This article was downloaded by: [George Mason University] On: 24 December 2014, At: 09:40 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Biofouling: The Journal of Bioadhesion and Biofilm Research Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/gbif20
Proteome profile of a pandemic Vibrio parahaemolyticus SC192 strain in the planktonic and biofilm condition a
b
c
b
Akhilandeswarre Dharmaprakash , Eshita Mutt , Abdul Jaleel , Sowdhamini Ramanathan & a
Sabu Thomas a
Cholera and Environmental Microbiology Laboratory, Pathogen Biology Program, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India b
National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, Karnataka, India
Click for updates
c
Proteomic Core Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India Published online: 23 May 2014.
To cite this article: Akhilandeswarre Dharmaprakash, Eshita Mutt, Abdul Jaleel, Sowdhamini Ramanathan & Sabu Thomas (2014) Proteome profile of a pandemic Vibrio parahaemolyticus SC192 strain in the planktonic and biofilm condition, Biofouling: The Journal of Bioadhesion and Biofilm Research, 30:6, 729-739, DOI: 10.1080/08927014.2014.916696 To link to this article: http://dx.doi.org/10.1080/08927014.2014.916696
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
Biofouling, 2014 Vol. 30, No. 6, 729–739, http://dx.doi.org/10.1080/08927014.2014.916696
Proteome profile of a pandemic Vibrio parahaemolyticus SC192 strain in the planktonic and biofilm condition Akhilandeswarre Dharmaprakasha, Eshita Muttb, Abdul Jaleelc, Sowdhamini Ramanathanb and Sabu Thomasa* a
Cholera and Environmental Microbiology Laboratory, Pathogen Biology Program, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India; bNational Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, Karnataka, India; cProteomic Core Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India
Downloaded by [George Mason University] at 09:40 24 December 2014
(Received 12 September 2013; accepted 11 April 2014) Vibrio parahaemolyticus is one of the leading causative agents of foodborne diseases in humans. In this study, the proteome profiles of the pandemic strain V. parahaemolyticus SC192 belonging to the O3:K6 serovar during the planktonic and biofilm stages were analyzed by two-dimensional liquid chromatography coupled to tandem mass spectrometry. This non-gel-based multidimensional protein identification technology approach identified 45.5% of the proteome in the reference genome V. parahaemolyticus RIMD 2210633. This is the largest proteome coverage obtained so far in V. parahaemolyticus and provides evidence for expression of 27% of the hypothetical proteins. Comparison of the planktonic and biofilm proteomes based on their cluster of orthologous groups, gene ontologies and KEGG pathways provides basic information on biofilm specific functions and pathways. To the authors’ knowledge, this is the first study to generate a global proteome profile of the pandemic strain of V. parahaemolyticus and the method reported here could be used to rapidly obtain a snapshot of the proteome of any microorganism at a given condition. Keywords: Vibrio parahaemolyticus; biofilm; planktonic stage; proteome; multidimensional protein identification technology; liquid chromatography tandem mass spectrometry
Introduction Vibrio parahaemolyticus is a Gram-negative halophilic bacterium, ubiquitous in marine and estuarine environments. It is one of the leading etiological agents of foodborne diseases in humans, mainly found to cause gastroenteritis and occasionally reported in wound infection and septicemia (Yeung & Boor 2004). The species is frequently reported to colonize marine organisms and is mostly delivered to human hosts through consumption of raw or undercooked contaminated seafood (Su & Liu 2007). Since 1996, strains belonging to the serotypes such as O3:K6, O4:K68, O1:K25 and O1:K untypeable (O1:KUT) have caused massive outbreaks of gastroenteritis in various parts of the world (Chowdhury et al. 2000; Nair et al. 2007). According to a survey conducted by the Center for Disease Control and Prevention (CDC), V. parahaemolyticus causes ~ 45,000 cases each year in the USA (Hoi Ho et al. 2011). The species has to encounter different environmental conditions between its native and host niches and its biofilm forming ability plays a crucial role in successful survival in the fluctuating parameters of the aquatic ecosystem and in transmission to the host (Yildiz & Visick 2009). These biofilms are surface associated bacterial communities enclosed by a self-produced, hydrated extracellular matrix which protects the bacterial cells *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
from deleterious conditions (Hall-Stoodley et al. 2004). Parsek and Singh (2003) discussed the crucial role of biofilm formation in the pathogenicity of microorganisms. According to Costerton et al. (1999), 80% of chronic infections are due to the involvement of bacterial biofilms. Moreover, a few studies conducted on Vibrio species have revealed the importance of biofilm formation in host colonization (Nyholm et al. 2000; Faruque et al. 2006; Yip et al. 2006). Although a few studies have been conducted to understand the biofilm formation mechanism in V. parahaemolyticus (Enos-Berlage et al. 2005; Shime-Hattori et al. 2006; Wang et al. 2013), none have provided the complete genetic requirements at various stages of biofilm formation. The availability of the complete genome sequence of O3:K6 pandemic strain, RIMD 2210633 (Makino et al. 2003) and the lack of global protein expression studies prompted the current authors to conduct a total proteome profile of V. parahaemolyticus. Hence, the aim of the present study was to gain insight into the physiological signature of the planktonic and biofilm stages of V. parahaemolyticus SC192, an O3:K6 pandemic strain, by profiling the total proteins in the planktonic and biofilm conditions employing multidimensional protein identification technology (MudPIT) (Chen et al. 2006). As the proteins in the total proteome of a microbe are
730
A. Dharmaprakash et al.
diverse in their molecular weight, charge and hydrophobicity, there is no single protein extraction protocol suitable for solubilizing the total proteome (Seneviratne et al. 2012). So, in the present study, two protocols were used with the objective of improving proteome coverage and profiling the proteins extracted at three different time points in the stationary planktonic stage, namely 12, 24 and 48 h, and two different time points in the biofilm stage, namely 24 and 48 h, employing MudPIT technology.
Downloaded by [George Mason University] at 09:40 24 December 2014
Materials and methods Bacterial strains and culture conditions The V. parahaemolyticus SC192 strain (03:K6 Serovar) used in this study was a kind gift from the National Institute of Cholera and Enteric Diseases (NICED), Kolkata, India (Sen et al. 2007). The strain was cultured at 30°C in Luria-Bertani broth (HiMedia, Mumbai, India) at 150 rpm for 5 h. Five ml of culture (optical density (OD) 1.0 at 600 nm) were distributed in 18 50-ml falcon tubes (Axygen, Union City, CA, USA). Six falcon tubes were allotted for three time points: 12, 24 and 48 h. For the falcon tubes at 24 and 48 h, a sterile rectangular cover glass of dimensions 24 × 50 mm (Heathrow Scientific LLC, Vernon Hills, IL, USA) was inserted in a vertical position for the growth of bacterial biofilm. All the falcon tubes were incubated at 30°C in a static condition. All chemicals were purchased from Sigma Aldrich (St Louis, MO, USA) unless indicated otherwise. Confocal laser scanning microscopy (CLSM) imaging After 24 and 48 h post incubation, the V. parahaemolyticus SC192 biofilm formed on both sides of the rectangular cover glass was washed by immersing in 0.9% saline. Biofilm formed on one side of the cover glass was wiped with wet cotton dipped in 70% ethanol and the other side was stained with SYTO-9 dye (Molecular Probes, Eugene, OR, USA) according to the manufacturer’s instructions. The side of the cover glass having the stained biofilm was placed over a cavity slide and sealed using DPX Mountant (Spectrochem, Mumbai, India). CLSM images were acquired using a Nikon A1R confocal laser scanning inverted microscope system (Nikon, Melville, NY, USA) using a 60× oil-immersion objective. SYTO-9 was excited using an argon laser at 488 nm and a 525 ± 25 nm band-pass filter was used to collect the emission. Then 40 μm image stacking was done with a 0.5 μm thickness for each stack. From the base of the biofilm, stacking was done 2 μm from the bottom and 38 μm from the top. Image processing and analysis were performed using NIS-Element AR software (V4.00.04; Nikon, Melville, NY, USA).
Sample preparation for mass spectrometry One hundred microliters of planktonic culture at the 12 h time point were added and centrifuged at 3,214 × g for 5 min at 4°C each time, until a 100 mg pellet was collected in two centrifuge tubes. The pellet was washed three times with 1 ml of 50 mM ammonium bicarbonate (ABC) (Biochem Lifesciences, New Delhi, India). The same procedure was repeated for harvesting planktonic cells at the 24 and 48 h time points. For harvesting biofilm at the 24 h time point, the cover glass was removed using forceps, immersed in 50 mM ABC and gently shaken for 30 s to get rid of planktonic cells. The washing step was followed by scraping the biofilm formed on both sides of the cover glass from all six falcon tubes and pooling them in 5 ml of 50 mM ABC. The biofilm associated cells were concentrated by centrifugation at 3,214 × g for 5 min at 4°C and suspended in 1 ml of 50 mM ammonium bicarbonate (ABC). From the 1 ml stock culture, 100 μl were taken and centrifuged at 3,214 × g for 5 min at 4°C each time, until a 100 mg pellet was collected in two centrifuge tubes. The same procedure was followed for harvesting biofilm associated cells at the 48 h time point. One hundred milligrams of pellet in two centrifuge tubes for three time points of the planktonic stage (12, 24 and 48 h) and two time points of the biofilm stage (24 and 48 h) were taken for further protein extraction using two protocols with different conditions as follows. In the first protocol (protocol 1), total proteins were prepared using Rapigest extraction buffer which was prepared by dissolving 5 mg of RapigestTM SF surfactant (Waters Corporation, Milford, MA, USA) in 1 ml of 50 mM ABC, 1 μl of 1 M phenyl methane sulfonyl fluoride (PMSF), 1 μl of 1 M magnesium chloride (HiMedia) and 1 μl of Benzonase®Nuclease (25U μl−1) (Novagen, San Diego, CA, USA). The pellet from each condition was resuspended in 60 μl of Rapigest extraction buffer and sonicated with a probeless sonicator three times without ice for 30 min with an interval of 10 min. The centrifuge tubes were placed in liquid nitrogen for 1 min and thawed on ice for 3 min and this step was repeated thrice. The tubes were centrifuged at 17,949 × g for 10 min and the supernatant was stored at –20°C. For the second protocol (protocol 2), the pellet from each condition was resuspended in 4 ml of 50 mM ABC containing 5 μl of 1 M PMSF and sonicated using a probe for 10 s 15 times with 60 s intermittent cooling. To this, 100 μl of Rapigest extraction buffer, 5 μl of 1 M MgCl2 and 1 μl of benzonase nuclease (25 U μl−1) were added and the mixture incubated for 15 min at room temperature. The suspension was centrifuged at 18,514 × g for 15 min at 4°C and the supernatant was concentrated in a 3 K-D cutoff (Millipore, Carrigtwohill, Co. Cork, Ireland) filter by spinning at 2,962 × g for 100 min and stored at –20°C.
