Towards a computational model of a methane producing archaeum

Physiological and transcriptomic response to methyl-coenzyme M reductase limitation in Methanosarcina acetivorans

Methyl-coenzyme M reductase (MCR) catalyzes the final step of methanogenesis, the microbial metabolism responsible for nearly all biological methane emissions to the atmosphere. Decades of biochemical and structural research studies have generated detailed insights into MCR function in vitro, yet very little is known about the interplay between MCR and methanogen physiology. For instance, while it is routinely stated that MCR catalyzes the rate-limiting step of methanogenesis, this has not been categorically tested. In this study, to gain a more direct understanding of MCR's control on the growth of Methanosarcina acetivorans, we generate a strain with an inducible mcr operon on the chromosome, allowing for careful control of MCR expression. We show that MCR is not growth rate-limiting in substrate-replete batch cultures. However, through careful titration of MCR expression, growth-limiting state(s) can be obtained. Transcriptomic analysis of M. acetivorans experiencing MCR limitation reveals a global response with hundreds of differentially expressed genes across diverse functional categories. Notably, MCR limitation leads to strong induction of methylsulfide methyltransferases, likely due to insufficient recycling of metabolic intermediates. In addition, the mcr operon is not transcriptionally regulated, i.e., it is constitutively expressed, suggesting that the overabundance of MCR might be beneficial when cells experience nutrient limitation or stressful conditions. Altogether, we show that there is a wide range of cellular MCR concentrations that can sustain optimal growth, suggesting that other factors such as anabolic reactions might be rate-limiting for methanogenic growth.

IMPORTANCE

Methane is a potent greenhouse gas that has contributed to ca. 25% of global warming in the post-industrial era. Atmospheric methane is primarily of biogenic origin, mostly produced by microorganisms called methanogens. Methyl-coenzyme M reductase (MCR) catalyzes methane formatio in methanogens. Even though MCR comprises ca. 10% of the cellular proteome, it is hypothesized to be growth-limiting during methanogenesis. In this study, we show that Methanosarcina acetivorans cells grown in substrate-replicate batch cultures produce more MCR than its cellular demand for optimal growth. The tools outlined in this study can be used to refine metabolic models of methanogenesis and assay lesions in MCR in a higher-throughput manner than isolation and biochemical characterization of pure protein.

Limited Mechanistic Link Between the Monod Equation and Methanogen Growth: a Perspective from Metabolic Modeling

The Monod equation has been widely applied as the general rate law of microbial growth, but its applications are not always successful. By drawing on the frameworks of kinetic and stoichiometric metabolic models and metabolic control analysis, the modeling reported here simulated the growth kinetics of a methanogenic microorganism and illustrated that different enzymes and metabolites control growth rate to various extents and that their controls peak at either very low, intermediate, or very high substrate concentrations. In comparison, with a single term and two parameters, the Monod equation only approximately accounts for the controls of rate-determining enzymes and metabolites at very high and very low substrate concentrations, but neglects the enzymes and metabolites whose controls are most notable at intermediate concentrations. These findings support a limited link between the Monod equation and methanogen growth, and unify the competing views regarding enzyme roles in shaping growth kinetics. The results also preclude a mechanistic derivation of the Monod equation from methanogen metabolic networks and highlight a fundamental challenge in microbiology: single-term expressions may not be sufficient for accurate prediction of microbial growth. IMPORTANCE The Monod equation has been widely applied to predict the rate of microbial growth, but its application is not always successful. Using a novel metabolic modeling approach, we simulated the growth of a methanogen and uncovered a limited mechanistic link between the Monod equation and the methanogen's metabolic network. Specifically, the equation provides an approximation to the controls by rate-determining metabolites and enzymes at very low and very high substrate concentrations, but it is missing the remaining enzymes and metabolites whose controls are most notable at intermediate concentrations. These results support the Monod equation as a useful approximation of growth rates and highlight a fundamental challenge in microbial kinetics: single-term rate expressions may not be sufficient for accurate prediction of microbial growth.

Methanosarcina acetivorans: A Model for Mechanistic Understanding of Aceticlastic and Reverse Methanogenesis

Acetate-utilizing methanogens are responsible for approximately two-thirds of the one billion metric tons of methane produced annually in Earth's anaerobic environments. Methanosarcina acetivorans has emerged as a model organism for the mechanistic understanding of aceticlastic methanogenesis and reverse methanogenesis applicable to understanding the methane and carbon cycles in nature. It has the largest genome in the Archaea, supporting a metabolic complexity that enables a remarkable ability for adapting to environmental opportunities and challenges. Biochemical investigations have revealed an aceticlastic pathway capable of fermentative and respiratory energy conservation that explains how Ms. acetivorans is able to grow and compete in the environment. The mechanism of respiratory energy conservation also plays a role in overcoming endothermic reactions that are key to reversing methanogenesis.

