Ciprofloxacin consumption and phenomenal transformation to a culturable nanometer-sized- bacterium by a Klebsiella strain SG01

Ciprofloxacin consumption and phenomenal transformation to a culturable nanometer-sized- bacterium by a Klebsiella strain SG01 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Ciprofloxacin consumption and phenomenal transformation to a culturable nanometer-sized- bacterium by a Klebsiella strain SG01 Sriradha Ganguli, Ranadhir Chakraborty This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5801647/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Antimicrobial resistance is a global crisis. Biodegradation by bacteria is an effective strategy to remove the micropollutant from the environment. In this study, we demonstrate that a persistent fluoroquinolone, ciprofloxacin (CIP) can be degraded by a multidrug-resistant Klebsiella sp. SG01 and used as its only carbon source. SG01’s ability to consume or degrade more than 50% of CIP (~2g/L) within 48 h exceeded previous published data on CIP-biodegradation. The degradation was quantified using UV-vis spectroscopy and the degraded product was less toxic than the parent compound as tested against a susceptible Escherichia coli K12. SG01 changes into nano-sized cells as culturable nanobacterium, passes through a 0.22 µm pore-size filter while growing on ciprofloxacin, and shows a shorter generation time than cells grown on glucose or rich medium. The nano-sized-bacterium reverses to its micrometer-sized form within an hour of culture transfer to nutrient-rich Luria broth. The basis for the changed growth phenotype of nano-SG01 cells and metabolic changes was partially established by the whole-genome transcriptome. Figures Figure 1 Introduction Antibiotic development is regarded as one of the most important advances in medicine. India ranks third in delivering antibiotics (20%) and 50% of vaccines to meet global demand, becoming the "Pharmacy of the Global" 1 . Antimicrobial usage in 2020 (during COVID-19) increased by 11.2% globally 2 and is expected to rise by 200% by 2030 3 , with antibiotics accounting for 83% of total consumption. Incredibly, up to 70% of antibiotics that are administered are eliminated unmetabolized and eventually find their way into natural ecosystems through wastewater discharge.These pharmaceutical residues build up in the environment because wastewater treatment plants (WWTPs) are typically inefficient at eliminating them. The emergence and spread of antibiotic resistance genes (ARGs)⁴, a growing threat to the environment and public health that emphasizes the need for sustainable mitigation strategies, are facilitated by the selective pressure this places on microbial communities. Fluoroquinolones, or FQs, are a common class of synthetic antibiotics. A second-generation FQ with broad-spectrum activity against both Gram-positive and Gram-negative pathogens is ciprofloxacin (CIP)⁵. The stability of the carbon–fluorine (C–F) bond and fluorine's high electronegativity, which also increases its antimicrobial potency, are responsible for its environmental persistence 6 . CIP has been found at concentrations ranging from 5 µg/kg to 45.5 mg/kg⁷ , ⁸ in a variety of environmental matrices, such as freshwater bodies, manure, sewage sludge, and agricultural soils. A number of bacterial strains have been shown to biodegrade CIP, including Labrys portucalensis F1110, Thermus sp. C41911, Bradyrhizobium sp. GLC_0112, Paraclostridium sp13, and more recently, Stutzerimonasstutzeri and Exiguobacterium indicum (isolated from pharmaceutical wastewater) 9 , 10 , 11 , 12 , 13 . However, the process is frequently left unfinished, which may result in the production of hazardous metabolites and secondary pollution 14 . Although they have been investigated, physical and chemical degradation techniques like photolysis, advanced oxidation, and electrochemical treatment are frequently inefficient, energy-intensive, or not economically feasible. Numerous xenobiotic substances, such as antibiotics, phenols, s-triazine herbicides, polycyclic aromatic hydrocarbons (PAHs), and synthetic polymers like polyethylene, have been shown to be biodegradable by Klebsiella spp. Catabolic genes encoded by chromosomes and plasmids mediate this metabolic versatility. For example, K. pneumoniae can metabolize s-triazine herbicides and PAHs like pyrene and benzo[a]pyrene 15 , 16 , 17 , while Klebsiellaoxytoca breaks down phenol using a TOL-like plasmid. Similarly, the chromosome genes ( atzC, trzN, and trzD) present in K. variicola strain FH-1, which was isolated from atrazine-contaminated soil, allow it to use atrazine as its only source of nitrogen 16 .Other strains, like Klebsiella sp. YB1 and K. pneumoniae PL1, have shown effective degradation of PAHs and antibiotics under stress, frequently made possible by mobile genetic elements and horizontal gene transfer 15 , 16 ,17 , 18 . Klebsiella species are positioned as promising agents for the bioremediation of environments contaminated by antibiotics because of these traits. In this study, we examined the capacity of Klebsiella sp. SG01, which was isolated from a waste-water sludge sample, to tolerate and break down high concentrations of ciprofloxacin (2 g/L) in the presence of limitednutrients Remarkably, SG01 demonstrated a significant capacity for degradation along with a reversible morphological change into a nanometric form, an adaptation that has never been documented in relation to antibiotic degradation. This is the first report of CIP biodegradation by a nanometric bacterial form that we are aware of, and it provides new information about the plasticity of microbes under xenobiotic stress. To further our knowledge of bacterial tactics for environmental detoxification, we also suggest a putative catabolic pathway for CIP degradation in SG01. These results demonstrate the potential of SG01 as a model organism to study microbial adaptabilityin ecosystems fluoroquinolone-contaminated environments and creating cell-free bioremediation systems. Results Enrichment An acclimatization strategy was adopted by repeated passages of culture, every seven days of incubation, into fresh mineral salts medium supplemented with increasing concentrations of CIP (from 0.5 to 2.0 g/L) to allow the enrichment of strain(s) able to degrade and consume CIP with simultaneous elimination of non-degraders of CIP in every passage. At the end of the fourth passage, after seven days of incubation, a cell density of 10 7 c.f.u / mL was obtained from culture containing 2.0 g/L CIP. Serial dilution followed by plating on MSM-CIP agar yielded colonies with identical colony morphology. Purified single colonies were obtained after repeated dilution streaking on Luria-agar plates. Phenotypic and Genotypic Characterization of the isolate The isolate, SG01, is a Gram-negative and rod-shaped bacterium. It is catalase-positive and oxidase-negative, capable of fixing nitrogen and utilizing a wide range of sugars such as lactose, sucrose, mannose, inositol, glycerol, raffinose, and dextrose (Tables S1, S2). The strain is confirmed to be a multidrug resistant bacterium by its antibiotic susceptibility profile (Table S3). Sequencing and de novo assembly resultsrevealed that SG01's genome had a G+C content of 57% and a length of 5,583,874 bp. The genome encodes a total of 5229 CDS, 77 tRNA, 3rRNA, and 1 tmRNA genes. In addition, the genome contains 242 phage genes. The whole genome sequence was submitted to NCBI under accession no JAQSIQ000000000. Evolutionarily SG01 and Klebsiella pneumoniae subsp. pneumonia DSM 30104 AJJID00000000 share a common ancestor (Fig.1a) and the genomic features are summarised in Fig.1b. Growth characteristics of SG01 in MSM-CIP medium in comparison to its growth in MSM-glucose and LB medium Growth of SG01 in mineral salts medium (MSM) containing CIP (2g/L) as the sole source of carbon and energy was monitored in terms of increment in viable cell count. In MSM -CIP, SG01cells grew exponentially reaching 10,000 times its original cell count at 20 h of incubation. For comparison, prototrophic and heterotrophic growth of SG01 was measured in MSM supplemented with glucose (5 g/L), and in Luria Bertani (LB) medium, respectively. In LB, cells continued to grow exponentially with a 1,000 times increase in cell number at 8 h of incubation; on the other hand, the stationary phase was attained at 9 h after a 100-times increase in CFU/ml when cultured in MSM-glucose (5g/L) (Fig.2.a-c). Cells incubated in MSM without a carbon source showed no increase in cell number (Fig.S1a). Shigella sp AP55, a CIP resistant strain also showed no increase in CFU/ml when incubated in MSM supplemented with CIP as the sole carbon source (Fig. S1b). Quantification of CIP in spent MSM-CIP medium using UV-vis Spectroscopy Since its functional groups have been protonated, pure ciprofloxacin diluted in MSM medium at pH 4.0 (pH adjusted with 0.1 N HCl) is largely in a cationic form, which has broadened the absorption spectra and produced three absorption maxima at 315 nm, 326 nm, and 332 nm, respectively. Linearity for the spectrophotometric method at 315 nm was noted over a concentration range of 2 -20 µg/ mL with a correlation coefficient of 0.992 (Fig S2). A standard curve was plotted based on the best-fit curve (r 2 value=0.992) at 315nm. We observed a 57 % reduction in CIP concentration after 54 h. Interestingly, initiation of CIP degradation took place during the lag phase; approximately 27% reduction in CIP concentration was observed after 6 hours of incubation (Fig.2.d). Residual Antibacterial activity of the degraded products The residual antibacterial activity of the degraded CIP was checked against an antibiotic- susceptible wild-type bacterium, Escherichia coli K12. The inhibition-zone diameter decreased with fixed volume of culture filtrate collected from aging culture over increasing incubation periods, suggesting that the degraded CIP due to bacterial activity is less toxic as compared to the parent molecule (Fig.S3). Cellular plasticity Growth assays revealed that turbidometric measurements (optical density, OD), commonly used to estimate bacterial proliferation, were unreliable for SG01 cells cultured in ciprofloxacin-supplemented medium (MSM-CIP). Despite the presence of viable cells, as confirmed by standard plate counts, OD measurements failed to detect growth, suggesting an unusual cellular phenotype. To investigate whether this refractoriness to OD detection was due to reduced cell size, log-phase cultures of SG01 grown in different media were passed through 0.22 µm bacterial filters. Viable cell counts in both unfiltered and filtrate fractions were compared. In MSM-glucose and LB, less than 0.01% of cells passed through the filter, whereas over 98% of MSM-CIP-grown cells did. A t-test confirmed that significantly fewer cells from MSM-glucose (P = 0.0207) and LB (P = 0.025) passed through the filter, compared to MSM-CIP, where no significant difference was observed between unfiltered and filtrate counts (P = 0.914). These results suggest that most SG01 cells grown in MSM-CIP are smaller than 0.22 µm. Negative staining with Nigrosin revealed distinct nanoscale cells (Fig. S4). Cell size estimation from oil immersion micrographs was performed using ImageJ, following calibration with a stage micrometer (10 divisions = 100 µm). The labelled 1000 µm scale spans 1406 pixels (based on direct measurement of the image). Therefore, 1 μm=14.06 pixels,and 1 pixel=0.0711μm (Supplementary file 1). The length of LB grown cells ranged 1.19 - 1.65 µm and mean ~1.458 ± 0.194 µm; MSM-glucose grown cells was 1.04-1.59 µm and mean ~1.253 + 0.156 whereas in MSM-CIP, it was significantly reduced to the size range 0.19 -0.31 µm and mean ~0.22 ± 0.08 µm. For this reason,when MSM-CIP grown bacterial culture was passed through a 0.22 µm (220 nm) pore-size filter , ; any cells found in the filtrate were presumed to be smaller than the effective pore size, typically less than ~0.22 µm (220 nm) in diameter.Although they might be longer in one dimension, SG01 cells cultured in MSM-CIP that have significantly shrunk in size-likely less than 0.22 µm in diameter-passed through a 0.22 µm filter. In this study, the DLS data showed a peak at 193.78 nm (±29.75 nm), suggesting the majority of cells in the filtrate are within the 164-224 nm range.Consistently, in LB medium (Fig. 3a-b and Fig. S6) and MSM with glucose (Fig. 3c, d), Klebsiella sp.SG01 was mostly rod-shaped with cells measured along their lengths. But with MSM containing added ciprofloxacin (CIP), the morphology underwent a drastic change. Cells were shorter and irregular in both liquid (Fig. 3e, f, g) and solid MSM-CIP medium (Fig. 3h). Interestingly, nanocells filtered with a 0.22 µm filter showed a tiny, rounded coccoid shape (Fig. 3g). MSM-CIP liquid culture cells cultured on LB agar (Fig. 3i) had a rounder or more coccobacillary in appearance, indicating that this changed morphology persisted even after being moved to a non-antibiotic, nutritionally rich growth medium. These findingssuggest that cell shape plasticity is inherent, and that environmental conditions like as medium composition, antibiotic stress, and growth surface have a major impact on morphological states. . The narrow peak dominance (86%) indicates a relatively homogeneous primary population, assuming no excessive scattering artifacts (Fig.3j). Cell morphology was further examined by observing cells retained on membrane filter and by plating them onto MSM supplemented with ciprofloxacin (MSM-CIP) agar and Luria-Bertani (LB) agar. The summarised process in parallel with the morphological images is illustrated in Fig. 3. Zeta potential measurements were conducted to assess surface charge variations under different growth conditions. SG01 cells in LB displayed a highly negative surface charge (-13.5 mV), typical of Gram-negative bacteria. This value was moderately less negative in MSM-glucose (–9.4 mV), while MSM-CIP-grown cells showed a near-neutral zeta potential (–0.9 mV), with the distribution peak shifting toward a slightly positive value (+8 mV). Correspondingly, electrophoretic mobility declined from –1.0524 µm•cm/V•s in LB to –0.0698 µm•cm/V•s in MSM-CIP (Table 1). These trends, accompanied by standard deviations of 1.6–3.2 mV, point to substantial alterations in outer membrane characteristics, possibly involving lipid bilayer remodeling or surface protein modifications in response to ciprofloxacin-induced stress. Additionally, the average length of each cell grown in MSM-glucose and MSM-CIP was found to be 1.35 μm (in isolated cell images) and 0.16 μm (160 nm; in aggregated form when nano-sized cells were concentrated to obtain a visible pellet enabling processing for electron microscopy), respectively, according to scanning electron microscopy (Fig.S5a,b). Reversible transformation of micrometer-sized SG01 cells (grown in nutrient-rich medium) to nanometer-sized bacterium (when grown in MSM-CIP medium), and effect of pH, temperature, and inoculum density on ciprofloxacin degradation As described in the previous section, SG01, when grown in MSM-CIP undergoes morphological transition to a culturable nanobacterium. We examined that these nanobacterial cells revert to their original morphology when transferred to LB. Fig.S6 demonstrates that cells grown in MSM-CIP inoculated into LB revert to the micrometer-sizedforms in one hour of incubation. Effect of pH on ciprofloxacin degradation The impact of pH on CIP degradation was evaluated across a range of pH 2 to pH 6 . Degradation was minimal at acidic pH values of 2 and 3 , with higher absorbance persisting throughout the 72-hour period. In contrast, greater degradation occurred at pH 4, 5,and 6 , with pH 5 and pH 6 showing the most effective and sustained decrease in absorbance , suggesting optimal enzymatic and microbial activity near-neutral pH (Fig. 4a). Effect of temperature on ciprofloxacin degradation CIP degradation was also assessed at three temperatures: 4°C, 30°C, and 37°C . As shown in Fig. 4b , minimal degradation was observed at 4°C , with absorbance values remaining nearly constant over the 72-hour period, indicating limited metabolic activity at this temperature. In contrast, significant CIP degradation was recorded at both 30°C and 37°C , with 30°C showing the highest degradation efficiency . Effect of inoculum density on ciprofloxacin degradation Ciprofloxacin degradation was monitored over a 72-hour incubation period at varying inoculum densities ranging from 1% to 5% (v/v). All inoculum concentrations initiated CIP degradation effectively, with a rapid decline in absorbance during the first 24 hours. Among the tested densities, 5% inoculum exhibited the fastest initial reduction in absorbance . However, after 48 hours, the rate of degradation plateaued, and the final absorbance values at 72 hours were similar across 2–5% inoculum conditions, suggesting that increasing inoculum density beyond 2% did not significantly enhance final degradation levels. 