Gene Interaction Networks Research Articles

Characterization of genomic variants associated with congenital heart disease in patients from southwestern Colombian

Congenital heart diseases correspond to errors during embryogenesis, generating structural and functional malformations. Congenital heart diseases are the most prevalent congenital malformations and are responsible for the highest infant morbidity and mortality. Among these cases, 8 % can be attributed to variants in genes associated with cardiac development. To establish the population frequency of genomic variants that cause congenital heart disease, a review of the scope of prevalent genes was carried out, complete exome sequencing results of 320 patients without suspicion of congenital heart disease were used, the exome sequencing is a technique based on DNA extraction using a Qiagen kit, with massive sequencing of Nextera TM libraries using an Illumina platform with 100X coverage, alignment with reference genome GRCh38/hg19, and analysis with the CRAVAT program; clinical characterization, significance classification, and gene interaction networks were performed. The scope analysis allowed to determine that the genes NKX2-5, TBX20, GATA4, NOTCH1, PTPN11 are the most prevalent, the variants with the highest allelic frequency were c.63A > G (0.2844), c.39T > C (0.3406), c.1132A > G (0.0406), c.1669+9T > C (0.3531) and c.854-30T > C (0.0875) respectively; 4 variants were reclassified by in silico tools, in the NKX2-5 gene c.335-311_335-303del from benign to probably pathogenic, in the NOTCH1 gene c.2354-24C > T from benign to pathogenic, and in the PTPN11 gene c.2354-24C > T and c.854-30T > C from benign to pathogenic. 17 % of intronic variants and 4.8 % of missense variants were identified. This work contributes to knowledge about the frequency with which genomic variants associated with congenital heart disease are present in the population, generating a tool for early diagnosis, early treatment, thus reducing morbidity and mortality, with a view to future universal molecular neonatal screening of congenital heart disease.

Bioinformatic analysis identified common pathogenetic processes between epilepsy and COVID-19.

With the ongoing progression of SARS-CoV-2-induced COVID-19, the post-acute COVID-19 syndrome (long COVID) has garnered increasing attention as a novel multisystem disorder. Long COVID-19 has been shown to impact the nervous system, leading to various neurological manifestations, including epilepsy and seizures. Current studies have reported a significant increase in the prevalence and mortality rate of epilepsy in COVID-19 patients. Additionally, COVID-19 exacerbates seizures in patients with epilepsy. However, the mechanisms underlying the impact of COVID-19 on epilepsy remain elusive. This research focused on further identifying and elucidating the molecular mechanisms and biological processes underlying the induction of epilepsy by COVID-19 through bioinformatic methods. We retrieved four gene expression datasets related to COVID-19 and epilepsy patients from the GEO and ArrayExpress databases. By crossing the major modules of weighted gene co-expression network analysis (WGCNA), the commonly expressed genes of epilepsy and COVID-19 were identified. By establishing the protein-protein interaction (PPI) network of the common genes, 20 hub genes were recognized through CytoHubba. Furthermore, functional enrichment and immune cell infiltration analyses were conducted to explore the potential mechanisms of COVID-19-related epilepsy. We identified a total of 373 common genes between the two diseases. The functional enrichment analysis revealed that the common genes were mainly involved in biological processes related to the immune response. Further analysis of the Hub genes revealed the important role of abnormal lipid metabolism in the crosstalk between COVID-19 and epilepsy. LASSO regression identified CD38 and PRKCA as the potential shared diagnostic candidates, which also exhibited excellent diagnostic value in the validation dataset. The immune infiltration analysis showed that activated dendritic cells (DCs) were positively correlated with the phenotypes of both diseases. This research revealed the potential mechanisms of COVID-19-related epilepsy, providing novel insights for the prevention, diagnosis, and clinical management strategies of COVID-19-related epilepsy.

Diagnostic model based on key autophagy-related genes in intervertebral disc degeneration

BackgroundCurrent research on autophagy is mainly focused on intervertebral disc tissues and cells, while there is few on human peripheral blood sample. therefore, this study constructed a diagnostic model to identify autophagy-related markers of intervertebral disc degeneration (IVDD).MethodsGSE150408 and GSE124272 datasets were acquired from the Gene Expression Omnibus database, and differential expression analysis was performed. The IVDD-autophagy genes were obtained using Weighted Gene Coexpression Network Analysis, and a diagnostic model was constructed and validated, followed by Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA). Meanwhile, miRNA–gene and transcription factor–gene interaction networks were constructed. In addition, drug-gene interactions and target genes of methylprednisolone and glucosamine were analyzed.ResultsA total of 1,776 differentially expressed genes were identified between IVDD and control samples, and the composition of the four immune cell types was significantly different between the IVDD and control samples. The Meturquoise and Mebrown modules were significantly related to immune cells, with significant differences between the control and IVDD samples. A diagnostic model was constructed using five key IVDD-autophagy genes. The area under the curve values of the model in the training and validation datasets were 0.907 and 0.984, respectively. The enrichment scores of the two pathways were significantly different between the IVDD and healthy groups. Eight pathways in the IVDD and healthy groups had significant differences. A total of 16 miRNAs and 3 transcription factors were predicted to be of great value. In total, 84 significantly related drugs were screened for five key IVDD-autophagy genes in the diagnostic model, and three common autophagy-related target genes of methylprednisolone and glucosamine were predicted.ConclusionThis study constructs a reliable autophagy-related diagnostic model that is strongly related to the immune microenvironment of IVD. Autophagy-related genes, including PHF23, RAB24, STAT3, TOMM5, and DNAJB9, may participate in IVDD pathogenesis. In addition, methylprednisolone and glucosamine may exert therapeutic effects on IVDD by targeting CTSD, VEGFA, and BAX genes through apoptosis, as well as the sphingolipid and AGE-RAGE signaling pathways in diabetic complications.

