{"paper_id":"0af476a7-d5e2-4620-9cff-87380d33ad05","body_text":"1\n1 Key genes and pathways in asparagine metabolism in \n2 Alzheimer’s Disease: a bioinformatics approach\n3 Xiaoqian Lan 1, Guangli Feng2, Qing Li3, Yuting Shi2, Shiyi Qin1, Lianmei Zhong2*\n4 1. Department of Neurology, The First Affiliated Hospital of Kunming Medical University, \n5 Kunming, China\n6 2. Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China\n7 3. Yunnan Key Laboratory of Stem Cell and Regenerative Medicine, School of Rehabilitation, \n8 Kunming Medical University, Kunming, China\n9 * Corresponding author\n10 E-mail: 13888967787@163.com (LZ)\n11 Abstract\n12 Background: Asparagine (Asn) metabolism is essential for maintaining cellular homeostasis and \n13 supporting neuronal energy demands. Recent studies have suggested its dysregulation may contribute \n14 to Alzheimer’s disease (AD) pathogenesis; however, the specific genes and regulatory mechanisms \n15 involved remain incompletely understood.\n16 Methods: Four publicly available microarray datasets (GSE5281, GSE29378, GSE36980, and \n17 GSE138260) were utilized to investigate genes with differential expression between control and AD \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n2\n18 samples. Asparagine metabolism-related genes (AMGs) were retrieved from the GeneCards database, \n19 and their intersection with DEGs yielded candidate asparagine metabolism-related differentially \n20 expressed genes (AMG-DEGs). Functional enrichment analysis (Gene Set Enrichment Analysis, Gene \n21 Ontology and Kyoto Encyclopedia of Genes and Genomes), protein–protein interaction (PPI) network \n22 analysis, and centrality scoring identified hub genes. Regulatory mechanisms were investigated \n23 through construction of competing endogenous RNA and transcription factor networks. Potential \n24 therapeutic compounds were predicted via drug–gene enrichment and evaluated using molecular \n25 docking simulations.\n26 Results: Thirty-nine AMG-DEGs were identified and found to be enriched in neurodevelopmental, \n27 synaptic transmission, and inflammatory signaling pathways. PPI analysis and centrality screening \n28 revealed seven hub genes (HPRT1, GAD2, TUBB3, GFAP, CD44, CCL2, and NFKBIA). Regulatory \n29 network analysis highlighted specific miRNAs, long non-coding RNAs, and transcription factors \n30 involved in their modulation. Drug screening and docking identified Bathocuproine disulfonate, DL-\n31 Mevalonic acid, and Phenethyl isothiocyanate as promising compounds with strong binding affinities \n32 to hub proteins.\n33 Conclusion: This study comprehensively maps the dysregulation of asparagine metabolism in \n34 Alzheimer’s disease and reveals a set of hub genes and regulatory elements potentially involved in \n35 disease progression. The predicted therapeutic compounds provide a foundation for further \n36 experimental validation and may contribute to the development of novel metabolism-targeted strategies \n37 for AD treatment. \n38 Keywords: Alzheimer’s disease; asparagine metabolism; drug screening; molecular docking; \n39 Bioinformatics\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n3\n40 1. Introduction\n41 Alzheimer’s disease (AD) is a chronic neurodegenerative condition marked by progressive \n42 cognitive impairment, memory deterioration, behavioral abnormalities, and eventual loss of \n43 autonomy(1, 2). Pathologically, AD is characterized by the deposition of extracellular β-amyloid (Aβ) \n44 plaques, the formation of intracellular neurofibrillary tangles consisting of hyperphosphorylated tau, \n45 and extensive synaptic dysfunction(3). Due to increasing global longevity, AD has become a leading \n46 cause of morbidity among older adults, with over 50 million cases reported worldwide—a number \n47 projected to triple by 2050(4, 5). This growing burden underscores the imperative for novel therapeutic \n48 strategies and effective preventive measures.\n49 Emerging evidence suggests that metabolic disturbances are key contributors to AD pathogenesis \n50 beyond classical amyloid and tau pathology(6). Patients with AD often exhibit systemic dysregulation \n51 of glucose utilization, lipid processing, and amino acid metabolism(7), which may trigger \n52 neuroinflammation, synaptic breakdown, and vascular impairment(8). These metabolic disruptions can \n53 modulate transcriptional programs through regulators such as NF-κB and Nrf2(9), and may also alter \n54 non-coding RNA networks, further exacerbating neuronal dysfunction and disease progression(10). \n55 Among multiple metabolic abnormalities, altered amino acid metabolism—especially involving \n56 asparagine (Asn)—has drawn increasing research attention. Asn, synthesized by asparagine synthetase \n57 and catabolized by asparaginase, is implicated in cellular immunity(11) and diverse neuronal functions, \n58 including bioenergetics, neurotransmitter regulation, and protein post-translational modification(12). \n59 Observations from both sporadic and autosomal-dominant AD brain tissues demonstrate disrupted Asn \n60 homeostasis, suggesting a common metabolic signature associated with disease progression(13). \n61 Clinical studies have shown that plasma Asn concentrations are markedly decreased in AD patients \n62 and positively correlate with neurodegeneration-associated markers such as neurofilament light chain, \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n4\n63 