Isolation, identification, and slow-growth phenotype characterization of a clinically derived Escherichia coli from urinary tract infection

Wenqiang Yu; Yao Ou; Shanshan Dong; Xiangyu Deng; Siqiang Niu; Xiongyu Jiang; Ranran Tang; Shuqi Zhang; Qiongyuan Zhang; Dinghua Liu; Chunhong Zou; Jiangling Wu; Yumei Zhao

doi:10.1371/journal.pone.0358948

Abstract

A slow-growth Escherichia coli isolate, designated E. coli 105X, was recovered from a patient with urinary-tract infection. Initial biochemical profiling misidentified it as Shigella boydii; however, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and average nucleotide identity analysis (98.67% relative to uropathogenic E. coli CFT073) unequivocally reclassified the strain as E. coli. Antimicrobial susceptibility testing revealed broad-spectrum resistance to β-lactams, aminoglycosides and fluoroquinolones, which was mediated by 53 acquired resistance determinants, including the extended-spectrum β-lactamase genes bla_CTX-M-55 and bla_TEM-1, together with an expanded repertoire of efflux pump systems. Whole-genome sequencing (WGS) disclosed 45399 single-nucleotide polymorphisms and 1736 insertions/deletions relative to E. coli CFT073. Functional enrichment analyses demonstrated that non-synonymous mutations in 11 flagellar biosynthesis genes (e.g., flgA, fliK), the peptidoglycan synthase-encoding mrcB, and 29 growth-associated loci collectively perturb energy allocation and compromise biofilm formation, resulting in a markedly prolonged doubling time. Concurrently, E. coli 105X lacks canonical virulence factors such as the siderophore enterobactin and P fimbriae, but has acquired temperature-sensitive hemagglutinin and type IV pili. Taken together, E. coli 105X exemplifies a distinctive adaptive strategy in which metabolic bottlenecks synergize with structural mutations to establish a “slow-growth, high-resistance, re-engineered virulence” phenotype. These findings underscore the critical necessity of molecular diagnostic tools for accurate identification of morphologically atypical clinical E. coli isolates, and provide a theoretical rationale for developing targeted combination therapies that interfere with core bacterial energy-metabolism pathways.

Citation: Yu W, Ou Y, Dong S, Deng X, Niu S, Jiang X, et al. (2026) Isolation, identification, and slow-growth phenotype characterization of a clinically derived Escherichia coli from urinary tract infection. PLoS One 21(10): e0358948. https://doi.org/10.1371/journal.pone.0358948

Editor: Dwij Raj Bhatta, Tribhuvan University, NEPAL

Received: May 29, 2026; Accepted: September 8, 2026; Published: October 29, 2026

Copyright: © 2026 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The datasets presented in this study have been deposited in NCBI under BioProject number PRJNA1282872.

Funding: This study was supported by Chongqing Municipal Health Commission (Grant No. 2024QNXM037), Chongqing Municipal Science and Technology Bureau (Grant No. CSTB2023NSCQ-MSX0175), Chongqing Municipal Education Commission (Grant No. KJQN202300426), Chongqing Medical University (Grant No. W0012), University-Town Hospital of Chongqing Medical University (Grant No. 2021LC07), and China Postdoctoral Science Foundation (Grant No. 2024M763904). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Escherichia coli is a Gram-negative, non-spore-forming rod equipped with peritrichous flagella that confer motility, and it constitutes the dominant facultative anaerobe of the human intestinal microbiota. It is a leading cause of diverse infections in humans and animals, including urinary-tract infections, enteric diseases, and systemic infections [1,2]. Its marked phenotypic heterogeneity and rapid evolution of antimicrobial resistance pose substantial challenges for accurate diagnosis and infection control.

Conventional identification relies on biochemical profiling; however, E. coli and the genus Shigella share >99% 16S rRNA sequence similarity, resulting in a 15–30% misidentification rate [3,4]. Recent advances in molecular technologies, namely matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and whole-genome sequencing (WGS), have demonstrated that E. coli and Shigella spp. represent distinct ecotypes of the same biological species, with average nucleotide identity (ANI) values exceeding 96% [5–7]. Therefore, routine laboratories report frequent biochemical misclassification, underscoring the limitations of traditional methods and suggesting that slow-growth strains may further confound phenotypic identification through metabolic pathway rewiring [8].

Slow-growth phenotypes, operationally defined as a significant prolongation of doubling time and reduction in overall biomass yield relative to wild-type or reference strains under identical culture conditions [8,9], have been linked to dysregulation of metabolic control networks. Mutations within the ATP-binding motif of the DEAD-box helicase DbpA impair ATPase activity. For instance, the arginine-to-alanine substitution R331A causes dominant-negative interference with the wild-type protein function. Consequently, cells exhibit pronounced colony morphotype alterations [10,11]. Moreover, aberrant assembly of the cell-cycle control complex disrupts the coordination between DNA replication and cytokinesis, driving the bacterium into a trade-off state between metabolic maintenance and growth [12,13]. Collectively, these mechanisms confer unique adaptive advantages upon slow-growth strains, yet the co-evolutionary dynamics linking slow growth with antimicrobial resistance and virulence remain poorly defined.

In this study, we isolated a slow-growth E. coli, designated E. coli 105X, from a patient with urinary-tract infection. Compared with typical E. coli, E. coli 105X exhibited markedly reduced growth and biochemical profiles indistinguishable from Shigella boydii. Using comprehensive WGS, we characterized the genetic determinants underlying the slow-growth phenotype and delineated their interplay with antimicrobial resistance and virulence evolution. Our findings provide a molecular framework for accurate clinical identification and for precise control strategies against multidrug-resistant E. coli.

Materials and Methods

Clinical strain acquisition and primary isolation

In October 2023, a slow-growth E. coli strain (E. coli 105X) was isolated from the urine of a 27-year-old male patient with urinary tract infection at University Town Hospital, Chongqing Medical University. The patient presented with chills, rigors, and fever; abdominal CT indicated nephroureterolithiasis with hydronephrosis and concurrent infection, alongside markedly elevated CRP and PCT. Urine specimens were Gram-stained, microscopically examined, and aerobically cultured on blood and MacConkey agar for 24 h at 37°C. Pure single colonies (white, circular, smooth, moist) were obtained through three-sector streaking and sub-cultured for ten passages on blood agar to ensure clonal purity. This study was approved by the Ethical Review Committee of University-Town Hospital of Chongqing Medical University (protocol No. LL-202323). Written informed consent was obtained from the participant after full explanation of the study procedures.

