Next Article in Journal
Conventional Soliton and Noise-Like Pulse Generated in an Er-Doped Fiber Laser with Carbon Nanotube Saturable Absorbers
Previous Article in Journal
Electromagnetic Acoustic Detection of Steel Plate Defects Based on High-Energy Pulse Excitation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 10
Issue 16
10.3390/app10165535
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Establishment of Optogenetic Modulation of cAMP for Analyzing Growth, Biofilm Formation, and Virulence Pathways of Bacteria Using a Light-Gated Cyclase

by
Manish Singh Kaushik
,
Swaroop Ranjan Pati
,
Shivanika Soni
,
Ayushi Mishra
,
Kumari Sushmita
and
Suneel Kateriya
*
Laboratory of Optobiology, School of Biotechnology, Jawaharlal Nehru University, New Delhi 110067, India
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(16), 5535; https://doi.org/10.3390/app10165535
Submission received: 27 February 2020 / Revised: 17 April 2020 / Accepted: 22 April 2020 / Published: 11 August 2020
(This article belongs to the Section Applied Biosciences and Bioengineering)
Browse Figures
Versions Notes

Abstract

:
In bacteria, cyclic adenosine monophosphate (cAMP) signaling plays an essential regulatory role whose modulation via optogenetic tools would provide researchers an immense opportunity to control biological processes simply by illumination. The cAMP signaling in bacteria is a complex network of regulatory pathways, which utilizes distinct proteomic resources under different nutrient environments. We established an optogenetic modulation of cAMP and studied important cellular process of growth, biofilm formation, and virulence in the model bacterium E. coli using a light-gated adenylate cyclase (LgAC) from Beggiatoa sp. Blue light-induced activation of LgAC elevated the cAMP level in a blue light-dependent manner in E. coli. Quantitative proteomics revealed a decrease in the level of certain proteins governing growth (PTS, Adk, AckA, GlnA, and EFP), biofilm formation (IhfA, flagellin, YajQ, YeaG, and HlfC), and virulence (ClpP, YebC, KatE, BtuE, and Zur) in E. coli cells expressing LgAC upon blue light illumination. This optogenetic modulation of cAMP would be useful for deciphering cAMP-associated host–pathogen signaling of bacterial systems. Proteome knowledge established by this research work would also be useful for the scientific community while adapting LgAC-based optogenetic modulation for studying other relevant cAMP-driven bacterial physiology (e.g., energy metabolism). The systematic utilization of the established method and more extensively designed experiments regarding bacterial growth, biofilm, survival, and virulence might provide a road map for the identification of new targets for developing novel antibacterial drugs.

1. Introduction

Cyclic adenosine monophosphate (cAMP) is a secondary messenger, which plays a central role in a myriad of signaling processes and is synthesized by a specific membrane or soluble protein, adenylate cyclase. This secondary messenger coordinates complex signaling by controlling intracellular metabolites like fructose 1,6-bisphosphate, phosphoenolpyruvate, acetyl-Coenzyme A, α-ketoglutaric acid, pyruvate, and oxaloacetate, known to be essential for enzyme-level regulation, transcriptional control, and nutrient homeostasis [1,2]. In bacteria, cAMP directly binds and activates the transcription factor of the cAMP receptor protein (CRP) family [3]. In Escherichia coli, cAMP is primarily known to be involved in mediating the glucose response or catabolite repression [4], but its role has now been expanding into the domains of biofilm formation, microbial virulence, and pathogenesis [3]. The cAMP-CRP complex controls extracellular matrix production and biofilm formation through transcriptional regulation of the csgD gene [5]. Hufnagel and coworker showed that cAMP may act as a signaling molecule, which drives the uropathogenic E. coli invasion from the bladder lumen to the interior of epithelial cells [5]. Muller et al. [6] suggested that the metabolic sensor cAMP-CRP regulates type 1 fimbriation, which is required for attachment and colonization of the uroepithelium. The surface attachment and biofilm formation are key factors for the onset of microbial pathogenesis and are also the major contributors to human diseases. The biofilm growth of E. coli might be a cAMP-dependent (mediated by phosphoenolpyruvate-dependent phosphotransferase, PTS) or -independent (pH-dependent) process [7]. Recently, Ritzert et al. [8] also demonstrated the association of cAMP-CRP complex with virulence, metal acquisition, and quorum sensing in addition to carbon metabolism. Several crucial bacterial processes may be managed by the modulation of the intracellular cAMP concentration, which might play an important role in the development of novel antibacterial drugs.
Researchers have tried to modulate the cAMP level in cellular systems to regulate the cyclic nucleotide(s)-mediated signaling in a controlled manner. Agonists activating adenylate cyclases [9], and phosphodiesterase (PDE) inhibitors [10], have been employed to modulate cAMP-dependent signaling; however, these methods lack spatial and temporal resolution. It was suggested that genetically encoded photo-switchable cyclases would provide scientists an opportunity to achieve cell-targeted control of cAMP levels by illumination [11]. In early 2005, channelrhodopsin (ChR) and other rhodopsins were used as genetically encoded photo-switches, for controlling the neural activity simply by illumination and such applications have paved the way for the development of the field of optogenetics [12].The successful application of these light-sensitive proteins as an optogenetic tool has encouraged scientists to identify and apply photoreceptor proteins for light-dependent regulation of cyclic nucleotide(s), protein–protein interaction, and protein translocation events in a cell.
The photoactivated adenylate cyclases (PACs) were genetically engineered into the heterologous systems and blue light illumination was used to alter the cAMP-mediated cellular and behavioral responses of the systems [13,14,15]. PACs possess a BLUF (blue light using flavin) domain-coupled adenylate cyclase and is utilized for blue light-dependent modulation of cAMP [16,17]. In 2007, Euglena PAC (ePAC) was expressed in a cell model system as well as in transgenic animals, and the cAMP level was manipulated simply by illumination with blue light [15,18]. However, these large-sized (110kDa) PACs showed significant cyclase activity in the dark. Tanwar and coworkers characterized smaller-sized (38kDa) PAC, which showed fast kinetics for light-regulated cyclase activity [13]. PACs have been used for altering cAMP levels in Drosophila [15,17] and for finding the mechanism underlying cAMP-dependent axonal morphogenesis [14]. In the current study, a light-gated adenylate cyclase (LgAC) from Beggiatoa sp. (bPAC) was utilized as an optogenetic tool to decipher the cAMP-modulated bacterial processes like growth, biofilm formation, and virulence in E. coli. A label-free quantitative proteomic approach was adapted to analyze the light-induced cAMP-dependent variations in the proteome regulating growth, biofilm formation, and virulence in E. coli.

2. Materials and Methodology

2.1. Bioinformatics Analysis of LgACs

Protein sequences having a BLUF-cyclase domain were retrieved from the NCBI database (https://www.ncbi.nlm.nih.gov/), and were subjected to a conserved domain search using the Conserved Domain Architecture Retrieval Tool (CDART; https://www.ncbi.nlm.nih.gov/Structure/lexington/lexington.cgi) [19]. Subsequently, 22 BLUF-cyclase domain-containing proteins were selected and aligned with a well-characterized PAC protein for the structure–function analysis using the BioEdit 7.2 [20]. The three-dimensional structure of LgAC from Beggiatoa sp. was predicted by I-Tasser (http://zhang.bioinformatics.ku.edu/I-TASSER) by employing an integrated approach comprising of threading, structural assembly, model selection, refinement of predicted structure, and structure-based functional annotation [21]. The accuracy of the predicted structure was estimated in terms of a confidence score (C-score) of the modeling. Docking analysis of ATP with the predicted 3D structure was performed using COACH I-TASSER to find the amino acid residues important for the interaction of ATP with LgAC. The docking output was visualized using Discovery studio visualizer version 16 (Dassault systems Biovia Corp., San Diego, CA, USA) [22].

2.2. Biochemical and Functional Characterization of the Recombinant LgAC

The commercially synthesized LgAC gene was cloned in the pASK vector (gifted by Prof. Peter Hegemann, Humboldt University, Germany) and was further confirmed by automated sequencing. The LgAC was expressed in Escherichia coli BL21 (λDE3). The recombinant protein was purified and the photophysical and biochemical characterizations of the purified protein were performed as per the methods employed previously [18]. The recombinant protein of interest was purified from the soluble fraction obtained after centrifugation of the cell lysate in the dark by immobilized metal affinity chromatography (IMAC) using Co2+-IMAC resins (Clontech Laboratories Inc., Mountain View, CA, USA). Subsequently, the purified protein was further separated using SDS-PAGE and transferred to a nitrocellulose membrane (Mdi Membrane Technologies, Harrisburg, PA, USA). Blotted membranes were blocked using 5% fat-free milk (Titan Biotech Ltd., Delhi, India) and incubated with a penta-His antibody. The membrane was washed with 1× PBS containing 0.1% Tween-20 (Sigma, St. Louis, MO, USA), followed by immunolabelling detection using an HRP-conjugated anti-mouse secondary antibody (Sigma, St. Louis, MO, USA). Signals were visualized by the chemiluminescence method and captured on X-ray films.

