Analysis of Pseudomonas putida growth on non-trivial carbon sources using transcriptomics and genome-scale modelling.

Pseudomonas putida is characterized by a versatile metabolism and stress tolerance traits that allow the bacterium to cope with different environmental conditions. In this work, the mechanisms that allow P. putida KT2440 to grow in the presence of four sole carbon sources (glucose, citrate, ferulic acid, serine) were investigated by RNA sequencing (RNA-seq) and genome-scale metabolic modelling. Transcriptomic data identified uptake systems for the four carbon sources, and candidates were subjected to preliminary experimental characterization by mutant strain growth to test their involvement in substrate assimilation. The OpdH and BenF-like porins were involved in citrate and ferulic acid uptake respectively. The citrate transporter (encoded by PP_0147) and the TctABC system were important for supporting cell growth in citrate; PcaT and VanK were associated with ferulic acid uptake; and the ABC transporter AapJPQM was involved in serine transport. A genome-scale metabolic model of P. putida KT2440 was used to integrate and analyze the transcriptomic data, identifying and confirming the active catabolic pathways for each carbon source. This study reveals novel information about transporters that are essential for understanding bacterial adaptation to different environments.

MARSI: metabolite analogues for rational strain improvement.

Summary: Metabolite analogues (MAs) mimic the structure of native metabolites, can competitively inhibit their utilization in enzymatic reactions, and are commonly used as selection tools for isolating desirable mutants of industrial microorganisms. Genome-scale metabolic models representing all biochemical reactions in an organism can be used to predict effects of MAs on cellular phenotypes. Here, we present the metabolite analogues for rational strain improvement (MARSI) framework. MARSI provides a rational approach to strain improvement by searching for metabolites as targets instead of genes or reactions. The designs found by MARSI can be implemented by supplying MAs in the culture media, enabling metabolic rewiring without the use of recombinant DNA technologies that cannot always be used due to regulations. To facilitate experimental implementation, MARSI provides tools to identify candidate MAs to a target metabolite from a database of known drugs and analogues. Availability and implementation: The code is freely available at https://github.com/biosustain/marsi under the Apache License V2. MARSI is implemented in Python. Supplementary information: Supplementary data are available at Bioinformatics online.

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories.

Computational systems biology methods enable rational design of cell factories on a genome-scale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, the majority of these methods' implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knockout, knock-in, overexpression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated Web site including documentation, examples, and installation instructions can be found at http://cameo.bio . Users can also give cameo a try at http://try.cameo.bio .

Analysis of genetic variation and potential applications in genome-scale metabolic modeling.

Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.