A structural mixed model to shrink covariance matrices for time-course differential gene expression studies

2009 ◽  
Vol 53 (5) ◽  
pp. 1630-1638 ◽  
Author(s):  
Guillemette Marot ◽  
Jean-Louis Foulley ◽  
Florence Jaffrézic
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Time Course ◽  
Mixed Model ◽  
Covariance Matrices ◽  
Expression Studies ◽  
Differential Gene ◽  
Gene Expression Studies

scholarly journals No differential gene expression for CD4+ T cells of MS patients and healthy controls

2019 ◽  
Vol 5 (2) ◽  
pp. 205521731985690 ◽  
Author(s):  
Ina S Brorson ◽  
Anna Eriksson ◽  
Ingvild S Leikfoss ◽  
Elisabeth G Celius ◽  
Pål Berg-Hansen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
T Cells ◽  
Differential Gene Expression ◽  
Genetic Variants ◽  
Healthy Controls ◽  
Expression Studies ◽  
Differential Gene ◽  
Gene Expression Studies ◽  
Multiple Sclerosis Patients

Background Multiple sclerosis-associated genetic variants indicate that the adaptive immune system plays an important role in the risk of developing multiple sclerosis. It is currently not well understood how these multiple sclerosis-associated genetic variants contribute to multiple sclerosis risk. CD4+ T cells are suggested to be involved in multiple sclerosis disease processes. Objective We aim to identify CD4+ T cell differential gene expression between multiple sclerosis patients and healthy controls in order to understand better the role of these cells in multiple sclerosis. Methods We applied RNA sequencing on CD4+ T cells from multiple sclerosis patients and healthy controls. Results We did not identify significantly differentially expressed genes in CD4+ T cells from multiple sclerosis patients. Furthermore, pathway analyses did not identify enrichment for specific pathways in multiple sclerosis. When we investigated genes near multiple sclerosis-associated genetic variants, we did not observe significant enrichment of differentially expressed genes. Conclusion We conclude that CD4+ T cells from multiple sclerosis patients do not show significant differential gene expression. Therefore, gene expression studies of all circulating CD4+ T cells may not result in viable biomarkers. Gene expression studies of more specific subsets of CD4+ T cells remain justified to understand better which CD4+ T cell subsets contribute to multiple sclerosis pathology.

Time course of myogenic and metabolic gene expression in response to acute exercise in human skeletal muscle

2005 ◽  
Vol 98 (5) ◽  
pp. 1745-1752 ◽  
Author(s):  
Yifan Yang ◽  
Andrew Creer ◽  
Bozena Jemiolo ◽  
Scott Trappe
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Acute Exercise ◽  
Vastus Lateralis ◽  
Gene Induction ◽  
Mrna Levels ◽  
Metabolic Gene ◽  
Expression Studies ◽  
Metabolic Gene Expression ◽  
Gene Expression Studies

The aim of this study was to examine the time course activation of select myogenic (MRF4, Myf5, MyoD, myogenin) and metabolic (CD36, CPT1, HKII, and PDK4) genes after an acute bout of resistance (RE) or run (Run) exercise. Six RE subjects [25 ± 4 yr (mean ± SD), 74 ± 14 kg, 1.71 ± 0.11 m] and six Run subjects (25 ± 4 yr, 72 ± 5 kg, 1.81 ± 0.07 m, 63 ± 8 ml·kg−1·min−1) were studied. Eight muscle biopsies were taken from the vastus lateralis (RE) and gastrocnemius (Run) before, immediately after, and 1, 2, 4, 8, 12 and 24 h after exercise. RE increased mRNA of MRF4 (3.7- to 4.5-fold 2–4 h post), MyoD (5.8-fold 8 h post), myogenin (2.6- and 3.5-fold 8–12 h post), HKII (3.6- to 10.5-fold 2–12 h post), and PDK4 (14- to 26-fold 2–8 h post). There were no differences in Myf5, CD36, and CPT1 mRNA levels 0–24 h post-RE. Run increased mRNA of MyoD (5.0- to 8.0-fold), HKII (12- to 16-fold), and PDK4 (32- to 52-fold) at 8–12 h postexercise. There were no differences in MRF4, Myf5, myogenin, CD36 and CPT1 mRNA levels 0–24 h post-Run. These data indicate a myogenic and metabolic gene induction with RE and Run exercise. The timing of the gene induction is variable and generally peaks 4–8 h postexercise with all gene expression not significantly different from the preexercise levels by 24 h postexercise. These data provide basic information for the timing of human muscle biopsy samples for gene-expression studies involving exercise.

scholarly journals High-resolution Expression Profiling of Selected Gene Sets during Plant Immune Activation

2019 ◽  
Author(s):  
Pingtao Ding ◽  
Bruno Pok Man Ngou ◽  
Oliver J. Furzer ◽  
Toshiyuki Sakai ◽  
Ram Krishna Shrestha ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Resolution ◽  
Differential Gene Expression ◽  
Time Course ◽  
High Throughput Sequencing ◽  
Cost Effective ◽  
Biological Processes ◽  
Rna Seq ◽  
Sequence Capture ◽  
Differential Gene

SUMMARYSequence capture followed by next-generation sequencing has broad applications in cost-effective exploration of biological processes at high resolution [1, 2]. Genome-wide RNA sequencing (RNA-seq) over a time course can reveal the dynamics of differential gene expression. However, in many cases, only a limited set of genes are of interest, and are repeatedly used as markers for certain biological processes. Sequence capture can help generate high-resolution quantitative datasets to assess changes in abundance of selected genes. We previously used sequence capture to accelerate Resistance gene cloning [1, 3, 4], investigate immune receptor gene diversity [5] and investigate pathogen diversity and evolution [6, 7].The plant immune system involves detection of pathogens via both cell-surface and intracellular receptors. Both receptor classes can induce transcriptional reprogramming that elevates disease resistance [8]. To assess differential gene expression during plant immunity, we developed and deployed quantitative sequence capture (CAP-I). We designed and synthesized biotinylated single-strand RNA bait libraries targeted to a subset of defense genes, and generated sequence capture data from 99 RNA-seq libraries. We built a data processing pipeline to quantify the RNA-CAP-I-seq data, and visualize differential gene expression. Sequence capture in combination with quantitative RNA-seq enabled cost-effective assessment of the expression profile of a specified subset of genes. Quantitative sequence capture is not limited to RNA-seq or any specific organism and can potentially be incorporated into automated platforms for high-throughput sequencing.

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