Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 110
Filter
1.
Hypertension ; 76(5): 1526-1536, 2020 11.
Article in English | MEDLINE | ID: covidwho-2153220

ABSTRACT

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.


Subject(s)
Coronavirus Infections/epidemiology , Disease Outbreaks/statistics & numerical data , Disease Progression , Heart Failure/enzymology , Heart Failure/physiopathology , Peptidyl-Dipeptidase A/blood , Pneumonia, Viral/epidemiology , Academic Medical Centers , Analysis of Variance , Angiotensin-Converting Enzyme 2 , Biomarkers/metabolism , COVID-19 , Cohort Studies , Coronavirus Infections/prevention & control , Female , Humans , Linear Models , Male , Middle Aged , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Prognosis , Proportional Hazards Models , Proteomics/methods , Retrospective Studies , Sensitivity and Specificity , Severity of Illness Index , United States
2.
Int J Mol Sci ; 23(20)2022 Oct 11.
Article in English | MEDLINE | ID: covidwho-2071503

ABSTRACT

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.


Subject(s)
COVID-19 , Glucocorticoids , Humans , Glucocorticoids/pharmacology , Glucocorticoids/therapeutic use , Proteomics/methods , COVID-19/drug therapy , Hydrocortisone , Metabolomics/methods , Amino Acids/metabolism , Tyrosine , Arginine , Bile Acids and Salts
3.
J Proteome Res ; 21(11): 2810-2814, 2022 Nov 04.
Article in English | MEDLINE | ID: covidwho-2050250

ABSTRACT

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.


Subject(s)
COVID-19 , Humans , Chromatography, Liquid/methods , Proteomics/methods , Mass Spectrometry/methods , Proteome/analysis
4.
Molecules ; 27(17)2022 Aug 26.
Article in English | MEDLINE | ID: covidwho-2023945

ABSTRACT

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.


Subject(s)
Diabetes, Gestational , Exosomes , Blood Proteins/metabolism , Complement C4b-Binding Protein/metabolism , Complement System Proteins/metabolism , Exosomes/metabolism , Female , Humans , Lipid Metabolism , Pregnancy , Proteomics/methods
5.
Lancet Digit Health ; 4(9): e632-e645, 2022 09.
Article in English | MEDLINE | ID: covidwho-2016308

ABSTRACT

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.


Subject(s)
COVID-19 , Biomarkers , COVID-19/diagnosis , Cohort Studies , Cytokines , Humans , Lipidomics/methods , Lipids , Metabolomics/methods , Pandemics , Prognosis , Proteomics/methods , Retrospective Studies , SARS-CoV-2
6.
Virus Res ; 321: 198916, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2008180

ABSTRACT

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.


Subject(s)
Coronavirus Infections , Porcine epidemic diarrhea virus , Swine Diseases , Animals , Chlorocebus aethiops , Porcine epidemic diarrhea virus/genetics , Proteomics/methods , Ribosomal Proteins/genetics , Ribosomal Proteins/metabolism , Swine , Thymidine/metabolism , Tumor Suppressor Protein p53/metabolism , Vero Cells , Virus Replication
7.
Stomatologiia (Mosk) ; 101(4): 34-37, 2022.
Article in Russian | MEDLINE | ID: covidwho-1988653

ABSTRACT

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.


Subject(s)
COVID-19 , Proteomics , Humans , Proteome/analysis , Proteome/metabolism , Proteomics/methods , Saliva/chemistry
8.
Front Cell Infect Microbiol ; 12: 926352, 2022.
Article in English | MEDLINE | ID: covidwho-1987473

ABSTRACT

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.


Subject(s)
COVID-19 , Extracellular Vesicles , Extracellular Vesicles/metabolism , Humans , Proteome/metabolism , Proteomics/methods , SARS-CoV-2 , Tandem Mass Spectrometry
9.
Adv Protein Chem Struct Biol ; 131: 311-339, 2022.
Article in English | MEDLINE | ID: covidwho-1959234

ABSTRACT

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.


Subject(s)
COVID-19 , COVID-19/genetics , Computational Biology , Humans , Proteome/metabolism , Proteomics/methods , SARS-CoV-2/genetics
10.
Methods Mol Biol ; 2511: 175-182, 2022.
Article in English | MEDLINE | ID: covidwho-1941375

ABSTRACT

Matrix-assisted laser desorption/ionization source coupled with time-of-flight mass analyzer mass spectrometry (MALDI-TOF MS) is being widely used to obtain proteomic profiles for clinical purposes, as a fast, low-cost, robust, and efficient technique. Here we describe a method for biofluid analysis using MALDI-TOF MS for rapid acquisition of proteomic signatures of COVID-19 infected patients. By using solid-phase extraction, the method allows the analysis of biofluids in less than 15 min.


