicity was determined on
first 100 CDS positions, first 100 3’-UTR and last 100
5’-UTR positions from average pairing probabilities of
451 transcripts. The highest amplitude was observed
at a frequency of 0.33 i.e. periodicity of 3 bases in
the CDS region. However, the UTRs have very low
amplitude relative to CDS and no periodicity was
seen at 3 bases as shown in Fig. 4d. When the CDS
positions were binned in codons, every position in a
codon had pairing probability significantly different
from the other two positions (p-value = 1.702 × 10
−8
)
(Fig. 4e). The first position had the highest pairing
probability compared to the second position, while
third position had the least ability to be present in a
paired conformation (p-value = 1.9 × 10
−7
). This is
repeated in a cycle of three, suggesting that similar to
primary sequence, pairing probabilities in a codon
also display a pattern.
Furthermore, these 451 transcripts were categorised in
40 classes of gene ontology based on their molecular
function to survey any similar structural features within
the same Gene Ontology (GO) class. Out of forty, only
ten classes showed significantly similar pattern of pairing
probability within a category. A heatmap of these ten
classes in Fig. 4f displays four classes, which are over
structured with respect to the mean PARS scores of the
total 451 transcripts. Genes with ‘structural molecule ac-
tivity’,‘structural constituent of ribosomes’and ‘NADH
dehydrogenase activity’are highly structured in CDS
relative to UTRs. However, ‘rRNA binding’genes have
higher structure in 3’UTR than CDS. On the contrary,
‘translation factors’and ‘organic cycle compound bind-
ing’have negligible secondary structural features in
UTRs compared to CDS. Genes with ‘nucleic acid bind-
ing’have lower pairing probability in CDS and 5’UTR
than 3’UTR. Secondary structures were present in
5’UTR region of genes with ‘unfolded protein activity’
with single stranded features in 3’UTR. Thus, the ten
classes of gene ontology displayed similar secondary
structural pattern amongst transcripts of the same
group, across CDS and UTRs.
PARS-enabled pairing probability at single nucleotide
resolution reveals secondary structures of candidate
non-coding RNAs in zebrafish
After deciphering the secondary structure pattern across
functional units of mRNAs, we utilised PARS to determine
the structures of non-coding RNAs. The efficiency of
PARS, an enzyme based probing method was evaluated by
comparing with structures derived from chemical probing
methods for non-coding RNAs. The human lncRNA,
HOTAIR was used as a positive control to validate PARS
scores for non-coding RNAs. Approximately, 2.2 kb long
HOTAIR was structure probed with RNase V1 and S1
nuclease to obtain PARS scores for 2039 positions. These
scores were plotted and are illustrated in Fig. 5a. In recent
years, HOTAIR structure has been elucidated by three dif-
ferent methods namely, SHAPE (Selective 2′-hydroxyl
acylation analysed by primer extension), DMS (Dimethyl
sulfate) probing and Terbium chloride probing [19]. PARS
structure was compared with SHAPE and DMS derived
structure (Fig. 5b) for one of the domains (Domain I) of
HOTAIR. The three techniques compared here have differ-
ent mechanisms namely, PARS is enzyme based method,
while SHAPE and DMS are chemical probing methods.
PARS scores were obtained for all 525 positions in domain
IofHOTAIR, while SHAPE captured 518 positions and
265 positions were obtained from DMS probing. Pairing
probabilities as determined from SHAPE and DMS have
207 positions in consensus with each other. Amongst these
207 positions, PARS has 47% positions in consensus with
SHAPE and DMS based methods (Fig. 5b).
Having determined the single nucleotide pairing prob-
ability of HOTAIR, next we investigated secondary struc-
ture of the candidate non-coding RNAs in zebrafish,
evolutionary conserved y-rna and tie1-as (antisense
lncRNA to tyrosine kinase containing immunoglobulin
and epidermal growth factor homology domain-1).
Pairing probabilities of these non-coding RNAs were re-
solved using PARS to correlate the secondary structural
patterns with their functional properties. y-rnas are
non-coding transcripts of 106 bases in zebrafish and are
evolutionary conserved in vertebrates (human, Xenopus
and zebrafish) [39]. They regulate the initiation of DNA
replication after mid-blastula transition by associating
with origin recognition complex and factors like CDT1
[39]. Positions 51–75 are regulatory regions that are
involved in interaction with other partners. Pairing prob-
ability at single nucleotide resolution will ease the better
understanding of the role of zebrafish y-rna as displayed
in Fig. 6a with the regulatory nucleotide positions
highlighted. PARS captured 83 positions out of 106, of
which 40 positions are unpaired and 43 positions as
paired. Furthermore, the RNAfold predicted structure of
y-rna (Fig. 6b) highlights 58% concordance with PARS
derived secondary structure in the regulatory region.
