simplifyEnrichment:A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results

Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest.However,it may produce a long list of significant terms with highly redundant information that is difficult to summarize.Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters.We propose a new method named binary cut for clustering similarity matrices of functional terms.Through comprehensive benchmarks on both simulated and real-world datasets,we demonstrated that binary cut could efficiently cluster functional terms into groups where terms showed consistent similarities within groups and were mutually exclusive between groups.We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with binary cut,while similarity matrices based on gene overlap showed less consistent patterns.We implemented the binary cut algorithm in the R package simplifyEnrichment,which additionally provides functionalities for visualizing,summarizing,and comparing the clustering.The simplifyEnrichment package and the documentation are available at https://bioconductor.org/packages/simplifyEnrichment/.

WocEA:The visualization of functional enrichment results in word clouds

The integration, analysis and visualization of the big omics data are critical for addressing a broad spectrum of biological questions. One of the most frequently conducted procedures is enrichment analysis, which statistically tests whether individual functional an- notations of Gent Ontology （GO） or Kyoto Encyclopedia of Genes and Genomes （KEGG） are significantly over-or under-represented in an ＂interesting＂ gene or protein list against the reference set （Tavazoie et al., 1999）.

Bayesian functional enrichment analysis for the Reactome database

The first step in the analysis of high-throughput experiment results is often to identify genes orproteins with certain characteristics, such as genes being differentially expressed (DE). To gainmore insights into the underlying biology, functional enrichment analysis is then conductedto provide functional interpretation for the identified genes or proteins. The hypergeometricP value has been widely used to investigate whether genes from predefined functional terms,e.g., Reactome, are enriched in the DE genes. The hypergeometric P value has several limitations: (1) computed independently for each term, thus neglecting biological dependence;(2) subject to a size constraint that leads to the tendency of selecting less-specific terms. In this paper,a Bayesian approach is proposed to overcome these limitations by incorporating the interconnected dependence structure of biological functions in the Reactome database through a CARprior in a Bayesian hierarchical logistic model. The inference on functional enrichment is thenbased on posterior probabilities that are immune to the size constraint. This method can detectmoderate but consistent enrichment signals and identify sets of closely related and biologicallymeaningful functional terms rather than isolated terms. The performance of the Bayesian methodis demonstrated via a simulation study and a real data application.

Gene signatures to therapeutics:Assessing the potential of ivermectin against t(4;14)multiple myeloma

BACKGROUND Multiple myeloma(MM)is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow.The translocation,(t)(4;14),results in high-risk MM with limited treatment alternatives.Thus,there is an urgent need for identification and validation of potential treatments for this MM subtype.Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.AIM To elucidate the molecular basis and search for potential effective drugs of t(4;14)MM subtype by employing a comprehensive approach.METHODS The transcriptional signature of t(4;14)MM was sourced from the Gene Expression Omnibus.Two datasets,GSE16558 and GSE116294,which included 17 and 15 t(4;14)MM bone marrow samples,and five and four normal bone marrow samples,respectively.After the differentially expressed genes were identified,the Cytohubba tool was used to screen for hub genes.Then,the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis.Using the STRING database and Cytoscape,protein–protein interaction networks and core targets were identified.Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis,respectively.RESULTS In this study,a total of 258 differentially expressed genes with enriched functions in cancer pathways,namely cytokine receptor interactions,nuclear factor(NF)-κB signaling pathway,lipid metabolism,atherosclerosis,and Hippo signaling pathway,were identified.Ten hub genes(cd45,vcam1,ccl3,cd56,app,cd48,btk,ccr2,cybb,and cxcl12)were identified.Nine drugs,including ivermectin,deforolimus,and isoliquiritigenin,were predicted by the Connectivity Map database to have potential therapeutic effects on t(4;14)MM.In molecular docking,ivermectin showed strong binding affinity to all 10 identified targets,especially cd45 and cybb.Ivermectin inhibited t(4;14)MM cell growth via the NF-κB pathway and induced MM cell apoptosis in vitro.Furthermore,ivermectin increased reactive oxygen species accumulation and altered the mitochondrial membrane potential in t(4;14)MM cells.CONCLUSION Collectively,the findings offer valuable molecular insights for biomarker validation and potential drug development in t(4;14)MM diagnosis and treatment,with ivermectin emerging as a potential therapeutic alternative.

