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<journal-id journal-id-type="publisher-id">Front. Environ. Sci.</journal-id>
<journal-title>Frontiers in Environmental Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Environ. Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-665X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-id pub-id-type="publisher-id">1668462</article-id>
<article-id pub-id-type="doi">10.3389/fenvs.2025.1668462</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Environmental Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Carbon metabolism and multidrug resistance in <italic>Bacillus mobilis</italic> and <italic>Cupriavidus campinensis</italic> isolated from cadmium-spiked soils</article-title>
<alt-title alt-title-type="left-running-head">Akinwole et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fenvs.2025.1668462">10.3389/fenvs.2025.1668462</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Akinwole</surname>
<given-names>Philips O.</given-names>
</name>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
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<contrib contrib-type="author">
<name>
<surname>Jacobs</surname>
<given-names>Emma E. C.</given-names>
</name>
<xref ref-type="author-notes" rid="fn001">
<sup>&#x2020;</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Shaffer</surname>
<given-names>Nina G.</given-names>
</name>
<xref ref-type="author-notes" rid="fn001">
<sup>&#x2020;</sup>
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<aff id="aff1">
<institution>Biology Department, DePauw University</institution>, <addr-line>Greencastle</addr-line>, <addr-line>IN</addr-line>, <country>United States</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/992173/overview">Paula S Tourinho</ext-link>, Masaryk University, Czechia</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2536492/overview">Nirosha Ruwani Amarasekara</ext-link>, Agricultural Research Service (USDA), United States</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3156950/overview">Sunita Singh</ext-link>, DY Patil Deemed to be University, India</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Philips O. Akinwole, <email>philipsakinwole@depauw.edu</email>
</corresp>
<fn fn-type="present-address" id="fn001">
<label>
<sup>&#x2020;</sup>
</label>
<p>
<bold>Present address:</bold> Emma E. C. Jacobs, Ecology and Evolutionary Biology Department, Tulane University, New Orleans, LA, United States</p>
<p>Nina G. Shaffer, Plant and Soil Sciences Department, University of Kentucky, Lexington, KY, United States</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>26</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>13</volume>
<elocation-id>1668462</elocation-id>
<history>
<date date-type="received">
<day>25</day>
<month>07</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>15</day>
<month>09</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Akinwole, Jacobs and Shaffer.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Akinwole, Jacobs and Shaffer</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Heavy metal contamination and antibiotic resistance are critical environmental and public health challenges, often exacerbated by co-selection pressures in polluted environments. This study identifies and characterizes <italic>Bacillus mobilis and Cupriavidus campinensis</italic>, two cadmium-tolerant bacterial species isolated from cadmium-amended soils with cadmium (Cd<sup>2&#x2b;</sup>) concentrations exceeding those typically found in highly contaminated soils. Both species exhibited multidrug resistance and the ability to metabolize specific carbon substrates, including pyruvic acid methyl ester, itaconic acid, D-galactonic acid-&#x3b3;-lactone, Tween-40, and Tween-80. These substrates enhance microbial activity and heavy metal bioavailability, supporting their potential roles in bioremediation, especially through the targeted introduction of optimal carbon substrates. Antibiotic susceptibility testing revealed distinct growth dynamics under exposure to antibiotics such as ceftriaxone, ciprofloxacin, gentamicin, and tetracycline. Notably, <italic>C. campinensis</italic> displayed extended lag phases and concentration-dependent growth inhibition, with delayed recovery observed for ceftriaxone and doripenem. In contrast, <italic>B. mobilis</italic> exhibited resistance to several antibiotics, including erythromycin and vancomycin, and adaptive responses to ciprofloxacin, levofloxacin and nitrofurantoin, suggesting robust resistance mechanisms. These findings highlight the limitations of standard 24-h testing protocols, which fail to capture delayed adaptive responses critical for understanding resistance in complex environments. <italic>In silico</italic> resistome profiling of the isolates confirmed high-risk resistance genes, including &#x3b2;-lactamases (<italic>blaZ</italic>, <italic>mecA</italic>), fluoroquinolone targets (<italic>gyrA</italic>, <italic>parC</italic>), macrolide resistance genes (<italic>ermB</italic>, <italic>ermC</italic>), and tetracycline efflux pumps (<italic>tetK</italic>, <italic>tetL</italic>), consistent with environmental persistence and potential horizontal gene acquisition. Our study underscores the potential of <italic>B. mobilis</italic> and <italic>C. campinensis</italic> in bioremediation strategies for heavy metal-contaminated soils. Additionally, the co-selection of resistance to both Cd<sup>2&#x2b;</sup>and antibiotics highlights the ecological complexity of contaminated environments. Future work should explore the molecular pathways driving these adaptive traits and extend susceptibility testing protocols to better assess bacterial responses under prolonged environmental and antibiotic stress.</p>
</abstract>
<kwd-group>
<kwd>
<italic>Bacillus</italic> mobilis</kwd>
<kwd>Cupriavidus campinensis</kwd>
<kwd>cadmium remediation</kwd>
<kwd>antibiotic susceptibilitytesting</kwd>
<kwd>growth dynamics</kwd>
<kwd>carbon metabolism</kwd>
<kwd>environmental pollution</kwd>
</kwd-group>
<counts>
<page-count count="19"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Biogeochemical Dynamics</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p>Cadmium (Cd<sup>2&#x2b;</sup>) is one of the most toxic nonessential heavy metals and its contamination poses significant risks to human health and ecosystems. Chronic exposure to Cd<sup>2&#x2b;</sup>can lead to severe health issues in humans, including kidney damage, bone fractures, and cancers (<xref ref-type="bibr" rid="B31">Jarup et al., 1998</xref>; <xref ref-type="bibr" rid="B81">Waalkes, 2000</xref>). Due to its classification as persistent, bioaccumulative, and toxic, Cd<sup>2&#x2b;</sup> has been identified as one of the 31 Priority Chemicals for action under the U.S. Environmental Protection Agency&#x2019;s (EPA) National Partnership for Environmental Priorities (<xref ref-type="bibr" rid="B79">US EPA, 2024</xref>). Together with lead and mercury, Cd<sup>2&#x2b;</sup> is one of only three heavy metals designated by the U.S. Environmental Protection Agency as priority hazardous air pollutants for which emissions must be eliminated or considerably reduced in industrial processes such as coal- and oil-fired power generation (<xref ref-type="bibr" rid="B79">US EPA, 2024</xref>). In ecosystems, Cd<sup>2&#x2b;</sup>disrupts soil microbial activity and plant growth, reducing agricultural productivity and biodiversity (<xref ref-type="bibr" rid="B56">McLaughlin and Singh, 1999</xref>). Current policies aim to limit Cd<sup>2&#x2b;</sup>emissions through regulations on industrial discharges and the use of Cd<sup>2&#x2b;</sup>-containing fertilizers (<xref ref-type="bibr" rid="B78">United Nations Environment Programme, 2010</xref>). Despite these efforts, continuous monitoring and stricter enforcement of regulations are necessary to protect human health and the environment from Cd<sup>2&#x2b;</sup>exposure.</p>
<p>Techniques for the removal and immobilization of heavy metals in contaminated soils have included physical, chemical, and biological methods, as well as a mixture of multiple methods (<xref ref-type="bibr" rid="B43">Lee et al., 2009</xref>; <xref ref-type="bibr" rid="B68">S&#xe1;nchez-Castro et al., 2023</xref>). Physical methods include encapsulation (<xref ref-type="bibr" rid="B62">Pandey et al., 2012</xref>) and electrokinetic removal (<xref ref-type="bibr" rid="B27">Hawal et al., 2023</xref>); however, encapsulation only contains the contaminants in the soil (<xref ref-type="bibr" rid="B47">Liu et al., 2018</xref>) and electrokinetic removal is expensive and difficult to implement on a large scale (<xref ref-type="bibr" rid="B75">Sun et al., 2023</xref>). Chemical processes include stabilization (<xref ref-type="bibr" rid="B43">Lee et al., 2009</xref>), but changes in soil pH could decrease the effectiveness of the treatment since the metal is not removed (<xref ref-type="bibr" rid="B50">Lombi et al., 2003</xref>). Soil properties such as pH and organic matter content play crucial roles in Cd<sup>2&#x2b;</sup>mobility and bioavailability (<xref ref-type="bibr" rid="B34">Kabata-Pendias, 2010</xref>). A combination of biological methods including phytoremediation and bio-augmentation poses a green method of removal and are being promoted to mitigate Cd<sup>2&#x2b;</sup>contamination and restore soil health (<xref ref-type="bibr" rid="B67">Salt et al., 1995</xref>; <xref ref-type="bibr" rid="B16">Clemens, 2006</xref>; <xref ref-type="bibr" rid="B71">Simmer and Schnoor, 2022</xref>). For instance, species of cadmium-resistant microbes have been shown to have high removal efficiencies and increase seed germination and growth rates of plants (<xref ref-type="bibr" rid="B41">Lata et al., 2021</xref>; <xref ref-type="bibr" rid="B7">Arce-Inga et al., 2022</xref>). For heavy metal alleviation, a sustainable alternative to mitigate Cd<sup>2&#x2b;</sup>soil toxicity includes the use of beneficial microbes with heavy metal removal abilities. Cadmium-tolerant bacteria (CdtB) such as <italic>Enterobacter</italic> sp., <italic>Bacillus</italic> sp., sulfate-reducing bacteria, and plant growth-promoting rhizobacteria (<xref ref-type="bibr" rid="B7">Arce-Inga et al., 2022</xref>; <xref ref-type="bibr" rid="B11">Bravo and Braissant, 2022</xref>) have received more attention in recent years. Future research should focus on enhancing the efficiency of remediation techniques and developing sustainable agricultural practices to prevent Cd<sup>2&#x2b;</sup>accumulation in soils. Moreover, it is important to identify diverse CdtB for use in green and cost-effective remediation of contaminated soils. Indeed, the application of metal-resistant bacteria in bioremediation presents promising opportunities for wastewater treatment, the restoration of contaminated soils, and bioprecipitation (<xref ref-type="bibr" rid="B20">Diels et al., 1995</xref>; <xref ref-type="bibr" rid="B73">Sreedevi et al., 2022</xref>; <xref ref-type="bibr" rid="B59">Nnaji et al., 2024</xref>; <xref ref-type="bibr" rid="B1">Abbas et al., 2025</xref>; <xref ref-type="bibr" rid="B64">Qattan, 2025</xref>).</p>
