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Enregistrement W7102390667 · doi:10.5281/zenodo.17470546

Antibiotic Resistance in Salmonella Typhi

2025· article· W7102390667 sur OpenAlexaboutno aff

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Langue
DomaineAgricultural and Biological Sciences
ThématiqueSalmonella and Campylobacter epidemiology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTyphoid feverAntibiotic resistanceSalmonella typhiAntibioticsPublic healthSerotypeSanitationDisease

Résumé

récupéré en direct d'OpenAlex

Antibiotic Resistance in Salmonella Typhi **Abstract** Typhoid fever caused by *Salmonella enterica* serovar *Typhi* (*S. Typhi*) remains a severe public health problem globally, particularly in low- and middle-income nations. The emergence and global spread of antibiotic-resistant strains have significantly increased the difficulty of treatment, morbidity, and the risk of outbreaks. Previously, first-line agents such as chloramphenicol, ampicillin, and trimethoprim-sulfamethoxazole succeeded with treatment. Multidrug-resistant (MDR) strains resistant to all three agents emerged in the 1980s, followed by the emergence of fluoroquinolone-resistant strains in the 1990s and increasingly over the past few years, extensively drug-resistant (XDR) isolates resistant to nearly all oral agents. This article presents a comprehensive review of the molecular dynamics, epidemiology, clinical relevance, diagnostic problems, and methods of control and prevention of antibiotic resistance in *S. Typhi*. It is crucial to understand these dynamics in order to develop novel therapeutic and preventive strategies, improve surveillance, and promote global typhoid fever control. **Keywords:** *Salmonella Typhi*, typhoid fever, antibiotic resistance, MDR, XDR, fluoroquinolones, azithromycin, ceftriaxone, antimicrobial stewardship. --- ## **1. Introduction** *Salmonella enterica* serovar *Typhi* is a human-limited bacterium that causes typhoid fever—a systemic infection with sustained fever, abdominal cramps, and potentially life-threatening complications like intestinal perforation or encephalopathy. The World Health Organization (WHO, 2023) estimates 11–20 million cases and 120,000–160,000 deaths annually worldwide. South Asia, sub-Saharan Africa, and parts of Southeast Asia bear the disease burden disproportionately heavy because of inadequate sanitation and restricted access to clean water, permitting spread of the disease. Antibiotics have been the cornerstone of typhoid therapy for decades. However, *S. Typhi* has been extremely plastic, acquiring and transferring resistance genes that have rendered most conventional therapies ineffective. This emerging antibiotic resistance not only complicates therapy but also poses a serious threat to public health via enhanced transmission, prolonged illness duration, and increased healthcare costs (Andrews et al., 2021). --- ## **2. Historical Background of Antibiotic Treatment and Resistance in *S. Typhi*** ### **2.1 Pre-antibiotic Era** Before the antibiotic period, typhoid fever was managed primarily by means of supportive treatment. The discovery of chloramphenicol in 1948 revolutionized treatment, reducing mortality from approximately 20% to below 1% (Woodward et al., 1950). Chloramphenicol remained the drug of first choice until the late 1970s. ### **2.2 Emergence of Multidrug Resistance (MDR)** In the 1980s, chloramphenicol-resistant, ampicillin-resistant, and trimethoprim-sulfamethoxazole-resistant *S. Typhi* strains collectively known as multidrug-resistant (MDR) *S. Typhi* began to appear in India, Pakistan, Vietnam, and Africa. The resistance was plasmid-mediated with *cat*, *blaTEM-1*, and *dhfrA* genes (Crump & Mintz, 2010). As a result of the appearance of MDR strains, traditional first-line medications yielded to fluoroquinolones. ### **2.3 The Fluoroquinolone Era** Ciprofloxacin and ofloxacin subsequently became the drug of choice during the 1990s. They were highly effective initially, but these drugs quickly lost their effectiveness with chromosomal mutations in the *gyrA* and *parC* genes that encode DNA gyrase and topoisomerase IV (Parry et al., 2002). This rendered them resistant and led to clinical treatment failures, prompting third-generation cephalosporins such as ceftriaxone and cefixime. ### **2.4 The Emergence of Extensively Drug-Resistant (XDR) *S. Typhi*** In 2016, Pakistan experienced the first chloramphenicol-resistant, ampicillin-resistant, trimethoprim-sulfamethoxazole-resistant, fluoroquinolone-resistant, and third-generation cephalosporin-resistant XDR *S. Typhi* outbreak, with only azithromycin and carbapenems being the remaining options (Klemm et al., 2018). This was a landmark point in typhoid control globally, highlighting the importance of new antibiotics and vaccines. --- ## **3. Mechanisms of Antibiotic Resistance** ### **3.1 Resistance to First-Line Agents * **Chloramphenicol Resistance:** Mediated by *cat* genes encoding chloramphenicol acetyltransferase, which inactivates the antibiotic through acetylation. * **Ampicillin Resistance:** Due to *blaTEM-1* and *blaSHV* β-lactamase genes that hydrolyze the β-lactam ring. * **Trimethoprim-Sulfamethoxazole