A Genome Scale Model of Geobacillus thermoglucosidasius (C56-YS93) reveals its biotechnological potential on rice straw hydrolysate

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.

A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications

Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard Cupriavidus necator H16 represents a particularly promising microbial chassis due to its ability to grow on a wide range of low-cost feedstocks, including the waste gas carbon dioxide, whilst also naturally producing large quantities of polyhydroxybutyrate (PHB) during nutrient-limited conditions. Understanding the complex metabolic behaviour of this bacterium is a prerequisite for the design of successful engineering strategies for optimising product yields. We present a genome-scale metabolic model (GSM) of C. necator H16 (denoted iCN1361), which is directly constructed from the BioCyc database to improve the readability and reusability of the model. After the initial automated construction, we have performed extensive curation and both theoretical and experimental validation. By carrying out a genome-wide essentiality screening using a Transposon-directed Insertion site Sequencing (TraDIS) approach, we showed that the model could predict gene knockout phenotypes with a high level of accuracy. Importantly, we indicate how experimental and computational predictions can be used to improve model structure and, thus, model accuracy as well as to evaluate potential false positives identified in the experiments. Finally, by integrating transcriptomics data with iCN1361 we create a condition-specific model, which, importantly, better reflects PHB production in C. necator H16. Observed changes in the omics data and in-silico-estimated alterations in fluxes were then used to predict the regulatory control of key cellular processes. The results presented demonstrate that iCN1361 is a valuable tool for unravelling the system-level metabolic behaviour of C. necator H16 and can provide useful insights for designing metabolic engineering strategies.

Genome-scale metabolic models (GSMs) provide a tool for unravelling the complex metabolic behaviour of bacteria and how they adapt to changing environments and genetic perturbations, and thus offer invaluable insights for biotechnology applications. For a GSM to be used efficiently for strain development purposes, however, the model must be easily readable and reusable by other researchers, whilst being able to predict metabolic behaviour with a high level of accuracy. In this work, we developed a GSM for Cupriavidus necator H16 that is linked to the BioCyc database, which provides an efficient way of application, model update, integration of experimental data and network visualisation for other researchers. Using our model, we demonstrate how integrating experimental observations, including Transposon-directed Insertion site Sequencing (TraDIS) and omics data, can be used to compensate for the lack of regulatory, kinetic and thermodynamic information in GSMs, and thus improve model accuracy. Importantly, we found that TraDIS in vivo screening and GSM analysis are complementary approaches, which can be used in combination to provide reliable gene essentiality predictions. Overall, our results offer an informed strategy for the deliberate manipulation of C. necator H16 metabolic capabilities, towards its industrial application to convert greenhouse gases into biochemicals and biofuels.

Current status and applications of genus Geobacillus in the production of industrially important products-a review

The genus Geobacillus is one of the most important genera which mainly comprises gram-positive thermophilic bacterial strains including obligate aerobes, denitrifiers and facultative anaerobes having capability of endospore formation as well. The genus Geobacillus is widely distributed in nature and mostly abundant in extreme locations such as cool soils, hot springs, hydrothermal vents, marine trenches, hay composts and dairy plants. Due to plasticity towards environmental adaptation, the Geobacillus sp. shows remarkable genome diversification and acquired many beneficial properties, which facilitates their exploitation for many biotechnological applications. Many thermophiles are of biotechnological importance and having considerable interest in commercial applications for the production of industrially important products. Recently, due to catabolic versatility especially in the degradation of hemicellulose and starch containing agricultural waste and rapid growth rates, these microorganisms show potential for the production of biofuels, thermostable enzymes and bioremediation. This review mainly summarizes the status of Geobacillus sp. including its notable properties, biotechnological studies and its potential application in the production of industrially important products.

Genome-Scale Metabolic Modelling Approach to Understand the Metabolism of the Opportunistic Human Pathogen Staphylococcus epidermidis RP62A

Staphylococcus epidermidis is a common commensal of collagen-rich regions of the body, such as the skin, but also represents a threat to patients with medical implants (joints and heart), and to preterm babies. Far less studied than Staphylococcus aureus, the mechanisms behind this increasingly recognised pathogenicity are yet to be fully understood. Improving our knowledge of the metabolic processes that allow S. epidermidis to colonise different body sites is key to defining its pathogenic potential. Thus, we have constructed a fully curated, genome-scale metabolic model for S. epidermidis RP62A, and investigated its metabolic properties with a focus on substrate auxotrophies and its utilisation for energy and biomass production. Our results show that, although glucose is available in the medium, only a small portion of it enters the glycolytic pathways, whils most is utilised for the production of biofilm, storage and the structural components of biomass. Amino acids, proline, valine, alanine, glutamate and arginine, are preferred sources of energy and biomass production. In contrast to previous studies, we have shown that this strain has no real substrate auxotrophies, although removal of proline from the media has the highest impact on the model and the experimental growth characteristics. Further study is needed to determine the significance of proline, an abundant amino acid in collagen, in S. epidermidis colonisation.

