COVIDomics: The Proteomic and Metabolomic Signatures of COVID-19

Recent advances in nutritional metabolism studies on SARS-CoV-2 infection

In the context of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), metabolic research has become crucial for in-depth exploration of viral infection mechanisms and in searching for therapeutic strategies. This paper summarizes the interrelationships between carbohydrate, lipid, and amino acid metabolism and COVID-19 infection, discussing their roles in infection progression. SARS-CoV-2 infection leads to insulin resistance and increased glycolysis, reducing glucose utilization and shifting metabolism to use fat as an energy source. Fat is crucial for viral replication, and imbalances in amino acid metabolism may interfere with immune regulation. Consequently, metabolic changes such as hyperglycemia, hypolipidemia, and deficiency of certain amino acids following SARS-CoV-2 infection can contribute to progression toward severe conditions. These metabolic pathways not only have potential value in prediction and diagnosis but also provide new perspectives for the development of therapeutic strategies. By monitoring metabolic changes, infection severity can be predicted early, and modulating these metabolic pathways may help reduce inflammatory responses, improve immune responses, and reduce the risk of thrombosis. Research on the relationship between metabolism and SARS-CoV-2 infection provides an important scientific basis for addressing the global challenge posed by COVID-19, however, further studies are needed to validate these findings and provide more effective strategies for disease control.

Comparative metabolomic analysis reveals shared and unique features of COVID-19 cytokine storm and surgical sepsis

The clinical manifestations of the cytokine storm (CS) associated with COVID-19 resemble the acute phase of sepsis. Metabolomics may contribute to understanding the specific pathobiology of these two syndromes. The aim of this study was to compare serum metabolomic profiles in CS associated with COVID-19 vs. septic surgery patients. In a retrospective cross-sectional study, serum samples from patients with CS associated with COVID-19, with and without comorbidity, as well as serum samples from patients with surgical sepsis were investigated. Targeted metabolomic analysis was performed on all samples using LC-MS/MS. Analysis revealed that similar alterations in the serum metabolome of patients with COVID-19 and surgical septic patients were associated with amino acid metabolism, nitrogen metabolism, inflammatory status, methionine cycle and glycolysis. The most significant difference was found for serum levels of metabolites of kynurenine synthesis, tricarboxylic acid cycle, gamma-aminobutyric acid and niacinamide. The metabolic pathway of cysteine and methionine metabolism was significantly disturbed in COVID-19 and surgical septic patients. For the first time, the similarities and differences between the serum metabolomic profiles of patients with CS associated with COVID-19 and patients with surgical sepsis were investigated for patients from the Northwest of the Russian Federation.

Mapping the knowledge of omics in myocardial infarction: A scientometric analysis in R Studio, VOSviewer, Citespace, and SciMAT

Many researchers nowadays choose multi-omics techniques for myocardial infarction studies. However, there's yet to be a review article integrating myocardial infarction multi-omics. Hence, this study adopts the popular bibliometrics. Based on its principles, we use software like R Studio, Vosviewer, Citespace, and SciMAT to analyze literature data of myocardial infarction omics research (1991-2022) from Web of Science. By extracting key information and calculating weights, we conduct analyses from 4 aspects: Collaboration Network Analysis, Co-word Analysis, Citing and Cited Journal Analysis, and Co-citation and Clustering Analysis, aiming to understand the field's cooperation, research topic evolution, and knowledge flow. The results show that myocardial infarction omics research is still in its early stage with limited international cooperation. In terms of knowledge flow, there's no significant difference within the discipline, but non-biomedical disciplines have joined, indicating an interdisciplinary integration trend. In the overall research field, genomics remains the main topic with many breakthroughs identifying susceptibility sites. Meanwhile, other omics fields like lipidomics and proteomics are also progressing, clarifying the pathogenesis. The cooperation details in this article enable researchers to connect with others, facilitating their research. The evolution trend of subject terms helps them set goals and directions, quickly grasp the development context, and read relevant literature. Journal analysis offers submission suggestions, and the analysis of research base and frontier provides references for the research's future development.

Immunometabolic chaos in septic shock

Septic shock is associated with over 40% mortality. The immune response in septic shock is tightly regulated by cellular metabolism and transitions from early hyper-inflammation to later hypo-inflammation. Patients are susceptible to secondary infections during hypo-inflammation. The magnitude of the metabolic dysregulation and the effect of plasma metabolites on the circulating immune cells in septic shock are not reported. We hypothesized that the accumulated plasma metabolites affect the immune response in septic shock during hypo-inflammation. Our study took a unique approach. Using peripheral blood from adult septic shock patients and healthy controls, we studied: (i) Whole blood stimulation ± E. Coli lipopolysaccharide (LPS: endotoxin) to analyze plasma TNF protein, and (ii). Plasma metabolomic profile by Metabolon. Inc. (iii) We exposed peripheral blood mononuclear cells (PBMCs) from healthy controls to commercially available carbohydrate, amino acid, and fatty acid metabolites and studied the response to LPS. We report that: (i) The whole blood stimulation of the healthy control group showed a significantly upregulated TNF protein, while the septic shock group remained endotoxin tolerant, a biomarker for hypo-inflammation. (ii) A significant accumulation of carbohydrate, amino acid, fatty acid, ceramide, sphingomyelin, and TCA cycle pathway metabolites in septic shock plasma. (iii) In vitro exposure to 5 metabolites repressed while 2 metabolites upregulated the inflammatory response of PBMCs to LPS. We conclude that the endotoxin-tolerant phenotype of septic shock is associated with a simultaneous accumulation of plasma metabolites from multiple metabolic pathways, and these metabolites fundamentally influence the immune response profile of circulating cells.

Long-term dysregulation of plasma peptidome in mild and multiple COVID-19 recovered patients revealed by a novel efficient peptidomics workflow

After recovering from COVID-19, many patients experience "long COVID" symptoms. Existing research has predominantly focused on moderate to severe cases, with limited studies examining mild cases and recurrent infections. The circulating low-molecular-weight (LMW) peptidome, involving lipid metabolism, coagulation, and immune pathways, is crucial for understanding COVID-19's long-term effects. We developed a peptidomics workflow utilizing solid-phase extraction with highly wrinkled GO-Fe3O4 composite materials (HWGO-F) and nanoLC-MS/MS detection. By altering the pH, HWGO-F enhances plasma peptide adsorption and purification. Compared to traditional methods, our workflow offers improved detection depth and reproducibility for over 70% of peptide signals with CV < 20%. We investigated plasma peptide profiles in mild COVID-19 patients post-recovery from single or second infections. The findings indicate persistent abnormalities in initial COVID-19 infections' plasma peptide profiles, gradually diminishing over time. Secondary infections prolong recovery. Disrupted functions include lipid metabolism, coagulation and complement cascades, and infection-related pathways. Lipid metabolism may normalize within 3 months, while coagulation and immune abnormalities can last 3-6 months. After secondary infections, lipid metabolism irregularities may last at least 1 month, with extended coagulation and immune imbalances. These results provide a theoretical foundation for understanding the widespread occurrence of long COVID and guide recovery care for mild cases.

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

Introduction

The 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.

Methods

Here, metabolomics combined with transcriptomics was performed to elucidate the immunometabolic changes in the lung of hamsters infected with delta and omicron variants.

Results

Both 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.

Discussion

The study suggests that multi-omics is a valuable approach for understanding immunometabolic responses to infectious diseases, and providing insights for effective treatment strategies.

The BNT162b2 mRNA vaccine demonstrates reduced age-associated TH1 support in vitro and in vivo

mRNA vaccines demonstrate impaired immunogenicity and durability in vulnerable older populations. We hypothesized that human in vitro modeling and proteomics could elucidate age-specific mRNA vaccine actions. BNT162b2-stimulation changed the plasma proteome of blood samples from young (18-50Y) and older adult (≥60Y) participants, assessed by mass spectrometry, proximity extension assay, and multiplex. Young adult up-regulation (e.g., PSMC6, CPN1) contrasted reduced induction in older adults (e.g., TPM4, APOF, APOC2, CPN1, PI16). 30-85% lower TH1-polarizing cytokines and chemokines were induced in elderly blood (e.g., IFNγ, CXCL10). Analytes lower in older adult samples included human in vivo mRNA immunogenicity biomarkers (e.g., IFNγ, CXCL10, CCL4, IL-1RA). BNT162b2 also demonstrated reduced CD4+ TH1 responses in aged vs. young adult mice. Our study demonstrates the utility of human in vitro platforms modeling age-specific mRNA vaccine immunogenicity, highlights impaired support of TH1 polarization in older adults, and provides a rationale for precision mRNA vaccine adjuvantation to induce greater immunogenicity.