Phenotypic and molecular identification

Colonies were identified via the BD Phoenix™ automated system after suspension in 0.45% saline (0.50–0.63 McFarland). Serological typing was performed by slide agglutination using 0.45% saline suspensions of the isolate and commercial Shigella antisera (Tianrun, Ningbo, China; Cat. No. TR201). The antisera panel included polyvalent antisera covering Shigella dysenteriae (groups 1–4, 5–8, 9–12), Shigella flexneri (serotypes I–VI and X/Y variants), S. boydii (groups 1–5, 6–10, 11–14, 15–18), and Shigella sonnei (phases 1 and 2), as well as corresponding monovalent antisera targeting individual O-antigens of each serotype. Agglutination was assessed macroscopically within 1 min, with physiological saline serving as a negative control. MALDI-TOF MS was performed by QuanID Microbial Identification System (Rongzh, Qingdao, China). For sample preparation, pure colonies of the target strains were picked and uniformly smeared onto the target plate. Subsequently, 2 µL of B reagent (Rongzhi, Qingdao, China, Cat. No. QT-SJ01-L) was dispensed onto the smear and allowed to air dry. Finally, 1.5 µL of A reagent was added to the same spot, air dried, and the plate was subjected to spectral acquisition. The acquired mass spectra were analyzed and identified using the dedicated software provided with the instrument. The identification results were generated by comparing the obtained spectra against an in-house curated database specific to clinical pathogens.

Growth kinetics and antimicrobial susceptibility

Growth curves were constructed in beef-peptone-glucose medium (37 °C, 180 rpm) by measuring OD₆₀₀ every 2 hours, with sterile medium incubated in parallel as a blank control. Susceptibility testing was carried out with the BD Phoenix™ automated system. MICs were determined by broth microdilution per CLSI guidelines and interpreted using CLSI M100-2025 breakpoints [14], with E. coli ATCC 25922 as the quality control strain.

Genomic DNA extraction and sequencing

Overnight Luria-Bertani (LB) broth cultures (37 °C, 180 rpm) were processed using the Hieff NGS® MaxUp II DNA Library Prep Kit for Illumina®. DNA quality (Qubit™ dsDNA HS Assay) and integrity (Agilent 2100 Bioanalyzer) were assessed before paired-end sequencing on Illumina NovaSeq. Raw reads were quality-trimmed and filtered using Fastp (version 0.11.2). Briefly, adapter sequences were automatically detected and trimmed; global trimming was applied to the 5′ and 3′ ends of each read; sliding-window trimming was performed with a window size of 4 bp, discarding any window with mean base quality below Q20; base correction was conducted based on read-overlap consensus; and low-quality reads—defined as those containing >40% bases with Phred quality scores < Q15 or with a post-trimming length < 35 nt—were removed. Hybrid assembly of short- and long-read data employed SPAdes v3.5.0 and Canu v1.3 [15,16].

Evolutionary relationship and phylogenomic analysis

Whole-genome CDSs were aligned with MAFFT; phylogenetic trees were inferred with FastTree. Pairwise ANI values were calculated via the JSpeciesWS web server for taxonomic confirmation. Functional annotation used Prokka v1.10 and the NCBI PGAP pipeline to catalog resistance, virulence, and metabolic genes [17]. Core- and pan-genome analyses were performed with Roary v3.11.2 against closely related reference strains [18].

Results

Isolation and Identification of E. coli 105X

On blood agar, E. coli 105X produced highly translucent colonies with a mean diameter of ~1 mm, markedly smaller than the 6-mm colonies of the control strain E. coli ATCC 25922 (Fig 1A,1B). Biochemical profiling showed E. coli 105X to be positive for L-leucine-7-amido-4-methyl-coumarin and negative for L-arabinose, L-arginine-7-amido-4-methyl-coumarin, galacturonic acid, acetate, malonate, ornithine, p-nitrophenyl phosphate, L-rhamnose, N-acetyl-glucosamine, and citrate. This biochemical pattern showed a closer match to the standard biochemical fingerprint of S. boydii (S1 Table). Serological testing with Shigella antisera yielded no agglutination, indicating the absence of Shigella-specific antigens (S1 Fig). MALDI-TOF MS unequivocally identified the isolate as E. coli (S2 Fig). The quantitative analysis of growth curves using the logistic growth model (GrowthCurver package) revealed that E. coli 105X had a significantly lower growth rate (r = 0.226 h⁻¹) compared to E. coli ATCC 25922 (r = 0.294 h⁻¹), representing a 23.1% reduction. Additionally, the area under the curve (AUC) for E. coli 105X (3.439) was 31.8% lower than that of E. coli ATCC 25922 (5.040), indicating reduced overall growth capacity (Fig 1C).

Download:

Fig 1. Growth characteristics of E. coli 105X.

(A) Colony morphology of E. coli 105X cultured on blood agar medium. (B) Colony morphology of parallel subcultured quality control strain E. coli ATCC 25922 on blood agar. (C) Comparison of the growth curves between E. coli 105X and E. coli ATCC 25922.

https://doi.org/10.1371/journal.pone.0358948.g001

Antimicrobial susceptibility testing

The broth microdilution susceptibility testing showed that E. coli 105X was resistant to Amikacin (MIC > 16 µg/mL), gentamicin (MIC > 8 µg/mL), cefazolin (MIC > 16 µg/mL), cefotaxime (MIC > 32 µg/mL), cefepime (MIC = 16 µg/mL), aztreonam (MIC > 16 µg/mL), ampicillin (MIC > 16 µg/mL), piperacillin (MIC > 64 µg/mL), ciprofloxacin (MIC > 2 µg/mL), Levofloxacin (MIC > 8 µg/mL), and Moxifloxacin (MIC > 4 µg/mL). Ceftazidime was intermediate (MIC = 8 µg/mL) (Table 1).

Download:

Table 1. Antimicrobial susceptibility testing results for E. coli 105X.

https://doi.org/10.1371/journal.pone.0358948.t001

General genomic characteristics and phylogeny

After trimming raw reads with Fastp, Illumina-based sequencing revealed that the slow-growth E. coli 105X possesses a genome of 4,894,031 bp with a GC content of 50.9% and 97.86% of bases achieving a Q20 quality score or higher. Annotation via the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) predicted 4,890 protein-coding genes, spanning 4,476,981 bp (87.948% of the genome) with a mean length of 916 bp (S2 Table).

Functional annotation against the COG database classified 4,034 proteins into functional categories. The most abundant classes were carbohydrate transport and metabolism (G) and amino-acid transport and metabolism (E), together accounting for 17.70% of assigned proteins. Additional highly represented functional modules included energy production and conversion (C, 7.29%), transcription (K, 7.19%), cell wall/membrane/envelope biogenesis (M, 6.00%), inorganic ion transport and metabolism (P, 5.55%), translation/ribosomal structure and biogenesis (J, 4.61%), and replication/recombination/repair (L, 4.54%) (Fig 2A).

Download:

Fig 2. General genomic and phylogenetic characteristics of E. coli 105X.

