Exploring differentially expressed genes in the ovaries of uniparous and multiparous goats using the RNA-Seq (Quantification) method

Fecundity improvement is one of the most important objectives for goat breeders as it greatly increases production efficiency. To investigate the genes associated with litter sizes in the Anhui White goat (AWG), gene expression differences in the ovaries of uniparous and multiparous AWG were assessed using the RNA-Seq (Quantification) method. This analysis generated 6,027,714 and 5,884,062 clean reads in uniparous and multiparous libraries, respectively. A total of 2201 differentially expressed genes (DEGs) were thereby identified (FDR ≤ 0.001, |log₂Ratio| ≥ 1). There were 1583 up-regulated and 618 down-regulated genes in the multiparous samples compared with the uniparous samples. A large number of these DEGs were related to the terms cellular process, cell & cell part and binding. Twelve genes which may be associated with the high prolificacy of AWG were identified using a bioinformatic screen. In addition, pathway analysis revealed that these DEGs were significantly enriched in 11 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including ECM–receptor interactions, focal adhesion, and regulation of the actin cytoskeleton among others. This suggested a role for these pathways in the prolificacy of AWG. These results provide a list of new candidate genes for goat prolificacy.     

The identification of dysfunctional crosstalk of pathways in Parkinson disease

Parkinson disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease, affecting 1–2% of the population over the age of 65. Both genetic and environmental factors trigger risks of and protection from PD. However, the molecular mechanism of PD is far from being clear. In this study, we downloaded the gene expression profile of PD from Gene Expression Omnibus and identified differentially expressed genes (DEGs) and dysfunctional pathways in PD patients compared with controls. To further understand how these pathways act together to account for the initiation of PD, we constructed a pathway crosstalk network by calculating the Jaccard index among pathways. A total of 873 DEGs and 16 dysfunctional pathways between PD patients and controls were identified. Through constructing a network of pathways, the relationships among PD pathways were visually presented by their interactions. Our results demonstrate the existence of crosstalk between different pathways in PD pathogenesis. These results not only may explain the causes of PD, but could also open the door to new therapeutic approaches for this disease.     

Comparison of different normalization assumptions for analyses of DNA methylation data from the cancer genome

Nowadays, some researchers normalized DNA methylation arrays data in order to remove the technical artifacts introduced by experimental differences in sample preparation, array processing and other factors. However, other researchers analyzed DNA methylation arrays without performing data normalization considering that current normalizations for methylation data may distort real differences between normal and cancer samples because cancer genomes may be extensively subject to hypomethylation and the total amount of CpG methylation might differ substantially among samples. In this study, using eight datasets by Infinium HumanMethylation27 assay, we systemically analyzed the global distribution of DNA methylation changes in cancer compared to normal control and its effect on data normalization for selecting differentially methylated (DM) genes. We showed more differentially methylated (DM) genes could be found in the Quantile/Lowess-normalized data than in the non-normalized data. We found the DM genes additionally selected in the Quantile/Lowess-normalized data showed significantly consistent methylation states in another independent dataset for the same cancer, indicating these extra DM genes were effective biological signals related to the disease. These results suggested normalization can increase the power of detecting DM genes in the context of diagnostic markers which were usually characterized by relatively large effect sizes. Besides, we evaluated the reproducibility of DM discoveries for a particular cancer type, and we found most of the DM genes additionally detected in one dataset showed the same methylation directions in the other dataset for the same cancer type, indicating that these DM genes were effective biological signals in the other dataset. Furthermore, we showed that some DM genes detected from different studies for a particular cancer type were significantly reproducible at the functional level.     

