Editorial: Metabolomics in Infectious Diseases

Rahman, Mahbuba; Schellhorn, Herb E.; Jithesh, Puthen V.; Rahman, Md Mizanur

doi:10.3389/fgene.2022.875835

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Metabolomics offers a comprehensive and unbiased view of the biochemical alterations occurring during viral infections portraying the complex interactions between the viral pathogen and the host response 13,14 . Notably, this approach has been proven successful in uncovering distinct metabolic signatures associated with various infectious diseases 15 , including COVID-19 16 and Post-COVID-19 17,18 . However, metabolomics profiles have high dimensionality nature 19 and can exhibit nonlinear interactions.…”

Section: Introductionmentioning

confidence: 99%

Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence

Oropeza-Valdez,

Padron-Manrique,

Vázquez-Jiménez

et al. 2024

Preprint

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The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of metabolic reprogramming in the infection's long-term consequences. This study employs a novel approach utilizing machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients. By integrating ML with SHAP (SHapley Additive exPlanations) values, we aimed to uncover metabolomic signatures and identify potential biomarkers for these conditions. Our analysis included a cohort of 142 COVID-19, 48 Post-COVID-19 samples and 38 CONTROL patients, with 111 identified metabolites. Traditional analysis methods like PCA and PLS-DA were compared with advanced ML techniques to discern metabolic changes. Notably, XGBoost models, enhanced by SHAP for explainability, outperformed traditional methods, demonstrating superior predictive performance and providing different insights into the metabolic basis of the disease's progression and its aftermath, the analysis revealed several metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions, suggesting heterogeneous metabolic responses to the infection and its long-term impacts. This study highlights the potential of integrating ML and XAI in metabolomics research.

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Section: Introductionmentioning

confidence: 99%

Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence

Oropeza-Valdez,

Padron-Manrique,

Vázquez-Jiménez

et al. 2024

Preprint

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“…The main therapeutic approach for the treatment of infectious diseases are antimicrobial drugs, followed by vaccines as the preventive measures for infectious diseases. However, antimicrobial drug resistance, increased side effects of drug toxicity, and the incidence of vaccine non-responders are increasing ( Zimmermann and Curtis, 2019 ; Szymański et al, 2020 ; Rahman et al, 2022 ). To overcome these problems, an in-depth understanding of the hosts’ immune systems and new drug formulations or drug repurposing is essential.…”

Section: Introductionmentioning

confidence: 99%

Metabolomics of infectious diseases in the era of personalized medicine

Rahman

Schellhorn

2023

Front. Mol. Biosci.

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Infectious diseases continue to be a major cause of morbidity and mortality worldwide. Diseases cause perturbation of the host’s immune system provoking a response that involves genes, proteins and metabolites. While genes are regulated by epigenetic or other host factors, proteins can undergo post-translational modification to enable/modify function. As a result, it is difficult to correlate the disease phenotype based solely on genetic and proteomic information only. Metabolites, however, can provide direct information on the biochemical activity during diseased state. Therefore, metabolites may, potentially, represent a phenotypic signature of a diseased state. Measuring and assessing metabolites in large scale falls under the omics technology known as “metabolomics”. Comprehensive and/or specific metabolic profiling in biological fluids can be used as biomarkers of disease diagnosis. In addition, metabolomics together with genomics can be used to differentiate patients with differential treatment response and development of host targeted therapy instead of pathogen targeted therapy where pathogens are more prone to mutation and lead to antimicrobial resistance. Thus, metabolomics can be used for patient stratification, personalized drug formulation and disease control and management. Currently, several therapeutics and in vitro diagnostics kits have been approved by US Food and Drug Administration (FDA) for personalized treatment and diagnosis of infectious diseases. However, the actual number of therapeutics or diagnostics kits required for tailored treatment is limited as metabolomics and personalized medicine require the involvement of personnel from multidisciplinary fields ranging from technological development, bioscience, bioinformatics, biostatistics, clinicians, and biotechnology companies. Given the significance of metabolomics, in this review, we discussed different aspects of metabolomics particularly potentials of metabolomics as diagnostic biomarkers and use of small molecules for host targeted treatment for infectious diseases, and their scopes and challenges in personalized medicine.

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Unraveling metabolic signatures in SARS-CoV-2 variant infections using multiomics analysis

Lee,

Lyoo

et al. 2024

Front. Immunol.

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IntroductionThe emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, notably delta and omicron, has significantly accelerated the global pandemic, worsening conditions worldwide. However, there is a lack of research concerning the molecular mechanisms related to immune responses and metabolism induced by these variants. MethodsHere, metabolomics combined with transcriptomics was performed to elucidate the immunometabolic changes in the lung of hamsters infected with delta and omicron variants.ResultsBoth variants caused acute inflammation and lung pathology in intranasally infected hamsters. Principal component analysis uncovered the delta variant significantly altered lung metabolite levels between the pre- and post-infection states. Additionally, metabolic pathways determined by assessment of metabolites and genes in lung revealed significant alterations in arginine biosynthesis, glutathione metabolism, and tryptophan metabolism upon infection with both variants and closely linked to inflammatory cytokines, indicating immune activation and oxidative stress in response to both variants. These metabolic changes were also evident in the serum, validating the presence of systemic alterations corresponding to those identified in lung. Notably, the delta variant induced a more robust metabolic regulation than the omicron variant. DiscussionThe study suggests that multi-omics is a valuable approach for understanding immunometabolic responses to infectious diseases, and providing insights for effective treatment strategies.

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Editorial: Metabolomics in Infectious Diseases

Cited by 3 publications

References 10 publications

Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence

Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence

Metabolomics of infectious diseases in the era of personalized medicine

Unraveling metabolic signatures in SARS-CoV-2 variant infections using multiomics analysis

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