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Results for “synthetic biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 289 records · Page 16Linked to original sources

bioRxiv2019

Engineered probiotics for local tumor delivery of checkpoint blockade nanobodies

Immunotherapies such as checkpoint inhibitors have revolutionized cancer therapy yet lead to a multitude of immune-related adverse events, suggesting the need for more targeted delivery systems. Due to their preferential colonization of tumors and advances in engineering capabilities from synthetic biology, microbes are a natural platform for the local delivery of cancer therapeutics. Here, we present an engineered probiotic bacteria system for the controlled production and release of novel immune checkpoint targeting nanobodies from within tumors. Specifically, we engineered genetic lysis circuit variants to effectively release nanobodies and safely control bacteria populations. To maximize therapeutic efficacy of the system, we used computational modeling coupled with experimental validation of circuit dynamics and found that lower copy number variants provide optimal nanobody release. Thus, we subsequently integrated the lysis circuit operon into the genome of a probiotic E. coli Nissle 1917, and confirmed lysis dynamics in a syngeneic mouse model using in vivo bioluminescent imaging. Expressing a nanobody against PD-L1 in this strain demonstrated enhanced efficacy compared to a plasmid-based lysing variant, and similar efficacy to a clinically relevant monoclonal antibody against PD-L1. Expanding upon this therapeutic platform, we produced a nanobody against cytotoxic T-lymphocyte associated protein -4 (CTLA-4), which reduced growth rate or completely cleared tumors when combined with a probiotically-expressed PD-L1 nanobody in multiple syngeneic mouse models. Together, these results demonstrate that our engineered probiotic system combines innovations in synthetic biology and immunotherapy to improve upon the delivery of checkpoint inhibitors. SENTENCE SUMMARYWe designed a probiotic platform to locally deliver checkpoint blockade nanobodies to tumors using a controlled lysing mechanism for therapeutic release.

synthetic biology↗

bioRxiv2026

Engineering Binding Efficiency and Interaction Stability of a Thermostable Cohesin-Dockerin Pair on the Bacterial Cell Surface

Efficient conversion of polymeric feedstocks for sustainable bioprocessing requires robust strategies for enzyme assembly and cell-surface attachment. In nature, cellulosomes achieve highly efficient lignocellulosic polysaccharide deconstruction through scaffoldin-mediated organization of carbohydrate-active enzymes via specific cohesin-dockerin interactions. These modular binding pairs are therefore attractive tools for synthetic biology and engineered whole-cell biocatalysis, yet their performance has been studied mainly in vitro or in yeast or Gram-positive bacteria. The factors governing their function on the microbial surfaces - particularly those of Gram-negative bacteria - remain incompletely understood. Here, we investigated the binding efficiency and interaction stability of two thermophilic cohesin-dockerin pairs from Acetivibrio thermocellus and Acetivibrio clariflavus displayed on the surface of the genome-streamlined strain Pseudomonas putida EM371 using an Ag43-based display system from Escherichia coli and a dockerin-tagged fluorescent reporter. We show that binding efficiency is strongly affected by the temperature at which the cohesin-dockerin complex is formed. We further demonstrate that the interaction stability of the A. clariflavus pair can be substantially improved by targeted amino acid substitutions in the dockerin domain guided by molecular dynamics simulations and free-energy calculations. These results identify key parameters controlling the performance of thermophilic cohesin-dockerin modules on living bacterial cell surfaces and establish a computation-guided strategy for engineering more stable cellulosome-derived assembly interfaces, advancing the development of modular whole-cell platforms for sustainable biotechnology applications. TOC graphics O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/743725v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@1a0d637org.highwire.dtl.DTLVardef@4fc620org.highwire.dtl.DTLVardef@15882f0org.highwire.dtl.DTLVardef@1fc6aa3_HPS_FORMAT_FIGEXP M_FIG C_FIG Cohesin-dockerin pairs provide strong and modular non-covalent interactions for synthetic biology and biotechnology applications. We establish an experimental and computational pipeline to improve their two key properties - binding efficiency and interaction stability - on the surface of Pseudomonas putida, enabling more robust cell-surface assembly systems.

synthetic biology↗

bioRxiv2024

Chloroplast Cell-Free Systems from Different Plant Species as a Rapid Prototyping Platform

