Endy:Translation demand: Difference between revisions

Revision as of 17:13, 4 September 2006

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Overview

Motivation

Our ability to reliably engineer biological systems is very limited. One reason for this is because we have a crude understanding of the interaction between an engineered biological system and the cell that hosts it, here called a cellular chassis. It is my goal to elucidate and quantify the interaction between engineered biological system and cellular chassis.

Demand & chassis response

An engineered biological system places many different demands on its cellular chassis. For example, an engineered biological system will compete with chassis systems for machinery such as polymerases and ribosomes and for "raw materials" such as nucleotides and amino acids. When these demands are placed on a cellular chassis, it may lead to changes in the physiology of the chassis (e.g. growth rates, protein synthesis rates etc.). I would like to examine the relationship between applied demand and chassis response.

Goal

We expect that as translation demand increases, the chassis response will become more pronounced (decreasing growth rate, triggering of stress response pathways etc.). I am interested in examining what range of translational demands can be applied to a cellular chassis before the chassis response to that demand adversely affects the performance of the engineered biological system.

Approach

Getting at these issues requires me to achieve two intermediate goals. Firstly, I need to develop methods to place a specified range of demands on the chassis. Secondly, I need to develop relevant measures of the chassis response to that applied demand.

Placing a Range of Demands on the Chassis

In order to develop a quantitative relationship between protein synthesis rate and cellular growth rate, I have to place a range of demands on the cell. I'm employing two methods in this experiment. First, I am using both a low and high copy vector. pSB4A3, the low copy vector, produces approximately 10-12 copies for cell. The high copy vector, pSB1A3, produces 100-300 copies per cell. This is a means of roughly but dramatically increasing the demand on a cell. In order to gain more intermediate steps in demand, I am also utilizing several different ribosome binding sites chosen to represent a range of strengths. I would expect that a stronger RBS would place a greater demand on the cell. The strenth of the RBS has been determined by the ranking system established by Ron Weiss, data taken from experiment conducted with the T7 Bacteriophage, and data from Heather Keller's work.

My experiment will utilize fluoroescent proteins to measure protein synthesis rate. By measuring GFP and Mcherry Counts with respect to time and determining the slope at selected points, I can determine the net synthesis rate of protein. By then taking optical density measurements, I can determine the number of cells at these selected times and determine rate of protein synthesis per cell. I will also use the OD vs. time data to determine the growth rate of the cells in culture. These analyses will allow me to directly compare the rate of protein synthesis and growth rate for a cell.

Measuring relevant chassis responses to an applied demand

I need to determine the appropriate metrics of the chassis response to an applied demand. For example, if the application of a demand on the chassis reduces chassis growth rate, then that will affect the behavior of the engineered biological system and hence growth rate would be a relevant chassis response. Conversely, if an applied demand leads to an increase in the synthesis of some heat shock proteins but these merely serve to ensure that protein synthesis is unaffected by the increase in demand, then those heat shock proteins would not be a very relevant chassis response.

Materials & Methods

Demand Constructs

The GFP and Mcherry scaffolds I'm using were constructed by Heather Keller of the Endy Lab. They include a LacI regulated version of the lambda pL promoter (BBaR0011), two hair pins on either side of the coding sequence to increase stability, GFP (BBa0040) or Mcherry (BBaJ06504), and an RBS that I intend to vary. As I stated earlier, I'm using the Biobricks vectors pSB1A3 and pSB4A3. For those unfamiliar with Biobricks and the Registry for Standard Biological Parts, they are both very successful efforts to make biological engineering more standardized and modular by making and recording various interchangable parts to be used in the construction of DNA. It is this modularity that allows me to place Heather's scaffolds on both the high and low copy vectors and switch RBS's without having to syntheisize each construct from scratch.

Bacterial strains

My initial characterization work will be done in the E. coli strain MG1655 (No, I did not discover the strain. I just happen to share my initials with a bacterium), though DB 3.1 will be used in an intermediary step to construct the recombinant plasmids.