Transcriptomic Analysis of Metastatic Colorectal Tumor with Low Mutational Burden

Objective:To identify potential drug targets for metastasis colorectal cancer(CRC)patients with low mutational burden by examining differences in immune-related gene expression.Methods:For this study,623 samples were collected from The Cancer Genome Atlas(TCGA)database,comprising tumor mutational burden(TMB),RNA sequencing(RNA-Seq),and clinical data.Differential gene expression analysis,Gene Ontology(GO),and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis of the identified genes were conducted using the R package.Additionally,a comparative analysis of immune infiltrating cell composition in metastatic and non-metastatic groups was performed.Hub genes,exhibiting high levels of interaction,were selected using the Search Tool for the Retrieval of Interacting Genes/Proteins(STRING)database.The Drug Gene Interaction Database(DGIdb)was then utilized to estimate drugs targeting the identified hub genes.Results:The transcriptome data of 326 colorectal cancer patients with low TMB were analyzed,comprising 58 patients with metastasis and 268 patients without metastasis.Among the differential expression in 1,111 genes for patients with metastasis compared to those without metastasis,733 genes were upregulated,and 378 genes were downregulated.KEGG and GO enrichment analysis indicated significant differences in gene expression in CRC metastatic patients with low TMB compared to non-metastasis patients with low TMB.Enriched pathways included humoral immune response,immunoglobulin production,and regulation of AMPA receptor activity.Two genes related to interleukin-12 were identified through secondary enrichment for immune-related genes.Analysis of tumor-infiltrating immune cell data revealed significant differences in memory-activated T cell CD4 and T cell CD8.Conclusions:This analysis of RNA sequencing data and immune-filtrating cell data revealed significant differences between metastatic colorectal cancer patients with low TMB and their non-metastatic counterparts.These distinctions suggest the possibility of identifying more effective drugs or therapies for metastatic colorectal cancer patients with low TMB.

Soft Tissue Deformation Model Based on Marquardt Algorithm and Enrichment Function

In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marquardt algorithm and enrichment function.The model is based on the element-free Galerkin method,in which Kelvin viscoelastic model and adjustment function are integrated.Marquardt algorithm is applied to fit the relation between force and displacement caused by surface deformation,and the enrichment function is applied to deal with the discontinuity in the meshless method.To verify the validity of the model,the Sensable Phantom Omni force tactile interactive device is used to simulate the deformations of stomach and heart.Experimental results show that the proposed model improves the real-time performance and accuracy of soft tissue deformation simulation,which provides a new perspective for the application of the meshless method in virtual surgery.

Hub genes and their key effects on prognosis of Burkitt lymphoma

BACKGROUND Burkitt lymphoma(BL)is an exceptionally aggressive malignant neoplasm that arises from either the germinal center or post-germinal center B cells.Patients with BL often present with rapid tumor growth and require high-intensity multidrug therapy combined with adequate intrathecal chemotherapy prophylaxis,however,a standard treatment program for BL has not yet been established.It is important to identify biomarkers for predicting the prognosis of BLs and discriminating patients who might benefit from the therapy.Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.AIM To identify hub genes and perform gene ontology(GO)and survival analysis in BL.METHODS Gene expression profiles and clinical traits of BL patients were collected from the Gene Expression Omnibus database.Weighted gene co-expression network analysis(WGCNA)was applied to construct gene co-expression modules,and the cytoHubba tool was used to find the hub genes.Then,the hub genes were analyzed using GO and Kyoto Encyclopedia of Genes and Genomes analysis.Additionally,a Protein-Protein Interaction network and a Genetic Interaction network were constructed.Prognostic candidate genes were identified through overall survival analysis.Finally,a nomogram was established to assess the predictive value of hub genes,and drug-gene interactions were also constructed.RESULTS In this study,we obtained 8 modules through WGCNA analysis,and there was a significant correlation between the yellow module and age.Then we identified 10 hub genes(SRC,TLR4,CD40,STAT3,SELL,CXCL10,IL2RA,IL10RA,CCR7 and FCGR2B)by cytoHubba tool.Within these hubs,two genes were found to be associated with OS(CXCL10,P=0.029 and IL2RA,P=0.0066)by survival analysis.Additionally,we combined these two hub genes and age to build a nomogram.Moreover,the drugs related to IL2RA and CXCL10 might have a potential therapeutic role in relapsed and refractory BL.CONCLUSION From WGCNA and survival analysis,we identified CXCL10 and IL2RA that might be prognostic markers for BL.

