Next generation plasma proteome profiling of COVID-19 patients with mild to moderate symptoms

Immune status assessment based on plasma proteomics with meta graph convolutional networks

Plasma proteins, especially immune-related proteins, are vital for assessing immune health and predicting disease risks. Despite their significance, the link between these proteins and systemic immune function remains unclear. To bridge this gap, researchers developed ProMetaGCN, a model integrating meta-learning, graph convolutional networks, and protein-protein interaction (PPI) data to evaluate immune status via plasma proteomics. This framework identified 309 immune-related factors with associated biological functions and pathways. Using six machine learning methods, four algorithms (Random Forest, LightGBM, XGBoost, Lasso) were selected for immune profiling and aging analysis, revealing ADAMTS13, GDF15, and SERPINF2 as key biomarkers. Validation across two COVID-19 cohorts confirmed the model's robustness, showing immune status correlates with infection progression and recovery. Furthermore, the study proposed ImmuneAgeGap, a novel metric linking immune profiles to survival rates in non-small-cell lung cancer (NSCLC) patients. These insights advance personalized immune health strategies and disease prevention.

Plasma proteomic profiles correlate with organ dysfunction in COVID-19 ARDS

Severe COVID-19 is often complicated by hypoxemic respiratory failure and acute respiratory distress syndrome (ARDS). Mechanisms governing lung injury and repair in ARDS remain poorly understood. We hypothesized that plasma proteomics may uncover protein biomarkers correlated with COVID-19 ARDS severity. We analyzed the plasma proteome from 32 patients with ARDS and COVID-19 using an aptamer-based platform of 7289 proteins, and correlated protein measurements with sequential organ failure assessment (SOFA) scores at days 1 and 7 of ICU admission. We identified 184 differentially abundant proteins correlated with SOFA at day 1 and 46 proteins at day 7. In a longitudinal analysis, we correlated dynamic changes in protein abundance and SOFA between days 1 and 7 and identified 40 significant proteins. Pathway analysis of significant proteins identified increased ephrin signaling and acute phase response signaling correlated with increased SOFA scores between days 1 and 7, while pathways related to pulmonary fibrosis signaling and wound healing had a negative correlation. These findings suggest that persistent inflammation may drive disease severity, while repair processes correlate with improvements in organ dysfunction. This approach is generalizable to future ARDS cohorts for identification of biomarkers and disease mechanisms as we strive towards targeted therapies in ARDS.

Longitudinal Assessment of Nasopharyngeal Biomarkers Post-COVID-19: Unveiling Persistent Markers and Severity Correlations

SARS-CoV-19 infection provokes a variety of symptoms; most patients present mild/moderate symptoms, whereas a small proportion of patients progress to severe illness with multiorgan failure accompanied by metabolic disturbances requiring ICU-level care. Given the importance of the disease, researchers focused on identifying severity-associated biomarkers in infected patients as well as markers associated with patients suffering long-COVID. However, little is known about the presence of biomarkers that remain a few years after SARS-CoV-2 infection once the patients fully recover of the symptoms. In this study, we evaluated the presence of persistent biomarkers in the nasopharyngeal tract two years after SARS-Cov-2 infection in fully asymptomatic patients, taking into account the severity of their infection (mild/moderate and severe infections). In addition to the direct identification of several components of the Coronavirus Infection Pathway in those individuals that suffered severe infections, we describe herein 371 proteins and their associated canonical pathways that define the different adverse effects of SARS-CoV-2 infections. The persistence of these biomarkers for up to two years after infection, along with their ability to distinguish the severity of the infection endured, highlights the surprising presence of persistent nasopharyngeal exudate changes in fully recovered patients.

Alterations in the plasma proteome persist ten months after recovery from mild to moderate SARS-CoV-2 infection

Background

Limited data are available describing the effects of SARS-CoV-2 breakthrough infections on the plasma proteome.

