Bacterial pigments represent a diverse group of secondary metabolites offering advantages to the producers in terms of survival and replication in communities. The bioactive potential of such metabolites including antimicrobial, anticancer and immune-suppressive properties are being explored. Reckoning that several of such pigments are produced in response to quorum sensing mediated expression of biosynthetic gene clusters and do influence cell-cell communication while residing in communities, systemic profiling of the pigments for possible impact on quorum sensing appears crucial; particularly in the quest of novel alternatives to confront drug nonresponsive pathogens. In this context, a series of bacterial pigments are clustered based on their physicochemical properties and representatives of the clusters are screened for quorum sensing inhibition. The screen highlighted prodigiosin as a potent quorum quencher although its production from Serratia marcescens apparently is QS-independent. In silico analysis indicated potential interaction with AbaI and AbaR, two major QS regulator in Acinetobacter baumannii. While developing multi-bacterial biofilm, prodigiosin producer S. marcescens significantly impaired fitness of A. baumannii and accentuated responsiveness against colistin under co-culture. Prodigiosin impaired a major QS dependent process, biofilm formation, in A. baumannii and also enhanced antibiotic action against A. baumannii biofilms. Collectively, the results underpin the prospect of prodigiosin-based therapeutic strategy in combating A. baumanii infection.
Research Article
A comprehensive profiling of quorum quenching by bacterial pigments identifies quorum sensing inhibition and anti-biofilm action of prodigiosin against Acinetobacter baumannii
https://doi.org/10.21203/rs.3.rs-2732625/v1
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bacterial pigments
prodigiosin
quorum sensing inhibition
biofilm
antibiotic resistance
Secondary metabolites, like pigments, alkaloids and antibiotics, produced by diverse groups of bacteria confer benefits to the producer organism with profound impact on survival, replication and cellular communication (Abdelghani et al. 2021). Of the secondary metabolites, pigments, for their significance in determining bacterial community function, remains one of the important-yet-less explored by-products of bacterial metabolism (Narsing Rao et al. 2017). While pigment production in bacteria offers advantages like protection from ultraviolet radiation, oxidants, antimicrobials and extreme and adverse temperature, for the competitive counterparts, it can be deleterious (Celedon and Diaz 2021). The expression of pigment biosynthetic genes is under precise regulation of multiple transcription factors that respond to environmental cues (Ramesh et al. 2021). Considering the immense amount of genetic load and energy invested in pigment synthesis (Ramesh et al. 2021), it can be assumed that such metabolites are crucial during interspecies communication and competition. Bacterial pigments are chemically diverse in nature belonging to groups like carotenoids, melanins, phenazines, quinones, indoles and pyrroles. Pyocyanin (PCN), a quorum sensing (QS) modulated phenazine-derivative produced by Pseudomoans aeruginosna (Pa) has been the most intensively studied bacterial pigment. The redox active pigment is released by the producer in the environment and exerts antimicrobial action by triggering reactive oxygen species in target cells offering advantage to the producer in communities (Jayaseelan et al. 2014; Noto et al. 2017) . PCN and other phenazine can induce tolerance to various antibiotics while it induced sensitivity against polymyxinB (Zhu et al. 2019). Violacein (VIO), an indole derivative produced by Chromobacterium violaceum (Cv) in response to QS-signals, shows excellent antimicrobial activity against Staphylococcus aureus and Staphylococcus epidermidis in planktonic and biofilmed form (Batista et al. 2017). Possible synergistic effect of VIO in combination with a series of antibiotics has been profiled against Klebsiella pneumoniae, Pa and Salmonella typhi (Subramaniam et al. 2014). Its impact on microbiome has also been studied where varied microbial population enrichment was estimated under low or high VIO regimen (Pauer et al. 2018). Prodigiosin (PDG), a red linear tripyrrole, syntheiszed by Gram-negative γ-proteobacteria like Serratia marcescens and Gram-positive Actinobacteria like Streptomyces coelicolor, possesses antimicrobial properties (Yip et al. 2019) as evidenced by its potential to augment extreme membrane leakage with depleted respiration rate (Danevcic et al. 2016). Though initially antibacterial action of PDG was considered to be restricted to Gram-positive pathogens like S. aureus and its resistant isolates like oxacillin-resistant S. aureus (ORSA) (Lapenda et al. 2015), in recent years inhibitory action against E. coli (Danevcic et al. 2016) and Pa has been documented. Similarly, therapeutic application of other bacterial pigments have been also been examined in recent years (Numan et al. 2018). Assessing bacterial pigments as prospective intervention strategy against drug non-responsive pathogens needs systemic exploration of these secondary-metabolites against resistance prone bacteria and resistant isolates, which might offer crucial clues for combating antimicrobial resistance emergence.
