Pseudomonas aeruginosa strain CD3 implements cadmium resistance through multimodal systems and its regulatory networking

Pseudomonas aeruginosa strain CD3 implements cadmium resistance through multimodal systems and its regulatory networking | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pseudomonas aeruginosa strain CD3 implements cadmium resistance through multimodal systems and its regulatory networking Soumya Chatterjee, Partha Barman, Chandan Barman, Sukanta Majumdar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4733845/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cadmium, a toxic heavy metal, poses a significant global concern. Out of 26 cadmium-resistant bacteria isolated from Malda, West Bengal, India, 10 exhibited significant resistance to cadmium. The study hypothesized that the net availability of metal salt molecules in a dissolved state would determine the tolerance limit of a given bacterium towards a particular heavy metal. Experiments were conducted using a modified medium that supported maximum bioavailability of cadmium, and strain CD3 was selected for studying the growth and induction of cadmium resistance. The resistance levels of CD3 cells increased with increasing initial cell numbers. Biofilm formation increased at lower concentrations of CdCl 2 .H 2 O but decreased as concentrations exceeded 0.75 mM. Atomic-absorption-spectrophotometry data confirmed that the efflux pump played a critical role in cadmium resistance at higher concentrations. Using whole-genome-based phylogenetic tools, strain CD3 was found to be the closest relative to Pseudomonas aeruginosa DSM50071 T among the type strains of Pseudomonas spp., highlighting its unique evolutionary path. The STRING database was used to uncover an intricate web of protein-protein interactions. Hence, bioinformatic analyses revealed a complex network of regulations, with BfmR playing a crucial role in the functions of CzcR and CzcS, essential for biofilm formation and receptor signalling pathways. Cadmium Pseudomonas aeruginosa CD3 Biofilm CzcCBA Induction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Cadmium, conventionally anticipated as a biologically insignificant element, has emerged as a significant environmental pollutant over the course of centuries, mainly due to anthropogenic industrial activities (Briffa et al. 2020 ). This significant rise substantially implies a shift from the evolutionary interplay commonly witnessed between life forms and elements, positioning cadmium’s biological engagement as predominantly motivated by its inorganic chemistry under physiological circumstances. Exclusively existing in its divalent cationic form, Cd 2+ , cadmium determines its interplay with biomolecules through two apparently contrary aspects that underlie its biological and toxicological consequences. Cd 2+ is known as a "soft" ion within Group 12 of the periodic table, displaying a strong affinity for thiols and other “soft” ligands (resulting from soft acids and bases). This orientation clears the way for its binding to key biomolecules, regulating various biochemical pathways. Metallothioneins, thiol-rich Cd 2+ -binding proteins, surface as critical modulators of intracellular cadmium levels, alleviating its bioavailability and emphasizing the complex interplay between Cd 2+ and Zn 2+ ions and cellular defence mechanisms. Its strong affinity for sulphur sites is relevant to biology since it implies, for example, that Cd 2+ concentrations in metallothionein containing ZnS 4 units only need to be 1/1000th of Zn 2+ concentrations to effectively compete (Templeton 2023 ). One major hypothesis is that most of cadmium toxicity occurs through interference with the control of cellular zinc homeostasis.Against all odds, certain phytoplankton species have progressed to leverage cadmium as an advantageous element under zinc-limited conditions, underscoring the adaptive strategies living organisms may acquire with regard to metal stress (Xu and Morel 2013 ). Owing to its dual ability to interact with thiol groups and imitate Ca 2+ ions, cadmium can affect a wide spectrum of proteins that are involved in DNA repair, signal transduction, and cytoskeletal stability. These interplays have extreme inferences for cellular function, highlighting the pleiotropic and predominantly negative impacts of cadmium (Maret and Moulis 2013 ). In prokaryotes, gradient-driven Ca 2+ exchangers, ATPases, and non-proteinaceous polyhydroxybutyrate-polyphosphate (PHB-PP) channels are the three main categories of Ca 2+ transport mechanisms that have been identified. Many bacterial taxa have been found to include Ca 2+ exchangers, which are assumed to be a key component of the Ca 2+ transport machinery in prokaryotes. These are low-affinity Ca 2+ transporters that can function in both directions, depending on the gradient. They do this by using the energy contained in the electrochemical gradient of ions (Sarkisova et al. 2005 ). Contrarily, the ionic radius of cadmium has a close resemblance to that of the "hard" ion calcium (Ca 2+ ), allowing it to imitate Ca 2+ ions and occupy its binding sites, especially in oxygen-rich environments. This mimicry disturbs cellular calcium homeostasis, distressing a wide array of signaling cascades and affecting the viability, proliferation, and apoptosis of cells. Bacteria have been exposed to cadmium in proportion to the element's presence in various ecological contexts over the course of evolution. But there is no proof that life has ever evolved a specific mechanism to deal with cadmium. Cadmium has the ability to hitchhike with an unexpectedly large number of biological compounds. The evolution of cadmium resistance in bacteria is an intriguing instance of how microorganisms can adjust to environmental challenges, especially heavy metal toxicity. Despite its acute toxicity, certain bacteria have evolved sophisticated machinery to resist and even thrive in cadmium-contaminated environments (Fardami et al. 2023 ).This evolutionary adaptation can be recognized as a consequence of the selection pressure exerted by environments heavily contaminated with cadmium. Microbial populations subjected to toxic levels of cadmium or other heavy metals go through remarkable survival challenges. In such circumstances, natural selection supports individuals with mutations or gene acquisitions that offer metal resistance. In the course of time, these favourable traits present frequently within the population contribute to the advent of cadmium-resistant bacterial strains (Woyda et al. 2023 ). To cope with cadmium toxicity, bacterial strains have spread out a variety of machinery, including efflux systems that pump cadmium ions out of the cell, sequestration of cadmium ions by metal-binding proteins such as metallothioneins, and enzymatic conversion of cadmium into less toxic or more easily sequestered forms (Saluja and Sharma 2014 ; Khan et al. 2015 ). Furthermore, some bacteria have a repair system to alleviate the damage caused due to oxidative stress, induced by cadmium (Fan et al. 2023 ).The development of cadmium resistance in bacteria is not only an instance of the outstanding adaptability of microbes, but it likewise provides valuable insights into the mechanisms of heavy metal resistance, which can be adopted in bioremediation processes to clean up cadmium and other heavy metal pollutants from the environment. Comprehending these mechanisms at a molecular level not only provides valuable insights into the wider context of microbial evolution in response to toxic substances but also helps in the application prospects of these adaptive strategies in biotechnological and environmental management practices (Jebril et al. 2022 ). However, research on bacterial cadmium resistance has identified several knowledge gaps. Firstly, the specific mechanisms of cadmium resistance in bacteria are not understood comprehensibly. While certain genes associated with cadmium resistance, such as czcC , czcB , czcA , czcR , czcS , cadA and cadR , have been identified, there might be many other genes involved (Abbas et al. 2018 ; Qin et al. 2019 ; Chen et al. 2022 ). Secondly, the role of time and dosage related to cadmium resistance is still questionable. It has been observed that the expression of genes associated with cadmium resistance can alter over time and in accordance to exposure to different cadmium concentrations (Hoogewerf et al. 2015 ; Qin et al. 2017 ). Moreover, it is unclear how cadmium resistance is influenced by the crosstalk between different cadmium resistance genes. It has been proposed that cadmium resistance is regulated by gene systems with time- and dose-dependent functions. Further research is required to address these lacunae and improve our understanding of cadmium resistance mechanisms in bacteria. Moreover, the MIC values in bacterial cadmium resistance may be overestimated due to issues with the media composition. Most of the metals being used in laboratory studies can be bound or chelated by the organic components in the media, resulting in erroneously high MICs. Standard media for determining MICs of heavy metals often remove significant amounts of free Cd 2+ ions from the media, leading to unreliable findings (Angle and Chaney 1989 ; Qin et al. 2019 ). Utilizing modified media, the uncertainties associated with other media could be avoided and the MICs could be precisely estimated (Angle et al. 1992 ). Certain modifications in the concentrations of the media components, such as phosphate, and yeast extract, can remarkably reduce the degree of error in estimating MICs of cadmium. These findings imply that the media composition used in toxicity-tolerance testing can influence the estimation of MIC values for cadmium resistance in bacteria. The principal objective of this research is to address the existing loopholes regarding bacterial cadmium resistance. By delving into the evolutionary pathways and mechanisms bacteria have developed to counteract cadmium toxicity, this study intends to better comprehend microbial adaptability and resilience towards cadmium, with potential implications for its effective bioremediation strategies. Materials and methods Isolation of cadmium-resistant bacteria Cadmium-resistant bacteria were isolated from soil samples collected from four distinct places (cadmium abundance in the samples ranged between 16 and 22 ppm) situated at Malda, West Bengal, India (GPS coordinates: 24.9091 N 88.0869 E, 25.071 N 87.873 E, 24.990057 N 88.1496345 E, 24.9882814 N 88.1454309 E). A 10 g soil sample was added to 100 mL Luria Broth (LB) medium supplemented with 0.5 mM cadmium chloride monohydrate (CdCl 2 . H 2 O), and the mixture was incubated at 30°C for 96 h. 10% inoculum was taken and then transferred into graded concentrations of CdCl 2 . H 2 O(0.75, 1.0, 2.0, 3.0 mM) supplemented medium. The soil-LB mixtures, after 4 days of enrichment, were serially diluted in sterilised 0.85% NaCl solution, and appropriate dilutions were spread on Luria Agar (LA) plates supplemented with sterilized 1 mM CdCl 2 . H 2 O (500 mM stock solution of CdCl 2 . H 2 Owas sterilized separately)(Fan et al. 2023 ). Plates were incubated at 30°C for overnight. Later, morphologically distinct bacterial colonies were identified and clonally purified in cadmium supplemented (1 mM CdCl 2 . H 2 O) LA plates. Isolates were stored in LA slants (supplemented with 0.01 mM CdCl 2 . H 2 O) and kept at 4 o C until further use. Formulation of a proper bacteriological medium suitable for determination of the cadmium resistant phenotype in bacteria After first round of clonal purification of bacterial colonies from enrichment medium, isolates were streaked in LA plates supplemented with different concentration(s) of CdCl 2 . H 2 O (separately ranging from 1–15 mM) and incubated at 30°C for 48 h for preliminary screening of potent cadmium-resistant bacteria. The strains exhibiting significant growth in CdCl 2 . H 2 O -supplemented plates were taken for another round of screening for demonstrating growth (1% inoculum) in minimal salt medium (MSM), which was formulated after several rounds of modification (medium composition specified in Table 1 ). Appropriate volume(s) from sterile CdCl 2 . H 2 O stock solution was added to sterile MSM to obtain the defined concentrations of Cd 2+ (1–15 mM with a graded elevation of 2 mM). Cell pellet was obtained by spinning the LB grown overnight culture at 4000 rpm in a centrifuge for 10 min and washed twice with sterile MSM. Then, 10 ml MSM supplemented with a defined concentration ofCdCl 2 . H 2 O was inoculated (1% inoculum) in 100 ml Erlenmeyer flasks and kept in a shaking incubator (GeNei™) at 30 ºC and 100 rpm for 48 h. After 48 h of incubation, visible turbidity of the culture confirmed growth vis-à-vis resistance of the isolates in the presence of the respective concentrations of CdCl 2 . H 2 O in each flask. Table 1 Composition of formulated liquid minimal salt medium (in g/l) used in this study: MSM components Amount (g/l) Ammonium chloride (NH 4 Cl) 1.5 Calcium chloride (CaCl 2 ) 0.02 Magnesium sulphate heptahydrate (MgSO 4 .7H 2 O) 0.2 Sodium chloride (NaCl) 0.5 Sodium sulphate (Na 2 SO 4 ) 3 Potassium nitrate (KNO 3 ) 0.875 D-glucose 8 Yeast extract 0.1 Determination of minimum inhibitory concentration (MIC) of CdCl 2. H 2 O to ascertain the upper limit of cadmium resistance in selected cadmium-resistant isolates The upper limit of cadmium resistance (in terms of MIC of cadmium) was determined for the individual isolates in the formulated MSM. This test involved graded elevation of CdCl 2 . H 2 Oconcentration in the MSM and the lowest concentration of the metal, which inhibits the growth of the isolate, are considered the MIC of cadmium. A1% (v/v) inoculum from log phase MSM culture of CD3 was added to individual Erlenmeyer flasks containing MSM supplemented with different concentrations of CdCl 2 . H 2 O. After 72 h of incubation, growth was assessed spectrophotometrically at 600 nm. Besides the determination of MIC, growth curves were generated by plotting log c.f.u. /ml values (enumerated by dilution plating technique) versus time in cultures containing different concentrations of CdCl 2 . H 2 O (0.5-3 mM). This study was conducted by growing the cells in modified MSM in triplicates for statistical validation (Sen et al. 2022 ). Determination of resistance to other metals and metalloids of the potential cadmium-resistant isolate, CD3 Resistance of CD3 to a few heavy metals and metalloids (nickel, zinc, cobalt, chromium, arsenate and arsenite) was determined by growing the bacteria in MSM supplemented with respective metal and metalloid salts. Stock solutions of 500 mM concentrations of heavy metal and metalloid salts (nickel chloride, zinc sulfate, cobalt chloride, potassium dichromate, sodium arsenate and sodium arsenite) were used. The concentrations of metal ions were increased in a stepwise manner until the bacterial growth ceased. Growth was determined spectrometrically after 96 h of incubation at 30°C. Culture grown in the absence of metals/metalloids was treated as a control. All studies were performed in triplicates (Khan et al. 2016 ). Re-examination of the heavy metal resistance including cadmium-resistance phenotype of the strain CD3 expressed as MIC values in relation to varying inoculum density Influence of the inoculum density on the cadmium-resistance phenotype shown by CD3 was performed to prove or disprove the hypothesis that resistance is determined by the degree of intracellular free import of cadmium per cell which can be reduced when there is an increase in the number of input cells, resulting in an overestimation of MIC values. To test this hypothesis, log-phase cells grown in MSM were inoculated (1% inoculum) in fresh MSM supplemented with varying concentrations of CdCl 2 . H 2 O or CoCl 2 . 6H 2 O or ZnSO 4 . 7H 2 O. Primarily, the initial cell counts at 0 h post- inoculation was found to be ≈ 10 5 c.f.u. /ml.; similar experiments were conducted by taking the initial cell count at 10 4 or 10 3 or 10 2 c.f.u. /ml at 0 h post- inoculation.The cultures were incubated at 30 o C under shaking conditions (100 rpm). After 7 days, cultures were taken and spread in LA to check viability and growth. Control experiments were conducted with E. coli K12 MTCC 1302. All studies were performed in triplicates. Induction of cadmium resistance in CD3, pre-exposed to low concentrations of cadmium or zinc or cobalt salt To study the nature of cadmium resistance (inducible or not), growth curves were studied in the modified MSM medium amended with 1.5 mM CdCl 2 . H 2 O by measuring the viable cell numbers (c.f.u. / ml) of the cultures at different time intervals (Bhadra et al. 2006 , 2007 ; Sen et al. 2022 ).A culture was originally prepared in MSM, free of CdCl 2 . H 2 O. 1% of the inoculum from the log-phase culture (not exposed to Cd 2+ ) was transferred to fresh MSM supplemented with 1.5 mM CdCl 2 . H 2 O (= half the MIC concentration) and viable cell counts (by dilution plating technique) at different time points were recorded to generate a growth curve and determine the length of the lag phase. A second set of cultures was prepared in MSM supplemented with 0.5 or 0.25 or 0.125 or 0.0625 or 0.03125 mM CdCl 2 . H 2 O, and 1% inoculum from each log-phase culture (exposed to low concentrations of Cd 2+ ) was transferred to fresh MSM supplemented with 1.5 mM CdCl 2 . H 2 O. Viable cell counts at different time points were recorded to generate a growth curve for determining the length of the lag phase. The lengths of the lag phases of the individual test cultures were compared with each other. A positive control set was run side by side, where 1% inoculum from the log-phase culture (cells grown in MSM supplemented with 1.5 mM CdCl 2 . H 2 O) was transferred to fresh MSM supplemented with 1.5 mM CdCl 2 . H 2 O.To investigate the role of Zn 2+ or Co 2+ in inducing cadmium resistance, log phase culture cells (1% inoculum) grown in MSM supplemented with 0.25 mM ZnSO 4 . 7H 2 O or CoCl 2 . 6H 2 O was transferred to sterile MSM containing 1.5 mM CdCl 2 . H 2 O; growth curves were generated to confirm shortening of lag-phase lengths as proof for induction of cadmium resistance. Quantification of cadmium concentrations in living and heat-killed CD3 cells using atomic absorption spectroscopy Concentration of extra-cellular, cell-surface-bound, and intra-cellular Cd 2+ was determined by a PerkinElmer Flame Atomic Absorption Spectrometer PinAAcle 900F (FAAS) following a standard protocol(Fang et al. 2011 ). CD3 was first grown up to the log phase of the culture in 10 mL MSM. A 1% (v/v) inoculum from its log phase MSM culture of CD3 was added to individual 10 ml MSM batches supplemented with 1 mM concentration of CdCl 2 . H 2 O (111.73 ppm of Cd 2+ ).At late exponential phase, cells were harvested by centrifugation (10000 rpm for 10 min), and the supernatant was analysed for extra-cellular Cd 2+ concentrations. The cell pellet obtained was resuspended in 0.1 M EDTA and vortexed gently for 15 s. After 2 h of incubation at room temperature the mixture was centrifuged at 10000 rpm for 10 min. Following this, the supernatant was analysed for the potential concentration of Cd 2+ bound to the cell surface. The final cell pellet obtained after the second round of centrifugation was resuspended in sterilized distilled water and sonicated at 60% amplitude for 5 minutes (10 s followed by rest for 10 s), centrifuged at 10000 rpm for 10 min, and the cell lysate was analysed for the quantification of the intra-cellular Cd 2+ concentrations. A sterile MSM supplemented with 1 mM concentration of CdCl 2 . H 2 O was taken as a control. For AAS analysis, a 1 ml sample was taken and mixed with 10 ml diacid solution (HNO 3 :HClO 4 = 9:4) overnight. After overnight incubation, the mixture was digested with sand bath and filtered by Whatman 42 filter paper. Then the filtrate was analysed to determine Cd 2+ concentrations. For comparison,10 ml MSM grown cells (late exponential) of CD3 were harvested by centrifugation, resuspended in 1.5 ml Eppendorf tube and heat killed in a water bath at 70°C for 2 min (Jill van Kessel et al. 2021). After centrifugation at 5000 rpm for 10 min, the pellet was resuspended in 10 ml MSM containing 1 mM CdCl 2 . H 2 O and incubated for 12 h. After 12 h of incubation, the cells were harvested and the supernatant as well as cells were prepared for AAS to analyse the Cd 2+ concentrations in extracellular, bound, and intracellular circumstances following the same protocol. Effect of pH to evaluate changes in toxicity affecting growth of CD3 in presence of 1 mM CdCl 2 . H 2 O In order to identify the pH range that supports normal growth of CD3, liquid MSM with a pH range of 3–12 was initially created for this investigation with a progressive increase of pH 1. To ascertain the correlation between cadmium toxicity and pH in growth, the log phase culture of CD3 was subsequently inoculated in liquid MSM supplemented with 1 mM CdCl 2 . H 2 O, with a pH range of 6–9, and allowed to grow at 30 o C in an orbital shaker fixed at 100 rpm. Control sets were made in liquid MSM with varying pH levels but no CdCl 2 . H 2 O addition (Babich and Stotzky 1977 ). Finally, the growing cultures were serially diluted at regular intervals using 0.85% NaCl solutions. The diluted bacterial suspensions were then spread onto LA plates and incubated for overnight at 30°C. Growth curves generated by plotting the log c.f.u. /ml versus time were used for comparison. Spectrophotometric quantification of biofilm formation in CD3 in response to various CdCl 2 . H 2 O concentrations The ability of the most potent isolate to produce biofilms was examined in liquid MSM using techniques previously reported (O’Toole 2011 ; Baugh et al. 2014 ; Sen et al. 2022 ). In order to accomplish this, CD3 cells were cultured in the wells of a microtiter plate using different concentrations of CdCl 2 . H 2 O. After the incubation period, the wells were washed with sterile double-distilled water to remove any remaining media. Subsequently, the wells were inundated with crystal violet and allowed to remain undisturbed for a minute. Following the removal of excess colour, the wells were dried and subsequently filled with a solution of 30% acetic acid. The absorbance of the suspensions was measured at a wavelength of 540 nm. Whole genome sequencing and analysis of bacterium CD3 The CD3 genomic DNA from overnight grown cells in Luria broth (Himedia) incubated at 30°C under shaking conditions (100 rpm) was isolated, sequenced in Illumina NovaSeq platform, contig-assembled, sequence deposited and annotated following a standard protocol described elsewhere (Barman et al. 2022 ). Preliminary taxonomic identification of strain CD3 was based on 16S rRNA gene sequence homology with legitimate bacterial species. The CD3 whole genome tree was also generated using the type strain genome server (TYGS) (Meier-Kolthoff and Göker 2019 ; Basak and Chakraborty 2023 ) to corroborate the findings. A single-nucleotide-polymorphism tree was also prepared employing the CSIPhylogeny tool (Kaas et al. 2014 ) from the Center for Genomic Epidemiology online server(Deng et al. 2016 ). The Pseudomonas genome database (Winsor et al. 2016 ) was also used to fish out the responsible genes for cadmium resistance. Sequence comparisons at the nucleotide level with Pseudomonas aeruginosa DSM 50071 and at both nucleotide and protein levels with other cadmium-resistant bacterial genera were performed using the online EMBOSS WATER tool (Rice et al. 2000 ). Evolutionary divergence in terms of nucleotide and amino acid sequences of cadmium efflux pump components, CzcC, CzcB, and CzcA Nucleotide and protein sequences of CzcC, CzcB, and CzcA from strain CD3 were used for the retrieval of the homologous sequences from other bacterial strains from the NCBI and Pseudomonas genome database employing blastn and blastp (Johnson et al. 2008 ) programs and top blast hits were considered for the phylogenetic analyses, wherein the homologous sequences were concatenated one after another (CzcC-CzcB-CzcA), aligned with the query sequences using ClustalW (for nucleotide sequences) (Thompson et al. 2003 ) and Muscle (for protein sequences) (Edgar 2004 ) programs in MEGA11(Tamura et al. 2021 ) and all the multiple sequence alignment (MSA) data were used for phylogenetic analysis. Construction of phylogenetic trees was carried out using the Maximum-likelihood approach, while the calculation of evolutionary distances was performed using the Kimura 2-parameter model (for nucleotide sequences)(Kimura 1980 ) and Jones-Taylor-Thornton (JTT) model (for protein sequences) (Jones et al. 1992 ) using the bootstrap method with 1000 replicates (Felsenstein 1985 )in MEGA11. Moreover, the intragenic nucleotide region between czcR and czcC of strain CD3 has been aligned with that of strain PA01, as it is thought to serve a regulatory role in the expression of the cadmium efflux pump CzcCBA in Pseudomonas aeruginosa . Assessment of genetic diversity from the analysis of mutational landscape of cadmium resistance genes in strain CD3 