1% inoculum showed the slowest degradation profile , with a relatively higher residual absorbance after 72 hours (Fig. 4c) Effect of different CIP-concentrations on the growth of SG01 The ciprofloxacin (CIP) degradation potential of SG01 and the impact of varying CIP concentrations on its growth were evaluated. As shown in Fig. 4d, SG01 exhibited a marked growth response across a CIP concentration range of 0.05–2 g/L, achieving a ~1000-fold increase in CFU/ml before reaching a plateau and experiencing a slight decline. Notably, even at a high concentration of 10 g/L, a ~100-fold increase in CFU/mL was observed. These findings indicate that SG01's growth is not inhibited by CIP concentrations typically suppressive to non-degrading or even certain CIP-resistant bacteria strains. Metabolome Analysis of lag and log phase cells grown in minimal salts medium (MSM) with ciprofloxacin as the sole carbon source LC-MS metabolomic Profiling We used untargeted LC-MS-based metabolomic profiling of cells sampled during both the lag and log phases of growth to examine how bacterial metabolism adjusts to ciprofloxacin, an unusual and stress-inducing single carbon source (Fig. 1c; Tables S7 and S8). Extracted Ion Chromatograms (EICs) were generated to show the abundance and distribution of ciprofloxacin-derived metabolites over lag (black) and log (yellow) phases (Fig S7a-b). These chromatograms show distinct retention time differences and relative peak intensities for each ion of interest, ascertaining the presence and separation of individual metabolites even in a composite profile. Bacteria are metabolically active but not yet dividing during the lag phase, which may indicate a period of metabolic rewiring to deal with the stress caused by ciprofloxacin. We postulated that this adaptation might entail the use of energy-saving techniques, the induction of alternative degradation pathways, the activation of efflux systems, and the modification of stress response pathways. A real-time window into these dynamic modifications is provided by metabolomics, which may also show which metabolic pathways are being suppressed, reprogrammed, or freshly activated. The log phase, on the other hand, shows that cells have successfully adapted, either by metabolizing ciprofloxacin or by creating defenses against it. We anticipated seeing metabolic markers linked to improved redox balancing, changes in central carbon metabolism (such as glycolysis and the TCA cycle), and the appearance of intermediates from the breakdown of ciprofloxacin at this period. Additional insights were predicted in the form of alterations in amino acid, lipid, and nucleotide pools, as well as the presence of enzyme degradation products. Ciprofloxacin degradation dynamics Different ciprofloxacin (CIP) degradation profiles were found by LC-MS analysis for each growth phase. Lag phase cells had higher abundances of early-stage degradation intermediates 9 (fold change <1), including m/z 231.0554, 245.1028, 288.1516, and 314.1279 (Fig. 1d). This suggests that degradation starts early during adaptation. The hydroxylated form (m/z 333.1444) and the parent molecule (CIP, m/z 332.1411), on the other hand, displayed higher levels in the log phase (fold changes of 3.36 and 2.0, respectively), indicating enhanced ciprofloxacin persistence and potential accumulation as growth proceeds (Tables S4, S5 and S6). Reactive oxygen species and lipid peroxidation When exposed to ciprofloxacin, lipid peroxidation markers such as PE(2OH(5S,6R)/22:6) and PC(2OH(5S,6R)/22:6) accumulated. Both the lag and log phases showed detectable levels of these oxidized lipids, although the log phase showed higher intensities, suggesting prolonged oxidative stress (Table S4). Lipidomic profile shifts Using lipidomic profiling, clear variations between growth phases were found (Fig. 1e). During the lag phase cells exhibited higher concentrations of monounsaturated and saturated ether-linked phospholipids, such as PE(O-18:0/17:1) and PC(P-16:0/16:0). There were also long-chain fatty acids including FA 22:3, FA 20:4, and FA 16:0, some of which were in wax ester (WE) and semi-volatile ester (SFE) forms. The log phase, on the other hand, revealed a shift toward polyunsaturated and hydroxylated phospholipids, which are consistent with membrane remodeling during active growth. These phospholipids included derivatives of phosphatidylglycerol (PG) and phosphatidylserine (PS), such as PG(22:6-2OH/i-16:0) and PG(20:4-2OH/18:2) (Table S4). Central metabolic intermediates Short-chain oxidized fatty acids (like C5H6O3, C4H6O4), carnitine-conjugated intermediates (like CAR 22:5, CAR 22:4), and TCA cycle constituents like succinate were among the metabolites linked to fundamental metabolic functions that were found. Methylmalonic acid and allyl acetoacetate were detected, which supports the idea of metabolic flexibility under ciprofloxacin-induced stress by indicating the activation of propionate pathways(Table S4). Global gene expression changes in response to ciprofloxacin as the sole carbon source, relative to glucose: transcriptome analysis We hypothesized that:(i) Efflux system gene upregulation in the transcriptome (MSM-CIP vs. MSM-glucose) would correlate with persistence of efflux-related metabolites (e.g., lipid or redox-associated changes) in the metabolome; (ii) Upregulation of stress-response genes (e.g., oxidative or envelope stress) would correlate with markers of lipid peroxidation or ROS detoxification; and (iii) Suppression of genes involved in the TCA cycle or glycolysis would correlate with metabolite accumulation upstream of these pathways. Significant transcriptional reprogramming was found when Klebsiella sp. SG01 was grown in minimal salts medium (MSM) with ciprofloxacin (CIP) as the sole carbon source as opposed to MSM with glucose, according to RNA-seq analysis. 5,367 differentially expressed genes (DEGs) were found through the mapping of sequencing reads. 2,232 genes were downregulated and 1,637 genes were significantly upregulated according to significance thresholds (p 2 or < -2) (Tables S9-S18). Transcriptome analysis was viewed using ipath 3.0 to significantly illustrate the altered metabolic pathways (Fig. 1f). Relation to hypothesis (i): Transporter and efflux gene responses There was a widespread induction of transporter genes: 15 genes had strong upregulation (Log₂FC> 2), while 64 genes had Log₂FC> 1. These included several ABC transporters (PRL05_00415, PRL05_00400, and PRL05_00405), glpT (glycerol-3-phosphate transporter gene, Log₂FC = 4.79), and PRL05_08210 (MFS family transporter, Log₂FC = 5.04). The compensatory increase in alternative transporters suggests functional redundancy or a shift in transporter specificity, potentially favoring the import or efflux of CIP metabolites, even though the classical AcrAB-TolC efflux system was downregulated along with its activator gene robA and its repressor acrR . Though through non-canonical systems, this lends some credence to the idea of efflux-related gene upregulation. Relation to hypothesis (ii): Oxidative and envelope stress responses Significant upregulation of peroxidase-related genes was observed (e.g., Log₂FC = 2.72), suggesting an active reaction to oxidative stress. Remarkably, the genes for sodC and sodB , which encode superoxide dismutases, were downregulated (Log₂FC = -2.56 and -1.97, respectively). This unusual pattern points to a non-canonical reaction to superoxide stress, which might favor the build up of reactive oxygen species (ROS) as a metabolic adaptation or degradation tactic. In accordance with metabolomic indicators of oxidative stress, ROS-induced lipid oxidation may be connected to the upregulation of peroxidases in the absence of the classical SOD response. Transcriptomic analysis revealed that ciprofloxacin treatment caused differential expression of several genes involved in cell wall production and cell division in Klebsiella sp. SG01. . Specifically, multipledivisome-related genes were upregulated including ldtD, zapA, ftsE, yceG, minC, cedA, ftsP, zapC, ftsL, sulA, ftsW , and ftsA (table S10). These genes are involved in peptidoglycan cross-linking, Z-ring regulation, and the suppression or stimulation of septum development. In contrast, a specific group of cell wall synthesis and division genes, such as zipA, damX, ftsB, zapE, rodZ, ftsZ, minE, cpoB, ldtA, mreB, mreD, minD, zapD , and pbpG were all downregulated(table S10). This suggests that division machinery and envelope biosynthesismay be reprogrammed in response to ciprofloxacin stress. Relation to hypothesis (iii): Metabolic pathway suppression and lipid turnover Fatty acid degradation genes ( fadE , fadJ , fadA , fadL , and genes coding for acetyl-CoA acetyltransferase) were upregulated (Log₂FC> 1), whereas genes linked to fatty acid biosynthesis ( fabI , f abF , accB , PRL05_04495) were downregulated (Log₂FC< -1), in accordance with carbon source stress. This pattern points to a shift toward lipid catabolism, which may be necessary to meet energy demands or to reduce stress through lipid remodeling. The idea that glycerol-like intermediates might build up as byproducts of ciprofloxacin breakdown or cell membrane lipids is supported by the strong upregulation of the glp operon, which is involved in glycerol metabolism. These findings are consistent with the observed inhibition of TCA/glycolysis, two pathways involved in central carbon metabolism, which most likely results in the accumulation of upstream metabolites. A CIP degradation mechanism has been proposed by integrating metabolic and transcriptomic evidence (Fig.5) Network analysis and identification of Hub genes We used the STRING database to conduct a thorough Protein-Protein Interaction (PPI) network analysis of the differentially expressed genes (DEGs) in order to identify the regulatory and functional architecture behind the noticed transcriptomic and metabolomic changes during ciprofloxacin (CIP) use. The CytoHubba plugin in Cytoscape was used to identify important hubs and bottlenecks after networks for upregulated and downregulated genes were constructed independently. Upregulated gene network: Adaptive remodeling under CIP Stress Densely connected clusters enriched for pathways linked to fatty acid degradation, glycerol metabolism, stress response, and translation machinery were found in the upregulated gene network (Fig.S8a). In line with metabolomic evidence of glycerol intermediates, lipid catabolism, and oxidative stress markers, this represents a coordinated reprogramming of bacterial physiology under CIP-induced metabolic stress. Notably, a key node that links to both energy production and membrane remodeling is the glp operon, which is involved in glycerol uptake and metabolism. Ten hub genes, including the ribosomal proteins rpmA, rplS, rpmG, rpsQ, rplP, rpsI, rpmB, rplT , and rpsT , were found within this upregulated network. Their prominence highlights the need for the synthesis of stress-associated proteins as well as increased translational activity during active growth. Furthermore, glycerol kinase ( glpK ) and glycerol-3-phosphate dehydrogenase complex component ( glpC ) were identified as top bottleneck genes, indicating that they function as important modulators of metabolic flux via glycerol-related pathways. These results imply that transcriptomic control and adaptive lipid remodeling are integrated, as they are consistent with the metabolomic detection of glycerol-phosphate and oxidized lipid intermediates. Downregulated gene network: Suppression of central carbon and redox metabolism Clusters enriched in central carbon metabolism, such as pyruvate metabolism, TCA cycle, electron transport, DNA repair, and cell morphogenesis, were visible in the downregulated gene network (Fig.S8b). The metabolomic finding of upstream intermediates (such as succinate and methylmalonic acid) building up during both the lag and log phases, which is suggestive of flux rerouting away from the TCA cycle or bottlenecks, is consistent with these transcriptional patterns. The nuo operon, which codes for the subunits of NADH:quinone oxidoreductase (complex I of the respiratory chain), became the main hub of this suppressed network; nuoA, nuoB, nuoE, nuoF, nuoH, nuoI, nuoJ, nuoL, nuoM , and nuoN were hub genes. In keeping with the downregulation of sodB/C and the buildup of ROS and lipid peroxidation markers in the metabolome, the downregulation of this complex indicates decreased respiratory activity, possibly to limit intracellular ROS production or in favor of alternative redox-balancing strategies. Additionally, two bottleneck genes were found: eno (enolase of the glycolytic pathway) and nuoCD (a fusion of complex I subunits). The observed rerouting of flux through alternative pathways such as propionate and fatty acid metabolism, which are both supported by metabolomic data, may be attributed to these central nodes connecting suppressed energy metabolism with larger regulatory networks. Functional integration and regulatory implications When combined, the network analysis shows a contrast between energy-intensive, oxidative modules (such as respiration, glycolysis, and DNA repair) that are selectively suppressed and growth-promoting, adaptive modules (such as glycerol metabolism, translation, and stress defense). The discovery of bottleneck genes identifies particular molecular regulators that may mediate the equilibrium between biomass accumulation, survival, and detoxification. These hub and bottleneck genes show important targets for preventing persistence or encouraging biotransformation in environmental or therapeutic settings, in addition to reflecting the bacterial strategy for metabolizing an unusual carbon source like ciprofloxacin. Discussion The broad-spectrum fluoroquinolone antibiotic ciprofloxacin (CIP) is extremely persistent in the environment and presents a serious problem because it contributes to antibiotic resistance. The ‘One Health’ framework is in line with addressing its removal through microbial biotransformation. Our work shows that Klebsiella sp. SG01, a novel strain that can degrade CIP and use it as the only carbon and energy source in mineral salts medium (MSM), was successfully enriched, isolated, and characterized (Figs. 1 and 2; Tables S1, S2, and S3). A robust degrader population was produced by the progressive enrichment method, and one isolate, SG01, was found to have metabolic versatility and multidrug resistance. Its versatility was characterized by the presence of a 5.6 Mb genome with 57% G+C content that encodes multiple transporters, phage-associated genes, and entire pathways for lipid metabolism and nitrogen fixation. The presence of multiple prophage genes may indicate previous horizontal gene transfer events supporting adaptive evolution, while phylogenomic analysis placed SG01 in close alignment with K. pneumoniae DSM 30104 (Figs. 1a and b). SG01 demonstrated effective growth in MSM-CIP, outperforming growth in MSM-glucose and even LB medium, with a 10,000-fold increase in cell number by 20 hours (Fig. 2). Plate counts, microscopy, filtration assays, and DLS analyses further supported the physiological transformation suggested by the inability of optical density (OD) to reflect growth in MSM-CIP. This transformation revealed that SG01 adopts a nanometric form (<0.22 μm) under CIP stress (Fig. 3). Under harsh circumstances, this size reduction—which is reversible upon transfer to LB—probably improves the surface area-to-volume ratio, improving nutrient uptake and metabolic efficiency (Supplementary file 1; Fig. S4). During the lag phase, CIP started to degrade early; UV-vis spectroscopy confirmed a 27% reduction at 6 hours and a 57% reduction by 54 hours (Fig. 4). This suggests a quick physiological reaction that comes before cellular proliferation. The reduced antibacterial