<p>Bacteria exposed to heavy metals like Cd<sup>2&#x2b;</sup>often develop resistance through similar genetic pathways used for antibiotic resistance, including efflux pumps and resistance genes (<xref ref-type="bibr" rid="B69">Seiler and Berendonk, 2012</xref>; <xref ref-type="bibr" rid="B11">Bravo and Braissant, 2022</xref>). These shared mechanisms can lead to the co-selection of CdtB and antibiotic-resistant bacteria in environments contaminated with heavy metals (<xref ref-type="bibr" rid="B8">Baker-Austin et al., 2006</xref>). Studies have shown that CdtB strains frequently exhibit resistance to multiple antibiotics, indicating a co-selection between metal resistance and multidrug resistance (<xref ref-type="bibr" rid="B74">Stepanauskas et al., 2006</xref>). Furthermore, horizontal gene transfer facilitated by mobile genetic elements such as plasmids can disseminate both metal-resistance and antibiotic-resistance genes among bacterial populations (<xref ref-type="bibr" rid="B8">Baker-Austin et al., 2006</xref>; <xref ref-type="bibr" rid="B69">Seiler and Berendonk, 2012</xref>). This relationship between metal and antibiotic resistance underscores the need for integrated management strategies addressing pollutants. Effective regulation of heavy metal emissions and prudent use of antibiotics are crucial to mitigating the spread of resistant bacterial strains in the environment.</p>
<p>The microbial metal reduction reaction is energy-dependent, and detoxifying bacteria such as CdtB require nutrients that may be limited in soils depleted of organic matter (<xref ref-type="bibr" rid="B82">Wagner-D&#xf6;bler, 2003</xref>). Soil properties, particularly carbon and nutrient availability, and pH, significantly influence the success of bacterial reduction and subsequent volatilization and immobilization during bioremediation (<xref ref-type="bibr" rid="B82">Wagner-D&#xf6;bler, 2003</xref>; <xref ref-type="bibr" rid="B12">Cattani et al., 2009</xref>). It has been reported that organic matter addition significantly increased As, Hg and Cd volatilization and immobilization from investigated soils (<xref ref-type="bibr" rid="B84">Yang et al., 2007</xref>; <xref ref-type="bibr" rid="B29">Huang et al., 2012</xref>; <xref ref-type="bibr" rid="B48">Liu et al., 2019</xref>). Also, adding phosphorus compounds to manage Cd<sup>2&#x2b;</sup> and Pb<sup>2&#x2b;</sup>-contaminated soils provides microorganisms with an essential nutrient, thereby enhancing microbial activity in these soils (<xref ref-type="bibr" rid="B63">Park et al., 2011</xref>). Given that microbial metal reduction can be utilized to remove heavy metals from contaminated soils by bio-augmentation and nutrient supplementation, it is essential to identify nutrients such as carbon substrates that enhance microbial growth when cultured in the laboratory.</p>
<p>The average concentration of Cd<sup>2&#x2b;</sup> in uncontaminated soil worldwide is 0.36&#xa0;mg/kg, but it can range from 0.01 to 1&#xa0;mg/kg (<xref ref-type="bibr" rid="B39">Kubier et al., 2019</xref>). Depending on the type of pollution, soil Cd<sup>2&#x2b;</sup> concentrations can be up to 344&#xa0;mg/kg (by leaching of solid waste) and 74&#xa0;mg/kg (by atmospheric deposition) in contaminated soils (<xref ref-type="bibr" rid="B80">Voglar and Lestan, 2010</xref>; <xref ref-type="bibr" rid="B9">Bi et al., 2006</xref>). In this study, we characterized bacterial strains isolated from Cd<sup>2&#x2b;</sup> spiked soil (2,043&#xa0;mg/kg) of about one order of magnitude compared to average prevailing concentration levels in highly contaminated soils yielding CdtB species. Given the reported higher frequencies of antibiotic resistance in bacteria within metal-contaminated ecosystems (<xref ref-type="bibr" rid="B55">McArthur and Tuckfield, 2000</xref>; <xref ref-type="bibr" rid="B74">Stepanauskas et al., 2006</xref>) it is crucial to monitor the selection for antibiotic resistance in bacteria with bioremediation potential, as this has significant implications for human health. <xref ref-type="bibr" rid="B22">Forsberg et al. (2012)</xref> found evidence of the lateral transfer of multidrug-resistant genes from soil bacteria to human pathogens, highlighting the importance of monitoring antibiotic-resistance in bacteria.</p>
<p>Antibiotic susceptibility testing has been done routinely using several methods such as the measurement of inhibition zones in agar diffusion tests (e.g., Kirby-Bauer), turbidity-based measurements, and the counting of colony-forming units after serial dilution (<xref ref-type="bibr" rid="B33">Jorgensen and Ferraro, 2009</xref>) or determination of the minimal inhibitory concentration (MIC) in serial dilutions as recommended by the Clinical and Laboratory Standards Institute (Clinical and Laboratory Standards Institute [<xref ref-type="bibr" rid="B17">CLSI], 2023</xref>). These conventional methods rely on static acquisition of single data points or regular-interval growth determinations (off-line susceptibility testing) and overlook the antibiotic-specific dynamic growth profiles of target microorganisms. <xref ref-type="bibr" rid="B76">Theophel et al. (2014)</xref> demonstrated that different microorganisms exhibit varying susceptibility to different antibiotics at different growth stages and times. In this study, we utilized on-line analysis of antibiotic susceptibility testing to provide robust information about CdtB growth kinetics with high temporal resolution in the presence of varying antibiotic concentrations in 96-well Sensititre microplates. Moreover, the on-line susceptibility analysis integrates simultaneous cultivation and assessment of antibiotic impact on bacterial growth dynamics. By combining these approaches with carbon substrate utilization profiles (BIOLOG<sup>&#xae;</sup> EcoPlate), a better understanding of CdtB growth kinetics and metabolic potential could be achieved.</p>
<p>The primary objectives of this study were to identify and characterize CdtB species inhabiting elevated Cd<sup>2&#x2b;</sup>amended soil, as well as to evaluate their antibiotic resistance and carbon utilization profiles, in order to enhance the efficiency of Cd<sup>2&#x2b;</sup>remediation in contaminated soils. In addition, we aimed at using the Comprehensive Antibiotic Resistance Database Resistance Gene Identifier (CARD-RGI) tool and Python applications to predict the antibiotic resistance genes (ARG) in isolated CdtB species. The findings of this study hold significant implications for the use of novel microorganisms in immobilization or detoxification of Cd<sup>2&#x2b;</sup>in polluted soils and for the potential augmentation of the remediation process through biostimulation with optimal carbon substrates.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>Materials and methods</title>
<sec id="s2-1">
<title>Isolation and identification of CdtB species from elevated cadmium amended soil</title>
<p>Two 50g soil samples collected from a temperate forest (Greencastle, IN) were incubated for 2&#xa0;weeks at room temperature with and without Cd<sup>2&#x2b;</sup>amendment. The amended soil received 2&#xa0;mL of 19.5&#xa0;mM Cd<sup>2&#x2b;</sup>solution [6g of Cd(NO<sub>3</sub>)<sub>2</sub>.4H<sub>2</sub>O/L (Mallinckrodt, St. Louis, USA)] to establish a high Cd<sup>2&#x2b;</sup>stress comparable to cadmium concentrations reported in zinc sulfide ore and smelting dust (430&#x2013;1500&#xa0;mg&#xa0;kg<sup>-1</sup>; <xref ref-type="bibr" rid="B9">Bi et al., 2006</xref>), thereby simulating an ecologically relevant high-stress environment representative of metal-contaminated sites. After incubation, we vortexed 2&#xa0;g of control and Cd<sup>2&#x2b;</sup>-amended soils in 18&#xa0;mL of Remel TM Butterfield&#x2019;s Phosphate Buffer (Hardy Diagnostics, California, USA). The resulting suspension was diluted at 10<sup>&#x2212;3</sup> and 100&#xa0;<italic>&#x3bc;</italic>L of this latter was inoculated on Peptone Yeast Cd<sup>
<italic>2&#x2b;</italic>
</sup>-amended agar plates in duplicate. Culture media used to select for Cd<sup>
<italic>2&#x2b;</italic>
</sup> resistant bacterial growth were as follows: Peptone Yeast (PY) medium consisted of 5&#xa0;g peptone, 3&#xa0;g yeast extract, and 15&#xa0;g/L agar powder maintaining pH 7. 10&#xa0;mL of 1.0M CaCl<sub>2</sub> was added to make the solution 10&#xa0;mM CaCl<sub>2</sub>, and amended with 1&#xa0;mL of 6g of Cd(NO<sub>3</sub>)<sub>2</sub>&#xb7;4H<sub>2</sub>O/L to make the medium select for Cd<sup>2&#x2b;</sup>resistant microbes. All plates were incubated aerobically at 37&#xa0;C for 48&#xa0;h. Culturable Cd isolates used in this study were identified by colony PCR followed by 16S rRNA gene sequencing. Purified PCR products were submitted for sequencing to Genewiz Inc. (South Plainfield, NJ, USA). For colony PCR, single colonies from nutrient agar plates were screened using OneTaq 2X Master Mix with Standard Buffer (New England Biolabs, Ipswich, MA; Cat. No. M0482), following the manufacturer&#x2019;s instructions. Each 50&#xa0;&#x3bc;L PCR reaction contained 25&#xa0;&#x3bc;L OneTaq 2X Master Mix, 1&#xa0;&#x3bc;L of 10&#xa0;&#x3bc;M forward primer, 1&#xa0;&#x3bc;L of 10&#xa0;&#x3bc;M reverse primer, 22&#xa0;&#x3bc;L ultrapure H<sub>2</sub>O, and 1&#xa0;&#x3bc;L of cell suspension as the DNA template. Thermocycling conditions were: initial denaturation at 94&#xa0;C for 2&#xa0;min; 35 cycles of 94&#xa0;C for 30 s, 57&#xa0;C for 30&#xa0;s, and 68&#xa0;C for 2&#xa0;min; and a final extension at 68&#xa0;C for 5&#xa0;min, followed by a 4&#xa0;C hold.</p>
<p>The control and spiked soils were oven-dried at 35&#xa0;C and grounded for metal analysis to assess the concentrations (ppm) of heavy metals in the soil samples. X-ray fluorescence (XRF) analysis was conducted on each soil sample using a Bruker Tracer 5&#xa0;g handheld XRF unit fitted with a rhodium (Rh) source, a graphene window silicon drift detector (SDD), and an 8&#xa0;mm collimator as described in <xref ref-type="bibr" rid="B4">Akinwole et al. (2024)</xref>.</p>
</sec>
<sec id="s2-2">
<title>Evaluation of antibiotic resistance of CdtB colonies by using Sensititre&#x2122; GN4F and GPALL1F microplates</title>
<p>The Sensititre method for testing antibiotic susceptibility was performed according to the manufacturer&#x2019;s instructions. Sensititre Gram-Positive GPALL1F (Thermo Scientific, USA) plate was used for <italic>Bacillus mobilis</italic> (<xref ref-type="bibr" rid="B46">Liu et al., 2017</xref>), while Sensititre Gram-Negative GN4F (Thermo Scientific, USA) plate was used for <italic>Cupriavidus campinensis</italic> (<xref ref-type="bibr" rid="B24">Goris et al., 2001</xref>). In brief, a pure culture of the CdtB isolate, which had been grown for 48&#xa0;h on nutrient agar, was used with sterile water to make a 0.5 McFarland turbidity suspension. The Sensititre plate GN4F consisted of serial 2-fold dilutions of 24 antimicrobials and the Sensititre plate GPALL1F consisted of serial 2-fold dilutions of 23 antimicrobials (<xref ref-type="sec" rid="s12">Supplementary Table S1</xref>).</p>
<p>From the bacterial suspension, 20&#xa0;&#x3bc;L was transferred into a 5.5&#xa0;mL Muller Hinton broth tube, and then 50&#xa0;&#x3bc;L was transferred into each well of the GN4F and GPALL1F plate using an 8-channel pipette. The plate was covered with an adhesive seal provided with the kit and incubated on a SPECTROstar Nano (BMG LABTECH) plate reader for automated reading for 72&#xa0;h at 35&#xa0;C. During incubation, orbital shaking conditions were selected (4&#xa0;mm amplitude and 30&#xa0;s shaking cycles), and measurements were taken every 25&#xa0;min at a wavelength of 660&#xa0;nm. Growth analysis was accompanied by controls cultured in the absence of antibiotics to obtain reference curves and sterile media controls without antibiotics for quality control testing.</p>
</sec>
<sec id="s2-3">
<title>Evaluation of metabolic profiles of Cd-resistant colonies by BIOLOG&#x2122; Ecoplates</title>
