IsoCor: isotope correction for high-resolution MS labeling experiments.

SUMMARY: Mass spectrometry (MS) is widely used for isotopic studies of metabolism and other (bio)chemical processes. Quantitative applications in systems and synthetic biology require to correct the raw MS data for the contribution of naturally occurring isotopes. Several tools are available to correct low-resolution MS data, and recent developments made substantial improvements by introducing resolution-dependent correction methods, hence opening the way to the correction of high-resolution MS (HRMS) data. Nevertheless, current HRMS correction methods partly fail to determine which isotopic species are resolved from the tracer isotopologues and should thus be corrected. We present an updated version of our isotope correction software (IsoCor) with a novel correction algorithm which ensures to accurately exploit any chemical species with any isotopic tracer, at any MS resolution. IsoCor v2 also includes a novel graphical user interface for intuitive use by end-users and a command-line interface to streamline integration into existing pipelines. AVAILABILITY AND IMPLEMENTATION: IsoCor v2 is implemented in Python 3 and was tested on Windows, Unix and MacOS platforms. The source code and the documentation are freely distributed under GPL3 license at https://github.com/MetaSys-LISBP/IsoCor/ and https://isocor.readthedocs.io/.

RetroPath2.0: A retrosynthesis workflow for metabolic engineers.

Synthetic biology applied to industrial biotechnology is transforming the way we produce chemicals. However, despite advances in the scale and scope of metabolic engineering, the research and development process still remains costly. In order to expand the chemical repertoire for the production of next generation compounds, a major engineering biology effort is required in the development of novel design tools that target chemical diversity through rapid and predictable protocols. Addressing that goal involves retrosynthesis approaches that explore the chemical biosynthetic space. However, the complexity associated with the large combinatorial retrosynthesis design space has often been recognized as the main challenge hindering the approach. Here, we provide RetroPath2.0, an automated open source workflow for retrosynthesis based on generalized reaction rules that perform the retrosynthesis search from chassis to target through an efficient and well-controlled protocol. Its easiness of use and the versatility of its applications make this tool a valuable addition to the biological engineer bench desk. We show through several examples the application of the workflow to biotechnological relevant problems, including the identification of alternative biosynthetic routes through enzyme promiscuity or the development of biosensors. We demonstrate in that way the ability of the workflow to streamline retrosynthesis pathway design and its major role in reshaping the design, build, test and learn pipeline by driving the process toward the objective of optimizing bioproduction. The RetroPath2.0 workflow is built using tools developed by the bioinformatics and cheminformatics community, because it is open source we anticipate community contributions will likely expand further the features of the workflow.

SensiPath: computer-aided design of sensing-enabling metabolic pathways.

Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design for medical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable. Here, we present the SensiPath web server, accessible at http://sensipath.micalis.fr SensiPath implements a strategy to enlarge the set of detectable compounds by screening for multi-step enzymatic transformations converting non-detectable compounds into detectable ones. The SensiPath approach is based on the encoding of reactions through signature descriptors to explore sensing-enabling metabolic pathways, which are putative biochemical transformations of the target compound leading to known effectors of transcription factors. In that way, SensiPath enlarges the design space by broadening the potential use of biosensors in synthetic biology applications.

Evaluation of Heterologous Biosynthetic Pathways for Methanol-Based 5-Aminovalerate Production by Thermophilic Bacillus methanolicus.

The use of methanol as carbon source for biotechnological processes has recently attracted great interest due to its relatively low price, high abundance, high purity, and the fact that it is a non-food raw material. In this study, methanol-based production of 5-aminovalerate (5AVA) was established using recombinant Bacillus methanolicus strains. 5AVA is a building block of polyamides and a candidate to become the C5 platform chemical for the production of, among others, δ-valerolactam, 5-hydroxy-valerate, glutarate, and 1,5-pentanediol. In this study, we test five different 5AVA biosynthesis pathways, whereof two directly convert L-lysine to 5AVA and three use cadaverine as an intermediate. The conversion of L-lysine to 5AVA employs lysine 2-monooxygenase (DavB) and 5-aminovaleramidase (DavA), encoded by the well-known Pseudomonas putida cluster davBA, among others, or lysine α-oxidase (RaiP) in the presence of hydrogen peroxide. Cadaverine is converted either to γ-glutamine-cadaverine by glutamine synthetase (SpuI) or to 5-aminopentanal through activity of putrescine oxidase (Puo) or putrescine transaminase (PatA). Our efforts resulted in proof-of-concept 5AVA production from methanol at 50°C, enabled by two pathways out of the five tested with the highest titer of 0.02 g l-1. To our knowledge, this is the first report of 5AVA production from methanol in methylotrophic bacteria, and the recombinant strains and knowledge generated should represent a valuable basis for further improved 5AVA production from methanol.

Complete Genome Sequence of Sphingobium xenophagum PH3-15, Isolated from La Roche-Posay Thermal Water Sources.

We report here the complete genome sequence of Sphingobium xenophagum strain PH3-15, which was isolated from La Roche-Posay thermal water sources. The assembled 4.6-Mbp genome consisted of two chromosomes and three plasmids. These data will provide valuable information and important insights into the physiology and metabolism of this Sphingobium organism.

