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

Mesoplasma florum: a near-minimal model organism for systems and synthetic biology

Mesoplasma florum is an emerging model organism for systems and synthetic biology due to its small genome (∼800 kb) and fast growth rate. While M. florum was isolated and first described almost 40 years ago, many important aspects of its biology have long remained uncharacterized due to technological limitations, the absence of dedicated molecular tools, and since this bacterial species has not been associated with any disease. However, the publication of the first M. florum genome in 2004 paved the way for a new era of research fueled by the rise of systems and synthetic biology. Some of the most important studies included the characterization and heterologous use of M. florum regulatory elements, the development of the first replicable plasmids, comparative genomics and transposon mutagenesis, whole-genome cloning in yeast, genome transplantation, in-depth characterization of the M. florum cell, as well as the development of a high-quality genome-scale metabolic model. The acquired data, knowledge, and tools will greatly facilitate future genome engineering efforts in M. florum, which could next be exploited to rationally design and create synthetic cells to advance fundamental knowledge or for specific applications.

Comparative untargeted and targeted metabonomics reveal discriminations in metabolite profiles between Mycoplasma capricolum subsp. capripneumoniae and Mycoplasma capricolum subsp. capricolum

Background

Mycoplasmas are among the smallest prokaryotic microbes that can grow and proliferate on non-living media. They have reduced genomes, which may be associated with a concomitant reduction in their metabolic capacity. Mycoplasma capricolum subsp. capripneumoniae (Mccp) and Mycoplasma capricolum subsp. capricolum (Mcc), both belong to the Mycoplasma mycoides cluster, are significant important pathogenic Mycoplasma species in veterinary research field. They share high degree of genome homology but Mcc grows markedly faster and has higher growth titer than Mccp.

Methods

This study investigated the metabolites of these two pathogenic bacteria from the middle and late stages of the logarithmic growth phase through liquid chromatography-mass spectrometry-based metabolomics and targeted energy metabolomics. The multivariate analysis was conducted to identify significant differences between the two important Mycoplasma species.

Results

A total of 173 metabolites were identified. Of them, 33 and 34 metabolites involved in purine and pyrimidine, pyruvate metabolism, and amino acid synthesis were found to significantly differ in the middle and late stages, respectively. The abundance of fructose 1,6-bisphosphate, ADP, and pyruvate was higher in Mcc than in Mccp during the whole logarithmic period. Lactate was upregulated in slow-growing Mccp. The pH buffering agent N-[2-hydroxyethyl]piperazine-N'-[2-ethanesulfonic acid] added to media effectively prevented pH reduction and increase bacterial viability and protein biomass. The multivariate analysis revealed that the two Mycoplasma species significantly differed in glucose metabolism, growth factor transport and metabolism, cholesterol utilization, and environmental regulation.

Conclusion

The study data are beneficial for understanding the metabolomic characteristics of these two crucial Mycoplasma species and shedding more light on mycoplasma metabolism, and serve as a resource for the pathogenesis and development of related vaccines.

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.

UHPLC/MS-Based Untargeted Metabolomics Reveals Metabolic Characteristics of Clinical Strain of Mycoplasma bovis

Mycoplasma bovis is a global concern for the cattle industry owing to its high rates of infection and resulting morbidity, but its pathogenesis remains poorly understood. Metabolic pathways and characteristics of M. bovis clinical strain were elucidated by comparing the differential expression of metabolites between M. bovis clinical strain NX114 and M. bovis international reference strain PG45. Metabolites of M. bovis in the logarithmic stage were analyzed based on the non-targeted metabolomic technology of ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). We found 596 metabolites with variable expression, of which, 190 had substantial differences. Differential metabolite analysis of M. bovis NX114 showed organic acids and their derivatives, nucleosides, and nucleotide analogs as important components. We found O-Phospho-L-serine (SEP) as a potential signature metabolite and indicator of pathogenicity. The difference in nucleic acid metabolites reflects the difference in growth phenotypes between both strains of M. bovis. According to KEGG enrichment analysis, the ABC transporter synthesis route had the most differential metabolites of the first 15 differential enrichment pathways. This study reflects the species-specific differences between two strains of M. bovis and further enriches our understanding of its metabolism, paving the way for further research into its pathogenesis.

Engineered live bacteria suppress Pseudomonas aeruginosa infection in mouse lung and dissolve endotracheal-tube biofilms

Engineered live bacteria could provide a new modality for treating lung infections, a major cause of mortality worldwide. In the present study, we engineered a genome-reduced human lung bacterium, Mycoplasma pneumoniae, to treat ventilator-associated pneumonia, a disease with high hospital mortality when associated with Pseudomonas aeruginosa biofilms. After validating the biosafety of an attenuated M. pneumoniae chassis in mice, we introduced four transgenes into the chromosome by transposition to implement bactericidal and biofilm degradation activities. We show that this engineered strain has high efficacy against an acute P. aeruginosa lung infection in a mouse model. In addition, we demonstrated that the engineered strain could dissolve biofilms formed in endotracheal tubes of patients with ventilator-associated pneumonia and be combined with antibiotics targeting the peptidoglycan layer to increase efficacy against Gram-positive and Gram-negative bacteria. We expect our M. pneumoniae-engineered strain to be able to treat biofilm-associated infections in the respiratory tract.

How much (ATP) does it cost to build a trypanosome? A theoretical study on the quantity of ATP needed to maintain and duplicate a bloodstream-form Trypanosoma brucei cell

ATP hydrolysis is required for the synthesis, transport and polymerization of monomers for macromolecules as well as for the assembly of the latter into cellular structures. Other cellular processes not directly related to synthesis of biomass, such as maintenance of membrane potential and cellular shape, also require ATP. The unicellular flagellated parasite Trypanosoma brucei has a complex digenetic life cycle. The primary energy source for this parasite in its bloodstream form (BSF) is glucose, which is abundant in the host's bloodstream. Here, we made a detailed estimation of the energy budget during the BSF cell cycle. As glycolysis is the source of most produced ATP, we calculated that a single parasite produces 6.0 x 1011 molecules of ATP/cell cycle. Total biomass production (which involves biomass maintenance and duplication) accounts for ~63% of the total energy budget, while the total biomass duplication accounts for the remaining ~37% of the ATP consumption, with in both cases translation being the most expensive process. These values allowed us to estimate a theoretical YATP of 10.1 (g biomass)/mole ATP and a theoretical [Formula: see text] of 28.6 (g biomass)/mole ATP. Flagellar motility, variant surface glycoprotein recycling, transport and maintenance of transmembrane potential account for less than 30% of the consumed ATP. Finally, there is still ~5.5% available in the budget that is being used for other cellular processes of as yet unknown cost. These data put a new perspective on the assumptions about the relative energetic weight of the processes a BSF trypanosome undergoes during its cell cycle.

