Identification and Subtyping of Salmonella Isolates Using Matrix-Assisted Laser Desorption–Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF)

Rapid and sensitive detection of Salmonella in agro-Food and environmental samples: A review of advances in rapid tests and biosensors

Salmonella is as an intracellular bacterium, causing many human fatalities when the host-specific serotypes reach the host gastrointestinal tract. Nontyphoidal Salmonella are responsible for numerous foodborne outbreaks and product recalls worldwide whereas typhoidal Salmonella are responsible for Typhoid fever cases in developing countries. Yet, Salmonella-related foodborne disease outbreaks through its food and water contaminations have urged the advancement of rapid and sensitive Salmonella-detecting methods for public health protection. While conventional detection methods are time-consuming and ineffective for monitoring foodstuffs with short shelf lives, advances in microbiology, molecular biology and biosensor methods have hastened the detection. Here, the review discusses Salmonella pathogenic mechanisms and its detection technology advancements (fundamental concepts, features, implementations, efficiency, benefits, limitations and prospects). The time-efficiency of each rapid test method is discussed in relation to their limit of detections (LODs) and time required from sample enrichment to final data analysis. Importantly, the matrix effects (LODs and sample enrichments) were compared within the methods to potentially speculate Salmonella detection from environmental, clinical or food matrices using certain techniques. Although biotechnological advancements have led to various time-efficient Salmonella-detecting techniques, one should consider the usage of sophisticated equipment to run the analysis by moderately to highly trained personnel. Ultimately, a fast, accurate Salmonella screening that is readily executed by untrained personnels from various matrices, is desired for public health procurement.

Pyelonephritis with bacteremia caused by Salmonella Choleraesuis in a Japanese patient with carcinoma of unknown primary origin: A case report

Salmonella enterica subspecies enterica serovar Choleraesuis (S. Choleraesuis) is a nontyphoidal Salmonella pathogen that causes swine paratyphoids. S. Choleraesuis is a zoonotic pathogen transmitted to humans via contaminated food and causes sepsis. Here, we report a rare case of pyelonephritis caused by S. Choleraesuis in a Japanese patient with a carcinoma of unknown primary origin. On the day of admission, the patient was diagnosed with pyelonephritis associated with ureteral stent obstruction. He had no history of raw pork consumption or gastrointestinal symptoms. Gram-negative rods were isolated from urine and blood cultures, identified as Salmonella enterica subsp. enterica using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The serological typing results were O7: -: 1 and 5; however, the serotypes could not be determined. The isolate was identified as S. Choleraesuis using multilocus sequence typing, nucleotide sequence analysis of the fliC gene, and biochemical examination. Four days after a 14-day course of intravenous piperacillin-tazobactam (9 g/day), the patient showed relapse of the condition. Subsequently, the patient was treated with intravenous ceftriaxone (2 g/day) and oral amoxicillin (1000 mg/day) for 14 days each; recurrence was not observed. This novel case of pyelonephritis with bacteremia was caused by S. Choleraesuis in Japan. Conventional testing methods could not identify the serotypes; however, the case highlights the importance of adopting advanced diagnostic techniques based on molecular biology to ensure accurate pathogen identification.

Rapid identification of Salmonella serovars Enteritidis and Typhimurium using whole cell matrix assisted laser desorption ionization - Time of flight mass spectrometry (MALDI-TOF MS) coupled with multivariate analysis and artificial intelligence

Salmonella is a common food-borne pathogen with Enteritidis and Typhimurium being among the most important serovars causing numerous outbreaks. A rapid method was investigated to identify these serovars using whole-cell MALDI-TOF MS coupled with multivariate analysis and artificial intelligence and 113 Salmonella strains, including 38 Enteritidis (SE), 38 Typhimurium (ST) and 37 strains from 32 other Salmonella serovars (SG). Datasets of ions (presence/absence) with high discriminative power were created using newly developed criteria and subject to multivariate analyses and eight artificial intelligence (AI) tools. Principal Component Analysis based on 55 or 88 selected ions separated SE, ST and SG without overlap on the first three principal components. Datasets were partitioned using five partitioning methods with 70% of samples for AI model training and 30% for validation. Of the eight AI models evaluated, high performance (HP) SVM and HP Neural were the top performers, identified three serovar groups 97% correctly on average (range 82%-100%) according to the validation results. Selection of serovar specific ions facilitated differentiation of serotypes using unsupervised model PCA and improved the accuracy of classification using AI significantly (p < 0.01). MALDI-TOF MS incorporated with advanced data processing and classification tools is a promising method to allow rapid identification of Salmonella serovars of concern in routine diagnostic laboratories.