Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling.

Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint-based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time-dependent changes, albeit using a static model. By performing an in silico knock-out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms.

In-house three-dimensional printing for surgical planning: learning curve from a case series of temporomandibular joint and related disorders.

Three-dimensional (3D) printed models can improve the understanding of the structural anatomic changes in cases of temporomandibular joint ankylosis and pseudoankylosis leading to closed jaw locking. Their use in pre-surgical planning and intraoperative guidance has been reported, contributing to the predictability and success of these surgery procedures, which can be quite complex, especially in small animal patients. The use and production of 3D tools and models remain challenging and are so far limited to institutions with high (economical and human) resources. This study aims to propose simplified workflows using open-source software to facilitate an in-house 3D printing process. To illustrate this, three cases of temporomandibular joint ankylosis and one of pseudoankylosis were reviewed, where in-house 3D printed models were used for client communication and surgical management. The 3D models were segmented from computed tomography and printed via stereolithography. They were used to support discussion with clients (n = 4), to allow surgeons to pre-surgical plan and practice (n = 4) and for intraoperative guidance during surgery (n = 2). Surgical cutting guides were produced in one case to improve precision and define more accurately osteotomy lines. It is essential to consider the initial time and financial investment required for establishing an in-house 3D printing production, particularly when there is a need to produce biocompatible tools, such as surgical cutting guides. However, efficient and streamlined workflows encourage the integration of this technology, by accelerating the printing process and reducing the steep learning curves, while open-source software enhances accessibility to these resources.

The regulatory logic of m-xylene biodegradation by Pseudomonas putida mt-2 exposed by dynamic modelling of the principal node Ps/Pr of the TOL plasmid.

The structure of the extant transcriptional control network of the TOL plasmid pWW0 born by Pseudomonas putida mt-2 for biodegradation of m-xylene is far more complex than one would consider necessary from a mere engineering point of view. In order to penetrate the underlying logic of such a network, which controls a major environmental cleanup bioprocess, we have developed a dynamic model of the key regulatory node formed by the Ps/Pr promoters of pWW0, where the clustering of control elements is maximal. The model layout was validated with batch cultures estimating parameter values and its predictive capability was confirmed with independent sets of experimental data. The model revealed how regulatory outputs originated in the divergent and overlapping Ps/Pr segment, which expresses the transcription factors XylS and XylR respectively, are computed into distinct instructions to the upper and lower catabolic xyl operons for either simultaneous or stepwise consumption of m-xylene and/or succinate. In this respect, the model reveals that the architecture of the Ps/Pr is poised to discriminate the abundance of alternative and competing C sources, in particular m-xylene versus succinate. The proposed framework provides a first systemic understanding of the causality and connectivity of the regulatory elements that shape this exemplary regulatory network, facilitating the use of model analysis towards genetic circuit optimization.

Effect of the earthworms Lumbricus terrestris and Aporrectodea caliginosa on bacterial diversity in soil.

Earthworms ingest large amounts of soil and have the potential to radically alter the biomass, activity, and structure of the soil microbial community. In this study, the diversity of eight bacterial groups from fresh soil, gut, and casts of the earthworms Lumbricus terrestris and Aporrectodea caliginosa were studied by single-strand conformation polymorphism (SSCP) analysis using both newly designed 16S rRNA gene-specific primer sets targeting Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Deltaproteobacteria, Bacteroidetes, Verrucomicrobia, Planctomycetes, and Firmicutes and a conventional universal primer set for SSCP, with RNA and DNA as templates. In parallel, the study of the relative abundance of these taxonomic groups in the same samples was performed using fluorescence in situ hybridization. Bacteroidetes, Alphaproteobacteria, and Betaproteobacteria were predominant in communities from the soil and worm cast samples. Representatives of classes Flavobacteria and Sphingobacteria (Bacteroidetes) and Pseudomonas spp. (low-abundant Gammaproteobacteria) were detected in soil and worm cast samples with conventional and taxon-targeting SSCP and through the sequence analysis of 16S rRNA clone libraries. Physiologically active unclassified Sphingomonadaceae (Alphaproteobacteria) and Alcaligenes spp. (Betaproteobacteria) also maintained their diversities during transit through the earthworm intestine and were found on taxon-targeting SSCP profiles from the soil and worm cast samples. In conclusion, our results suggest that some specific bacterial taxonomic groups maintain their diversity and even increase their relative numbers during transit through the gastrointestinal tract of earthworms.

Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13)C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential.

Programmable bacterial catalysis - designing cells for biosynthesis of value-added compounds.

Bacteria have long been used for the synthesis of a wide range of useful proteins and compounds. The developments of new bioprocesses and improvements of existing strategies for syntheses of valuable products in various bacterial cell hosts have their own challenges and limitations. The field of synthetic biology has combined knowledge from different science and engineering disciplines and facilitated the advancement of novel biological components which has inspired the design of targeted biosynthesis. Here we discuss recent advances in synthetic biology with relevance to biosynthesis in bacteria and the applications of computational algorithms and tools for manipulation of cellular components. Continuous improvements are necessary to keep up with increasing demands in terms of complexity, scale, and predictability of biosynthesis products.

Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction.

BACKGROUND: Burkholderia cenocepacia is a threatening nosocomial epidemic pathogen in patients with cystic fibrosis (CF) or a compromised immune system. Its high level of antibiotic resistance is an increasing concern in treatments against its infection. Strain B. cenocepacia J2315 is the most infectious isolate from CF patients. There is a strong demand to reconstruct a genome-scale metabolic network of B. cenocepacia J2315 to systematically analyze its metabolic capabilities and its virulence traits, and to search for potential clinical therapy targets. RESULTS: We reconstructed the genome-scale metabolic network of B. cenocepacia J2315. An iterative reconstruction process led to the establishment of a robust model, iKF1028, which accounts for 1,028 genes, 859 internal reactions, and 834 metabolites. The model iKF1028 captures important metabolic capabilities of B. cenocepacia J2315 with a particular focus on the biosyntheses of key metabolic virulence factors to assist in understanding the mechanism of disease infection and identifying potential drug targets. The model was tested through BIOLOG assays. Based on the model, the genome annotation of B. cenocepacia J2315 was refined and 24 genes were properly re-annotated. Gene and enzyme essentiality were analyzed to provide further insights into the genome function and architecture. A total of 45 essential enzymes were identified as potential therapeutic targets. CONCLUSIONS: As the first genome-scale metabolic network of B. cenocepacia J2315, iKF1028 allows a systematic study of the metabolic properties of B. cenocepacia and its key metabolic virulence factors affecting the CF community. The model can be used as a discovery tool to design novel drugs against diseases caused by this notorious pathogen.