We sequenced the genomes of 3,615 strains of serotype Emm protein 1 (M1) group A Streptococcus to unravel the nature and timing of molecular events contributing to the emergence, dissemination, and genetic diversification of an unusually virulent clone that now causes epidemic human infections worldwide. We discovered that the contemporary epidemic clone emerged in stepwise fashion from a precursor cell that first contained the phage encoding an extracellular DNase virulence factor (streptococcal DNase D2, SdaD2) and subsequently acquired the phage encoding the SpeA1 variant of the streptococcal pyrogenic exotoxin A superantigen. The SpeA2 toxin variant evolved from SpeA1 by a single-nucleotide change in the M1 progenitor strain before acquisition by horizontal gene transfer of a large chromosomal region encoding secreted toxins NAD + -glycohydrolase and streptolysin O. Acquisition of this 36-kb region in the early 1980s into just one cell containing the phage-encoded sdaD2 and speA2 genes was the final major molecular event preceding the emergence and rapid intercontinental spread of the contemporary epidemic clone. Thus, we resolve a decades-old controversy about the type and sequence of genomic alterations that produced this explosive epidemic. Analysis of comprehensive, population-based contemporary invasive strains from seven countries identified strong patterns of temporal population structure. Compared with a preepidemic reference strain, the contemporary clone is significantly more virulent in nonhuman primate models of pharyngitis and necrotizing fasciitis. A key finding is that the molecular evolutionary events transpiring in just one bacterial cell ultimately have produced millions of human infections worldwide.pathogenesis | phylogeography | mobile genetic element | flesh-eating disease | molecular clock

Klebsiella pneumoniae is a serious human pathogen associated with resistance to multiple antibiotics and high mortality. K. variicola and K. quasipneumoniae are closely related organisms that are generally considered to be less-virulent opportunistic pathogens. We used a large, comprehensive, population-based strain collection and whole-genome sequencing to investigate infections caused by these organisms in our hospital system. We discovered that K. variicola and K. quasipneumoniae isolates are often misidentified as K. pneumoniae by routine clinical microbiology diagnostics and frequently cause severe life-threatening infections similar to K. pneumoniae. The presence of KPC in K. variicola and K. quasipneumoniae strains as well as NDM-1 metallo-beta-lactamase in one K. variicola strain is particularly concerning because these genes confer resistance to many different beta-lactam antibiotics. The sharing of plasmids, as well as evidence of homologous recombination, between these three species of Klebsiella is cause for additional concern.

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ±1 two-fold dilution factor, is 92%. Individual accuracies are ≥90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

The newly emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlights the urgent need for assays that detect protective levels of neutralizing antibodies. We studied the relationship among anti-spike ectodomain (anti-ECD), antireceptor-binding domain (anti-RBD) IgG titers, and SARS-CoV-2 virus neutralization (VN) titers generated by 2 in vitro assays using convalescent plasma samples from 68 patients with COVID-19. We report a strong positive correlation between both plasma anti-RBD and anti-ECD IgG titers and in vitro VN titers. The probability of a VN titer of ≥160, the FDA-recommended level for convalescent plasma used for COVID-19 treatment, was ≥80% when anti-RBD or anti-ECD titers were ≥1:1350. Of all donors, 37% lacked VN titers of ≥160. Dyspnea, hospitalization, and disease severity were significantly associated with higher VN titer. Frequent donation of convalescent plasma did not significantly decrease VN or IgG titers. Analysis of 2814 asymptomatic adults found 73 individuals with anti-ECD IgG titers of ≥1:50 and strong positive correlation with anti-RBD and VN titers. Fourteen of these individuals had VN titers of ≥1:160, and all of them had anti-RBD titers of ≥1:1350. We conclude that anti-RBD or anti-ECD IgG titers can serve as a surrogate for VN titers to identify suitable plasma donors. Plasma anti-RBD or anti-ECD titers of ≥1:1350 may provide critical information about protection against COVID-19 disease.

