Semi-automated curation of metabolic models via flux balance analysis: a case study with Mycoplasma gallisepticum

Characterization of serum metabolome and respiratory microbiota in children with influenza A virus infection

The risk of children being infected with Influenza A virus (IAV) is high, and if not treated promptly, it can lead to serious illness. Compared with control group, IAV infection decreased the contents of platelet, white blood cell, lymphocyte, eosinophil, basophil, CD3+ T cells, CD4+ T cells, CD8+ T cells, and B cells, while increasing the number of red blood cell. Additionally, IAV infection increased serum concentrations of total protein, albumin and lipase, while decreasing the contents of calcium, triglyceride, total bilirubin, direct bilirubin, indirect bilirubin and gamma-glutamyltransferase. However, the interactions between the respiratory microbiome and metabolites and their impact on IAV in children remains unclear. Ultra performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-QTOF/MS) and 16S rRNA gene sequencing were employed to analysis the respiratory microbiome and serum metabolic characteristics of 85 patients with IAV infection and age-matched 55 controls with respiratory disease who tested negative for 13 types of respiratory pathogens. The serum metabolic profile of IAV patients was significantly changed, and the purine metabolism was destroyed. Purine metabolism was also enriched in H3N2 patients compared to H1N1, with increased xanthine, deoxyguanosine, and inosine. The respiratory microbiome structure in children with IAV, including H1N1 and H3N2, was significantly different from that of the control, with significantly increased Chao index. The Mantel test revealed the correlation and consistency in the trends of Haemophilus, Ureaplasma and Inosine. This study revealed the characteristics of the respiratory microbiome and serum metabolites in pediatric patients with IAV, providing a new direction for exploring the pathogenesis of IAV in children.

Genome-scale metabolic modeling reveals key features of a minimal gene set

Mesoplasma florum, a fast-growing near-minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein-coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species-specific constraints to produce the functional iJL208 genome-scale model (GEM) of metabolism. Genome-wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI-syn3.0 and its parent JCVI-syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping-stone toward model-driven whole-genome engineering.

Comparative Metabolomics of Mycoplasma bovis and Mycoplasma gallisepticum Reveals Fundamental Differences in Active Metabolic Pathways and Suggests Novel Gene Annotations

Mycoplasmas are pathogenic bacteria that cause serious chronic infections in production animals, resulting in considerable losses worldwide, as well as causing disease in humans. These bacteria have extremely reduced genomes and are thought to have limited metabolic flexibility, even though they are highly successful persistent parasites in a diverse number of species. The extent to which different Mycoplasma species are capable of catabolizing host carbon sources and nutrients, or synthesizing essential metabolites, remains poorly defined. We have used advanced metabolomic techniques to identify metabolic pathways that are active in two species of Mycoplasma that infect distinct hosts (poultry and cattle). We show that these species exhibit marked differences in metabolite steady-state levels and carbon source utilization. This information has been used to functionally characterize previously unknown genes in the genomes of these pathogens. These species-specific differences are likely to reflect important differences in host nutrient levels and pathogenic mechanisms.

Mycoplasmas are simple, but successful parasites that have the smallest genome of any free-living cell and are thought to have a highly streamlined cellular metabolism. Here, we have undertaken a detailed metabolomic analysis of two species, Mycoplasma bovis and Mycoplasma gallisepticum, which cause economically important diseases in cattle and poultry, respectively. Untargeted gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry analyses of mycoplasma metabolite extracts revealed significant differences in the steady-state levels of many metabolites in central carbon metabolism, while ¹³C stable isotope labeling studies revealed marked differences in carbon source utilization. These data were mapped onto in silico metabolic networks predicted from genome wide annotations. The analyses elucidated distinct differences, including a clear difference in glucose utilization, with a marked decrease in glucose uptake and glycolysis in M. bovis compared to M. gallisepticum, which may reflect differing host nutrient availabilities. The ¹³C-labeling patterns also revealed several functional metabolic pathways that were previously unannotated in these species, allowing us to assign putative enzyme functions to the products of a number of genes of unknown function, especially in M. bovis. This study demonstrates the considerable potential of metabolomic analyses to assist in characterizing significant differences in the metabolism of different bacterial species and in improving genome annotation.

IMPORTANCE Mycoplasmas are pathogenic bacteria that cause serious chronic infections in production animals, resulting in considerable losses worldwide, as well as causing disease in humans. These bacteria have extremely reduced genomes and are thought to have limited metabolic flexibility, even though they are highly successful persistent parasites in a diverse number of species. The extent to which different Mycoplasma species are capable of catabolizing host carbon sources and nutrients, or synthesizing essential metabolites, remains poorly defined. We have used advanced metabolomic techniques to identify metabolic pathways that are active in two species of Mycoplasma that infect distinct hosts (poultry and cattle). We show that these species exhibit marked differences in metabolite steady-state levels and carbon source utilization. This information has been used to functionally characterize previously unknown genes in the genomes of these pathogens. These species-specific differences are likely to reflect important differences in host nutrient levels and pathogenic mechanisms.

