DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

Using system biology and bioinformatics to identify the influences of COVID-19 co-infection with influenza virus on COPD

Coronavirus disease 2019 (COVID-19) has speedily increased mortality globally. Although they are risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), less is known about the common molecular mechanisms behind COVID-19, influenza virus A (IAV), and chronic obstructive pulmonary disease (COPD). This research used bioinformatics and systems biology to find possible medications for treating COVID-19, IAV, and COPD via identifying differentially expressed genes (DEGs) from gene expression datasets (GSE171110, GSE76925, GSE106986, and GSE185576). A total of 78 DEGs were subjected to functional enrichment, pathway analysis, protein-protein interaction (PPI) network construct, hub gene extraction, and other potentially relevant disorders. Then, DEGs were discovered in networks including transcription factor (TF)-gene connections, protein-drug interactions, and DEG-microRNA (miRNA) coregulatory networks by using NetworkAnalyst. The top 12 hub genes were MPO, MMP9, CD8A, HP, ELANE, CD5, CR2, PLA2G7, PIK3R1, SLAMF1, PEX3, and TNFRSF17. We found that 44 TFs-genes, as well as 118 miRNAs, are directly linked to hub genes. Additionally, we searched the Drug Signatures Database (DSigDB) and identified 10 drugs that could potentially treat COVID-19, IAV, and COPD. Therefore, we evaluated the top 12 hub genes that could be promising DEGs for targeted therapy for SARS-CoV-2 and identified several prospective medications that may benefit COPD patients with COVID-19 and IAV co-infection.

Juvenile idiopathic arthritis-associated genetic loci exhibit spatially constrained gene regulatory effects across multiple tissues and immune cell types

Juvenile idiopathic arthritis (JIA) is an autoimmune, inflammatory joint disease with complex genetic etiology. Previous GWAS have found many genetic loci associated with JIA. However, the biological mechanism behind JIA remains unknown mainly because most risk loci are located in non-coding genetic regions. Interestingly, increasing evidence has found that regulatory elements in the non-coding regions can regulate the expression of distant target genes through spatial (physical) interactions. Here, we used information on the 3D genome organization (Hi-C data) to identify target genes that physically interact with SNPs within JIA risk loci. Subsequent analysis of these SNP-gene pairs using data from tissue and immune cell type-specific expression quantitative trait loci (eQTL) databases allowed the identification of risk loci that regulate the expression of their target genes. In total, we identified 59 JIA-risk loci that regulate the expression of 210 target genes across diverse tissues and immune cell types. Functional annotation of spatial eQTLs within JIA risk loci identified significant overlap with gene regulatory elements (i.e., enhancers and transcription factor binding sites). We found target genes involved in immune-related pathways such as antigen processing and presentation (e.g., ERAP2, HLA class I and II), the release of pro-inflammatory cytokines (e.g., LTBR, TYK2), proliferation and differentiation of specific immune cell types (e.g., AURKA in Th17 cells), and genes involved in physiological mechanisms related to pathological joint inflammation (e.g., LRG1 in arteries). Notably, many of the tissues where JIA-risk loci act as spatial eQTLs are not classically considered central to JIA pathology. Overall, our findings highlight the potential tissue and immune cell type-specific regulatory changes contributing to JIA pathogenesis. Future integration of our data with clinical studies can contribute to the development of improved JIA therapy.

Analysis of the potential biological mechanisms of diosmin against renal fibrosis based on network pharmacology and molecular docking approach

BACKGROUND

Interstitial fibrosis is involved in the progression of various chronic kidney diseases and renal failure. Diosmin is a naturally occurring flavonoid glycoside that has antioxidant, anti-inflammatory, and antifibrotic activities. However, whether diosmin protects kidneys by inhibiting renal fibrosis is unknown.

METHODS

The molecular formula of diosmin was obtained, targets related to diosmin and renal fibrosis were screened, and interactions among overlapping genes were analyzed. Overlapping genes were used for gene function and KEGG pathway enrichment analysis. TGF-β1 was used to induce fibrosis in HK-2 cells, and diosmin treatment was administered. The expression levels of relevant mRNA were then detected.

RESULTS

Network analysis identified 295 potential target genes for diosmin, 6828 for renal fibrosis, and 150 hub genes. Protein-protein interaction network results showed that CASP3, SRC, ANXA5, MMP9, HSP90AA1, IGF1, RHOA, ESR1, EGFR, and CDC42 were identified as key therapeutic targets. GO analysis revealed that these key targets may be involved in the negative regulation of apoptosis and protein phosphorylation. KEGG indicated that pathways in cancer, MAPK signaling pathway, Ras signaling pathway, PI3K-Akt signaling pathway, and HIF-1 signaling pathway were key pathways for renal fibrosis treatment. Molecular docking results showed that CASP3, ANXA5, MMP9, and HSP90AA1 stably bind to diosmin. Diosmin treatment inhibited the protein and mRNA levels of CASP3, MMP9, ANXA5, and HSP90AA1. Network pharmacology analysis and experimental results suggest that diosmin ameliorates renal fibrosis by decreasing the expression of CASP3, ANXA5, MMP9, and HSP90AA1.

CONCLUSIONS

Diosmin has a potential multi-component, multi-target, and multi-pathway molecular mechanism of action in the treatment of renal fibrosis. CASP3, MMP9, ANXA5, and HSP90AA1 might be the most important direct targets of diosmin.

Prioritization of genes associated with type 2 diabetes mellitus for functional studies

Existing therapies for type 2 diabetes mellitus (T2DM) show limited efficacy or have adverse effects. Numerous genetic variants associated with T2DM have been identified, but progress in translating these findings into potential drug targets has been limited. Here, we describe the tools and platforms available to identify effector genes from T2DM-associated coding and non-coding variants and prioritize them for functional studies. We discuss QSER1 and SLC12A8 as examples of genes that have been identified as possible T2DM candidate genes using these tools and platforms. We suggest further approaches, including the use of sequencing data with increased sample size and ethnic diversity, single-cell omics data for analyses, glycaemic trait associations to predict gene function and, potentially, human induced pluripotent stem cell 'village' cultures, to strengthen current gene functionalization workflows. Effective prioritization of T2DM-associated genes for experimental validation could expedite our understanding of the genetic mechanisms responsible for T2DM to facilitate the use of precision medicine in its treatment.

Cellular Diversity in Human Subgenual Anterior Cingulate and Dorsolateral Prefrontal Cortex by Single-Nucleus RNA-Sequencing

Regional cellular heterogeneity is a fundamental feature of the human neocortex; however, details of this heterogeneity are still undefined. We used single-nucleus RNA-sequencing to examine cell-specific transcriptional features in the dorsolateral PFC (DLPFC) and the subgenual anterior cingulate cortex (sgACC), regions implicated in major psychiatric disorders. Droplet-based nuclei-capture and library preparation were performed on replicate samples from 8 male donors without history of psychiatric or neurologic disorder. Unsupervised clustering identified major neural cell classes. Subsequent iterative clustering of neurons further revealed 20 excitatory and 22 inhibitory subclasses. Inhibitory cells were consistently more abundant in the sgACC and excitatory neuron subclusters exhibited considerable variability across brain regions. Excitatory cell subclasses also exhibited greater within-class transcriptional differences between the two regions. We used these molecular definitions to determine which cell classes might be enriched in loci carrying a genetic signal in genome-wide association studies or for differentially expressed genes in mental illness. We found that the heritable signals of psychiatric disorders were enriched in neurons and that, while the gene expression changes detected in bulk-RNA-sequencing studies were dominated by glial cells, some alterations could be identified in specific classes of excitatory and inhibitory neurons. Intriguingly, only two excitatory cell classes exhibited concomitant region-specific enrichment for both genome-wide association study loci and transcriptional dysregulation. In sum, by detailing the molecular and cellular diversity of the DLPFC and sgACC, we were able to generate hypotheses on regional and cell-specific dysfunctions that may contribute to the development of mental illness.SIGNIFICANCE STATEMENT Dysfunction of the subgenual anterior cingulate cortex has been implicated in mood disorders, particularly major depressive disorder, and the dorsolateral PFC, a subsection of the PFC involved in executive functioning, has been implicated in schizophrenia. Understanding the cellular composition of these regions is critical to elucidating the neurobiology underlying psychiatric and neurologic disorders. We studied cell type diversity of the subgenual anterior cingulate cortex and dorsolateral PFC of humans with no neuropsychiatric illness using a clustering analysis of single-nuclei RNA-sequencing data. Defining the transcriptomic profile of cellular subpopulations in these cortical regions is a first step to demystifying the cellular and molecular pathways involved in psychiatric disorders.