Gene co‐expression network analyses in Mild cognitive impairment

AbstractBackgroundMultiomic data analysis has been extensively developed in recent years. Particularly, microarray data can be used as biomarkers of disease of progression specially in pathologies as cancer, autoimmunity and others. (Dahinden et al., 2010; Soleimani Zakeri et al., 2020). This modality of data analysis can also be employed in other multifactorial diseases as Alzheimer’s disease including the progression from normal cognition to mild cognitive impairment and finally to dementia. These genes work in intricate relationship with each other. The establishment of computational gene interaction networks shows the intricacy of the biological systems but increases the complexity of the analysis, for this reason, the removal of redundant edges leaves a network backbone revealing important driver nodes that are essential for the observed interactions, reducing the number of genes of interest and narrowing potential diagnostic or therapeutic targets. (Gates et al., 2021)MethodsWe used data from the ImaGENE study and the Weighted gene co‐expression network analysis (WGCNA) pipeline to analyze gene‐gene interactions of mRNA transcripts obtaining hierarchical clustering of genes and eigengenes in a sample of 160 individuals (Table 1). The backbones were obtained using the shortest path computation. (Gates et al., 2021; Simas et al., 2021). Gene ontology was used to classify processes associated with these genes.ResultsWe identified 42 hubs of differentially expressed genes and we analyzed the top 5 groups with the strongest association with amnestic mild cognitive impairment (MCI) phenotype (pfdr<0.0001). This cluster consisting of 46 genes was further classified according to function and interactions with other nodes in the backbone identifying processes like calcium signaling, nucleotide excision repair, fatty acid metabolism among others.ConclusionThere is an identifiable difference in gene expression in individuals with MCI compared to normal controls. The analysis of the backbone of the gene hubs allowed to reduce the number of genes of interest. This approach can help to elucidate important biological interactions, potential therapeutic targets and could eventually be used as risk factors markers for the development of MCI and AD.

Dysregulated bioenergetics pathways in the Alzheimer’s mouse brain

AbstractBackgroundAlzheimer’s disease (AD) is the most common cause of dementia in the aging population. The pathological characteristics include extracellular senile plaques and intracellular neurofibrillary tangles. In addition, mitochondrial dysfunction, oxidative stress, and neuroinflammation contribute to AD pathogenesis.MethodRNA‐seq analysis was done to compare the transcriptome in the brain of 5XFAD mice to the WT brain.ResultThere was significant changes in the expression of genes related to pathways such as, oxidation‐reduction, oxidative phosphorylation, innate immune response, ribosomal protein synthesis, and ubiquitin proteosome system. The most striking feature was the alteration of the oxidation‐reduction process with changes in the expression of a large number of mitochondrial genes. We also observed an upregulation of several immune response genes. Gene interaction network of oxidation‐reduction related genes further confirmed a tight “Gordian knot” of mitochondrial genes. Furthermore, gene interaction analysis of all the 1288 genes showed at least three distinct interaction clusters, with the predominant one relating to cellular energeticsConclusionWe identified 1288 genes distinctly different in the 5XFAD brain compared to the WT brain and found cellular energetics to be the most distinct gene cluster in the AD mouse brain compared to the WT.

Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival

The plasma cell cancer multiple myeloma (MM) varies significantly in genomic characteristics, response to therapy, and long-term prognosis. To investigate global interactions in MM, we combined a known protein interaction network with a large clinically annotated MM dataset. We hypothesized that an unbiased network analysis method based on large-scale similarities in gene expression, copy number aberration, and protein interactions may provide novel biological insights. Applying a novel measure of network robustness, Ollivier-Ricci Curvature, we examined patterns in the RNA-Seq gene expression and CNA data and how they relate to clinical outcomes. Hierarchical clustering using ORC differentiated high-risk subtypes with low progression free survival. Differential gene expression analysis defined 118 genes with significantly aberrant expression. These genes, while not previously associated with MM, were associated with DNA repair, apoptosis, and the immune system. Univariate analysis identified 8/118 to be prognostic genes; all associated with the immune system. A network topology analysis identified both hub and bridge genes which connect known genes of biological significance of MM. Taken together, gene interaction network analysis in MM uses a novel method of global assessment to demonstrate complex immune dysregulation associated with shorter survival.

