New insights into genetic characteristics between multiple myeloma and COVID-19: An integrative bioinformatics analysis of gene expression omnibus microarray and the cancer genome atlas data

Natural selection directing molecular evolution in vertebrate viral sensors

Diseases caused by pathogens contribute to molecular adaptations in host immunity. Variety of viral pathogens challenging animal immunity can drive positive selection diversifying receptors recognising the infections. However, whether distinct virus sensing systems differ across animals in their evolutionary modes remains unclear. Our review provides a comparative overview of natural selection shaping molecular evolution in vertebrate viral-binding pattern recognition receptors (PRRs). Despite prevailing negative selection arising from the functional constraints, multiple lines of evidence now suggest diversifying selection in the Toll-like receptors (TLRs), NOD-like receptors (NLRs), RIG-I-like receptors (RLRs) and oligoadenylate synthetases (OASs). In several cases, location of the positively selected sites in the ligand-binding regions suggests effects on viral detection although experimental support is lacking. Unfortunately, in most other PRR families including the AIM2-like receptor family, C-type lectin receptors (CLRs), and cyclic GMP-AMP synthetase studies characterising their molecular evolution are rare, preventing comparative insight. We indicate shared characteristics of the viral sensor evolution and highlight priorities for future research.

From Co-Infections to Autoimmune Disease via Hyperactivated Innate Immunity: COVID-19 Autoimmune Coagulopathies, Autoimmune Myocarditis and Multisystem Inflammatory Syndrome in Children

Neutrophilia and the production of neutrophil extracellular traps (NETs) are two of many measures of increased inflammation in severe COVID-19 that also accompany its autoimmune complications, including coagulopathies, myocarditis and multisystem inflammatory syndrome in children (MIS-C). This paper integrates currently disparate measures of innate hyperactivation in severe COVID-19 and its autoimmune complications, and relates these to SARS-CoV-2 activation of innate immunity. Aggregated data include activation of Toll-like receptors (TLRs), nucleotide-binding oligomerization domain (NOD) receptors, NOD leucine-rich repeat and pyrin-domain-containing receptors (NLRPs), retinoic acid-inducible gene I (RIG-I) and melanoma-differentiation-associated gene 5 (MDA-5). SARS-CoV-2 mainly activates the virus-associated innate receptors TLR3, TLR7, TLR8, NLRP3, RIG-1 and MDA-5. Severe COVID-19, however, is characterized by additional activation of TLR1, TLR2, TLR4, TLR5, TLR6, NOD1 and NOD2, which are primarily responsive to bacterial antigens. The innate activation patterns in autoimmune coagulopathies, myocarditis and Kawasaki disease, or MIS-C, mimic those of severe COVID-19 rather than SARS-CoV-2 alone suggesting that autoimmunity follows combined SARS-CoV-2-bacterial infections. Viral and bacterial receptors are known to synergize to produce the increased inflammation required to support autoimmune disease pathology. Additional studies demonstrate that anti-bacterial antibodies are also required to account for known autoantigen targets in COVID-19 autoimmune complications.

Integrative Analysis of Bulk RNA-Seq and Single-Cell RNA-Seq Unveils Novel Prognostic Biomarkers in Multiple Myeloma

Molecular heterogeneity has great significance in the disease biology of multiple myeloma (MM). Thus, the analysis combined single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data were performed to investigate the clonal evolution characteristics and to find novel prognostic targets in MM. The scRNA-seq data were analyzed by the Seurat pipeline and Monocle 2 to identify MM cell branches with different differentiation states. Marker genes in each branch were uploaded to the STRING database to construct the Protein-Protein Interaction (PPI) network, followed by the detection of hub genes by Cytoscape software. Using bulk RNA-seq data, Kaplan-Meier (K-M) survival analysis was then carried out to determine prognostic biomarkers in MM. A total of 342 marker genes in two branches with different differentiation states were identified, and the top 20 marker genes with the highest scores in the network calculated by the MCC algorithm were selected as hub genes in MM. Furthermore, K-M survival analysis revealed that higher NDUFB8, COX6C, NDUFA6, USMG5, and COX5B expression correlated closely with a worse prognosis in MM patients. Moreover, ssGSEA and Pearson analyses showed that their expression had a significant negative correlation with the proportion of Tcm (central memory cell) immune cells. Our findings identified NDUFB8, COX6C, NDUFA6, USMG5, and COX5B as novel prognostic biomarkers in MM, and also revealed the significance of genetic heterogeneity during cell differentiation in MM prognosis.

