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Metabolites
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Application of Metabolomics in Toxicology Research
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Application of Metabolomics in Toxicology Research

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Pharmacology and Drug Metabolism".

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 17310

Special Issue Editors

Dr. Sarah Wille

E-Mail Website
Guest Editor
Department Toxicology, National Institute for Criminalistics and Criminology (NICC), 1120 Brussels, Belgium
Interests: toxicological analysis; metabolites; forensics
Dr. Andrea E. Steuer

E-Mail Website
Guest Editor
Department of Forensic Pharmacology and Toxicology, Zurich Institute of Forensic Medicine, The University of Zurich, Winterthurerstrasse 190/52, CH-8057 Zurich, Switzerland
Interests: enzyme kinetics; toxicology; pharmacokinetics; LC-MS/MS; MDMA; postmortem analytical considerations for (un)‐targeted metabolomic studies with special focus on forensic applications

Special Issue Information

Dear Colleagues,

Toxicology has faced many challenges for many years, both concerning the analytical detection of drugs in relation to the ever-increasing potential drugs of interest, and concerning the final interpretation of the toxicological result. The development of highly sensitive detection techniques and appropriate data processing has enabled this progress. About a decade ago, metabolomics was introduced into the fields of forensic, clinical and environmental toxicology. The increasing use of metabolomic studies can identify changes between different states (e.g., toxic versus non-toxic), leading to the potential identification of more suitable biomarkers of toxicity. Another interesting aspect of metabolomics is the possibility of detecting biomarkers for interpretation concerning the state of the samples themselves (e.g., determination of PM interval, the impact of storage, adulteration). Moreover, mapping of the biochemical changes after drug use can lead to the detection of new metabolites in a time- and dose-dependent manner, and could result in knowledge concerning the pharmacodynamic targets and differences in acute versus long-term use. 

The purpose of this Special Issue is to look into the current metabolomic research that is done in the context of forensic, clinical or environmental toxicology. The aim is to give an overview of what is yet possible and what can be expected towards the future concerning new biomarkers for drug detection and interpretation. The purpose is to be informative for toxicologists dealing with case work (forensic, clinical and environmental), but also for more fundamental scientists looking to improve their research whilst investigating the opportunities of metabolomics for better evidence-based toxicological solutions.

Dr. Sarah Wille
Dr. Andrea E. Steuer
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolomics
  • biomarkers
  • interpretation
  • metabolites
  • forensic toxicology
  • clinical toxicology
  • environmental toxicology

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Published Papers (6 papers)

