scholarly journals Gene Co-expression Networks Identifies Common Hub Genes Between Cutaneous Sarcoidosis and Discoid Lupus Erythematosus

2020 ◽  
Vol 7 ◽  
Author(s):  
Melissa A. Nickles ◽  
Kai Huang ◽  
Yi-Shin Chang ◽  
Maria M. Tsoukas ◽  
Nadera J. Sweiss ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lupus Erythematosus ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Gene Expression Patterns ◽  
Discoid Lupus Erythematosus ◽  
Cutaneous Sarcoidosis ◽  
Hub Genes ◽  
Common Gene ◽  
Gene Similarity

In this study we analyzed gene co-expression networks of three immune-related skin diseases: cutaneous sarcoidosis (CS), discoid lupus erythematosus (DLE), and psoriasis. We propose that investigation of gene co-expression networks may provide insights into underlying disease mechanisms. Microarray expression data from two cohorts of patients with CS, DLE, or psoriasis skin lesions were analyzed. We applied weighted gene correlation network analysis (WGCNA) to construct gene-gene similarity networks and cluster genes into modules based on similar expression profiles. A module of interest that was preserved between datasets and corresponded with case/control status was identified. This module was related to immune activation, specifically leukocyte activation, and was significantly increased in both CS lesions and DLE lesions compared to their respective controls. Protein-protein interaction (PPI) networks constructed for this module revealed seven common hub genes between CS lesions and DLE lesions: TLR1, ITGAL, TNFRSF1B, CD86, SPI1, BTK, and IL10RA. Common hub genes were highly upregulated in CS lesions and DLE lesions compared to their respective controls in a differential expression analysis. Our results indicate common gene expression patterns in the immune processes of CS and DLE, which may have indications for future therapeutic targets and serve as Th1-mediated disease biomarkers. Additionally, we identified hub genes unique to CS and DLE, which can help differentiate these diseases from one another and may serve as unique therapeutic targets and biomarkers. Notably, we find common gene expression patterns in the immune processes of CS and DLE through utilization of WGCNA.

Common Gene Expression Patterns in the Generation of Induced Neurons From Fibroblasts

2017 ◽  
Vol 119 (1) ◽  
pp. 237-239 ◽  
Author(s):  
Abdulshakour Mohammadnia ◽  
Moein Yaqubi ◽  
Ping Wee
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Common Gene ◽  
Induced Neurons

scholarly journals An Actinidia chinensis (kiwifruit) eFP browser and network analysis of transcription factors

2020 ◽  
Author(s):  
Lara Brian ◽  
Ben Warren ◽  
Peter McAtee ◽  
Jessica Rodrigues ◽  
Niels Nieuwenhuizen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Fruit Development ◽  
Transcriptional Control ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Actinidia Chinensis ◽  
Knox Gene ◽  
Hub Genes ◽  
Flower And Fruit Development

Abstract BackgroundTranscriptomic studies combined with a well annotated genome have laid the foundations for new understanding of molecular processes. Tools which visualise gene expression patterns have further added to these resources. The manual annotation of the Actinidia chinensis (kiwifruit) genome has resulted in a high quality set of 33,044 genes. Here we investigate gene expression patterns in diverse tissues, visualised in an Electronic Fluorescent Pictograph (eFP) browser, to study the relationship of transcription factor (TF) expression using network analysis. ResultsSixty-one samples covering diverse tissues at different developmental time points were selected for RNAseq analysis and an eFP browser was generated to visualise this dataset. 2,839 TFs representing 57 different classes were identified and named. Network analysis of the TF expression patterns separated TFs into 14 different modules. Two modules consisting of 237 TFs were correlated with floral bud and flower development, a further two modules containing 160 TFs were associated with fruit development and maturation. A single module of 480 TFs was associated with ethylene-induced fruit ripening. Three “hub” genes correlated with flower and fruit development consisted of a HAF-like gene central to gynoecium development, an ERF and a DOF gene. Maturing and ripening hub genes included a KNOX gene that was associated with seed maturation, and a GRAS-like TF.ConclusionsThis study provides an insight into the complexity of the transcriptional control of flower and fruit development, as well as providing a new resource to the plant community. The eFP browser is provided in an accessible format that allows researchers to download and work internally.