compared to phylogenetically related P. aeruginosa strains, PA01, MR 41, and San_ai Whole genome sequences of P. aeruginosa strain PA01, P. aeruginosa strain MR41, and P. aeruginosa strain San_ai (NCAIM B.001380) were downloaded from NCBI Genome database and were annotated using the RAST server. Loci of several cadmium resistance-related genes ( czcC, czcB, czcA, czcR, czcS, cadA, cadR ) were identified from these bacterial genomes. Nucleotide sequences of these genes from strain CD3 were aligned and compared individually with the sequences present in those three genomes using BioEdit 7.7.1(Hall 1999 ) and EMBOSS WATER to identify the point mutations in the CD3 genome. Next, sequences were aligned in the Expasy translate tool (Gasteiger et al. 2003 ), which translates a given nucleotide in all six possible reading frames. Then, we considered only those reading frames, which resulted in amino acid sequences matching with the annotated protein sequences from the RAST server. These in-frame translations (codons along with the resultant amino acids) are then compared side by side to identify the dominant (non-synonymous)as well as silent (synonymous) mutations for each gene. Finally, the dN/dS ratios (ratio between non-synonymous and synonymous mutations) (Roy et al. 2020 ) were calculated to throw some light on the genome evolution in the context of P. aeruginosa cadmium-resistance. Functional protein network analysis of strain CD3 using STRING database The software program STRING (v12.0) was utilized to forecast protein-protein interactions. The STRING database may be accessed using either the protein identifier or the protein sequence. The confidence level of the functional partners for a certain protein is used for ordering them (Mering et al. 2003 ).The predictions are derived from genetic information, the transfer of associations or interactions among organisms, advanced data collection, database and literature research, and gene co-expression analysis. Every source is presented as evidence. Each individual score for an interaction or connected data in STRING is separately emphasized and displayed with coloured lines. Each of these associations is assigned a probabilistic confidence score. Confidence ratings indicate the level of interaction between nodes that are connected by a multitude of evidence. A high aggregate score indicates the utmost degree of assurance, typically surpassing the individual score. STRING utilizes a distinctive scoring method that relies on benchmarks of different types of connections compared to a shared reference set. There are four categories of confidence scores in STRING: highest (0.9), high (0.7), medium (0.4), and low (0.1)(Anitha et al. 2016 ). In this study, we chose multiple proteins as our input format and BfmR, PA2523 (CzcR) and CzcC as our protein queries. P. aeruginosa PA01 (NCBI taxonomic ID: 208964) was chosen as the model organism. To generate the functional protein interaction network, the network type was set to ‘full’ STRING network, ‘meaning of network edges’ was set to evidence, all the active sources of interactions were allowed, minimum required interaction score was set at the default value of 0.4, and for display purpose, no more than 10 interactions were allowed in the 1st shell and no more than 20 interactions were allowed in the 2nd shell in the basic settings. In the clustering options, k-means clustering was chosen with a number of clusters set at 4, and edges between clusters were represented as solid lines. Finally, the generated STRING network was exported in PNG format for visual interpretation and analysis. Network interpretation and functional enrichment analysis via Cytoscape The open-source software platform called Cytoscape (Kohl et al. 2011 ) allows for the integration of annotations, gene expression profiles, and other state data into molecular interaction networks and biological pathways. Although originally designed for biological study, Cytoscape has now become a widely used tool for advanced network analysis and visualization. The Cytoscape core distribution provides a fundamental set of features for data integration, analysis, and visualization. By utilizing Cytoscape plugins, one can incorporate other features at no expense. Additional plugins encompass software applications for generating novel designs, transforming data formats, scripting, and examining molecular profiles (Saito et al. 2012 ). ClueGO is a plugin in Cytoscape that carries out this function for the significant genes in the network. A pragmatic Cytoscape plug-in was utilized to significantly enhance the biological investigation of extensive gene sets. ClueGO integrates KEGG/BioCarta pathways with Gene Ontology (GO) ideas to provide a functionally structured network of GO/pathway terms. Unlike BiNGO or PIPE, ClueGO assesses overrepresented Gene Ontology (GO) concepts instead of employing kappa statistics to establish connections between terms in the hierarchical ontology tree. ClueGO is capable of conducting functional comparisons between gene clusters, highlighting their unique functional aspects (Bindea et al. 2009 ). ClueGo functional analysis in Cytoscape (v3.10.1) using ClueGo (v2.5.10) and CluePedia (v1.5.10) plugins was performed using BfmR, BfmS, CzcS (PA2524) and CzcR (PA2423) as input queries, where P. aeruginosa strain PA01 genome was used in the Marker List as the model organism and gene identifiers and ontologies were kept as automatic. The visual style was set at Groups. In the gene ontologies/pathways section, biological processes, cellular components and molecular functions were selected. Network specificity was set to medium. In the Grouping options, GO Term Grouping (functional grouping), group colouring was set at random; the leading group term was based on the highest significance. Functional grouping was based on the kappa score. CluePedia parameters were set to default (Bindea et al. 2013 ). Statistical analysis A two-sample t test was done for comparing datasets to see if their means are statistically different. The p-value corresponding to the T test was determined to suggest significant difference ( https://www.graphpad.com/quickcalcs/ttest1/?format=S ). Results Isolation of cadmium-resistant bacteria Post enrichment, cultivable bacteria were isolated and counted from LA plates supplemented with 1 mM CdCl 2 .H 2 O. Total 26 bacterial strains were isolated and identified as CD1, CD2, CD3, CD4, CD5, CD6, CD7, CD8, CD9, CD10, CD11, CD12, CD13, CD14, CD15, CD16, CD17, CD18, CD19, CD20, CD21, CD22, CD23, CD24, CD25, and CD26. The isolates were stored in LA slants for further investigations. Re-evaluation of the cadmium-resistant phenotype of the cadmium-resistant isolates in newly modified MS medium After obtaining 26 cadmium-resistant strains (as described above), they were dilution-streaked in LA plates supplemented with increasing concentrations of CdCl 2 . H 2 O (ranging from 1–15 mM). After 96 hours of incubation at 30 ºC, only 10 bacterial isolates showed growth in LA plates containing 15 mM CdCl 2 . H 2 O. The rest of the strains have shown varying ranges of cadmium-resistance phenotypes (Table S1 ). The 10 strains that grew in LA containing 15 mM CdCl 2 . H 2 O was then tested for its ability to grow in liquid MSM supplemented with increasing concentrations of CdCl 2 . H 2 O (1–15 mM with a graded elevation of 1 mM). It is to be mentioned here that cells grown overnight (12 h) in LB were harvested, washed, and re-suspended in the same volume of sterile MSM to be used as inoculum (1%). The resistance phenotype was evaluated by observing the turbidity of the cultures after 96 h of incubation at 30 ºC. It was found that only five among the 10 strains showed the ability to grow up to 9 mM CdCl 2 . H 2 O-containingliquid MSM (Table S2). Determination of minimum inhibitory concentration (MIC) of CdCl 2 . H 2 O to ascertain the upper limit of cadmium resistance in selected cadmium-resistant isolates When MSM-grown log-phase cells (5 h grown culture) were used as inoculum, only one (strain CD3) among the five isolates (those demonstrated visible growth in the presence of 9 mM CdCl 2 . H 2 O when inocula were derived from 12 h grown Luria broth), could grow demonstrating resistance up to 3 mM CdCl 2 . H 2 O. The remaining isolates were not able to resist beyond 1.75 mM CdCl 2 . H 2 O. The results obtained using LB- or MSM-grown cells as inocula were repeated three times to confirm this phenomenon. Hence, in order to solve this apparent paradox, we hypothesized that the initial cell number confronting the cadmium stress could be the limiting or deciding factor for determining the MIC. The results of the experiments to prove or disprove this hypothesis have been detailed under the next sub-heading. So, the strain CD3 was selected to work out its growth curve. MSM was supplemented with 0.5 or 1.0 or 2.0 or 3 mM CdCl 2 . H 2 O, CD3 having an initial cell density of log c.f.u. /ml = 5.0, after 28, 32, 52 and 72 h of incubation, grew to reach maximum log c.f.u. /ml equal to 7.26, 7.012, 7.017, and 7.02 respectively (Fig. 1a). Determination of resistance to other metals and metalloids of the potential cadmium-resistant isolate, CD3 The isolate CD3 was also able to resist diverse heavy metals and metalloids, so it was considered multi-metal/metalloid resistant. It has tolerated up to 1 mM chromium (Cr 6+ ), 1.25 mM Nickel (Ni 2+ ), 13 mM zinc (Zn 2+ ), 1 mM cobalt (Co 2+ ), 10.25 mM Arsenate (As 5+ ) and 18 mM Arsenite (As 3+ ) respectively. Re-examination of the heavy metal resistance, including cadmium-resistance phenotype of the strain CD3, expressed as MIC values in relation to varying inoculum density The amount of the inoculum had a significant impact on the MIC values. Our research indicated a decrease in MIC values for cadmium, zinc and cobalt salts with low inoculum densities (Table 2 ). When an initial inoculum of 10 5 c.f.u./ml was introduced to the MSM supplemented with CdCl 2 . H 2 O concentrations of 1 mM, 3 mM and 5 mM, turbidity was observed after 24, 48 and 120 hours of incubation respectively. However, at a concentration of 7 mM CdCl 2 . H 2 O, no turbidity was seen; yet, there was an increase in c.f.u./ml value. At 9 mM CdCl 2 . H2O concentration, there was no turbidity observed. Beyond this concentration, there was no detectable growth in the medium.When there were 10 3 cells in the medium, turbidity was observed in 1 mM and 3 mM concentrations after incubation of 48 h and 96 h, respectively. Similar to the previous findings, in 5 mM CdCl 2 . H 2 O, no turbidity was observed, but it was found that the cells were resistant; but above 5 mM CdCl 2 . H 2 O, cells were no longer viable. When initial input cells in MSM were in the multiples of 10 2 c.f.u./ ml (but less than10 3 c.f.u./ ml), turbidity was noticed only in 1 mM CdCl 2 . H 2 O, and beyond 3 mM concentration, no viable cells remained in the medium to grow into colonies in solid medium. In the case of cobalt and zinc salts containing MSM, similar trends of growth were observed (Table 2 ). In the control set ( E. coli K12 MTCC1302), when the cell number was decreased, the MIC value towards cadmium cobalt and zinc decreased as well (Table S3). Table 2 Growth in the presence of variable concentrations of heavy metal salt of cadmium/zinc/cobalt in response to varying inoculum density of isolate CD3. Attainment of visible turbidity in presence of variable concentrations of different heavy metal salts with varying inoculum density Heavy metal salt Inoculum density (c.f.u./ml) Concentrations of heavy metal salts in the medium (mM) 24 h 48 h 72 h 96 h 120 h 144 h 168 h CdCl 2 . H 2 O 2.32×10 5 0 +++ 1 +++ 3 +/- + + ++ ++ ++ ++ 5 - - - +/- + + + 7 - - - - - - NVT 1 9 - - - - - - NVT 2 11 - - - - - - - 3.95×10 3 0 +++ 1 +/- + ++ +++ 3 - - +/- + ++ ++ ++ 5 - - - - - - NVT 3 7 - - - - - - - 9 - - - - - - - 11 - - - - - - - 4.6×10 2 0 +++ 1 +/- + + + ++ ++ ++ 3 - - - - - - NVT 4 5 - - - - - - - 7 - - - - - - - 9 - - - - - - - 11 - - - - - - - ZnSO 4 . 7H 2 O 3.78×10 5 0 +++ 24 - +/- + ++ +++ 26 - - +/- + ++ +++ 28 - - +/- + + + ++ 30 - - - - - - NVT 5 32 - - - - - - NVT 6 34 - - - - - - NVT 7 36 - - - - - - - 38 - - - - - - - 4.1×10 2 0 +++ 24 - - - +/- + ++ ++ 26 - - - - - +/- + 28 - - - - - +/- + 30 - - - - - - NVT 8 32 - - - - - - NVT 9 34 - - - - - - - CoCl 2 . 6H 2 O 3.45×10 5 0 +++ 1 - +/- + ++ ++ ++ ++ 2 - - - - +/- + + 3 - - - - - - NVT 10 4 - - - - - - NVT 11 5 - - - - - - NVT 12 6 - - - - - - - 7 - - - - - - - 8 - - - - - - - 6.2×10 2 0 +++ 1 - - - - - - NVT 13 2 - - - - - - NVT 14 3 - - - - - - - 4 - - - - - - - 5 - - - - - - - +++ indicates high turbidity; ++ indicates moderate turbidity; + indicates just visible turbidity; +/- denotes uncertain growth; - denotes no turbidity, NVT indicates no visible turbidity but c.f.u. /ml count has shown viability with marginal increase [NVT 1 - 4.45×10 5 ; NVT 2 - 2.43×10 5 ; NVT 3 - 4.3×10 3 ; NVT 4 - 5.5×10 2 ; NVT 5 - 5.34×10 5 ; NVT 6 - 5.07×10 5 ; NVT 7 - 4.03×10 5 ; NVT 8 - 3.9×10 2 {cell number decreased}; NVT 9 - 2.3×10 2 {cell number decreased}; NVT 10 - 4.63×10 5 ; NVT 11 - 3.81×10 5 ; NVT 12 - 3.52×10 5 ; NVT 13 - 6.7×10 2 ; NVT 14 - 5.8×10 2 {cell number decreased}]. Induction of cadmium resistance in CD3, pre-exposed to low concentrations of cadmium or zinc or cobalt salt A 12-hour lag phase was detected when CD3 inoculum was transferred from CdCl 2 . H 2 O-unexposed MSM seed culture to fresh MSM containing 1.5 mM CdCl 2 . H 2 O. To determine how efficiently the strain CD3 acclimated to different amounts of cadmium chloride (CdCl 2 . H 2 O), it was cultured in 1.5, 0.5, 0.25, 0.125, 0.0625, and 0.03125 mM CdCl 2 . H 2 O and then 1% (v/v) inocula were transferred to new MSM with 1.5mM. Resulting growth curves reflected that pre-exposure to increasing CdCl 2 . H 2 O concentrations reduced the lag phase in experimental cultures.Additionally, pre-exposure to as little as 0.03125 mM CdCl 2 . H 2 O concentration was shown to induce cadmium resistance in CD3 (Fig. 1b). Similarly, a reduction in the lag phase was seen upon inoculating CD3 cells into fresh MSM fortified with 1.5 mM CdCl 2 . H 2 O after they had been pre-grown in MSM containing 0.25 mM of ZnSO 4 . 7H 2 O or CoCl 2 . 6H 2 O. This suggests that cadmium resistance can also be induced by Zn 2+ or Co 2+ . Zn 2+ induced cadmium resistance in CD3 more strongly than Co 2+ did (Fig. S1 ). Quantification of cadmium concentrations in living and heat-killed CD3 cells using atomic absorption spectroscopy The Cd 2+ efflux capability of CD3 was assessed by growing it in liquid MSM supplemented with 1 mM CdCl 2 . H 2 O. When CD3 cells were grown in the presence of 1 mM CdCl 2 . H 2 O in MSM, maximum Cd 2+ concentration (average 85.33 ppm) was detected in the extracellular milieu after late exponential phase. On the contrary, a minute fraction of Cd 2+ adhered to the cells (average 9 ppm) was detected. An average 13 ppm intra-cellular Cd 2+ concentration was detected (Fig. 2 ). Heat killed CD3 cells incubated for 12 hours in MSM containing 1 mM CdCl 2 . H2O showed a 3 times higher cell surface bound Cd 2+ concentration (average 29 ppm) than living cells and a very low intra-cellular Cd 2+ concentration (average 2.66 ppm) (Fig. 2 ). Effect of pH to evaluate changes in toxicity affecting growth of CD3 in presence of 1 mM CdCl 2 . H 2 O Bacterium CD3 could grow in MSM in a wide pH range, ranging from 6 to 11. The tolerance towards CdCl 2 . H 2 O varied with the increasing pH. At pH 8 of MSM supplemented without or with 1 mM CdCl 2 . H 2 O, CD3 cells entered into the log phase within 2 h and 8 h post-inoculation respectively. On the other hand, at pH 9 of MSM supplemented withoutor with 1 mM CdCl 2 . H 2 O, CD3 cells entered into the log phase within 4 h and 16 h post-inoculation respectively. At neutral (pH 7) or acidic pH (pH 6), CD3 cells in MSM supplemented withoutor with 1 mM CdCl 2 . H 2 O attained its log phase within 2 h and 4 h respectively. Though time of entry into log phase by CD3 cells have shown no difference, but it reached mid-log phase after 16 h and 12 h in 1 mM CdCl 2 . H 2 O containing MSM of pH 7 and 6 respectively (Fig. 3 a). Spectrophotometric quantification of biofilm formation in CD3 in response to various CdCl 2 . H 2 O concentrations In MSM, CD3 cells formed biofilm at varying concentrations of CdCl 2 . H 2 O, which could be measured by spectrophotometry. Biofilm quantities were observed to rise as the concentration of Cd 2+ in MSM increased from 0 to 0.75 mM CdCl 2 . H 2 O, while all other growth conditions stayed the same. At concentrations greater than 0.75 mM CdCl 2 . H 2 O, biofilm production was declined (Fig. 3 b). Whole genome sequencing and analysis of bacterium CD3 The whole genome sequence of CD3 was deposited at NCBI (Bioproject ID-PRJNA948186; Biosample ID-SAMN33879544) (Table 3 ). Table 3 Genomic summary of cadmium-resistant bacterium, P. aeruginosa strain CD3 Characteristics P. aeruginosa strain CD3 Bioproject ID: PRJNA948186 SRA accession no.: SRR24718582 BioSample ID: SAMN33879544 GenBank accession no.: JARWAQ000000000 No. of contigs: 91 Genome size(bp): 6239438 (6.2 Mbp) Genome coverage: 98.0x GC content (%): 66.5 N50: 472668 N75: 281478 L50: 5 Total no. of genes: 5,825 No. of coding genes: 5,733 No. of tRNAs: 57 No. of non-coding RNAs: 4 No. of pseudogenes 28 A whole genome-based phylogenetic tree was prepared in TYGS to determine the closest type strains. It was revealed that strain CD3 belongs to the genus Pseudomonas , and its nearestneighbour is P. aeruginosa DSM 50071. In the SNP-based tree, it was seen that strain CD3 had a position, distinct and distant from that of both P. aeruginosa DSM50071 and P. aeruginosa PA01 (Figs. 4 and 5 ). The draft genome of P. aeruginosa strain CD3 disclosed 44 genes associated with overall metal resistance and transport (Table S4). The presence of czcC, czcB, czcA, czcR, czcS, czcD, cadA , and cadR genes were confirmed. The genetic organization of the czcC , czcB , czcA , czcR , and czcS genes verified that they reside in one operon,while cadA and cadR were locatedin another operon. Pairwise sequence alignment of CD3’s czcC, czcB, czcA, czcR, czcS, czcD, cadA , and cadR genes with that of its closest relative, P. aeruginosa DSM 50071, yielded identities of 99.69%, 99.86%, 99.81%, 99.85%, 99.22%, 99.78%, 99.73% and 99.85% respectively (Table S5). A comparative analysis of the nucleotide and translated protein sequences of czcC , czcB and czcA of reported cadmium-resistant genera have been presented in tabular form (Tables S6 and S7). Evolutionary divergence in terms of nucleotide and amino acid sequences of cadmium efflux pump components, CzcC, CzcB, and CzcA Since the CzcCBA pump is mainly responsible for bacterial cadmium resistance, phylogenetic analyses based on the concatenated nucleotide sequences of the czcCBA genes and their translated protein sequences showed that strain CD3 has the highest similarity with Pseudomonas aeruginosa PA01. Results have also shown that czcCBA genes and their translated protein sequences of all the members of the Pseudomonadaceae family, including strainCD3, formed a common cluster distinguishing this clad from Caulobacter and Cupriavidus (including C. necator strainN1, where czc operon system was discovered) (Fig. 6 a and 6 b). Additionally, the intragenic nucleotide region (513 bp) between czcR and czcC genes of strain CD3 aligned perfectly (identity 100%) with that of P. aeruginosa PA01. Subtle genetic diversity ascertained from the analysis of the mutational landscape of cadmium resistance genes in strain CD3 compared to phylogenetically related P. aeruginosa strains, PA01, MR 41, and San_ai The genes responsible for cadmium resistance, czcC, czcB, czcA, czcS, czcR, cadA , and cadR , in strain CD3 showed a total of 34, 63, and 37 events of point mutations when aligned separately with corresponding nucleotide sequences of the P. aeruginosa strains PA01, MR41 and San_ai respectively. A total of 10, 11, and 9 transverse mutations and 24, 52, and 28 transition mutations have been identified in CD3 when compared with sequences derived from strains PA01, MR41 and San_ai, respectively. Surprisingly, as an outcome of these point mutations distributed in the seven genes only 5, 10, and 4 non-synonymous mutations leading to alterations in amino acids were noted in CD3 sequences compared to PA01, MR41 and San_ai, respectively (Fig. 7 ), while the rest point mutations were silent (coded for the same amino acids; Fig. S2). Finally, a dN/dS value of < 1 was found in every gene studied, indicating a convergent evolution of the CD3 genome in terms of cadmium-resistance-associated genes. Functional protein network analysis of strain CD3 using STRING database A highly interacting network of four protein clusters was created using the STRING database. BfmR, BfmS, PA4103, PA4104, PA4105, PA4106, and PA4107 (EFhP) proteins formed the first yellow cluster, which is involved in biofilm development and maturation. CopR, CopS, IrlR, PtrA, ParR, PcoA, PA1437, PA1438, PA4886, PA2807, PA2523 (CzcR) and PA2524 (CzcS) were two-component signal transduction proteins in the second cluster (red). CzcC, CzcB, and CzcA proteins, the CzcCBA pump components of the bacterial cadmium efflux system, formed the third cluster (green). Finally, PA3689 (CadR) and PA3690 (CadA) produced the fourth cluster (blue), which effluxes cadmium from the cytoplasm to the periplasm (Fig. 8 ). Network interpretation and functional enrichment analysis via Cytoscape The diagram shows the activities associated with BfmR, BfmS, CzcR and CzcS. BfmR and CzcR are both associated with each other through the positive regulation of single-species biofilm formation. CzcR and CzcS are associated with each other through receptor signalling activity. BfmR and BfmS share receptor signalling and molecular signal transduction activity with each other, while BfmS and CzcS are both associated with phosphorelay sensor kinase activity, among other roles (Fig. 9 ). Discussion Cadmium resistance and its possible mechanism(s) have been reported in bacteria from a wide range of taxonomies and habitats (Khan et al. 2016 ; Qin et al. 2019 ; Wang et al. 2019 ). The majority of organisms that fit this profile can withstand concentrations of cadmium between 1 and 7 mM (Izrael-Živković et al. 2018 ), possibly through adsorption or efflux mechanisms. Twenty-six cadmium-resistant bacteria were isolated from Malda, West Bengal, India. Out of these, 10 exhibited significant resistance to cadmium (15 mM CdCl 2 . H 2 O) when grown in a traditional bacteriological medium (LA). However, the bioavailability of heavy metals in solid media can pose a challenge, leading to a potential overestimation of Cd 2+ resistance when measured on bacteriological media gelled with agar (Agarwal et al. 2020 ). This has induced the investigators of this study to raise doubts about the methodology and inferences drawn from the experiments that have been reported earlier in terms of determining actual MIC of cadmium salts. Hence, the foremost challenge was to determine the actual maximum tolerated concentration of cadmium salts for "cadmium-resistant" isolates. The initial strategy adopted in this study was to shift from a solid medium to a liquid medium for determining the MIC of Cd 2+ . Eventually, we encountered a remarkable problem while conducting experiments in conventional rich growth media like Luria-broth (LB), Nutrient broth (NB), Plate count broth (PCB), and Tryptone Soya broth (TSB). In each of this media, adding cadmium salt turned the medium opaque due to formation of insoluble fine suspension with time, leading to deceptive interpretations. Therefore, in order to address the issue of media opaqueness, we searched for alternative media where precipitation and cross-reaction of media components in presence of cadmium salt could be avoided (Fig. S3). Several bacteriological media were tried to find a most suitable medium that would support the maximum bio-availability of cadmium and enable bacteria to experience the highest uptake of dissolved cadmium. While using standard mineral salts medium (MSM) for MIC determination, free phosphate in MSM reacted with cadmium and formed a precipitate in the medium. It was also revealedthat the presence of organic compounds in the media leads to the chelation of heavy metals. Additionally, the degree of chelation or precipitation is directly proportional to the amount of organic compound in the medium (Angle and Chaney 1989 ). After careful adjustments, we modified the organic ingredient (yeast extract) content in the formulated or modified medium to the ideal concentration of 0.1 g/L. This ensured that the cadmium chloride monohydrate remained dissolved completely and was thus readily available for bacterial growth and MIC determination, even at concentrations as high as 100 mM. When the newly modified medium was used, out of the 10 isolates initially screened to have been cadmium resistant, five showed resistance to cadmium up to 9 mM. The selected cadmium-resistant strains have also shown resistance to Ni 2+ , Cr 6+ , Zn 2+ , Co 2+ , As (V), and As (III). Isolate CD3 could tolerate a comparatively higher concentration for arsenic (in terms of MIC) than cadmium. This is because arsenic is a common metalloid in the environment where bacteria have evolved much stronger mechanism to evade their toxic effects. The global average arsenic content in soil is 5 mg/ Kg, in open sea water 1–2 µg/l (0.001–0.002 ppm), and in unpolluted surface and ground water is below 10 µg/l (< 0.01 ppm) (Raju 2022 ). In contrast, Cd 2+ concentration in unpolluted water is usually below 1 µg/l (< 0.001 ppm) (Friberg et al. 1986 ). Again, cadmium is extremely toxic and has not been associated with any known biological functions in living organisms, except for diatoms in rare cases (Templeton 2023 ). Understanding the binding of metal ions to the biological system relies on factors such as the electronegativity and ionic radius of the metal ions(Naja et al. 2010 ). The size of an ion plays a significant role in its adsorption strength, while high electronegativity helps the ion bind more securely to surface functional groups (Sulaymon et al. 2011 ). Based on the findings of the multi-metal/metalloid resistance study, it can be inferred that isolate CD3 demonstrates a high level of tolerance to every tested metal/metalloid. Exploration of microbial resistance to heavy metals, in particular cadmium, zinc, and cobalt, is paramount to deciphering the role of inoculum density on microbial tolerance levels. We hypothesized that net availability of metal salt molecules in a dissolved state, through active or passive diffusion, per bacterial cell would determine the tolerance limit vis-a-vis the resistance phenotype of a given bacterium towards a particular heavy metal. In other words, when cell density in a metal-containing medium is high and the net available number of metal salt molecules per cell is less, the MIC value will be high and vice-versa. We tested this hypothesis in the present study by exposing cadmium-resistant isolate CD3 at varying cell densities to a varying concentration of CdCl 2 . H 2 O or CoCl 2 . 