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Tie1-as, is an antisense lncRNA to the protein coding
gene tyrosine kinase containing immunoglobulin and
epidermal growth factor homology domain-1 (tie-1) [40].
Like several other antisense RNAs, tie-1as also binds to
tie-1 RNA and regulates its levels. Keguo and co-workers
have shown the hybrid structure of tie1 and tie1-as by
computational predictions. PARS assisted structure
probing at single nucleotide resolution (Fig. 6c) may aid
in better understanding of this hybrid. Out of 819 posi-
tions of tie1-as, 803 are captured by PARS assay. Of
these, 449 positions are unpaired and 354 positions are
paired. The RNAfold predicted structure of tie1-as
shows 53% concordance with PARS assisted structure in
the binding region of this lncRNA (Fig. 6d).
Transcriptome-wide single nucleotide resolved secondary
structure map of zebrafish
We have developed a web based online resource that pro-
vides pairing probabilities of zebrafish transcriptome at single
nucleotide resolution. The normalised read start counts for
every position generated from RNase V1 and S1 Nuclease
catalysed fragments in the genome has been provided as
bigwig files. This can be uploaded on UCSC genome browser
(http://genome.ucsc.edu/cgi-bin/hgTracks?db=danRer7&hu-
bUrl=http://genome.igib.res.in/upload_files/zf_pars_hub/
hub.txt) under zv9 assembly. A snapshot of ubiquitin c
(Fig. 7a)andtie1-as (Fig. 7b), displays an example of pairing
probabilities of zebrafish transcriptome (Additional files 2,3,
4,5,6,7,8and 9).
Discussion
Multiple genome scale sequencing projects have
highlighted that a large proportion of the genome actively
contributes towards the transcriptome, most of which is
engaged in regulatory activities. In order to gain insights
into the functional aspect of the regulatory transcriptome,
it is important to understand their ability to interact with
other biomolecules by the virtue of their structure. The
primary information for intramolecular pairing is embed-
ded in the RNA sequence [37]. The Watson-Crick base
pair driven secondary structure thereby provides a
template for the RNA to fold upon itself and sets the stage
for long range interactions. Therefore, it is important to
uncover the hidden layer of information in the RNA sec-
ondary structure, to further understand the tertiary interac-
tions. Conventional gel based methods of assessing RNA
secondary structure focused on single RNA species in
isolation. However, it is well-known that the structures are
influenced in presence of a heterogeneous pool of
transcripts. Lately, several RNA probing methods [36,37,
41–44] are coupled with high throughput sequencing to
decipher the transcriptome-wide secondary structure in
Fig. 5 Secondary structure of human non-coding RNA, HOTAIR. a. Bar graph representing PARS scores of 2062 positions of HOTAIR. Red bars with
negative PARS scores are unpaired, while positive scores in green are paired positions. b. Heatmap with comparison of the paired and unpaired
positions in domain 1 (1 to 525 positions) of HOTAIR. PARS scores are compared with structure data obtained from SHAPE and DMS probing. Red
represents unpaired, green represents paired positions while yellow represents no data for that position. Upon comparison, 207 out of 518 positions
were correlated by SHAPE and DMS, while PARS has consensus of 96 positions amongst these 207 positions
Kaushik et al. BMC Genomics (2018) 19:147 Page 8 of 17
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diverse organisms. Amongst these, PARS has been applied
to yeast and human transcriptomes to determine pairing
probability at single nucleotide resolution. Zebrafish has
emerged as a model organism to study various biological
processes including human diseases. Understanding RNA
secondary structure organisation and its features across
functional units of transcripts would provide insights into
the functioning of transcripts in zebrafish.
Pairing probabilities obtained from RNase V1 and S1
Nuclease cleavage of zebrafish transcriptome suggested
that the transcriptome was equally paired and unpaired.
However, the structure maps of other eukaryotic tran-
scriptome such as, A. thaliana [45] revealed that a larger
part of the transcriptome was unpaired. In the zebrafish
transcriptome, the RNase V1 and S1 Nuclease enabled
cleavages displayed distinct signatures with only 4%
overlap of the structure signals, hinting to the ambiguous
nature of the pairing probabilities at these nucleotides.
Previously, structure profiling of the yeast transcriptome
reported 7% nucleotide positions [36], while that of hu-
man transcriptome constituted 3.7% nucleotide positions
to have ambiguous pairing probability respectively [37].
Out of the total transcripts (n= 54,083), a majority
(77%) mapped to protein coding genes, while lncRNAs
were relatively low (16%). This could be possible as
lncRNAs possess a lower expression than protein-coding
genes, and hence are not sequenced at a greater depth.
This was similar to findings from PARS probed human
transcriptome [37].
