Elevated expression of proteins, such as those involved in native antibiotic resistance pathways or introduced to enable biosynthesis of a metabolic engineering target, frequently leads to increased fitness cost. This can result in reduced growth and places selective pressure on cells. In conditions where there is diversity in expression within the population, this can result in cells with higher fitness out-competing their low-fitness counterparts. In the antibiotic resistance context, differential fitness costs caused by antibiotic resistance machinery can be exploited to select against resistant bacteria. However, in biotechnology applications, introducing burdensome synthetic constructs often requires additional engineering to increase genetic stability and maintain production. In this thesis, we investigate the origin of fitness costs and strategies for either exploiting or reducing it, focusing on specific examples related to antibiotic resistance and metabolic engineering. In the resistance work, we study the multiple antibiotic resistance activator MarA and related proteins in Escherichia coli. We quantify the differential fitness cost impacts of salicylate on E. coli antibiotic resistance variants. We demonstrate that salicylate, the natural inducer of MarA, imposes a higher fitness cost on resistant cells compared to the susceptible counterparts, making it possible to bias bacterial population membership towards those cells that are susceptible. In a second study, we focus on the role of salicylate in antibiotic tolerant persister cell formation, finding that salicylate induces reactive oxygen species and consequently persistence. In the metabolic engineering parts of the thesis we first review the mechanisms of fitness cost and existing strategies to ameliorate cost and cell-to-cell variation. Next, we present a technique for reducing fitness cost while maintaining production that takes advantage of transcription factor decoy sites to regulate biosynthesis in E. coli. Using arginine production as a model system, the transcription factor decoy is able to increase production by 16-fold without detectable growth differences. Together, the thesis provides an understanding of the origins and mechanisms of fitness cost in the context of antibiotic resistance and metabolic engineering. It also introduces strategies to exploit fitness costs to select against resistant bacteria and engineering strategies to ameliorate cost while increasing production and genetic stability.

Precise engineering of genomes holds great promise to advance our understanding of gene function and biotechnological applications. DNA double strand breaks are repaired via imprecise non-homologous end joining repair or via precise homology-directed repair processes. Therefore, we could harness the DSBs to engineer the genomes with a variety of genetic outcomes and with singlebase- level precision. The major barrier for genome engineering was the generation of site-specific DNA DSBs. Programmable DNA enzymes capable of making a complete and site-specific cut in the genome do not exist in nature. However, these enzymes can be made in in vitro as chimeric fusions of two modules, a DNA binding module and a DNA cleaving module. The DNA cleaving module can be programmed to bind to any user-defined sequence and the DNA cleaving module would generate DSBs in the target sequence. These enzymes called molecular scissors include zinc finger nucleases (ZFNs) and transcriptional activator like effector nucleases (TALENs). The programmability of these enzymes depends on protein engineering for DNA binding specificity which may be complicated, recourse intensive and suffer from reproducibility issues. Recently, clustered regularly interspaced palindromic repeats (CRISPR)/ CRISPR associated endonuclease 9 (Cas9) an adaptive immune system of bacterial and archaeal species has been developed for genome engineering applications. CRISPR/Cas9 is an RNA-guided DNA endonuclease and can be reprogrammed through the engineering of single guide RNA molecule (sgRNA). CRISPR/Cas9 activity has been shown across eukaryotic species including plants. Although the engineering of CRISPR/Cas9 is quite predictable and reproducible, there are many technological challenges and improvements that need to be made to achieve robust, specific, and efficient plant genome engineering. Here in this thesis, I developed a number of technologies to improve specificity, delivery and expression and heritability of CRISRP/Cas9-modification in planta. Moreover, I used these technologies to answer basic questions to understand the molecular underpinning of the interplay between splicing and abiotic stress. To improve Cas9 specificity, I designed and constructed a chimeric fusion between catalytically dead Cas9 (dCas9) and FOKI catalytic DNA cleaving domain (dCas9.FoKI). This synthetic chimeric fusion enzyme improved Cas9 specificity which enable precision genome engineering. Delivery of genome engineering reagents into plant cells is quite challenging, I developed a virus-based system to deliver sgRNAs into plants which facilitates plant genome engineering and could bypass the need for tissue culture in engineering plant genomes. To improve the expression of the CRISPR/Cas9 machinery in plant species, I developed a meiotically-driven expression of CRISPR/Cas9 which improved genome editing and heritability of editing in seed progeny, thereby facilitating robust genome engineering applications. To understand the molecular basis of the interplay between splicing stress and abiotic stress, I used the CRISPR/Cas9 machinery to engineer components of the U2snRNP complex coupled which chemical genomics to understand the splicing stress regulation in response to abiotic stress conditions. Finally, I harnessed the technological improvements and developments I have achieved with CRISPR/Cas9 system to develop a directed evolution platform for targeted trait engineering which expands and accelerates trait discovery and engineering of plant species resilient to climate change conditions.

