A new closeness centrality measure via effective distance in complex networks

Identifying crucial lncRNAs and mRNAs in hypoxia-induced A549 lung cancer cells and investigating their underlying mechanisms via high-throughput sequencing

BACKGROUND

Rapid proliferation and outgrowth of tumor cells frequently result in localized hypoxia, which has been implicated in the progression of lung cancer. The present study aimed to identify key long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) involved in hypoxia-induced A549 lung cancer cells, and to investigate their potential underlying mechanisms of action.

METHODS

High-throughput sequencing was utilized to obtain the expression profiles of lncRNA and mRNA in both hypoxia-induced and normoxia A549 lung cancer cells. Subsequently, a bioinformatics analysis was conducted on the differentially expressed molecules, encompassing functional enrichment analysis, protein-protein interaction (PPI) network analysis, and competitive endogenous RNA (ceRNA) analysis. Finally, the alterations in the expression of key lncRNAs and mRNAs were validated using real-time quantitative PCR (qPCR).

RESULTS

In the study, 1155 mRNAs and 215 lncRNAs were identified as differentially expressed between the hypoxia group and the normoxia group. Functional enrichment analysis revealed that the differentially expressed mRNAs were significantly enriched in various pathways, including the p53 signaling pathway, DNA replication, and the cell cycle. Additionally, key lncRNA-miRNA-mRNA relationships, such as RP11-58O9.2-hsa-miR-6749-3p-XRCC2 and SNAP25-AS1-hsa-miR-6749-3p-TENM4, were identified. Notably, the qPCR assay demonstrated that the expression of SNAP25-AS1, RP11-58O9.2, TENM4, and XRCC2 was downregulated in the hypoxia group compared to the normoxia group. Conversely, the expression of LINC01164, VLDLR-AS1, RP11-14I17.2, and CDKN1A was upregulated.

CONCLUSION

Our findings suggest a potential involvement of SNAP25-AS1, RP11-58O9.2, TENM4, XRCC2, LINC01164, VLDLR-AS1, RP11-14I17.2, and CDKN1A in the development of hypoxia-induced lung cancer. These key lncRNAs and mRNAs exert their functions through diverse mechanisms, including the competitive endogenous RNA (ceRNA) pathway.

Identification of Critical Links Based on Electrical Betweenness and Neighborhood Similarity in Cyber-Physical Power Systems

Identifying critical links is of great importance for ensuring the safety of the cyber-physical power system. Traditional electrical betweenness only considers power flow distribution on the link itself, while ignoring the local influence of neighborhood links and the coupled reaction of information flow on energy flow. An identification method based on electrical betweenness centrality and neighborhood similarity is proposed to consider the internal power flow dynamic influence existing in multi-neighborhood nodes and the topological structure interdependence between power nodes and communication nodes. Firstly, for the power network, the electrical topological overlap is proposed to quantify the vulnerability of the links. This approach comprehensively considers the local contribution of neighborhood nodes, power transmission characteristics, generator capacity, and load. Secondly, in communication networks, effective distance closeness centrality is defined to evaluate the importance of communication links, simultaneously taking into account factors such as the information equipment function and spatial relationships. Next, under the influence of coupled factors, a comprehensive model is constructed based on the dependency relationships between information flow and energy flow to more accurately assess the critical links in the power network. Finally, the simulation results show the effectiveness of the proposed method under dynamic and static attacks.

An Improved Genome-Wide Association Procedure Explores Gene-Allele Constitutions and Evolutionary Drives of Growth Period Traits in the Global Soybean Germplasm Population

In soybeans (Glycine max (L.) Merr.), their growth periods, DSF (days of sowing-to-flowering), and DFM (days of flowering-to-maturity) are determined by their required accumulative day-length (ADL) and active temperature (AAT). A sample of 354 soybean varieties from five world eco-regions was tested in four seasons in Nanjing, China. The ADL and AAT of DSF and DFM were calculated from daily day-lengths and temperatures provided by the Nanjing Meteorological Bureau. The improved restricted two-stage multi-locus genome-wide association study using gene-allele sequences as markers (coded GASM-RTM-GWAS) was performed. (i) For DSF and its related ADL_DSF and AAT_DSF, 130-141 genes with 384-406 alleles were explored, and for DFM and its related ADL_DFM and AAT_DFM, 124-135 genes with 362-384 alleles were explored, in a total of six gene-allele systems. DSF shared more ADL and AAT contributions than DFM. (ii) Comparisons between the eco-region gene-allele submatrices indicated that the genetic adaptation from the origin to the geographic sub-regions was characterized by allele emergence (mutation), while genetic expansion from primary maturity group (MG)-sets to early/late MG-sets featured allele exclusion (selection) without allele emergence in addition to inheritance (migration). (iii) Optimal crosses with transgressive segregations in both directions were predicted and recommended for breeding purposes, indicating that allele recombination in soybean is an important evolutionary drive. (iv) Genes of the six traits were mostly trait-specific involved in four categories of 10 groups of biological functions. GASM-RTM-GWAS showed potential in detecting directly causal genes with their alleles, identifying differential trait evolutionary drives, predicting recombination breeding potentials, and revealing population gene networks.