The well-known protein-coding gene (rpl35) in zebra-
fish was found to possess sequence and structure hom-
ology with its human orthologue Rpl35. We also studied
another conserved gene - nucleoplasmin 1a (npm1a, 852
bases, ZFIN:ZDB-GENE-021028-1). This gene showed
Fig. 6 Secondary structure of zebrafish non-coding RNA as determined by PARS. a. Bar plot representing PARS scores of y-rna for 83 positions out of 106
positions. b. Heatmap with comparisons of pairing probability of the binding region of y-rna as determined by PARS and computational predictions by
RNAfold. c. Bar plot representing PARS scores of tie1-as for 803 positions out of 819 positions. d. Heatmap with comparison of pairing probability of tie1-as
as determined by PARS and RNAfold
Kaushik et al. BMC Genomics (2018) 19:147 Page 9 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
62% sequence homology in the CDS region with its
human ortholog NPM (NCBI ID: 4869). We observed an
overall structure conservation of 329 bases (38%) out of
which 197 positions (60%) were also conserved at the
sequence level. In summary, we observed that although
npm1a has similar sequence conservation as rpl35 with
its human ortholog, the structure conservation does not
follow any specific trend. Given that the extent of over-
structuring or under-structuring is enriched for specific
gene ontology categories in a species as shown in Fig. 4f,
the same might hold true across species and may not al-
ways be simply a function of the sequence conservation.
Regulation of gene expression at post-transcriptional
and translational levels is governed by the functional
units of mRNA such as translation start and stop sites,
CDS, splice sites, UTRs and poly-adenylation sites. Dis-
tinct RNA secondary structure features in zebrafish tran-
scriptome were observed corresponding to the different
functional units of highly expressed protein coding tran-
scripts. A sharp decrease in the PARS scores was noticed
at translational start and stop signals. This may suggest
ribosome accessibility to coding regions and initiation of
translation. Similar features were also observed in other
transcriptomes studied such as humans [37], yeast [36]
and Arabidopsis [43]. This is in accordance to the
presence of IRES (internal ribosome entry sites) present
at translational start signals in eukaryotes [46] which are
structured elements. As observed in our study, structure
signals with low pairing probability at translational stop
sites were also reported in humans [37] and yeast [36].
Zebrafish CDS region had higher pairing probability
than 5’-UTRs but displays lesser pairing probability than
3’-UTRs. However, structure signals in the CDS of yeast
transcriptome showed higher pairing probability
compared to UTRs [36]. The three base periodicity
observed in the CDS region of zebrafish transcriptome
was correlated to that observed in yeast [36] and Arabi-
dopsis [43] suggesting a common universal regulatory
feature in translating regions in eukaryotes. This was
similar to the three base sequence periodicity in coding
exons of DNA [47]. There have been reports suggesting
(RNY)
n
sequence periodicity in CDS of various genomes.
The pattern of structure signals within a zebrafish codon
was also consistent, such that every first base in a codon
Fig. 7 UCSC snapshot of single nucleotide resolved RNA secondary structure map for (a)ubiquitin c and (b)tie1-as
Kaushik et al. BMC Genomics (2018) 19:147 Page 10 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
had the highest pairing probability suggesting structural
constraints relative to the second base, while the last
base has the lowest pairing probability suggesting struc-
tural flexibility. The structural constraint observed in the
first position of the codon in zebrafish mRNAs might
create a steric hindrance so that the subsequent codon
positions have more steric flexibility. This is in contrast
to what Kertesz et al. reported within the yeast, where
the second base of the codon had highest pairing prob-
ability followed by third and first. The triplet periodicity
in the protein coding regions of transcript suggests the
translational efficiency of the transcripts [43]. In our
study, we observed a three base structure periodicity in
the CDS, suggesting a righteous conformation for the
occupancy of ribosomes. Additionally, as periodic struc-
ture signals are distinct in coding and non-coding UTRs,
the structure signals can be employed to annotate the
unknown regions in the zebrafish transcriptome.
Distinct structure signals were observed at the splice
site junctions of the zebrafish transcriptome. The dinu-
cleotides at the end of the 5’exon possess structure sig-
nals with low pairing probability and first dinucleotides
of 3’exon have structure signals with higher pairing
probability relative to the rest of the positions in a tran-
script. This was similar to the findings observed at splice
junctions of mRNAs in human transcriptome [37].
The 5’UTR regions in zebrafish mRNAs on average
possess lowest pairing probability compared to CDS and
3’-UTRs. This is also true for yeast [36], Arabidopsis (in
vivo) [43] and mouse (in silico) [48]. The 3’UTR regions
display higher pairing probability compared to CDS on
average, as 3’UTRs constitute several regulatory ele-
ments. Albeit, GC% of the bases rules the structure sig-
nals, the UTRs in zebrafish have lower GC content than
CDS. Similar to the findings in humans, a low consensus
between GC content and RNA secondary structure sig-
nals was observed [37]. Moreover, the analysis of struc-
ture signals at poly-A sites revealed that A-rich elements
(−10 to −30 nt) [49–51] endure structure signals with
lower pairing probability relative to upstream stimulat-
ing element (USE), suggesting the accessibility of Cleav-
age and Polyadenylation Specificity Factor (CPSF)
protein [51].
