scholarly journals Time Course of Renal Transcriptomics after Subchronic Exposure to Ochratoxin A in Fisher Rats

Toxins ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 177
Author(s):  
Laura Pastor ◽  
Ariane Vettorazzi ◽  
Elizabeth Guruceaga ◽  
López de Cerain A.
Keyword(s):  
Gene Expression ◽  
Ochratoxin A ◽  
Time Course ◽  
Early Response ◽  
Male Rats ◽  
Female Response ◽  
Metabolism Enzymes ◽  
F344 Rats ◽  
Expression Time ◽  
Over Time

The mycotoxin ochratoxin A (OTA) is a potent nephrocarcinogen, mainly in male rats. The aim of this study was to determine the time course of gene expression (GeneChip® Rat Gene 2.0 ST Array, Affymetrix) in kidney samples from male and female F344 rats, treated daily (p.o) with 0.50 mg/kg b.w. (body weight) of OTA for 7 or 21 days, and evaluate if there were differences between both sexes. After OTA treatment, there was an evolution of gene expression in the kidney over time, with more differentially expressed genes (DEG) at 21 days. The gene expression time course was different between sexes with respect to the number of DEG and the direction of expression (up or down): the female response was progressive and consistent over time, whereas males had a different early response with more DEG, most of them up-regulated. The statistically most significant DEG corresponded to metabolism enzymes (Akr1b7, Akr1c2, Adh6 down-regulated in females; Cyp2c11, Dhrs7, Cyp2d1, Cyp2d5 down-regulated in males) or transporters (Slc17a9 down-regulated in females; Slco1a1 (OATP-1) and Slc51b and Slc22a22 (OAT) down-regulated in males). Some of these genes had also a basal sex difference and were over-expressed in males or females with respect to the other sex.

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

scholarly journals Gene Expression Time Course in the Human Skin during Elicitation of Allergic Contact Dermatitis

2007 ◽  
Vol 127 (11) ◽  
pp. 2585-2595 ◽  
Author(s):  
Malene B. Pedersen ◽  
Lone Skov ◽  
Torkil Menné ◽  
Jeanne D. Johansen ◽  
Jørgen Olsen
Keyword(s):  
Gene Expression ◽  
Contact Dermatitis ◽  
Allergic Contact Dermatitis ◽  
Human Skin ◽  
Time Course ◽  
Allergic Contact ◽  
Expression Time

scholarly journals Comparative analysis of clustering methods for gene expression time course data

2004 ◽  
Vol 27 (4) ◽  
pp. 623-631 ◽  
Author(s):  
Ivan G. Costa ◽  
Francisco de A. T. de Carvalho ◽  
Marcílio C. P. de Souto
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Time Course ◽  
Clustering Methods ◽  
Time Course Data ◽  
Expression Time

Sex-dependent dose response of gene expression modulation after ochratoxin A insult in F344 rats

2017 ◽  
Vol 280 ◽  
pp. S88
Author(s):  
Ariane Vettorazzi ◽  
Laura Pastor ◽  
Adela López de Cerain
Keyword(s):  
Gene Expression ◽  
Ochratoxin A ◽  
Dose Response ◽  
Expression Modulation ◽  
F344 Rats

Sex-dependent gene expression of kidney transporters after Ochratoxin A exposure in F344 rats

2014 ◽  
Vol 229 ◽  
pp. S77
Author(s):  
Laura Pastor ◽  
Ariane Vettorazzi ◽  
Adela López de Cerain
Keyword(s):  
Gene Expression ◽  
Ochratoxin A ◽  
F344 Rats

A Bayesian approach to virus-gene expression time course data

2005 ◽  
Author(s):  
I.-S. Chang ◽  
Chi-Chung Wen ◽  
Yuh-Jenn Wu ◽  
P.K. Gupta ◽  
Shih Sheng Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bayesian Approach ◽  
Time Course ◽  
Virus Gene ◽  
Virus Gene Expression ◽  
Time Course Data ◽  
Expression Time

scholarly journals Modeling Spatio-temporal Dynamics of Chromatin Marks

2017 ◽  
Author(s):  
Petko Fiziev ◽  
Jason Ernst
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Temporal Dynamics ◽  
Spatial Dynamics ◽  
Computational Method ◽  
Time Points ◽  
Signal Density ◽  
Spatial Changes ◽  
Spatio Temporal ◽  
Over Time

ABSTRACTTo model spatial changes of chromatin mark peaks over time we developed and applied ChromTime, a computational method that predicts regions for which peaks either expand or contract significantly or hold steady between time points. Predicted expanding and contracting peaks can mark regulatory regions associated with transcription factor binding and gene expression changes. Spatial dynamics of peaks provided information about gene expression changes beyond localized signal density changes. ChromTime detected asymmetric expansions and contractions, which for some marks associated with the direction of transcription. ChromTime facilitates the analysis of time course chromatin data in a range of biological systems.

Sign in / Sign up

Export Citation Format

Share Document