scholarly journals Gene Expression Network Analysis Provides Potential Targets Against SARS-CoV-2

2020 ◽  
Author(s):  
Ana I. Hernández Cordero ◽  
Xuan Li ◽  
Chen Xi Yang ◽  
Stephen Milne ◽  
Yohan Bossé ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Host Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tissue Samples ◽  
Drug Compounds ◽  
Bioinformatics Databases ◽  
Novel Coronavirus ◽  
Transmembrane Serine Protease

ABSTRACTBACKGROUNDCell entry of SARS-CoV-2, the novel coronavirus causing COVID-19, is facilitated by host cell angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2). We aimed to identify and characterize genes that are co-expressed with ACE2 and TMPRSS2, and to further explore their biological functions and potential as druggable targets.METHODSUsing the gene expression profiles of 1,038 lung tissue samples, we performed a weighted gene correlation network analysis (WGCNA) to identify modules of co-expressed genes. We explored the biology of co-expressed genes using bioinformatics databases, and identified known drug-gene interactions.RESULTSACE2 was in a module of 681 co-expressed genes; 12 genes with moderate-high correlation with ACE2 (r>0.3, FDR<0.05) had known interactions with existing drug compounds. TMPRSS2 was in a module of 1,086 co-expressed genes; 15 of these genes were enriched in the gene ontology biologic process ‘Entry into host cell’, and 53 TMPRSS2-correlated genes had known interactions with drug compounds.CONCLUSIONDozens of genes are co-expressed with ACE2 and TMPRSS2, many of which have plausible links to COVID-19 pathophysiology. Many of the co-expressed genes are potentially targetable with existing drugs, which may help to fast-track the development of COVID-19 therapeutics.

scholarly journals Gene expression network analysis provides potential targets against SARS-CoV-2

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ana I. Hernández Cordero ◽  
Xuan Li ◽  
Chen Xi Yang ◽  
Stephen Milne ◽  
Yohan Bossé ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tissue Samples ◽  
Drug Compounds ◽  
Bioinformatics Databases ◽  
Biologic Process ◽  
Novel Coronavirus ◽  
Transmembrane Serine Protease

AbstractCell entry of SARS-CoV-2, the novel coronavirus causing COVID-19, is facilitated by host cell angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2). We aimed to identify and characterize genes that are co-expressed with ACE2 and TMPRSS2, and to further explore their biological functions and potential as druggable targets. Using the gene expression profiles of 1,038 lung tissue samples, we performed a weighted gene correlation network analysis (WGCNA) to identify modules of co-expressed genes. We explored the biology of co-expressed genes using bioinformatics databases, and identified known drug-gene interactions. ACE2 was in a module of 681 co-expressed genes; 10 genes with moderate-high correlation with ACE2 (r > 0.3, FDR < 0.05) had known interactions with existing drug compounds. TMPRSS2 was in a module of 1,086 co-expressed genes; 31 of these genes were enriched in the gene ontology biologic process ‘receptor-mediated endocytosis’, and 52 TMPRSS2-correlated genes had known interactions with drug compounds. Dozens of genes are co-expressed with ACE2 and TMPRSS2, many of which have plausible links to COVID-19 pathophysiology. Many of the co-expressed genes are potentially targetable with existing drugs, which may accelerate the development of COVID-19 therapeutics.

scholarly journals A Study of Gene Expression Changes in Human Spinal and Oculomotor Neurons; Identifying Potential Links to Sporadic ALS

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 448
Author(s):  
Aayan N. Patel ◽  
Dennis Mathew
Keyword(s):  
Gene Expression ◽  
Disease Progression ◽  
Motor Neurons ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tissue Samples ◽  
Disease Symptoms ◽  
Sporadic Als ◽  
Oculomotor Neurons ◽  
Als Patients

