Multi-Omics Reveals Mechanisms of Partial Modulation of COVID-19 Dysregulation by Glucocorticoid Treatment

Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review

Systems biology is a holistic approach to biological sciences that combines experimental and computational strategies, aimed at integrating information from different scales of biological processes to unravel pathophysiological mechanisms and behaviours. In this scenario, high-throughput technologies have been playing a major role in providing huge amounts of omics data, whose integration would offer unprecedented possibilities in gaining insights on diseases and identifying potential biomarkers. In the present review, we focus on strategies that have been applied in literature to integrate genomics, transcriptomics, proteomics, and metabolomics in the year range 2018-2024. Integration approaches were divided into three main categories: statistical-based approaches, multivariate methods, and machine learning/artificial intelligence techniques. Among them, statistical approaches (mainly based on correlation) were the ones with a slightly higher prevalence, followed by multivariate approaches, and machine learning techniques. Integrating multiple biological layers has shown great potential in uncovering molecular mechanisms, identifying putative biomarkers, and aid classification, most of the time resulting in better performances when compared to single omics analyses. However, significant challenges remain. The high-throughput nature of omics platforms introduces issues such as variable data quality, missing values, collinearity, and dimensionality. These challenges further increase when combining multiple omics datasets, as the complexity and heterogeneity of the data increase with integration. We report different strategies that have been found in literature to cope with these challenges, but some open issues still remain and should be addressed to disclose the full potential of omics integration.

SARS-CoV2 variants differentially impact on the plasma metabolome

INTRODUCTION

Infection with severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) leads to COVID19 disease and caused a worldwide pandemic in 2019. Since the first wave of infections, there has been significant antigenic shifts, leading to the emergence of new variants. Today, infections have shifted away from the severe, fatal infection seen in 2019.

OBJECTIVE

This study aimed to assess how the plasma metabolomes from patients varied with infection with different strains and could reflect disease severity.

METHODS

Patients with COVID19 not requiring intensive care were recruited between January 2021 and May 2022 from the Queen Elizabeth Hospital Birmingham; 33 patients with alpha, 13 delta and 14 omicron variants. These were compared to 26 age matched contemporaneously recruited controls. Plasma samples were extracted into chloroform/methanol/water (1:2.5/1 v/v) and assessed by flow injection electrospray mass spectrometry (FIE-MS) using an Exactive Orbitrap mass spectrometer. Derived data were assessed using the R based MetaboAnalyst platform.

RESULTS

Plasma metabolomes from COVID19 patients were clearly different from controls. Metabolite variation could be related to infection with different SARS-CoV2 variants. Variant showed different levels of some phospholipids, ganglioside GD1a and a dihydroxyvitamin D3 derivative. Correlations of the plasma metabolomes indicated negative correlations between selected phospholipids and the levels of C-reactive protein, creatinine, neutrophil and D-dimer.

CONCLUSION

The plasma metabolomes of COVID19 patients show changes, particularly in phospholipids, which could reflect disease severity and SARS-CoV2 variant infection.

Drug Repurposing for COVID-19 by Constructing a Comorbidity Network with Central Nervous System Disorders

In the post-COVID-19 era, treatment options for potential SARS-CoV-2 outbreaks remain limited. An increased incidence of central nervous system (CNS) disorders has been observed in long-term COVID-19 patients. Understanding the shared molecular mechanisms between these conditions may provide new insights for developing effective therapies. This study developed an integrative drug-repurposing framework for COVID-19, leveraging comorbidity data with CNS disorders, network-based modular analysis, and dynamic perturbation analysis to identify potential drug targets and candidates against SARS-CoV-2. We constructed a comorbidity network based on the literature and data collection, including COVID-19-related proteins and genes associated with Alzheimer's disease, Parkinson's disease, multiple sclerosis, and autism spectrum disorder. Functional module detection and annotation identified a module primarily involved in protein synthesis as a key target module, utilizing connectivity map drug perturbation data. Through the construction of a weighted drug-target network and dynamic network-based drug-repurposing analysis, ubiquitin-carboxy-terminal hydrolase L1 emerged as a potential drug target. Molecular dynamics simulations suggested pregnenolone and BRD-K87426499 as two drug candidates for COVID-19. This study introduces a dynamic-perturbation-network-based drug-repurposing approach to identify COVID-19 drug targets and candidates by incorporating the comorbidity conditions of CNS disorders.