scholarly journals Emerging molecular subtypes and therapeutic targets in B-cell precursor acute lymphoblastic leukemia

2021 ◽  
Author(s):  
Jianfeng Li ◽  
Yuting Dai ◽  
Liang Wu ◽  
Ming Zhang ◽  
Wen Ouyang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
B Cell ◽  
Molecular Subtypes ◽  
Lymphoblastic Leukemia ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Genetic Alterations ◽  
Gene Expression Patterns ◽  
Cell Precursor

AbstractB-cell precursor acute lymphoblastic leukemia (BCP-ALL) is characterized by genetic alterations with high heterogeneity. Precise subtypes with distinct genomic and/or gene expression patterns have been recently revealed using high-throughput sequencing technology. Most of these profiles are associated with recurrent non-overlapping rearrangements or hotspot point mutations that are analogous to the established subtypes, such as DUX4 rearrangements, MEF2D rearrangements, ZNF384/ZNF362 rearrangements, NUTM1 rearrangements, BCL2/MYC and/or BCL6 rearrangements, ETV6-RUNX1-like gene expression, PAX5alt (diverse PAX5 alterations, including rearrangements, intragenic amplifications, or mutations), and hotspot mutations PAX5 (p.Pro80Arg) with biallelic PAX5 alterations, IKZF1 (p.Asn159Tyr), and ZEB2 (p.His1038Arg). These molecular subtypes could be classified by gene expression patterns with RNA-seq technology. Refined molecular classification greatly improved the treatment strategy. Multiagent therapy regimens, including target inhibitors (e.g., imatinib), immunomodulators, monoclonal antibodies, and chimeric antigen receptor T-cell (CAR-T) therapy, are transforming the clinical practice from chemotherapy drugs to personalized medicine in the field of risk-directed disease management. We provide an update on our knowledge of emerging molecular subtypes and therapeutic targets in BCP-ALL.

Identification of novel therapeutic targets for neuropathic pain based on gene expression patterns

2019 ◽  
Vol 234 (11) ◽  
pp. 19494-19501
Author(s):  
Di Zhu ◽  
Kang Liu ◽  
Cheng‐Liang Wan ◽  
Jangnin Lu ◽  
Hong‐Xia Zhao
Keyword(s):  
Gene Expression ◽  
Neuropathic Pain ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Gene Expression Patterns ◽  
Novel Therapeutic

scholarly journals A gene expression atlas for kiwifruit (Actinidia chinensis) and network analysis of transcription factors

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lara Brian ◽  
Ben Warren ◽  
Peter McAtee ◽  
Jessica Rodrigues ◽  
Niels Nieuwenhuizen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Fruit Development ◽  
Transcriptional Control ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Actinidia Chinensis ◽  
Knox Gene ◽  
Hub Genes ◽  
Flower And Fruit Development

Abstract Background Transcriptomic studies combined with a well annotated genome have laid the foundations for new understanding of molecular processes. Tools which visualise gene expression patterns have further added to these resources. The manual annotation of the Actinidia chinensis (kiwifruit) genome has resulted in a high quality set of 33,044 genes. Here we investigate gene expression patterns in diverse tissues, visualised in an Electronic Fluorescent Pictograph (eFP) browser, to study the relationship of transcription factor (TF) expression using network analysis. Results Sixty-one samples covering diverse tissues at different developmental time points were selected for RNA-seq analysis and an eFP browser was generated to visualise this dataset. 2839 TFs representing 57 different classes were identified and named. Network analysis of the TF expression patterns separated TFs into 14 different modules. Two modules consisting of 237 TFs were correlated with floral bud and flower development, a further two modules containing 160 TFs were associated with fruit development and maturation. A single module of 480 TFs was associated with ethylene-induced fruit ripening. Three “hub” genes correlated with flower and fruit development consisted of a HAF-like gene central to gynoecium development, an ERF and a DOF gene. Maturing and ripening hub genes included a KNOX gene that was associated with seed maturation, and a GRAS-like TF. Conclusions This study provides an insight into the complexity of the transcriptional control of flower and fruit development, as well as providing a new resource to the plant community. The Actinidia eFP browser is provided in an accessible format that allows researchers to download and work internally.

