scholarly journals Gene Network Exploration of Crosstalk between Apoptosis and Autophagy in Chronic Myelogenous Leukemia

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Fengfeng Wang ◽  
William C. S. Cho ◽  
Lawrence W. C. Chan ◽  
S. C. Cesar Wong ◽  
Nancy B. Y. Tsui ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Expression Profiles ◽  
Microarray Dataset ◽  
Underlying Mechanism ◽  
Gene Expression Omnibus ◽  
Myelogenous Leukemia ◽  
Underlying Mechanisms ◽  
Graphical Illustration

Background. Gene expression levels change to adapt the stress, such as starvation, toxin, and radiation. The changes are signals transmitted through molecular interactions, eventually leading to two cellular fates, apoptosis and autophagy. Due to genetic variations, the signals may not be effectively transmitted to modulate apoptotic and autophagic responses. Such aberrant modulation may lead to carcinogenesis and drug resistance. The balance between apoptosis and autophagy becomes very crucial in coping with the stress. Though there have been evidences illustrating the apoptosis-autophagy interplay, the underlying mechanism and the participation of the regulators including transcription factors (TFs) and microRNAs (miRNAs) remain unclear.Results. Gene network is a graphical illustration for exploring the functional linkages and the potential coordinate regulations of genes. Microarray dataset for the study of chronic myeloid leukemia was obtained from Gene Expression Omnibus. The expression profiles of those genes related to apoptosis and autophagy, including MCL1, BCL2, ATG, beclin-1, BAX, BAK, E2F, cMYC, PI3K, AKT, BAD, and LC3, were extracted from the dataset to construct the gene networks.Conclusion. The network analysis of these genes explored the underlying mechanisms and the roles of TFs and miRNAs for the crosstalk between apoptosis and autophagy.

EFFECTIVE MODELS FOR GENE NETWORKS AND THEIR APPLICATIONS

2012 ◽  
Vol 07 (01n02) ◽  
pp. 41-70 ◽  
Author(s):  
JASON SHULMAN ◽  
LARS SEEMANN ◽  
GREGG W. ROMAN ◽  
GEMUNU H. GUNARATNE
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Stationary Solutions ◽  
The State ◽  
Effective Model ◽  
Knockout Mutant ◽  
Double Knockout

Networks are used to abstract large, highly-coupled sets of objects. Their analyses have included network classification into a few broad classes and selection of small substructures that perform simple yet common tasks. One issue that has received little attention is how the state of a network can be moved according to a pre-specified set of guidelines. In this paper, we address this question in the context of gene networks. In general, neither the full membership of the gene network associated with a biological process nor the precise form of interactions between nodes is known. What is available, through microarrays or sequencing, are gene expression profiles of an organism or its viable mutants. Our approach relies only on these expression profiles, and not on the availability of an accurate model for the network. The first step is to select a small set of core- or master- nodes, such as transcription factors or microRNAs, that can be used to alter the levels of many of the remaining genes in the network. We ask how the state — or solution — of the gene network changes as the levels of these master nodes are altered externally. The object of our study is, not the network, but the surface of these solutions. We argue that it can be approximated using gene expression profiles of the organism and single manipulation of master node activity. This is done through an "effective model." The effective model as well as error estimates for its predictions can be derived from experimental data. The method is validated using synthetic gene networks that have stationary solutions and those that are periodically driven, e.g., circadian networks. An effective model for the oxygen-deprivation network of E.coli is constructed using previously published gene expression profiles, and used to predict the expression levels in a double knockout mutant. Less that 30% of the predictions lie outside the 5% confidence level. We propose the use of the effective model methodology to compute how Drosophila melanogaster in the normal state can be genetically altered into a pre-defined sleep deprived-like state.

scholarly journals Evidence of Highly Regulated Genes (in-Hubs) in Gene Networks of Saccharomyces Cerevisiae

2008 ◽  
Vol 2 ◽  
pp. BBI.S853 ◽  
Author(s):  
Jesper Lundström ◽  
Johan Björkegren ◽  
Jesper Tegnér
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Repeated Measurements ◽  
Experimental Conditions ◽  
Cellular Processes ◽  
Binding Data ◽  
External Stimuli

