The Molecular Mechanism of Multiple Organ Dysfunction and Targeted Intervention of COVID-19 Based on Time-Order Transcriptomic Analysis

Exploring the Role of Neutrophil-Related Genes in Osteosarcoma via an Integrative Analysis of Single-Cell and Bulk Transcriptome

BACKGROUND

The involvement of neutrophil-related genes (NRGs) in patients with osteosarcoma (OS) has not been adequately explored. In this study, we aimed to examine the association between NRGs and the prognosis as well as the tumor microenvironment of OS.

METHODS

The OS data were obtained from the TARGET-OS and GEO database. Initially, we extracted NRGs by intersecting 538 NRGs from single-cell RNA sequencing (scRNA-seq) data between aneuploid and diploid groups, as well as 161 up-regulated differentially expressed genes (DEGs) from the TARGET-OS datasets. Subsequently, we conducted Least Absolute Shrinkage and Selection Operator (Lasso) analyses to identify the hub genes for constructing the NRG-score and NRG-signature. To assess the prognostic value of the NRG signatures in OS, we performed Kaplan-Meier analysis and generated time-dependent receiver operating characteristic (ROC) curves. Gene enrichment analysis (GSEA) and gene set variation analysis (GSVA) were utilized to ascertain the presence of tumor immune microenvironments (TIMEs) and immunomodulators (IMs). Additionally, the KEGG neutrophil signaling pathway was evaluated using ssGSEA. Subsequently, PCR and IHC were conducted to validate the expression of hub genes and transcription factors (TFs) in K7M2-induced OS mice.

RESULTS

FCER1G and C3AR1 have been identified as prognostic biomarkers for overall survival. The findings indicate a significantly improved prognosis for OS patients. The effectiveness and precision of the NRG signature in prognosticating OS patients were validated through survival ROC curves and an external validation dataset. The results clearly demonstrate that patients with elevated NRG scores exhibit decreased levels of immunomodulators, stromal score, immune score, ESTIMATE score, and infiltrating immune cell populations. Furthermore, our findings substantiate the potential role of SPI1 as a transcription factor in the regulation of the two central genes involved in osteosarcoma development. Moreover, our analysis unveiled a significant correlation and activation of the KEGG neutrophil signaling pathway with FCER1G and C3AR1. Notably, PCR and IHC demonstrated a significantly higher expression of C3AR1, FCER1G, and SPI1 in Balb/c mice induced with K7M2.

CONCLUSIONS

Our research emphasizes the significant contribution of neutrophils within the TIME of osteosarcoma. The newly developed NRG signature could serve as a good instrument for evaluating the prognosis and therapeutic approach for OS.

Integration of transcriptomics and metabolomics to reveal the effect of ginsenoside Rg3 on allergic rhinitis in mice

Increasing studies have demonstrated that ginsenoside Rg3 (Rg3) plays an important role in the prevention and treatment of various diseases, including allergic lower airway inflammation such as asthma. To investigate the role of Rg3 in allergic upper airway disease, the effect and therapeutic mechanism of Rg3 in allergic rhinitis (AR) were studied. Ovalbumin-induced AR model mice were intragastrically administered with Rg3. Nasal symptoms, levels of IgE, IL-4, IL-5, IL-13, SOD and MDA in serum, and histopathological analysis of nasal mucosa were used to evaluate the effect of Rg3 on ameliorating AR in mice. Moreover, nasal mucosa samples from the normal control group, AR model group and high dosage of Rg3 were collected to perform omics analysis. The differentially expressed genes and significantly changed metabolites were screened based on transcriptomics and metabolomics analyses, respectively. Integrative analysis was further performed to confirm the hub genes, metabolites and pathways. After Rg3 intervention, the nasal symptoms and inflammatory infiltration were effectively improved, the levels of IgE, IL-4, IL-5, IL-13 and MDA were significantly reduced, and the level of SOD was obviously increased. The results of the qRT-PCR assay complemented the transcriptomic findings. Integrated analysis showed that Rg3 played an anti-AR role mainly by regulating the interaction network, which was constructed by 12 genes, 8 metabolites and 4 pathways. Our findings suggested that Rg3 had a therapeutic effect on ovalbumin-induced AR in mice by inhibiting inflammation development and reducing oxidative stress. The present study could provide a potential natural agent for the treatment of AR.

