Influence of continuous light or dark on <i>V. campbellii</i> WT and Δ<i>pR</i> growth in minimal medium and <i>pR</i> transcription.

<p>White squares–<i>V. campbellii</i> WT in continuous light; black squares–<i>V. campbellii</i> WT in continuous dark; white circles–<i>V. campbellii</i> Δ<i>pR</i> in continuous light; black circles–<i>V. campbellii</i> Δ<i>pR</i> in continuous dark. (a) Optical density measurements (OD₆₀₀) and (b) Flow cytometry-based assessment of bacterial growth in M9 minimal medium cultures. Data shown represent means ± SD of duplicate determinations from two experiments. (c) Time course analysis of <i>V. campbellii</i> WT <i>pR</i> transcription via reverse-transcription real-time PCR from M9 minimal medium cultures grown in continuous light or dark.</p

Method Development for Metaproteomic Analyses of Marine Biofilms

The large-scale identification and quantitation of proteins via nanoliquid chromatography (LC)-tandem mass spectrometry (MS/MS) offers a unique opportunity to gain unprecedented insight into the microbial composition and biomolecular activity of true environmental samples. However, in order to realize this potential for marine biofilms, new methods of protein extraction must be developed as many compounds naturally present in biofilms are known to interfere with common proteomic manipulations and LC-MS/MS techniques. In this study, we used amino acid analyses (AAA) and LC-MS/MS to compare the efficacy of three sample preparation methods [6 M guanidine hydrochloride (GuHCl) protein extraction + in-solution digestion + 2D LC; sodium dodecyl sulfate (SDS) protein extraction + 1D gel LC; phenol protein extraction + 1D gel LC] for the metaproteomic analyses of an environmental marine biofilm. The AAA demonstrated that proteins constitute 1.24% of the biofilm wet weight and that the compared methods varied in their protein extraction efficiencies (0.85–15.15%). Subsequent LC-MS/MS analyses revealed that the GuHCl method resulted in the greatest number of proteins identified by one or more peptides whereas the phenol method provided the greatest sequence coverage of identified proteins. As expected, metagenomic sequencing of the same biofilm sample enabled the creation of a searchable database that increased the number of protein identifications by 48.7% (≥1 peptide) or 54.7% (≥2 peptides) when compared to SwissProt database identifications. Taken together, our results provide methods and evidence-based recommendations to consider for qualitative or quantitative biofilm metaproteome experimental design

Method Development for Metaproteomic Analyses of Marine Biofilms

The large-scale identification and quantitation of proteins via nanoliquid chromatography (LC)-tandem mass spectrometry (MS/MS) offers a unique opportunity to gain unprecedented insight into the microbial composition and biomolecular activity of true environmental samples. However, in order to realize this potential for marine biofilms, new methods of protein extraction must be developed as many compounds naturally present in biofilms are known to interfere with common proteomic manipulations and LC-MS/MS techniques. In this study, we used amino acid analyses (AAA) and LC-MS/MS to compare the efficacy of three sample preparation methods [6 M guanidine hydrochloride (GuHCl) protein extraction + in-solution digestion + 2D LC; sodium dodecyl sulfate (SDS) protein extraction + 1D gel LC; phenol protein extraction + 1D gel LC] for the metaproteomic analyses of an environmental marine biofilm. The AAA demonstrated that proteins constitute 1.24% of the biofilm wet weight and that the compared methods varied in their protein extraction efficiencies (0.85–15.15%). Subsequent LC-MS/MS analyses revealed that the GuHCl method resulted in the greatest number of proteins identified by one or more peptides whereas the phenol method provided the greatest sequence coverage of identified proteins. As expected, metagenomic sequencing of the same biofilm sample enabled the creation of a searchable database that increased the number of protein identifications by 48.7% (≥1 peptide) or 54.7% (≥2 peptides) when compared to SwissProt database identifications. Taken together, our results provide methods and evidence-based recommendations to consider for qualitative or quantitative biofilm metaproteome experimental design

RpoS1 as an activator of <i>pR</i> expression.

<p>(a) <i>V. campbellii</i> WT, Δ<i>pR</i>, Δ<i>rpoS1</i> and Δ<i>rpoS2</i> M9 minimal medium stationary phase cultures (36 hr) that were either continuously illuminated or dark were assessed by reverse-transcription real-time PCR for <i>pR</i> expression or (b) <i>rpoS1</i> expression. White bars–continuously illuminated cultures; black bars–continuously dark cultures; n.d. – none detected. Data shown represent means ± SD of two technical replicates from two independent experiments. (c) Comparison of PR-mediated pigmentation in <i>V. campbellii</i> WT, Δ<i>pR</i>, Δ<i>rpoS1</i>, and Δ<i>rpoS2</i> stationary phase cells grown under continuous illumination.</p

Biofilm community structure and the associated drag penalties of a groomed fouling release ship hull coating

