Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential

Plasma Proteins Associated with COVID-19 Severity in Puerto Rico

Viral strains, age, and host factors are associated with variable immune responses against SARS-CoV-2 and disease severity. Puerto Ricans have a genetic mixture of races: European, African, and Native American. We hypothesized that unique host proteins/pathways are associated with COVID-19 disease severity in Puerto Rico. Following IRB approval, a total of 95 unvaccinated men and women aged 21-71 years old were recruited in Puerto Rico from 2020-2021. Plasma samples were collected from COVID-19-positive subjects (n = 39) and COVID-19-negative individuals (n = 56) during acute disease. COVID-19-positive individuals were stratified based on symptomatology as follows: mild (n = 18), moderate (n = 13), and severe (n = 8). Quantitative proteomics was performed in plasma samples using tandem mass tag (TMT) labeling. Labeled peptides were subjected to LC/MS/MS and analyzed by Proteome Discoverer (version 2.5), Limma software (version 3.41.15), and Ingenuity Pathways Analysis (IPA, version 22.0.2). Cytokines were quantified using a human cytokine array. Proteomics analyses of severely affected COVID-19-positive individuals revealed 58 differentially expressed proteins. Cadherin-13, which participates in synaptogenesis, was downregulated in severe patients and validated by ELISA. Cytokine immunoassay showed that TNF-α levels decreased with disease severity. This study uncovers potential host predictors of COVID-19 severity and new avenues for treatment in Puerto Ricans.

COVID-19 and Comorbidities: What Has Been Unveiled by Metabolomics?

The COVID-19 pandemic has brought about diverse impacts on the global population. Individuals with comorbidities were more susceptible to the severe symptoms caused by the virus. Within the crisis scenario, metabolomics represents a potential area of science capable of providing relevant information for understanding the metabolic pathways associated with the intricate interaction between the viral disease and previous comorbidities. This work aims to provide a comprehensive description of the scientific production pertaining to metabolomics within the specific context of COVID-19 and comorbidities, while highlighting promising areas for exploration by those interested in the subject. In this review, we highlighted the studies of metabolomics that indicated a variety of metabolites associated with comorbidities and COVID-19. Furthermore, we observed that the understanding of the metabolic processes involved between comorbidities and COVID-19 is limited due to the urgent need to report disease outcomes in individuals with comorbidities. The overlap of two or more comorbidities associated with the severity of COVID-19 hinders the comprehension of the significance of each condition. Most identified studies are observational, with a restricted number of patients, due to challenges in sample collection amidst the emergent situation.

Identification of genetic biomarkers, drug targets and agents for respiratory diseases utilising integrated bioinformatics approaches

Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD.

Exploration of the common pathogenic link between COVID-19 and diabetic foot ulcers: An in silico approach

Background and Aims

The Coronavirus Disease-19 (COVID-19) is posing an ongoing threat to human health. Patients of diabetic foot ulcer (DFU) are susceptible to COVID-19-induced adverse outcomes. Nevertheless, investigations into their mutual molecular mechanisms have been limited to date. In the present work, we tried to uncover the shared pathogenesis and regulatory gene targets of COVID-19 and DFU.

Methods

In this study, we chose GSE161281 as the COVID-19 data set, which contained severe acute respiratory syndrome coronavirus 2 infected human induced embryonic stem cell-derived peripheral neurons (n = 2) with uninfected controls (n = 2). The GSE134431 designated as the DFU data set, comprising full-thickness DFU (n = 13) and diabetic foot skin (n = 8) samples from diabetic patients. The differential expressed genes (DEGs) were identified from GSE161281 and GSE134431, and the common DEGs between COVID-19 and DFU were extracted. Multifactor regulatory network and co-expression network of the common DEGs were analyzed, along with candidate drug prediction.

Results

Altogether, six common DEGs (dickkopf-related protein 1 [DKK1], serine proteinase inhibitor A3 [SERPINA3], ras homolog family member D [RHOD], myelin protein zero like 3 [MPZL3], Claudin-11 [CLDN11], and epidermal growth factor receptor pathway substrate 8-like 1 [EPS8L1]) were found between COVID-19 and DFU. Functional analyses indicated that pathways of apoptotic and Wnt signaling may contribute to progression of COVID-19. Gene co-expression network implied the shared pathways of immune regulation and cytokine response participated collectively in the development of DFU and COVID-19. A multifactor regulatory network was constructed integrating the corresponding microRNAs (miRNAs) and transcription factors. Additionally, we proposed potential drug objects for the combined therapy.

