Microarray analysis of genes with differential expression of m6A methylation in lung cancer

Purpose: N6-methyladenosine (m6A) is among the most abundant mRNA modifications in eukaryote. The aim of the present study was to investigate function of m6A mRNA methylation in lung cancer and the underlying mechanism.Methods: Microarray analysis was performed to detect the differences in RNA expression between cancerous and adjacent non-cancerous tissue samples. The target mRNAs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Hierarchical clustering of RNAs was conducted to identify distinct m6A methylation or expression patterns between the samples.Results: In the present study, some differentially expressed genes (DEGs) of mRNAs were identified, including up-regulated secret phosphoprotein 1 (SPP1) and down-regulated pRB. Functional enrichment analysis revealed that while differential hypermethylation was related to cell cycle, intracellular part and protein binding, the main pathway involved herpes simplex virus 1 infection related to down-regulated AKT, Araf1 and BCL2A1. In the meantime, sexual reproduction, cohesin complex and protein C-terminus binding was functionally linked to differential hypomethylation, while fluid shear stress and atherosclerosis were identified as the main pathways related to up-regulated GST and CNP.Conclusions: We showed that lung cancer development involved differential expression of SPP1 and pRB mRNA, as well as m6A mRNA methylation in AKT, APAF1, BCL2A1, GST and CNP genes.     

Candidate agents for pancreatic ductal adenocarcinoma identified by a sub-pathway based method

Aim

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer death worldwide. This study aims to explore the molecular mechanism of PDAC and identify biologically active small molecules capable of targeting the sub-pathways which were dysregulated in the development of PDAC.

Methods

The gene expression profile of GSE28735 microarray data (including 45 matching pairs of pancreatic tumor and adjacent non-tumor tissues) was downloaded from GEO (Gene Expression Omnibus) database. Differentially expressed genes (DEGs) between pancreatic tumor tissues and non-tumor tissues were identified, and then the sub-pathway enrichment analysis was performed. Moreover, an approach based on targeting sub-pathways was used to reveal potential agents for PDAC.

Results

A total of 5315 DEGs were identified between pancreatic tumor tissues and non-tumor tissues with a false discovery rate of 0.01. Genes of collagen family and integrin receptor family which were involved in pathways of focal adhesion and ECM-receptor interaction respectively were differentially expressed in the pancreatic tumor tissue. Besides, a total of 85 small molecules including fludrocortisone, latamoxef and metronidazole were revealed by bioinformatics analysis.

Conclusion

This study proposed the use of an approach based on targeting sub-pathways to identify potential agents for PDAC. The sub-pathways and small molecules discovered in this study were not only related to PDAC but also play a role in perturbing the development of PDAC.     

MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein–Protein Network Mapping

Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO, to deduce Gene Ontology attributes of proteins by combining sequence homology-based annotation with low-resolution structure prediction and comparison, and partner's homology-based protein–protein network mapping. The pipeline was tested on a large-scale set of 1000 non-redundant proteins from the CAFA3 experiment. Under the stringent benchmark conditions where templates with > 30% sequence identity to the query are excluded, MetaGO achieves average F-measures of 0.487, 0.408, and 0.598, for Molecular Function, Biological Process, and Cellular Component, respectively, which are significantly higher than those achieved by other state-of-the-art function annotations methods. Detailed data analysis shows that the major advantage of the MetaGO lies in the new functional homolog detections from partner's homology-based network mapping and structure-based local and global structure alignments, the confidence scores of which can be optimally combined through logistic regression. These data demonstrate the power of using a hybrid model incorporating protein structure and interaction networks to deduce new functional insights beyond traditional sequence homology-based referrals, especially for proteins that lack homologous function templates. The MetaGO pipeline is available at http://zhanglab.ccmb.med.umich.edu/MetaGO/.     

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.     

