Note: Descriptions are shown in the official language in which they were submitted.
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METHOD FOR IDENTIFYING SMALL RNAS
FIELD OF THE INVENTION
The present invention relates to small RNAs and, in particular, to in silico
method
for identifying small RNAs.
BRIEF DESCRIPTION OF THE PRIOR ART
In Eubacterial cell metabolism, a growing number of cell regulatory pathways
were
subject to control by small ribonucleic acids. These non messenger, non
transfer and
non ribosomal RNAs were also called small RNAs (sRNA) or non coding RNAs
(ncRNA) or regulatory RNAs (regRNA) because they are small molecules and not
often
translated. These ones are involved in regulation of gene expression at the
post
transcriptionnai level by modulating mRNA traductability and stability
(Gottesman 2005),
protein catalytic activity (Pichon & Felden 2007) and protein synthesis
quality control
(Gillet & Felden 2001) as well as in the general cell metabolism (Gottesman
2005) and
in the control of virulence expression (Romby et al. 2006). Small RNA genes
are often
located in the core genome of bacteria (Wassarman et al. 2001) but could be in
the
mobile genetic elements including transposable elements, plasmids and phages
(Brantl
2007 for a review), and in pathogenicity islands (Pichon & Felden 2005).
In the case of protein coding genes, several software had been developed and
showed a strong accuracy in open reading frame characterization. But
identification of
sRNA genes with in silico methodologies was a more recent quest (Pichon &
Felden
2008). At the end of the twenty's century, known sRNAs of the Escherichia coli
(E.
co/i)bacterium were identified "by chance" during in vitro or in vivo
experiments. In
consequence of, sRNA diversity and importance for the eubacterial phylum were
unexplored although they were already known as global regulators of the cell
metabolism (Wassarman et al. 1999) and controlled of virulence genes (Novick
et al.
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1993). First in silico experimentations used to identify new sRNA genes in
bacterial
genome exploited two main hypotheses, reported for the first time in the
pioneering
work of Karen Wassarman and colleagues (Wassarman et al. 2001). They
hypothesised
that sRNA genes were conserved among closed relative bacteria genome,
especially in
the inter-genic regions (IGR) and found 17 new sRNAs in the E. coli genome.
Since,
numerous in silico methods based on comparative genomics (Argaman et al. 2001,
Rivas et al. 2001, Pichon & Felden 2003, Axmann et al. 2005, Livny et al.
2005),
statistics/probabilities analysis (Carter et al. 2001, Schattner 2002, Saetom
et al. 2005,
Yachie et al. 2006, Wang et al. 2006) and RNA secondary structure analysis
(Rivas et
al. 2001, Uzilov et al. 2007) had emerged with variable effectiveness (Pichon
& Felden
2008 for a review). In spite of several efforts, exiting methods could be
perfectible and
some strategies had not been tested.
Identification of sRNA secondary structures by sRNA genefinders had been used
to but displayed weak effectiveness (Rivas et al. 2000) or no in vivo
validation (Uzilov et
i 5 ai. 2006). Past studies about RNA secondary structures elucidation had
been showed
that full or partial structure conservation of sRNAs between closed relative
bacteria was
not fully mediated by nucleic sequence conservation and some slightly
different
sequences could fold into the same RNA structure thanks to the appearance of
compensatory mutations (Chen et al. 1999) (Figure 1A&B). Following Kimura's
definition
(Kimura 1985), by compensatory mutations we mean a pair of mutations at
different
nucleotide sites that may be individually deleterious but are neutral in
appropriate
combinations. Thus, individual mutations occurring at nucleotide sites
invoived in
Watson Crick pairs within RNA stems are expected to be deleterious if they
break up
the pairing of double strand structures; however, fitness can be restored,
when a
second, compensatory mutation occurs at the appropriate position on the
opposite
strand of the stem and reestablishes the pairing. Few RNA structure finder
algorithms
identified compensatory mutations, a strong phylogenetic proof of the
existence of a
stem structure and could be a necessary step in the finding of a general sRNA
genefinder algorithm as proposed in the past.
