Endy:Translation demand: Difference between revisions

Revision as of 07:36, 11 August 2006

Overview

Furthering the Discipline

Biological engineering has great potential to solve many of the world's toughest problems. The ability to replicate, discern, measure, judiciously express chemicals, and evolve are all characteristics of engineered organisms. That said, our ability to reliably design and build such organisms is, to say the least, lacking. My project will explore one of many frontiers by attempting to establish a more concrete and quantitative relationship between the demand placed on an engineered organism by foreign DNA synthesis and cellular response and use this information to increase the efficiency of bioreactors.

Translational Demand

The term "demand" refers to the many loads placed on a cell by an engineered biological system. Examples include the use of cellular machinery such as polymerases and ribosomes and/or "raw materials" such as nucleotides and amino acids. To measure these various demands are all singular challenges. For the purposes of this project, I've elected to focus on "translational demand," or the rate of foreign protein synthesis, because fluorescent proteins facilitate its measurement. The demand placed on cells by transcription and other processes that do not manifest themselves in polypeptide products are significant, but their measurements constitute separate experiments that I may decide to explore in the future.

Cellular Response

As engineers ask cells to perform recombinant functions, one has to expect that the cells will respond to the foreign stressors. Just as demand has many facets to consider, so too does the cellular response. One can look at cell physiology, morphology, transcriptomes, proteomes, or any number of means of examining the cellular state while operating under the stress of recombinant DNA. Again, to narrow this term to a measurable quantity and to take advantage of the literature involving growth rates and stress responses, I've elected to examine the cellular growth rate. As another, more qualitative means of measuring cellular response, I also intend to look at cell morphology through microscopy.

Main Goal

The main goal in all of this is to tease out the point where foreign protein causes a significant growth hit in its bacterial host. Eliminating the growth hit is important both for industrial bioreactors and for engineering projects that are characterized by a need for reliable function. Bioreactors, or cultures of cells used to produce some protein product, are composed of subpopulations with varied characteristics. Some cells produce more of the protein product while others produce less for various reasons. After a certain protein synthesis threshold, the cells will take a growth hit. The problem is that in an assymetric culture (various production levels), some cells will take growth hits of various magnitudes while others will not. In a culture where metabolic resources are finite, those cells that grow and multiply more quickly will deplete the resources and soon represent the majority of the subpopulations. In effect, there is a selection for those cells that produce less of the desired product. The result is that a culture exhibiting a range of growth rates will naturally become less efficient in time. By eliminating the growth hit, we can eliminate one of many factors that tend towards inefficiency in bioreactors. Biological engineers, though concerned with efficiency, are equally concerned with reliable function. A growth hit is a macroscopic signal that the host cell has changed its state and that the engineered system may begin to act unreliably. Many changes in host cell metabolism and physiology will manifest themselves macroscopically as changes in growth rate, so by investigating mechanisms of avoiding growth hits, I am also developing means of maintaining the cellualar environment for optimum operation of an engineered system.

Contact

Approach

Placing a Range of Demands on the Cell

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.

Measuring Protein Synthesis and Growth Rates

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.

Specifics

The fluoroescent proteins will be measured in the bacterial 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.

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.

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 untranformed 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 for optimum production capacity Rebuilding T7.

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 selection for subpopulations with lesser or no plasmids. With regard to the Voltmeter, it seems that the strain I'm using, C86 (derived from X90), cannot repress expression from the plasmid. Our friends at the Sauer Lab have suggested that this may be due to the loss of an F Plasmid expressing the repressor.

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. 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 instability, there are other, more subtle forms. The mechanism of plasmid segregration in bacteria isn't clearly undestood. Kristala Jones Prather has suggested that especially when using high copy vectors (100 - 300 copies/cell), it is very possible that there is an asymmeteric segregation of plasmids during cell division. Until daughter cells are receiving roughly equal 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 and if I can somehow maintain the range of demands allowed by using the high copy vector but reduce the variability of copy number in a particular cell.

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, 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.

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.

Modeling

Code modules

References

Data

Graphs and Images

Bacterial Morphology While Expressing Foreign Protein

Date	Raw Data	Data Processing script (.m)	Description
6/23/06	MG-GFP-1series-2.csv	translationdemand1trial1.m	First Run of the Primary Exeperiment
		TranslationDemandRun1growthrate.m	Growth Rate Generator for the First Run of the Primary Exeperiment
	Plate_Layout_Run_1.xls		First Run Plate Layout
6/30/06	MG-GFP-1series-3.csv	translationdemand1trial2.m	Second Run of the Primary Exeperiment
		TranslationDemandRun2growthrate.m	Growth Rate Generator for the Second Run of the Primary Exeperiment
	Plate_Layout_Run_2.xls		Second Run Plate Layout
7/20/06	MG-GFP-1series-4.csv	translationdemand1trial3.m	Third Run of the Primary Exeperiment
		TranslationDemandRun3growthrate.m	Growth Rate Generator for the Third Run of the Primary Exeperiment
	Plate_Layout_Run_3.xls		Third Run Plate Layout
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	translationDemand2trial1.m	First Run "Voltmeter"
		TranslationDemand2C86andX90growthraterun1.m	Growth Rate Generator for the First Run of the "Voltmeter"
	Plate_Layout_X90_and_C86_Run_1.xls		First Run "Voltmeter" Plate Layout
7/31/06	GFPandMcherrytrial2.xls	translationDemand2trial2.m	Second Run "Voltmeter"
		TranslationDemand2C86andX90growthraterun2.m	Growth Rate Generator for the Second Run of the "Voltmeter"
	Plate_Layout_X90_and_C86_Run_1.xls		Second Run "Voltmeter" Plate Layout

@@ Line 41: / Line 41: @@
 ===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 selection for subpopulations with lesser or no plasmids. With regard to the Voltmeter, it seems that the strain I'm using, C86 (derived from X90), cannot repress expression from the plasmid. Our friends at the Sauer Lab have suggested that this may be due to the loss of an F Plasmid expressing the repressor.
 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. 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 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 very possible that there is an asymmeteric segregation of plasmids during cell division. Until daughter cells are receiving roughly equal 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 and if I can somehow maintain the range of demands allowed by using the high copy vector but reduce the variability of copy number in a particular cell.
+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.
 ==To Dos==