scholarly journals m6A Regulators in Human Adipose Tissue - Depot-Specificity and Correlation With Obesity

2021 ◽  
Vol 12 ◽  
Author(s):  
Torunn Rønningen ◽  
Mai Britt Dahl ◽  
Tone Gretland Valderhaug ◽  
Akin Cayir ◽  
Maria Keller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adipose Tissue ◽  
Gene Expression Data ◽  
Human Adipose Tissue ◽  
Snp Markers ◽  
Specific Gene ◽  
Expression Data ◽  
Clinical Variables ◽  
Specific Gene Expression ◽  
Adipose Tissue Depot

BackgroundN6-methyladenosine (m6A) is one of the most abundant post-transcriptional modifications on mRNA influencing mRNA metabolism. There is emerging evidence for its implication in metabolic disease. No comprehensive analyses on gene expression of m6A regulators in human adipose tissue, especially in paired adipose tissue depots, and its correlation with clinical variables were reported so far. We hypothesized that inter-depot specific gene expression of m6A regulators may differentially correlate with clinical variables related to obesity and fat distribution.MethodsWe extracted intra-individually paired gene expression data (omental visceral adipose tissue (OVAT) N=48; subcutaneous adipose tissue (SAT) N=56) of m6A regulators from an existing microarray dataset. We also measured gene expression in another sample set of paired OVAT and SAT (N=46) using RT-qPCR. Finally, we extracted existing gene expression data from peripheral mononuclear blood cells (PBMCs) and single nucleotide polymorphisms (SNPs) in METTL3 and YTHDF3 from genome wide data from the Sorbs population (N=1049). The data were analysed for differential gene expression between OVAT and SAT; and for association with obesity and clinical variables. We further tested for association of SNP markers with gene expression and clinical traits.ResultsIn adipose tissue we observed that several m6A regulators (WTAP, VIRMA, YTHDC1 and ALKBH5) correlate with obesity and clinical variables. Moreover, we found adipose tissue depot specific gene expression for METTL3, WTAP, VIRMA, FTO and YTHDC1. In PBMCs, we identified ALKBH5 and YTHDF3 correlated with obesity. Genetic markers in METTL3 associate with BMI whilst SNPs in YTHDF3 are associated with its gene expression.ConclusionsOur data show that expression of m6A regulators correlates with obesity, is adipose tissue depot-specific and related to clinical traits. Genetic variation in m6A regulators adds an additional layer of variability to the functional consequences.

scholarly journals Model of Tryptophan Metabolism, Readily Scalable Using Tissue-specific Gene Expression Data

2013 ◽  
Vol 288 (48) ◽  
pp. 34555-34566 ◽  
Author(s):  
Anne-Kristin Stavrum ◽  
Ines Heiland ◽  
Stefan Schuster ◽  
Pål Puntervoll ◽  
Mathias Ziegler
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Tryptophan Metabolism ◽  
Specific Gene ◽  
Expression Data ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression

Tu-P7:3 Sex- and site-specific gene expression in the adipose tissue of OB/OB mice

2006 ◽  
Vol 7 (3) ◽  
pp. 185
Author(s):  
S. Shiozaki ◽  
T. Chiba ◽  
K. Kokame ◽  
T. Miyata ◽  
M. Ai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adipose Tissue ◽  
Specific Gene ◽  
Site Specific ◽  
Specific Gene Expression

scholarly journals dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using single cell time-course gene expression data

2021 ◽  
Author(s):  
Yu Xu ◽  
Jiaxing Chen ◽  
Aiping Lyu ◽  
William K Cheung ◽  
Lu Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Time Course ◽  
Target Genes ◽  
Neighborhood Context ◽  
Specific Gene ◽  
Expression Data ◽  
Regulatory Interactions ◽  
Gene Functions

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely applied to reconstruct the cell-type-specific gene regulatory networks by exploring the dynamic changes of gene expression between transcription factors (TFs) and their target genes. The existing algorithms were commonly designed to analyze bulk gene expression data and could not deal with the dropouts and cell heterogeneity in scRNA-seq data. In this paper, we developed dynDeepDRIM that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. dynDeepDRIM integrated the primary image, neighbor images with time-course into a four-dimensional tensor and trained a convolutional neural network to predict the direct regulatory interactions between TFs and genes. We evaluated the performance of dynDeepDRIM on five time-course gene expression datasets. dynDeepDRIM outperformed the state-of-the-art methods for predicting TF-gene direct interactions and gene functions. We also observed gene functions could be better performed if more neighbor images were involved.

