Effect of model methanogens on the electrochemical activity, stability, and microbial community structure of Geobacter spp. dominated biofilm anodes.

Combining anaerobic digestion (AD) and microbial electrochemical technologies (MET) in AD-MET holds great potential. Methanogens have been identified as one cause of decreased electrochemical activity and deterioration of Geobacter spp. biofilm anodes. A better understanding of the different interactions between methanogenic genera/species and Geobacter spp. biofilms is needed to shed light on the observed reduction in electrochemical activity and stability of Geobacter spp. dominated biofilms as well as observed changes in microbial communities of AD-MET. Here, we have analyzed electrochemical parameters and changes in the microbial community of Geobacter spp. biofilm anodes when exposed to three representative methanogens with different metabolic pathways, i.e., Methanosarcina barkeri, Methanobacterium formicicum, and Methanothrix soehngenii. M. barkeri negatively affected the performance and stability of Geobacter spp. biofilm anodes only in the initial batches. In contrast, M. formicicum did not affect the stability of Geobacter spp. biofilm anodes but caused a decrease in maximum current density of ~37%. M. soehngenii induced a coloration change of Geobacter spp. biofilm anodes and a decrease in the total transferred charge by ~40%. Characterization of biofilm samples after each experiment by 16S rRNA metabarcoding, whole metagenome nanopore sequencing, and shotgun sequencing showed a higher relative abundance of Geobacter spp. after exposure to M. barkeri as opposed to M. formicicum or M. soehngenii, despite the massive biofilm dispersal observed during initial exposure to M. barkeri.

Breaking down microbial hierarchies.

Microbial communities that degrade natural polysaccharides are thought to have a hierarchical organization and one-way positive interactions from higher to lower trophic levels. Daniels et al. have recently shown that reciprocal interactions between trophic levels can occur and that these interactions change over the duration of a batch culture.

A unified and simple medium for growing model methanogens.

Apart from their archetypic use in anaerobic digestion (AD) methanogenic archaea are targeted for a wide range of applications. Using different methanogenic archaea for one specific application requires the optimization of culture media to enable the growth of different strains under identical environmental conditions, e.g., in microbial electrochemical technologies (MET) for (bio)electromethanation. Here we present a new culture medium (BFS01) adapted from the DSM-120 medium by omitting resazurin, yeast extract, casitone, and using a low salt concentration, that was optimized for Methanosarcina barkeri, Methanobacterium formicicum, and Methanothrix soehngenii. The aim was to provide a medium for follow-up co-culture studies using specific methanogens and Geobacter spp. dominated biofilm anodes. All three methanogens showed growth and activity in the BFS01 medium. This was demonstrated by estimating the specific growth rates ( µ ) and doubling times ( t d ) of each methanogen. The µ and t d based on methane accumulation in the headspace showed values consistent with literature values for M. barkeri and M. soehngenii. However, µ and t d based on methane accumulation in the headspace differed from literature data for M. formicicum but still allowed sufficient growth. The lowered salt concentration and the omission of chemically complex organic components in the medium may have led to the observed deviation from µ and t d for M. formicicum as well as the changed morphology. 16S rRNA gene-based amplicon sequencing and whole genome nanopore sequencing further confirmed purity and species identity.

Enhancing Microbiome Research through Genome-Scale Metabolic Modeling.

Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research. Here, we briefly outline these opportunities, present current rate-limiting challenges for the trustworthy application of GEMs to microbiome research, and suggest approaches for moving the field forward.

A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS).

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. This protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis.

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

Dynamic Allocation of Carbon Storage and Nutrient-Dependent Exudation in a Revised Genome-Scale Model of Prochlorococcus.

Microbial life in the oceans impacts the entire marine ecosystem, global biogeochemistry and climate. The marine cyanobacterium Prochlorococcus, an abundant component of this ecosystem, releases a significant fraction of the carbon fixed through photosynthesis, but the amount, timing and molecular composition of released carbon are still poorly understood. These depend on several factors, including nutrient availability, light intensity and glycogen storage. Here we combine multiple computational approaches to provide insight into carbon storage and exudation in Prochlorococcus. First, with the aid of a new algorithm for recursive filling of metabolic gaps (ReFill), and through substantial manual curation, we extended an existing genome-scale metabolic model of Prochlorococcus MED4. In this revised model (iSO595), we decoupled glycogen biosynthesis/degradation from growth, thus enabling dynamic allocation of carbon storage. In contrast to standard implementations of flux balance modeling, we made use of forced influx of carbon and light into the cell, to recapitulate overflow metabolism due to the decoupling of photosynthesis and carbon fixation from growth during nutrient limitation. By using random sampling in the ensuing flux space, we found that storage of glycogen or exudation of organic acids are favored when the growth is nitrogen limited, while exudation of amino acids becomes more likely when phosphate is the limiting resource. We next used COMETS to simulate day-night cycles and found that the model displays dynamic glycogen allocation and exudation of organic acids. The switch from photosynthesis and glycogen storage to glycogen depletion is associated with a redistribution of fluxes from the Entner-Doudoroff to the Pentose Phosphate pathway. Finally, we show that specific gene knockouts in iSO595 exhibit dynamic anomalies compatible with experimental observations, further demonstrating the value of this model as a tool to probe the metabolic dynamic of Prochlorococcus.