<p>Each isolate was cultured in nutrient agar broth at 37&#xa0;&#xb0;C for 48h and 150&#xa0;&#xb5;L of 1:1000 dilution of each isolate was pipetted into BIOLOG Ecoplate (BIOLOG Inc., Hayward, CA). Ecoplates were incubated aerobically at room temperature for 7 days to assess metabolic profile analysis. The rate of utilization was indicated by the reduction of the tetrazolium salt (a redox indicator dye) that changes from colorless into purple in the Ecoplate wells. The color development was monitored every 24&#xa0;h as optical density (OD) with SPECTROstar Nano (BMG LABTECH) microplate reader at a wavelength of 590&#xa0;nm. The OD values of Ecoplate at 120&#xa0;h were used to analyze bacterial carbon source utilization since these represented the optimal range of OD readings in our study in the absence of fungal growth biases (<xref ref-type="bibr" rid="B87">Zhang et al., 2013</xref>). The metabolic profile of each isolate was calculated according to the average well color development (AWCD) defined as the arithmetic average of the absorbance values for each substrate (<xref ref-type="bibr" rid="B26">Harch et al., 1997</xref>), AWCD indicates the total metabolic capacity of each bacterial species in terms of carbon-source utilization. The five carbon substrate guilds proposed by <xref ref-type="bibr" rid="B83">Weber and Legge (2009)</xref> were utilized: 1) carbohydrates, 2) carboxylic and acetic acids, 3) amino acids, 4) polymers, and 5) amines and amides. For each guild, the blank-corrected absorbance values of the substrates at 120&#xa0;h were summarized and expressed as a percentage of the total absorbance value of the plate (<xref ref-type="bibr" rid="B83">Weber and Legge, 2009</xref>).</p>
</sec>
<sec id="s2-4">
<title>Data visualization and statistics of the comprehensive antibiotic resistance database (CARD) antibiotic resistance ontology (ARO) of isolates</title>
<p>ARGs were identified <italic>in silico</italic> from the genome assemblies corresponding to our isolates (<italic>B</italic>. <italic>mobilis</italic>, GCA_001884045.1; <italic>C. campinensis</italic>, GCF_042653745.1). Isolate identity was confirmed via partial 16S rRNA sequencing and BLAST analysis, which showed 100% identity to <italic>B. mobilis</italic> strain MCCC 1A05942 (NR_157731.1) and <italic>C. campinensis</italic> strain WS2 (NR_025137.1) (<xref ref-type="bibr" rid="B46">Liu et al., 2017</xref>; <xref ref-type="bibr" rid="B24">Goris et al., 2001</xref>). ARGs were detected using the CARD Resistance Gene Identifier (CARD-RGI; <xref ref-type="bibr" rid="B5">Alcock et al., 2023</xref>) and custom Python scripts. In this study Matplotlib (version 3.9.4) and Seaborn (version 0.13.2) Python libraries widely used for data visualization were used to generate sunburst classification plots based on the CARD antibiotic resistance ontology, helping to visually represent the hierarchical structure and distribution of potential resistance genes present in <italic>B. mobilis</italic> and <italic>C. campinensis</italic>. Matplotlib is a core plotting library that works with NumPy (version 2.0.2) and provides an object-oriented application programming interface for embedding plots into applications. It supports various graphical user interface toolkits such as Tkinter, wxPython, Qt, and GTK. Seaborn builds on Matplotlib and integrates seamlessly with pandas (version 2.3.0). It offers a high-level, declarative interface for creating statistical graphics, making it easier to translate complex data into clear visual insights (<xref ref-type="bibr" rid="B10">Bisong, 2019</xref>).</p>
</sec>
<sec id="s2-5">
<title>Data analysis</title>
<p>The values in the figures and tables correspond to the average of triplicate data &#xb1;standard error (SE) for both antibiotic susceptibility testing and carbon substrate utilization data. For the carbon substrate utilization data, the AWCD index was calculated with the formula:<disp-formula id="equ1">
<mml:math id="m1">
<mml:mrow>
<mml:mi>A</mml:mi>
<mml:mi>W</mml:mi>
<mml:mi>C</mml:mi>
<mml:mi>D</mml:mi>
<mml:mo>&#x3d;</mml:mo>
<mml:mi mathvariant="normal">&#x3a3;</mml:mi>
<mml:mrow>
<mml:mfenced open="(" close=")" separators="|">
<mml:mrow>
<mml:mi>C</mml:mi>
<mml:mtext>&#x2009;</mml:mtext>
<mml:mi>i</mml:mi>
<mml:mo>&#x2212;</mml:mo>
<mml:mi>R</mml:mi>
</mml:mrow>
</mml:mfenced>
</mml:mrow>
<mml:mo>/</mml:mo>
<mml:mi>n</mml:mi>
<mml:mo>,</mml:mo>
</mml:mrow>
</mml:math>
</disp-formula>where Ci is the absorbance of the carbon source, R is the absorbance of the control well, and n is the number of carbon substrates (31 for EcoPlates). When Ci&#x2013;R &#x3c; 0, the values were set to 0 to minimize bias. Assessing the utilization profiles involves grouping the 31 carbon sources into five guilds according to <xref ref-type="bibr" rid="B83">Weber and Legge (2009)</xref> and evaluating changes in the percent guild utilizations over the study period. This method effectively reduces the 31-dimensional space of each plate into five dimensions, facilitating easier plotting and interpretation. In addition, the growth dynamic of cells exposed to carbon substrate reaching an OD &#x3e; 1.8&#xa0;at the end of the incubation period is termed high utilization, growth dynamic at OD &#x3c; 1.8 but &#x3e;0.2 is termed medium utilization and OD &#x2264; 0.2 is inhibitory.</p>
<p>Resistance analysis was performed using CARD-RGI with Perfect, Strict, and Loose detection paradigms. Perfect hits indicate &#x3e;95% identity and coverage to known resistance genes. Strict hits show &#x3e;40% identity with coverage criteria. Loose hits represent &#x3c;40% identity but maintain biological relevance. Statistical analysis and visualization were performed using Python with matplotlib and seaborn libraries.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<sec id="s3-1">
<title>Strain identification and morphology characterization</title>
<p>To provide a quantitative assessment of heavy metals present in both control and amended soils, the results of bulk soil XRF analysis conducted on each soil sample are shown in <xref ref-type="table" rid="T1">Table 1</xref>a. There were no significant differences in spiked and control soils for major heavy metals (Pb, Cu, Cr, and Zn) in this region, however, Cd<sup>2&#x2b;</sup>spiked soil showed an extreme increase in the concentration of 2,043 &#xb1; 43&#xa0;ppm compared to below the level of detection in the control soil. This demonstrated that the spiked soil was adequately amended as a cadmium-elevated soil environment.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>(a) Heavy metal analysis showing soil concentration means (&#xb1;2SE; n &#x3d; 5), and (b) blastn results, according to the NCBI database.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">a) Soil type</th>
<th align="left">Cd (ppm)</th>
<th align="left">Pb (ppm)</th>
<th align="left">Cu (ppm)</th>
<th align="left">Zn (ppm)</th>
<th align="left">Cr (ppm)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Control soil</td>
<td align="left">&#x3c;LOD&#x2a;</td>
<td align="left">22 &#xb1; 11</td>
<td align="left">16 &#xb1; 6</td>
<td align="left">80 &#xb1; 6</td>
<td align="left">80 &#xb1; 15</td>
</tr>
<tr>
<td align="left">Cadmium-spiked soil</td>
<td align="left">2043 &#xb1; 43</td>
<td align="left">20 &#xb1; 6</td>
<td align="left">18 &#xb1; 7</td>
<td align="left">78 &#xb1; 7</td>
<td align="left">73 &#xb1; 25</td>
</tr>
</tbody>
</table>
<table>
<thead valign="top">
<tr style="background-color:#D9D9D9">
<th align="left">b) Strain Description</th>
<th align="left">Accession No</th>
<th align="left">Max Identity (%)</th>
<th align="left">Max score</th>
<th align="left">Total score</th>
<th align="left">Notes</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">
<italic>Bacillus mobilis strain MCCC 1A05942</italic>
</td>
<td align="left">
<ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/sites/entrez?cmd=Search&amp;db=nucleotide&amp;term=NR_157731.1&amp;dopt=GenBank">NR_157731.1</ext-link>
</td>
<td align="left">100</td>
<td align="left">2787</td>
<td align="left">2787</td>
<td align="left">Within the <italic>B. cereus</italic> group (<xref ref-type="bibr" rid="B46">Liu et al., 2017</xref>)</td>
</tr>
<tr>
<td align="left">
<italic>Cupriavidus campinensis</italic> strain WS2</td>
<td align="left">
<ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/sites/entrez?cmd=Search&amp;db=nucleotide&amp;term=NR_025137.1&amp;dopt=GenBank">NR_025137.1</ext-link>
</td>
<td align="left">100</td>
<td align="left">2741</td>
<td align="left">2741</td>
<td align="left">Formerly known as <italic>Ralstonia campinensis</italic> (<xref ref-type="bibr" rid="B24">Goris et al., 2001</xref>)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>&#x2a; below level of detection.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>A total of six culturable bacteria exhibiting distinct colony characteristics such as opacity, texture, form, size, and margin surface were identified, however, the two most metabolic active on Biolog Ecoplate were selected for further biochemical and molecular characterizations. For the two colonies selected, a BLAST search of the commercially determined sequences against publicly available sequences in the NCBI database indicated 100% identity to <italic>B. mobilis strain MCCC 1A05942</italic> (accession no. <ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/sites/entrez?cmd=Search&amp;db=nucleotide&amp;term=NR_157731.1&amp;dopt=GenBank">NR_157731.1</ext-link>) and 100% to <italic>C. campinensis</italic> strain WS2 (accession no. <ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/sites/entrez?cmd=Search&amp;db=nucleotide&amp;term=NR_025137.1&amp;dopt=GenBank">NR_025137.1</ext-link>) (<xref ref-type="table" rid="T1">Table 1</xref>b) formerly known as <italic>Ralstonia campinensis</italic> (<xref ref-type="bibr" rid="B46">Liu et al., 2017</xref>; <xref ref-type="bibr" rid="B24">Goris et al., 2001</xref>). <italic>B. mobilis</italic> produces milky-white, circular, opaque colonies measuring 2&#x2013;3&#xa0;mm in diameter after 48&#xa0;h of incubation at 32&#xa0;&#xb0;C on LB medium (<xref ref-type="bibr" rid="B46">Liu et al., 2017</xref>). In contrast, <italic>C. campinensis</italic> forms small, round colonies (sometimes with slightly scalloped margins) that are smooth, convex, and transparent, with an average diameter of &#x223c;0.5&#xa0;mm after 24&#xa0;h of incubation on TSA at 30&#xa0;C (<xref ref-type="bibr" rid="B24">Goris et al., 2001</xref>).</p>
</sec>
<sec id="s3-2">
<title>Effects of various antibiotics on B. mobilis and C. campinensis growth dynamics</title>
<p>To examine the dynamic effects of antibiotics over time, we implemented a method that integrates simultaneous cultivation and assessment of antibiotic impact on bacterial growth through automated OD measurements taken at 25-min intervals. This approach revealed varying effective exposure times and concentration-dependent effects on the growth dynamics of <italic>B. mobilis</italic> and <italic>C. campinensis</italic> isolated from cadmium-amended soil.</p>
