RESEARCH



The mission of the Wright lab is to advance medicine by developing innovative solutions to the problem of antibiotic resistance. To accomplish this, we combine experimental and computational approaches that leverage the power of large-scale data.


Evolution of biosynthetic gene clusters


The continued rise of antibiotic resistance is a major threat to public health and portends a return to the pre-antibiotic era. 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 benefit by being able to survive in the presence of an antibiotic but often pay a substantial price in the form of reduced growth rate. Over time, the genomes of newly-resistant bacteria acquire "compensatory" mutations that mitigate the initial fitness cost. These 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


Electronic health records have been an important part of our healthcare system for over a decade. With years of accumulated data, we have reached a point where we can harness this rich data source to improve healthcare. The Wright Lab is mining de-identified patient-level data in order to better inform antibiotic prescribing for both individual patients and hospital-level antibiotic stewardship interventions. We are particularly interested in determining treatment strategies that counteract antibiotic resistance, minimize side effects, and maximize treatment success.


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.


Decrypting the language of the microbiome


Cell-to-cell communication in the microbiome is a complex process that occurs through contact-dependent and contact-independent mechanisms using an immense repertoire of small molecules. We are developing a 3D printed platform that will enable us to use high-throughput mass spectrometry to study the language of the microbiome. The goal of this project is to detect signaling and metabolic transformations occuring within the microbiome of the human gut.


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.