(A) COG functional classification bar chart of E. coli 105X genes. (B) Phylogenetic tree based on single-nucleotide polymorphisms (SNPs). (C) Heatmap displaying the ANI between the phylogenetically close species. The color bar on the right indicates the ANI value calculated using fastANI.

https://doi.org/10.1371/journal.pone.0358948.g002

To delineate the precise genetic divergence of E. coli 105X, we constructed a whole-genome SNP-based phylogeny and calculated ANI. The phylogenetic tree placed E. coli 105X in the same clade as E. coli CFT073, E. coli O157:H7 Sakai, S. dysenteriae SWHEFF 49, S. flexneri 2a str. 301, E. coli K-12 MG1655, S. boydii ESBL-W3-2, and S. sonnei SE6−1 (Fig 2B). ANI values determined by fastANI corroborated this topology, with the highest identities observed for E. coli CFT073 (98.67%), E. coli K-12 MG1655 (96.86%), S. dysenteriae SWHEFF 49 (96.72%), S. sonnei SE6−1 (96.71%), S. boydii ESBL-W3-2 (96.66%), E. coli O157:H7 Sakai (96.54%), and S. flexneri 2a str. 301 (96.44%), thereby confirming the phylogenetic placement (Fig 2C).

Enrichment Analysis and Prediction of Resistance and Virulence Determinants

Using E. coli CFT073 as the reference, we identified 45,399 SNPs and 1,736 indels in E. coli 105X. After excluding synonymous changes, 2,284 genes harbored non-synonymous mutations. GO enrichment analyses showed that these mutated genes were significantly over-represented in biological processes related to the metabolism of organic acids, alcohols, and amino acids (Fig 3A). At the cellular-component level, they were enriched in the cytoplasmic membrane, periplasmic space, and transporter complexes, implying roles in membrane architecture and substrate transport. For molecular functions, enrichment was evident in ATP binding, oxidoreductase activity, and transferase activity, reflecting alterations in cellular energy metabolism, redox homeostasis, and phosphoryl-group transfer. KEGG pathway enrichment further demonstrated that the mutated genes are enriched in four principal areas (Fig 3B). First, antimicrobial resistance: cationic antimicrobial peptide (CAMP) resistance and β-lactam resistance pathways. Second, metabolic reprogramming: lysine biosynthesis, glycine–serine–threonine metabolism, and glutamate/aspartate/alanine metabolism. Third, DNA repair: base-excision repair, suggesting compromised genomic stability. Fourth, motility and chemotaxis: bacterial chemotaxis and flagellar assembly pathways, indicating potential defects in motility and surface adherence.

Download:

Fig 3. Enrichment analysis and prediction of resistance and virulence determinants.

(A) The mutated genes of E. coli 105X were analyzed for enrichment in three GO ontologies: biological process (BP), cellular component (CC), and molecular function (MF). The top 10 enriched GO terms sorted by number were visualized as bar graph. (B) Top 30 KEGG pathways sorted by predicted Q-value for mutated genes of E. coli 105X in each strain. (C) Comparison of antimicrobial resistance genes in E. coli 105X, E. coli ATCC 25922 and E. coli CFT073. (D) Comparison of virulence factors in E. coli 105X and E. coli CFT073.

https://doi.org/10.1371/journal.pone.0358948.g003

The entire genome of E. coli 105X was uploaded to the Comprehensive Antibiotic Resistance Database (CARD) for resistance gene identification. The results showed that the E. coli 105X genome carries 58 resistance genes, which confer resistance via four mechanisms: antibiotic efflux, antibiotic inactivation, target modification, and reduced permeability (Table 2). It comprises 43 genes (81%) encoding antibiotic efflux pumps. These include 21 resistance–nodulation–division (RND) family components such as acrA, 13 major facilitator superfamily (MFS) transporters exemplified by mdtH, 2 ATP-binding cassette (ABC) systems, and the small multidrug resistance (SMR) pump emrE. In addition, four transcriptional regulators (marA, soxS, leuO, and kdpE) reduce permeability. Twelve genes modify antibiotic targets, including pmrF, bacA, eptA, ugd, arnT, gyrA, tufA, and vanG. Five genes mediate antibiotic inactivation, including aph(4)-Ia, bla_TEM⁃1, aac(3)-IV, bla_CTX-M-55 and bla_EC-5. Compared with E. coli CFT073, E. coli 105X carries an additional inactivation determinant (bla_CTX-M-55); relative to E. coli ATCC 25922, it has acquired both ESBL genes (bla_CTX-M-55 and bla_TEM⁃1) and lost the mdtM efflux locus (Fig 3C).

Download:

Table 2. Classification of antimicrobial resistance genes identified in E. coli 105X based on functional resistance mechanisms.

https://doi.org/10.1371/journal.pone.0358948.t002

Whole-genome comparison of E. coli 105X and E. coli CFT073 within the Virulence Factors of Pathogenic Bacteria (VFDB) revealed marked differences in their virulence repertoires. E. coli 105X completely lacks enterobactin (salmochelin), P fimbriae and α-haemolysin, and carries fewer copies of yersiniabactin, type I fimbriae, the SCI-I type VI secretion system, EspL1 and EaeH. Conversely, the haemorrhagic E. coli fimbrial locus is expanded, while E. coli 105X uniquely harbours temperature-sensitive haemagglutinin, Yersinia-type IV pili, lateral flagella (Aeromonas hydrophila origin), Bordetella-type flagella and the ACE type VI secretion system (Fig 3D).

Slow-growth-associated genes

Comprehensive GO annotation revealed 29 genes whose nonsynonymous variants are linked to the slow-growth phenotype in E. coli 105X. These include one metabolic gene (frdB). Eleven flagellar genes (flgA, flgD, flgK, flgJ, flgN, fliK, fliT, fliH, fliS, fliR, flhA) encode structural and regulatory components of the flagellar basal body and motor. Twelve fimbrial genes (yadL, htrE, csgF, csgB, ydeR, ydeS, fimA, fimD, fimF, fimH, fimI, yehB) are involved in type I fimbriae and curli biosynthesis. Additionally, there is one peptidoglycan-synthetic gene (mrcB), three toxin–antitoxin system genes (hipA, yhaV, prlF), and one further gene (hofN) (S3 Table).

Discussion

In the present study, a clinically derived E. coli strain designated E. coli 105X was isolated from a urinary tract infection. E. coli CFT073 is a prototype uropathogenic E. coli (UPEC) originally isolated from the blood and urine of a woman suffering from acute pyelonephritis; it elaborates a diverse array of toxins and enterotoxins and is a well-established model for studies of urinary-tract infections, bacteraemia and pyelonephritis [19,20]. This strain, E. coli 105X, exhibited the highest average nucleotide identity (ANI) when compared to the internationally recognized quality control reference strain CFT073 (Fig 2C). Therefore, E. coli CFT073 was selected as the control strain for subsequent comparative analysis. E. coli ATCC strain 25922 is widely utilized as a standard quality-control organism in clinical microbiology laboratories [21]. Compared to the normally growing E. coli ATCC 25922, E. coli 105X exhibits a slow growth phenotype (Fig 1). Genomic analysis revealed that this slow-growth phenotype arises from a complex adaptive mechanism involving nonsynonymous mutations in key structural genes, a significantly expanded repertoire of antibiotic resistance determinants, and a reprogrammed virulence strategy (Fig 3).

