Open Access

RNA-seq analysis of the influence of anaerobiosis and FNR on Shigella flexneri

  • Marta Vergara-Irigaray1, 2,
  • Maria C Fookes3,
  • Nicholas R Thomson3 and
  • Christoph M Tang1, 2Email author
BMC Genomics201415:438

DOI: 10.1186/1471-2164-15-438

Received: 19 September 2013

Accepted: 23 May 2014

Published: 6 June 2014

Abstract

Background

Shigella flexneri is an important human pathogen that has to adapt to the anaerobic environment in the gastrointestinal tract to cause dysentery. To define the influence of anaerobiosis on the virulence of Shigella, we performed deep RNA sequencing to identify transcriptomic differences that are induced by anaerobiosis and modulated by the anaerobic Fumarate and Nitrate Reduction regulator, FNR.

Results

We found that 528 chromosomal genes were differentially expressed in response to anaerobic conditions; of these, 228 genes were also influenced by FNR. Genes that were up-regulated in anaerobic conditions are involved in carbon transport and metabolism (e.g. ptsG, manX, murQ, cysP, cra), DNA topology and regulation (e.g. ygiP, stpA, hns), host interactions (e.g. yciD, nmpC, slyB, gapA, shf, msbB) and survival within the gastrointestinal tract (e.g. shiA, ospI, adiY, cysP). Interestingly, there was a marked effect of available oxygen on genes involved in Type III secretion system (T3SS), which is required for host cell invasion and pathogenesis. These genes, located on the large Shigella virulence plasmid, were down regulated in anaerobiosis in an FNR-dependent manner. We also confirmed anaerobic induction of csrB and csrC small RNAs in an FNR-independent manner.

Conclusions

Anaerobiosis promotes survival and adaption strategies of Shigella, while modulating virulence plasmid genes involved in T3SS-mediated host cell invasion. The influence of FNR on this process is more extensive than previously appreciated, although aside from the virulence plasmid, this transcriptional regulator does not govern expression of genes on other horizontally acquired sequences on the chromosome such as pathogenicity islands.

Background

Shigella flexneri is a Gram-negative bacterium that causes dysentery, an acute human rectocolitis that usually results in destruction of the intestinal mucosa and bloody diarrhoea. The ability of this pathogen to invade epithelial cells at the colonic and rectal mucosal surface is a key determinant in the establishment of the disease. This is mediated by a Type III secretion system (T3SS) encoded on the large Shigella virulence plasmid [1, 2]. The T3SS acts like a molecular syringe that delivers molecules directly from the bacterial cytoplasm into host cells via a needle-like structure [1, 2]. However, before the bacterium reaches the large intestine and invades mucosal epithelial cells, Shigella must successfully survive the hostile conditions found in the gastrointestinal tract. Therefore the capacity of the bacterium to adapt to anaerobiosis, changes in pH, resist antimicrobial peptides, and acquire nutrients is essential for its pathogenesis [3, 4].

Anaerobiosis is known to influence the virulence of several enteric pathogens including Shigella, Escherichia coli, Salmonella spp., Vibrio cholerae and Yersinia enterocolitica[513]. In particular, S. flexneri has been shown to be primed for invasion in anaerobic conditions, in which it expresses longer T3SS needles while reducing Ipa (invasion plasmid antigen) effector secretion; this results from FNR-mediated repression of the virulence plasmid genes, spa32 and spa33[7]. FNR is a major regulator of anaerobic metabolism that is inactivated by the presence of oxygen. Its function depends on the integrity of its O2-sensitive [4Fe–4S] cluster, which is required for FNR dimerization and thence site-specific DNA binding and transcriptional regulation [14]. One RNA deep sequencing (RNA-seq) and several microarray studies have been performed to characterise the extent of the FNR regulon in E. coli and other Gram negative pathogens such as Salmonella enterica and Neisseria gonorrhoeae[1520]. In E. coli, there were significant discrepancies between studies even when the same strain was examined. However some differences could be attributed to the use of media containing high levels of glucose, which represses expression from some FNR-activated promoters, and the delayed growth rate of mutants lacking FNR compared with wild-type strains under anaerobic conditions [16].

Here we define the regulatory role of oxygen and FNR in S. flexneri. We have applied two powerful whole-transcriptome approaches, RNA-seq complemented with Flow cell Reverse Transcription sequencing (FRT-seq), in which there is no amplification during library preparation, to quantify differences in gene expression induced by anaerobiosis and to define the contribution of FNR in this process. We found that Shigella grown anaerobically exhibits global transcriptional changes compared to when grown aerobically, with marked changes in metabolic and transport genes, as well as those involved in regulatory and virulence functions. Importantly, transcription from the Shigella virulence plasmid is extensively modified in anaerobiosis, with most of T3SS-related genes being down regulated in the absence of oxygen in an FNR-dependent manner, demonstrating that this highly conserved regulator of metabolism also controls the horizontally-acquired virulence genes on the plasmid, but not on the chromosome, in this important human pathogen.

Results

Growth conditions and RNA sequencing strategies

To determine the response of Shigella to anaerobiosis and the role of FNR in this process, we employed RNA-seq to compare the transcriptional profiles of wild type S. flexneri M90T and its Δfnr mutant grown in Luria-Bertani (LB) medium in the presence and absence of oxygen. Constantinidou et al. designed a supplemented, minimal salts medium (including LB) in which an E. coli fnr mutant exhibited similar growth as the parental strain in the absence of oxygen [16]. However, this medium did not support the growth of S. flexneri M90T. On the other hand, enriched-glucose media have been shown to repress some FNR-activated promoters [16]. Therefore, we chose LB with no added glucose for our experiments. Particular attention was paid to ensure that the culture volume, agitation, temperature and the growth stage of bacteria did not differ in aerobic and anaerobic conditions. Cultures were grown to an Optical Density at 600 nm (OD600) of 0.2 to avoid a reduction in the concentration of dissolved oxygen tension and total depletion of sugars that occurs during exponential growth [21, 22]. Furthermore until reach OD600 of 0.2 under anaerobiosis, there was no obvious delay in growth rate of the Δfnr mutant in relation to the wild-type strain (See Additional file 1: Figure S1). Three biological replicates were performed per strain in each condition, and differential expression between conditions was analysed with the DESeq R statistical package.

To assess the reproducibility of results obtained with RNA-seq data and to further characterise the role of FNR, the Shigella FNR regulon under anaerobiosis was also examined using FRT-seq, an alternative sequencing approach in which cDNA synthesis is performed on the sequencing flowcell thereby avoiding the possible PCR biases generated during library preparation using standard RNA-seq methods [23]. FRT-seq confirmed 77% of the genes found differentially expressed by RNA-seq, showing a robust concordance between the two techniques. Due to its higher sensitivity, FRT-seq detected more genes whose transcription was significantly influenced by the absence of FNR than RNA-seq (See Additional file 1: Table S2). A complete catalogue of significant differences is shown in Additional material (See Additional file 1: Tables S1 and S2) as well as a summary of the mapping statistics (See Additional file 1: Table S3). To confirm the results obtained by global analysis of the transcriptional profile, we performed strand-specific qRT-PCR to analyse mRNA levels of several genes found to be differentially expressed under anaerobic and aerobic growth conditions.

Identification of novel chromosomal genes influenced by the absence of oxygen in S. flexneri

Analysis of the RNA-seq data revealed that 528 chromosomal genes were differentially expressed by wild-type S. flexneri M90T grown under anaerobic conditions compared with aerobic conditions, with 363 genes being up-regulated, and 165 genes down-regulated. Additional file 1: Table S1 shows these genes classified into functional categories based on the database of Clusters of Orthologous Groups (COGs) [24]. As expected, most of the genes differentially expressed were related to energy production and metabolism (53%). The remaining genes were involved in cellular processes and signalling (15%), information storage and processing (8%) or were poorly characterized (24%). RNA-seq data also showed that from the above 528 differentially expressed genes, 228 genes (43%) were influenced by the absence of FNR under anaerobic conditions (See Additional file 1: Table S1).

