Bioinformatics analysis reveals disturbance mechanism of MAPK signaling pathway and cell cycle in Glioblastoma multiforme

Background & objectives

To analyze the reversal gene pairs and identify featured reversal genes related to mitogen-activated protein kinases (MAPK) signaling pathway and cell cycle in Glioblastoma multiforme (GBM) to reveal its pathogenetic mechanism.

Methods

We downloaded the gene expression profile GSE4290 from the Gene Expression Omnibus database, including 81 gene chips of GBM and 23 gene chips of controls. The t test was used to analyze the DEGs (differentially expressed genes) between 23 normal and 81 GBM samples. Then some perturbing metabolic pathways, including MAPK (mitogen-activated protein kinases) and cell cycle signaling pathway, were extracted from KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway database. Cancer genes were obtained from the database of Cancer Gene Census. The reversal gene pairs between DEGs and cancer genes were further analyzed in MAPK and cell cycle signaling pathway.

Results

A total 8523 DEGs were obtained including 4090 up-regulated and 4433 down-regulated genes. Among them, ras-related protein rab-13(RAB13), neuroblastoma breakpoint family member 10 (NBPF10) and disks large homologue 4 (DLG4) were found to be involved in GBM for the first time. We obtained MAPK and cell cycle signaling pathways from KEGG database. By analyzing perturbing mechanism in these two pathways, we identified several reversal gene pairs, including NRAS (neuroblastoma RAS) and CDK2 (cyclin-dependent kinase 2), CCND1 (cyclin D1) and FGFR (fibroblast growth factor receptor). Further analysis showed that NRAS and CDK2 were positively related with GBM. However, FGFR2 and CCND1 were negatively related with GBM.

Interpretation & conclusions

These findings suggest that newly identified DEGs and featured reversal gene pairs participated in MAPK and cell cycle signaling pathway may provide a new therapeutic line of approach to GBM.     

Green tea polyphenols boost gut-microbiota-dependent mitochondrial TCA and urea cycles in Sprague–Dawley rats

Green tea polyphenols (GTPs) were found to boost mammal energy conversion by modulating gut-microbial community structure, gene orthologs and metabolic pathways. Here we examined the metabolites present in the gut-microbiota-dependent mitochondrial tricarboxylic acid (TCA) cycle and urea cycle using hydrophilic interaction liquid chromatography (HILIC)-heated electrospray ionization (HESI)-tandem liquid chromatogram mass spectrometry (LC–MS). Six groups (n=12) of Sprague–Dawley rats (6-mo, ~250 g) were administered with water containing 0%, 0.5%, and 1.5% GTPs (wt/vol or g/dL). Gut-content samples were collected at 3- and 6-mo. Untargeted metabolomics detected 2177 features, with 91 features demonstrating significant dose- and time-dependencies on the GTPs treatment. Targeted metabolomics analysis revealed remarkable changes of 39 metabolites in the mitochondrial TCA cycle and urea cycle, including argininosuccunic acid (0.9-fold vs control), dihydrouracil (1.14-fold vs control), fumaric acid (1.19-fold vs control), malic acid (2.17-fold vs control), citrulline (1.86-fold vs control), and succinic acid (0.4-fold vs control). The untargeted metabolomics data were mined using bioinformatics approaches, such as analysis of variance-simultaneous component analysis (ASCA), enrichment pathway analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping analysis. The results of 16S rRNA survey, metagenomics analysis, and metabolomics analysis were extrapolated and integrated using databases of Integrated Microbial Genomes and Microbiomes (IMG/M) and KEGG. Our analysis demonstrates that GTPs enhance energy conversion by boosting mitochondrial TCA cycle and urea cycle of gut-microbiota in rats. This metabolic modulation is achieved by enriching many gene orthologs, following the increase of beneficial microbials in families C. Ruminococcaceae, C. Lachnospiraceae and B. Bacteroidaceae.     