Resistance:** Involves *dfrA* and *sul* genes, which lead to altered dihydrofolate reductase and dihydropteroate synthase enzymes. ### **3.2 Fluoroquinolone Resistance** Fluoroquinolone resistance is mediated by: * Point mutations in *gyrA* (Ser83→Phe/Tyr) and *parC* (Ser80→Ile) genes. * Plasmid-mediated quinolone resistance (PMQR) genes such as *qnr*, *aac(6')-Ib-cr*, and *qepA*. They decrease fluoroquinolone binding to target enzymes, lessening the effectiveness of the drugs (Das et al., 2019). ### **3.3 Cephalosporin Resistance** Resistance to ceftriaxone and cefixime is mediated by extended-spectrum β-lactamases (ESBLs), particularly *blaCTX-M-15* and *blaTEM-1* genes that hydrolyze third-generation cephalosporins (Wong et al., 2019). ### **3.4 Azithromycin Resistance** Azithromycin resistance, through mutation in the *acrB* efflux pump gene and gaining the *mphA* macrolide phosphotransferase gene, has led to decreased intracellular concentration of the antibiotic (Hooda et al., 2019). ### **3.5 Carbapenem Resistance** Although rare, carbapenem-resistant *S. Typhi* isolates have been reported. Resistance is through gain of carbapenemase genes such as *blaNDM-1* on transmissible plasmids, creating a serious therapeutic issue (Kumar et al., 2021). --- ## **4.4 Epidemiology of Resistant *S. Typhi*** ### **4.1 Global Distribution** MDR and XDR *S. Typhi* strains are spread worldwide through travel, migration, and poor sanitation. The epicenter is South Asia, particularly Pakistan and India. Imported UK, USA, and Canadian infections frequently have a history of travel to endemic regions (Wong et al., 2019). ### **4.2 The XDR Outbreak in Pakistan** The 2016 Sindh outbreak of XDR, due to the H58 haplotype, was a turning point. Genomic studies identified the resistance determinants carried on an IncY plasmid harboring *blaCTX-M-15* and *qnrS* genes (Klemm et al., 2018). Over 10,000 cases had been reported by 2020, with worldwide distribution to the UK, USA, and Canada. ### **4.3 Regional Trends** * **Africa:** MDR strains are common, but ceftriaxone resistance is low. * **South Asia:** XDR and azithromycin-resistant isolates are prevalent. * **Southeast Asia:** Resistance to fluoroquinolones has emerged in Vietnam and Indonesia. * **Middle East and Europe:** Sporadic imported cases of XDR typhoid have been reported. --- ## **5. Clinical Implications** Resistance to antibiotics leads to: * Prolonged clearance of fever. * Increased relapse rates. * Increased complications like intestinal perforation. * Decreased treatment options and increased hospitalization duration. Fluoroquinolone resistance, on the other hand, raises median fever clearance time from 3 to 6 days (Parry et al., 2002). XDR cases, in turn, tend to need intravenous carbapenems or high-dose azithromycin, putting more healthcare burden. --- ## **6. Diagnosis and Detection of Resistance** ### **6.1 Conventional Culture and Sensitivity Blood culture remains the gold standard for diagnosis, but sensitivity is only 40–60%. Antimicrobial susceptibility testing (AST) on disk diffusion or automated systems like VITEK-2 identifies resistant phenotypes. ### **6.2 Molecular Methods** PCR and whole-genome sequencing (WGS) facilitate the quick identification of resistance genes (*bla*, *qnr*, *cat*, *mphA*) and track transmission. WGS has played a crucial role in identifying global spread of the H58 lineage (Wong et al., 2019). ### **6.3 Emerging Diagnostic Tools** Loop-mediated isothermal amplification (LAMP) and CRISPR-based assays are being developed for field-friendly resistance testing, with cost-effective and quick alternatives. --- ## **7. Treatment Strategies** ### **7.1 Current Recommendations** WHO (2022) recommends: * **Uncomplicated typhoid:** Azithromycin (10 mg/kg/day for 7 days) for oral therapy. * **Severe or XDR typhoid:** Carbapenems (meropenem/imipenem) or intravenous azithromycin. ### **7.2 Role of Combination Therapy Combination therapy, for example, azithromycin plus ceftriaxone, may avoid the development of further resistance but current evidence is sparse (Andrews et al., 2021). ### **7.3 Future Antimicrobials** New molecules like tebipenem, cefiderocol, and aztreonam-avibactam are being considered against resistant *S. Typhi* strains. Their accessibility and cost in endemic nations remain barriers, however. --- ## **8. Prevention and Control** ### **8.1 Vaccination** Typhoid conjugate vaccines such as Typbar-TCV provide long-term immunity and are WHO-preferred in children above 6 months in endemic regions (WHO, 2023). Mass vaccination is the backbone to relieve antibiotic pressure. ### **8.2 Sanitation and Hygiene** Improved sanitation, safe water, and food hygiene are the pillars of prevention even now. Community-based educational campaigns significantly decrease transmission. ### **8.3 Surveillance and Stewardship** Global initiatives like the Global Antimicrobial Resistance Surveillance System (GLASS) track resistance patterns. Antimicrobial stewa

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,035
Tête enseignante GPT0,253
Écart entre enseignants0,218 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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