Transcriptional abnormalities of hamstring muscle contractures in children with cerebral palsy

Abstract

Cerebral palsy (CP) is an upper motor neuron disease that results in a spectrum of movement disorders. Secondary to the neurological lesion, muscles from patients with CP are often spastic and form debilitating contractures that limit range of motion and joint function. With no genetic component, the pathology of skeletal muscle in CP is a response to aberrant complex neurological input in ways that are not fully understood. This study was designed to gain further understanding of the skeletal muscle response in CP using transcriptional profiling correlated with functional measures to broadly investigate muscle adaptations leading to mechanical deficits.Biopsies were obtained from both the gracilis and semitendinosus muscles from a cohort of patients with CP (n = 10) and typically developing patients (n = 10) undergoing surgery. Biopsies were obtained to define the unique expression profile of the contractures and passive mechanical testing was conducted to determine stiffness values in previously published work. Affymetrix HG-U133A 2.0 chips (n = 40) generated expression data, which was validated for selected transcripts using quantitative real-time PCR. Chips were clustered based on their expression and those from patients with CP clustered separately. Significant genes were determined conservatively based on the overlap of three summarization algorithms (n = 1,398). Significantly altered genes were analyzed for over-representation among gene ontologies and muscle specific networks.The majority of altered transcripts were related to increased extracellular matrix expression in CP and a decrease in metabolism and ubiquitin ligase activity. The increase in extracellular matrix products was correlated with mechanical measures demonstrating the importance in disability. These data lay a framework for further studies and development of novel therapies.

Figure 1. Condition tree from Pearson correlation clustering algorithm.

Individual genes are colored according to expression ratio. CP samples cluster separately from typically developing controls, except for one subject. Both subject sample being clustered together indicates relatively little variability between gracilis and semitendinosus biopsies from the same subject. Table contains Subject ID in row 1 and either typically developing (TD) or CP (CP) in row 2.

Figure 2. Comparison of quantitative real-time PCR data to microarray data.

The microarray data from MAS5 summarization algorithm is used. Each gene in A–F has a significant correlation (p<0.05). (G) atrogin-1 (FBXO32) and (H) MURF-1 (TRIM63) are quantified based on disease state and muscle as they are not present on the microarray.

Figure 3. Heatmap of functional muscle gene networks.

Heatmap based on expression ratio and separated by Entrez gene symbols are used for individual entities with the exception of gene families found in Table S3 in which case geometric means of multiple genes determine expression ratio. Gene symbols found to be significantly different in CP are colored based on direction of regulation. Gene families are colored if any individual gene in the family is significantly altered in CP. Genes with N/A were either not present on the chip or did not have expression to qualify as present in analysis.

Figure 4. Correlation between transcript levels and stiffness.

Examples of significant correlation (p<0.05) between mRNA expression levels and passive mechanical stiffness measurements. (A) ubiquitin-conjugating enzyme E2I (UBE2I) has a negative correlation with fiber stiffness. (B) Collagen XXI alpha I (COL21A1) has a positive correlation with fiber bundle stiffness. (C) adenylate kinase 2 (AK2) and a mitochondrial intermembrane transcript has a negative correlation with bundle stiffness.