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SUMMARY
The present invention concerns an in silico method for identifying small
RNAs. More particularly, the present invention relates to a method for the
identification of small RNAs, inside the sequence of a first nucleic acid
molecule,
comprising the following steps:
A) Preparation of a test sequence dataset of candidate sequences comprising:
i) identification of termination sites of transcription elongation complexes
in
said first nucleic acid molecule,
ii) elimination of all termination sites whose sequences are located near the
stop codon of an open reading frame with the same nucleic acid strand
nriantatinn
iii) gathering of nucleic acid sequences comprising the sequence, between 50
to 500 nucleotides, upstream each remaining termination sites after step (ii)
plus the. termination site sequences themselves;
B) Identification of at least one nucleic acid sequence sharing significant
sequence identities for each sequence from the test sequence dataset,
C) Multi alignment of all nucleic acid sequences sharing significant
identities with
a sequence from the test sequences dataset,
D) Identification of those nucleic acid sequences having each one at least two
partially conserved stem loops (small RNAs) and further identification of at
least
one putative compensatory mutation in each stem,
E) Analysis of the putative expression of each candidate small RNA genes.
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BRIEF DESCRIPTION OF THE FIGURES
Figure 1: Definition of a compensatory mutation. (A) Multi-alignment of a
theorical
sRNA. (B) Secondary structures from the previous sRNA showing compensatory
mutations.
Figure 2: Workflow of sRNA genefinder based on MASR and CSSR utilization.
Figure 3: Observation of 7 candidates that are positively expressed with the
5S gene as
expression reference.
Figure 4 illustrates the sequence of 10 small RNAs, identified in the genome
of E. coli
strain 55989.
Figure 5: Northern blot analysis of ten candidates from Streptococcus
agalactiae
NEM316. At left, fractionization by gel electrophoresis (denaturing 8%
acrylamide gel) of
total RNA extracted from bacteria cultured in Todd Hewitt (TH) broth at
exponential (E)
and stationary phases (S) or culture in RPMI1640 medium at stationary phase.
L, RNA
ladder. The 10 northern membranes were displayed with the same total RNA
samples
disposition. (>) shows the positive hybridization of a candidate. All results
were detailed
in Table 6.
DETAILED DESCRIPTION OF AN EMBODIMENT OF THE INVENTION
The inventors described the design and validation of a new in silico strategy
which was able to identify known and new sRNAs in the genome of some
Escherichia
coli strains. The inventors implement a package of two softwares which are
able to
analyze automatically a set of sequences ended by a predicted rho-independent
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terminator. The program combined comparative genomic and RNA structure
prediction
based on identification of compensatory mutations especially in the stem of
rho-
independent terminators which led to the discovery of new sRNA genes without
taking
care of its genome locus. This method used less genetic dependent features
than any
5 other in silico identification methodologies and enabled rapid and efficient
analysis of
any bacterial genomes. As previously showed for Staphylococcus aureus (Pichon
et a!.
2005) and Salmonella typhymurium (Pfeiffer et al. 2007), the inventors have
identified
new sRNA genes in the pathogenicity islands of EAEC and ExPEC strains which
can be
used to control a present virulence of E. coli.
Example 1
Materials & Methods
Nucleic sequences
All available genomic sequences of Enterobacterae were obtained from Genbank
database (http://www.ncbi.nlm.nih.gov/genbank/) and listed in Table 1. All
know sRNAs
previously identified in Eubacteria core genomes were listed in Table 2. The
sequence
from these sRNAs was get from genome sequence where it was first
characterized. All
bioinformatics data and biochimical experiements available in particular 5'
and 3' rapid
amplification of chimeric ends (RACE), primer extension analysis, and Northern
blot
were used for characterization of each sRNA primary transcript ends, promoter
and
terminator localisation. At default, when no sufficient data were available,
bioinformatic
predictions of sRNA sequences were used.
Summary of the MASR program
Multiple alignement of Small RNAs (MASR) software was composed of two perl
script whose functions were to select and align putative sRNA sequences.
Sequences
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suspected to contained sRNA gene (called the reference sequence) was analysed
with
the FASTA 3 program (Pearson 2000) or any identities search programs (e.g.
BLAST)
against a database containing DNA sequences (e.g. genomic sequence). The
resulting
hit sequences was analysed by a first script called PPSA (Post Process of
Simple
Alignments) in order to characterize the best significant hit results in
function of 25
different parameters alone or in combination, including e-value, size of the
hit sequence
and percentage of conservation. The PPSA script generated an output file
specific of
the MASR (Multi Alignment of Small RNAs) script. The principle of MASR was
based on
the following affirmations; when the kt" nucleotide of the reference sequence
noted Nk
and the ith nucleotide include in the alignment area of the sequence j (noted
N;j) were at
the same position in a simple alignment (generated by FASTA3), they must be
aligned
in the multiple alignment. When the Nijth nucleotide had no nucleotide match
in the
reference sequence, a gap was created.
