Burden of salmonellosis, campylobacteriosis and listeriosis: a time series analysis, Belgium, 2012 to 2020

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

A changing landscape: Tracking and analysis of the international HDV epidemiology 1999-2020

The international epidemiology of Hepatitis Delta Virus (HDV) is challenging to accurately estimate due to limited active surveillance for this rare infectious disease. Prior HDV epidemiological studies have relied on meta-analysis of aggregated and static datasets. These limitations restrict the capacity to actively detect low-level and/or geographically dispersed changes in the incidence of HDV diagnoses. This study was designed to provide a resource to track and analyze the international HDV epidemiology. Datasets analyzed collectively consisted of >700,000 HBV and >9,000 HDV reported cases ranging between 1999-2020. Datasets mined from government publications were identified for Argentina, Australia, Austria, Brazil, Bulgaria, Canada, Finland, Germany, Macao, Netherlands, New Zealand, Norway, Sweden, Taiwan, Thailand, United Kingdom, and United States. Time series analyses, including Mann-Kendall (MK) trend test, Bayesian Information Criterion (BIC), and hierarchal clustering, were performed to characterize trends in the HDV timelines. An aggregated prevalence of 2,560 HDV/HBV100,000 cases (95% CI 180-4940) or 2.56% HDV/HBV cases was identified, ranging from 0.26% in Canada to 20% in the United States. Structural breaks in the timeline of HDV incidence were identified in 2002, 2012, and 2017, with a significant increase occurring between 2013-2017. Significant increasing trends in reported HDV and HBV cases were observed in 47% and 24% of datasets, respectively. Analyses of the HDV incidence timeline identified four distinct temporal clusters, including Cluster I (Macao, Taiwan), Cluster II (Argentina, Brazil, Germany, Thailand), Cluster III (Bulgaria, Netherlands, New Zealand, United Kingdom, United States) and Cluster IV (Australia, Austria, Canada, Finland, Norway, Sweden). Tracking of HDV and HBV cases on an international scale is essential in defining the global impact of viral hepatitis. Significant disruptions of HDV and HBV epidemiology have been identified. Increased surveillance of HDV is warranted to further define the etiology of the recent breakpoints in the international HDV incidence.

Virulence and Antibiotic Resistance Genes in Listeria monocytogenes Strains Isolated From Ready-to-Eat Foods in Chile

Listeria monocytogenes is causing listeriosis, a rare but severe foodborne infection. Listeriosis affects pregnant women, newborns, older adults, and immunocompromised individuals. Ready-to-eat (RTE) foods are the most common sources of transmission of the pathogen This study explored the virulence factors and antibiotic resistance in L. monocytogenes strains isolated from ready-to-eat (RTE) foods through in vitro and in silico testing by whole-genome sequencing (WGS). The overall positivity of L. monocytogenes in RTE food samples was 3.1% and 14 strains were isolated. L. monocytogenes ST8, ST2763, ST1, ST3, ST5, ST7, ST9, ST14, ST193, and ST451 sequence types were identified by average nucleotide identity, ribosomal multilocus sequence typing (rMLST), and core genome MLST. Seven isolates had serotype 1/2a, five 1/2b, one 4b, and one 1/2c. Three strains exhibited in vitro resistance to ampicillin and 100% of the strains carried the fosX, lin, norB, mprF, tetA, and tetC resistance genes. In addition, the arsBC, bcrBC, and clpL genes were detected, which conferred resistance to stress and disinfectants. All strains harbored hlyA, prfA, and inlA genes almost thirty-two the showed the bsh, clpCEP, hly, hpt, iap/cwhA, inlA, inlB, ipeA, lspA, mpl, plcA, pclB, oat, pdgA, and prfA genes. One isolate exhibited a type 11 premature stop codon (PMSC) in the inlA gene and another isolate a new mutation (deletion of A in position 819). The Inc18(rep25), Inc18(rep26), and N1011A plasmids and MGEs were found in nine isolates. Ten isolates showed CAS-Type II-B systems; in addition, Anti-CRISPR AcrIIA1 and AcrIIA3 phage-associated systems were detected in three genomes. These virulence and antibiotic resistance traits in the strains isolated in the RTE foods indicate a potential public health risk for consumers.

