Engineering Quadrangle, G204-206
|Engineering Quadrangle, A423||
Lab (609) 258-5661
With the ever-increasing incidence of antibiotic-resistant infections and a weak pipeline of new antibiotics, our antibiotic arsenal is in danger of becoming obsolete. Since conventional antibiotic discovery is failing to keep pace with the rise of resistance, fresh perspectives and novel methodologies are needed to address this critical public health issue. The main focus of our group is to use both computational and experimental techniques in systems biology, synthetic biology, and metabolic engineering to understand and combat infectious disease. We focus on three key areas: host-pathogen interactions, bacterial persistence, and biofilms.
The increase in the frequency of antibiotic-resistant strains has researchers searching for new antimicrobials or novel ways to potentiate current therapeutics. One exciting approach with great potential is antivirulence therapy, which focuses on disrupting the ability of a pathogen to infect a host. Rather than targeting essential bacterial functions as current antibiotics do, antivirulence therapy targets essential host-pathogen interactions required for infection such as adhesion, quorum sensing, and susceptibility to immune attack. These therapies are less prone to resistance development due to their ability to provide selective pressure only within the host, and have the potential to greatly expand our antimicrobial capabilities. In this area, we aim to leverage our knowledge and understanding of bacterial metabolism to increase the susceptibility of pathogens to killing by various immune antimicrobials, including reactive oxygen species, reactive nitrogen species, and antimicrobial peptides.
Bacterial persistence is a non-genetic, non-inherited (epigenetic) ability in bacteria to tolerate antibiotics and other stress. This distinct physiological state is thought to cause chronic and recurrent infection, and represents an insurance policy in which a small portion of cells enter dormancy and sacrifice their ability to replicate in order to survive stress at a future time. The proportion of persisters in a population varies by strain and environment (generally 1 in 100 to 1 in 1,000,000 cells), and the mechanism of persister formation as well as the content of their physiology remain elusive. A major goal of our group is the reconstruction of persister physiology using systems biology to identify active portions of their metabolic, signal transduction, and transcriptional regulatory networks. This work will provide the first cellular-level persister network and direct efforts to eliminate persisters as a source of chronic infection.
Biofilms are communities of bacterial cells embedded in a self-generated extracellular polymer matrix (EPM) that protects them from exogenous stress. The pathogenicities of a number of organisms including Pseudomonas aeruginosa, Streptococcus pneumoniae, Staphylococcus aureus, and uropathogenic Escherichia coli have been linked to biofilm formation. Most research in this area has focused on the genetics, signaling events, and surface modifications that affect initial adhesion or biofilm maturation. Interestingly, despite the fact that EPM is a metabolic product of bacteria, little research has focused on how to metabolically impair an organism’s ability to synthesize EPM. Our group uses metabolic engineering techniques employed for metabolic optimization to identify strategies that minimize the production or negatively impact the integrity of biofilms (e.g., suboptimal composition). This work will lay the foundation for a novel class of antibiofilm therapies based on biosynthetic limitation.
Orman MA, Brynildsen MP. (2013) Dormancy is not necessary or sufficient for bacterial persistence. Antimicrob Agents Chemother. 57: 3230-9. Pubmed
Orman MA, Brynildsen MP. (2013) Establishment of a method to rapidly assay bacterial persister metabolism. Antimicrob Agents Chemother. 57: 4398-409. Pubmed
Amato SM, Orman MA, Brynildsen MP. (2013) Metabolic control of persister formation in Escherichia coli. Mol Cell. 50: 475-87. Pubmed
Robinson JL, Brynildsen MP. (2013) A kinetic platform to determine the fate of nitric oxide in Escherichia coli. PLoS Comput Biol. 9: e1003049. Pubmed
Brynildsen MP, Winkler JA, Spina CS, Macdonald IC, Collins JJ. (2013) Potentiating antibacterial activity by predictably enhancing endogenous microbial ROS production. Nat Biotechnol. 31 :160-5. Pubmed
Allison KR, Brynildsen MP, Collins JJ. (2011) Heterogeneous bacterial persisters and engineering approaches to eliminate them. Curr Opin Microbiol. 14: 593-598. PubMed
Allison KR, Brynildsen MP, Collins JJ. (2011) Metabolite-enabled eradication of bacterial persisters by aminoglycosides. Nature 473: 216-220. PubMed
Brynildsen MP, Liao JC. (2009) An integrated network approach identifies the isobutanol response network of Escherichia coli. Mol Syst Biol. 5: 277 PubMed
Brynildsen MP, Collins JJ. (2009) Systems biology makes it personal. Mol Cell. 34: 137-138. PubMed
Atsumi S, Cann AF, Connor MR, Shen CR, Smith KM, Brynildsen MP, Chou KJ, Hanai T, Liao JC. (2008) Metabolic engineering of Escherichia coli for 1-butanol production. Metab Eng. 10: 305-311. PubMed
Brynildsen MP, Wu TY, Jang SS, Liao JC. (2007) Biological network mapping and source signal deduction. Bioinformatics 23: 1783-1791. PubMed
Brynildsen MP, Tran LM, Liao JC. (2006) A Gibbs sampler for the identification of gene expression and network connectivity consistency. Bioinformatics 22: 3040-3046. PubMed
Brynildsen MP, Tran LM, Liao JC. (2006) Versatility and connectivity efficiency of bipartite transcription networks. Biophys J. 91: 2749-2759. PubMed
Brynildsen MP, Wong WW, Liao JC. (2005) Transcriptional regulation and metabolism. Biochem Soc Trans. 33: 1423-1426. PubMed
Yang YL, Suen J, Brynildsen MP, Galbraith S, Liao JC. (2005) Inferring yeast cell cycle regulators and interactions using transcription factor activities. BMC Genomics. 6: 90. PubMed
Tran LM, Brynildsen MP, Kao KC, Suen JK, Liao JC. (2005) gNCA: A framework for determining transcription factor activity based on transcriptome: Identifiability and numerical implementation. Metab Eng. 7: 128-141. PubMed