Network pharmacology reveals that Berberine may function against Alzheimer's disease via the AKT signaling pathway

Objective

To investigate the mechanism underlying the effects of berberine (BBR) in the treatment of Alzheimer's disease (AD).

Methods

3 × Tg AD mice were treated with BBR for 3 months, then the open field test (OFT), the novel object recognition test (NOR) and the Morris water maze (MWM) test were performed to assess behavioral performance. Hematoxylin-eosin (HE) staining, Nissl staining were used to examine histopathological changes. The pharmacological and molecular properties of BBR were obtained from the TCMSP database. BBR-associated AD targets were identified using the PharmMapper (PM), the comparative toxicogenomics database (CTD), DisGeNet and the human gene database (GeneCards). Core networks and BBR targets for the treatment of AD were identified using PPI network and functional enrichment analyses. AutoDock software was used to model the interaction between BBR and potential targets. Finally, RT-qPCR, western blotting were used to validate the expression of core targets.

Results

Behavioral experiments, HE staining and Nissl staining have shown that BBR can improve memory task performance and neuronal damage in the hippocampus of AD mice. 117 BBR-associated targets for the treatment of AD were identified, and 43 genes were used for downstream functional enrichment analysis in combination with the results of protein-protein interaction (PPI) network analysis. 2,230 biological processes (BP) terms, 67 cell components (CC) terms, 243 molecular function (MF) terms and 118 KEGG terms were identified. ALB, EGFR, CASP3 and five targets in the PI3K-AKT signaling pathway including AKT1, HSP90AA1, SRC, HRAS, IGF1 were selected by PPI network analysis, validated by molecular docking analysis and RT-q PCR as core targets for further analysis. Akt1 mRNA expression levels were significantly decreased in AD mice and significantly increased after BBR treatment (p < 0.05). Besides, AKT and ERK phosphorylation decreased in the model group, and BBR significantly increased their phosphorylation levels.

Conclusion

AKT1, HSP90AA1, SRC, HRAS, IGF1 and ALB, EGFR, CASP3 were core targets of BBR in the treatment of AD. BBR may exert a neuroprotective effect by modulating the ERK and AKT signaling pathways.

Ontology characterization, enrichment analysis, and similarity calculation-based evaluation of disease-syndrome-formula associations by applying SoFDA

Clinical symptom-based diagnosis and therapy play a crucial role in personalized medicine and drug discovery. The syndromes, distinctive groups of clinical symptoms summarized by traditional Chinese medicine (TCM) theories and clinical experiences, are used as the core diagnostic criteria and therapeutic guidance in TCM. However, there is still a lack of standardized data, information, and intrinsic molecular basis to help TCM syndromes better classify diseases and guide tailored medications. To address this problem, we built the first integrated web platform, SoFDA (http://www.tcmip.cn/Syndrome/front/), with a curated ontology of 319 TCM syndromes, 8045 diseases, and 1359 TCM herbal formulas and their relationships with genes, diseases, and formulas. This platform proposed an association measurement by calculating Jaccard/Cosine similarities between TCM syndromes and their related biomedical entities with case and control validations. On this basis, the SoFDA platform enables biomedical and pharmaceutical scientists to rank and filter the most promising associations for disease diagnosis and tailored interventions. Conversely, the targeted gene sets and symptom sets can also be associated with TCM syndromes, formulas, and diseases for function illustration. Notably, SoFDA explores the multi-way associations among diseases, TCM syndromes, symptom genes, herbal formulas, drug targets, and pathways in heterogeneous biomedical networks with lots of customization. The protocol here implements all the analyses above using the SoFDA platform. Collectively, SoFDA may provide insights into the biological basis of disease-specific TCM syndromes and the underlying molecular mechanisms, as well as a tailored treatment for single or multiple symptoms within a syndrome.

A Descriptive Whole-Genome Transcriptomics Study in a Stem Cell-Based Tool Predicts Multiple Tissue-Specific Beneficial Potential and Molecular Targets of Carnosic Acid

Carnosic acid (CA) is a phenolic diterpene widely distributed in herbal plants, rosemary and sage. Although its medicinal properties, such as antioxidant, antimicrobial, and neuroprotective effects, have been well-documented, its relevant biochemical processes and molecular targets have not been fully explored yet. In the present study, we conducted an untargeted whole-genome transcriptomics analysis to investigate CA-induced early biological and molecular events in human amniotic epithelial stem cells (hAESCs) with the aim of exploring its multiple tissue-specific functionalities and potential molecular targets. We found that seven days of CA treatment in hAESCs could induce mesoderm-lineage-specific differentiation. Tissue enrichment analysis revealed that CA significantly enriched lateral plate mesoderm-originated cardiovascular and adipose tissues. Further tissue-specific PPI analysis and kinase and transcription factor enrichment analyses identified potential upstream regulators and molecular targets of CA in a tissue-specific manner. Gene ontology enrichment analyses revealed the metabolic, antioxidant, and antifibrotic activities of CA. Altogether, our comprehensive whole-genome transcriptomics analyses offer a thorough understanding of the possible underlying molecular mechanism of CA.

Network Pharmacology and Molecular Docking Analysis of Active Compounds in Tualang Honey against Atherosclerosis

Atherosclerosis, a pathological condition marked by the accumulation of lipids and fibrous substances in the arterial walls, is a leading cause of heart failure and death. The present study aimed to utilize network pharmacology to assess the potential pharmacological effects of bioactive compounds in Tualang honey on atherosclerosis. This is significant as previous studies have indicated the cardioprotective effects of Tualang honey, yet a comprehensive evaluation using network pharmacology has yet to be conducted. The bioactive compounds in Tualang honey were screened and the potential gene targets for these compounds were predicted through Swiss Target Prediction and SuperPred databases. Atherosclerosis genes were retrieved from the OMIM, DisGeNet, and GeneCards databases. The interaction between these compounds and atherosclerosis genes was established through protein-protein interaction, gene ontology, and KEGG pathway analysis. The results of these analyses were then further confirmed through molecular docking studies using the AutoDock Tools software. The results revealed that 6 out of 103 compounds in Tualang honey met the screening criteria, with a total of 336 potential gene targets, 238 of which were shared with atherosclerosis. Further analysis showed that these active compounds had a good affinity with key targets and were associated with biological processes related to protein phosphorylation and inflammation as well as pathways related to lipid and atherosclerosis and other signaling pathways. In conclusion, the study provides insight into the potential pharmacological effects of Tualang honey bioactive compounds on atherosclerosis, supporting its use as a promising treatment for the disease.

Keystone pathobionts associated with colorectal cancer promote oncogenic reprograming

Fusobacterium nucleatum (Fn) and enterotoxigenic Bacteroides fragilis (ETBF) are two pathobionts consistently enriched in the gut microbiomes of patients with colorectal cancer (CRC) compared to healthy counterparts and frequently observed for their direct association within tumors. Although several molecular mechanisms have been identified that directly link these organisms to features of CRC in specific cell types, their specific effects on the epithelium and local immune compartment are not well-understood. To fill this gap, we leveraged single-cell RNA sequencing (scRNA-seq) on wildtype mice and mouse model of CRC. We find that Fn and ETBF exacerbate cancer-like transcriptional phenotypes in transit-amplifying and mature enterocytes in a mouse model of CRC. We also observed increased T cells in the pathobiont-exposed mice, but these pathobiont-specific differences observed in wildtype mice were abrogated in the mouse model of CRC. Although there are similarities in the responses provoked by each organism, we find pathobiont-specific effects in Myc-signaling and fatty acid metabolism. These findings support a role for Fn and ETBF in potentiating tumorigenesis via the induction of a cancer stem cell-like transit-amplifying and enterocyte population and the disruption of CTL cytotoxic function.