Local Network Topology in Human Protein Interaction Data Predicts Functional Association

The use of high-throughput techniques to generate large volumes of protein-protein interaction (PPI) data has increased the need for methods that systematically and automatically suggest functional relationships among proteins. In a yeast PPI network, previous work has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional association. In this study we improved the prediction scheme by developing a new algorithm and applied it on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting function-associated protein pairs. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as benchmarks to compare and evaluate the function relevance. The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins. Further functional comparisons between them allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made functional inferences from detailed analysis on one subcluster highly enriched in the TGF-β signaling pathway (P<10⁻⁵⁰). Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era.     

Identifying pathways affected by cancer mutations

Mutations in 15 cancers, sourced from the COSMIC Whole Genomes database, and 297 human pathways, arranged into pathway groups based on the processes they orchestrate, and sourced from the KEGG pathway database, have together been used to identify pathways affected by cancer mutations. Genes studied in ≥ 15, and mutated in ≥ 10 samples of a cancer have been considered recurrently mutated, and pathways with recurrently mutated genes have been considered affected in the cancer. Novel doughnut plots have been presented which enable visualization of the extent to which pathways and genes, in each pathway group, are targeted, in each cancer. The ‘organismal systems’ pathway group (including organism-level pathways; e.g., nervous system) is the most targeted, more than even the well-recognized signal transduction, cell-cycle and apoptosis, and DNA repair pathway groups. The important, yet poorly-recognized, role played by the group merits attention. Pathways affected in ≥ 7 cancers yielded insights into processes affected.     

Gene–smoking interactions in multiple Rho-GTPase pathway genes in an early-onset coronary artery disease cohort

We performed a gene–smoking interaction analysis using families from an early-onset coronary artery disease cohort (GENECARD). This analysis was focused on validating and expanding results from previous studies implicating single nucleotide polymorphisms (SNPs) on chromosome 3 in smoking-mediated coronary artery disease. We analyzed 430 SNPs on chromosome 3 and identified 16 SNPs that showed a gene–smoking interaction at P < 0.05 using association in the presence of linkage—ordered subset analysis, a method that uses permutations of the data to empirically estimate the strength of the association signal. Seven of the 16 SNPs were in the Rho-GTPase pathway indicating a 1.87-fold enrichment for this pathway. A meta-analysis of gene–smoking interactions in three independent studies revealed that rs9289231 in KALRN had a Fisher’s combined P value of 0.0017 for the interaction with smoking. In a gene-based meta-analysis KALRN had a P value of 0.026. Finally, a pathway-based analysis of the association results using WebGestalt revealed several enriched pathways including the regulation of the actin cytoskeleton pathway as defined by the Kyoto Encyclopedia of Genes and Genomes.     

FELLA: an R package to enrich metabolomics data

Background

Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects.

Results

We introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets –three from Homo sapiens, two from Danio rerio and one from Mus musculus– and has reproduced findings from the original studies and from independent literature.

Conclusions

The R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.

     

Biological Pathway‐Based Genome‐Wide Association Analysis Identified the Vasoactive Intestinal Peptide (VIP) Pathway Important for Obesity

Recent genome‐wide association (GWA) studies have identified a number of novel genes/variants predisposing to obesity. However, most GWA studies have focused on individual single‐nucleotide polymorphism (SNPs)/genes with a strong statistical association with a phenotypic trait without considering potential biological interplay of the tested genes. In this study, we performed biological pathway‐based GWA analysis for BMI and body fat mass. We used individual level genotype data generated from 1,000 unrelated US whites that were genotyped for ～500,000 SNPs. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm. A total of 963 pathways extracted from the BioCarta, Kyoto Encyclopedia of Genes and Genomes (KEGG), Ambion GeneAssist, and Gene Ontology (GO) databases were analyzed. Among all of the pathways analyzed, the vasoactive intestinal peptide (VIP) pathway was most strongly associated with fat mass (nominal P = 0.0009) and was the third most strongly associated pathway with BMI (nominal P = 0.0006). After multiple testing correction, the VIP pathway achieved false‐discovery rate (FDR) q values of 0.042 and 0.120 for fat mass and BMI, respectively. Our study is the first to demonstrate that the VIP pathway may play an important role in development of obesity. The study also highlights the importance of pathway‐based GWA analysis in identification of additional genes/variants for complex human diseases.     

