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Characterization of Basal Transcriptomes Identifies Potential Metabolic and Virulence-Associated Adaptations Among Diverse Nontyphoidal Salmonella enterica Serovars.

ABSTRACT: The zoonotic pathogen Salmonella enterica includes >2,600 serovars, which differ in the range of hosts they infect and the severity of disease they cause. To further elucidate the mechanisms behind these differences, we performed transcriptomic comparisons of nontyphoidal Salmonella (NTS) serovars with the model for NTS pathogenesis, S. Typhimurium. Specifically, we used RNA-seq to characterize the understudied NTS serovars S. Javiana and S. Cerro, representing a serovar frequently attributed to human infection via contact with amphibians and reptiles, and a serovar primarily associated with cattle, respectively. Whole-genome sequence (WGS) data were utilized to ensure that strains characterized with RNA-seq were representative of their respective serovars. RNA extracted from representative strains of each serovar grown to late exponential phase in Luria-Bertani (LB) broth showed that transcript abundances of core genes were significantly higher (p<0.001) than those of accessory genes for all three serovars. Inter-serovar comparisons identified that transcript abundances of genes in Salmonella Pathogenicity Island (SPI) 1 were significantly higher in both S. Javiana and S. Typhimurium compared to S. Cerro. Together, our data highlight potential transcriptional mechanisms that may facilitate S. Cerro and S. Javiana survival in and adaptation to their respective hosts and impact their ability to cause disease in others. Furthermore, our analyses demonstrate the utility of omics approaches in advancing our understanding of the diversity of metabolic and virulence mechanisms of different NTS serovars.

SUBMITTER: Cohn AR

PROVIDER: S-EPMC8552914 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Characterization of Basal Transcriptomes Identifies Potential Metabolic and Virulence-Associated Adaptations Among Diverse Nontyphoidal Salmonella enterica Serovars.

Cohn Alexa R AR Orsi Renato H RH Carroll Laura M LM Chen Ruixi R Wiedmann Martin M Cheng Rachel A RA

Frontiers in microbiology 20211013

The zoonotic pathogen Salmonella enterica includes >2,600 serovars, which differ in the range of hosts they infect and the severity of disease they cause. To further elucidate the mechanisms behind these differences, we performed transcriptomic comparisons of nontyphoidal Salmonella (NTS) serovars with the model for NTS pathogenesis, S. Typhimurium. Specifically, we used RNA-seq to characterize the understudied NTS serovars S. Javiana and S. Cerro, representing ...[more]

PMID: 34721328

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Project description:Nontyphoidal Salmonella (NTS) is a leading global cause of bacterial foodborne morbidity and mortality. Our ability to treat severe NTS infections has been impaired by increasing antimicrobial resistance (AMR). To understand and mitigate the global health crisis AMR represents, we need to link the observed resistance phenotypes with their underlying genomic mechanisms. Broiler chickens represent a key reservoir and vector for NTS infections, but isolates from this setting have been characterized in only very low numbers relative to clinical isolates. In this study, we sequenced and assembled 97 genomes encompassing 7 serotypes isolated from broiler chicken in farms in British Columbia between 2005 and 2008. Through application of machine learning (ML) models to predict the observed AMR phenotype from this genomic data, we were able to generate highly (0.92 to 0.99) precise logistic regression models using known AMR gene annotations as features for 7 antibiotics (amoxicillin-clavulanic acid, ampicillin, cefoxitin, ceftiofur, ceftriaxone, streptomycin, and tetracycline). Similarly, we also trained "reference-free" k-mer-based set-covering machine phenotypic prediction models (0.91 to 1.0 precision) for these antibiotics. By combining the inferred k-mers and logistic regression weights, we identified the primary drivers of AMR for the 7 studied antibiotics in these isolates. With our research representing one of the largest studies of a diverse set of NTS isolates from broiler chicken, we can thus confirm that the AmpC-like CMY-2 β-lactamase is a primary driver of β-lactam resistance and that the phosphotransferases APH(6)-Id and APH(3″-Ib) are the principal drivers of streptomycin resistance in this important ecosystem.IMPORTANCE Antimicrobial resistance (AMR) represents an existential threat to the function of modern medicine. Genomics and machine learning methods are being increasingly used to analyze and predict AMR. This type of surveillance is very important to try to reduce the impact of AMR. Machine learning models are typically trained using genomic data, but the aspects of the genomes that they use to make predictions are rarely analyzed. In this work, we showed how, by using different types of machine learning models and performing this analysis, it is possible to identify the key genes underlying AMR in nontyphoidal Salmonella (NTS). NTS is among the leading cause of foodborne illness globally; however, AMR in NTS has not been heavily studied within the food chain itself. Therefore, in this work we performed a broad-scale analysis of the AMR in NTS isolates from commercial chicken farms and identified some priority AMR genes for surveillance.

