Bifidobacterium thermophilum RBL67 impacts on growth and virulence gene expression of Salmonella enterica subsp. enterica serovar Typhimurium
© Tanner et al. 2016
Received: 30 July 2015
Accepted: 2 March 2016
Published: 18 March 2016
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.
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.
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.
Probiotics are live organism that, when administered in adequate amounts, confer a health benefit on the host . 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 . 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 .
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 . The infant feces isolate B. thermophilum RBL67 (RBL67) is a promising probiotic candidate which genome was sequenced . 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, 11–14]. Furthermore, RBL67 decreases S. Typhimurium counts in an in vitro fermentation model of the gastrointestinal tract , reduces severity of rotavirus-associated diarrhea in suckling mice , 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 . 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 . 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 . 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 . 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 . 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 . 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 . 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.
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 , 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  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 . 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 , 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 % . 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 , 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 , 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 , 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 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  included in the Bioconductor EdgeR software package [32–34] 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 . Proteins of RBL67 and LT2 were assigned to gene ontology categories (GO) using Blast2GO at standard settings . GO categories enrichment analyses were performed and visualized using the BiNGO plugin  in Cytoscape (v.3.0.1, ) 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) , 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  and are accessible through GEO Series accession number GSE65716 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65716).
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).
Growth characteristics of RBL67 in mono- and co-culture with N-15
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
Bifidobacterium thermophilum RBL67 genes higher expressed in co-culture with N-15 compared to mono-culture
Oligopeptide transport ATP-binding protein OppF (TC 3.A.1.5.1)
Transcriptional regulator, MarR family
Lipid A export ATP-binding/permease protein MsbA
FIG 00672402: hypothetical protein
possible conserved integral membrane protein
SOS-response repressor and protease LexA (EC 220.127.116.11)
Inner membrane protein
Zinc ABC transporter, periplasmic-binding protein ZnuA
Cystathionine beta-synthase (EC 18.104.22.168)
Glutamate 5-kinase (EC 22.214.171.124)
COG0536: GTP-binding protein Obg
DNA recombination protein RmuC
Maltodextrin glucosidase (EC 126.96.36.199)
ABC-type sugar transport system, permease component
MSM (multiple sugar metabolism) operon regulatory protein
Sortase A, LPXTG specific
COG family: predicted phosphohydrolases
Transcriptional regulator, HxlR family
Rrf2-linked NADH-flavin reductase
COG2110, Macro domain, possibly ADP-ribose binding module
HspR, transcriptional repressor of DnaK operon
Bifidobacterium thermophilum RBL67 genes higher expressed in mono-culture compared to co-culture with N-15
FIG 00519111: hypothetical protein
HTH domain protein
Ribonucleotide reductase of class Ib (aerobic), alpha subunit (EC 188.8.131.52)
Transcriptional regulator, GntR family domain/Aspartate aminotransferase (EC 184.108.40.206)
Manganese transport protein MntH
Glycosyl transferase, group 2 family protein
glycosyl transferase, group 1 family protein
Glycosyltransferase (EC 2.4.1.-)
D-lactate dehydrogenase (EC 220.127.116.11)
Aspartate aminotransferase (EC 18.104.22.168)
Oligopeptide transport system permease protein OppC (EC 3.A.1.5.1)
Acetyltransferase, GNAT family
putative TraA-like conjugal transfer protein
Ferric iron ABC transporter, iron-binding protein
Methionine ABC transporter permease protein
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 22.214.171.124) 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
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).
