scholarly journals Co-Expression Network-based Analysis associated with potato initial resistance

2018 ◽  
Author(s):  
Lang Yan ◽  
Xianjun Lai ◽  
Yan Wu ◽  
Xuemei Tan ◽  
Yizheng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Evolutionary Divergence ◽  
Regulation Mechanism ◽  
Rna Seq ◽  
Functional Studies ◽  
Study Gene Expression ◽  
Gene Modules ◽  
Dramatic Shift

RNA sequencing (RNA-seq) providing genome-wide expression datasets has been successfully used to study gene expression patterns and regulation mechanism among multiple samples. Gene co-expression networks (GCNs) studies within or across species showed that coordinated genes in expression patterns are often functionally related. For potatoes, a large amount of publicly available transcriptome datasets have been generated but an optimal GCN detecting expression patterns in different genotypes, tissues and environmental conditions, is lacking. We constructed a potato GCN using 16 published RNA-Seq datasets covering 11 cultivars from native habitat worldwide. The correlations of gene expression were assessed pair-wisely and biologically meaningful gene modules which are highly connected in GCN were identified. One of the primitively native-farmer-selected cultivars in the Andes, ssp.Andigena, had relative far distance in gene expression patterns with other modern varieties. GCN in further enriched 134 highly and specifically co-expressed genes in ssp.Andigena associated with potato disease and stress resistance, which underlying the dramatic shift in evolutionary pressures during potato artificial domestication. In total, the network was consisted of into 14 gene models that involves in a variety of plant processes, which sheds light on how gene modules organized intra- and inter-varieties in the context of evolutionary divergence and provides a basis of information resource for potato gene functional studies.

Gene Expression Patterns Identify Novel Biologically Relevant Signaling and Transcriptional Pathways Involved in Terminal Erythroid Differentiation and Polycythemia Vera.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 3524-3524
Author(s):  
Anil Potti ◽  
Holly K. Dressman ◽  
Murat O. Arcasoy
Keyword(s):  
Gene Expression ◽  
Polycythemia Vera ◽  
Hierarchical Clustering ◽  
Expression Patterns ◽  
Terminal Differentiation ◽  
Erythroid Differentiation ◽  
Gene Expression Patterns ◽  
Biologically Relevant ◽  
Distinct Gene ◽  
Study Gene Expression

Abstract Hematopoietic proliferation, lineage commitment, and terminal differentiation are characterized by the emergence of a cell type-specific gene expression and transcriptional programs that determine the specific phenotype and function of cells in the erythroid lineage. Our objectives in this study were to identify unique gene expression patterns that characterize the transcriptional program of normal primary human erythroid precursors during terminal differentiation, and define the gene expression patterns seen in erythroblasts (EBL) of patients with polycythemia vera (PV). Homogenous populations of primary proEBL were generated from purified liquid cultures of CD34+ cells collected from healthy volunteers and PV patients. All patients with PV were diagnosed based on established criteria and had the JAK2-V617F mutation. Morphologic examination and surface expression of CD71 confirmed the purity of proEBL cell populations. ProEBL from normal individuals were induced to terminally differentiate generating orthochromatic EBL. RNA was extracted from normal proEBL, PV proEBL, and normal orthochromatic EBL. Affymetrix U133 Plus 2.0 arrays representing approximately 39,000 human genes were used for gene expression analysis. Four replicates from four independent primary cell cultures were analyzed for each comparison group (e.g. undifferentiated proEBL versus terminally differentiated orthochromatic EBL). Unsupervised hierarchical clustering showed distinct gene expression profiles in the proEBL and terminally differentiated EBL lineages. 1109 genes (2.0 fold change, P<0.01) were found to be differentially expressed. Numerous erythroid genes were found to be upregulated during terminal differentiation [e.g. globin genes, erythropoietin receptor, heme synthesis enzymes (ferrochelatase, ALAS2) erythrocyte membrane proteins (band 3, ankyrin, protein 4.1) and transcription factors (NFE2, Kruppel-like factors, myb, GATA2)]. As a proof of validation, the differential expression of 7 genes was verified by Northern blotting. To better understand the biologic role of the gene sets identified, using Ingenuity pathway analysis, individual genes were integrated into specific regulatory and signaling pathway networks. A total of 19 networks with significant scores (>23) were identified. Biological functions of the identified networks included RNA post-transcriptional regulation, cell cycle control, translational regulation, DNA replication and repair and cellular assembly/organization. In a proof of principle study, gene expression patterns in PV proEBL (n=6) were compared to normal proEBL (n=5). Unsupervised hierarchical clustering showed a distinct gene expression profile for PV. A binary regression predictive model was also developed to find gene expression patterns predictive for PV. Using this model a 150 gene predictor was found that could predict PV patients from control at 100% accuracy. Ingenuity pathways analysis of a subset of gene subsets demonstrated several biologically relevant networks that were distinct in patients with PV, including myc, CDC2, and JAK2. Deregulation of normal transcriptional mechanisms in hematopoietic cells is associated with the pathogenesis of PV. Further, our data shows that genomic studies provide new insights into transcriptional programs that govern erythroid differentiation, and identify biologically relevant deregulated pathways as potential targets for therapy in PV.