Identifying important nodes in complex networks based on extended degree and E-shell hierarchy decomposition

The identification of important nodes is a hot topic in complex networks. Many methods have been proposed in different fields for solving this problem. Most previous work emphasized the role of a single feature and, as a result, rarely made full use of multiple items. This paper proposes a new method that utilizes multiple characteristics of nodes for the evaluation of their importance. First, an extended degree is defined to improve the classical degree. And E-shell hierarchy decomposition is put forward for determining nodes' position through the network's hierarchical structure. Then, based on the combination of these two components, a hybrid characteristic centrality and its extended version are proposed for evaluating the importance of nodes. Extensive experiments are conducted in six real networks, and the susceptible-infected-recovered model and monotonicity criterion are introduced to test the performance of the new approach. The comparison results demonstrate that the proposed new approach exposes more competitive advantages in both accuracy and resolution compared to the other five approaches.

Identification of signaling pathways associated with achaete-scute homolog 1 in glioblastomas through ChIP-seq data bioinformatics

Background: Achaete-scute homolog 1 transcription factors were important in the differentiation of neuronal-like glioblastoma (GBM) cancer stem cells (CSCs). To gain a better understanding of the role of ASCL1 in GBM, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) data can be analyzed to construct their gene transcription regulation network.

Methods: GSE87618 was downloaded from the Gene Expression Omnibus, which is a famous database, in the field of biology. The filtered clean reads were mapped to the human genome utilizing the software of bowtie2. Then, differential peak analysis was performed by diffbind. Finally, the annotated gene functions and signaling pathways were investigated by Gene ontology function and kyoto encyclopedia of genes genomes (KEGG) pathway enrichment analysis. Moreover, the protein–protein interaction network (PPI) analysis of genes obtained from ASCL1 was carried out to explore the hub genes influenced by ASCL1.

Results: A total of 516 differential peaks were selected. GO analysis of functions revealed that promoter, untranslated region (UTR), exon, intron, and intergenic genes were mainly enriched in biological pathways such as keratinization, regulation of cAMP metabolic process, blood coagulation, fibrin clot formation, midgut development, and synapse assembly. Genes were mainly enriched in KEGG pathways including pentose phosphate pathway, glycosphingolipid biosynthesis—globo and isoglobo series, ECM–receptor interaction, and adherens junction. In total, 244 nodes and 475 interaction pairs were included in the PPI network with the hub genes including EGFR, CTNNB1, and SPTAN1.

Conclusion: EGFR, SPTAN1, and CTNN1B might be the potential down-stream genes of ASCL1 in GBM development, and CTNN1B might make contributions to GBM progression on regulating the cAMP pathway.

Identification of Key Endometrial MicroRNAs and Their Target Genes Associated With Pathogenesis of Recurrent Implantation Failure by Integrated Bioinformatics Analysis

Purpose: Recurrent implantation failure (RIF) is an enormous challenge for in vitro fertilization (IVF) clinicians. An understanding of the molecular mechanisms of RIF helps to predict prognosis and develop new therapeutic strategies. The study is designed to identify diagnostic biomarkers for RIF as well as the potential mechanisms underlying RIF by utilizing public databases together with experimental validation.

Methods: Two microarray datasets of RIF patients and the healthy control endometrium were downloaded from the Gene Expression Omnibus (GEO) database. First, differentially expressed microRNAs (miRNAs) (DEMs) were identified and their target genes were predicted. Then, we identified differentially expressed genes (DEGs) and selected hub genes through protein-protein interaction (PPI) analyses. Functional enrichment analyses of DEGs and DEMs were conducted. Furthermore, the key DEMs which targeted these hub genes were selected to obtain the key miRNA–target gene network. The key genes in the miRNA-target gene network were validated by a single-cell RNA-sequencing dataset of endometrium from GEO. Finally, we selected two miRNA–target gene pairs for further experimental validation using dual-luciferase assay and quantitative polymerase chain reaction (qPCR).

Results: We identified 49 DEMs between RIF patients and the fertile group and found 136,678 target genes. Then, 325 DEGs were totally used to construct the PPI network, and 33 hub genes were selected. Also, 25 DEMs targeted 16 key DEGs were obtained to establish a key miRNA–target gene network, and 16 key DEGs were validated by a single-cell RNA-sequencing dataset. Finally, the target relationship of hsa-miR-199a-5p-PDPN and hsa-miR-4306-PAX2 was verified by dual-luciferase assay, and there were significant differences in the expression of those genes between the RIF and fertile group by PCR (p < 0.05).

Conclusion: We constructed miRNA–target gene regulatory networks associated with RIF which provide new insights regarding the underlying pathogenesis of RIF; hsa-miR-199a-5p-PDPN and hsa-miR-4306-PAX2 could be further explored as potential biomarkers for RIF, and their detection in the endometrium could be applied in clinics to estimate the probability of successful embryo transfer.

The impact of the COVID-19 pandemic on O-D flow and airport networks in the origin country and in Northeast Asia

The ongoing COVID-19 pandemic has posed a global threat to human health. In order to prevent the spread of this virus, many countries have imposed travel restrictions. This difficult situation has dramatically affected the airline industry by reducing the passenger volume, number of flights, airline flow patterns, and even has changed the entire airport network, especially in Northeast Asia (because it includes the original disease seed). However, although most scholars have used conventional statistical analysis to describe the changes in passenger volume before and during the COVID-19 outbreak, very few of them have applied statistical assessment or time series analysis, and have not even examined how the impact may be different from place to place. Therefore, the purpose of this study was to identify the impact of COVID-19 on the airline industry and affected areas (including the origin-destination flow and the airport network). First, a Clustering Large Applications (CLARA) algorithm was used to group numerous origin-destination (O-D) flow patterns based on their characteristics and to determine if these characteristics have changed the severity of the impact of each cluster during the COVID-19 outbreak. Second, two statistical tests (the paired t-test and the Wilcoxon signed-rank test) were utilized to determine if the entire airport network and the top 30 hub airports changed during COVID-19. Four centrality measurement indices (degree, closeness, eigenvector, and betweenness centrality) of the airports were used to assess the entire network and ranking of individual hub airports. The study data, provided by The Official Aviation Guide (OAG) from December 2019 to April 2020, indicated that during the COVID-19 outbreak, there was a decrease in passenger volume (60%-98.4%) as well as the number of flights (1.5%-82.6%). However, there were no such significant changes regarding the popularity ranking of most airports during the outbreak. Before this occurred (December 2019), most hub airports were in China (April 2020), and this trend remain similar during the COVID-19 outbreak. However, the values of the centrality measurement decreased significantly for most hub airports due to travel restrictions issued by the government.

Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy

Identifying influential nodes in complex networks has attracted the attention of many researchers in recent years. However, due to the high time complexity, methods based on global attributes have become unsuitable for large-scale complex networks. In addition, compared with methods considering only a single attribute, considering multiple attributes can enhance the performance of the method used. Therefore, this paper proposes a new multiple local attributes-weighted centrality (LWC) based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered for evaluating the influence of nodes and identifying influential nodes in complex networks. Firstly, the influence of a node in a complex network is divided into direct influence and indirect influence. The degree and clustering coefficient are selected as direct influence measures. Secondly, based on the two direct influence measures, we define two indirect influence measures: two-hop degree and two-hop clustering coefficient. Then, the information entropy is used to weight the above four influence measures, and the LWC of each node is obtained by calculating the weighted sum of these measures. Finally, all the nodes are ranked based on the value of the LWC, and the influential nodes can be identified. The proposed LWC method is applied to identify influential nodes in four real-world networks and is compared with five well-known methods. The experimental results demonstrate the good performance of the proposed method on discrimination capability and accuracy.

Identification of Critical Genes and Proteins for Stent Restenosis Induced by Esophageal Benign Hyperplasia in Esophageal Cancer

This study was conducted to explore the potential genes and proteins associated with esophagus benign hyperplasia induced by esophageal stents. Five patients with esophageal cancer subjected to esophageal stent placement were enrolled in this study. Long non-coding RNA (lncRNA) sequencing and tandem mass tag quantitative proteomics analysis were performed by using the collected hyperplastic samples and adjacent non-hyperplastic tissues. Differentially expressed (DE) RNAs and proteins were analyzed, followed by functional enrichment analysis, protein-protein interaction (PPI) network analysis, and competitive endogenous RNA (ceRNA) network construction. Venn analysis was performed to extract the overlaps between DE mRNAs and DE proteins and the expression correlations between DE mRNA and proteins were analyzed. Results showed that total 642 DE RNAs (457 mRNA and 185 lncRNAs) and 256 DE proteins were detected. DE mRNAs (such as MAOB, SDR16C5, and FOSL1) were enriched in oxidation-reduction process-associated functions. PPI network was comprised of 175 nodes and 425 edges. VEGFA was a significant node with the highest degree. LncRNA-mRNA network with three subnetworks (C1, C2, C3) was constructed for lncRNAs with more than 15 gene targets. RP11-58O9.2 was a significant lncRNA with the most target genes and RP11-667F14.1 regulated more than 20 targets. FOSL1 was a common target of the two lncRNAs. Function analysis showed that DE lncRNAs were involved in the HTLV-I infection (RP11-58O9.2 and RP11-667F14.1) and IL-17 signaling pathways (RP11-5O24.1 and RP11-58O9.2). Total 11 DE mRNAs were overlapped with DE proteins, among which MAOB and SDR16C5 showed positive correlations between mRNA and protein expression. Function analysis showed that MAOB was enriched in oxidation-reduction process and its protein was closely related with response to lipopolysaccharide. VEGFA, FOSL1, MAOB, SDR16C5, RP11-58O9.2, RP11-667F14.1, and RP11-288A5.2 may be served as genetic targets for preventing stent restenosis in esophageal cancer.

Signaling pathways of genetic variants and miRNAs in the pathogenesis of myasthenia gravis

BACKGROUND

Myasthenia gravis (MG) is a chronic autoimmune neuromuscular disorder causing muscle weakness and characterized by a defect in synaptic transmission at the neuromuscular junction. The pathogenesis of this disease remains unclear. We aimed to predict the key signaling pathways of genetic variants and miRNAs in the pathogenesis of MG, and identify the key genes among them.

METHODS

We searched published information regarding associated single nucleotide polymorphisms (SNPs) and differentially-expressed miRNAs in MG cases. We search of SNPs and miRNAs in literature databases about MG, then we used bioinformatic tools to predict target genes of miRNAs. Moreover, functional enrichment analysis for key genes was carried out utilizing the Cytoscape-plugin, known as ClueGO. These key genes were mapped to STRING database to construct a protein-protein interaction (PPI) network. Then a miRNA-target gene regulatory network was established to screen key genes.

RESULTS

Five genes containing SNPs associated with MG risk were involved in the inflammatory bowel disease (IBD) signaling pathway, and FoxP3 was the key gene. MAPK1, SMAD4, SMAD2 and BCL2 were predicted to be targeted by the 18 miRNAs and to act as the key genes in adherens, junctions, apoptosis, or cancer-related pathways respectively. These five key genes containing SNPs or targeted by miRNAs were found to be involved in negative regulation of T cell differentiation.

CONCLUSIONS

We speculate that SNPs cause the genes to be defective or the miRNAs to downregulate the factors that subsequently negatively regulate regulatory T cells and trigger the onset of MG.