Synthetic biology, a field that sits between Biology and Engineering disciplines, has come into its own in the last decade. The decreasing cost of DNA synthesis has lead to the creation of larger and more complex synthetic gene networks, engineered with functional goals rather than simple demonstration. While many methods have been developed to reduce the time required to produce complex networks, none focus upon the considerable tuning needed to turn structurally correct networks into functional gene networks. To this end, we created a Plug-and-Play synthetic gene network assembly that emphasizes character-driven iteration for producing functional synthetic gene networks. This platform enables post-construction modification and easy tuning of networks through its ability to swap individual parts. To demonstrate this system, we constructed a functional bistable genetic toggle and transformed it into two functionally distinct synthetic networks. Once these networks have been created and tuned at the bench, they next must be delivered to bacteria in their target environment. While this is easy for industrial applications, delivering synthetic networks as medical therapeutics has a host of problems, such as competing microbes, the host immune system, and harsh microenvironments. Therefore, we employed bacteriophage technologies to deliver functional synthetic gene networks to specific bacterial strains in various microenvironments. We first sought to deliver functional genetic networks to bacteria present in the gut microbiome. This allows for functionalization of these bacteria to eventually sense disease states and secrete therapeutics. As a proof of concept a simple circuit was created using the Plug-and-Play platform and tested before being moved into the replicative form plasmid of the M13 bacteriophage. Bacteriophage particles carrying this network were used to infect gut bacteria of mice. Infection and functionality of the synthetic network was monitored from screening fecal samples. Next, we employed phagemid technologies to deliver high copy plasmids expressing antibacterial networks to target bacteria. This allows for sustained expression of antibacterial genes that cause non-lytic bacterial death without reliance upon traditional small molecule antibiotics. Phagemid particles carrying our antibacterial networks were then tested against wild type and antibiotic-resistant bacteria in an in vitro and in vivo environment.