MultiSimNeNc: A network representation learning-based module identification method by network embedding and clustering

Accurate identification of gene modules based on biological networks is an effective approach to understanding gene patterns of cancer from a module-level perspective. However, most graph clustering algorithms just consider low-order topological connectivity, which limits their accuracy in gene module identification. In this study, we propose a novel network-based method, MultiSimNeNc, to identify modules in various types of networks by integrating network representation learning (NRL) and clustering algorithms. In this method, we first obtain the multi-order similarity of the network using graph convolution (GC). Then, we aggregate the multi-order similarity to characterize the network structure and use non-negative matrix factorization (NMF) to achieve low-dimensional node characterization. Finally, we predict the number of modules based on the bayesian information criterion (BIC) and use the gaussian mixture model (GMM) to identify modules. To testify to the efficacy of MultiSimeNc in module identification, we apply this method to two types of biological networks and six benchmark networks, where the biological networks are constructed based on the fusion of multi-omics data from glioblastoma (GBM). The analysis shows that MultiSimNeNc outperforms several state-of-the-art module identification algorithms in identification accuracy, which is an effective method for understanding biomolecular mechanisms of pathogenesis from a module-level perspective.

High resolution genomes of multiple Xiphophorus species provide new insights into microevolution, hybrid incompatibility, and epistasis

Because of diverged adaptative phenotypes, fish species of the genus Xiphophorus have contributed to a wide range of research for a century. Existing Xiphophorus genome assemblies are not at the chromosomal level and are prone to sequence gaps, thus hindering advancement of the intra- and inter-species differences for evolutionary, comparative, and translational biomedical studies. Herein, we assembled high-quality chromosome-level genome assemblies for three distantly related Xiphophorus species, namely, X. maculatus, X. couchianus, and X. hellerii Our overall goal is to precisely assess microevolutionary processes in the clade to ascertain molecular events that led to the divergence of the Xiphophorus species and to progress understanding of genetic incompatibility to disease. In particular, we measured intra- and inter-species divergence and assessed gene expression dysregulation in reciprocal interspecies hybrids among the three species. We found expanded gene families and positively selected genes associated with live bearing, a special mode of reproduction. We also found positively selected gene families are significantly enriched in nonpolymorphic transposable elements, suggesting the dispersal of these nonpolymorphic transposable elements has accompanied the evolution of the genes, possibly by incorporating new regulatory elements in support of the Britten-Davidson hypothesis. We characterized inter-specific polymorphisms, structural variants, and polymorphic transposable element insertions and assessed their association to interspecies hybridization-induced gene expression dysregulation related to specific disease states in humans.

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing

Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein-protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.

Total Flavonoids of Polygala fallax Hemsl Induce Apoptosis of Human Ectopic Endometrial Stromal Cells through PI3K/AKT/Bcl-2 Signaling Pathway

OBJECTIVE

The objective of this study was to explore the inhibitory effect of total flavonoids of Polygala fallax Hemsl (PFHF) on human ectopic endometrial stromal cells (HEcESCs) and its mechanism.

DESIGN

The apoptosis, cell cycle, migration, and invasion ability of HEcESCs (Fresh human ovarian endometriosis tissue was used for primary culture) after PFHF treatment were detected, and the mechanism of action was explored.

MATERIALS

The Polygala fallax Hemsl (PFH), RPMI 1640 culture medium, Dulbecco's modified Eagle's medium (DMEM)/F-12, fetal bovine serum, penicillin/streptomycin, cell counting kit-8 (CCK-8) kit, trypsin, phenylmethylsulfonyl fluoride, radioimmunoprecipitation assay tissue/cell lysate, bicinchoninic acid protein concentration detection kits, protein loading buffer, the apoptosis and cell cycle extraction kits, the matrix glue, TRIzol Universal Reagent, the reverse transcription kit, AB HS Green qPCR Mix, the ECL chromogenic solution, enzyme labeling instrument, flow cytometry, automatic real-time fluorescence quantitative PCR instrument, Goat anti-rabbit, rabbit anti-β-actin, vimentin, phosphatidylinositol 3 kinase (PI3K), protein kinase B (AKT), B-cell lymphoma-2 (Bcl-2), Bcl-extra long (Bcl-xl), Bcl-2 associated death promoter (Bad) antibody, Alexa Fluor 594-labeled secondary antibody, the inverted microscope, the constant temperature carbon dioxide cell incubator.

SETTING

Five parts included introduction, materials and methods, results, discussion, and conclusion.

METHODS

The potential targets and pathways of PFHF in the treatment of endometriosis were predicted by network pharmacology. The effect of PFHF on the proliferation, apoptosis and cell cycle, migration, and invasion of HEcESCs was detected by CCK-8 method, flow cytometry, and Transwell chamber experiment. Label-free quantitative proteomics based on mass spectrometry was used to analyze the protein mass spectrum of differential expression of HEcESCs before and after PFHF, and the biological information was analyzed. The effects of PFHF on the mRNA and protein expression of pathway-related genes predicted in HEcESCs were detected by reverse transcription-quantitative polymerase chain reaction and Western blotting.

RESULTS

The network pharmacology predicts that PFHF treats endometriosis through PI3K/AKT signaling pathway. Compared with control group (DMEM/F-12 medium alone), the high dose PFHF can significantly reduce the viability, migration, and invasion of HEcESCs, increase the apoptosis rate of HEcESCs, and make the HEcESCs accumulated in G0/G1 phase in a time- and dose-dependent manner (p < 0.05). The analysis of label-free quantitative proteomics indicated that PFHF flavonoids may induce apoptosis of EESCs through PI3K/AKT signaling pathway. The results of RT-qPCR and Western blotting showed that the expressions of PI3K, AKT, Bcl-2, and Bcl-xl were significantly downregulated, while the bad expression was upregulated in HEcESCs treated with PFHF (p < 0.05).

LIMITATIONS

This research investigated the effects of PFHF on the stromal endometriotic cells only. So it is unknown how PFHF can affect the entire endometriotic lesion. And the research is carried out in vitro, which gives no impression about the bioavailability of the flavonoids.

CONCLUSION

PFHF reduces the expression of PI3K, AKT, Bcl-2, and Bcl-xl through the PI3K/AKT/Bcl-2 signaling pathway to inhibit HEcESCs proliferation, migration, and invasion and promote their apoptosis.

Genome-wide screening and identification of potential kinases involved in endoplasmic reticulum stress responses

AIM

This study aims to identify endoplasmic reticulum stress response elements (ERSE) in the human genome to explore potentially regulated genes, including kinases and transcription factors, involved in the endoplasmic reticulum (ER) stress and its related diseases.

MATERIALS AND METHODS

Python-based whole genome screening of ERSE was performed using the Amazon Web Services elastic computing system. The Kinome database was used to filter out the kinases from the extracted list of ERSE-related genes. Additionally, network analysis and genome enrichment were achieved using NDEx, the Network and Data Exchange software, and web-based computational tools. To validate the gene expression, quantitative RT-PCR was performed for selected kinases from the list by exposing the HeLa cells to tunicamycin and brefeldin, ER stress inducers, for various time points.

KEY FINDINGS

The overall number of ERSE-associated genes follows a similar pattern in humans, mice, and rats, demonstrating the ERSE's conservation in mammals. A total of 2705 ERSE sequences were discovered in the human genome (GRCh38.p14), from which we identified 36 kinases encoding genes. Gene expression analysis has shown a significant change in the expression of selected genes under ER stress conditions in HeLa cells, supporting our finding.

SIGNIFICANCE

In this study, we have introduced a rapid method using Amazon cloud-based services for genome-wide screening of ERSE sequences from both positive and negative strands, which covers the entire genome reference sequences. Approximately 10 % of human protein-protein interactomes were found to be associated with ERSE-related genes. Our study also provides a rich resource of human ER stress-response-based protein networks and transcription factor interactions and a reference point for future research aiming at targeted therapeutics.

Dihydrotanshinone I inhibits hepatocellular carcinoma cells proliferation through DNA damage and EGFR pathway

Background

The incidence and mortality of hepatocellular carcinoma (HCC) are globally on the rise. Dihydrotanshinone I, a natural product isolated from Salvia miltiorrhiza Bunge, has attracted extensive attention in recent years for its anti-tumour proliferation efficiency.

Methods

Cell proliferations in hepatoma cells (Huh-7 and HepG2) were evaluated by MTT and colony formation assays. Immunofluorescence (IF) of 53BP1 and flow cytometry analysis were performed to detect DNA damage and cell apoptosis. Furthermore, network pharmacological analysis was applied to explore the potential therapeutic targets and pathway of dihydrotanshinone I.

Results

The results showed that dihydrotanshinone I effectively inhibited the proliferation of Huh-7 and HepG2 cells. Moreover, dihydrotanshinone I dose-dependently induced DNA-damage and apoptosis in vitro. Network pharmacological analysis and molecular simulation results indicated that EGFR might be a potential therapeutic target of dihydrotanshinone I in HCC. Collectively, our findings suggested that dihydrotanshinone I is a novel candidate therapeutic agent for HCC treatment.