Development of a Serum-Free Medium To Aid Large-Scale Production of Mycoplasma-Based Therapies

To assist in the advancement of the large-scale production of safe Mycoplasma vaccines and other Mycoplasma-based therapies, we developed a culture medium free of animal serum and other animal components for Mycoplasma pneumoniae growth. By establishing a workflow method to systematically test different compounds and concentrations, we provide optimized formulations capable of supporting serial passaging and robust growth reaching 60 to 70% of the biomass obtained in rich medium. Global transcriptomic and proteomic analysis showed minor physiological changes upon cell culture in the animal component-free medium, supporting its suitability for the production of M. pneumoniae-based therapies. The major contributors to growth performance were found to be glucose as a carbon source, glycerol, cholesterol, and phospholipids as a source of fatty acids. Bovine serum albumin or cyclodextrin (in the animal component-free medium) were required as lipid carriers to prevent lipid toxicity. Connaught Medical Research Laboratories medium (CMRL) used to simplify medium preparation as a source of amino acids, nucleotide precursors, vitamins, and other cofactors could be substituted by cysteine. In fact, the presence of protein hydrolysates such as yeastolate or peptones was found to be essential and preferred over free amino acids, except for the cysteine. Supplementation of nucleotide precursors and vitamins is not strictly necessary in the presence of yeastolate, suggesting that this animal origin-free hydrolysate serves as an efficient source for these compounds. Finally, we adapted the serum-free medium formulation to support growth of Mycoplasma hyopneumoniae, a swine pathogen for which inactivated whole-cell vaccines are available. IMPORTANCE Mycoplasma infections have a significant negative impact on both livestock production and human health. Vaccination is often the first option to control disease and alleviate the economic impact that some Mycoplasma infections cause on milk production, weight gain, and animal health. The fastidious nutrient requirements of these bacteria, however, challenges the industrial production of attenuated or inactivated whole-cell vaccines, which depends on the use of animal serum and other animal raw materials. Apart from their clinical relevance, some Mycoplasma species have become cellular models for systems and synthetic biology, owing to the small size of their genomes and the absence of a cell wall, which offers unique opportunities for the secretion and delivery of biotherapeutics. This study proposes medium formulations free of serum and animal components with the potential of supporting large-scale production upon industrial optimization, thus contributing to the development of safe vaccines and other Mycoplasma-based therapies.

Bacterial expression of a designed single-chain IL-10 prevents severe lung inflammation

Interleukin-10 (IL-10) is an anti-inflammatory cytokine that is active as a swapped domain dimer and is used in bacterial therapy of gut inflammation. IL-10 can be used as treatment of a wide range of pulmonary diseases. Here we have developed a non-pathogenic chassis (CV8) of the human lung bacterium Mycoplasma pneumoniae (MPN) to treat lung diseases. We find that IL-10 expression by MPN has a limited impact on the lung inflammatory response in mice. To solve these issues, we rationally designed a single-chain IL-10 (SC-IL10) with or without surface mutations, using our protein design software (ModelX and FoldX). As compared to the IL-10 WT, the designed SC-IL10 molecules increase the effective expression in MPN four-fold, and the activity in mouse and human cell lines between 10 and 60 times, depending on the cell line. The SC-IL10 molecules expressed in the mouse lung by CV8 in vivo have a powerful anti-inflammatory effect on Pseudomonas aeruginosa lung infection. This rational design strategy could be used to other molecules with immunomodulatory properties used in bacterial therapy.

MycoWiki: Functional annotation of the minimal model organism Mycoplasma pneumoniae

The human pathogen Mycoplasma pneumoniae is viable independently from host cells or organisms, despite its strongly reduced genome with only about 700 protein-coding genes. The investigation of M. pneumoniae can therefore help to obtain general insights concerning the basic requirements for cellular life. Accordingly, M. pneumoniae has become a model organism for systems biology in the past decade. To support the investigation of the components of this minimal bacterium, we have generated the database MycoWiki. (http://mycowiki.uni-goettingen.de) MycoWiki organizes data under a relational database and provides access to curated and state-of-the-art information on the genes and proteins of M. pneumoniae. Interestingly, M. pneumoniae has undergone an evolution that resulted in the limited similarity of many proteins to proteins of model organisms. To facilitate the analysis of the functions of M. pneumoniae proteins, we have integrated structure predictions from the AlphaFold Protein Structure Database for most proteins, structural information resulting from in vivo cross-linking, and protein-protein interactions based on a global in vivo study. MycoWiki is an important tool for the systems and synthetic biology community that will support the comprehensive understanding of a minimal organism and the functional annotation of so far uncharacterized proteins.

A genetic toolkit and gene switches to limit Mycoplasma growth for biosafety applications

Mycoplasmas have exceptionally streamlined genomes and are strongly adapted to their many hosts, which provide them with essential nutrients. Owing to their relative genomic simplicity, Mycoplasmas have been used to develop chassis for biotechnological applications. However, the dearth of robust and precise toolkits for genomic manipulation and tight regulation has hindered any substantial advance. Herein we describe the construction of a robust genetic toolkit for M. pneumoniae, and its successful deployment to engineer synthetic gene switches that control and limit Mycoplasma growth, for biosafety containment applications. We found these synthetic gene circuits to be stable and robust in the long-term, in the context of a minimal cell. With this work, we lay a foundation to develop viable and robust biosafety systems to exploit a synthetic Mycoplasma chassis for live attenuated vectors for therapeutic applications.

Mycoplasmas are minimal cell model organisms but lack genetic tools. Here the authors provide a robust genetic toolkit for Mycoplasma demonstrating gene circuit engineering applications.

