Open Access

Bifidobacterium thermophilum RBL67 impacts on growth and virulence gene expression of Salmonella enterica subsp. enterica serovar Typhimurium

  • Sabine A. Tanner1,
  • Christophe Chassard1, 2,
  • Eugenia Rigozzi1,
  • Christophe Lacroix1 and
  • Marc J. A. Stevens1Email author
BMC MicrobiologyBMC series – open, inclusive and trusted201616:46

https://doi.org/10.1186/s12866-016-0659-x

Received: 30 July 2015

Accepted: 2 March 2016

Published: 18 March 2016

Abstract

Background

Bifidobacterium thermophilum RBL67 (RBL67), a human fecal isolate and health promoting candidate shows antagonistic and protective effects against Salmonella and Listeria spec. in vitro. However, the underlying mechanisms fostering these effects remain unknown. In this study, the interactions of RBL67 and Salmonella enterica subsp. enterica serovar Typhimurium N-15 (N-15) were explored by global transcriptional analysis.

Results

Growth experiments were performed in a complex nutritive medium with controlled pH of 6.0 and suitable for balanced growth of both RBL67 and N-15. RBL67 growth was slightly enhanced in presence of N-15. Conversely, N-15 showed reduced growth in the presence of RBL67. Transcriptional analyses revealed higher expression of stress genes and amino acid related function in RBL67 in co-culture with N-15 when compared to mono-culture. Repression of the PhoP regulator was observed in N-15 in presence of RBL67. Further, RBL67 activated virulence genes located on the Salmonella pathogenicity islands 1 and 2. Flagellar genes, however, were repressed by RBL67. Sequential expression of flagellar, SPI 1 and fimbrial genes is essential for Salmonella infection. Our data revealed that RBL67 triggers expression of SPI 1 and fimbrial determinants prematurely, potentially leading to redundant energy expenditure. In the competitive environment of the gut such energy expenditure could lead to enhanced clearing of Salmonella.

Conclusion

Our study provides first insights into probiotic-pathogen interactions on global transcriptional level and suggests that deregulation of virulence gene expression might be an additional protective mechanism of probiotica against infections of the host.

Keywords

Bifidobacterium thermophilum Salmonella Typhimurium Co-culture RNA-seq Probiotic Anti-microbial Virulence

Background

Probiotics are live organism that, when administered in adequate amounts, confer a health benefit on the host [1]. They exert their beneficial effect via a wide array of mechanisms including direct and indirect antagonism with enteropathogens, improvement of the intestinal barrier function and activation of the mucosal immune system [2, 3]. Direct antagonism with enteropathogens is mediated via production of antimicrobial compounds such as organic acids and bacteriocins, competition for nutrients and minerals, and occupation of adhesion sites [2]. Bifidobacteria and lactobacilli are important constituents of the human gut microbiota and have been associated with a good health status of the host [3, 4]. They are the two major genera used for probiotic applications and have a long history of safe use. Specific strains from bifidobacteria and lactobacilli have been shown to protect against pathogens, with strain specific effects [4].

Bifidobacterium thermophilum is a relatively oxygen tolerant Bifidobacterium species that has been isolated from bovine rumen, sewage, and from piglet, calf and baby feces [5, 6]. Peptidoglycans from B. thermophilum strain P2-91 protect mice against Escherichia coli infections and improve cytotoxic activity of mice lymphocytes [7, 8]. Furthermore, chicken were more resistant to E. coli infection after oral administration of B. thermophilum [9]. The infant feces isolate B. thermophilum RBL67 (RBL67) is a promising probiotic candidate which genome was sequenced [10]. The strain can grow under low oxygen, at pHs as low as 4.0 and at temperatures up to 47 °C. Further, it can reach high cell yield numbers in fermentation which makes it suitable to be applied in industrial fermentations [6, 1114]. Furthermore, RBL67 decreases S. Typhimurium counts in an in vitro fermentation model of the gastrointestinal tract [14], reduces severity of rotavirus-associated diarrhea in suckling mice [15], and blocks invasion of S. Typhimurium and L. monocytogenes to human intestinal cell lines [13, 16]. However, the underlying mechanisms of RBL67-Salmonella interaction are not elucidated yet.

Salmonella species are a major cause of food-borne diseases with an estimated world-wide annual infection rate of 93.8 million cases and 155,000 deaths [17]. Salmonella usually infect humans after ingestion of contaminated food products [18, 19]. Salmonella enterica subsp. enterica serovar Typhimurium (S. Typhimurium) is a Salmonella serotype frequently encountered in clinical cases [18]. Its pathogenesis depends on multiple factors including motility and chemotaxis, adhesion, invasion and persistence. The majority of relevant virulence determinants are located on Salmonella pathogenicity islands (SPIs) and are regulated by a complex molecular network that transmits environmental signals of conditions prevailing in the host [18]. Salmonella invasion is dependent on the gut environment and is enhanced by low oxygen tension, high osmolarity, neutral pH and acetate, whereas cationic peptides, bile, propionate and butyrate suppress invasion [18, 20]. One of the key regulators for Salmonella invasion is HilA [21]. HilA expression is affected by environmental signals and enables Salmonella to express different invasive phenotypes under different conditions [18, 22, 23]. Modulation of the gut environment via pre- and/or probiotic treatments may alter the gene expression of pathogens like Salmonella, either indirectly via production of organic acids or directly via microbe-microbe interactions [2]. Indeed, probiotic strains were reported to modulate the transcriptional response of Salmonella. PhoP, a postulated repressor of hilA expression was activated and HilA was repressed during growth in the presence of supernatant of Lactobacillus rhamnosus GG [23]. However, information about modulation of gene expression in enteropathogens due to direct microbe-microbe interaction is still scarce and unraveling the transcriptomic response of these multifactorial interactions is challenging.

RNA-sequencing (RNA-seq) is a powerful tool to determine the transcriptional response of an organism in a complex culture because interference of signals from other organisms is limited [24]. In this study we investigated the potential of B. thermophilum RBL67 to modulate the transcriptome of S. Typhimurium N-15. The response of RBL67 and Salmonella Typhimurium N-15 in the co-culture was compared to mono-cultures using RNA-seq in attempt to provide insight in the protective mechanism of RBL67 against Salmonella infections.

Methods

Bacterial strains

Salmonella Typhimurium N-15 was isolated from a clinical case in Switzerland in 2007 and obtained from the National Reference Centre for Enteropathogenic Bacteria and Listeria (NENT; Zurich, Switzerland). Bifidobacterium thermophilum RBL67 (=LMG S-23614), originally isolated from infant feces [6], was obtained from our own culture collection.

