Team:Penn State

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WELCOME TO PENN STATE iGEM 2014!

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Welcome!

You have reached the 2014 Penn State iGEM page.
Here you will find information about our projects, daily and weekly summaries of our wet laboratory activities, and information about our community outreach initiatives.

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Meet the Team!

Our Projects

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Engineering a Biodetoxification Pathway
for Lignocellulosic Feedstock
Codon Optimization

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. 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. Based on our recent experiments, the pathway also does not function in Pseudomonas fluorescens, a microbial relative of P. putida.

We want to determine the genomic differences that allow the pathway to function in one organism versus another. We intend to do this using a novel approach, combinatorial dCas9 gene knockdown. The final objective of this research is to engineer the HMF pathway in E. coli and bring us one step closer to sustainable biofuels 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.