scholarly journals Gene Expression Data Mining Reveals the Involvement of GPR55 and Its Endogenous Ligands in Immune Response, Cancer, and Differentiation

2021 ◽  
Vol 22 (24) ◽  
pp. 13328
Author(s):  
Artur Wnorowski ◽  
Jakub Wójcik ◽  
Maciej Maj
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Immune Cell ◽  
Cell Lineage ◽  
Expression Data ◽  
Experimental Conditions ◽  
Pathological Conditions ◽  
Pituitary Adenylate Cyclase ◽  
G Protein Coupled

G protein-coupled receptor 55 (GPR55) is a recently deorphanized lipid- and peptide-sensing receptor. Its lipidic endogenous agonists belong to lysoglycerophospholipids, with lysophosphatidylinositol (LPI) being the most studied. Peptide agonists derive from fragmentation of pituitary adenylate cyclase-activating polypeptide (PACAP). Although GPR55 and its ligands were implicated in several physiological and pathological conditions, their biological function remains unclear. Thus, the aim of the study was to conduct a large-scale re-analysis of publicly available gene expression datasets to identify physiological and pathological conditions affecting the expression of GPR55 and the production of its ligands. The study revealed that regulation of GPR55 occurs predominantly in the context of immune activation pointing towards the role of the receptor in response to pathogens and in immune cell lineage determination. Additionally, it was revealed that there is almost no overlap between the experimental conditions affecting the expression of GPR55 and those modulating agonist production. The capacity to synthesize LPI was enhanced in various types of tumors, indicating that cancer cells can hijack the motility-related activity of GPR55 to increase aggressiveness. Conditions favoring accumulation of PACAP-derived peptides were different than those for LPI and were mainly related to differentiation. This indicates a different function of the two agonist classes and possibly the existence of a signaling bias.

scholarly journals ImmuCellDB: An Indicative Database of Immune Cell Composition From Different Tissues and Disease Conditions in Mouse and Human

2021 ◽  
Vol 12 ◽  
Author(s):  
Ziyi Chen ◽  
Han Na ◽  
Aiping Wu
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Computational Tools ◽  
Cell Composition ◽  
Cell Abundance ◽  
Different Tissues

Immune cell composition is highly divergent across different tissues and diseases. A comprehensive resource of tissue immune cells across different conditions in mouse and human will thus provide great understanding of the immune microenvironment of many diseases. Recently, computational methods for estimating immune cell abundance from tissue transcriptome data have been developed and are now widely used. Using these computational tools, large-scale estimation of immune cell composition across tissues and conditions should be possible using gene expression data collected from public databases. In total, 266 tissue types and 706 disease types in humans, as well as 143 tissue types and 61 disease types, and 206 genotypes in mouse had been included in a database we have named ImmuCellDB (http://wap-lab.org:3200/ImmuCellDB/). In ImmuCellDB, users can search and browse immune cell proportions based on tissues, disease or genotype in mouse or humans. Additionally, the variation and correlation of immune cell abundance and gene expression level between different conditions can be compared and viewed in this database. We believe that ImmuCellDB provides not only an indicative view of tissue-dependent or disease-dependent immune cell profiles, but also represents an easy way to pre-determine immune cell abundance and gene expression profiles for specific situations.

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

scholarly journals Stress Modifies the Expression of Glucocorticoid-Responsive Genes by Acting at Epigenetic Levels in the Rat Prefrontal Cortex: Modulatory Activity of Lurasidone

2021 ◽  
Vol 22 (12) ◽  
pp. 6197
Author(s):  
Paola Brivio ◽  
Giulia Sbrini ◽  
Letizia Tarantini ◽  
Chiara Parravicini ◽  
Piotr Gruca ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Chronic Stress ◽  
Chronic Mild Stress ◽  
Male Rats ◽  
Mild Stress ◽  
Experimental Conditions ◽  
Glucocorticoid Responsive Element ◽  
Responsive Element

