Lipidomic alteration of plasma in cured COVID-19 patients using ultra high-performance liquid chromatography with high-resolution mass spectrometry

Mass spectrometric approaches in discovering lipid biomarkers for COVID-19 by lipidomics: Future challenges and perspectives

Coronavirus disease 2019 (COVID-19) has emerged as a global health threat and has rapidly spread worldwide. Significant changes in the lipid profile before and after COVID-19 confirmed the significance of lipid metabolism in regulating the response to viral infection. Therefore, understanding the role of lipid metabolism may facilitate the development of new therapeutics for COVID-19. Owing to their high sensitivity and accuracy, mass spectrometry (MS)-based methods are widely used for rapidly identifying and quantifying of thousands of lipid species present in a small amount of sample. To enhance the capabilities of MS for the qualitative and quantitative analysis of lipids, different platforms have been combined to cover a wide range of lipidomes with high sensitivity, specificity, and accuracy. Currently, MS-based technologies are being established as efficient methods for discovering potential diagnostic biomarkers for COVID-19 and related diseases. As the lipidome of the host cell is drastically affected by the viral replication process, investigating lipid profile alterations in patients with COVID-19 and targeting lipid metabolism pathways are considered to be crucial steps in host-directed drug targeting to develop better therapeutic strategies. This review summarizes various MS-based strategies that have been developed for lipidomic analyzes and biomarker discoveries to combat COVID-19 by integrating various other potential approaches using different human samples. Furthermore, this review discusses the challenges in using MS technologies and future perspectives in terms of drug discovery and diagnosis of COVID-19.

Lipidome is a valuable tool for the severity prediction of coronavirus disease 2019

Objective

To describe the lipid metabolic profile of different patients with coronavirus disease 2019 (COVID-19) and contribute new evidence on the progression and severity prediction of COVID-19.

Methods

This case-control study was conducted in Peking University Third Hospital, China. The laboratory-confirmed COVID-19 patients aged ≥18 years old and diagnosed as pneumonia from December 2022 to January 2023 were included. Serum lipids were detected. The discrimination ability was calculated with the area under the curve (AUC). A random forest (RF) model was conducted to determine the significance of different lipids.

Results

Totally, 44 COVID-19 patients were enrolled with 16 mild and 28 severe patients. The top 5 super classes were triacylglycerols (TAG, 55.9%), phosphatidylethanolamines (PE, 10.9%), phosphatidylcholines (PC, 6.8%), diacylglycerols (DAG, 5.9%) and free fatty acids (FFA, 3.6%) among the 778 detected lipids from the serum of COVID-19 patients. Certain lipids, especially lysophosphatidylcholines (LPCs), turned to have significant correlations with certain immune/cytokine indexes. Reduced level of LPC 20:0 was observed in severe patients particularly in acute stage. The AUC of LPC 20:0 reached 0.940 in discriminating mild and severe patients and 0.807 in discriminating acute and recovery stages in the severe patients. The results of RF models also suggested the significance of LPCs in predicting the severity and progression of COVID-19.

Conclusion

Lipids probably have the potential to differentiate and forecast the severity, progression, and clinical outcomes of COVID-19 patients, with implications for immune/inflammatory responses. LPC 20:0 might be a potential target in predicting the progression and outcome and the treatment of COVID-19.

Lipidomics signature in post-COVID patient sera and its influence on the prolonged inflammatory response

BACKGROUND

The ongoing issues with post-COVID conditions (PCC), where symptoms persist long after the initial infection, highlight the need for research into blood lipid changes in these patients. While most studies focus on the acute phase of COVID-19, there's a significant lack of information on the lipidomic changes that occur in the later stages of the disease. Addressing this knowledge gap is critical for understanding the long-term effects of COVID-19 and could be key to developing personalized treatments for those suffering from PCC.

METHODS

We employed untargeted lipidomics to analyze plasma samples from 147 PCC patients, assessing nearly 400 polar lipids. Data mining (DM) and machine learning (ML) tools were utilized to decode the results and ascertain significant lipidomic patterns.

