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Article

Genetic Characterization of Salmonella and Analysis of Ciprofloxacin Resistance Using Sanger Technique in Romania, 2024

by
Elena Roxana Buzilă
1,2,
Raluca Gatej
3,*,
Cristina Trifan
1,
Teodora Vremera
3,
Mihaela Leustean
3,
Adina David
3,
Daniela Cosmina Bosogea
3,
Georgiana Barbu
3,
Adina Gatea
3,
Ciprian Ilie
3 and
Luminița Smaranda Iancu
1,2
1
Iasi Regional Center for Public Health, National Institute of Public Health, 700465 Iasi, Romania
2
Microbiology Discipline, Preventive Medicine and Interdisciplinarity Department, “Grigore T. Popa”, University of Medicine and Pharmacy, 700115 Iasi, Romania
3
National Institute of Public Health, 050463 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Bacteria 2025, 4(3), 43; https://doi.org/10.3390/bacteria4030043 (registering DOI)
Submission received: 13 June 2025 / Revised: 18 August 2025 / Accepted: 26 August 2025 / Published: 1 September 2025
Versions Notes

Abstract

Salmonella is a major foodborne pathogen, representing a significant public health concern across the European Union (EU), accounting for 39% of foodborne illness-related hospitalizations in 2022, with the highest rates observed in Romania, Cyprus, Greece, and Lithuania. This pilot study aimed to enhance the surveillance and characterization of Salmonella by implementing both phenotypic and genotypic methods for strain typing, as well as for the detection and confirmation of resistance to ciprofloxacin. Materials and methods: A total of 109 Salmonella strains from acute diarrheal cases in North-Eastern Romania were collected (January–August 2024). From these, 19 representative isolates were selected for molecular characterization, including Multi-Locus Sequence Typing (MLST) and the detection of ciprofloxacin resistance determinants. Whole-Genome Sequencing (WGS) was subsequently performed to confirm serotype identity and resistance markers. Results: The 19 isolates underwent Multi-Locus Sequence Typing (MLST) and ciprofloxacin resistance profiling, with Whole-Genome Sequencing (WGS) for confirmation. MLST identified S. Enteritidis (42.1%) as the predominant serotype, followed by S. Typhimurium, S. Livingstone, and S. Infantis. WGS confirmed serotypes in 15 isolates; 2 showed discrepancies with phenotypic results. Phenotypic resistance to ciprofloxacin was detected in 12/19 (63.2%) of the isolates, 6/12 presenting gyrA mutations (S83Y, D87G), and 2/12 strains presenting the plasmid-mediated qnrB19 gene.