Biofouling
Downloaded by [George Mason University] at 09:40 24 December 2014
Trypsin digestion Protein concentration was determined using the Bradford protein assay reagent according to the manufacturer’s instructions. From each condition 100 μg of total protein were made up to 100 μl with 50 mM ABC and incubated at 80°C for 15 min at 300 rpm in a Thermomixer comfort (Eppendorf, Hamburg, Germany). The proteins were reduced by adding 5 μl of 100 mM DL-dithiothreitol at 60°C for 30 min and alkylated by the addition of 5 μl of 200 mM iodoacetamide at 37°C for 30 min. Subsequently, the proteins were digested with sequence grade modified trypsin (1:25, w/w) by incubating overnight at 37°C. Trypsinization was blocked by adding 1 μl of 100% formic acid and incubation at 37°C for 20 min. The resulting tryptic peptides were centrifuged at 20,817 × g for 12 min and the supernatant was stored at –20°C for further LC-MS/MS analysis. Multidimensional protein identification technology (MudPIT) The peptide samples were analyzed employing twodimensional (2D) nanoacquity UPLC® system (Waters Corporation) coupled to Quadrupole-time of flight (Q-TOF) mass spectrometer (SYNAPT-G2-HDMS, Waters Corporation). Both systems were operated and controlled by MassLynx (Waters Corporation) software. The peptide level fractionation was done using reverse phase (RP) column 1 at high pH (pH 8) in the first dimension, followed by RP column 2 at low pH (pH 2) in the second dimension. Two binary solvent managers (BSM) were used for altering the pH and dilution of high pH to low pH in a 20 mm long Symmetry® C18trap column (Waters Corporation) having an internal diameter of 180 μm and a particle size of 5 μm. The four buffer solutions used for chromatography were 20 mM ammonium formate in Milli-Q water at pH 8 (buffer A), 100% acetonitrile (buffer B), 0.1% formic acid in Milli-Q water (buffer C) and 0.1% formic acid in acetonitrile (buffer D). The sample was injected in partial loop mode and was loaded onto a 50 mm long XBridge™ BEH130 (Ethylene Bridged Hybrid) (Waters Corporation) C18 column having an inner diameter of 300 μm and particle size of 5 μm with buffer A. Peptides were fractionated into 10 fractions sequentially based on a different percentage of buffer B (7.4, 10.8, 12.6, 14.0, 15.3, 16.7, 18.3, 20.4, 23.5 and 50% respectively) and were eluted employing a discontinuous step gradient at a flow rate of 2 μl min−1 from the RP column 1 by first-dimension BSM. The system employs an online dilution of the effluent after the first dimension where de-salting and cleaning-up of peptides takes place and the process also ensure that no peptides were lost during trapping prior to
731
the second dimension. For that, buffer C at a flow rate of 20 μl min−1 was delivered by second-dimension BSM to the trap column. Each fraction was resolved further in a 100 mm long BEH C18 column having an inner diameter of 75 μm and a particle size of 1.7 μm particle (RP column 2) with 1–40% buffer D in a 50 min gradient at 0.3 μl min−1 flow rate. After separation the column was washed with 85% buffer D for 4 min and re-equilibrated with 1% buffer D for 14 min. The column temperature was maintained at 40°C. In addition, 500 fmole μl−1 of human [Glu1]-fibrinopeptide B were infused at 500 nl min−1 as the reference compound using the auxiliary solvent manager (ASM). Mass spectrometry Mass spectrometric analysis of eluting peptides was performed employing traveling wave ionmobility separation (TWIMS) by data-independent acquisition (MSE) on a SYNAPT® G2 high definition Q-TOF mass spectrometer™ system. All the analyses were performed on positive mode electrospray ionization using a nano-lock spray source. The optimal capillary voltage was set at 3.5 kV and the cone voltage at 35 V. The source temperature was set at 80°C. The data were acquired by rapidly alternating between two functions: the first acquires low energy spectra (MS) and the second acquires mass spectra at elevated collision energy (MSE) with ion mobility. Here, both the precursor and fragment ions are generated by the mass spectrometer simultaneously while securing enough data points across each component peak to ensure correct peak integration and quantitative accuracy. The data acquisition was done in Continuum format. The mass spectrometer was operated in resolution mode with a resolving power of 18,000 FWHM. The m/z acquisition range was set from 50 to 2000 with an acquisition rate of 0.9 s per spectrum and an interscan delay of 0.024 s. To perform the mobility separation, the IMS T-Wave™ (Waters Corporation) pulse height was set to 40 V during transmission and the IMS T-Wave™ velocity was set to 700 m s−1. Nitrogen was used as the drift gas at 90 ml min−1. In low energy mode, the collision energy was set to 4 V in the trap region and 2 V in the transfer region. In high energy mode, the collision energy was set to 4 V in the trap region and was ramped from 20 to 45 V in the transfer region. To ensure accuracy and reproducibility during the MS analysis, the reference compound human [Glu1]-fibrinopeptide B (positive ion mode [M+2H] 2+ = 785.8426) for mass correction was sampled every 30 s through the reference sprayer of the nano-lock spray source. MassLynx V4.1 SCN781 was used to acquire and process all the data.
732
A. Dharmaprakash et al.
Downloaded by [George Mason University] at 09:40 24 December 2014
Data analysis For protein identification, the acquired ion mobility enhanced MSE spectra were analyzed using a ProteinLynx Global Server™ v2.5.3 (PLGS) (Waters Corporation) against the NCBI reference sequence V. parahaemolyticus RIMD 2210633 database (NC_004603.faa and NC_004605 dated 22 December 2012) concatenated to a decoy database in which the sequence was reversed for each entry in the original database. Noise reduction thresholds for low energy scan ion, high energy scan ion and peptide intensity were fixed at 150, 50 and 500 counts respectively. Data processing included lock mass correction post acquisition. During the database search, the protein false discovery rate was set to 4%. Work flow was designed in such a way that a protein was required to have at least three fragment ion matches and one peptide match, whereas a peptide was required to have at least one fragment ion match. Trypsin was selected as the primary digest reagent, used with a specificity of one missed cleavage. Oxidation of methionine was selected as the variable modification and carbamidomethylation of cysteine was selected as the fixed modification. Mass tolerance was set to 10 ppm for precursor ions and 20 ppm for fragment ions. Bioinformatic analysis Analyzing the physico-chemical properties of the total proteomes The total proteomes were obtained by compiling the total number of distinct proteins identified at five stages employing both the protocols. Physico-chemical properties such as molecular weight (MW), isoelectric point (pI) and grand average of hydropathicity (GRAVY) were calculated using the EXPASY – Compute pI/MW tool and the GRAVY calculator respectively and the values were plotted as a scatter plot using Perlscript and R (Gasteiger et al. 2005). Subcellular locations were predicted using PSORTb v. 3.0 (Yu et al. 2010). Comparison of the planktonic and biofilm proteomes The identified total proteomes at each stage using protocol 1 and protocol 2 were compiled to come up with the total number of proteins identified in that particular stage. The total proteins identified at five stages were analyzed using the Venny tool to identify proteins specific to the planktonic and biofilm stages (Oliveros 2007). Comparison of the planktonic and biofilm proteomes by functional roles The total proteomes of the planktonic stage were obtained by compiling the total proteins identified at the three
different time points of the planktonic stage, 12, 24 and 48 h. Similarly the total proteomes of the biofilm stage were obtained by compiling the total proteins identified at the two different time points of the biofilm stage, 24 and 48 h. The 4,832 protein coding genes in the V. parahaemolyticus RIMD 2210633 genome were sorted to their respective clusters of orthologous groups (COG) and were made available on the Integrated Microbial genomes website (Markowitz et al. 2012). This information was used for comparing the planktonic and biofilm proteomes based on their COG categories. DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources 6.7 was used for comparing the planktonic and biofilm proteomes based on their gene ontology (GO) terms and KEGG (Kyoto Encyclopedia of Gene and Genomes) pathways. DAVID generates an EASE score, a modified Fisher’s Exact p-value for each term and the GO and KEGG pathway terms with a p-value less than or equal to 0.1 were considered as enriched (Huang et al. 2009). The GO terms corresponding to the biological processes at the hierarchical level (DAVID category – GOTERM_BP_4) were chosen for comparison of planktonic and biofilm proteomes to identify the biological processes specific to the biofilm stage. The dissimilarity analysis based on Euclidean distance was calculated for enrichment values of the GO and KEGG pathway terms obtained using the DAVID tool employing the R esp. dist function of the R package. The results were visualized in the form of heat maps and a clustering dendrogram employing heat map and hclust functions of the R package. Results and discussion Proteome profile In order to improve proteome coverage, two protocols that differ in their detergent concentration and cell lysis method were adapted to extract total proteins from V. parahaemolyticus SC192 at three different time points of the stationary planktonic stage, namely 12, 24 and 48 h, and two different time points of the biofilm stage, namely 24 and 48 h (Figure 1A and B). The proteins identified employing protocol 1 and protocol 2 at five different stages employing MudPIT technology were compiled; 2,199 proteins were identified from a total of 4,832 protein coding genes in the V. parahaemolyticus RIMD 2210633 reference genome. This represents 45.5% of the total proteome in the reference genome. Among the 2,199 total proteins identified, 1,507 were present in both the protocols whereas 565 and 127 were identified exclusively in protocol 1 and protocol 2 respectively. According to the reference genome annotation, 1,885 open reading frames were annotated as hypothetical proteins, of which 510 proteins were identified in the present analysis. To access the protein coverage of the adapted protocols, the number of identified
Biofouling
733
Downloaded by [George Mason University] at 09:40 24 December 2014
protocols favored the extraction of proteins within the pI range of 4–10, a molecular weight of 10–110 kDa and GRAVY index range between –1 and 1 (Figures 2 and 3). When the cellular localization of the identified proteins were predicted using PSORTb, 52.01% (cytoplasmic protein), 64.8% (periplasmic protein), 50.4% (outer membrane protein), 29.23% (extracellular protein) and 28.51% of protein with unknown cellular localization were found to be extracted using the adapted method (Table 1). From the analysis, it was revealed that protocol 1 with a 0.5% detergent concentration showed more proteome coverage than protocol 2 with a 0.001% detergent concentration. The maximum proteome coverage achieved in protocol 1 emphasizes the increase in the solubilization efficiency of a wide range of proteins at higher detergent concentration. The in silico results of the total proteomes identified employing protocols 1 and 2 revealed that protocol 1 favored the extraction of hydrophobic proteins whereas protocol 2 favored the extraction of hydrophilic proteins. So, the protein profiling method adapted here could be used to rapidly obtain the total proteome of any microorganism at a given condition. In the present study, protein profiling was conducted on biofilm grown on a cover glass which was a chitin free surface, and a mannose-sensitive hemagglutinin (MSHA) pilus was identified in all the four stages except the 48 h biofilm stage, whereas a chitin-regulated pilus (ChiRP) was not detected in any of the analyzed conditions. The present observation is in agreement with the finding of Shime-Hattori et al. (2006) which revealed
Figure 1. CLSM images of V. parahaemolyticus SC192 biofilm formed on a sterile rectangular cover glass stained with SYTO-9. The side frame on the right side of the image is the overlay of the x–y plane in the z-axis showing the thickness of the biofilm and the red line in the z-stack indicates the level at which the projected x–y plane was taken. A, 24 h biofilm; B, 48 h biofilm.