<p>In the Sensititre Gram-Negative GN4F plate for <italic>C. campinensis,</italic> after a lag phase of approximately 10&#xa0;h, the control culture (without antibiotics) exhibited a sharp increase in OD until about the 24-h mark, followed by a gradual decrease and plateau for the remainder of the incubation period (<xref ref-type="fig" rid="F1">Figures 1</xref>, <xref ref-type="fig" rid="F2">2</xref>). Compared with the antibiotic-free control, <italic>C. campinensis</italic> cells exposed to imipenem, tigecycline, levofloxacin, ceftriaxone (<xref ref-type="fig" rid="F1">Figure 1</xref>) and 10 more antibiotics (<xref ref-type="table" rid="T2">Table 2</xref>) were unable to grow (i.e., inhibited) at any concentrations. However, in the case of ceftriaxone, after a prolonged lag phase up to 50 h, growth comparable to that of the antibiotic-free control was observed at the lowest concentration of 0.5&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> (<xref ref-type="fig" rid="F1">Figure 1d</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Inhibitory effects of antibiotics tested on the growth of <italic>Cupriavidus campinensis</italic>. Results for only <bold>(a)</bold> imipenem, <bold>(b)</bold> tigecycline, <bold>(c)</bold> levofloxacin and <bold>(d)</bold> ceftriaxone are shown, but inhibitory effects with similar patterns (except for Ceftriaxone) were observed for 15 out of 24 antibiotics tested (<xref ref-type="table" rid="T2">Table 2</xref>). For visual clarity, standard error bars (n &#x3d; 3) are only presented for selected time points every 6&#xa0;h. GC &#x3d; Growth Control. Note the extended lag phase and partial growth promotion at low concentration (0.5&#xa0;&#x3bc;g&#xa0;mL<sup>-1</sup>) for Ceftriaxone.</p>
</caption>
<graphic xlink:href="fenvs-13-1668462-g001.tif">
<alt-text content-type="machine-generated">Line graphs displaying the change in optical density at 660 nanometers over 72 hours for four antibiotics: a) Imipenem, b) Tigecycline, c) Levofloxacin, and d) Ceftriaxone. Each graph compares different concentrations of the antibiotics against a control (GC), highlighting variations in bacterial growth inhibition. Error bars indicate variability, with the control showing the highest growth across all panels.</alt-text>
</graphic>
</fig>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>The growth dynamics of <italic>Cupriavidus campinensis</italic> in the presence of selected antibiotics <bold>(a-f)</bold>. Each data point (time resolution over 72&#xa0;h) represents mean values of triplicate cultivations, normalized with data from identical incubations without bacterial cells (sterile controls). Using a time resolution of 25 min, each curve represents 576 single data points. For visual clarity, standard errors bars (n &#x3d; 3) are only presented for selected time points every 6&#xa0;h. GC &#x3d; Growth Control. Note the extended lag phase for some antibiotics (e.g., piperacillin and ampicillin) and partial growth promotion at low concentrations for some antibiotics.</p>
</caption>
<graphic xlink:href="fenvs-13-1668462-g002.tif">
<alt-text content-type="machine-generated">Six graphs labeled a) to f) display the change in optical density at 660 nm over time for different antibiotics. Each graph shows multiple lines representing different concentrations of antibiotics and a standard growth control (GC). Graph a) explores nitrofurantoin, b) ertapenem, c) ticarcillin/clavulanic acid, d) piperacillin, e) doripenem, and f) aztreonam. The x-axis represents time in hours, and the y-axis shows the change in OD at 660 nm. Error bars indicate variability in measurements.</alt-text>
</graphic>
</fig>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Sensitivity of <italic>Bacillus mobilis</italic> and <italic>Cupriavidus campinensis</italic> to various antibiotics and their cellular targets. Susceptible (S), Resistance (R), potential categorization (PC) is based on MIC (<italic>&#xb5;g mL</italic>
<sup>
<italic>-</italic>
<bold>
<italic>1</italic>
</bold>
</sup>).</p>
</caption>
<table>
<thead valign="top">
<tr>
<th colspan="4" align="left">C. Campinensis</th>
</tr>
<tr>
<th align="left">Antibiotic</th>
<th align="left">Antibiotic&#x2019;s cellular targets</th>
<th align="left">S Or R based on MIC (<italic>&#xb5;g mL-</italic>
<sup>
<italic>1</italic>
</sup>) at &#x3c;36h</th>
<th align="left">PC based on MIC (<italic>&#xb5;g mL-</italic>
<sup>
<italic>1</italic>
</sup>) at &#x3e;36h</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Amikacin, Tigecycline<break/>Tetracycline, Minocycline, Gentamicin, Tobramycin</td>
<td align="left">30S ribosomal subunit (inhibits protein synthesis)</td>
<td align="left">S</td>
<td align="left">S</td>
</tr>
<tr>
<td align="left">Piperacillin/Tazobactam constant 4</td>
<td align="left">Penicillin-binding proteins (PBPs) (Piperacillin inhibits cell wall synthesis, Tazobactam is a beta-lactamase inhibitor)</td>
<td align="left">S</td>
<td align="left">S</td>
</tr>
<tr>
<td align="left">Ticarcillin/Clavulanic acid constant 2</td>
<td align="left">PBPs (Ticarcillin inhibits cell wall synthesis, Clavulanic acid is a beta-lactamase inhibitor)</td>
<td align="left">R at MIC &#x2265;64/2, S</td>
<td align="left">R at MIC &#x2265;64/2, S</td>
</tr>
<tr>
<td align="left">Levofloxacin, Ciprofloxacin</td>
<td align="left">DNA gyrase and topoisomerase IV (inhibits DNA replication)</td>
<td align="left">S</td>
<td align="left">S</td>
</tr>
<tr>
<td align="left">Nitrofurantoin</td>
<td align="left">Multiple targets, including ribosomal proteins and DNA (inhibits bacterial growth)</td>
<td align="left">R</td>
<td align="left">R</td>
</tr>
<tr>
<td align="left">Doripenem</td>
<td align="left">PBPs (inhibits cell wall synthesis)</td>
<td align="left">R at MIC &#x2265;2, S</td>
<td align="left">at MIC &#x3d; 2, R<break/>at MIC &#x2265;4, S</td>
</tr>
<tr>
<td align="left">Ertapenem, Meropenem<break/>Cefazolin</td>
<td align="left">PBPs (inhibits cell wall synthesis)</td>
<td align="left">R</td>
<td align="left">R</td>
</tr>
<tr>
<td align="left">Trimethoprim/Sulfamethoxazole</td>
<td align="left">Dihydrofolate reductase (Trimethoprim) and dihydropteroate synthase (Sulfamethoxazole) (inhibit folic acid synthesis)</td>
<td align="left">S</td>
<td align="left">S</td>
</tr>
<tr>
<td align="left">Imipenem<break/>Cefepime</td>
<td align="left">PBPs (inhibits cell wall synthesis)</td>
<td align="left">S</td>
<td align="left">S</td>
</tr>
<tr>
<td align="left">Piperacillin<break/>Ampicillin</td>
<td align="left">PBPs (inhibits cell wall synthesis)</td>
<td align="left">S</td>
<td align="left">R</td>
</tr>
<tr>
<td align="left">Ceftazidime</td>
<td align="left">PBPs (inhibits cell wall synthesis)</td>
<td align="left">R at MIC &#x2265;4, S</td>
<td align="left">R at MIC &#x2265;4, S</td>
</tr>
<tr>
<td align="left">Ampicillin/Sulbactam 2:1 ratio</td>
<td align="left">PBPs (Ampicillin inhibits cell wall synthesis, Sulbactam is a beta-lactamase inhibitor)</td>
<td align="left">S</td>
<td align="left">S</td>
</tr>
<tr>
<td align="left">Aztreonam</td>
<td align="left">PBPs (inhibits cell wall synthesis)</td>
<td align="left">R</td>
<td align="left">at MIC &#x2265;16, S</td>
</tr>
<tr>
<td align="left">Ceftriaxone</td>
<td align="left">PBPs (inhibits cell wall synthesis)</td>
<td align="left">S</td>
<td align="left">at MIC &#x2264;0.5, R</td>
</tr>
</tbody>
</table>
<table>
<thead valign="bottom">
<tr>
<th colspan="4" align="left">
<italic>B. mobilis</italic>
</th>
</tr>
<tr>
<th align="left">Antibiotic</th>
<th align="left">Antibiotic&#x2019;s cellular targets</th>
<th align="left">S Or R based on MIC (<italic>&#xb5;g mL-</italic>
<sup>
<italic>1</italic>
</sup>) at &#x3c;36h</th>
<th align="left">PC based on MIC (<italic>&#xb5;g mL-</italic>
<sup>
<italic>1</italic>
</sup>) at &#x3e;36h</th>
</tr>
</thead>
<tbody valign="bottom">
<tr>
<td align="left">Chloramphenicol</td>
<td align="left">50S ribosomal subunit (inhibits protein synthesis)</td>
<td align="left">S</td>
<td align="left">at MIC &#x2264;4, R</td>
</tr>
<tr>
<td align="left">Daptomycin</td>
<td align="left">Cell membrane (causes depolarization and cell death)</td>
<td align="left">R</td>
<td align="left">R</td>
</tr>
<tr>
<td align="left">Gentamicin</td>
<td align="left">30S ribosomal subunit (inhibits protein synthesis)</td>
<td align="left">S</td>
<td align="left">at MIC &#x2264;4, R</td>
</tr>
<tr>
<td align="left">Rifampin</td>
<td align="left">RNA polymerase (inhibits RNA synthesis)</td>
<td align="left">R</td>
<td align="left">R</td>
</tr>
<tr>
<td align="left">Trimethoprim/Sulfamethoxazole</td>
<td align="left">Inhibits dihydrofolate reductase (Trimethoprim) and dihydropteroate synthase (Sulfamethoxazole) (inhibit nucleic acid and protein synthesis)</td>
<td align="left">R at MIC &#x2265;4/76, S</td>
<td align="left">at MIC &#x2265;4/76, S</td>
</tr>
<tr>
<td align="left">Tetracycline</td>
<td align="left">30S ribosomal subunit (inhibits protein synthesis)</td>
<td align="left">R at MIC &#x3d; 16, S</td>
<td align="left">R at MIC &#x3d; 16, S</td>
</tr>
<tr>
<td align="left">Erythromycin, Clindamycin, Linezolid, Quinupristin/Dalfopristin</td>
<td align="left">50S ribosomal subunit (inhibits protein synthesis)</td>
<td align="left">R</td>
<td align="left">R</td>
</tr>
<tr>
<td align="left">Ampicillin, Penicillin<break/>Oxacillin &#x2b;2% NaCl</td>
<td align="left">PBPs (inhibits cell wall synthesis)</td>
<td align="left">R</td>
<td align="left">R</td>
</tr>
<tr>
<td align="left">Vancomycin</td>
<td align="left">D-Ala-D-Ala terminus of cell wall precursors (inhibits cell wall synthesis)</td>
<td align="left">R</td>
<td align="left">R</td>
</tr>
<tr>
<td align="left">Levofloxacin</td>
<td align="left">DNA gyrase and topoisomerase IV (inhibits DNA replication)</td>
<td align="left">R at MIC &#x2265;1, S</td>
<td align="left">R at MIC &#x2265;2, S</td>
</tr>
<tr>
<td align="left">Tigecycline</td>
<td align="left">30S ribosomal subunit (inhibits protein synthesis)</td>
<td align="left">R at MIC &#x2265;0.25, S</td>
<td align="left">at MIC &#x2265;0.25, S</td>
</tr>
<tr>
<td align="left">Moxifloxacin</td>
<td align="left">DNA gyrase and topoisomerase IV (inhibits DNA replication)</td>
<td align="left">R at MIC &#x2265;1, S</td>
<td align="left">at MIC &#x2265;2, S</td>
</tr>
<tr>
<td align="left">Streptomycin</td>
<td align="left">30S ribosomal subunit (inhibits protein synthesis)</td>
<td align="left">S</td>
<td align="left">S</td>
</tr>
<tr>
<td align="left">Ciprofloxacin</td>
<td align="left">DNA gyrase and topoisomerase IV (inhibits DNA replication)</td>
<td align="left">S</td>
<td align="left">R</td>
</tr>
<tr>
<td align="left">Nitrofurantoin</td>
<td align="left">Multiple targets, including ribosomal proteins and DNA (inhibits bacterial growth)</td>
<td align="left">S</td>
<td align="left">R</td>
</tr>
<tr>
<td align="left">D Test 1<break/>D Test 2</td>
<td align="left">tests for inducible clindamycin resistance in the presence of erythromycin</td>
<td align="left">R</td>
<td align="left">R</td>
</tr>
<tr>
<td align="left">Cefoxitin screen</td>
<td align="left">PBPs (inhibits cell wall synthesis)</td>
<td align="left">R</td>
<td align="left">R</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>