Composition of Archaea in Seawater,Sediment, and Sponges in the Kepulauan Seribu Reef System,Indonesia

Coral reefs are among the most diverse and productive ecosystems in the world. Most research has, however, focused on eukaryotes such as corals and fishes. Recently, there has been increasing interest in the composition of prokaryotes, particularly those inhabiting corals and sponges, but these have mainly focused on bacteria. There have been very few studies of coral reef Archaea, despite the fact that Archaea have been shown to play crucial roles in nutrient dynamics, including nitrification and methanogenesis, of oligotrophic environments such as coral reefs. Here, we present the first study to assess Archaea in four different coral reef biotopes (seawater, sediment, and two sponge species, Stylissa massa and Xestospongia testudinaria). The archaeal community of both sponge species and sediment was dominated by Crenarchaeota, while the seawater community was dominated by Euryarchaeota. The biotope explained more than 72 % of the variation in archaeal composition. The number of operational taxonomic units (OTUs) was highest in sediment and seawater biotopes and substantially lower in both sponge hosts. No “sponge-specific” archaeal OTUs were found, i.e., OTUs found in both sponge species but absent from nonhost biotopes. Despite both sponge species hosting phylogenetically distinct microbial assemblages, there were only minor differences in Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways. In contrast, most functional pathways differed significantly between microbiomes from sponges and nonhost biotopes including all energy metabolic pathways. With the exception of the methane and nitrogen metabolic pathway, all energy metabolic pathways were enriched in sponges when compared to nonhost biotopes.     

Analysis of dysregulation of immune system in pancreatic cancer based on gene expression profile

The aim of this study was to explore the dysregulated expression of the immune system in pancreatic cancer and clarify the pathogenesis of pancreatic cancer. The Dataset GSE15471 was downloaded from GEO database, Student’s t test was used to screen differentially expressed genes (DEGs) between the pancreatic cancer group and the normal control group. Kyoto Encyclopedia of Genes and Genomes (KEGG) provides functional annotation was employed to explore the significant DEGs involved in biological functions. We got 988 significantly DEGs, including 832 up-regulated genes and 156 down-regulated genes. The ratio of up-regulated genes and down-regulated genes was 5.3. Total 13 biological pathways which were significant enriched with DEGs by KEGG pathway enrichment analysis. Finally, we constructed a overall network of the immune system in pancreatic cancer with these biological pathways information. Our study reveals that dysregulated pathways in pancreatic cancer associated with the immune system. Besides, we also identify some important molecular biomarkers of the pancreatic cancer, including CXCR4 and CD4. Dysfunctional pathways and important molecular biomarkers of pancreatic cancer will provide useful information for potential treatment of pancreatic cancer.     

High-abundance mRNAs in Apis mellifera: comparison between nurses and foragers

A large volume of honey bee (Apis mellifera) tag-seq was obtained to identify differential gene expression via Solexa/lllumina Digital Gene Expression tag profiling (DGE) based on next generation sequencing. In total, 4,286,250 (foragers) and 3,422,327 (nurses) clean tags were sequenced, 24,568 (foragers) and 13,134 (nurses) distinct clean tags could not be match to the reference database, and 7508 and 6875 mapped genes were detected in foragers and nurses respectively. 7045 genes were found differentially expressed between foragers and nurses. Of those genes, 1621genes had significantly different expression, that is, they showed an expression ratio (foragers/nurses) of more than 2 and FDR (False Discovery Rate) of less than 0.001. We identified 101 genes that were uniquely expressed in foragers, and 9 genes that were only expressed in nurses. We performed the Gene Ontology (GO) category and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and found 415 genes with annotation terms linked to the GO cellular component category. 200 components of KEGG pathways were obtained, including 21 signaling pathways. The PPAR signaling pathway was the most highly enriched, with the lowest Q-value.     