1. Introduction

Salmonella infections constitute a substantial global public health concern, ranking among the most frequently reported foodborne pathogens and as the second leading cause of foodborne gastrointestinal infections worldwide, following campylobacteriosis [1,2,3]. The costs associated with preventing, surveillance, and treatment of Salmonella infections impose a substantial financial burden on healthcare systems in both developed and developing countries [1]. In 2022, a total of 29,712 cases of salmonellosis were reported in the European Union (EU), of which 39% resulted in hospitalization. The highest hospitalization rates were recorded in Romania, Cyprus, Greece, and Lithuania. The COVID-19 pandemic influenced case reporting, with a decrease in 2020, followed by an increase in 2021 and 2022; however, case numbers remained below pre-pandemic levels observed in 2018–2019. During the period 2018–2022, no significant EU-wide trends in salmonellosis incidence were identified, although five member states reported a statistically significant decrease in case numbers [4].
Salmonella enterica species are broadly classified into typhoid and non-typhoidal groups based on their pathogenic profiles and clinical manifestations [5]. Among the non-typhoidal Salmonella (NTS) serovars, S. Typhimurium and S. Enteritidis are the serovars most frequently associated with invasive disease in humans [6]. S. Enteritidis, in particular, is a major cause of foodborne infections, typically transmitted through the consumption of contaminated poultry products and eggs. It is commonly associated with self-limiting gastroenteritis, but can also lead to invasive non-typhoidal Salmonella (iNTS) infections, especially in immunocompromised individuals, infants, and the elderly [7,8,9].
Serovar classification of Salmonella strains is traditionally based on the White–Kauffmann–Le Minor scheme, which relies on agglutination assays targeting somatic (O) and flagellar (H) antigens. This serotyping approach currently encompasses approximately 2660 recognized serovars [10,11]. Furthermore, S. enterica is subdivided into six subspecies—S. enterica subspecies enterica (I); S. enterica subspecies salama (II); S. enterica subspecies arizonae (IIIa); S. enterica subspecies diarizonae (IIIb); S. enterica subspecies houtenae (IV); and S. enterica subspecies indica (VI)—based on distinct biochemical properties and genomic relationships [1].
Although slide and tube agglutination assays remain the conventional method for serovar identification, they are labor-intensive and time-consuming. Consequently, there is an increasing demand for rapid and efficient screening methods, particularly for the detection of the most clinically and epidemiologically relevant Salmonella serovars [11]. One promising alternative is Fourier Transform Infrared Spectroscopy (FT-IR), which has been employed for the epidemiological analysis of bacterial isolates. FT-IR operates by detecting molecular vibrations resulting from infrared light absorption, generating unique spectral fingerprints that reflect the chemical composition of each microorganism [11,12,13].
Systems such as the IR Biotyper (Bruker Daltonics) facilitate rapid strain-level typing [14], following species identification via MALDI Biotyper (MALDI Biotyper® Sirius System, Bruker Daltonics, Bremen, Germany,) [15]. Each microorganism exhibits a distinct absorption spectrum, enabling discrimination down to the subspecies level. As a phenotypic, non-DNA-based method, FT-IR offers rapid outbreak screening capabilities in clinical settings and has demonstrated high discriminatory power for precise bacterial identification at fine taxonomic resolution [16,17].
To characterize the evolutionary relationships and clonal structures of bacterial populations, molecular methods are essential. Multi-Locus Sequence Typing (MLST) is a well-established technique used to investigate the genetic relatedness and lineage structure of bacterial strains. It differentiates isolates based on the sequence variations across seven conserved housekeeping genes, providing a reliable level of discrimination through allelic profiling [18]. The MLST workflow typically involves PCR amplification using specific primers for loci in the MLST scheme, followed by Sanger sequencing [19]. Molecular subtyping of Salmonella strains belonging to the same serovar is crucial for outbreak investigating and understanding bacterial epidemiology. With the increasing availability of whole-genome subtyping technologies, genomics has become an integral component of epidemiological surveillance. Advances in sequencing technologies and decreasing costs have made it feasible to generate large-scale genomic datasets, enabling the routine tracking of pathogens based on their genome sequences. High-resolution Whole-Genome Sequencing (WGS)-based molecular typing methods now offer exceptional accuracy and are widely used for detailed bacterial fingerprinting and outbreak source attribution [20].
The primary objective of the study was to expand the recently acquired expertise of the National Institute of Public Health, Romania (NIPH) laboratory, in the field of genomic surveillance, to more effectively support disease prevention, preparedness and response. This activity was associated with the acquisition of new capacities in the microbiology department, by improving confirmatory testing, cluster analysis and investigation of antimicrobial resistance (AMR).
This pilot study aimed to implement phenotypic and genotypic methods for Salmonella strain typing, cluster analysis, and detection of the most frequent ciprofloxacin resistance markers at NIPH.
This study was conducted within the project “Implementing the Pandemic Preparedness Plan using Integrated Genomic Surveillance Programs” (PANDOMIC), which is co-funded by the EU, under the EU4Health 2021–2027 program, being a binational collaborative project between Laboratoire National de Santé (LNS, Luxembourg), NIPH, Romania and the National Institute for Medical-Military Research and Development Cantacuzino (IC, Romania) as an affiliated entity. PANDOMIC is a 24-month project (from July 2023 to June 2025) and coordinated by LNS.
Ethics statement: The project was approved by the Scientific Council of the National Institute of Public Health (NIPH), Romania, under Decision No. 16343/19.12.2022.

2. Materials and Methods

2.1. Bacterial Strains

The study included 109 Salmonella strains, which were collected as part of a microbiological surveillance program between January and August 2024 through a network of nine public hospitals in North-Eastern Romania. The isolates were obtained from the Regional Public Health Center Iasi (RCPH Iasi), which receives Salmonella isolates from District Public Health Authorities in Moldova, following the procedure outlined in the acute diarrheal disease surveillance methodology developed by NIPH, Romania.
All strains were isolated from fecal samples of patients presenting with acute diarrheal disease, as part of routine diagnostic investigations for gastrointestinal symptoms. The isolates collected by RPHC Iasi were preserved in soft agar and subsequently sent to the National Public Health Laboratory Bucharest (NPHL) for Sanger genotyping.
Due to resource limitations and the pilot nature of this study, a subset of 19 isolates was selected out of 109 total strains. The selection was based on their antimicrobial susceptibility profiles, diversity in phenotypic clustering by IR Biotyper® (Bruker Daltonics, Bremen, Germany), and preliminary serogroup distribution, in order to ensure representation of both prevalent and atypical serovars. The reduced number of isolates allowed for a focused validation of Sanger-based MLST and resistance profiling before scaling up genomic surveillance. In addition, the 19 strains were submitted to LNS Luxemburg for WGS.