Rosini Silva A, Brites C, Angelo da Silva Miyaguti N, Raposo Passos Mansoldo F, Vaz Nunes S, Henrique Godoy Sanches P, Regiani Cataldi T, Pais de Carvalho C, Reis da Silva A, Ribeiro da Rosa J, Magalhães Borges M, Vilarindo Oliveira W, Canevari TC, Beatriz Vermelho A, Nogueira Eberlin M, M. Porcari A. Mass Spectrometry-Based Metabolomics Reveals a Salivary Signature for Low-Severity COVID-19

Omics approaches were extensively applied during the coronavirus disease 2019 (COVID-19) pandemic to understand the disease, identify biomarkers with diagnostic and prognostic value, and discover new molecular targets for medications. COVID-19 continues to challenge the healthcare system as the virus mutates, becoming more transmissible or adept at evading the immune system, causing resurgent epidemic waves over the last few years. In this study, we used saliva from volunteers who were negative and positive for COVID-19 when Omicron and its variants became dominant. We applied a direct solid-phase extraction approach followed by non-target metabolomics analysis to identify potential salivary signatures of hospital-recruited volunteers to establish a model for COVID-19 screening. Our model, which aimed to differentiate COVID-19-positive individuals from controls in a hospital setting, was based on 39 compounds and achieved high sensitivity (85%/100%), specificity (82%/84%), and accuracy (84%/92%) in training and validation sets, respectively. The salivary diagnostic signatures were mainly composed of amino acids and lipids and were related to a heightened innate immune antiviral response and an attenuated inflammatory profile. The higher abundance of thyrotropin-releasing hormone in the COVID-19 positive group highlighted the endocrine imbalance in low-severity disease, as first reported here, underscoring the need for further studies in this area.

Identification of gene and protein signatures associated with long-term effects of COVID-19 on the immune system after patient recovery by analyzing single-cell multi-omics data using a machine learning approach

Viral infections significantly impact the immune system, and impact will persist until recovery. However, the influence of severe acute respiratory syndrome coronavirus 2 infection on the homeostatic immune status and secondary immune response in recovered patients remains unclear. To investigate these persistent alterations, we employed five feature-ranking algorithms (LASSO, MCFS, RF, CATBoost, and XGBoost), incremental feature selection, synthetic minority oversampling technique and two classification algorithms (decision tree and k-nearest neighbors) to analyze multi-omics data (surface proteins and transcriptome) from coronavirus disease 2019 (COVID-19) recovered patients and healthy controls post-influenza vaccination. The single-cell multi-omics dataset was divided into five subsets corresponding to five immune cell subtypes: B cells, CD4+ T cells, CD8+ T cells, Monocytes, and Natural Killer cells. Each cell was represented by 28,402 scRNA-seq (RNA) features, 3 Hash Tag Oligo (HTO) features, 138 Cellular indexing of transcriptomes and epitopes by sequencing (CITE) features and 23,569 Single Cell Transform (SCT) features. Some multi-omics markers were identified and effective classifiers were constructed. Our findings indicate a distinct immune status in COVID-19 recovered patients, characterized by low expression of ribosomal protein (RPS26) and high expression of immune cell surface proteins (CD33, CD48). Notably, TMEM176B, a membrane protein, was highly expressed in monocytes of COVID-19 convalescent patients. These observations aid in discerning molecular differences among immune cell subtypes and contribute to understanding the prolonged effects of COVID-19 on the immune system, which is valuable for treating infectious diseases like COVID-19.

Unraveling the relevance of SARS-Cov-2 infection and ferroptosis within the heart of COVID-19 patients

Background

The coronavirus disease 2019 (COVID-19) was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to a huge mortality rate and imposed significant costs on the health system, causing severe damage to the cells of different organs such as the heart. However, the exact details and mechanisms behind this damage are not clarified. Therefore, we aimed to identify the cell and molecular mechanism behind the heart damage caused by SARS-Cov-2 infection.

Methods

RNA-seq data for COVID-19 patients' hearts was analyzed to obtain differentially expressed genes (DEGs) and differentially expressed ferroptosis-related genes (DEFRGs). Then, DEFRGs were used for analyzing GO and KEGG enrichment, and perdition of metabolites and drugs. we also constructed a PPI network and identified hub genes and functional modules for the DEFRGs. Subsequently, the hub genes were validated using two independent RNA-seq datasets. Finally, the miRNA-gene interaction networks were predicted in addition to a miRNA-TF co-regulatory network, and important miRNAs and transcription factors (TFs) were highlighted.

Findings

We found ferroptosis transcriptomic alterations within the hearts of COVID-19 patients. The enrichment analyses suggested the involvement of DEFRGs in the citrate cycle pathway, ferroptosis, carbon metabolism, amino acid biosynthesis, and response to oxidative stress. IL6, CDH1, AR, EGR1, SIRT3, GPT2, VDR, PCK2, VDR, and MUC1 were identified as the ferroptosis-related hub genes. The important miRNAs and TFs were miR-124-3P, miR-26b-5p, miR-183-5p, miR-34a-5p and miR-155-5p; EGR1, AR, IL6, HNF4A, SRC, EZH2, PPARA, and VDR.

Conclusion

These results provide a useful context and a cellular snapshot of how ferroptosis affects cardiomyocytes (CMs) in COVID-19 patients' hearts. Besides, suppressing ferroptosis seems to be a beneficial therapeutic approach to mitigate heart damage in COVID-19.

The Biology and Biochemistry of Kynurenic Acid, a Potential Nutraceutical with Multiple Biological Effects

Kynurenic acid (KYNA) is an antioxidant degradation product of tryptophan that has been shown to have a variety of cytoprotective, neuroprotective and neuronal signalling properties. However, mammalian transporters and receptors display micromolar binding constants; these are consistent with its typically micromolar tissue concentrations but far above its serum/plasma concentration (normally tens of nanomolar), suggesting large gaps in our knowledge of its transport and mechanisms of action, in that the main influx transporters characterized to date are equilibrative, not concentrative. In addition, it is a substrate of a known anion efflux pump (ABCC4), whose in vivo activity is largely unknown. Exogeneous addition of L-tryptophan or L-kynurenine leads to the production of KYNA but also to that of many other co-metabolites (including some such as 3-hydroxy-L-kynurenine and quinolinic acid that may be toxic). With the exception of chestnut honey, KYNA exists at relatively low levels in natural foodstuffs. However, its bioavailability is reasonable, and as the terminal element of an irreversible reaction of most tryptophan degradation pathways, it might be added exogenously without disturbing upstream metabolism significantly. Many examples, which we review, show that it has valuable bioactivity. Given the above, we review its potential utility as a nutraceutical, finding it significantly worthy of further study and development.

The Impact of Serum/Plasma Proteomics on SARS-CoV-2 Diagnosis and Prognosis

While COVID-19's urgency has diminished since its emergence in late 2019, it remains a significant public health challenge. Recent research reveals that the molecular intricacies of this virus are far more complex than initially understood, with numerous post-translational modifications leading to diverse proteoforms and viral particle heterogeneity. Mass spectrometry-based proteomics of patient serum/plasma emerges as a promising complementary approach to traditional diagnostic methods, offering insights into SARS-CoV-2 protein dynamics and enhancing understanding of the disease and its long-term consequences. This article highlights key findings from three years of pandemic-era proteomics research. It delves into biomarker discovery, diagnostic advancements, and drug development efforts aimed at monitoring COVID-19 onset and progression and exploring treatment options. Additionally, it examines global protein abundance and post-translational modification profiling to elucidate signaling pathway alterations and protein-protein interactions during infection. Finally, it explores the potential of emerging multi-omics analytic strategies in combatting SARS-CoV-2.

