Determination of Microbial Maintenance in Acetogenesis and Methanogenesis by Experimental and Modeling Techniques

Physiological basis for atmospheric methane oxidation and methanotrophic growth on air

Atmospheric methane oxidizing bacteria (atmMOB) constitute the sole biological sink for atmospheric methane. Still, the physiological basis allowing atmMOB to grow on air is not well understood. Here we assess the ability and strategies of seven methanotrophic species to grow with air as sole energy, carbon, and nitrogen source. Four species, including three outside the canonical atmMOB group USCα, enduringly oxidized atmospheric methane, carbon monoxide, and hydrogen during 12 months of growth on air. These four species exhibited distinct substrate preferences implying the existence of multiple metabolic strategies to grow on air. The estimated energy yields of the atmMOB were substantially lower than previously assumed necessary for cellular maintenance in atmMOB and other aerobic microorganisms. Moreover, the atmMOB also covered their nitrogen requirements from air. During growth on air, the atmMOB decreased investments in biosynthesis while increasing investments in trace gas oxidation. Furthermore, we confirm that a high apparent specific affinity for methane is a key characteristic of atmMOB. Our work shows that atmMOB grow on the trace concentrations of methane, carbon monoxide, and hydrogen present in air and outlines the metabolic strategies that enable atmMOB to mitigate greenhouse gases.

Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture

BACKGROUND

The ability to quantitatively predict ecophysiological functions of microbial communities provides an important step to engineer microbiota for desired functions related to specific biochemical conversions. Here, we present the quantitative prediction of medium-chain carboxylate production in two continuous anaerobic bioreactors from 16S rRNA gene dynamics in enriched communities.

RESULTS

By progressively shortening the hydraulic retention time (HRT) from 8 to 2 days with different temporal schemes in two bioreactors operated for 211 days, we achieved higher productivities and yields of the target products n-caproate and n-caprylate. The datasets generated from each bioreactor were applied independently for training and testing machine learning algorithms using 16S rRNA genes to predict n-caproate and n-caprylate productivities. Our dataset consisted of 14 and 40 samples from HRT of 8 and 2 days, respectively. Because of the size and balance of our dataset, we compared linear regression, support vector machine and random forest regression algorithms using the original and balanced datasets generated using synthetic minority oversampling. Further, we performed cross-validation to estimate model stability. The random forest regression was the best algorithm producing more consistent results with median of error rates below 8%. More than 90% accuracy in the prediction of n-caproate and n-caprylate productivities was achieved. Four inferred bioindicators belonging to the genera Olsenella, Lactobacillus, Syntrophococcus and Clostridium IV suggest their relevance to the higher carboxylate productivity at shorter HRT. The recovery of metagenome-assembled genomes of these bioindicators confirmed their genetic potential to perform key steps of medium-chain carboxylate production.

CONCLUSIONS

Shortening the hydraulic retention time of the continuous bioreactor systems allows to shape the communities with desired chain elongation functions. Using machine learning, we demonstrated that 16S rRNA amplicon sequencing data can be used to predict bioreactor process performance quantitatively and accurately. Characterizing and harnessing bioindicators holds promise to manage reactor microbiota towards selection of the target processes. Our mathematical framework is transferrable to other ecosystem processes and microbial systems where community dynamics is linked to key functions. The general methodology used here can be adapted to data types of other functional categories such as genes, transcripts, proteins or metabolites. Video Abstract.

Pre-incubation conditions determine the fermentation pattern and microbial community structure in fermenters at mild hydrostatic pressure

Fermentation at elevated hydrostatic pressure is a novel strategy targeting product selectivity. However, the role of inoculum history and cross-resistance, that is, acquired tolerance from incubation under distinctive environmental stress, remains unclear in high-pressure operation. In our here presented work, we studied fermentation and microbial community responses of halotolerant marine sediment inoculum (MSI) and anaerobic digester inoculum (ADI), pre-incubated in serum bottles at different temperatures and subsequently exposed to mild hydrostatic pressure (MHP; < 10 MPa) in stainless steel reactors. Results showed that MHP effects on microbial growth, activity, and community structure were strongly temperature-dependent. At moderate temperature (20°C), biomass yield and fermentation were not limited by MHP; suggesting a cross-resistance effect from incubation temperature and halotolerance. Low temperatures (10°C) and MHP imposed kinetic and bioenergetic limitations, constraining growth and product formation. Fermentation remained favorable in MSI at 28°C and ADI at 37°C, despite reduced biomass yield resulting from maintenance and decay proportionally increasing with temperature. Microbial community structure was modified by temperature during the enrichment, and slight differences observed after MHP-exposure did not compromise functionality. Results showed that the relation incubation temperature-halotolerance proved to be a modifier of microbial responses to MHP and could be potentially exploited in fermentations to modulate product/biomass ratio.