NontyphoidalSalmonellaspecies are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available forSalmonellastrains because of surveillance efforts from public health agencies. In this study, a collection of 5,278 nontyphoidalSalmonellagenomes, collected over 15 years in the United States, was used to generate extreme gradient boosting (XGBoost)-based machine learning models for predicting MICs for 15 antibiotics. The MIC prediction models had an overall average accuracy of 95% within ±1 2-fold dilution step (confidence interval, 95% to 95%), an average very major error rate of 2.7% (confidence interval, 2.4% to 3.0%), and an average major error rate of 0.1% (confidence interval, 0.1% to 0.2%). The model predicted MICs with noa prioriinformation about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for the training sets, we show that highly accurate MIC prediction models can be generated with less than 500 genomes. We also show that our approach for predicting MICs is stable over time, despite annual fluctuations in antimicrobial resistance gene content in the sampled genomes. Finally, using feature selection, we explore the important genomic regions identified by the models for predicting MICs. To date, this is one of the largest MIC modeling studies to be published. Our strategy for developing whole-genome sequence-based models for surveillance and clinical diagnostics can be readily applied to other important human pathogens.

Klebsiella pneumoniae is a major human pathogen responsible for high morbidity and mortality rates. The emergence and spread of strains resistant to multiple antimicrobial agents and documented large nosocomial outbreaks are especially concerning. To develop new therapeutic strategies for K. pneumoniae, it is imperative to understand the population genomic structure of strains causing human infections. To address this knowledge gap, we sequenced the genomes of 1,777 extended-spectrum beta-lactamase-producing K. pneumoniae strains cultured from patients in the 2,000-bed Houston Methodist Hospital system between September 2011 and May 2015, representing a comprehensive, population-based strain sample. Strains of largely uncharacterized clonal group 307 (CG307) caused more infections than those of well-studied epidemic CG258. Strains varied markedly in gene content and had an extensive array of small and very large plasmids, often containing antimicrobial resistance genes. Some patients with multiple strains cultured over time were infected with genetically distinct clones. We identified 15 strains expressing the New Delhi metallo-beta-lactamase 1 (NDM-1) enzyme that confers broad resistance to nearly all beta-lactam antibiotics. Transcriptome sequencing analysis of 10 phylogenetically diverse strains showed that the global transcriptome of each strain was unique and highly variable. Experimental mouse infection provided new information about immunological parameters of host-pathogen interaction. We exploited the large data set to develop whole-genome sequence-based classifiers that accurately predict clinical antimicrobial resistance for 12 of the 16 antibiotics tested. We conclude that analysis of large, comprehensive, population-based strain samples can assist understanding of the molecular diversity of these organisms and contribute to enhanced translational research.IMPORTANCE Klebsiella pneumoniae causes human infections that are increasingly difficult to treat because many strains are resistant to multiple antibiotics. Clonal group 258 (CG258) organisms have caused outbreaks in health care settings worldwide. Using a comprehensive population-based sample of extended-spectrum betalactamase (ESBL)-producing K. pneumoniae strains, we show that a relatively uncommon clonal type, CG307, caused the plurality of ESBL-producing K. pneumoniae infections in our patients. We discovered that CG307 strains have been abundant in Houston for many years. As assessed by experimental mouse infection, CG307 strains

Ceftaroline is the first member of a novel class of cephalosporins approved for use in the United States. Although prior studies have identified eight ceftaroline-resistant methicillin-resistant Staphylococcus aureus (MRSA) isolates in Europe and Asia with MICs ranging from 4 to 8 mg/liter, high-level resistance to ceftaroline (>32 mg/liter) has not been described in MRSA strains isolated in the United States. We isolated a ceftaroline-resistant (MIC > 32 mg/liter) MRSA strain from the blood of a cystic fibrosis patient and five MRSA strains from the respiratory tract of this patient. Whole-genome sequencing identified two amino acid-altering mutations uniquely present in the ceftaroline-binding pocket of the transpeptidase region of penicillin-binding protein 2a (PBP2a) in ceftaroline-resistant isolates. Biochemical analyses and the study of isogenic mutant strains confirmed that these changes caused ceftaroline resistance. Thus, we identified the molecular mechanism of ceftaroline resistance in the first MRSA strain with high-level ceftaroline resistance isolated in the United States.

Staphylococcus aureus small-colony variants (SCVs) are implicated in chronic and relapsing infections that are difficult to diagnose and treat. Despite many years of study, the underlying molecular mechanisms and virulence effect of the small-colony phenotype remain incompletely understood. We sequenced the genomes of five S. aureus SCV strains recovered from human patients and discovered previously unidentified nonsynonymous point mutations in three genes encoding proteins in the menadione biosynthesis pathway. Analysis of genetic revertants and complementation with wild-type alleles confirmed that these mutations caused the SCV phenotype and decreased virulence for mice.