scholarly journals Large-Scale Gene Expression Signatures Reveal a Microbicidal Pattern of Activation in Mycobacterium leprae-Infected Monocyte-Derived Macrophages With Low Multiplicity of Infection

2021 ◽  
Vol 12 ◽  
Author(s):  
Thyago Leal-Calvo ◽  
Bruna Leticia Martins ◽  
Daniele Ferreira Bertoluci ◽  
Patricia Sammarco Rosa ◽  
Rodrigo Mendes de Camargo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Schwann Cells ◽  
Large Scale ◽  
Cholesterol Metabolism ◽  
Mycobacterium Leprae ◽  
Giant Cells ◽  
Gene Expression Omnibus ◽  
Cell Phenotype ◽  
Multiplicity Of Infection ◽  
Granulomatous Formation

Leprosy is a disease with a clinical spectrum of presentations that is also manifested in diverse histological features. At one pole, lepromatous lesions (L-pole) have phagocytic foamy macrophages heavily parasitized with freely multiplying intracellular Mycobacterium leprae. At the other pole, the presence of epithelioid giant cells and granulomatous formation in tuberculoid lesions (T-pole) lead to the control of M. leprae replication and the containment of its spread. The mechanism that triggers this polarization is unknown, but macrophages are central in this process. Over the past few years, leprosy has been studied using large scale techniques to shed light on the basic pathways that, upon infection, rewire the host cellular metabolism and gene expression. M. leprae is particularly peculiar as it invades Schwann cells in the nerves, reprogramming their gene expression leading to a stem-like cell phenotype. This modulatory behavior exerted by M. leprae is also observed in skin macrophages. Here, we used live M. leprae to infect (10:1 multiplicity of infection) monocyte-derived macrophages (MDMs) for 48 h and analyzed the whole gene expression profile using microarrays. In this model, we observe an intense upregulation of genes consistent with a cellular immune response, with enriched pathways including peptide and protein secretion, leukocyte activation, inflammation, and cellular divalent inorganic cation homeostasis. Among the most differentially expressed genes (DEGs) are CCL5/RANTES and CYP27B1, and several members of the metallothionein and metalloproteinase families. This is consistent with a proinflammatory state that would resemble macrophage rewiring toward granulomatous formation observed at the T-pole. Furthermore, a comparison with a dataset retrieved from the Gene Expression Omnibus of M. leprae-infected Schwann cells (MOI 100:1) showed that the patterns among the DEGs are highly distinct, as the Schwann cells under these conditions had a scavenging and phagocytic gene profile similar to M2-like macrophages, with enriched pathways rearrangements in the cytoskeleton, lipid and cholesterol metabolism and upregulated genes including MVK, MSMO1, and LACC1/FAMIN. In summary, macrophages may have a central role in defining the paradigmatic cellular (T-pole) vs. humoral (L-pole) responses and it is likely that the multiplicity of infection and genetic polymorphisms in key genes are gearing this polarization.

scholarly journals GEOMetaCuration: A web-based application for accurate manual curation of Gene Expression Omnibus metadata

2018 ◽  
Author(s):  
Zhao Li ◽  
Jin Li ◽  
Peng Yu
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Gene Expression Omnibus ◽  
Biological Data ◽  
Use Case ◽  
Web Based ◽  
Link Type ◽  
Manual Curation ◽  
Development Framework ◽  
Biological Discovery

AbstractMetadata curation has become increasingly important for biological discovery and biomedical research because a large amount of heterogeneous biological data is currently freely available. To facilitate efficient metadata curation, we developed an easy-to-use web-based curation application, GEOMetaCuration, for curating the metadata of Gene Expression Omnibus datasets. It can eliminate mechanical operations that consume precious curation time and can help coordinate curation efforts among multiple curators. It improves the curation process by introducing various features that are critical to metadata curation, such as a back-end curation management system and a curator-friendly front-end. The application is based on a commonly used web development framework of Python/Django and is open-sourced under the GNU General Public License V3. GEOMetaCuration is expected to benefit the biocuration community and to contribute to computational generation of biological insights using large-scale biological data. An example use case can be found at the demo website: http://geometacuration.yubiolab.org. Source code URL: https://bitbucket.com/yubiolab/GEOMetaCuration

scholarly journals A curated transcriptome dataset collection to investigate the immunobiology of HIV infection