Summary of the CSSR program
The Covariation Search in Small RNAs (CSSR) software is designed to search
structurally conserved stem loops in a set of aligned sequences (make by MASR
program). When one of them is found, it searched for compensatory mutations
into and
discarded all fully conserved stems. The CSSR program requires one or more RNA
secondary structure generated by any kind of prediction softwares or get
available
structural data. The software generates a file containing all stem loops with
putative
compensatory mutation locations aligned along the reference sequence. All
Watson
Crick pairs which change to or from a GU weak pair are added to the results as
a
putative site for appearance of compensatory mutations.
Identification of rho-independent terminators
Rho-independent terminator localisation were predicted with the RNAMotif
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program (Macke et aL. 2001) using the previously described method of Elena
Lesnik and
co-workers with modifications (Lesnik et al. 2001). In order to calculated
score, the free
Gibbs energy (AG 37) of the RNA stems were calculated with the efn2 subroutine
of the
RNAMotif program. The OG 37 of the RNA:DNA hybrid duplex of the predicted poly
uracyl tail of the terminator and its corresponding genomic DNA sequences was
calculated with the Melting 4 program (Le Novere 2001) and use of the nearest
neighbour thermodynamic parameters for RNA:DNA duplex formation (Sugimoto et
al.
1995). The score for all the terminators were calculated as described (Lesnik
et al.
2001) and those with a score > -4.0 kcal/mol were removed.
In silico identification of small RNAs in E. coli genomic sequences.
A reference sequence dataset composed of the sequence of predicted rho-
independent terminator (see upper section) with their 200 upstream nucleotides
were
analyzed for sequence conservations against a genome sequences database (Table
1)
with the FASTA 3.4 program (Pearson 2000). Hit results were analyzed with PPSA
in
order to exclude those with an e-value greater than 0.0001. The MASR program
was
applied to transform individual alignment in a multi alignment with it default
parameters.
The global RNA structures of reference sequences were predicted with the Mfold
3.2
program (Mathews et al. 1999). The CSSR program combined the multi alignments
of
MASR and RNA secondary structure predictions in order to find at least two
conserved
structures which harboured compensatory mutations including the rho-
independent
terminator stem.
Evaluation of efficiency of the in silico method.
84 sRNAs had been characterized in the genome of the E. coli MG1655 strain and
approximatively 70% of them used rho-independent termination of the
transcription
(Table 2). Efficiency of MASR/CSSR was evaluated by calculating the percentage
of
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rho-independent terminated sRNAs found by them in the E. coli MG1655 genome by
applying the upper section workflow (Figure 2).
Bacterial strain and growth conditions
All E. coli strains (Table 3) were cultivated in liquid Luria Bertani Broth
(LB) or M9
medium supplemented with 0.4% of sodium pyruvate.
RNA sample preparation
Small precultures of the E. coli 55989 (CNCM deposit number 1-3144) strain was
done in 40 ml of LB medium in a 100 ml erlen for 19h (overnight) without
antibiotic
selection at 37 C under constant 120 rpm agitation. Culture medium was removed
by
centrifugation 4000 rpm, 10 min at room temperature and resuspended in 40 m{
of fresh
LB or M9 pyruvate media. Erlens with 400 ml of LB or M9 pyruvate media were
inoculated with 8 ml (1/50th dilution) of LB or M9 pyruvate preculture,
respectively, and
incubated at 37 C, 120 rpm. E. coli total RNAs was isolated at exponential
(sample E)
and stationary phase (sample S) when DO600 reached 0.52 and 4.29 (24h),
respectively,
for LB medium cultures and reached 0.51 and 1.28 (24h) for M9 pyruvate medium
cultures.
Total RNAs were isolated immediately with Trizol (Invitrogen) according to the
manufacturer protocol excepted bacteria were harvested by centrifugation at
4000 rpm
for 5 min at room temperature for avoiding cold shock stress. After all
protocol steps,
traces of ethanol were removed by air dry and RNA samples were resuspended
with
Tris buffer (10 mM Tris HCI pH 7.5). RNA samples were treated two times with
30 units
of FPLC pure DNase I (Amersham) in 10 mM Tris HCI pH 7.5, 6 mM MgCI2 buffer
for
1h30 at 37 C in order to digest DNA contaminants, phenol/chloroform extracted
and
ethanol precipited. RNA samples were resuspended with Tris buffer, quantified
by UV
spectrophotometry and checked for putative degradations on 2% agarose gel.
