Current understanding of how antibiotics induce bacterial cell death is centered on the essential bacterial cell function that is inhibited. However, antibiotic-mediated cell death is a complex, multi-factorial process that begins with the physical interaction between a drug molecule and its specific target, and involves alterations to the affected bacterium at the biochemical, molecular, regulatory and structural levels. A deeper understanding of the complexity of interactions between the drug, the target and the rest of the genome, and thus of the specific underlying mechanisms that lead to antibiotic resistance, is essential for the successful development of new treatment strategies to kill multi-drug resistant bacteria as well as strategies to prevent the emergence and spread of antibiotic resistance.
We utilize cutting edge genome-wide, experimental and bioinformatics systems approaches, of which we have recently developed several ourselves, in order to construct drug/gene interaction networks that mediate the bacterial antibiotic responses. These networks are subsequently used to direct the development of new therapeutic treatments.
Microbes are extremists, being found on the most inhospitable places on earth; they live on the slopes of the highest mountains; the edges of volcanoes; in deep-sea ocean vents; and they can even survive solitarily deep under ground. Living on and inside the human body they outnumber human cells 10:1, raising the philosophical question of what defines a human being. The robustness and impressive evolutionary potential of bacteria gives them the amazing ability to deal with almost any environment they are confronted with. In the lab we uncover how bacteria overcome stress in their environment, which include drugs and the host immune system. By mapping-out stress on several different organizational levels by means of different 'omics' approaches, including RNA-Seq, Tn-Seq, Metabolomics and Experimental Evolution we create network models with the objective to predict the survival outcome of an infection, and thus whether a bacterial pathogen will survive and cause disease or will be eradicated.
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An important goal in modern biology is to understand the relationship between genotype and phenotype; what constitutes a phenotype, which genes are involved and how do they interact to provide an efficient yet robust response to environmental change. With respect to pathogenic microorganisms, the goal of uncovering genotype-phenotype relationships is especially relevant, because the lack of understanding about the function of a significant part of the (pan-)genome currently hampering the design of novel strategies to battle infectious diseases. Developing high-throughput approaches for non-model (pathogenic) organisms that can match genotypes to phenotypes under in vitro and in vivo (infection) conditions is therefore crucial.
We developed the now widely used massively parallel sequencing technique, Tn-Seq (van Opijnen et al., 2009), and have drawn up a detailed roadmap to link genotypes to phenotypes (Van Opijnen and Camilli 2012). New work in the lab includes the development of strategies that automate the discovery of genotype-phenotype links and the placement of genes in their pathways, different microfluidics approaches, and high-throughput genome-wide genetic interaction mapping tools.
The misuse and overuse of antibacterial agents is one of the most vexing issues facing modern medicine. There are at least two underlying reasons for this mis/overuse: 1) Rapid identification of the infectious agent is limited, often leading to prescription that are based on deductive reasoning: 2) It is mostly not possible to determine what state an infections is in, e.g. whether it will get worse and needs treatment or whether it would resolve itself. To solve the latter problem we integrate our experimental work into computational/network models (as described above). To solve the first problem we collaborate with chemists, physicists and computer scientists, both within Boston College (e.g. Drs. Ken Burch, Jianmin Gao and José Bento) and outside, to develop exciting new tools and strategies including a graphene/microfluidics-based sensing device and bacterial strain specific chemical probes.
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Produced by Ravi Jain