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.

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 understanding the 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 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 (Suvarna et al., 2021).

Slit2 signaling stimulates Ewing sarcoma growth.

Ewing sarcoma is a cancer of bone and soft tissue in children driven by EWS::ETS fusion, most commonly EWS::FLI1. Because current cytotoxic chemotherapies are not improving the survival of those with metastatic or recurrent Ewing sarcoma cases, there is a need for novel and more effective targeted therapies. While EWS::FLI1 is the major driver of Ewing sarcoma, EWS::FLI1 has been difficult to target. A promising alternative approach is to identify and target the molecular vulnerabilities created by EWS::FLI1. Here we report that EWS::FLI1 induces the expression of Slit2, the ligand of Roundabout (Robo) receptors implicated in axon guidance and multiple other developmental processes. EWS::FLI1 binds to the Slit2 gene promoter and stimulates the expression of Slit2. Slit2 inactivates cdc42 and stabilizes the BAF chromatin remodeling complexes, enhancing EWS::FLI1 transcriptional output. Silencing of Slit2 strongly inhibited anchorage-dependent and anchorage-independent growth of Ewing sarcoma cells. Silencing of Slit2 receptors, Robo1 and Robo2, inhibited Ewing sarcoma growth as well. These results uncover a new role for Slit2 signaling in stimulating Ewing sarcoma growth and suggest that this pathway can be targeted therapeutically.

Downregulation of METTL6 mitigates cell progression, migration, invasion and adhesion in hepatocellular carcinoma by inhibiting cell adhesion molecules.

RNA modifications have attracted increasing interest in recent years because they have been frequently implicated in various human diseases, including cancer, highlighting the importance of dynamic post­transcriptional modifications. Methyltransferase­like 6 (METTL6) is a member of the RNA methyltransferase family that has been identified in many cancers; however, little is known about its specific role or mechanism of action. In the present study, we aimed to study the expression levels and functional role of METTL6 in hepatocellular carcinoma (HCC), and further investigate the relevant pathways. To this end, we systematically conducted bioinformatics analysis of METTL6 in HCC using gene expression data and clinical information from a publicly available dataset. The mRNA expression levels of METTL6 were significantly upregulated in HCC tumor tissues compared to that in adjacent non­tumor tissues and strongly associated with poorer survival outcomes in patients with HCC. CRISPR/Cas9­mediated knockout of METTL6 in HCC cell lines remarkably inhibited colony formation, cell proliferation, cell migration, cell invasion and cell attachment ability. RNA sequencing analysis demonstrated that knockout of METTL6 significantly suppressed the expression of cell adhesion­related genes. However, chromatin immunoprecipitation sequencing results revealed no significant differences in enhancer activities between cells, which suggests that METTL6 may regulate genes of interest post­transcriptionally. In addition, it was demonstrated for the first time that METTL6 was localized in the cytosol as detected by immunofluorescence analysis, which indicates the plausible location of RNA modification mediated by METTL6. Our findings provide further insight into the function of RNA modifications in cancer and suggest a possible role of METTL6 as a therapeutic target in HCC.

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.

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

The pestilential pathogen SARS-CoV-2 has led to a seemingly ceaseless pandemic of COVID-19. The healthcare sector is under a tremendous burden, thus necessitating the prognosis of COVID-19 severity. This in-depth study of plasma proteome alteration provides insights into the host physiological response towards the infection and also reveals the potential prognostic markers of the disease. Using label-free quantitative proteomics, we performed deep plasma proteome analysis in a cohort of 71 patients (20 COVID-19 negative, 18 COVID-19 non-severe, and 33 severe) to understand the disease dynamics. Of the 1200 proteins detected in the patient plasma, 38 proteins were identified to be differentially expressed between non-severe and severe groups. The altered plasma proteome revealed significant dysregulation in the pathways related to peptidase activity, regulated exocytosis, blood coagulation, complement activation, leukocyte activation involved in immune response, and response to glucocorticoid biological processes in severe cases of SARS-CoV-2 infection. Furthermore, we employed supervised machine learning (ML) approaches using a linear support vector machine model to identify the classifiers of patients with non-severe and severe COVID-19. The model used a selected panel of 20 proteins and classified the samples based on the severity with a classification accuracy of 0.84. Putative biomarkers such as angiotensinogen and SERPING1 and ML-derived classifiers including the apolipoprotein B, SERPINA3, and fibrinogen gamma chain were validated by targeted mass spectrometry-based multiple reaction monitoring (MRM) assays. We also employed an in silico screening approach against the identified target proteins for the therapeutic management of COVID-19. We shortlisted two FDA-approved drugs, namely, selinexor and ponatinib, which showed the potential of being repurposed for COVID-19 therapeutics. Overall, this is the first most comprehensive plasma proteome investigation of COVID-19 patients from the Indian population, and provides a set of potential biomarkers for the disease severity progression and targets for therapeutic interventions.

Proteomic investigation reveals dominant alterations of neutrophil degranulation and mRNA translation pathways in patients with COVID-19.

The altered molecular proteins and pathways in response to COVID-19 infection are still unclear. Here, we performed a comprehensive proteomics-based investigation of nasopharyngeal swab samples from patients with COVID-19 to study the host response by employing simple extraction strategies. Few of the host proteins such as interleukin-6, L-lactate dehydrogenase, C-reactive protein, Ferritin, and aspartate aminotransferase were found to be upregulated only in COVID-19-positive patients using targeted multiple reaction monitoring studies. The most important pathways identified by enrichment analysis were neutrophil degranulation, interleukin-12 signaling pathways, and mRNA translation of proteins thus providing the detailed investigation of host response in COVID-19 infection. Thus, we conclude that mass spectrometry-detected host proteins have a potential for disease severity progression; however, suitable validation strategies should be deployed for the clinical translation. Furthermore, the in silico docking of potential drugs with host proteins involved in the interleukin-12 signaling pathway might aid in COVID-19 therapeutic interventions.