In extension to this, when the highly expressed protein
coding transcripts were grouped on the basis of their
molecular function, genes with functions of ‘structure
molecule activity’and ‘structural constituent’of ribosomes
such as rRNA had higher pairing probabilities across CDS
and UTRs. While, genes with ‘translation factor activity’
had lower pairing probability in CDS than UTRs as they
need to be actively translated and highly regulated by do-
mains in UTRs. The presence of structured elements (high
pairing probability) at 5’-UTRs signify regulation at
translation level, whereas structures at 3’-UTRs represent
post-transcriptional processing. Similarly, structure profil-
ing of yeast transcriptome [36] and Arabidopsis transcrip-
tome [43] revealed correlation between RNA secondary
structure signals and biological function of mRNAs.
PARS scores generated for zebrafish non-coding RNAs
were endorsed using known structure of human HOTAIR
determined by chemically probing using SHAPE and
DMS [19]. PARS scores showed positive correlation with
the other two derived structures, thereby confirming the
broad utility of PARS for determining secondary structure
of non-coding RNAs. The difference in consensus be-
tween PARS and the other two techniques could be due
to the usage of different structure sensitive reagents in
these studies. Nucleases possess steric hindrance in cata-
lysing large structured elements. The efficiency of RNase
V1 is limited by helix length whereas chemical probing
reagents are much more specific due to smaller size.
However, chemicals such as DMS can probe only
unpaired adenine or cytosine, therefore not providing
information about uracil or guanine. SHAPE, utilises re-
agents that interact with sterically flexible nucleotides, but
can be carried out only for known sequences. In compari-
son, PARS an enzyme-based probing method, provides
structure signals for every position in the transcript.
In the recent few years, non-coding RNAs especially
lncRNAs have been extensively studied in zebrafish
cataloguing lncRNA transcripts expressed in different de-
velopmental stages [16,33] and adult tissues [34]. Of these,
13–35% lncRNAs are overlapping to protein coding genes
in sense or antisense direction [35]. One of the earliest
studied lncRNA in zebrafish was tie1-as,antisenseto
tyrosine kinase containing immunoglobulin and epidermal
growth factor homology domain-1 (tie-1) [40]. Similarly,
y-rna is a small non-coding RNA, which interacts with
DNA replication machinery at maternal to zygotic transi-
tion. Both of these non-coding transcripts play a pivotal
role in the developmental stages of zebrafish. Therefore, in
order to aid in investigating the function and binding part-
ners of the transcripts and the mechanism of regulation,
the secondary structural trends in these transcripts were
visualised using PARS. This presents the first experimen-
tally validated structures of non-coding RNAs in zebrafish.
Furthermore, the single nucleotide resolved pairing
probability map of zebrafish transcriptome could be evalu-
ated to predict miRNA binding sites. Strong AGO binding
sites display lower pairing probabilities at −1to3nt
upstream of miRNA target sites [37]. Pairing probabilities
at the UTRs of transcripts can be availed to verify the
miRNA target sites.
In addition, the single nucleotide pairing probabilities
could also be utilised to identify riboSNitches [37], which
are secondary structure elements that change in the pres-
ence of single nucleotide variations. Previous studies have
documented approximately 15 million Single Nucleotide
Kaushik et al. BMC Genomics (2018) 19:147 Page 11 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Variations (SNVs) in different strains of zebrafish [52,53].
The regulation of gene expression by riboSNitches could
be evaluated by studying the structure signals across these
SNVs, which could provide insights on how SNVs contrib-
ute to modulating gene expression.
Conclusion
We present the first PARS-enabled secondary structure
transcriptome map of zebrafish, which documents pairing
probability of RNA at single nucleotide precision. This has
facilitated the identification of unique structural patterns
across functional units of mRNA. We also present the en-
zyme probed structures of selected regions of candidate
non-coding RNAs such as tie1-as and y-rna in zebrafish.
This study is not without limitation. Currently, PARS has
been executed by folding the transcripts in-vitro with the
consensus that most of the structure signals are embedded
in the sequence. However, future studies may be carried
out on native de-proteinised transcripts to see the extent
to which in vivo structures deviate from the present ones.
The technique can be further explored to determine RNA
structure of full length candidate lncRNAs in zebrafish.