Methods

PCR-positive SARS-CoV-2 patients, enrolled in a natural history study, underwent analysis of the plasma proteome. A prospective cohort of 66 unvaccinated and 24 vaccinated persons with different degrees of infection severity were evaluated acutely (within 40 days of symptom onset), and at three and ten months. Comparisons based on vaccination status alone and unsupervised hierarchical clustering were performed. A second cohort of vaccinated Omicron patients were evaluated acutely and at ten months.

Results

Acutely, unvaccinated patients manifested overexpression of proteins involved in immune and inflammatory responses, while vaccinated patients exhibited adaptive immune responses without significant inflammation. At three and ten months, only unvaccinated patients had diminished but sustained inflammatory (C3b, CCL15, IL17RE) and immune responses (DEFA5,TREM1). Both groups had underexpression of pathways essential for cellular function, signaling, and angiogenesis (AKT1, MAPK14, HSPB1) across phases. Unsupervised clustering, based on protein expression, identified four groups of patients with variable vaccination rates demonstrating that additional clinical factors influence the plasma proteome. The proteome of vaccinated Omicron patients did not differ from vaccinated pre-Omicron patients.

Conclusions

Vaccination attenuates the inflammatory response to SARS-CoV-2 infection across phases. However, at ten months after symptom onset, changes in the plasma proteome persist in both vaccinated and unvaccinated individuals, which may be relevant to post-acute sequelae of SARS-CoV-2 and other viral infections associated with post-acute infection syndromes.

Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases

Background

COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures.

Aim

To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis.

Method

We leveraged a multi-omics network-based approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization Ordinal Scale WHO Ordinal Scale was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state specific omics-graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis.

Results

Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4 (CCL4), and Interferon Regulatory Factor 1 (IRF1). Hepatocyte Growth Factor (HGF), Matrix Metallopeptidase 12 (MMP12), Interleukin 10 (IL10), Nuclear Factor Kappa B Subunit 1 (NFKB1), and suberoylcarnitine form hubs in the omics network that characterizes the moderate disease state. The severe cases were marked by biosignatures such as Signal Transducer and Activator of Transcription 1 (STAT1), Superoxide Dismutase 2 (SOD2), HGF, taurine, lysophosphatidylcholine, diacylglycerol, triglycerides, and sphingomyelin that characterize the disease state.

Conclusion

This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions (such as protein-protein, protein-transcript, protein-metabolite, transcript-metabolite, and lipid-lipid interactions) that are unique to mild, moderate, and severe COVID-19 disease states. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. The approach implemented here can be used to find associations between transcripts, proteins, lipids, and metabolites in other diseases.

Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by highly heterogeneous manifestations ranging from asymptomatic cases to death for still incompletely understood reasons. As part of the IMmunoPhenotyping Assessment in a COVID-19 Cohort study, we mapped the plasma proteomes of 1117 hospitalized patients with COVID-19 from 15 hospitals across the United States. Up to six samples were collected within ~28 days of hospitalization resulting in one of the largest COVID-19 plasma proteomics cohorts with 2934 samples. Using perchloric acid to deplete the most abundant plasma proteins allowed for detecting 2910 proteins. Our findings show that increased levels of neutrophil extracellular trap and heart damage markers are associated with fatal outcomes. Our analysis also identified prognostic biomarkers for worsening severity and death. Our comprehensive longitudinal plasma proteomics study, involving 1117 participants and 2934 samples, allowed for testing the generalizability of the findings of many previous COVID-19 plasma proteomics studies using much smaller cohorts.

Proteomic snapshot of saliva samples predicts new pathways implicated in SARS-CoV-2 pathogenesis

BACKGROUND

Information on the microbiome's human pathways and active members that can affect SARS-CoV-2 susceptibility and pathogenesis in the salivary proteome is very scarce. Here, we studied a unique collection of samples harvested from April to June 2020 from unvaccinated patients.

