High-throughput molecular identification of Staphylococcus spp. isolated from a clean room facility in an environmental monitoring program

<p>Abstract</p> <p>Background</p> <p>The staphylococci are one of the most common environmental isolates found in clean room facility. Consequently, isolation followed by comprehensive and accurate identification is an essential step in any environmental monitoring program.</p> <p>Findings</p> <p>We have used the API Staph identification kit (bioMérieux, France) which depends on the expression of metabolic activities and or morphological features to identify the <it>Staphylococcus </it>isolates. The API staphylococci showed low sensitivity in the identification of some species, so we performed molecular methods based on PCR based fingerprinting of glyceraldehyde-3-phosphate dehydrogenase encoding gene as useful taxonomic tool for examining <it>Staphylococcus </it>isolates.</p> <p>Conclusions</p> <p>Our results showed that PCR protocol used in this study which depends on genotypic features was relatively accurate, rapid, sensitive and superior in the identification of at least 7 species of <it>Staphylococcus </it>than API Staph which depends on phenotypic features.</p

Comparative Genome-Scale Metabolic Modeling of Metallo-Beta-Lactamase–Producing Multidrug-Resistant Klebsiella pneumoniae Clinical Isolates

The emergence and spread of metallo-beta-lactamase–producing multidrug-resistant (MDR) Klebsiella pneumoniae is a serious public health threat, which is further complicated by the increased prevalence of colistin resistance. The link between antimicrobial resistance acquired by strains of Klebsiella and their unique metabolic capabilities has not been determined. Here, we reconstruct genome-scale metabolic models for 22 K. pneumoniae strains with various resistance profiles to different antibiotics, including two strains exhibiting colistin resistance isolated from Cairo, Egypt. We use the models to predict growth capabilities on 265 different sole carbon, nitrogen, sulfur, and phosphorus sources for all 22 strains. Alternate nitrogen source utilization of glutamate, arginine, histidine, and ethanolamine among others provided discriminatory power for identifying resistance to amikacin, tetracycline, and gentamicin. Thus, genome-scale model based predictions of growth capabilities on alternative substrates may lead to construction of classification trees that are indicative of antibiotic resistance in Klebsiella isolates

Utilization of Crude Glycerol as a Substrate for the Production of Rhamnolipid by Pseudomonas aeruginosa

Biosurfactants are produced by bacteria or yeast utilizing different substrates as sugars, glycerol, or oils. They have important applications in the detergent, oil, and pharmaceutical industries. Glycerol is the product of biodiesel industry and the existing glycerol market cannot accommodate the excess amounts generated; consequently, new markets for refined glycerol need to be developed. The aim of present work is to optimize the production of microbial rhamnolipid using waste glycerol. We have developed a process for the production of rhamnolipid biosurfactants using glycerol as the sole carbon source by a local Pseudomonas aeruginosa isolate that was obtained from an extensive screening program. A factorial design was applied with the goal of optimizing the rhamnolipid production. The highest production yield was obtained after 2 days when cells were grown in minimal salt media at pH 6, containing 1% (v/v) glycerol and 2% (w/v) sodium nitrate as nitrogen source, at 37°C and at 180 rpm, and reached 2.164 g/L after 54 hours (0.04 g/L h). Analysis of the produced rhamnolipids by TLC, HPLC, and FTIR confirmed the nature of the biosurfactant as monorhamnolipid. Glycerol can serve as a source for the production of rhamnolipid from microbial isolates providing a cheap and reliable substrate

Optimization of prodigiosin production by Serratia marcescens using crude glycerol and enhancing production using gamma radiation

Prodigiosin is a red pigment produced by Serratia marcescens. Prodigiosin is regarded as a promising drug owing to its reported characteristics of possessing anti-microbial, anti-cancer, and immunosuppressive activity. A factorial design was applied to generate a set of 32 experimental combinations to study the optimal conditions for pigment production using crude glycerol obtained from local biodiesel facility as carbon source for the growth of Serratia marcescens. The maximum production (870 unit/cell) was achieved at 22 °C, at pH 9 with the addition of 1% (w/v) peptone and 109 cell/ml inoculum size after 6 days of incubation. Gamma radiation at dose 200 Gy was capable of doubling the production of the pigment using the optimized conditions and manipulating production temperature. Our results indicate that we have designed an economic medium supporting enhanced Serratia marcescens MN5 prodigiosin production giving an added value for crude glycerol obtained from biodiesel industry

Sequence alignment of insert sequence region in representative eukaryotic IF2_mt sequences.