particularly among individuals with elevated cerebral Aβ load(14). Mechanistically, Asn can be \n64 converted to aspartate via transamination, thereby entering the tricarboxylic acid (TCA) cycle and \n65 contributing to cellular energy metabolism(15). Metabolomics studies have corroborated these \n66 findings, showing significantly decreased Asn levels in the plasma of AD patients, which may \n67 contribute to an energy-deficient and neurotoxic microenvironment(16). Asn is also essential for \n68 protein N-glycosylation, a key post-translational modification process. Depletion of Asn may disrupt \n69 glycosylation events(17); notably, aberrant glycosylation at the Asn368 site of tau protein has been \n70 associated with impaired synaptic signaling and reduced plasticity, potentially intensifying AD-related \n71 neuropathology(18). Although asparagine endopeptidase (AEP) does not participate directly in Asn \n72 metabolism, many of its substrate proteins include Asn residues, suggesting its activity may be \n73 modulated by Asn availability(19). AEP has been implicated in the proteolytic cleavage of both \n74 amyloid precursor protein and tau, leading to the generation of neurotoxic fragments(20, 21) and \n75 influences microglial activation(21), and inflammatory cascades that impair neuronal integrity and \n76 cognitive performance(22). Although existing studies support a link between altered Asn metabolism \n77 and AD, the molecular mechanisms underlying this association remain poorly understood. In \n78 particular, the regulatory interactions of asparagine metabolism-related genes (AMGs)—especially \n79 those involved in neuroinflammation and synaptic integrity—and their potential as therapeutic targets \n80 require in-depth, systematic investigation.\n81 This study employed bioinformatics approaches to systematically analyze the characteristics of \n82 Asn metabolic dysregulation in AD. By integrating multiple public databases, we successfully \n83 identified asparagine metabolism-related differentially expressed genes (AMG-DEGs). Furthermore, \n84 we elucidated how these genes participate in the molecular mechanisms underlying AD pathogenesis \n85 by regulating key biological processes, including neuroinflammation, synaptic dysfunction, and energy \n86 metabolism. Through functional enrichment analyses, such as Gene Set Enrichment Analysis (GSEA), \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n5\n87 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), we systematically \n88 characterized the biological functions of Asn metabolism-related genes. Additionally, by constructing \n89 protein-protein interaction (PPI) and gene regulatory networks, we identified several hub genes that \n90 play critical roles throughout the progression of AD. Notably, through drug screening and molecular \n91 docking simulations, we identified multiple high-affinity small molecules that target key pathways \n92 involved in Asn metabolism, providing significant theoretical insights and potential drug candidates \n93 for the development of novel AD therapeutic strategies based on Asn metabolism modulation.\n94 Fig 1. Research design flow chart.\n95 2. Materials and methods\n96 2.1 Datasets and preprocessing \n97 Microarray datasets associated with AD, including GSE5281, GSE29378, GSE36980, and \n98 GSE138260, ere accessed from the Gene Expression Omnibus (GEO) repository \n99 (https://www.ncbi.nlm.nih.gov/geo/). Corresponding platform annotation files were obtained to \n100 facilitate probe-to-gene symbol mapping. Clinical metadata including age, sex, and group assignment \n101 were also extracted. A list of AMGs was retrieved from the GeneCards human gene database \n102 (https://www.genecards.org/) by searching for the keyword 'asparagine metabolism'. Genes with a \n103 relevance score > 6 and that were protein-coding were selected for analysis. A total of 1,294 AMGs \n104 were compiled for subsequent analyses. Data preprocessing was performed on each dataset, including \n105 background correction, log2 transformation, and quantile normalization of the raw expression values. \n106 Gene-level analysis was performed by mapping probe IDs to gene symbols, with the average \n107 expression value applied when multiple probes corresponded to a single gene. The data utilized in this \n108 study were publicly accessible, and no experimental work was carried out by the authors.\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n6\n109 2.2 Identification of DEGs\n110 Expression data were processed in R version 4.4.1 using the limma and sva packages. Batch \n111 effects were first assessed via principal component analysis (PCA), which allowed for the identification \n112 of significant inter-dataset variability. Genes with an adjusted p-value < 0.05 and absolute log2 fold \n113 change (|log2FC|) > 0.585 were considered significant. The DEGs from all datasets were intersected \n114 to identify common AD-related genes. DEG visualization was performed using the pheatmap package \n115 for heatmap generation and ggplot2 for volcano plot visualization. Venn diagram analyses were \n116 conducted using the VennDiagram package to intersect DEGs with the list of AMGs, ultimately \n117 identifying 39 candidate AMG-DEGs.\n118 2.3 Functional enrichment analyses: GSEA, GO and KEGG.