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Multiple small RNAs identified in Mycobacterium
bovis BCG are also expressed in Mycobacterium
tuberculosis and Mycobacterium smegmatis
Jeanne M. DiChiara
1
, Lydia M. Contreras-Martinez
1
, Jonathan Livny
2,3
, Dorie Smith
1
,
Kathleen A. McDonough
1,4
and Marlene Belfort
1,4,
*
1
Wadsworth Center, New York State Department of Health, PO Box 22002, Albany, NY 12201-2002,
2
Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142,
3
Channing Laboratory,
Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115 and
4
School of Public Health, State University of New York at Albany, Albany, NY 12201-0509
Received December 17, 2009; Revised February 2, 2010; Accepted February 3, 2010
ABSTRACT
Tuberculosis (TB) is a major global health problem,
infecting millions of people each year. The causative
agent of TB, Mycobacterium tuberculosis, is one
of the world’s most ancient and successful patho-
gens. However, until recently, no work on small
regulatory RNAs had been performed in this
organism. Regulatory RNAs are found in all three
domains of life, and have already been shown to
regulate virulence in well-known pathogens, such
as Staphylococcus aureus and Vibrio cholera. Here
we report the discovery of 34 novel small RNAs
(sRNAs) in the TB-complex M. bovis BCG, using a
combination of experimental and computational
approaches. Putative homologues of many of
these sRNAs were also identified in M. tuberculosis
and/or M. smegmatis. Those sRNAs that are also
expressed in the non-pathogenic M. smegmatis
could be functioning to regulate conserved cellular
functions. In contrast, those sRNAs identified spe-
cifically in M. tuberculosis could be functioning in
mediation of virulence, thus rendering them poten-
tial targets for novel antimycobacterials. Various
features and regulatory aspects of some of these
sRNAs are discussed.
INTRODUCTION
Mycobacterium tuberculosis (Mtb), the causative agent of
tuberculosis (TB), is one of the world’s most succesful
pathogens. Treatment of TB has become increasingly
difficult, due to the emergence of multiple drug resistant
Mtb strains (1–5), and thus the development of new and
more effective treatments for TB is imperative. One area of
TB research that has only recently begun to garner atten-
tion is that of small noncoding RNAs (sRNAs) and their
possible roles in virulence (6).
sRNAs are generally small and untranslated; they can
originate from their own independent genes or through the
processing of larger transcripts (7). They have been
recognized in recent years as a major class of gene regu-
lators in bacteria. These regulatory transcripts have been
found in all three domains of life, including a diverse set of
bacteria (8). sRNAs allow bacteria to respond quickly to
their environment, by causing global changes in gene
expression. This is particularly important for pathogenic
bacteria, which regulate their virulence in response to
rapidly shifting conditions and external signals in the
host environment, such as temperature and pH (9).
sRNA-mediated regulation has been shown to play
a central role in the virulence of several bacteria. These
pathogens include Clamydia trachomatis,Clostridium
perfringens,Pseudomonas aeruginosa,Salmonella
typhimurium,Staphylococcus aureus,Streptococcus
pyogenes,Vibrio cholerae and Yersinia pestis, the causative
agents of sexually transmitted genital infections, food poi-
soning, burn and wound infections, gastroenteritis,
various respiratory and skin diseases, scarlet fever,
cholera and plague, respectively (10,11).
While sRNA-mediated gene regulation has been
demonstrated in both Gram-negative (G
–
) and Gram-
positive (G
+
) bacteria, diffferences in the mode of
sRNA action are likely to exist between G
–
and G
+
species. Hfq, an RNA chaperone, plays a central role in
sRNA–mRNA interactions in G
bacteria. However, this
*To whom correspondence should be addressed. Tel: +1 518 473 3345; Fax: +1 518 474 3181; Email: belfort@wadsworth.org
Published online 24 February 2010 Nucleic Acids Research, 2010, Vol. 38, No. 12 4067–4078
doi:10.1093/nar/gkq101
ßThe Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
does not appear to occur in all G
+
bacteria; in some cases
there is no known Hfq homolog, including in the
Actinomycetes-Deinococcus-Cyanobacteria clade (12,13),
of which Mtb is a member. In G
+
bacteria that do have
an Hfq homolog, such as S. aureus, the protein is not
required for sRNA activity (14). Recently, evidence of a
role for Hfq in sRNA antisense regulation was shown
in Listeria monocytogenes, a low-GC G
+
bacterium (15).
However, despite this breakthough, the majority of
sRNAs in L. monocytogenes do not seem to require Hfq
for stability or target interactions in vitro. It is therefore
possible that some G
+
bacteria, including mycobacteria,
with a 65% GC content, utilize an RNA chaperone
either related to or distinct from Hfq (13), or use a differ-
ent mechanism for sRNA–mRNA interactions.
To begin to explore the role of sRNAs in Mtb and other
G
+
bacteria, and to identify sRNAs that will provide
insight into Mtb virulence, we undertook a search for
sRNAs in mycobacteria. We employed a two-pronged
approach that utilized both a cloning-based screen and a
computational search, to identify sRNAs in the TB-
complex bacterium Mycobacterium bovis BCG (BCG).
While cloning-based approaches have proven successful
in identifying novel sRNAs in various organisms, they
present several limitations. First, they are ineffective in
identifying non-abundant sRNAs or sRNAs expressed
under specific growth conditions that differ from those
used for RNA isolation (16,17). Second, these approaches
are time intensive, relatively expensive and can present
technical challenges. Several computational algorithms
have recently been developed to identify putative
sRNAs, based on the presence of a predicted Rho-
independent terminator downstream of a conserved
intergenic region (18-20). These algorithms have been
effective in identifying sRNAs in diverse species.