Proteomic and metabolomic profiling of methicillin resistant versus methicillin sensitive Staphylococcus aureus using a simultaneous extraction protocol

Background

Understanding the biology of methicillin resistant Staphylococcus aureus (MRSA) is crucial to unlocking insights for new targets in our fight against this antimicrobial resistant priority pathogen. Although proteomics and metabolomic profiling offer the potential to elucidating such biological markers, reports of methodological approaches for carrying this out in S. aureus isolates remain limited. We describe the use of a dual-functionality methanol extraction method for the concurrent extraction of protein and metabolites from S. aureus and report on the comparative analysis of the proteomic and metabolomic profiles of MRSA versus methicillin sensitive S. aureus (MSSA).

Methods

Bacterial reference strains MRSA ATCC43300 and MSSA ATCC25923 were used. The conventional urea methodology was used for protein extraction and a methanol based method was used for concurrent proteins and metabolites extraction. Proteomic and metabolomic profiling was carried out using TimsTOF mass spectrometry. Data processing was carried out using the MaxQuant version 2.1.4.0.

Results

This study represents the first report on the utilization of the methanol extraction method for concurrent protein and metabolite extraction in Gram positive bacteria. Our findings demonstrate good performance of the method for the dual extraction of proteins and metabolites from S. aureus with demonstration of reproducibility. Comparison of MRSA and MSSA strains revealed 407 proteins with significantly different expression levels. Enrichment analysis of those proteins revealed distinct pathways involved in fatty acid degradation, metabolism and beta-lactam resistance. Penicillin-binding protein PBP2a, the key determinant of MRSA resistance, exhibited distinct expression patterns in MRSA isolates. Metabolomic analysis identified 146 metabolites with only one exclusive to the MRSA. The enriched pathways identified were related to arginine metabolism and biosynthesis.

Conclusion

Our findings demonstrate the effectiveness of the methanol-based dual-extraction method, providing simultaneous insights into the proteomic and metabolomic landscapes of S. aureus strains. These findings demonstrate the utility of proteomic and metabolomic profiling for elucidating the biological basis of antimicrobial resistance.

Acoustic ejection mass spectrometry empowers ultra-fast protein biomarker quantification

The global scientific response to COVID 19 highlighted the urgent need for increased throughput and capacity in bioanalytical laboratories, especially for the precise quantification of proteins that pertain to health and disease. Acoustic ejection mass spectrometry (AEMS) represents a much-needed paradigm shift for ultra-fast biomarker screening. Here, a quantitative AEMS assays is presented, employing peptide immunocapture to enrich (i) 10 acute phase response (APR) protein markers from plasma, and (ii) SARS-CoV-2 NCAP peptides from nasopharyngeal swabs. The APR proteins were quantified in 267 plasma samples, in triplicate in 4.8 h, with %CV from 4.2% to 10.5%. SARS-CoV-2 peptides were quantified in triplicate from 145 viral swabs in 10 min. This assay represents a 15-fold speed improvement over LC-MS, with instrument stability demonstrated across 10,000 peptide measurements. The combination of speed from AEMS and selectivity from peptide immunocapture enables ultra-high throughput, reproducible quantitative biomarker screening in very large cohorts.

Multi-cohort study on cytokine and chemokine profiles in the progression of COVID-19

Various substances in the blood plasma serve as prognostic indicators of the progression of COVID-19. Consequently, multi-omics studies, such as proteomic and metabolomics, are ongoing to identify accurate biomarkers. Cytokines and chemokines, which are crucial components of immune and inflammatory responses, play pivotal roles in the transition from mild to severe illness. To determine the relationship between plasma cytokines and the progression of COVID-19, we used four study cohorts to perform a systematic study of cytokine levels in patients with different disease stages. We observed differential cytokine expression between patients with persistent-mild disease and patients with mild-to-severe transformation. For instance, IL-4 and IL-17 levels significantly increased in patients with mild-to-severe transformation, indicating differences within the mild disease group. Subsequently, we analysed the changes in cytokine and chemokine expression in the plasma of patients undergoing two opposing processes: the transition from mild to severe illness and the transition from severe to mild illness. We identified several factors, such as reduced expression of IL-16 and IL-18 during the severe phase of the disease and up-regulated expression of IL-10, IP-10, and SCGF-β during the same period, indicative of the deterioration or improvement of patients' conditions. These factors obtained from fine-tuned research cohorts could provide auxiliary indications for changes in the condition of COVID-19 patients.

Developmental Impacts of Epigenetics and Metabolism in COVID-19

Developmental biology is intricately regulated by epigenetics and metabolism but the mechanisms are not completely understood. The situation becomes even more complicated during diseases where all three phenomena are dysregulated. A salient example is COVID-19, where the death toll exceeded 6.96 million in 4 years, while the virus continues to mutate into different variants and infect people. Early evidence during the pandemic showed that the host's immune and inflammatory responses to COVID-19 (like the cytokine storm) impacted the host's metabolism, causing damage to the host's organs and overall physiology. The involvement of angiotensin-converting enzyme 2 (ACE2), the pivotal host receptor for the SARS-CoV-2 virus, was identified and linked to epigenetic abnormalities along with other contributing factors. Recently, studies have revealed stronger connections between epigenetics and metabolism in COVID-19 that impact development and accelerate aging. Patients manifest systemic toxicity, immune dysfunction and multi-organ failure. Single-cell multiomics and other state-of-the-art high-throughput studies are only just beginning to demonstrate the extent of dysregulation and damage. As epigenetics and metabolism directly impact development, there is a crucial need for research implementing cutting-edge technology, next-generation sequencing, bioinformatics analysis, the identification of biomarkers and clinical trials to help with prevention and therapeutic interventions against similar threats in the future.

Identification of key genes associated with persistent immune changes and secondary immune activation responses induced by influenza vaccination after COVID-19 recovery by machine learning methods

COVID-19 is hypothesized to exert enduring effects on the immune systems of patients, leading to alterations in immune-related gene expression. This study aimed to scrutinize the persistent implications of SARS-CoV-2 infection on gene expression and its influence on subsequent immune activation responses. We designed a machine learning-based approach to analyze transcriptomic data from both healthy individuals and patients who had recovered from COVID-19. Patients were categorized based on their influenza vaccination status and then compared with healthy controls. The initial sample set encompassed 86 blood samples from healthy controls and 72 blood samples from recuperated COVID-19 patients prior to influenza vaccination. The second sample set included 123 blood samples from healthy controls and 106 blood samples from recovered COVID-19 patients who had been vaccinated against influenza. For each sample, the dataset captured expression levels of 17,060 genes. Above two sample sets were first analyzed by seven feature ranking algorithms, yielding seven feature lists for each dataset. Then, each list was fed into the incremental feature selection method, incorporating three classic classification algorithms, to extract essential genes, classification rules and build efficient classifiers. The genes and rules were analyzed in this study. The main findings included that NEXN and ZNF354A were highly expressed in recovered COVID-19 patients, whereas MKI67 and GZMB were highly expressed in patients with secondary immune activation post-COVID-19 recovery. These pivotal genes could provide valuable insights for future health monitoring of COVID-19 patients and guide the creation of continued treatment regimens.

Short-term predictor for COVID-19 severity from a longitudinal multi-omics study for practical application in intensive care units

BACKGROUND

The COVID-19 pandemic challenged the management of technical and human resources in intensive care units (ICU) across the world. Several long-term predictors for COVID-19 disease progression have been discovered. However, predictors to support short-term planning of resources and medication that can be translated to future pandemics are still missing. A workflow was established to identify a predictor for short-term COVID-19 disease progression in the acute phase of intensive care patients to support clinical decision-making.

METHODS

Thirty-two patients with SARS-CoV-2 infection were recruited on admission to the ICU and clinical data collected. During their hospitalization, plasma samples were acquired from each patient on multiple occasions, excepting one patient for which only one time point was possible, and the proteome (Inflammation, Immune Response and Organ Damage panels from Olink® Target 96), metabolome and lipidome (flow injection analysis and liquid chromatography-mass spectrometry) analyzed for each sample. Patient visits were grouped according to changes in disease severity based on their respiratory and organ function, and evaluated using a combination of statistical analysis and machine learning. The resulting short-term predictor from this multi-omics approach was compared to the human assessment of disease progression. Furthermore, the potential markers were compared to the baseline levels of 50 healthy subjects with no known SARS-CoV-2 or other viral infections.

