Large-Scale Multi-omic Analysis of COVID-19 Severity

mvlearnR and Shiny App for multiview learning

Summary

The package mvlearnR and accompanying Shiny App is intended for integrating data from multiple sources or views or modalities (e.g. genomics, proteomics, clinical, and demographic data). Most existing software packages for multiview learning are decentralized and offer limited capabilities, making it difficult for users to perform comprehensive integrative analysis. The new package wraps statistical and machine learning methods and graphical tools, providing a convenient and easy data integration workflow. For users with limited programming language, we provide a Shiny Application to facilitate data integration anywhere and on any device. The methods have potential to offer deeper insights into complex disease mechanisms.

Availability and implementation

mvlearnR is available from the following GitHub repository: https://github.com/lasandrall/mvlearnR. The web application is hosted on shinyapps.io and available at: https://multi-viewlearn.shinyapps.io/MultiView_Modeling/.

Interferon response and profiling of interferon response genes in peripheral blood of vaccine-naive COVID-19 patients

Introduction

There is insufficient understanding on systemic interferon (IFN) responses during COVID-19 infection. Early reports indicated that interferon responses were suppressed by the coronavirus (SARS-CoV-2) and clinical trials of administration of various kinds of interferons had been disappointing. Expression of interferon-stimulated genes (ISGs) in peripheral blood (better known as interferon score) has been a well-established bioassay marker of systemic IFN responses in autoimmune diseases. Therefore, with archival samples of a cohort of COVID-19 patients collected before the availability of vaccination, we aimed to better understand this innate immune response by studying the IFN score and related ISGs expression in bulk and single cell RNAs sequencing expression datasets.

Methods

In this study, we recruited 105 patients with COVID-19 and 30 healthy controls in Hong Kong. Clinical risk factors, disease course, and blood sampling times were recovered. Based on a set of five commonly used ISGs (IFIT1, IFIT2, IFI27, SIGLEC1, IFI44L), the IFN score was determined in blood leukocytes collected within 10 days after onset. The analysis was confined to those blood samples collected within 10 days after disease onset. Additional public datasets of bulk gene and single cell RNA sequencing of blood samples were used for the validation of IFN score results.

Results

Compared to the healthy controls, we showed that ISGs expression and IFN score were significantly increased during the first 10 days after COVID infection in majority of patients (71%). Among those low IFN responders, they were more commonly asymptomatic patients (71% vs 25%). 22 patients did not mount an overall significant IFN response and were classified as low IFN responders (IFN score < 1). However, early IFN score or ISGs level was not a prognostic biomarker and could not predict subsequent disease severity. Both IFI27 and SIGLEC1 were monocyte-predominant expressing ISGs and IFI27 were activated even among those low IFN responders as defined by IFN score. In conclusion, a substantial IFN response was documented in this cohort of COVID-19 patients who experience a natural infection before the vaccination era. Like innate immunity towards other virus, the ISGs activation was observed largely during the early course of infection (before day 10). Single-cell RNA sequencing data suggested monocytes were the cell-type that primarily accounted for the activation of two highly responsive ISGs (IFI44L and IFI27).

Discussion

As sampling time and age were two major confounders of ISG expression, they may account for contradicting observations among previous studies. On the other hand, the IFN score was not associated with the severity of the disease.

Proteomics-based mass spectrometry profiling of SARS-CoV-2 infection from human nasopharyngeal samples

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the on-going global pandemic of coronavirus disease 2019 (COVID-19) that continues to pose a significant threat to public health worldwide. SARS-CoV-2 encodes four structural proteins namely membrane, nucleocapsid, spike, and envelope proteins that play essential roles in viral entry, fusion, and attachment to the host cell. Extensively glycosylated spike protein efficiently binds to the host angiotensin-converting enzyme 2 initiating viral entry and pathogenesis. Reverse transcriptase polymerase chain reaction on nasopharyngeal swab is the preferred method of sample collection and viral detection because it is a rapid, specific, and high-throughput technique. Alternate strategies such as proteomics and glycoproteomics-based mass spectrometry enable a more detailed and holistic view of the viral proteins and host-pathogen interactions and help in detection of potential disease markers. In this review, we highlight the use of mass spectrometry methods to profile the SARS-CoV-2 proteome from clinical nasopharyngeal swab samples. We also highlight the necessity for a comprehensive glycoproteomics mapping of SARS-CoV-2 from biological complex matrices to identify potential COVID-19 markers.

Variable selection in linear regression models: Choosing the best subset is not always the best choice

We consider the question of variable selection in linear regressions, in the sense of identifying the correct direct predictors (those variables that have nonzero coefficients given all candidate predictors). Best subset selection (BSS) is often considered the "gold standard," with its use being restricted only by its NP-hard nature. Alternatives such as the least absolute shrinkage and selection operator (Lasso) or the Elastic net (Enet) have become methods of choice in high-dimensional settings. A recent proposal represents BSS as a mixed-integer optimization problem so that large problems have become computationally feasible. We present an extensive neutral comparison assessing the ability to select the correct direct predictors of BSS compared to forward stepwise selection (FSS), Lasso, and Enet. The simulation considers a range of settings that are challenging regarding dimensionality (number of observations and variables), signal-to-noise ratios, and correlations between predictors. As fair measure of performance, we primarily used the best possible F1-score for each method, and results were confirmed by alternative performance measures and practical criteria for choosing the tuning parameters and subset sizes. Surprisingly, it was only in settings where the signal-to-noise ratio was high and the variables were uncorrelated that BSS reliably outperformed the other methods, even in low-dimensional settings. Furthermore, FSS performed almost identically to BSS. Our results shed new light on the usual presumption of BSS being, in principle, the best choice for selecting the correct direct predictors. Especially for correlated variables, alternatives like Enet are faster and appear to perform better in practical settings.

Biological analysis of the potential pathogenic mechanisms of Infectious COVID-19 and Guillain-Barré syndrome

Background

Guillain-Barré syndrome (GBS) is a medical condition characterized by the immune system of the body attacking the peripheral nerves, including those in the spinal nerve roots, peripheral nerves, and cranial nerves. It can cause limb weakness, abnormal sensations, and facial nerve paralysis. Some studies have reported clinical cases associated with the severe coronavirus disease 2019 (COVID-19) and GBS, but how COVID-19 affects GBS is unclear.

Methods

We utilized bioinformatics techniques to explore the potential genetic connection between COVID-19 and GBS. Differential expression of genes (DEGs) related to COVID-19 and GBS was collected from the Gene Expression Omnibus (GEO) database. By taking the intersection, we obtained shared DEGs for COVID-19 and GBS. Subsequently, we utilized bioinformatics analysis tools to analyze common DEGs, conducting functional enrichment analysis and constructing Protein-protein interaction networks (PPI), Transcription factors (TF) -gene networks, and TF-miRNA networks. Finally, we validated our findings by constructing the Receiver Operating Characteristic (ROC) curves.

Results

This study utilizes bioinformatics tools for the first time to investigate the close genetic relationship between COVID-19 and GBS. CAMP, LTF, DEFA1B, SAMD9, GBP1, DDX60, DEFA4, and OAS3 are identified as the most significant interacting genes between COVID-19 and GBS. In addition, the signaling pathway of NOD-like receptors is believed to be essential in the link between COVID-19 and GBS.