Moreover, computational searches can be completed
quickly, are inexpensive and enable identification of
putative sRNAs independent of their relative abundance
or condition-dependent expression. However, unlike
cloning-based approaches, these alogorithms cannot be
used to identify non-canonical sRNAs, such as those
not associated with a Rho-independent terminator,
those located within open reading frames (ORFs) or in
mis-annotated ORFs, or those not conserved in closely
related species. This last limitation is particularly problem-
atic for our analysis, as an sRNA that has recently
emerged in a pathogenic species of Mycobacterium to
help mediate virulence or growth in a particular host
niche might not be well conserved, and thus would be
missed in a bioinformatic screen.
We therefore reasoned that utilization of combined
experimental and computational approaches in our
analysis would yield the most comprehensive annotation
for sRNAs in BCG. Indeed, using these two approaches,
we identified 37 sRNAs, very few of which were identified
in both screens. Thus, the present work not only identifies
sRNAs in BCG, but also sheds light on the evolutionary
conservation of these sRNAs: some of whose expression
is restricted to TB-complex bacteria (BCG and Mtb),
while others are expressed both in TB-complex and
non-TB-complex mycobacteria, including the non-
pathogenic Mycobacterium smegmatis.
MATERIALS AND METHODS
Strains and plasmids
Recombinant M. bovis BCG (Pasteur strain, Trudeau
Institute) and Mtb H37Rv were grown in mycomedium
(Middlebrook 7H9 medium supplemented with 0.5%
glycerol, 10% oleic acid-albumin-dextrose-catalase
(OADC) and 0.05% Tween-80) as described previously
(21). Cultures were grown for the specified number of
days in either 25 or 50 cm
2
tissue culture flasks, either
shaking or standing. Low-oxygen (1.3% O
2
+5%CO
2
)
and low-pH cultures were grown as described previously
(21–23). M. smegmatis MC
2
155 was grown shaking at
37C, in trypticase soy broth supplemented with 0.05%
Tween-80.
Total RNA isolation
Cells were pelleted at 4C and then either stored at –80C
or directly used for total RNA preparation. All
centrifugation was performed at 4C. The cell pellets
were resuspended in 1 ml TRIzol reagent (Invitrogen),
transferred to screw cap tubes containing 0.1 mm
diameter zirconia beads (BioSpec Products) and incubated
at 25C for 5 min. The cells were then lysed using a
mini-beadbeater, with two 100-s pulses. The cells were
kept on ice for 10 min between the two 100-s treatments.
The beads and cellular debris were then spun out at 4C
for 2 min. The supernatant was transferred to a clean,
siliconized microfuge tube, 300 ml of a chloroform:isoamyl
alcohol mix (v/v 24:1) was added, the samples were
vortexed for 15 s, then incubated at 25C for 3 min. The
tubes were then spun at 14 000 r.p.m. for 10 min, the
aqueous phase transferred to a siliconized 1.5 ml tube,
and 270 ml isopropanol and 270 ml of a sodium citrate
and sodium chloride mix (0.8 and 1.2 M, respectively)
was added to the tubes. The samples were mixed well,
and then incubated on ice for 10 min. The RNA was
sedimented by centrifugation at 14 000 r.p.m. for 15 min.
The pellet was washed with 1 ml 95% ethanol and
centrifuged for 5 min. The pelleted RNA was allowed to
air-dry for 5 min, and was then resuspended in 30 ml
RNase-free water (Ambion); like samples were then
combined. RNA concentration was measured by
spectrophotometry. Samples were stored at –20C. Total
RNA was prepared from M. smegmatis MC
2
155 in the
same manner from either log phase (OD
600
= 0.6) or
stationary phase cultures (OD
600
1). Mtb cultures were
treated with 500 ml 5 M guanidinium thiocyanate (GTC)
prior to pelleting.
sRNA cloning
The sRNA cloning was performed using the miRCat
TM
microRNA Cloning Kit (IDT), with slight modifications.
Total RNA (100 mg) was separated on a 10% denaturing
polyacrylamide gel (Sequagel, National Diagnostics) and
RNA in the ranges of <80 nt, between 100 and 200 nt,
4068 Nucleic Acids Research, 2010, Vol. 38, No. 12
between 200 and 300 nt and >300 nt were gel extracted
using the DTR column method as described in the
miRCat
TM
technical manual. The 30cloning linker was
ligated to the gel-extracted RNA using T4 RNA ligase.
This cloning linker is preactivated for ligation due to a
50adenylation (rApp), which allows the ligation to occur
in the absence of ATP (24), thus eliminating the need to
dephosphorylate the RNA prior to the first ligation, which
is otherwise necessary to prevent RNA circularization.
The 30cloning linker also contains a blocking group at
its 30end (ddC) to minimize multimerization of the
oligonucleotide. Following gel extraction, as above, of
the 30-linkered RNA, the 50-linker was then ligated to
the gel extraced RNA, again using T4 RNA ligase. The
primer complementary to the 30linker sequence was then
used to reverse transcribe the RNA into cDNA, utilizing
SuperScript III Reverse Transcriptase (Invitrogen). The
cDNA produced was amplified by PCR, using
Fermentas PCR Master Mix, with the PCR cycling as
described in the miRCat
TM
technical manual. The PCR
products were then pooled, extracted with phenol:chloro-
form:isoamyl alcohol (v/v 25:24:1), and ethanol
precipitated, followed by agarose gel extraction to
remove any ligation and PCR artifacts. The gel-extracted
cDNA library was subsequently reamplified by another
round of PCR. The resulting cDNA product was then
either cloned directly into the pCRÕII TOPO vector
(Invitrogen) or concatemerized, as described in the
miRCat
TM
technical manual, and then cloned. Colonies
resulting from chemical transformation of TOP10 cells
(Invitrogen) were screened for inserts both by blue/white
screening and by colony PCR, using the M13 Forward
(20) Primer (IDT) and M13 Reverse Primer (IDT) to
the pCRÕII vector. While the blue/white screen facilitated
the detection of cloned inserts, any biologically active
and/or potentially toxic cloned BCG fragments would
be counterselected for, and thus missed in our screen.