Genomic
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DNA contaminations were analyzed by PCR amplification of the multicopy 5S
ribosomal
gene with the 5S.Fw and 5S.RT primers (Table 4). PCR were done with 2 units of
Taq
polymerase (QBiogen) in 1X supplied buffer, 200 pM dNTP (QBiogen), 200 nM of
each
primers (Sigma Proligo) and 10 pg of RNA samples. DNA contaminations were
checked
in a 2% agarose gel and considered unsignificant if no PCR products were
observed by
ethidium bromide staining.
Semiquantitative RT-PCR
Chimeric DNA (cDNA) were synthetized from lOpg of total RNA with 200 units of
Superscript III reverse transcriptase enzyme (Invitrogen) at 55 C for 1 hour
with 2 pmol
of specific primer (named gene.RT) (Sigma Proligo) (Table 4) and after enzyme
was
heat inactivated according to supplier protocol. PCR amplification of cDNA was
done
with 0.4 unit of Taq polymerase (Qbiogen), 100 nM of each primers (named
gene.RT
and gene.Fw), 200 pM of dNTP, 2 pl of the RT reaction with thermal cycling of
94 C, 3
minutes, 40 cycles of 94 C, 30 sec.; 55 C, 30 sec.; 72 C for 30 sec. and a
final
extension of 72 C, 7 minutes. Identical reactions were done for same RNA
samples with
the tmRNA.Fw and tmRNA.RT primers used as positive expression control. PCR
products were analysed with a 2% agarose gel by ethidium staining.
Northern blot hybridization
Northern blot membrane preparation and hybridization were done as previously
described (Pichon & Felden 2005). Shortly, poly acrylamide gel electrophoresis
of RNA
samples were done in urea denaturing 8% bis-tris polyacrylamide (Sigma) gel
and
electro-transferred to Zeta probe GT membrane (Biorad). Membrane were
hybridized
with 32P 5' end labelled oligonucleotides with ExpressHyb solution (Clontech)
as
described (Pichon & Felden 2005). Hybridization of the TmRNA molecules with
the
TmRNA.RT primer was used as positive expression control.
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Results
Identification of small RNAs.
5 Comparative genomic of sRNA sequences showed that compensatory mutations
may be observed in the rho-independent terminator (RIT) stem of two related
sequences (Figure 1). The inventors hypothetized such phenomenum could be
observed in any RIT structures. The inventors applied the workflow described
in Figure
2 on the genomes of straon of E. coli from the Table 3. The inventors focused
on the E.
10 coli 55989 results and took 9 candidates (Table 5) randomly and done
detection of
putative sRNA transcript by RT-PCR. The inventors observed that 7 candidates
are
positively expressed (Figure3) with the 5S gene as expression reference.
Accuracy of the in silico approach.
The inventors gathered sRNA gene coordinates and presence for rho-independent
terminator (RIT) data from a set of publications (Wassarman et al. 2001,
Agarmann et
al. 2001, Rivas et al. 2001, Chen et al. 2002, Tjaden et al. 2002, Vogel et
al. 2003,
Kawano et al. 2005, Yachie et al. 2006) and their corresponding sequences from
E. coli
MG1655 genome. Among the 84 non redundant sRNAs, 58 have a confirmed or a
predicted RIT according to available data, i.e. 69,0% of the sRNA genes used
this kind
of termination as previously reported. The inventors applied the method of
identifying
sRNAs of the present invention (see upper paragraph; Figure 2) except E. coli
55989
genome is replaced by E. coli MG1655 and show that 47 (81.0%) of the RIT are
predicted with the present method. The 11 unpredicted ones correspond to
terminators
which do not have a perfect stem loop (Lesnik et al. 2001). If the imperfect
model for
RIT prediction (tolerating one maximum mismatch and bulged stem) is used, the
inventors found 10 of the 11 others RIT which increased the success rate of
prediction
system to 98.3%. The inventors assessed for presence of compensatory mutations
in
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the 57 predicted RIT and show that 49 candidates (82,7%) correspond to the
inventors'
selection criterion. The inventors hypothetized that absence of covariation
are caused
by low sequence homologies in this region of the genome. As same the inventors
applied sRNA analyse of the E. coli 55989 genome and found identical results
as same
as E. coli MG1655 except that the c0293, sokW and rdlC sRNA genes were absents
from the E. coli 55989 genome.