Pilot genome-wide association study of antibody response to inactivated SARS-CoV-2 vaccines

Vaccines are a key weapon against the COVID-19 pandemic caused by SARS-CoV-2. However, there are inter-individual differences in immune response to SARS-CoV-2 vaccines and genetic contributions to these differences have barely been investigated. Here, we performed genome-wide association study (GWAS) of antibody levels in 168 inactivated SARS-CoV-2 vaccine recipients. A total of 177 SNPs, corresponding to 41 independent loci, were identified to be associated with IgG, total antibodies or neutral antibodies. Specifically, the rs4543780, the intronic variant of FAM89A gene, was associated with total antibodies level and was annotated as a potential regulatory variant affecting gene expression of FAM89A, a biomarker differentiating bacterial from viral infections in febrile children. These findings might advance our knowledge of the molecular mechanisms driving immunity to SARS-CoV-2 vaccine.

Construction of a Prognosis Model of the Pyroptosis-Related Gene in Multiple Myeloma and Screening of Core Genes

Pyroptosis is an important factor affecting the proliferation, invasion, and metastasis of tumor cells. However, in multiple myeloma (MM), there are few studies on whether the occurrence of pyroptosis is related to the occurrence and prognosis of the disease. Based on the Gene Expression Omnibus and Cancer Genome Atlas database search dataset, this study identified pyroptosis-related genes with a specific prognosis, constructed and verified the prediction model by stepwise Cox regression analysis and time receiver operating characteristic curve analysis, and predicted specific functions by single-sample gene set enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes. Dataset analysis identified key genes, which were used to construct a risk scoring system for the prognosis of MM. The entire test set and external verification set verified the results. The expression levels of related genes in the clinical samples were detected using fluorescence quantitative PCR. A prognostic gene model based on six pyroptosis-related genes (CYCS, NLRP9, AIM2, NOD2, CHMP3, and GSDME) was constructed. The model has an excellent prognostic ability and can be popularized in the external validation set. The predictive prognostic nomogram integrating clinical information can effectively evaluate the risk score of each patient and predict their survival. After sample validation, our study found three potential key pyroptosis-related genes in multiple myeloma. GSDME, NOD2, and CHMP3 were significantly different between MM and healthy subjects, suggesting that they are pyroptosis-related protective genes. This study shows that the key pyroptosis-related gene in the model can be used as a marker for predicting the prognosis of myeloma, which may provide a basis for clinical individualized stratification therapy.

Differentially Expressed Genes Study Shown Potential for BCG Stimulation in Reducing the Severity of COVID-19

The incidence of COVID-19 infection and death is known to be lower in tuberculosis (TB) endemic countries than in nonendemic countries. The Bacillus Calmette-Guerin (BCG) vaccination, which is commonly administered in TB endemic countries, was previously reported to have a nonspecific protective effect against several infections, including COVID-19. In this study, we used a differentially expressed genes (DEG) approach to analyze the genes modulated by BCG vaccination and COVID-19 infection. The Gene Expression Omnibus (GEO) database was used to select a COVID-19 gene expression data set with GSE164805, GSE14408, and GSE58636, and DEG in each data set were identified using the GEO2R online tools and selected using the adjusted p value (padj) 0.05 criteria. The protein-protein interaction (PPI) network was constructed from DEGs with the same trend of expression (upregulation or downregulation) using STRING version 11. The PPI network was performed by using the highest confidence number (0.9). DEGs that have a high-trust network were collected and functional cluster analysis of biological processes from Gene Ontology (GO), pathway analysis from the Human KEGG pathway, and COVID-19-related gene analysis was carried out using the Enrichr database. We found that either BCG or tuberculin increased the expression of several genes related to hyperinflammation, such as CCL3, CCL4, CSF2, IL1B, and LTA. In severe COVID-19, these genes were downregulated. This leads to the hypothesis that revaccination may have a protective effect against the severity of COVID-19 by reducing the hyperinflammatory status.

