Introduction

Urinary tract infections (UTIs) represent one of the most common bacterial infections globally, imposing a significant burden on healthcare systems.1 In clinical practice, the nitrite dipstick test2 is widely used as a rapid screening tool for bacteriuria, primarily detecting nitrate-reducing Enterobacteriaceae such as Escherichia coli. However, this reliance creates a diagnostic “blind spot”: Gram-positive pathogens, including Enterococcus spp. and Staphylococcus spp., typically do not reduce nitrate and thus yield negative nitrite results.3 Recent epidemiological studies indicate that Gram-positive uropathogens are frequently underestimated, yet they account for a substantial proportion—ranging from 20% to over 30%—of culture-positive urinary tract infections, particularly in complicated or hospital-acquired cases.4,5 Furthermore, studies focusing on nitrite-negative UTIs confirm that Enterococcus species are a leading etiology among these missed diagnoses.6 Consequently, these infections are frequently miscategorized as “sterile pyuria” or non-bacterial inflammation, leading to delayed diagnosis and inappropriate empirical therapy. This diagnostic gap is particularly concerning given the increasing prevalence of multidrug-resistant (MDR) Gram-positive bacteria in complicated UTIs.

The therapeutic landscape for UTIs is rapidly deteriorating due to the dissemination of resistance genes against last-resort antibiotics. Linezolid (an oxazolidinone7) and tigecycline8 (a glycylcycline) serve as final lines of defense against vancomycin-resistant enterococci (VRE)9,10 and methicillin-resistant Staphylococcus aureus (MRSA). However, the emergence of plasmid-mediated resistance genes has challenged their efficacy. The gene optrA, encoding an ABC-F ribosomal protection protein, confers transferable resistance to linezolid and phenicols.7 Similarly, the plasmid-borne tet(X) variants encode monooxygenases that degrade tigecycline.

Compounding this threat is the phenomenon of “co-selection” a potent evolutionary mechanism where the use of one antimicrobial agent indirectly selects for resistance to another. For instance, the fexA gene, which encodes a specific phenicol exporter, is frequently found physically linked with optrA on the same mobile genetic elements. Due to this genetic linkage, exposure to widely accessible phenicol antibiotics (such as florfenicol, heavily used in agricultural and veterinary settings) can inadvertently co-select and maintain linezolid resistance within bacterial populations, bypassing the need for direct linezolid exposure.

While comprehensive surveillance of multidrug-resistant (MDR) pathogens is well-established in tertiary care hospitals, molecular epidemiological data from primary healthcare settings remain alarmingly scarce. Nitrite-negative UTIs in these community settings are frequently managed with empirical oral antibiotics, bypassing rigorous microbiological diagnostics, thereby turning these settings into unrecognized potential sources for the dissemination of critical resistance determinants. Therefore, the specific aim of this study was to characterize the resistome and investigate the genomic context of high-risk resistance genes in nitrite-negative UTIs within a primary healthcare setting. Using deep metagenomic sequencing, we sought to determine the prevalence of optrA and tet(X) in this diagnostically “silent” cohort and to identify genomic evidence of multidrug-resistance clustering—specifically the co-localization of optrA and fexA—to better understand the mechanisms driving their persistence and spread in the community.

Materials and Methods Study Design and Patient Recruitment

This study was approved by the Medical Ethics Committee of Jiashan County Traditional Chinese Medicine Hospital (Approval No. 2026–001). The first author’s affiliation, Jiashan County Huimin Street Community Health Service Center, is a primary healthcare facility that does not have an independent medical ethics committee. Within the local healthcare system, this center is part of a consolidated medical consortium led by Jiashan County Traditional Chinese Medicine Hospital, which serves as the central ethical review body for all member institutions. Consequently, ethical approval for research conducted within the consortium is uniformly managed by the leading hospital’s ethics committee. The requirement for informed consent was waived by the Ethics Committee due to the retrospective nature of the study and the use of anonymized data.