ribosomal proteins and physico-chemical parameters such as isoelectric point (pI), molecular weight (MW) and hydrophobicity (GRAVY) were calculated for the identified proteomes. Of the total 55 ribosomal proteins, 53 were identified with high confidence, representing 96% of the total ribosomal proteins. The Compute pI/MW and GRAVY calculator results revealed that the adapted
Figure 2. Distribution of the predicted molecular weight (MW) and isoelectric point (pI) for the identified proteins in protocol 1 and protocol 2. Protocol 1 is represented as black dots and protocol 2 as gray dots. The scatter plot shows the majority of extracted proteins between the pI range of 4–10 and a MW of 10–110 kDa.
Downloaded by [George Mason University] at 09:40 24 December 2014
734
A. Dharmaprakash et al.
Figure 3. Distribution of GRAVY values for the identified proteins in Protocol 1 and Protocol 2. Protocol 1 is represented as black dots and Protocol 2 as gray dots. The scatter plot shows the majority of extracted proteins between a GRAVY index range of -1 and 1.
that MSHA pilus (VPA0747) is required for attachment of V. parahaemolyticus to any surface and if it attaches to a chitin surface, ChiRP (VP2523) expression is upregulated and interconnects the bacteria, creating a stable biofilm architecture. When the identified total proteomes were analyzed for the proteins related to V. parahaemolyticus pathogenicity, 17 proteins in the tdh pathogenicity island (VPA1310-VPA1398) and 11 proteins in the super integron region (VP1787–1865) were identified and further investigation of these proteins may provide novel antivirulent drug targets for combating V. parahaemolyticus infection. Moreover, the ORF8 protein (VP1561) of the f237 prophage was identified in all the analyzed conditions; this was shown to be a genetic marker for detection of the O3:K6 pandemic strain by Iida et al. (2001). This observation was in agreement with the pandemic categorization of the clinical strain used in the present investigation.
Figure 4. A, Venn diagram generated for the identified total proteomes of the 12 h planktonic stage (1,436 proteins) vs the 24 h planktonic stage (1,545) vs the 48 h planktonic stage (1,513) bacteria. A total of 1,953 proteins were identified in the planktonic stage. B, Venn diagram generated for the identified total proteomes of the 24 h biofilm stage (1,332) vs the 48 h biofilm stage (1,457) bacteria. A total of 1,816 proteins were identified in the biofilm stage.
Table 1. Predicted localization for the total number of proteins in the reference genome and the identified proteomes of the reference genome. Total number of identified proteins of the reference genome Localization Cytoplasm Periplasm Outer membrane Extracellular Unknown
Total number of proteins in the reference genome
Protocol 1
Protocol 2
Protocols 1 and 2 compiled
3,176 125 123 65 1,343
1,586 77 56 18 333
1,253 61 42 10 266
1,652 81 62 19 383
Biofouling
735
Downloaded by [George Mason University] at 09:40 24 December 2014
the planktonic and the biofilm stages, respectively. The change in the proteome profile at each stage clearly indicates a temporal variation in protein expression during the course of biofilm formation (Figures 4A, B, 5). When the 246 biofilm specific proteins were analyzed, the majority were found to be hypothetical proteins. In addition, proteins involved in motility, transport, carbohydrate metabolism and transcription regulation were also reported, which correlates with the previous global transcriptomic studies conducted on the planktonic and biofilm stages of Pseudomonas aeruginosa (Whiteley et al. 2001), Escherichia coli (Schembri et al. 2003) and Vibrio cholerae (Moorthy & Watnick 2005). Figure 5. Venn diagram generated for the identified total proteomes of the planktonic stage (1,953) vs the biofilm stage (1,816) of V. parahaemolyticus SC192. A total of 2,199 proteins were identified in the five analyzed conditions. Of these, 383 and 246 proteins were present exclusively in the planktonic and biofilm stages, respectively.
Comparison of the planktonic and biofilm proteomes The total numbers of proteins identified at each stage employing both the protocols were compiled to get the total proteomes at each stage. When the total proteomes present in the planktonic (12, 24 and 48 h) and biofilm stages (24 and 48 h) were analyzed using the Venny tool, 953 proteins were detected in all the conditions. These proteins were involved in housekeeping functions and were designated as the core proteomes of V. parahaemolyticus SC192. Of the 2,199 identified total proteins, 383 and 246 proteins were specific to
Analyzing the biofilm proteomes by functional roles For analyzing the biofilm proteomes by functional roles, the total proteomes of the planktonic and biofilm stages were compared on the basis of clusters of orthologous groups (COG) and gene ontology (GO) categories. On comparison based on their COG categories, it was found that the COG classes needed for house-keeping functions such as information processing (J and K), cellular processes (M and O) and metabolism (C, E, and G) were over-represented in the core proteome. The COG grouping of the biofilm specific proteins revealed that most of the proteins were sorted to COG classes such as T (signal transduction mechanism), P (inorganic ion transport and metabolism) and E (amino acid transport and metabolism) next to the poorly characterized COG categories (Figure 6).
Figure 6. Comparison of the identified total proteomes of the planktonic and the biofilm stage based on their corresponding COG functional categories. COG categories are as follows: K, transcription; J, translation, ribosomal structure and biogenesis; L, DNA replication, recombination and repair; A, RNA processing and modification; T, signal transduction mechanisms; M, outer membrane and cell envelope biogenesis; N, cell motility and secretion; O, post translational modification, protein turnover and chaperones; U, intracellular trafficking, secretion and vesicular transport; V, defense mechanism; D, cell division and chromosome partitioning; E, amino acid transport and metabolism; G, carbohydrate transport and metabolism; P, inorganic ion transport and metabolism; C, energy production and conversion; H, coenzyme metabolism; I, lipid metabolism; F, nucleotide transport and metabolism; Q, secondary metabolite biosynthesis, transport and catabolism; R and S, poorly characterized; X, no affiliated COG category.
Downloaded by [George Mason University] at 09:40 24 December 2014
736
A. Dharmaprakash et al.
Figure 7. Comparison of enriched gene ontology (GO) terms in the planktonic and the biofilm stage by clustering the DAVID results using a Euclidean distance matrix. The heat map shows the specific enrichment of GO terms such as the aromatic amino acid family metabolic process, the aromatic compound biosynthetic process, the fatty acid metabolic process, the heterocycle biosynthetic process and the lipid biosynthetic process in the biofilm stage.