Synthetic genetic regulatory networks (or genetic circuits) can operate in complex biochemical environments to process and manipulate biological information to produce a desired behavior. The ability to engineer such genetic circuits has wide-ranging applications in various fields such as therapeutics, energy, agriculture, and environmental remediation. However, engineering multilevel genetic circuits quickly and reliably is a big challenge in the field of synthetic biology. This difficulty can partly be attributed to the growing complexity of biology. But some of the predominant challenges include the absence of formal specifications -- that describe precise desired behavior of these biological systems, as well as a lack of computational and mathematical frameworks -- that enable rapid in-silico design and synthesis of genetic circuits. This thesis introduces two major frameworks to reliably design genetic circuits. The first implementation focuses on a framework that enables synthetic biologists to encode Boolean logic functions into living cells. Using high-level hardware description language to specify the desired behavior of a genetic logic circuit, this framework describes how, given a library of genetic gates, logic synthesis can be applied to synthesize a multilevel genetic circuit, while accounting for biological constraints such as 'signal matching', 'crosstalk', and 'genetic context effects'. This framework has been implemented in a tool called Cello, which was applied to design 60 circuits for Escherichia coli, where the circuit function was specified using Verilog code and transformed to a DNA sequence. Across all these circuits, 92% of the output states functioned as predicted. The second implementation focuses on a framework to design complex genetic systems where the focus is on how the system behaves over time instead of its behavior at steady-state. Using Signal Temporal Logic (STL) -- a formalism used to specify properties of dense-time real-valued signals, biologists can specify very precise temporal behaviors of a genetic system. The framework describes how genetic circuits that are built from a well characterized library of DNA parts, can be scored by quantifying the 'degree of robustness' of in-silico simulations against an STL formula. Using formal verification, experimental data can be used to validate these in-silico designs. In this framework, the design space is also explored to predict external controls (such as approximate small molecule concentrations) that might be required to achieve a desired temporal behavior. This framework has been implemented in a tool called Phoenix. / 2021-02-28T00:00:00Z

Depuis plus de 80 ans, le métabolisme spécialisé nous fournit de nombreuses molécules utilisées en médecine, en particulier comme anti-infectieux. Aujourd’hui, avec l’augmentation mondiale de la résistance aux antimicrobiens, de nouveaux antibiotiques sont indispensables. Une des réponses à cette pénurie grave pourrait provenir de la biologie synthétique. Dans le domaine du métabolisme spécialisé, la biologie synthétique est utilisée en particulier pour la biosynthèse de métabolites non naturels. Parmi les métabolites spécialisés, les peptides non ribosomiques constituent une cible attrayante, car ils nous ont déjà fourni des molécules à haute valeur clinique (ex. les antibiotiques vancomycine et daptomycine). De plus, la plupart sont synthétisés par des enzymes multimodulaires appelées synthétases de peptides non ribosomiques (NRPS), et sont diversifiés davantage par des enzymes de décoration. Ainsi, ces voies de biosynthèse se prêtent particulièrement à la biosynthèse combinatoire, consistant à combiner des gènes de biosynthèse provenant de divers groupes de gènes ou, dans le cas des NRPS, à combiner des modules ou domaines pour créer de nouvelles enzymes. Cependant, si plusieurs études ont établi la faisabilité de telles approches, de nombreux obstacles subsistent avant que les approches combinatoires de biosynthèse soient totalement efficaces pour la synthèse de nouveaux métabolites. Les travaux présentés ici s’inscrivent dans le cadre d’un projet visant à comprendre les facteurs limitant les approches de biosynthèse combinatoire basées sur les NRPS, en utilisant une approche de biologie synthétique. Nous avons choisi de travailler avec les NRPS responsables de la biosynthèse des pyrrolamides. En effet, ces NRPS sont constitués uniquement de modules et de domaines autonomes, et donc particulièrement adaptés aux manipulations génétiques et biochimiques. La caractérisation du groupe de gènes de biosynthèse du pyrrolamide anthelvencine constitue la première partie de cette thèse et nous a fourni de nouveaux gènes pour notre étude. La deuxième partie a consisté à construire de vecteurs intégratifs modulaires, outils essentiels pour la construction et l’assemblage de cassettes génétiques. La dernière partie présente la reconstruction du groupe de gènes du pyrrolamide congocidine, basée sur