Uncovering interactions between genes, gene networks, is important to increase our understanding of intrinsic cellular processes and responses to external stimuli such as drugs. Gene networks can be computationally inferred from repeated measurements of gene expression, using algorithms, which assume that each gene is controlled by only a small number of other proteins. Here, by extending the transcription network with cofactors (defined from protein-protein binding data) as active regulators, we identified the effective gene network, providing evidence of in-hubs in the gene regulatory networks of yeast. Then, using the notion that in-hub genes will be differentially expressed over several experimental conditions, we designed an algorithm, the HubDetector, enabling identification of in-hubs directly from gene expression data. Applying the HubDetector to 488 genome-wide expression profiles from two independent datasets, we identified putative in-hubs overlapping significantly with in-hubs in the effective gene network.

scholarly journals CircFAM13B promotes the proliferation of hepatocellular carcinoma by sponging miR-212, upregulating E2F5 expression and activating the P53 pathway

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ying Xie ◽  
Xiaofeng Hang ◽  
Wensheng Xu ◽  
Jing Gu ◽  
Yuanjing Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
In Vivo Studies ◽  
Circular Rnas ◽  
Novel Target ◽  
Underlying Mechanisms

Abstract Background Most of the biological functions of circular RNAs (circRNAs) and the potential underlying mechanisms in hepatocellular carcinoma (HCC) have not yet been discovered. Methods In this study, using circRNA expression data from HCC tumor tissues and adjacent tissues from the Gene Expression Omnibus database, we identified out differentially expressed circRNAs and verified them by qRT-PCT. Functional experiments were performed to evaluate the effects of circFAM13B in HCC in vitro and in vivo. Results We found that circFAM13B was the most significantly differentially expressed circRNA in HCC tissue. Subsequently, in vitro and in vivo studies also demonstrated that circFAM13B promoted the proliferation of HCC. Further studies revealed that circFAM13B, a sponge of miR-212, is involved in the regulation of E2F5 gene expression by competitively binding to miR-212, inhibits the activation of the P53 signalling pathway, and promotes the proliferation of HCC cells. Conclusions Our findings revealed the mechanism underlying the regulatory role played by circFAM13B, miR-212 and E2F5 in HCC. This study provides a new theoretical basis and novel target for the clinical prevention and treatment of HCC.

scholarly journals Comprehensive analysis of IgA nephropathy expression profiles: identification of potential biomarkers and therapeutic agents

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alieh Gholaminejad ◽  
Yousof Gheisari ◽  
Sedigheh Jalali ◽  
Amir Roointan
Keyword(s):  
Iga Nephropathy ◽  
Regulatory Network ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Integrative Approach ◽  
Potential Factors ◽  
Ngf Signaling ◽  
Underlying Mechanisms

Abstract Background IgA nephropathy (IgAN) is a kidney disease recognized by the presence of IgA antibody depositions in kidneys. The underlying mechanisms of this complicated disease are remained to be explored and still, there is an urgent need for the discovery of noninvasive biomarkers for its diagnosis. In this investigation, an integrative approach was applied to mRNA and miRNA expression profiles in PBMCs to discover a gene signature and novel potential targets/biomarkers in IgAN. Methods Datasets were selected from gene expression omnibus database. After quality control checking, two datasets were analyzed by Limma to identify differentially expressed genes/miRNAs (DEGs and DEmiRs). Following identification of DEmiR-target genes and data integration, intersecting mRNAs were subjected to different bioinformatic analyses. The intersecting mRNAs, DEmiRs, related transcription factors (from TRRUST database), and long-non coding RNAs (from LncTarD database) were used for the construction of a multilayer regulatory network via Cytoscape. Result “GSE25590” (miRNA) and “GSE73953” (mRNA) datasets were analyzed and after integration, 628 intersecting mRNAs were identified. The mRNAs were mainly associated with “Innate immune system”, “Apoptosis”, as well as “NGF signaling” pathways. A multilayer regulatory network was constructed and several hub-DEGs (Tp53, STAT3, Jun, etc.), DEmiRs (miR-124, let-7b, etc.), TFs (NF-kB, etc.), and lncRNAs (HOTAIR, etc.) were introduced as potential factors in the pathogenesis of IgAN. Conclusion Integration of two different expression datasets and construction of a multilayer regulatory network not only provided a deeper insight into the pathogenesis of IgAN, but also introduced several key molecules as potential therapeutic target/non-invasive biomarkers.

scholarly journals 975. Why Sex Make a Difference in HIV Clinical Course? Bioinformatics Analysis of Differential Expressed Gene in Females and Males with HIV Disease

