Metabolite reanalysis revealed potential biomarkers for COVID-19: a potential link with immune response

Multi-omics investigation of prospective therapeutic targets for type 1 diabetes

Background

In recent years, the incidence of type 1 diabetes has been rising steadily, positioning its prevention and treatment as a central focus of global public health initiatives. Previous Mendelian randomization (MR) studies have investigated the relationship between proteomics and type 1 diabetes. Consequently, this study aims to identify prospective therapeutic targets for type 1 diabetes through a comprehensive multi-omics analysis.

Methods

This study primarily utilized the MR method, drawing on genetic data from several large-scale, publicly accessible genome-wide association studies. Within this framework, we applied two-sample MR to evaluate the relationship between five omics components and type 1 diabetes. Finally, we conducted various sensitivity analyses and bidirectional MR to ensure the robustness and reliability of our findings.

Results

The inverse variance weighted method revealed that, following false discovery rate correction, 39 plasma proteins and 3 plasma protein ratios exhibited significant associations with type 1 diabetes. The genetically predicted risk of type 1 diabetes ranged from 0.05 for RBP2 to 394.51 for FMNL1. Furthermore, 4-chlorobenzoic acid levels demonstrated a potential association with type 1 diabetes.

Conclusion

Our research identified numerous omics components associated with type 1 diabetes. These findings offer novel insights into the disease's etiology, diagnosis, and treatment.

Exploring metabolic anomalies in COVID-19 and post-COVID-19: a machine learning approach with explainable artificial intelligence

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. Samples were taken from a cohort of 142 COVID-19, 48 Post-COVID-19, and 38 control patients, comprising 111 identified metabolites. Traditional analysis methods, like PCA and PLS-DA, were compared with ML techniques, particularly eXtreme Gradient Boosting (XGBoost) enhanced by SHAP (SHapley Additive exPlanations) values for explainability. XGBoost, combined with SHAP, outperformed traditional methods, demonstrating superior predictive performance and providing new insights into the metabolic basis of the disease's progression and aftermath. The analysis revealed metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions, suggesting heterogeneous metabolic responses to the infection and its long-term impacts. Key metabolic signatures in Post-COVID-19 include taurine, glutamine, alpha-Ketoglutaric acid, and LysoPC a C16:0. This study highlights the potential of integrating ML and XAI for a fine-grained description in metabolomics research, offering a more detailed understanding of metabolic anomalies in COVID-19 and Post-COVID-19 conditions.

Bile acids and coronavirus disease 2019

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been significantly alleviated. However, long-term health effects and prevention strategy remain unresolved. Thus, it is essential to explore the pathophysiological mechanisms and intervention for SARS-CoV-2 infection. Emerging research indicates a link between COVID-19 and bile acids, traditionally known for facilitating dietary fat absorption. The bile acid ursodeoxycholic acid potentially protects against SARS-CoV-2 infection by inhibiting the farnesoid X receptor, a bile acid nuclear receptor. The activation of G-protein-coupled bile acid receptor, another membrane receptor for bile acids, has also been found to regulate the expression of angiotensin-converting enzyme 2, the receptor through which the virus enters human cells. Here, we review the latest basic and clinical evidence linking bile acids to SARS-CoV-2, and reveal their complicated pathophysiological mechanisms.

Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers

The established efficacy of electronic volatile organic compound (VOC) detection technologies as diagnostic tools for noninvasive early detection of COVID-19 and related coronaviruses has been demonstrated from multiple studies using a variety of experimental and commercial electronic devices capable of detecting precise mixtures of VOC emissions in human breath. The activities of numerous global research teams, developing novel electronic-nose (e-nose) devices and diagnostic methods, have generated empirical laboratory and clinical trial test results based on the detection of different types of host VOC-biomarker metabolites from specific chemical classes. COVID-19-specific volatile biomarkers are derived from disease-induced changes in host metabolic pathways by SARS-CoV-2 viral pathogenesis. The unique mechanisms proposed from recent researchers to explain how COVID-19 causes damage to multiple organ systems throughout the body are associated with unique symptom combinations, cytokine storms and physiological cascades that disrupt normal biochemical processes through gene dysregulation to generate disease-specific VOC metabolites targeted for e-nose detection. This paper reviewed recent methods and applications of e-nose and related VOC-detection devices for early, noninvasive diagnosis of SARS-CoV-2 infections. In addition, metabolomic (quantitative) COVID-19 disease-specific chemical biomarkers, consisting of host-derived VOCs identified from exhaled breath of patients, were summarized as possible sources of volatile metabolic biomarkers useful for confirming and supporting e-nose diagnoses.

