[Genetic characterization and drug resistance analysis of Salmonella Kentucky ST314 in Shenzhen in 2010-2021].

OBJECTIVE: To understand the prevalence, genetic characteristics and drug resistance features of Salmonella Kentucky ST314 in Shenzhen. METHODS: Whole genome sequencing of 14 strains of Salmonella Kentucky ST314 collected from 2010-2021 by the Foodborne Disease Surveillance Network of Shenzhen Center for Disease Control and Prevention for phylogenetic evolutionary analysis, drug resistance gene and plasmid detection; drug susceptibility experiments were performed by micro-broth dilution method. RESULTS: A total of 57 strains of Salmonella Kentucky were collected from the foodborne disease surveillance network, 14 of which were ST314. The Shenzhen isolates were clustered with isolates from Southeast Asian countries such as Vietnam and Thailand on clade 314.2, and the single nucleotide polymorphism distance between local strains in Shenzhen was large, indicating dissemination. In this study, a total of 17 drug resistance genes/mutations in 9 categories were detected in the genome of Salmonella Kentucky ST314, carrying 3 extended spectrum beta-lactamases(ESBLs), including bla_(CTX-M-24)(14.3%, 2/14), bla_(CTX-M-55)(7.1%, 1/14), and bla_(CTX-M-130)(14.3%, 2/14), all located on plasmids. Regarding quinolone resistance factors, two plasmid-mediated quinolone resistance(PMQR) genes were identified in the genome: qnrB6(71.4%, 10/14) and aac(6')Ib-cr(78.6%, 11/14), a quinolone resistance quinolone resistance-determining regions(QRDR) mutation T57 S(100%, 14/14). The multi-drug resistance rate of Salmonella Kentucky ST314 in Shenzhen was 92.86%(13/14)with the highest rate of resistance to tetracycline and cotrimoxazole(100%, 14/14), followed by chloramphenicol(92.86%, 13/14), cefotaxime and ampicillin(78.57%, 11/14), ciprofloxacin and nalidixic acid(71.43%, 10/14), and ampicillin-sulbactam had the lowest resistance rate(21.43%, 3/14). CONCLUSION: ST314 is the second most prevalent ST type among Salmonella Kentucky in Shenzhen, mainly isolated from food, especially poultry; phylogenetic analysis suggests that ST314 is a disseminated infection and the genome shows a highly genetically conserved phenotype. Drug resistance of Salmonella Kentucky ST314 is very serious, especially QRDR mutation, PMQR gene co-mediated quinolone resistance and plasmid-mediated cephalosporin resistance are prominent and deserve extensive attention.

Estimating epidemic trajectories of SARS-CoV-2 and influenza A virus based on wastewater monitoring and a novel machine learning algorithm.

The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS-CoV-2 and influenza A virus (IAV) in three key port cities in China through real-time quantitative PCR (RT-qPCR). Next, a novel machine learning algorithm (MLA) based on Gaussian model and random forest model was used to predict the epidemic trajectories of SARS-CoV-2 and IAV. The results showed that from February 2023 to January 2024, three port cities experienced two waves of SARS-CoV-2 infection, which peaked in late-May and late-August 2023, respectively. Two waves of IAV were observed in the spring and winter of 2023, respectively with considerable variations in terms of onset/offset date and duration. Furthermore, we employed MLA to extract the key features of epidemic trajectories of SARS-CoV-2 and IAV from February 3rd, to October 15th, 2023, and thereby predicted the epidemic trends of SARS-CoV-2 and IAV from October 16th, 2023 to April 22nd, 2024, which showed high consistency with the observed values. These collective findings offer an important understanding of SARS-CoV-2 and IAV epidemics, suggesting that wastewater surveillance together with MLA emerges as a powerful tool for risk assessment of respiratory viral diseases and improving public health preparedness.

The viral trends and genotype diversity of norovirus in the wastewater of Shenzhen, China.

