Streptococcus pneumoniae is a common occupant of the nasopharynx and a major etiology of illness worldwide. The bacterium causes tens of millions of episodes of invasive pneumococcal disease (IPD) and ~1.5 million deaths each year, and as with most bacterial pathogens, antibiotic resistance is an increasing problem. Instrumental in the development of resistance is a bacterium's inherent ability to survive low-level exposure to antibiotics giving the population the opportunity to accumulate genomic changes that eventually lead to full clinical resistance. However, we know very little about the mechanisms that underlie this intrinsic ability to withstand antibiotics, how these mechanisms affect antibiotic resistance and how genetic changes in these mechanisms influence bacterial adaptability. We hypothesize that a diverse set of genes, pathways and non-coding RNAs (ncRNAs), are involved in the intrinsic ability of S. pneumoniae to withstand antibiotics, that these genetic components and their regulation is only partially conserved across strains and that changes within these components can lead to higher antibiotic resistance. Here we propose to determine in detail the mechanisms behind the bacterium's ability to withstand antibiotics by identifying how a bacterium responds mechanistically and transcriptionally to antibiotic stress and how changes in these responses contribute to antibiotic resistance in clinical IPD strains. With the introduction of massively parallel sequencing techniques it has become feasible to perform a study like this in high-throughput and on a genome-wide scale for a non- model pathogenic organism such as S. pneumoniae. We apply several of these techniques to achieve the following:
aim 1) determine the genes, pathways and ncRNAs that are involved in antibiotic stress and whether the same genetic components are used in different bacterial strains;
aim 2) determine which of the antibiotic response genes also play a role in inducing disease and how they contribute to the emergence of resistance in clinical IPD strains;
aim 3) determine how antibiotic responses are regulated and how conserved these responses are across bacterial strains. We expect that this work will generate an unprecedented view of how S. pneumoniae withstands antibiotic stress, both on a phenotypic level as well as on a transcriptional level. Successful completion of several parts of this project will enable the rational design of antibioti combinations that have the lowest probability of developing resistance mutations. Additionally, the networks can be applied to small molecule screens directed against intrinsic ability genes. Compounds coming out of such screens could function as adjuvants that modulate the response and make antibiotics more efficacious while slowing the evolution of resistance. Thereby this proposal fits into the major long-term goal of the lab, which is to understand how bacteria respond to (environmental) stress in order to use this knowledge to come up with novel antimicrobial strategies.

Public Health Relevance

Bacterial pathogens cause millions of deaths each year and are becoming increasingly resistant to the antibiotics that should curb their infections. Here we develop a detailed view of how a bacterium withstands antibiotic stress and how this response is involved in the emergence of antibiotic resistance.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
Project #
Application #
Study Section
Drug Discovery and Mechanisms of Antimicrobial Resistance Study Section (DDR)
Program Officer
Lu, Kristina
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Boston College
Schools of Arts and Sciences
Chestnut Hill
United States
Zip Code
Jensen, Paul A; Zhu, Zeyu; van Opijnen, Tim (2017) Antibiotics Disrupt Coordination between Transcriptional and Phenotypic Stress Responses in Pathogenic Bacteria. Cell Rep 20:1705-1716
McCoy, Katherine Maia; Antonio, Margaret L; van Opijnen, Tim (2017) MAGenTA: a Galaxy implemented tool for complete Tn-Seq analysis and data visualization. Bioinformatics 33:2781-2783
van Opijnen, Tim; Dedrick, Sandra; Bento, José (2016) Strain Dependent Genetic Networks for Antibiotic-Sensitivity in a Bacterial Pathogen with a Large Pan-Genome. PLoS Pathog 12:e1005869