Design of Biological Systems for Tools and Applications · essential to achieving desired functions, we begin by describing development of a system for ... Although several methods

Design of Biological Systems for Tools and Applications

By

Joshua Tyler Kittleson

A dissertation submitted in partial satisfaction of the requirements for the degree of

Joint Doctor of Philosophy

with University of California, San Francisco

in

Bioengineering

in the

Graduate Division

of the

University of California, Berkeley

Committee in Charge:

Professor J.Christopher Anderson, Chair

Professor Adam Arkin

Professor Daniel Portnoy

Professor Francis Szoka

Fall 2012

- 1 -

Abstract

Design of Biological Systems for Tools and Applications

by

Joshua Tyler Kittleson

Joint Doctor of Philosophy in Bioengineering with UCSF

University of California, Berkeley

Professor J. Christopher Anderson, Chair

Early efforts to genetically engineer biological systems were restricted to relatively crude

cut and paste operations, with the sequence of even model organisms’ genomes inaccessible for

detailed study. Exponential improvement in our ability to both read and write DNA sequences

has dramatically altered the landscape of genetic engineering, rendering de novo synthesis of

entire genomes possible [1]. However, our ability to design functional systems has not kept pace

with our ability to fabricate them. The field of synthetic biology seeks to address this

shortcoming and provide a theoretical framework for rationally manipulating biological systems.

Of particular interest is exploration of how core engineering principles such as modularity,

abstraction, and standardization can be applied to the development of genetic systems. Despite

considerable recent progress toward answering that question, imperfect and incomplete

knowledge about biological systems necessitates exploration of numerous variants to craft even

relatively simple systems. In this work, we first explore development of biological systems that

make engineering biology easier, then pursue development of a complex system for a demanding

application to probe the limits of our design capability, and conclude with a discussion of key

challenges in designing biological systems and how to address them.

Motivated by the observation that tuning the expression of system components is often

essential to achieving desired functions, we begin by describing development of a system for

rapidly prototyping genetic constructs to determine an optimal expression level. Although

several methods exist for manipulating the transcription or translation of a desired gene, they

require fabrication of numerous distinct variants. To work around this limitation, we instead

explore manipulation of plasmid copy number and develop a set of strains that are capable of

maintaining the same plasmid at a desired level in the range of 1-250 copies per cell. We

demonstrate that this system can be applied to rapidly find the optimal expression level for a

model biosynthetic pathway, regardless of the transcriptional activity of the pathway.

Recognizing that fabricating and assaying distinct variants is in some cases essential, we then

examined the problem of enabling high-throughput manipulation of DNA. Existing methods are

limited by difficult to automate operations, such as centrifugation, application of a vacuum, or

gel electrophoresis. Development of alternative methods that only require liquid handling

operations would enable utilization of very high throughput automation platforms. We therefore

engineer a P1 phage-based system for transferring plasmids between cells using only liquid

- 2 -

handling operations. After establishing exogenous control of phage lysis through addition of a

small molecule inducer, we incorporate phage cis elements that enable desired DNA to be

transferred 1600-fold more efficiently than other cellular DNA. The system is capable of

transferring large libraries of DNA up to 25 kilobases in length at small volumes. This provides

an important first step toward development of a highly automatable suite of tools for biological

engineering.

Biological engineers ultimately want to manipulate biological systems to solve human

specified problems. Although a number of potential applications of synthetic biology have been

described [2-4], most projects rely on relatively simple designs to achieve a desired outcome. To

identify bottlenecks in the development of more complex systems, we investigate engineering a

laboratory strain of E. coli to localize to, invade, and kill cancer cells. After attempting to

transfer capsular polysaccharide synthesis clusters to a laboratory strain to improve bacterial

survival in an animal model, we generate a strain capable of delivering a cytotoxic ribonuclease

to and subsequently killing cultured cancer cells. Although the strain did not inhibit tumor

growth in an animal model, the lessons learned during the execution of this and other projects

guided our thinking about current bottlenecks and the path forward. We conclude with a

discussion of the potential for modular design to enable routine development of complex

biological systems, and identify six failure modes that hinder current efforts. By architecting

software to codify existing biological knowledge and investigating the basis of important

phenomena such as load and stress, we can harness the power of improved DNA sequencing and

synthesis capabilities to build novel, useful biological systems.

i

Table of Contents

Table of Contents.................................................................................................................... i

Table of Figures...................................................................................................................... iii

Table of Tables........................................................................................................................ iv

Acknowledgements................................................................................................................. v

Chapter 1 – Introduction....................................................................................................... 1

1.1 – Synthetic biology and biological engineering................................................................ 1

1.2 – Expression level optimization........................................................................................ 1

1.3 – High throughput screening to overcome incomplete information................................. 2

1.4 – Application development............................................................................................... 2

1.5 – Remaining challenges.................................................................................................... 3

Chapter 2 – Rapid Optimization of Gene Dosage in E. coli using DIAL Strains............ 4

2.1 – Abstract......................................................................................................................... 4

2.2 – Background................................................................................................................... 4

2.3 – Results and Discussion.................................................................................................. 5

2.3.1 – ColE2 as a Trans-Activated Origin....................................................................... 5

2.3.2 – Orthogonality of R6K and ColE2......................................................................... 6

2.3.3 – Construction of DIAL Strains............................................................................... 6

2.3.4 – Characterization of DIAL Strains: Copy Number, Cell-to-Cell Variability, and

Stability................................................................................................................ 7

2.3.5 – Optimization of Violacein Expression.................................................................. 9

2.4 – Conclusions................................................................................................................... 10

2.5 – Methods......................................................................................................................... 11

2.5.1 – Media..................................................................................................................... 11

2.5.2 – Plasmids................................................................................................................. 12

2.5.3 – Genomic RBS Library Construction..................................................................... 12

2.5.4 – Plasmid Copy Number Estimation by qPCR......................................................... 13

2.5.5 – Flow Cytometry..................................................................................................... 13

2.5.6 – Stability Analysis................................................................................................... 13

2.6 – Acknowledgements........................................................................................................ 13

Chapter 3 – Scalable Plasmid Transfer using Engineered P1-based Phagemids............ 14

3.1 – Abstract......................................................................................................................... 14

3.2 – Introduction................................................................................................................... 14

3.3 – Results and Discussion.................................................................................................. 16

3.3.1 – Transcription of coi Triggers the P1 Lytic Cycle.................................................. 16

3.3.2 – cin is a cis Element that Improves Phagemid Transduction.................................. 17

3.3.3 – Phagemids Exhibit Robust and Faithful Transduction.......................................... 18

3.4 – Summary and Conclusions............................................................................................ 21

3.5 – Methods......................................................................................................................... 22

3.5.1 – Plasmids, Strains, Phage, and Growth Media....................................................... 22

3.5.2 – Generation of Phage Lysates................................................................................. 23

3.5.3 – Transduction of Phage Lysates.............................................................................. 23

ii

3.5.4 – Timecourse of Phage Lysis.................................................................................... 23

3.5.5 – Measurement of Relative Promoter Units (RPU).................................................. 23

3.5.6 – Visualization of Serial Lysis.................................................................................. 23

3.5.7 – Phagemid Library Construction and Enrichment.................................................. 24

3.5.8 – Competition Between Phagemids.......................................................................... 24

3.5.9 – Construction of Library of GFP-Expressing Phagemids....................................... 24


Chapter 4 – Toward Engineering E. coli as a Cancer Therapeutic................................... 26 4.1 – Abstract......................................................................................................................... 26

4.2 – Introduction................................................................................................................... 26

4.3 – Results........................................................................................................................... 27

4.3.1 – Encapsulation of E. coli......................................................................................... 27

4.3.2 – Localization to Tumors and Plasmid Maintenance................................................ 31

4.3.3 – Engineered E. coli Kill Tumor Cells In Vitro, but not In Vivo.............................. 32

4.4 – Discussion...................................................................................................................... 33

4.5 – Materials and Methods................................................................................................... 35

4.5.1 – Plasmids, Strains, Phage, and Growth Media....................................................... 35

4.5.2 – Serum Resistance.................................................................................................. 35

4.5.3 – Phage Sensitivity and Resistance.......................................................................... 35

4.5.4 – Cell Culture Cytotoxicity...................................................................................... 36

4.5.5 – Preparation of Bacteria for Animal Experiments.................................................. 36

4.5.6 – Bacteria Clearance In Vivo.................................................................................... 36

4.5.7 – Tumor Models and Bacterial Infections................................................................ 36

Chapter 5 – Successes and Failures in Modular Genetic Engineering............................. 37 5.1 – Abstract......................................................................................................................... 37

5.2 – Introduction................................................................................................................... 37

5.3 – Modular Design............................................................................................................. 38

5.4 – Failure Modes................................................................................................................. 40

5.4.1 – Syntactic Errors: Ensuring desired features are present......................................... 40

5.4.2 – Semantic Errors: Getting the molecular interactions right..................................... 40

5.4.3 – Mismatched Parameters: Tuning behaviors........................................................... 41

5.4.4 – Contextual Sensitivity: Influence of factors beyond primary sequence................ 41

5.4.5 – Noise and Evolution: System behavior in a population......................................... 42

5.4.6 – Chassis Failure: The role of load and stress.......................................................... 43

5.5 – The Path Forward........................................................................................................... 43


Chapter 6 – Plasmids and Strains ........................................................................................ 45

Chapter 7 – References.......................................................................................................... 50

iii

Table of Figures

Figure 2.1. qPCR estimation of plasmid copy number.............................................................. 6

Figure 2.2. Flow cytometric analysis of cell-to-cell variation.................................................... 8

Figure 2.3. Stability of plasmids in DIAL strains..................................................................... 9

Figure 2.4. Optimization of violacein production..................................................................... 10

Figure 2.5. Schematic of templates constructed for genomic integration.................................... 12

Figure 3.1. Genetic interaction of phage lysogen and phagemid......................................... 16

Figure 3.2. Expression of coi induces lysis.......................................................................... 17

Figure 3.3. Impact of cis elements on packaging................................................................. 18

Figure 3.4. Impact of volume on phagemid efficiency........................................................ 19

Figure 3.5. Impact of phagemid size on efficiency.............................................................. 20

Figure 3.6. Library transfer using the phagemid.................................................................. 21

Figure 4.1. Strategies for manipulation of capsular polysaccharides................................... 28

Figure 4.2. Survival of encapsulated bacteria in a mouse.................................................... 29

Figure 4.3. In vitro killing of tumor cells............................................................................. 32

Figure 4.4. Bacterial treatment of tumors in mice................................................................ 33

Figure 5.1. Failure modes of genetically engineered systems.............................................. 39

iv

Table of Tables

Table 2.1. RBSs of repA and pir expression variants........................................................... 7

Table 2.2. Oligos used for library construction.................................................................... 12

Table 4.1. In vitro assays of capsular polysaccharides......................................................... 30

Table 4.2. Bacterial Tumor Colonization and Plasmid Maintenance................................... 31

Table 6.1. Basic Parts............................................................................................................ 45

Table 6.2. Composite Parts.................................................................................................... 46

Table 6.3. Plasmid Backbones............................................................................................... 48

Table 6.4. Strains................................................................................................................... 48

v

Acknowledgements

I am deeply grateful to the dedicated individuals who have either directly contributed to

my development as a scientist and the work presented here or supported me as I navigated

through that process. First and foremost, I want to thank my advisor, Chris Anderson, for his

thoughtful guidance and tutelage over the past six and half years. From teaching me how to

perform the technical operations required of a molecular biologist to providing insight into

developing informed plans for tackling complex problems and broadening my perspective on

what it means to be a biological engineer, his constant and patient direction has been an

invaluable and formative part of my experience. I have also been fortunate to have had

significant interactions with both John Dueber and Adam Arkin, whose insight and ideas have

energized my thinking about synthetic biology. The other members of the Anderson lab have

been a pleasure to work with and be around, with Tim Hsiau and Jin Huh in particular facilitating

this work. The ecosystem of other synthetic biology labs, including the Arkin and Dueber labs,

has provided an excellent academic environment, such as numerous thoughtful conversations

with Weston Whitaker. Sherine Cheung effectively implemented several of the experiments

reported in Chapter 2, while Will DeLoache was instrumental in kicking off the phagemid

research reported in Chapter 3 and Lindsey Saldin put a lot of time and effort into the capsular

polysaccharide material presented in Chapter 4. Gabriel Wu both focused the discussion

presented in Chapter 5 and helped provide some balance to my experience as a graduate student.

I would also like to thank the Joint Graduate Group in Bioengineering, SynBERC, the National

Science Foundation, and the Siebel Scholars for funding this work.

Finally, I want to thank my family for their support these past several years. Though

somewhat convinced that my work might cause a zombie apocalypse, my parents and siblings

have helped keep me focused and productive. My wife, Jennifer Hogan, has persevered through

a long-distance relationship, an erratic schedule, and more talk about moving clear liquids

around than any sane person should be subjected to, and has been nothing but supportive and

patient throughout. I’ve been very fortunate to have been surrounded by understanding and

encouraging people.

1

Chapter 1 – Introduction

1.1 – Synthetic Biology and Biological Engineering

Biological systems are highly complex, with interesting and relevant phenomena

occurring across several time and space scales. Further, such systems can behave stochastically,

are frequently out of equilibrium, and are imperfectly self-replicating [5]. Biologists have been

trying to observe, describe, and understand these complex natural biological systems for decades,

despite only recently having acquired the technical capability to observe specific sub-cellular

events in real-time in a living system [6]. Out of this ongoing work to develop a molecular level

understanding of how cells and organisms function, some investigators have turned toward

altering biological systems to achieve a human specified purpose. Fortunately for such

engineers, the intricate molecular dance of living cells is largely orchestrated by DNA, a

relatively easy to observe central information repository. Early manipulations of DNA were

limited to simple cut and paste operations [7], or mutation of a specific DNA sequence [8]. Even

this limited capacity for manipulation, however, was sufficient to enable development of the

biotechnology industry [9]. Since the inception of recombinant DNA, our ability to both read and

write the genetic code has expanded exponentially [10]. The field of synthetic biology has

emerged in recognition of the fact that we lack a formal theoretical framework for selecting what

DNA sequence to write to achieve a specified goal [11]. In particular, we have not yet

established how key engineering concepts such as abstraction, standardization, and modularity

can be applied to the design of complex biological systems. Early work in the field focused on

demonstrating our capability to build interesting genetic systems, showing that we have

sufficient control over basic cellular processes to rationally generate a DNA sequence that elicits

a desired set of non-native behaviors [2]. Subsequent work has turned toward finding

applications for these new kinds of genetic circuits [2-4]. Importantly, it remains clear that in the

absence of a complete understanding of biological behaviors, examination of numerous variants

is required to achieve even modest engineering goals.

In this work, we focus on applying important ideas from synthetic biology to the

development of tools that make engineering biology easier, pushing on the limits of our design

capability, and reflecting on remaining challenges in engineering biological systems. First, we

develop a system for rapidly prototyping genetic constructs to determine an optimal expression

level. By enabling different cell lines to maintain the same plasmid at different copy numbers,

we reduce the necessary operations down to a single simple transformation. Second, we

engineer a phage-based system for transferring plasmids between cells. Because the method

requires only liquid handling operations, it enables automation using standard, extremely high

throughput robotics platforms. After developing these tools, we try to engineer a laboratory

strain of bacteria to localize to, invade, and destroy tumor cells. The complexity of the pathways

and interactions required challenges our ability as biological engineers. Finally, we draw from

these and other experiences to outline limitations in our ability to engineer biological systems.

By highlighting specific failure modes and suggesting potential solutions, we offer a path

forward.

1.2 – Expression Level Optimization

2

Biological engineers frequently need to optimize the expression level of one or more

genes, whether for improving the flux through a metabolic pathway [12], or for tuning the

behavior of a genetic circuit [13]. Typically, this has been approached by cloning a number of

distinct plasmid variants. However, development of a more efficient strategy would accelerate

the engineering process. Chapter 2 describes the development of a novel biological tool for

rapidly scanning through almost two orders of magnitude of expression levels using only a single

plasmid and a simple protocol. A crucial part of engineering the system was replacing the native

regulation of two proteins responsible for controlling plasmid replication with synthetic

constitutive promoters. Because biological engineers largely draw from existing biological

functions and native systems usually have transcriptional and/or translational regulation, the need

to re-engineer regulatory signals is a common practice in synthetic biology. This process,

referred to as refactoring, is also employed in chapter 3 to link a biochemical event to a human

specified signal, and to a lesser extent in chapter 4 to control an important cellular phenotype.

Chapter 5 expands on the idea to discuss how understanding the context and regulation of a

genetic system are critical to system function.

1.3 – High throughput screening to overcome incomplete information

Because biological engineers typically have insufficient information to specify the

precise components, configuration, and regulation necessary to implement a desired system as

basepair level precision, it is usually necessary to construct and test numerous variants. In

chapter 2, for example, hundreds of ribosome binding site variants are tested to find the desired

behavior, and in chapter 4, several proteins are tested for their ability to cause cytotoxicity in

cancer cells. The field would therefore benefit from improvements to the throughput of the

fundamental operations required to construct and test new designs. Chapter 3 argues that

conversion of routine procedures to liquid handling operations is essential to achieving a

transformative increase in the throughput of construct fabrication and analysis. It proceeds to

describe the development of a phage-based system for transferring plasmids between cells using

only liquid handling operations. In order to improve the efficiency of the system, millions of

variants are competed against each other, utilizing the fact that more efficient systems amplify

faster than less efficient ones. For other design efforts, where it may be more difficult to link a

growth advantage to a desired behavior, development of these kinds of tools is essential. This

limitation is further explored in chapter 5, which notes that until we can develop a more

comprehensive way of predicting cellular behaviors, we have to resort to this type of trial and

error.

1.4 – Application development

Ultimately, development of biological tools is intended to facilitate engineering of

biological systems to address a problem. Chapter 4 presents our efforts to engineer tumor killing

bacteria, a problem selected to explore the limitations of our current engineering capabilities. We

first manipulate large biosynthetic operons to confer a laboratory strain of E. coli with a

controllable way of expressing protective polysaccharide capsules. We then engineer strains to

deliver cytotoxic proteins to cancer cells, and demonstrate that they are capable of killing

cultivated tumor cells. Challenges encountered during the engineering process prompted

3

discussion of several failure modes in chapter 5, including syntactic error, semantic errors,

context dependence, and concerns about load and stress.

1.5 – Remaining Challenges

In the course of implementing the projects described in chapters 2-4, we observed that

failures during development of a biological system generally fall into one of a handful of

categories, and that we could take steps to help minimize those failures. Chapter 5 describes the

potential of applying a modular engineering strategy, and then considers six general types of

system failure. The failure modes encompass a broad spectrum of problems, ranging from

simple human design error to population heterogeneity. We then describe how computer aided

design can help mitigate some errors, while others can only currently be addressed by trial and

error. Finally, chapter 5 argues that continued development of DNA synthesis and manipulation

techniques, coupled to further research into the causes of cellular load and stress, will enable

better a priori design.

4

Chapter 2 – Rapid Optimization of Gene Dosage in E. coli using

DIAL Strains

Authors:

Joshua T Kittleson

Department of Bioengineering, University of California, Berkeley CA, United States

Sherine Cheung

Department of Bioengineering, University of California, San Diego, USA

J. Christopher Anderson (corresponding author: [email protected])


Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, United

States

Reprinted from Journal of Biological Engineering, JT Kittleson, S Cheung, JC Anderson, “Rapid

optimization of gene dosage in E. coli using DIAL strains”, Vol. 5:10, (2011) under the terms of

the Creative Commons Attribution License

2.1 – Abstract

Engineers frequently vary design parameters to optimize the behaviour of a system. However,

synthetic biologists lack the tools to rapidly explore a critical design parameter, gene expression

level, and have no means of systematically varying the dosage of an entire genetic circuit. As a

step toward overcoming this shortfall, we have developed a technology that enables the same

plasmid to be maintained at different copy numbers in a set of closely related cells. This

provides a rapid method for exploring gene or cassette dosage effects. We engineered two sets of

strains to constitutively provide a trans-acting replication factor, either Pi of the R6K plasmid or

RepA of the ColE2 plasmid, at different doses. Each DIAL (different allele) strain supports the

replication of a corresponding plasmid at a constant level between 1 and 250 copies per cell. The

plasmids exhibit cell-to-cell variability comparable to other popular replicons, but with improved

stability. Since the origins are orthogonal, both replication factors can be incorporated into the

same cell. We demonstrate the utility of these strains by rapidly assessing the optimal expression

level of a model biosynthetic pathway for violacein. The DIAL strains can rapidly optimize

single gene expression levels, help balance expression of functionally coupled genetic elements,

improve investigation of gene and circuit dosage effects, and enable faster development of

metabolic pathways.

2.2 – Background

Optimizing desired outcomes by varying a design parameter is a staple of almost every

engineering field, from mechanical engineers tweaking blade angles on a wind turbine to civil

engineers altering the timing of traffic lights. Similarly, genetic engineers alter gene expression

levels to optimize some desirable phenotype. Strong overproduction of single proteins can

5

impose a metabolic burden on E. coli, and often a lower expression level leads to improved

phenotype [14]. In multi-subunit proteins and genetic circuits, expression of particular proteins

often needs to be balanced for proper function (e.g. [15],[16], and [17]). Extensive work has

established methods for achieving expression of a gene or operon at a particular level, including

control of transcription using standard promoter sets [18], modulation of RNA processing [19],

and control of translation through ribosome binding site (RBS) manipulation [20]. However,

using these tools to investigate the desired expression level of a single gene or operon requires

cloning for each level to be tested. Using inducible promoter systems to probe multiple

expression levels can rapidly determine an approximate desired expression level, but does not

provide a genetically encoded solution, which can be useful for downstream applications. For

optimizing multi-operon constructs, fewer tools exist. Generating large numbers of repeats in

the genome is labor intensive[21], while strategies that increase plasmid copy number upon

induction provide a narrow range of copy numbers [22], cause runaway replication [23, 24], or

are incompatible with constructs using the common PBAD promoter [25]. To address these

shortcomings and allow researchers to explore the effect of copy number on genetic devices, we

have exploited an underutilized control point: plasmid copy number.

Genetic engineers working in E. coli are blessed with a wide range of plasmid systems and

plasmid copy numbers to choose from, ranging from single copy BACs to ~500 copy pUC

plasmids[26]. However, to take advantage of copy number differences, each gene or device of

interest has to be cloned into different plasmids, necessarily changing the local genetic context

along with the copy number. A notable exception to this rule is the gamma origin of the R6K

plasmid, which requires the trans-acting Pi protein to initiate replication [27]. Different alleles

of pir integrated into the E. coli genome are known to support R6K plasmids at different copy

numbers (e.g. pir+ and pir116 [28]), meaning that the genetic context on the plasmid itself

remains unaltered. Similarly, the orthogonal replicon of ColE2-P9 also uses a trans-acting

factor, RepA, to support the origin of replication [29].

In this work, we generate two sets of strains bearing different alleles (DIAL) of pir or repA to

support the same plasmid at a wide range of copy numbers. We then characterize the copy

number, cell-to-cell variability, and stability of plasmids in both sets of DIAL strains. We

illustrate their utility on a model system by examining expression of the violacein biosynthesis

pathway [30] at different copy numbers. The results demonstrate that artificially re-regulating

replication factor expression from the genome can produce stable plasmid copy numbers, that

phenotype varies with copy number, and that DIAL strains can accelerate development of

genetic devices.

2.3 – Results and Discussion

2.3.1 – ColE2 as a Trans-Activated Origin

A previous report[31] identified a minimal 32 bp region of ColE2 sufficient to support

replication of a plasmid when repA is provided in trans. We first recapitulated this behaviour by

transforming a plasmid bearing the ColE2 minimal origin (BBa_J72203-BBa_J72158) into cells

constitutively expressing the trans-acting repA gene from a plasmid (MC1061 + BBa_J72109-

BBa_J72182). Although we observed transformants, they exhibited small colony morphologies.

Presuming this observation to reflect plasmid instability (as suggested by data in [32]), we

repeated the experiment using a larger 470 bp fragment of the ColE2 plasmid as the origin

6

(BBa_J72203-BBa_J72161) in the hope that any context dependent influence on the minimal

origin would be eliminated, or that non-essential factors contributing to robustness would be

captured. This yielded colonies with morphologies indistinguishable from untransformed cells

(data not shown).

2.3.2 – Orthogonality of R6K and ColE2

We next examined if ColE2 and R6K were orthogonal origins of replication. Plasmids bearing

either an R6K or ColE2 origin of replication were transformed or cotransformed into cells

expressing pir, repA, or both. We observed colonies only when a plasmid was transformed into

cells possessing the cognate replication factor, as expected.

Figure 2.1 – qPCR estimation of plasmid copy number. JTK160 (repA) and JTK164 (pir)

variants were transformed with BBa_J72205-BBa_J72048 and BBa_J72206-BBa_J72048,

respectively. JTK160J and JTK164J were also transformed with BBa_J72202-BBa_J72048

(p15a origin) and BBa_J72207-BBa_J72048 (pUC origin). Two biological replicates of each

sample were prepared and analyzed using qPCR. Error bars represent standard deviation.

2.3.3 – Construction of DIAL Strains

To generate cells expressing diverse levels of the trans-acting factors Pir and RepA, we

integrated expression cassettes with randomized ribosome binding sites (RBSs) into the genome,

thereby creating strain sets JTK160 and JTK164 for pir and repA, respectively. We subsequently

visualized copy number variation by transforming the libraries with reporter plasmids

constitutively expressing sfGFP [33] (BBa_J72111-BBa_J72183 or BBa_J72112-BBa_J72183).

7

After a preliminary fluorescence analysis of 380 clones of each type (data not shown), 24 were

selected for further investigation. After a second round of fluorescence measurement, 10

variants of each type that spanned the range of observed fluorescence levels were chosen for full

characterization. The sequences of the selected RBSs are reported in Table 2.1.

2.3.4 – Characterization of DIAL Strains: Copy Number, Cell-to-Cell Variability, and

Stability

We characterized three important properties of plasmids in the DIAL strains: copy

number, cell-to-cell variation, and stability. To estimate the copy number supported by each

strain, we employed qPCR to examine plasmid content both at mid log and at stationary phase

(Figure 2.1). Based on this analysis, a ColE2 plasmid in the DIAL strains spans the range of ~1-

60 copies per genome equivalent, while an R6K plasmid in the DIAL strains spans the range of

~5-250 copies per genome equivalent. This covers nearly the entire range of reported plasmid

copy numbers, from single copy to nearly pUC levels. We observed that the pUC plasmid

exhibited 4-5 fold increased copy numbers at stationary phase, while the p15a, R6K, and ColE2

plasmids showed ~2-fold or lower changes.

Table 2.1 – RBSs of repA and pir expression variants

Strain Variant Sequence

JTK160 (repA) A TCTAGAATACCTGATG

B GTGAGAAGTGAACGTG

C CCGAGAACGTAGGATG

D GGCAGAAAGTTGAATG

E GTGAGAAAGCTCTGTG

F CTCAGAACCGAATATG

G GTGAGAATGCTTTATG

H TTGAGAAAAACAGATG

I GGGAGAAAGATGGGTG

J GGGAGAAAACAAAATG

JTK164 (pir) A GCTGGAACAGGTGGTG

B TAAGGAATTAGGTGTG

C GGGGGAAGGGCATGTG

D TTGGGAACAATTCATG

E TCCGGAAGACTAGGTG

F GTCGGAAAGGGCTGTG

G GTGGGAATAGAATATG

H ACGGGAATGTAACGTG

I ACTGGAACTGCATGTG

J ATCGGAAACATAGGTG

Start codons are underlined

We next examined sfGFP expression in samples of each of the cells by flow cytometry

(Figure 2.2) to determine cell-to-cell variability. The ColE2 and R6K plasmids generally exhibit

similar distributions to p15a and pUC origins. Strains JTK160I and JTK164E, which have mean

8

expression levels within 25% of p15a levels, have coefficients of variance that fall within 25% of

p15a levels. Similarly, JTK160J and JTK164I, which have mean expression levels within 25%

of pUC levels, have coefficients of variance that fall within 25% of pUC levels. It is unclear if

the relatively high coefficients of variance for JTK160A and JTK164A are a result of noise at

low fluorescence (as evidenced by the very high MC1061 coefficients of variance) or due to true

variance in plasmid copy number or GFP expression.

Figure 2.2 – Flow cytometric analysis of cell-to-cell variation. JTK160 strains expressing

repA (A) or JTK164 cells expressing pir (B) bearing GFP expressing plasmids (BBa_J72111-

BBa_J72185 or BBa_J72112-BBa_J72185, respectively) were analyzed by flow cytometry.

MC1061 alone or bearing p15a (BBa_J72202-BBa_J72185) or pUC (BBa_J72109-BBa_J72185)

plasmids expressing GFP were also analyzed. Data is presented as forward scatter versus GFP

fluorescence, with the mean and coefficient of variance listed below.

Finally, we monitored the stability of plasmids in mid-level expression variants of both

pir and repA after 100 generations without selection (Figure 2.3). Both pir and repA variants

9

exhibited high stability, losing the plasmid in only 5.2% or 0.5% of cells, respectively. These

numbers fall between the stability of the control p15a and pUC plasmids, which lost the plasmid

in 23.5% or .25% of cells, respectively.

2.3.5 – Optimization of Violacein Expression

As a simple demonstration of the utility of the DIAL strains, we optimized the expression

level of the violacein biosynthesis operon, VioABCDE. While moderate production levels of the

deeply purple metabolite are tolerated in E. coli, high levels can be toxic or cause instability [30].

We cloned the operon behind both a weak and a strong constitutive promoter to illustrate the

flexibility afforded by controlling copy number. Figure 2.4 shows the colonies that result from

transforming both weakly and strongly expressed operons (BBa_J72188 and BBa_J72189,

respectively) into all of the DIAL strains. Production of violacein, as indicated by purple

coloration, clearly increases as copy number increases, up to some threshold level. Beyond that

threshold, colonies begin to grow at reduced rates or not at all. The large colonies in the high

copy strains (JTK160G, JTK160I, and JTK164H) with a strong promoter were sequenced and

confirmed to be escape mutants in which a fragment of the strong constitutive promoter has

recombined out, demonstrating the risk of overstressing the cells.

Figure 2.3 – Stability of plasmids in DIAL strains. JTK165EI (pir repA) bearing BBa_J72202-

BBa_J72183 (p15a origin), BBa_J72207-BBa_J72183 (pUC origin), BBa_J72111-BBa_J72183

(ColE2 origin), or BBa_J72112-BBa_J72183 (R6K origin) was serially propagated for 100

generations and then analysed for plasmid loss. Four biological replicates of each plasmid were

analysed, and error bars represent standard deviation.

10

Figure 2.4 – Optimization of violacein production. Variants of JTK160 (repA) or JTK164

(pir) were transformed with plasmids containing either a weak promoter (BBa_J72111-

BBa_J72188 or BBa_J72112-BBa_J72188) or strong promoter (BBa_J72111-BBa_J72189 or

BBa_J72112-BBa_J72189) driving expression of the violacein synthesis pathway.

Transformants were plated as 5 µl spots. Small circles for a given strain set/plasmid

combination are cropped from the same original plate image taken on a flatbed scanner.

Gradient indicates the trend of copy number in strain variants.

2.4 – Conclusions

We have developed and characterized two sets of strains that support the R6K and ColE2 origin

of replication at a wide range of different copy numbers enabling rapid exploration of gene and

circuit dosage. To accomplish this, we placed the trans-acting replication factors from each

replicon under artificial transcriptional regulation in the genome, leaving only the origins of

replication on the plasmids themselves. Although negative feedback relying on elements 5’ of

the trans-acting factor open reading frame has been implicated as one factor helping to maintain

stable copy numbers in both ColE2 [34] and R6K [27], we found that engineered cells stably

maintained plasmid copy numbers despite removal of all 5’ regulatory elements. This is

consistent with the existence of additional feedback mechanisms, as has been suggested for both

R6K [35] and ColE2 [36].

To generate copy number diversity in the DIAL strains, we created a library of RBS variants of

the trans-acting replication factor in the genome. Although other strategies, such as the use of an

inducible promoter or a library of promoters, could also have achieved diverse levels of trans-

acting factor expression, varying the RBS enables compatibility with genetic circuits employing

any promoter and maintains a consistent noise profile across strains due to stochastic

transcription effects [37]. We employed a novel mechanism of generating RBS libraries in the

genome: lambda red based integration of Splicing by Overlap Extension (SOEing) [38] PCR

11

products. Multiplex automated genome engineering [39] has also been employed for creating

libraries of modified genomic RBSs. While that process is a powerful method for modifying

genes already in the genome, this work required simultaneous modification and introduction into

the genome of an exogenous gene. In such cases, PCR based integration is an excellent option

for library construction, particularly where a relatively small number of variants (<10,000) is

sufficient to isolate the desired functionality.

The DIAL strains are the first tool capable of systematically varying genetic circuit dosage

without altering the local genetic context. Previous studies examining the impact of circuit

dosage in prokaryotes have been largely theoretical (e.g. [40, 41]), and in eukaryotes focus only

on low (~1-2) copy numbers (e.g. [42]). Because the theoretical predictions suggest that circuit

dosage has a significant impact on the function of some genetic circuits, it is important to

empirically verify the robustness or fragility of different circuit architectures. Using the DIAL

strains, network behaviour and expression noise can be rapidly assessed at a wide variety of

different circuit dosages.

Of great practical use, the DIAL strains offer a rapid, facile mechanism for determining desired

expression levels, making it a tool with broad applicability in genetic engineering. The trivial

operation of transforming the same plasmid into different strains is sufficient to provide

information on the maximum tolerated expression level for a given protein, pathway, or circuit,

and screening of viable colonies reveals the optimal expression level for a desired phenotype.

We demonstrated this simple capability by optimizing expression of the violacein biosynthesis

pathway, which in excess produces moderate toxicity in E. coli. Regardless of whether that

starting point was a weakly or a strongly expressed operon, deeply purple yet healthy cells were

isolated when matched with the appropriate strain. Knowing the optimal gene dosage can be

leveraged to change the context of a gene or operon without altering the phenotype. Since the

copy number is known, any change in protein dosage resulting from changing the context of the

system (such as by integration into the genome) can be compensated for by using existing tools

such as the RBS calculator [20] or a set of standard promoters [18].

Importantly, the ColE2 and R6K origins are orthogonal and can co-exist in the same cell, and the

two sets of DIAL strains were designed to enable ready combination of both trans factors into a

single strain by P1 transduction. Having a single set of cells with both orthogonal origins allows

both the relative and absolute levels of two genes or sets of genes to be optimized by, for

example, co-transformation into a pool of competent cells. Although R6K has already seen

widespread use because of its ability to split into trans and cis elements, having a variety of copy

number variants available for both R6K and ColE2 provides an even more powerful toolset for

expression level optimization and balancing, circuit dosage investigation, and novel selection

schemes.

2.5 – Methods

2.5.1 – Media

Strains were propagated in LB broth and LB agar plates, with addition of 100 µg/ml

ampicillin sodium salt, 50 µg/ml spectinomycin dihydrochloride pentahydrate, 25 µg/ml

kanamycin sulphate, and/or 10 µg/ml trimethoprim if appropriate.

12

2.5.2 – Plasmids

Plasmids were constructed using BglBrick standard assembly[43]. Plasmid and strain

descriptions are available in Chapter 6, and complete sequences are available through the

Registry of Standard Biological Parts (http://partsregistry.org) [44]. The replicon of ColE2-

P9[29] is referred to as ColE2 for convenience. The gamma origin of R6K is similarly referred

to simply as the R6K origin.

2.5.3 – Genomic RBS Library Construction

Template plasmids BBa_J72204-BBa_J72184 and BBa_J72111-BBa_J72186, illustrated

schematically in Figure 2.5, were first constructed. Splicing by overlap extension SOEing PCR

[38] with degenerate oligos (Table 2.2) was used to generate RBS variants

(NNNGGAANNNNNNRTG for pir and NNNAGAANNNNNNRTG for repA) of the cassettes

on the template plasmids. The final PCR products were gel purified using Zymo columns

according to the manufacturer’s instructions, and then used to modify the genome of strain

MC1061 [45] by the procedure of Datsenko and Wanner [46]. The resulting libraries consisted

of 10,000 members each, and were pooled before preliminary transformation with fluorescent

protein expressing plasmids and analysis of fluorescence levels. Variants ultimately selected for

full characterization were P1 transduced into MC1061 cells before further analysis to eliminate

the initial fluorescent plasmid.

Figure 2.5 – Schematic of templates constructed for genomic integration. Plasmids

BBa_J72204-BBa_J72184 and BBa_J72111-BBa_J72186 both contain: 1) a ~500 bp homology

arm (HA) matching the 5’ end of the genomic insertion site 2) a constitutive promoter (PCON) 3)

the ORF of the trans-acting replication factor (Trans) 4) a terminator 5) a kanamycin resistance

cassette (KnR) flanked by FRT sites and 6) a ~500 bp HA matching the 3’ end of the genomic

insertion site.

Table 2.2 – Oligos used for library construction

pir

BBa_J72111-

BBa_J72186 5’-3’ oligos

Outer oligo F CACATGGCGACCAGATCAATAC

Outer oligo R ATTGCCGCAGGTGGAAAC

Inner oligo F GGTACAGTGCTAGCGGATCTNNNGGAANNNNNNRTGAGACTCAAGGTCATGATGG

Inner oligo R GATCCGCTAGCACTGTACC

repA BBa_J72204-BBa_J72184 5’-3’ oligos

Outer oligo F GGATTTTCCTTGTTTCCAGAG

Outer oligo R GCTTACGGCTTTATATTACGGG

Inner oligo F CTCGTCAGTAACGAGCCCTNNNAGAANNNNNNRTGAGCGCCGTACTTC

Inner oligo R AGGGCTCGTTACTGACGAG

13

2.5.4 – Plasmid Copy Number Estimation by qPCR

Plasmid copy number per genome equivalent was estimated using the relative

quantitation method described previously [47]. Briefly, cells were subcultured 1:100 into fresh

media and grown until mid-log or stationary phase before total DNA isolation using QIAamp

DNA Mini kits (Qiagen) according to the manufacturer’s instructions. DNA samples and 10-

fold serial dilutions of a purified calibrator plasmid bearing a single copy of both bla and dxs

(BBa_J72109-BBa_J72187) were then amplified on an iCycler with iQ5 real-time PCR detection

system (Biorad) using previously validated primer pairs [47] for both bla and dxs (bla F: 5’-

CTACGATACGGGAGGGCTTA-3’ blaR: 5’-ATAAATCTGGAGCCGGTGAG-3’ dxsF: 5’-

CGAGAAACTGGCGATCCTTA-3’ dxsR: 5’-CTTCATCAAGCGGTTTCACA-3’). Each

reaction contained 25 µl: 12.5 µl Absolute QPCR SYBR Green Fluorescein Mix (Thermo

Scientific), 1.25 µl each primer (10 µM), 3.75 µl H2O, and 5 µl sample DNA. Reaction

conditions were as follows: an initial denaturation at 95ºC for 10 minutes, followed by 40 cycles

of 95ºC for 10 seconds, 63ºC for 15 seconds, and 72ºC for 15 seconds. Measurements were

taken at the end of each extension step. Copy numbers were calculated by using the calibrator

standard curves to determine the quantity of plasmid (bla) and genome (dxs) dna for a given

sample in arbitrary units, and then calculating their ratio.

2.5.5 – Flow Cytometry

Cells grown to stationary phage were subcultured 1:100 in fresh media, grown until mid-

log, resuspended in PBS, and then examined on a Coulter Epics Xl-MCl instrument with a 488

nm excitation wavelength and 525 nm emission bandpass filter.

2.5.6 – Stability Analysis

Single colonies were picked and grown to stationary phase under selection. Cells were

then subcultured 1:106 and grown back to stationary phase without selection, which corresponds

to 20 generations of growth. The dilution and regrowth was repeated serially for 100

generations, at which point dilutions of cells were plated on non-selective media, and colonies

were examined for sfGFP fluorescence as an indicator of plasmid presence.

2.6 – Acknowledgements

The authors would like to thank Dr. Tateo Itoh for providing the ColE2-P9 replicon. This work

was supported by the National Science Foundation Synthetic Biology Engineering Research

Center (SynBERC). JTK was supported by a National Science Foundation Graduate Research

Fellowship.

14

Chapter 3 – Scalable Plasmid Transfer using Engineered P1-based

Phagemids

Authors:

Joshua T Kittleson


Will DeLoache


Hsiao-Ying Cheng





States

Reprinted with permission from ACS Synthetic Biology, JT Kittleson, W DeLoache, H Cheng, JC

Anderson, “Scalable Plasmid Transfer using Engineered P1-based Phagemids”, DOI:

10.1021/sb300054p (2012). Copyright 2012 American Chemical Society.

3.1 – Abstract

Dramatic improvements to computational, robotic, and biological tools have enabled

genetic engineers to conduct increasingly sophisticated experiments. Further development of

biological tools offers a route to bypass complex or expensive mechanical operations, thereby

reducing the time and cost of highly parallelized experiments. Here, we engineer a system based

on bacteriophage P1 to transfer DNA from one E. coli cell to another, bypassing the need for

intermediate DNA isolation (e.g. minipreps). To initiate plasmid transfer, we refactored a native

phage element into a DNA module capable of heterologously inducing phage lysis. After

incorporating known cis-acting elements, we identified a novel cis-acting element that further

improves transduction efficiency, exemplifying the ability of synthetic systems to offer insight

into native ones. The system transfers DNAs up to 25 kilobases, the maximum assayed size, and

operates well at microliter volumes, enabling manipulation of most routinely used DNAs. The

system’s large DNA capacity and physical coupling of phage particles to phagemid DNA

suggest applicability to biosynthetic pathway evolution, functional proteomics, and, ultimately,

diverse molecular biology operations including DNA fabrication.

3.2 – Introduction

Biological discovery and design relies on a wide array of software, hardware, and

bioware tools. The last describes biological entities or their derivatives, such as DNA

15

manipulation enzymes, recombinant cell lines, and general tranducing phages. Importantly,

bioware such as yeast two-hybrid systems (Y2H) [48] reduce otherwise complex and expensive

operations into simple, highly parallelized procedures. Absent bioware such as Y2H, identifying

interactions between a target protein and ~3000 other proteins requires time- and resource-

consuming expression and purification of each protein, followed by an appropriate in vitro

binding assay. By contrast, traditional Y2H requires only plasmid transformation, population

selection, and interaction pair sequencing. With the advent of next generation sequencing, Y2H

has been extended to provide data on an entire interactome from a single experiment [44].

Similarly, the mating-assisted genetically integrated cloning (MAGIC) strategy takes advantage

of bacterial conjugation, recombination, and selection to simplify the cloning process [49].

Incorporation of bioware into MAGIC eliminated the costs and complications associated with

lysing cells, purifying DNA from the lysate, and prepping cells for DNA transformation, thereby

achieving greatly improved throughput. These examples highlight how transformative jumps in

throughput can be accomplished by performing otherwise slow, laborious, or expensive unit

operations with appropriately designed bioware. Development of bioware tools compatible with

high-throughput robotics for routine operations could dramatically reduce the costs and improve

the throughput of diverse experimental approaches.

In particular, routine methods of DNA manipulation rely principally on E. coli for

plasmid amplification and propagation, taking advantage of its fast growth, high competency,

and high DNA yields. While generally effective, such methods are still subject to a number of

constraints: isolation of high quality DNA from E. coli usually requires either centrifugation or

vacuum application, analysis of the DNA frequently relies on gel electrophoresis, growth cycles

of E. coli in liquid and solid media require on the order of 12 hours, and clonal selection methods

require isolation of a single colony from a plate. These difficult hardware operations limit

automatability, with expensive, specialized robotics required to achieve even moderate

throughput using a 96-well format (e.g. the Qiagen BioRobot 8000). If the operations were

instead implemented as bioware designed to function using purely liquid handling manipulations,

they could instead be conducted at the nanoliter scale with throughput in the hundreds of

thousands using technologies such as acoustic liquid handlers and microfluidics [50]. To

overcome the inherent limitations of relying on bacteria alone, here we investigate using phage-

based DNA transfer to address the essential problem of moving DNAs efficiently between cells.

Many phage can infect, reproduce, and lyse their hosts in less than an hour [51], and they

have evolved to transmit genetic information from one host to another through aqueous

intermediates. We selected phage P1 to use as a starting point to take advantage of its extensive

characterization [52] and ability to package large DNAs from a well-defined packaging site.

Because P1 employs a headful packing mechanism, any sized DNA up to 90 kb can be packaged

into a P1 particle, since smaller DNAs can either be concatenated before uptake or packaged

serially [53]. Further, another group has already demonstrated that a P1 phagemid, a plasmid

containing cis phage elements, can be successfully packaged and delivered into new cells [54]. In

that study, a temperature inducible P1 lysogen was used to generate particles containing a

plasmid with a packaging site and a P1 lytic origin of replication. In this work, we employ a

transcriptionally activated mechanism for inducing lysis to generate a small molecule responsive

phagemid system. In an effort to improve efficiency, we also isolate an additional cis element

that enhances phagemid transduction. Finally, we characterize behaviors of the system relevant

to its potential applications. The resulting DNA transfer system operates at low volumes under

isothermal conditions, and should find application in improving continuous selection schemes,

16

identifying protein-protein interactions, and streamlining diverse molecular biology operations

including DNA fabrication.

Figure 3.1 – Genetic interaction of phage lysogen and phagemid. The interaction between a

complete phagemid, J72103, and a simplified representation of the P1 genome is shown. For

simplicity, only interactions relevant to the system behavior or mentioned in the main text are

presented here. C1 is the master repressor of lysis, and can be inhibited by Coi. It also

downregulates expression of the native coi, but not the refactored version on the phagemid. The

phage lysogen provides both repL and pacA, which act on sites within their respective coding

regions. The phagemid also expresses pacA, but has a truncated version of repL encoding only

the cis element.

3.3 – Results and Discussion

3.3.1 – Transcription of coi Triggers the P1 Lytic Cycle

We sought a trans-acting protein capable of inducing the P1 lytic cycle, reasoning that

transcription of such a protein would provide a mechanism for controlling phage induction. The

P1 lysis/lysogeny decision is regulated by the activity a master repressor, C1. Although a

complex genetic circuit controls C1 synthesis and activity during P1 infection [52], heterologous

expression of a C1 inhibitory protein, Coi, blocks C1 activity, thereby inducing the lytic cycle

[55]. Here, we similarly employ overexpression of coi from a phagemid to induce the P1 lytic

cycle, and the interactions between a P1 lysogen and a coi-based phagemid are summarized in

Figure 3.1. To validate this approach, we first refactored coi into a synthetic module activated by

transcription, removing all sequence 5’ of the ribosome binding site, taken to be the 17 base pairs

17

immediately 5’ of the start codon [56], and adding a terminator 3’ of the stop codon. We then

constructed plasmids with the refactored coi under the transcriptional control of one of two small

molecule inducible promoters, PBAD or PRHA, which are induced by arabinose or rhamnose,

respectively. Addition of the appropriate inducer to P1 lysogens transformed with these plasmids

resulted in cell lysis, while uninduced controls and controls lacking a phagemid failed to lyse

(Figure 3.2a). Consistent with previous results [55], this confirms that induced transcription of

coi from phagemids initiates lysis.

Figure 3.2 – Expression of coi induces lysis. (a) MC1061 P1 lysogens harboring a plasmid with

an arabinose-inducible coi (PBAD-coi, J72110- J72094), a plasmid with a rhamnose inducible coi

(PRHA-coi, J72110- J72097), or no plasmid were incubated in the presence or absence of inducer

and the OD600 monitored. Each construct was run in triplicate; error bars have been omitted for

clarity, with all standard deviations falling below 16% of the mean, except the PBAD-coi construct

induced with arabinose, which had standard deviations as high as 67% of the mean. (b) P1

lysogenized MC1061 containing either a plasmid with only an arabinose-inducible coi (PBAD-coi

, J72110-J72094) or a complete phagemid (PBAD- coi + cin + repL + pacA, J72110-J72103) were

induced to lyse by addition of arabinose. The corresponding arabinose promoter activity,

reported in relative promoter units (RPU), is plotted against the resulting percent lysis, calculated

as 1 – (OD600 sample / OD600 uninduced control). Error bars indicate the standard deviation of

quadruplicate samples.

Although we focus here on the use of small-molecule inducible promoters to control

lysis, other applications may require the use of a different triggering mechanism. To aid in the

development of alternative coi-based lysis circuits, we quantified lysis induction efficiency as a

function of PBAD transcriptional activity. As illustrated in Figure 3.2b, lysis of both an arabinose-

driven coi-only phagemid and a phagemid bearing additional cis elements (coi + cin + repL +

18

pacA, discussed below) exhibited dose dependence, appearing to saturate at the maximal

observed promoter strength of 0.4 relative promoter units (RPUs) [57]. We expect that any

alternative phagemid design that provides the same level coi transcription would also function to

induce lysis.

Figure 3.3 – Impact of cis elements on packaging. Test phagemids harboring various

combinations of putative cis packaging elements were competed against a reference phagemid

containing all of the elements (J72109-J72105: cin + repL + pacA). The resulting ratio of test

phagemid to reference phagemid indicates the relative efficiency of test phagemid packaging.

Error bars indicate the standard deviation of triplicate biological samples. Starred pairs show a

statistically significant difference (p<0.05 in an unpaired two-tailed t-test). The test phagemids

are J72113-J72106: coi + rfp, J72113-J72094: coi, J72113-J72102: coi + cin, J72113-J72095: coi

+ repL, J72113-J72115: coi + cin + repL, J72113-J72101: coi + pacA, J72113-J72116: coi + cin

+ pacA, J72113-J72096: coi + pacA + repL, and J72113-J72103: coi + cin + repL + pacA.

3.3.2 – cin is a cis Element that Improves Phagemid Transduction

Motivated by the observation that plasmids not harboring phage cis elements are also

transduced, albeit infrequently, we sought to improve the relative efficiency with which desired

plasmid DNA is transduced. Incorporation of a packaging site (which resides within pacA) and a

lytic origin of replication (which resides within repL) both enhance packaging [54]. Although no

other packaging or delivery enhancers have been reported, we reasoned that there might be

additional regions of the P1 genome that improve DNA packaging or delivery. We therefore

inserted a library of P1 genomic fragments into a phagemid harboring complete pacA and repL

ORFs. The library was enriched for elements that improve transduction by generating phagemid

19

lysate from pooled library members, and then infecting naïve lysogens with the lysate. After

several rounds of such enrichment, individual clones were isolated and sequenced to identify the

inserted genomic fragment. Sequences of interest were PCR-amplified out of the P1 genome and

cloned into phagemids, which were subsequently tested individually for improved function. One

region, which contained the cin ORF, consistently showed improvement (data not shown).

Although there is no published cis role for cin in the packaging or delivery of P1 phage DNA, it

has been shown to encode a site-specific recombinase responsible for switching the host

specificity of the phage [58]. To determine if the improvement was an unexpected function of the

coding sequence or an overlying cis element, a noncoding version of the ORF lacking a start

codon and bearing a nonsense mutation was created. Inclusion of the noncoding cin DNA

improved the relative transduction efficiency of phagemids harboring coi and any combination of

repL and pacA by 3-11 fold (Figure 3.3), suggesting that cin operates as a cis element. We also

examined the possibility that increased plasmid size alone might lead to improved transduction,

but inclusion of a gene coding for red fluorescent protein had no effect on transduction

efficiency. Importantly, a phagemid harboring all three cis elements showed an approximately

1600 fold improved yield over a plasmid lacking any cis elements. This suggests that the system

very specifically transduces desired phagemid DNA.

Figure 3.4 – Impact of volume on phagemid efficiency. (a) MC1061 P1 lysogens containing a

complete phagemid (J72110-J72103) were induced to lyse at 2000 µL, 200 µL, 50 µL, and 10

µL scale in 24 well blocks, 96 well plates, 384 well plates, and 1536 well plates, respectively,

and the lysate used to transduce naïve JTK029 cells for titer determination. Error bars indicate

the standard deviation of biological triplicates. Differences between conditions are not

statistically significant (p>0.05 in an unpaired two-tailed t-test). (b) P1 lysogenized JTK160C

harboring plasmid J72111-J72098 was transduced with two-fold serial dilutions of J72110-

J72103 lysate from (a) in the presence of two-fold serial dilutions of arabinose (highest

concentration, 13 mM) in a 384 well plate. Tetrazolium violet was added to visualize unlysed

cells.

3.3.3 – Phagemids Exhibit Robust and Faithful Transduction

20

Because we envision utilization of the phagemid in high-throughput liquid handling

operations with diverse genetic material, it is important for phagemid transduction to be robust to

both reaction volume and phagemid size. To establish the effect of reaction volume on lysis, we

generated lysate from P1 lysogenized cells bearing phagemids in different vessels, and used the

lysate to transduce naïve cells to titer viable phagemid-carrying phage particles (Figure 3.4a).

Although the average titer dropped approximately 2-fold between a 2000 µL lysis volume and a

200 µL lysis volume, this difference is not statistically significant (p>0.05), and there is little

impact on the average titer from further scale reduction. To ascertain if transduction also

operates at small volumes, we tested the ability of phagemid lysate to transduce naïve lysogens

and initiate lysis in a 384 well plate (50 µL scale). Figure 3.4b illustrates that a high titer of

phagemid, in the presence of sufficient inducer, leads to complete lysis of the recipient cells.

This suggests that transduction functions at small volumes, as expected.

To probe the size constraints of the system, we tested the transduction efficiency of a

modest (10kb), a medium (14kb), and a large (25kb) phagemid; larger constructs are not readily

supported by the p15a origin of replication [26]. We observed that although the smallest

phagemid transduced with 2.5 fold higher efficiency than the larger phagemids, all of the

phagemids generated hundreds to thousands of transductants (Figure 3.5). Therefore, this system

is capable of operating on large DNAs, such as those encoding multi-gene genetic circuits.

Figure 3.5 – Impact of phagemid size on efficiency. MC1061 P1 lysogens containing one of

three phagemids (J72114-J72100 – 9729 bp, J72114-J72090 – 13938 bp, or J72114-J72104 –

25114 bp) were induced to lyse with arabinose, and the lysate used to transduce naïve MC1061

cells for titer determination. Error bars indicate the standard deviation of biological triplicates.

The 9729 bp phagemid is significantly different from the other two phagemids (p<0.05 in an

unpaired two-tailed t test), which are not significantly different from each other (p>0.05 in an

unpaired two-tailed t test).

Some applications of the phagemid system may involve manipulation of libraries of

genetic constructs. Under conditions such as those in Figure 3.4a, 1 µL of phagemid lysate is

sufficient to generate up to 10,000 transductants. Scaled up to higher volumes, this implies that

100 ml of lysate could generate up to 109 clones. However, library manipulations should also

preserve the relative abundance of each clone. As a simple probe of this functionality, we

generated a library of phagemids expressing different levels of green fluorescent protein (GFP)

in P1-lysogenized cells, illustrated in Figure 3.6a. We pooled the clones, induced lysis, and

21

transduced naïve P1-lysogenized cells with the lysate. The resulting clones (Figure 3.6b) show a

similar diversity of GFP expression levels. To better quantify the fidelity of library transfer, we

used a mixture of GFP-expressing and non GFP-expressing phagemids to generate a library with

10% GFP-expressing clones. Lysate generated from pooled library members was used to

transduce naïve P1-lysogenized cells, and resulted in an average of 9% GFP-expressing clones,

which is not significantly different from the original library (p>0.05, Figure 3.6c). The ability to

produce large numbers of clones without significantly perturbing the ratio of variants confirms

the utility of phagemids for transduction of genetic libraries.

Figure 3.6 – Library transfer using the phagemid. MC1061 P1 lysogens containing a library

of GFP expressing phagemids (J72110-J72152 library) (a) were pooled, induced to lyse with

arabinose, and the lysate used to transduce naïve MC1061 P1 lysogens (b). (c) MC1061 P1

lysogens were transformed with a mixture of GFP expressing (J72113-J72152) and non-GFP

expressing (J72113-J72103) phagemids, and the percent of GFP expressing clones counted (the

original library). The clones were pooled, induced to lyse, and the lysate used to transduce naïve

MC1061 P1 lysogens. The resulting clones (the transduced library) were counted, and the

percent expressing GFP calculated. Error bars indicate the standard deviation from three

replicates. The difference between the original library and the transduced library is not

statistically significant (p>0.05 in an unpaired two-tailed t test).

3.4 – Summary and conclusions

We have engineered and characterized a P1-based phagemid that requires only liquid

handling for the isothermal, high efficiency transfer of plasmids from one cell to another. As part

of this process, we isolated an additional cis element that lies inside of the cin gene and enhances

transduction of the phagemid. Because there is no known role for cin in the packaging or

delivery of P1 phage DNA, the biological mechanism of improved transduction remains to be

elucidated, illustrating how engineering novel synthetic systems can probe our understanding of

native systems.

22

By refactoring the natural transcriptional regulation of coi, we gain control of the

lysogeny-to-lysis switch. Here, we demonstrated simple control by a heterologous input,

arabinose or rhamnose, using an inducible promoter to drive coi expression. In theory, however,

any genetic circuit could be used. For example, recent work by Esvelt and colleagues [59]

demonstrated the power of connecting phage viability to an encoded gene function with the

development of phage-assisted continuous evolution (PACE). An analogous approach could be

used to evolve an entire biosynthetic pathway encoded on a P1 phagemid by using a biosensor

[60] for the product of the pathway to trigger expression of coi.

Addition of cin to the phagemid improved transduction efficiency, enabling a phagemid

bearing all relevant cis elements to be transduced 1600-fold more efficiently than a plasmid

lacking any such elements. Although further work is required to elucidate the relative packaging

efficiency of phagemids and wild-type P1 phage genomes, which may be important to

continuous selection schemes such as PACE, the current system is clearly sufficient to transfer

both individual clones and libraries of genetic elements. The ability to specifically couple at least

25 kb of DNA to a phage particle takes a step toward enabling P1 phage display for functional

proteomics. Based on work with other well characterized phages [56, 61-63], we speculate that

phagemid-encoded protein complexes could be attached to the particle surface, e.g. via a short

tag [64], allowing functional complexes to be enriched by targeted binding to individual complex

components.

Transcriptional control of lysis provides a final advantage: isothermal phage induction,

which confers compatibility with a simple liquid handling robotics including microfluidic

technologies. The robustness of the system at microliter volumes suggests the potential for

operation on nanoliter scale platforms. DNA transfer, in combination with as yet undeveloped

bioware operations such as restriction and ligation, may ultimately provide a mechanism for fast,

cheap DNA fabrication. Although development of such bioware is challenging to engineer today

due to the general difficulties of creating complex genetic systems [2], improvements in our

ability to design [65] and fabricate [10] genetic systems will continue to make bioware

development easier. An initial investment in the development of additional liquid handling-only

bioware tools for common procedures would reduce the cost of genetic engineering to the

purchase of a standard liquid handling platform, accelerating development of useful

biotechnologies.

3.5 – Methods

3.5.1 – Plasmids, Strains, Phage, and Growth Media

Plasmids used in this study are described in Chapter 6, and were constructed using

BglBrick standard assembly [43]. E. coli strain MC1061 [45] or derivatives were used for all

studies. Derivatives were generated by the procedure of Datsenko and Wanner [62]. Full

descriptions of plasmids and strain modifications are available in Chapter 6, and DNA sequences

are available at the Registry of Standard Biological Parts (http://partsregistry.org) [44]. For

brevity, the BBa_ prefix of sequence names has been omitted throughout the manuscript. P1kc

[66] (obtained in strain KL739 from the Yale Coli Genetic Stock Center) was used for

experiments involving phage lysogens, and will be referred to simply as P1 for convenience.

P1vir [67] (gift from Carlos Bustamante) DNA was used for library preparation. Bacterial cells

23

were grown in Luria-Bertani medium (LB) or phage lysis medium (PLM; LB containing 100

mM MgCl2 and 5 mM CaCl2).

3.5.2 – Generation of Phage Lysates

Stationary phase cultures of a P1 lysogenized strain harboring a phagemid were diluted

100-fold into PLM and incubated for 1 hour at 37°C. Lysis was then induced by addition of

either 13 mM arabinose or 10 mM rhamnose. After 1-4 hours of further incubation at 37°C,

chloroform (2.5% v/v) was added and the culture was vortexed for 30 seconds. The culture was

then clarified by centrifugation (12100 g for 90 seconds), and the supernatant recovered. For

characterization of lysis efficiency in different volumes, the chloroform and clarification steps

were omitted, and the crude lysate was instead used directly for subsequent transduction.

3.5.3 – Transduction of Phage Lysates

A stationary phase culture of the host strain was isolated by centrifugation and

resuspended to an OD600 of 2 in PLM. An equal volume of phage lysate was mixed with

resuspended cells and allowed to adsorb at 37°C without agitation for 30 minutes. The reaction

was quenched with 5 volumes of 2YT medium containing 200 mM sodium citrate, pH 5.5, and

the infected cells were incubated for an additional hour at 37°C before titering transductants on

plates with appropriate antibiotics.

3.5.4 – Timecourse of Phage Lysis

Stationary phase cultures of MC1061 P1 lysogens were diluted 100 fold into PLM. After

growth for 1 hour at 37°C with agitation, 200 µL of cells were transferred to a Corning flat-

bottom 96 well microplate and induced by addition of either 13 mM arabinose or 10 mM

rhamnose. Culture OD600 was then monitored every 5 minutes in a Tecan Sapphire instrument

with continued incubation at 37°C with agitation. OD600 measurements were then normalized to

the first OD600 reading (OD600 at time t divided by OD600 at time 0).

3.5.5 – Measurement of Relative Promoter Units (RPU)

The RPU of the PBAD promoter with different levels of arabinose induction (0-13 mM)

was measured essentially as previously described [57]. Briefly, samples of stationary phase

MC1061 P1 lysogen cells harboring a p15a plasmid with either a constitutive reference promoter

(PREF, J72110-J72107) or PBAD (J72110-J72108) driving expression of green fluorescent protein

were diluted 100-fold into fresh inducing media and grown until mid exponential phase. At that

point, the OD600 (OD) and fluorescence (F) were measured. RPU was then calculated using

equation 10 from [57], with the approximation that all relevant growth rates are equal:

[F(PBAD)/OD(PBAD)]/[ F(PREF)/OD(PREF)].

3.5.6 – Visualization of Serial Lysis

Stationary phase culture of the host strain was diluted 100 fold into PLM and incubated

for 75 minutes at 37°C. Subsequently, 25 µL aliquots of cells were arrayed into a Genetix flat-

24

bottom 384 well microplate and incubated with 25 µL of phagemid lysate for 2 hours at 37°C in

the presence of arabinose. Cells were then stained by addition of 0.04% w/v tetrazolium violet,

and the plate incubated for an additional 2 hours at 37°C before image acquisition.

3.5.7 – Phagemid Library Construction and Enrichment

To isolate P1 vir DNA, 250 µL P1 vir lysate was treated with 5 µL of Qiagen buffer L1

(300 mM NaCl; 100mM Tris-Cl, pH 7.5; 10 mM EDTA; 0.2 mg/ml BSA; 20 mg/ml RNase A; 6

mg/ml DNase I) and incubated for 30 minutes at 37°C. After incubation, 400 µL 4% SDS was

added, and the mixture further incubated for 15 minutes at 55°C. 350 µL of Qiagen buffer N3

was then added, and the mixture applied to a Qiagen Qiaprep Spin Column. DNA isolation then

proceeded according to the manufacturer’s instructions for plasmid DNA purification.

Purified P1 vir DNA was then amplified using Phi29 polymerase. A typical reaction

consisted of 2 µL 10x Phi29 buffer, 5 U Phi29 polymerase, 400 ng thiophosphate protected

random hexamers [68], 500 µM each dNTP, approximately 25 ng template DNA, and water to

20 µL. The reaction mixture was incubated at 30°C for 16 hours, and then purified using Zymo-

Spin I columns according the manufacturer’s instructions.

Amplified P1 vir DNA was then partially digested with Sau3AI, and fragments of 1-4

kilobases ligated into BamHI digested and CIP treated J72112- J72099 plasmid DNA. Plasmids

were inserted into a JTK030 P1kc lysogen by transformation, generating a library of 3 x 106

members. Library members were pooled and then used to generate phagemid lysate. This lysate

was used to transduce phagemids into naïve JTK030 P1kc lysogen cells, and the cycle of

pooling, lysis, and transduction repeated.

3.5.8 – Competition between phagemids

MC1061 P1 lysogens were co-transformed with two phagemids, a non-inducible

“reference” phagemid (J72109 -J72105) composed of cin, pacA, and repL, and a “test” phagemid

(in vector J72113) composed of arabinose-inducible coi and a subset of the elements pacA, repL,

and cin. Phage lysate was generated as described above, and then used to transduce naïve

MC1061 cells for titering of each phagemid.

3.5.9 – Construction of library of GFP expressing phagemids

Splicing by overlap extension SOEing PCR [38] with degenerate oligos (Outer forward:

5’- GTATCACGAGGCAGAATTTCAG-3’, Outer reverse: 5’-

ATTACCGCCTTTGAGTGAGC-3’, Inner forward: 5’-

GCACGGATCTTAGCTACTAGAGAAAGANNNGAAANNNNNNATGCGTAAAGGCGAA

GAGC-3’, Inner reverse: 5’-TCTTTCTCTAGTAGCTAAGATCCGTGC-3’) on template

plasmid J72111-J72155 was used to generate RBS variants (NNNGAAANNNNNNATG) of the

GFP expression cassette. The SOEing PCR product was digested with EcoRI and BamHI and

cloned into the EcoRI and BamHI sites of vector J72154. The resulting library of clones was

pooled before DNA extraction and subsequent assembly with plasmid J72153-J72103 using the

2ab strategy [69] to generate a library of variants of J72110-J72152.

25


The authors would like to thank Caroline Westwater for thermoinducible P1 phage

(P1Cm c1ts.100), and Carlos Bustamante for P1vir. This work was supported by the National

Science Foundation Synthetic Biology Engineering Research Center (SynBERC). JTK received

support from a National Science Foundation Graduate Research Fellowship and a Siebel Scholar

award. WD received support through the Amgen Scholars Program.

26

Chapter 4 – Toward Engineering E. coli as a Cancer Therapeutic

4.1 – Abstract

The ability of bacteria to specifically target tumor tissue and intimately interact with host

cells has led to development of several bacterial cancer therapies effective in animal models.

However, human clinical trials suggest that further bacterial engineering is required to create a

clinically viable bacterial therapeutic. In this study, we work toward engineering a laboratory

strain of E. coli that both exhibits an extended lifetime after systemic administration in a mouse

model and interacts with and kills tumor cells. First, we attempt to engineer a strain to express

both an O-antigen and a K-capsule to protect against attack by the immune system. Although we

both confirm that capsular polysaccharides are essential for extended survival and transfer

functional O-antigens into a laboratory strain, transfer of a protective K-capsule remains

technically challenging. Second, we demonstrate that a strain engineered to invade tumor cells

and release a ribonuclease is capable of killing cultured tumor cells in vitro. However, the strain

failed to affect tumor growth in a mouse model, and further development will be required to

identify and overcome shortcomings in the current design.


Although several compounds are effective at killing cancer cells, their therapeutic

potential is limited by off-target activity and inadequate distribution to tumor tissue [70].

Recognizing these limitations, several next-generation drug delivery strategies have been

developed to more precisely deliver payload molecules to target cells, thereby avoiding toxic

side effects. For example, complex branched molecules called dendrimers can be designed to

interact with specific cell types and release a payload only under specific conditions [71]. An

alternative strategy employs liposomes, which enclose a drug payload inside of a lipid

membrane. The membrane can then be modified by incorporation of targeting elements to

ensure more specific delivery to tumor cells [72]. While promising, these targeting approaches

face important challenges, including expensive synthesis, reliance on passive distribution, and a

limited set of functions that can be statically encoded in the structure of the delivery vehicle. A

novel approach to overcome these limitations is the use of bacteria as a therapeutic delivery

vehicle. Because they can dynamically respond to a variety of physiologically relevant stimuli,

self-replicate, and intimately interact with host cells and systems, bacteria offer a powerful

platform for therapeutic development.

Bacteria are particularly well suited to development as therapeutics for cancer. A diverse

group of both anaerobes and facultative anaerobes selectively localize to solid tumors after

systemic administration [73-76]. For facultative anaerobes, this likely stems from an enhanced

delivery and retention effect, coupled with a lack of immune surveillance in tumors [77].

Researchers have taken advantage of this natural localization ability to engineering bacteria as

tumor visualizing agents. For example, E. coli expressing luciferase genes have been used to

identify small metastatic nodules in mice [76]. Beyond localization and imaging, bacteria are

known to be capable of causing tumor regression. In studies in the late 1800’s [78], it was

observed that some patients bearing tumors, upon being deliberately infected with bacteria,

would experience tumor regression. However, the treatment had considerable negative side

27

effects and only applied to a narrow range of tumor types, and was therefore abandoned in favor

of chemotherapy.

Recent renewed interest in exploring unconventional cancer therapeutics has led to novel

approaches for using bacteria as cancer therapeutics (reviewed in [77]). Non-pathogenic (or

attenuated) bacteria have been used directly [79], in combination with conventional therapy [80],

engineered to express a tumoricidal agent [81, 82], and engineered to express an enzyme that

converts a prodrug into an active drug [83-86]. These approaches have yielded considerable

improvements in mouse models, but failed to provide significant improvement in the clinic [77].

The case of attenuated variants of Salmonella spp. is particularly relevant: they have shown

considerable effectiveness in mouse models, including complete regression of solid tumors [79]

and elimination of hundreds of lung metastases [87]. However, in clinical trials [88, 89], these

salmonella failed to robustly localize to patient tumors, and no effect on tumor growth was

observed. One plausible explanation for this failure is that the immune system of humans is

much more efficient at clearing the attenuated Salmonella, prevent distribution to tumor sites.

Failures in the clinic motivate us to engineer a strain to be more resistant to clearance by

the immune system. Because of its safety and genetic tractability, we focus on engineering a

non-pathogenic laboratory strain of E. coli to have a reduced immune clearance rate, and then

confer it with a tumor destroying capability. In previous work[90], we developed E. coli capable

of invading cancer cells in vitro and delivering benign reporter proteins. Here, we present

progress on extending the in vivo survival of E. coli by incorporation of capsular

polysaccharides. We then assess the ability of E. coli bearing a cytotoxic payload to destroy

tumor cells both in vitro and in vivo.

4.3 – Results

4.3.1 – Encapsulation of E. coli

E. coli causing extra-intestinal infections have two layers of polysaccharide coating that

protect them from host defenses (reviewed in [91]). The O-antigen, an oligosaccharide extension

of lipopolysaccharide, and the K-capsule, an additional external polysaccharide coat, function in

concert to provide protection against several innate host immune responses, including both

classical and alternative complement pathways and opsonophagocytosis. Strains made deficient

in these protective coatings are consistently cleared from the host faster than their wild-type

counterparts [92, 93].

There are numerous variants (serotypes) of both O-antigens and K-capsules, each with a

unique biochemical composition. Of particular interest is the K1 capsule, a serotype

overrepresented in patients suffering from meningitis [91] and causing high levels of bacteria in

the bloodstream as part of its pathogenesis [94]. The K1 capsule is an effective defense

mechanism because it is composed of repeating units of 2,8 linked sialic acid, a structure that

both binds inhibitors of the complement cascade and mimics structures naturally found in the

host [91]. Bacteria bearing K1 capsules are usually found with O-antigens of the type O1, O7,

O16, or O18, though the reason for this association is unknown [95]. The genes clusters required

for synthesis of all of these capsules are well-understood [96], making them excellent candidates

for further manipulation.

Several lines of evidence suggest that constitutive expression of the protective layers is

undesirable. While the polysaccharide coats sterically shield the bacteria from attack by the

28

immune system [97], it also hides underlying factors required for intimate interaction with host

cells [98]. Consequently, capsule bearing bacteria often shed their protective layering during

later stages of pathogenesis [99, 100]. Similarly, we expect that applications involving cell-cell

adhesion or receptor mediated signaling will demand more intimate interactions than are

permitted by the outer bacterial coats. Anticipating a need for dynamic control of capsule

expression, we take an approach of placing the O-antigen and K-capsule under heterologous

control by deleting a single gene from each gene cluster required for capsule synthesis, and

complementing that gene on a plasmid.

Figure 4.1 – Strategies for manipulation of capsular polysaccharides. (a) To make a lab

strain bearing K1 and O16, the neuS and wbbL genes were replaced with an antibiotic resistance

marker in strains EV36 and BOS12, respectively. The genomic regions containing the marked

biosynthetic pathways were then serially P1 transduced into MC1061. (b) An O7:K1 natural

isolate was modified by deletion of either neuS or manB to make derivatives missing either the

K1 capsule or O7 antigen, respectively. The neuS deletion strain was then used to make a double

neuS manB deletion strain. In parallel, the genome of the O7:K1 isolate was used as a template

to make plasmids bearing either the complete O7 or K1 synthesis cluster. Strains used in this

study are listed in Chapter 6.

Because it remains unclear if encapsulation is sufficient or merely necessary to confer an

extended survival phenotype, we approached capsule synthesis using both additive and

subtractive strategies (Figure 4.1). First, organisms reported to express either a K1 capsule

(EV36)[101] or O16 (Bos12)[102] were modified to replace either neuS or wbbL, respectively,

with an antibiotic resistance marker. Transduction by bacteriophage P1 was then used to transfer

both synthesis clusters into a lab strain of E. coli, MC1061. Capsule synthesis in this strain was

controlled by addition of a plasmid expressing neuS, wbbL, or both. Second, plasmids

containing complete synthesis clusters for O7 and K1 were constructed, using a natural O7:K1

isolate as a template. Finally, the natural O7:K1 isolate was modified by deletion of neuS, manB

(required for O7 synthesis), or both. Capsule synthesis in these strains was similarly controlled

by addition of a plasmid expressing one or both of the deleted genes.

To detect the presence of polysialic acid on the bacterial surface, strains were tested for

sensitivity to bacteriophage K1E, which requires polysialic acid to adsorb to its host [103, 104].

To test for protection of the bacterial membrane by encapsulation, strains were assayed for their

29

ability to survive treatment with horse serum, bacteriophage T4, and/or bacteriophage T7 (table

1). As expected, strains bearing a complete biosynthetic pathway for either O16 or O7 show

protection against membrane-mediated attacks, while unencapsulated strains are consistently

killed. The only exception is expression of the O7 biosynthesis cluster in MC1061, which failed

to confer protection. This can be readily explained by documented mutations in the galactose

synthesis machinery of MC1061 [105], since galactose is a subunit of the O7 structure [106].

MG1655, a K12 strain with intact galactose synthesis machinery [107], shows protection with

the O7 biosynthetic pathway. K1 capsule expression, however, proved more challenging.

Although the K1 capsule transferred from EV36 into MC1061 led to K1E sensitivity, it did not

confer protection to membrane attack. The biosynthetic cluster for K1 cloned onto a plasmid did

not confer K1E sensitivity to MC1061 or MG1655. Because it only conferred partial K1E

sensitivity to an O7:K1 neuS knockout strain, we suspect that an undetected sequence error or

complex regulation may account for a lack of activity in laboratory strains. Only the O7:K1

natural isolate, and derivatives thereof, exhibited both K1E sensitivity and protection from

membrane attack in the absence of a functional O-antigen.

Although the in vitro assays suggested that the K1 capsule was only partially functional

in laboratory bacteria, we proceeded to assess whether a functional O-antigen and partially

functional K-capsule would provide an extended half-life in vivo. Survival of selected strains 120

minutes after systemic administration to a mouse is illustrated in Figure 4.2. The results are

largely consistent with the in vitro assays: less than 0.05% of MC1061, either unaltered or with

O16 and K1 capsules, survived, while up to 72% of derivatives of the O7:K1 survived. Deletion

of a gene required for either K1 capsule or O7 antigen synthesis from the O7:K1 isolate led to

~100 fold reduced survival, while complementation of the deleted gene restored a high survival

rate, as expected. Further K-capsule manipulation is required to improve the survival of a lab

strain.

Figure 4.2 – Survival of encapsulated bacteria in a mouse. Strains were systemically injected

into mice, and the mouse blood sampled 120 minutes later for cfu. Error bars are the standard

error of the mean for at least 3 replicates. Strains and plasmids used, from left to right, are:

MC1061; MC828E + pSB1A2-BBa_J72199; ATCC 23503; JTK041; JTK011; JTK041; JTK035

+ BBa_J72109- BBa_J72193; JTK011 + pSB1A2-BBa_J72190; JTK041 + BBa_J72109-

BBa_J72192.

30

Table 1 – In vitro assays of capsular polysaccharides

Strain Strain Description Plasmid

Plasmid

description K1Ea S/T4/T7

b

MC1061 + -

EV36 K1 - +

Bos12 O16:K92 + +

MC828Q

MC1061 O16(∆wbbL) K1(∆neuS)

∆msbB ∆fim ∆tonB

BBa_J72208-

BBa_J72190 neuS - -

MC828Q



pSB1A2-

BBa_J72191 wbbL + +

MC828Q



BBa_J72208-

I716023 wbbl, neus - +

ATCC

23503 O7:K1 - +

JTK011 ATCC 23503 ∆neuS + nd

JTK011 ATCC 23503 ∆neuS

pSB1A2-

BBa_J72190 neuS - nd

JTK035 ATCC 23503 ∆manB nd +

JTK037 ATCC 23503 ∆manB ∆neuS kanR nd -

JTK041 ATCC 23503 ∆manB ∆neuS + -

JTK041 ATCC 23503 ∆manB ∆neuS

BBa_J72109-

BBa_J72192 manB, neuS - +

JTK050 ATCC 23503 ∆manB ∆neuS transc + -

JTK050 ATCC 23503 ∆manB ∆neuS trans

BBa_J72109-

BBa_J72193 manB + +


BBa_J72109-

BBa_J72194 neuS - +


BBa_J72109-

BBa_J72192 manB, neuS - +

MG1655 nd -

MG1655

BBa_J72110-

BBa_J72169 O7 nd +

MC1061

BBa_J72110-

BBa_J72169 O7 nd -

MC1061

BBa_J72209-

BBa_J72170 K1 + nd

MG1655

BBa_J72209-

BBa_J72170 K1 + nd


BBa_J72209-

BBa_J72170 K1 +/- nd a + indicates bacterial survival, or lack of a K1 capsule, while - indicates bacterial killing, or

presence of a K1 capsule. +/- indicates a partial killing phenotype. nd not determined. b + indicates bacterial survival when challenged with one of: serum, T4 phage, or T7 phage. -

indicates bacterial killing when challenged with one of: serum, T4 phage, or T7 phage. nd not

determined. c trans is a highly transformable phenotype.

31

4.3.2 – Localization to Tumors and Plasmid Maintenance

In mouse tumor models, unencapsulated lab strains of E. coli have been reported to

localize to solid tumors [108]. However, plasmids relying on antibiotic selection are sometimes

lost during bacterial growth in the tumor. To overcome this limitation, other groups have

exploited E. coli’s requirement for diaminopimelic acid (DAP) [109, 110]. If the genomic DAP

biosynthesis pathway is disrupted by deletion of a gene, the resulting E. coli require either

exogenous addition of DAP or complementation of the deleted gene on a plasmid. Since DAP is

not known to be produced in mammalian systems, this provides a powerful selective force in

vivo. We therefore deleted dapD from the genome, and provided a complementing copy on the

plasmid backbone. To validate bacterial localization to tumors and plasmid maintenance,

laboratory E. coli bearing various plasmids were administered to mice bearing solid tumors. In

mice where bacteria were administered systemically (i.v. injection), 42% of tumors showed

intensive bacterial colonization, while in mice where bacteria were administered directly into

tumors, 86% showed intensive colonization (table 2). Without the use of the dapD plasmid

maintenance system, none of the bacterial populations maintained all of their plasmids.

However, using the dapD system, all of the bacteria maintained the dapD carrying plasmid, as

expected. These results confirm that our bacteria can successfully colonize tumors and maintain

a desired plasmid.

Table 2 – Bacterial Tumor Colonization and Plasmid Maintenance

Strain

Strain

Description Plasmids Injection

tumors with

bacteria

bacteria with

all resistances

MC1061 i.v. 2 of 6 n/a

MC1061

BBa_J72109-

BBa_J72195

BBa_J72210-

BBa_J72176

BBa_J72113-

BBa_J72196 i.v. 2 of 5 0 of 2

MC1061 i.t. 8 of 11 n/a

JH5-18 MC1061 inva

BBa_J72109-

BBa_J72195

BBa_J72113-

BBa_J72196 i.t. 4 of 5 0 of 4

JTK174

MC1061 inv2

∆dapD

BBa_J72211-

BBa_J72198 i.t. 6 of 6 6 of 6

JTK176

MC1061 inv3

∆dapD

BBa_J72211-

BBa_J72198 i.t. 6 of 6 6 of 6 a inv, inv2, and inv3 denote alleles of invasin

32

Figure 4.3 – In vitro killing of tumor cells. HeLa cells were exposed either to bacteria (JH4-C7)

carrying the payload delivery plasmid (BBa_J72218-BBa_J72197) and a payload plasmid

(nothing, Cytolysin: BBa_J72212-BBa_J72200, BamHI: BBa_J72109-BBa_J72201, Barnase:

BBa_J72212-BBa_J72195), or to staurosporine. Cell killing, as evidenced by detachment of

cells, was determined 24 hours later. aBarnase was later tested against B16F10 and HCT116 cells

and found to be equally effective.

4.3.3 – Engineered E. coli Kill Tumor Cells In Vitro, but not In Vivo

We have previously described development of a bacterial system for delivering proteins

to mammalian cell in vitro[90]. Briefly, E. coli was engineered to express Invasin from Yersinia

pseudotuberculosis, enabling uptake by cancer cells overexpressing β integrin. Upon entry into

an endosome, the bacteria express Holin from phage Lambda, causing self-lysis. Lysis released

Perfringolysin O (pfo) and Phospholipase C (plc) from Clostridium perfringens, in turn causes

rupture of the endosome. To demonstrate system functionality, fluorescent proteins were

delivered to recipient mammalian cells. To enable E. coli to effectively kill tumor cells, we

sought to identify an appropriate protein payload to deliver this system. We selected an

endonuclease (BamHI), a ribonuclease (Barnase [111]), and a pore-forming hemolysin (HlyE

[112]) for investigation. Expression of BamHI would be expected to irreparably damage the

tumor cell DNA, thereby leading to cell death. The bacterial strain expressing BamHI carries the

corresponding DNA methylase to prevent digestion of its own genome. Exposure to Barnase

should lead to cleavage of cellular mRNA and subsequent cell death. To express Barnase in E.

coli, it has been fused to a PelB export tag [113] and is expressed in a strain also expressing its

inhibitor, Barstar, in the cytoplasm. Bacteria overexpressing HlyE have been previously

reported to be toxic to mammalian cells [114]. Strains carrying the payload delivery device and

each of these payloads were incubated with cancer cells in vitro, and their cytotoxicity assessed

(Figure 4.3). While no killing was observed for BamHI or HlyE, substantial cellular detachment

33

was observed for cells exposed to Barnase expressing bacteria. We consider this to be evidence

of bacterial mediated tumor cell killing.

Figure 4.4 – Bacterial treatment of tumors in mice. (a) Mice carrying tumors of implanted

B16F10 cells were injected intravenously with either control bacterial (MC1061) or cytotoxic

bacteria (JTK176 + BBa_J72211-BBa_J72198). Error bars represent the standard deviation of

three replicates. (b) Mice carrying tumors of HCT116 cells were injected either intravenously

(i.v.) or intratumorally (i.t.) with control bacteria (MC1061) or cytotoxic bacteria (JTK176 +

BBa_J72211-BBa_J72198). Error bars represent the standard deviation of at least four replicates.

To follow up on the in vitro results, we sought to observe an effect on tumor growth in a

mouse model. Mice bearing established subcutaneous B16F10 tumors were injected with

bacteria carrying Barnase as a payload. However, no significant effect on tumor growth was

observed (Figure 4.4a), and tumor sizes exhibited considerable variability. In an attempt to

reduce the variability, a slower growing cell line, HCT116, was also tested, and in some cases

bacteria were injected directly into the tumors to ensure colonization (Figure 4.4b). Although

there may be a modest reduction in variability, the average tumor growth rates don not

significantly differ, suggesting that further engineering is required to make E. coli an effective

cancer therapeutic.

4.4 – Discussion

Our strategy for engineering a lab strain of E. coli to treat solid tumors entails making

several modifications, including addition of protective capsular polysaccharides and

incorporation of a direct cell-killing mechanism. The results presented here demonstrate

substantial progress towards achieving both of those goals.

We have successfully modified a lab strain of E. coli to have either an O16 antigen or an

O7 antigen, and demonstrated that they confer resistance to membrane mediated killing. For

both O-antigens and the K1 capsule, omission of one of the required biosynthetic genes (wbbL,

34

manB, or neuS for O16, O7, and K1, respectively) proved to be an effective mechanism for

controlling capsule expression. Unlike the O-antigens, however, we were ultimately unable to

confer a protective K1 phenotype to a lab strain of E. coli. Considerable effort was required to

effect a genomic transfer of the biosynthetic genes from a hybrid strain, EV36, to a lab strain,

MC1061. Even though the resulting strain exhibited sensitivity to a K1 specific phage, it was not

resistant to other bacteriophage, unlike a natural K1 isolate where the O-antigen synthesis had

been disabled. Similarly, attempts to clone the K1 capsule onto a plasmid failed, with poor

complementation of K1 biosynthesis even in a natural isolate missing only a single K1

biosynthetic gene, and no apparent K1 biosynthesis in a lab strain. Although the mode of failure

remains unclear, our difficulties working with K1 are not unique. A plasmid previously reported

to confer a K1 phenotype (pRS23 [115]) failed to do so in our hands, and was described as

“unstable” by the provider of the material. The polysialic acid biosynthesis cluster from

Neisseria meningitidis serogroup B is known to undergo frameshift mutations due to slipped-

strand mispairing during replication [116], and it is possible that some similar mechanism leads

to frequent loss of K1 cluster function in E. coli. We suspect that recoding the K1 biosynthesis

cluster may remove such obscure regulation and enable more controlled manipulation.

Although manipulation of capsule expression in a lab strain remains technically

challenging, it stay may prove to be an effective approach for enabling therapeutic applications

in humans. Even in a mouse model, where the complement is unable to directly kill E. coli

[117], the presence of a functional O-antigen and K-capsule was essential for maintaining

substantially higher survival rates. We presume this extended lifetime would translate into more

effective tumor localization, although this remains to be tested by exploring the dose of bacteria

required to achieve robust localization behavior in encapsulated versus non-encapsulated strains.

Because even unprotected bacteria can localize to tumors in a mouse model, we were still

able to investigate whether engineered E. coli can affect tumor growth. We first assessed the

ability of bacteria bearing both a toxic payload and a payload delivery device to kill cultured

tumor cells in vitro. Expression of a ribonuclease, Barnase, conferred a killing phenotype to E.

coli, although expression of an endonuclease, BamHI, and a hemolysin, HlyE, did not. These

observations might be explained by more robust ability of mammalian cells to repair DNA

damage[118], and an insufficient dose of HlyE to affect cell viability, although further

investigation would be required to confirm those limitations. Because Barnase carries a

periplasmic export signal sequence, it is possible that indirect tumor cell killing may have

resulted from uptake of extracellular Barnase in vitro. Confirmation of the mechanism of

Barnase-mediated killing should be explored by omitting different components of the payload

delivery device. Further, Barnase-mediated killing, as well as other killing mechanisms, should

be explored with a range of bacterial concentrations, since a lower multiplicity of infection may

help differentiate between more and less effective cytotoxic strategies. It may be the case that

the relatively high concentrations of bacteria used to do the in vitro studies here are not

representative of the bacterial exposure of typical cells in a tumor.

After establishing that Barnase expressing bacteria are capable of cell killing in vitro, we

proceeded to test for an effect on tumor growth in a mouse model in vivo. However, we observed

no reduction in tumor growth rates after administering engineered E. coli. There are a number of

possible explanations for this failure, including poor distribution of E. coli within the tumor,

insufficient bacteria interacting with tumor cells to cause killing, and differences in bacterial

behavior in a real tumor microenvironment. With Salmonella spp., it was found that

modification of the chemotactic machinery improved distribution throughout tumor tissue [119];

35

similar work may be required to improve E. coli function. As described above, a high MOI was

used to assess bacterial killing capability in vitro, while only a few bacteria might interact with

each tumor cell in vivo. The payload delivery device was developed and tested on cancer cells in

vitro; it is conceivable that changes in the microenvironment of the tumor, such as reduced

oxygen, could affect the behavior of the device in vivo. Preliminary evidence suggests that none

of the tumor cells receive the payload, implying that improving the robustness of the payload

delivery device to conditions in tumor tissue should be explored. Such an investigation may be

facilitated by the use of a 3D cell culture model, which more closely represents conditions in

actual tissue [120-122].

Clearly, engineering E. coli to act as a cancer therapeutic is an ambitious and challenging

goal. Although we have made considerable progress toward this goal, several important obstacles

remain. As both our technical ability to manipulate DNA at the scale of genomes [123, 124] and

the fidelity of in vitro assays improve, these obstacles should become easier to overcome.

4.5 –Materials and Methods

4.5.1 –Plasmids, Strains, Phage, and Growth Media

Plasmids used in this study are described in Chapter 6, and were constructed using

BglBrick and BioBrick standard assembly [43, 125]. E. coli strains MC1061 [45], MG1655

[107], EV36 [101], BOS12 [102], ATCC 23503 (referred to as O7:K1 natural isolate) or

derivatives thereof were used for experiments. Derivatives were generated by the procedure of

Datsenko and Wanner [62], by random mutagenesis [126], and by P1 transduction [127]. Full

descriptions of plasmids and strain modifications are available in Chapter 6, and DNA sequences

have been deposited in the Registry of Standard Biological Parts (http://partsregistry.org) [44].

Bacteriophages T7 and T4 were the kind gift of Richard Calendar. Bacteriohage K1E [104] was

the kind gift of Dean Scholl. Bacterial cells were grown in Luria-Bertani medium (LB), or LB +

0.2% v/w arabinose for strains carrying pSB1A2-BBa_J72199, or LB + 40 mM MgSO4 for

strains carrying BBa_J72113-BBa_J72196 or BBa_J72218-BBa_J72197.

4.5.2 – Serum Resistance

Stationary phase cultures of strains to be tested were diluted to approximately 1 x 105

cfu/mL in LB. 5 µl of the diluted bacterial culture was added to both 100 µl of sterile filtered

horse serum (Thermo Scientific # SH30074 03) and 100 µl of LB. After 1 hour incubation at

37°C, 50 ul of each mixture was plated for cfu determination. Bacteria were scored as resistant if

the count of colonies from serum treated bacteria was at least 50% of the LB only treated

bacteria, and scored as sensitive if the count of serum treated bacteria was less than 5% of the LB

only treated bacteria.

4.5.3 – Phage Sensitivity and Resistance

Stationary phase cultures of strains to be tested were diluted 40-fold into LB and

incubated for 1 hour at 37°C. Phage lysate was added, and bacteria incubated for an additional 1-

2 hours at 37°C. Cultures were then inspected visually for clarification.

36

4.5.4 – Cell Culture Cytotoxicity

A monolayer of HeLa ,B16F10 (ATCC: CRL-6475), or HCT116 (ATCC: CCL-247) cells

was prepared 24 hours prior to experimentation in a 24-well culture plate in growth medium

(DMEM or RPMI supplemented with 10% fetal bovine serum) with penicillin and streptomycin

antibiotics. The medium was replaced with fresh medium without antibiotics, and either 3 µl of

stationary phase bacterial culture or 1 µM (final concentration) staurosporine was added to each

well, representing a multiplicity of infection of approximately 120. After 3 hours of incubation at

37 °C, cells were washed twice into growth medium with 100 µg/mL gentamicin, and the plate

incubated for a further 24 hours. The cells were then washed once with DMEM before

examination by microscopy. Detachment of most or all cells was considered to be evidence of

cellular killing.

4.5.5 – Preparation of Bacteria for Animal Experiments

Bacteria were harvested at mid-logarithmic phase, mixed 1:1 with 50% glycerol,

aliquoted into 1.5 mL microcentrifuge tubes, and frozen at -80oC. One aliquot was removed, the

bacteria washed with PBS, and serial dilutions plated on LB-agar plates to estimate bacterial

concentration. Prior to injection, a second bacterial aliquot was removed, washed with PBS, and

resuspended at a final concentration of 1 * 108 cfu/ml. Serial dilutions of the final solution were

plated on LB-agar plates to confirm the concentration of bacteria used for injection.

4.5.6 – Bacteria Clearance In Vivo

To assess bacterial survival, four- to six-week old female mice BALB/c mice (Jackson

Laboratories) were injected into the lateral tail vein with 100 µl of bacterial suspension. After

120 minutes, 5 µl of blood was collected from the tail tip, immediately diluted into 100 µl of LB,

and serial dilutions of the mixture plated on LB agar to determine cfu.

4.5.7 – Tumor Models and Bacterial Infections

Six- to seven-week-old male athymic nude mice (Jackson Laboratories Nu/J strain

002019) were injected subcutaneously on the right flank with 1.5 * 106 B16F10 murine

melanoma cells (ATCC: CRL-6475) re-suspended in 100 µl PBS or 1.5 * 106 HCT116 human

carcinoma cells (ATCC: CCL-247) re-suspended in 50 µl PBS and mixed 1:1 with growth factor

reduced matrigel (BD Biosciences) for a total volume of 100 µl. Tumor-bearing nude mice were

injected with bacteria after tumors reached a size of ~100-150 mm3, typically around 1 week post

cell implantation for B16F10 and 2 weeks post cell implantation for HCT116. Injections were

either a single injection of 100 µl into the lateral tail vein or 2 injections of 50 µl directly into the

tumor mass. Tumor volumes (calculated as L * W * W / 2) were monitored at least 3 times per

week until they exceeded 1500 mm3, at which point the study was terminated. To assess bacterial

colonization of the tumor, mice were sacrificed, the tumors were excised and homogenized, and

serial dilutions were plated on LB agar plates with or without appropriate single antibiotics. All

animal experiments were carried out in accordance with protocols approved by the Animal Care

and Use Committee (ACUC) of UC Berkeley.

37

Chapter 5 – Successes and Failures in Modular Genetic Engineering

Authors:

Joshua T Kittleson


Gabriel C. Wu

Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology,

University of Texas at Austin, Austin, Texas, USA




States

Reprinted from Current Opinion in Chemical biology, JT Kittleson, GC Wu, JC Anderson,

“Successes and Failures in Modular Genetic Engineering”, Vol. 16, 329-336 (2012) with

permission from Elsevier.

5.1 – Abstract

Synthetic biology relies on engineering concepts such as abstraction, standardization, and

decoupling to develop systems that address environmental, clinical, and industrial needs. Recent

advances in applying modular design to system development have enabled creation of

increasingly complex systems. However, several challenges to module and system development

remain, including syntactic errors, semantic errors, parameter mismatches, contextual sensitivity,

noise and evolution, and load and stress. To combat these challenges, researchers should develop

a framework for describing and reasoning about biological information, design systems with

modularity in mind, and investigate how to predictively describe the diverse sources and

consequences of metabolic load and stress.


Synthetic biologists engineer systems both to understand design principles relevant to

natural systems (reviewed in [128]) and to address important environmental, clinical, and

industrial needs (reviewed in [3, 4, 129]). Drawing on important concepts including abstraction,

standardization, and decoupling [11], investigators have developed both novel systems that

operate with little direct interaction with the host and synthetic systems intentionally embedded

into native networks [130]. Here, we review how modular design, an important engineering

strategy, has enabled development of more complex systems, and describe some of the failure

modes that still challenge biological engineers, as well as offer strategies to address such design

challenges.

38

5.3 – Modular Design

It was recognized several decades ago [131] that proteins are composed of modular

domains, independently folding polypeptides that can be linked together to form a functional

enzyme. More recently, biologists recognized that interacting collections of biomolecules that

carry out discrete functions can also be seen as modules [132], and identified biological modules

based on correlated protein activities [133-135]. These descriptive methods simplify analysis of

native networks, but fail to provide a framework for predicting the behavior of engineered

genetic networks. Although a formal theoretical framework for quantifying the robustness of a

module to external module connections has been developed [136], it offers no insight into how to

describe or characterize the biological operations performed by a module, or its in different

cellular contexts. Absent theoretical guidance, biological engineers have focused on building a

repertoire of modules with human defined functions [2] and characterizing modules in a way that

allows their reuse [137].

Modular design envisions complex systems as compositions of functional modules that

have been independently fabricated and characterized [138]. In mature engineering disciplines,

module behavior is well-characterized, robust in expected use scenarios, and independent of

other system elements. System design is reduced to specifying the necessary modules and

connectivity needed to generate a desired higher order function. Currently, the characterization,

reliability, and independence of biological modules are suspect [139]. The quantitative

interrogation of biological systems is technically challenging and exacerbated by the fact that

biological systems exist within the context of a cell. Some isolated biochemical entities such as

green fluorescent protein (GFP) exhibit robust behavior across multiple contexts, but designing

multicomponent modules that are sufficiently independent of cellular interaction remains a

challenge.

Nevertheless, several groups have developed increasingly complex systems through the

explicit use of modular design. In early work, Kobayashi and colleagues [140] framed system

design as development of three modules: an input/sensing module, a regulatory/computing

module, and an output/response module. Employing that framework, they engineered a cell-

density dependent protein expression platform. They developed a sensing module using quorum

sensing proteins from V. fischeri, a computing module using a transcriptional regulator that

behaves like a genetic toggle, and a response module that outputs GFP. Tabor and coworkers

followed a similar strategy to develop a light-responsive genetic edge detection program [141].

They employed a previously developed dark sensor [142] as an input module, which was

transcriptionally coupled to a regulatory module implementing edge detection logic. The

transcriptional output of the edge detecting module served as an input to the response module,

which expressed beta-galactosidase to enable visualization. The work illustrated how iterative

testing of a system built up from constitutive components can enable successful development of

a complex behavior.

As an alternative to describing expression levels as a signal that passes between genetic

modules, metabolic engineers use metabolites as signals that pass between independently tested

and fabricated biosynthetic modules. Martin and colleagues [143] engineered E. coli to produce

amorphadiene, a precursor to the antimalarial drug artemisinin, by first dividing the pathway into

“top” and “bottom” operons. They constructed the bottom operon and confirmed production of

amorphadiene from mevalonate, a stable intermediate that E. coli can obtain exogenously, before

providing the top operon and confirming function of the entire pathway. Similarly, Ajikumar and

39

coworkers [144] greatly improved biosynthesis of taxadiene, the first committed step towards

synthesizing the anticancer drug Taxol, by separating the biosynthetic pathway into upstream

and downstream modules. They joined the two modules by co-varying their expression and

searching for a global optimum. Although the above examples demonstrate the potential for

modular engineering, such systems are not generalizable, and the most elaborate synthetic

systems developed to date are dwarfed by even a small natural developmental program [145].

To explain this gap, we consider some of the challenges relevant to module design, connection,

and robustness.

Figure 5.1 – Failure modes of genetically engineered systems. (a) Syntactic errors, including

truncations, mutations, and omissions, result in missing or incorrect components. (b) Semantic

errors result from unintended or overlooked molecular interactions that adversely affect system

or cellular function. (c) Mismatched parameters, such as imbalanced expression levels, can cause

qualitative failures (e.g. dampened oscillations), fail to activate a desired phenotype, or trigger

responses outside of specified conditions. (d) Changing the context of a system, as by changing

the strain, copy number, or genomic location, can affect system behavior. (e) Populations of cells

exhibit a variety of behaviors due to stochastic noise, and mutations may abrogate function

entirely. (f) The additive metabolic load from one or more modules can manifest as either stress

or toxicity.

40

5.4 –Failure Modes

5.4.1 – Syntactic Errors: Ensuring desired features are present

Function of biological modules and systems requires that all components are present in

the DNA. However, user and database error can result in incorrect or missing components,

called syntactic errors. While a powerful tool, automated gene annotation software can mis-

annotate more than 10% of gene start sites [146, 147]. Naïve incorporation of an automatically-

annotated open reading frame (ORF) into an engineered genetic system can either truncate the

protein product, potentially abrogating function, or mislead designers about where to exert

translational control.

5.4.2 – Semantic Errors: Getting the molecular interactions right

Semantic errors consist of unintended molecular interactions that adversely affect system

function. For example, combining a module that employs a promoter with the widespread tet

operator (tetO) sequence [148] as a constitutive source of transcription will likely fail if

combined with a module that utilizes TetR (e.g. [149]). Similarly, introduction of a lox-based

circuit (e.g. [150]) into a P1 lysogen will likely fail due to native Cre expression [151]. More

difficult to anticipate than these well-characterized interactions are functions reported in the

literature, but not widely cited. For example, CpG methyltransferases are toxic to wild type E.

coli K12. Reports of cloning CpG methyltransferases explicitly cite the need to work in mcrA-

and mcrB- strains [152-154], since those genes restrict methylated cytosines [155]. However, the

mrr gene also restricts CpG methylated DNA [156] and while a mrr- strain was used in two of

the CpG methylase studies [152, 153], no comment about the need for this genotype appears in

the text. The large and rapidly growing body of scientific literature makes it challenging and

impractical to manually consider all possible interactions a priori and perform effective searches

for information about those interactions.

Similarly difficult to anticipate are interactions that require a quantitative rather than

qualitative description. In particular, cellular functions that are saturable or titratable will behave

differently depending on the extent of their usage, and prediction of the switching point is

challenging. For example protein degradation of SsrA-tagged proteins in prokaryotes normally

follows Michaelis-Menton kinetics [157], but shift to a first order decay rate when the

degradation machinery saturates. This phenomenon actually stabilized an oscillator system

developed in E. coli [158]. The twin arginine translocation pathway can also be saturated by

heterologous expression of tagged proteins, leading to accumulation of tagged proteins in the

cytoplasm [159]. This may lead to the failure of a synthetic system relying on efficient export.

Inversely, increased occupancy can affect system behavior. For example, introduction of lac

operator binding sites on a multi-copy plasmid effectively reduces the concentration of available

LacI regulator, enabling expression from an otherwise repressed genomic gene [160].

Unknown interactions are the most challenging for system designers. Substantial, but still

incomplete, progress has been made mapping the metabolic, regulatory, and signaling networks

of various organisms ([161-165]). Integration of heterologous elements exacerbates the problem.

While engineering E. coli to overexpress the non-native metabolite mevalonate, toxicity from the

accumulation of a pathway intermediate was observed [166]. Subsequent analysis demonstrated

that the intermediate inhibited fatty acid biosynthesis, and that toxicity could be alleviated by

41

exogenous addition of palmitic acid [167] or scaffolding of pathway enzymes [168]. The scope

of heterologous toxicity is hinted at by a recent survey of Sanger sequencing data, which

indicated that over 10,000 sequenced genes from a variety of organisms have toxic interactions

with E. coli [169].

One approach to minimizing unwanted interactions is segregation of system components

away from other cellular entities. Localization to the inside of a protein shell offers a mechanism

for accomplishing this segregation [170]. For example, the propanediol compartment from C.

freundii can be expressed in E. coli, and desired proteins targeted to its interior [171]. Progress in

the development of synthetic membrane bound organelles may ultimately offer a powerful

method for compartmentalizing synthetic systems, preventing undesirable interactions [172].

5.4.3 – Mismatched Parameters: Tuning behaviors Engineered systems will only exhibit the desired behavior if connected modules have matched

input/output characteristics. In a process analogous to signal conditioning in electrical

engineering, the output range of transcription or biomolecule production from one module must

be in the range that causes a desired phenotype or triggers appropriate behavior in a downstream

module. In early pioneering work, Yokobayashi and coworkers [13] illustrated this concept by

connecting a genetic IMPLIES gate to an inverter gate. When the initial circuit failed to work

properly, modification of the expression level or activity of the repressor carrying a signal

between the two gates resulted in a functional design. Another group developed bacteria that

release a toxic protein only when inside the anoxic environment of a tumor. Because the basal

output level of the oxygen-sensitive promoter caused undesired synthesis of the toxic protein

even in non-inducing conditions, the promoter was mutated to reduce its output level [82].

Similarly, Anderson and coworkers [173] engineered expression of invasin, which enables

bacterial uptake into mammalian cells, to trigger only in the presence of a low oxygen or high

bacterial concentration. To reduce undesired invasion in the absence of an appropriate signal, the

RBS of invasin was mutated to reduce its basal expression. The paradigm of matching module

activity is readily extended to engineering metabolic systems, where the importance of balancing

pathway activity has been increasingly recognized [12]. In a recent ambitious engineering effort,

simultaneous randomization of 20 ribosome binding sites in E. coli achieved a 390% increase in

lycopene production [39]. Application of expression tuning is widespread in biosynthetic

pathways and genetic networks. The recognition that appropriate coupling of modules and

components is critical for establishing both qualitative and quantitative function has led to the

development of several tools that allow relative expression levels of the same protein to be

selected rationally [56-59].

5.4.4 – Contextual Sensitivity: Influence of factors beyond primary sequence

Changing the genetic context of a system also changes both the expression level and gene

dosage. In mammalian systems, positional effects of different integration sites are known to alter

expression levels [174]. When Kramer and colleagues constructed a hysteric switch in

mammalian cells, they integrated two sets of genes into separate loci [175]. The positional

expression variability caused imbalanced production of system components in many screened

clones, and prevented the desired switching behavior. Use of insulator elements can protect

transgenes against both outside enhancers and gene silencing [176], as demonstrated by the

improvement of transgene expression reliability in mice following inclusion of insulator

42

elements into the expression cassette [177]. In bacteria and yeast, transcriptional interference can

alter expression [178], as can expression from upstream promoters. To enable more precise

measurement of promoter activities in E. coli, promoter-probe vectors were created using

terminators to insulate against transcription from both 5’ and 3’ elements [179]. Importantly,

altering gene dosage by placing a system on a multi-copy plasmid both increases the level of

gene expression and changes the number of cis DNA elements, such as operators or initiation

sites. Theoretical work has demonstrated that behavior of a genetic circuit at single copy differs

from the behavior at higher copy [180, 181]. Native systems employ feedback to mitigate the

effects of gene dosage [42], inspiring development of an engineered system in yeast that uses

feedback to ensure consistent protein expression levels regardless of the system copy number

[182].

Environmental context changes, such as altered pH or temperature, can directly affect the

activities of both biomolecules and small molecules. For example, acyl-homoserine lactone

degradation rates vary with both pH and temperature [183], making systems employing quorum

sensing potentially sensitive to corresponding environmental changes [184]. Activity changes in

response to environmental shifts have also been deliberately incorporated into engineered

systems [72-74].

5.4.5 – Noise and Evolution: System behavior in a population

Genetic systems exhibit behavioral diversity across a population, a consequence of both

stochastic processes and the mutability of underlying genetic programs. Improvement in the

ability to monitor the behavior of single cells has provided considerable insight into how

stochastic processes generate noise in isogenic populations [185-187]. Fluctuations in the

availability of shared resources, such as polymerase and ribosomes, has a global influence on

expression levels, while the biochemical processes responsible for expression of individual genes

causes further variation [188]. In prokaryotes, the balance between transcription and translation

strength strongly influences expression noise, with low transcription and high translation

generating the most variability [189]. In eukaryotes, bursts of transcription [190], potentially

attributable to chromatin remodeling, also contribute to expression noise.

Variability in gene expression affects the behavior of several published synthetic systems.

An engineered prokaryotic oscillator exhibited cell-to-cell variability in period and amplitude

attributable to stochastic effects, requiring single cell observations to confirm oscillatory

behavior [191]. In a eukaryotic regulatory switch constructed through positive feedback,

stochastic variations in expression levels led to spontaneous state switching [192]. By contrast,

natural systems both mitigate [185] and exploit [193, 194] noise and stochastic processes. A

study of the E. coli chemotaxis pathway found that it has the smallest topology that offers

sufficient noise suppression for detection of chemical signals [195], while B. subtilis maintains a

competency decision network architecture that causes significant population heterogeneity as a

bet hedging strategy [196]. As engineers, we can adopt strategies to similarly mitigate or exploit

noise in the design of genetic systems. Coupling a slow, short-range interaction via a quorum

sensing system to a fast, long-range interaction via a redox sensing system achieved the

synchronized oscillation of millions of E. coli cells [197]. Elucidation of the principles behind

engineering noisy promoters in yeast [198, 199] provides a tool for deliberate introduction of

noise for probabilistic decision making.

43

Spontaneous genetic mutations arise in biological systems, introducing a confounding

factor into genetic system design faced by few other engineering disciplines [11]. Although

misbehavior of a single cell in large population is of little consequence when the desired system

behavior relies on aggregate properties and requires little cellular growth, it can become

problematic for both large-scale production and applications beyond the bioreactor [5]. Any

burden imposed by an introduced genetic system (see discussion of load and stress below) could

potentially be removed by a disabling mutation. Given enough growth cycles, the non-functional

mutant will dominant the population [5]. While direct reduction of mutation rates due to DNA

replication is challenging, elimination of mobile DNA elements can stabilize introduced genetic

material. Systemic removal of insertion sequence elements from E. coli reduced mutation of

toxic plasmids, enabling recovery of DNA with desired sequences [200]. While undesirable

mutations usually remain problematic, engineers have taken advantage of spontaneous mutation

and applied selective pressure to evolve desired behaviors. Computational analysis of a whole-

genome metabolic reconstruction in E. coli identified a set of target genes that, upon removal,

coupled bacterial growth to production of lactate. Subsequent adaptive evolution of the resulting

strains by continuous growth substantially improved lactate titer [201].

5.4.6 – Chassis Failure: The role of load and stress

Indirect effects of an introduced system can alter the cellular state. Strong overexpression

of an otherwise non-toxic protein causes competition for ribosomes and leads to cell death [202].

Moderate overexpression, can still effect the cellular environment, potentially resulting in growth

inhibition, toxic accumulation of acetate, shutdown of the oxidative branch of the pentose

phosphate pathway, reduced expression of housekeeping proteins, or stress responses [203].

Although phenomenological models exist to describe protein load’s effect on growth [204],

consequences of cellular stress are not well understood. In yeast, for example, it has been

predicted that decreased free ribosome concentration (as from expression of competing

exogenous mRNAs) can lead to an ultrasensitive drop in translation of a subset of host mRNA

[205]. Overexpression of membrane proteins also indirectly affects the host, likely saturating

protein translocation machinery, consequently reducing respiratory chain complex formation and

causing respiratory stress [206]. In cases where the biochemical action of a produced protein

consumes energy [207] or subtly interacts with core host functions, the effect on the cellular

environment is even less clear.

5.5 – The Path Forward

Natural systems often demonstrate remarkable modularity and robustness, with complex

genetic programs [208, 209] and structures [210-212] readily transferrable between species.

Focused effort in a few key areas can facilitate rapid progress towards that goal. First, knowledge

about biological entities and systems, both natural and engineered, should be encoded in a

computer-understandable format using a controlled vocabulary. Recognizing the need for

codification in systems biology, several ontologies have been developed that enable description

of system components, simulation and analysis methodology, and numerical results [213].

However, the synthetic biology community has yet to select or develop an ontology that meets

the needs of biological engineers. Formal descriptions of current knowledge, and intelligent

44

inference algorithms that use it, are needed to scale designs beyond what is achievable through

human intuition alone.

Because device engineering requires a significant time and resource investment, reuse of

engineered components, modules, and systems is essential for rapid progress. Ideally, parts

should be selected from large orthogonal sets, instead of reusing workhorses like TetR, to

minimize collisions between devices [214]. For example, a set of RNA regulators was recently

described in which rational changes to the RNA generates orthogonal variants [215].

Additionally, recent reductions to the cost of large-batch gene synthesis [216] will enable data

mining of the metagenome for functionally equivalent but orthogonal sets of genetic

components. Modules should also avoid dependence on host functions, just as broad host range

plasmids and some phages encode most needed functions [217-220]. Devices not intended for

bioproduction should minimize metabolic load on the host, avoiding unnecessarily draining

components such as very strong promoters [221]. When possible, components should be placed

at single copy on the genome flanked by insulating elements, thereby both reducing load and

providing a more consistent gene dosage and expression level. Development of a framework for

predictively assessing the effects of stress and load will be critical to avoiding system failure

resulting from the additive effects of several modules. The synergistic effects of improvements to

engineering methodology coupled with the advancement of core technologies such as DNA

synthesis [10, 216, 222] and single cell manipulation and measurement [223-226] will enable

development of increasingly complex and useful biological systems.


This work was supported by the National Science Foundation Synthetic Biology

Engineering Research Center (SynBERC). J.T.K. received support from a Siebel Scholar award.

G.C.W. is supported by a National Science Foundation Graduate Research Fellowship.