Importantly the majority of genes that we found to be anaerobically induced/repressed have been identified in previous microarray studies with other enteric pathogens examining the effect of oxygen on the transcriptome and/or the two main anaerobic regulators, FNR and ArcA [6, 1620, 25]. Consistent with previous work, we found increased expression of genes involved in anoxic carbon metabolism (focA-pfl, yfiD, fdnG, gldA, aspA, fumB, ansB), respiratory pathways (glpABC, nap, nir, ccm, nrfABC, frd), production of hydrogenases (hyb, hya, hyc, hyp), fermentation (adhE, ackA-pta, fdhF) and acid response (adiA, adiY, yjdE, gadA, hdeAB) under anaerobiosis (Additional file 1: Table S1) [6, 1618, 20, 25, 26]. Our analysis also identified several anaerobically repressed genes that have been previously characterised [6, 1620, 25]. These genes encode enzymes of the tricarboxylic acid cycle (ace, gltA, acn, icdA, sdh), aerobic dehydrogenases (glpD, betBA, gcd, aldA), transhydrogenases (udhA) and iron acquisition systems (exb, iuc, iutA, sit, suf, fep, fhu), and others (Additional file 1: Table S1) [6, 1618, 20, 25, 27, 28].

The sensitivity of the direct sequencing approaches, RNA-seq and FRT-seq, compared with array-based methods enabled us to extend the repertoire of Shigella genes modulated by ambient oxygen. Table 1 shows all genes influenced by the presence of oxygen and not detected in previous microarray studies on E. coli and S. flexneri[6, 1618, 20, 25]. The effect of FNR mutation on the transcription of previous genes under anaerobiosis (assessed by RNA-seq and FRT-seq) is also shown in Table 1. Several members of the phosphoenolpyruvate–carbohydrate phosphotransferase system (PTS), involved in the transport and phosphorylation of sugars, were up-regulated under anaerobic conditions. Examples include ptsHI, which encode the general PTS components phosphohistidine carrier protein (HPr) and Enzyme I (EI) respectively, and sugar-specific PTS components like ptsG and manXYZ (involved in glucose transport), treBC (trehalose transport and hydrolysis), mtlA (mannitol) and murQP that contribute to the uptake and catabolism of N-acetylmuramic acid [2932]. Of note, the murQP operon, which is also involved in peptidoglycan recycling, showed an FNR-dependent expression pattern (Table 1, Figure 1A) [31].
Table 1

Chromosomal genes differentially expressed in response to anaerobic conditions not previously published in E. coli and S. flexneri microarray analysis

ORF IDab

Gene

Description

RNA-seqclog2FC

RNA-seqclog2FC

FRT-seqclog2FC

   

WT no O2/O2

Δfnr/WT no O2

Δfnr/WT no O2

Metabolism

     

Energy production and conversion

SF5M90T_1519

 

putative oxidoreductase, major subunit

3.80

-4.97

-3.30

SF5M90T_3856

yiaY

putative oxidoreductase

3.18

-2.73

-2.67

SF5M90T_1560

 

putative oxidoreductase, major subunit

3.06

 

-3.87

SF5M90T_3333

pckA

phosphoenolpyruvate carboxykinase

1.99

 

0.62

SF5M90T_3877

yiaK

putative dehydrogenase

1.93

1.22

0.93

SF5M90T_3374

ugpQ

glycerophosphodiester phosphodiesterase, cytosolic

1.89

  

SF5M90T_2534

hmpA

dihydropteridine reductase, ferrisiderophore reductase activity

1.47

5.38

5.85

SF5M90T_33

caiB

l-carnitine dehydratase

1.32

  

SF5M90T_3679

atpF

membrane-bound ATP synthase, F0 sector, subunit b

1.04

  

SF5M90T_3680

atpE

membrane-bound ATP synthase, F0 sector, subunit c

1.04

  

SF5M90T_3937

ppc

phosphoenolpyruvate carboxylase

0.91

 

0.64

SF5M90T_579

galT

galactose-1-phosphate uridylyltransferase

0.77

  

SF5M90T_1419

ydjA

predicted oxidoreductase

-1.17

1.69

1.52

SF5M90T_2771

ygaF

hydroxyglutarate oxidase

-1.31

4.12

2.99

SF5M90T_4044

gltP

glutamate-aspartate symport protein

-1.32

  

SF5M90T_1603

rnfB

electron transport complex protein

-1.46

  

SF5M90T_2869

fldB

flavodoxin 2

-1.56

  

SF5M90T_1602

rnfA

Na + -translocating NADH-quinone reductase subunit E

-1.75

  

SF5M90T_1011

rutA

pyrimidine monooxygenase

-3.07

  

Carbohydrate transport and metabolism

SF4250

treB

PTS system trehalose(maltose)-specific transporter subunits IIBC

3.66

  

SF5M90T_4160

treC

trehalase 6-P hydrolase

3.56

  

SF5M90T_1379

manX

PTS enzyme IIAB, mannose-specific

3.36

  

SF5M90T_1378

manY

PTS enzyme IIC, mannose-specific

3.11

  

SF5M90T_1377

manZ

PTS enzyme IID, mannose-specific

2.89

  

SF5M90T_3670

rbsD

high affinity ribose transport protein

2.71

  

SF5M90T_1101

ptsG

PTS system, glucose-specific IIBC component

2.27

  

SF5M90T_3491

treF

cytoplasmic trehalase

2.12

  

SF5M90T_2419

murP

PTS system N-acetylmuramic acid transporter subunits EIIBC

2.09

 

-0.71

SF5M90T_3499

pfkA

6-phosphofructokinase I

2.08

  

SF5M90T_2096

 

fructose-bisphosphate aldolase

2.02

2.08

1.86

SF5M90T_1001

agp

periplasmic glucose-1-phosphatase

2.00

1.40

1.57

SF5M90T_2887

rpiA

ribosephosphate isomerase, constitutive

1.84

  

SF5M90T_2898

pgk

phosphoglycerate kinase

1.84

  

SF5M90T_1403

gapA

glyceraldehyde-3-phosphate dehydrogenase A

1.83

  

SF5M90T_2097

yegT

putative nucleoside permease protein

1.74

  

SF5M90T_2897

fba

fructose-bisphosphate aldolase, class II

1.56

  

SF5M90T_2404

ptsH

PTS system protein HPr

1.56

  

SF5M90T_3850

mtlA

PTS system, mannitol-specific enzyme IIABC components

1.52

  

SF5M90T_1640

ydhC

putative transport protein

1.51

 

0.69

SF5M90T_2359

 

beta-fructosidase

1.49

 

0.72

SF5M90T_3496

tpiA

triosephosphate isomerase

1.45

  

SF5M90T_2405

ptsI

PEP-protein phosphotransferase system enzyme I

1.42

  

SF5M90T_2808

fucI

L-fucose isomerase

1.41

  

SF5M90T_2875

bglA

6-phospho-beta-glucosidase A

1.27

  

SF5M90T_3348

malP

maltodextrin phosphorylase

1.16

 

0.78

SF5M90T_1107

ycfO

beta-hexosaminidase

1.13

  

SF5M90T_8

talB

transaldolase B

1.10

  

SF5M90T_2033

gnd

gluconate-6-phosphate dehydrogenase

1.01

  

SF5M90T_581

galM

galactose-1-epimerase

1.01

1.41

1.00

SF5M90T_1805

eda

keto-hydroxyglutarate-aldolase/keto-deoxy-phosphogluconate aldolase

0.97

  

SF5M90T_580

galK

galactokinase

0.95

 

0.62

SF5M90T_2913

tktA

transketolase 1 isozyme

0.80

  

SF5M90T_2187

fruB

PTS system fructose-specific transporter subunit IIA/HPr protein

-1.33

  

SF5M90T_2186

fruK

fructose-1-phosphate kinase

-1.75

  

SF5M90T_3161

ptsO

phosphocarrier protein NPr

-1.76

  

SF5M90T_1637

 

putative transport protein

-1.93

1.58

 

SF5M90T_2185

fruA

PTS system, fructose-specific transport protein

-1.99

  

Aminoacid transport and metabolism

SF5M90T_2823

argA

N-acetylglutamate synthase

1.94

  

SF5M90T_1910

fliY

putative periplasmic binding transport protein

1.80

  

SF5M90T_625

ybgH

peptide transporter

1.64

-1.60

-1.21

SF5M90T_292

pepD

aminoacyl-histidine dipeptidase (peptidase D)

1.54

1.78

1.75

SF5M90T_2879

gcvT

aminomethyltransferase

1.53

 

1.44

SF5M90T_284

proA

gamma-glutamylphosphate reductase

1.48

  

SF5M90T_1121

potD

spermidine/putrescine periplasmic transport protein

1.44

 