<xref ref-type="fig" rid="F2">Figure 2</xref> displayed various growth dynamics and concentration-dependent effects in the case of six antibiotics. Cells exposed to different concentrations of nitrofurantoin, ertapenem (<xref ref-type="fig" rid="F2">Figures 2a,b</xref>), and cefazolin (<xref ref-type="table" rid="T2">Table 2</xref>) had similar growth patterns with antibiotic-free control; however, the final OD values did not reach that of the control culture. Despite a lag phase of <italic>&#x3e;</italic>24 h, cells exposed to 8/2, 16/2, and 32/2&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> of ticarcillin/clavulanic acid constant two began to grow slightly below antibiotic-free control. At 64/2&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> inhibitory effects of ticarcillin/clavulanic acid, constant two was seen on the growth of <italic>C. campinensis</italic> (<xref ref-type="fig" rid="F2">Figure 2c</xref>). Extended lag phases (approximately 36&#xa0;h) were observed in cultures containing beta-lactam class; piperacillin (<xref ref-type="fig" rid="F2">Figure 2d</xref>) and ampicillin (<xref ref-type="table" rid="T2">Table 2</xref>) at all concentrations and began to grow exponentially at a rate slightly below that of the antibiotic-free control. More complex concentration-dependent effects were observed in the case of doripenem, aztreonam (<xref ref-type="fig" rid="F2">Figures 2e,f</xref>) and meropenem (<xref ref-type="table" rid="T2">Table 2</xref>). 4&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> of doripenem was inhibitory on cells for the entire incubation period while after an extended lag phase of 36&#xa0;h at a concentration of 2&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> cells began to grow and plateau approximately at 52&#xa0;h for the remainder of the incubation period. Cells exposed to 0.5 and 1&#xa0;&#x3bc;g&#xa0;mL<sup>-1</sup> showed a similar pattern to the control with a lag phase of 10&#xa0;h, a sharp increase in OD until 24&#xa0;h, and a gradual decrease/plateau for the remainder of the incubation period (<xref ref-type="fig" rid="F2">Figure 2e</xref>). The lag phase was similar to the control culture at all concentrations for aztreonam, however, after 8&#xa0;h exponential growth, cells exposed to 16&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> of aztreonam plummeted and did not show bacterial growth for the remainder of the incubation period (<xref ref-type="fig" rid="F2">Figure 2f</xref>). After a lag phase of 12 h, cells exposed to 1, 2, 4 and 8&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> of aztreonam began to grow and decline gradually at 24&#xa0;h for the remainder of the incubation period.</p>
<p>With a similar pattern of cells&#x2019; growth dynamics in Ertapenem&#x2019;s (<xref ref-type="fig" rid="F2">Figure 2b</xref>), cells exposed to meropenem were resistant at low concentrations (0.5, 1, and 2&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup>), but growth curves at concentrations of four and 8&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> exhibited extended lag phases (approximately 32&#xa0;h), accompanied by a slight increase in growth with lower OD values but not significantly different from samples with 0.5, one and 2&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup>. Growth curves at all concentrations were at a rate slightly below that of the antibiotic-free control (<xref ref-type="table" rid="T2">Table 2</xref>).</p>
<p>Ceftazidime concentrations of 4&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> and above were inhibitory on cells for the entire incubation period (<xref ref-type="table" rid="T2">Table 2</xref>). Cells in 2&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> exhibited extended lag phase up to approximately 36 h, accompanied by a shallower growth curve slope and lower OD values than control cells. For cells exposed to 1&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup>, after a lag phase of approximately 10&#xa0;h as the control culture, exhibited a gradual increase in OD until about the 48-h mark, followed by a gradual decrease and plateau for the remainder of the incubation period.</p>
<p>In the Sensititre Gram-Positive GPALL1F plate for B. <italic>mobilis,</italic> after a lag phase of approximately 6&#xa0;h, the control culture without antibiotics exhibited a sharp increase in OD until about the 15-h mark, followed by a plateau for the remainder of the incubation period (<xref ref-type="fig" rid="F3">Figures 3</xref>, <xref ref-type="fig" rid="F4">4</xref>). Approximately 50% of (a total of ten) antibiotics showed limited activity against <italic>B. mobilis,</italic> at all concentrations tested and five agents were ineffective at lower concentrations (<xref ref-type="table" rid="T2">Table 2</xref>). Representative growth dynamics of cells exposed to different concentrations of erythromycin, ampicillin, clindamycin, and vancomycin (<xref ref-type="fig" rid="F3">Figure 3</xref>) had identical growth patterns similar to antibiotic-free control; however, with a slightly extended lag phase of about 12&#xa0;h. Generally, growth curves at lower concentrations were not significantly different from that of the antibiotic-free control while curves at higher concentrations showed more deviation from controls. Other similar antibiotics that did not inhibit the growth of <italic>B. mobilis</italic> are shown in <xref ref-type="table" rid="T2">Table 2</xref>.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>The growth dynamics of <italic>Bacillus mobilis</italic> in the presence of selected antibiotics: <bold>(a)</bold> Levofloxacin, <bold>(b)</bold> Nitrofurantoin, <bold>(c)</bold> Ciprofloxacin, and <bold>(d)</bold> Streptomycin and selected tests. Each data point (time resolution over 72&#xa0;h) represents mean values of triplicate cultivations, normalized with data from identical incubations without bacterial cells (sterile controls). Using a time resolution of 25 min, each curve represents 576 single data points. For visual clarity, standard error bars (n &#x3d; 3) are only presented for selected time points every 6&#xa0;h. GC &#x3d; Growth Control. Note the extended lag phase for some antibiotics (e.g., Levofloxacin and Ciprofloxacin) and partial growth promotion at high concentrations for some antibiotics.</p>
</caption>
<graphic xlink:href="fenvs-13-1668462-g003.tif">
<alt-text content-type="machine-generated">Four graphs display changes in optical density at 660 nm over time, examining different antibiotics: (a) Levofloxacin, (b) Nitrofurantoin, (c) Ciprofloxacin, and (d) Streptomycin and selected tests. Each graph shows various concentrations and their growth curves over 72 hours, with distinct lines representing different treatments and control conditions. Error bars are included, indicating variability in the data.</alt-text>
</graphic>
</fig>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>The growth dynamics of <italic>Bacillus mobilis</italic> in the presence of selected antibiotics: <bold>(a)</bold> Erythromycin, <bold>(b)</bold> Ampicillin, <bold>(c)</bold> Clindamycin and <bold>(d)</bold> Vancomycin. Each data point (time resolution over 72&#xa0;h) represents mean values of triplicate cultivations, normalized with data from identical incubations without bacterial cells (sterile controls). Using a time resolution of 25 min, each curve represents 576 single data points. For visual clarity, standard error bars (n &#x3d; 3) are only presented for selected time points every 6&#xa0;h. GC &#x3d; Growth Control. Note the extended lag phase for some antibiotics at higher concentrations (e.g., ampicillin).</p>
</caption>
<graphic xlink:href="fenvs-13-1668462-g004.tif">
<alt-text content-type="machine-generated">Four line graphs showing the effect of different antibiotic concentrations on optical density at six hundred sixty nanometers over seventy-two hours. Graphs labeled a) Erythromycin, b) Ampicillin, c) Clindamycin, and d) Vancomycin. Each graph features multiple lines representing varying concentrations and a control labeled GC. The optical density generally increases over time with variations depending on antibiotic and concentration. Error bars indicate standard deviation.</alt-text>
</graphic>
</fig>
<p>More complex concentration-dependent effects were observed for the other 10 antibiotics, cefoxitin screen, and D Tests. Representative growth dynamics of cells exposed to different concentrations of levofloxacin, nitrofurantoin, ciprofloxacin, streptomycin and some selected screening tests are shown in <xref ref-type="fig" rid="F4">Figure 4</xref>. Levofloxacin concentrations of two and 4&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> were inhibitory on cells for the entire incubation period (<xref ref-type="fig" rid="F4">Figure 4a</xref>). Cells in 0.25&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup> exhibited extended lag phase up to approximately 22 h, accompanied by an exponential growth curve and lower OD values than control cells. For cells exposed to 0.5 and 1&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup>, after a lag phase of approximately 36&#xa0;h, exhibited a gradual increase in OD but lower values than control cells. This growth pattern is similar to that observed in cells exposed to tigecycline, chloramphenicol, gentamicin, and moxifloxacin (<xref ref-type="table" rid="T2">Table 2</xref>).</p>
<p>Cells exposed to nitrofurantoin exhibited an extended lag phase of up to 24&#xa0;h followed by exponential growth and the final OD values were not significantly different from that of the control culture (<xref ref-type="fig" rid="F4">Figure 4b</xref>). In the case of ciprofloxacin, the extended lag phase reached 48h followed by a gradual increase in OD but with lower values than control cells (<xref ref-type="fig" rid="F4">Figure 4c</xref>). Cells exposed to streptomycin (1000&#xa0;&#xa0;&#x3bc;g&#xa0;mL<sup>&#x2212;1</sup>) exhibited extended lag phase up to 55&#xa0;h followed by a slight increase in OD significantly lower than that of the antibiotic-free control (<xref ref-type="fig" rid="F4">Figure 4d</xref>). However, Cefoxitin screen (FOXS), D test one and test two showed limited activity against <italic>B. mobilis</italic> (<xref ref-type="fig" rid="F4">Figure 4d</xref>).</p>
</sec>
<sec id="s3-3">
<title>Metabolic and physiological profiles of B. mobilis and C. campinensis</title>
<p>In this study, we investigated the growth dynamics and metabolic profiles of <italic>B. mobilis</italic> and <italic>C. campinensis</italic> when exposed to various carbon substrates. <xref ref-type="fig" rid="F5">Figure 5</xref> indicates a broad utilization of various carbon substrates by the bacterial isolates from cadmium-amended soil. For <italic>B. mobilis</italic>, the growth patterns showed significant variation depending on the carbon substrate provided. Pyruvic Acid Methyl Ester (carbohydrate), D- Malic Acid, Itaconic Acid, D-Galactonic acid &#x3b3;-Lactone (carboxylic and acetic acids) and Phenylethylamine (amine/amides) exhibited the highest growth rates of <italic>B. mobilis</italic>, with OD values exceeding 1.8 (high utilization) over the 7 days incubation period (<xref ref-type="fig" rid="F5">Figure 5a</xref>). After a lag phase of approximately 1 day, <italic>B. mobilis</italic> culture exhibited an exponential growth in these substrates. However, cells in L-Serine (amino acids) had an extended lag phase of 4 days before showing an increase in growth rate and reaching OD value of &#x3e;1.8 on day 7. D, L-&#x3b1;-Glycerol Phosphate, &#x3b3;-Hydroxybutyric Acid, L-Phenylalanine, Glycyl-L Glutamic Acid, L-Threonine, and L-Asparagine showed medium utilization by <italic>B. mobilis</italic> with OD values &#x3c;1.8 but greater than 0.2 estimate for inhibitory effect (<xref ref-type="fig" rid="F5">Figure 5c</xref>). Lag phase varied between 1 and 3 days for L-Phenylalanine, Glycyl-L Glutamic Acid, L-Threonine, and L-Asparagine. However, <italic>B. mobilis</italic> exhibited an extended lag phase of 5&#xa0;days in D, L-&#x3b1;-Glycerol Phosphate and &#x3b3;-Hydroxybutyric Acid before reaching 0.4 &#xb1; 0.02 and 0.6 &#xb1; 0.05 OD values respectively.</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Growth dynamics of bacterial isolates exposed to 31 carbon substrates in Ecoplates, measured as OD<sub>590nm</sub> over 7 days. Panels show high carbon utilization in <bold>(a)</bold> <italic>Bacillus mobilis</italic> and <bold>(b)</bold> <italic>Cupriavidus campinensis</italic> (final OD<sub>590</sub> &#x3e; 1.8 on day 7), and medium carbon utilization in <bold>(c)</bold> <italic>B. mobilis</italic> and <bold>(d)</bold> <italic>C. campinensis</italic> (0.2 &#x3c; final OD<sub>590</sub> &#x3c; 1.8 on day 7). Error bars represent the standard error of the mean (n &#x3d; 3).</p>