Norovirus (NoV) is the primary cause of acute gastroenteritis (AGE) on a global scale. Numerous studies have demonstrated the immense potential of wastewater surveillance in monitoring the prevalence and spread of NoV within communities. This study employed a one-step reverse transcription-quantitative PCR to quantify NoV GI/GII in wastewater samples (n = 2574), which were collected once or twice a week from 38 wastewater treatment plants from March 2023 to February 2024 in Shenzhen. The concentrations of NoV GI and GII ranged from 5.0 × 104 to 1.7 × 106 copies/L and 4.1 × 105 to 4.5 × 106 copies/L, respectively. The concentrations of NoV GII were higher than those of NoV GI. Spearman's correlation analysis revealed a moderate correlation between the concentration of NoV in wastewater and the detection rates of NoV infections in sentinel hospitals. Baseline values were established for NoV concentrations in Shenzhen's wastewater, providing a crucial reference point for implementing early warning systems and nonpharmaceutical interventions to mitigate the impact of potential outbreaks. A total of 24 NoV genotypes were identified in 100 wastewater samples by sequencing. Nine genotypes of NoV GI were detected, with the major genotypes being GI.4 (38.6 %) and GI.3 (21.8 %); Fifteen genotypes of NoV GII were identified, with GII.4 (53.6 %) and GII.17 (26.0 %) being dominant. The trends in the relative abundance of NoV GI/GII were significantly different, and the trends in the relative abundance of NoV GII.4 over time were similar across all districts, suggesting a potential risk of cross-regional spread. Our findings underscore the effectiveness of wastewater surveillance in reflecting population-level NoV infections, capturing the diverse array of NoV genotypes, and utilizing NoV RNA in wastewater as a specific indicator to supplement clinical surveillance data, ultimately enhancing our ability to predict the timing and intensity of NoV epidemics.

Early Detection of the Emerging SARS-CoV-2 BA.2.86 Lineage Through Wastewater Surveillance Using a Mediator Probe PCR Assay - Shenzhen City, Guangdong Province, China, 2023.

Introduction: The emergence of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron sublineage, BA.2.86, has sparked global public health concerns for its potential heightened transmissibility and immune evasion. Utilizing data from Shenzhen's city-wide wastewater surveillance system, we highlight the presence of the BA.2.86 lineage in Shenzhen. Methods: A mediator probe polymerase chain reaction (PCR) assay was developed to detect the BA.2.86 lineage in wastewater by targeting a specific mutation (Spike: A264D). Between September 19 and December 10, 2023, 781 wastewater samples from 38 wastewater treatment plants (WWTPs) and 9 pump stations in ten districts of Shenzhen were examined. Through multiple short-amplicon sequencing, three positive samples were identified. Results: The BA.2.86 lineage was identified in the wastewater of Futian and Nanshan districts in Shenzhen on December 2, 2023. From December 2 to 10, a total of 21 BA.2.86-positive wastewater samples were found across 6 districts (Futian, Nanshan, Longhua, Baoan, Longgang, and Luohu) in Shenzhen. The weighted average viral load of the BA.2.86 lineage in Shenzhen's wastewater was 43.5 copies/L on December 2, increased to 219.8 copies/L on December 4, and then decreased to approximately 100 copies/L on December 6, 8, and 10. Conclusions: The mediator probe PCR assay, designed for swift detection of low viral concentrations of the BA.2.86 lineage in wastewater samples, shows promise for detecting different SARS-CoV-2 variants. Wastewater surveillance could serve as an early detection system for promptly identifying specific SARS-CoV-2 variants as they emerge.

Epidemiological Insights into the Omicron Outbreak via MeltArray-Assisted Real-Time Tracking of SARS-CoV-2 Variants.

The prolonged course of the COVID-19 pandemic necessitates sustained surveillance of emerging variants. This study aimed to develop a multiplex real-time polymerase chain reaction (rt-PCR) suitable for the real-time tracking of Omicron subvariants in clinical and wastewater samples. Plasmids containing variant-specific mutations were used to develop a MeltArray assay. After a comprehensive evaluation of both analytical and clinical performance, the established assay was used to detect Omicron variants in clinical and wastewater samples, and the results were compared with those of next-generation sequencing (NGS) and droplet digital PCR (ddPCR). The MeltArray assay identified 14 variant-specific mutations, enabling the detection of five Omicron sublineages (BA.2*, BA.5.2*, BA.2.75*, BQ.1*, and XBB.1*) and eight subvariants (BF.7, BN.1, BR.2, BQ.1.1, XBB.1.5, XBB.1.16, XBB.1.9, and BA.4.6). The limit of detection (LOD) of the assay was 50 copies/reaction, and no cross-reactivity was observed with 15 other respiratory viruses. Using NGS as the reference method, the clinical evaluation of 232 swab samples exhibited a clinical sensitivity of > 95.12% (95% CI 89.77-97.75%) and a specificity of > 95.21% (95% CI, 91.15-97.46%). When used to evaluate the Omicron outbreak from late 2022 to early 2023, the MeltArray assay performed on 1408 samples revealed that the epidemic was driven by BA.5.2* (883, 62.71%) and BF.7 (525, 37.29%). Additionally, the MeltArray assay demonstrated potential for estimating variant abundance in wastewater samples. The MeltArray assay is a rapid and scalable method for identifying SARS-CoV-2 variants. Integrating this approach with NGS and ddPCR will improve variant surveillance capabilities and ensure preparedness for future variants.