Subject(s)
COVID-19 , Proteomics , Biomarkers , COVID-19/diagnosis , Humans , Proteomics/methods , Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods
11.
Sci Rep ; 12(1): 12204, 2022 07 16.
Article in English | MEDLINE | ID: covidwho-1937450

ABSTRACT

Proteins are direct products of the genome and metabolites are functional products of interactions between the host and other factors such as environment, disease state, clinical information, etc. Omics data, including proteins and metabolites, are useful in characterizing biological processes underlying COVID-19 along with patient data and clinical information, yet few methods are available to effectively analyze such diverse and unstructured data. Using an integrated approach that combines proteomics and metabolomics data, we investigated the changes in metabolites and proteins in relation to patient characteristics (e.g., age, gender, and health outcome) and clinical information (e.g., metabolic panel and complete blood count test results). We found significant enrichment of biological indicators of lung, liver, and gastrointestinal dysfunction associated with disease severity using publicly available metabolite and protein profiles. Our analyses specifically identified enriched proteins that play a critical role in responses to injury or infection within these anatomical sites, but may contribute to excessive systemic inflammation within the context of COVID-19. Furthermore, we have used this information in conjunction with machine learning algorithms to predict the health status of patients presenting symptoms of COVID-19. This work provides a roadmap for understanding the biochemical pathways and molecular mechanisms that drive disease severity, progression, and treatment of COVID-19.


Subject(s)
COVID-19 , COVID-19/complications , Humans , Lung , Metabolomics/methods , Proteomics/methods , Severity of Illness Index
12.
Nat Commun ; 13(1): 2391, 2022 05 02.
Article in English | MEDLINE | ID: covidwho-1890171

ABSTRACT

COVID-19 has infected more than 275 million worldwide (at the beginning of 2022). Children appear less susceptible to COVID-19 and present with milder symptoms. Cases of children with COVID-19 developing clinical features of Kawasaki-disease have been described. Here we utilise Mass Spectrometry proteomics to determine the plasma proteins expressed in healthy children pre-pandemic, children with multisystem inflammatory syndrome (MIS-C) and children with COVID-19 induced ARDS. Pathway analyses were performed to determine the affected pathways. 76 proteins are differentially expressed across the groups, with 85 and 52 proteins specific to MIS-C and COVID-19 ARDS, respectively. Complement and coagulation activation are implicated in these clinical phenotypes, however there was significant contribution of FcGR and BCR activation in MIS-C and scavenging of haem and retinoid metabolism in COVID-19 ARDS. We show global proteomic differences in MIS-C and COVID-ARDS, although both show complement and coagulation dysregulation. The results contribute to our understanding of MIS-C and COVID-19 ARDS in children.


Subject(s)
COVID-19 , Respiratory Distress Syndrome , COVID-19/complications , Complement System Proteins , Humans , Proteomics/methods , Systemic Inflammatory Response Syndrome
13.
Nat Commun ; 13(1): 3108, 2022 06 07.
Article in English | MEDLINE | ID: covidwho-1878525

ABSTRACT

Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously reported results. We also discover six T-cell epitopes from SARS-CoV-2 immunopeptidome that might represent potential targets for COVID-19 vaccine development. The platform supports data formats from all major instruments and is implemented with the distributed high-performance computing technology, allowing analysis of tera-scale datasets of thousands of samples for clinical applications.


Subject(s)
COVID-19 , Proteomics , COVID-19 Vaccines , DDT/analogs & derivatives , Humans , Mass Spectrometry/methods , Peptides/analysis , Proteomics/methods , SARS-CoV-2
14.
Expert Rev Proteomics ; 19(3): 197-212, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1873769

ABSTRACT

INTRODUCTION: The challenges posed by emergent strains of SARS-CoV-2 need to be tackled by contemporary scientific approaches, with proteomics playing a significant role. AREAS COVERED: In this review, we provide a brief synthesis of the impact of proteomics technologies in elucidating disease pathogenesis and classifiers for the prognosis of COVID-19 and propose proteomics methodologies that could play a crucial role in understanding emerging variants and their altered disease pathology. From aiding the design of novel drug candidates to facilitating the identification of T cell vaccine targets, we have discussed the impact of proteomics methods in COVID-19 research. Techniques varied as mass spectrometry, single-cell proteomics, multiplexed ELISA arrays, high-density proteome arrays, surface plasmon resonance, immunopeptidomics, and in silico docking studies that have helped augment the fight against existing diseases were useful in preparing us to tackle SARS-CoV-2 variants. We also propose an action plan for a pipeline to combat emerging pandemics using proteomics technology by adopting uniform standard operating procedures and unified data analysis paradigms. EXPERT OPINION: The knowledge about the use of diverse proteomics approaches for COVID-19 investigation will provide a framework for future basic research, better infectious disease prevention strategies, improved diagnostics, and targeted therapeutics.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , COVID-19/diagnosis , Proteomics/methods , Proteome/genetics
15.
Int J Mol Sci ; 23(10)2022 May 19.
Article in English | MEDLINE | ID: covidwho-1862814