Suppressing IKZF1 Deletion-Mediated Activation of Ras Oncogenic Signaling By the Combination of Azacitidine and Flumatinib in High-Risk B-ALL

Background IKZF1 deletion is frequently observed in Ph + acute lymphoblastic leukemia (Ph + ALL), which results in poor prognosis and affects clinical efficacy, and possible resistance to tyrosine kinase inhibitors (TKIs). Ikaros transcriptionally regulates the gene expression of multiple oncogenic pathways including Ras/MAPK signaling. However, it is undefined about the role of IKZF1 genetic defects in the activation of Ras/MAPK signaling and their relations with the efficacy of therapeutic drugs in ALL. This study is to investigate the sensitivity of IKZF1-deletion-mediated activation of RAS signaling to the new combined therapy of TKI Flumatinib (FLU) and the Azacitidine (AZA) in B-ALL cells. Methods The patients' samples were obtained from Zhongda Hospital Southeast University with the approval of the institute's Ethics Committee. The gene expression was tested by qPCR or Western blot. CCK8 Cell proliferation, PI staining cell cycle analysis, and apoptosis assay (Annexin V-FITC/ PI staining) were performed in SUP-B15, the Ph + ALL cells with IKZF1 deletion treated with DMSO control, FLU only, AZA only, and FLU+AZA. The overlapped differential expression genes (DEGs）with the pathways (GO term, KEGG) analysis and gene interaction networks were achieved in the potential oncogenic genes in ALL through databases (OMIM, TTD, and GeneCards), and drug targets over that of SwissTargetPrediction, SuperPred, and SEA. Results AZA or FLU treatment induced the cell proliferation arrest of SUP-B15 cells in a dose-/time-dependent manner (Fig. 1A). The combination of IC50 AZA with various doses of FLU significantly enhanced the effect on cell proliferation arrest versus single-drug controls, and CalcuSyn analysis demonstrated their synergistic effect in the cells (Fig. 1B). Also, the combination significantly increased the G0/G1 phase arrest (Fig. 1C, D), and % of apoptotic cells (Fig. 1E, F) versus the single-drug controls. Consistently, the combination up-regulated the mRNA and protein levels of P53, BAX, and P21, but decreased the MDM2, BCL2, and CyclinE (Fig. 1G, H). A total of 118 overlapped DEGs were identified between the potential oncogenes in ALL and FLU-/AZA-associated genes, and they predominantly enriched in Ras/MAPK oncogenic pathways, in which PTPN11 is at the top of the pathways. Indeed, the combination significantly decreased the mRNA and protein levels of PTPN11 (Fig. 2A, B). PTPN11 is significantly higher expressed in 20 newly-diagnosed Ph +ALL patients versus 20 healthy bone marrow control (Fig. 2C), and it was validated in the GSE13159 dataset (Fig. 2D). PTPN11 high expression is significantly associated with lower overall survival and relapse-free survival compared to that of low expression in our cohorts (Fig. 2E). These data together indicate the combination exerts anti-tumor effect by suppressing RAS/MAPK signaling through down-regulation of PTPN11, and also PTPN11 promotes Ras activation in Ph + B-ALL. Moreover, about 80% of Ph +B-ALL patients have the IKZF1 deletion, our ChIP-seq data showed that IKZF1-encoded Ikaros protein binds to the promoter region of PTPN11 and CK2 inhibitor CX4945 as Ikaros function activator dramatically increase the Ikaros binding to the promoter of PTPN11 in B-ALL cells and Ikaros directly suppresses its promoter activity (Fig. 2F, G). We have reported that CX4945 demonstrated therapeutic efficacy for high-risk B-ALL by restoring Ikaros' function. Here, we observed that pre-treatment of SUP-B15 cells significantly increases the effect of AZA+FLU combination on cell proliferation arrest, and cell cycle arrest (Fig. 2H, I). Taken together, these results reveal a model that IKZF1 deletion-mediated Ras/MAPK activation via PTPN11 has the oncogenic role in Ph + B-ALL and the combination of AZA+FLU exerts the anti-tumor effect by targeting Ikaros/PTPN11/Ras oncogenic signaling. The model is summarized in (Fig. 2J). Conclusions The combination of AZA and FLU has a synergistic anti-leukemia effect on cell proliferation arrest and apoptosis in Ph +ALL cells with IKZF1-deletion by targeting Ikaros/PTPN11/Ras oncogenic signaling. Our data provide experimental evidence for a new potential combination of AZA with FLU in the therapy of Ph +ALL and highlight the likelihood of the novel combination in ALL patients.

