Association of CRISPR-Cas Systems with Antimicrobial Resistance and MLST Types in Neonatal Invasive Escherichia coli

Association of CRISPR-Cas Systems with Antimicrobial Resistance and MLST Types in Neonatal Invasive Escherichia coli | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of CRISPR-Cas Systems with Antimicrobial Resistance and MLST Types in Neonatal Invasive Escherichia coli Peicen Zou, Yijun Ding, Ying Chen, Panpan Xu, Dongmiao Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8280865/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background To investigate the distribution of CRISPR-Cas systems in Escherichia coli ( E. coli ) isolates and evaluate their associations with multilocus sequence types (MLST), antimicrobial resistance genes (ARGs), and plasmid features. Results ST1193 (16/65, 24.6%) and ST95 (11/65, 16.9%) were the predominant lineages. ST1193 showed a higher resistance gene burden than ST95, and bla TEM−1 was detected in 87.5% of ST1193 isolates. CRISPR-Cas systems were detected in 22 isolates (33.8%), including 11 with type I-F (50.0%), 10 with type I-E (45.5%), and one with both types. Spacer sequences were primarily directed against plasmid DNA. Plasmid replicons were frequently detected, and plasmid burden varied across lineages. All the ST1193 isolates lacked detectable CRISPR-Cas systems, whereas 90.9% (10/11) of ST95 isolates harbored type I-F systems. Conclusions CRISPR-Cas carriage was strongly lineage-dependent and showed an inverse association with predicted resistance gene burden in this cohort; this pattern should be interpreted as a lineage-structured correlation rather than mechanistic evidence of CRISPR-mediated restriction of ARG acquisition. Escherichia coli CRISPR-cas Bacterial resistance MLST Newborn Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Neonatal infectious diseases, particularly sepsis and bacterial meningitis, remain among the leading causes of neonatal mortality(1). In developing countries, the mortality rate of neonatal bacterial meningitis can reach as high as 40% − 58%, significantly higher than in developed regions(2). In recent years, with the widespread use of intrapartum prophylactic antibiotics, Escherichia coli ( E. coli ) has gradually replaced Group B Streptococcus as the primary pathogen of neonatal infections, especially in preterm infants(3). A multicenter study conducted in China revealed that 87.4% of neonatal E. coli isolates were resistant to at least one antimicrobial agent, 48% were positive for extended-spectrum β-lactamase (ESBL) production, and 42.2% exhibited multidrug resistance, underscoring the growing antimicrobial threat(4). Similarly, epidemiological data from the United States indicate decreasing susceptibility and increasing multidrug resistance among neonatal E. coli isolates toward commonly used antibiotics(5). Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) are widely distributed in the genomes of bacteria and archaea, functioning as an adaptive immune system against exogenous genetic elements. Population-level studies have reported inverse associations between CRISPR-Cas carriage and the acquisition of antimicrobial resistance genes in some species(6, 7). A complete CRISPR-Cas system consists of the CRISPR array, a leader sequence, and associated Cas (CRISPR-associated) proteins, which work together to recognize and eliminate foreign genetic material. The immune response of CRISPR-Cas systems is typically divided into three stages: adaptation, expression, and interference. During the adaptation phase, short DNA fragments (known as spacers) derived from invading plasmids or phages are integrated into the CRISPR array, typically at the leader-proximal end, forming new repeat-spacer units. In the expression phase, the CRISPR array is transcribed and processed into small CRISPR RNAs (crRNAs), which combine with Cas proteins to form a surveillance complex. Upon a secondary invasion, the interference complex recognizes and cleaves the corresponding nucleic acid, thereby providing sequence-specific immunity and maintaining genomic stability(8). In E. coli , the CRISPR-Cas systems are mainly classified into type I-E and type I-F subtypes(9, 10). Multilocus sequence typing (MLST) is a widely used for molecular epidemiological typing and provides insights into genetic structure and evolutionary relationships among microbial populations. However, data remain limited on the distribution of CRISPR-Cas systems and their relationships with resistance gene profiles and clonal lineages among invasive neonatal E. coli isolates. Therefore, this study aimed to characterize CRISPR-Cas subtypes, MLST lineages, and resistance gene repertoires in neonatal invasive E. coli isolates and to assess their associations at the population level. Methods Study Population This retrospective study retrospectively analyzed clinical data and bacterial isolates recovered from sterile body fluids (cerebrospinal fluid and blood) of hospitalized neonates from the neonatal ward of children's hospital, collected since 2009. Bacterial species were identified from positive cultures using an automated microbiological analyzer (Auto Scan-4, USA), yielding a total of 65 clinical E. coli isolates. Only one isolate per patient episode was included. All isolates were cultured in Luria-Bertani (LB) broth at 37°C and stored at – 80°C in 20% glycerol for long-term preservation. Invasive E. coli infection was defined as the presence of clinical signs of infection in neonates along with isolation of E. coli from blood or cerebrospinal fluid. Cases were categorized based on the age of onset into: Early-onset E. coli invasive disease: 0–6 days of life; Late-onset E. coli invasive disease: ≥7 days of life(11). This study was approved by the Ethics Committee of Capital Institute of Pediatrics (Approval Number: SHERLLM2025011). Whole-genome sequencing Genomic DNA was extracted from neonatal invasive E. coli isolates using a magnetic bead-based method (Xi'an Tianlong Science and Technology, T132). DNA purity and concentration were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA), and DNA integrity was evaluated by 1% agarose gel electrophoresis. Samples passing quality control were subjected to paired-end 150 bp high-throughput sequencing on the Illumina NovaSeq PE150 platform (Illumina, San Diego, CA, USA). Raw reads were quality-controlled and preprocessed using Trimmomatic v0.39, including adaptor removal, low-quality base trimming, and filtering of short reads. Clean reads were then assembled de novo using SPAdes v3.13.0, and assembly quality was assessed using QUAST, with metrics including N50, number of contigs, and total genome length. The final assembled genome was submitted to the CNCB database (BioProject number: PRJCA046074, BioSample accession numbers and genome accessions for each isolate are listed in Supplementary Table S1 ). Multilocus sequence typing (MLST) and serotyping Multilocus sequence typing (MLST) of E. coli isolates was performed using the MLST 2.0 web-based tool ( https://cge.food.dtu.dk/services/MLST/ ). The typing was based on the E. coli #1 scheme, using the following seven housekeeping genes: adk , fumC , gyrB , icd , mdh , purA , and recA . Serotypes (O:H) were inferred in silico using ABRicate with the EcOH database under default/consistent thresholds. Identification of antibiotic resistance genes Antibiotic resistance genes (ARGs) in E. coli genomes were identified using the Comprehensive Antibiotic Resistance Database (CARD) online tool ( https://card.mcmaster.ca/ ). According to CARD criteria, the annotation results are categorized into three levels based on sequence similarity: Perfect match: The query sequence is 100% identical to a reference resistance gene and covers the entire curated region required for functional resistance. Strict match: The sequence shows ≥ 95% similarity to a reference gene and includes coverage of key functional domains. Loose match: The sequence has lower similarity, with partial or uncertain functional annotation. In this study, only perfect and strict matches were included in the downstream analysis. Resistance genes annotated from protein homolog models were flagged separately for further evaluation. CRISPR-Cas system and spacer target analysis The CRISPR-Cas systems in E. coli isolates were identified and annotated using the CRISPRCasFinder web tool. Only CRISPR arrays with an evidence level of ≥ 3 were considered valid(12, 13). For isolates lacking subtype classification in CRISPRCasFinder, subtyping was supplemented using CRISPRDetect. CRISPR gene maps were visualized using the Proksee platform(14). Spacer sequences were aligned against the PLSDB (plasmid database), phage databases (NCBI viral RefSeq and PhagesDB) using BLAST to identify putative protospacer targets and infer the CRISPR immune history. For each spacer, the best-supported database match was recorded at the isolate level and summarized as plasmid-targeting or phage-targeting. Plasmid replicons and plasmid burden Plasmid replicon types were identified using PlasmidFinder (via the abricate pipeline) with default settings. Plasmid burden was summarized as plasmid count per isolate, defined as the number of reconstructed plasmids reported by MOB-suite (mob_recon) under default settings. Core-genome cladogram construction Genome annotation was performed using Prokka v1.14.6(15), and the resulting GFF files were analyzed with Roary v3.13.0 (16)to perform pan-genome analysis and generate a core-gene alignment. The core gene alignment was used to infer a maximum-likelihood core-genome cladogram using IQ-TREE v2.4.0 under the GTR + G substitution model, with branch support assessed by 1,000 ultrafast bootstrap replicates. The consensus tree was visualized and annotated using iTOL(18). Statistical analysis Statistical analysis was conducted using SPSS software version 23.0. Continuous variables were presented as median and interquartile range (IQR), and categorical variables were expressed as counts (n) and percentages (%). Group comparisons were performed using the chi-squared (χ²) test, and P < 0.05 was considered statistically significant. Results Clinical characteristics of E. coli isolates A total of 65 clinical E. coli isolates were obtained from sterile body fluids of neonates, including 15 isolates from cerebrospinal fluid and 50 isolates from blood cultures. Among the 65 neonates, 13 (20.0%) were diagnosed with early-onset infection (0–6 days of life), and 52 (80.0%) with late-onset infection (≥ 7 days). The youngest patient was 9 hours old, and the oldest was 60 days old at the time of infection. Genotypic characteristics of E. coli isolates MLST identified 20 distinct sequence types (STs) among the 65 clinical E. coli isolates. The most prevalent type was ST1193 (16/65, 24.6%), followed by ST95 (11/65, 16.9%), ST4702 (6/65, 9.2%), ST62 (5/65, 7.7%), and ST131 (4/65, 6.2%). The remaining STs were found in 3 or fewer isolates, and two isolates could not be assigned to any known ST. Serotyping revealed 22 different O:H combinations, among which O75:H5 (16/65, 24.6%), O4:H5 (8/65, 12.3%), and O7:H45 (5/65, 7.7%) were the most frequent. Consistent with known lineage-associated serotypes, ST1193 was predominantly associated with O75:H5 and ST62 with O7:H45 in this cohort. Comparative analysis of resistance gene profiles among MLST types Resistance genes in all 65 E. coli isolates were predicted using the CARD database. Among β-lactamase genes, bla TEM−1 was detected in 27 isolates (41.5%), and bla CTX−M−14 in 10 isolates (15.4%). Additionally, 19 isolates (29.2%) were positive for a putative EC-5 β-lactamase–encoding gene based on CARD protein homolog models. Although this variant has not yet been formally classified, it may confer extended-spectrum resistance. Two isolates (3.1%) carried the broad-spectrum β-lactamase SFO-1 . A comparison between the two predominant sequence types, ST1193 and ST95, revealed that all ST1193 isolates were positive for the putative EC-5 β-lactamase gene, and that ST1193 isolates exhibited a significantly higher burden of resistance genes than ST95 isolates. Significant differences ( P < 0.05) were observed in the presence of bla TEM−1 , mphA , mrx , sul1 , sul2 , aadA5 , and AAC(3)-IId . In contrast, no significant difference was noted in the prevalence of bla CTX−M−14 and bla CTX−M−55 (Table 1 ). Additionally, high detection rates were observed for regulatory genes implicated in multidrug resistance and envelope stress responses. The global regulator H-NS gene was perfectly matched in 100% (65/65) of isolates, followed by cpxA (96.9%), and evgA (92.3%). These findings may indicate that neonatal invasive E. coli isolates although functional consequences were not assessed envelope stress and multi drug resistance. Table 1 Distribution of major ARGs in E. coli ST1193 and ST95 isolates. Category Resistance Gene ST1193 ST95 P-value No. of Isolates (n) - 16 11 - Perfect resistance genes, Median (IQR) - 17 (5) 8 (0) < 0.001 Strict resistance genes , Median (IQR) - 45 (1) 43 (1) 0.044 β-lactamases bla TEM−1 14 (87.5%) 2 (18.2%) 0.001 bla CTX−M−14 1 (6.3%) 1 (9.1%) 1.000 bla CTX−M−55 2 (12.5%) 0 (0%) 0.638 EC-5 * 16 (100%) 0 (0%) < 0.001 Macrolides mphA 13 (81.3%) 0 (0%) < 0.001 mrx 12 (75%) 0 (0%) 0.001 Sulfonamides sul1 11 (68.8%) 0 (0%) 0.002 sul2 10 (62.5%) 0 (0%) 0.004 Aminoglycosides aadA5 12 (75%) 0 (0%) 0.001 AAC(3)-IId 10 (62.5%) 0 (0%) 0.004 *: “EC-5” denotes a putative β-lactamase gene predicted using CARD protein homolog models. Antimicrobial resistance gene and plasmid profiles of E. coli isolates Analysis of ARG repertoires and plasmid profiles in the 65 E. coli isolates revealed marked differences between MLST lineages in ARG abundance, ESBL carriage and plasmid architecture. ST1193 represented the lineage with the highest resistance and plasmid burden, with a median plasmid burden of 6 reconstructed plasmids per isolate, and a 93.8% carriage rate of IncFIB replicons. Notably, all ESBL gene–positive ST1193 isolates simultaneously carried IncFIB replicons, indicating a highly homogeneous ESBL plasmid background in this clone. ST4702 exhibited the highest ESBL positivity rate (66.7%) but showed a lower frequency of IncFIB carriage and no co-occurrence of ESBL and IncFIB, suggesting that ESBL determinants in this clone are likely mobilized by non-IncFIB plasmids. ST62 was characterized by a high plasmid count and 100% IncFIB carriage but a relatively low ESBL positivity rate (20%), implying a stable plasmid background that does not primarily mediate ESBL dissemination. In contrast to these resistant lineages, classical sequence types such as ST95 and ST131 harbored a lower ARG burden, had lower ESBL carriage rates and carried fewer plasmids. Together, these findings indicate that dominant lineages such as ST1193, ST4702 and ST62 show stronger enrichment of resistance genes and a preference for specific plasmid types, suggesting potential selective advantages during the evolution of antimicrobial resistance (Fig. 1 ). Distribution of CRISPR-Cas systems and their MLST associations Among the 65 neonatal invasive E. coli isolates from neonates, 22 isolates (33.8%) harbored CRISPR-Cas systems with an evidence level ≥ 3. Type I-F (11/22, 50.0%) and type I-E (10/22, 45.5%) were the predominant subtypes, and one isolate, EXPEC045 (ST448), carried a dual type I-E + I-F configuration (Fig. 2 ). CRISPR-Cas carriage was strongly lineage-dependent (Fig. 3 ): all ST1193 isolates (16/16) were CRISPR-negative, whereas 90.9% (10/11) of ST95 isolates were CRISPR-positive and all belonged to subtype I-F. Distribution of CRISPR-Cas systems and their association with resistance genes The relationship between CRISPR-Cas system presence and antibiotic resistance genes carriage is summarized in Table 2 . Overall, CRISPR-negative E. coli isolates carried a significantly higher number of resistance genes than CRISPR-positive isolates ( P < 0.05). This difference was primarily observed in the category of strictly matched resistance genes. In contrast, for perfectly matched genes, no statistically significant difference was found between the two groups. Notably, most commonly detected resistance genes—particularly those conferring resistance to β-lactams, macrolides, sulfonamides, and aminoglycosides—were classified as perfect matches, which may explain the lack of significant differences in these categories between CRISPR-positive and -negative isolates. Table 2 Comparison of resistance gene profiles between CRISPR-positive and CRISPR-negative E. coli isolates resistance gene CRISPR-negative CRISPR-positive P value Number of isolates (n = 65) / 43 (66.2%) 22 (33.8%) / Total genes (median, IQR) / 53 (10) 52 (3.5) 0.027 Perfect genes (median, IQR) / 11 (9) 12 (10) 0.244 Strict genes (median, IQR) / 44 (3) 43 (7.5) < 0.001 β-lactam ampC 0 (0%) 3 (13.6%) 0.064 bla TEM−1 21 (48.8%) 6 (27.3%) 0.095 bla CTX−M−14 7 (16.2%) 3 (13.6%) 1.000 bla CTX−M−27 0 (0%) 1 (4.5%) 0.731 bla CTX−M−55 3 (7.0%) 0 (0%) 0.520 bla CTX−M−19 1 (2.3%) 0 (0%) 1.000 EC-5 19 (44.2%) 0 (0%) < 0.001 Broad-spectrum β-lactamases SFO-1 2 (4.7%) 0 (0%) 0.788 Macrolide mphA 17 (39.5%) 3 (13.6%) 0.032 mrx 16 (37.2%) 4 (18.2%) 0.116 Sulfonamide sul1 13 (30.2%) 5 (22.7%) 0.522 sul2 12 (27.9%) 2 (9.1%) 0.153 Aminoglycoside aadA5 13 (30.2%) 3 (13.6%) 0.142 AAC(3)-IId 12 (27.9%) 2 (9.1%) 0.153 Association of resistance gene profiles and CRISPR-Cas systems in ST1193 and ST95 E. coli isolates A comparative analysis was conducted between the two most prevalent sequence types in this study—ST1193 and ST95. The ST1193 isolates exhibited a higher burden of resistance genes, yet none of the 16 ST1193 isolates carried CRISPR-Cas systems. In contrast, 90.9% (10/11) of ST95 isolates harbored CRISPR-Cas systems, predominantly type I-F, and tended to carry a lower resistance gene burden. Figure 4 shows the core-genome cladogram annotated with MLST types, CRISPR-Cas system subtypes, and resistance gene profiles among all 65 neonatal E. coli isolates. Together, these patterns indicate an inverse association between CRISPR-Cas carriage and resistance gene burden in this cohort, and suggest that both traits may be linked to lineage background (Fig. 4 ). CRISPR spacer repertoire and targeting profiles Among the 22 isolates carrying a CRISPR-Cas system, a total of 151 CRISPR arrays were identified, of which 38 arrays had an evidence level ≥ 3. In these high-confidence arrays, each isolate harbored 1–4 CRISPR arrays, with individual arrays containing 4–20 spacers. The repeat consensus sequences were 26–30 bp in length, consistent with canonical type I-E/I-F CRISPR-Cas systems. In total, 278 spacer records were detected, corresponding to 208 unique spacer sequences after deduplication. The number of unique spacers per isolate ranged from 4 to 45, with a median of 10 (IQR 5–14). Most isolates carried 5–14 spacers, whereas only a few exhibited long CRISPR arrays containing 26–45 spacers. Among the 208 unique spacers, 24 were shared across multiple arrays or isolates, suggesting a small subset of conserved CRISPR “memory”. Of the spacer-carrying isolates, 16 (16/22, 72.8%) possessed at least one spacer targeting plasmid sequences in PLSDB, whereas only 2 (2/22, 9.1%) harbored spacers targeting phage genomes in RefSeq/PhagesDB, indicating that the CRISPR systems in this cohort are more biased toward plasmid rather than phage targeting (Fig. 5 ). Discussion E. coli is a major pathogen responsible for invasive neonatal infections. Previous studies have shown that ST95 and ST1193 E. coli are high-risk clones associated with neonatal purulent meningitis and recurrent infections(19). Consistent with those findings, ST1193 and ST95 were also the predominant sequence types in our study among neonatal cases of sepsis and purulent meningitis. These two ST types exhibited considerable differences in antimicrobial resistance. ST1193 