Identification of Novel Gene Signatures using Next-Generation Sequencing Data from COVID-19 Infection Models: Focus on Neuro-COVID and Potential Therapeutics

Recent Advances in and Application of Fluorescent Microspheres for Multiple Nucleic Acid Detection

Traditional single nucleic acid assays can only detect one target while multiple nucleic acid assays can detect multiple targets simultaneously, providing comprehensive and accurate information. Fluorescent microspheres in multiplexed nucleic acid detection offer high sensitivity, specificity, multiplexing, flexibility, and scalability advantages, enabling precise, real-time results and supporting clinical diagnosis and research. However, multiplexed assays face challenges like complexity, costs, and sample handling issues. The review explores the recent advancements and applications of fluorescent microspheres in multiple nucleic acid detection. It discusses the versatility of fluorescent microspheres in various fields, such as disease diagnosis, drug screening, and personalized medicine. The review highlights the possibility of adjusting the performance of fluorescent microspheres by modifying concentrations and carrier forms, allowing for tailored applications. It emphasizes the potential of fluorescent microsphere technology in revolutionizing nucleic acid detection and advancing health, disease treatment, and medical research.

Deciphering aging-associated molecular mechanisms in bone marrow derived hematopoietic stem cells in the elderly using NGS data

Aging is a complex process that is not yet fully understood. Despite advancements in research, a deeper understanding of the underlying biological mechanisms is necessary to develop interventions that promote healthy longevity. The aim of this study was to elucidate the complex mechanisms associated with healthy aging and longevity in healthy elderly individuals. The RNA sequencing (RNA-seq) data used in this study was obtained from the Gene Expression Omnibus (GEO) database (accession number GSE104406), which was collected from Fluorescent Activated Cell Sorting (FACS) of human bone marrow derived human hematopoietic stem cells (BM-HSCs) (Lineage-, CD34+, CD38-) young (18-30 years old) and aged (65-75 years old) donors who had no known hematological malignancy, with 10 biological replicates per group. The GEO RNA-seq Experiments Interactive Navigator (GREIN) software was used to obtain raw gene-level counts and filtered metadata for this dataset. Next generation knowledge discovery (NGKD) tools provided by BioJupies were used to obtain differentially regulated pathways, gene ontologies (GO), and gene signatures in the BM-HSCs. Finally, the L1000 Characteristic Direction Signature Search Engine (L1000CDS2) tool was used to identify specific drugs that reverse aging-associated gene signatures in old but healthy individuals. The down-regulation of signaling pathways such as longevity regulation, proteasome, Notch, apoptosis, nuclear factor kappa B (NFkB), and peroxisome proliferator-activated receptors (PPAR) signaling pathways in the BM-HSCs of healthy elderly. GO functions related to negative regulation of bone morphogenetic protein (BMP), telomeric DNA binding, nucleoside binding, calcium -dependent protein binding, chromatin-DNA binding, SMAD binding, and demethylase activity were significantly downregulated in the BM-HSCs of the elderly compared to the healthy young group. Importantly, potential drugs such as salermide, celestrol, cercosporin, dorsomorphin dihydrochloride, and LDN-193189 monohydrochloride that can reverse the aging-associated signatures in HSCs from healthy elderly were identified. The analysis of RNA-seq data based on NGKD techniques revealed a plethora of differentially regulated pathways, gene ontologies, and drugs with anti-aging potential to promote healthspan in the elderly.

Characterization of human umbilical cord blood-derived mast cells using high-throughput expression profiling and next-generation knowledge discovery platforms

Mast cells (MCs) are tissue-resident innate immune cells that express the high-affinity receptor for immunoglobulin E and are responsible for host defense and an array of diseases related to immune system. We aimed in this study to characterize the pathways and gene signatures of human cord blood-derived MCs (hCBMCs) in comparison to cells originating from CD34- progenitors using next-generation knowledge discovery methods. CD34+ cells were isolated from human umbilical cord blood using magnetic activated cell sorting and differentiated into MCs with rhIL-6 and rhSCF supplementation for 6-8 weeks. The purity of hCBMCs was analyzed by flow cytometry exhibiting the surface markers CD117+CD34-CD45-CD23-FcεR1αdim. Total RNA from hCBMCs and CD34- cells were isolated and hybridized using microarray. Differentially expressed genes were analyzed using iPathway Guide and Pre-Ranked Gene Set Enrichment Analysis. Next-generation knowledge discovery platforms revealed MC-specific gene signatures and molecular pathways enriched in hCBMCs and pertain the immunological response repertoire.

Exploration of potential molecular mechanisms and genotoxicity of anti-cancer drugs using next generation knowledge discovery methods

Background & Objectives

Accurate identification of molecular and toxicological functions of potential drug candidates is crucial for drug discovery and development. This may aid in the evaluation of the risks of genotoxicity and carcinogenesis. In addition, in silico characterization of existing and new drugs might offer clues for future investigations and aid in the development of anticancer treatments. Using next-generation knowledge discovery (NGKD) methodology, we endeavored to establish a risk assessment of anticancer drugs for their molecular mechanism(s) and genotoxicity.