Climate change poses a significant threat to global agriculture, necessitating innovative solutions. Plant synthetic biology, particularly chloroplast engineering, holds promise as a viable approach to this challenge. Chloroplasts present a variety of advantageous traits for genetic engineering, but the development of genetic tools and genetic part characterization in these organelles is hindered by the lengthy timescales required to generate transplastomic organisms. To address these challenges, we have established a versatile protocol for generating chloroplast-based cell-free gene expression (CFE) systems derived from a diverse range of plant species, including wheat (monocot), spinach, and poplar trees (dicots). We show that these systems work with conventionally used T7 RNA polymerase, as well as the endogenous chloroplast polymerases, allowing for detailed characterization and prototyping of regulatory sequences at both transcription and translation levels. To demonstrate the platform for characterization of promoters and 5 and 3 untranslated regions (UTRs) in higher plant chloroplast gene expression, we analyze a collection of 23 5UTRs, 10 3UTRs, and 6 chloroplast promoters, assessed their expression in spinach and wheat extracts, and found consistency in expression patterns, suggesting cross-species compatibility. Looking forward, our chloroplast CFE systems open new avenues for plant synthetic biology, offering prototyping tools for both understanding gene expression and developing engineered plants, which could help meet the demands of a changing global climate. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/580994v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@a6b24org.highwire.dtl.DTLVardef@2b62org.highwire.dtl.DTLVardef@1203308org.highwire.dtl.DTLVardef@f674a5_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2021

Low-cost drug discovery with engineered E. coli reveals an anti-mycobacterial activity of benazepril

Whole-cell screening for Mycobacterium tuberculosis (Mtb) inhibitors is complicated by the pathogens slow growth and biocontainment requirements. Here we present a synthetic biology framework for assaying Mtb drug targets in engineered E. coli. We construct Target Essential Surrogate E. coli (TESEC) in which an essential metabolic enzyme is deleted and replaced with an Mtb-derived functional analog, linking bacterial growth to the activity of the target enzyme. High throughput screening of a TESEC model for Mtb alanine racemase (ALR) revealed benazepril as a targeted inhibitor. In vitro biochemical assays indicated a noncompetitive mechanism unlike that of clinical ALR inhibitors. This is the first report of an antimicrobial activity in an approved Angiotensin Converting Enzyme (ACE) inhibitor and may explain clinical data associating use of ACE inhibitors with reduced Mtb infection risk. We establish the scalability of TESEC for drug discovery by characterizing TESEC strains for four additional targets. SIGNIFICANCE STATEMENTThe challenge of discovering new antibiotics is both scientific and economic. No simple test can determine if a given molecule will be safe and effective in real human patients. Many drug candidates must therefore be advanced for each new antibiotic that reaches the market - a risky and expensive process. In this work we use synthetic biology to engineer the common laboratory model bacterium E. coli as a tool for early stage antibiotic discovery. As a proof of concept we expressed a known tuberculosis drug target and found a novel inhibitor: benazepril. Many other drug targets could be screened similarly using the system that we describe. Because E. coli can be grown safely and cheaply, this approach may help to reduce costs and make drug discovery more accessible.

synthetic biology↗

bioRxiv2023

Literate programming for iterative design-build-test-learn cycles in bioengineering

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of complex biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of a metabolic pathway designs for production of a key precursor to medicinal alkaloids. The teemi platform is publicly available at PyPi and GitHub.

bioinformatics↗

bioRxiv2026

A comprehensive reference database to support untargeted metabolomics in Pseuudomonas putida

Pseudomonas putida strain KT2440 is a crucial model organism for synthetic biology and bioengineering applications, yet there currently exists no comprehensive metabolomics database comparable to those available for other model organisms. This gap hinders the use of untargeted metabolomics for exploratory analyses in this system. We developed the P. putida metabolome reference database (PPMDB v1) to address this limitation by consolidating metabolite information from multiple sources and expanding coverage through computational predictions. The database was constructed by curating metabolites from BioCyc, BiGG, and other literature sources, then computationally expanding this collection using BioTransformer environmental transformation predictions to generate additional predicted metabolites. We enhanced the databases utility for molecular annotation in metabolomics studies by incorporating analytical properties including collision cross-sections, tandem mass spectra, and gas-phase infrared spectra. These analytical properties were gathered from existing measurement data or predicted using computational tools. We further augmented the database through inclusion of reaction information and pathway annotations, facilitating biological interpretation of metabolomics data. This publicly available resource fills a critical gap in P. putida research infrastructure, supporting metabolite annotation and biological interpretation in untargeted metabolomics studies and enabling in-depth exploratory analyses of this important synthetic biology platform at the molecular level. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/713193v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@c8828forg.highwire.dtl.DTLVardef@1f3a5c5org.highwire.dtl.DTLVardef@1084535org.highwire.dtl.DTLVardef@1f7ca4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