A platelet lipidomics signature in patients with COVID-19

Ischemic cardiovascular and venous thromboembolic events are a frequent cause of death in severe COVID-19 patients. Platelet activation plays a key role in these complications, however platelet lipidomics have not been studied yet. The aim of our pilot investigation was to perform a preliminary study of platelet lipidomics in COVID-19 patients compared to healthy subjects. Lipid extraction and identification of ultrapurified platelets from eight hospitalized COVID-19 patients and eight age- and sex-matched healthy controls showed a lipidomic pattern almost completely separating COVID-19 patients from healthy controls. In particular, a significant decrease of ether phospholipids and increased levels of ganglioside GM3 were observed in platelets from COVID-19 patients. In conclusion, our study shows for the first time that platelets from COVID-19 patients display a different lipidomics signature distinguishing them from healthy controls, and suggests that altered platelet lipid metabolism may play a role in viral spreading and in the thrombotic complications of COVID-19.

Elevated A-to-I RNA editing in COVID-19 infected individuals

Given the current status of coronavirus disease 2019 (COVID-19) as a global pandemic, it is of high priority to gain a deeper understanding of the disease's development and how the virus impacts its host. Adenosine (A)-to-Inosine (I) RNA editing is a post-transcriptional modification, catalyzed by the ADAR family of enzymes, that can be considered part of the inherent cellular defense mechanism as it affects the innate immune response in a complex manner. It was previously reported that various viruses could interact with the host's ADAR enzymes, resulting in epigenetic changes both to the virus and the host. Here, we analyze RNA-seq of nasopharyngeal swab specimens as well as whole-blood samples of COVID-19 infected individuals and show a significant elevation in the global RNA editing activity in COVID-19 compared to healthy controls. We also detect specific coding sites that exhibit higher editing activity. We further show that the increment in editing activity during the disease is temporary and returns to baseline shortly after the symptomatic period. These significant epigenetic changes may contribute to the immune system response and affect adverse outcomes seen in post-viral cases.

Serum CRP interacts with SPARC and regulate immune response in severe cases of COVID-19 infection

Serum C-reactive protein (CRP) has been found elevated during COVID-19 infection, and associated with systematic inflammation as well as a poor clinical outcome. However, how did CRP participated in the COVID-19 pathogenesis remains poorly understood. Here, we report that serum C-reactive protein (CRP) levels are correlated with megakaryocyte marker genes and could regulate immune response through interaction with megakaryocytes. Molecular dynamics simulation through ColabFold showed a reliable interaction between monomeric form of CRP (mCRP) and the secreted protein acidic and rich in cysteine (SPARC). The interaction does not affect the physiological activities of SPARC while would be disturbed by pentamerization of CRP. Interplay between SPARC and mCRP results in a more intense immune response which may led to poor prognosis. This study highlights the complex interplay between inflammatory markers, megakaryocytes, and immune regulation in COVID-19 and sheds light on potential therapeutic targets.

Sequential multi-omics analysis identifies clinical phenotypes and predictive biomarkers for long COVID

The post-acute sequelae of COVID-19 (PASC), also known as long COVID, is often associated with debilitating symptoms and adverse multisystem consequences. We obtain plasma samples from 117 individuals during and 6 months following their acute phase of infection to comprehensively profile and assess changes in cytokines, proteome, and metabolome. Network analysis reveals sustained inflammatory response, platelet degranulation, and cellular activation during convalescence accompanied by dysregulation in arginine biosynthesis, methionine metabolism, taurine metabolism, and tricarboxylic acid (TCA) cycle processes. Furthermore, we develop a prognostic model composed of 20 molecules involved in regulating T cell exhaustion and energy metabolism that can reliably predict adverse clinical outcomes following discharge from acute infection with 83% accuracy and an area under the curve (AUC) of 0.96. Our study reveals pertinent biological processes during convalescence that differ from acute infection, and it supports the development of specific therapies and biomarkers for patients suffering from long COVID.

COVID-19 induces more pronounced extracellular matrix deposition than other causes of ARDS

BACKGROUND

Lung fibrosis is a major concern in severe COVID-19 patients undergoing mechanical ventilation (MV). Lung fibrosis frequency in post-COVID syndrome is highly variable and even if the risk is proportionally small, many patients could be affected. However, there is still no data on lung extracellular matrix (ECM) composition in severe COVID-19 and whether it is different from other aetiologies of ARDS.

METHODS

We have quantified different ECM elements and TGF-β expression in lung tissue of 28 fatal COVID-19 cases and compared to 27 patients that died of other causes of ARDS, divided according to MV duration (up to six days or seven days or more). In COVID-19 cases, ECM elements were correlated with lung transcriptomics and cytokines profile.

RESULTS

We observed that COVID-19 cases presented significant increased deposition of collagen, fibronectin, versican, and TGF-β, and decreased decorin density when compared to non-COVID-19 cases of similar MV duration. TGF-β was precociously increased in COVID-19 patients with MV duration up to six days. Lung collagen was higher in women with COVID-19, with a transition of upregulated genes related to fibrillogenesis to collagen production and ECM disassembly along the MV course.

CONCLUSIONS

Fatal COVID-19 is associated with an early TGF-β expression lung environment after the MV onset, followed by a disordered ECM assembly. This uncontrolled process resulted in a prominent collagen deposition when compared to other causes of ARDS. Our data provides pathological substrates to better understand the high prevalence of pulmonary abnormalities in patients surviving COVID-19.

Comprehensive Overview of Bottom-Up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.

COVID-19 and Alzheimer's Disease Share Common Neurological and Ophthalmological Manifestations: A Bidirectional Risk in the Post-Pandemic Future

A growing body of evidence indicates that a neuropathological cross-talk takes place between the coronavirus disease 2019 (COVID-19) -the pandemic severe pneumonia that has had a tremendous impact on the global economy and health since three years after its outbreak in December 2019- and Alzheimer's Disease (AD), the leading cause of dementia among human beings, reaching 139 million by the year 2050. Even though COVID-19 is a primary respiratory disease, its causative agent, the so-called Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), is also endowed with high neuro-invasive potential (Neurocovid). The neurological complications of COVID-19, resulting from the direct viral entry into the Central Nervous System (CNS) and/or indirect systemic inflammation and dysregulated activation of immune response, encompass memory decline and anosmia which are typically associated with AD symptomatology. In addition, patients diagnosed with AD are more vulnerable to SARS-CoV-2 infection and are inclined to more severe clinical outcomes. In the present review, we better elucidate the intimate connection between COVID-19 and AD by summarizing the involved risk factors/targets and the underlying biological mechanisms shared by these two disorders with a particular focus on the Angiotensin-Converting Enzyme 2 (ACE2) receptor, APOlipoprotein E (APOE), aging, neuroinflammation and cellular pathways associated with the Amyloid Precursor Protein (APP)/Amyloid beta (Aβ) and tau neuropathologies. Finally, the involvement of ophthalmological manifestations, including vitreo-retinal abnormalities and visual deficits, in both COVID-19 and AD are also discussed. Understanding the common physiopathological aspects linking COVID-19 and AD will pave the way to novel management and diagnostic/therapeutic approaches to cope with them in the post-pandemic future.

