The continued rise of antibiotic resistance is a major threat to public health and portends a return to the pre-antibiotic era. We seek to tackle the problem of antibiotic resistance through understanding the evolution of both antibiotic-producing microbes and antibiotic-resistant pathogens.

Evolution of biosynthetic gene clusters

About 80% of antibiotics used in medicine were derived from microorganisms that naturally produce antibiotics. Over half of these antibiotics come from a single genus of bacteria, the Streptomyces. Despite their contribution to our antibiotic arsenal, we still know relatively little about the role antibiotics have played in their ecology and evolution.

The information for producing antibiotics is encoded in biosynthetic gene clusters that vary markedly from strain-to-strain. In many cases, biosynthetic gene clusters have unknown function, potentially offering a wealth of untapped antibiotics. Using comparative genomics, we study the principles underlying the evolution of biosynthetic gene clusters in antibiotic producing organisms. The goal of this research is to uncover new antibiotics with the potential to be applied in the clinic.

Evolution of compensatory adaptations

Pathogens that acquire antibiotic resistance often pay a substantial price in the form of reduced growth rate. Yet, after a few generations their growth rate sometimes rebounds due to adaptations that compensate for the costs of antibiotic resistance. Such adaptations make it more difficult for resistance to be lost even in the absence of antibiotic pressure.

Using experimental evolution followed by genome sequencing we study the role of compensatory mutations in irreversibly "locking-in" resistance. In this way, we hope to understand how genomic context evolves to accommodate newly acquired traits, such as resistance to an antibiotic, and how these complex adaptations "unwind" when they are no longer useful.

Treatment strategies that consider evolution

Increasing levels of antibiotic resistance pose a major challenge in the struggle against infectious disease. As the number of new antibiotics has dwindled, many pathogens have evolved multi-drug resistance that make them impossible to treat with standard paradigms. We use a combination of experimental evolution and bioinformatics to find alternative treatment strategies for combatting resistance in the clinic with existing antibiotics. In essence, we seek to guide pathogens through a metaphorical fitness landscape to prevent them from reaching the peak of multi-drug resistance. The goal of this research is to develop treatment strategies with immediate relevance to tackling antibiotic resistance in the clinic.

The role of spatial structuring in evolution

Although microbiologists often study evolution using single organisms grown in well-mixed liquid environments, natural circumstances are never this simple. Here we ask: how does evolution differ at 1µm, the scale of most microorganisms? To answer this question, we are developing methods for creating spatially structured environments and observing evolution in action. We are also studying the spatial structuring of naturally occurring microbial communities, such as those in the soil, to better understand how microbes view the world. The goal of this research is to develop an intuition for how spatial structuring shapes the ecology and evolution of microorganisms.

Development of tools for comparative genomics

The Wright lab is home to DECIPHER, an R package for efficiently curating, analyzing, and manipulating massive amounts of biological sequence data. We are extending DECIPHER by developing algorithms for comparative genomics, including: large-scale multiple sequence alignment, taxonomic classification, phylogenetics, detecting horizontal gene transfer, estimating selective pressures, and identifying phenotype-genotype correlations. Furthermore, we are particularly interested in methods for displaying large-scale sequence information, because this is a major way that biologists (such as ourselves) interact with DECIPHER.