Blood proteomics of COVID-19 infection: An update

Despite significant progress in understanding SARS-CoV-2 and its impact, a deeper comprehension of the molecular changes in the host following infection is still lacking. Proteomic analysis of COVID-19 infected patients can provide valuable data about the events and mechanisms related to the initial, progression, and terminal stages of the disease. Such information can lead to better prevention, treatment, and convalescence strategies. In this review, we discuss blood proteomic studies based on mass spectrometry in COVID-19 patients. The analysis focuses on various blood components including plasma, platelets, serum, red blood cells, and peripheral blood mononuclear cells. Scientific papers published since 2022 are covered, with approaches categorised based on whether they use in-gel, in-solution, or on-beads/on-filter digestion modes. Additionally, candidate protein biomarkers related to COVID-19 are surveyed and discussed.

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.

Multi-Omics Profiling Reveals Phenotypic and Functional Heterogeneity of Neutrophils in COVID-19

Accumulating evidence has revealed unexpected phenotypic heterogeneity and diverse functions of neutrophils in several diseases. Coronavirus disease (COVID-19) can alter the leukocyte phenotype based on disease severity, including neutrophil activation in severe cases. However, the plasticity of neutrophil phenotypes and their relative impact on COVID-19 pathogenesis has not been well addressed. This study aimed to identify and validate the heterogeneity of neutrophils in COVID-19 and evaluate the functions of each subpopulation. We analyzed public single-cell RNA-seq, bulk RNA-seq, and proteome data from healthy donors and patients with COVID-19 to investigate neutrophil subpopulations and their response to disease pathogenesis. We identified eight neutrophil subtypes: pro-neutrophil, pre-neutrophil, immature neutrophil, and five mature neutrophil subpopulations. The subtypes exhibited distinct features, including diverse activation signatures and multiple enriched pathways. The pro-neutrophil subtype was associated with severe and fatal disease, while the pre-neutrophil subtype was particularly abundant in mild/moderate disease. One of the mature neutrophil subtypes showed consistently large fractions in patients with different disease severity. Bulk RNA-seq dataset analyses using a cellular deconvolution approach validated the relative abundances of neutrophil subtypes and the expansion of pro-neutrophils in severe COVID-19 patients. Cell-cell communication analysis revealed representative ligand-receptor interactions among the identified neutrophil subtypes. Further investigation into transcription factors and differential protein abundance revealed the regulatory network differences between healthy donors and patients with severe COVID-19. Overall, we demonstrated the complex interactions among heterogeneous neutrophil subtypes and other blood cell types during COVID-19 disease. Our work has great value in terms of both clinical and public health as it furthers our understanding of the phenotypic and functional heterogeneity of neutrophils and other cell populations in multiple diseases.

Viral PIC-pocketing: RSV sequestration of translational preinitiation complexes into bi-phasic biomolecular condensates