Identification of genes related to immune enhancement caused by heterologous ChAdOx1-BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods

The widely used ChAdOx1 nCoV-19 (ChAd) vector and BNT162b2 (BNT) mRNA vaccines have been shown to induce robust immune responses. Recent studies demonstrated that the immune responses of people who received one dose of ChAdOx1 and one dose of BNT were better than those of people who received vaccines with two homologous ChAdOx1 or two BNT doses. However, how heterologous vaccines function has not been extensively investigated. In this study, single-cell RNA sequencing data from three classes of samples: volunteers vaccinated with heterologous ChAdOx1-BNT and volunteers vaccinated with homologous ChAd-ChAd and BNT-BNT vaccinations after 7 days were divided into three types of immune cells (3654 B, 8212 CD4+ T, and 5608 CD8+ T cells). To identify differences in gene expression in various cell types induced by vaccines administered through different vaccination strategies, multiple advanced feature selection methods (max-relevance and min-redundancy, Monte Carlo feature selection, least absolute shrinkage and selection operator, light gradient boosting machine, and permutation feature importance) and classification algorithms (decision tree and random forest) were integrated into a computational framework. Feature selection methods were in charge of analyzing the importance of gene features, yielding multiple gene lists. These lists were fed into incremental feature selection, incorporating decision tree and random forest, to extract essential genes, classification rules and build efficient classifiers. Highly ranked genes include PLCG2, whose differential expression is important to the B cell immune pathway and is positively correlated with immune cells, such as CD8+ T cells, and B2M, which is associated with thymic T cell differentiation. This study gave an important contribution to the mechanistic explanation of results showing the stronger immune response of a heterologous ChAdOx1-BNT vaccination schedule than two doses of either BNT or ChAdOx1, offering a theoretical foundation for vaccine modification.

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Longitudinal transcriptional analysis of peripheral blood leukocytes in COVID-19 convalescent donors

BACKGROUND

SARS-CoV2 can induce a strong host immune response. Many studies have evaluated antibody response following SARS-CoV2 infections. This study investigated the immune response and T cell receptor diversity in people who had recovered from SARS-CoV2 infection (COVID-19).

METHODS

Using the nCounter platform, we compared transcriptomic profiles of 162 COVID-19 convalescent donors (CCD) and 40 healthy donors (HD). 69 of the 162 CCDs had two or more time points sampled.

RESULTS

After eliminating the effects of demographic factors, we found extensive differential gene expression up to 241 days into the convalescent period. The differentially expressed genes were involved in several pathways, including virus-host interaction, interleukin and JAK-STAT signaling, T-cell co-stimulation, and immune exhaustion. A subset of 21 CCD samples was found to be highly "perturbed," characterized by overexpression of PLAU, IL1B, NFKB1, PLEK, LCP2, IRF3, MTOR, IL18BP, RACK1, TGFB1, and others. In addition, one of the clusters, P1 (n = 8) CCD samples, showed enhanced TCR diversity in 7 VJ pairs (TRAV9.1_TCRVA_014.1, TRBV6.8_TCRVB_016.1, TRAV7_TCRVA_008.1, TRGV9_ENST00000444775.1, TRAV18_TCRVA_026.1, TRGV4_ENST00000390345.1, TRAV11_TCRVA_017.1). Multiplexed cytokine analysis revealed anomalies in SCF, SCGF-b, and MCP-1 expression in this subset.

CONCLUSIONS

Persistent alterations in inflammatory pathways and T-cell activation/exhaustion markers for months after active infection may help shed light on the pathophysiology of a prolonged post-viral syndrome observed following recovery from COVID-19 infection. Future studies may inform the ability to identify druggable targets involving these pathways to mitigate the long-term effects of COVID-19 infection.

TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT04360278 Registered April 24, 2020.

Relationships between SARS-CoV-2 in Wastewater and COVID-19 Clinical Cases and Hospitalizations, with and without Normalization against Indicators of Human Waste

