Transcriptome analysis of sevoflurane exposure effects at the different brain regions

Backgrounds Sevoflurane is a most frequently used volatile anesthetics, but its molecular mechanisms of action remain unclear. We hypothesized that specific genes play regulatory roles in brain exposed to sevoflurane. Thus, we aimed to evaluate the effects of sevoflurane inhalation and identify potential regulatory genes by RNA-seq analysis. Methods Eight-week old mice were exposed to sevoflurane. RNA from medial prefrontal cortex, striatum, hypothalamus, and hippocampus were analysed using RNA-seq. Differently expressed genes were extracted and their gene ontology terms were analysed using Metascape. These our anesthetized mouse data and the transcriptome array data of the cerebral cortex of sleeping mice were compared. Finally, the activities of transcription factors were evaluated using a weighted parametric gene set analysis (wPGSA). JASPAR was used to confirm the existence of binding motifs in the upstream sequences of the differently expressed genes. Results The gene ontology term enrichment analysis result suggests that sevoflurane inhalation upregulated angiogenesis and downregulated neural differentiation in each region of brain. The comparison with the brains of sleeping mice showed that the gene expression changes were specific to anesthetized mice. Focusing on individual genes, sevoflurane induced Klf4 upregulation in all sampled parts of brain. wPGSA supported the function of KLF4 as a transcription factor, and KLF4-binding motifs were present in many regulatory regions of the differentially expressed genes. Conclusions Klf4 was upregulated by sevoflurane inhalation in the mouse brain. The roles of KLF4 might be key to elucidating the mechanisms of sevoflurane induced functional modification in the brain.

Proteomics provides insights into the inhibition of Chinese hamster V79 cell proliferation in the deep underground environment

Background: To characterize the environment of deep underground laboratory (DUGL) with a rock cover of 1470m and observe the effect of the DUGL environment on the growth and metabolism of Chinese hamster V79 cells. Results: Six environmental parameters in the DUGL and an above ground laboratory (AGL; control) were monitored. Compared to the AGL, O2 concentration was not significantly different, total γ ray dose rate was significantly lower (p=0.005), and relative humidity (p<0.001), air pressure (p<0.001), and concentration of CO2 and radon gas (p<0.001) were significantly higher in the DUGL. The growth curves of cultured V79 cells showed cell proliferation was slower in the DUGL. Tandem mass tag (TMT) proteomics analysis was performed to identify differentially abundant proteins (DAPs) in V79 cells cultured in the DUGL and AGL. Parallel Reaction Monitoring (PRM) was conducted to verify TMT results. TMT detected 980 DAPs, defined as proteins with a ≥1.2- absolute fold change in relative abundance (p <0.05) between V79 cells cultured in the DUGL and AGL. Of these, 576 proteins were up-regulated and 404 proteins were down-regulated in V79 cells cultured in the DUGL. GO term enrichment analysis of up-regulated proteins revealed enrichment of proteins involved in translation, ribosome, proton-transporting ATP synthase activity, oxygen binding, and oxygen transporter activity et al. GO term enrichment analysis of down-regulated proteins demonstrated enrichment of proteins involved in the endoplasmic reticulum lumen and respiratory chain. KEGG pathway analysis revealed that ribosome (p<0.001), base excision repair (p<0.001), RNA transport (p=0.009), Huntington's disease (p=0.023), and oxidative phosphorylation (OXPPL) (p=0.035) pathways were significantly enriched. Conclusion: Proliferation of V79 cells was inhibited in the DUGL, likely because cells were exposed to reduced cosmic ray muons flux. There were apparent changes in the proteome profile of the V79 cells cultured in the DUGL, which affected proteins related to the ribosome, RNA transport, translation, energy metabolism, and DNA repair. These proteins may have induced cellular changes that delayed proliferation but enhanced survival, making the V79 cells adaptable to the changing environment. Our findings provide insight into the cellular stress response that is triggered in the absence of normal levels of radiation.

DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis

Summary Although various tools for Gene Ontology (GO) term enrichment analysis are available, there is still room for improvement. Hence, we present DiNGO, a standalone application based on an open source code from BiNGO, a widely-used application to assess the overrepresentation of GO categories. Besides facilitating GO term enrichment analyses, DiNGO has been developed to allow for convenient Human Phenotype Ontology (HPO) term overrepresentation investigation. This is an important contribution considering the increasing interest in HPO in scientific research and its potential in clinical settings. DiNGO supports gene/protein identifier conversion and an automatic updating of GO and HPO annotation resources. Finally, DiNGO can rapidly process a large amount of data due to its multithread design. Availability and Implementation DiNGO is implemented in the JAVA language, and its source code, example datasets and instructions are available on GitHub: https://github.com/radoslav180/DiNGO. A pre-compiled jar file is available at: https://www.vin.bg.ac.rs/180/tools/DiNGO.php Supplementary information Supplementary data are available at Bioinformatics online.

Implementation of BiClusO and its comparison with other biclustering algorithms

This paper describes the implementation of biclustering algorithm BiClusO using graphical user interface and different parameters to generate overlapping biclusters from a binary sparse matrix. We compare our algorithm with several other biclustering algorithms in the context of two different types of biological datasets and four synthetic datasets with known embedded biclusters. Biclustering technique is widely used in different fields of studies for analyzing bipartite relationship dataset. Over the past decade, different biclustering algorithms have been proposed by researchers which are mainly used for biological data analysis. The performance of these algorithms differs depending on dataset size, pattern, and property. These issues create difficulties for a researcher to take the right decision for selecting a good biclustering algorithm. Two different scoring methods along with Gene Ontology(GO) term enrichment analysis have been used to measure and compare the performance of our algorithm. Our algorithm shows the best performance over some other well-known biclustering algorithms.

