scholarly journals Hybrid analysis of gene dynamics predicts context-specific expression and offers regulatory insights

2019 ◽  
Vol 35 (22) ◽  
pp. 4671-4678
Author(s):  
Justin D Finkle ◽  
Neda Bagheri
Keyword(s):  
Gene Expression ◽  
Differential Equation ◽  
Time Series ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Supplementary Information ◽  
Series Data ◽  
Expression Data ◽  
Regulatory Pathways

Abstract Motivation To understand the regulatory pathways underlying diseases, studies often investigate the differential gene expression between genetically or chemically differing cell populations. Differential expression analysis identifies global changes in transcription and enables the inference of functional roles of applied perturbations. This approach has transformed the discovery of genetic drivers of disease and possible therapies. However, differential expression analysis does not provide quantitative predictions of gene expression in untested conditions. We present a hybrid approach, termed Differential Expression in Python (DiffExPy), that uniquely combines discrete, differential expression analysis with in silico differential equation simulations to yield accurate, quantitative predictions of gene expression from time-series data. Results To demonstrate the distinct insight provided by DiffExpy, we applied it to published, in vitro, time-series RNA-seq data from several genetic PI3K/PTEN variants of MCF10a cells stimulated with epidermal growth factor. DiffExPy proposed ensembles of several minimal differential equation systems for each differentially expressed gene. These systems provide quantitative models of expression for several previously uncharacterized genes and uncover new regulation by the PI3K/PTEN pathways. We validated model predictions on expression data from conditions that were not used for model training. Our discrete, differential expression analysis also identified SUZ12 and FOXA1 as possible regulators of specific groups of genes that exhibit late changes in expression. Our work reveals how DiffExPy generates quantitatively predictive models with testable, biological hypotheses from time-series expression data. Availability and implementation DiffExPy is available on GitHub (https://github.com/bagherilab/diffexpy). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DECODE: an integrated differential co-expression and differential expression analysis of gene expression data

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Thomas WH Lui ◽  
Nancy BY Tsui ◽  
Lawrence WC Chan ◽  
Cesar SC Wong ◽  
Parco MF Siu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Expression Data

scholarly journals Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

2017 ◽  
Author(s):  
Anthony Szedlak ◽  
Spencer Sims ◽  
Nicholas Smith ◽  
Giovanni Paternostro ◽  
Carlo Piermarocchi
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Cell Cycle ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Human Cervical Cancer

AbstractModern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.Author SummaryCell cycle – the process in which a parent cell replicates its DNA and divides into two daughter cells – is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

Abstract 545: Differential mRNA Expression is Influenced by Apolipoprotein A-I in Order to Promote Foam Cell Regression

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Elisa C Maruko ◽  
Hao Xu ◽  
Sushma Kaul ◽  
Brian J Capaldo ◽  
Nathalie Pamir ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Immune Cell ◽  
Foam Cell ◽  
Differential Expression Analysis ◽  
Ldl Receptor ◽  
Gene Set Enrichment Analysis ◽  
Control Group ◽  
Apolipoprotein A

Atherosclerosis is a disease of both lipids and inflammatory immune cells. More specifically, elevated plasma levels of low-density lipoproteins (LDL) leads to migration of circulating monocytes into the artery wall. Lipid loaded monocyte cells subsequently proliferate in the arterial walls becoming macrophage foam cells; a hallmark of atherosclerotic lesions. A proposed mechanism of the protective effects of high-density lipoprotein (HDL) is apolipoprotein A-I (apo A-I) acting as a mediator of cholesterol efflux and subsequent foam cell regression. To better understand the biological changes stimulated by apo A-I treatment, differential expression analysis of microarray data was performed on spleen cells from apo A-I treated mice. LDL receptor null (LDLr -/- ) and LDL receptor and apo A-I null (LDLr -/- , apoA-I -/- ) mice were fed a western diet consisting of 0.2% cholesterol and 42% of calories as fat for 12 weeks. After 6 weeks of diet, a subset of mice for each genotype was subcutaneously injected with 200 micrograms of apo A-I 3 times a week for the remaining 6 weeks. The control group mice were subcutaneously injected with 200 micrograms of saline or BSA. Spleen cell RNA was isolated, purified, and analyzed for differential expression analysis using Illumina BeadArray Microarray Technology Analysis. Individual gene expression analysis for LDLr -/- , apoA-I -/- apo A-I treated mice showed 281 significantly differentially expressed genes compared to BSA treated mice. LDLr -/- A-I treated mice had 1502. Of the significant genes, 189 intersected across both genotypes. LDLr -/- , apoA-I -/- A-I mice showed 73 up-regulated and 116 down-regulated genes. Similarly, LDLr -/- A-I mice had 71 up-regulated and 118 down-regulated. One-directional Gene Set Enrichment Analysis (GSEA) of LDLr -/- , apoA-I -/- A-I mice revealed 49 significant pathways while a total of 63 were found for LDLr -/- . Of these pathways, 21 were up-regulated and 13 were down-regulated in both genotypes. Eight of the top 10 most significant up-regulated pathways in both genotypes were immune cell related. Their functions involve receptor, adhesion, and chemokine signaling. Overall, preliminary analysis suggests A-I treatment induces similar gene expression changes across different genotypes.