Batch fermentation conditions

Two sets of fermentations were performed, each set consisting of six fermentations. The first set was composed of three RBL67 mono-cultures and three RBL67-N-15 co-cultures. The second set consisted of three N-15 mono-cultures and another set of three RBL67-N-15co-cultures. The first set of three co-cultures was used for sampling RBL67-RNA at t = 5 h and the second for sampling N15-RNA at t = 4 h. Bacteria were cultured in 350 mL scale Sixfors bioreactors (Infors AG, Bottmingen, Switzerland) using 310 mL YCFA medium [25] supplemented with 6 g/L glucose (Sigma-Aldrich Chemie GmbH, Buchs, Switzerland). Fermentations were performed at 38 °C with stirring at 200 rpm for 24 h. A constant pH of 6.0 was maintained by automated addition of 2.5 M NaOH. Anaerobic conditions were ensured by purging the headspace with CO2. Fermentations were inoculated with 4 % (v/v) of a 16 h grown pre-culture. Pre-cultures were prepared by propagating strains twice in 10 mL YCFA medium in Hungate tubes to adapt the strains to the medium and anaerobic conditions. The pre-cultures were centrifuged (6000 × g, 5 min), washed in 0.1 % peptone water reduced with 0.05 % L-cysteine hydrochloride (VWR International AG, Dietikon, Switzerland) and resuspended in 2 mL peptone water before inoculation to the fermenter.

Growth was monitored by optical density measurements at 600 nm (OD600) using a Biochrom WPA CO8000 cell density meter (Biochrom, Cambridge, United Kingdom). Samples were taken hourly until the stationary growth phase was reached, with a final sample taken after 24 h. Metabolite and sugar concentrations were determined by HPLC analysis (Thermo Fisher Scientific, Wohlen, Switzerland) as described previously [26]. Carbon balance as % of carbons recovered was calculated on the basis of consumed glucose and produced organic acids. Viable cell counts of RBL67 were determined by plating appropriate dilutions on MRS agar (Biolife, Milan, Italy), supplemented with 0.05 % L-cysteine hydrochloride (MRS-C). Viable cell counts of N-15 were determined on MacConkey Agar No. 2 (Oxoid AG, Pratteln, Switzerland). Co-culture effluent samples were plated on MRS-C agar supplemented with 5 g L−1 mupirocin (VWR International AG, Dietikon, Switzerland) to select for RBL67 [27], and on MacConkey Agar No. 2 to select for N-15. MRS plates were incubated anaerobically using anaerobic gas pack systems (AnaeroGen TM, Oxoid AG) at 37 °C for 48 h. MacConkey Agar plates were incubated aerobically at 37 °C for 24 h.

Maximum specific growth rates were calculated for each replication separately (N = 3) from the slope of the curve of the log cell counts versus time during the exponential growth phase.

Sampling for RNA extraction

RBL67 and N-15 mono- and co-culture samples were subjected to different procedures to allow optimal RNA extraction of both RBL67 and N-15.

Mono- and co-culture samples of N-15 cultures (20 mL each) were directly transferred to 20 mL 60 % glycerol (Sigma-Aldrich Chemie GmbH, Buchs, Switzerland) at −40 °C, kept on ice for 20 min and centrifuged for 15 min (3220 × g, 4 °C). The supernatant was discarded and the resulting pellets were immediately frozen at −80 °C until RNA extraction. Mono- and co-culture samples of RBL67 cultures were shortly centrifuged (10,000 × g, 20 s). The RBL67 mono-culture pellets were resuspended in 400 μl MRS-C and transferred to a pre-chilled screw cap tube, containing 500 mg glass beads (0.1 mm; Biospec Products Inc., Bartlesville, USA), 500 μl chloroform/phenol (1:1, v/v), 30 μl 3 M Na-acetate (pH 5.2) and 30 μl SDS 10 % [28]. The pellets of the RBL67 co-culture were resuspended in 12 mL of RNAprotect® Bacteria Reagent (Qiagen AG, Basel, Switzerland), incubated for 5 min at room temperature and centrifuged again (10,000 × g, 20 s). Both samples were then rapidly frozen in liquid nitrogen and stored at −80 °C until RNA extraction.

RNA-extraction and ribosomal RNA depletion

Frozen pellets from N-15 samples were resuspended in 200 μl 10 mM Tris-buffer (pH 8.0). Total RNA was extracted using the High Pure RNA isolation kit (Roche Diagnostics, Rotkreuz, Switzerland), according to the manufacturer’s instructions. Total RNA of RBL67 mono- and co-culture samples was extracted using a phenol/chloroform extraction method [28], followed by a purification using the High Pure RNA isolation kit (Roche Diagnostics). Prior to RNA extraction the sample from the RBL67 co-culture was resuspended in MRS-C medium and transferred to a pre-chilled mix of 500 mg glass beads (Biospec Products Inc.) and TRI Reagent® (Life Technologies Europe BV, Zug, Switzerland).

RNA quantity and purity was determined on a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Washington, USA) and RNA integrity was tested with an Agilent 2100 Bioanalyzer (Agilent, Basel, Switzerland). RBL67 samples with a RNA integrity number (RIN) ≥ 9.5 and a 16S/23S-rRNA ratio ≥1.6 were used for ribosomal RNA depletion and subsequent RNA-sequencing. Due to the aberrant nature of ribosomal RNA of S. Typhimurium [29], the RIN value and the 16S/23S-rRNA ratio could not be calculated for N-15. Hence we selected samples which were comparable to the profiles reported previously for Salmonella [30], i.e. a straight zero line (indicating no RNA degradation), absence of 23S RNA and appearance of two additional peaks neighboring the 16S peak.

Depletion of ribosomal RNA from 10 μg total RNA was performed using the MICROBExpress™ Bacterial mRNA Enrichment Kit (Life Technologies Europe BV, Zug, Switzerland) according to the manufacturer’s instructions. Additionally, EDTA (1 mM) was added to chelate divalent cations present in the RNA solution.

RNA-sequencing

RNA-sequencing was performed on an Illumina HiSeq 2000 sequencer (Illumina Inc., California, USA) at the Functional Genomics Center Zurich (FGCZ). Libraries were prepared using the TruSeq Stranded mRNA Sample Prep Kit (Illumina) according to the manufacturer’s protocol. The libraries were qualitatively and quantitatively checked using a Qubit® (1.0) Fluorometer (Life Technologies Europe BV, Zug, Switzerland) and a Bioanalyzer 2100 (Agilent, Basel, Switzerland) and were subsequently normalized at 10 nM in Tris-Cl (10 mM, pH 8.5) containing 0.1 % Tween20. Cluster generation was performed using the TruSeq SR Cluster Kit v3-cBot-HS (Illumina) using 8 pM of pooled normalized libraries on the cBOT and stranded sequencing of 100 bp was done using the TruSeq SBS Kit v3-HS (Illumina). Each set of samples (N = 6) was analyzed in a separate sequencing lane.

RNA-Seq data analysis

Illumina raw data reads (100 bp) were separated by barcode and mapped against the genome of RBL67 (GenBank accession no. CP004346) or Salmonella Typhimurium LT2 (GenBank accession no. AE006468) using the CLC Genomics Workbench 6.5.1 (CLCbio, Aarhus, Denmark) applying the default settings. Maximum allowance of mismatches was set at 2, minimum length fraction at 0.9 and minimum similarity fraction at 0.8.

