Genome-scale metabolic network reconstruction and in silico flux analysis of the thermophilic bacterium Thermus thermophilus HB27

Genome-scale metabolic modelling of extremophiles and its applications in astrobiological environments

Metabolic modelling approaches have become the powerful tools in modern biology. These mathematical models are widely used to predict metabolic phenotypes of the organisms or communities of interest, and to identify metabolic targets in metabolic engineering. Apart from a broad range of industrial applications, the possibility of using metabolic modelling in the contexts of astrobiology are poorly explored. In this mini-review, we consolidated the concepts and related applications of applying metabolic modelling in studying organisms in space-related environments, specifically the extremophilic microbes. We recapitulated the current state of the art in metabolic modelling approaches and their advantages in the astrobiological context. Our review encompassed the applications of metabolic modelling in the theoretical investigation of the origin of life within prebiotic environments, as well as the compilation of existing uses of genome-scale metabolic models of extremophiles. Furthermore, we emphasize the current challenges associated with applying this technique in extreme environments, and conclude this review by discussing the potential implementation of metabolic models to explore theoretically optimal metabolic networks under various space conditions. Through this mini-review, our aim is to highlight the potential of metabolic modelling in advancing the study of astrobiology.

Reconstruction of a Genome-Scale Metabolic Network for Shewanella oneidensis MR-1 and Analysis of its Metabolic Potential for Bioelectrochemical Systems

Bioelectrochemical systems (BESs) based on Shewanella oneidensis MR-1 offer great promise for sustainable energy/chemical production, but the low rate of electron generation remains a crucial bottleneck preventing their industrial application. Here, we reconstructed a genome-scale metabolic model of MR-1 to provide a strong theoretical basis for novel BES applications. The model iLJ1162, comprising 1,162 genes, 1,818 metabolites and 2,084 reactions, accurately predicted cellular growth using a variety of substrates with 86.9% agreement with experimental results, which is significantly higher than the previously published models iMR1_799 and iSO783. The simulation of microbial fuel cells indicated that expanding the substrate spectrum of MR-1 to highly reduced feedstocks, such as glucose and glycerol, would be beneficial for electron generation. In addition, 31 metabolic engineering targets were predicted to improve electricity production, three of which have been experimentally demonstrated, while the remainder are potential targets for modification. Two potential electron transfer pathways were identified, which could be new engineering targets for increasing the electricity production capacity of MR-1. Finally, the iLJ1162 model was used to simulate the optimal biosynthetic pathways for six platform chemicals based on the MR-1 chassis in microbial electrosynthesis systems. These results offer guidance for rational design of novel BESs.

Construction and Validation of a Genome-Scale Metabolic Network of Thermotoga sp. Strain RQ7

Thermotoga are anaerobic hyperthermophiles that have a deep lineage to the last universal ancestor and produce biological hydrogen gas accompanying cell growth. In recent years, systems-level approaches have been used to elucidate their metabolic capacities, by integrating mathematical modeling and experimental results. To assist biochemical engineering studies of T. sp. strain RQ7, this work aims at building a metabolic model of the bacterium that quantitatively simulates its metabolism at the genome scale. The constructed model, RQ7_iJG408, consists of 408 genes, 692 reactions, and 538 metabolites. Constraint-based flux balance analyses were used to simulate cell growth in both the complex and defined media. Quantitative comparison of the predicted and measured growth rates resulted in good agreements. This model serves as a foundation for an integrated biochemical description of T. sp. strain RQ7. It is a useful tool in designing growth media, identifying metabolic engineering strategies, and exploiting the physiological potentials of this biotechnologically significant organism.