6H 2 O or ZnSO 4 . 7H 2 O. We have presented the CD3’s results in comparison to the control strain, E. coli K12 MTCC 1302 to validate our hypothesis. It was revealed that there is a significant effect of inoculum density on the MIC values of Cd, Zn, and Co. The resistance levels of the CD3 or the ability of CD3 to grow in presence of increasing metal salt concentrations, expressed in MIC values, increased with increasing initial cell numbers (= inoculum density) in a given test medium. Consequently, the intracellular metal ion concentration per cell reduces at high initial cell density, which is accompanied by reduced metal ion toxicity in an individual cell. As a result, the overall resistance of the population increases as they divide. At a lower density of initial cell input in the growth medium and a high import of cadmium ions into the cell, the influence of efflux pumps and other mechanisms aimed at metal sequestration fails to bring down the intracellular cadmium concentration to a level that can prevent Cd 2+ ions from out-competing other divalent cations to bind to their respective enzymes. In low cell densities, the influx of the number of Cd 2+ , or Zn 2+ and Co 2+ ions per cell increases in liquid medium containing salts of the heavy metal following the principle of diffusion and randomness of interaction between cells and dissolved or bio-available metal ions. Hence, the intracellular metal ion concentration may overwhelm the cell’s detoxification capacity, in contrast to the condition when intracellular concentration per cell goes down under an identical concentration of heavy metal in the medium when the initial input cells are log-fold higher. In fact, we have demonstrated that MIC values of heavy metal(s) are indirectly proportional to the inoculum density, establishing enhanced collective defence in higher numbers of cells. Furthermore, this phenomenon is not limited to the CD3 strain but has been unequivocally demonstrated by E. coli K12 MTCC 1302, indicating that the biological principles are universal. The results, therefore, have novel implications for the traditional microbiological methods for determining MIC of heavy metals shown by any experimental bacterium. Future research directions, therefore, must involve system biology tools in understanding the molecular mechanisms that drive density-dependent resistance. Additionally, it would be valuable to investigate how these findings can be applied to bioremediation strategies and the enhancement of microbial strains for heavy metal detoxification. In order to explore more about the physiological basis of cadmium resistance, it was found that pre-exposing CD3 cells to low concentrations of cadmium ions can induce resistance to higher concentrations of cadmium. Induced cells (cells pre-grown in the presence of low concentrations of cadmium salt) enter the logarithmic phase of growth earlier than the un-induced cells (cells grown in absence of cadmium salt). The CD3 cells pre-grown separately at increasing concentrations of CdCl 2 . H 2 O from 0.03125 to 0.5 mM has shown a gradual reduction of the lag phase when grown in the presence of 1.5 mM CdCl 2 . H 2 O. The cellular response in making the defence system ready for cadmium assaults is modulated differentially when grown in lower concentrations of Cd 2+ , indicating that lower cadmium concentrations can activate efflux mechanisms more effectively in lesser preparatory time (Chen et al. 2022 ), especially when shifted to a fresh medium with abruptly high cadmium (1.5 mM).This study has also described the effect of zinc or cobalt concentrations in the pre-culture (i.e., zinc or cobalt-induced cells) on cadmium resistance in CD3. It was shown that the cells' resistance to cadmium was influenced by pre-growing them in the presence of ZnSO 4 .7H 2 O (0.25 mM). Nevertheless, cells previously cultured in 0.25 mM zinc or 0.5 mM cadmium have demonstrated a somewhat comparable impact on bacterial growth in relation to the stimulation of cadmium resistance. Therefore, cells induced by 0.25 mM zinc have a greater impact on cadmium resistance in terms of reduction in lag-phase duration when compared to cells induced by an equivalent concentration of cadmium. The Cd 2+ transporting P-type ATPase, also known as the CadA transporter, facilitates the movement of Zn 2+ ions from the cytoplasm to the periplasm. The periplasmic adapter domain of CzcS forms a strong bond with Zn 2+ ions, exhibiting a high affinity. This interaction leads to the activation of the adaptor domain, which in turn activates CzcR. Subsequently, CzcR stimulates the transcription of the czcCBA operon, facilitating the expulsion of Cd 2+ ions from the cytoplasm and periplasm of CD3 cells (Liu et al. 2021 ). However, the impact of cobalt-induced cells mirrored that of cadmium-induced cells (Fig. S1 ).The findings of the AAS analyses indicate that efflux pumps are pivotal in the cadmium resistance of CD3. The significant extracellular accumulation of Cd 2+ (average 85.33 ppm) observed after exposure to 1 mM CdCl 2 .H 2 O suggests that CD3 efficiently transports cadmium out of the cell into the surrounding environment. This process is crucial for maintaining a lower intracellular cadmium concentration (average 13 ppm) despite external exposure, highlighting the effectiveness of efflux mechanisms in resisting cadmium toxicity. Despite this fact, it is also important to note that this intracellular accumulation suggests that CD3 is capable of taking up cadmium from its environment. The CD3 genome contains several zinc transporters, metallothionein, and some thiol-rich proteins that help in cadmium sequestration (Table S8). The relatively small but detectable fraction of Cd 2+ (average 9 ppm) bound to the cell surface underscores the role of efflux pumps in preventing excessive accumulation within the cell. This binding likely represents an equilibrium between cadmium uptake and efflux, with efflux pumps continuously exporting cadmium ions to maintain cellular homeostasis. The mechanisms by which cell surface sorption occurs are not influenced by cell metabolism. Instead, they rely on the physicochemical interactions between heavy metal ions and the functional groups present on the cell walls of microorganisms. Biomass possesses the characteristic of working as a chemical substance and a biological ion exchanger. The effect was revealed to be caused by the specific cell wall structure of some bacteria (Hassan et al. 2010 ). Furthermore, the cell wall of the microbe mostly comprises polysaccharides, lipids, and proteins, which offer several opportunities for metal binding. These compounds have many functional groups, such as carboxylate, hydroxide, amine, imidazole, sulfate, and sulfhydryl, with different charge distributions and geometries. Functional groups have the ability to selectively attach to specific metal ions. In this section, the process of binding is ascribed to many mechanisms such as ion exchange, adsorption, complexing, microprecipitation, and crystallization, which take place on the cell wall (Veglio’ and Beolchini 1997 ; Davis et al. 2003 ; Malik 2004 ; Sheng et al. 2004 ). The elements of the cell wall are pivotal in the sequestration of metals due to the intricate nature of the biomaterials used. Dead cells often exhibit a lower isoelectric point compared to living cells(Çolak et al. 2011 ; Huang et al. 2013 ). This difference in isoelectric point may be a key factor contributing to the increased biosorption of Cd 2+ in heat-killed CD3 cells. Electrostatic contact is a crucial factor in the biosorption process(Huang et al. 2013 ). Additionally, it was shown that deceased bacterial cells had a higher level of negative charge on their cell surface and demonstrated a larger capacity for biosorption compared to living cells. This indicates that deceased cells possess a higher affinity for Cd 2+ binding compared to live cells. Lowering the pH of the growth medium from 7 to 6 resulted in a shorter lag phase for isolate CD3. Previously, a decrease in the uptake of cadmium, cobalt, copper, manganese, and nickel by encapsulated Klebsiella pneumoniae in acidic pH was reported (Rudd et al. 1983 ). It remains uncertain whether a decrease in pH solely decreases cadmium accumulation and/ or enhances cadmium efflux from within the cell. Specific metal efflux pumps are powered by the proton motive force, while others rely on adenosine triphosphate (ATP) for their function (Nies 1999 ). Decreasing the pH raises the concentration gradient of protons across the bacterial cell wall, enhancing the proton motive force and enabling faster ATP synthesis. In addition, earlier it was observed that exposure of E. coli K12 to cadmium in an acidic pH environment for 5 minutes resulted in the activation of many stress response genes (Worden et al.2008). Another explanation was proposed, stating that the higher concentration of hydrogen ions at low pH levels intensifies the rivalry between hydrogen and metal ions for attachment sites on the cell surface. This ultimately results in decreased toxicity of cadmium(Franklin et al. 2000 ) .It remains uncertain how the increase in pH enhances metal toxicity, but it could be related to metal speciation into a more harmful form(Babich and Stotzky 1985 ) and/or an elevation in metal adsorption and uptake, specifically by microorganisms. As an illustration, the theory suggests that the transformation of cadmium into a single-charged, hydroxylated form is responsible for the enhanced toxicity of cadmium to fungi, bacteria, and actinomycetes at alkaline pH. Other cadmium species, apart from Cd 2+ , could potentially enhance cadmium toxicity at pH ≥ 7. As the pH increases, concentrations of cadmium species like CdOH + also increase. Studies have shown that certain ions, such as CdOH + , can be more hazardous compared to the more commonly found Cd 2+ ions (Babich and Stotzky 1985 ; Collins and Stotzky 1992 ; Ivanov et al. 1997 ). It is believed that the alteration in charge leads to the destabilization of the bacterial cell membrane, resulting from CdOH + toxicity. Microbial biofilm is widely recognized as a crucial factor in bacterial heavy metal resistance(Patel et al. 2016 ; Yang et al. 2018 ). Microorganisms generate extracellular polysaccharides (EPSs), a fundamental element of the biofilm that provides protection against heavy metal stress (Nocelli et al. 2016 ). Enhancing EPS production can increase heavy metal resistance in certain strains. Studies have shown that the environment's heavy metal content can impact the development of biofilms in specific types of bacteria, as evidenced by earlier research (Oknin et al. 2015 ; Hao et al. 2016 ; Nocelli et al. 2016 ; Alviz-Gazitua et al. 2019 ). However, the effect of heavy metals on biofilm formation can differ based on the unique interaction between a particular heavy metal and bacterial species. For example, divalent cations such as Mg 2+ and Ca 2+ can significantly impact the development of biofilms. They have the ability to directly alter electrostatic interactions and indirectly influence attachment processes based on physiology. They play crucial roles as cellular cations and are necessary for enzyme function, as mentioned by various researchers (Fletcher 1988 ; Malik and Kakii 2003 ; Song and Leff 2006 ). Microorganisms in biofilms can shield themselves from the harmful effects of heavy metals(Nocelli et al. 2016 ). Additionally, biofilms can even absorb certain heavy metals (Azizi et al. 2016 ). Despite these findings, the impact of heavy metal ions on biofilm formation still needs to be fully understood. According to a study, it was found that cadmium, a frequently found soil-contaminant, has the ability to directly hinder the growth of bacteria, leading to a reduction in biofilm formation (Rau et al. 2009 ). Our study revealed that at lower concentrations of CdCl 2 . H 2 O (up to 0.75 mM), there was an observed increase in biofilm formation. However, as the concentration of CdCl 2 . H 2 O exceeded 0.75 mM, a decrease in biofilm formation was observed (Fig. 3 b). Similar kind of results were observed when studying the impact of Cd 2+ on the biofilm formation of the Bacillus subtilis strain 1JN2(Yang et al. 2018 ). When bacterial cells are damaged or injured, their functions can be disrupted or inhibited, causing the contents inside the cells to leak or be released. This disruption can impede the bacteria's capacity to generate additional polymeric substances (Bouhdid et al. 2010 ). The presence of heavy metals can have a negative impact on bacterial biofilms. This is because they can disrupt the water channels that are essential for the transportation of nutrients within the biofilm (Syed et al. 2021 ). At higher concentrations of Cd 2+ , a reduction in the expression of genes associated with biofilm formation was observed (Yang et al. 2022 ). Studying the whole genome sequence (WGS) is an incredibly valuable approach to assessing genetic potential of a bacterium to combat metal assault and corroborate with the phenotypic and physiological data. WGS analysis pinpoints specific genes that may play different role in developing resistance to toxic metals, metalloids, and antibiotics and helps understand adapting to different environments (Adetunji et al. 2022 ). Comparative WGS analyses enable valuable insights into the evolutionary dynamics of bacteria, enhancing our understanding of microbial interactions in diverse ecosystems. It also enables the identification of new resistance mechanisms against antibiotics and, as emphasized in recent studies, heavy metals, which present significant challenges in environmental and clinical settings (Garza-Ramos et al. 2023 ). Through a comprehensive examination of the CD3 genome, it was determined that it belongs to a Pseudomonas species known for its remarkable resistance and ability to produce biofilms. Additionally, the analysis revealed the presence of a unique enzymatic system that enables the strain to efficiently remove cadmium ions, further enhancing its resistance to this particular metal. Minor variations were observed in the genes associated with cadmium efflux when compared to the type strain DSM50071. The whole-genome tree generated by the Type Strain Genome Server (TYGS) offers a state-of-the-art method for comprehending the relationships and evolution of microorganisms. With the help of whole-genome sequencing data, TYGS can generate phylogenetic trees that depict the genetic relationships between different microbial strains, including type strains. This methodology provides a precise and thorough approach to categorising and recognising microorganisms, surpassing conventional methods that depend on restricted genetic markers or phenotypic characteristics. The utilization of the whole-genome tree approach allows researchers to track the evolutionary lineage of microorganisms, discover new species, and gain insights into the genomic aspects of microbial diversity. Using whole-genome (WG)- based phylogenetic studies, strain CD3 was found to be the closest relative to P. aeruginosa DSM50071 T among the type strains of Pseudomonas spp., as determined by the TYGS method (Fig. 4 ). The SNP tree is an essential tool for studying intraspecies genetic variations. It provides a detailed view of the subtle genetic differences within a species, allowing for greater comprehension of these variations. By examining SNPs, which are genetic variations at individual nucleotide positions in the genome, scientists can create phylogenetic trees that unveil the genetic connections and evolutionary past of diverse populations within a given species. This approach offers valuable insights into the genetic diversity, population structure, and evolutionary dynamics of species, showcasing the role of genetic variations in shaping phenotypic diversity, adapting to environmental changes, and combating diseases. In microbial genomics, SNP trees play an integral part in monitoring the transmission of microbial pathogens, gaining insights into the spread of antibiotic resistance, and pinpointing the genetic factors behind virulence. SNP trees are pivotal in revealing the genetic foundation of variations within a species, which has far-reaching implications for disciplines such as evolutionary biology, epidemiology, and conservation (Zhang and Liu 2023 ; Bu et al. 2023 ). Through SNP-based phylogenetic analysis, strain CD3 was found to be genetically closely related to P. aeruginosa strains LESB58 and LES431. On the other hand, P. aeruginosa PA01 was found to be genetically more distant, residing on a distinct branch in the evolutionary tree. Although there is significant sequence homology with PA01, the analysis reveals that strain CD3 has a distinct genetic composition compared to PA01. This revelation emphasizes the distinct genetic identity of CD3 within the P. aeruginosa lineage, showcasing its unique evolutionary path (Fig. 5 ). The czcCBA sequence-based phylogenetic analysis and gene comparison between strain CD3 and other cadmium-resistant bacteria enabled us to contextualize these findings regarding evolution of bacterial metal-resistance mechanisms. The phylogenomic investigation using the concatenated nucleotide and translated amino acid sequences of the czcC , czcB , czcA genes has revealed valuable insights into the evolutionary relationships and genetic preservation of these resistance mechanisms within the Pseudomonadaceae family (Fig. 6 a and 6 b). The remarkable similarity between strain CD3 and P. aeruginosa PA01 in the CzcCBA pump sequence indicates a significant evolutionary conservation of the cadmium resistance mechanism in certain strains of P. aeruginosa . This finding highlights the vital role of the CzcCBA efflux pump in conferring bacterial resistance to cadmium, an essential adaptive benefit in habitats polluted with heavy metals. The convergence of all members of the Pseudomonadaceae family, including strain CD3, into a single cluster, highlights their collective evolutionary background and potential occupation of similar ecological niches. The unique placement of Cupriavidus necator strain N1, the pioneer strain in possessing the CzcCBA pump, emphasizes the evolutionary divergence and possible horizontal gene transfer incidences that could have permitted the dissemination of cadmium resistance genes among several bacterial families (Fig. 6 a and 6 b). The complete matching of the intragenic nucleotide region between czcR and czcC in strain CD3 and strain PA01 indicates a strongly conserved regulatory mechanism for the activation of the CzcCBA efflux pump in P. aeruginosa . The conservation of this region indicates its crucial function in controlling resistance to cadmium, possibly through shared transcriptional regulatory mechanisms in various strains. Regulatory elements play an essential part in enabling bacteria to adjust and endure in environments polluted with heavy metals. In addition, a deep understanding of the genetic and regulatory mechanisms that drive heavy metal resistance can provide valuable insights for devising effective strategies to address the proliferation of resistance genes (Demircan and Memon 2022 ; Fardami et al. 2023 ; Garg et al. 2024 ). This is especially crucial in environments where antibiotic and heavy metal resistance work together to create multidrug-resistant pathogens. Investigating the genetic diversity and mutation landscape of cadmium resistance genes in P. aeruginosa strain CD3, alongside strains PA01, MR41, and San_ai, highlights the intricate processes in which bacteria adapt and evolve in the face of environmental challenges like exposure to heavy metals. Through careful analysis of nucleotide sequences for important cadmium resistance-related genes, the mutation spectrum within these loci has been revealed. This sheds light on commonalities as well as uniqueness in genetic responses to cadmium-mediated stress among various strains of P. aeruginosa . The abundance of various mutations, encompassing both transverse and transition types, within the cadmium resistance genes, along with a distinct pattern of dominant and silent mutations, suggests an array of genetic variation (Fig. S2). This restraineddiversity is most likely a result of the evolutionary stresses caused by exposure to cadmium, which requires genetic versatility for survival. The prevailing mutations, which lead to alterations in amino acids, may play a crucial role in modifying the function or effectiveness of the related mechanisms of resistance, potentially increasing the bacterium's ability to detoxify or sequester cadmium ions (Yu et al. 2022 ; Zhao et al. 2023 ). The rate at which a population approaches its fitness optimum can be significantly impacted by the presence of even a few substantial effect mutations. However, the finding that the dN/dS ratio is less than 1 for all the genes examined implies a situation of purifying selection rather than positive selection. This contradicts the initial assumption of adaptive evolution caused by cadmium exposure (Wolf et al. 2009 ; Roy et al. 2020 ; Zwonitzer et al. 2023 ). Based on the synonymous mutations, it appears that most of the mutations in the cadmium resistance genesdid not affect the amino acid sequences of the proteins they encode. Nevertheless, there are some non-synonymous mutations that may potentially enhance protein function, which is yet to be explored. It seems that the core functions of the proteins coded by these genes remained unchanged in the face of sustained selective pressure. This conservation may be attributed to their crucial role in maintaining cellular homeostasis when exposed to toxic cadmium concentrations. It could indicate a delicate equilibrium between the need to adjust to the environment and the importance of maintaining essential cellular processes, ensuring the bacterium's survival while maintaining its overall fitness (Sendolo et al. 2022 ). In simple words, studying the evolution of the CD3 genome in relation to cadmium resistance reveals the intricate relationship between genetic diversity, types of mutations, and evolutionary forces. It highlights the intricate strategies utilised by P. aeruginosa to overcome the obstacles presented by toxic metal exposure, underscoring the significance of preserving genetic integrity to maintain vital biological processes in unfavourable circumstances. In this study, we used the STRING database (v12.0) to uncover an intricate web of protein-protein interactions within P. aeruginosa PAO1. This provided us with valuable knowledge regarding bacterial resistance mechanisms and the formation of biofilms. Through the integration of various sources of evidence, such as genomic context, experimental data, and database mining, we have successfully identified four distinct clusters of proteins with different levels of interaction confidence (Fig. 8 ). These clusters showcase the multifaceted features of proteins associated with bacterial cadmium efflux, signal transduction, and biofilm formation and maturation. The initial cluster, primarily linked to the formation and development of