ABSTRACT

The identification of markers of inflammatory activity at the early stages of pulmonary diseases which share common characteristics that prevent their clear differentiation is of great significance to avoid misdiagnosis, and to understand the intrinsic molecular mechanism of the disorder. The combination of electrophoretic/chromatographic methods with mass spectrometry is currently a promising approach for the identification of candidate biomarkers of a disease. Since the fluid phase of sputum is a rich source of proteins which could provide an early diagnosis of specific lung disorders, it is frequently used in these studies. This report focuses on the state-of-the-art of the application, over the last ten years (2011-2021), of sputum proteomics in the investigation of severe lung disorders such as COPD; asthma; cystic fibrosis; lung cancer and those caused by COVID-19 infection. Analysis of the complete set of proteins found in sputum of patients affected by these disorders has allowed the identification of proteins whose levels change in response to the organism's condition. Understanding proteome dynamism may help in associating these proteins with alterations in the physiology or progression of diseases investigated.


Subject(s)
Lung Diseases , Proteome , Sputum , Biomarkers/metabolism , Humans , Lung/metabolism , Lung Diseases/diagnosis , Proteome/metabolism , Proteomics/methods , Sputum/chemistry
16.
Nat Commun ; 12(1): 6073, 2021 10 18.
Article in English | MEDLINE | ID: covidwho-1860369

ABSTRACT

Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further utilize a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies.


Subject(s)
Glycopeptides/analysis , Proteome/analysis , Proteomics/methods , Algorithms , Blood Proteins/chemistry , Glycoproteins/chemistry , Humans , Mass Spectrometry , Polysaccharides/chemistry , Schizosaccharomyces pombe Proteins/chemistry , Workflow
17.
Methods Mol Biol ; 2452: 167-182, 2022.
Article in English | MEDLINE | ID: covidwho-1844266

ABSTRACT

A comprehensive cartography of viral and host proteins expressed during the different stages of SARS-CoV-2 infection is key to decipher the molecular mechanisms of pathogenesis. For the most detailed analysis, proteins should be first purified and then proteolyzed with trypsin in the presence of detergents. The resulting peptide mixtures are resolved by reverse phase ultrahigh pressure liquid chromatography and then identified by a high-resolution tandem mass spectrometer. The thousands of spectra acquired for each fraction can then be assigned to peptide sequences using a relevant protein sequence database, comprising viral and host proteins and potential contaminants from the growth medium or from the operator. The peptides are evidencing proteins and their intensities are used to infer the abundance of their corresponding proteins. Data analysis allows for highlighting the viral and host proteins dynamics. Here, we describe the sample preparation method adapted to profile SARS-CoV-2 -infected cell models, the shotgun proteomics pipeline to record experimental data, and the workflow for data interpretation to analyze infection-induced proteomic changes in a time-resolved manner.


Subject(s)
COVID-19 , Proteomics , Humans , Peptides , Proteomics/methods , SARS-CoV-2 , Tandem Mass Spectrometry
18.
Proteomics Clin Appl ; 16(6): e2100097, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-1826106

ABSTRACT

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.


Subject(s)
COVID-19 , Proteomics , Humans , Proteomics/methods , Proteome/metabolism , Precision Medicine/methods , COVID-19/diagnosis , Biomarkers/metabolism
19.
PLoS One ; 17(4): e0267047, 2022.
Article in English | MEDLINE | ID: covidwho-1808567

ABSTRACT

COVID-19 is a disease characterized by its seemingly unpredictable clinical outcomes. In order to better understand the molecular signature of the disease, a recent multi-omics study was done which looked at correlations between biomolecules and used a tree- based machine learning approach to predict clinical outcomes. This study specifically looked at patients admitted to the hospital experiencing COVID-19 or COVID-19 like symptoms. In this paper we examine the same multi-omics data, however we take a different approach, and we identify stable molecules of interest for further pathway analysis. We used stability selection, regularized regression models, enrichment analysis, and principal components analysis on proteomics, metabolomics, lipidomics, and RNA sequencing data, and we determined key molecules and biological pathways in disease severity, and disease status. In addition to the individual omics analyses, we perform the integrative method Sparse Multiple Canonical Correlation Analysis to analyse relationships of the different view of data. Our findings suggest that COVID-19 status is associated with the cell cycle and death, as well as the inflammatory response. This relationship is reflected in all four sets of molecules analyzed. We further observe that the metabolic processes, particularly processes to do with vitamin absorption and cholesterol are implicated in COVID-19 status and severity.


Subject(s)
COVID-19 , Humans , Machine Learning , Metabolomics/methods , Proteomics/methods
20.
Cell Death Dis ; 13(3): 235, 2022 03 14.
Article in English | MEDLINE | ID: covidwho-1740434

ABSTRACT

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


Subject(s)
COVID-19/mortality , Metabolomics/methods , Patient Discharge/statistics & numerical data , Proteomics/methods , SARS Virus/pathogenicity , Female , Humans , Male , Middle Aged , Survivors , Time Factors
SELECTION OF CITATIONS
SEARCH DETAIL