\n119 GSEA was performed using the clusterProfiler package in R, based on the reference gene set \n120 collections c5.go.Hs.symbols.gmt. The DEGs were ranked by their log2FC, and enrichment scores \n121 were computed for each gene set using the GSEA function. A threshold of an adjusted p-value < 0.05 \n122 was considered statistically significant.\n123 GO and KEGG pathway enrichment analyses were performed using the “clusterProfiler” R \n124 package to explore cellular components (CC), molecular functions (MF), biological processes (BP), \n125 and signaling pathways associated with the intersecting AMG-DEGs. GO and KEGG enrichment \n126 analyses were conducted with the enrichGO and enrichKEGG functions, respectively, using the \n127 parameters: p-value < 0.05 and adjusted p-value < 0.05 as the significance thresholds. \n128 2.4 Construction of the PPI network\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n7\n129 To explore the interactions among the 39 AMG-DEGs, a PPI network was generated using the \n130 Retrieval of Interacting Genes (STRING) database (https://string-db.org/) for gene interaction \n131 retrieval, with a confidence score cutoff > 0.4. Unrelated nodes were removed to improve network \n132 clarity. The network was visualized using Cytoscape software (version 3.10.0), and hub modules were \n133 identified using the MCODE plugin with the following parameters: K-core = 2, node score cutoff = \n134 0.2, degree cutoff = 2 and maximum depth = 100.\n135 2.5 Hub gene identification and functional interaction analysis\n136 The cytoHubba plugin in Cytoscape was employed to identify core AMG-DEGs using the \n137 Maximal Clique Centrality algorithm. Additionally, Gene Multiple Association Network Integration \n138 Algorithm (GeneMANIA) (http://www.genemania.org/) was used to construct gene co-expression \n139 networks, providing insight into potential functional relationships between hub genes.\n140 2.6 Construction of the competing endogenous RNA (ceRNA) regulatory \n141 network\n142 MiRNA-mRNA interactions targeting hub genes were predicted using multiple databases, \n143 including miRanda (http://www.microrna.org/), miRTarBase (http://mirtarbase.cuhk.edu.cn/), miRDB \n144 (http://mirdb.org/), and TargetScan (http://www.targetscan.org/). Long non-coding RNAs (lncRNAs)–\n145 miRNA interactions were obtained via SpongeScan (http://spongescan.rc.ufl.edu/). All predicted \n146 interactions were visualized in Cytoscape to generate the final ceRNA network, integrating lncRNA–\n147 miRNA and miRNA-mRNA regulatory axes.\n148 2.7 Transcription factor regulatory network construction\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n8\n149 Transcription factor (TF) regulating the identified hub genes were retrieved from the TRRUST \n150 database (https://www.grnpedia.org/trrust/). The TF-mRNA regulatory network was then visualized \n151 using Cytoscape to elucidate potential upstream regulatory mechanisms.\n152 2.8 Drug enrichment analysis\n153 Candidate drugs targeting the hub genes were identified from the Drug-Gene Interaction Database \n154 (DGIdb, https://www.dgidb.org/). Gene–compound association enrichment was performed using Drug \n155 Signatures Database (DSigDB) (https://maayanlab.cloud/DSigDB/), and results were visualized using \n156 the enrichplot package in R.\n157 2.9 Molecular docking\n158 Molecular docking was employed to assess the binding affinity between predicted small-molecule \n159 compounds and their target proteins. The top five candidate drugs—ranked by adjusted p-values from \n160 drug enrichment analysis—included DL-Mevalonic acid (MVA), Bathocuproine disulfonate (BCS), \n161 Phenethyl isothiocyanate (PEITC), MELAMINE, and CHLOROBENZENE. Corresponding protein \n162 structures were downloaded from the RCSB Protein Data Bank (PDB, https://www.rcsb.org/) for CD44 \n163 (PDB ID: 1UUH), CCL2 (PDB ID: 4USP), and TUBB3 (PDB ID: 5IJ0). The protein structure of \n164 NFKBIA was predicted using the AlphaFold Protein Structure Database \n165 (https://www.alphafold.ebi.ac.uk/entry/P25963). 3D molecular docking simulations were performed \n166 with CB-Dock2 (https://cadd.labshare.cn/cb-dock2/php/index.php), and Vina score (binding energy ≤ \n167 −5.0 kcal/mol) were used to prioritize ligand–receptor interactions.\n168 3. Results\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n9\n169 3.1 Identification of AMG-DEGs in AD\n170 To identify candidate genes associated with asparagine metabolism in AD, we analyzed four \n171 publicly available microarray datasets (GSE5281, GSE29378, GSE36980, and GSE138260). Clinical \n172 information including age, sex, and sample group distributions for each dataset is summarized in Table \n173 1. PCA was performed to evaluate batch effects. Before correction, samples clustered separately by \n174 dataset, indicating strong batch effects (Fig 2A). After batch effect adjustment, the samples showed \n175 improved clustering consistency (Fig 2B), confirming effective normalization. Differential expression \n176 analysis revealed a total of 363 DEGs between AD and control samples, with 156 genes significantly \n177 upregulated and 207 downregulated (Fig 2C, S1 Fig). A Venn diagram analysis identified 39 genes \n178 overlapping between DEGs and AMGs (Fig 2D). The expression patterns of these 39 overlapping \n179 genes were visualized using a heatmap, which demonstrated distinct clustering between control and \n180 AD groups (Fig 2E). The complete list of these AMG-DEGs is provided in S1 Table.