<p>Low sequence conservation region indicated by red bar, the first sequence shows the position of the 49 aa insert region as dashes.</p

The occupation of <i>E. coli</i> and <i>T. thermophilus</i> IF1 and IF2 ribosomal binding sites by IF2_mt.

<p>(A) Final optimized IF2_mt model bound to the <i>E. coli</i> 70S ribosome. (B) Bacterial IF1 and IF2 bound to the <i>E.coli</i> 70S ribosome, (C) IF2_mt model bound to the <i>T. thermophilus</i> 30S ribosomal subunit. (D) Bacterial IF1 and IF2 bound to the <i>T. thermophilus</i> 30S ribosomal subunit. Small subunit landmarks indicated: h - head, sh - shoulder, s - spur. Large subunit landmarks indicated: CP - central protuberance, SB - stalk base. The mesh density shown corresponds to the previously published <i>E. coli</i> 70S initiation complex (EMD 1248) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-Allen2" target="_blank">[24]</a> and the <i>T. thermophilus</i> 30S initiation complex (EMD 1523) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-Simonetti2" target="_blank">[26]</a>. In (C) and (D), the initiator tRNA density is indicated by an asterisk. The color scheme is as follows: domain G: orange, domain V: green, domain VI-C1: cornflower blue, domain VI-C2: cyan, and insert region: red, bacterial IF1 in dark green, small ribosomal subunit: transparent yellow, and large ribosomal subunit: transparent blue.</p

Computationally predicted quasi-atomic IF2_mt models and their flexible fitting into the IF2_mt cryo-EM map.

<p>(A) Comparison of IF2_mt model obtained by fitting into the <i>E. coli</i> IF2 cryo-EM map (in blue, left) with recently published IF2_mt model obtained by fitting into the IF2_mt cryo-EM map <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-Yassin1" target="_blank">[27]</a> (in red, right). The initial CCC value with the IF2_mt cryo-EM map is 0.73, but flexible fitting improves the CCC value to 0.84 (blue, center). (B) An alternate topologically variant model for IF2_mt (blue, left) and secondary structure differences between the alternate model (center) and the recently published IF2_mt model (right) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-Yassin1" target="_blank">[27]</a>. The alternate model is obtained through reorientation of an α-helix (residues 446–460, in region indicated by red arrow) that results in additional helices being maintained in the insert region (indicated by a blue arrow). Color scheme for secondary structure: α-helices in purple, β-sheets in yellow, loops in cyan or white, and alternate helices in blue.</p

Secondary and tertiary structure prediction of the 49 aa insert region in IF2_mt.

<p>(A) Five secondary structure prediction protocols: JPRED <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-Cole1" target="_blank">[35]</a>, SCRATCH <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-Cheng1" target="_blank">[36]</a>, PSIPRED <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-McGuffin1" target="_blank">[37]</a>, PREDICTPROTEIN <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-Rost1" target="_blank">[38]</a>, and NETSURFP <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-Petersen1" target="_blank">[39]</a>; were used to predict the internal secondary structure of the 49 aa insert region in IF2_mt. H represents α-Helix, C represents Coil, and E represents Extended strand. (B) Tertiary structure of the 49 aa insert as predicted by I-TASSER.</p

Final optimized composite IF2_mt models showing fit into excised IF2 cryo-EM densities and the structural overlay of the 49 aa insert with bacterial IF1.

<p>A–B represent the optimized IF2_mt model based on flexible fitting into the excised <i>E. coli</i> IF2 density map <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-Allen2" target="_blank">[24]</a>, C–D represent the optimized IF2_mt model based on flexible fitting into the excised <i>T. thermophilus</i> IF2 density map <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-Simonetti2" target="_blank">[26]</a>. The insert is shown in red and the bacterial IF1 is shown in green. The position of IF1 is predicted based on the manual rigid-body fit of <i>T. thermophilus</i> 30S subunit with bound IF1 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021871#pone.0021871-Carter1" target="_blank">[15]</a>.</p