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GSE106865
Homo sapiens
40 Downloadable Samples
Illumina HumanHT-12 V4.0 expression beadchip
Description
Skeletal muscle adapts to resistance exercise (RE) performance acutely and chronically. An important regulatory step of muscle adaptation to RE is gene expression. Microarray analysis can be used as an exploratory method to investigate how genes and gene clusters are modulated acutely and chronically by RE. The purpose of the present study was to investigate the effect of training status in the basal (rested) and pre- to 24h post-RE on the global transcriptome in vastus lateralis muscle biopsies of young men. Muscle biopsies of nine young men who undertook RE training for 10-wks were collected pre and 24h post-RE at the first (W1) and last (W10) weeks of training and analysed using microarray. An unaccustomed RE bout (at W1) up-regulated muscle gene transcripts related to stress (e.g., heat shock proteins), damage and inflammation, structural remodelling, protein turnover and increased translational capacity. Trained muscles (at W10) became more efficient metabolically, as training favoured a more oxidative metabolism, refined response to stress, showed by genes suppression related to RE-induced stress and inflammation, and up-regulated genes indicating greater muscle contractile efficiency and contribution to promote muscle growth and development. These data highlight that chronic repetition of RE increases muscle efficiency and adapt muscles to respond more specifically and accurately to RE-induced stress.
Publication Title
Resistance training in young men induces muscle transcriptome-wide changes associated with muscle structure and metabolism refining the response to exercise-induced stress.
Sample Metadata Fields
Sex, Specimen part
View Samples- Drosophila melanogaster
- 2 Downloadable Samples
- Affymetrix Drosophila Genome 2.0 Array (drosophila2)
Description
Loss of Ck1alpha produces 'flyabetic' larvae that are feeding defective. In addition we found other larvae with glucose elevations show feeding aversion.
Publication Title
Circulating glucose levels inversely correlate with <i>Drosophila</i> larval feeding through insulin signaling and SLC5A11.
Sample Metadata Fields
Sex, Specimen part
View Samples- Homo sapiens
- 119 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Liposarcoma is the most common soft tissue sarcoma, accounting for about 20% of cases. Liposarcoma is classified into 5 histologic subtypes that fall into 3 biological groups characterized by specific genetic alterations. To identify genes that contribute to liposarcomagenesis and to better predict outcome for patients with the disease, we undertook expression profiling of liposarcoma. U133A expression profiling was performed on 140 primary liposarcoma samples, which were randomly split into training set (n=95) and test set (n=45). A multi-gene predictor for distant recurrence-free survival (DRFS) was developed using the supervised principal component method. Expression levels of the 588 genes in the predictor were used to calculate a risk score for each patient. In validation of the predictor in the test set, patients with low risk score had a 3-year DRFS of 83% vs. 45% for high risk score patients (P=0.001). The hazard ratio for high vs. low score, adjusted for histologic subtype, was 4.42 (95% confidence interval 1.26-15.55; P=0.021). The concordance probability for risk score was 0.732. Genes related to adipogenesis, DNA replication, mitosis, and spindle assembly checkpoint control were all highly represented in the multi-gene predictor. Three genes from the predictor, TOP2A, PTK7, and CHEK1, were found to be overexpressed in liposarcoma samples of all five subtypes and in liposarcoma cell lines. Knockdown of these genes in liposarcoma cell lines reduced proliferation and invasiveness and increased apoptosis. Thus, genes identified from this predictor appear to have roles in liposarcomagenesis and have promise as therapeutic targets. In addition, the multi-gene predictor will improve risk stratification for individual patients with liposarcoma.
Publication Title
Expression profiling of liposarcoma yields a multigene predictor of patient outcome and identifies genes that contribute to liposarcomagenesis.
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Specimen part
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