Statistical analysis for differential gene expression of the mono- and co-cultures was done with the statistical software R (http://www.R-project.org) using the GLM method [31] included in the Bioconductor EdgeR software package [3234] based on negative binomial distribution. Genes with low read numbers (sum of reads in all samples <3 counts per million (cpm)) or with high read numbers (number of reads >50,000 cpm in each sample) were filtered out before data normalization. A false discovery rate (FDR) value <0.05 and a differential expression of at least 2 fold (1 < log2 ratio < −1) was used as cut off for significant differentially expressed genes in mono-culture and co-culture [35]. Proteins of RBL67 and LT2 were assigned to gene ontology categories (GO) using Blast2GO at standard settings [36]. GO categories enrichment analyses were performed and visualized using the BiNGO plugin [37] in Cytoscape (v.3.0.1, [38]) applying the hypergeometric test with Benjamini and Hochberg false discovery rate correction option. The significance cutoff for overrepresented gene ontology categories was a corrected p-value of <0.05.

Virulence factors of Salmonella LT2 were identified by genome wide blast against the virulence factor database (VFDB) [39], using a cut off E-value of 1−20. Significant enrichment of virulence factors was calculated using the Fisher’s Exact Test Calculator for 2 × 2 Contingency at www.research.microsoft.com/en-us/um/redmond/projects/mscompbio/fisherexacttest/.

The RNAseq data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus [40] and are accessible through GEO Series accession number GSE65716 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65716).

Statistical analysis

Statistical analysis for cell counts (log10 transformation) and growth rates were performed using JMP 10.0 (SAS Institute., Cary, NC). Cell counts and maximum specific growth rates of mono-and co-cultures were tested for significant differences using the non-parametric Kruskal-Wallis (P-value <0.05).

Results

Growth characteristics of RBL67 in mono- and co-culture with N-15

To analyze interactions between B. thermophilum RBL67 and Salmonella N-15, both strains were grown in pH controlled mono- and co-cultures (pH 6.0) and growth characteristics were compared. The maximum specific growth rate of RBL67 in mono-culture (μmax = 0.26 ± 0.05 h−1) was significantly lower compared to that in co-culture (μmax = 0.33 ± 0.01 h−1). The stationary growth phase was reached after approximately 8 h in both cultures, with final cell counts of 8.88 ± 0.10 log10 cfu mL−1 and 9.12 ± 0.14 log10 cfu mL−1 in the mono- and co-culture, respectively (Fig. 1a).
Fig. 1

Cell counts and metabolic profiles of RBL67 in mono-culture and in co-cultures with N-15. a Cell counts in mono- (open symbols) and co-culture (closed symbols). b Metabolite concentration in mono- (open symbols) and co-culture (closed symbols). Means ± SD from three biological replicates are presented. *Cell counts significantly different between mono- and co-culture with the non-parametric Kruskal-Wallis Test (P < 0.05); square: glucose; circle: acetate; triangle: lactate and diamonds: formate

Glucose consumption and metabolite profiles were similar for RBL67 in mono- and co-culture (Fig. 1b). In both cultures, glucose was depleted after 8 h which corresponded to the onset of the stationary growth phase, indicating growth limitation by the carbon source. The main metabolites produced in mono-cultures were 50 ± 3 mM acetate, 15 ± 1 mM lactate, and 9 ± 0.3 mM formate after 8 h, corresponding to a calculated carbon recovery of 103 %. A slightly lower acetate concentration was observed in co-culture: 45 ± 2 mM acetate. Further, 16 ± 2 mM lactate and 7 ± 2 mM formate were produced after 8 h, corresponding to a carbon recovery of 100 %.

Taken together, RBL67 growth was slightly enhanced in the co-culture with Salmonella compared to mono-culture and only small differences in organic acid production were observed.

Global transcriptional response of RBL67 to co-culture with N-15

To elucidate the response of RBL67 to N-15 on a global level, the transcriptome profiles of B. thermophilum RBL67 grown in mono- and in co-culture were compared. Samples were taken after 5 h of growth, a time point at which RBL67 displayed exponential growth in both cultures (Fig. 1). Viable RBL67 cell counts at harvesting point were 8.07 ± 0.07 and 8.53 ± 0.04 log10 cfu mL−1, in the mono- and in co-culture, respectively. RNA sequencing of RBL67 cultures resulted in a mean read number of 37,365,651 and 31,752,403 for mono- and co-cultures, respectively. Thereof, 93 % of the reads deriving from the mono-cultures and 79 % of the reads from the co-cultures could be mapped onto the RBL67 genome. Differential gene expression analysis revealed 57 genes being significantly differentially expressed in mono- compared to co-cultures (Tables 1 and 2). An operon involved in lipid export (D805_0155-D805_0157), sugar transport (D805_1600-D805_1602), and an operon of undefined function (D805_1659-D805_1660), together with its putative regulator of the HxlR family (D805_1658) were higher expressed in co- culture (Table 1). Further, a stress response was triggered in co-cultures as revealed by higher expression of the heat shock protein regulator HspR (D805_1678), the SOS-response repressor and protease LexA (D805_0599) and the protease ClpB (D805_1594). The latter gene harbors a HspR-associated inverted repeat (HAIR) in its upstream region and is therefore likely activated by HspR. Additional functions of RBL67 genes higher expressed in co-cultures with Salmonella N-15 were related to metal transport (D805_1209) and amino acid metabolism (D805_1238 and D805_1530), including a glutamate-5-kinase (D805_1238) which catalyzes the first step for proline biosynthesis from glutamate.
Table 1

Bifidobacterium thermophilum RBL67 genes higher expressed in co-culture with N-15 compared to mono-culture

Locus tag

Function

logFCa

logCPMb

FDRc

D805_0058

Oligopeptide transport ATP-binding protein OppF (TC 3.A.1.5.1)

−1.52

4.09

4E-06

D805_0077

hypothetical protein

−1.17

6.36

4.8E-07

D805_0155

Transcriptional regulator, MarR family

−1.95

6.80

1.2E-17

D805_0156

hypothetical protein

−1.50

3.64

0.00401

D805_0157

Lipid A export ATP-binding/permease protein MsbA

−1.17

8.10

6.2E-06

D805_0382

hypothetical protein

−1.13

7.94

1.4E-06

D805_0466

FIG 00672402: hypothetical protein

−1.00

7.21

9.5E-08

D805_0503

possible conserved integral membrane protein

−1.14

3.08

0.01355

D805_0599

SOS-response repressor and protease LexA (EC 3.4.21.88)

−1.24

6.59

7.9E-09

D805_0600

hypothetical protein

−2.14

6.18

2.6E-27

D805_0707

Inner membrane protein

−1.14

6.16

3.4E-06

D805_1209

Zinc ABC transporter, periplasmic-binding protein ZnuA

−1.15

4.69

1.3E-05

D805_1238

Cystathionine beta-synthase (EC 4.2.1.22)

