scholarly journals Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform

10.3791/54693 ◽  
2016 ◽  
Author(s):  
Nicolaas H. Fourie ◽  
Ralph M. Peace ◽  
Sarah K. Abey ◽  
LeeAnne B. Sherwin ◽  
John W. Wiley ◽  
...  
Keyword(s):  
Gene Expression ◽  
Irritable Bowel Syndrome ◽  
High Throughput ◽  
Circulating Mirnas ◽  
Expression Platform ◽  
Gene Expression Platform ◽  
Irritable Bowel ◽  
High Throughput Gene Expression

scholarly journals Identification of potential biomarkers for abdominal pain in IBS patients by bioinformatics approach

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhongyuan Lin ◽  
Yimin Wang ◽  
Shiqing Lin ◽  
Decheng Liu ◽  
Guohui Mo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Irritable Bowel Syndrome ◽  
Abdominal Pain ◽  
Gastrointestinal Disease ◽  
Rectal Mucosa ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Irritable Bowel ◽  
Potential Biomarkers

Abstract Background Irritable bowel syndrome (IBS) is the most common functional gastrointestinal disease characterized by chronic abdominal discomfort and pain. The mechanisms of abdominal pain, as a relevant symptom, in IBS are still unclear. We aimed to explore the key genes and neurobiological changes specially involved in abdominal pain in IBS. Methods Gene expression data (GSE36701) was downloaded from Gene Expression Omnibus database. Fifty-three rectal mucosa samples from 27 irritable bowel syndrome with diarrhea (IBS-D) patients and 40 samples from 21 healthy volunteers as controls were included. Differentially expressed genes (DEGs) between two groups were identified using the GEO2R online tool. Functional enrichment analysis of DEGs was performed on the DAVID database. Then a protein–protein interaction network was constructed and visualized using STRING database and Cytoscape. Results The microarray analysis demonstrated a subset of genes (CCKBR, CCL13, ACPP, BDKRB2, GRPR, SLC1A2, NPFF, P2RX4, TRPA1, CCKBR, TLX2, MRGPRX3, PAX2, CXCR1) specially involved in pain transmission. Among these genes, we identified GRPR, NPFF and TRPA1 genes as potential biomarkers for irritating abdominal pain of IBS patients. Conclusions Overexpression of certain pain-related genes (GRPR, NPFF and TRPA1) may contribute to chronic visceral hypersensitivity, therefore be partly responsible for recurrent abdominal pain or discomfort in IBS patients. Several synapses modification and biological process of psychological distress may be risk factors of IBS.

Co-expressional conservation in virulence and stress related genes of three Gammaproteobacterial species: Escherichia coli, Salmonella enterica and Pseudomonas aeruginosa

2015 ◽  
Vol 11 (11) ◽  
pp. 3137-3148
Author(s):  
Nazanin Hosseinkhan ◽  
Peyman Zarrineh ◽  
Hassan Rokni-Zadeh ◽  
Mohammad Reza Ashouri ◽  
Ali Masoudi-Nejad
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Pseudomonas Aeruginosa ◽  
Systems Biology ◽  
Gene Expression Data ◽  
High Throughput ◽  
Expression Data ◽  
P Gene ◽  
Stress Related Genes ◽  
High Throughput Gene Expression

Gene co-expression analysis is one of the main aspects of systems biology that uses high-throughput gene expression data.

scholarly journals High Throughput Gene Expression Analysis Identifies Reliable Expression Markers of Human Corneal Endothelial Cells

PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e67546 ◽  
Author(s):  
Zhenzhi Chng ◽  
Gary S. L. Peh ◽  
Wishva B. Herath ◽  
Terence Y. D. Cheng ◽  
Heng-Pei Ang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Expression Analysis ◽  
High Throughput ◽  
Gene Expression Analysis ◽  
Corneal Endothelial Cells ◽  
Human Corneal Endothelial Cells ◽  
High Throughput Gene Expression

scholarly journals BBCancer: an expression atlas of blood-based biomarkers in the early diagnosis of cancers

2019 ◽  
Author(s):  
Zhixiang Zuo ◽  
Huanjing Hu ◽  
Qingxian Xu ◽  
Xiaotong Luo ◽  
Di Peng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Early Detection ◽  
Cancer Patients ◽  
High Throughput ◽  
Early Detection Of Cancer ◽  
Circular Rnas ◽  
Expression Data ◽  
Blood Samples ◽  
Cancer Types ◽  
High Throughput Gene Expression

