Modelling of microbial interactions in anaerobic digestion: from black to glass box.

Anaerobic and microaerophilic environments are pervasive in nature, providing essential contributions to the maintenance of human health, biogeochemical cycles and the Earth's climate. These ecological niches are characterised by low free oxygen and oxidants, or lack thereof. Under these conditions, interactions between species are essential for supporting the growth of syntrophic species and maintaining thermodynamic feasibility of anaerobic fermentation. Kinetic models provide a simplified view of complex metabolic networks, while genome-scale metabolic models and flux-balance analysis (FBA) aim to unravel these systems as a whole. The target of this review is to outline the main similarities, differences and challenges associated with kinetic and metabolic modelling, and describe state-of-the-art modelling practices for studying syntrophies in the anaerobic digestion (AD) case study.

Short sludge age denitrification as alternative process for energy and nutrient recovery.

High rate activated sludge (HRAS) systems redirect organics into highly biodegradable sludge and nutrients into microbial proteins. This study evaluates anoxic HRAS for nitrogen and carbon recovery. The reactor treated synthetic wastewater at solids retention times (SRTs) of 5, 3 and 1 days. Denitrification rates varied between 0.15 and 0.19 g-NO3-N g-TSS-1 d-1 (total suspended solids per day) and all conditions showed favourable settling. The highest sludge yield, obtained at SRT 1 d, was 0.75 g-TSS g-CODremoved-1, double that observed for aerobic HRAS. The highest methane yield (322 mL-CH4 g-VSsludge-1) was obtained from sludge wasted at 3 d SRT. Both 1 d and 3 d SRTs showed favourable energy recovery, with 14 % of the organics recovered as methane. All conditions yielded sludge with protein content ranging between 24 and 27 % of dry weight and similar amino acid profile, comparable to traditional proteins. Thus, denitrifying HRAS recovers resources as its aerobic counterpart, allowing for nitrogen removal via denitrification, more stable compared to mainstream partial nitritation anammox typically combined with aerobic HRAS.

Integrating metagenomic binning with flux balance analysis to unravel syntrophies in anaerobic CO2 methanation.

BACKGROUND: Carbon fixation through biological methanation has emerged as a promising technology to produce renewable energy in the context of the circular economy. The anaerobic digestion microbiome is the fundamental biological system operating biogas upgrading and is paramount in power-to-gas conversion. Carbon dioxide (CO2) methanation is frequently performed by microbiota attached to solid supports generating biofilms. Despite the apparent simplicity of the microbial community involved in biogas upgrading, the dynamics behind most of the interspecies interaction remain obscure. To understand the role of the microbial species in CO2 fixation, the biofilm generated during the biogas upgrading process has been selected as a case study. The present work investigates via genome-centric metagenomics, based on a hybrid Nanopore-Illumina approach the biofilm developed on the diffusion devices of four ex situ biogas upgrading reactors. Moreover, genome-guided metabolic reconstruction and flux balance analysis were used to propose a biological role for the dominant microbes. RESULTS: The combined microbiome was composed of 59 species, with five being dominant (> 70% of total abundance); the metagenome-assembled genomes representing these species were refined to reach a high level of completeness. Genome-guided metabolic analysis appointed Firmicutes sp. GSMM966 as the main responsible for biofilm formation. Additionally, species interactions were investigated considering their co-occurrence in 134 samples, and in terms of metabolic exchanges through flux balance simulation in a simplified medium. Some of the most abundant species (e.g., Limnochordia sp. GSMM975) were widespread (~ 67% of tested experiments), while others (e.g., Methanothermobacter wolfeii GSMM957) had a scattered distribution. Genome-scale metabolic models of the microbial community were built with boundary conditions taken from the biochemical data and showed the presence of a flexible interaction network mainly based on hydrogen and carbon dioxide uptake and formate exchange. CONCLUSIONS: Our work investigated the interplay between five dominant species within the biofilm and showed their importance in a large spectrum of anaerobic biogas reactor samples. Flux balance analysis provided a deeper insight into the potential syntrophic interaction between species, especially Limnochordia sp. GSMM975 and Methanothermobacter wolfeii GSMM957. Finally, it suggested species interactions to be based on formate and amino acids exchanges. Video Abstract.

Ex-situ biogas upgrading in thermophilic up-flow reactors: The effect of different gas diffusers and gas retention times.

Four different types of ceramic gas distributors (Al2O3 of 1.2 µm and SiC of 0.5, 7 and 14 µm) were evaluated to increase biomethane formation during ex-situ biogas upgrading process. Each type of gas diffuser was tested independently at three different gas retention times of 10, 5 and 2.5 h, at thermophilic conditions. CH4 production rate increased by increasing input gas flow rate for all type of distributors, whereas CH4 concentration declined. Reactors equipped with SiC gas distributors effectively improved biomethane content fulfilling natural gas standards. Microbial analysis showed high abundance of hydrogenotrophic methanogens and proliferated syntrophic bacteria, i.e. syntrophic acetate oxidizers and homoacetogens, confirming the effect of H2 to alternate anaerobic digestion microbiome and enhance hydrogenotrophic methanogenesis. A detailed anaerobic bioconversion model was adapted to simulate the operation of the R1-R4 reactors. The model was shown to be effective for the simulation of biogas upgrading process in up-flow reactors.

