Greenhouse gas emissions and dwindling fossil fuel reserves have pushed developed countries like the United States to explore renewable fuel sources. “Biofuels” are an attractive sustainable energy technology because they can be produced from plant biomass, which includes wood, grasses, and agricultural waste. Bioethanol and biodiesel can be blended or used as automobile fuel, among other uses. One way to produce biofuels from biomass is by using bacteria to ferment the sugars in plant matter to fuel alcohols. However, the bioenergy industry faces problems converting this inexpensive plant matter into high value fuels. Biomass is tough to break down and requires costly pretreatment processes before it can be converted to fuel. Pretreatment produces toxic byproducts, including furfural and 5-hydroxymethyl furfural (HMF), which will kill cell cultures and inhibit the conversion of biomass to usable sugars.

To solve this problem, we intend to engineer bacteria with a recently discovered metabolic pathway that consumes furfural and HMF. Koopman et. al. identified the six enzyme pathway from Cupriavidus basilensis and showed that it functions in Pseudomonas putida. In C. basilensis or P. putida, HMF can be used as the sole carbon source. Engineering bacteria with this pathway would allow them to survive and produce biofuels but also use the toxic HMF as an energy source. However, this pathway does not function in Esherichia coli is commonly used to manufacture valuable chemicals, including fuels, because it is fast growing, easily manipulated, and well-studied. Based on our recent experiments, the pathway also does not function in Pseudomonas fluorescens, a microbial relative of P. putida. The first and foremost objective of our research is to identify the genomic differences that allow the pathway to function in one organism, P. putida, but not E. coli or P. fluorescens. We will do this using a novel approach, combinatorial dCas9 gene knockdown. Cas9 is an RNA-guided DNA endonuclease that cleaves DNA at precise locations. Cas9 binds CRISPR RNAs (clustered regularly spaced short palindromic repeats) in order to bind corresponding DNA sties. The deactivated form of Cas9, dCas9, contains two mutations that block its endonuclease activity, but it still can stably bind DNA and block transcription. Gene expression can be lowered 100- to 1000-fold. To carry out combinatorial gene knockdown, we will construct CRISPR RNA libraries containing all possible 3-gene combinations for the 19 target genes we have identified. These libraries and the dCas9 system will be co-transformed into P. putida construct containing the HMF pathway. These colonies will be grown for the furoic acid assay and we can determine from absorbance measurements whether the HMF pathway is functioning. We will sequence the genomes of non-functioning strains and identify which genes were turned off via dCas9 knockdown.

The 19 target genes in P. putida were identified through manual genome comparison with the help of graduate student Iman Farasat. These are genes that are likely involved in furfural catabolism and are present in P. putida but not present in E. coli. Many of these genes encode cofactors, chaperone proteins, ATPases, and several other possible transport proteins. Our hypothesis is that a key oxidoreductase in the HMF pathway requires a molybdenum-containing cofactor, which is produced by a separate pathway and inserted with a chaperone protein. Manual comparison of genomes is time consuming, and another objective of our research is to develop a program that can optimize genome comparison. This program would employ “BLAST”, Basic Local Alignment Search Tool from the National Center for Biotechnology Information, to identify homologs between species and potential target genes that are contained in one genome but not another. Optimizing genome comparison would allow industrial and academic researchers to identify the likely missing genes in any pathway.

The final objective of this research is to engineer the HMF pathway in E. coli. This is a late-stage goal, providing the missing ingredients of the HMF pathway are identified. But if this objective is completed, it could be one step closer to sustainable fuels produced by bacteria

Numerous bioproducts are important in our lives. Examples include medicines, fuels, and industrial chemicals. All of these are derived from biological sources, and the ability to engineer their production is vital to a wide variety of industries. Codon optimization is an important area of research because it has the potential to give engineers an additional point of control over protein synthesis, and proteins(a broad class of macromolecules that includes enzymes)are vital components of countless bioproducts.

Our codon optimization research is important for the additional reason that it will help future researchers to develop more comprehensive models of translation. A better understanding of translation is an example of a foundational advance in biology that will lead to faster, more efficient research in many areas of biology. If, for example, our research shows clearly that certain degenerate codons are preferred because they can be translated more efficiently this will allow scientists to search for a mechanism that predicts these effects, and will invite engineers to redesign genes to be translated more efficiently.

Codon optimization refers to the idea that the individual codons of a gene in a specific organism can be changed in order to alter the behavior of that organism. This relies on an understanding of the central dogma of biology, which states that any organism produces proteins by first transcribing genetic material in the form of DNA to RNA, which is then “read” by ribosomes which produce proteins based on the sequence of amino acids in that RNA. The reading of the RNA is done three nucleotides at a time, and these three letter series of nucleotides are called codons. Codons specify to the ribosome which amino acid to add to a growing amino acid chain. There are 4 nucleotides, thus 43, or 64 codons are possible. Since there are only 20 amino acids, there is redundancy in the codons, that is, some amino acids are specified by multiple codons. There is no ambiguity, however, meaning that each codon specifies only one amino acid. Codons that code for the same amino acid are called degenerate codons, and even though these degenerate codons code for the same amino acid, they do not necessarily lead to the same expression levels of that amino acid.

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