2.3. Measurement of Cyclase Activity of Recombinant LgAC Expressed in E. coli BL21 (λDE3) Cells

The cAMP levels were quantified immunologically to determine the light-regulated adenylate cyclase activity in E. coli cells expressing recombinant LgAC incubated in the dark and illuminated with blue light (BL). The bacterial cells were harvested by centrifugation at 6000 rpm for 10 min and resuspended in 1× phosphate buffer saline (PBS) containing lysozyme (50 µgmL−1; Bio Basic Inc., Markham, ON, Canada) and phenylmethylsulfonyl fluoride (PMSF) (200 µM; Sigma, St. Louis, MO, USA). The cells were then sonicated and clarified by centrifugation at 13,000 rpm for 1 h. The soluble fraction containing 60 µg of total cell protein was used for measuring the cyclase activity. All samples were acetylated to increase the sensitivity of the assay. The light-dependent cyclase activity was then quantified in terms of the levels of cAMP produced in the cell by a competitive ELISA-based kit (Cayman, Ann Arbor, MI, USA) using the manufacturer’s instructions. The concentration of cAMP was quantified using a standard curve and the data are presented as mean ± SD. The data were statistically tested employing student’s t test using SPSS version 16.0.

2.4. Confocal Microscopy of E. coli Cells Expressing LgAC

Harvested cells of E. coli grown until the late exponential phase were incubated in the dark and BL and were pelleted and washed 5 times with 1× PBS. The cells were fixed with 0.4% paraformaldehyde (Sigma, St. Louis, MO, USA; prepared in 1× PBS) for 20 min at room temperature after which they were pelleted down by centrifugation and re-dissolved in 1× PBS. Then, 200 μL of cells were seeded on poly-L-lysine (Sigma, St. Louis, MO, USA)-coated coverslips. After 1 h, the coverslips were washed 3 times with 1× PBS and mounted onto clean slides with glycerol and visualized by exploiting the intrinsic fluorescence property of the LgAC. For the detection of biofilm formation, E. coli cells expressing LgAC were grown on fresh cover slips with the addition of glucose (Hi Media, West Chester, PA, USA) in media and incubated at 37 °C for 24 h in the dark and blue light. After incubation, cover slips were washed mildly with 1× PBS followed by fixation with 0.4% paraformaldehyde (Sigma, St. Louis, MO, USA; in 1× PBS) for 20 min. Mounting and visualization was done as mentioned previously [10].

2.5. Label-Free Quantitative Proteomics

The comparative label-free quantitative proteomics of E. coli cells expressing LgAC incubated in the dark and illuminated with BL was performed. The cells were lysed using lysis buffer (8.0 M urea in 25 mM ammonium bicarbonate) supplemented with protease inhibitors (Roche Applied Science, Penzberg, BY, Germany). The total protein in the lysate was quantitated using Brad-ford assay (Bio-Rad, Hercules, CA, USA). The sample was first reduced using 5 mM tris (2-carboxyethyl) phosphine and later alkylated using 50mM iodoacetamide. The sample was then digested using trypsin (1:50 trypsin: lysate ratio) for 16 h at 37 °C. The digested sample was cleaned using a C18 silica cartridge (The Nest Group, Southborough, MA, USA) according to the manufacturer’s protocol and dried using a speed vac. The dried pellet was resuspended in buffer A (5% acetonitrile, 0.1% formic acid) and analyzed on nano-LC/MSMS. Label-free sample was analyzed using an EASY-nLC system (Thermo Fisher Scientific, Waltham, MA, USA) coupled to an LTQ Orbitrap-Velos mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a nano-electrospray ion source as per the method described in Jhingan et al. [23]. Four biological replicates were processed for each sample, and the RAW files were analyzed with MaxQuant (version. 1.4.1.2) against the E. coli UniProt reference proteome database. Functional annotation and pathway validation of differentially expressed proteins (DEPs) were conducted using the KEGG pathway analysis (http://kobas.cbi.pku.edu.cn/anno_iden.php). Protein networking of selected DEPs were performed using String version 11 (Academic Consortium 2020; [24]) and the output was analyzed with Cytoscape 3.7.2 (San Diego, CA, USA) using the betweenness centrality algorithm [25]. A Venn diagram representing the distribution of proteins identified by label-free comparative quantitative proteomics was created using Venny 2.1 (Calle Darwin, MD, Spain) [26]. A heat map was constructed with Multi Experiment Viewer (MeV 4.9.0, Rockville, MD, USA) to show the comparative expression profile of differentially expressed proteins [27]. A pie diagram was created utilizing Past 3.11 (Natural History Museum, University of Oslo, Sars gt., Oslo) [28].

2.6. Growth of E. coli Expressing LgAC

Overnight grown cultures of E. coli cells expressing LgAC were inoculated in 200 mL of terrific broth medium (TBM) supplemented with ampicillin (100 μgmL−1; Hi Media, West Chester, PA, USA) and induced by anhydrous tetracycline (0.3 mM; IBA Lifesciences, Göttingen, LS, Germany). After 1h incubation, cells were incubated in the dark and illuminated with BL for 30 min. Bacterial culture(s) incubated in the dark and illuminated with BL were sampled at regular time intervals (0 min, 30 min, 1 h, 2 h, 3 h, 4 h, 5 h, 6 h, and 7 h) and the growth was measured as the optical density (O.D.) at 600 nm with a Perkin Elmer Lambda 465 Spectrophotometer (Waltham, MA, USA).

2.7. Biofilm of E. coli Expressing LgAC

The biofilm formation by E. coli cells in the dark and BL was estimated as described by O’Toole and Kolter [29]. An overnight grown culture (100μL) was inoculated in 900 μL of LB medium containing ampicillin (10 μgmL−1; Hi Media, West Chester, PA, USA) and anhydrous tetracycline (0.3 mM; IBA Lifesciences, Göttingen, LS, Germany) in a polystyrene plate, and incubated at 37 °C for 24 h in the dark and BL. After 24 h of incubation, the cell suspension was removed, and the walls of the plate were rinsed with autoclaved distilled water and dried at room temperature. Further, 1mL of crystal violet (0.1% v/v) was added to the plate for 20 min. The stained wells were later washed several times with autoclaved distilled water and allowed to dry at room temperature. The stained cells were extracted with 1mL of 20% (v/v) acetone in ethanol and the optical density at 550 nm (OD550) was estimated using the Perkin Elmer Lambda 465 Spectrophotometer (Waltham, MA, USA). Experiments were performed with three biological triplicates and two technical replicates for each biological replicate (n = 6) and data was statistically verified by ANOVA using SPSS (ver. 16.0). The qualitative assay for biofilm detection was also performed under dark and BL following the protocol of Christensen et al. [30] with slight modifications. An overnight grown culture [100 μL culture + 900 μL of LB medium supplemented with 100 μg mL−1 ampicillin (Hi Media, West Chester, PA, USA) and 3 mM anhydrous tetracycline (IBA Lifesciences, Göttingen, LS, Germany)] was incubated at 37 °C for 24 h. After 24 h of incubation, the cells were removed, and the plate was washed several times with phosphate saline buffer (1× PBS). The biofilm formed on the walls of the polystyrene plate was stained with safranin for 1 h. The stained plates were later rinsed twice with PBS to remove the excess stain and further air dried. Finally, the stained biofilm formed was visualized on the wall and the bottom of the plates.

3. Results and Discussion

3.1. LgAC is an Optogenetic Modulator of cAMP in E. coli

The homology analysis of the BLUF domain (Figure S1A), linker region (Figure S1B), and cyclase domain (Figure S1C) from 22 BLUF cyclases was performed against the well-characterized PAC from Oscillatoria acuminata. The residues important for BLUF photodynamics and ATP binding pocket of the cyclase domain were mostly found to be conserved. LgAC from Beggiatoa sp. showed the maximum conservation for the ATP binding pocket residues (ASP6, LEU8, ALA9, PHE10, SER11, THR12, ILE48, GLY49, ASP50, CYS51, ARG133) (Figure S1C and S2; [31]). Therefore, the LgAC from Beggiatoa sp. was selected as the PAC to be used as an optogenetic tool for the regulation of cAMP-dependent bacterial physiological processes. Further, to study the impact of light-gated adenylate cyclase on cAMP-dependent processes in bacteria, the heterologous expression of recombinant LgAC was induced in E. coli. The LgAC was expressed in E. coli cells as an N-terminal Histidine (His) tag fusion protein (5X His:LgAC). The spectroscopic analysis of the purified recombinant LgAC suggested the functional expression of LgAC in E. coli (Figure 1A). In addition, Western blotting with an anti-His antibody (Figure 1B) and confocal analysis (Figure 1C–E) displayed a robust expression of recombinant LgAC in E. coli cells. An ELISA-based immunoassay was performed to estimate the cAMP level in the total lysates prepared from the LgAC-expressing cells incubated under dark and BL to confirm the photoactivation of LgAC. A significant increase (~10 fold; p < 0.001) in the cAMP level was detected in E. coli after BL illumination when compared to dark-adapted cells (Figure 1F); however, insignificant cyclase activity of LgAC under dark corresponding to the basal cyclase activity in E. coli cells was also observed.

3.2. Label-Free Quantitative Proteomics Revealed cAMP-Dependent Modulation of Growth, Biofilm Formation, and Virulence in E. coli

The comparative proteomics data of LgAC-carrying E. coli cells incubated in the dark and illuminated with BL revealed quantitative changes in the protein profile. A total of 732 proteins were identified (Figure 2A), out of which 57 were differentially expressed, and are represented by a heat map (Figure 2B; Table 1). The KEGG pathway analysis categorically linked differentially expressed proteins (DEPs) with 29 different pathways (Figure 2C; Table 2). The DEPs involved in growth, biofilm formation, and virulence were studied in detail.