RESULTS

A total of 124 clinical parameters, 271 proteins and 782 unique metabolites and lipids were assessed. The dimensionality of the dataset was reduced, selecting 47 from the 1177 parameters available following down-selection, to build the machine learning model. Subsequently, two proteins (C-C motif chemokine 7 (CCL7) and carbonic anhydrase 14 (CA14)) and one lipid (hexosylceramide 18:2; O2/20:0) were linked to disease progression in the studied SARS-CoV-2 infections. Thus, a predictor delivering the prognosis of an upcoming worsening of the patient's condition up to five days in advance with a reasonable accuracy (79 % three days prior to event, 84 % four to five days prior to event) was found. Interestingly, the predictor's performance was complementary to the clinicians' capabilities to foresee a worsening of a patient.

CONCLUSION

This study presents a workflow to identify omics-based biomarkers to support clinical decision-making and resource management in the ICU. This was successfully applied to develop a short-term predictor for aggravation of COVID-19 symptoms. The applied methods can be adapted for future small cohort studies.

Nucleotide, Phospholipid, and Kynurenine Metabolites Are Robustly Associated with COVID-19 Severity and Time of Plasma Sample Collection in a Prospective Cohort Study

Understanding the molecular underpinnings of disease severity and progression in human studies is necessary to develop metabolism-related preventative strategies for severe COVID-19. Metabolites and metabolic pathways that predispose individuals to severe disease are not well understood. In this study, we generated comprehensive plasma metabolomic profiles in >550 patients from the Longitudinal EMR and Omics COVID-19 Cohort. Samples were collected before (n = 441), during (n = 86), and after (n = 82) COVID-19 diagnosis, representing 555 distinct patients, most of which had single timepoints. Regression models adjusted for demographics, risk factors, and comorbidities, were used to determine metabolites associated with predisposition to and/or persistent effects of COVID-19 severity, and metabolite changes that were transient/lingering over the disease course. Sphingolipids/phospholipids were negatively associated with severity and exhibited lingering elevations after disease, while modified nucleotides were positively associated with severity and had lingering decreases after disease. Cytidine and uridine metabolites, which were positively and negatively associated with COVID-19 severity, respectively, were acutely elevated, reflecting the particular importance of pyrimidine metabolism in active COVID-19. This is the first large metabolomics study using COVID-19 plasma samples before, during, and/or after disease. Our results lay the groundwork for identifying putative biomarkers and preventive strategies for severe COVID-19.

Using the power of innate immunoprofiling to understand vaccine design, infection, and immunity

In the field of immunology, a systems biology approach is crucial to understanding the immune response to infection and vaccination considering the complex interplay between genetic, epigenetic, and environmental factors. Significant progress has been made in understanding the innate immune response, including cell players and critical signaling pathways, but many questions remain unanswered, including how the innate immune response dictates host/pathogen responses and responses to vaccines. To complicate things further, it is becoming increasingly clear that the innate immune response is not a linear pathway but is formed from complex networks and interactions. To further our understanding of the crosstalk and complexities, systems-level analyses and expanded experimental technologies are now needed. In this review, we discuss the most recent immunoprofiling techniques and discuss systems approaches to studying the global innate immune landscape which will inform on the development of personalized medicine and innovative vaccine strategies.

Proteomics and Metabolomics in Biomedicine

The technological advances of recent years have significantly enhanced medical discoveries [...].

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

Dysregulations in hemostasis, metabolism, immune response, and angiogenesis in post-acute COVID-19 syndrome with and without postural orthostatic tachycardia syndrome: a multi-omic profiling study

Post-acute COVID-19 (PACS) are associated with cardiovascular dysfunction, especially postural orthostatic tachycardia syndrome (POTS). Patients with PACS, both in the absence or presence of POTS, exhibit a wide range of persisting symptoms long after the acute infection. Some of these symptoms may stem from alterations in cardiovascular homeostasis, but the exact mechanisms are poorly understood. The aim of this study was to provide a broad molecular characterization of patients with PACS with (PACS + POTS) and without (PACS-POTS) POTS compared to healthy subjects, including a broad proteomic characterization with a focus on plasma cardiometabolic proteins, quantification of cytokines/chemokines and determination of plasma sphingolipid levels. Twenty-one healthy subjects without a prior COVID-19 infection (mean age 43 years, 95% females), 20 non-hospitalized patients with PACS + POTS (mean age 39 years, 95% females) and 22 non-hospitalized patients with PACS-POTS (mean age 44 years, 100% females) were studied. PACS patients were non-hospitalized and recruited ≈18 months after the acute infection. Cardiometabolic proteomic analyses revealed a dysregulation of ≈200 out of 700 analyzed proteins in both PACS groups vs. healthy subjects with the majority (> 90%) being upregulated. There was a large overlap (> 90%) with no major differences between the PACS groups. Gene ontology enrichment analysis revealed alterations in hemostasis/coagulation, metabolism, immune responses, and angiogenesis in PACS vs. healthy controls. Furthermore, 11 out of 33 cytokines/chemokines were significantly upregulated both in PACS + POTS and PACS-POTS vs. healthy controls and none of the cytokines were downregulated. There were no differences in between the PACS groups in the cytokine levels. Lastly, 16 and 19 out of 88 sphingolipids were significantly dysregulated in PACS + POTS and PACS-POTS, respectively, compared to controls with no differences between the groups. Collectively, these observations suggest a clear and distinct dysregulation in the proteome, cytokines/chemokines, and sphingolipid levels in PACS patients compared to healthy subjects without any clear signature associated with POTS. This enhances our understanding and might pave the way for future experimental and clinical investigations to elucidate and/or target resolution of inflammation and micro-clots and restore the hemostasis and immunity in PACS.

Quantitative LC-MS study of compounds found predictive of COVID-19 severity and outcome

INTRODUCTION

Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients.

OBJECTIVES

In this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome.

METHODS

A targeted LC-MS method was used in 46 control and 95 acute COVID-19 patient samples to quantitate the selected metabolites. These compounds included tryptophan and its degradation products kynurenine and kynurenic acid (reflective of immune response), butyrylcarnitine and its isomer (reflective of energy metabolism) and finally 3',4'-didehydro-3'-deoxycytidine, a deoxycytidine analogue, (reflective of host viral defence response). We subsequently examine changes in those markers by disease severity and outcome relative to those of control patients' levels.

RESULTS & CONCLUSION

Finally, we demonstrate the added value of the kynurenic acid/tryptophan ratio for severity and outcome prediction and highlight the viral detection potential of ddhC.

Complement factor D targeting protects endotheliopathy in organoid and monkey models of COVID-19

COVID-19 is linked to endotheliopathy and coagulopathy, which can result in multi-organ failure. The mechanisms causing endothelial damage due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain elusive. Here, we developed an infection-competent human vascular organoid from pluripotent stem cells for modeling endotheliopathy. Longitudinal serum proteome analysis identified aberrant complement signature in critically ill patients driven by the amplification cycle regulated by complement factor B and D (CFD). This deviant complement pattern initiates endothelial damage, neutrophil activation, and thrombosis specific to organoid-derived human blood vessels, as verified through intravital imaging. We examined a new long-acting, pH-sensitive (acid-switched) antibody targeting CFD. In both human and macaque COVID-19 models, this long-acting anti-CFD monoclonal antibody mitigated abnormal complement activation, protected endothelial cells, and curtailed the innate immune response post-viral exposure. Collectively, our findings suggest that the complement alternative pathway exacerbates endothelial injury and inflammation. This underscores the potential of CFD-targeted therapeutics against severe viral-induced inflammathrombotic outcomes.