Metabolomics analysis identifies glutamic acid and cystine imbalances in COVID-19 patients without comorbid conditions. Implications on redox homeostasis and COVID-19 pathophysiology

It is well known that the presence of comorbidities and age-related health issues may hide biochemical and metabolic features triggered by SARS-CoV-2 infection and other diseases associated to hypoxia, as they are by themselves chronic inflammatory conditions that may potentially disturb metabolic homeostasis and thereby negatively impact on COVID-19 progression. To unveil the metabolic abnormalities inherent to hypoxemia caused by COVID-19, we here applied gas chromatography coupled to mass spectrometry to analyze the main metabolic changes exhibited by a population of male patients less than 50 years of age with mild/moderate and severe COVID-19 without pre-existing comorbidities known to predispose to life-threatening complications from this infection. Several differences in serum levels of particular metabolites between normal controls and patients with COVID-19 as well as between mild/moderate and severe COVID-19 were identified. These included increased glutamic acid and reduced glutamine, cystine, threonic acid, and proline levels. In particular, using the entire metabolomic fingerprint obtained, we observed that glutamine/glutamate metabolism was associated with disease severity as patients in the severe COVID-19 group presented the lowest and higher serum levels of these amino acids, respectively. These data highlight the hypoxia-derived metabolic alterations provoked by SARS-CoV-2 infection in the absence of pre-existing co-morbidities as well as the value of amino acid metabolism in determining reactive oxygen species recycling pathways, which when impaired may lead to increased oxidation of proteins and cell damage. They also provide insights on new supportive therapies for COVID-19 and other disorders that involve altered redox homeostasis and lower oxygen levels that may lead to better outcomes of disease severity.

Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning

OBJECTIVE

To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes.

MATERIAL AND METHODS

Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG and KNN.

RESULTS

The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity and fatality of COVID-19 of 93%, 94% and 97%, respectively. Low levels of the metabolites ribothymidine, 4-hydroxyphenylacetoylcarnitine and uridine were associated with COVID-19 positivity, whereas high levels of N-acetyl-glucosamine-1-phosphate, cysteinylglycine, methyl isobutyrate, l-ornithine and 5,6-dihydro-5-methyluracil were significantly related to greater severity and fatality from COVID-19.

CONCLUSION

The PLS-DA model can help to predict SARS-CoV-2 diagnosis, severity and fatality in daily practice. Some biomarkers typically increased in COVID-19 patients' serum or plasma (i.e. ribothymidine, N-acetyl-glucosamine-1-phosphate, l-ornithine, 5,6-dihydro-5-methyluracil) should be further evaluated as prognostic indicators of the disease.

Metabolomics in the Diagnosis and Prognosis of COVID-19

Coronavirus disease 2019 (COVID-19) pandemic triggered an unprecedented global effort in developing rapid and inexpensive diagnostic and prognostic tools. Since the genome of SARS-CoV-2 was uncovered, detection of viral RNA by RT-qPCR has played the most significant role in preventing the spread of the virus through early detection and tracing of suspected COVID-19 cases and through screening of at-risk population. However, a large number of alternative test methods based on SARS-CoV-2 RNA or proteins or host factors associated with SARS-CoV-2 infection have been developed and evaluated. The application of metabolomics in infectious disease diagnostics is an evolving area of science that was boosted by the urgency of COVID-19 pandemic. Metabolomics approaches that rely on the analysis of volatile organic compounds exhaled by COVID-19 patients hold promise for applications in a large-scale screening of population in point-of-care (POC) setting. On the other hand, successful application of mass-spectrometry to detect specific spectral signatures associated with COVID-19 in nasopharyngeal swab specimens may significantly save the cost and turnaround time of COVID-19 testing in the diagnostic microbiology and virology laboratories. Active research is also ongoing on the discovery of potential metabolomics-based prognostic markers for the disease that can be applied to serum or plasma specimens. Several metabolic pathways related to amino acid, lipid and energy metabolism were found to be affected by severe disease with COVID-19. In particular, tryptophan metabolism via the kynurenine pathway were persistently dysregulated in several independent studies, suggesting the roles of several metabolites of this pathway such as tryptophan, kynurenine and 3-hydroxykynurenine as potential prognostic markers of the disease. However, standardization of the test methods and large-scale clinical validation are necessary before these tests can be applied in a clinical setting. With rapidly expanding data on the metabolic profiles of COVID-19 patients with varying degrees of severity, it is likely that metabolomics will play an important role in near future in predicting the outcome of the disease with a greater degree of certainty.