Conclusion

Our study revealed the shared molecular mechanisms underlying COVID-19 and DFU. The identified pivotal targets and common pathways can provide new perspectives for further research and assist the development of management strategies in patients of DFU complicated with COVID-19.

Plasma proteomics show altered inflammatory and mitochondrial proteins in patients with neurologic symptoms of post-acute sequelae of SARS-CoV-2 infection

Persistent symptoms of COVID-19 survivors constitute long COVID syndrome, also called post-acute sequelae of SARS-CoV-2 infection (PASC). Neurologic manifestations of PASC (Neuro-PASC) are particularly debilitating, long lasting, and poorly understood. To gain insight into the pathogenesis of PASC, we leveraged a well-characterized group of Neuro-PASC (NP) patients seen at our Neuro-COVID-19 clinic who had mild acute COVID-19 and never required hospitalization to investigate their plasma proteome. Using the SomaLogic platform, SomaScan, the plasma concentration of >7000 proteins was measured from 92 unvaccinated individuals, including 48 NP patients, 20 COVID-19 convalescents (CC) without lingering symptoms, and 24 unexposed healthy controls (HC) to interrogate underlying pathobiology and potential biomarkers of PASC. We analyzed the plasma proteome based on post-COVID-19 status, neurologic and non-neurologic symptoms, as well as subjective and objective standardized tests for changes in quality-of-life (QoL) and cognition associated with Neuro-PASC. The plasma proteome of NP patients differed from CC and HC subjects more substantially than post-COVID-19 groups (NP and CC combined) differed from HC. Proteomic differences in NP patients 3-9 months following acute COVID-19 showed alterations in inflammatory proteins and pathways relative to CC and HC subjects. Proteomic associations with Neuro-PASC symptoms of brain fog and fatigue included changes in markers of DNA repair, oxidative stress, and neutrophil degranulation. Furthermore, we discovered a correlation between NP patients lower subjective impression of recovery to pre-COVID-19 baseline with an increase in the concentration of the oxidative phosphorylation protein COX7A1, which was also associated with neurologic symptoms and fatigue, as well as impairment in QoL and cognitive dysfunction. Finally, we identified other oxidative phosphorylation-associated proteins correlating with central nervous system symptoms. Our results suggest ongoing inflammatory changes and mitochondrial involvement in Neuro-PASC and pave the way for biomarker validation for use in monitoring and development of therapeutic intervention for this debilitating condition.

A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors

BACKGROUND

Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective.

METHODS

A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins.

RESULTS

A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades.

CONCLUSIONS

The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.

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.

Changes in the Proteome of Platelets from Patients with Critical Progression of COVID-19

Platelets, the smallest cells in human blood, known for their role in primary hemostasis, are also able to interact with pathogens and play a crucial role in the immune response. In severe coronavirus disease 2019 (COVID-19) cases, platelets become overactivated, resulting in the release of granules, exacerbating inflammation and contributing to the cytokine storm. This study aims to further elucidate the role of platelets in COVID-19 progression and to identify predictive biomarkers for disease outcomes. A comparative proteome analysis of highly purified platelets from critically diseased COVID-19 patients with different outcomes (survivors and non-survivors) and age- and sex-matched controls was performed. Platelets from critically diseased COVID-19 patients exhibited significant changes in the levels of proteins associated with protein folding. In addition, a number of proteins with isomerase activity were found to be more highly abundant in patient samples, apparently exerting an influence on platelet activity via the non-genomic properties of the glucocorticoid receptor (GR) and the nuclear factor κ-light-chain-enhancer of activated B cells (NFκB). Moreover, carbonic anhydrase 1 (CA-1) was found to be a candidate biomarker in platelets, showing a significant increase in COVID-19 patients.

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.

