Extraction methods for untargeted metabolomics influence enzymatic activity in diverse soils.

Soil organic matter (SOM) is the largest carbon pool in terrestrial ecosystems and underpins the health and productivity of soil. Accurate characterization of its chemical composition will improve our understanding of biotic and abiotic processes regulating its stabilization. Our purpose in this study was to estimate the loss of SOM by microbial and exoenzymatic activity that might occur when soil is extracted for analysis of representative low molecular weight mass features using untargeted metabolomics. Two mined clays (kaolinite, montmorillonite) and three diverse soils (varying in texture, specific surface area and cation exchange capacity) were used to assess the extraction efficiency and loss of three enzymatic activity indicators (2,6-dichloroindophenol sodium salt hydrate [DCIP], 4-methylumbelliferyl phosphate [MUBph] and 3,4-dihydroxy-L-phenylalanine [LDOPA]) during extraction with two different solvents (water and methanol). Losses of the indicators were attributed to extraction method (ultrasonication, shaking, or shaking following chloroform fumigation), physical properties associated with the soil/clay type, and microbial activity. Soil/clay type strongly influenced indicator recovery and hence, SOM recovery. Choice of extraction method strongly influenced the composition and recovery of representative SOM mass features, while the choice of solvent determined whether the soil type or extraction method had a greater influence of compositional differences in the SOM mass features extracted. Extraction following chloroform fumigation had the greatest loss of the indicators, due to enzymatic activity and/or adsorption onto the soil matrix. Minimal variation in composition and loss of SOM mass features occurred during extraction by shaking for the soils tested; we therefore recommend it as the method of choice for untargeted SOM extraction studies.

A continental scale spatial investigation of lake sediment organic compositions using sedimentomics.

Sedimentomics is a new method used to investigate carbon cycling in sediment organic matter. This untargeted method, based on metabolomics workflows, was used to investigate the molecular composition of sediment organic matter across northern Canada (Nunavut and Northwest Territories). Unique "lake districts" were defined using unsupervised clustering based on changes in sediment organic carbon compositions across space. Supervised machine learning analyses were used to compare the "lake districts" to commonly used regional classification systems like the treeline, ecozones, and/or georegions. Treeline was the best model to explain the compositional variance of sediment organic carbon from lakes across Canada, closely followed by the georegions model. A novel sediment metaphenomics analysis was also applied to determine how well environmental constraints explain the variation of sediment organic matter composition across a continent. We determined that sedimentomics is more informative than traditional measurements (such as total organic carbon) and can be integrated with other "omics" techniques.

Identifying novel treeline biomarkers in lake sediments using an untargeted screening approach.

Paleolimnology uses sedimentary biomarkers as proxies to reconstruct long-term changes in environmental conditions from lake sediment cores. This work describes an untargeted metabolomics-based approach and uniquely applies it to the field of paleolimnology to identify novel sediment biomarkers to track long-term patterns in treeline dynamics. We identified new potential biomarkers across the Canadian northern Arctic, non-alpine, treeline using high-resolution accurate mass spectrometry, and pattern recognition analysis. This method was applied to 120 sediment core extracts from 14 boreal, 25 forest-tundra, and 21 tundra lakes to assess long-term fluctuations in treeline position. High resolution accurate mass spectrometry resolved many compounds from complex mixtures with low mass accuracy errors. This generated a large dataset that required metabolomics styled statistical analyses to identify potential biomarkers. In total, 29 potential biomarkers discriminated between boreal and tundra lakes. Tetrapyrrole-type phorbides and squalene derivatives dominated in boreal regions, while biohopane-type lipids were in the tundra regions. Tetrapyrroles were in both surface and subsurface sediments of boreal lakes indicating these compounds can survive long-term burial in sediments. At the ecozone level, tetrapyrroles were more abundant in boreal Taiga Shield, and Taiga Plains. Boreal plant extracts belonging to Pinaceae and Ericaceae also contained tetrapyrroles. Squalene derivatives demonstrated long-term preservation, but wider distribution than tetrapyrroles. Hopanoids were present in tundra and forest-tundra lake regions, specifically the Low Arctic and Taiga Shield, and were absent in all boreal lake sediments. Herein, we describe a method that can systematically identify new paleolimnological biomarkers. Novel biomarkers would facilitate multi-proxy paleolimnological studies and potentially lead to more accurate paleoenvironmental reconstructions.