Acinetobacter baumannii (Ab) has been listed among “ESKAPE” pathogens which resists diverse spectrum of antimicrobials by virtue of heritable and transmissible genomic alterations and can also persist as dormant cells with in biofilms (Asif et al. 2018). Owing to prompt acquisition of resistance determinants, multi (MDR), extreme (XDR) and even pan drug-resistant (PDR) isolates of it have now been described (Nowak et al. 2017). QS modulates bacterial collective behaviours such as biofilm formation, motility, virulence, and even drug resistance mechanisms in a number of bacteria including Ab (Mayer et al. 2020; Saipriya et al. 2020). In Gram-negative bacteria QS is predominantly mediated by the synthesis, release and effector function of a group of diffusible signalling molecules- N-acyl-homoserine lactones (AHLs) (Abisado et al. 2018; Zhao et al. 2020). Acinetobacter spp. encodes an AHL synthase (AbaI) and a transcriptional regulator (AbaR) which constitute a canonical LuxR/LuxI QS system commonly functional in Gram-negative bacteria (Lopez-Martin et al. 2021). The complete genome sequence of Ab delineate AbaI as the solitary AHL-synthase for production of AHLs with varying acyl chains of which N-3-hydroxy dodecanoyl-HSL (OH-C12-HSL) is the major modulator (Mayer et al. 2020). In a recent transcriptomic analysis it was revealed that deletion of AbaI down regulates genes linked to biofilm formation, purine metabolism and TypeVI secretion system while upregulating genes linked to fatty acid metabolism and amino acid metabolism (Xiong et al. 2022). AbaM, a gene encoded in the same locus functions as a key regulator of the production of AHLs (Lopez-Martin et al. 2021). Like other Gram-negative pathogens, disruption of QS, quorum quenching (QQ), is projected as anti-virulence strategy with immense therapeutic potential against Ab (Raad et al. 1993). Several natural mechanisms have evolved as an inhibitor of QS for QS-emitting organisms and communities in terms of competitive relationship which includes AHL-lactonases, acylases and oxidoreductases, metabolites like S-adenosyl methionine or AHL analogues and natural compounds like plant secondary metabolites (Paluch et al. 2020). Exploration of such mechanisms might offer crucial clue to combat infections caused by invading bacterial species or communities. In this context, here an attempt to screen and profile bacterial pigments for possible quorum quenching (QQ) potential exploiting established markers of AHL-mediated QS. Prospect of the identified pigment for QQ is introspected in silico and in terms of bacterial biofilm formation by Ab. Alongside, augmenting antibiotic action against Ab in biofilm and possible impact on fitness in community culture with pigment producer are also analysed.
Strains and reagents
Acinetobacter baumannii (ATCC19606) (Ab), Pseudomonas aeruginosa PA14 (Pa), Chromobacterium violaceum MTCC2656 (Cv) and Serratia marcescens MTCC4822 (Sm) are maintained in Luria-Bertani agar and broth (Himedia). For biofilm assays LB broth with 1% glucose is used. Antimicrobial susceptibility tests were performed in Mueller Hinton Broth (Himedia). Leeds Acinetobacter Agar (Himedia) was used for selective assays involving Ab.
All the reagents including pigments like pyocyanin (PCN), prodigiosin (PDG), violaceine (VIO), doxorubicin (DOX), pyoverdin (PVD) and antibiotics like colistin (COL) were purchased from Sigma unless mentioned otherwise.
Biofilm formation
The biofilm formation by the bacterial strains of Ab were assessed using crystal violet (CV) by a method described earlier with required modifications (Paul Bhattacharya et al. 2020). Briefly, cells were inoculated and grown for attaining late-log phase in Luria-Bertani broth at 37°C until 0.6 OD600 nm was reached. Aliquots of 200 µl from this culture were then distributed into the wells of 96-well polystyrene plates (Himedia) in the presence or absence of the pigments. After an exposure for 24 h at 37°C and the medium was discarded carefully and the wells were washed thrice with sterile distilled water (O'Toole 2011). To each well, 0.1% (w/v) CV preparation was added, incubated for 5 min and washed to remove excess stains. CV retained by the biofilm was solubilized with 70% and finally O.D. was measured at 570 nm using a plate reader (Biorad, iMarkMicroplate Absorbance Reader). Non-inoculated wells and wells freshly filled with overnight culture were used in each experiment, as non-biofilm negative control.