RESULTS

The study uncovered substantial changes in various lipid subclasses, presenting a detailed profile of the polar lipid fraction in PCC patients. These alterations correlated with ongoing inflammation and immune response. Notably, there were elevated levels of lysophosphatidylglycerols (LPGs) and phosphatidylethanolamines (PEs), and reduced levels of lysophosphatidylcholines (LPCs), suggesting these as potential lipid biomarkers for PCC. The lipidomic signatures indicated specific anionic lipid changes, implicating antimicrobial peptides (AMPs) in inflammation. Associations between particular medications and symptoms were also suggested. Classification models, such as multinomial regression (MR) and random forest (RF), successfully differentiated between symptomatic and asymptomatic PCC groups using lipidomic profiles.

CONCLUSIONS

The study's groundbreaking discovery of specific lipidomic disruptions in PCC patients marks a significant stride in the quest to comprehend and combat this condition. The identified lipid biomarkers not only pave the way for novel diagnostic tools but also hold the promise to tailor individualized therapeutic strategies, potentially revolutionizing the clinical approach to managing PCC and improving patient care.

Exploring salivary lipid profile changes in COVID-19 patients: Insights from mass spectrometry analysis

The most common COVID-19 testing relies on the use of nasopharyngeal swabs. However, this sampling step is very uncomfortable and is one of the biggest challenges regarding population testing. In the present study, the use of saliva as an alternative sample for COVID-19 diagnosis was investigated. Therefore, high-resolution mass spectrometry analysis and chemometric approaches were applied to salivary lipid extracts. Two data organizations were used: classical MS data and pseudo-MS image datasets. The latter transformed MS data into pseudo-images, simplifying data interpretation. Classification models achieved high accuracy, with pseudo-MS image data performing exceptionally well. PLS-DA with OPSDA successfully separated COVID-19 and healthy groups, serving as a potential diagnostic tool. The most important lipids for COVID-19 classification were elucidated and include sphingolipids, ceramides, phospholipids, and glycerolipids. These lipids play a crucial role in viral replication and the inflammatory response. While pseudo-MS image data excelled in classification, it lacked the ability to annotate important variables, which was performed using classical MS data. These findings have the potential to improve clinical diagnosis using rapid, non-invasive testing methods and accurate high-volume results.

Unraveling lipid and inflammation interplay in cancer, aging and infection for novel theranostic approaches

The synergistic relationships between Cancer, Aging, and Infection, here referred to as the CAIn Triangle, are significant determinants in numerous health maladies and mortality rates. The CAIn-related pathologies exhibit close correlations with each other and share two common underlying factors: persistent inflammation and anomalous lipid concentration profiles in the membranes of affected cells. This study provides a comprehensive evaluation of the most pertinent interconnections within the CAIn Triangle, in addition to examining the relationship between chronic inflammation and specific lipidic compositions in cellular membranes. To tackle the CAIn-associated diseases, a suite of complementary strategies aimed at diagnosis, prevention, and treatment is proffered. Our holistic approach is expected to augment the understanding of the fundamental mechanisms underlying these diseases and highlight the potential of shared features to facilitate the development of novel theranostic strategies.

COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

Metabolomics as a powerful tool for diagnostic, pronostic and drug intervention analysis in COVID-19

COVID-19 currently represents one of the major health challenges worldwide. Albeit its infectious character, with onset affectation mainly at the respiratory track, it is clear that the pathophysiology of COVID-19 has a systemic character, ultimately affecting many organs. This feature enables the possibility of investigating SARS-CoV-2 infection using multi-omic techniques, including metabolomic studies by chromatography coupled to mass spectrometry or by nuclear magnetic resonance (NMR) spectroscopy. Here we review the extensive literature on metabolomics in COVID-19, that unraveled many aspects of the disease including: a characteristic metabotipic signature associated to COVID-19, discrimination of patients according to severity, effect of drugs and vaccination treatments and the characterization of the natural history of the metabolic evolution associated to the disease, from the infection onset to full recovery or long-term and long sequelae of COVID.