−1.23

6.23

2.7E-09

D805_1392

putative aminotransferase

−1.23

4.66

1.2E-05

D805_1393

hypothetical protein

−1.03

4.30

0.00118

D805_1530

Glutamate 5-kinase (EC 2.7.2.11)

−1.08

8.00

3.9E-09

D805_1531

COG0536: GTP-binding protein Obg

−1.03

9.16

2.5E-06

D805_1591

DNA recombination protein RmuC

−1.27

7.58

3.6E-12

D805_1594

ClpB protein

−1.06

8.68

2.1E-06

D805_1600

Maltodextrin glucosidase (EC 3.2.1.20)

−1.17

6.23

5.6E-08

D805_1601

ABC-type sugar transport system, permease component

−1.85

4.02

3.9E-09

D805_1602

MSM (multiple sugar metabolism) operon regulatory protein

−1.68

3.28

1.1E-05

D805_1621

Sortase A, LPXTG specific

−1.09

3.52

0.00557

D805_1622

hypothetical protein

−1.44

4.42

5.6E-08

D805_1637

COG family: predicted phosphohydrolases

−1.63

6.91

1.8E-21

D805_1658

Transcriptional regulator, HxlR family

−1.07

4.37

0.00624

D805_1659

Rrf2-linked NADH-flavin reductase

−2.01

5.23

2.9E-14

D805_1660

COG2110, Macro domain, possibly ADP-ribose binding module

−1.83

4.90

2.4E-16

D805_1678

HspR, transcriptional repressor of DnaK operon

−1.08

5.37

4.9E-08

D805_1702

transport protein

−1.80

6.58

3.5E-23

a logFC log2 fold change, b logCPM log2 counts per million, c FDR false discovery rate

Table 2

Bifidobacterium thermophilum RBL67 genes higher expressed in mono-culture compared to co-culture with N-15

Locus tag

Function

logFCa

logCPMb

FDRc

D805_0063

FIG 00519111: hypothetical protein

1.76

5.67

1.8E-12

D805_0064

HTH domain protein

2.18

3.98

4.1E-09

D805_0075

hypothetical protein

1.04

5.15

0.00011

D805_0178

Ribonucleotide reductase of class Ib (aerobic), alpha subunit (EC 1.17.4.1)

1.21

7.11

0.04089

D805_0341

Transcriptional regulator, GntR family domain/Aspartate aminotransferase (EC 2.6.1.1)

1.93

3.72

0.02045

D805_0345

Manganese transport protein MntH

1.84

3.71

0.04055

D805_0351

Glycosyl transferase, group 2 family protein

1.49

4.48

0.00041

D805_0352

Glycosyltransferase (EC:2.4.1.-)

1.74

4.96

1.3E-07

D805_0354

glycosyl transferase, group 1 family protein

2.07

4.79

3.0E-10

D805_0355

hypothetical protein

1.34

6.14

4.5E-06

D805_0356

Glycosyltransferase (EC 2.4.1.-)

2.14

4.96

1.5E-15

D805_0512

hypothetical protein

2.01

2.39

0.03026

D805_0524

D-lactate dehydrogenase (EC 1.1.1.28)

2.85

3.15

0.00049

D805_0525

Aspartate aminotransferase (EC 2.6.1.1)

1.91

3.11

0.01352

D805_0652

Oligopeptide transport system permease protein OppC (EC 3.A.1.5.1)

2.25

2.47

0.00227

D805_0656

hypothetical protein

1.57

3.01

0.01267

D805_0665

hypothetical protein

1.18

5.85

2.0E-05

D805_0693

Acetyltransferase, GNAT family

2.23

6.96

2.7E-31

D805_0694

hypothetical protein

2.80

3.46

1.5E-14

D805_0698

hypothetical protein

1.19

4.02

0.00369

D805_0837

putative TraA-like conjugal transfer protein

2.75

3.13

0.00038

D805_0885

Ferric iron ABC transporter, iron-binding protein

1.63

2.52

0.00624

D805_0928

hypothetical protein

1.90

2.59

0.00374

D805_0948

hypothetical protein

2.08

2.96

0.00118

D805_1220

hypothetical protein

2.60

3.03

0.00015

D805_1313

Methionine ABC transporter permease protein

1.52

2.03

0.04091

D805_1771

hypothetical protein

1.01

4.56

0.01571

a logFC log2 fold change, b logCPM log2 counts per million, c FDR false discovery rate

Twenty-seven genes were down regulated in co-cultures compared to monoculture, of which 12 were classified as hypothetical proteins (Table 2). A putative operon encoding glycosyltransferases (ORF D805_0351-D805_0356), three genes involved in amino acid metabolism (D805_0341, D805_0525 and D805_1313), including the glutamate producing enzyme aspartate aminotransferase (EC 2.6.1.1) and two metal transporters (D805_0345 and D805_0885) were higher expressed.

Mapping the co-culture reads to the Salmonella Typhimurium LT2 genome resulted in less than 5 million reads mapped (data not shown), indicating that the majority of the RNA isolated form the co-culture after 5 h consisted of bifidobacterial RNA.

Growth characteristics of N-15 in mono- and co-culture with RBL67

In a next step Salmonella N-15 was grown in a mono- and co-culture with RBL67, the latter being a repetition of the co-culture presented above. Salmonella N-15 had similar maximum specific growth rates of μmax = 0.39 ± 0.02 h−1 and 0.38 ± 0.04 h−1 in mono- and co-culture, respectively. In the late exponential phase after approximately 5 h of fermentation, the growth rate in the co-culture was smaller compared to mono-culture (Fig. 2a). The difference in growth resulted in a higher Salmonella cell count of 9.10 ± 0.16 log10 cfu mL−1 in the mono-culture compared to 8.82 ± 0.08 log10 cfu mL−1 in the co-culture. Glucose was depleted after 10 and 8 h for mono- and co-cultures, respectively (Fig. 2b). The main metabolites produced by N-15 in mono-culture were 27 ± 0.4 mM acetate, 23 ± 2 mM lactate and 12 ± 2 mM formate, corresponding to a calculated carbon recovery of 93 %. The metabolite production in the co-culture was: 42 ± 4 mM acetate, 17 ± 2 mM lactate and 8 ± 3 mM formate. These values are virtually the same to those from the first co-culture experiments (Fig. 1b). The calculated carbon mass balance in the co-culture was 99 %.
Fig. 2

Cell counts and metabolic profiles of N-15 in mono-culture and in co-cultures with RBL67. a Cell counts in mono- (open symbols) and co-culture (closed symbols). b Metabolite concentration in mono- (open symbols) and co-culture (closed symbols). Means ± SD from three biological replicates are presented. *Cell counts significantly different between mono- and co-culture with the non-parametric Kruskal-Wallis Test (P < 0.05); square: glucose; circle: acetate; triangle: lactate and diamonds: formate

Salmonella reached slightly lower cell numbers in the co-culture with RBL67 compared to its mono-culture, but was further not affected by the presence of RBL67 concerning growth speed.