Detection of Mutant Peptides of SARS-CoV-2 Variants by LC/MS in the DDA Approach Using an In-House Database

Equipped with a dramatically high mutation rate, which happens to be a signature of RNA viruses, SARS-CoV-2 trampled across the globe infecting individuals of all ages and ethnicities. As the variants of concern (VOC) loomed large, definitive detection of SARS-CoV-2 strains became a matter of utmost importance in epidemiological and clinical research. Besides, unveiling the disease pathogenesis at the molecular level and deciphering the therapeutic targets became key priorities since the emergence of the pandemic. Mass spectrometry has been largely used in this regard. A critical part of mass spectrometric analyses is the proteome database required for the identification of peptides. Presently, the mutational information on proteins available on SARS-CoV-2 databases cannot be used to analyze data extracted from mass spectrometers. Hence, we developed the novel Mutant Peptide Database (MPD) for the mass spectrometry (MS)-based identification of mutated peptides, which contains information from 11 proteins of SARS-CoV-2 from a total of 21,549 SARS-CoV-2 variants across different regions of India. The database was validated using clinical samples, and its applicability was also demonstrated with the mutated peptides extracted from the literature. We believe that MPD will support broad-spectrum MS-based studies like viral detection, disease pathogenesis, and therapeutics with respect to SARS-CoV-2 and its variants.

The serum proteome of VA-ECMO patients changes over time and allows differentiation of survivors and non-survivors: an observational study

BACKGROUND

Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is applied in patients with refractory hemodynamic failure. Exposure of blood components to high shear stress and the large extracorporeal surfaces in the ECMO circuit trigger a complex inflammatory response syndrome and coagulopathy which are believed to worsen the already poor prognosis of these patients. Mass spectrometry-based proteomics allow a detailed characterization of the serum proteome as it provides the identity and concentration of large numbers of individual proteins at the same time. In this study, we aimed to characterize the serum proteome of patients receiving VA-ECMO.

METHODS

Serum samples were collected on day 1 and day 3 after initiation of VA-ECMO. Samples underwent immunoaffinity based depletion for the 14 most abundant serum proteins, in-solution digestion and PreOmics clean-up. A spectral library was built with multiple measurements of a master-mix sample using variable mass windows. Individual samples were measured in data independent acquisition (DIA) mode. Raw files were analyzed by DIA-neural network. Unique proteins were log transformed and quantile normalized. Differential expression analysis was conducted with the LIMMA-R package. ROAST was applied to generate gene ontology enrichment analyses.

RESULTS

Fourteen VA-ECMO patients and six healthy controls were recruited. Seven patients survived. Three hundred and fifty-one unique proteins were identified. One hundred and thirty-seven proteins were differentially expressed between VA-ECMO patients and controls. One hundred and forty-five proteins were differentially expressed on day 3 compared to day 1. Many of the differentially expressed proteins were involved in coagulation and the inflammatory response. The serum proteomes of survivors and non-survivors on day 3 differed from each other according to partial least-squares discriminant analysis (PLS-DA) and 48 proteins were differentially expressed. Many of these proteins have also been ascribed to processes in coagulation and inflammation (e.g., Factor IX, Protein-C, Kallikrein, SERPINA10, SEMA4B, Complement C3, Complement Factor D and MASP-1).

CONCLUSION

The serum proteome of VA-ECMO patients displays major changes compared to controls and changes from day 1 until day 3. Many changes in the serum proteome are related to inflammation and coagulation. Survivors and non-survivors can be differentiated according to their serum proteomes using PLS-DA analysis on day 3. Our results build the basis for future studies using mass-spectrometry based serum proteomics as a tool to identify novel prognostic biomarkers.

TRIAL REGISTRATION

DRKS00011106.

Challenges and Opportunities in Clinical Diagnostic Routine of Envenomation Using Blood Plasma Proteomics

Specific and sensitive tools for the diagnosis and monitoring of accidents by venomous animals are urgently needed. Several diagnostic and monitoring assays have been developed; however, they have not yet reached the clinic. This has resulted in late diagnoses, which represents one of the main causes of progression from mild to severe disease. Human blood is a protein-rich biological fluid that is routinely collected in hospital settings for diagnostic purposes, which can translate research progress from the laboratory to the clinic. Although it is a limited view, blood plasma proteins provide information about the clinical picture of envenomation. Proteome disturbances in response to envenomation by venomous animals have been identified, allowing mass spectrometry (MS)-based plasma proteomics to emerge as a tool in a range of clinical diagnostics and disease management that can be applied to cases of venomous animal envenomation. Here, we provide a review of the state of the art on routine laboratory diagnoses of envenomation by snakes, scorpions, bees, and spiders, as well as a review of the diagnostic methods and the challenges encountered. We present the state of the art on clinical proteomics as the standardization of procedures to be performed within and between research laboratories, favoring a more excellent peptide coverage of candidate proteins for biomarkers. Therefore, the selection of a sample type and method of preparation should be very specific and based on the discovery of biomarkers in specific approaches. However, the sample collection protocol (e.g., collection tube type) and the processing procedure of the sample (e.g., clotting temperature, time allowed for clotting, and anticoagulant used) are equally important to eliminate any bias.

