Environmentally friendly strategy for discovering the toxicity and mechanisms of nerve injury induced by acrylamide via network toxicology combined with molecular dynamics simulation

doi:10.21203/rs.3.rs-4553439/v1

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Environmentally friendly strategy for discovering the toxicity and mechanisms of nerve injury induced by acrylamide via network toxicology combined with molecular dynamics simulation

https://doi.org/10.21203/rs.3.rs-4553439/v1

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This study aimed to explore an efficient and low-cost toxicological analysis method for environmental pollutants by taking the mechanism of acrylamide induced nerve injury as an example. Potential targets of acrylamide were retrieved by combining the ChEMBL, Super-PRED, SwissTargetPrediction, Similarity ensemble approach, and STITCH databases. The GeneCards and OMIM databases were searched to identify the potential gene pool related to neurotoxicity and to identify intersecting genes. These genes were subsequently entered into the STRING database to construct a protein interaction network. GO and KEGG analyses were conducted by using the DAVID platform, and the molecular docking of intersection targets was assessed by using AutoDock 1.5.7 software. Finally, molecular dynamics simulation was used to verify the stability of the optimal binding model for molecular docking. After screening, 142 intersection targets were obtained, with TP53, PIK3CA, PIK3R1, PTK2, and GRB2 being the key targets of acrylamide-induced nerve injury. GO and KEGG enrichment analyses results showed that the mechanism of action is related mainly to the PI3K-Akt signaling pathway and microRNAs involved in cancer pathogenesis. Molecular docking confirmed that acrylamide was strongly bound to key targets. The stability of the interaction between acrylamide and TP53 was verified by molecular dynamics simulation. The proposed strategy not only reduces the initial experimental cost of identifying new pollutants and increases the amount of information on the toxic effects of environmental pollutants but also improves the efficiency of regulatory authorities in identifying environmental pollutant hazards.

Biological sciences/Computational biology and bioinformatics

Biological sciences/Ecology

Earth and environmental sciences/Ecology

Health sciences/Diseases

acrylamide

network toxicology

molecular docking

molecular dynamics simulation

nerve damage

Humans and animals are continuously exposed to environmental pollutants throughout their lives through manufacturing operations, product consumption, and drug use. Long-term exposure to environmental pollutants with different structures has adverse effects on humans and animals, such as reproductive disorders, cognitive defects, metabolic disorders, and tissue cancer^[1–3]. Acrylamide is a vinyl monomer that is widely used in water treatment, food packaging, and plastic products, and in industrial production, and it is one of the by-products of the Maillard reaction in food thermal processing. When acrylamide and its polymer are used, acrylamide monomers are released into the environment as vapor or particles, which can be easily adsorbed on particles in the air or washed into the water and soil environment by sedimentation and rain after entering the atmosphere. acrylamide in soil infiltrates groundwater easily; thus, the vast majority of acrylamide in the environment will eventually enter the water environment and cause potential exposure risk^[4–5]. Studies have shown that acrylamide can penetrate the blood–brain barrier and the placental barrier. acrylamide can be widely distributed throughout tissues and organs and in the blood after it is absorbed by the body, where it can eventually cause neurotoxicity, hepatotoxicity, reproductive toxicity, and other toxic effects^[6–7]. However, the molecular mechanism of acrylamide-induced nerve injury has not yet been elucidated, thus requiring further studies^[8–9].

In the face of the potentially harmful effects of environmental pollutants on human existence, regulatory authorities around the world require the integration of epidemiological, in vivo toxicology, and in vitro mechanistic data to provide the necessary information for hazard classification, labeling, and risk management of chemicals. Many regulatory agencies have established in vivo animal-based tests to detect various forms of chemical toxicity (e.g., carcinogenicity, reproductive toxicity, etc.). Problems such as limited information on the relevant mechanism, long screening times, violations of animal ethics, and high experimental costs mean that traditional low-throughput toxicology tests cannot meet the demand for toxicity screening of a large number of chemicals^[10–11]. Therefore, comprehensive, effective, and low-cost toxicological methods are needed to evaluate the potential health risks of the growing number of environmental pollutants.