In this study, E. coli 105X was initially identified as S. boydii based on biochemical reactions, whereas MALDI-TOF MS unequivocally assigned it to E. coli, highlighting the risk of misidentifying invasive E. coli as Shigella by conventional phenotypic tests. Phylogenomic reconstruction and fastANI confirmed that E. coli 105X clusters with both E. coli and Shigella species, exhibiting ANI values ≥ 95% relative to S. dysenteriae SWHEFF 49, S. sonnei SE6−1, S. boydii ESBL-W3-2 and S. flexneri 2a str. 301 (ANI = 96.44%) (Fig 2C). E. coli and Shigella spp. share >99% 16S rRNA sequence identity and similar proteomic signatures, yet differ markedly in pathogenicity and clinical outcome [3,4]. Dif et al. reported ANI > 96% and dDDH > 70% between E. coli, supporting the re-classification of Shigella as an E. coli [5].

The slow-growth phenotype of E. coli 105X is not a simple growth defect but a multifaceted adaptation rooted in widespread genomic alterations. Enrichment analyses of nonsynonymous variants revealed pronounced perturbations in carbon and energy metabolism (Fig 3A). Metabolic bottlenecks, particularly within the purine nucleotide biosynthetic pathway, have been demonstrated to decelerate growth and simultaneously enhance β-lactam tolerance [9,22], suggesting that analogous lesions in E. coli 105X underlie its slow-growth and altered resistance phenotypes. Additionally, the over-representation of ABC transporters is noteworthy; these permeases govern iron and amino-acid uptake and are pivotal for UPEC fitness and virulence [23]. KEGG pathway enrichment identified mutations within CAMP-resistance and β-lactam-resistance modules, suggesting a potential role in evading host innate immunity and antibiotic challenge [22]. Alterations in amino acid metabolism are inferred to reprogram biosynthetic fluxes, promoting bacterial survival under nutrient deprivation [23,24]. Similarly, enrichment of fatty acid degradation and pyruvate metabolism genes indicates metabolic adaptation to oxidative and nutrient stress [25]. Finally, the accumulation of mutations in the flagellar assembly pathway suggests structural or regulatory modifications that could influence motility, biofilm formation and host colonisation, thereby modulating the bacterium’s environmental and host adaptability [24,25].

Crucially, we identified 29 specific genes directly implicated in growth regulation. Mutations in 11 flagellar assembly genes (e.g., flgA, fliK) likely disrupt structure assembly [26,27], diverting cellular energy (ATP) from proliferation to futile repair cycles [28,29]. Similarly, mutations in 12 fimbrial genes (e.g., fimA, fimH) and the peptidoglycan synthase gene mrcB are predicted to compromise biofilm integrity [30–33]. The mrcB mutation is particularly significant, as PBP1b deficiency is directly linked to prolonged doubling times [13], and impaired biofilms diminish nutrient sequestration, further attenuating growth [34]. Collectively, these lesions illustrate a sophisticated trade-off: E. coli 105X sacrifices rapid proliferation to reallocate energy towards stress survival, forming the foundation of its persistent phenotype.

Unlike the prototype UPEC strain CFT073, E. coli 105X jettisons enterobactin, P fimbriae, and α-haemolysin-factors that trigger robust immune responses [35,36]. Instead, it acquires Yersinia-type IV pili, lateral flagella, and an expanded ACE T6SS, potentially enhancing adherence, motility, and immune evasion [25,36–39]. A missense mutation in fimH may optimize this shift from biofilm formation to dissemination [32]. This re-engineered virulence suite is complemented by a formidable resistance portfolio. Over 80% of its 53 resistance genes encode efflux pumps (e.g., acrA, acrB, tolC), whose overexpression is synergistically regulated by global regulators (marA, soxS) [40,41] and potentiated by the slow-growth state, which reduces antibiotic target activity [9]^. Among these mechanisms, overexpression of the RND-type AcrAB-TolC efflux system not only facilitates the extrusion of β-lactams and fluoroquinolones but also reduces membrane permeability, thereby contributing to enhanced resistance to aminoglycosides [23]; marR and acrR products possess dual roles in target modification and efflux regulation [42,43]; Furthermore, the EvgA/EvgS and H-NS regulatory systems are known to induce distinct sets of eff pump genes [44]. The isolate’s broad resistance suggests that slow growth and high resistance are co-adaptive traits, enabling persistence under antibiotic and host pressures. The presence of ESBL genes CTX-M-55 and TEM-1 matches the predominance of CTX-M + TEM genotypes in clinical isolates [45]. This highlights the need for molecular-guided antimicrobial stewardship to limit the spread of multidrug-resistant strains.

Collectively, the slow-growing, multidrug-resistant clinical E. coli strain profiled in this study delivers robust evidence to optimize routine clinical pathogen identification pipelines. Our results further highlight the imperative to substitute conventional biochemical assays with more robust molecular platforms, including MALDI-TOF mass spectrometry and WGS.

Conclusion

This case highlights that slow-growth E. coli strains can be misidentified as S. boydii by conventional biochemical profiling, underscoring the critical need for genomic or MALDI-TOF MS‑based identification in clinical microbiology. The delayed growth phenotype was attributed to mutations in genes involved in purine metabolism (impA, purH), cell wall integrity (mrcB), and motility (flgA, fliK, fimH), which collectively impair nucleotide synthesis, biofilm formation, and energy distribution. This adaptive trade-off may reflect a virulence-metabolism balance commonly observed in pathogenic Enterobacteriaceae. They suggest that energy-targeted therapeutic strategies, such as the combination of fosfomycin and efflux pump inhibitors, could be effective against multidrug-resistant pathogens exhibiting slow-growth phenotypes.

Supporting information

S1 Fig. Serological analysis of E. coli 105X.

Shown are the serological test results of E. coli 105X (A) and the corresponding positive controls (B).

https://doi.org/10.1371/journal.pone.0358948.s001

(TIF)

S2 Fig. MALDI-TOF MS analysis of strain 105X.

The MALDI-TOF MS results suggest that strain 105X is E. coli.

https://doi.org/10.1371/journal.pone.0358948.s002

(TIF)

S1 Table. Biochemical test results of E. coli 105X.

The biochemical profile of E. coli 105X is shown alongside the expected results for E. coli and S. boydii as provided by the manufacturer’s reference database.

https://doi.org/10.1371/journal.pone.0358948.s003

(DOCX)

S2 Table. Quality-controlled data statistics of E. coli 105X.