Characteristics of ABCC4 and ABCG2 High Expression Subpopulations in CRC—A New Opportunity to Predict Therapy Response

Background: Our previous findings proved that ABCC4 and ABCG2 proteins present much more complex roles in colorectal cancer (CRC) than typically cancer-associated functions as drug exporters. Our objective was to evaluate their predictive/diagnostic potential. Methods: CRC patients’ transcriptomic data from the Gene Expression Omnibus database (GSE18105, GSE21510 and GSE41568) were discriminated into two subpopulations presenting either high expression levels of ABCC4 (ABCC4 High) or ABCG2 (ABCG2 High). Subpopulations were analysed using various bioinformatical tools and platforms (KEEG, Gene Ontology, FunRich v3.1.3, TIMER2.0 and STRING 12.0). Results: The analysed subpopulations present different gene expression patterns. The protein–protein interaction network of subpopulation-specific genes revealed the top hub proteins in ABCC4 High: RPS27A, SRSF1, DDX3X, BPTF, RBBP7, POLR1B, HNRNPA2B1, PSMD14, NOP58 and EIF2S3 and in ABCG2 High: MAPK3, HIST2H2BE, LMNA, HIST1H2BD, HIST1H2BK, HIST1H2AC, FYN, TLR4, FLNA and HIST1H2AJ. Additionally, our multi-omics analysis proved that the ABCC4 expression correlates with substantially increased tumour-associated macrophage infiltration and sensitivity to FOLFOX treatment. Conclusions: ABCC4 and ABCG2 may be used to distinguish CRC subpopulations that present different molecular and physiological functions. The ABCC4 High subpopulation demonstrates significant EMT reprogramming, RNA metabolism and high response to DNA damage stimuli. The ABCG2 High subpopulation may resist the anti-EGFR therapy, presenting higher proteolytical activity.

Unveiling Key Biomarkers and Therapeutic Drugs in Polycystic Ovary Syndrome (PCOS) Through Pathway Enrichment Analysis and Hub Gene-miRNA Networks.

Polycystic ovary syndrome (PCOS) affects women of reproductive age globally with an incidence rate of 5% - 26%. Growing evidence reports important roles for microRNAs (miRNAs) in the pathophysiology of granulosa cells (GCs) in PCOS. The objectives of this study were to identify the top differentially expressed miRNAs (DE-miRNAs) and their corresponding targets in hub gene-miRNA networks, as well as identify novel DE-miRNAs by analyzing three distinct microarray datasets. Additionally, functional enrichment analysis was performed using bioinformatics approaches. Finally, interactions between the 5 top-ranked hub genes and drugs were investigated. Using bioinformatics approaches, three GC profiles from the gene expression omnibus (GEO), namely gene expression omnibus series (GSE)-34526, GSE114419, and GSE137684, were analyzed. Targets of the top DE-miRNAs were predicted using the multiMiR R package, and only miRNAs with validated results were retrieved. Genes that were common between the "DE-miRNA prediction results" and the "existing tissue DE-mRNAs" were designated as differentially expressed genes (DEGs). Gene ontology (GO) and pathway enrichment analyses were implemented for DEGs. In order to identify hub genes and hub DE-miRNAs, the protein-protein interaction (PPI) network and miRNA-mRNA interaction network were constructed using Cytoscape software. The drug-gene interaction database (DGIdb) database was utilized to identify interactions between the top-ranked hub genes and drugs. Out of the top 20 DE-miRNAs that were retrieved from the GSE114419 and GSE34526 microarray datasets, only 13 of them had "validated results" through the multiMiR prediction method. Among the 13 DE-miRNAs investigated, only 5, namely hsa-miR-8085, hsa-miR-548w, hsa-miR-612, hsa-miR-1470, and hsa-miR-644a, demonstrated interactions with the 10 hub genes in the hub gene-miRNA networks in our study. Except for hsa-miR-612, the other 4 DE-miRNAs, including hsa-miR-8085, hsa-miR-548w, hsa-miR-1470, and hsa-miR-644a, are novel and had not been reported in PCOS pathogenesis before. Also, GO and pathway enrichment analyses identified "pathogenic E. coli infection" in the Kyoto encyclopedia of genes and genomes (KEGG) and "regulation of Rac1 activity" in FunRich as the top pathways. The drug-hub gene interaction network identified ACTB, JUN, PTEN, KRAS, and MAPK1 as potential targets to treat PCOS with therapeutic drugs. The findings from this study might assist researchers in uncovering new biomarkers and potential therapeutic drug targets in PCOS treatment.

Bioinformatics and experimental studies jointly reveal that Sacubitril Valsartan improves myocardial oxidative stress and inflammation by regulating the MAPK signaling pathway to treat chemotherapy related cardiotoxicity

BackgroundCRC is a common but serious complication or sequela of tumor treatment, and new coping strategies are urgently needed. SV is a classic clinical cardiovascular protective drug, which has been widely used in the treatment of heart failure, hypertension and other diseases. It has good therapeutic effect in other cardiovascular diseases such as diabetes cardiomyopathy, ischemic cardiomyopathy and vascular disease, but it has not been proved by research that SV can prevent and treat CRC. MethodIn this study, DOX was used to induce a rat CRC model and evaluate the therapeutic effect of SV on it. Subsequently, R software was applied to analyze the control group, SV group, and DOX group in databases GSE207283 and GSE22369, and to screen for common differentially expressed genes. Use the DAVID website for enrichment analysis and visualization. Use STRING website to analyze and visualize protein interaction networks of key genes. Finally, experimental verification was conducted on key genes. ResultOur research results show that SV has a protective effect on DOX induced myocardial injury by alleviating Weight loss, increasing Ejection fraction, and reducing the level of biomarkers of myocardial injury. Meanwhile, SV can effectively alleviate the above abnormalities. Bioinformatics and KEGG pathway analysis showed significant enrichment of metabolic and MAPK signaling pathways, suggesting that they may be the main regulatory pathway for SV treatment of CRC. Subsequent studies have also confirmed that SV can inhibit DOX induced myocardial injury through the MAPK signaling pathway, and alleviate DOX induced oxidative stress and inflammatory states. ConclusionOur research indicates that SV is a potential drug for treating CRC and preliminarily elucidates its molecular mechanism of regulating the MAPK pathway to improve oxidative stress and inflammation.

