scholarly journals Kalirin-RAC controls nucleokinetic migration in ADRN-type neuroblastoma

2021 ◽  
Vol 4 (5) ◽  
pp. e201900332
Author(s):  
Elena A Afanasyeva ◽  
Moritz Gartlgruber ◽  
Tatsiana Ryl ◽  
Bieke Decaesteker ◽  
Geertrui Denecker ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Time Lapse ◽  
Dna Methyltransferases ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Risk Characteristics ◽  
Weak Points ◽  
Treatment Research ◽  
Time Lapse Imaging

The migrational propensity of neuroblastoma is affected by cell identity, but the mechanisms behind the divergence remain unknown. Using RNAi and time-lapse imaging, we show that ADRN-type NB cells exhibit RAC1- and kalirin-dependent nucleokinetic (NUC) migration that relies on several integral components of neuronal migration. Inhibition of NUC migration by RAC1 and kalirin-GEF1 inhibitors occurs without hampering cell proliferation and ADRN identity. Using three clinically relevant expression dichotomies, we reveal that most of up-regulated mRNAs in RAC1- and kalirin–GEF1–suppressed ADRN-type NB cells are associated with low-risk characteristics. The computational analysis shows that, in a context of overall gene set poverty, the upregulomes in RAC1- and kalirin–GEF1–suppressed ADRN-type cells are a batch of AU-rich element–containing mRNAs, which suggests a link between NUC migration and mRNA stability. Gene set enrichment analysis–based search for vulnerabilities reveals prospective weak points in RAC1- and kalirin–GEF1–suppressed ADRN-type NB cells, including activities of H3K27- and DNA methyltransferases. Altogether, these data support the introduction of NUC inhibitors into cancer treatment research.

scholarly journals Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Scrub Typhus ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Acid Base ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.

scholarly journals Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mike Fang ◽  
Brian Richardson ◽  
Cheryl M. Cameron ◽  
Jean-Eudes Dazard ◽  
Mark J. Cameron
Keyword(s):  
Drug Targets ◽  
T Regulatory Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Regulatory Cells ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Gastroenteropancreatic Neuroendocrine Tumor ◽  
Public Datasets

Abstract Background In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets. Results We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting. Conclusions dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.

scholarly journals Key pathways involved in prostate cancer based on gene set enrichment analysis and meta analysis

2011 ◽  
Vol 10 (4) ◽  
pp. 3856-3887 ◽  
Author(s):  
Q.Y. Ning ◽  
J.Z. Wu ◽  
N. Zang ◽  
J. Liang ◽  
Y.L. Hu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Key Pathways

scholarly journals A Novel Prognostic Model Based On Cytosolic DNA Sensing-Related Gene Signature for Pancreatic Cancer

2021 ◽  
Author(s):  
Chuan-Qi Xu ◽  
Kui-Sheng Yang ◽  
Shu-Xian Zhao ◽  
Jian Lv
Keyword(s):  
Pancreatic Cancer ◽  
Prognostic Factor ◽  
Prognostic Model ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Independent Prognostic Factor ◽  
Gene Set Enrichment Analysis ◽  
Dna Sensing ◽  
Gene Set Enrichment ◽  
Gene Set

Abstract Objective: Pancreatic cancer (PC) is one of the most malignant tumors. Cytosolic DNA sensing have been found to play an essential role in tumor. In this study, a cytosolic DNA sensing-related genes (CDSRGs) signature was constructed and the potential mechanisms also been discussed.Methods: The RNA expression and clinical data of PC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Subsequently, univariate (UCR) and multivariate Cox regression (MCR) analyses were conducted to establish a prognostic model in the TCGA patients, which was verified by GEO patients. Cancer immune infiltrates were investigated via single sample gene set enrichment analysis (ssGSEA) and Tumor Immune Estimation Resource (TIMER). Finally, Gene Set Enrichment Analysis (GSEA) was used to investigate the related signaling pathways.Results: A prognostic model comprising four genes (POLR2E,IL18, MAVS, and FADD) was established. The survival rate of patients in the low-risk group was significantly higher than that of patients in the high-risk group. In addition, CDSRGs-risk score was proved as an independent prognostic factor in PC. Immune infiltrates and drug sensitivity are associated with POLR2E,IL18, MAVS, and FADD expression.Conclusions: In summary, we present and validated a CDSRGs risk model that is an independent prognostic factor and indicates the immune characteristics of PC. This prognostic model may facilitate the personalized treatment and monitoring.

scholarly journals DPYSL2 as potential diagnostic and prognostic biomarker linked to immune infiltration in lung adenocarcinoma

