New insights into the protein aggregation pathology in myotilinopathy by combined proteomic and immunolocalization analyses.

INTRODUCTION: Myofibrillar myopathies are characterized by progressive muscle weakness and impressive abnormal protein aggregation in muscle fibers. In about 10 % of patients, the disease is caused by mutations in the MYOT gene encoding myotilin. The aim of our study was to decipher the composition of protein deposits in myotilinopathy to get new information about aggregate pathology. RESULTS: Skeletal muscle samples from 15 myotilinopathy patients were included in the study. Aggregate and control samples were collected from muscle sections by laser microdissection and subsequently analyzed by a highly sensitive proteomic approach that enables a relative protein quantification. In total 1002 different proteins were detected. Seventy-six proteins showed a significant over-representation in aggregate samples including 66 newly identified aggregate proteins. Z-disc-associated proteins were the most abundant aggregate components, followed by sarcolemmal and extracellular matrix proteins, proteins involved in protein quality control and degradation, and proteins with a function in actin dynamics or cytoskeletal transport. Forty over-represented proteins were evaluated by immunolocalization studies. These analyses validated our mass spectrometric data and revealed different regions of protein accumulation in abnormal muscle fibers. Comparison of data from our proteomic analysis in myotilinopathy with findings in other myofibrillar myopathy subtypes indicates a characteristic basic pattern of aggregate composition and resulted in identification of a highly sensitive and specific diagnostic marker for myotilinopathy. CONCLUSIONS: Our findings i) indicate that main protein components of aggregates belong to a network of interacting proteins, ii) provide new insights into the complex regulation of protein degradation in myotilinopathy that may be relevant for new treatment strategies, iii) imply a combination of a toxic gain-of-function leading to myotilin-positive protein aggregates and a loss-of-function caused by a shift in subcellular distribution with a deficiency of myotilin at Z-discs that impairs the integrity of myofibrils, and iv) demonstrate that proteomic analysis can be helpful in differential diagnosis of protein aggregate myopathies.

Differential proteomic analysis of abnormal intramyoplasmic aggregates in desminopathy.

Desminopathy is a subtype of myofibrillar myopathy caused by desmin mutations and characterized by protein aggregates accumulating in muscle fibers. The aim of this study was to assess the protein composition of these aggregates. Aggregates and intact myofiber sections were obtained from skeletal muscle biopsies of five desminopathy patients by laser microdissection and analyzed by a label-free spectral count-based proteomic approach. We identified 397 proteins with 22 showing significantly higher spectral indices in aggregates (ratio >1.8, p<0.05). Fifteen of these proteins not previously reported as specific aggregate components provide new insights regarding pathomechanisms of desminopathy. Results of proteomic analysis were supported by immunolocalization studies and parallel reaction monitoring. Three mutant desmin variants were detected directly on the protein level as components of the aggregates, suggesting their direct involvement in aggregate-formation and demonstrating for the first time that proteomic analysis can be used for direct identification of a disease-causing mutation in myofibrillar myopathy. Comparison of the proteomic results in desminopathy with our previous analysis of aggregate composition in filaminopathy, another myofibrillar myopathy subtype, allows to determine subtype-specific proteomic profile that facilitates identification of the specific disorder. BIOLOGICAL SIGNIFICANCE: Our proteomic analysis provides essential new insights in the composition of pathological protein aggregates in skeletal muscle fibers of desminopathy patients. The results contribute to a better understanding of pathomechanisms in myofibrillar myopathies and provide the basis for hypothesis-driven studies. The detection of specific proteomic profiles in different myofibrillar myopathy subtypes indicates that proteomic analysis may become a useful tool in differential diagnosis of protein aggregate myopathies.

Automated nuclear segmentation in the determination of the Ki-67 labeling index in meningiomas.

OBJECTIVE: Assessing the Ki-67 labeling index (LI) is laborious and time consuming. Therefore, an automated computer-based method was developed, which is able to identify and analyze immunolabeled and hematoxylin-stained nuclei in digital images of routine immunohistochemical slides. MATERIAL AND METHODS: The method is based on a plugin for the public domain image analysis software ImageJ, which runs on every operating system (free download at http://rsb.info.nih.gov/ij/). Percentage of Ki-67 immunostained nuclei were determined in 5 high power fields (x40) of immunostained slides (DAB detection technique, hematoxylin counterstain) of 20 Grade I, 20 Grade II, and 10 Grade III meningiomas conventionally by two independent investigators and automatically, respectively. The time effort was measured for each counting procedure. RESULTS: Enumerating conventionally or automatically did not reveal any significant differences in the mean labeling indices. Ki-67 LIs discriminated sufficiently between meningiomas of Grade I (median 1.7% Investigator 1 and 1.5% Investigator 2 vs. 1.5% automatically), Grade II (7.6%, 8% vs. 7.3%), and Grade III meningiomas (22%, 21% vs. 22%). The computer-based results correlated very closely with those obtained by manual counting (correlation coefficient = 0.98). The mean time effort for counting procedure per image was 374 s (130 s-435 s) for the conventional and 11 s (7 s-12 s) for the automated method. CONCLUSIONS: The described method can reliably assess the Ki-67 LI much faster than conventional enumerating. The computerized method has the advantages of objectivity, accuracy, repeatability, and ease of use. There is no request for special stains nor special image acquiring systems. The plugin can be downloaded at the "Morphometrie" section of http://www.uniklinikum-saarland.de/neuropathologie.