Circulating Peptidome Is Strongly Altered in COVID-19 Patients

Whilst the impact of coronavirus disease 2019 (COVID-19) on the host proteome, metabolome, and lipidome has been largely investigated in different bio-fluids, to date, the circulating peptidome remains unexplored. Thus, the present study aimed to apply an untargeted peptidomic approach to provide insight into alterations of circulating peptides in the development and severity of SARS-CoV-2 infection. The circulating peptidome from COVID-19 severe and mildly symptomatic patients and negative controls was characterized using LC-MS/MS analysis for identification and quantification purposes. Database search and statistical analysis allowed a complete characterization of the plasma peptidome and the detection of the most significant modulated peptides that were impacted by the infection. Our results highlighted not only that peptide abundance inversely correlates with disease severity, but also the involvement of biomolecules belonging to inflammatory, immune-response, and coagulation proteins/processes. Moreover, our data suggested a possible involvement of changes in protein degradation patterns. In the present research, for the first time, the untargeted peptidomic approach enabled the identification of circulating peptides potentially playing a crucial role in the progression of COVID-19.

Untargeted plasma metabolomic fingerprinting highlights several biomarkers for the diagnosis and prognosis of coronavirus disease 19

Objectives

The COVID-19 pandemic has been a serious worldwide public health crisis since 2020 and is still challenging healthcare systems. New tools for the prognosis and diagnosis of COVID-19 patients remain important issues.

Design

Here, we studied the metabolome of plasma samples of COVID-19 patients for the identification of prognosis biomarkers.

Patients

Plasma samples of eighty-six SARS-CoV-2-infected subjects and 24 healthy controls were collected during the first peak of the COVID-19 pandemic in France in 2020.

Main results

Plasma metabolome fingerprinting allowed the successful discrimination of healthy controls, mild SARS-CoV-2 subjects, and moderate and severe COVID-19 patients at hospital admission. We found a strong effect of SARS-CoV-2 infection on the plasma metabolome in mild cases. Our results revealed that plasma lipids and alterations in their saturation level are important biomarkers for the detection of the infection. We also identified deoxy-fructosyl-amino acids as new putative plasma biomarkers for SARS-CoV-2 infection and COVID-19 severity. Finally, our results highlight a key role for plasma levels of tryptophan and kynurenine in the symptoms of COVID-19 patients.

Conclusion

Our results showed that plasma metabolome profiling is an efficient tool for the diagnosis and prognosis of SARS-CoV-2 infection.

Additional Evidence for Commonalities between COVID-19 and Radiation Injury: Novel Insight into COVID-19 Candidate Drugs

COVID-19 is a challenge to biosecurity and public health. The speed of vaccine development lags behind that of virus evolution and mutation. To date, no agent has been demonstrated to be fully effective against COVID-19. Therefore, it remains of great urgency to rapidly develop promising therapeutic and diagnostic candidates. Intriguingly, mounting evidence hints at parallel etiologies between SARS-CoV-2 infection and radiation injury. Herein, from the perspectives of immunogenic pathway activation and metabolic alterations, we provide novel evidence of commonalities between these two pathological conditions based on the most recent findings. Since numerous agents have been developed to prevent or reverse radiation injury in the past 70 years to ensure nuclear safety, we also advocate investigating the promising function of radioprotectors and radiomitigators against COVID-19 in clinical settings.