METHODS

We compared 10 infected and hospitalized patients with severe (n = 5) and moderate (n = 5) coronavirus disease (COVID-19) with 10 uninfected individuals, including non-COVID-19 but susceptible individuals (n = 5) and non-COVID-19 and nonsusceptible healthcare workers with repeated high-risk exposures (n = 5).

RESULTS

By performing high-throughput proteomic profiling in saliva samples, we detected 226 unique differentially expressed (DE) human proteins between groups (q-value ≤ 0.05) out of 3376 unambiguously identified proteins (false discovery rate ≤ 1%). Major differences were observed between the non-COVID-19 and nonsusceptible groups. Bioinformatics analysis of DE proteins revealed human proteomic signatures related to inflammatory responses, central cellular processes, and antiviral activity associated with the saliva of SARS-CoV-2-infected patients (p-value ≤ 0.0004). Discriminatory biomarker signatures from human saliva include cystatins, protective molecules present in the oral cavity, calprotectins, involved in cell cycle progression, and histones, related to nucleosome functions. The expression levels of two human proteins related to protein transport in the cytoplasm, DYNC1 (p-value, 0.0021) and MAPRE1 (p-value, 0.047), correlated with angiotensin-converting enzyme 2 (ACE2) plasma activity. Finally, the proteomes of microorganisms present in the saliva samples showed 4 main microbial functional features related to ribosome functioning that were overrepresented in the infected group.

CONCLUSION

Our study explores potential candidates involved in pathways implicated in SARS-CoV-2 susceptibility, although further studies in larger cohorts will be necessary.

Proximity extension assay-based proteomics studies in neurodegenerative disorders and multiple sclerosis

Neurodegenerative diseases impact the structure and operation of the nervous system, causing progressive and irreparable harm. Efforts for distinguishing neurodegenerative diseases in their early stages are continuing. Despite several biomarkers being identified, there is always search for more accurate and abundant ones. Additionally, it can be difficult to pinpoint the precise neurodegenerative disorder affecting a patient as the symptoms of these conditions frequently overlap. Numerous studies have shown that pathological changes occur years before clinical signs appear. Therefore, it is crucial to discover blood-based biomarkers for neurodegenerative diseases for easier and earlier diagnosis. Proximity extension assay is a unique proteomics method that uses antibodies linked to oligonucleotides for quantifying proteins with real-time PCR. Proximity extension assay can identify even low-quantity proteins using a small volume of specimens with increased sensitivity compared to conventional methods. In this article, we reviewed the employment of proximity extension assay technology to detect biomarkers or protein profiles for several neurodegenerative diseases.

Complex changes in serum protein levels in COVID-19 convalescents

The COVID-19 pandemic, triggered by severe acute respiratory syndrome coronavirus 2, has affected millions of people worldwide. Much research has been dedicated to our understanding of COVID-19 disease heterogeneity and severity, but less is known about recovery associated changes. To address this gap in knowledge, we quantified the proteome from serum samples from 29 COVID-19 convalescents and 29 age-, race-, and sex-matched healthy controls. Samples were acquired within the first months of the pandemic. Many proteins from pathways known to change during acute COVID-19 illness, such as from the complement cascade, coagulation system, inflammation and adaptive immune system, had returned to levels seen in healthy controls. In comparison, we identified 22 and 15 proteins with significantly elevated and lowered levels, respectively, amongst COVID-19 convalescents compared to healthy controls. Some of the changes were similar to those observed for the acute phase of the disease, i.e. elevated levels of proteins from hemolysis, the adaptive immune systems, and inflammation. In contrast, some alterations opposed those in the acute phase, e.g. elevated levels of CETP and APOA1 which function in lipid/cholesterol metabolism, and decreased levels of proteins from the complement cascade (e.g. C1R, C1S, and VWF), the coagulation system (e.g. THBS1 and VWF), and the regulation of the actin cytoskeleton (e.g. PFN1 and CFL1) amongst COVID-19 convalescents. We speculate that some of these shifts might originate from a transient decrease in platelet counts upon recovery from the disease. Finally, we observed race-specific changes, e.g. with respect to immunoglobulins and proteins related to cholesterol metabolism.