Antimicrobial susceptibility assays
Minimum bactericidal concentration (MBC) was determined fom treated bacterial population by scoring colony forming units (CFU) according to a previously described protocol (Lee et al. 2017). The MBC was designated as the concentration at which no viable bacteria could be recovered. For each pigment-treated system respective solvent (dimethyl sulfoxide, DMSO; Sigma) control was used.
Minimum inhibitory concentration (MIC) was determined according to CLSI-microdilution method (Kowalska-Krochmal and Dudek-Wicher 2021) on 96-well polystyrene plates (Himedia). MIC was defined as concentration at which no visible growth was observed.
QS assay with C. violaceum
Measurement of synthesis of VIO by Cv has been considered as a marker for QS activity (Singh et al. 2009). In this study, VIO production by Cv MTCC2656 in the presence and absence of the pigments was assessed according an earlier described method with suitable modifications (Blosser and Gray 2000). Briefly, log phase cultures (OD600 nm = 0.6) were allowed to grow in the absence or presence of the pigments for 24 h. The pellets of Cv cells were dissolved in DMSO. The supernatant containing soluble VIO was measured at 595 nm using a U-2910 Spectrophotometer (Hitachi) spectrophotometer and iMarkMicroplate Absorbance Reader (Biorad) depending on assay volume. For each treatment cell density was recorded by measuring OD600 nm.
Quantitative assay for pyocyanin
PCN assay was performed according to Chong et al. (2017) with minor modifications (Chong et al. 2018). Briefly, pigments were added in different concentrations to the log phase culture of Pa and incubated overnight at 37 °C. The culture supernatant was chloroform extracted on ice with of 0.2 M HCl. O. D of the chloroform layer containing PCN was measured at 520 nm using a U-2910 Spectrophotometer (Hitachi) spectrophotometer and iMarkMicroplate Absorbance Reader (Biorad) depending on assay volume. For each treatment cell density was recorded by measuring OD600 nm.
Quantitative assay for prodigiosin
PDG production was estimated according to Elkenawy et al., 2017 with necessary modifications. Briefly, following growth in culture twice volume of acidified ethanol (4% of 1 M HCl) was added and the mixture was vortexed well and centrifuged at 6000 rpm for 5 mins at 4 ºC to obtain a clear extract. The extract was immediately analyzed spectrophotometriacally for PDG content by measuring OD540 nm. Relative PDG concentration was expressed in terms of an arbitrary unit (A.U)- OD540 nm of extract/ OD600 nm of source culture.
Fitness in community biofilms
To estimate competition in biofilm communities of pigment formers and Ab, community biofilms were allowed to form for 24 h with equivalent number of late-log phase cultures (OD600 mn = 0.6). Subsequently broth containing planktonic cells were removed and the biofilm was washed with 1XPBS and suspended in LB broth. Viability of each bacteria was measured in terms of CFU after diluting the suspension and spreading on LB-agar plates. Colony for Ab and Sm was counted based of their distinctive colony morphology using digital colony counter (EI).
Fitness was expressed in terms of Competition Index calculated with the following formula:
Developing hyper-biofilm former strains
A hyperbiofilm former strain of Ab ATCC19606, AbHBF was developed using adaptive selection approach. Late log phase Ab cells were allowed to develop biofilms for 24 h. Following the removal of planktonic cell containing medium the biofilms were resuspended in LB broth and grown for 24 h. The late log culture was again allowed to form biofilm and the adaptive selection was perpetuated for 15 cycles. The population retrieved after the final selection was compared for biofilm forming potential with Ab ATCC19606. The purity of the strain under election was confirmed after every five selection cycles by PCR amplification of genomic DNA for gapdh and 16S rRNA gene using primer pairs -AbgapdhF: ATGCAACGTATCGCCATT, AbgapdhR: TCGTACATGACACACTCGAT and Ab16SF: GAATAAGCACCGGCTAACTCTGT and Ab16SR: TAAGGTTCTTCGCGTTGCAT using SimpliAmp Thermal Cycler (Applied Biosystems).
Viability in biofilm cells
Biofilms of Ab was allowed to develop in liquid-polystyrene substratum interface for 24 h. The impact of antibiotic-pigment combinations on viability of biofilm cells were estimated by treating the preformed biofilms, with different doses of antibiotic alone or in combination with PDG in fresh medium. CFU for the biofilms were scored by suspending the biofilm cells, dilution and further plating.
