Guillaume Cambray

Lactic acid bacteria (LAB) are important for many biotechnological applications such as bioproduction and engineered probiotics for therapy. Inducible promoters are key gene expression control elements, yet those available in LAB are mainly based on bacteriocin systems and have many drawbacks, including large gene clusters, costly inducer peptides, and little portability to in vivo settings. Using Lactobacillus gasseri, a model commensal bacteria from the human gut, we report the engineering of synthetic LactoSpanks promoters (Pls), a collection of variable strength inducible promoters controlled by the LacI repressor from E. coli and induced by isopropyl β-d-1-thiogalactopyranoside (IPTG). We first show that the Phyper-spank promoter from Bacillus subtilis is functional in L. gasseri, albeit with substantial leakage. We then construct and screen a semirational library of Phyper-spank variants to select a set of four IPTG-inducible promoters that span a range of expression levels and exhibit reduced leakages and operational dynamic ranges (from ca. 9 to 28 fold-change). With their low genetic footprint and simplicity of use, LactoSpanks will support many applications in L. gasseri, and potentially other lactic acid and Gram-positive bacteria.

In the dynamic dance of evolution, organisms are often faced with fluctuating environments to which adaptation through selection of traditional heritable genetic mutations can be limiting. In this study, we unveil a refined mechanism of non-heritable variability in a virus with a compact DNA genome. We discovered that the genome of the Junona cœnia densovirus (JcDV) experiences a 10-fold lower transcription fidelity than the rest of the host’s transcriptome, despite the shared transcription machinery. We found that the virus’ capsid proteins interact with the host’s RNA Polymerase II, and further show that truncating these proteins through early stop codons largely restore the transcriptional fidelity of viral genes. These observations suggest a potential mechanism for the selective manipulation of transcription accuracy. We also pinpoint specific sequence contexts that may provide other knobs to finely control the local transcription fidelity. We estimate that this lower transcriptional fidelity results in more than 7% of the viral proteome bearing at least one non-synonymous mutation. The production of non-heritable functional diversity by hijacking the transcriptional machinery might be a refined strategy to enhance short term adaptation within the complex and ever-changing host-parasite interface, and might be shared by other genetic parasites. Our findings shed light on a virus-specific transcription fidelity control mechanism, expanding our understanding of adaptive strategies in biological entities.

Lactic acid bacteria (LAB) are important for many biotechnological applications, such as bioproduction and engineered probiotics for therapy. Inducible promoters are key gene expression control elements, yet those available in LAB are mainly based on bacteriocin systems and have many drawbacks, including large gene clusters, costly inducer peptides and little portability to in vivo settings. Using Lactobacillus gasseri , a model commensal bacteria from the human gut, we report the engineering of Lactospanks promoters (Pls), a collection of variable strength inducible promoters controlled by the LacI repressor from B. subtilis and induced by isopropyl β-D-1-thiogalactopyranoside (IPTG). We first show that the Phyper-spank promoter from Bacillus subtilis is functional in L. gasseri , albeit with substantial leakage. We then construct and screen a semi-rational library of Phyper-spank variants to select a set of four IPTG-inducible promoters that span a range of expression levels and exhibit reduced leakages and operational dynamic ranges (from ca. 9 to 28 fold-change). With their low genetic footprint and simplicity of use, Lactospanks will support many applications in L. gasseri , and potentially other lactic acid and gram-positive bacteria.

Engineered bacteria are promising candidates for in situ detection and treatment of diseases. The female uro-genital tract presents several pathologies, such as sexually transmitted diseases or genital cancer, that could benefit from such technology. While bacteria from the gut microbiome are increasingly engineered, the use of chassis isolated from the female uro-genital resident flora has been limited. A major hurdle to implement the experimental throughput required for efficient engineering in these non-model bacteria is their low transformability. Here we report an optimized electrotransformation protocol for Lactobacillus jensenii, one the most widespread species across vaginal microflora. Starting from classical conditions, we optimized buffers, electric field parameters, cuvette type and DNA quantity to achieve an 80-fold improvement in transformation efficiency, with up to 3.5·10³ CFUs/μg of DNA in L. jensenii ATCC 25258. We also identify several plasmids that are maintained and support reporter gene expression in L. jensenii. Finally, we demonstrate that our protocol provides increased transformability in three independent clinical isolates of L. jensenii. This work will facilitate the genetic engineering of L. jensenii and enable its use for addressing challenges in gynecological healthcare.

Engineered bacteria are promising candidates for in situ detection and treatment of diseases. The female uro-genital tract presents several pathologies, such as sexually transmitted diseases or genital cancer, that could benefit from such technology. While bacteria from the gut microbiome are increasingly engineered, the use of chassis isolated from the female uro-genital resident flora has been limited. A major hurdle to implement the experimental throughput required for efficient engineering in these non-model bacteria is their low transformability. Here we report an optimized electrotransformation protocol for Lactobacillus jensenii , one the most widespread species across vaginal microflora. Starting from classical conditions, we optimized buffers, electric field parameters, cuvette type and DNA quantity to achieve an 80-fold improvement in transformation efficiency, with up to 3.5·10 ³ CFUs/µg of DNA in L. jensenii ATCC 25258 . We also identify several plasmids that are maintained and support reporter gene expression in L. jensenii . Finally, we demonstrate that our protocol provides increased transformability in three independent clinical isolates of L. jensenii . This work will facilitate the genetic engineering of L. jensenii and enable its use for addressing challenges in gynecological healthcare.

