No Substrate Left behind-Mining of Shotgun Proteomics Datasets Rescues Evidence of Proteolysis by SARS-CoV-2 3CLpro Main Protease.

Proteolytic processing is the most ubiquitous post-translational modification and regulator of protein function. To identify protease substrates, and hence the function of proteases, terminomics workflows have been developed to enrich and detect proteolytically generated protein termini from mass spectrometry data. The mining of shotgun proteomics datasets for such 'neo'-termini, to increase the understanding of proteolytic processing, is an underutilized opportunity. However, to date, this approach has been hindered by the lack of software with sufficient speed to make searching for the relatively low numbers of protease-generated semi-tryptic peptides present in non-enriched samples viable. We reanalyzed published shotgun proteomics datasets for evidence of proteolytic processing in COVID-19 using the recently upgraded MSFragger/FragPipe software, which searches data with a speed that is an order of magnitude greater than many equivalent tools. The number of protein termini identified was higher than expected and constituted around half the number of termini detected by two different N-terminomics methods. We identified neo-N- and C-termini generated during SARS-CoV-2 infection that were indicative of proteolysis and were mediated by both viral and host proteases-a number of which had been recently validated by in vitro assays. Thus, re-analyzing existing shotgun proteomics data is a valuable adjunct for terminomics research that can be readily tapped (for example, in the next pandemic where data would be scarce) to increase the understanding of protease function and virus-host interactions, or other diverse biological processes.

Proteomic profiling of single extracellular vesicles reveals colocalization of SARS-CoV-2 with a CD81/integrin-rich EV subpopulation in sputum from COVID-19 severe patients.

Background: The global outbreak of COVID-19, and the limited availability of clinical treatments, forced researchers around the world to search for the pathogenesis and potential treatments. Understanding the pathogenesis of SARS-CoV-2 is crucial to respond better to the current coronavirus disease 2019 (COVID-19) pandemic. Methods: We collected sputum samples from 20 COVID-19 patients and healthy controls. Transmission electron microscopy was used to observe the morphology of SARS-CoV-2. Extracellular vesicles (EVs) were isolated from sputum and the supernatant of VeroE6 cells, and were characterized by transmission electron microscopy, nanoparticle tracking analysis and Western-Blotting. Furthermore, a proximity barcoding assay was used to investigate immune-related proteins in single EV, and the relationship between EVs and SARS-CoV-2. Result: Transmission electron microscopy images of SARS-COV-2 virus reveal EV-like vesicles around the virion, and western blot analysis of EVs extracted from the supernatant of SARS-COV-2-infected VeroE6 cells showed that they expressed SARS-COV-2 protein. These EVs have the infectivity of SARS-COV-2, and the addition can cause the infection and damage of normal VeroE6 cells. In addition, EVs derived from the sputum of patients infected with SARS-COV-2 expressed high levels of IL6 and TGF-ß, which correlated strongly with expression of the SARS-CoV-2 N protein. Among 40 EV subpopulations identified, 18 differed significantly between patients and controls. The EV subpopulation regulated by CD81 was the most likely to correlate with changes in the pulmonary microenvironment after SARS-CoV-2 infection. Single extracellular vesicles in the sputum of COVID-19 patients harbor infection-mediated alterations in host and virus-derived proteins. Conclusions: These results demonstrate that EVs derived from the sputum of patients participate in virus infection and immune responses. This study provides evidence of an association between EVs and SARS-CoV-2, providing insight into the possible pathogenesis of SARS-CoV-2 infection and the possibility of developing nanoparticle-based antiviral drugs.

PepGM: a probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores.

MOTIVATION: Inferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides, which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, the correct taxonomic inference is crucial when identifying different viral strains with high-sequence homology-considering, e.g., the different epidemiological characteristics of the various strains of severe acute respiratory syndrome-related coronavirus-2. Additionally, many viruses mutate frequently, further complicating the correct identification of viral proteomic samples. RESULTS: We present PepGM, a probabilistic graphical model for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence scores, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on the species level, which PepGM clearly indicates by lower confidence scores. AVAILABILITY AND IMPLEMENTATION: PepGM is written in Python and embedded into a Snakemake workflow. It is available at https://github.com/BAMeScience/PepGM.

BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data.

Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.

Multi-Omics Profiling for Health.

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Quantitative protein biomarker panels: a path to improved clinical practice through proteomics.

The utilisation of protein biomarker panels, rather than individual protein biomarkers, offers a more comprehensive representation of human physiology. It thus has the potential to improve diagnosis, prognosis and the differentiation of responders from nonresponders in the context of precision medicine. Although several proteomic techniques exist for measuring biomarker panels, the integration of proteomics into clinical practice has been limited. In this Commentary, we highlight the significance of quantitative protein biomarker panels in clinical medicine and outline the challenges that must be addressed in order to identify the most promising panels and implement them in clinical routines to realise their medical potential. Furthermore, we argue that the absolute quantification of protein panels through targeted mass spectrometric assays remains the most promising technology for translating proteomics into routine clinical applications due to its high flexibility, low sample costs, independence from affinity reagents and low entry barriers for its integration into existing laboratory workflows.

Evolution of Mass Spectrometry Instruments and Techniques for Blood Proteomics.

Mass spectrometry (MS)-based blood proteomics is a crucial research focus in identifying disease biomarkers. Blood serum or plasma is the most commonly used sample for such analysis; however, it presents challenges due to the complexity and dynamic range of protein abundance. Despite these difficulties, the development of high-resolution MS instruments has made comprehensive investigation of blood proteomics possible. The evolution of time-of-flight (TOF) or Orbitrap MS instruments has played a significant role in the field of blood proteomics. These instruments are now among the most prominent techniques for blood proteomics due to their sensitivity, selectivity, fast response, and stability. For optimal results, it is necessary to eliminate high-abundance proteins from the blood sample, to maximize the depth coverage of the blood proteomics analysis. This can be achieved through various methods, including commercial kits, chemically synthesized materials, and MS technologies. This paper reviews recent advancements in MS technology and its remarkable applications in biomarker discovery, particularly in the areas of cancer and COVID-19 studies.

Saliva and Saliva Extracellular Vesicles for Biomarker Candidate Identification-Assay Development and Pilot Study in Amyotrophic Lateral Sclerosis.

Saliva is gaining increasing attention as a source of biomarkers due to non-invasive and undemanding collection access. Extracellular vesicles (EVs) are nano-sized, cell-released particles that contain molecular information about their parent cells. In this study, we developed methods for saliva biomarker candidate identification using EV-isolation and proteomic evaluation. We used pooled saliva samples for assay development. EVs were isolated using membrane affinity-based methods followed by their characterization using nanoparticle tracking analysis and transmission electron microscopy. Subsequently, both saliva and saliva-EVs were successfully analyzed using proximity extension assay and label-free quantitative proteomics. Saliva-EVs had a higher purity than plasma-EVs, based on the expression of EV-proteins and albumin. The developed methods could be used for the analysis of individual saliva samples from amyotrophic lateral sclerosis (ALS) patients and controls (n = 10 each). The starting volume ranged from 2.1 to 4.9 mL and the amount of total isolated EV-proteins ranged from 5.1 to 42.6 µg. Although no proteins were significantly differentially expressed between the two groups, there was a trend for a downregulation of ZNF428 in ALS-saliva-EVs and an upregulation of IGLL1 in ALS saliva. In conclusion, we have developed a robust workflow for saliva and saliva-EV analysis and demonstrated its technical feasibility for biomarker discovery.

Metabolomic and Proteomic Profiling of Porcine Intestinal Epithelial Cells Infected with Porcine Epidemic Diarrhea Virus.