Bioinformatics Analysis of Genes and Pathways of CD11b^＋/Ly6C^intermediate Macrophages after Renal Ischemia-Reperfusion Injury

Renal ischemia-reperfusion injury（IRI） is a major cause of acute kidney injury（AKI）,which could induce the poor prognosis.The purpose of this study was to characterize the molecular mechanism of the functional changes of CD11 b＋/Ly6 Cintermediate macrophages after renal IRI.The gene expression profiles of CD11 b＋/Ly6 Cintermediate macrophages of the sham surgery mice,and the mice 4 h,24 h and 9 days after renal IRI were downloaded from the Gene Expression Omnibus database.Analysis of m RNA expression profiles was conducted to identify differentially expressed genes（DEGs）,biological processes and pathways by the series test of cluster.Protein-protein interaction network was constructed and analysed to discover the key genes.A total of 6738 DEGs were identified and assigned to 20 model profiles.DEGs in profile 13 were one of the predominant expression profiles,which are involved in immune cell chemotaxis and proliferation.Signet analysis showed that Atp5 a1,Atp5 o,Cox4 i,Cdc42,Rac2 and Nhp2 were the key genes involved in oxidation-reduction,apoptosis,migration,M1-M2 differentiation,and proliferation of macrophages.RPS18 may be an appreciate reference gene as it was stable in macrophages.The identified DEGs and their enriched pathways investigate factors that may participate in the functional changes of CD11 b＋/Ly6 Cintermediate macrophages after renal IRI.Moreover,the vital gene Nhp2 may involve the polarization of macrophages,which may be a new target to affect the process of AKI.

Transcriptome and carotenoid profiling of different varieties of Coffea arabica provides insights into fruit color formation

The processability and ultimate quality of coffee(Coffea arabica)are determined by the composition of the matured fruits.The basis of genetic variation in coffee fruit quality could be explained by studying color formation during fruit maturation.Transcriptome profiling was conducted on matured fruits of four C.arabica varieties(orange colored fruits(ORF);purple colored fruits(PF);red colored fruits(RF)and yellow colored fruits(YF))to identify key color-regulating genes,biosynthesis pathways and transcription factors implicated in fruit color formation.A total of 39,938 genes were identified in the transcriptomes of the four C.arabica varieties.In all,2745,781 and 1224 differentially expressed genes(DEGs)were detected in YF_vs_PF,YF_vs_RF and YF_vs_ORF,respectively,with 1732 DEGs conserved among the three pairwise groups.Functional annotation of the DEGs led to the detection of 28 and 82 key genes involved in the biosynthesis of carotenoids and anthocyanins,respectively.Key transcription factors bHLH,MYB,NAC,MADS,and WRKY implicated in fruit color regulation were detected.The high expression levels of gene-LOC113688784(PSY),gene-LOC113730013(b-CHY),gene-LOC113728842(CCD7),gene-LOC113689681(NCED)and gene-LOC113729473(ABA2)in YF may have accounted for the yellow coloration.The differential expression of several anthocyanin and carotenoid-specific genes in the fruits substantially account for the purple(PF),red(RF),and orange(ORF)colorations.This study provides important insights into fruit color formation and variations in C.arabica and will help to develop coffee varieties with specific color and quality traits.