A total of 24 patients presenting with symptoms of complicated urinary tract infections (cUTIs) were initially screened. The inclusion criteria were: (1) clinical symptoms of UTI (eg, dysuria, frequency, urgency); (2) significant pyuria (WBC >100 cells/µL) confirmed by urinalysis; and (3) consistently negative results for urinary nitrite. Exclusion criteria included insufficient sequencing depth (< 150,000 optimized microbial reads) or library construction failure. Based on these criteria, 4 samples were excluded, resulting in a final cohort of 20 patients.

Clinical metadata were retrospectively extracted from electronic medical records. These comprehensive variables included demographic data, pregnancy status, recent antibiotic history (within 3 months), prior hospitalization, presence of indwelling urinary catheters, and a history of recurrent UTIs. Within this cohort, 35.0% (7/20) of the patients had a history of recurrent UTIs, and 20.0% (4/20) had been hospitalized within the past 3 months. Notably, none of the patients (0.0%) had an indwelling urinary catheter, reflecting a typical community-acquired outpatient population.

Sample Processing and Metagenomic Sequencing

Midstream urine samples (~15 mL per patient) were collected and processed within 2 hours. Samples were centrifuged, the bulk supernatant was removed, and the sediment was thoroughly resuspended in a minimal volume of the remaining basal urine to guarantee sufficient microbial biomass recovery. Host DNA depletion was performed using the QIAamp DNA Microbiome Kit (Qiagen, Germany). This protocol yielded an average of 2.01 × 107 optimized microbial reads per sample, with non-host reads constituting an average of 14.33% of the raw sequence output, demonstrating robust background depletion.

Total genomic DNA was extracted and used for library construction. Metagenomic shotgun sequencing was performed on the Illumina NovaSeq 6000 platform (Pair-End 150 bp). Raw sequencing data were processed using fastp (v0.23.0) to remove adapters and low-quality reads. Host contamination was removed by mapping clean reads against the human reference genome (GRCh38) using BWA-MEM.

Resistome and Virulence Profiling Taxonomic Classification

Taxonomic classification of filtered reads was performed using Kraken211 (v2.1.2) against the standard bacterial database. Species-level abundances were subsequently re-estimated using Bracken12 (v2.6.2) to determine the dominant pathogens in the nitrite-negative cohort.

Read-Based Resistome Quantification

To identify the presence of the tigecycline resistance gene tet(X) amid potential assembly fragmentation, an a priori conservative empirical threshold of >50 mapped paired-end reads was applied. This cutoff was designed to filter out potential background sequencing noise, index hopping, or transient cross-contamination. Given that tet(X) variants span approximately 1100 to 1200 bp, 50 paired-end reads (150 bp each) provide a theoretical minimum coverage depth of 5× to 6×, ensuring high-confidence alignment across the core flavin-dependent monooxygenase domains rather than non-specific mapping to short conserved motifs. To accurately quantify antibiotic resistance genes (ARGs) despite potential assembly fragmentation, we employed a read-mapping approach. Clean microbial reads were aligned against the CARD database13 (Comprehensive Antibiotic Resistance Database, v3.2.9) using Diamond.14 Gene abundance was normalized using the Transcripts Per Million (TPM) metric to account for variations in sequencing depth and gene length. The TPM was calculated as follows:

where Ri represents the number of reads mapped to gene i, and Li represents the nucleotide length of gene i. Unlike RPKM or FPKM, where the sum of normalized values varies across samples, TPM ensures that the sum of all relative abundance values in each sample is strictly identical (1,000,000). This unique mathematical property allows for a direct and reliable comparison of ARG relative abundances across different patient samples with varying sequencing depths, effectively avoiding the sample-dependent scaling biases inherent to RPKM.

