Research

Engineering Superior Strains for Lignocellulosic Biomass Conversion to Butanol

Although butanol contains more energy than ethanol and is a drop-in fuel, use of biobutanol remains uneconomical compared to bioethanol. Solventogenic Clostridium naturally produce butanol at titers comparable to non-native producers with engineered pathways. Strain improvement represents an attractive target for viable biobutanol but the state-of-the-art in engineering Clostridium is laborious and time-consuming, allowing single-edit events after multiple cloning rounds. For optimal production, new tools must be developed for predictable control of protein expression, ensuring maximum carbon flux through desired pathways.

Our group studies the effect of perturbations in gene expression in Clostridium pasteurianum (Cpa), on butanol production using a rapid, high-throughput approach.  High throughput loss-of-function and gain-of-function screen have never been performed in Clostridium. However, the development of CRISPR-based tools and the reduced cost of synthesizing oligos and DNA sequencing allows for rapid screening across entire genomes. We aim analyze the effect of gene expression on the ability to metabolize various sugars, resistance to lignocellulosic inhibitors, and butanol production.  The methods developed will be applicable not only to Cpa, but to several members of the Clostridium genus with the ability to produce butanol from several substrates and will contribute significantly to making Clostridium robust platform organisms for the industrial biobutanol production.

Engineering Biosensors for Controlled Input-Output Relationships 

Rapid depletion and environmental impact of fossil fuels have pushed a mandate to seek clean alternative energy sources. While ethanol is a renewable biofuel, butanol is superior due to its higher energy density, lower volatility and hygroscopicity, and fungibility to the current infrastructure. However, low production yield and high substrate costs limit butanol’s use. Furthermore, strain improvement to identify butanol hyperproducers and other biochemicals are hindered due to low-throughput screening methods.

Transcription factor–based biosensors coupled with flow cytometry enable rapid screening of large genetic libraries to identify desired microbial phenotypes. Therefore, a biosensor must be able to differentiate strains exhibiting different biochemical production yields to identify hyperproducing strains. Our lab investigates design properties to fine-tune biosensor’s basal activity, dynamic range, sensitivity, and linearity of a butanol-responsive transcription factor as a high-throughput screening platform.

Understanding genetic basis for Chinese hamster ovary (CHO) genome stability

Recombinant therapeutic protein production often requires culturing in mammalian cells, most commonly Chinese hamster ovary (CHO) cells, for proper folding and posttranslational modification. These ‘biotherapeutics’ are a large and growing market, accounting for over $98 billion (with a ‘b’) in sales in 2017 alone. One major issue plaguing recombinant therapeutic protein production is production instability of the host CHO cells. This instability can be a result of gene silencing over time due to methylation of the promoter and histone deacetylation, as well as a progressive loss of recombinant gene expression in a proliferating CHO culture, compounded with inherent chromosomal instability, which all leads to a reduction in specific productivity.

 

Our lab’s research aims to address the issue of production instability in collaboration with our collaborators at Clemson, Delaware, and Delaware State. We aim to elucidate genotype-to-phenotype relationships between genes and the mechanisms of instability and genes and the effect on glycosylation. Our lab specializes in using glycan profiling technology and CRISPR-based genome editing techniques. Additionally, we are interested in studying gene silencing through the engineering of promoters with a reduced susceptibility to methylation using high-throughput techniques.

Cell Free Production of Therapeutic Phage

Phage therapy has the potential to cure and prevent bacterial infections that are otherwise resistant to the most advanced antibiotics, but current limitations in phage production limit productivity and give rise to safety and regulatory concerns. Antibiotic resistance is a major international emerging threat, as over 23,000 deaths in the U.S. alone are attributed to antibiotic-resistant infections. Bacteriophages, or ‘phages’, are viruses that target and kill specific strains of bacteria. Phage can specifically target pathogenic strains of bacteria to be eliminated, avoiding gut dysbiosis associated with brute force antibiotic treatment that eliminates the “good” gut bacteria. However, scaling up phage therapy to industrial levels is limited by safety and regulatory concerns due to the requirement of growing phage on their respective pathogenic host bacteria.

An in vitro cell-free transcription and translation production method can avoid the need for live bacterial hosts by providing the biological machinery required for phage production from bacterial lysates to a well-characterized phage genome. Current cell-free production systems largely rely on E. coli-based bacterial machinery, which may not be compatible with production of phage intended to target phylogenetically distant pathogens.

Our lab is working toward the long-term goal to develop a robust synthetic biology-based platform to produce a broad variety of phage. The overall objective of this specific project is to produce phage targeting non-model bacteria through engineering robust cell-free production platforms, and in so doing, compare the relevant product characteristics such as genome stability, production rates and titers, and endotoxin burden to standard cell free production systems.

Department of Chemical and Biomolecular Engineering | School of Science and Engineering | Tulane University

326 Lindy Boggs Building | 6823 St. Charles Avenue | New Orleans, LA 70118 | (504) 862-3261

© 2017 by the Sandoval Lab