activity of the degraded products against E. coli K12 demonstrated SG01's capacity for detoxification (Fig. S3). Significant changes in surface charge were found by zeta potential analysis: SG01 in MSM-CIP showed a nearly neutral zeta potential (~-0.9 mV), in contrast to more negative values in LB and MSM-glucose. To survive under antibiotic pressure, this shift implies significant remodeling of the outer membrane, potentially involving lipid composition or OMV expression 19 , 20 . Distinct metabolic rewiring in lag versus log phases was revealed by LC-MS-based metabolomics. Early-stage degradation products (m/z 231.05–314.12) and oxidative stress indicators (such as hydroxylated lipids and ROS signatures) were predominant during the lag phase. The parent compound and its hydroxylated derivatives (m/z 332.14, 333.14) accumulated during the log phase, while energy-producing intermediates such as glycerol-3-phosphate and dihydroxyacetone phosphate (DHAP) increased. These findings show a temporal transition from survival and detoxification to growth driven by metabolism. Adaptive remodeling was indicated by lipidomic profiles. While the log phase favored polyunsaturated and hydroxylated lipids, which promoted membrane fluidity and transport, the lag phase was enriched in saturated and ether-linked phospholipids, which may have provided rigidity and resistance to membrane perturbation. The simultaneous detection of fatty acid esters and wax esters suggests lipid-based energy turnover and storage. Global reprogramming was revealed by transcriptomic profiling. There was a significant suppression of central carbon metabolism and an upregulation of stress response, lipid catabolism, and glycerol metabolism pathways among the 5,367 genes that showed differential expression between MSM-CIP and MSM-glucose conditions. Crucially, metabolic rechanneling into glycerol utilization was supported by the highly induced glp operon ( glpK, glpD , and glpT ; Log₂FC = 4.79 for glpT ). Furthermore, 64 transporter genes—including those from the ABC and MFS families—were upregulated, indicating altered import/export dynamics, possibly for stress reduction or CIP metabolites 21 , 22 . The downregulation of cytoskeletal genes ( mreB, mreD, minD, and zapD ) was associated with the transition from rod-shaped to spherical and nanosized morphologies, whereas the expression of ftsZ was moderately maintained, permitting cell division. A simplified approach to reducing energy expenditure under nutrient stress is reflected in these morphological changes as well as decreased expression of genes for cell wall biosynthesis and envelope stress regulators 23 , 24 , 25 , 26 . While nuoCD 27 and eno , which are essential for NADH oxidation and glycolysis, were among the most downregulated nodes.Network analysis revealed that glpK and glpC were upregulated bottleneck genes that were probably essential to carbon flux through glycerol metabolism (Fig. S8). As evidenced by high lipid peroxidation and the presence of ROS-related degradation products in the metabolome, downregulation of NADH dehydrogenase (complex I) suggests decreased respiratory chain activity, possibly to prevent ROS accumulation. It is interesting to note that the oxidative stress response involved selectively upregulating peroxidases while downregulating sod genes, which may indicate a tactic to promote CIP degradation and Fenton-type reactions. These results lend credence to the notion that SG01 deliberately uses ROS for antibiotic breakdown as well as survival 28 . A reversible morpho-physiological adaptation is confirmed by the return of the nanoscale cells to normal-sized rods in LB medium after just one hour (Fig. S6). This adaptability could make SG01 a viable option for bioremediation and enable it to flourish in changing conditions. When taken together, these structural, metabolic, and transcriptional changes offer a thorough adaptive approach to CIP use. As far as we are aware, no prior study has documented bacterial growth and ciprofloxacin degradation at such high concentrations (up to 2 g/L). A comprehensive picture of the coordinated metabolic remodeling of SG01 is provided by the integrative data presented here. Targeted knockouts or isotope-labeling studies are still needed to fully validate the putative degradation pathway (Fig. 5), which was deduced from metabolomic and transcriptomic evidence.These omics-derived observations are by inherently preliminary and hypothesis-generating. Further study is being conducted to determine the possible small RNAs and metabolic nodes involved in the reported phenotypic plasticity. . Our research provides a foundation for developing biotechnological approaches to reduce pharmaceutical pollution and advances our knowledge of how microorganisms adapt to antibiotic pressure. Though Klebsiella species are opportunistic pathogens and hence unsuitable for direct use in bioremediation, their capacity to breakdown drugs such as ciprofloxacin in our work demonstrates considerable environmental and evolutionary dynamics. . The presence of such metabolic capability in wastewater conditions suggests that these bacteria are adjusting to high selective pressures. To address biosafety issues, future research may involve transplanting the ciprofloxacin breakdown pathway or functional genes for this activity into non-pathogenic organisms such as Bacillus subtilis or Pseudomonas putida . As an alternative, researchers might use immobilized enzymes or cell-free enzymatic systems from Klebsiella in controlled and safe bioremediation methods. Although Klebsiella's pathogenicity limits its direct utility, understanding its breakdown pathways can help lead the development of safer synthetic biology-derived treatments for environmental detoxification. The discovery of ciprofloxacin-degrading Klebsiella sp. SG01 in wastewater not only demonstrates the great metabolic plasticity of microbial communities, but also serves as a stark reminder of the widespread pharmaceutical contamination in these environments. This finding is particularly concerning for wastewater treatment facilities (WWTPs), as it highlights the crucial need for improved monitoring and control mechanisms to deal with rising pollution and its related dangers. Methods Chemicals Culture media were purchased from Himedia (Mumbai, India). All standard chemicals were purchased from Sigma-Aldrich (St. Louis, MO, USA).Ciprofloxacin(>98% purity) was obtained from Tokyo Chemical Industry (TCI), India (CAS NO. 85721-33-1). Other antibiotic discs were obtained from Himedia, additional reagents used were of analytical grade. Enrichment culture. A wastewater sludge sample was collected from North Bengal Medical College and Hospital (NBMCH), West Bengal, India. 5 g of the sludge sample was inoculated into a 500 ml Erlenmeyer flask containing 100 ml modified mineral salts medium (MSM) supplemented with ciprofloxacin (CIP) [constituents (g/L): KH 2 PO 4 , 3.0; NaCl, 5.0;MgSO 4 . 7 H 2 O, 0.01; NH 4 Cl, 0.5; and CIP, 1.0; pH, 5.0 (adjusted with 0.1 N HCl) ] and left in static condition for 7 d in the dark at 30 0 C. The culture (1ml) was further transferred to a fresh 100 mL MSM, supplemented with 2 g/L of CIP, and left in static condition for 7 d in the dark, at 30 0 C, and sub-cultured thrice under similar conditions. Isolation and identification of the strain capable of degrading CIP . After final sub-culturing, the culture was serially diluted with sterile phosphate buffer solution (PBS; pH 7.4), from which 0.1mL suspension was spread on the MSM-CIP-agar plate (CIP concentration = 0.5 g/L). The plates were incubated at 30 0 C for 24 h until the emergence of visible colonies. The single colony purification was done twice in MSM-CIP agar plates before being routinely maintained in slants. The purified culture was subsequently used for all physiological experiments including growth assays. Bergey's Manual of Systematic Bacteriology 29 was followed to compare the results of biochemical tests. Genomic DNA was prepared and whole genome sequencing was performed using the IlluminaNovaseq 6000 platform 30 . The whole genome sequence was used to construct the phylogenetic tree after aligning sequences of closely related type strains via the Type Strain Genome Server ( https://tygs.dsmz.de/ ) 31 . Growth assays in different media. The growth studies were conducted in Luria Bertani (LB) medium, a modified mineral salt medium supplemented with 5.0 g/L glucose (MSM-glucose) or 2 g/L ciprofloxacin (MSM-CIP) and MSM without any carbon source. Before performing detailed growth studies in LB or MSM-glucose, standard procedure was used to calibrate OD 600 to colony forming unit (CFU) counts, which are directly correspond to the cell concentration of the culture, i.e. viable cell counts per mL. Following the protocol, 10 mL of overnight grown cells of a pre-culture in LB was centrifuged, pellet was re-suspended in 10 mL sterile PBS and serially diluted. From 10 -3 diluted bacterial suspensions, cells were inoculated (1 %) into fresh sterile LB for enumerating growth using the standard plate technique. For conducting a growth assay in MSM-glucose, 10 mL of log phase grown cells (8 h grown culture) of a pre-culture in LB was centrifuged, and the pellet was re-suspended in 10 mL sterile PBS and diluted. From 10 -1 diluted bacterial suspension, cells were inoculated (1 %) into fresh sterile MSM-glucose for enumerating growth using the standard plate technique. Similarly, for growth studies in MSM-CIP, 10 mL of log phase grown cells (7 h grown culture) of a pre-culture in MSM-glucose was centrifuged, and the pellet was re-suspended in 10 mL sterile PBS and diluted. From 10 -1 diluted bacterial suspension, cells were inoculated (1 %) into fresh sterile MSM-CIP for enumerating growth using the standard plate technique. A CIP-resistant strain Shigella sp AP55 (NCBI Accession No. PV652741) was evaluated for its CIP-consumption capabilities. UV-vis Spectroscopic quantification of CIP Spectrometric determination of residual CIP in the culture medium was done by using a Lambda 25 UV/VIS spectrometer. Absorption maxima (λ max ) was determined before generating a standard curve using varying concentrations of CIP (10–90 μg/mL). For quantifying the concentration of CIP in the culture medium following inoculation of bacterium, the growing culture medium was withdrawn at specific intervals and centrifuged at 10,000 rpm to pellet cells for collection of cell-free supernatant. The optical density of each cell-free supernatant sample (derived from samples at different time intervals) at λ max was obtained to ascertain the CIP concentration from the standard curve 14 . Residual Antibacterial activity of the degraded products The residual antibacterial potential of intermediates formed in the course of degradation of CIP by SG01 was evaluated for inhibitory activity of culture medium minus cells (cell-free supernatant) against CIP-sensitive Escherichia coli K12 on Luria agar using the disc diffusion method 32 . Studying plasticity in cell dimensions of SG01 when grown in different media SG01 was grown in three different media, LB, MSM-glucose, and MSM-CIP up to the mid-log phase. Cell harvesting time was ascertained from individual growth curves in a specific medium. Immediately before passing the log phase cells through a 0.22μm bacterial filter, unfiltered culture was serially diluted and plated, and incubated overnight to determine the cell count. Side-by-side, the filtrate was also serially diluted and plated, and incubated overnight to determine the cell count (CFU/mL). Light microscopy of the log-phase cells grown in three different media was done following Gram staining 33 and negative staining using nigrosin 34 . Scanning electron microscopy (SEM) was performed to confirm a reduction in cell size when grown in MSM-CIP 35 . To further validate the presence and size quantification of nano-sized cells as observed under light microscope and photographed, we performed Dynamic Light Scattering (DLS) 36 , also known as Photon Correlation Spectroscopy (PCS) or Quasi-Elastic Light Scattering (QELS). It is a widely used technique for determining the size distribution of particles in the nanometer range suspended in a liquid. DLS measurements were performed using standard protocol with suitable optimizations. Briefly, cells grown in MSM-CIP were harvested from mid-log phase and washed twice using sterile PBS. The pellet was dissolved in sterile PBS and passed through a bacterial membrane filter. The final suspension was diluted to achieve appropriate concentration for analysis. Cell size measurements were performed using an Anton PaarLitesizer 500 instrument at 25°C, using standard polystyrene cuvettes. The system was set to measure at a scattering angle of 175° (backscatter detection). Each sample was equilibrated in the instrument for 30 seconds before measurement. Peak mean intensity and standard deviation were exported from the Anton Paar software for documentation. Investigating the reversal phenomenon of nanobacterium transformation to micrometer-sized bacterium SG01 was grown in MSM-glucose upto mid-log phase and harvested. The cells were centrifuged at 4000 rpm and the supernatant was discarded. The pellet was washed with sterile PBS twice by centrifugation and 1% inoculum was added to MSM-CIP (2g/L), allowed to incubate at 30 0 C under gyrotary shaking (100 r.p.m.). From the mid-log phase, cells were harvested, washed with PBS twice and reinoculated in LB medium. The cell morphology was determined by Gram’s staining at different time intervals. Since nano-sized cells are not distinctly visible with Gram’s stain, the initial 0-hour cells were viewed with nigrosin staining. Optimization of CIP Degradation Parameters Klebsiella sp. SG01 was cultured overnight in MSM-glucose (5g/L) at 30 °C with shaking at 150 rpm. Cells were harvested by centrifugation at 5000 × g for 10 minutes, washed twice with sterile phosphate-buffered saline (PBS), and resuspended to prepare inocula of different for optimization studies. pH Optimization To study the effect of pH on CIP degradation, minimal media were adjusted to five different pH values: 2, 3, 4, 5, and 6with 1 N HCl or NaOH. Each pH condition was inoculated with 1% bacterial culture and incubated at 30 °C. Temperature Optimization The effect of incubation temperature on CIP degradation was assessed by incubating cultures at three different temperatures: 4 °C, 30 °C, and 37 °C , with fixed pH (7.0) and inoculum density (1%). Inoculum Density Optimization Inoculum density was varied from 1% to 5% (v/v) to evaluate its effect on degradation. The experiments were conducted at 30 °C and pH 7.0. For each set of the above three conditions, samples were collected at different time intervals (e.g., 0, 15, 24, 48, and 72 hours). CIP degradation was monitored using UV-Visible spectroscopy by measuring absorbance at the CIP λmax and degradation was calculated based on the reduction in absorbance relative to the initial concentration. Effect of Different CIP Concentrations on Bacterial Growth To assess the impact of CIP concentration on bacterial viability and growth, cultures were exposed to a range of CIP concentrations: 0.025, 0.05, 0.25, 0.5, 1, 2, 5, and 10 mg/mL in minimal media at 30 °C. At intervals of 24 hours, bacterial growth was quantified by measuring CFU/mL through serial dilution and plating on Luria-Bertani agar. Plates were incubated at 30 °C overnight before colony counting. Metabolite extraction Klebsiella sp. SG01 was cultured in Minimal Salt Medium (MSM) supplemented with ciprofloxacin (2g/L) to assess its metabolic profile. Cells were harvested from mid-lag phase (~6hours) and mid-log phase (~17 hours) by centrifugation at 4°C (e.g., 8000 × g for 10 min), washed twice with ice-cold phosphate-buffered saline (PBS), and quenched using cold methanol to halt metabolic activity. The homogenates were then incubated at –20°C for 60 minutes to enhance metabolite precipitation, followed by centrifugation at 14,000 × g for 20 minutes at 4°C. The supernatants were carefully transferred to 1.5-mL Eppendorf tubes, flash-frozen, and dried under vacuum using a centrifugal evaporator. The dried metabolite residues were reconstituted in 100 µL of a complex solvent system suitable for LC-MS analysis, followed by vortexing and gently shaking. Samples were centrifuged again at 14,000 × g for 15 minutes at 4°C, and the resulting supernatants were filtered through 0.22-μm membrane filters. The final extracts were transferred into liquid chromatography- mass spectrometry (LC-MS) vials for analysis. To ensure consistency and monitor system performance, equal aliquots of each processed sample were pooled to prepare quality control (QC) samples. Blank samples, consisting of the same solvents and reagents used in extraction but without biological material, were processed identically and analyzed alongside experimental samples to identify and subtract background signals. Ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) analysis was conducted using an Agilent UHPLC system coupled with a Thermo Q Exactive™ HF-X Orbitrap mass spectrometer. Metabolite separation was performed on an Accucore HILIC column (50 mm × 2.1 mm, 2.6 μm, Thermo Fisher Scientific, USA) using a gradient elution of two mobile phases: phase A consisted of 0.1% formic acid and 10 mM ammonium acetate in 95% acetonitrile, and phase B consisted of 0.1% formic acid and 10 mM ammonium acetate in 50% acetonitrile. The flow rate was maintained at 0.3 mL/min with an injection volume of 5 µL per sample. The column and autosampler were maintained at 40°C and 4°C, respectively. The elution program began with 2% B for 0–1 min, followed by a linear increase to 50% B from 1–17 min, held at 50% B until 17.5 min, and re-equilibrated at 2% B from 18–20 min. Samples were injected in random order to minimize analytical variability, with one quality control (QC) sample and one blank sample run after every five injections. Mass spectrometric detection was carried out in both positive and negative electrospray ionization (ESI) modes with a spray voltage of 3.2 kV. The sheath and auxiliary gas flow rates were set to 35 and 10 arbitrary units, respectively, and the capillary temperature was maintained at 320°C. The system operated in data-dependent acquisition mode, alternating between full MS scans and MS/MS fragmentation with dynamic exclusion, covering a scan range of 100–1500 m/z at a scan rate of 40 Hz. Metabolomics data processing Raw LC-MS data were converted to open formats (e.g., mzXML or mzML) and processed using MZmine 37 for peak detection, alignment, and normalization.Metabolites were identified based on accurate mass, retention time, and MS/MS fragmentation, using databases like HMDB, METLIN, and LIPID MAPS 38 , 39 , 40 . Transcriptome study Prior to transcriptome analyses, genomic DNA was isolated and sequenced as per the protocol described earlier 41 . For transcriptome, the strain SG01 was grown in MSM-glucose (control) and MSM-CIP (test) media at 30 0 C under gyrotary shaking (100 r.p.m.). Cells were harvested from the mid-log phase by centrifugation at 4000 r.p.m. Spent media was discarded, and pelleted cells were washed twice with sterile PBS and immediately quenched with liquid nitrogen and stored at -80 0 C. Total RNA was isolated from both control and test bacterial samples, and after ascertaining qualities and quantities of the isolated RNA samples, RNA-seq libraries were prepared from the purified RNA samples using IlluminaTrueSeq mRNA sample Prep kit (Illumina, U.S.) and MICROBExpress kit (Invitrogen™, USA) as per the manufacturer’s protocol. Following sequencing, raw sequence reads were processed with Trimmomatic v0.39, which allowed for the removal of low-quality and adapter sequences from the raw data. The sequenced raw reads of the two samples, the control and test sample, were processed to obtain high-quality clean reads and were mapped on the reference genome of Klebsiella sp. SG01, using STAR (v 2.7.10a) with default parameters. Feature Counts (version 2.0.3) was used to count the number of reads mapped on each gene. Differential gene expression analysis was performed using the DEGSeqR package between control and test samples. Log2Fold change (log2 FC) values greater than zero were considered up-regulated whereas less than zero were down-regulated along the P-value threshold of 0.05 for significant results. Using KAAS (KEGG Automatic Annotation Server), functional annotations of every gene were performed against the curated KEGG genes database by the previously mentioned methods 42 , 43 , 44 , 45 , 46 . Network analysis and Identification of the hub genes The STRING database version 12.0 was used to construct a PPI network based on experimental results, automated text-mining, co-expression data, and other curated databases 20 . The upregulated DEGs > 1 log 2FC and downregulated DEGs < -1 log 2FC were chosen to study the interaction. The PPI networks were functionally categorized using the CLueGO plugin of Cytoscape v3.8.0 47 , 48 . The hub genes and bottleneck genes were also identified by the Cytohubba plugin 41 , 49 . On identification of all metabolites through integratedtranscriptomics and metabolomic analyses, PubChem Sketcher V2.4 (https://pubchem.ncbi.nlm.nih.gov//edit3/index.html) was used to draw the chemical structures and prepare a hypothesised degradation pathway 50 . Statistical analysis The statistical analysis was performed using GraphPad Prism (https://www.graphpad.com/quickcalcs/ttest/?format=SD). A two-tailed unpaired Student’s t -test was used to compare the means between two groups. All experiments were conducted with three independent biological replicates . Where applicable, at least 50 individual bacterial cells were measured per condition in microscopy-based assays. Declarations Acknowledgments We would like to acknowledge the University of North Bengal, Raja Rammohanpur Campus, India-734010 for their support in conducting this study. We are indebted to the Department of Biotechnology, Government of India for funding a part of our work (BT/PR40383/BCE/8/1561/2020). S.G. is thankful to the Government of West Bengal (WBP211629117511) for providing financial aid. Author’s Contribution S.G. participated in designing the experiments, performed the studies, analysed data, wrote and reviewed the manuscript; R.C. conceived the idea, supervised and designed the experiments, analysed data, and wrote and reviewed the manuscript. Data availability Sequence data in the form of Raw sequence reads that support the findings of this study have been deposited in NCBI and are available at SRA: PRJNA931810 under accessions SRR24804248 for the draft genome sequence of Klebsiella sp. SG01 and SRR29374586 for the whole transcriptome sequence. Preprint version of the manuscript is available with doi: https://doi.org/10.1101/2024.11.20.624549. Supplementary Material Supplemental material for this article is provided in Supplementary file 1,2 and 3 Competing interests The authors declare no competing interests. References Cherian, J. J. et al. 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Cells retained on the filter measured ~0.64 µm, whereas cells that passed through the 0.22 µm filter (filtrate) were significantly smaller (~0.23 µm), confirmed by DLS (193 ± 29.75 nm). Cells regrown from the filtrate on MSM-CIP and LA agar showed varying degrees of size Media Medium/condition Average cell diameter using light microscopy (μm) Advanced size characterization(μm) Zeta Potential (mV) LB Liquid 1.458 + 0.194 1.282 *(SEM) -13.5 MSM-Glucose Liquid 1.253 + 0.156 1.115 *(SEM) -9.4 MSM-CIP Retained on filter 0.638 + 0.047 - - Passed through filter (filtrate) 0.226 + 0.088 0.193 + 29.75 **(DLS) -0.9 Filtrate grown on MSM-CIP agar plated 0.289 + 0.062 - - Filtrate grown on LA plate 0.649 + 0.058 0.598 *(SEM) - Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5801647","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":497965183,"identity":"b010c4d1-28b4-475b-84f7-3162ec71122b","order_by":0,"name":"Sriradha Ganguli","email":"","orcid":"","institution":"University of North Bengal","correspondingAuthor":false,"prefix":"","firstName":"Sriradha","middleName":"","lastName":"Ganguli","suffix":""},{"id":497965184,"identity":"e551c365-10a7-42c8-8be9-66d769cf3916","order_by":1,"name":"Ranadhir Chakraborty","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYHACNiCWkAGRDAkVQIKZuYEoLTwQLWdAWhiJ0sLAA2YytoFJ/FrM288ee8zzx4KHj3+N2YeH82qj+duBWn5UbMOpReZMXroxbxvQYRJvjGckbjueO+MwYwNjz5nbOLVISPCYSfM2gLScMWZI3HYstwGohZmxjYAWnj8wLXOO5c4nTgsbUAt/D1BLQ03uBoJaeHLMDeeC/cJWzJBw7EDuRqCWg3j9wn7G7MGbP3Vy8v2HNzP+qKnLnXf+8MEHPypwa0HSnAAiD4PZB4hQDwT8YHV1xCkeBaNgFIyCEQUA/HtOyGNEumAAAAAASUVORK5CYII=","orcid":"","institution":"University of North Bengal","correspondingAuthor":true,"prefix":"","firstName":"Ranadhir","middleName":"","lastName":"Chakraborty","suffix":""}],"badges":[],"createdAt":"2025-01-10 07:53:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5801647/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5801647/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88918440,"identity":"b6a80888-dd97-4171-bb81-791c597b699e","added_by":"auto","created_at":"2025-08-12 16:50:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":410072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothetical pathway for ciprofloxacin degradation by SG01.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proposed degradation pathway involves two phases of metabolic activity.\u003cbr\u003e\nIn the lag phase (blue box), ciprofloxacin is initially degraded by hydroxyl radicals, monooxygenases, and short-chain dehydrogenase/reductase (SDR) family oxidoreductases, resulting in dehalogenated, demethylated, and oxidized intermediates.\u003cbr\u003e\nThese intermediates, including acetyl coenzyme A, feed into downstream central metabolic pathways during the log phase (orange box), which involve fatty acid degradation, glycerophospholipid biosynthesis, and carnitine-linked oxidation. Key enzymes include glycerol kinase, methylglyoxal synthase, and 1,3-propanediol dehydrogenase, among others.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5801647/v1/918e5caced4858e945f905a5.png"},{"id":88920457,"identity":"dae67354-1b3c-4a12-b05b-86c560172610","added_by":"auto","created_at":"2025-08-12 17:09:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2036054,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5801647/v1/c546fa68-a9b8-4bd9-a0f2-28ef171009b6.pdf"},{"id":88859809,"identity":"ea6b632e-7544-43c9-82c8-00654789c763","added_by":"auto","created_at":"2025-08-12 07:31:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6052715,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5801647/v1/b2b45707efb93e25c636c641.docx"},{"id":88859807,"identity":"08ab6bef-cb0e-454a-afb2-39f70765165e","added_by":"auto","created_at":"2025-08-12 07:31:09","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":46143,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5801647/v1/72c69b910bd9b0e110bee50f.xlsx"},{"id":88859808,"identity":"68be922a-221d-453b-b88b-51f0a27ae076","added_by":"auto","created_at":"2025-08-12 07:31:09","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":141012,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5801647/v1/d9dcc78340913bc9200886a0.xlsx"},{"id":88918150,"identity":"e7175413-6b83-43a6-a948-68fba4e22e8e","added_by":"auto","created_at":"2025-08-12 16:42:25","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3000648,"visible":true,"origin":"","legend":"","description":"","filename":"Figures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5801647/v1/cffefabf465f52cb593c8e9e.docx"},{"id":88918280,"identity":"feab247f-a062-40c6-9430-68f937e2d944","added_by":"auto","created_at":"2025-08-12 16:45:47","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":542717,"visible":true,"origin":"","legend":"","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5801647/v1/03940ebf6169ff6ee4fabee1.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ciprofloxacin consumption and phenomenal transformation to a culturable nanometer-sized- bacterium by a Klebsiella strain SG01","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntibiotic development is regarded as one of the most important advances in medicine. India ranks third in delivering antibiotics (20%) and 50% of vaccines to meet global demand, becoming the \"Pharmacy of the Global\"\u003csup\u003e1\u003c/sup\u003e. Antimicrobial usage in 2020 (during COVID-19) increased by 11.2% globally\u003csup\u003e2\u003c/sup\u003e and is expected to rise by 200% by 2030\u003csup\u003e3\u003c/sup\u003e, with antibiotics accounting for 83% of total consumption. Incredibly, up to 70% of antibiotics that are administered are eliminated unmetabolized and eventually find their way into natural ecosystems through wastewater discharge.These pharmaceutical residues build up in the environment because wastewater treatment plants (WWTPs) are typically inefficient at eliminating them. The emergence and spread of antibiotic resistance genes (ARGs)⁴, a growing threat to the environment and public health that emphasizes the need for sustainable mitigation strategies, are facilitated by the selective pressure this places on microbial communities.\u003c/p\u003e\n\u003cp\u003eFluoroquinolones, or FQs, are a common class of synthetic antibiotics. A second-generation FQ with broad-spectrum activity against both Gram-positive and Gram-negative pathogens is ciprofloxacin (CIP)⁵. The stability of the carbon\u0026ndash;fluorine (C\u0026ndash;F) bond and fluorine's high electronegativity, which also increases its antimicrobial potency, are responsible for its environmental persistence\u003csup\u003e6\u003c/sup\u003e. CIP has been found at concentrations ranging from 5 \u0026micro;g/kg to 45.5 mg/kg⁷\u003csup\u003e,\u003c/sup\u003e⁸ in a variety of environmental matrices, such as freshwater bodies, manure, sewage sludge, and agricultural soils. A number of bacterial strains have been shown to biodegrade CIP, including \u003cem\u003eLabrys portucalensis\u003c/em\u003e F1110, \u003cem\u003eThermus\u003c/em\u003e sp. C41911, \u003cem\u003eBradyrhizobium\u003c/em\u003e sp. GLC_0112, \u003cem\u003eParaclostridium\u003c/em\u003e sp13, and more recently, \u003cem\u003eStutzerimonasstutzeri\u003c/em\u003e and \u003cem\u003eExiguobacterium indicum\u003c/em\u003e (isolated from pharmaceutical wastewater)\u003csup\u003e9\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e11\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e13\u003c/sup\u003e. However, the process is frequently left unfinished, which may result in the production of hazardous metabolites and secondary pollution\u003csup\u003e14\u003c/sup\u003e. Although they have been investigated, physical and chemical degradation techniques like photolysis, advanced oxidation, and electrochemical treatment are frequently inefficient, energy-intensive, or not economically feasible.\u003c/p\u003e\n\u003cp\u003eNumerous xenobiotic substances, such as antibiotics, phenols, s-triazine herbicides, polycyclic aromatic hydrocarbons (PAHs), and synthetic polymers like polyethylene, have been shown to be biodegradable by \u003cem\u003eKlebsiella\u003c/em\u003e spp. Catabolic genes encoded by chromosomes and plasmids mediate this metabolic versatility. For example, \u003cem\u003eK. pneumoniae\u003c/em\u003e can metabolize s-triazine herbicides and PAHs like pyrene and benzo[a]pyrene\u003csup\u003e15\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e16\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e17\u003c/sup\u003e, while \u003cem\u003eKlebsiellaoxytoca\u003c/em\u003e breaks down phenol using a TOL-like plasmid. Similarly, the chromosome genes (\u003cem\u003eatzC, trzN, and trzD)\u003c/em\u003e present in \u003cem\u003eK. variicola\u003c/em\u003e strain FH-1, which was isolated from atrazine-contaminated soil, allow it to use atrazine as its only source of nitrogen\u003csup\u003e16\u003c/sup\u003e.Other strains, like \u003cem\u003eKlebsiella\u003c/em\u003e sp. YB1 and \u003cem\u003eK. pneumoniae\u003c/em\u003e PL1, have shown effective degradation of PAHs and antibiotics under stress, frequently made possible by mobile genetic elements and horizontal gene transfer\u003csup\u003e15\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e16\u003c/sup\u003e\u003csup\u003e,17\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e. \u003cem\u003eKlebsiella\u003c/em\u003e species are positioned as promising agents for the bioremediation of environments contaminated by antibiotics because of these traits.