A role for cytoglobin in regulating intracellular hydrogen peroxide and redox signals in the vasculature

The oxidant hydrogen peroxide serves as a signaling molecule that alters many aspects of cardiovascular functions. Recent studies suggest that cytoglobin - a hemoglobin expressed in the vasculature - may promote electron transfer reactions with proposed functions in hydrogen peroxide decomposition. Here, we determined the extent to which cytoglobin regulates intracellular hydrogen peroxide and established mechanisms. We found that cytoglobin decreased the hyperoxidation of peroxiredoxins and maintained the activity of peroxiredoxin 2 following challenge with exogenous hydrogen peroxide. Cytoglobin promoted a reduced intracellular environment and facilitated the reduction of the thiol-based hydrogen peroxide sensor Hyper7 after bolus addition of hydrogen peroxide. Cytoglobin also limited the inhibitory effect of hydrogen peroxide on glycolysis and reversed the oxidative inactivation of the glycolytic enzyme GAPDH. Our results indicate that cytoglobin in cells exists primarily as oxyferrous cytoglobin (CygbFe 2+ -O 2 ) with its cysteine residues in the reduced form. We found that the specific substitution of one of two cysteine residues on cytoglobin (C83A) inhibited the reductive activity of cytoglobin on Hyper7 and GAPDH. Carotid arteries from cytoglobin knockout mice were more sensitive to glycolytic inhibition by hydrogen peroxide than arteries from wildtype mice. Together, these results support a role for cytoglobin in regulating intracellular redox signals associated with hydrogen peroxide through oxidation of its cysteine residues, independent of hydrogen peroxide reaction at its heme center.

Immune inhibitory receptor-mediated immune response, metabolic adaptation, and clinical characterization in patients with COVID-19

Immune inhibitory receptors (IRs) play a critical role in the regulation of immune responses to various respiratory viral infections. However, in coronavirus disease 2019 (COVID-19), the roles of these IRs in immune modulation, metabolic reprogramming, and clinical characterization remain to be determined. Through consensus clustering analysis of IR transcription in the peripheral blood of patients with COVID-19, we identified two distinct IR patterns in patients with COVID-19, which were named IR_cluster1 and IR_cluster2. Compared to IR_cluster1 patients, IR_cluster2 patients with lower expressions of immune inhibitory receptors presented with a suppressed immune response, lower nutrient metabolism, and worse clinical manifestations or prognosis. Considering the critical influence of the integrated regulation of multiple IRs on disease severity, we established a scoring system named IRscore, which was based on principal component analysis, to evaluate the combined effect of multiple IRs on the disease status of individual patients with COVID-19. Similar to IR_cluster2 patients, patients with high IRscores had longer hospital-free days at day 45, required ICU admission and mechanical ventilatory support, and presented higher Charlson comorbidity index and SOFA scores. A high IRscore was also linked to acute infection phase and absence of drug intervention. Our investigation comprehensively elucidates the potential role of IR patterns in regulating the immune response, modulating metabolic processes, and shaping clinical manifestations of COVID-19. All of this evidence suggests the essential role of prognostic stratification and biomarker screening based on IR patterns in the clinical management and drug development of future emerging infectious diseases such as COVID-19.

Integrated longitudinal multi-omics study identifies immune programs associated with COVID-19 severity and mortality in 1152 hospitalized participants

Hospitalized COVID-19 patients exhibit diverse clinical outcomes, with some individuals diverging over time even though their initial disease severity appears similar. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity. In this study, we carried out deep immunophenotyping and conducted longitudinal multi-omics modeling integrating ten distinct assays on a total of 1,152 IMPACC participants and identified several immune cascades that were significant drivers of differential clinical outcomes. Increasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, NETosis, and T-cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma immunoglobulins and B cells, as well as dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to the failure of viral clearance in patients with fatal illness. Our longitudinal multi-omics profiling study revealed novel temporal coordination across diverse omics that potentially explain disease progression, providing insights that inform the targeted development of therapies for hospitalized COVID-19 patients, especially those critically ill.

A ketogenic diet can mitigate SARS-CoV-2 induced systemic reprogramming and inflammation

The ketogenic diet (KD) has demonstrated benefits in numerous clinical studies and animal models of disease in modulating the immune response and promoting a systemic anti-inflammatory state. Here we investigate the effects of a KD on systemic toxicity in mice following SARS-CoV-2 infection. Our data indicate that under KD, SARS-CoV-2 reduces weight loss with overall improved animal survival. Muted multi-organ transcriptional reprogramming and metabolism rewiring suggest that a KD initiates and mitigates systemic changes induced by the virus. We observed reduced metalloproteases and increased inflammatory homeostatic protein transcription in the heart, with decreased serum pro-inflammatory cytokines (i.e., TNF-α, IL-15, IL-22, G-CSF, M-CSF, MCP-1), metabolic markers of inflammation (i.e., kynurenine/tryptophane ratio), and inflammatory prostaglandins, indicative of reduced systemic inflammation in animals infected under a KD. Taken together, these data suggest that a KD can alter the transcriptional and metabolic response in animals following SARS-CoV-2 infection with improved mice health, reduced inflammation, and restored amino acid, nucleotide, lipid, and energy currency metabolism.

Plasma metabolomics supports non-fasted sampling for metabolic profiling across a spectrum of glucose tolerance in the Nile rat model for type 2 diabetes

Type 2 diabetes is a challenge in modern healthcare, and animal models are necessary to identify underlying mechanisms. The Nile rat (Arvicanthis niloticus) develops diet-induced diabetes rapidly on a conventional rodent chow diet without genetic or chemical manipulation. Unlike common laboratory models, the outbred Nile rat model is diurnal and has a wide range of overt diabetes onset and diabetes progression patterns in both sexes, better mimicking the heterogeneous diabetic phenotype in humans. While fasted blood glucose has historically been used to monitor diabetic progression, postprandial blood glucose is more sensitive to the initial stages of diabetes. However, there is a long-held assumption that ad libitum feeding in rodent models leads to increased variance, thus masking diabetes-related metabolic changes in the plasma. Here we compared repeatability within triplicates of non-fasted or fasted plasma samples and assessed metabolic changes relevant to glucose tolerance in fasted and non-fasted plasma of 8-10-week-old male Nile rats. We used liquid chromatography-mass spectrometry lipidomics and polar metabolomics to measure relative metabolite abundances in the plasma samples. We found that, compared to fasted metabolites, non-fasted plasma metabolites are not only more strongly associated with glucose tolerance on the basis of unsupervised clustering and elastic net regression model, but also have a lower replicate variance. Between the two sampling groups, we detected 66 non-fasted metabolites and 32 fasted metabolites that were associated with glucose tolerance using a combined approach with multivariable elastic net and individual metabolite linear models. Further, to test if metabolite replicate variance is affected by age and sex, we measured non-fasted replicate variance in a cohort of mature 30-week-old male and female Nile rats. Our results support using non-fasted plasma metabolomics to study glucose tolerance in Nile rats across the progression of diabetes.

Integrative proteomics and metabolomics study reveal enhanced immune responses by COVID-19 vaccine booster shot against Omicron SARS-CoV-2 infection

Since its outbreak in late 2021, the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widely reported to be able to evade neutralizing antibodies, becoming more transmissible while causing milder symptoms than previous SARS-CoV-2 strains. Understanding the underlying molecular changes of Omicron SARS-CoV-2 infection and corresponding host responses are important to the control of Omicron COVID-19 pandemic. In this study, we report an integrative proteomics and metabolomics investigation of serum samples from 80 COVID-19 patients infected with Omicron SARS-CoV-2, as well as 160 control serum samples from 80 healthy individuals and 80 patients who had flu-like symptoms but were negative for SARS-CoV-2 infection. The multiomics results indicated that Omicron SARS-CoV-2 infection caused significant changes to host serum proteome and metabolome comparing to the healthy controls and patients who had flu-like symptoms without COVID-19. Protein and metabolite changes also pointed to liver dysfunctions and potential damage to other host organs by Omicron SARS-CoV-2 infection. The Omicron COVID-19 patients could be roughly divided into two subgroups based on their proteome differences. Interestingly, the subgroup who mostly had received full vaccination with booster shot had fewer coughing symptom, changed sphingomyelin lipid metabolism, and stronger immune responses including higher numbers of lymphocytes, monocytes, neutrophils, and upregulated proteins related to CD4+ T cells, CD8+ effector memory T cells (Tem), and conventional dendritic cells, revealing beneficial effects of full COVID-19 vaccination against Omicron SARS-CoV-2 infection through molecular changes.