Global transcriptional response of N-15 to co-culture with RBL67

Because RNA-seq analyses of the co-culture after 5 h growth resulted in low read mapping the transcriptome of N-15 in mono-a-culture and in co-culture with RBL67 was analyzed after 4 h growth. This time-point corresponds to cell counts of 8.42 ± 0.12 and 8.02 ± 0.06 log10 cfu mL−1 for mono- and co-cultures, respectively. Acetate concentrations at sampling point was 17.8 ± 2.2 mM in the co-culture, slightly higher than the 12.5 ± 1.1 mM in the mono-culture Moreover, at this point Salmonella is growing exponentially and at comparable speed in both cultures (Fig. 2b). From the total mean read numbers of 38,838,013 (mono-culture) and 30,020,491 (co-culture), 91 and 52 % could be mapped onto the genome and plasmid of the sequenced strain Salmonella Typhimurium LT2, respectively. In total 701 genes were higher expressed in mono- culture and 1278 genes in the co-culture (Additional file 1: Tables S1 and S2).

GO category enrichment analysis revealed 88 categories significantly overrepresented in co-culture of which 47 belonged to the cluster “biological processes”, 29 to “molecular function” and 29 to “cellular component”. Within the cluster biological processes the categories “localization” (GO:051179), “establishment of localization” (GO:051234) and “transport” (GO:006810) were significantly overrepresented (Table 3). Detailed analysis of these categories revealed that they each contained the same 281 genes. At a lower hierarchical level, the category “protein secretion by the type III secretion system” was highly overrepresented (GO:030254, N = 49 genes). Further, “siderophore transport” and “carbohydrate transport systems”, including “PEP-dependent sugar phosphotransferase systems” (GO:009401, N = 33) were overrepresented in co-culture. Other categories overrepresented in biological processes included “multi-organism process” (GO:051704, N = 47), “pathogenesis” (GO:009405, N = 26) and “interspecies interaction between organisms” (GO:044419, N = 35). The majority of the genes (N = 26) in the latter category were also found in GO:052049: “interaction with host via protein secreted by type III secretion system”. The 26 genes assigned to this category were also present in already mentioned GO:030254: “type III secretion system” category.
Table 3

Gene Ontology (GO) categories of the Salmonella Typhimurium N-15 transcriptome significantly overrepresented in the co-culture with RBL67 compared to mono-culture

GO category

p-value

Ngenes in category

Description of category

Biological process

 GO:051234

4.77E-28

281

establishment of localization

 GO:006810

4.77E-28

281

transport

 GO:051179

6.29E-25

281

localization

 GO:030254

8.34E-15

42

protein secretion by the type III secretion system

 GO:051704

1.80E-14

47

multi-organism process

 GO:051701

3.20E-12

35

interaction with host

 GO:044419

3.20E-12

35

interspecies interaction between organisms

 GO:044403

3.20E-12

35

symbiosis, encompassing mutualism through parasitism

 GO:008643

8.15E-11

52

carbohydrate transport

 GO:046903

4.51E-10

49

secretion

 GO:032940

4.51E-10

49

secretion by cell

 GO:009306

4.51E-10

49

protein secretion

 GO:052047

5.68E-10

26

interaction with other organism via secreted substance involved in symbiotic interaction

 GO:052049

5.68E-10

26

interaction with host via protein secreted by type III secretion system

 GO:052048

5.68E-10

26

interaction with host via secreted substance involved in symbiotic interaction

 GO:052210

5.68E-10

26

interaction with other organism via protein secreted by type III secretion system involved in symbiotic interaction