Homology modelling and structural superimposition
Homology models of the AbaI and AbaR were obtained from SWISS-MODEL (Waterhouse et al. 2018) and MODELLER 9.22 (Webb and Sali 2016) using prepared using 2.00 Å crystal structure of TofI from Burkholderia glumae (3P2H) and 2.5 Å-crystal structure of QscR from Pa (6CC0) as templates for each protein respectively. The models were energy minimized through Swiss-PdbViewer (Guex and Peitsch 1997). The resulting homology models were validated using Ramachandran plot using PROCHECK (https://servicesn.mbi.ucla.edu/PROCHECK/) (Laskowski et al., 1993). All figures were generated using either BIOVIA Discovery Studio Visualizer Tool or PyMOL. Structural superimposition analysis were performed in PyMOL.
Molecular docking analysis
Molecular docking experiments were carried out by PyRx virtual screening software (Dallakyan and Olson 2015), which includes both AutoDock and AutoDockVina with the Lamarckian genetic algorithm (LGA) as scoring function. Resultant docked structures with best binding affinity (kcal/ mol) were retrieved and visualized by using BIOVIA Discovery Studio Visualizer Tool.
Molecular Dynamics Simulation
Using PRODRUG web tool (Schuttelkopf and van Aalten 2004), the topology file for PDG was generated. The solvated systems of AbaI-PDG and AbaR-PDG complexes were subjected to 50000 steps of energy minimization with steepest descent integrator. The systems were then equilibrated for NVT/NPT equilibration and after completion of the equilibration phase; the system was prepared for the production of MD under constant temperature (300 K) and pressure (1 bar). The molecular simulations were passed out through GROMOS96 54a7 force field with a time-step of 20ns for simulation time, in WEBGRO simulation server (Oostenbrink et al. 2004; Pronk et al. 2013). During production dynamics, the number of frame per simulation was 5000, and RMSD and RMSF plots were generated.
Heat map
The web interface of heatmapper (http://www.heatmapper.ca/), was used to generate heat maps (Babicki et al. 2016).
Statistical analysis
Statistical analysis was performed using Graphpad Prism. Two tailed paired Student´s t-test on data obtained from at least three independent experiments were implemented for majority of the analysis, unless mentioned otherwise.
Chemical clustering indicated diversity of bacterial pigments in terms of physicochemical properties.
To envision the diversity among structural and physicochemical properties in bacterial pigments synthesized by various groups of bacteria (enlisted in Table-S1) were analyzed by Chemmine tools (Backman et al. 2011). Chemmine toolbox integrates cheminformatic algorithms with data mining to accomplish systematic structure and activity based analyses of compound sets. Here, with a set of 11 bacterial pigments, hierarchical clustering was performed for Openbabel descriptors (Fig. 1A) and ChemmineR (Fig. 1B) properties imperative for bioactivity profiling. As depicted in Figs. 1A and 1B, the pigments demonstrated similar clustering pattern for both sets of properties, as depicted by the Z-values; with pyoverdin (PVD) and rubrolone (RUB) forming a cluster distinct from two clusters. One formed by phenazine (PHE), toxoflavin (TXF) and pyocyanin (PCN), the other constituted by and indigoidine (IND), melanin (MEL), prodigiosin (PDG) and violacein (VIO). Zeaxanthin (ZEA), astaxanthin (AST) and flexirubin (FLR) & actinorhodin (ACT), doxorubicin (DOX) and rubrolone (RUB) formed two divergent clusters. The results highlighted the diversity of physicochemical properties among bacterial pigments and underpins their bioactive potential to interact with diverse group of biomolecules.
Screening of bacterial pigments as quorum quenchers
QS-triggered expression of biosynthetic gene clusters linked to pigment production in many pathogenic bacteria. C4-C8-HSL dependent VIO production by Cv has been established as a standard quantitative assay for ASL-based QS (Rehman and Leiknes 2018). Similarly, C4-HSL and 3-oxoC12-HSL induces expression of a set of genes including PCN synthetic cassette in Pa. To introspect whether the pigments can affect C4-AHL and 3-oxo-C12-AHL signaling, PCN production by Pa was analyzed (Lee and Zhang 2015). Satisfying the obtained chemical clusters upon Openbabel and ChemmineR descriptors, Six bacterial pigments-namely PCN (0.0625-0.5 µg/ ml) , PDG (0.625-5µg/ ml), VIO (0.625-5µg/ ml), ZEA (6.25-50 µg/ ml), DOX (6.25-50 µg/ ml) and PVD (0.625-5 µg/ ml) were selected as representatives of distinct clusters and screened at Sub-MIC concentrations for possible impact on VIO production by Cv and PCN production by Pa. PCN exerted significant modulation on VIO production by Cv at 0.125 µg/ ml, 0.25 µg/ ml and 0.5µg/ ml with 52.90±3.42%, 31.88±4.92 and 15.25±3.81% of the solvent control treated cells (100%) respectively (Fig. 2A). At 25 µg/ ml and 50 µg/ ml ZEA exerted modest impairment of PCN production from Pa with 70.96±2.51% and 61.00±3.53% of the control (Fig. 2A). PDG impeded VIO biosynthesis from Cv at 0.625 µg/ ml, 1.25 µg/ ml, 2.5 µg/ ml and 5 µg/ ml with 36.24±6.39%, 14.73±1.63%, 16.17±1.47% and 12.71±1.55% of the control respectively (Figs. 1A and 1B). PDG also inhibited PCN production in Pa with 32.98±2.33%, 23.17±3.73%, 21.13±4.38% and 18.89±3.00% receptively at 0.625, 1.25, 2.5 and 5 µg/ ml (Figs. 1A and 1C). The results highlighted PDG as potential inhibitor of AHL-mediated QS.