Enzymes in the time of COVID-19: An overview about the effects in the human body, enzyme market, and perspectives for new drugs

The rising pandemic caused by a coronavirus, resulted in a scientific quest to discover some effective treatments against its etiologic agent, the severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2). This research represented a significant scientific landmark and resulted in many medical advances. However, efforts to understand the viral mechanism of action and how the human body machinery is subverted during the infection are still ongoing. Herein, we contributed to this field with this compilation of the roles of both viral and human enzymes in the context of SARS‐CoV‐2 infection. In this sense, this overview reports that proteases are vital for the infection to take place: from SARS‐CoV‐2 perspective, the main protease (M^pro) and papain‐like protease (PL^pro) are highlighted; from the human body, angiotensin‐converting enzyme‐2, transmembrane serine protease‐2, and cathepsins (CatB/L) are pointed out. In addition, the influence of the virus on other enzymes is reported as the JAK/STAT pathway and the levels of lipase, enzymes from the cholesterol metabolism pathway, amylase, aspartate aminotransferase, alanine aminotransferase, lactate dehydrogenase, and glyceraldehyde 3‐phosphate dehydrogenase are also be disturbed in SARS‐CoV‐2 infection. Finally, this paper discusses the importance of detailed enzymatic studies for future treatments against SARS‐CoV‐2, and how some issues related to the syndrome treatment can create opportunities in the biotechnological market of enzymes and the development of new drugs.

Plasma proteomic and metabolomic characterization of COVID-19 survivors 6 months after discharge

Coronavirus disease 2019 (COVID-19) has gained prominence as a global pandemic. Studies have suggested that systemic alterations persist in a considerable proportion of COVID-19 patients after hospital discharge. We used proteomic and metabolomic approaches to analyze plasma samples obtained from 30 healthy subjects and 54 COVID-19 survivors 6 months after discharge from the hospital, including 30 non-severe and 24 severe patients. Through this analysis, we identified 1019 proteins and 1091 metabolites. The differentially expressed proteins and metabolites were then subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Among the patients evaluated, 41% of COVID-19 survivors reported at least one clinical symptom and 26.5% showed lung imaging abnormalities at 6 months after discharge. Plasma proteomics and metabolomics analysis showed that COVID-19 survivors differed from healthy control subjects in terms of the extracellular matrix, immune response, and hemostasis pathways. COVID-19 survivors also exhibited abnormal lipid metabolism, disordered immune response, and changes in pulmonary fibrosis-related proteins. COVID-19 survivors show persistent proteomic and metabolomic abnormalities 6 months after discharge from the hospital. Hence, the recovery period for COVID-19 survivors may be longer.

Syrian hamsters as a model of lung injury with SARS-CoV-2 infection: Pathologic, physiologic, and detailed molecular profiling

The acute respiratory distress syndrome (ARDS) is a common complication of severe COVID-19 (coronavirus disease 2019) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. Knowledge of molecular mechanisms driving host responses to SARS-CoV-2 is limited by the lack of reliable preclinical models of COVID-19 that recapitulate human illness. Further, existing COVID-19 animal models are not characterized as models of experimental acute lung injury (ALI) or ARDS. Acknowledging differences in experimental lung injury in animal models and human ARDS, here we systematically evaluate a model of experimental acute lung injury as a result of SARS-CoV-2 infection in Syrian golden hamsters. Following intranasal inoculation, hamsters demonstrate acute SARS-CoV-2 infection, viral pneumonia, and systemic illness but survive infection with clearance of virus. Hamsters exposed to SARS-CoV-2 exhibited key features of experimental ALI, including histologic evidence of lung injury, increased pulmonary permeability, acute inflammation, and hypoxemia. RNA sequencing of lungs indicated upregulation of inflammatory mediators that persisted after infection clearance. Lipidomic analysis demonstrated significant differences in hamster phospholipidome with SARS-CoV-2 infection. Lungs infected with SARS-CoV-2 showed increased apoptosis and ferroptosis. Thus, SARS-CoV-2 infected hamsters exhibit key features of experimental lung injury supporting their use as a preclinical model of COVID-19 ARDS.

