Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function

Methanogens and what they tell us about how life might survive on Mars

Space exploration and research are uncovering the potential for terrestrial life to survive in outer space, as well as the environmental factors that affect life during interplanetary transfer. The presence of methane in the Martian atmosphere suggests the possibility of methanogens, either extant or extinct, on Mars. Understanding how methanogens survive and adapt under space-exposed conditions is crucial for understanding the implications of extraterrestrial life. In this article, we discuss methanogens as model organisms for obtaining energy transducers and producing methane in a simulated Martian environment. We also explore the chemical evolution of cellular composition and growth maintenance to support survival in extraterrestrial environments. Neutral selective pressure is imposed on the chemical composition of cellular components to increase cell survival and reduce growth under physiological conditions. Energy limitation is an evolutionary driver of macromolecular polymerization, growth maintenance, and survival fitness of methanogens. Methanogens grown in a Martian environment may exhibit global alterations in their metabolic function and gene expression at the system scale. A space systems biology approach would further elucidate molecular survival mechanisms and adaptation to a drastic outer space environment. Therefore, identifying a genetically stable methanogenic community is essential for biomethane production from waste recycling to achieve sustainable space-life support functions.

Machine learning classification of archaea and bacteria identifies novel predictive genomic features

BACKGROUND

Archaea and Bacteria are distinct domains of life that are adapted to a variety of ecological niches. Several genome-based methods have been developed for their accurate classification, yet many aspects of the specific genomic features that determine these differences are not fully understood. In this study, we used publicly available whole-genome sequences from bacteria ( N = 2546 ) and archaea ( N = 109 ). From these, a set of genomic features (nucleotide frequencies and proportions, coding sequences (CDS), non-coding, ribosomal and transfer RNA genes (ncRNA, rRNA, tRNA), Chargaff's, topological entropy and Shannon's entropy scores) was extracted and used as input data to develop machine learning models for the classification of archaea and bacteria.

RESULTS

The classification accuracy ranged from 0.993 (Random Forest) to 0.998 (Neural Networks). Over the four models, only 11 examples were misclassified, especially those belonging to the minority class (Archaea). From variable importance, tRNA topological and Shannon's entropy, nucleotide frequencies in tRNA, rRNA and ncRNA, CDS, tRNA and rRNA Chargaff's scores have emerged as the top discriminating factors. In particular, tRNA entropy (both topological and Shannon's) was the most important genomic feature for classification, pointing at the complex interactions between the genetic code, tRNAs and the translational machinery.

CONCLUSIONS

tRNA, rRNA and ncRNA genes emerged as the key genomic elements that underpin the classification of archaea and bacteria. In particular, higher nucleotide diversity was found in tRNA from bacteria compared to archaea. The analysis of the few classification errors reflects the complex phylogenetic relationships between bacteria, archaea and eukaryotes.

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.

An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter

Methanogenesis allows methanogenic archaea to generate cellular energy for their growth while producing methane. Thermophilic hydrogenotrophic species of the genus Methanothermobacter have been recognized as robust biocatalysts for a circular carbon economy and are already applied in power-to-gas technology with biomethanation, which is a platform to store renewable energy and utilize captured carbon dioxide. Here, we generated curated genome-scale metabolic reconstructions for three Methanothermobacter strains and investigated differences in the growth performance of these same strains in chemostat bioreactor experiments with hydrogen and carbon dioxide or formate as substrates. Using an integrated systems biology approach, we identified differences in formate anabolism between the strains and revealed that formate anabolism influences the diversion of carbon between biomass and methane. This finding, together with the omics datasets and the metabolic models we generated, can be implemented for biotechnological applications of Methanothermobacter in power-to-gas technology, and as a perspective, for value-added chemical production.

Optimizing Strategies for Bio-Based Ethanol Production Using Genome-Scale Metabolic Modeling of the Hyperthermophilic Archaeon, Pyrococcus furiosus

A genome-scale metabolic model, encompassing a total of 623 genes, 727 reactions, and 865 metabolites, was developed for Pyrococcus furiosus, an archaeon that grows optimally at 100°C by carbohydrate and peptide fermentation. The model uses subsystem-based genome annotation, along with extensive manual curation of 237 gene-reaction associations including those involved in central carbon metabolism, amino acid metabolism, and energy metabolism. The redox and energy balance of P. furiosus was investigated through random sampling of flux distributions in the model during growth on disaccharides. The core energy balance of the model was shown to depend on high acetate production and the coupling of a sodium-dependent ATP synthase and membrane-bound hydrogenase, which generates a sodium gradient in a ferredoxin-dependent manner, aligning with existing understanding of P. furiosus metabolism. The model was utilized to inform genetic engineering designs that favor the production of ethanol over acetate by implementing an NADPH and CO-dependent energy economy. The P. furiosus model is a powerful tool for understanding the relationship between generation of end products and redox/energy balance at a systems-level that will aid in the design of optimal engineering strategies for production of bio-based chemicals and fuels. IMPORTANCE The bio-based production of organic chemicals provides a sustainable alternative to fossil-based production in the face of today's climate challenges. In this work, we present a genome-scale metabolic reconstruction of Pyrococcus furiosus, a well-established platform organism that has been engineered to produce a variety of chemicals and fuels. The metabolic model was used to design optimal engineering strategies to produce ethanol. The redox and energy balance of P. furiosus was examined in detail, which provided useful insights that will guide future engineering designs.