Prodigiosin can interact with quorum sensing regulators in A. baumannii
To assess the potential of PDG to act as QQ in Ab possible interaction of PDG with the orthologues for AHL-synthase orthologue (AbaI) and AHL dependent transcriptional activator (AbaR) was analyzed. Homology models of AbaI and AbaR was prepared using 2.00 Å crystal structure of TofI from Burkholderia glumae (3P2H) and 2.5 Å-crystal structure of QscR from Pa (6CC0). Quality of the models were validated with Ramachandran Plot mapping 1 and 0 residues in disallowed region respectively. Molecular docking study revealed possible interaction of PDG with AbaI within the catalytic domain (ΔG= -7.2 kcal/ mol) through one H-bond with D160 and several Pi-sigma and Van der Walls interactions (Figs. 3A and 3C). With AbaR (ΔG= -6.6 kcal/ mol), possible biding was suggested at core of AHL binding domains with one H-bond with M54 and several Pi-sigma and Van der Walls interactions (Fig. 3B and 3D). In order to further introspect the atomic details of molecular interactions of AbaI-PDG and AbaR-PDG, MD simulations were performed for 50 ns using the docked conformation of the complexes. The RMSD was calculated for the backbone atoms of AbaI and AbaR relative to the docked structures to evaluate the dynamic stabilization in the time scale of the simulation period. From the RMSD plot of AbaI-PDG Fig. 3E, the simulation reached convergence at around 10 ns and attained a stable RMSD value of 0.35 nm from 0.10 nm (Fig. 3E). Similarly for AbaR-PDG, it can be observed that the simulation reached convergence at around 15 ns and attained a stable RMSD value of 0.60 nm from 0.16 nm (Fig. 3F). The simulation suggested a more stable interaction between PDG with AbaR compared to AbaI.
Prodigiosin biosynthesis in S. marcescens is quorum sensing independent
Short chain AHLs like C4-HS and C6-HSL have been identified as the major effector for all the LuxI/ LuxR ortholgues identified in Serratia spp. including SwiI/ SwiR and SmaI/ SmaR. Albeit earlier PDG production has been ascribed to QS in Sm, in recent years whole genome sequencing of several strains revealed that C4-HSL/ C6-HSL dependent QS mediated PDG production is S. marcescens is a strain specific event as indicated by (Sakuraoka et al. 2019; Van Houdt et al. 2007). In order to reexamine whether PDG synthesis in the test strain of Sm (MTCC4822) is QS dependent, log-phase Sm cells were exposed to N4-butyryl HSL at various concentrations ranging from 6.25 μg/ ml to 50 μg/ ml for 24 hours at 30ºC. When PDG production was compared in terms of a population density normalized arbitrary unit according to a standard method, no significant alteration in PDG production was noted (Fig. 4A). Temperature has been reported as a factor in PDG production by Sm and OmpR family transcription factor, CpxR has been attributed in such thermoregulation of PDG biosynthetic gene cluster (Sun et al. 2020). Sm MTCC4822 is a psychrotolerant isolate, where PDG production is diminished at temperature >34 ºC (Chatterjee et al., unpublished data). While divulging the impact of N4-butyryl HSL, temperature dependent PDG production was examined as control. As depicted in Fig. 4B, profound PDG production was detected when grown in 30 ºC which was diminished by ~6.14-fold when Sm was grown at 37 ºC. The results indicated that PDG production in Sm is QS independent and QQ activity of PDG possibly have no feed-back influence on its production.
