Subarachnoid hemorrhage (SAH) is a severe subtype of stroke caused mainly by ruptured aneurysms. The disease is characterized by high morbidity and mortality and is increasingly more common in young adults. Survivors, often face a myriad of long-term challenges, including issues incise and separate the neck skin and muscles, exposing the external carotid artery (ECA), internal carotid artery (ICA), and their branches. The distal end of the ECA was ligated and cut, and then the common carotid artery and ICA were temporarily closed with an arterial clip. Subsequently, a blunt-ended 4-0 nylon monofilament was inserted into the ICA through a small incision, the arterial clip was released, and the filament was slowly advanced towards the skull until resistance was felt, indicating the filament had reached the junction of the anterior cerebral artery and the middle cerebral artery. The filament was then further advanced 4-5mm to perforate the artery and left in place for 10 seconds before being slowly withdrawn, thus inducing SAH. Afterward, the neck incision was disinfected and sutured, and the rats were allowed to recover on a warming blanket until the end of the surgery. Rats in the sham surgery group underwent the same surgical procedure, but the perforation step was not performed. At predetermined time points, samples of the entire cerebral cortex from the model side of the experimental rats were collected. Initially, these samples were rinsed with 0. 9% saline to remove blood and connective tissue, followed by drying off surface liquid. Representative photos of the bottom of the rat brain in different groups are shown in Supplementary Figure 1. Subsequently, the samples were rapidly frozen in liquid nitrogen and stored in sterile preservation tubes at -80°C for subsequent transcriptomic and metabolomic analyses. We utilized Trizol reagent (Takara Biomedical Technology, Beijing, China) to extract total RNA from cerebral cortex samples and assessed the integrity and purity of RNA using NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE, USA). Approximately 1μg of total RNA per sample was used to construct, purify, and pool the cDNA libraries. These libraries were then sequenced on the Illumina HiSeq2500 platform (Bemec Biotechnology Co. , Ltd. , Wuhan, China). The entire RNA-Seq analysis process was managed by Biomarker Technologies (Beijing). Raw data (in fastq format) were processed to remove adapter-containing reads and low-quality reads based on Q20 (base error rate ≤1%) to ensure clean and high-quality data. The screening of differentially expressed genes (DEGs) was performed on the BMKCloud platform (http: //www. biocloud. net/), using an adjusted p-value for false discovery rate (FDR), with p 1. Finally, the hypergeometric distribution test is used for enrichment analysis of differential metabolites in KEGG pathways, utilizing MetaboAnalyst (https: //www. metaboanalyst. ca/, version 5. 0) to identify related metabolic pathways. In this study, we integrate transcriptomics and metabolomics data to perform joint pathway analysis through MetaboAnalyst5. 0, aiming to obtain enriched maps of metabolic pathways. Additionally, the STRING database and the Cytoscape plugin CytoHubba are used to predict protein-protein interaction (PPI) networks and the top 15 central hub genes are identified. Furthermore, Spearman's correlation analysis is employed to explore the relationships between the selected genes and metabolites. The raw RNA-seq and metabolomics data will soon be uploaded to the public database -GEO and MetaboLights, respectively. 2. 7 Statistical analysis All data are presented as mean ± standard error of the mean (SEM). Statistical analyses are performed using GraphPad Prism 9. 0 (GraphPad Software, USA). Student's t-test (two-tailed) or two-way analysis of variance (ANOVA) is appropriately used for comparisons between different groups. P-value 0. 6, suggesting the model is stable and reliable. Through 200 iterations of permutation testing, the results indicate that the R² value is greater than Q², and the intercept of Q² with the y-axis is less than 0. These results hint that the OPLS-DA model is neither overfitting nor underfitting, demonstrating stability and good predictive capability, effectively explaining the differences between the two groups of samples (Figure 1A). Using the OPLS-DA model, differentially expressed metabolites (DEMs) are selected based on the combined criteria of P value 1. Compared with the sham surgery group, SAH found 540 DEMs on day 1; Compared with SAH 1d, SAH 7d discovered 254 DEMs, totaling 136 DEMs (Figure 1B, Supplementary Table 1). Utilizing metaboAnalyst5. 