Network Pharmacology Prediction and Experimental Verification for Anti-Ferroptosis of Edaravone After Experimental Intracerebral Hemorrhage

Neuronal ferroptosis plays an important role in secondary brain injuries after intracerebral hemorrhage (ICH). Edaravone (Eda) is a promising free radical scavenger that inhibits ferroptosis in neurological diseases. However, its protective effects and underlying mechanisms in ameliorating post-ICH ferroptosis remain unclear. We employed a network pharmacology approach to determine the core targets of Eda against ICH. Forty-two rats were subjected to successful striatal autologous whole blood injection (n=28) or sham operation (n=14). The 28 blood-injected rats were randomly assigned to either the Eda or vehicle group (n=14) for immediate administration and then for 3 consecutive days. Hemin-induced HT22 cells were used for in vitro studies. The effects of Eda in ICH on ferroptosis and the MEK/ERK pathway were investigated in vivo and in vitro. Network pharmacology-based analysis revealed that candidate targets of Eda-treated ICH might be related to ferroptosis; among which prostaglandin G/H synthase 2 (PTGS2) was a ferroptosis marker. In vivo experiments showed that Eda alleviated sensorimotor deficits and decreased PTGS2 expression (all p<0.05) after ICH. Eda rescued neuron pathological changes after ICH (increased NeuN⁺ cells and decreased FJC⁺ cells, all p<0.01). In vitro experiments showed that Eda reduced intracellular reactive oxygen species and reversed mitochondria damage. Eda repressed ferroptosis by decreasing malondialdehyde and iron deposition and by influencing ferroptosis-related protein expression (all p<0.05) in ICH rats and hemin-induced HT22 cells. Mechanically, Eda significantly suppressed phosphorylated-MEK and phosphorylated-ERK1/2 expression. These results indicate that Eda has protective effects on ICH injury through ferroptosis and MEK/ERK pathway suppression.

A crowdsourcing database for the copy-number variation of the Spanish population

BACKGROUND

Despite being a very common type of genetic variation, the distribution of copy-number variations (CNVs) in the population is still poorly understood. The knowledge of the genetic variability, especially at the level of the local population, is a critical factor for distinguishing pathogenic from non-pathogenic variation in the discovery of new disease variants.

RESULTS

Here, we present the SPAnish Copy Number Alterations Collaborative Server (SPACNACS), which currently contains copy number variation profiles obtained from more than 400 genomes and exomes of unrelated Spanish individuals. By means of a collaborative crowdsourcing effort whole genome and whole exome sequencing data, produced by local genomic projects and for other purposes, is continuously collected. Once checked both, the Spanish ancestry and the lack of kinship with other individuals in the SPACNACS, the CNVs are inferred for these sequences and they are used to populate the database. A web interface allows querying the database with different filters that include ICD10 upper categories. This allows discarding samples from the disease under study and obtaining pseudo-control CNV profiles from the local population. We also show here additional studies on the local impact of CNVs in some phenotypes and on pharmacogenomic variants. SPACNACS can be accessed at: http://csvs.clinbioinfosspa.es/spacnacs/ .

CONCLUSION

SPACNACS facilitates disease gene discovery by providing detailed information of the local variability of the population and exemplifies how to reuse genomic data produced for other purposes to build a local reference database.

Prospects for the application of traditional Chinese medicine network pharmacology in food science research

There has always been a particular difficulty with in-depth research on the mechanisms of food nutrition and bioactivity. The main function of food is to meet the nutritional needs of the human body, rather than to exert a therapeutic effect. Its relatively modest biological activity makes it difficult to study from the perspective of general pharmacological models. With the popularity of functional foods and the concept of dietary therapy, and the development of information and multi-omics technology in food research, research into these mechanisms is moving towards a more microscopic future. Network pharmacology has accumulated nearly 20 years of research experience in traditional Chinese medicine (TCM), and there has been no shortage of work from this perspective on the medicinal functions of food. Given the similarity between the concept of 'multi-component-multi-target' properties of food and TCM, we think that network pharmacology is applicable to the study of the complex mechanisms of food. Here we review the development of network pharmacology, summarize its application to 'medicine and food homology', and propose a methodology based on food characteristics for the first time, demonstrating its feasibility for food research. © 2023 Society of Chemical Industry.

Mechanism of anti-hyperuricemia of isobavachin based on network pharmacology and molecular docking

BACKGROUND

Hyperuricemia is a more popular metabolic disease caused by a disorder of purine metabolism. Our previous study firstly screened out a natural product Isobavachin as anti-hyperuricemia targeted hURAT1 from a Chinese medicine Haitongpi (Cortex Erythrinae). In view of Isobavachin's diverse pharmacological activities, similar to the Tranilast (as another hURAT1 inhibitor), our study focused on its potential targets and molecular mechanisms of Isobavachin anti-hyperuricemia based on network pharmacology and molecular docking.

METHODS

First of all, the putative target genes of compounds were screen out based on the public databases with different methods, such as SwissTargetPerdiction, PharmMapper and TargetNet,etc. Then the compound-pathways were obtained by the compounds' targets gene from David database for Gene Ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. The cross pathways of compound-pathways and the diseases pathways of hyperuricemia from Comparative Toxicogenomics Database were be considered as the compound-disease pathways. Next, based on the compound-disease pathways and the PPI network, the core targets were identified based on the retrieved disease-genes. Finally, the compound-target-pathway-disease network was constructed by Cytoscape and the mechanism of isobavachin anti-hyperuricemia was discussed based on the network analysis.

RESULTS

Our study demonstrated that there were five pathways involved in Isobavachin against hyperuricemia, including Drug metabolism-other enzymes, Metabolic pathways, Bile secretion, Renin-angiotensin system and Renin secretion. Among the proteins involved in these pathways, HPRT1, REN and ABCG2 were identified as the core targets associated with hyperuricemia, which regulated the five pathways mentioned above. It is quite different from that of Tranilast, which involved in the same pathways except Bile secretion instead of purine metabolism.

CONCLUSION

This study revealed Isobavachin could regulate the pathways including Drug metabolism-other enzymes, Metabolic pathways, Bile secretion, Renin-angiotensin system, Renin secretion by core targets HPRT1, REN and ABCG2, in the treatment of hyperuricemia effect. Among them, the Bile secretion regulated by ABCG2 probably would be a novel pathway. Our work provided a theoretical basis for the pharmacological study of Isobavachin in lowering uric acid and further basic research.

Cardiac Transcriptome Remodeling and Impaired Bioenergetics in Single-Ventricle Congenital Heart Disease

The mechanisms responsible for heart failure in single-ventricle congenital heart disease are unknown. Using explanted heart tissue, we showed that failing single-ventricle hearts have dysregulated metabolic pathways, impaired mitochondrial function, decreased activity of carnitine palmitoyltransferase activity, and altered functioning of the tricarboxylic acid cycle. Interestingly, nonfailing single-ventricle hearts demonstrated an intermediate metabolic phenotype suggesting that they are vulnerable to development of heart failure in the future. Mitochondrial targeted therapies and treatments aimed at normalizing energy generation could represent a novel approach to the treatment or prevention of heart failure in this vulnerable group of patients.

Role of Oligodendrocyte Lineage Cells in Multiple System Atrophy

Multiple system atrophy (MSA) is a debilitating movement disorder with unknown etiology. Patients present characteristic parkinsonism and/or cerebellar dysfunction in the clinical phase, resulting from progressive deterioration in the nigrostriatal and olivopontocerebellar regions. MSA patients have a prodromal phase subsequent to the insidious onset of neuropathology. Therefore, understanding the early pathological events is important in determining the pathogenesis, which will assist with developing disease-modifying therapy. Although the definite diagnosis of MSA relies on the positive post-mortem finding of oligodendroglial inclusions composed of α-synuclein, only recently has MSA been verified as an oligodendrogliopathy with secondary neuronal degeneration. We review up-to-date knowledge of human oligodendrocyte lineage cells and their association with α-synuclein, and discuss the postulated mechanisms of how oligodendrogliopathy develops, oligodendrocyte progenitor cells as the potential origins of the toxic seeds of α-synuclein, and the possible networks through which oligodendrogliopathy induces neuronal loss. Our insights will shed new light on the research directions for future MSA studies.