HCG18, LEF1AS1 and lncCEACAM21 as biomarkers of disease severity in the peripheral blood mononuclear cells of COVID-19 patients

BACKGROUND

Even after 3 years from SARS-CoV-2 identification, COVID-19 is still a persistent and dangerous global infectious disease. Significant improvements in our understanding of the disease pathophysiology have now been achieved. Nonetheless, reliable and accurate biomarkers for the early stratification of COVID-19 severity are still lacking. Long noncoding RNAs (LncRNAs) are ncRNAs longer than 200 nucleotides, regulating the transcription and translation of protein-coding genes and they can be found in the peripheral blood, thus holding a promising biomarker potential. Specifically, peripheral blood mononuclear cells (PBMCs) have emerged as a source of indirect biomarkers mirroring the conditions of tissues: they include monocytes, B and T lymphocytes, and natural killer T cells (NKT), being highly informative for immune-related events.

METHODS

We profiled by RNA-Sequencing a panel of 2906 lncRNAs to investigate their modulation in PBMCs of a pilot group of COVID-19 patients, followed by qPCR validation in 111 hospitalized COVID-19 patients.

RESULTS

The levels of four lncRNAs were found to be decreased in association with COVID-19 mortality and disease severity: HLA Complex Group 18-242 and -244 (HCG18-242 and HCG18-244), Lymphoid Enhancer Binding Factor 1-antisense 1 (LEF1-AS1) and lncCEACAM21 (i.e. ENST00000601116.5, a lncRNA in the CEACAM21 locus). Interestingly, these deregulations were confirmed in an independent patient group of hospitalized patients and by the re-analysis of publicly available single-cell transcriptome datasets. The identified lncRNAs were expressed in all of the PBMC cell types and inversely correlated with the neutrophil/lymphocyte ratio (NLR), an inflammatory marker. In vitro, the expression of LEF1-AS1 and lncCEACAM21 was decreased upon THP-1 monocytes exposure to a relevant stimulus, hypoxia.

CONCLUSION

The identified COVID-19-lncRNAs are proposed as potential innovative biomarkers of COVID-19 severity and mortality.

Single-cell RNA-seq public data reveal the gene regulatory network landscape of respiratory epithelial and peripheral immune cells in COVID-19 patients

Introduction

Infection with SARS-CoV-2 leads to coronavirus disease 2019 (COVID-19), which can result in acute respiratory distress syndrome and multiple organ failure. However, its comprehensive influence on pathological immune responses in the respiratory epithelium and peripheral immune cells is not yet fully understood.

Methods

In this study, we analyzed multiple public scRNA-seq datasets of nasopharyngeal swabs and peripheral blood to investigate the gene regulatory networks (GRNs) of healthy individuals and COVID-19 patients with mild/moderate and severe disease, respectively. Cell-cell communication networks among cell types were also inferred. Finally, validations were conducted using bulk RNA-seq and proteome data.

Results

Similar and dissimilar regulons were identified within or between epithelial and immune cells during COVID-19 severity progression. The relative transcription factors (TFs) and their targets were used to construct GRNs among different infection sites and conditions. Between respiratory epithelial and peripheral immune cells, different TFs tended to be used to regulate the activity of a cell between healthy individuals and COVID-19 patients, although they had some TFs in common. For example, XBP1, FOS, STAT1, and STAT2 were activated in both the epithelial and immune cells of virus-infected individuals. In contrast, severe COVID-19 cases exhibited activation of CEBPD in peripheral immune cells, while CEBPB was exclusively activated in respiratory epithelial cells. Moreover, in patients with severe COVID-19, although some inflammatory genes, such as S100A8/A9, were found to be upregulated in both respiratory epithelial and peripheral immune cells, their relative regulators can differ in terms of cell types. The cell-cell communication analysis suggested that epidermal growth factor receptor signaling among epithelia contributes to mild/moderate disease, and chemokine signaling among immune cells contributes to severe disease.

Conclusion

This study identified cell type- and condition-specific regulons in a wide range of cell types from the initial infection site to the peripheral blood, and clarified the diverse mechanisms of maladaptive responses to SARS-CoV-2 infection.

Differentiation between Descending Thoracic Aortic Diseases using Machine Learning and Plasma Proteomic Signatures

Background

Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection.

Methods

This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using 5-fold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis.

Results

Of the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p-value <0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important correlated groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation.

Conclusions

We conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

Understanding COVID-19 progression with longitudinal peripheral blood mononuclear cell proteomics: Changes in the cellular proteome over time

The clinical presentation of COVID-19 is highly variable, and understanding the underlying biological processes is crucial. This study utilized a proteomic analysis to investigate dysregulated processes in the peripheral blood mononuclear cells of patients with COVID-19 compared to healthy volunteers. Samples were collected at different stages of the disease, including hospital admission, after 7 days of hospitalization, and 30 days after discharge. Metabolic pathway alterations and increased abundance of neutrophil-related proteins were observed in patients. Patients progressing to critical illness had significantly low-abundance proteins in the pentose phosphate and glycolysis pathways compared with those presenting clinical recovery. Important biological processes, such as fatty acid concentration and glucose metabolism disorder, remained altered even after 30 days of hospital discharge. Temporal proteomic changes revealed distinct pathways in critically ill and non-critically ill patients. Our study emphasizes the significance of longitudinal cellular proteomic studies in identifying disease progression-related pathways and persistent protein changes post-hospitalization.

Niclosamide as a chemical probe for analyzing SARS-CoV-2 modulation of host cell lipid metabolism

Introduction

SARS-CoV-2 subverts host cell processes to facilitate rapid replication and dissemination, and this leads to pathological inflammation.

Methods

We used niclosamide (NIC), a poorly soluble anti-helminth drug identified initially for repurposed treatment of COVID-19, which activates the cells' autophagic and lipophagic processes as a chemical probe to determine if it can modulate the host cell's total lipid profile that would otherwise be either amplified or reduced during SARS-CoV-2 infection.

Results

Through parallel lipidomic and transcriptomic analyses we observed massive reorganization of lipid profiles of SARS-CoV-2 infected Vero E6 cells, especially with triglycerides, which were elevated early during virus replication, but decreased thereafter, as well as plasmalogens, which were elevated at later timepoints during virus replication, but were also elevated under normal cell growth. These findings suggested a complex interplay of lipid profile reorganization involving plasmalogen metabolism. We also observed that NIC treatment of both low and high viral loads does not affect virus entry. Instead, NIC treatment reduced the abundance of plasmalogens, diacylglycerides, and ceramides, which we found elevated during virus infection in the absence of NIC, resulting in a significant reduction in the production of infectious virions. Unexpectedly, at higher viral loads, NIC treatment also resulted in elevated triglyceride levels, and induced significant changes in phospholipid metabolism.

Discussion

We posit that future screens of approved or new partner drugs should prioritize compounds that effectively counter SARS-CoV-2 subversion of lipid metabolism, thereby reducing virus replication, egress, and the subsequent regulation of key lipid mediators of pathological inflammation.