Beware of Mycoplasma Anti-immunoglobulin Strategies

Mycoplasmas are small, genome-reduced bacteria. They are obligate parasites that can be found in a wide range of host species, including the majority of livestock animals and humans. Colonization of the host can result in a wide spectrum of outcomes. In many cases, these successful parasites are considered commensal, as they are found in the microbiota of asymptomatic carriers. Conversely, mycoplasmas can also be pathogenic, as they are associated with a range of both acute and chronic inflammatory diseases which are problematic in veterinary and human medicine. The chronicity of mycoplasma infections and the ability of these bacteria to infect even recently vaccinated individuals clearly indicate that they are able to successfully evade their host’s humoral immune response. Over the years, multiple strategies of immune evasion have been identified in mycoplasmas, with a number of them aimed at generating important antigenic diversity. More recently, mycoplasma-specific anti-immunoglobulin strategies have also been characterized. Through the expression of the immunoglobulin-binding proteins protein M or mycoplasma immunoglobulin binding (MIB), mycoplasmas have the ability to target the host’s antibodies and to prevent them from interacting with their cognate antigens. In this review, we discuss how these discoveries shed new light on the relationship between mycoplasmas and their host’s immune system. We also propose that these strategies should be taken into consideration for future studies, as they are key to our understanding of mycoplasma diseases' chronic and inflammatory nature and are probably a contributing factor to reduce vaccine efficacy.

Engineering a genome-reduced bacterium to eliminate Staphylococcus aureus biofilms in vivo

Bacteria present a promising delivery system for treating human diseases. Here, we engineered the genome‐reduced human lung pathogen Mycoplasma pneumoniae as a live biotherapeutic to treat biofilm‐associated bacterial infections. This strain has a unique genetic code, which hinders gene transfer to most other bacterial genera, and it lacks a cell wall, which allows it to express proteins that target peptidoglycans of pathogenic bacteria. We first determined that removal of the pathogenic factors fully attenuated the chassis strain in vivo. We then designed synthetic promoters and identified an endogenous peptide signal sequence that, when fused to heterologous proteins, promotes efficient secretion. Based on this, we equipped the chassis strain with a genetic platform designed to secrete antibiofilm and bactericidal enzymes, resulting in a strain capable of dissolving Staphylococcus aureus biofilms preformed on catheters in vitro, ex vivo, and in vivo. To our knowledge, this is the first engineered genome‐reduced bacterium that can fight against clinically relevant biofilm‐associated bacterial infections.

Silver nanoparticles boost charge-extraction efficiency in Shewanella microbial fuel cells

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The Role of Fatty Acid Metabolites in Vaginal Health and Disease: Application to Candidiasis

Although the vast majority of women encounters at least one vaginal infection during their life, the amount of microbiome-related research performed in this area lags behind compared to alternative niches such as the intestinal tract. As a result, effective means of diagnosis and treatment, especially of recurrent infections, are limited. The role of the metabolome in vaginal health is largely elusive. It has been shown that lactate produced by the numerous lactobacilli present promotes health by limiting the chance of infection. Short chain fatty acids (SCFA) have been mainly linked to dysbiosis, although the causality of this relationship is still under debate. In this review, we aim to bring together information on the role of the vaginal metabolome and microbiome in infections caused by Candida. Vulvovaginal candidiasis affects near to 70% of all women at least once in their life with a significant proportion of women suffering from the recurrent variant. We assess the role of fatty acid metabolites, mainly SCFA and lactate, in onset of infection and virulence of the fungal pathogen. In addition, we pinpoint where lack of research limits our understanding of the molecular processes involved and restricts the possibility of developing novel treatment strategies.

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.

Microbiome Analysis Reveals Diversity and Function of Mollicutes Associated with the Eastern Oyster, Crassostrea virginica

Despite their biological and ecological significance, a mechanistic characterization of microbiome function is frequently missing from many nonmodel marine invertebrates. As an initial step toward filling this gap for the eastern oyster, Crassostrea virginica, this study provides an integrated taxonomic and functional analysis of the oyster microbiome using samples from a coastal salt pond in August 2017.

Marine invertebrate microbiomes play important roles in diverse host and ecological processes. However, a mechanistic understanding of host-microbe interactions is currently available for a small number of model organisms. Here, an integrated taxonomic and functional analysis of the microbiome of the eastern oyster, Crassostrea virginica, was performed using 16S rRNA gene-based amplicon profiling, shotgun metagenomics, and genome-scale metabolic reconstruction. Relatively high variability of the microbiome was observed across individual oysters and among different tissue types. Specifically, a significantly higher alpha diversity was observed in the inner shell than in the gut, gill, mantle, and pallial fluid samples, and a distinct microbiome composition was revealed in the gut compared to other tissues examined in this study. Targeted metagenomic sequencing of the gut microbiota led to further characterization of a dominant bacterial taxon, the class Mollicutes, which was captured by the reconstruction of a metagenome-assembled genome (MAG). Genome-scale metabolic reconstruction of the oyster Mollicutes MAG revealed a reduced set of metabolic functions and a high reliance on the uptake of host-derived nutrients. A chitin degradation and an arginine deiminase pathway were unique to the MAG compared to closely related genomes of Mollicutes isolates, indicating distinct mechanisms of carbon and energy acquisition by the oyster-associated Mollicutes. A systematic reanalysis of public eastern oyster-derived microbiome data revealed a high prevalence of the Mollicutes among adult oyster guts and a significantly lower relative abundance of the Mollicutes in oyster larvae and adult oyster biodeposits.

IMPORTANCE Despite their biological and ecological significance, a mechanistic characterization of microbiome function is frequently missing from many nonmodel marine invertebrates. As an initial step toward filling this gap for the eastern oyster, Crassostrea virginica, this study provides an integrated taxonomic and functional analysis of the oyster microbiome using samples from a coastal salt pond in August 2017. The study identified high variability of the microbiome across tissue types and among individual oysters, with some dominant taxa showing higher relative abundance in specific tissues. A high prevalence of Mollicutes in the adult oyster gut was revealed by comparative analysis of the gut, biodeposit, and larva microbiomes. Phylogenomic analysis and metabolic reconstruction suggested the oyster-associated Mollicutes is closely related but functionally distinct from Mollicutes isolated from other marine invertebrates. To the best of our knowledge, this study represents the first metagenomics-derived functional inference of Mollicutes in the eastern oyster microbiome.