Many applications of synthetic biology involve engineering microbial strains to express high-value proteins. Thanks to advances in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain design and optimization. Such models, however, require large amounts of training data that are costly to acquire, which creates substantial entry barriers for many laboratories. Here, we study the relation between model accuracy and data efficiency in a large panel of machine learning models of varied complexity, from penalized linear regressors to deep neural networks. Our analysis is based on data from a large genotype-phenotype screen in Escherichia coli , which was generated with a design-of-experiments approach to balance coverage and depth of the genotypic space. We sampled these data to emulate scenarios with a limited number of DNA sequences for training, as commonly encountered in strain engineering applications. Our results suggest that classic, non-deep, models can achieve good prediction accuracy with much smaller datasets than previously thought, and provide robust evidence that convolutional neural networks further improve performance with the same amount of data. Using methods from Explainable AI and model benchmarking, we show that convolutional neural networks have an improved ability to discriminate between input sequences and extract sequence features that are highly predictive of protein expression. We moreover show that controlled sequence diversity leads to important gains in data efficiency, and validated this principle in a separate genotype-phenotype screen in Saccharomyces cerevisiae. These results provide practitioners with guidelines for designing experimental screens that strike a balance between cost and quality of training data, laying the groundwork for wider adoption of deep learning across the biotechnology sector.

Bacterial biosensors, or bactosensors, are promising agents for medical and environmental diagnostics. However, the lack of scalable frameworks to systematically program ligand detection limits their applications. Here we show how novel, clinically relevant sensing modalities can be introduced into bactosensors in a modular fashion. To do so, we have leveraged a synthetic receptor platform, termed EMeRALD (Engineered Modularized Receptors Activated via Ligand-induced Dimerization) which supports the modular assembly of sensing modules onto a high-performance, generic signaling scaffold controlling gene expression in E. coli. We apply EMeRALD to detect bile salts, a biomarker of liver dysfunction, by repurposing sensing modules from enteropathogenic Vibrio species. We improve the sensitivity and lower the limit-of-detection of the sensing module by directed evolution. We then engineer a colorimetric bactosensor detecting pathological bile salt levels in serum from patients having undergone liver transplant, providing an output detectable by the naked-eye. The EMeRALD technology enables functional exploration of natural sensing modules and rapid engineering of synthetic receptors for diagnostics, environmental monitoring, and control of therapeutic microbes.

Densoviruses could be used as biological control agents to manage insect pests. Such applications require an in-depth biological understanding and associated molecular tools.

Bacterial biosensors, or bactosensors, are promising field-deployable agents for medical and environmental diagnostics. However, the lack of scalable frameworks to systematically program ligand detection limits their applications. Here we present a synthetic receptor platform, termed EMeRALD ( E ngineered M odularized R eceptors A ctivated via L igand-induced D imerization) which supports the modular assembly of sensing modules onto a high-performance, generic signaling scaffold controlling gene expression in E. coli . We applied EMeRALD to detect bile salts, a biomarker of liver dysfunction, by repurposing sensing modules from enteropathogenic Vibrio species. We improved the sensitivity and lowered the limit-of-detection of the sensing module by directed evolution. We then engineered a colorimetric bactosensor detecting pathological bile salt levels in serum from patients having undergone liver transplant, providing an output detectable by the naked-eye. The EMeRALD technology enables functional exploration of natural sensing modules and rapid engineering of synthetic receptors for diagnostics, environmental monitoring, and control of therapeutic microbes.

A bstract: Despite tight genetic compression, viral genomes are often organized in functional gene clusters, a modular structure that might favor their evolvability. This has greatly facilitated biotechnological developments, such as the recombinant Adeno-Associated Virus (AAV) systems for gene therapy. Following this lead, we endeavored to engineer the related insect parvovirus Junonia coenia densovirus (JcDV) to create addressable vectors for insect pest biocontrol. To enable safer manipulation of capsid mutants, we translocated the non-structural ( ns ) gene cluster outside the viral genome. To our dismay, this yielded a virtually non-replicable clone. We linked the replication defect to an unexpected modularity breach, as ns translocation truncated the overlapping 3’ UTR of the capsid transcript ( vp ). We found that native vp 3’UTR is necessary to high VP production, but that decreased expression do not adversely impact the expression of NS proteins, which are known replication effectors. As nonsense vp mutations recapitulate the replication defect, VP proteins appear directly implicated in the replication process. Our findings suggest intricate replication-encapsidation couplings that favor maintenance of genetic integrity. We discuss possible connections with an intriguing cis-packaging phenomenon previously observed in parvoviruses, whereby capsids preferentially package the genome from which they were expressed. I mportance: Densoviruses could be used as biological control agents to manage insect pests. Such applications require in depth biological understanding and associated molecular tools. However, the genomes of these viruses remain hard to manipulate due too poorly tractable secondary structures at their extremities. We devised a construction strategy that enable precise and efficient molecular modifications. Using this approach, we endeavored to create a split clone of the Junonia coenia densovirus (JcDV) that can be used to safely study the impact of capsid mutations on host specificity. Our original construct proved to be non-functional. Fixing this defect led us to uncover that capsid proteins and their correct expression are essential for continued rolling-hairpin replication. This points to an intriguing link between replication and packaging, which might be shared with related viruses. This serendipitous discovery illustrates the power of synthetic biology approaches to advance our knowledge of biological systems.