Listed are the total reads count, total base count, average read length, Q20 base count, Q20 base ratio, GC base batio, etc.

https://doi.org/10.1371/journal.pone.0358948.s004

(DOCX)

S3 Table. Growth-related gene mutations in E. coli 105X.

Listed are the gene name, mutation type, impact level, annotation, and HGVS p.

https://doi.org/10.1371/journal.pone.0358948.s005

(XLSX)

References

1. Allocati N, Masulli M, Alexeyev MF, Di Ilio C. Escherichia coli in Europe: An overview. Int J Environ Res Public Health. 2013;10(12):6235–54. pmid:24287850
- View Article
- PubMed/NCBI
- Google Scholar
2. Bonten M, Johnson JR, van den Biggelaar AHJ, Georgalis L, Geurtsen J, de Palacios PI, et al. Epidemiology of Escherichia coli bacteremia: a systematic literature review. Clinical Infectious Diseases. 2021;72(7):1211–9. pmid:32406495
- View Article
- PubMed/NCBI
- Google Scholar
3. Chang M, Lee H, Kim JY, Lee W, Moon S. Comparison of 16S rRNA gene sequencing, API 20E, and RT-PCR for identification of autopsy-derived Escherichia coli and Shigella spp. J Science Criminal Investigation. 2022;16(3):163–77.
- View Article
- Google Scholar
4. Khalil IA, Troeger C, Blacker BF, Rao PC, Brown A, Atherly DE, et al. Morbidity and mortality due to shigella and enterotoxigenic Escherichia coli diarrhoea: The global burden of disease study 1990-2016. Lancet Infect Dis. 2018;18(11):1229–40. pmid:30266330
- View Article
- PubMed/NCBI
- Google Scholar
5. Dif G, Djemouai N, Bouras N, Zitouni A. In-Depth Genome-Based Analysis of Shigella spp. and Escherichia spp.: Resolving Ambiguities and Unveiling Phylogenetic Relationships. Curr Microbiol. 2025;82(4):170. pmid:40045049
- View Article
- PubMed/NCBI
- Google Scholar
6. Huls PG, Vischer NOE, Woldringh CL. Different amounts of DNA in newborn cells of escherichia coli preclude a role for the chromosome in size control according to the “Adder” Model. Front Microbiol. 2018;9:664. pmid:29675011
- View Article
- PubMed/NCBI
- Google Scholar
7. Zheng H, Bai Y, Jiang M, Tokuyasu TA, Huang X, Zhong F, et al. General quantitative relations linking cell growth and the cell cycle in Escherichia coli. Nat Microbiol. 2020;5(8):995–1001. pmid:32424336
- View Article
- PubMed/NCBI
- Google Scholar
8. Elles LMS, Uhlenbeck OC. Mutation of the arginine finger in the active site of Escherichia coli DbpA abolishes ATPase and helicase activity and confers a dominant slow growth phenotype. Nucleic Acids Res. 2008;36(1):41–50. pmid:17986459
- View Article
- PubMed/NCBI
- Google Scholar
9. Lubrano P, Smollich F, Schramm T, Lorenz E, Alvarado A, Eigenmann SC, et al. Metabolic mutations reduce antibiotic susceptibility of E. coli by pathway-specific bottlenecks. Mol Syst Biol. 2025;21(3):274–93. pmid:39748127
- View Article
- PubMed/NCBI
- Google Scholar
10. Song Y, Sun M, Mu G, Tuo Y. Exopolysaccharide produced by Lactiplantibacillus plantarum Y12 exhibits inhibitory effect on the Shigella flexneri genes expression related to biofilm formation. Int J Biol Macromol. 2023;253(Pt 5):127048. pmid:37748596
- View Article
- PubMed/NCBI
- Google Scholar
11. Pifer R, Russell RM, Kumar A, Curtis MM, Sperandio V. Redox, amino acid, and fatty acid metabolism intersect with bacterial virulence in the gut. Proc Natl Acad Sci U S A. 2018;115(45):E10712–9. pmid:30348782
- View Article
- PubMed/NCBI
- Google Scholar
12. Kleckner NE, Chatzi K, White MA, Fisher JK, Stouf M. Coordination of growth, chromosome replication/segregation, and cell division in E-coli. Front Microbiol. 2018. pmid:30038602
- View Article
- PubMed/NCBI
- Google Scholar
13. Pepper ED, Farrell MJ, Finkel SE. Role of penicillin-binding protein 1b in competitive stationary-phase survival of Escherichia coli. FEMS Microbiol Lett. 2006;263(1):61–7. pmid:16958852
- View Article
- PubMed/NCBI
- Google Scholar
14. Humphries R, Bobenchik AM, Hindler JA, Schuetz AN. Overview of Changes to the Clinical and Laboratory Standards Institute Performance Standards for Antimicrobial Susceptibility Testing, M100, 31st Edition. J Clin Microbiol. 2021;59(12):e0021321. pmid:34550809
- View Article
- PubMed/NCBI
- Google Scholar
15. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–77. pmid:22506599
- View Article
- PubMed/NCBI
- Google Scholar
16. Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 2017;27(5):722–36. pmid:28298431
- View Article
- PubMed/NCBI
- Google Scholar
17. Seemann T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068–9. pmid:24642063
- View Article
- PubMed/NCBI
- Google Scholar
18. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, et al. Roary: Rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31(22):3691–3. pmid:26198102
- View Article
- PubMed/NCBI
- Google Scholar
19. Sarshar M, Scribano D, Limongi D, Zagaglia C, Palamara AT, Ambrosi C. Adaptive strategies of uropathogenic Escherichia coli CFT073: From growth in lab media to virulence during host cell adhesion. Int Microbiol. 2022;25(3):481–94. pmid:35106679
- View Article
- PubMed/NCBI
- Google Scholar
20. Shea AE, Marzoa J, Himpsl SD, Smith SN, Zhao L, Tran L, et al. Escherichia coli CFT073 Fitness Factors during Urinary Tract Infection: Identification Using an Ordered Transposon Library. Appl Environ Microbiol. 2020;86(13):e00691-20. pmid:32358013
- View Article
- PubMed/NCBI
- Google Scholar
21. Feßler AT, Turnidge J, Schwarz S. Quality control ranges for cefoperazone 30 μg disks for Staphylococcus aureus ATCC® 25923 and Escherichia coli ATCC® 25922. Vet Microbiol. 2014;171(3–4):284–9. pmid:24685239
- View Article
- PubMed/NCBI
- Google Scholar
22. Sulaiman JE. Proteomic study of antibiotic tolerance and resistance in bacterial populations from adaptive laboratory evolution. 2022.
23. Shea AE, Forsyth VS, Stocki JA, Mitchell TJ, Frick-Cheng AE, Smith SN, et al. Emerging roles for ABC transporters as virulence factors in uropathogenic Escherichia coli. Proc Natl Acad Sci U S A. 2024;121(16):e2310693121. pmid:38607934
- View Article
- PubMed/NCBI
- Google Scholar
24. Zhang C, Li C, Aziz T, Alharbi M, Cui H, Lin L. Screening of E. coli O157:H7 AI-2 QS inhibitors and their inhibitory effect on biofilm formation in combination with disinfectants. Food Bioscience. 2024;58:103821.
- View Article
- Google Scholar
