The cross-species bacterial communication signal autoinducer 2 (AI-2), produced by the purified enzymes Pfs (nucleosidase) and LuxS (terminal synthase) from S-adenosylhomocysteine, directly increased Escherichia coli biofilm mass 30-fold. Continuous-flow cells coupled with confocal microscopy corroborated these results by showing the addition of AI-2 significantly increased both biofilm mass and thickness and reduced the interstitial space between microcolonies. As expected, the addition of AI-2 to cells which lack the ability to transport AI-2 (lsr null mutant) failed to stimulate biofilm formation. Since the addition of AI-2 increased cell motility through enhanced transcription of five motility genes, we propose that AI-2 stimulates biofilm formation and alters its architecture by stimulating flagellar motion and motility. It was also found that the uncharacterized protein B3022 regulates this AI-2-mediated motility and biofilm phenotype through the two-component motility regulatory system QseBC. Deletion of b3022 abolished motility, which was restored by expressing b3022 in trans. Deletion of b3022 also decreased biofilm formation significantly, relative to the wild-type strain in three media (46 to 74%) in 96-well plates, as well as decreased biomass (8-fold) and substratum coverage (19-fold) in continuous-flow cells with minimal medium (growth rate not altered and biofilm restored by expressing b3022 in trans). Deleting b3022 changed the wild-type biofilm architecture from a thick (54-m) complex structure to one that contained only a few microcolonies. B3022 positively regulates expression of qseBC, flhD, fliA, and motA, since deleting b3022 decreased their transcription by 61-, 25-, 2.4-, and 18-fold, respectively. Transcriptome analysis also revealed that B3022 induces crl (26-fold) and flhCD (8-to 27-fold). Adding AI-2 (6.4 M) increased biofilm formation of wild-type K-12 MG1655 but not that of the isogenic b3022, qseBC, fliA, and motA mutants. Adding AI-2 also increased motA transcription for the wild-type strain but did not stimulate motA transcription for the b3022 and qseB mutants. Together, these results indicate AI-2 induces biofilm formation in E. coli through B3022, which then regulates QseBC and motility; hence, b3022 has been renamed the motility quorum-sensing regulator gene (the mqsR gene).There is an explosive amount of research on biofilms with the ultimate aim of their control (24); however, little is known about the regulation of this complex process of cell attachment leading to exquisite architecture (11). Since 65% of human bacterial infections involve biofilms (31), understanding the genetic basis of biofilm formation to find effective ways to prevent biofilms is important for combating disease and for engineering applications. To this end, we have studied the whole bacterial genome with DNA microarrays by two complementary approaches: studying biofilm gene expression relative to planktonic cells (34, 35) and studying plant-derived biofilm inhibitors that do not alter the bacterial g...

Eight Escherichia coli strains were studied in minimal medium with a continuous flow system using confocal microscopy. K12 wild-type strains ATCC 25404 and MG1655 formed the best biofilms ( approximately 43 microm thick, 21 to 34% surface coverage). JM109, DH5alpha, and MG1655 motA formed intermediate biofilms ( approximately 13 microm thick, 41 to 58% surface coverage). BW25113, MG1655 qseB, and MG1655 fliA had poor biofilms (surface coverage less than 5%). The best biofilm-formers, ATCC 25404 and MG1655, displayed the highest motility, whereas the worst biofilm former, BW25113, was motility-impaired. The differences in motility were due to differences in expression of the motility loci qseB, flhD, fliA, fliC, and motA (e.g., qseB expression in MG1655 was 139-fold higher than BW25113 and 209-fold higher than JM109). Motility affected the biofilm architecture as those strains which had poor motility (E. coli JM109, E. coli MG1655 motA, and DH5alpha) formed flatter microcolonies compared with MG1655 and ATCC 25404, which had more dramatic vertical structures as a result of their enhanced motility. The presence of flagella was also found to be important as qseB and fliA mutants (which lack flagella) had less biofilm than the isogenic paralyzed motA strain (threefold less thickness and 15-fold less surface coverage).

