Periodic Reporting for period 3 - PROSPECTOMICS (Using Omics Techniques for Hydrocarbon Prospecting)
Okres sprawozdawczy: 2023-01-01 do 2024-12-31
As part of the “Future and Emerging Technologies (FET)” program of Horizon 2020, PROSPECTOMICS proposes a completely new approach to hydrocarbon exploration that has the potential to reduce its environmental impact and financial burden by providing cleaner and cheaper alternatives. The project’s main objective is to develop a biomolecular tool for rapid, minimally invasive screening of marine sediments for minor hydrocarbon seepage from underlying reservoirs, and thereby guide hydrocarbon exploration with unprecedented sensitivity and precision. By combining high-throughput “multi-omics” data with the biogeochemical context of the surrounding sediments in which microorganisms live and feed, PROSPECTOMICS aims to detect inconspicuous sites with underlying oil and gas resources but without visible surface manifestation.
PROSPECTOMICS outcomes help determine biogeochemical reactions related to hydrocarbon seepage in the shallow subsurface, identify microbial fingerprints diagnostic of metabolic activities in the sediment and streamline their analysis via machine learning to deliver predictive models for prospecting. This can significantly reduce the amount of work, time and costs per sample and allow for screening large numbers of samples. By relying on gravity coring of the seabed and the use of biological instead of geological tools, PROSPECTOMICS’ approach minimizes environmental disturbances and financial costs related to hydrocarbon prospecting in European waters. The key objectives of PROSPECTOMICS are reached through several consecutive tasks, namely:
(I) Develop an optimized protocol for DNA, RNA and protein extraction from replicate sediment samples
(II) Streamline analyses of large biogeochemical “multi-omics” datasets via machine learning
(III) Identify “fingerprints” of hydrocarbon-related microbial taxa and hydrocarbon-fueled metabolisms that could lead to rapid omics-based detection methods for hydrocarbon seepage.
Statistical integration of the resulting large multi-omics dataset is achieved via machine learning to detect selective parameters for rapid identification of hydrocarbon seepage. PROSPECTOMICS has thereby identified several hitherto unknown key features that can serve as robust indicators of underlying hydrocarbon reservoirs, such as biogeochemical and metabolic processes related to molecular subsystems by active taxa. Combining multi-omics with already existing sedimentary data provides a holistic understanding of seabed processes related to naturally-occurring petroleum seepage in marine sediments and its effects on benthic microbial ecosystems.
PROSPECTOMICS outcomes help determine biogeochemical reactions related to hydrocarbon seepage in the shallow subsurface, identify microbial fingerprints diagnostic of metabolic activities in the sediment and streamline their analysis via machine learning to deliver predictive models for prospecting. This can significantly reduce the amount of work, time and costs per sample and allow for screening large numbers of samples. By relying on gravity coring of the seabed and the use of biological instead of geological tools, PROSPECTOMICS’ approach minimizes environmental disturbances and financial costs related to hydrocarbon prospecting in European waters. The key objectives of PROSPECTOMICS are reached through several consecutive tasks, namely:
(I) Develop an optimized protocol for DNA, RNA and protein extraction from replicate sediment samples
(II) Streamline analyses of large biogeochemical “multi-omics” datasets via machine learning
(III) Identify “fingerprints” of hydrocarbon-related microbial taxa and hydrocarbon-fueled metabolisms that could lead to rapid omics-based detection methods for hydrocarbon seepage.
Statistical integration of the resulting large multi-omics dataset is achieved via machine learning to detect selective parameters for rapid identification of hydrocarbon seepage. PROSPECTOMICS has thereby identified several hitherto unknown key features that can serve as robust indicators of underlying hydrocarbon reservoirs, such as biogeochemical and metabolic processes related to molecular subsystems by active taxa. Combining multi-omics with already existing sedimentary data provides a holistic understanding of seabed processes related to naturally-occurring petroleum seepage in marine sediments and its effects on benthic microbial ecosystems.