2.2. Serotyping by Agglutination

Conventional Salmonella serotyping typically entails an initial inoculation on tryptone soy agar (TSA), followed by subculturing onto selective differential media (Salmonella Shigella and Drigalski agar). Presumptive Salmonella colonies are then subjected to biochemical characterization and subsequently confirmed through serological testing using antisera targeting polyvalent flagellar and somatic antigens [21]. The routine testing was carried out at RCPH Iasi by agglutination screening with polyvalent O antisera: OMA plus O:4 (together with antigen H:i), O:9 (together with antigen H:m), OMB plus O:6,7, 8 and O:7,8 (together with H:r).

2.3. Serogroup Clustering Through Proteomic Fingerprinting

Salmonella isolates were tested by MALDI-TOF-MS (MALDI Biotyper® Sirius System, Bruker Daltonics, Bremen, Germany) [22]. IR Biotyper® was used for subsequent strain typing of the strains due to its capability of cluster phenotypic analysis [23].

2.4. Antimicrobial Susceptibility Testing

Panel NMIC-502 (EUCAST) on BD Phoenix™ M50 (Becton, Dickinson and Company, Baltimore, MD, USA) was used for antimicrobial susceptibility testing [24]. The panel allows biochemical identification and susceptibility testing for the following antibiotics: Imipenem, Ertapenem, Meropenem, Cefixime, Ceftazidime, Ceftazidime-Avibactam, Ceftriaxone, Cefepime, Aztreonam, Ampicilin, Piperacilin, Amoxicilin-Clavulanate, Piperacilin-Tazobactam, Colistin, Trimethoprim-Sulfamethoxazole, Ciprofloxacin, and Levofloxacin.

2.5. MLST and gyrA Sanger Sequencing

Sanger sequencing was used to analyze the seven housekeeping genes included in the Achtman MLST scheme [25]—thrA, purE, sucA, hisD, aroC, hemD, dnaN—in addition to the gyrA gene associated with fluoroquinolone resistance. The obtained sequences were aligned using Ugene software (versions 49.1, 64 bit, 25 November 2023) (https://ugene.net/ accessed on 12 December 2024) alongside the reference genome sequence NC_0031981. All sequences were translated into amino acids sequences to assess mutations at codon positions 83 and 87 of gyrA, which are commonly linked to ciprofloxacin resistance, as reported in the literature [26]. At codon 83, serine (S) may be substituted by phenylalanine (F) or tyrosine (Y) resulting in gyrAS83F or gyrAS83Y, respectively. At codon 87, aspartic acid (D) may be replaced by asparagine (N), glycine (G), or tyrosine (Y), corresponding to gyrAD87N, gyrAD87G or gyrAD87Y in resistant strains.
Genomic DNA from Salmonella isolates was extracted using an automated extractor KingFisher™ Purification System with 96 Deep-Well head, (Thermo Fisher Scientific Marsiling Industrial Estate, Singapore), in combination with the MagMAX Core Nucleic Acids Purification Kit (Thermo Fisher Scientific, Ref: A32700). Target gene fragments were amplified via conventional PCR, using the GoTaq®G2 Master Mix, (Promega, Madison, WI, USA, Ref: M7822). DNA concentration was determined using Qubit 1X dsDNA High Sensitivity Assay kit (Invitrogen, Waltham, MA, USA, Ref: Q33230). The resulting amplicons were further sequenced using Sanger Sequencing kit (Thermo Fisher Scientific, Ref: A38073) on the SeqStudioTM capillary electrophoresis system.
Sequence data analysis was performed using Ridom SeqSphere+ software [Client Version 10.5.5 (2025-03), Server Version 10.5.0 (2025-05)]. All sequence type (ST) profiles were identified and cross-referenced with corresponding serotypes in EnteroBase, knowing that MLST analysis predicts Salmonella serovar [27].
The strains were also submitted to LNS Luxembourg for confirming the ST for independent validation. DNA extraction was carried out using the QIAsymphony DSP Virus/Pathogen Mini Kit (QIAGEN 937036, Hilden, Germany). NGS library was prepared with the Illumina DNA Prep Library Kit (Illumina, San Diego, CA, USA, Ref: 20060059) and was sequenced on a MiniSeq platform (Illumina) using the MiniSeq High Output Kit (Illumina, Ref: FC-420-1003).