Carbon Monoxide Induced Metabolic Shift in the Carboxydotrophic Parageobacillus thermoglucosidasius DSM 6285

Parageobacillus thermoglucosidasius is known to catalyse the biological water gas shift (WGS) reaction, a pathway that serves as a source of alternative energy and carbon to a wide variety of bacteria. Despite increasing interest in this bacterium due to its ability to produce biological hydrogen through carbon monoxide (CO) oxidation, there are no data on the effect of toxic CO gas on its physiology. Due to its general requirement of O₂, the organism is often grown aerobically to generate biomass. Here, we show that carbon monoxide (CO) induces metabolic changes linked to distortion of redox balance, evidenced by increased accumulation of organic acids such as acetate and lactate. This suggests that P. thermoglucosidasius survives by expressing several alternative pathways, including conversion of pyruvate to lactate, which balances reducing equivalents (oxidation of NADH to NAD⁺), and acetyl-CoA to acetate, which directly generates energy, while CO is binding terminal oxidases. The data also revealed clearly that P. thermoglucosidasius gained energy and grew during the WGS reaction. Combined, the data provide critical information essential for further development of the biotechnological potential of P. thermoglucosidasius.

Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism

Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published ¹³C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.

Process scale-up of an efficient acid-catalyzed steam pretreatment of rice straw for xylitol production by C. Tropicalis MTCC 6192

The present study focuses on operational parameters for the efficient acid catalyzed rice straw pretreatment process for xylitol production. 75.77 % xylose yield was attained when the 24 h presoaked rice straw (≤10 mm or ≤ 15 mm) in 1.5 % (v/v) H₂SO₄ was pretreated in the same reactor at 121 °C for 30 min. Neutralization with barium hydroxide produced insoluble salt and noticeably reduced HMF and furfurals. Xylitol yield of 0.6 g/g of xylose, was achieved by fermenting rice straw hydrolysate medium with C. tropicalis MTCC 6192. This two-step process of production of xylitol from xylose rich hydrolysate is much simpler and produced minimal inhibitors including organic acids such as acetic acid. This process is modified for upscaling at optimized parameters and will simultaneously minimize the pollution problem caused by rice straw and is also promising for commercial scale.

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.

A Genome-Scale Metabolic Model of 2,3-Butanediol Production by Thermophilic Bacteria Geobacillus icigianus

The thermophilic strain of the genus Geobacillus, Geobacillus icigianus is a promising bacterial chassis for a wide range of biotechnological applications. In this study, we explored the metabolic potential of Geobacillus icigianus for the production of 2,3-butanediol (2,3-BTD), one of the cost-effective commodity chemicals. Here we present a genome-scale metabolic model iMK1321 for Geobacillus icigianus constructed using an auto-generating pipeline with consequent thorough manual curation. The model contains 1321 genes and includes 1676 reactions and 1589 metabolites, representing the most-complete and publicly available model of the genus Geobacillus. The developed model provides new insights into thermophilic bacterial metabolism and highlights new strategies for biotechnological applications of the strain. Our analysis suggests that Geobacillus icigianus has a potential for 2,3-butanediol production from a variety of utilized carbon sources, including glycerine, a common byproduct of biofuel production. We identified a set of solutions for enhancing 2,3-BTD production, including cultivation under anaerobic or microaerophilic conditions and decreasing the TCA flux to succinate via reducing citrate synthase activity. Both in silico predicted metabolic alternatives have been previously experimentally verified for closely related strains including the genus Bacillus.

Genome-Scale Metabolic Model Driven Design of a Defined Medium for Campylobacter jejuni M1cam

Campylobacter jejuni, the most frequent cause of food-borne bacterial gastroenteritis, is a fastidious organism when grown in the laboratory. Oxygen is required for growth, despite the presence of the metabolic mechanism for anaerobic respiration. Amino acid auxotrophies are variably reported and energy metabolism can occur through several electron donor/acceptor combinations. Overall, the picture is one of a flexible, but vulnerable metabolism. To understand Campylobacter metabolism, we have constructed a fully curated, metabolic model for the reference organism M1 (our variant is M1cam) and validated it through laboratory experiments. Our results show that M1cam is auxotrophic for methionine, niacinamide, and pantothenate. There are complete biosynthesis pathways for all amino acids except methionine and it can produce energy, but not biomass, in the absence of oxygen. M1cam will grow in DMEM/F-12 defined media but not in the previously published Campylobacter specific defined media tested. Using the model, we identified potential auxotrophies and substrates that may improve growth. With this information, we designed simple defined media containing inorganic salts, the auxotrophic substrates, L-methionine, niacinamide, and pantothenate, pyruvate and additional amino acids L-cysteine, L-serine, and L-glutamine for growth enhancement. Our defined media supports a 1.75-fold higher growth rate than Brucella broth after 48 h at 37°C and sustains the growth of other Campylobacter jejuni strains. This media can be used to design reproducible assays that can help in better understanding the adaptation, stress resistance, and the virulence mechanisms of this pathogen. We have shown that with a well-curated metabolic model it is possible to design a media to grow this fastidious organism. This has implications for the investigation of new Campylobacter species defined through metagenomics, such as C. infans.