\n181 Table 1 Microarrays datasets clinical characteristics.\nDataset GSE5281 GSE29378 GSE36980 GSE138260\nGroups Control AD Control AD Control AD Control AD\nNumbe\nr\n74 87 32 31 47 33 19 17\nAge\n73.30±18.3\n5\n79.53±6.8\n4\n81.65±6.7\n7\n76.64±8.9\n6\n78.09±9.3\n5\n91.70±5.8\n3\n64.31±17.1\n5\n79.82±9.3\n4\nMale 53 50 22 16 22 15 9 7\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n10\nFemale 21 37 10 15 25 18 9 10\n182\n183 Fig 2. Expression profile analysis of overlapping genes between DEGs and AMGs.\n184 (A–B) PCA for batch effect assessment: (A) Before batch correction; (B) After batch correction. \n185 (C) Volcano plot displaying the results of DEGs analysis in AD, with red and blue dots representing \n186 upregulated and downregulated genes, respectively. (D) Venn diagram illustrating the intersection \n187 between DEGs and AMGs. (E) Heatmap of AMG-DEGs, with red and blue representing high and low \n188 expression levels, respectively.\n189 3.2 Functional enrichment analyses \n190 GSEA revealed that in control samples, energy production and neurotransmission-related \n191 pathways were enriched, including ATP synthesis coupled electron transport, GABA ergic synapse, \n192 inner mitochondrial membrane protein complex, mitochondrial protein containing complex and \n193 synaptic vesicle membrane (Fig 3A, S2 Table). In contrast, AD samples exhibited enrichment in \n194 pathways associated with positive regulation of vasculature development, regulation of epithelial cell \n195 differentiation, regulation of vasculature development, collagen containing extracellular matrix and \n196 growth factor binding (Fig 3B, S2 Table).\n197 To further understand the biological roles of the 39 AMG-DEGs, we conducted GO and KEGG \n198 pathway enrichment analyses. The GO analysis revealed that these genes were significantly enriched \n199 in BP such as response to steroid hormones, regulation of neurogenesis, and nervous system \n200 development. For CC, the AMG-DEGs were found to be associated with the neuronal cell body, \n201 GABAergic synapses, and clathrin-coated vesicle membranes (Fig 3C-D, Table 2), emphasizing their \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n11\n202 involvement in maintaining neuronal function and synaptic communication. In terms of MF, the \n203 enriched terms included carbon-carbon lyase activity, carboxy-lyase activity, and pyridoxal phosphate \n204 (PLP) binding, all of which are essential for energy and neurotransmitter regulation (Fig 3C-D). KEGG \n205 pathway enrichment revealed that AMG-DEGs were significantly involved in pathways like \n206 GABAergic synapses (hsa04727), alanine, aspartate, and glutamate metabolism (hsa00250), and IL-\n207 17 signaling (hsa04657). Interestingly, pathways related to butanoate metabolism (hsa00650) and β-\n208 alanine metabolism (hsa00410) were found to be downregulated, while inflammation-related pathways, \n209 including IL-17 signaling and rheumatoid arthritis (hsa05323), were upregulated (Fig 3E-F).\n210 Fig 3. Functional enrichment analyses.\n211 (A-B) GSEA: (A) GO terms enriched in the control group; (B) GO terms enriched in the AD group; \n212 (C) GO enrichment bar plot showing the top 10 significantly enriched MF, CC, and BP. (D) GO \n213 enrichment bubble plot. (E) Circular plot of KEGG pathway enrichment analysis. (F) KEGG pathway \n214 enrichment bubble plot.\n215 Table 2. GO and KEGG enrichment analysis of AMG‐DEGs.\nTerm ID Description GeneRatio p.Value\nGO:0048545 response to steroid \nhormone\n8/39 3.67E-07BP\nGO:0043649 dicarboxylic acid \ncatabolic process\n3/39 5.42E-06\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n12\nGO:0050767 regulation of \nneurogenesis\n7/39 1.39E-05\nGO:0009065 glutamine family amino \nacid catabolic process\n3/39 2.05E-05\nGO:0016188 synaptic vesicle \nmaturation\n3/39 2.57E-05\nGO:0098982 GABA-ergic synapse 5/39 4.50E-07\nGO:0030665 clathrin-coated vesicle \nmembrane\n5/39 7.11E-06\nGO:0043025 neuronal cell body 7/39 3.99E-05\nGO:0030662 coated vesicle \nmembrane\n5/39 4.79E-05\nCC\nGO:0030136 clathrin-coated vesicle 5/39 6.41E-05\nGO:0016831 carboxy-lyase activity 3/39 4.94E-05MF\nGO:0016830 carbon-carbon lyase \nactivity\n3/39 1.68E-04\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n13\nGO:0030170 pyridoxal phosphate \nbinding\n3/39 2.21E-04\nGO:0070279 vitamin B6 binding 3/39 2.33E-04\nGO:0008503 benzodiazepine receptor \nactivity\n2/39 2.35E-04\nhsa04727 GABAergic synapse 4/34 4.02E-04\nhsa00250 Alanine, aspartate and \nglutamate metabolism\n3/34 4.08E-04\nhsa00430 Taurine and hypotaurine \nmetabolism\n2/34 2.01E-03\nhsa05120 Epithelial cell signaling \nin Helicobacter pylori \ninfection\n3/34 2.74E-03\nKEGG\nhsa00650 Butanoate metabolism 2/34 5.07E-03\n216 3.3 Construction of PPI network and module analysis\n217 A PPI network was constructed for the 39 AMG-DEGs using the STRING database with a \n218 confidence score threshold of >0.4. The network was visualized in Cytoscape, which revealed a \n219 network consisting of 34 nodes and 78 edges, with 15 upregulated genes highlighted in pink and 19 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n14\n220 downregulated genes in green (Fig 4A). The MCODE plugin was used to identify key network modules, \n221 which revealed two significant clusters. Module 1 contained 7 hub genes and 21 interactions, which \n222 were primarily enriched in genes related to GABAergic neurotransmission, such as SST, GAD1, and \n223 GAD2. This suggests a central role for these genes in neurotransmitter regulation (Fig 4B). Module 2 \n224 was composed of immune-related genes, including CCL2, CXCR4, and CD44, which are implicated in \n225 immune response and inflammatory signaling (Fig 4C). These results highlight the involvement of \n226 AMG-DEGs in both neurotransmission and immune regulation, suggesting that disrupted pathways in \n227 AD may affect both neural and inflammatory processes.