Porcine epidemic diarrhea virus (PEDV) infection results in severe epidemic diarrhea and the death of suckling pigs. Although new knowledge about the pathogenesis of PEDV has been improved, alterations in metabolic processes and the functional regulators involved in PEDV infection with host cells remain largely unknow. To identify cellular metabolites and proteins related to PEDV pathogenesis, we synergistically investigated the metabolome and proteome profiles of PEDV-infected porcine intestinal epithelial cells by liquid chromatography tandem mass spectrometry and isobaric tags for relative and absolute quantification techniques. We identified 522 differential metabolites in positive and negative ion modes and 295 differentially expressed proteins after PEDV infection. Pathways of cysteine and methionine metabolism, glycine, serine and threonine metabolism, and mineral absorption were significantly enriched by differential metabolites and differentially expressed proteins. The betaine-homocysteine S-methyltransferase (BHMT) was indicated as a potential regulator involved in these metabolic processes. We then knocked down the BHMT gene and observed that down-expression of BHMT obviously decreased copy numbers of PEDV and virus titers (p < 0.01). Our findings provide new insights into the metabolic and proteomic profiles in PEDV-infected host cells and contribute to our further understanding of PEDV pathogenesis.

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

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

Robust single-cell matching and multimodal analysis using shared and distinct features.

The ability to align individual cellular information from multiple experimental sources is fundamental for a systems-level understanding of biological processes. However, currently available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely on a large number of shared features across datasets for cell matching. This approach underperforms when applied to single-cell proteomic datasets due to the limited number of parameters simultaneously accessed and lack of shared markers across these experiments. Here, we introduce a cell-matching algorithm, matching with partial overlap (MARIO) that accounts for both shared and distinct features, while consisting of vital filtering steps to avoid suboptimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multimodal methods, including spatial techniques and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via codetection by indexing imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid by cellular indexing of transcriptomes and epitopes by sequencing, revealing unique immune responses within the lung microenvironment of patients with COVID.

Clinical and Proteomic Correlates of Plasma ACE2 (Angiotensin-Converting Enzyme 2) in Human Heart Failure.

ACE2 (angiotensin-converting enzyme 2) is a key component of the renin-angiotensin-aldosterone system. Yet, little is known about the clinical and biologic correlates of circulating ACE2 levels in humans. We assessed the clinical and proteomic correlates of plasma (soluble) ACE2 protein levels in human heart failure. We measured plasma ACE2 using a modified aptamer assay among PHFS (Penn Heart Failure Study) participants (n=2248). We performed an association study of ACE2 against ≈5000 other plasma proteins measured with the SomaScan platform. Plasma ACE2 was not associated with ACE inhibitor and angiotensin-receptor blocker use. Plasma ACE2 was associated with older age, male sex, diabetes mellitus, a lower estimated glomerular filtration rate, worse New York Heart Association class, a history of coronary artery bypass surgery, and higher pro-BNP (pro-B-type natriuretic peptide) levels. Plasma ACE2 exhibited associations with 1011 other plasma proteins. In pathway overrepresentation analyses, top canonical pathways associated with plasma ACE2 included clathrin-mediated endocytosis signaling, actin cytoskeleton signaling, mechanisms of viral exit from host cells, EIF2 (eukaryotic initiation factor 2) signaling, and the protein ubiquitination pathway. In conclusion, in humans with heart failure, plasma ACE2 is associated with various clinical factors known to be associated with severe coronavirus disease 2019 (COVID-19), including older age, male sex, and diabetes mellitus, but is not associated with ACE inhibitor and angiotensin-receptor blocker use. Plasma ACE2 protein levels are prominently associated with multiple cellular pathways involved in cellular endocytosis, exocytosis, and intracellular protein trafficking. Whether these have a causal relationship with ACE2 or are relevant to novel coronavirus-2 infection remains to be assessed in future studies.

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

Treatments for COVID-19 infections have improved dramatically since the beginning of the pandemic, and glucocorticoids have been a key tool in improving mortality rates. The UK's National Institute for Health and Care Excellence guidance is for treatment to be targeted only at those requiring oxygen supplementation, however, and the interactions between glucocorticoids and COVID-19 are not completely understood. In this work, a multi-omic analysis of 98 inpatient-recruited participants was performed by quantitative metabolomics (using targeted liquid chromatography-mass spectrometry) and data-independent acquisition proteomics. Both 'omics datasets were analysed for statistically significant features and pathways differentiating participants whose treatment regimens did or did not include glucocorticoids. Metabolomic differences in glucocorticoid-treated patients included the modulation of cortisol and bile acid concentrations in serum, but no alleviation of serum dyslipidemia or increased amino acid concentrations (including tyrosine and arginine) in the glucocorticoid-treated cohort relative to the untreated cohort. Proteomic pathway analysis indicated neutrophil and platelet degranulation as influenced by glucocorticoid treatment. These results are in keeping with the key role of platelet-associated pathways and neutrophils in COVID-19 pathogenesis and provide opportunity for further understanding of glucocorticoid action. The findings also, however, highlight that glucocorticoids are not fully effective across the wide range of 'omics dysregulation caused by COVID-19 infections.