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that causes compromised function of motor neurons and neuronal death. However, oculomotor neurons are largely spared from disease symptoms. The underlying causes for sporadic ALS as well as for the resistance of oculomotor neurons to disease symptoms remain poorly understood. In this bioinformatic-analysis, we compared the gene expression profiles of spinal and oculomotor tissue samples from control individuals and sporadic ALS patients. We show that the genes GAD2 and GABRE (involved in GABA signaling), and CALB1 (involved in intracellular Ca2+ ion buffering) are downregulated in the spinal tissues of ALS patients, but their endogenous levels are higher in oculomotor tissues relative to the spinal tissues. Our results suggest that the downregulation of these genes and processes in spinal tissues are related to sporadic ALS disease progression and their upregulation in oculomotor neurons confer upon them resistance to ALS symptoms. These results build upon prevailing models of excitotoxicity that are relevant to sporadic ALS disease progression and point out unique opportunities for better understanding the progression of neurodegenerative properties associated with sporadic ALS.

scholarly journals A large-scale screening of the normalized mammalian mitochondrial gene expression profiles

2005 ◽  
Vol 86 (2) ◽  
pp. 127-138 ◽  
Author(s):  
SERGEY V. ANISIMOV
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Mitochondrial Gene ◽  
Gene Expression Profiles ◽  
Vital Role ◽  
Mitochondrial Genomes ◽  
Mitochondrial Gene Expression ◽  
Tissue Samples ◽  
Large Scale Screening

Mammalian mitochondrial genomes are organized in a conserved and extremely compact manner, encoding molecules that play a vital role in oxidative phosphorylation (OXPHOS) and carry out a number of other important biological functions. A large-scale screening of the normalized mitochondrial gene expression profiles generated from publicly available mammalian serial analysis of gene expression (SAGE) datasets (over 17·7 millions of tags) was performed in this study. Acquired SAGE libraries represent an extensive range of human, mouse, rat, bovine and swine cell and tissue samples (normal and pathological) in a variety of conditions. Using a straightforward in silico algorithm, variations in total mitochondrial gene expression, as well as in the expression of individual genes encoded by mitochondrial genomes are addressed, and common patterns in the species- and tissue-specific mitochondrial gene expression profiles are discussed.

scholarly journals Identification of Novel Hub Genes Associated With Gastric Cancer Using Integrated Bioinformatics Analysis

2020 ◽  
Author(s):  
Xiao-Qing Lu ◽  
Jia-qian Zhang ◽  
Jun Qiao ◽  
Sheng-Xiao Zhang ◽  
Meng-Ting Qiu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
High Recurrence Rate ◽  
Hub Genes ◽  
Tissue Samples ◽  
Qrt Pcr

Abstract Background: Gastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high-recurrence-rate. Identifying the molecular signatures and specific biomarkers of GC might provide novel clues for GC prognosis and targeted therapy.Methods: Gene expression profiles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytoHubba plugin. Their prognostic values were assessed by Kaplan–Meier survival analyses and the gene expression profiling interactive analysis (GEPIA). Finally, qRT-PCR in GC tissue samples was established to validate these DEGs. Results: Total of 295 DEGs were identified between GC and their corresponding normal adjacent tissue samples in E-MTAB-1440, GSE79973, GSE19826, GSE13911, GSE27342, GSE33335 and GSE56807 datasets, including 117 up-regulated and 178 down-regulated genes. Among them, 7 vital upregulated genes (HMMR, SPP1, FN1, CCNB1, CXCL8, MAD2L1 and CCNA2) were selected. Most of them had a significantly worse prognosis except SPP1. Using qRT-PCR, we validated that their transcriptions in our GC tumor tissue were upregulated except SPP1 and FN1, which correlated with tumor relapse and predicts poorer prognosis in GC patients.Discussion: We have identified 5 upregulated DEGs (HMMR, CCNB1, CXCL8, MAD2L1, and CCNA2) in GC patients with poor prognosis using integrated bioinformatical methods, which could be potential biomarkers and therapeutic targets for GC treatment.

scholarly journals Automated identification of the mouse brain’s spatial compartments from in situ sequencing data