Metabolic modeling of energy balances in Mycoplasma hyopneumoniae shows that pyruvate addition increases growth rate

Mycoplasma hyopneumoniae is cultured on large-scale to produce antigen for inactivated whole-cell vaccines against respiratory disease in pigs. However, the fastidious nutrient requirements of this minimal bacterium and the low growth rate make it challenging to reach sufficient biomass yield for antigen production. In this study, we sequenced the genome of M. hyopneumoniae strain 11 and constructed a high quality constraint-based genome-scale metabolic model of 284 chemical reactions and 298 metabolites. We validated the model with time-series data of duplicate fermentation cultures to aim for an integrated model describing the dynamic profiles measured in fermentations. The model predicted that 84% of cellular energy in a standard M. hyopneumoniae cultivation was used for non-growth associated maintenance and only 16% of cellular energy was used for growth and growth associated maintenance. Following a cycle of model-driven experimentation in dedicated fermentation experiments, we were able to increase the fraction of cellular energy used for growth through pyruvate addition to the medium. This increase in turn led to an increase in growth rate and a 2.3 times increase in the total biomass concentration reached after 3-4 days of fermentation, enhancing the productivity of the overall process. The model presented provides a solid basis to understand and further improve M. hyopneumoniae fermentation processes. Biotechnol. Bioeng. 2017;114: 2339-2347. © 2017 Wiley Periodicals, Inc.

Synthetic biology advances for pharmaceutical production

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Synthetic biology is quickly moving from proof of concept to industrial application.

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Pharmaceuticals are a promising target for advanced genetic engineering.

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Genome sequence data indicate vast underexploited biosynthetic capacity.

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Synthetic biology can create libraries of novel chemicals enriched for bioactivity.

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Synthetic biology expands the range of available chassis organisms for industry.

Synthetic biology enables a new generation of microbial engineering for the biotechnological production of pharmaceuticals and other high-value chemicals. This review presents an overview of recent advances in the field, describing new computational and experimental tools for the discovery, optimization and production of bioactive molecules, and outlining progress towards the application of these tools to pharmaceutical production systems.

Hydrogen peroxide production from glycerol metabolism is dispensable for virulence of Mycoplasma gallisepticum in the tracheas of chickens

Hydrogen peroxide (H2O2) is a by-product of glycerol metabolism in mycoplasmas and has been shown to cause cytotoxicity for cocultured eukaryotic cells. There appears to be selective pressure for mycoplasmas to retain the genes needed for glycerol metabolism. This has generated interest and speculation as to their function during infection. However, the actual effects of glycerol metabolism and H2O2 production on virulence in vivo have never been assessed in any Mycoplasma species. To this end, we determined that the wild-type (WT) R(low) strain of the avian pathogen Mycoplasma gallisepticum is capable of producing H2O2 when grown in glycerol and is cytotoxic to eukaryotic cells in culture. Transposon mutants with mutations in the genes present in the glycerol transport and utilization pathway, namely, glpO, glpK, and glpF, were identified. All mutants assessed were incapable of producing H2O2 and were not cytotoxic when grown in glycerol. We also determined that vaccine strains ts-11 and 6/85 produce little to no H2O2 when grown in glycerol, while the naturally attenuated F strain does produce H2O2. Chickens were infected with one of two glpO mutants, a glpK mutant, R(low), or growth medium, and tracheal mucosal thickness and lesion scores were assessed. Interestingly, all glp mutants were reproducibly virulent in the respiratory tracts of the chickens. Thus, there appears to be no link between glycerol metabolism/H2O2 production/cytotoxicity and virulence for this Mycoplasma species in its natural host. However, it is possible that glycerol metabolism is required by M. gallisepticum in a niche that we have yet to study.

Functional metabolic map of Faecalibacterium prausnitzii, a beneficial human gut microbe

The human gut microbiota plays a central role in human well-being and disease. In this study, we present an integrated, iterative approach of computational modeling, in vitro experiments, metabolomics, and genomic analysis to accelerate the identification of metabolic capabilities for poorly characterized (anaerobic) microorganisms. We demonstrate this approach for the beneficial human gut microbe Faecalibacterium prausnitzii strain A2-165. We generated an automated draft reconstruction, which we curated against the limited biochemical data. This reconstruction modeling was used to develop in silico and in vitro a chemically defined medium (CDM), which was validated experimentally. Subsequent metabolomic analysis of the spent medium for growth on CDM was performed. We refined our metabolic reconstruction according to in vitro observed metabolite consumption and secretion and propose improvements to the current genome annotation of F. prausnitzii A2-165. We then used the reconstruction to systematically characterize its metabolic properties. Novel carbon source utilization capabilities and inabilities were predicted based on metabolic modeling and validated experimentally. This study resulted in a functional metabolic map of F. prausnitzii, which is available for further applications. The presented workflow can be readily extended to other poorly characterized and uncharacterized organisms to yield novel biochemical insights about the target organism.

Promise and reality in the expanding field of network interaction analysis: metabolic networks

In the last few decades, metabolic networks revealed their capabilities as powerful tools to analyze the cellular metabolism. Many research fields (eg, metabolic engineering, diagnostic medicine, pharmacology, biochemistry, biology and physiology) improved the understanding of the cell combining experimental assays and metabolic network-based computations. This process led to the rise of the “systems biology” approach, where the theory meets experiments and where two complementary perspectives cooperate in the study of biological phenomena. Here, the reconstruction of metabolic networks is presented, along with established and new algorithms to improve the description of cellular metabolism. Then, advantages and limitations of modeling algorithms and network reconstruction are discussed.