</caption>
<graphic xlink:href="fenvs-13-1668462-g005.tif">
<alt-text content-type="machine-generated">Four line graphs labeled a, b, c, and d show the optical density at 590 nm over 8 days for different substances. Graph a includes Pyruvic Acid, Methyl Ester, D-Malic Acid, Itaconic Acid, D-Galactonic Acid, &#x3B3;-Lactone, Phenylethylamine, and L-Serine. Graph b features Tween 40, Tween 80, Pyruvic Acid Methyl Ester, and L-Asparagine. Graph c displays D,L-&#x3B1;-Glycerol Phosphate, &#x3B3;-Hydroxybutyric Acid, L-Phenylalanine, Glycyl-L Glutamic Acid, L-Threonine, and L-Asparagine. Graph d includes &#x3B3;-Hydroxybutyric Acid, Itaconic Acid, 4-Hydroxy Benzoic Acid, Glycyl-L Glutamic Acid, L-Threonine, D-Malic Acid, and L-Phenylalanine. Optical density increases over time in all graphs.</alt-text>
</graphic>
</fig>
<p>For <italic>C. campinensis</italic>, the growth dynamics also varied significantly with different carbon substrates. Tween 40, Tween 80 (polymers), L-Asparagine (amino acid) and Pyruvic Acid Methyl Ester (carbohydrate) supported the highest growth, reaching an OD value of over 1.8 (high utilization) by the seventh day (<xref ref-type="fig" rid="F5">Figure 5b</xref>). <italic>C. campinensis</italic> exhibited medium utilization of &#x3b3;-Hydroxybutyric Acid, Itaconic Acid, 4-HydroxyBenzoic Acid, Glycyl-L Glutamic Acid, L-Threonine, D-Malic Acid and L-Phenylalanine with maximum OD between 0.2 and 1.8. However, cells in Glycyl-L Glutamic Acid exhibited reduced growth and only reached 0.22 &#xb1; 0.01 on day 7 (<xref ref-type="fig" rid="F5">Figure 5d</xref>).</p>
<p>Inhibitory effects (OD &#x3c; 0.2) were observed for both isolates when exposed to specific carbon substrates that accounts for about 65% of total carbon substrates in Ecoplates. <italic>B. mobilis</italic> exhibited inhibited growth in Glycogen, Tween 40, Tween 80, &#x3b1;-Cyclodextrin (polymers), L-Arginine (amino acid), Putrescine (amine/amides), several carbohydrates, and carboxylic and acetic acids (<xref ref-type="table" rid="T3">Table 3</xref>). Similarly, <italic>C. campinensis</italic> showed minimal or no growth response to Glycogen and &#x3b1;-Cyclodextrin (polymers), L-Arginine and L-Serine (amino acids), Putrescine and Phenylethylamine (Amine/amides) several carbohydrates, and carboxylic and acetic acids (<xref ref-type="table" rid="T3">Table 3</xref>).</p>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Percentage total carbon substrate utilization per guild and substrates with inhibitory effects on <italic>B. mobillis and Cupriavidus campinensis.</italic>
</p>
</caption>
<table>
<thead valign="top">
<tr>
<th colspan="2" align="left">
<italic>B. mobillis</italic>
</th>
<th colspan="2" align="left">
<italic>C. campinensis</italic>
</th>
</tr>
<tr>
<th align="left">Guild utilization (%)</th>
<th align="left">Inhibitory carbon substrate within guild</th>
<th align="left">Guild utilization (%)</th>
<th align="left">Inhibitory carbon substrate within guild</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Polymer (0.88)</td>
<td align="left">Glycogen, &#x3b1;-Cyclodextrin <bold>Tween 40, Tween 80</bold>
</td>
<td align="left">Polymer (36.31)</td>
<td align="left">Glycogen, &#x3b1;-Cyclodextrin</td>
</tr>
<tr>
<td align="left">Carbohydrates (20.95)</td>
<td align="left">D-Cellobiose, D-Mannitol, D-Xylose, Glucose-1-Phosphate, i-Erythritol<break/>N-Acetyl-D-Glucosamine, &#x3b1;-D-Lactose<break/>&#x3b2;-Methyl-D-Glucoside</td>
<td align="left">Carbohydrates (24.19)</td>
<td align="left">D-Cellobiose, D-Mannitol, D-Xylose, Glucose-1-Phosphate, i-Erythritol, N-Acetyl-D-Glucosamin<break/>&#x3b1;-D-Lactose, &#x3b2;-Methyl-D-Glucoside, <bold>D, L-&#x3b1;-Glycerol Phosphate</bold>
</td>
</tr>
<tr>
<td align="left">Carboxylic and Acetic acids (52.26)</td>
<td align="left">2-Hydroxy Benzoic Acid, <bold>4-Hydroxy Benzoic Acid</bold>, D-Galacturonic Acid<break/>D-Glucosaminic Acid<break/>&#x3b1;-Ketobutyric Acid</td>
<td align="left">Carboxylic and Acetic acids (13.62)</td>
<td align="left">2-Hydroxy Benzoic Acid, D-Galacturonic Acid, D-Glucosaminic Acid<break/>&#x3b1;-Ketobutyric Acid<break/>
<bold>D-Galactonic Acid &#x3b3;-Lactone</bold>
</td>
</tr>
<tr>
<td align="left">Amino acids (14.59)</td>
<td align="left">L-Arginine</td>
<td align="left">Amino acids (25.74)</td>
<td align="left">L-Arginine, <bold>L-Serine</bold>
</td>
</tr>
<tr>
<td align="left">Amine/amides (11.32)</td>
<td align="left">Putrescine</td>
<td align="left">Amine/amides (0.14)</td>
<td align="left">Putrescine, <bold>Phenylethylamine</bold>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>&#x2a;<bold>bold</bold> indicates substrates that were inhibitory in only a single isolate.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>The overall percent utilization of different carbon substrates grouped by guilds by both isolates highlighted their metabolic preferences. <italic>C. campinensis</italic> metabolized polymers and amino acids at 36.31% and 25.74% respectively compared to <italic>B. mobilis,</italic> with polymer utilization at 0.88% and amino acid utilization at 14.5%. In contrast, <italic>B. mobilis</italic> disproportionately utilized carboxylic and acetic acids (52.26%) and amines/amides (11.32%), compared to <italic>C. campinensis</italic> at 13.62% and 0.14% respectively (<xref ref-type="table" rid="T3">Table 3</xref>).</p>
</sec>
<sec id="s3-4">
<title>
<italic>In silico</italic> identification of ARGs found in <italic>Bacillus mobilis</italic> and <italic>Cupriavidus campinensis</italic>
</title>
<p>Antibiotic susceptibility testing of <italic>B</italic>. <italic>mobilis</italic> using the GPALL1F panel revealed resistance to 15 of the 23 antibiotics tested, corresponding to a 65.2% overall resistance rate, with susceptibility maintained for 34.8% of the agents. CARD-based resistome analysis identified 15 resistance determinants, with high-confidence (&#x201c;perfect&#x201d;) matches comprising 46.7%, strict hits 26.7%, and loose hits 26.7% of the predictions (<xref ref-type="fig" rid="F6">Figure 6a</xref>). Mechanistically, resistance was primarily mediated by target alteration (53.3%), followed by antibiotic inactivation (33.3%), efflux (13.3%), and target replacement (6.7%) (<xref ref-type="fig" rid="F6">Figure 6c</xref>). Notably, key resistance genes included <italic>mecA</italic> (methicillin resistance, MRSA-like phenotype), <italic>cfr</italic> (linezolid resistance, oxazolidinone class), and rpoB (rifampicin resistance), mprF (daptomycin resistance, lipopeptides). A multidrug-resistant profile was evident, with complete resistance across the beta-lactam class (e.g., <italic>blaZ</italic>, <italic>mecA</italic>), fluoroquinolones (<italic>gyrA</italic>, <italic>parC</italic>), macrolide-lincosamide antibiotics (<italic>ermB</italic>, <italic>ermC</italic>), and tetracyclines (<italic>tetK</italic>, <italic>tetL</italic>) (<xref ref-type="fig" rid="F6">Figure 6c</xref>). A detailed listing of the individual ARGs identified in <italic>B. mobilis</italic> is shown in <xref ref-type="sec" rid="s12">Supplementary Table S2</xref>.</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>CARD Gene Annotation Quality Assessment (Identity vs Quality coverage plot) for <bold>(a)</bold> <italic>Bacillus mobilis</italic> and <bold>(b)</bold> <italic>Cupriavidus campinensis</italic>, and CARD antibiotic resistance ontology sunburst classification for <bold>(c)</bold> <italic>Bacillus mobilis</italic> and <bold>(d)</bold> <italic>Cupriavidus campinensis</italic> multi-level hierarchical gene organization which generates an annotation organized by the Perfect, Strict and Loose paradigm and resistance mechanisms. For visual clarity, aac (3)-I &#x2026; , aac (6&#x2032;)&#x2026;, aph (3&#x2032;)&#x2026; are aac (3)-IIa, aac (6&#x2032;)-Ib, aph (3&#x2032;)-VI, respectively, while TEM-1, IMP-1, GES-1, AmpC, CTX-M-15, VIM-2, NDM-1, SHV-1, OXA-23/48/58, and KPC-2 are prefixed by bla (<xref ref-type="sec" rid="s12">Supplementary Tables S1 and S2</xref>). <italic>Target Prote/Alter &#x3d; Target Protection and Target Alteration respectively.</italic>
</p>
</caption>
<graphic xlink:href="fenvs-13-1668462-g006.tif">
<alt-text content-type="machine-generated">Graph a shows sequence identity versus coverage percentage for strict, perfect, and loose categories with high and moderate quality thresholds. Graph b presents similar data with additional quality thresholds. Diagrams c and d illustrate antibiotic resistance ontology, with concentric rings showing CARD confidence, resistance mechanisms, and gene names.</alt-text>
</graphic>
</fig>
<p>Antibiotic resistance profiling of C. <italic>campinensis</italic> revealed a clinically significant carbapenem-resistant Enterobacterales (CRE)-like profile. Phenotypic testing using the GN4F panel indicated resistance to 11 of 24 antibiotics (45.8%), including complete resistance to all three tested carbapenems (ertapenem, meropenem, doripenem), and susceptibility to 12 agents (50%). CARD-RGI analysis identified 27 resistance genes, with high-confidence matches (perfect hits) comprising 22.2%, strict hits 44.4%, and loose hits 33.3% (<xref ref-type="fig" rid="F6">Figure 6b</xref>). Mechanistically, resistance was primarily mediated by Antibiotic Inactivation (63%), followed by Target Alteration (14.8%), efflux pump (14.8%), and target protection (7.4%) (<xref ref-type="fig" rid="F6">Figure 6d</xref>; <xref ref-type="sec" rid="s12">Supplementary Table S2</xref>). High-risk carbapenemase genes included <italic>blaKPC-2</italic> (ertapenem resistance, Class A), blaNDM-1 (meropenem resistance, Class B), blaVIM-2 (imipenem, meropenem, and ertapenem resistance) and <italic>blaOXA-48</italic> (doripenem resistance, Class D), each identified with strict to perfect confidence. Additional beta-lactam resistance genes such as <italic>blaTEM-1, blaCTX-M, blaSHV-1,</italic> and <italic>blaAmpC</italic> contributed to extensive resistance against penicillins and cephalosporins (<xref ref-type="fig" rid="F6">Figure 6d</xref>). A detailed listing of the individual ARGs identified in <italic>C. campinensis</italic> is shown in <xref ref-type="sec" rid="s12">Supplementary Table S3</xref>.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>In this study, the bacterial species <italic>B. mobilis</italic> and <italic>C. campinensis</italic> were isolated from cadmium-amended soil with Cd<sup>2&#x2b;</sup> levels approximately one order of magnitude higher than those typically found in soils highly contaminated with Cd<sup>2&#x2b;</sup>. This categorizes them as Cd<sup>2&#x2b;</sup> tolerant bacterial species, indicating their remarkable ability to survive in high Cd<sup>2&#x2b;</sup> concentrations.</p>
<p>