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Research

28 pages, 3033 KiB  
Article
Key Therapeutic Targets to Treat Hyperglycemia-Induced Atherosclerosis Analyzed Using a Petri Net-Based Model
by Agnieszka Rybarczyk, Dorota Formanowicz and Piotr Formanowicz
Metabolites 2023, 13(12), 1191; https://doi.org/10.3390/metabo13121191 - 8 Dec 2023
Cited by 2 | Viewed by 1644
Abstract
Chronic superphysiological glucose concentration is a hallmark of diabetes mellitus (DM) and a cause of damage to many types of cells. Atherosclerosis coexists with glucose metabolism disturbances, constituting a significant problem and exacerbating its complications. Atherosclerosis in DM is accelerated, so it is [...] Read more.
Chronic superphysiological glucose concentration is a hallmark of diabetes mellitus (DM) and a cause of damage to many types of cells. Atherosclerosis coexists with glucose metabolism disturbances, constituting a significant problem and exacerbating its complications. Atherosclerosis in DM is accelerated, so it is vital to slow its progression. However, from the complex network of interdependencies, molecules, and processes involved, choosing which ones should be inhibited without blocking the pathways crucial for the organism’s functioning is challenging. To conduct this type of analysis, in silicotesting comes in handy. In our study, to identify sites in the network that need to be blocked to have an inhibitory effect on atherosclerosis in hyperglycemia, which is toxic for the human organism, we created a model using Petri net theory and performed analyses. We have found that blocking isoforms of protein kinase C (PKC)—PKCβ and PKCγ—in diabetic patients can contribute to the inhibition of atherosclerosis progression. In addition, we have discovered that aldose reductase inhibition can slow down atherosclerosis progression, and this has been shown to reduce PKC (β and γ) expression in DM. It has also been observed that diminishing oxidative stress through the inhibitory effect on the AGE-RAGE axis may be a promising therapeutic approach in treating hyperglycemia-induced atherosclerosis. Moreover, the blockade of NADPH oxidase, the key enzyme responsible for the formation of reactive oxygen species (ROS) in blood vessels, only moderately slowed down atherosclerosis development. However, unlike aldose reductase blockade, or direct PKC (β and γ), the increased production of mitochondrial ROS associated with mitochondrial dysfunction effectively stopped after NADPH oxidase blockade. The results obtained may constitute the basis for further in-depth research. Full article
(This article belongs to the Special Issue Application of Metabolomics in Toxicology Research)
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13 pages, 2592 KiB  
Article
Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics
by Marie Lenski, Saïd Maallem, Gianni Zarcone, Guillaume Garçon, Jean-Marc Lo-Guidice, Sébastien Anthérieu and Delphine Allorge
Metabolites 2023, 13(2), 282; https://doi.org/10.3390/metabo13020282 - 15 Feb 2023
Cited by 7 | Viewed by 4155
Abstract
Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic [...] Read more.
Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R2 score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models’ reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data. Full article
(This article belongs to the Special Issue Application of Metabolomics in Toxicology Research)
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17 pages, 1165 KiB  
Article
Postmortem Metabolomics of Insulin Intoxications and the Potential Application to Find Hypoglycemia-Related Deaths
by Liam J. Ward, Gustav Engvall, Henrik Green, Fredrik C. Kugelberg, Carl Söderberg and Albert Elmsjö
Metabolites 2023, 13(1), 5; https://doi.org/10.3390/metabo13010005 - 20 Dec 2022
Cited by 6 | Viewed by 3889
Abstract
Postmortem metabolomics can assist death investigations by characterizing metabolic fingerprints differentiating causes of death. Hypoglycemia-related deaths, including insulin intoxications, are difficult to identify and, thus, presumably underdiagnosed. This investigation aims to differentiate insulin intoxication deaths by metabolomics, and identify a metabolic fingerprint to [...] Read more.
Postmortem metabolomics can assist death investigations by characterizing metabolic fingerprints differentiating causes of death. Hypoglycemia-related deaths, including insulin intoxications, are difficult to identify and, thus, presumably underdiagnosed. This investigation aims to differentiate insulin intoxication deaths by metabolomics, and identify a metabolic fingerprint to screen for unknown hypoglycemia-related deaths. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry data were obtained from 19 insulin intoxications (hypo), 19 diabetic comas (hyper), and 38 hangings (control). Screening for potentially unknown hypoglycemia-related deaths was performed using 776 random postmortem cases. Data were processed using XCMS and SIMCA. Multivariate modeling revealed group separations between hypo, hyper, and control groups. A metabolic fingerprint for the hypo group was identified, and analyses revealed significant decreases in 12 acylcarnitines, including nine hydroxylated-acylcarnitines. Screening of random postmortem cases identified 46 cases (5.9%) as potentially hypoglycemia-related, including six with unknown causes of death. Autopsy report review revealed plausible hypoglycemia-cause for five unknown cases. Additionally, two diabetic cases were found, with a metformin intoxication and a suspicious but unverified insulin intoxication, respectively. Further studies are required to expand on the potential of postmortem metabolomics as a tool in hypoglycemia-related death investigations, and the future application of screening for potential insulin intoxications. Full article
(This article belongs to the Special Issue Application of Metabolomics in Toxicology Research)
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13 pages, 611 KiB  
Article
In Vitro and In Vivo Toxicometabolomics of the Synthetic Cathinone PCYP Studied by Means of LC-HRMS/MS
by Selina Hemmer, Lea Wagmann, Benedikt Pulver, Folker Westphal and Markus R. Meyer
Metabolites 2022, 12(12), 1209; https://doi.org/10.3390/metabo12121209 - 2 Dec 2022
Cited by 4 | Viewed by 2408
Abstract
Synthetic cathinones are one important group amongst new psychoactive substances (NPS) and limited information is available regarding their toxicokinetics and -dynamics. Over the past few years, nontargeted toxicometabolomics has been increasingly used to study compound-related effects of NPS to identify important exogenous and [...] Read more.
