Pathological Stages of Abnormally Processed Tau Protein During Its Aggregation into Fibrillary Structures in Alzheimer’s Disease
Bibliographic record
Abstract
The description about the progression of the neurofibrillary pathology in AD has been proposed by Braak and Braak (Braak et al., 1993, 1996), which states that there is a stereotype in the appearance and distribution of NFTs along entorhinal, limbic and isocortical areas.It is well known that in AD, NFTs and dystrophic neurites (DNs) are mostly composed of tau protein which has undergone several posttranslational modifications such as abnormal phosphorylation, conformational changes and truncation (Du et al., 2007;Novak et al., 1991; Wischik et al., 1988a).In this regard, we have previously proposed that a continuous and specific pathway of conformational changes and truncation of tau protein is occurring during the maturation of NFTs (Binder et al., 2005;Guillozet-Bongaarts et al., 2005, Mondragon-Rodriguez et al., 2009).The sum of our data support that not only the number of NFTs defines the progression of AD, but also the state of proteolysis of the C-terminus which is associated with conformational changes, i.e. structural modification along the tau molecule (Garcia-Sierra, Mondragon-Rodriguez & Basurto-Islas, 2008).In distinct populations of NFTs, the occurrence of mutually exclusive truncations at either acid aspartic-421 (Asp 421 ) or glutamic acid-391 (Glu 391 ) correlated well with the evolution of the disease (Basurto-Islas et al., 2008).This cascade of pathological molecular events may give a better correlation with the neuropathological progression of the disease.In our model of pathologic processing of tau we pointed out the existence of chimeric NFTs which are composed of diverse molecules of tau characteristically in different stages of proteolysis. The neurofibrillary pathology of ADThe observed neuropathological changes in patients with AD are at least partly the result of the accumulation of NFTs (Fig. 1, arrows) and amyloid-β (Aβ) deposits (Aβ-plaques) (Fig. 1, asterisk) around the hippocampal area (Braak & Braak, 1994;Tseng, Kitazawa & LaFerla, 2004).However, the numeric correlation between Aβ-plaques and clinical symptoms in AD barely represents what is happening in the neurodegenerative process.Also the simple Aβaccumulation per se is not the only etiological factor to trigger AD abnormalities (Lee et al., 2005).Cited studies explain why many aged individuals, despite the presence of high numbers of senile plaques, show little or no cognitive decline (Lee et al., 2004).With these data in mind, the belief that the Aβ-accumulation is a consequence rather that a cause is gaining more support.However, the pathology of the NFTs, mainly composed of tau protein, remains as a relevant criterion for the diagnosis of AD after death, because of the spatial correlation, albeit not perfect, between the number of NFTs and the clinical symptoms of this disease (Braak & Braak, 1991;Murayama & Saito, 2004).The support for this asseveration is mainly based on Braak's study, in which the occurrence and progression of NFTs along allocortical and isocortical areas was described (Braak & Braak, 1991).This study evaluates the density and distribution of NFTs along the brain and classifies them into I, II, entorhinal; III, IV, limbic; and V, VI, neocortical stages.These three groups of stages correspond to normal cognition, cognitive impairment, and dementia (Braak & Braak, 1997).Note that this correlation is not sufficient to demonstrate a cause and effect relationship between NFTs and cognitive deficits.In this regard, the obvious question raised is why do brains of AD patients show the pathology of NFTs?Despite all the information available the answer is not even close to being addressed.However, a huge number of strategies focused on the mechanisms governing the aggregation of the tau protein into PHFs and NFTs (Kosik, Joachim & Selkoe, 1986) have attempted to address this.Those www.intechopen.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".