Metabolomics-directed nanotechnology in viral diseases management: COVID-19 a case study

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently regarded as the twenty-first century's plague accounting for coronavirus disease 2019 (COVID-19). Besides its reported symptoms affecting the respiratory tract, it was found to alter several metabolic pathways inside the body. Nanoparticles proved to combat viral infections including COVID-19 to demonstrate great success in developing vaccines based on mRNA technology. However, various types of nanoparticles can affect the host metabolome. Considering the increasing proportion of nano-based vaccines, this review compiles and analyses how COVID-19 and nanoparticles affect lipids, amino acids, and carbohydrates metabolism. A search was conducted on PubMed, ScienceDirect, Web of Science for available information on the interrelationship between metabolomics and immunity in the context of SARS-CoV-2 infection and the effect of nanoparticles on metabolite levels. It was clear that SARS-CoV-2 disrupted several pathways to ensure a sufficient supply of its building blocks to facilitate its replication. Such information can help in developing treatment strategies against viral infections and COVID-19 based on interventions that overcome these metabolic changes. Furthermore, it showed that even drug-free nanoparticles can exert an influence on biological systems as evidenced by metabolomics.

Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction

An integrative multi-modal metabolic phenotyping model was developed to assess the systemic plasma sequelae of SARS-CoV-2 (rRT-PCR positive) induced COVID-19 disease in patients with different respiratory severity levels. Plasma samples from 306 unvaccinated COVID-19 patients were collected in 2020 and classified into four levels of severity ranging from mild symptoms to severe ventilated cases. These samples were investigated using a combination of quantitative Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) platforms to give broad lipoprotein, lipidomic and amino acid, tryptophan-kynurenine pathway, and biogenic amine pathway coverage. All platforms revealed highly significant differences in metabolite patterns between patients and controls (n = 89) that had been collected prior to the COVID-19 pandemic. The total number of significant metabolites increased with severity with 344 out of the 1034 quantitative variables being common to all severity classes. Metabolic signatures showed a continuum of changes across the respiratory severity levels with the most significant and extensive changes being in the most severely affected patients. Even mildly affected respiratory patients showed multiple highly significant abnormal biochemical signatures reflecting serious metabolic deficiencies of the type observed in Post-acute COVID-19 syndrome patients. The most severe respiratory patients had a high mortality (56.1%) and we found that we could predict mortality in this patient sub-group with high accuracy in some cases up to 61 days prior to death, based on a separate metabolic model, which highlighted a different set of metabolites to those defining the basic disease. Specifically, hexosylceramides (HCER 16:0, HCER 20:0, HCER 24:1, HCER 26:0, HCER 26:1) were markedly elevated in the non-surviving patient group (Cliff's delta 0.91-0.95) and two phosphoethanolamines (PE.O 18:0/18:1, Cliff's delta = -0.98 and PE.P 16:0/18:1, Cliff's delta = -0.93) were markedly lower in the non-survivors. These results indicate that patient morbidity to mortality trajectories is determined relatively soon after infection, opening the opportunity to select more intensive therapeutic interventions to these "high risk" patients in the early disease stages.

Metabolic Adaptations Correlated with Antibody Response after Immunization with Inactivated SARS-CoV-2 in Brazilian Subjects

The adsorbed vaccine SARS-CoV-2 (inactivated) produced by Sinovac (SV) was the first vaccine against COVID-19 to be used in Brazil. To understand the metabolic effects of SV in Brazilian subjects, NMR-based metabolomics was used, and the immune response was studied in Brazilian subjects. Forty adults without (group^-, n = 23) and with previous COVID-19 infection (group⁺, n = 17) were followed-up for 90 days postcompletion of the vaccine regimen. After 90 days, our results showed that subjects had increased levels of lipoproteins, lipids, and N-acetylation of glycoproteins (NAG) as well as decreased levels of amino acids, lactate, citrate, and 3-hydroxypropionate. NAG and threonine were the highest correlated metabolites with N and S proteins, and neutralizing Ab levels. This study sheds light on the immunometabolism associated with the use of SV in Brazilian subjects from Rio de Janeiro and identifies potential metabolic markers associated with the immune status.

Maintained imbalance of triglycerides, apolipoproteins, energy metabolites and cytokines in long-term COVID-19 syndrome patients

Background

Deep metabolomic, proteomic and immunologic phenotyping of patients suffering from an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have matched a wide diversity of clinical symptoms with potential biomarkers for coronavirus disease 2019 (COVID-19). Several studies have described the role of small as well as complex molecules such as metabolites, cytokines, chemokines and lipoproteins during infection and in recovered patients. In fact, after an acute SARS-CoV-2 viral infection almost 10-20% of patients experience persistent symptoms post 12 weeks of recovery defined as long-term COVID-19 syndrome (LTCS) or long post-acute COVID-19 syndrome (PACS). Emerging evidence revealed that a dysregulated immune system and persisting inflammation could be one of the key drivers of LTCS. However, how these biomolecules altogether govern pathophysiology is largely underexplored. Thus, a clear understanding of how these parameters within an integrated fashion could predict the disease course would help to stratify LTCS patients from acute COVID-19 or recovered patients. This could even allow to elucidation of a potential mechanistic role of these biomolecules during the disease course.

Methods

This study comprised subjects with acute COVID-19 (n=7; longitudinal), LTCS (n=33), Recov (n=12), and no history of positive testing (n=73). ¹H-NMR-based metabolomics with IVDr standard operating procedures verified and phenotyped all blood samples by quantifying 38 metabolites and 112 lipoprotein properties. Univariate and multivariate statistics identified NMR-based and cytokine changes.

Results

Here, we report on an integrated analysis of serum/plasma by NMR spectroscopy and flow cytometry-based cytokines/chemokines quantification in LTCS patients. We identified that in LTCS patients lactate and pyruvate were significantly different from either healthy controls (HC) or acute COVID-19 patients. Subsequently, correlation analysis in LTCS group only among cytokines and amino acids revealed that histidine and glutamine were uniquely attributed mainly with pro-inflammatory cytokines. Of note, triglycerides and several lipoproteins (apolipoproteins Apo-A1 and A2) in LTCS patients demonstrate COVID-19-like alterations compared with HC. Interestingly, LTCS and acute COVID-19 samples were distinguished mostly by their phenylalanine, 3-hydroxybutyrate (3-HB) and glucose concentrations, illustrating an imbalanced energy metabolism. Most of the cytokines and chemokines were present at low levels in LTCS patients compared with HC except for IL-18 chemokine, which tended to be higher in LTCS patients.

Conclusion

The identification of these persisting plasma metabolites, lipoprotein and inflammation alterations will help to better stratify LTCS patients from other diseases and could help to predict ongoing severity of LTCS patients.

SARS-CoV-2 infection results in upregulation of Plasminogen Activator Inhibitor-1 and Neuroserpin in the lungs, and an increase in fibrinolysis inhibitors associated with disease severity

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection results in coagulation activation although it is usually not associated with consumption coagulopathy. D-dimers are also commonly elevated despite systemic hypofibrinolysis. To understand these unusual features of coronavirus disease 2019 (COVID-19) coagulopathy, 64 adult patients with SARS-CoV-2 infection (36 moderate and 28 severe) and 16 controls were studied. We evaluated the repertoire of plasma protease inhibitors (Serpins, Kunitz, Kazal, Cystatin-like) targeting the fibrinolytic system: Plasminogen Activator Inhibitor-1 (PAI-1), Tissue Plasminogen Activator/Plasminogen Activator Inhibitor-1 complex (t-PA/PAI-1), α-2-Antiplasmin, Plasmin-α2-Antiplasmin Complex, Thrombin-activatable Fibrinolysis Inhibitor (TAFI)/TAFIa, Protease Nexin-1 (PN-1), and Neuroserpin (the main t-PA inhibitor of the central nervous system). Inhibitors of the common (Antithrombin, Thrombin-antithrombin complex, Protein Z [PZ]/PZ inhibitor, Heparin Cofactor II, and α2-Macroglobulin), Protein C ([PC], Protein C inhibitor, and Protein S), contact (Kallistatin, Protease Nexin-2/Amyloid Beta Precursor Protein, and α-1-Antitrypsin), and complement (C1-Inhibitor) pathways, in addition to Factor XIII, Histidine-rich glycoprotein (HRG) and Vaspin were also investigated by enzyme-linked immunosorbent assay. The association of these markers with disease severity was evaluated by logistic regression. Pulmonary expression of PAI-1 and Neuroserpin in the lungs from eight post-mortem cases was assessed by immunohistochemistry. Results show that six patients (10%) developed thrombotic events, and mortality was 11%. There was no significant reduction in plasma anticoagulants, in keeping with a compensated state. However, an increase in fibrinolysis inhibitors (PAI-1, Neuroserpin, PN-1, PAP, and t-PA/PAI-1) was consistently observed, while HRG was reduced. Furthermore, these markers were associated with moderate and/or severe disease. Notably, immunostains demonstrated overexpression of PAI-1 in epithelial cells, macrophages, and endothelial cells of fatal COVID-19, while Neuroserpin was found in intraalveolar macrophages only. These results imply that the lungs in SARS-CoV-2 infection provide anti-fibrinolytic activity resulting in a shift toward a local and systemic hypofibrinolytic state predisposing to (immuno)thrombosis, often in a background of compensated disseminated intravascular coagulation.