Plasmids containing cloned inserts based on the colony
PCR screen were then sequenced. The resulting sequences
were used to perform BLAST searches to the nucleotide
collection on NCBI.
Northern blot analysis
Northern blots were used to verify expression of both the
potential sRNA sequences determined by the linker
cloning and the computationally predicted candidates.
DNA oligonucleotide probes specific for each candidate
sRNA (Supplementary Table S3) were end-labeled using
20 pmoles of oligonucleotide in a 20-ml kinase reaction
containing 25 mMg-P
32
ATP and 20 units T4 poly-
nucleotide kinase (NEB) at 37C for 1 h. Markers were
labeled in the same manner.
Total RNA (5mg, except for 10 mg for Mcr7 and
Mcr9) was separated on a 10% denaturing polyacrylamide
gel alongside either labeled 1 kb Plus DNA ladder
(Invitrogen) or labeled X174 DNA/Hinf I ladder
(Promega), which was transferred to a positively charged
membrane (Hybond N+, GE Life Sciences) for blotting.
Hybridization was performed using Amersham Rapid-hyb
buffer (GE Healthcare), following their recommended
protocol for oligonucleotide probes, with a 3-h incuba-
tion, and moderately stringent conditions, as described
in Supplementary materials and methods. Membranes
were exposed to a phosphor screen overnight and
visualized with a phosphorimager (Typhoon 9400
Variable Mode Imager, Amersham Biosciences).
Quantitation was performed using ImageQuantÕ
software, Version 5.2 (MolecularÕDynamics). Statistical
analysis was performed using InStatÕsoftware, Version
3.0b (GraphPad).
Phylogenetic seletion of computationally predicted
sRNA candidates
Candidate sRNAs were predicted using the SIPHT
program, as described previously (20). Briefly, SIPHT
identifies candidate regulatory RNA-encoding based
on the presence of intergenic sequence conservation
upstream of a putative Rho-independent terminator.
These loci are then annotated for several features,
including their position relative to their flanking ORFs
and whether they share conserved primary sequence or
synteny with previously annotated regulatory RNAs.
The database nucleotide collection (nr/nt) was used with
the preset parameters for blastn searches, with the
computationally predicted candidate sRNA sequence.
Only results with Evalues <1 were used in this study,
unless otherwise indicated. The query search was limited
to Mycobacterium (taxid1763) only if results from using
the nr/nt collection did not include M. smegmatis.
Co-transcription experiments
Total RNA, prepared as above, was treated with 2 U of
TURBO
TM
DNase (Ambion) according to the manufac-
turer’s protocol, followed by extraction with phenol:
chloroform:isoamyl alcohol (v/v 25:24:1), and ethanol pre-
cipitation. The DNase treatment was then repeated, and
the final RNA pellet was resuspended and the RNA was
quantitated as above. Approximately 200 ng of RNA was
used for first strand cDNA synthesis, at 45C, using the
RevertAid
TM
First Strand Synthesis Kit (Fermentas), with
primers designed to amplify the junction between the
sRNA and adjacent ORF (Supplementary Table S3).
The cDNA produced was amplified by PCR, using
Fermentas PCR Master Mix (10 ml RT reaction in a
50 ml PCR reaction). The PCR cycling was as follows:
95C/3 min, then 95C/30 s, 60Cor56
C/30 s (Mcr,
Mpr annealing temperatures, respectively), 72C/45 s for
30 cycles, followed by a 9 min 15 s final extension at 72C.
The PCR products were then visualized on 1 or 1.5%
agarose gels.
RESULTS
sRNAs identified by cloning
Several independent cDNA libraries were constructed
using total BCG RNA from cells grown at 37C with
shaking for 7 days (late log phase). From these libraries,
116 clones with inserts were obtained and sequenced
(Figure 1). Of these 116 candidates, 56 were eliminated: 26
Nucleic Acids Research, 2010, Vol. 38, No. 12 4069
contained rRNA fragments, 22 contained tRNAs, 4 con-
tained the 10Sa RNA, 3 contained rnpB fragments (RNA
component of RnaseP) and 1 contained the rRNA internal
transcribed spacer (ITS1) sequence (25,26). Of the
remaining 60 candidates, 13 contained fragments located
in intergenic regions of the BCG genome and 47 contained
sequences located within annotated ORFs, as determined
by megablast (BLAST; http://blast.ncbi.nlm.nih.gov/
Blast.cgi). Some candidate sRNAs were cloned more
than once (Supplementary Table S1), suggesting that
these sRNAs may be more abundant or easier to clone
than others.
All 60 of the sRNA candidates were tested for expres-
sion using northern blot hybridization. The Mycobac-
terium cloned RNAs (Mcr) verified by northern blot are
designated Mcr1–Mcr19 (Figure 2). Each strand of the
potential sRNA was probed independently, to ensure
both that the sRNA was expressed and that expression
was only observed from one strand. For candidates
located within ORFs this was particularly important, to
establish whether a positive Northern blot signal resulted
from a bona fide sRNA, rather than an mRNA degrada-
tion product. We have defined the ‘class’ of sRNA based
on several criteria, including its position relative to
adjacent ORFs, evidence of processing versus an indepen-
dent transcript, and its direction relative to any known or
predicted overlapping transcripts. sRNAs that are >60 bp
downstream of the closest 30ORF and >100 bp upstream
from the closest 50ORF are designated as intergenic, and
are presumed to act in trans to a distal target. sRNAs that
are cotranscribed with the upstream ORF and/or the
downstream ORF are designated as 30or 50UTRs, respec-
tively. Finally, RNAs that are <60 bp away from the 30
end of an opposing ORF are potentially antisense to a
30UTR, while sRNAs <100 bp away from the 50-end of
an opposing ORF are potentially antisense to a 50UTR.