A Parallelization Strategy for the Time Efficient Analysis of Thousands of LC/MS Runs in High-Performance Computing Environment.

Combining robust proteomics instrumentation with high-throughput enabling liquid chromatography (LC) systems (e.g., timsTOF Pro and the Evosep One system, respectively) enabled mapping the proteomes of 1000s of samples. Fragpipe is one of the few computational protein identification and quantification frameworks that allows for the time-efficient analysis of such large data sets. However, it requires large amounts of computational power and data storage space that leave even state-of-the-art workstations underpowered when it comes to the analysis of proteomics data sets with 1000s of LC mass spectrometry runs. To address this issue, we developed and optimized a Fragpipe-based analysis strategy for a high-performance computing environment and analyzed 3348 plasma samples (6.4 TB) that were longitudinally collected from hospitalized COVID-19 patients under the auspice of the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study. Our parallelization strategy reduced the total runtime by ∼90% from 116 (theoretical) days to just 9 days in the high-performance computing environment. All code is open-source and can be deployed in any Simple Linux Utility for Resource Management (SLURM) high-performance computing environment, enabling the analysis of large-scale high-throughput proteomics studies.

Exosome Proteomics Reveals the Deregulation of Coagulation, Complement and Lipid Metabolism Proteins in Gestational Diabetes Mellitus.

Exosomes are small extracellular vesicles with a variable protein cargo in consonance with cell origin and pathophysiological conditions. Gestational diabetes mellitus (GDM) is characterized by different levels of chronic low-grade inflammation and vascular dysfunction; however, there are few data characterizing the serum exosomal protein cargo of GDM patients and associated signaling pathways. Eighteen pregnant women were enrolled in the study: 8 controls (CG) and 10 patients with GDM. Blood samples were collected from patients, for exosomes' concentration. Protein abundance alterations were demonstrated by relative mass spectrometric analysis and their association with clinical parameters in GDM patients was performed using Pearson's correlation analysis. The proteomics analysis revealed 78 significantly altered proteins when comparing GDM to CG, related to complement and coagulation cascades, platelet activation, prothrombotic factors and cholesterol metabolism. Down-regulation of Complement C3 (C3), Complement C5 (C5), C4-B (C4B), C4b-binding protein beta chain (C4BPB) and C4b-binding protein alpha chain (C4BPA), and up-regulation of C7, C9 and F12 were found in GDM. Our data indicated significant correlations between factors involved in the pathogenesis of GDM and clinical parameters that may improve the understanding of GDM pathophysiology. Data are available via ProteomeXchange with identifier PXD035673.

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.

Up-regulated 60S ribosomal protein L18 in PEDV N protein-induced S-phase arrested host cells promotes viral replication.

Coronavirus subverts the host cell cycle to create a favorable cellular environment that enhances viral replication in host cells. Previous studies have revealed that nucleocapsid (N) protein of the coronavirus porcine epidemic diarrhea virus (PEDV) interacts with p53 to induce cell cycle arrest in S-phase and promotes viral replication. However, the mechanism by which viral replication is increased in the PEDV N protein-induced S-phase arrested cells remains unknown. In the current study, the protein expression profiles of PEDV N protein-induced S-phase arrested Vero E6 cells and thymidine-induced S-phase arrested Vero E6 cells were characterized by tandem mass tag-labeled quantitative proteomic technology. The effect of differentially expressed proteins (DEPs) on PEDV replication was investigated. The results indicated that a total of 5709 proteins, including 20,560 peptides, were identified, of which 58 and 26 DEPs were identified in the PEDV N group and thymidine group, respectively (P < 0.05; ratio ≥ 1.2 or ≤ 0.8). The unique DEPs identified in the PEDV N group were mainly involved in DNA replication, transcription, and protein synthesis, of which 60S ribosomal protein L18 (RPL18) exhibited significantly up-regulated expression in the PEDV N protein-induced S-phase arrested Vero E6/IPEC-J2 cells and PEDV-infected IPEC-J2 cells (P < 0.05). Further studies revealed that the RPL18 protein could significantly enhance PEDV replication (P < 0.05). Our findings reveal a mechanism regarding increased viral replication when the PEDV N protein-induced host cells are in S-phase arrest. These data also provide evidence that PEDV maintains its own replication by utilizing protein synthesis-associated ribosomal proteins.

