Biology subjects

Levassor, L.

Publications and source records attributed to Levassor, L..

3 recordsLinked to original sources

bioRxiv2024

StreptoCAD: An open-source software toolbox automating genome engineering workflows in streptomycetes

Streptomycetes hold immense potential for discovering novel bioactive molecules for applications in medicine or sustainable agriculture. However, high-throughput exploration is hampered by the current Streptomyces genetic engineering methods that involve the manual design of complex experimental molecular biological engineering strategies for each targeted gene. Here, we introduce StreptoCAD, an open-source software toolbox that automates and streamlines the design of genome engineering strategies in Streptomyces, supporting various CRISPR-based and gene overexpression methods. Once initiated, StreptoCAD designs all necessary DNA primers and CRISPR guide sequences, simulates plasmid assemblies (cloning) and the resulting modification of the genomic target(s), and further summarizes the information needed for laboratory implementation and documentation. StreptoCAD currently offers six design workflows, including the construction of overexpression libraries, base-editing, including multiplexed CRISPR-BEST plasmid generation, and genome engineering using CRISPR-Cas9, CRISPR-Cas3, and CRISPRi systems. In addition to automating the design process, StreptoCAD further secures compliance with the FAIR principles, ensuring reproducibility and ease of data management via standardized output files. To experimentally demonstrate the design process and output of StreptoCAD, we designed and constructed a series of gene overexpression strains in Streptomyces Go40/10, underscoring the tools efficiency and user-friendliness. This tool simplifies complex genetic engineering tasks and promotes collaboration through standardized workflows and design parameters. StreptoCAD is set to transform genome engineering in Streptomyces, making sophisticated genetic manipulations accessible for all and accelerating natural product discovery. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=170 SRC="FIGDIR/small/629370v1_ufig1.gif" ALT="Figure 1000"> View larger version (56K): org.highwire.dtl.DTLVardef@42a7deorg.highwire.dtl.DTLVardef@1aef57org.highwire.dtl.DTLVardef@574e59org.highwire.dtl.DTLVardef@1d8dd3d_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

EXTRA-seq: a genome-integrated extended massively parallel reporter assay to quantify enhancer-promoter communication

Precise control of gene expression is essential for cellular function, but the mechanisms by which enhancers communicate with promoters to coordinate this process are not fully understood. While sequence-based deep learning models show promise in predicting enhancer-driven gene expression, experimental validation and human-interpretable mechanistic insights lag behind. Here, we present EXTRA-seq, a novel EXTended Reporter Assay followed by sequencing designed to quantify enhancer activity in endogenous contexts over kilobase-scale distances. We demonstrate that EXTRA-seq can be targeted to disease-relevant loci and captures expression changes at the resolution of individual transcription factor binding sites, enabling mechanistic discovery. Using engineered synthetic enhancer-promoter combinations, we reveal that the TATA-box acts as a dynamic range amplifier, modulating expression levels in function of enhancer strength. Importantly, we find that integrating state-of-the-art deep learning models with plasmid-based enhancer assays improves the prediction of gene expression as measured by EXTRA-seq. These findings open new avenues for predictive modeling and therapeutic applications. Overall, our work provides a powerful experimental platform to interrogate the complex interplay between enhancers and promoters, bridging the gap between in silico predictions and human-interpretable biological mechanisms.

genomics↗

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↗