NAD+-boosting compounds enhance nitric oxide production and prevent oxidative stress in endothelial cells exposed to plasma from patients with COVID-19

SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), induces vascular endothelial dysfunction, but the mechanisms are unknown. We tested the hypothesis that the "circulating milieu" (plasma) of patients with COVID-19 would cause endothelial cell dysfunction (characterized by lower nitric oxide (NO) production), which would be linked to greater reactive oxygen species (ROS) bioactivity and depletion of the critical metabolic co-substrate, nicotinamide adenine dinucleotide (NAD+). We also investigated if treatment with NAD+-boosting compounds would prevent COVID-19-induced reductions in endothelial cell NO bioavailability and oxidative stress. Human aortic endothelial cells (HAECs) were exposed to plasma from men and women (age 18-85 years) who were hospitalized and tested positive (n = 34; 20 M) or negative (n = 13; 10 M) for COVID-19. HAECs exposed to plasma from patients with COVID-19 also were co-incubated with NAD+ precursors nicotinamide riboside (NR) or nicotinamide mononucleotide (NMN). Acetylcholine-stimulated NO production was 27% lower and ROS bioactivity was 54% higher in HAECs exposed to plasma from patients with COVID-19 (both p < 0.001 vs. control); these responses were independent of age and sex. NAD+ concentrations were 30% lower in HAECs exposed to plasma from patients with COVID-19 (p = 0.001 vs. control). Co-incubation with NR abolished COVID-19-induced reductions in NO production and oxidative stress (both p > 0.05 vs. control). Co-treatment with NMN produced similar results. Our findings suggest the circulating milieu of patients with COVID-19 promotes endothelial cell dysfunction, characterized by lower NO bioavailability, greater ROS bioactivity, and NAD+ depletion. Supplementation with NAD+ precursors may exert a protective effect against COVID-19-evoked endothelial cell dysfunction and oxidative stress.

Potential relevance of salivary legumain for the clinical diagnostic of hand, foot, and mouth disease

The fight against hand, foot, and mouth disease (HFMD) remains an arduous challenge without existing point-of-care (POC) diagnostic platforms for accurate diagnosis and prompt case quarantine. Hence, the purpose of this salivary biomarker discovery study is to set the fundamentals for the realization of POC diagnostics for HFMD. Whole salivary proteome profiling was performed on the saliva obtained from children with HFMD and healthy children, using a reductive dimethylation chemical labeling method coupled with high-resolution mass spectrometry-based quantitative proteomics technology. We identified 19 upregulated (fold change = 1.5-5.8) and 51 downregulated proteins (fold change = 0.1-0.6) in the saliva samples of HFMD patients in comparison to that of healthy volunteers. Four upregulated protein candidates were selected for dot blot-based validation assay, based on novelty as biomarkers and exclusions in oral diseases and cancers. Salivary legumain was validated in the Singapore (n = 43 healthy, 28 HFMD cases) and Taiwan (n = 60 healthy, 47 HFMD cases) cohorts with an area under the receiver operating characteristic curve of 0.7583 and 0.8028, respectively. This study demonstrates the feasibility of a broad-spectrum HFMD POC diagnostic test based on legumain, a virus-specific host systemic signature, in saliva.

Analysis of inflammatory protein profiles in the circulation of COVID-19 patients identifies patients with severe disease phenotypes

BACKGROUND

The coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) can present with a broad range of clinical manifestations, ranging from asymptomatic to severe multiple organ failure. The severity of the disease can vary depending on factors such as age, sex, ethnicity, and pre-existing medical conditions. Despite multiple efforts to identify reliable prognostic factors and biomarkers, the predictive capacity of these markers for clinical outcomes remains poor. Circulating proteins, which reflect the active mechanisms in an individual, can be easily measured in clinical practice and therefore may be useful as biomarkers for COVID-19 disease severity. In this study, we sought to identify protein biomarkers and endotypes for COVID-19 severity and evaluate their reproducibility in an independent cohort.