Gene Ontology (GO) categories of the Salmonella Typhimurium N-15 transcriptome significantly overrepresented in the co-culture with RBL67 compared to mono-culture
Ngenes in category
Description of category
establishment of localization
protein secretion by the type III secretion system
interaction with host
interspecies interaction between organisms
symbiosis, encompassing mutualism through parasitism
secretion by cell
interaction with other organism via secreted substance involved in symbiotic interaction
interaction with host via protein secreted by type III secretion system
interaction with host via secreted substance involved in symbiotic interaction
interaction with other organism via protein secreted by type III secretion system involved in symbiotic interaction
interaction with host via substance released outside of symbiont
establishment of localization in cell
establishment of protein localization
phosphoenolpyruvate-dependent sugar phosphotransferase system
cellular cell wall organization
external encapsulating structure organization
cell wall organization
colanic acid biosynthetic process
movement in host environment
movement in environment of other organism involved in symbiotic interaction
entry into host
colanic acid metabolic process
entry into other organism involved in symbiotic interaction
metal ion transport
sodium ion transport
cobalamin metabolic process
cobalamin biosynthetic process
nonribosomal peptide biosynthetic process
monovalent inorganic cation transport
porphyrin metabolic process
porphyrin biosynthetic process
carbohydrate transmembrane transporter activity
substrate-specific transporter activity
substrate-specific transmembrane transporter activity
sugar transmembrane transporter activity
transmembrane transporter activity
cation transmembrane transporter activity
cation:sugar symporter activity
ion transmembrane transporter activity
secondary active transmembrane transporter activity
solute:cation symporter activity
solute:hydrogen symporter activity
sugar:hydrogen symporter activity
active transmembrane transporter activity
di-, tri-valent inorganic cation transmembrane transporter activity
metal ion transmembrane transporter activity
inorganic cation transmembrane transporter activity
hexose transmembrane transporter activity
monosaccharide transmembrane transporter activity
siderophore-iron transmembrane transporter activity
siderophore transporter activity
iron ion transmembrane transporter activity
transition metal ion transmembrane transporter activity
aldonate transmembrane transporter activity
gluconate transmembrane transporter activity
iron ion binding
carboxylic acid transmembrane transporter activity
organic acid transmembrane transporter activity
type III protein secretion system complex
integral to membrane
intrinsic to membrane
cell outer membrane
external encapsulating structure part
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).
Gene Ontology (GO) categories of Salmonella Typhimurium N-15 transcriptome significantly overrepresented in the mono-culture compared to co-culture with RBL67
Ngenes in category
Description of category
cellular biosynthetic process
cellular metabolic process
primary metabolic process
cellular macromolecule biosynthetic process
macromolecule biosynthetic process
cellular protein metabolic process
fatty acid biosynthetic process
protein metabolic process
cellular macromolecule metabolic process
isoprenoid biosynthetic process
isoprenoid metabolic process
lipid biosynthetic process
cellular lipid metabolic process
macromolecule metabolic process
lipid metabolic process
fatty acid metabolic process
localization of cell
ciliary or flagellar motility
small molecule biosynthetic process
cellular component movement
nucleoside triphosphate metabolic process
RNA metabolic process
coenzyme biosynthetic process
cell projection organization
nucleoside triphosphate biosynthetic process
small molecule metabolic process
cellular nitrogen compound metabolic process
nucleobase, nucleoside, nucleotide and nucleic acid metabolic process
RNA biosynthetic process
Mo-molybdopterin cofactor metabolic process
molybdopterin cofactor biosynthetic process
molybdopterin cofactor metabolic process
prosthetic group metabolic process
Mo-molybdopterin cofactor biosynthetic process
organic acid biosynthetic process
carboxylic acid biosynthetic process
pyrimidine deoxyribonucleotide metabolic process
2′-deoxyribonucleotide metabolic process
cellular ketone metabolic process
cell projection assembly
coenzyme metabolic process
pteridine and derivative biosynthetic process
pteridine and derivative metabolic process
ATP metabolic process
energy coupled proton transport, down electrochemical gradient
ATP synthesis coupled proton transport
ATP biosynthetic process
cellular nitrogen compound biosynthetic process
response to stress
cellular component assembly
cellular component biogenesis
purine ribonucleotide biosynthetic process
ribonucleoside triphosphate biosynthetic process
purine ribonucleoside triphosphate biosynthetic process
purine nucleoside triphosphate biosynthetic process
nitrogen compound metabolic process
secondary metabolic process
menaquinone biosynthetic process
menaquinone metabolic process
fat-soluble vitamin biosynthetic process
vitamin K biosynthetic process
vitamin K metabolic process
fat-soluble vitamin metabolic process
purine ribonucleotide metabolic process
purine nucleotide biosynthetic process
nucleotide biosynthetic process
purine ribonucleoside triphosphate metabolic process
ribonucleoside triphosphate metabolic process
purine nucleoside triphosphate metabolic process
carboxylic acid metabolic process
oxoacid metabolic process
pyrimidine deoxyribonucleoside triphosphate metabolic process
deoxyribonucleoside triphosphate metabolic process
deoxyribonucleoside metabolic process
pyrimidine deoxyribonucleoside metabolic process
pyrimidine deoxyribonucleotide biosynthetic process
deoxyribonucleotide biosynthetic process
2′-deoxyribonucleotide biosynthetic process
ribonucleotide biosynthetic process
purine nucleotide metabolic process
aromatic compound biosynthetic process
organic acid metabolic process
nucleic acid metabolic process
dicarboxylic acid metabolic process
cellular component organization
ribonucleotide metabolic process
monocarboxylic acid metabolic process
structural molecule activity
structural constituent of ribosome
protein dimerization activity
hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds
hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines
intracellular non-membrane-bounded organelle
proton-transporting two-sector ATPase complex
intracellular organelle part
large ribosomal subunit
bacterial-type flagellum basal body, distal rod
bacterial-type flagellum hook
acetyl-CoA carboxylase complex
cell projection part
bacterial-type flagellum part
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 . 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  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.