Assembly-Based Genomic Context Analysis

De novo assembly was performed using MEGAHIT15 (v1.2.9) in multi-k-mer mode. The assembly pipeline achieved high quality across the cohort, yielding an average contig N50 of 26,030 bp. The assembled contigs were annotated using Abricate (v1.0.1) against multiple databases to characterize the genomic context: CARD for ARGs, VFDB16 for virulence factors, and PlasmidFinder17 /ISfinder18 for mobile genetic elements (MGEs).19 Only hits with >80% coverage and >90% identity were considered significant. To visualize the genetic environment of key multidrug-resistance clusters (e.g., optrA-fexA), linear comparison figures were generated using custom Python scripts based on the Matplotlib library.

Statistical Analysis

Descriptive statistics were used to summarize clinical and genomic features. The prevalence of resistance genes was calculated as the percentage of positive samples within the cohort (N=20). All analyses were performed using Python (v3.8) and R software (v4.1.0).

Results Clinical Characteristics of the Nitrite-Negative Cohort

The final study cohort comprised 20 patients with clinically diagnosed complicated UTIs. Despite presenting with significant pyuria (Mean WBC > 100 cells/µL) and typical symptoms, 100% (20/20) of the patients tested negative for urinary nitrite (Table 1 and Supplementary Table 1). This clinical signature, which typically rules out nitrate-reducing Enterobacteriaceae (e.g., E. coli),20 was corroborated by metagenomic taxonomic classification. Analysis confirmed that Gram-positive pathogens dominated the microbiome in 90% (18/20) of the samples, with Enterococcus spp. (predominantly E. faecalis) identified as the most frequent genus. This taxonomic profile aligns with the ubiquitous detection of the optrA gene found in this cohort. Notably, the study included 4 pregnant women (20%), a vulnerable subgroup for whom tetracyclines are strictly contraindicated, yet who harbored these high-risk resistance determinants. Review of clinical records indicated that none of the patients had a history of linezolid or tigecycline usage in the preceding 3 months.

Table 1 Clinical Characteristics and Urinalysis Profile of the Study Cohort

The “Silent” Resistome: Ubiquity of optrA and Hidden Tet(X)

Metagenomic profiling revealed a reservoir of multidrug resistance genes (ARGs) typically associated with Gram-positive pathogens (Supplementary Data 1).

Linezolid Resistance

The oxazolidinone resistance gene optrA was ubiquitous, detected in 100% (20/20) of samples. This suggests a widespread distribution of transferable linezolid resistance in this nitrite-negative population.

Tigecycline Resistance

Although full-length assembly of tet(X) variants was limited by fragmentation, read-mapping analysis revealed a high prevalence of this gene. 55% (11/20) of samples contained reads mapping specifically to tet(X), with sample JX-NY004 exhibiting an exceptionally high abundance (944 reads).

MDR Background

High abundances of macrolide resistance genes (ermA, ermB, ermC) and other tetracycline resistance determinants (tetM, tetL) were also observed (Figure 1, Supplementary Figure 1 and Supplementary Data 2), confirming the multidrug-resistant nature of the microbiome.

Figure 1 Normalized abundance (TPM) of key resistance genes in the study cohort. The stacked bar plot displays the abundance of the linezolid resistance gene optrA (blue) and the tigecycline resistance gene tet(X) (red) across 20 nitrite-negative urine samples. Abundance is expressed as Transcripts Per Million (TPM) to normalize for sequencing depth and gene length civariation. Note that tet(X) exhibits significant abundance in multiple samples (e.g., JX-NY022, JX-NY014), highlighting its potential impact despite assembly limitations.

Genomic Architecture: Co-Localization of optrA and fexA

To investigate the mechanisms of co-selection, we analyzed the genomic context of the identified ARGs. Overall, the de novo assembly across the cohort yielded robust structural data, with a mean N50 of 26,030 bp. In sample JX-NY001, facilitated by its exceptionally high microbial read yield (>105 million optimized reads; 75.3% of raw data), we successfully assembled a specific contig (k97_12924; length: 12,173 bp, average depth of coverage: 585×) that harbored both the linezolid resistance gene optrA and the phenicol exporter gene fexA. As shown in Figure 2, these two genes were located in close physical proximity, separated by a short intergenic spacer. This physical linkage provides direct genomic evidence that resistance to oxazolidinones and phenicols can be co-selected and co-transferred on the same genetic element. Additionally, analysis of flanking regions in other contigs revealed the presence of mobile genetic elements, including IS26 and Tn554,19,21 further supporting the potential for horizontal gene transfer (Supplementary Data 3).