0, we analyze 136 DEMs and their metabolic pathways (Figure 1C，Supplementary Table 2), discovering that these DEMs are enriched in pathways such as folate biosynthesis, the pentose phosphate pathway, arachidonic acid metabolism, sphingolipid metabolism, cysteine and methionine metabolism, pyruvate metabolism, arginine and proline metabolism and pyrimidine metabolism. Metabolites within these pathways may serve as potential biomarkers. Volcano plots of differentially expressed ions between diverse groups (sham group vs. SAH 1d group, SAH 1d group vs. SAH 7d group) are shown in Figure D andFigure E, respectively. Additionally, Figure 2 shows the expression of the 10 DEMs that may have the highest disease severity among the 136 DEMs. Before data analysis, we implemented a series of stringent quality control procedures, removing connector sequences and low-quality reads, resulting in approximately 2 million high-quality clean reads per sample. The proportions of quality scores Q20 and Q30 for each sample exceed 98% and 94%, respectively, with the GC content ranging between 47% to 49%, indicating the high quality of the data. The clean reads are aligned with the reference genome (Rattusₙorvegicus. Rnor₆. 0ᵣelease95. genome. fa), achieving an alignment efficiency ranging from 94. 99% to 95. 65%. This indicates a high utilization rate of the transcriptomic data (Supplementary Table 3). To compare gene expression levels across different samples, the FPKM (Fragments Per Kilobase of transcript per Million mapped reads) method is utilized. The results demonstrate that the distribution of gene expression levels is consistent across different samples (Supplementary Figure 3A, B). PCA is conducted on different samples, showing good separation between sample groups, indicating high intra-group correlation (Figure 3A). This is further confirmed through Spearman correlation analysis, validating good repeatability and reliability among different samples (Supplementary Figure 3C). The above results emphasize the robustness and consistency of the gene expression data, providing support for the reliability of the samples. A total of 1198 differentially expressed genes (DEGs, |Fold Change| ≥ 2 and FDR < 0. 05) are identified. Between the sham group and the SAH 1d group, 942 DEGs are identified, including 799 upregulated genes and 143 downregulated genes. Between the SAH 1d group and the SAH 7d group, 292 DEGs are identified, including 133 upregulated genes and 159 downregulated genes. Between the sham group and the SAH 7d group, 399 DEGs are identified, with both upregulated and downregulated genes totaling 390 and 9, respectively (Figure 3B). The overall distribution of DEGs is illustrated by volcano plots (Figure 3C). Hierarchical clustering analysis of the DEGs reveals distinct gene clusters and intergroup differences (Figure 3D). To elucidate the specific biological functions of the DEGs between the sham group and the SAH 1d group, as well as between the SAH 1d group and the SAH 7d group, we performed Gene Ontology (GO) enrichment analysis. Through GO enrichment analysis, we gain a deeper understanding of the biological processes (BPs), cellular components (CCs), and molecular functions (MFs) that may be altered at different time points post-SAH. In the SAH 1d group versus the Sham group, DEGs are enriched in various BPs, predominantly involving inflammatory and immune responses, regulation of angiogenesis, complement activation, cellular response to lipopolysaccharide, and defense response to viruses. In terms of CCs, the DEGs show enrichment in the extracellular space and matrix, MHC class II protein complex, MCM complex, and cell surface, which suggests their involvement in regulating these structural components. Regarding MFs, the DEGs are primarily implicated in pathways related to integrin binding, cytokine receptor activity, chemokine activity, and cell adhesion molecule binding (Figure 4A, B, C). Besides, in the SAH 7d group versus the SAH 1d group, DEGs are remarkably enriched in BPs related to the regulation of phagocytosis, engulfment, regulation of endothelial cell proliferation and cell adhesion, regulation of the inflammatory response, complement activation, and mediated synaptic pruning. These DEGs play a role in the negative regulation of inflammation and immunity, as well as positive regulation related to repair mechanisms. In terms of CCs, the DEGs are involved in pathways regulating the extracellular space, MHC class II protein complex, lysosome, collagen-containing extracellular matrix, and cell surface. For MFs, the DEGs are implicated in pathways modulating sulfiredoxin activity, cytokine activity, heme binding, and growth factor activity (Supplementary Figures 4A, B, C). Figure4Further analysis of the DEGs is conducted through KEGG pathway analysis. The results reveal that, compared to the sham group, DEGs at SAH 1d are enriched in multiple inflammatory and immune pathways including Complement and coagulation cascades, Cytokine-cytokine receptor interaction, Chemokine signaling pathway, and the majority of these DEGs were upregulated (Figure 4D). This analysis underscores the significant activation of inflammation and immune response pathways shortly after SAH, highlighting the body's immediate molecular response to injury and the potential targets for therapeutic intervention to modulate these responses. Compared to SAH 1d, the DEGs at SAH 7d are also dramatically