Transcriptional Regulation in Archaea: From Individual Genes to Global Regulatory Networks

Archaea are major contributors to biogeochemical cycles, possess unique metabolic capabilities, and resist extreme stress. To regulate the expression of genes encoding these unique programs, archaeal cells use gene regulatory networks (GRNs) composed of transcription factor proteins and their target genes. Recent developments in genetics, genomics, and computational methods used with archaeal model organisms have enabled the mapping and prediction of global GRN structures. Experimental tests of these predictions have revealed the dynamical function of GRNs in response to environmental variation. Here, we review recent progress made in this area, from investigating the mechanisms of transcriptional regulation of individual genes to small-scale subnetworks and genome-wide global networks. At each level, archaeal GRNs consist of a hybrid of bacterial, eukaryotic, and uniquely archaeal mechanisms. We discuss this theme from the perspective of the role of individual transcription factors in genome-wide regulation, how these proteins interact to compile GRN topological structures, and how these topologies lead to emergent, high-level GRN functions. We conclude by discussing how systems biology approaches are a fruitful avenue for addressing remaining challenges, such as discovering gene function and the evolution of GRNs.

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

Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism's biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria.

Genome-wide gene expression and RNA half-life measurements allow predictions of regulation and metabolic behavior in Methanosarcina acetivorans

BACKGROUND

While a few studies on the variations in mRNA expression and half-lives measured under different growth conditions have been used to predict patterns of regulation in bacterial organisms, the extent to which this information can also play a role in defining metabolic phenotypes has yet to be examined systematically. Here we present the first comprehensive study for a model methanogen.

RESULTS

We use expression and half-life data for the methanogen Methanosarcina acetivorans growing on fast- and slow-growth substrates to examine the regulation of its genes. Unlike Escherichia coli where only small shifts in half-lives were observed, we found that most mRNA have significantly longer half-lives for slow growth on acetate compared to fast growth on methanol or trimethylamine. Interestingly, half-life shifts are not uniform across functional classes of enzymes, suggesting the existence of a selective stabilization mechanism for mRNAs. Using the transcriptomics data we determined whether transcription or degradation rate controls the change in transcript abundance. Degradation was found to control abundance for about half of the metabolic genes underscoring its role in regulating metabolism. Genes involved in half of the metabolic reactions were found to be differentially expressed among the substrates suggesting the existence of drastically different metabolic phenotypes that extend beyond just the methanogenesis pathways. By integrating expression data with an updated metabolic model of the organism (iST807) significant differences in pathway flux and production of metabolites were predicted for the three growth substrates.

CONCLUSIONS

This study provides the first global picture of differential expression and half-lives for a class II methanogen, as well as provides the first evidence in a single organism that drastic genome-wide shifts in RNA half-lives can be modulated by growth substrate. We determined which genes in each metabolic pathway control the flux and classified them as regulated by transcription (e.g. transcription factor) or degradation (e.g. post-transcriptional modification). We found that more than half of genes in metabolism were controlled by degradation. Our results suggest that M. acetivorans employs extensive post-transcriptional regulation to optimize key metabolic steps, and more generally that degradation could play a much greater role in optimizing an organism's metabolism than previously thought.

Perspective: On the importance of hydrodynamic interactions in the subcellular dynamics of macromolecules

An outstanding challenge in computational biophysics is the simulation of a living cell at molecular detail. Over the past several years, using Stokesian dynamics, progress has been made in simulating coarse grained molecular models of the cytoplasm. Since macromolecules comprise 20%-40% of the volume of a cell, one would expect that steric interactions dominate macromolecular diffusion. However, the reduction in cellular diffusion rates relative to infinite dilution is due, roughly equally, to steric and hydrodynamic interactions, HI, with nonspecific attractive interactions likely playing rather a minor role. HI not only serve to slow down long time diffusion rates but also cause a considerable reduction in the magnitude of the short time diffusion coefficient relative to that at infinite dilution. More importantly, the long range contribution of the Rotne-Prager-Yamakawa diffusion tensor results in temporal and spatial correlations that persist up to microseconds and for intermolecular distances on the order of protein radii. While HI slow down the bimolecular association rate in the early stages of lipid bilayer formation, they accelerate the rate of large scale assembly of lipid aggregates. This is suggestive of an important role for HI in the self-assembly kinetics of large macromolecular complexes such as tubulin. Since HI are important, questions as to whether continuum models of HI are adequate as well as improved simulation methodologies that will make simulations of more complex cellular processes practical need to be addressed. Nevertheless, the stage is set for the molecular simulations of ever more complex subcellular processes.