After 13,000 samples of compounds purified from plants were screened, a new biofilm inhibitor, ursolic acid, has been discovered and identified. Using both 96-well microtiter plates and a continuous flow chamber with COMSTAT analysis, 10 g of ursolic acid/ml inhibited Escherichia coli biofilm formation 6-to 20-fold when added upon inoculation and when added to a 24-h biofilm; however, ursolic acid was not toxic to E. coli, Pseudomonas aeruginosa, Vibrio harveyi, and hepatocytes. Similarly, 10 g of ursolic acid/ml inhibited biofilm formation by >87% for P. aeruginosa in both complex and minimal medium and by 57% for V. harveyi in minimal medium. To investigate the mechanism of this nontoxic inhibition on a global genetic basis, DNA microarrays were used to study the gene expression profiles of E. coli K-12 grown with or without ursolic acid. Ursolic acid at 10 and 30 g/ml induced significantly (P < 0.05) 32 and 61 genes, respectively, and 19 genes were consistently induced. The consistently induced genes have functions for chemotaxis and mobility (cheA, tap, tar, and motAB), heat shock response (hslSTV and mopAB), and unknown functions (such as b1566 and yrfHI). There were 31 and 17 genes repressed by 10 and 30 g of ursolic acid/ml, respectively, and 12 genes were consistently repressed that have functions in cysteine synthesis (cysK) and sulfur metabolism (cysD), as well as unknown functions (such as hdeAB and yhaDFG). Ursolic acid inhibited biofilms without interfering with quorum sensing, as shown with the V. harveyi AI-1 and AI-2 reporter systems. As predicted by the differential gene expression, deleting motAB counteracts ursolic acid inhibition (the paralyzed cells no longer become too motile). Based on the differential gene expression, it was also discovered that sulfur metabolism (through cysB) affects biofilm formation (in the absence of ursolic acid).

Escherichia coli Hha is an environmental-response regulator of the pathogenic hemolysin operon, and Hha and the contiguous YbaJ are both induced 30-fold in E. coli biofilms (Appl. Microbiol. Biotechnol. 64:515, 2004). Here it is shown that Hha and YbaJ regulate biofilm formation since the hha/ybaJ deletion reduces biofilm mass in microtitre plates (81% in minimal medium, 50% in complex medium) and in flow cells (1,000-fold less surface coverage in minimal medium). The addition of the derepressed conjugative plasmid R1drd19, which increases significantly biofilm formation, eliminated motility completely in wild-type E. coli K12, promoted cell aggregation 27.18 +/- 0.05-fold, and produced a flatter biofilm. Deletion of hha/ybaJ or ybaJ restored motility (this motility phenotype may be complemented by providing hha(+)/ybaJ(+) or ybaJ(+) in trans) and reduced cell aggregation to that of the wild-type strain that lacks the conjugation plasmid. This increase in motility due to deleting hha/ybaJ was found to be due to 8-fold induction of fliA transcription. In addition, deletion of ompA reduced biofilm mass by 80% in both LB medium and LB medium with glucose. Also, Hha/YbaJ promotes conjugation since there was five-fold less conjugation in the hha/ybaJ mutant. It appears that conjugation plasmids promote biofilm formation by promoting cell aggregation, and that Hha and YbaJ increase biofilm formation by increasing conjugation and by decreasing motility when a conjugative plasmid (R1drd19) is present (YbaJ plays the most important role in this regulation of motility). When hha/ybaJ are deleted, there is less conjugation, less aggregation, more motility, and less biofilm.

