Mechanistic Insights into the Effects of Astaxanthin on Asthma in Mice: A Combined Transcriptomic and Metabolomic Analysis

Mechanistic Insights into the Effects of Astaxanthin on Asthma in Mice: A Combined Transcriptomic and Metabolomic Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mechanistic Insights into the Effects of Astaxanthin on Asthma in Mice: A Combined Transcriptomic and Metabolomic Analysis Zi Hui Ma, Yuteng Ma, Hongquan Xie, Yue Li, Guoliang Wang, Haoyu Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6366020/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Asthma is a complex disease characterised by chronic airway inflammation and airway remodelling, and its pathogenesis involves a variety of factors such as inflammatory response, oxidative stress and immunomodulatory imbalance. Although existing treatments (e.g., glucocorticosteroids and β2 agonists) are effective in controlling symptoms, some patients still suffer from treatment resistance or drug side effects, so it is important to explore new therapeutic strategies and targets. In recent years, astaxanthin has received much attention for its potent antioxidant and anti-inflammatory activities. Astaxanthin is able to play a protective role in a variety of inflammatory, immune diseases by scavenging free radicals and inhibiting inflammatory pathways such as NF-κB. However, studies on astaxanthin in asthma are still relatively limited and mostly focus on a single mechanism using a single histological technique, which makes it difficult to comprehensively reveal the regulatory network of astaxanthin at the gene and metabolic levels. The aim of this study was to investigate the ameliorative effects of oral astaxanthin on asthma symptoms by integrating metabolomics and transcriptomics analyses, and to screen potential asthma-related biomarkers and therapeutic targets. Method Seventy-five female BALB/C mice (SPF grade, 6-8 weeks old) were divided into five groups on the basis of randomisation: the blank control group (NC), the model group (OVA), the dexamethasone group (DEX), the astaxanthin low-dose group (ASTA-L), and the astaxanthin high-dose group (ASTA-H), and the mice in the remaining four groups were injected intraperitoneally with 0.01% ovoacidin (OVA) and 0.01% ovoacidin (OVA) on days 0, 7, and 14, respectively. The asthma model was constructed by intraperitoneal injection of 0.01% ovalbumin on days 0, 7 and 14, and nebulisation of 2.5% ovalbumin every other day from day 21 onwards. dexamethasone and astaxanthin were administered to the DEX group, the ASTA-L group (25 mg/kg), and the ASTA-H group (50 mg/kg) by gavage 3 hours before each nebulisation. The body weight and food intake of mice in each group were observed weekly. After 4 weeks of continuous nebulisation, 5 mice in each group were anaesthetised and bronchoalveolar lavage was performed to collect the lavage fluid to calculate the total cell count. The remaining 10 eyes were blood sampled and killed, and serum interleukin 4 (IL-4), interleukin 6 (IL-6), interleukin 13 (IL-13), immunoglobulin E (IgE), superoxide dismutase (SOD) levels were measured, and the lung wet/dry ratios were calculated, and the lung tissues were subjected to histological staining by HE, MASSON, and PAS, and analyses of transcriptomics and nontarget metabolomics. Screening of differentially expressed genes(DEGs) and metabolites(DEGs), joint study to construct gene metabolic network to analyse core pathways. Result (1) Compared with the OVA group, the lung dry-to-wet ratio and the total number of cells in the bronchoalveolar lavage fluid were reduced in the ASTA-L and ASTA-H groups (P<0.001) ASTA-H group was more obvious. (2) HE, MASSON, and PAS staining analysis showed that compared with the OVA group, the airway wall inflammatory cell infiltration, pulmonary septal thickness, collagen deposition, and glycogen deposition area were improved in the ASTA-L and ASTA-H groups, but the improvement was more significant in the ASTA-H group compared with the ASTA-L group. (3) Serum levels of IL-4, IL-6, IL-13, and IgE were reduced and SOD activity was increased in the ASTA-H group compared with the OVA group (P<0.001). (4) Transcriptomics analysed 20 genes including Gstt1, Gstm1 and Adh1, and metabolomics analysed 8 metabolites including glyceraldehyde-1,3-bisphosphate. These genes and metabolites were mainly involved in key processes such as inflammatory