With the development of technology in the field of toxicology, several in vitro experiments have emerged as replacements for animal experiments. Combining network toxicology with molecular dynamics simulation is a promising strategy. Network toxicology integrates multiple disciplines, such as network pharmacology, network biology, and computational biology, and combines drug action and biological networks to systematically analyze the interactions of target proteins and predict the molecular mechanism of diseases^[12]. Molecular docking is a mature molecular simulation method that explores the interaction between biological macromolecules and small molecules to predict their binding mode and binding affinity^[13]. Unlike molecular docking, which achieves static binding, molecular dynamics can simulate the flexible binding process of environmental pollutants and biological macromolecules, as well as the dynamic allosteric process of the complex, providing more comprehensive information about the interaction mechanism. The mutual complement and collaboration of network toxicology and molecular dynamics simulation ensures a comprehensive and effective assessment of the impact of environmental pollutants on organisms, revealing their potential toxic mechanisms^[14]. In this paper, the potential mechanism of acrylamide-induced neurotoxicity was investigated by using network toxicology and molecular dynamics simulations to provide an efficient and low-cost strategy for assessing the toxicity of environmental pollutants.

2.1 Acrylamide target acquisition

The standard structure of acrylamide and the SMILE number were determined by searching for “acrylamide” using the PubChem database. On the basis of this search, potential targets of acrylamide were retrieved from the ChEMBL database, Super-PRED database, SwissTargetPrediction (STP) database, Similarity ensemble approach (SEA) database, and STITCH database. The keyword “acrylamide” was used to limit the species to “Homo sapiens.” The structure of the search results was carefully examined, and their consistency was compared. The collected potential targets were consolidated and deduplicated, and the obtained target names were standardized using the UniProt database. These combined targets were subsequently used to build a database of potential acrylamide targets.

2.2 Selection of target networks associated with neurotoxicity

Through a comprehensive search of the GeneCards and OMIM databases, we identified relevant targets by searching for “nerve damage” (ND), “neurotoxic disease,” and “neurodegenerative disease.” To ensure that the obtained genes are strongly correlated with ND and toxic effects, we set the “score” threshold as the median and selected genes with a score higher than the median to establish a gene pool for the target of neuro-related diseases. In addition, we used Venn diagrams to screen for common potential targets between acrylamide targets and nerve injury targets and identified these intersecting targets as relevant targets for acrylamide-induced nerve injury.

2.3 Construction of protein–protein interaction networks and screening of core targets

The STRING database contains a large number of known or predicted protein–protein interaction (PPI) data. The above intersecting genes were entered into the STRING database. The analysis was performed by limiting the species to “Homo sapiens,” setting the “minimum required interaction score” to “HighConfidence > 0.9,” and removing discrete targets. These parameters ensure that our analysis of the active target proteins correspond to the target genes. STRING’s results were subsequently imported into Cytoscape software (version 3.8.2), a network biology visual analysis application that calculates parameters for each node in a network diagram and visualizes molecular connections. This process enables the calculation of the topological properties of the network nodes and edges, resulting in a PPI network diagram. The CytoHubba plug-in was subsequently used to determine the combination of the maximum cluster centrality (MCC), maximum neighborhood component (MNC), and degree algorithm to further determine the top 20 core genes in the network. The three algorithms will intersect with the core genes to increase the accuracy of the core targets.

2.4 Gene function and pathway enrichment analysis of target proteins

The data were collected using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) platform for GO analysis and KEGG pathway enrichment analysis to investigate the biological function of potential targets in acrylamide-induced neuro-related diseases. We also used DAVID to perform KEGG enrichment analysis of the core targets of acrylamide-induced neurotoxicity. This analysis aimed to further investigate the pathways involved in the core targets of ND to elucidate and highlight important signaling pathways involved in biological processes. Finally, we performed a visual analysis to effectively interpret and present the GO and KEGG analyses results. The top 10 KEGG pathways with high GO enrichment and the top 20 KEGG pathways with high counts were selected for further analysis. After that, the important GO terms and KEGG pathways were visualized by using Weisheng online mapping software.