isolates harbored more resistance genes than ST95 isolates. This finding is consistent with results from a multicenter study in China, which showed that ST1193, ST410, and ST131 E. coli had higher rates of multidrug resistance and ESBL positivity than ST95 and ST73 isolates(4). EC-5 was identified as a putative class C β-lactamase–like hit predicted by the CARD protein homolog model; however, its functional role in cephalosporin resistance remains to be validated(20). In this study, all ST1193 isolates carried an EC-5–like β-lactamase hit predicted by the CARD homology model. We did not perform functional validation, so the contribution of this putative allele to the resistance phenotype remains uncertain. The most common mechanism of antimicrobial resistance dissemination in E. coli is horizontal gene transfer via mobile genetic elements such as plasmids or transposons(21). Previous studies have reported inverse associations between CRISPR-Cas carriage and resistance gene burden; however, mechanistic evidence for CRISPR-mediated defense against incoming DNA in non-engineered E. coli remains limited. In our neonatal cohort, CRISPR-positive isolates tended to carry fewer acquired resistance genes, consistent with these population-level correlations(22). Palmer et al. reported that multidrug-resistant enterococci often lacked CRISPR-Cas systems, supporting a negative association between CRISPR-Cas and antibiotic resistance [18] . Similarly, a global analysis on Klebsiella pneumoniae revealed an inverse relationship between CRISPR-Cas presence and carbapenem resistance(23). These findings suggest that CRISPR-Cas carriage is often inversely associated with ARG burden at the population level. However, some studies have reported no association between the presence of CRISPR systems and resistance in E. coli (24). Our results showed that approximately one-third of the collected invasive neonatal E. coli isolates were CRISPR-positive, and that CRISPR-positive isolates carried fewer resistance genes than CRISPR-negative isolates. This inverse association should be interpreted cautiously because both CRISPR-Cas carriage and ARG burden were structured by lineage background in our dataset. Notably, several CRISPR-Cas–positive isolates also carried multiple resistance genes, indicating that CRISPR-Cas presence alone is insufficient to predict low ARG content. The type I-E and I-F are the major CRISPR-Cas subtypes in E. coli , and their CRISPR structures and interference modules differ. Previous studies have suggested that the I-E type systems more frequently target phage DNA, whereas the I-F system can display robust interference in some contexts, partly through regulatory features affecting cas expression(25, 26). In our study, I-E and I-F CRISPR-positive isolates each accounted for approximately half of the CRISPR-positive isolates, suggesting a broad distribution of both subtypes in neonatal invasive E. coli . However, spacer sequence analysis revealed that spacers from both subtypes matched plasmid sequences more often than phage genomes in our dataset, indicating a plasmid-biased targeting spectrum in this cohort. Furthermore, no CRISPR systems were detected in ST1193 and ST131 isolates, which have been reported as resistance-enriched lineages in multiple settings(4, 27), whereas ST95 isolates, which are relatively more susceptible, had a high CRISPR-positive rate—all of which were of the I-F type. This aligns with previous findings suggesting that type I-F CRISPR systems are more commonly found in antibiotic-susceptible E. coli (6). Notably, several ST95 isolates still carried multiple resistance genes, indicating that CRISPR-Cas presence does not preclude ARG carriage. Potential explanations include strain-specific variation in CRISPR activity, spacer content, and mobile element dynamics; however, these mechanisms were not evaluated in this study(28). Touchon M and García-Gutiérrez E have suggested that E. coli typically harbors either the I-E or I-F CRISPR-Cas system, and that the two are mutually exclusive due to functional antagonism(9) (29). However, in our study, one strain (EXPEC045, ST448) harbored both I-E and I-F loci. This dual configuration was uncommon in our dataset and should be interpreted as a rare lineage feature rather than a general rule. Additionally, previous studies have pointed out that most prokaryotes contain only 1–3 CRISPR arrays, and that more than three arrays are rare(30). In this study, we identified one strain with two evidence level ≥ 3 CRISPR loci for both I-E and I-F systems, suggesting that different CRISPR-Cas types may coexist in specific isolates. This warrants further investigation into their evolutionary significance and potential functional interactions. This study has limitations. First, the sample size was modest (n = 65) and dominated by a small number of sequence types, which limits generalizability and may confound associations between CRISPR-Cas carriage and ARG burden. The number of isolates for some STs (e.g., ST131) was relatively small, potentially underpowering subgroup analysis. Moreover, the identification of CRISPR-Cas systems was based solely on bioinformatics predictions without functional validation or phenotypic antimicrobial susceptibility testing, which limits inference about activity and clinical impact. Conclusions This study provides a population-level view of the association between CRISPR-Cas systems, MLST types, plasmid features, and predicted resistance gene profiles in neonatal invasive E. coli isolates. Significant differences were observed between ST1193 and ST95 isolates in terms of CRISPR prevalence and resistance gene carriage. Overall, our findings support a lineage-structured inverse association between CRISPR-Cas carriage and predicted resistance gene burden in this cohort, and provide a rationale for future studies integrating larger datasets and functional assays to test causality. Abbreviations ARGs antimicrobial resistance genes CARD Comprehensive Antibiotic Resistance Database CRISPR clustered regularly interspaced short palindromic repeats Cas CRISPR-associated ESBL extended-spectrum β-lactamase IQR interquartile range MLST multilocus sequence typing. Declarations Ethics approval and consent to participate: The E. coli isolates analyzed in this study were obtained from cerebrospinal fluid and blood cultures of hospitalized neonates as part of routine clinical diagnostics. No additional procedures or interventions were performed. Ethical approval was granted by the Ethics Committee of the Capital Institute of Pediatrics (Approval Number: SHERLLM2025011), with informed consent waived as only anonymized bacterial isolates were used. Consent for publication: Not applicable. Competing interests: The authors declare that there are no conflicts of interest. Funding: This work was supported by Natural Science Foundation of Beijing Municipality (Grant Nos. 7232009, 7244289), the National Natural Science Foundation of China (Grant No. 8257121766), the Cross-cooperation project of Beijing Science and Technology New Star Program (Grant Nos. 20240484724), the high level public health technical personnel construction Project (Subject leaders-03-02). Author Contribution Peicen Zou : Conceptualization, Data curation, Formal analysis, Validation, Visualization, Writing-original draft; Yijun Ding Conceptualization, Data curation, Formal analysis, Validation, Visualization, Writing-original draft; Ying Chen : Data curation, Validation; Panpan Xu : Validation; Dongmiao Zhang : Formal analysis; Peipei Zhang, Pan Huang, Yue Du : Data curation; Yueqiao Gao : Formal analysis; Yajuan Wang : Conceptualization, Funding acquisition; Resources; Writing – review & editing. Acknowledgements: Not applicable. 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Association of clustered regularly interspaced short palindromic repeat (CRISPR) elements with specific serotypes and virulence potential of shiga toxin-producing Escherichia coli. Appl Environ Microbiol. 2014;80(4):1411-20. Nami Y, Rostampour M, Panahi B. CRISPR-Cas systems and diversity of targeting phages in Lactobacillus johnsonii isolates; insights from genome mining approach. Infect Genet Evol. 2023;114:105500. Malone LM, Hampton HG, Morgan XC, Fineran PC. Type I CRISPR-Cas provides robust immunity but incomplete attenuation of phage-induced cellular stress. Nucleic Acids Res. 2022;50(1):160 − 74. Ding Y, Zhang J, Yao K, Gao W, Wang Y. Molecular characteristics of the new emerging global clone ST1193 among clinical isolates of Escherichia coli from neonatal invasive infections in China. Eur J Clin Microbiol Infect Dis. 2021;40(4):833 − 40. Pawluk A, Davidson AR, Maxwell KL. Anti-CRISPR: discovery, mechanism and function. Nat Rev Microbiol. 2018;16(1):12 − 7. García-Gutiérrez E, Almendros C, Mojica FJ, Guzmán NM, García-Martínez J. CRISPR Content Correlates with the Pathogenic Potential of Escherichia coli. PLoS One. 2015;10(7):e0131935. Makarova KS, Wolf YI, Alkhnbashi OS, Costa F, Shah SA, Saunders SJ, et al. An updated evolutionary classification of CRISPR-Cas systems. Nat Rev Microbiol. 2015;13(11):722 − 36. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.docx Supplementarydata.csv Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviewers invited by journal 02 Feb, 2026 Editor invited by journal 02 Feb, 2026 Editor assigned by journal 04 Dec, 2025 Submission checks completed at journal 04 Dec, 2025 First submitted to journal 04 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8280865","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584998519,"identity":"dfa23004-91ba-4d67-9f92-c10013cba961","order_by":0,"name":"Peicen Zou","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Peicen","middleName":"","lastName":"Zou","suffix":""},{"id":584998520,"identity":"007f705a-db81-4a6e-9298-1f63816f64b7","order_by":1,"name":"Yijun Ding","email":"","orcid":"","institution":"Capital Medical University, National Center for Children’s Health","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Ding","suffix":""},{"id":584998521,"identity":"94d65e92-22ea-4097-ad1e-68edb1634905","order_by":2,"name":"Ying Chen","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Chen","suffix":""},{"id":584998522,"identity":"72696d27-992d-4db0-bc85-3915e20fe31c","order_by":3,"name":"Panpan Xu","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Panpan","middleName":"","lastName":"Xu","suffix":""},{"id":584998523,"identity":"5ac14f3c-cfb7-4022-b0d0-83a25a503634","order_by":4,"name":"Dongmiao Zhang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Dongmiao","middleName":"","lastName":"Zhang","suffix":""},{"id":584998524,"identity":"3f95103a-6022-4cc4-be93-2af6640dd287","order_by":5,"name":"Peipei Zhang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Peipei","middleName":"","lastName":"Zhang","suffix":""},{"id":584998525,"identity":"53589be2-f84f-4749-b213-4962732eef6e","order_by":6,"name":"Pan Huang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Pan","middleName":"","lastName":"Huang","suffix":""},{"id":584998526,"identity":"2fed3c46-caba-4e26-918f-6d7f1dd9b1a8","order_by":7,"name":"Yue Du","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Du","suffix":""},{"id":584998527,"identity":"5b3228d7-8bf5-49fc-8120-2200575fdcea","order_by":8,"name":"Yueqiao Gao","email":"","orcid":"","institution":"York House School","correspondingAuthor":false,"prefix":"","firstName":"Yueqiao","middleName":"","lastName":"Gao","suffix":""},{"id":584998528,"identity":"1c26d76c-005c-4b58-917c-82e9e072c87d","order_by":9,"name":"Yajuan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYNACHhsGNhCdQIKWNJK1MBwmQS3/jPRn0jwy5xP7pBuYPzzcYcfA396N3zKJM2fMpHl4bie2yRxgk0g8kwwUObsBrxYD9h6220AtxmwSCWwMiW3MDAYSuQS0MLM/A2o5B9LC/CGxrZ4ILewNZkAtB+SAWhgkEtsOE9YC9Iv5zzk8yXJsMgfbgFqO8xD0CzDEHhu87bHjkZ/dfPjjz7ZqOf72XvxaQICJtwdkH2MDiMNDUDkIMP74AdJClNpRMApGwSgYiQAAHBw+6VBzjhQAAAAASUVORK5CYII=","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Yajuan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-12-04 15:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8280865/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8280865/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101850892,"identity":"328a8794-0339-4fa4-9945-8b5c293da6aa","added_by":"auto","created_at":"2026-02-04 09:59:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17837,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of MLST, ARG load, IncFIB carriage and plasmid burden in 65 invasive \u003cem\u003eE. coli\u003c/em\u003eisolates. Each column represents one isolate. From top to bottom, the tracks show MLST sequence type, total number of CARD-predicted resistance genes, presence of IncFIB plasmid replicons, and number of reconstructed plasmids by MOB-suite. Colour intensity reflects increasing gene and plasmid counts, as indicated in the legends.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8280865/v1/d5668381c9c6d62982562df9.png"},{"id":101850875,"identity":"4d6a887b-2ddf-494e-b9ef-7cc644f6b01e","added_by":"auto","created_at":"2026-02-04 09:59:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2028576,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic structure of CRISPR-Cas systems in \u003cem\u003eE. coli\u003c/em\u003estrain EXPEC045. The left circular map shows CRISPR-Cas and antibiotic resistance gene features in EXPEC045. From outer to inner rings: (1) CRISPR arrays (brown) and cas gene clusters (purple); (2) antibiotic resistance genes annotated by the CARD database (red); (3) GC content (black); and (4) GC skew (green: positive; purple: negative). The right panel illustrates two CRISPR-Cas systems: The type I-F system includes CRISPR1 and CRISPR2 arrays, and a contiguous operon with \u003cem\u003ecas1\u003c/em\u003e, \u003cem\u003ecas2-3\u003c/em\u003e, \u003cem\u003ecsy1–3\u003c/em\u003e, and \u003cem\u003ecas6\u003c/em\u003e. The type I-E system comprises two separate CRISPR arrays (CRISPR3 and CRISPR4) flanking a complete \u003cem\u003ecas\u003c/em\u003e operon (\u003cem\u003ecse1\u003c/em\u003e, \u003cem\u003ecse2\u003c/em\u003e, \u003cem\u003ecas7\u003c/em\u003e, \u003cem\u003ecas5\u003c/em\u003e, \u003cem\u003ecas6\u003c/em\u003e, \u003cem\u003ecas1\u003c/em\u003e, \u003cem\u003ecas2\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8280865/v1/cda5c3d6bc0856df5b70a20b.png"},{"id":101850960,"identity":"37fd99e3-af8f-43e6-8eb0-477e7de40da3","added_by":"auto","created_at":"2026-02-04 09:59:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38885,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of CRISPR-Cas subtypes across MLST lineages in 65 neonatal invasive \u003cem\u003eE. coli\u003c/em\u003e isolates. The heatmap summarizes the number of isolates in each MLST group by CRISPR-Cas category. Cell labels indicate isolate counts, and colour intensity reflects the same.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8280865/v1/8f63e58c17587adc4f89e306.png"},{"id":101851086,"identity":"659bb083-eebe-4ef8-baba-2d02e980df43","added_by":"auto","created_at":"2026-02-04 10:00:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2585943,"visible":true,"origin":"","legend":"\u003cp\u003eCore-genome cladogram of 65 neonatal \u003cem\u003eE. coli\u003c/em\u003eisolates annotated with MLST, CRISPR-Cas subtype, and major resistance genes. From inner to outer rings: MLST (colour-coded), CRISPR-Cas subtype (grey, none; yellow, I-E; purple, I-F; red, I-E+I-F), and presence/absence of selected resistance genes (green, present; white, absent).\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8280865/v1/7322fa02de421bfd9cc0848a.jpg"},{"id":101850883,"identity":"23c6faa9-a86d-449e-a94d-5cfd793a984d","added_by":"auto","created_at":"2026-02-04 09:59:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":403366,"visible":true,"origin":"","legend":"\u003cp\u003eCRISPR spacer repertoire and targeting profiles in 22 CRISPR-Cas–positive \u003cem\u003eE. coli \u003c/em\u003eisolates. (A) Number of total and unique spacers per isolate based on high-confidence (evidence level ≥3) CRISPR arrays. (B) Counts of spacers per isolate with matches to plasmid sequences in PLSDB and to phage genomes in RefSeq/PhagesDB, illustrating the predominance of plasmid- over phage-targeting spacers in this cohort.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8280865/v1/2934001f5ba483f95b683ee2.jpg"},{"id":101851100,"identity":"86bb96f1-7cc4-4d72-a17f-9978d7bf6685","added_by":"auto","created_at":"2026-02-04 10:00:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4798266,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8280865/v1/b7cd2a11-f16e-40da-9693-42d1d4383b9a.pdf"},{"id":101850880,"identity":"bb372874-3852-4c33-8bd1-c34e65beb50b","added_by":"auto","created_at":"2026-02-04 09:59:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24764,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8280865/v1/33f103d1e272615d11f1edde.docx"},{"id":101850955,"identity":"090aec1a-3b00-47d1-b52d-9f0123c0748b","added_by":"auto","created_at":"2026-02-04 09:59:54","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9046,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.csv","url":"https://assets-eu.researchsquare.com/files/rs-8280865/v1/363347742fe7637acf405ed7.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of CRISPR-Cas Systems with Antimicrobial Resistance and MLST Types in Neonatal Invasive Escherichia coli","fulltext":[{"header":"Background","content":"\u003cp\u003eNeonatal infectious diseases, particularly sepsis and bacterial meningitis, remain among the leading causes of neonatal mortality(1). In developing countries, the mortality rate of neonatal bacterial meningitis can reach as high as 40% \u0026minus;\u0026thinsp;58%, significantly higher than in developed regions(2). In recent years, with the widespread use of intrapartum prophylactic antibiotics, \u003cem\u003eEscherichia coli\u003c/em\u003e (\u003cem\u003eE. coli\u003c/em\u003e) has gradually replaced Group B \u003cem\u003eStreptococcus\u003c/em\u003e as the primary pathogen of neonatal infections, especially in preterm infants(3). A multicenter study conducted in China revealed that 87.4% of neonatal \u003cem\u003eE. coli\u003c/em\u003e isolates were resistant to at least one antimicrobial agent, 48% were positive for extended-spectrum β-lactamase (ESBL) production, and 42.2% exhibited multidrug resistance, underscoring the growing antimicrobial threat(4). Similarly, epidemiological data from the United States indicate decreasing susceptibility and increasing multidrug resistance among neonatal \u003cem\u003eE. coli\u003c/em\u003e isolates toward commonly used antibiotics(5).\u003c/p\u003e \u003cp\u003eClustered Regularly Interspaced Short Palindromic Repeats (CRISPR) are widely distributed in the genomes of bacteria and archaea, functioning as an adaptive immune system against exogenous genetic elements. Population-level studies have reported inverse associations between CRISPR-Cas carriage and the acquisition of antimicrobial resistance genes in some species(6, 7). A complete CRISPR-Cas system consists of the CRISPR array, a leader sequence, and associated Cas (CRISPR-associated) proteins, which work together to recognize and eliminate foreign genetic material. The immune response of CRISPR-Cas systems is typically divided into three stages: adaptation, expression, and interference. During the adaptation phase, short DNA fragments (known as spacers) derived from invading plasmids or phages are integrated into the CRISPR array, typically at the leader-proximal end, forming new repeat-spacer units. In the expression phase, the CRISPR array is transcribed and processed into small CRISPR RNAs (crRNAs), which combine with Cas proteins to form a surveillance complex. Upon a secondary invasion, the interference complex recognizes and cleaves the corresponding nucleic acid, thereby providing sequence-specific immunity and maintaining genomic stability(8). In \u003cem\u003eE. coli\u003c/em\u003e, the CRISPR-Cas systems are mainly classified into type I-E and type I-F subtypes(9, 10).