 GO:044046

5.68E-10

26

interaction with host via substance released outside of symbiont

 GO:051649

4.63E-09

49

establishment of localization in cell

 GO:051641

8.25E-09

49

cellular localization

 GO:009405

9.76E-08

26

pathogenesis

 GO:015031

2.55E-06

49

protein transport

 GO:045184

2.55E-06

49

establishment of protein localization

 GO:033036

2.96E-06

50

macromolecule localization

 GO:008104

3.67E-06

49

protein localization

 GO:009401

8.19E-06

33

phosphoenolpyruvate-dependent sugar phosphotransferase system

 GO:007047

2.10E-04

12

cellular cell wall organization

 GO:045229

2.10E-04

12

external encapsulating structure organization

 GO:071555

7.60E-04

12

cell wall organization

 GO:009242

2.19E-03

7

colanic acid biosynthetic process

 GO:052126

2.19E-03

7

movement in host environment

 GO:052192

2.19E-03

7

movement in environment of other organism involved in symbiotic interaction

 GO:044409

2.19E-03

7

entry into host

 GO:046377

2.19E-03

7

colanic acid metabolic process

 GO:051828

2.19E-03

7

entry into other organism involved in symbiotic interaction

 GO:022610

3.83E-03

17

biological adhesion

 GO:007155

3.83E-03

17

cell adhesion

 GO:030001

6.91E-03

33

metal ion transport

 GO:006814

8.26E-03

16

sodium ion transport

 GO:009235

1.23E-02

14

cobalamin metabolic process

 GO:009236

1.23E-02

14

cobalamin biosynthetic process

 GO:015891

2.34E-02

5

siderophore transport

 GO:019184

2.34E-02

5

nonribosomal peptide biosynthetic process

 GO:006811

2.34E-02

47

ion transport

 GO:015672

2.61E-02

27

monovalent inorganic cation transport

 GO:006812

3.45E-02

37

cation transport

 GO:006778

4.86E-02

16

porphyrin metabolic process

 GO:006779

4.86E-02

16

porphyrin biosynthetic process

Molecular function

 GO:005215

4.79E-20

215

transporter activity

 GO:015144

1.57E-09

39

carbohydrate transmembrane transporter activity

 GO:022892

1.31E-08

111

substrate-specific transporter activity

 GO:022891

9.42E-08

99

substrate-specific transmembrane transporter activity

 GO:051119

9.81E-08

34

sugar transmembrane transporter activity

 GO:022857

1.61E-07

107

transmembrane transporter activity

 GO:008324

1.82E-07

64

cation transmembrane transporter activity

 GO:005402

1.79E-06

29

cation:sugar symporter activity

 GO:015075

5.82E-06

70

ion transmembrane transporter activity

 GO:015291

6.89E-06

44

secondary active transmembrane transporter activity

 GO:015294

6.89E-06

34

solute:cation symporter activity

 GO:015293

6.89E-06

34

symporter activity

 GO:015295

6.89E-06

27

solute:hydrogen symporter activity

 GO:005351

6.89E-06

27

sugar:hydrogen symporter activity

 GO:022804

5.84E-05

71

active transmembrane transporter activity

 GO:015082

6.07E-03

13

di-, tri-valent inorganic cation transmembrane transporter activity

 GO:046873

7.20E-03

23

metal ion transmembrane transporter activity

 GO:022890

9.53E-03

28

inorganic cation transmembrane transporter activity

 GO:015149

1.06E-02

8

hexose transmembrane transporter activity

 GO:015145

1.06E-02

8

monosaccharide transmembrane transporter activity

 GO:015343

2.20E-02

5

siderophore-iron transmembrane transporter activity

 GO:042927

2.20E-02

5

siderophore transporter activity

 GO:005381

2.59E-02

8

iron ion transmembrane transporter activity

 GO:046915

2.59E-02

11

transition metal ion transmembrane transporter activity

 GO:042879

2.59E-02

6

aldonate transmembrane transporter activity

 GO:015128

2.59E-02

6

gluconate transmembrane transporter activity

 GO:005506

3.93E-02

16

iron ion binding

 GO:046943

4.82E-02

26

carboxylic acid transmembrane transporter activity

 GO:005342

4.82E-02

26

organic acid transmembrane transporter activity

Cellular component

 GO:016020

6.15E-12

381

membrane

 GO:030257

3.05E-10

26

type III protein secretion system complex

 GO:005886

2.84E-08

314

plasma membrane

 GO:044425

5.28E-06

129

membrane part

 GO:016021

5.28E-06

124

integral to membrane

 GO:031224

5.28E-06

124

intrinsic to membrane

 GO:009279

4.20E-05

47

cell outer membrane

 GO:019867

3.67E-04

47

outer membrane

 GO:043234

4.76E-04

50

protein complex

 GO:009289

9.12E-04

15

pilus

 GO:044462

2.45E-03

104

external encapsulating structure part

 GO:043190

3.32E-02

4

ATP-binding cassette (ABC) transporter complex

In the “molecular function” cluster, “transporter activity” was significantly overrepresented (GO:005215, N = 215), with transmembrane transporters being highly abundant (Table 3) The “cellular component” cluster included membrane-associated functions (GO:016020, N = 381) including again the overrepresented “type III protein secretion system complex” (GO:030257, N = 26) (Table 3). The GO enrichment in the “molecular function” and “cellular component” clusters was similar to that of the “biological processes” cluster.

Summarizing, the transcriptomic analyses of N-15 in co-culture compared to mono-culture revealed responses involved in carbohydrate and metal transport and in extracellular function, mainly secretion of proteins (secretion, cell wall organization, interaction with other organisms).

In the mono-culture, 133 GO categories were significantly enriched of which 101 were assigned to “biological processes”, 6 into “molecular functions” and 26 into “cellular components” (Table 4). Overrepresented categories included “cellular process” in the biological processes cluster (GO:009987; N = 276) and “structural molecule activity” in the molecular function cluster (GO:005198; N = 39). In the cellular components cluster the “intracellular parts” (GO:044424; N = 391), which includes the categories “ribosome” (GO:005840; N = 33) and “flagellum” (GO:019861; N = 19), were overrepresented. GO category 019861 (“flagellum”) includes the flagellar biosynthesis encoding operons flg and fli and cheZ. Overall, GO-categories related to flagella and to cell growth like “cellular processes” and “ribosomes” and were significantly overrepresented in the mono-culture transcriptome of Salmonella N-15.
Table 4

Gene Ontology (GO) categories of Salmonella Typhimurium N-15 transcriptome significantly overrepresented in the mono-culture compared to co-culture with RBL67