\n228 Fig 4. PPI network of AMG-DEGs and functional subclusters.\n229 (A) PPI network constructed from AMG-DEGs. (B-C) Subclusters extracted from the PPI network. \n230 Red nodes indicate upregulated genes, green nodes indicate downregulated genes.\n231 3.4 Identification and characterization of core AMG-DEGs\n232 Using the UpSetR package and 10 centrality metrics in the cytoHubba plugin, we identified the \n233 top 20 genes per metric and obtained the intersection, resulting in 7 consensus hub genes: GFAP, CCL2, \n234 NFKBIA, TUBB3, GAD2, CD44, and HPRT1 (Fig 5A, S3 Table). Table 3 summarizes their full names \n235 and functional annotations. GeneMANIA analysis of these hub genes revealed a complex co-\n236 expression network dominated by co-expression interactions (80.85%), with co-localization \n237 relationships accounting for the remaining 19.15% (Fig 5B). Key pathways identified as significantly \n238 enriched included responses to bacterial molecules and lipopolysaccharides, regulation of leukocyte \n239 cell-cell adhesion, cellular reactions to biotic stimuli, as well as negative regulation of apoptotic \n240 signaling, among others. \n241 Fig 5. Identification of key hub genes among AMG - DEGs.\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n15\n242 (A) UpSet plot showing the intersection of seven hub genes identified by 10 different computational \n243 algorithms. (B) GeneMANIA analysis of core AMG-DEGs and their co-expressed genes.\n244 Table 3. The details of the core AMG-DEGs.\nGene \nsymbol\nFull name Function of genea\nGFAP Glial \nFibrillary \nAcidic \nProtein\nA class-III intermediate filament serves as a cell-specific \nmarker, distinguishing astrocytes from other glial cells during \ncentral nervous system development.\nGAD2 Glutamate \nDecarboxylas\ne 2\nCatalyzes the production of GABA.\nCD44 CD44 \nMolecule (IN \nBlood Group)\nCD44 plays a key role in immune regulation, cell adhesion \nand migration, and signal transduction.\nCCL2 C-C Motif \nChemokine \nLigand 2\nThe CCL2 gene encodes a protein that acts as a ligand for the \nCCR2 receptor. By activating CCR2, it triggers calcium ion \ninflux and chemotactic responses, specifically recruiting \nmonocytes and basophils (but not neutrophils or eosinophils). \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n16\nIt may play a role in atherosclerosis by promoting monocyte \nmigration into arterial walls.\nNFKBI\nA\nNFKB \nInhibitor \nAlpha\nThe NFKBIA gene encodes a protein that inhibits NF-κB \ndimeric complexes (e.g., RELA/p65 and NFKB1/p50) by \nmasking their nuclear localization signals, trapping them in \nthe cytoplasm. Upon immune or inflammatory stimulation, \nNFKBIA is phosphorylated and degraded, releasing NF-κB to \ntranslocate into the nucleus and activate target gene \ntranscription.\nHPRT1 Hypoxanthin\ne \nPhosphoribos\nyltransferase \n1\nCatalyzes the conversion of guanine to guanosine \nmonophosphate and hypoxanthine to inosine monophosphate. \nTransfers the 5-phosphoribosyl group from 5-\nphosphoribosylpyrophosphate to the purine. Essential for \npurine nucleotide synthesis via the purine salvage pathway.\nTUBB3 Tubulin Beta \n3 Class III\nThe TUBB3 gene encodes β-tubulin, a core component of \nmicrotubules that regulates dynamic assembly (GTP-bound \nstate promotes growth while GDP-bound triggers \ndisassembly). It is essential for axon guidance and \nmaintenance by modulating microtubule dynamics (e.g., \ndorsal root ganglion axon projection) and mediates axon \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n17\nrepulsion through interaction with the Netrin-1/UNC5C \nsignaling pathway.\n245 aGene functional annotations were obtained from GeneCards (https://www.genecards.org/).\n246 3.5 Construction of a ceRNA regulatory network\n247 To further elucidate the post-transcriptional regulatory mechanisms associated with AD, we \n248 constructed a ceRNA regulatory network (Fig 6A). In this network, HPRT1, CD44, and CCL2 emerged \n249 as central nodes, suggesting their potential roles in asparagine metabolism-related ceRNA regulation. \n250 lncRNAs including RP4-539M6.22, CITF22-1A6.3, LA16c-306A4.2, and SNHG14 were predicted to \n251 regulate the expression of HPRT1 mRNA by competitively binding to hsa-miR-130a-3p. Similarly, \n252 CTC-459F4.1 was found to potentially modulate HPRT1 through competition for hsa-miR-576-5p. In \n253 the regulation of CCL2, lncRNAs LINC01043, GNG12-AS1, and RP3-470B24.5 were identified as \n254 ceRNAs via their interaction with hsa-miR-1-3p. Moreover, lncRNAs GS1-251I9.3, CTC-457E21.1, \n255 and RP11-486P11.1 were predicted to regulate CD44 expression through competition for hsa-miR-\n256 130b-5p. These lncRNA-miRNA-mRNA regulatory axes may play critical roles in the pathogenesis of \n257 AD.