Example 2
Material & Methods
Identification & validation processes of new sRNAs in Streptococcus agalactiae
strain NEM316 was done as same as in E. coli MG1655 except for rho-independent
terminators. Indeed, we used a different software called TranstermHP in order
to predict
the same RNA structure in all Gram positive bacteria (Kingsford et al. 2007).
Expression analysis was done as described previously in Example 1.
Results
In silico analysis
The sequence of the genome of Streptococcus agalactiae NEM316 strain was
investigated with TransTermHP software in order to identify rho-idependent
terminators
(RIT). We excluded from detected RITs those corresponding to termination of
CDSs as
previously described for E. coli MG1655. The use of MASR/CSSR software
permitted to
identify 81 sRNA candidates including 20 ones, which are located in
pathogenicity
islands described in the genome (See Table 6).
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Expression analysis
Screening of the expression of 29 sRNA candidates among the 81 found in silico
was analyzed by RT-PCR as described. Results showed that 25 candidates were
expressed in Todd Hewitt broth or RPMI1640 medium (Table 6). These candidates
were analyzed by Northern blot and we showed that 10 of them were small RNAs
(Figure 5). Other candidates may be long mRNA leaders (e.g. riboswitches, RNA
stability elements, etc ...), in silico false positive candidates or not
efficient antisense
oligo probes used for northern hybridization.
Conclusions
The inventors concluded that MASR/CSSR softwares were useful to find new
small regulatory RNAs in genome of Gram-positive bacteria.
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Table 1: Genomic sequences used in this study.
Bacteria Strain Accession Database
Buchnera aphidicola Bp NC_004545 Genbank
Buchnera aphidicola Cc NC_008513 Genbank
Buchnera aphidicola Sg NC_004061 Genbank
Buchnera aphidicola APS NC_002528 Genbank
Enterobacter 638 NC_009436 Genbank
Erwinia carotovora SCRI 1043 NC_004547 Genbank
Escherichia coli MG1655 NC_000913 Genbank
Escherichia coli EDL933 NC_002655 Genbank
Escherichia coli Sakai NC_002695 Genbank
Escherichia coli CFT073 NC_004431 Genbank
Escherichia coli S88 N.A. Coliscope
Escherichia coli UMN026 N.A. Co4iscope
Escherichia coli ED1 a N.A. Coliscope
Escherichia coli IAI1 N.A. Coliscope
Escherichia coli IAI39 N.A. Coliscope
Escherichia coli 55989 N.A. Coliscope
Escherichia co/i 042 N.A. Sanger
Escherichia coli W3110 AC_000091 Genbank
Escherichia coli APEC01 NC008563 Genbank
Escherichia co/i UT189 NC_007946 Genbank
Escherichia coli 536 NC_008253 Genbank
Escherichia fergusonnii ATCC35469 N.A. Coliscope
Photorhabdus
luminescens TTO1 NC_005126 Genbank
Salmonella enterica Ty2 NC_004631 Genbank
Salmonella enterica CT18 NC_003198 Genbank
Salmonella enterica SC-B67 NC_006905 Genbank
Salmonella enterica ATCC9150 NC_006511 Genbank
Salmonella typhimurium LT2 NC_003197 Genbank
Shigella boydii Sb227 NC_007613 Genbank
Shigella dysenteriae Sd197 NC_007606 Genbank
Shigella flexneri 301 NC_004337 Genbank
Shigella flexneri 2457T NC_004741 Genbank
Shigel/a flexneri 8401 NC_008258 Genbank
Shigella sonnei Ss046 NC_007384 Genbank
Sodalis glossinidius morsitans NC007712 Genbank
Wigglesworthia
glossinidia Gb NC_004344 Genbank
Yersinia enterocolitica 8081 NC_008800 Genbank
Yersinia pestis C092 NC003143 Genbank
Yersinia pestis Antiqua NC_008150 Genbank
Yersinia pestis 91001 NC_005810 Genbank
Yersinia pestis KIM NC004088 Genbank
Yersinia pestis Nepa1516 NC_008149 Genbank
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Yersinia pestis Pestoides F NC009381 Genbank
Table 2: List of know sRNA genes from Escherichia coli species.