BMC Biology ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Gabriele Partel ◽  
Markus M. Hilscher ◽  
Giorgia Milli ◽  
Leslie Solorzano ◽  
Anna H. Klemm ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Gene Markers ◽  
Smooth Transitions

Abstract Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments. Results Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles. Conclusion We show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.

scholarly journals Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis

2020 ◽  
Author(s):  
Jianing Tang ◽  
Yongwen Luo ◽  
Gaosong Wu
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Proliferation ◽  
Network Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Validation Dataset ◽  
Pcr Analysis ◽  
Normal Tissues

Abstract Background Breast cancer is the mostly diagnosed malignance in female worldwide. However, the mechanisms of its pathogenesis remain largely unknown. Methods In this study, we used weighted gene co-expression network analysis (WGCNA) to identify novel biomarkers associated with the prognosis of breast cancer. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Results A total of 5 modules were identified via the average linkage hierarchical clustering. And a module significantly with the pathological grade was screened out. 33 genes with high connectivity in the clinically significant module were identified as hub genes. Among them, CASC5 and RAD51 were negatively associated with the overall survival and disease-specific survival. Similar results were observed in the validation dataset. Protein levels of CACS5 and RAD51 were also significantly higher in tumor tissues compared with normal tissues based on the analysis of the Human Protein Atlas. Convincingly, qRT-PCR analysis of breast cancer tissues and matched paracancerous tissue demonstrated that CACS5 and RAD51 were significantly upregulated in in breast cancer compared to paracancerous tissues. Further cell proliferation assay indicated that CACS5 and RAD51 depletion decreased cell proliferation capability. Conclusion In conclusion, our findings suggested that CASC5 and RAD51 could serve as biomarkers related to the prognosis of breast cancer and may be helpful for revealing pathogenic mechanism and developing further research.

scholarly journals Stage specific classification of DEGs via statistical profiling and network analysis reveals potential biomarker associated with various stages of TB

2018 ◽  
Author(s):  
Romana Ishrat
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Body Part ◽  
Specific Pattern ◽  
Host Responses ◽  
Data Set ◽  
Potential Biomarker

AbstractBackgroundTuberculosis (TB) is a deadly transmissible disease that can infect almost any body-part of the host but is mostly infect the lungs. It is one of the top 10 causes of death worldwide. In the 30 high TB burden countries, 87% of new TB cases occurred in 2016. Seven countries: India, Indonesia, China, Philippines, Pakistan, Nigeria, and South Africa accounted for 64% of the new TB cases. To stop the infection and progression of the disease, early detection of TB is important. In our study, we used microarray data set and compared the gene expression profiles obtained from blood samples of patients with different datasets of Healthy control, Latent infection, Active TB and performed network-based analysis of DEGs to identify potential biomarker.ObjectivesWe want to observe the transition of genes from normal condition to different stages of the TB and identify, annotate those genes/pathways/processes that play key role in the progression of TB disease during its cyclic interventions in human body.ResultsWe identified 319 genes that are differentially expressed in various stages of TB (Normal to LTTB, Normal to Active TB and LTTB to active TB) and allocated to pathways from multiple databases which comprised of curated class of associated genes. These pathway’s importance was then evaluated according to the no. of DEGs present in the pathway and these genes show the broad spectrum of processes that take part in every state. In addition, we studied the regulatory networks of these classified genes, network analysis does consider the interactions between genes (specific for TB) or proteins provide us new facts about TB disease, which in turn can be used for potential biomarkers identification. We identified total 29 biomarkers from various comparison groups of TB stages in which 14 genes are over expressed as host responses against pathogen, but 15 genes are down regulated that means these genes has allowed the process of host defense to cease and give time to pathogen for its progression.ConclusionsThis study revealed that gene-expression profiles can be used to identify and classified the genes on stage specific pattern among normal, LTTB and active TB and network modules associated with various stages of TB were elucidated, which in turn provided a basis for the identification of potential pathways and key regulatory genes that may be involved in progression of TB disease.

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