33 research outputs found

    Genetic Features of Resident Biofilms Determine Attachment of Listeria monocytogenes▿

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    Planktonic Listeria monocytogenes cells in food-processing environments tend most frequently to adhere to solid surfaces. Under these conditions, they are likely to encounter resident biofilms rather than a raw solid surface. Although metabolic interactions between L. monocytogenes and resident microflora have been widely studied, little is known about the biofilm properties that influence the initial fixation of L. monocytogenes to the biofilm interface. To study these properties, we created a set of model resident Lactococcus lactis biofilms with various architectures, types of matrices, and individual cell surface properties. This was achieved using cell wall mutants that affect bacterial chain formation, exopolysaccharide (EPS) synthesis and surface hydrophobicity. The dynamics of the formation of these biofilm structures were analyzed in flow cell chambers using in situ time course confocal laser scanning microscopy imaging. All the L. lactis biofilms tested reduced the initial immobilization of L. monocytogenes compared to the glass substratum of the flow cell. Significant differences were seen in L. monocytogenes settlement as a function of the genetic background of resident lactococcal biofilm cells. In particular, biofilms of the L. lactis chain-forming mutant resulted in a marked increase in L. monocytogenes settlement, while biofilms of the EPS-secreting mutant efficiently prevented pathogen fixation. These results offer new insights into the role of resident biofilms in governing the settlement of pathogens on food chain surfaces and could be of relevance in the field of food safety controls

    Variations in the Degree of d-Alanylation of Teichoic Acids in Lactococcus lactis Alter Resistance to Cationic Antimicrobials but Have No Effect on Bacterial Surface Hydrophobicity and Charge▿

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    An increase of the degree of d-alanylation of teichoic acids in Lactococcus lactis resulted in a significant increase of bacterial resistance toward the cationic antimicrobials nisin and lysozyme, whereas the absence of d-alanylation led to a decreased resistance toward the same compounds. In contrast, the same variations of the d-alanylation degree did not modify bacterial cell surface charge and hydrophobicity. Bacterial adhesion to polystyrene and glass surfaces was not modified either

    Proteomic Signature of Lactococcus lactis NCDO763 Cultivated in Milk

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    We have compared the proteomic profiles of L. lactis subsp. cremoris NCDO763 growing in the synthetic medium M17Lac, skim milk microfiltrate (SMM), and skim milk. SMM was used as a simple model medium to reproduce the initial phase of growth of L. lactis in milk. To widen the analysis of the cytoplasmic proteome, we used two different gel systems (pH ranges of 4 to 7 and 4.5 to 5.5), and the proteins associated with the cell envelopes were also studied by two-dimensional electrophoresis. In the course of the study, we analyzed about 800 spots and identified 330 proteins by mass spectrometry. We observed that the levels of more than 50 and 30 proteins were significantly increased upon growth in SMM and milk, respectively. The large redeployment of protein synthesis was essentially associated with an activation of pathways involved in the metabolism of nitrogenous compounds: peptidolytic and peptide transport systems, amino acid biosynthesis and interconversion, and de novo biosynthesis of purines. We also showed that enzymes involved in reactions feeding the purine biosynthetic pathway in one-carbon units and amino acids have an increased level in SMM and milk. The analysis of the proteomic data suggested that the glutamine synthetase (GS) would play a pivotal role in the adaptation to SMM and milk. The analysis of glnA expression during growth in milk and the construction of a glnA-defective mutant confirmed that GS is an essential enzyme for the development of L. lactis in dairy media. This analysis thus provides a proteomic signature of L. lactis, a model lactic acid bacterium, growing in its technological environment

    Analysis of the peptidoglycan hydrolase complement of **Lactobacillus casei** and characterization of the major <tex>\gamma$</tex>-D-glutamyl-L-lysyl-endopeptidase