response, oxidative stress and immune regulation. (5) Combined analyses further revealed that astaxanthin up-regulates Adh1 expression by activating the expression of glyceraldehyde-1,3-bisphosphate, and the up-regulation of Adh1 further contributes to the synthesis of retinyl acetate. Conclusion Astaxanthin attenuates airway inflammation, oxidative stress and immune stress in asthmatic mice. The mechanism may be related to the activation of glyceraldehyde-1,3-bisphosphate up-regulation of Adh1 expression contributing to the synthesis of retinyl acetate. astaxanthin asthma transcriptomics metabonomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Asthma a major common chronic non-communicable diseases in children and adults. Epidemiological studies indicate its global prevalence in this cohort is increasing by 50% per decade, especially in developing countries [1] . Asthma is a complex multifactorial disease with heterogeneous phenotypes of varying etiology and prognosis outcomes, with etiology mostly attributed to interactions between genetic susceptibility, host factors, and environmental exposures. Current asthma treatments mainly include inhaled corticosteroids. In children, oral or systemic corticosteroid use increases fracture risk in a dose-dependent manner. The medication can also impact on height in adulthood, as well as poorly controlling asthma [2] . Prolonged exposure to systemic corticosteroid use also increases osteoporosis and cardiovascular disease risks [3] . Importantly, disease symptoms in patients with mild to moderate allergic asthma can be controlled using combined inhaled corticosteroids and long-term acting β-agonists [4] . However, these medications only treat the symptoms, while underlying disease characteristics remain unaltered. Asthma involves many inflammatory mediators, and targeting a single mediator or receptor is not effective, so identifying new therapeutic agents is necessary. Astaxanthin is a lutein carotenoid found in various microorganisms and marine animals. Its potent antioxidant activity, which reduces oxidative/nitrative stress and protects cells/tissues from free radical damage, has excellent potential for clinical applications [5] . In terms of anti-inflammation processes, astaxanthin inhibits atopic dermatitis in mice [6] . Continuous astaxanthin supplementation (4 mg/day) for 4 weeks also significantly reduces plasma malondialdehyde (oxidative stress marker) concentrations, suggesting an ability to reduce systemic oxidative stress [7] . A recent study reported a role for astaxanthin in treating respiratory diseases, suggesting that it potentially treats asthma by inhibiting Th2-mediated cytokines and enhancing Th1-mediated cytokines [8] . Lung cyclic nucleotide levels are also significantly lowered in astaxanthin-fed animals compared to controls, reducing contractility and relaxing airways, thereby reducing asthma severity [9] . However, there no research examined astaxanthin mechanisms during asthma treatment. Combined multi-omics analyses have been used to study respiratory diseases such as asthma [10] , and provide insights on the asthma pathogenesis and astaxanthin’s therapeutic mechanisms. For example, genomics, metabolomics, and epigenomics have been used to identify candidate biomarkers for asthma [11] . This study aimed to evaluate the therapeutic effects of oral astaxanthin on asthma symptoms and elucidate its underlying mechanisms. Using an ovalbumin (OVA)-induced murine asthma model, we investigated the impact of astaxanthin administration through transcriptomic and non-targeted metabolomic analyses. These approaches enabled identification of differentially expressed genes (DEGs) and differential metabolites (DEMs), revealing potential asthma-related biomarkers and therapeutic targets. Our integrated multi-omics analysis provides insights into astaxanthin's mechanism of action, suggesting its potential as a natural therapeutic intervention for asthma management. Methods Materials and reagents Astaxanthin (Aladdin, A114383-250 mg, 99%, China), dexamethasone (DEX) (MCE, HY-14648 500mg, China). Albumin from chicken egg white grades V (Sigma-Aldrich, A5503-10g, USA) and II (Sigma-Aldrich, A5253-500g, USA), Aluminum oxide hexahydrate (Aladdin, A112509-100 g, China), mouse immunoglobulin E (IgE), interleukin IL-4, IL-6, IL-13, and superoxide dismutase (SOD) enzyme-linked immunosorbent assay (ELISA) kits (Elabscience