Quantitative analysis of proteins of metabolism by reverse phase protein microarrays identifies potential biomarkers of rare neuromuscular diseases

Abstract

Background: Muscle diseases have been associated with changes in the expression of proteins involved in energy metabolism. To this aim we have developed a number of monoclonal antibodies against proteins of energy metabolism.

Methods: Herein, we have used Reverse Phase Protein Microarrays (RPMA), a high throughput technique, to investigate quantitative changes in protein expression with the aim of identifying potential biomarkers in rare neuromuscular diseases. A cohort of 73 muscle biopsies that included samples from patients diagnosed of Duchenne (DMD), Becker (BMD), symptomatic forms of DMD and BMD in female carriers (Xp21 Carriers), Limb Girdle Muscular Dystrophy Type 2C (LGMD2C), neuronal ceroid lipofuscinosis (NCL), glycogenosis type V (Mc Ardle disease), isolated mitochondrial complex I deficiency, intensive care unit myopathy and control donors were investigated. The nineteen proteins of energy metabolism studied included members of the mitochondrial oxidation of pyruvate, the tricarboxylic acid cycle, β-oxidation of fatty acids, electron transport and oxidative phosphorylation, glycogen metabolism, glycolysis and oxidative stress using highly specific antibodies.

Results: The results indicate that the phenotype of energy metabolism offers potential biomarkers that could be implemented to refine the understanding of the biological principles of rare diseases and, eventually, the management of these patients.

Conclusions: We suggest that some biomarkers of energy metabolism could be translated into the clinics to contribute to the improvement of the clinical handling of patients affected by rare diseases.

Figure 1

Validation of the antibodies used for application in RPMA. 30–40 μg of protein derived from human muscle (M) were fractionated on SDS-PAGE gels (see Coomasie blue stained track on top-left), blotted against the indicated antibodies and processed for western blotting. Only antibodies that recognize a single protein band of the expected molecular mass were used in the study. The migration of molecular mass markers is indicated to the left.

Figure 2

Printing of RPMA. A, Scheme of RPMA printing processed for anti-LDH-A is shown magnified. One nl samples were spotted in quadruplicate. Black boxed: negative controls of BSA; Blue boxed: standard curves of HCT116 cells; Green boxed: tissue samples from control donors; Red boxed: tissue samples from patients. Below are shown representative RPMAs processed with other antibodies. B, The plot illustrates the linear correlation that exists between the fluorescence intensity (arbitrary units, a.u.) and the amount of LDH-A protein in HCT116 cell lysates. Protein concentrations in the biopsies were calculated according to the fluorescence intensity obtained in the linear plot of HCT116 cells. For other details see Additional file 2: Figure S2.

Figure 3

Hierarchical clustering analyses of the biopsies using enzymes of energy metabolism. Rows indicate type of sample, columns, proteins and derived ratios. Protein expression scores are shown normalized to the mean relative expression level in normal samples, according to a color scale (below panels): red indicates high; black, normal; and green, low expression. The dendogram (to the right of the matrix) represents overall similarities in expression profiles. The maximum and minimum values of the markers for each cluster are shown. A, Clustering of normal (CTR), isolated deficit of Complex I activity (MITO) and myophosphorylase (GlycV) biopsies using β-F1-ATPase/GAPDH ratio, PYGM and NADHs9 as biomarkers. B, Clustering of normal (CTR) and DMD, BMD, Xp21 and LGMD2C muscular dystrophies using LDH-A, BEC index and PYGM as biomarkers. C, Clustering of normal (CTR) and Neuronal Ceroid Lipofuscinosis (NCL) biopsies using PYGM, GPD1 and β-F1-ATPase/LDHA ratio as biomarkers.