Integrated analysis of transcriptomic and metabolomic data demonstrates the significant role of pyruvate carboxylase in the progression of ovarian cancer

The aim of this study was to explore prognosis-related biomarkers and underlying mechanisms during ovarian carcinoma progression and development. mRNA expression profiles and GSE49997 dataset were downloaded. Survival analyses were performed for genes with high expression levels. Expression level of candidate genes was explored in four ovarian cancer cells lines. Pyruvate carboxylase (PC) was found to be one of significantly differentially expressed gene (DEG). The role of PC knockdown was analyzed in SKOV cells using cell proliferation, flow cytometric, and Transwell migration and invasion assays. DEGs and metabolites in PC-shRNA (shPC)-treated samples vs. control groups were identified. PC was a prognosis-related gene and related to metabolic pathway. Knockdown of PC regulated cell proliferation, cell cycle progression, and migration and invasion of SKOV-3 cells. Transcriptome sequencing analyses showed STAT1 and TP53 gained higher degrees in PPI network. A total of 44 metabolites were identified. These DEGs and metabolites in PC samples were related with neuroactive ligands receptor interaction, glycine, serine and threonine metabolism, and ABC transporter pathways. PC may affect the tumor biology of ovarian cancer through the dysregulation of glycine, serine, and threonine metabolism, and ABC transporter pathways, as well as STAT1 and TP53 expression.

An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma

BACKGROUND

Melanoma phenotype and the dynamics underlying its progression are determined by a complex interplay between different types of regulatory molecules. In particular, transcription factors (TFs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) interact in layers that coalesce into large molecular interaction networks. Our goal here is to study molecules associated with the cross-talk between various network layers, and their impact on tumor progression.

RESULTS

To elucidate their contribution to disease, we developed an integrative computational pipeline to construct and analyze a melanoma network focusing on lncRNAs, their miRNA and protein targets, miRNA target genes, and TFs regulating miRNAs. In the network, we identified three-node regulatory loops each composed of lncRNA, miRNA, and TF. To prioritize these motifs for their role in melanoma progression, we integrated patient-derived RNAseq dataset from TCGA (SKCM) melanoma cohort, using a weighted multi-objective function. We investigated the expression profile of the top-ranked motifs and used them to classify patients into metastatic and non-metastatic phenotypes.

CONCLUSIONS

The results of this study showed that network motif UCA1/AKT1/hsa-miR-125b-1 has the highest prediction accuracy (ACC = 0.88) for discriminating metastatic and non-metastatic melanoma phenotypes. The observation is also confirmed by the progression-free survival analysis where the patient group characterized by the metastatic-type expression profile of the motif suffers a significant reduction in survival. The finding suggests a prognostic value of network motifs for the classification and treatment of melanoma.

Identification of MicroRNA-Target Gene-Transcription Factor Regulatory Networks in Colorectal Adenoma Using Microarray Expression Data

Objective

The aim of the study was to find the key genes, microRNAs (miRNAs) and transcription factors (TFs) and construct miRNA-target gene-TF regulatory networks to investigate the underlying molecular mechanism in colorectal adenoma (CRA).

Methods

Four mRNA expression datasets and one miRNA expression dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were identified between CRA and normal samples. Moreover, functional enrichment analysis for DEGs was carried out utilizing the Cytoscape-plugin, known as ClueGO. These DEGs were mapped to STRING database to construct a protein-protein interaction (PPI) network. Then, a miRNA-target gene regulatory network was established to screen key DEMs. In addition, similar workflow of the analyses were also performed comparing the CRC samples with CRA ones to screen key DEMs. Finally, miRNA-target gene-TF regulatory networks were constructed for these key DEMs using iRegulon plug-in in Cytoscape.

Results

We identified 514 DEGs and 167 DEMs in CRA samples compared to healthy samples. Functional enrichment analysis revealed that these DEGs were significantly enriched in several terms and pathways, such as regulation of cell migration and bile secretion pathway. A PPI network was constructed including 325 nodes as well as 890 edges. A total of 59 DEGs and 65 DEMs were identified in CRC samples compared to CRA ones. In addition, Two key DEMs in CRA samples compared to healthy samples were identified, such as hsa-miR-34a and hsa-miR-96. One key DEM, hsa-miR-29c, which was identified when we compared the differentially expressed molecules found in the comparison CRA versus normal samples to the ones obtained in the comparison CRC versus CRA, was also identified in CRC samples compared to CRA ones. The miRNA-target gene-TF regulatory networks for these key miRNAs included two TFs, one TF and five TFs, respectively.

Conclusion

These identified key genes, miRNA, TFs and miRNA-target gene-TF regulatory networks associated with CRA, to a certain degree, may provide some hints to enable us to better understand the underlying pathogenesis of CRA.

Centrality metric for the vulnerability of urban networks to toxic releases

The dispersion of airborne pollutants in the urban atmosphere is a complex, canopy-driven process. The intricate structure of the city, the high number of potential sources, and the large spatial domain make it difficult to predict dispersion patterns, to simulate a great number of scenarios, and to identify the high-impact emission areas. Here we show that these complex transport dynamics can be efficiently characterized by adopting a complex network approach. The urban canopy layer is represented as a complex network. Street canyons and their intersections shape the spatial structure of the network. The direction and the transport capacity of the flow in the streets define the direction and the weight of the links. Within this perspective, pollutant contamination from a source is modeled as a spreading process on a network, and the most dangerous areas in a city are identified as the best spreading nodes. To this aim, we derive a centrality metric tailored to mass transport in flow networks. By means of the proposed approach, vulnerability maps of cities are rapidly depicted, revealing the nontrivial relation between urban topology, transport capacity of the street canyons, and forcing of the external wind. The network formalism provides promising insight in the comprehensive analysis of the fragility of cities to air pollution.

