Barrick Lab :: Research

Engineering Insect Symbionts

BEES
We created the bee microbiome toolkit (BTK) that can be used to engineer bacteria found in various insects and their gut microbiomes, as demonstrated in honey bees (Apis mellifera) and bumble bees (Bombus species). These tools were used to engineer a native gut bacterium, Snodgrassella alvi, to produce dsRNA targeting two causes of colony collapse: Varroa mites and deformed wing virus. Engineered S. alvi was shown to kill the mites and improve bee health. This symbiont-mediated RNAi approach is helpful to protect insects that are beneficial to human health, but this approach could also be applied to pest species to improve food security or study the biology of the relationship between host and symbiont.

Representative Publications

• Leonard et al. (2020) Science PMID: 32001655
• Leonard et al. (2018) ACS Synthetic Biol. PMID: 29608282

APHIDS
In aphids, we are engineering strains of the symbiotic species Serratia symbiotica, cultured from the aphid gut, to enable improved understanding of its relationship with aphids and the development of new tools for food security. Genetic tools for the study and manipulation of aphids have been lacking, but engineering a symbiont for “paratransgenesis” can serve as a useful alternative. We engineered the strain, recolonized aphids with it, and used it to induce aphid mortality. We also showed that cultured S. symbiotica strains are capable of maternal transmission within aphids, suggesting that they possess a latent capacity for a long-term symbiotic relationship with their hosts.

LEAFHOPPERS
A newer project in the lab involves another pest insect known as the leafhopper. Leafhoppers have unique nanostructures, called brochosomes, that they anoint onto their wings and sometimes eggs. Brochosomes have special properties, including superhydrophobicity and omnidirectional antireflectivity. We are collecting leafhoppers across Texas with the goal of identifying and characterizing natural variation in brochosome structure across species, ecoregions and seasons. We are working towards engineering leafhopper symbionts to control the production of brochosomes and enhancing their properties for the creation of novel biomaterials using RNAi.

Funding: DARPA BRICS, DARPA AEPHID, MURI ARO

Preventing Evolutionary Failure in Synthetic Biology

Synthetic biology applies engineering principles to create living systems with predictable and useful behaviors from collections of standardized genetic parts. However, living systems – unlike mechanical devices – inevitably evolve when their DNA sequences accumulate copying errors, often resulting in "broken" cells that no longer function as they were programmed. We are addressing this challenge by better characterizing how engineered cells evolve and using this information to design DNA sequences and host cells that are more robust against unwanted evolution. This work includes: (1) the development of the Evolutionary Failure Mode (EFM) Calculator software for identifying mutational hotspots in a designed DNA sequence; (2) using experimental evolution to identify "antimutator" variants of host organisms that lead to lower-than-natural mutation rates; and (3) designing genetic circuits that kill those cells within a population that are most likely to accumulate mutations.

Resources

• Evolutionary Failure Mode (EFM) Calculator website

Representative Publications

• Jack et al. (2015) ACS Synthetic Biol. PMID:26096262
• Renda et al. (2014) Mol. Biosyst. PMID:24556867

Funding: DARPA BRICS

Dynamics of Microbial Genome Evolution

We develop the breseq computational pipeline for identifying mutations in laboratory-evolved microbial genomes from next-generation sequencing data. We have used this tool to extensively study rates of genome evolution in the 30-year Lenski long-term evolution experiment (LTEE) with E. coli. We continue to develop breseq so that it can be used for more additional applications related to strain engineering and medicine. For example, we are interested in how tracking rare variants within populations of microorganisms (such as oncoviruses) can anticipate further evolutionary trajectories and how this information might be used to better diagnose disease outcomes.

Resources

• breseq project page
• breseq code and downloads (GitHub)
• LTEE genome resources (GitHub)

Representative Publications

• Deatherage et al. (2017) Proc. Natl. Acad. Sci. PMID: 28202733
• Tenaillon et al. (2016) Nature PMID: 27479321
• Deatherage and Barrick (2014) Methods Mol. Biol PMID:24838886
• Barrick and Lenski (2014) Nat. Rev. Genet. PMID:24166031

Funding: NIH K99/R00, NSF, NSF BEACON Center, CPRIT

Evolution and Engineering of Naturally Transformable Bacteria

Naturally competent bacteria have expanded evolutionary potential because they can readily acquire new DNA from their environment. We are using experimental evolution of the model organism Acinetobacter baylyi ADP1 to understand how horizontally acquired genes and mobile genetic elements become domesticated after their incorporation into a new genome and the broader effects of gene acquisition on the rest of the genome. We are also studying the role of chemical specificity in determining the fate of acquired DNA as either nutrition or genetic information.

These bacteria also provide an improved platform for studying microbial genome engineering due to the ease of reconstructing mutations and introducing new genes. We are investigating sources of genetic instability in ADP1 and engineering a clean genome version of this strain by deleting transposable elements and prophages. This will promote the use of ADP1 in synthetic biology by reducing rates of mutations that lead to inactivation of introduced genes. Further, we are using ADP1 as a platform to understand the limits to streamlining bacterial genomes and identifying adaptations to overcome the fitness costs of reduced genomes.

Representative Publications

• Suárez et al. (2020) Nucleic Acids Res. PMID:32232367
• Suárez et al. (2017) Appl. Env. Microbiol. PMID:28667117
• Renda et al. (2015) J. Bacteriol. PMID:25512307

Funding: Welch Foundation

Previous Research Projects