BIOMARKER DISCOVERY AND VISUALIZATION IN GENE EXPRESSION DATA WITH EFFICIENT GENERALIZED MATRIX APPROXIMATIONS

2007 ◽  
Vol 05 (02a) ◽  
pp. 251-279 ◽  
Author(s):  
WENYUAN LI ◽  
YANXIONG PENG ◽  
HUNG-CHUNG HUANG ◽  
YING LIU
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Biomarker Discovery ◽  
Attribute Selection ◽  
Specific Gene ◽  
Expression Data ◽  
Selection Methods ◽  
Data Set ◽  
Generalized Matrix ◽  
Matrix Approximations

In most real-world gene expression data sets, there are often multiple sample classes with ordinals, which are categorized into the normal or diseased type. The traditional feature or attribute selection methods consider multiple classes equally without paying attention to the up/down regulation across the normal and diseased types of classes, while the specific gene selection methods particularly consider the differential expressions across the normal and diseased, but ignore the existence of multiple classes. In this paper, to improve the biomarker discovery, we propose to make the best use of these two aspects: the differential expressions (that can be viewed as the domain knowledge of gene expression data) and the multiple classes (that can be viewed as a kind of data set characteristic). Therefore, we simultaneously take into account these two aspects by employing the 1-rank generalized matrix approximations (GMA). Our results show that GMA cannot only improve the accuracy of classifying the samples, but also provide a visualization method to effectively analyze the gene expression data on both genes and samples. Based on the mechanism of matrix approximation, we further propose an algorithm, CBiomarker, to discover compact biomarker by reducing the redundancy.

scholarly journals Supervised Classification by Filter Methods and Recursive Feature Elimination Predicts Risk of Radiotherapy-Related Fatigue in Patients with Prostate Cancer

2014 ◽  
Vol 13 ◽  
pp. CIN.S19745 ◽  
Author(s):  
Leorey N. Saligan ◽  
Juan Luis Fernández-Martínez ◽  
Enrique J. deAndrés-Galiana ◽  
Stephen Sonis
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Patients ◽  
Gene Expression Data ◽  
Fold Change ◽  
Learning Phase ◽  
Small Scale ◽  
Specific Gene ◽  
Expression Data ◽  
Validation Phase

Background Fatigue is a common side effect of cancer (CA) treatment. We used a novel analytical method to identify and validate a specific gene cluster that is predictive of fatigue risk in prostate cancer patients (PCP) treated with radiotherapy (RT). Methods A total of 44 PCP were categorized into high-fatigue (HF) and low-fatigue (LF) cohorts based on fatigue score change from baseline to RT completion. Fold-change differential and Fisher's linear discriminant analyses (LDA) from 27 subjects with gene expression data at baseline and RT completion generated a reduced base of most discriminatory genes (learning phase). A nearest-neighbor risk (k-NN) prediction model was developed based on small-scale prognostic signatures. The predictive model validity was tested in another 17 subjects using baseline gene expression data (validation phase). Result The model generated in the learning phase predicted HF classification at RT completion in the validation phase with 76.5% accuracy. Conclusion The results suggest that a novel analytical algorithm that incorporates fold-change differential analysis, LDA, and a k-NN may have applicability in predicting regimen-related toxicity in cancer patients with high reliability, if we take into account these results and the limited amount of data that we had at disposal. It is expected that the accuracy will be improved by increasing data sampling in the learning phase.

scholarly journals Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Kristina M Hettne ◽  
André Boorsma ◽  
Dorien A M van Dartel ◽  
Jelle J Goeman ◽  
Esther de Jong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Text Mining ◽  
Gene Expression Data ◽  
Specific Gene ◽  
Expression Data ◽  
Next Generation ◽  
Gene Sets ◽  
Chemical Response

CONTEXT-SPECIFIC GENE REGULATIONS IN CANCER GENE EXPRESSION DATA

2008 ◽  
Author(s):  
INA SEN ◽  
MICHAEL P. VERDICCHIO ◽  
SUNGWON JUNG ◽  
ROBERT TREVINO ◽  
MICHAEL BITTNER ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Specific Gene ◽  
Cancer Gene ◽  
Expression Data ◽  
Context Specific ◽  
Gene Regulations

CASCADING SVMS AS A TOOL FOR MEDICAL DIAGNOSIS USING MULTI-CLASS GENE EXPRESSION DATA

2006 ◽  
Vol 15 (03) ◽  
pp. 335-352
Author(s):  
ILIAS N. FLAOUNAS ◽  
DIMITRIS K. IAKOVIDIS ◽  
DIMITRIS E. MAROULIS
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Medical Diagnosis ◽  
Gene Selection ◽  
Selection Process ◽  
Support Vector ◽  
Specific Gene ◽  
Processing Unit ◽  
Expression Data ◽  
Medical Diagnostic

In this paper we propose a novel Support Vector Machines-based architecture for medical diagnosis using multi-class gene expression data. It consists of a pre-processing unit and N-1 sequentially ordered blocks capable of classifying N classes in a cascading manner. Each block embodies both a gene selection and a classification module. It offers the flexibility of constructing block-specific gene expression spaces and hypersurfaces for the discrimination of the different classes. The proposed architecture was applied for medical diagnostic tasks including prostate and lung cancer diagnosis. Its performance was evaluated by using a leave-one-out cross validation approach which avoids the bias introduced by the gene selection process. The results show that it provides high accuracy which in most cases exceeds the accuracy achieved by the popular one-vs-one and one-vs-all SVM combination schemes and Nearest-Neighbor classifiers. The cascading SVMs can be successfully applied as a medical diagnostic tool.

Sign in / Sign up

Export Citation Format

Share Document