The northern analysis indicated that many of the sRNAs
were not full length (Supplementary Table S1). For
example, only 46 nt of the Mcr4 sRNA were cloned, but
the northern blot signal corresponded to an >200 nt
species (Figure 2A and B). Where possible, we therefore
mapped the 50-end utilizing 50RLM-RACE (RNA
ligase-mediated rapid amplification of cDNA ends)
and/or primer extension (Supplementary Table S1).
Additionally, we checked whether there was any
co-transcription with neighboring ORFs, using reverse
transcription and PCR amplification (RT–PCR). The
RT–PCR was designed to amplify the junction between
the sRNA and its adjacent ORF(s). Seven sRNAs were
co-transcribed with either one or both of their adjacent
ORFs, as listed in Figure 2A and shown in Figure 2C.
Of particular interest are the following sRNAs (with
genes referred to using their Mtb H37Rv annotations):
First, Mcr9 lies upstream of ilvB1, which has recently
been shown to play a role in Mtb virulence in mice (27).
Mcr9 is co-transcribed with ilvB1 (Figure 2A and C).
This RNA most likely is derived from an mRNA leader
containing a T-box. The 14-nt conserved sequence of
the T-box is part of a tRNA-directed antitermination
mechanism, where tRNA
leu
acts as a direct effector (a
riboswitch). This was first described in Bacillus subtilis,
but is also present in other G
+
bacteria (28–30). Second,
Mcr11, which is located between an adenylyl cyclase
(Rv1264) and the cyclic AMP-induced gene Rv1265 (23),
whose function is unknown. Finally, there are three poten-
tially interesting sRNAs located within ORFs: Mcr16,
located in fabD, which plays a role in fatty acid synthesis
in Mtb (31); Mcr18, located in nuoC (Rv3147), which is
involved in respiration (32,33); and sRNA Mcr19, located
in the gene for the transcriptional regulatory protein
Rv0485. This protein is required for virulence in Mtb
(34). Two of the intergenic sRNAs identified, Mcr6 and
Mcr14, were previously identified in Mtb, as sRNAs C8
(4.5S RNA) and F6, respectively (6).
sRNAs identified in silico
We reasoned that predicted loci that are conserved among
species that are more distantly related represented strong
candidates for functional transcripts. We therefore ini-
tially used the SIPHT program (20) to identify 144 candi-
date sRNAs in BCG (Refseq: NC_008769). We then
applied an additional ‘filter’ using BLAST, and compiled
a list of 67 BCG candidate sRNAs that were partially
conserved in mycobacterial species outside of the
TB-complex (Figure 1; Supplementary Table S2).
All potential candidates were tested using multiple
probes (Supplementary Table 3), with RNA prepared
Cloning
Approach
(Mcr)
Potential sRNAs
BLAST
annotation
In and out
of TB
complex
TB
complex
only
Test in Mtb
(Northern) Test in Mtb and
M. smegmatis
(Northern)
Computational
Approach
(Mpr)
Predicted sRNAs
BLAST
conservation
Discard Test in BCG
(Northern)
Conserved in and
out of TB complex
[116]
[12] [7]
[21]
[67][77]
[144]
[60][56] NoYes
Discard Test in BCG
(Northern)
[19]
Conservation
Yes
No
Figure 1. Strategy for sRNA discovery. This schematic shows the com-
bination of cloning and computational approaches used to identify
sRNAs in BCG, Mtb and M. smegmatis.
4070 Nucleic Acids Research, 2010, Vol. 38, No. 12
A
B
C
Figure 2. sRNAs identified in BCG by cloning. (A) Properties of Mcr1-Mcr19. The approximate sRNA size observed by northern blot (predominant
band), orientation relative to its flanking genes, corresponding Mtb H37Rv genes, co-transcription information and the type of sRNA are given.
All of the gene names and numbers in the arrows are BCG designations. The black arrows represent the sRNA. "= upstream ORF; #= down-
stream ORF; Ind = independent transcript; Co = co-transcribed; n/d = not determined; intergenic = intergenic/trans-acting sRNA; 50UTR = 50
untranslated region; 30UTR = 30untranslated region; 50UTR AS = antisense to potential 50UTR; 30UTR AS = antisense to potential 30UTR;
cds = overlapping coding sequence; RS = riboswitch. *See ref. (6). (B) Representative northern blots of cloned sRNAs. In each case the marker
lane is on the left, labeled in nucleotides, while the sRNA lane is on the right. For Mcr5, s is the sRNA, while t is a tRNA control. All northerns
shown were performed with 7-day shaking BCG RNA run on a 10% denaturing PAGE, except for Mcr4 and Mcr8, which were run on a 6%
denaturing PAGE. Probes are given in Supplementary Table S3. (C) Representative co-transcription. The seven co-transcribed RNAs are shown,
with the chromosomal DNA positive PCR control (C), and RT-PCR reactions spanning the junction of the sRNA and adjacent ORF with (+) or
without () reverse transcriptase. The RT-PCR results for sRNAs that did not display co-transcription are not shown.
Nucleic Acids Research, 2010, Vol. 38, No. 12 4071
from 6- (mid-log phase) and 8-day (early stationary phase)
shaking and standing cultures, as well as 7-day late log
phase shaking cultures, in an effort to maximize our
chances of detecting expression. All candidates were also
tested in both orientations.