The enriched gene ontology categories in the biofilm condition were identified by comparing the total planktonic and biofilm proteomes employing the DAVID bioinformatics resource. The analysis showed that the gene ontology terms pertaining to biological processes, such as the aromatic amino acid family metabolic process, the aromatic compound biosynthetic process, the fatty acid metabolic process, the heterocycle biosynthetic process and the lipid biosynthetic process, were found to be enriched in the biofilm proteome (Figure 7). According to Yildiz and Visick (2009), aromatic compounds such as acyl homoserine lactone (AHL) was reported to regulate the biofilm formation mechanism in some Vibrio
spp., and the enrichment of the gene ontologies pertaining to aromatic compound biosynthesis and metabolism during the biofilm stage suggests the role of these aromatic signaling molecules in regulating the biofilm formation mechanism. So, the DAVID result can be taken as strong evidence for conducting future studies in elucidating the role of quorum sensing (QS) in regulating biofilm formation in V. parahaemolyticus. Pathways specific to the biofilm stage On analyzing the 2,199 identified total proteins using DAVID, ~ 39.1% were assigned to defined KEGG
Downloaded by [George Mason University] at 09:40 24 December 2014
Biofouling
737
Figure 8. Comparison of enriched KEGG pathways in the planktonic and biofilm stage by clustering the DAVID results using a Euclidean distance matrix. The heat map shows the specific enrichment of KEGG pathways such as arginine and proline metabolism, fatty acid biosynthesis and sulfur metabolism in the biofilm stage.
pathways. More than 80% of the proteins were reported for the following 11 KEGG pathways: amino acid metabolism (176 hits), ribosome (53 hits), bacterial chemotaxis (43 hits), pyruvate metabolism (43 hits), butanoate metabolism (35 hits), glycolysis/gluconeogenesis (34 hits), propanoate metabolism (32 hits), citrate cycle (24 hits), aminoacyl tRNA biosynthesis (24 hits), fatty acid metabolism (23 hits) and folate metabolism (14 hits), which further ensured the maximum coverage achieved using the current method. Furthermore, it was revealed that the KEGG pathways of arginine and proline metabolism, fatty acid biosynthesis and sulfur metabolism were statistically enriched in the biofilm con-
dition (Figure 8). The requirement of proteins involved in sulfur metabolism is strong evidence of anaerobic metabolism and reveals the metabolic heterogeneity of surface associated bacteria. Besides, the similar enrichment observed for KEGG pathways such as glycolysis, citrate cycle, purine metabolism, pyruvate metabolism and ribosomes in the planktonic and biofilm stages revealed that the biofilm associated cells were viable and engaged in division like their planktonic counterparts. To summarize, in the present study, MudPIT was used to document the proteome of the stationary planktonic stage and the mature biofilm stage of V. parahaemolyticus SC192. The adapted protocol identified 45.5%
738
A. Dharmaprakash et al.
Downloaded by [George Mason University] at 09:40 24 December 2014
of the total proteome of the reference genome, thereby providing large-scale proteome coverage of V. parahaemolyticus obtained up to the present. The protein profiling method reported here could therefore be used to rapidly obtain a snapshot of the total proteome of any microorganism at a given condition. Indeed, the protein profiling data reported in this paper has provided fundamental information and a set of interesting targets for a multifaceted analysis of the biofilm formation mechanism in V. parahaemolyticus. To the best of the authors’ knowledge, this is the first attempt to generate a global proteome profile of a pandemic strain of V. parahaemolyticus during the planktonic and biofilm stages.
Acknowledgements The authors thank Prof. M. Radhakrishna Pillai, Director, Rajiv Gandhi Centre for Biotechnology (RGCB), for the facilities provided. They also thank Dr Ramamurthy of the National Institute of Cholera and Enteric Diseases (NICED), Kolkata, for providing the pandemic strain used in this work and Mr Arun Surendran, Mr Srikanth Jandhyam and Mr Saravana Kumar of RGCB for their technical help in carrying out the mass spectrometry analysis. D. Akhilandeswarre is grateful to the Department of Science and Technology (DST), Government of India, New Delhi for providing the INSPIRE fellowship [Fellow code: IF 110672].
References Chen EI, Hewel J, Felding-Habermann B, Yates JR 3rd. 2006. Large scale protein profiling by combination of protein fractionation and multidimensional protein identification technology (MudPIT). Mol Cell Proteomics. 5:53–56. Chowdhury NR, Chakraborty S, Ramamurthy T, Nishibuchi M, Yamasaki S, Takeda Y, Nair GB. 2000. Molecular evidence of clonal Vibrio parahaemolyticus pandemic strains. Emerg Infect Dis. 6:631–636. Costerton JW, Stewart PS, Greenberg EP. 1999. Bacterial biofilms: a common cause of persistent infections. Sci. 284:1318–1322. Enos-Berlage JL, Guvener ZT, Keenan CE, McCarter LL. 2005. Genetic determinants of biofilm development of opaque and translucent Vibrio parahaemolyticus. Mol Microbiol. 55:1160–1182. Faruque SM, Biswas K, Udden SM, Ahmad QS, Sack DA, Nair GB, Mekalanos JJ. 2006. Transmissibility of cholera: in vivo-formed biofilms and their relationship to infectivity and persistence in the environment. Proc Natl Acad Sci USA. 103:6350–6355. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A. 2005. Protein identification and analysis tools on the ExPASyServer. In: Walker JM, editor. The proteomics protocols handbook. Totowa (NJ): Humana Press; p. 571–607. EXPASY – Compute pI/MW (http:// web.expasy.org/compute_pi/) and GRAVY calculator (http://www.gravy-calculator.de/). Hall-Stoodley L, Costerton JW, Stoodley P. 2004. Bacterial biofilms: from the natural environment to infectious diseases. Nat Rev Microbiol. 2:95–108.
Ho H, Karl Alozie O, Tran S-Y, Ho TT, Do TH. 2011. Vibrio infections. Available from: http://emedicine.medscape.com/ article/232038-overview Huang Da W, Sherman BT, Lempicki RA. 2009. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. Available from: http://david.abcc.ncifcrf.gov/ Iida T, Hattori A, Tagomori K, Nasu H, Naim R, Honda T. 2001. Filamentous phage associated with recent pandemic strains of Vibrio parahaemolyticus. Emerg Infect Dis. 7:477–478. Makino K, Oshima K, Kurokawa K, Yokoyama K, Uda T, Tagomori K, Iijima Y, Najima M, Nakano M, Yamashita A, et al. 2003. Genome sequence of Vibrio parahaemolyticus: a pathogenic mechanism distinct from that of V. cholerae. Lancet. 361:743–749. Markowitz VM, Chen IM, Palaniappan K, Chu K, Szeto E, Grechkin Y, Ratner A, Jacob B, Huang J, Williams P, et al. 2012. IMG: the integrated microbial genomes database and comparative analysis system. Nucleic Acids Res. 40 (Database issue):D115–D122. Available from: http://img.jgi.doe. gov/ cgi-bin/w/main.cgi?section=TaxonDetail&page=taxonDetail &taxon_oid =637000335 Moorthy S, Watnick PI. 2005. Identification of novel stagespecific genetic requirements through whole genome transcription profiling of Vibrio cholerae biofilm development. Mol Microbiol. 57:1623–1635. Nair GB, Ramamurthy T, Bhattacharya SK, Dutta B, Takeda Y, Sack DA. 2007. Global dissemination of Vibrio parahaemolyticus serotype O3:K6 and its serovariants. Clin Microbiol Rev. 20:39–48. Nyholm SV, Stabb EV, Ruby EG, McFall-Ngai MJ. 2000. Establishment of an animal-bacterial association: recruiting symbiotic vibrios from the environment. Proc Natl Acad Sci USA. 97:10231–10235. Oliveros JC. 2007. VENNY. An interactive tool for comparing lists with Venn diagrams. Available from: http://bioinfogp. cnb.csic.es/tools/venny/index.html Parsek MR, Singh PK. 2003. Bacterial biofilms: an emerging link to disease pathogenesis. Annu Rev Microbiol. 57:677–701. Schembri MA, Kjaergaard K, Klemm P. 2003. Global gene expression in Escherichia coli biofilms. Mol Microbiol. 48:253–267. Sen B, Dutta B, Chatterjee S, Bhattacharya MK, Nandy RK, Mukhopadhyay AK, Gangopadhyay DN, Bhattacharya SK, Ramamurthy T. 2007. The first outbreak of acute diarrhea due to a pandemic strain of Vibrio parahaemolyticus O3: K6 in Kolkata, India. Int J Infect Dis. 11:185–187. Seneviratne CJ, Wang Y, Jin L, Wong SS, Herath TD, Samaranayake LP. 2012. Unraveling the resistance of microbial biofilms: has proteomics been helpful? Proteomics. 12:651–665. Shime-Hattori A, Iida T, Arita M, Park KS, Kodama T, Honda T. 2006. Two type IV pili of Vibrio parahaemolyticus play different roles in biofilm formation. FEMS Microbiol Lett. 264:89–97. Su YC, Liu C. 2007. Vibrio parahaemolyticus: a concern of seafood safety. Food Microbiol. 24:549–558. Wang L, Ling Y, Jiang H, Qiu Y, Qiu J, Chen H, Yang R, Zhou D. 2013. AphA is required for biofilm formation, motility, and virulence in pandemic Vibrio parahaemolyticus. Int J Food Microbiol. 160:245–251. Whiteley M, Bangera MG, Bumgarner RE, Parsek MR, Teitzel GM, Lory S, Greenberg EP. 2001. Gene expression in Pseudomonas aeruginosa biofilms. Nature. 413:860–864.
Biofouling
Downloaded by [George Mason University] at 09:40 24 December 2014
Yeung PS, Boor KJ. 2004. Epidemiology, pathogenesis, and prevention of foodborne Vibrio parahaemolyticus infections. Foodborne Pathog Dis. 1:74–88. Yildiz FH, Visick KL. 2009. Vibrio biofilms: so much the same yet so different. Trends Microbiol. 17:109–118. Yip ES, Geszvain K, DeLoney-Marino CR, Visick KL. 2006. The symbiosis regulator rscS controls the syp gene locus, biofilm formation and symbiotic aggregation by Vibrio fischeri. Mol Microbiol. 62:1586–1600.
