environmental metabolomics
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Metabolites ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 485
Author(s):  
Hyung Min Kim ◽  
Jong Seong Kang

Environmental pollution causes significant toxicity to ecosystems. Thus, acquiring a deeper understanding of the concentration of environmental pollutants in ecosystems and, clarifying their potential toxicities is of great significance. Environmental metabolomics is a powerful technique in investigating the effects of pollutants on living organisms in the environment. In this review, we cover the different aspects of the environmental metabolomics approach, which allows the acquisition of reliable data. A step-by-step procedure from sample preparation to data interpretation is also discussed. Additionally, other factors, including model organisms and various types of emerging environmental toxicants are discussed. Moreover, we cover the considerations for successful environmental metabolomics as well as the identification of toxic effects based on data interpretation in combination with phenotype assays. Finally, the effects induced by various types of environmental toxicants in model organisms based on the application of environmental metabolomics are also discussed.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Galen O’Shea-Stone ◽  
Rachelle Lambert ◽  
Brian Tripet ◽  
James Berardinelli ◽  
Jennifer Thomson ◽  
...  

AbstractEnvironmental metabolomics has the potential to facilitate the establishment of a new suite of tools for assessing the physiological status of important wildlife species. A first step in developing such tools is to evaluate the impacts of various capture techniques on metabolic profiles as capture is necessary to obtain the biological samples required for assays. This study employed 1H nuclear magnetic resonance (NMR)-based metabolite profiling of 562 blood serum samples from wild bighorn sheep to identify characteristic molecular serum makers of three capture techniques (dart, dropnet, and helicopter-based captures) to inform future sampling protocols for metabolomics studies, and to provide insights into the physiological impacts of capture. We found that different capture techniques induce distinct changes in amino acid serum profiles, the urea cycle, and glycolysis, and attribute the differences in metabolic patterns to differences in physical activity and stress caused by the different capture methods. These results suggest that when designing experiments involving the capture of wild animals, it may be prudent to employ a single capture technique to reduce confounding factors. Our results also supports administration of tranquilizers as soon as animals are restrained to mitigate short-term physiological and metabolic responses when using pursuit and physical restraint capture techniques.


2021 ◽  
pp. 117214
Author(s):  
Nyuk Ling Ma ◽  
Su Datt Lam ◽  
Wan Afifudeen Che Lah ◽  
Aziz Ahmad ◽  
Jörg Rinklebe ◽  
...  

2021 ◽  
Author(s):  
Galen O’Shea-Stone ◽  
Rachelle Lambert ◽  
Brian Tripet ◽  
James Berardinelli ◽  
Jennifer Thomson ◽  
...  

Abstract Environmental metabolomics has the potential to facilitate the establishment of a new suite of tools for assessing the physiological status of important wildlife species. A first step in developing such tools is to evaluate the impacts of various capture techniques on metabolic profiles as capture is necessary to obtain the biological samples required for assays. This study employed 1H nuclear magnetic resonance (NMR)-based metabolite profiling of 562 blood serum samples from wild bighorn sheep to identify characteristic molecular serum makers of three capture techniques (dart, dropnet, and helicopter-based captures) to inform future sampling protocols for metabolomics studies, and to provide insights into the physiological impacts of capture. We found that different capture techniques induce distinct changes in amino acid serum profiles, the urea cycle, and glycolysis, and attribute the differences in metabolic patterns to differences in physical activity and stress caused by the different capture methods. These results suggest that when designing experiments involving the capture of wild animals, it may be prudent to employ a single capture technique to reduce confounding factors. It also supports administration of tranquilizers as soon as animals are restrained to mitigate stress and other physiological and metabolic responses.


2021 ◽  
Vol 5 ◽  
pp. 100081
Author(s):  
Li-Juan Zhang ◽  
Lu Qian ◽  
Ling-Yun Ding ◽  
Lei Wang ◽  
Ming Hung Wong ◽  
...  

Author(s):  
Robert E. Danczak ◽  
Rosalie K. Chu ◽  
Sarah J. Fansler ◽  
Amy E. Goldman ◽  
Emily B. Graham ◽  
...  

AbstractEnvironmental metabolomics, enabled by high-resolution mass spectrometric techniques, have demonstrated the biogeochemical importance of the metabolites which comprise natural organic matter (NOM). However, significant gaps exist in our understanding of the spatiotemporal organization of NOM composition. We suggest that the underlying mechanisms governing NOM can be revealed by applying tools and concepts from metacommunity ecology to environmental metabolomics. After illustrating the similarities between metabolomes and ecological communities, we call this conceptual synthesis ‘meta-metabolome ecology’ and demonstrate its potential utility using a freshwater mass spectrometry dataset. Specifically, we developed three relational metabolite dendrograms using combinations of molecular properties (i.e., aromaticity index, double-bond equivalents, etc.) and putative biochemical transformations. Using these dendrograms, which are similar to phylogenetic or functional trait trees in ecological communities, we illustrate potential analytical techniques by investigating relationally-informed α-diversity and β-diversity metrics (e.g., MPD, MNTD, UniFrac), and null model analyses (e.g., NRI, NTI, and βNTI). Furthermore, we demonstrate that this synthesis allows ecological communities (e.g., microbes) and the metabolites they produce and consume using the same framework. We propose that applying this framework to a broad range of ecosystems will reveal generalizable principles that can advance our predictive capabilities regarding NOM dynamics.


Author(s):  
Giovanni Mastroianni ◽  
Monica Scognamiglio ◽  
Chiara Russo ◽  
Antonio Fiorentino ◽  
Margherita Lavorgna

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