-0.69

SF5M90T_2674

cysD

ATP:sulfurylase (ATP:sulfate adenylyltransferase), subunit 2

1.39

-2.30

-2.21

SF5M90T_1514

dcp

dipeptidyl carboxypeptidase II

1.35

 

0.63

SF5M90T_285

proB

gamma-glutamate kinase

1.26

 

-0.58

SF5M90T_2533

glyA

serine hydroxymethyltransferase

1.16

  

SF5M90T_2967

gsp

glutathionylspermidine synthetase/amidase

1.16

 

0.70

SF5M90T_1806

edd

6-phosphogluconate dehydratase

1.15

 

-0.72

SF5M90T_807

 

glutathione transporter ATP-binding protein

1.08

  

SF5M90T_2317

hisJ

histidine-binding periplasmic protein of high-affinity histidine transport system

1.05

  

SF5M90T_806

ybiK

putative asparaginase

1.02

  

SF5M90T_1122

potC

spermidine/putrescine transport system permease

0.97

-1.09

-0.92

SF5M90T_2882

pepP

proline aminopeptidase P II

0.94

  

SF5M90T_2877

gcvP

glycine decarboxylase

0.90

2.13

1.69

SF5M90T_3687

asnA

asparagine synthetase A

-1.23

 

-1.61

SF5M90T_4099

lysC

aspartokinase III, lysine sensitive

-1.33

1.37

1.14

SF5M90T_1253

trpE

anthranilate synthase component I

-1.57

  

SF5M90T_4187

cycA

transport of D-alanine, D-serine, and glycine

-1.69

  

SF5M90T_1946

yedA

putative transmembrane subunit

-1.79

  

SF5M90T_3626

yifK

putative amino acid/amine transport protein

-1.94

  

SF5M90T_3385

livJ

Leu/Ile/Val-binding protein precursor

-1.95

  

SF5M90T_2843

lysA

diaminopimelate decarboxylase

-2.11

2.44

 

SF5M90T_4029

proP

low-affinity transport system; proline permease II

-2.65

1.05

0.75

SF5M90T_4185

ytfF

putative transmembrane subunit

-3.70

2.71

 

Nucleotide transport and metabolism

   

SF5M90T_3587

udp

uridine phosphorylase

3.12

 

0.59

SF5M90T_2387

nupC

permease of transport system for 3 nucleosides

2.81

  

SF5M90T_2949

nupG

nucleoside permease

2.47

  

SF5M90T_674

ybeK

putative tRNA synthetase

1.60

  

SF5M90T_444

adk

adenylate kinase

1.51

 

-0.67

SF5M90T_2456

purC

phosphoribosylaminoimidazole-succinocarboxamidesynthetase

1.35

  

SF5M90T_4182

cpdB

2′:3′-cyclic-nucleotide 2′-phosphodiesterase

1.16

  

SF5M90T_291

gpt

guanine-hypoxanthine phosphoribosyltransferase

1.10

 

-0.89

SF5M90T_1598

add

adenosine deaminase

0.95

  

SF5M90T_478

purE

phosphoribosylaminoimidazole carboxylase

-1.41

  

Coenzyme transport and metabolism

SF5M90T_2274

menB

dihydroxynaphtoic acid synthetase

2.63

-1.75

-1.65

SF5M90T_2687

 

phenylacrylic acid decarboxylase-like protein

1.83

  

SF5M90T_2276

menD

2-oxoglutarate decarboxylase

1.76

-1.63

-1.75

SF5M90T_2273

menC

O-succinylbenzoate synthase

1.76

-1.72

-1.57

SF5M90T_3142

ispB

octaprenyl diphosphate synthase

1.06

  

SF5M90T_1613

pdxH

pyridoxinephosphate oxidase

1.06

  

SF5M90T_2880

visC

putative FAD-dependent oxidoreductase

0.89

  

SF5M90T_3011

ribB

3,4 dihydroxy-2-butanone-4-phosphate synthase

-1.10

  

SF5M90T_3577

yigC

putative oxidoreductase

-1.31

  

SF5M90T_2885

ygfA

putative ligase

-1.59

  

SF5M90T_3957

birA

biotin--protein ligase

-1.62

 

-0.51

SF5M90T_2103

thiM

hydoxyethylthiazole kinase

-1.95

  

Lipid transport and metabolism

SF5M90T_1094

acpP

acyl carrier protein

1.70

-3.04

 

SF5M90T_2272

menE

o-succinylbenzoate-CoA ligase

1.60

-1.54

-1.80

SF5M90T_2416

ucpA

putative oxidoreductase

1.45

 

-0.56

SF5M90T_339

sbmA

sensitivity to microcin B17, possibly envelope protein

-1.64

  

Inorganic ion transport and metabolism

SF5M90T_2903

 

hypothetical lipoprotein

3.41

  

SF5M90T_929

ycbO

alkanesulfonate transporter substrate-binding subunit

3.04

  

SF5M90T_2415

cysP

thiosulfate binding protein

2.81

-1.51

-1.88

SF5M90T_1636

sodB

superoxide dismutase

2.52

2.13

1.67

SF5M90T_1187

 

putative ATP-binding protein of ABC transporter

2.14

  

SF5M90T_454

copA

copper exporting ATPase

1.95

  

SF5M90T_1186

 

putative iron compound ABC transporter permease

1.69

  

SF5M90T_1185

 

iron ABC transporter ATP-binding protein

1.52

  

SF5M90T_4057

yjcE

predicted cation/proton antiporter

1.49

  

SF5M90T_2675

cysN

ATP-sulfurylase (ATP:sulfate adenylyltransferase), subunit 1

1.08

-2.08

-2.05

SF5M90T_448

ybaL

putative transport protein

-1.01

 

-0.50

SF5M90T_2386

mntH

divalent metal cation transporter

-1.93

1.61

1.68

SF5M90T_330

tauC

taurine transport system permease protein

-2.09

  

SF5M90T_3769

shiF

putative membrane transport protein

-2.16

  

SF5M90T_3054

ygjT

putative transport protein

-2.41

  

SF5M90T_1102

fhuE

outer membrane receptor for ferric iron uptake

-2.46

  

SF5M90T_1483

ydiE

hemin uptake protein

-2.93

-2.20

-1.55

SF5M90T_1572

mdtI

spermidine export protein

-3.61

  

Secondary metabolites biosynthesis, transport and catabolism

SF5M90T_1184

 

putative SAM-dependent methyltransferase

2.12

  

SF5M90T_331

tauD

taurine dioxygenase, 2-oxoglutarate-dependent

-2.97

1.94

 

Cellular processes and signalling

Cell cycle control, cell division, chromosome partitioning

SF5M90T_1243

yciB

probable intracellular septation protein A

0.93

 

-0.80

Defense mechanisms

SF5M90T_4215

ampC

beta-lactamase; penicillin resistance

1.55

 

-1.69

SF5M90T_3751

emrD

multidrug resistance protein D

1.41

  

SF5M90T_4273

 

putative restriction modification enzyme R subunit

1.41

-0.94

-0.99

SF5M90T_3781

shiA

virulence factor

1.30

  

SF5M90T_101

ampD

N-acetyl-anhydromuranmyl-L-alanine amidase

1.18

  

SF5M90T_772

ybhF

putative ABC-type multidrug transport system component

1.16

 

0.54

SF5M90T_771

ybhS

putative ABC-type multidrug transport system component

1.15

  

SF5M90T_770

ybhR

putative ABC-type multidrug transport system component

0.90

  

SF5M90T_418

mdlA

ATP-binding component of a transport system

-1.29

  

Signal transduction mechanisms

SF5M90T_2126

yehU

putative 2-component sensor protein

1.36

  

SF5M90T_3428

uspA

universal stress protein

0.86

  

SF5M90T_2388

yfeA

predicted diguanylate cyclase

-1.20

 

-0.81

SF5M90T_4339

creC

sensory histidine kinase

-1.63

1.43

 

Cell wall/membrane/envelope biogenesis

SF5M90T_1923

nmpC

outer membrane porin protein

2.04

-1.34

-1.13

SF5M90T_1618

slyB

putative outer membrane protein

1.82

-1.43

-0.92

SF5M90T_952

ompA

outer membrane protein 3a

1.59

  

SF5M90T_374

tsx

outer membrane protein

1.53

  

SF5M90T_256

gtrB

bactoprenol glucosyl transferase

1.36

-2.59

 