739
Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, Dao P, Sahinalp SC, Ester M, Foster LJ, Brinkman FS. 2010. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics. 26:1608–1615. Available from: http://www.psort.org/
Biofouling: The Journal of Bioadhesion and Biofilm Research Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/gbif20
Proteome profile of a pandemic Vibrio parahaemolyticus SC192 strain in the planktonic and biofilm condition a
b
c
b
Akhilandeswarre Dharmaprakash , Eshita Mutt , Abdul Jaleel , Sowdhamini Ramanathan & a
Sabu Thomas a
Cholera and Environmental Microbiology Laboratory, Pathogen Biology Program, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India b
National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, Karnataka, India
Click for updates
c
Proteomic Core Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India Published online: 23 May 2014.
To cite this article: Akhilandeswarre Dharmaprakash, Eshita Mutt, Abdul Jaleel, Sowdhamini Ramanathan & Sabu Thomas (2014) Proteome profile of a pandemic Vibrio parahaemolyticus SC192 strain in the planktonic and biofilm condition, Biofouling: The Journal of Bioadhesion and Biofilm Research, 30:6, 729-739, DOI: 10.1080/08927014.2014.916696 To link to this article: http://dx.doi.org/10.1080/08927014.2014.916696
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
Biofouling, 2014 Vol. 30, No. 6, 729–739, http://dx.doi.org/10.1080/08927014.2014.916696
Proteome profile of a pandemic Vibrio parahaemolyticus SC192 strain in the planktonic and biofilm condition Akhilandeswarre Dharmaprakasha, Eshita Muttb, Abdul Jaleelc, Sowdhamini Ramanathanb and Sabu Thomasa* a
Cholera and Environmental Microbiology Laboratory, Pathogen Biology Program, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India; bNational Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, Karnataka, India; cProteomic Core Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India
Downloaded by [George Mason University] at 09:40 24 December 2014
(Received 12 September 2013; accepted 11 April 2014) Vibrio parahaemolyticus is one of the leading causative agents of foodborne diseases in humans. In this study, the proteome profiles of the pandemic strain V. parahaemolyticus SC192 belonging to the O3:K6 serovar during the planktonic and biofilm stages were analyzed by two-dimensional liquid chromatography coupled to tandem mass spectrometry. This non-gel-based multidimensional protein identification technology approach identified 45.5% of the proteome in the reference genome V. parahaemolyticus RIMD 2210633. This is the largest proteome coverage obtained so far in V. parahaemolyticus and provides evidence for expression of 27% of the hypothetical proteins. Comparison of the planktonic and biofilm proteomes based on their cluster of orthologous groups, gene ontologies and KEGG pathways provides basic information on biofilm specific functions and pathways. To the authors’ knowledge, this is the first study to generate a global proteome profile of the pandemic strain of V. parahaemolyticus and the method reported here could be used to rapidly obtain a snapshot of the proteome of any microorganism at a given condition. Keywords: Vibrio parahaemolyticus; biofilm; planktonic stage; proteome; multidimensional protein identification technology; liquid chromatography tandem mass spectrometry
Introduction Vibrio parahaemolyticus is a Gram-negative halophilic bacterium, ubiquitous in marine and estuarine environments. It is one of the leading etiological agents of foodborne diseases in humans, mainly found to cause gastroenteritis and occasionally reported in wound infection and septicemia (Yeung & Boor 2004). The species is frequently reported to colonize marine organisms and is mostly delivered to human hosts through consumption of raw or undercooked contaminated seafood (Su & Liu 2007). Since 1996, strains belonging to the serotypes such as O3:K6, O4:K68, O1:K25 and O1:K untypeable (O1:KUT) have caused massive outbreaks of gastroenteritis in various parts of the world (Chowdhury et al. 2000; Nair et al. 2007). According to a survey conducted by the Center for Disease Control and Prevention (CDC), V. parahaemolyticus causes ~ 45,000 cases each year in the USA (Hoi Ho et al. 2011). The species has to encounter different environmental conditions between its native and host niches and its biofilm forming ability plays a crucial role in successful survival in the fluctuating parameters of the aquatic ecosystem and in transmission to the host (Yildiz & Visick 2009). These biofilms are surface associated bacterial communities enclosed by a self-produced, hydrated extracellular matrix which protects the bacterial cells *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
from deleterious conditions (Hall-Stoodley et al. 2004). Parsek and Singh (2003) discussed the crucial role of biofilm formation in the pathogenicity of microorganisms. According to Costerton et al. (1999), 80% of chronic infections are due to the involvement of bacterial biofilms. Moreover, a few studies conducted on Vibrio species have revealed the importance of biofilm formation in host colonization (Nyholm et al. 2000; Faruque et al. 2006; Yip et al. 2006). Although a few studies have been conducted to understand the biofilm formation mechanism in V. parahaemolyticus (Enos-Berlage et al. 2005; Shime-Hattori et al. 2006; Wang et al. 2013), none have provided the complete genetic requirements at various stages of biofilm formation. The availability of the complete genome sequence of O3:K6 pandemic strain, RIMD 2210633 (Makino et al. 2003) and the lack of global protein expression studies prompted the current authors to conduct a total proteome profile of V. parahaemolyticus. Hence, the aim of the present study was to gain insight into the physiological signature of the planktonic and biofilm stages of V. parahaemolyticus SC192, an O3:K6 pandemic strain, by profiling the total proteins in the planktonic and biofilm conditions employing multidimensional protein identification technology (MudPIT) (Chen et al. 2006). As the proteins in the total proteome of a microbe are
730
A. Dharmaprakash et al.
diverse in their molecular weight, charge and hydrophobicity, there is no single protein extraction protocol suitable for solubilizing the total proteome (Seneviratne et al. 2012). So, in the present study, two protocols were used with the objective of improving proteome coverage and profiling the proteins extracted at three different time points in the stationary planktonic stage, namely 12, 24 and 48 h, and two different time points in the biofilm stage, namely 24 and 48 h, employing MudPIT technology.
Downloaded by [George Mason University] at 09:40 24 December 2014
Materials and methods Bacterial strains and culture conditions The V. parahaemolyticus SC192 strain (03:K6 Serovar) used in this study was a kind gift from the National Institute of Cholera and Enteric Diseases (NICED), Kolkata, India (Sen et al. 2007). The strain was cultured at 30°C in Luria-Bertani broth (HiMedia, Mumbai, India) at 150 rpm for 5 h. Five ml of culture (optical density (OD) 1.0 at 600 nm) were distributed in 18 50-ml falcon tubes (Axygen, Union City, CA, USA). Six falcon tubes were allotted for three time points: 12, 24 and 48 h. For the falcon tubes at 24 and 48 h, a sterile rectangular cover glass of dimensions 24 × 50 mm (Heathrow Scientific LLC, Vernon Hills, IL, USA) was inserted in a vertical position for the growth of bacterial biofilm. All the falcon tubes were incubated at 30°C in a static condition. All chemicals were purchased from Sigma Aldrich (St Louis, MO, USA) unless indicated otherwise. Confocal laser scanning microscopy (CLSM) imaging After 24 and 48 h post incubation, the V. parahaemolyticus SC192 biofilm formed on both sides of the rectangular cover glass was washed by immersing in 0.9% saline. Biofilm formed on one side of the cover glass was wiped with wet cotton dipped in 70% ethanol and the other side was stained with SYTO-9 dye (Molecular Probes, Eugene, OR, USA) according to the manufacturer’s instructions. The side of the cover glass having the stained biofilm was placed over a cavity slide and sealed using DPX Mountant (Spectrochem, Mumbai, India). CLSM images were acquired using a Nikon A1R confocal laser scanning inverted microscope system (Nikon, Melville, NY, USA) using a 60× oil-immersion objective. SYTO-9 was excited using an argon laser at 488 nm and a 525 ± 25 nm band-pass filter was used to collect the emission. Then 40 μm image stacking was done with a 0.5 μm thickness for each stack. From the base of the biofilm, stacking was done 2 μm from the bottom and 38 μm from the top. Image processing and analysis were performed using NIS-Element AR software (V4.00.04; Nikon, Melville, NY, USA).
Sample preparation for mass spectrometry One hundred microliters of planktonic culture at the 12 h time point were added and centrifuged at 3,214 × g for 5 min at 4°C each time, until a 100 mg pellet was collected in two centrifuge tubes. The pellet was washed three times with 1 ml of 50 mM ammonium bicarbonate (ABC) (Biochem Lifesciences, New Delhi, India). The same procedure was repeated for harvesting planktonic cells at the 24 and 48 h time points. For harvesting biofilm at the 24 h time point, the cover glass was removed using forceps, immersed in 50 mM ABC and gently shaken for 30 s to get rid of planktonic cells. The washing step was followed by scraping the biofilm formed on both sides of the cover glass from all six falcon tubes and pooling them in 5 ml of 50 mM ABC. The biofilm associated cells were concentrated by centrifugation at 3,214 × g for 5 min at 4°C and suspended in 1 ml of 50 mM ammonium bicarbonate (ABC). From the 1 ml stock culture, 100 μl were taken and centrifuged at 3,214 × g for 5 min at 4°C each time, until a 100 mg pellet was collected in two centrifuge tubes. The same procedure was followed for harvesting biofilm associated cells at the 48 h time point. One hundred milligrams of pellet in two centrifuge tubes for three time points of the planktonic stage (12, 24 and 48 h) and two time points of the biofilm stage (24 and 48 h) were taken for further protein extraction using two protocols with different conditions as follows. In the first protocol (protocol 1), total proteins were prepared using Rapigest extraction buffer which was prepared by dissolving 5 mg of RapigestTM SF surfactant (Waters Corporation, Milford, MA, USA) in 1 ml of 50 mM ABC, 1 μl of 1 M phenyl methane sulfonyl fluoride (PMSF), 1 μl of 1 M magnesium chloride (HiMedia) and 1 μl of Benzonase®Nuclease (25U μl−1) (Novagen, San Diego, CA, USA). The pellet from each condition was resuspended in 60 μl of Rapigest extraction buffer and sonicated with a probeless sonicator three times without ice for 30 min with an interval of 10 min. The centrifuge tubes were placed in liquid nitrogen for 1 min and thawed on ice for 3 min and this step was repeated thrice. The tubes were centrifuged at 17,949 × g for 10 min and the supernatant was stored at –20°C. For the second protocol (protocol 2), the pellet from each condition was resuspended in 4 ml of 50 mM ABC containing 5 μl of 1 M PMSF and sonicated using a probe for 10 s 15 times with 60 s intermittent cooling. To this, 100 μl of Rapigest extraction buffer, 5 μl of 1 M MgCl2 and 1 μl of benzonase nuclease (25 U μl−1) were added and the mixture incubated for 15 min at room temperature. The suspension was centrifuged at 18,514 × g for 15 min at 4°C and the supernatant was concentrated in a 3 K-D cutoff (Millipore, Carrigtwohill, Co. Cork, Ireland) filter by spinning at 2,962 × g for 100 min and stored at –20°C.