Revealing metabolic mechanisms of interaction in the anaerobic digestion microbiome by flux balance analysis.

Anaerobic digestion is a key biological process for renewable energy, yet the mechanistic knowledge on its hidden microbial dynamics is still limited. The present work charted the interaction network in the anaerobic digestion microbiome via the full characterization of pairwise interactions and the associated metabolite exchanges. To this goal, a novel collection of 836 genome-scale metabolic models was built to represent the functional capabilities of bacteria and archaea species derived from genome-centric metagenomics. Dominant microbes were shown to prefer mutualistic, parasitic and commensalistic interactions over neutralism, amensalism and competition, and are more likely to behave as metabolite importers and profiteers of the coexistence. Additionally, external hydrogen injection positively influences microbiome dynamics by promoting commensalism over amensalism. Finally, exchanges of glucogenic amino acids were shown to overcome auxotrophies caused by an incomplete tricarboxylic acid cycle. Our novel strategy predicted the most favourable growth conditions for the microbes, overall suggesting strategies to increasing the biogas production efficiency. In principle, this approach could also be applied to microbial populations of biomedical importance, such as the gut microbiome, to allow a broad inspection of the microbial interplays.

New insights from the biogas microbiome by comprehensive genome-resolved metagenomics of nearly 1600 species originating from multiple anaerobic digesters.

BACKGROUND: Microorganisms in biogas reactors are essential for degradation of organic matter and methane production. However, a comprehensive genome-centric comparison, including relevant metadata for each sample, is still needed to identify the globally distributed biogas community members and serve as a reliable repository. RESULTS: Here, 134 publicly available metagenomes derived from different biogas reactors were used to recover 1635 metagenome-assembled genomes (MAGs) representing different biogas bacterial and archaeal species. All genomes were estimated to be > 50% complete and nearly half ≥ 90% complete with ≤ 5% contamination. In most samples, specialized microbial communities were established, while only a few taxa were widespread among the different reactor systems. Metabolic reconstruction of the MAGs enabled the prediction of functional traits related to biomass degradation and methane production from waste biomass. An extensive evaluation of the replication index provided an estimation of the growth dynamics for microbes involved in different steps of the food chain. CONCLUSIONS: The outcome of this study highlights a high flexibility of the biogas microbiome, allowing it to modify its composition and to adapt to the environmental conditions, including temperatures and a wide range of substrates. Our findings enhance our mechanistic understanding of the AD microbiome and substantially extend the existing repository of genomes. The established database represents a relevant resource for future studies related to this engineered ecosystem.

Early warning indicators for mesophilic anaerobic digestion of corn stalk: a combined experimental and simulation approach.

BACKGROUND: Monitoring and providing early warning are essential operations in the anaerobic digestion (AD) process. However, there are still several challenges for identifying the early warning indicators and their thresholds. One particular challenge is that proposed strategies are only valid under certain conditions. Another is the feasibility and universality of the detailed threshold values obtained from different AD systems. In this article, we report a novel strategy for identifying early warning indicators and defining threshold values via a combined experimental and simulation approach. RESULTS: The AD of corn stalk (CS) was conducted using mesophilic, completely stirred anaerobic reactors. Two overload modes (organic and hydraulic) and overload types (sudden and gradual) were applied in order to identify early warning indicators of the process and determine their threshold values. To verify the selection of experimental indicators, a combined experimental and simulation approach was adopted, using a modified anaerobic bioconversion mathematical model (BioModel). Results revealed that the model simulations agreed well with the experimental data. Furthermore, the ratio of intermediate alkalinity to bicarbonate alkalinity (IA/BA) and volatile fatty acids (VFAs) were selected as the most potent early warning indicators, with warning times of 7 days and 5-8 days, respectively. In addition, IA, BA, and VFA/BA were identified as potential auxiliary indicators for diagnosing imbalances in the AD system. The relative variations for indicators based on that of steady state were observed instead of the absolute threshold values, which make the early warning more feasible and universal. CONCLUSION: The strategy of a combined approach presented that the model is promising tool for selecting and monitoring early warning indicators in various corn stalk AD scenarios. This study may offer insight into industrial application of early warning in AD system with mathematical model.

Co-digestion of Laminaria digitata with cattle manure: A unimodel simulation study of both batch and continuous experiments.

