Amyloidosis is a disease with many faces.

Amyloidosis is a severe disease caused by protein misfolding and deposition in tissues and organs. Thirty-eight different proteins are known to be amyloidogenic. Amyloidosis is categorized into inherited or acquired, and systemic or localized. Light-chain (AL)- and transthyretin (ATTR) amyloidosis are the two most common subtypes. Awareness, early diagnosis, accurate subtyping and relevant treatment are crucial for the management. Novel therapies of systemic AL and ATTR amyloidosis have considerably improved outcome and survival. The aim of this review is to increase awareness and knowledge on diagnosing 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.

Immunoelectron microscopy and mass spectrometry for classification of amyloid deposits.

Amyloidosis is a shared name for several rare, complex and serious diseases caused by extra-cellular deposits of different misfolded proteins. Accurate characterization of the amyloid protein is essential for patient care. Immunoelectron microscopy (IEM) and laser microdissection followed by tandem mass spectrometry (LMD-MS) are new gold standards for molecular subtyping. Both methods perform superiorly to immunohistochemistry, but their complementarities, strengths and weaknesses across amyloid subtypes and organ biopsy origin remain undefined. Therefore, we performed a retrospective study of 106 Congo Red positive biopsies from different involved organs; heart, kidney, lung, gut mucosa, skin and bone marrow. IEM, performed with gold-labelled antibodies against kappa light chains, lambda light chains, transthyretin and amyloid A, identified specific staining of amyloid fibrils in 91.6%; in six biopsies amyloid fibrils were not identified, and in two, the fibril subtype could not be established. LMD-MS identified amyloid protein signature in 98.1%, but in nine the amyloid protein could not be clearly identified. MS identified protein subtype in 89.6%. Corresponding specificities ranged at organ level from 94-100%. Concordance was 89.6-100% for different amyloid subtypes. Importantly, combined use of both methods increased the diagnostic classification to 100%. Some variety in performances at organ level was observed.

Defective glycerol metabolism in aquaporin 9 (AQP9) knockout mice.

Aquaporin-9 (AQP9) is an aquaglyceroporin membrane channel shown biophysically to conduct water, glycerol, and other small solutes. Because the physiological role/s of AQP9 remain undefined and the expression sites of AQP9 remain incomplete and conflicting, we generated AQP9 knockout mice. In the absence of physiological stress, knockout mice did not display any visible behavioral or severe physical abnormalities. Immunohistochemical analyses using multiple antibodies revealed AQP9 specific labeling in hepatocytes, epididymis, vas deferens, and in epidermis of wild type mice, but a complete absence of labeling in AQP9(-/-) mice. In brain, no detectable labeling was observed. Compared with control mice, plasma levels of glycerol and triglycerides were markedly increased in AQP9(-/-) mice, whereas glucose, urea, free fatty acids, alkaline phosphatase, and cholesterol were not significantly different. Oral administration of glycerol to fasted mice resulted in an acute rise in blood glucose levels in both AQP9(-/-) and AQP9(+/-) mice, revealing no defect in utilization of exogenous glycerol as a gluconeogenic substrate and indicating a high gluconeogenic capacity in nonhepatic organs. Obese Lepr(db)/Lepr(db) AQP9(-/-) and obese Lepr(db)/Lepr(db) AQP9(+/-) mice showed similar body weight, whereas the glycerol levels in obese Lepr(db)/Lepr(db) AQP9(-/-) mice were dramatically increased. Consistent with a role of AQP9 in hepatic uptake of glycerol, blood glucose levels were significantly reduced in Lepr(db)/Lepr(db) AQP9(-/-) mice compared with Lepr(db)/Lepr(db) AQP9(+/-) in response to 3 h of fasting. Thus, AQP9 is important for hepatic glycerol metabolism and may play a role in glycerol and glucose metabolism in diabetes mellitus.