Estrogen Promotes cAMP Production in Mesenchymal Stem Cells by Regulating ADCY2

Background and Objectives

The maternal-fetal interface is an important source of mesenchymal stem cells (MSCs), and it is influenced by high levels of estradiol (E2) during pregnancy. It is highly important to study the role of E2 in MSCs for both clinical application and understanding of the mechanisms underlying pregnancy related diseases.

Methods and Results

In this study, differently expressed genes (DEGs) were found in the MSCs after exposure to E2. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs was performed and the integrated regulatory network of DEGs-miRNA was constructed. A total of 390 DEGs were found in the MSCs exposed to E2, including 164 upregulated DEGs (e.g. ADCY2, VEGFA and PPY) and 226 downregulated DEGs (e.g. KNG1, AGT and NPY). Additionally, 10 miRNAs (such as miR-148A/B, miR-152, miR-182) identified the integrated regulatory network of DEGs-miRNAs. Among them, the expression of ADCY2 was significantly upregulated, and this was associated with multiple changed genes. We confirmed that the expression of ADCY2 is significantly promoted by E2 and subsequently promoted the production of cAMP in MSCs. We also found that E2 promoted ADCY2 expression by inhibiting miR-152 and miR-148a.

Conclusions

E2 promotes the expression of cAMP through miR-148a/152-ADCY2 in MSCs. It is suggested that E2 plays a key role in the growth and function of MSCs.

Analysis of key genes and their functions in placental tissue of patients with gestational diabetes mellitus

BACKGROUND

This study was aimed at screening out the potential key genes and pathways associated with gestational diabetes mellitus (GDM).

METHODS

The GSE70493 dataset used for this study was obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in the placental tissue of women with GDM in relation to the control tissue samples were identified and submitted to protein-protein interaction (PPI) network analysis and subnetwork module mining. Functional enrichment analyses of the PPI network and subnetworks were subsequently carried out. Finally, the integrated miRNA-transcription factor (TF)-DEG regulatory network was analyzed.

RESULTS

In total, 238 DEGs were identified, of which 162 were upregulated and 76 were downregulated. Through PPI network construction, 108 nodes and 278 gene pairs were obtained, from which chemokine (C-X-C motif) ligand 9 (CXCL9), CXCL10, protein tyrosine phosphatase, receptor type C (PTPRC), and human leukocyte antigen (HLA) were screened out as hub genes. Moreover, genes associated with the immune-related pathway and immune responses were found to be significantly enriched in the process of GDM. Finally, miRNAs and TFs that target the DEGs were predicted.

CONCLUSIONS

Four candidate genes (viz., CXCL9, CXCL10, PTPRC, and HLA) are closely related to GDM. miR-223-3p, miR-520, and thioredoxin-binding protein may play important roles in the pathogenesis of this disease.

Comparative transcriptomic analysis reveals beneficial effect of dietary mulberry leaves on the muscle quality of finishing pigs

Background

The aim of this study was to investigate the effect of dietary mulberry leaves on the transcriptome profiles of finishing pigs. RNA‐Seq was used to identify differentially expressed genes (DEGs) in the longissimus dorsi of 56 pigs fed either a traditional diet or diets supplemented with 3%, 6% or 9% mulberry leaf powder, and both gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were performed. Furthermore, protein–protein interaction (PPI) network and the subnet module analysis were used to identify genes with beneficial potential, and quantitative real‐time polymerase chain reaction (qRT‐PCR) was used to validate the expression patterns revealed by RNA‐Seq.

Results

Pigs fed with the 6% mulberry diet exhibited greater average daily gain, lower water loss and lower shear force than the control group and yielded 531 DEGs, including 271 and 260 upregulated and downregulated genes, respectively. Function analysis revealed that the DEGs were significantly enriched in functions related to muscle growth and development. Furthermore, several genes (i.e. ACOT4, ECHS1, HACD1, NPR1, ADCY2, MGLL and IRS1) were enriched in a KEGG pathway that was associated with fatty acid metabolism, and in the PPI subnet module, four of eight node genes, namely TNNC1, MYL3, TCAP and TNNT1, were associated with muscle formation and development. The upregulation of these genes, including TNNC1, TNNT1 and MYL3, was confirmed by qRT‐PCR.

Conclusions

Dietary mulberry leaves (6%) may improve the muscle quality of pigs by modulating the expression of several key genes, such as TNNC1, MYL3 and TNNT1.

The study was aimed to explain the effect of the inclusion of mulberry in the diet of pigs on transcriptome profiling. The inclusion of mulberry in the diet might be helpful in muscle formation and development of pigs by modulating the expression levels of three genes including TNNC1, MYL3 and TNNT1