Figure 2 Genetic organization of the optrA-fexA cluster in sample JX-NY001. Linear map of the assembled contig k97_12924 (12,173 bp). The linezolid resistance gene optrA (red arrow) and the phenicol exporter gene fexA (blue arrow) are co-localized with a short intergenic spacer (688 bp), indicating a physically linked multidrug-resistance cluster. The macrolide resistance gene erm(A) (green arrow) is located downstream on the reverse strand. Arrows indicate the direction of transcription.

Virulence Potential and Mobile Genetic Element

Beyond resistance, the resistome-carrying microbiomes exhibited significant virulence potential.5

Virulence profiling revealed heterogeneous distribution across the cohort. Specific clusters of samples, notably JX-NY001 and JX-NY004, exhibited high pathogenic potential, harboring complete gene clusters encoding capsular polysaccharides (cpsA-K) and flagellar assembly (flgB-J) (Supplementary Data 4).22,23 However, these determinants were absent in the majority of the remaining samples, as visualized in Figure 3 and Supplementary Figure 2. The co-occurrence of these virulence factors with critical ARGs (optrA, tet(X) reads) paints a picture of “high-risk” pathogens capable of both immune evasion and antibiotic survival, explaining the clinical severity (high WBC) despite the negative nitrite results.

Figure 3 Heatmap profiling of virulence factors in the nitrite-negative cohort. The binary heatmap visualizes the presence (red) or absence (white) of key virulence determinants across the 20 patient samples. Genes are grouped by functional categories: Capsule/Immune Evasion (cpsA-K), Biofilm Formation (bopD), and Adhesion/Motility (flg and fim clusters). The widespread detection of the capsule biosynthetic operon (cps) and flagellar assembly genes (flg) indicates a high pathogenic potential for immune evasion and ascending infection.

Discussion Redefining the Risk of Nitrite-Negative UTIs

The clinical reliance on urinary nitrite positivity as a proxy for bacteriuria often leads to the underdiagnosis of Gram-positive infections.24 Our study challenges this paradigm by revealing that nitrite-negative UTIs are not necessarily “low-risk” or “sterile” conditions but can represent a diagnostically silent reservoir of multidrug resistance. Despite negative nitrite results, all patients in our cohort exhibited severe pyuria and harbored complex microbiomes dominated by optrA-carrying pathogens. Notably, while optrA has been reported in Enterobacteriaceae, the 100% prevalence observed in our nitrite-negative (Gram-positive dominant) cohort is significantly higher than the 2–15% prevalence typically reported in unselected UTI cohorts. This striking disparity highlights this specific patient group as a concentrated reservoir for multidrug resistance. The inability of standard dipstick screening to detect non-nitrate-reducing bacteria (e.g., Enterococcus and Staphylococcus spp.) thus creates a critical diagnostic blind spot, potentially delaying appropriate antibiotic therapy and facilitating the silent spread of resistance.

optrA and fexA: A Co-Selected MDR Cluster

A key finding of this study is the 100% prevalence of optrA,25 a gene conferring transferable resistance to linezolid, a last-resort antibiotic.26 The ubiquity of optrA in this cohort is alarming, as it exceeds prevalence rates typically reported in hospital settings. Furthermore, our genomic assembly provided direct evidence of physical linkage between optrA and fexA (a phenicol exporter) on the same genetic element. This co-localization27 suggests a mechanism of co-selection: the use of phenicols (or environmental exposure to related compounds) could inadvertently select for the maintenance of linezolid resistance. This “genetic hitchhiking” mechanism explains the persistence of optrA even in the absence of direct linezolid pressure.