Project description:IntroductionWhole genome sequencing (WGS) is increasingly used for characterizing foodborne pathogens and it has become a standard typing technique for surveillance and research purposes. WGS data can help assessing microbial risks and defining risk mitigating strategies for foodborne pathogens, including Salmonella enterica.MethodsTo test the hypothesis that (combinations of) different genes can predict the probability of infection [P(inf)] given exposure to a certain pathogen strain, we determined P(inf) based on invasion potential of 87 S. enterica strains belonging to 15 serovars isolated from animals, foodstuffs and human patients, in an in vitro gastrointestinal tract (GIT) model system. These genomes were sequenced with WGS and screened for genes potentially involved in virulence. A random forest (RF) model was applied to assess whether P(inf) of a strain could be predicted based on the presence/absence of those genes. Moreover, the association between P(inf) and biofilm formation in different experimental conditions was assessed.Results and discussionP(inf) values ranged from 6.7E-05 to 5.2E-01, showing variability both among and within serovars. P(inf) values also varied between isolation sources, but no unambiguous pattern was observed in the tested serovars. Interestingly, serovars causing the highest number of human infections did not show better ability to invade cells in the GIT model system, with strains belonging to other serovars displaying even higher infectivity. The RF model did not identify any virulence factor as significant P(inf) predictors. Significant associations of P(inf) with biofilm formation were found in all the different conditions for a limited number of serovars, indicating that the two phenotypes are governed by different mechanisms and that the ability to form biofilm does not correlate with the ability to invade epithelial cells. Other omics techniques therefore seem more promising as alternatives to identify genes associated with P(inf), and different hypotheses, such as gene expression rather than presence/absence, could be tested to explain phenotypic virulence [P(inf)].

Project description:BackgroundThe impact of S. enterica colonization in cattle is highly variable and often serovar-dependent. The aim of this study was to compare the global transcriptomes of highly pathogenic bovine-adapted S. enterica serovar Dublin and the less pathogenic, bovine-adapted, serovar Cerro during interactions with bovine epithelial cells, to identify genes that impact serovar-related outcomes of S. enterica infections in dairy animals.ResultBovine epithelial cells were infected with S. enterica strains from serovars Dublin and Cerro, and the bacterial RNA was extracted and sequenced. The total number of paired-end reads uniquely mapped to non-rRNA and non-tRNA genes in the reference genomes ranged between 12.1 M (Million) and 23.4 M (median: 15.7 M). In total, 360 differentially expressed genes (DEGs) were identified with at least two-fold differences in the transcript abundances between S. Dublin and S. Cerro (false discovery rate ≤ 5%). The highest number of DEGs (17.5%, 63 of 360 genes) between the two serovars were located on the genomic regions potentially associated with Salmonella Pathogenicity Islands (SPIs). DEGs potentially located in the SPI-regions that were upregulated (≥ 2-fold) in the S. Dublin compared with S. Cerro included: 37 SPI-1 genes encoding mostly Type 3 Secretion System (T3SS) apparatus and effectors; all of the six SPI-4 genes encoding type I secretion apparatus (siiABCDEF); T3SS effectors and chaperone (sopB, pipB, and sigE) located in SPI-5; type VI secretion system associated protein coding genes (sciJKNOR) located in SPI-6; and T3SS effector sopF in SPI-11. Additional major functional categories of DEGs included transcription regulators (n = 25), amino acid transport and metabolism (n = 20), carbohydrate transport and metabolism (n = 20), energy production and metabolism (n = 19), cell membrane biogenesis (n = 18), and coenzyme transport and metabolism (n = 15). DEGs were further mapped to the metabolic pathways listed in the KEGG database; most genes of the fatty acid β-oxidation pathway were upregulated/uniquely present in the S. Dublin strains compared with the S. Cerro strains.ConclusionsThis study identified S. enterica genes that may be responsible for symptomatic or asymptomatic infection and colonization of two bovine-adapted serovars in cattle.

Project description:Nontyphoidal strains of Salmonella enterica are a major cause of foodborne illnesses, and infection with these bacteria results in inflammatory gastroenteritis. Polymorphonuclear leukocytes (PMNs), also known as neutrophils, are a dominant immune cell type found at the site of infection in Salmonella-infected individuals, but how they regulate infection outcome is not well understood. Here, we used a co-culture model of primary human PMNs and human intestinal organoids to probe the role of PMNs during infection with two of the most prevalent Salmonella serovars: Salmonella enterica serovar Enteritidis and Typhimurium. Using a transcriptomics approach, we identified a dominant role for PMNs in mounting differential immune responses including production of pro-inflammatory cytokines, chemokines, and antimicrobial peptides. We also identified specific gene sets that were induced by PMNs in response to Enteritidis or Typhimurium infection. By comparing host responses to these serovars, we uncovered differential regulation of host metabolic pathways particularly induction of cholesterol biosynthetic pathways during Typhimurium infection and suppression of RNA metabolism during Enteritidis infection. Together, these findings provide insight into the role of human PMNs in modulating different host responses to pathogens that cause similar disease in humans.IMPORTANCENontyphoidal serovars of Salmonella enterica are known to induce robust recruitment of polymorphonuclear leukocytes (PMNs) in the gut during early stages of infection, but the specific role of PMNs in regulating infection outcome of different serovars is poorly understood. Due to differences in human infection progression compared to small animal models, characterizing the role of PMNs during infection has been challenging. Here, we used a co-culture model of human intestinal organoids with human primary PMNs to study the role of PMNs during infection of human intestinal epithelium. Using a transcriptomics approach, we define PMN-dependent reprogramming of the host response to Salmonella, establishing a clear role in amplifying pro-inflammatory gene expression. Additionally, the host response driven by PMNs differed between two similar nontyphoidal Salmonella serovars. These findings highlight the importance of building more physiological infection models to replicate human infection conditions to study host responses specific to individual pathogens.

Dataset Information

Characterization of Basal Transcriptomes Identifies Potential Metabolic and Virulence-Associated Adaptations Among Diverse Nontyphoidal Salmonella enterica Serovars.

Publications

Characterization of Basal Transcriptomes Identifies Potential Metabolic and Virulence-Associated Adaptations Among Diverse Nontyphoidal <i>Salmonella enterica</i> Serovars.

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