Nontyphoidal Salmonella species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available for Salmonella strains because of surveillance efforts from public health agencies.

Significance: Epidemics of microbial infections are a considerable threat to human and animal health. Analysis of 3,615 genome sequences, coupled with virulence studies in animals, permitted us to delineate the nature and timing of molecular events that contributed to an ongoing global human epidemic of infections caused by group A Streptococcus , the “flesh-eating” pathogen. We clarified decades-long uncertainty about the timing and sequence of genomic alterations that underpinned the global epidemic. Analyses of this type are crucial for developing better strategies to predict and monitor strain emergence and epidemics, formulate effective protective public health maneuvers, and develop or modify vaccines.

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.

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), anti–receptor-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.

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.

Klebsiella pneumoniae has been classified into two types, classical K. pneumoniae (cKP) and hypervirulent K. pneumoniae (hvKP). cKP isolates are highly diverse and important causes of nosocomial infections; they include globally disseminated antibiotic-resistant clones. hvKP isolates are sensitive to most antibiotics but are highly virulent, causing community-acquired infections in healthy individuals. The virulence phenotype of hvKP is associated with pathogenicity loci responsible for siderophore and hypermucoid capsule production. Recently, convergent strains of K. pneumoniae, which possess features of both cKP and hvKP, have emerged and are cause of much concern. Here, we screen the genomes of 2,608 multidrug-resistant K. pneumoniae isolates from the United States and identify 47 convergent isolates. We perform phenotypic and genomic characterization of 12 representative isolates. These 12 convergent isolates contain a variety of antimicrobial resistance plasmids and virulence plasmids. Most convergent isolates contain aerobactin biosynthesis genes and produce more siderophores than cKP isolates but not more capsule. Unexpectedly, only 1 of the 12 tested convergent isolates has a level of virulence consistent with hvKP isolates in a murine pneumonia model. These findings suggest that additional studies should be performed to clarify whether convergent strains are indeed more virulent than cKP in mouse and human infections.