GO category

p-value

Ngenes in category

Description of category

Biological process

 GO:044249

1.69E-20

168

cellular biosynthetic process

 GO:009058

2.27E-18

174

biosynthetic process

 GO:010467

1.16E-11

70

gene expression

 GO:009987

2.05E-11

276

cellular process

 GO:044237

1.12E-10

236

cellular metabolic process

 GO:006412

1.07E-09

49

translation

 GO:044238

2.26E-08

227

primary metabolic process

 GO:034645

5.65E-08

74

cellular macromolecule biosynthetic process

 GO:009059

1.43E-07

75

macromolecule biosynthetic process

 GO:044267

2.58E-07

66

cellular protein metabolic process

 GO:006633

1.86E-06

12

fatty acid biosynthetic process

 GO:019538

4.61E-06

82

protein metabolic process

 GO:044260

5.80E-06

118

cellular macromolecule metabolic process

 GO:008299

5.80E-06

12

isoprenoid biosynthetic process

 GO:006720

5.80E-06

12

isoprenoid metabolic process

 GO:008610

7.04E-06

32

lipid biosynthetic process

 GO:008152

1.73E-05

286

metabolic process

 GO:044255

1.80E-05

32

cellular lipid metabolic process

 GO:043170

1.93E-05

138

macromolecule metabolic process

 GO:006629

2.55E-05

34

lipid metabolic process

 GO:006631

4.06E-05

12

fatty acid metabolic process

 GO:048870

4.72E-04

13

cell motility

 GO:051674

4.72E-04

13

localization of cell

 GO:001539

4.72E-04

13

ciliary or flagellar motility

 GO:044283

6.74E-04

62

small molecule biosynthetic process

 GO:006928

8.07E-04

13

cellular component movement

 GO:006350

9.66E-04

9

transcription

 GO:043064

1.53E-03

8

flagellum organization

 GO:009141

2.48E-03

10

nucleoside triphosphate metabolic process

 GO:016070

2.77E-03

30

RNA metabolic process

 GO:009108

3.33E-03

17

coenzyme biosynthetic process

 GO:030030

3.43E-03

8

cell projection organization

 GO:009142

4.02E-03

9

nucleoside triphosphate biosynthetic process

 GO:006351

5.55E-03

7

transcription, DNA-dependent

 GO:040011

5.89E-03

17

locomotion

 GO:044281

8.06E-03

106

small molecule metabolic process

 GO:009296

8.41E-03

6

flagellum assembly

 GO:034641

9.65E-03

117

cellular nitrogen compound metabolic process

 GO:006139

9.87E-03

77

nucleobase, nucleoside, nucleotide and nucleic acid metabolic process

 GO:032774

9.87E-03

7

RNA biosynthetic process

 GO:019720

9.87E-03

7

Mo-molybdopterin cofactor metabolic process

 GO:032324

9.87E-03

7

molybdopterin cofactor biosynthetic process

 GO:043545

9.87E-03

7

molybdopterin cofactor metabolic process

 GO:051189

9.87E-03

7

prosthetic group metabolic process

 GO:006777

9.87E-03

7

Mo-molybdopterin cofactor biosynthetic process

 GO:016053

1.01E-02

31

organic acid biosynthetic process

 GO:046394

1.01E-02

31

carboxylic acid biosynthetic process

 GO:009219

1.10E-02

4

pyrimidine deoxyribonucleotide metabolic process

 GO:009394

1.10E-02

4

2′-deoxyribonucleotide metabolic process

 GO:042180

1.26E-02

58

cellular ketone metabolic process

 GO:030031

1.55E-02

6

cell projection assembly

 GO:006732

1.55E-02

20

coenzyme metabolic process

 GO:042559

1.55E-02

7

pteridine and derivative biosynthetic process

 GO:042558

1.55E-02

7

pteridine and derivative metabolic process

 GO:046034

1.55E-02

7

ATP metabolic process

 GO:015985

1.55E-02

7

energy coupled proton transport, down electrochemical gradient

 GO:015986

1.55E-02

7

ATP synthesis coupled proton transport

 GO:006119

1.55E-02

7

oxidative phosphorylation

 GO:006754

1.55E-02

7

ATP biosynthetic process

 GO:044271

1.65E-02

52

cellular nitrogen compound biosynthetic process

 GO:006950

1.72E-02

26

response to stress

 GO:022607

1.93E-02

16

cellular component assembly

 GO:044085

2.28E-02

22

cellular component biogenesis

 GO:009152

2.52E-02

10

purine ribonucleotide biosynthetic process

 GO:009201

2.53E-02

7

ribonucleoside triphosphate biosynthetic process

 GO:009206

2.53E-02

7

purine ribonucleoside triphosphate biosynthetic process

 GO:009145

2.53E-02

7

purine nucleoside triphosphate biosynthetic process

 GO:006807

2.99E-02

121

nitrogen compound metabolic process

 GO:019748

3.10E-02

4

secondary metabolic process

 GO:009234

3.10E-02

4

menaquinone biosynthetic process

 GO:009233

3.10E-02

4

menaquinone metabolic process

 GO:042362

3.10E-02

4

fat-soluble vitamin biosynthetic process

 GO:042371

3.10E-02

4

vitamin K biosynthetic process

 GO:042373

3.10E-02

4

vitamin K metabolic process

 GO:006775

3.10E-02

4

fat-soluble vitamin metabolic process

 GO:009150

3.10E-02

10

purine ribonucleotide metabolic process

 GO:006164

3.35E-02

11

purine nucleotide biosynthetic process

 GO:009165

3.40E-02

15

nucleotide biosynthetic process

 GO:009205

3.58E-02

7

purine ribonucleoside triphosphate metabolic process

 GO:009199

3.58E-02

7

ribonucleoside triphosphate metabolic process

 GO:009144

3.58E-02

7

purine nucleoside triphosphate metabolic process

 GO:019752

3.59E-02

53

carboxylic acid metabolic process

 GO:043436

3.59E-02

53

oxoacid metabolic process

 GO:009211

3.59E-02

3

pyrimidine deoxyribonucleoside triphosphate metabolic process

 GO:009200

3.59E-02

3

deoxyribonucleoside triphosphate metabolic process

 GO:009120

3.59E-02

3

deoxyribonucleoside metabolic process

 GO:046125

3.59E-02

3

pyrimidine deoxyribonucleoside metabolic process

 GO:009221

3.59E-02

3

pyrimidine deoxyribonucleotide biosynthetic process

 GO:009263

3.59E-02

3

deoxyribonucleotide biosynthetic process

 GO:009265

3.59E-02

3

2′-deoxyribonucleotide biosynthetic process

 GO:009260

3.68E-02

10

ribonucleotide biosynthetic process

 GO:006163

3.85E-02

11

purine nucleotide metabolic process

 GO:019438

3.88E-02

12

aromatic compound biosynthetic process

 GO:015992

3.97E-02

8

proton transport

 GO:006818

3.97E-02

8

hydrogen transport

 GO:006082

4.00E-02

54

organic acid metabolic process

 GO:090304

4.05E-02

52

nucleic acid metabolic process

 GO:043648

4.38E-02

9

dicarboxylic acid metabolic process

 GO:016043

4.38E-02

22

cellular component organization

 GO:009259

4.64E-02

10

ribonucleotide metabolic process

 GO:032787

4.92E-02

16

monocarboxylic acid metabolic process

Molecular function

 GO:005198

2.25E-10

39

structural molecule activity

 GO:003735

1.05E-08

32

structural constituent of ribosome

 GO:046983

1.50E-02

10

protein dimerization activity

 GO:003774

2.16E-02

10

motor activity

 GO:016810

2.24E-02

16

hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds

 GO:016814

3.39E-02

6

hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines

Cellular component

 GO:044424

7.14E-22

391

intracellular part

 GO:005622

8.72E-21

395

intracellular

 GO:005737

1.19E-16

371

cytoplasm

 GO:043228

7.58E-16

53

non-membrane-bounded organelle

 GO:043232

7.58E-16

53

intracellular non-membrane-bounded organelle

 GO:043229

7.58E-16

57

intracellular organelle

 GO:043226

7.58E-16

57

organelle

 GO:005840

1.95E-10

33

ribosome

 GO:030529

5.54E-10

33

ribonucleoprotein complex

 GO:044444

1.68E-09

41

cytoplasmic part

 GO:032991

2.06E-07

59

macromolecular complex

 GO:019861

1.39E-06

19

flagellum

 GO:009288

1.47E-05

15

bacterial-type flagellum

 GO:044422

3.09E-04

16

organelle part

 GO:033279

9.66E-04

9

ribosomal subunit

 GO:016469

3.31E-03

8

proton-transporting two-sector ATPase complex

 GO:042995

7.19E-03

19

cell projection

 GO:044446

7.85E-03

9

intracellular organelle part

 GO:015934

8.09E-03

5

large ribosomal subunit

 GO:009426

1.60E-02

3

bacterial-type flagellum basal body, distal rod

 GO:009424

1.60E-02

3

bacterial-type flagellum hook

 GO:009317

1.60E-02

3

acetyl-CoA carboxylase complex

 GO:044463

3.10E-02

7

cell projection part

 GO:044461

3.10E-02

7

bacterial-type flagellum part

 GO:044460

3.10E-02

7

flagellum part

 GO:030694

4.73E-02

3

bacterial-type flagellum basal body, rod

Effect of RBL67 to the virulence response of N-15

GO enrichment analysis revealed enriched differential expression of some virulence genes, such as the 42 genes belonging to “protein secretion by the type III secretion system” (GO:030254) in the co-culture. Therefore we analyzed the regulation of all putative virulence factors of Salmonella LT2. A comparison to the virulence database VFDB revealed 151 genes in LT2 putatively involved in virulence [39]. Of these 151 genes, one was higher expressed in mono-culture, i.e. phoP encoding the transcriptional regulator PhoP, a member of the two-component system PhoQ-PhoP. The PhoQ encoding gene was also overexpressed in the mono-culture, although not significant (FDR = 0.063, Additional file 1: Table S2).

In the co-culture, 122 virulence genes were higher expressed, a significant enrichment of expressed virulence genes (p = 7 × 10−39 in Fisher’s test). The large majority of genes were involved in secretion systems (N  = 66) and fimbrial adherence determinants (N = 51). The pathogeny island 1 (SPI-1) encodes 39 genes [39] of which 30 were significantly higher expressed in the co-culture including the complete type III secretion system 1 (TTSS-1) consisting of sipB, sipD, prgIHK, invACBGH, spaSRQPO, and sicAP. Only avrAI, sprB, hilC, orgC and hilD were not higher expressed in co-culture. Additionally genes located on SPI-2 were higher expressed in co-culture, including the TTSS-2 genes ssrAB, ssaBCDE, ssaGHIJKLMVNOPQRSTU, sseAB, sseCDE, sseFG, sscA, and sscB. Further the main activation complex of type 1 fimbriae fimY, fimW and fimZ where higher expressed in co-culture, albeit the latter not significant.

