BIO PRODUCTION
Laura Maria Gonzalez
April 27, 2021
First part of our two week assignment was to design and run an experiment to understand how different factors influence bioproduction. To do so we
worked with two genes, the pAC-LYC (lycopene) and pAC-BETA (beta carotene), media (LB, 2YT), fructose, temperature, and shaking. I initially set up my
experiment to test for media and vibration with both plasmids. I hypothesized that vibration would increase production of lycopene and beta
carotene over not shaking across different mediums. After a slight road bump with the experiment I also tested adding fructose with no shake to
understand the role it played vs no fructose in the other two sets of experiments. See below for the full experiment chart.
PART A: TELE-EXPERIMENT
Plasmids diagram provided in class
The results showed the fructose increased the lycopene and beta-carotene production. The control appeared deeper in color than the various
shaking tests. The first run also demonstrated increased production in the 2YT over the LB medium.
The second run is harder to visually compare. Perhaps 24hrs isn't enough to compare the results. Based on the first run I expected to see a gradient
of lycopene and beta-carotene production with the least production occurring in LB medium and the most occurring in 2YT + Fructose. However, a
visual inspection doesn't show noticeable difference among the different conditions.
For the last part of this week we needed to think about what we might do with the capabilities of Ginkgo Bioworks Foundry. I am interested in using
Ginkgo capabilities to realize architecture and infrastructure type projects.
Ginkgo's fast prototyping model of designing, building, testing, and failing a lot is not unlike design and fabrication methods that are so crucial to
realizing architecture projects today. Predictability and reliability are key to brining engineering in biology to this application and by testing
thousands of designs and parameters with ginkgo's software you can overcome challenges by exploring a larger design space.
One specific project that came to mind was the E. glowli project and several others like it that propose to replace electrical lighting with plant
bioluminescence. However the idea falls short because the glow is weak, the glow is not sustained and incorporating bacterial bioluminescence
genes can also be toxic to plants.
Only recently has more progress been made through a characterization of a metabolic pathway responsible for luminescence in fungi (Mitiouchkina,
Tatiana, et al.). Fungi and plants both contain caffeic acid, a compound used to synthesize luciferin, and could therefore use it to glow. They found
that a tobacco plant engineered with four fungus genes associated with bioluminescence glowed in a manner visible to the naked eye. The glow
occurs from seedling to maturity and without toxicity to the plant. It is still not bright enough to read with, but is clearly visible.
In this example I am wondering if ginkgo's codebase of cells, enzymes, and genetic programs can be used to find other efficient pathways to brighter
and reliable glow or optimize ones such as the recently discovered fungal metabolic pathway - not only drawing from nature, as was the case with
the fungi, but finding new solutions through digital technologies such as machine learning and using automation to accelerate the testing process.
For the second part of this week we needed to identify a cool gene related to our final project. While trying to identify a cool gene I became lost in
the world of genetic circuits. for the final project I would like to build a light sensor using a two component system (TCS) that controls promoters and
repressors in order to activate a chromoprotein. As a simple introduction to genetic circuits the TAs suggested looking into the lac operon and using
the vector pet-21(+) with Ampicillin resistance gene.
This vector contains a lac repressor and lac operator to inhibit transcription in E.coli. However, expression can be induced by lactose or IPTG.
Therefore I could use this vector to test/ learn how a circuit would work. In the presence of lactose or IPTG my gene of choice will be expressed. The
gene's I chose to initially test twist ordering and plasmid design were amilCP (a chromoprotein) and mCherry (a fluorescent protein). In my final
project I am interested in using amilCP but recognize that while light sensing occurs rapidly, gene expression of chromoproteins is slow 20h+. While
fluorescent proteins can express in minutes to hours. So I thought it would be a fun mini side test.
The pET-21(+) vector is designed for expression from bacterial translation signals carried within a cloned insert and lacks a ribosome binding site and
ATG start codon present on the pET translation vectors which raised a few errors during the twist ordering, but the TAs helped to clarify this.
An operon is a sequence of DNA in bacteria containing a cluster of genes under the control of a single promoter. Genes within an operon will always
be expressed together or not at all. Some operons are inducible - they can be turned on by the presence of a particular small molecule. Others are
repressible - they are on by default, but can be turned off by a small molecule. They allow cells to express sets of genes whose products are
needed at the same time.
Operons contain regulatory DNA sequences. These can be promoters or site where RNA polymerase binds or the repressors, that bind to operators
(DNA where repressor binds) to reduce transcription by blocking promoters.
A well-studied operon is the lac operon. It contains genes that encode proteins involved in uptake and metabolism of lactose (a sugar). E. coli
prefers to use glucose, but if lactose is the only available sugar, the e. coli will express the lac operon genes to enable lactose uptake. To be
efficient, E. coli will only express the lac operon when lactose is available and glucose is not.
In the absence of lactose a lac repressor protein is bound to the operator preventing the transcription of subsequent genes (no transcription). In the
presence of lactose you will also have allolactose that acts as an inducer of transcription by binding to the lac repressor preventing it from binding
to the operator.
To include mCherry into a vector I first found an mCherry gene in addgene that contained an RBS. The one I found Is located
HERE.I selected the RBS and gene and copied the DNA sequence which was then imported into the Twist Bioscience clonal genes type. I chose the vector pET-21(+) with Ampicillin resistance gene.
I then selected codon optimization and selected E. coli as a host to optimize for.
No restriction enzyme sites were added.
And the RBS area was selected as a region to preserve.
RESULTS
PART C: GINKGO BIOWORKS CAPABILITIES
PART B: COOL GENE
MY FIRST CIRCUIT: LAC OPERON
MCHERRY
Down Shaker!
Control - Shaking + Fructose
Shaking - 24HR
No Shaking - 24HR
No Shaking - 24HR
Lac Operon, Khan Academy
plasmid with mCherry in Benchling
plasmid with amilCP in Benchling
pAC-LYC
LB
pAC-LYC
LB
pAC-LYC
LB
+Fructose
pAC-BETA
LB
+Fructose
pAC-BETA
LB
pAC-LYC
2YT
pAC-LYC
2YT
pAC-LYC
2YT
+Fructose
pAC-BETA
2YT
+Fructose
pAC-BETA
2YT
pAC-BETA
LB
pAC-BETA
2YT
amilCP expression, Iowa State University 2020
mCherry expression, Penn Igem 2012
RBS area in Benchling
RBS area imported and preserved in Twist
RBS area imported and preserved in Twist
E.glowli igem project, 2010
The process for amilCP was very similar to mCherry. Here I used the muav plasmid from a previous week's assignment and imported into benchling.
I selected the RBS and gene and copied the DNA sequence which was then imported into the Twist Bioscience clonal genes type. I chose the vector pET-21(+) with Ampicillin resistance gene.
I then selected codon optimization and selected E. coli as a host to optimize for.
No restriction enzyme sites were added.
And the RBS area was selected as a region to preserve. The RBS for mCherry and amilCP ended up being the same.
AMILCP
RBS area in Benchling