la construction et l’assemblage de cassettes de gènes synthétiques. Dans l’ensemble, ces travaux ouvrent la voie à de futures expériences de biosynthèse combinatoire, expériences qui devraient contribuer à une meilleure compréhension du fonctionnement précis des NRPS. / For more than 80 years, specialized metabolism has provided us with many molecules used in medicine, especially as anti-infectives. Yet today, with the rise of antimicrobial resistance worldwide, new antibiotics are crucially needed. One of the answers to this serious shortage could arise from synthetic biology. In the field of specialized metabolism, synthetic biology is used in particular to biosynthesize unnatural metabolites. Among specialized metabolites, non-ribosomal peptides constitute an attractive target as they have already provided us with clinically valuable molecules (e.g. the vancomycin and daptomycin antibiotics). In addition, most are synthesized by multimodular enzymes called non-ribosomal peptide synthetases (NRPS) and further diversified by tailoring enzymes. Thus, such biosynthetic pathways are particularly amenable to combinatorial biosynthesis, which consists in combining biosynthetic genes coming from various gene clusters or, in the case of NRPSs, combining modules or domains to create a new enzyme. Yet, if several studies have established the feasibility of such approaches, many obstacles remain before combinatorial biosynthesis approaches are fully effective for the synthesis of new metabolites. The work presented here is part of a project aiming at understanding the limiting factors impeding NRPS-based combinatorial biosynthesis approaches, using a synthetic biology approach. We chose to work with the NRPSs involved in the biosynthesis of pyrrolamides. Indeed, these NRPS are solely constituted of stand-alone modules and domains, and thus, particularly amenable to genetic and biochemical manipulations. The characterization of the biosynthetic gene cluster of the pyrrolamide anthelvencin constitutes the first part of this thesis, and provided us with new genes for our study. The second part involved the construction of modular integrative vectors, essential tools for the construction and assembly of gene cassettes. The final part presents the successful refactoring of the congocidine pyrrolamide gene cluster, based on the construction and assembly of synthetic gene cassettes. Altogether, this work paves the way for future combinatorial biosynthesis experiments that should help deciphering the detailed functioning of NRPSs.

La biologie synthétique est le domaine de la bioingénierie permettant de concevoir, de construire et de tester de nouveaux systèmes biologiques en réécrire le code génétique. Les circuits biologiques synthétiques sont des outils sophistiqués permettant de construire des réseaux biologiques pour des applications médicales, industrielles et environnementales. Cette thèse de doctorat porte sur le développement de voies métaboliques synthétiques conçues à l'aide d'outils informatiques. Ces voies métaboliques sont intégrées à la couche de régulation transcriptionnelle pour développer des biocircuits pour la bioproduction, la biodétection et la biocalcul dans des systèmes cellulaires et acellulaires. Les résultats obtenus durant cette thèse de doctorat révèlent le nouveau potentiel des voies métaboliques dans l'établissement de biocircuits synthétiques. Le volet bioproduction-biodétection de la thèse vise à développer un nouveau biocapteur pour un sucre rare utilisé pour améliorer l'activité catalytique d’enzyme dans la cellule (in vivo). Ce biocapteur a ensuite été implémenté dans un système acellulaire (in vitro) pour découvrir et optimiser le comportement de biocapteurs à base de répresseurs. Une fois optimisé en système acellulaire, notre biocapteur a été utilisé pour surveiller la production enzymatique de sucre rare. Le développement de biocapteurs procaryotes acellulaires, qui reposent principalement sur des répresseurs, permet d'accélérer et de rendre plus efficace le cycle “design-build-test” dans le prototypage des voies métaboliques dans les systèmes acellulaires. L'application de la biodétection des circuits métaboliques pour le diagnostic est la mise en œuvre et l'optimisation des transducteurs métaboliques dans le système acellulaire. Les transducteurs sont des voies métaboliques composées d'au moins une enzyme catalysant un métabolite indétectable en un inducteur transcriptionnel, augmentant ainsi le nombre de petites molécules biologiquement détectables. En tant que nouvelle approche pour effectuer des biocalculs, des circuits métaboliques ont été appliqués pour construire des additionneurs métaboliques et des perceptrons métaboliques. Dans la cellule, trois transducteurs métaboliques et un additionneur métabolique ont été construits et caractérisés. Les systèmes acellulaires permettent d’accélérer la caractérisation de circuits biologiques, de finement régler le niveau d’expression d’un ou