In vivo rate-determining steps of tau seed accumulation in Alzheimer's disease

Mapping the Spatial Distribution of Fibrillar Polymorphs in Human Brain Tissue

Alzheimer's disease (AD) is a neurodegenerative disorder defined by the progressive formation and spread of fibrillar aggregates of Aβ peptide and tau protein. Polymorphic forms of these aggregates may contribute to disease in varying ways since different neuropathologies appear to be associated with different sets of fibrillar structures and follow distinct pathological trajectories that elicit characteristic clinical phenotypes. The molecular mechanisms underlying the spread of these aggregates in disease may include nucleation, replication, and migration all of which could vary with polymorphic form, stage of disease, and region of brain. Given the linkage between mechanisms of progression and distribution of polymorphs, mapping the distribution of fibrillar structures in situ has the potential to discriminate between mechanisms of progression. However, the means of carrying out this mapping are limited. Optical microscopy lacks the resolution to discriminate between polymorphs in situ, and higher resolution tools such as ssNMR and cryoEM require the isolation of fibrils from tissue, destroying relevant spatial information. Here, we demonstrate the use of scanning x-ray microdiffraction (XMD) to map the locations of fibrillar polymorphs of Aβ peptides and tau protein in histological thin sections of human brain tissue. Coordinated examination of serial sections by immunohistochemistry was used to aid in the interpretation of scattering patterns and to put the observations in a broader anatomical context. Scattering from lesions in tissue shown to be rich in Aβ fibrils by immunohistochemistry exhibited scattering patterns with a prototypical 4.7 Å cross-β peak, and overall intensity distribution that compared well with that predicted from high resolution structures. Scattering from lesions in tissue with extensive tau pathology also exhibited a 4.7 Å cross-β peak but with intensity distributions that were distinct from those seen in Aβ-rich regions. In summary, these observations demonstrate that XMD is a rich source of information on the distribution of fibrillar polymorphs in diseased human brain tissue. When used in coordination with neuropathological examination it has the potential to provide novel insights into the molecular mechanisms underlying disease.

Fluent molecular mixing of Tau isoforms in Alzheimer's disease neurofibrillary tangles

Alzheimer's disease (AD) is defined by intracellular neurofibrillary tangles formed by the microtubule-associated protein tau and extracellular plaques formed by the β-amyloid peptide. AD tau tangles contain a mixture of tau isoforms with either four (4R) or three (3R) microtubule-binding repeats. Here we use solid-state NMR to determine how 4R and 3R tau isoforms mix at the molecular level in AD tau aggregates. By seeding differentially isotopically labeled 4R and 3R tau monomers with AD brain-derived tau, we measured intermolecular contacts of the two isoforms. The NMR data indicate that 4R and 3R tau are well mixed in the AD-tau seeded fibrils, with a 60:40 incorporation ratio of 4R to 3R tau and a small homotypic preference. The AD-tau templated 4R tau, 3R tau, and mixed 4R and 3R tau fibrils exhibit no structural differences in the rigid β-sheet core or the mobile domains. Therefore, 4R and 3R tau are fluently recruited into the pathological fold of AD tau aggregates, which may explain the predominance of AD among neurodegenerative disorders.

Nonlocal models in the analysis of brain neurodegenerative protein dynamics with application to Alzheimer's disease

It is well known that today nearly one in six of the world’s population has to deal with neurodegenerative disorders. While a number of medical devices have been developed for the detection, prevention, and treatments of such disorders, some fundamentals of the progression of associated diseases are in urgent need of further clarification. In this paper, we focus on Alzheimer’s disease, where it is believed that the concentration changes in amyloid-beta and tau proteins play a central role in its onset and development. A multiscale model is proposed to analyze the propagation of these concentrations in the brain connectome. In particular, we consider a modified heterodimer model for the protein–protein interactions. Higher toxic concentrations of amyloid-beta and tau proteins destroy the brain cell. We have studied these propagations for the primary and secondary and their mixed tauopathies. We model the damage of a brain cell by the nonlocal contributions of these toxic loads present in the brain cells. With the help of rigorous analysis, we check the stability behaviour of the stationary points corresponding to the homogeneous system. After integrating the brain connectome data into the developed model, we see that the spreading patterns of the toxic concentrations for the whole brain are the same, but their concentrations are different in different regions. Also, the time to propagate the damage in each region of the brain connectome is different.