scholarly journals In Silico Analysis of the Gene Expression Patterns between Aldosterone-Producing Adenoma and Nonfunctional Adrenocortical Adenoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yongfa Dai ◽  
Jing Li ◽  
Hong Wen ◽  
Jie Liu ◽  
Jianling Li
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Primary Aldosteronism ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Pathophysiological Mechanism ◽  
Adrenocortical Adenoma ◽  
Hub Genes ◽  
Aldosterone Producing Adenoma

Primary aldosteronism is the most common form of secondary hypertension, and aldosteronoma makes up a significant proportion of primary aldosteronism cases. Aldosteronoma is also called aldosterone-producing adenoma (APA). Although there have been many studies about APA, the pathogenesis of this disease is not yet fully understood. In this study, we aimed to find out the difference of gene expression patterns between APA and nonfunctional adrenocortical adenoma (NFAA) using a weighted gene coexpression network (WGCNA) and differentially expressed gene (DEG) analysis; only the genes that meet the corresponding standards of both methods were defined as real hub genes and then used for further analysis. Twenty-nine real hub genes were found out, most of which were enriched in the phospholipid metabolic process. WISP2, S100A10, SSTR5-AS1, SLC29A1, APOC1, and SLITRK4 are six real hub genes with the same gene expression pattern between the combined and validation datasets, three of which indirectly or directly participate in lipid metabolism including WISP2, S100A10, and APOC1. According to the gene expression pattern of DEGs, we speculated five candidate drugs with potential therapeutic value for APA, one of which is cycloheximide, an inhibitor for phospholipid biosynthesis. All the evidence suggests that phospholipid metabolism may be an important pathophysiological mechanism for APA. Our study provides a new perspective regarding the pathophysiological mechanism of APA and offers some small molecules that may possibly be effective drugs against APA.

Evaluation of common gene expression patterns in the rat nervous system

2003 ◽  
Vol 16 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Sergio Kaiser ◽  
Laura K. Nisenbaum
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Transcript Profiling ◽  
Nerve Tissue ◽  
Gene Expression Patterns ◽  
Published Data ◽  
Common Gene ◽  
Neuropsychiatric Diseases ◽  
Combining Data ◽  
Microarray Studies

In the postgenomic era, integrating data obtained from array technologies (e.g., oligonucleotide microarrays) with published information on eukaryotic genomes is beginning to yield biomarkers and therapeutic targets that are key for the diagnosis and treatment of disease. Nevertheless, identifying and validating these drug targets has not been a trivial task. Although a plethora of bioinformatics tools and databases are available, major bottlenecks for this approach reside in the interpretation of vast amounts of data, its integration into biologically representative models, and ultimately the identification of pathophysiologically and therapeutically useful information. In the field of neuroscience, accomplishing these goals has been particularly challenging because of the complex nature of nerve tissue, the relatively small adaptive nature of induced-gene expression changes, as well as the polygenic etiology of most neuropsychiatric diseases. This report combines published data sets from multiple transcript profiling studies that used GeneChip microarrays to illustrate a postanalysis approach for the interpretation of data from neuroscience microarray studies. By defining common gene expression patterns triggered by diverse events (administration of psychoactive drugs and trauma) in different nerve tissues (telencephalic brain areas and spinal cord), we broaden the conclusions derived from each of the original studies. In addition, the evaluation of the identified overlapping gene lists provides a foundation for generating hypotheses relating alterations in specific sets of genes to common physiological processes. Our approach demonstrates the significance of interpreting transcript profiling data within the context of common pathways and mechanisms rather than specific to a given tissue or stimulus. We also highlight the use of gene expression patterns in predictive biology (e.g., in toxicogenomics) as well as the utility of combining data derived from multiple microarray studies that examine diverse biological events for a broader interpretation of data from a particular microarray study.

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