\u003c/p\u003e\n\u003cp\u003eIn this study, we examined the capacity of \u003cem\u003eKlebsiella\u003c/em\u003e sp. SG01, which was isolated from a waste-water sludge sample, to tolerate and break down high concentrations of ciprofloxacin (2 g/L) in the presence of limitednutrients Remarkably, SG01 demonstrated a significant capacity for degradation along with a reversible morphological change into a nanometric form, an adaptation that has never been documented in relation to antibiotic degradation. This is the first report of CIP biodegradation by a nanometric bacterial form that we are aware of, and it provides new information about the plasticity of microbes under xenobiotic stress. To further our knowledge of bacterial tactics for environmental detoxification, we also suggest a putative catabolic pathway for CIP degradation in SG01. These results demonstrate the potential of SG01 as a model organism to study microbial adaptabilityin ecosystems fluoroquinolone-contaminated environments and creating cell-free bioremediation systems.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eEnrichment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn acclimatization strategy was adopted by repeated passages of culture, every seven days of incubation, into fresh mineral salts medium supplemented with increasing concentrations of CIP (from 0.5 to 2.0 g/L) to allow the enrichment of strain(s) able to degrade and consume CIP with simultaneous elimination of non-degraders of CIP in every passage. At the end of the fourth passage, after seven days of incubation, a cell density of 10\u003csup\u003e7\u003c/sup\u003ec.f.u / mL was obtained from culture containing 2.0 g/L CIP. Serial dilution followed by plating on MSM-CIP agar yielded colonies with identical colony morphology. Purified single colonies were obtained after repeated dilution streaking on Luria-agar plates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotypic and Genotypic Characterization of the isolate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe isolate, SG01, is a Gram-negative and rod-shaped bacterium. It is catalase-positive and oxidase-negative, capable of fixing nitrogen and utilizing a wide range of sugars such as lactose, sucrose, mannose, inositol, glycerol, raffinose, and dextrose (Tables S1, S2). The strain is confirmed to be a multidrug resistant bacterium by its antibiotic susceptibility profile (Table S3). Sequencing and de novo assembly resultsrevealed that SG01's genome had a G+C content of 57% and a length of 5,583,874 bp. The genome encodes a total of 5229 CDS, 77 tRNA, 3rRNA, and 1 tmRNA genes. In addition, the genome contains 242 phage genes. The whole genome sequence was submitted to NCBI under accession no JAQSIQ000000000. Evolutionarily SG01 and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e subsp. \u003cem\u003epneumonia\u003c/em\u003e DSM 30104 AJJID00000000 share a common ancestor (Fig.1a) and the genomic features are summarised in Fig.1b.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrowth characteristics of SG01 in MSM-CIP medium in comparison to its growth in MSM-glucose and LB medium\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGrowth of SG01 in mineral salts medium (MSM) containing CIP (2g/L) as the sole source of carbon and energy was monitored in terms of increment in viable cell count. In MSM -CIP, SG01cells grew exponentially reaching 10,000 times its original cell count at 20 h of incubation. For comparison, prototrophic and heterotrophic growth of SG01 was measured in MSM supplemented with glucose (5 g/L), and in Luria Bertani (LB) medium, respectively. In LB, cells continued to grow exponentially with a 1,000 times increase in cell number at 8 h of incubation; on the other hand, the stationary phase was attained at 9 h after a 100-times increase in CFU/ml when cultured in MSM-glucose (5g/L) (Fig.2.a-c). Cells incubated in MSM without a carbon source showed no increase in cell number (Fig.S1a). \u003cem\u003eShigella\u0026nbsp;\u003c/em\u003esp AP55, a CIP resistant strain also showed no increase in CFU/ml when incubated in MSM supplemented with CIP as the sole carbon source (Fig. S1b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of CIP in spent MSM-CIP medium using UV-vis Spectroscopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince its functional groups have been protonated, pure ciprofloxacin diluted in MSM medium at pH 4.0 (pH adjusted with 0.1 N HCl) is largely in a cationic form, which has broadened the absorption spectra and produced three absorption maxima at 315 nm, 326 nm, and 332 nm, respectively. Linearity for the spectrophotometric method at 315 nm was noted over a concentration range of 2 -20 µg/ mL with a correlation coefficient of 0.992 (Fig S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA standard curve was plotted based on the best-fit curve (r\u003csup\u003e2\u003c/sup\u003evalue=0.992) at 315nm. We observed a 57 % reduction in CIP concentration after 54 h. Interestingly, initiation of CIP degradation took place during the lag phase; approximately 27% reduction in CIP concentration was observed after 6 hours of incubation (Fig.2.d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResidual Antibacterial activity of the degraded products\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe residual antibacterial activity of the degraded CIP was checked against an antibiotic- susceptible wild-type bacterium, \u003cem\u003eEscherichia coli\u003c/em\u003e K12. The inhibition-zone diameter decreased with fixed volume of culture filtrate collected from aging culture over increasing incubation periods, suggesting that the degraded CIP due to bacterial activity is less toxic as compared to the parent molecule (Fig.S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCellular plasticity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGrowth assays revealed that turbidometric measurements (optical density, OD), commonly used to estimate bacterial proliferation, were unreliable for SG01 cells cultured in ciprofloxacin-supplemented medium (MSM-CIP). Despite the presence of viable cells, as confirmed by standard plate counts, OD measurements failed to detect growth, suggesting an unusual cellular phenotype.\u003c/p\u003e\n\u003cp\u003eTo investigate whether this refractoriness to OD detection was due to reduced cell size, log-phase cultures of SG01 grown in different media were passed through 0.22 µm bacterial filters. Viable cell counts in both unfiltered and filtrate fractions were compared. In MSM-glucose and LB, less than 0.01% of cells passed through the filter, whereas over 98% of MSM-CIP-grown cells did. A t-test confirmed that significantly fewer cells from MSM-glucose (P = 0.0207) and LB (P = 0.025) passed through the filter, compared to MSM-CIP, where no significant difference was observed between unfiltered and filtrate counts (P = 0.914). These results suggest that most SG01 cells grown in MSM-CIP are smaller than 0.22 µm. Negative staining with Nigrosin revealed distinct nanoscale cells (Fig. S4). Cell size estimation from oil immersion micrographs was performed using ImageJ, following calibration with a stage micrometer (10 divisions = 100 µm). The labelled 1000 µm scale spans 1406 pixels (based on direct measurement of the image). Therefore, 1 μm=14.06 pixels,and 1 pixel=0.0711μm (Supplementary file 1). The length of LB grown cells ranged 1.19 - 1.65 µm and mean ~1.458 ± 0.194 µm; MSM-glucose grown cells was 1.04-1.59 µm and mean ~1.253\u003cu\u003e+\u003c/u\u003e0.156 whereas in MSM-CIP, it was significantly reduced to the size range 0.19 -0.31 µm and mean ~0.22 ± 0.08 µm. For this reason,when MSM-CIP grown bacterial culture was passed through a 0.22 µm (220 nm) pore-size filter\u003cs\u003e,\u003c/s\u003e; any cells found in the filtrate were presumed to be smaller than the effective pore size, typically less than ~0.22 µm (220 nm) in diameter.Although they might be longer in one dimension, SG01 cells cultured in MSM-CIP that have significantly shrunk in size-likely less than 0.22 µm in diameter-passed through a 0.22 µm filter. In this study, the DLS data showed a peak at 193.78 nm (±29.75 nm), suggesting the majority of cells in the filtrate are within the 164-224 nm range.Consistently, in LB medium (Fig. 3a-b and Fig. S6) and MSM with glucose (Fig. 3c, d), \u003cem\u003eKlebsiella\u003c/em\u003e sp.SG01 was mostly rod-shaped with cells measured along their lengths. But with MSM containing added ciprofloxacin (CIP), the morphology underwent a drastic change. Cells were shorter and irregular in both liquid (Fig. 3e, f, g) and solid MSM-CIP medium (Fig. 3h). Interestingly, nanocells filtered with a 0.22 µm filter showed a tiny, rounded coccoid shape (Fig. 3g). MSM-CIP liquid culture cells cultured on LB agar (Fig. 3i) had a rounder or more coccobacillary in appearance, indicating that this changed morphology persisted even after being moved to a non-antibiotic, nutritionally rich growth medium. These findingssuggest that cell shape plasticity is inherent, and that environmental conditions like as medium composition, antibiotic stress, and growth surface have a major impact on morphological states.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e. The narrow peak dominance (86%) indicates a relatively homogeneous primary population, assuming no excessive scattering artifacts (Fig.3j). Cell morphology was further examined by observing cells retained on membrane filter and by plating them onto MSM supplemented with ciprofloxacin (MSM-CIP) agar and Luria-Bertani (LB) agar. The summarised process in parallel with the morphological images is illustrated in Fig. 3.\u003c/p\u003e\n\u003cp\u003eZeta potential measurements were conducted to assess surface charge variations under different growth conditions. SG01 cells in LB displayed a highly negative surface charge (-13.5 mV), typical of Gram-negative bacteria. This value was moderately less negative in MSM-glucose (–9.4 mV), while MSM-CIP-grown cells showed a near-neutral zeta potential (–0.9 mV), with the distribution peak shifting toward a slightly positive value (+8 mV). Correspondingly, electrophoretic mobility declined from –1.0524 µm•cm/V•s in LB to –0.0698 µm•cm/V•s in MSM-CIP (Table 1). These trends, accompanied by standard deviations of 1.6–3.2 mV, point to substantial alterations in outer membrane characteristics, possibly involving lipid bilayer remodeling or surface protein modifications in response to ciprofloxacin-induced stress.\u003c/p\u003e\n\u003cp\u003eAdditionally, the average length of each cell grown in MSM-glucose and MSM-CIP was found to be 1.35 μm (in isolated cell images) and 0.16 μm (160 nm; in aggregated form when nano-sized cells were concentrated to obtain a visible pellet enabling processing for electron microscopy), respectively, according to scanning electron microscopy (Fig.S5a,b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReversible transformation of micrometer-sized SG01 cells (grown in nutrient-rich medium) to nanometer-sized bacterium (when grown in MSM-CIP medium), and effect of pH, temperature, and inoculum density on ciprofloxacin degradation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;As described in the previous section, SG01, when grown in MSM-CIP undergoes morphological transition to a culturable nanobacterium. We examined that these nanobacterial cells revert to their original morphology when transferred to LB. Fig.S6 demonstrates that cells grown in MSM-CIP inoculated into LB revert to the micrometer-sizedforms \u0026nbsp;in one hour of incubation. \u0026nbsp;\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eEffect of pH on ciprofloxacin degradation\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe impact of pH on CIP degradation was evaluated across a range of \u003cstrong\u003epH 2 to pH 6\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Degradation was minimal at acidic pH values of \u003cstrong\u003e2 and 3\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e with higher absorbance persisting throughout the 72-hour period. In contrast, \u003cstrong\u003egreater degradation occurred at pH 4, 5,and 6\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e with \u003cstrong\u003epH 5 and pH 6 showing the most effective and sustained decrease in absorbance\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e suggesting optimal enzymatic and microbial activity near-neutral pH (Fig. 4a).\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eEffect of temperature on ciprofloxacin degradation\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eCIP degradation was also assessed at three temperatures: \u003cstrong\u003e4°C, 30°C, and 37°C\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e As shown in \u003cstrong\u003eFig. 4b\u003c/strong\u003e, minimal degradation was observed at \u003cstrong\u003e4°C\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e with absorbance values remaining nearly constant over the 72-hour period, indicating limited metabolic activity at this temperature. In contrast, \u003cstrong\u003esignificant CIP degradation was recorded at both 30°C and 37°C\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003ewith\u003cstrong\u003e30°C showing the highest degradation efficiency\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eEffect of inoculum density on ciprofloxacin degradation\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eCiprofloxacin degradation was monitored over a 72-hour incubation period at varying inoculum densities ranging from 1% to 5% (v/v). All inoculum concentrations initiated CIP degradation effectively, with a rapid decline in absorbance during the first 24 hours. Among the tested densities, \u003cstrong\u003e5% inoculum exhibited the fastest initial reduction in absorbance\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e However, after 48 hours, the rate of degradation plateaued, and the final absorbance values at 72 hours were similar across 2–5% inoculum conditions, suggesting that increasing inoculum density beyond 2% did not significantly enhance final degradation levels. \u003cstrong\u003e1% inoculum showed the slowest degradation profile\u003c/strong\u003e, with a relatively higher residual absorbance after 72 hours (Fig. 4c)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of different CIP-concentrations on the growth of SG01\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ciprofloxacin (CIP) degradation potential of SG01 and the impact of varying CIP concentrations on its growth were evaluated. As shown in Fig. 4d, SG01 exhibited a marked growth response across a CIP concentration range of 0.05–2 g/L, achieving a ~1000-fold increase in CFU/ml before reaching a plateau and experiencing a slight decline. Notably, even at a high concentration of 10 g/L, a ~100-fold increase in CFU/mL was observed. These findings indicate that SG01's growth is not inhibited by CIP concentrations typically suppressive to non-degrading or even certain CIP-resistant bacteria strains.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolome Analysis of lag and log phase cells grown in minimal salts medium (MSM) with ciprofloxacin as the sole carbon source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLC-MS metabolomic Profiling\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used untargeted LC-MS-based metabolomic profiling of cells sampled during both the lag and log phases of growth to examine how bacterial metabolism adjusts to ciprofloxacin, an unusual and stress-inducing single carbon source (Fig. 1c; Tables S7 and S8). Extracted Ion Chromatograms (EICs) were generated to show \u0026nbsp;the abundance and distribution of ciprofloxacin-derived metabolites over lag (black) and log (yellow) phases (Fig S7a-b). These chromatograms show distinct retention time differences and relative peak intensities for each ion of interest, ascertaining the presence and separation of individual metabolites even in a composite profile. Bacteria are metabolically active but not yet dividing during the lag phase, which may indicate a period of metabolic rewiring to deal with the stress caused by ciprofloxacin. We postulated that this adaptation might entail the use of energy-saving techniques, the induction of alternative degradation pathways, the activation of efflux systems, and the modification of stress response pathways.