METHODS

We investigated a cohort of 153 Greek patients with confirmed SARS-CoV-2 infection in which plasma protein levels were measured using the Olink Explore 1536 panel, which consists of 1472 proteins. We compared the protein profiles from severe and moderate COVID-19 patients to identify proteins associated with disease severity. To evaluate the reproducibility of our findings, we compared the protein profiles of 174 patients with comparable COVID-19 severities in a US COVID-19 cohort to identify proteins consistently correlated with COVID-19 severity in both groups.

RESULTS

We identified 218 differentially regulated proteins associated with severity, 20 proteins were also replicated in an external cohort which we used for validation. Moreover, we performed unsupervised clustering of patients based on 97 proteins with the highest log2 fold changes in order to identify COVID-19 endotypes. Clustering of patients based on differentially regulated proteins revealed the presence of three clinical endotypes. While endotypes 2 and 3 were enriched for severe COVID-19 patients, endotypes 3 represented the most severe form of the disease.

CONCLUSIONS

These results suggest that identified circulating proteins may be useful for identifying COVID-19 patients with worse outcomes, and this potential utility may extend to other populations.

TRIAL REGISTRATION

NCT04357366.

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.

COVIDpro: Database for Mining Protein Dysregulation in Patients with COVID-19

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 still has limited treatment options. Our understanding of the molecular dysregulations that occur in response to infection remains incomplete. We developed a web application COVIDpro (https://www.guomics.com/covidPro/) that includes proteomics data obtained from 41 original studies conducted in 32 hospitals worldwide, involving 3077 patients and covering 19 types of clinical specimens, predominantly plasma and serum. The data set encompasses 53 protein expression matrices, comprising a total of 5434 samples and 14,403 unique proteins. We identified a panel of proteins that exhibit significant dysregulation, enabling the classification of COVID-19 patients into severe and non-severe disease categories. The proteomic signatures achieved promising results in distinguishing severe cases, with a mean area under the curve of 0.87 and accuracy of 0.80 across five independent test sets. COVIDpro serves as a valuable resource for testing hypotheses and exploring potential targets for novel treatments in COVID-19 patients.

Persistent immune and clotting dysfunction detected in saliva and blood plasma after COVID-19

A growing number of studies indicate that coronavirus disease 2019 (COVID-19) is associated with inflammatory sequelae, but molecular signatures governing the normal versus pathologic convalescence process have not been well-delineated. Here, we characterized global immune and proteome responses in matched plasma and saliva samples obtained from COVID-19 patients collected between 20 and 90 days after initial clinical symptoms resolved. Convalescent subjects showed robust total IgA and IgG responses and positive antibody correlations in saliva and plasma samples. Shotgun proteomics revealed persistent inflammatory patterns in convalescent samples including dysfunction of salivary innate immune cells, such as neutrophil markers (e.g., myeloperoxidase), and clotting factors in plasma (e.g., fibrinogen), with positive correlations to acute COVID-19 disease severity. Saliva samples were characterized by higher concentrations of IgA, and proteomics showed altered myeloid-derived pathways that correlated positively with SARS-CoV-2 IgA levels. Beyond plasma, our study positions saliva as a viable fluid to monitor normal and aberrant immune responses including vascular, inflammatory, and coagulation-related sequelae.

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.