The Cryptic Threat of Tet(X) and Vulnerable Populations

While short-read sequencing limited the full assembly of tet(X) variants, the detection of high-abundance reads mapping to this tigecycline-degrading gene28 in 55% of samples represents a significant signal. Crucially, although assembly was fragmented, the read coverage was distributed across the conserved flavin-dependent monooxygenase domains, supporting the specific presence of tet(X) variants rather than non-specific mapping.29 This finding is clinically alarming as tigecycline serves as one of the few remaining options for treating MDR infections.30 The presence of tet(X) signatures in pregnant women—a population in whom tetracyclines are strictly contraindicated—is particularly concerning. This observation suggests that the acquisition of tet(X) in this community is likely driven by environmental transmission or food chain exposure rather than direct clinical antibiotic selection pressure.31,32

Virulence Factors as Drivers of Persistence

The severity of inflammation (high WBC counts) observed in our cohort correlates with the genomic detection of potent virulence factors. The prevalence of biofilm-associated genes (icaADBC, bopD) indicates that these pathogens are capable of forming persistent communities on the uroepithelium, making them recalcitrant to antibiotic treatment and host immune clearance.33 This virulence profile, combined with the MDR genotype, defines a specific “high-risk” pathotype that masquerades behind a negative nitrite test.

The high prevalence of optrA (100%) and the identification of the optrA-fexA co-localization cluster in our cohort are particularly concerning given that these samples were associated with a primary healthcare setting. Traditionally, linezolid-nonsusceptible enterococci are regarded as nosocomial threats confined to intensive care units. Our metagenomic evidence indicates a substantial “spillover” effect, where these plasmid-mediated high-risk genes are actively circulating within community-acquired infections. This underscores the urgent need to extend advanced molecular surveillance (such as mNGS) beyond tertiary hospitals and into frontline community clinics to curb the silent transmission of MDR reservoirs.

Limitations

This study has limitations inherent to a short report. First, the sample size (N=20) is small, although the high sequencing depth partially compensates by providing comprehensive resistome resolution. Second, due to the retrospective nature of the study and the reliance on short-read Illumina metagenomic data, we could not definitively determine whether the assembled optrA-fexA contig was located on a mobile plasmid or integrated into the chromosome. Furthermore, due to assembly fragmentation in other samples, this direct physical co-localization could only be successfully resolved in a single sample (JX-NY001). Finally, this study lacks parallel culture-based isolation, phenotypic antimicrobial susceptibility testing (AST) confirmation, and functional expression validation of the key resistance determinants, such as tet(X). Future studies employing hybrid assembly approaches (e.g., Oxford Nanopore combined with short reads) and phenotypic verification are warranted to fully resolve the mobile genetic elements driving this resistome.

Conclusion

In conclusion, this metagenomic study characterizes a diagnostically “silent” reservoir of multidrug resistance in nitrite-negative UTI patients. We demonstrate that despite the absence of typical markers for bacteriuria, these patients harbor high-risk pathogens carrying the linezolid resistance gene optrA (100% prevalence) and significant relative abundances of the tigecycline resistance gene tet(X) (55% prevalence). The identification of a physically linked optrA-fexA cluster provides genomic evidence for co-selection mechanisms independent of linezolid usage. Our findings strongly advocate for the inclusion of molecular surveillance for Gram-positive MDR pathogens in routine UTI diagnostics, particularly for nitrite-negative cases.

Data Sharing Statement

The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.

Ethics Approval

This study was reviewed and approved by the Medical Ethics Committee of Jiashan County Traditional Chinese Medicine Hospital (Approval No. 2026-001). The requirement for informed consent was waived by the Ethics Committee due to the retrospective nature of the study and the use of anonymized data. This study is part of the project “Construction of a Comprehensive Biological Sample Biobank for Systems Biology and Human Microbiome Research”, which received ethical approval.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Disclosure

The authors declare no conflicts of interest in this work.

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