Staphylococcus aureus is a bacterium that can live on the human body harmlessly (i.e., colonization), but it is also among the most common causes of skin and soft tissue infections (SSTIs), bloodstream infections, and pneumonia. Methicillin-resistant S. aureus (MRSA) strains that are resistant to nearly all antibiotics related to penicillin are particularly concerning, killing more than 11,000 people each year in the U.S. Furthermore, up to 50% of patients with an initial MRSA SSTI suffer from a recurrent SSTI within 12 months. It would be useful to know which patients are at high risk of recurrence so that they could be treated in the best way to prevent the recurrent infection. However, now we do not know who is at high risk of recurrence. The USA300 MRSA strain is the most common cause of MRSA infections in the U.S., especially SSTIs. New methods using whole genome sequencing (WGS) are available to track the evolutionary change in MRSA over time as it grows on the human body. Little is known about the communities of MRSA that develop over time, how diverse they are, how they are affected when a person takes an antibiotic, and what genetic changes in MRSA are associated with the onset of a recurrent infection or prolonged colonization. We propose a WGS study of 7,000 MRSA isolates obtained from 400 people with a MRSA SSTI collected over a one-year period. We will test the 400 subjects in the proposed study at 3 body sites quarterly over a year to address these unknowns for the first time, determining which bacterial genes change over time as USA300 and other MRSA strain types grow and evolve on the body. Colonizing bacteria are constantly interacting with their human hosts and the environment. We will therefore assess these gene changes in the colonizing bacteria over time as well as the demographic, behavioral, antibiotic exposure, and medical characteristics of studied human subjects to determine their relative impacts on the risk of a recurrent infection. Our central hypothesis is that USA300 and closely related (CC8) strains, independent of host characteristics: 1) colonize the skin for a longer period of time and cause recurrent infections; 2) more likely cause infections that require medical intervention; and 3) are more likely to survive as colonizers after antibiotic treatment than other strain types. We also hypothesize that a higher level of diversity among colonizing MRSA is a predictor of long-term colonization and ability to survive challenge with antibiotics. Our hypotheses will be assessed using a combination of analysis of genome data and computer modeling. We will also test the MRSA isolates that we collect to determine if specific genetic changes lead to changes in the fitness of MRSA, as measured by changes in their growth rate, relative to strains cultured earlier from the same subject.
We aim to identify which clinical treatments lead to the most dramatic reduction in fitness of the surviving S. aureus population, and whether this is associated with lower likelihood of recurrent infections. We will apply the discoveries of the proposed study to identify MRSA patients at high risk of recurrent infections in order to give them the best preventive treatment.

Public Health Relevance

MRSA is among the most common causes of skin and fatal bloodstream infections, and there are few effective interventions to prevent recurrent infections. Using cutting edge technology, we will investigate how the genetic makeup of MRSA, patient characteristics, and antibiotic use during treatment affect the likelihood of recurrent infections. With this information, targeted preventive interventions could be directed at patients who are at the highest risk for developing recurrent infections.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI139188-03
Application #
9970408
Study Section
Clinical Research and Field Studies of Infectious Diseases Study Section (CRFS)
Program Officer
Huntley, Clayton C
Project Start
2018-08-16
Project End
2023-07-31
Budget Start
2020-08-01
Budget End
2021-07-31
Support Year
3
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104