RNA‑seq analyses of antibiotic resistance mechanisms in Serratia marcescens

The present study aimed to further clarify the genetic mechanisms responsible for the antimicrobial resistance of Serratia marcescens (S. marcescens) using RNA sequencing. Three drug-susceptible S. marcescens strains (named MYQT1, MYQT2, and MYQT3) and three multidrug-resistant S. marcescens strains (named MYQT4, MYQT5, and MYQT6) were isolated from six different patients and subjected to RNA sequencing. Differentially expressed genes (DEGs) between the multidrug-resistant S. marcescens strains and drug-susceptible strains were screened and compared, followed by functional enrichment analysis. In addition, a protein-protein interaction (PPI) network was constructed, and significant modules were extracted from it. Genes enriched in the significant modules were subjected to further enrichment analysis. MYQT3 had very a different expression pattern from MYQT1 and MYQT2, and thus, MYQT3 was excluded from the following analysis. A total of 225 DEGs were identified, of which SMDB11_RS09300 (GTP cyclohydrolase FolE2) was the most significantly upregulated with a log₂ FC of 6.4; these DEGs were enriched in different GO terms, including hydrogen sulfide biosynthetic process, sulfur compound transmembrane transporter activity, and ABC transporter complex. Additionally, several genes were identified to be important genes in the PPI network, including SMDB11_RS17755 (upregulated; glutamate synthase large subunit), SMDB11_RS00590 (upregulated; sulfite reductase subunit α), and SMDB11_RS04505 (upregulated; cystathionine β-synthase). Thus, SMDB11_RS09300, SMDB11_RS17755, SMDB11_RS00590, and SMDB11_RS04505 may play significant roles in the antimicrobial resistance of S. marcescens by participating in folate metabolism or the integrity of cell membranes. However, further experiments are required to clarify these findings.

Knockdown of RAD18 inhibits glioblastoma development

This study aimed at investigating the role of RAD18 in the regulation of glioblastoma development as well as the underlying mechanisms. The human glioblastoma U251 and U87MG cells were transfected with siRNAs specifically targeting RAD18, and the effects of knockdown of RAD18 on the viability, apoptosis, migration, and invasion of U251 and U87MG cells were investigated. Transcriptome sequencing of the siRNA-RAD18-tranfected and siRNA-NC-transfected U251 cells was performed, followed by bioinformatic analyses for sequencing data. The results showed that knockdown of RAD18 significantly inhibited cell viability, promoted apoptosis, and suppressed migration and invasion of U251 and U87MG cells. Bioinformatic analyses of sequencing data identified 1,051 differentially expressed genes (DEGs) (369 up- and 682 downregulated genes) in the siRNA-RAD18-transfected U251 cells compared with siRNA-NC-transfected U251 cells. Eleven DEGs, including nerve growth factor (NGF), colony-stimulating factor 2 (CSF2), matrix metallopeptidase 1 (MMP1), platelet-derived growth factor receptor α (PDGFRA), and heme oxygenase 1 (HMOX1), were identified as the hub nodes in protein-protein interaction (PPI) network. Moreover, the aforementioned 11 hub genes were significantly enriched in PI3K-Akt signaling pathway and GO functions associated with the extracellular region. Notably, quantitative real-time polymerase chain reaction further confirmed that the expression levels of NGF, CSF2, HMOX1, and MMP1 were significantly downregulated, while that of PDGFRA was markedly upregulated in the siRNA-RAD18-transfected U251 cells than in the siRNA-NC cells. In conclusion, the knockdown of RAD18 may inhibit glioblastoma development by regulating the expression of the aforementioned key DEGs.

Comprehensive bioinformatics analysis of critical lncRNAs, mRNAs and miRNAs in non‑alcoholic fatty liver disease

Non‑alcoholic fatty liver disease (NAFLD) is the most common fatty liver disease in developed countries, in which fat accumulation in the liver is induced by non‑alcoholic factors. The present study was conducted to identify NAFLD‑associated long non‑coding RNAs (lncRNAs), mRNAs and microRNAs (miRNAs). The microarray dataset GSE72756, which included 5 NAFLD liver tissues and 5 controls, was acquired from the Gene Expression Omnibus database. Differentially expressed lncRNAs (DE‑lncRNAs) and mRNAs (DE‑mRNAs) were detected using the pheatmap package. Using the clusterProfiler package and Cytoscape software, enrichment and protein‑protein interaction (PPI) network analyses were conducted to evaluate the DE‑mRNAs. Next, the miRNA‑lncRNA‑mRNA interaction network was visualized using Cytoscape software. Additionally, RP11‑279F6.1 and AC004540.4 expression levels were analyzed by reverse transcription quantitative polymerase chain reaction. There were 318 DE‑lncRNAs and 609 DE‑mRNAs identified in the NAFLD tissues compared with the normal tissues. Jun proto‑oncogene, AP‑1 transcription factor subunit (JUN), which is regulated by AC004540.4 and RP11‑279F6.1, exhibited higher degree compared with other nodes in the PPI network. Furthermore, miR‑409‑3p and miR‑139 (targeting JUN) were predicted as PPI network nodes. In the miRNA‑lncRNA‑mRNA network, miR‑20a and B‑cell lymphoma 2‑like 11 (BCL2L11) were among the top 10 nodes. Additionally, BCL2L11, AC004540.4 and RP11‑279F6.1 were targeted by miR‑20a, miR‑409‑3p and miR‑139 in the miRNA‑lncRNA‑mRNA network, respectively. RP11‑279F6.1 and AC004540.4 expression was markedly enhanced in NAFLD liver tissues. These key RNAs may be involved in the pathogenic mechanisms of NAFLD.

Cycle Based Network Centrality

Paths and cycles are the two pivotal elements in a network. Here, we demonstrate that paths, particularly the shortest ones, are incomplete in information network. However, based on such paths, many network centrality measures are designed. While extensive explorations on paths have been made, modest studies focus on the cycles on measuring network centrality. We study the relationship between the shortest cycle and the shortest path from extensive real-world networks. The results illustrate the incompleteness of the shortest paths on measuring network centrality. Noticing that the shortest cycle is much more robust than the shortest path, we propose two novel cycle-based network centrality measures to address the incompleteness of paths: the shortest cycle closeness centrality (SCC) and the all cycle betweenness centrality (ACC). Notwithstanding we focus on the network centrality problem, our findings on cycles can be applied to explain the incompleteness of paths in applications and could improve the applicability into more scenarios where the paths are employed in network science.

