Insulin Regulates AKT/GSK-3β Signalling, Tau Phosphorylation, and Redox Homeostasis in SH-SY5Y Neuroblastoma Cells

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Insulin is important for the growth, differentiation, and survival processes of neural tissue. It also regulates the phosphorylation of tau protein, maintains energy homeostasis and glucose metabolism, and shields neurons from oxidative stress, which is mediated by the expression of insulin receptors on neural tissue cells. Insulin plays an important role in the growth, differentiation, and survival processes of neural tissue. It also maintains energy homeostasis and glucose metabolism, regulates tau protein phosphorylation, and shields neurons from oxidative stress, which is mediated by the expression of insulin receptors on neural tissue cells. It is well known that insulin is produced in neurons as well as entering the brain through the bloodstream. Neurodegenerative illnesses, such as Alzheimer's disease, which is classified as type 3 diabetes, are caused by impaired cell receptors and insulin sensitivity. Phosphorylation activity in the signaling system from insulin receptors, such as IRS1, Akt, and mTOR, plays a significant role in the pathophysiology of neurodegeneration  (Kciuk M, Kruczkowska W, Gałęziewska J, Wanke K, Kałuzińska-Kołat Ż, Aleksandrowicz M, Kontek R. Alzheimer's Disease as Type 3 Diabetes: Understanding the Link and Implications. Int J Mol Sci. 2024 Nov 7;25(22):11955. doi: 10.3390/ijms252211955.).   The purpose of this study was to investigate how pre-treatment of neuroblastoma cells affected their critical function, such as survival and modifications in signaling molecule phosphorylation. I think this research is pertinent and promising given the critical importance of supplying glucose to nervous system cells and the role of impaired insulin sensitivity, especially in light of the growing body of evidence regarding the beneficial effects of antidiabetic medications on the functional status of nervous system cells. Interestingly, the authors describe how insulin affects neuroblastoma cell viability and how a certain insulin dosage alters the phosphorylation of signaling molecules. The authors' suggested study design is appropriate and enabled them to gather detailed information on how insulin at the chosen dosage affects the viability and activity of signaling molecules in human neuroblastoma cells. The findings can be replicated in other labs. Figures and tables are used to display the work, and as the study's findings are given, the information is adequately understood. The information gathered enabled the scientists to make an argument on how insulin affects the signaling pathways of neuroblastoma cells that are important in oxidative stress regulation and cell survival. Since the study focuses on human neuroblastoma cell culture rather than humans or animals, it does not need ethical committee permission.

 

Remarks regarding the work:
1. The MTT test is interpreted by the authors as only a measure of metabolic activity for some reason, but it is actually a measure of intracellular activity of NADPH-dependent oxidoreductases, cytotoxicity, and indirectly, the potential for cell proliferation due to the intensification of synthetic processes in cells.

2. The authors explain how the important dye trypan blue is used to measure cell growth in the Abstract section. However, this is untrue; instead, it serves as a primary indicator of cell viability and shows how well cells' cytoplasmic membranes are preserved. As a result, "proliferation" is not fully reflected in the final column of Table 1. Use radioactive labeling or other kits (such as the MTT test or Click-iT EdU-647 or CELL COUNTING KIT-8).
3. Since the ordinate axis' label is incorrect, I advise the authors to fix it. Leaving only (%) is preferable. When a parameter is normalized by control, it is customary to take the control values for 1 (for absolute values) or 100% (for relative values) and create a graph with either positive or negative values relative to the abscissa axis, where the abscissa axis represents the norm.
   4. To make their work more noteworthy and show that they have a thorough mastery of the most recent research in this field, authors should increase the number of referenced publications from 2021 to 2026.

 

Work-related queries:
1. Why is there no information on the dosage-dependent effect of insulin, at least on some functional tests for neuroblastoma cells, and why is the insulin dose chosen? These facts may serve as the rationale for the insulin dosage that was selected, or it may be important to inform other researchers of these impacts in advance of reporting the findings.
2. Why do most research choose different cell incubation intervals? Yes, it is known that signaling in the cell can begin within a few minutes of interaction with the substance, but it would make sense to investigate how the length of incubation affects these pathways—whether they are enhanced or decreased, and why.

Author Response

We thank the reviewer for the careful and constructive evaluation of our manuscript and for the valuable suggestions, which have helped us improve the clarity and accuracy of the work.

Regarding the interpretation of the MTT assay, we agree with the reviewer that this method reflects cellular metabolic activity associated with NADPH-dependent oxidoreductase activity, which can be indirectly related to cell viability and proliferation. In the revised manuscript, we have clarified the wording to avoid overinterpretation and now refer to MTT results as “cell metabolic activity” rather than proliferation.