SF5M90T_2039

rfbC

dTDP-4-dehydrorhamnose 3,5-epimerase

1.20

-2.64

 

SF5M90T_4332

slt

soluble lytic murein transglycosylase

0.96

1.54

1.46

SF5M90T_3951

murI

glutamate racemase

0.93

 

-0.48

SF5M90T_3821

rfaD

ADP-L-glycero-D-mannoheptose-6-epimerase

0.82

  

SF5M90T_1241

tonB

transport protein

-1.72

  

SF5M90T_3956

murB

UDP-N-acetylenolpyruvoylglucosamine reductase

-2.23

 

-0.60

Cell motility

SF5M90T_1938

fliQ

flagellar biosynthetic protein

3.55

  

Intracellular trafficking, secretion and vesicular transport

   

SF5M90T_3964

secE

preprotein translocase

0.87

-1.06

-0.76

SF5M90T_3580

tatC

Sec-independent protein translocase

0.84

 

-0.57

SF5M90T_3501

yiiO

uncharacterized periplasmic protein

-4.72

 

2.64

Posttranslational modification, protein turnover, chaperones

SF5M90T_4204

mopB

co-chaperonin GroES

1.41

  

SF5M90T_3279

slyD

FKBP-type peptidyl-prolyl cis-trans isomerase

1.31

  

SF5M90T_462

ybbN

putative thioredoxin-like protein

0.90

  

SF5M90T_407

clpP

ATP-dependent proteolytic subunit of clpA-clpP serine protease

0.84

  

SF5M90T_3738

ibpA

heat shock protein

-1.17

  

SF5M90T_2074

yegD

putative heat shock protein

-2.80

 

-1.11

Information storage and processing

Translation, ribosomal structure and biogenesis

SF5M90T_2801

yfiA

translation inhibitor protein RaiA

2.70

  

SF5M90T_2392

gltX

glutamate tRNA synthetase, catalytic subunit

1.65

  

SF5M90T_155

frr

ribosome releasing factor

1.21

  

SF5M90T_650

glnS

glutamine tRNA synthetase

1.08

  

SF5M90T_3893

glyQ

glycine tRNA synthetase, alpha subunit

1.06

 

-0.50

SF5M90T_4220

yjeA

putative lysyl-tRNA synthetase

0.95

-1.75

-1.72

SF5M90T_3894

glyS

glycine tRNA synthetase, beta subunit

0.81

  

Transcription

SF5M90T_3025

ygiP

putative transcriptional regulator/nucleoid-associated protein

3.04

-4.57

-2.45

SF5M90T_2417

murR

HTH-type transcriptional regulator

2.35

  

SF5M90T_3510

rhaR

positive regulator for rhaRS operon

2.33

  

SF5M90T_1595

malI

repressor of malX and Y genes

1.98

  

SF5M90T_2125

yehT

putative two-component response regulator

1.80

  

SF5M90T_1373

cspC

cold shock protein

1.59

-1.38

 

SF5M90T_3349

malT

positive regulator of mal regulon

1.58

 

0.58

SF5M90T_3335

ompR

osmolarity response regulator

1.42

  

SF5M90T_3453

yiaG

putative transcriptional regulator

1.38

 

3.65

SF5M90T_2089

gatR

galactitol utilization operon repressor

1.33

  

SF5M90T_71

cra

transcriptional repressor of fru operon and others

1.16

  

SF5M90T_4197

yjdC

putative transcriptional regulator

1.15

 

1.37

SF5M90T_1370

 

putative regulator

1.09

  

SF5M90T_3578

rfaH

transcriptional activator

-1.57

 

-0.96

SF5M90T_4242

yjeB

HTH-type transcriptional repressor

-1.95

  

SF5M90T_984

cspH

cold shock-like protein

-3.35

  

Replication, recombination and repair

SF5M90T_2925

endA

DNA-specific endonuclease I

1.42

-3.26

-2.74

SF5M90T_3034

ygjF

G/U mismatch-specific DNA glycosylase

1.23

 

1.10

SF5M90T_410

hupB

DNA-binding protein HU-beta

1.08

  

SF5M90T_775

rhlE

putative ATP-dependent RNA helicase

-1.16

  

SF5M90T_3117

deaD

inducible ATP-independent RNA helicase

-1.20

  

SF5M90T_1769

dbpA

ATP-dependent RNA helicase

-1.86

  

Poorly characterized

General function prediction only

SF5M90T_2762

stpA

DNA-binding protein

3.51

-3.00

-2.60

SF5M90T_275

 

putative crossover junction endodeoxyribonuclease

2.84

  

SF5M90T_2418

muQ

N-acetylmuramic acid 6-phosphate etherase

2.77

 

-0.93

SF5M90T_1724

 

putative acetyltransferase

2.06

-2.41

 

SF5M90T_2435

 

putative amino acid antiporter

2.03

1.90

1.58

SF5M90T_2301

yfbT

putative phosphatase

2.00

  

SF5M90T_773

ybhG

putative membrane protein

1.78

1.02

 

SF5M90T_1227

hns

DNA-binding protein

1.69

-1.24

 

SF5M90T_2275

yfbB

putative enzyme

1.62

-1.90

-1.85

SF5M90T_3225

yrdA

putative transferase

1.51

  

SF5M90T_4236

hfq

RNA-binding protein

1.45

-2.10

 

SF5M90T_3315

gph

phosphoglycolate phosphatase

1.25

  

SF5M90T_2192

yeiR

putative GTPases

1.03

  

SF5M90T_1919

yedE

putative transport system permease protein

1.03

-1.83

-1.96

SF5M90T_3295

yhfC

putative transport

0.97

-1.30

-1.25

SF5M90T_2205

yejK

nucleoid-associated protein

0.95

  

SF5M90T_2066

yegH

putative transport protein

0.84

  

SF5M90T_3344

yhgH

putative gluconate periplasmic binding protein

0.81

  

SF5M90T_3102

yraM

putative glycosylase

0.73

  

SF5M90T_794

ybiP

putative enzyme

-1.14

  

SF5M90T_2207

yejM

putative sulfatase

-1.36

  

SF5M90T_3139

yhbE

putative permeases of drug/metabolite transporter superfamily

-1.36

 

-0.90

SF5M90T_966

yccA

putative carrier/transport protein

-1.38

 

-1.18

SF5M90T_2742

yqaB

putative phosphatase

-1.51

  

SF5M90T_3882

bax

putative ATP-binding protein

-1.70

 

-0.77

SF5M90T_3370

yhhX

putative regulator

-1.97

 

1.30

SF5M90T_3621

aslB

putative arylsulfatase regulator

-2.73

  

SF5M90T_2516

 

putative enzyme

-3.91

  

Function unknown

SFxv_3833

 

conserved hypothetical protein

3.59

-4.23

 

SF5M90T_2431

 

conserved hypothetical protein

2.89

  

SF5M90T_2432

 

conserved hypothetical protein

2.69

  

SF5M90T_11

 

uncharacterized protein

2.57

  

SF5M90T_1402

yeaD

conserved hypothetical protein

2.45

  

SF5M90T_828

ybjO

conserved hypothetical protein

2.23

 

-2.52

SF5M90T_1941

dsrB

conserved hypothetical protein

2.03

-2.43

 

SF5M90T_2302

yfbU

conserved hypothetical protein

1.87

  

SF5M90T_451

ybaK

conserved hypothetical protein

1.86

-1.21

-1.10

SSJG_00311

 

conserved hypothetical protein

1.75

 

-1.60

SF5M90T_5

yaaA

conserved hypothetical protein

1.54

  

SF5M90T_1387

 

conserved hypothetical protein

1.51

  

SF5M90T_3911

yiiU

conserved hypothetical protein

1.45

-2.49

 

SF5M90T_957

 

conserved hypothetical protein

1.37

  

SF5M90T_4146

yjgD

conserved hypothetical protein

1.28

-2.30

 

SF5M90T_2622

 

conserved hypothetical protein

1.24

-1.46

 

SF5M90T_3155

yhbN

conserved hypothetical protein

0.81

  

SF5M90T_479

ybbF

conserved hypothetical protein

-1.19

  

SF5M90T_2195

rtn

conserved hypothetical protein

-1.50

  

SF5M90T_438

ybaN

conserved hypothetical protein

-1.73

  

SF5M90T_1853

 

conserved hypothetical protein

-2.03

-2.58

 

SF5M90T_1647

 

conserved hypothetical protein

-2.11

  