sRNA Gene sRNA synonym Localization Previous Next Strand Strain
name gene gene
4.5S ffs 475611 475786 ybaZ ybaA <>> MG 1655
6S ssrS 3053956 3054187 zapA ygfA >>> MG1655
C0067 c0067 238411 238623 yafT yafU >>< MG1655
C0293 c0293 1195889 1196009 fcd ymfD >>< MG 1655
C0299 c0299 1229852 1229930 hlyE umuD <>> MG1655
C0343 c0343 1407387 1407461 ydaN dbpA >>> MG1655
C0362 c0362 1549943 1550423 cue0 gcd > < < MG1655
C0465 c0465 1970719 1970843 tar cheW <>< MG1655
C0614 c0614 2651472 2651558 sseA 1s128 ><> MG1655
C0664 c0664 2833077 2833189 norW hypF >>< MG1655
C0719 c0719 3119303 3119648 yghK glcB <>< MG 1655
CrpTic crpTic 3483855 3484014 yhfA crp <<> MG 1655
CsrB csrB 2922178 2922581 yqcC syd <<< MG1655
CsrC csrC SraK / RyiB / Tpk2 / IS198 4049020 4049304 yihA yihl <>> MG1655
DicF dicF 1647406 1647458 rzpQ dicB >>> MG1655
DsrA dsrA 2023242 2023370 dsrB yedP <<> MG 1655
DsrB dsrB 2022661 2022867 rcsA dsrA > < < Mr3 165fi
GadY gadY IS183 3662852 3662991 gadW gadX <> < MG1655
GcvB gcvB IS145 2940683 2940923 gcvA ydll <> < MG1655
GImY glmY Tkel I SroF 2689177 2689389 yfhK purL <<< MG1655
GImZ glmZ SraJ / K19 / RyiA 3984419 3984665 aslA hemY <>< MG1655
IS128 is128 2651506 2651734 C0614 ryfA <>> MG1655
Isf isf 1019490 1019890 ompA sulA <>< MG1655
IsrA isrA IS061 1403680 1403866 abgR ydaL > < < MG1655
IsrB isrB IS092 1985865 1986059 yecJ yecR <<> MG1655
IsrC isrC IS102 2069307 2069538 yeeP flu > > > MG1655
1stR istR 3851141 3851316 ivbL tisA <<> MG1655
M1 rnpB 10Sb ! M1 3268199 3268650 yhaC yhaK ><< MG1655
MicA micA SraD 2812790 2812901 luxS gshA <> < MG1655
MicC micC Tke8 / IS063 1435110 1435253 ompN yjcD ><> MG1655
MicF micF 2311070 2311198 ompC rcsD < > > MG1655
NC092 nc092 3069272 3069486 fbaA pgk <>< MG1655
OmrA omrA RygA / T59 / PAIR2 2974124 2974246 aas gal <<> MG1655
OmrB omrB RygB / T59 / PAIR2 / SraE 2974326 2974440 aas gal <<> MG1655
OxyS oxyS 4156301 4156455 argH oxyR ><> MG1655
RdIA rdlA 1268511 1268615 ldrA ldrB <>< MG1655
RdIB rd1B 1269046 1269150 ldrB ldrC <>< MG1655
RdIC rdlC 1269581 1269685 ldrC chaA <> < MG1655
RdID rdlD 3698124 3698228 ldrD yhjV <>> MG 1655
RprA rprA IS083 1768361 1768501 ydiK ydiL >>> MG1655
RseX rseX 2031637 2031763 yedR yedS <>> MG1655
Rtt rtt RttR / RtV1 1286289 1286459 dppA proK <<< MG1655
RybA rybA 852175 852263 ybiL mntR > < > MG1655
CA 02670835 2009-06-25
sRNA Gene sRNA synonym Localization Previous Next Strand Strain
name gene gene
RybB rybB P25 887199 887314 ybjK ybjL > < < MG1655
RybC rybC SroB 506393 506511 ybaK ybaP <>< MG1655
RybD rybD 764212 764373 sucD mngR >>< MG1655
RydB rydB Tpe7 / IS082 1762737 1762804 sufA ydiH <<< MG1655
RydC rydC 1489466 1489562 cybB ydcA > < > MG1655
RyeA ryeA SraC / Tpke79 / IS091 1921041 1921362 pphA yebY <>< MG1655