biofilms, highlights the keyimportance of BfmR and its associated proteins (BfmS, PA4103-PA4107) in maintaining the structural integrity and resilience of biofilms. Biofilms play a vital role in the survival and virulence of bacteria, as they help bacteria resist antibiotics and evade the host's immune responses (Al-Tayawi et al. 2023 ). Our research supports previous studies that highlight the importance of BfmR in regulating biofilm development in P. aeruginosa (Harmsen et al. 2010 ). Identification of this cluster not only confirms the important role of BfmR but also indicates potential protein interactions that could be focused on disrupting biofilm formation. The second cluster emphasises the importance of two-component signal transduction systems, which play a decisive role in bacterial response to environmental stimuli, especially metal ions. Proteins like CopR, CopS, PA2523 (CzcR), and PA2524 (CzcS) are key players in this process. These proteins play a crucial role in regulating gene expression in response to metal ion concentrations, which is vital for the survival of bacteria in challenging environments. Our findings align with previous research that has shown the role of CzcR and CzcS in the regulation of the czc operon, facilitating the removal of heavy metals and enhancing resistance to metal toxicity (Liu et al. 2021 ). The third cluster, which includes the CzcCBA pump components (CzcC, CzcB, CzcA), plays a crucial role in bacterial resistance to cadmium toxicity by directly facilitating cadmium efflux. Thenetwork analysis presented (Fig. 8 ) sheds light on the interaction between the CzcCBA pump and other protein clusters, indicating a coordinated response to cadmium stress and providing additional contexts in this extensively characterized resistance mechanism. Lastly, the fourth cluster, which includes CadR and CadA, plays a crucial role in driving out cadmium to the periplasmic space, providing an additional defense mechanism against cadmium toxicity. This finding adds to our understanding of the processes behind cadmium resistance in P. aeruginosa and supports the possibility of a cooperative relationship between the CzcCBA pump and CadA-CadR mediated efflux systems. The results of this study confirm previously reported interactions and reveals potential new connections between important proteins involved in biofilm formation, signal transduction, and heavy metal efflux. These interactions provide a more profound insight into the intricate regulatory networks that govern bacterial survival strategies. Future research should focus on conducting experiments to validate the predicted interactions and further investigate their impact on bacterial physiology and pathogenicity. In addition, focusing on particular interactions within these clusters could offer innovative methods for managing bacterial resistance and biofilm-mediated infections. The ClueGO and CluePedia plugins were used in Cytoscape to analyze the complex network of gene interactions and functional enrichments linked to the regulatory proteins BfmR, BfmS, CzcR, and CzcS in P. aeruginosa . The analysis reveals an intricate network of regulations, highlighting the crucial influence of BfmR on the functions of CzcR and CzcS, which are essential for biofilm formation and receptor signalling pathways (Modrzejewska et al. 2021 ; Kim et al. 2022 ). It is evident that BfmR plays a crucial role in the positive regulation of single-species biofilm formation, closely interacting with CzcR.Understanding biofilm formation is essential in studying the virulence of P. aeruginosa , as it allows the bacterium to establish prolonged infections and evade antimicrobial therapies (Kristensen et al. 2022 ). The connection between BfmR and CzcR highlights an overlooked aspect of regulatory control, which could provide fresh perspectives on how biofilm regulatory networks incorporate environmental signals. In addition, the connection between BfmR and receptor signalling activities, which are additionally observed in BfmS, implies that BfmR may have a wider role in regulating signal transduction mechanisms that control biofilm formation and bacterial response to environmental stress. The close connection between BfmR and BfmS in receptor signalling and molecular signal transduction activities emphasises a coordinated regulatory mechanism that could finely adjust the bacterium's adaptive responses. The coordination is further demonstrated by the shared roles of BfmS and CzcS in phosphorelay sensor kinase activity, a crucial process in bacterial signal transduction pathways. Based on the results, it appears that BfmR has an impact on the activity of CzcS, possibly through its regulatory interactions with BfmS. This could potentially affect the bacterium's ability to detect and respond to heavy metal stress, considering the known involvement of CzcS in the czc operon. Therefore, the observation of associations confirms a hierarchical regulatory connection; BfmR serves to control CzcR and CzcS activities. This hierarchy indicates a potentially vital role for BfmR in coordinating environmental cues and biofilm formation as well as heavy-metal resistance. BfmR not only positively regulates biofilm formation but also uses CzcR and CzcS receptor signalling pathways as a regulator, which means that an intricate regulatory network is at play, positioning BfmR as a central hub in coordinating the bacterium’s response to various environmental stimuli. Summarizing all the interpretations of results obtained from this study we put forward a hypothesized model of the molecular mechanism of cadmium resistance in strain CD3 (Fig. 10 ). Conclusions Taken together, our data present a comprehensive overview of the complex regulatory network where BfmR heavily influences the behaviours of CzcR and CzcS, such that BfmR can be seen as a master regulator of biofilm formation and environmental sensing in P. aeruginosa (Fan et al. 2021 ). As such, this analysis of the regulatory interactions can guide further molecular studies of the mechanisms of biofilm formation and antibiotic resistance, which could be translated into targeted strategies against P. aeruginosa infection. Future work will also be devoted to unravelling the molecular details of how BfmR coordinates these complicated networks, as it could help identify novel therapeutic targets in the battle against bacterial pathogenesis. Declarations Availability of data and materials: Data is available in the NCBI database under Genbank accession ID: JARWAQ000000000, Biosample accession ID: SAMN33879544, Bioproject accession ID: PRJNA948186 and SRA accession ID: SRR24718582 Competing interests: There are no financial or academic compete of interest for the authors concerning the publication of this work. Authors' contributions: Methodology: Soumya Chatterjee; Formal analysis: Soumya Chatterjee and Partha Barman; Writing-original draft preparation: Soumya Chatterjee, Partha Barman and Ranadhir Chakraborty; Writing-review and editing: Chandan Barman, Sukanta Majumdar and Ranadhir Chakraborty; Data curation: Chandan Barman, Sukanta Majumdar and Ranadhir Chakraborty; Conceptualization: Ranadhir Chakraborty; Supervision: Ranadhir Chakraborty Acknowledgements: Soumya Chatterjee, Chandan Barman and Sukanta Majumdar acknowledge the assistance of the University of Gour Banga administration in Malda, India. Soumya Chatterjee, Partha Barman and Ranadhir Chakraborty thank the University of North Bengal (Siliguri, India) administration for their assistance. We thank Mr. Rinku Das (Central Instrumentation Centre [CIC], Quality Control Laboratory (QCL) and RKVY Soil Testing Laboratory, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal: 736165, India) for AAS support. Soumya Chatterjee and Partha Barman sincerely thank Council of Scientific and Industrial Research (CSIR, New Delhi, Govt. of India) as they received research grants in the form of Senior Research Fellowship during this work (CSIR-SRF; Award no. 09/1151(0006)/2019-EMR-I, 09/285(0086)/2019-EMR-I respectively). References Abbas SZ, Rafatullah M, Hossain K, et al (2018) A review on mechanism and future perspectives of cadmium-resistant bacteria. 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Science of The Total Environment 865:161243. https://doi.org/10.1016/j.scitotenv.2022.161243 Zwonitzer KD, Iverson ENK, Sterling JE, et al (2023) Disentangling Positive Selection from Relaxed Selection in Animal Mitochondrial Genomes. Am Nat 202:E121–E129. https://doi.org/10.1086/725805 Additional Declarations No competing interests reported. Supplementary Files graphicalabstract.tiff.tif Supplementaryinformation.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4733845","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329344305,"identity":"2204dd51-8489-422d-9b21-55b0796127fe","order_by":0,"name":"Soumya Chatterjee","email":"","orcid":"","institution":"University of Gour Banga","correspondingAuthor":false,"prefix":"","firstName":"Soumya","middleName":"","lastName":"Chatterjee","suffix":""},{"id":329344306,"identity":"a6cd6760-fb7c-4b40-aeb5-48df6fef0ed4","order_by":1,"name":"Partha Barman","email":"","orcid":"","institution":"University of North Bengal","correspondingAuthor":false,"prefix":"","firstName":"Partha","middleName":"","lastName":"Barman","suffix":""},{"id":329344307,"identity":"855e4ad2-bec3-495d-ac97-779ec87fb6d7","order_by":2,"name":"Chandan Barman","email":"","orcid":"","institution":"University of Gour Banga","correspondingAuthor":false,"prefix":"","firstName":"Chandan","middleName":"","lastName":"Barman","suffix":""},{"id":329344308,"identity":"c0c8de85-cab9-4fc3-9e1c-520c8b391271","order_by":3,"name":"Sukanta Majumdar","email":"","orcid":"","institution":"University of Gour Banga","correspondingAuthor":false,"prefix":"","firstName":"Sukanta","middleName":"","lastName":"Majumdar","suffix":""},{"id":329344309,"identity":"b0025890-e273-4d13-8448-35f6167d9b4e","order_by":4,"name":"Ranadhir Chakraborty","email":"","orcid":"","institution":"University of North Bengal","correspondingAuthor":false,"prefix":"","firstName":"Ranadhir","middleName":"","lastName":"Chakraborty","suffix":""},{"id":329344310,"identity":"5f6473f3-f3d2-4804-96b1-30b000a53129","order_by":5,"name":"Ranadhir Chakraborty","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYHACNiCWkAGRDAkVQIKZuYEoLTwQLWdAWhiJ0sLAA2YytoFJ/FrM288ee8zzx4KHj3+N2YeH82qj+duBWn5UbMOpReZMXroxbxvQYRJvjGckbjueO+MwYwNjz5nbOLVISPCYSfM2gLScMWZI3HYstwGohZmxjYAWnj8wLXOO5c4nTgsbUAt/D1BLQ03uBoJaeHLMDeeC/cJWzJBw7EDuRqCWg3j9wn7G7MGbP3Vy8v2HNzP+qKnLnXf+8MEHPypwa0HSnAAiD4PZB4hQDwT8YHV1xCkeBaNgFIyCEQUA/HtOyGNEumAAAAAASUVORK5CYII=","orcid":"","institution":"University of North Bengal","correspondingAuthor":true,"prefix":"","firstName":"Ranadhir","middleName":"","lastName":"Chakraborty","suffix":""}],"badges":[],"createdAt":"2024-07-13 06:45:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4733845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4733845/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61731777,"identity":"7ab2d305-ceb7-447d-8291-5f585a04699a","added_by":"auto","created_at":"2024-08-05 01:17:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":276168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e1a:\u003c/strong\u003e Growth responses of CD3 in liquid MSM supplemented with a graded elevation of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO concentration; \u003cstrong\u003e1b:\u003c/strong\u003e Potential inducibility of cadmium resistance in CD3 through preexposure in different concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/5e7c226d8699f3a9d2309150.png"},{"id":61731783,"identity":"d539464d-4908-4ba2-bf80-ecca6460b2a6","added_by":"auto","created_at":"2024-08-05 01:17:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65711,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential accumulation of cadmium in supernatant, cell-bound, and intracellular compartments of living and heat-killed CD3 cells. The difference between cell bound Cd\u003csup\u003e2+\u003c/sup\u003e of living cells and heat killed cells of CD3 is statistically very significant (** P = 0.0015)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/52edfe54901397e2cc5f256f.png"},{"id":61731773,"identity":"ebd413b9-71d7-40ed-a210-b00e75ca5c26","added_by":"auto","created_at":"2024-08-05 01:17:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":231979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3a:\u003c/strong\u003e Comparative growth curves of CD3 in liquid MSM in response to varying pH and supplementation of 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO; \u003cstrong\u003e3b: \u003c/strong\u003eSpectrophotometric quantification of the biofilm formation in CD3 in relation to different CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO concentrations. The difference of biofilm formation in 0 mM and 0.5 mM is statistically significant (* P = 0.0306); 0 mM and 0.75 mM is statistically very significant (** P = 0.0063); 0 mM and 1 mM is statistically very significant (** P = 0.014); 0 mM and 1.5 mM is statistically very significant (** P = 0.0042); 0.75 mM and 1.5 mM is statistically significant (* P = 0.0480).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/2b8b67f0128b9b092993d0e3.png"},{"id":61731943,"identity":"f2d3db79-d0c0-431d-a967-a42ef823cc87","added_by":"auto","created_at":"2024-08-05 01:25:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73764,"visible":true,"origin":"","legend":"\u003cp\u003eWhole-genome-based phylogenetic tree of \u003cem\u003eP. aeruginosa\u003c/em\u003e strain CD3 prepared in TYGS server\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/239e6b9f0b60a66cb0673177.png"},{"id":61731775,"identity":"cbca9827-c017-40b2-8fbc-f4966c4c59d9","added_by":"auto","created_at":"2024-08-05 01:17:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110916,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-nucleotide-polymorphism (SNP)-based phylogenetic tree of strain CD3 with other members of \u003cem\u003eP. aeruginosa\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/5fdd892370c4824c8925a6b4.png"},{"id":61731784,"identity":"89154683-5457-4bde-90a6-118bbaf390a7","added_by":"auto","created_at":"2024-08-05 01:17:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":467488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e6a:\u003c/strong\u003e Molecular phylogeny of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e strain CD3 on the basis of concatenated nucleotide sequences of \u003cem\u003eczcC\u003c/em\u003e, \u003cem\u003eczcB\u003c/em\u003e, and \u003cem\u003eczcA \u003c/em\u003e(Evolutionary analysis by Maximum Likelihood method: The evolutionary history was inferred by using the Maximum Likelihood method and Kimura 2-parameter model. The tree with the highest log likelihood (-54347.14) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. This analysis involved 16 nucleotide sequences. Codon positions included were 1st+2nd+3rd+Noncoding. There were a total of 6109 positions in the final dataset. Evolutionary analyses were conducted in MEGA11); \u003cstrong\u003e6b:\u003c/strong\u003e Molecular phylogeny of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e strain CD3 on the basis of concatenated protein sequences of czcC, czcB, and czcA (Evolutionary analysis by Maximum Likelihood method: The evolutionary history was inferred by using the Maximum Likelihood method and JTT matrix-based model. The tree with the highest log likelihood (-27689.44) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the JTT model, and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. This analysis involved 16 amino acid sequences. There were a total of 2060 positions in the final dataset. Evolutionary analyses were conducted in MEGA11).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/1b361e6aaf17ab19be69c7bb.png"},{"id":61731944,"identity":"c568558f-3bf9-435d-9c29-0d018bb4f386","added_by":"auto","created_at":"2024-08-05 01:25:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":405112,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of dominant (non-synonymous) point mutations in cadmium-resistance-related gene loci of the CD3 genome as compared to \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e strains PA01, MR41 (JCM5962) and San_ai (NCAIM B.001380)\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/8b7ebf706df5c0e7cdea0090.png"},{"id":61731778,"identity":"d94d7095-962e-41b2-804e-410374763d8f","added_by":"auto","created_at":"2024-08-05 01:17:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":597703,"visible":true,"origin":"","legend":"\u003cp\u003eProtein–protein interaction network constructed using STRING v12 using BfmR, CzcR and CzcC as multiple “protein query sequence” queries with \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e PA01 (NCBI taxonomy ID: 208964) as model organism. Network nodes represent proteins, splice isoforms, or post-translational modifications are collapsed, i.e., each node represents all the proteins produced by a single, protein-coding gene locus; Edges: Edges represent specific and meaningful protein-protein associations, i.e., proteins jointly contribute to a shared function\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/0a2ddf9e10252b6284e64fab.png"},{"id":61731942,"identity":"69487488-0901-4e31-b17c-9907f728a9cf","added_by":"auto","created_at":"2024-08-05 01:25:43","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":318278,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork interpretation and functional enrichment analysis of BfmR, BfmS, CzcR and CzcS proteins to explore their multifaced roles and database-determined, shared activities in \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e biology in relation to regulation of biofilm formation, phosphorelay sensor-kinase activity and receptor signaling activity employing ClueGo (v2.5.10) and CluePedia (v1.5.10) plug-ins in the open-source software platform Cytoscape (v3.10.1)\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/43b89fefd1583cc6dd9954d9.png"},{"id":61731781,"identity":"c04987be-ea7d-405c-a79e-fdb439a3e30e","added_by":"auto","created_at":"2024-08-05 01:17:44","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":192889,"visible":true,"origin":"","legend":"\u003cp\u003eA hypothesized model of the molecular mechanism of cadmium resistance in strain CD3. A. Scenario under low cadmium stress condition, where BfmR is bound near the CzcCBA promoter region inhibiting CzcR promoter binding, Czc pump is inactive and cell is protecting itself against cadmium stress solely through vigorous biofilm formation; B. Scenario under high cadmium stress condition: 1. Adsorption of cadmium ions at the cell surface and diffusion of cadmium ions inside the cell, 2. Change in the cell wall integrity/net charge collectively sends stimulus (in a yet unknown mechanism) to BfmS and it gets autophosphorylated; CadR binds to 4 cadmium ions and activates CadA, 3. Phosphorylated BfmS phosphorylates BfmR, which has a lower binding affinity towards DNA; activated CadA effectively transfers cadmium ions into periplasmic space along with CzcD, a cation-diffusion-family protein serving the same purpose, 4. Phosphorylated BfmR gets detached from DNA. This event indicates a decrease in biofilm development, which reallocates cellular resources towards the activation of the Czc efflux mechanism; periplasmic cadmium ions bind to CzcS, 5. CzcS gets autophosphorylated upon binding to cadmium ions. 6. Phosphorylated CzcS phosphorylates CzcR protein, 7. Phosphorylated CzcR, binds to the CzcCBA promoter region in the absence of BfmR. The detachment of BfmR and the subsequent binding of CzcR is crucial for the functioning of the CzcCBA efflux pump, which aids in the process of detoxification, as previously anticipated (Fan et al. 2021). 8. Active transcription of CzcCBA operon under the operational control of CzcR and activation of CzcCBA pump, 9. Active efflux of cadmium ions through the CzcCBA pump.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/8cabc33887d7378f35236522.png"},{"id":61732422,"identity":"55fd17af-c1f6-48f6-a73d-97798ab70f13","added_by":"auto","created_at":"2024-08-05 01:41:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4432314,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/433a6feb-4d9a-4fca-b6b8-5f637148d126.pdf"},{"id":61731774,"identity":"4b72d12a-0cf8-4700-a316-fbe75d5de90b","added_by":"auto","created_at":"2024-08-05 01:17:43","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":372926,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.tiff.tif","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/e1df64fb3d9da702f6e3f3ef.tif"},{"id":61731941,"identity":"93ac174b-48a5-4c51-b61b-21297e4249f8","added_by":"auto","created_at":"2024-08-05 01:25:43","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":863530,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4733845/v1/412f543347c97385e06cf312.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pseudomonas aeruginosa strain CD3 implements cadmium resistance through multimodal systems and its regulatory networking","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCadmium, conventionally anticipated as a biologically insignificant element, has emerged as a significant environmental pollutant over the course of centuries, mainly due to anthropogenic industrial activities (Briffa et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This significant rise substantially implies a shift from the evolutionary interplay commonly witnessed between life forms and elements, positioning cadmium\u0026rsquo;s biological engagement as predominantly motivated by its inorganic chemistry under physiological circumstances. Exclusively existing in its divalent cationic form, Cd\u003csup\u003e2+\u003c/sup\u003e, cadmium determines its interplay with biomolecules through two apparently contrary aspects that underlie its biological and toxicological consequences. Cd\u003csup\u003e2+\u003c/sup\u003eis known as a \"soft\" ion within Group 12 of the periodic table, displaying a strong affinity for thiols and other \u0026ldquo;soft\u0026rdquo; ligands (resulting from soft acids and bases). This orientation clears the way for its binding to key biomolecules, regulating various biochemical pathways. Metallothioneins, thiol-rich Cd\u003csup\u003e2+\u003c/sup\u003e-binding proteins, surface as critical modulators of intracellular cadmium levels, alleviating its bioavailability and emphasizing the complex interplay between Cd\u003csup\u003e2+\u003c/sup\u003e and Zn\u003csup\u003e2+\u003c/sup\u003e ions and cellular defence mechanisms. Its strong affinity for sulphur sites is relevant to biology since it implies, for example, that Cd\u003csup\u003e2+\u003c/sup\u003e concentrations in metallothionein containing ZnS\u003csub\u003e4\u003c/sub\u003e units only need to be 1/1000th of Zn\u003csup\u003e2+\u003c/sup\u003e concentrations to effectively compete (Templeton \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). One major hypothesis is that most of cadmium toxicity occurs through interference with the control of cellular zinc homeostasis.Against all odds, certain phytoplankton species have progressed to leverage cadmium as an advantageous element under zinc-limited conditions, underscoring the adaptive strategies living organisms may acquire with regard to metal stress (Xu and Morel \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOwing to its dual ability to interact with thiol groups and imitate Ca\u003csup\u003e2+\u003c/sup\u003e ions, cadmium can affect a wide spectrum of proteins that are involved in DNA repair, signal transduction, and cytoskeletal stability. These interplays have extreme inferences for cellular function, highlighting the pleiotropic and predominantly negative impacts of cadmium (Maret and Moulis \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In prokaryotes, gradient-driven Ca\u003csup\u003e2+\u003c/sup\u003e exchangers, ATPases, and non-proteinaceous polyhydroxybutyrate-polyphosphate (PHB-PP) channels are the three main categories of Ca\u003csup\u003e2+\u003c/sup\u003e transport mechanisms that have been identified. Many bacterial taxa have been found to include Ca\u003csup\u003e2+\u003c/sup\u003e exchangers, which are assumed to be a key component of the Ca\u003csup\u003e2+\u003c/sup\u003e transport machinery in prokaryotes. These are low-affinity Ca\u003csup\u003e2+\u003c/sup\u003e transporters that can function in both directions, depending on the gradient. They do this by using the energy contained in the electrochemical gradient of ions (Sarkisova et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Contrarily, the ionic radius of cadmium has a close resemblance to that of the \"hard\" ion calcium (Ca\u003csup\u003e2+\u003c/sup\u003e), allowing it to imitate Ca\u003csup\u003e2+\u003c/sup\u003e ions and occupy its binding sites, especially in oxygen-rich environments. This mimicry disturbs cellular calcium homeostasis, distressing a wide array of signaling cascades and affecting the viability, proliferation, and apoptosis of cells.