Abstract The early detection of cancer holds the key to combat and control the increasing global burden of cancer morbidity and mortality. Blood-based screenings using circulating DNAs (ctDNAs), circulating RNA (ctRNAs), circulating tumor cells (CTCs) and extracellular vesicles (EVs) have shown promising prospects in the early detection of cancer. Recent high-throughput gene expression profiling of blood samples from cancer patients has provided a valuable resource for developing new biomarkers for the early detection of cancer. However, a well-organized online repository for these blood-based high-throughput gene expression data is still not available. Here, we present BBCancer (http://bbcancer.renlab.org/), a web-accessible and comprehensive open resource for providing the expression landscape of six types of RNAs, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), microRNAs (miRNAs), circular RNAs (circRNAs), tRNA-derived fragments (tRFRNAs) and Piwi-interacting RNAs (piRNAs) in blood samples, including plasma, CTCs and EVs, from cancer patients with various cancer types. Currently, BBCancer contains expression data of the six RNA types from 5040 normal and tumor blood samples across 15 cancer types. We believe this database will serve as a powerful platform for developing blood biomarkers.

Leveraging Automation toward Development of a High-Throughput Gene Expression Profiling Platform

2020 ◽  
pp. 247255522095659
Author(s):  
Jing Chen ◽  
Alan Futran ◽  
Austin Crithary ◽  
Sha Li ◽  
Alex Wolicki ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Discovery ◽  
High Throughput ◽  
Screening Strategy ◽  
Qrt Pcr ◽  
Reverse Transcription Pcr ◽  
Expression Platform ◽  
Program Support ◽  
Drug Discovery Program ◽  
One Step

We previously developed a panel of one-step real-time quantitative reverse transcription PCR (one-step qRT-PCR; hereafter referred to as qRT-PCR) assays to assess compound efficacy. However, these high-cost, conventional qRT-PCR manual assays are not amenable to high-throughput screen (HTS) analysis in a time-sensitive and complex drug discovery process. Here, we report the establishment of an automated gene expression platform using in-house lysis conditions that allows the study of various cell lines, including primary T cells. This process innovation provides the opportunity to perform genotypic profiling in both immunology and oncology therapeutic areas with quantitative studies as part of routine drug discovery program support. This newly instituted platform also enables a panel screening strategy to efficiently connect HTS, lead identification, and lead optimization in parallel.

scholarly journals Pheo-Type: A Diagnostic Gene-expression Assay for the Classification of Pheochromocytoma and Paraganglioma

2016 ◽  
Vol 101 (3) ◽  
pp. 1034-1043 ◽  
Author(s):  
Aidan Flynn ◽  
Trisha Dwight ◽  
Jessica Harris ◽  
Diana Benn ◽  
Li Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Classification Accuracy ◽  
Microarray Dataset ◽  
Support Vector ◽  
Gene Expression Assay ◽  
Expression Platform ◽  
Gene Expression Platform ◽  
Surveillance Programs ◽  
Sporadic Cases ◽  
Type Gene

Abstract Context: Pheochromocytomas and paragangliomas (PPGLs) are heritable neoplasms that can be classified into gene-expression subtypes corresponding to their underlying specific genetic drivers. Objective: This study aimed to develop a diagnostic and research tool (Pheo-type) capable of classifying PPGL tumors into gene-expression subtypes that could be used to guide and interpret genetic testing, determine surveillance programs, and aid in elucidation of PPGL biology. Design: A compendium of published microarray data representing 205 PPGL tumors was used for the selection of subtype-specific genes that were then translated to the Nanostring gene-expression platform. A support vector machine was trained on the microarray dataset and then tested on an independent Nanostring dataset representing 38 familial and sporadic cases of PPGL of known genotype (RET, NF1, TMEM127, MAX, HRAS, VHL, and SDHx). Different classifier models involving between three and six subtypes were compared for their discrimination potential. Results: A gene set of 46 genes and six endogenous controls was selected representing six known PPGL subtypes; RTK1–3 (RET, NF1, TMEM127, and HRAS), MAX-like, VHL, and SDHx. Of 38 test cases, 34 (90%) were correctly predicted to six subtypes based on the known genotype to gene-expression subtype association. Removal of the RTK2 subtype from training, characterized by an admixture of tumor and normal adrenal cortex, improved the classification accuracy (35/38). Consolidation of RTK and pseudohypoxic PPGL subtypes to four- and then three-class architectures improved the classification accuracy for clinical application. Conclusions: The Pheo-type gene-expression assay is a reliable method for predicting PPGL genotype using routine diagnostic tumor samples.

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