In silico Identification of Serovar-Specific Genes for Salmonella Serotyping

A Proof-of-Concept Protein Microarray-Based Approach for Serotyping of Salmonella enterica Strains

Salmonella enterica, a bacterium causing foodborne illnesses like salmonellosis, is prevalent in Europe and globally. It is found in food, water, and soil, leading to symptoms like diarrhea and fever. Annually, it results in about 95 million cases worldwide, with increasing antibiotic resistance posing a public health challenge. Therefore, it is necessary to detect and serotype Salmonella for several reasons. The identification of the serovars of Salmonella enterica isolates is crucial to detect and trace outbreaks and to implement effective control measures. Our work presents a protein-based microarray for the rapid and accurate determination of Salmonella serovars. The microarray carries a set of antibodies that can detect different Salmonella O- and H-antigens, allowing for the identification of multiple serovars, including Typhimurium and Enteritidis, in a single miniaturized assay. The system is fast, economical, accurate, and requires only small sample volumes. Also, it is not required to maintain an extensive collection of sera for the serotyping of Salmonella enterica serovars and can be easily expanded and adapted to new serovars and sera. The scientific state of the art in Salmonella serotyping involves the comparison of traditional, molecular, and in silico methods, with a focus on economy, multiplexing, accuracy, rapidity, and adaptability to new serovars and sera. The development of protein-based microarrays, such as the one presented in our work, contributes to the ongoing advancements in this field.

Emergence of Poultry-Associated Human Salmonella enterica Serovar Abortusovis Infections, New South Wales, Australia

Salmonella enterica serovar Abortusovis is a ovine-adapted pathogen that causes spontaneous abortion. Salmonella Abortusovis was reported in poultry in 2009 and has since been reported in human infections in New South Wales, Australia. Phylogenomic analysis revealed a clade of 51 closely related isolates from Australia originating in 2004. That clade was genetically distinct from ovine-associated isolates. The clade was widespread in New South Wales poultry production facilities but was only responsible for sporadic human infections. Some known virulence factors associated with human infections were only found in the poultry-associated clade, some of which were acquired through prophages and plasmids. Furthermore, the ovine-associated clade showed signs of genome decay, but the poultry-associated clade did not. Those genomic changes most likely led to differences in host range and disease type. Surveillance using the newly identified genetic markers will be vital for tracking Salmonella Abortusovis transmission in animals and to humans and preventing future outbreaks.

Improved Genomic Identification, Clustering, and Serotyping of Shiga Toxin-Producing Escherichia coli Using Cluster/Serotype-Specific Gene Markers

Shiga toxin-producing Escherichia coli (STEC) have more than 470 serotypes. The well-known STEC O157:H7 serotype is a leading cause of STEC infections in humans. However, the incidence of non-O157:H7 STEC serotypes associated with foodborne outbreaks and human infections has increased in recent years. Current detection and serotyping assays are focusing on O157 and top six (“Big six”) non-O157 STEC serogroups. In this study, we performed phylogenetic analysis of nearly 41,000 publicly available STEC genomes representing 460 different STEC serotypes and identified 19 major and 229 minor STEC clusters. STEC cluster-specific gene markers were then identified through comparative genomic analysis. We further identified serotype-specific gene markers for the top 10 most frequent non-O157:H7 STEC serotypes. The cluster or serotype specific gene markers had 99.54% accuracy and more than 97.25% specificity when tested using 38,534 STEC and 14,216 non-STEC E. coli genomes, respectively. In addition, we developed a freely available in silico serotyping pipeline named STECFinder that combined these robust gene markers with established E. coli serotype specific O and H antigen genes and stx genes for accurate identification, cluster determination and serotyping of STEC. STECFinder can assign 99.85% and 99.83% of 38,534 STEC isolates to STEC clusters using assembled genomes and Illumina reads respectively and can simultaneously predict stx subtypes and STEC serotypes. Using shotgun metagenomic sequencing reads of STEC spiked food samples from a published study, we demonstrated that STECFinder can detect the spiked STEC serotypes, accurately. The cluster/serotype-specific gene markers could also be adapted for culture independent typing, facilitating rapid STEC typing. STECFinder is available as an installable package (https://github.com/LanLab/STECFinder) and will be useful for in silico STEC cluster identification and serotyping using genome data.

