Searching for an <i>in vivo</i> site for nascent amyloid fibril formation
Bibliographic record
Abstract
Research in Alzheimer’s disease (AD), more than in any other amyloid-related diseases, has brought together the concerted efforts of scientists from diverse disciplines. As such, it has advanced our understanding of the in vitro molecular and biochemical parameters required for amyloid fibril formation. Evidence shows that synthetic or recombinant amyloidogenic proteins yield amyloid fibrils in vitro both in physiological and acidic conditions [6,21,23,66,73]. Importantly, addition of amyloid “nucleus” to solutions containing amyloidogenic peptides significantly reduces the lag time and accelerates fibrillogenesis. Mechanistically amyloidogenesis appears to be a nucleation-dependent process [35]. Such an event could conceivably account for relatively robust expansion of amyloid deposition in vivo and thus constitute a central event in the genesis of amyloid-related diseases. However, the question that still remains unanswered is the conditions that might affect nascent amyloid “nucleus” formation in vivo. Clearly, both for the understanding of the biology of amyloidogenesis in vivo and the development of therapeutic strategies, it is essential to understand the conditions and identify the cellular compartment(s) in which nascent amyloid fibrils are formed. Based on in vitro findings,Kelly et al. proposed “acid denaturation pathway” as a fundamental mechanism for amyloidogenesis [34,35]. Experimental evidence supports this concept, particularly the ease with which various amyloid precursor proteins or their cleaved products yield amyloid fibrils in vitro. Some of the examples are light chain of immunoglobulin, transthyretin, various fragments of the Alzheimer amyloid (Aβ) polypeptide, prion protein and serum amyloid A (SAA) etc [6, 21,23,34,35,46,66,73]. Recently, Dobson’s group introduced a more provocative concept of amyloidogenesis [11,27]. Using non-amyloidogenic proteins they were able to generate amyloid fibrils via acid denaturation pathway in vitro and propose that amyloid fibril formation may not be restricted only to certain protein sequences but a property common to many natural polypeptide chains provided that target proteins are exposed to certain “appropriate conditions”. Precise clues to the “appropriate conditions” are unclear at this time and thus a subject of intense investigation. Conceptually, acid conditions in endosomes-lysosomes (EL) in monocytoid cells, simulating the in vitro conditions, have been suggested to provide in vivo one of the requisite conditions for amyloidogenesis [11,27,34, 35]. Other accessory “appropriate conditions”, such as overloading of the EL with amyloidogenic precursor proteins, its partial degradation (although, not fully established in all cases), followed by oxidation may further augment the fibrillogenesis process [2,10,41]. Activated monocytoid cells (macrophage, MA; reticuloendothelial, RE, cells; microglia) can provide some or all of the requisite amyloid inducing conditions in vivo. Our hypothesis on the induction of inflammationassociated AA amyloidosis is that incessant intake of amyloidogenic serum amyloid A (SAA), by MA or RE cells, may cause over-loading of EL and consequently compromise their degradation activities [1,13]. As depicted in Fig. 1, reactive oxygen radicals (ROR) gener-
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".