\n258 3.6 Construction of a transcription factor regulatory network\n259 A transcriptional regulatory network consisting of 9 nodes and 12 interactions was established \n260 based on known transcription factor (TF)-target relationships. Our analysis revealed that CCL2, a key \n261 pro-inflammatory cytokine, is regulated by multiple TFs, including NFKB1, STAT3, SP1, NFIC, and \n262 RELA. Likewise, the expression of GFAP is modulated by NFKB1, STAT3, NFIC, and RELA. NFKBIA \n263 was found to be jointly regulated by NFKB1 and RELA, while CD44 is transcriptionally controlled by \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n18\n264 SP1. These results highlight NFKB1, STAT3, SP1, NFIC, and RELA as core transcriptional regulators \n265 of AMG-DEGs in AD (Fig 6B).\n266 Fig 6. Regulatory networks of ceRNAs and transcription factors.\n267 (A) ceRNA regulatory network: green hexagons represent miRNAs, blue rhomboids represent \n268 lncRNAs, and red circles represent mRNAs. (B) TF regulatory network: green rectangles represent TF, \n269 red ellipses represent target genes.\n270 3.7 Identification of potential therapeutic compounds\n271 To explore therapeutic strategies targeting key AMG-DEGs, we conducted drug enrichment \n272 analysis. The top candidate compounds with the highest statistical significance included MVA, BCS, \n273 PEITC, MELAMINE, and CHLOROBENZENE (Fig 7A). A drug-gene interaction network was \n274 constructed to further elucidate the molecular mechanisms underlying these compounds (Fig 7B). The \n275 network revealed potential interactions between the identified compounds and the hub genes, \n276 suggesting that these molecules may exert therapeutic effects by modulating inflammation- or \n277 neurofunction-related pathways involved in AD pathogenesis.\n278 Fig 7. Drug enrichment analysis and gene-drug interaction network.\n279 (A) Bar plot of drug enrichment results. (B) Drug-gene interaction network illustrating the potential \n280 associations between identified drugs and target genes.\n281 3.8 Molecular docking analysis\n282 To validate the therapeutic potential of the candidate compounds, molecular docking analysis was \n283 performed using CB-Dock, and the binding affinities were evaluated using Vina scores (Table 4, Fig \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n19\n284 8). Among the compounds, BCS exhibited the strongest binding affinity, particularly with CD44, \n285 showing a Vina score of -9.3 kcal/mol (Fig 8C), outperforming MVA (-5.7 kcal/mol, Fig 8A). \n286 Additionally, BCS demonstrated high binding affinities with CCL2 and NFKBIA (Vina scores: -7.6 \n287 and -7.1 kcal/mol, respectively; Fig 8B, D), supporting its potential as a multi-target modulator. PEITC \n288 showed a favorable binding affinity to TUBB3 (-6.2 kcal/mol) with a relatively large cavity volume \n289 (1608 ų, Fig 8E), although its interaction with CCL2 and NFKBIA was relatively weak (Vina scores: \n290 -4.2 and -4.4 kcal/mol, respectively; S2 Fig). In contrast, MELAMINE and CHLOROBENZENE \n291 exhibited poor binding capacities across all docking analyses, with the highest Vina scores of -4.2 and \n292 -3.8 kcal/mol, respectively, suggesting limited therapeutic potential (S2 Fig).\n293 Fig 8. Molecular docking results of small-molecule compounds with target proteins.\n294 (A) Molecular docking of DL-Mevalonic acid with CD44 protein. (B) Docking result of Bathocuproine \n295 disulfonate with NFKBIA protein. (C) Docking result of Bathocuproine disulfonate with CD44 protein. \n296 (D) Docking result of Bathocuproine disulfonate with CCL2 protein. (E) Docking result of Phenethyl \n297 isothiocyanate with TUBB3 protein.\n298 Table 4. Binding affinity and pocket volume of drug-protein complexes identified by molecular \n299 docking.\nDrug Protein Vina score \n(kcal/mol)\nCavity volume \n(ų)\nDL-Mevalonic acid CD44 -5.7 121\nDL-Mevalonic acid CCL2 -4.1 82\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n20\nDL-Mevalonic acid NFKBIA -3.7 285\nBathocuproine \ndisulfonate\nCD44 -9.3 308\nBathocuproine \ndisulfonate\nCCL2 -7.6 102\nBathocuproine \ndisulfonate\nNFKBIA -7.1 72\nPhenethyl \nisothiocyanate\nCCL2 -4.2 107\nPhenethyl \nisothiocyanate\nNFKBIA -4.4 72\nPhenethyl \nisothiocyanate\nTUBB3 -6.2 1608\nMELAMINE CCL2 -4.2 107\nMELAMINE NFKBIA -4.2 285\nCHLOROBENZENE CCL2 -3.5 102\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n21\nCHLOROBENZENE NFKBIA -3.8 72\n300\n301 Discussion\n302 AD remains one of the most widespread neurodegenerative conditions globally, particularly \n303 affecting the aging population. Despite recent advances in AD diagnosis and treatment, the disease \n304 remains incurable, with current therapeutic strategies primarily aimed at alleviating symptoms rather \n305 than halting disease progression(23). Identifying new molecular targets involved in the core processes \n306 of AD is increasingly important. This study offers fresh insights into how Asn metabolism may \n307 contribute to AD progression. Our results emphasize the role of key genes and regulatory mechanisms \n308 that impact neuroinflammation, synaptic function, and energy balance in AD.