Inferring Active Metabolic Pathways from Proteomics and Essentiality Data

Here, we propose an approach to identify active metabolic pathways by integrating gene essentiality analysis and protein abundance. We use two bacterial species (Mycoplasma pneumoniae and Mycoplasma agalactiae) that share a high gene content similarity yet show significant metabolic differences. First, we build detailed metabolic maps of their carbon metabolism, the most striking difference being the absence of two key enzymes for glucose metabolism in M. agalactiae. We then determine carbon sources that allow growth in M. agalactiae, and we introduce glucose-dependent growth to show the functionality of its remaining glycolytic enzymes. By analyzing gene essentiality and performing quantitative proteomics, we can predict the active metabolic pathways connected to carbon metabolism and show significant differences in use and direction of key pathways despite sharing the large majority of genes. Gene essentiality combined with quantitative proteomics and metabolic maps can be used to determine activity and directionality of metabolic pathways.

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Active metabolic bacterial pathways are identified

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Integration of gene essentiality and proteomics allow prediction of active pathways

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Glucose-dependent growth is restored in Mycoplasma agalactiae

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Two Mycoplasma species show different usage of metabolic pathways

Determination of metabolic activity in planktonic and biofilm cells of Mycoplasma fermentans and Mycoplasma pneumoniae by nuclear magnetic resonance

Mycoplasmas are fastidious microorganisms, typically characterised by their restricted metabolism and minimalist genome. Although there is reported evidence that some mycoplasmas can develop biofilms little is known about any differences in metabolism in these organisms between the growth states. A systematic metabolomics approach may help clarify differences associated between planktonic and biofilm associated mycoplasmas. In the current study, the metabolomics of two different mycoplasmas of clinical importance (Mycoplasma pneumoniae and Mycoplasma fermentans) were examined using a novel approach involving nuclear magnetic resonance spectroscopy and principle component analysis. Characterisation of metabolic changes was facilitated through the generation of high-density metabolite data and diffusion-ordered spectroscopy that provided the size and structural information of the molecules under examination. This enabled the discrimination between biofilms and planktonic states for the metabolomic profiles of both organisms. This work identified clear biofilm/planktonic differences in metabolite composition for both clinical mycoplasmas and the outcomes serve to establish a baseline understanding of the changes in metabolism observed in these pathogens in their different growth states. This may offer insight into how these organisms are capable of exploiting and persisting in different niches and so facilitate their survival in the clinical setting.

Integrative characterization of the near-minimal bacterium Mesoplasma florum

The near-minimal bacterium Mesoplasma florum is an interesting model for synthetic genomics and systems biology due to its small genome (~ 800 kb), fast growth rate, and lack of pathogenic potential. However, fundamental aspects of its biology remain largely unexplored. Here, we report a broad yet remarkably detailed characterization of M. florum by combining a wide variety of experimental approaches. We investigated several physical and physiological parameters of this bacterium, including cell size, growth kinetics, and biomass composition of the cell. We also performed the first genome-wide analysis of its transcriptome and proteome, notably revealing a conserved promoter motif, the organization of transcription units, and the transcription and protein expression levels of all protein-coding sequences. We converted gene transcription and expression levels into absolute molecular abundances using biomass quantification results, generating an unprecedented view of the M. florum cellular composition and functions. These characterization efforts provide a strong experimental foundation for the development of a genome-scale model for M. florum and will guide future genome engineering endeavors in this simple organism.

Model-driven design allows growth of Mycoplasma pneumoniae on serum-free media

Mycoplasma pneumoniae is a slow-growing, human pathogen that causes atypical pneumonia. Because it lacks a cell wall, many antibiotics are ineffective. Due to its reduced genome and dearth of many biosynthetic pathways, this fastidious bacterium depends on rich, undefined medium for growth, which makes large-scale cultivation challenging and expensive. To understand factors limiting growth, we developed a genome-scale, constraint-based model of M. pneumoniae called iEG158_mpn to describe the metabolic potential of this bacterium. We have put special emphasis on cell membrane formation to identify key lipid components to maximize bacterial growth. We have used this knowledge to predict essential components validated with in vitro serum-free media able to sustain growth. Our findings also show that glycolysis and lipid metabolism are much less efficient under hypoxia; these findings suggest that factors other than metabolism and membrane formation alone affect the growth of M. pneumoniae. Altogether, our modelling approach allowed us to optimize medium composition, enabled growth in defined media and streamlined operational requirements, thereby providing the basis for stable, reproducible and less expensive production.

Improving Mycoplasma ovipneumoniae culture medium by a comparative transcriptome method

Mycoplasma ovipneumoniae (Mo) is difficult to culture, resulting in many difficulties in related research and application. Since nucleotide metabolism is a basic metabolism affects growth, this study conducted a “point-to-point” comparison of the corresponding growth phases between the Mo NM151 strain and the Mycoplasma mycoides subsp. capri (Mmc) PG3 strain. The results showed that the largest difference in nucleotide metabolism was found in the stationary phase. Nucleotide synthesis in PG3 was mostly de novo, while nucleotide synthesis in NM151 was primarily based on salvage synthesis. Compared with PG3, the missing reactions of NM151 referred to the synthesis of deoxythymine monophosphate. We proposed and validated a culture medium with added serine to fill this gap and prolong the stationary phase of NM151. This solved the problem of the fast death of Mo, which is significant for related research and application.

Enzymes as Parts in Need of Replacement - and How to Extend Their Working Life

Enzymes catalyze reactions in vivo at different rates and each enzyme molecule has a lifetime limit before it is degraded and replaced to enable catalysis to continue. Considering these rates together as a unitless ratio of catalytic cycles until replacement (CCR) provides a new quantitative tool to assess the replacement schedule of and energy investment into enzymes as they relate to function. Here, we outline the challenges of determining CCRs and new approaches to overcome them and then assess the CCRs of selected enzymes in bacteria and plants to reveal a range of seven orders of magnitude for this ratio. Modifying CCRs in plants holds promise to lower cellular costs, to tailor enzymes for particular environments, and to breed enzyme improvements for crop productivity.