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.

Heterologous biosynthesis and characterization of a glycocin from a thermophilic bacterium

The genome of the thermophilic bacterium, Aeribacillus pallidus 8, encodes the bacteriocin pallidocin. It belongs to the small class of glycocins and is posttranslationally modified, containing an S-linked glucose on a specific Cys residue. In this study, the pallidocin biosynthetic machinery is cloned and expressed in Escherichia coli to achieve its full biosynthesis and modification. It targets other thermophilic bacteria with potent activity, demonstrated by a low minimum inhibitory concentration (MIC) value. Moreover, the characterized biosynthetic machinery is employed to produce two other glycopeptides Hyp1 and Hyp2. Pallidocin and Hyp1 exhibit antibacterial activity against closely related thermophilic bacteria and some Bacillus sp. strains. Thus, heterologous expression of a glycocin biosynthetic gene cluster including an S-glycosyltransferase provides a good tool for production of hypothetical glycocins encoded by various bacterial genomes and allows rapid in vivo screening.

Heterologous production of the glycocins, posttranslationally modified peptide bacteriocins containing a sugar moiety, has not been achieved. Here, the authors express a thermophilic bacterium glycocin biosynthetic gene cluster and S-glycosyltransferase for the production of antibacterial glycocins in E. coli.

Methods for automated genome-scale metabolic model reconstruction

In the era of next-generation sequencing and ubiquitous assembly and binning of metagenomes, new putative genome sequences are being produced from isolate and microbiome samples at ever-increasing rates. Genome-scale metabolic models have enormous utility for supporting the analysis and predictive characterization of these genomes based on sequence data. As a result, tools for rapid automated reconstruction of metabolic models are becoming critically important for supporting the analysis of new genome sequences. Many tools and algorithms have now emerged to support rapid model reconstruction and analysis. Here, we are comparing and contrasting the capabilities and output of a variety of these tools, including ModelSEED, Raven Toolbox, PathwayTools, SuBliMinal Toolbox and merlin.

In vivo selection of sfGFP variants with improved and reliable functionality in industrially important thermophilic bacteria

BACKGROUND

Fluorescent reporter proteins (FP) have become an indispensable tool for the optimization of microbial cell factories and in synthetic biology per se. The applicability of the currently available FPs is, however, constrained by species-dependent performance and misfolding at elevated temperatures. To obtain functional reporters for thermophilic, biotechnologically important bacteria such as Parageobacillus thermoglucosidasius, an in vivo screening approach based on a mutational library of superfolder GFP was applied.

RESULTS

Flow cytometry-based benchmarking of a set of GFPs, sfGFPs and species-specific codon-optimized variants revealed that none of the proteins was satisfyingly detectable in P. thermoglucosidasius at its optimal growth temperature of 60 °C. An undirected mutagenesis approach coupled to fluorescence-activated cell sorting allowed the isolation of sfGFP variants that were extremely well expressed in the chassis background at 60 °C. Notably, a few nucleotide substitutions, including silent mutations, significantly improved the functionality and brightness. The best mutant sfGFP(N39D/A179A) showed an 885-fold enhanced mean fluorescence intensity (MFI) at 60 °C and is the most reliable reporter protein with respect to cell-to-cell variation and signal intensity reported so far. The in vitro spectral and thermostability properties were unaltered as compared to the parental sfGFP protein, strongly indicating that the combination of the amino acid exchange and an altered translation or folding speed, or protection from degradation, contribute to the strongly improved in vivo performance. Furthermore, sfGFP(N39D/A179A) and the newly developed cyan and yellow derivatives were successfully used for labeling several industrially relevant thermophilic bacilli, thus proving their broad applicability.

CONCLUSIONS

This study illustrates the power of in vivo isolation of thermostable proteins to obtain reporters for highly efficient fluorescence labeling. Successful expression in a variety of thermophilic bacteria proved that the novel FPs are highly suitable for imaging and flow cytometry-based studies. This enables a reliable cell tracking and single-cell-based real-time monitoring of biological processes that are of industrial and biotechnological interest.