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yang-Jie Wu ◽  
Ai-Tao Nai ◽  
Gui-Cheng He ◽  
Fei Xiao ◽  
Zhi-Min Li ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Roc Curve ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
List Type ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Background Dihydropyrimidinase like 2 (DPYSL2) has been linked to tumor metastasis. However, the function of DPSY2L in lung adenocarcinoma (LUAD) is yet to be explored. Methods Herein, we assessed DPYSL2 expression in various tumor types via online databases such as Oncomine and Tumor Immune Estimation Resource (TIMER). Further, we verified the low protein and mRNA expressions of DPYSL2 in LUAD via the ULCAN, The TCGA and GEPIA databases. We applied the ROC curve to examine the role of DPYSL2 in diagnosis. The prognostic significance of DPYSL2 was established through the Kaplan–Meier plotter and the Cox analyses (univariate and multivariate). TIMER was used to explore DPYSL2 expression and its connection to immune infiltrated cells. Through Gene Set Enrichment Analysis, the possible mechanism of DPYSL2 in LUAD was investigated. Results In this study, database analysis revealed lower DPYSL2 expression in LUAD than in normal tissues. The ROC curve suggested that expression of DPYSL2 had high diagnostic efficiency in LUAD. The DPYSL2 expression had an association with the survival time of LUAD patients in the Kaplan–Meier plotter and the Cox analyses. The results from TIMER depicted a markedly positive correlation of DPYSL2 expression with immune cells infiltrated in LUAD, such as macrophages, dendritic cells, CD4+ T cells, and neutrophils. Additionally, many gene markers for the immune system had similar positive correlations in the TIMER analysis. In Gene Set Enrichment Analysis, six immune-related signaling pathways were associated with DPYSL2. Conclusions In summary, DPYSL2 is a novel biomarker with diagnostic and prognostic potential for LUAD as well as an immunotherapy target. Highlights Expression of DPYSL2 was considerably lower in LUAD than in normal tissues. Investigation of multiple databases showed a high diagnostic value of DPYSL2 in LUAD. DPYSL2 can independently predict the LUAD outcomes. Immune-related mechanisms may be potential ways for DPYSL2 to play a role in LUAD.

scholarly journals Arkas: Rapid reproducible RNAseq analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 586 ◽  
Author(s):  
Anthony R. Colombo ◽  
Timothy J. Triche Jr ◽  
Giridharan Ramsingh
Keyword(s):  
De Novo ◽  
Transcriptome Assembly ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Special Focus ◽  
Gene Set Enrichment ◽  
Cloud Computing Environment ◽  
Gene Set ◽  
Structured Information ◽  
Computing Platforms

The recently introduced Kallisto pseudoaligner has radically simplified the quantification of transcripts in RNA-sequencing experiments.  We offer cloud-scale RNAseq pipelines Arkas-Quantification, which deploys Kallisto for parallel cloud computations, and Arkas-Analysis, which annotates the Kallisto results by extracting structured information directly from source FASTA files with per-contig metadata and calculates the differential expression and gene-set enrichment analysis on both coding genes and transcripts. The biologically informative downstream gene-set analysis maintains special focus on Reactome annotations while supporting ENSEMBL transcriptomes. The Arkas cloud quantification pipeline includes support for custom user-uploaded FASTA files, selection for bias correction and pseudoBAM output. The option to retain pseudoBAM output for structural variant detection and annotation provides a middle ground between de novo transcriptome assembly and routine quantification, while consuming a fraction of the resources used by popular fusion detection pipelines.  Illumina's BaseSpace cloud computing environment, where these two applications are hosted, offers a massively parallel distributive quantification step for users where investigators are better served by cloud-based computing platforms due to inherent efficiencies of scale.

scholarly journals Interactive Gene Expression Between Metarhizium anisopliae JEF-290 and Longhorned Tick Haemaphysalis longicornis at Early Stage of Infection

2021 ◽  
Vol 12 ◽  
Author(s):  
Mi Rong Lee ◽  
Jong Cheol Kim ◽  
So Eun Park ◽  
Se Jin Lee ◽  
Woo Jin Kim ◽  
...  
Keyword(s):  
Metarhizium Anisopliae ◽  
Early Stage ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Transcriptional Level ◽  
Hard Tick ◽  
Haemaphysalis Longicornis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Number Of Patients

The longhorned tick, Haemaphysalis longicornis (Acari: Ixodidae), is a hard tick and a vector for severe fever with thrombocytopenia syndrome (SFTS) virus. The number of patients infected with SFTS is rapidly increasing. Recently, the invertebrate pathogen Metarhizium anisopliae JEF-290 was reported to be useful to control the tick as an alternative to chemical acaricides, which are not easily applicable in human living areas where the tick is widely spread. In this study, we analyzed how the tick and the fungal pathogen interact at the transcriptional level. Field-collected tick nymphs were treated with JEF-290 conidia at 1 × 108 conidia/ml. In the early stage of infection with 2.5% mortality, the infected ticks were subjected to RNA sequencing, and non-infected ticks and fungal masses served as controls. Fungus and tick genes were mostly up-regulated at the early stage of infection. In the gene set enrichment analysis of the infecting fungus, catabolic processes that included lipids, phospholipids, and detoxification processes, the response to oxidative stress, and toxic substances were significantly up-regulated. In this fungal up-regulation, various lipase, antioxidant enzyme, and hydrolase genes were highly transcribed. The gene set enrichment analysis of the infected tick showed that many peptide synthesis processes including translation, peptide metabolism, ribonucleotide metabolism, and energy production processes that included ATP generation and ADP metabolism were significantly up-regulated. Structurally, mitochondria and ribosome subunit genes in ticks were highly transcribed to upregulate these processes. Together these results indicate that JEF-290 initiates process that infects the tick while the tick actively defends against the fungal attack. This work provides background to improve our understanding of the early stage of fungal infection in longhorned tick.

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