Numerical and experimental investigation of multi-species bacterial co-aggregation

This paper deals with the mathematical modeling of bacterial co-aggregation and its numerical implementation in a FEM framework. Since the concept of co-aggregation refers to the physical binding between cells of different microbial species, a system composed of two species is considered in the modeling framework. The extension of the model to an arbitrary number of species is straightforward. In addition to two-species (multi-species growth) dynamics, the transport of a nutritional substance and the extent of co-aggregation are introduced into the model as the third and fourth primary variables. A phase-field modeling approach is employed to describe the co-aggregation between the two species. The mathematical model is three-dimensional and fully based on the continuum description of the problem without any need for discrete agents which are the key elements of the individual-based modeling approach. It is shown that the use of a phase-field-based model is equivalent to a particular form of classical diffusion-reaction systems. Unlike the so-called mixture models, the evolution of each component of the multi-species system is captured thanks to the inherent capability of phase-field modeling in treating systems consisting of distinct multi-phases. The details of numerical implementation in a FEM framework are also presented. Indeed, a new multi-field user element is developed and implemented in ANSYS for this multiphysics problem. Predictions of the model are compared with the experimental observations. By that, the versatility and applicability of the model and the numerical tool are well established

Screening of Compounds against Gardnerella vaginalis Biofilms.

Bacterial vaginosis (BV) is a common infection in reproductive age woman and is characterized by dysbiosis of the healthy vaginal flora which is dominated by Lactobacilli, followed by growth of bacteria like Gardnerella vaginalis. The ability of G. vaginalis to form biofilms contributes to the high rates of recurrence that are typical for BV and which unfortunately make repeated antibiotic therapy inevitable. Here we developed a biofilm model for G. vaginalis and screened a large spectrum of compounds for their ability to prevent biofilm formation and to resolve an existing G. vaginalis biofilm. The antibiotics metronidazole and tobramycin were highly effective in preventing biofilm formation, but had no effect on an established biofilm. The application of the amphoteric tenside sodium cocoamphoacetate (SCAA) led to disintegration of existing biofilms, reducing biomass by 51% and viability by 61% and it was able to increase the effect of metronidazole by 40% (biomass) and 61% (viability). Our data show that attacking the biofilm and the bacterial cells by the combination of an amphoteric tenside with the antibiotic metronidazole might be a useful strategy against BV

Biofilm mass and viability after treatment with combinations of tensides and antibiotics.

<p>Biofilm mass was determined by crystal violet staining, and inhibition of biofilm viability was determined by live/dead staining. The tenside SCAA (1 mg/ml) was combined with 25 mg/ml TOB, 0.1 mg/ml MET, 0.05 mg/ml CPC or applied alone and compared to the untreated control. Mean and standard deviation from triplicate cultures are shown.</p

Chemical structures of tested compounds.

<p>CLC (A), CPC (B); PBI (C), SCAA (D), LEC (E), TOB (F) and MET (G).</p

Viability of <i>G</i>. <i>vaginalis</i> biofilms after treatment with an antibiotic, enzyme or tenside.

<p>Live/dead staining of <i>G</i>. <i>vaginalis</i> biofilms treated with 0.1 mg/ml MET, 0.5 mg/ml LYS or 1 mg/ml SCAA is shown for the 20 h experiment and the 40 h experiment in comparison to the untreated control.</p

Effect of pH on growth of <i>G</i>. <i>vaginalis</i> in planktonic and biofilm culture.

<p>(A) Planktonic growth in media adjusted to pH 7, 5.5, 5 or 4.5 measured via OD_600nm (B) Biofilm formation after 20 h at pH 7 (20 h, pH 7) and after 40 h with a medium change after 20 h. After the medium change, the pH was either kept at pH 7 (40 h, pH 7), changed to pH 4.5 (40 h, pH 4.5) or buffered with citrate phosphate buffer (CPB) to pH 4.5 (40 h, pH 4.5 CPB). Biofilm formation was measured using colony forming units (CFUs). Mean and standard deviation from triplicate cultures are shown.</p

Effect of medium and culture conditions on the biomass of <i>G</i>. <i>vaginalis</i> biofilms.

<p>(A) Biomass development in different media after 20 h and 40 h of growth. (B) Effect of glucose concentration and oxygen. Biofilm mass was determined by crystal violet staining. Mean and standard deviation from twelve replicate cultures are shown. (C) Aerobic growth (5% CO₂) at 37°C in sBHIG was measured by crystal violet staining. For the 40 h mc value, the growth medium had been replaced by fresh medium after 20 h of incubation (mc = medium change).</p

Effect of test compounds on <i>G</i>. <i>vaginalis</i> biofilm mass and viability.

<p>(A) antibiotics, (B) enzymes and peptides, (C) antiseptics and (D) tensides. Biofilm mass was determined by CV staining and is shown on the primary y-axis. Inhibition of biofilm viability [%] was measured via live/dead staining and is shown on the secondary y-axis. Mean and standard deviation from triplicate cultures are shown.</p

High-resolution taxonomic profiling of the subgingival microbiome for biomarker discovery and periodontitis diagnosis.

The oral microbiome plays a key role for caries, periodontitis, and systemic diseases. A method for rapid, high-resolution, robust taxonomic profiling of subgingival bacterial communities for early detection of periodontitis biomarkers would therefore be a useful tool for individualized medicine. Here, we used Illumina sequencing of the V1-V2 and V5-V6 hypervariable regions of the 16S rRNA gene. A sample stratification pipeline was developed in a pilot study of 19 individuals, 9 of whom had been diagnosed with chronic periodontitis. Five hundred twenty-three operational taxonomic units (OTUs) were obtained from the V1-V2 region and 432 from the V5-V6 region. Key periodontal pathogens like Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia could be identified at the species level with both primer sets. Principal coordinate analysis identified two outliers that were consistently independent of the hypervariable region and method of DNA extraction used. The linear discriminant analysis (LDA) effect size algorithm (LEfSe) identified 80 OTU-level biomarkers of periodontitis and 17 of health. Health- and periodontitis-related clusters of OTUs were identified using a connectivity analysis, and the results confirmed previous studies with several thousands of samples. A machine learning algorithm was developed which was trained on all but one sample and then predicted the diagnosis of the left-out sample (jackknife method). Using a combination of the 10 best biomarkers, 15 of 17 samples were correctly diagnosed. Training the algorithm on time-resolved community profiles might provide a highly sensitive tool to detect the onset of periodontitis