Identification of circRNA-lncRNA-miRNA-mRNA competitive endogenous RNA networks as prognostic bio-markers in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent neoplasms of malignancy. The pathogenesis of HNSCC is involved in the change of gene expression and the abnormal interaction between genes. The purpose of our research is to find genes that affect the survival time of patients and construct circRNA-lncRNA-miRNA-mRNA network. This paper aims to reveal the development mechanism of HNSCC and provide new ideas for clinical prognosis prediction. Transcriptome data were obtained from TCGA and GEO databases. A total of 556 samples were included. We screened the differentially expressed lncRNAs, circRNAs and mRNAs to predict interactions between lncRNA-miRNA, circRNA-miRNA and miRNA-mRNA. Then, we selected the hub mRNAs to predict the prognosis of patients. We identified 8 hub prognostic mRNAs (FAM111A, IFIT2, CAV1, KLF9, OCIAD2, SLCO3A1, S100A16 and APOL3) by a series of bioinformatics analyses and established the prognostic ceRNA network of 8 mRNAs, 3 miRNAs, 2 circRNAs and 1 lncRNAs according to the targeting relationship by using databases. We established the circRNA-lncRNA-miRNA-mRNA gene interaction network in HNSCC. We illuminated the molecular mechanism underlying the gene regulation associated with the pathogenesis of HNSCC and predicted the biomarkers related to prognosis.

Network pharmacology to explore the molecular mechanisms of Prunella vulgaris for treating thyroid cancer.

Thyroid cancer (TC) is the most common endocrine malignancy that has rapidly increased in global incidence. Prunella vulgaris (PV) has manifested therapeutic effects in patients with TC. We aimed to investigate its molecular mechanisms against TC and provide potential drug targets by using network pharmacology and molecular docking. The ingredients of PV were retrieved from Traditional Chinese Medicine Systematic Pharmacology Database. TC-related gene sets were established using the GeneCard and OMIM databases. The establishment of the TC-PV target gene interaction network was accomplished using the STRING database. Cytoscape constructed networks for visualization. Protein-protein interaction, gene ontology and the biological pathway Kyoto encyclopedia of genes and genomes enrichment analyses were performed to discover the potential mechanism. Molecular docking technology was used to analyze the effective compounds from PV for treating TC. 11 active compounds and 192 target genes were screened from PV. 177 potential targets were obtained by intersecting PV and TC gene sets. Network pharmacological analysis showed that the PV active ingredients including Vulgaxanthin-I, quercetin, Morin, Stigmasterol, poriferasterol monoglucoside, Spinasterol, kaempferol, delphinidin, stigmast-7-enol, beta-sitosterol and luteolin showed better correlation with TC target genes such as JUN, AKT1, mitogen-activated protein kinase 1, IL-6 and RELA. The gene ontology and Kyoto encyclopedia of genes and genomes indicated that PV can act by regulating the host defense and response to oxidative stress immune response and several signaling pathways are closely associated with TC, such as the TNF and IL-17. Protein-protein interaction network identified 8 hub genes. The molecular docking was conducted on the most significant gene MYC. Eleven active compounds of PV can enter the active pocket of MYC, namely poriferasterol monoglucoside, stigmasterol, beta-sitosterol, vulgaxanthin-I, spinasterol, stigmast-7-enol, luteolin, delphinidin, morin, quercetin and kaempferol. Further analysis showed that oriferasterol monoglucoside, followed by tigmasterol, were the potential therapeutic compound identified in PV for the treatment of TC. The network pharmacological strategy integrates molecular docking to unravel the molecular mechanism of PV. MYC is a promising drug target to reduce oxidative stress damage and potential anti-tumor effect. Oriferasterol monoglucoside and kaempferol were 2 bioactive compounds of PV to treat TC. This provides a basis to understand the mechanism of the anti-TC activity of PV.