[Proteomic saliva assay in patients with COVID-19]. / Proteomnyi analiz slyuny u patsientov s COVID-19.

THE AIM OF THE STUDY: Was to perform proteomic saliva assay in order to reveal mechanisms of the oral pathology caused by COVID-19. MATERIALS AND METHODS: Proteomic analysis was performed to compare saliva proteins profile in healthy individuals (10 samples) and patients with COVID-19 (30 samples). RESULTS: The obtained results of the saliva samples study in patients with COVID-19 indicate activation in the oral tissues the pathways of the cell renewal, apoptosis, DNA exchange processes and chromatin remodelling; there are also marked signs of immune response reactivation and immunostimulation. CONCLUSION: Of all the proteins presented, the saliva of patients with COVID-19 33 proteins have an intersection with GO-annotated proteins of inflammation and epithelial cornification.

Proteomic Profile of Procoagulant Extracellular Vesicles Reflects Complement System Activation and Platelet Hyperreactivity of Patients with Severe COVID-19.

Background: Extracellular vesicles (EVs) are a valuable source of biomarkers and display the pathophysiological status of various diseases. In COVID-19, EVs have been explored in several studies for their ability to reflect molecular changes caused by SARS-CoV-2. Here we provide insights into the roles of EVs in pathological processes associated with the progression and severity of COVID-19. Methods: In this study, we used a label-free shotgun proteomic approach to identify and quantify alterations in EV protein abundance in severe COVID-19 patients. We isolated plasma extracellular vesicles from healthy donors and patients with severe COVID-19 by size exclusion chromatography (SEC). Then, flow cytometry was performed to assess the origin of EVs and to investigate the presence of circulating procoagulant EVs in COVID-19 patients. A total protein extraction was performed, and samples were analyzed by nLC-MS/MS in a Q-Exactive HF-X. Finally, computational analysis was applied to signify biological processes related to disease pathogenesis. Results: We report significant changes in the proteome of EVs from patients with severe COVID-19. Flow cytometry experiments indicated an increase in total circulating EVs and with tissue factor (TF) dependent procoagulant activity. Differentially expressed proteins in the disease groups were associated with complement and coagulation cascades, platelet degranulation, and acute inflammatory response. Conclusions: The proteomic data reinforce the changes in the proteome of extracellular vesicles from patients infected with SARS-CoV-2 and suggest a role for EVs in severe COVID-19.

Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in late 2019 in Wuhan, China, and has proven to be highly pathogenic, making it a global public health threat. The immediate need to understand the mechanisms and impact of the virus made omics techniques stand out, as they can offer a holistic and comprehensive view of thousands of molecules in a single experiment. Mastering bioinformatics tools to process, analyze, integrate, and interpret omics data is a powerful knowledge to enrich results. We present a robust and open access computational pipeline for extracting information from quantitative proteomics and transcriptomics public data. We present the entire pipeline from raw data to differentially expressed genes. We explore processes and pathways related to mapped transcripts and proteins. A pipeline is presented to integrate and compare proteomics and transcriptomics data using also packages available in the Bioconductor and providing the codes used. Cholesterol metabolism, immune system activity, ECM, and proteasomal degradation pathways increased in infected patients. Leukocyte activation profile was overrepresented in both proteomics and transcriptomics data. Finally, we found a panel of proteins and transcripts regulated in the same direction in the lung transcriptome and plasma proteome that distinguish healthy and infected individuals. This panel of markers was confirmed in another cohort of patients, thus validating the robustness and functionality of the tools presented.