Complement 9 in amyloid deposits

Prevalence of amyloid in ligamentum flavum of patients with lumbar spinal stenosis

BACKGROUND

Transthyretin (ATTR) amyloidosis is often diagnosed in an advanced stage, when irreversible cardiac damage has occurred. Lumbar spinal stenosis (LSS) may precede cardiac ATTR amyloidosis by many years, offering the opportunity to detect ATTR already at the time of LSS surgery. We prospectively assessed the prevalence of ATTR in the ligamentum flavum by tissue biopsy in patients aged >50 years undergoing surgery for LSS.

METHODS

Ligamentum flavum thickness was assessed pre-operatively on axial T2 magnetic resonance imaging (MRI) slices. Tissue samples from ligamentum flavum were screened centrally by Congo red staining and immunohistochemistry (IHC).

RESULTS

Amyloid in the ligamentum flavum was detected in 74/94 patients (78.7%). IHC revealed ATTR in 61 (64.9%), whereas amyloid subtyping was inconclusive in 13 (13.8%). Mean thickness of ligamentum flavum was significantly higher at all levels in patients with amyloid (p < .05). Patients with amyloid deposits were older (73.1 ± 9.2 vs. 64.6 ± 10.1 years, p = .01). No differences in sex, comorbidities, previous surgery for carpal tunnel syndrome or LSS were observed.

CONCLUSIONS

Amyloid, mostly of the ATTR subtype, was found in four out of five patients with LSS and is associated with age and ligamentum flavum thickness. Histopathological work-up of ligamentum flavum might inform future decision making.

Quantitative proteome profiling provides evidence of an activation of the complement cascade in ATTR amyloidosis

Amyloidosis is a disease group caused by pathological aggregation and deposition of peptides in diverse tissue sites. Apart from the fibril protein, amyloid deposits frequently enclose non-fibrillar constituents. In this study, carpal tunnel tissue sections with ATTR amyloid were analysed by quantitative mass spectrometry-based proteomics. Following manual dissection, tissue samples of equal size and with heterogeneous amyloid load were dissected and forwarded to bottom-up proteome analysis and label-free protein profiling. The amyloid-associated proteins showed significant correlations of label-free intensity profiles. A comprehensive list of 83 proteins specifically enriched in amyloid deposits was discovered. In addition to well-known signature proteins (e.g. apolipoprotein E, apolipoprotein A-IV, and vitronectin), 22 members of the complement system, including all seven components of the membrane attack complex could be associated to the disease. These data lend support to the hypothesis that the complement system is activated in ATTR amyloidosis.

Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics

Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically throughout the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril-forming protein, and its accurate identification is essential to the choice of treatment. Mass spectrometry-based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective interpretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model-assisted method for the unbiased identification of amyloid-containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid-containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid-containing biopsies and 78 Congo Red negative biopsies to identify novel “amyloid signature” proteins that included clusterin, fibulin-1, vitronectin complement component C9 and also three collagen proteins, as well as the well-known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid-negative control biopsies. The trained algorithm performed superior in the discrimination of amyloid-containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected amyloid-containing biopsies. Moreover, our method successfully classified amyloidosis patients according to the subtype in 102 out of 103 blinded cases. Collectively, our model-assisted approach identified novel amyloid-associated proteins and demonstrated the use of mass spectrometry-based data in clinical diagnostics of disease by the unbiased and reliable model-assisted classification of amyloid deposits and of the specific amyloid subtype.