SF5M90T_983

ymcD

conserved hypothetical protein

-2.34

  

SF5M90T_4094

yjbA

P-starvation inducible protein PsiE

-2.51

1.69

 

SF5M90T_1110

ycfJ

conserved hypothetical protein

-2.52

 

0.99

SF2861

 

hypothetical protein remnant

-2.64

  

SF5M90T_2146

yohO

membrane protein

-2.96

  

SF5M90T_1952

 

putative outer membrane pore protein

-2.98

  

SF5M90T_4307

 

putative inner membrane protein

-3.40

  

SF1231

 

conserved hypothetical protein

-3.71

 

-1.60

SF5M90T_427

ybaA

conserved hypothetical protein

-3.88

  

Phage related

    

S1668

relF

prophage maintenance protein

1.75

  

SF5M90T_1793

 

putative phage integrase protein

1.45

-1.60

 

SF5M90T_1056

 

hypothetical bacteriophage protein

1.14

  

SF5M90T_740

 

putative bacteriophage protein

-1.93

  

aGenomes used as reference are: S. flexneri 5a str. M90T, S. flexneri 2a str. 301, S. flexneri 2002017, Shigella sp. D9 and S. flexneri 2457 T with GenBank accession numbers AGNM00000000, NC_004337, NC_017328, NZ_GG657384 and NC_004741 respectively.

bGenes are classified in functional categories based on the database of Clusters of Orthologous Groups (COGs). http://www.ncbi.nlm.nih.gov/COG/. Inside each subgroup, genes are arranged in descending order in relation to Log2 of Fold Change values of WT no O2/WT O2 comparison.

cLog2 of Fold Change values of WT no O2/WT O2 and Δfnr no O2/WT no O2 comparisons are presented. Only values considered differentially expressed are shown (p adjust <0.05).

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-438/MediaObjects/12864_2013_Article_7055_Fig1_HTML.jpg
Figure 1

qRT-PCR verification of S. flexneri chromosomal genes induced under anaerobic growth conditions and the role of FNR in the process. Strand specific qRT-PCR analysis of mRNA levels of S. flexneri M90T chromosomal genes shown to be induced under anaerobiosis in RNA-seq analysis. Panel A shows transport and metabolic genes, and panel B acid resistance, OMP and regulatory genes. Data were calculated as the n-fold difference relative to polA (2Ct, where ΔCt represents the difference in threshold cycle between the target and control genes). Results are shown in relation to the wild-type strain 2Ct levels under aerobic conditions, here referred to as 1. Thus, values greater than 1 indicate increased transcription under anaerobiosis, and lower than 1 indicate the opposite. Significant differences were detected when wild-type 2Ct levels under aerobic and anaerobic conditions, or wild-type vs. Δfnr 2Ct levels under anaerobiosis were compared. ns = non-significant, P < 0.05, *; P <0.01, **; n = 4; Mann–Whitney test. Error bars show Standard Deviation (SD).

The expression of other genes involved in transport displayed altered expression in anaerobiosis. For instance, emrD, coding for a drug transporter, cysP, involved in the binding and uptake of sulfate and thiosulfate, yjcE, coding for a Na+/H+ exchanger, ybgH, which encodes a peptide transporter and genes involved in nucleoside transport and catabolism (tsx, nupC, nupG and udp) are induced in anaerobiosis (Table 1, Figure 1A) [3340].

We found several metabolic genes induced under anaerobic growth such as cra, coding for the catabolite repressor/activator protein, Cra, tpiA, encoding a key enzyme of the gluconeogenic and glycolytic pathways, gapA, involved in glycolysis, yehU/yehT, coding for a two component system involved in responses to carbon starvation, malT, the transcriptional activator of the genes responsible for uptake and metabolism of maltodextrins and proA, which encodes an enzyme in proline biosynthesis [4147]. The expression of these genes was not FNR-dependent (Table 1, Figure 1A).

In addition to metabolism, we observed anaerobic up-regulation of: genes involved in stress response such as cspC; genes coding for outer membrane proteins (OMPs) such as NmpC, OmpA and SlyB; genes with global regulatory functions such as yjgD that codes for RraB, which interacts with the endonuclease RNase E; yfiA, encoding a ribosome-associated protein that inhibits protein translation; and yejK, hns and its paralogue stpA coding for nucleoid-associated proteins responsible for chromosomal DNA compaction and global gene regulation [4856]. Interestingly, anaerobic induction of cspC, nmpC, slyB, yjgD, hns and stpA was dependent, at least in part, on FNR (Table 1, Figure 1B). Anaerobiosis can also down-regulate transcription. This is the case for fruBKA, encoding the fructose PTS [29] (Table 1, Figure 2).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-438/MediaObjects/12864_2013_Article_7055_Fig2_HTML.jpg
Figure 2

qRT-PCR verification of S. flexneri chromosomal genes repressed under anaerobic growth conditions and the role of FNR in the repression. Strand specific qRT-PCR analysis of mRNA levels of S. flexneri M90T chromosomal genes shown to be repressed in RNA-seq analysis. Data were calculated as the n-fold difference relative to polA (2Ct, where ΔCt represents the difference in threshold cycle between the target and control genes). Results are shown in relation to wild-type 2Ct levels under aerobic conditions, here referred to as 1. Thus, values greater than 1 indicate increased transcription under anaerobiosis and lower than 1 indicates the opposite. Significant differences were detected when wild-type 2Ct levels under aerobic and anaerobic conditions or wild-type vs. Δfnr 2Ct levels under anaerobiosis were compared. P <0.01, **; n = 4; Mann–Whitney test. Error bars show Standard Deviation (SD).

The analysis of genes known to be influenced by anaerobiosis revealed further functions of FNR. This is the case for ygiP, encoding a nucleoid-associated protein induced under anaerobic growth conditions, which we found is FNR-dependent [57]. Furthermore, we observed that menDBCE, genes required for the biosynthesis of quinones with essential roles in anaerobic electron transport systems, are affected by the presence of FNR in contrast to E. coli (Table 1, Figure 1A and B) [5860].

Our study revealed extended regulatory roles for FNR, such as in the biosynthesis of L-cysteine. Previous work has demonstrated that cysK, which encodes an enzyme in L-cysteine biosynthesis, is subject to FNR regulation and identified an FNR-like domain in cysJ, which encodes a component of the sulfite reductase [16, 61]. Here, we found that loss of FNR affects the entire L-cysteine biosynthetic pathway including genes involved in the uptake and transport of sulfate (i.e. cysPUWAM), sulfate activation (cysDN), reduction to sulfide (cysJIH) and transformation into L-cysteine (cysK) (Table 1, Figure 1A, see Additional file 1: Table S2) [6264].

Reprogramming of T3SS related genes under anaerobic conditions

Analysis of genes involved in Shigella virulence revealed that multiple genes on the Shigella virulence plasmid, including ipa-mxi-spa genes, were repressed under anaerobic growth in an FNR-dependent manner (Table 2). In contrast, only seven genes on the plasmid (yigB, ospI, shf, rfbU, virK, msbB and parA) were up-regulated in the absence of oxygen; all of these are regulated by FNR except parA and yigB (Table 2). Figure 3 shows effect of oxygen on expression of genes on the virulence plasmid genes. These findings were confirmed by strand specific qRT-PCR for several genes (Figures 4 and 5). Since excess ParA levels compared with ParB can affect plasmid partitioning, we also examined the transcription profile of parB[65]. Similar to parA, mRNA levels of parB are elevated during anaerobic growth (Figure 5). Consistent with this finding, there was no significant difference in loss of the virulence plasmid from bacteria grown in aerobic and anaerobic conditions (not shown).
Table 2

Virulence plasmid genes differentially expressed in response to anaerobic conditions

ORF IDab

Gene

Description

RNA-seqclog2FC

RNA-seqclog2FC

FRT-seqclog2FC

   

WT no O2/O2

Δfnr/WT no O2

Δfnr/WT no O2

pWR501_0265

yigB

hypothetical protein

2.56

  

pWR501_0225

ospI

T3SS effector

2.00

-2.21

 

pWR501_0250

shf

peptidoglycan deacetylase

1.24

-3.42

-1.30

pWR501_0251

rfbU

glycosiltransferase

1.21

-1.65

-1.16

pWR501_0252

virK

virulence protein

1.15

-2.16

-1.03

pWR501_0039

parA

plasmid segregation protein

1.13

  

pWR501_0253

msbB

acyltransferase

1.07

-3.03

-1.08

pWR501_0074

sepA

secreted protease

-1.12

1.34

1.52

pWR501_0177

 

hypothetical protein

-1.55

 