2.5 Pathway enrichment analysis of the core genes

The 18 core targets of acrylamide-induced ND that were intersected by the three algorithms were subjected to KEGG pathway analysis via the DAVID platform, and the top 20 pathways were selected for further analysis and visualized using Weisheng online mapping software.

2.6 Molecular docking

Molecular docking methods were used to further analyze the intermolecular interactions between acrylamide and the core target proteins identified in this study by predicting binding patterns and affinities. acrylamide was selected as the ligand, and the key target selected after network topology analysis was chosen as the receptor. The PDB file of the core target 3D structure was obtained from the PDB database, and the SDF file of the acrylamide 3D structure from the PubChem database was downloaded and converted into a Mol2 format file by using Chem3D 21.0.0 software. AutoDock Vina software was used to complete the molecular pair analysis of acrylamide and the core targets, and PyMOL and Discovery Studio 4.5 were used to visualize the results. The binding energy of a component to a target and the number of hydrogen bonds that formed are generally believed to be important factors for evaluating molecular docking results. A low binding energy corresponds to a great number of hydrogen bonds, more stable binding, and a greater likelihood that the target will interact with the molecule.

2.7 Molecular dynamics simulation

The GROMACS2020.6 software package, AMBER99SB force field, and SPC water model were used for molecular dynamics simulation. Then, GAFF force fields were applied to the ligands. A neutral total charge of the system was ensured by adding the corresponding number of chloride ions to replace the water molecules to obtain a suitable solvent box. A balance of acceptor, ligand, and solvent was obtained by molecular dynamics simulations constrained by position and by constant number, volume, and temperature of particles and constant number, pressure, and temperature of particles. Finally, the root mean square deviation (RMSD) and the root mean square fluctuation (RMSF) of the atomic position of the system were analyzed. The RMSD can reflect the dispersion degree of the system to realize the stability of the complex, and the RMSF can reflect the change in an atom relative to the reference conception to indicate the flexibility of the atom.

3.1 Target prediction

A total of 179 samples were screened from the ChEMBL, Super-PRED, STP, SEA, and STITCH databases, targeting information corresponding to acrylamide. For the target screening of neuro-related diseases, 8,188 targets were screened from GeneCards, and 616 targets were screened from OMIM. A total of 8,628 targets and neuro-related diseases were obtained after the information from the two databases was summarized and deweighted. A Venn diagram was generated to screen the common potential targets between acrylamide and neuro-related diseases, and the intersecting genes were regarded as the key targets of neuro-related diseases caused by acrylamide. Finally, 142 crossover targets were obtained; these were regarded as important targets of acrylamide and ND (Fig. 1A).

3.2 PPI network construction and core target screening

We constructed a PPI network using the STRING database (Fig. 1B) and analyzed the topological properties of the network nodes, including degree, mediateness, and centrality, using Cytoscape. A visually optimized PPI network was generated (Fig. 1C), which included 71 nodes and 185 edges. The average node degree is 5.21, with 24 nodes having a degree value exceeding the average. These highly connected nodes may be important targets that are strongly correlated with ND. Through the intersection of the MCC, MNC, and degree algorithms, 18 core genes related to ND were identified (Fig. 2): JUN, KDR, PIK3CD, PIK3CA, PTK2, TP53, AKT2, EGFR, STAT1, PDGFRB, PIK3R1, JAK3, PIK3CB, PTPN11, PDGFRA, FYN, ERBB2, and GRB.