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine.

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, "precision medicine," a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

Measurement of ATPase Activity of Valosin-containing Protein/p97.

Valosin-containing protein (VCP; also known as p97) is a type II ATPase regulating several cellular processes. Using proteomic techniques, we identified a chemical compound that binds to the D1 ATPase domain of VCP. The protocol described here was to study the effect of the compound on ATPase activity in vitro of purified VCP protein. ATPases are enzymes that hydrolyze ATP in a reaction resulting the release of an inorganic phosphate. This reaction can be measured using several methods, such as colorimetric, fluorescence, and radiometric assays, in addition to the bioluminescence assay mentioned here. Since the remaining ATP level after the reaction was detected using a luciferase assay, the luminescent signal indicates the ATPase activity inversely. This protocol is sensitive, rapid, and can be used for high-throughput screening assays to study the effect of compounds on ATPase function.

Identification of Target Protein for Bio-active Small Molecule Using Photo-cross Linked Beads and MALDI-TOF Mass Spectrometry.

Development of methods for protein identification is one of the important aspects of proteomics. Here, we report a protocol for the preparation of compound conjugated beads by photo-crosslinking, affinity purification, gel electrophoresis, and highly sensitive mass spectrometric assay for drug-target identification. Although there are several other methods used for drug-target identification, such as biochemical fractionation or radioactive ligand binding assay, affinity purification is widely used for its straight-forward and easy approach. To identify the target protein of an inhibitor of cancer cell-accelerated fibroblast migration, we prepared the inhibitor-conjugated beads by photo-crosslinking. Proteins were pulled down from cell lysates by the compound beads and separated by SDS-PAGE, and a specifically pulled down protein was cut out, trypsin-digested, analyzed using matrix-assisted laser desorption ionization/time of flight mass spectrometry (MALDI-TOF-MS) and identified by peptide mass fingerprinting (PMF) method. Since the photo-crosslinking enables the immobilization of ligands on an affinity matrix in a functional group-independent manner, we do not have to determine the functional group of the compound to conjugate the matrix. In addition, as compared to other MS techniques such as electrospray ionization, MALDI offers a less complex sample preparation procedure and higher sensitivity, and thus is better suited for the rapid identification of proteins isolated by gel electrophoresis.

A small-molecule ligand of valosin-containing protein/p97 inhibits cancer cell-accelerated fibroblast migration.

Carcinoma-associated fibroblasts are fibroblasts activated by surrounding cancer cells. Carcinoma-associated fibroblasts exhibit enhanced cell migration, which plays an important role in cancer metastasis. Previously, we demonstrated enhanced migration of NIH3T3 fibroblasts when they were cultured in the presence of MCF7 breast cancer cells. Human fibroblasts displayed a similar phenomenon even when they were co-cultured with cancer cells other than MCF7 cells. In this study, we screened ∼16,000 compounds from the RIKEN Natural Products Depository chemical library for inhibitors of enhanced NIH3T3 cell migration in the presence of MCF7. We identified NPD8733 as an inhibitor of cancer cell-enhanced fibroblast migration. This inhibition was observed not only in a wound-healing co-culture assay but also in a Transwell migration assay. Using NPD8733 and a structurally similar but inactive derivative, NPD8126, on immobilized beads, we found that NPD8733, but not NPD8126, specifically binds to valosin-containing protein (VCP)/p97, a member of the ATPase-associated with diverse cellular activities (AAA+) protein family. Using VCP truncation variants, we found that NPD8733 binds to the D1 domain of VCP. Because VCP's D1 domain is important for its function, we concluded that NPD8733 may act on VCP by binding to this domain. siRNA-mediated silencing of VCP in NIH3T3 fibroblasts, but not in MCF7 cells, reduced the migration of the co-cultured NIH3T3 fibroblasts. These results indicate that MCF7 activates the migration of NIH3T3 cells through VCP and that NPD8733 binds VCP and thereby inhibits its activity.

Identification of a small-molecule ligand of ß-arrestin1 as an inhibitor of stromal fibroblast cell migration accelerated by cancer cells.

Stromal fibroblasts, which occupy a major portion of the tumor microenvironment, play an important role in cancer metastasis. Thus, targeting of these fibroblasts activated by cancer cells (carcinoma-associated fibroblasts; CAFs) might aid in the improved treatment of cancer metastasis. NIH3T3 fibroblasts cocultured with MCF7 cells displayed enhanced migration compared to NIH3T3 fibroblasts cultured alone. We used this system to identify the small-molecule inhibitors responsible for their enhanced migration, a characteristic of CAFs. We selected ß-arrestin1, which showed high expression in cocultured cells, as a molecular target for such inhibitors. Cofilin, a protein downstream of ß-arrestin1, is activated/dephosphorylated in this condition. The small-molecule ligands of ß-arrestin1 obtained by chemical array were then examined using a wound healing coculture assay. RKN5755 was identified as a selective inhibitor of activated fibroblasts. RKN5755 inhibited the enhanced migration of fibroblasts cocultured with cancer cells by binding to ß-arrestin1 and interfering with ß-arrestin1-mediated cofilin signaling pathways. Therefore, these results demonstrate the role of ß-arrestin1 in the activation of fibroblasts and inhibiting this protein by small molecule inhibitor might be a potential therapeutic target for the stromal fibroblast activation (cancer-stroma interaction).