Antagonism and protective effects of selected B. thermophilum strains against enterobacteriaceae have been observed in several studies [7–9, 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 . 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 . 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 . 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 . 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  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 . 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 . Further, environmental factors such as acetate can trigger virulence gene expression in Salmonella . 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 . 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 . 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 . 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 . 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 . This results in situ in reduced invasion of human intestinal cells and ultimately eliminates Salmonella from the lumen . 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 [7–9, 13–15]. 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.
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).
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.
- Hill C, Guarner F, Reid G, Gibson GR, Merenstein DJ, Pot B, et al. Expert consensus document. The International Scientific Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic. Nat Rev Gastroenterol Hepatol. 2014;11(8):506–14.View ArticlePubMedGoogle Scholar
- O’Toole PW, Cooney JC. Probiotic bacteria influence the composition and function of the intestinal microbiota. Interdiscip Perspect Infect Dis. 2008;2008:175285.PubMedPubMed CentralGoogle Scholar
- Walsh CJ, Guinane CM, O’Toole PW, Cotter PD. Beneficial modulation of the gut microbiota. FEBS Lett. 2014;17;588(22):4120–30.Google Scholar
- Gaggia F, Mattarelli P, Biavati B. Probiotics and prebiotics in animal feeding for safe food production. Int J Food Microbiol. 2010;141 Suppl 1:15–28.View ArticleGoogle Scholar
- Biavati B, Mattarelli P. Genus I. Bifidobacterium. In: Whitman WB, Kämpfer P, Goodfellow M, Garrity GM, Ludwig W, editors. Bergey’s manual of systematic bacteriology The Actinobacteria, vol. 5. 2nd ed. New York, USA: Springer Verlag; 2009. p. 171–206.Google Scholar
- Toure R, Kheadr E, Lacroix C, Moroni O, Fliss I. Production of antibacterial substances by bifidobacterial isolates from infant stool active against Listeria monocytogenes. J Appl Microbiol. 2003;95(5):1058–69.View ArticlePubMedGoogle Scholar
- Sasaki T, Fukami S, Namioka S. Enhanced resistance of mice to Escherichia coli infection induced by administration of peptidoglycan derived from Bifidobacterium thermophilum. J Vet Med Sci. 1994;56(3):433–7.View ArticlePubMedGoogle Scholar
- Sasaki T, Fukami S, Namioka S. Enhancement of cytotoxic activity of lymphocytes in mice by oral administration of peptidoglycan (PG) derived from Bifidobacterium thermophilum. J Vet Med Sci. 1994;56(6):1129–33.View ArticlePubMedGoogle Scholar
- Kobayashi C, Yokoyama H, Nguyen SV, Hashi T, Kuroki M, Kodama Y. Enhancement of chicken resistance against Escherichia coli infection by oral administration of Bifidobacterium thermophilum preparations. Avian Dis. 2002;46(3):542–6.View ArticlePubMedGoogle Scholar
- Jans C, Lacroix C, Follador R, Stevens MJ. Complete genome sequence of the probiotic Bifidobacterium thermophilum strain RBL67. Genome Announc. 2013;1(3):e00191-13.View ArticlePubMedPubMed CentralGoogle Scholar
- von Ah U. Identification of Bifidobacterium thermophilum RBL67 isolated from baby feces and partial purification of its bacteriocin. PhD thesis. Zurich, Switzerland: ETH Zurich, Switzerland; 2006.Google Scholar
- von Ah U, Mozzetti V, Lacroix C, Kheadr EE, Fliss I, Meile L. Classification of a moderately oxygen-tolerant isolate from baby faeces as Bifidobacterium thermophilum. BMC Microbiol. 2007;7:79.View ArticleGoogle Scholar