Rosa26 docking sites for investigating genetic circuit silencing in stem cells

ABSTRACTApproaches in mammalian synthetic biology have transformed how cells can be programmed to have reliable and predictable behaviour, however, the majority of mammalian synthetic biology has been accomplished using immortalized cell lines that are easy to grow and easy to transfect. Genetic circuits that integrate into the genome of these immortalized cell lines remain functional for many generations, often for the lifetime of the cells, yet when genetic circuits are integrated into the genome of stem cells gene silencing is observed within a few generations. To investigate the reactivation of silenced genetic circuits in stem cells, the Rosa26 locus of mouse pluripotent stem cells was modified to contain docking sites for site-specific integration of genetic circuits. We show that the silencing of genetic circuits can be reversed with the addition of sodium butyrate, a histone deacetylase inhibitor. These findings demonstrate an approach to reactivate the function of genetic circuits in pluripotent stem cells to ensure robust function over many generations. Altogether, this work introduces an approach to overcome the silencing of genetic circuits in pluripotent stem cells that may enable the use of genetic circuits in pluripotent stem cells for long-term function.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cell biology↗

bioRxiv2023

Laws of genome nucleotide composition

Genome nucleotide composition is of fundamental significance in molecular evolution, genome organization and synthetic biology. Albeit studied for decades, it remains unclear whether there is any theoretical law underlying variable genome nucleotide composition across different species. From the mathematical viewpoint, here we propose three laws of genome nucleotide composition and validate their effectiveness on a large collection of empirical genome sequences across three domains of life. The three laws together provide a unifying framework that is able to unravel the mystery of genome-wide nucleotide composition variation across diverse species, paving the way towards a new era with quantitative insights for deciphering basic principles of life and further advancing theoretical biology. One-Sentence SummaryThe three laws of genome nucleotide composition provide a fundamental framework for molecular evolution, genome organization and synthetic biology.

bioinformatics↗

bioRxiv2024

Effects of multistability, absorbing boundaries and growth on Turing pattern formation

Turing patterns are a fundamental concept in developmental biology, describing how homogeneous tissues develop into self-organized spatial patterns. However, the classical Turing mechanism, which relies on linear stability analysis, often fails to capture the complexities of real biological systems, such as multistability, non-linearities, growth, and boundary conditions. Here, we explore the impact of these factors on Turing pattern formation, contrasting linear stability analysis with numerical simulations based on a simple reaction-diffusion model, motivated by synthetic gene-regulatory pathways. We demonstrate how non-linearities introduce multistability, leading to unexpected pattern outcomes not predicted by the traditional Turing theory. The study also examines how growth and realistic boundary conditions influence pattern robustness, revealing that different growth regimes and boundary conditions can disrupt or stabilize pattern formation. Our findings are critical for understanding pattern formation in both natural and synthetic biological systems, providing insights into engineering robust patterns for applications in synthetic biology. Author summaryDuring development, tissues self-organize to go from a single cell to a structured organism. In this process, simple chemical reactions lead to the emergence of the intricate designs we see in nature, like the stripes on a zebra or the labyrinths on a brain cortex. Although multiple theories have been proposed to model this phenomenon, one of the most simple and popular ones was introduced in the 1950s by the mathematician Alan Turing. However, his theory oversimplifies the biological conditions and ignores properties such as non-linearities, boundary effects, or growth in the tissue. In this work, we used a combination of mathematical models and computer simulations to investigate how these real-world factors influence pattern formation. Our findings show that when we account for these realistic effects, the patterns that emerge can be very different from what Turings theory would predict. Thus, this work may help us better understand the laws behind pattern formation and could have practical applications in tissue engineering for medical or environmental applications.

biophysics↗

bioRxiv2017

Computational Re-Design of Synthetic Genetic Oscillators for Independent Amplitude and Frequency Modulation