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S516-S517
Author(s):  
Kulachanya Suwanwongse ◽  
Nehad Shabarek
Keyword(s):  
Gene Expression ◽  
Bioinformatics Analysis ◽  
Underlying Mechanism ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Gender Inequalities ◽  
Hiv Disease ◽  
Go Analysis ◽  
Hiv Progression ◽  
Pathway Analyses

Abstract Background Human immunodeficiency virus (HIV) disease progression are different among genders, in which women usually progress to acquired immunodeficiency syndrome (AIDS) faster than men. The mechanisms resulting in the gender biases of HIV progression are unclear. We conducted a bioinformatics analysis of differentially expressed genes (DEGs) in women and men with HIV disease to understand the sex-based differences in HIV pathogenesis. Methods We obtained microarray data from the Gene Expression Omnibus (GEO) database using our pre-defined search strategy and analyzed data using the GEO2R platform. The t-test was done to compare DEGs between females and males with HIV diseases. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was implemented to systematically extract biological features and processes of retrieving DEGs via gene ontology (GO) analysis. A Systemic search was performed to evaluate each DEG function and its possible association with HIV. Results One gene expression profiling data were retrieved: GSE 140713, composed of 40 males and 10 females with HIV1 infected samples. A GEO2R analysis yielded 19 DEGs (Table 1). The GO analysis result was demonstrated in Tables 2 and 3. Following a systemic search, we found two DEGs, which have previous studies reported an association with HIV: DDX3X (20 studies) and PDS5 (1 study). We proposed DDX3X (t 5.3, p 0.0037) is responsible for gender inequalities of HIV progression because of: 1. DDX3X is needed in the HIV1 life cycle. 2. Several studies confirmed a positive correlation between DDX3X expression and HIV1 replication. 3. Our study found an up-regulated DDX3X expression in women corresponded to the fact that women progress to AIDS faster than men. 4. Our GO analysis showed female up-regulated genes were enriched in positive regulation of the gene expression pathway, which can be explained by DDX3X and its underlying mechanism. Table 1: DEGs in women and men with HIV1 disease Table 2: GO functional enrichment pathway analyses of overall retrieving DEGs Table 3: GO functional enrichment pathway analyses of down- and up-regulated clusters of DEGs Conclusion Aberrant DDX3X expression may contribute to sex-based differences in HIV disease. Drugs modifying DDX3X gene expression will be beneficial in the treatment of HIV especially resolving the HIV drug resistance problem because current anti-HIV drugs target viral components posed the risk of viral mutation. Disclosures All Authors: No reported disclosures

scholarly journals Analysis of blood-based gene expression in idiopathic Parkinson disease

Neurology ◽  
2017 ◽  
Vol 89 (16) ◽  
pp. 1676-1683 ◽  
Author(s):  
Ron Shamir ◽  
Christine Klein ◽  
David Amar ◽  
Eva-Juliane Vollstedt ◽  
Michael Bonin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson Disease ◽  
Gene Networks ◽  
Large Scale ◽  
Expression Profiles ◽  
Area Under The Curve ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
Gene Profiles ◽  
Independent Test

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

Comprehensive Analysis of Differential Expression Profiles of Long Non-coding RNAs with Associated Co-expression and Competing Endogenous RNA Networks in the Hippocampus of Patients with Alzheimer's Disease

2021 ◽  
Vol 18 ◽  
Author(s):  
Jian-Jun Zhang ◽  
Ze-Xuan-Zhu ◽  
Guang-Min-Xu ◽  
Peng Su ◽  
Qian Lei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Competing Endogenous Rna ◽  
Pathogenic Genes ◽  
Non Coding Rna ◽  
Using Data ◽  
Trans Regulation