- Zihler A, Gagnon M, Chassard C, Lacroix C. Protective effect of probiotics on Salmonella infectivity assessed with combined in vitro gut fermentation-cellular models. BMC Microbiol. 2011;11:264.View ArticlePubMedPubMed CentralGoogle Scholar
- Zihler A, Le Blay G, Chassard C, Braegger C, Lacroix C. Bifidobacterium thermophilum RBL67 inhibits S. Typhimurium in an in vitro model of Salmonella infection in children. J Food Nutr Disord. 2014, in press.Google Scholar
- Gagnon M. Rôle des probiotiques lors d’infections entériques d’origine bactérienne et virale: analyses in vitro et études in vivo chez des modèles murines. PhD thesis. Québec: Université de Laval; 2007.Google Scholar
- Moroni O, Kheadr E, Boutin Y, Lacroix C, Fliss I. Inactivation of adhesion and invasion of food-borne Listeria monocytogenes by bacteriocin-producing Bifidobacterium strains of human origin. Appl Environ Microbiol. 2006;72(11):6894–901.View ArticlePubMedPubMed CentralGoogle Scholar
- Majowicz SE, Musto J, Scallan E, Angulo FJ, Kirk M, O’Brien SJ, et al. The global burden of nontyphoidal Salmonella gastroenteritis. Clin Infect Dis. 2010;50(6):882–9.View ArticlePubMedGoogle Scholar
- Fabrega A, Vila J. Salmonella enterica serovar Typhimurium skills to succeed in the host: virulence and regulation. Clin Microbiol Rev. 2013;26(2):308–41.View ArticlePubMedPubMed CentralGoogle Scholar
- EFSA, ECDC. The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2012. EFSA Journal. 2014;12(2):3547–859.Google Scholar
- Altier C. Genetic and environmental control of Salmonella invasion. J Microbiol. 2005;43 Spec No:85–92.PubMedGoogle Scholar
- Ellermeier JR, Slauch JM. Adaptation to the host environment: regulation of the SPI1 type III secretion system in Salmonella enterica serovar Typhimurium. Curr Opin Microbiol. 2007;10(1):24–9.View ArticlePubMedGoogle Scholar
- Lucas RL, Lee CA. Unravelling the mysteries of virulence gene regulation in Salmonella typhimurium. Mol Microbiol. 2000;36(5):1024–33.View ArticlePubMedGoogle Scholar
- de Keersmaecker SC, Marchal K, Verhoeven TL, Engelen K, Vanderleyden J, Detweiler CS. Microarray analysis and motif detection reveal new targets of the Salmonella enterica serovar Typhimurium HilA regulatory protein, including hilA itself. J Bacteriol. 2005;187(13):4381–91.View ArticlePubMedPubMed CentralGoogle Scholar
- Gong J, Yang CB. Advances in the methods for studying gut microbiota and their relevance to the research of dietary fiber functions. Food Res Int. 2012;48(2):916–29.View ArticleGoogle Scholar
- Duncan SH, Hold GL, Barcenilla A, Stewart CS, Flint HJ. Roseburia intestinalis sp. nov., a novel saccharolytic, butyrate-producing bacterium from human faeces. Int J Syst Evol Microbiol. 2002;52(Pt 5):1615–20.PubMedGoogle Scholar
- Tanner SA, Zihler Berner A, Rigozzi E, Grattepanche F, Chassard C, Lacroix C. In vitro continuous fermentation model (PolyFermS) of the swine proximal colon for simultaneous testing on the same gut microbiota. PLoS One. 2014;9(4), e94123.View ArticlePubMedPubMed CentralGoogle Scholar
- Rada V, Sirotek K, Petr J. Evaluation of selective media for bifidobacteria in poultry and rabbit caecal samples. J Veterinary Med Ser B. 1999;46(6):369–73.View ArticleGoogle Scholar
- Stevens MJ, Wiersma A, de Vos WM, Kuipers OP, Smid EJ, Molenaar D, et al. Improvement of Lactobacillus plantarum aerobic growth as directed by comprehensive transcriptome analysis. Appl Environ Microbiol. 2008;74(15):4776–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Winkler ME. Ribosomal ribonucleic acid isolated from Salmonella typhimurium: absence of the intact 23S species. J Bacteriol. 1979;139(3):842–9.PubMedPubMed CentralGoogle Scholar