Meditation-induced bloodborne factors as an adjuvant treatment to COVID-19 disease

The COVID-19 pandemic has resulted in significant morbidity and mortality worldwide. Management of the pandemic has relied mainly on SARS-CoV-2 vaccines, while alternative approaches such as meditation, shown to improve immunity, have been largely unexplored. Here, we probe the relationship between meditation and COVID-19 disease and directly test the impact of meditation on the induction of a blood environment that modulates viral infection. We found a significant inverse correlation between length of meditation practice and SARS-CoV-2 infection as well as accelerated resolution of symptomology of those infected. A meditation "dosing" effect was also observed. In cultured human lung cells, blood from experienced meditators induced factors that prevented entry of pseudotyped viruses for SARS-CoV-2 spike protein of both the wild-type Wuhan-1 virus and the Delta variant. We identified and validated SERPINA5, a serine protease inhibitor, as one possible protein factor in the blood of meditators that is necessary and sufficient for limiting pseudovirus entry into cells. In summary, we conclude that meditation can enhance resiliency to viral infection and may serve as a possible adjuvant therapy in the management of the COVID-19 pandemic.

Peripheral Blood Omics and Other Multiplex-based Systems in Pulmonary and Critical Care Medicine

Over the last years, the use of peripheral blood-derived big datasets in combination with machine learning technology has accelerated the understanding, prediction, and management of pulmonary and critical care conditions. The goal of this article is to provide readers with an introduction to the methods and applications of blood omics and other multiplex-based technologies in the pulmonary and critical care medicine setting to better appreciate the current literature in the field. To accomplish that, we provide essential concepts needed to rationalize this approach and introduce readers to the types of molecules that can be obtained from the circulating blood to generate big datasets; elaborate on the differences between bulk, sorted, and single-cell approaches; and the basic analytical pipelines required for clinical interpretation. Examples of peripheral blood-derived big datasets used in recent literature are presented, and limitations of that technology are highlighted to qualify both the current and future value of these methodologies.

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.

Enhanced production of eicosanoids in plasma and activation of DNA damage pathways in PBMCs are correlated with the severity of ancestral COVID-19 infection

Background

Many questions remain unanswered regarding the implication of lipid metabolites in severe SARS-CoV-2 infections. By re-analyzed sequencing data from the nasopharynx of a previously published cohort, we found that alox genes, involved in eicosanoid synthesis, were up-regulated in high WHO score patients, especially in goblet cells. Herein, we aimed to further understand the roles played by eicosanoids during severe SARS-CoV-2 infection.

Methods and findings

We performed a total fatty acid panel on plasma and bulk RNA-seq analysis on peripheral blood mononuclear cells (PBMCs) collected from 10 infected and 10 uninfected patients. Univariate comparison of lipid metabolites revealed that lipid metabolites were increased in SARS-CoV-2 patients including the lipid mediators Arachidonic Acid (AA) and Eicosapentaenoic Acid (EPA). AA, EPA and the fatty acids Docosahexaenoic acid (DHA) and Docosapentaenoic acid (DPA), were positively correlated to WHO disease severity score. Transcriptomic analysis demonstrated that COVID-19 patients can be segregated based on WHO scores. Ontology, KEGG and Reactome analysis identified pathways enriched for genes related to innate immunity, interactions between lymphoid and nonlymphoid cells, interleukin signaling and, cell cycling pathways.

Conclusions

Our study offers an association between nasopharynx mucosa eicosanoid genes expression, specific serum inflammatory lipids and, subsequent DNA damage pathways activation in PBMCs to severity of COVID-19 infection.

Quantitative multiorgan proteomics of fatal COVID-19 uncovers tissue-specific effects beyond inflammation

SARS-CoV-2 may directly and indirectly damage lung tissue and other host organs, but there are few system-wide, untargeted studies of these effects on the human body. Here, we developed a parallelized mass spectrometry (MS) proteomics workflow enabling the rapid, quantitative analysis of hundreds of virus-infected FFPE tissues. The first layer of response to SARS-CoV-2 in all tissues was dominated by circulating inflammatory molecules. Beyond systemic inflammation, we differentiated between systemic and true tissue-specific effects to reflect distinct COVID-19-associated damage patterns. Proteomic changes in the lungs resembled those of diffuse alveolar damage (DAD) in non-COVID-19 patients. Extensive organ-specific changes were also evident in the kidneys, liver, and lymphatic and vascular systems. Secondary inflammatory effects in the brain were related to rearrangements in neurotransmitter receptors and myelin degradation. These MS-proteomics-derived results contribute substantially to our understanding of COVID-19 pathomechanisms and suggest strategies for organ-specific therapeutic interventions.

The citrate transporters SLC13A5 and SLC25A1 elicit different metabolic responses and phenotypes in the mouse

Cytosolic citrate is imported from the mitochondria by SLC25A1, and from the extracellular milieu by SLC13A5. In the cytosol, citrate is used by ACLY to generate acetyl-CoA, which can then be exported to the endoplasmic reticulum (ER) by SLC33A1. Here, we report the generation of mice with systemic overexpression (sTg) of SLC25A1 or SLC13A5. Both animals displayed increased cytosolic levels of citrate and acetyl-CoA; however, SLC13A5 sTg mice developed a progeria-like phenotype with premature death, while SLC25A1 sTg mice did not. Analysis of the metabolic profile revealed widespread differences. Furthermore, SLC13A5 sTg mice displayed increased engagement of the ER acetylation machinery through SLC33A1, while SLC25A1 sTg mice did not. In conclusion, our findings point to different biological responses to SLC13A5- or SLC25A1-mediated import of citrate and suggest that the directionality of the citrate/acetyl-CoA pathway can transduce different signals.

Decreased plasma cartilage acidic protein 1 in COVID-19

Cartilage acidic protein-1 (CRTAC1) is produced by several cell types, including Type 2 alveolar epithelial (T2AE) cells that are targeted by SARS-CoV2. Plasma CRTAC1 is known based on proteomic surveys to be low in patients with severe COVID-19. Using an ELISA, we found that patients treated for COVID-19 in an ICU almost uniformly had plasma concentrations of CRTAC1 below those of healthy controls. Magnitude of decrease in CRTAC1 distinguished COVID-19 from other causes of acute respiratory decompensation and correlated with established metrics of COVID-19 severity. CRTAC1 concentrations below those of controls were found in some patients a year after hospitalization with COVID-19, long COVID after less severe COVID-19, or chronic obstructive pulmonary disease. Decreases in CRTAC1 in severe COVID-19 correlated (r = 0.37, p = 0.0001) with decreases in CFP (properdin), which interacts with CRTAC1. Thus, decreases of CRTAC1 associated with severe COVID-19 may result from loss of production by T2AE cells or co-depletion with CFP. Determination of significance of and reasons behind decreased CRTAC1 concentration in a subset of patients with long COVID will require analysis of roles of preexisting lung disease, impact of prior acute COVID-19, age, and other confounding variables in a larger number of patients.

Comparative Shotgun Proteomics Reveals the Characteristic Protein Signature of Osteosarcoma Subtypes

Osteosarcoma is a primary malignant bone tumor affecting adolescents and young adults. This study aimed to identify proteomic signatures that distinguish between different osteosarcoma subtypes, providing insights into their molecular heterogeneity and potential implications for personalized treatment approaches. Using advanced proteomic techniques, we analyzed FFPE tumor samples from a cohort of pediatric osteosarcoma patients representing four various subtypes. Differential expression analysis revealed a significant proteomic signature that discriminated between these subtypes, highlighting distinct molecular profiles associated with different tumor characteristics. In contrast, clinical determinants did not correlate with the proteome signature of pediatric osteosarcoma. The identified proteomics signature encompassed a diverse array of proteins involved in focal adhesion, ECM-receptor interaction, PI3K-Akt signaling pathways, and proteoglycans in cancer, among the top enriched pathways. These findings underscore the importance of considering the molecular heterogeneity of osteosarcoma during diagnosis or even when developing personalized treatment strategies. By identifying subtype-specific proteomics signatures, clinicians may be able to tailor therapy regimens to individual patients, optimizing treatment efficacy and minimizing adverse effects.