scholarly journals Identifying core biological processes distinguishing human eye tissues with precise systems-level gene expression analyses and weighted correlation networks

2017 ◽  
Author(s):  
John M Bryan ◽  
Temesgen D Fufa ◽  
Kapil Bharti ◽  
Brian P Brooks ◽  
Robert B Hufnagel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Mouse Retina ◽  
Human Tissues ◽  
Biological Processes ◽  
Rna Seq ◽  
Human Eye ◽  
Correlation Networks ◽  
Eye Tissues ◽  
Study Gene Expression

AbstractThe human eye is built from several specialized tissues which direct, capture, and pre-process information to provide vision. The gene expression of the different eye tissues has been extensively profiled with RNA-seq across numerous studies. Large consortium projects have also used RNA-seq to study gene expression patterning across many different human tissues, minus the eye. There has not been an integrated study of expression patterns from multiple eye tissues compared to other human body tissues. We have collated all publicly available healthy human eye RNA-seq datasets as well as dozens of other tissues. We use this fully integrated dataset to probe the biological processes and pan expression relationships between the cornea, retina, RPE-choroid complex, and the rest of the human tissues with differential expression, clustering, and GO term enrichment tools. We also leverage our large collection of retina and RPE-choroid tissues to build the first human weighted gene correlation networks and use them to highlight known biological pathways and eye gene disease enrichment. We also have integrated publicly available single cell RNA-seq data from mouse retina into our framework for validation and discovery. Finally, we make all these data, analyses, and visualizations available via a powerful interactive web application (https://eyeintegration.nei.nih.gov/).

scholarly journals Raw sequence to target gene prediction: An integrated inference pipeline for ChIP-seq and RNA-seq datasets

2017 ◽  
Author(s):  
Nisar Wani ◽  
Khalid Raza
Keyword(s):  
Gene Expression ◽  
Target Gene ◽  
Target Genes ◽  
Gene Prediction ◽  
Expression Patterns ◽  
Dna Binding Proteins ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cellular Processes ◽  
Regulatory Effects

AbstractGene expression patterns determine the manner whereby organisms regulate various cellular processes and therefore their organ functions.These patterns do not emerge on their own, but as a result of diverse regulatory factors such as, DNA binding proteins known as transcription factors (TF), chromatin structure and various other environmental factors. TFs play a pivotal role in gene regulation by binding to different locations on the genome and influencing the expression of their target genes. Therefore, predicting target genes and their regulation becomes an important task for understanding mechanisms that control cellular processes governing both healthy and diseased cells.In this paper, we propose an integrated inference pipeline for predicting target genes and their regulatory effects for a specific TF using next-generation data analysis tools.