This study provides a basal data to plan experiments for
long range intra- and inter-molecular interactions of tran-
scripts using Psoralen Analysis of RNA Interactions and
Structures (PARIS) [54]. This transcriptome-wide second-
ary structure map at single nucleotide resolution adds to
the ever increasing genomics resources for zebrafish and
would aid in improving our understanding of the zebrafish
transcriptome.
Methods
Transcriptome-wide RNA secondary structure profiles in
developing zebrafish was captured using Parallel Analysis
of RNA Structure (PARS) [36,37]. The transcriptome
from one-day-old (24 hpf) zebrafish embryos was sub-
jected to enzymatic cleavage by RNase V1 and S1 nuclease
catalysis (Additional file 1: Figure S1). The enzyme-based
cleavage reaction was tightly regulated to derive single-hit
kinetics. In-vitro reconstituted zebrafish transcriptome
was subjected to cleavage by RNase V1 at 0.000125 U for
45 s (s) and by S1 Nuclease cleaved at 10,000 U for
10 min (min) respectively in RNA structure buffer (1X).
The enzyme cleaved fragments were adapter ligated and
processed for next generation sequencing using semicon-
ductor based chemistry on Ion Proton Platform as
described below. A detailed schematic of Parallel Analysis
of RNA Structure is shown in Fig. 8.
RNA isolation
Assam wild type (ASWT) strains of zebrafish maintained
at CSIR-Institute of Genomics and Integrative Biology
were used in this study [52]. One-day old zebrafish em-
bryos were collected in the eppendorf tubes and water
was decanted. The vials were immediately snap frozen at
−80 °C and processed for RNA isolation. Approximately,
200 μg of total RNA was isolated from 24 hpf ASWT
zebrafish (n=250) using RNeasy kit (Qiagen, USA) as
previously described [55]. Poly-A RNA was enriched using
oligo-dT Dynabeads (Life Technologies, USA) using man-
ufacturer’s protocol to yield 4 μg of processed transcripts
from the initial pool. RNA pool of Poly-A transcripts was
divided into two parts (2 μg each) for individual catalysis
by RNase V1 (Life Technologies, USA) and S1 nuclease
(Thermo Fisher Scientific, USA). Likewise, five biological
replicates were put using 1250 embryos in total.
Enzymatic probing of RNA
The resulting RNA pool was processed for PARS heated at
90 °C for 2 min followed by snap-chilling in ice (at 0–4 °C).
Further, the poly-A pool was folded in RNA Structure
Buffer (Life Technologies, USA) containing 10 mM Tris
pH 7, 100 mM KCl, and 10 mM MgCl
2
slowly from 4 °C
to 28 °C for 25 min. Each 2 μg of Poly-A RNA was digested
with 10 μl (0.000125 U) of RNase V1 (Life Technologies,
USA) for 45 s and 10 μl (10,000 U) of S1 Nuclease
(Thermo Fisher Scientific, USA) for 10 min to achieve sin-
gle hit kinetics. Enzyme-cleaved fragments were further
purified using equal volume (100 μl) of phenol: chloroform:
isoamyl alcohol (Invitrogen, USA) at 13,000 rpm (4 °C) for
10 min. RNase V1 cleaved RNA pool in the top aqueous
layer was extracted and 20 μl of Inactivation/Precipitation
buffer (Life Technologies, USA) was added, followed by
1 h (h) incubation at -80 °C. Further, 20 μlof3Msodium
acetate, 1 μl of glycogen and 300 μl of cold ethanol was
added to precipitate RNA at -80 °C for 1 h. S1 Nuclease
cleaved RNA pool in the top aqueous layer was extracted
and 20 μl of 3 M sodium acetate, 1 μlofglycogenand
300 μl of cold ethanol was added to precipitate RNA at
-80 °C for 1 h. Further, the purified RNase V1/S1 Nuclease
digested samples were fragmented by alkaline hydrolysis
buffer (Life Technologies, USA) containing 500 mM So-
dium bicarbonate at 95 °C, for 1.5 min which generated
3’phosphate groups at the enzyme cleaved fragments. The
reaction was stopped using 2 μl of 3 M sodium acetate,
and further precipitated using 2 μl of glycogen and 300 μl
of 100% ethanol at -80 °C for 3–4 h. A detailed schematic
of Parallel Analysis of RNA Structure is shown in Fig. 8.