Rapid Detection and Differentiating of the Predominant Salmonella Serovars in Chicken Farm by TaqMan Multiplex Real-Time PCR Assay

Salmonella has been known as an important zoonotic pathogen that can cause a variety of diseases in both animals and humans. Poultry are the main reservoir for the Salmonella serovars Salmonella Pullorum (S. Pullorum), Salmonella Gallinarum (S. Gallinarum), Salmonella Enteritidis (S. Enteritidis), and Salmonella Typhimurium (S. Typhimurium). The conventional serotyping methods for differentiating Salmonella serovars are complicated, time-consuming, laborious, and expensive; therefore, rapid and accurate molecular diagnostic methods are needed for effective detection and prevention of contamination. This study developed and evaluated a TaqMan multiplex real-time PCR assay for simultaneous detection and differentiation of the S. Pullorum, S. Gallinarum, S. Enteritidis, and S. Typhimurium. In results, the optimized multiplex real-time PCR assay was highly specific and reliable for all four target genes. The analytical sensitivity corresponded to three colony-forming units (CFUs) for these four Salmonella serovars, respectively. The detection limit for the multiplex real-time PCR assay in artificially contaminated samples was 500 CFU/g without enrichment, while 10 CFU/g after pre-enrichment. Moreover, the multiplex real-time PCR was applied to the poultry clinical samples, which achieved comparable results to the traditional bacteriological examination. Taken together, these results indicated that the optimized TaqMan multiplex real-time PCR assay will be a promising tool for clinical diagnostics and epidemiologic study of Salmonella in chicken farm and poultry products.

Real-time PCR method for qualitative and quantitative detection of Lactobacillus sakei group species targeting novel markers based on bioinformatics analysis

Latilactobacillus sakei group comprises four closely related species, making it difficult to accurately distinguish them with standard markers such as the 16S rRNA gene. The objective of our study was to mine novel markers for PCR detection and discrimination of L. sakei group species and L. sakei subspecies by comparative pan-genomic analysis. A total of 63 genome sequences of L. sakei group species consisted of 119,899 coding genes, yielding 5741 pan-genomes, 831 core-genomes, 3347 accessory-genomes, and 1563 unique-genomes. The accessory-genome was compared to extract unique candidate genes common only to genomes of the same species. The candidate genes were then aligned with the other bacterial genomes to select marker genes present in all genomes of a given species, but not in the genomes of other species. We identified the arginine/ornithine antiporter, putative cell surface protein precursor, sodium:solute symporter, PRD domain protein, PTS sugar transporter subunit IIC, and phosphoenolpyruvate-dependent sugar phosphotransferase system EIIC as marker genes for L. sakei, L. sakei subsp. sakei, L. sakei subsp. carnosus, L. curvatus, L. graminis, and L. fuchuensis, respectively. Primer pairs were designed for each marker and showed 100% specificity for 48 lactic acid bacterial reference strains. The PCR method developed in this study was used to evaluate 106 strains isolated from fermented foods to demonstrate that the marker genes provided a viable alternative to the 16S rRNA gene. We also applied the method to the monitoring of kimchi samples to quantify L. sakei group species or subspecies. Our PCR method based on novel markers can rapidly identify L. sakei group with high accuracy and high throughput.

Genomic characterization and phylogenetic analysis of Salmonella enterica serovar Javiana

Salmonella enterica serovar Javiana is the fourth most reported serovar of laboratory-confirmed human Salmonella infections in the U.S. and in Tennessee (TN). Although Salmonella ser. Javiana is a common cause of human infection, the majority of cases are sporadic in nature rather than outbreak-associated. To better understand Salmonella ser. Javiana microbial population structure in TN, we completed a phylogenetic analysis of 111 Salmonella ser. Javiana clinical isolates from TN collected from Jan. 2017 to Oct. 2018. We identified mobile genetic elements and genes known to confer antibiotic resistance present in the isolates, and performed a pan-genome-wide association study (pan-GWAS) to compare gene content between clades identified in this study. The population structure of TN Salmonella ser. Javiana clinical isolates consisted of three genetic clades: TN clade I (n = 54), TN clade II (n = 4), and TN clade III (n = 48). Using a 5, 10, and 25 hqSNP distance threshold for cluster identification, nine, 12, and 10 potential epidemiologically-relevant clusters were identified, respectively. The majority of genes that were found to be over-represented in specific clades were located in mobile genetic element (MGE) regions, including genes encoding integrases and phage structures (91.5%). Additionally, a large portion of the over-represented genes from TN clade II (44.9%) were located on an 87.5 kb plasmid containing genes encoding a toxin/antitoxin system (ccdAB). Additionally, we completed phylogenetic analyses of global Salmonella ser. Javiana datasets to gain a broader insight into the population structure of this serovar. We found that the global phylogeny consisted of three major clades (one of which all of the TN isolates belonged to) and two cgMLST eBurstGroups (ceBGs) and that the branch length between the two Salmonella ser. Javiana ceBGs (1,423 allelic differences) was comparable to those from other serovars that have been reported as polyphyletic (929–2,850 allelic differences). This study demonstrates the population structure of TN and global Salmonella ser. Javiana isolates, a clinically important Salmonella serovar and can provide guidance for phylogenetic cluster analyses for public health surveillance and response.