Engineering robust and tuneable genetic clocks is a topic of current interest in Systems and Synthetic Biology with wide applications in biotechnology. Synthetic genetic oscillators share a common structure based on a negative feedback loop with a time delay, and generally display only limited tuneability. Recently, the dual-feedback oscillator was demonstrated to be robust and tuneable, to some extent, by the use of chemical inducers. Yet no engineered genetic oscillator currently allows for the independent modulation of amplitude and period. In this work, we demonstrate computationally how recent advances in tuneable synthetic degradation can be used to decouple the frequency and amplitude modulation in synthetic genetic oscillators. We show how the range of tuneability can be increased by connecting additional input dials, e.g. orthogonal transcription factors that respond to chemical, temperature or even light signals. Modelling and numerical simulations predict that our proposed re-designs enable amplitude tuning without period modulation, coupled modulation of both period and amplitude, or period adjustment with near-constant amplitude. We illustrate our work through computational re-designs of both the dual-feedback oscillator and the repressilator, and show that the repressilator is more flexible and can allow for independent amplitude and near-independent period modulation.

synthetic biology↗

bioRxiv2023

Screening and characterization of aging regulators using synthesized yeast chromosome XIII

Yeast, a single eukaryotic cell model organism, demonstrates a progressive aging process. In the era of synthetic biology, study of the impact of synthetic chromosomes and aging is urgent and intriguing. Herein, we successfully constructed the 884 Kb synXIII of Saccharomyces cerevisiae and conducted replicative aging studies using the synthetic strains. We verified that the rRNA-related transcriptional factor RRN9 is a major positive controller of replicative lifespan. Using SCRaMbLE and an HSP104 reporter as a biomarker for mutant discovery, we screened 135 SCRaMbLEd synXIII strains with extended lifespan and identified 10 genes on synXIII that potentially serve as aging regulators. In addition, the genome-scale regression analysis of long-replicative lifespan SCRaMbLEd strains revealed distinct dysregulation of nucleus, ribosome, and mitochondrion function networks. Our findings suggest that Sc2.0 yeast has potential for unveiling new aging-related genes and gene-gene interactions underlying replicative lifespan.

synthetic biology↗

bioRxiv2026

Gene expression noise is reduced in communicating synthetic cell populations

A major goal in bottom-up synthetic biology is the construction of multicellular synthetic systems capable of coordinated and robust collective behaviours. However, robustness is often limited by noise and variability arising from increased molecular complexity. Whilst communication has been implemented in synthetic multi-cellular systems, the ability for communication to suppress cell free gene expression variability in populations of synthetic cells remain unexplored. To address this, we encapsulated the Lux and Las quorum sensing gene circuits in lipid vesicles under cell-free conditions to test the effect of communication on reducing cell-free gene expression variability across the population. Our results show that communication, limiting expression resources, and membrane surface effects can reduce gene expression variability. Resource limited Gillespie simulations for transcription and translation show that communication-mediated coupling reduces population-level expression noise under constrained and excess resource conditions. Together, our work provides simple strategies to reduce gene expression variability and thereby improve robustness in synthetic multicellular systems, an important criteria for the future applications of synthetic cells.

synthetic biology↗

bioRxiv2021

NLoed: A Python package for nonlinear optimal experimental design in systems biology

MotivationModelling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data, and on how well-suited the available data is to a particular modelling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modelling objective. However, implementation of OED is limited by currently available software tools that are not well-suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. ResultsHere we present the NLoed software package. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models, and facilitates modelling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modelling workflow. NLoed offers an accessible, modular, and flexible OED tool-set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLOEDs capabilities by applying it to experimental design for characterization of a bacterial optogenetic system. AvailabilityNLoed is available via pip from the PyPi repository; https://pypi.org/project/nloed/. Source code, documentation and examples can be found on Github at https://github.com/ingallslab/NLoed. Contactbingalls@uwaterloo.ca Supplementary informationSupplementary materials are available online.

systems biology↗

bioRxiv2023

Deep neural networks for predicting single cell responses and probability landscapes

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.

synthetic biology↗

bioRxiv2024

Engineering programmable CAR-T cells with tunable controls as fail-safe mechanisms during cancer immunotherapy