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

Proteomic Investigation of COVID-19 Severity During the Tsunamic Second Wave in Mumbai

Maharashtra was severely affected during the noxious second wave of COVID-19, with the highest number of cases recorded across India. The emergence of new symptoms and dysregulation of multiple organs resulted in high disease severity during the second wave which led to increased difficulties in understanding the molecular mechanisms behind the disease pathology. Exploring the underlying factors can help to relieve the burden on the medical communities to some extent by prioritizing the patients and, at the same time, opening avenues for improved treatments. In the current study, we have performed a mass-spectrometry-based proteomic analysis to investigate the disease pathology using nasopharyngeal swab samples collected from the COVID-19 patients in the Mumbai region of Maharashtra over the period of March-June 2021, the peak of the second wave. A total of 59 patients, including 32 non-severe and 27 severe cases, were considered for this proteomic study. We identified 23 differentially regulated proteins in severe patients as a host response to infection. In addition to the previously identified innate mechanisms of neutrophil and platelet degranulation, this study revealed significant alterations of anti-microbial peptide pathways in severe conditions, illustrating its role in the severity of the infectious strain of COVID-19 during the second wave. Furthermore, myeloperoxidase, cathepsin G, and profilin-1 were identified as potential therapeutic targets of the FDA-approved drugs dabrafenib, ZINC4097343, and ritonavir. This study has enlightened the role of the anti-microbial peptide pathway associated with the second wave in India and proposed its importance in potential therapeutics for COVID-19.

COVIDpro: Database for mining protein dysregulation in patients with COVID-19

Background

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers.

Methods

We built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers.

Findings

We built and maintain a web server COVIDpro ( https://www.guomics.com/covidPro/ ) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80.

Interpretation

COVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients.

Funding

National Key R&D Program of China: Key PDPM technologies (2021YFA1301602, 2021YFA1301601, 2021YFA1301603), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107), National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS-P41 GM103504).

Research in context

Evidence before this study: Although an increasing number of therapies against COVID-19 are being developed, they are still insufficient, especially with the rise of new variants of concern. This is partially due to our incomplete understanding of the disease’s mechanisms. As data have been collected worldwide, several questions are now worth addressing via meta-analyses. Most COVID-19 drugs function by targeting or affecting proteins. Effectiveness and resistance to therapeutics can be effectively assessed via protein measurements. Empowered by mass spectrometry-based proteomics, protein expression has been characterized in a variety of patient specimens, including body fluids (e.g., serum, plasma, urea) and tissue (i.e., formalin-fixed and paraffin-embedded (FFPE)). We expert-curated proteomic expression data from COVID-19 patients published before May 2022, from the largest proteomic data repository ProteomeXhange as well as from literature search engines. Using this resource, a COVID-19 proteome meta-analysis could provide useful insights into the mechanisms of the disease and identify new potential drug targets.Added value of this study: We integrated many published datasets from patients with COVID-19 from 11 nations, with over 3000 patients and more than 5434 proteome measurements. We collected these datasets in an online database, and generated a toolbox to easily explore, analyze, and visualize the data. Next, we used the database and its associated toolbox to identify new proteins of diagnostic and therapeutic value for COVID-19 treatment. In particular, we identified a set of significantly dysregulated proteins for distinguishing severe from non-severe patients using serum samples.Implications of all the available evidence: COVIDpro will support the navigation and analysis of patterns of dysregulated proteins in various COVID-19 clinical specimens for identification and verification of protein biomarkers and potential therapeutic targets.

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

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

Integration of protein context improves protein-based COVID-19 patient stratification

BACKGROUND

Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19.

METHODS

We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort.

RESULTS

Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type.

CONCLUSION

Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Genome-Wide Association Study of COVID-19 Outcomes Reveals Novel Host Genetic Risk Loci in the Serbian Population