Determination of the Gene Regulatory Network of a Genome-Reduced Bacterium Highlights Alternative Regulation Independent of Transcription Factors

Here, we determined the relative importance of different transcriptional mechanisms in the genome-reduced bacterium Mycoplasma pneumoniae, by employing an array of experimental techniques under multiple genetic and environmental perturbations. Of the 143 genes tested (21% of the bacterium’s annotated proteins), only 55% showed an altered phenotype, highlighting the robustness of biological systems. We identified nine transcription factors (TFs) and their targets, representing 43% of the genome, and 16 regulators that indirectly affect transcription. Only 20% of transcriptional regulation is mediated by canonical TFs when responding to perturbations. Using a Random Forest, we quantified the non-redundant contribution of different mechanisms such as supercoiling, metabolic control, RNA degradation, and chromosome topology to transcriptional changes. Model-predicted gene changes correlate well with experimental data in 95% of the tested perturbations, explaining up to 70% of the total variance when also considering noise. This analysis highlights the importance of considering non-TF-mediated regulation when engineering bacteria.

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Full comprehensive reconstruction of a bacterial gene regulatory network achieved

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Genome-reduced bacterium Mycoplasma pneumoniae is robust to genetic perturbations

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Large part of transcription regulation in bacteria is transcription-factor independent

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Transcription-factor-independent regulation has a smaller dynamic range

Finding MEMo: minimum sets of elementary flux modes

Metabolic network reconstructions are widely used in computational systems biology for in silico studies of cellular metabolism. A common approach to analyse these models are elementary flux modes (EFMs), which correspond to minimal functional units in the network. Already for medium-sized networks, it is often impossible to compute the set of all EFMs, due to their huge number. From a practical point of view, this might also not be necessary because a subset of EFMs may already be sufficient to answer relevant biological questions. In this article, we study MEMos or minimum sets of EFMs that can generate all possible steady-state behaviours of a metabolic network. The number of EFMs in a MEMo may be by several orders of magnitude smaller than the total number of EFMs. Using MEMos, we can compute generating sets of EFMs in metabolic networks where the whole set of EFMs is too large to be enumerated.

Essential metabolism for a minimal cell

JCVI-syn3A, a robust minimal cell with a 543 kbp genome and 493 genes, provides a versatile platform to study the basics of life. Using the vast amount of experimental information available on its precursor, Mycoplasma mycoides capri, we assembled a near-complete metabolic network with 98% of enzymatic reactions supported by annotation or experiment. The model agrees well with genome-scale in vivo transposon mutagenesis experiments, showing a Matthews correlation coefficient of 0.59. The genes in the reconstruction have a high in vivo essentiality or quasi-essentiality of 92% (68% essential), compared to 79% in silico essentiality. This coherent model of the minimal metabolism in JCVI-syn3A at the same time also points toward specific open questions regarding the minimal genome of JCVI-syn3A, which still contains many genes of generic or completely unclear function. In particular, the model, its comparison to in vivo essentiality and proteomics data yield specific hypotheses on gene functions and metabolic capabilities; and provide suggestions for several further gene removals. In this way, the model and its accompanying data guide future investigations of the minimal cell. Finally, the identification of 30 essential genes with unclear function will motivate the search for new biological mechanisms beyond metabolism.

One way that researchers can test whether they understand a biological system is to see if they can accurately recreate it as a computer model. The more they learn about living things, the more the researchers can improve their models and the closer the models become to simulating the original. In this approach, it is best to start by trying to model a simple system.

Biologists have previously succeeded in creating ‘minimal bacterial cells’. These synthetic cells contain fewer genes than almost all other living things and they are believed to be among the simplest possible forms of life that can grow on their own. The minimal cells can produce all the chemicals that they need to survive – in other words, they have a metabolism. Accurately recreating one of these cells in a computer is a key first step towards simulating a complete living system.

Breuer et al. have developed a computer model to simulate the network of the biochemical reactions going on inside a minimal cell with just 493 genes. By altering the parameters of their model and comparing the results to experimental data, Breuer et al. explored the accuracy of their model. Overall, the model reproduces experimental results, but it is not yet perfect. The differences between the model and the experiments suggest new questions and tests that could advance our understanding of biology. In particular, Breuer et al. identified 30 genes that are essential for life in these cells but that currently have no known purpose.

Continuing to develop and expand models like these to reproduce more complex living systems provides a tool to test current knowledge of biology. These models may become so advanced that they could predict how living things will respond to changing situations. This would allow scientists to test ideas sooner and make much faster progress in understanding life on Earth. Ultimately, these models could one day help to accelerate medical and industrial processes to save lives and enhance productivity.

Genomic diversity, lifestyles and evolutionary origins of DPANN archaea

Archaea-a primary domain of life besides Bacteria-have for a long time been regarded as peculiar organisms that play marginal roles in biogeochemical cycles. However, this picture changed with the discovery of a large diversity of archaea in non-extreme environments enabled by the use of cultivation-independent methods. These approaches have allowed the reconstruction of genomes of uncultivated microorganisms and revealed that archaea are diverse and broadly distributed in the biosphere and seemingly include a large diversity of putative symbiotic organisms, most of which belong to the tentative archaeal superphylum referred to as DPANN. This archaeal group encompasses at least 10 different lineages and includes organisms with extremely small cell and genome sizes and limited metabolic capabilities. Therefore, many members of DPANN may be obligately dependent on symbiotic interactions with other organisms and may even include novel parasites. In this contribution, we review the current knowledge of the gene repertoires and lifestyles of members of this group and discuss their placement in the tree of life, which is the basis for our understanding of the deep microbial roots and the role of symbiosis in the evolution of life on Earth.

Genome-scale biological models for industrial microbial systems

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.

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.

Detecting the Significant Flux Backbone of Escherichia coli metabolism

The heterogeneity of computationally predicted reaction fluxes in metabolic networks within a single flux state can be exploited to detect their significant flux backbone. Here, we disclose the backbone of Escherichia coli, and compare it with the backbones of other bacteria. We find that, in general, the core of the backbones is mainly composed of reactions in energy metabolism corresponding to ancient pathways. In E. coli, the synthesis of nucleotides and the metabolism of lipids form smaller cores which rely critically on energy metabolism. Moreover, the consideration of different media leads to the identification of pathways sensitive to environmental changes. The metabolic backbone of an organism is thus useful to trace simultaneously both its evolution and adaptation fingerprints.