Background: Alzheimer's disease (AD) is still one of the major threats to human health. Although a satisfactory treatment for AD has not yet been discovered, it is necessary to continue to search for novel approaches to deal with this insidious and debilitating disease. Although numerous studies have shown that long non-coding RNA (lncRNA) occupy a significant role in a variety of diseases, their roles in AD remain unclear. Objectives: Using data analysis to explore the role of lncRNA in the course of AD, to further our understanding of AD, and to look forward to finding a new breakthrough for the treatment of AD. Methods: We downloaded and screened expression data of the hippocampal regions of patients with AD from the Gene Expression Omnibus database. We generated lncRNA-miRNA-mRNA networks based on the competing endogenous RNA (ceRNA) hypothesis, and according to gene expression level, we constructed a coding-noncoding co-expression (CNC) network and then executed cis- and trans-regulation analyses. Results: Through comprehensive and systematic analyses, we found that lncRNAs MALAT1, OIP5-AS1, LINC00657, and lnc-NUMB-1 regulated the expression of the key AD pathogenic genes APP, PSEN1, BACE1; and that these lncRNAs may promote the distribution of β-amyloid (Aβ protein) in the brain through exosomes. In addition, lncRNAs were found to adjust viral transcriptional expression, thereby further supporting viral pathogenesis for AD. Conclusions: The lncRNAs MALAT1, OIP5-AS1, LINC00657, and lnc-NUMB-1 that are present in the hippocampus of AD patients exert an important influence on the development of this disease.

Assessment of cancer prevention effect of exercise

2021 ◽  
pp. 1-6
Author(s):  
Reza Vafaee ◽  
Mostafa Rezaei Tavirani ◽  
Sina Rezaei Tavirani ◽  
Mohammadreza Razzaghi
Keyword(s):  
Gene Expression ◽  
Cancer Prevention ◽  
Human Health ◽  
Expression Profiles ◽  
Vastus Lateralis ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Vastus Lateralis Muscle ◽  
Before And After ◽  
Exercise Training Intervention

There are many documents about benefits of exercise on human health. However, evidences indicate to positive effect of exercise on disease prevention, understanding of many aspects of this mechanism need more investigations. Determination of critical genes which effect human health. GSE156249 including 12 gene expression profiles of healthy individual biopsy from vastus lateralis muscle before and after 12-week combined exercise training intervention were extracted from gene expression omnibus (GEO) database. The significant DEGs were included in interactome unit by Cytoscape software and STRING database. The network was analyzed to find the central nodes subnetwork clusters. The nodes of prominent cluster were assessed via gene ontology by using ClueGO. Number of 8 significant DEGs and 100 first neighbors analyzed via network analysis. The network includes 2 clusters and COL3A1, BGN, and LOX were determined as central DEGs. The critical DEGs were involved in cancer prevention process.

Simulated microgravity using the Random Positioning Machine inhibits differentiation and alters gene expression profiles of 2T3 preosteoblasts

2005 ◽  
Vol 288 (6) ◽  
pp. C1211-C1221 ◽  
Author(s):  
Steven J. Pardo ◽  
Mamta J. Patel ◽  
Michelle C. Sykes ◽  
Manu O. Platt ◽  
Nolan L. Boyd ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alkaline Phosphatase ◽  
Bone Loss ◽  
Simulated Microgravity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Underlying Mechanism ◽  
Bone Biology ◽  
Random Positioning Machine

Exposure to microgravity causes bone loss in humans, and the underlying mechanism is thought to be at least partially due to a decrease in bone formation by osteoblasts. In the present study, we examined the hypothesis that microgravity changes osteoblast gene expression profiles, resulting in bone loss. For this study, we developed an in vitro system that simulates microgravity using the Random Positioning Machine (RPM) to study the effects of microgravity on 2T3 preosteoblast cells grown in gas-permeable culture disks. Exposure of 2T3 cells to simulated microgravity using the RPM for up to 9 days significantly inhibited alkaline phosphatase activity, recapitulating a bone loss response that occurs in real microgravity conditions without altering cell proliferation and shape. Next, we performed DNA microarray analysis to determine the gene expression profile of 2T3 cells exposed to 3 days of simulated microgravity. Among 10,000 genes examined using the microarray, 88 were downregulated and 52 were upregulated significantly more than twofold using simulated microgravity compared with the static 1-g condition. We then verified the microarray data for some of the genes relevant in bone biology using real-time PCR assays and immunoblotting. We confirmed that microgravity downregulated levels of alkaline phosphatase, runt-related transcription factor 2, osteomodulin, and parathyroid hormone receptor 1 mRNA; upregulated cathepsin K mRNA; and did not significantly affect bone morphogenic protein 4 and cystatin C protein levels. The identification of gravisensitive genes provides useful insight that may lead to further hypotheses regarding their roles in not only microgravity-induced bone loss but also the general patient population with similar pathological conditions, such as osteoporosis.

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