Biofouling
Downloaded by [George Mason University] at 09:40 24 December 2014
Trypsin digestion Protein concentration was determined using the Bradford protein assay reagent according to the manufacturer’s instructions. From each condition 100 μg of total protein were made up to 100 μl with 50 mM ABC and incubated at 80°C for 15 min at 300 rpm in a Thermomixer comfort (Eppendorf, Hamburg, Germany). The proteins were reduced by adding 5 μl of 100 mM DL-dithiothreitol at 60°C for 30 min and alkylated by the addition of 5 μl of 200 mM iodoacetamide at 37°C for 30 min. Subsequently, the proteins were digested with sequence grade modified trypsin (1:25, w/w) by incubating overnight at 37°C. Trypsinization was blocked by adding 1 μl of 100% formic acid and incubation at 37°C for 20 min. The resulting tryptic peptides were centrifuged at 20,817 × g for 12 min and the supernatant was stored at –20°C for further LC-MS/MS analysis. Multidimensional protein identification technology (MudPIT) The peptide samples were analyzed employing twodimensional (2D) nanoacquity UPLC® system (Waters Corporation) coupled to Quadrupole-time of flight (Q-TOF) mass spectrometer (SYNAPT-G2-HDMS, Waters Corporation). Both systems were operated and controlled by MassLynx (Waters Corporation) software. The peptide level fractionation was done using reverse phase (RP) column 1 at high pH (pH 8) in the first dimension, followed by RP column 2 at low pH (pH 2) in the second dimension. Two binary solvent managers (BSM) were used for altering the pH and dilution of high pH to low pH in a 20 mm long Symmetry® C18trap column (Waters Corporation) having an internal diameter of 180 μm and a particle size of 5 μm. The four buffer solutions used for chromatography were 20 mM ammonium formate in Milli-Q water at pH 8 (buffer A), 100% acetonitrile (buffer B), 0.1% formic acid in Milli-Q water (buffer C) and 0.1% formic acid in acetonitrile (buffer D). The sample was injected in partial loop mode and was loaded onto a 50 mm long XBridge™ BEH130 (Ethylene Bridged Hybrid) (Waters Corporation) C18 column having an inner diameter of 300 μm and particle size of 5 μm with buffer A. Peptides were fractionated into 10 fractions sequentially based on a different percentage of buffer B (7.4, 10.8, 12.6, 14.0, 15.3, 16.7, 18.3, 20.4, 23.5 and 50% respectively) and were eluted employing a discontinuous step gradient at a flow rate of 2 μl min−1 from the RP column 1 by first-dimension BSM. The system employs an online dilution of the effluent after the first dimension where de-salting and cleaning-up of peptides takes place and the process also ensure that no peptides were lost during trapping prior to
731
the second dimension. For that, buffer C at a flow rate of 20 μl min−1 was delivered by second-dimension BSM to the trap column. Each fraction was resolved further in a 100 mm long BEH C18 column having an inner diameter of 75 μm and a particle size of 1.7 μm particle (RP column 2) with 1–40% buffer D in a 50 min gradient at 0.3 μl min−1 flow rate. After separation the column was washed with 85% buffer D for 4 min and re-equilibrated with 1% buffer D for 14 min. The column temperature was maintained at 40°C. In addition, 500 fmole μl−1 of human [Glu1]-fibrinopeptide B were infused at 500 nl min−1 as the reference compound using the auxiliary solvent manager (ASM). Mass spectrometry Mass spectrometric analysis of eluting peptides was performed employing traveling wave ionmobility separation (TWIMS) by data-independent acquisition (MSE) on a SYNAPT® G2 high definition Q-TOF mass spectrometer™ system. All the analyses were performed on positive mode electrospray ionization using a nano-lock spray source. The optimal capillary voltage was set at 3.5 kV and the cone voltage at 35 V. The source temperature was set at 80°C. The data were acquired by rapidly alternating between two functions: the first acquires low energy spectra (MS) and the second acquires mass spectra at elevated collision energy (MSE) with ion mobility. Here, both the precursor and fragment ions are generated by the mass spectrometer simultaneously while securing enough data points across each component peak to ensure correct peak integration and quantitative accuracy. The data acquisition was done in Continuum format. The mass spectrometer was operated in resolution mode with a resolving power of 18,000 FWHM. The m/z acquisition range was set from 50 to 2000 with an acquisition rate of 0.9 s per spectrum and an interscan delay of 0.024 s. To perform the mobility separation, the IMS T-Wave™ (Waters Corporation) pulse height was set to 40 V during transmission and the IMS T-Wave™ velocity was set to 700 m s−1. Nitrogen was used as the drift gas at 90 ml min−1. In low energy mode, the collision energy was set to 4 V in the trap region and 2 V in the transfer region. In high energy mode, the collision energy was set to 4 V in the trap region and was ramped from 20 to 45 V in the transfer region. To ensure accuracy and reproducibility during the MS analysis, the reference compound human [Glu1]-fibrinopeptide B (positive ion mode [M+2H] 2+ = 785.8426) for mass correction was sampled every 30 s through the reference sprayer of the nano-lock spray source. MassLynx V4.1 SCN781 was used to acquire and process all the data.
732
A. Dharmaprakash et al.
Downloaded by [George Mason University] at 09:40 24 December 2014
Data analysis For protein identification, the acquired ion mobility enhanced MSE spectra were analyzed using a ProteinLynx Global Server™ v2.5.3 (PLGS) (Waters Corporation) against the NCBI reference sequence V. parahaemolyticus RIMD 2210633 database (NC_004603.faa and NC_004605 dated 22 December 2012) concatenated to a decoy database in which the sequence was reversed for each entry in the original database. Noise reduction thresholds for low energy scan ion, high energy scan ion and peptide intensity were fixed at 150, 50 and 500 counts respectively. Data processing included lock mass correction post acquisition. During the database search, the protein false discovery rate was set to 4%. Work flow was designed in such a way that a protein was required to have at least three fragment ion matches and one peptide match, whereas a peptide was required to have at least one fragment ion match. Trypsin was selected as the primary digest reagent, used with a specificity of one missed cleavage. Oxidation of methionine was selected as the variable modification and carbamidomethylation of cysteine was selected as the fixed modification. Mass tolerance was set to 10 ppm for precursor ions and 20 ppm for fragment ions. Bioinformatic analysis Analyzing the physico-chemical properties of the total proteomes The total proteomes were obtained by compiling the total number of distinct proteins identified at five stages employing both the protocols. Physico-chemical properties such as molecular weight (MW), isoelectric point (pI) and grand average of hydropathicity (GRAVY) were calculated using the EXPASY – Compute pI/MW tool and the GRAVY calculator respectively and the values were plotted as a scatter plot using Perlscript and R (Gasteiger et al. 2005). Subcellular locations were predicted using PSORTb v. 3.0 (Yu et al. 2010). Comparison of the planktonic and biofilm proteomes The identified total proteomes at each stage using protocol 1 and protocol 2 were compiled to come up with the total number of proteins identified in that particular stage. The total proteins identified at five stages were analyzed using the Venny tool to identify proteins specific to the planktonic and biofilm stages (Oliveros 2007). Comparison of the planktonic and biofilm proteomes by functional roles The total proteomes of the planktonic stage were obtained by compiling the total proteins identified at the three
different time points of the planktonic stage, 12, 24 and 48 h. Similarly the total proteomes of the biofilm stage were obtained by compiling the total proteins identified at the two different time points of the biofilm stage, 24 and 48 h. The 4,832 protein coding genes in the V. parahaemolyticus RIMD 2210633 genome were sorted to their respective clusters of orthologous groups (COG) and were made available on the Integrated Microbial genomes website (Markowitz et al. 2012). This information was used for comparing the planktonic and biofilm proteomes based on their COG categories. DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources 6.7 was used for comparing the planktonic and biofilm proteomes based on their gene ontology (GO) terms and KEGG (Kyoto Encyclopedia of Gene and Genomes) pathways. DAVID generates an EASE score, a modified Fisher’s Exact p-value for each term and the GO and KEGG pathway terms with a p-value less than or equal to 0.1 were considered as enriched (Huang et al. 2009). The GO terms corresponding to the biological processes at the hierarchical level (DAVID category – GOTERM_BP_4) were chosen for comparison of planktonic and biofilm proteomes to identify the biological processes specific to the biofilm stage. The dissimilarity analysis based on Euclidean distance was calculated for enrichment values of the GO and KEGG pathway terms obtained using the DAVID tool employing the R esp. dist function of the R package. The results were visualized in the form of heat maps and a clustering dendrogram employing heat map and hclust functions of the R package. Results and discussion Proteome profile In order to improve proteome coverage, two protocols that differ in their detergent concentration and cell lysis method were adapted to extract total proteins from V. parahaemolyticus SC192 at three different time points of the stationary planktonic stage, namely 12, 24 and 48 h, and two different time points of the biofilm stage, namely 24 and 48 h (Figure 1A and B). The proteins identified employing protocol 1 and protocol 2 at five different stages employing MudPIT technology were compiled; 2,199 proteins were identified from a total of 4,832 protein coding genes in the V. parahaemolyticus RIMD 2210633 reference genome. This represents 45.5% of the total proteome in the reference genome. Among the 2,199 total proteins identified, 1,507 were present in both the protocols whereas 565 and 127 were identified exclusively in protocol 1 and protocol 2 respectively. According to the reference genome annotation, 1,885 open reading frames were annotated as hypothetical proteins, of which 510 proteins were identified in the present analysis. To access the protein coverage of the adapted protocols, the number of identified
Biofouling
733
Downloaded by [George Mason University] at 09:40 24 December 2014
protocols favored the extraction of proteins within the pI range of 4–10, a molecular weight of 10–110 kDa and GRAVY index range between –1 and 1 (Figures 2 and 3). When the cellular localization of the identified proteins were predicted using PSORTb, 52.01% (cytoplasmic protein), 64.8% (periplasmic protein), 50.4% (outer membrane protein), 29.23% (extracellular protein) and 28.51% of protein with unknown cellular localization were found to be extracted using the adapted method (Table 1). From the analysis, it was revealed that protocol 1 with a 0.5% detergent concentration showed more proteome coverage than protocol 2 with a 0.001% detergent concentration. The maximum proteome coverage achieved in protocol 1 emphasizes the increase in the solubilization efficiency of a wide range of proteins at higher detergent concentration. The in silico results of the total proteomes identified employing protocols 1 and 2 revealed that protocol 1 favored the extraction of hydrophobic proteins whereas protocol 2 favored the extraction of hydrophilic proteins. So, the protein profiling method adapted here could be used to rapidly obtain the total proteome of any microorganism at a given condition. In the present study, protein profiling was conducted on biofilm grown on a cover glass which was a chitin free surface, and a mannose-sensitive hemagglutinin (MSHA) pilus was identified in all the four stages except the 48 h biofilm stage, whereas a chitin-regulated pilus (ChiRP) was not detected in any of the analyzed conditions. The present observation is in agreement with the finding of Shime-Hattori et al. (2006) which revealed
Figure 1. CLSM images of V. parahaemolyticus SC192 biofilm formed on a sterile rectangular cover glass stained with SYTO-9. The side frame on the right side of the image is the overlay of the x–y plane in the z-axis showing the thickness of the biofilm and the red line in the z-stack indicates the level at which the projected x–y plane was taken. A, 24 h biofilm; B, 48 h biofilm.