Synthetic cathinones are one important group amongst new psychoactive substances (NPS) and limited information is available regarding their toxicokinetics and -dynamics. Over the past few years, nontargeted toxicometabolomics has been increasingly used to study compound-related effects of NPS to identify important exogenous and endogenous biomarkers. In this study, the effects of the synthetic cathinone PCYP (2-cyclohexyl-1-phenyl-2-(1-pyrrolidinyl)-ethanone) on in vitro and in vivo metabolomes were investigated. Pooled human-liver microsomes and blood and urine of male Wistar rats were used to generate in vitro and in vivo data, respectively. Samples were analyzed by liquid chromatography and high-resolution mass spectrometry using an untargeted metabolomics workflow. Statistical evaluation was performed using univariate and multivariate statistics. In total, sixteen phase I and one phase II metabolite of PCYP could be identified as exogenous biomarkers. Five endogenous biomarkers (e.g., adenosine and metabolites of tryptophan metabolism) related to PCYP intake could be identified in rat samples. The present data on the exogenous biomarker of PCYP are crucial for setting up analytical screening procedures. The data on the endogenous biomarker are important for further studies to better understand the physiological changes associated with cathinone abuse but may also serve in the future as additional markers for an intake. Full article
(This article belongs to the Special Issue Application of Metabolomics in Toxicology Research)
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17 pages, 5014 KiB  
Article
Dynamic Changes in Plasma Metabolic Profiles Reveal a Potential Metabolite Panel for Interpretation of Fatal Intoxication by Chlorpromazine or Olanzapine in Mice
by Rui Bai, Xiaohui Dai, Xingang Miao, Bing Xie, Feng Yu, Bin Cong, Di Wen and Chunling Ma
Metabolites 2022, 12(12), 1184; https://doi.org/10.3390/metabo12121184 - 27 Nov 2022
Cited by 2 | Viewed by 1725
Abstract
Diagnosing the cause of fatal intoxication by antipsychotic agents is an important task in forensic practice. In the 2020 Annual Report of the American Association of Poison Control Centers, among 40 deaths caused by antipsychotics, 21 cases were diagnosed as “probably responsible”, thereby [...] Read more.
Diagnosing the cause of fatal intoxication by antipsychotic agents is an important task in forensic practice. In the 2020 Annual Report of the American Association of Poison Control Centers, among 40 deaths caused by antipsychotics, 21 cases were diagnosed as “probably responsible”, thereby indicating that more objective diagnostic tools are needed. We used liquid chromatography-mass spectrometry-based integrated metabolomics analysis to measure changes in metabolic profiles in the plasma of mice that died from fatal intoxication due to chlorpromazine (CPZ) or olanzapine (OLA). These results were used to construct a stable discriminative classification model (DCM) comprising L-acetylcarnitine, succinic acid, and propionylcarnitine between fatal intoxication caused by CPZ/OLA and cervical dislocation (control). Performance evaluation of the classification model in mice that suffered fatal intoxication showed relative specificity for different pharmacodynamic drugs and relative sensitivity in different life states (normal, intoxication, fatal intoxication). A stable level of L-acetylcarnitine and variable levels of succinic acid and propionylcarnitine between fatal-intoxication and intoxication groups revealed procedural perturbations in metabolic pathways related to fatal intoxication by CPZ/OLA. Additional stability studies revealed that decomposition of succinic acid in fatal-intoxication samples (especially in the OLA group) could weaken the prediction performance of the binary-classification model; however, levels of these three potential metabolites measured within 6 days in fresh samples kept at 4 °C revealed a good performance of our model. Our findings suggest that metabolomics analysis can be used to explore metabolic alterations during fatal intoxication due to use of antipsychotic agents and provide evidence for the cause of death. Full article
(This article belongs to the Special Issue Application of Metabolomics in Toxicology Research)
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10 pages, 1506 KiB  
Article
Orthogonality in Principal Component Analysis Allows the Discovery of Lipids in the Jejunum That Are Independent of Ad Libitum Feeding
by David Balgoma, Fredrik Kullenberg, Karsten Peters, David Dahlgren, Femke Heindryckx, Hans Lennernäs and Mikael Hedeland
Metabolites 2022, 12(9), 866; https://doi.org/10.3390/metabo12090866 - 14 Sep 2022
Cited by 3 | Viewed by 2385
Abstract
Ad libitum feeding of experimental animals is preferred because of medical relevance together with technical and practical considerations. In addition, ethical committees may require ad libitum feeding. However, feeding affects the metabolism so ad libitum feeding may mask the effects of drugs on [...] Read more.
Ad libitum feeding of experimental animals is preferred because of medical relevance together with technical and practical considerations. In addition, ethical committees may require ad libitum feeding. However, feeding affects the metabolism so ad libitum feeding may mask the effects of drugs on tissues directly involved in the digestion process (e.g., jejunum and liver). Despite this effect, principal component analysis has the potential of identifying metabolic traits that are statistically independent (orthogonal) to ad libitum feeding. Consequently, we used principal component analysis to discover the metabolic effects of doxorubicin independent of ad libitum feeding. First, we analyzed the lipidome of the jejunum and the liver of rats treated with vehicle or doxorubicin. Subsequently, we performed principal component analysis. We could identify a principal component associated to the hydrolysis of lipids during digestion and a group of lipids that were orthogonal. These lipids in the jejunum increased with the treatment time and presented a polyunsaturated fatty acid as common structural trait. This characteristic suggests that doxorubicin increases polyunsaturated fatty acids. This behavior agrees with our previous in vitro results and suggests that doxorubicin sensitized the jejunum to ferroptosis, which may partially explain the toxicity of doxorubicin in the intestines. Full article
(This article belongs to the Special Issue Application of Metabolomics in Toxicology Research)
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