- Smith NH, Crichton PB, Old DC, Higgins CF. Ribosomal-RNA patterns of Escherichia coli, Salmonella typhimurium and related Enterobacteriaceae. J Med Microbiol. 1988;26(3):223–8.View ArticlePubMedGoogle Scholar
- McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40(10):4288–97.View ArticlePubMedPubMed CentralGoogle Scholar
- Robinson MD, Smyth GK. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics. 2007;23(21):2881–7.View ArticlePubMedGoogle Scholar
- Robinson MD, Smyth GK. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics. 2008;9(2):321–32.View ArticlePubMedGoogle Scholar
- Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40.View ArticlePubMedPubMed CentralGoogle Scholar
- Rosenthal AZ, Matson EG, Eldar A, Leadbetter JR. RNA-seq reveals cooperative metabolic interactions between two termite-gut spirochete species in co-culture. ISME J. 2011;5(7):1133–42.View ArticlePubMedPubMed CentralGoogle Scholar
- Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21(18):3674–6.View ArticlePubMedGoogle Scholar
- Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005;21(16):3448–9.View ArticlePubMedGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.View ArticlePubMedPubMed CentralGoogle Scholar
- Chen L, Xiong Z, Sun L, Yang J, Jin Q. VFDB 2012 update: toward the genetic diversity and molecular evolution of bacterial virulence factors. Nucleic Acids Res. 2012;40(Database issue):D641–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.View ArticlePubMedPubMed CentralGoogle Scholar
- next-generation-sequencing-guide (2015). Available at https://genohub.com/next-generation-sequencing-guide/. Accessed 15 Oct 2015.
- Dhanani AS, Block G, Dewar K, Forgetta V, Topp E, Beiko RG, et al. Genomic comparison of non-typhoidal Salmonella enterica Serovars Typhimurium, Enteritidis, Heidelberg, Hadar and Kentucky isolates from broiler chickens. PLoS One. 2015;10(6), e0128773.View ArticlePubMedPubMed CentralGoogle Scholar
- Bielecka M, Biedrzycka E, Smoragiewicz W, Smieszek M. Interaction of Bifidobacterium and Salmonella during associated growth. Int J Food Microbiol. 1998;45(2):151–5.View ArticlePubMedGoogle Scholar
- Yan D, Ikeda TP, Shauger AE, Kustu S. Glutamate is required to maintain the steady-state potassium pool in Salmonella typhimurium. Proc Natl Acad Sci U S A. 1996;93(13):6527–31.View ArticlePubMedPubMed CentralGoogle Scholar
- Kato A, Groisman EA. The PhoQ/PhoP regulatory network of Salmonella enterica. In: Utsumi R, editor. Bacterial signal transduction: networks and drug targets. New York, USA: Springer Science + Business Media, LLC Landes Bioscience; 2008.Google Scholar
- Lawhon SD, Maurer R, Suyemoto M, Altier C. Intestinal short-chain fatty acids alter Salmonella typhimurium invasion gene expression and virulence through BarA/SirA. Mol Microbiol. 2002;46(5):1451–64.View ArticlePubMedGoogle Scholar
- Saini S, Slauch JM, Aldridge PD, Rao CV. Role of cross talk in regulating the dynamic expression of the flagellar Salmonella pathogenicity island 1 and type 1 fimbrial genes. J Bacteriol. 2010;192(21):5767–77.View ArticlePubMedPubMed CentralGoogle Scholar
- Nunez-Hernandez C, Tierrez A, Ortega AD, Pucciarelli MG, Godoy M, Eisman B, et al. Genome expression analysis of nonproliferating intracellular Salmonella enterica serovar Typhimurium unravels an acid pH-dependent PhoP-PhoQ response essential for dormancy. Infect Immun. 2013;81(1):154–65.View ArticlePubMedPubMed CentralGoogle Scholar
- Sturm A, Heinemann M, Arnoldini M, Benecke A, Ackermann M, Benz M, et al. The cost of virulence: retarded growth of Salmonella Typhimurium cells expressing type III secretion system 1. PLoS Pathog. 2011;7(7), e1002143.View ArticlePubMedPubMed CentralGoogle Scholar
- Diard M, Garcia V, Maier L, Remus-Emsermann MN, Regoes RR, Ackermann M, et al. Stabilization of cooperative virulence by the expression of an avirulent phenotype. Nature. 2013;494(7437):353–6.View ArticlePubMedGoogle Scholar