3. Results

Based on MLST analysis, the predicted Salmonella serovars were as follows: 8/19 (42.15%) S. Enteritidis, 3/19 (15.8%) S. Typhimurium, 2/19 (10.5%) S. Livingstone, 1/19 (5.3%) S. Montevideo, 1/19 (5.3%) S. Boismorbificans, 2/19 (10.5%) S. Infantis, and 2/19 (10.5%) non-typable (Table 1). Results were confirmed by WGS for 15 strains. Two strains (232166 and 232107) that could not identified by Sanger sequencing were successfully identified by WGS. One strain (232193) was assigned a different serovar by WGS compared to the initial Sanger-based MLST prediction, but WGS confirmed the serotype identified by agglutination for this strain. For strain 232097, the Biotyper result was concordant with the predicted serogroup/serovar predicted by ST (O:4), the discrepancy arose from the agglutination assay. In 4 cases (232126, 232128, 232191, and 232168), the serotypes predicted by STs confirmed the serogroups identified by agglutination, but were different from the presumptive ones (S. Livingstone and S. Montevideo instead of S. Infantis).
Antimicrobial susceptibility testing demonstrated that 12/19 (63.2%) strains exhibited resistance to ciprofloxacin; among these, 1 strain was resistant, while 2 were categorized as susceptible with increased exposure to levofloxacin. Additionally, 1 strain displayed concurrent resistance to both ampicillin and piperacillin. All strains were susceptible to the remaining antibiotics tested.
For gyrA gene, 18 out of 19 isolates could be sequenced. Of the 12/19 (63.2%) strains phenotypically resistant to ciprofloxacin, only 6/12 (50%) strains had a known amino-acid substitution in gyrA conferring this phenotype (S83Y, N = 5; D87G, N = 1). Notably, all known mutations detected in gyrA were correlated with phenotypic ciprofloxacin resistance; i.e., no false positives were identified by analyzing gyrA sequence data. However, 6/12 (50%) of the strains with a MIC indicating fluoroquinolone resistance could not be associated with the gyrA mutations. In two of these six cases, the phenotypic resistance profile determined by NMIC-502 panel (EUCAST) using the BD Phoenix™ M50 system (Becton, Dickinson and Company, Maryland USA) was attributed to the presence of the plasmid-mediated borne quinolone resistance gene qnrB19, as confirmed by WGS.

