Novel Approaches for Metabolomics in Drugs and Biomarkers Discovery

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 5390

Special Issue Editor


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Guest Editor
Department of Chemical Engineering, ISEL- Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Lisbon, Portugal
Interests: biomarkers discovery; drugs discovery; metabolomics; FTIR spectroscopy; diagnostic; prognosis
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Special Issue Information

Dear Colleagues,

Metabolomics, by providing a direct vision of the functional metabolic outcome of a given biological system, can enable the discovery of diverse sub-classes of pathophysiological states. This potentiates the discovery of new drug targets and biomarkers, enabling a more precise disease diagnosis and prognosis. The holistic molecular signature captured by metabolomics can also be used to evaluate the effect of drugs and, consequently, to monitor and optimize drug therapies. All of this will contribute towards a more precise and efficient use of medicine.

To further potentiate these discoveries, this Special Issue focuses on new approaches in metabolomics enabling the classification of the biological system towards the discovery of drug and disease biomarkers. The topics covered by this Special Issue will include (not exclusively) advances on:

  1. New methodologies to capture the physiological state of the system.
  2. Metabolome perturbation according to pathophysiological states.
  3. Metabolome perturbation due to drugs, and other environmental variables.

Dr. Cecília R.C. Calado
Guest Editor

Manuscript Submission Information

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Keywords

  • metabolomics
  • drugs discovery
  • biomarkers discovery
  • diagnostic
  • prognosis

Published Papers (4 papers)

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Research

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19 pages, 1045 KiB  
Article
Unraveling Metabolic Changes following Stroke: Insights from a Urinary Metabolomics Analysis
by Jamie N. Petersson, Elani A. Bykowski, Chelsea Ekstrand, Sean P. Dukelow, Chester Ho, Chantel T. Debert, Tony Montina and Gerlinde A. S. Metz
Metabolites 2024, 14(3), 145; https://doi.org/10.3390/metabo14030145 - 28 Feb 2024
Viewed by 961
Abstract
The neuropathological sequelae of stroke and subsequent recovery are incompletely understood. Here, we investigated the metabolic dynamics following stroke to advance the understanding of the pathophysiological mechanisms orchestrating stroke recovery. Using a nuclear magnetic resonance (NMR)-driven metabolomic profiling approach for urine samples obtained [...] Read more.
The neuropathological sequelae of stroke and subsequent recovery are incompletely understood. Here, we investigated the metabolic dynamics following stroke to advance the understanding of the pathophysiological mechanisms orchestrating stroke recovery. Using a nuclear magnetic resonance (NMR)-driven metabolomic profiling approach for urine samples obtained from a clinical group, the objective of this research was to (1) identify novel biomarkers indicative of severity and recovery following stroke, and (2) uncover the biochemical pathways underlying repair and functional recovery after stroke. Urine samples and clinical stroke assessments were collected during the acute (2–11 days) and chronic phases (6 months) of stroke. Using a 700 MHz 1H NMR spectrometer, metabolomic profiles were acquired followed by a combination of univariate and multivariate statistical analyses, along with biological pathway analysis and clinical correlations. The results revealed changes in phenylalanine, tyrosine, tryptophan, purine, and glycerophospholipid biosynthesis and metabolism during stroke recovery. Pseudouridine was associated with a change in post-stroke motor recovery. Thus, NMR-based metabolomics is able to provide novel insights into post-stroke cellular functions and establish a foundational framework for future investigations to develop targeted therapeutic interventions, advance stroke diagnosis and management, and enhance overall quality of life for individuals with stroke. Full article
(This article belongs to the Special Issue Novel Approaches for Metabolomics in Drugs and Biomarkers Discovery)
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17 pages, 2527 KiB  
Article
Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics
by Fatma Hilal Yagin, Seyma Yasar, Yasin Gormez, Burak Yagin, Abdulvahap Pinar, Abedalrhman Alkhateeb and Luca Paolo Ardigò
Metabolites 2023, 13(12), 1204; https://doi.org/10.3390/metabo13121204 - 18 Dec 2023
Cited by 1 | Viewed by 1586
Abstract
Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, [...] Read more.
Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 ± 1.88) % accuracy, (89.33 ± 1.80) % precision, (91.24 ± 1.67) % recall, (89.37 ± 1.52) % F1-Score, and (97.00 ± 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies. Full article
(This article belongs to the Special Issue Novel Approaches for Metabolomics in Drugs and Biomarkers Discovery)
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13 pages, 1185 KiB  
Article
Metabolomic Profiling of Hormonal Contraceptive Use in Young Females Using a Commercially Available LC-MS/MS Kit
by Tania Grobler, Monique Opperman, Janette Bester, Albe Carina Swanepoel and Ilse du Preez
Metabolites 2023, 13(10), 1092; https://doi.org/10.3390/metabo13101092 - 18 Oct 2023
Cited by 1 | Viewed by 1012
Abstract
Oral hormonal contraceptive users carry the risk of venous thrombosis and increased mortality. This study aimed to comprehensively profile the serum metabolome of participants using a combination of drospirenone (DRSP) and ethinyl estradiol (EE) containing oral contraceptives (COCs). The MxP Quant 500 kit [...] Read more.
Oral hormonal contraceptive users carry the risk of venous thrombosis and increased mortality. This study aimed to comprehensively profile the serum metabolome of participants using a combination of drospirenone (DRSP) and ethinyl estradiol (EE) containing oral contraceptives (COCs). The MxP Quant 500 kit for liquid chromatography mass tandem spectrometry (LC-MS/MS) was used to analyse the 22 controls and 44 COC users (22 on a low EE dose (DRSP/20EE) and 22 on a higher EE dose (DRSP/30EE)). The kit’s results were compared to our internally developed untargeted and targeted metabolomics methods previously applied to this cohort. Of the 630 metabolites included in the method, 277 provided desirable results (consistently detected above their detection limits), and of these, 5 had p-values < 0.05, including betaine, glutamine, cortisol, glycine, and choline. Notably, these variations were observed between the control and COC groups, rather than among the two COC groups. Partial least squares-discriminant analysis revealed 49 compounds with VIP values ≥ 1, including amino acids and their derivatives, ceramides, phosphatidylcholines, and triglycerides, among others. Ten differential compounds were consistent with our previous studies, reinforcing the notion of COCs inducing a prothrombotic state and increased oxidative stress. Although only a limited number of compounds were deemed usable, these were quantified with high reliability and facilitated the identification of meaningful biological differences among the sample groups. In addition to substantiating known drug-induced variations, new hypotheses were also generated. Full article
(This article belongs to the Special Issue Novel Approaches for Metabolomics in Drugs and Biomarkers Discovery)
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Review

