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Raw gene Expression data is sourced from GEO, and the appropriate db package for mapping probes to gene symbols was sourced from the Bioconductor AnnotationData packages.
You can read more about microarray data here.
Select conditions below to toggle them from the plot:
| GROUP | CONDITION | SAMPLES |
|---|---|---|
| Female subjects |
GSM3595783 GSM3595784 GSM3595785 GSM3595786 GSM3595787 GSM3595788 GSM3595789
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GSM3595769 GSM3595770 GSM3595771 GSM3595772 GSM3595773 GSM3595774 GSM3595775
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| Male subjects |
GSM3595776 GSM3595777 GSM3595778 GSM3595779 GSM3595780 GSM3595781 GSM3595782
|
|
|
GSM3595762 GSM3595763 GSM3595764 GSM3595765 GSM3595766 GSM3595767 GSM3595768
|
Submission Date: Feb 08, 2019
Summary: Aim: To examine the global gene expression response to the profound metabolic and hormonal stress induced by acute sprint exercise.
Methods: Healthy women and men (n=14) performed 3 all-out cycle sprints interspersed by 20 min recovery. Muscle biopsies were obtained before the first and 2h and 20 min after the last sprint. Microarray analysis was performed to analyse the acute gene expression response. Repeated blood sample were also obtained.
Results: In skeletal muscle, a set of immediate early genes, FOS, NR4A3, MAFF, EGR1, JUNB were markedly upregulated after sprint exercise. These genes were also upregulated, but to a lesser extent, after other exercise modes and after insulin exposure. Gene ontology analysis from 879 differentially expressed genes revealed predicted activation of extracellular remodelling and fat metabolism, which was in common with the gene expression response to endurance and resistance exercise. Moreover, the gene expression pattern indicated an increased turnover of skeletal muscle mass after sprint exercise, which was in common with data on resistance exercise and GH exposure. Insulin and GH may be involved in sprint-induced changes in the transcriptome, based on the overlap noted between sprint- and hormone exposure-related signatures. Furthermore, there were correlations between sprint-induced changes in hormonal levels and changes in gene expression.
Conclusion: This is the first study reporting on global gene expression in skeletal muscle in response to acute sprint exercise. Several novel findings are presented related to novel sprint exercise - regulated genes as well as to shared gene signatures between sprint exercise, other exercise modes and exogenous hormone exposure.
GEO Accession ID: GSE126296
PMID: 31647849
Submission Date: Feb 08, 2019
Summary: Aim: To examine the global gene expression response to the profound metabolic and hormonal stress induced by acute sprint exercise.
Methods: Healthy women and men (n=14) performed 3 all-out cycle sprints interspersed by 20 min recovery. Muscle biopsies were obtained before the first and 2h and 20 min after the last sprint. Microarray analysis was performed to analyse the acute gene expression response. Repeated blood sample were also obtained.
Results: In skeletal muscle, a set of immediate early genes, FOS, NR4A3, MAFF, EGR1, JUNB were markedly upregulated after sprint exercise. These genes were also upregulated, but to a lesser extent, after other exercise modes and after insulin exposure. Gene ontology analysis from 879 differentially expressed genes revealed predicted activation of extracellular remodelling and fat metabolism, which was in common with the gene expression response to endurance and resistance exercise. Moreover, the gene expression pattern indicated an increased turnover of skeletal muscle mass after sprint exercise, which was in common with data on resistance exercise and GH exposure. Insulin and GH may be involved in sprint-induced changes in the transcriptome, based on the overlap noted between sprint- and hormone exposure-related signatures. Furthermore, there were correlations between sprint-induced changes in hormonal levels and changes in gene expression.
Conclusion: This is the first study reporting on global gene expression in skeletal muscle in response to acute sprint exercise. Several novel findings are presented related to novel sprint exercise - regulated genes as well as to shared gene signatures between sprint exercise, other exercise modes and exogenous hormone exposure.
GEO Accession ID: GSE126296
PMID: 31647849
Visualize Samples
Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.
Precomputed Differential Gene Expression
Differential expression signatures are automatically computed using the limma R package.
More options for differential expression are available to compute below.
Signatures:
Select conditions:
Control Condition
Perturbation Condition
Only conditions with at least 1 replicate are available to select
Differential expression signatures can be computed using DESeq2 or characteristic direction.