Northern blot analysis of the potential sRNAs
resulted in the confirmation of 21 sRNAs expressed in
BCG, Mpr1–Mpr21 (Mycobacterium predicted RNA;
Figure 3). Of the 21 sRNAs identified and confirmed
through the use of computational algorithms, only three
were also identified by cloning (Mcr3/Mpr7, Mcr9/Mpr14
and Mcr14/Mpr13; Figure 3A). Therefore, the comple-
mentary computational approach led to 17 novel sRNA
candidates that were not identified using our cloning
alone, possibly because a relatively small number of
clones were employed [Mpr19 was previously identified
in Mtb as sRNA B11 (6)]. Interestingly, the three
sRNAs identified by both methods produced strong
signals by northern blots, relative to the other sRNAs
identified in silico (Figure 3 and Supplementary Figure
1), suggesting a potential bias of the cloning-based
screen for abundant transcripts.
A comparison of the northern blot results and the
computational predictions of the sRNAs revealed
discrepancies in their sizes and orientations. Only five
sRNAs detected were close to their predicted sizes; most
of the candidates were significantly larger or smaller than
their estimated sizes. An underestimate of sRNA size
could result from only a portion of the sRNA sequence
being conserved. In contrast, overestimates of sRNA size
could result from sequence conservation that extends
beyond the actual sRNA gene. Moreover, for the two
sRNAs Mcr14/Mpr13 and Mpr17, two distinct bands
were detected for each by northern analysis (Figure 3B),
suggesting the presence of multiple, possibly processed,
sRNAs. In these cases, only one sRNA was predicted
computationally. Furthermore, we found that seven of
the 21 sRNAs ran in the opposite orientation to the one
predicted. These results likely arise from the GC-richness
of the genome, leading to false prediction of a putative
Rho-independent terminator associated with a real
conserved intergenic locus on the opposite strand.
We again checked for co-transcription with adjacent
ORFs. As shown in Figure 3A and C, several of the
computationally identified sRNAs were also
co-transcribed. Mpr8 is co-transcribed with its down-
stream ORF, infC (Figure 3A; data not shown). Rfam
(http://rfam.sanger.ac.uk) shows that Mpr8 is likely a
member of the L20 leader family. The L20 leader was
shown to control expression of the infC operon in
Bacillus subtilis through transcriptional attenuation (35).
Evolutionary conservation of sRNAs
Out of the 37 total sRNAs identified in BCG, 15 were also
expressed in the fast-growing, non-pathogenic bacterium
M. smegmatis (Figure 4A and B). M. smegmatis is related
to Mtb and BCG, but is not a member of the TB-complex
bacteria (Figure 4D) (36). The majority of sRNAs that are
also expressed in M. smegmatis were only identified
through computational means (12 out of 15), and are
also predicted to be in a wide range of mycobacterial
species (Figure 4A and D). From this evolutionary con-
servation analysis we hypothesize that these sRNAs likely
regulate highly conserved cellular functions, but that they
may not be involved in regulating pathogenic activities or
specific functions exclusive to other mycobacteria (e.g.
hydrocarbon degradation).
Many sRNAs in BCG are also expressed in Mtb, as one
might expect in closely related bacteria (Figure 4A, B and
D). Based on homology with BCG, 17 novel candidates
were confirmed in Mtb by northern blot. Of these, eight
were solely from the computational predictions, thus
underscoring the success of our phylogenetic approach
in aiding the identification of novel sRNAs among
evolutionarily related organisms.
Mcr11 is differentially expressed
We selected Mcr11 to explore differential expression of
sRNAs. This sRNA lies between two genes, Rv1264 and
Rv1265 (corresponding to BCG1323 and BCG1324 in
Figure 2A), that are of particular interest to us, due to
their relationship to cAMP metabolism (23). The cyclic
AMP-induced gene Rv1265 is upregulated at low pH in
BCG, but not Mtb; Rv1265 is also upregulated in both
BCG and Mtb during macrophage infection (37,38). We
therefore tested whether Mcr11 is regulated by conditions
Mtb encounters during macrophage infection.
Indeed, Mcr11 showed differential expression in both
BCG and Mtb under conditions associated with the host
environments of macrophages and granulomas during
infection, such as low pH and hypoxia (39,40).
Expression of Mcr11 in BCG is 3-fold higher
(P= 0.02) in 8-day cultures than 7-day ones, when the
cells are transitioning from late log phase into stationary
phase (Figure 5A). Eight-day cultures grown under CO
2
-
supplemented low oxygen conditions would still be in an
extended log phase (22), and showed an approximately
3-fold decrease (P= 0.002) in Mcr11 expression relative
to the 8-day stationary phase cultures, but not the 7-day
log phase cultures (Figure 5A). These results indicate that
the expression of Mcr11 in BCG is responsive to the
growth phase and possibly also to a hypoxic environment.
However, expression of Mcr11 in Mtb is only dependent
on the growth phase (Figure 5B). As in BCG, expression
in Mtb was approximately 2-fold higher in 8-day than
7-day cultures (P= 0.05).
