Classification of pathogenic microbes using a minimal set of single nucleotide polymorphisms derived from whole genome sequences

Roychowdhury, Tanmoy; Singh, V. P.; Bhattacharya, Alok

doi:10.1016/j.ygeno.2018.02.004

Cited by 2 publications

(3 citation statements)

References 34 publications

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“…This strategy can be used as a model for other detailed reconstructions of the evolutionary history of any other microorganism with a sufficient number of available sequenced genomes in databases. Recently, a similar strategy based on SNPs and gain/loss of genes was used [45]. Future research on the staggered bacterial diversification will certainly provide deeper knowledge for understanding the effect of environmental changes in microbial evolution.…”

Section: Discussionmentioning

confidence: 99%

Stratified reconstruction of ancestral Escherichia coli diversification

2019

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BackgroundPhylogenetic analyses of the bacterial genomes based on the simple classification in core- genes and accessory genes pools could offer an incomplete view of the evolutionary processes, of which some are still unresolved. A combined strategy based on stratified phylogeny and ancient molecular polymorphisms is proposed to infer detailed evolutionary reconstructions by using a large number of whole genomes. This strategy, based on the highest number of genomes available in public databases, was evaluated for improving knowledge of the ancient diversification of E. coli. This staggered evolutionary scenario was also used to investigate whether the diversification of the ancient E. coli lineages could be associated with particular lifestyles and adaptive strategies.ResultsPhylogenetic reconstructions, exploiting 6220 available genomes in Genbank, established the E. coli core genome in 1023 genes, representing about 20% of the complete genome. The combined strategy using stratified phylogeny plus molecular polymorphisms inferred three ancient lineages (D, EB1A and FGB2). Lineage D was the closest to E. coli root. A staggered diversification could also be proposed in EB1A and FGB2 lineages and the phylogroups into these lineages. Several molecular markers suggest that each lineage had different adaptive trajectories. The analysis of gained and lost genes in the main lineages showed that functions of carbohydrates utilization (uptake of and metabolism) were gained principally in EB1A lineage, whereas loss of environmental-adaptive functions in FGB2 lineage were observed, but this lineage showed higher accumulated mutations and ancient recombination events. The population structure of E. coli was re-evaluated including up to 7561 new sequenced genomes, showing a more complex population structure of E. coli, as a new phylogroup, phylogroup I, was proposed.ConclusionsA staggered reconstruction of E. coli phylogeny is proposed, indicating evolution from three ancestral lineages to reach all main known phylogroups. New phylogroups were confirmed, suggesting an increasingly complex population structure of E. coli. However these new phylogroups represent < 1% of the global E. coli population. A few key evolutionary forces have driven the diversification of the two main E. coli lineages, metabolic flexibility in one of them and colonization-virulence in the other.

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Section: Discussionmentioning

confidence: 99%

Stratified reconstruction of ancestral Escherichia coli diversification

2019

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“…2003), E. coli (Roychowdhury et al . 2019) and Bacillus subtilis (Juhas et al . 2014), 41 of the 47 obtained proteins are possibly essential proteins required for survival, and 30 of these proteins have been identified as drug targets in other bacteria according to the DrugBank database (Table ).…”

Section: Resultsmentioning

confidence: 99%

“…Most of these proteins belong to the KEGG categories 'Translation' (17/47), 'Membrane transport' (8/47), 'Folding, sorting and degradation' (7/47) and 'Replication and repair' (6/47). According to the essential genes present in the DEG database of other bacteria, such as M. tuberculosis (Sassetti et al 2003), E. coli (Roychowdhury et al 2019) and Bacillus subtilis (Juhas et al 2014), 41 of the 47 obtained proteins are possibly essential proteins required for survival, and 30 of these proteins have been identified as drug targets in other bacteria according to the Drug-Bank database (Table S3). Drugs targeting the homologues of these 30 proteins are in the experimental stage or have been approved (Table S3).…”

Section: Identification Of Potential Drug Targetsmentioning

confidence: 99%

Protein–protein interaction network and potential drug target candidates ofStreptococcus suis

et al. 2021

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Aims: This study aimed to explore potential drug targets of Streptococcus suis at the system level. Methods and Results: A homologous protein mapping method was used in the construction of a protein-protein interaction (PPI) network of S. suis, which presented 1147 non-redundant interaction pairs among 286 proteins. The parameters of PPI networks were calculated and showed scale-free network properties. In all, 41 possibly essential proteins identified from 47 highly connected proteins were selected as potential drug target candidates. Of these proteins, 30 were already regarded as drug targets in other bacterial species. Six transporters with high connections to other functional proteins were identified as probably not essential but important functional proteins. Afterward, the subnetwork centred with cell division protein FtsZ was used in confirming the PPI network through bacterial two-hybrid analysis. Conclusions: The predicted PPI network covers 13Á04% of the proteome in S. suis. The selected 41 potential drug target candidates are conserved between S. suis and several model bacteria. Significance and Impact of the Study: The predictions included proteins known to be drug targets, and a verifying experiment confirmed the reliability of predicted interactions. This work is the first to present systematic computational PPI data for S. suis and provides potential drug targets, which are valuable in exploring novel anti-streptococcus drugs.Studies on PPIs reveal the underlying molecular mechanism in living cells, predict protein functions, separate functional modules, identify essential genes and pathways, and search novel drug targets (Peng et al. 2017). With the increasing availability of genomics, proteomics, metabolomics and bioinformatics, the amount of proteome level PPI data from experiments based on various highthroughput experimental methods is growing rapidly in many model organisms (Lees et al. 2011).Exploring the PPI networks of pathogens at the system level can be a useful and effective strategy for investigating potential drug targets, such as species-specific genes, uncharacterized essential genes, virulence genes and membrane transporters (Hasan et al. 2016). A series of

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Classification of pathogenic microbes using a minimal set of single nucleotide polymorphisms derived from whole genome sequences

Cited by 2 publications

References 34 publications

Stratified reconstruction of ancestral Escherichia coli diversification

Stratified reconstruction of ancestral Escherichia coli diversification

Protein–protein interaction network and potential drug target candidates ofStreptococcus suis

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