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Model-Guided Design of Mammalian Genetic Programs Exclusive Licensee J SCIENCE ADVANCES | RESEARCH ARTICLE GENETICS Copyright © 2021 The Authors, some rights reserved; Model-guided design of mammalian genetic programs exclusive licensee J. J. Muldoon1,2, V. Kandula3, M. Hong2, P. S. Donahue1,2,4, J. D. Boucher1,2, American Association 1,2,5,6 1,2,5 for the Advancement N. Bagheri , J. N. Leonard * of Science. No claim to original U.S. Government Genetically engineering cells to perform customizable functions is an emerging frontier with numerous techno- Works. Distributed logical and translational applications. However, it remains challenging to systematically engineer mammalian cells under a Creative to execute complex functions. To address this need, we developed a method enabling accurate genetic program Commons Attribution design using high-performing genetic parts and predictive computational models. We built multifunctional pro- License 4.0 (CC BY). teins integrating both transcriptional and posttranslational control, validated models for describing these mechanisms, implemented digital and analog processing, and effectively linked genetic circuits with sensors for multi-input evaluations. The functional modularity and compositional versatility of these parts enable one to satisfy a given design objective via multiple synonymous programs. Our approach empowers bioengineers to predictively de- sign mammalian cellular functions that perform as expected even at high levels of biological complexity. Downloaded from INTRODUCTION these parts (25), and computational and conceptual tools that facilitate Early demonstrations of genetically engineering customized func- the identification of designs that function robustly despite biological tions in mammalian cells indicate a vast potential to benefit applica- variability and cross-talk (26–28). In this study, we sought to address tions including directed stem cell differentiation (1, 2) and cell-based these issues by developing a model-driven process that enables one to therapies (3). In general, most applications require precise control propose a tractable set of candidate circuits for construction and of gene expression and the capability to sense and respond to exter- testing without the need for empirical trial-and-error tuning. We http://advances.sciencemag.org/ nal cues (4–8). Despite the growing availability of biological parts validated this framework by using it to implement a variety of func- (such as libraries of promoters and regulatory proteins) that could tions including digital and analog information processing, as well as be used to control cell states, assembling parts to compose customized sense-and-respond circuits. genetic programs that function as intended remains a challenge, and it often requires iterative experimental tuning or down- selection to identify functional configurations. This highly empirical process limits RESULTS the scope of programs that one can feasibly compose and fine-tune, Biological parts for integrating transcriptional as well as the performance of functional programs identified in and posttranslational control of gene expression this manner. Thus, the need for systematic and precise design pro- The strategy that we pursued for genetic program design was cesses represents a grand challenge in the field of mammalian syn- uniquely enabled by the COmposable Mammalian Elements of on August 8, 2021 thetic biology. Transcription (COMET): a toolkit of TFs and promoters with tun- Model-guided predictive design has been demonstrated in the able properties enabling precise and orthogonal control of gene composition of some cellular functions, including transcriptional expression (13). These TFs comprise a ZF DNA-binding domain logic in bacteria (9) as well as logical (10) and analog behaviors in and a functional domain, e.g., VP16 or VP64, which are activation yeast (11); however, this type of approach is less developed in mam- domains (ADs) that with a ZF form an activator (ZFa). A protein malian systems. To date, transcription factors (TFs) based on zinc including a ZF domain but lacking an AD functions as an inhibitor fingers (ZFs) (12, 13), transcription activator-like effectors (TALEs) by competing with the cognate ZFa for binding. Promoters in this (14–17), dCas9 (18, 19), and other proteins (20) have been used to library contain ZF binding sites arranged in different configurations implement transcriptional logic in mammalian cells. Some of these (e.g., ZF1x6-C has six compactly arranged ZF1 sites). Each combi- studies make use of protein splicing (12, 14, 18). Other studies have nation of a promoter and a ZFa (and potentially an inhibitor) con- used RNA-binding proteins (21), proteases (22, 23), and synthetic fers a characteristic level of transcriptional activity (fig. S1, A to E), protein-binding domains (17). Yet, it remains difficult to predict and as part of this previous work, we developed mathematical models circuit performance based only upon descriptions of the components. to characterize these relationships (13). Here, we investigate whether Associated challenges include