Preparation of library and sequencing
Purified enzyme-cleaved RNA from 24 hpf zebrafish was
adapter-ligated using Ion total RNA-Seq kit v2 (Life
Technologies, USA) using manufacturer’s protocol. The
phosphate groups from the 3′-ends of the alkaline hydro-
lysed enzyme-cleaved fragments were removed by Antarc-
tic phosphatase treatment to generate 3’-OH ends for
adapter ligation. The 5′adapter ligated products were
treated with 5 μl of 10× Antarctic phosphatase buffer
Kaushik et al. BMC Genomics (2018) 19:147 Page 12 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
(NEB), 2.5 μl of Superasin RNase inhibitor (Life Technolo-
gies, USA) and 2.5 μl of Antarctic phosphatase enzyme
(NEB). The volume was made to 50 μl using nuclease free
water (Ambion, USA). This was followed by adapter
ligation at 3’OH ends generated after phosphatase
treatment. The adapter ligated products were reverse
transcribed to obtain cDNA and amplified by PCR to
generate the sequencing library. At each step, purification
was carried out using nucleic acid beads enrichment proto-
col compatible with standard sequencing techniques. The
libraries were sequenced on Ion Proton platform (Life
Technologies, CA, US) employing semiconductor based
chemistry after quality check to generate single end reads.
Data analysis
Sequencing was done for five technical replicates each
for RNase V1 and S1 Nuclease probed samples
(Additional file 1: Table S1).The raw single-end reads
generated by Ion Proton sequencing were trimmed with
BWA algorithm at a threshold of Q13 (p-value = 0.05)
and length-sorted with a threshold of 25 nucleotides as
implemented by SolexaQA version 2.2 [56]. The pre-
processed reads were mapped back to the zebrafish
transcriptome assembly downloaded from Ensembl (v79,
Zv9) comprising of 56,754 transcripts (33,737 genes)
using a two-stepped approach involving STAR aligner
[57] and Bowtie2 [58] as prescribed by Life technologies
(http://www.thermofisher.com/order/catalog/product/
4476610?ICID=search-product&CID=fl-ion-proton-
docs). First, the reads were mapped using STAR (with
parameters –outReadsUnmapped Fastx –outSAMstrand-
Field intronMotif ). The unmapped reads obtained were
then aligned locally using Bowtie2 (with parameters
–local –no-unal -k 10). The bam files obtained from the
above steps were merged using Samtools [59]. In order
to select only uniquely mapped reads, those reads
mapping more than once in the zebrafish reference gen-
ome (Zv9/danRer7) were removed. Aligned reads with
erroneous read starts (5′ends) were further removed to
retain only high-confidence reads with perfectly aligned
read starts.
Calculation of load, position coverage and ratio scores
Load for each transcript was estimated by the total num-
ber of reads mapping to the transcript relative to the
effective (mapped) length of the transcript. Load score
determines the transcript abundance in the sample [36].
The transcripts with load ≥1 (atleast one read per base)
were considered.
The position coverage for every transcript was also
computed by summing the total number of positions
with read starts obtained from both RNase V1 and S1
Nuclease data relative to the length of the transcript. The
read starts define the enzymatic cleavage for the respective
Fig. 8 Schematic of RNA structure probing by PARS in zebrafish. Poly-A RNA from zebrafish is folded in-vitro. The folded RNA is cleaved by RNase
V1 and S1 nuclease separately. The enzyme cut sites generate 5’Pendsand3’OH ends at the cleaved sites. Long fragments generated by single-hit
kinetics are further fragmented by alkaline hydrolysis, which blocks the 3′site of the enzyme-cut fragments. Sequencing adapters are ligated to the 5′
end followed by alkaline phosphatase treatment to 3’P group. Adapters are ligated to 3’ends followed cDNA synthesis and PCR purification of
the library. Appropriate size of the library is maintained by purification by nucleic acid beads. Sequenced reads are aligned back to the genome and
only unique reads with the correct read start positions are considered for PARS score calculation
Kaushik et al. BMC Genomics (2018) 19:147 Page 13 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
position and the pairing probability of the prior nucleotide
as the RNase V1 and S1 Nuclease enzymes cleave at 3′
phosphodiester bond of the paired and unpaired position
respectively. The transcripts that had load score of more
than one and at least 85% positions covered with read
starts were considered for further analysis.
Ratio score for every position in each of the RNase V1
(henceforth represented as V1 dataset) and S1 Nuclease
(henceforth represented as S1 dataset) datasets were
calculated by read start coverage for each nucleotide
relative to the load of the transcript. Only those positions,
which have more than one ratio score, were called as
peaks. A peak could be present only in one of the dataset
(V1 or S1) and confirmed that a position can be either
paired or unpaired. If a position displayed a peak in both
the (V1 and S1) datasets, it was termed as overlapping
peak and corresponded to dynamic regions with multi-
conformations, which were not able to acquire a stable
structure. Any transcript with more than five such
positions was termed as a multi-conformation transcript.