Proline and glucose metabolic reprogramming supports vascular endothelial and medial biomass in pulmonary arterial hypertension

In pulmonary arterial hypertension (PAH), inflammation promotes a fibroproliferative pulmonary vasculopathy. Reductionist studies emphasizing single biochemical reactions suggest a shift toward glycolytic metabolism in PAH; however, key questions remain regarding the metabolic profile of specific cell types within PAH vascular lesions in vivo. We used RNA-Seq to profile the transcriptome of pulmonary artery endothelial cells (PAECs) freshly isolated from an inflammatory vascular injury model of PAH ex vivo, and these data were integrated with information from human gene ontology pathways. Network medicine was then used to map all aa and glucose pathways to the consolidated human interactome, which includes data on 233,957 physical protein-protein interactions. Glucose and proline pathways were significantly close to the human PAH disease module, suggesting that these pathways are functionally relevant to PAH pathobiology. To test this observation in vivo, we used multi-isotope imaging mass spectrometry to map and quantify utilization of glucose and proline in the PAH pulmonary vasculature at subcellular resolution. Our findings suggest that elevated glucose and proline avidity underlie increased biomass in PAECs and the media of fibrosed PAH pulmonary arterioles. Overall, these data show that anabolic utilization of glucose and proline are fundamental to the vascular pathology of PAH.

Deafness gene screening based on a multilevel cascaded BPNN model

Sudden sensorineural hearing loss is a common and frequently occurring condition in otolaryngology. Existing studies have shown that sudden sensorineural hearing loss is closely associated with mutations in genes for inherited deafness. To identify these genes associated with deafness, researchers have mostly used biological experiments, which are accurate but time-consuming and laborious. In this paper, we proposed a computational method based on machine learning to predict deafness-associated genes. The model is based on several basic backpropagation neural networks (BPNNs), which were cascaded as multiple-level BPNN models. The cascaded BPNN model showed a stronger ability for screening deafness-associated genes than the conventional BPNN. A total of 211 of 214 deafness-associated genes from the deafness variant database (DVD v9.0) were used as positive data, and 2110 genes extracted from chromosomes were used as negative data to train our model. The test achieved a mean AUC higher than 0.98. Furthermore, to illustrate the predictive performance of the model for suspected deafness-associated genes, we analyzed the remaining 17,711 genes in the human genome and screened the 20 genes with the highest scores as highly suspected deafness-associated genes. Among these 20 predicted genes, three genes were mentioned as deafness-associated genes in the literature. The analysis showed that our approach has the potential to screen out highly suspected deafness-associated genes from a large number of genes, and our predictions could be valuable for future research and discovery of deafness-associated genes.

Identification of Potential Serum Protein Biomarkers in Thymoma with Myasthenia Gravis After Docetaxel Treatment

INTRODUCTION

Myasthenia gravis (MG) is a devastating acquired autoimmune disease that can seriously affect the patient's quality of life. It is also a common complication of thymoma. Previous studies have shown that docetaxel alleviates myasthenic symptoms in thymoma with MG (TMG). However, little is known about the protein expression profiles and biomarkers for efficacy after docetaxel treatment.

METHODS

We recruited 9 healthy controls and 30 patients with TMG for the serum proteomics study with data-independent acquisition (DIA) technology. We further recruited additional 30 patients for the key protein validation by enzyme-linked immunosorbent assay (ELISA).

RESULTS

We identified 43 proteins by trend analysis and analyzed the interaction between these proteins and MG pathogenic proteins from the DisGNET database and the correlation analysis with clinical data of patients with TMG. Among these, KRAS and SELP were screened out and validated. KRAS and SELP increased in patients with TMG and decreased significantly after docetaxel treatment.

CONCLUSIONS

Our study revealed that the serum proteins were differentially expressed after docetaxel treatment, suggesting their important role in patients with TMG, as well as the critical role of KRAS and SELP as biomarkers in evaluating the efficacy of docetaxel treatment.

Network Pharmacological Analysis of a New Herbal Combination Targeting Hyperlipidemia and Efficacy Validation In Vitro

The network pharmacology (NP) approach is a valuable novel methodology for understanding the complex pharmacological mechanisms of medicinal herbs. In addition, various in silico analysis techniques combined with the NP can improve the understanding of various issues used in natural product research. This study assessed the therapeutic effects of Arum ternata (AT), Poria cocos (PC), and Zingiber officinale (ZO) on hyperlipidemia after network pharmacologic analysis. A protein-protein interaction (PPI) network of forty-one key targets was analyzed to discover core functional clusters of the herbal compounds. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) term enrichment analysis identified significant categories of hypolipidemic mechanisms. The STITCH database indicated a high connection with several statin drugs, deduced by the similarity in targets. AT, PC, and ZO regulated the genes related to the energy metabolism and lipogenesis in HepG2 cells loaded with free fatty acids (FFAs). Furthermore, the mixture of three herbs had a combinational effect. The herbal combination exerted superior efficacy compared to a single herb, particularly in regulating acetyl-CoA carboxylase (ACC) and carnitine palmitoyltransferase 1 (CPT-1). In conclusion, the network pharmacologic approach was used to assess potential targets of the herbal combination for treatment. Experimental data from FFA-induced HepG2 cells suggested that the combination of AT, PC, and ZO might attenuate hyperlipidemia and its associated hepatic steatosis.

Skeletal Muscles of Sedentary and Physically Active Aged People Have Distinctive Genic Extrachromosomal Circular DNA Profiles

To bring new extrachromosomal circular DNA (eccDNA) enrichment technologies closer to the clinic, specifically for screening, early diagnosis, and monitoring of diseases or lifestyle conditions, it is paramount to identify the differential pattern of the genic eccDNA signal between two states. Current studies using short-read sequenced purified eccDNA data are based on absolute numbers of unique eccDNAs per sample or per gene, length distributions, or standard methods for RNA-seq differential analysis. Previous analyses of RNA-seq data found significant transcriptomics difference between sedentary and active life style skeletal muscle (SkM) in young people but very few in old. The first attempt using circulomics data from SkM and blood of aged lifelong sedentary and physically active males found no difference at eccDNA level. To improve the capability of finding differences between circulomics data groups, we designed a computational method to identify Differentially Produced per Gene Circles (DPpGCs) from short-read sequenced purified eccDNA data based on the circular junction, split-read signal, of the eccDNA, and implemented it into a software tool DifCir in Matlab. We employed DifCir to find to the distinctive features of the influence of the physical activity or inactivity in the aged SkM that would have remained undetected by transcriptomics methods. We mapped the data from tissue from SkM and blood from two groups of aged lifelong sedentary and physically active males using Circle_finder and subsequent merging and filtering, to find the number and length distribution of the unique eccDNA. Next, we used DifCir to find up-DPpGCs in the SkM of the sedentary and active groups. We assessed the functional enrichment of the DPpGCs using Disease Gene Network and Gene Set Enrichment Analysis. To find genes that produce eccDNA in a group without comparison with another group, we introduced a method to find Common PpGCs (CPpGCs) and used it to find CPpGCs in the SkM of the sedentary and active group. Finally, we found the eccDNA that carries whole genes. We discovered that the eccDNA in the SkM of the sedentary group is not statistically different from that of physically active aged men in terms of number and length distribution of eccDNA. In contrast, with DifCir we found distinctive gene-associated eccDNA fingerprints. We identified statistically significant up-DPpGCs in the two groups, with the top up-DPpGCs shed by the genes AGBL4, RNF213, DNAH7, MED13, and WWTR1 in the sedentary group, and ZBTB7C, TBCD, ITPR2, and DDX11-AS1 in the active group. The up-DPpGCs in both groups carry mostly gene fragments rather than whole genes. Though the subtle transcriptomics difference, we found RYR1 to be both transcriptionally up-regulated and up-DPpGCs gene in sedentary SkM. DifCir emphasizes the high sensitivity of the circulome compared to the transcriptome to detect the molecular fingerprints of exercise in aged SkM. It allows efficient identification of gene hotspots that excise more eccDNA in a health state or disease compared to a control condition.