RyeB tyeB Tpke79 1921188 1921308 pphA yebY <<< MG1655
RyeC ryeC Tp11/QUAD1a 2151299 2151475 yegL yegM < MG1655
RyeD ryeD Tpe60 / QUAD1 b 2151634 2151803 yegL yegM <>> MG1655
RyeE ryeE 2165079 2165224 yegQ orgK >>< MG1655
RyeF RyeF 1956465 1956584 torY cutC <<< MG1655
RyfA ryfA Tp1 / PAIR3 2651828 2652180 Is128 sseB >>< MG1655
RyfB ryfB 2698078 2698435 yfhL ryfC > < > MG 1655
RyfC ryfC 2698505 2698620 ryfB acpS <> < MG1655
RyfD ryfD 2732175 2732343 c/pB yfiH <<< MG1655
RygC rygC T27 / QUAD1c 3054837 3055016 ygfA serA > > < MG1655
C0730 / QUAD1d / IS156 / 3192738 3192922 yqiK rfaE ><<
RygD rygD Tp8 MG1655
RygE rygE QUAD1 3193114 3193297 yqiK rfaE ><< MG1655
RyhA ryhA SraH 3348564 3348722 elbB arcB < > < MG1655
RyhB ryhB Sral / IS176 3578945 3579075 yhhX yhhY <<> MG1655
RyjA ryjA SraL 4275946 4276124 soxR yjcD > < > MG1655
RyjB ryjB 4525965 4526089 sgcA sgcQ <> < MG1655
SgrS sgrS RyaA 77331 77593 sgrR setA <>> MG1655
SokA sokA -3718623 3720136 hokA insJ <>> MG1655
SokB sokB 1490107 1490205 hokB trg <>> MG1655
SokC sokC Sof 16917 17012 mokC nhaA <>> MG1655
SokE sokE 606956 607051 ydbK hokE <<> MG1655
SokW sokW 2777339 2777409 mokW Z3118 < > < 0157
SokX sokX 2885339 2885429 ygcB cysH <>< MG1655
Spot42 spf IS197 4047922 4048032 potA yihA >>< MG1655
SraA sraA T15 457949 458104 clpX Ion > < > MG1655
SraB sraB Pke20 1145812 1145980 yceF yceD < > > MG1655
SraF sraF Tpk1 / IS160 3236396 3236583 ygjR ygjT > > > MG1655
SraG sraG P3 3309247 3309420 pnp rps0 <> < MG1655
SroH sroH 4188342 4188545 htrC thiH > < < MG1655
SymR symR RyjC 4577822 4577953 yjiW hsdS < > < MG1655
T44 t44 Tff 189676 189860 map rpsB < > > MG1655
TmRNA ssrA 10Sa RNA / M2 2753571 2754056 smpB intA >>> MG1655
Tp2 tp2 122857 123023 pdhR aceE > < > MG1655
Tpkell tpkell 14077 14444 dnaK dnaJ >>> MG1655
Tpke70 tpke70 2494609 2494649 ddg yfdZ > < < MG1655
Notes :
sokA gene in E. coli MG1655 strain is interupted by an IS150 insertion.
CA 02670835 2009-06-25
16
Table 3: Strains and plasmids used in this study.
Name Pathotype Origin' Origin' Pg. Relevant Reference
enot e
Strains
E. coli MG1655 N.A. Com. fecal A hf , Blattnet et al. 1997
E. coli UT189 UPEC Cys. urine B2 Chen et a1. 2006
E. coli IAI39 UPEC urine D Picard et a!. 1999
E. coli CFT073 UPEC P I. urine B2 Welch et al. 2002
E. coli AL862 Unknow blood Lalioui et al. 2001
E. co/i55989 EAEC Diarrhea fecal B1 Bernier et al. 2002
E. coli 536 UPEC P I. urine B2 Hochhut et al. 2006
Plasmids
pBR322 cat, b/a Lab. Collection
Human clinical isolates: Commensal (Com.), Cystitis (Cys.), Pyelonephritis
(Pyl.) and Laboratory Strain
(Ls.).
2 Phylogenetic group.
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17
Table 4 : Oligo used in this study.