Deciphering the Relationship between SARS-CoV-2 and Cancer

Some viruses are known to be associated with the onset of specific cancers. These microorganisms, oncogenic viruses or oncoviruses, can convert normal cells into cancer cells by modulating the central metabolic pathways or hampering genomic integrity mechanisms, consequently inhibiting the apoptotic machinery and/or enhancing cell proliferation. Seven oncogenic viruses are known to promote tumorigenesis in humans: human papillomavirus (HPV), hepatitis B and C viruses (HBV, HCV), Epstein-Barr virus (EBV), human T-cell leukemia virus 1 (HTLV-1), Kaposi sarcoma-associated herpesvirus (KSHV), and Merkel cell polyomavirus (MCPyV). Recent research indicates that SARS-CoV-2 infection and COVID-19 progression may predispose recovered patients to cancer onset and accelerate cancer development. This hypothesis is based on the growing evidence regarding the ability of SARS-CoV-2 to modulate oncogenic pathways, promoting chronic low-grade inflammation and causing tissue damage. Herein, we summarize the main relationships known to date between virus infection and cancer, providing a summary of the proposed biochemical mechanisms behind the cellular transformation. Mechanistically, DNA viruses (such as HPV, HBV, EBV, and MCPyV) encode their virus oncogenes. In contrast, RNA viruses (like HCV, HTLV-1) may encode oncogenes or trigger host oncogenes through cis-/-trans activation leading to different types of cancer. As for SARS-CoV-2, its role as an oncogenic virus seems to occur through the inhibition of oncosuppressors or controlling the metabolic and autophagy pathways in the infected cells. However, these effects could be significant in particular scenarios like those linked to severe COVID-19 or long COVID. On the other hand, looking at the SARS-CoV-2─cancer relationship from an opposite perspective, oncolytic effects and anti-tumor immune response were triggered by SARS-CoV-2 infection in some cases. In summary, our work aims to recall comprehensive attention from the scientific community to elucidate the effects of SARS-CoV-2 and, more in general, β-coronavirus infection on cancer susceptibility for cancer prevention or supporting therapeutic approaches.

Targeted lipidomics data of COVID-19 patients

The dataset provided with this article describes a targeted lipidomics analysis performed on the serum of COVID-19 patients characterized by different degree of severity. As the ongoing pandemic has posed a challenging threat for humanity, the data here presented belong to one of the first lipidomics studies carried out on COVID-19 patients’ samples collected during the first pandemic waves. Serum samples were obtained from hospitalized patients with a molecular diagnosis of SARS-CoV-2 infection detected after nasal swab, and categorized as mild, moderate, or severe according to pre-established clinical descriptors. The MS-based targeted lipidomic analysis was performed by MRM using a Triple Quad 5500+ mass spectrometer, and the quantitative data were acquired on a panel of 483 lipids. The characterization of this lipidomic dataset has been outlined using multivariate and univariate descriptive statistics and bioinformatics tools.

New Insights into the Identification of Metabolites and Cytokines Predictive of Outcome for Patients with Severe SARS-CoV-2 Infection Showed Similarity with Cancer

SARS-CoV-2 infection is characterized by several clinical manifestations, ranging from the absence of symptoms to severe forms that necessitate intensive care treatment. It is known that the patients with the highest rate of mortality develop increased levels of proinflammatory cytokines, called the "cytokine storm", which is similar to inflammatory processes that occur in cancer. Additionally, SARS-CoV-2 infection induces modifications in host metabolism leading to metabolic reprogramming, which is closely linked to metabolic changes in cancer. A better understanding of the correlation between perturbed metabolism and inflammatory responses is necessary. We evaluated untargeted plasma metabolomics and cytokine profiling via ¹H-NMR (proton nuclear magnetic resonance) and multiplex Luminex assay, respectively, in a training set of a limited number of patients with severe SARS-CoV-2 infection classified on the basis of their outcome. Univariate analysis and Kaplan-Meier curves related to hospitalization time showed that lower levels of several metabolites and cytokines/growth factors, correlated with a good outcome in these patients and these data were confirmed in a validation set of patients with similar characteristics. However, after the multivariate analysis, only the growth factor HGF, lactate and phenylalanine retained a significant prediction of survival. Finally, the combined analysis of lactate and phenylalanine levels correctly predicted the outcome of 83.3% of patients in both the training and the validation set. We highlighted that the cytokines and metabolites involved in COVID-19 patients' poor outcomes are similar to those responsible for cancer development and progression, suggesting the possibility of targeting them by repurposing anticancer drugs as a therapeutic strategy against severe SARS-CoV-2 infection.

The role of peroxisome proliferator-activated receptors in the modulation of hyperinflammation induced by SARS-CoV-2 infection: A perspective for COVID-19 therapy

Coronavirus disease 2019 (COVID-19) is a severe respiratory disease caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that affects the lower and upper respiratory tract in humans. SARS-CoV-2 infection is associated with the induction of a cascade of uncontrolled inflammatory responses in the host, ultimately leading to hyperinflammation or cytokine storm. Indeed, cytokine storm is a hallmark of SARS-CoV-2 immunopathogenesis, directly related to the severity of the disease and mortality in COVID-19 patients. Considering the lack of any definitive treatment for COVID-19, targeting key inflammatory factors to regulate the inflammatory response in COVID-19 patients could be a fundamental step to developing effective therapeutic strategies against SARS-CoV-2 infection. Currently, in addition to well-defined metabolic actions, especially lipid metabolism and glucose utilization, there is growing evidence of a central role of the ligand-dependent nuclear receptors and peroxisome proliferator-activated receptors (PPARs) including PPARα, PPARβ/δ, and PPARγ in the control of inflammatory signals in various human inflammatory diseases. This makes them attractive targets for developing therapeutic approaches to control/suppress the hyperinflammatory response in patients with severe COVID-19. In this review, we (1) investigate the anti-inflammatory mechanisms mediated by PPARs and their ligands during SARS-CoV-2 infection, and (2) on the basis of the recent literature, highlight the importance of PPAR subtypes for the development of promising therapeutic approaches against the cytokine storm in severe COVID-19 patients.

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.

Plasma Proteomic Variables Related to COVID-19 Severity: An Untargeted nLC-MS/MS Investigation

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection leads to a wide range of clinical manifestations and determines the need for personalized and precision medicine. To better understand the biological determinants of this heterogeneity, we explored the plasma proteome of 43 COVID-19 patients with different outcomes by an untargeted liquid chromatography-mass spectrometry approach. The comparison between asymptomatic or pauci-symptomatic subjects (MILDs), and hospitalised patients in need of oxygen support therapy (SEVEREs) highlighted 29 proteins emerged as differentially expressed: 12 overexpressed in MILDs and 17 in SEVEREs. Moreover, a supervised analysis based on a decision-tree recognised three proteins (Fetuin-A, Ig lambda-2chain-C-region, Vitronectin) that are able to robustly discriminate between the two classes independently from the infection stage. In silico functional annotation of the 29 deregulated proteins pinpointed several functions possibly related to the severity; no pathway was associated exclusively to MILDs, while several only to SEVEREs, and some associated to both MILDs and SEVEREs; SARS-CoV-2 signalling pathway was significantly enriched by proteins up-expressed in SEVEREs (SAA1/2, CRP, HP, LRG1) and in MILDs (GSN, HRG). In conclusion, our analysis could provide key information for 'proteomically' defining possible upstream mechanisms and mediators triggering or limiting the domino effect of the immune-related response and characterizing severe exacerbations.