A Genome-Scale Metabolic Model of Methanoperedens nitroreducens: Assessing Bioenergetics and Thermodynamic Feasibility

Methane is an abundant low-carbon fuel that provides a valuable energy resource, but it is also a potent greenhouse gas. Therefore, anaerobic oxidation of methane (AOM) is an essential process with central features in controlling the carbon cycle. Candidatus 'Methanoperedens nitroreducens' (M. nitroreducens) is a recently discovered methanotrophic archaeon capable of performing AOM via a reverse methanogenesis pathway utilizing nitrate as the terminal electron acceptor. Recently, reverse methanogenic pathways and energy metabolism among anaerobic methane-oxidizing archaea (ANME) have gained significant interest. However, the energetics and the mechanism for electron transport in nitrate-dependent AOM performed by M. nitroreducens is unclear. This paper presents a genome-scale metabolic model of M. nitroreducens, iMN22HE, which contains 813 reactions and 684 metabolites. The model describes its cellular metabolism and can quantitatively predict its growth phenotypes. The essentiality of the cytoplasmic heterodisulfide reductase HdrABC in the reverse methanogenesis pathway is examined by modeling the electron transfer direction and the specific energy-coupling mechanism. Furthermore, based on better understanding electron transport by modeling, a new energy transfer mechanism is suggested. The new mechanism involves reactions capable of driving the endergonic reactions in nitrate-dependent AOM, including the step reactions in reverse canonical methanogenesis and the novel electron-confurcating reaction HdrABC. The genome metabolic model not only provides an in silico tool for understanding the fundamental metabolism of ANME but also helps to better understand the reverse methanogenesis energetics and its thermodynamic feasibility.

Molecular Microbial Community Analysis as an Analysis Tool for Optimal Biogas Production

The microbial diversity in anaerobic digestion (AD) is important because it affects process robustness. High-throughput sequencing offers high-resolution data regarding the microbial diversity and robustness of biological systems including AD; however, to understand the dynamics of microbial processes, knowing the microbial diversity is not adequate alone. Advanced meta-omic techniques have been established to determine the activity and interactions among organisms in biological processes like AD. Results of these methods can be used to identify biomarkers for AD states. This can aid a better understanding of system dynamics and be applied to producing comprehensive models for AD. The paper provides valuable knowledge regarding the possibility of integration of molecular methods in AD. Although meta-genomic methods are not suitable for on-line use due to long operating time and high costs, they provide extensive insight into the microbial phylogeny in AD. Meta-proteomics can also be explored in the demonstration projects for failure prediction. However, for these methods to be fully realised in AD, a biomarker database needs to be developed.

Nature's recyclers: anaerobic microbial communities drive crude biomass deconstruction

Microbial communities within anaerobic ecosystems have evolved to degrade and recycle carbon throughout the earth. A number of strains have been isolated from anaerobic microbial communities, which are rich in carbohydrate active enzymes (CAZymes) to liberate fermentable sugars from crude plant biomass (lignocellulose). However, natural anaerobic communities host a wealth of microbial diversity that has yet to be harnessed for biotechnological applications to hydrolyze crude biomass into sugars and value-added products. This review highlights recent advances in 'omics' techniques to sequence anaerobic microbial genomes, decipher microbial membership, and characterize CAZyme diversity in anaerobic microbiomes. With a focus on the herbivore rumen, we further discuss methods to discover new CAZymes, including those found within multi-enzyme fungal cellulosomes. Emerging techniques to characterize the interwoven metabolism and spatial interactions between anaerobes are also reviewed, which will prove critical to developing a predictive understanding of anaerobic communities to guide in microbiome engineering.

Gains and losses of metabolic function inferred from a phylotranscriptomic analysis of algae

Hidden Markov models representing 167 protein sequence families were used to infer the presence or absence of homologs within the transcriptomes of 183 algal species/strains. Statistical analyses of the distribution of HMM hits across major clades of algae, or at branch points on the phylogenetic tree of 98 chlorophytes, confirmed and extended known cases of metabolic loss and gain, most notably the loss of the mevalonate pathway for terpenoid synthesis in green algae but not, as we show here, in the streptophyte algae. Evidence for novel events was found as well, most remarkably in the recurrent and coordinated gain or loss of enzymes for the glyoxylate shunt. We find, as well, a curious pattern of retention (or re-gain) of HMG-CoA synthase in chlorophytes that have otherwise lost the mevalonate pathway, suggesting a novel, co-opted function for this enzyme in select lineages. Finally, we find striking, phylogenetically linked distributions of coding sequences for three pathways that synthesize the major membrane lipid phosphatidylcholine, and a complementary phylogenetic distribution pattern for the non-phospholipid DGTS (diacyl-glyceryl-trimethylhomoserine). Mass spectrometric analysis of lipids from 25 species was used to validate the inference of DGTS synthesis from sequence data.