Molecular mechanism of Ferula asafoetida for the treatment of asthma: Network pharmacology and molecular docking approach

Asthma is a significant health-care burden that has great impact on the quality of life of patients and their families. The limited amount of previously reported data and complicated pathophysiology of asthma make it a difficult to treat and significant economic burden on public healthcare systems. Ferula asafoetida is an herbaceous, monoecious, perennial plant of the Umbelliferae family. In Asia, F. asafoetida is used to treat a range of diseases and disorders, including asthma. Several in vitro studies demonstrated the therapeutic efficacy of F. asafoetida against asthma. Nevertheless, the precise molecular mechanism is yet to be discovered. In the framework of current study, network pharmacology approach was used to identify the bioactive compounds of F. asafoetida in order to better understand its molecular mechanism for the treatment of asthma. In present work, we explored a compound-target-pathway network and discovered that assafoetidin, cynaroside, farnesiferol-B, farnesiferol-C, galbanic-acid, and luteolin significantly influenced the development of asthma by targeting MAPK3, AKT1 and TNF genes. Later, docking analysis revealed that active constituents of F. asafoetida bind stably with three target proteins and function as asthma repressor by regulating the expression of MAPK3, AKT1 and TNF genes. Thus, integration of network pharmacology with molecular docking revealed that F. asafoetida prevent asthma by modulating asthma-related signaling pathways. This study lays the basis for establishing the efficacy of multi-component, multi-target compound formulae, as well as investigating new therapeutic targets for asthma.

MicroRNA and Messenger RNA Expression Profiles in Canine Mammary Gland Tumor

Canine mammary gland tumor (CMT) is the most frequently diagnosed neoplasm in intact female dogs. As prognosis depends on the malignancy of tumors and metastasis levels, early and accurate diagnosis are crucial for prolongation of life expectancy. The genetic similarity of dogs with humans in addition to environmental and physiological similarities make them ideal models for the study of cancer. In this study, we analyzed differentially expressed microRNAs followed by RNA-Seq to investigate the alterations in mRNA levels based on the malignancy (benign, malignant) and the biopsy locations (tumors, surrounding normal tissues). We identified multiple breast cancer-related genes regardless of malignancy. We found cfa-miR-503 to be the only miRNA that showed altered expression in response to malignancy in CMTs. Although further validation is needed, cfa-miR-503 could be used as a potential diagnostic biomarker as well as a potential RNA-based anti-tumor drug in malignant CMTs.

Network pharmacology- and molecular simulation-based exploration of therapeutic targets and mechanisms of heparin for the treatment of sepsis/COVID-19

Critically infected patients with COVID-19 (coronavirus disease 2019) are prone to develop sepsis-related coagulopathy as a result of a robust immune response. The mechanism underlying the relationship between sepsis and COVID-19 is largely unknown. LMWH (low molecular weight heparin) exhibits both anti-inflammatory and anti-coagulating properties that result in a better prognosis of severely ill patients with COVID-19 co-associated with sepsis-induced coagulopathy or with a higher D-dimer value. Heparin-associated molecular targets and their mechanism of action in sepsis/COVID-19 are not well understood. In this work, we characterize the pharmacological targets, biological functions and therapeutic actions of heparin in sepsis/COVID-19 from the perspective of network pharmacology. A total of 38 potential targets for heparin action against sepsis/COVID-19 and 8 core pharmacological targets were identified, including IL6, KNG1, CXCL8, ALB, VEGFA, F2, IL10 and TNF. Moreover, enrichment analysis showed that heparin could help in treating sepsis/COVID-19 through immunomodulation, inhibition of the inflammatory response, regulation of angiogenesis and antiviral activity. The pharmacological effects of heparin against these targets were further confirmed by molecular docking and simulation analysis, suggesting that heparin exerts effective binding capacity by targeting the essential residues in sepsis/COVID-19. Prospective clinical practice evaluations may consider the use of these key prognostic indicators for the treatment of sepsis/COVID-19.Communicated by Ramaswamy H. Sarma.

Defects and asymmetries in the visual pathway of non-human primates with natural strabismus and amblyopia

Strabismus and amblyopia are common ophthalmologic developmental diseases caused by abnormal visual experiences. However, the underlying pathogenesis and visual defects are still not fully understood. Most studies have used experimental interference to establish disease-associated animal models, while ignoring the natural pathophysiological mechanisms. This study was designed to investigate whether natural strabismus and amblyopia are associated with abnormal neurological defects. We screened one natural strabismic monkey ( Macaca fascicularis) and one natural amblyopic monkey from hundreds of monkeys, and retrospectively analyzed one human strabismus case. Neuroimaging, behavioral, neurophysiological, neurostructural, and genovariation features were systematically evaluated using magnetic resonance imaging (MRI), behavioral tasks, flash visual evoked potentials (FVEP), electroretinogram (ERG), optical coherence tomography (OCT), and whole-genome sequencing (WGS), respectively. Results showed that the strabismic patient and natural strabismic and amblyopic monkeys exhibited similar abnormal asymmetries in brain structure, i.e., ipsilateral impaired right hemisphere. Visual behavior, visual function, retinal structure, and fundus of the monkeys were impaired. Aberrant asymmetry in binocular visual function and structure between the strabismic and amblyopic monkeys was closely related, with greater impairment of the left visual pathway. Several similar known mutant genes for strabismus and amblyopia were also identified. In conclusion, natural strabismus and amblyopia are accompanied by abnormal asymmetries of the visual system, especially visual neurophysiological and neurostructural defects. Our results suggest that future therapeutic and mechanistic studies should consider defects and asymmetries throughout the entire visual system.

Prioritization of New Candidate Genes for Rare Genetic Diseases by a Disease-Aware Evaluation of Heterogeneous Molecular Networks

Screening for pathogenic variants in the diagnosis of rare genetic diseases can now be performed on all genes thanks to the application of whole exome and genome sequencing (WES, WGS). Yet the repertoire of gene-disease associations is not complete. Several computer-based algorithms and databases integrate distinct gene-gene functional networks to accelerate the discovery of gene-disease associations. We hypothesize that the ability of every type of information to extract relevant insights is disease-dependent. We compiled 33 functional networks classified into 13 knowledge categories (KCs) and observed large variability in their ability to recover genes associated with 91 genetic diseases, as measured using efficiency and exclusivity. We developed GLOWgenes, a network-based algorithm that applies random walk with restart to evaluate KCs' ability to recover genes from a given list associated with a phenotype and modulates the prediction of new candidates accordingly. Comparison with other integration strategies and tools shows that our disease-aware approach can boost the discovery of new gene-disease associations, especially for the less obvious ones. KC contribution also varies if obtained using recently discovered genes. Applied to 15 unsolved WES, GLOWgenes proposed three new genes to be involved in the phenotypes of patients with syndromic inherited retinal dystrophies.

The Potential of Ameliorating COVID-19 and Sequelae From Andrographis paniculata via Bioinformatics

The current coronavirus disease 2019 (COVID-19) outbreak is alarmingly escalating and raises challenges in finding efficient compounds for treatment. Repurposing phytochemicals in herbs is an ideal and economical approach for screening potential herbal components against COVID-19. Andrographis paniculata, also known as Chuan Xin Lian, has traditionally been used as an anti-inflammatory and antibacterial herb for centuries and has recently been classified as a promising herbal remedy for adjuvant therapy in treating respiratory diseases. This study aimed to screen Chuan Xin Lian's bioactive components and elicit the potential pharmacological mechanisms and plausible pathways for treating COVID-19 using network pharmacology combined with molecular docking. The results found terpenoid (andrographolide) and flavonoid (luteolin, quercetin, kaempferol, and wogonin) derivatives had remarkable potential against COVID-19 and sequelae owing to their high degrees in the component-target-pathway network and strong binding capacities in docking scores. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the PI3K-AKT signaling pathway might be the most vital molecular pathway in the pathophysiology of COVID-19 and long-term sequelae whereby therapeutic strategies can intervene.

Functional Analysis of Monkeypox and Interrelationship between Monkeypox and COVID-19 by Bioinformatic Analysis

The outbreak of monkeypox may be considered a novel and urgent threat after the coronavirus disease (COVID-19). No wide-ranging studies have been conducted on this disease since it was first reported. We systematically assessed the functional role of gene expression in cells infected with the monkeypox virus using transcriptome profiling and compared the functional relation with that of COVID-19. Based on the Gene Expression Omnibus database, we obtained 212 differentially expressed genes (DEGs) of GSE36854 and GSE21001 of monkeypox datasets. Enrichment analyses, including KEGG and gene ontology (GO) analyses, were performed to identify the common function of 212 DEGs of GSE36854 and GSE21001. CytoHubba and Molecular Complex Detection were performed to determine the core genes after a protein-protein interaction (PPI). Metascape/COVID-19 was used to compare DEGs of monkeypox and COVID-19. GO analysis of 212 DEGs of GSE36854 and GSE21001 for monkeypox infection showed cellular response to cytokine stimulus, cell activation, and cell differentiation regulation. KEGG analysis of 212 DEGs of GSE36854 and GSE21001 for monkeypox infection showed involvement of monkeypox in COVID-19, cytokine-cytokine receptor interaction, inflammatory bowel disease, atherosclerosis, TNF signaling, and T cell receptor signaling. By comparing our data with published transcriptome of severe acute respiratory syndrome coronavirus 2 infections in other cell lines, the common function of monkeypox and COVID-19 includes cytokine signaling in the immune system, TNF signaling, and MAPK cascade regulation. Thus, our data suggest that the molecular connections identified between COVID-19 and monkeypox elucidate the causes of monkeypox.