1.04

pWR501_0283

ipaH1.4

T3SS effector

-1.56

  

pWR501_0175

 

hypothetical protein

-1.58

 

2.29

pWR501_0176

 

hypothetical protein

-1.76

 

2.02

pWR501_0015

 

hypothetical protein

-2.00

1.52

1.05

pWR501_0002

 

putative resolvase

-2.04

1.97

 

pWR501_0007

 

hypothetical protein

-2.25

1.63

 

pWR501_0014

 

hypothetical protein

-2.25

1.60

1.27

pWR501_0192

virG

invasion protein

-2.25

 

2.36

pWR501_0144

ipgF

unknown function

-2.38

 

3.20

pWR501_0051

virF

transcriptional activator of virulence

-2.47

  

pWR501_0006

 

hypothetical protein

-2.54

  

pWR501_0143

ipgE

chaperon

-2.55

 

3.39

pWR501_0146

mxiH

T3SS component

-2.56

1.55

4.10

pWR501_0122

 

hypothetical protein

-2.58

1.53

1.95

pWR501_0121

 

hypothetical protein

-2.65

  

pWR501_0191

virA

T3SS effector

-2.66

 

2.42

pWR501_0147

mxiI

T3SS component

-2.80

2.00

4.01

pWR501_0013

mkaD

mouse killing factor

-2.81

1.97

3.33

pWR501_0145

mxiG

T3SS component

-3.02

 

3.45

pWR501_0148

mxiJ

T3SS component

-3.06

2.17

4.33

pWR501_0031

 

hypothetical protein

-3.14

  

pWR501_0005

 

hypothetical protein

-3.14

 

2.49

pWR501_0292

sopA

VirG-specific protease

-3.25

 

2.49

pWR501_0291

 

hypothetical protein

-3.31

  

pWR501_0138

ipgB

invasion protein

-3.34

 

3.66

pWR501_0157

spa15

chaperon

-3.34

3.52

4.43

pWR501_0012

shET2-2

enterotoxin

-3.38

 

3.41

pWR501_0156

mxiA

T3SS component

-3.44

 

4.67

pWR501_0160

spa32

invasion protein

-3.45

1.34

4.63

pWR501_0141

 

hypothetical protein

-3.50

 

3.71

pWR501_0004

phoN2

apyrase

-3.52

2.38

3.25

pWR501_0150

mxiL

hypothetical protein

-3.59

2.07

4.64

pWR501_0132

acp

hypothetical protein

-3.62

 

5.19

pWR501_0166

spa-orf10

hypothetical protein

-3.64

1.97

4.52

pWR501_0158

spa47

T3SS component

-3.65

3.19

4.69

pWR501_0140

icsB

T3SS effector

-3.65

2.16

4.43

pWR501_0162

spa24

T3SS component

-3.70

2.28

3.71

pWR501_0159

spa13

T3SS component

-3.75

 

5.13

pWR501_0161

spa33

T3SS component

-3.75

1.99

4.03

pWR501_0151

mxiM

T3SS component

-3.81

2.40

4.25

pWR501_0155

mxiC

T3SS component

-3.86

2.43

4.95

pWR501_0290

 

hypothetical protein

-3.86

 

2.00

pWR501_0030

 

putative enterotoxin fragment

-3.90

2.20

4.54

pWR501_0163

spa9

T3SS component

-3.92

1.93

3.56

pWR501_0137

ipgC

chaperon

-3.93

1.86

4.04

pWR501_0135

ipaC

T3SS effector

-3.93

2.65

5.01

pWR501_0165

spa40

T3SS component

-3.94

 

4.02

pWR501_0139

ipgA

chaperon

-3.95

2.45

4.25

pWR501_0153

mxiD

T3SS component

-3.98

2.33

4.81

pWR501_0152

mxiE

transcriptional activator

-4.06

2.51

4.48

pWR501_0154

mxiD

T3SS component

-4.08

2.66

4.54

pWR501_0134

ipaD

T3SS effector

-4.16

2.99

4.92

pWR501_0167

spa-orf11

hypothetical protein

-4.19

2.85

4.07

pWR501_0136

ipaB

T3SS effector

-4.24

2.97

4.59

pWR501_0133

ipaA

T3SS effector

-4.24

3.09

4.98

pWR501_0003

 

hypothetical protein

-4.57

 

3.29

pWR501_0164

spa29

T3SS component

-5.06

2.36

3.32

pWR501_0131

virB

transcriptional activator

-5.17

2.40

4.26

aS. flexneri 5a str. M90T pWR501 virulence plasmid sequence was used as reference GenBank accession numbers AF348706.

bGenes are arranged in descending order in relation to Log2 of Fold Change values of WT no O2/WT O2 comparison.

cLog2 of Fold Change values of WT no O2/WT O2 and Δfnr no O2/WT no O2 comparisons are presented. Only values considered differentially expressed are shown (p adjust <0.05).

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-438/MediaObjects/12864_2013_Article_7055_Fig3_HTML.jpg
Figure 3

Circular map of genes differentially expressed in the virulence plasmid under anaerobiosis. Outer ring shows ORFs and their orientations. Genes differentially repressed and induced in the wild type M90T strain under anaerobiosis in relation to aerobic conditions were marked in deep blue and red respectively. Scale is in base pairs. The figure was generated with DNAPlotter.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-438/MediaObjects/12864_2013_Article_7055_Fig4_HTML.jpg
Figure 4

qRT-PCR verification of S. flexneri virulence plasmid genes repressed under anaerobic growth conditions and the role of FNR in the repression. Strand specific qRT-PCR analysis of S. flexneri M90T virulence plasmid genes mRNA levels shown to be repressed in RNA-seq analysis. Data were calculated as the n-fold difference relative to polA (2Ct, where ΔCt represents the difference in threshold cycle between the target and control genes). Results are shown in relation to the wild-type 2Ct levels under aerobic conditions (referred to as 1). Values greater than 1 indicate increased transcription under anaerobiosis, while lower than 1 indicate the opposite. Significant differences were detected with the wild-type strain 2Ct levels under aerobic and anaerobic conditions, or wild-type vs. Δfnr 2Ct levels under anaerobiosis were compared. P <0.01, **; n = 4; Mann–Whitney test. Error bars show Standard Deviation (SD).

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-438/MediaObjects/12864_2013_Article_7055_Fig5_HTML.jpg
Figure 5

qRT-PCR verification of S. flexneri virulence plasmid genes induced under anaerobic growth conditions and the role of FNR in the induction. Strand specific qRT-PCR analysis of S. flexneri M90T virulence genes mRNA levels shown to be induced in RNA-seq analysis. Data were calculated as the n-fold difference relative to polA (2Ct, where ΔCt represents the difference in threshold cycle between the target and control genes). Results are shown in relation to the wild-type 2Ct levels under aerobic conditions (referred to as 1). Values greater than 1 indicate increased transcription under anaerobiosis, while lower than 1 indicate the opposite. Significant differences were detected with the wild-type strain 2Ct levels under aerobic and anaerobic conditions, or wild-type vs. Δfnr 2Ct levels under anaerobiosis were compared. P < 0.05, *; P <0.01, **; n = 4; Mann–Whitney test. Error bars show standard deviation (SD).

The Shigella pathogenicity island SHI-1 is not present in S. flexneri M90T. Therefore, we examined the transcriptional profile of the SHI-2 pathogenicity island that includes the aerobactin, iron-uptake system [66]. As previously reported, we found that genes encoding the aerobactin system (iucABCD and iutA) were down-regulated under anaerobic conditions, as was shiF, a gene which is also involved in iron acquisition [6, 67]. In contrast, shiA, a SHI-2 gene involved in attenuating host inflammatory responses, was over-expressed under anaerobic conditions when compared to aerobic conditions [68]. Of note, no SHI-2 gene is subject to FNR regulation (Table 1, Figure 5, see Additional file 1: Table S1).

csrB and csrC sRNAs are induced in the absence of oxygen in S. flexneriM90T

Little is known about the small RNAs (sRNAs) in Shigella or their expression under anaerobic conditions. We analysed the sRNAs already described in Shigella as well as potential sRNAs homologues to those described in S. enterica serovar Typhimurium and found that anaerobic growth conditions induce the expression of csrB and csrC in an FNR-independent manner (Table 3, Figure 6) [6971].
Table 3

sRNAs differentially expressed in response to anaerobic conditions

sRNAa

Adjacent genes

Description/class

Length (nt)

RNA-seqblog2FC

RNA-seqblog2FC

    

WT no O2/O2

Δfnr/WT no O2

csrB

syd/SF5M90T_2595

protein-binding sRNA

360

4.97

 

csrC

yihi/yihA

protein-binding sRNA

245

3.38

 

asRNAs are arranged in descending order in relation to Log2 of Fold Change values of WT no O2/WT O2 comparison.

bLog2 of Fold Change values of WT no O2/WT O2 and Δfnr no O2/WT no O2 comparisons are presented. Only values considered differentially expressed are shown (p adjust <0.05).

