Genomic Analyses of DNA Transformation and Penicillin Resistance in Streptococcus pneumoniae Clinical Isolates

Fani, Fereshteh; Leprohon, Philippe; Zhanel, George G.; Bergeron, Michel G.; Ouellette, Marc

doi:10.1128/aac.01311-13

Cited by 24 publications

(25 citation statements)

References 39 publications

(47 reference statements)

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“…This yielded a model with an error rate of 1.7% that targeted the pbp2b gene. These results are consistent with the literature, since they indicate that alterations in both genes are equally predictive of a high-level of resistance and thus, that they occur simultaneously in isolates that are highly resistant to penicillin (Fani et al, 2014).…”

Section: The Scm Can Predict the Level Of Resistancesupporting

confidence: 92%

“…To further demonstrate how the SCM can be used to explore the relationship between genotypes and phenotypes, it was used to predict the level of benzylpenicillin resistance in S. pneumoniae. For this bacterium, penicillin resistance is often mediated by alterations that reduce the affinity of penicillin-binding proteins (Fani et al, 2014). Moderate-level resistance is due to alterations in PBP2b and PBP2x, whereas high-level resistance is due to additional alterations in PBP1a.…”

Section: The Scm Can Predict the Level Of Resistancementioning

confidence: 99%

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Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

Drouin

Giguère

Déraspe

et al. 2016

Preprint

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Background:The identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies. Results: The method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery. Conclusions: Our method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes (http://github. com/aldro61/kover/).

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Section: The Scm Can Predict the Level Of Resistancesupporting

confidence: 92%

Section: The Scm Can Predict the Level Of Resistancementioning

confidence: 99%

Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

Drouin

Giguère

Déraspe

et al. 2016

Preprint

View full text Add to dashboard Cite

show abstract

“…To further demonstrate how the SCM can be used to explore the relationship between genotypes and phenotypes, it was used to predict the level of benzylpenicillin resistance in S. pneumoniae . For this bacterium, penicillin resistance is often mediated by alterations that reduce the affinity of penicillin-binding proteins [ 50 ]. Moderate-level resistance is due to alterations in PBP2b and PBP2x, whereas high-level resistance is due to additional alterations in PBP1a.…”

Section: Resultsmentioning

confidence: 99%

Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

et al. 2016

View full text Add to dashboard Cite

BackgroundThe identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies.ResultsThe method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery.ConclusionsOur method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes (http://github.com/aldro61/kover/).Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2889-6) contains supplementary material, which is available to authorized users.

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“…DNA transformation. S. pneumoniae transformation was performed as previously described (50). Transformants were selected on casein tryptone (CAT) agar with 5% (vol/vol) sheep blood.…”

Section: Methodsmentioning

confidence: 99%

Gain- and Loss-of-Function Screens Coupled to Next-Generation Sequencing for Antibiotic Mode of Action and Resistance Studies in Streptococcus pneumoniae

Gingras

Patron

Bhattacharya

et al. 2019

Antimicrob Agents Chemother

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Two whole-genome screening approaches are described for studying the mode of action and the mechanisms of resistance to trimethoprim (TMP) in the Gram-positive Streptococcus pneumoniae. The gain-of-function approach (Int-Seq) relies on a genomic library of DNA fragments integrated into a fucose-inducible cassette. The second approach, leading to both gain-and loss-of-function mutation, is based on chemical mutagenesis coupled to next-generation sequencing (Mut-Seq). Both approaches pointed at the drug target dihydrofolate reductase (DHFR) as a major resistance mechanism to TMP. Resistance was achieved by dhfr overexpression either through the addition of fucose (Int-Seq) or by mutations upstream of the gene (Mut-Seq). Three types of mutations increased expression by disrupting a predicted Rho-independent terminator upstream of dhfr. Known and novel DHFR mutations were also detected by Mut-Seq, and these were functionally validated for TMP resistance. The two approaches also suggested that an increase in the metabolic flux from purine synthesis to GTP and then to folate can modulate the susceptibility to TMP. Finally, we provide evidence for a novel role of the ABC transporter PatAB in TMP susceptibility. Our genomic screens highlighted novel aspects on the mode of action and mechanisms of resistance to antibiotics.

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Genomic Analyses of DNA Transformation and Penicillin Resistance in Streptococcus pneumoniae Clinical Isolates

Cited by 24 publications

References 39 publications

Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

Gain- and Loss-of-Function Screens Coupled to Next-Generation Sequencing for Antibiotic Mode of Action and Resistance Studies in Streptococcus pneumoniae

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