Aminoglycoside Resistance and Possible Mechanisms in Campylobacter Spp. Isolated From Chicken and Swine in Jiangsu, China

Campylobacter is a major food-borne pathogen in humans, and previous studies reported a high prevalence of gentamicin-resistant Campylobacter isolates from food-producing animals in China. This study aimed to investigate the aminoglycoside resistance of Campylobacter isolated from chicken and swine in Jiangsu province, China and understand the possible mechanisms responsible for aminoglycoside resistance. One hundred and eighty-five Campylobacter isolates of chicken and swine origins in 2017 and 2018 were analyzed for gentamicin and kanamycin resistance. Some aminoglycoside resistance genes were selected for PCR detection in all strains. The genomic DNAs of two strains with high resistance to gentamicin were used as donors to subject C. jejuni NCTC11168 to natural transformation. The transformants were investigated by whole-genome sequencing and analyzed comparatively with C. jejuni NCTC11168. In total, 30.5% (29/95) of C. jejuni isolates and 42.2% (38/90) of C. coli isolates were resistant to gentamicin and kanamycin. The prevalence of the aph(2")-If gene and aac(6')-Ie/aph(2")-Ia gene was 65.4% (121/185) and 36.2% (67/185) in Campylobacter isolates, respectively. The aadE-sat4-aphA-3 cluster was identified in 8.7% (8/92) and 20.4% (19/93) of all Campylobacter isolates in each year. With each donor DNA, aminoglycoside-resistant transformants were obtained. The transformants showed ≥128-fold increases in the MICs of gentamicin, kanamycin, and tobramycin. A 5200-bp segment was found to be inserted between the highly conserved genes Cj0299 and panB of Campylobacter. A total of 9.7% (18/185) strains showing high resistance to aminoglycosides had this segment by PCR detection. The genetic diversity of the insertion-fragment positive strains was determined by MLST, and seven sequence types were identified for these strains.

Coverage of the national surveillance system for human Salmonella infections, Belgium, 2016-2020

Introduction

The surveillance of human salmonellosis in Belgium is dependent on the referral of human Salmonella isolates to the National Reference Center (NRC). Knowledge of current diagnostic practices and the coverage of the national Salmonella surveillance system are important to correctly interpret surveillance data and trends over time, to estimate the true burden of salmonellosis in Belgium, and to evaluate the appropriateness of implementing whole-genome sequencing (WGS) at this central level.

Methods

The coverage of the NRC was defined as the proportion of all diagnosed human Salmonella cases in Belgium reported to the NRC and was assessed for 2019 via a survey among all licensed Belgian medical laboratories in 2019, and for 2016–2020 via a capture-recapture study using the Sentinel Network of Laboratories (SNL) as the external source. In addition, the survey was used to assess the impact of the implementation of culture-independent diagnostic tests (CIDTs) at the level of peripheral laboratory sites, as a potential threat to national public health surveillance programs.

Results

The coverage of the NRC surveillance system was estimated to be 83% and 85%, based on the results of the survey and on the two-source capture-recapture study, respectively. Further, the results of the survey indicated a limited use of CIDTs by peripheral laboratories in 2019.

Conclusion

Given the high coverage and the limited impact of CIDTs on the referral of isolates, we may conclude that the NRC can confidently monitor the epidemiological situation and identify outbreaks throughout the country. These findings may guide the decision to implement WGS at the level of the NRC and may improve estimates of the true burden of salmonellosis in Belgium.

Burden of foodborne diseases: think global, act local

National burden of foodborne disease (FBD) studies are essential to establish food safety as a public health priority, rank diseases, and inform interventions. In recent years, various countries have taken steps to implement them. Despite progress, the current burden of disease landscape remains scattered, and researchers struggle to translate findings to input for policy. We describe the current knowledge base on burden of FBDs, highlight examples of well-established studies, and how results have been used for decision-making. We discuss challenges in estimating burden of FBD in low-resource settings, and the experience and opportunities deriving from a large-scale research project in these settings. Lastly, we highlight the role of international organizations and initiatives in supporting countries to develop capacity and conduct studies.

Genomic diversity of Listeria monocytogenes isolates from seafood, horticulture and factory environments in New Zealand

Listeria monocytogenes is a foodborne human pathogen that causes systemic infection, fetal-placental infection in pregnant women causing abortion and stillbirth and meningoencephalitis in elderly and immunocompromised individuals. This study aimed to analyse L. monocytogenes from different sources from New Zealand (NZ) and to compare them with international strains. We used pulsed-field gel electrophoresis (PFGE), multilocus sequence typing (MLST) and whole-genome single nucleotide polymorphisms (SNP) to study the population structure of the NZ L. monocytogenes isolates and their relationship with the international strains. The NZ isolates formed unique clusters in PFGE, MLST and whole-genome SNP comparisons compared to the international isolates for which data were available. PFGE identified 31 AscI and 29 ApaI PFGE patterns with indistinguishable pulsotypes being present in seafood, horticultural products and environmental samples. Apart from the Asc0002:Apa0002 pulsotype which was distributed across different sources, other pulsotypes were site or factory associated. Whole-genome analysis of 200 randomly selected L. monocytogenes isolates revealed that lineage II dominated the NZ L. monocytogenes populations. MLST comparison of international and NZ isolates with lineage II accounted for 89% (177 of 200) of the total L. monocytogenes population, while the international representation was 45.3% (1674 of 3473). Rarefaction analysis showed that sequence type richness was greater in NZ isolates compared to international trend, however, it should be noted that NZ isolates predominantly came from seafood, horticulture and their respective processing environments or factories, unlike international isolates where there was a good mixture of clinical, food and environmental isolates.