\u003c/p\u003e\n\u003cp\u003eA real-time window into these dynamic modifications is provided by metabolomics, which may also show which metabolic pathways are being suppressed, reprogrammed, or freshly activated. The log phase, on the other hand, shows that cells have successfully adapted, either by metabolizing ciprofloxacin or by creating defenses against it. We anticipated seeing metabolic markers linked to improved redox balancing, changes in central carbon metabolism (such as glycolysis and the TCA cycle), and the appearance of intermediates from the breakdown of ciprofloxacin at this period. Additional insights were predicted in the form of alterations in amino acid, lipid, and nucleotide pools, as well as the presence of enzyme degradation products.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCiprofloxacin degradation dynamics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDifferent ciprofloxacin (CIP) degradation profiles were found by LC-MS analysis for each growth phase. Lag phase cells had higher abundances of early-stage degradation intermediates\u003csup\u003e9\u003c/sup\u003e\u0026nbsp;(fold change \u0026lt;1), including m/z 231.0554, 245.1028, 288.1516, and 314.1279 (Fig. 1d). This suggests that degradation starts early during adaptation. The hydroxylated form (m/z 333.1444) and the parent molecule (CIP, m/z 332.1411), on the other hand, displayed higher levels in the log phase (fold changes of 3.36 and 2.0, respectively), indicating enhanced ciprofloxacin persistence and potential accumulation as growth proceeds (Tables S4, S5 and S6).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eReactive oxygen species and lipid peroxidation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhen exposed to ciprofloxacin, lipid peroxidation markers such as PE(2OH(5S,6R)/22:6) and PC(2OH(5S,6R)/22:6) accumulated. Both the lag and log phases showed detectable levels of these oxidized lipids, although the log phase showed higher intensities, suggesting prolonged oxidative stress (Table S4).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLipidomic profile shifts\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing lipidomic profiling, clear variations between growth phases were found (Fig. 1e). During the lag phase cells exhibited higher concentrations of monounsaturated and saturated ether-linked phospholipids, such as PE(O-18:0/17:1) and PC(P-16:0/16:0). There were also long-chain fatty acids including FA 22:3, FA 20:4, and FA 16:0, some of which were in wax ester (WE) and semi-volatile ester (SFE) forms. The log phase, on the other hand, revealed a shift toward polyunsaturated and hydroxylated phospholipids, which are consistent with membrane remodeling during active growth. These phospholipids included derivatives of phosphatidylglycerol (PG) and phosphatidylserine (PS), such as PG(22:6-2OH/i-16:0) and PG(20:4-2OH/18:2) (Table S4).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCentral metabolic intermediates\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eShort-chain oxidized fatty acids (like C5H6O3, C4H6O4), carnitine-conjugated intermediates (like CAR 22:5, CAR 22:4), and TCA cycle constituents like succinate were among the metabolites linked to fundamental metabolic functions that were found. Methylmalonic acid and allyl acetoacetate were detected, which supports the idea of metabolic flexibility under ciprofloxacin-induced stress by indicating the activation of propionate pathways(Table S4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal gene expression changes in response to ciprofloxacin as the sole carbon source, relative to glucose: transcriptome analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe hypothesized that:(i) Efflux system gene upregulation in the transcriptome (MSM-CIP vs. MSM-glucose) would correlate with persistence of efflux-related metabolites (e.g., lipid or redox-associated changes) in the metabolome; (ii) Upregulation of stress-response genes (e.g., oxidative or envelope stress) would correlate with markers of lipid peroxidation or ROS detoxification; and (iii) Suppression of genes involved in the TCA cycle or glycolysis would correlate with metabolite accumulation upstream of these pathways.\u003c/p\u003e\n\u003cp\u003eSignificant transcriptional reprogramming was found when \u003cem\u003eKlebsiella\u003c/em\u003e sp. SG01 was grown in minimal salts medium (MSM) with ciprofloxacin (CIP) as the sole carbon source as opposed to MSM with glucose, according to RNA-seq analysis. 5,367 differentially expressed genes (DEGs) were found through the mapping of sequencing reads. 2,232 genes were downregulated and 1,637 genes were significantly upregulated according to significance thresholds (p \u0026lt; 0.05). A global trend of transcriptional repression under CIP-utilizing conditions was indicated by the strong upregulation of 163 genes and the strong downregulation of 259 genes (log₂FC\u0026gt; 2 or \u0026lt; -2) (Tables S9-S18). Transcriptome analysis was viewed using ipath 3.0 to significantly illustrate the altered metabolic pathways (Fig. 1f).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelation to hypothesis (i): Transporter and efflux gene responses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThere was a widespread induction of transporter genes: 15 genes had strong upregulation (Log₂FC\u0026gt; 2), while 64 genes had Log₂FC\u0026gt; 1. These included several ABC transporters (PRL05_00415, PRL05_00400, and PRL05_00405), \u003cem\u003eglpT\u003c/em\u003e (glycerol-3-phosphate transporter gene, Log₂FC = 4.79), and PRL05_08210 (MFS family transporter, Log₂FC = 5.04). The compensatory increase in alternative transporters suggests functional redundancy or a shift in transporter specificity, potentially favoring the import or efflux of CIP metabolites, even though the classical AcrAB-TolC efflux system was downregulated along with its activator gene\u003cem\u003erobA\u003c/em\u003e and its repressor \u003cem\u003eacrR\u003c/em\u003e. Though through non-canonical systems, this lends some credence to the idea of efflux-related gene upregulation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelation to hypothesis (ii): Oxidative and envelope stress responses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSignificant upregulation of peroxidase-related genes was observed (e.g., Log₂FC = 2.72), suggesting an active reaction to oxidative stress. Remarkably, the genes for \u003cem\u003esodC\u003c/em\u003e and \u003cem\u003esodB\u003c/em\u003e, which encode superoxide dismutases, were downregulated (Log₂FC = -2.56 and -1.97, respectively). This unusual pattern points to a non-canonical reaction to superoxide stress, which might favor the build up of reactive oxygen species (ROS) as a metabolic adaptation or degradation tactic. In accordance with metabolomic indicators of oxidative stress, ROS-induced lipid oxidation may be connected to the upregulation of peroxidases in the absence of the classical SOD response.\u003c/p\u003e\n\u003cp\u003eTranscriptomic analysis revealed that ciprofloxacin treatment caused differential expression of several genes involved in cell wall production and cell division in \u003cem\u003eKlebsiella\u003c/em\u003e sp. SG01.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e. Specifically, multipledivisome-related genes were upregulated including\u003cem\u003eldtD, zapA, ftsE, yceG, minC, cedA, ftsP, zapC, ftsL, sulA, ftsW\u003c/em\u003e, and \u003cem\u003eftsA\u003c/em\u003e(table S10). These genes are involved in peptidoglycan cross-linking, Z-ring regulation, and the suppression or stimulation of septum development. In contrast, a specific group of cell wall synthesis and division genes, such as \u003cem\u003ezipA, damX, ftsB, zapE, rodZ, ftsZ, minE, cpoB, ldtA, mreB, mreD, minD, zapD\u003c/em\u003e, and \u003cem\u003epbpG\u003c/em\u003ewere all downregulated(table S10). This suggests that division machinery and envelope biosynthesismay be reprogrammed in response to ciprofloxacin stress.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelation to hypothesis (iii): Metabolic pathway suppression and lipid turnover\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFatty acid degradation genes (\u003cem\u003efadE\u003c/em\u003e,\u003cem\u003efadJ\u003c/em\u003e, \u003cem\u003efadA\u003c/em\u003e, \u003cem\u003efadL\u003c/em\u003e, and genes coding for acetyl-CoA acetyltransferase) were upregulated (Log₂FC\u0026gt; 1), whereas genes linked to fatty acid biosynthesis (\u003cem\u003efabI\u003c/em\u003e, f\u003cem\u003eabF\u003c/em\u003e, \u003cem\u003eaccB\u003c/em\u003e, PRL05_04495) were downregulated (Log₂FC\u0026lt; -1), in accordance with carbon source stress. This pattern points to a shift toward lipid catabolism, which may be necessary to meet energy demands or to reduce stress through lipid remodeling. The idea that glycerol-like intermediates might build up as byproducts of ciprofloxacin breakdown or cell membrane lipids is supported by the strong upregulation of the \u003cem\u003eglp\u003c/em\u003e operon, which is involved in glycerol metabolism. These findings are consistent with the observed inhibition of TCA/glycolysis, two pathways involved in central carbon metabolism, which most likely results in the accumulation of upstream metabolites. A CIP degradation mechanism has been proposed by integrating metabolic and transcriptomic evidence (Fig.5)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork analysis and identification of Hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the STRING database to conduct a thorough Protein-Protein Interaction (PPI) network analysis of the differentially expressed genes (DEGs) in order to identify the regulatory and functional architecture behind the noticed transcriptomic and metabolomic changes during ciprofloxacin (CIP) use. The CytoHubba plugin in Cytoscape was used to identify important hubs and bottlenecks after networks for upregulated and downregulated genes were constructed independently.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUpregulated gene network: Adaptive remodeling under CIP Stress\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDensely connected clusters enriched for pathways linked to fatty acid degradation, glycerol metabolism, stress response, and translation machinery were found in the upregulated gene network (Fig.S8a). In line with metabolomic evidence of glycerol intermediates, lipid catabolism, and oxidative stress markers, this represents a coordinated reprogramming of bacterial physiology under CIP-induced metabolic stress. Notably, a key node that links to both energy production and membrane remodeling is the \u003cem\u003eglp\u003c/em\u003e operon, which is involved in glycerol uptake and metabolism.\u003c/p\u003e\n\u003cp\u003eTen hub genes, including the ribosomal proteins \u003cem\u003erpmA, rplS, rpmG, rpsQ, rplP, rpsI, rpmB, rplT\u003c/em\u003e, and\u003cem\u003erpsT\u003c/em\u003e, were found within this upregulated network. Their prominence highlights the need for the synthesis of stress-associated proteins as well as increased translational activity during active growth. Furthermore, glycerol kinase (\u003cem\u003eglpK\u003c/em\u003e) and glycerol-3-phosphate dehydrogenase complex component (\u003cem\u003eglpC\u003c/em\u003e) were identified as top bottleneck genes, indicating that they function as important modulators of metabolic flux via glycerol-related pathways. These results imply that transcriptomic control and adaptive lipid remodeling are integrated, as they are consistent with the metabolomic detection of glycerol-phosphate and oxidized lipid intermediates.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDownregulated gene network: Suppression of central carbon and redox metabolism\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eClusters enriched in central carbon metabolism, such as pyruvate metabolism, TCA cycle, electron transport, DNA repair, and cell morphogenesis, were visible in the downregulated gene network (Fig.S8b). The metabolomic finding of upstream intermediates (such as succinate and methylmalonic acid) building up during both the lag and log phases, which is suggestive of flux rerouting away from the TCA cycle or bottlenecks, is consistent with these transcriptional patterns.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003enuo\u003c/em\u003e operon, which codes for the subunits of NADH:quinone oxidoreductase (complex I of the respiratory chain), became the main hub of this suppressed network; \u003cem\u003enuoA, nuoB, nuoE, nuoF, nuoH, nuoI, nuoJ, nuoL, nuoM\u003c/em\u003e, and \u003cem\u003enuoN\u003c/em\u003e were hub genes. In keeping with the downregulation of \u003cem\u003esodB/C\u003c/em\u003e and the buildup of ROS and lipid peroxidation markers in the metabolome, the downregulation of this complex indicates decreased respiratory activity, possibly to limit intracellular ROS production or in favor of alternative redox-balancing strategies.\u003c/p\u003e\n\u003cp\u003eAdditionally, two bottleneck genes were found: \u003cem\u003eeno\u003c/em\u003e (enolase of the glycolytic pathway) and \u003cem\u003enuoCD\u003c/em\u003e (a fusion of complex I subunits). The observed rerouting of flux through alternative pathways such as propionate and fatty acid metabolism, which are both supported by metabolomic data, may be attributed to these central nodes connecting suppressed energy metabolism with larger regulatory networks.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunctional integration and regulatory implications\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhen combined, the network analysis shows a contrast between energy-intensive, oxidative modules (such as respiration, glycolysis, and DNA repair) that are selectively suppressed and growth-promoting, adaptive modules (such as glycerol metabolism, translation, and stress defense). The discovery of bottleneck genes identifies particular molecular regulators that may mediate the equilibrium between biomass accumulation, survival, and detoxification. These hub and bottleneck genes show important targets for preventing persistence or encouraging biotransformation in environmental or therapeutic settings, in addition to reflecting the bacterial strategy for metabolizing an unusual carbon source like ciprofloxacin.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe broad-spectrum fluoroquinolone antibiotic ciprofloxacin (CIP) is extremely persistent in the environment and presents a serious problem because it contributes to antibiotic resistance. The \u0026lsquo;One Health\u0026rsquo; framework is in line with addressing its removal through microbial biotransformation. Our work shows that \u003cem\u003eKlebsiella\u003c/em\u003e sp. SG01, a novel strain that can degrade CIP and use it as the only carbon and energy source in mineral salts medium (MSM), was successfully enriched, isolated, and characterized (Figs. 1 and 2; Tables S1, S2, and S3).\u003c/p\u003e\n\u003cp\u003eA robust degrader population was produced by the progressive enrichment method, and one isolate, SG01, was found to have metabolic versatility and multidrug resistance. Its versatility was characterized by the presence of a 5.6 Mb genome with 57% G+C content that encodes multiple transporters, phage-associated genes, and entire pathways for lipid metabolism and nitrogen fixation. The presence of multiple prophage genes may indicate previous horizontal gene transfer events supporting adaptive evolution, while phylogenomic analysis placed SG01 in close alignment with \u003cem\u003eK. pneumoniae\u003c/em\u003e DSM 30104 (Figs. 1a and b).\u003c/p\u003e\n\u003cp\u003eSG01 demonstrated effective growth in MSM-CIP, outperforming growth in MSM-glucose and even LB medium, with a 10,000-fold increase in cell number by 20 hours (Fig. 2). Plate counts, microscopy, filtration assays, and DLS analyses further supported the physiological transformation suggested by the inability of optical density (OD) to reflect growth in MSM-CIP. This transformation revealed that SG01 adopts a nanometric form (\u0026lt;0.22 \u0026mu;m) under CIP stress (Fig. 3). Under harsh circumstances, this size reduction\u0026mdash;which is reversible upon transfer to LB\u0026mdash;probably improves the surface area-to-volume ratio, improving nutrient uptake and metabolic efficiency (Supplementary file 1; Fig. S4).\u003c/p\u003e\n\u003cp\u003eDuring the lag phase, CIP started to degrade early; UV-vis spectroscopy confirmed a 27% reduction at 6 hours and a 57% reduction by 54 hours (Fig. 4). This suggests a quick physiological reaction that comes before cellular proliferation. The reduced antibacterial activity of the degraded products against \u003cem\u003eE. coli\u003c/em\u003e K12 demonstrated SG01's capacity for detoxification (Fig. S3).