plusieurs gènes et facilite l’expression de plusieurs plasmides simultanément. Ceci a permis de construire de multiples transducteurs pondérés et des additionneurs métaboliques. Le modèle basé sur des données expérimentales a permis de concevoir un perceptron métabolique pour construire des classificateurs binaires à quatre entrées. Les additionneurs, perceptrons et classificateurs peuvent être utilisés dans des applications avancées telles que la détection de précision et dans le développement de souches pour le génie métabolique ou la thérapeutique intelligente. / Synthetic biology is the field of engineerable life science and technology to design-build-test novel biological systems through reprogramming the code of DNA. Synthetic biocircuits are sophisticated tools to reconstruct biological networks for medical, industrial, and environmental applications. This doctoral thesis focuses on the development of synthetic metabolic pathways designed by computer-aided tools integrated with the transcriptional regulatory layer enabling bioproduction, biosensing, and biocomputation in whole-cell and cell-free systems. The achievements of this doctoral thesis bring attention to new potentials of metabolic pathways in the development of synthetic biocircuits. The bioproduction-biosensing section of the thesis is to build a novel sensor for a rare sugar used to improve the catalytic activity of its producing enzyme in the whole-cell system (in vivo). This sensor was then implemented in a TX-TL cell-free system (in vitro) as a proof of concept of a repressor based biosensor to discover and optimize the behavior of repressor based biosensors in the cell-free system that suffer from low fold repression. The optimized cell-free biosensor was then used to monitor the enzymatic production of the rare sugar. The development of cell-free prokaryotic biosensors which are mostly relying on repressors enables faster and more efficient design-build-test cycle in metabolic pathways prototyping in cell-free systems. The biosensing application of the metabolic circuits for diagnosis is the implementation and optimization of cell-free metabolic transducers. The transducers are metabolic pathways composed of at least one enzyme catalyzing an undetectable metabolite to a transcriptional inducer, hence expanding the number of biologically detectable small molecules in cell-free systems. Finally, as a radical approach to perform biocomputation, metabolic circuits were applied to build metabolic adders and metabolic perceptrons. In whole-cell system, three metabolic transducers and a metabolic adder (multiple transducers receiving multiple input metabolites and transform them into a common metabolite) were built and characterized. By taking advantage of cell-free systems in rapid characterization, high tunability, and the possibility of using tightly controlled multiple DNA parts, multiple weighted transducers and metabolic adders were implemented. The integrated model trained on the experimental data enabled the designing of a metabolic perceptron for building four-input binary classifiers. The adders, perceptrons and classifiers can be applied in advanced applications such as multiplex detection/precision medicine and in the development of designer strains for metabolic engineering or smart therapeutics.

Les buts de cette thèse sont doubles, et concernent les circuits métaboliques synthétiques, qui permettent de détecter des composants chimiques par transmission de signal et de faire du calcul en utilisant des enzymes. La première partie a consisté à développer des outils d’apprentissage actif et par renforcement pour améliorer la conception de circuits métaboliques et optimiser la biodétection et la bioproduction. Pour atteindre cet objectif, un nouvel algorithme (RetroPath3.0) fondé sur une recherche arborescente de Monte Carlo guidée par similarité est présenté. Cet algorithme, combiné à des règles de réaction apprises sur des données et des niveaux différents de promiscuité enzymatique, permet de focaliser l’exploration sur les composés et les chemins les plus prometteurs en bio-rétrosynthèse. Les chemins obtenus par rétrosynthèse peuvent être implémentés dans des cellules ou des systèmes acellulaires. Afin de concevoir le meilleur milieu pour optimiser la productivité du système, une méthode d’apprentissage actif qui explore efficacement l’espace combinatoire des composants du milieu a été développée.La deuxième partie a consisté à développer des méthodes d’analyse, pour générer des connaissances à partir de données biologiques, et modéliser les réponses de biocapteurs. Dans un premier temps, l’effet du nombre de copies de plasmides sur la sensibilité d’un biocapteur utilisant un facteur de transcription a été modélisé. Ensuite, en utilisant des systèmes acellulaires qui permettent un meilleur contrôle des variables expérimentales comme la concentration d’ADN, l’utilisation des ressources a été modélisée pour assurer que notre compréhension