Systematic analysis of various RNA transcripts and construction of biological regulatory networks at the post-transcriptional level for chronic obstructive pulmonary disease

BackgroundAlthough chronic inflammation, oxidative stress, airway remodeling, and protease-antiprotease imbalance have been implicated in chronic obstructive pulmonary disease (COPD), the exact pathogenesis is still obscure. Gene transcription and post-transcriptional regulation have been taken into account as key regulators of COPD occurrence and development. Identifying the hub genes and constructing biological regulatory networks at the post-transcriptional level will help extend current knowledge on COPD pathogenesis and develop potential drugs.MethodsAll lung tissues from non-smokers (n = 6), smokers without COPD (smokers, n = 7), and smokers with COPD (COPD, n = 7) were collected to detect messenger RNA (mRNA), microRNA (miRNA), circular RNA (circRNA), and long non-coding RNA (lncRNA) expression and identify the hub genes. Biological regulatory networks were constructed at the post-transcriptional level, including the RNA-binding protein (RBP)-hub gene interaction network and the competitive endogenous RNA (ceRNA) network. In addition, we assessed the composition and abundance of immune cells in COPD lung tissue and predicted potential therapeutic drugs for COPD. Finally, the hub genes were confirmed at both the RNA and protein levels.ResultsAmong the 20 participants, a total of 121169 mRNA transcripts, 1871 miRNA transcripts, 4244 circRNA transcripts, and 122130 lncRNA transcripts were detected. There were differences in the expression of 1561 mRNAs, 48 miRNAs, 33 circRNAs, and 545 lncRNAs between smokers and non-smokers, as well as 1289 mRNAs, 69 miRNAs, 32 circRNAs, and 433 lncRNAs between smokers and COPD patients. 18 hub genes were identified in COPD. TGF-β signaling and Wnt/β-catenin signaling may be involved in the development of COPD. Furthermore, the circRNA/lncRNA-miRNA-mRNA ceRNA networks and the RBP-hub gene interaction network were also constructed. Analysis of the immune cell infiltration level revealed that M2 macrophages and activated NK cells were increased in COPD lung tissues. Finally, we identified that the ITK inhibitor and oxybutynin chloride may be effective in treating COPD.ConclusionsWe identified several novel hub genes involved in COPD pathogenesis. TGF-β signaling and Wnt/β-catenin signaling were the most dysregulated pathways in COPD patients. Our study constructed post-transcriptional biological regulatory networks and predicted small-molecule drugs for the treatment of COPD, which enhanced the existing understanding of COPD pathogenesis and suggested an innovative direction for the therapeutic intervention of the disease.

The role of miR-223 in breast cancer; an integrated analysis.

Breast cancer (BRCA) is the most common and leading cause of cancer-related death in women. MicroRNAs (miRNAs) are short non-coding RNA fragments that play a role in regulating gene expression including the cancer-related pathways. Although dysregulation of miR-223 has been demonstrated in recent studies to have prognostic value in various cancers, its diagnostic and prognostic role in BRCA remains unknown. The expression and the prognostic value of miR-223 were evaluated using the TCGA data and verified by qRT-PCR. Subsequently, potential oncogenic targets of miR-223 were identified by using three different miRNA target prediction tools and the GEPIA database. In addition to these databases, protein-protein interaction network, molecular functions, prognostic value, and the expression level of miR-223 targets were included by using several other bioinformatics tools and databases; such as, UALCAN, GeneMANIA and Metascape. The bioinformatic results demonstrated that miR-223 downregulated in BRCA and associated with poor prognosis of patients. In vitro experiments validated that miR-223 significantly downregulated in BRCA cells, MCF-7, SK-BR3, MDA-MB-231 and HCC1500, compared to normal breast cell line hTERT-HME1. Furthermore, ANLN, DYNLT1, LRRC59, SLC12A8 and TPM3 genes were identified as the potential oncogenic target genes of miR-223 based on their expression and prognosis in BRCA. Additionally, protein-protein interaction network of these target genes was mainly enriched in dynein intermediate chain binding, cell division, regulation of cell cycle process, and positive regulation of cellular component biogenesis. The results suggests that miR-223 and its targets, ANLN, DYNLT1, LRRC59, SLC12A8 and TPM3, might be reliable potential prognostic biomarkers in BRCA patients.

MiR-135a-5p overexpression in peripheral blood-derived exosomes mediates vascular injury in type 2 diabetes patients.

Diabetes pathology relies on exosomes (Exos). This study investigated how peripheral blood Exo-containing microRNAs (miRNAs) cause vascular injury in type 2 diabetes (T2D). We removed DEmiRNA from T2D chip data from the GEO database. We isolated Exo from 15 peripheral blood samples from T2D patients and 15 healthy controls and measured Exo DEmiRNA levels. We employed the intersection of Geneards and mirWALK database queries to find T2D peripheral blood mRNA-related chip target genes. Next, we created a STRING database candidate target gene interaction network map. Next, we performed GO and KEGG enrichment analysis on T2D-related potential target genes using the ClusterProfiler R package. Finally, we selected T2D vascular damage core genes and signaling pathways using GSEA and PPI analysis. Finally, we used HEK293 cells for luciferase assays, co-cultured T2D peripheral blood-derived Exo with HVSMC, and detected HVSMC movement alterations. We found 12 T2D-related DEmiRNAs in GEO. T2D patient-derived peripheral blood Exo exhibited significantly up-regulated miR-135a-3p by qRT-PCR. Next, we projected miR-135a-3p's downstream target mRNA and screened 715 DEmRNAs to create a regulatory network diagram. DEmRNAs regulated biological enzyme activity and vascular endothelial cells according to GO function and KEGG pathway analysis. ErbB signaling pathway differences stood out. PPI network study demonstrated that DEmRNA ATM genes regulate the ErbB signaling pathway. The luciferase experiment validated miR-135a-3p and ATM target-binding. Co-culture of T2D patient-derived peripheral blood Exo with HVSMC cells increases HVSMC migration, ErbB2, Bcl-2, and VEGF production, and decreases BAX and ATM. However, miR-135a-3p can reverse the production of the aforesaid functional proteins and impair HVSMC cell movement. T2D patient-derived peripheral blood Exo carrying miR-135a-3p enter HVSMC, possibly targeting and inhibiting ATM, activating the ErbB signaling pathway, promoting abnormal HVSMC proliferation and migration, and aggravating vascular damage.