4. Discussion

Conventional serotyping remains a fundamental tool for the surveillance of Salmonella and the investigation of associated outbreaks, aiding in source tracking and risk assessment [28,29,30]. However, molecular biology techniques also offer valuable approaches for the typing and identification of microorganisms [28]. Therefore, the integration of the appropriate molecular typing method with serotyping is recommended to ensure comprehensive epidemiological investigations of Salmonella [29].
This study aimed to evaluate the performance of the Sanger sequencing technique in identifying Salmonella clusters, including their serotype assignment and the detection of most common ciprofloxacin resistance markers, in comparison with conventional serotyping and the IR Biotyper method. The ultimate objective of the analysis was to assess the feasibility of implementing this technique as a standardized approach for the identification of non-typhoidal Salmonella strains at the national level, thereby enabling comparative studies across different districts and regions.
The results of our study indicated that the most prevalent serotypes in the North-East region of Romania are S. Enteritidis 8/19 (42.15%) ST11 and S. Typhimurium 3/19 (15.8%) ST19, a finding consistent with previous studies [4,31,32,33]. These are currently the most common serovars involved in human salmonellosis [34]. This observation may, in part, be attributed to the preliminary strategy employed in accordance with the national surveillance program, which prioritizes differentiation between these two serotypes. Other strains are only approximately classified using a limited panel of agglutination antisera, primarily due to financial constraints.
The Sanger-based MLST technique proved to be a reliable and informative tool, complementing conventional serotyping methods. Despite two discrepancies (samples ID 232193 and 232097) with agglutination results, the WGS resolved both. MLST accurately identified serovars in cases with partial phenotypic classification. These results support MLST’s utility in improving Salmonella serotyping accuracy, especially when traditional methods are inconclusive.
Aslo, Tewolde et al. conducted a study on 250 strains and compared serotyping with WGS and the results showed that 245 (98%) isolates had identical results by the two methods and only 5 isolates were different. The results proved once again that genome-based Salmonella serotype prediction is concordant with serovar by serum agglutination test, except for Typhimurium and I 4, [5], 12:i:-; hence, WGS-derived serotyping can replace the agglutination test to some extent and can be applied in typing, identification, and traceability of Salmonella [35].
These discrepancies observed between traditional serotyping and MLST may be attributed to several factors, particularly given that conventional serotyping relies on immunological reactions involving somatic O, flagellar H and capsular Vi antigens. This method presents several limitations: it is labor-intensive and costly, requires skilled personnel, and depends on the availability of high-quality antisera. In the absence of these conditions, serotyping is susceptible to errors arising from subjective interpretation of agglutination profiles and issues such as incomplete expression of H antigens [20,21,28,36]. This test uses over 250 different antisera to differentiate more than 2600 serovars, and accurate interpretation demands significant expertise [21,28,36]. However, this does not mean that serotyping will be quickly replaced, as it has become a traditional method of classifying microbial phenotyping by microbiologists and public health organizations. Its continued utility in bacterial surveillance and epidemiological tracking remains essential [20].
The observed differences may also be explained by the presence of S. Typhimurium variants that express only a single flagellin. These monophasic variants share the same antigenic formula as biphasic strains and therefore require additional testing for unambiguous identification. The typical antigenic formula of S. Typhimurium is 1,4, [5], 12:i:1,2; however, monophasic S. Typhimurium variants lack expression of the second-phase flagellin. In contrast, S. Enteritidis does not possess a second flagellin itself, which is reflected in its antigenic formula: 1,9,12:g,m:- [37].
Another possible explanation for the discrepancies observed between traditional serotyping and MLST may relate to the influence of culture media composition on the degree of bacterial flagellation. Literature data suggest that different types of agar can promote or inhibit flagellar expression. The addition of glucose to Hektoen agar enhances flagellation in Salmonella, while gelatin media supplemented with tyrosine and glucose similarly induce the formation of flagellated organisms. In contrast, the casein C2 medium, which contains high concentrations of tyrosine, phenylalanine, and histidine, inhibits the formation of flagellated organisms by blocking uptake due to the competitive presence of phenylalanine and histidine [38].
Although other rapid molecular methods, such as multiplex PCR, were considered, Sanger sequencing proved to be more suitable for the objectives of this study. Multiplex PCR assays are designed for the rapid molecular identification of the most common pathogenic Salmonella serovars [11]. However, these assays are limited to a small number of targets (two to five targets usually) and are prone to a high rate of false-positive results when distinguishing closely related serovars. Until date, the most reliable method for molecular typing of Salmonella species and serotypes is WGS, but this is time-consuming and expensive. In routine surveillance or event-driven investigations, only a small proportion of samples can typically be sequenced using high-throughput sequencing (HTS) technologies, which poses a significant challenge for public health systems. In this context, Sanger sequencing offers a practical and accessible alternative, capable of generating reliable genotyping data in a low-throughput format.
The MLST method analyzes the sequences of multiple housekeeping genes, which are involved in the organism’s primary metabolism and present in all bacteria within a species, because these genes are not under large selective pressure and allelic profile is suitable to classify the ST [39]. MLST is particularly appropriate for long-term studies of bacterial population structures, especially when a high rate of genetic recombination species is subtyped [40]. However the technique lacks sufficient discriminatory power for routine application in outbreak investigations and targeted pathogen surveillance [41]. In the context of cluster analysis, Sanger sequencing MLST provides limited resolution compared to WGS-based core genome MLST, which can hinder precise discrimination between closely related isolates.
A study by Pearce et al. demonstrated that the cgMLST scheme approach provides sufficient resolution to detect multinational outbreaks caused by genetically similar S. enterica serovar Enteritidis strains [42].
In the recent literature, MALDI-TOF-MS is described as a potential real-time screening tool for outbreak investigations [16]. Once a confident and living database is established by WGS, implementation of the IR Biotyper as a screening tool could reduce the number of isolates requiring further WGS analysis by 50% [43].
FT-IR biotyping is a faster method for screening after the O antigen, with the potential to be a valuable tool for real-time outbreak management [13]. The IR Biotyper (IRBT) is an automated, simple, rapid, and reliable method with high discriminatory power in identifying clonal relationships between bacterial isolates. Recently, IRBT has been used for transmission pathway analysis and outbreak investigation of bacterial strains [12]. A retrospective study by Martak et al. demonstrated that the results obtained with IRBT were congruent with those of standard methods, such as MLST and pulsed-field gel electrophoresis (PFGE) [44]. Furthermore, two studies also highlighted a higher discriminatory power of IRBT compared to WGS [15,45].
Antimicrobial resistance was commonly observed, with 22% of isolates resistant to at least three classes of antimicrobials. Resistance was highest in monophasic S. Typhimurium, S. Kentucky, and S. Infantis. Resistance to fluoroquinolones was also 19%, and to third-generation cephalosporins at reduced percentages (1.2–1.4%). Trends showed a decrease in resistance to ampicillin and tetracycline in some EU countries, while the percentage of strains producing extended-spectrum β-lactamases (ESBLs) remained low and stable between 2018 and 2022 [4].
Antibiotic susceptibility testing revealed that 12 strains exhibited resistance to ciprofloxacin; however, mutations in the gyrA gene—commonly associated with ciprofloxacin resistance—were detected in only 6 of these strains (50%). For the remaining 6 strains, we assume other mechanisms that confer resistance to ciprofloxacin are involved, and analyzing this fragment from gyrase subunit A alone is insufficient to predict quinolone resistance in Salmonella. Substitutions in GyrA for S83Y were identified in five strains, and for D87G in one strain. Bai et al. observed in most isolates double amino acid substitutions in GyrA (S83F and D87N)/GyrA (S83F and D87G) and ParC (T57S and S80R) [46].
These findings suggest that additional mechanisms contribute to ciprofloxacin resistance in Salmonella, and that targeting only a specific gene fragment via Sanger sequencing is insufficient for accurately predicting quinolone resistance.
Several studies in the literature show that fluoroquinolone resistance develops through horizontal transfer of plasmids carrying qnr genes (e.g., qnrA, qnrB, qnrS, qnrC, and qnrD). Resistance also frequently occurs through de novo mutations in regions that regulate quinolone resistance, particularly in the gyrA and parC genes, the tolC efflux system, and genes encoding regulators of this efflux system [34,47,48]. Other antimicrobial resistance determinants frequently identified include tetA, floR, sul1, dfrA1, aph(3′)-Ia, in addition to double mutations in gyrA and parC, which are known to confer high levels of resistance to ciprofloxacin [49,50].