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    Peptidoglycan (PG) is the major component of Gram positive bacteria cell wall and is essential for bacterial integrity and shape. Bacteria synthesize PG hydrolases (PGHs) which are able to cleave bonds in their own PG and play major roles in PG remodelling required for bacterial growth and division. Our aim was to identify the main PGHs in Lactobacillus casei BL23, a lactic acid bacterium with probiotic properties. The PGH complement was first identified in silico by amino acid sequence similarity searches of the BL23 genome sequence. Thirteen PGHs were detected with different predicted hydrolytic specificities. Transcription of the genes was confirmed by RT-PCR. A proteomic analysis combining the use of SDS-PAGE and LC-MS/MS revealed the main seven PGHs synthesized during growth of L. casei BL23. Among these PGHs, LCABL_02770 (renamed Lc-p75) was identified as the major one. This protein is the homolog of p75 (Msp1) major secreted protein of Lactobacillus rhamnosus GG, which was shown to promote survival and growth of intestinal epithelial cells. We identified its hydrolytic specificity on PG and showed that it is a c-D-glutamyl-L-lysyl-endopeptidase. It has a marked specificity towards PG tetrapeptide chains versus tripeptide chains and for oligomers rather than monomers. Immunofluorescence experiments demonstrated that Lc-p75 localizes at cell septa in agreement with its role in daughter cell separation. It is also secreted under an active form as detected in zymogram. Comparison of the muropeptide profiles of wild type and Lc-p75-negative mutant revealed a decrease of the amount of disaccharide-dipeptide in the mutant PG in agreement with Lc-p75 activity. A

    Evaluating the Efficiency of Isotope Transmission for Improved Panel Design and a Comparison of the Detection Sensitivities of Mass Cytometer Instruments

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    International audienceThe recent introduction of mass cytometry, a technique coupling a cell introduction system generating a stream of single cells with mass spectrometry, has greatly increased the number of parameters that can be measured per single cell. As with all new technology there is a need for dissemination of standardization and quality control procedures. Here, we characterize variations in sensitivity observed across the mass range of a mass cytometer, using different lanthanide tags. We observed a five-fold difference in lanthanide detection over the mass range and demonstrated that each instrument has its own sensitivity pattern. Therefore, the selection of lanthanide combinations is a key step in the establishment of a staining panel for mass cytometry-based experiments, particularly for multicenter studies. We propose the sensitivity pattern as the basis for panel design, instrument standardization and future implementation of normalization algorithms

    Proteomic identification by 1D SDS-PAGE and LC-MS/MS of the PGHs present in <i>L. casei</i> BL23 extracts and estimation of relative amounts by PAI calculation.

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    a<p>As defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032301#pone-0032301-t001" target="_blank">Table 1</a>.</p>b<p>SP, signal peptide predicted with SignalP tool <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032301#pone.0032301-Emanuelsson1" target="_blank">[40]</a>.</p>c<p>Protein log(E-value) is the log of the product of validated unique peptide E-values and was calculated by X!tandem PAPPSO pipeline.</p>d<p>PAI (Protein Abundance Index) was calculated according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032301#pone.0032301-Rappsilber1" target="_blank">[13]</a> as the number of observed spectra divided by the number of calculated observable peptides and calculated with the X!Tandem pipeline.</p

    The PGH complement of <i>L. casei</i> BL23 predicted <i>in silico</i> on whole genome sequence.

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    a<p>Calculated molecular mass.</p>b<p>SP, signal peptide predicted with SignalP tool <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032301#pone.0032301-Emanuelsson1" target="_blank">[40]</a>.</p>c<p>Catalytic domains were predicted with Pfam domain prediction <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032301#pone.0032301-Finn1" target="_blank">[41]</a>. Glucosaminidase (PF01832), muramidase (glyco_hydro_25; PF01183), Amidase_2 (PF01510), Amidase_3 (PF01520), CHAP (cysteine, histidine-dependant amidohydrolase/peptidase) domain (amidase or peptidase) (PF05257), NlpC_P60 (PF00877) (including γ-glutamyl-diamino-acid endopeptidases), Peptidase_S11 (PF00768).</p>d<p>SH3, SH3-domain (PF08460); LysM, LysM-domain (PF01476); PBP5_C (PF07943).</p>e<p>Putative prophage-encoded PGH.</p
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