NUTRIC Score Is Not Superior to mNUTRIC Score in Prediction of Mortality of COVID-19 Patients

OBJECTIVES

The NUTRIC (nutrition risk in the critically ill) score and the modified NUTRIC score are two scoring systems that show the nutritional risk status and severity of acute disease of patients. The only difference between them is the examination of interleukin-6 (IL-6) level. The aim of this study was to investigate whether or not the NUTRIC score is superior to the mNUTRIC score in the prediction of mortality of patients with COVID-19 followed up in the Intensive Care Unit (ICU). Material and Method. This retrospective study included 322 patients followed up in ICU with a diagnosis of COVID-19. A record was made of demographic data, laboratory values, clinical results, and mortality status. All the data of the patients were compared between high and low variations of the NUTRIC score and the mNUTRIC score.

RESULTS

A high NUTRIC score was determined in 62 patients and a high mNUTRIC score in 86 patients. The need for invasive mechanical ventilation, the use of vasopressors in ICU, the development of acute kidney injury, and mortality rates were statistically significantly higher in the patients with high NUTRIC and high mNUTRIC scores than in those with low scores (p = 0.0001 for all). The AUC values were 0.791 for high NUTRIC score and 0.786 for high mNUTRIC score (p = 0.0001 for both). No statistically significant difference was determined between the two scoring systems.

CONCLUSION

Although the NUTRIC score was seen to be superior to the mNUTRIC score, no statistically significant difference was determined. Therefore, when IL-6 cannot be examined, the mNUTRIC score can be considered safe and effective for the prediction of mortality in COVID-19 patients.

Plasma characteristic metabolites of pediatric community-acquired pneumonia in traditional Chinese medicine syndrome differentiation

Community-acquired pneumonia (CAP) is the leading cause of lower respiratory tract infections in children. Heat syndrome (HS) and cold syndrome (CS) are two main syndrome types of pediatric CAP in traditional Chinese medicine (TCM). This study aimed to identify plasma metabolic profiles in pediatric CAP and to further select potential biomarkers to distinguish between HS and CS. An ultra-performance liquid chromatography coupled with linear ion trap quadrupole-orbitrap mass spectrometry method was applied to plasma samples of 296 patients and 55 healthy controls (HC). The samples were divided into the discovery group (n = 213, HS = 160, CS = 23, HC = 30) and the validation group (n = 138, HS = 93, CS = 20, HC = 25). The orthogonal partial least-squares discriminant analysis, the value of fold change, and Kruskal-Wallis test with false discovery rate correction (q-value <0.05) were applied to identify differential plasma metabolites. The area under the ROC curve (AUC) was used to evaluate the diagnostic performance of the screened metabolites. The results showed that the plasma levels of aspartic acid, phenylalanine, arginine, lysoPC20:1, lysoPE16:0, lysoPE18:0, and PE (16:0_22:6) were increased in CS compared with HC. The plasma levels of PC (18:1_18:1), PC (20:4_20:4), PE (16:0_18:2), lysoPE20:4, lysoPE18:2, and lysoPE22:6 were decreased, whereas, the plasma level of ceramide (d18:1_24:1) was increased in HS compared with HC. There were 13 differential metabolites in CS (AUC = 0.995) and 15 differential metabolites in HS (AUC = 0.954), compared with HC. A panel of seven biomarkers, including LysoPC20:1, lysoPE16:0, lysoPE18:2, lysoPE20:4, lysoPE22:6, PC (18:1_18:1), and PC (20:4_20:4) showed good discrimination between HS and CS with an AUC of 0.982. Altered plasma amino acids and lipids may provide an objective basis for TCM syndrome differentiation in pediatric CAP.

A lipidomic view of SARS-CoV-2

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which started in late 2019, has caused huge social and economic losses. A growing number of investigators are focusing on understanding the interaction of SARS-CoV-2 with host cellular processes to find therapeutic approaches. New data suggest that lipid metabolism may play a significant role in regulating the response of immune cells like macrophages to viral infection, thereby affecting the outcome of the disease. Therefore, understanding the role of lipid metabolism could help develop new therapeutic approaches to mitigate the social and economic cost of coronavirus disease 2019 (COVID-19).