Single-cell RNA sequencing and multiple bioinformatics methods to identify the immunity and ferroptosis-related biomarkers of SARS-CoV-2 infections to ischemic stroke

BACKGROUND

Since December 2019, Coronavirus disease 2019 (COVID-19) induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in significant morbidity and mortality worldwide. There is an increased risk of ischemic stroke (IS) associated with COVID-19. However, few studies have been reported to explain the potential correlation between COVID-19 and IS.

METHODS

We investigated the relationship and relevant mechanisms between COVID-19 and IS using single-cell RNA sequencing and multiple bioinformatics approaches.

RESULTS

By intersecting differentially expressed genes and WGCNA critical module genes, we obtained 73 COVID-19-related IS genes. According to the KEGG pathway analysis, the COVID-19-related IS disease genes were significantly enriched in the hematopoietic cell lineage pathway, ribosome pathway, COVID-19 pathway and primary immunodeficiency pathway. Finally, three genes associated with immunity (B4GALT5, CRISPLD2, F5) and two genes associated with ferroptosis (ACSL1, CREB5) were identified up-regulated in COVID-19-related IS. Significantly, it was found that all five genes were highly expressed in monocytes by single cell RNA sequencing.

CONCLUSION

We believe these genes (B4GALT5, CRISPLD2, F5, ACSL1, CREB5) may regulate the immune response and ferroptosis of multiple immune cells, mainly including monocytes, which may contribute to the development of COVID-19-related IS. In addition, these genes may be potential targets for the treatment of COVID-19-related IS.

Cross-Linking Mass Spectrometry Uncovers Interactions Between High-Density Lipoproteins and the SARS-CoV-2 Spike Glycoprotein

High-density lipoprotein (HDL) levels are reduced in patients with coronavirus disease 2019 (COVID-19), and the extent of this reduction is associated with poor clinical outcomes. While lipoproteins are known to play a key role during the life cycle of the hepatitis C virus, their influence on coronavirus (CoV) infections is poorly understood. In this study, we utilize cross-linking mass spectrometry (XL-MS) to determine circulating protein interactors of the severe acute respiratory syndrome (SARS)-CoV-2 spike glycoprotein. XL-MS of plasma isolated from patients with COVID-19 uncovered HDL protein interaction networks, dominated by acute-phase serum amyloid proteins, whereby serum amyloid A2 was shown to bind to apolipoprotein (Apo) D. XL-MS on isolated HDL confirmed ApoD to interact with SARS-CoV-2 spike but not SARS-CoV-1 spike. Other direct interactions of SARS-CoV-2 spike upon HDL included ApoA1 and ApoC3. The interaction between ApoD and spike was further validated in cells using immunoprecipitation-MS, which uncovered a novel interaction between both ApoD and spike with membrane-associated progesterone receptor component 1. Mechanistically, XL-MS coupled with data-driven structural modeling determined that ApoD may interact within the receptor-binding domain of the spike. However, ApoD overexpression in multiple cell-based assays had no effect upon viral replication or infectivity. Thus, SARS-CoV-2 spike can bind to apolipoproteins on HDL, but these interactions do not appear to alter infectivity.

Mass Spectrometry-Based Multi-omics Integration with a Single Set of C. elegans Samples

Mass spectrometry-based large-scale multi-omics research has proven to be powerful in answering biological questions; nonetheless, it faces many challenges from sample preparation to downstream data integration. To efficiently extract biomolecules of different physicochemical properties, preparation of various sample type needs specific tailoring, especially of difficult ones, such as Caenorhabditis elegans. In this study, we sought to develop a multi-omics sample preparation method starting with a single set ofC. elegans samples to save time, minimize variability, expand biomolecule coverage, and promote multi-omics integration. We investigated tissue disruption methods to effectively release biomolecules and optimized extraction strategies to achieve broader and more reproducible biomolecule coverage in proteomics, lipidomics, and metabolomics workflows. In our assessment, we also considered speediness and usability of the approaches. The developed method was validated through a study of 16C. elegans samples designed to shine light on mitochondrial unfolded protein response (UPRmt), induced by three unique stressors─knocking down electron transfer chain element cco-1, mitochondrial ribosome protein S5 mrps-5, and antibiotic treatment Doxycycline. Our findings suggested that the method achieved great coverage of proteome, lipidome, and metabolome with high reproducibility and validated that all stressors triggered UPRmt in C. elegans, although generating unique molecular signatures. Innate immune response was activated, and triglycerides were decreased under all three stressor conditions. Additionally, Doxycycline treatment elicited more distinct proteomic, lipidomic, and metabolomic response than the other two treatments. This method has been successfully used to process Saccharomyces cerevisiae (data not shown) and can likely be applied to other organisms for multi-omics research.

Identification of cuproptosis-related molecular subtypes and a novel predictive model of COVID-19 based on machine learning

Background

To explicate the pathogenic mechanisms of cuproptosis, a newly observed copper induced cell death pattern, in Coronavirus disease 2019 (COVID-19).

Methods

Cuproptosis-related subtypes were distinguished in COVID-19 patients and associations between subtypes and immune microenvironment were probed. Three machine algorithms, including LASSO, random forest, and support vector machine, were employed to identify differentially expressed genes between subtypes, which were subsequently used for constructing cuproptosis-related risk score model in the GSE157103 cohort to predict the occurrence of COVID-19. The predictive values of the cuproptosis-related risk score were verified in the GSE163151 cohort, GSE152418 cohort and GSE171110 cohort. A nomogram was created to facilitate the clinical use of this risk score, and its validity was validated through a calibration plot. Finally, the model genes were validated using lung proteomics data from COVID-19 cases and single-cell data.

Results

Patients with COVID-19 had higher significantly cuproptosis level in blood leukocytes compared to patients without COVID-19. Two cuproptosis clusters were identified by unsupervised clustering approach and cuproptosis cluster A characterized by T cell receptor signaling pathway had a better prognosis than cuproptosis cluster B. We constructed a cuproptosis-related risk score, based on PDHA1, PDHB, MTF1 and CDKN2A, and a nomogram was created, which both showed excellent predictive values for COVID-19. And the results of proteomics showed that the expression levels of PDHA1 and PDHB were significantly increased in COVID-19 patient samples.

Conclusion

Our study constructed and validated an cuproptosis-associated risk model and the risk score can be used as a powerful biomarker for predicting the existence of SARS-CoV-2 infection.

COVID-19 of differing severity: from bulk to single-cell expression data analysis

Coronavirus disease 2019 (COVID-19) is raging worldwide and causes an immense disease burden. Despite this, the biomarkers and targeting drugs of COVID-19 of differing severity remain largely unknown. Based on the GSE164805 dataset, we identified modules most critical for mild COVID-19 (mCOVID-19) and severe COVID-19 (sCOVID-19) through WGCNA, respectively. We subsequently constructed a protein-protein interaction network, and detected 16 hub genes for mCOVID-19 and 10 hub genes for sCOVID-19, followed by the prediction of upstream transcription factors (TFs) and kinases. The enrichment analysis then showed downregulation of TNFA signaling via NFKB for mCOVID-19, as well as downregulation of MYC targets V1 for sCOVID-19. Infiltration degrees of many immune cells, such as macrophages, were also sharply different between mCOVID-19 and sCOVID-19 samples. Predicted protein targeting drugs with the highest scores nearly all belong to naturally derived or synthetic glucocorticoids. For the two single-cell RNA-seq datasets, we explored the expression distribution of hub genes for mCOVID-19/sCOVID-19 in each cell type. The expression levels of PRKCA, MCM5, TYMS, RBBP4, BCL6, FLOT1, KDM6B, and TLR2 were found to be cell-type-specific. Furthermore, the expression levels of 10 hub genes for mCOVID-19 were significantly upregulated in PBMCs between eight healthy controls and eight mCOVID-19 patients at our institution. Collectively, we detected critical modules, pathways, TFs, kinases, immune cells, targeting drugs, hub genes, and their expression distributions in different cell types that may involve the pathogenesis of COVID-19 of differing severity, which may propel earlier diagnosis and more effective treatment of this intractable disease in the future.