Epigenetics is one of the mechanisms by which environmental factors can alter brain function and may contribute to central nervous system disorders. Alterations of DNA methylation and miRNA expression can induce long-lasting changes in neurobiological processes. Hence, we investigated the effect of chronic stress, by employing the chronic mild stress (CMS) and the chronic restraint stress protocol, in adult male rats, on the glucocorticoid receptor (GR) function. We focused on DNA methylation specifically in the proximity of the glucocorticoid responsive element (GRE) of the GR responsive genes Gadd45β, Sgk1, and Gilz and on selected miRNA targeting these genes. Moreover, we assessed the role of the antipsychotic lurasidone in modulating these alterations. Chronic stress downregulated Gadd45β and Gilz gene expression and lurasidone normalized the Gadd45β modification. At the epigenetic level, CMS induced hypermethylation of the GRE of Gadd45β gene, an effect prevented by lurasidone treatment. These stress-induced alterations were still present even after a period of rest from stress, indicating the enduring nature of such changes. However, the contribution of miRNA to the alterations in gene expression was moderate in our experimental conditions. Our results demonstrated that chronic stress mainly affects Gadd45β expression and methylation, effects that are prolonged over time, suggesting that stress leads to changes in DNA methylation that last also after the cessation of stress procedure, and that lurasidone is a modifier of such mechanisms.

scholarly journals Predicting Host Immune Cell Dynamics and Key Disease-Associated Genes Using Tissue Transcriptional Profiles

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 301
Author(s):  
Muying Wang ◽  
Satoshi Fukuyama ◽  
Yoshihiro Kawaoka ◽  
Jason E. Shoemaker
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Mean Squared Error ◽  
Expression Profiles ◽  
Statistical Tests ◽  
Critical Factor ◽  
Expression Data ◽  
Cell Dynamics ◽  
Cell Counts

Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R 2 ) but a poor absolute fit (normalized mean squared error; NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests.

scholarly journals The Role of Prokineticin 2 in Oxidative Stress and in Neuropathological Processes

2021 ◽  
Vol 12 ◽  
Author(s):  
Roberta Lattanzi ◽  
Cinzia Severini ◽  
Daniela Maftei ◽  
Luciano Saso ◽  
Aldo Badiani
Keyword(s):  
Pain Perception ◽  
G Protein Coupled Receptors ◽  
Cellular Processes ◽  
Prokineticin 2 ◽  
Physiological Processes ◽  
Pathological Conditions ◽  
Double Function ◽  
The Central Nervous System ◽  
G Protein Coupled

The prokineticin (PK) family, prokineticin 1 and Bv8/prokineticin 2 (PROK2), initially discovered as regulators of gastrointestinal motility, interacts with two G protein-coupled receptors, PKR1 and PKR2, regulating important biological functions such as circadian rhythms, metabolism, angiogenesis, neurogenesis, muscle contractility, hematopoiesis, immune response, reproduction and pain perception. PROK2 and PK receptors, in particular PKR2, are widespread distributed in the central nervous system, in both neurons and glial cells. The PROK2 expression levels can be increased by a series of pathological insults, such as hypoxia, reactive oxygen species, beta amyloid and excitotoxic glutamate. This suggests that the PK system, participating in different cellular processes that cause neuronal death, can be a key mediator in neurological/neurodegenerative diseases. While many PROK2/PKRs effects in physiological processes have been documented, their role in neuropathological conditions is not fully clarified, since PROK2 can have a double function in the mechanisms underlying to neurodegeneration or neuroprotection. Here, we briefly outline the latest findings on the modulation of PROK2 and its cognate receptors following different pathological insults, providing information about their opposite neurotoxic and neuroprotective role in different pathological conditions.

scholarly journals Collaborative Cross Founder Expression Analysis (CCFEA)

2021 ◽  
Author(s):  
Richard R Green ◽  
Renee C Ireton ◽  
Martin Ferris ◽  
Kathleen Muenzen ◽  
David R Crosslin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Gene Expression Data ◽  
Viral Infections ◽  
Collaborative Cross ◽  
Visualization Tool ◽  
Expression Data ◽  
Individual Gene ◽  
Rnaseq Data

To understand the role of genetic variation in SARS and Influenza infections we developed CCFEA, a shiny visualization tool using public RNAseq data from the collaborative cross (CC) founder strains (A/J, C57BL/6J, 129s1/SvImJ, NOD/ShILtJ, NZO/HILtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ). Individual gene expression data is displayed across founders, viral infections and days post infection.

scholarly journals LSTrAP-Crowd: Prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data

2020 ◽  
Author(s):  
Benedict Hew ◽  
Qiao Wen Tan ◽  
William Goh ◽  
Jonathan Wei Xiong Ng ◽  
Kenny Koh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Rna Sequencing ◽  
Gene Expression Data ◽  
Large Scale ◽  
Bacterial Resistance ◽  
Expression Data ◽  
Sequencing Data ◽  
Novel Proteins ◽  
Novel Antibiotics

AbstractBacterial resistance to antibiotics is a growing problem that is projected to cause more deaths than cancer in 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the bacterial ribosomes, proteins that are involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. The data can be used to identify other vulnerabilities or bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowdsourced.

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