\u003c/p\u003e\n\u003cp\u003eSignificant changes in surface charge were found by zeta potential analysis: SG01 in MSM-CIP showed a nearly neutral zeta potential (~-0.9 mV), in contrast to more negative values in LB and MSM-glucose. To survive under antibiotic pressure, this shift implies significant remodeling of the outer membrane, potentially involving lipid composition or OMV expression\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDistinct metabolic rewiring in lag versus log phases was revealed by LC-MS-based metabolomics. Early-stage degradation products (m/z 231.05\u0026ndash;314.12) and oxidative stress indicators (such as hydroxylated lipids and ROS signatures) were predominant during the lag phase. The parent compound and its hydroxylated derivatives (m/z 332.14, 333.14) accumulated during the log phase, while energy-producing intermediates such as glycerol-3-phosphate and dihydroxyacetone phosphate (DHAP) increased. These findings show a temporal transition from survival and detoxification to growth driven by metabolism.\u003c/p\u003e\n\u003cp\u003eAdaptive remodeling was indicated by lipidomic profiles. While the log phase favored polyunsaturated and hydroxylated lipids, which promoted membrane fluidity and transport, the lag phase was enriched in saturated and ether-linked phospholipids, which may have provided rigidity and resistance to membrane perturbation. The simultaneous detection of fatty acid esters and wax esters suggests lipid-based energy turnover and storage.\u003c/p\u003e\n\u003cp\u003eGlobal reprogramming was revealed by transcriptomic profiling. There was a significant suppression of central carbon metabolism and an upregulation of stress response, lipid catabolism, and glycerol metabolism pathways among the 5,367 genes that showed differential expression between MSM-CIP and MSM-glucose conditions. Crucially, metabolic rechanneling into glycerol utilization was supported by the highly induced glp operon (\u003cem\u003eglpK, glpD\u003c/em\u003e, and \u003cem\u003eglpT\u003c/em\u003e; Log₂FC = 4.79 for \u003cem\u003eglpT\u003c/em\u003e). Furthermore, 64 transporter genes\u0026mdash;including those from the ABC and MFS families\u0026mdash;were upregulated, indicating altered import/export dynamics, possibly for stress reduction or CIP metabolites \u003csup\u003e21\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe downregulation of cytoskeletal genes (\u003cem\u003emreB, mreD, minD, and zapD\u003c/em\u003e) was associated with the transition from rod-shaped to spherical and nanosized morphologies, whereas the expression of \u003cem\u003eftsZ\u003c/em\u003e was moderately maintained, permitting cell division. A simplified approach to reducing energy expenditure under nutrient stress is reflected in these morphological changes as well as decreased expression of genes for cell wall biosynthesis and envelope stress regulators\u003csup\u003e23\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e24\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e25\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile \u003cem\u003enuoCD\u003c/em\u003e\u003csup\u003e27\u003c/sup\u003e and \u003cem\u003eeno\u003c/em\u003e, which are essential for NADH oxidation and glycolysis, were among the most downregulated nodes.Network analysis revealed that \u003cem\u003eglpK\u003c/em\u003e and \u003cem\u003eglpC\u003c/em\u003e were upregulated bottleneck genes that were probably essential to carbon flux through glycerol metabolism (Fig. S8). As evidenced by high lipid peroxidation and the presence of ROS-related degradation products in the metabolome, downregulation of NADH dehydrogenase (complex I) suggests decreased respiratory chain activity, possibly to prevent ROS accumulation.\u003c/p\u003e\n\u003cp\u003eIt is interesting to note that the oxidative stress response involved selectively upregulating peroxidases while downregulating \u003cem\u003esod\u003c/em\u003e genes, which may indicate a tactic to promote CIP degradation and Fenton-type reactions. These results lend credence to the notion that SG01 deliberately uses ROS for antibiotic breakdown as well as survival\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA reversible morpho-physiological adaptation is confirmed by the return of the nanoscale cells to normal-sized rods in LB medium after just one hour (Fig. S6). This adaptability could make SG01 a viable option for bioremediation and enable it to flourish in changing conditions. When taken together, these structural, metabolic, and transcriptional changes offer a thorough adaptive approach to CIP use.\u003c/p\u003e\n\u003cp\u003eAs far as we are aware, no prior study has documented bacterial growth and ciprofloxacin degradation at such high concentrations (up to 2 g/L). A comprehensive picture of the coordinated metabolic remodeling of SG01 is provided by the integrative data presented here. Targeted knockouts or isotope-labeling studies are still needed to fully validate the putative degradation pathway (Fig. 5), which was deduced from metabolomic and transcriptomic evidence.These omics-derived observations are by inherently preliminary and hypothesis-generating. Further study is being conducted to determine the possible small RNAs and metabolic nodes involved in the reported phenotypic plasticity.\u003c/p\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003cp\u003eOur research provides a foundation for developing biotechnological approaches to reduce pharmaceutical pollution and advances our knowledge of how microorganisms adapt to antibiotic pressure. Though \u003cem\u003eKlebsiella\u003c/em\u003e species are opportunistic pathogens and hence unsuitable for direct use in bioremediation, their capacity to breakdown drugs such as ciprofloxacin in our work demonstrates considerable environmental and evolutionary dynamics. . The presence of such metabolic capability in wastewater conditions suggests that these bacteria are adjusting to high selective pressures. To address biosafety issues, future research may involve transplanting the ciprofloxacin breakdown pathway or functional genes for this activity into non-pathogenic organisms such as \u003cem\u003eBacillus subtilis \u003c/em\u003eor \u003cem\u003ePseudomonas putida\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eAs an alternative, researchers might use immobilized enzymes or cell-free enzymatic systems from \u003cem\u003eKlebsiella\u003c/em\u003e in controlled and safe bioremediation methods. Although Klebsiella's pathogenicity limits its direct utility, understanding its breakdown pathways can help lead the development of safer synthetic biology-derived treatments for environmental detoxification. \u003cbr /\u003e The discovery of ciprofloxacin-degrading \u003cem\u003eKlebsiella\u003c/em\u003e sp. SG01 in wastewater not only demonstrates the great metabolic plasticity of microbial communities, but also serves as a stark reminder of the widespread pharmaceutical contamination in these environments. This finding is particularly concerning for wastewater treatment facilities (WWTPs), as it highlights the crucial need for improved monitoring and control mechanisms to deal with rising pollution and its related dangers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eChemicals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCulture media were purchased from Himedia (Mumbai, India). All standard chemicals were purchased from Sigma-Aldrich (St. Louis, MO, USA).Ciprofloxacin(\u0026gt;98% purity) was obtained from Tokyo Chemical Industry (TCI), India (CAS NO. 85721-33-1). Other antibiotic discs were obtained from Himedia, additional reagents used were of analytical grade.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnrichment culture.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA wastewater sludge sample was collected from North Bengal Medical College and Hospital (NBMCH), West Bengal, India. 5 g of the sludge sample was inoculated into a 500 ml Erlenmeyer flask containing 100 ml modified mineral salts medium (MSM) supplemented with ciprofloxacin (CIP) [constituents (g/L): KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 3.0; NaCl, 5.0;MgSO\u003csub\u003e4\u003c/sub\u003e. 7 H\u003csub\u003e2\u003c/sub\u003eO, 0.01; NH\u003csub\u003e4\u003c/sub\u003eCl, 0.5; and CIP, 1.0; pH, 5.0 (adjusted with 0.1 N HCl) ] and left in static condition for 7 d in the dark at 30 \u003csup\u003e0\u003c/sup\u003eC. The culture (1ml) was further transferred to a fresh 100 mL MSM, supplemented with 2 g/L of CIP, and left in static condition for 7 d in the dark, at 30 \u003csup\u003e0\u003c/sup\u003eC, and sub-cultured thrice under similar conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsolation and identification of the strain capable of degrading CIP\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eAfter final sub-culturing, the culture was serially diluted with sterile phosphate buffer solution (PBS; pH 7.4), from which 0.1mL suspension was spread on the MSM-CIP-agar plate (CIP concentration = 0.5 g/L). The plates were incubated at 30 \u003csup\u003e0\u003c/sup\u003eC for 24 h until the emergence of visible colonies. The single colony purification was done twice in MSM-CIP agar plates before being routinely maintained in slants. The purified culture was subsequently used for all physiological experiments including growth assays. Bergey's Manual of Systematic Bacteriology\u003csup\u003e29\u003c/sup\u003e was followed to compare the results of biochemical tests. Genomic DNA was prepared and whole genome sequencing was performed using the IlluminaNovaseq 6000 platform\u003csup\u003e30\u003c/sup\u003e. The whole genome sequence was used to construct the phylogenetic tree after aligning sequences of closely related type strains via the Type Strain Genome Server (\u003cu\u003ehttps://tygs.dsmz.de/\u003c/u\u003e)\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrowth assays in different media.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe growth studies were conducted in Luria Bertani (LB) medium, a modified mineral salt medium supplemented with 5.0 g/L glucose (MSM-glucose) or 2 g/L ciprofloxacin (MSM-CIP) and MSM without any carbon source. Before performing detailed growth studies in LB or MSM-glucose, standard procedure was used to calibrate OD\u003csub\u003e600\u003c/sub\u003e to colony forming unit (CFU) counts, which are directly correspond to the cell concentration of the culture, i.e. viable cell counts per mL. Following the protocol, 10 mL of overnight grown cells of a pre-culture in LB was centrifuged, pellet was re-suspended in 10 mL sterile PBS and serially diluted. From 10\u003csup\u003e-3\u003c/sup\u003e diluted bacterial suspensions, cells were inoculated (1 %) into fresh sterile LB for enumerating growth using the standard plate technique. For conducting a growth assay in MSM-glucose, 10 mL of log phase grown cells (8 h grown culture) of a pre-culture in LB was centrifuged, and the pellet was re-suspended in 10 mL sterile PBS and diluted. From 10\u003csup\u003e-1\u003c/sup\u003e diluted bacterial suspension, cells were inoculated (1 %) into fresh sterile MSM-glucose for enumerating growth using the standard plate technique. Similarly, for growth studies in MSM-CIP, 10 mL of log phase grown cells (7 h grown culture) of a pre-culture in MSM-glucose was centrifuged, and the pellet was re-suspended in 10 mL sterile PBS and diluted. From 10\u003csup\u003e-1\u003c/sup\u003e diluted bacterial suspension, cells were inoculated (1 %) into fresh sterile MSM-CIP for enumerating growth using the standard plate technique. A CIP-resistant strain \u003cem\u003eShigella\u003c/em\u003esp AP55 (NCBI Accession No. PV652741) was evaluated for its CIP-consumption capabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUV-vis Spectroscopic quantification of CIP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpectrometric determination of residual CIP in the culture medium was done by using a Lambda 25 UV/VIS spectrometer. Absorption maxima (\u0026lambda;\u003csub\u003emax\u003c/sub\u003e) was determined before generating a standard curve using varying concentrations of CIP (10\u0026ndash;90 \u0026mu;g/mL). For quantifying the concentration of CIP in the culture medium following inoculation of bacterium, the growing culture medium was withdrawn at specific intervals and centrifuged at 10,000 rpm to pellet cells for collection of cell-free supernatant. The optical density of each cell-free supernatant sample (derived from samples at different time intervals) at \u0026lambda;\u003csub\u003emax\u003c/sub\u003e was obtained to ascertain the CIP concentration from the standard curve\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResidual Antibacterial activity of the degraded products\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe residual antibacterial potential of intermediates formed in the course of degradation of CIP by SG01 was evaluated for inhibitory activity of culture medium minus cells (cell-free supernatant) against CIP-sensitive \u003cem\u003eEscherichia coli\u003c/em\u003e K12 on Luria agar using the disc diffusion method\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudying plasticity in cell dimensions of SG01 when grown in different media\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSG01 was grown in three different media, LB, MSM-glucose, and MSM-CIP up to the mid-log phase. Cell harvesting time was ascertained from individual growth curves in a specific medium. Immediately before passing the log phase cells through a 0.22\u0026mu;m bacterial filter, unfiltered culture was serially diluted and plated, and incubated overnight to determine the cell count. Side-by-side, the filtrate was also serially diluted and plated, and incubated overnight to determine the cell count (CFU/mL). Light microscopy of the log-phase cells grown in three different media was done following Gram staining\u003cu\u003e\u003csup\u003e33\u003c/sup\u003e\u003c/u\u003e and negative staining using nigrosin\u003cu\u003e\u003csup\u003e34\u003c/sup\u003e\u003c/u\u003e. Scanning electron microscopy (SEM) was performed to confirm a reduction in cell size when grown in MSM-CIP\u003csup\u003e35\u003c/sup\u003e. To further validate the presence and size quantification of nano-sized cells as observed under light microscope and photographed, we performed Dynamic Light Scattering (DLS)\u003csup\u003e36\u003c/sup\u003e, also known as Photon Correlation Spectroscopy (PCS) or Quasi-Elastic Light Scattering (QELS). It is a widely used technique for determining the size distribution of particles in the nanometer range suspended in a liquid. DLS measurements were performed using standard protocol with suitable optimizations. Briefly, cells grown in MSM-CIP were harvested from mid-log phase and washed twice using sterile PBS. The pellet was dissolved in sterile PBS and passed through a bacterial membrane filter. The final suspension was diluted to achieve appropriate concentration for analysis. Cell size measurements were performed using an Anton PaarLitesizer 500 instrument at 25\u0026deg;C, using standard polystyrene cuvettes. The system was set to measure at a scattering angle of 175\u0026deg; (backscatter detection). Each sample was equilibrated in the instrument for 30 seconds before measurement. Peak mean intensity and standard deviation were exported from the Anton Paar software for documentation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInvestigating the reversal phenomenon of nanobacterium transformation to micrometer-sized bacterium\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSG01 was grown in MSM-glucose upto mid-log phase and harvested. The cells were centrifuged at 4000 rpm and the supernatant was discarded. The pellet was washed with sterile PBS twice by centrifugation and 1% inoculum was added to MSM-CIP (2g/L), allowed to incubate at 30\u003csup\u003e0\u003c/sup\u003eC under gyrotary shaking (100 r.p.m.). From the mid-log phase, cells were harvested, washed with PBS twice and reinoculated in LB medium. The cell morphology was determined by Gram\u0026rsquo;s staining at different time intervals. Since nano-sized cells are not distinctly visible with Gram\u0026rsquo;s stain, the initial 0-hour cells were viewed with nigrosin staining.