<p>Grooming is a proactive method to keep a ship’s hull free of fouling. This approach uses a frequent and gentle wiping of the hull surface to prevent the recruitment of fouling organisms. A study was designed to compare the community composition and the drag associated with biofilms formed on a groomed and ungroomed fouling release coating. The groomed biofilms were dominated by members of the Gammaproteobacteria and Alphaproteobacteria as well the diatoms <i>Navicula</i>, <i>Gomphonemopsis</i>, <i>Cocconeis</i>, and <i>Amphora.</i> Ungroomed biofilms were characterized by Phyllobacteriaceae, Xenococcaceae, Rhodobacteraceae, and the pennate diatoms <i>Cyclophora</i>, <i>Cocconeis</i>, and <i>Amphora.</i> The drag forces associated with a groomed biofilm (0.75 ± 0.09 N) were significantly less than the ungroomed biofilm (1.09 ± 0.06 N). Knowledge gained from this study has helped the design of additional testing which will improve grooming tool design, minimizing the growth of biofilms and thus lowering the frictional drag forces associated with groomed surfaces.</p

Molecular Characterization of Multidrug Resistant Hospital Isolates Using the Antimicrobial Resistance Determinant Microarray

<div><p>Molecular methods that enable the detection of antimicrobial resistance determinants are critical surveillance tools that are necessary to aid in curbing the spread of antibiotic resistance. In this study, we describe the use of the Antimicrobial Resistance Determinant Microarray (ARDM) that targets 239 unique genes that confer resistance to 12 classes of antimicrobial compounds, quaternary amines and streptothricin for the determination of multidrug resistance (MDR) gene profiles. Fourteen reference MDR strains, which either were genome, sequenced or possessed well characterized drug resistance profiles were used to optimize detection algorithms and threshold criteria to ensure the microarray's effectiveness for unbiased characterization of antimicrobial resistance determinants in MDR strains. The subsequent testing of <i>Acinetobacter baumannii</i>, <i>Escherichia coli</i> and <i>Klebsiella pneumoniae</i> hospital isolates revealed the presence of several antibiotic resistance genes [e.g. belonging to TEM, SHV, OXA and CTX-M classes (and OXA and CTX-M subfamilies) of β-lactamases] and their assemblages which were confirmed by PCR and DNA sequence analysis. When combined with results from the reference strains, ∼25% of the ARDM content was confirmed as effective for representing allelic content from both Gram-positive and –negative species. Taken together, the ARDM identified MDR assemblages containing six to 18 unique resistance genes in each strain tested, demonstrating its utility as a powerful tool for molecular epidemiological investigations of antimicrobial resistance in clinically relevant bacterial pathogens.</p></div

Electrochemical signals from MDR reference strain, <i>A. baumannii</i> BAA-1710 (A) and antibiotic sensitive reference strain, <i>A. baumannii</i> 17978 (B).

<p>Each data point represents one probe; data are sorted according to alleles represented by each probe. In each panel, the horizontal dotted line indicates the “probe threshold” used to determine whether probes were considered positive (mean of lowest 95% probes+3 SD). For panel A, peaks correspond to the following alleles: <b><i>a</i></b>: <i>tet(G)</i> [9/9 probes positive]; <b><i>b</i></b>: <i>cat4</i> [5/7 probes positive]; <b><i>c</i></b>: <i>dfrA1</i> [9/9 probes positive]; <b><i>d</i></b>: <i>catA1</i> [6/8 probes positive]; <b><i>e</i></b>: <i>arr-3</i> [8/8 probes positive]; <b><i>f</i></b><i>: aph6/str(B)</i> [8/10 probes positive], <i>aph3″/str(A)</i> [10/10 probes positive], <i>aadA1b</i> [10/10 probes positive], <i>aadB</i> [10/10 probes positive]; <b><i>g</i></b>: <i>qacEΔ1</i> [10/10 probes positive]; <b><i>h</i></b>: <i>aadA2</i> [2/9 probes positive - allele deemed negative], <i>aadA1</i> [7/7 probes positive]; <b><i>i</i></b>: <i>bla</i>_VEB [9/9 probes positive]; <b><i>j</i></b>: <i>bla</i>_OXA-10 [7/7 probes positive]; <b><i>k</i></b>: <i>dfrA10</i> [8/10 probes positive]; <b><i>l</i></b>: <i>bla</i>_PSE-1/CARB [2/10 probes positive – allele deemed negative]; <b><i>m</i></b>: <i>aacC1</i> [9/9 probes positive], <i>aphA1</i> [10/10 probes positive]; <b><i>n</i></b>: <i>ant(2′)-Ia</i> [10/10 probes positive]; <b><i>o</i></b>: <i>sulI</i> [10/10 probes positive]; <b><i>p</i></b>: <i>tet(A)</i> [9/9 probes positive]. For panel B, the <i>sulII</i> allele (peak indicated) was deemed positive with 7/7 probes positive; other alleles had one or two positive probes, but were deemed negative.</p

ARDM analysis of reference strains.

a<p>ARDM-positive genes that have been validated by sequencing or PCR for strains whose genomes have not been fully sequenced are shown in bold.</p

ARDM content allocations for genes conferring resistance to different classes of antimicrobial compounds.

<p>Two hundred thirty-nine different resistance determinants are represented by probes on each sub-array.</p

ARDM v.1, PCR and phenotypic β-lactamase/ESBL profiles of the clinical and reference strains used in this study.

<p>N/A – not available. R – resistant, I – intermediate, S – sensitive.</p>a<p>The identified family to which the CTX-M allele belongs is indicated in parentheses.</p>b<p>While no official ATM resistance criteria for <i>A. baumannii</i> are available, all isolates of this species designated as resistant (R) in the above table showed no zone of inhibition around ATM disks.</p>c<p>Weakly positive detection by PCR.</p