Methods

This study was performed at the Faculty of Applied Medical Sciences, King Abdulaziz University (KAU), Jeddah, Saudi Arabia, in November 2022. Using innovative in silico model systems, we assessed the molecular mechanism of action and toxicity of around 20 distinct substances such as Deguelin, Etoposide, Camptothecin, Cytarabine (Ara-C), Cisplatin, Hydroxyurea, Trichostain A, Antimycin, Colchicine, 2-deoxyglucose, Tunicamycin, Thapsigargin, Vinblastin, Docetaxel, Oxaliplatin, Methotrexate, 5-flurouracil, Bleomycin, Taxol (Paclitaxel), and Apicidin. Using the Ingenuity Pathway Analysis (IPA) knowledge base, the number of targets for each compound was determined in silico. Subsequently, they were examined using Fisher's exact test and Benjamini Hochberg Multiple Testing Correction (P<0.05) and submitted to core analysis with IPA to decode the biological and toxicological activities differently controlled by these drugs. In addition, a multiple comparison module in IPA was used to compare the core analyses of each molecule. In addition, we obtained the top 100 protein targets of Etoposide, Camptothecin, and Ara-C using SwissTargetPrediction, as well as the key pathways and gene ontologies affected by these drugs and disease associations using the WebGestalt tool.

Results

We identified distinct toxicological signatures and canonical signaling pathways in tumor cell lines regulated by these 20 anticancer drugs. These signaling pathways included cell death and apoptosis in addition to molecular processes, p53 signaling, and aryl hydrocarbon receptor signaling. The TP53 signaling pathway is utilized by these agents to effectively trigger cell death and apoptosis, and p53 functions as a master regulator in a variety of cellular stress responses, including genotoxic stress.

Conclusion

Our research has laid the groundwork for the discovery of additional biomarkers that assess both the safety and effectiveness of treatment. Our mechanism based "NGKD" tools have more relevance for the identification of safer therapies and has the potential to lead to the rational screening of drug candidates targeting specific molecular networks and canonical pathways implicated in cancer and genotoxicity. In addition, the combination of protein, microRNA and metabolome profiles may be essential for the development of translatable biomarkers for the safety and efficacy of pharmacotherapeutic agents.Our research has laid the groundwork for the discovery of additional biomarkers that assess both the safety and the effectiveness of a treatment.

Immune Factors Drive Expression of SARS-CoV-2 Receptor Genes Amid Sexual Disparity

The emergence of COVID-19 has led to significant morbidity and mortality, with around seven million deaths worldwide as of February 2023. There are several risk factors such as age and sex that are associated with the development of severe symptoms due to COVID-19. There have been limited studies that have explored the role of sex differences in SARS-CoV-2 infection. As a result, there is an urgent need to identify molecular features associated with sex and COVID-19 pathogenesis to develop more effective interventions to combat the ongoing pandemic. To address this gap, we explored sex-specific molecular factors in both mouse and human datasets. The host immune targets such as TLR7, IRF7, IRF5, and IL6, which are involved in the immune response against viral infections, and the sex-specific targets such as AR and ESSR were taken to investigate any possible link with the SARS-CoV-2 host receptors ACE2 and TMPRSS2. For the mouse analysis, a single-cell RNA sequencing dataset was used, while bulk RNA-Seq datasets were used to analyze the human clinical data. Additional databases such as the Database of Transcription Start Sites (DBTS), STRING-DB, and the Swiss Regulon Portal were used for further analysis. We identified a 6-gene signature that showed differential expression in males and females. Additionally, this gene signature showed potential prognostic utility by differentiating ICU patients from non-ICU patients due to COVID-19. Our study highlights the importance of assessing sex differences in SARS-CoV-2 infection, which can assist in the optimal treatment and better vaccination strategies.

The Natural Products Withaferin A and Withanone from the Medicinal Herb Withania somnifera Are Covalent Inhibitors of the SARS-CoV-2 Main Protease

The current COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) created a global health crisis. The ability of vaccines to protect immunocompromised individuals and from emerging new strains are major concerns. Hence antiviral drugs against SARS-CoV-2 are essential. The SARS-CoV-2 main protease Mpro is vital for replication and an important target for antivirals. Using CMap analysis and docking studies, withaferin A (wifA) and withanone (win), two natural products from the medicinal herb Withania somnifera (ashwagandha), were identified as promising candidates that can covalently inhibit the viral protease Mpro. Cell culture, enzymatic, LC-MS/MS, computational, and equilibrium dialysis based assays were performed. DFT calculations indicated that wifA and win can form stable adducts with thiols. The cytotoxicity of Mpro was significantly reduced by wifA and win. Both wifA and win were found to irreversibly inhibit 0.5 μM Mpro with IC50 values of 0.54 and 1.8 μM, respectively. LC-MS/MS analysis revealed covalent adduct formation with wifA at cysteines 145 and 300 of Mpro. The natural products wifA and win can irreversibly inhibit the SARS-CoV-2 main protease Mpro. Based on the work presented here we propose that both wifA and win have the potential to be safely used as preventative and therapeutic interventions for COVID-19.

Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine

The coronavirus disease 2019 (COVID-19) pandemic causes many morbidity and mortality cases. Despite several developed vaccines and antiviral therapies, some patients experience severe conditions that need intensive care units (ICU); therefore, precision medicine is necessary to predict and treat these patients using novel biomarkers and targeted drugs. In this study, we proposed a multi-level biological network analysis framework to identify key genes via protein–protein interaction (PPI) network analysis as well as survival analysis based on differentially expressed genes (DEGs) in leukocyte transcriptomic profiles, discover novel biomarkers using microRNAs (miRNA) from regulatory network analysis, and provide candidate drugs targeting the key genes using drug–gene interaction network and structural analysis. The results show that upregulated DEGs were mainly enriched in cell division, cell cycle, and innate immune signaling pathways. Downregulated DEGs were primarily concentrated in the cellular response to stress, lysosome, glycosaminoglycan catabolic process, and mature B cell differentiation. Regulatory network analysis revealed that hsa-miR-6792-5p, hsa-let-7b-5p, hsa-miR-34a-5p, hsa-miR-92a-3p, and hsa-miR-146a-5p were predicted biomarkers. CDC25A, GUSB, MYBL2, and SDAD1 were identified as key genes in severe COVID-19. In addition, drug repurposing from drug–gene and drug–protein database searching and molecular docking showed that camptothecin and doxorubicin were candidate drugs interacting with the key genes. In conclusion, multi-level systems biology analysis plays an important role in precision medicine by finding novel biomarkers and targeted drugs based on key gene identification.

Decoding the Role of Astrocytes in the Entorhinal Cortex in Alzheimer’s Disease Using High-Dimensional Single-Nucleus RNA Sequencing Data and Next-Generation Knowledge Discovery Methodologies: Focus on Drugs and Natural Product Remedies for Dementia

Introduction: Alzheimer’s disease (AD) is a major cause of the development of cognitive decline and dementia. AD and associated dementias (ADRD) are the major contributors to the enormous burden of morbidity and mortality worldwide. To date, there are no robust therapies to alleviate or cure this debilitating disease. Most drug treatments focus on restoring the normal function of neurons and the cells that cause inflammation, such as microglia in the brain. However, the role of astrocytes, the brain’s housekeeping cells, in the development of AD and the initiation of dementia is still not well understood.

Objective: To decipher the role of astrocytes in the entorhinal cortex of AD patients using single nuclear RNA sequencing (snRNASeq) datasets from the Single Cell RNA-seq Database for Alzheimer’s Disease (scREAD). The datasets were originally derived from astrocytes, isolated from the entorhinal cortex of AD brain and healthy brain to decipher disease-specific signaling pathways as well as drugs and natural products that reverse AD-specific signatures in astrocytes.

Methods: We used snRNASeq datasets from the scREAD database originally derived from astrocytes isolated from the entorhinal cortex of AD and healthy brains from the Gene Expression Omnibus (GEO) (GSE138852 and GSE147528) and analyzed them using next-generation knowledge discovery (NGKD) platforms. scREAD is a user-friendly open-source interface available at https://bmbls.bmi.osumc.edu/scread/that enables more discovery-oriented strategies. snRNASeq data and metadata can also be visualized and downloaded via an interactive web application at adsn.ddnetbio.com. Differentially expressed genes (DEGs) for each snRNASeq dataset were analyzed using iPathwayGuide to compare and derive disease-specific pathways, gene ontologies, and in silico predictions of drugs and natural products that regulate AD -specific signatures in astrocytes. In addition, DEGs were analyzed using the L1000FWD and L1000CDS2 signature search programming interfaces (APIs) to identify additional drugs and natural products that mimic or reverse AD-specific gene signatures in astrocytes.

Results: We found that PI3K/AKT signaling, Wnt signaling, neuroactive ligand-receptor interaction pathways, neurodegeneration pathways, etc. were significantly impaired in astrocytes from the entorhinal cortex of AD patients. Biological processes such as glutamate receptor signaling pathway, regulation of synapse organization, cell-cell adhesion via plasma membrane adhesion molecules, and chylomicrons were negatively enriched in the astrocytes from the entorhinal cortex of AD patients. Gene sets involved in cellular components such as postsynaptic membrane, synaptic membrane, postsynapse, and synapse part were negatively enriched (p < 0.01). Moreover, molecular functions such as glutamate receptor activity, neurotransmitter receptor activity, and extracellular ligand-gated ion channels were negatively regulated in the astrocytes of the entorhinal cortex of AD patients (p < 0.01). Moreover, the application of NGKD platforms revealed that antirheumatic drugs, vitamin-E, emetine, narciclasine, cephaeline, trichostatin A, withaferin A, dasatinib, etc. can potentially reverse gene signatures associated with AD.

Conclusions: The present study highlights an innovative approach to use NGKD platforms to find unique disease-associated signaling pathways and specific synthetic drugs and natural products that can potentially reverse AD and ADRD-associated gene signatures.

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.