Relative molecular masses [kDa] and polymerization states (n) of substituted AbrB-variants (for detailed chromatograms see Figure S1 and Figure S2).

a<p>Q81K corresponds to the wild type of <i>B. amyloliquefaciens</i> FZB45.</p>b<p>Q81E showed a plateau at lower retention volumes containing octamers or larger polymeric forms.</p>c<p>Q55E showed aggregation under different conditions and was stable only in presence of 5 mM β-mercaptoethanol.</p

Substitutional Analysis of the C-Terminal Domain of AbrB Revealed Its Essential Role in DNA-Binding Activity

<div><p>The global transition state regulator AbrB controls more than 100 genes of the <i>Bacillus</i> relatives and is known to interact with varying DNA-sequences. The DNA-binding domain of the AbrB-like proteins was proposed to be located exclusively within the amino-terminal ends. However, the recognition of DNA, and specificity of the binding mechanism, remains elusive still in view of highly differing recognition sites. Here we present a substitutional analysis to examine the role of the carboxy-terminal domain of AbrB from <i>Bacillus subtilis</i> and <i>Bacillus amyloliquefaciens</i>. Our results demonstrate that the carboxy-terminal domains of AbrB affect the DNA-binding properties of the tetrameric AbrB. Most likely, the C-termini are responsible for the cooperative character observed for AbrB interaction with some DNA targets like <i>tycA</i> and <i>phyC</i>.</p></div

Amino acid alignment of AbrB-like proteins of <i>Bacillus</i> related species.

<p>N- and C-terminal domains are indicated on the top. Known conserved structures of the DNA-binding domain (β1-4 and α) are highlighted in gray according to (7). The structured regions (β1-2 and α) of the C-terminal domain of AbrB from <i>B. subtilis</i> are highlighted in dark gray according to Olson <i>et al. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097254#pone.0097254-Olson1" target="_blank">[28]</a>. Amino acid residues that were subjected to the substitution are framed. The consensus sequence resulting from this alignment is given on the bottom and the sizes of the amino acid code indicate the percentage of the similarity (100%, 77%, 65% and 42%). Abh_BS  =  Abh protein of <i>B. subtilis</i> 168, B_sub  =  <i>B. subtilis</i> 168, B_amy_FZB45  =  <i>B. amyloliquefaciens</i> FZB45, B_amy_TA208 =  <i>B. amyloliquefaciens</i> TA208, B_lich  =  <i>B. licheniformis</i> ATCC14580, B_pum  =  <i>B. pumilus</i> SARF-032, A_flavith  =  <i>Anoxybacillus flavithermus</i> WK1, B_thuring  =  <i>B. thuringensis</i> Al Hakam, B_weihenstef  =  <i>B. weihenstefanansis</i> KBAB4, B_anth  =  <i>B. anthracis</i> A0248, B_claus  =  <i>B. clausii</i> KSM-K16, B_cer  =  <i>B. cereus</i> ATCC14579, S_silvest  =  <i>Solibacillus silvestris</i> StLB046, L_shearic  =  <i>Lysinibacillus sphearicus</i> C3-41, B_halodu  =  <i>B. halodurans</i> C-125, S_new  =  <i>Sporosarcina newyorkensis</i> 2681, L_monoc  =  <i>Listeria monocytogenes</i> L312</p

Strains and plasmids used in this study

<p>Strains and plasmids used in this study</p

Accuracy of cell counts of a simulated 3D biofilm containing 995 dead cells.

<p>Accuracy of cell counts of a simulated 3D biofilm containing 995 dead cells.</p

A Novel Computerized Cell Count Algorithm for Biofilm Analysis

<div><p>Biofilms are the preferred sessile and matrix-embedded life form of most microorganisms on surfaces. In the medical field, biofilms are a frequent cause of treatment failure because they protect the bacteria from antibiotics and immune cells. Antibiotics are selected according to the minimal inhibitory concentration (MIC) based on the planktonic form of bacteria. Determination of the minimal biofilm eradicating concentration (MBEC), which can be up to 1,000-fold greater than the MIC, is not currently conducted as routine diagnostic testing, primarily because of the methodical hurdles of available biofilm assessing protocols that are time- and cost-consuming. Comparative analysis of biofilms is also limited as most quantitative methods such as crystal violet staining are indirect and highly imprecise. In this paper, we present a novel algorithm for assessing biofilm resistance to antibiotics that overcomes several of the limitations of alternative methods. This algorithm aims for a computer-based analysis of confocal microscope 3D images of biofilms after live/dead stains providing various biofilm parameters such as numbers of viable and dead cells and their vertical distributions within the biofilm, or biofilm thickness. The performance of this algorithm was evaluated using computer-simulated 2D and 3D images of coccal and rodent cells varying different parameters such as cell density, shading or cell size. Finally, genuine biofilms that were untreated or treated with nitroxoline or colistin were analyzed and the results were compared with quantitative microbiological standard methods. This novel algorithm allows a direct, fast and reproducible analysis of biofilms after live/dead staining. It performed well in biofilms of moderate cell densities in a 2D set-up however the 3D analysis remains still imperfect and difficult to evaluate. Nevertheless, this is a first try to develop an easy but conclusive tool that eventually might be implemented into routine diagnostics to determine the MBEC and to improve outcomes of patients with biofilm-associated infections.</p></div

Accuracy of the cell counting (<i>N</i>) and the calculation of the biomass (<i>A</i>) depending on coloration of the cells.

<p>Accuracy of the cell counting (<i>N</i>) and the calculation of the biomass (<i>A</i>) depending on coloration of the cells.</p

Comparison of simulated images with genuine biofilms.

<p>(A) <i>S</i>. <i>aureus</i> (cocci) biofilm and (B) <i>P</i>. <i>aeruginosa</i> (rods) biofilm. Simulations of 10,000 coccal (C) or rod (D) cells at a minimum and maximum declension = 1. All 2D-images of single layers are shown as section of similar resolution with an approximately edge length of 42 μm.</p

Accuracy of cell counting per Z-layer and of the total cell number of simulated 3D biofilms depending on <i>I</i> and <i>P</i> filters.

<p>Accuracy of cell counting per Z-layer and of the total cell number of simulated 3D biofilms depending on <i>I</i> and <i>P</i> filters.</p

Comparison of a 2D and 3D analysis by qBA of an <i>E</i>. <i>coli</i> biofilm.

<p>(A) Analyzed biofilm layers scanned by CLSM (green and red channels overlapping). (B) Histogram of the viable (green) and dead (red) cells estimated in a 2D (dotted lines) and 3D (solid lines) setting. (C) Allocated (red crosses) local grayscale maxima in three neighboring layers (as indicated by the red dotted square in A). Biofilm images in A and B were processed by increasing the intensity and contrast of the signals for illustrative purpose.</p