DISCUSSION
Importance of sRNAs in mycobacteria
We have identified and described 37 sRNAs in BCG, 34 of
which are novel. We have further shown that out of all the
sRNAs listed in Figure 4A, eight (21%) were expressed in
BCG and Mtb only (TB-complex bacteria), with no
expression observed in M. smegmatis; three (8%) were
expressed in BCG and M. smegmatis, with no expression
observed in Mtb; 12 (32%) were expressed in all three
bacteria, leaving 14 (38%) that were expressed in BCG
alone under the conditions tested. All 37 RNAs are pre-
dicted to be in Mtb, but only 20 (54%) were expressed
4072 Nucleic Acids Research, 2010, Vol. 38, No. 12
Mpr1
Mpr2
Mpr3
Mpr4
Mpr5
Mpr6
Mpr7
Mpr8
Mpr9
Mpr10
Mpr11
Mpr12
(Mcr14)
Mpr14
Mpr15
Mpr16
Mpr17
Mpr18
Mpr19
(Mcr3)
Mpr13
(Mcr9)
Mpr20
Mpr21
ab
~66-82
~50; F6*
c6452saf
3207c 3208
0862c
0863c
c02739173
1109c 1110
sigE 1282
lysX infC
apa 1897
Nbocc0802
48523852
58524852
5Edaf2Adaf
3209 3210
3415c
PPE55b
clpC lsr2
3709
PE_PGRS60
9173c8173
0699 rplJ
63705370
6pfc1Bvli
~68-82
~68-82
~118/66
~311
~118
~118
~200
~100-118
~100
~80-100
~118
~66-82
~118
~82-118
~82-100
~82, B11*
~82
~100
ab
ab
ab
ab
ab
a
ab
ab
b
ab
ab
ab
ab
ab
ab
ab
ab
ab
ab
a-2813128
b-2813648
a-3505334
b-3505565
a-935509
b-935656
a-4073623
b-4074378
a-1205186
b-1206209
a-1394906
b-1395064
a-1857282
b-1857519
a-2109671
b-2110123
a-2300737
b-2300821
a-2845503
b-2845860
a-2846597
b-2846740
a-3506235
b-3506773
a-3731024
b-3731280
a-4019618
b-4019895
a-4072260
b-4072760
a-4065899
b-4066653
a-779354
b-779685
a-818355
b-818508
ab
~118
murA rrs
Coord-
inates
fas Rv2525c
Rv0810c Rv0811c
Rv3661
Rv1222
apa Rv1861
fadA2 fadE5
Rv3183
clpC1
Rv3181c RV3182
Rv1051c
sigE
lysX
Rv2061c
Rv2560
Rv2561-
Rv2562
ilvB1
Rv3364c
Rv3651
Rv3662c
infC
Rv2561
Rv2563
cfp6
Mtb gene Trans-
cription Class
intergenic
5„ UTR AS
5„ UTR, 3„ UTR
3„ UTR, 3„ UTR AS
3„UTR
A
B
C
Rv1052
murA rrs
cobN
Rv3188
Rv3660c
Rv0650
Rv0686
PPE55
lsr2
PE_PGRS60
Rv3661
rplJ
Rv0687
intergenic
integenic
intergenic
intergenic
5„ UTR
intergenic
5„ UTR
5„ UTR AS, 3„ UTR AS
5„ UTR
3„ UTR
5„ UTR/RS
3„ UTR AS
5„ UTR
3„ UTR
intergenic
82
118
100
141
150
Mpr5
140
Mpr6
118
100
82
151
100
82
118
151
140
Mpr11
311
200
249
151
140
Mpr8
100
200
118
151
140
Mpr9
118
100
82
Mpr17
Ind
Co
↓
Co
Co*
Ind
Ind
Co
↑↓
Co
↑↓
Ind
Ind
Ind
Ind
a-1498091
b-1498360
Mpr18
down
Ind
Ind
Ind
Co
↓
5„ UTR AS, 3„ UTR AS
C+- C+- C+- C+- C+- C+-
Mpr6
u
p
Mpr6
down
Mpr11
u
p
Mpr12
u
p
Mpr20
u
p
Co
↑
Ind*
Ind
Co
↑
a
Co
b
a-321523
b-321829
a-3317473
b-3317835
100
118
82
66
M sRNA
Mpr2
Figure 3. sRNAs identified in BCG through computational predictions. (A) Properties of Mpr1-Mpr21 are depicted as for the Mcr RNAs in Figure
2A. Mpr7, Mpr13, and Mpr14 coincide with the cloned sRNAs Mcr3, Mcr14 and Mcr9, respectively. *See ref. (6). (B) Representative
computationally predicted sRNAs verified in BCG by northern blot, depicted as in Figure 2B. Mpr5-11 were identified with 7-day shaking BCG
RNA, while Mpr13 and Mpr17 were identified with 8-day shaking BCG RNA and 6-day standing BCG RNA, respectively. All northerns shown
were run on 10% denaturing PAGE. Probes are given in Supplementary Table S3. (C) Representative co-transcription. Six of the nine co-transcribed
RNAs are shown, with the chromosomal DNA positive PCR control (C), and RT-PCR reactions spanning the junction of the sRNA and adjacent
ORF with (+) or without (–) reverse transcriptase.
Nucleic Acids Research, 2010, Vol. 38, No. 12 4073
under the conditions tested. This could result from differ-
ent regulation of the same RNA between Mtb and BCG,
or from idiosyncrasies of the RNA preparations (i.e. lower
sensitivity of detection in Mtb due to reduced RNA
yields). It is also worth noting that, although BCG and
Mtb are both TB-complex mycobacteria, Mtb is a virulent
pathogen, while BCG is an attenuated vaccine strain
derived from pathogenic M. bovis. Therefore Mtb and
BCG may use different mechanisms to regulate expression
and/or stability of homologous sRNAs. Virulence-
associated sRNA regulatory differences could be signifi-
cant for identifying new targets for novel TB therapeutics.
On a broader level, these sRNAs can be used to help elu-
cidate how sRNAs work in a G
+
system.