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in wastewater has been used to track community infections of coronavirus disease-2019 (COVID-19), providing critical information for public health interventions. Since levels in wastewater are dependent upon human inputs, we hypothesize that tracking infections can be improved by normalizing wastewater concentrations against indicators of human waste [Pepper Mild Mottle Virus (PMMoV), β-2 Microglobulin (B2M), and fecal coliform]. In this study, we analyzed SARS-CoV-2 and indicators of human waste in wastewater from two sewersheds of different scales: a University campus and a wastewater treatment plant. Wastewater data were combined with complementary COVID-19 case tracking to evaluate the efficiency of wastewater surveillance for forecasting new COVID-19 cases and, for the larger scale, hospitalizations. Results show that the normalization of SARS-CoV-2 levels by PMMoV and B2M resulted in improved correlations with COVID-19 cases for campus data using volcano second generation (V2G)-qPCR chemistry (r s = 0.69 without normalization, r s = 0.73 with normalization). Mixed results were obtained for normalization by PMMoV for samples collected at the community scale. Overall benefits from normalizing with measures of human waste depend upon qPCR chemistry and improves with smaller sewershed scale. We recommend further studies that evaluate the efficacy of additional normalization targets.

Proteomic insights into SARS-CoV-2 infection mechanisms, diagnosis, therapies and prognostic monitoring methods

At the end of 2019, the COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection, seriously damaged world public health security. Several protein markers associated with virus infection have been extensively explored to combat the ever-increasing challenge posed by SARS-CoV-2. The proteomics of COVID-19 deepened our understanding of viral particles and their mechanisms of host invasion, providing us with information on protein changes in host tissues, cells and body fluids following infection in COVID-19 patients. In this review, we summarize the proteomic studies of SARS-CoV-2 infection and review the current understanding of COVID-19 in terms of the quantitative and qualitative proteomics of viral particles and host entry factors from the perspective of protein pathological changes in the organism following host infection.

A comprehensive SARS-CoV-2-human protein-protein interactome network identifies pathobiology and host-targeting therapies for COVID-19

Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host's immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line [half maximal effective concentration (EC ₅₀ ) value of 4.1 µM], suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.

What Is Currently Known about the Role of CXCL10 in SARS-CoV-2 Infection?

Dysregulation of the immune response plays an important role in the progression of SARS-CoV-2 infection. A "cytokine storm", which is a phenomenon associated with uncontrolled production of large amounts of cytokines, very often affects patients with COVID-19. Elevated activity of chemotactic cytokines, called chemokines, can lead to serious consequences. CXCL10 has an ability to activate its receptor CXCR3, predominantly expressed on macrophages, T lymphocytes, dendritic cells, natural killer cells, and B cells. So, it has been suggested that the chemokine CXCL10, through CXCR3, is associated with inflammatory diseases and may be involved in the development of COVID-19. Therefore, in this review paper, we focus on the role of CXCL10 overactivity in the pathogenesis of COVID-19. We performed an extensive literature search for our investigation using the MEDLINE/PubMed database. Increased concentrations of CXCL10 were observed in COVID-19. Elevated levels of CXCL10 were reported to be associated with a severe course and disease progression. Published studies revealed that CXCL10 may be a very good predictive biomarker of patient outcome in COVID-19, and that markedly elevated CXCL10 levels are connected with ARDS and neurological complications. It has been observed that an effective treatment for SARS-CoV-2 leads to inhibition of "cytokine storm", as well as reduction of CXCL10 concentrations. It seems that modulation of the CXCL10-CXCR3 axis may be an effective therapeutic target of COVID-19. This review describes the potential role of CXCL10 in the pathogenesis of COVID-19, as well as its potential immune-therapeutic significance. However, future studies should aim to confirm the prognostic, clinical, and therapeutic role of CXCL10 in SARS-CoV-2 infection.

Global Research Status of Multiple Organ Dysfunction Syndrome During 2001-2021: A 20-Year Bibliometric Analysis

Background

Multiple Organ Dysfunction Syndrome (MODS) is a major cause of high morbidity and mortality among patients in intensive care units (ICU). Although numerous basic and clinical researches on MODS have been conducted, there is still a long way to go to prevent patients from entering this stage. To our knowledge, no bibliometric analyses of MODS have been reported, this study, therefore, was conducted to reveal MODS research status and trends during 2001–2021.

Methods

All relevant literature covering MODS during 2001–2021 were extracted from Web of Science. An online analysis platform of literature metrology was used to analyze the publication trends. VOSviewer software was used to collect and analyze the keywords and research hotspots related to MODS.

Results

As of July 31, 2021, a total of 994 MODS-related articles from 2001 to 2021 were identified. The United States accounted for the largest number of publications (31.1%), followed by China and Germany, with 186 and 75 publications, respectively. Among all the institutions, the University of Pittsburgh published the most papers related to MODS (21). Critical Care Medicine published the most papers in this field (106). Professor Moore EE, who had the most citation frequency (1847), made great achievements in MODS research. Moreover, analysis of the keywords identified three MODS research hotspot clusters: “mechanism-related research,” “clinical research,” and “diagnostic research.”