5. Study Limitations

The small sample size (19/109 strains) limits the statistical power and representativeness of the findings; however, isolates were purposively selected to capture maximum diversity based on proteomic fingerprinting and resistance phenotypes.
Consequently, further research involving a larger and more geographically diverse collection of isolates is necessary to validate the robustness and broader applicability of this method within large-scale epidemiological surveillance efforts.

6. Challenges and Solutions

We encountered several challenges that were related to technical and analysis issues. Firstly, we probably underestimated the quality requirement of the DNA extracted for sequencing. While it did not affect the overall procedure, for samples that needed to be repeated, the DNA could progressively be degraded after several freeze–thaw cycles.
Traditional agglutination shortcomings: screening agglutination was performed with a limited number of antisera and none of the flagellar antigens was determined. The technique is subjective because of operator experience, reagents used, performance variation, stock availability, and age of bacterial culture. The partial identification of the antigenic formulae, i.e., the serogroup only, is not appropriate to detect an early signal of human clustering.
Detecting AMR using Sanger sequencing requires prior knowledge of resistance-associated genes or mutations and may miss novel or unexpected mechanisms. The gyrA typing method is able to detect only 50% of the ciprofloxacin-resistant strains and is not validated as a good screening tool. Multiple genes associated with resistance should be sequenced to ensure comprehensive coverage. Additionally, these results have to be compared with phenotypic susceptibility testing.
Sanger sequencing is cost-effective for small-scale projects but becomes expensive when analyzing large sample sets. So, different protocols should be set up as alternative methods and a prioritization of samples based on epidemiological significance should be performed (clinical relevance or outbreak potential). Also, when large datasets are implied, an HTS method should be used.