Calculation of parallel analysis of RNA structure (PARS)
scores
The number of reads initiating at every position in the
transcriptome were calculated in both V1 and S1 data-
sets. Normalisation constants were calculated for both
the datasets as K
v
and K
s
as per the following formula:
Kv¼V1þS1ðÞ=2
V1 Ks¼V1þS1ðÞ=2
S1
V1 and S1 are total read starts for all positions covered
by uniquely mapped reads in V1 and S1 datasets. Read
counts for every position are further normalised by
multiplying them by normalisation constants. This was
done to eliminate the read disparity in the two datasets.
V1i¼KvRaw V1i
ðÞS1i¼KsRaw S1i
ðÞ
PARS score for every position was calculated by the
following formula.
Scorei‐1¼log2
V1iþ5
S1iþ5
Where iis any nucleotide position, PARS score for a
position defines the pairing probability of the previous
position.
Enzymatic Footprinting
In vitro synthesised transcript of ubiquitin C UTR was
generated using T7 Megascript kit according to manufac-
turer’s instructions (Life Technologies, USA). The RNA
was checked for single RNA species using 12% denaturing
polyacrylamide gel electrophoresis (PAGE). The composition
of the gel was 40% 29:1 acrylamide:bisacrylamide, 8 M urea,
133.5 mM TBE. The gel mix is polymerized using 10% APS
and 0.05% TEMED. The RNA products were visualized by
UV shadowing and eluted from the gel using RNA elution
buffer containing 300 mM Sodium acetate + 1 mM EDTA
[60].
Gel purified RNA was radiolabelled using the Kinase
max kit (Life Technologies, USA) as per the protocol pro-
vided by the manufacturer. Briefly, the RNA was dephos-
phorylated using Calf Intestinal phosphatase (CIP) and
purified using Phosphatase Removal Reagent (PRR). The
transcript was further incubated with T4 polynucleotide
kinase and [γ-
32
P] ATP (BARC, India) for overnight at
37 °C [60]. The labelled RNA was further purified using
NucAway columns (Life Technologies, USA).
5′-end-radiolabelled RNA (50,000 counts per lane) was
added to 1 μg of unlabelled zebrafish RNA and was sub-
jected to heating at 90 °C for 5 min and then allowed to
cool to 28 °C in RNA structure buffer (Life Technologies)
and 5 mM MgCl
2
for overnight to facilitate structure
formation. Folded RNA was subjected to digestion with
RNase V1 (1:1600 U for 45 s) and S1 Nuclease (1:100 U
for 1 min) at 28 °C respectively. RNA ladder for G
residues was obtained by digesting the RNA with 1 U of
RNase T1 (Fermentas) in the presence of 1 M LiCl and
100 mM MgCl
2
for 2.5 min at 37 °C. Alkaline hydrolysis
of RNA was performed at 90 °C in 0.5 M sodium bicar-
bonate buffer for 8 min. All reactions were stopped using
equal volumes of gel loading buffer II (Life Technologies,
USA) containing 95% formamide and 18 mM EDTA and
snap-chilled on ice. Equal counts of digested products
were separated on a 12% denaturing gel in 0.5× Tris-bo-
rate EDTA buffer and exposed to a phosphorimager
screen. The gel images were scanned on a Typhoon scan-
ner (GE Healthcare). Cleavage profiles were visualised
using ImageQuant 5.2 software (GE Healthcare).
Validation of PARS in zebrafish using rpl35
Several studies have highlighted that protein-coding genes
are well conserved across species based on nucleotide se-
quence and function. The RNA secondary structures of
such conserved genes are also known to be preserved. We
tested the validity of PARS based pairing probability in
zebrafish using a well-conserved protein-coding gene
across human and zebrafish. Pairing probability of rpl35
(ribosomal protein large subunit 35), a candidate gene en-
coding the protein component of 60S ribosome subunit
was compared with its human homolog.
Region-wise RNA structures across enriched gene
ontology terms
Gene ontology enrichment analysis was performed for
the transcripts showing position coverage of least 85% of
the transcript length (209 genes) using WebGEStalt [61].
Kaushik et al. BMC Genomics (2018) 19:147 Page 14 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The enrichment analysis was performed for Molecular
Functions gene ontology using default statistical test
options and a significance level threshold of 0.05. In
order to assess the extent of over-structuring or under-
structuring of the RNA within the UTRs and CDS of the
transcripts belonging to the enriched GO terms, we
employed single sample Wilcoxon rank sum test (with
mu = average PARS score for the respective regions - 5’-
UTR, CDS and 3’-UTR). The resulting significant
p-values were plotted as a heatmap for further inference.
Periodicity pattern in coding regions of mRNAs
Periodicity across CDS regions was determined using
Discrete Fourier Transform analysis [36]. PARS scores
across first 100 positions in the CDS of 451 transcripts
were averaged and were checked for periodicity.
Codon-wise pairing probability
Average PARS scores of first 100 CDS positions were
separated into 33 codons. Anova Test (not assuming
equal variances) was used to affirm that the PARS score
for every position in a codon differs from the rest of the
two positions. If significant, pairwise comparisons of
PARS scores using t-test with pooled Standard Deviation
(SD) was performed.