Biotechnology, China), UPLC-Q/TOF-MS, Sartorius provided a 1 in 10,000 balance (BSA224S-CW, Germany), ultrasonic cleaner (KQ-500E, Kunshan Ultrasonic Instrument Co, China), Watson water purifier, MS acetonitrile, and MS formic acid (Fisher, USA). Ethical statement All experiments complied with (ARRIVE) 2.0 guidelines and were approved by the Ethics Committee of Harbin Medical University (ID: HMUIRB2024019). Animal management Female-specific pathogen-free (SPF) BALB/C mice (Beijing Viton Lever, China) were housed in the Laboratory Animal Center of Harbin Medical University under controlled conditions (24 ± 2°C, 45%–60% relative humidity, 12 h light/dark cycle) with ad libitum access to water. After a 7-day acclimatization period, the mice were randomly assigned to five groups: 1. Normal control (NC) 2. OVA-induced (OVA) 3. Dexamethasone-treated (DEX, 0.01 mg/kg) 4. Low-dose astaxanthin (ASTA-L, 25 mg/kg/day) 5. High-dose astaxanthin (ASTA-H, 50 mg/kg/day) All groups except NC received intraperitoneal injections of 0.2 mL sensitization solution (0.01% OVA, 500 μg OVA + 25 mg Al(OH)₃ in PBS) on days 0, 7, and 14. From day 21 onward, they were subjected to alternate-day nebulization with 2.5% OVA (2.5 g OVA in 100 mL PBS) for 4 weeks. NC mice received equivalent PBS treatments. Pharmacological interventions (DEX or astaxanthin) were administered daily via oral gavage starting on day 21, with treatments given 3 h prior to nebulization. The OVA and NC groups received PBS as a vehicle control. This protocol successfully established the mouse model (Fig. 1). Evaluating astaxanthin effects on asthma Body weight and food intake were measured weekly. Upon completion of the intervention, mice were anesthetized via intraperitoneal injection of 1% pentobarbital sodium (50 mg/kg). Blood samples were collected from the orbital sinus of 10 mice per group and centrifuged at 4000 rpm for 10 min to obtain serum. Levels of IgE, SOD, IL-4, IL-6, and IL-13 were quantified using commercial ELISA kits (vendor, city, country). Bronchoalveolar lavage fluid (BALF) was collected, and total cell counts were determined using a hemocytometer. Lung tissues were harvested post-lavage for wet/dry weight ratio analysis. The remaining tissues were snap-frozen and stored at −80°C for subsequent transcriptomic and metabolomic studies. Transcriptomics analyses We used lung tissues from NC, OVA, and ASTA-H groups (n=5) for transcriptomics analyses. RNA was extracted according to the manufacturer’s instructions, with mRNA’s enriched with oligo (dT) magnetic beads. Fragmentation of mRNA enrichment processed RNA products, reverse transcription to generate the first-strand cRNA, and dUTP were used to synthesize double-stranded cDNA. The role of each polymerase on the double-stranded cDNA for end repair and ligase ligation of the Ilumina-specific junction, the final principal component analysis (PCA) amplification purification to get the final RNA sequencing library. PCA analysis was performed by analyzing genes with significantly different mean values (one-way analysis of variance (ANOVA)) ( p- value ≤ 0.05) in three sample sets. Correlation coefficients were calculated using FPKM expression data at gene levels, and correlation analyses were performed by squaring Pearson’s coefficients from biological replicate samples > 0.92 according to ENCODE_RNAseq_Standard metrics. A threshold of 1 was set, with a 5-fold difference, p -value ≤ 0.05, and mean FPKM value ≥ 0.5 to screen for DEGs. Metabolomics analysis We used NC, OVA, and ASTA-H lung tissues (n=6) for metabolomics analysis. Lung tissue samples were divided into six groups. Then, 400 μL of pre-cooled methanol/acetonitrile/water solution [4:4:2 (v/v)] was added, samples were vortexed by mixing, then incubated at -20℃ for 60 min, centrifuged at 14000 g at 4℃ for 20 min, and supernatants taken for drying under nitrogen. Then, mass spectrometry (MS) was performed as follows: 100 μL of aqueous acetonitrile (acetonitrile: water = 1:1 v/v) was added to re-dissolve samples, then samples were vortexed and sonicated in an ice-water bath for 1 min. Samples were centrifuged at 14000 g for 15 min at 4℃, from which a 2 μL aliquot underwent UPLC-MS analysis. Chromatographic conditions were as follows: an UPLC ® BEH C18 column (1.7 μm, 2.1 × 100 mm) with mobile phase consisting of acetonitrile (A)-0.1% formic acid water (B) was used. Gradient elution conditions are shown (Tab.1) The