Simultaneous Oxidation of Atmospheric Methane, Carbon Monoxide and Hydrogen for Bacterial Growth

The second largest sink for atmospheric methane (CH₄) is atmospheric methane oxidizing-bacteria (atmMOB). How atmMOB are able to sustain life on the low CH₄ concentrations in air is unknown. Here, we show that during growth, with air as its only source for energy and carbon, the recently isolated atmospheric methane-oxidizer Methylocapsa gorgona MG08 (USCα) oxidizes three atmospheric energy sources: CH₄, carbon monoxide (CO), and hydrogen (H₂) to support growth. The cell-specific CH₄ oxidation rate of M. gorgona MG08 was estimated at ~0.7 × 10⁻¹⁸ mol cell⁻¹ h⁻¹, which, together with the oxidation of CO and H₂, supplies 0.38 kJ Cmol⁻¹ h⁻¹ during growth in air. This is seven times lower than previously assumed necessary to support bacterial maintenance. We conclude that atmospheric methane-oxidation is supported by a metabolic flexibility that enables the simultaneous harvest of CH₄, H₂ and CO from air, but the key characteristic of atmospheric CH₄ oxidizing bacteria might be very low energy requirements.

Microbial Resource Management for Ex Situ Biomethanation of Hydrogen at Alkaline pH

Biomethanation is a promising solution to convert H₂ (produced from surplus electricity) and CO₂ to CH₄ by using hydrogenotrophic methanogens. In ex situ biomethanation with mixed cultures, homoacetogens and methanogens compete for H₂/CO₂. We enriched a hydrogenotrophic microbiota on CO₂ and H₂ as sole carbon and energy sources, respectively, to investigate these competing reactions. The microbial community structure and dynamics of bacteria and methanogenic archaea were evaluated through 16S rRNA and mcrA gene amplicon sequencing, respectively. Hydrogenotrophic methanogens and homoacetogens were enriched, as acetate was concomitantly produced alongside CH₄. By controlling the media composition, especially changing the reducing agent, the formation of acetate was lowered and grid quality CH₄ (≥97%) was obtained. Formate was identified as an intermediate that was produced and consumed during the bioprocess. Stirring intensities ≥ 1000 rpm were detrimental, probably due to shear force stress. The predominating methanogens belonged to the genera Methanobacterium and Methanoculleus. The bacterial community was dominated by Lutispora. The methanogenic community was stable, whereas the bacterial community was more dynamic. Our results suggest that hydrogenotrophic communities can be steered towards the selective production of CH₄ from H₂/CO₂ by adapting the media composition, the reducing agent and the stirring intensity.

Augmenting Biogas Process Modeling by Resolving Intracellular Metabolic Activity

The process of anaerobic digestion in which waste biomass is transformed to methane by complex microbial communities has been modeled for more than 16 years by parametric gray box approaches that simplify process biology and do not resolve intracellular microbial activity. Information on such activity, however, has become available in unprecedented detail by recent experimental advances in metatranscriptomics and metaproteomics. The inclusion of such data could lead to more powerful process models of anaerobic digestion that more faithfully represent the activity of microbial communities. We augmented the Anaerobic Digestion Model No. 1 (ADM1) as the standard kinetic model of anaerobic digestion by coupling it to Flux-Balance-Analysis (FBA) models of methanogenic species. Steady-state results of coupled models are comparable to standard ADM1 simulations if the energy demand for non-growth associated maintenance (NGAM) is chosen adequately. When changing a constant feed of maize silage from continuous to pulsed feeding, the final average methane production remains very similar for both standard and coupled models, while both the initial response of the methanogenic population at the onset of pulsed feeding as well as its dynamics between pulses deviates considerably. In contrast to ADM1, the coupled models deliver predictions of up to 1,000s of intracellular metabolic fluxes per species, describing intracellular metabolic pathway activity in much higher detail. Furthermore, yield coefficients which need to be specified in ADM1 are no longer required as they are implicitly encoded in the topology of the species’ metabolic network. We show the feasibility of augmenting ADM1, an ordinary differential equation-based model for simulating biogas production, by FBA models implementing individual steps of anaerobic digestion. While cellular maintenance is introduced as a new parameter, the total number of parameters is reduced as yield coefficients no longer need to be specified. The coupled models provide detailed predictions on intracellular activity of microbial species which are compatible with experimental data on enzyme synthesis activity or abundance as obtained by metatranscriptomics or metaproteomics. By providing predictions of intracellular fluxes of individual community members, the presented approach advances the simulation of microbial community driven processes and provides a direct link to validation by state-of-the-art experimental techniques.