Prodigiosin can impair biofilm formation by A. baumannii
QS and biofilm development by Ab depends on longer (C10-C16) acyl HSLs of which the most predominant one is 3‐hydroxy‐C12‐homoserine lactone (Saipriya et al. 2020). Though compared to other model biofilm formers like Pa, late-log/ stationary phase Ab cells form thinner pellicular biofilm on polystyrene surface, the biofilms are stable enough for assaying implementing standard quantitation procedures. PDG demonstrated potential antimicrobial action against Ab with MIC and MBC values of 12.5 µg/ ml. Drug-likeness, physicochemical, ADME/T properties of PDG was calculated with the help of SwissADME and ADMETlab 2.0 online tools (Daina et al. 2017; Xiong et al. 2021) (Figs. S1A and S1B) and toxicity was predicted with Protox-II (Banerjee et al. 2018) (Fig. S1C). Though SwissADME prediction tags PDG positive in terms of drug-likeliness as per Lipinski filter with an Abbott Bioavailability Score of 0.55, and Protox-II categorize it as Predicted Toxicity Class: 6 with only predicted immunotoxicity (Fig. S1C); predictions reinforced by earlier demonstrations of low or no toxicity on normal cell lines (Sumathi et al. 2014). Such predictions suggest PDG as a prospective therapeutic option provided strategies for selective delivery/ controlled release could be optimized.
To introspect the effect of PDG on biofilm development, mid-log phase Ab cells were exposed to various sub-MIC concentrations of PDG (0.75 μg/ ml, 1/16XMIC; 1.5 μg/ ml, 1/8XMIC; 3.0 μg/ ml, 1/4XMIC and 6.0 μg/ ml, 1/2XMIC) while forming biofilm and biofilm development were quantified by conventional CV-staining. As depicted in Fig. 5A dose depended impairment in biofilm formation was observed from 1.5 μg/ ml (26.77±3.13% reduction compared to control), which further reduced at higher concentrations (55.95±6.32 reduction for 3.0 μg/ ml and 80.10±2.15% reduction for 6.0 μg/ ml compared to control, Fig. 5A). A more stable and robust biofilm former strain, AbHBF was developed by adaptive selection of Ab ATCC19606 for biofilm formation on polystyrene substratum, which demonstrated ~3.71 fold greater CV retention (Fig. S2). When AbHBF was allowed to develop biofilm in presence of various concentrations of PDG, a more prominent impact on biofilm formation was envisioned with 59.83±7.18% reduction at 0.75 μg/ ml of PDG; attaining a maximum of 87.52±2.47% for 6 μg/ ml of PDG (Fig. 5B). Potentiation of antimicrobial action of antibiotics against bacterial biofilms has been the hall mark of several anti-biofilm agents (Hawas et al. 2022). To test whether PDG can enhance antibiotic action against biofilms, preformed biofilms of Ab were exposed to 1XMIC of ciprofloxacin (0.5 μg/ ml, MIC vs. Ab) in combination with 0.25X and 0.5XMIC of PDG (3.0 μg/ ml and 6.0 μg/ ml, respectively) for 16 h. While at 0.5XMIC PDG modestly reduce the viability of Ab-biofilms by ~1.58-fold, CIP mitigated it by ~2.26-fold. In combination with 0.25XMIC of PDG and 0.5XMIC of PDG, CIP resulted ~2.6-fold and ~10.2-fold depreciation in biofilm viability compared to CIP-only exposure (Fig. 5C); suggesting considerable potentiation of antibiotic action against Ab-biofilms.
Prodigiosin producer can attenuate fitness of A. baumannii in community biofilms and enhance susceptibility against colistin in community culture.
PDG has been projected as a broad spectrum antimicrobial (Yip et al. 2021) and often such metabolites elevates fitness of the producer in multispecies communities. To explore whether PDG producer indeed exploit some fitness advantage in communities to restrict the ability of other organisms to replicate and survive in a competitive environment, fitness of Ab in community biofilm with Sm was enumerated in terms of viability of biofilmed cells. Independent cultures of Sm and Ab with equivalent population size of late-log phase cells, were allowed to form biofilms on polystyrene substratum. Alongside, a co-culture system comprising equivalent amount of late-log Sm and Ab cells was allowed to form community biofilms. Viability of Ab and Sm from each of the system was scored in terms of CFU based on distinctive colony apprearence (chromogenic for Sm and white for Ab) and expressed in terms of competition index. As illustrated in Fig. 6A, fitness for Ab in community biofilms was substantially compromised in community biofilm (CI=0.221±0.022). Since PDG production by Sm diminishes at 37 ºC, community biofilms with Ab were allowed to develop in 37 ºC with equivalent population size of late-log phase cells grown in 37 ºC. Fitness of Ab enhanced significantly (CI=0.468±0.055, p=0.0044, pair-wise student t-test).