Diverse lineages of multidrug resistant clinical Salmonella enterica and a cryptic outbreak in New Hampshire, USA revealed from a year-long genomic surveillance

Salmonella enterica, the causative agent of gastrointestinal diseases and typhoid fever, is a human and animal pathogen that causes significant mortality and morbidity worldwide. In this study, we examine the genomic diversity and phylogenetic relationships of 63 S. enterica isolates from human-derived clinical specimens submitted to the Department of Health and Human Services (DHHS) in the state of New Hampshire, USA in 2017. We found a remarkably large genomic, phylogenetic and serotype variation among the S. enterica isolates, dominated by serotypes Enteritidis (sequence type [ST] 11), Heidelberg (ST 15) and Typhimurium (ST 19). Analysis of the distribution of single nucleotide polymorphisms in the core genome suggests that the ST 15 cluster is likely a previously undetected or cryptic outbreak event that occurred in the south/southeastern part of New Hampshire in August-September. We found that nearly all of the clinical S. enterica isolates carried horizontally acquired genes that confer resistance to multiple classes of antimicrobials, most notably aminoglycosides, fluoroquinolones and macrolides. Majority of the isolates (76.2%) carry at least four resistance determinants per genome. We also detected the genes mdtK and mdsABC that encode multidrug efflux pumps and the gene sdiA that encodes a regulator for a third multidrug resistance pump. Our results indicate rapid microevolution and geographical dissemination of multidrug resistant lineages over a short time span. These findings are critical to aid the DHHS and similar public health laboratories in the development of effective disease control measures, epidemiological studies and treatment options for serious Salmonella infections.

Virulence and resistance genes profiles and clonal relationships of non-typhoidal food-borne Salmonella strains isolated in Tunisia by whole genome sequencing

Whole genome sequencing (WGS) has made impressive progress in the field of molecular biology. Its most common application for public health is in the area of surveillance of food-borne diseases. WGS has the potential for providing a large amount of information, such as the identification of the strain type, the characterization of antibiotic resistance and virulence, and phylogeny. In our study, thirty-nine non-typhoidal Salmonella strains were isolated from diverse sources in Tunisia. Non-typhoidal Salmonella are among the most common pathogens contaminating food animals. The presence of virulence and antimicrobial resistance determinants in those strains were investigated using whole genome sequencing (WGS) and appropriate data analysis. The genomes were screened for several Salmonella virulence genes using the Virulence Factor Database VFDB. Twelve different virulence profiles, which correspond to the 12 identified serovars, were recognized. Several antimicrobial resistance genes were also detected: aac (6')-Iaa, sul1, tetA, bla-TEM and qnrS genes. Phylogenetic relationships among the strains were further assessed by a cgMLST analysis. The resulting phylogenetic tree consisted of several clusters consistently with the in silico multilocus sequence typing (MLST) and serotyping. Our findings demonstrated that WGS and subsequent data analysis provided an accurate tool for genetic characterization of bacterial strains compared to usual molecular typing techniques. To the best of our knowledge, this is the first report of an application of WGS for genetic characterization of food-borne Tunisian strains.