Topology Driven Analysis of Protein - Protein Interactome for Prioritizing Key Comorbid Genes via Sub Graph Based Average Path Length Centrality

In gene-based therapies, local perturbations associated with one disease can lead to comorbidity as it influences the pathways involved with the other diseases. The key genes orchestrating the common biological mechanisms are need to be prioritized for addressing the challenges introduced by the cross talks between disease modules. Here, a local centrality measure named Sub graph based Average Path length Double Specific Betweenness centrality (SAPDSB) for prioritizing the comorbid genes via Protein-Protein Interaction Network (PPIN) analysis is presented. This approach can be used to identify putative biomarkers which can be repurposed for the management of comorbidity. Proposed network based topological measure is designed specifically to prioritize the comorbid genes that are most likely to be present in the overlap of disease modules. In order to attain this, the estimated average path length of the seed network which holds Protein-Protein Interactions (PPIs) of the disease genes is exploited. Prioritized comorbid genes are further pruned using centrality-based cut-off values and specificity scores. The biological significance of the resultant genes is corroborated with connectivity analysis using leave-one-out method, pathway enrichment analysis and a comparative analysis using single disease-based gene prioritization tools. For performance analysis, proposed approach is tested using case studies involving common diseases and rare neurodegenerative diseases. For case study1, diseases such as Diabetes, Carcinoma and Alzheimer's are considered in a pairwise manner while for case study2, Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) are considered. As outcome, prioritized candidate genes and biological pathways associated with respective disease pairs have been found. The associations from top 10 candidate genes in different disease pair combinations of Diabetes-Carcinoma-Alzheimer's revealed common genes like CREBBP, TP53, HSP90AA1 and the common pathway namely p53 pathway feedback loops 2. Out of the pathways retrieved from the top 10 genes associated with ALS-SMA disease pair, 60% of unique pathways are found to be leading to both diseases and its comorbidities. Comparative analysis of the proposed method with recent similar approach also reported a clear degree of benefits in performance.

Niaoduqing alleviates podocyte injury in high glucose model via regulating multiple targets and AGE/RAGE pathway: Network pharmacology and experimental validation

Purpose: The aim of present study was to explore the pharmacological mechanisms of Niaoduqing granules on the treatment of podocyte injury in diabetic nephropathy (DN) via network pharmacology and experimental validation. Methods: Active ingredients and related targets of Niaoduqing, as well as related genes of podocyte injury, proteinuria and DN, were obtained from public databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network analysis were performed to investigate the potential mechanisms. High glucose (HG) -induced MPC5 cell injury model was treated with the major core active ingredients of Niaoduqing and used to validate the predicted targets and signaling pathways. Results: Totally, 16 potential therapeutic targets were identified by intersecting the targets of Niaoduqing and disease, in which 7 of them were considered as the core targets via PPI network analysis. KEGG enrichment analysis showed that AGE-RAGE signaling pathway was identified as the most crucial signaling pathway. The results of in vitro experiments revealed that the treatment of Niaoduqing active ingredients significantly protected MPC5 cells from HG-induced apoptosis. Moreover, Niaoduqing could significantly attenuate the HG-induced activation of AGE-RAGE signaling pathway, whereas inhibited the over-expression of VEGF-A, ICAM-1, PTGS-2 and ACE in HG-induced MPC5 cells. Conclusion: Niaoduqing might protect against podocyte injury in DN through regulating the activity of AGE/RAGE pathway and expression of multiple genes. Further clinical and animal experimental studies are necessary to confirm present findings.

Network Pharmacology and Molecular Docking Study on the Multi-Target Mechanisms of Aloe vera for Non-Alcoholic Steatohepatitis Treatment

Non-alcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease with limited treatment options. The widely distributed plant Aloe vera has shown protective effects against NASH in animals, yet the precise mechanism remains unknown. In this study, we investigated the potential mechanisms underlying the anti-NASH effects of Aloe vera using a network pharmacology and molecular docking approach. By searching online databases and analyzing the Gene Expression Omnibus dataset, we obtained 260 Aloe vera-NASH common targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses showed that the common targets were strongly associated with the key pathological processes implicated in NASH, including lipid and glucose metabolism, inflammation, apoptosis, oxidative stress, and liver fibrosis. Four core proteins, AKT serine/threonine kinase 1 (AKT1), tumor necrosis factor alpha (TNFα), transcription factor c-Jun, and tumor suppressor protein p53, were identified from compound-target-pathway and protein-protein interaction networks. Molecular docking analysis verified that the active ingredients of Aloe vera were able to interact with the core proteins, especially AKT1 and TNFα. The results demonstrate the multi-compound, multi-target, and multi-pathway mechanisms of Aloe vera against NASH. Our study has shown the scientific basis for further experiments in terms of the mechanism to develop Aloe vera-based natural products as complementary treatments for NASH. Furthermore, it identifies novel drug candidates based on the structures of Aloe vera's active compounds.

Systematic comparison of differential expression networks in MTB mono-, HIV mono- and MTB/HIV co-infections for drug repurposing

The synergy between human immunodeficiency virus (HIV) and Mycobacterium tuberculosis (MTB) could accelerate the deterioration of immunological functions. Previous studies have explored the pathogenic mechanisms of HIV mono-infection (HMI), MTB mono-infection (MMI) and MTB/HIV co-infection (MHCI), but their similarities and specificities remain to be profoundly investigated. We thus designed a computational framework named IDEN to identify gene pairs related to these states, which were then compared from different perspectives. MMI-related genes showed the highest enrichment level on a greater number of chromosomes. Genes shared by more states tended to be more evolutionarily conserved, posttranslationally modified and topologically important. At the expression level, HMI-specific gene pairs yielded higher correlations, while the overlapping pairs involved in MHCI had significantly lower correlations. The correlation changes of common gene pairs showed that MHCI shared more similarities with MMI. Moreover, MMI- and MHCI-related genes were enriched in more identical pathways and biological processes, further illustrating that MTB may play a dominant role in co-infection. Hub genes specific to each state could promote pathogen infections, while those shared by two states could enhance immune responses. Finally, we improved the network proximity measure for drug repurposing by considering the importance of gene pairs, and approximately ten drug candidates were identified for each disease state.

Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer's disease

Background

A factor leading to Alzheimer's Disease (AD), portrayed by peripheral insulin resistance, is Type 2 diabetes mellitus (T2D). The likelihood of T2D cases would be at boosted danger in alternating AD cases has severe social consequences. Several genes have been detected via gene expression profiling or different techniques; despite the consideration of the utility of numerous of these genes stays insufficient.

Methods

This project is designed to uncover the mutual genomics motifs between AD and T2D via non-negative matrix factorization (NMF) of differentially expressed genes (DEGs) of T2D Mellitus of human cortical neurons of the neurovascular unit gene expression data. A rank factorization value is calculated by employing the combination of the NMF model with the unit invariant knee (UIK) point method. The metagenes are further determined by remarking the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) enrichment tools. In this study, the most highly expressed genes of metagenes are subjected to protein-protein interaction (PPI) network study to discover the most significant biomarkers of T2D Mellitus in the ageing brain.

Results

We screened the most important shared genes (CDKN1A, COL22A1, EIF4A, GFAP, SLC1A1, and VIM) and essential human molecular pathways that motivate these diseases. The study aimed to validate the most significant hub genes using network-based methods which detected the corresponding relationship between AD and T2D.

Conclusions

Using in silico tools, the computational pipeline has broadly examined transformed pathways and discovered promising biomarkers and drug targets. We validated the most significant hub genes using network-based methods which detected the corresponding relationship between AD and T2D. These consequences on brain cells hypothetically reserve to diabetic Alzheimer's so-called type 3 diabetes (T3D) and may offer promising methodologies for curative intrusion.