Application of Massively Parallel Sequencing to Genetic Diagnosis in Multiplex Families with Idiopathic Sensorineural Hearing Impairment

Despite the clinical utility of genetic diagnosis to address idiopathic sensorineural hearing impairment (SNHI), the current strategy for screening mutations via Sanger sequencing suffers from the limitation that only a limited number of DNA fragments associated with common deafness mutations can be genotyped. Consequently, a definitive genetic diagnosis cannot be achieved in many families with discernible family history. To investigate the diagnostic utility of massively parallel sequencing (MPS), we applied the MPS technique to 12 multiplex families with idiopathic SNHI in which common deafness mutations had previously been ruled out. NimbleGen sequence capture array was designed to target all protein coding sequences (CDSs) and 100 bp of the flanking sequence of 80 common deafness genes. We performed MPS on the Illumina HiSeq2000, and applied BWA, SAMtools, Picard, GATK, Variant Tools, ANNOVAR, and IGV for bioinformatics analyses. Initial data filtering with allele frequencies (<5% in the 1000 Genomes Project and 5400 NHLBI exomes) and PolyPhen2/SIFT scores (>0.95) prioritized 5 indels (insertions/deletions) and 36 missense variants in the 12 multiplex families. After further validation by Sanger sequencing, segregation pattern, and evolutionary conservation of amino acid residues, we identified 4 variants in 4 different genes, which might lead to SNHI in 4 families compatible with autosomal dominant inheritance. These included GJB2 p.R75Q, MYO7A p.T381M, KCNQ4 p.S680F, and MYH9 p.E1256K. Among them, KCNQ4 p.S680F and MYH9 p.E1256K were novel. In conclusion, MPS allows genetic diagnosis in multiplex families with idiopathic SNHI by detecting mutations in relatively uncommon deafness genes.     

Discovery of core gene families associated with liver metastasis in colorectal cancer and regulatory roles in tumor cell immune infiltration

In this study, we aimed to uncover genes that drive the pathogenesis of liver metastasis in colorectal cancer (CRC), and identify effective genes that could serve as potential therapeutic targets for treating with colorectal liver metastasis patients based on two GEO datasets. Several bioinformatics approaches were implemented. First, differential expression analysis screened out key differentially expressed genes (DEGs) across the two GEO datasets. Based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we identified the enrichment functions and pathways of the DEGs that were associated with liver metastasis in CRC. Second, immune infiltration analysis identified key immune signature gene sets associated with CRC liver metastasis, among which two key immune gene families (CD and CCL) identified as key DEGs were filtered by protein-protein interaction (PPI) network. Some of the members in these gene families were associated with disease free survival (DFS) or overall survival (OS) in two subtypes of CRC, namely COAD and READ. Finally, functional enrichment analysis of the two gene families and their neighboring genes revealed that they were closely associated with cytokine, leukocyte proliferation and chemotaxis. These results are valuable in comprehending the pathogenesis of liver metastasis in CRC, and are of seminal importance in understanding the role of immune tumor infiltration in CRC. Our study also identified potentially effective therapeutic targets for liver metastasis in CRC including CCL20, CCL24 and CD70.     

Function of CAZymes encoded by highly abundant genes in rhizosphere microbiome of Moringa oleifera

Metagenomic analysis referring to CAZymes (Carbohydrate-Active enZymes) of CAZy classes encoded by the most abundant genes in rhizosphere versus bulk soil microbes of the wild plant Moringa oleifera was conducted. Results indicated that microbiome signatures and corresponding CAZy datasets differ between the two soil types. CAZy class glycoside hydrolases (GH) and its α-amylase family GH13 in rhizobiome were proven to be the most abundant among CAZy classes and families. The most abundant bacteria harboring these CAZymes include phylum Actinobacteria and its genus Streptomyces and phylum Proteobacteria and its genus Microvirga. These CAZymes participate in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway “Starch and sucrose metabolism” and mainly use the “double displacement catalytic mechanism” in their reactions. We assume that microbiome of the wild plant Moringa oleifera is a good source of industrially important enzymes that act on starch hydrolysis and/or biosynthesis. In addition, metabolic engineering and integration of certain microbes of this microbiomes can also be used in improving growth of domestic plants and their ability to tolerate adverse environmental conditions.