During a sampling cruise to the Barents Sea in 10/2021, 50 gravity cores were collected in the Barents Sea from three areas with known hydrocarbon (HC) anomalies and two reference areas. Cores were subsampled and conditioned on site using GFZ’s mobile geomicrobiology laboratory (BugLab). Biogeochemical characterization of the samples included high-resolution pore water profiles, sulfate reduction rates, dissolved gas concentrations, and microbial cell counts. Combined results showed that HC anomalies result in more pronounced biogeochemical trends and steeper gradients in pore water chemistry. Biomolecule extractions were individually optimized on paired samples to reach sufficient yield and quality of DNA, RNA and protein extracts. Metagenomic sequencing of Illumina short- and Oxford Nanopore Technology long-reads was performed on all 50 sediment cores. Bioinformatics processing delivered 165 archaeal and 358 bacterial high-quality metagenome-assembled genomes (MAGs), and >6000 bins via hybrid assembly, achieving thorough characterization of subseafloor microbial taxonomic and functional diversity. Several MAGs prevalent at HC-affected sites exhibited key functions in the anaerobic degradation of alkanes and aromatic HCs. Metatranscriptomic data were obtained for 31 sediment cores, mostly from HC-affected sites. DNA and RNA sequencing data were compiled into a comprehensive database of predicted proteins. For metaproteomics, continuous elution electrophoresis from sample slurry improved the yield and quality of protein extracts. Mass spectrometric data searches resulted in >1,300 taxonomically and functionally assigned peptides per gravity core, highlighting metabolic activities by archaea related to methane and C1 compounds. Statistics on the combined dataset selectively extracted diagnostic features for integration into predictive models. Key markers for inconspicuous seepage included pore water sulfate, trace elements and alkalinity, alongside 9 taxonomic groups and 14 marker genes.
To validate these diagnostic features, 6 gravity cores were collected south-west of the initial sampling area in fall 2023. These were analyzed for biogeochemistry, metagenomics and metaproteomics. Pore water sulfate and alkalinity, microbial reduction rates, predicted functions of prevalent MAGs, and expressed proteins related to C1 metabolism were validated as indicators of thermogenic methane anomalies. Such biogeochemical and omics features were all indicative of sulfate-driven anaerobic oxidation of methane, with a suite of taxa cross-feeding on simple HCs and metabolites. In conclusion, minute supplies of electron donors through seepage result in slightly increased consumption of electron acceptors.
Project dissemination was achieved through conferences, publications and data deposition in public repositories. The sampling campaign video documentary is posted on PROSPECTOMICS social media channels.
To validate these diagnostic features, 6 gravity cores were collected south-west of the initial sampling area in fall 2023. These were analyzed for biogeochemistry, metagenomics and metaproteomics. Pore water sulfate and alkalinity, microbial reduction rates, predicted functions of prevalent MAGs, and expressed proteins related to C1 metabolism were validated as indicators of thermogenic methane anomalies. Such biogeochemical and omics features were all indicative of sulfate-driven anaerobic oxidation of methane, with a suite of taxa cross-feeding on simple HCs and metabolites. In conclusion, minute supplies of electron donors through seepage result in slightly increased consumption of electron acceptors.
Project dissemination was achieved through conferences, publications and data deposition in public repositories. The sampling campaign video documentary is posted on PROSPECTOMICS social media channels.
The project produced a large multi-omics dataset with its geochemical context. Integration via machine learning enabled prediction of biogeochemical and biomolecular features in the shallow subseafloor that are diagnostic of inconspicuous seepage from deep underlying hydrocarbon (HC) resources. PROSPECTOMICS included a validation phase, during which a set of samples, without any a-priori knowledge of the degree of HC exposure, were screened applying streamlined procedures. Blind tests confirmed that a selection of statistically extracted features enables the detection of thermogenic methane anomalies to constitute indicators of microbial activity metabolizing simple HCs. The developed protocols thereby help to efficiently identify the presence-absence of HC seepage, and the data produced to train predictive models for samples from unknown sources.
Beyond proof-of-concept, this approach has the potential to reduce the financial burden of HC exploration and provide cleaner alternatives. PROSPECTOMICS’ main contribution is the establishment of a streamlined multi-omics lab procedure, the design of a bioinformatics pipeline and statistical protocol for industrial applications. By relying on gravity coring in shallow sediment and biological indicators instead of deep drilling, PROSPECTOMICS’ final product will minimize environmental disturbances related to HC prospecting in European waters. AKER BP’s connections to the oil and gas industry provide direct contact to the expected users of this new technology.
Adding biogeochemical and multi-omics data to existing knowledge of the seafloor will provide a truly holistic understanding of microbial processes related to petroleum leakage and its effects on benthic ecosystems. Thus, environmental protection agencies and similar entities are likely to be interested in PROSPECTOMICS’ findings to monitor HC contamination and bioremediation.
Beyond proof-of-concept, this approach has the potential to reduce the financial burden of HC exploration and provide cleaner alternatives. PROSPECTOMICS’ main contribution is the establishment of a streamlined multi-omics lab procedure, the design of a bioinformatics pipeline and statistical protocol for industrial applications. By relying on gravity coring in shallow sediment and biological indicators instead of deep drilling, PROSPECTOMICS’ final product will minimize environmental disturbances related to HC prospecting in European waters. AKER BP’s connections to the oil and gas industry provide direct contact to the expected users of this new technology.
Adding biogeochemical and multi-omics data to existing knowledge of the seafloor will provide a truly holistic understanding of microbial processes related to petroleum leakage and its effects on benthic ecosystems. Thus, environmental protection agencies and similar entities are likely to be interested in PROSPECTOMICS’ findings to monitor HC contamination and bioremediation.