A Genome-Scale Metabolic Model of Thalassiosira pseudonana CCMP 1335 for a Systems-Level Understanding of Its Metabolism and Biotechnological Potential

Thalassiosira pseudonana is a transformable and biotechnologically promising model diatom with an ability to synthesise nutraceuticals such as fucoxanthin and store a significant amount of polyglucans and lipids including omega-3 fatty acids. While it was the first diatom to be sequenced, a systems-level analysis of its metabolism has not been done yet. This work presents first comprehensive, compartmentalized, and functional genome-scale metabolic model of the marine diatom Thalassiosira pseudonana CCMP 1335, which we have termed iThaps987. The model includes 987 genes, 2477 reactions, and 2456 metabolites. Comparison with the model of another diatom Phaeodactylum tricornutum revealed presence of 183 unique enzymes (belonging primarily to amino acid, carbohydrate, and lipid metabolism) in iThaps987. Model simulations showed a typical C3-type photosynthetic carbon fixation and suggested a preference of violaxanthin-diadinoxanthin pathway over violaxanthin-neoxanthin pathway for the production of fucoxanthin. Linear electron flow was found be active and cyclic electron flow was inactive under normal phototrophic conditions (unlike green algae and plants), validating the model predictions with previous reports. Investigation of the model for the potential of Thalassiosira pseudonana CCMP 1335 to produce other industrially useful compounds suggest iso-butanol as a foreign compound that can be synthesized by a single-gene addition. This work provides novel insights about the metabolism and potential of the organism and will be helpful to further investigate its metabolism and devise metabolic engineering strategies for the production of various compounds.

Biochemical Characteristics and a Genome-Scale Metabolic Model of an Indian Euryhaline Cyanobacterium with High Polyglucan Content

Marine cyanobacteria are promising microbes to capture and convert atmospheric CO₂ and light into biomass and valuable industrial bio-products. Yet, reports on metabolic characteristics of non-model cyanobacteria are scarce. In this report, we show that an Indian euryhaline Synechococcus sp. BDU 130192 has biomass accumulation comparable to a model marine cyanobacterium and contains approximately double the amount of total carbohydrates, but significantly lower protein levels compared to Synechococcus sp. PCC 7002 cells. Based on its annotated chromosomal genome sequence, we present a genome scale metabolic model (GSMM) of this cyanobacterium, which we have named as iSyn706. The model includes 706 genes, 908 reactions, and 900 metabolites. The difference in the flux balance analysis (FBA) predicted flux distributions between Synechococcus sp. PCC 7002 and Synechococcus sp. BDU130192 strains mimicked the differences in their biomass compositions. Model-predicted oxygen evolution rate for Synechococcus sp. BDU130192 was found to be close to the experimentally-measured value. The model was analyzed to determine the potential of the strain for the production of various industrially-useful products without affecting growth significantly. This model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for the production of industrially-relevant compounds.

Genomic comparison of anoxybacillus flavithermus AK1, a thermophilic bacteria, with other strains

From ecological and industrial perspectives, Anoxybacillus flavithermus species that lives in a thermophilic environment, are extremely important bacteria due to their potential in producing highly interesting compounds and enzymes. In order to understand the genetic makeup of these thermophiles, we have performed a comparative genomics study of 12 genome-sequenced strains of Anoxybacillus flavithermus bacteria. The genome size of Anoxybacillus flavithermus strains is from 2.5Mbp to 3.7Mbp and on average containing a low percentage of G + C genomic content (˜41.9%). We show that, on the basis of the total gene-content, Anoxybacillus flavithermus strains are grouped in three different subgroups. In the future, it would be interesting to explore these strain subgroups to further understand the lifestyle of thermophilic bacteria. Focussing on the Anoxybacillus flavithermus AK1 strain, which was isolated from a Hot Spring in Saudi Arabia and closely related to A. flavithermus NBRC strain, we identified a unique list of 75 genes specific to AK1 strain, of which 63 of them have homologs in other taxonomically related species. We speculate that these AK1-specific genes might be resulted due to horizontal gene transfer from other bacteria in order to adapt to the extreme environmental conditions. Moreover, we predicted three potential secondary metabolite gene clusters in the AK1 strain that further need to be experimentally characterised. Genomic annotation, secondary metabolite gene clusters and outcomes of the strain genomic comparisons from this study would be the basis for the strain-specific mathematical model for exploiting the metabolism for the industrial and ecological applications.

Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotroph Methylococcus capsulatus str. Bath

The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model (GSMM) of an obligate methanotroph, Methylococcus capsulatus str. Bath was reconstructed. The model contains 535 genes, 899 reactions and 865 metabolites and is named iMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner–Duodoroff pathway to be essential for the growth of this bacterium, whereas the Embden–Meyerhof–Parnas pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found that M. capsulatus can grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis, 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a GSMM of a methanotroph Methylococcus capsulatus str. Bath. This is the first high quality GSMM of a Methylococcus strain which can serve as an important resource for further strain-specific models of the Methylococcus genus, as well as identifying the biotechnological potential of M. capsulatus Bath.

Correlating enzyme annotations with a large set of microbial growth temperatures reveals metabolic adaptations to growth at diverse temperatures

Background

The ambient temperature of all habitats is a key physical property that shapes the biology of microbes inhabiting them. The optimal growth temperature (OGT) of a microbe, is therefore a key piece of data needed to understand evolutionary adaptations manifested in their genome sequence. Unfortunately there is no growth temperature database or easily downloadable dataset encompassing the majority of cultured microorganisms. We are thus limited in interpreting genomic data to identify temperature adaptations in microbes.

Results

In this work I significantly contribute to closing this gap by mining data from major culture collection centres to obtain growth temperature data for a nonredundant set of 21,498 microbes. The dataset (10.5281/zenodo.1175608) contains mainly bacteria and archaea and spans psychrophiles, mesophiles, thermophiles and hyperthermophiles. Using this data a full 43% of all protein entries in the UniProt database can be annotated with the growth temperature of the species from which they originate. I validate the dataset by showing a Pearson correlation of up to 0.89 between growth temperature and mean enzyme optima, a physiological property directly influenced by the growth temperature. Using the temperature dataset I correlate the genomic occurance of enzyme functional annotations with growth temperature. I identify 319 enzyme functions that either increase or decrease in occurrence with temperature. Eight metabolic pathways were statistically enriched for these enzyme functions. Furthermore, I establish a correlation between 33 domains of unknown function (DUFs) with growth temperature in microbes, four of which (DUF438, DUF1524, DUF1957 and DUF3458_C) were significant in both archaea and bacteria.

Conclusions

The growth temperature dataset enables large-scale correlation analysis with enzyme function- and domain-level annotations. Growth-temperature dependent changes in their occurrence highlight potential evolutionary adaptations. A few of the identified changes are previously known, such as the preference for menaquinone biosynthesis through the futalosine pathway in bacteria growing at high temperatures. Others represent important starting points for future studies, such as DUFs where their occurrence change with temperature. The growth temperature dataset should become a valuable community resource and will find additional, important, uses in correlating genomic, transcriptomic, proteomic, metabolomic, phenotypic or taxonomic properties with temperature in future studies.

Electronic supplementary material

The online version of this article (10.1186/s12866-018-1320-7) contains supplementary material, which is available to authorized users.

Clostridioides difficile 630Δerm in silico and in vivo - quantitative growth and extensive polysaccharide secretion

Antibiotic-associated infections with Clostridioides difficile are a severe and often lethal risk for hospitalized patients, and can also affect populations without these classical risk factors. For a rational design of therapeutical concepts, a better knowledge of the metabolism of the pathogen is crucial. Metabolic modeling can provide a simulation of quantitative growth and usage of metabolic pathways, leading to a deeper understanding of the organism. Here, we present an elaborate genome-scale metabolic model of C. difficile 630Δerm. The model iHD992 includes experimentally determined product and substrate uptake rates and is able to simulate the energy metabolism and quantitative growth of C. difficile. Dynamic flux balance analysis was used for time-resolved simulations of the quantitative growth in two different media. The model predicts oxidative Stickland reactions and glucose degradation as main sources of energy, while the resulting reduction potential is mostly used for acetogenesis via the Wood-Ljungdahl pathway. Initial modeling experiments did not reproduce the observed growth behavior before the production of large quantities of a previously unknown polysaccharide was detected. Combined genome analysis and laboratory experiments indicated that the polysaccharide is an acetylated glucose polymer. Time-resolved simulations showed that polysaccharide secretion was coupled to growth even during unstable glucose uptake in minimal medium. This is accomplished by metabolic shifts between active glycolysis and gluconeogenesis which were also observed in laboratory experiments.