X90/C86

Media & growth conditions

As I build my constructs and grow cells, I'll use LB with Ampicillin for both cultures and plates. In preparation for the plate reader, however, I'll innoculate culture in Neidhardt EZ Rich Defined and induce with IPTG. The plate will have samples of each RBS on the high and low copy vectors, and as controls I will have "empty" vectors (just the vector without the scaffold), RBSes constructed to be insignificantly weak, the untransformed strain MG1655, and blanks of the EZ media with both Ampicillin and IPTG.

Future Pursuits

The major thrust of this project is to better characterize the relationship between recombinant load and growth rate and eventually tease out the point at which the strain MG1655 takes a significant growth hit. A natural progression of this project would be to examine the variables that determine this point. Perhaps MG1655 and a host of other strains could be "refactored" simliar to T7 Rebuilding T7 for optimum production capacity and reliable function.

Modeling

Code modules

References

Data

Graphs and Images

Bacterial Morphology While Expressing Foreign Protein

Date	Raw Data	Plate Layout	Data Processing script (.m)	Description
6/23/06	MG-GFP-1series-2.csv	Plate_Layout_Run_1.xls	translationdemand1trial1.m	First Run of the Primary Exeperiment
			TranslationDemandRun1growthrate.m	Growth Rate Generator for the First Run of the Primary Exeperiment
6/30/06	MG-GFP-1series-3.csv	Plate_Layout_Run_2.xls	translationdemand1trial2.m	Second Run of the Primary Exeperiment
			TranslationDemandRun2growthrate.m	Growth Rate Generator for the Second Run of the Primary Exeperiment
7/20/06	MG-GFP-1series-4.csv	Plate_Layout_Run_3.xls	translationdemand1trial3.m	Third Run of the Primary Exeperiment
			TranslationDemandRun3growthrate.m	Growth Rate Generator for the Third Run of the Primary Exeperiment
7/13/06	Morevariablesblanktestall.csv		TranslationDemandmvblanktest.m	First "Blank" Test
7/17/06	Full_Plate_Same_Media_Blank_Test.csv		TranslationDemandplategradientFullPlate.m	Second "Blank" Test
7/17/06	Empty_Plate_Test.csv		TranslationDemandplategradientEmpty.m	Empty Plate Test
7/11/06	GFPandMcherrytrial.xls	Plate_Layout_X90_and_C86_Run_1.xls	translationDemand2trial1.m	First Run "Voltmeter"
			TranslationDemand2C86andX90growthraterun1.m	Growth Rate Generator for the First Run of the "Voltmeter"
7/31/06	GFPandMcherrytrial2.xls	Plate_Layout_X90_and_C86_Run_2.xls	translationDemand2trial2.m	Second Run "Voltmeter"
			TranslationDemand2C86andX90growthraterun2.m	Growth Rate Generator for the Second Run of the "Voltmeter"
8/16/06	081506 mCherry Comparison.csv	Plate_Layout_X90_and_C86_Run_3.xls	translationDemand2trial3.m	Third Run "Voltmeter"
			TranslationDemand2C86andX90growthraterun3.m	Growth Rate Generator for the Third Run of the "Voltmeter"
8/16/06	081706 Blank Test.csv		translationDemandplategradientFullPlate2.m	"Turning" Blank Test
	081706 Blank Test Turned.csv		translationDemandplategradientFullPlate2.m	"Turning" Blank Test
9/2/06	090206 3K3 Plate Run.csv		translationDemand1trial4.m	Fourth Run of Primary Experiment (First use of Mid-Copy Vector pSB3K3)

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 The major thrust of this project is to better characterize the relationship between recombinant load and growth rate and eventually tease out the point at which the strain MG1655 takes a significant growth hit. A natural progression of this project would be to examine the variables that determine this point. Perhaps MG1655 and a host of other strains could be "refactored" simliar to T7 [[Rebuilding T7]] for optimum production capacity and reliable function.
-==Current Status==
-===8/11/06===
-After 3 runs of the primary experiment (GFP in MG1655) and 2 runs of the "Voltmeter", I have successfully been able to place a range of demands on the cell as evidenced by the clear and consistent levels of fluorescent expression from each RBS, but I have not been able to establish a clear pattern of growth rates. I think this could be due to the existence subpopulations with lesser or no plasmids. Regarding the Voltmeter, it seems that the strain I'm using, C86 (derived from X90), cannot repress expression of mCherry. Our friends at the [http://www.openwetware.org/wiki/Sauer_Lab Sauer Lab] have suggested that this may be due to the loss of an F Plasmid expressing the repressor. I'll be looking into these two problems over the next few weeks.
-Cells expressing [[Ampicillin]] resistance do so by excreting Beta-Lactamase into the extracellular environment to destroy the antibiotic. This allows one cell to "protect" another by creating a pocket free of antibiotic. When one combines the growth advantage given to plasmid free cells with this abilitiy to survive without providing resistance for itself, the possibilty of losing the plasimd generationally becomes stronger. In order to test for plasmid instability, I've grown cultures and plated them in media both with and without antibiotics. The idea is that if subpopulations of cells within the culture have lost the plasmid, they will grow only on the plate without antibiotics. In my first run of this plating experiment, I plated high copy mCherry constructs in C86, a strain of bacteria with a GFP chromosomal insert. The result was as expected; the constructs with stronger RBS's grew more colonies on the LB plate (no antibiotic) than the Ampicillin plate meaning that conditions were present to select for plasmid free populations. A disparity in the number of colonies became indistinguishable in the mid and low strength RBS's meaning perhaps that the demand placed on the cell was too weak to cause this negative selection. When this experiment was repeated with high copy GFP constructs in MG1655, there seemed to be little or no difference in the number of colonies suggesting a more stable plasmid situation.
-Though plasmid loss is perhaps the most striking manifestation of instability, there are other, more subtle forms. The mechanism of plasmid segregration in bacteria isn't clearly undestood. [http://web.mit.edu/prathergroup/ Kristala Jones Prather] has suggested that especially when using high copy vectors (100 - 300 copies/cell), it is possible that there is an asymmeteric segregation of plasmids during cell division. If daughter cells are receiving disproportionate amounts of the plasmid, the variation in copy number will produce too many unique subpopulations to pin down a pattern in growth rates for a particular construct. I'll be reading some literature on the mechanism of plasmid segregation to see to what degree this phenomenon may be affecting my data. If it proves to be significant, I'll look into reducing the variability of copy number in a particular cell while somehow maintaining the range of demands allowed by using the high copy vector.
-In the mean time, I'm going to make two major changes to my experiment to see if I can get more consistent growth rate data. I'm going to move my GFP and mCherry scaffolds to a mid copy vector with [[Kanamycin]] resistance, [http://parts.mit.edu/registry/index.php/Part:pSB3K3 pSB3K3]. It's my hope that by moving to a mid-copy vector, the variability in plasmid segregation will be much reduced, but protein yield will be enough to produce a range of growth hits on the cell. As far as I understand, Kanamycin works intracellularly (not excreted into the media), thereby eliminating the potential for plasmid free colonies to survive by clustering around KanR+ subpopulations. I hope to run these constructs in the plate reader within the next week or so.
-As for the Voltmeter, the Sauer Lab has given us the parent strain of C86 (X90) lacking the F Plasmid. If the Voltmeter's inability to repress my constructs is due to the loss of the plasmid, I would expect that this X90 F- strain would demonstrate the same phenotype. After transforming the X90 F- with my mCherry constructs, I grew cultures and induced half with IPTG and not the other. I found that both inuduced and uninduced cultures expressed mCherry at the same level (qualitative observation), but that this level isn't nearly as high as the mCherry expression observed in the Voltmeter. I'd like to run the plate reader in the near future with X90, X90 F-, C86, and perhaps MG1655 expressing high copy mCherry constructs to see how the expression levels quantitatively compare.
-==To Dos==
-#Test for plasmid instability
-#*Grow cultures used for plate reader expts. 1-3 and plate them on Amp and on LB plates.
-#*Use the same growth protocol as used for the full experiments.
-#*Do this for the 5 RBSs.
-#Select non-pink colonies from the voltmeter plates and see if some of those have the F-plasmid.
-#Compare mCherry expression levels in X90, X90 F-, and C86 in a plate reader run.
-#*Modify the plate reader protocol to have lesser time between repeats.
-#*See if the Voltmeter was unable to repress the mCherry constructs on low copy.
-#Construct mCherry/GFP-pSB3K3 constructs.
 ==Modeling==