Charting the Metabolic Landscape of the Facultative Methylotroph Bacillus methanolicus.

Bacillus methanolicus MGA3 is a thermotolerant and relatively fast-growing methylotroph able to secrete large quantities of glutamate and lysine. These natural characteristics make B. methanolicus a good candidate to become a new industrial chassis organism, especially in a methanol-based economy. Intriguingly, the only substrates known to support B. methanolicus growth as sole sources of carbon and energy are methanol, mannitol, and, to a lesser extent, glucose and arabitol. Because fluxomics provides the most direct readout of the cellular phenotype, we hypothesized that comparing methylotrophic and nonmethylotrophic metabolic states at the flux level would yield new insights into MGA3 metabolism. In this study, we designed and performed a 13C metabolic flux analysis (13C-MFA) of the facultative methylotroph B. methanolicus MGA3 growing on methanol, mannitol, and arabitol to compare the associated metabolic states. On methanol, results showed a greater flux in the ribulose monophosphate (RuMP) pathway than in the tricarboxylic acid (TCA) cycle, thus validating previous findings on the methylotrophy of B. methanolicus New insights related to the utilization of cyclic RuMP versus linear dissimilation pathways and between the RuMP variants were generated. Importantly, we demonstrated that the linear detoxification pathways and the malic enzyme shared with the pentose phosphate pathway have an important role in cofactor regeneration. Finally, we identified, for the first time, the metabolic pathway used to assimilate arabitol. Overall, those data provide a better understanding of this strain under various environmental conditions.IMPORTANCE Methanol is inexpensive, is easy to transport, and can be produced both from renewable and from fossil resources without mobilizing arable lands. As such, it is regarded as a potential carbon source to transition toward a greener industrial chemistry. Metabolic engineering of bacteria and yeast able to efficiently consume methanol is expected to provide cell factories that will transform methanol into higher-value chemicals in the so-called methanol economy. Toward that goal, the study of natural methylotrophs such as Bacillus methanolicus is critical to understand the origin of their efficient methylotrophy. This knowledge will then be leveraged to transform such natural strains into new cell factories or to design methylotrophic capability in other strains already used by the industry.

Extended Metabolic Space Modeling.

Determining the fraction of the chemical space that can be processed in vivo by using natural and synthetic biology devices is crucial for the development of advanced synthetic biology applications. The extended metabolic space is a coding system based on molecular signatures that enables the derivation of reaction rules for metabolic reactions and the enumeration of all possible substrates and products corresponding to the rules. The extended metabolic space expands capabilities for controlling the production, processing, sensing, and the release of specific molecules in chassis organisms.

A dataset of small molecules triggering transcriptional and translational cellular responses.

The aim of this dataset is to identify and collect compounds that are known for being detectable by a living cell, through the action of a genetically encoded biosensor and is centred on bacterial transcription factors. Such a dataset should open the possibility to consider a wide range of applications in synthetic biology. The reader will find in this dataset the name of the compounds, their InChI (molecular structure), the publication where the detection was reported, the organism in which this was detected or engineered, the type of detection and experiment that was performed as well as the name of the biosensor. A comment field is also provided that explains why the compound was included in the dataset, based on quotes from the reference publication or the database it was extracted from. Manual curation of ACS Synthetic Biology abstracts (Volumes 1 to 6 and Volume 7 issue 1) was performed as well as extraction from the following databases: Bionemo v6.0 (Carbajosa et al., 2009) [1], RegTransbase r20120406 (Cipriano et al., 2013) [2], RegulonDB v9.0 (Gama-Castro et al., 2016) [3], RegPrecise v4.0 (Novichkov et al., 2013) [4] and Sigmol v20180122 (Rajput et al., 2016) [5].

Expanding Biosensing Abilities through Computer-Aided Design of Metabolic Pathways.

Detection of chemical signals is critical for cells in nature as well as in synthetic biology, where they serve as inputs for designer circuits. Important progress has been made in the design of signal processing circuits triggering complex biological behaviors, but the range of small molecules recognized by sensors as inputs is limited. The ability to detect new molecules will increase the number of synthetic biology applications, but direct engineering of tailor-made sensors takes time. Here we describe a way to immediately expand the range of biologically detectable molecules by systematically designing metabolic pathways that transform nondetectable molecules into molecules for which sensors already exist. We leveraged computer-aided design to predict such sensing-enabling metabolic pathways, and we built several new whole-cell biosensors for molecules such as cocaine, parathion, hippuric acid, and nitroglycerin.

Sensing new chemicals with bacterial transcription factors.

Bacteria rely on allosteric transcription factors (aTFs) to sense a wide range of chemicals. The variety of effectors has contributed in making aTFs the most used input system in synthetic biological circuits. Considering their enabling role in biotechnology, an important question concerns the size of the chemical space that can potentially be detected by these biosensors. From digging into the ever changing repertoire of natural regulatory circuits, to advances in aTF engineering, we review here different strategies that are pushing the boundaries of this chemical space. We also review natural and synthetic cases of indirect sensing, where aTFs work in combination with metabolism to enable detection of new molecules.