Humoral Immune Response Profile of COVID-19 Reveals Severity and Variant-Specific Epitopes: Lessons from SARS-CoV-2 Peptide Microarray

The amaranthine scale of the COVID-19 pandemic and unpredictable disease severity is of grave concern. Serological diagnostic aids are an excellent choice for clinicians for rapid and easy prognosis of the disease. To this end, we studied the humoral immune response to SARS-CoV-2 infection to map immunogenic regions in the SARS-CoV-2 proteome at amino acid resolution using a high-density SARS-CoV-2 proteome peptide microarray. The microarray has 4932 overlapping peptides printed in duplicates spanning the entire SARS-CoV-2 proteome. We found 204 and 676 immunogenic peptides against IgA and IgG, corresponding to 137 and 412 IgA and IgG epitopes, respectively. Of these, 6 and 307 epitopes could discriminate between disease severity. The emergence of variants has added to the complexity of the disease. Using the mutation panel available, we could detect 5 and 10 immunogenic peptides against IgA and IgG with mutations belonging to SAR-CoV-2 variants. The study revealed severity-based epitopes that could be presented as potential prognostic serological markers. Further, the mutant epitope immunogenicity could indicate the putative use of these markers for diagnosing variants responsible for the infection.

SERPINA3: Stimulator or Inhibitor of Pathological Changes

SERPINA3, also called α-1-antichymotrypsin (AACT, ACT), is one of the inhibitors of serine proteases, one of which is cathepsin G. As an acute-phase protein secreted into the plasma by liver cells, it plays an important role in the anti-inflammatory response and antiviral response. Elevated levels of SERPINA3 have been observed in heart failure and neurological diseases such as Alzheimer's disease or Creutzfeldt-Jakob disease. Many studies have shown increased expression levels of the SERPINA3 gene in various types of cancer, such as glioblastoma, colorectal cancer, endometrial cancer, breast cancer, or melanoma. In this case, the SERPINA3 protein is associated with an antiapoptotic function implemented by adjusting the PI3K/AKT or MAPK/ERK 1/2 signal pathways. However, the functions of the SERPINA3 protein are still only partially understood, mainly in the context of cancerogenesis, so it seems necessary to summarize the available information and describe its mechanism of action. In particular, we sought to amass the existing body of research focusing on the description of the underlying mechanisms of various diseases not related to cancer. Our goal was to present an overview of the correct function of SERPINA3 as part of the defense system, which unfortunately easily becomes the "Fifth Column" and begins to support processes of destruction.

promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling

Summary

We present promor, a comprehensive, user-friendly R package that streamlines label-free quantification proteomics data analysis and building machine learning-based predictive models with top protein candidates.

Availability and implementation

promor is freely available as an open source R package on the Comprehensive R Archive Network (CRAN) (https://CRAN.R-project.org/package=promor) and distributed under the Lesser General Public License (version 2.1 or later). Development version of promor is maintained on GitHub (https://github.com/caranathunge/promor) and additional documentation and tutorials are provided on the package website (https://caranathunge.github.io/promor/).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

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.

Serum Proteomic Analysis for New Types of Long-Term Persistent COVID-19 Patients in Wuhan