Identification of potential key molecules and signaling pathways for psoriasis based on weighted gene co-expression network analysis

BACKGROUND Psoriasis is a chronic inflammatory skin disease,the pathogenesis of which is more complicated and often requires long-term treatment.In particular,moderate to severe psoriasis usually requires systemic treatment.Psoriasis is also associated with many diseases,such as cardiometabolic diseases,malignant tumors,infections,and mood disorders.Psoriasis can appear at any age,and lead to a substantial burden for individuals and society.At present,psoriasis is still a treatable,but incurable,disease.Previous studies have found that micro RNAs(mi RNAs)play an important regulatory role in the progression of various diseases.Currently,mi RNAs studies in psoriasis and dermatology are relatively new.Therefore,the identification of key mi RNAs in psoriasis is helpful to elucidate the molecular mechanism of psoriasis.AIM To identify key molecular markers and signaling pathways to provide potential basis for the treatment and management of psoriasis.METHODS The mi RNA and m RNA data were obtained from the Gene Expression Omnibus database.Then,differentially expressed m RNAs(DEm RNAs)and differentially expressed mi RNAs(DEmi RNAs)were screened out by limma R package.Subsequently,DEm RNAs were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomics functional enrichment.The“WGCNA”R package was used to analyze the co-expression network of all mi RNAs.In addition,we constructed mi RNA-m RNA regulatory networks based on identified hub mi RNAs.Finally,in vitro validation was performed.All experimental procedures were approved by the ethics committee of Chinese PLA General Hospital(S2021-012-01).RESULTS A total of 639 DEm RNAs and 84 DEmi RNAs were identified.DEm RNAs screening criteria were adjusted P(adj.P)value<0.01 and|log Fold Change|(|log FC|)>1.DEmi RNAs screening criteria were adj.P value<0.01 and|logFC|>1.5.KEGG functional analysis demonstrated that DEm RNAs were significantly enriched in immune-related biological functions,for example,tolllike receptor signaling pathway,cytokine-cytokine receptor interaction,and chemokine signaling pathway.In weighted gene co-expression network analysis,turquoise module was the hub module.Moreover,10 hub mi RNAs were identified.Among these 10 hub mi RNAs,only 8 hub mi RNAs predicted the corresponding target m RNAs.97 negatively regulated mi RNA-m RNA pairs were involved in the mi RNA-m RNA regulatory network,for example,hsa-mi R-21-5 pclaudin 8(CLDN8),hsa-mi R-30 a-3 p-interleukin-1 B(IL-1 B),and hsa-mi R-181 a-5 p/hsa-mi R-30 c-2-3 p-C-X-C motif chemokine ligand 9(CXCL9).Real-time polymerase chain reaction results showed that IL-1 B and CXCL9 were up-regulated and CLDN8 was down-regulated in psoriasis with statistically significant differences.CONCLUSION The identification of potential key molecular markers and signaling pathways provides potential research directions for further understanding the molecular mechanisms of psoriasis.This may also provide new research ideas for the prevention and treatment of psoriasis in the future.

Key genes of deficiency of liver and kidney type chronic pain through bioinformatics analyses

Objective To screen the key genes of chronic pain and provide a reference for the treatment of chronic pain.Methods We performed comprehensive bioinformatics analysis by screening chronic primary pain-related datasets to obtain differentially expressed genes(DEGs)and then imported DEGs into the DAVID database for Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis.Gene Set Enrichment Analysis(GESA)analysis was done by GSEA_4.1.0 software.At the same time,we imported the intersecting genes into the STRING database and processed them by Cytoscape_3.8.1 software to obtain the protein-protein interaction(PPI)network and the central gene.Results As a result,a total of 54 DEGs were screened,including 47 up-regulated genes,1 down-regulated gene,and 6 genes that were expressed differently in different datasets.23 GO terms and 8 KEGG pathways were enriched by DAVID.PPI network analysis found that SPI1,STAT3,TNFRSF1B,PTGS2,and CXCL1 genes interacted more strongly with other genes,and were predicted to be key genes in chronic primary pain.Conclusion Our results suggested that 5 DEGs,STAT3,SPI1,TNFRSF1B,PTGS2,and CXCL1,have the potential to be used as prognostic and predictive markers for the clinical management of patients with this disease.