25. Guan W, Wang T, Huang Q, Tian E, Liu B, Yang Y, et al. A LuxR-type regulator, AcrR, regulates flagellar assembly and contributes to virulence, motility, biofilm formation, and growth ability of Acidovorax citrulli. Mol Plant Pathol. 2020;21(4):489–501. pmid:31943660
- View Article
- PubMed/NCBI
- Google Scholar
26. Zhou G, Wang Y-S, Peng H, Liu H-Z, Feng J, Li S-J, et al. Outer membrane protein of OmpF contributes to swimming motility, biofilm formation, osmotic response as well as the transcription of maltose metabolic genes in Citrobacter werkmanii. World J Microbiol Biotechnol. 2022;39(1):15. pmid:36401137
- View Article
- PubMed/NCBI
- Google Scholar
27. Zhou Y, Chang J, Li F, He M, Li R, Hou Y, et al. H-NS-Mediated Regulation of Swimming Motility and Polar Flagellar Gene Expression in Vibrio parahaemolyticus. Curr Microbiol. 2024;82(1):5. pmid:39579231
- View Article
- PubMed/NCBI
- Google Scholar
28. Komeda Y. Transcriptional control of flagellar genes in Escherichia coli K-12. J Bacteriol. 1986;168(3):1315–8. pmid:3536871
- View Article
- PubMed/NCBI
- Google Scholar
29. Lisevich I, Colin R, Yang HY, Ni B, Sourjik V. Physics of swimming and its fitness cost determine strategies of bacterial investment in flagellar motility. Nat Commun. 2025;16(1):1731. pmid:39966405
- View Article
- PubMed/NCBI
- Google Scholar
30. Berne C, Ducret A, Hardy GG, Brun YV. Adhesins Involved in Attachment to Abiotic Surfaces by Gram-Negative Bacteria. Microbiol Spectr. 2015;3(4):10.1128/microbiolspec.MB-0018–2015. pmid:26350310
- View Article
- PubMed/NCBI
- Google Scholar
31. Kumar A, Sarkar SK, Ghosh D, Ghosh AS. Deletion of penicillin-binding protein 1b impairs biofilm formation and motility in Escherichia coli. Res Microbiol. 2012;163(4):254–7. pmid:22349271
- View Article
- PubMed/NCBI
- Google Scholar
32. Liu Q, Zhu J, Liu N, Sun W, Yu B, Niu H, et al. Type I fimbriae subunit fimA enhances Escherichia coli biofilm formation but affects L-threonine carbon distribution. Front Bioeng Biotechnol. 2022;10:904636. pmid:36338119
- View Article
- PubMed/NCBI
- Google Scholar
33. Nair VG, Srinandan CS, Rajesh YBRD, Narbhavi D, Anupriya A, Prabhusaran N, et al. Biogenic amine tryptamine in human vaginal probiotic isolates mediates matrix inhibition and thwarts uropathogenic E. coli biofilm. Sci Rep. 2024;14(1):15387. pmid:38965339
- View Article
- PubMed/NCBI
- Google Scholar
34. Ramesh R, Rekha ND, Gopal S. Pseudomonas aeruginosa biofilm: treatment strategies to combat infection. Arch Microbiol. 2025;207(6):141. pmid:40348909
- View Article
- PubMed/NCBI
- Google Scholar
35. Fagerberg SK, Skals M, Leipziger J, Praetorius HA. P2X receptor-dependent erythrocyte damage by α-hemolysin from Escherichia coli triggers phagocytosis by THP-1 cells. Toxins (Basel). 2013;5(3):472–87. pmid:23462688
- View Article
- PubMed/NCBI
- Google Scholar
36. Izquierdo M, Navarro-Garcia F, Nava-Acosta R, Nataro JP, Ruiz-Perez F, Farfan MJ. Identification of cell surface-exposed proteins involved in the fimbria-mediated adherence of enteroaggregative Escherichia coli to intestinal cells. Infect Immun. 2014;82(4):1719–24. pmid:24516112
- View Article
- PubMed/NCBI
- Google Scholar
37. McKee RW, Aleksanyan N, Garrett EM, Tamayo R. Type IV pili promote Clostridium difficile adherence and persistence in a mouse model of infection. Infection and Immunity. 2018;86(5). pmid:29483294
- View Article
- PubMed/NCBI
- Google Scholar
38. Murakami K, Kimura S, Nagafuchi O, Sekizuka T, Onozuka D, Mizukoshi F, et al. Flagellum expression and swimming activity by the zoonotic pathogen Escherichia albertii. Environ Microbiol Rep. 2020;12(1):92–6. pmid:31845481
- View Article
- PubMed/NCBI
- Google Scholar
39. Ouyang Z, Zhao H, Zhao M, Yang Y, Zhao J. Type IV pili are involved in phenotypes associated with Clostridioides difficile pathogenesis. Crit Rev Microbiol. 2024;50(6):1011–9. pmid:37452617
- View Article
- PubMed/NCBI
- Google Scholar
40. Camp J, Schuster S, Vavra M, Schweigger T, Rossen JWA, Reuter S. Limited multidrug resistance efflux pump overexpression among multidrug-resistant Escherichia coli strains of ST131. Antimicrobial Agents and Chemotherapy. 2021;65(4). pmid:33468485
- View Article
- PubMed/NCBI
- Google Scholar
41. Middlemiss AD, Haycocks JRJ, Stringer AM, Piddock LJV, Wade JT, Grainger DC. Mapping direct and indirect MarA/SoxS/Rob/RamA regulons in Salmonella Typhimurium reveals repression of csgD and biofilm formation. Microbiology (Reading). 2023;169(5):001330. pmid:37204124
- View Article
- PubMed/NCBI
- Google Scholar
42. Giuliano S, Fox V, Ferin S, Martini L, Angelini J, Bulfoni M, et al. Potential role of avibactam in restoring susceptibility in Escherichia coli with two copies of blaKPC-3 and PBP3 mutations. Sci Rep. 2025;15(1):21161. pmid:40595049
- View Article
- PubMed/NCBI
- Google Scholar
43. Maldonado J, Czarnecka B, Harmon DE, Ruiz C. The multidrug efflux pump regulator AcrR directly represses motility in Escherichia coli. mSphere. 2023;8(5):e0043023. pmid:37787551
- View Article
- PubMed/NCBI
- Google Scholar
44. Zhang R, Wang Y. EvgS/EvgA, the unorthodox two-component system regulating bacterial multiple resistance. Appl Environ Microbiol. 2023;89(12):e0157723. pmid:38019025
- View Article
- PubMed/NCBI
- Google Scholar
45. Jena J, Sahoo RK, Debata NK, Subudhi E. Prevalence of TEM, SHV, and CTX-M genes of extended-spectrum β-lactamase-producing Escherichia coli strains isolated from urinary tract infections in adults. 3 Biotech. 2017;7. pmid:28710743
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Allocati N, Masulli M, Alexeyev MF, Di Ilio C. Escherichia coli in Europe: An overview. Int J Environ Res Public Health. 2013;10(12):6235–54. pmid:24287850
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Bonten M, Johnson JR, van den Biggelaar AHJ, Georgalis L, Geurtsen J, de Palacios PI, et al. Epidemiology of Escherichia coli bacteremia: a systematic literature review. Clinical Infectious Diseases. 2021;72(7):1211–9. pmid:32406495
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Chang M, Lee H, Kim JY, Lee W, Moon S. Comparison of 16S rRNA gene sequencing, API 20E, and RT-PCR for identification of autopsy-derived Escherichia coli and Shigella spp. J Science Criminal Investigation. 2022;16(3):163–77.
View Article
Google Scholar