7. Conclusions

The results showed that, in regional settings where agglutination shortcomings are prevalent, screening can be performed in the first phase followed by MLST genotyping for rare serotypes and for serotypes that showed a higher risk of agglutination error, as observed in our case for S. Infantis.
Sanger sequencing for cluster analysis and AMR detection was implemented in NIPH Romania laboratory with results for 17 out of 19 samples tested. While we faced many challenges, we obtained sequences that could be used for MLST analysis, and allelic profiles were assigned to the isolates. The method will be validated by characterizing the samples tested for MLST by WGS, to confirm the serotypes. MLST scheme analysis improves understanding of Salmonella genetic diversity and gyrA sequencing contributes to the understanding of fluoroquinolone resistance mechanisms. Implementation is just the start, workflows can be refined to reduce time and cost per sample. Also, other targets for AMR resistance should be considered. Transitioning to a large-scale sequencing technique could respond to higher-resolution datasets.
This study demonstrates the feasibility and utility of integrating Sanger sequencing into Salmonella surveillance programs. The ability to perform accurate cluster analysis and detect AMR genes enhances NIPH capacity to respond to public health threats posed by resistant strains.

Author Contributions

Conceptualization, E.R.B., R.G. and A.D.; methodology, T.V. and R.G.; software, C.I. and A.G.; validation, M.L. and A.D.; formal analysis, E.R.B. and C.T.; investigation, D.C.B. and G.B.; resources, D.C.B.; data curation, C.I. and A.G.; writing—original draft preparation, E.R.B. and C.T.; writing—review and editing, T.V. and L.S.I.; visualization, T.V. and L.S.I.; supervision T.V. and L.S.I.; project administration, T.V. and R.G.; funding acquisition, T.V. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the European Union through the Project 101112724—PANDOMIC- Implementation of Pandemic Preparedness Plan using Integrated Genomic Surveillance programs funded under the EU4H-2022-DGA-MS-IBA-01-02 call. The project is implemented in partnership with the ‘Cantacuzino’ National Institute for Medical-Military Research and Development and the National Health Laboratory of Luxembourg. The funder was not involved in the design of the study, the collection or interpretation of data, or the decision to submit the manuscript for publication.

Institutional Review Board Statement

This study is based on a descriptive analysis of strains obtained through routine surveillance, with all data anonymized. It did not involve human participants, animals, or tissue, and there was no possibility of identifying patients or linking them to the bacterial isolates.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

This research was supported by the EU co-funded project PANDOMIC under Grant Agreement Nr. 101112724. Views and opinions expressed in this document are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency. Neither the European Union nor the European Health and Digital Executive Agency can be held responsible for them. We gratefully acknowledge the experts from LNS for their technical support in processing the strains using Whole-Genome Sequencing (WGS) and for their assistance in interpreting the resulting data.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MLSTMulti-Locus Sequence Typing
WGSWhole-Genome Sequencing
LNSLaboratoire National de Santé
EUEuropean Union
ESBLExtended-Spectrum β-lactamases
NTSNon-Typhoid Salmonella
iNTSInvasive Non-Typhoid Infections
FTIRFourier Transform Infrared Spectroscopy
NIPHNational Institute of Public Health Romania
AMRAntimicrobial Resistance
PANDOMICPlan using Integrated Genomic Surveillance Programs
STSequence Type
RCPH IasiRegional Public Health Center Iasi
TSATryptone Soy Agar
NIPHLNational Public Health Laboratory Bucharest
HTSHigh-Throughput Sequencing
IRBTIR Biotyper
PFGEPulsed-Field Gel Electrophoresis
OMA Official Methods of Analysis