The emergence of a new type of COVID-19 patients, who were retested positive after hospital discharge with long-term persistent SARS-CoV-2 infection but without COVID-19 clinical symptoms (hereinafter, LTPPs), poses novel challenges to COVID-19 treatment and prevention. Why was there such a contradictory phenomenon in LTPPs? To explore the mechanism underlying this phenomenon, we performed quantitative proteomic analyses using the sera of 12 LTPPs (Wuhan Pulmonary Hospital), with the longest carrying history of 132 days, and mainly focused on 7 LTPPs without hypertension (LTPPs-NH). The results showed differential serum protein profiles between LTPPs/LTPPs-NH and health controls. Further analysis identified 174 differentially-expressed-proteins (DEPs) for LTPPs, and 165 DEPs for LTPPs-NH, most of which were shared. GO and KEGG analyses for these DEPs revealed significant enrichment of "coagulation" and "immune response" in both LTPPs and LTPPs-NH. A unity of contradictory genotypes in the 2 aspects were then observed: some DEPs showed the same dysregulated expressed trend as that previously reported for patients in the acute phase of COVID-19, which might be caused by long-term stimulation of persistent SARS-CoV-2 infection in LTPPs, further preventing them from complete elimination; in contrast, some DEPs showed the opposite expression trend in expression, so as to retain control of COVID-19 clinical symptoms in LTPPs. Overall, the contrary effects of these DEPs worked together to maintain the balance of LTPPs, further endowing their contradictory steady-state with long-term persistent SARS-CoV-2 infection but without symptoms. Additionally, our study revealed some potential therapeutic targets of COVID-19. Further studies on these are warranted. IMPORTANCE This study reported a new type of COVID-19 patients and explored the underlying molecular mechanism by quantitative proteomic analyses. DEPs were significantly enriched in "coagulation" and "immune response". Importantly, we identified 7 "coagulation system"- and 9 "immune response"-related DEPs, the expression levels of which were consistent with those previously reported for patients in the acute phase of COVID-19, which appeared to play a role in avoiding the complete elimination of SARS-CoV-2 in LTPPs. On the contrary, 6 "coagulation system"- and 5 "immune response"-related DEPs showed the opposite trend in expression. The 11 inconsistent serum proteins seem to play a key role in the fight against long-term persistent SARS-CoV-2 infection, further retaining control of COVID-19 clinical symptom of LTPPs. The 26 proteins can serve as potential therapeutic targets and are thus valuable for the treatment of LTPPs; further studies on them are warranted.

C1 esterase inhibitor-mediated immunosuppression in COVID-19: Friend or foe?

From asymptomatic to severe, SARS-CoV-2, causative agent of COVID-19, elicits varying disease severities. Moreover, understanding innate and adaptive immune responses to SARS-CoV-2 is imperative since variants such as Omicron negatively impact adaptive antibody neutralization. Severe COVID-19 is, in part, associated with aberrant activation of complement and Factor XII (FXIIa), initiator of contact system activation. Paradoxically, a protein that inhibits the three known pathways of complement activation and FXIIa, C1 esterase inhibitor (C1-INH), is increased in COVID-19 patient plasma and is associated with disease severity. Here we review the role of C1-INH in the regulation of innate and adaptive immune responses. Additionally, we contextualize regulation of C1-INH and SERPING1, the gene encoding C1-INH, by other pathogens and SARS viruses and propose that viral proteins bind to C1-INH to inhibit its function in severe COVID-19. Finally, we review the current clinical trials and published results of exogenous C1-INH treatment in COVID-19 patients.

Multiomics Data Analysis Workflow to Assess Severity in Longitudinal Plasma Samples of COVID-19 Patients

Elucidation of molecular markers related to the mounted immune response is crucial for the understanding of disease pathogenesis. In this article, we present the mass-spectrometry-based metabolomic and proteomic data of blood plasma of COVID-19 patients collected at two-time points, which showed a transition from non-severe to severe conditions during these time points. Metabolites were extracted and subjected to mass spectrometric analysis using the Q-Exactive mass spectrometer. For proteomic analysis, depleted plasma samples were tryptic digested and subjected to mass spectrometry analysis. The expression of a few significant targeted proteins was also validated by employing the targeted proteomic approach of multiple reaction monitoring (MRM). Integrative pathway analysis was performed with the significant proteins to obtain biological insights into disease severity. For discussion and more information on the dataset creation, please refer to the related full-length article [1].

Heterologous expression of hAID in E. coli leads to the production of a splice isoform of AID: hAIDδC, a mystery to be explored

Activation-induced cytidine deaminase (AID) is a key player that initiates antibody diversification in activated B-cell. AID mediates somatic hypermutation (SHM) and class switch recombination (CSR) via the deamination of cytosine to uracil at the Ig locus, resulting in the production of high-affinity antibodies. AID is predominantly restricted to Ig genes, whereas off-targeting of AID leads to lymphocyte-related malignancies. Interestingly, apart from FL-AID other splice isoforms of AID are highly expressed in the lymphocyte malignancies. In our study, we found that the heterologous expression of hAID-FL in E. coli cells produced two induced bands of hAID as demonstrated by SDS-PAGE and western blotting. Remarkably, peptide mapping data predicted that one band is hAID-FL and the other is its splice isoform, hAIDδE4a. To get an insight into why E. coli cells expressed hAID-FL and hAID variant, we mutated the 5' and 3' splice site of a putative intron of hAID, but it failed to produce only hAID-FL. Incidentally, hAID expressed with fusion partners also displayed two bands, and peptide mapping data strongly suggest that besides hAID-FL, the lower band showed a significant number of amino acids missing towards the C-terminal domain (named as hAIDδC). Our results are the first report to show that expression of recombinant hAID alone or irrespective of solubilization tags in E. coli cells produced hAID-FL and hAIDδC. It will be fascinating to explore the potential mechanism underlying the expression of hAIDδC from recombinant hAID plasmid in E. coli cells.