Physiological, metabolic and biotechnological features of extremely thermophilic microorganisms

The current upper thermal limit for life as we know it is approximately 120°C. Microorganisms that grow optimally at temperatures of 75°C and above are usually referred to as 'extreme thermophiles' and include both bacteria and archaea. For over a century, there has been great scientific curiosity in the basic tenets that support life in thermal biotopes on earth and potentially on other solar bodies. Extreme thermophiles can be aerobes, anaerobes, autotrophs, heterotrophs, or chemolithotrophs, and are found in diverse environments including shallow marine fissures, deep sea hydrothermal vents, terrestrial hot springs-basically, anywhere there is hot water. Initial efforts to study extreme thermophiles faced challenges with their isolation from difficult to access locales, problems with their cultivation in laboratories, and lack of molecular tools. Fortunately, because of their relatively small genomes, many extreme thermophiles were among the first organisms to be sequenced, thereby opening up the application of systems biology-based methods to probe their unique physiological, metabolic and biotechnological features. The bacterial genera Caldicellulosiruptor, Thermotoga and Thermus, and the archaea belonging to the orders Thermococcales and Sulfolobales, are among the most studied extreme thermophiles to date. The recent emergence of genetic tools for many of these organisms provides the opportunity to move beyond basic discovery and manipulation to biotechnologically relevant applications of metabolic engineering. WIREs Syst Biol Med 2017, 9:e1377. doi: 10.1002/wsbm.1377 For further resources related to this article, please visit the WIREs website.

Genome-Scale Modeling of Thermophilic Microorganisms

Thermophilic microorganisms are of increasing interest for many industries as their enzymes and metabolisms are highly efficient at elevated temperatures. However, their metabolic processes are often largely different from their mesophilic counterparts. These differences can lead to metabolic engineering strategies that are doomed to fail. Genome-scale metabolic modeling is an effective and highly utilized way to investigate cellular phenotypes and to test metabolic engineering strategies. In this review we chronicle a number of thermophilic organisms that have recently been studied with genome-scale models. The microorganisms spread across archaea and bacteria domains, and their study gives insights that can be applied in a broader context than just the species they describe. We end with a perspective on the future development and applications of genome-scale models of thermophilic organisms.

Co-utilization of glucose and xylose by evolved Thermus thermophilus LC113 strain elucidated by (13)C metabolic flux analysis and whole genome sequencing

We evolved Thermus thermophilus to efficiently co-utilize glucose and xylose, the two most abundant sugars in lignocellulosic biomass, at high temperatures without carbon catabolite repression. To generate the strain, T. thermophilus HB8 was first evolved on glucose to improve its growth characteristics, followed by evolution on xylose. The resulting strain, T. thermophilus LC113, was characterized in growth studies, by whole genome sequencing, and (13)C-metabolic flux analysis ((13)C-MFA) with [1,6-(13)C]glucose, [5-(13)C]xylose, and [1,6-(13)C]glucose+[5-(13)C]xylose as isotopic tracers. Compared to the starting strain, the evolved strain had an increased growth rate (~2-fold), increased biomass yield, increased tolerance to high temperatures up to 90°C, and gained the ability to grow on xylose in minimal medium. At the optimal growth temperature of 81°C, the maximum growth rate on glucose and xylose was 0.44 and 0.46h(-1), respectively. In medium containing glucose and xylose the strain efficiently co-utilized the two sugars. (13)C-MFA results provided insights into the metabolism of T. thermophilus LC113 that allows efficient co-utilization of glucose and xylose. Specifically, (13)C-MFA revealed that metabolic fluxes in the upper part of metabolism adjust flexibly to sugar availability, while fluxes in the lower part of metabolism remain relatively constant. Whole genome sequence analysis revealed two large structural changes that can help explain the physiology of the evolved strain: a duplication of a chromosome region that contains many sugar transporters, and a 5x multiplication of a region on the pVV8 plasmid that contains xylose isomerase and xylulokinase genes, the first two enzymes of xylose catabolism. Taken together, (13)C-MFA and genome sequence analysis provided complementary insights into the physiology of the evolved strain.

Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach

Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information.

Cancer cell metabolism as new targets for novel designed therapies

Metabolic processes are altered in cancer cells, which obtain advantages from this metabolic reprogramming in terms of energy production and synthesis of biomolecules that sustain their uncontrolled proliferation. Due to the conceptual progresses in the last decade, metabolic reprogramming was recently included as one of the new hallmarks of cancer. The advent of high-throughput technologies to amass an abundance of omic data, together with the development of new computational methods that allow the integration and analysis of omic data by using genome-scale reconstructions of human metabolism, have increased and accelerated the discovery and development of anticancer drugs and tumor-specific metabolic biomarkers. Here we review and discuss the latest advances in the context of metabolic reprogramming and the future in cancer research.

Protein Thermostability Is Owing to Their Preferences to Non-Polar Smaller Volume Amino Acids, Variations in Residual Physico-Chemical Properties and More Salt-Bridges

Introduction

Protein thermostability is an important field for its evolutionary perspective of mesophilic versus thermophilic relationship and for its industrial/ therapeutic applications.

Methods

Presently, a total 400 (200 thermophilic and 200 mesophilic homologue) proteins were studied utilizing several software/databases to evaluate their amino acid preferences. Randomly selected 50 homologous proteins with available PDB-structure of each group were explored for the understanding of the protein charges, isoelectric-points, hydrophilicity, hydrophobicity, tyrosine phosphorylation and salt-bridge occurrences. These 100 proteins were further probed to generate Ramachandran plot/data for the gross secondary structure prediction in and comparison between the thermophilic and mesophilic proteins.

Results

Present results strongly suggest that nonpolar smaller volume amino acids Ala (χ² = 238.54, p<0.001) and Gly (χ² = 73.35, p<0.001) are highly and Val moderately (χ² = 144.43, p<0.001) occurring in the 85% of thermophilic proteins. Phospho-regulated Tyr and redox-sensitive Cys are also moderately distributed (χ²~20.0, p<0.01) in a larger number of thermophilic proteins. A consistent lower distribution of thermophilicity and discretely higher distribution of hydrophobicity is noticed in a large number of thermophilic versus their mesophilic protein homolog. The mean differences of isoelectric points and charges are found to be significantly less (7.11 vs. 6.39, p<0.05 and 1 vs. -0.6, p<0.01, respectively) in thermophilic proteins compared to their mesophilic counterpart. The possible sites for Tyr phosphorylation are noticed to be 25% higher (p<0.05) in thermophilic proteins. The 60% thermophiles are found with higher number of salt bridges in this study. The average percentage of salt-bridge of thermophiles is found to be higher by 20% than their mesophilic homologue. The GLU-HIS and GLU-LYS salt-bridge dyads are calculated to be significantly higher (p<0.05 and p<0.001, respectively) in thermophilic and GLU-ARG is higher in the mesophilic proteins. The Ramachandran plot/ data suggest a higher abundance of the helix, left-handed helix, sheet, nonplanar peptide and lower occurrence of cis peptide, loop/ turn and outlier in thermophiles. Pearson’s correlation result suggests that the isoelectric points of mesophilic and thermophilic proteins are positively correlated (r = 0.93 and 0.84, respectively; p<0.001) to their corresponding charges. And their hydrophilicity is negatively associated with the corresponding hydrophobicity (r = -0.493, p<0.001 and r = -0.324, p<0.05) suggesting their reciprocal evolvement.

Conclusions

Present results for the first time with this large amount of datasets and multiple contributing factors suggest the greater occurrence of hydrophobicity, salt-bridges and smaller volume nonpolar residues (Gly, Ala and Val) and lesser occurrence of bulky polar residues in the thermophilic proteins. A more stoichiometric relationship amongst these factors minimized the hindrance due to side chain burial and increased compactness and secondary structural stability in thermophilic proteins.