At a late stage in spore development inBacillus subtilis, the mother cell directs synthesis of a layer of peptidoglycan known as the cortex between the two forespore membranes, as well as the assembly of a protective protein coat at the surface of the forespore outer membrane. SafA, the key determinant of inner coat assembly, is first recruited to the surface of the developing spore and then encases the spore under the control of the morphogenetic protein SpoVID. SafA has a LysM peptidoglycan-binding domain, SafALysM, and localizes to the cortex-coat interface in mature spores. SafALysMis followed by a region, A, required for an interaction with SpoVID and encasement. We now show that residues D10 and N30 in SafALysM, while involved in the interaction with peptidoglycan, are also required for the interaction with SpoVID and encasement. We further show that single alanine substitutions on residues S11, L12, and I39 of SafALysMthat strongly impair binding to purified cortex peptidoglycan affect a later stage in the localization of SafA that is also dependent on the activity of SpoVE, a transglycosylase required for cortex formation. The assembly of SafA thus involves sequential protein-protein and protein-peptidoglycan interactions, mediated by the LysM domain, which are required first for encasement then for the final localization of the protein in mature spores.IMPORTANCEBacillus subtilisspores are encased in a multiprotein coat that surrounds an underlying peptidoglycan layer, the cortex. How the connection between the two layers is enforced is not well established. Here, we elucidate the role of the peptidoglycan-binding LysM domain, present in two proteins, SafA and SpoVID, that govern the localization of additional proteins to the coat. We found that SafALysMis a protein-protein interaction module during the early stages of coat assembly and a cortex-binding module at late stages in morphogenesis, with the cortex-binding function promoting a tight connection between the cortex and the coat. In contrast, SpoVIDLysMfunctions only as a protein-protein interaction domain that targets SpoVID to the spore surface at the onset of coat assembly.

Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms’ metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models’ reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.

Genome-scale metabolic models have been recognized as useful tools for better understanding living organism's metabolism. Merlin (https://merlin-sysbio.org/) is an open-source and user-friendly resource that hastens these models' reconstruction process, conjugating manual, and automatic procedures, while leveraging user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features were implemented in merlin, along with profound changes in the software architecture, operating flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates led to an increase in the user-base, resulting in multiple published works including genome metabolic (re-)annotation and model reconstruction of multiple (lower and higher) eukaryotes and prokaryotes.

Summary Metabolic Engineering aims to favour the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical approaches able to identify potential metabolic modifications to achieve the defined production goals. We present MEWpy, a Python workbench for metabolic engineering, which covers a wide range of metabolic and regulatory modelling approaches, as well as phenotype simulation and CSO algorithms. Availability and implementation MEWpy can be installed from PyPi (pip install mewpy), the source code being available at https://github.com/BioSystemsUM/mewpy under the GPL license.

Alpha-linolenic acid and stearidonic acid are precursors of omega-3 polyunsaturated fatty acids, essential nutrients in the human diet. The ability of cyanobacteria to directly convert atmospheric carbon dioxide into bio-based compounds makes them promising microbial chassis to sustainably produce omega-3 fatty acids. However, their potential in this area remains unexploited, mainly due to important gaps in our knowledge of fatty acid synthesis pathways. To gain insight into the cyanobacterial fatty acid biosynthesis pathways, we analyzed two enzymes involved in the elongation cycle, FabG and FabZ, in Synechococcus elongatus PCC 7942. Overexpression of these two enzymes led to an increase in C18 fatty acids, key intermediates in omega-3 fatty acid production. Nevertheless, coexpression of these enzymes with desaturases DesA and DesB from Synechococcus sp. PCC 7002 did not improve alpha-linolenic acid production, possibly due to their limited role in fatty acid synthesis. In any case, efficient production of stearidonic acid was not achieved by cloning DesD from Synechocystis sp. PCC 6803 in combination with the aforementioned DesA and DesB, reaching maximum production at 48 h post induction. According to current knowledge, this is the first report demonstrating that S. elongatus PCC 7942 can be used as an autotrophic chassis to produce stearidonic acid.

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.

Background As genome sequencing projects grow rapidly, the diversity of organisms with recently assembled genome sequences peaks at an unprecedented scale, thereby highlighting the need to make gene functional annotations fast and efficient. However, the (high) quality of such annotations must be guaranteed, as this is the first indicator of the genomic potential of every organism. Automatic procedures help accelerating the annotation process, though decreasing the confidence and reliability of the outcomes. Manually curating a genome-wide annotation of genes, enzymes and transporter proteins function is a highly time-consuming, tedious and impractical task, even for the most proficient curator. Hence, a semi-automated procedure, which balances the two approaches, will increase the reliability of the annotation, while speeding up the process. In fact, a prior analysis of the annotation algorithm may leverage its performance, by manipulating its parameters, hastening the downstream processing and the manual curation of assigning functions to genes encoding proteins. Results Here SamPler , a novel strategy to select parameters for gene functional annotation routines is presented. This semi-automated method is based on the manual curation of a randomly selected set of genes/proteins. Then, in a multi-dimensional array, this sample is used to assess the automatic annotations for all possible combinations of the algorithm’s parameters. These assessments allow creating an array of confusion matrices, for which several metrics are calculated (accuracy, precision and negative predictive value) and used to reach optimal values for the parameters. Conclusions The potential of this methodology is demonstrated with four genome functional annotations performed in merlin , an in-house user-friendly computational framework for genome-scale metabolic annotation and model reconstruction. For that, SamPler was implemented as a new plugin for the merlin tool. Electronic supplementary material The online version of this article (10.1186/s12859-019-3038-4) contains supplementary material, which is available to authorized users.

Genome-Scale metabolic models (GEMs) are a relevant tool in systems biology for in silico strain optimisation and drug discovery. An easier way to reconstruct a model is to use available GEMs as templates to create the initial draft, which can be curated up until a simulation-ready model is obtained. This approach is implemented in merlin's BiGG Integration Tool, which reconstructs models from existing GEMs present in the BiGG Models database. This study aims to assess draft models generated using models from BiGG as templates for three distinct organisms, namely, Streptococcus thermophilus, Xylella fastidiosa and Mycobacterium tuberculosis. Several draft models were reconstructed using the BiGG Integration Tool and different templates (all, selected and random). The variability of the models was assessed using the reactions and metabolic functions associated with the model's genes. This analysis showed that, even though the models shared a significant portion of reactions and metabolic functions, models from different organisms are still differentiated. Moreover, there also seems to be variability among the templates used to generate the draft models to a lower extent. This study concluded that the BiGG Integration Tool provides a fast and reliable alternative for draft reconstruction for bacteria.