Cell cycle and complement inhibitors may be specific for treatment of spinal cord injury in aged and young mice: transcriptomic analyses

Previous studies have reported age-specific pathological and functional outcomes in young and aged patients suffering spinal cord injury,but the mechanisms remain poorly understood. In this study, we examined mice with spinal cord injury. Gene expression profiles from the Gene Expression Omnibus database （accession number GSE93561） were used, including spinal cord samples from 3 young injured mice （2–3-months old, induced by Impactor at Th9 level） and 3 control mice （2–3-months old, no treatment）, as well as 2 aged injured mice （15–18-months old, induced by Impactor at Th9 level） and 2 control mice （15–18-months old, no treatment）. Differentially expressed genes （DEGs） in spinal cord tissue from injured and control mice were identified using the Linear Models for Microarray data method,with a threshold of adjusted P 〈 0.05 and ｜logFC（fold change）｜ 〉 1.5. Protein–protein interaction networks were constructed using data from the STRING database, followed by module analysis by Cytoscape software to screen crucial genes. Kyoto encyclopedia of genes and genomes pathway and Gene Ontology enrichment analyses were performed to investigate the underlying functions of DEGs using Database for Annotation, Visualization and Integrated Discovery. Consequently, 1,604 and 1,153 DEGs were identified between injured and normal control mice in spinal cord tissue of aged and young mice, respectively. Furthermore, a Venn diagram showed that 960 DEGs were shared among aged and young mice, while 644 and 193 DEGs were specific to aged and young mice, respectively. Functional enrichment indicates that shared DEGs are involved in osteoclast differentiation, extracellular matrix–receptor interaction, nuclear factor-kappa B signaling pathway, and focal adhesion. Unique genes for aged and young injured groups were involved in the cell cycle （upregulation of PLK1） and complement （upregulation of C3） activation, respectively. These findings were confirmed by functional analysis of genes in modules （common, 4; aged, 2; young, 1） screened from protein–protein interaction networks. Accordingly, cell cycle and complement inhibitors may be specific treatments for spinal cord injury in aged and young mice, respectively.

Association between chromosomal aberration of COX8C and tethered spinal cord syndrome:array-based comparative genomic hybridization analysis

Copy number variations have been found in patients with neural tube abnormalities.In this study,we performed genome-wide screening using high-resolution array-based comparative genomic hybridization in three children with tethered spinal cord syndrome and two healthy parents.Of eight copy number variations,four were non-polymorphic.These non-polymorphic copy number variations were associated with Angelman and Prader-Willi syndromes,and microcephaly.Gene function enrichment analysis revealed that COX8 C,a gene associated with metabolic disorders of the nervous system,was located in the copy number variation region of Patient 1.Our results indicate that array-based comparative genomic hybridization can be used to diagnose tethered spinal cord syndrome.Our results may help determine the pathogenesis of tethered spinal cord syndrome and prevent occurrence of this disease.

Bioinformatics prediction of potential mechanisms and biomarkers underlying dilated cardiomyopathy