\u003c/p\u003e \u003cp\u003eMultilocus sequence typing (MLST) is a widely used for molecular epidemiological typing and provides insights into genetic structure and evolutionary relationships among microbial populations. However, data remain limited on the distribution of CRISPR-Cas systems and their relationships with resistance gene profiles and clonal lineages among invasive neonatal \u003cem\u003eE. coli\u003c/em\u003e isolates. Therefore, this study aimed to characterize CRISPR-Cas subtypes, MLST lineages, and resistance gene repertoires in neonatal invasive \u003cem\u003eE. coli\u003c/em\u003e isolates and to assess their associations at the population level.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis retrospective study retrospectively analyzed clinical data and bacterial isolates recovered from sterile body fluids (cerebrospinal fluid and blood) of hospitalized neonates from the neonatal ward of children's hospital, collected since 2009. Bacterial species were identified from positive cultures using an automated microbiological analyzer (Auto Scan-4, USA), yielding a total of 65 clinical \u003cem\u003eE. coli\u003c/em\u003e isolates. Only one isolate per patient episode was included. All isolates were cultured in Luria-Bertani (LB) broth at 37\u0026deg;C and stored at \u0026ndash; 80\u0026deg;C in 20% glycerol for long-term preservation. Invasive \u003cem\u003eE. coli\u003c/em\u003e infection was defined as the presence of clinical signs of infection in neonates along with isolation of \u003cem\u003eE. coli\u003c/em\u003e from blood or cerebrospinal fluid. Cases were categorized based on the age of onset into: Early-onset \u003cem\u003eE. coli\u003c/em\u003e invasive disease: 0\u0026ndash;6 days of life; Late-onset \u003cem\u003eE. coli\u003c/em\u003e invasive disease: \u0026ge;7 days of life(11). This study was approved by the Ethics Committee of Capital Institute of Pediatrics (Approval Number: SHERLLM2025011).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWhole-genome sequencing\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from neonatal invasive \u003cem\u003eE. coli\u003c/em\u003e isolates using a magnetic bead-based method (Xi'an Tianlong Science and Technology, T132). DNA purity and concentration were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA), and DNA integrity was evaluated by 1% agarose gel electrophoresis. Samples passing quality control were subjected to paired-end 150 bp high-throughput sequencing on the Illumina NovaSeq PE150 platform (Illumina, San Diego, CA, USA). Raw reads were quality-controlled and preprocessed using Trimmomatic v0.39, including adaptor removal, low-quality base trimming, and filtering of short reads. Clean reads were then assembled de novo using SPAdes v3.13.0, and assembly quality was assessed using QUAST, with metrics including N50, number of contigs, and total genome length. The final assembled genome was submitted to the CNCB database (BioProject number: PRJCA046074, BioSample accession numbers and genome accessions for each isolate are listed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMultilocus sequence typing (MLST) and serotyping\u003c/h3\u003e\n\u003cp\u003eMultilocus sequence typing (MLST) of \u003cem\u003eE. coli\u003c/em\u003e isolates was performed using the MLST 2.0 web-based tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cge.food.dtu.dk/services/MLST/\u003c/span\u003e\u003cspan address=\"https://cge.food.dtu.dk/services/MLST/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The typing was based on the \u003cem\u003eE. coli #1\u003c/em\u003e scheme, using the following seven housekeeping genes: \u003cem\u003eadk\u003c/em\u003e, \u003cem\u003efumC\u003c/em\u003e, \u003cem\u003egyrB\u003c/em\u003e, \u003cem\u003eicd\u003c/em\u003e, \u003cem\u003emdh\u003c/em\u003e, \u003cem\u003epurA\u003c/em\u003e, and \u003cem\u003erecA\u003c/em\u003e. Serotypes (O:H) were inferred in silico using ABRicate with the EcOH database under default/consistent thresholds.\u003c/p\u003e\n\u003ch3\u003eIdentification of antibiotic resistance genes\u003c/h3\u003e\n\u003cp\u003eAntibiotic resistance genes (ARGs) in \u003cem\u003eE. coli\u003c/em\u003e genomes were identified using the Comprehensive Antibiotic Resistance Database (CARD) online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://card.mcmaster.ca/\u003c/span\u003e\u003cspan address=\"https://card.mcmaster.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). According to CARD criteria, the annotation results are categorized into three levels based on sequence similarity: Perfect match: The query sequence is 100% identical to a reference resistance gene and covers the entire curated region required for functional resistance. Strict match: The sequence shows\u0026thinsp;\u0026ge;\u0026thinsp;95% similarity to a reference gene and includes coverage of key functional domains. Loose match: The sequence has lower similarity, with partial or uncertain functional annotation. In this study, only perfect and strict matches were included in the downstream analysis. Resistance genes annotated from protein homolog models were flagged separately for further evaluation.\u003c/p\u003e\n\u003ch3\u003eCRISPR-Cas system and spacer target analysis\u003c/h3\u003e\n\u003cp\u003eThe CRISPR-Cas systems in \u003cem\u003eE. coli\u003c/em\u003e isolates were identified and annotated using the CRISPRCasFinder web tool. Only CRISPR arrays with an evidence level of \u0026ge;\u0026thinsp;3 were considered valid(12, 13). For isolates lacking subtype classification in CRISPRCasFinder, subtyping was supplemented using CRISPRDetect. CRISPR gene maps were visualized using the Proksee platform(14). Spacer sequences were aligned against the PLSDB (plasmid database), phage databases (NCBI viral RefSeq and PhagesDB) using BLAST to identify putative protospacer targets and infer the CRISPR immune history. For each spacer, the best-supported database match was recorded at the isolate level and summarized as plasmid-targeting or phage-targeting.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePlasmid replicons and plasmid burden\u003c/h2\u003e \u003cp\u003ePlasmid replicon types were identified using PlasmidFinder (via the abricate pipeline) with default settings. Plasmid burden was summarized as plasmid count per isolate, defined as the number of reconstructed plasmids reported by MOB-suite (mob_recon) under default settings.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCore-genome cladogram construction\u003c/h3\u003e\n\u003cp\u003eGenome annotation was performed using Prokka v1.14.6(15), and the resulting GFF files were analyzed with Roary v3.13.0 (16)to perform pan-genome analysis and generate a core-gene alignment. The core gene alignment was used to infer a maximum-likelihood core-genome cladogram using IQ-TREE v2.4.0 under the GTR\u0026thinsp;+\u0026thinsp;G substitution model, with branch support assessed by 1,000 ultrafast bootstrap replicates. The consensus tree was visualized and annotated using iTOL(18).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using SPSS software version 23.0. Continuous variables were presented as median and interquartile range (IQR), and categorical variables were expressed as counts (n) and percentages (%). Group comparisons were performed using the chi-squared (χ\u0026sup2;) test, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of E. coli isolates\u003c/h2\u003e \u003cp\u003eA total of 65 clinical \u003cem\u003eE. coli\u003c/em\u003e isolates were obtained from sterile body fluids of neonates, including 15 isolates from cerebrospinal fluid and 50 isolates from blood cultures. Among the 65 neonates, 13 (20.0%) were diagnosed with early-onset infection (0\u0026ndash;6 days of life), and 52 (80.0%) with late-onset infection (\u0026ge;\u0026thinsp;7 days). The youngest patient was 9 hours old, and the oldest was 60 days old at the time of infection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenotypic characteristics of E. coli isolates\u003c/h2\u003e \u003cp\u003eMLST identified 20 distinct sequence types (STs) among the 65 clinical \u003cem\u003eE. coli\u003c/em\u003e isolates. The most prevalent type was ST1193 (16/65, 24.6%), followed by ST95 (11/65, 16.9%), ST4702 (6/65, 9.2%), ST62 (5/65, 7.7%), and ST131 (4/65, 6.2%). The remaining STs were found in 3 or fewer isolates, and two isolates could not be assigned to any known ST. Serotyping revealed 22 different O:H combinations, among which O75:H5 (16/65, 24.6%), O4:H5 (8/65, 12.3%), and O7:H45 (5/65, 7.7%) were the most frequent. Consistent with known lineage-associated serotypes, ST1193 was predominantly associated with O75:H5 and ST62 with O7:H45 in this cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparative analysis of resistance gene profiles among MLST types\u003c/h2\u003e \u003cp\u003eResistance genes in all 65 \u003cem\u003eE. coli\u003c/em\u003e isolates were predicted using the CARD database. Among β-lactamase genes, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eTEM\u0026minus;1\u003c/em\u003e\u003c/sub\u003e was detected in 27 isolates (41.5%), and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eCTX\u0026minus;M\u0026minus;14\u003c/em\u003e\u003c/sub\u003e in 10 isolates (15.4%). Additionally, 19 isolates (29.2%) were positive for a putative EC-5 β-lactamase\u0026ndash;encoding gene based on CARD protein homolog models. Although this variant has not yet been formally classified, it may confer extended-spectrum resistance. Two isolates (3.1%) carried the broad-spectrum β-lactamase \u003cem\u003eSFO-1\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eA comparison between the two predominant sequence types, ST1193 and ST95, revealed that all ST1193 isolates were positive for the putative EC-5 β-lactamase gene, and