[10] View Article

[11] Google Scholar

[ref4] 4. Khalil IA, Troeger C, Blacker BF, Rao PC, Brown A, Atherly DE, et al. Morbidity and mortality due to shigella and enterotoxigenic Escherichia coli diarrhoea: The global burden of disease study 1990-2016. Lancet Infect Dis. 2018;18(11):1229–40. pmid:30266330
View Article
PubMed/NCBI
Google Scholar

[13] View Article

[14] PubMed/NCBI

[15] Google Scholar

[ref5] 5. Dif G, Djemouai N, Bouras N, Zitouni A. In-Depth Genome-Based Analysis of Shigella spp. and Escherichia spp.: Resolving Ambiguities and Unveiling Phylogenetic Relationships. Curr Microbiol. 2025;82(4):170. pmid:40045049
View Article
PubMed/NCBI
Google Scholar

[17] View Article

[18] PubMed/NCBI

[19] Google Scholar

[ref6] 6. Huls PG, Vischer NOE, Woldringh CL. Different amounts of DNA in newborn cells of escherichia coli preclude a role for the chromosome in size control according to the “Adder” Model. Front Microbiol. 2018;9:664. pmid:29675011
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref7] 7. Zheng H, Bai Y, Jiang M, Tokuyasu TA, Huang X, Zhong F, et al. General quantitative relations linking cell growth and the cell cycle in Escherichia coli. Nat Microbiol. 2020;5(8):995–1001. pmid:32424336
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref8] 8. Elles LMS, Uhlenbeck OC. Mutation of the arginine finger in the active site of Escherichia coli DbpA abolishes ATPase and helicase activity and confers a dominant slow growth phenotype. Nucleic Acids Res. 2008;36(1):41–50. pmid:17986459
View Article
PubMed/NCBI
Google Scholar

[29] View Article

[30] PubMed/NCBI

[31] Google Scholar

[ref9] 9. Lubrano P, Smollich F, Schramm T, Lorenz E, Alvarado A, Eigenmann SC, et al. Metabolic mutations reduce antibiotic susceptibility of E. coli by pathway-specific bottlenecks. Mol Syst Biol. 2025;21(3):274–93. pmid:39748127
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref10] 10. Song Y, Sun M, Mu G, Tuo Y. Exopolysaccharide produced by Lactiplantibacillus plantarum Y12 exhibits inhibitory effect on the Shigella flexneri genes expression related to biofilm formation. Int J Biol Macromol. 2023;253(Pt 5):127048. pmid:37748596
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref11] 11. Pifer R, Russell RM, Kumar A, Curtis MM, Sperandio V. Redox, amino acid, and fatty acid metabolism intersect with bacterial virulence in the gut. Proc Natl Acad Sci U S A. 2018;115(45):E10712–9. pmid:30348782
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref12] 12. Kleckner NE, Chatzi K, White MA, Fisher JK, Stouf M. Coordination of growth, chromosome replication/segregation, and cell division in E-coli. Front Microbiol. 2018. pmid:30038602
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref13] 13. Pepper ED, Farrell MJ, Finkel SE. Role of penicillin-binding protein 1b in competitive stationary-phase survival of Escherichia coli. FEMS Microbiol Lett. 2006;263(1):61–7. pmid:16958852
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref14] 14. Humphries R, Bobenchik AM, Hindler JA, Schuetz AN. Overview of Changes to the Clinical and Laboratory Standards Institute Performance Standards for Antimicrobial Susceptibility Testing, M100, 31st Edition. J Clin Microbiol. 2021;59(12):e0021321. pmid:34550809
View Article
PubMed/NCBI
Google Scholar

[53] View Article

[54] PubMed/NCBI

[55] Google Scholar

[ref15] 15. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–77. pmid:22506599
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref16] 16. Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 2017;27(5):722–36. pmid:28298431
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref17] 17. Seemann T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068–9. pmid:24642063
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref18] 18. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, et al. Roary: Rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31(22):3691–3. pmid:26198102
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref19] 19. Sarshar M, Scribano D, Limongi D, Zagaglia C, Palamara AT, Ambrosi C. Adaptive strategies of uropathogenic Escherichia coli CFT073: From growth in lab media to virulence during host cell adhesion. Int Microbiol. 2022;25(3):481–94. pmid:35106679
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref20] 20. Shea AE, Marzoa J, Himpsl SD, Smith SN, Zhao L, Tran L, et al. Escherichia coli CFT073 Fitness Factors during Urinary Tract Infection: Identification Using an Ordered Transposon Library. Appl Environ Microbiol. 2020;86(13):e00691-20. pmid:32358013
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref21] 21. Feßler AT, Turnidge J, Schwarz S. Quality control ranges for cefoperazone 30 μg disks for Staphylococcus aureus ATCC® 25923 and Escherichia coli ATCC® 25922. Vet Microbiol. 2014;171(3–4):284–9. pmid:24685239
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref22] 22. Sulaiman JE. Proteomic study of antibiotic tolerance and resistance in bacterial populations from adaptive laboratory evolution. 2022.