flow rate = 0.2 mL/min, cuvette temperature = 4℃, column temperature = 45℃, and injection volume = 1 μL. Table 1. Gradient elution conditions Time Acetonitrile Water 0 19 81 4 21 79 8.5 24 76 14 26 74 17 60 40 23 70 30 24 95 5 26 95 5 Samples were measured using an electrospray ionization source. All data were collected in MSE mode and mass spectral data collected in positive and negative ion and resolution modes. The instrument was calibrated in real time for mass numbers using LE standards with positive ion mode m/z = 556.2771 and negative ion mode m/z = 554.2615 at 10 μL/min. MS data acquisition parameters are shown (Tab.2) Table 2. Mass spectrum parameters Conditions Negative mode Positive mode Capillary voltage (kV) 2.5 3.0 Sample cone voltage (V) 40 40 Source offset voltage (V) 80 80 Source temperature (°C) 80 80 Desolvation temperature (°C) 150 150 Cone gas flow rate (L/h) 50 50 Desolvation gas flow rate (L/h) 800 800 Collision energy (V) 6.5 6.5 Ramp collision energy (V) 0 - 40 0 - 40 Acquisition mass range (Da) 100 - 1500 100 - 1500 MS survey scan time (s) 0.2 0.2 We used MassLynx V4.1 software to collect MSE data, construct a compound database based on the literature and a DPN 24.2 natural product dictionary, and created a table to import all information into UNIFI software for peak identification and fingerprinting. Multivariate statistical analysis was performed on data using PCA, orthogonal projection by potential structure discriminant analysis (OPLS-DA), and permutation tests. Metabolites with VIP > 1.0 and p < 0.05 values were considered potential differential metabolites. Receiver operating characteristic curves were generated to assess data.Data processing MassLynx V4.1 software was used to collect MSE data, compound database was constructed according to literature and DPN24.2 dictionary of natural products, and a table was established to import it into UNIFI software for peak identification and peak identification. Data analysis Metabolomics data were processed using Simca14.1. Principal Component Analysis (PCA) and Orthogonal Partial Least Squares-Discriminant Analysis were used. OPLS-DA) two multivariate statistical analysis methods. PCA analysis can directly reflect the overall distribution characteristics of sample data and effectively distinguish the metabolic differences among different groups. By constructing OPLS-DA model, the explanatory ability and predictive performance of the model were further evaluated. R2 and Q2 were used to measure the interpretability and predictability of the model, respectively. When R2 and Q2 values are both greater than 0.05, it indicates that the established model has good robustness and reliability, in which Q2>0.4 is considered to be an effective model, and Q2>0.9 is considered to be an excellent model. The Variable Importance in Projection (VIP) value calculated based on OPLS-DA model, Combined with the criteria of statistical significance (P1) and the Change factor of metabolite concentration (|Fold Change|>1.0), Differentially Expressed Metabolites (DEMs) with significant differences were screened. The selected DEMs data were imported into the Metabioalyst 5.0 pathway analysis module for metabolic pathway enrichment analysis. Paths with P < 0.05 were considered statistically significant. Joint analysis of transcriptomics and metabolomics Core regulatory networks based on gene and metabolite correlation analyses of transcriptomics and metabolomics data were generated. Network construction Screened genes and metabolites were imported into Cytoscape software to create gene-metabolite networks. Joint-pathway analysis The Kyoto Encyclopedia of Genes and Genomes-based joint-pathway analysis was performed to analyze central genes and metabolites from the network, and major astaxanthin pathways were mined based on pathway impact values enriched by joint-pathway analysis. Statistical analysis The experimental data were analyzed by SPSS 26.0. t test was used for comparison between the two groups, and one-way analysis of variance was used for multiple groups. The results were represented by mean ± standard deviation and plotted by origin. P<0.05 and P<0.001 were considered statistically significant. * means P<0.05 compared with the blank control group; # means P<0.05 compared with the model group; ## means P<0.001 compared with the model group. Results General conditions When compared with NC mice, OVA mice showed