The genetic associations of COVID-19 on genitourinary symptoms

Background

Recently emerged reports indicated that patients with coronavirus disease 2019 (COVID-19) might experience novo genitourinary symptoms after discharge. Nevertheless, the causal associations and underlying mechanisms remain largely unclear.

Methods

Genome-wide association study (GWAS) statistics for COVID-19 and 28 genitourinary symptoms with consistent definitions were collected from the COVID-19 Host Genetic Initiative, FinnGen, and UK Biobanks. Mendelian randomization (MR) analyses were applied to explore the causal effects of COVID-19 on genitourinary symptoms by selecting single-nucleotide polymorphisms as instrumental variables. Meta-analyses were conducted to evaluate the combined causal effect. Molecular pathways connecting COVID-19 and its associated disorders were evaluated by weighted gene co-expression network analysis (WGCNA) and enrichment analyses to extract insights into the potential mechanisms underlying the connection.

Results

The MR and meta-analyses indicated that COVID-19 was causally associated with increased risk for calculus of the lower urinary tract (LUTC, OR: 1.2984 per doubling in odds of COVID-19, 95% CI: 1.0752-1.5680, p = 0.007) and sexual dysfunction (SD, OR: 1.0931, 95% CI: 1.0292-1.1610, p = 0.004). Intriguingly, COVID-19 might exert a slight causal protective effect on the progression of urinary tract infections (UTIs) and bladder cancer (BLCA). These results were robust to sensitivity analyses. Bioinformatic analyses indicated that the inflammatory-immune response module may mediate the links between COVID-19 and its associated disorders at the molecular level.

Conclusions

In response to post-COVID-19 symptoms, we recommend that COVID-19 patients should strengthen the prevention of LUTC and the monitoring of sexual function. Meanwhile, the positive effects of COVID-19 on UTIs and BLCA should attach equal importance.

Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status

BACKGROUND

There is still more to learn about the pathobiology of COVID-19. A multi-omic approach offers a holistic view to better understand the mechanisms of COVID-19. We used state-of-the-art statistical learning methods to integrate genomics, metabolomics, proteomics, and lipidomics data obtained from 123 patients experiencing COVID-19 or COVID-19-like symptoms for the purpose of identifying molecular signatures and corresponding pathways associated with the disease.

RESULTS

We constructed and validated molecular scores and evaluated their utility beyond clinical factors known to impact disease status and severity. We identified inflammation- and immune response-related pathways, and other pathways, providing insights into possible consequences of the disease.

CONCLUSIONS

The molecular scores we derived were strongly associated with disease status and severity and can be used to identify individuals at a higher risk for developing severe disease. These findings have the potential to provide further, and needed, insights into why certain individuals develop worse outcomes.

Analysis of Plasma Proteins Involved in Inflammation, Immune Response/Complement System, and Blood Coagulation upon Admission of COVID-19 Patients to Hospital May Help to Predict the Prognosis of the Disease

The development of new approaches allowing for the early assessment of COVID-19 cases that are likely to become critical and the discovery of new therapeutic targets are urgently required. In this prospective cohort study, we performed proteomic and laboratory profiling of plasma from 163 COVID-19 patients admitted to Bauru State Hospital (Brazil) between 4 May 2020 and 4 July 2020. Plasma samples were collected upon admission for routine laboratory analyses and shotgun quantitative label-free proteomics. Based on the course of the disease, the patients were divided into three groups: (a) mild (n = 76) and (b) severe (n = 56) symptoms, whose patients were discharged without or with admission to an intensive care unit (ICU), respectively, and (c) critical (n = 31), a group consisting of patients who died after admission to an ICU. Based on our data, potential therapies for COVID-19 should target proteins involved in inflammation, the immune response and complement system, and blood coagulation. Other proteins that could potentially be employed in therapies against COVID-19 but that so far have not been associated with the disease are CD5L, VDBP, A1BG, C4BPA, PGLYRP2, SERPINC1, and APOH. Targeting these proteins' pathways might constitute potential new therapies or biomarkers of prognosis of the disease.

Exploration of the potential common pathogenic mechanisms in COVID-19 and silicosis by using bioinformatics and system biology

Silicosis is an occupational lung disease that is common worldwide. In recent years, coronavirus disease 2019 (COVID-19) has provided daunting challenges to public healthcare systems globally. Although multiple studies have shown a close link between COVID-19 and other respiratory diseases, the inter-relational mechanisms between COVID-19 and silicosis remain unclear. This study aimed to explore the shared molecular mechanisms and drug targets of COVID-19 and silicosis. Gene expression profiling identified four modules that were most closely associated with both diseases. Furthermore, we performed functional analysis and constructed a protein-protein interaction network. Seven hub genes (budding uninhibited by benzimidazoles 1 [BUB1], protein regulator of cytokinesis 1 [PRC1], kinesin family member C1 [KIFC1], ribonucleotide reductase regulatory subunit M2 [RRM2], cyclin-dependent kinase inhibitor 3 [CDKN3], Cyclin B2 [CCNB2], and minichromosome maintenance complex component 6 [MCM6]) were involved in the interaction between COVID-19 and silicosis. We investigated how diverse microRNAs and transcription factors regulate these seven genes. Subsequently, the correlation between the hub genes and infiltrating immune cells was explored. Further in-depth analyses were performed based on single-cell transcriptomic data from COVID-19, and the expression of hub-shared genes was characterized and located in multiple cell clusters. Finally, molecular docking results reveal small molecular compounds that may improve COVID-19 and silicosis. The current study reveals the common pathogenesis of COVID-19 and silicosis, which may provide a novel reference for further research.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Differential metabolic host response to pathogens associated with community-acquired pneumonia

Background

Metabolic changes induced by the host immune response to pathogens found in patients with community-acquired pneumonia (CAP) may provide insight into its pathogenesis. In this study, we characterized differences in the host metabolic response to common CAP-associated pathogens.

Method

Targeted metabolomic profiling was performed on serum samples obtained from hospitalized CAP patients (n = 119) at admission. We quantified 347 unique metabolites across multiple biochemical classes, including amines, acylcarnitines, and signaling lipids. We evaluated if unique associations between metabolite levels and specific CAP-associated pathogens could be identified.

Results

Several acylcarnitines were found to be elevated in C. burnetii and herpes simplex virus and lowered in M. pneumoniae as compared to other pathogens. Phenylalanine and kynurenine were found elevated in L. pneumophila as compared to other pathogens. S-methylcysteine was elevated in patients with M. pneumoniae, and these patients also showed lowered cortisol levels in comparison to almost all other pathogens. For the herpes simplex virus, we observed a unique elevation of eicosanoids and several amines. Many lysophosphatidylcholines showed an altered profile in C. burnetii versus S. pneumoniae, L. pneumophila, and respiratory syncytial virus. Finally, phosphatidylcholines were negatively affected by the influenza virus in comparison to S. pneumoniae.

Conclusions

In this exploratory analysis, metabolites from different biochemical classes were found to be altered in serum samples from patients with different CAP-associated pathogens, which may be used for hypothesis generation in studies on differences in pathogen host response and pathogenesis of CAP.