ribosomal proteins and physico-chemical parameters such as isoelectric point (pI), molecular weight (MW) and hydrophobicity (GRAVY) were calculated for the identified proteomes. Of the total 55 ribosomal proteins, 53 were identified with high confidence, representing 96% of the total ribosomal proteins. The Compute pI/MW and GRAVY calculator results revealed that the adapted
Figure 2. Distribution of the predicted molecular weight (MW) and isoelectric point (pI) for the identified proteins in protocol 1 and protocol 2. Protocol 1 is represented as black dots and protocol 2 as gray dots. The scatter plot shows the majority of extracted proteins between the pI range of 4–10 and a MW of 10–110 kDa.
Downloaded by [George Mason University] at 09:40 24 December 2014
734
A. Dharmaprakash et al.
Figure 3. Distribution of GRAVY values for the identified proteins in Protocol 1 and Protocol 2. Protocol 1 is represented as black dots and Protocol 2 as gray dots. The scatter plot shows the majority of extracted proteins between a GRAVY index range of -1 and 1.
that MSHA pilus (VPA0747) is required for attachment of V. parahaemolyticus to any surface and if it attaches to a chitin surface, ChiRP (VP2523) expression is upregulated and interconnects the bacteria, creating a stable biofilm architecture. When the identified total proteomes were analyzed for the proteins related to V. parahaemolyticus pathogenicity, 17 proteins in the tdh pathogenicity island (VPA1310-VPA1398) and 11 proteins in the super integron region (VP1787–1865) were identified and further investigation of these proteins may provide novel antivirulent drug targets for combating V. parahaemolyticus infection. Moreover, the ORF8 protein (VP1561) of the f237 prophage was identified in all the analyzed conditions; this was shown to be a genetic marker for detection of the O3:K6 pandemic strain by Iida et al. (2001). This observation was in agreement with the pandemic categorization of the clinical strain used in the present investigation.
Figure 4. A, Venn diagram generated for the identified total proteomes of the 12 h planktonic stage (1,436 proteins) vs the 24 h planktonic stage (1,545) vs the 48 h planktonic stage (1,513) bacteria. A total of 1,953 proteins were identified in the planktonic stage. B, Venn diagram generated for the identified total proteomes of the 24 h biofilm stage (1,332) vs the 48 h biofilm stage (1,457) bacteria. A total of 1,816 proteins were identified in the biofilm stage.
Table 1. Predicted localization for the total number of proteins in the reference genome and the identified proteomes of the reference genome. Total number of identified proteins of the reference genome Localization Cytoplasm Periplasm Outer membrane Extracellular Unknown
Total number of proteins in the reference genome
Protocol 1
Protocol 2
Protocols 1 and 2 compiled
3,176 125 123 65 1,343
1,586 77 56 18 333
1,253 61 42 10 266
1,652 81 62 19 383
Biofouling
735
Downloaded by [George Mason University] at 09:40 24 December 2014
the planktonic and the biofilm stages, respectively. The change in the proteome profile at each stage clearly indicates a temporal variation in protein expression during the course of biofilm formation (Figures 4A, B, 5). When the 246 biofilm specific proteins were analyzed, the majority were found to be hypothetical proteins. In addition, proteins involved in motility, transport, carbohydrate metabolism and transcription regulation were also reported, which correlates with the previous global transcriptomic studies conducted on the planktonic and biofilm stages of Pseudomonas aeruginosa (Whiteley et al. 2001), Escherichia coli (Schembri et al. 2003) and Vibrio cholerae (Moorthy & Watnick 2005). Figure 5. Venn diagram generated for the identified total proteomes of the planktonic stage (1,953) vs the biofilm stage (1,816) of V. parahaemolyticus SC192. A total of 2,199 proteins were identified in the five analyzed conditions. Of these, 383 and 246 proteins were present exclusively in the planktonic and biofilm stages, respectively.
Comparison of the planktonic and biofilm proteomes The total numbers of proteins identified at each stage employing both the protocols were compiled to get the total proteomes at each stage. When the total proteomes present in the planktonic (12, 24 and 48 h) and biofilm stages (24 and 48 h) were analyzed using the Venny tool, 953 proteins were detected in all the conditions. These proteins were involved in housekeeping functions and were designated as the core proteomes of V. parahaemolyticus SC192. Of the 2,199 identified total proteins, 383 and 246 proteins were specific to
Analyzing the biofilm proteomes by functional roles For analyzing the biofilm proteomes by functional roles, the total proteomes of the planktonic and biofilm stages were compared on the basis of clusters of orthologous groups (COG) and gene ontology (GO) categories. On comparison based on their COG categories, it was found that the COG classes needed for house-keeping functions such as information processing (J and K), cellular processes (M and O) and metabolism (C, E, and G) were over-represented in the core proteome. The COG grouping of the biofilm specific proteins revealed that most of the proteins were sorted to COG classes such as T (signal transduction mechanism), P (inorganic ion transport and metabolism) and E (amino acid transport and metabolism) next to the poorly characterized COG categories (Figure 6).
Figure 6. Comparison of the identified total proteomes of the planktonic and the biofilm stage based on their corresponding COG functional categories. COG categories are as follows: K, transcription; J, translation, ribosomal structure and biogenesis; L, DNA replication, recombination and repair; A, RNA processing and modification; T, signal transduction mechanisms; M, outer membrane and cell envelope biogenesis; N, cell motility and secretion; O, post translational modification, protein turnover and chaperones; U, intracellular trafficking, secretion and vesicular transport; V, defense mechanism; D, cell division and chromosome partitioning; E, amino acid transport and metabolism; G, carbohydrate transport and metabolism; P, inorganic ion transport and metabolism; C, energy production and conversion; H, coenzyme metabolism; I, lipid metabolism; F, nucleotide transport and metabolism; Q, secondary metabolite biosynthesis, transport and catabolism; R and S, poorly characterized; X, no affiliated COG category.
Downloaded by [George Mason University] at 09:40 24 December 2014
736
A. Dharmaprakash et al.
Figure 7. Comparison of enriched gene ontology (GO) terms in the planktonic and the biofilm stage by clustering the DAVID results using a Euclidean distance matrix. The heat map shows the specific enrichment of GO terms such as the aromatic amino acid family metabolic process, the aromatic compound biosynthetic process, the fatty acid metabolic process, the heterocycle biosynthetic process and the lipid biosynthetic process in the biofilm stage.