obvious sneezing, nose rubbing, and back arching, indicating successful OVA-induced asthma mouse model establishment. Compared with OVA mice, DEX, ASTA-H, and ASTA-L mice showed different level of improvement in the above phenomena, but the improvement of ASTA-L group was worse than the other two groups. (Fig.2a-b). The morphological effects of astaxanthin on lung tissue Mice were humanely euthanized after 4 weeks of continuous intervention, with lung tissues taken for Hematoxylin-eosin staining (HE), Masson’s trichrome staining (MASSON), and Periodic Acid-Schiff stain (PAS) staining. When compared with NC mice, OVA mice showed increased inflammatory cell infiltration and components, including eosinophils (Fig.3a), increased collagen fiber deposition (Fig.3b), and increased mucus secretion with lung septa thickening (Fig.3c). The area of blue collagen deposition indicated by the arrow was calculated using image j image processing software (Fig.3d), the collagen deposition in the OVA group of mice was significantly higher than that in the other groups ( p <0.001), and it was significantly reduced after astaxanthin treatment, and increased mucus secretion, glycogen deposition, and thickening of the pulmonary septum. The area of glycogen deposition was calculated as indicated by the arrow (Fig.3c), in accordance with the results of collagen deposition area (Fig.3e) ( p <0.001). Inflammatory cell counts Total cell numbers in bronchial cell lavage fluid are shown (Fig.4a). Total inflammatory cell numbers in lavage fluid in OVA mice were significantly higher than the other groups ( p <0.05), which indicated successful modeling. The number of inflammatory cells in the ASTA-H ( p <0.001) was less than ASTA-L ( p <0.001), suggesting that astaxanthin inhibited inflammatory response. Lung dry-wet ratios After bronchial lavage, the lungs were analyzed to measure wet/dry ratios. As shown (Fig.4b), compared with mice in the NC group, the OVA group had a 7% increase in the lung wet-to-dry ratio ( p <0.05) and severe injury from elevated lung water content. In contrast, compared with the OVA group, the ASTA-H, ASTA-L, and DEX groups decreased by 6.8%, 5.2%, and 6.9% ( p <0.001), respectively. These results indicated that astaxanthin reduced the risk of lung injury. Oxidative stress, inflammatory factors, and IgE analyses Serum IL-4, IL-6, IL-13, and Ig-E levels were significantly higher in OVA mice (Fig.5a-d) when compared with NC mice, while levels were substantially lower in ASTA-H mice when compared with OVA mice ( p <0.001). SOD activity levels were significantly lower in OVA mice (Fig.5e) ( p <0.05). when compared with NC mice, whereas levels were significantly higher in ASTA-H and DEX mice ( p <0.001), suggesting that astaxanthin attenuated oxidative damage associated with asthma. Astaxanthin alters the transcriptome in asthmatic mice. As shown in Figure 6a-b, down-regulated Gene Enrichment Score in OVA and ASTA-H groups was obtained according to KEGG analysis to reflect the overall enrichment degree and Gene Ratio of gene set under specific conditions: The proportion of significant genes in a specific function or pathway is analyzed by combining the two values to analyze the pathway where the genes are mainly enriched. High Enrichment Score + high Gene Ratio indicates that this function and pathway are not only significantly enriched on the whole, but also the proportion of significant genes is relatively large. The up-regulated genes are mainly concentrated in Glutathione metabolism, Ribosome, Chemical carcinogens-reactive oxygen species and Hepatocellular carcinoma, Fluid shear stress and atherosclerosis, Drug metabolism other enzymes, Drug metabolism − cytochrome p450, Metabolism of xenobiotics by cytochrome. Down-regulated genes are mainly enriched in Cytosolic DNA-sensing pathway, Parkinson disease, Salivary secretion, Neurotrophin signaling pathway and Estrogen signaling pathway, Vascular smooth muscle contraction and other pathways. Transcriptomics altered with ASTA treatment in asthmatic mice. Using PCA, NC, OVA, and ASTA-H lung tissue genes were grouped into different regions (Fig.7a). To identify DEGs in groups, we used the DEseq2 algorithm to process transcriptome data. Venn diagrams were used to screen for DEGs similar to NC and ASTA-H groups. We identified 198 DEGs (Fig.7b), which were visualized in a heatmap (Fig.7c). Astaxanthin treatment alters metabolism in