This work investigated the thermophilic (55 °C) co-digestion performance both in batch and continuous mode operation. The biochemical methane potentials of L. digitata and cattle manure were 308 ±â€¯24 and 203 ±â€¯33 mL CH4/g VS, respectively. The optimum co-digestion feedstock ratio was found to be 80% macroalgae: 20% manure on a volatile solids basis, which produced 290 ±â€¯19 mL CH4/g VS under long-term and stable continuous operation at an organic loading rate of 2 g VS/L/d and hydraulic retention time of 15 days. Simulations of the batch and continuous experiments were, for the first time, carried out using an integrated anaerobic bioconversion model without structural modifications. Close fits between measured and simulated data provided mutual confirmation of experimental reliability and model robustness, and provided new perspectives for the use of the software tool.

Microbial activity response to hydrogen injection in thermophilic anaerobic digesters revealed by genome-centric metatranscriptomics.

BACKGROUND: The expansion of renewable energy produced by windmills and photovoltaic panels has generated a considerable electricity surplus, which can be utilized in water electrolysis systems for hydrogen production. The resulting hydrogen can then be funneled to anaerobic digesters for biogas upgrading (biomethanation) purposes (power-to-methane) or to produce high value-added compounds such as short-chain fatty acids (power-to-chemicals). Genome-centric metagenomics and metatranscriptomic analyses were performed to better understand the metabolic dynamics associated with H2 injection in two different configurations of anaerobic digesters treating acidic wastes, specifically cheese manufacturing byproducts. These approaches revealed the key-genes involved in methanation and carbon fixation pathways at species level. RESULTS: The biogas upgrading process in the single-stage configuration increased the CH4 content by 7%. The dominant methanogenic species responsible for the upregulation of the hydrogenotrophic pathway in this reactor was Methanothermobacter wolfeii UC0008. In the two-stage configuration, H2 injection induced an upregulation of CO2 fixation pathways producing short-chain fatty acids, mainly acetate and butyrate. In this configuration, the abundant species Anaerobaculum hydrogeniformans UC0046 and Defluviitoga tunisiensis UC0050 primarily upregulated genes related to electron transport chains, suggesting putative syntrophisms with hydrogen scavenger microbes. Interestingly, Tepidanaerobacter acetatoxydans UC0018 did not act as an acetate-oxidizer in either reactor configurations, and instead regulated pathways involved in acetate production and uptake. A putative syntrophic association between Coprothermobacter proteolyticus UC0011 and M. wolfeii UC0008 was proposed in the two-stage reactor. In order to support the transcriptomic findings regarding the hydrogen utilization routes, an advanced bioconversion model was adapted for the simulation of the single- and two-stage reactor setups. CONCLUSIONS: This is the first study investigating biogas reactor metatranscriptome dynamics following hydrogen injection for biomethanation and carbon fixation to short-chain fatty acids purposes. The same microbes showed different patterns of metabolic regulation in the two reactor configurations. It was observed an effect of the specialized acidogenic reactor on the overall microbial consortium composition and activity in the two-stage digester. There were also suggested the main species responsible for methanation, short-chain fatty acids production, and electron transport chain mechanisms, in both reactor configurations.

In-situ biogas upgrading process: Modeling and simulations aspects.

Biogas upgrading processes by in-situ hydrogen (H2) injection are still challenging and could benefit from a mathematical model to predict system performance. Therefore, a previous model on anaerobic digestion was updated and expanded to include the effect of H2 injection into the liquid phase of a fermenter with the aim of modeling and simulating these processes. This was done by including hydrogenotrophic methanogen kinetics for H2 consumption and inhibition effect on the acetogenic steps. Special attention was paid to gas to liquid transfer of H2. The final model was successfully validated considering a set of Case Studies. Biogas composition and H2 utilization were correctly predicted, with overall deviation below 10% compared to experimental measurements. Parameter sensitivity analysis revealed that the model is highly sensitive to the H2 injection rate and mass transfer coefficient. The model developed is an effective tool for predicting process performance in scenarios with biogas upgrading.

A systematic methodology to extend the applicability of a bioconversion model for the simulation of various co-digestion scenarios.

Detailed simulation of anaerobic digestion (AD) requires complex mathematical models and the optimization of numerous model parameters. By performing a systematic methodology and identifying parameters with the highest impact on process variables in a well-established AD model, its applicability was extended to various co-digestion scenarios. More specifically, the application of the step-by-step methodology led to the estimation of a general and reduced set of parameters, for the simulation of scenarios where either manure or wastewater were co-digested with different organic substrates. Validation of the general parameter set involved the simulation of laboratory-scale data from three continuous co-digestion experiments, treating mixtures of different organic residues either at thermophilic or mesophilic conditions. Evaluation of the results showed that simulations using the general parameter set fitted experimental data quite well, indicating that it offers a reliable reference point for future simulations of anaerobic co-digestion scenarios.