Systems biology approaches to defining transcription regulatory networks in halophilic archaea

To survive complex and changing environmental conditions, microorganisms use gene regulatory networks (GRNs) composed of interacting regulatory transcription factors (TFs) to control the timing and magnitude of gene expression. Genome-wide datasets; such as transcriptomics and protein-DNA interactions; and experiments such as high throughput growth curves; facilitate the construction of GRNs and provide insight into TF interactions occurring under stress. Systems biology approaches integrate these datasets into models of GRN architecture as well as statistical and/or dynamical models to understand the function of networks occurring in cells. Previously, these types of studies have focused on traditional model organisms (e.g. Escherichia coli, yeast). However, recent advances in archaeal genetics and other tools have enabled a systems approach to understanding GRNs in these relatively less studied archaeal model organisms. In this report, we outline a systems biology workflow for generating and integrating data focusing on the TF regulator. We discuss experimental design, outline the process of data collection, and provide the tools required to produce high confidence regulons for the TFs of interest. We provide a case study as an example of this workflow, describing the construction of a GRN centered on multi-TF coordinate control of gene expression governing the oxidative stress response in the hypersaline-adapted archaeon Halobacterium salinarum.

Spatially-resolved metabolic cooperativity within dense bacterial colonies

Background

The exchange of metabolites and the reprogramming of metabolism in response to shifting microenvironmental conditions can drive subpopulations of cells within colonies toward divergent behaviors. Understanding the interactions of these subpopulations—their potential for competition as well as cooperation—requires both a metabolic model capable of accounting for a wide range of environmental conditions, and a detailed dynamic description of the cells’ shared extracellular space.

Results

Here we show that a cell’s position within an in silicoEscherichia coli colony grown on glucose minimal agar can drastically affect its metabolism: “pioneer” cells at the outer edge engage in rapid growth that expands the colony, while dormant cells in the interior separate two spatially distinct subpopulations linked by a cooperative form of acetate crossfeeding that has so far gone unnoticed. Our hybrid simulation technique integrates 3D reaction-diffusion modeling with genome-scale flux balance analysis (FBA) to describe the position-dependent metabolism and growth of cells within a colony. Our results are supported by imaging experiments involving strains of fluorescently-labeled E. coli. The spatial patterns of fluorescence within these experimental colonies identify cells with upregulated genes associated with acetate crossfeeding and are in excellent agreement with the predictions. Furthermore, the height-to-width ratios of both the experimental and simulated colonies are in good agreement over a growth period of 48 hours.

Conclusions

Our modeling paradigm can accurately reproduce a number of known features of E. coli colony growth, as well as predict a novel one that had until now gone unrecognized. The acetate crossfeeding we see has a direct analogue in a form of lactate crossfeeding observed in certain forms of cancer, and we anticipate future application of our methodology to models of tissues and tumors.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0155-1) contains supplementary material, which is available to authorized users.

Single-Molecule Pull-Down FRET to Dissect the Mechanisms of Biomolecular Machines

Spliceosomes are multimegadalton RNA-protein complexes responsible for the faithful removal of noncoding segments (introns) from pre-messenger RNAs (pre-mRNAs), a process critical for the maturation of eukaryotic mRNAs for subsequent translation by the ribosome. Both the spliceosome and ribosome, as well as many other RNA and DNA processing machineries, contain central RNA components that endow biomolecular complexes with precise, sequence-specific nucleic acid recognition, and versatile structural dynamics. Single-molecule fluorescence (or Förster) resonance energy transfer (smFRET) microscopy is a powerful tool for the study of local and global conformational changes of both simple and complex biomolecular systems involving RNA. The integration of biochemical tools such as immunoprecipitation with advanced methods in smFRET microscopy and data analysis has opened up entirely new avenues toward studying the mechanisms of biomolecular machines isolated directly from complex biological specimens, such as cell extracts. Here, we detail the general steps for using prism-based total internal reflection fluorescence microscopy in exemplary single-molecule pull-down FRET studies of the yeast spliceosome and discuss the broad application potential of this technique.

Achievements and challenges in structural bioinformatics and computational biophysics

Motivation: The field of structural bioinformatics and computational biophysics has undergone a revolution in the last 10 years. Developments that are captured annually through the 3DSIG meeting, upon which this article reflects.

Results: An increase in the accessible data, computational resources and methodology has resulted in an increase in the size and resolution of studied systems and the complexity of the questions amenable to research. Concomitantly, the parameterization and efficiency of the methods have markedly improved along with their cross-validation with other computational and experimental results.

Conclusion: The field exhibits an ever-increasing integration with biochemistry, biophysics and other disciplines. In this article, we discuss recent achievements along with current challenges within the field.

Contact:Rafael.Najmanovich@USherbrooke.ca