Network pharmacology analysis of a patented Chinese herbal medicine for alleviating anxiety disorder in in vitro fertilization-embryo transfer

ObjectiveQu's formula 3 (QUF3) is a patented Chinese herbal medicine used to alleviate anxiety disorders during in vitro fertilization-embryo transfer (IVF-ET). This study aimed to identify the potential active constituents and molecular mechanisms of action of QUF3 in alleviating anxiety disorders during IVF-ET. MethodsThe active constituents of QUF3 were identified from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and literatures. Potential targets of anxiety disorder and IVF-ET were identified using GeneCards, Online Mendelian Inheritance in Man, and the UniProt Database. Protein-protein interaction (PPI) network, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the potential mechanisms. Molecular docking and molecular dynamics (MD) simulations were performed to visualize and verify the results. ResultsQuercetin, sophoranol, luteolin, kaempferol, and neurotoxin inhibitors were identified as the TOP 5 active constituents of QUF3. Forty common targets were shared among QUF3, anxiety disorders, and IVF-ET. Tumour necrosis factor, interleukin-6, vascular endothelial growth factor A, epidermal growth factor, interleukin-1B, cellular tumour antigen p53, matrix metalloproteinase-9, and oestrogen receptor were identified as the TOP 8 potential targets through PPI analysis. A total of 697 biological processes, 20 cellular components, and 54 molecular functions were identified. Further, 91 KEGG pathways were revealed to be enriched. The TOP 5 active constituents were verified to have good binding activity with the TOP 8 potential targets using molecular docking and MD simulations. ConclusionsThe mechanism of QUF3 in alleviating anxiety disorders in patients undergoing IVF-ET may be related to the interleukin-17 and tumour necrosis factor signalling pathways, inhibiting inflammatory responses and antioxidants, which may provide a solid foundation for the clinical application and further study of QUF3.

Differential expression of serum miR-486 and miR-25 in ulcerative colitis and Crohn’s disease: Correlations with disease activity, extent, and location

Novel reliable biomarkers of inflammatory bowel disease (IBD) are clinically imperative due to potential limitations of endoscopic techniques. MicroRNAs (miRNAs) have emerged as non-invasive biomarkers of IBD; however, the full disease-specific miRNAs signature for IBD subtypes remains elusive. We evaluated the diagnostic role of circulating miR-486 and miR-25 in IBD patients and their potential ability to discriminate IBD subtypes; ulcerative colitis (UC) and Crohn’s disease (CD). Sixty UC patients, 60 CD patients, and 60 healthy controls were recruited. Serum miRNA expression was determined using RT-qPCR. Bioinformatics was employed for target gene and protein-protein interaction (PPI) network analyses. Serum miR-486 was upregulated in CD patients, but didn’t change in UC patients compared to controls. Conversely, serum miR-25 was decreased in both CD and UC patients compared to controls. Only miR-486 was differentially expressed between UC and CD patients. Receiver-operating characteristic analysis revealed that serum miR-486 was superior in CD diagnosis (AUC=0.945) and significantly distinguished CD and UC patients, whereas miR-25 showed discriminative potential for both UC and CD from controls. In the multivariate logistic analysis only miR-486 was associated with the risk of CD diagnosis. Serum miR-486 was correlated with CD activity index and location of disease in CD patients, whereas miR-25 was correlated with the type/extent of UC. PPI network analysis revealed common target genes and signaling pathways for both miRNAs. Conclusively, serum miR-486 and miR-25 might serve as new biomarkers of IBD, with serum miR-486 could be employed in risk stratification of IBD subtypes and has the ground for clinical utility in CD diagnosis, whereas miR-25 has potential for UC and CD diagnosis.