Discussion

Antagonism and protective effects of selected B. thermophilum strains against enterobacteriaceae have been observed in several studies [79, 13, 14], but the underlying mechanisms of this antagonism are unknown. In this study we used RNA-sequencing to investigate the global transcriptional response of RBL67 and Salmonella N-15 in mono- and co-culture. To our knowledge we present the first study investigating the interaction of a probiotic Bifidobacterium strain with enteropathogenic S. Typhimurium using RNA-sequencing.

RNA-sequencing was previously shown to be a powerful method to investigate genome-wide transcript analysis in mixed-culture experiments [35]. In our study we could map at least 10 million reads specifically to one of the genomes, which is clearly above the 5 million reads needed for differential expression analyses in bacterial genomes [41]. The transcriptome of N-15 mapped to the genome of Salmonella Typhimurium LT2 had a similar efficiency as the mapping of the transcriptome of RBL67 to the RBL67 genome, suggesting that mapping reads to a closely related genome is possible. The pathogenicity islands of Salmonella Typhimurium strains are conserved and difference in virulence factors contents mainly occurs on plasmid [42]. We could map RNAseq reads against the plasmid of LT-2, indicating that N-15 has a virulence-genes-encoding plasmid similar to that of LT2. Hence, both strains seem highly similar and the RNAseq data presented resemble closely the transcriptome profile of Salmonella Typhimurium strain N-15. Sampling points were chosen when growth speed, cell number and metabolite concentrations were similar in both cultures to allow accurate transcriptomic profiling. Further, fermentations were performed under pH controlled condition to exclude low pH effects.

RBL67 growth was slightly but significantly enhanced in presence of Salmonella N-15. Also growth of other Bifidobacterium species (B. globosum, B. animalis, B. breve) was shown to be stimulated by S. Typhimurium and S. Enteriditis, albeit under pH uncontrolled conditions [43]. A glutamate producing enzyme was repressed and a glutamate consuming enzyme activated in RBL67 in co-culture, suggesting a change in glutamate availability in the presence of N-15. Interestingly, Salmonella accumulates glutamate under various conditions [44] and lysing Salmonella cells could provide B. thermophilum with additional glutamate resulting in the change in amino acid metabolism and possibly also in the observed growth rate. The elevated expression of 3 stress genes suggests that RBL67 is exposed to weak stress in the presence of Salmonella N-15, but the enhanced growth performance indicates that the microbe was able to cope with the stress in the co-culture.

The Salmonella N-15 transcriptome was clearly affected by presence of RBL67. Many virulence genes were higher expressed in Salmonella N-15 during co-culture with RBL67 and such increased expression may enhance infection rate. However, this would contradict with previous results showing reduced invasion capacity of Salmonella to HT29-MTX cells in presence of RBL67 [13]. Salmonella virulence is tightly controlled and the activity of virulence factors at the right time, correct place and in appropriate amounts is crucial for virulence [45]. Further, environmental factors such as acetate can trigger virulence gene expression in Salmonella [20]. A low concentration of 15 mM acetate at pH 6.7 induces the three invasion determinants hilA, invF and sipC in S. Typhimurium and the induction is dependent on acetate kinase (ackA) and phosphotransacetylase (pta) activity [46]. The genes hilA, and invF were higher expressed in co-culture but ackA and pta were down regulated in co-cultures (Additional file 1: Table S2) and therefore hilA was likely not activated by acetate. The two-component system PhoQ-PhoP was down-regulated in the co-culture. PhoQ-PhoP is a repressor of hilA, a key regulator for Salmonella invasion [18, 21] and the higher expression of hilA observed in the co-culture seems therefore due to a repressor release mediated by PhoQ-PhoP.

Invasion of Salmonella follows sequential expression of first flagellar genes, followed by genes encoded on SPI-1, and eventually type 1 fimbrial genes [47]. Flagellar genes were repressed while genes of SPI-1 and type 1 fimbriae genes were activated in co-culture compared to mono-culture. This shows that N-15 in co-culture is further progressed in the sequential expression for infection and the balance in virulence gene expression is disturbed by the presence of RBL67. In fact, the expression of SPI-1 and SPI-2 and repression of flagellar genes observed in the co-culture resembles the transcriptional profile of Salmonella cells in fibroblast after infection [48]. Further, an early activation of the type III secretion system-1 (TTSS-1) located on SPI-1 was observed in co-culture. A TTSS-1 expressing S. Typhimurium subpopulation is essential for infection, but this subpopulation is also vulnerable to overgrowth by the non-TTSS-1 expressing subpopulation [49]. An imbalance in the regulation of TTSS-1 results in an inappropriate fraction of TTSS-1 expressing cells and eventually to a decreased infection rate [50]. This results in situ in reduced invasion of human intestinal cells and ultimately eliminates Salmonella from the lumen [50]. In vitro, a reduced infection of human intestinal cells by Salmonella in presence of RBL67 and repression of Salmonella by RBL67 in a continuous intestinal fermentation model was reported [13, 14].

Our data provide a first clue on a possible mechanism that could contributes to the antagonistic effects of RBL67 against Salmonella spec and other pathogens [79, 1315]. The expression of virulence gene at early stage is a burden for the pathogen and may result in lower infection rate and subsequent wash-out from the lumen. In addition, the repression of flagellar genes reduces motility thereby preventing colonization of other areas. Whether the imbalance in virulence gene expression observed in vitro also occurs in situ remains to be elucidated. The effect may be reinforced by simultaneous protection by other probiotic mechanisms such as competition for adhesion sites and nutrients, and acetate production.

Conclusion

Our study provides first insights into the transcriptome response of B. thermophilum RBL67 and S. Typhimurium grown in co-cultures under simplified conditions and reveals possible molecular mechanisms of probiotic-pathogen interaction. Our data show that RBL67 has a huge impact on the transcriptome of Salmonella and causes in an imbalanced virulence gene expression. This imbalance in the cascade pathway of virulence could represent a novel possible mechanism of how probiotic organisms can protect the host against infections.

Availability of data and materials

Data presented in this study are available under NCBI BioProject Record PRJNA274782 accessible through http://www.ncbi.nlm.nih.gov/bioproject/PRJNA274782. Gene expression data are directly accessible through GEO Series accession number GSE65716 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65716).

Declarations

Acknowledgements

We thank Dr. Hubert Rehraurer and Dr. Lucy Poveda from the Functional Genomics Center Zurich for RNA-Sequencing and support in statistical analysis. This project was financed by the Commission for Technology and Innovation (CTI), Bern, Switzerland, under project no. 11962.1 PFLS-LS

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Laboratory of Food Biotechnology, ETH Zurich, Institute of Food, Nutrition and Health
(2)
Present Address: Institut National de la Recherche Agronomique, UR 545 URF

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Copyright

© Tanner et al. 2016