The types of transcripts identified here include the
gamut of regulatory RNAs that have been described in
Predicted Tested
sRNA
Mcr1 +
Mcr2 +
Mcr3, Mpr7 + +
Mcr4 +
Mcr5 + +
Mcr6 (4.5S**) + + +
Mcr7 +
Mcr8 ++
Mcr9, Mpr14 +
Mcr10 + +
Mcr11 + +
Mcr12 +
Mcr13 +
Mcr14, Mpr13(F6**) ++
Mcr15 +
Mcr16 +
Mcr17 +
Mcr18 +
Mcr19 ++
Mpr1 +
Mpr2 +
Mpr3 ++
Mpr4 +++
Mpr5 +++
Mpr6 +++
Mpr8 +
Mpr9 ++
Mpr10 +
Mpr11 + + +
Mpr12 + + +
Mpr15 +++
Mpr16 +
Mpr17 +++
Mpr18 +++
Mpr19 (B11**) +++
Mpr20 + +
Mpr21 +
TB-complex*
M. abscessus
M. avium 104
M. avium paratuberculosis
M. gilvum PYR-GCK
M. leprae TN
M. marinum M
M. smegmatis MC
2
155
M. sp. JLS
M. sp. KMS
M. sp. MCS
M. ulcerans Agy99
M vanbaalenii PYR-1
GC
B
r
u
e
tsaPsivob
.M
vR73Hsisoluc
r
ebut.M
M. smegmatis MC2 155
A
slow-growing fast-growing
TB-complex
M. tuberculosis
M. bovis (including BCG)
Non TB-complex
M. avium
M. leprae
M. marinum
M. ulcerans
M. abscessus
M. smegmatis
Hydrocarbon-degading
M. gilvum PYR-GCK
M. sp. JLS
M. sp.KMS
M. sp. MCS
M. vanbaalenii PYR-1
D
B
C
82
100
118
140
151
200
100
140
151
118
82
66
Mpr3
MM12
118
100
151 M 1 2
M
140
Mpr6
140
151
100
118
82
Mpr18
M21
Mcr10
100
140
151
118
Mcr5
82
100
66
M sRNA
249
200
311
Mcr8
Mcr6 (4.5S RNA)
M2
1
+
Mcr16
100
140
118
Mpr5
100
140
151
118
Mpr15
100
66
82
+
+
+
+
(pathogenic) (non-pathogenic)
Figure 4. The Mcr and Mpr sRNAs are conserved within mycobacteria. (A) Summary of the BCG sRNAs and their presence in other mycobacterial
genomes. For Mcr1-19, homology was determined by megablast using the cloned sRNA sequence, while for Mpr1-21 homology was determined by
blastn using the candidate sRNA sequence (only results with an Evalue <1 are shown). sRNAs that did not show homology to M. smegmatis
(Mpr5, 6, 9, 12, 13, 17, 18 and 20) were then rechecked by blastn restricted to Mycobacterium (taxid1763); a few of these had Evalues >1 (Mpr9,
E= 5.0; Mpr11, E= 4.5; and Mpr17, E= 2.4). sRNAs were tested for expression by Northern blot in the three genomes listed; shaded boxes were
not tested. For clarity, duplicate sRNAs are listed only once. + = positive signal by northern; – = negative signal by northern. *In this study
TB-complex includes M. tuberculosis H37Rv, M. bovis subsp. bovis AF2122/97 and M. bovis Pasteur BCG; **previously identified in Mtb (6).
(B) Representative sRNAs that are expressed in M. smegmatis. The lanes are as follows: M. Marker, in nucleotides, 1. log-phase BCG RNA, 2.
log-phase M. smegmatis RNA. (C) Representative sRNAs that are expressed in Mtb. M. Marker, in nucleotides, 1. 7-day shaking Mtb RNA.
(D) Schematic of the general division of mycobacteria. Species correspond to those in panel A,with homology to the Mcr and Mpr sRNAs.
4074 Nucleic Acids Research, 2010, Vol. 38, No. 12
other bacteria, corresponding to intergenic, antisense and
sense sRNAs, as well as potential regulatory UTRs and
possibly multiple riboswitches. The very definition of a
sRNA is ‘a matter of perspective’, since sRNAs can orig-
inate from independent sRNA genes, 50UTR attenuation
or processing, 30UTR processing, and possibly even
overlapping protein coding regions (7). Thus, distinct
sRNAs are not necessarily due to independent RNA syn-
thesis (7,41,42). Intergenic sRNAs usually act in trans to
regulate a distal mRNA target, while antisense sRNAs
presumably regulate their cognate mRNAs. Sense
sRNAs may regulate in cis or in trans, depending on
their sequence. It will be interesting to determine
whether, and how, the sRNAs, found in this study that
are sense to protein coding regions, such as Mcr5 and
Mcr12 (Figure 2A) act as regulators.
Phylogeny aids in computational predictions of sRNAs
Several factors limit the use of bioinformatics and
computational algorithms. For instance, Dynalign predic-
tions, which rely on the stable formation of secondary
structures from intergenic sequences (43), are challenged
by genomes that are high in GC content; this is particu-
larly problematic with mycobacterial genomes, since their
GC content is >65%. Attempts to confirm sRNAs that
were predicted by the Dynalign method repeatedly yielded
northern blots without signals, or with signals corre-
sponding to mycobacterial tRNAs (J. DiChiara and
D. Mathews, unpublished results). Another typical chal-
lenge with computational predictions is the need to rely on
transcription factor binding sites and terminators that are
often species specific, and may be unknown in organisms
like M. bovis. Although promoter and terminator regions
are better studied in Mtb and M. smegmatis (44,45) the
recent finding of unpredicted promoters for sRNA expres-
sion in Mtb indicates the immaturity of alogorithms
within the mycobacterial species (6). Hence, initial
results from predictive analyses often comprise long lists
of potential sRNA candidates that are impractical to test,
due to the inherent high numbers of false positives. The
challenge then becomes how to reduce the number of pre-
dictions so as to increase the efficiency of confirming
positive candidates, without reducing the sensitivity of
the method. Here, a phylogeny-based strategy was used
as an additional filter to select a smaller subset of BCG
sRNA predictions for testing; that step aided in identify-
ing novel sRNAs in mycobacteria. A recent study took a
similar phylogenetic approach to identify noncoding
RNAs on a much larger scale, by looking for conservation
in 422 bacterial genomes (46). Additionally,analysis of the
evolutionary conservation of sRNAs across species could
be highly beneficial in future functional studies, particu-
larly when looking at the biological relevance of an sRNA
between different mycobacterial species.