3.3 GO and KEGG enrichment analyses based on intersection targets

To further understand the biological processes, cellular components, and molecular functions of acrylamide in NDs, we used the DAVID platform to conduct GO and KEGG enrichment analyses of 142 intersecting genes. A total of 27 biological pathways, 127 cell component expression processes, 44 molecular function-related processes, and 115 KEGG pathways were obtained. The GO enrichment analysis revealed the top 10 GO enrichment items for BC, CC, and MF. The top 20 pathways were selected based on the KEGG-enriched pathways as potential pathways associated with ND, and the main pathways included the PI3K-Akt signaling pathway, prostate cancer, central carbon metabolism in cancer, the VEGF signaling pathway, and non-small cell lung cancer (Table 1). The visualization result of Weisheng online mapping software is presented as a bubble diagram (Fig. 3 and Fig. 4).

Table 1

KEGG pathway enrichment analysis of targets at the intersection of acrylamide and ND.
No.	ID	Term	Count	PValue
1	hsa04151	PI3K-Akt signaling pathway	25	1.10E-10
2	hsa05206	MicroRNAs in cancer	21	8.88E-09
3	hsa05417	Lipid and atherosclerosis	18	6.38E-09
4	hsa05161	Hepatitis B	17	7.21E-10
5	hsa05215	Prostate cancer	16	3.80E-12
6	hsa04210	Apoptosis	16	5.39E-10
7	hsa04072	Phospholipase D signaling pathway	16	1.78E-09
8	hsa04380	Osteoclast differentiation	15	4.76E-09
9	hsa05230	Central carbon metabolism in cancer	14	9.99E-12
10	hsa04722	Neurotrophin signaling pathway	14	9.25E-09
11	hsa04370	VEGF signaling pathway	13	2.09E-11
12	hsa05223	Non-small cell lung cancer	13	2.47E-10
13	hsa05220	Chronic myeloid leukemia	13	4.75E-10
14	hsa01521	EGFR tyrosine kinase inhibitor resistance	13	7.57E-10
15	hsa05210	Colorectal cancer	13	2.08E-09
16	hsa05235	PD-L1 expression and PD-1 checkpoint pathway in cancer	13	3.12E-09
17	hsa01522	Endocrine resistance	13	9.61E-09
18	hsa05214	Glioma	12	5.88E-09
19	hsa05212	Pancreatic cancer	12	6.80E-09
20	hsa05213	Endometrial cancer	11	5.88E-09

3.4 KEGG pathway analysis based on core targets

The 18 core targets of acrylamide-induced ND that were intersected by the three algorithms were subjected to pathway analysis via the DAVID platform. A total of 155 signaling pathways were enriched, and bubble maps were drawn (Fig. 5) The main pathways of acrylamide against the development of ND are integrated in Fig. 6.

3.5 Molecular docking

The interaction between acrylamide and the core target was further verified by molecular docking analysis. The analysis of the interconnection data revealed that the binding energies of acrylamide with TP53, PIK3CA, PIK3R1, PTK2, and GRB2 are low and that these proteins can form hydrogen bonds, indicating that acrylamide strongly binds to each core target and plays an important role in the molecular mechanism of the toxicology of ND induced by acrylamide. Images visualized using Discovery Studio 4.5 and PyMOL software visually demonstrate their conformational relationships (Fig. 7).

3.6 Molecular dynamics simulation

Molecular dynamics techniques were further used to simulate the stability and flexibility of the acrylamide and PIK3R1 complex, and the RMSD and RMSF of PIK3R1 and acrylamide were obtained for the proteins, as shown in Fig. 8. Compared with those in the first frame, PIK3R1 and acrylamide reached equilibrium at 10 ns in the 100 ns simulation, indicating that the system quickly reached a stable state without excessive fluctuations (Fig. 8A). The RMSF is in the range of 0.06–0.70 nm, and the RMSF of residue VAL828 is 0.1 nm, indicating the effect of hydrogen bonding on limiting VAL828 (Fig. 8B).

