Application of Whole Genome Sequencing (WGS) Approach Against Identification of Foodborne Bacteria

Bharadwaj, Shiv; Dwivedi, Vivek Dhar; Kirtipal, Nikhil

doi:10.1007/978-981-13-8739-5_7

Cited by 4 publications

(3 citation statements)

References 49 publications

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“…Similar to whole genome sequencing (WGS) analysis, CMA is a high-resolution technique to screen the entire genome and identify CNVs [15]. While WGS can identify CNVs, SNPs, and other genetic variations, providing all-encompassing information of the entire genome, the cost and time requirements of WGS surpass those of microarray analysis, making it less feasible for regular clinical testing purposes [16,17]. Using an array with probes designed to selectively bind with DNA extracted from a sample, CMA demonstrates the capability to detect CNVs as small as 50-100 kb in size.…”

Section: Introductionmentioning

confidence: 99%

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Goh,

Kwon,

Kim

et al. 2023

Diagnostics

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Copy number variation (CNV) is a primary source of structural variation in the human genome, leading to several disorders. Therefore, analyzing neonatal CNVs is crucial for managing CNV-related chromosomal disabilities. However, genomic waves can hinder accurate CNV analysis. To mitigate the influences of the waves, we adopted a machine learning approach and developed a new method that uses a modified log R ratio instead of the commonly used log R ratio. Validation results using samples with known CNVs demonstrated the superior performance of our method. We analyzed a total of 16,046 Korean newborn samples using the new method and identified CNVs related to 39 genetic disorders were identified in 342 cases. The most frequently detected CNV-related disorder was Joubert syndrome 4. The accuracy of our method was further confirmed by analyzing a subset of the detected results using NGS and comparing them with our results. The utilization of a genome-wide single nucleotide polymorphism array with wave offset was shown to be a powerful method for identifying CNVs in neonatal cases. The accurate screening and the ability to identify various disease susceptibilities offered by our new method could facilitate the identification of CNV-associated chromosomal disease etiologies.

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Section: Introductionmentioning

confidence: 99%

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Goh,

Kwon,

Kim

et al. 2023

Diagnostics

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“…This is becoming the de facto main framework for the surveillance and control of antimicrobial resistance. Food-related pathogens are key players under this framework [24,25], and using genome information to identify new resistance determinants and monitor the spread of those already known is a major improvement over previous methodologies [26,27]. However, its implementation must consider not only the technological issues but also the subsequent analyses and the comparison with the regular methods used in each laboratory [28].…”

Section: Introductionmentioning

confidence: 99%

Genomic Surveillance of Salmonella from the Comunitat Valenciana (Spain)

Sánchez-Serrano,

Mejía,

Camaró

et al. 2023

Antibiotics

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Salmonella enterica subspecies enterica is one of the most important foodborne pathogens and the causative agent of salmonellosis, which affects both humans and animals producing numerous infections every year. The study and understanding of its epidemiology are key to monitoring and controlling these bacteria. With the development of whole-genome sequencing (WGS) technologies, surveillance based on traditional serotyping and phenotypic tests of resistance is being replaced by genomic surveillance. To introduce WGS as a routine methodology for the surveillance of food-borne Salmonella in the region, we applied this technology to analyze a set of 141 S. enterica isolates obtained from various food sources between 2010 and 2017 in the Comunitat Valenciana (Spain). For this, we performed an evaluation of the most relevant Salmonella typing methods, serotyping and sequence typing, using both traditional and in silico approaches. We extended the use of WGS to detect antimicrobial resistance determinants and predicted minimum inhibitory concentrations (MICs). Finally, to understand possible contaminant sources in this region and their relationship to antimicrobial resistance (AMR), we performed cluster detection combining single-nucleotide polymorphism (SNP) pairwise distances and phylogenetic and epidemiological data. The results of in silico serotyping with WGS data were highly congruent with those of serological analyses (98.5% concordance). Multi-locus sequence typing (MLST) profiles obtained with WGS information were also highly congruent with the sequence type (ST) assignment based on Sanger sequencing (91.9% coincidence). In silico identification of antimicrobial resistance determinants and minimum inhibitory concentrations revealed a high number of resistance genes and possible resistant isolates. A combined phylogenetic and epidemiological analysis with complete genome sequences revealed relationships among isolates indicative of possible common sources for isolates with separate sampling in time and space that had not been detected from epidemiological information. As a result, we demonstrate the usefulness of WGS and in silico methods to obtain an improved characterization of S. enterica enterica isolates, allowing better surveillance of the pathogen in food products and in potential environmental and clinical samples of related interest.

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“…WGS provides more comprehensive information with regard to bacterial species. This strategy facilitates the identification of pathogens, the identification of antimicrobial‐resistant genes, and more comprehensive information allowing the identification of bacterial outbreaks (Bharadwaj, Dwivedi, & Kirtipal, 2019; Hurley et al., 2019; Ronholm, Nasheri, Petronella, & Pagotto, 2016). WGS is also useful as it does not require specific targeting as PCR does, so there is no need for the continuous development of primers as bacteria mutate.…”

Section: Introductionmentioning

confidence: 99%

Microbial detection and identification methods: Bench top assays to omics approaches

Ferone

Gowen

Fanning

et al. 2020

Comp Rev Food Sci Food Safe

166

111

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Rapid detection of foodborne pathogens, spoilage microbes, and other biological contaminants in complex food matrices is essential to maintain food quality and ensure consumer safety. Traditional methods involve culturing microbes using a range of nonselective and selective enrichment methods, followed by biochemical confirmation among others. The time‐to‐detection is a key limitation when testing foods, particularly those with short shelf lives, such as fresh meat, fish, dairy products, and vegetables. Some recent detection methods developed include the use of spectroscopic techniques, such as matrix‐assisted laser desorption ionization‐time of flight along with hyperspectral imaging protocols.This review presents a comprehensive overview comparing insights into the principles, characteristics, and applications of newer and emerging techniques methods applied to the detection and identification of microbes in food matrices, to more traditional benchtop approaches. The content has been developed to provide specialist scientists a broad view of bacterial identification methods available in terms of their benefits and limitations, which may be useful in the development of future experimental design. The case is also made for incorporating some of these emerging methods into the mainstream, for example, underutilized potential of spectroscopic techniques and hyperspectral imaging.

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Application of Whole Genome Sequencing (WGS) Approach Against Identification of Foodborne Bacteria

Cited by 4 publications

References 49 publications

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Genomic Surveillance of Salmonella from the Comunitat Valenciana (Spain)

Microbial detection and identification methods: Bench top assays to omics approaches

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