In Vitro Anti-Influenza Virus Activity of Non-Polar Primula veris subsp. veris Extract

Medicinal plants have long been recognized as a tremendous source of candidate compounds for the development of pharmaceuticals, including anti-viral agents. Herein, we report the identification of anti-influenza virus activity in non-polar Primula veris L. subsp. veris extracts. We show that P. veris subsp. veris flower extracts, obtained using supercritical fluid or ultrasound-based extraction, possess virucidal/virus inactivation properties and confer prophylactic and therapeutic effects against influenza virus-induced cytolysis in vitro. By GC-MS and UPLC-HRMS analysis of non-polar P. veris subsp. veris extracts we identified terpenes, flavones, tocopherols, and other classes of phytochemicals with known or putative anti-influenza properties. In silico prediction of cellular functions and molecular pathways affected by these phytochemicals suggests putative effects on signal transduction, inflammasome, and cell death pathways that are relevant to influenza virus pathogenesis. Combining P. veris subsp. veris with extracts of medicinal plants with proven anti-influenza activity such as Echinacea purpurea (L.) Moench and Cistus creticus L. subsp. creticus achieves an impressive protective effect against infection by influenza virus H1N1 in vitro and reduced progeny virus production by infected cells. Collectively, these findings uncover a previously uncharted biological property of non-polar P. veris flower extracts that warrants further studies to assess clinical efficacy.

Pacific Biosciences Fusion and Long Isoform Pipeline for Cancer Transcriptome-Based Resolution of Isoform Complexity

Genomic profiling using short-read sequencing has utility in detecting disease-associated variation in both DNA and RNA. However, given the frequent occurrence of structural variation in cancer, molecular profiling using long-read sequencing improves the resolution of such events. For example, the Pacific Biosciences long-read RNA-sequencing (Iso-Seq) transcriptome protocol provides full-length isoform characterization, discernment of allelic phasing, and isoform discovery, and identifies expressed fusion partners. The Pacific Biosciences Fusion and Long Isoform Pipeline (PB_FLIP) incorporates a suite of RNA-sequencing software analysis tools and scripts to identify expressed fusion partners and isoforms. In addition, sequencing of a commercial reference (Spike-In RNA Variants) with known isoform complexity was performed and demonstrated high recall of the Iso-Seq and PB_FLIP workflow to benchmark our protocol and analysis performance. This study describes the utility of Iso-Seq and PB_FLIP analysis in improving deconvolution of complex structural variants and isoform detection within an institutional pediatric and adolescent/young adult translational cancer research cohort. The exemplar case studies demonstrate that Iso-Seq and PB_FLIP discover novel expressed fusion partners, resolve complex intragenic alterations, and discriminate between allele-specific expression profiles.

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.

Vitamin D Deficiency and COVID-19: A Biological Database Study on Pathways and Gene-Disease Associations

Vitamin D (VD) is a fat-soluble vitamin, and pivotal for maintaining health. Several genetic markers have been related to a deficient VD status; these markers could confer an increased risk to develop osteoporosis and other chronic diseases. A VD deficiency could also be a determinant of a severe COVID-19 disease. This study aimed to interrogate genetic/biological databases on the biological implications of a VD deficiency and its association with diseases, to further explore its link with COVID-19. The genetic variants of both a VD deficiency and COVID-19 were identified in the genome-wide association studies (GWAS) catalog and other sources. We conducted enrichment analyses (considering corrected p-values < 0.05 as statistically significant) of the pathways, and gene-disease associations using tools, such as FUMA, REVIGO, DAVID and DisGeNET, and databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). There were 26 and 46 genes associated with a VD deficiency and COVID-19, respectively. However, there were no genes shared between the two. Genes related to a VD deficiency were involved in the metabolism of carbohydrates, retinol, drugs and xenobiotics, and were associated with the metabolic syndrome and related factors (obesity, hypertension and diabetes mellitus), as well as with neoplasms. There were few enriched pathways and disease connections for the COVID-19-related genes, among which some of the aforementioned comorbidities were also present. In conclusion, genetic factors that influence the VD levels in the body are most prominently associated with nutritional and metabolic diseases. A VD deficiency in high-risk populations could be therefore relevant in a severe COVID-19, underlining the need to examine whether a VD supplementation could reduce the severity of this disease.

Gene set predictor for post-treatment Lyme disease

Lyme disease (LD) is tick-borne disease whose post-treatment sequelae are not well understood. For this study, we enrolled 152 individuals with symptoms of post-treatment LD (PTLD) to profile their peripheral blood mononuclear cells (PBMCs) with RNA sequencing (RNA-seq). Combined with RNA-seq data from 72 individuals with acute LD and 44 uninfected controls, we investigated differences in differential gene expression. We observe that most individuals with PTLD have an inflammatory signature that is distinguished from the acute LD group. By distilling gene sets from this study with gene sets from other sources, we identify a subset of genes that are highly expressed in the cohorts but are not already established as biomarkers for inflammatory response or other viral or bacterial infections. We further reduce this gene set by feature importance to establish an mRNA biomarker set capable of distinguishing healthy individuals from those with acute LD or PTLD as a candidate for translation into an LD diagnostic.

Identification of key candidate genes and biological pathways in neuropathic pain

BACKGROUND

Neuropathic pain is a common chronic pain, characterized by spontaneous pain and mechanical allodynia. The incidence of neuropathic pain is on the rise due to infections, higher rates of diabetes and stroke, and increased use of chemotherapy drugs in cancer patients. At present, due to its pathophysiological process and molecular mechanism remaining unclear, there is a lack of effective treatment and prevention methods in clinical practice. Now, we use bioinformatics technology to integrate and filter hub genes that may be related to the pathogenesis of neuropathic pain, and explore their possible molecular mechanism by functional annotation and pathway enrichment analysis.

METHODS

The expression profiles of GSE24982, GSE2884, GSE2636 and GSE30691 were downloaded from the Gene Expression Omnibus（GEO）database, and these datasets include 93 neuropathic pain Rattus norvegicus and 59 shame controls. After the four datasets were all standardized by quantiles, the differentially expressed genes (DEGs) between NPP Rattus norvegicus and the shame controls were finally identified by the robust rank aggregation (RRA) analysis method. In order to reveal the possible underlying biological function of DEGs, the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of DEGs were performed. In addition, a Protein-protein Interaction (PPI) network was also established. At the end of our study, a high throughput sequencing dataset GSE117526 was used to corroborate our calculation results.

RESULTS

Through RRA analysis of the above four datasets GSE24982, GSE2884, GSE2636, and GSE30691, we finally obtained 231 DEGs, including 183 up-regulated genes and 47 down-regulated genes. Arranging 231 DEGs in descending order according to |log2 fold change (FC)|, we found that the top 20 key genes include 14 up-regulated genes and 6 down-regulated genes. The most down-regulated hub gene abnormal expressed in NPP was Egf17 (P-value = 0.008), Camk2n2 (P-value = 0.002), and Lep (P-value = 0.02), and the most up-regulated hub gene abnormal expressed in NPP was Nefm (P-value = 1.08E-06), Prx (P-value = 2.68E-07), and Stip1 (P-value = 4.40E-07). In GO functional annotation analysis results, regulation of ion transmembrane transport (GO:0034765; P-value = 1.45E-09) was the most remarkable enriched for biological process, synaptic membrane (GO:0097060; P-value = 2.95E-08) was the most significantly enriched for cellular component, channel activity (GO:0015267; P-value = 2.44E-06) was the most prominent enriched for molecular function. In KEGG pathway enrichment analysis results, the top three notable enrichment pathways were Neuroactive ligand-receptor interaction (rno04080; P-value = 3.46E-08), Calcium signaling pathway (rno04020; P-value = 5.37E-05), and Osteoclast differentiation (rno04380; P-value = 0.000459927). Cav1 and Lep appeared in the top 20 genes in both RRA analysis and PPI analysis, while Nefm appeared in RRA analysis and datasets GSE117526 validation analysis, so we finally identified these three genes as hub genes.

CONCLUSIONS

Our research identified the hub genes and signal pathways of neuropathic pain, enriched the pathophysiological mechanism of neuropathic pain to some extent, and provided a possible basis for the targeted therapy of neuropathic pain.

Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV

BACKGROUND

The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body's ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients.

METHODS

COVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the "Deseq2" package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the "limma" package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification.

RESULTS

In this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients.

CONCLUSION

In this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.