scholarly journals Bioinformatics Analyses of the Transcriptome Reveal Ube3a-Dependent Effects on Mitochondrial-Related Pathways

2020 ◽  
Vol 21 (11) ◽  
pp. 4156 ◽  
Author(s):  
Julia Panov ◽  
Lilach Simchi ◽  
Yonatan Feuermann ◽  
Hanoch Kaphzan
Keyword(s):  
Oxidative Stress ◽  
Angelman Syndrome ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Learning Approaches ◽  
Bioinformatics Analyses ◽  
Ubiquitin E3 Ligase ◽  
15Q Duplication ◽  
Ube3a Gene ◽  
Baseline State

The UBE3A gene encodes the ubiquitin E3-ligase protein, UBE3A, which is implicated in severe neurodevelopmental disorders. Lack of UBE3A expression results in Angelman syndrome, while UBE3A overexpression, due to genomic 15q duplication, results in autism. The cellular roles of UBE3A are not fully understood, yet a growing body of evidence indicates that these disorders involve mitochondrial dysfunction and increased oxidative stress. We utilized bioinformatics approaches to delineate the effects of murine Ube3a deletion on the expression of mitochondrial-related genes and pathways. For this, we generated an mRNA sequencing dataset from mouse embryonic fibroblasts (MEFs) in which both alleles of Ube3a gene were deleted and their wild-type controls. Since oxidative stress and mitochondrial dysregulation might not be exhibited in the resting baseline state, we also activated mitochondrial functioning in the cells of these two genotypes using TNFα application. Transcriptomes of the four groups of MEFs, Ube3a+/+ and Ube3a−/−, with or without the application of TNFα, were analyzed using various bioinformatics tools and machine learning approaches. Our results indicate that Ube3a deletion affects the gene expression profiles of mitochondrial-associated pathways. We further confirmed these results by analyzing other publicly available human transcriptome datasets of Angelman syndrome and 15q duplication syndrome.

scholarly journals Keap1 deletion accelerates mutant K-ras/p53-driven cholangiocarcinoma

2020 ◽  
Vol 318 (3) ◽  
pp. G419-G427 ◽  
Author(s):  
Tatsuhide Nabeshima ◽  
Shin Hamada ◽  
Keiko Taguchi ◽  
Yu Tanaka ◽  
Ryotaro Matsumoto ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Cancer Progression ◽  
Stress Responses ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Related Factor ◽  
Loss Of Function ◽  
P53 Expression ◽  
Nrf2 Activation

The activation of the Kelch-like ECH-associated protein 1 (Keap1)-NF-E2-related factor 2 (Nrf2) pathway contributes to cancer progression in addition to oxidative stress responses. Loss-of-function Keap1 mutations were reported to activate Nrf2, leading to cancer progression. We examined the effects of Keap1 deletion in a cholangiocarcinoma mouse model using a mutant K-ras/ p53 mouse. Introduction of the Keap1 deletion into liver-specific mutant K-ras/ p53 expression resulted in the formation of invasive cholangiocarcinoma. Comprehensive analyses of the gene expression profiles identified broad upregulation of Nrf2-target genes such as Nqo1 and Gstm1 in the Keap1-deleted mutant K-ras/ p53 expressing livers, accompanied by upregulation of cholangiocyte-related genes. Among these genes, the transcriptional factor Sox9 was highly expressed in the dysplastic bile duct. The Keap-Nrf2-Sox9 axis might serve as a novel therapeutic target for cholangiocarcinoma. NEW & NOTEWORTHY The Keap1-Nrf2 system has a wide variety of effects in addition to the oxidative stress response in cancer cells. Addition of the liver-specific Keap1 deletion to mice harboring mutant K-ras and p53 accelerated cholangiocarcinoma formation, together with the hallmarks of Nrf2 activation. This process involved the expansion of Sox9-positive cells, indicating increased differentiation toward the cholangiocyte phenotype.

scholarly journals Novel Insights into the Role of UBE3A in Regulating Apoptosis and Proliferation

2020 ◽  
Vol 9 (5) ◽  
pp. 1573 ◽  
Author(s):  
Lilach Simchi ◽  
Julia Panov ◽  
Olla Morsy ◽  
Yonatan Feuermann ◽  
Hanoch Kaphzan
Keyword(s):  
Angelman Syndrome ◽  
Critical Role ◽  
Embryonic Fibroblasts ◽  
Ubiquitin E3 Ligase ◽  
Proliferation And Apoptosis ◽  
Ube3a Gene ◽  
Ubiquitin Binding ◽  
Molecular Effects ◽  
Substrate Proteins