<italic>C. campinensis</italic> (basionym: <italic>R. campinensis</italic>) was initially isolated from various heavy metal-contaminated industrial biotopes in Campine, northeastern Belgium (<xref ref-type="bibr" rid="B24">Goris et al., 2001</xref>), and from a heavy metal-contaminated playground in Salg&#xf3;tarj&#xe1;n, Hungary (<xref ref-type="bibr" rid="B2">Abbaszade et al., 2020</xref>), demonstrating its adaptation and resistance to environmental stresses. <xref ref-type="bibr" rid="B2">Abbaszade et al. (2020)</xref> documented several typical metal-resistant genes and gene clusters in <italic>C. campinensis</italic> strain S14E4C, including <italic>cadA</italic> (cadmium-translocating P-type ATPase), <italic>czcB</italic> (cobalt-zinc-cadmium efflux RND transporter, membrane fusion protein), <italic>czcR</italic> (cobalt-zinc-cadmium resistance protein), among others. The <italic>B. mobilis strain MCCC 1A05942</italic> was isolated from sediment of the Indian Ocean (<xref ref-type="bibr" rid="B46">Liu et al., 2017</xref>) and a closely related strain <italic>B. mobilis</italic> CR3 strain isolated from Cr-polluted soil sample in Qinghai, China, can remove Cr(VI) mainly through bio-reduction by reductase (<xref ref-type="bibr" rid="B86">Ye et al., 2024</xref>). Various reductase enzymes are involved in heavy metal reduction (<xref ref-type="bibr" rid="B60">Ojha et al., 2023</xref>) and APS reductase was shown to be efficient in immobilizing soil Cd<sup>2&#x2b;</sup> in cadmium-contaminated paddy soils (<xref ref-type="bibr" rid="B49">Liu et al., 2024</xref>). The identification and characterization of these Cd<sup>2&#x2b;</sup>-tolerant bacterial strains highlight their potential use in the remediation of heavy metal-contaminated environments. Their ability to thrive in extreme conditions suggests they could be pivotal in developing biotechnological applications for the detoxification of cadmium-polluted soils. Additionally, understanding the genetic mechanisms underlying their resistance offers valuable insights for engineering more robust microbial strains for environmental clean-up strategies.</p>
<p>In recent years, a few microorganisms with the potential to absorb Cd<sup>2&#x2b;</sup> with varying efficiencies and limitations have been documented. For instance, <italic>Pseudomonas aeruginosa</italic> has been highlighted as a versatile microbe and a suitable biosorbent for the removal of Cd<sup>2&#x2b;</sup>. It also promotes plant growth and enhances heavy metal accumulation by plants (<xref ref-type="bibr" rid="B13">Chellaiah, 2018</xref>). Additionally, <italic>Bacillus cereus</italic>, when associated with lawn plants, showed approximately 33% removal efficiency of Cd<sup>2&#x2b;</sup> from contaminated soil (<xref ref-type="bibr" rid="B88">Zhou et al., 2024</xref>). <italic>Rhizobium leguminosarum</italic> forms a symbiotic relationship with legumes, reducing Cd<sup>2&#x2b;</sup> toxicity in plants and potentially enhancing Cd<sup>2&#x2b;</sup> uptake and sequestration (<xref ref-type="bibr" rid="B30">Jach et al., 2022</xref>). <italic>Klebsiella planticola</italic> strain was shown to convert S<sub>2</sub>O<sub>3</sub> into H<sub>2</sub>S and precipitates Cd ions as insoluble sulfides (<xref ref-type="bibr" rid="B37">Kour et al., 2021</xref>). Additionally, in agricultural applications, <italic>Bacillus subtilis</italic> has been shown to promote plant growth and Cd<sup>2&#x2b;</sup> uptake in host plants, assisting phytoremediation processes by altering soil conditions to make Cd<sup>2&#x2b;</sup> more bioavailable (<xref ref-type="bibr" rid="B44">Li et al., 2022</xref>). Despite the promising strategy of bioremediation through microbial activity, the known versatile Cd<sup>2&#x2b;</sup>resistant bacteria remain limited. Therefore, we recommend that the potential of Cd<sup>2&#x2b;</sup> tolerant bacterial strains <italic>B. mobilis</italic> and <italic>C. campinensis</italic> in this study should be explored for their effectiveness in Cd<sup>2&#x2b;</sup> bioremediation, potentially contributing to innovative solutions for soil contamination.</p>
<p>It is not surprising that these Cd-tolerant species utilized limited substrates needed for growth in extreme environments and might devote energy to producing resistance determinants allowing them to live in extreme environments. The results showed that both bacterial strains can metabolize a low variety of carbon substrates with different efficiencies, reflecting their ability to thrive in environments with limited nutrient availability. Such metabolic flexibility is critical for bacteria inhabiting heavy metal-contaminated soils, where nutrient sources may be limited or irregular. <italic>B. mobilis</italic> exhibited high metabolic activity when exposed to substrates like pyruvic acid methyl ester (carbohydrate), D-malic acid, Itaconic acid, D-Galactonic acid &#x3b3;-Lactone (carboxylic and acetic acids) and phenylethylamine (amine/amides), suggesting that <italic>B. mobilis</italic> can efficiently utilize for instance both simple carbohydrates (itaconic acid) and more complex carbon substrates (Phenylethylamine, D-Galactonic acid &#x3b3;-Lactone). The significantly utilized substrates for <italic>C. campinensis</italic> were complex polymers (Tween-40 and Tween-80), an amino acid (L. Asparagine), and a simple carbohydrate (pyruvic acid methyl ester).</p>
<p>Microbial metabolism of specific carbon substrates plays a critical role in shaping community responses to heavy metal stress, as these substrates can influence both metal mobility and microbial tolerance mechanisms. Organic acids, amino acids, and surfactants have been linked to enhanced microbial activity, exopolysaccharide production, and increased solubility or uptake of metals in contaminated soils (<xref ref-type="bibr" rid="B72">Skorokhodova et al., 2021</xref>; <xref ref-type="bibr" rid="B35">Kenarova and Boteva, 2015</xref>; <xref ref-type="bibr" rid="B57">Memarian and Ramamurthy, 2013</xref>; <xref ref-type="bibr" rid="B15">Cheng et al., 2017</xref>; <xref ref-type="bibr" rid="B25">Harati et al., 2023</xref>). In this study, we found that <italic>B. mobilis</italic> and <italic>C. campinensis</italic> disproportionately utilized several carbon substrates under Cd<sup>2&#x2b;</sup> stress, and these utilization patterns are consistent with mechanisms previously linked to microbial remediation of heavy metals. For example, pyruvic acid and D-malic acid, which were among the most highly utilized substrates in our assays, are known to enhance microbial respiration and biomass production, as well as stimulate exopolysaccharide production that can immobilize heavy metals (<xref ref-type="bibr" rid="B72">Skorokhodova et al., 2021</xref>; <xref ref-type="bibr" rid="B70">Si et al., 2022</xref>; <xref ref-type="bibr" rid="B3">Adedayo et al., 2023</xref>; <xref ref-type="bibr" rid="B51">Lou et al., 2023</xref>). Similarly, itaconic acid and D-galactonic acid-&#x3b3;-lactone, also strongly metabolized in our study, have been shown to promote the growth of metal-binding microbes and to support microbial resistance to heavy metals (<xref ref-type="bibr" rid="B38">Krishnamoorthy et al., 2021</xref>; <xref ref-type="bibr" rid="B35">Kenarova and Boteva, 2015</xref>; <xref ref-type="bibr" rid="B54">Mart&#xed;nez-Toledo et al., 2021</xref>). Our data further revealed that phenylethylamine and L-asparagine supported the growth of metal-tolerant taxa, in line with earlier reports connecting these substrates to contaminant degradation pathways and reductions in bioavailable Zn (<xref ref-type="bibr" rid="B36">Koner et al., 2022</xref>; <xref ref-type="bibr" rid="B23">Ghorbanzadeh et al., 2022</xref>). Finally, we observed notable utilization of surfactants such as Tween-40 and Tween-80, which studies have shown their role in enhancing the solubility and microbial uptake of polycyclic aromatic hydrocarbons and metals, including Cd<sup>2&#x2b;</sup> and Pb<sup>2&#x2b;</sup> (<xref ref-type="bibr" rid="B19">Dhenain et al., 2006</xref>; <xref ref-type="bibr" rid="B45">Lima et al., 2011</xref>; <xref ref-type="bibr" rid="B57">Memarian and Ramamurthy, 2013</xref>; <xref ref-type="bibr" rid="B15">Cheng et al., 2017</xref>; <xref ref-type="bibr" rid="B25">Harati et al., 2023</xref>). Collectively, these findings demonstrate that the substrate preferences identified in our isolates align with known biostimulation mechanisms and highlight their potential for enhancing Cd<sup>2&#x2b;</sup> uptake and reduction in contaminated soils.</p>
<p>Bioaugmentation and biostimulation are well-established strategies for accelerating the on-site degradation of organic pollutants and facilitating metal reduction. Introducing specific carbon substrates such as organic acids or surfactants can be crucial in promoting the activity of native, metal-tolerant microorganisms, among which particular species may enhance the rate and efficiency of bioremediation. In this context, the six carbon substrates identified as highly utilized by <italic>B. mobilis</italic> and the four substrates identified for <italic>C. campinensis</italic> represent promising carbon substrates for further investigation. The above findings provide a foundational characterization of the Cd<sup>2&#x2b;</sup>-tolerance and metabolic capacity of <italic>B. mobilis</italic> and <italic>C. campinensis</italic> for potential targeted biostimulation to boost <italic>in-situ</italic> bioremediation processes for heavy metals at contaminated sites. Although we did not evaluate their direct effects on plants under Cd<sup>2&#x2b;</sup> stress, evidence from related <italic>Bacillus</italic> and <italic>Cupriavidus</italic> strains suggests potential applicability in rhizosphere colonization, cadmium immobilization, and mitigation of metal-induced phytotoxicity (<xref ref-type="bibr" rid="B14">Chen et al., 2008</xref>; <xref ref-type="bibr" rid="B65">Rajkumar et al., 2010</xref>; <xref ref-type="bibr" rid="B52">Ma et al., 2016</xref>; <xref ref-type="bibr" rid="B58">Nayak et al., 2018</xref>; <xref ref-type="bibr" rid="B85">Yang et al., 2024</xref>). Future studies should specifically assess the plant-growth&#x2013;promoting and stress-alleviating capacities of these isolates in cadmium-contaminated soils.</p>
<p>The dynamic effects of antibiotics on <italic>B. mobilis</italic> and <italic>C. campinensis</italic> growth patterns, as observed in this study, provide critical insights into how bacterial isolates from cadmium-amended soils respond to diverse antibiotic pressures. These results reveal significant differences in growth phases and rates, reflecting varying degrees of susceptibility and adaptive responses. Heavy metals like Cd<sup>2&#x2b;</sup>are known to co-select for antibiotic resistance, as resistance genes often co-localize with metal resistance determinants on mobile genetic elements such as plasmids and transposons (<xref ref-type="bibr" rid="B61">Pal et al., 2015</xref>). This co-selection could explain the elevated baseline resistance observed in <italic>B. mobilis</italic> and <italic>C. campinensis</italic>, especially to antibiotics with overlapping resistance mechanisms, such as beta-lactams and aminoglycosides.</p>
<p>For <italic>C. campinensis</italic>, most tested antibiotics, including imipenem, tigecycline, tetracycline, gentamicin, levofloxacin, and ceftriaxone, were inhibitory at all concentrations. However, notable exceptions, such as the recovery of growth at low ceftriaxone concentrations (0.5&#xa0;&#x3bc;g/mL), suggest concentration-dependent adaptation mechanisms. The extended lag phase observed at higher antibiotic concentrations, such as those of piperacillin and doripenem, likely represents a survival strategy allowing cells to endure sublethal antibiotic stress before resuming growth. Such lag phase extensions may involve upregulation of efflux pumps, stress response pathways, or the activation of antibiotic resistance genes, as previously described in resistant bacteria (<xref ref-type="bibr" rid="B40">Lambert, 2002</xref>; <xref ref-type="bibr" rid="B8">Baker-Austin et al., 2006</xref>; <xref ref-type="bibr" rid="B53">Martinez, 2009</xref>).</p>
