Cerebral amyloid angiopathy-linked ß-amyloid mutations promote cerebral fibrin deposits via increased binding affinity for fibrinogen.

Cerebral amyloid angiopathy (CAA), where beta-amyloid (Aß) deposits around cerebral blood vessels, is a major contributor of vascular dysfunction in Alzheimer's disease (AD) patients. However, the molecular mechanism underlying CAA formation and CAA-induced cerebrovascular pathology is unclear. Hereditary cerebral amyloid angiopathy (HCAA) is a rare familial form of CAA in which mutations within the (Aß) peptide cause an increase in vascular deposits. Since the interaction between Aß and fibrinogen increases CAA and plays an important role in cerebrovascular damage in AD, we investigated the role of the Aß-fibrinogen interaction in HCAA pathology. Our work revealed the most common forms of HCAA-linked mutations, Dutch (E22Q) and Iowa (D23N), resulted in up to a 50-fold stronger binding affinity of Aß for fibrinogen. In addition, the stronger interaction between fibrinogen and mutant Aßs led to a dramatic perturbation of clot structure and delayed fibrinolysis. Immunofluorescence analysis of the occipital cortex showed an increase of fibrin(ogen)/Aß codeposition, as well as fibrin deposits in HCAA patients, compared to early-onset AD patients and nondemented individuals. Our results suggest the HCAA-type Dutch and Iowa mutations increase the interaction between fibrinogen and Aß, which might be central to cerebrovascular pathologies observed in HCAA.

Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients.

BACKGROUND: The objective of this study was to investigate cellular bioenergetics in primary skin fibroblasts derived from patients with amyotrophic lateral sclerosis (ALS) and to determine if they can be used as classifiers for patient stratification. METHODS: We assembled a collection of unprecedented size of fibroblasts from patients with sporadic ALS (sALS, n = 171), primary lateral sclerosis (PLS, n = 34), ALS/PLS with C9orf72 mutations (n = 13), and healthy controls (n = 91). In search for novel ALS classifiers, we performed extensive studies of fibroblast bioenergetics, including mitochondrial membrane potential, respiration, glycolysis, and ATP content. Next, we developed a machine learning approach to determine whether fibroblast bioenergetic features could be used to stratify patients. RESULTS: Compared to controls, sALS and PLS fibroblasts had higher average mitochondrial membrane potential, respiration, and glycolysis, suggesting that they were in a hypermetabolic state. Only membrane potential was elevated in C9Orf72 lines. ATP steady state levels did not correlate with respiration and glycolysis in sALS and PLS lines. Based on bioenergetic profiles, a support vector machine (SVM) was trained to classify sALS and PLS with 99% specificity and 70% sensitivity. CONCLUSIONS: sALS, PLS, and C9Orf72 fibroblasts share hypermetabolic features, while presenting differences of bioenergetics. The absence of correlation between energy metabolism activation and ATP levels in sALS and PLS fibroblasts suggests that in these cells hypermetabolism is a mechanism to adapt to energy dissipation. Results from SVM support the use of metabolic characteristics of ALS fibroblasts and multivariate analysis to develop classifiers for patient stratification.