RNA sequencing reveals significant miRNAs in Atypical endometrial hyperplasia

PURPOSE

In this paper, we aimed to investigate the miRNAs that played a regulatory role in the development of atypical endometrial hyperplasia (AEH).

METHODS

RNA sequencing was performed for endometrial tissues from 3 AEH patients and 3 endometrial normal hyperplasia patients. RNA sequencing data were processed and differentially expressed (DE) miRNAs were identified between AEH and controls. The target genes for DE miRNAs were identified and mapped to the protein-protein interaction (PPI) network. The miRNA related functions were predicted and miRNA-disease gene network was constructed.

RESULTS

Total 18 DE miRNAs were overlapped in three sample groups, among which hsa-miR-577, hsa-miR-182-5p and hsa-miR-183-5p were top three miRNAs that targeting largest number of genes. Function analysis showed that the 18 overlapped miRNAs mainly related with cancer and signaling transduction related pathways. PPI network showed that total 12 genes were among top 20 genes based on three network topological features including BCL2, UMPS, MAPK13, PRKCB, CREB1, IGF1, SP1, SMAD3, IGF1R, NOTCH2, WNT5A, TK2. Top 10 miRNAs in miRNA-disease gene network were identified such as hsa-miR-577 (degree = 17), hsa-miR-182-5p (degree = 16) and hsa-miR-3609 (degree = 13).

CONCLUSION

hsa-miR-577 and hsa-miR-182-5p may play regulatory role in AEH through AMPK signal pathway and Wnt signaling pathway.

Identification of key microRNAs, transcription factors and genes associated with congenital obstructive nephropathy in a mouse model of megabladder

OBJECTIVE

The present study aimed to investigate the molecular mechanism underlying congenital obstructive nephropathy (CON).

METHODS

The microarray dataset GSE70879 was downloaded from the Gene Expression Omnibus, including 3 kidney samples of megabladder mice and 4 control kidneys. Using this dataset, differentially expressed miRNAs (DEMs) were identified between the kidney samples from megabladder mice and controls, followed by identification of the target genes for these DEMs and construction of a DEM and target gene interaction network. Additionally, the target genes were subjected to Gene Ontology and pathway enrichment analyses, and were used for construction of a protein-protein interaction (PPI) network. Finally, regulatory networks were constructed to analyze transcription factors for the key miRNAs.

RESULTS

From 17 DEMs identified between kidney samples of megabladder mice and controls, 3 key miRNAs were screened, including mmu-miR-150-5p, mmu-miR-374b-5p and mmu-miR-126a-5p. The regulatory networks identified vascular endothelial growth factor A (Vegfa) as the common target gene of mmu-miR-150-5p and five transcription factors, including nuclear receptor subfamily 4, group A, member 2 (Nr4a2), Jun dimerisation protein 2 (Jdp2), Kruppel-like factor 6 (Klf6), Neurexophilin-3 (Nxph3) and RNA binding motif protein 17 (Rbm17). The gene encoding phosphatase and tensin homolog (Pten) was found to be co-regulated by mmu-miR-374b-5p and high mobility group protein A1 (Hmga1), whereas the kirsten rat sarcoma viral oncogene (Kras) was identified as a common target gene of mmu-miR-126a-5p and paired box 6 (Pax6).

CONCLUSIONS

In summary, the above-listed key miRNAs, transcription factors and key genes may be involved in the development of CON.

FGF2 and FAM201A affect the development of osteonecrosis of the femoral head after femoral neck fracture

Osteonecrosis of the femoral head (ONFH) is a common orthopedic disease associated with high disability, and femoral neck fracture (FNF) is one of the most common reasons for traumatic ONFH. This study was designed to reveal the mechanisms underlying ONFH. Using fastx_toolkit and prinseq-lite tools, quality control was conducted for the sequencing data. The differentially expressed genes (DEGs, including both mRNAs and lncRNAs) between ONFH and FNF samples were identified using the edgeR package in R, and were then subjected to enrichment analysis using the BioCloud platform. Subsequently, protein-protein interaction (PPI) networks were constructed using Cytoscape software. After the target genes of DE-lncRNAs were predicted based on Spearman's rank correlation coefficient, lncRNA-gene coexpression network was visualized using the Cytoscape software. Furthermore, functional enrichment analysis was carried out for the target genes using the clusterprofiler package in R. Additionally, the key genes were detected by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 2965 DEGs were identified from the ONFH samples, including 602 DE-lncRNAs (such as downregulated FAM201A). In the PPI networks, eight upregulated genes (including FGF2, IGF1, SOX9, and COL2A1) and 11 downregulated genes were among the top 20 genes according to all of the scores, such as degree centrality, closeness centrality, and betweenness centrality scores. Functional enrichment analysis showed that IGF1, SOX9, and COL2A1 were significantly enriched during skeletal system development. Moreover, qRT-PCR experiments detected the upregulation of FGF2 and downregulation of FAM201A in ONFH samples. FGF2 and FAM201A were correlated with the development of ONFH. Besides, IGF1, SOX9, and COL2A1 might also affect the pathogenesis of ONFH.

RNA Sequencing Uncovers Molecular Mechanisms Underlying Pathological Complete Response to Chemotherapy in Patients with Operable Breast Cancer

Background

This study aimed to identify key genes contributing to pathological complete response (pCR) to chemotherapy by mRNA sequencing (RNA-seq).