To explore the molecular mechanism of acrylamide-induced ND, we searched the ChEMBL, Super-PRED, STP, SEA, STITCH, GeneCards, and OMIM databases and identified 142 potential targets of ND associated with acrylamide. We subsequently used the STRING database and Cytoscape to screen 18 core targets, including TP53, PIK3CA, PIK3R1, PTK2, and GRB2, which are considered core targets of acrylamide-induced ND and have been associated with ND. We subsequently constructed the PPI network. Using the DAVID platform, we performed GO and KEGG enrichment analyses for the core targets. The results showed that acrylamide induced ND mainly through the PI3K-Akt and PLD signaling pathways. Molecular docking results showed that the five core target proteins could bind stably to acrylamide with a binding energy less than 0 kcal/mol.

The TP53 gene, which is also known as P53 and is located on chromosome 17, is an important tumor suppressor gene that prevents normal cells from becoming cancer cells and plays a crucial role in promoting cell apoptosis and inhibiting tumor formation. Previous studies showed that TP53 mutations exist in the somatic cells of diseased tissues in patients with various nerve injuries, such as nervous system tumors and malignant peripheral schwannomas^[15–16]. Qu Shuang reported that TP53 deletion was present in two newly treated patients with multiple myeloma with intracranial invasion and was a clinical manifestation of impaired central nervous system function^[17]. In addition, the TP53 subtype has been shown to modulate the toxic and protective effects of astrocytes on neurons. Abnormal expression of TP53 may be related to neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s syndrome^[18–19]. In human neuroblastoma cells, acrylamide exert cytotoxic effects by phosphorylating the P53 protein at Ser15^[20]. Molecular docking experiments showed that acrylamide could easily bind to the TP53 protein.

PIK3CA is an oncogene that is expressed in the brain, mammary gland, and intestine of normal humans and plays an important role in regulating somatic cell proliferation, differentiation, and other physiological functions. As a catalytic subunit of PI3K, PIK3CA plays a key role in the PI3K/AKT signaling pathway and protects against Aβ toxicity by inhibiting tyrosine phosphatase, thereby affecting the regulation of AD pathogenesis^[21]. According to relevant studies, the PI3KCA mutation is associated with peripheral neurolipomatosis^[22].

PI3K is located downstream of tyrosine kinase, G protein-coupled receptors, and GTPase and is involved in regulating cell metabolism, proliferation, and migration^[23]. In vivo, PI3KR1 and its gene product p85α inhibit the catalytic activity of PI3K in tumors and exert a tumor suppressive effect^[24]. In addition, PI3KR1 is underexpressed in most tumors, and its deletion may play a role in abnormal cell proliferation, increased invasion, and decreased apoptosis^[25]. According to relevant research, miR-153-3p causes insulin resistance in the brain through PIK3R1 and participates in the neurotoxicity induced by cigarette smoke^[26]. PIK3R1 has also been shown to be associated with the development of neurodegenerative diseases^[27].

The PTK2 protein, which is also known as FAK, is involved in cell adhesion and diffusion and is related to the normal apoptosis of cells. The upregulation of PTK2 expression, which includes high PTK2 protein expression and overactivation, occurs in almost all tumor tissues, such as common lung cancer, stomach cancer, colorectal cancer, uterine cancer, and melanoma tissues^[28]. Existing studies have shown that PTK2 is associated with axon growth^[29–30]. The PTK2B protein, which is the expression product of the PTK2B gene, may be a risk factor for AD. The expression level of PTK2B in the hippocampus of rats with Aβ-induced cognitive dysfunction is increased, accompanied by increased phosphorylation of the Aβ1–42 and Tau proteins^[31].

The GRB2 protein, which is also known as the ASH protein, is involved in EGF receptor-mediated signal transduction and is indirectly involved in insulin receptor-mediated signal transduction by binding to the Shc phosphorylated tyrosine. The GRB2 protein can also bind to the Shc-SOS complex and activate the Ras protein, thus participating in various cell development processes^[32–33]. GRB2-associated binding protein 1 has been shown to be critical for peripheral nerve myelination, and it regulates the formation of neuromuscular synapses in skeletal muscle^[34–35]. Studies have shown that GRB2-associated binder 2 gene knockout mice exhibit neural repair defects after nerve transection^[36].