Orthopneumoviruses characteristically form membrane-less cytoplasmic inclusion bodies (IBs) wherein RNA replication and transcription occur. Here, we report a strategy whereby the orthopneumoviruses sequester various components of the translational preinitiation complex machinery into viral inclusion bodies to facilitate translation of their own mRNAs-PIC-pocketing. Electron microscopy of respiratory syncytial virus (RSV)-infected cells revealed bi-phasic organization of IBs, specifically, spherical "droplets" nested within the larger inclusion. Using correlative light and electron microscopy, combined with fluorescence in situ hybridization, we showed that the observed bi-phasic morphology represents functional compartmentalization of the inclusion body and that these domains are synonymous with the previously reported inclusion body-associated granules (IBAGs). Detailed analysis demonstrated that IBAGs concentrate nascent viral mRNA, the viral M2-1 protein as well as components of eukaryotic translation initiation factors (eIF), eIF4F and eIF3, and 40S complexes involved in translation initiation. Interestingly, although ribopuromycylation-based imaging indicates that the majority of viral mRNA translation occurs in the cytoplasm, there was some evidence for intra-IBAG translation, consistent with the likely presence of ribosomes in a subset of IBAGs imaged by electron microscopy. Mass spectrometry analysis of sub-cellular fractions from RSV-infected cells identified significant modification of the cellular translation machinery; however, interestingly, ribopuromycylation assays showed no changes to global levels of translation. The mechanistic basis for this pathway was subsequently determined to involve the viral M2-1 protein interacting with eIF4G, likely to facilitate its transport between the cytoplasm and the separate phases of the viral inclusion body. In summary, our data show that these viral organelles function to spatially regulate early steps in viral translation within a highly selective bi-phasic biomolecular condensate.

IMPORTANCE

Respiratory syncytial viruses (RSVs) of cows and humans are a significant cause of morbidity and mortality in their respective populations. These RNA viruses replicate in the infected cells by compartmentalizing the cell's cytoplasm into distinct viral microdomains called inclusion bodies (IBs). In this paper, we show that these IBs are further compartmentalized into smaller structures that have significantly different density, as observed by electron microscopy. Within smaller intra-IB structures, we observed ribosomal components and evidence for active translation. These findings highlight that RSV may additionally compartmentalize translation to favor its own replication in the cell. These data contribute to our understanding of how RNA viruses hijack the cell to favor replication of their own genomes and may provide new targets for antiviral therapeutics in vivo.

Persisting Gastrointestinal Symptoms in Children with SARS-CoV-2: Temporal Evolution over 18 Months and Possible Role of Lactoferrin

BACKGROUND AND AIM

Persisting gastrointestinal symptoms are reported to be relatively common in children with long COVID; however, their detailed characterization and long-term outcomes have not yet been described.

METHODS

We performed a retrospective study aiming to investigate the temporal evolution of gastrointestinal symptoms in children with SARS-CoV-2, from acute infection to 18-months follow-up. To further investigate possible therapeutic strategies, we evaluated the role of lactoferrin in improving gastrointestinal symptoms in these children, compared with those not treated.

RESULTS

A total of 1224 patients (47.7% females) were included. Of these participants, 246 (19.8%) were vaccinated and 143 (11.5%) presented with comorbidities. A total of 175 patients (14.1%) presented gastrointestinal symptoms during acute infection, 54 (4.4%) at three months, 23 (1.9%) at six months, 6 (3.3%) at twelve months, and 2 (2.3%) at eighteen months follow-up. At six months follow-up, children who were treated with 3 months oral lactoferrin had less persisting symptoms compared to those who did not receive lactoferrin, although this difference was not statistically significant (three patients (25%) in the lactoferrin group vs. fourteen patients (33.3%) not treated, p = 0.73), probably due to the low number of patients with persisting GI symptoms.

CONCLUSIONS

GI symptoms are relatively common during acute SARS-CoV-2 infection in children, and a non-negligible proportion of these children reported persisting symptoms for up to 12-18 months after the acute infection. In addition, we found a trend even if statistically nonsignificant toward faster improvement of persisting gastrointestinal symptoms in children with long COVID treated with lactoferrin. Despite the limitations relating to the present study's design, given the significant burden of gastrointestinal symptoms in children with long COVID, our findings provide the basis to perform a prospective, placebo-controlled study.