Development of Real-Time PCR Assay to Specifically Detect 22 Bifidobacterium Species and Subspecies Using Comparative Genomics

Bifidobacterium species are used as probiotics to provide beneficial effects to humans. These effects are specific to some species or subspecies of Bifidobacterium. However, some Bifidobacterium species or subspecies are not distinguished because similarity of 16S rRNA and housekeeping gene sequences within Bifidobacterium species is very high. In this study, we developed a real-time polymerase chain reaction (PCR) assay to rapidly and accurately detect 22 Bifidobacterium species by selecting genetic markers using comparative genomic analysis. A total of 210 Bifidobacterium genome sequences were compared to select species- or subspecies-specific genetic markers. A phylogenetic tree based on pan-genomes generated clusters according to Bifidobacterium species or subspecies except that two strains were not grouped with their subspecies. Based on pan-genomes constructed, species- or subspecies-specific genetic markers were selected. The specificity of these markers was confirmed by aligning these genes against 210 genome sequences. Real-time PCR could detect 22 Bifidobacterium specifically. We constructed the criterion for quantification by standard curves. To further test the developed assay for commercial food products, we monitored 26 probiotic products and 7 dairy products. Real-time PCR results and labeling data were then compared. Most of these products (21/33, 63.6%) were consistent with their label claims. Some products labeled at species level only can be detected up to subspecies level through our developed assay.

Prevalence and risk factors of Salmonella in commercial poultry farms in Nigeria

Salmonella is an important human pathogen and poultry products constitute an important source of human infections. This study investigated prevalence; identified serotypes based on whole genome sequence, described spatial distribution of Salmonella serotypes and predicted risk factors that could influence the prevalence of Salmonella infection in commercial poultry farms in Nigeria. A cross sectional approach was employed to collect 558 pooled shoe socks and dust samples from 165 commercial poultry farms in North West Nigeria. On-farm visitation questionnaires were administered to obtain information on farm management practices in order to assess risk factors for Salmonella prevalence. Salmonella was identified by culture, biotyping, serology and polymerase chain reaction (PCR). PCR confirmed isolates were paired-end Illumina- sequenced. Following de novo genome assembly, draft genomes were used to obtain serotypes by SeqSero2 and SISTR pipeline and sequence types by SISTR and Enterobase. Risk factor analysis was performed using the logit model. A farm prevalence of 47.9% (CI95 [40.3-55.5]) for Salmonella was observed, with a sample level prevalence of 15.9% (CI95 [12.9-18.9]). Twenty-three different serotypes were identified, with S. Kentucky and S. Isangi as the most prevalent (32.9% and 11%). Serotypes showed some geographic variation. Salmonella detection was strongly associated with disposal of poultry waste and with presence of other livestock on the farm. Salmonella was commonly detected on commercial poultry farms in North West Nigeria and S. Kentucky was found to be ubiquitous in the farms.

Highly Sensitive and Specific Detection and Serotyping of Five Prevalent Salmonella Serovars by Multiple Cross-Displacement Amplification

Salmonella is a common cause of foodborne disease worldwide, including Australia. More than 85% of outbreaks of human salmonellosis in Australia were caused by five Salmonella serovars. Rapid, accurate, and sensitive identification of Salmonella serovars is vital for diagnosis and public health surveillance. Recently, an isothermal amplification technique, termed multiple cross-displacement amplification (MCDA), has been employed to detect Salmonella at the species level. Herein, seven MCDA assays were developed and evaluated for rapid detection and differentiation of the five most common Salmonella serovars in Australia: Typhimurium, Enteritidis, Virchow, Saintpaul, and Infantis. MCDA primer sets were designed by targeting seven serovar/lineage-specific gene markers identified through genomic comparisons. The sensitivity and specificity of the seven MCDA assays were evaluated using 79 target strains and 32 nontarget strains. The assays were all highly sensitive and specific to target serovars, with the sensitivity ranging from 92.9% to 100% and the specificity from 93.3% to 100%. The limit of detection of the seven MCDA assays was 50 fg per reaction (10 copies) from pure DNA, and positive results were detected in as little as 8 minutes. These seven MCDA assays offer a rapid, accurate, and sensitive serotyping method. With further validation in clinically relevant conditions, these assays could be used for culture-independent serotyping of common Salmonella serovars directly from clinical samples.

Performance and Accuracy of Four Open-Source Tools for In Silico Serotyping of Salmonella spp. Based on Whole-Genome Short-Read Sequencing Data