\u003c/p\u003e \u003cp\u003eBacteria have been exposed to cadmium in proportion to the element's presence in various ecological contexts over the course of evolution. But there is no proof that life has ever evolved a specific mechanism to deal with cadmium. Cadmium has the ability to hitchhike with an unexpectedly large number of biological compounds. The evolution of cadmium resistance in bacteria is an intriguing instance of how microorganisms can adjust to environmental challenges, especially heavy metal toxicity. Despite its acute toxicity, certain bacteria have evolved sophisticated machinery to resist and even thrive in cadmium-contaminated environments (Fardami et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).This evolutionary adaptation can be recognized as a consequence of the selection pressure exerted by environments heavily contaminated with cadmium. Microbial populations subjected to toxic levels of cadmium or other heavy metals go through remarkable survival challenges. In such circumstances, natural selection supports individuals with mutations or gene acquisitions that offer metal resistance. In the course of time, these favourable traits present frequently within the population contribute to the advent of cadmium-resistant bacterial strains (Woyda et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo cope with cadmium toxicity, bacterial strains have spread out a variety of machinery, including efflux systems that pump cadmium ions out of the cell, sequestration of cadmium ions by metal-binding proteins such as metallothioneins, and enzymatic conversion of cadmium into less toxic or more easily sequestered forms (Saluja and Sharma \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, some bacteria have a repair system to alleviate the damage caused due to oxidative stress, induced by cadmium (Fan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).The development of cadmium resistance in bacteria is not only an instance of the outstanding adaptability of microbes, but it likewise provides valuable insights into the mechanisms of heavy metal resistance, which can be adopted in bioremediation processes to clean up cadmium and other heavy metal pollutants from the environment. Comprehending these mechanisms at a molecular level not only provides valuable insights into the wider context of microbial evolution in response to toxic substances but also helps in the application prospects of these adaptive strategies in biotechnological and environmental management practices (Jebril et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, research on bacterial cadmium resistance has identified several knowledge gaps. Firstly, the specific mechanisms of cadmium resistance in bacteria are not understood comprehensibly. While certain genes associated with cadmium resistance, such as \u003cem\u003eczcC\u003c/em\u003e, \u003cem\u003eczcB\u003c/em\u003e, \u003cem\u003eczcA\u003c/em\u003e, \u003cem\u003eczcR\u003c/em\u003e, \u003cem\u003eczcS\u003c/em\u003e, \u003cem\u003ecadA\u003c/em\u003e and \u003cem\u003ecadR\u003c/em\u003e, have been identified, there might be many other genes involved (Abbas et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Secondly, the role of time and dosage related to cadmium resistance is still questionable. It has been observed that the expression of genes associated with cadmium resistance can alter over time and in accordance to exposure to different cadmium concentrations (Hoogewerf et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, it is unclear how cadmium resistance is influenced by the crosstalk between different cadmium resistance genes. It has been proposed that cadmium resistance is regulated by gene systems with time- and dose-dependent functions. Further research is required to address these lacunae and improve our understanding of cadmium resistance mechanisms in bacteria. Moreover, the MIC values in bacterial cadmium resistance may be overestimated due to issues with the media composition. Most of the metals being used in laboratory studies can be bound or chelated by the organic components in the media, resulting in erroneously high MICs. Standard media for determining MICs of heavy metals often remove significant amounts of free Cd\u003csup\u003e2+\u003c/sup\u003e ions from the media, leading to unreliable findings (Angle and Chaney \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Utilizing modified media, the uncertainties associated with other media could be avoided and the MICs could be precisely estimated (Angle et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Certain modifications in the concentrations of the media components, such as phosphate, and yeast extract, can remarkably reduce the degree of error in estimating MICs of cadmium. These findings imply that the media composition used in toxicity-tolerance testing can influence the estimation of MIC values for cadmium resistance in bacteria.\u003c/p\u003e \u003cp\u003eThe principal objective of this research is to address the existing loopholes regarding bacterial cadmium resistance. By delving into the evolutionary pathways and mechanisms bacteria have developed to counteract cadmium toxicity, this study intends to better comprehend microbial adaptability and resilience towards cadmium, with potential implications for its effective bioremediation strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIsolation of cadmium-resistant bacteria\u003c/h2\u003e \u003cp\u003eCadmium-resistant bacteria were isolated from soil samples collected from four distinct places (cadmium abundance in the samples ranged between 16 and 22 ppm) situated at Malda, West Bengal, India (GPS coordinates: 24.9091 N 88.0869 E, 25.071 N 87.873 E, 24.990057 N 88.1496345 E, 24.9882814 N 88.1454309 E). A 10 g soil sample was added to 100 mL Luria Broth (LB) medium supplemented with 0.5 mM cadmium chloride monohydrate (CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO), and the mixture was incubated at 30\u0026deg;C for 96 h. 10% inoculum was taken and then transferred into graded concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO(0.75, 1.0, 2.0, 3.0 mM) supplemented medium. The soil-LB mixtures, after 4 days of enrichment, were serially diluted in sterilised 0.85% NaCl solution, and appropriate dilutions were spread on Luria Agar (LA) plates supplemented with sterilized 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO (500 mM stock solution of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eOwas sterilized separately)(Fan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Plates were incubated at 30\u0026deg;C for overnight. Later, morphologically distinct bacterial colonies were identified and clonally purified in cadmium supplemented (1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO) LA plates. Isolates were stored in LA slants (supplemented with 0.01 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO) and kept at 4 \u003csup\u003eo\u003c/sup\u003eC until further use.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFormulation of a proper bacteriological medium suitable for determination of the cadmium resistant phenotype in bacteria\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter first round of clonal purification of bacterial colonies from enrichment medium, isolates were streaked in LA plates supplemented with different concentration(s) of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO (separately ranging from 1\u0026ndash;15 mM) and incubated at 30\u0026deg;C for 48 h for preliminary screening of potent cadmium-resistant bacteria. The strains exhibiting significant growth in CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO -supplemented plates were taken for another round of screening for demonstrating growth (1% inoculum) in minimal salt medium (MSM), which was formulated after several rounds of modification (medium composition specified in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Appropriate volume(s) from sterile CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO stock solution was added to sterile MSM to obtain the defined concentrations of Cd\u003csup\u003e2+\u003c/sup\u003e (1\u0026ndash;15 mM with a graded elevation of 2 mM). Cell pellet was obtained by spinning the LB grown overnight culture at 4000 rpm in a centrifuge for 10 min and washed twice with sterile MSM. Then, 10 ml MSM supplemented with a defined concentration ofCdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO was inoculated (1% inoculum) in 100 ml Erlenmeyer flasks and kept in a shaking incubator (GeNei\u0026trade;) at 30 \u0026ordm;C and 100 rpm for 48 h. After 48 h of incubation, visible turbidity of the culture confirmed growth vis-\u0026agrave;-vis resistance of the isolates in the presence of the respective concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO in each flask.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComposition of formulated liquid minimal salt medium (in g/l) used in this study:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSM components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmount (g/l)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmmonium chloride (NH\u003csub\u003e4\u003c/sub\u003eCl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium chloride (CaCl\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium sulphate heptahydrate (MgSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium chloride (NaCl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium sulphate (Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium nitrate (KNO\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYeast extract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDetermination of minimum inhibitory concentration (MIC) of CdCl\u003c/b\u003e \u003csub\u003e \u003cb\u003e2.\u003c/b\u003e \u003c/sub\u003e \u003cb\u003eH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO to ascertain the upper limit of cadmium resistance in selected cadmium-resistant isolates\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe upper limit of cadmium resistance (in terms of MIC of cadmium) was determined for the individual isolates in the formulated MSM. This test involved graded elevation of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eOconcentration in the MSM and the lowest concentration of the metal, which inhibits the growth of the isolate, are considered the MIC of cadmium. A1% (v/v) inoculum from log phase MSM culture of CD3 was added to individual Erlenmeyer flasks containing MSM supplemented with different concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO. After 72 h of incubation, growth was assessed spectrophotometrically at 600 nm.\u003c/p\u003e \u003cp\u003eBesides the determination of MIC, growth curves were generated by plotting log c.f.u. /ml values (enumerated by dilution plating technique) versus time in cultures containing different concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO (0.5-3 mM). This study was conducted by growing the cells in modified MSM in triplicates for statistical validation (Sen et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of resistance to other metals and metalloids of the potential cadmium-resistant isolate, CD3\u003c/h2\u003e \u003cp\u003eResistance of CD3 to a few heavy metals and metalloids (nickel, zinc, cobalt, chromium, arsenate and arsenite) was determined by growing the bacteria in MSM supplemented with respective metal and metalloid salts. Stock solutions of 500 mM concentrations of heavy metal and metalloid salts (nickel chloride, zinc sulfate, cobalt chloride, potassium dichromate, sodium arsenate and sodium arsenite) were used. The concentrations of metal ions were increased in a stepwise manner until the bacterial growth ceased. Growth was determined spectrometrically after 96 h of incubation at 30\u0026deg;C. Culture grown in the absence of metals/metalloids was treated as a control. All studies were performed in triplicates (Khan et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRe-examination of the heavy metal resistance including cadmium-resistance phenotype of the strain CD3 expressed as MIC values in relation to varying inoculum density\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInfluence of the inoculum density on the cadmium-resistance phenotype shown by CD3 was performed to prove or disprove the hypothesis that resistance is determined by the degree of intracellular free import of cadmium per cell which can be reduced when there is an increase in the number of input cells, resulting in an overestimation of MIC values. To test this hypothesis, log-phase cells grown in MSM were inoculated (1% inoculum) in fresh MSM supplemented with varying concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO or CoCl\u003csub\u003e2\u003c/sub\u003e. 6H\u003csub\u003e2\u003c/sub\u003eO or ZnSO\u003csub\u003e4\u003c/sub\u003e. 7H\u003csub\u003e2\u003c/sub\u003eO. Primarily, the initial cell counts at 0 h post- inoculation was found to be \u0026asymp;\u0026thinsp;10\u003csup\u003e5\u003c/sup\u003e c.f.u. /ml.; similar experiments were conducted by taking the initial cell count at 10\u003csup\u003e4\u003c/sup\u003e or 10\u003csup\u003e3\u003c/sup\u003e or 10\u003csup\u003e2\u003c/sup\u003e c.f.u. /ml at 0 h post- inoculation.The cultures were incubated at 30 \u003csup\u003eo\u003c/sup\u003eC under shaking conditions (100 rpm). After 7 days, cultures were taken and spread in LA to check viability and growth. Control experiments were conducted with \u003cem\u003eE. coli\u003c/em\u003e K12 MTCC 1302. All studies were performed in triplicates.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInduction of cadmium resistance in CD3, pre-exposed to low concentrations of cadmium or zinc or cobalt salt\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo study the nature of cadmium resistance (inducible or not), growth curves were studied in the modified MSM medium amended with 1.5 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO by measuring the viable cell numbers (c.f.u. / ml) of the cultures at different time intervals (Bhadra et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sen et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).A culture was originally prepared in MSM, free of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO. 1% of the inoculum from the log-phase culture (not exposed to Cd\u003csup\u003e2+\u003c/sup\u003e) was transferred to fresh MSM supplemented with 1.5 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO (=\u0026thinsp;half the MIC concentration) and viable cell counts (by dilution plating technique) at different time points were recorded to generate a growth curve and determine the length of the lag phase. A second set of cultures was prepared in MSM supplemented with 0.5 or 0.25 or 0.125 or 0.0625 or 0.03125 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, and 1% inoculum from each log-phase culture (exposed to low concentrations of Cd\u003csup\u003e2+\u003c/sup\u003e) was transferred to fresh MSM supplemented with 1.5 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO. Viable cell counts at different time points were recorded to generate a growth curve for determining the length of the lag phase. The lengths of the lag phases of the individual test cultures were compared with each other. A positive control set was run side by side, where 1% inoculum from the log-phase culture (cells grown in MSM supplemented with 1.5 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO) was transferred to fresh MSM supplemented with 1.5 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO.To investigate the role of Zn\u003csup\u003e2+\u003c/sup\u003e or Co\u003csup\u003e2+\u003c/sup\u003e in inducing cadmium resistance, log phase culture cells (1% inoculum) grown in MSM supplemented with 0.25 mM ZnSO\u003csub\u003e4\u003c/sub\u003e. 7H\u003csub\u003e2\u003c/sub\u003eO or CoCl\u003csub\u003e2\u003c/sub\u003e. 6H\u003csub\u003e2\u003c/sub\u003eO was transferred to sterile MSM containing 1.5 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO; growth curves were generated to confirm shortening of lag-phase lengths as proof for induction of cadmium resistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of cadmium concentrations in living and heat-killed CD3 cells using atomic absorption spectroscopy\u003c/h2\u003e \u003cp\u003eConcentration of extra-cellular, cell-surface-bound, and intra-cellular Cd\u003csup\u003e2+\u003c/sup\u003e was determined by a PerkinElmer Flame Atomic Absorption Spectrometer PinAAcle 900F (FAAS) following a standard protocol(Fang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). CD3 was first grown up to the log phase of the culture in 10 mL MSM. A 1% (v/v) inoculum from its log phase MSM culture of CD3 was added to individual 10 ml MSM batches supplemented with 1 mM concentration of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO (111.73 ppm of Cd\u003csup\u003e2+\u003c/sup\u003e).At late exponential phase, cells were harvested by centrifugation (10000 rpm for 10 min), and the supernatant was analysed for extra-cellular Cd\u003csup\u003e2+\u003c/sup\u003e concentrations.\u003c/p\u003e \u003cp\u003eThe cell pellet obtained was resuspended in 0.1 M EDTA and vortexed gently for 15 s. After 2 h of incubation at room temperature the mixture was centrifuged at 10000 rpm for 10 min. Following this, the supernatant was analysed for the potential concentration of Cd\u003csup\u003e2+\u003c/sup\u003e bound to the cell surface.\u003c/p\u003e \u003cp\u003eThe final cell pellet obtained after the second round of centrifugation was resuspended in sterilized distilled water and sonicated at 60% amplitude for 5 minutes (10 s followed by rest for 10 s), centrifuged at 10000 rpm for 10 min, and the cell lysate was analysed for the quantification of the intra-cellular Cd\u003csup\u003e2+\u003c/sup\u003e concentrations. A sterile MSM supplemented with 1 mM concentration of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO was taken as a control. For AAS analysis, a 1 ml sample was taken and mixed with 10 ml diacid solution (HNO\u003csub\u003e3\u003c/sub\u003e:HClO\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;9:4) overnight. After overnight incubation, the mixture was digested with sand bath and filtered by Whatman 42 filter paper. Then the filtrate was analysed to determine Cd\u003csup\u003e2+\u003c/sup\u003e concentrations.\u003c/p\u003e \u003cp\u003eFor comparison,10 ml MSM grown cells (late exponential) of CD3 were harvested by centrifugation, resuspended in 1.5 ml Eppendorf tube and heat killed in a water bath at 70\u0026deg;C for 2 min (Jill van Kessel et al. 2021). After centrifugation at 5000 rpm for 10 min, the pellet was resuspended in 10 ml MSM containing 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO and incubated for 12 h. After 12 h of incubation, the cells were harvested and the supernatant as well as cells were prepared for AAS to analyse the Cd\u003csup\u003e2+\u003c/sup\u003e concentrations in extracellular, bound, and intracellular circumstances following the same protocol.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEffect of pH to evaluate changes in toxicity affecting growth of CD3 in presence of 1 mM CdCl\u003c/b\u003e \u003csub\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sub\u003e. \u003cb\u003eH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn order to identify the pH range that supports normal growth of CD3, liquid MSM with a pH range of 3\u0026ndash;12 was initially created for this investigation with a progressive increase of pH 1. To ascertain the correlation between cadmium toxicity and pH in growth, the log phase culture of CD3 was subsequently inoculated in liquid MSM supplemented with 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, with a pH range of 6\u0026ndash;9, and allowed to grow at 30 \u003csup\u003eo\u003c/sup\u003eC in an orbital shaker fixed at 100 rpm. Control sets were made in liquid MSM with varying pH levels but no CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO addition (Babich and Stotzky \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Finally, the growing cultures were serially diluted at regular intervals using 0.85% NaCl solutions. The diluted bacterial suspensions were then spread onto LA plates and incubated for overnight at 30\u0026deg;C. Growth curves generated by plotting the log c.f.u. /ml versus time were used for comparison.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSpectrophotometric quantification of biofilm formation in CD3 in response to various CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO concentrations\u003c/h2\u003e \u003cp\u003eThe ability of the most potent isolate to produce biofilms was examined in liquid MSM using techniques previously reported (O\u0026rsquo;Toole \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Baugh et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sen et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In order to accomplish this, CD3 cells were cultured in the wells of a microtiter plate using different concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO. After the incubation period, the wells were washed with sterile double-distilled water to remove any remaining media. Subsequently, the wells were inundated with crystal violet and allowed to remain undisturbed for a minute. Following the removal of excess colour, the wells were dried and subsequently filled with a solution of 30% acetic acid. The absorbance of the suspensions was measured at a wavelength of 540 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eWhole genome sequencing and analysis of bacterium CD3\u003c/h2\u003e \u003cp\u003eThe CD3 genomic DNA from overnight grown cells in Luria broth (Himedia) incubated at 30\u0026deg;C under shaking conditions (100 rpm) was isolated, sequenced in Illumina NovaSeq platform, contig-assembled, sequence deposited and annotated following a standard protocol described elsewhere (Barman et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePreliminary taxonomic identification of strain CD3 was based on 16S rRNA gene sequence homology with legitimate bacterial species. The CD3 whole genome tree was also generated using the type strain genome server (TYGS) (Meier-Kolthoff and G\u0026ouml;ker \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Basak and Chakraborty \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to corroborate the findings. A single-nucleotide-polymorphism tree was also prepared employing the CSIPhylogeny tool (Kaas et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) from the Center for Genomic Epidemiology online server(Deng et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The \u003cem\u003ePseudomonas\u003c/em\u003e genome database (Winsor et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) was also used to fish out the responsible genes for cadmium resistance. Sequence comparisons at the nucleotide level with \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e DSM 50071 and at both nucleotide and protein levels with other cadmium-resistant bacterial genera were performed using the online EMBOSS WATER tool (Rice et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEvolutionary divergence in terms of nucleotide and amino acid sequences of cadmium efflux pump components, CzcC, CzcB, and CzcA\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNucleotide and protein sequences of CzcC, CzcB, and CzcA from strain CD3 were used for the retrieval of the homologous sequences from other bacterial strains from the NCBI and Pseudomonas genome database employing blastn and blastp (Johnson et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) programs and top blast hits were considered for the phylogenetic analyses, wherein the homologous sequences were concatenated one after another (CzcC-CzcB-CzcA), aligned with the query sequences using ClustalW (for nucleotide sequences) (Thompson et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Muscle (for protein sequences) (Edgar \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) programs in MEGA11(Tamura et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and all the multiple sequence alignment (MSA) data were used for phylogenetic analysis. Construction of phylogenetic trees was carried out using the Maximum-likelihood approach, while the calculation of evolutionary distances was performed using the Kimura 2-parameter model (for nucleotide sequences)(Kimura \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) and Jones-Taylor-Thornton (JTT) model (for protein sequences) (Jones et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) using the bootstrap method with 1000 replicates (Felsenstein \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1985\u003c/span\u003e)in MEGA11. Moreover, the intragenic nucleotide region between \u003cem\u003eczcR\u003c/em\u003e and \u003cem\u003eczcC\u003c/em\u003e of strain CD3 has been aligned with that of strain PA01, as it is thought to serve a regulatory role in the expression of the cadmium efflux pump CzcCBA in \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssessment of genetic diversity from the analysis of mutational landscape of cadmium resistance genes in strain CD3 compared to phylogenetically related\u003c/b\u003e \u003cb\u003eP. aeruginosa\u003c/b\u003e \u003cb\u003estrains, PA01, MR 41, and San_ai\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhole genome sequences of \u003cem\u003eP. aeruginosa\u003c/em\u003e strain PA01, \u003cem\u003eP. aeruginosa\u003c/em\u003e strain MR41, and \u003cem\u003eP. aeruginosa\u003c/em\u003e strain San_ai (NCAIM B.001380) were downloaded from NCBI Genome database and were annotated using the RAST server. Loci of several cadmium resistance-related genes (\u003cem\u003eczcC, czcB, czcA, czcR, czcS, cadA, cadR\u003c/em\u003e) were identified from these bacterial genomes. Nucleotide sequences of these genes from strain CD3 were aligned and compared individually with the sequences present in those three genomes using BioEdit 7.7.1(Hall \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and EMBOSS WATER to identify the point mutations in the CD3 genome. Next, sequences were aligned in the Expasy translate tool (Gasteiger et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), which translates a given nucleotide in all six possible reading frames. Then, we considered only those reading frames, which resulted in amino acid sequences matching with the annotated protein sequences from the RAST server. These in-frame translations (codons along with the resultant amino acids) are then compared side by side to identify the dominant (non-synonymous)as well as silent (synonymous) mutations for each gene. Finally, the dN/dS ratios (ratio between non-synonymous and synonymous mutations) (Roy et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) were calculated to throw some light on the genome evolution in the context of \u003cem\u003eP. aeruginosa\u003c/em\u003e cadmium-resistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional protein network analysis of strain CD3 using STRING database\u003c/h2\u003e \u003cp\u003eThe software program STRING (v12.0) was utilized to forecast protein-protein interactions. The STRING database may be accessed using either the protein identifier or the protein sequence. The confidence level of the functional partners for a certain protein is used for ordering them (Mering et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).The predictions are derived from genetic information, the transfer of associations or interactions among organisms, advanced data collection, database and literature research, and gene co-expression analysis. Every source is presented as evidence. Each individual score for an interaction or connected data in STRING is separately emphasized and displayed with coloured lines. Each of these associations is assigned a probabilistic confidence score. Confidence ratings indicate the level of interaction between nodes that are connected by a multitude of evidence. A high aggregate score indicates the utmost degree of assurance, typically surpassing the individual score. STRING utilizes a distinctive scoring method that relies on benchmarks of different types of connections compared to a shared reference set. There are four categories of confidence scores in STRING: highest (0.9), high (0.7), medium (0.4), and low (0.1)(Anitha et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we chose multiple proteins as our input format and BfmR, PA2523 (CzcR) and CzcC as our protein queries. \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 (NCBI taxonomic ID: 208964) was chosen as the model organism. To generate the functional protein interaction network, the network type was set to \u0026lsquo;full\u0026rsquo; STRING network, \u0026lsquo;meaning of network edges\u0026rsquo; was set to evidence, all the active sources of interactions were allowed, minimum required interaction score was set at the default value of 0.4, and for display purpose, no more than 10 interactions were allowed in the 1st shell and no more than 20 interactions were allowed in the 2nd shell in the basic settings.\u003c/p\u003e \u003cp\u003eIn the clustering options, k-means clustering was chosen with a number of clusters set at 4, and edges between clusters were represented as solid lines. Finally, the generated STRING network was exported in PNG format for visual interpretation and analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eNetwork interpretation and functional enrichment analysis via Cytoscape\u003c/h2\u003e \u003cp\u003eThe open-source software platform called Cytoscape (Kohl et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) allows for the integration of annotations, gene expression profiles, and other state data into molecular interaction networks and biological pathways. Although originally designed for biological study, Cytoscape has now become a widely used tool for advanced network analysis and visualization. The Cytoscape core distribution provides a fundamental set of features for data integration, analysis, and visualization. By utilizing Cytoscape plugins, one can incorporate other features at no expense. Additional plugins encompass software applications for generating novel designs, transforming data formats, scripting, and examining molecular profiles (Saito et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClueGO is a plugin in Cytoscape that carries out this function for the significant genes in the network. A pragmatic Cytoscape plug-in was utilized to significantly enhance the biological investigation of extensive gene sets. ClueGO integrates KEGG/BioCarta pathways with Gene Ontology (GO) ideas to provide a functionally structured network of GO/pathway terms. Unlike BiNGO or PIPE, ClueGO assesses overrepresented Gene Ontology (GO) concepts instead of employing kappa statistics to establish connections between terms in the hierarchical ontology tree. ClueGO is capable of conducting functional comparisons between gene clusters, highlighting their unique functional aspects (Bindea et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClueGo functional analysis in Cytoscape (v3.10.1) using ClueGo (v2.5.10) and CluePedia (v1.5.10) plugins was performed using BfmR, BfmS, CzcS (PA2524) and CzcR (PA2423) as input queries, where \u003cem\u003eP. aeruginosa\u003c/em\u003e strain PA01 genome was used in the Marker List as the model organism and gene identifiers and ontologies were kept as automatic. The visual style was set at Groups. In the gene ontologies/pathways section, biological processes, cellular components and molecular functions were selected. Network specificity was set to medium. In the Grouping options, GO Term Grouping (functional grouping), group colouring was set at random; the leading group term was based on the highest significance. Functional grouping was based on the kappa score. CluePedia parameters were set to default (Bindea et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eA two-sample t test was done for comparing datasets to see if their means are statistically different. The p-value corresponding to the T test was determined to suggest significant difference (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.graphpad.com/quickcalcs/ttest1/?format=S\u003c/span\u003e\u003cspan address=\"https://www.graphpad.com/quickcalcs/ttest1/?format=S\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIsolation of cadmium-resistant bacteria\u003c/h2\u003e \u003cp\u003ePost enrichment, cultivable bacteria were isolated and counted from LA plates supplemented with 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e.H\u003csub\u003e2\u003c/sub\u003eO. Total 26 bacterial strains were isolated and identified as CD1, CD2, CD3, CD4, CD5, CD6, CD7, CD8, CD9, CD10, CD11, CD12, CD13, CD14, CD15, CD16, CD17, CD18, CD19, CD20, CD21, CD22, CD23, CD24, CD25, and CD26. The isolates were stored in LA slants for further investigations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRe-evaluation of the cadmium-resistant phenotype of the cadmium-resistant isolates in newly modified MS medium\u003c/h2\u003e \u003cp\u003eAfter obtaining 26 cadmium-resistant strains (as described above), they were dilution-streaked in LA plates supplemented with increasing concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO (ranging from 1\u0026ndash;15 mM). After 96 hours of incubation at 30 \u0026ordm;C, only 10 bacterial isolates showed growth in LA plates containing 15 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO. The rest of the strains have shown varying ranges of cadmium-resistance phenotypes (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The 10 strains that grew in LA containing 15 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO was then tested for its ability to grow in liquid MSM supplemented with increasing concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO (1\u0026ndash;15 mM with a graded elevation of 1 mM). It is to be mentioned here that cells grown overnight (12 h) in LB were harvested, washed, and re-suspended in the same volume of sterile MSM to be used as inoculum (1%). The resistance phenotype was evaluated by observing the turbidity of the cultures after 96 h of incubation at 30 \u0026ordm;C. It was found that only five among the 10 strains showed the ability to grow up to 9 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO-containingliquid MSM (Table S2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDetermination of minimum inhibitory concentration (MIC) of CdCl\u003c/b\u003e \u003csub\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sub\u003e. \u003cb\u003eH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO to ascertain the upper limit of cadmium resistance in selected cadmium-resistant isolates\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhen MSM-grown log-phase cells (5 h grown culture) were used as inoculum, only one (strain CD3) among the five isolates (those demonstrated visible growth in the presence of 9 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO when inocula were derived from 12 h grown Luria broth), could grow demonstrating resistance up to 3 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO. The remaining isolates were not able to resist beyond 1.75 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO. The results obtained using LB- or MSM-grown cells as inocula were repeated three times to confirm this phenomenon. Hence, in order to solve this apparent paradox, we hypothesized that the initial cell number confronting the cadmium stress could be the limiting or deciding factor for determining the MIC. The results of the experiments to prove or disprove this hypothesis have been detailed under the next sub-heading. So, the strain CD3 was selected to work out its growth curve. MSM was supplemented with 0.5 or 1.0 or 2.0 or 3 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, CD3 having an initial cell density of log c.f.u. /ml\u0026thinsp;=\u0026thinsp;5.0, after 28, 32, 52 and 72 h of incubation, grew to reach maximum log c.f.u. /ml equal to 7.26, 7.012, 7.017, and 7.02 respectively (Fig.\u0026nbsp;1a).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of resistance to other metals and metalloids of the potential cadmium-resistant isolate, CD3\u003c/h2\u003e \u003cp\u003eThe isolate CD3 was also able to resist diverse heavy metals and metalloids, so it was considered multi-metal/metalloid resistant. It has tolerated up to 1 mM chromium (Cr\u003csup\u003e6+\u003c/sup\u003e), 1.25 mM Nickel (Ni\u003csup\u003e2+\u003c/sup\u003e), 13 mM zinc (Zn\u003csup\u003e2+\u003c/sup\u003e), 1 mM cobalt (Co\u003csup\u003e2+\u003c/sup\u003e), 10.25 mM Arsenate (As\u003csup\u003e5+\u003c/sup\u003e) and 18 mM Arsenite (As\u003csup\u003e3+\u003c/sup\u003e) respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRe-examination of the heavy metal resistance, including cadmium-resistance phenotype of the strain CD3, expressed as MIC values in relation to varying inoculum density\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe amount of the inoculum had a significant impact on the MIC values. Our research indicated a decrease in MIC values for cadmium, zinc and cobalt salts with low inoculum densities (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When an initial inoculum of 10\u003csup\u003e5\u003c/sup\u003e c.f.u./ml was introduced to the MSM supplemented with CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO concentrations of 1 mM, 3 mM and 5 mM, turbidity was observed after 24, 48 and 120 hours of incubation respectively. However, at a concentration of 7 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, no turbidity was seen; yet, there was an increase in c.f.u./ml value. At 9 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H2O concentration, there was no turbidity observed. Beyond this concentration, there was no detectable growth in the medium.When there were 10\u003csup\u003e3\u003c/sup\u003e cells in the medium, turbidity was observed in 1 mM and 3 mM concentrations after incubation of 48 h and 96 h, respectively. Similar to the previous findings, in 5 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, no turbidity was observed, but it was found that the cells were resistant; but above 5 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, cells were no longer viable. When initial input cells in MSM were in the multiples of 10\u003csup\u003e2\u003c/sup\u003ec.f.u./ ml (but less than10\u003csup\u003e3\u003c/sup\u003ec.f.u./ ml), turbidity was noticed only in 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, and beyond 3 mM concentration, no viable cells remained in the medium to grow into colonies in solid medium. In the case of cobalt and zinc salts containing MSM, similar trends of growth were observed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the control set (\u003cem\u003eE. coli\u003c/em\u003e K12 MTCC1302), when the cell number was decreased, the MIC value towards cadmium cobalt and zinc decreased as well (Table S3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGrowth in the presence of variable concentrations of heavy metal salt of cadmium/zinc/cobalt in response to varying inoculum density of isolate CD3.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c10\" namest=\"c4\"\u003e \u003cp\u003eAttainment of visible turbidity in presence of variable concentrations of different heavy metal salts with varying inoculum density\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy metal salt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInoculum density (c.f.u./ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConcentrations of heavy metal salts in the medium (mM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72 h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96 h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e120 h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e144 h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e168 h\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"20\" rowspan=\"21\"\u003e \u003cp\u003eCdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e2.32\u0026times;10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e3.95\u0026times;10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e4.6\u0026times;10\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"15\" rowspan=\"16\"\u003e \u003cp\u003eZnSO\u003csub\u003e4\u003c/sub\u003e. 7H\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e3.78\u0026times;10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e4.1\u0026times;10\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eCoCl\u003csub\u003e2\u003c/sub\u003e. 6H\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e3.45\u0026times;10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e6.2\u0026times;10\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNVT\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e+++ indicates high turbidity; ++ indicates moderate turbidity; + indicates just visible turbidity; +/- denotes uncertain growth; - denotes no turbidity, NVT indicates no visible turbidity but c.f.u. /ml count has shown viability with marginal increase [NVT\u003csup\u003e1\u003c/sup\u003e- 4.45\u0026times;10\u003csup\u003e5\u003c/sup\u003e; NVT\u003csup\u003e2\u003c/sup\u003e- 2.43\u0026times;10\u003csup\u003e5\u003c/sup\u003e; NVT\u003csup\u003e3\u003c/sup\u003e- 4.3\u0026times;10\u003csup\u003e3\u003c/sup\u003e; NVT\u003csup\u003e4\u003c/sup\u003e- 5.5\u0026times;10\u003csup\u003e2\u003c/sup\u003e; NVT\u003csup\u003e5\u003c/sup\u003e- 5.34\u0026times;10\u003csup\u003e5\u003c/sup\u003e; NVT\u003csup\u003e6\u003c/sup\u003e- 5.07\u0026times;10\u003csup\u003e5\u003c/sup\u003e; NVT\u003csup\u003e7\u003c/sup\u003e- 4.03\u0026times;10\u003csup\u003e5\u003c/sup\u003e; NVT\u003csup\u003e8\u003c/sup\u003e- 3.9\u0026times;10\u003csup\u003e2\u003c/sup\u003e {cell number decreased}; NVT\u003csup\u003e9\u003c/sup\u003e- 2.3\u0026times;10\u003csup\u003e2\u003c/sup\u003e {cell number decreased}; NVT\u003csup\u003e10\u003c/sup\u003e- 4.63\u0026times;10\u003csup\u003e5\u003c/sup\u003e; NVT\u003csup\u003e11\u003c/sup\u003e- 3.81\u0026times;10\u003csup\u003e5\u003c/sup\u003e; NVT\u003csup\u003e12\u003c/sup\u003e- 3.52\u0026times;10\u003csup\u003e5\u003c/sup\u003e; NVT\u003csup\u003e13\u003c/sup\u003e- 6.7\u0026times;10\u003csup\u003e2\u003c/sup\u003e; NVT\u003csup\u003e14\u003c/sup\u003e- 5.8\u0026times;10\u003csup\u003e2\u003c/sup\u003e{cell number decreased}].\u003c/p\u003e \u003cp\u003e \u003cb\u003eInduction of cadmium resistance in CD3, pre-exposed to low concentrations of cadmium or zinc or cobalt salt\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA 12-hour lag phase was detected when CD3 inoculum was transferred from CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO-unexposed MSM seed culture to fresh MSM containing 1.5 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO. To determine how efficiently the strain CD3 acclimated to different amounts of cadmium chloride (CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO), it was cultured in 1.5, 0.5, 0.25, 0.125, 0.0625, and 0.03125 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO and then 1% (v/v) inocula were transferred to new MSM with 1.5mM. Resulting growth curves reflected that pre-exposure to increasing CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO concentrations reduced the lag phase in experimental cultures.Additionally, pre-exposure to as little as 0.03125 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO concentration was shown to induce cadmium resistance in CD3 (Fig.\u0026nbsp;1b). Similarly, a reduction in the lag phase was seen upon inoculating CD3 cells into fresh MSM fortified with 1.5 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO after they had been pre-grown in MSM containing 0.25 mM of ZnSO\u003csub\u003e4\u003c/sub\u003e. 7H\u003csub\u003e2\u003c/sub\u003eO or CoCl\u003csub\u003e2\u003c/sub\u003e. 6H\u003csub\u003e2\u003c/sub\u003eO. This suggests that cadmium resistance can also be induced by Zn\u003csup\u003e2+\u003c/sup\u003e or Co\u003csup\u003e2+\u003c/sup\u003e. Zn\u003csup\u003e2+\u003c/sup\u003e induced cadmium resistance in CD3 more strongly than Co\u003csup\u003e2+\u003c/sup\u003e did (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of cadmium concentrations in living and heat-killed CD3 cells using atomic absorption spectroscopy\u003c/h2\u003e \u003cp\u003eThe Cd\u003csup\u003e2+\u003c/sup\u003e efflux capability of CD3 was assessed by growing it in liquid MSM supplemented with 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO. When CD3 cells were grown in the presence of 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO in MSM, maximum Cd\u003csup\u003e2+\u003c/sup\u003e concentration (average 85.33 ppm) was detected in the extracellular milieu after late exponential phase. On the contrary, a minute fraction of Cd\u003csup\u003e2+\u003c/sup\u003eadhered to the cells (average 9 ppm) was detected. An average 13 ppm intra-cellular Cd\u003csup\u003e2+\u003c/sup\u003e concentration was detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHeat killed CD3 cells incubated for 12 hours in MSM containing 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H2O showed a 3 times higher cell surface bound Cd\u003csup\u003e2+\u003c/sup\u003e concentration (average 29 ppm) than living cells and a very low intra-cellular Cd\u003csup\u003e2+\u003c/sup\u003e concentration (average 2.66 ppm) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEffect of pH to evaluate changes in toxicity affecting growth of CD3 in presence of 1 mM CdCl\u003c/b\u003e \u003csub\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sub\u003e. \u003cb\u003eH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBacterium CD3 could grow in MSM in a wide pH range, ranging from 6 to 11. The tolerance towards CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO varied with the increasing pH. At pH 8 of MSM supplemented without or with 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, CD3 cells entered into the log phase within 2 h and 8 h post-inoculation respectively. On the other hand, at pH 9 of MSM supplemented withoutor with 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, CD3 cells entered into the log phase within 4 h and 16 h post-inoculation respectively. At neutral (pH 7) or acidic pH (pH 6), CD3 cells in MSM supplemented withoutor with 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO attained its log phase within 2 h and 4 h respectively. Though time of entry into log phase by CD3 cells have shown no difference, but it reached mid-log phase after 16 h and 12 h in 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO containing MSM of pH 7 and 6 respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSpectrophotometric quantification of biofilm formation in CD3 in response to various CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO concentrations\u003c/h2\u003e \u003cp\u003eIn MSM, CD3 cells formed biofilm at varying concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, which could be measured by spectrophotometry. Biofilm quantities were observed to rise as the concentration of Cd\u003csup\u003e2+\u003c/sup\u003e in MSM increased from 0 to 0.75 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, while all other growth conditions stayed the same. At concentrations greater than 0.75 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO, biofilm production was declined (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWhole genome sequencing and analysis of bacterium CD3\u003c/h2\u003e \u003cp\u003eThe whole genome sequence of CD3 was deposited at NCBI (Bioproject ID-PRJNA948186; Biosample ID-SAMN33879544) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenomic summary of cadmium-resistant bacterium, \u003cem\u003eP. aeruginosa\u003c/em\u003e strain CD3\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e strain CD3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBioproject ID:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRJNA948186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRA accession no.:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRR24718582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBioSample ID:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSAMN33879544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenBank accession no.:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJARWAQ000000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of contigs:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenome size(bp):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6239438 (6.2 Mbp)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenome coverage:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.0x\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGC content (%):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN50:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e472668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN75:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL50:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal no. of genes:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of coding genes:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of tRNAs:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of non-coding RNAs:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of pseudogenes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA whole genome-based phylogenetic tree was prepared in TYGS to determine the closest type strains. It was revealed that strain CD3 belongs to the genus \u003cem\u003ePseudomonas\u003c/em\u003e, and its nearestneighbour is \u003cem\u003eP. aeruginosa\u003c/em\u003e DSM 50071. In the SNP-based tree, it was seen that strain CD3 had a position, distinct and distant from that of both \u003cem\u003eP. aeruginosa\u003c/em\u003e DSM50071 and \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe draft genome of \u003cem\u003eP. aeruginosa\u003c/em\u003e strain CD3 disclosed 44 genes associated with overall metal resistance and transport (Table S4). The presence of \u003cem\u003eczcC, czcB, czcA, czcR, czcS, czcD, cadA\u003c/em\u003e, and \u003cem\u003ecadR\u003c/em\u003e genes were confirmed. The genetic organization of the \u003cem\u003eczcC\u003c/em\u003e, \u003cem\u003eczcB\u003c/em\u003e, \u003cem\u003eczcA\u003c/em\u003e, \u003cem\u003eczcR\u003c/em\u003e, and \u003cem\u003eczcS\u003c/em\u003e genes verified that they reside in one operon,while \u003cem\u003ecadA\u003c/em\u003e and \u003cem\u003ecadR\u003c/em\u003e were locatedin another operon. Pairwise sequence alignment of CD3\u0026rsquo;s\u003cem\u003eczcC, czcB, czcA, czcR, czcS, czcD, cadA\u003c/em\u003e, and \u003cem\u003ecadR\u003c/em\u003e genes with that of its closest relative, \u003cem\u003eP. aeruginosa\u003c/em\u003e DSM 50071, yielded identities of 99.69%, 99.86%, 99.81%, 99.85%, 99.22%, 99.78%, 99.73% and 99.85% respectively (Table S5). A comparative analysis of the nucleotide and translated protein sequences of \u003cem\u003eczcC\u003c/em\u003e, \u003cem\u003eczcB\u003c/em\u003e and \u003cem\u003eczcA\u003c/em\u003e of reported cadmium-resistant genera have been presented in tabular form (Tables S6 and S7).