Marked alteration in bacterial population response against antibiotics has been observed in a multi-bacterial communities (Galera-Laporta and Garcia-Ojalvo 2020). In order to examine whether Sm can affect response of Ab against COL in community culture, pure 107 log-phase Ab cells were mixed with equivalent number of log-phase Sm cells and the co-culture system was exposed to 1XMIC of COL (0.6 µg/ ml, MIC vs. Ab). At various time points post drug exposure, viability of Ab was examined on Leeds Acinetobacter Agar plates where Ab form typical pink colony with mauve back ground. Though no significant effect was observed till 30 mins of COL-exposure, a 4.65±0.14 –fold (p=0.0112, pair-wise student t-test) reduction in viability of Ab was observed at 60 mins post COL treatment in community culture against pure Ab culture under similar treatment condition with equivalent seeding population size (Fig. 6B). Conversely, time-dependent tracking foe viability of Ab in pure and mixed planktonic culture of Ab and Sm did not indicate such impact of fitness of Ab when probed for 2 hrs (Fig. 6C). The result indicated augmentation of antibiotic action against Ab by Sm in multibacterial community.
Most common bacterial pigments belong to the groups of carotenoids, melanins, phenazines, quinones, indoles and pyrroles. Majority of such pigments are reported for their bioactive potential like antimicrobial, antioxidant, UV protection properties with potential clinical and biomedical applications (Celedon and Diaz 2021). Though phenazines have previously explores as a terminal component of QS system in Pa (Zakharenko 1991) influencing gene expression regulation by the quinolone-signal (PQS), systemic profiling of bacterial pigment for their impact on QS was lacking. In an attempt to perform systemic profiling for impact of bacterial pigments, a chemical cluster analysis was performed with 15 major bacterial pigments (Celedon and Diaz 2021) and six pigments were chosen as representative of different clusters. We implemented two independent screening methods, C4-C8-HSL dependent VIO production from Cv, and C4-HSL and 3-oxo-C12-HSL induced PCN production from Pa, to test possible impact of the pigments on ASL dependent QS (Lee and Zhang 2015; Rehman and Leiknes 2018). PDG elicited substantial attenuation in pigment production in both systems.
PDG is a heterocyclic tripyrole bacterial secondary metabolite synthesized by few Actinomycetes and eubacteria including Sm (Williams 1973). The pigment PDG is a signature of Sm strains isolated from environment or pathogenic strains isolated from invertebrates, other animals and human (Abreo and Altier 2019; Raymann et al. 2018). PDG is synthesized in a bifurcated pathway where mono- and bipyrrole precursors are synthesized in parallel and subsequently coupled. Whole genome sequencing of Sm strains identified pig (pigA to pigO) genes, a set of 14-15 genes dedicated for PDG biosynthesis. Several of the genes are involved in 2-methyl-3-n-amyl-pyrrole (MAP) synthesis; others some are linked to production of 4-methoxy-2,2'-bipyrrole-5-carbaldehyde (MBC), and rest are involved in the coupling steps (Williamson et al. 2006). Recently a transposon mediated mutagenesis screen identified 33 genes, some of which are essential genes, encoding transcriptional regulator, membrane proteins and metabolic enzymes out-side of PDG biosynthetic gene cluster involved in PDG biosynthesis and regulation (Jia et al. 2021). Such genetic and metabolic load illuminates significance of PDG in survival and pathogenicity of the bacteria. Though PDG is stored in Sm as an intracellular pigment, according to one hypothesis by Yip et al., in response to interspecies competition PDG might get secreted in the environment (Yip et al. 2021). In this context, effect of Sm on fitness of Ab in community culture and in community biofilms was analysed, which was significantly compromised in community biofilms under condition where PDG is synthesized. However at 37ºC where PDG biosynthesis is impaired in Sm, fitness of Ab improved substantially. Such observation can also be an outcome of intrinsic biofilm forming potential of Ab and Sm or due to production of other metabolic intermediates. However, biofilm specific competition might also result from autolysis of Sm cells and subsequent release of PDG. Autolysis has been found to induct specific advantages in biofilm development for Enterococcus faecalis and Pseudoalteromonas tunicata (Mai-Prochnow et al. 2006; Thomas et al. 2008). Though phenotypic diversification was noted in biofilms formation by Sm (Koh et al. 2007), significance of autolysis remains to be explored in the bacteria. Co-culture or cross feeding condition results in altered antimicrobial responsiveness profile and resistance acquisition dynamics (Adamowicz et al. 2020; Galera-Laporta and Garcia-Ojalvo 2020). Co-culture with Sm accentuated action of COL against Ab. Further exploration of community response between Sm and Ab might offer insights into dynamics of interaction between pigment former and non-former bacteria.