BACKGROUND Heart failure is a health burden responsible for high morbidity and mortality worldwide, and dilated cardiomyopathy(DCM) is one of the most common causes of heart failure. DCM is a disease of the heart muscle and is characterized by enlargement and dilation of at least one ventricle alongside impaired contractility with left ventricular ejection fraction < 40%. It is also associated with abnormalities in cytoskeletal proteins, mitochondrial ATP transporter, microvasculature, and fibrosis. However, the pathogenesis and potential biomarkers of DCM remain to be investigated.AIM To investigate the candidate genes and pathways involved in DCM patients.METHODS Two expression datasets(GSE3585 and GSE5406) were downloaded from the Gene Expression Omnibus database. The differentially expressed genes(DEGs) between the DCM patients and healthy individuals were identified using the R package “linear models for microarray data.” The pathways with common DEGs were analyzed via Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes(KEGG), and gene set enrichment analyses. Moreover, a protein-protein interaction network(PPI) was constructed to identify the hub genes and modules. The MicroRNA Database was applied to predict the microRNAs(miRNAs) targeting the hub genes. Additionally, immune cell infiltration in DCM was analyzed using CIBERSORT.RESULTS In total, 97 DEGs(47 upregulated and 50 downregulated) were identified. GO analysis showed that the DEGs were mainly enriched in “response to growth factor,” “extracellular matrix,” and “extracellular matrix structural constituent.” KEGG pathway analysis indicated that the DEGs were mainly enriched in “protein digestion and absorption” and “interleukin 17(IL-17) signaling pathway.” The PPI network suggested that collagen type Ⅲ alpha 1 chain(COL3A1) and COL1A2 contribute to the pathogenesis of DCM. Additionally, visualization of the interactions between miRNAs and the hub genes revealed that hsa-miR-5682 and hsa-miR-4500 interacted with both COL3A1 and COL1A2, and thus these miRNAs might play roles in DCM. Immune cell infiltration analysis revealed that DCM patients had more infiltrated plasma cells and fewer infiltrated B memory cells, T follicular helper cells, and resting dendritic cells.CONCLUSION COL1A2 and COL3A1 and their targeting miRNAs, hsa-miR-5682 and hsa-miR-4500, may play critical roles in the pathogenesis of DCM, which are closely related to the IL-17 signaling pathway and acute inflammatory response. These results may provide useful clues for the diagnosis and treatment of DCM.

Early Detection of Temporal Lobe Epilepsy: Identification of Novel Candidate Genes and Potential Biomarkers Using Integrative Genomics Analysis

Currently afflicting more than 50 million people worldwide, epilepsy is the spectrum disorder characterizing seizures that occur without other plausible medical explanations. Temporal lobe epilepsy (TLE) is one of the most common forms of epilepsy. Current clinical methods;including MRI scans, EEG tests, and doctor visits;can take upwards of several months to confirm a TLE diagnosis;during this time, patients may experience additional seizures and are at an increased risk for other psychiatric disorders. The purpose of this study is to identify candidate genetic biomarkers to facilitate the earlier detection and diagnosis of TLE through gene-based testing (e.g., genomic heatmap analysis or genetic and/or microarray testing). It was hypothesized that potential biomarkers could be identified by analyzing genes that are normally significantly overexpressed in the temporal lobe relative to the gray matter. Statistical and functional analysis was performed on significantly overexpressed genes (≥3.000 fold change) in the gene expression profiles of four donors without epilepsy. The experimental-evidence-based STRING protein interactions analysis showed associations between genes found in DAVID keyword search and other genes facilitating network interconnectivity. After evaluation of the genes’ STRING enriched functions, changes in the expression of the genes CAMK2A, NPY, DLG4, MEF2C, and MAPK7 were concluded to be potential biomarkers for TLE, confirming the original hypothesis. Specifically, the identification of MEF2C and MAPK7 for this purpose is relatively novel in the fields of bioinformatics and neurogenetics. Future work includes investigating the utility of the candidate genes in real-world gene-based diagnostic methods.

DNA methylation changes induced by BDE-209 are related to DNA damage response and germ cell development in GC-2spd