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-438/MediaObjects/12864_2013_Article_7055_Fig6_HTML.jpg
Figure 6

Verification of sRNAs results by Northern Blot. Northern blot analysis of csrB and crsC sRNAs expression under aerobic/anaerobic conditions. 10 μg of total RNA obtained from S. flexneri M90T wild‒type strain and its isogenic Δfnr mutant grown under aerobic and anaerobic conditions until OD600 = 0.2 were separated in 1,25% MOPS‒agarose gels, transferred to membranes and detected using probes specific for the sense strand.

Discussion

In vitro studies have several limitations in relation to in vivo studies; e.g., they cannot mimic the amount and type of carbon sources available for bacteria and lack the signals derived from the interaction with intestinal epithelium, human immune system or other bacteria present in the gut. However, if conducted accurately can provide valuable information.

In the current study we have, for the first time, employed RNA-sequencing to identify oxygen regulated genes in an enteric pathogen. Our findings confirm previous results, but as this method is more sensitive than array based approaches, we identified an extended repertoire of genes modulated by oxygen in an FNR-dependent or -independent manner. For instance, little is known about the role of Cra, a transcriptional regulator of carbon flux (that represses glycolysis and activates gluconeogenesis) here shown to be induced under anaerobic conditions [41]. Interestingly, mutation of cra increases both epithelial cell attachment and invasion by Shigella in aerobic conditions [72]. However, Cra has an entirely distinct role in the virulence of enterohemorrhagic E. coli (EHEC), a close relative of Shigella, when investigated under conditions mimicking the anaerobic environment of the intestinal tract. Under these circumstances, loss of Cra reduces attachment of bacteria to enterocytes [73]. Additionally, Salmonella cra mutants are avirulent when administered orally, indicating that Cra may have key roles in enteric pathogens in anaerobic conditions [74].

While there is an increasing recognition that carbon metabolism affects microbial virulence, it is still not clear whether distinct carbon energy sources are important or preferable for different members of the Enterobacteriaceae[72, 7580]. For example, our results show that the expression pattern under anaerobic conditions of ptsG, manXYC and fruBKA involved in the transport of sugars is opposite in Shigella to that observed in E. coli[18, 20]. This could be simply due to the different growth medium used in the experiments or to distinct metabolic strategies between Shigella and other Enterobacteriaceae. In favour of the latter and its relationship with virulence it has been shown that mutation of ptsG induces the adherence and invasive capacity of enteroinvasive E. coli (EIEC) strains but not in Salmonella[81]. Further differences between Shigella and other Enterobacteriaceae include adiY, an AraC-like regulator, which activates expression of adiA and adiC, encoding the arginine-dependent acid resistance system (AR3). In Salmonella adiY expression is elevated under aerobic conditions, whereas in Shigella and in E. coli, increased expression of adiY occurs in anaerobiosis [20, 82]. These differences could be due to the strikingly different acid survival strategies that these bacteria seem to develop in spite of being close relatives [83]. Deletion of cad locus, a typical pathoadaptive mutation in Shigella spp., also induces the AR3 system suggesting that this system contributes to the survival of Shigella in its particular niche in the intestinal tract [84, 85].

Interestingly, we observed an FNR-dependent elevated expression under anaerobiosis of hns and overall of stpA and ygiP that encode nucleoid-associated proteins responsible for DNA compaction and global gene regulation, indicating that lack of oxygen profoundly modifies DNA topology in Shigella. Recently, it has been shown that FNR function is strongly inhibited by this kind of nucleoid-associated proteins, which block FNR access to many binding sites [20]. Our findings suggest that FNR is involved in this inhibition, probably indirectly, due to the absence of putative FNR binding-boxes in the promoter region of these genes [20].

To distinguish between direct and indirect effects of FNR, in vivo approaches based in chromatin immunoprecipitation followed by micro-array hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) have been performed in E. coli[20, 86]. Correlation of FNR ChIP-seq peaks with transcriptomic data showed that less than half of the FNR-regulated operons could be attributed to direct FNR binding. Of note, FNR occupancy does not always correlate with the presence of a consensus FNR binding site or a change in expression [20, 86]. A total of 19 of E. coli ChIP-seq peaks are located in promoter regions of genes identified in Table 1 (i.e. ptsG, pfkA, gapA, yegT, ptsH, tpiA, lysC, menD, ribB, uspA, slyB, ompA, tonB, yjeA, cspH, deaD, dbpA, yccA and yhhX); only one of these, dbpA, has a canonical FNR binding sequence in its promoter region. Consistent with previous findings, only six of these 19 genes (lysC, menD, slyB, yjeA, yccA and yhhX) were influenced by FNR in our transcriptomic analysis. This result suggests that many FNR effects in Table 1 are likely to be indirect. However, we cannot rule out differences in regulation between E. coli and Shigella that could affect FNR function. Of note, this is the first time that menD, slyB, yjeA and yhhX have been identified as FNR regulated by transcriptome analysis, corroborating previous ChIP findings performed in E. coli.

sRNAs are widespread in bacteria and play critical roles in regulating physiological processes [87]. In Shigella, putative sRNAs have been identified by bioinformatics [69, 70]. However, the expression of these sRNAs has not been confirmed in all cases and little is known about their function or the physiological conditions that induce their expression. Here, we found that anaerobic growth induces expression of two sRNAs, csrB and csrC, independently of FNR. In E. coli csrB and csrC regulate the activity of CsrA, the carbon storage regulator although their function in Shigella has not been characterised so far [88, 89].

For genes directly involved in host:pathogen interactions, we found that oxygen influences the expression of almost all genes in the mxi-spa operon. These T3SS-related genes were down-regulated in the absence of oxygen in an FNR-dependent manner. This is likely to be mediated by VirB as this transcription factor controls many genes in this operon, is influenced by H-NS dependent DNA supercoiling and our findings demonstrate that virB gene is repressed in anaerobiosis [90]. The effect of oxygen on the Shigella T3SS is opposite to Salmonella in which FNR induces expression of invasion genes, and probably reflects the different sites occupied in the host by these two related intestinal pathogens [19]. The results further emphasise that the Shigella T3SS is inactive in anaerobic environments as we previously reported [7].

Inflammation at the site of invasive infection is a hallmark of intestinal shigellosis [91, 92]. Of note, expression of shiA is induced under anaerobiosis. This gene in the SHI-2 pathogenicity island encodes a factor that attenuates the intestinal inflammatory response in shigellosis by decreasing the recruitment of polymorphonuclear leukocytes and T-cells [68, 93]. Similarly OspI is the only T3SS-effector protein that was overexpressed in anaerobiosis; it also serves to dampen inflammatory responses by deaminating a glutamine in host ubiquitin-conjugating enzyme (UBC13) [94]. Thus, expression of both ShiA and OspI under low oxygen tension might dampen the extent of inflammatory responses to Shigella while it is in the anoxic environment of the intestinal lumen, impairing immune responses. Only one operon on the virulence plasmid, shf-rfbU-virK-msbB, was induced under anaerobiosis in an FNR-dependent manner. Interestingly, all these genes are implicated in modification of Shigella lipopolysaccharide (LPS), an important pro-inflammatory mediator [9599].

The transcription of several genes encoding OMPs was induced under anaerobic growth. Both OmpA and OmpC have been implicated in Shigella virulence, while our results suggest that Tsx, Slp, NmpC, SlyB and YciD (OmpW) could also contribute to pathogenesis and be considered as potential vaccine targets [100, 101]. Indeed, Salmonella OmpW, Tsx and NmpC have already been demonstrated to be immunogenic [102, 103]. In addition to OMPs, transcription of gapA, which encodes glyceraldehyde-3-phosphate dehydrogenase, was induced under anaerobic conditions. Interestingly, this enzyme is exported by EHEC and enteropathogenic E. coli (EPEC) strains but not by non-pathogenic strains. Due to its ability to interact with plasminogen, fibrinogen and intestinal epithelial cells, it has been suggested that GapA might contribute in vivo to the interaction of EHEC and EPEC with the gut epithelium [104].