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 327 ◽  
Author(s):  
Jana Blazkova ◽  
Sabri Boughorbel ◽  
Scott Presnell ◽  
Charlie Quinn ◽  
Damien Chaussabel
Keyword(s):  
Gene Expression ◽  
Hiv Infection ◽  
Web Application ◽  
Large Scale ◽  
Single Gene ◽  
Gene Expression Omnibus ◽  
Biomedical Knowledge ◽  
Ncbi Gene Expression Omnibus ◽  
Interactive Query ◽  
Public Repositories

Compendia of large-scale datasets available in public repositories provide an opportunity to identify and fill current gaps in biomedical knowledge. But first, these data need to be readily accessible to research investigators for interpretation. Here, we make available a collection of transcriptome datasets relevant to HIV infection. A total of 2717 unique transcriptional profiles distributed among 34 datasets were identified, retrieved from the NCBI Gene Expression Omnibus (GEO), and loaded in a custom web application, the Gene Expression Browser (GXB), designed for interactive query and visualization of integrated large-scale data. Multiple sample groupings and rank lists were created to facilitate dataset query and interpretation via this interface. Web links to customized graphical views can be generated by users and subsequently inserted in manuscripts reporting novel findings, such as discovery notes. The tool also enables browsing of a single gene across projects, which can provide new perspectives on the role of a given molecule across biological systems. This curated dataset collection is available at:http://hiv.gxbsidra.org/dm3/geneBrowser/list.

scholarly journals Pathway and network embedding methods for prioritizing psychiatric drugs

2019 ◽  
Author(s):  
Yash Pershad ◽  
Margaret Guo ◽  
Russ B. Altman
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Disease Diagnosis ◽  
Gene Expression Omnibus ◽  
Diagnosis And Treatment ◽  
Psychiatric Disease ◽  
Specific Gene ◽  
Psychiatric Diseases ◽  
Expression Data ◽  
New Genes

One in five Americans experience mental illness, and roughly 75% of psychiatric prescriptions do not successfully treat the patient’s condition. Extensive evidence implicates genetic factors and signaling disruption in the pathophysiology of these diseases. Changes in transcription often underlie this molecular pathway dysregulation; individual patient transcriptional data can improve the efficacy of diagnosis and treatment. Recent large-scale genomic studies have uncovered shared genetic modules across multiple psychiatric disorders—providing an opportunity for an integrated multi-disease approach for diagnosis. Moreover, network-based models informed by gene expression can represent pathological biological mechanisms and suggest new genes for diagnosis and treatment. Here, we use patient gene expression data from multiple studies to classify psychiatric diseases, integrate knowledge from expert-curated databases and publicly available experimental data to create augmented disease-specific gene sets, and use these to recommend disease-relevant drugs. From Gene Expression Omnibus, we extract expression data from 145 cases of schizophrenia, 82 cases of bipolar disorder, 190 cases of major depressive disorder, and 307 shared controls. We use pathway-based approaches to predict psychiatric disease diagnosis with a random forest model (78% accuracy) and derive important features to augment available drug and disease signatures. Using protein-protein-interaction networks and embedding-based methods, we build a pipeline to prioritize treatments for psychiatric diseases that achieves a 3.4-fold improvement over a background model. Thus, we demonstrate that gene-expression-derived pathway features can diagnose psychiatric diseases and that molecular insights derived from this classification task can inform treatment prioritization for psychiatric diseases.

scholarly journals Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes

2019 ◽  
Vol 35 (18) ◽  
pp. 3357-3364 ◽  
Author(s):  
Holger Weishaupt ◽  
Patrik Johansson ◽  
Anders Sundström ◽  
Zelmina Lubovac-Pilav ◽  
Björn Olsson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Gene Expression Omnibus ◽  
Negative Control ◽  
Supplementary Information ◽  
Normal Brain ◽  
Expression Data ◽  
Batch Effects ◽  
Treatment Side Effects ◽  
Cure Rates

Abstract Motivation Medulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts. To improve cure rates and reduce treatment side effects, accurate integration of such data to increase analytical power will be important, if not essential. Results We have integrated 23 transcription datasets, spanning 1350 MB and 291 normal brain samples. To remove batch effects, we combined the Removal of Unwanted Variation (RUV) method with a novel pipeline for determining empirical negative control genes and a panel of metrics to evaluate normalization performance. The documented approach enabled the removal of a majority of batch effects, producing a large-scale, integrative dataset of MB and cerebellar expression data. The proposed strategy will be broadly applicable for accurate integration of data and incorporation of normal reference samples for studies of various diseases. We hope that the integrated dataset will improve current research in the field of MB by allowing more large-scale gene expression analyses. Availability and implementation The RUV-normalized expression data is available through the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) and can be accessed via the GSE series number GSE124814. Supplementary information Supplementary data are available at Bioinformatics online.