Considering the results of GO and KEGG enrichment analyses, we found that several key signaling pathways play a role in acrylamide-induced neurotoxicity. For example, the PI3K-Akt signaling pathway plays a role in the expression of microRNAs in cancer, lipid and atherosclerosis, hepatitis B, and phospholipase D (PLD) signaling. The PI3K-Akt signaling pathway is activated by various types of cell stimulation or toxic damage and regulates basic cell functions such as transcription, translation, proliferation, growth, and survival^[37]. PI3K catalyzes the production of PIP3 on the cell membrane. PIP3 acts as a second messenger to activate Akt. Activated Akt controls key cellular processes by phosphorylating substrates involved in apoptosis, protein synthesis, metabolism, and the cell cycle^[38]. Studies have shown that nerve growth factor (NGF) promotes the formation of filamentous pseudopods and branches of axons through the PI3K-Akt signaling pathway^[39]. The PI3K-Akt signaling pathway has been shown to be associated with ND^[40–42]. Considering the regulatory effect of the PI3K-Akt signaling pathway and key targets such as TP53, PI3KCA, PIK3R1, RTK2, and GRB2 on nerve injury and combined with the findings of previous studies, acrylamide was found to regulate the PI3K-Akt signaling pathway^[43–44]. acrylamide may have a substantial effect on nerve injury by regulating the PI3K-AKT signaling pathway. Glioblastoma is the most common primary malignant tumor in the nervous system of humans and is formed by the malignant transformation of astrocytes^[45]. Studies have shown that microRNA-3938 inhibits glioblastoma by downregulating multiple oncogenes and upregulating p53, suggesting a close relationship between microRNAs and nerve injury^[46]. In addition to stimulating neuron differentiation and survival, NGF plays an important role in lipid metabolism and atherosclerosis progression^[47]. Moreover, studies have shown that acrylamide is closely related to the development of atherosclerosis^[48]. Current studies have indicated that hepatitis B can cause ND^[49]. PLDs are responsible for the production of the lipid second messenger phosphatidic acid (PA) in cells, and PAs are involved in basic cellular processes such as membrane transport, actin cytoskeleton remodeling, cell proliferation, and cell survival^[50]. Previous studies have shown that PLD is related to the stretch sensitivity of sensory nerve endings, which is crucial for maintaining the stretch reactivity of the primary end of the mechanical sensor of the muscle spindle^[51]. Moreover, the regulation of the PLD signaling pathway is closely related to nerve injury^[52].

Despite evidence indicating that acrylamide can cause neurological damage, the underlying mechanism of its toxicity remains unclear. acrylamide is widely used in a variety of industries, which is why residual acrylamide monomers enter environmental media such as water, soil, and the atmosphere through industrial wastewater and waste residue during production and polyacrylamide production. Smoking and high-temperature cooking processes, especially frying or baking high-starch food, can also produce acrylamide. In addition, acrylamide can be metabolized into its carcinogenic active metabolite, epoxide propanamide, by animals and humans. Long-term exposure to or ingestion of acrylamide may increase the risk of cancer.

In addition to revealing the molecular mechanism underlying the potential neurological damage caused by acrylamide, we also attempted to develop an effective and rapid strategy for screening and studying the toxicity of environmental pollutants. Traditional toxicological studies rely on animal models, and pathological and immunological experimental techniques are used to identify toxic effects of drugs and environmental factors. However, certain limitations exist, not least in dealing with potential chemical poisons that quickly emerge in the environment. Network toxicology involves describing the toxicological properties of research objects by constructing network models that can quickly construct network models of chemical-toxicity target effect pathway interactions and provide strong technical support for exploring the toxicity mechanism of chemical substances. In addition, molecular docking and molecular dynamics simulation techniques can explain the interactions between molecules in depth and visually explain the mechanism of interaction. These approaches have become important research methods for explaining biological mechanisms. Molecular docking and molecular dynamics techniques also provide important tools for predicting the binding types and interaction patterns of biomacromolecular complexes and provide useful references and theoretical support for further experiments. The use of network-based toxicology and molecular docking paradigms may expand the efficiency, depth, and prediction accuracy of toxicological screening and provide a new perspective for the assessment and mechanism prediction of a large number of understudied emergent environmental toxicants.

