Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer's disease

Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer's disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini-Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer's disease, which supports its potential utility as a clinically useful biomarker

Aβ42 Protofibrils Interact With, and Are Trafficked Through, Microglial-Derived Microvesicles

Microvesicles (MVs) and exosomes comprise a class of cell-secreted particles termed extracellular vesicles (EVs). These cargo-holding vesicles mediate cell-to-cell communication and have recently been implicated in neurodegenerative diseases such as Alzheimer’s disease (AD). The two types of EVs are distinguished by the mechanism of cell release and their size, with the smaller exosomes and the larger MVs ranging from 30 to 100 nm and 100 nm to 1 μm in diameter, respectively. MV numbers are increased in AD and appear to interact with amyloid-β peptide (Aβ), the primary protein component of the neuritic plaques in the AD brain. Because microglial cells play such an important role in AD-linked neuroinflammation, we sought to characterize MVs shed from microglial cells, better understand MV interactions with Aβ, and determine whether internalized Aβ may be incorporated into secreted MVs. Multiple strategies were used to characterize MVs shed from BV-2 microglia after ATP stimulation. Confocal images of isolated MVs bound to fluorescently labeled annexin-V via externalized phosphatidylserine revealed a polydisperse population of small spherical structures. Dynamic light scattering measurements yielded MV diameters ranging from 150 to 600 nm. Electron microscopy of resin-embedded MVs cut into thin slices showed well-defined uranyl acetate-stained ring-like structures in a similar diameter range. The use of a fluorescently labeled membrane insertion probe, NBD C6–HPC, effectively tracked MVs in binding experiments, and an Aβ ELISA confirmed a strong interaction between MVs and Aβ protofibrils but not Aβ monomers. Despite the lesser monomer interaction, MVs had an inhibitory effect on monomer aggregation. Primary microglia rapidly internalized Aβ protofibrils, and subsequent stimulation of the microglia with ATP resulted in the release of MVs containing the internalized Aβ protofibrils. The role of MVs in neurodegeneration and inflammation is an emerging area, and further knowledge of MV interaction with Aβ may shed light on extracellular spread and influence on neurotoxicity and neuroinflammation

The Mechanism of Enantioselective Neurosteroid Actions on GABA_A Receptors

The neurosteroid allopregnanolone (ALLO) and pregnanolone (PREG), are equally effective positive allosteric modulators (PAMs) of GABAA receptors. Interestingly, the PAM effects of ALLO are strongly enantioselective, whereas those of PREG are not. This study was aimed at determining the basis for this difference in enantioselectivity. The oocyte electrophysiology studies showed that ent-ALLO potentiates GABA-elicited currents in α1β3 GABAA receptors with lower potency and efficacy than ALLO, PREG or ent-PREG. The small PAM effect of ent-ALLO was prevented by the α1(Q242L) mutation in the intersubunit neurosteroid binding site between the β3 and α1 subunits. Consistent with this result, neurosteroid analogue photolabeling with mass spectrometric readout, showed that ent-ALLO binds weakly to the β3-α1 intersubunit binding site in comparison to ALLO, PREG and ent-PREG. Rigid body docking predicted that ent-ALLO binds in the intersubunit site with a preferred orientation 180° different than ALLO, PREG or ent-PREG, potentially explaining its weak binding and effect. Photolabeling studies did not identify differences between ALLO and ent-ALLO binding to the α1 or β3 intrasubunit binding sites that also mediate neurosteroid modulation of GABAA receptors. The results demonstrate that differential binding of ent-ALLO and ent-PREG to the β3-α1 intersubunit site accounts for the difference in enantioselectivity between ALLO and PREG

Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease

Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer's disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer's disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset-9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer's disease, which is alike sporadic Alzheimer's disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer's disease

Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease

Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer's disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer's disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset-9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer's disease, which is alike sporadic Alzheimer's disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer's disease