bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.06.09.731101

Dairy wastewater grease stabilizes in situ mesophilic biomethanation for H2-to-CH4 conversion

Ruiz-Lorenzo, M. L.·Angela, L.-Z.·Moreno, A. D.·Ferrari, F.·Diaz, I.·Contreras, J.·Iglesias, R.·Suarez, S.·Acedos, M. G.
View original source ↗Export metadata ↓

Abstract

Power-to-Gas technologies are emerging as a key strategy to integrate surplus renewable electricity into energy systems, through the conversion of green hydrogen into methane. However, the practical implementation of biological in situ biomethanation is still constrained by operational and design requirements that are incompatible with most existing anaerobic digestion infrastructures. This study demonstrates a stable and efficient mesophilic (37{degrees}C) in situ biomethanation process driven by substrate-induced microbial selection rather than relying on continuous hydrogen supply. Anaerobic digesters co-digesting sewage sludge from a wastewater treatment plant with lipid-rich greases recovered from dairy wastewater developed a pre-adapted hydrogenotrophic consortium capable of effective CO2-H2 conversion under mesophilic conditions. Long-term operation confirmed the robustness and persistence of this microbial structure. Upon H2 addition, methane concentrations up to 82 % were achieved under atmospheric pressure, without biogas recirculation, with hydrogen-to-methane conversion efficiencies up to 90% and methane productivities of 1.64 NLCH4.L-1d-1. 16SrRNA-based microbial community analysis revealed that dairy grease co-digestion selectively enriched hydrogenotrophic methanogens, particularly Methanospirillum, together with syntrophic fatty-acid-degrading bacteria such as Syntrophomonas, promoting efficient interspecies hydrogen transfer. Importantly, the lipid co-substrate enabled the establishment and long-term stability of the hydrogenotrophic pathway independently of hydrogen availability, mitigating challenges associated with intermittent renewable energy supply. Overall, these findings challenge the common reliance on thermophilic conditions, continuous hydrogen input, pressurization, and gas recirculation in in situ biomethanation, demonstrating that substrate-driven microbial selection can replace conventional engineering requirements such as thermophilic operation or reactor modifications, providing a simpler and scalable strategy for mesophilic in situ biomethanation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/731101v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@c62086org.highwire.dtl.DTLVardef@1813ed6org.highwire.dtl.DTLVardef@4462bcorg.highwire.dtl.DTLVardef@1ae2cfb_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG Highlights- Lipid-assisted co-digestion promotes stable biogas and biomethane production - Dairy wastewater greases enable mesophilic in situ biomethanation - An enriched hydrogenotrophic methanogenic consortium yields >82% CH4 - 70-90% H2-to-CH4 conversion efficiency under mesophilic, unpressurized conditions - Substrate-driven microbial selection enables in situ biomethanation in WWTP digesters

Explore related subjects

bioengineeringPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ruiz-Lorenzo, M. L., Angela, L.-Z., Moreno, A. D., Ferrari, F., Diaz, I., Contreras, J., Iglesias, R., Suarez, S., Acedos, M. G.. 2026-06-11. Dairy wastewater grease stabilizes in situ mesophilic biomethanation for H2-to-CH4 conversion. https://doi.org/10.64898/2026.06.09.731101

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

RNASeek: A Cross-Phyla Generative Foundation Model for Multipurpose RNA Modeling and Reinforcement Learning-Based Design

RNA plays central roles in regulating information flow and provides a versatile substrate for engineering biological functions. While large language models (LLMs) have transformed natural language processing and protein design, a general framework connecting RNA foundation models to functional sequence design remains limited. Here, we present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus for RNA sequence representation and generation. Natural-language tokens enable flexible conditional prediction and sequence design using a unified backbone. RNASeek captures species-specific transcript features and intron-exon boundaries in a zero-shot setting. We then fine-tune RNASeek to predict ribozyme self-cleavage activity and viral mRNA stability, revealing interpretable sequence features associated with function, including ribozyme loop flexibility and stem stability, as well as AU-rich motifs associated with mRNA stability. We use these functional predictors as reward models and apply Group Relative Policy Optimization (GRPO) to update the generation policy of RNASeek toward sequences with desired properties. GRPO-guided generation produces faster-cleaving ribozymes and stability-enhancing 3' UTRs while satisfying user-specified IUPAC constraints. Experimentally validated RNASeek-generated ribozymes achieve wild-type levels of activity, while RNASeek-generated 3' UTR sequences exceed the performance of the training data and benchmarked AI-generated 3' UTRs. Together, RNASeek establishes a unified pretrain-predict-optimize framework that connects learned RNA function to controllable de novo sequence design and provides a general strategy for engineering regulatory RNAs with desired properties.

