Metabolomics: Basic Principles and Strategies

Nalbantoglu, Sinem

doi:10.5772/intechopen.88563

Cited by 56 publications

(42 citation statements)

References 71 publications

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“…Of the three fields, genomics has been the most utilized for clinical virology purposes; however, there is significant interest in leveraging alternative approaches such as metabolomics and proteomics to address remaining gaps. Metabolomics, or the large-scale study of small molecules, represents a change in paradigm from routine clinical virology diagnostics as it detects host metabolic response rather than directly detecting the pathogen [17] . Metabolomics theoretically holds promise for infectious diseases applications as it can be performed directly from patient specimens from minimal sample volume, is inexpensive to run, provides a real-time assessment of host response and may accurately differentiate active infection from colonization [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza

et al. 2021

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Background Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flight mass spectrometry (LC/Q-TOF) combined with machine learning for the diagnosis of influenza infection. Methods We analyzed nasopharyngeal swab samples by LC/Q-TOF to identify distinct metabolic signatures for diagnosis of acute illness. Machine learning models were performed for classification, followed by Shapley additive explanation (SHAP) analysis to analyze feature importance and for biomarker discovery. Findings A total of 236 samples were tested in the discovery phase by LC/Q-TOF, including 118 positive samples (40 influenza A 2009 H1N1, 39 influenza H3 and 39 influenza B) as well as 118 age and sex-matched negative controls with acute respiratory illness. Analysis showed an area under the receiver operating characteristic curve (AUC) of 1.00 (95% confidence interval [95% CI] 0.99, 1.00), sensitivity of 1.00 (95% CI 0.86, 1.00) and specificity of 0.96 (95% CI 0.81, 0.99). The metabolite most strongly associated with differential classification was pyroglutamic acid. Independent validation of a biomarker signature based on the top 20 differentiating ion features was performed in a prospective cohort of 96 symptomatic individuals including 48 positive samples (24 influenza A 2009 H1N1, 5 influenza H3 and 19 influenza B) and 48 negative samples. Testing performed using a clinically-applicable targeted approach, liquid chromatography triple quadrupole mass spectrometry, showed an AUC of 1.00 (95% CI 0.998, 1.00), sensitivity of 0.94 (95% CI 0.83, 0.98), and specificity of 1.00 (95% CI 0.93, 1.00). Limitations include lack of sample suitability assessment, and need to validate these findings in additional patient populations. Interpretation This metabolomic approach has potential for diagnostic applications in infectious diseases testing, including other respiratory viruses, and may eventually be adapted for point-of-care testing.

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Section: Introductionmentioning

confidence: 99%

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza

et al. 2021

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“…Metabolites are small molecules (<1000 Da) that play a crucial role in managing pathways in any organism. 36 They include nucleotides, nucleosides, amino acids, glucose moieties, and more. Metabolites work at all the levels of central dogma to manage interrelated pathways meticulously.…”

Section: Omics-based Technology Platform For Covid-19 Researchmentioning

confidence: 99%

Role of Multiomics Data to Understand Host–Pathogen Interactions in COVID-19 Pathogenesis

et al. 2021

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Human infectious diseases are contributed equally by the host immune system’s efficiency and any pathogens’ infectivity. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the coronavirus strain causing the respiratory pandemic coronavirus disease 2019 (COVID-19). To understand the pathobiology of SARS-CoV-2, one needs to unravel the intricacies of host immune response to the virus, the viral pathogen’s mode of transmission, and alterations in specific biological pathways in the host allowing viral survival. This review critically analyzes recent research using high-throughput “omics” technologies (including proteomics and metabolomics) on various biospecimens that allow an increased understanding of the pathobiology of SARS-CoV-2 in humans. The altered biomolecule profile facilitates an understanding of altered biological pathways. Further, we have performed a meta-analysis of significantly altered biomolecular profiles in COVID-19 patients using bioinformatics tools. Our analysis deciphered alterations in the immune response, fatty acid, and amino acid metabolism and other pathways that cumulatively result in COVID-19 disease, including symptoms such as hyperglycemic and hypoxic sequelae.

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“…79 The metabolome is defined as the set of metabolites present in a given biological system, fluid, cell, or tissue at a given time. 80 The topological connections among these molecules define the groups, and the characteristic displays how the system evolves concerning metabolic fluxes and environmental stimuli. 81 Numerous metabolomics studies confirmed an attempt to define higher the pathways altered in schizophrenia and its therapeutics.…”

Section: Metabolic Biomarkersmentioning

confidence: 99%

Nanotechnology Approaches for Enhanced CNS Drug Delivery in the Management of Schizophrenia

Rajalakshmi¹,

Menon²,

Nair³

2021

Adv Pharm Bull

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Schizophrenia is a neuropsychiatric disorder mainly affecting the central nervous system, presented with auditory and visual hallucinations, delusion and withdrawal from society. Abnormal dopamine levels mainly characterise the disease; various theories of neurotransmitters explain the pathophysiology of the disease. The current therapeutic approach deals with the systemic administration of drugs other than the enteral route, altering the neurotransmitter levels within the brain and providing symptomatic relief. Fluid biomarkers help in the early detection of the disease, which would improve the therapeutic efficacy. However, the major challenge faced in CNS drug delivery is the blood-brain barrier. Nanotherapeutic approaches may overcome these limitations, which will improve safety, efficacy, and targeted drug delivery. This review article addresses the main challenges faced in CNS drug delivery and the significance of current therapeutic strategies and nanotherapeutic approaches for a better understanding and enhanced drug delivery to the brain, which improve the quality of life of schizophrenia patients.

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Metabolomics: Basic Principles and Strategies

Cited by 56 publications

References 71 publications

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza

Role of Multiomics Data to Understand Host–Pathogen Interactions in COVID-19 Pathogenesis

Nanotechnology Approaches for Enhanced CNS Drug Delivery in the Management of Schizophrenia

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