Identifying Genetic Signatures from Single-Cell RNA Sequencing Data by Matrix Imputation and Reduced Set Gene Clustering

In this current era, the identification of both known and novel cell types, the representation of cells, predicting cell fates, classifying various tumor types, and studying heterogeneity in various cells are the key areas of interest in the analysis of single-cell RNA sequencing (scRNA-seq) data. Due to the nature of the data, cluster identification in single-cell sequencing data with high dimensions presents several difficulties. In this paper, we introduce a new framework that combines various strategies such as imputed matrix, minimum redundancy maximum relevance (MRMR) feature selection, and shrinkage clustering to discover gene signatures from scRNA-seq data. Firstly, we conducted the pre-filtering of the “drop-out” value in the data focusing solely on imputing the identified “drop-out” values. Next, we applied the MRMR feature selection method to the imputed data and obtained the top 100 features based on the MRMR feature selection optimization scores for further downstream analysis. Thereafter, we employed shrinkage clustering on the selected feature matrix to identify the cell clusters using a global optimization approach. Finally, we applied the Limma-Voom R tool employing voom normalization and an empirical Bayes test to detect differentially expressed features with a false discovery rate (FDR) < 0.001. In addition, we performed the KEGG pathway and gene ontology enrichment analysis of the identified biomarkers using David 6.8 software. Furthermore, we conducted miRNA target detection for the top gene markers and performed miRNA target gene interaction network analysis using the Cytoscape online tool. Subsequently, we compared our detected 100 markers with our previously detected top 100 cluster-specified markers ranked by FDR of the latest published article and discovered three common markers; namely, Cyp2b10, Mt1, Alpi, along with 97 novel markers. In addition, the Gene Set Enrichment Analysis (GSEA) of both marker sets also yields similar outcomes. Apart from this, we performed another comparative study with another published method, demonstrating that our model detects more significant markers than that model. To assess the efficiency of our framework, we apply it to another dataset and identify 20 strongly significant up-regulated markers. Additionally, we perform a comparative study of different imputation methods and include an ablation study to prove that every key phase of our framework is essential and strongly recommended. In summary, our proposed integrated framework efficiently discovers differentially expressed stronger gene signatures as well as up-regulated markers in single-cell RNA sequencing data.

Elucidating the Differentiation Synthesis Mechanisms of Differently Colored Resistance Quinoa Seedings Using Metabolite Profiling and Transcriptome Analysis.

Quinoa (Chenopodium quinoa wild.), a dicotyledonous plant native to the Andes, is an increasingly popular pseudograin owing to its high nutritional value, stress resistance capabilities, and gluten-free properties. In this study, we aimed to explore the dynamic changes in different varieties of quinoa at the seedling stage and their regulatory networks. Here, we found that the leaves of quinoa showed obvious coloration after 45 days, and four quinoa seedling types (red, white, yellow, and black) were subjected to ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and transcriptome sequencing to identify their differentially expressed genes and metabolites. A total of 29 differential metabolites and 19 genes (14 structural and 5 regulatory genes) were identified, and consistent differences were observed in the flavonoid, phenolic acid, and alkaloid metabolites in the different quinoa types. These differential metabolites were significantly enriched in flavonoid and flavonol biosynthesis, flavonoid biosynthesis, and phenylpropanoid biosynthesis pathways. In addition, real-time fluorescence quantitative PCR (RT-qPCR) technology was used to detect the expression of four structural genes involved in the flavonoid biosynthesis pathway and four regulatory genes (interaction network). The results revealed that the structural and regulatory gene transcript levels in the flavonoid pathway were higher in the red quinoa cultivars than in the white, yellow, and black. Additionally, the differences in the leaves of these four quinoa cultivars were mainly due to differences in flavonoid, phenolic acid, and alkaloid accumulation. Our findings provide a basis for understanding the accumulation and coloration mechanisms of flavonoids, phenolic acids, and alkaloids in quinoa seedlings of different colors and also provide a theoretical basis for future investigations.

Integrated bioinformatics analysis and network pharmacology to explore the potential mechanism of Patrinia heterophylla Bunge against acute promyelocytic leukemia.

Current treatment with arsenic trioxide and all-trans retinoic acid has greatly improved the therapeutic efficacy and prognosis of acute promyelocytic leukemia (APL), but may cause numerous adverse effects. Patrinia heterophylla Bunge (PHEB), commonly known as "Mu-Tou-Hui" in China, is effective in treating leukemia. However, no studies have reported the use of PHEB for APL treatment. In this study, we aimed to investigate the potential anticancer mechanism of PHEB against APL. Public databases were used to search for bioactive compounds in PHEB, their potential targets, differentially expressed genes associated with APL, and therapeutic targets for APL. The core targets and signaling pathways of PHEB against APL were identified by the protein-protein interaction network, Kaplan-Meier curves, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and compound-target-pathway network analysis. Molecular docking was performed to predict the binding activity between the most active compounds and the key targets. Quercetin and 2 other active components of PHEB may exert anti-APL effects through proteoglycans in cancer, estrogen signaling, and acute myeloid leukemia pathways. We also identified 6 core targets of the bioactive compounds of PHEB, including protein tyrosine phosphatase receptor type C, proto-oncogene tyrosine-protein kinase Src, mitogen-activated protein kinase phosphatase 3 (MAPK3), matrix metalloproteinase-9, vascular endothelial growth factor receptor-2, and myeloperoxidase, most of which were validated to improve the 5-year survival of patients. Molecular docking results showed that the active compound bound well to key targets. The results not only predict the active ingredients and potential molecular mechanisms of PHEB against APL, but also help to guide further investigation into the anti-APL application of PHEB.