3.2.1. LgAC Regulates Growth and Energy Metabolism in E. coli

The KEGG pathway analysis suggested that most DEPs are involved in growth and energy metabolism (Figure 2C; Table 2). Proteomic analysis showed a ~2-fold reduction in the expression of growth-related proteins, i.e., phosphoenolpyruvate-dependent phosphotransferase (PTS), adenylate kinase (Adk), and acetate kinase (AckA) in recombinant E. coli illuminated with BL (Figure 2B; Table 1). In E. coli, the cyclic AMP (cAMP)-catabolite receptor protein (CRP) complex regulates (positively or negatively) the catabolic repression and transcription of several genes involved in carbohydrate catabolism [2,32]. The inactivation of either adenylate cyclase or CRP would lead to highly pleiotropic phenotypes [33]. Bettenbrock and coworkers showed the correlation between the intracellular cAMP concentration and growth in E. coli [33]. Phosphoenolpyruvate-dependent phosphotransferase (PTS) regulates carbon catabolism via phosphoenolpyruvate-dependent protein kinase enzyme IIACrr (EIIACrr)-mediated inducer exclusion and cAMP-CRP-based catabolic repression in E. coli. A reduction in PTS expression is explained by the fact that phosphorylated EIIACrr activates bacterial adenylate cyclase, which further synthesizes cAMP [33]. However, in the present experiment, adenylate cyclase activation was light induced, which bypasses the requirement of EIIACrr. Adenylate kinase is another crucial enzyme involved in regulating the growth of E. coli by controlling the cellular ATP levels [34]. In Streptococcus pneumoniae, the adenylate kinase (SpAdk) level and growth rate were correlated, where mutation in SpAdk caused growth defects in vivo [35]. The ratio of phosphorylated and non-phosphorylated AckA on the other hand regulates the phosphoenzyme I/enzyme I ratio of PTS as well as the ATP/ADP ratio, respectively. This in turn controls the rate of sugar transport and cellular growth [36]. Apart from regulating bacterial growth, AckA is also considered abroad-spectrum novel drug target in several pathogenic bacteria [37].
The cAMP modulation also controls the molecular complexes that regulate coordination between carbon metabolism and other nutrient homeostasis that are essential for the growth and survival of E. coli [1]. The downregulation of glutamine synthetase (GlnA; ~2 fold) and a specific potassium ion (K+)-binding protein (Kbp; ~2.5 fold) was observed after BL illumination (Figure 2B; Table 1). In many bacteria, GlnA is an important enzyme of the central nitrogen metabolic circuit other than glutamate synthase (GOGAT), and glutamate dehydrogenase (GDH), which are responsible for the assimilation of ammonia/ammonium (NH4+) into glutamine [38]. In bacteria, the glutamate/glutamine ratio serves as a sensor of external N availability and is essential for their proper growth and sustenance [39]. Yan et al. [38] reported a glnA mutant SK3112 that showed slow growth on NH4+ as an N-source due to a limiting internal glutamine pool. In adverse conditions, GlnA in coordination with Adk and Ack also provide a phosphorelay system, which behaves as a potent ATP generator [40]. This phospho-network helps in acquiring ATP by oxidative phosphorylation for bacterial growth in adverse conditions [41]. Potassium ion is involved in several key processes like the maintenance of intracellular pH and osmolarity [42]. The cytoplasmic protein Kbp, a member of RpoS regulon, is essential for normal growth in the presence of high levels of external K+. Ashraf et al. [42] compared the growth of wild-type and ∆kbp strains at different K+ concentrations and found that at higher K+ concentrations, a mutation in kbp resulted in a delayed exponential phase. Non-canonical elongation factor (EF-P) was downregulated (~2.5 fold) in E. coli illuminated with BL (Figure 2B; Table 1). In many organisms, short sequences ending with a Pro residue (PPG, PPP, or longer Pro clusters) in nascent peptides may lead to ribosome pausing (stall the translation process), which can be alleviated by EF-P [43,44]. The regulated synthesis of proteins having terminal Pro residues in the nascent peptide by EF-P is essential for different processes. Inactivation of EF-P or its modifying enzymes YjeA or YjeK causes pleiotropic phenotypes, including a reduced growth rate and motility [45].
Growth curve analysis of LgAC-expressing E. coli incubated in the dark and illuminated with BL was performed, and the results obtained showed a reduction in the growth of BL-illuminated E. coli cells (Figure 3A). The untransformed E. coli cells (without LgAC expression; negative control) did not show any pronounced changes in growth between the cells grown in the dark and under BL illumination (Figure S3). For E. coli, it is already established that glucose modulates the intracellular concentration of cAMP [46]; therefore, we considered the glucose-supplemented condition as a positive control. When the growth of E. coli cells supplemented with glucose (0.1%) was compared with dark-adapted and BL-illuminated E. coli cells expressing LgAC, the growth of E. coli cells in the glucose-supplemented condition is comparable to dark-adapted cells (Figure S3). In E. coli, adenylate cyclase catalyzes the cyclization of adenosine triphosphates (ATP) into cyclic adenosine monophosphates (cAMP) by releasing a pyrophosphate (PPi) molecule. Therefore, activation of adenylate cyclase and cAMP generation may indirectly inhibit bacterial growth by reducing cellular ATP levels.
A comparative quantitative Western blot analysis using protein-specific antibodies of the differentially expressed protein(s) (DEP) and quantitative proteomics as well as transcriptomics (RNA-seq) would be required for targeted validation of the quantity of these protein(s). These studies would establish a direct molecular correlation (transcriptional and/or translational) between the optogenetic modulation of cAMP levels and its role in alteration of the quantity of the protein(s) associated with biofilm formation, growth, and virulence of E. coli.

3.2.2. Functional Expression of LgAC Modulates Biofilm Formation and Associated Processes in E. coli

The cAMP-based regulation of the expression of genes controlling biofilm formation in many bacteria is well established [47]. In Vibrio cholerae thriving under a low glucose concentration, the cAMP-CRP complex regulates biofilm formation by modulating CdgA and HapR (quorum sensing regulator) [48,49]. Biofilm formation is an important transitional event occurring in the bacterial lifestyle, wherein the inhibition of motility promotes biofilm formation. Motility-to-biofilm transition is regulated by controlling either flagellar function or genes of the flagella-biofilm regulatory network [50]. Proteomic analysis revealed a ~2-fold downregulation of two proteins associated with the flagella-biofilm network (i.e., IhfA [51] and flagellin (FliC) [52]) in BL-illuminated E. coli (Figure 2B; Table 1). IhfA is one of the global regulators (GRs) of biofilm formation in E. coli and modulates the expression of genes (rpoS, matA, and csgD) involved in the flagella-biofilm network [51]. In E. coli, flagellin is a structural protein encoded by the fliC gene, which is involved in the polymerization of the long helical filament of bacterial flagella [52]. Mutation in the fliC gene causes a severe defect in biofilm formation [53]. In Pseudomonas aeruginosa PAO1, the FliC protein is associated with the dispersal of the biofilm [54], where post-translational modification (phosphorylation of T27 and S28 residues) of FliC alters biofilm formation and dispersal. Proteomic analysis revealed a significant reduction (~2 fold) in the expression of the YeaG protein in BL-illuminated E. coli (Figure 2B; Table 1). Ibanez-Ruiz et al. [55] reported the yeaG gene as a member of the rpoS regulon that plays a crucial role in biofilm formation and its regulation in Salmonella entrica Serovar Typhimurium. Further, yeaG was identified as a putative stress response gene that was associated with the stress response occurring during biofilm growth [56].
A c-di-GMP receptor protein, YajQ, showed a significant decrease (~2 fold) in expression in BL-illuminated E. coli (Figure 2B; Table 1). An and coworkers performed an affinity pull-down assay using cyclic di-GMP-coupled magnetic beads and identified the YajQ family protein as a potential cyclic di-GMP receptor, and mutation of the gene manifested a reduction in biofilm formation [57]. A reduction (~2 fold) in expression of the HflC protein (FtsH modulator) was observed in E. coli after BL illumination (Figure 2B; Table 1). In bacteria, the matrix-producing cells are essential for effective biofilm formation [58]. In E. coli cells, HflC and HflK (chaperons) oligomerizes FtsH protein and make it a functional unit [59]. Yepes and coworkers showed a direct interaction between FloT/YqfA (HflC/HflK homologues) and FtsH. Inhibition of FloT/YqfA negatively affected the FtsH concentration and further reduced the differentiation of matrix producer cells and biofilm formation [59].
In this study, the cAMP-dependent biofilm formation ability of E. coli cells expressing LgAC was estimated, and the result showed a significant reduction (p < 0.05) in the biofilm formation ability in BL-illuminated cells (Figure 3B). However, the alteration in the cAMP-dependent biofilm formation ability of untransformed E. coli culture (negative control) was not significant (p > 0.069) when compared between dark-adapted and BL-illuminated conditions (Figure S3), suggesting that the reduction in the biofilm formation ability of E. coli was partially associated with the light-dependent elevation of cAMP. Safranin-based qualitative detection and confocal microscopy revealed a reduction in the biofilm formation ability of E. coli expressing LgAC upon BL illumination (Figure 3C,D). In the confocal microscopy, the fluorescent property of the protein was exploited for the visualization of biofilm formation. Cell aggregation, which is characteristic of biofilm-forming microorganisms, was reduced or absent in the BL-illuminated LgAC-expressing E. coli culture (Figure 3D).