BackgroundThe increase in glycerol obtained as a byproduct of biodiesel has encouraged the production of new industrial products, such as 1,3-propanediol (PDO), using biotechnological transformation via bacteria like Clostridium butyricum. However, despite the increasing role of Clostridium butyricum as a bio-production platform, its metabolism remains poorly modeled.ResultsWe reconstructed iCbu641, the first genome-scale metabolic (GSM) model of a PDO producer Clostridium strain, which included 641 genes, 365 enzymes, 891 reactions, and 701 metabolites. We found an enzyme expression prediction of nearly 84% after comparison of proteomic data with flux distribution estimation using flux balance analysis (FBA). The remaining 16% corresponded to enzymes directionally coupled to growth, according to flux coupling findings (FCF). The fermentation data validation also revealed different phenotype states that depended on culture media conditions; for example, Clostridium maximizes its biomass yield per enzyme usage under glycerol limitation. By contrast, under glycerol excess conditions, Clostridium grows sub-optimally, maximizing biomass yield while minimizing both enzyme usage and ATP production. We further evaluated perturbations in the GSM model through enzyme deletions and variations in biomass composition. The GSM predictions showed no significant increase in PDO production, suggesting a robustness to perturbations in the GSM model. We used the experimental results to predict that co-fermentation was a better alternative than iCbu641 perturbations for improving PDO yields.ConclusionsThe agreement between the predicted and experimental values allows the use of the GSM model constructed for the PDO-producing Clostridium strain to propose new scenarios for PDO production, such as dynamic simulations, thereby reducing the time and costs associated with experimentation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0434-0) contains supplementary material, which is available to authorized users.

Pseudomonas aeruginosa is one of the Multi-Drug-Resistant organisms most frequently isolated worldwide and, because of a shortage of new antibiotics, bacteriophages are considered an alternative for its treatment. Previously, P. aeruginosa phages were isolated and best candidates were chosen based on their ability to form clear plaques and their host range. This work aimed to characterize one of those phages, ΦPan70, preliminarily identified as a good candidate for phage-therapy. We performed infection curves, biofilm removal assays, transmission-electron-microscopy, pulsed-field-gel-electrophoresis, and studied the in vivo ΦPan70 biological activity in the burned mouse model. ΦPan70 was classified as a member of the Myoviridae family and, in both planktonic cells and biofilms, was responsible for a significant reduction in the bacterial population. The burned mouse model showed an animal survival between 80% and 100%, significantly different from the control animals (0%). However, analysis of the ΦPan70 genome revealed that it was 64% identical to F10, a temperate P. aeruginosa phage. Gene annotation indicated ΦPan70 as a new, but possible temperate phage, therefore not ideal for phage-therapy. Based on this, we recommend genome sequence analysis as an early step to select candidate phages for potential application in phage-therapy, before entering into a more intensive characterization.

Malassezia species are lipophilic and lipid-dependent yeasts belonging to the human and animal microbiota. Typically, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis, pityriasis versicolor, atopic dermatitis, and folliculitis. The genomes of Malassezia globosa, Malassezia sympodialis, and Malassezia pachydermatis lack the genes related to fatty acid synthesis. Here, the lipid-synthesis pathways of these species, as well as of Malassezia furfur, and of an atypical M. furfur variant were reconstructed using genome data and Constraints Based Reconstruction and Analysis. To this end, the genomes of M. furfur CBS 1878 and the atypical M. furfur 4DS were sequenced and annotated. The resulting Enzyme Commission numbers and predicted reactions were similar to the other Malassezia strains despite the differences in their genome size. Proteomic profiling was utilized to validate flux distributions. Flux differences were observed in the production of steroids in M. furfur and in the metabolism of butanoate in M. pachydermatis. The predictions obtained via these metabolic reconstructions also suggested defects in the assimilation of palmitic acid in M. globosa, M. sympodialis, M. pachydermatis, and the atypical variant of M. furfur, but not in M. furfur. These predictions were validated via physiological characterization, showing the predictive power of metabolic network reconstructions to provide new clues about the metabolic versatility of Malassezia.

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