the availability of appropriate parts these biological parts and descriptive computational tools can be (24), suitably descriptive models that support predictions using adapted and applied to achieve predictive genetic program design. Although COMET includes many parts for implementing tran- scriptional regulation, we hypothesized that complex genetic program 1Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, design could be facilitated by introducing a mechanism for regula- IL 60208, USA. 2Department of Chemical and Biological Engineering, Northwestern tion at the posttranslational level (Fig. 1, A and B). To investigate University, Evanston, IL 60208, USA. 3Honors Program in Medical Education, North- western University Feinberg School of Medicine, Chicago, IL 60611, USA. 4Medical this strategy, we evaluated new parts based on split inteins: comple- Scientist Training Program, Northwestern University Feinberg School of Medicine, mentary domains that fold and trans-splice to covalently ligate Chicago, IL 60611, USA. 5Center for Synthetic Biology, Chemistry of Life Processes flanking domains (exteins) (29). We selected the split intein gp41-1 Institute, and Robert H. Lurie Comprehensive Cancer Center, Northwestern University, for its rapid splicing kinetics (30). To test an application of this Evanston, IL 60208, USA. 6Departments of Biology and Chemical Engineering, Uni- versity of Washington, Seattle, WA 98195, USA. mechanism, we appended an AD to the gp41-1 N-terminal fragment *Corresponding author. Email: [email protected] (intN) and a ZF to the C-terminal fragment (intC). These parts were Muldoon et al., Sci. Adv. 2021; 7 : eabe9375 19 February 2021 1 of 12 SCIENCE ADVANCES | RESEARCH ARTICLE + + AND – IMPLY – A B C – + D – + Functions to implement in cells Genetic program design VP64- VP64- VP64- NIMPLY intN VP64- intN ZF1 AND IMPLY NAND ZF1 VP64- Design Synthetic Predictive intC- DsDed- + ZF1 intC- ZF1 objective components model Input 2 ZF1 – –+ dx dt / Input 1 Output: OFF ON EF1α EF1α N C ... EF1α EF1α VP64 int DsDed int ZF1 N C VP64 int int ZF1 Mechanisms to use in composing genetic programs intN C Transcriptional regulation TF Enzymatic Multiple layers DsDed int VP64 ZF1 intN Activator Inhibitor Strong inhibitor VP64 ZF1 ZF1x6-C EF1α competition conversion of regulation C int ZF1x6-C mKate2 VP64 ZF1 ZF DsDed ZF AD ZF mKate2 Splicing of an inhibitor into an activatorSplicing of an inhibitor into an activator COMET promoter Target ExperimentFit Prediction Experiment Simulations to explore an expanded design space 4 4 Posttranslational regulation Base caseIncreasing complexity 1 1 N C A int B A’ int B’ 1/4 1/4 intC-ZF1 N Output Output int B Input Input A B’ 0 DsDed-intC-ZF1 0 A’ intC One response Multiple possible context- Stable folding Ligation profile dependent response profiles 01/401/10 2/5 01/401/102/5 01/801/201/5 01/801/201/5 VP64-intN Output: VP64-intN (x109 gene copies) 0 1.5 mKate2 signal 0 0.85 (x106 MEPTRs) + + + + NAND – NIMPLY – NIMPLY – NIMPLY – E – + F – + G – + H – + DsDed- DsDed- DsDed- DsDed- DsDed- intN ZF1 intN ZF1 ZF1 ZF1 Downloaded from intC- VP64- VP64- VP64- ZF1 VP64- intC- ZF1 ZF1 ZF1 ZF1 EF1α EF1α N C EF1α DsDed int int ZF1 EF1α EF1α EF1α N C ZF1 DsDed ZF1 DsDed int VP64 int ZF1 N DsDed ZF1 int intC EF1α EF1α intN VP64 ZF1 ZF1x6-C VP64 ZF1 ZF1x6-C C EF1α VP64 int DsDed ZF1 ZF1x6-C mKate2 VP64 ZF1 ZF1x6-C mKate2 mKate2 mKate2 http://advances.sciencemag.org/ Splicing to a more potent inhibitor Steric inhibition Dual mechanism of inhibition Splicing of an activator into an inhibitor Prediction Experiment Prediction Experiment Prediction Experiment Prediction Experiment 2 2/5 2/5 1/10 1/2 1/10 1/10 1/40 1/8 1/40 1/40 1/160 intC-ZF1 VP64-ZF1 VP64-ZF1 0 0 0 VP64-intC-ZF1 0 01/4 14 01/4 14 01/4 14 01/4 14 01/4 14 01/4 14 0 1/81/2 2 01/8 1/22 DsDed-intN ZF1 DsDed-ZF1 DsDed-intN 0 1.3 0 1.1 0 1.0 0 0.45 + + NIMPLY – AND – I – + J – + K Activation Double inversion DsDed- DsDed- DsDed- DsDed- DsDed- ZF1- intN DsDed- ZF10 ZF10 ZF1- DsDed- VP64-ZF1 PEST ZF10 VP64- PEST intC- ZF1- VP64- PEST ZF10 VP64- ZF10 DsDed- ZF10 VP64- ZF1 ZF1 VP64- ZF10 VP64- on August 8, 2021 ZF1 ZF1 EF1α DsDed ZF10 EF1α EF1α N C DsDed int int ZF10 EF1α EF1α EF1α EF1α VP64 ZF10 ZF10x6-C VP64 ZF1 ZF1x6-C DsDed ZF10 DsDed ZF1 DsDed ZF1 PEST intN mKate2 DsDed ZF10 C EF1α int EF1α EF1α VP64 ZF1 ZF1x6-C VP64 ZF10 ZF10x6-C VP64 ZF10 ZF10x6-C mKate2 DsDed ZF1 PEST Standard activation Inhibition cascade DsDed ZF1 PEST EF1α Fit Prediction VP64 ZF1 ZF1x6-C EF1α 1 0.5 mKate2 VP64 ZF1 ZF1x6-C n = 0.9 n = 2.4 mKate2 Ultrasensitive Inhibition cascade Splicing upstream of an inhibition cascade Prediction Experiment Prediction Experiment Reporter signal Reporter signal 0 (max.-normalized) (max.-normalized) 0 10 10 0.10.2 0.4124 10 0.10.2 0.4124 10 Experiment Experiment 2.5 2.5 1.2 6 n = 1.0 n = 2.8 5/8 5/8 Ultrasensitive 0.8 intC-ZF10 4 DsDed-ZF10 0 MEPTRs) 0 MEPTRs) 6 2 6 0.4 01/4 14 01/4 14 05/8 2.510 05/8 2.510 mKate2 signal mKate2 signal (x10 (x10 DsDed-ZF1 0 0 DsDed-intN 0.1 0.2 0.4 12410 0.10.2 0.4124 10 0 0.40 0 0.5 VP64-ZF1 (x109 gene copies) DsDed-ZF10 (x109 gene copies) Fig.
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