"-Omics" workflow for paleolimnological and geological archives: A review

Exploring within-ecodistrict lake organic matter variability and identifying possible environmental contaminant biomarkers using sedimentomics

Sedimentomics methods offer insight into the physiological parameters that influence freshwater sediment organic matter (sedOM). To date, most sedimentomics studies characterized variations across large spatial and environmental gradients; here we examine whether sedimentomics methods capture subtle sedOM variations within a relatively homogeneous study area in southwestern Nova Scotia, Canada. Additionally, we explore the lake sedimentome for candidate biomarkers related to ongoing carnivorous animal farming in the region. Sediment cores were recovered from seven lakes across a trophic (oligo- to eu- trophic) and anthropogenic land use gradient (carnivorous animal farming in catchment, downstream of farming, no farming nearby). Subsamples that dated prior to 1910 (pre-carnivorous animal farming) and later than 2010 (during carnivorous animal farming) were analyzed using UHPLC-HRMS in both negative (ESI-) and positive (ESI+) electrospray ionization modes. Cluster analysis (k-means) showed replicate samples from a given lake clustered distinctly from one another in both ESI modes, indicating sedOM captured subtle variations between lake systems. PCA combined with multiple linear regression indicated carnivorous animal farming and OM source explained most of the observed variation in lake sedOM. Principal component analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) of ESI- and ESI+ data sets identified 103 unique candidate biomarkers. Ten strong candidate biomarkers were identified using graphical methods; more research is required for biomarker verification and molecular characterization. Our results indicate sedimentomics could be used in environmentally homogeneous areas, offering insight into the controls of sedOM cycling. Additionally, we identified prospective biomarkers related to carnivorous animal farming that could be used to understand relative contributions of farming to ongoing eutrophication issues in southwestern Nova Scotia.

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.

Antibiotic drug discovery: Challenges and perspectives in the light of emerging antibiotic resistance

Amid a rising threat of antimicrobial resistance in a global scenario, our huge investments and high-throughput technologies injected for rejuvenating the key therapeutic scaffolds to suppress these rising superbugs has been diminishing severely. This has grasped world-wide attention, with increased consideration being given to the discovery of new chemical entities. Research has now proven that the relatively tiny and simpler microbes possess enhanced capability of generating novel and diverse chemical constituents with huge therapeutic leads. The usage of these beneficial organisms could help in producing new chemical scaffolds that govern the power to suppress the spread of obnoxious superbugs. Here in this review, we have explicitly focused on several appealing strategies employed for the generation of new chemical scaffolds. Also, efforts on providing novel insights on some of the unresolved questions in the production of metabolites, metabolic profiling and also the serendipity of getting "hit molecules" have been rigorously discussed. However, we are highly aware that biosynthetic pathway of different classes of secondary metabolites and their biosynthetic route is a vast topic, thus we have avoided discussion on this topic.

Investigation of α-Glucosidase Inhibitory Metabolites from Tetracera scandens Leaves by GC-MS Metabolite Profiling and Docking Studies

Stone leaf (Tetracera scandens) is a Southeast Asian medicinal plant that has been traditionally used for the management of diabetes mellitus. The underlying mechanisms of the antidiabetic activity have not been fully explored yet. Hence, this study aimed to evaluate the α-glucosidase inhibitory potential of the hydromethanolic extracts of T. scandens leaves and to characterize the metabolites responsible for such activity through gas chromatography-mass spectrometry (GC-MS) metabolomics. Crude hydromethanolic extracts of different strengths were prepared and in vitro assayed for α-glucosidase inhibition. GC-MS analysis was further carried out and the mass spectral data were correlated to the corresponding α-glucosidase inhibitory IC₅₀ values via an orthogonal partial least squares (OPLS) model. The 100%, 80%, 60% and 40% methanol extracts displayed potent α-glucosidase inhibitory potentials. Moreover, the established model identified 16 metabolites to be responsible for the α-glucosidase inhibitory activity of T. scandens. The putative α-glucosidase inhibitory metabolites showed moderate to high affinities (binding energies of -5.9 to -9.8 kcal/mol) upon docking into the active site of Saccharomyces cerevisiae isomaltase. To sum up, an OPLS model was developed as a rapid method to characterize the α-glucosidase inhibitory metabolites existing in the hydromethanolic extracts of T. scandens leaves based on GC-MS metabolite profiling.

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.

Grouping of complex substances using analytical chemistry data: A framework for quantitative evaluation and visualization

A detailed characterization of the chemical composition of complex substances, such as products of petroleum refining and environmental mixtures, is greatly needed in exposure assessment and manufacturing. The inherent complexity and variability in the composition of complex substances obfuscate the choices for their detailed analytical characterization. Yet, in lieu of exact chemical composition of complex substances, evaluation of the degree of similarity is a sensible path toward decision-making in environmental health regulations. Grouping of similar complex substances is a challenge that can be addressed via advanced analytical methods and streamlined data analysis and visualization techniques. Here, we propose a framework with unsupervised and supervised analyses to optimally group complex substances based on their analytical features. We test two data sets of complex oil-derived substances. The first data set is from gas chromatography-mass spectrometry (GC-MS) analysis of 20 Standard Reference Materials representing crude oils and oil refining products. The second data set consists of 15 samples of various gas oils analyzed using three analytical techniques: GC-MS, GC×GC-flame ionization detection (FID), and ion mobility spectrometry-mass spectrometry (IM-MS). We use hierarchical clustering using Pearson correlation as a similarity metric for the unsupervised analysis and build classification models using the Random Forest algorithm for the supervised analysis. We present a quantitative comparative assessment of clustering results via Fowlkes-Mallows index, and classification results via model accuracies in predicting the group of an unknown complex substance. We demonstrate the effect of (i) different grouping methodologies, (ii) data set size, and (iii) dimensionality reduction on the grouping quality, and (iv) different analytical techniques on the characterization of the complex substances. While the complexity and variability in chemical composition are an inherent feature of complex substances, we demonstrate how the choices of the data analysis and visualization methods can impact the communication of their characteristics to delineate sufficient similarity.

Under the radar: long-term perspectives on ecological changes in lakes

Aquatic ecosystems are constantly changing due to natural and anthropogenic stressors. When dealing with such 'moving targets', one of the greatest challenges faced by scientists, managers and policy makers is to use appropriate time scales for environmental assessments. However, most aquatic systems lack monitoring data, and if a programme does exist, rarely have data been collected for more than a few years. Hence, it is often difficult or impossible to determine the nature and timing of ecosystem changes based on these short-term datasets. Furthermore, as environmental assessments are typically performed after a problem is identified, critical data regarding pre-disturbance (or reference) conditions are rarely available. Here, I summarize some recent studies employing lake sediment analyses (i.e. palaeolimnology) that have provided retrospective assessments of ecosystem changes that have been emerging slowly and often innocuously 'under the radar'. My examples include the identification of legacy effects of acid rain and logging, namely long-term declines in calcium concentrations in softwater lakes, which have led to significant repercussions for ecosystem services. I then show that past trajectories of aerial pollution from the burgeoning oil sands operations of western Canada can be tracked using environmental proxies preserved in dated sediment cores, and how these data can be used to determine the relative contributions of natural versus industrial sources of pollutants. I conclude by reviewing how palaeolimnological analyses have linked climate change with the proliferation of harmful blue-green algal (cyanobacterial) blooms, even without the addition of limiting nutrients. Collectively, these studies show that effective ecosystem management, particularly for incremental environmental stressors, requires temporal sampling windows that are not readily available with standard monitoring, but can be supplemented with high-resolution lake sediment analyses.