A metabolic readout of the urine metabolome of COVID-19 patients

Analysis of urine samples from COVID-19 patients by ¹H NMR reveals important metabolic alterations due to SAR-CoV-2 infection. Previous studies have identified biomarkers in urine that reflect metabolic alterations in COVID-19 patients. We have used ¹H NMR to better define these metabolic alterations since this technique allows us to obtain a broad profile of the metabolites present in urine. This technique offers the advantage that sample preparation is very simple and gives us very complete information on the metabolites present. To detect these alterations, we have compared urine samples from COVID-19 patients (n = 35) with healthy people (n = 18). We used unsupervised (Robust PCA) and supervised (PLS-LDA) multivariate analysis methods to evaluate the differences between the two groups: COVID-19 and healthy controls. The differences focus on a group of metabolites related to energy metabolism (glucose, ketone bodies, glycine, creatinine, and citrate) and other processes related to bacterial flora (TMAO and formic acid) and detoxification (hippuric acid). The alterations in the urinary metabolome shown in this work indicate that SARS-CoV-2 causes a metabolic change from a normal situation of glucose consumption towards a gluconeogenic situation and possible insulin resistance.

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.

Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19

Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alterations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospitalized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19-negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chromatography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites altered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid segregated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with the severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify the potential biomarkers for the diagnosis and prognosis of the disease.

NMR-Metabolomics in COVID-19 Research

COVID-19 stands for Corona Virus Disease 2019, which starts as a viral infection that provokes illness with different symptoms and severity. The infected individuals can be asymptomatic or present with mild, moderate, severe, and critical illness with acute respiratory distress syndrome (ARDS), acute cardiac injury, and multiorgan failure. When the virus enters the cells, it replicates and provokes responses. Most diseased individuals resolve the problems in a short time but unfortunately, some may die, and almost 3 years after the first reported cases, COVID-19 still kills thousands per day worldwide. One of the problems in not curing the viral infection is that the virus passes by undetected in cells. This can be caused by the lack of pathogen-associated molecular patterns (PAMPs) that start an orchestrated immune response, such as activation of type 1 interferons (IFNs), inflammatory cytokines, chemokines, and antiviral defenses. Before all of these events can happen, the virus uses the infected cells and numerous small molecules as sources of energy and building blocks for newly synthesized viral nanoparticles that travel to and infect other host cells. Therefore, studying the cell metabolome and metabolomic changes in biofluids might give insights into the state of the viral infection, viral loads, and defense response. NMR-metabolomics can help in solving the real-time host interactions by monitoring concentration changes in metabolites. This chapter addresses the state of the art of COVIDomics by NMR analyses and presents exemplified biomolecules identified in different world regions and gravities of illness as potential biomarkers.

Heritability of Protein and Metabolite Biomarkers Associated with COVID-19 Severity: A Metabolomics and Proteomics Analysis

OBJECTIVES

Prior studies have characterized protein and metabolite changes associated with SARS-CoV-2 infection; we hypothesized that these biomarkers may be part of heritable metabolic pathways in erythrocytes.

METHODS

Using a twin study of erythrocyte protein and metabolite levels, we describe the heritability of, and correlations among, previously identified biomarkers that correlate with COVID-19 severity. We used gene ontology and pathway enrichment analysis tools to identify pathways and biological processes enriched among these biomarkers.

RESULTS

Many COVID-19 biomarkers are highly heritable in erythrocytes. Among heritable metabolites downregulated in COVID-19, metabolites involved in amino acid metabolism and biosynthesis are enriched. Specific amino acid metabolism pathways (valine, leucine, and isoleucine biosynthesis; glycine, serine, and threonine metabolism; and arginine biosynthesis) are heritable in erythrocytes.

CONCLUSIONS

Metabolic pathways downregulated in COVID-19, particularly amino acid biosynthesis and metabolism pathways, are heritable in erythrocytes. This finding suggests that a component of the variation in COVID-19 severity may be the result of phenotypic variation in heritable metabolic pathways; future studies will be necessary to determine whether individual variation in amino acid metabolism pathways correlates with heritable outcomes of COVID-19.

Serum Proteomic Analysis for New Types of Long-Term Persistent COVID-19 Patients in Wuhan

The emergence of a new type of COVID-19 patients, who were retested positive after hospital discharge with long-term persistent SARS-CoV-2 infection but without COVID-19 clinical symptoms (hereinafter, LTPPs), poses novel challenges to COVID-19 treatment and prevention. Why was there such a contradictory phenomenon in LTPPs? To explore the mechanism underlying this phenomenon, we performed quantitative proteomic analyses using the sera of 12 LTPPs (Wuhan Pulmonary Hospital), with the longest carrying history of 132 days, and mainly focused on 7 LTPPs without hypertension (LTPPs-NH). The results showed differential serum protein profiles between LTPPs/LTPPs-NH and health controls. Further analysis identified 174 differentially-expressed-proteins (DEPs) for LTPPs, and 165 DEPs for LTPPs-NH, most of which were shared. GO and KEGG analyses for these DEPs revealed significant enrichment of "coagulation" and "immune response" in both LTPPs and LTPPs-NH. A unity of contradictory genotypes in the 2 aspects were then observed: some DEPs showed the same dysregulated expressed trend as that previously reported for patients in the acute phase of COVID-19, which might be caused by long-term stimulation of persistent SARS-CoV-2 infection in LTPPs, further preventing them from complete elimination; in contrast, some DEPs showed the opposite expression trend in expression, so as to retain control of COVID-19 clinical symptoms in LTPPs. Overall, the contrary effects of these DEPs worked together to maintain the balance of LTPPs, further endowing their contradictory steady-state with long-term persistent SARS-CoV-2 infection but without symptoms. Additionally, our study revealed some potential therapeutic targets of COVID-19. Further studies on these are warranted. IMPORTANCE This study reported a new type of COVID-19 patients and explored the underlying molecular mechanism by quantitative proteomic analyses. DEPs were significantly enriched in "coagulation" and "immune response". Importantly, we identified 7 "coagulation system"- and 9 "immune response"-related DEPs, the expression levels of which were consistent with those previously reported for patients in the acute phase of COVID-19, which appeared to play a role in avoiding the complete elimination of SARS-CoV-2 in LTPPs. On the contrary, 6 "coagulation system"- and 5 "immune response"-related DEPs showed the opposite trend in expression. The 11 inconsistent serum proteins seem to play a key role in the fight against long-term persistent SARS-CoV-2 infection, further retaining control of COVID-19 clinical symptom of LTPPs. The 26 proteins can serve as potential therapeutic targets and are thus valuable for the treatment of LTPPs; further studies on them are warranted.

The mRNA vaccine BNT162b2 demonstrates impaired TH1 immunogenicity in human elders in vitro and aged mice in vivo

mRNA vaccines have been key to addressing the SARS-CoV-2 pandemic but have impaired immunogenicity and durability in vulnerable older populations. We evaluated the mRNA vaccine BNT162b2 in human in vitro whole blood assays with supernatants from adult (18-50 years) and elder (≥60 years) participants measured by mass spectrometry and proximity extension assay proteomics. BNT162b2 induced increased expression of soluble proteins in adult blood (e.g., C1S, PSMC6, CPN1), but demonstrated reduced proteins in elder blood (e.g., TPM4, APOF, APOC2, CPN1, and PI16), including 30-85% lower induction of TH1-polarizing cytokines and chemokines (e.g., IFNγ, and CXCL10). Elder TH1 impairment was validated in mice in vivo and associated with impaired humoral and cellular immunogenicity. Our study demonstrates the utility of a human in vitro platform to model age-specific mRNA vaccine activity, highlights impaired TH1 immunogenicity in older adults, and provides rationale for developing enhanced mRNA vaccines with greater immunogenicity in vulnerable populations.