The UBE3A gene codes for a protein with two known functions, a ubiquitin E3-ligase which catalyzes ubiquitin binding to substrate proteins and a steroid hormone receptor coactivator. UBE3A is most famous for its critical role in neuronal functioning. Lack of UBE3A protein expression leads to Angelman syndrome (AS), while its overexpression is associated with autism. In spite of extensive research, our understanding of UBE3A roles is still limited. We investigated the cellular and molecular effects of Ube3a deletion in mouse embryonic fibroblasts (MEFs) and Angelman syndrome (AS) mouse model hippocampi. Cell cultures of MEFs exhibited enhanced proliferation together with reduced apoptosis when Ube3a was deleted. These findings were supported by transcriptome and proteome analyses. Furthermore, transcriptome analyses revealed alterations in mitochondria-related genes. Moreover, an analysis of adult AS model mice hippocampi also found alterations in the expression of apoptosis- and proliferation-associated genes. Our findings emphasize the role UBE3A plays in regulating proliferation and apoptosis and sheds light into the possible effects UBE3A has on mitochondrial involvement in governing this balance.

scholarly journals Exploring the Mechanism of Skeletal Muscle in a Tacrolimus-Induced Posttransplantation Diabetes Mellitus Model on Gene Expression Profiles

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chenlei Zheng ◽  
Cheng Wang ◽  
Tan Zhang ◽  
Ding Li ◽  
Xiao-feng Ni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Skeletal Muscle ◽  
Muscle Development ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Quadriceps Muscle ◽  
Control Group ◽  
Bioinformatics Analyses

Objective. Posttransplantation diabetes mellitus (PTDM) is a known complication of transplantation that affects the prognosis. Tacrolimus (Tac or FK506) is a widely used immunosuppressant that has been reported to be a risk factor for PTDM and to further induce complications in heart and skeletal muscles, but the mechanism is still largely unknown. In our preliminary experiments, we found that after Tac treatment, blood glucose increased, and the weight of skeletal muscle declined. Here, we hypothesize that tacrolimus can induce PTDM and influence the atrophy of skeletal muscle. Methods. We designed preliminary experiments to establish a tacrolimus-induced PTDM model. Gene expression profiles in quadriceps muscle from this rat model were characterized by oligonucleotide microarrays. Then, differences in gene expression profiles in muscle from PTDM rats that received tacrolimus and control subjects were analyzed by using GeneSpring GX 11.0 software (Agilent). Functional annotation and enrichment analysis of differentially expressed genes (DEGs) helped us identify clues for the side effects of tacrolimus. Results. Our experiments found that the quadriceps in tacrolimus-induced PTDM group were smaller than those in the control group. The study identified 275 DEGs that may be responsible for insulin resistance and the progression of PTDM, including 86 upregulated genes and 199 downregulated genes. GO and KEGG functional analysis of the DEGs showed a significant correlation between PTDM and muscle development. PPI network analysis screened eight hub genes and found that they were related to troponin and tropomyosin. Conclusions. This study explored the molecular mechanism of muscle atrophy in a tacrolimus-induced PTDM model by bioinformatics analyses. We identified 275 DEGs and identified significant biomarkers for predicting the development and progression of tacrolimus-induced PTDM.

scholarly journals Effect of Sulforaphane and 5-Aza-2’-Deoxycytidine on Melanoma Cell Growth

Medicines ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 71 ◽  
Author(s):  
Tung-chin Chiang ◽  
Brian Koss ◽  
L. Joseph Su ◽  
Charity L. Washam ◽  
Stephanie D. Byrum ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Cell Growth ◽  
Gene Transcription ◽  
Melanoma Cell ◽  
Dna Methyltransferase ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Epigenetic Modifications ◽  
Driving Mechanism ◽  
Epigenetic Drug

Background: UV exposure-induced oxidative stress is implicated as a driving mechanism for melanoma. Increased oxidative stress results in DNA damage and epigenetic dysregulation. Accordingly, we explored whether a low dose of the antioxidant sulforaphane (SFN) in combination with the epigenetic drug 5-aza-2’-deoxycytidine (DAC) could slow melanoma cell growth. SFN is a natural bioactivated product of the cruciferous family, while DAC is a DNA methyltransferase inhibitor. Methods: Melanoma cell growth characteristics, gene transcription profiles, and histone epigenetic modifications were measured after single and combination treatments with SFN and DAC. Results: We detected melanoma cell growth inhibition and specific changes in gene expression profiles upon combinational treatments with SFN and DAC, while no significant alterations in histone epigenetic modifications were observed. Dysregulated gene transcription of a key immunoregulator cytokine—C-C motif ligand 5 (CCL-5)—was validated. Conclusions: These results indicate a potential combinatorial effect of a dietary antioxidant and an FDA-approved epigenetic drug in controlling melanoma cell growth.

scholarly journals Gene Expression Profiles Controlled by the Alternative Splicing Factor Nova2 in Endothelial Cells

Cells ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1498 ◽  
Author(s):  
Elisa Belloni ◽  
Anna Di Matteo ◽  
Davide Pradella ◽  
Margherita Vacca ◽  
Christopher D. R. Wyatt ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Protein Isoforms ◽  
Peroxisome Proliferator ◽  
Bioinformatics Analyses ◽  
Peroxisome Proliferator Activated Receptor ◽  
Ppar Γ ◽  
Significant Enrichment ◽  
Steady State Levels