scholarly journals Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

2020 ◽  
Author(s):  
Timothy J. Durham ◽  
Riza M. Daza ◽  
Louis Gevirtzman ◽  
Darren A. Cusanovich ◽  
William Stafford Noble ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Specific ◽  
C Elegans

AbstractRecently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

Differences in gene expression patterns, revealed by RNA-seq analysis, between various Sanfilippo and Morquio disease subtypes

Gene ◽  
2021 ◽  
pp. 146090
Author(s):  
Karolina Wiśniewska ◽  
Lidia Gaffke ◽  
Karolina Krzelowska ◽  
Grzegorz Węgrzyn ◽  
Karolina Pierzynowska
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Morquio Disease ◽  
Disease Subtypes

scholarly journals Pseudo-Location: A novel predictor for predicting pseudo-temporal gene expression patterns using spatial functional regression

2020 ◽  
Author(s):  
Kyungmin Ahn ◽  
Hironobu Fujiwara
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Functional Regression ◽  
Rna Seq ◽  
Temporal Gene Expression ◽  
Early Stages ◽  
The Future

Statement of withdrawalThe authors have withdrawn version 1 of this manuscript because a draft manuscript, which was still in the early stages of preparation and required major revisions including the replacement of the source RNA-seq datasets, was erroneously submitted. The authors do not wish this version to be cited as reference for this study. We will post a revised manuscript in the future. If you have any questions, please contact the corresponding author.

scholarly journals Single cell transcriptomics reveals a heterogeneous cellular response to BKV infection

2020 ◽  
Author(s):  
Ping An ◽  
Paul G. Cantalupo ◽  
Wenshan Zheng ◽  
Maria Teresa Sáenz-Robles ◽  
Alexis M. Duray ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Viral Mrna ◽  
Rna Seq ◽  
Cellular Gene ◽  
Viral Mrnas ◽  
Infected Cells ◽  
Low Levels

BKV is a human polyomavirus that is generally harmless but can cause devastating disease in immunosuppressed individuals. BKV infection of renal cells is a common problem for kidney transplant patients undergoing immunosuppressive therapy. In cultured primary human renal proximal tubule epithelial cells (RPTE), BKV undergoes a productive infection. The BKV-encoded large T antigen (LT) induces cell cycle entry, resulting in the upregulation of numerous genes associated with cell proliferation. Consistently, microarray and RNA-seq experiments performed in bulk infected cell populations identified several proliferation-related pathways that are upregulated by BKV. These studies revealed few genes that are downregulated. In this study, we analyzed viral and cellular transcripts in single mock or BKV-infected cells. We found that the levels of viral mRNAs vary widely among infected cells, resulting in different levels of LT and viral capsid protein expression. Cells expressing the highest levels of viral transcripts account for approximately 20% of the culture and have a gene expression pattern that is distinct from cells expressing lower levels of viral mRNAs. Surprisingly, cells expressing low levels of viral mRNA do not progress with time to high expression, suggesting that the two cellular responses are determined prior to or shortly following infection. Finally, comparison of cellular gene expression patterns of cells expressing high levels of viral mRNA with mock-infected cells, or with cells expressing low levels of viral mRNA, revealed previously unidentified pathways that are downregulated by BKV. Among these are pathways associated with drug metabolism and detoxification, TNF-signaling, energy metabolism, and translation. IMPORTANCE The outcome of viral infection is determined by the ability of the virus to redirect cellular systems towards progeny production countered by the ability of the cell to block these viral actions. Thus, an infected culture consists of thousands of cells, each fighting their own individual battle. Bulk measurements, such as PCR or RNA-seq, measure the average of these individual responses to infection. Single cell transcriptomics provides a window to the one-on-one battle between BKV and each cell. Our studies reveal that only a minority of infected cells are overwhelmed by the virus and produce large amounts of BKV mRNAs and proteins, while the infection appears to be restricted in the remaining cells. Correlation of viral transcript levels with cellular gene expression patterns reveals pathways manipulated by BKV that may play a role in limiting infection.

Sign in / Sign up

Export Citation Format

Share Document