scholarly journals B-Cell and Monocyte Contribution to Systemic Lupus Erythematosus Identified by Cell-Type-Specific Differential Expression Analysis in RNA-Seq Data

2015 ◽  
Vol 9s3 ◽  
pp. BBI.S29470 ◽  
Author(s):  
Mikhail G. Dozmorov ◽  
Nicolas Dominguez ◽  
Krista Bean ◽  
Susan R. Macwana ◽  
Virginia Roberts ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Differential Expression ◽  
Expression Analysis ◽  
Lupus Erythematosus ◽  
Differential Expression Analysis ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by complex interplay among immune cell types. SLE activity is experimentally assessed by several blood tests, including gene expression profiling of heterogeneous populations of cells in peripheral blood. To better understand the contribution of different cell types in SLE pathogenesis, we applied the two methods in cell-type-specific differential expression analysis, csSAM and DSection, to identify cell-type-specific gene expression differences in heterogeneous gene expression measures obtained using RNA-seq technology. We identified B-cell-, monocyte-, and neutrophil-specific gene expression differences. Immunoglobulin-coding gene expression was altered in B-cells, while a ribosomal signature was prominent in monocytes. On the contrary, genes differentially expressed in the heterogeneous mixture of cells did not show any functional enrichment. Our results identify antigen binding and structural constituents of ribosomes as functions altered by B-cell- and monocyte-specific gene expression differences, respectively. Finally, these results position both csSAM and DSection methods as viable techniques for cell-type-specific differential expression analysis, which may help uncover pathogenic, cell-type-specific processes in SLE.

scholarly journals Easy and efficient ensemble gene set testing with EGSEA

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2010 ◽  
Author(s):  
Monther Alhamdoosh ◽  
Charity W. Law ◽  
Luyi Tian ◽  
Julie M. Sheridan ◽  
Milica Ng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
P Gene ◽  
Wide Range ◽  
Gene Set Testing

Gene set enrichment analysis is a popular approach for prioritising the biological processes perturbed in genomic datasets. The Bioconductor project hosts over 80 software packages capable of gene set analysis. Most of these packages search for enriched signatures amongst differentially regulated genes to reveal higher level biological themes that may be missed when focusing only on evidence from individual genes. With so many different methods on offer, choosing the best algorithm and visualization approach can be challenging. The EGSEA package solves this problem by combining results from up to 12 prominent gene set testing algorithms to obtain a consensus ranking of biologically relevant results.This workflow demonstrates how EGSEA can extend limma-based differential expression analyses for RNA-seq and microarray data using experiments that profile 3 distinct cell populations important for studying the origins of breast cancer. Following data normalization and set-up of an appropriate linear model for differential expression analysis, EGSEA builds gene signature specific indexes that link a wide range of mouse or human gene set collections obtained from MSigDB, GeneSetDB and KEGG to the gene expression data being investigated. EGSEA is then configured and the ensemble enrichment analysis run, returning an object that can be queried using several S4 methods for ranking gene sets and visualizing results via heatmaps, KEGG pathway views, GO graphs, scatter plots and bar plots. Finally, an HTML report that combines these displays can fast-track the sharing of results with collaborators, and thus expedite downstream biological validation. EGSEA is simple to use and can be easily integrated with existing gene expression analysis pipelines for both human and mouse data.

scholarly journals Signatures and Prognostic Values of Related Immune Targets in Tongue Cancer

2021 ◽  
Author(s):  
Xi Yu ◽  
Xiaofei Lv
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Tongue Cancer ◽  
Differential Expression Analysis ◽  
Marker Genes ◽  
Data Sets ◽  
Expression Data ◽  
Oral Cancers ◽  
Limma Package ◽  
Cancer Bioinformatics

Abstract Tongue cancer, as one of the most malignant oral cancers, is highly invasive and has a high risk of recurrence. At present, tongue cancer in the advanced stage is not obvious, easy to miss the opportunity of early diagnosis. It is important to find markers that can predict the occurrence and progression of tongue cancer. Bioinformatics analysis plays an important role in the acquisition of marker genes. GEO and TCGA data are very important public databases. In addition to expression data, TCGA database also contains corresponding clinical data. In this study, we screened three GEO datasets included GSE13601, GSE34105 and GSE34106 that met the standard. These data sets were combined using the SVA package to prepare the data for differential expression analysis, and then the LIMMA package was used to set the standard to p<0.05 and |log2 (FC)| ≥1.5. We got 170 DEGs (104, raised 66 downgrade). Besides, the DEseq package was used for differential expression analysis using the same criteria for samples in TCGA database. It ended up with 1589 DEGs (644 up-regulated, 945 down-regulated). By merging these two sets of DEGs, 5 common up-regulated DEGs (CCL20, SCG5, SPP1, KRT75 and FOLR3) and 15 common down-regulated DEGs were obtained. Further functional analysis of the DEGs showed that CCL20, SCG5 and SPP1 is closely related to prognosis and may be a therapeutic target of TSCC.

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