The Longitudinal Analysis of Convergent Antibody VDJ Regions in SARS-CoV-2-Positive Patients Using RNA-Seq

Severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) is an ongoing pandemic that continues to evolve and reinfect individuals. To understand the convergent antibody responses that evolved over the course of the pandemic, we evaluated the immunoglobulin repertoire of individuals infected by different SARS-CoV-2 variants for similarity between patients. We utilized four public RNA-seq data sets collected between March 2020 and March 2022 from the Gene Expression Omnibus (GEO) in our longitudinal analysis. This covered individuals infected with Alpha and Omicron variants. In total, from 269 SARS-CoV-2-positive patients and 26 negative patients, 629,133 immunoglobulin heavy-chain variable region V(D)J sequences were reconstructed from sequencing data. We grouped samples based on the SARS-CoV-2 variant type and/or the time they were collected from patients. Our comparison of patients within each SARS-CoV-2-positive group found 1011 common V(D)Js (same V gene, J gene and CDR3 amino acid sequence) shared by more than one patient and no common V(D)Js in the noninfected group. Taking convergence into account, we clustered based on similar CDR3 sequence and identified 129 convergent clusters from the SARS-CoV-2-positive groups. Within the top 15 clusters, 4 contain known anti-SARS-CoV-2 immunoglobulin sequences with 1 cluster confirmed to cross-neutralize variants from Alpha to Omicron. In our analysis of longitudinal groups that include Alpha and Omicron variants, we find that 2.7% of the common CDR3s found within groups were also present in more than one group. Our analysis reveals common and convergent antibodies, which include anti-SARS-CoV-2 antibodies, in patient groups over various stages of the pandemic.

Machine learning and integrative analysis identify the common pathogenesis of azoospermia complicated with COVID-19

Background

Although more recent evidence has indicated COVID-19 is prone to azoospermia, the common molecular mechanism of its occurrence remains to be elucidated. The aim of the present study is to further investigate the mechanism of this complication.

Methods

To discover the common differentially expressed genes (DEGs) and pathways of azoospermia and COVID-19, integrated weighted co-expression network (WGCNA), multiple machine learning analyses, and single-cell RNA-sequencing (scRNA-seq) were performed.

Results

Therefore, we screened two key network modules in the obstructive azoospermia (OA) and non-obstructive azoospermia (NOA) samples. The differentially expressed genes were mainly related to the immune system and infectious virus diseases. We then used multiple machine learning methods to detect biomarkers that differentiated OA from NOA. Enrichment analysis showed that azoospermia patients and COVID-19 patients shared a common IL-17 signaling pathway. In addition, GLO1, GPR135, DYNLL2, and EPB41L3 were identified as significant hub genes in these two diseases. Screening of two different molecular subtypes revealed that azoospermia-related genes were associated with clinicopathological characteristics of age, hospital-free-days, ventilator-free-days, charlson score, and d-dimer of patients with COVID-19 (P < 0.05). Finally, we used the Xsum method to predict potential drugs and single-cell sequencing data to further characterize whether azoospermia-related genes could validate the biological patterns of impaired spermatogenesis in cryptozoospermia patients.

Conclusion

Our study performs a comprehensive and integrated bioinformatics analysis of azoospermia and COVID-19. These hub genes and common pathways may provide new insights for further mechanism research.

Dysregulated early transcriptional signatures linked to mast cell and interferon responses are implicated in COVID-19 severity

Background

Dysregulated immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are thought to underlie the progression of coronavirus disease 2019 (COVID-19) to severe disease. We sought to determine whether early host immune-related gene expression could predict clinical progression to severe disease.

Methods

We analysed the expression of 579 immunological genes in peripheral blood mononuclear cells taken early after symptom onset using the NanoString nCounter and compared SARS-CoV-2 negative controls with SARS-CoV-2 positive subjects with mild (SARS+ Mild) and Moderate/Severe disease to evaluate disease outcomes. Biobanked plasma samples were also assessed for type I (IFN-α2a and IFN-β), type II (IFN-γ) and type III (IFN-λ1) interferons (IFNs) as well as 10 additional cytokines using multiplex immunoassays.

Results

We identified 19 significantly deregulated genes in 62 SARS-CoV-2 positive subject samples within 5 days of symptom onset and 58 SARS-CoV-2 negative controls and found that type I interferon (IFN) signalling (MX1, IRF7, IFITM1, IFI35, STAT2, IRF4, PML, BST2, STAT1) and genes encoding proinflammatory cytokines (TNF, TNFSF4, PTGS2 and IL1B) were upregulated in both SARS+ groups. Moreover, we found that FCER1, involved in mast cell activation, was upregulated in the SARS+ Mild group but significantly downregulated in the SARS+ Moderate/Severe group. In both SARS+ groups we discovered elevated interferon type I IFN-α2a, type II IFN and type III IFN λ1 plasma levels together with higher IL-10 and IL-6. These results indicate that those with moderate or severe disease are characterised by deficiencies in a mast cell response together with IFN hyper-responsiveness, suggesting that early host antiviral immune responses could be a cause and not a consequence of severe COVID-19.

Conclusions

This study suggests that early host immune responses linking defects in mast cell activation with host interferon responses correlates with more severe outcomes in COVID-19. Further characterisation of this pathway could help inform better treatment for vulnerable individuals.

Immunogenic cell death-led discovery of COVID-19 biomarkers and inflammatory infiltrates

Immunogenic cell death (ICD) serves a critical role in regulating cell death adequate to activate an adaptive immune response, and it is associated with various inflammation-related diseases. However, the specific role of ICD-related genes in COVID-19 remains unclear. We acquired COVID-19-related information from the GEO database and a total of 14 ICD-related differentially expressed genes (DEGs) were identified. These ICD-related DEGs were closely associated with inflammation and immune activity. Afterward, CASP1, CD4, and EIF2AK3 among the 14 DEGs were selected as feature genes based on LASSO, Random Forest, and SVM-RFE algorithms, which had reliable diagnostic abilities. Moreover, functional enrichment analysis indicated that these feature genes may have a potential role in COVID-19 by being involved in the regulation of immune response and metabolism. Further CIBERSORT analysis demonstrated that the variations in the immune microenvironment of COVID-19 patients may be correlated with CASP1, CD4, and EIF2AK3. Additionally, 33 drugs targeting 3 feature genes had been identified, and the ceRNA network demonstrated a complicated regulative association based on these feature genes. Our work identified that CASP1, CD4, and EIF2AK3 were diagnostic genes of COVID-19 and correlated with immune activity. This study presents a reliable diagnostic signature and offers an overview to investigate the mechanism of COVID-19.

Plasma proteome dynamics of COVID-19 severity learnt by a graph convolutional network of multi-scale topology

Efforts to understand the molecular mechanisms of COVID-19 have led to the identification of ACE2 as the main receptor for the SARS-CoV-2 spike protein on cell surfaces. However, there are still important questions about the role of other proteins in disease progression. To address these questions, we modelled the plasma proteome of 384 COVID-19 patients using protein level measurements taken at three different times and incorporating comprehensive clinical evaluation data collected 28 d after hospitalisation. Our analysis can accurately assess the severity of the illness using a metric based on WHO scores. By using topological vectorisation, we identified proteins that vary most in expression based on disease severity, and then utilised these findings to construct a graph convolutional network. This dynamic model allows us to learn the molecular interactions between these proteins, providing a tool to determine the severity of a COVID-19 infection at an early stage and identify potential pharmacological treatments by studying the dynamic interactions between the most relevant proteins.