Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript attempts to link Spreading Depolarization (SD) with Aβ42 aggregation kinetics through a multiscale model. While the intersection of metabolic stress and Alzheimer’s pathology is of high interest, the current work is characterized by a lack of technical and fundamental rigor, systematic errors in presentation, and a modeling framework that relies on ungrounded assumptions. In its current state, the manuscript is not scientifically sound and requires a drastic reassessment.

EXPERIMENTAL BASIS AND RELIABILITY

1. Atypical Kinetics: The authors take a "progressively biphasic" ThT kinetic profile for granted, citing their own past work. However, this phenomenon is rarely reported for Aβ42 by other independent groups. In particular, diphasic behavior has been associated with specific triggers, such as lipid-induced aggregation (e.g.,https://doi.org/10.1039/C8CC03002B). The authors must provide a rigorous explanation for why in their hands Aβ42 displays this behavior. Otherwise, alternative causes such as poor temperature control during the experiment onset may be equally presented as likely explanations.

2. Absence of Statistics: The traces in Figs 2A–2C lack error bars, replicates, or any form of statistical analysis. It is unclear if these are single measurements.

3. Missing Data: The text mentions measurements at 2, 5, 10, 15, and 30 μM, but only the first three are presented. The omission of higher-concentration data suggests a selective reporting of results that fit the model.

FLAWS IN THE COMPUTATIONAL MODEL

4. Overparameterization: The model is excessively complex without a credible rationale for its parameters. For example, the fundamental basis for Eq 2 and the numerical parameters in Eqs 2 and 6 are entirely missing.

5. Reductionist Assumptions: The authors ascribe aggregation modulation by SD almost exclusively to volume-driven concentration changes. This is highly speculative and ignores the massive physiological shifts inherent to SD, including variations in ionic strength, pH, macromolecular crowding, or glutamate release. Despite the evident overparameterization, the model additionally ignores mechanisms of Aβ42 production and clearance, which are at least as important as the volume shrinkage in extracellular space. By omitting key thermodynamic, kinetic and physiological drivers, the simulations in Figs 3–6 lack any meaningful predictive value. Even if the authors acknowledge the existence of some of these limitations in the Discussion, their existence is overly detrimental for the authors’ objectives.

6. Reproducibility: Code must be provided as Supplementary Information. Stating it will be "archived on the authors' website" at a later date is not recommended for peer-reviewed literature.

UNSATISFACTORY FORMAL RIGOR 

I am sorry to note that the manuscript has several editorial errors that suggest, once again, lack of rigor. Some examples include:

7. Reckless Referencing: There are systematic errors in figure calls (e.g., Fig 3D does not show what is claimed, viz. that “increasing monomer concentrations promote rapid oligomer formation”) and bibliography calls (e.g., Ref [1] does not contain the model details mentioned in pg 3).

8. Presentation: Many graphs lack axis titles. The terminology is imprecise, with the authors using "fibrils" and "fibers" interchangeably.

RECOMMENDATION

Rejection. The underlying experimental data is insufficiently validated, and the model is built upon a speculative foundation that ignores established biochemical drivers of Aβ aggregation.

 

Author Response

See attached.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

manuscript by De Oliveira et al. describes important aspoects of aB oligomerization under varying conditions using both mathematical modelling and experiment.

 

I have some comments/suggestions:

On pathaway and off-pathway oligomers mentioned in abstract should be explained in the introduction.

Measuring IDP aBeta concentration at 280 nm may not be that accurate.

It is not stated clearly whether the incubation in FluoStar is quiescent.

 

The manuscript lacks clear and separate conclusion section

 

 

 

Author Response

See attached.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Alzheimer’s disease (AD) is threating the health of elderly people all over the world and pathological states associated with metabolic stress are regarded as the elevated risk factors for the AD. In their work, the authors probed the mechanism underlying the effect of these conditions on the progression of the AD. The authors probed the how metabolic stress modulates amyloid β (Aβ42) aggregation kinetics through dy-namic changes in extracellular space (ECS). Their work revealed a mechanistic link between SD-induced microenvironmental changes and Aβ aggregation dynamics, which is of significance for understanding early AD pathogenesis. Their work can be accepted after addressing the following issues.

1. In their introduction, the authors mentioned that the production of Aβ monomers are related with β- and γ- secretases.  Work relating with β- secretases should be mentioned.
2. The authors had better provide a figure to clarify the different aggregate forms of Aβ42 so that their work can be better be understood.
3. The author proposed neuronal model. Although the authors clarified this mode in their previous work, the authors had better simply re-describe this model.
4. Their figure 5 shows increasing the intensity and duration of metabolic stress, suggesting that the authors performed a deeper discussion on the aggregation kinetics of Aβ42.

Author Response

See attached.

Author Response File: Author Response.pdf