Decabrominated diphenyl ether(BDE-209)is generally utilized in multiple polymer materials as common brominated flame retardant.BDE-209 has been listed as persistent organic pollutants(POPs),which was considered to be reproductive toxin in the environment.But it still remains unclear about the effects of BDE-209 on DNA methylation and the inducedmale reproductive toxicity.Due to the extensive epigenetic regulation in germ line development,we hypothesize that BDE-209 exposure impacts the statue of DNA methylation in spermatocytes in vitro.Therefore,the mouse GC-2spd(GC-2)cells were used for the genome wide DNA methylation analysis after treated with 32μg/mL BDE-209 for 24 hr.The results showed that BDE-209 caused genomic methylation changes with 32,083 differentially methylated CpGs in GC-2 cells,including 16,164(50.38%)hypermethylated and 15,919(49.62%)hypomethylated sites.With integrated analysis ofDNAmethylation data and functional enrichment,we found that BDE-209 might affect the functional transcription in cell growth and sperm development by differential gene methylation.qRT-PCR validation demonstrated the involvement of p53-dependent DNA damage response in the GC-2 cells after BDE-209 exposure.In general,our findings indicated that BDE-209-induced genome wide methylation changes could be interrelated with reproductive dysfunction.This study might provide new insights into the mechanisms of male reproductive toxicity under the environmental exposure to BDE-209.

NORMA:The Network Makeup Artist—A Web Tool for Network Annotation Visualization

The Network Makeup Artist(NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations(e.g., Gene Ontology, Pathway enrichment, community detection,or clustering results) can be uploaded and visualized in a network, either as colored pie-chart nodes or as color-filled areas in a 2D/3D Venn-diagram-like style. In the case where no annotation exists,algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefy, with NORMA, users can encode three types of information simultaneously. These are 1) the network, 2) the communities or annotations of interest, and 3) node categories or expression values. Finally, NORMA offers basic topological analysis and direct topological comparison across any of the selected networks. NORMA service is available at http://norma.pavlopouloslab.info, whereas the code is available at https://github.com/Pavlopoulos Lab/NORMA.

Bioinformatic identification of key genes and pathways that may be involved in the pathogenesis of HBV-associated acute liver failure

In order to explore the molecular mechanisms behind the pathogenesis of acute liver failure(ALF)associated with hepatitis B virus(HBV)infection,the present study aimed to identify potential key genes and pathways involved using samples from patients with HBV-associated ALF.The GSE38941 array dataset was downloaded from the Gene Expression Omnibus database,and differentially expressed genes(DEGs)between 10 liver samples from 10 healthy donors and 17 liver specimens from 4 patients with HBV-associated ALF were analyzed using the Linear Models for Microarray Data package.Gene Ontology and KEGG pathway enrichment analyses of the DEGs were performed,followed by functional annotation of the genes and construction of a proteineprotein interaction(PPI)network.Subnetwork modules were subsequently identified and analyzed.In total,3142 DEGs were identified,of which 1755 were upregulated and 1387 were downregulated.The extracellular exosome,immune response,and inflammatory response pathways may potentially be used as biomarkers of ALF pathogenesis.In total,17 genes(including CCR5,CXCR4,ALB,C3,VGEFA,and IGF1)were identified as hub genes in the PPI network and may therefore be potential marker genes for HBV-associated ALF.

A novel multi-level model for quasi-brittle cracking analysis with complex microstructure

The paper presents a novel multi-level model for quasi-brittle cracking analysis.Based on the partition of unity and information transmission technology,it provides a new non-re-meshing way to describe the cracking phenomenon in structures constructed from materials with complex microstructures.In the global model,the concept of the material particle is defined and the basic unknowns are the boundary displacements of these particles,which is different from the concept of the traditional displacement field.A series of enrichment functions with continuous steps is proposed,describing the boundary displacement affected by crack bands and allowing the intersections of crack bands with particle boundaries a priori unknown.Simultaneously,additional equations are introduced to determine element status and make the degrees of freedom of the global model remain at a stable level.Compared with previous research by our group,where the local description is equal to the global description on the boundary of a material particle,the introduced enrichment functions enable more accurate capture of the characteristics of the crack band.The model avoids the complex and dynamic model adjustments due to the activation and exit of representative volume elements(RVEs)and the accuracy of the description of the crack pattern can be ensured.The RVEs are activated at first,but then many of them exit the computation due to the unloading which reduces many of the degrees of freedom.Two examples of concrete specimens are analyzed,and the concrete fracture experiment and the digital image correlation(DIC)test are conducted.Compared with the reference solutions and the experimental data,even though the microstructure of concrete is very complex,the cracking process and crack pattern can be obtained accurately.