Earlier, antibacterial activity of PDG has been reported to be somewhat restricted to Gram positive bacteria like MRSA, E. faecalis and S. epidermidis while Gram negative bacteria like Pa and S. typhi were observed to be non-restricted by PDG (Jardak et al. 2022; Lapenda et al. 2015; Yip et al. 2021). In recent years antibacterial action of PDG against Gram negative bacteria like E. coli (Danevcic et al. 2016) and Pa with MIC values ranging between 10-20 µg/ ml (Darshan and Manonmani 2016) has been evidenced. In this study, PDG showed considerable antibacterial action against Ab ATCC19606 with an MIC of 12.5 µg/ ml. Alongside, in silico analysis suggested PDG as possible quorum quencher for Ab with potential interaction with AbaR. Biofilm formation in Ab is depended on N-3-hydroxy dodecanoyl-HSL (OH-C12-HSL) mediated QS-signal (Mayer et al. 2020). Biofilm formation was assessed in order to confirm QQ action of PDG against Ab and at sub-MIC concentrations PDG attenuated biofilm development on polystyrene surface. PDG also potentiated antibiotic action of CIP against preformed biofilms of Ab, confirming its antibiofilm potential. Intriguingly, PDG production by Sm is not regulated by AHL-mediated QS as PDG biosynthesis remained unaffected by short chain-ASL. Hence PDG synthesis possibly does not involve a feed-back regulatory loop involving QS. Though the exact mechanism of antibacterial action for PDG remains obscure, independent studies using different target organisms revealed affecting pH, disruption of the plasma membrane, degradation of DNA and ROS generations as generic mode of bactericidal action of PDG (Araujo et al. 2022). Also, the mode of action seems to vary between different groups of bacteria as disruption of cell wall and subsequent lysis has been speculated as the major event in Gram positive bacteria, while in the case of Gram-negative bacteria, it affect metabolism and gene expression (Araujo et al. 2022). Observation from the present study indicates that QQ activity of PDG might also be a possible mechanism against Gram negative organisms.
While predicting prospective therapeutic application, SwissADME prediction tagged PDG as positive in terms of drug-likeliness as per Lipinski filter and Protox-II categorize it as Predicted Toxicity Class: 6. Also, a number of study described PDG as cytotoxic against tumor and cancer cell lines including NCHI-292, HEp-2, MCF-7 and HL-60 with IC50 between <5 µg/ ml (Lapenda et al. 2020) and non-toxic to normal cells (Sumathi et al. 2014). Though cytotoxicity against normal non-malignant cells warrants further exploration; selective cytotoxic effect of PDG has been accomplished by formulating PDG-loaded halloysite nanotubes (Guryanov et al. 2020). Localized release of PDG has been achieved via adsorption in Poly(N-isopropylacrylamide) (PNIPA) gels further encapsulated in poly-di-methyl-siloxane (PDMS) (Danyuo et al. 2014) and PLGA/Ge pluronic F127 loaded nanofibers (Akpan et al. 2020). PDG incorporated in Poly(lactic acid) (PLA) has been characterized as effective nanoformulation against biofilms formed by Klebsiella aerogenes and S. aureus (Mudenur et al. 2022). With its potential action against Ab biofilms, development of selective nanoformulation might offer a proper clinical implementation of the pigment.
Acknowledgement:
The authors acknowledge all the open source software and server providers. The authors also acknowledge Mr. Saikat Samanta, Adamas University for his constant support in laboratory activities. AB is funded by Startup research Grant- SRG/2020/000702 (SERB, Govt. of India) and SEED grant, Adamas University.
Funding:
AB is funded by Startup research Grant- SRG/2020/000702 (SERB, Govt. of India).
Conflict of interest:
The authors declare no conflict of interest. None of the authors were paid from the funding of the project.
Availability of data and material:
NA
Author contribution:
Kusumita Acharya performed most of the experiments, analysed the data and wrote the manuscript.
Sonjukta Borborah performed the in silico analysis
Abhishek Chatterjee performed experiments
Arijit Bhattacharya conceptualized the work, designed experiments, analysed data and prepared the manuscript.
All approved the final draft.
Ethical approval:
The study does not involve any human and/or animal subjects or clinical isolates. No personally identifiable patient/ human subject information was disclosed to the researchers.
Consent to participate:
NA
Consent for publication:
NA
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