Global Network Analysis of Lipid-Raft-Related Proteins Reveals Their Centrality in the Network and Their Roles in Multiple Biological Processes

Lipid rafts are specialized cholesterol-enriched microdomains in the cell membrane. They have been known as a platform for protein-protein interactions and to take part in multiple biological processes. Nevertheless, how lipid rafts influence protein properties at the proteomic level is still an open question for researchers using traditional biochemical approaches. Here, by annotating the lipid raft localization of proteins in human protein-protein interaction networks, we performed a systematic analysis of the function of proteins related to lipid rafts. Our results demonstrated that lipid raft proteins and their interactions were critical for the structure and stability of the whole network, and that the interactions between them were significantly enriched. Furthermore, for each protein in the network, we calculated its “lipid raft dependency (LRD),” which indicates how close it is topologically associated with lipid rafts, and we then uncovered the connection between LRD and protein functions. Proteins with high LRD tended to be essential for mammalian development, and malfunction of these proteins was inclined to cause human diseases. Coordinated with their neighbors, high-LRD proteins participated in multiple biological processes and targeted many pathways in diseases pathogenesis. High-LRD proteins were also found to have tissue specificity of expression. In summary, our network-based analysis denotes that lipid raft proteins have higher centrality in the network, and that lipid-raft-related proteins have multiple functions and are probably concerned with many biological processes in disease development.     

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.     

Morphological and proteomic analysis of early stage of osteoblast differentiation in osteoblastic progenitor cells

Bone remodeling relies on a dynamic balance between bone formation and resorption, mediated by osteoblasts and osteoclasts, respectively. Under certain stimuli, osteoprogenitor cells may differentiate into premature osteoblasts and further into mature osteoblasts. This process is marked by increased alkaline phosphatase (ALP) activity and mineralized nodule formation. In this study, we induced osteoblast differentiation in mouse osteoprogenitor MC3T3-E1 cells and divided the process into three stages. In the first stage (day 3), the MC3T3-E1 cell under osteoblast differentiation did not express ALP or deposit a mineralized nodule. In the second stage, the MC3T3-E1 cell expressed ALP but did not form a mineralized nodule. In the third stage, the MC3T3-E1 cell had ALP activity and formed mineralized nodules. In the present study, we focused on morphological and proteomic changes of MC3T3-E1 cells in the early stage of osteoblast differentiation — a period when premature osteoblasts transform into mature osteoblasts. We found that mean cell area and mean stress fiber density were increased in this stage due to enhanced cell spreading and decreased cell proliferation. We further analyzed the proteins in the signaling pathway of regulation of the cytoskeleton using a proteomic approach and found upregulation of IQGAP1, gelsolin, moesin, radixin, and Cfl1. After analyzing the focal adhesion signaling pathway, we found the upregulation of FLNA, LAMA1, LAMA5, COL1A1, COL3A1, COL4A6, and COL5A2 as well as the downregulation of COL4A1, COL4A2, and COL4A4. In conclusion, the signaling pathway of regulation of the cytoskeleton and focal adhesion play critical roles in regulating cell spreading and actin skeleton formation in the early stage of osteoblast differentiation.     

Production of bulk chemicals via novel metabolic pathways in microorganisms

Metabolic engineering has been playing important roles in developing high performance microorganisms capable of producing various chemicals and materials from renewable biomass in a sustainable manner. Synthetic and systems biology are also contributing significantly to the creation of novel pathways and the whole cell-wide optimization of metabolic performance, respectively. In order to expand the spectrum of chemicals that can be produced biotechnologically, it is necessary to broaden the metabolic capacities of microorganisms. Expanding the metabolic pathways for biosynthesizing the target chemicals requires not only the enumeration of a series of known enzymes, but also the identification of biochemical gaps whose corresponding enzymes might not actually exist in nature; this issue is the focus of this paper. First, pathway prediction tools, effectively combining reactions that lead to the production of a target chemical, are analyzed in terms of logics representing chemical information, and designing and ranking the proposed metabolic pathways. Then, several approaches for potentially filling in the gaps of the novel metabolic pathway are suggested along with relevant examples, including the use of promiscuous enzymes that flexibly utilize different substrates, design of novel enzymes for non-natural reactions, and exploration of hypothetical proteins. Finally, strain optimization by systems metabolic engineering in the context of novel metabolic pathways constructed is briefly described. It is hoped that this review paper will provide logical ways of efficiently utilizing ‘big’ biological data to design and develop novel metabolic pathways for the production of various bulk chemicals that are currently produced from fossil resources.