3.2.3. Optogenetic Modulation of cAMP Regulates Virulence Signaling in E. coli

The involvement of cAMP in the regulation of bacterial virulence is a well-known phenomenon [60]. In Mycobacterium tuberculosis, 10 functional adenylate cyclases (ACs) and CRP were reported to be involved in the complex regulation of virulence [61]. Quantitative proteomics showed a reduced expression of proteins (ClpP, YebC, KatG, BtuE, and Zur) associated with virulence in E. coli after BL illumination. The caseinolytic protease P (ClpP), a serine protease, was significantly reduced (~2 fold) in the LgAC-expressing BL-illuminated E. coli (Figure 2B; Table 1). In bacterial pathogens, ClpP plays an important role in the onset of pathogenesis and virulence [62]. The Clp mutant of Salmonella typhimurium was unable to grow in peritoneal macrophages, which is a crucial step for the establishment of S. typhimurium virulence [63]. Knudsen and coworkers demonstrated that S. typhimurium virulence was attenuated in the Clp mutant due to the mis-regulation of RpoS as well as the indirect regulation of CsrA and the genes of the Salmonella pathogenicity island 1 [64]. YebC, a PmpR (pqsR-mediated PQS regulator) family protein, which regulates quorum sensing in the bacterial system [65], was significantly downregulated (~1.5 fold) in E. coli after BL illumination (Figure 2B; Table 1). Bacterial virulence depends on the type III secretion system (T3SS), type VI secretion system (T6SS), and quorum sensing system (QS), which controls cell-to-cell communication and helps the bacterial population to invade and interact with hosts [66,67,68]. Liang et al. [49,69] also reported the role of the cAMP-CRP complex in the regulation of HapR expression (a major regulator of quorum sensing) in a cell density-dependent manner in V. cholerae via control of the Fis and RpoS pathways. Therefore, modulating cAMP-dependent quorum sensing may control the bacterial pathogenesis and compromise host–pathogen interactions.
Bacterial pathogenesis elicits a host immune response to generate and release toxic reactive oxygen species (ROS) by phagocyte cells. However, many pathogens have evolved an arsenal of effective oxidative stress defense systems to cope with such conditions, such as molecular scavengers, proteins, and DNA repair enzymes [70]. Quantitative proteomics in the current study revealed the downregulation of proteins associated with oxidative stress in the LgAC-expressing E. coli after BL illumination. An oxidative stress defense protein was significantly downregulated (~1.5 fold) in E. coli after BL illumination. Furthermore, a ~2-fold downregulation of BtuE protein (glutathione peroxidase) and specific catalase (KatE) was also observed (Figure 2B; Table 1). In E. coli, BtuE has non-specific peroxide activity, and it utilizes thioredoxin A for the decomposition of different hydroperoxides [71]. In Xanthomonas axonopodis pv. citris, KatE is required for full virulence in Citris plant, where KatE is involved in the colonization and survival of X. axonopodis during pathogenesis [72]. Significant downregulation (~4 fold) of the Zur protein (zinc uptake regulator) was observed in E. coli after BL illumination (Figure 2B; Table 1). Nutritional immunity is one of the most common responses posed by the host upon pathogen invasion, wherein the host intoxicates the invading microbes with essential metals, of which zinc is an important component. Several microbes have evolved ways to adapt hypozincemic or hyperzincemic conditions [73]. In bacteria, Zur regulated the expression of genes involved in the control of zinc homeostasis and the onset of oxidative stress [74].

3.3. Protein Networking Deciphers Functional Links among the DEPs

We constructed a protein network of DEPs associated with growth, biofilm, and pathogenesis, and the resulting network revealed different nodes showing connections between proteins within as well as between the relevant pathways (Figure 4). The betweenness centrality analysis depicted dominating nodes and paths in the constructed protein network. Interestingly, most of the DEPs (HflC, IhfA, ClpP, BtuE, GlnA, Adk, and AckA) identified through quantitative proteomics were principal effectors that modulate the functioning of respective bacterial signaling pathways. Among the growth regulating proteins, Adk, AckA, and GlnA were the main node, which exert a determining impact on the protein network (Figure 4). In bacteria, cellular stoichiometry between Adk, AckA, and GlnA is crucial for the maintenance of the energy budget by invoking alternative ATP-generating pathways, which is an important factor regulating bacterial growth [40]. Similarly, IhfA, HflC, and MotA (flagellar motor) were the main nodes influencing the biofilm network (Figure 4). IhfA and MotA are important regulators of the flagella-biofilm network, whereas HflC regulates the differentiation of matrix-producing cells, two of the important steps of bacterial biofilm formation. ClpP and BtuE are the main nodes influencing the bacterial pathogenicity network (Figure 4). Michel et al. [75] demonstrated the regulatory role of ClpP protease in the oxidative stress response by controlling the antioxidative enzymes like catalase and peroxidases, respectively. In bacteria, as in other organisms, physiological processes do not occur in an isolated and exclusive manner. They are part of an interlinked and complex network driven by different metabolic pathways in parallel. Likewise, growth, biofilm formation, and pathogenesis are interrelated phenomena in bacteria; therefore, the machinery regulating these processes might be linked via some regulatory proteins.

4. Conclusions

The establishment of a new optogenetic modulation is an innovative approach to reprogram gene expression and cell signaling with spatiotemporal precision. A variety of optogenetic tools have been developed for controlling neural activity, behavioral response, and cellular events in cells and the whole organism. In bacteria, cAMP signaling regulates a variety of crucial processes essential for its growth, survival, and pathogenesis. We established optogenetic modulation of cAMP in a model bacterium (E. coli) and studied the optogenetically modulated cAMP-dependent bacterial processes like growth, biofilm formation, and virulence using a light-gated adenylate cyclase (LgAC). The blue light-dependent activation of LgAC and the corresponding increase in cellular cAMP level demonstrated a significant decrease in the growth and levels of relevant proteins (PTS, Adk, AckA, GlnA, Kbp, and EF-P). Proteins regulating the motility to biofilm transition and flagella-biofilm network were modulated upon optogenetic control of cAMP. Photoactivation of LgAC modulated the amount of IhfA, YajQ, and HflC, which regulates crucial steps in biofilm formation. Photoinduced elevation in the cAMP level influenced the levels of proteins (ClpP, YebC, BtuE, KatE, and Zur), which are responsible for conferring virulence. ClpP and YebC proteins are known to regulate different aspects of bacterial virulence and pathogenesis. BtuE and Zur represent the group of proteins that helps pathogens counter oxidative stress imposed by the host immune system. It is evident that LgAC is an optogenetic photoswitch, which can modulate cAMP-dependent bacterial growth, biofilm formation, and pathogenesis simply by illumination with blue light. The established optogenetic modulation of bacterial cAMP opens avenues for studying cAMP-driven physiological aspects, including the host–pathogen signaling of bacterial systems. The systematic utilization of the established method and more extensively designed experiments regarding the bacterial growth, biofilm, survival, and virulence might help in providing a road map for the identification of new targets for developing antibacterial drugs.

Supplementary Materials

The following are available online at https://www.mdpi.com/2076-3417/10/16/5535/s1, Figure S1: Multiple sequence alignment of the BLUF (blue light using flavin) domain (A), linker region (B), and cyclase domain (C) from different organisms with that of the well-characterized BLUF-Cyclase proteins from Oscilatoria accuminata depicting the functionally important amino acids in the BLUF domain (red star represent the amino acids important for photocycle and photodynamics of the BLUF while the orange square denotes the residues essential for the flavin binding pocket), in the cyclase domain [green stars represent important amino acids required for ATP binding, red arrows depict metal-binding residues (MBR), blue arrows correspond to the essential adenine-binding residues (ABR), and orange arrows denote transition state-stabilizing residues (TSSR)], respectively. The length of the linker region is crucial for light-gated cyclase activity of all the BLUF; cyclases were shown to be similar except in Leptonema illini, where the length of the linker region was surprisingly very short, Figure S2: LgAC–ATP interaction analysis. (A) A 3-D model of the cyclase domain of LgAC docked with the ATP as the ligand (encircled). (B) Interaction between ATP and amino acid residues forming the ATP binding pocket within the cyclase domain (Inset). (C) 2-D interaction image depicting the amino acid residues and the nature of interaction involved in the binding of ATP. The green, yellow, and violet dashed lines represent conventional hydrogen bonding, attractive charge-based bonding, and pi-alkyl bonding, respectively, Figure S3: (A) Growth curve of E. coli expressing recombinant LgAC in the dark and BL. Growth was estimated at 600 nm (B) Biofilm formation by E. coli carrying LgAC under dark and BL. The biofilm formation of E. coli was estimated at 550 nm. Data are presented as mean ± SD (n = 3). Statistical comparisons were performed using ANOVA using SPSS version 16.0. Asterisks (*) represents the level of significance difference as compared to control: * p < 0.05, ** p < 0.01, *** p < 0.001 and “ns”, not significant. Positive control was E. coli cells supplemented with 0.1% glucose and negative control was untransformed E. coli cells only.