Structural progression of Alzheimer’s disease over decades: the MRI staging scheme

The chronological progression of brain atrophy over decades, from pre-symptomatic to dementia stages, has never been formally depicted in Alzheimer’s disease. This is mainly due to the lack of cohorts with long enough MRI follow-ups in cognitively unimpaired young participants at baseline. To describe a spatiotemporal atrophy staging of Alzheimer’s disease at the whole-brain level, we built extrapolated lifetime volumetric models of healthy and Alzheimer’s disease brain structures by combining multiple large-scale databases (n = 3512 quality controlled MRI from 9 cohorts of subjects covering the entire lifespan, including 415 MRI from ADNI1, ADNI2 and AIBL for Alzheimer’s disease patients). Then, we validated dynamic models based on cross-sectional data using external longitudinal data. Finally, we assessed the sequential divergence between normal aging and Alzheimer’s disease volumetric trajectories and described the following staging of brain atrophy progression in Alzheimer’s disease: (i) hippocampus and amygdala; (ii) middle temporal gyrus; (iii) entorhinal cortex, parahippocampal cortex and other temporal areas; (iv) striatum and thalamus and (v) middle frontal, cingular, parietal, insular cortices and pallidum. We concluded that this MRI scheme of atrophy progression in Alzheimer’s disease was close but did not entirely overlap with Braak staging of tauopathy, with a ‘reverse chronology’ between limbic and entorhinal stages. Alzheimer’s disease structural progression may be associated with local tau accumulation but may also be related to axonal degeneration in remote sites and other limbic-predominant associated proteinopathies.

Amyloid and Tau Positron Emission Tomography Imaging in Alzheimer’s Disease and Other Tauopathies

The detection and staging of Alzheimer’s disease (AD) using non-invasive imaging biomarkers is of substantial clinical importance. Positron emission tomography (PET) provides readouts to uncover molecular alterations in the brains of AD patients with high sensitivity and specificity. A variety of amyloid-β (Aβ) and tau PET tracers are already available for the clinical diagnosis of AD, but there is still a lack of imaging biomarkers with high affinity and selectivity for tau inclusions in primary tauopathies, such as progressive supranuclear palsy (PSP), corticobasal degeneration (CBD) and Pick’s disease (PiD). This review aims to provide an overview of the existing Aβ and tau PET imaging biomarkers and their binding properties from in silico, in vitro, and in vivo assessment. Imaging biomarkers for pathologic proteins are vital for clinical diagnosis, disease staging and monitoring of the potential therapeutic approaches of AD. Off-target binding of radiolabeled tracers to white matter or other neural structures is one confounding factor when interpreting images. To improve binding properties such as binding affinity and to eliminate off-target binding, second generation of tau PET tracers have been developed. To conclude, we further provide an outlook for imaging tauopathies and other pathological features of AD and primary tauopathies.

Divergent Cortical Tau Positron Emission Tomography Patterns Among Patients With Preclinical Alzheimer Disease

Importance

Characterization of early tau deposition in individuals with preclinical Alzheimer disease (AD) is critical for prevention trials that aim to select individuals at risk for AD and halt the progression of disease.

Objective

To evaluate the prevalence of cortical tau positron emission tomography (PET) heterogeneity in a large cohort of clinically unimpaired older adults with elevated β-amyloid (A+).

Design, Setting, and Participants

This cross-sectional study examined prerandomized tau PET, amyloid PET, structural magnetic resonance imaging, demographic, and cognitive data from the Anti-Amyloid Treatment in Asymptomatic AD (A4) Study from April 2014 to December 2017. Follow-up analyses used observational tau PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Harvard Aging Brain Study (HABS), and the Wisconsin Registry for Alzheimer's Prevention and the Wisconsin Alzheimer's Disease Research Center (together hereinafter referred to as Wisconsin) to evaluate consistency. Participants were clinically unimpaired at the study visit closest to the tau PET scan and had available amyloid and tau PET data (A4 Study, n = 447; ADNI, n = 433; HABS, n = 190; and Wisconsin, n = 328). No participants who met eligibility criteria were excluded. Data were analyzed from May 11, 2021, to January 25, 2022.

Main Outcomes and Measures

Individuals with preclinical AD with heterogeneous cortical tau PET patterns (A+T cortical+) were identified by examining asymmetrical cortical tau signal and disproportionate cortical tau signal relative to medial temporal lobe (MTL) tau. Voxelwise tau patterns, amyloid, neurodegeneration, cognition, and demographic characteristics were examined.

