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Whole genome sequencing provides insights into the genetic determinants of invasiveness in Salmonella Dublin.

ABSTRACT: Salmonella enterica subsp. enterica serovar Dublin (S. Dublin) is one of the non-typhoidal Salmonella (NTS); however, a relatively high proportion of human infections are associated with invasive disease. We applied whole genome sequencing to representative invasive and non-invasive clinical isolates of S. Dublin to determine the genomic variations among them and to investigate the underlying genetic determinants associated with invasiveness in S. Dublin. Although no particular genomic variation was found to differentiate in invasive and non-invasive isolates four virulence factors were detected within the genome of all isolates including two different type VI secretion systems (T6SS) encoded on two Salmonella pathogenicity islands (SPI), including SPI-6 (T6SSSPI-6) and SPI-19 (T6SSSPI-19), an intact lambdoid prophage (Gifsy-2-like prophage) that contributes significantly to the virulence and pathogenesis of Salmonella serotypes in addition to a virulence plasmid. These four virulence factors may all contribute to the potential of S. Dublin to cause invasive disease in humans.

SUBMITTER: Mohammed M

PROVIDER: S-EPMC9150539 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Whole genome sequencing provides insights into the genetic determinants of invasiveness in Salmonella Dublin.

Mohammed M M Cormican M M

Epidemiology and infection 20160321 11

Salmonella enterica subsp. enterica serovar Dublin (S. Dublin) is one of the non-typhoidal Salmonella (NTS); however, a relatively high proportion of human infections are associated with invasive disease. We applied whole genome sequencing to representative invasive and non-invasive clinical isolates of S. Dublin to determine the genomic variations among them and to investigate the underlying genetic determinants associated with invasiveness in S. Dublin. Although no particular genomic variation ...[more]

PMID: 26996313

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Project description:BackgroundDue to a significant decline in the costs associated with next-generation sequencing, it has become possible to decipher the genetic architecture of a population by sequencing a large number of individuals to a deep coverage. The Korean Personal Genomes Project (KPGP) recently sequenced 35 Korean genomes at high coverage using the Illumina Hiseq platform and made the deep sequencing data publicly available, providing the scientific community opportunities to decipher the genetic architecture of the Korean population.MethodsIn this study, we used two single nucleotide variant (SNV) calling pipelines: mapping the raw reads obtained from whole genome sequencing of 35 Korean individuals in KPGP using BWA and SOAP2 followed by SNV calling using SAMtools and SOAPsnp, respectively. The consensus SNVs obtained from the two SNV pipelines were used to represent the SNVs of the Korean population. We compared these SNVs to those from 17 other populations provided by the HapMap consortium and the 1000 Genomes Project (1KGP) and identified SNVs that were only present in the Korean population. We studied the mutation spectrum and analyzed the genes of non-synonymous SNVs only detected in the Korean population.ResultsWe detected a total of 8,555,726 SNVs in the 35 Korean individuals and identified 1,213,613 SNVs detected in at least one Korean individual (SNV-1) and 12,640 in all of 35 Korean individuals (SNV-35) but not in 17 other populations. In contrast with the SNVs common to other populations in HapMap and 1KGP, the Korean only SNVs had high percentages of non-silent variants, emphasizing the unique roles of these Korean only SNVs in the Korean population. Specifically, we identified 8,361 non-synonymous Korean only SNVs, of which 58 SNVs existed in all 35 Korean individuals. The 5,754 genes of non-synonymous Korean only SNVs were highly enriched in some metabolic pathways. We found adhesion is the top disease term associated with SNV-1 and Nelson syndrome is the only disease term associated with SNV-35. We found that a significant number of Korean only SNVs are in genes that are associated with the drug term of adenosine.ConclusionWe identified the SNVs that were found in the Korean population but not seen in other populations, and explored the corresponding genes and pathways as well as the associated disease terms and drug terms. The results expand our knowledge of the genetic architecture of the Korean population, which will benefit the implementation of personalized medicine for the Korean population.