Host genetics, an important contributor to the COVID-19 clinical susceptibility and severity, currently is the focus of multiple genome-wide association studies (GWAS) in populations affected by the pandemic. This is the first study from Serbia that performed a GWAS of COVID-19 outcomes to identify genetic risk markers of disease severity. A group of 128 hospitalized COVID-19 patients from the Serbian population was enrolled in the study. We conducted a GWAS comparing (1) patients with pneumonia (n = 80) against patients without pneumonia (n = 48), and (2) severe (n = 34) against mild disease (n = 48) patients, using a genotyping array followed by imputation of missing genotypes. We have detected a significant signal associated with COVID-19 related pneumonia at locus 13q21.33, with a peak residing upstream of the gene KLHL1 (p = 1.91 × 10-8). Our study also replicated a previously reported COVID-19 risk locus at 3p21.31, identifying lead variants in SACM1L and LZTFL1 genes suggestively associated with pneumonia (p = 7.54 × 10-6) and severe COVID-19 (p = 6.88 × 10-7), respectively. Suggestive association with COVID-19 pneumonia has also been observed at chromosomes 5p15.33 (IRX, NDUFS6, MRPL36, p = 2.81 × 10-6), 5q11.2 (ESM1, p = 6.59 × 10-6), and 9p23 (TYRP1, LURAP1L, p = 8.69 × 10-6). The genes located in or near the risk loci are expressed in neural or lung tissues, and have been previously associated with respiratory diseases such as asthma and COVID-19 or reported as differentially expressed in COVID-19 gene expression profiling studies. Our results revealed novel risk loci for pneumonia and severe COVID-19 disease which could contribute to a better understanding of the COVID-19 host genetics in different populations.

Mapping the Serum Proteome of COVID-19 Patients; Guidance for Severity Assessment

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), whose outbreak in 2019 led to an ongoing pandemic with devastating consequences for the global economy and human health. According to the World Health Organization, COVID-19 has affected more than 481 million people worldwide, with 6 million confirmed deaths. The joint efforts of the scientific community have undoubtedly increased the pace of production of COVID-19 vaccines, but there is still so much uncharted ground to cover regarding the mechanisms of SARS-CoV-2 infection, replication and host response. These issues can be approached by proteomics with unprecedented capacity paving the way for the development of more efficient strategies for patient care. In this study, we present a deep proteome analysis that has been performed on a cohort of 72 COVID-19 patients aiming to identify serum proteins assessing the dynamics of the disease at different age ranges. A panel of 53 proteins that participate in several functions such as acute-phase response and inflammation, blood coagulation, cell adhesion, complement cascade, endocytosis, immune response, oxidative stress and tissue injury, have been correlated with patient severity, suggesting a molecular basis for their clinical stratification. Eighteen protein candidates were further validated by targeted proteomics in an independent cohort of 84 patients including a group of individuals that had satisfactorily resolved SARS-CoV-2 infection. Remarkably, all protein alterations were normalized 100 days after leaving the hospital, which further supports the reliability of the selected proteins as hallmarks of COVID-19 progression and grading. The optimized protein panel may prove its value for optimal severity assessment as well as in the follow up of COVID-19 patients.

HYGIEIA: HYpothesizing the Genesis of Infectious Diseases and Epidemics through an Integrated Systems Biology Approach

More than two years on, the COVID-19 pandemic continues to wreak havoc around the world and has battle-tested the pandemic-situation responses of all major global governments. Two key areas of investigation that are still unclear are: the molecular mechanisms that lead to heterogenic patient outcomes, and the causes of Post COVID condition (AKA Long-COVID). In this paper, we introduce the HYGIEIA project, designed to respond to the enormous challenges of the COVID-19 pandemic through a multi-omic approach supported by network medicine. It is hoped that in addition to investigating COVID-19, the logistics deployed within this project will be applicable to other infectious agents, pandemic-type situations, and also other complex, non-infectious diseases. Here, we first look at previous research into COVID-19 in the context of the proteome, metabolome, transcriptome, microbiome, host genome, and viral genome. We then discuss a proposed methodology for a large-scale multi-omic longitudinal study to investigate the aforementioned biological strata through high-throughput sequencing (HTS) and mass-spectrometry (MS) technologies. Lastly, we discuss how a network medicine approach can be used to analyze the data and make meaningful discoveries, with the final aim being the translation of these discoveries into the clinics to improve patient care.