<p>In contrast, <italic>B. mobilis</italic> exhibited greater resilience, with nearly 50% of tested antibiotics showing limited activity across all concentrations. This limited susceptibility could be attributed to intrinsic resistance mechanisms or the physiological robustness of <italic>B. mobilis</italic> under stress conditions. Notably, antibiotics such as erythromycin, clindamycin, daptomycin, ampicillin, and vancomycin were ineffective, with growth dynamics comparable to antibiotic-free controls. These observations are consistent with reports that Gram-positive bacteria, particularly environmental isolates, often possess structural barriers, such as thick peptidoglycan layers, and constitutive efflux pumps that confer broad-spectrum resistance (<xref ref-type="bibr" rid="B8">Baker-Austin et al., 2006</xref>; <xref ref-type="bibr" rid="B53">Martinez, 2009</xref>). The distinct growth patterns observed for levofloxacin, nitrofurantoin, ciprofloxacin, and streptomycin highlight the complexity of antibiotic concentration-dependent effects. For instance, <italic>B. mobilis</italic> exposed to levofloxacin exhibited an extended lag phase of up to 48&#xa0;h at 1&#xa0;&#x3bc;g&#xa0;mL<sup>-1</sup>, followed by gradual growth, whereas above 2&#xa0;&#x3bc;g&#xa0;mL<sup>-1</sup> there was complete inhibitory effect. This prolonged lag phase may indicate the activation of DNA repair systems, as levofloxacin targets bacterial DNA gyrase (<xref ref-type="table" rid="T2">Table 2</xref>), inducing double-strand breaks that necessitate extensive repair before replication can resume (<xref ref-type="bibr" rid="B28">Hooper and Jacoby, 2015</xref>). Similarly, the delayed growth of cells exposed to streptomycin underscores the potential for ribosomal protein modification or aminoglycoside inactivation enzymes to mitigate translational inhibition (<xref ref-type="bibr" rid="B18">Davies and Davies, 2010</xref>).</p>
<p>Our <italic>in silico</italic> analyses revealed comprehensive multidrug resistance (MDR) profiles in both strains, complementing their high Cd<sup>2&#x2b;</sup>-tolerance; a hallmark of <italic>C. campinensis</italic> noted in other strains tolerating Cd levels up to &#x223c;19&#xa0;mM (<xref ref-type="bibr" rid="B2">Abbaszade et al., 2020</xref>)<italic>. B. mobilis</italic> exhibited a broad ARG burden, including &#x3b2;-lactamases (<italic>blaZ</italic>, <italic>mecA</italic>), fluoroquinolone targets (<italic>gyrA</italic>, <italic>parC</italic>), macrolide-clindamycin methylases (<italic>ermB</italic>, <italic>ermC</italic>), and tetracycline efflux pumps (<italic>tetK</italic>, <italic>tetL</italic>). Such a profile reflects its environmental adaptability and may point to horizontal gene transfer from commensals or pathogens (<xref ref-type="bibr" rid="B8">Baker-Austin et al., 2006</xref>). The presence of <italic>mprF</italic> highlights its capacity to resist lipopeptide antibiotics like daptomycin (<xref ref-type="bibr" rid="B77">Thitiananpakorn et al., 2020</xref>), underlining the need for caution when considering its use in bioremediation.</p>
<p>Similarly, <italic>C. campinensis</italic> displayed a clinically alarming resistome profile, defined by strict to perfect matches to <italic>blaKPC-2</italic>, <italic>blaNDM-1</italic>, blaVIM-2, and <italic>blaOXA-48</italic>. These enzymes confer resistance to all tested carbapenems and are recognized as high-risk carbapenemase globally (<xref ref-type="bibr" rid="B32">Jean et al., 2022</xref>). Class A&#x2013;D &#x3b2;-lactamases were well-represented, aligning with previously observed intrinsic resistome signatures in <italic>Cupriavidus</italic>. The detection of RND family efflux pumps -key mediators of broad-spectrum resistance-further underscores their potential clinical threat (<xref ref-type="bibr" rid="B6">Anes et al., 2015</xref>).</p>
<p>A particularly significant finding of this study is the persistence of growth effects well beyond the standard 24-h incubation period used in most clinical and environmental susceptibility testing protocols (<xref ref-type="bibr" rid="B17">CLSI, 2023</xref>). For example, in <italic>C. campinensis</italic>, antibiotics such as ceftriaxone, piperacillin, and ampicillin exhibited delayed inhibitory effects or adaptive growth after extended lag phases of 36&#x2013;50&#xa0;h. Similarly, in <italic>B. mobilis</italic>, lag phases of up to 48&#xa0;h for ciprofloxacin and 30&#xa0;h for nitrofurantoin reveal the potential for delayed bacterial adaptation to antibiotic stress. The lack of complete inhibition at higher concentrations highlights the potential for resistance mechanisms to confer partial protection, even under substantial antibiotic stress. These observations suggest that traditional susceptibility testing methods, which typically assess growth inhibition within 24&#xa0;h, may overlook critical adaptive responses and underestimate the resilience of certain bacterial isolates. Extending susceptibility testing beyond the standard 24-h timeframe is crucial for understanding the full spectrum of bacterial responses to antibiotics (<xref ref-type="bibr" rid="B76">Theophel et al., 2014</xref>). Delayed growth recovery, as observed in this study, could have significant implications for treatment efficacy and the emergence of resistance. For instance, patients receiving antibiotics may initially appear to respond to treatment, but delayed bacterial recovery could lead to treatment failure or relapse. Additionally, sublethal antibiotic exposure during prolonged lag phases might facilitate the development of tolerance or resistance through selection of adaptive mutants or the activation of stress-induced resistance mechanisms (<xref ref-type="bibr" rid="B76">Theophel et al., 2014</xref>; <xref ref-type="bibr" rid="B21">Drawz and Bonomo, 2010</xref>). Ecologically, in contaminated ecosystems such as cadmium-amended soils, bacterial isolates are likely subjected to prolonged exposure to sublethal antibiotic concentrations due to environmental persistence of antibiotics and heavy metal co-selection pressures. The extended lag phases observed in this study could represent an ecologically relevant adaptation, allowing bacteria to survive fluctuating antibiotic concentrations. This highlights the need for susceptibility testing protocols that reflect environmental conditions more accurately, particularly in ecosystems where co-selection for antibiotic and metal resistance is likely (<xref ref-type="bibr" rid="B8">Baker-Austin et al., 2006</xref>; <xref ref-type="bibr" rid="B61">Pal et al., 2015</xref>).</p>
<p>Our findings emphasize the importance of dynamic, time-resolved assessments of bacterial growth in both clinical and environmental contexts. Automated OD measurements, as implemented in this study, provide a valuable tool for capturing differential growth dynamics and resistance profiles of <italic>B. mobilis</italic> and <italic>C. campinensis</italic> over extended time periods, offering a more comprehensive understanding of bacterial responses to antibiotics. Future studies should explore the molecular mechanisms underlying extended lag phases and delayed growth recovery, particularly in relation to heavy metal stress and resistance gene expression.</p>
</sec>
<sec sec-type="conclusion" id="s5">
<title>Conclusion</title>
<p>This study identified <italic>B. mobilis</italic> and <italic>C. campinensis</italic> as multidrug resistant and cadmium-tolerant bacteria with potential for bioremediation of heavy metal-contaminated soils. Both strains exhibited limited substrate utilization profiles, indicative of metabolic adaptations for survival in nutrient-poor, metal-stressed environments. Their ability to metabolize key substrates, such as organic acids and surfactants, supports their role in enhancing heavy metal bioavailability and mobilization in contaminated soils. Antibiotic susceptibility testing revealed delayed growth responses beyond the standard 24-h timeframe, highlighting the limitations of traditional testing protocols in capturing adaptive resistance mechanisms. We summarize predicted resistance genes <italic>in silico</italic> from available genomic data entered in CARD-RGI. Using a higher ARG-target coverage cutoff (&#x3e;95%) predicted presence of seven and six perfect hits in <italic>B. mobilis</italic> and <italic>C. campinensis</italic> respectively. <italic>C. campinensis</italic> exhibited critical resistance to all tested carbapenems revealing a concerning carbapenem-resistant Enterobacteriaceae (CRE) profile. These findings underscore the ecological relevance of extended testing periods, particularly in environments where co-selection pressures from heavy metals and antibiotics persist. Also, the observed co-selection of resistance to both Cd<sup>2&#x2b;</sup>and antibiotics further underscores the ecological complexity of contaminated environments, where heavy metal pressures and residual antibiotic concentrations may synergistically drive resistance evolution. Our study demonstrates that <italic>B. mobilis</italic> and <italic>C. campinensis</italic> isolated from cadmium-spiked soils exhibit both functional diversity in carbon metabolism and multidrug resistance traits, underscoring their ability to adapt and persist under high Cd<sup>2&#x2b;</sup> stress. These findings highlight the potential of these strains as candidates for application in bioremediation of heavy metal&#x2013;contaminated environments, where metabolic versatility and resistance determinants are critical for survival and ecological function. While additional studies could further elucidate the genetic pathways underlying these traits, the present work provides clear evidence of their ecological resilience and biotechnological promise.</p>
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</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="s12">Supplementary Material</xref>, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec sec-type="author-contributions" id="s7">
<title>Author contributions</title>
<p>PA: Project administration, Writing &#x2013; review and editing, Formal Analysis, Investigation, Methodology, Writing &#x2013; original draft, Funding acquisition, Supervision, Software, Data curation, Conceptualization, Resources. EJ: Methodology, Investigation, Writing &#x2013; review and editing, Resources. NS: Methodology, Writing &#x2013; review and editing, Investigation.</p>
</sec>
<sec sec-type="funding-information" id="s8">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This work was partly funded by generous support from the J. William Asher and Melanie J. Norton Endowed Fund in the Sciences, DePauw University, Greencastle, IN.</p>
</sec>
<ack>
<p>We thank Kennett Brown of Department of Geology and Environmental Geosciences, DePauw University, Greencastle, IN for metal analysis of the amended soil. Jenni McGaughey of the Biology Department at DePauw, helped with laboratory logistics and support.</p>
</ack>
<sec sec-type="COI-statement" id="s9">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s10">
<title>Generative AI statement</title>
<p>The author(s) declare that Generative AI was used in the creation of this manuscript. Generative AI was employed to assist in debugging and optimizing Python-integrated scripts used for CARD Antibiotic Resistance Ontology sunburst classification.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="s11">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="s12">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fenvs.2025.1668462/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fenvs.2025.1668462/full&#x23;supplementary-material</ext-link>
</p>
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