Conclusions

The United States maintained a top position worldwide and made the most outstanding contribution in the MODS field. In terms of publication, China was next only to the United States, but there was a disproportion between the quantity of publications and citation frequency. The institution University of Pittsburgh and journal Critical Care Medicine represent the highest level of research in this field. During the 20 years from 2001 to 2021, basic MODS research has been in-depth yet progressed relatively slowly recently, but the outbreak of COVID-19 has to some extent set off an upsurge of clinical research in MODS field.

Estradiol reduces ACE2 and TMPRSS2 mRNA levels in A549 human lung epithelial cells

Epidemiologic studies suggest slightly higher risk of severe Covid‐19 symptoms and fatalities following SARS‐CoV‐2 infection in men compared with women from similar age groups. This bias was suggested to reflect differences in the male and female immune system regulation, driven by different sex hormone levels in men and women, in particular, higher plasma estradiol in women. SARS‐CoV‐2 infects respiratory tract epithelial cells by binding to their cell membrane ACE2, followed by priming for cell entry by the host cell membrane serine protease TMPRSS2. The cell protease FURIN facilitates cell exit of mature SARS‐CoV‐2 virions. Our study examined the effects of in vitro treatment of A549 human lung epithelial cells with 17‐β‐estradiol on mRNA expression of genes coding for these proteins. Treatment of A549 human lung epithelial cells with 17‐β‐estradiol reduced the cellular mRNA levels of ACE2 and TMPRSS2 mRNA, while not affecting FURIN expression. Our findings suggest that 17‐β‐estradiol may reduce SARS‐CoV‐2 infection of lung epithelial cells, which may in part explain the reduced incidence of severe Covid‐19 and fatalities among women compared with men of similar age. Studies into the molecular pathways by which 17‐β‐estradiol reduces ACE2 and TMPRSS2 mRNA expression in lung epithelial cells are needed for assessing its potential protective value against severe Covid‐19.

Thrombocytopenia in COVID‑19 and vaccine‑induced thrombotic thrombocytopenia

The highly heterogeneous symptomatology and unpredictable progress of COVID-19 triggered unprecedented intensive biomedical research and a number of clinical research projects. Although the pathophysiology of the disease is being progressively clarified, its complexity remains vast. Moreover, some extremely infrequent cases of thrombotic thrombocytopenia following vaccination against SARS-CoV-2 infection have been observed. The present study aimed to map the signaling pathways of thrombocytopenia implicated in COVID-19, as well as in vaccine-induced thrombotic thrombocytopenia (VITT). The biomedical literature database, MEDLINE/PubMed, was thoroughly searched using artificial intelligence techniques for the semantic relations among the top 50 similar words (>0.9) implicated in COVID-19-mediated human infection or VITT. Additionally, STRING, a database of primary and predicted associations among genes and proteins (collected from diverse resources, such as documented pathway knowledge, high-throughput experimental studies, cross-species extrapolated information, automated text mining results, computationally predicted interactions, etc.), was employed, with the confidence threshold set at 0.7. In addition, two interactomes were constructed: i) A network including 119 and 56 nodes relevant to COVID-19 and thrombocytopenia, respectively; and ii) a second network containing 60 nodes relevant to VITT. Although thrombocytopenia is a dominant morbidity in both entities, three nodes were observed that corresponded to genes (AURKA, CD46 and CD19) expressed only in VITT, whilst ADAM10, CDC20, SHC1 and STXBP2 are silenced in VITT, but are commonly expressed in both COVID-19 and thrombocytopenia. The calculated average node degree was immense (11.9 in COVID-19 and 6.43 in VITT), illustrating the complexity of COVID-19 and VITT pathologies and confirming the importance of cytokines, as well as of pathways activated following hypoxic events. In addition, PYCARD, NLP3 and P2RX7 are key potential therapeutic targets for all three morbid entities, meriting further research. This interactome was based on wild-type genes, revealing the predisposition of the body to hypoxia-induced thrombosis, leading to the acute COVID-19 phenotype, the 'long-COVID syndrome', and/or VITT. Thus, common nodes appear to be key players in illness prevention, progression and treatment.