[ref23] 23. Shea AE, Forsyth VS, Stocki JA, Mitchell TJ, Frick-Cheng AE, Smith SN, et al. Emerging roles for ABC transporters as virulence factors in uropathogenic Escherichia coli. Proc Natl Acad Sci U S A. 2024;121(16):e2310693121. pmid:38607934
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref24] 24. Zhang C, Li C, Aziz T, Alharbi M, Cui H, Lin L. Screening of E. coli O157:H7 AI-2 QS inhibitors and their inhibitory effect on biofilm formation in combination with disinfectants. Food Bioscience. 2024;58:103821.
View Article
Google Scholar

[90] View Article

[91] Google Scholar

[ref25] 25. Guan W, Wang T, Huang Q, Tian E, Liu B, Yang Y, et al. A LuxR-type regulator, AcrR, regulates flagellar assembly and contributes to virulence, motility, biofilm formation, and growth ability of Acidovorax citrulli. Mol Plant Pathol. 2020;21(4):489–501. pmid:31943660
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref26] 26. Zhou G, Wang Y-S, Peng H, Liu H-Z, Feng J, Li S-J, et al. Outer membrane protein of OmpF contributes to swimming motility, biofilm formation, osmotic response as well as the transcription of maltose metabolic genes in Citrobacter werkmanii. World J Microbiol Biotechnol. 2022;39(1):15. pmid:36401137
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref27] 27. Zhou Y, Chang J, Li F, He M, Li R, Hou Y, et al. H-NS-Mediated Regulation of Swimming Motility and Polar Flagellar Gene Expression in Vibrio parahaemolyticus. Curr Microbiol. 2024;82(1):5. pmid:39579231
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref28] 28. Komeda Y. Transcriptional control of flagellar genes in Escherichia coli K-12. J Bacteriol. 1986;168(3):1315–8. pmid:3536871
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref29] 29. Lisevich I, Colin R, Yang HY, Ni B, Sourjik V. Physics of swimming and its fitness cost determine strategies of bacterial investment in flagellar motility. Nat Commun. 2025;16(1):1731. pmid:39966405
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref30] 30. Berne C, Ducret A, Hardy GG, Brun YV. Adhesins Involved in Attachment to Abiotic Surfaces by Gram-Negative Bacteria. Microbiol Spectr. 2015;3(4):10.1128/microbiolspec.MB-0018–2015. pmid:26350310
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref31] 31. Kumar A, Sarkar SK, Ghosh D, Ghosh AS. Deletion of penicillin-binding protein 1b impairs biofilm formation and motility in Escherichia coli. Res Microbiol. 2012;163(4):254–7. pmid:22349271
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref32] 32. Liu Q, Zhu J, Liu N, Sun W, Yu B, Niu H, et al. Type I fimbriae subunit fimA enhances Escherichia coli biofilm formation but affects L-threonine carbon distribution. Front Bioeng Biotechnol. 2022;10:904636. pmid:36338119
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref33] 33. Nair VG, Srinandan CS, Rajesh YBRD, Narbhavi D, Anupriya A, Prabhusaran N, et al. Biogenic amine tryptamine in human vaginal probiotic isolates mediates matrix inhibition and thwarts uropathogenic E. coli biofilm. Sci Rep. 2024;14(1):15387. pmid:38965339
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref34] 34. Ramesh R, Rekha ND, Gopal S. Pseudomonas aeruginosa biofilm: treatment strategies to combat infection. Arch Microbiol. 2025;207(6):141. pmid:40348909
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref35] 35. Fagerberg SK, Skals M, Leipziger J, Praetorius HA. P2X receptor-dependent erythrocyte damage by α-hemolysin from Escherichia coli triggers phagocytosis by THP-1 cells. Toxins (Basel). 2013;5(3):472–87. pmid:23462688
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref36] 36. Izquierdo M, Navarro-Garcia F, Nava-Acosta R, Nataro JP, Ruiz-Perez F, Farfan MJ. Identification of cell surface-exposed proteins involved in the fimbria-mediated adherence of enteroaggregative Escherichia coli to intestinal cells. Infect Immun. 2014;82(4):1719–24. pmid:24516112
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref37] 37. McKee RW, Aleksanyan N, Garrett EM, Tamayo R. Type IV pili promote Clostridium difficile adherence and persistence in a mouse model of infection. Infection and Immunity. 2018;86(5). pmid:29483294
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref38] 38. Murakami K, Kimura S, Nagafuchi O, Sekizuka T, Onozuka D, Mizukoshi F, et al. Flagellum expression and swimming activity by the zoonotic pathogen Escherichia albertii. Environ Microbiol Rep. 2020;12(1):92–6. pmid:31845481
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref39] 39. Ouyang Z, Zhao H, Zhao M, Yang Y, Zhao J. Type IV pili are involved in phenotypes associated with Clostridioides difficile pathogenesis. Crit Rev Microbiol. 2024;50(6):1011–9. pmid:37452617
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref40] 40. Camp J, Schuster S, Vavra M, Schweigger T, Rossen JWA, Reuter S. Limited multidrug resistance efflux pump overexpression among multidrug-resistant Escherichia coli strains of ST131. Antimicrobial Agents and Chemotherapy. 2021;65(4). pmid:33468485
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref41] 41. Middlemiss AD, Haycocks JRJ, Stringer AM, Piddock LJV, Wade JT, Grainger DC. Mapping direct and indirect MarA/SoxS/Rob/RamA regulons in Salmonella Typhimurium reveals repression of csgD and biofilm formation. Microbiology (Reading). 2023;169(5):001330. pmid:37204124
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref42] 42. Giuliano S, Fox V, Ferin S, Martini L, Angelini J, Bulfoni M, et al. Potential role of avibactam in restoring susceptibility in Escherichia coli with two copies of blaKPC-3 and PBP3 mutations. Sci Rep. 2025;15(1):21161. pmid:40595049
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref43] 43. Maldonado J, Czarnecka B, Harmon DE, Ruiz C. The multidrug efflux pump regulator AcrR directly represses motility in Escherichia coli. mSphere. 2023;8(5):e0043023. pmid:37787551
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref44] 44. Zhang R, Wang Y. EvgS/EvgA, the unorthodox two-component system regulating bacterial multiple resistance. Appl Environ Microbiol. 2023;89(12):e0157723. pmid:38019025
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref45] 45. Jena J, Sahoo RK, Debata NK, Subudhi E. Prevalence of TEM, SHV, and CTX-M genes of extended-spectrum β-lactamase-producing Escherichia coli strains isolated from urinary tract infections in adults. 3 Biotech. 2017;7. pmid:28710743
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

Figures

Abstract

Introduction

Materials and Methods

Clinical strain acquisition and primary isolation

Phenotypic and molecular identification

Growth kinetics and antimicrobial susceptibility

Genomic DNA extraction and sequencing

Evolutionary relationship and phylogenomic analysis

Results

Isolation and Identification of E. coli 105X

Antimicrobial susceptibility testing

General genomic characteristics and phylogeny

Enrichment Analysis and Prediction of Resistance and Virulence Determinants

Slow-growth-associated genes

Discussion

Conclusion

Supporting information

S1 Fig. Serological analysis of E. coli 105X.

S2 Fig. MALDI-TOF MS analysis of strain 105X.

S1 Table. Biochemical test results of E. coli 105X.

S2 Table. Quality-controlled data statistics of E. coli 105X.

S3 Table. Growth-related gene mutations in E. coli 105X.

References