The snapshot of metabolic health in evaluating micronutrient status, the risk of infection and clinical outcome of COVID-19

COVID-19 has re-established the significance of analyzing the organism through a metabolic perspective to uncover the dynamic interconnections within the biological systems. The role of micronutrient status and metabolic health emerge as pivotal in COVID-19 pathogenesis and the immune system's response. Metabolic disruption, proceeding from modifiable factors, has been proposed as a significant risk factor accounting for infection susceptibility, disease severity and risk for post-COVID complications. Metabolomics, the comprehensive study and quantification of intermediates and products of metabolism, is a rapidly evolving field and a novel tool in biomarker discovery. In this article, we propose that leveraging insulin resistance biomarkers along with biomarkers of micronutrient deficiencies, will allow for a diagnostic window and provide functional therapeutic targets. Specifically, metabolomics can be applied as: a. At-home test to assess the risk of infection and propose nutritional support, b. A screening tool for high-risk COVID-19 patients to develop serious illness during hospital admission and prioritize medical support, c(i). A tool to match nutritional support with specific nutrient requirements for mildly ill patients to reduce the risk for hospitalization, and c(ii). for critically ill patients to reduce recovery time and risk of post-COVID complications, d. At-home test to monitor metabolic health and reduce post-COVID symptomatology. Metabolic rewiring offers potential virtues towards disease prevention, dissection of high-risk patients, taking actionable therapeutic measures, as well as shielding against post-COVID syndrome.

Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection

Improved methods are required for investigating the systemic metabolic effects of SARS-CoV-2 infection and patient stratification for precision treatment. We aimed to develop an effective method using lipid profiles for discriminating between SARS-CoV-2 infection, healthy controls, and non-SARS-CoV-2 respiratory infections. Targeted liquid chromatography-mass spectrometry lipid profiling was performed on discovery (20 SARS-CoV-2-positive; 37 healthy controls; 22 COVID-19 symptoms but SARS-CoV-2negative) and validation (312 SARS-CoV-2-positive; 100 healthy controls) cohorts. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and Kruskal-Wallis tests were applied to establish discriminant lipids, significance, and effect size, followed by logistic regression to evaluate classification performance. OPLS-DA reported separation of SARS-CoV-2 infection from healthy controls in the discovery cohort, with an area under the curve (AUC) of 1.000. A refined panel of discriminant features consisted of six lipids from different subclasses (PE, PC, LPC, HCER, CER, and DCER). Logistic regression in the discovery cohort returned a training ROC AUC of 1.000 (sensitivity = 1.000, specificity = 1.000) and a test ROC AUC of 1.000. The validation cohort produced a training ROC AUC of 0.977 (sensitivity = 0.855, specificity = 0.948) and a test ROC AUC of 0.978 (sensitivity = 0.948, specificity = 0.922). The lipid panel was also able to differentiate SARS-CoV-2-positive individuals from SARS-CoV-2-negative individuals with COVID-19-like symptoms (specificity = 0.818). Lipid profiling and multivariate modelling revealed a signature offering mechanistic insights into SARS-CoV-2, with strong predictive power, and the potential to facilitate effective diagnosis and clinical management.

Lymphopenia in COVID-19: γδ T Cells-Based Therapeutic Opportunities

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection dysregulates the immune system by lymphopenia of B cells, monocytes, eosinophils, basophils, and cytotoxic cells such as CD8, γδ T cells, and natural killer (NK) cells. Despite many studies being conducted to better understand the effects of SARS-CoV-2 on the immune system, many mechanisms still remain unclear, hindering the development of novel therapeutic approaches and strategies to improve the host’s immune defense. This mini-review summarizes the findings on the role of γδ T cells in coronavirus disease 2019 (COVID-19), providing an overview of the excellent anti-viral therapeutic potential of γδ T cells, that had not yet been exploited in depth.