Transplanting the pathway engineering toolbox to methanogens

Biological methanogenesis evolved early in Earth's history and was likely already a major process by 3.5 Ga. Modern methanogenesis is now a key process in virtually all anaerobic microbial communities, such as marine and lake sediments, wetland and rice soils, and human and cattle digestive tracts. Owing to their long evolution and extensive adaptations to various habitats, methanogens possess enormous metabolic and physiological diversity. Not only does this diversity offers unique opportunities for biotechnology applications, but also reveals their direct impact on the environment, agriculture, and human and animal health. These efforts are facilitated by an advanced genetic toolbox, emerging new molecular tools, and systems-level modelling for methanogens. Further developments and convergence of these technical advancements provide new opportunities for bioengineering methanogens.

Biotechnology of extremely thermophilic archaea

Although the extremely thermophilic archaea (Topt ≥ 70°C) may be the most primitive extant forms of life, they have been studied to a limited extent relative to mesophilic microorganisms. Many of these organisms have unique biochemical and physiological characteristics with important biotechnological implications. These include methanogens that generate methane, fermentative anaerobes that produce hydrogen gas with high efficiency, and acidophiles that can mobilize base, precious and strategic metals from mineral ores. Extremely thermophilic archaea have also been a valuable source of thermoactive, thermostable biocatalysts, but their use as cellular systems has been limited because of the general lack of facile genetics tools. This situation has changed recently, however, thereby providing an important avenue for understanding their metabolic and physiological details and also opening up opportunities for metabolic engineering efforts. Along these lines, extremely thermophilic archaea have recently been engineered to produce a variety of alcohols and industrial chemicals, in some cases incorporating CO2 into the final product. There are barriers and challenges to these organisms reaching their full potential as industrial microorganisms but, if these can be overcome, a new dimension for biotechnology will be forthcoming that strategically exploits biology at high temperatures.

Conserved principles of transcriptional networks controlling metabolic flexibility in archaea

Gene regulation is intimately connected with metabolism, enabling the appropriate timing and tuning of biochemical pathways to substrate availability. In microorganisms, such as archaea and bacteria, transcription factors (TFs) often directly sense external cues such as nutrient substrates, metabolic intermediates, or redox status to regulate gene expression. Intense recent interest has characterized the functions of a large number of such regulatory TFs in archaea, which regulate a diverse array of unique archaeal metabolic capabilities. However, it remains unclear how the co-ordinated activity of the interconnected metabolic and transcription networks produces the dynamic flexibility so frequently observed in archaeal cells as they respond to energy limitation and intermittent substrate availability. In this review, we communicate the current state of the art regarding these archaeal networks and their dynamic properties. We compare the topology of these archaeal networks to those known for bacteria to highlight conserved and unique aspects. We present a new computational model for an exemplar archaeal network, aiming to lay the groundwork toward understanding general principles that unify the dynamic function of integrated metabolic-transcription networks across archaea and bacteria.

Fine-Tuned Protein Production in Methanosarcina acetivorans C2A

Methanogenic archaea can be integrated into a sustainable, carbon-neutral cycle for producing organic chemicals from C₁ compounds if the rate, yield, and titer of product synthesis can be improved using metabolic engineering. However, metabolic engineering techniques are limited in methanogens by insufficient methods for controlling cellular protein levels. We conducted a systematic approach to tune protein levels in Methanosarcina acetivorans C2A, a model methanogen, by regulating transcription and translation initiation. Rationally designed core promoter and ribosome binding site mutations in M. acetivorans C2A resulted in a predicable change in protein levels over a 60 fold range. The overall range of protein levels was increased an additional 3 fold by introducing the 5' untranslated region of the mcrB transcript. This work demonstrates a wide range of precisely controlled protein levels in M. acetivorans C2A, which will help facilitate systematic metabolic engineering efforts in methanogens.

Genome-scale metabolic model analysis indicates low energy production efficiency in marine ammonia-oxidizing archaea

Marine ammonia-oxidizing archaea (AOA) play an important role in the global nitrogen cycle by obtaining energy for biomass production from CO₂ via oxidation of ammonium. The isolation of Candidatus “Nitrosopumilus maritimus” strain SCM1, which represents the globally distributed AOA in the ocean, provided an opportunity for uncovering the contributions of those AOA to carbon and nitrogen cycles in ocean. Although several ammonia oxidation pathways have been proposed for SCM1, little is known about its ATP production efficiency. Here, based on the published genome of Nitrosopumilus maritimus SCM1, a genome-scale metabolic model named NmrFL413 was reconstructed. Based on the model NmrFL413, the estimated ATP/NH₄⁺ yield (0.149–0.276 ATP/NH₄⁺) is tenfold lower than the calculated theoretical yield of the proposed ammonia oxidation pathways in marine AOA (1.5–1.75 ATP/NH₄⁺), indicating a low energy production efficiency of SCM1. Our model also suggested the minor contribution of marine AOA to carbon cycle comparing with their significant contribution to nitrogen cycle in the ocean.

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.