that ST1193 isolates exhibited a significantly higher burden of resistance genes than ST95 isolates. Significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed in the presence of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eTEM\u0026minus;1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003emphA\u003c/em\u003e, \u003cem\u003emrx\u003c/em\u003e, \u003cem\u003esul1\u003c/em\u003e, \u003cem\u003esul2\u003c/em\u003e, \u003cem\u003eaadA5\u003c/em\u003e, and \u003cem\u003eAAC(3)-IId\u003c/em\u003e. In contrast, no significant difference was noted in the prevalence of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eCTX\u0026minus;M\u0026minus;14\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eCTX\u0026minus;M\u0026minus;55\u003c/em\u003e\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, high detection rates were observed for regulatory genes implicated in multidrug resistance and envelope stress responses. The global regulator \u003cem\u003eH-NS\u003c/em\u003e gene was perfectly matched in 100% (65/65) of isolates, followed by \u003cem\u003ecpxA\u003c/em\u003e (96.9%), and \u003cem\u003eevgA\u003c/em\u003e (92.3%). These findings may indicate that neonatal invasive \u003cem\u003eE. coli\u003c/em\u003e isolates although functional consequences were not assessed envelope stress and multi drug resistance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of major ARGs in \u003cem\u003eE. coli\u003c/em\u003e ST1193 and ST95 isolates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResistance Gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST1193\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eST95\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Isolates (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfect resistance genes, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrict resistance genes\u003c/p\u003e \u003cp\u003e, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-lactamases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eTEM\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eCTX\u0026minus;M\u0026minus;14\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eCTX\u0026minus;M\u0026minus;55\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-5\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrolides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003emphA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (81.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003emrx\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfonamides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esul1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (68.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esul2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAminoglycosides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eaadA5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAC(3)-IId\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*: \u0026ldquo;EC-5\u0026rdquo; denotes a putative β-lactamase gene predicted using CARD protein homolog models.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAntimicrobial resistance gene and plasmid profiles of\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e \u003cb\u003eisolates\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAnalysis of ARG repertoires and plasmid profiles in the 65 E. coli isolates revealed marked differences between MLST lineages in ARG abundance, ESBL carriage and plasmid architecture. ST1193 represented the lineage with the highest resistance and plasmid burden, with a median plasmid burden of 6 reconstructed plasmids per isolate, and a 93.8% carriage rate of IncFIB replicons. Notably, all ESBL gene\u0026ndash;positive ST1193 isolates simultaneously carried IncFIB replicons, indicating a highly homogeneous ESBL plasmid background in this clone. ST4702 exhibited the highest ESBL positivity rate (66.7%) but showed a lower frequency of IncFIB carriage and no co-occurrence of ESBL and IncFIB, suggesting that ESBL determinants in this clone are likely mobilized by non-IncFIB plasmids. ST62 was characterized by a high plasmid count and 100% IncFIB carriage but a relatively low ESBL positivity rate (20%), implying a stable plasmid background that does not primarily mediate ESBL dissemination. In contrast to these resistant lineages, classical sequence types such as ST95 and ST131 harbored a lower ARG burden, had lower ESBL carriage rates and carried fewer plasmids. Together, these findings indicate that dominant lineages such as ST1193, ST4702 and ST62 show stronger enrichment of resistance genes and a preference for specific plasmid types, suggesting potential selective advantages during the evolution of antimicrobial resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of CRISPR-Cas systems and their MLST associations\u003c/h2\u003e \u003cp\u003eAmong the 65 neonatal invasive \u003cem\u003eE. coli\u003c/em\u003e isolates from neonates, 22 isolates (33.8%) harbored CRISPR-Cas systems with an evidence level\u0026thinsp;\u0026ge;\u0026thinsp;3. Type I-F (11/22, 50.0%) and type I-E (10/22, 45.5%) were the predominant subtypes, and one isolate, EXPEC045 (ST448), carried a dual type I-E\u0026thinsp;+\u0026thinsp;I-F configuration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). CRISPR-Cas carriage was strongly lineage-dependent (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): all ST1193 isolates (16/16) were CRISPR-negative, whereas 90.9% (10/11) of ST95 isolates were CRISPR-positive and all belonged to subtype I-F.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of CRISPR-Cas systems and their association with resistance genes\u003c/h2\u003e \u003cp\u003eThe relationship between CRISPR-Cas system presence and antibiotic resistance genes carriage is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, CRISPR-negative \u003cem\u003eE. coli\u003c/em\u003e isolates carried a significantly higher number of resistance genes than CRISPR-positive isolates (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This difference was primarily observed in the category of strictly matched resistance genes. In contrast, for perfectly matched genes, no statistically significant difference was found between the two groups.\u003c/p\u003e \u003cp\u003eNotably, most commonly detected resistance genes\u0026mdash;particularly those conferring resistance to β-lactams, macrolides, sulfonamides, and aminoglycosides\u0026mdash;were classified as perfect matches, which may explain the lack of significant differences in these categories between CRISPR-positive and -negative isolates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of resistance gene profiles between CRISPR-positive and CRISPR-negative \u003cem\u003eE. coli\u003c/em\u003e isolates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eresistance gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRISPR-negative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCRISPR-positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of isolates (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (66.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal genes (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfect genes (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrict genes (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-lactam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eampC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eTEM\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eCTX\u0026minus;M\u0026minus;14\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eCTX\u0026minus;M\u0026minus;27\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eCTX\u0026minus;M\u0026minus;55\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eCTX\u0026minus;M\u0026minus;19\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEC-5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBroad-spectrum β-lactamases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSFO-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrolide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003emphA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003emrx\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfonamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esul1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esul2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAminoglycoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eaadA5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAAC(3)-IId\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of resistance gene profiles and CRISPR-Cas systems in ST1193 and ST95 E. coli isolates\u003c/h2\u003e \u003cp\u003eA comparative analysis was conducted between the two most prevalent sequence types in this study\u0026mdash;ST1193 and ST95. The ST1193 isolates exhibited a higher burden of resistance genes, yet none of the 16 ST1193 isolates carried CRISPR-Cas systems. In contrast, 90.9% (10/11) of ST95 isolates harbored CRISPR-Cas systems, predominantly type I-F, and tended to carry a lower resistance gene burden. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the core-genome cladogram annotated with MLST types, CRISPR-Cas system subtypes, and resistance gene profiles among all 65 neonatal \u003cem\u003eE. coli\u003c/em\u003e isolates. Together, these patterns indicate an inverse association between CRISPR-Cas carriage and resistance gene burden in this cohort, and suggest that both traits may be linked to lineage background (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCRISPR spacer repertoire and targeting profiles\u003c/h2\u003e \u003cp\u003eAmong the 22 isolates carrying a CRISPR-Cas system, a total of 151 CRISPR arrays were identified, of which 38 arrays had an evidence level\u0026thinsp;\u0026ge;\u0026thinsp;3. In these high-confidence arrays, each isolate harbored 1\u0026ndash;4 CRISPR arrays, with individual arrays containing 4\u0026ndash;20 spacers. The repeat consensus sequences were 26\u0026ndash;30 bp in length, consistent with canonical type I-E/I-F CRISPR-Cas systems. In total, 278 spacer records were detected, corresponding to 208 unique spacer sequences after deduplication. The number of unique spacers per isolate ranged from 4 to 45, with a median of 10 (IQR 5\u0026ndash;14). Most isolates carried 5\u0026ndash;14 spacers, whereas only a few exhibited long CRISPR arrays containing 26\u0026ndash;45 spacers. Among the 208 unique spacers, 24 were shared across multiple arrays or isolates, suggesting a small subset of conserved CRISPR \u0026ldquo;memory\u0026rdquo;. Of the spacer-carrying isolates, 16 (16/22, 72.8%) possessed at least one spacer targeting plasmid sequences in PLSDB, whereas only 2 (2/22, 9.1%) harbored spacers targeting phage genomes in RefSeq/PhagesDB, indicating that the CRISPR systems in this cohort are more biased toward plasmid rather than phage targeting (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cem\u003eE. coli\u003c/em\u003e is a major pathogen responsible for invasive neonatal infections. Previous studies have shown that ST95 and ST1193 \u003cem\u003eE. coli\u003c/em\u003e are high-risk clones associated with neonatal purulent meningitis and recurrent infections(19). Consistent with those findings, ST1193 and ST95 were also the predominant sequence types in our study among neonatal cases of sepsis and purulent meningitis. These two ST types exhibited considerable differences in antimicrobial resistance. ST1193 isolates harbored more resistance genes than ST95 isolates. This finding is consistent with results from a multicenter study in China, which showed that ST1193, ST410, and ST131 \u003cem\u003eE. coli\u003c/em\u003e had higher rates of multidrug resistance and ESBL positivity than ST95 and ST73 isolates(4). EC-5 was identified as a putative class C β-lactamase\u0026ndash;like hit predicted by the CARD protein homolog model; however, its functional role in cephalosporin resistance remains to be validated(20). In this study, all ST1193 isolates carried an EC-5\u0026ndash;like β-lactamase hit predicted by the CARD homology model. We did not perform functional validation, so the contribution of this putative allele to the resistance phenotype remains uncertain.\u003c/p\u003e \u003cp\u003eThe most common mechanism of antimicrobial resistance dissemination in \u003cem\u003eE. coli\u003c/em\u003e is horizontal gene transfer via mobile genetic elements such as plasmids or transposons(21). Previous studies have reported inverse associations between CRISPR-Cas carriage and resistance gene burden; however, mechanistic evidence for CRISPR-mediated defense against incoming DNA in non-engineered \u003cem\u003eE. coli\u003c/em\u003e remains limited. In our neonatal cohort, CRISPR-positive isolates tended to carry fewer acquired resistance genes, consistent with these population-level correlations(22). Palmer et al. reported that multidrug-resistant enterococci often lacked CRISPR-Cas systems, supporting a negative association between CRISPR-Cas and antibiotic resistance\u003csup\u003e[18]\u003c/sup\u003e. Similarly, a global analysis on \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e revealed an inverse relationship between CRISPR-Cas presence and carbapenem resistance(23). These findings suggest that CRISPR-Cas carriage is often inversely associated with ARG burden at the population level. However, some studies have reported no association between the presence of CRISPR systems and resistance in \u003cem\u003eE. coli\u003c/em\u003e (24). Our results showed that approximately one-third of the collected invasive neonatal \u003cem\u003eE. coli\u003c/em\u003e isolates were CRISPR-positive, and that CRISPR-positive isolates carried fewer resistance genes than CRISPR-negative isolates. This inverse association should be interpreted cautiously because both CRISPR-Cas carriage and ARG burden were structured by lineage background in our dataset. Notably, several CRISPR-Cas\u0026ndash;positive isolates also carried multiple resistance genes, indicating that CRISPR-Cas presence alone is insufficient to predict low ARG content.\u003c/p\u003e \u003cp\u003eThe type I-E and I-F are the major CRISPR-Cas subtypes in \u003cem\u003eE. coli\u003c/em\u003e, and their CRISPR structures and interference modules differ. Previous studies have suggested that the I-E type systems more frequently target phage DNA, whereas the I-F system can display robust interference in some contexts, partly through regulatory features affecting cas expression(25, 26). In our study, I-E and I-F CRISPR-positive isolates each accounted for approximately half of the CRISPR-positive isolates, suggesting a broad distribution of both subtypes in neonatal invasive \u003cem\u003eE. coli\u003c/em\u003e. However, spacer sequence analysis revealed that spacers from both subtypes matched plasmid sequences more often than phage genomes in our dataset, indicating a plasmid-biased targeting spectrum in this cohort.\u003c/p\u003e \u003cp\u003eFurthermore, no CRISPR systems were detected in ST1193 and ST131 isolates, which have been reported as resistance-enriched lineages in multiple settings(4, 27), whereas ST95 isolates, which are relatively more susceptible, had a high CRISPR-positive rate\u0026mdash;all of which were of the I-F type. This aligns with previous findings suggesting that type I-F CRISPR systems are more commonly found in antibiotic-susceptible \u003cem\u003eE. coli\u003c/em\u003e(6). Notably, several ST95 isolates still carried multiple resistance genes, indicating that CRISPR-Cas presence does not preclude ARG carriage. Potential explanations include strain-specific variation in CRISPR activity, spacer content, and mobile element dynamics; however, these mechanisms were not evaluated in this study(28).\u003c/p\u003e \u003cp\u003eTouchon M and Garc\u0026iacute;a-Guti\u0026eacute;rrez E have suggested that \u003cem\u003eE. coli\u003c/em\u003e typically harbors either the I-E or I-F CRISPR-Cas system, and that the two are mutually exclusive due to functional antagonism(9) (29). However, in our study, one strain (EXPEC045, ST448) harbored both I-E and I-F loci. This dual configuration was uncommon in our dataset and should be interpreted as a rare lineage feature rather than a general rule. Additionally, previous studies have pointed out that most prokaryotes contain only 1\u0026ndash;3 CRISPR arrays, and that more than three arrays are rare(30). In this study, we identified one strain with two evidence level\u0026thinsp;\u0026ge;\u0026thinsp;3 CRISPR loci for both I-E and I-F systems, suggesting that different CRISPR-Cas types may coexist in specific isolates. This warrants further investigation into their evolutionary significance and potential functional interactions.\u003c/p\u003e \u003cp\u003eThis study has limitations. First, the sample size was modest (n\u0026thinsp;=\u0026thinsp;65) and dominated by a small number of sequence types, which limits generalizability and may confound associations between CRISPR-Cas carriage and ARG burden. The number of isolates for some STs (e.g., ST131) was relatively small, potentially underpowering subgroup analysis. Moreover, the identification of CRISPR-Cas systems was based solely on bioinformatics predictions without functional validation or phenotypic antimicrobial susceptibility testing, which limits inference about activity and clinical impact.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides a population-level view of the association between CRISPR-Cas systems, MLST types, plasmid features, and predicted resistance gene profiles in neonatal invasive \u003cem\u003eE. coli\u003c/em\u003e isolates. Significant differences were observed between ST1193 and ST95 isolates in terms of CRISPR prevalence and resistance gene carriage. Overall, our findings support a lineage-structured inverse association between CRISPR-Cas carriage and predicted resistance gene burden in this cohort, and provide a rationale for future studies integrating larger datasets and functional assays to test causality.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eantimicrobial resistance genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCARD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComprehensive Antibiotic Resistance Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRISPR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eclustered regularly interspaced short palindromic repeats\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCas\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCRISPR-associated\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESBL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eextended-spectrum β-lactamase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emultilocus sequence typing.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003eThe \u003cem\u003eE. coli\u003c/em\u003e isolates analyzed in this study were obtained from cerebrospinal fluid and blood cultures of hospitalized neonates as part of routine clinical diagnostics. No additional procedures or interventions were performed. Ethical approval was granted by the Ethics Committee of the Capital Institute of Pediatrics (Approval Number: SHERLLM2025011), with informed consent waived as only anonymized bacterial isolates were used.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by Natural Science Foundation of Beijing Municipality (Grant Nos. 7232009, 7244289), the National Natural Science Foundation of China (Grant No. 8257121766), the Cross-cooperation project of Beijing Science and Technology New Star Program (Grant Nos. 20240484724), the high level public health technical personnel construction Project (Subject leaders-03-02).