\n309 GSEA in this study revealed impairments in energy metabolism, synaptic dysfunction, and \n310 vascular dysfunction of AD, which aligns with previous reports(24, 25). GO analysis further supported \n311 this, indicating that genes involved in Asn metabolism are primarily engaged in neurogenesis, nervous \n312 system development, and synaptic plasticity—processes tightly linked to cognitive decline in AD(26, \n313 27). In particular, the CC enrichment results showed significant AMG-DEGs localization in neuronal \n314 cell bodies, GABAergic synapses, and clathrin-coated vesicle membranes, indicating that asparagine \n315 metabolism may impact synaptic signaling and vesicle transport. Previous studies have demonstrated \n316 that GABAergic synapses are essential for inhibitory neural regulation(28), and disruptions in this \n317 system have been associated with synaptic abnormalities in AD and other neurodegenerative \n318 diseases(29, 30). Interestingly, MF enrichment analysis revealed a significant association with carbon-\n319 carbon lyase activity and pyridoxal phosphate binding. Pyridoxal phosphate is a crucial cofactor for \n320 neurotransmitter biosynthesis(31), indicating that alterations in Asn metabolism may disrupt \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n22\n321 neurotransmitter balance in AD. KEGG pathway analysis revealed a downregulation in butyrate and \n322 β-alanine metabolism pathways, while inflammation-related pathways, such as the IL-17 signaling \n323 pathway, were upregulated. This inverse regulation between metabolism and inflammation suggests \n324 an imbalance in the immune-metabolic axis in AD. Although previous studies demonstrated the \n325 dynamic interplay between neuroinflammation and disturbances in lipid, glucose, and amino acid \n326 metabolism in AD(32, 33), our study identified asparagine metabolism as a previously unrecognized \n327 key node within the immune-metabolic axis. This metabolite-specific mechanism not only supports the \n328 concept of the immune-metabolic axis but also provides further evidence for how discrete metabolic \n329 dysregulation can modulate neuroinflammation, thus refining the theoretical framework of immune-\n330 metabolism in AD.\n331 Among the seven hub genes identified in our study, GFAP, CD44, CCL2, and NFKBIA were \n332 upregulated, while GAD2, TUBB3, and HPRT1 were downregulated. These genes play pivotal roles in \n333 inflammation, synaptic stability, and neurotransmitter regulation, which are critical to AD \n334 pathophysiology. The upregulation of neuroinflammatory markers like GFAP(34), CD44(35), and \n335 CCL2(36) in AD corroborates findings from prior studies linking chronic inflammation to disease \n336 progression(35, 37-39). Upregulation of CCL2(40) and NFKBIA(41) is associated with the IL-17 \n337 signaling pathway, further validating the results from the KEGG pathway analysis. Furthermore, the \n338 downregulation of GAD2, which encodes glutamate decarboxylase responsible for GABA synthesis, \n339 is consistent with the known imbalance in excitatory-inhibitory neurotransmission in AD(42, 43). Our \n340 findings show that both TUBB3 and HPRT1 are downregulated in AD. Research has shown that \n341 downregulation of TUBB3 may destabilize microtubules, leading to Tau hyperphosphorylation and \n342 neuronal structural damage in AD(44). Additionally, consistent with extensive literature on energy \n343 metabolism imbalances in AD(45, 46), our results suggest that the downregulation of HPRT1 could \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n23\n344 impair purine metabolism, leading to insufficient energy supply and mitochondrial dysfunction in \n345 neurons.\n346 The regulatory networks constructed in this study also highlighted the complexity of post-\n347 transcriptional regulation in AD. The ceRNA network analysis suggested that multiple lncRNAs and \n348 miRNAs regulate hub genes such as HPRT1, CD44, and CCL2. These interactions may modulate the \n349 inflammatory and energy metabolism in AD. Although previous studies have shown that under \n350 ischemic conditions, SNHG14 exacerbates neuronal damage through the miR-182-5p/BINP3 signaling \n351 axis(47), our study identifies a novel mechanism in AD, wherein SNHG14 modulates the miR-130a-\n352 3p/HPRT1 pathway to influence purine metabolism. This differential regulatory pattern suggests that \n353 SNHG14 may act as a 'metabolic switch', maintaining energy homeostasis by regulating mitochondrial \n354 autophagy during acute injury, while modulating energy supply through the purine metabolism \n355 pathway in chronic neurodegenerative conditions. The TF network revealed the involvement of NFKB1, \n356 STAT3, and RELA in regulating these pathways, emphasizing their potential as therapeutic targets in \n357 modulating the immune response and synaptic plasticity. Our analysis revealed that CD44 is uniquely \n358 regulated by the transcription factor SP1, suggesting that CD44 may influence inflammatory responses \n359 through an independent pathway(48). Notably, NFKBIA, a negative regulator of NF-κB signaling, \n360 appears to act as a compensatory mechanism to suppress excessive inflammation. However, this \n361 inhibition may be insufficient to counterbalance the chronic neuroinflammation observed in AD \n362 progression(49).