bioengineering↗
bioRxiv2026

Joint Vector Flow Mapping and Segmentation: Ill-Posedness,Differentiable Bayesian Inference, and Synthetic Vortex-FlowBenchmarks

Vector flow mapping (VFM) reconstructs left-ventricular (LV) blood velocity from color-Doppler echocardiography by combining the measured beamwise component with physical and regularizing constraints. Analysis of the discrete VFM formulation shows that the inverse problem is intrinsically ill posed: the occurrence of singular modes can be predicted from the geometry of the segmented blood-pool domain, the imposed boundary conditions, and the degree of smoothing. These modes can propagate uncertainty along entire transverse bands of the reconstructed velocity field, yet conventional VFM neither quantifies this uncertainty nor allows for correcting the blood-pool segmentation. We introduce Bayesian VFM (B--VFM), a hierarchical framework that jointly infers radial and transverse velocities, a probabilistic blood-pool mask, their spatially resolved uncertainties, and hyperparameters weighting Doppler and segmentation fidelity, mass conservation, boundary conditions, and smoothness. The discretized posterior admits a closed-form gradient and exact Hessian, enabling computationally efficient, gradient-based MAP estimation, sampling, and direct analysis of ill-posed modes. Posterior inference combines Gibbs sampling of conjugate Gamma-distributed hyperparameters with conditional maximum-a-posteriori estimation and a Laplace approximation for the high-dimensional velocity and mask fields. To accommodate systematic departures from planar mass conservation, B-VFM can learn the covariance of the planar divergence residual from an ensemble of flows and incorporate it as a structured model-discrepancy prior. Independent chains converged reproducibly, while covariance priors learned from flow ensembles illustrated how model discrepancies can be incorporated into the inference. B--VFM was evaluated using Lamb-Chaplygin dipoles under ideal conditions and with Doppler corruption, Doppler voids, and segmentation defects, and using the Hicks-Moffatt family of spherical vortices to assess violations of planar mass conservation. The method produced smooth reconstructions, localized uncertainty near unreliable measurements and regions of model inconsistency, and used flow information to correct segmentation errors. Within the tested vortex family, the data-informed planar divergence prior reduced velocity bias and mask distortion. B--VFM thus provides an uncertainty-aware reconstruction method and a flexible foundation for future VFM formulations incorporating additional priors, observations, and physical models. Future work will evaluate the method using clinical data and more complex three-dimensional benchmark flows.

bioengineering↗
bioRxiv2026

Computational design of a versatile, zero-radius proximity labeling enzyme

The ability to map protein interactomes and organelle proteomes is foundational for achieving a molecular understanding of living cells. Proximity labeling (PL) provides a powerful strategy for this, but existing enzymes and photocatalysts are limited by their spatial resolution, reliance on biotin, and/or in vivo compatibility. Here we report FlexID, an engineered promiscuous ligase that catalyzes the rapid attachment of diverse small-molecule probes to proximal endogenous proteins. Critically, FlexID operates through a zero-radius, direct-contact mechanism, offering superior spatial precision compared to existing PL tools. We engineered FlexID by combining the strengths of sequence- and structure-trained computational models to enhance its catalytic activity and structural stability. Biophysical analysis revealed that specific conformational changes in FlexID improve its ability to recognize diverse target proteins while simultaneously preventing the premature release of the reactive intermediate. We demonstrate FlexID's versatility through in vivo proximity labeling, comprehensive organelle proteome mapping, and a high-throughput, fluorescence-based screen for molecular glues. Our work shows that computational methods can be harnessed to create mechanistically distinct PL enzymes and establishes FlexID as a flexible, high-resolution tool for mapping protein interactions and proteomes in living cells.

bioengineering↗