Similarly, we acknowledge the reviewer’s comment regarding the Trypan Blue exclusion assay. We have revised the manuscript to clarify that this assay reflects cell viability based on membrane integrity and does not directly measure proliferation. The term “proliferation” has been corrected throughout the manuscript, including Table 1, to avoid misleading interpretation.

Concerning data presentation, we have revised the graphical representation to ensure that normalized values are expressed as percentage of control (100%), with appropriate labeling of axes in accordance with standard practice.

We agree with the reviewer that including more recent references strengthens the manuscript. The bibliography has been updated to include relevant literature from 2021–2026.

Regarding the rationale for insulin concentration, the selected dose (10⁻⁸ M) was based on previous studies in SH-SY5Y cells and neuronal models reporting activation of insulin signaling pathways, including AKT and downstream targets, without inducing cytotoxic effects. This has now been clarified in the revised Methods section. We also acknowledge that dose–response analyses could provide additional information; however, the present study was designed to characterize signaling responses at a physiologically relevant and widely used concentration.

With respect to incubation times, these were selected based on the known temporal dynamics of insulin signaling. Short-term exposure (30 min) was used to assess phosphorylation events (AKT and GSK-3β), while longer incubation (24 h) was used for downstream effects on tau phosphorylation, oxidative stress markers, and cell viability parameters. This rationale has now been clarified in the Methods section. We agree that future studies including detailed time-course analyses may further refine the understanding of these signaling dynamics.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors of IJMS-4382343 studied the role of insulin in relevance of neurodegenerative disease treatment. The SH-SY5Y neuroblastoma cells were chosen as the target; 11 types of experiments were examined in quadruple runs. Insulin treatment was found to significantly increase p-AKT, p-GSK-3β levels and decrease pTAU level through phosphorylation. In addition, insulin treatments increase NRF2 expression, Cu/Zn-SOD, and Mn-SOD levels but reduce MDA level. Thus, insulin modulates signaling pathways and change oxidative stress-related markers. It is a valuable study to know another role of insulin to treat AD in addition to its popular role for blood sugar related disease. The manuscript can be published after minor revisions.

Comments:

1)Insulin is a vital hormone produced by the pancreas that regulates blood sugar by allowing cells to absorb and use glucose for energy. Will the new role of insulin to treat Alzheimer disease interfere the fundamental biological role of insulin?

2)The authors mentioned the shortcomings of SH-SY5Y cells in the discussions; How far can we believe the current results? Do we have to wait for more robust biological model to be used for more proper conclusions?

Author Response

We thank the reviewer for this important comment. The present study was not designed to evaluate the therapeutic use of insulin or its systemic metabolic effects, but rather to investigate the cellular signaling pathways modulated by insulin in SH-SY5Y cells. In the brain, insulin regulates neuronal survival, synaptic plasticity, energy metabolism, and intracellular signaling pathways independently of its classical role in peripheral glucose homeostasis. Therefore, the signaling effects described in our study are not necessarily incompatible with the physiological metabolic actions of insulin. Nevertheless, systemic administration of insulin may influence glucose metabolism and potentially increase the risk of hypoglycaemia. For this reason, alternative approaches, such as intranasal insulin administration, have been explored to target the central nervous system while minimizing peripheral metabolic effects. We have clarified this point in the Discussion section.

We agree with the reviewer that SH-SY5Y neuroblastoma cells represent a simplified and non-differentiated in vitro model that does not fully reproduce the complexity of mature human neurons or in vivo brain physiology. Accordingly, the findings of this study should be interpreted as mechanistic cellular responses under controlled experimental conditions, rather than as direct evidence of therapeutic efficacy in Alzheimer’s disease.

As stated in the manuscript, further studies using more physiologically relevant models, including differentiated neuronal systems, primary cultures, or vivo models, will be necessary to validate the functional relevance of these observations. Nevertheless, SH-SY5Y cells provide a useful and widely accepted first-step model for dissecting insulin related signaling pathways, which supports the exploratory nature of the present work.

Reviewer 3 Report

Comments and Suggestions for Authors

The present study describes the effects of insulin on AKT/GSK-3β signaling and some components of the antioxidant system. The effect of insulin on phosphorylation of the PI3K/AKT/GSK-3β/tau axis shown by the authors generally confirms previously known effects of insulin on this pathway. The second part of the work, concerning effects on the antioxidant system, has been less studied; however, the results of this part are not reflected at all in the title of the article. What is the reason for this?