Ossification of the posterior longitudinal ligament related genes identification using microarray gene expression profiling and bioinformatics analysis

Ossification of the posterior longitudinal ligament (OPLL) is a kind of disease with physical barriers and neurological disorders. The objective of this study was to explore the differentially expressed genes (DEGs) in OPLL patient ligament cells and identify the target sites for the prevention and treatment of OPLL in clinic. Gene expression data GSE5464 was downloaded from Gene Expression Omnibus; then DEGs were screened by limma package in R language, and changed functions and pathways of OPLL cells compared to normal cells were identified by DAVID (The Database for Annotation, Visualization and Integrated Discovery); finally, an interaction network of DEGs was constructed by string. A total of 1536 DEGs were screened, with 31 down-regulated and 1505 up-regulated genes. Response to wounding function and Toll-like receptor signaling pathway may involve in the development of OPLL. Genes, such as PDGFB, PRDX2 may involve in OPLL through response to wounding function. Toll-like receptor signaling pathway enriched genes such as TLR1, TLR5, and TLR7 may involve in spine cord injury in OPLL. PIK3R1 was the hub gene in the network of DEGs with the highest degree; INSR was one of the most closely related genes of it. OPLL related genes screened by microarray gene expression profiling and bioinformatics analysis may be helpful for elucidating the mechanism of OPLL.     

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.     

Analysis of microRNA expression profile induced by AICAR in mouse hepatocytes

AMP-activated protein kinase (AMPK) has been proposed to act as a key energy sensor mediating the metabolism of glucose and lipids, and pharmacological activation of AMPK may provide a new strategy for the management of type 2 diabetes. MicroRNAs (miRNAs) are a group of endogenous noncoding RNA that play important roles in many biological processes including energy metabolism. Whether miRNAs mediate AMPK action in regulating metabolic process is not clear. In this study, 0.5 mM 5-aminoimidazole-4-carboxamide-1-β-d-ribofuranoside (AICAR) was added to increase activation of AMPK in 8 week old C57BL/6 mice primary hepatocytes. MiRNA microarray was performed to compare the miRNA expression profiles of hepatocytes treated with or without AICAR. We discovered that 41 miRNAs were significantly altered in AICAR-treated sample (fold change: > 2) compared with untreated control sample. Among them, 19 miRNAs were upregulated. MiRNA targets were predicted by TargetScan. Further bioinformatic analysis indicated that these predicted targets might be mainly involved in pathways of cellular metabolism and tumor pathogenesis. FUNDO analysis suggested that these predicted targets were enriched in cancer, diabetes mellitus, hypertension, obesity and heart failure (P < 0.01). A series of miRNAs could be regulated by the activation of AMPK and might mediate the action of AMPK during metabolic processes and tumor pathogenesis. Predicted target genes discovered in this study and pathway analysis provide new insights into hepatic metabolism and tumor pathogenesis regulated by AMPK signaling and clues to the possible molecular mechanism underlying the effect of AMPK.     

Protein–protein interaction and SNP analysis in intraductal papillary mucinous neoplasm

Intraductal papillary mucinous neoplasm (IPMN) is a type of tumor that grows within the pancreatic ducts. It is a progress from hyperplasia to intraductal adenoma (IPMA), to noninvasive carcinoma, and ultimately to invasive carcinoma (IPMC). The objective of this study was to explore the molecular mechanism of the progression from IPMA to IPMC. By using the GSE19650 affymetrix microarray data accessible from Gene Expression Omnibus (GEO) database, we first identified the differentially expressed genes (DEGs) between IPMA and IPMC, followed by the protein–protein interaction and single-nucleotide polymorphism (SNP) analysis of the DEGs. Our study identified thousands of DEGs which involved regulation of cell cycle and apoptosis in this progression from IPMA to IPMC. Protein–protein interaction network construction found that MYC, IL6ST, NR3C1, CREBBP, GATA1 and LRP1 might play an important role in the progression. Furthermore, the SNP analysis confirmed the association between BRAC1 and pancreas cancer. In conclusion, our data provide a comprehensive bioinformatics analysis of genes and pathways which may be involved in the progression of IPMN from IPMA to IPMC.