Longitudinal serum proteomics analyses identify unique and overlapping host response pathways in Lyme disease and West Nile virus infection

Advancement in proteomics methods for interrogating biological samples has helped identify disease biomarkers for early diagnostics and unravel underlying molecular mechanisms of disease. Herein, we examined the serum proteomes of 23 study participants presenting with one of two common arthropod-borne infections: Lyme disease (LD), an extracellular bacterial infection or West Nile virus infection (WNV), an intracellular viral infection. The LC/MS based serum proteomes of samples collected at the time of diagnosis and during convalescence were assessed using a depletion-based high-throughput shotgun proteomics (dHSP) pipeline as well as a non-depleting blotting-based low-throughput platform (MStern). The LC/MS integrated analyses identified host proteome responses in the acute and recovery phases shared by LD and WNV infections, as well as differentially abundant proteins that were unique to each infection. Notably, we also detected proteins that distinguished localized from disseminated LD and asymptomatic from symptomatic WNV infection. The proteins detected in both diseases with the dHSP pipeline identified unique and overlapping proteins detected with the non-depleting MStern platform, supporting the utility of both detection methods. Machine learning confirmed the use of the serum proteome to distinguish the infection from healthy control sera but could not develop discriminatory models between LD and WNV at current sample numbers. Our study is the first to compare the serum proteomes in two arthropod-borne infections and highlights the similarities in host responses even though the pathogens and the vectors themselves are different.

Dynamic Interactomics by Cross-Linking Mass Spectrometry: Mapping the Daily Cell Life in Postgenomic Era

The majority of processes that occur in daily cell life are modulated by hundreds to thousands of dynamic protein-protein interactions (PPI). The resulting protein complexes constitute a tangled network that, with its continuous remodeling, builds up highly organized functional units. Thus, defining the dynamic interactome of one or more proteins allows determining the full range of biological activities these proteins are capable of. This conceptual approach is poised to gain further traction and significance in the current postgenomic era wherein the treatment of severe diseases needs to be tackled at both genomic and PPI levels. This also holds true for COVID-19, a multisystemic disease affecting biological networks across the biological hierarchy from genome to proteome to metabolome. In this overarching context and the current historical moment of the COVID-19 pandemic where systems biology increasingly comes to the fore, cross-linking mass spectrometry (XL-MS) has become highly relevant, emerging as a powerful tool for PPI discovery and characterization. This expert review highlights the advanced XL-MS approaches that provide in vivo insights into the three-dimensional protein complexes, overcoming the static nature of common interactomics data and embracing the dynamics of the cell proteome landscape. Many XL-MS applications based on the use of diverse cross-linkers, MS detection methods, and predictive bioinformatic tools for single proteins or proteome-wide interactions were shown. We conclude with a future outlook on XL-MS applications in the field of structural proteomics and ways to sustain the remarkable flexibility of XL-MS for dynamic interactomics and structural studies in systems biology and planetary health.

COVID-19 Salivary Protein Profile: Unravelling Molecular Aspects of SARS-CoV-2 Infection

COVID-19 is the most impacting global pandemic of all time, with over 600 million infected and 6.5 million deaths worldwide, in addition to an unprecedented economic impact. Despite the many advances in scientific knowledge about the disease, much remains to be clarified about the molecular alterations induced by SARS-CoV-2 infection. In this work, we present a hybrid proteomics and in silico interactomics strategy to establish a COVID-19 salivary protein profile. Data are available via ProteomeXchange with identifier PXD036571. The differential proteome was narrowed down by the Partial Least-Squares Discriminant Analysis and enrichment analysis was performed with FunRich. In parallel, OralInt was used to determine interspecies Protein-Protein Interactions between humans and SARS-CoV-2. Five dysregulated biological processes were identified in the COVID-19 proteome profile: Apoptosis, Energy Pathways, Immune Response, Protein Metabolism and Transport. We identified 10 proteins (KLK 11, IMPA2, ANXA7, PLP2, IGLV2-11, IGHV3-43D, IGKV2-24, TMEM165, VSIG10 and PHB2) that had never been associated with SARS-CoV-2 infection, representing new evidence of the impact of COVID-19. Interactomics analysis showed viral influence on the host immune response, mainly through interaction with the degranulation of neutrophils. The virus alters the host’s energy metabolism and interferes with apoptosis mechanisms.

Dysregulation of glutamine/glutamate metabolism in COVID-19 patients: A metabolism study in African population and mini meta-analysis

Coronavirus disease 2019 (COVID-19) remains a serious global threat. The metabolic analysis had been successfully applied in the efforts to uncover the pathological mechanisms and biomarkers of disease severity. Here we performed a quasi-targeted metabolomic analysis on 56 COVID-19 patients from Sierra Leone in western Africa, revealing the metabolomic profiles and the association with disease severity, which was confirmed by the targeted metabolomic analysis of 19 pairs of COVID-19 patients. A meta-analysis was performed on published metabolic data of COVID-19 to verify our findings. Of the 596 identified metabolites, 58 showed significant differences between severe and nonsevere groups. The pathway enrichment of these differential metabolites revealed glutamine and glutamate metabolism as the most significant metabolic pathway (Impact = 0.5; -log10P = 1.959). Further targeted metabolic analysis revealed six metabolites with significant intergroup differences, with glutamine/glutamate ratio significantly associated with severe disease, negatively correlated with 10 clinical parameters and positively correlated with SPO₂ (r_s = 0.442, p = 0.005). Mini meta-analysis indicated elevated glutamate was related to increased risk of COVID-19 infection (pooled odd ratio [OR] = 2.02; 95% confidence interval [CI]: 1.17-3.50) and severe COVID-19 (pooled OR = 2.28; 95% CI: 1.14-4.56). In contrast, elevated glutamine related to decreased risk of infection and severe COVID-19, the pooled OR were 0.30 (95% CI: 0.20-0.44), and 0.44 (95% CI: 0.19-0.98), respectively. Glutamine and glutamate metabolism are associated with COVID-19 severity in multiple populations, which might confer potential therapeutic target of COVID-19, especially for severe patients.

Serum Proteome Signatures of Anti-SARS-CoV-2 Vaccinated Healthcare Workers in Greece Associated with Their Prior Infection Status

Over the course of the pandemic, proteomics, being in the frontline of anti-COVID-19 research, has massively contributed to the investigation of molecular pathogenic properties of the virus. However, data on the proteome on anti-SARS-CoV-2 vaccinated individuals remain scarce. This study aimed to identify the serum proteome characteristics of anti-SARS-CoV-2 vaccinated individuals who had previously contracted the virus and comparatively assess them against those of virus-naïve vaccine recipients. Blood samples of n = 252 individuals, out of whom n = 35 had been previously infected, were collected in the "G. Gennimatas" General Hospital of Thessaloniki, from 4 January 2021 to 31 August 2021. All participants received the BNT162b2 mRNA COVID-19 vaccine (Pfizer/BioNTech). A label-free quantitative proteomics LC-MS/MS approach was undertaken, and the identified proteins were analyzed using the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes) databases as well as processed by bioinformatics tools. Titers of total RBD-specific IgGs against SARS-CoV-2 were also determined using the SARS-CoV-2 IgG II Quant assay. A total of 47 proteins were significantly differentially expressed, the majority of which were down-regulated in sera of previously infected patients compared to virus-naïve controls. Several pathways were affected supporting the crucial role of the humoral immune response in the protection against SARS-CoV-2 infection provided by COVID-19 vaccination. Overall, our comprehensive proteome profiling analysis contributes novel knowledge of the mechanisms of immune response induced by anti-SARS-CoV-2 vaccination and identified protein signatures reflecting the immune status of vaccine recipients.

Serum metabolomic abnormalities in survivors of non-severe COVID-19

Metabolic reprogramming is a distinctive characteristic of SARS-CoV-2 infection, which refers to metabolic changes in hosts triggered by viruses for their survival and spread. It is current urgent to understand the metabolic health status of COVID-19 survivors and its association with long-term health consequences of infection, especially for the predominant non-severe patients. Herein, we show systemic metabolic signatures of survivors of non-severe COVID-19 from Wuhan, China at six months after discharge using metabolomics approaches. The serum amino acids, organic acids, purine, fatty acids and lipid metabolism were still abnormal in the survivors, but the kynurenine pathway and the level of itaconic acid have returned to normal. These metabolic abnormalities are associated with liver injury, mental health, energy production, and inflammatory responses. Our findings identify and highlight the metabolic abnormalities in survivors of non-severe COVID-19, which provide information on biomarkers and therapeutic targets of infection and cues for post-hospital care and intervention strategies centered on metabolism reprogramming.