Structure probing of non-coding RNAs
Human HOTAIR [62] transcript with well-defined RNA
secondary structure [19] was employed (approx. 1 pico-
mole) as a positive control. HOTAIR was folded and probed
at 37 °C with 10 μl of (1:1600 U) of RNase V1 for 45 s and
10 μl of (10,000 U) S1 Nuclease for 5 min. Similarly, RNA
structures of two zebrafish candidate non-coding RNAs viz.
y-rna and tie-1as were elucidated using PARS. Approxi-
mately, one picomole of the above mentioned in-vitro syn-
thesised transcripts were pooled with zebrafish poly-A RNA
to constitute 2 μg of the starting material. They were enzy-
matically probed by both RNase V1 and S1 nuclease as
mentioned above and RNA libraries were prepared for se-
quencing. The data analysis was performed for the candi-
date ncRNAs using the pipeline described previously. The
PARS scores were computed for each ncRNA using the
method described in the above section. The oligo sequences
used in the study are provided in Additional file 1:TableS2.
Additional files
Additional file 1: Supplementary Tables and Figures. (PDF 454 kb)
Additional file 2: Load score and percentage coverage of 54,083
transcripts. (TXT 3635 kb)
Additional file 3: Multi-conformation position counts in transcripts with
overlapping peaks. (TXT 712 kb)
Additional file 4: Number of read starts for every position covered in
RNase V1 sample. (TXT 32051 kb)
Additional file 5: Number of read starts for every position covered in S1
nuclease sample. (TXT 45980 kb)
Additional file 6: Total number of read starts for every position in both
RNase V1 and S1 nuclease sample. (TXT 64960 kb)
Additional file 7: Positions with ratio score more than one in V1
dataset. (TXT 15410 kb)
Additional file 8: Positions with ratio score more than one in S1
dataset. (TXT 15865 kb)
Additional file 9: PARS scores of 54,083 transcripts and non-coding
RNAs. (TXT 35418 kb)
Abbreviations
CDS: Coding DNA Sequence; DMS: Dimethyl sulfate; GO: Gene Ontology;
h: Hours; min: Minutes; PARS: Parallel Analysis of RNA Structure; s: Seconds;
SD: Standard Deviation; SHAPE: Selective 2’-Hydroxyl Acylation Analyzed by
Primer Extension; SNV: Single Nucleotide Variation; UTR: Untranslated Region
Acknowledgements
We thank members of the zebrafish facility of CSIR-Institute of Genomics and
Integrative Biology (CSIR-IGIB) for the excellent maintenance of zebrafish.
Authors would like to acknowledge Dr. Hemant Gautam for radioactivity
facility at CSIR-IGIB. We also thank the NGS and supercomputer facility at
CSIR-IGIB.
Funding
This work was supported by the Council of Scientific and Industrial Research
(CSIR), India [Project code BSC0123]. KK and SP acknowledge CSIR Senior
Research Fellowship. TS is thankful to the support provided by the Wellcome
Trust/DBT India Alliance grant IA/CPHE/14/1/501504.
Availability of data and materials
The sequencing data have been deposited in the NCBI Sequence Read
Archive (SRA) under accession SRR3521405 to SRR3521419.
Authors’contributions
KK, SM, VS and SSB conceived and supervised the entire study. KK
standardized the assays and performed molecular biology experiments for
the study. SKV, AV and RJ conducted the RNA sequencing and performed
the quality control experiments. KK and SP conducted footprinting
experiments for the specific transcripts and along with SM interpreted the
findings. AS, KK and TS performed the entire bioinformatics and statistical
analysis for the study. All authors were involved in drafting of the
manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Fish experiments were performed in strict accordance with the
recommendations and guidelines laid down by the CSIR Institute of
Genomics and Integrative Biology, India. The protocol was approved by the
Institutional Animal Ethics Committee (IAEC) of the CSIR Institute of
Genomics and Integrative Biology, India. All efforts were made to minimize
animal suffering.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Genomics and Molecular Medicine, CSIR Institute of Genomics and
Integrative Biology, Sukhdev Vihar, Mathura Road, New Delhi 110025, India.
2
G.N. Ramachandran Knowledge Centre for Genome Informatics, CSIR
Institute of Genomics and Integrative Biology, Sukhdev Vihar, Mathura Road,
New Delhi 110025, India.
3
Indraprastha Institute of Information Technology,
Kaushik et al. BMC Genomics (2018) 19:147 Page 15 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Delhi 110020, India.
4
Academy of Scientific and Innovative Research (AcSIR),
New Delhi 110025, India.
Received: 2 March 2017 Accepted: 28 January 2018
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