Antimicrobial resistance (AMR) and its impact on health has been recognized as one of the most serious public health threats facing society. Significantly, the vast majority of acquired AMR genes are carried on mobile genetic elements (MGEs), which include plasmids, insertion sequences, and transposons. One of the most important hospital and community-associated pathogens harboring resistance genes is Staphylococcus aureus, which causes more than 80,000 infections and 11,000 deaths each year in the United States. S. aureus readily acquire MGEs, which encompass more than 20% of the genome for most strains, and these elements have been central to the establishment and broad spread of resistance to antibiotics such as oxacillin and vancomycin. Additionally, S. aureus is capable of causing a wide range of infections, including bacteremia, with a mortality rate up to 30%. Utilizing an unparalleled collection of S. aureus strains from a cohort of patients in Central and South America with bacteremia, I seek to develop a framework for the identification and comparison of circulating MGEs through the use of bacterial phylogenetics, clinical epidemiology, and machine learning. This dataset will serve as the foundation for my training to become an independent scientist. To begin interrogating this exceptional strain collection, we have generated Illumina short-read whole genome sequencing data on 1,087 S. aureus bacteremia isolates to identify MGEs, and will leverage novel ultra-long read sequencing methodologies to fully characterize the position and variations of these elements. The three aims within this proposal are designed to elucidate the role of MGEs in driving the genetic diversification of endemic S. aureus clades, and identify if they serve as adaptation hotspots when put under selective pressure from the host immune system or antibiotics. First, I will characterize the repertoire of MGEs within this large cohort of isolates and apply gene-order and Bayesian time-measured phylogenetics to identify the predominant MGEs within each clade and how frequently they are acquired and lost. Second, I will assess the MGE diversity within isolates collected serially from the same individual. I hypothesize these MGEs will be the primary variation points and will be more important than single nucleotide polymorphisms (SNPs) to the adaptation to selective pressures such as antibiotics. Third, I will identify the clinical (age, BMI, and present comorbidities) and genomic (SNPs, MGEs, and genes) features that are predictive of the 30-day mortality in the predominant clades of S. aureus within our dataset. I theorize these genetic and clinical signatures will be different for each clade as they possess different repertoires of MGEs. The Center for Antimicrobial Resistance and Microbial Genomics in The University of Texas Health Sciences Center at Houston has a firm commitment to understanding and reducing AMR and AMR infections. This provides an exceptional environment to conduct my research elucidating the role of MGEs in the clinical outcomes of S. aureus bacteremia, and to develop as an investigator and participate in the training offered to make the transition to independence.
Antimicrobial resistance (AMR) and its impact on health has been recognized as one of the most serious global public health threats facing our society. This proposal aims to clarify the role mobile genetic elements play in transmitting antimicrobial resistance and driving genetic diversity within endemic Staphylococcus aureus lineages causing bacteremia. Additionally, this research will provide the platform for the career development of a young scientist focused on bacterial phylogenetics, clinical epidemiology, and machine learning methods to tackle the dire consequences of AMR.