We compared the performance of four open-source in silico Salmonella typing tools (SeqSero, SeqSero2, Salmonella In Silico Typing Resource [SISTR], and Metric Oriented Sequence Typer [MOST]) to assess their potential for replacing laboratory serological testing with serovar predictions from whole-genome sequencing data. We conducted a retrospective analysis of 1,624 Salmonella isolates of 72 serovars submitted to the German National Salmonella Reference Laboratory between 1999 and 2019. All isolates are derived from animal and foodstuff origins. We conducted Illumina short-read sequencing and compared the in silico serovar prediction results with the results of routine laboratory serotyping. We found the best-performing in silico serovar prediction tool to be SISTR, with 94% correctly typed isolates, followed by SeqSero2 (87%), SeqSero (81%), and MOST (79%). Furthermore, we found that mapping-based tools like SeqSero and SeqSero2 (allele mode) were more reliable for the prediction of monophasic variants, while sequence type and cluster-based methods like MOST and SISTR (core-genome multilocus sequence type [cgMLST]), showed greater resilience when confronted with GC-biased sequencing data. We showed that the choice of library preparation kit could substantially affect O antigen detection, due to the low GC content of the wzx and wzy genes. Although the accuracy of computational serovar predictions is still not quite on par with traditional serotyping by Salmonella reference laboratories, the command-line tools investigated in this study perform a rapid, efficient, inexpensive, and reproducible analysis, which can be integrated into in-house characterization pipelines. Based on our results, we find SISTR most suitable for automated, routine serotyping for public health surveillance of Salmonella IMPORTANCE Salmonella spp. are important foodborne pathogens. To reduce the number of infected patients, it is essential to understand which subtypes of the bacteria cause disease outbreaks. Traditionally, characterization of Salmonella requires serological testing, a laboratory method by which Salmonella isolates can be classified into over 2,600 distinct subtypes, called serovars. Due to recent advances in whole-genome sequencing, many tools have been developed to replace traditional testing methods with computational analysis of genome sequences. It is crucial to validate that these tools, many already in use for routine surveillance, deliver accurate and reliable serovar information. In this study, we set out to compare which of the currently available open-source command-line tools is most suitable to replace serological testing. A thorough evaluation of the differing computational approaches is highly important to ensure the backward compatibility of serotyping data and to maintain comparability between laboratories.

Typing methods based on whole genome sequencing data

Whole genome sequencing (WGS) of foodborne pathogens has become an effective method for investigating the information contained in the genome sequence of bacterial pathogens. In addition, its highly discriminative power enables the comparison of genetic relatedness between bacteria even on a sub-species level. For this reason, WGS is being implemented worldwide and across sectors (human, veterinary, food, and environment) for the investigation of disease outbreaks, source attribution, and improved risk characterization models. In order to extract relevant information from the large quantity and complex data produced by WGS, a host of bioinformatics tools has been developed, allowing users to analyze and interpret sequencing data, starting from simple gene-searches to complex phylogenetic studies. Depending on the research question, the complexity of the dataset and their bioinformatics skill set, users can choose between a great variety of tools for the analysis of WGS data. In this review, we describe the relevant approaches for phylogenomic studies for outbreak studies and give an overview of selected tools for the characterization of foodborne pathogens based on WGS data. Despite the efforts of the last years, harmonization and standardization of typing tools are still urgently needed to allow for an easy comparison of data between laboratories, moving towards a one health worldwide surveillance system for foodborne pathogens.

Antibiotic resistance: bioinformatics-based understanding as a functional strategy for drug design

The use of antibiotics to manage infectious diseases dates back to ancient civilization, but the lack of a clear distinction between the therapeutic and toxic dose has been a major challenge. This precipitates the notion that antibiotic resistance was from time immemorial, principally because of a lack of adequate knowledge of therapeutic doses and continuous exposure of these bacteria to suboptimal plasma concentration of antibiotics. With the discovery of penicillin by Alexander Fleming in 1924, a milestone in bacterial infections' treatment was achieved. This forms the foundation for the modern era of antibiotic drugs. Antibiotics such as penicillins, cephalosporins, quinolones, tetracycline, macrolides, sulphonamides, aminoglycosides and glycopeptides are the mainstay in managing severe bacterial infections, but resistant strains of bacteria have emerged and hampered the progress of research in this field. Recently, new approaches to research involving bacteria resistance to antibiotics have appeared; these involve combining the molecular understanding of bacteria systems with the knowledge of bioinformatics. Consequently, many molecules have been developed to curb resistance associated with different bacterial infections. However, because of increased emphasis on the clinical relevance of antibiotics, the synergy between in silico study and in vivo study is well cemented and this facilitates the discovery of potent antibiotics. In this review, we seek to give an overview of earlier reviews and molecular and structural understanding of bacteria resistance to antibiotics, while focusing on the recent bioinformatics approach to antibacterial drug discovery.

Understanding the evolution of antibiotic resistance at the molecular level as a functional tool for bioinformatic-based drug design.