Molecular Epidemiology and Biomarkers in Etiologic Cancer Research: The New in Light of the Old

2020 ◽  
Author(s):  
Lungwani Muungo
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Molecular Epidemiology ◽  
Exposure Assessment ◽  
Large Scale ◽  
First Generation ◽  
Cancer Epidemiology ◽  
Policy Changes ◽  
The Past ◽  
New Generation

The purpose of this review is to evaluate progress inmolecular epidemiology over the past 24 years in canceretiology and prevention to draw lessons for futureresearch incorporating the new generation of biomarkers.Molecular epidemiology was introduced inthe study of cancer in the early 1980s, with theexpectation that it would help overcome some majorlimitations of epidemiology and facilitate cancerprevention. The expectation was that biomarkerswould improve exposure assessment, document earlychanges preceding disease, and identify subgroupsin the population with greater susceptibility to cancer,thereby increasing the ability of epidemiologic studiesto identify causes and elucidate mechanisms incarcinogenesis. The first generation of biomarkers hasindeed contributed to our understanding of riskandsusceptibility related largely to genotoxic carcinogens.Consequently, interventions and policy changes havebeen mounted to reduce riskfrom several importantenvironmental carcinogens. Several new and promisingbiomarkers are now becoming available for epidemiologicstudies, thanks to the development of highthroughputtechnologies and theoretical advances inbiology. These include toxicogenomics, alterations ingene methylation and gene expression, proteomics, andmetabonomics, which allow large-scale studies, includingdiscovery-oriented as well as hypothesis-testinginvestigations. However, most of these newer biomarkershave not been adequately validated, and theirrole in the causal paradigm is not clear. There is a needfor their systematic validation using principles andcriteria established over the past several decades inmolecular cancer epidemiology.

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

2020 ◽  
Vol 26 (29) ◽  
pp. 3619-3630
Author(s):  
Saumya Choudhary ◽  
Dibyabhaba Pradhan ◽  
Noor S. Khan ◽  
Harpreet Singh ◽  
George Thomas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Gene ◽  
Therapeutic Targets ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Keratinocyte Differentiation ◽  
Hub Genes ◽  
Lesional Skin ◽  
Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

scholarly journals The combined detection of Amphiregulin, Cyclin A1 and DDX20/Gemin3 expression predicts aggressive forms of oral squamous cell carcinoma

2021 ◽  
Author(s):  
Ekaterina Bourova-Flin ◽  
Samira Derakhshan ◽  
Afsaneh Goudarzi ◽  
Tao Wang ◽  
Anne-Laure Vitte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Gene Expression Signature ◽  
Predictive Biomarkers ◽  
Specific Gene ◽  
Specific Expression ◽  
Intrinsic Nature ◽  
Independent Cohort

Abstract Background Large-scale genetic and epigenetic deregulations enable cancer cells to ectopically activate tissue-specific expression programmes. A specifically designed strategy was applied to oral squamous cell carcinomas (OSCC) in order to detect ectopic gene activations and develop a prognostic stratification test. Methods A dedicated original prognosis biomarker discovery approach was implemented using genome-wide transcriptomic data of OSCC, including training and validation cohorts. Abnormal expressions of silent genes were systematically detected, correlated with survival probabilities and evaluated as predictive biomarkers. The resulting stratification test was confirmed in an independent cohort using immunohistochemistry. Results A specific gene expression signature, including a combination of three genes, AREG, CCNA1 and DDX20, was found associated with high-risk OSCC in univariate and multivariate analyses. It was translated into an immunohistochemistry-based test, which successfully stratified patients of our own independent cohort. Discussion The exploration of the whole gene expression profile characterising aggressive OSCC tumours highlights their enhanced proliferative and poorly differentiated intrinsic nature. Experimental targeting of CCNA1 in OSCC cells is associated with a shift of transcriptomic signature towards the less aggressive form of OSCC, suggesting that CCNA1 could be a good target for therapeutic approaches.

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.

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