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptimization of CIP Degradation Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eKlebsiella\u003c/em\u003e sp. SG01 was cultured overnight in MSM-glucose (5g/L) at 30 \u0026deg;C with shaking at 150 rpm. Cells were harvested by centrifugation at 5000 \u0026times; g for 10 minutes, washed twice with sterile phosphate-buffered saline (PBS), and resuspended to prepare inocula of different for optimization studies.\u003c/p\u003e\n\u003cp\u003epH Optimization\u003c/p\u003e\n\u003cp\u003eTo study the effect of pH on CIP degradation, minimal media were adjusted to five different pH values: 2, 3, 4, 5, and 6with 1 N HCl or NaOH. Each pH condition was inoculated with 1% bacterial culture and incubated at 30 \u0026deg;C.\u003c/p\u003e\n\u003ch5\u003e\u003cstrong\u003eTemperature Optimization\u003c/strong\u003e\u003c/h5\u003e\n\u003cp\u003eThe effect of incubation temperature on CIP degradation was assessed by incubating cultures at three different temperatures: \u003cstrong\u003e4 \u0026deg;C, 30 \u0026deg;C, and 37 \u0026deg;C\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e with fixed pH (7.0) and inoculum density (1%).\u003c/p\u003e\n\u003ch5\u003e\u003cstrong\u003e\u0026nbsp;Inoculum Density Optimization\u003c/strong\u003e\u003c/h5\u003e\n\u003cp\u003eInoculum density was varied from \u003cstrong\u003e1% to 5%\u003c/strong\u003e (v/v) to evaluate its effect on degradation. The experiments were conducted at 30 \u0026deg;C and pH 7.0.\u003c/p\u003e\n\u003cp\u003eFor each set of the above three conditions, samples were collected at different time intervals (e.g., 0, 15, 24, 48, and 72 hours). \u003cstrong\u003eCIP degradation was monitored using UV-Visible spectroscopy\u003c/strong\u003e by measuring absorbance at the CIP \u0026lambda;max and degradation was calculated based on the reduction in absorbance relative to the initial concentration.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eEffect of Different CIP Concentrations on Bacterial Growth\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eTo assess the impact of CIP concentration on bacterial viability and growth, cultures were exposed to a range of CIP concentrations: \u003cstrong\u003e0.025, 0.05, 0.25, 0.5, 1, 2, 5, and 10 mg/mL\u003c/strong\u003e in minimal media at 30 \u0026deg;C. At intervals of 24 hours, \u003cstrong\u003ebacterial growth was quantified by measuring CFU/mL \u003c/strong\u003ethrough serial dilution and plating on Luria-Bertani agar. Plates were incubated at 30 \u0026deg;C overnight before colony counting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolite extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eKlebsiella \u003c/em\u003esp. SG01 was cultured in Minimal Salt Medium (MSM) supplemented with ciprofloxacin (2g/L) to assess its metabolic profile. Cells were harvested from mid-lag phase (~6hours) and mid-log phase (~17 hours) by centrifugation at 4\u0026deg;C (e.g., 8000 \u0026times; g for 10 min), washed twice with ice-cold phosphate-buffered saline (PBS), and quenched using cold methanol to halt metabolic activity. The homogenates were then incubated at \u0026ndash;20\u0026deg;C for 60 minutes to enhance metabolite precipitation, followed by centrifugation at 14,000 \u0026times; g for 20 minutes at 4\u0026deg;C. The supernatants were carefully transferred to 1.5-mL Eppendorf tubes, flash-frozen, and dried under vacuum using a centrifugal evaporator. The dried metabolite residues were reconstituted in 100 \u0026micro;L of a complex solvent system suitable for LC-MS analysis, followed by vortexing and gently shaking. Samples were centrifuged again at 14,000 \u0026times; g for 15 minutes at 4\u0026deg;C, and the resulting supernatants were filtered through 0.22-\u0026mu;m membrane filters. The final extracts were transferred into liquid chromatography- mass spectrometry (LC-MS) vials for analysis. To ensure consistency and monitor system performance, equal aliquots of each processed sample were pooled to prepare quality control (QC) samples. Blank samples, consisting of the same solvents and reagents used in extraction but without biological material, were processed identically and analyzed alongside experimental samples to identify and subtract background signals.\u003c/p\u003e\n\u003cp\u003eUltra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) analysis was conducted using an Agilent UHPLC system coupled with a Thermo Q Exactive\u0026trade; HF-X Orbitrap mass spectrometer. Metabolite separation was performed on an Accucore HILIC column (50 mm \u0026times; 2.1 mm, 2.6 \u0026mu;m, Thermo Fisher Scientific, USA) using a gradient elution of two mobile phases: phase A consisted of 0.1% formic acid and 10 mM ammonium acetate in 95% acetonitrile, and phase B consisted of 0.1% formic acid and 10 mM ammonium acetate in 50% acetonitrile. The flow rate was maintained at 0.3 mL/min with an injection volume of 5 \u0026micro;L per sample. The column and autosampler were maintained at 40\u0026deg;C and 4\u0026deg;C, respectively. The elution program began with 2% B for 0\u0026ndash;1 min, followed by a linear increase to 50% B from 1\u0026ndash;17 min, held at 50% B until 17.5 min, and re-equilibrated at 2% B from 18\u0026ndash;20 min. Samples were injected in random order to minimize analytical variability, with one quality control (QC) sample and one blank sample run after every five injections. Mass spectrometric detection was carried out in both positive and negative electrospray ionization (ESI) modes with a spray voltage of 3.2 kV. The sheath and auxiliary gas flow rates were set to 35 and 10 arbitrary units, respectively, and the capillary temperature was maintained at 320\u0026deg;C. The system operated in data-dependent acquisition mode, alternating between full MS scans and MS/MS fragmentation with dynamic exclusion, covering a scan range of 100\u0026ndash;1500 m/z at a scan rate of 40 Hz.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMetabolomics data processing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRaw LC-MS data were converted to open formats (e.g., mzXML or mzML) and processed using MZmine\u003csup\u003e37\u003c/sup\u003e for peak detection, alignment, and normalization.Metabolites were identified based on accurate mass, retention time, and MS/MS fragmentation, using databases like HMDB, METLIN, and LIPID MAPS\u003csup\u003e38\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e39\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e40\u003c/sup\u003e\u003csup\u003e.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome study \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to transcriptome analyses, genomic DNA was isolated and sequenced as per the protocol described earlier\u003csup\u003e41\u003c/sup\u003e. For transcriptome, the strain SG01 was grown in MSM-glucose (control) and MSM-CIP (test) media at 30\u003csup\u003e0\u003c/sup\u003eC under gyrotary shaking (100 r.p.m.). Cells were harvested from the mid-log phase by centrifugation at 4000 r.p.m. Spent media was discarded, and pelleted cells were washed twice with sterile PBS and immediately quenched with liquid nitrogen and stored at -80\u003csup\u003e0\u003c/sup\u003eC. Total RNA was isolated from both control and test bacterial samples, and after ascertaining qualities and quantities of the isolated RNA samples, RNA-seq libraries were prepared from the purified RNA samples using IlluminaTrueSeq mRNA sample Prep kit (Illumina, U.S.) and MICROBExpress kit (Invitrogen\u0026trade;, USA) as per the manufacturer\u0026rsquo;s protocol. Following sequencing, raw sequence reads were processed with Trimmomatic v0.39, which allowed for the removal of low-quality and adapter sequences from the raw data. The sequenced raw reads of the two samples, the control and test sample, were processed to obtain high-quality clean reads and were mapped on the reference genome of \u003cem\u003eKlebsiella\u003c/em\u003e sp. SG01, using STAR (v 2.7.10a) with default parameters. Feature Counts (version 2.0.3) was used to count the number of reads mapped on each gene. Differential gene expression analysis was performed using the DEGSeqR package between control and test samples. Log2Fold change (log2 FC) values greater than zero were considered up-regulated whereas less than zero were down-regulated along the P-value threshold of 0.05 for significant results. Using KAAS (KEGG Automatic Annotation Server), functional annotations of every gene were performed against the curated KEGG genes database by the previously mentioned methods\u003csup\u003e42\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e43\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e44\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e45\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e46\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork analysis and Identification of the hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe STRING database version 12.0 was used to construct a PPI network based on experimental results, automated text-mining, co-expression data, and other curated databases\u003csup\u003e20\u003c/sup\u003e. The upregulated DEGs \u0026gt; 1 log 2FC and downregulated DEGs \u0026lt; -1 log 2FC were chosen to study the interaction. The PPI networks were functionally categorized using the CLueGO plugin of Cytoscape v3.8.0\u003csup\u003e47\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e48\u003c/sup\u003e. The hub genes and bottleneck genes were also identified by the Cytohubba plugin\u003csup\u003e41\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e49\u003c/sup\u003e. On identification of all metabolites through integratedtranscriptomics and metabolomic analyses, PubChem Sketcher V2.4 (https://pubchem.ncbi.nlm.nih.gov//edit3/index.html) was used to draw the chemical structures and prepare a hypothesised degradation pathway\u003csup\u003e50\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003estatistical analysis\u003c/strong\u003e was performed using \u003cstrong\u003eGraphPad Prism\u003c/strong\u003e(https://www.graphpad.com/quickcalcs/ttest/?format=SD). A \u003cstrong\u003etwo-tailed unpaired Student\u0026rsquo;s\u003c/strong\u003e\u003cem\u003et\u003c/em\u003e\u003cstrong\u003e-test\u003c/strong\u003e was used to compare the means between two groups. All experiments were conducted with \u003cstrong\u003ethree independent biological replicates\u003c/strong\u003e. Where applicable, \u003cstrong\u003eat least 50 individual bacterial cells\u003c/strong\u003e were measured per condition in microscopy-based assays.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the University of North Bengal, Raja Rammohanpur Campus, India-734010 for their support in conducting this study. We are indebted to the Department of Biotechnology, Government of India for funding a part of our work (BT/PR40383/BCE/8/1561/2020). S.G. is thankful to the Government of West Bengal (WBP211629117511) for providing financial aid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.G. participated in designing the experiments, performed the studies, analysed data, wrote and reviewed the manuscript; R.C. conceived the idea, supervised and designed the experiments, analysed data, and wrote and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequence data in the form of Raw sequence reads that support the findings of this study have been deposited in NCBI and are available at SRA: PRJNA931810 under accessions SRR24804248 for the draft genome sequence of \u003cem\u003eKlebsiella\u003c/em\u003e sp. SG01 and SRR29374586 for the whole transcriptome sequence. Preprint version of the manuscript is available with doi: https://doi.org/10.1101/2024.11.20.624549.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Material \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplemental material for this article is provided in Supplementary file 1,2 and 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCherian, J. J. et al. India\u0026rsquo;s Road to Independence in Manufacturing Active Pharmaceutical Ingredients: Focus on Essential Medicines. Economies 9, 71 (2021).\u003c/li\u003e\n\u003cli\u003eKhouja, T., Mitsantisuk, K., Tadrous, M. \u0026amp; Suda, K. J. 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Cells retained on the filter measured ~0.64 \u0026micro;m, whereas cells that passed through the 0.22 \u0026micro;m filter (filtrate) were significantly smaller (~0.23 \u0026micro;m), confirmed by DLS (193 \u0026plusmn; 29.75 nm). Cells regrown from the filtrate on MSM-CIP and LA agar showed varying degrees of size\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMedia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMedium/condition\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eAverage cell diameter using light microscopy (\u0026mu;m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eAdvanced \u0026nbsp;size characterization(\u0026mu;m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eZeta Potential (mV)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLiquid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1.458\u003cu\u003e+\u003c/u\u003e0.194\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.282 *(SEM)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e-13.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMSM-Glucose\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLiquid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1.253\u003cu\u003e+\u003c/u\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.115 *(SEM)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e-9.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMSM-CIP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eRetained on filter\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.638\u003cu\u003e+\u003c/u\u003e0.047\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePassed through filter (filtrate)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.226\u003cu\u003e+\u003c/u\u003e0.088\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.193\u003cu\u003e+\u003c/u\u003e29.75\u003c/p\u003e\n \u003cp\u003e**(DLS)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFiltrate grown on MSM-CIP agar plated\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.289\u003cu\u003e+\u003c/u\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFiltrate grown on LA plate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.649\u003cu\u003e+\u003c/u\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.598 *(SEM)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5801647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5801647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Antimicrobial resistance is a global crisis. Biodegradation by bacteria is an effective strategy to remove the micropollutant from the environment. In this study, we demonstrate that a persistent fluoroquinolone, ciprofloxacin (CIP) can be degraded by a multidrug-resistant Klebsiella sp. SG01 and used as its only carbon source. SG01’s ability to consume or degrade more than 50% of CIP (~2g/L) within 48 h exceeded previous published data on CIP-biodegradation. The degradation was quantified using UV-vis spectroscopy and the degraded product was less toxic than the parent compound as tested against a susceptible Escherichia coli K12. SG01 changes into nano-sized cells as culturable nanobacterium, passes through a 0.22 µm pore-size filter while growing on ciprofloxacin, and shows a shorter generation time than cells grown on glucose or rich medium. The nano-sized-bacterium reverses to its micrometer-sized form within an hour of culture transfer to nutrient-rich Luria broth. The basis for the changed growth phenotype of nano-SG01 cells and metabolic changes was partially established by the whole-genome transcriptome.","manuscriptTitle":"Ciprofloxacin consumption and phenomenal transformation to a culturable nanometer-sized- bacterium by a Klebsiella strain SG01","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 07:31:04","doi":"10.21203/rs.3.rs-5801647/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe95e549-1480-4e7a-a7e3-5f985679abf5","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-12T07:31:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 07:31:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5801647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5801647","identity":"rs-5801647","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}