Small Primer
RNA Name Sequence PCR Size (bp)
Positive control
5S 5S.Fw 5'-GGTGGTCCCACCTGACC-3' 101
5S.RT 5'-ATGCCTGGCAGTTCCCTACT-3'
TmRNA tmRNA.Fw 5'-TCTGGATTCGACGGGATTTG-3' 193
tmRNA.RT 5'-CGCGATCTCTTTTGGGTTTG-3'
Small RNA candidates of E. coli 55989 (CNCM deposit number I-
3144)
213 213.Fw 5'-CACCGCCAGGAAGGTGTAT-3' 105
213. RT 5'-ACACATGCAGGGCGTCTAAC-3'
597 597.Fw 5'-TACCGCTTACGTTGAGAGCA-3' 105
597.RT 5'-TAAAACAAAACCCGCCGTAG-3'
1069 1069.Fw 5'-TAGGAGATCAGCCCGTCAAG-3' 127
1069.RT 5'-CGGGGCATTTTTGTACAGGT-3'
1079 1079. Fw 5'-TGCGTTAGTGTTTTTTTGCC-3' 97
1079.RT 5'-AAAAATCCCGCAGTGATCG-3'
1325 1325.Fw 5'-GAGAGAATCTATTGAAGTGCATGG-3' 101
1325.RT 5'-GGGCAATCAGCGAGTAGGTA-3'
1663 1663.Fw 5'-CGCTGTGTGAAATACGGATG-3' 100
16b3.K I 5'-A(;C T TAGCAACCGATTGACG-3'
1757 1757.Fw 5'-TAAATACAGCCCCAGCCATT-3' 101
1757.RT 5'-CCTGACGGGTGAAATGAATAA-3'
2037 2037.Fw 5'-AGTAGTTGGCTTTGGGGTGA-3' 88
2037.RT 5'-GAATTGACTTTGGCGGTGAC-3'
2046 2046.Fw 5'-AATTCGCAGGACCGTGATAC-3' 117
2046.RT 5'-CGCCTCATTCATGTTCTGGT-3'
2531 2531.Fw 5'-CTTCGCGGTCTCTTTTCCTC-3' 92
2531.RT 5'-CGCGAGCGCGCCATTG-3'
Small RNA candidates of E. coli AL862 (CNCM deposit number I-
4051)
RIG10.Fw 5'-ATCAGTCCGTTGTGTGCAAT-3' 117
RIG 10. RT 5'--CGATCGATAAAACAGGTATCG3'
11 RIG11.Fw 5'-TCAGCATTCAGTGCAGGAAC-3' 110
RIG11.RT 5'-CTGCCGGGAAGAATCATAAA-3'
12 RIG12.Fw 5'-AGTTCCAGCCTGCGACTTT-3' 147
RIG12.RT 5'-TTTCAGGGAAGCTGGTATCC-3'
RIG14.Fw 5'-GGCATGATGAGAACGCAGTA-3'
14 RIG14.RT 5'-CGCTCAGACGGATGCTTAAT-3' 121
53 RIG53.Fw 5'-AATGTAAGTGTAAACTGAGTGCCGTA-3' 100
RIG53.RT 5'-ATGTTCCATAACAGACGTCCAC-3'
vpe vpe. Fw 5'-TTAATTAATGTGATGATTGTCG-3'
vpe.RT 5'-TAGCGCTATCACAAAGATTG-3' 71
CA 02670835 2009-06-25
18
Table 5: Expressed sRNA in ExPEC.
Flanking Flanking Strand
Souches Localisation ORF ORF orientation
sRNA candidates
14 AL862 16870 17298 0RF00049 ORF00051 > > >
53 AL862 56273 56806 afaD afaE > < >
213 55989 300904 301451 Hyp. Prot. Hyp. Prot. < < <
597 55989 822633 822919 Hyp. Prot. Hyp. Prot. > < >
1069 55989 1430311 1430726 Rz Hyp. Prot. > < >
1079 55989 1448272 1448613 I J > < >
1325 55989 1785228 1785593 tolA Hyp. Prot. < > <
1663 55989 2176098 2176920 D Hyp. Prot. > > <
1757 55989 2305688 2305907 Hyp. Prot. Hyp. Prot. > > >
2037 55989 2699641 2700054 Hyp. Prot. N < < >
2046 55989 2705703 2706231 Hyp. Prot. cscB > > <
2531 55989 3343375 3343569 Hyp. Prot. Hyp. Prot. < < >
sRNA used as positive expression control
5S AL862 N.A. N.A. N.A. N.A. N.A.
ARNtm AL862 N.A. N.A. N.A. N.A. N.A.
5S 55989
ARNtm 55989 2978731 2979092 smpB EC55_2909 > > >
N.A. : Not Applicable.
CA 02670835 2009-06-25
19
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