Arginine deiminase pathway provides ATP and boosts growth of the gas-fermenting acetogen Clostridium autoethanogenum

Acetogens are attractive organisms for the production of chemicals and fuels from inexpensive and non-food feedstocks such as syngas (CO, CO₂ and H₂). Expanding their product spectrum beyond native compounds is dictated by energetics, particularly ATP availability. Acetogens have evolved sophisticated strategies to conserve energy from reduction potential differences between major redox couples, however, this coupling is sensitive to small changes in thermodynamic equilibria. To accelerate the development of strains for energy-intensive products from gases, we used a genome-scale metabolic model (GEM) to explore alternative ATP-generating pathways in the gas-fermenting acetogen Clostridium autoethanogenum. Shadow price analysis revealed a preference of C. autoethanogenum for nine amino acids. This prediction was experimentally confirmed under heterotrophic conditions. Subsequent in silico simulations identified arginine (ARG) as a key enhancer for growth. Predictions were experimentally validated, and faster growth was measured in media containing ARG (t_D~4h) compared to growth on yeast extract (t_D~9h). The growth-boosting effect of ARG was confirmed during autotrophic growth. Metabolic modelling and experiments showed that acetate production is nearly abolished and fast growth is realised by a three-fold increase in ATP production through the arginine deiminase (ADI) pathway. The involvement of the ADI pathway was confirmed by metabolomics and RNA-sequencing which revealed a ~500-fold up-regulation of the ADI pathway with an unexpected down-regulation of the Wood-Ljungdahl pathway. The data presented here offer a potential route for supplying cells with ATP, while demonstrating the usefulness of metabolic modelling for the discovery of native pathways for stimulating growth or enhancing energy availability.

Defined chromosome structure in the genome-reduced bacterium Mycoplasma pneumoniae

DNA-binding proteins are central regulators of chromosome organization; however, in genome-reduced bacteria their diversity is largely diminished. Whether the chromosomes of such bacteria adopt defined three-dimensional structures remains unexplored. Here we combine Hi-C and super-resolution microscopy to determine the structure of the Mycoplasma pneumoniae chromosome at a 10 kb resolution. We find a defined structure, with a global symmetry between two arms that connect opposite poles, one bearing the chromosomal Ori and the other the midpoint. Analysis of local structures at a 3 kb resolution indicates that the chromosome is organized into domains ranging from 15 to 33 kb. We provide evidence that genes within the same domain tend to be co-regulated, suggesting that chromosome organization influences transcriptional regulation, and that supercoiling regulates local organization. This study extends the current understanding of bacterial genome organization and demonstrates that a defined chromosomal structure is a universal feature of living systems.

Persistence of Functional Protein Domains in Mycoplasma Species and their Role in Host Specificity and Synthetic Minimal Life

Mycoplasmas are the smallest self-replicating organisms and obligate parasites of a specific vertebrate host. An in-depth analysis of the functional capabilities of mycoplasma species is fundamental to understand how some of simplest forms of life on Earth succeeded in subverting complex hosts with highly sophisticated immune systems. In this study we present a genome-scale comparison, focused on identification of functional protein domains, of 80 publically available mycoplasma genomes which were consistently re-annotated using a standardized annotation pipeline embedded in a semantic framework to keep track of the data provenance. We examined the pan- and core-domainome and studied predicted functional capability in relation to host specificity and phylogenetic distance. We show that the pan- and core-domainome of mycoplasma species is closed. A comparison with the proteome of the “minimal” synthetic bacterium JCVI-Syn3.0 allowed us to classify domains and proteins essential for minimal life. Many of those essential protein domains, essential Domains of Unknown Function (DUFs) and essential hypothetical proteins are not persistent across mycoplasma genomes suggesting that mycoplasma species support alternative domain configurations that bypass their essentiality. Based on the protein domain composition, we could separate mycoplasma species infecting blood and tissue. For selected genomes of tissue infecting mycoplasmas, we could also predict whether the host is ruminant, pig or human. Functionally closely related mycoplasma species, which have a highly similar protein domain repertoire, but different hosts could not be separated. This study provides a concise overview of the functional capabilities of mycoplasma species, which can be used as a basis to further understand host-pathogen interaction or to design synthetic minimal life.

Control of fluxes in metabolic networks

Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.

Insights on the virulence of swine respiratory tract mycoplasmas through genome-scale metabolic modeling

Background

The respiratory tract of swine is colonized by several bacteria among which are three Mycoplasma species: Mycoplasma flocculare, Mycoplasma hyopneumoniae and Mycoplasma hyorhinis. While colonization by M. flocculare is virtually asymptomatic, M. hyopneumoniae is the causative agent of enzootic pneumonia and M. hyorhinis is present in cases of pneumonia, polyserositis and arthritis. The genomic resemblance among these three Mycoplasma species combined with their different levels of pathogenicity is an indication that they have unknown mechanisms of virulence and differential expression, as for most mycoplasmas.

Methods

In this work, we performed whole-genome metabolic network reconstructions for these three mycoplasmas. Cultivation tests and metabolomic experiments through nuclear magnetic resonance spectroscopy (NMR) were also performed to acquire experimental data and further refine the models reconstructed in silico.

Results

Even though the refined models have similar metabolic capabilities, interesting differences include a wider range of carbohydrate uptake in M. hyorhinis, which in turn may also explain why this species is a widely contaminant in cell cultures. In addition, the myo-inositol catabolism is exclusive to M. hyopneumoniae and may be an important trait for virulence. However, the most important difference seems to be related to glycerol conversion to dihydroxyacetone-phosphate, which produces toxic hydrogen peroxide. This activity, missing only in M. flocculare, may be directly involved in cytotoxicity, as already described for two lung pathogenic mycoplasmas, namely Mycoplasma pneumoniae in human and Mycoplasma mycoides subsp. mycoides in ruminants. Metabolomic data suggest that even though these mycoplasmas are extremely similar in terms of genome and metabolism, distinct products and reaction rates may be the result of differential expression throughout the species.