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14 pages, 1908 KiB  
Review
Chikungunya Virus, Metabolism, and Circadian Rhythmicity Interplay in Phagocytic Cells
by Linamary Alvarez-García, F. Javier Sánchez-García, Mauricio Vázquez-Pichardo and M. Maximina Moreno-Altamirano
Metabolites 2023, 13(11), 1143; https://doi.org/10.3390/metabo13111143 - 11 Nov 2023
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Abstract
Chikungunya virus (CHIKV) is transmitted to humans by mosquitoes of the genus Aedes, causing the chikungunya fever disease, associated with inflammation and severe articular incapacitating pain. There has been a worldwide reemergence of chikungunya and the number of cases increased to 271,006 [...] Read more.
Chikungunya virus (CHIKV) is transmitted to humans by mosquitoes of the genus Aedes, causing the chikungunya fever disease, associated with inflammation and severe articular incapacitating pain. There has been a worldwide reemergence of chikungunya and the number of cases increased to 271,006 in 2022 in the Americas alone. The replication of CHIKV takes place in several cell types, including phagocytic cells. Monocytes and macrophages are susceptible to infection by CHIKV; at the same time, they provide protection as components of the innate immune system. However, in host–pathogen interactions, CHIKV might have the ability to alter the function of immune cells, partly by rewiring the tricarboxylic acid cycle. Some viral evasion mechanisms depend on the metabolic reprogramming of immune cells, and the cell metabolism is intertwined with circadian rhythmicity; thus, a circadian immunovirometabolism axis may influence viral pathogenicity. Therefore, analyzing the interplay between viral infection, circadian rhythmicity, and cellular metabolic reprogramming in human macrophages could shed some light on the new field of immunovirometabolism and eventually contribute to the development of novel drugs and therapeutic approaches based on circadian rhythmicity and metabolic reprogramming. Full article
(This article belongs to the Special Issue Novel Approaches for Metabolomics in Drugs and Biomarkers Discovery)
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