The exploration of glucocorticoid pathway based on disease severity in COVID-19 patients

Systemic inflammation is a hallmark of Coronavirus Disease 2019 (COVID-19) and is the key to the pathophysiology of its severe cases with host cytokine involvement. Glucocorticoids can moderate this inflammatory effect due to receptor binding (NRC31-the gene encoded), influencing the expression of effector genes and pro-inflammatory cytokines. Another important pathway in the processes of the immune and inflammatory responses is nuclear factor-κB (NF-κB) signaling (NFKBIA-the gene encoded). We aimed to explore the expression of genes in the glucocorticoid pathway in mild and severe COVID-19. We performed a cross-sectional, observational study on COVID-19 cases, assessing the expression of RNA in white blood cells. The Illumina® platform was used for RNA sequencing, and FASTQ data were quality-checked with Multiqc. The raw data were analyzed using CLC Genomics Workbench®. Our study included 23 patients with severe COVID-19 and 21 patients with mild COVID-19 with an average age of 49.9 ± 18.2 years old. The NR3C1 and NFKBIA expressions did not show a significantly significant difference between groups (log2 fold change 0.5, p = 0.1; 0.82, p = 0.09). However, the expressions of TSC22D3, DUSP-1, JAK-1 and MAPK-1 were significantly higher in mild cases (log2 fold change 1.3, p < 0.001; 2.6, p < 0.001; 0.9, p < 0.001; 1.48, p-value<0.001; respectively). Furthermore, the TNF, IL-1β, and IL-6 expressions were significantly lower in mild cases (log2 fold change 4.05, p < 0.001; 3.33, p < 0.001; 6.86, p < 0.001; respectively). In conclusion, our results showed that although the NRC31 and NFKBIA expressions did not show a statistically significant difference between groups, the expression of TSC22D3 was higher in mild cases. These results highlight the importance of effector genes, specifically TSC22D3, in combatting systemic inflammation. Our recent findings have the potential to lead to the identification of novel pharmacological targets that could prove to be vital in the fight against diseases associated with inflammation.

Methylprednisolone therapy induces differential metabolic trajectories in severe COVID-19 patients

IMPORTANCE

The SARS-CoV-2 virus infection in humans induces significant inflammatory and systemic reactions and complications of which corticosteroids like methylprednisolone have been recommended as treatment. Our understanding of the metabolic and metabolomic pathway dysregulations while using intravenous corticosteroids in COVID-19 is limited. This study will help enlighten the metabolic and metabolomic pathway dysregulations underlying high daily doses of intravenous methylprednisolone in COVID-19 patients compared to those receiving placebo. The information on key metabolites and pathways identified in this study together with the crosstalk with the inflammation and biochemistry components may be used, in the future, to leverage the use of methylprednisolone in any future pandemics from the coronavirus family.

Comprehensive proteomics and meta-analysis of COVID-19 host response

COVID-19 is characterised by systemic immunological perturbations in the human body, which can lead to multi-organ damage. Many of these processes are considered to be mediated by the blood. Therefore, to better understand the systemic host response to SARS-CoV-2 infection, we performed systematic analyses of the circulating, soluble proteins in the blood through global proteomics by mass-spectrometry (MS) proteomics. Here, we show that a large part of the soluble blood proteome is altered in COVID-19, among them elevated levels of interferon-induced and proteasomal proteins. Some proteins that have alternating levels in human cells after a SARS-CoV-2 infection in vitro and in different organs of COVID-19 patients are deregulated in the blood, suggesting shared infection-related changes.The availability of different public proteomic resources on soluble blood proteome alterations leaves uncertainty about the change of a given protein during COVID-19. Hence, we performed a systematic review and meta-analysis of MS global proteomics studies of soluble blood proteomes, including up to 1706 individuals (1039 COVID-19 patients), to provide concluding estimates for the alteration of 1517 soluble blood proteins in COVID-19. Finally, based on the meta-analysis we developed CoViMAPP, an open-access resource for effect sizes of alterations and diagnostic potential of soluble blood proteins in COVID-19, which is publicly available for the research, clinical, and academic community.

Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers

The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography-Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting. We further evaluate a crowdsourced panel comprising the COVID-19 metabolomics biomarkers most commonly mentioned in the literature between 2020 and 2023. The best-performing panel in the independent dataset-measured by F1 score (0.76) and AUROC (0.77)-included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, β-alanine, ornithine, arachidonic acid, choline, and hypoxanthine. Panels comprising fewer metabolites performed less well, showing weaker statistical significance in the independent cohort than originally reported in their respective discovery studies. Whilst the studies reviewed here were small and may be subject to confounders, it is desirable that biomarker panels be resilient across cohorts if they are to find use in the clinic, highlighting the importance of assessing the robustness and reproducibility of metabolomics analyses in independent populations.

A Systematic Review of the Role of Purinergic Signalling Pathway in the Treatment of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global health concern. Three years since its origin, despite the approval of vaccines and specific treatments against this new coronavirus, there are still high rates of infection, hospitalization, and mortality in some countries. COVID-19 is characterised by a high inflammatory state and coagulation disturbances that may be linked to purinergic signalling molecules such as adenosine triphosphate (ATP), adenosine diphosphate (ADP), adenosine (ADO), and purinergic receptors (P1 and P2). These nucleotides/nucleosides play important roles in cellular processes, such as immunomodulation, blood clot formation, and vasodilation, which are affected during SARS-CoV-2 infection. Therefore, drugs targeting this purinergic pathway, currently used for other pathologies, are being evaluated in preclinical and clinical trials for COVID-19. In this review, we focus on the potential of these drugs to control the release, degradation, and reuptake of these extracellular nucleotides and nucleosides to treat COVID-19. Drugs targeting the P1 receptors could have therapeutic efficacy due to their capacity to modulate the cytokine storm and the immune response. Those acting in P2X7, which is linked to NLRP3 inflammasome activation, are also valuable candidates as they can reduce the release of pro-inflammatory cytokines. However, according to the available preclinical and clinical data, the most promising medications to be used for COVID-19 treatment are those that modulate platelets behaviour and blood coagulation factors, mainly through the P2Y12 receptor.

Untargeted serum metabolomics analysis of Trichinella spiralis-infected mouse

BACKGROUND

Trichinellosis, caused by a parasitic nematode of the genus Trichinella, is a zoonosis that affects people worldwide. After ingesting raw meat containing Trichinella spp. larvae, patients show signs of myalgia, headaches, and facial and periorbital edema, and severe cases may die from myocarditis and heart failure. The molecular mechanisms of trichinellosis are unclear, and the sensitivity of the diagnostic methods used for this disease are unsatisfactory. Metabolomics is an excellent tool for studying disease progression and biomarkers; however, it has never been applied to trichinellosis. We aimed to elucidate the impacts of Trichinella infection on the host body and identify potential biomarkers using metabolomics.

METHODOLOGY/PRINCIPAL FINDINGS

Mice were infected with T. spiralis larvae, and sera were collected before and 2, 4, and 8 weeks after infection. Metabolites in the sera were extracted and identified using untargeted mass spectrometry. Metabolomic data were annotated via the XCMS online platform and analyzed with Metaboanalyst version 5.0. A total of 10,221 metabolomic features were identified, and the levels of 566, 330, and 418 features were significantly changed at 2-, 4-, and 8-weeks post-infection, respectively. The altered metabolites were used for further pathway analysis and biomarker selection. A major pathway affected by Trichinella infection was glycerophospholipid metabolism, and glycerophospholipids comprised the main metabolite class identified. Receiver operating characteristic revealed 244 molecules with diagnostic power for trichinellosis, with phosphatidylserines (PS) being the primary lipid class. Some lipid molecules, e.g., PS (18:0/19:0)[U] and PA (O-16:0/21:0), were not present in metabolome databases of humans and mice, thus they may have been secreted by the parasites.

CONCLUSIONS/SIGNIFICANCE

Our study highlighted glycerophospholipid metabolism as the major pathway affected by trichinellosis, hence glycerophospholipid species are potential markers of trichinellosis. The findings of this study represent the initial steps in biomarker discovery that may benefit future trichinellosis diagnosis.

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.

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.