Project description:Combating the spread of drug resistant tuberculosis is a global health priority. Whole genome association studies are being applied to identify genetic determinants of resistance to anti-tuberculosis drugs. Protein structure and interaction modelling are used to understand the functional effects of putative mutations and provide insight into the molecular mechanisms leading to resistance.To investigate the potential utility of these approaches, we analysed the genomes of 144 Mycobacterium tuberculosis clinical isolates from The Special Programme for Research and Training in Tropical Diseases (TDR) collection sourced from 20 countries in four continents. A genome-wide approach was applied to 127 isolates to identify polymorphisms associated with minimum inhibitory concentrations for first-line anti-tuberculosis drugs. In addition, the effect of identified candidate mutations on protein stability and interactions was assessed quantitatively with well-established computational methods.The analysis revealed that mutations in the genes rpoB (rifampicin), katG (isoniazid), inhA-promoter (isoniazid), rpsL (streptomycin) and embB (ethambutol) were responsible for the majority of resistance observed. A subset of the mutations identified in rpoB and katG were predicted to affect protein stability. Further, a strong direct correlation was observed between the minimum inhibitory concentration values and the distance of the mutated residues in the three-dimensional structures of rpoB and katG to their respective drugs binding sites.Using the TDR resource, we demonstrate the usefulness of whole genome association and convergent evolution approaches to detect known and potentially novel mutations associated with drug resistance. Further, protein structural modelling could provide a means of predicting the impact of polymorphisms on drug efficacy in the absence of phenotypic data. These approaches could ultimately lead to novel resistance mutations to improve the design of tuberculosis control measures, such as diagnostics, and inform patient management.

Project description:Salmonella enterica subsp. enterica serovar Dublin is a cattle-adapted serovar that causes enteritis and systemic diseases in animals. In Germany, S. Dublin is not detected or is very rarely detected in some federal states but is endemic in certain regions. Information on detailed genetic characteristics of S. Dublin is not available. An understanding of the paths and spreading of S. Dublin within and between regions and over time is essential to establish effective control strategies. Whole-genome sequencing (WGS) and bioinformatic analysis were used to explore the genetic traits of S. Dublin and to determine their epidemiological context. Seventy-four S. Dublin strains collected in 2005 to 2018 from 10 federal states were studied. The phylogeny was analyzed using core-genome single-nucleotide polymorphisms (cgSNPs) and core-genome multilocus sequence typing. Genomic clusters at 100 cgSNPs, 40 cgSNPs, and 15 cgSNPs were selected for molecular epidemiology. WGS-based genoserotyping confirmed serotyping. Important specific virulence determinants were detected in all strains, but multidrug resistance in German S. Dublin organisms is uncommon. Use of different thresholds for cgSNP analysis enabled a broad view and also a detailed view of the occurrence of S. Dublin in Germany. Genomic clusters could be allocated nationwide, to a limited number of federal states, or to special regions only. Results indicate both persistence and spread of S. Dublin within and between federal states in short and longer time periods. However, to detect possible routes of infection or persistence of S. Dublin indicated by genomic analysis, information on the management of the cattle farms and contacts with corresponding farms are essential. IMPORTANCE Salmonella enterica subsp. enterica serovar Dublin is a bovine host-adapted serovar that causes up to 50% of all registered outbreaks of salmonellosis in cattle in Germany. S. Dublin is not detected or is only rarely detected in some federal states but has been endemic in certain regions of the country for a long time. Information on genetic traits of the causative strains is essential to determine routes of infection. WGS and bioinformatic analysis should be used to explore the genetic characteristics of S. Dublin. Combining the genomic features of S. Dublin strains with information on the management of the cattle farms concerned should enable the detection of possible routes of infection or persistence of S. Dublin. This approach is regarded as a prerequisite to developing effective intervention strategies.

Dataset Information

Whole genome sequencing provides insights into the genetic determinants of invasiveness in Salmonella Dublin.

Publications

Whole genome sequencing provides insights into the genetic determinants of invasiveness in Salmonella Dublin.

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