Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections

Al-Sulaiti, Haya; Almaliti, Jehad; Naman, C. Benjamin; Thani, Asmaa A. Al; Yassine, Hadi M.

doi:10.3390/metabo13080948

Cited by 6 publications

(4 citation statements)

References 87 publications

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“…Early disease diagnosis is a prominent application area. Metabolomics can detect changes in metabolites within the body, aiding in the identification of early signs of various conditions such as diabetes, cancer, and cardiovascular diseases [177]. Early diagnosis often offers better treatment opportunities and enhances the likelihood of successful outcomes.…”

Section: Disease Diagnosismentioning

confidence: 99%

“…By studying metabolic differences among different subgroups, a better understanding of disease diversity and individual variations is achieved, supporting the field of precision medicine. During treatment, metabolomics is also utilized to monitor changes in disease progression and treatment effectiveness [177,178]. This helps physicians adjust treatment plans to ensure optimal patient outcomes.…”

Section: Disease Diagnosismentioning

confidence: 99%

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Review on analytical technologies and applications in metabolomics

MENG,

LIU,

et al. 2024

BIOCELL

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Over the past decade, the swift advancement of metabolomics can be credited to significant progress in technologies such as mass spectrometry, nuclear magnetic resonance, and multivariate statistics. Currently, metabolomics garners widespread application across diverse fields including drug research and development, early disease detection, toxicology, food and nutrition science, biology, prescription, and chinmedomics, among others.Metabolomics serves as an effective characterization technique, offering insights into physiological process alterations in vivo. These changes may result from various exogenous factors like environmental conditions, stress, medications, as well as endogenous elements including genetic and protein-based influences. The potential scientific outcomes gleaned from these insights have catalyzed the formulation of innovative methods, poised to further broaden the scope of this domain. Today, metabolomics has evolved into a valuable and widely accepted instrument in the life sciences. However, comprehensive reviews focusing on the sample preparation and analytical methodologies employed in metabolomics within the life sciences are surprisingly scant. This review aims to fill that gap, providing an overview of current trends and recent advancements in metabolomics. Particular emphasis is placed on sample preparation, sophisticated analytical techniques, and their applications in life science research.

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Section: Disease Diagnosismentioning

confidence: 99%

Section: Disease Diagnosismentioning

confidence: 99%

Review on analytical technologies and applications in metabolomics

MENG,

LIU,

et al. 2024

BIOCELL

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“…Viral infections could induce changes in the concentration of various metabolites, and metabolomics approaches have been proposed for the discovery of biomarkers as a way to provide better diagnoses and clinical treatments [52,53]. The global metabolome of CHIKV-infected patient sera has identified the TCA cycle as a relevant metabolic pathway at play in the course of infection [54].…”

Section: Viral Infection-related Cell Metabolismmentioning

confidence: 99%

Chikungunya Virus, Metabolism, and Circadian Rhythmicity Interplay in Phagocytic Cells

Alvarez-García,

Sánchez-García,

Vázquez-Pichardo

et al. 2023

Metabolites

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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.

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“…Between 2007 and 2017, the number of publications mentioning the terms 'metabolomic' and 'biomarker' increased significantly [15], and this increase has continued in recent years, with the number of publications doubling since 2017. This growth has been attributed to advances in liquid chromatography-mass spectrometry (LC-MS), which has allowed the untargeted metabolomic analysis of biological fluids [15][16][17]. Several types of mass spectrometers, such as time-of-flight (TOF), ion trap TOF, hybrid quadrupole TOF, and Orbitraps, routinely achieve high mass accuracy [15,18,19].…”

Section: Introductionmentioning

confidence: 99%

Untargeted Plasma Metabolomic Profiling in Patients with Depressive Disorders: A Preliminary Study

Chernonosov,

Mednova,

Levchuk

et al. 2024

Metabolites

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Depressive disorder is a multifactorial disease that is based on dysfunctions in mental and biological processes. The search for biomarkers can improve its diagnosis, personalize therapy, and lead to a deep understanding of the biochemical processes underlying depression. The purpose of this work was a metabolomic analysis of blood serum to classify patients with depressive disorders and healthy individuals using Compound Discoverer software. Using high-resolution mass spectrometry, blood plasma samples from 60 people were analyzed, of which 30 were included in a comparison group (healthy donors), and 30 were patients with a depressive episode (F32.11) and recurrent depressive disorder (F33.11). Differences between patient and control groups were identified using the built-in utilities in Compound Discoverer software. Compounds were identified by their accurate mass and fragment patterns using the mzCloud database and tentatively identified by their exact mass using the ChemSpider search engine and the KEGG, ChEBI, FDA UNII-NLM, Human Metabolome and LipidMAPS databases. We identified 18 metabolites that could divide patients with depressive disorders from healthy donors. Of these, only two compounds were tentatively identified using the mzCloud database (betaine and piperine) based on their fragmentation spectra. For three compounds ((4S,5S,8S,10R)-4,5,8-trihydroxy-10-methyl-3,4,5,8,9,10-hexahydro-2H-oxecin-2-one, (2E,4E)-N-(2-hydroxy-2-methylpropyl)-2,4-tetradecadienamide and 17α-methyl-androstan-3-hydroxyimine-17β-ol), matches were found in the mzCloud database but with low score, which could not serve as reliable evidence of their structure. Another 13 compounds were identified by their exact mass in the ChemSpider database, 9 (g-butyrobetaine, 6-diazonio-5-oxo-L-norleucine, 11-aminoundecanoic acid, methyl N-acetyl-2-diazonionorleucinate, glycyl-glycyl-argininal, dilaurylmethylamine, 12-ketodeoxycholic acid, dicetylamine, 1-linoleoyl-2-hydroxy-sn-glycero-3-PC) had only molecular formulas proposed, and 4 were unidentified. Thus, the use of Compound Discoverer software alone was not sufficient to identify all revealed metabolites. Nevertheless, the combination of the found metabolites made it possible to divide patients with depressive disorders from healthy donors.

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Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections

Cited by 6 publications

References 87 publications

Review on analytical technologies and applications in metabolomics

Review on analytical technologies and applications in metabolomics

Chikungunya Virus, Metabolism, and Circadian Rhythmicity Interplay in Phagocytic Cells

Untargeted Plasma Metabolomic Profiling in Patients with Depressive Disorders: A Preliminary Study

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