actuelle des phénomènes sous-jacents est suffisante pour rendre compte du comportement du circuit, en utilisant des modèles empiriques ou mécanistiques. Couplés aux outils de conception de circuits métaboliques, ces modèles ont ensuite permis de développer une nouvelle approche de calcul biologique, appelée perceptrons métaboliques.Dans l’ensemble, cette thèse présente des outils de conception et d’analyse pour les circuits métaboliques synthétiques. Ces outils ont été utilisés pour développer une nouvelle méthode permettant d’effectuer des calculs en biologie synthétique. / The aims of this thesis are two-fold, and centered on synthetic metabolic circuits, which perform sensing and computation using enzymes.The first part consisted in developing reinforcement and active learning tools to improve the design of metabolic circuits and optimize biosensing and bioproduction. In order to do this, a novel algorithm (RetroPath3.0) based on similarity-guided Monte Carlo Tree Search to improve the exploration of the search space is presented. This algorithm, combined with data-derived reaction rules and varying levels of enzyme promiscuity, allows to focus exploration on the most promising compounds and pathways for bio-retrosynthesis. As retrosynthesis-based pathways can be implemented in whole cell or cell-free systems, an active learning method to efficiently explore the combinatorial space of components for rational media optimization was also developed, to design the best media maximizing cell-free productivity.The second part consisted in developing analysis tools, to generate knowledge from biological data and model biosensor response. First, the effect of plasmid copy number on sensitivity of a transcription-factor based biosensor was modeled. Then, using cell-free systems allowing for broader control over the experimental factors such as DNA concentration, resource usage was modeled to ensure our current knowledge of underlying phenomenons is sufficient to account for circuit behavior, using either empirical models or mechanistic models. Coupled with metabolic circuit design, those models allowed us to develop a new biocomputation approach, called metabolic perceptrons.Overall, this thesis presents tools to design and analyse synthetic metabolic circuits, which are a novel way to perform computation in synthetic biology.

abstract: The fundamental building blocks for constructing complex synthetic gene networks are effective biological parts with wide dynamic range, low crosstalk, and modularity. RNA-based components are promising sources of such parts since they can provide regulation at the level of transcription and translation and their predictable base pairing properties enable large libraries to be generated through in silico design. This dissertation studies two different approaches for initiating interactions between RNA molecules to implement RNA-based components that achieve translational regulation. First, single-stranded domains known as toeholds were employed for detection of the highly prevalent foodborne pathogen norovirus. Toehold switch riboregulators activated by trigger RNAs from the norovirus RNA genome are designed, validated, and coupled with paper-based cell-free transcription-translation systems. Integration of paper-based reactions with synbody enrichment and isothermal RNA amplification enables as few as 160 copies/mL of norovirus from clinical samples to be detected in reactions that do not require sophisticated equipment and can be read directly by eye. Second, a new type of riboregulator that initiates RNA-RNA interactions through the loop portions of RNA stem-loop structures was developed. These loop-initiated RNA activators (LIRAs) provide multiple advantages compared to toehold-based riboregulators, exhibiting ultralow signal leakage in vivo, lacking any trigger RNA sequence constraints, and appending no additional residues to the output protein. Harnessing LIRAs as modular parts, logic gates that exploit loop-mediated control of mRNA folding state to implement AND and OR operations with up to three sequence-independent input RNAs were constructed. LIRA circuits can also be ported to paper-based cell-free reactions to implement portable systems with molecular computing and sensing capabilities. LIRAs can detect RNAs from a variety of different pathogens, such as HIV, Zika, dengue, yellow fever, and norovirus, and after coupling to isothermal amplification reactions, provide visible test results down to concentrations of 20 aM (12 RNA copies/µL). And the logic functionality of LIRA circuits can be used to specifically identify different HIV strains and influenza A subtypes. These findings demonstrate that toehold- and loop-mediated RNA-RNA interactions are both powerful strategies for implementing RNA-based computing systems for intracellular and diagnostic applications. / Dissertation/Thesis / Doctoral Dissertation Biochemistry 2019

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