Conclusions

We were able to infer from the reconstructed networks that the lack of pathogenicity of M. flocculare if compared to the highly pathogenic M. hyopneumoniae may be related to its incapacity to produce cytotoxic hydrogen peroxide. Moreover, the ability of M. hyorhinis to grow in diverse sites and even in different hosts may be a reflection of its enhanced and wider carbohydrate uptake. Altogether, the metabolic differences highlighted in silico and in vitro provide important insights to the different levels of pathogenicity observed in each of the studied species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2644-z) contains supplementary material, which is available to authorized users.

Dynamic metabolic models in context: biomass backtracking

Mathematical modeling has proven to be a powerful tool to understand and predict functional and regulatory properties of metabolic processes. High accuracy dynamic modeling of individual pathways is thereby opposed by simplified but genome scale constraint based approaches. A method that links these two powerful techniques would greatly enhance predictive power but is so far lacking. We present biomass backtracking, a workflow that integrates the cellular context in existing dynamic metabolic models via stoichiometrically exact drain reactions based on a genome scale metabolic model. With comprehensive examples, for different species and environmental contexts, we show the importance and scope of applications and highlight the improvement compared to common boundary formulations in existing metabolic models. Our method allows for the contextualization of dynamic metabolic models based on all available information. We anticipate this to greatly increase their accuracy and predictive power for basic research and also for drug development and industrial applications.

Exploring membrane respiratory chains

Acquisition of energy is central to life. In addition to the synthesis of ATP, organisms need energy for the establishment and maintenance of a transmembrane difference in electrochemical potential, in order to import and export metabolites or to their motility. The membrane potential is established by a variety of membrane bound respiratory complexes. In this work we explored the diversity of membrane respiratory chains and the presence of the different enzyme complexes in the several phyla of life. We performed taxonomic profiles of the several membrane bound respiratory proteins and complexes evaluating the presence of their respective coding genes in all species deposited in KEGG database. We evaluated 26 quinone reductases, 5 quinol:electron carriers oxidoreductases and 18 terminal electron acceptor reductases. We further included in the analyses enzymes performing redox or decarboxylation driven ion translocation, ATP synthase and transhydrogenase and we also investigated the electron carriers that perform functional connection between the membrane complexes, quinones or soluble proteins. Our results bring a novel, broad and integrated perspective of membrane bound respiratory complexes and thus of the several energetic metabolisms of living systems. This article is part of a Special Issue entitled 'EBEC 2016: 19th European Bioenergetics Conference, Riva del Garda, Italy, July 2-6, 2016', edited by Prof. Paolo Bernardi.

All-in-one construct for genome engineering using Cre-lox technology

Mycoplasma genitalium is an appealing model of a minimal cell and synthetic biology study, and it was one of the first organisms whose genome was fully sequenced and chemically synthesized. Despite its usefulness as a model organism, many genetic tools well established for other microorganisms are not currently available in mycoplasmas. We have developed several vectors to adapt the Cre-lox technology for genome engineering in M. genitalium, providing an all-in-one construct that could be also useful to obtain unmarked genetic modifications in many other slow growing microorganisms. This construct contains a modified promoter sequence based in TetR system that exhibits an enhanced control on Cre recombinase expression, virtually abolishing the presence of this recombinase in the absence of inducer. This allows to introduce the Cre recombinase gene and the desired genetic modification in a single transformation step. In addition, this inducible promoter may be a very promising tool for a wide range of molecular applications.

Bacterial antisense RNAs are mainly the product of transcriptional noise

cis-Encoded antisense RNAs (asRNAs) are widespread along bacterial transcriptomes. However, the role of most of these RNAs remains unknown, and there is an ongoing discussion as to what extent these transcripts are the result of transcriptional noise. We show, by comparative transcriptomics of 20 bacterial species and one chloroplast, that the number of asRNAs is exponentially dependent on the genomic AT content and that expression of asRNA at low levels exerts little impact in terms of energy consumption. A transcription model simulating mRNA and asRNA production indicates that the asRNA regulatory effect is only observed above certain expression thresholds, substantially higher than physiological transcript levels. These predictions were verified experimentally by overexpressing nine different asRNAs in Mycoplasma pneumoniae. Our results suggest that most of the antisense transcripts found in bacteria are the consequence of transcriptional noise, arising at spurious promoters throughout the genome.

Synthetic biology: insights into biological computation

Synthetic biology attempts to rationally engineer biological systems in order to perform desired functions. Our increasing understanding of biological systems guides this rational design, while the huge background in electronics for building circuits defines the methodology.

Rescuing discarded spectra: Full comprehensive analysis of a minimal proteome

A common problem encountered when performing large-scale MS proteome analysis is the loss of information due to the high percentage of unassigned spectra. To determine the causes behind this loss we have analyzed the proteome of one of the smallest living bacteria that can be grown axenically, Mycoplasma pneumoniae (729 ORFs). The proteome of M. pneumoniae cells, grown in defined media, was analyzed by MS. An initial search with both Mascot and a species-specific NCBInr database with common contaminants (NCBImpn), resulted in around 79% of the acquired spectra not having an assignment. The percentage of non-assigned spectra was reduced to 27% after re-analysis of the data with the PEAKS software, thereby increasing the proteome coverage of M. pneumoniae from the initial 60% to over 76%. Nonetheless, 33,413 spectra with assigned amino acid sequences could not be mapped to any NCBInr database protein sequence. Approximately, 1% of these unassigned peptides corresponded to PTMs and 4% to M. pneumoniae protein variants (deamidation and translation inaccuracies). The most abundant peptide sequence variants (Phe-Tyr and Ala-Ser) could be explained by alterations in the editing capacity of the corresponding tRNA synthases. About another 1% of the peptides not associated to any protein had repetitions of the same aromatic/hydrophobic amino acid at the N-terminus, or had Arg/Lys at the C-terminus. Thus, in a model system, we have maximized the number of assigned spectra to 73% (51,453 out of the 70,040 initial acquired spectra). All MS data have been deposited in the ProteomeXchange with identifier PXD002779 (http://proteomecentral.proteomexchange.org/dataset/PXD002779).