NOD1, NOD2, and NLRC5 Receptors in Antiviral and Antimycobacterial Immunity

The innate immune system recognizes pathogen-associated molecular motifs through pattern recognition receptors (PRRs) that induce inflammasome assembly in macrophages and trigger signal transduction pathways, thereby leading to the transcription of inflammatory cytokine genes. Nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) represent a family of cytosolic PRRs involved in the detection of intracellular pathogens such as mycobacteria or viruses. In this review, we discuss the role of NOD1, NOD2, and NLRC5 receptors in regulating antiviral and antimycobacterial immune responses by providing insight into molecular mechanisms as well as their potential health and disease implications.

SARS-CoV-2/COVID-19 and its relationship with NOD2 and ubiquitination

COVID-19 infection activates the immune system to cause autoimmune and autoinflammatory diseases. We provide a comprehensive review of the relationship between SARS-CoV-2, NOD2 and ubiquitination. COVID-19 infection partly results from host inborn errors and genetic factors and can lead to autoinflammatory disease. The interaction between defective NOD2 and viral infection may trigger NOD2-associated disease. SARS-CoV-2 can alter UBA1 and abnormal ubiquitination leading to VEXAS syndrome. Both NOD2 and ubiquitination play important roles in controlling inflammatory process. Receptor interacting protein kinase 2 is a key component of the NOD2 activation pathway and becomes ubiquitinated to recruit downstream effector proteins. NOD2 mutations result in loss of ubiquitin binding and increase ligand-stimulated NOD2 signaling. During viral infection, mutations of either NOD2 or UBA1 genes or in combination can facilitate autoinflammatory disease. COVID-19 infection can cause autoinflammatory disease. There are reciprocal interactions between SARS-CoV-2, NOD2 and ubiquitination.

New insights into genetic characteristics between multiple myeloma and COVID-19: An integrative bioinformatics analysis of gene expression omnibus microarray and the cancer genome atlas data

Background

Multiple myeloma (MM) is a hematological malignancy. Coronavirus disease 2019 (COVID‐19) infection correlates with MM features. This study aimed to identify MM prognostic biomarkers with potential association with COVID‐19.

Methods

Differentially expressed genes (DEGs) in five MM data sets (GSE47552, GSE16558, GSE13591, GSE6477, and GSE39754) with the same expression trends were screened out. Functional enrichment analysis and the protein‐protein interaction network were performed for all DEGs. Prognosis‐associated DEGs were screened using the stepwise Cox regression analysis in the cancer genome atlas (TCGA) MMRF‐CoMMpass cohort and the GSE24080 data set. Prognosis‐associated DEGs associated with COVID‐19 infection in the GSE164805 data set were also identified.

Results

A total of 98 DEGs with the same expression trends in five data sets were identified, and 83 DEGs were included in the protein‐protein interaction network. Cox regression analysis identified 16 DEGs were associated with MM prognosis in the TCGA cohort, and only the cytochrome c oxidase subunit 6C (COX6C) gene (HR = 1.717, 95% CI 1.231–2.428, p = .002) and the nucleotide‐binding oligomerization domain containing 2 (NOD2) gene (HR = 0.882, 95% CI 0.798–0.975, p = .014) were independent factors related to MM prognosis in the GSE24080 data set. Both of them were downregulated in patients with mild COVID‐19 infection compared with controls but were upregulated in patients with severe COVID‐19 compared with patients with mild illness.

Conclusions

The NOD2 and COX6C genes might be used as prognostic biomarkers in MM. The two genes might be associated with the development of COVID‐19 infection.