Results

The 447 A4 participants (A+ group, 392; and normal β-amyloid group, 55), with a mean (SD) age of 71.8 (4.8) years, included 239 women (54%). A total of 36 individuals in the A+ group (9% of the A+ group) exhibited heterogeneous cortical tau patterns and were further categorized into 3 subtypes: asymmetrical left, precuneus dominant, and asymmetrical right. A total of 116 individuals in the A+ group (30% of the A+ group) showed elevated MTL tau (A+T MTL+). Individuals in the A+T cortical+ group were younger than those in the A+T MTL+ group (t61.867 = -2.597; P = .03). Across the A+T cortical+ and A+T MTL+ groups, increased regional tau was associated with reduced hippocampal volume and MTL thickness but not with cortical thickness. Memory scores were comparable between the A+T cortical+ and A+T MTL+ groups, whereas executive functioning scores were lower for the A+T cortical+ group than for the A+T MTL+ group. The prevalence of the A+T cortical+ group and tau patterns within the A+T cortical+ group were consistent in ADNI, HABS, and Wisconsin.

Conclusions and Relevance

This study suggests that early tau deposition may follow multiple trajectories during preclinical AD and may involve several cortical regions. Staging procedures, especially those based on neuropathology, that assume a uniform trajectory across individuals are insufficient for disease monitoring with tau imaging.

Stochasticity, Entropy and Neurodegeneration

We previously suggested that stochastic processes are fundamental in the development of sporadic adult onset neurodegenerative disorders. In this study, we develop a theoretical framework to explain stochastic processes at the protein, DNA and RNA levels. We propose that probability determines random sequencing changes, some of which favor neurodegeneration in particular anatomical spaces, and that more than one protein may be affected simultaneously. The stochastic protein changes happen in three-dimensional space and can be considered to be vectors in a space-time continuum, their trajectories and kinetics modified by physiological variables in the manifold of intra- and extra-cellular space. The molecular velocity of these degenerative proteins must obey the second law of thermodynamics, in which entropy is the driver of the inexorable progression of neurodegeneration in the context of the N-body problem of interacting proteins, time-space manifold of protein-protein interactions in phase space, and compounded by the intrinsic disorder of protein-protein networks. This model helps to elucidate the existence of multiple misfolded proteinopathies in adult sporadic neurodegenerative disorders.

The common incidence-age multistep model of neurodegenerative diseases revisited: wider general age range of incidence corresponds to fewer disease steps

Background

Previously, we collected age-stratified incidence data of 404 epidemiological datasets of 10 neurodegenerative diseases (NDs), namely Amyotrophic Lateral Sclerosis (ALS), Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), Fronto Temporal Dementia (FTD), Dementia with Lewy Bodies (DLB), Parkinsonism (PDM), Parkinson’s disease with Dementia (PDD), Creutzfeldt–Jakob disease (CJD), and Multiple Sclerosis (MS). We tested whether each ND follows a multistep model, found the number of steps necessary for the onset of each ND, found the number of common steps with other NDs and the number of specific steps of each ND, and built a parsimony tree of the genealogy of the NDs. The tree disclosed three groups of NDs: the stem NDs with less than 3 steps; the trunk NDs with 5–7 steps; and the crown NDs with more than 7 steps.

Methods

We made a multidimensional reduction of the previously collected age-stratified incidence epidemiological data of the 10 NDs. We studied the general range of incidence of the 10 NDs using the age- and sex-stratified incidence data. First, we calculated the log of the incidence versus the log of the age for each ND. Next, we calculated the age intervals of the spread of the incidence of each ND. We calculated the regression of the steps obtained with the multistep model versus the age of incidence of the NDs.

Results

We found that the number of steps of the NDs is inversely correlated with the age of incidence of the NDs, and calculated the number of years required for a single step for each ND. Based on these results, we extended the genealogy tree model of the NDs to account for the time needed for a ND step to occur.

Conclusion

The extended genealogy tree disclosed three groups of NDs according to the estimated time needed for a step to occur: the stem ND, HD, with 32.5 years/step, the trunk NDs ALS, FTD, PD and CJD, with 6.7–13.7 years/step; and the crown NDs PDM, PDD, AD and DLB, with 2.3–3.8 years/step. Thus, the NDs cluster into three groups according to both the number of steps and the number of years for a step to occur.