Chimeric antigen receptor (CAR) T-cell therapies have demonstrated remarkable efficacy in treating various cancers, but significant risks of severe adverse effects remain. In this study, we present a synthetic biology toolbox for engineering CAR-T cells with tunable and controllable features to mitigate these risks. Using this toolbox, we programmed cytotoxic CAR-T cells to be turned on and off using synthetic genes with tetracycline response elements, referred to as iOnCAR and iOffCAR. We achieved temporal regulation of CAR expression using the synthetic Notch receptor system and "AND-gate" programs, which allow for additional control using doxycycline. We also engineered cooperation among T cells through CD4 help to CD8 T cells in targeting cancer cells via synthetic Notch receptors controlled by doxycycline treatment. Additionally, we developed a T cell fratricide system by triggering a genetic "Battle Royale" mechanism that enables CAR-T cells to eliminate each other. In this system, surviving T cells become activated, proliferate, and display high cytotoxicity to target cells. In conclusion, our synthetic biology toolbox provides targeted fail-safe solutions for improving the safety and potency of CAR-T cell therapies by integrating different genetic circuits with a commonly used antibiotic.

immunology↗

bioRxiv2021

Independent control of mean and noise by convolution of gene expression distributions

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from a single promoter. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

synthetic biology↗

bioRxiv2026

A modular bacterial platform for tunable double-stranded RNA delivery and RNA interference in nematode Caenorhabditis elegans

RNA interference (RNAi), one of the major molecular tools in model nematode Ceanorhabditis elegans, relies on feeding engineered bacteria that express double-stranded RNA (dsRNA), which modulates animal host gene expression in a programmable manner. Currently no synthetic biology toolkit exits in C. elegans RNAi, while dsRNA circuits construction is restricted to one type of architecture. To facilitate systematic strategies for dsRNA circuit design and expression, here, we performed strain-specific screen of synthetic promoters, based on which we developed seventeen modular genetic parts compatible for rapid assembly of dsRNA expression constructs in Escherichia coli HT115(DE3). We validated dsRNA production in vitro and assessed RNAi efficiency in live animals by feeding. As a proof of concept, a constitutive dsRNA circuit achieved rapid and near-complete gene knockdown, whereas a Ptac-driven circuit enabled tunable, partial silencing while minimizing the leakiness commonly observed in standard feeding RNAi systems. Together, this work establishes a synthetic biology toolkit for programmable dsRNA delivery, enabling precise control of RNAi outcomes from partial to complete gene silencing in C. elegans.

bioengineering↗

bioRxiv2019

Removal of a subset of non-essential genes fully attenuates a highly virulent Mycoplasma strain

Mycoplasmas are the smallest free-living organisms and cause a number of economically important diseases affecting humans, animals, insects and plants. Here, we demonstrate that highly virulent Mycoplasma mycoides subspecies capri (Mmc) can be fully attenuated via targeted deletion of non-essential genes encoding, among others, potential virulence traits. Five genomic regions, representing approximately ten percent of the original Mmc genome, were successively deleted using Saccharomyces cerevisiae as an engineering platform. Specifically, a total of 68 genes out of the 432 genes verified to be individually nonessential in the JCVI-Syn3.0 minimal cell, were excised from the genome. In vitro characterization showed that this mutant was similar to its parental strain in terms of its doubling time, even though ten percent of the genome content were removed. A novel in vivo challenge model in goats revealed that the wild-type parental strain caused marked necrotizing inflammation at the site of inoculation, septicemia and all animals reaching endpoint criteria within seven days after experimental infection. This is in contrast to the mutant strain, which caused no clinical signs nor pathomorphological lesions. These results highlight, for the first time, the rational design, construction and complete attenuation of a Mycoplasma strain via synthetic genomics tools. Trait addition using the yeast-based genome engineering platform and subsequent in vitro or in vivo trials employing the Mycoplasma chassis will allow us to dissect the role of individual candidate Mycoplasma virulence factors and lead the way for the development of an attenuated designer vaccine.\n\nIMPORTANCEMembers of the Mycoplasma mycoides cluster cause important animal plaques in Africa and Asia, which impact animal welfare, provision of food and the life of thousands of small-scale farmers. We applied synthetic biology tools to Mycoplasma mycoides in order to design and create a fully attenuated Mycoplasma strain that was subsequently confirmed in vivo using a novel caprine infection model. This is the first time that a Mycoplasma mutant developed by applying synthetic biology tools has been tested in vivo to pin point candidate virulence traits. The mutant strain is similar to \"apathogenic E. coli K12\" strains that boosted the research on host-pathogen interactions for the genus Escherichia and other bacterial genera.

microbiology↗