Conclusions

Overall, our RNA-seq based analysis revealed that in the anaerobic lumen of the intestine Shigella is predicted to prompt both survival and anti-host immune-modulatory activities of the bacterium. This occurs through a reprogramming of bacterial metabolism including altered transcription of genes encoding transport systems and metabolic pathways (Figure 7), likely reflecting the carbon energy sources available in the intestine. Modulation of LPS, along with ShiA and OspI may enable Shigella to subvert inflammatory responses prior to mucosal invasion. Our results highlight the central role of oxygen and FNR in these processes and how it governs bacterial interactions and entry into host cells [7, 68].
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-438/MediaObjects/12864_2013_Article_7055_Fig7_HTML.jpg
Figure 7

Summary of novel genes influenced by the absence of oxygen in Shigella identified by RNA-seq.

Methods

Bacterial strains and culture conditions

Bacterial strains and plasmids used in this study are shown in Additional file 1: Table S4. E. coli strains were grown in Luria-Bertani (LB; Invitrogen) broth or on LB agar plates while S. flexneri was propagated either in LB broth, tryptic soy broth (TCS; Sigma) or on TCS plates with Congo red (0.01%, Sigma). Experiments under anaerobiosis were performed in an anaerobic workstation (Whitley A35). When required, antibiotics were added at the following concentrations: chloramphenicol 20 μg/ml, ampicillin 100 μg/ml.

Deletion of fnrgene and complementation experiments

The fnr deletion mutant was generated by allelic exchange using pKO3blue plasmid as previously described [105]. Oligonucleotide primers used in this study are listed in Additional file 1: Table S5. Complementation of Δfnr mutant was performed with pBM2, a derivative of pBBR1MCS-4 plasmid that carries a copy of fnr gene under the control of its native promoter. The plasmid pBBR1MCS-4 was used as a control (See Additional file 1: Table S4). The absence of FNR in the Δfnr mutant and its presence in the complemented strain was confirmed by western blot using polyclonal antibodies against FNR as previously described [7] (See Additional file 1: Figure S1).

DNA and RNA extraction methods

S. flexneri M90T genomic DNA for sequencing was isolated as previously described [106]. For RNA extraction bacteria were grown in LB medium with and without oxygen. A 5 ml pre-inoculum was grown over night aerobically or anaerobically with shaking conditions. The pre-inoculums were diluted proportionally to their OD600nm to standardize the input of bacteria to a starting OD600nm of 0.005. Cultures (volume, 175 ml in 1 L flasks) were grown at 37°C, under shaking conditions (200 rpm) until the OD600nm reached 0.2. Three biological replicates were performed for each condition. Total RNA from bacterial pellets was extracted using TRIzol reagent method as previously described [107]. RNA qualities were determined using Agilent RNA Nano Chips (Agilent Technologies).

Genomic DNA was removed from RNA samples using TURBO DNase (Ambion) followed by a second DNase treatment with DNase I (Roche). DNase I treatment was repeated until DNA was not detected by genome-specific PCRs targeting four housekeeping genes (trpB, thrB, purN and mdh) (Additional file 1: Table S5). The RNA quality after DNase treatments was checked using Agilent RNA Nano Chips.

For RNA-seq, total RNA was reverse transcribed using SuperScript III reverse transcriptase (Invitrogen). Actinomycin D (6 μg/ml, Sigma) was added to the reaction to avoid spurious second-strand cDNA synthesis [108]. cDNA was purified using QIAquick PCR purification kit (Qiagen) and used for single stranded cDNA library construction as previously [109, 110]. FRT-seq Illumina libraries were constructed as previously described [111].

Reference genome, sequencing, read mapping and statistic analysis

The genome of S. flexneri M90T was sequenced at Wellcome Trust Sanger Institute using an Illumina HiSeq 2000 sequencer. A total of 0.7 Gb sequence data, in 75-bp paired reads, was obtained (acc. no. ERS033387) and assembled de novo using Velvet [112]. This assembled sequence, which is rich in IS1 elements and for which no attempt of gap closure was performed, is comprised of 501 contigs with a total size of 4.43 Mb. A M90T draft annotated genome was prepared and the annotation transferred from S. flexneri strain 8401 (acc. no. CP000266). Rfam searches were performed and the features identified were included in the annotation as well as Shigella published sRNAs [69, 70]. This draft genome was used as reference for the mapping of RNA-seq reads [113]. During the course of our study the S. flexneri M90T genome was published [114]. Therefore, final expression results are given using this latter locus tag systematic names for coding sequences.

RNA Sequencing was performed using an Illumina HiSeq 2000 sequencer. Raw data as well as mapped reads obtained per replicate were averaged per sample/condition and summarized, together with other interesting quality control parameters, in Additional file 1: Table S3. Processing of reads after mapping included the unmarking of duplicate reads followed by correction to allow for directional fidelity of the data [115]. Output files included per sample, a matrix of readcounts and RPKM values on both sense and antisense strands for genes as well as for automatic 50 bp+/- trimmed intergenic features created in the + strand. The R package DESeq, which implements negative binomial distribution statistics for RNA-seq data was used for statistical analysis [116]. A logarithmic transformed version of the count data (log(x + 1)) was used to avoid zero count values [117]. A p adjust value <0.05, which controls false discovery rate, was used for the cut-off calling of differential expression between conditions. Independent runs of analysis were carried out for sense and antisense directions. Ribosomal genes and repeated sequences, such as transposases or insertion sequences, were filtered out from final tables.

Strand-specific quantitative RT-PCR and Northern blot

A StepOnePlus Real Time PCR system (Applied Biosystems) was used to monitor real-time quantitative PCR. First-strand cDNA was synthesized as previously described but using genome specific primers carrying a tag sequence in the 5′-end instead of random primers. This tag sequence was unique and not found in the genome of S. flexneri M90T. Subsequent PCRs were performed using Power SYBR Green PCR Master Mix (Applied Biosystems) and the tag sequence as one of the paired primers (See Additional file 1: Table S5). As a result, only cDNAs synthesized with a 5′-end tagged primer were amplified. Results are the average of triplicate experiments performed, on at least four independent occasions. Data were expressed relative to polA mRNA levels. To monitor the specificity, final PCR products were analyzed by melting curves. Only samples with no amplification in the control aliquots (not subjected to reverse transcription) were included in the study. The amount of transcripts was expressed as the n-fold difference relative to the control gene (2Ct where ΔCt represents the difference in threshold cycles between the target and control genes). Results were shown in relation to wild type 2-ΔCt levels under aerobic conditions, which were referred as 1. Thus, values greater than 1 indicate increased transcription in relation to the wild-type under aerobic conditions, and lower than 1 indicate the opposite. Significant differences were detected with Mann–Whitney test; values with P <0.05 were considered as significant.

Northern blots were performed as previously described [118]. Radiolabeled RNA probes synthesized with the MAXIscript kit (Ambion) were used to detect specifically the sense of the RNA-targets. The primers used for probes synthesis are listed in Additional file 1: Table S5.

Availability of supporting data

RNA-seq data has been submitted to the European Nucleotide Archive with accession code ERP003817 and the experiment has an ArrayExpress acc. no. E-ERAD-204.

Declarations

Acknowledgements

We thank Jeffrey A. Cole for his advice and suggestions, Iñigo Lasa for pKO3blue plasmid, David Harris and Nathalie Smerdon for data and ArrayExpress submissions; Lira Mamanova for the FRT-seq Illumina library construction; Lesley A. H. Bowman, Malene Cohen, Haifang Zhang and Nuria Vergara for their thoughtful reading of the manuscript and members of Tang group for their help.

M. Vergara-Irigaray was funded by FP7 Marie Curie EIMID-IAPP-217768 grant, and Stopenterics EU grant no. 261472. M. Fookes and N. Thomson are supported by Wellcome Trust grant 098051.

Authors’ Affiliations

(1)
Sir William Dunn School of Pathology, Oxford University
(2)
Centre for Molecular Microbiology and Infection, Imperial College London
(3)
The Wellcome Trust Sanger Institute

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