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Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models.

ABSTRACT: Group A Streptococcus (GAS) is a globally significant bacterial pathogen. The GAS genotyping gold standard characterises the nucleotide variation of emm, which encodes a surface-exposed protein that is recombinogenic and under immune-based selection pressure. Within a supervised learning methodology, we tested three random forest (RF) algorithms (Guided, Ordinary, and Regularized) and 53 GAS response regulator (RR) allele types to infer six genomic traits (emm-type, emm-subtype, tissue and country of sample, clinical outcomes, and isolate invasiveness). The Guided, Ordinary, and Regularized RF classifiers inferred the emm-type with accuracies of 96.7%, 95.7%, and 95.2%, using ten, three, and four RR alleles in the feature set, respectively. Notably, we inferred the emm-type with 93.7% accuracy using only mga2 and lrp. We demonstrated a utility for inferring emm-subtype (89.9%), country (88.6%), invasiveness (84.7%), but not clinical (56.9%), or tissue (56.4%), which is consistent with the complexity of GAS pathophysiology. We identified a novel cell wall-spanning domain (SF5), and proposed evolutionary pathways depicting the 'contrariwise' and 'likewise' chimeric deletion-fusion of emm and enn. We identified an intermediate strain, which provides evidence of the time-dependent excision of mga regulon genes. Overall, our workflow advances the understanding of the GAS mga regulon and its plasticity.

SUBMITTER: Buckley SJ

PROVIDER: S-EPMC8209152 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models.

Buckley Sean J SJ Harvey Robert J RJ Shan Zack Z

Scientific reports 20210616 1

Group A Streptococcus (GAS) is a globally significant bacterial pathogen. The GAS genotyping gold standard characterises the nucleotide variation of emm, which encodes a surface-exposed protein that is recombinogenic and under immune-based selection pressure. Within a supervised learning methodology, we tested three random forest (RF) algorithms (Guided, Ordinary, and Regularized) and 53 GAS response regulator (RR) allele types to infer six genomic traits (emm-type, emm-subtype, tissue and count ...[more]

PMID: 34135390

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Project description:A recent clinical report has linked Streptococcus pyogenes ?-lactam antibiotic resistance to mutation in the penicillin binding protein (PBP) PBP2x. To determine whether this is an isolated case or reflects a broader prevalence of mutations that might confer reduced ?-lactam susceptibility, we investigated the relative frequency of PBP sequence variation within a global database of 9,667 S. pyogenes isolates. We found that mutations in S. pyogenes PBPs (PBP2x, PBP1a, PBP1b, and PBP2a) occur infrequently across this global database, with fewer than 3 amino acid changes differing between >99% of the global population. Only 4 of the 9,667 strains contained mutations near transpeptidase active sites of PBP2x or PBP1a. The reported PBP2x T553K substitution was not identified. These findings are in contrast to those of 2,520 S. pneumococcus sequences where PBP mutations are relatively frequent and are often located in key ?-lactam binding pockets. These data, combined with the general lack of penicillin resistance reported in S. pyogenes worldwide, suggests that extensive, unknown constraints restrict S. pyogenes PBP sequence plasticity. Our findings imply that while heavy antibiotic pressure may select for mutations in the PBPs, there is currently no evidence of such mutations becoming fixed in the S. pyogenes population or that mutations are being sequentially acquired in the PBPs.IMPORTANCE ?-Lactam antibiotics are the first-line therapeutic option for Streptococcus pyogenes infections. Despite the global high prevalence of S. pyogenes infections and widespread use of ?-lactams worldwide, reports of resistance to ?-lactam antibiotics, such as penicillin, have been incredibly rare. Recently, ?-lactam resistance, as defined by clinical breakpoints, was detected in two clinical S. pyogenes isolates with accompanying mutations in the active site of the penicillin binding protein PBP2x, raising concerns that ?-lactam resistance will become more widespread. We screened a global database of S. pyogenes genome sequences to investigate the frequency of PBP mutations, identifying that PBP mutations are uncommon relative to those of Streptococcus pneumoniae These findings support clinical observations that ?-lactam resistance is rare in S. pyogenes and suggest that there are considerable constraints on S. pyogenes PBP sequence variation.

Project description:Background and aimsThis study aimed to develop an interpretable random forest model for predicting severe acute pancreatitis (SAP).MethodsClinical and laboratory data of 648 patients with acute pancreatitis were retrospectively reviewed and randomly assigned to the training set and test set in a 3:1 ratio. Univariate analysis was used to select candidate predictors for the SAP. Random forest (RF) and logistic regression (LR) models were developed on the training sample. The prediction models were then applied to the test sample. The performance of the risk models was measured by calculating the area under the receiver operating characteristic (ROC) curves (AUC) and area under precision recall curve. We provide visualized interpretation by using local interpretable model-agnostic explanations (LIME).ResultsThe LR model was developed to predict SAP as the following function: -1.10-0.13×albumin (g/L) + 0.016 × serum creatinine (μmol/L) + 0.14 × glucose (mmol/L) + 1.63 × pleural effusion (0/1)(No/Yes). The coefficients of this formula were utilized to build a nomogram. The RF model consists of 16 variables identified by univariate analysis. It was developed and validated by a tenfold cross-validation on the training sample. Variables importance analysis suggested that blood urea nitrogen, serum creatinine, albumin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, calcium, and glucose were the most important seven predictors of SAP. The AUCs of RF model in tenfold cross-validation of the training set and the test set was 0.89 and 0.96, respectively. Both the area under precision recall curve and the diagnostic accuracy of the RF model were higher than that of both the LR model and the BISAP score. LIME plots were used to explain individualized prediction of the RF model.ConclusionsAn interpretable RF model exhibited the highest discriminatory performance in predicting SAP. Interpretation with LIME plots could be useful for individualized prediction in a clinical setting. A nomogram consisting of albumin, serum creatinine, glucose, and pleural effusion was useful for prediction of SAP.

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Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models.

Publications

Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models.

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