The enriched gene ontology categories in the biofilm condition were identified by comparing the total planktonic and biofilm proteomes employing the DAVID bioinformatics resource. The analysis showed that the gene ontology terms pertaining to biological processes, such as the aromatic amino acid family metabolic process, the aromatic compound biosynthetic process, the fatty acid metabolic process, the heterocycle biosynthetic process and the lipid biosynthetic process, were found to be enriched in the biofilm proteome (Figure 7). According to Yildiz and Visick (2009), aromatic compounds such as acyl homoserine lactone (AHL) was reported to regulate the biofilm formation mechanism in some Vibrio
spp., and the enrichment of the gene ontologies pertaining to aromatic compound biosynthesis and metabolism during the biofilm stage suggests the role of these aromatic signaling molecules in regulating the biofilm formation mechanism. So, the DAVID result can be taken as strong evidence for conducting future studies in elucidating the role of quorum sensing (QS) in regulating biofilm formation in V. parahaemolyticus. Pathways specific to the biofilm stage On analyzing the 2,199 identified total proteins using DAVID, ~ 39.1% were assigned to defined KEGG
Downloaded by [George Mason University] at 09:40 24 December 2014
Biofouling
737
Figure 8. Comparison of enriched KEGG pathways in the planktonic and biofilm stage by clustering the DAVID results using a Euclidean distance matrix. The heat map shows the specific enrichment of KEGG pathways such as arginine and proline metabolism, fatty acid biosynthesis and sulfur metabolism in the biofilm stage.
pathways. More than 80% of the proteins were reported for the following 11 KEGG pathways: amino acid metabolism (176 hits), ribosome (53 hits), bacterial chemotaxis (43 hits), pyruvate metabolism (43 hits), butanoate metabolism (35 hits), glycolysis/gluconeogenesis (34 hits), propanoate metabolism (32 hits), citrate cycle (24 hits), aminoacyl tRNA biosynthesis (24 hits), fatty acid metabolism (23 hits) and folate metabolism (14 hits), which further ensured the maximum coverage achieved using the current method. Furthermore, it was revealed that the KEGG pathways of arginine and proline metabolism, fatty acid biosynthesis and sulfur metabolism were statistically enriched in the biofilm con-
dition (Figure 8). The requirement of proteins involved in sulfur metabolism is strong evidence of anaerobic metabolism and reveals the metabolic heterogeneity of surface associated bacteria. Besides, the similar enrichment observed for KEGG pathways such as glycolysis, citrate cycle, purine metabolism, pyruvate metabolism and ribosomes in the planktonic and biofilm stages revealed that the biofilm associated cells were viable and engaged in division like their planktonic counterparts. To summarize, in the present study, MudPIT was used to document the proteome of the stationary planktonic stage and the mature biofilm stage of V. parahaemolyticus SC192. The adapted protocol identified 45.5%
738
A. Dharmaprakash et al.
Downloaded by [George Mason University] at 09:40 24 December 2014
of the total proteome of the reference genome, thereby providing large-scale proteome coverage of V. parahaemolyticus obtained up to the present. The protein profiling method reported here could therefore be used to rapidly obtain a snapshot of the total proteome of any microorganism at a given condition. Indeed, the protein profiling data reported in this paper has provided fundamental information and a set of interesting targets for a multifaceted analysis of the biofilm formation mechanism in V. parahaemolyticus. To the best of the authors’ knowledge, this is the first attempt to generate a global proteome profile of a pandemic strain of V. parahaemolyticus during the planktonic and biofilm stages.
Acknowledgements The authors thank Prof. M. Radhakrishna Pillai, Director, Rajiv Gandhi Centre for Biotechnology (RGCB), for the facilities provided. They also thank Dr Ramamurthy of the National Institute of Cholera and Enteric Diseases (NICED), Kolkata, for providing the pandemic strain used in this work and Mr Arun Surendran, Mr Srikanth Jandhyam and Mr Saravana Kumar of RGCB for their technical help in carrying out the mass spectrometry analysis. D. Akhilandeswarre is grateful to the Department of Science and Technology (DST), Government of India, New Delhi for providing the INSPIRE fellowship [Fellow code: IF 110672].
References Chen EI, Hewel J, Felding-Habermann B, Yates JR 3rd. 2006. Large scale protein profiling by combination of protein fractionation and multidimensional protein identification technology (MudPIT). Mol Cell Proteomics. 5:53–56. Chowdhury NR, Chakraborty S, Ramamurthy T, Nishibuchi M, Yamasaki S, Takeda Y, Nair GB. 2000. Molecular evidence of clonal Vibrio parahaemolyticus pandemic strains. Emerg Infect Dis. 6:631–636. Costerton JW, Stewart PS, Greenberg EP. 1999. Bacterial biofilms: a common cause of persistent infections. Sci. 284:1318–1322. Enos-Berlage JL, Guvener ZT, Keenan CE, McCarter LL. 2005. Genetic determinants of biofilm development of opaque and translucent Vibrio parahaemolyticus. Mol Microbiol. 55:1160–1182. Faruque SM, Biswas K, Udden SM, Ahmad QS, Sack DA, Nair GB, Mekalanos JJ. 2006. Transmissibility of cholera: in vivo-formed biofilms and their relationship to infectivity and persistence in the environment. Proc Natl Acad Sci USA. 103:6350–6355. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A. 2005. Protein identification and analysis tools on the ExPASyServer. In: Walker JM, editor. The proteomics protocols handbook. Totowa (NJ): Humana Press; p. 571–607. EXPASY – Compute pI/MW (http:// web.expasy.org/compute_pi/) and GRAVY calculator (http://www.gravy-calculator.de/). Hall-Stoodley L, Costerton JW, Stoodley P. 2004. Bacterial biofilms: from the natural environment to infectious diseases. Nat Rev Microbiol. 2:95–108.
Ho H, Karl Alozie O, Tran S-Y, Ho TT, Do TH. 2011. Vibrio infections. Available from: http://emedicine.medscape.com/ article/232038-overview Huang Da W, Sherman BT, Lempicki RA. 2009. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. Available from: http://david.abcc.ncifcrf.gov/ Iida T, Hattori A, Tagomori K, Nasu H, Naim R, Honda T. 2001. Filamentous phage associated with recent pandemic strains of Vibrio parahaemolyticus. Emerg Infect Dis. 7:477–478. Makino K, Oshima K, Kurokawa K, Yokoyama K, Uda T, Tagomori K, Iijima Y, Najima M, Nakano M, Yamashita A, et al. 2003. Genome sequence of Vibrio parahaemolyticus: a pathogenic mechanism distinct from that of V. cholerae. Lancet. 361:743–749. Markowitz VM, Chen IM, Palaniappan K, Chu K, Szeto E, Grechkin Y, Ratner A, Jacob B, Huang J, Williams P, et al. 2012. IMG: the integrated microbial genomes database and comparative analysis system. Nucleic Acids Res. 40 (Database issue):D115–D122. Available from: http://img.jgi.doe. gov/ cgi-bin/w/main.cgi?section=TaxonDetail&page=taxonDetail &taxon_oid =637000335 Moorthy S, Watnick PI. 2005. Identification of novel stagespecific genetic requirements through whole genome transcription profiling of Vibrio cholerae biofilm development. Mol Microbiol. 57:1623–1635. Nair GB, Ramamurthy T, Bhattacharya SK, Dutta B, Takeda Y, Sack DA. 2007. Global dissemination of Vibrio parahaemolyticus serotype O3:K6 and its serovariants. Clin Microbiol Rev. 20:39–48. Nyholm SV, Stabb EV, Ruby EG, McFall-Ngai MJ. 2000. Establishment of an animal-bacterial association: recruiting symbiotic vibrios from the environment. Proc Natl Acad Sci USA. 97:10231–10235. Oliveros JC. 2007. VENNY. An interactive tool for comparing lists with Venn diagrams. Available from: http://bioinfogp. cnb.csic.es/tools/venny/index.html Parsek MR, Singh PK. 2003. Bacterial biofilms: an emerging link to disease pathogenesis. Annu Rev Microbiol. 57:677–701. Schembri MA, Kjaergaard K, Klemm P. 2003. Global gene expression in Escherichia coli biofilms. Mol Microbiol. 48:253–267. Sen B, Dutta B, Chatterjee S, Bhattacharya MK, Nandy RK, Mukhopadhyay AK, Gangopadhyay DN, Bhattacharya SK, Ramamurthy T. 2007. The first outbreak of acute diarrhea due to a pandemic strain of Vibrio parahaemolyticus O3: K6 in Kolkata, India. Int J Infect Dis. 11:185–187. Seneviratne CJ, Wang Y, Jin L, Wong SS, Herath TD, Samaranayake LP. 2012. Unraveling the resistance of microbial biofilms: has proteomics been helpful? Proteomics. 12:651–665. Shime-Hattori A, Iida T, Arita M, Park KS, Kodama T, Honda T. 2006. Two type IV pili of Vibrio parahaemolyticus play different roles in biofilm formation. FEMS Microbiol Lett. 264:89–97. Su YC, Liu C. 2007. Vibrio parahaemolyticus: a concern of seafood safety. Food Microbiol. 24:549–558. Wang L, Ling Y, Jiang H, Qiu Y, Qiu J, Chen H, Yang R, Zhou D. 2013. AphA is required for biofilm formation, motility, and virulence in pandemic Vibrio parahaemolyticus. Int J Food Microbiol. 160:245–251. Whiteley M, Bangera MG, Bumgarner RE, Parsek MR, Teitzel GM, Lory S, Greenberg EP. 2001. Gene expression in Pseudomonas aeruginosa biofilms. Nature. 413:860–864.
Biofouling
Downloaded by [George Mason University] at 09:40 24 December 2014
Yeung PS, Boor KJ. 2004. Epidemiology, pathogenesis, and prevention of foodborne Vibrio parahaemolyticus infections. Foodborne Pathog Dis. 1:74–88. Yildiz FH, Visick KL. 2009. Vibrio biofilms: so much the same yet so different. Trends Microbiol. 17:109–118. Yip ES, Geszvain K, DeLoney-Marino CR, Visick KL. 2006. The symbiosis regulator rscS controls the syp gene locus, biofilm formation and symbiotic aggregation by Vibrio fischeri. Mol Microbiol. 62:1586–1600.
739
Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, Dao P, Sahinalp SC, Ester M, Foster LJ, Brinkman FS. 2010. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics. 26:1608–1615. Available from: http://www.psort.org/