Alternative splicing (AS) plays an important role in expanding the complexity of the human genome through the production of specialized proteins regulating organ development and physiological functions, as well as contributing to several pathological conditions. How AS programs impact on the signaling pathways controlling endothelial cell (EC) functions and vascular development is largely unknown. Here we identified, through RNA-seq, changes in mRNA steady-state levels in ECs caused by the neuro-oncological ventral antigen 2 (Nova2), a key AS regulator of the vascular morphogenesis. Bioinformatics analyses identified significant enrichment for genes regulated by peroxisome proliferator-activated receptor-gamma (Ppar-γ) and E2F1 transcription factors. We also showed that Nova2 in ECs controlled the AS profiles of Ppar-γ and E2F dimerization partner 2 (Tfdp2), thus generating different protein isoforms with distinct function (Ppar-γ) or subcellular localization (Tfdp2). Collectively, our results supported a mechanism whereby Nova2 integrated splicing decisions in order to regulate Ppar-γ and E2F1 activities. Our data added a layer to the sequential series of events controlled by Nova2 in ECs to orchestrate vascular biology.

scholarly journals Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zheng-yuan Wu ◽  
Gang Du ◽  
Yi-cai Lin
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Cell Activation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Joint Space ◽  
Mast Cell Activation ◽  
Healthy Controls ◽  
Bioinformatics Analyses ◽  
Immune Infiltration

Abstract Background Osteoarthritis (OA) is the most common chronic degenerative joint disorder globally that is characterized by synovitis, cartilage degeneration, joint space stenosis, and sub-cartilage bone hyperplasia. However, the pathophysiologic mechanisms of OA have not been thoroughly investigated. Methods In this study, we conducted various bioinformatics analyses to identify hub biomarkers and immune infiltration in OA. The gene expression profiles of synovial tissues from 29 healthy controls and 36 OA samples were obtained from the gene expression omnibus database to identify differentially expressed genes (DEGs). The CIBERSORT algorithm was used to explore the association between immune infiltration and arthritis. Results Eighteen hub DEGs were identified as critical biomarkers for OA. Through gene ontology and pathway enrichment analyses, it was found that these DEGs were primarily involved in PI3K-Akt signaling pathway and Rap1 signaling pathway. Furthermore, immune infiltration analysis revealed differences in immune infiltration between patients with OA and healthy controls. The hub gene ZNF160 was closely related to immune cells, especially mast cell activation in OA. Conclusion Overall, this study presented a novel method to identify hub DEGs and their correlation with immune infiltration, which may provide novel insights into the diagnosis and treatment of patients with OA.

scholarly journals On the use of topological features of metabolic networks for the classification of cancer samples

2021 ◽  
Vol 22 ◽  
Author(s):  
Jeaneth Machicao ◽  
Francesco Craighero ◽  
Davide Maspero ◽  
Fabrizio Angaroni ◽  
Chiara Damiani ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Data Science ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Learning Approaches ◽  
Topological Properties ◽  
Cancer Dataset ◽  
Topological Features

Background: The increasing availability of omics data collected from patients affected by severe pathologies, such as cancer, is fostering the development of data science methods for their analysis. Introduction: The combination of data integration and machine learning approaches can provide new powerful instruments to tackle the complexity of cancer development and deliver effective diagnostic and prognostic strategies. Methods: We explore the possibility of exploiting the topological properties of sample-specific metabolic networks as features in a supervised classification task. Such networks are obtained by projecting transcriptomic data from RNA-seq experiments on genome-wide metabolic models to define weighted networks modeling the overall metabolic activity of a given sample. Results: We show the classification results on a labeled breast cancer dataset from the TCGA database, including 210 samples (cancer vs. normal). In particular, we investigate how the performance is affected by a threshold-based pruning of the networks by comparing Artificial Neural Networks, Support Vector Machines and Random Forests. Interestingly, the best classification performance is achieved within a small threshold range for all methods, suggesting that it might represent an effective choice to recover useful information while filtering out noise from data. Overall, the best accuracy is achieved with SVMs, which exhibit performances similar to those obtained when gene expression profiles are used as features. Conclusion: These findings demonstrate that the topological properties of sample-specific metabolic networks are effective in classifying cancer and normal samples, suggesting that useful information can be extracted from a relatively limited number of features.

scholarly journals A Survey and Systematic Assessment of Computational Methods for Drug Response Prediction

2019 ◽  
Author(s):  
Jinyu Chen ◽  
Louxin Zhang
Keyword(s):  
Drug Response ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response Prediction ◽  
Future Research ◽  
Learning Approaches ◽  
Systematic Assessment ◽  
The Past ◽  
Public Datasets ◽  
Prediction Problems

AbstractDrug response prediction arises from both basic and clinical research of personalized therapy, as well as drug discovery for cancer and other diseases. With gene expression profiles and other omics data being available for over 1000 cancer cell lines and tissues, different machine learning approaches have been applied to solve drug response prediction problems. These methods appear in a body of literature and have been evaluated on different datasets with only one or two accuracy metrics. We systematically assessed 17 representative methods for drug response prediction, which have been developed in the past five years, on four large public datasets in nine metrics. This study provides insights and lessons for future research into drug response prediction.

Sign in / Sign up

Export Citation Format

Share Document