Author Contributions

M.S.K. and S.R.P. have executed and analyzed proteomics experiments. S.S. and A.M. have performed bioinformatic analysis and LgAC expression. K.S. had done functional characterization of LgAC. All authors have written relevant section of the manuscript. S.K. had conceptualized, outlined experiments, analyzed data and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

M.S.K. is financially supported by University Grant Commission (UGC)-D.S. Kothari Postdoctoral Fellowship, India. S.S. is funded by University Grant Commission (UGC) non-NET fellowship. S.R.P., A.M. and K.S. are thankful to Council of Scientific and Industrial Research (CSIR), government of India and Department of Biotechnology (DBT), government of India for providing fellowship(s). S.K. is thankful to Department of Science and Technology (DST)-Science and Engineering Research Board (SERB)-India [SERB(EEQ)/2018/000781], University with potential of Excellence (UPE)-II Jawaharlal Nehru University (JNU) and Department of Science and Technology (DST)-Promotion of University Research and Scientific Excellence (PURSE) Jawaharlal Nehru University (JNU) for the research grant.

Acknowledgments

We are also thankful to Sindhu K. Veetil and Adivitiya for valuable editing of the manuscript.

Conflicts of Interest

The authors have no conflict of interest.

References

  1. Shimizu, K. Metabolic regulation and coordination of the metabolism in bacteria in response to a variety of growth conditions. Adv. Biochem. Eng. Biotechnol. 2016, 155, 1–54. [Google Scholar]
  2. You, C.; Okano, H.; Hui, S.; Zhang, Z.; Kim, M.; Gunderson, C.W.; Wang, Y.P.; Lenz, P.; Yan, D.; Hwa, T. Coordination of bacterial proteome with metabolism by cyclic AMP signalling. Nature 2013, 500, 301–306. [Google Scholar] [CrossRef] [Green Version]
  3. McDonough, K.A.; Rodriguez, A. The myriad roles of cyclic AMP in microbial pathogens: From signal to sword. Nature Rev. Microbiol. 2012, 10, 27–38. [Google Scholar] [CrossRef] [Green Version]
  4. Görke, B.; Stülke, J. Carbon catabolite repression in bacteria: Many ways to make the most out of nutrients. Nature Rev. Microbiol. 2008, 6, 613–624. [Google Scholar] [CrossRef]
  5. Hufnagel, D.A.; Evans, M.L.; Greene, S.E.; Pinkner, J.S.; Hultgren, S.J.; Chapman, M.R. The catabolite repressor protein-cyclic AMP complex regulates csgD and biofilm formation in uropathogenic Escherichia coli. J. Bacteriol. 2016, 198, 329–3334. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Müller, C.M.; Åberg, A.; Straseviçiene, J.; Emődy, L.; Uhlin, B.E.; Balsalobre, C. Type 1 fimbriae, a colonization factor of uropathogenic Escherichia coli, are controlled by the metabolic sensor CRP-cAMP. PLoS Pathog. 2009, 5, e1000303. [Google Scholar] [CrossRef] [Green Version]
  7. Sutrina, S.L.; Daniel, K.; Lewis, M.; Charles, N.T.; Anselm, C.K.; Holder, N. Biofilm growth of Escherichia coli is subject to cAMP-dependent and cAMP-independent inhibition. J. Mol. Microbiol. Biotechnol. 2015, 25, 209–225. [Google Scholar] [CrossRef] [PubMed]
  8. Ritzert, J.T.; Minasov, G.; Embry, R.; Schipma, M.J.; Satchell, K.J. The Cyclic AMP Receptor Protein Regulates Quorum Sensing and Global Gene Expression in Yersinia pestis During Planktonic Growth and Growth in Biofilms. mBio 2019, 10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Alasbahi, R.H.; Melzig, M.F. Forskolin and derivatives as tools for studying the role of cAMP. Pharmazie 2012, 67, 5–13. [Google Scholar] [PubMed]
  10. Martinez, A.; Gil, C. cAMP-specific phosphodiesterase inhibitors: Promising drugs for inflammatory and neurological diseases. Expert Opin. Ther. Pat. 2014, 24, 1311–1321. [Google Scholar] [CrossRef]
  11. Levskaya, A.; Weiner, O.D.; Lim, W.A.; Voigt, C.A. Spatiotemporal control of cell signalling using a light-switchable protein interaction. Nature 2009, 461, 997–1001. [Google Scholar] [CrossRef] [Green Version]
  12. Boyden, E.S.; Zhang, F.; Bamberg, E.; Nagel, G.; Deisseroth, K. Millisecond-time scale, genetically targeted optical control of neural activity. Nature Neurosci. 2005, 8, 1263–1268. [Google Scholar] [CrossRef] [PubMed]
  13. Tanwar, M.; Khera, L.; Haokip, N.; Kaul, R.; Naorem, A.; Kateriya, S. Modulation of cyclic nucleotide-mediated cellular signaling and gene expression using photoactivated adenylyl cyclase as an optogenetic tool. Sci. Rep. 2017, 7, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Zhou, Z.; Tanaka, K.F.; Matsunaga, S.; Iseki, M.; Watanabe, M.; Matsuki, N.; Ikegaya, Y.; Koyama, R. Photoactivated adenylyl cyclase (PAC) reveals novel mechanisms underlying cAMP-dependent axonal morphogenesis. Sci. Rep. 2016, 5, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Schröder-Lang, S.; Schwärzel, M.; Seifert, R.; Strünker, T.; Kateriya, S.; Looser, J.; Watanabe, M.; Kaupp, U.B.; Hegemann, P.; Nagel, G. Fast manipulation of cellular cAMP level by light in vivo. Nat. Methods 2007, 4, 39–42. [Google Scholar] [CrossRef] [PubMed]
  16. Kaushik, M.; Sharma, R.; Veetil, S.; Srivastava, S.; Kateriya, S. Modular Diversity of the BLUF Proteins and Their Potential for the Development of Diverse Optogenetic Tools. Appl. Sci. 2019, 9, 3924. [Google Scholar] [CrossRef] [Green Version]
  17. Stierl, M.; Stumpf, P.; Udwari, D.; Gueta, R.; Hagedorn, R.; Losi, A.; Gärtner, W.; Petereit, L.; Efetova, M.; Schwarzel, M.; et al. Light modulation of cellular cAMP by a small bacterial photoactivated adenylyl cyclase, bPAC, of the soil bacterium Beggiatoa. J. Biol. Chem. 2011, 286, 1181–1188. [Google Scholar] [CrossRef] [Green Version]
  18. Tanwar, M.; Sharma, K.; Moar, P.; Kateriya, S. Biochemical Characterization of the Engineered Soluble PhotoactivatedGuanylateCyclases from Microbes Expands Optogenetic Tools. Appl. Biochem. Biotechnol. 2018, 185, 1014–1028. [Google Scholar] [CrossRef]
  19. Geer, L.Y.; Domrachev, M.; Lipman, D.J.; Bryant, S.H. CDART: Protein homology by domain architecture. Genome Res. 2002, 12, 1619–1623. [Google Scholar] [CrossRef] [Green Version]
  20. Hall, T.A. BIoedit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/ NT. Nucleic Acids Symp. Ser. 1999, 41, 95–98. [Google Scholar]
  21. Roy, A.; Kucukural, A.; Zhang, Y. I-TASSER: A unified platform for automated protein structure and function prediction. Nat. Protoc. 2010, 5, 725–738. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. BIOvIA, D.S. Discovery Studio Modeling Environment; Dassault Systemes: San Diego, CA, USA, 2015; Volume 4. [Google Scholar]
  23. Jhingan, G.D.; Kumari, S.; Jamwal, S.V.; Kalam, H.; Arora, D.; Jain, N.; Kumaar, L.K.; Samal, A.; Rao, K.V.S.; Kumar, D.; et al. Comparative Proteomic Analyses of Avirulent, Virulent, and Clinical Strains of Mycobacterium tuberculosis Identify Strain-specific Patterns. J. Biol. Chem. 2016, 291, 14257–14273. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Szklarczyk, D.; Morris, J.H.; Cook, H.; Kuhn, M.; Wyder, S.; Simonovic, M.; Santos, A.; Doncheva, N.T.; Roth, A.; Bork, P.; et al. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017, 45, D362–D368. [Google Scholar] [CrossRef] [PubMed]
  25. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software Environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef] [PubMed]
  26. Oliveros, J.C. VENNY. An Interactive Tool for Comparing Lists with Venn Diagrams. Available online: http://bioinfogp.cnb.csic.es/tools/venny/index.html (accessed on 18 January 2020).
  27. Saeed, A.I.; Sharov, V.; White, J.; Li, J.; Liang, W.; Bhagabati, N.; Braisted, J.; Klapa, M.; Currier, T.; Thiagarajan, M.; et al. TM4: A free, open-source system for microarray data management and analysis. Biotechniques 2003, 34, 374–378. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Hammer, Ø.; Harper, D.A.; Ryan, P.D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 2001, 4, 1–9. [Google Scholar]
  29. O’Toole, G.A.; Kolter, R. Initiation of biofilm formation in Pseudomonas fluorescens WCS365 proceeds via multiple, convergent signalling pathways: A genetic analysis. Mol. Microbiol. 1998, 28, 449–461. [Google Scholar] [CrossRef]
  30. Christensen, G.D.; Simpson, W.A.; Younger, J.J.; Baddour, L.M.; Barrett, F.F.; Melton, D.M.; Beachey, E.H. Adherence of coagulase-negative staphylococci to plastic tissue culture plates: A quantitative model for the adherence of staphylococci to medical devices. J. Clin. Microbiol. 1985, 22, 996–1006. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Kleinboelting, S.; Diaz, A.; Moniot, S.; Van Den Heuvel, J.; Weyand, M.; Levin, L.R.; Buck, J.; Steegborn, C. Crystal structures of human soluble adenylyl cyclase reveal mechanisms of catalysis and of its activation through bicarbonate. Proc. Natl. Acad. Sci. USA 2014, 111, 3727–3732. [Google Scholar] [CrossRef] [Green Version]
  32. Kolb, A.; Busby, S.; Buc, H.; Garges, S.; Adhya, S. Transcriptional regulation by cAMP and its receptor protein. Ann. Rev. Biochem. 1993, 62, 749–797. [Google Scholar] [CrossRef]
  33. Bettenbrock, K.; Sauter, T.; Jahreis, K.; Kremling, A.; Lengeler, J.W.; Gilles, E.D. Correlation between growth rates, EIIACrr phosphorylation, and intracellular cyclic AMP levels in Escherichia coli K-12. J. Bacteriol. 2007, 189, 6891–6900. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Kim, J.H.; Shen, R.; Olcott, M.C.; Rajagopal, I.; Mathews, C.K. Adenylate kinase of Escherichia coli, a component of the phage T4 dNTPsynthetase complex. J. Biol. Chem. 2005, 280, 28221–28229. [Google Scholar] [CrossRef] [Green Version]
  35. Thach, T.T.; Luong, T.T.; Lee, S.; Rhee, D.K. Adenylate kinase from Streptococcus pneumoniae is essential for growth through its catalytic activity. FEBS Open Bio 2014, 4, 672–682. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Fox, D.K.; Meadow, N.D.; Roseman, S. Phosphate transfer between acetate kinase and Enzyme I of the bacterial phosphotransferase system. J. Biol. Chem. 1986, 261, 13498–13503. [Google Scholar] [PubMed]
  37. Tiwari, S.; Barh, D.; Imchen, M.; Rao, E.; Kumavath, R.K.; Seenivasan, S.P.; Jaiswal, A.K.; Jamal, S.B.; Kumar, V.; Ghosh, P.; et al. Acetate Kinase (AcK) is Essential for Microbial Growth and Betel-derived Compounds Potentially Target AcK, PhoP and MDR Proteins in M. tuberculosis, V. cholerae and Pathogenic, E. coli: An in silico and in vitro Study. Curr. Top. Med. Chem. 2018, 18, 2731. [Google Scholar] [CrossRef]
  38. Yan, D. Protection of the glutamate pool concentration in enteric bacteria. Proc. Natl. Acad. Sci. USA 2007, 104, 9475–9480. [Google Scholar] [CrossRef] [Green Version]