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEvolutionary divergence in terms of nucleotide and amino acid sequences of cadmium efflux pump components, CzcC, CzcB, and CzcA\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSince the CzcCBA pump is mainly responsible for bacterial cadmium resistance, phylogenetic analyses based on the concatenated nucleotide sequences of the \u003cem\u003eczcCBA\u003c/em\u003e genes and their translated protein sequences showed that strain CD3 has the highest similarity with \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e PA01. Results have also shown that \u003cem\u003eczcCBA\u003c/em\u003e genes and their translated protein sequences of all the members of the \u003cem\u003ePseudomonadaceae\u003c/em\u003e family, including strainCD3, formed a common cluster distinguishing this clad from \u003cem\u003eCaulobacter and Cupriavidus\u003c/em\u003e (including \u003cem\u003eC. necator\u003c/em\u003e strainN1, where \u003cem\u003eczc\u003c/em\u003e operon system was discovered) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Additionally, the intragenic nucleotide region (513 bp) between \u003cem\u003eczcR\u003c/em\u003e and \u003cem\u003eczcC\u003c/em\u003e genes of strain CD3 aligned perfectly (identity 100%) with that of \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSubtle genetic diversity ascertained from the analysis of the mutational landscape of cadmium resistance genes in strain CD3 compared to phylogenetically related\u003c/b\u003e \u003cb\u003eP. aeruginosa\u003c/b\u003e \u003cb\u003estrains, PA01, MR 41, and San_ai\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe genes responsible for cadmium resistance, \u003cem\u003eczcC, czcB, czcA, czcS, czcR, cadA\u003c/em\u003e, and \u003cem\u003ecadR\u003c/em\u003e, in strain CD3 showed a total of 34, 63, and 37 events of point mutations when aligned separately with corresponding nucleotide sequences of the \u003cem\u003eP. aeruginosa\u003c/em\u003e strains PA01, MR41 and San_ai respectively. A total of 10, 11, and 9 transverse mutations and 24, 52, and 28 transition mutations have been identified in CD3 when compared with sequences derived from strains PA01, MR41 and San_ai, respectively. Surprisingly, as an outcome of these point mutations distributed in the seven genes only 5, 10, and 4 non-synonymous mutations leading to alterations in amino acids were noted in CD3 sequences compared to PA01, MR41 and San_ai, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e), while the rest point mutations were silent (coded for the same amino acids; Fig. S2). Finally, a dN/dS value of \u0026lt;\u0026thinsp;1 was found in every gene studied, indicating a convergent evolution of the CD3 genome in terms of cadmium-resistance-associated genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFunctional protein network analysis of strain CD3 using STRING database\u003c/h2\u003e \u003cp\u003eA highly interacting network of four protein clusters was created using the STRING database. BfmR, BfmS, PA4103, PA4104, PA4105, PA4106, and PA4107 (EFhP) proteins formed the first yellow cluster, which is involved in biofilm development and maturation. CopR, CopS, IrlR, PtrA, ParR, PcoA, PA1437, PA1438, PA4886, PA2807, PA2523 (CzcR) and PA2524 (CzcS) were two-component signal transduction proteins in the second cluster (red). CzcC, CzcB, and CzcA proteins, the CzcCBA pump components of the bacterial cadmium efflux system, formed the third cluster (green). Finally, PA3689 (CadR) and PA3690 (CadA) produced the fourth cluster (blue), which effluxes cadmium from the cytoplasm to the periplasm (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eNetwork interpretation and functional enrichment analysis via Cytoscape\u003c/h2\u003e \u003cp\u003eThe diagram shows the activities associated with BfmR, BfmS, CzcR and CzcS. BfmR and CzcR are both associated with each other through the positive regulation of single-species biofilm formation. CzcR and CzcS are associated with each other through receptor signalling activity. BfmR and BfmS share receptor signalling and molecular signal transduction activity with each other, while BfmS and CzcS are both associated with phosphorelay sensor kinase activity, among other roles (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCadmium resistance and its possible mechanism(s) have been reported in bacteria from a wide range of taxonomies and habitats (Khan et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The majority of organisms that fit this profile can withstand concentrations of cadmium between 1 and 7 mM (Izrael-Živković et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), possibly through adsorption or efflux mechanisms. Twenty-six cadmium-resistant bacteria were isolated from Malda, West Bengal, India. Out of these, 10 exhibited significant resistance to cadmium (15 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO) when grown in a traditional bacteriological medium (LA). However, the bioavailability of heavy metals in solid media can pose a challenge, leading to a potential overestimation of Cd\u003csup\u003e2+\u003c/sup\u003eresistance when measured on bacteriological media gelled with agar (Agarwal et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This has induced the investigators of this study to raise doubts about the methodology and inferences drawn from the experiments that have been reported earlier in terms of determining actual MIC of cadmium salts. Hence, the foremost challenge was to determine the actual maximum tolerated concentration of cadmium salts for \"cadmium-resistant\" isolates. The initial strategy adopted in this study was to shift from a solid medium to a liquid medium for determining the MIC of Cd\u003csup\u003e2+\u003c/sup\u003e. Eventually, we encountered a remarkable problem while conducting experiments in conventional rich growth media like Luria-broth (LB), Nutrient broth (NB), Plate count broth (PCB), and Tryptone Soya broth (TSB). In each of this media, adding cadmium salt turned the medium opaque due to formation of insoluble fine suspension with time, leading to deceptive interpretations. Therefore, in order to address the issue of media opaqueness, we searched for alternative media where precipitation and cross-reaction of media components in presence of cadmium salt could be avoided (Fig. S3). Several bacteriological media were tried to find a most suitable medium that would support the maximum bio-availability of cadmium and enable bacteria to experience the highest uptake of dissolved cadmium. While using standard mineral salts medium (MSM) for MIC determination, free phosphate in MSM reacted with cadmium and formed a precipitate in the medium. It was also revealedthat the presence of organic compounds in the media leads to the chelation of heavy metals. Additionally, the degree of chelation or precipitation is directly proportional to the amount of organic compound in the medium (Angle and Chaney \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). After careful adjustments, we modified the organic ingredient (yeast extract) content in the formulated or modified medium to the ideal concentration of 0.1 g/L. This ensured that the cadmium chloride monohydrate remained dissolved completely and was thus readily available for bacterial growth and MIC determination, even at concentrations as high as 100 mM. When the newly modified medium was used, out of the 10 isolates initially screened to have been cadmium resistant, five showed resistance to cadmium up to 9 mM.\u003c/p\u003e \u003cp\u003eThe selected cadmium-resistant strains have also shown resistance to Ni\u003csup\u003e2+\u003c/sup\u003e, Cr\u003csup\u003e6+\u003c/sup\u003e, Zn\u003csup\u003e2+\u003c/sup\u003e, Co\u003csup\u003e2+\u003c/sup\u003e, As (V), and As (III). Isolate CD3 could tolerate a comparatively higher concentration for arsenic (in terms of MIC) than cadmium. This is because arsenic is a common metalloid in the environment where bacteria have evolved much stronger mechanism to evade their toxic effects. The global average arsenic content in soil is 5 mg/ Kg, in open sea water 1\u0026ndash;2 \u0026micro;g/l (0.001\u0026ndash;0.002 ppm), and in unpolluted surface and ground water is below 10 \u0026micro;g/l (\u0026lt;\u0026thinsp;0.01 ppm) (Raju \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, Cd\u003csup\u003e2+\u003c/sup\u003e concentration in unpolluted water is usually below 1 \u0026micro;g/l (\u0026lt;\u0026thinsp;0.001 ppm) (Friberg et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Again, cadmium is extremely toxic and has not been associated with any known biological functions in living organisms, except for diatoms in rare cases (Templeton \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Understanding the binding of metal ions to the biological system relies on factors such as the electronegativity and ionic radius of the metal ions(Naja et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The size of an ion plays a significant role in its adsorption strength, while high electronegativity helps the ion bind more securely to surface functional groups (Sulaymon et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Based on the findings of the multi-metal/metalloid resistance study, it can be inferred that isolate CD3 demonstrates a high level of tolerance to every tested metal/metalloid.\u003c/p\u003e \u003cp\u003eExploration of microbial resistance to heavy metals, in particular cadmium, zinc, and cobalt, is paramount to deciphering the role of inoculum density on microbial tolerance levels. We hypothesized that net availability of metal salt molecules in a dissolved state, through active or passive diffusion, per bacterial cell would determine the tolerance limit vis-a-vis the resistance phenotype of a given bacterium towards a particular heavy metal. In other words, when cell density in a metal-containing medium is high and the net available number of metal salt molecules per cell is less, the MIC value will be high and vice-versa. We tested this hypothesis in the present study by exposing cadmium-resistant isolate CD3 at varying cell densities to a varying concentration of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO or CoCl\u003csub\u003e2\u003c/sub\u003e. 6H\u003csub\u003e2\u003c/sub\u003eO or ZnSO\u003csub\u003e4\u003c/sub\u003e. 7H\u003csub\u003e2\u003c/sub\u003eO. We have presented the CD3\u0026rsquo;s results in comparison to the control strain, \u003cem\u003eE. coli\u003c/em\u003e K12 MTCC 1302 to validate our hypothesis.\u003c/p\u003e \u003cp\u003eIt was revealed that there is a significant effect of inoculum density on the MIC values of Cd, Zn, and Co. The resistance levels of the CD3 or the ability of CD3 to grow in presence of increasing metal salt concentrations, expressed in MIC values, increased with increasing initial cell numbers (=\u0026thinsp;inoculum density) in a given test medium. Consequently, the intracellular metal ion concentration per cell reduces at high initial cell density, which is accompanied by reduced metal ion toxicity in an individual cell. As a result, the overall resistance of the population increases as they divide. At a lower density of initial cell input in the growth medium and a high import of cadmium ions into the cell, the influence of efflux pumps and other mechanisms aimed at metal sequestration fails to bring down the intracellular cadmium concentration to a level that can prevent Cd\u003csup\u003e2+\u003c/sup\u003e ions from out-competing other divalent cations to bind to their respective enzymes. In low cell densities, the influx of the number of Cd\u003csup\u003e2+\u003c/sup\u003e, or Zn\u003csup\u003e2+\u003c/sup\u003e and Co\u003csup\u003e2+\u003c/sup\u003e ions per cell increases in liquid medium containing salts of the heavy metal following the principle of diffusion and randomness of interaction between cells and dissolved or bio-available metal ions. Hence, the intracellular metal ion concentration may overwhelm the cell\u0026rsquo;s detoxification capacity, in contrast to the condition when intracellular concentration per cell goes down under an identical concentration of heavy metal in the medium when the initial input cells are log-fold higher. In fact, we have demonstrated that MIC values of heavy metal(s) are indirectly proportional to the inoculum density, establishing enhanced collective defence in higher numbers of cells. Furthermore, this phenomenon is not limited to the CD3 strain but has been unequivocally demonstrated by \u003cem\u003eE. coli\u003c/em\u003e K12 MTCC 1302, indicating that the biological principles are universal. The results, therefore, have novel implications for the traditional microbiological methods for determining MIC of heavy metals shown by any experimental bacterium. Future research directions, therefore, must involve system biology tools in understanding the molecular mechanisms that drive density-dependent resistance. Additionally, it would be valuable to investigate how these findings can be applied to bioremediation strategies and the enhancement of microbial strains for heavy metal detoxification.\u003c/p\u003e \u003cp\u003eIn order to explore more about the physiological basis of cadmium resistance, it was found that pre-exposing CD3 cells to low concentrations of cadmium ions can induce resistance to higher concentrations of cadmium. Induced cells (cells pre-grown in the presence of low concentrations of cadmium salt) enter the logarithmic phase of growth earlier than the un-induced cells (cells grown in absence of cadmium salt). The CD3 cells pre-grown separately at increasing concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO from 0.03125 to 0.5 mM has shown a gradual reduction of the lag phase when grown in the presence of 1.5 mM CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO. The cellular response in making the defence system ready for cadmium assaults is modulated differentially when grown in lower concentrations of Cd\u003csup\u003e2+\u003c/sup\u003e, indicating that lower cadmium concentrations can activate efflux mechanisms more effectively in lesser preparatory time (Chen et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), especially when shifted to a fresh medium with abruptly high cadmium (1.5 mM).This study has also described the effect of zinc or cobalt concentrations in the pre-culture (i.e., zinc or cobalt-induced cells) on cadmium resistance in CD3. It was shown that the cells' resistance to cadmium was influenced by pre-growing them in the presence of ZnSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO (0.25 mM). Nevertheless, cells previously cultured in 0.25 mM zinc or 0.5 mM cadmium have demonstrated a somewhat comparable impact on bacterial growth in relation to the stimulation of cadmium resistance. Therefore, cells induced by 0.25 mM zinc have a greater impact on cadmium resistance in terms of reduction in lag-phase duration when compared to cells induced by an equivalent concentration of cadmium. The Cd\u003csup\u003e2+\u003c/sup\u003e transporting P-type ATPase, also known as the CadA transporter, facilitates the movement of Zn\u003csup\u003e2+\u003c/sup\u003e ions from the cytoplasm to the periplasm. The periplasmic adapter domain of CzcS forms a strong bond with Zn\u003csup\u003e2+\u003c/sup\u003e ions, exhibiting a high affinity. This interaction leads to the activation of the adaptor domain, which in turn activates CzcR. Subsequently, CzcR stimulates the transcription of the \u003cem\u003eczcCBA\u003c/em\u003e operon, facilitating the expulsion of Cd\u003csup\u003e2+\u003c/sup\u003e ions from the cytoplasm and periplasm of CD3 cells (Liu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the impact of cobalt-induced cells mirrored that of cadmium-induced cells (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).The findings of the AAS analyses indicate that efflux pumps are pivotal in the cadmium resistance of CD3. The significant extracellular accumulation of Cd\u003csup\u003e2+\u003c/sup\u003e(average 85.33 ppm) observed after exposure to 1 mM CdCl\u003csub\u003e2\u003c/sub\u003e.H\u003csub\u003e2\u003c/sub\u003eO suggests that CD3 efficiently transports cadmium out of the cell into the surrounding environment. This process is crucial for maintaining a lower intracellular cadmium concentration (average 13 ppm) despite external exposure, highlighting the effectiveness of efflux mechanisms in resisting cadmium toxicity. Despite this fact, it is also important to note that this intracellular accumulation suggests that CD3 is capable of taking up cadmium from its environment. The CD3 genome contains several zinc transporters, metallothionein, and some thiol-rich proteins that help in cadmium sequestration (Table S8).\u003c/p\u003e \u003cp\u003eThe relatively small but detectable fraction of Cd\u003csup\u003e2+\u003c/sup\u003e (average 9 ppm) bound to the cell surface underscores the role of efflux pumps in preventing excessive accumulation within the cell. This binding likely represents an equilibrium between cadmium uptake and efflux, with efflux pumps continuously exporting cadmium ions to maintain cellular homeostasis. The mechanisms by which cell surface sorption occurs are not influenced by cell metabolism. Instead, they rely on the physicochemical interactions between heavy metal ions and the functional groups present on the cell walls of microorganisms. Biomass possesses the characteristic of working as a chemical substance and a biological ion exchanger. The effect was revealed to be caused by the specific cell wall structure of some bacteria (Hassan et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Furthermore, the cell wall of the microbe mostly comprises polysaccharides, lipids, and proteins, which offer several opportunities for metal binding. These compounds have many functional groups, such as carboxylate, hydroxide, amine, imidazole, sulfate, and sulfhydryl, with different charge distributions and geometries. Functional groups have the ability to selectively attach to specific metal ions. In this section, the process of binding is ascribed to many mechanisms such as ion exchange, adsorption, complexing, microprecipitation, and crystallization, which take place on the cell wall (Veglio\u0026rsquo; and Beolchini \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Davis et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Malik \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sheng et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The elements of the cell wall are pivotal in the sequestration of metals due to the intricate nature of the biomaterials used.\u003c/p\u003e \u003cp\u003eDead cells often exhibit a lower isoelectric point compared to living cells(\u0026Ccedil;olak et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This difference in isoelectric point may be a key factor contributing to the increased biosorption of Cd\u003csup\u003e2+\u003c/sup\u003e in heat-killed CD3 cells. Electrostatic contact is a crucial factor in the biosorption process(Huang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, it was shown that deceased bacterial cells had a higher level of negative charge on their cell surface and demonstrated a larger capacity for biosorption compared to living cells. This indicates that deceased cells possess a higher affinity for Cd\u003csup\u003e2+\u003c/sup\u003e binding compared to live cells.\u003c/p\u003e \u003cp\u003eLowering the pH of the growth medium from 7 to 6 resulted in a shorter lag phase for isolate CD3. Previously, a decrease in the uptake of cadmium, cobalt, copper, manganese, and nickel by encapsulated \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e in acidic pH was reported (Rudd et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). It remains uncertain whether a decrease in pH solely decreases cadmium accumulation and/ or enhances cadmium efflux from within the cell. Specific metal efflux pumps are powered by the proton motive force, while others rely on adenosine triphosphate (ATP) for their function (Nies \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Decreasing the pH raises the concentration gradient of protons across the bacterial cell wall, enhancing the proton motive force and enabling faster ATP synthesis. In addition, earlier it was observed that exposure of \u003cem\u003eE. coli\u003c/em\u003e K12 to cadmium in an acidic pH environment for 5 minutes resulted in the activation of many stress response genes (Worden et al.2008). Another explanation was proposed, stating that the higher concentration of hydrogen ions at low pH levels intensifies the rivalry between hydrogen and metal ions for attachment sites on the cell surface. This ultimately results in decreased toxicity of cadmium(Franklin et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) .It remains uncertain how the increase in pH enhances metal toxicity, but it could be related to metal speciation into a more harmful form(Babich and Stotzky \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) and/or an elevation in metal adsorption and uptake, specifically by microorganisms. As an illustration, the theory suggests that the transformation of cadmium into a single-charged, hydroxylated form is responsible for the enhanced toxicity of cadmium to fungi, bacteria, and actinomycetes at alkaline pH. Other cadmium species, apart from Cd\u003csup\u003e2+\u003c/sup\u003e, could potentially enhance cadmium toxicity at pH\u0026thinsp;\u0026ge;\u0026thinsp;7. As the pH increases, concentrations of cadmium species like CdOH\u003csup\u003e+\u003c/sup\u003e also increase. Studies have shown that certain ions, such as CdOH\u003csup\u003e+\u003c/sup\u003e, can be more hazardous compared to the more commonly found Cd\u003csup\u003e2+\u003c/sup\u003e ions (Babich and Stotzky \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Collins and Stotzky \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Ivanov et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). It is believed that the alteration in charge leads to the destabilization of the bacterial cell membrane, resulting from CdOH\u003csup\u003e+\u003c/sup\u003e toxicity.