Material/Methods

RNA was extracted from the frozen biopsy tissue of patients with pathological complete response and patients with non-pathological complete response. Sequencing was performed on the HiSeq2000 platform. Differentially expressed genes (DEGs) were identified between the pCR group and non-pCR (NpCR) group. Pathway enrichment analysis of DEGs was performed. A protein-protein interaction network was constructed, then module analysis was performed to identify a subnetwork. Finally, transcription factors were predicted.

Results

A total of 673 DEGs were identified, including 419 upregulated ones and 254 downregulated ones. The PPI network constructed consisted of 276 proteins forming 471 PPI pairs, and a subnetwork containing 18 protein nodes was obtained. Pathway enrichment analysis revealed that PLCB4 and ADCY6 were enriched in pathways renin secretion, gastric acid secretion, gap junction, inflammatory mediator regulation of TRP channels, retrograde endocannabinoid signaling, melanogenesis, cGMP-PKG signaling pathway, calcium signaling pathway, chemokine signaling pathway, cAMP signaling pathway, and rap1 signaling pathway. CNR1 was enriched in the neuroactive ligand-receptor interaction pathway, retrograde endocannabinoid signaling pathway, and rap1 signaling pathway. The transcription factor-gene network consists of 15 transcription factors and 16 targeted genes, of which 5 were downregulated and 10 were upregulated.

Conclusions

We found key genes that may contribute to pCR to chemotherapy, such as PLCB4, ADCY6, and CNR1, as well as some transcription factors.

Network-based analysis of the molecular mechanisms of multiple myeloma and monoclonal gammopathy of undetermined significance

The present study aimed to reveal the molecular mechanisms of multiple myeloma (MM) and monoclonal gammopathy of undetermined significance (MGUS). This was a secondary study on microarray dataset GSE80608, downloaded from the Gene Expression Omnibus database, which included 10 control samples, 10 MGUS samples and 10 MM samples. Differentially expressed genes (DEGs) were identified between control and MGUS samples, and between control and MM samples. A protein-protein interaction (PPI) network was built for studying the interactions between the DEGs. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for the genes in a gene co-expression network. A microRNA (miRNA/miR)-gene network was built to the evaluate possible the miRNAs and genes involved in the diseases. The present study identified 136 common upregulated DEGs and 165 common downregulated DEGs between MM and MGUS. Pathway enrichment analysis of the genes in the gene co-expression network revealed that the complement and coagulation cascades pathway was significantly enriched for certain complement and coagulation-associated genes. Endothelin-1 (EDN1) was significantly enriched in the hypoxia inducible factor-1 (HIF-1) and tumor necrosis factor signaling pathways. EDN1 was an important node in the PPI network, and a target gene of let-7e, let-7b and miR-19a in the miRNA-gene network. The results of the present study indicate that complement and coagulation-associated genes, the complement and coagulation cascades pathway, EDN1, let-7e, let-7b-5p, miR-19a, and the tumor necrosis factor and HIF-1 signaling pathways may all be implicated in MM and MGUS.

Long-term variability of importance of brain regions in evolving epileptic brain networks

We investigate the temporal and spatial variability of the importance of brain regions in evolving epileptic brain networks. We construct these networks from multiday, multichannel electroencephalographic data recorded from 17 epilepsy patients and use centrality indices to assess the importance of brain regions. Time-resolved indications of highest importance fluctuate over time to a greater or lesser extent, however, with some periodic temporal structure that can mostly be attributed to phenomena unrelated to the disease. In contrast, relevant aspects of the epileptic process contribute only marginally. Indications of highest importance also exhibit pronounced alternations between various brain regions that are of relevance for studies aiming at an improved understanding of the epileptic process with graph-theoretical approaches. Nonetheless, these findings may guide new developments for individualized diagnosis, treatment, and control.

Mining dynamic noteworthy functions in software execution sequences

As the quality of crucial entities can directly affect that of software, their identification and protection become an important premise for effective software development, management, maintenance and testing, which thus contribute to improving the software quality and its attack-defending ability. Most analysis and evaluation on important entities like codes-based static structure analysis are on the destruction of the actual software running. In this paper, from the perspective of software execution process, we proposed an approach to mine dynamic noteworthy functions (DNFM)in software execution sequences. First, according to software decompiling and tracking stack changes, the execution traces composed of a series of function addresses were acquired. Then these traces were modeled as execution sequences and then simplified so as to get simplified sequences (SFS), followed by the extraction of patterns through pattern extraction (PE) algorithm from SFS. After that, evaluating indicators inner-importance and inter-importance were designed to measure the noteworthiness of functions in DNFM algorithm. Finally, these functions were sorted by their noteworthiness. Comparison and contrast were conducted on the experiment results from two traditional complex network-based node mining methods, namely PageRank and DegreeRank. The results show that the DNFM method can mine noteworthy functions in software effectively and precisely.

Local immunization strategy based on the scores of nodes

The problem of finding a better immunization strategy for controlling the spreading of the epidemic with limited resources has attracted much attention because of its great theoretical significance and wide application. In this paper, we propose a successful immunization strategy only depending on local information. Our strategy initializes the scores of nodes with the values of their degree and recalculates the score of a certain immunized node based on its local information, and then replaces the certain immunized node with its nonimmunized higher-score neighbor. To test the effectiveness of the proposed strategy, we conduct the experiments on several synthetic networks and real-world networks. The results show that the proposed strategy outperforms the existing well-known local strategies, even the degree centrality targeted strategy.