  39. Ikeda, T.P.; Shauger, A.E.; Kustu, S. Salmonella typhimurium apparently perceives external nitrogen limitation as internal glutamine limitation. J. Mol. Biol. 1996, 259, 589–607. [Google Scholar] [CrossRef] [PubMed]
  40. Aldarini, N.; Alhasawi, A.A.; Thomas, S.C.; Appanna, V.D. The role of glutamine synthetase in energy production and glutamine metabolism during oxidative stress. Antonie van Leeuwenhoek. Int. J. Gen. Mol. Microbiol. 2017, 110, 629–639. [Google Scholar]
  41. Auger, C.; Appanna, V.D. A novel ATP-generating machinery to counter nitrosative stress is mediated by substrate-level phosphorylation. Biochim. Biophys. Acta 2015, 1850, 43–50. [Google Scholar] [CrossRef]
  42. Ashraf, K.U.; Josts, I.; Mosbahi, K.; Kelly, S.M.; Byron, O.; Smith, B.O.; Walker, D. The Potassium Binding Protein Kbp Is a Cytoplasmic Potassium Sensor. Structure 2016, 24, 741–749. [Google Scholar] [CrossRef] [Green Version]
  43. Woolstenhulme, C.J.; Guydosh, N.R.; Green, R.; Buskirk, A.R. High-precision analysis of translational pausing by ribosome profiling in bacteria lacking EFP. Cell Rep. 2015, 11, 13–21. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Johansson, M.; Ieong, K.W.; Trobro, S.; Strazewski, P.; Åqvist, J.; Pavlov, M.Y.; Ehrenberg, M. pH-sensitivity of the ribosomal peptidyl transfer reaction dependent on the identity of the A-site aminoacyl-tRNA. Proc. Natl. Acad. Sci. USA 2011, 108, 79–84. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Doerfel, L.K.; Rodnina, M.V. Elongation factor P: Function and effects on bacterial fitness. Biopolymers 2013, 99, 837–845. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Kuo, J.T.; Chang, Y.J.; Tseng, C.P. Growth rate regulation of lac operon expression in Escherichia coli is cyclic AMP dependent. FEBS Lett. 2003, 553, 397–402. [Google Scholar] [CrossRef] [Green Version]
  47. Diaz, M.R.; King, J.M.; Yahr, T.L. Intrinsic and extrinsic regulation of type III secretion gene expression in Pseudomonas aeruginosa. Front. Microbiol. 2011, 2, 89. [Google Scholar] [CrossRef] [Green Version]
  48. Fong, J.C.; Yildiz, F.H. Interplay between cyclic AMP-cyclic AMP receptor protein and cyclic di-GMP signaling in Vibrio cholerae biofilm formation. J. Bacteriol. 2008, 190, 6646–6659. [Google Scholar] [CrossRef] [Green Version]
  49. Liang, W.; Pascual-Montano, A.; Silva, A.J.; Benitez, J.A. The cyclic AMP receptor protein modulates quorum sensing, motility and multiple genes that affect intestinal colonization in Vibrio cholerae. Appl. Environ. Microbiol. 2007, 153, 2964–2975. [Google Scholar] [CrossRef] [Green Version]
  50. Laverty, G.; Gorman, S.P.; Gilmore, B.F. Biomolecular mechanisms of Pseudomonas aeruginosa and Escherichia coli biofilm formation. Pathogens 2014, 3, 596–632. [Google Scholar] [CrossRef] [Green Version]
  51. Amores, G.R.; De Las Heras, A.; Sanches-Medeiros, A.; Elfick, A.; Silva-Rocha, R. Systematic identification of novel regulatory interactions controlling biofilm formation in the bacterium Escherichia coli. Sci. Rep. 2017, 7, 1–14. [Google Scholar] [CrossRef] [Green Version]
  52. Guttenplan, S.B.; Shaw, S.; Kearns, D.B. The cell biology of peritrichous flagella in Bacillus subtilis. Mol. Microbiol. 2013, 87, 211–229. [Google Scholar] [CrossRef] [Green Version]
  53. Pratt, L.A.; Kolter, R. Genetic analysis of Escherichia coli biofilm formation: Roles of flagella, motility, chemotaxis and type I pili. Mol. Microbiol. 1998, 30, 285–293. [Google Scholar] [CrossRef] [PubMed]
  54. Suriyanarayanan, T.; Periasamy, S.; Lin, M.H.; Ishihama, Y.; Swarup, S. FlagellinFliC phosphorylation affects type 2 protease secretion and biofilm dispersal in Pseudomonas aeruginosa PAO1. PLoS ONE 2016, 11, 1–19. [Google Scholar] [CrossRef]
  55. Ibanez-Ruiz, M.; Robbe-Saule, V.; Hermant, D.; Labrude, S.; Norel, F. Identification of RpoS(S)-Regulated Genes in Salmonella entericaSerovarTyphimurium. J. Bacteriol. 2000, 182, 5749–5756. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Schembri, M.A.; Kjærgaard, K.; Klemm, P. Global gene expression in Escherichia coli biofilms. Mol. Microbiol. 2003, 48, 253–267. [Google Scholar] [CrossRef] [PubMed]
  57. An, S.Q.; Caly, D.L.; McCarthy, Y.; Murdoch, S.L.; Ward, J.; Febrer, M.; Dow, J.M.; Ryan, R.P. Novel Cyclic di-GMP Effectors of the YajQ Protein Family Control Bacterial Virulence. PLoS Pathog. 2014, 10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Chai, Y.; Chu, F.; Kolter, R.; Losick, R. Bistability and biofilm formation in Bacillus subtilis. Mol. Microbiol. 2008, 67, 254–263. [Google Scholar] [CrossRef] [Green Version]
  59. Yepes, A.; Schneider, J.; Mielich, B.; Koch, G.; García-Betancur, J.C.; Ramamurthi, K.S.; Vlamakis, H.; López, D. The biofilm formation defect of a Bacillus subtilisflotillin-defective mutant involves the protease FtsH. Mol. Microbiol. 2012, 86, 457–471. [Google Scholar] [CrossRef] [Green Version]
  60. Kalia, D.; Merey, G.; Nakayama, S.; Zheng, Y.; Zhou, J.; Luo, Y.; Guo, M.; Roembke, B.T.; Sintim, H.O. Nucleotide, c-di-GMP, c-di-AMP, cGMP, cAMP,(p) ppGpp signaling in bacteria and implications in pathogenesis. Chem. Soc. Rev. 2013, 42, 305–341. [Google Scholar] [CrossRef]
  61. McCue, L.A.; McDonough, K.A.; Lawrence, C.E. Functional classification of cNMP-binding proteins and nucleotide cyclases with implications for novel regulatory pathways in Mycobacterium tuberculosis. Genome Res. 2000, 10, 204–219. [Google Scholar] [CrossRef] [Green Version]
  62. Bhandari, V.; Wong, K.S.; Zhou, J.L.; Mabanglo, M.F.; Batey, R.A.; Houry, W.A. The Role of ClpP Protease in Bacterial Pathogenesis and Human Diseases. ACS Chem. Biol. 2018, 13, 1413–1425. [Google Scholar] [CrossRef]
  63. Yamamoto, T.; Sashinami, H.; Takaya, A.; Tomoyasu, T.; Matsui, H.; Kikuchi, Y.; Hanawa, T.; Kamiya, S.; Nakane, A. Disruption of the genes for ClpXP protease in Salmonella enterica serovarTyphimurium results in persistent infection in mice, and development of persistence requires endogenous gamma interferon and tumor necrosis factor alpha. Infect. Immun. 2001, 69, 3164–3174. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Knudsen, G.M.; Olsen, J.E.; Aabo, S.; Barrow, P.; Rychlik, I.; Thomsen, L.E. ClpP deletion causes attenuation of Salmonella typhimurium virulence through mis-regulation of rpoS and indirect control of CsrA and the SPI genes. Microbiology (UK) 2013, 159, 1497–1509. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Liang, H.; Li, N.; Dong, Z.; Surette, M.G.; Duan, K. The YebC family protein PA0964 negatively regulates the Pseudomonas aeruginosa quinolone signal system and pyocyanin production. J. Bacteriol. 2008, 190, 6217–6227. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Passos da Silva, D.; Schofield, M.C.; Parsek, M.R.; Tseng, B.S. An update on the sociomicrobiology of quorum sensing in gram-negative biofilm development. Pathogens 2017, 6, 51. [Google Scholar] [CrossRef] [PubMed]
  67. Lopez, J.; Feldman, M.F. Expanding the molecular weaponry of bacterial species. J. Biol. Chem. 2018, 293, 1515–1516. [Google Scholar] [CrossRef] [Green Version]
  68. Nguyen, V.S.; Douzi, B.; Durand, E.; Roussel, A.; Cascales, E.; Cambillau, C. Towards a complete structural deciphering of Type VI secretion system. Curr. Opin. Struct. Biol. 2018, 49, 77–84. [Google Scholar] [CrossRef] [Green Version]
  69. Liang, W.; Sultan, S.Z.; Silva, A.J.; Benitez, J.A. Cyclic AMP post-transcriptionally regulates the biosynthesis of a major bacterial autoinducer to modulate the cell density required to activate quorum sensing. FEBS Lett. 2008, 582, 3744–3750. [Google Scholar] [CrossRef] [Green Version]
  70. Neumann, W.; Hadley, R.C.; Nolan, E.M. Transition metals at the host–pathogen interface: How Neisseria exploit human metalloproteins for acquiring iron and zinc. Essays Biochem. 2017, 61, 211–223. [Google Scholar]
  71. Arenas, F.A.; Díaz, W.A.; Leal, C.A.; Pérez-Donoso, J.M.; Imlay, J.A.; Vásquez, C.C. The Escherichia coli btuE gene, encodes a glutathione peroxidase that is induced under oxidative stress conditions. Biochem. Biophys. Res. Commun. 2010, 398, 690–694. [Google Scholar] [CrossRef] [Green Version]
  72. Tondo, M.L.; Petrocelli, S.; Ottado, J.; Orellano, E.G. The monofunctional catalase kate of Xanthomonasaxonopodispv. citri is required for full virulence in citrus plants. PLoS ONE 2010, 5, e10803. [Google Scholar] [CrossRef] [Green Version]
  73. Subramanian Vignesh, K.; Deepe, G.S. Immunological orchestration of zinc homeostasis: The battle between host mechanisms and pathogen defenses. Arch. Biochem. Biophys. 2016, 611, 66–78. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Capdevila, D.A.; Wang, J.; Giedroc, D.P. Bacterial strategies to maintain zinc metallostasis at the host-pathogen interface. J. Biol. Chem. 2016, 291, 20858–20868. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Michel, A.; Agerer, F.; Hauck, C.R.; Herrmann, M.; Ullrich, J.; Hacker, J.; Ohlsen, K. Global regulatory impact of ClpP protease of Staphylococcus aureus on regulons involved in virulence, oxidative stress response, autolysis, and DNA repair. J. Bacteriol. 2006, 188, 5783–5796. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Applsci 10 05535 g001 550
Figure 1. Photoactivation of light-gated adenylate cyclase (LgAC). (A) Spectral properties of recombinant LgAC in the dark (solid line) and blue light-adapted (dotted line) states. A difference spectrum between blue light and dark-adapted LgAC is represented by the dashed line. (B) Immunoblot analysis of recombinant LgAC protein. E1, E2, and E3 are the eluted fractions of the purified recombinant LgAC probed with anti-His antibody (C) Visualization of intracellular expression of LgAC in E. coli cells. (D) A differential interference contrast (DIC) image and merged images (E) are also presented. Scale bars represent 10 μm. (F) Variation in the total cAMP level in the cell lysates extracted from E. coli cells expressing LgAC in the dark and BL. The data are represented as mean ± SD and were statistically tested employing student’s t test using SPSS version 16.0. Asterisks (*) represents the level of significance of the difference as compared to the control (dark): *** p < 0.001.