\u003c/p\u003e \u003cp\u003eMicrobial biofilm is widely recognized as a crucial factor in bacterial heavy metal resistance(Patel et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Microorganisms generate extracellular polysaccharides (EPSs), a fundamental element of the biofilm that provides protection against heavy metal stress (Nocelli et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Enhancing EPS production can increase heavy metal resistance in certain strains. Studies have shown that the environment's heavy metal content can impact the development of biofilms in specific types of bacteria, as evidenced by earlier research (Oknin et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hao et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nocelli et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Alviz-Gazitua et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the effect of heavy metals on biofilm formation can differ based on the unique interaction between a particular heavy metal and bacterial species. For example, divalent cations such as Mg\u003csup\u003e2+\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e can significantly impact the development of biofilms. They have the ability to directly alter electrostatic interactions and indirectly influence attachment processes based on physiology. They play crucial roles as cellular cations and are necessary for enzyme function, as mentioned by various researchers (Fletcher \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Malik and Kakii \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Song and Leff \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Microorganisms in biofilms can shield themselves from the harmful effects of heavy metals(Nocelli et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, biofilms can even absorb certain heavy metals (Azizi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite these findings, the impact of heavy metal ions on biofilm formation still needs to be fully understood. According to a study, it was found that cadmium, a frequently found soil-contaminant, has the ability to directly hinder the growth of bacteria, leading to a reduction in biofilm formation (Rau et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Our study revealed that at lower concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO (up to 0.75 mM), there was an observed increase in biofilm formation. However, as the concentration of CdCl\u003csub\u003e2\u003c/sub\u003e. H\u003csub\u003e2\u003c/sub\u003eO exceeded 0.75 mM, a decrease in biofilm formation was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Similar kind of results were observed when studying the impact of Cd\u003csup\u003e2+\u003c/sup\u003e on the biofilm formation of the \u003cem\u003eBacillus subtilis\u003c/em\u003e strain 1JN2(Yang et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). When bacterial cells are damaged or injured, their functions can be disrupted or inhibited, causing the contents inside the cells to leak or be released. This disruption can impede the bacteria's capacity to generate additional polymeric substances (Bouhdid et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The presence of heavy metals can have a negative impact on bacterial biofilms. This is because they can disrupt the water channels that are essential for the transportation of nutrients within the biofilm (Syed et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At higher concentrations of Cd\u003csup\u003e2+\u003c/sup\u003e, a reduction in the expression of genes associated with biofilm formation was observed (Yang et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudying the whole genome sequence (WGS) is an incredibly valuable approach to assessing genetic potential of a bacterium to combat metal assault and corroborate with the phenotypic and physiological data. WGS analysis pinpoints specific genes that may play different role in developing resistance to toxic metals, metalloids, and antibiotics and helps understand adapting to different environments (Adetunji et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Comparative WGS analyses enable valuable insights into the evolutionary dynamics of bacteria, enhancing our understanding of microbial interactions in diverse ecosystems. It also enables the identification of new resistance mechanisms against antibiotics and, as emphasized in recent studies, heavy metals, which present significant challenges in environmental and clinical settings (Garza-Ramos et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThrough a comprehensive examination of the CD3 genome, it was determined that it belongs to a \u003cem\u003ePseudomonas\u003c/em\u003e species known for its remarkable resistance and ability to produce biofilms. Additionally, the analysis revealed the presence of a unique enzymatic system that enables the strain to efficiently remove cadmium ions, further enhancing its resistance to this particular metal. Minor variations were observed in the genes associated with cadmium efflux when compared to the type strain DSM50071. The whole-genome tree generated by the Type Strain Genome Server (TYGS) offers a state-of-the-art method for comprehending the relationships and evolution of microorganisms. With the help of whole-genome sequencing data, TYGS can generate phylogenetic trees that depict the genetic relationships between different microbial strains, including type strains. This methodology provides a precise and thorough approach to categorising and recognising microorganisms, surpassing conventional methods that depend on restricted genetic markers or phenotypic characteristics. The utilization of the whole-genome tree approach allows researchers to track the evolutionary lineage of microorganisms, discover new species, and gain insights into the genomic aspects of microbial diversity. Using whole-genome (WG)- based phylogenetic studies, strain CD3 was found to be the closest relative to \u003cem\u003eP. aeruginosa\u003c/em\u003e DSM50071\u003csup\u003eT\u003c/sup\u003e among the type strains of \u003cem\u003ePseudomonas\u003c/em\u003e spp., as determined by the TYGS method (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe SNP tree is an essential tool for studying intraspecies genetic variations. It provides a detailed view of the subtle genetic differences within a species, allowing for greater comprehension of these variations. By examining SNPs, which are genetic variations at individual nucleotide positions in the genome, scientists can create phylogenetic trees that unveil the genetic connections and evolutionary past of diverse populations within a given species. This approach offers valuable insights into the genetic diversity, population structure, and evolutionary dynamics of species, showcasing the role of genetic variations in shaping phenotypic diversity, adapting to environmental changes, and combating diseases. In microbial genomics, SNP trees play an integral part in monitoring the transmission of microbial pathogens, gaining insights into the spread of antibiotic resistance, and pinpointing the genetic factors behind virulence. SNP trees are pivotal in revealing the genetic foundation of variations within a species, which has far-reaching implications for disciplines such as evolutionary biology, epidemiology, and conservation (Zhang and Liu \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Through SNP-based phylogenetic analysis, strain CD3 was found to be genetically closely related to \u003cem\u003eP. aeruginosa\u003c/em\u003e strains LESB58 and LES431. On the other hand, \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 was found to be genetically more distant, residing on a distinct branch in the evolutionary tree. Although there is significant sequence homology with PA01, the analysis reveals that strain CD3 has a distinct genetic composition compared to PA01. This revelation emphasizes the distinct genetic identity of CD3 within the \u003cem\u003eP. aeruginosa\u003c/em\u003e lineage, showcasing its unique evolutionary path (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eczcCBA\u003c/em\u003e sequence-based phylogenetic analysis and gene comparison between strain CD3 and other cadmium-resistant bacteria enabled us to contextualize these findings regarding evolution of bacterial metal-resistance mechanisms. The phylogenomic investigation using the concatenated nucleotide and translated amino acid sequences of the \u003cem\u003eczcC\u003c/em\u003e, \u003cem\u003eczcB\u003c/em\u003e, \u003cem\u003eczcA\u003c/em\u003e genes has revealed valuable insights into the evolutionary relationships and genetic preservation of these resistance mechanisms within the \u003cem\u003ePseudomonadaceae\u003c/em\u003e family (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The remarkable similarity between strain CD3 and \u003cem\u003eP. aeruginosa\u003c/em\u003e PA01 in the CzcCBA pump sequence indicates a significant evolutionary conservation of the cadmium resistance mechanism in certain strains of \u003cem\u003eP. aeruginosa\u003c/em\u003e. This finding highlights the vital role of the CzcCBA efflux pump in conferring bacterial resistance to cadmium, an essential adaptive benefit in habitats polluted with heavy metals.\u003c/p\u003e \u003cp\u003eThe convergence of all members of the \u003cem\u003ePseudomonadaceae\u003c/em\u003e family, including strain CD3, into a single cluster, highlights their collective evolutionary background and potential occupation of similar ecological niches. The unique placement of \u003cem\u003eCupriavidus necator\u003c/em\u003e strain N1, the pioneer strain in possessing the CzcCBA pump, emphasizes the evolutionary divergence and possible horizontal gene transfer incidences that could have permitted the dissemination of cadmium resistance genes among several bacterial families (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe complete matching of the intragenic nucleotide region between \u003cem\u003eczcR\u003c/em\u003e and \u003cem\u003eczcC\u003c/em\u003e in strain CD3 and strain PA01 indicates a strongly conserved regulatory mechanism for the activation of the CzcCBA efflux pump in \u003cem\u003eP. aeruginosa\u003c/em\u003e. The conservation of this region indicates its crucial function in controlling resistance to cadmium, possibly through shared transcriptional regulatory mechanisms in various strains. Regulatory elements play an essential part in enabling bacteria to adjust and endure in environments polluted with heavy metals. In addition, a deep understanding of the genetic and regulatory mechanisms that drive heavy metal resistance can provide valuable insights for devising effective strategies to address the proliferation of resistance genes (Demircan and Memon \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fardami et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Garg et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is especially crucial in environments where antibiotic and heavy metal resistance work together to create multidrug-resistant pathogens.\u003c/p\u003e \u003cp\u003eInvestigating the genetic diversity and mutation landscape of cadmium resistance genes in \u003cem\u003eP. aeruginosa\u003c/em\u003e strain CD3, alongside strains PA01, MR41, and San_ai, highlights the intricate processes in which bacteria adapt and evolve in the face of environmental challenges like exposure to heavy metals. Through careful analysis of nucleotide sequences for important cadmium resistance-related genes, the mutation spectrum within these loci has been revealed. This sheds light on commonalities as well as uniqueness in genetic responses to cadmium-mediated stress among various strains of \u003cem\u003eP. aeruginosa\u003c/em\u003e. The abundance of various mutations, encompassing both transverse and transition types, within the cadmium resistance genes, along with a distinct pattern of dominant and silent mutations, suggests an array of genetic variation (Fig. S2). This restraineddiversity is most likely a result of the evolutionary stresses caused by exposure to cadmium, which requires genetic versatility for survival. The prevailing mutations, which lead to alterations in amino acids, may play a crucial role in modifying the function or effectiveness of the related mechanisms of resistance, potentially increasing the bacterium's ability to detoxify or sequester cadmium ions (Yu et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The rate at which a population approaches its fitness optimum can be significantly impacted by the presence of even a few substantial effect mutations. However, the finding that the dN/dS ratio is less than 1 for all the genes examined implies a situation of purifying selection rather than positive selection. This contradicts the initial assumption of adaptive evolution caused by cadmium exposure (Wolf et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Roy et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zwonitzer et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on the synonymous mutations, it appears that most of the mutations in the cadmium resistance genesdid not affect the amino acid sequences of the proteins they encode. Nevertheless, there are some non-synonymous mutations that may potentially enhance protein function, which is yet to be explored. It seems that the core functions of the proteins coded by these genes remained unchanged in the face of sustained selective pressure. This conservation may be attributed to their crucial role in maintaining cellular homeostasis when exposed to toxic cadmium concentrations. It could indicate a delicate equilibrium between the need to adjust to the environment and the importance of maintaining essential cellular processes, ensuring the bacterium's survival while maintaining its overall fitness (Sendolo et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In simple words, studying the evolution of the CD3 genome in relation to cadmium resistance reveals the intricate relationship between genetic diversity, types of mutations, and evolutionary forces. It highlights the intricate strategies utilised by \u003cem\u003eP. aeruginosa\u003c/em\u003e to overcome the obstacles presented by toxic metal exposure, underscoring the significance of preserving genetic integrity to maintain vital biological processes in unfavourable circumstances.\u003c/p\u003e \u003cp\u003eIn this study, we used the STRING database (v12.0) to uncover an intricate web of protein-protein interactions within \u003cem\u003eP. aeruginosa\u003c/em\u003e PAO1. This provided us with valuable knowledge regarding bacterial resistance mechanisms and the formation of biofilms. Through the integration of various sources of evidence, such as genomic context, experimental data, and database mining, we have successfully identified four distinct clusters of proteins with different levels of interaction confidence (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These clusters showcase the multifaceted features of proteins associated with bacterial cadmium efflux, signal transduction, and biofilm formation and maturation.\u003c/p\u003e \u003cp\u003eThe initial cluster, primarily linked to the formation and development of biofilms, highlights the keyimportance of BfmR and its associated proteins (BfmS, PA4103-PA4107) in maintaining the structural integrity and resilience of biofilms. Biofilms play a vital role in the survival and virulence of bacteria, as they help bacteria resist antibiotics and evade the host's immune responses (Al-Tayawi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our research supports previous studies that highlight the importance of BfmR in regulating biofilm development in \u003cem\u003eP. aeruginosa\u003c/em\u003e(Harmsen et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Identification of this cluster not only confirms the important role of BfmR but also indicates potential protein interactions that could be focused on disrupting biofilm formation.\u003c/p\u003e \u003cp\u003eThe second cluster emphasises the importance of two-component signal transduction systems, which play a decisive role in bacterial response to environmental stimuli, especially metal ions. Proteins like CopR, CopS, PA2523 (CzcR), and PA2524 (CzcS) are key players in this process. These proteins play a crucial role in regulating gene expression in response to metal ion concentrations, which is vital for the survival of bacteria in challenging environments. Our findings align with previous research that has shown the role of CzcR and CzcS in the regulation of the \u003cem\u003eczc\u003c/em\u003e operon, facilitating the removal of heavy metals and enhancing resistance to metal toxicity (Liu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe third cluster, which includes the CzcCBA pump components (CzcC, CzcB, CzcA), plays a crucial role in bacterial resistance to cadmium toxicity by directly facilitating cadmium efflux. Thenetwork analysis presented (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e) sheds light on the interaction between the CzcCBA pump and other protein clusters, indicating a coordinated response to cadmium stress and providing additional contexts in this extensively characterized resistance mechanism.\u003c/p\u003e \u003cp\u003eLastly, the fourth cluster, which includes CadR and CadA, plays a crucial role in driving out cadmium to the periplasmic space, providing an additional defense mechanism against cadmium toxicity. This finding adds to our understanding of the processes behind cadmium resistance in \u003cem\u003eP. aeruginosa\u003c/em\u003e and supports the possibility of a cooperative relationship between the CzcCBA pump and CadA-CadR mediated efflux systems.\u003c/p\u003e \u003cp\u003eThe results of this study confirm previously reported interactions and reveals potential new connections between important proteins involved in biofilm formation, signal transduction, and heavy metal efflux. These interactions provide a more profound insight into the intricate regulatory networks that govern bacterial survival strategies. Future research should focus on conducting experiments to validate the predicted interactions and further investigate their impact on bacterial physiology and pathogenicity. In addition, focusing on particular interactions within these clusters could offer innovative methods for managing bacterial resistance and biofilm-mediated infections.\u003c/p\u003e \u003cp\u003eThe ClueGO and CluePedia plugins were used in Cytoscape to analyze the complex network of gene interactions and functional enrichments linked to the regulatory proteins BfmR, BfmS, CzcR, and CzcS in \u003cem\u003eP. aeruginosa\u003c/em\u003e. The analysis reveals an intricate network of regulations, highlighting the crucial influence of BfmR on the functions of CzcR and CzcS, which are essential for biofilm formation and receptor signalling pathways (Modrzejewska et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is evident that BfmR plays a crucial role in the positive regulation of single-species biofilm formation, closely interacting with CzcR.Understanding biofilm formation is essential in studying the virulence of \u003cem\u003eP. aeruginosa\u003c/em\u003e, as it allows the bacterium to establish prolonged infections and evade antimicrobial therapies (Kristensen et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The connection between BfmR and CzcR highlights an overlooked aspect of regulatory control, which could provide fresh perspectives on how biofilm regulatory networks incorporate environmental signals. In addition, the connection between BfmR and receptor signalling activities, which are additionally observed in BfmS, implies that BfmR may have a wider role in regulating signal transduction mechanisms that control biofilm formation and bacterial response to environmental stress. The close connection between BfmR and BfmS in receptor signalling and molecular signal transduction activities emphasises a coordinated regulatory mechanism that could finely adjust the bacterium's adaptive responses. The coordination is further demonstrated by the shared roles of BfmS and CzcS in phosphorelay sensor kinase activity, a crucial process in bacterial signal transduction pathways. Based on the results, it appears that BfmR has an impact on the activity of CzcS, possibly through its regulatory interactions with BfmS. This could potentially affect the bacterium's ability to detect and respond to heavy metal stress, considering the known involvement of CzcS in the \u003cem\u003eczc\u003c/em\u003eoperon. Therefore, the observation of associations confirms a hierarchical regulatory connection; BfmR serves to control CzcR and CzcS activities. This hierarchy indicates a potentially vital role for BfmR in coordinating environmental cues and biofilm formation as well as heavy-metal resistance. BfmR not only positively regulates biofilm formation but also uses CzcR and CzcS receptor signalling pathways as a regulator, which means that an intricate regulatory network is at play, positioning BfmR as a central hub in coordinating the bacterium\u0026rsquo;s response to various environmental stimuli.\u003c/p\u003e \u003cp\u003eSummarizing all the interpretations of results obtained from this study we put forward a hypothesized model of the molecular mechanism of cadmium resistance in strain CD3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTaken together, our data present a comprehensive overview of the complex regulatory network where BfmR heavily influences the behaviours of CzcR and CzcS, such that BfmR can be seen as a master regulator of biofilm formation and environmental sensing in \u003cem\u003eP. aeruginosa\u003c/em\u003e (Fan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As such, this analysis of the regulatory interactions can guide further molecular studies of the mechanisms of biofilm formation and antibiotic resistance, which could be translated into targeted strategies against \u003cem\u003eP. aeruginosa\u003c/em\u003e infection. Future work will also be devoted to unravelling the molecular details of how BfmR coordinates these complicated networks, as it could help identify novel therapeutic targets in the battle against bacterial pathogenesis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eData is available in the NCBI database under Genbank accession ID: JARWAQ000000000, Biosample accession ID: \u0026nbsp;SAMN33879544, Bioproject accession ID: PRJNA948186 and SRA accession ID: SRR24718582\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThere are no financial or academic compete of interest for the authors concerning the publication of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eMethodology: Soumya Chatterjee; Formal analysis: Soumya Chatterjee and Partha Barman; Writing-original draft preparation: Soumya Chatterjee, Partha Barman and Ranadhir Chakraborty; Writing-review and editing: Chandan Barman, Sukanta Majumdar and Ranadhir Chakraborty; Data curation: Chandan Barman, Sukanta Majumdar and Ranadhir Chakraborty; Conceptualization: Ranadhir Chakraborty; Supervision: Ranadhir Chakraborty\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eSoumya Chatterjee, Chandan Barman and Sukanta Majumdar acknowledge the assistance of the University of Gour Banga administration in Malda, India. Soumya Chatterjee, Partha Barman and Ranadhir Chakraborty thank the University of North Bengal (Siliguri, India) administration for their assistance. We thank Mr. Rinku Das (Central Instrumentation Centre\u0026nbsp;[CIC], Quality Control Laboratory (QCL) and RKVY Soil Testing Laboratory, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal: 736165, India) for AAS support.\u003c/p\u003e\n\u003cp\u003eSoumya Chatterjee and Partha Barman sincerely thank Council of Scientific and Industrial Research (CSIR, New Delhi, Govt. of India) as they received research grants in the form of Senior Research Fellowship during this work (CSIR-SRF; Award no. 09/1151(0006)/2019-EMR-I, 09/285(0086)/2019-EMR-I respectively).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbas SZ, Rafatullah M, Hossain K, et al (2018) A review on mechanism and future perspectives of cadmium-resistant bacteria. 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Am Nat 202:E121\u0026ndash;E129. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/725805\u003c/span\u003e\u003cspan address=\"10.1086/725805\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cadmium, Pseudomonas aeruginosa, CD3, Biofilm, CzcCBA, Induction","lastPublishedDoi":"10.21203/rs.3.rs-4733845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4733845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCadmium, a toxic heavy metal, poses a significant global concern. Out of 26 cadmium-resistant bacteria isolated from Malda, West Bengal, India, 10 exhibited significant resistance to cadmium. The study hypothesized that the net availability of metal salt molecules in a dissolved state would determine the tolerance limit of a given bacterium towards a particular heavy metal. Experiments were conducted using a modified medium that supported maximum bioavailability of cadmium, and strain CD3 was selected for studying the growth and induction of cadmium resistance. The resistance levels of CD3 cells increased with increasing initial cell numbers. Biofilm formation increased at lower concentrations of CdCl\u003csub\u003e2\u003c/sub\u003e.H\u003csub\u003e2\u003c/sub\u003eO but decreased as concentrations exceeded 0.75 mM. Atomic-absorption-spectrophotometry data confirmed that the efflux pump played a critical role in cadmium resistance at higher concentrations. Using whole-genome-based phylogenetic tools, strain CD3 was found to be the closest relative to \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e DSM50071\u003csup\u003eT\u003c/sup\u003e among the type strains of \u003cem\u003ePseudomonas\u003c/em\u003e spp., highlighting its unique evolutionary path. The STRING database was used to uncover an intricate web of protein-protein interactions. Hence, bioinformatic analyses revealed a complex network of regulations, with BfmR playing a crucial role in the functions of CzcR and CzcS, essential for biofilm formation and receptor signalling pathways.\u003c/p\u003e","manuscriptTitle":"Pseudomonas aeruginosa strain CD3 implements cadmium resistance through multimodal systems and its regulatory networking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 01:17:38","doi":"10.21203/rs.3.rs-4733845/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe95e549-1480-4e7a-a7e3-5f985679abf5","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T01:17:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-05 01:17:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4733845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4733845","identity":"rs-4733845","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}