Also, several clarifying questions arose while reading:

1. In Section 4, a paragraph on statistical data processing must be added. The figure captions must indicate which statistical method was used for the analysis. Either in the figure caption or in the text, the full values of the statistical criteria must be provided, not just the p-value.
2. The manuscript presents percentage values for the changes in various assessed parameters. Since a semi-quantitative method was used, this raises a number of questions. Was it experimentally proven that the amount of protein loaded on the gel falls within the linear range for the antibodies and detection system used? The methods do not specify the antibody dilutions used; this needs to be added. Was it proven that the level of the normalization protein (actin) does not change? In the western blot results, it is also advisable to show individual data points on the graphs so that it is clear on how many samples the conclusions are based.

Author Response

We thank the reviewer for the careful evaluation of our manuscript and for the constructive comments.

Regarding the inclusion of oxidative stress-related results in the manuscript, these were included because they provide complementary mechanistic insight into insulin signalling beyond the classical AKT/GSK-3β/TAU axis. Although these parameters have been less extensively studied in this specific context, they are directly linked to the signalling pathways analysed and therefore contribute to a more comprehensive understanding of insulin’s effects in SH-SY5Y cells. We agree, however, that their relevance was not sufficiently reflected in the original title, and the title has been revised accordingly to better represent the full scope of the study.

New title: Insulin Regulates AKT/GSK-3β Signaling, Tau Phosphorylation, and Redox Homeostasis in SH-SY5Y Neuroblastoma Cells

We also appreciate the detailed methodological and statistical comments. In the revised manuscript, a dedicated section describing the statistical analysis has been added, and all figure legends have been updated to clearly indicate the statistical tests used, as well as the corresponding statistical parameters.

We added a new point:

Statistical analyses were performed using GraphPad Prism (version 10). Data are presented as mean ± standard deviation (SD) of at least four independent biological replicates per group. Normality of data distribution was assessed using the Shapiro–Wilk test. For comparisons between two groups (control vs. insulin-treated cells), statistical significance was determined using an unpaired two-tailed Student’s t-test. A p value < 0.05 was considered statistically significant. Individual data points representing independent biological replicates are displayed in all quantification graphs to ensure transparency and visualization of biological variability.

Protein loading was optimized to ensure that signal detection remained within the linear range of the chemiluminescent system and antibody performance. Antibody dilutions have been specified in the Methods section as requested. β-actin was used as a loading control and was assessed across all experimental conditions, showing no significant variation between groups, thereby supporting its suitability for normalization; this statement has been explicitly included in the revised manuscript. Finally, individual data points have been incorporated into all quantitative graphs to improve transparency and allow visualization of biological variability and sample size.

We added in the point material y methods:

Cells were washed twice with ice-cold phosphate-buffered saline (PBS) and lysed in SDS sample buffer containing 0.125 M Tris-HCl (pH 6.8), 2% SDS, 0.5% β-mercaptoethanol, 1% bromophenol blue, and 19% glycerol. Protein concentrations were determined using the modified Lowry method.
Equal amounts of protein (20–40 μg) were separated by SDS-PAGE and transferred onto nitrocellulose membranes using standard procedures. Membranes were blocked with 5% non-fat dry milk in Tris-buffered saline containing 0.05% Tween-20 (TBS-T) for 1 h at room temperature and incubated overnight at 4°C with the corresponding primary antibodies. Exposure times were adjusted to ensure signal acquisition within the linear range of detection. After washing with TBS-T, membranes were incubated for 1 h at room temperature with horseradish peroxidase-conjugated goat anti-mouse or goat anti-rabbit secondary antibodies. Immunoreactive bands were visualized using an enhanced chemiluminescence (ECL) detection system and quantified by densitometric analysis using Bio-Rad image analysis software. Protein expression levels were normalized to β-actin.
The following primary antibodies were used: AKT (Cat. No. GRW10110, GenoChem World) (1:1000), phospho-AKT (Cat. No. 07-789, Millipore) (1:1000), NRF2 (Cat. No. SAB5700720, Sigma-Aldrich) (1:1000), GSK-3β (Cat. No. GRW10122, GenoChem World) (1:1000), phospho-GSK-3β (Ser9) (Cat. No. ab75814, Abcam) (1:1000), total TAU (Cat. No. GRW10158, GenoChem World) (1:1000), phospho-TAU (Ser396) (Cat. No. A34931, Antibodies.com) (1:1000), Cu/Zn-SOD (Cat. No. SAB5200083, Sigma-Aldrich) (1:1000), Mn-SOD (Cat. No. SAB2102261, Sigma-Aldrich) (1:1000), and β-actin (Cat. No. A2228, Sigma-Aldrich) (1:5000). Secondary antibodies included goat anti-mouse IgG (Cat. No. ab205719, Abcam) (1:2000) or goat anti-rabbit IgG (Cat. No. ab97080, Abcam) (1:2000).