Figure 1. Photoactivation of light-gated adenylate cyclase (LgAC). (A) Spectral properties of recombinant LgAC in the dark (solid line) and blue light-adapted (dotted line) states. A difference spectrum between blue light and dark-adapted LgAC is represented by the dashed line. (B) Immunoblot analysis of recombinant LgAC protein. E1, E2, and E3 are the eluted fractions of the purified recombinant LgAC probed with anti-His antibody (C) Visualization of intracellular expression of LgAC in E. coli cells. (D) A differential interference contrast (DIC) image and merged images (E) are also presented. Scale bars represent 10 μm. (F) Variation in the total cAMP level in the cell lysates extracted from E. coli cells expressing LgAC in the dark and BL. The data are represented as mean ± SD and were statistically tested employing student’s t test using SPSS version 16.0. Asterisks (*) represents the level of significance of the difference as compared to the control (dark): *** p < 0.001.
Applsci 10 05535 g001
Applsci 10 05535 g002 550
Figure 2. Comparative proteomics of LgAC-expressing E. coli incubated in the dark and illuminated with BL. (A) Venn diagram representing the distribution of proteins identified by label-free comparative quantitative proteomics as total protein, dark condition specific, BL specific, and differentially expressed proteins (DEPs) using Venny 2.1. (B) Heat map showing comparative expression patterns of the DEPs. Details of the DEPs are given in Table 1. (C) A pie diagram representing the KEGG (Kyoto Encyclopedia of Gene and Genome) pathway analysis output data; tick labels represent the KEGG identifiers (IDs) of the corresponding pathways. Details of the pathways are given in Table 2.
Figure 2. Comparative proteomics of LgAC-expressing E. coli incubated in the dark and illuminated with BL. (A) Venn diagram representing the distribution of proteins identified by label-free comparative quantitative proteomics as total protein, dark condition specific, BL specific, and differentially expressed proteins (DEPs) using Venny 2.1. (B) Heat map showing comparative expression patterns of the DEPs. Details of the DEPs are given in Table 1. (C) A pie diagram representing the KEGG (Kyoto Encyclopedia of Gene and Genome) pathway analysis output data; tick labels represent the KEGG identifiers (IDs) of the corresponding pathways. Details of the pathways are given in Table 2.
Applsci 10 05535 g002
Applsci 10 05535 g003 550
Figure 3. Impact of LgAC expression on growth and biofilm formation in E. coli. (A) Growth curve of E. coli expressing LgAC in the dark and BL. Growth was estimated at 600 nm. (B) Biofilm formation by E. coli expressing LgAC in the dark and BL. The biofilm formation of E. coli was estimated at 550 nm. These data are presented as mean ± SD (n = 6). Statistical comparisons were performed using ANOVA by tukey’s b test using SPSS version 16.0. Asterisks (*) represents the level of significance difference as compared to control: ** p < 0.01 and “ns”, not significant. (C) Visualization of biofilm formation after staining with safranin. (D) Visualization of biofilm formation using a confocal microscopy. For each representative image, DIC and merged images are also presented. White arrowhead marked the biofilm formation. Scale bars represent 10 μm.
Figure 3. Impact of LgAC expression on growth and biofilm formation in E. coli. (A) Growth curve of E. coli expressing LgAC in the dark and BL. Growth was estimated at 600 nm. (B) Biofilm formation by E. coli expressing LgAC in the dark and BL. The biofilm formation of E. coli was estimated at 550 nm. These data are presented as mean ± SD (n = 6). Statistical comparisons were performed using ANOVA by tukey’s b test using SPSS version 16.0. Asterisks (*) represents the level of significance difference as compared to control: ** p < 0.01 and “ns”, not significant. (C) Visualization of biofilm formation after staining with safranin. (D) Visualization of biofilm formation using a confocal microscopy. For each representative image, DIC and merged images are also presented. White arrowhead marked the biofilm formation. Scale bars represent 10 μm.
Applsci 10 05535 g003
Applsci 10 05535 g004 550
Figure 4. Growth-biofilm-pathogenicity protein network. Protein–protein interaction analysis was performed by using String version 11 (https://string-db.org/;) and further analyzed by Cytoscape 3.4.1 applying the betweenness centrality algorithm. Color range represents betweenness values in the network. Protein network framed under blue, pink, and green lines belongs to biofilm formation, virulence, and growth, respectively. Blue, pink, and green solid arrows represent proteins that showed differential expression in quantitative proteomic analysis of LgAC-expressing E. coli incubated in the dark and under blue light illumination, respectively.
Figure 4. Growth-biofilm-pathogenicity protein network. Protein–protein interaction analysis was performed by using String version 11 (https://string-db.org/;) and further analyzed by Cytoscape 3.4.1 applying the betweenness centrality algorithm. Color range represents betweenness values in the network. Protein network framed under blue, pink, and green lines belongs to biofilm formation, virulence, and growth, respectively. Blue, pink, and green solid arrows represent proteins that showed differential expression in quantitative proteomic analysis of LgAC-expressing E. coli incubated in the dark and under blue light illumination, respectively.
Applsci 10 05535 g004
Table
Table 1. Details of the differentially expressed proteins from light-gated adenylate cyclase expressing E. coli incubated in the dark and illuminated with blue light.
Table 1. Details of the differentially expressed proteins from light-gated adenylate cyclase expressing E. coli incubated in the dark and illuminated with blue light.
Accession No.Differentially Expressed Protein (DEPs)Abv.Log Student’s t-Test p-ValueAbundance (Log10)
Dark Blue Light
P0A9B2Glyceraldehyde-3-phosphate dehydrogenase AGapA2.519.629.43
A0A387CPK3*Phosphoenolpyruvate-protein phosphotransferasePTS*2.228.458.23
P21179CatalaseKatE$2.039.068.84
B1XFR1*Adenylate kinaseAdk*2.398.508.28
P0ABK5Cysteine synthase ACysK2.298.548.41
A0A387CZE1*Acetate kinaseAckA*2.018.328.11
P0A991Fructose-bisphosphate aldolase class 1FbaB1.988.818.68
P0ACY3Uncharacterized proteinYeaG#1.898.548.22
P0A993Fructose-1,6-bisphosphatase class 1Fbp1.967.997.79
Q1PI72Isocitrate dehydrogenase (Fragment)Icd1.878.548.32
P0AC33Fumarate hydratase class I, aerobicFumA1.907.937.80
A0A387CZB23-oxoacyl-ACP synthase IKasA2.158.047.80
A0A387CVS4Citrate synthaseGltA2.397.987.65
P0AFM6Phage shock protein APspA2.577.737.40
P30859Putative ABC transporter arginine-binding protein 2ArtI1.977.667.30
P0A6G7ATP-dependent Clp protease proteolytic subunitClpP$2.668.147.86
P0A9C5Glutamine synthetaseGlnA*2.108.117.96
P0A9G6Isocitrate lyaseAceA2.057.957.50
A0A387CYG6Pyruvate dehydrogenase [ubiquinone]Pdh2.287.727.42
P0AC47Fumarate reductase iron-sulfur subunitFrdB2.028.127.91
P0A8E7UPF0234 proteinYajQ#1.898.157.94
A0A387CXU5Glutamine--tRNA ligaseGlnS2.147.777.59
P76558NADP-dependent malic enzymeMaeB2.757.887.62
A0A387CQ94Oxidative stress defense proteinnk$2.068.648.48
A0A387D0J5FlagellinFliC#2.398.308.00
P0A8A0Probable transcriptional regulatory proteinYebC$2.337.967.84
P00363Fumarate reductase flavoprotein subunitFrdA3.078.077.70
P25516Aconitate hydratase AAcnA2.277.937.52
C4ZYH6Phenylalanine--tRNA ligase alpha subunitPheS2.147.807.41
A0A387D1K8TransaldolaseTal2.518.127.98
A0A387CSZ5TransketolaseTkt2.667.777.45
P06610Thioredoxin/glutathione peroxidaseBtuE$1.887.767.43
P0ABC3Modulator of FtsH proteaseHflC#2.827.877.61
P0A6X7Integration host factor subunit alphaIhfA#3.018.618.43
A0A387D6G1ATP-dependent protease subunitHslV2.537.657.80
A0A387CWB1Aldehyde reductaseAhr2.597.907.55
A0A387CLW3Glycogen synthaseGlgA2.037.457.29
C4ZZ96NH (3)-dependent NAD(+) synthetaseNadE2.627.657.35
A0A387CQM6Tryptophan--tRNA ligaseTrpS1.897.156.95
P0A887Ubiquinone/menaquinone biosynthesis C-methyltransferaseUbiE3.377.366.74
A0A387CRM5AsmA family proteinAsmA1.957.307.05
A0A387CZG61,4-dihydroxy-2-naphthoyl-CoA synthaseMenB1.917.767.52
B1X953ADP-L-glycero-D-manno-heptose-6-epimeraseHldD2.027.537.12
A0A387CLV4OxidoreductaseNk1.997.587.06
A0A387D459D-alanyl-D-alanine carboxypeptidaseNk2.007.507.15
A0A387D1C0Dihydroxy-acid dehydrataseIlvD1.957.187.02
A0A387CTN4Elongation factor P-like proteinEfp*2.827.226.85
C4ZXK8L-threonine 3-dehydrogenaseTdh2.297.226.71
P27550Acetyl-coenzyme A synthetaseAcs2.906.806.31
B1XEI5tRNA-modifying proteinYgfZ3.066.936.47
P0ADE6Potassium binding proteinKbp*2.088.417.96
B1XDY2EsteraseFrsA2.067.096.61
P31448Uncharacterized symporterYidK2.847.317.09
P0AC51Zinc uptake regulation proteinZur$2.486.686.13
P376472-dehydro-3-deoxygluconokinaseKdgK2.137.096.88
A0A387CRW2LipoproteinNk1.896.246.87
A0A387D3P1Endonuclease VNfi3.406.746.51
Abv.—Abbreviations, nk—not known, *—growth associated proteins, #—biofilm associated proteins, $—virulence associated proteins.
Table
Table 2. Uniquely enriched KEGG (Kyoto Encyclopedia of Gene and Genome) pathway terms corresponding to the differentially expressed proteins.
Table 2. Uniquely enriched KEGG (Kyoto Encyclopedia of Gene and Genome) pathway terms corresponding to the differentially expressed proteins.
KEGG TERMKEGG IDInput Numberp-Value
Carbon metabolismeco01200141.76 × 10−13
Microbial metabolism in diverse environmentseco01120144.91 × 10−9
Biosynthesis of secondary metaboliteseco01110146.47 × 10−8
Pyruvate metabolismeco0062065.29 × 10−6
Biosynthesis of antibioticseco01130105.58 × 10−6
Metabolic pathwayseco01100171.61 × 10−5
Glyoxylate and dicarboxylate metabolismeco0063052.99 × 10−5
Citrate cycle (TCA cycle)eco0002040.000102
Glycolysis / Gluconeogenesiseco0001040.000487
Methane metabolismeco0068030.001768
Pentose phosphate pathwayeco0003030.002336
Biosynthesis of amino acidseco0123050.002993
Ubiquinone and other terpenoid-quinone biosynthesiseco0013020.013632
Butanoate metabolismeco0065020.040092
Propanoate metabolismeco0064020.041988
Fructose and mannose metabolismeco0005120.047874
Oxidative phosphorylationeco0019020.047874
Tryptophan metabolismeco0038010.079139
Two-component systemeco0202030.127058
Arginine biosynthesiseco0022010.145133
Glutathione metabolismeco0048010.152176
2-Oxocarboxylic acid metabolismeco0121010.199917
Nitrogen metabolismeco0091010.199917
Alanine, aspartate and glutamate metabolismeco0025010.232407
Cysteine and methionine metabolismeco0027010.238749
Sulfur metabolismeco0092010.245041
Glycine, serine and threonine metabolismeco0026010.263613
Quorum sensingeco0202410.386875
ABC transporterseco0201010.766284