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.

COVIDomics: The Proteomic and Metabolomic Signatures of COVID-19

Omics-based technologies have been largely adopted during this unprecedented global COVID-19 pandemic, allowing the scientific community to perform research on a large scale to understand the pathobiology of the SARS-CoV-2 infection and its replication into human cells. The application of omics techniques has been addressed to every level of application, from the detection of mutations, methods of diagnosis or monitoring, drug target discovery, and vaccine generation, to the basic definition of the pathophysiological processes and the biochemical mechanisms behind the infection and spread of SARS-CoV-2. Thus, the term COVIDomics wants to include those efforts provided by omics-scale investigations with application to the current COVID-19 research. This review summarizes the diverse pieces of knowledge acquired with the application of COVIDomics techniques, with the main focus on proteomics and metabolomics studies, in order to capture a common signature in terms of proteins, metabolites, and pathways dysregulated in COVID-19 disease. Exploring the multiomics perspective and the concurrent data integration may provide new suitable therapeutic solutions to combat the COVID-19 pandemic.

Elevated Cytokine Levels in Plasma of Patients with SARS-CoV-2 Do Not Contribute to Pulmonary Microvascular Endothelial Permeability

The vascular endothelial injury occurs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, but the mechanisms are poorly understood. We sought to determine the frequency and type of cytokine elevations and their relationship to endothelial injury induced by plasma from patients with SARS-CoV-2 versus controls. Plasma from eight consecutively enrolled patients hospitalized with acute SARS-CoV-2 infection was compared to controls. Endothelial cell (EC) barrier integrity was evaluated using ECIS (electric cell-substrate impedance sensing) on human lung microvascular EC. Plasma from all SARS-CoV-2 but none from controls decreased transendothelial resistance to a greater degree than that produced by tumor necrosis factor-alpha (TNF-α), the positive control for the assay. Thrombin, angiopoietin 2 (Ang2), and vascular endothelial growth factor (VEGF), complement factor C3a and C5a, and spike protein increased endothelial permeability, but to a lesser extent and a shorter duration when compared to SARS-CoV-2 plasma. Analysis of Ang2, VEGF, and 15 cytokines measured in plasma revealed striking patient-to-patient variability within the SARS-CoV-2 patients. Pretreatment with thrombin inhibitors, single, or combinations of neutralizing antibodies against cytokines, Ca3 and C5a receptor antagonists, or with ACE2 antibody failed to lessen the SARS-CoV-2 plasma-induced EC permeability. The EC barrier destructive effects of plasma from patients with SARS-CoV-2 were susceptible to heat inactivation. Plasma from patients hospitalized with acute SARS-CoV-2 infection uniformly disrupts lung microvascular integrity. No predicted single, or set of, cytokine(s) accounted for the enhanced vascular permeability, although the factor(s) were heat-labile. A still unidentified but potent circulating factor(s) appears to cause the EC disruption in SARS-CoV-2 infected patients.

IMPORTANCE Lung vascular endothelial injury in SARS-CoV-2 patients is one of the most important causes of morbidity and mortality and has been linked to more severe complications including acute respiratory distress syndrome (ARDS) and subsequent death due to multiorgan failure. We have demonstrated that in eight consecutive patients with SARS-CoV-2, who were not selected for evidence of endothelial injury, the diluted plasma-induced intense lung microvascular damage, in vitro. Known endothelial barrier-disruptive agents and proposed mediators of increased endothelial permeability in SARS-CoV-2, induced changes in permeability that were smaller in magnitude and shorter in duration than plasma from patients with SARS-CoV-2. The effect on endothelial cell permeability of plasma from patients with SARS-CoV-2 was heat-labile. The main plasma factor that causes the increased endothelial permeability remains to be identified. Our study provides a possible approach for future studies to understand the underlying mechanisms leading to vascular injury in SARS-CoV-2 infections.