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePeicen Zou : Conceptualization, Data curation, Formal analysis, Validation, Visualization, Writing-original draft; Yijun Ding Conceptualization, Data curation, Formal analysis, Validation, Visualization, Writing-original draft; Ying Chen : Data curation, Validation; Panpan Xu : Validation; Dongmiao Zhang : Formal analysis; Peipei Zhang, Pan Huang, Yue Du : Data curation; Yueqiao Gao : Formal analysis; Yajuan Wang : Conceptualization, Funding acquisition; Resources; Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll genome sequences have been submitted to CNCB under BioProject accession number PRJCA046074.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Liu L, Oza S, Hogan D, Perin J, Rudan I, Lawn JE, et al. Global, regional, and national causes of child mortality in 2000-13, with projections to inform post-2015 priorities: an updated systematic analysis. Lancet. 2015;385(9966):430\u0026thinsp;\u0026minus;\u0026thinsp;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Furyk JS, Swann O, Molyneux E. Systematic review: neonatal meningitis in the developing world. Trop Med Int Health. 2011;16(6):672-9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Stoll BJ, Puopolo KM, Hansen NI, S\u0026aacute;nchez PJ, Bell EF, Carlo WA, et al. Early-Onset Neonatal Sepsis 2015 to 2017, the Rise of Escherichia coli, and the Need for Novel Prevention Strategies. JAMA Pediatr. 2020;174(7):e200593.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Gu S, Lai J, Kang W, Li Y, Zhu X, Ji T, et al. Drug resistance characteristics and molecular typing of Escherichia coli isolates from neonates in class A tertiary hospitals: A multicentre study across China. 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Philos Trans R Soc Lond B Biol Sci. 2019;374(1772):20180384.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Makarova KS, Wolf YI, Iranzo J, Shmakov SA, Alkhnbashi OS, Brouns SJJ, et al. Evolutionary classification of CRISPR-Cas systems: a burst of class 2 and derived variants. Nat Rev Microbiol. 2020;18(2):67\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Touchon M, Rocha EP. The small, slow and specialized CRISPR and anti-CRISPR of Escherichia and Salmonella. PLoS One. 2010;5(6):e11126.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Bozic B, Repac J, Djordjevic M. Endogenous Gene Regulation as a Predicted Main Function of Type I-E CRISPR/Cas System in E. coli. Molecules. 2019;24(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Camacho-Gonzalez A, Spearman PW, Stoll BJ. Neonatal infectious diseases: evaluation of neonatal sepsis. Pediatr Clin North Am. 2013;60(2):367\u0026thinsp;\u0026minus;\u0026thinsp;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Couvin D, Bernheim A, Toffano-Nioche C, Touchon M, Michalik J, N\u0026eacute;ron B, et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 2018;46(W1):W246-w51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Pourcel C, Touchon M, Villeriot N, Vernadet JP, Couvin D, Toffano-Nioche C, et al. CRISPRCasdb a successor of CRISPRdb containing CRISPR arrays and cas genes from complete genome sequences, and tools to download and query lists of repeats and spacers. Nucleic Acids Res. 2020;48(D1):D535-d44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Grant JR, Enns E, Marinier E, Mandal A, Herman EK, Chen CY, et al. Proksee: in-depth characterization and visualization of bacterial genomes. Nucleic Acids Res. 2023;51(W1):W484-w92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068-9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MT, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31(22):3691-3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Mol Biol Evol. 2020;37(5):1530-4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49(W1):W293-w6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Nhu NTK, Phan MD, Hancock SJ, Peters KM, Alvarez-Fraga L, Forde BM, et al. High-risk Escherichia coli clones that cause neonatal meningitis and association with recrudescent infection. Elife. 2024;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e McArthur AG, Waglechner N, Nizam F, Yan A, Azad MA, Baylay AJ, et al. The comprehensive antibiotic resistance database. Antimicrob Agents Chemother. 2013;57(7):3348-57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Nasrollahian S, Graham JP, Halaji M. A review of the mechanisms that confer antibiotic resistance in pathotypes of E. coli. Front Cell Infect Microbiol. 2024;14:1387497.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Dziuba A, Dzierżak S, Sodo A, Wawszczak-Kasza M, Zegadło K, Białek J, et al. Comparative study of virulence potential, phylogenetic origin, CRISPR-Cas regions and drug resistance of Escherichia coli isolates from urine and other clinical materials. Front Microbiol. 2023;14:1289683.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Jiang J, Cienfuegos-Gallet AV, Long T, Peirano G, Chu T, Pitout JDD, et al. Intricate interplay of CRISPR-Cas systems, anti-CRISPR proteins, and antimicrobial resistance genes in a globally successful multi-drug resistant Klebsiella pneumoniae clone. Genome Med. 2025;17(1):9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Toro M, Cao G, Ju W, Allard M, Barrangou R, Zhao S, et al. Association of clustered regularly interspaced short palindromic repeat (CRISPR) elements with specific serotypes and virulence potential of shiga toxin-producing Escherichia coli. Appl Environ Microbiol. 2014;80(4):1411-20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Nami Y, Rostampour M, Panahi B. CRISPR-Cas systems and diversity of targeting phages in Lactobacillus johnsonii isolates; insights from genome mining approach. Infect Genet Evol. 2023;114:105500.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Malone LM, Hampton HG, Morgan XC, Fineran PC. Type I CRISPR-Cas provides robust immunity but incomplete attenuation of phage-induced cellular stress. Nucleic Acids Res. 2022;50(1):160\u0026thinsp;\u0026minus;\u0026thinsp;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Ding Y, Zhang J, Yao K, Gao W, Wang Y. Molecular characteristics of the new emerging global clone ST1193 among clinical isolates of Escherichia coli from neonatal invasive infections in China. Eur J Clin Microbiol Infect Dis. 2021;40(4):833\u0026thinsp;\u0026minus;\u0026thinsp;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Pawluk A, Davidson AR, Maxwell KL. Anti-CRISPR: discovery, mechanism and function. Nat Rev Microbiol. 2018;16(1):12\u0026thinsp;\u0026minus;\u0026thinsp;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Garc\u0026iacute;a-Guti\u0026eacute;rrez E, Almendros C, Mojica FJ, Guzm\u0026aacute;n NM, Garc\u0026iacute;a-Mart\u0026iacute;nez J. CRISPR Content Correlates with the Pathogenic Potential of Escherichia coli. PLoS One. 2015;10(7):e0131935.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Makarova KS, Wolf YI, Alkhnbashi OS, Costa F, Shah SA, Saunders SJ, et al. An updated evolutionary classification of CRISPR-Cas systems. Nat Rev Microbiol. 2015;13(11):722\u0026thinsp;\u0026minus;\u0026thinsp;36.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Escherichia coli, CRISPR-cas, Bacterial resistance, MLST, Newborn","lastPublishedDoi":"10.21203/rs.3.rs-8280865/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8280865/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo investigate the distribution of CRISPR-Cas systems in \u003cem\u003eEscherichia coli\u003c/em\u003e (\u003cem\u003eE. coli\u003c/em\u003e) isolates and evaluate their associations with multilocus sequence types (MLST), antimicrobial resistance genes (ARGs), and plasmid features.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eST1193 (16/65, 24.6%) and ST95 (11/65, 16.9%) were the predominant lineages. ST1193 showed a higher resistance gene burden than ST95, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eTEM\u0026minus;1\u003c/em\u003e\u003c/sub\u003e was detected in 87.5% of ST1193 isolates. CRISPR-Cas systems were detected in 22 isolates (33.8%), including 11 with type I-F (50.0%), 10 with type I-E (45.5%), and one with both types. Spacer sequences were primarily directed against plasmid DNA. Plasmid replicons were frequently detected, and plasmid burden varied across lineages. All the ST1193 isolates lacked detectable CRISPR-Cas systems, whereas 90.9% (10/11) of ST95 isolates harbored type I-F systems.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCRISPR-Cas carriage was strongly lineage-dependent and showed an inverse association with predicted resistance gene burden in this cohort; this pattern should be interpreted as a lineage-structured correlation rather than mechanistic evidence of CRISPR-mediated restriction of ARG acquisition.\u003c/p\u003e","manuscriptTitle":"Association of CRISPR-Cas Systems with Antimicrobial Resistance and MLST Types in Neonatal Invasive Escherichia coli","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 09:56:17","doi":"10.21203/rs.3.rs-8280865/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-11T13:05:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T18:25:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T17:28:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334125588600620172625877325425481036659","date":"2026-02-06T11:13:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62843738483236911915570802789364031701","date":"2026-02-03T11:16:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-02T11:24:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-02T07:38:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-05T04:18:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T04:17:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2025-12-04T15:17:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1ebd03a3-8c8c-44e3-8e86-bd8139580901","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T06:40:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 09:56:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8280865","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8280865","identity":"rs-8280865","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}