Despite the shortcomings of current algorithms in
mycobacteria, the methods used in this study had virtually
identical success rates in identifying sRNAs when
compared to the cloning-based approach (31.3 versus
31.7%, respectively; the number positive by northern
blot per the number of potential sRNAs tested in BCG,
Figure 1). A larger scale cloning approach, coupled with
deep sequencing, would likely identify a greater propor-
tion of sRNAs expressed under a certain condition.
However, by combining small-scale cloning and
computational methods we were able to identify conserved
sRNAs across many different mycobacteria. Combining
the data gathered on all the types of sRNAs identified in
this study, there is the possibility of improving the current
algorithm to have an even greater success rate. We are
currently analyzing in detail the advantages of this
method as a filter to large datasets of computational
predictions.
Locations and implications of sRNAs identified
Highly conserved sRNAs expressed in all three species
were Mcr3/Mpr7, Mcr14/Mpr13 and Mpr4, 5, 6, 11, 12,
15, 17, 18 and 19 (Figure 4A). Given that these sRNAs
7 day 8 day
311
249
200
151
140
118
100
82
66
Mnt 123 123
A
B
sRNA
tRNA
311
249
200
151
140
118
100
82
66
M123123nt
BCG
Mtb
7 day 8 day
17.3 18.6 12.4 52.4 51.2 16.5 % of tRNA
SD13.0 4.3 2.8 7.7 4.3 4.8
28.1 32.8 33.4 54.2 41.5 36.5 % of tRNA
SD
7.9 6.2 9.3 13.7 18.0 20.9
sRNA
tRNA
Figure 5. Differential expression of Mcr11. Representative blots
showing that Mcr11 expression is responsive to environmental growth
conditions, in both BCG (A) and Mtb (B). Lanes are as follows:
M. Marker, in nucleotides, 1: shaking, 2: shaking, acid-treated,
3: shaking, low O
2
. Differences in sRNA expression were determined
by the percentage of tRNA signal for each sample; the average of three
experiments is given, with the standard deviations listed below.
Nucleic Acids Research, 2010, Vol. 38, No. 12 4075
occur in the relatively divergent M. smegmatis
(Figure 4D), it will be informative to see if these sRNAs
are also expressed in the other mycobacteria predicted
in Figure 4A, such as the hydrocarbon-degrading
mycobacteria. These species are divergent from one
another and from other fast and slow-growing
mycobacteria (47–49). The above-listed sRNAs may
regulate conserved cellular functions, and it would be
interesting to establish if they regulate the same targets,
in the same manner, within the different mycobacteria.
For example, the ‘region of difference 1’ (RD1) of Mtb
encodes a specialized secretion system that is required for
virulence, and absent from many non-pathogenic
mycobacteria (50,51). Like the well-studied bacterial
pathogen Salmonella enterica serovar Typhimurium,
which is comprised of a mosaic genome, it is possible
that sRNAs outside RD1 act to regulate
virulence-associated genes within RD1 (52).
Mpr8, which likely encodes an L20 leader based on
Rfam analysis, is the first such instance of an L20 leader
identified in a high-GC G
+
bacterium. As mentioned
above, L20 acts as a riboswitch. This 200-bp leader has
only been identified in low-GC G
+
bacteria to date,
including, but not limited to, Clostridia, Lactobacillales
and Bacillales (http://rfam.sanger.ac.uk/family?acc=
RF00558#tabview=tab4).
sRNAs were identified in, or upstream of, several ORFs
that are known to affect virulence. These sRNAs include
Mcr9, located upstream of ilvB1 and Mcr19, located within
the regulatory protein Rv0485. Mcr9 is co-transcribed with
ilvB1, as mentioned in ‘Results’ section. IlvB1 is required
for the synthesis of branched chain amino acids and a
DilvB1 strain cannot grow in vitro without all three
branched chain amino acids added to the media.
Additionally, this Mtb deletion strain is attenuated for vir-
ulence in mice, but persists in the spleen and lungs, making
it a potential vaccine candidate (27). Mcr19 is located sense
to the gene for the regulatory protein Rv0485, which was
recently found to modulate the pe13/ppe18 genes. These
pe13/ppe18 genes are unique to mycobacteria and may
play a role in Mtb virulence (34).
Further studies are needed to assess Mcr11’s relevance
for Mtb pathogenesis. Although the target of this sRNA is
not yet known, Mcr11 transcription is responsive to
growth phase, and its regulation may differ between Mtb
and BCG under hypoxic conditions. A previous study
of Rv1265 expression showed pH had an effect in BCG,
but not in Mtb (23). Additionally, there is preliminary
evidence that Mcr11 is involved in cAMP metabolism
(J.M. DiChiara and K. A. McDonough, unpublished
observation). cAMP plays a role in the interaction of
TB-complex mycobacteria during macrophage infection
(53,54) and multiple proteins are involved in cAMP-
mediated gene regulation, such as CRP
Mt
and Cmr
(23,55). Thus, this work opens many possibilities for the
study of sRNAs as potential virulence regulators in Mtb.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.
ACKNOWLEDGEMENTS
The authors thank Damen D. Schaak for expert technical
assistance with the Mtb work, Guangchun Bai and Joe
Wade for insightful discussions and advice, and John
Dansereau for helping prepare figures. They also thank
Matthew K. Waldor for his time, effort and support that
greatly aided this study. DNA sequencing was performed
by the Wadsworth Center Molecular Genetics Core.
FUNDING
The National Institutes of Health (F32GM087251 to
J.M.D., GM39422 to M.B. and AI063499 to K.A.M.).
The content is solely the reponsibility of the authors
and does not necessarily represent the official views of
the NIH. Funding for open access charge: National
Institutes of Health grants GM39422 (M.B.) and
AI063499 (K.A.M.).
Conflict of interest statement. None declared.
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