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Table 1. Compared phenotypic and genotypic results obtained for 19 Salmonella samples tested.
Table 1. Compared phenotypic and genotypic results obtained for 19 Salmonella samples tested.
Crt.Sample
ID		Antigenic
Formula by Screening 		Presumptive
Classification 		Biotyper
Result		MLST Ridom Profile aroC/snaN/hemD/hisD/purE/sucA/thrARidom and Enterobase
ST		Enterobase SerotypeKauffman White O Antigenic Formula
1232166OMASalmonella enterica OMA0:4?/63/?/16/?/15/?NANANA
2232143OMA:09:mSalmonella Enteritidis0:95/2/3/7/6/6/11ST11Enteritidis1,9,12
3232126OMB:0:6,7,8:rSalmonella Infantis0:7117/135/21/12/76/162/38ST1941Livingstone6,7,14
4232193OMB:0:6,7,8:rSalmonella Infantis0:75/2/3/7/6/6/11ST11Enteritidis1,9,12
5232190OMA:09:mSalmonella Enteritidis0:95/2/3/7/6/6/11ST11Enteritidis1,9,12
6232189OMA:09:mSalmonella Enteritidis0:95/2/3/7/6/6/11ST11Enteritidis1,9,12
7232090OMA:09:mSalmonella Enteritidis0:95/2/3/7/6/6/11ST11Enteritidis1,9,12
8232181OMB:0:6,7,8:rSalmonella Infantis0:717/18/22/17/5/21/19ST32Infantis6,7,14
9232147OMA:09:mSalmonella Enteritidis0:95/2/3/7/6/6/11ST11Enteritidis1,9,12
10232144OMA:09:mSalmonella Enteritidis0:95/2/3/7/6/6/11ST11Enteritidis1,9,12
11232097OMA:09:mSalmonella Enteritidis0:410/7/12/9/5/9/2ST19Typhimurium1,4, [5],12
12232120OMA:04:iSalmonella Typhimurium0:410/7/12/9/5/9/2ST19Typhimurium1,4, [5],12
13232187OMA:09:mSalmonella Enteritidis0:95/2/3/7/6/6/11ST11Enteritidis1,9,12
14232194OMA:04:iSalmonella Typhimurium0:410/7/12/9/5/9/2ST19Typhimurium1,4, [5],12
15232128OMB:0:6,7,8:rSalmonella Infantis0.7117/135/21/12/76/162/38ST1941Livingstone6,7,14
16232191OMB:0:6,7,8:rSalmonella Infantis0:743/41/16/13/12/13/4ST195Montevideo6,7,14
17232086OMB:0:6,7,8:rSalmonella Infantis0:717/18/22/17/5/21/19ST32Infantis6,7,14
18232168OMBSalmonella spp. OMB0:82/59/23/64/38/61/12ST142Bovismorbificans6,8,20
19232107OMA:04:iSalmonella Typhimurium0:4NANANANA
Legend: Orange: Discordant results between serogroups identified by agglutination method and predicted serotypes obtained by MLST. Green: Misidentification in 4 cases in the agglutination assay; serotypes predicted by STs confirmed serogroups identified by agglutination but were different from the presumptive ones (S. Livingstone and S. Montevideo instead of S. Infantis). OMA—Official Methods of Analysis.
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Buzilă, E.R.; Gatej, R.; Trifan, C.; Vremera, T.; Leustean, M.; David, A.; Bosogea, D.C.; Barbu, G.; Gatea, A.; Ilie, C.; et al. Genetic Characterization of Salmonella and Analysis of Ciprofloxacin Resistance Using Sanger Technique in Romania, 2024. Bacteria 2025, 4, 43. https://doi.org/10.3390/bacteria4030043

AMA Style

Buzilă ER, Gatej R, Trifan C, Vremera T, Leustean M, David A, Bosogea DC, Barbu G, Gatea A, Ilie C, et al. Genetic Characterization of Salmonella and Analysis of Ciprofloxacin Resistance Using Sanger Technique in Romania, 2024. Bacteria. 2025; 4(3):43. https://doi.org/10.3390/bacteria4030043

Chicago/Turabian Style

Buzilă, Elena Roxana, Raluca Gatej, Cristina Trifan, Teodora Vremera, Mihaela Leustean, Adina David, Daniela Cosmina Bosogea, Georgiana Barbu, Adina Gatea, Ciprian Ilie, and et al. 2025. "Genetic Characterization of Salmonella and Analysis of Ciprofloxacin Resistance Using Sanger Technique in Romania, 2024" Bacteria 4, no. 3: 43. https://doi.org/10.3390/bacteria4030043

APA Style

Buzilă, E. R., Gatej, R., Trifan, C., Vremera, T., Leustean, M., David, A., Bosogea, D. C., Barbu, G., Gatea, A., Ilie, C., & Iancu, L. S. (2025). Genetic Characterization of Salmonella and Analysis of Ciprofloxacin Resistance Using Sanger Technique in Romania, 2024. Bacteria, 4(3), 43. https://doi.org/10.3390/bacteria4030043

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