Automatic reconstruction of metabolic pathways from identified biosynthetic gene clusters.

BACKGROUND: A wide range of bioactive compounds is produced by enzymes and enzymatic complexes encoded in biosynthetic gene clusters (BGCs). These BGCs can be identified and functionally annotated based on their DNA sequence. Candidates for further research and development may be prioritized based on properties such as their functional annotation, (dis)similarity to known BGCs, and bioactivity assays. Production of the target compound in the native strain is often not achievable, rendering heterologous expression in an optimized host strain as a promising alternative. Genome-scale metabolic models are frequently used to guide strain development, but large-scale incorporation and testing of heterologous production of complex natural products in this framework is hampered by the amount of manual work required to translate annotated BGCs to metabolic pathways. To this end, we have developed a pipeline for an automated reconstruction of BGC associated metabolic pathways responsible for the synthesis of non-ribosomal peptides and polyketides, two of the dominant classes of bioactive compounds. RESULTS: The developed pipeline correctly predicts 72.8% of the metabolic reactions in a detailed evaluation of 8 different BGCs comprising 228 functional domains. By introducing the reconstructed pathways into a genome-scale metabolic model we demonstrate that this level of accuracy is sufficient to make reliable in silico predictions with respect to production rate and gene knockout targets. Furthermore, we apply the pipeline to a large BGC database and reconstruct 943 metabolic pathways. We identify 17 enzymatic reactions using high-throughput assessment of potential knockout targets for increasing the production of any of the associated compounds. However, the targets only provide a relative increase of up to 6% compared to wild-type production rates. CONCLUSION: With this pipeline we pave the way for an extended use of genome-scale metabolic models in strain design of heterologous expression hosts. In this context, we identified generic knockout targets for the increased production of heterologous compounds. However, as the predicted increase is minor for any of the single-reaction knockout targets, these results indicate that more sophisticated strain-engineering strategies are necessary for the development of efficient BGC expression hosts.

Enzyme-Constrained Models and Omics Analysis of Streptomyces coelicolor Reveal Metabolic Changes that Enhance Heterologous Production.

Many biosynthetic gene clusters (BGCs) require heterologous expression to realize their genetic potential, including silent and metagenomic BGCs. Although the engineered Streptomyces coelicolor M1152 is a widely used host for heterologous expression of BGCs, a systemic understanding of how its genetic modifications affect the metabolism is lacking and limiting further development. We performed a comparative analysis of M1152 and its ancestor M145, connecting information from proteomics, transcriptomics, and cultivation data into a comprehensive picture of the metabolic differences between these strains. Instrumental to this comparison was the application of an improved consensus genome-scale metabolic model (GEM) of S. coelicolor. Although many metabolic patterns are retained in M1152, we find that this strain suffers from oxidative stress, possibly caused by increased oxidative metabolism. Furthermore, precursor availability is likely not limiting polyketide production, implying that other strategies could be beneficial for further development of S. coelicolor for heterologous production of novel compounds.

Predicting Strain Engineering Strategies Using iKS1317: A Genome-Scale Metabolic Model of Streptomyces coelicolor.

Streptomyces coelicolor is a model organism for the Actinobacteria, a phylum known to produce an extensive range of different bioactive compounds that include antibiotics currently used in the clinic. Biosynthetic gene clusters discovered in genomes of other Actinobacteria can be transferred to and expressed in S. coelicolor, making it a factory for heterologous production of secondary metabolites. Genome-scale metabolic reconstructions have successfully been used in several biotechnology applications to facilitate the over-production of target metabolites. Here, the authors present iKS1317, the most comprehensive and accurate reconstructed genome-scale metabolic model (GEM) for S. coelicolor. The model reconstruction is based on previous models, publicly available databases, and published literature and includes 1317 genes, 2119 reactions, and 1581 metabolites. It correctly predicts wild-type growth in 96.5% of the evaluated growth environments and gene knockout predictions in 78.4% when comparing with observed mutant growth phenotypes, with a total accuracy of 83.3%. However, using a minimal nutrient environment for the gene knockout predictions, iKS1317 has an accuracy of 87.1% in predicting mutant growth phenotypes. Furthermore, we used iKS1317 and existing strain design algorithms to suggest robust gene-knockout strategies to increase the production of acetyl-CoA. Since acetyl-CoA is the most important precursor for polyketide antibiotics, the suggested strategies may be implemented in vivo to improve the function of S. coelicolor as a heterologous expression host.