Comparative "-omics" in Mycoplasma pneumoniae Clinical Isolates Reveals Key Virulence Factors

The human respiratory tract pathogen M. pneumoniae is one of the best characterized minimal bacterium. Until now, two main groups of clinical isolates of this bacterium have been described (types 1 and 2), differing in the sequence of the P1 adhesin gene. Here, we have sequenced the genomes of 23 clinical isolates of M. pneumoniae. Studying SNPs, non-synonymous mutations, indels and genome rearrangements of these 23 strains and 4 previously sequenced ones, has revealed new subclasses in the two main groups, some of them being associated with the country of isolation. Integrative analysis of in vitro gene essentiality and mutation rates enabled the identification of several putative virulence factors and antigenic proteins; revealing recombination machinery, glycerol metabolism and peroxide production as possible factors in the genetics and physiology of these pathogenic strains. Additionally, the transcriptomes and proteomes of two representative strains, one from each of the two main groups, have been characterized to evaluate the impact of mutations on RNA and proteins levels. This study has revealed that type 2 strains show higher expression levels of CARDS toxin, a protein recently shown to be one of the major factors of inflammation. Thus, we propose that type 2 strains could be more toxigenic than type 1 strains of M. pneumoniae.

Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks

MOTIVATION

Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of flux balance analysis (FBA), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial reduction in running time, even for higher order synthetic lethals.

RESULTS

We performed synthetic reaction and gene lethality analysis, using Fast-SL, for genome-scale metabolic networks of Escherichia coli, Salmonella enterica Typhimurium and Mycobacterium tuberculosis. Fast-SL also rigorously identifies synthetic lethal gene deletions, uncovering synthetic lethal triplets that were not reported previously. We confirm that the triple lethal gene sets obtained for the three organisms have a precise match with the results obtained through exhaustive enumeration of lethals performed on a computer cluster. We also parallelized our algorithm, enabling the identification of synthetic lethal gene quadruplets for all three organisms in under 6 h. Overall, Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets.

AVAILABILITY AND IMPLEMENTATION

The MATLAB implementation of the algorithm, compatible with COBRA toolbox v2.0, is available at https://github.com/RamanLab/FastSL CONTACT: kraman@iitm.ac.in

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational prediction of essential metabolic genes using constraint-based approaches

In this chapter, we describe the application of constraint-based modeling to predict the impact of gene deletions on a metabolic phenotype. The metabolic reactions taking place inside cells form large networks, which have been reconstructed at a genome-scale for several organisms at increasing levels of detail. By integrating mathematical modeling techniques with biochemical principles, constraint-based approaches enable predictions of metabolite fluxes and growth under specific environmental conditions or for genetically modified microorganisms. Similar to the experimental knockout of a gene, predicting the essentiality of a metabolic gene for a phenotype further allows to generate hypotheses on its biological function and design of genetic engineering strategies for biotechnological applications. Here, we summarize the principles of constraint-based approaches and provide a detailed description of the procedure to predict the essentiality of metabolic genes with respect to a specific metabolic function. We exemplify the approach by predicting the essentiality of reactions in the citric acid cycle for the production of glucose from fatty acids.

MyMpn: a database for the systems biology model organism Mycoplasma pneumoniae

MyMpn (http://mympn.crg.eu) is an online resource devoted to studying the human pathogen Mycoplasma pneumoniae, a minimal bacterium causing lower respiratory tract infections. Due to its small size, its ability to grow in vitro, and the amount of data produced over the past decades, M. pneumoniae is an interesting model organisms for the development of systems biology approaches for unicellular organisms. Our database hosts a wealth of omics-scale datasets generated by hundreds of experimental and computational analyses. These include data obtained from gene expression profiling experiments, gene essentiality studies, protein abundance profiling, protein complex analysis, metabolic reactions and network modeling, cell growth experiments, comparative genomics and 3D tomography. In addition, the intuitive web interface provides access to several visualization and analysis tools as well as to different data search options. The availability and—even more relevant—the accessibility of properly structured and organized data are of up-most importance when aiming to understand the biology of an organism on a global scale. Therefore, MyMpn constitutes a unique and valuable new resource for the large systems biology and microbiology community.

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.

Essential plasticity and redundancy of metabolism unveiled by synthetic lethality analysis

We unravel how functional plasticity and redundancy are essential mechanisms underlying the ability to survive of metabolic networks. We perform an exhaustive computational screening of synthetic lethal reaction pairs in Escherichia coli in a minimal medium and we find that synthetic lethal pairs divide in two different groups depending on whether the synthetic lethal interaction works as a backup or as a parallel use mechanism, the first corresponding to essential plasticity and the second to essential redundancy. In E. coli, the analysis of pathways entanglement through essential redundancy supports the view that synthetic lethality affects preferentially a single function or pathway. In contrast, essential plasticity, the dominant class, tends to be inter-pathway but strongly localized and unveils Cell Envelope Biosynthesis as an essential backup for Membrane Lipid Metabolism. When comparing E. coli and Mycoplasma pneumoniae, we find that the metabolic networks of the two organisms exhibit a large difference in the relative importance of plasticity and redundancy which is consistent with the conjecture that plasticity is a sophisticated mechanism that requires a complex organization. Finally, coessential reaction pairs are explored in different environmental conditions to uncover the interplay between the two mechanisms. We find that synthetic lethal interactions and their classification in plasticity and redundancy are basically insensitive to medium composition, and are highly conserved even when the environment is enriched with nonessential compounds or overconstrained to decrease maximum biomass formation.

Plasticity and redundancy are essential features of biological systems, from brain to genome, that underlie the ability of organisms to survive. In metabolic networks, these essential mechanisms are unveiled by the analysis and categorization of synthetic lethal pairs of reactions. We find that plasticity acts as a backup mechanism that reorganizes metabolic fluxes, while redundancy corresponds to a simultaneous use of different flux channels that increases fitness. Both capacities ensure viability and are highly insensitive to environmental conditions, but plasticity seems a more sophisticated mechanism requiring a more complex functional organization. Our results have clear implications for biotechnology and biomedicine, since targeting a plasticity or a redundancy synthetic lethal pair will certainly have different requirements and peculiar specific effects.