Share and Cite

MDPI and ACS Style

Kaushik, M.S.; Pati, S.R.; Soni, S.; Mishra, A.; Sushmita, K.; Kateriya, S. Establishment of Optogenetic Modulation of cAMP for Analyzing Growth, Biofilm Formation, and Virulence Pathways of Bacteria Using a Light-Gated Cyclase. Appl. Sci. 2020, 10, 5535. https://doi.org/10.3390/app10165535

AMA Style

Kaushik MS, Pati SR, Soni S, Mishra A, Sushmita K, Kateriya S. Establishment of Optogenetic Modulation of cAMP for Analyzing Growth, Biofilm Formation, and Virulence Pathways of Bacteria Using a Light-Gated Cyclase. Applied Sciences. 2020; 10(16):5535. https://doi.org/10.3390/app10165535

Chicago/Turabian Style

Kaushik, Manish Singh, Swaroop Ranjan Pati, Shivanika Soni, Ayushi Mishra, Kumari Sushmita, and Suneel Kateriya. 2020. "Establishment of Optogenetic Modulation of cAMP for Analyzing Growth, Biofilm Formation, and Virulence Pathways of Bacteria Using a Light-Gated Cyclase" Applied Sciences 10, no. 16: 5535. https://doi.org/10.3390/app10165535

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop