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NORTHWESTERN UNIVERSITY

Engineering Multiparametric Evaluation of Environmental Cues by Mammalian Cell-based Devices

A DISSERTATION

SUBMITTED TO THE GRADUATE SCHOOL IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

for the degree

DOCTOR OF PHILOSOPHY

Field of Chemical Engineering

By

Rachel M. Dudek

EVANSTON, IL

August 2015

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Abstract

Engineering Multiparametric Evaluation of Environmental Cues by Mammalian

Cell-based Devices

Rachel M. Dudek

Engineered cell-based therapies are a promising emerging strategy for overcoming

existing barriers to treatment. Reaching the full potential of this powerful therapeutic strategy

requires new tools for engineering mammalian cells to sense and respond to their physiological

environment in programmable ways. In particular, the engineered cell should be able to 1) sense

the cues in its environment and 2) evaluate multiple cues such that activation of its therapeutic

function is conditional upon whether it senses a healthy or a diseased environment. This sensing

and evaluation cascade should furthermore be performed in a manner orthogonal to the native

signaling pathways of the cell, to avoid interference with or by these native pathways.

Orthogonality also confers cell type independence, such that the technology could be ported into

any cell type of interest with minimal modification. To meet these needs, we have previously

described a platform technology to transduce an extracellular sensing event into a change in cell

state. We have developed the first fully orthogonal cell surface biosensor platform, termed a

modular extracellular sensor architecture (MESA), and we have described the engineering of a

generic dimerization-dependent signal induction mechanism.

Here we present an expansion of that technology to activate alternative output modalities,

to sense extracellular species via a novel single chain antibody-derived binding domain, and to

perform multiparametric sensing and evaluation within mammalian cells. First we investigated

whether the MESA could be configured to activate an alternative output, by reconstituting an

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enzyme in response to ligand-induced dimerization. Second, we investigated whether we could

achieve sensing of exclusively extracellular ligands using the MESA platform. Leveraging a

novel protein binding domain, the nanobody, we demonstrated MESA that transduce an

extracellular ligand-binding event into an orthogonal intracellular signaling event. Moreover, we

demonstrated that these protein-binding MESA are readily adaptable to recognizing a distinct

cue, and that two MESA receptor pairs specific for distinct cues could be multiplexed into a

logic gate for multiparametric evaluation of the extracellular environment. As a whole, this work

fills an important gap in the mammalian synthetic biology toolbox and may enable novel

therapeutic strategies using engineered cells.

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Acknowledgments

I would like to thank the funding agencies that made this work possible, especially the

Defense Advanced Research Programs Agency (DARPA), the Robert H. Lurie Comprehensive

Cancer Center (RHLCC) Malkin Family Scholar program, and the National Academies Keck

Futures Initiative (NAKFI). Special thanks to the RHLCC Flow Cytometry Core for the facilities

and technical support that enabled this work.

I owe a great debt of gratitude to my adviser, Josh Leonard, for his guidance, patience,

quickness to both challenge and encourage me, and unabashed enthusiasm and optimism that

have formed me as a scientist and teacher. I also thank my committee, Bill Miller, Heather

Pinkett, and Lonnie Shea for their insightful and helpful discussion of my work over the years.

Thanks also to Linda Broadbelt, my Teaching Apprenticeship Mentor, for her guidance and

support.

To the members of the Leonard Lab, I am grateful for our years of collegiality and

camaraderie. I especially thank Nichole Daringer, my partner in crime in pioneering the project,

and Kelly Schwarz for continuing this work and taking it in new and exciting directions. I thank

Yishan Chuang and Andy Scarpelli (aka Johnny Raincloud), to whom I could always turn for

help in learning new skills, troubleshooting experiments, and being my sounding boards for all

things science and non-science. I will miss our adventures, convoluted inside jokes, and

whimsical pranks. I thank Michelle Hung and Andrew Younger for their unique contributions to

the lab’s vibrant prank culture, the institution of Iron Chef Leonard Lab, and of course for being

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insightful and supportive colleagues. To other former, new, and shared members of the Leonard

Lab (Shirley, Nichole, Katya, Brendon, Jennifer, Patrick, Ragan, Luke, Joe, Danny, Alia, Patrick,

and Taylor), thank you for your intellectual rigor and for being part of my academic family.

Thank you to Mark, Alaina, and Teresa in the Miller Lab for being good lab neighbors

and always having our back when incubators malfunctioned and when we ran out of FACS

tubes. This dissertation represents a truly staggering quantity of FACS tubes.

A special thanks to my good friend and kindred spirit Mirian Diop, for our many support

sessions on all things faith, family, science, and imposter syndrome. (They still haven’t found me

out, I think I might just get away with it…)

Thank you to the Sheil Catholic Center, and its priests, musicians, and people, for being

my spiritual home throughout my graduate career.

Thank you also to my sisters Anna, Sarah, Jessica, and Mary for your love and support,

and for always being interested and enthusiastic about my science. And thanks to my nephews

Luke and Noah, for being my favorite outside of lab distraction.

This work is dedicated to my parents, Karen Dudek and Kenneth Dudek, PhD, and ad

majorem Dei gloriam. I would be nothing without your constant love and support and sacrifices

for my sake. Thank you for giving me the world.

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Abbreviations

AAV – Adeno-associated virusCAR – chimeric antigen receptorCD28 – Cluster of differentiation 28CM – Conditioned mediaCREA – Conditional reconstitution of enzymatic activity CS – Cleavage sequenceCTL – Cytotoxic T-lymphocyteCTLA-4 – anti-cytotoxic T-lymphocyte antigen 4ECD – EctodomainGBP – GFP binding proteinHLA – Human leukocyte antigeniCAR – inhibitory chimeric antigen receptorLaM – llama antibody against mCherryLD – Intracellular linker domainMESA – Modular extracellular sensor architectureMHC-I – Major histocompatibility complexMOI – Multiplicity of infectionPC – Protease chainPPID – protein-peptide interaction domainPR – ProteaseRAP – rapamycinSCF – Extracellular scaffoldscFv – Short chain variable fragmentTAA – Tumor-associate antigenTC – Target chainTCR – T cell receptorTEV – Tobacco etch virus (protease)TIL – Tumor invading lymphocyteTF – Transcription factortTA – Tet transactivatorUAS – Upstream activator sequenceVEGF – Vascular endothelial growth factorVH – Variable heavyVHH – Variable heavy of heavy chain only antibodyVL – Variable light

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Table of Contents

Chapter 1: Introduction..............................................................................................................15

1.1 Introduction and significance..........................................................................................15

1.2 Engineered T cells for cancer immunotherapy...............................................................16

1.2.1 Adoptive T cell transfer...........................................................................................16

1.2.2 Engineered chimeric antigen receptor T cells.........................................................18

1.3 Logic gates and gene circuits..............................................................................20

1.4 Biosensor engineering.....................................................................................................22

1.4.1 Receptor engineering...............................................................................................22

1.4.2 Modular Extracellular Sensor Architecture.............................................................23

1.5 Single chain immunoglobulins and nanobodies..............................................................27

1.6 Engineered red blood cells..............................................................................................28

Chapter 2: Engineering a Cell-Based Biosensor that Activates a Transcriptionally Independent

Change in Cell State......................................................................................................................30

2.1 Introduction.....................................................................................................................30

2.2 Materials and Methods....................................................................................................32

2.2.1 DNA constructs.......................................................................................................32

2.2.2 Cell culture and transfection....................................................................................32

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2.2.3 Flow cytometry........................................................................................................33

2.3 Results.............................................................................................................................34

2.3.1 Engineering Conditional Reconstitution of Enzymatic Activity (CREA)...............34

2.3.2 Ligand-inducible CREA signaling via a small molecule........................................42

2.3.3 Ligand-inducible CREA signaling via protein-peptide interaction domains (PPID)...45

2.4 Discussion.......................................................................................................................50

2.5 Acknowledgments...........................................................................................................50

Chapter 3: Engineering Nanobody-based Biosensors that Sense and Respond to Extracellular

Cues...............................................................................................................................................52

3.1 Introduction.....................................................................................................................52

3.2 Materials and Methods....................................................................................................53

3.2.1 DNA constructs.......................................................................................................53


3.2.3 Adeno-associated virus production and titering......................................................54

3.2.4 AAV transduction of MESA and recombinant ligand stimulation..........................55

3.2.5 Flow cytometry........................................................................................................55

3.2.6 Immunolabeling.......................................................................................................56

3.2.7 Ligand binding assay...............................................................................................56

3.3 Results..................................................................................................................................57

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3.3.1 Design and characterization of MESA responsive to GFP......................................57

3.3.2 Design and characterization of MESA responsive to mCherry...............................66

3.4 Discussion.......................................................................................................................70

3.5 Supplemental information...............................................................................................72

3.6 Acknowledgements.........................................................................................................76

Chapter 4: Multiparametric extracellular cue evaluation via engineered AND gate reporters.....77

4.1 Introduction.....................................................................................................................77

4.2 Materials and methods....................................................................................................77

4.2.1 DNA constructs.......................................................................................................77


4.2.3 Flow cytometry........................................................................................................78

4.2.4 Microscopy..............................................................................................................78

4.3 Results.............................................................................................................................79

4.3.1 Design of hybrid TF reporter library.......................................................................79

4.3.2 Activation of hybrid promoter AND gate by membrane-bound TFs......................86

4.3.3 Activation of AND gate by MESA specific for distinct cues..................................88

4.4 Discussion.......................................................................................................................90

4.5 Acknowledgements.........................................................................................................91

Chapter 5: Conclusions and Recommendations............................................................................93

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5.1 Chapter 2: Engineering a Cell-Based Biosensor that Activates a Transcriptionally

Independent Change in Cell State..............................................................................................93

5.1.1 Conclusions...................................................................................................................93

5.1.2 Recommendations....................................................................................................94

5.2 Chapter 3: Engineering Nanobody-based Biosensors that Sense and Respond to

Extracellular Cues......................................................................................................................95

5.2.1 Conclusions..............................................................................................................95


5.3 Chapter 4: Multiparametric evaluation via engineered two-input dependent reporters..97

5.3.1 Conclusions...................................................................................................................97


Chapter 6 References...............................................................................................................99

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Table of Figures

Figure 1.1 Modular Extracellular Sensor Architecture (MESA) design concept. Proposed general mechanism: ligand binding-induced receptor dimerization causes the protease on the protease chain (PC) to cleave its cognate cleavage sequence on the target chain (TC), which releases the transcription factor (TF) to travel to the nucleus and modulate target gene expression by binding to a TF binding domain (TFBD) adjacent to a minimal promoter (Pmin) to drive expression of the output gene............................................................................................25

Figure 2.1 Conditional Reconstitution of Enzyme Activity (CREA) design. Proposed general mechanism: receptor dimerization causes the TEV N-terminal fragment (NTev) protease chain (PCN) to refold with its complementary fragment on the C-terminal fragment (CTev) protease chain (PCC), such that the reconstituted protease can cleave its cognate cleavage sequence on a third target chain (TC), which releases the transcription factor (TF) to travel to the nucleus and modulate target gene expression by binding to a TF binding domain (TFBD) adjacent to a minimal promoter (Pmin) to drive expression of the output gene.......................................35

Figure 2.2 Background characteristics of model receptor CREA. (a) Cleavage of target chain variants by cytosolic TEV. (b) Individual split TEV fragments lack proteolytic activity. (c) Geometric and kinetic analysis of contributors to sTEV background. Experiments were conducted in biological triplicate, mean fluorescence intensity (MFI) of YFP was measured for each sample after gating on transfected cells, measurements were normalized relative to the internal control (described in section 2.3.3), and error bars represent the scaled standard deviation............................................................................................................................37

Figure 2.3 Tuning design parameters of CREA. (a) Contributions of linker length and cleavage kinetics to dimerization-inducible CREA MESA signaling. (b) Effects of receptor stoichiometry on CREA performance. For target chain dilutions, fractions are defined relative to the starting amount of 1 µg of target chain plasmid vector DNA per sample, with empty vector plasmid used to keep the total amount of DNA transfected constant. For protease chain dilutions, fractions are again defined relative to the starting amount (1 µg each of PCN and PCC plasmid vectors), and empty vector plasmid was again used to keep the total amount of DNA transfected constant. Experiments were conducted in biological triplicate, MFI of YFP was measured for each sample after gating on transfected cells, measurements were normalized relative to the internal control (described in section 2.3.3), and error bars represent the scaled standard deviation. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001)..............................................................40

Figure 2.4 Rapamycin-induced CREA activation. (a) Schematic of CREA utilizing the rapamycin-binding Frb and FKBP domains. Refer to Figure 2.1 for mechanistic details. (b) Evaluation of background signaling for incomplete receptor configurations. (c) Ligand-inducible enzyme reconstitution. Reporter activation was measured for rapamycin CREA expressed transiently in cells cultured without rapamycin (light green) or with rapamycin (dark green). (d)

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Evaluating ligand-inducible enzyme reconstitution with linker-less target chains. Refer to figure 2.3 for measurement details. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001).................................44

Figure 2.5 Strategy for engineering PPID CREA. Schematic of CREA utilizing PPID ectodomains. Refer to Figure 2.1 for mechanistic details.................................................46

Figure 2.6 Characterizing peptide ligand-induced CREA. Combinatorial experiments were performed in which split-TEV receptors were fused to ectodomains bearing either homotypic (a) or heterotypic (b) PPID. Naming conventions indicate linker lengths within protease chains (e.g., SH3 10.6CTev = 10 aa between SH3 and TM and 6 aa between TM and CTev) and target chains (e.g., ILL1 = spacer between N terminal and C terminal ligand domains, ILL2 = spacer between C terminal ligand and anchor protein); purple bars indicate TC bearing both sh3 peptide ligands, green bars correspond to heterotypic ligand with sh3 and pdz peptides, and orange bars correspond to both pdz peptide ligands, with light shades indicating shorter linkers, and dark shades longer linkers. MFI of mCherry is presented (a) as a read-out for expression level of each TC / ligand. Experiments were conducted in biological triplicate, MFI of YFP was measured for each sample after gating on transfected cells, measurements were normalized relative to the internal control (described in section 2.2.3), and error bars represent the scaled standard deviation. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001)..............................................................48

Figure 3.1 Design and characterization of nanobody MESA responsive to GFP. (a) Schematic of GFP nanobody MESA and GFP nanobody (GBP) library information. Binding of ligand induces dimerization of the target chain (TC) and protease chain (PC) causing trans-cleavage of the cognate cleavage sequence and release of the transcription factor (TF), which binds to a TF binding domain (TFBD) immediately upstream of a minimal promoter (Pmin) to drive expression of the output gene. (b) Cell surface expression of HA-tagged nanobody MESA was verified by immunolabeling and flow cytometry. Shaded region represents control. (c) GFP-binding by nanobody MESA was assessed by labeling receptors with GFP followed by immunolabeling bound GFP (see section 3.2.5). (d) Reporter activity for GFP nanobody extracellular linker variants. Experiments were conducted in biological triplicate, mean fluorescence intensity (MFI) of DsRed was measured for each sample after gating on transfected cells, measurements were normalized relative to the internal control, and error bars represent the scaled standard deviation. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001).....................................60

Figure 2 Induction of nanobody MESA by exclusively extracellular protein. (a) Reporter activity for cells transfected with nanobody MESA in the presence of recombinant GFP added to culture media. See Figure 3.1 and Methods for measurement details. (b) Cells transduced with AAV MESA were evaluated for surface expression of MESA 7 days post transduction by immunolabeling and flow cytometry as described in Figure 3.1 and Methods. (c) Reporter activity for cells transduced with nanobody MESA and transfected with reporter plasmid in the presence of recombinant GFP added to the culture media. Measurement details are as in figure 1, with the pSecGFP co-transfected condition serving as the internal control for this experiment.........................................................................................................................65

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Figure S3.1Prediction of MESA subcellular localization using WoLF PSORT. By inputting the amino acid sequences of GBP1 TCs with the indicated peptides into the web-based program WoLF PSORT (http://www.genscript.com/psort/wolf_psort.html), we obtained the indicated weighted scores for preferential localization of the constructs. Only sp3 was predicted to confer surface localization preferentially over secretion or localization in intracellular compartments. In our hands, sp3 was indeed effective (see Figure 3.1b)......................................................72

Figure S3.2Assaying binding to soluble GFP by GBP MESA. Representative examples of constructs utilizing the 40α SCF domain were characterized for both surface expression (left column) and the capacity to bind soluble GFP (right column). Gray: cells transfected only with BFP (nonspecific binding control); white: cells transfected with the indicated GBP nanobody MESA. Assay details are described in 3.2.6 and Figure 3.1b...........................................73

Figure S3.3...Detection of secGFP visually and in conditioned culture medium. Cells expressing secGFP (top left) and expressing secGFP as well as a GBP1 PC (top right) were visualized ~40 hours post-transfection; both images were captures using the same microscope settings, with additional microscopy details as in section 4.2.4. Cells expressing either a 6xHis- or HA-tagged SecGFP construct were harvested ~40 hours post-transfection, along with corresponding conditioned media (CM). Lysate and CM were run at the dilutions from starting concentration shown with 30 µL loaded per well. Fresh media was also analyzed as a control (lane 1). Antibodies used were mouse anti-GFP mms-118 (Covance) and HRP-conjugated rabbit anti-mouse secondary (Life Technologies).......................................................................74

Figure S3.4Flow cytometry method for quantifying AAV titer. GBP6 target chain receptors with a C-terminal BFP fusion were packaged into AAV as described (section 3.2.3) so that the BFP could serve as a proxy for receptor expression. Viral crude lysate was used to transduce cells, and 48 hours post-transfection cells were harvested and analyzed by flow. The BFP positive population was determined by gating on negative control cells as shown, and MOI was calculated assuming that infection follows a Poisson process, such that MOI = -ln(1 - %BFP+)............................................................................................................................................75

Figure 4.1 Hybrid promoters for multiparametric evaluation using MESA. Schematic of hybrid promoter concept and library design. Capital letters represent pairs of transcription factor binding sites, whereas lower case letters denote single binding sites................................81

Figure 4.2 Hybrid promoters perform logical AND gate evaluation. Activation of hybrid promoter reporters by constitutively expressed transcription factors. Specific fold induction (“specific fold”, in this figure) is defined as the reporter output in the presence of both inputs divided by the highest reporter output conferred by either input alone. “Synergy” is defined as the reporter output in the presence of both inputs divided by the sum of the reporter outputs conferred by each individual input. PtTA is the two-tailed Student’s t-test value comparing reporter output induced by tTA alone to reporter output induced by both tTA and Gal4, and PGal4

is analogously defined. Experiments were conducted in biological triplicate, mean fluorescence

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intensity (MFI) of YFP was measured for each sample after gating on transfected cells, and error bars represent one standard deviation. Micrographs at bottom right show representative images from the pTU-YFP data set................................................................................................85

Figure 4.3 Transcription factors released from MESA can activate AND gate. (a) Schematic of rapamycin MESA and experimental set up. (b) Activity of hybrid promoters co-transfected with combinations of rapamycin MESA. Measurement details are as in Figure 3.1, with YFP serving as the fluorescent output and each reporter co-transfected with constitutive transcription factors serving as the internal control to which each sample was normalized (not shown)................................................................................................................................87

Figure 4.4 Multiparametric evaluation of extracellular cues by nanobody MESA coupled to a genetic AND gate. (a) Reporter activity of GFP nanobody MESA with Gal4 TF. (b) Reporter activity conferred by matched and mismatched nanobody PCs and TCs. (c) Reporter activity conferred by GFP and mCherry nanobodies co-transfected with 0, 1, or both secreted ligands. Measurement details are as in Figure 3.1, with BFP serving as the fluorescent output to avoid spectral overlap with ligands. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001)...............................89

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Chapter 1: Introduction

1.1 Introduction and significance

Engineered cell-based therapies have transformative potential for addressing unsolved

problems in human disease. Cells are uniquely able to sense and respond to their environment,

synthesize multiple bioactive molecules, and confer multifactorial effector functions in vivo. In

this way, cells can be considered “devices” that carry out sophisticated functions that cannot be

achieved with small molecule drugs or biomolecular therapeutics, and that can be “programmed”

to carry out designer functions using the tools of synthetic biology. A major limitation in our

ability to program cells as devices is the dearth of synthetic biology technologies for sensing

extracellular cues in the environment and relaying these into intracellular processing circuits

independently of the native processes of the cell.

Here we present a brief perspective on the emergence of engineered cell-based therapy

from the precursor field of adoptive cell transfer and as a direct response to advances in

understanding tumor-immune biology, availability of tools for genetic engineering, and the

intersection of synthetic biology strategy with translational research (section 1.2). We

demonstrate that further innovation in this field requires technologies that enable the cell-based

therapy to 1) sense exclusively extracellular species and 2) receive and process multiple inputs to

compute a response to its environment. While tools for developing sophisticated gene circuits in

cells (section 1.3) and engineering novel receptor technologies (section 1.4.1) have been

demonstrated, the ability to interface between these systems has largely remained unaddressed.

Our work in developing a Modular Extracellular Sensor Architecture (MESA) fills this gap

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(1.4.2) as it represents a core technology for relaying information across the barrier of the cell

membrane and converting any sensing event (input) into an intracellular state change (output).

We discuss the emergence of new tools (section 1.5) enabling expansion of the sensing

capabilities of MESA and applications (section 1.6) motivating the expansion of the output

modalities of MESA. We have published portions of this introduction elsewhere, specifically the

majority of sections 1.2-1.31 and 1.42.

These motivating and enabling examples from the literature and from our previous work

establish the feasibility of and highlight the need for biosensor technologies that: 1) give rise to

diverse (including transcriptionally independent) outputs and are amenable to sensing an array of

ligands for an array of applications (Chapter 2); 2) respond to exclusively extracellular inputs in

a modular and predictable manner (Chapter 3); and 3) can be multiplexed modularly to relay

information regarding extracellular input detection into an intracellular logical evaluator such

that cell output is contingent upon multiple extracellular cues (Chapter 4). The remaining

chapters of this dissertation describe our progress towards attaining these goals and

recommendations for next steps enabled by this work (Chapter 5).

1.2 Engineered T cells for cancer immunotherapy

1.2.1 Adoptive T cell transfer

Engineered immune cell therapy is a direct antecedent of the practice of adoptive transfer

of tumor reactive T cells as cancer immunotherapy1. First developed in the 1980’s, this

immunotherapy strategy3 is predicated on presumed natural mechanisms for controlling tumor

growth. The immunosurveillance theory, which was first formulated in the mid-twentieth

century, posits that during homeostasis, the adaptive arm of the immune system controls nascent

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tumors by recognizing mutant protein antigens expressed by tumor cells and targets these cells

for killing.4,5 This theory has been refined and expanded over half a century to now propose that

disease results from a gradual escape from immunological control during cancer progression, via

processes collectively termed immunoediting6. This overall conceptual model is supported by the

common observation of anergic or otherwise dysfunctional tumor-specific T cells in the vicinity

of established tumors.

Based on this understanding of tumor–immune interactions, early immunotherapy

approaches were motivated by the hypothesis that naturally occurring tumor-specific

CTL may be harnessed therapeutically. In this approach, autologous tumor infiltrating

lymphocytes (TIL) are isolated from a surgically accessible tumor, expanded, and activated ex

vivo, and re-infused into the patient.3 In clinical trials, autologous

TIL therapy has shown promise for treating melanoma, but efficacy has largely been limited to

this type of cancer7. More generally, experience with autologous TIL highlighted the importance

of generating sufficient quantities of T cells having both tumor antigen specificity and the

capacities to persist, proliferate, and induce cytotoxic functions at the tumor site upon re-

infusion. The advent of technologies for genetically modifying human cells opened the door to

potentially programming desired functionalities into a cell-based therapy. One approach for

applying genetic engineering to circumvent the challenge of isolating and expanding TIL is to

identify a T cell receptor (TCR) that is specific for a given TAA and then clone and express this

TCR as a transgene in autologous T cells.8 Such a model TCR is generally isolated from TIL of a

patient with a good response to TIL therapy. The hypothesis motivating this approach is that

when the engineered T cell encounters a tumor cell expressing the TAA, the transgenic TCR will

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induce downstream signaling through native pathways, resulting in proliferation and induction of

cytotoxicity.

Since this approach relies upon native TCR, it is limited in that the transgenic TCR only

recognizes TAA presented in the context of a compatible MHC-I. Thus, a given cloned TCR is

only effective in HLA-matched patients (i.e., those expressing compatible MHC-I), and overall

efficacy is diminished by low MHC-I expression on tumor cells. In addition, since the transgenic

TCR may be expressed alongside a native TCR within a single-engineered cell, mispairing

between TCR chains creates receptors with hybrid specificity, potentially limiting recognition of

the tumor or raising the risk of off-target activation and induction of harmful autoimmunity 9.

Although transgenic TCR-based approaches demonstrated the feasibility of genetically

modifying cells to create customized therapeutics, the challenges and limitations associated with

this particular strategy also motivated the development of a new technology platform that is

amenable to modular incorporation of specific functionalities.

1.2.2 Engineered chimeric antigen receptor T cells

The next wave of transgenic T cell therapies that utilize chimeric antigen receptors

(CAR) represents a fundamental shift in strategy from recapitulating natural functionalities to

designing novel therapeutics that may be described as cell-based “devices”, and marks the

entrance of synthetic biology into this area of translational research. Moving from the

complex TCR synapse to the CAR requires conceptualization of the TCR as a

series of “parts” including sensing and signal transduction modules that can

be substituted, streamlined, and rearranged to recapitulate the natural

mechanism but in a defined and simplified way10. The native TCR comprises

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alpha and beta chains that form a signaling complex with delta, gamma,

epsilon, and zeta chains to transmit a signal after the TCR binds its target

antigen presented on the MHC of another cell. Specificity for a novel antigen

may be conferred by creating a chimeric TCR11, in which the TCR variable

region is replaced by an antibody derived single chain variable fragment

(scFv), comprising variable heavy (VH) and variable light (VL) chains joined

by a linker. Importantly, the incorporation of the antibody sensing domain

both confers specificity and removes the requirement that the TAA be

contacted in a MHC-dependent fashion, thus overcoming a key limitation of

the transgenic TCR approach. Reduction of this chimeric TCR to a single

chain minimal model gave rise to the first generation (1G) CAR12, in which

the scFv is fused to a transmembrane domain and a single signal

transduction domain (typically the TCR zeta chain). Pilot studies with these

1G CAR T cells highlighted a need for costimulation to enhance expansion

and persistence of the engineered cells13-15, motivating the design of second

(2G) and third (3G) generation CAR incorporating two or three total signal

transduction modules, such as CD28 and CD137.16-22 Second and third

generation CAR have shown remarkable efficacy in both preclinical23 and

clinical settings24-30, and represent not only a powerful therapeutic strategy

but also demonstrate the potential of a design-driven synthetic biology

approach to engineering cell-based therapeutics to counter disease.

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An important next step in the design cycle of this therapeutic approach

will be in improving safety and efficacy31, particularly in decreasing both on-

target and off-target effects32-35. Limitations to the broader applicability of

CAR therapy include the risk of toxicity from aggressive inflammation and

tumor lysis syndrome upon administration of the therapeutic cells and the

potential to attack healthy tissues displaying antigens that overlap with the

tumor antigen recognized by the CAR. Thus CAR therapy has been most

successful to date in treating lymphomas and leukemias, which are B cell

cancers, because the elimination of all patient B cells is generally tolerable

and preferable to the presence of cancer. Thus technologies enabling

conditional activation of therapeutics such as CAR T cells are needed.

1.3 Logic gates and gene circuits

An attractive capability that would improve the safety and efficacy of

cell-based therapy would be the ability to program an engineered cell to

evaluate its environment and then become “activated” only under pre-

specified conditions. Such a cell-based therapy could be programmed to

travel throughout the body and deliver a potent immune stimulant only when

the engineered cell enters the tumor microenvironment. For example,

engineered logical evaluation could be used to prevent activation in healthy

tissue by programming the therapeutic to survey for both a TAA and a

second antigen that is expressed only on healthy tissue that might also

express the TAA at low levels. One version of such a strategy has been

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implemented in the form of the inhibitory CAR (iCAR), which combines an

extracellular antigen-binding domain fused to the intracellular domain of a

native T cell inhibitory receptor, PD-1 or CTLA-4.36 Expression of iCAR

effectively dampened T cell activation via either a native TCR primed against

a model antigen or a transduced CAR when both the activating and inhibitory

receptor were engaged, without impeding activation when only the

activating antigen was present. Similarly, specificity could be achieved by

engineering the cell-based therapy to become activated only when it

encounters two TAA, neither of which is uniquely expressed by tumor cells.

One approach to implementing this strategy has been to transduce a T cell

with both a suboptimal CAR specific for one antigen and also with a

costimulatory receptor (CCR) specific for a second antigen, thereby making

full T cell activation conditional upon binding to both antigens.37 In addition

to providing specificity, combinatorial antigen recognition strategies could

also be employed to circumvent tumor escape by antigen downregulation.28

However, these approaches depend on the presence and identification of co-

activating tumor and inhibitory healthy cell antigens, which may vary

considerably between cancer types and subsets and across variable genetic

makeups of individual patients. Moreover, such approaches also rely on

native signal transduction pathways.

An alternative strategy would be to program a cell therapy to detect

multiple diverse cues in its environment (including cell surface-presented

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antigens as well as soluble factors), and activate an orthogonal logic gate or

gates to process these cues and recognize signatures representative of

either diseased or healthy states, and respond accordingly. A similar concept

was demonstrated using a gene circuit that sensed cellular miRNA levels and

used this information to selectively activate the circuit only when it was

expressed in specific cancer cells having miRNA expression “fingerprints”

matching those programmed to be recognized by the gene circuit.38 In

principle, such a circuit could be delivered via a nontargeted gene therapy

vector, transducing both healthy and cancer cells, but the output of the gene

circuit (such as a toxin) would be expressed only in the diseased cells. Such

a capability is uniquely possible using synthetic biology approaches to

perform multiparametric evaluation of cellular features, and both analog and

digital (e.g., Boolean) evaluation have been performed in mammalian cells

using “parts” composed of RNA elements39-41, gene transcription networks42,

43, and other protein-based elements44. These examples establish the

feasibility of engineering complex logical computation systems in

mammalian cells for therapeutic purposes and highlight the need for

technologies that interface extracellular sensing with such intracellular

logical evaluation systems.

1.4 Biosensor engineering

1.4.1 Receptor engineering

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Engineering cell-based devices that interface robustly with host

physiology necessitates new technologies for engineering cell-surface

biosensors that transduce the detection of exclusively extracellular ligands

into changes in cell state. One approach to building a biosensor for a given

ligand is to modify an existing biosensor protein to recognize a new input.

For example, directed evolution of G-protein coupled receptors (GPCRs) has

generated receptors with novel specificities (receptors activated by solely

synthetic ligands, or RASSLs) for drug-like small molecules.45, 46 Chimeric

antigen receptor engineering, described above, represents a rational design

strategy, in which a known extracellular antigen-binding motif is fused to the

downstream signaling cascade of a native receptor system. A limitation of

all these approaches is that these novel receptors utilize endogenous

downstream signaling mechanisms to transduce a detection event into a

change in cell state. Therefore, signaling downstream from the engineered

receptors may be subject to cross-talk or regulation by native cellular

pathways and components. Moreover, these sensing events may be

transduced into signaling via complex biophysical mechanisms47, precluding

the straightforward redirection of receptor output into engineered gene

circuits. Thus, integrating such modified receptors into complex synthetic

biology “programs” will require new engineering strategies.

An alternative approach for coupling ligand-binding to changes in cell

state is to redirect native receptor sensing and signaling into orthogonal

24

pathways. Most notably, the Tango assay enables one to detect a ligand

binding-induced protein−protein interaction by transducing this association

into the release of an engineered transcription factor from an inactive

state.48 In this system, the transcription factor is genetically tethered to a cell

surface receptor protein via an amino acid sequence that is cleaved by the

Tobacco Etch Virus protease (TEV), and TEV is genetically tethered to an

adaptor protein that is recruited to the receptor when the receptor is in the

ligand-bound state. Thus, binding of ligand to the receptor brings TEV into

proximity with its target sequence, resulting in a trans-cleavage event that

liberates the transcription factor to translocate to the nucleus and regulate

expression of an engineered reporter gene. Other approaches for monitoring

native protein−protein interactions include split protein reconstitution, in

which a protein such as GFP49 or TEV50 is genetically split, with N- and C

terminal domains fused to each of two interaction partners, such that

association between the interaction partners enables the split GFP or TEV to

refold and reconstitute its activity. While these approaches do redirect ligand

binding-induced receptor signaling into orthogonal signaling pathways, they

nonetheless rely on native interactions and may interact with native cellular

components. Moreover, receptor redirection requires existing native

receptors and adapter proteins, potentially limiting the generalizability and

portability of this approach. Thus, while several useful tools for biosensing

exist, a general approach for engineering biosensors for exclusively

25

extracellular ligands represents an important technology gap in mammalian

synthetic biology.

1.4.2 Modular Extracellular Sensor Architecture

To address this need, we have developed a technology we term a

Modular Extracellular Sensor Architecture (MESA).2 We have previously

described the design and development of the MESA platform, comprising

independent, tunable modules, and the optimization of MESA performance

using design-based approaches. Through the systematic characterization of

this platform, we have provided a quantitative framework that should

streamline the adaptation of the MESA system to recognize novel ligands

and the integration of these sensors into various synthetic biology functional

programs.

The MESA design concept, characterization, and iterative improvement have been

published along with the content of Chapter 2 sections 2.3.1 and 2.3.2. Those design details and

a summary of findings are briefly recapitulated here as background to better contextualize

Chapters 2, 3, and 4.

1.4.2.1 MESA design concept

The MESA design concept (Figure 1.1) comprises a fully self-contained

sensing and signal transduction system, such that binding of ligand to the

receptor induces signaling via an orthogonal mechanism to regulate

expression of a target gene. In our initial MESA design, ligand binding-

induced receptor dimerization results in proteolytic trans-cleavage of the

26

target chain (TC) by the protease chain (PC), releasing a transcription factor

(TF) previously sequestered at the plasma membrane. The ectodomain (ECD)

confers both specificity and affinity for a ligand. Potential ectodomain

sources include ligand-binding domains from native receptors, scFv, or any

other protein(s) that dimerizes upon ligand binding. Ligand binding may be

homotypic in the case of multivalent ligands (e.g., many cytokines exist as

homodimers), such that the ectodomain on each MESA chain recognizes the

same epitope. Ligand binding may also by heterotypic, such that the

ectodomain on each MESA chain binds to a distinct epitope on a given

ligand. The transmembrane domain (TMD) confers cell surface localization.

27

Figure 1.1 Modular Extracellular Sensor Architecture (MESA) design concept. Proposed

general mechanism: ligand binding-induced receptor dimerization causes the protease on the

protease chain (PC) to cleave its cognate cleavage sequence on the target chain (TC), which

releases the transcription factor (TF) to travel to the nucleus and modulate target gene expression

by binding to a TF binding domain (TFBD) adjacent to a minimal promoter (Pmin) to drive

expression of the output gene.

28

1.4.2.2 Summary of MESA platform characterization

To initially evaluate the feasibility of the MESA concept, we developed

a strategy enabling us to decouple the two engineering goals required to

build a functional MESA: (1) achieve ligand binding-induced receptor

dimerization and (2) achieve receptor dimerization-induced signaling. To

pursue the latter goal first and identify intracellular receptor architectures

that confer dimerization-inducible signaling, a small library of model

receptors was constructed in which receptor dimerization was mediated by

interactions between receptor ectodomains and did not involve any ligands.

For these model receptors, ectodomains were derived from (a) mCherry, a

monomeric fluorescent protein51 or (b) dTomato, a fluorescent protein that is

of comparable size to mCherry but that exists as an obligate homodimer,

such that dTomato dimerization is essentially irreversible52. Thus, in this

model system, the mCherry-MESA represent monomeric receptors, which

only encounter one another transiently due to diffusion within the cell

membrane whereas the dTomato-MESA represent receptors that dimerize.

Therefore, by comparing the amount of reporter gene activation conferred

by mCherry-MESA versus dTomato-MESA having identical intracellular

architectures, we were able to assess the degree to which that particular

intracellular architecture conferred dimerization dependent signaling.

The remainder of this initial MESA system was constructed as follows. To simplify

preliminary design evaluations, no additional extracellular scaffold (SCF) was inserted.

29

Transmembrane domains derived from CD28 were utilized to mediate cell surface expression of

MESA, which is an approach used extensively for this purpose in fusion proteins such as CAR

(section 1.2.2). Linker domains comprised flexible glycine/serine spacers of various lengths. The

autolysis-resistant tobacco etch virus protease S219V mutant53 (hereafter, TEV) and its wild type

cleavage sequence (ENLYFQ/G) were selected as trans-cleavage partners based upon the

specificity of this system and its extensive use in mammalian cells.48, 50, 54 As indicated by a

forward slash in the protease cleavage sequence above, cleavage occurs between glutamine and

glycine residues, and the position following the slash is termed P1. All constructs utilized the tet

transactivator (tTA) as a constitutively active transcription factor, such that release of tTA from

the plasma membrane induced expression of YFP from a tTA-responsive reporter construct.55, 56

Utilizing this library of limiting case receptors, we were able to “solve” the parameters of

LD length, CS kinetics, and PR length/kinetics that would give rise to dimerization-inducible

signaling. Moreover, we were able to exchange the model ectodomains for ligand-inducible

rapamycin binding domains FKBP (FK506-binding protein of 12 kDa) and Frb (FKBP

rapamycin-binding)57 and rapidly converge on design parameters that were functional for this

ligand-inducible system. This approach for systematic design space exploration was utilized to

characterize an alternative MESA output system employing split protein reconstitution (Chapter

2). Moreover, this characterization enabled the design and demonstration of MESA able to bind

extracellular proteins (Chapter 3) and prompted investigation into whether the architecture could

be multiplexed for performing logical evaluation (Chapter 4).

1.5 Single chain immunoglobulins and nanobodies

30

An intuitive choice for a protein-binding ectodomain is the scFv derivative of the

canonical antibody. As described in section 1.2.2, scFvs have been employed as the sensor

domain for CARs. They have other uses as well in imaging (e.g. as radiolabeled probes to bind to

and enable visualization of a cell type of interest58) and for modulating immune responses (e.g.

by binding to and blocking the activity of biomolecules such as the cytokines vascular

endothelial growth factor or VEGF59 and interferon alpha60). A limitation of scFvs is the

potential for the VL of one scFv to associate with the VH of another scFv, rather than with the

VH to which it is linked, resulting in aggregation61.

Interestingly, the immune systems of camelids include both conventional heavy and light

chain immunoglobulins as well as immunoglobulins with a heavy chain only.62 The variable

regions of these unique heavy chain-only immunoglobulins, often abbreviated ‘VHH’ (for

variable heavy of heavy chain only antibody) or termed a ‘nanobody’, is truly single chain,

unlike the scFv, and therefore smaller, more modular, and less prone to aggregate63. Due to these

advantages, nanobodies have been used in a number of applications to date, including imaging64-

66, targeting67, and as high affinity nanotraps68. Thus pipelines for rapidly developing and

screening libraries for functional binders of proteins of interest are on the rise69. For all these

reasons, nanobodies are good candidates for use as ligand-binding modules in biosensors.

1.6 Engineered red blood cells

Erythrocytes, or red blood cells, are not only the most plentiful cell

type in the human body with the longest track record in the clinic, they also

have a plethora of phenotypes that make them attractive candidates for cell

based therapy.70, 71 They are abundant and have a long circulation time—

31

comprising about a quarter of the cells in the human body and having a half-

life of ~122 days in humans—and therefore accessible with a considerable

but finite timeframe to their therapeutic window. Upon undergoing eryptosis

(an erythrocyte-specific form of apoptosis), erythrocytes are cleared by the

reticuloendothelial system, which presents the eryptotic cell, along with any

engineered cargo it may be carrying, to the immune system in a tolerogenic

manner. This property has been leveraged to promote tolerance to

erythrocyte-conjugated antigens72 and is advantageous since it enables the

engineered erythrocyte therapeutic to avoid the induction of an unwanted

inflammatory response, thus promoting its survival and efficacy. Finally,

since erythrocytes are enucleated, they carry no genetic material in their

mature form and therefore no risk of tumorigenicity.

Current strategies to utilize red blood cells therapeutically have

consisted of using them as drug delivery vehicles, by entrapment73 or by

noncovalently attaching prodrugs to their surface74. While erythrocytes

cannot be genetically modified in their mature form, they may be generated

in vitro from nucleated progenitors. These progenitor cells may be

genetically engineered such that modifications to the genes encoding

proteins found in the plasma membranes of erythrocytes persist through

differentiation and enucleation71. Thus modification of an erythrocyte to bear

a surface-bound biosensor is challenging but feasible, and it would require

biosensor output to be transcriptionally independent. It is therefore highly

32

desirable to develop biosensor platform technologies that are cell-type

independent and can be applied to cell types as diverse as T cells and

erythrocytes with minimal modification.

33

Chapter 2: Engineering a Cell-Based Biosensor that Activates a

Transcriptionally Independent Change in Cell State

2.1 Introduction

The basic MESA mechanism (Figure 1.1) is well-suited to coupling MESA output to the

regulation of genetic circuits; however it is also of interest to design MESA receptors in which

receptor dimerization alters cell state via a transcription-independent mechanism: reconstitution

of enzymatic activity. We have termed this system Conditional Reconstitution of Enzyme

Activity, or CREA. In this system, N- and C-terminal fragments of TEV were each fused to

separate chains, such that ligand binding-induced dimerization should promote reconstitution of

split TEV protease (sTEV), which can be monitored by cleavage of a third “target” chain. Split

TEV has been used to monitor protein-protein interactions50, and this concept appears to be

generalizable to reconstitution of many proteins, as similar systems using split GFP49, 75, 76, split

luciferase77, or split beta-lactamase78, 79 have been developed. Hypothetically, CREA could

couple biosensing to metabolism, could enable biosensor-mediated control of processes in

enucleated cells, or could rapidly induce physiological processes such as caspase-induced

apoptosis. Thus, reconstitution of sTEV serves as a proof of principle for a wide range of

potential MESA-derived outputs. We also hypothesized that CREA might exhibit low

background and improved signal-to-noise, since diffusive encounters between partial TEV

fragments and the target chain would not result in a cleavage event.

34

Here we report the characterization of this split enzyme reconstitution system using

limiting case ectodomains representing constitutively monomeric and constitutively dimerized

receptor configurations. We further demonstrate ligand-inducible signaling by both small

molecule and peptide species. For small-molecule activation, CREA were outfitted with

ectodomains comprising the rapamycin-binding domains FKBP (FK506-binding protein of

12 kDa) and FRB (FKBP rapamycin-binding).57 These rapamycin-binding domains have been

used for many applications including ligand-induced protein splicing80-82 and regulation of gene

expression83-85. The two domains do not interact in the absence of rapamycin, and upon the

addition of rapamycin, a stable tertiary complex forms with Kd ≈ 2.5 nM.57, 86

To engineer peptide-activated CREA, we derived ectodomains from the peptide-binding

domains SH3 (a mouse Crk protein that binds peptide ligand PPPALPPKRRR)87 and PDZ (a

mouse α-syntrophin protein that binds peptide ligand GVKESLV)87. For characterization

purposes, the ligand was generated by fusing tandem repeats of the cognate peptide ligands for

the SH3 and PDZ domains to the fluorescent protein mCherry, which served as an anchor

protein. However, this system could be intuitively extended for instance by patterning these

peptide sequences onto a surface on which cells expressing the biosensor could be plated or to a

particle which could be delivered to biosensor cells, enabling new tools for engineering cell-

material interactions for applications in tissue engineering. Moreover, peptide-conjugated protein

scaffolds presented on the surface or secreted by “sender” cells could be introduced to biosensor-

expressing “receiver” cells for generating synthetic intercellular communication systems. Thus

this protein-peptide interaction domain (PPID) CREA system may be broadly used for a host of

applications including synthetic cell-cell communication, directed differentiation, and pattern

35

recognition, to name a few. Taken together, these systems demonstrate the modularity of the

CREA system and its potential to be adapted for detection of a variety of ligands for use in

numerous diverse biosensing applications.

2.2 Materials and Methods

2.2.1 DNA constructs

Constructs encoding CREA fusion proteins were assembled by PCR amplification and

standard molecular cloning. CREA constructs were cloned into the adeno-associated virus

expression vector plasmid pAAV GFP SN88, 89, although expression was achieved by transient

transfection (not viral packaging). pDSRedExpress2 was included as a transfection control.

Source plasmids for CREA components included: pCL-CTIG (Addgene plasmid 14901)90,

pRK1043 (Addgene plasmid 8835)53, pBI-MCS-EGFP (Addgene plasmid 16542)56, pBS mCD4

(Addgene plasmid 14613)91, AAV-FLEX-rev-ChR2-tdtomato (Addgene plasmid 18917)92,

pEBFP2-Nuc (Addgene plasmid 14893)93, YFP-FKBP (Addgene plasmid 20175)94 and YFP-

tagged FRB (YR) (Addgene plasmid 20148)94, pmCherry-C1 (Clontech 632524)52, and

PDZ(5)SH3(9)p1(Rnw392-501)pep187. Genes encoding the peptide ligands for PPID CREA

were synthesized by Integrated DNA Technologies and fused to existing mCherry TC constructs

by standard molecular cloning.

2.2.2 Cell culture and transfection

HEK293FT cells (Life Technologies) were maintained at 37oC in 5% CO2 in growth

medium (Dulbecco’s modified growth medium supplemented with 10% FBS, 1% penicillin-

streptomycin, and 4 mM L-glutamine). DNA expression experiments were performed via

36

transient transfection. Transfections were performed in 10 cm plates seeded with 6x106 cells in

10 mL media (for immunochemistry) or in 24-well plates seeded with 1.5x105 cells in 0.5-0.75

mL media (for receptor signaling experiments). Cells were seeded 8-12 hours before transfection

by the CaCl2-HEPES-buffered saline (HeBS) method. For rapamycin-induced signaling

experiments, media change occurred 16 hours post-transfection at which time rapamycin (Santa

Cruz Biotechnology Inc., 100 nM with 0.5% DMSO, final concentrations) or DMSO (0.5% final

concentration) was added to culture media, and cells were incubated for 24 hours before analysis.

2.2.3 Flow cytometry

Approximately 1x104 live cells from each transfected well were analyzed using an LSRII

flow cytometer (BD Bioscience) running FACSDiva software. Cells were harvested 36 hours

post-transfection by trypsinization with 0.15 mL trypsin-EDTA or PBS with 0.5 mM EDTA and

re-suspended in phosphate buffered saline (PBS) with 5% bovine serum albumin (BSA) and 0.5

mM EDTA to prevent formation of aggregates. Data were electronically compensated and

analyzed using FlowJo software (Tree Star). Live single cells were gated based on scatter, and

DsRedExpress2+ cells were gated as “transfected,” and reporter activity (YFP) was quantified

and normalized with respect to the internal control (reporter plasmid + constitutively expressed

tTA). Samples were collected and analyzed in biological triplicate, and data points and error bars

represent the mean and standard deviation, respectively, of the mean fluorescent intensity

measured for each biological replicate.

37

2.3 Results

2.3.1 Engineering Conditional Reconstitution of Enzymatic Activity (CREA)

CREA chiefly differs from MESA in that the ligand-induced dimerization event prompts

reconstitution of the TEV protease, which is then free to diffuse into a separate target chain to

release a transcription factor as a read-out for protease reconstitution (Figure 2.1). Because

CREA utilizes a different mechanism of activation than the basic MESA, we performed an

independent characterization of this design space. An initial library of CREA variants was

generated in which dTomato or mCherry ectodomains served as model receptors (see

Introduction section 1.4 for rationale), and 6 or 12 residue intracellular linker domains were

initially included on each chain because we anticipated that extra flexibility might be required to

allow protease reconstitution. The TEV protease was split into N- and C-terminal fragments to

partition the enzyme’s active site50: amino acid residues 1-118 (NTEV) on the protease chain

with NTEV (PCN) and residues 119-242 (CTEV) on the protease chain with CTEV (PCC) (Figure

2.1).

38

Figure 2.1 Conditional Reconstitution of Enzyme Activity (CREA) design. Proposed general

mechanism: receptor dimerization causes the TEV N-terminal fragment (NTev) protease chain

(PCN) to refold with its complementary fragment on the C-terminal fragment (CTev) protease

chain (PCC), such that the reconstituted protease can cleave its cognate cleavage sequence on a

third target chain (TC), which releases the transcription factor (TF) to travel to the nucleus and

modulate target gene expression by binding to a TF binding domain (TFBD) adjacent to a

minimal promoter (Pmin) to drive expression of the output gene.

39

A library of target chains was also generated in which mCherry served as the ectodomain

and various linkers and cleavage sequences separated the transmembrane domain from tTA.

None of the target chains induced reporter activation in the absence of TEV, and since the target

chain with G cleavage sequence and 6 amino acid linker signaled most strongly when co-

expressed with soluble TEV (Figure 2.2a), this construct was initially selected for evaluating the

sTEV MESA concept. This target chain was co-expressed with sTEV PCN or PCC individually,

confirming that neither sTEV chain alone induced detectable cleavage of the target chain (Figure

2.2b). When the target chain was co-expressed with surface-bound TEV (sb TEV; a mCherry

protease chain from the basic MESA system, Figure 1, with a zero residue LD), reporter

activation was evident. However, when monomeric mCherry-based PCN and PCC were co-

expressed, cleavage of target chains bearing either the most or least kinetically favorable

cleavage sequences (G and L, respectively) was also observed (Figure 2.2c). These data indicate

that diffusive encounters were sufficient to reconstitute sTEV in these constructs (which we

term, “spontaneous sTEV reconstitution”). Since Wehr et al. did not observe spontaneous

reconstitution of sTEV in membrane-bound constructs50, we hypothesized that this difference

could be due to expression level differences or our inclusion of long (6 or 12 amino acid)

unstructured linkers that facilitate sTEV refolding (Wehr et al. omitted such linkers).

40

Figure 2.2 Background characteristics of model receptor CREA. (a) Cleavage of target chain

variants by cytosolic TEV. (b) Individual split TEV fragments lack proteolytic activity. (c)

Geometric and kinetic analysis of contributors to sTEV background. Experiments were

conducted in biological triplicate, mean fluorescence intensity (MFI) of YFP was measured for

each sample after gating on transfected cells, measurements were normalized relative to the

internal control (described in section 2.3.3), and error bars represent the scaled standard

deviation.

41

To investigate strategies for reducing target chain cleavage due to spontaneous sTEV

reconstitution, a library of CREA variants were constructed including linkers of 0 or 6 amino

acids, and these were co-expressed with target chains including G or M cleavage sequences and

linkers of 0 or 6 amino acids. For PCC with 0 linkers, reporter activity was “de-inducible” upon

dimerization for all target chains (Figure 2.3a). To explain this phenomenon, we hypothesized

that dTomato-mediated dimerization of ectodomains may cause the protease chains to dimerize

in a conformation that precludes refolding of sTEV fragments, whereas the freely diffusing

mCherry constructs may have sufficient geometric freedom to permit reconstitution following

diffusive encounter. Receptors with 6 residue linkers on both PCs exhibited dimerization-

independent reporter activation, potentially due to spontaneous sTEV reconstitution during

transient diffusive encounters. However, when the PCN lacking intracellular linkers and the PCC

with 6 residue linkers were co-expressed with a target with 0 linkers and the G cleavage

sequence, a 2.5 fold induction upon dimerization was observed. Although it is certainly possible

that fold induction could be further increased by refinement of this scenario (e.g., by considering

target chain linker lengths between 0 and 6 amino acids), optimization of these constructs was

not the objective of this proof of principle investigation, and we opted to further characterize this

functional architecture.

Because each CREA signaling event requires interaction between three receptor chains,

we investigated how varying the stoichiometry of CREA components would impact signaling

(Figure 2.3b). While reducing the quantity of target chain transfected did not appreciably affect

fold induction, reducing the quantity of both PCN and PCC transfected increased fold induction

from 2.5 to 10.6. Similarly, reducing the amount of either PCN or PCC transfected also increased

42

fold induction to an intermediate degree. Together, these data demonstrate that this mechanism

for achieving dimerization-dependent signaling is robust to variations in relative CREA

expression levels, and fold induction may be optimized by tuning the expression of protease

chains to limit spontaneous sTEV reconstitution. Thus, reconstitution of enzymatic activity

provides an additional modality for coupling MESA-derived biosensing to regulation of cell

state.

43

Figure 2.3 Tuning design parameters of CREA. (a) Contributions of linker length and

cleavage kinetics to dimerization-inducible CREA MESA signaling. (b) Effects of receptor

stoichiometry on CREA performance. For target chain dilutions, fractions are defined relative to

the starting amount of 1 µg of target chain plasmid vector DNA per sample, with empty vector

plasmid used to keep the total amount of DNA transfected constant. For protease chain dilutions,

fractions are again defined relative to the starting amount (1 µg each of PCN and PCC plasmid

vectors), and empty vector plasmid was again used to keep the total amount of DNA transfected

44

constant. Experiments were conducted in biological triplicate, MFI of YFP was measured for



deviation. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001)

45

2.3.2 Ligand-inducible CREA signaling via a small molecule

We next investigated whether the CREA mechanism could be harnessed to achieve

ligand-inducible enzyme reconstitution. Thus, CREA chains were constructed in which the

heterodimeric rapamycin binding domains (FRB and FKBP) were utilized as ectodomains for the

protease chains (Figure 2.4a). Based upon results from model CREA (Figure 2.1), we evaluated

PCN with 0 and 6 linkers and PCC with 6 linkers, since PCC with 0 linkers appeared incompatible

with sTEV reconstitution. A flexible scaffold (2 or 6 amino acids) was also inserted between

transmembrane and rapamycin-binding domains on the protease chains, because we

hypothesized that some flexibility would be required to enable simultaneous dimerization of

rapamycin-binding domains and reconstitution of sTEV fragments. Since the geometric

constraints governing the mobility of reconstituted sTEV may differ when protease chain

dimerization is mediated by rapamycin-binding domains vs. dTomato domains, we investigated

target chains including either 0 or 6 amino acid intracellular linkers and a G cleavage sequence.

In control experiments, rapamycin-sTEV MESA performed similarly to model CREA – no

signaling was observed when the target chain was expressed alone or paired with only PCC or

PCN (Figure 2.4b).

When this small library of potential receptors was functionally evaluated, several

configurations exhibited significant rapamycin-inducible signaling (Figure 2.4c). The highest

fold induction (7.4) was observed for receptors with 2 extracellular scaffold linkers, and 6

intracellular linkers on both the PCN (FKBP) and PCC (FRB). However, no rapamycin-inducible

signaling was observed when these protease chains were expressed with target chains lacking an

intracellular linker (Figure 2.4d). This suggests that rapamycin-mediated sTEV reconstitution

46

resulted in protease chain complexes to which the linker-less target chain was sterically or

geometrically inaccessible. Although the number of design variations considered in this

experiment was limited, one general trend may be that inducible receptor configurations

involved a combination of protease chain linker lengths that somewhat constrained receptor

flexibility and potentially limited spontaneous sTEV reconstitution.

It may well be possible to further optimize receptor performance by modifying the

promising constructs reported here (e.g., by considering intermediate linker lengths).

Importantly, this design space may be explored by making such rational changes to the initial

constructs characterized here. Overall, this proof of principle experiment demonstrates that the

CREA platform may be adapted to engineer novel ligand-inducible receptor output modalities.

47

Figure 2.4 Rapamycin-induced CREA activation. (a) Schematic of CREA utilizing the

rapamycin-binding Frb and FKBP domains. Refer to Figure 2.1 for mechanistic details. (b)

Evaluation of background signaling for incomplete receptor configurations. (c) Ligand-inducible

enzyme reconstitution. Reporter activation was measured for rapamycin CREA expressed

transiently in cells cultured without rapamycin (light green) or with rapamycin (dark green). (d)

Evaluating ligand-inducible enzyme reconstitution with linker-less target chains. Refer to figure

2.3 for measurement details. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001)

48

2.3.3 Ligand-inducible CREA signaling via protein-peptide interaction domains (PPID)

Having demonstrated small molecule-inducible CREA signaling, we wished to

investigate whether CREA could be activated by a peptide-ligand recognized by a PPID

ectodomain. In preliminary experiments, MESA outfitted with SH3 and PDZ PPID ectodomains

and secreted peptide-conjugated mCherry ligands suffered from saturating background (data not

shown). Therefore, we investigated whether adaptation of these PPID MESA to the CREA

platform, with PPID ectodomains on the sTEV PCs and the peptides fused to the target chain

(Figure 2.5), might improve signal to noise. This configuration enabled us to map the design

space for these constructs and elucidate design parameters favorable for peptide ligand-induced

signaling.

49

Figure 2.5 Strategy for engineering PPID CREA. Schematic of CREA utilizing PPID

ectodomains. Refer to Figure 2.1 for mechanistic details.

50

Using this model system, we next performed combinatorial experiments in which CREA

PPID receptors were fused to ectodomains bearing either homotypic or heterotypic PPID (Figure

2.6a-b) and co-expressed with either matched or mismatched ligand TCs. In this experiment, we

also explored various ligand configurations in which the spacer between the two ligand domains

(ILL1) and the tether between the ligand domains and the surface-bound mCherry (ILL2) were

each varied in length.

Only ligands containing at least one ligand domain cognate to the receptor induced

MESA signaling; e.g. homotypic SH3 receptors do not induce signaling in the presence of TC

lacking peptide ligands or TC displaying only pdz and no sh3 peptides (Figure 2.6a). Thus,

ligand-inducibility appears to be specific. For homotypic SH3-based receptors, ligands with one

SH3-binding domain induced signaling, as did ligands with two SH3-binding domains (Figure

2.6a). Based upon these data, we speculated that it is possible that signaling occurs via reducing

a 3-chain chance encounter to a 2-chain chance encounter (with either PCN or PCC bound to the

TC ligand and the other merely diffusing into this complex), and we could not determine whether

any ligand actually promoted dimerization of the CREA PCN and PCC. Using heterotypic SH3

and PDZ-based receptors, we observed that matched ligands (those containing both SH3 & PDZ

ligand domains) induced signaling greater than did ligands based upon SH3 or PDZ ligand

domains only (Figure 2.6b). This presents strong evidence that ligand-mediated dimerization of

peptide-responsive CREA has occurred. Notably, this functional signaling was orientation-

dependent (compare left and right series within Figure 2.6b), and although signalizing was

achieved with both short and long ILL1/ILL2 domains, long linkers were generally more

effective.

51

Figure 2.6 Characterizing peptide ligand-induced CREA. Combinatorial experiments were

performed in which split-TEV receptors were fused to ectodomains bearing either homotypic (a)

or heterotypic (b) PPID. Naming conventions indicate linker lengths within protease chains (e.g.,

52

SH3 10.6CTev = 10 aa between SH3 and TM and 6 aa between TM and CTev) and target chains

(e.g., ILL1 = spacer between N terminal and C terminal ligand domains, ILL2 = spacer between

C terminal ligand and anchor protein); purple bars indicate TC bearing both sh3 peptide ligands,

green bars correspond to heterotypic ligand with sh3 and pdz peptides, and orange bars

correspond to both pdz peptide ligands, with light shades indicating shorter linkers, and dark

shades longer linkers. MFI of mCherry is presented (a) as a read-out for expression level of each

TC / ligand. Experiments were conducted in biological triplicate, MFI of YFP was measured for



deviation. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001)

53

2.4 Discussion

The CREA platform is a straightforward extension of the MESA design to give rise to an

alternative change in cell state, and together with its sister technology addresses a key

technological gap in the mammalian synthetic biology toolbox of enabling robust interfacing of

engineered cell-based devices with host physiology. Through design-based tuning and

quantitative exploration of design space, functional design parameters were elucidated and robust

signaling achieved for a variety of inputs. Thus CREA is highly versatile and may be adapted to

reconstituting other proteins of interest for therapeutic or imaging applications. Due to CREA’s

distinct mechanism of action and different background properties from MESA, it may also be

employed in transcriptionally independent settings (e.g. in enucleated red blood cells) or in

settings where a transcriptional output is desirable and low background is of particular

importance. In sum, CREA is a robust complementary technology to MESA that broadens the

scope of the platform to construct complex and customizable cell-based devices that enable new

and effective therapeutic strategies.

2.5 Acknowledgments

Nichole Daringer characterized the mCherry and dTomato receptors in the context of

MESA and formatted Figures 2.1-2.4 for publication. Kelly Schwarz developed the rapamycin-

binding domains to expand the CREA platform and generated the data that appears in Figure 2.4.

Both contributed helpful discussions concerning platform development, and are co-authors on

the publication in which this research has been made available to the scientific community2.

Plasmids encoding the SH3 and PDZ constructs were a generous gift of John Dueber, UC

Berkeley.

54

This work was supported by the Defense Advanced Research Projects Agency, Award

number W911NF-11-2-0066 (to JNL). This work was supported by the Northwestern University

Flow Cytometry Facility and a Cancer Center Support Grant (NCI CA060553). Traditional

sequencing services were performed at the Northwestern University Genomics Core Facility.

Additional support is acknowledged from the National Academies Keck Futures Initiative

(NAKFI-SB6 to JNL) and the Robert H. Lurie Comprehensive Cancer Center Malkin Family

Award (to RMD).

55

Chapter 3: Engineering Nanobody-based Biosensors that Sense and

Respond to Extracellular Cues.

3.1 Introduction

In the work described here, we investigated whether the MESA platform could be

generalized to recognize extracellular ligands via modular ligand binding domains termed

nanobodies. Derived from the variable region (VHH) of camelid single chain antibodies62,

nanobodies are promising candidates for MESA ECD as their single chain nature makes them

compact and modular, they are not prone to aggregation, and pipelines for facilely generating

and screening large libraries of nanobodies against proteins of interest are emerging69.

Additionally, these nanobody libraries may be screened to discover clones that bind distinct

epitopes on a single protein of interest95, enabling the generation of heterotypic MESA (TC

recognizes a distinct epitope from PC) and eliminating nonproductive complexes (e.g. a pair of

TCs or a pair of PCs that does not signal). Here we report the engineering of MESA utilizing

previously characterized nanobodies specific for the green fluorescent protein GFP96 as a model

protein input, and we demonstrate the activation of the MESA by exclusively extracellular GFP.

Moreover, we demonstrate that this nanobody MESA architecture can be adapted to a novel

input, by replacing the GFP-specific nanobody ECD with mCherry-specific ECDs. This system

is therefore highly modular and potentially suitable to adaptation to a variety of protein ligands

of interest, and represents the first completely orthogonal exclusively extracellular sensor to our

knowledge. This chapter will be published together with chapter 4 in a single paper (in

56

preparation for submission), on which I will be first author, as these chapters represent distinct

bodies of work that are complete on their own but complementary taken together.

3.2 Materials and Methods


Constructs encoding MESA fusion proteins and secretable ligands were assembled by

PCR amplification and standard molecular cloning. MESA constructs were cloned into the

adeno-associated virus expression vector plasmid pAAV GFP SN88, 89 (for both transient

transfection and viral packaging). A reporter plasmid was constructed by replacing the YFP

cassette in pBI-YFP (derived from pBI-MCS-EGFP, see section 2.2.1) with DsRedExpress2

(hereafter DsRed) from pDSRedExpress2. Blue fluorescent protein (BFP) utilized as a

transfection control or TC fusion partner was derived from pEBFP2-Nuc (Addgene plasmid

14893)93. The adeno-associated virus packaging plasmids pXX2 and pHelper97 were utilized for

viral vector production. GFP nanobody ectodomains were derived from plasmids pCAG-GBP1-

10gly-Gal4DBD and pCAG-p65AD-GBP695 contributed by Constance Cepko, Harvard. Genetic

constructs encoding mCherry nanobodies were designed and codon-optimized based on

published protein sequences69 and synthesized by GeneArt. Other source plasmids included:

pCL-CTIG (Addgene plasmid 14901)90, and pRK1043 (Addgene plasmid 8835)53.


HEK293FT and AAV 293 cells (Life Technologies) were maintained at 37oC in 5% CO2

in growth medium (Dulbecco’s modified growth medium supplemented with 10% FBS, 1%

penicillin-streptomycin, and 4 mM L-glutamine). Transient transfection experiments were

57

performed in HEK293FT and adeno-associated viral packaging was performed in AAV 293 cells

(see section 3.2.3). Transfections were performed in 6-well plates seeded with 7x105 cells in 1.5

mL media (for immunolabeling) or in 24-well plates seeded with 1.5x105 cells in 0.5 mL media

(for receptor signaling experiments). Cells were seeded 8-12 hours before transfection by the

CaCl2-HEPES-buffered saline (HeBS) method. Media change was performed 12-16 hours post-

transfection, and cells were incubated for another 24 hours prior to analysis.

3.2.3 Adeno-associated virus production and titering

For production of viral vectors encoding MESA, pAAV MESA plasmids were

transfected into AAV 293 cells along with the packaging plasmids pHelper and pXX2.

Transfections were performed in 10 cm dishes seeded with 6x106 AAV 293 cells in 10 mL of

media, using 7 µg of each plasmid and the CaCl2-HEPES-buffered saline (HeBS) method. Media

was changed 12-16 hours post-transfection, and AAV was harvested 3 days later or at the onset

of cell necrosis. Briefly, cells were removed from the plate by trituration, pelleted and re-

suspended in AAV lysis buffer (100 nM NaCl, 10 mM Tris-HCl, pH 8.5), and lysed over three

freeze-thaw cycles by transferring between a dry-ice ethanol bath and a 37◦C water bath.

Genomic DNA was removed from the lysate by incubation with 1 U/mL benzonase (EMD

Millipore) for 30 minutes at 37◦C. Cell debris was pelleted by a 15 minute centrifugation at 7000

RPM and 4◦C, and the supernatant was retained as “crude lysate”. The effective functional titer

of AAV crude lysate was determined by infecting HEK293FT cells with various volumes of

crude lysate and then quantifying the frequency of MESA cells (as determined by expression of

BFP fusion partner) by flow cytometry (Figure S3.4).

58

3.2.4 AAV transduction of MESA and recombinant ligand stimulation

For transduction by AAV, 6-well plates were seeded with 7x105 HEK293FT cells in 1.5

mL media, and viral supernatant was added ~6 hours later. Volume of virus to be added to each

sample was calculated based on AAV titer and desired multiplicity of infection (ratio of

infectious particles to cells, MOI) (see section 3.6). Cells were incubated with virus for at least

24 hours prior to media change or passaging. For transfection of cells transduced with MESA,

AAV-transduced cells were plated in 48 well plates at ~7x104 cells per well in 0.25 mL media

and transfected as previously described (3.2.2). For functional characterizations of MESA using

recombinant ligands, recombinant GFP (ab84191 from Abcam) or recombinant mCherry (4993-

100 from BioVision) was added to culture media immediately following transfection, and ligand

was replaced along with the media change at 12-16 hours post-transfection, after which cells

were incubated another 24 hours prior to analysis by flow cytometry.


Approximately 1x104 live cells from each transfected well were analyzed using an LSRII

flow cytometer (BD Bioscience) running FACSDiva software. Cells were harvested 40 hours

post-transfection by incubation with a FACS buffer (FB) comprising phosphate buffered saline

(PBS) with 1% bovine serum albumin (BSA) and 2.5 mM EDTA. Data were electronically

compensated and analyzed using FlowJo software (Tree Star). Live single cells were gated based

on forward- and side-scatter, BFP+ cells were gated as “transfected,” and reporter activity

(DsRed or YFP fluorescence) was quantified and normalized with respect to the internal control

(reporter plasmid + constitutively expressed tTA). Samples were collected and analyzed in

59

biological triplicate, and data points and error bars represent the mean and standard deviation,

respectively, of the mean fluorescent intensity measured for each biological replicate.

3.2.6 Immunolabeling

Cells transfected with N-terminal HA-tagged nanobody MESA and BFP transfection

control, or just BFP as a control, were harvested as previously described and blocked with 10 µg

human IgG. Both HA-tagged MESA expressing cells and either untransfected or “BFP only”

cells (as a control for nonspecific binding) were incubated with a rabbit monoclonal antibody

against the HA tag (3724S from Cell Signaling Technologies), washed with FB and centrifuged

three times to remove excess antibody, and incubated with an Alexa Fluor® 647 conjugated

polyclonal goat anti-rabbit secondary antibody (A-21244 from Life Technologies), and washed

again. Alexa Fluor® 647 labeling was quantified by flow cytometry and analyzed as previously

described.

3.2.7 Ligand binding assay

Cells transfected with nanobody MESA were either co-transfected with secretable ligand

and harvested as described previously, or transfected only with the MESA and incubated with

recombinant ligand (GFP ab84191 from Abcam) after harvesting 36 h post-transfection. Cells

were then washed as described (3.2.6) to remove excess ligand, and ligand binding was

quantified by labeling the ligand with a polyclonal rabbit antibody against GFP (ab290 from

Abcam) and flow cytometry analysis were performed as previously described. Again, cells

transfected with BFP only were treated identically to control for nonspecific binding of the

antibodies to the cells.

60

3.3 Results

3.3.1 Design and characterization of MESA responsive to GFP

In order to investigate whether MESA may be adapted to sensing an extracellular protein

species, the basic MESA architecture, described previously2, was modified to replace the

ectodomain with nanobodies specific for GFP as a model extracellular ligand (Figure 3.1a). A

library of six nanobodies specific for GFP, termed GBPs (for GFP binding proteins) were

previously characterized96 (Figure 3.1a) and screened for pairs that recognized non-overlapping

epitopes on GFP95 . Within this library of GFP co-binders, GBP1 was the most desirable for

testing in the context of MESA, as its affinity was known and it was also known to tolerate

fusion partners at its C terminus unlike the other GBP library members (Figure 3.1a). Therefore

GBP1 and its co-binder GBP6 (one GBP1 and one GBP6 can bind the same single molecule of

GFP at the same time) were chosen for MESA ectodomains. To achieve robust expression and

GFP binding by the nanobody MESA at the cell surface, we optimized the N terminal signal

peptide and extracellular linker domains. We utilized the WoLF PSORT algorithm to predict a

favorable signal peptide for conferring surface localization (Figure S3.1), and generated a library

of GFP nanobody MESA having a series of linker lengths. We hypothesized that long flexible

linkers would provide optimal binding of GFP and MESA dimer formation, but that excessively

long linkers might decrease stability of the MESA on the cell surface or prove detrimental to

formation of a functional MESA complex (e.g. by allowing the MESA to bind the ligand in a

conformation in which they are too far apart to signal efficiently). Therefore we included in our

initial library a series of 10-30 amino acid linkers comprising all flexible residues (glycine,

serine, threonine, alanine) as well as a 40 residue linker comprising 20 flexible residues at its N

61

terminus and 20 residues with alpha-helical secondary structure at its C terminus (abbreviated

40α). We wished to investigate whether the all-flexible linkers would suffice to confer stability

and conformational freedom or whether this 40α linker might confer more stable surface

localization via its alpha helical portion as well as conformational freedom for binding ligand via

its flexible portion. We chose 10 flexible linkers as the smallest length for the GBP1 MESA and

16 for the GBP6 MESA, as GBP6 was known to prefer N over C terminal fusion partners95

(Figure 3.1a) and would potentially benefit from larger spacing. This initial library was

expressed in HEK293 FT cells by transient transfection, and assayed approximately 40 hours

post-transfection to evaluate both surface expression of MESA and GFP-binding by MESA. All

library members were expressed on the cell surface, independently of linker length (Figure 3.1b).

The GBP6 TCs bound GFP in a manner that increased with linker length (Figure 3.1c), although

interestingly the GBP6 PCs did not demonstrably bind GFP (Figure S3.2). For the GBP1 MESA,

both the PCs (Figure 3.1c) and TC (Figure S3.2) were able to bind GFP robustly, with the

flexible linkers fairing somewhat better than the 40α. Therefore, (1) TCs with GBP6 for an

ectodomain and 30 or 40α linkers and (2) PCs with GBP1 for an ectodomain and 20 or 30

flexible linkers were taken forward for further characterization.

We next sought to evaluate the signaling of GFP nanobody MESA and establish their

responsiveness to ligand. Rather than add recombinant GFP exogenously to the media, we first

co-expressed a plasmid encoding a secretion-tagged GFP (pSecGFP, or secGFP for the protein

product) in the same cells as the MESA in order to most expediently evaluate feasibility (i.e., to

avoid having to optimize the variables of ligand dose and timing of ligand addition in this initial

analysis). Trafficking and secretion of secGFP were confirmed by microscopy and by western

62

blot of the conditioned medium (Figure S3.3). Thus MESA receptors were expressed in cells

along with reporter and reporter output was assayed in the presence or absence of co-expressed

pSecGFP. All MESA variants tested exhibited significantly higher reporter activation in the

presence of secreted ligand compared to in the absence of ligand, and the highest induction

(nearly 8-fold) resulted from PCs and TCs both having a long 30-residue flexible linker (Figure

3.1d). This long flexible linker configuration likely best enabled the nanobodies to bind their

epitopes in a conformation favorable for MESA signaling. We therefore proceeded to assess

whether these best performing MESA could be activated by recombinant exogenous GFP.

63

Figure 3.1 Design and characterization of nanobody MESA responsive to GFP. (a)

Schematic of GFP nanobody MESA and GFP nanobody (GBP) library information. Binding of

ligand induces dimerization of the target chain (TC) and protease chain (PC) causing trans-

cleavage of the cognate cleavage sequence and release of the transcription factor (TF), which

binds to a TF binding domain (TFBD) immediately upstream of a minimal promoter (Pmin) to

drive expression of the output gene. (b) Cell surface expression of HA-tagged nanobody MESA

was verified by immunolabeling and flow cytometry. Shaded region represents control. (c) GFP-

binding by nanobody MESA was assessed by labeling receptors with GFP followed by

64

immunolabeling bound GFP (see section 3.2.5). (d) Reporter activity for GFP nanobody

extracellular linker variants. Experiments were conducted in biological triplicate, mean

fluorescence intensity (MFI) of DsRed was measured for each sample after gating on transfected

cells, measurements were normalized relative to the internal control, and error bars represent the

scaled standard deviation. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001).

65

To characterize nanobody MESA responsiveness to exclusively extracellular ligand,

MESA receptors and reporter were transiently transfected as before, except this time

recombinant GFP ligand was added to the culture media. We explored a range of GFP doses

from 0.2 to 5 µg, adding ligand at the time of transfection so that it would be available as soon as

the MESA began to arrive at the cell surface. However, no exogenous ligand condition induced

reporter activity above background (Figure 3.2a).

To reconcile this observation with our previous findings, we postulated that most of the

signaling observed in the secGFP co-expression system may have been due to MESA receptor

chains and secGFP ligand interacting in the endoplasmic reticulum (ER) or during trafficking to

the cell surface, as the local concentration of all the protein components would be much higher in

the ER than in the plasma membrane. Moreover, we hypothesized that in the absence of secGFP,

this proposed crowding effect in the ER and/or during trafficking could lead to premature

cleavage of the target chains, such that at the surface, there would exist fewer uncleaved target

chains with which the protease chains could potentially interact upon addition of GFP.

Moreover, these prematurely cleaved “dead” target chains on the surface could also act as

competitive inhibitors, sequestering protease chains into nonproductive complexes upon the

addition of GFP and thereby further decreasing the sensitivity of the MESA-expressing cell to

extracellular ligand.

We next hypothesized that this ER crowding effect, if present, could be an artifact of the

transient transfection format used, since this approach can deliver up to 105-106 plasmids per cell

(given cell number and quantity of plasmid as per 3.2.2) and results in massive overexpression of

the transfected construct. Thus, we hypothesized that this crowding effect could be reduced by

66

reducing the quantity of MESA-expressing plasmids delivered to each cell. This lower number of

MESA expression cassettes present in the cell would then hypothetically result in a lower level

of MESA receptors being transcribed and trafficked at any given time, allowing them to avoid

contact in the ER and accumulate on the cell surface intact. In order to evaluate whether a

reduced and sustained rate of MESA expression could reduce background signaling and enable

recombinant GFP-mediated induction of MESA signaling, we investigated expression of MESA

from adeno-associated virus (AAV) vectors. AAV-mediated transgene expression levels can be

tuned by varying the multiplicity of infection (MOI) used, and expression of transgenes from

AAV constructs is relatively stable in a variety of cell types and known to persist for at least 7

days in mammalian cell culture even in the absence of drug selection.98

Thus, cassettes expressing the MESA TC and PC (30 flexible linker variants, see Figure

3.1d) were packaged into separate AAV vectors. Cells were transduced at an MOI of 4 for each

MESA chain and expanded over 3 to 5 days to obtain a sufficient quantity of cells for further

analysis. To ensure that the quantity of reporter would not limit our quantification of

extracellular ligand-induced nanobody MESA signaling, reporter plasmid was transfected into

the MESA AAV transduced cells along with a blue fluorescent protein (BFP) transfection

control at 3 to 5 days post transduction. At the time of transfection, either GFP ligand was added

to the culture media or pSecGFP was co-transfected as a positive control. Cells were harvested

approximately 40 hours post-transfection and assayed for persistence of MESA expression on the

surface as well as for reporter activity. Nearly 40% of the transduced cells maintained significant

surface expression of the MESA at up to 7 days post-transduction (Figure 3.2b). Of the

transfected cells, those co-transfected with the pSecGFP control experienced a nearly 7-fold

67

increase in reporter activity compared to cells receiving neither GFP protein nor pSecGFP

plasmid, and cells receiving recombinant GFP experienced 3- to 4-fold induction of reporter

activity (Figure 3.2c). These data support our hypotheses that the previously observed ligand

insensitivity was an artifact of the method by which MESA were expressed (transient

transfection), and that reduced and sustained expression of MESA receptors can overcome this

limitation. Thus, we have demonstrated that given a favorable mode of expression, nanobody

MESA are responsive to exclusively extracellular ligand and therefore the goal of modular,

orthogonal intracellular signal transduction in response to an extracellular cue is achievable via

the MESA platform.

68

Figure 2 Induction of nanobody MESA by exclusively extracellular protein. (a)

Reporter activity for cells transfected with nanobody MESA in the presence of recombinant GFP

added to culture media. See Figure 3.1 and Methods for measurement details. (b) Cells

transduced with AAV MESA were evaluated for surface expression of MESA 7 days post

transduction by immunolabeling and flow cytometry as described in Figure 3.1 and Methods. (c)

Reporter activity for cells transduced with nanobody MESA and transfected with reporter

plasmid in the presence of recombinant GFP added to the culture media. Measurement details are

as in figure 1, with the pSecGFP co-transfected condition serving as the internal control for this

experiment.

69

3.3.2 Design and characterization of MESA responsive to mCherry

In light of our finding that nanobody MESA could signal inducibly in response to GFP as

a model extracellular ligand, we next investigated whether this receptor system could be

extended to other nanobody-ligand pairs. We synthesized a library of six previously reported

mCherry-binding nanobodies (which the authors termed LaMs, or llama antibodies against

mCherry)69, and we incorporated these LaMs into the MESA as ectodomains (Figure 3.3a). All

target and protease chains comprising this mCherry nanobody MESA library were expressed

robustly on the cell surface (Figure 3.3b).

Since it was unknown whether any members of the mCherry nanobody library could bind

mCherry simultaneously (Figure 3.3a), we sought to screen the library for mCherry co-binders

that moreover could function as heterotypic MESA. A screen was designed to test pairwise

heterotypic combinations of the mCherry nanobody MESA for reporter activation in the presence

and absence of co-transfected secreted mCherry. To reduce our search space, we made use of the

observation that LaMs 3 and 4 can also bind the red fluorescent protein DsRed (a tetrameric red

fluorescent protein from which mCherry is derived and with which it shares 80% sequence

homology) whereas the remaining library members only bind mCherry. This observation may

indicate that the epitopes recognized by LaMs 3 and 4 lie within this region of shared homology

and are accessible in both the monomeric and tetrameric forms (of the fluorescent protein

ligand). Conversely, the mCherry-unique epitopes recognized by LaMs 1, 2, 6, and 8 may either

lie in regions where the mCherry and DsRed sequences diverge, or perhaps these epitopes are not

accessible when the constitutive monomers of DsRed tetramerize. We therefore paired all LaM 3

and 4 PCs against the LaM 1, 2, 6, and 8 TCs (and vice versa), and assessed reporter activity in

70

the presence and absence of ligand. Each pair was transiently transfected along with a reporter

plasmid driving yellow fluorescent protein (YFP) so as to avoid spectral overlap with ligand, and

secreted mCherry ligand (smCherry) or empty vector. Homotypic pairs with either LaM 3 or 4

on both the TC and PC were included as controls.

Interestingly, we observed one significantly inducible phenotype (fold induction > 1),

several un-inducible phenotypes (fold induction ≈ 1), as well as several de-inducible phenotypes

(fold induction < 1) (Figure 3.3c). A potential explanation for the de-inducible phenotypes is that

both chains are unable to simultaneously bind mCherry, such that ligand-induced dimerization is

precluded whereas the rate of transient encounter between chains individually bound to mCherry

is decreased due to steric hindrance by the bound ligand. The mildly de-inducible LaM 3

homotypic pair corroborates this explanation. Alternatively, some complexes may co-bind

mCherry in a conformation that does not enable signaling by the MESA intracellular

architecture, e.g. if the mCherry binding sites on a pair of ectodomains are situated such that on

the PC the active site of the protease cannot contact its cleavage sequence on the TC (see section

2.3.1, Figure 2.3a for another example of this phenomenon). All the mCherry nanobody MESA

exhibited surface ligand binding except for LaM 1 (Figure 3.3d). Most importantly, we did

identify a functional mCherry-inducible MESA receptor (the LaM 4 PC and LaM 8 TC) by

screening this limited library in which the mCherry nanobodies were simply substituted in place

of GFP nanobodies on a previously validated architecture, without requiring an additional

optimization step.

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Figure 3.3 Induction of nanobody MESA signaling by mCherry ligand (a) Schematic of

mCherry nanobody MESA and mCherry nanobody (LaM) library information. (b) Cell surface

expression of HA-tagged nanobody MESA was verified by immunolabeling and flow cytometry.

Shaded region represents non-specific binding control as described in 3.2.6. (c) Reporter activity

for mCherry nanobody MESA library members. LaM clones are listed by clone number (see

panel a). (d) Binding of recombinant mCherry ligand to nanobody MESA at the cell surface.

Experiments were conducted in biological triplicate, mean fluorescence intensity (MFI) of YFP

was measured for each sample after gating on transfected cells, measurements were normalized

relative to the internal control, and error bars represent the scaled standard deviation. (*p ≤ 0.05,

**p ≤ 0.01, ***p ≤ 0.001).

73

3.4 Discussion

The ability to robustly and predictably engineer biosensors for an exclusively

extracellular species of interest has been a previously unmet need in mammalian synthetic

biology. Here, we have demonstrated a modular sensor architecture that not only accomplishes

this goal of extracellular species sensing but can be readily converted into a sensor that

recognizes a distinct species as well.

While the intracellular parameters of the original MESA platform (e.g. the cleavage

sequence, intracellular linker configurations, and protease kinetics; see section 1.4.2) were

largely transferrable in adapting it to sense extracellular proteins via nanobodies, we here

optimized additional parameters of the architecture. Particularly, robust surface expression

hinged on optimizing the N terminal signal peptide, but once optimized, this signal peptide

conferred robust surface expression not only for the MESA with GFP nanobodies but also for the

same architecture with substitution of the mCherry nanobody ECD. The length and composition

of the SCF domain proved to be important for enabling binding of the ligand by nanobody

MESA, with long flexible linkers yielding the best results. For the linker lengths we assessed,

there did not seem to be a penalty for having flexible SCF that were too long.

We have observed that a transient transfection format has been disadvantageous for

achieving induction by an exogenously added ligand due to a proposed ER crowding effect, and

have circumvented this limitation by employing viral transduction to achieve a low sustained

expression of the MESA receptors. This observation indicates that desirable signal-to-noise

properties would likely be exhibited by nanobody MESA expressed at low to single copy number

by stable integration e.g. for expressing the platform in a therapeutic cell type of interest. In

74

future implementations, lentiviral expression of MESA may confer these expression properties

that are expected to be advantageous based upon our analysis.

Given the ease of redirecting the GFP nanobody MESA to a novel input, we anticipate

that it will also be straightforward to redirect the platform to a detect a disease marker or cell

surface antigen of interest, either using a previously described nanobody or given the ability to

generate a library of heterotypic nanobodies against the target antigen. Thus nanobody MESA

may ultimately facilitate the development of effective strategies for detecting and intervening in

disease environments via engineered cell-based therapeutics.

75

3.5 Supplemental information

Figure S3.1 Prediction of MESA subcellular localization using WoLF PSORT. By

inputting the amino acid sequences of GBP1 TCs with the indicated peptides into the web-based

program WoLF PSORT (http://www.genscript.com/psort/wolf_psort.html), we obtained the

indicated weighted scores for preferential localization of the constructs. Only sp3 was predicted

to confer surface localization preferentially over secretion or localization in intracellular

compartments. In our hands, sp3 was indeed effective (see Figure 3.1b).

76

Figure S3.2 Assaying binding to soluble GFP by GBP MESA. Representative examples of

constructs utilizing the 40α SCF domain were characterized for both surface expression (left

column) and the capacity to bind soluble GFP (right column). Gray: cells transfected only with

BFP (nonspecific binding control); white: cells transfected with the indicated GBP nanobody

MESA. Assay details are described in 3.2.6 and Figure 3.1b.

77

Figure S3.3 Detection of secGFP visually and in conditioned culture medium. Cells

expressing secGFP (top left) and expressing secGFP as well as a GBP1 PC (top right) were

visualized ~40 hours post-transfection; both images were captures using the same microscope

settings, with additional microscopy details as in section 4.2.4. Cells expressing either a 6xHis-

or HA-tagged SecGFP construct were harvested ~40 hours post-transfection, along with

corresponding conditioned media (CM). Lysate and CM were run at the dilutions from starting

concentration shown with 30 µL loaded per well. Fresh media was also analyzed as a control

(lane 1). Antibodies used were mouse anti-GFP mms-118 (Covance) and HRP-conjugated rabbit

anti-mouse secondary (Life Technologies).

78

Figure S3.4 Flow cytometry method for quantifying AAV titer. GBP6 target chain

receptors with a C-terminal BFP fusion were packaged into AAV as described (section 3.2.3) so

that the BFP could serve as a proxy for receptor expression. Viral crude lysate was used to

transduce cells, and 48 hours post-transfection cells were harvested and analyzed by flow. The

BFP positive population was determined by gating on negative control cells as shown, and MOI

was calculated assuming that infection follows a Poisson process, such that MOI = -ln(1 -

%BFP+).

79

3.6 Acknowledgements

Plasmids encoding the GBPs were generously contributed by Constance Cepko, Harvard

University. This work was supported by the Defense Advanced Research Projects Agency,

Award number W911NF-11-2-0066 (to JNL). This work was supported by the Northwestern

University Flow Cytometry Facility and a Cancer Center Support Grant (NCI CA060553).

Traditional sequencing services were performed at the Northwestern University Genomics Core

Facility. Additional support is acknowledged from the National Academies Keck Futures

Initiative (NAKFI-SB6 to JNL) and the Robert H. Lurie Comprehensive Cancer Center Malkin

Family Award (to RMD).

80

Chapter 4: Multiparametric extracellular cue evaluation via

engineered AND gate reporters

4.1 Introduction

In the previous chapter, we reported the engineering of MESA utilizing previously

characterized nanobodies specific for the green fluorescent protein GFP96 as a model protein

input. We furthermore demonstrated the activation of the MESA by exclusively extracellular

GFP and adaptation of the nanobody MESA architecture to a novel input by replacing the GFP-

specific nanobody ECDs with mCherry-specific ECDs. In this chapter, we investigated whether

such nanobody MESA with two separate specificities could be multiplexed to generate an overall

output contingent upon sensing both ligands (i.e., an AND gate). To achieve this, we postulated

that we could construct a hybrid promoter AND gate featuring interspersed binding sites for two

distinct transcription factors. We then engineered nanobody MESA to release two such distinct

TFs in the presence of their cognate ligands, thus activating the AND gate reporter. This system

represents the first completely orthogonal multiparametric extracellular sensor and logic gate to

our knowledge. Furthermore, due to its modularity, it may be adapted readily to additional

protein inputs and transcriptional regulator outputs for numerous applications in mammalian

cellular engineering. This chapter will be published together with chapter 3 (see 3.1).

4.2 Materials and methods


81

Constructs encoding hybrid promoter AND gates were assembled by PCR amplification

and standard molecular cloning. Briefly, the bidirectional TRE from pBI-YFP was removed and

tetO or UAS binding sites were appended to a minimal promoter region. Blue fluorescent

variants of reporters were generated by subcloning EBFP into this initial YFP library (to avoid

spectral overlap in downstream experiments utilizing GFP as a ligand). Gal4 MESA were

generated by replacing the tTA region of previously described Frb (section 1.4.2.2) and GBP6

(section 3.3.1) TCs with the gal4 DNA domain and VP16 activation domain99. Other nanobody

MESA were as previously described (see section 3.2.1).


Cell culture and transfection were performed as described in sections 2.2.2 and 3.2.2.

YFP reporters and BFP transfection controls were used for initial reporter characterization

experiments, whereas BFP reporters and YFP transfection controls were used for experiments

with secretable GFP ligands to avoid spectral overlap. Rapamycin-induced signaling experiments

were conducted as in 2.2.2.


Flow cytometry was conducted and analyzed as previously described (3.2.4) with the

reporter and transfection controls assigned as described above (4.2.2). For AND gate

experiments, an internal control was defined for each reporter as its activity in the presence of

both TF inputs constitutively expressed.

4.2.4 Microscopy

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Fluorescent cells were imaged using a Leica DM IL microscope with a Prior Lumen 2000

light source. Images were captured by a QICAM Fast 1394 camera and QCapture Pro 6.0

software and tinted in Microsoft ® Powerpoint.

4.3 Results

4.3.1 Design of hybrid TF reporter library

Having demonstrated that MESA receptors could be readily adaptable to recognizing a

distinct ligand via introduction of a new nanobody ectodomain (see Figure 3.3), we next

investigated whether MESA could be multiplexed in order to construct higher order, customized

cell functions. Thus, as a straightforward test of this question, we investigated whether two

distinct MESA receptors could be combined with a genetic AND gate to perform logical

evaluation of distinct extracellular cues.

To evaluate this question, we first designed a multi-input transcriptional AND gate,

building on the observation that engineered transcription factors require several copies of their

cognate DNA binding sequence for efficient activation. Our initial MESA libraries utilized the

transcription factor tTA, a hybrid of the tetR DNA binding domain and the VP16 activation

domain, and its cognate reporter consisting of 7 tandem repeats of the tetO DNA motif 55.

Similarly the gal4-VP16 (hereafter Gal4) transcription factor also utilizes the VP16 activation

domain but in combination with the gal4 DNA-binding domain (which recognizes the UAS

DNA motif) and efficiently activates a reporter containing 5 tandem UAS repeats99 . Thus, to

make a reporter efficiently activated by both transcription factors, we hypothesized that we could

generate a hybrid promoter, consisting of interspersed tetO and UAS sites, such that binding of

both tTA and Gal4 would be required to efficiently initiate transcription (Figure 4.1). Since the

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most well-characterized and frequently used Gal4 and tTA reporter promoters have 5 and 7

transcription factor binding motifs, respectively, we hypothesized that a library of hybrid

promoters having between 4 and 8 binding motifs total, comprising mixed tetO and UAS sites,

would potentially yield a construct with the desired phenotype (Figure 4.1). Since tTA and Gal4

both bind as dimers, we interspersed pairs of tetO and UAS sites. Seeing as both the 7x tetO

reporter and 5x UAS reporter feature an odd number of sites total, we also included one design

variant with an odd number of binding sites, having 3 continuous repeats of the UAS following a

tetO dimer.

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Figure 4.1 Hybrid promoters for multiparametric evaluation using MESA. Schematic of

hybrid promoter concept and library design. Capital letters represent pairs of transcription factor

binding sites, whereas lower case letters denote single binding sites.

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To characterize this engineered promoter library, we first transfected each of the reporter

constructs along with all combinations of constitutively expressed tTA and Gal4 transcription

factor “inputs”. To quantify promoter performance, we defined two metrics: “specific fold

induction” and “synergy”. “Specific fold induction” was defined as (a) mean fluorescence of

reporter protein expressed in the presence of both inputs, divided by (b) the highest mean

fluorescence of reporter protein expressed in the presence of either input alone. Thus specific

fold captures the sensitivity of the reporter to the less dominant input in the presence of the more

dominant input. “Synergy” was defined as (a) mean fluorescence of reporter protein expressed in

the presence of both inputs, divided by (b) the sum of the mean fluorescence of reporter protein

expressed in the presence of either input alone. Thus, a synergy of 1 denotes a purely additive

interaction between inputs, synergy less than 1 denotes a negatively synergistic interaction, and

synergy greater than 1 denotes a positively synergistic interaction.

Interestingly, the majority of the promoter designs evaluated exhibited substantial

specific fold induction as well as positive synergy in the presence of both transcription factor

inputs (Figure 4.2). Such trends were also readily evident by microscopy (Figure 4.2, lower

right). Interestingly, pUTT was only slightly more responsive to both inputs than it was to tTA

alone, but the addition of an extra UAS binding site pair in pUTTU increased the specific fold

and synergy. Also of interest is the fact that pTUu, although featuring only one more UAS

binding site than pTU, exhibited much higher total signaling in the presence of both inputs and

dramatically higher specific fold induction and synergy than did the rest of the reporters

evaluated. Since Gal4 is potentially a stronger transcriptional activator than is tTA99, our

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combined observation that the phenotypes of the hybrid promoters were more sensitive to the

number and orientation of UAS sites (compared to variations in tetO sites) seems reasonable.

88

Figure 4.2 Hybrid promoters perform logical AND gate evaluation. Activation of hybrid

promoter reporters by constitutively expressed transcription factors. Specific fold induction

(“specific fold”, in this figure) is defined as the reporter output in the presence of both inputs

divided by the highest reporter output conferred by either input alone. “Synergy” is defined as

the reporter output in the presence of both inputs divided by the sum of the reporter outputs

conferred by each individual input. PtTA is the two-tailed Student’s t-test value comparing

reporter output induced by tTA alone to reporter output induced by both tTA and Gal4, and PGal4

is analogously defined. Experiments were conducted in biological triplicate, mean fluorescence

intensity (MFI) of YFP was measured for each sample after gating on transfected cells, and error

bars represent one standard deviation. Micrographs at bottom right show representative images

from the pTU-YFP data set.

89

4.3.2 Activation of hybrid promoter AND gate by membrane-bound TFs

We next investigated whether the levels of transcription factor released from MESA

would be sufficient to activate the AND gate. The most promising reporters in terms of synergy,

specific fold, and total activation (pTUT, pTUu, pTUTU, and pUTTU) were co-transfected with

previously characterized rapamycin-responsive MESA (see section 2.3.2), in which the TF

domains of the TC comprised either tTA or Gal4, (Figure 4.3a). All four reporters exhibited

some background fluorescent protein expression in the presence of both tTA and Gal4 TCs

(presumably due to some background release of transcription factors from the receptors). Most

notably, three out of four promoters exhibited significant fold-induction when ligand was added

(Figure 4.3b). Therefore we concluded that our reporters were sensitive to changes in levels of

free transcription factor that distinguish ligand-free and ligand-induced levels of MESA receptor

signaling.

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Figure 4.3 Transcription factors released from MESA can activate AND gate. (a)

Schematic of rapamycin MESA and experimental set up. (b) Activity of hybrid promoters co-

transfected with combinations of rapamycin MESA. Measurement details are as in Figure 3.1,

with YFP serving as the fluorescent output and each reporter co-transfected with constitutive

transcription factors serving as the internal control to which each sample was normalized (not

shown).

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4.3.3 Activation of AND gate by MESA specific for distinct cues

Taking forward the three best performing AND gates, we assessed whether they could be

activated by MESA engineered with two distinct TFs released in response to two distinct ligands.

We first verified that GFP nanobody MESA with Gal4 substituted for tTA on the TC were

satisfactorily inducible (Figure 4.4a). We then assessed crosstalk between non-cognate nanobody

PCs and TCs (Figure 4.4b). The background levels of reporter output conferred by both matched

and mismatched TC:PC pairs were comparable. Therefore, we concluded that crosstalk between

these MESA receptors would not present a significant additional source of background reporter

induction, and thus our two nanobody MESA receptors are sufficiently independent to evaluate

the potential for multiplexing. To investigate this possibility, we first co-transfected each AND

gate reporter with both nanobody MESA receptors, in the presence or absence of co-expressed

secreted versions of their cognate ligands (SecGFP and SecmCherry) (Figure 4.4c). All three

reporters showed significantly higher activation in the presence of both ligands than in the

presence of either ligand alone, with pTUu exhibiting the most robust logic gate performance in

this assay – this promoter was not activated above background by either ligand alone, but the

reporter exhibited specific fold induction of ~2 and modest but positive synergy of 1.1.

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Figure 4.4 Multiparametric evaluation of extracellular cues by nanobody MESA coupled to

a genetic AND gate. (a) Reporter activity of GFP nanobody MESA with Gal4 TF. (b) Reporter

activity conferred by matched and mismatched nanobody PCs and TCs. (c) Reporter activity

conferred by GFP and mCherry nanobodies co-transfected with 0, 1, or both secreted ligands.

Measurement details are as in Figure 3.1, with BFP serving as the fluorescent output to avoid

spectral overlap with ligands. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001).

93

4.4 Discussion

In section 1.3, we highlighted the need for “smart” cell therapeutics that could sense cues

in their environment, logically evaluate them, and respond in defined and programmable ways.

We have here demonstrated a major step toward achieving that goal, by interfacing modular

extracellular sensors with a novel hybrid promoter logic gate capable of performing ‘AND’

computation upon receiving two distinct transcriptional inputs.

While others have engineered logical processing capabilities utilizing tandem

combinations of binding sites for transcription factors42 or chromatin modifiers100 upstream of a

gene of interest, this is the first demonstration to our knowledge that transcription factor binding

elements could be disassembled into minimal motifs and recombined to perform ‘AND’

computation. Using this design strategy, we achieved the highest specific fold induction and

synergy from a design variant featuring a single pair of tetO sites followed by a triad of UAS

sites. It is interesting that this triad arrangement of UAS sites should confer such an advantage

compared to a simple pair of UAS sites when Gal4 binds as a dimer. This advantage may be due

to an avidity affect, allowing the Gal4 to more readily rebind upon coming unbound from the

DNA, or perhaps the UAS repeat proximal to the tetO pair merely functions as a spacer,

improving the ability of the tTA and Gal4 dimers to simultaneously occupy the promoter. Thus

while the number and spacing of tetO and UAS repeats might be further optimized to maximize

reporter induction, specific fold, and synergy, the design strategy here presented nonetheless

yielded several functional promoter architectures that displayed significant specific fold

induction and synergy in the presence of constitutive and MESA-released transcription factors.

More importantly, we demonstrated that MESA receptors could be multiplexed into a modular

94

genetic logic gate, indicating that the system here described could readily be further modularly

interfaced with other gene circuit technologies to access additional logical evaluation

capabilities43 and outputs100.

Further corroborating this possibility, we showed that the MESA are amenable to

adaptation to a new transcriptional output, here the Gal4 TF, just as they were previously shown

to be adaptable to the novel ligand input, mCherry. Thus it is likely that MESA could

accommodate an alternative DNA activator or repressor element to achieve additional logical

evaluation functionalities in combination with a complementary reporter architecture. Another

key finding was that the background of PCs co-expressed with their non-cognate TCs exhibited

similar levels of background, thus the system does not incur additional background from cross

talk. All these observations speak to the modularity of nanobody MESA and the potential for this

proof of principle system to access a broad spectrum of extracellular sensing applications as

stands or interfaced with additional processor technologies. Thus the system presented here

represents an important step forward in enabling similar and complementary technologies for

programming sophisticated therapeutic functions in mammalian cells.

4.5 Acknowledgements

This work was supported by the Defense Advanced Research Projects Agency, Award

number W911NF-11-2-0066 (to JNL). This work was supported by the Northwestern University

Flow Cytometry Facility and a Cancer Center Support Grant (NCI CA060553). Traditional

sequencing services were performed at the Northwestern University Genomics Core Facility.

Additional support is acknowledged from the National Academies Keck Futures Initiative

95

(NAKFI-SB6 to JNL) and the Robert H. Lurie Comprehensive Cancer Center Malkin Family

Award (to RMD).

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Chapter 5: Conclusions and Recommendations

5.1 Chapter 2: Engineering a Cell-Based Biosensor that Activates a

Transcriptionally Independent Change in Cell State

5.1.1 Conclusions

To demonstrate the modularity and versatility of the MESA biosensor platform

technology and adapt it to new applications, we investigated a modification to the architecture in

which enzymatic activity reconstitution served as the output. While the demonstrated system

used a transcriptional event as a read-out for enzymatic reconstitution, the reconstitution itself is

transcriptionally independent; thus CREA may be employed in a setting in which either a

transcriptional output or a transcription-free output is desirable. By systematically exploring the

design space of this modified CREA system, we rapidly converged on a design that gave rise to

dimerization-induced signaling. A distinguishing feature of the transcriptionally dependent

CREA system is its decreased sensitivity to transient encounter-based background signaling due

to its requirement for the co-localization of three rather than only two chains in the cell

membrane. We were able to take advantage of this property to obtain inducible signaling with

PPID ectodomains that had suffered from saturating background in the context of MESA. We

also demonstrated that, by increasing the linkers on the respective CREA chains, we could

increase its sensitivity to achieve inducible signaling in the context of the rapamycin-binding

ectodomains, a system known from our MESA characterizations to inherently exhibit low

97

background. Thus CREA is suitable to implementation in systems with diverse background

signaling phenotypes.

5.1.2 Recommendations

An interesting future direction for the CREA platform would be to investigate its ability

to reconstitute an alternative enzyme or protein of interest instead of TEV in response to an

externally sensed ligand. For instance, CREA that reconstituted fragments of a caspase could be

utilized to achieve ligand-activated induction of apoptosis in a target cell. Such an output might

be desirable as a “kill switch” for a cell-based therapeutic gone awry, and additionally could

provide tolerance-induction since apoptotic cells are cleared by the reticuloendothelial system

and presented to the immune system in a tolerogenic manner.

Alternatively, the CREA mechanism could be used to reconstitute a reporter protein such

as GFP or near-infrared fluorescent protein (iRFP)101 for in vivo imaging applications. Such a

modality might be particularly advantageous for engineering erythrocytes (see Introduction

section 1.6), which lack a nucleus and require a transcriptionally independent output.

Finally, the peptide-activated system described may be suitable for a number of

applications in bio-patterning and synthetic intercellular communication. Use of peptide-

conjugated surfaces or particles to activate the CREA mechanism could enable new tools for

engineering cell-material interactions. Such peptide-conjugated materials might also be used to

oligomerize the CREA receptors (e.g. by inducing binding of all three chains rather than binding

of two and transient encounter of the third). Furthermore, cells engineered to secrete activator

peptides or peptide-conjugated synthetic cytokine analogs or express them on their surface could

also be used to activate PPID CREA for engineering such intercellular signaling platforms. Such

98

intercellular communication systems would be useful for interrogating and manipulating natural

cell-cell communication systems such as quorum sensing, engineering multicellular systems,

biocomputation, engineered pattern formation, and a host of other applications.102

5.2 Chapter 3: Engineering Nanobody-based Biosensors that Sense and Respond

to Extracellular Cues

5.2.1 Conclusions

The nanobody MESA system presented here is the first biosensor system to our

knowledge to successfully achieve orthogonal intracellular signaling in response to an

extracellular ligand. Nanobody MESA receptors were engineered to achieve stable surface

expression and ligand-binding, and proof of concept was demonstrated using a transient

transfection method in which the ligand was expressed from the same cells as the nanobody

MESA. An inability for the transiently transfected nanobody MESA to detect recombinant ligand

was overcome by transducing the MESA receptors using AAV as a vector, thus achieving a

reduced and sustained rate of expression and an inducible phenotype in the presence of

extracellular ligand. Finally, we demonstrated the modularity of this nanobody MESA platform

by substituting the GFP-specific nanobody ectodomains with a new pair of nanobodies specific

for a distinct ligand (mCherry) and achieved ligand-inducible signaling in a single step without

the requirement for additional optimization.

99


While an optimization step was not required for obtaining a functional architecture

utilizing the mCherry nanobodies, such an optimization step would be straightforward and might

yield a biosensor with improved signal to noise properties. For instance, exploring additional

lengths and compositions of the SCF domain to obtain the best biophysical parameters for

expression, low background, and optimal ligand binding may be of interest. Generating and

characterizing nanobody MESA with longer flexible linkers would be straightforward to

accomplish by rational design, whereas a thorough exploration of linker composition presents a

larger challenge and a vast design space that might be explored using a high throughput directed

evolution method.

An important future direction for the characterization of the nanobody MESA platform

will be determining and potentially optimizing a method of implementation. In our hands, AAV-

mediated delivery of the MESA coupled with transfection of reporter constructs considerably

altered and improved input/output behavior. For future applications, it may be necessary or

desirable to stably integrate both the MESA receptors and the reporter by lentiviral transduction

or by an integrase-mediated multi-gene delivery platform103.

As nanobody MESA receptors have demonstrated modularity and ease of adaptation to a

novel protein ligand, it would be of great interest to employ nanobodies specific for proteins of

therapeutic interest. Nanobodies have been developed for detection of cell surface antigens104, 105

and cytokines106, and incorporation of these into the MESA platform may be rapid and

straightforward, resulting in a biosensor specific for medically relevant environmental proteins.

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5.3 Chapter 4: Multiparametric evaluation via engineered two-input dependent

reporters

5.3.1 Conclusions

We have demonstrated the first instance of successful integration of an extracellular

biosensor technology with an intracellular logic gate. By engineering nanobody MESA specific

for distinct ligands to release distinct TFs and designing a hybrid promoter strategy to parse these

TFs into a concerted output, we have shown that MESA can be multiplexed to sense

combinations of cues in their environment. Using just our proof-of-concept library of hybrid

promoters, we were able to access an assortment of phenotypes and demonstrate multiple

functional designs.


To expand on the investigation of AND gate reporter architecture presented here,

additional permutations might be investigated, including architectures with odd numbers of TF

binding sites or with variations in the spacing between TF binding sites. Such an investigation

may yield reporters exhibiting even better signal-to-noise properties or interesting phenotypes

(e.g., additive response to ligands). Such an investigation might prove particularly desirable if

the reporter were to be implemented in a stable instead of transient context. Since fewer copies

of the reporter will be present in a given cell as opposed to the transient overexpression case, it

may be possible to increase transcription and consequently output by incorporating additional

repeats of TF binding sites.

We have observed that the nanobody MESA receptors are amenable to being redirected

to recognize a novel input and to release a novel transcription factor in a highly efficient and

101

straightforward manner. This finding importantly suggests that the system could be extended to

give rise to additional logical evaluation modalities and reporter outputs by incorporating other

transcriptional regulators such as transcription activator-like effectors (TALEs), zinc fingers

(ZF), chromatin modifiers100, and CRISPR Cas9 or dCas9 domains107. This potential to engineer

sophisticated logical modalities and potentially accept more than two inputs utilizing MESA

could enable a host of therapeutic applications. For instance, one could conceive of designing

one nanobody MESA with specificity for healthy tissue, another for a tumor antigen, and another

for an immune cytokine, and then multiplexing them into a logic circuit that might cause

activation of an inflammatory response in the presence of tumor but not healthy tissue, and only

in the absence of levels of cytokine that might be toxic. Thus the work here described represents

both first in-class breakthroughs in extracellular sensing and extracellular sensor-mediated

decision-making as well as important steps towards the goal of engineering safe and efficacious

smart cell therapies.

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