enriched in inflammatory and immune pathways, containing Antigen processing and presentation, ECM-receptor interaction, and Th17 cell differentiation signaling pathways (Supplementary Figure 4D). However, the majority of these DEGs are downregulated. This shift in gene expression indicates a transition in the inflammatory and immune response from the acute phase following injury to a more controlled state, hinting at the body's attempt to restore homeostasis and initiate repair processes. To further probe into the interrelationship between DEGs and DEMs, a joint analysis is conducted on the sham group and the SAH 1d group. The number of common pathways revealed by KEGG analysis is presented in Figure 5C, and some of the pathways prominently enriched with both DEGs and DEMs are displayed in Figure 5A. The results indicate that these common pathways are tightly linked to inflammation, immune and metabolism-related pathways. Further pathway enrichment analysis of DEGs and DEMs is conducted using the MetaboAnalyst5. 0 platform, resulting in a metabolic pathway enrichment map (Figure 5B, Supplementary Table 4). The joint analysis results of the SAH 1d group and SAH 7d group are shown in Supplementary Figures 4E, F, G, and Supplementary Table 5. Comparing these results with the enrichment outcomes between the SAH 1d group and the SAH 7d group reveals that both sets of data are closely associated with pathways such as Arginine and proline metabolism, Arachidonic acid metabolism, Folate biosynthesis, Sphingolipid metabolism, Cysteine, and methionine metabolism, Pyrimidine metabolism and Inositol phosphate metabolism, which are consistent with those identified in Figure 1C. Figure5 Joint analysis of transcriptomics and metabolomics (A) Enrichment pathway analysis of differentially expressed genes and metabolites in KEGG, with circles representing the transcriptome and triangles representing the metabolome between Sham group and SAH 1d group. The size of the bubble represents the number of differential metabolites or genes, and the larger the number, the larger the dot. (B) Pathway enrichment analysis of differentially expressed genes and metabolites between Sham group and SAH 1d group. (C) Venn diagram of metabolic pathways enriched by metabolomics and transcriptomics between Sham group and SAH 1d group. (D) 15 core genes were selected from the protein interaction network of immune inflammation-related differentially expressed genes. (E) Correlation between core genes and specific metabolites. (*p<0. 05, **p<0. 01, ***p<0. 001) The metabolites and their related genes within these enriched pathways above are detailed in Table 1, followed by representative metabolic pathway diagrams (Figures 6 and7). In the comparison between the sham group and the SAH 1d group, metabolites such as Hydroxyproline, N (omega) -Hydroxyarginine, Thromboxane B2, Precursor Z, L-Homocystine, and dCDP show increased expression. In contrast, Prostaglandin A2, Leukotriene C4, Prostaglandin H2, 9 (S) -HETE, Formamidopyrimidine nucleoside triphosphate, Barbiturate, Biopterin, 7-carboxy-7-carbaguanine, 2-Oxo-4-methylthiobutanoic acid, and CMP experience a decrease in expression levels. However, when comparing the SAH 1d group with the SAH 7d group, the expression levels of these metabolites are opposite (Figures 6 and7). To further explore the potential between metabolites and genes, this on comparisons between the sham group and the SAH 1d group. DEGs related to are from the KEGG database and combined with DEMs associated with the above pathways. Spearman correlation network analysis is conducted (Supplementary Figure Supplementary Table and the results were using the Cytoscape (Supplementary Figure The above analysis reveals significant between these DEGs and DEMs. using protein-protein interaction (PPI) networks and the CytoHubba top 15 hub genes associated with immune inflammation are identified, including and (Figures Figure shows further correlation analysis between these hub genes and specific DEMs. These further the close between genes and specific DEMs, providing a of information that hemorrhage (SAH) is the common of caused by the of aneurysms. is an acute and The to increased of cerebral cerebral and. In the of SAH, the activation of brain and the of inflammatory play a role in the and brain In the study, the results from KEGG and GO analyses indicate that DEGs within immune and inflammatory pathways are have core genes related to and found that these genes are associated with endothelial cell and of the in metabolites are for the of SAH, but while understanding reveals a close between DEGs and specific DEMs. This underscores the between and metabolomic in SAH, highlighting the importance of multiple to the of SAH and identify potential therapeutic demonstrate that the of SAH may be associated with in metabolic pathways related to arachidonic acid metabolism, cysteine and methionine metabolism, arginine and proline metabolism, folate biosynthesis, and metabolism. 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