However, some limitations remain with regard to analyzing the mechanism of action of acrylamide in producing neurotoxicity based on the existing limited database. Therefore, further validation experiments are needed to confirm the results of network-based analysis.

In conclusion, our study presents a comprehensive investigation into the neurotoxicity of acrylamide by employing the strategy of network toxicology and molecular dynamics simulation. We identified 142 crossover targets, which were regarded as the important target of acrylamide and ND. TP53, PIK3CA, PIK3R1, PTK2 and GRB2 were the key targets of acrylamide-induced nerve damage. Integrating the key enrichment pathways, such as Prostate cancer, PLD signaling pathway, VEGF signaling pathway, and PI3K-Akt signaling pathway, our findings suggest that AA impacts on ND through pathways related to PI3K-Akt signaling pathway and PLD signaling pathway. Molecular docking showed that acrylamide was firmly bound to key targets. The stability of acrylamide and PIK3R1 binding was verified by molecular dynamics simulation. This study not only provides a molecular mechanism for the potential ND of acrylamide, also suggests an effective and rapid strategy for studying the toxicity of environmental pollutants.

Data availability

The dataset used in this study can be downloaded from the following public databases and the results are provided within the manuscript.

PubChem : https://pubchem.ncbi.nlm.nih.gov/

ChEMBL : https://www.ebi.ac.uk/chembl/

Super-PRED : https://prediction.charite.de/

SwissTargetPrediction : http://www.swisstargetprediction.ch/

Similarity ensemble approach (SEA): https://sea.bkslab.org/

STITCH : http://stitch.embl.de/cgi/

UniProt : https://www.uniprot.org/

GeneCards : https://www.genecards.org/

OMIM : https://www.omim.org/

STRING : https://cn.string-db.org/

DAVID : https://david.ncifcrf.gov/

RCSB Protein Data Bank : https://www.rcsb.org/

Acknowledgements

We appreciate funding support from Doctoral Foundation Project of Huanggang normal University (No.2019057); Huanggang Science and Technology Bureau (ZDXM20220023).

Author contributions

J.X.P. : Data curation, Formal analysis, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing; H.Y.Z. : Data curation, Formal analysis, Investigation, Validation, Writing – review & editing; Z.Y : Data curation, Software, Validation, Visualization; H.W.T. : Data curation, Formal analysis, Writing – original draft; Y.J.H. : Formal analysis; W. W. : Funding acquisition, Investigation, Resources, Supervision. W.S.Z. : Funding acquisition, Resources, Supervision.

Competing interests

The authors declare no competing interests.

Author information

Hubei Key Laboratory of Economic Forest Germplasm Improvement and Resources Comprehensive Utilization, Hubei Collaborative Innovation Center for the Characteristic Resources Exploitation of Dabie Mountains, Huanggang Normal University, Huangzhou 438000, China.

Xupeng Jin , Yuanzhi Huang , Yan Zhang , Wanting Hu , Jiahui Yu, Wei Wu, Shuzheng Wang

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Environmentally friendly strategy for discovering the toxicity and mechanisms of nerve injury induced by acrylamide via network toxicology combined with molecular dynamics simulation

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Version 1

Abstract

Figures

1 Introduction

2 Methods

2.1 Acrylamide target acquisition

2.2 Selection of target networks associated with neurotoxicity

2.3 Construction of protein–protein interaction networks and screening of core targets

2.4 Gene function and pathway enrichment analysis of target proteins

2.5 Pathway enrichment analysis of the core genes

2.6 Molecular docking

2.7 Molecular dynamics simulation

3 Results

3.1 Target prediction

3.2 PPI network construction and core target screening

3.3 GO and KEGG enrichment analyses based on intersection targets

3.4 KEGG pathway analysis based on core targets

3.5 Molecular docking

3.6 Molecular dynamics simulation

4 Discussion

5 Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1