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The abundant proteolytic plasminogen (Plg)/plasmin system is important in several physiological functions in mammals and also engaged by a number of pathogenic microbial species to increase tissue invasiveness or to obtain nutrients. This paper reports that a commensal bacterium, Lactobacillus crispatus, interacts with the Pig system. Strain ST1 of L. crispatus enhanced activation of human Plg by the tissue-type Pig activator (tPA), whereas enhancement of the urokinase-mediated Plg activation was lower. ST1 cells bound Pig, plasmin and tPA only poorly, and the Plg-binding and activation-enhancing capacities were associated with extracellular material released from the bacteria into buffer. The extracellular proteome of L. crispatus ST1 contained enolase and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as major components. The enolase and the GAPDH genes of ST1 were cloned, sequenced and expressed in recombinant Escherichia coli as [His.sub.6]-fusion proteins, which bound Plg and enhanced its activation by tPA. Variable levels of secretion of enolase and GAPDH proteins as well as of the Pig activation cofactor function were detected in strains representing major taxonomic groups of the genus Lactobacillus. So far, interference with the Plg system has been addressed with pathogenic microbes. The results reported here demonstrate a novel interaction between a member of the microbiota and a major proteolytic system in humans.

Cell adhesion analysis shows that fresh lactobacillus cells adhere to the epithelium and in a strain-dependent manner, the cells adhere to follicle-associated epithelium and the apical surfaces of mature endocytes of intestinal villi.

Abstract Background A proper balance between different T helper (Th) cell subsets is necessary for normal functioning of the adaptive immune system. Revealing key genes and pathways driving the differentiation to distinct Th cell lineages provides important insight into underlying molecular mechanisms and new opportunities for modulating the immune response. Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior. Results We studied transcriptional dynamics of human umbilical cord blood T helper cells cultured in absence and presence of cytokines promoting Th1 or Th2 differentiation. To identify genes that exhibit distinct lineage commitment dynamics and are specific for initiating differentiation to different Th cell subsets, we developed a novel computational methodology (LIGAP) allowing integrative analysis and visualization of multiple lineages over whole time-course profiles. Applying LIGAP to time-course data from multiple Th cell lineages, we identified and experimentally validated several differentially regulated Th cell subset specific genes as well as reciprocally regulated genes. Combining differentially regulated transcriptional profiles with transcription factor binding site and pathway information, we identified previously known and new putative transcriptional mechanisms involved in Th cell subset differentiation. All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource. Conclusions The LIGAP method is widely applicable to quantify differential time-course dynamics of many types of datasets and generalizes to any number of conditions. It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes. Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation.

Lactobacillus crispatus is a common member of the beneficial microbiota present in the vertebrate gastrointestinal and human genitourinary tracts. Here, we report the genome sequence of L. crispatus ST1, a chicken isolate displaying strong adherence to vaginal epithelial cells. doi: 10.1128/JB.00399-10

Background: A proper balance between different T helper (Th) cell subsets is necessary for normal functioning of the adaptive immune system. Revealing key genes and pathways driving the differentiation to distinct Th cell lineages provides important insight into underlying molecular mechanisms and new opportunities for modulating the immune response. Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior. Results: We studied transcriptional dynamics of human umbilical cord blood T helper cells cultured in absence and presence of cytokines promoting Th1 or Th2 differentiation. To identify genes that exhibit distinct lineage commitment dynamics and are specific for initiating differentiation to different Th cell subsets, we developed a novel computational methodology (LIGAP) allowing integrative analysis and visualization of multiple lineages over whole time-course profiles. Applying LIGAP to time-course data from multiple Th cell lineages, we identified and experimentally validated several differentially regulated Th cell subset specific genes as well as reciprocally regulated genes. Combining differentially regulated transcriptional profiles with transcription factor binding site and pathway information, we identified previously known and new putative transcriptional mechanisms involved in Th cell subset differentiation. All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource. Conclusions: The LIGAP method is widely applicable to quantify differential time-course dynamics of many types of datasets and generalizes to any number of conditions. It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes. Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation. [ABSTRACT FROM AUTHOR]

Glutamine synthetase (GS) and glucose-6-phosphate isomerase (GPI) were identified as novel adhesive moonlighting proteins of Lactobacillus crispatus ST1. Both proteins were bound onto the bacterial surface at acidic pHs, whereas a suspension of the cells to pH 8 caused their release into the buffer, a pattern previously observed with surface-bound enolase and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) of L. crispatus. The pH shift was associated with a rapid and transient increase in cell wall permeability, as measured by cell staining with propidium iodide. A gradual increase in the release of the four moonlighting proteins was also observed after the treatment of L. crispatus ST1 cells with increasing concentrations of the antimicrobial cationic peptide LL-37, which kills bacteria by disturbing membrane integrity and was here observed to increase the cell wall permeability of L. crispatus ST1. At pH 4, the fusion proteins His(6)-GS, His(6)-GPI, His(6)-enolase, and His(6)-GAPDH showed localized binding to cell division septa and poles of L. crispatus ST1 cells, whereas no binding to Lactobacillus rhamnosus GG was detected. Strain ST1 showed a pH-dependent adherence to the basement membrane preparation Matrigel. Purified His(6)-GS and His(6)-GPI proteins bound to type I collagen, and His(6)-GS also bound to laminin, and their level of binding was higher at pH 5.5 than at pH 6.5. His(6)-GS also expressed a plasminogen receptor function. The results show the strain-dependent surface association of moonlighting proteins in lactobacilli and that these proteins are released from the L. crispatus surface after cell trauma, under conditions of alkaline stress, or in the presence of the antimicrobial peptide LL-37 produced by human cells.