Identification of key DNA methylation-driven genes in prostate adenocarcinoma: an integrative analysis of TCGA methylation data

Background Prostate cancer (PCa) remains the second leading cause of deaths due to cancer in the United States in men. The aim of this study was to perform an integrative epigenetic analysis of prostate adenocarcinoma to explore the epigenetic abnormalities involved in the development and progression of prostate adenocarcinoma. The key DNA methylation-driven genes were also identified. Methods Methylation and RNA-seq data were downloaded for The Cancer Genome Atlas (TCGA). Methylation and gene expression data from TCGA were incorporated and analyzed using MethylMix package. Methylation data from the Gene Expression Omnibus (GEO) were assessed by R package limma to obtain differentially methylated genes. Pathway analysis was performed on genes identified by MethylMix criteria using ConsensusPathDB. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also applied for the identification of pathways in which DNA methylation-driven genes significantly enriched. The protein–protein interaction (PPI) network and module analysis in Cytoscape software were used to find the hub genes. Two methylation profile (GSE112047 and GSE76938) datasets were utilized to validate screened hub genes. Immunohistochemistry of these hub genes were evaluated by the Human Protein Atlas. Results A total of 553 samples in TCGA database, 32 samples in GSE112047 and 136 samples in GSE76938 were included in this study. There were a total of 266 differentially methylated genes were identified by MethylMix. Plus, a total of 369 differentially methylated genes and 594 differentially methylated genes were identified by the R package limma in GSE112047 and GSE76938, respectively. GO term enrichment analysis suggested that DNA methylation-driven genes significantly enriched in oxidation–reduction process, extracellular exosome, electron carrier activity, response to reactive oxygen species, and aldehyde dehydrogenase [NAD(P)+] activity. KEGG pathway analysis found DNA methylation-driven genes significantly enriched in five pathways including drug metabolism—cytochrome P450, phenylalanine metabolism, histidine metabolism, glutathione metabolism, and tyrosine metabolism. The validated hub genes were MAOB and RTP4. Conclusions Methylated hub genes, including MAOB and RTP4, can be regarded as novel biomarkers for accurate PCa diagnosis and treatment. Further studies are needed to draw more attention to the roles of these hub genes in the occurrence and development of PCa.

Identification of 67 pleiotropic genes for seven autoimmune diseases using multivariate statistical analysis

Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitation in detecting complex genotype-phenotype correlations. It is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune diseases with a multivariate analysis. In this study, the GWAS summary statistics including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions was analyzed to identify shared variants of seven autoimmune diseases using metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological function of the identified genes. After metaCCA analysis, 4,962 SNPs (P<1.21×10−6) and 1,044 pleotropic genes (P<4.34×10−6) were identified. By screening the results of gene-based p-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes which achieved significance threshold in metaCCA analysis and were also associated with at least one autoimmune disease in the VEGAS2 analysis. The metaCCA method could identify novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for some common therapeutic approaches of autoimmune diseases based on the pleiotropic genes and common mechanisms identified.Author summaryAlthough previous researches have clearly indicated varying degrees of overlapping genetic sensitivities in autoimmune diseases, it has proven GWAS only explain small percent of heritability. Here, we take advantage of recent technical and methodological advances to identify pleiotropic genes that act on common biological mechanisms and the overlapping pathophysiological pathways of autoimmune diseases. After selection using multivariate analysis and verification using gene-based analyses, we successfully identified a total of 67 pleiotropic genes and performed the functional term enrichment analysis. In particularly, 27 genes were identified to be pleiotropic in previous different types of studies, which were validated by our present study. Forty significant genes (16 genes were associated with one disease earlier, and 24 were novel) might be the novel pleiotropic candidate genes for seven autoimmune diseases. The improved detection not only yielded the shared genetic components but also provided better understanding for exploring the potential common biological pathogenesis of these major autoimmune diseases.

High CO2 alters the hypoxia response of the Pacific whiteleg shrimp (Litopenaeus vannamei) transcriptome including known and novel hemocyanin isoforms

Acclimation to low O2 in many organisms involves changes at the level of the transcriptome. Here we used high-throughput RNA sequencing (RNA-Seq) to explore the global transcriptomic response and specific involvement of a suite of hemocyanin (Hc) subunits to low O2 alone and in combination with high CO2, which naturally co-occurs with low O2. Hepatopancreas mRNA of juvenile L. vannamei exposed to air-saturated water, low O2, or low O2/high CO2 for 4 or 24 h was pooled, sequenced (HiSeq 2500) and assembled (Trinity: 52,190 contigs) to create a deep strand-specific reference transcriptome. Annotation of the assembly revealed sequences encoding the previously described small Hc subunit (HcS), and three full-length isoforms of the large subunit (HcL1-3). In addition to this, a previously unidentified full-length Hc subunit was discovered. Phylogenetic analysis demonstrated the subunit to be a β-type Hc subunit (denoted HcB), making this the first report of a β-type hemocyanin subunit in the Penaeoidea. RNAs of individual shrimp were sequenced; regulated genes identified from pairwise comparisons demonstrated a distinct pattern of regulation between prolonged low O2 and low O2/high CO2 treatments by GO term enrichment analysis (Roff-Bentzen, P < 0.0001), showcasing the stabilization of energetically costly translational machinery, mobilization of energy stores, and downregulation of the ubiquitin/proteasomal degradation machinery. Exposure to hypoxia for 24 h resulted in an increase in all of the full-length hemocyanin subunits (HcS, HcL1, HcL2, HcL3, and HcB). The addition of CO2 to hypoxia muted the transcriptomic response of all the Hc subunits to low O2, except for the β-type subunit.