Levels of soluble complement regulators predict severity of COVID-19 symptoms

The SARS-CoV-2 virus continues to cause significant morbidity and mortality worldwide from COVID-19. One of the major challenges of patient management is the broad range of symptoms observed. While the majority of individuals experience relatively mild disease, a significant minority of patients require hospitalisation, with COVID-19 still proving fatal for some. As such, there remains a desperate need to better understand what drives this severe disease, both in terms of the underlying biology, but also to potentially predict at diagnosis which patients are likely to require further interventions, thus enabling better outcomes for both patients and healthcare systems. Several lines of evidence have pointed to dysregulation of the complement cascade as a major factor in severe COVID-19 outcomes. How this is underpinned mechanistically is not known. Here, we have focussed on the role of the soluble complement regulators Complement Factor H (FH), its splice variant Factor H-like 1 (FHL-1) and five Factor H-Related proteins (FHR1-5). Using a targeted mass spectrometry approach, we quantified these proteins in a cohort of 188 plasma samples from controls and SARS-CoV-2 patients taken at diagnosis. This analysis revealed significant elevations in all FHR proteins, but not FH, in patients with more severe disease, particularly FHR2 and FHR5 (FHR2: 1.97-fold, p<0.0001; FHR5: 2.4-fold, p<0.0001). Furthermore, for a subset of 77 SARS-CoV-2 +ve patients we also analysed time course samples taken approximately 28 days post-diagnosis. Here, we see complement regulator levels drop in all individuals with asymptomatic or mild disease, but regulators remain high in those with more severe outcomes, with elevations in FHR2 over baseline levels in this group. These data support the hypothesis that elevation of circulating levels of the FHR family of proteins could predict disease severity in COVID-19 patients, and that the duration of elevation (or lack of immune activation resolution) may be partly responsible for driving poor outcomes in 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.

Blood plasma proteome alteration after local tissue damage induced by Bothrops erythromelas snake venom in mice

Snakes of the genus Bothrops are responsible the most snakebites in the Brazil, causing a diverse and complex pathophysiological condition. Bothrops erythromelas is the main specie of medical relevance found in the Caatinga from the Brazilian Northeast region. The pathophysiological effects involving B. erythromelas snakebite as well as the organism reaction in response to this envenomation are not so explored. Thus, edema was induced in mice paws using 2.5 μg or 5.0 μg of B. erythromelas venom, and the percentage of edema was measured. Plasma was collected 30  minutes after the envenomation-induced in mice and analyzed by mass spectrometry. It was identified a total of 112 common plasma proteins differentially abundant among experimental groups, which are involved with the complement system and coagulation cascades, oxidative stress, neutrophil degranulation, platelets degranulation and inflammatory response. Apolipoprotein A1 (Apoa), serum amyloid protein A-4 (Saa4), adiponectin (Adipoq) showed up-regulated in mice plasma after injection of venom, while fibulin (Fbln1), factor XII (F12) and vitamin K-dependent protein Z (Proz) showed down-regulated. The results indicate a protein pattern of thrombo-inflammation to the B. erythromelas snakebite, evidencing potential biomarkers for monitoring this snakebite, new therapeutic targets and its correlations with the degree of envenomation once showed modulations in the abundance among the different groups according to the amount of venom injected into the mice.

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.

Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization

Coronavirus 2019 (COVID-19) is an extreme acute respiratory syndrome. Early diagnosis and accurate assessment of COVID-19 are not available, resulting in ineffective therapeutic therapy. This study designs an effective intelligence framework to early recognition and discrimination of COVID-19 severity from the perspective of coagulation indexes. The framework is proposed by integrating an enhanced new stochastic optimizer, a brain storm optimizing algorithm (EBSO), with an evolutionary machine learning algorithm called EBSO-SVM. Fast convergence and low risk of the local stagnant can be guaranteed for EBSO with added by Harris hawks optimization (HHO), and its property is verified on 23 benchmarks. Then, the EBSO is utilized to perform parameter optimization and feature selection simultaneously for support vector machine (SVM), and the presented EBSO-SVM early recognition and discrimination of COVID-19 severity in terms of coagulation indexes using COVID-19 clinical data. The classification performance of the EBSO-SVM is very promising, reaching 91.9195% accuracy, 90.529% Matthews correlation coefficient, 90.9912% Sensitivity and 88.5705% Specificity on COVID-19. Compared with other existing state-of-the-art methods, the EBSO-SVM in this paper still shows obvious advantages in multiple metrics. The statistical results demonstrate that the proposed EBSO-SVM shows predictive properties for all metrics and higher stability, which can be treated as a computer-aided technique for analysis of COVID-19 severity from the perspective of coagulation.

Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning

The recent surge of coronavirus disease 2019 (COVID-19) hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of hundreds of proteins and metabolites in plasma from hospitalized COVID-19 patients. We observed a clear distinction between COVID-19 patients and controls and, strikingly, a significant difference between survivors and nonsurvivors. With increasing length of hospitalization, the survivors' samples showed a trend toward normal concentrations, indicating a potential sensitive readout of treatment success. Building a machine learning multi-omic model that considers the concentrations of 10 proteins and five metabolites, we could predict patient survival with 92% accuracy (area under the receiver operating characteristic curve: 0.97) on the day of hospitalization. Hence, our standardized assays represent a unique opportunity for the early stratification of hospitalized COVID-19 patients.

A Meta-summary and Bioinformatic Analysis Identified Interleukin 6 as a Master Regulator of COVID-19 Severity Biomarkers

With the rising demand for improved COVID-19 disease monitoring and prognostic markers, studies have aimed to identify biomarkers using a range of screening methods. However, the selection of biomarkers for validation from large datasets may result in potentially important biomarkers being overlooked when datasets are considered in isolation. Here, we have utilized a meta-summary approach to investigate COVID-19 biomarker datasets to identify conserved biomarkers of COVID-19 severity. This approach identified a panel of 17 proteins that showed a consistent direction of change across two or more datasets. Furthermore, bioinformatics analysis of these proteins highlighted a range of enriched biological processes that include inflammatory responses and compromised integrity of physiological systems including cardiovascular, neurological, and metabolic. A panel of upstream regulators of the COVID-19 severity biomarkers were identified, including chemical compounds currently under investigation for COVID-19 treatment. One of the upstream regulators, interleukin 6 (IL6), was identified as a “master regulator” of the severity biomarkers. COVID-19 disease severity is intensified due to the extreme viral immunological reaction that results in increased inflammatory biomarkers and cytokine storm. Since IL6 is the primary stimulator of cytokines, it could be used independently as a biomarker in determining COVID-19 disease progression, in addition to a potential therapeutic approach targeting IL6. The array of upstream regulators of the severity biomarkers identified here serve as attractive candidates for the development of new therapeutic approaches to treating COVID-19. In addition, the findings from this study highlight COVID-19 severity biomarkers which represent promising, robust biomarkers for future validation studies for their use in defining and monitoring disease severity and patient prognosis.

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.

Semen Proteomics of COVID-19 Convalescent Men Reveals Disruption of Key Biological Pathways Relevant to Male Reproductive Function

A considerable section of males suffered from COVID-19, with many experiencing long-term repercussions. Recovered males have been documented to have compromised fertility, albeit the mechanisms remain unclear. We investigated the impact of COVID-19 on semen proteome following complete clinical recovery using mass spectrometry. A label-free quantitative proteomics study involved 10 healthy fertile subjects and 17 COVID-19-recovered men. With 1% false discovery rate and >1 unique peptide stringency, MaxQuant analysis found 1099 proteins and 8503 peptides. Of the 48 differentially expressed proteins between the healthy and COVID-19-recovered groups, 21 proteins were downregulated and 27 were upregulated in COVID-19-recovered males. The major pathways involved in reproductive functions, such as sperm-oocyte recognition, testosterone response, cell motility regulation, adhesion regulation, extracellular matrix adhesion, and endopeptidase activity, were downregulated in COVID-19-recovered patients according to bioinformatics analysis. Furthermore, the targeted approach revealed significant downregulation of semenogelin 1 and prosaposin, two proteins related to male fertility. Therefore, we demonstrate the alteration of semen proteome in response to COVID-19, thus disrupting the male reproductive function despite the patient's clinical remission. Hence, to understand fertility-related biological processes triggered by this infection, a protracted evaluation of the consequences of COVID-19 in recovered men is warranted.

COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments

Background: With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination. Methods: We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments. Result and Conclusions: Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis.

Rise of the SARS-CoV-2 Variants: can proteomics be the silver bullet?

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.

MALDI-TOF mass spectrometry of saliva samples as a prognostic tool for COVID-19

Background

Methods

Results

Conclusion

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection

RT-PCR is the primary method to diagnose COVID-19 and is also used to monitor the disease course. This approach, however, suffers from false negatives due to RNA instability and poses a high risk to medical practitioners. Here, we investigated the potential of using serum proteomics to predict viral nucleic acid positivity during COVID-19. We analyzed the proteome of 275 inactivated serum samples from 54 out of 144 COVID-19 patients and shortlisted 42 regulated proteins in the severe group and 12 in the non-severe group. Using these regulated proteins and several key clinical indexes, including days after symptoms onset, platelet counts, and magnesium, we developed two machine learning models to predict nucleic acid positivity, with an AUC of 0.94 in severe cases and 0.89 in non-severe cases, respectively. Our data suggest the potential of using a serum protein-based machine learning model to monitor COVID-19 progression, thus complementing swab RT-PCR tests. More efforts are required to promote this approach into clinical practice since mass spectrometry-based protein measurement is not currently widely accessible in clinic.

Multiplex Technologies in COVID-19 Research, Diagnostics, and Prognostics: Battling the Pandemic

Due to continuous technical developments and new insights into the high complexity of infectious diseases such as COVID-19, there is an increasing need for multiplex biomarkers to aid clinical management and support the development of new drugs and vaccines. COVID-19 disease requires rapid diagnosis and stratification to enable the most appropriate treatment course for the best possible outcomes for patients. In addition, these tests should be rapid, specific, and sensitive. They should rule out other potential causes of illness with simultaneous testing for other diseases. Elevated levels of specific biomarkers can be used to establish severity risks of chronic diseases so that patients can be provided the proper medication at the right time. This review describes the state-of-the-art technologies in proteomics, transcriptomics, and metabolomics, for multiplex biomarker approaches in COVID-19 research.

Proteomics advances towards developing SARS-CoV-2 therapeutics using in silico drug repurposing approaches

Standing amidst the COVID-19 pandemic, we have faced major medical and economic crisis in recent times which remains to be an unresolved issue till date. Although the scientific community has made significant progress towards diagnosis and understanding the disease; however, effective therapeutics are still lacking. Several omics-based studies, especially proteomics and interactomics, have contributed significantly in terms of identifying biomarker panels that can potentially be used for the disease prognosis. This has also paved the way to identify the targets for drug repurposing as a therapeutic alternative. US Food and Drug Administration (FDA) has set in motion more than 500 drug development programs on an emergency basis, most of them are focusing on repurposed drugs. Remdesivir is one such success of a robust and quick drug repurposing approach. The advancements in omics-based technologies has allowed to explore altered host proteins, which were earlier restricted to only SARS-CoV-2 protein signatures. In this article, we have reviewed major contributions of proteomics and interactomics techniques towards identifying therapeutic targets for COVID-19. Furthermore, in-silico molecular docking approaches to streamline potential drug candidates are also discussed.

A Multi-omics Longitudinal Study Reveals Alteration of the Leukocyte Activation Pathway in COVID-19 Patients

Severe coronavirus disease 2019 (COVID-19) infection may lead to lung injury, multi-organ failure, and eventually death. Cytokine storm due to excess cytokine production has been associated with fatality in severe infections. However, the specific molecular signatures associated with the elevated immune response are yet to be elucidated. We performed a mass-spectrometry-based proteomic and metabolomic analysis of COVID-19 plasma samples collected at two time points. Using Orbitrap Fusion LC-MS/MS-based label-free proteomic analysis, we identified around 10 significant proteins, 32 significant peptides, and 5 metabolites that were dysregulated at the severe time points. Few of these proteins identified by quantitative proteomics were validated using the multiple reaction monitoring (MRM) assay. Integrated pathway analysis using distinct proteomic and metabolomic signatures revealed alterations in complement and coagulation cascade, platelet aggregation, myeloid leukocyte activation pathway, and arginine metabolism. Further, we highlight the role of leukocyte activation and arginine metabolism in COVID-19 pathogenesis and targeting these pathways for COVID-19 therapeutics.

Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review

Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.