\n363 The potential therapeutic compounds identified through drug enrichment and molecular docking \n364 analysis, including BCS, MVA, and PEITC, show promising results in targeting key genes and \n365 pathways involved in AD. Molecular docking results revealed that Bisulfate-derived compound BCS \n366 exhibits a strong binding affinity to CD44, CCL2, and NFKBIA proteins, suggesting that BCS may \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n24\n367 alleviate the progression of AD through the inhibition of inflammatory pathways. Previous studies \n368 have indicated that excessive copper in Alzheimer's disease may exacerbate Aβ aggregation and \n369 oxidative stress(50). BCS, a copper chelator, has shown potential therapeutic value by restoring metal \n370 ion balance and may therefore provide beneficial effects in the context of AD(51, 52). MVA, a key \n371 intermediate in cholesterol biosynthesis, was significantly enriched in our analysis. Cholesterol \n372 metabolism dysregulation has been implicated in AD pathogenesis(53, 54). Statins, which target this \n373 pathway, have shown promise in AD prevention and treatment by reducing Aβ accumulation, \n374 suppressing inflammation, enhancing vascular function, and modulating Tau phosphorylation(55). \n375 Nonetheless, the optimal therapeutic window and specific statin formulations for AD remain under \n376 debate. Intriguingly, we found that MVA exhibits strong binding affinity to CD44 proteins, a finding \n377 not widely reported in previous studies. This suggests a potential link between MVA and CD44 \n378 signaling in AD, warranting further investigation. PEITC is a naturally occurring compound known \n379 for its antioxidant and anti-inflammatory properties(56). Our docking analysis indicated significant \n380 binding affinity between PEITC and TUBB3 proteins, suggesting that PEITC may help stabilize \n381 microtubule structures and mitigate neuronal damage associated with AD. Although the binding \n382 affinity of PEITC to CCL2 and NFKBIA proteins was relatively weaker, a considerable body of \n383 evidence suggests that PEITC not only inhibits Aβ aggregation in AD(57) ， but also exerts its \n384 therapeutic effects through antioxidant and anti-inflammatory mechanisms, thereby slowing the \n385 disease's progression(58, 59). However, despite promising preclinical findings, clinical studies \n386 investigating the efficacy of BCS, MVA, and PEITC in AD treatment are currently lacking, and further \n387 validation of their practical effects is required.\n388 This study comprehensively elucidated the potential involvement of asparagine metabolism in \n389 AD pathogenesis and identified several key molecular targets. Nevertheless, some limitations should \n390 be noted. First, our findings are primarily based on public datasets and bioinformatics predictions, \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n25\n391 requiring further validation through experiments. Second, the clinical translational potential of our \n392 molecular docking results needs to be further assessed. Future studies integrating single-cell \n393 sequencing, metabolomics, and experimental validation are warranted to clarify the role of asparagine \n394 metabolism in AD and evaluate its feasibility as a therapeutic target.\n395 Conclusion\n396 In conclusion, this study sheds light on the role of asparagine metabolism in AD. We identified \n397 key genes associated with neuroinflammation, synaptic function, and energy metabolism, suggesting \n398 they could be potential targets for therapy. Our analysis of regulatory networks also uncovered intricate \n399 interactions between miRNAs, lncRNAs, and transcription factors that influence these genes. \n400 Moreover, drug screening and molecular docking revealed several promising compounds that may \n401 offer therapeutic benefits for AD. These findings indicate that modulating asparagine metabolism could \n402 be a new strategy for AD treatment. However, further research is needed to validate these results \n403 experimentally.\n404 Acknowledgments\n405 We gratefully acknowledge all contributors for their valuable participation in this study.\n406 Funding Statement\n407 This study was supported by the Yunnan Science and Technology Program (Grant/Award \n408 Number: 202401AT070176).\n409 Abbreviations\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint \n\n26\n410 Aβ, β-amyloid; AD, Alzheimer’s disease; AEP, asparagine endopeptidase; AMGs, asparagine \n411 metabolism-related genes; AMG ‐DEGs, asparagine metabolism ‐differentially expressed genes; \n412 Asn, asparagine; BCS, Bathocuproine disulfonate; Betweenness, Betweenness Centrality; BottleNeck, \n413 BottleNeck Algorithm; BP, biological process; CC, cellular component; ceRNA, competing \n414 endogenous RNA; Closeness, Closeness Centrality; DEG, differentially expressed gene; Degree, \n415 Degree Centrality; DGIdb, Drug-Gene Interaction Database; DMNC, Density of Maximum \n416 Neighborhood Component; DSigDB, Drug Signatures Database; EcCentricity, Eccentricity Centrality; \n417 GeneMANIA, Gene Multiple Association Network Integration Algorithm; GO, Gene Ontology; \n418 KEGG, Kyoto Encyclopedia of Genes and Genomes; lncRNAs, Long non-coding RNAs; MCC, \n419 Maximal Clique Centrality; MF, molecular function; MNC, Maximum Neighborhood Component; \n420 MVA, DL-Mevalonic acid; PCA, principal component analysis; PEITC, Phenethyl isothiocyanate; PPI, \n421 protein-protein interaction; Radiality, Radiality Centrality; Stress, Stress Centrality; STRING, Search \n422 Tool for the Retrieval of Interacting Genes; TCA, tricarboxylic acid; TF, Transcription factor. \n423 References\n424 1. 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It is made \nThe copyright holder for this preprint (whichthis version posted April 28, 2025. ; https://doi.org/10.1101/2025.04.25.650586doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}