Hydrogen (H₂) will play a crucial role in Europe's green energy transition, necessitating efficient storage solutions such as underground storage in salt caverns or porous media. However, the potentia Show more
Hydrogen (H₂) will play a crucial role in Europe's green energy transition, necessitating efficient storage solutions such as underground storage in salt caverns or porous media. However, the potential microbial H₂ consumption in these subsurface environments poses risks to storage stability and safety, and its magnitude remains relatively unexplored. Within the HyLife-CETP project, we developed a brine sampling protocol for the field operators and tested a standardized laboratory procedure for estimating microbial hydrogen consumption rates in these original brine samples, combining precise gas, chemical, and genetic analyses. Four labs tested and compared the developed enrichment protocol in a round-robin-like test using artificial brine and the hydrogen-consuming, sulfate-reducer Oleidesulfovibrio alaskensis as a reference strain. This test revealed consistent trends in microbial hydrogen consumption and corresponding pH increase across labs, indicating that the developed protocol effectively captures the overall microbial activity. However, inter-laboratory variability in the reported H Show less
Taxonomic analysis of environmental microbial communities is now routinely performed thanks to advances in DNA sequencing. Determining the role of these communities in global biogeochemical cycles req Show more
Taxonomic analysis of environmental microbial communities is now routinely performed thanks to advances in DNA sequencing. Determining the role of these communities in global biogeochemical cycles requires the identification of their metabolic functions, such as hydrogen oxidation, sulfur reduction, and carbon fixation. These functions can be directly inferred from metagenomics data, but in many environmental applications metabarcoding is still the method of choice. The reconstruction of metabolic functions from metabarcoding data and their integration into coarse-grained representations of biogeochemical cycles remains a difficult bioinformatics problem today. We developed a pipeline, called Tabigecy, which exploits taxonomic affiliations to predict metabolic functions constituting biogeochemical cycles. In a first step, Tabigecy uses the tool EsMeCaTa to predict consensus proteomes from input affiliations. To optimize this process, we generated a precomputed database containing information about 2404 taxa from UniProt. The consensus proteomes are searched using bigecyhmm, a newly developed Python package relying on Hidden Markov Models to identify key enzymes involved in metabolic function of biogeochemical cycles. The metabolic functions are then projected on coarse-grained representation of the cycles. We applied Tabigecy to two salt cavern datasets and validated its predictions with microbial activity and hydrochemistry measurements performed on the samples. The results highlight the utility of the approach to investigate the impact of microbial communities on biogeochemical processes. The Tabigecy pipeline is available at https://github.com/ArnaudBelcour/tabigecy. The Python package bigecyhmm and the precomputed EsMeCaTa database are also separately available at https://github.com/ArnaudBelcour/bigecyhmm and https://doi.org/10.5281/zenodo.13354073, respectively. Show less