45

Chapter 6 – Plasmids and Strains

Table 6.1 – Basic Parts

Registry # Short

Description

Long Description/Source

BBa_J27001 FRT FLP recombinase recognition site, Saccharomyces cerevisiae [227]

BBa_J72005 PTET BglBrick standard version of BBa_R0040,TetR repressible promoter (see

http://partsregistry.org/Part:BBa_R0040)

BBa_J72046 rbs.GFP.double

terminator BglBrick standard version of (BBa_E0040 joined with BBa_B0015)

BBa_J72047 PCON J23101 BglBrick standard version of BBa_J23101, synthetic constitutive promoter (see

http://partsregistry.org/wiki/index.php?title=Part:BBa_J23101)

BBa_J72048 rbs.sfGFP Superfolder GFP mutant [228]. BglBrick standard version of BBa_I746916 (see

http://partsregistry.org/Part:BBa_I746916)

BBa_J72049 double terminator Tandem terminators; BglBrick standard version of BBa_B0015 (see

http://partsregistry.org/Part:BBa_B0015)

BBa_J72050 PCON-B5 BglBrick standard version of BBa_J72131

BBa_J72051 rnpB terminator BglBrick standard version of BBa_J61048

BBa_J72052 rbs_brp! A modified version of bacteriocin release protein from E. coli[229]

BBa_J72054 PphoP Mg2+ responsive promoter from E. coli[230]

BBa_J72056 PmgrBl Mg2+ responsive promoter from E. coli[230]

BBa_J72059 terminator BglBrick standard version of BBa_B1006, synthetic transcription terminator (see

http://partsregistry.org/Part:BBa_B1006)

BBa_J72065 rbs_pelB> Leader sequence of pelB from Erwinia carotovora CE[231]

BBa_J72066 rbs_pelB_R> Leader sequence that exposes Arg at N-terminus after cleavage[232]. From Erwinia

carotovora CE[231]

BBa_J72068 rbs_lamB_R> Leader sequence of lamB that expose Arg at N-terminus after cleavage[232]. From E. coli[233]

BBa_J72071 araC-PBAD Arabinose responsive regulator and corresponding promoter from E. Coli [234]

BBa_J72072 rhaRS-PRHA Rhamnose responsive regulators and corresponding promoter from E. Coli [235]

BBa_J72073 PCON Synthetic constitutive promoter

BBa_J72074 PCON-c5 Synthetic constitutive promoter

BBa_J72075 PCON-g6 Synthetic constitutive promoter

BBa_J72076 LPsit Late promoter, Bacteriophage P1 [236]

BBa_J72077 trnpB terminator Terminator from E. coli DH1

BBa_J72079 cin, no start, frameshift

Derived from Bacteriophage P1 [236]

BBa_J72080 repL, no start Lytic replication origin, Bacteriophage P1 [236]

BBa_J72082 rbs.coi Repressor inactivator, Bacteriophage P1 [236]

BBa_J72083 rbs.pacA DNA packaging enzyme subunit and DNA packaging sites, Bacteriophage P1 [236]

BBa_J72084 rbs.mRFP1 Monomeric red fluorescent protein derivative [237]

BBa_J72085 rbs.repL Initiates lytic replication, Bacteriophage P1 [236]

BBa_J72086 rbs.pir Factor required for replication of gamma origin of E. coli plasmid R6K [28]

BBa_J72087 rbs.lacZ Beta-galactosidase from E. coli MG1655

BBa_J72088 rbsC.ColE2 repA Replication initiator of E. coli/Shigella plasmid ColE2-P9 [238], used to support replication of

vector J72111 (referred to as pBjk2741 in [239])

BBa_J72089 rbs.c1 Master repressor, Bacteriophage P1 [236]

BBa_J72090 vioABCDE Synthetic (recoded) violacein synthesis operon from Chromobacterium violaceum. BglBrick

standard version of BBa_K274002 (see http://partsregistry.org/Part:BBa_K274002)

BBa_J72091 luxCDABE Luciferase and substrate synthesis operon from Photorhabdus luminescens(ATCC 29999) [240]

46

BBa_J72092 kanamycin

resistance cassette

BBa_J72128 <plc! Phospholipase C (plc) from Clostridium perfringens[241]

BBa_J72129 <pfo! Perfringolysin O (pfo) from Clostridium perfringens[241]

BBa_J72156 upp 5' UTR MG1655

BBa_J72157 upp 3' UTR MG1655

BBa_J72158 ColE2 minimal

origin Minimal sequence thought to support replication from plasmid ColE2-P9 [242]

BBa_J72159 rbs.repA Replication initiation protein from plasmid ColE2-P9 [242]

BBa_J72160 rbs2.repA Replication initiation protein from plasmid ColE2-P9 [242] with strong RBS

BBa_J72161 ColE2 origin Origin of replication from plasmid ColE2-P9 [242]

BBa_J72162 rbs.pir Replication initiation protein from E. coli R6K plasmid[27]

BBa_J72163 PglpT Promoter repressed by the presence of glucose [243]

BBa_J72164 dxs MG1655

BBa_J72165 rbs.dhfr Dihydrofolate reductase (type II) from plasmid R-388[244]. Confers trimethoprim resistance.

BBa_J72166 tR2 terminator A terminator from bacteriophage lambda with a >90% termination efficiency.[245]

BBa_J72167 rbs.neuS (BBa) Sialyltransferase responsible for chain elongation during synthesis of K1 capsule [246]

BBa_J72168 rbs.wbbL(BBa) Rhamnosyltransferase required for O16 antigen synthesis [247]

BBa_J72169 O7 biosynthetic

cluster Pathway for synthesis of O7 antigen [248] from ATCC 23503

BBa_J72170 K1 biosynthetic

cluster Pathway for synthesis of K1 capsule [246] from ATCC 23503

BBa_J72171 rbs.neuS Sialyltransferase responsible for chain elongation during synthesis of K1 capsule [246]

BBa_J72172 rbs.manB Required for synthesis of GDP-mannose for O7 biosynthesis[248]

BBa_J72173 <barnase! Ribonuclease from Bacillus amyloliquefaciens[249]

BBa_J72174 PCON J23100.2 Synthetic promoter (see http://partsregistry.org/wiki/index.php?title=Part:BBa_J23100)

BBa_J72175 rbs.barstar Barnase inactivator from Bacillus amyloliquefaciens[249]

BBa_J72176 rbs.invasin.termin

ator Invasin from Yersinia pseudotuberculosis [173]

BBa_J72177 PCON J23100 Synthetic promoter (see http://partsregistry.org/wiki/index.php?title=Part:BBa_J23100)

BBa_J72178 rbs.malE> Maltose Binding Protein (encoded by malE) can be fused to other polypeptides and direct their

export to the periplasm [250].

BBa_J72179 araC-PBAD (BBa) Arabinose responsive regulator and corresponding promoter from E. Coli [234]

BBa_J72180 rbs.hlyE Hemolysin protein from E. coli K12 [112]

BBa_J72181 rbs.NIS.R.BamHI Restriction endonuclease BamHI [251] with a 4x repeat of the SV40 NLS [252]

BBa_K112305 rbs_lLysozyme! Lysozyme from enterobacteria phage λ[253]

BBa_K112311 rbs_lholin! Holin from enterobacteria phage λ[253]

BBa_K112317 rbs_lantiholin! Antiholin from enterobacteria phage λ[253]

BBa_R0040 PTET (BBa) TetR repressible promoter (see http://partsregistry.org/Part:BBa_R0040)

Table 6.2 – Composite Parts

Registry

#

Description Composition

BBa_J72094 PBAD driven, coi only phagemid BBa_J72071.BBa_J72082.BBa_J72049

BBa_J72095 PBAD driven, coi + repL phagemid BBa_J72071.BBa_J72082.BBa_J72049.BBa_J72080

BBa_J72096 PBAD driven, coi + repL + pacA

phagemid

BBa_J72071.BBa_J72082.BBa_J72049.BBa_J72080.BBa_J72076.BBa_J72083

.BBa_J72085

BBa_J72097 PRHA driven, coi only phagemid BBa_J72072.BBa_J72082.BBa_J72049

47

BBa_J72098 Constitutively expressed c1 BBa_J72075.BBa_J72089

BBa_J72099 PBAD driven, coi + repL+pacA phagemid

for library fragment insertion BBa_J72071.BBa_J72082.BBa_J72049.BBa_J72076.BBa_J72083.BBa_J72085

BBa_J72100 Constitutively expressed lacZ BBa_J72074.BBa_J72087

BBa_J72101 PBAD driven, coi + pacA phagemid BBa_J72071.BBa_J72082.BBa_J72049.BBa_J72076.BBa_J72083.BBa_J72077

BBa_J72102 PBAD driven, coi + cin phagemid BBa_J72071.BBa_J72082.BBa_J72049.BBa_J72079

BBa_J72103 PBAD driven, coi + repL + cin + pacA phagemid


.

BBa_J72083.BBa_J72077

BBa_J72104 PRHA driven lux + vio operons,

constitutive lacZ expression BBa_J72072.BBa_J72091.BBa_J72090.BBa_J72049.BBa_J72074.BBa_J72087

BBa_J72105 Competitor phagemid; cin + repL + pacA BBa_J72079.BBa_J72080.BBa_J72083

BBa_J72106 PBAD driven, coi + RFP phagemid BBa_J72071.BBa_J72082.BBa_J72049.BBa_J72005.BBa_J72084.BBa_J72049

BBa_J72107 Reference promoter driving expression

of GFP BBa_J72047.BBa_J72046

BBa_J72108 PBAD driven GFP BBa_J72071.BBa_J72046

BBa_J72115 PBAD driven, coi + cin + repL phagemid BBa_J72071.BBa_J72082.BBa_J72049.BBa_J72079.BBa_J72080

BBa_J72116 PBAD driven, coi + cin + pacA phagemid BBa_J72071.BBa_J72082.BBa_J72049.BBa_J72079.BBa_J72076.BBa_J72083

.BBa_J72077

BBa_J72152 PBAD driven, coi + repL + cin + pacA phagemid expressing GFP

BBa_J72071.BBa_J72082.BBa_J72049.BBa_J72079.BBa_J72080.BBa_J72076.BBa_J72083.BBa_J72077.BBa_J72005.BBa_J72048.BBa_J72049

BBa_J72155 GFP expression cassette BBa_J72005.BBa_J72048.BBa_J72049

BBa_J72182 repA expression cassette BBa_J72005.BBa_J72159

BBa_J72183 GFP expression cassette BBa_J72163.BBa_J72048

BBa_J72184 repA expression cassette for genomic integration

BBa_J72073.BBa_J72160.BBa_J72049

BBa_J72185 GFP expression cassette BBa_J72005.BBa_J72048.BBa_J72049

BBa_J72186 Trimethoprim resistant, pir expression

cassette for genomic integration


.BBa_J72165.BBa_J72166.BBa_J27001.BBa_J72157

BBa_J72187 dxs containing sequence BBa_J72048.BBa_J72159.BBa_J72164

BBa_J72188 weak violacein expression cassette BBa_J72074.BBa_J72090

BBa_J72189 strong violacein expression cassette BBa_J72005.BBa_J72090

BBa_J72190 neuS expression cassette (BBa) BBa_r0040.BBa_J72167

BBa_J72191 wbbL expression cassette (BBa) BBa_r0040.BBa_J72168

BBa_J72192 manB and neuS expression cassette BBa_J72005.BBa_J72172.BBa_J72171

BBa_J72193 manB expression cassette BBa_J72005.BBa_J72172

BBa_J72194 neuS expression cassette BBa_J72005.BBa_J72171

BBa_J72195 Barnase/Barstar expression cassette BBa_J72073.BBa_J72065.BBa_J72173.BBa_J72077.BBa_J72174.BBa_J72175

.BBa_J72049

BBa_J72196 Payload Delivery Device BBa_J72056.BBa_J72052.BBa_K112305.BBa_K112311.BBa_J72049.BBa_J72050.BBa_K112317.BBa_J72051.BBa_J72005.BBa_J72066.BBa_J72129.BBa

_J72068. BBa_J72128.BBa_J72059

BBa_J72197 Payload Delivery Device BBa_J72054.BBa_J72052.BBa_K112305.BBa_K112311.BBa_J72049.BBa_J72050.BBa_K112317.BBa_J72051.BBa_J72005.BBa_J72066.

BBa_J72129.BBa_J72068. BBa_J72128.BBa_J72059

BBa_J72198 Payload Delivery Device with Barnase

Payload

BBa_J72054.BBa_J72052.BBa_K112305.BBa_K112311.BBa_J72049.BBa_J72050.BBa_K112317.BBa_J72051.BBa_J72005.BBa_J72066.BBa_J72129.BBa

_J72068.BBa_J72128.BBa_J72059.BBa_J72074.BBa_J72178.BBa_J72065.BB

a_J72173.BBa_J72077.BBa_J72174.BBa_J72175.BBa_J72049

BBa_J72199 wbbL neuS expression cassette (BBa) BBa_J72179.BBa_J72168.BBa_J72167

BBa_J72200 hlyE expresion cassette BBa_J72074.BBa_J72180.BBa_J72049

BBa_J72201 BamHI endonuclease expression cassette BBa_J72005.BBa_J72181

48

Table 6.3 – Plasmid Backbones

Registry # Description

BBa_J72109 ampicillin resistant pUC vector

BBa_J72110 ampicillin + chloramphenicol resistant p15A vector

BBa_J72111 spectinomycin resistant split ColE2 vector

BBa_J72112 spectinomycin resistant split R6K vector

BBa_J72113 chloramphenicol resistant p15A vector

BBa_J72114 complete, arabinose inducible phagemid, chloramphenicol resistant, p15a vector

BBa_J72153 ampicillin + kanamycin resistant p15A vector

BBa_J72154 kanamycin + chloramphenicol resistant p15A vector

BBa_J72202 ampicillin resistant p15A vector

BBa_J72203 kanamycin resistant split R6K vector

BBa_J72204 ampicillin resistant split R6K vector with O16 homology and kanamycin

resistance flanked by FRT sites

BBa_J72205 ampicillin resistant split ColE2 vector

BBa_J72206 ampicillin resistant split R6K vector

BBa_J72207 ampicillin resistant pUC vector

BBa_J72208 Kanamycin resistant BAC with R6K origin (BBa format)

BBa_J72209 kanamycin + ampicillin resistant p15A vector

BBa_J72210 Kanamycin resistant BAC with R6K origin, glucose driven promoter 5' of cloning

site

BBa_J72211 ampicillin resistant p15A vector with dapD

BBa_J72212 spectinomycin resistant pUC vector

BBa_J72218 kanamycin resistant p15A vector

pSB1A2 ampicillin resistant pUC vector (BBa format)

Table 6.4 – Strains

Name Description/Genotype Composition of insertions Registry #

ATCC 23503

O7:K1 isolate

BOS12 O16:K92 isolate

EV36

Hybrid K1-K12 strain derived from CGSC288

and RS1085 (which was derived from D699) [101]

JH5-18 MC1061 P21::Pflu-inv [90]

JTK011 ATCC 23503 ∆neuS

JTK029

MC1061 upp::pir+FRT-kanR-FRT

Cassette encoding constitutive expression of pir

and kanamycin resistance inserted between purM and uraA

purM - BBa_J72005.BBa_J72086.BBa_J72001.BBa_J72092.BBa_

J72001 - uraA

BBa_J72136

JTK030 MC1061 upp::pir+FRT Cassette encoding constitutive expression of pir

inserted between purM and uraA

purM - BBa_J72005.BBa_J72086.BBa_J72001 - uraA BBa_J72137

JTK035 ATCC 23503 ∆manB

JTK037 ATCC 23503 ∆manB ∆neuS kanR

JTK041 ATCC 23503 ∆manB ∆neuS


49

JTK160 MC1061 O16::repA+FRT-kanR-FRT

galF - BBa_J72073.rbslib-

BBa_J72160.BBa_J72049.BBa_J27001.BBa_J72092.BBa_J27001- wbbL

BBa_J72213

JTK160C

MC1061 O16::repA+FRT

Casette encoding constitutive expression of repA

(from ColE2) and kanamycin resistance inserted between galF and wbbL

galF - BBa_J72073.BBa_J72088. BBa_J72049.BBa_J72001.

BBa_J72092.BBa_J72001 - wbbL

BBa_J72138

JTK164 MC1061 upp::pir+FRT-trimethoprimR-FRT

purM - BBa_J72177.rbslib-

BBa_J72162.BBa_J72049.BBa_J27001.BBa_J72073.BBa_J72165.BBa_J72166.BBa_J27001 - uraA

BBa_J72214

JTK165EI MC1061 upp::rbsE.pir+FRT-trimethoprimR-FRT O16::rbsI.repA+FRT

purM - BBa_J72177.rbsE-

BBa_J72162.BBa_J72049.BBa_J27001.BBa_J72073.BBa_

J72165.BBa_J72166.BBa_J27001 – uraA

galF - BBa_J72073.rbsI-

BBa_J72160.BBa_J72049.BBa_J27001- wbbL

BBa_J72214

BBa_J72215

JTK174 MC1061 ∆dapD O16::PglpT-rbs1.inv galF- BBa_J72163.(rbs1)BBa_J72176.BBa_J27001 - wbbL

(composition is approximate - see sequence in registry) BBa_J72216

JTK176 MC1061 ∆dapD O16::PglpT-rbs2.inv galF- BBa_J72163.(rbs2)BBa_J72176.BBa_J27001 - wbbL

(composition is approximate - see sequence in registry) BBa_J72217

MC1061

F- araD139 ∆(araA-leu)7697 ∆(codB-lacI)3

∆(lac)X74 galK16 galE15λ- e14- mcrA0 relA1

rpsL150(strR) spT1 mcrB1 hsdR2

MC828E MC1061 O16(∆wbbL) K1(∆neuS)

MC828Q MC1061 O16(∆wbbL) K1(∆neuS) ∆msbB ∆fim

∆tonB

MG1655 F- λ- ilvG- rfb-50 rph-1

50

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