asthmatic mice. To perform metabolomics analysis on NC, OVA, and ASTA-H samples, we first validated UPLC-Q/TOF-MS stability, precision, reproducibility, and sample stability procedures, which indicated good strength, accuracy, and reproducibility. As shown Figure.8a, from PCA, NC, OVA, and ASTA-H samples were assigned to different areas. ASTA-H samples were assigned to the middle of the two groups, suggesting that astaxanthin altered the metabolic disorder in asthma model mice. OPLS-DA obtained maximum separation using OVA and ASTA-H group samples. As can be seen from FIG. 15, samples in the OVA group and ASTA-H group were well separated from each other, indicating significant metabolomic differences between the two groups. The OPLS-DA model was then subjected to 200 permutation tests to ensure that there was no risk of fit. As shown in Figure 8a-b OPLS-DA, Q2 >0.4 in the original model is considered to be an effective model, and Q2 >0.9 is considered to be an excellent model. The cationic original model R2=0.99, Q2 >0.9, and the anionic R2=0.99, Q2 >0.8 did not appear overfitting. Therefore, the original model had strong interpretation and prediction ability of data, and could well capture the differences and patterns between groups and make reliable prediction of unknown data. OPLS-DA arrangement diagram (Fig. 8c-d) R2 (0.0, 0.986), Q2 (0.0, -0.518) in cations, R2 (0.0, 0.65), Q2 (0.0, -0.951) in anions, but most of the stochastic models have low R² values, indicating that the stochastic models have poor ability to interpret and predict data. Therefore, R2 and Q2 in the original model were significantly higher than those in the random model, indicating that the model was statistically significant and could further effectively distinguish metabolic differences between different OVA and ASTA-H groups and screen out differential metabolites. In order to evaluate the diagnostic efficacy of 8 metabolites for target diseases, the receiver operating characteristic curve (ROC curve) of these 8 metabolites was predicted, and the area under the curve (AUC) was calculated, as shown in Figure 18g-h. The AUC area of metabolites glycerophospholipid, adenosine monophosphate, oleic acid and 2S-2-amino-5-amino-valeric acid were 0.9444, 0.8426, 0.8333, 0.8056, respectively, showing high diagnostic accuracy. Among them, the AUC value of glycerophospholipids was significantly higher than that of other metabolites (P<0.05), suggesting that glycerophospholipids could be used as potential biomarkers. And 8 differential metabolites were identified between groups (Tab.3) about 2S-2-amino-5-carbamoylaminopentanoicacid, Adenosine monophosphate, Glyceric acid 1,3-biphosphate, Colfosceril palmitate, Glycero phosphocholine, Retinyl ester, Chloro phenylalanine, Oleic acid. Table 3. 8 different metabolites identified in asthmatic mouse samples NO. Biomarkers Formula P Mass error (ppm) Formula VIP value 1 2S-2-amino-5- Carbamoylaminopentanoic acid C 6 H 13 N 3 O 3 3.61*10 -7 8.626974 +H 1.03824 2 Adenosine monophosphate C 10 H 14 N 5 O 7 P 0.000325 6.906834 +H 1.46173 3 Glyceric acid 1,3-biphosphate C 6 H 12 O 6 0.004567 2.706136 +N 1.46161 4 Colfosceril palmitate C 40 H 80 NO 8 P 0.0036298 -2.800583138 +H 1.32609 5 Glycero phosphocholine C 8 H 20 NO 6 P 0.0706894 8.7718527 +H, +Na 1.24251 6 Retinyl ester C 20 H 30 O 2 0.0170683 4.587158582 +H 1.22148 7 Chloro phenylalanine C 9 H 10 CINO 2 0.0413623 -8.89928 +H 1.081 Continuation of table 3 NO. Biomarkers Formula P Mass error (ppm) Formula VIP value 8 Oleic acid C 18 H 34 O 2 1.48*10 -11 -5.25492 +HCOO 1.53518 Typicality correlation analysis Typicality correlation analysis is used to show correlations between two data groups. For this, we analyzed 198 DEGs and eight differential metabolites. The total explained variance of the explanatory variable matrix (metabolites) to the response variable matrix (genes) was 90.48%, indicating the reliability of this analysis (Fig.9a). We screened 20 genes (Rabac1, Hbb−bs, Acta2, Dynlt1b, Clic3, Tuba1a, Banf1, Skp1a, Gstm2, Rnf187, Arl3, Clec3b, Gstt1, Gstm1, Tuba1b, Cnn2, Mgp, H2afz, Adh1, C130074G19Rik) and 8 metabolites (2S-2-amino-5-carbamoylaminopentanoicacid, Adenosine monophosphate, Glyceric acid 1,3-biphosphate, Colfosceril palmitate, Glycero phosphocholine, Retinyl ester, Chloro phenylalanine, Oleic acid).based on gene-metabolite interaction heatmap data. These identified molecules may be implicated in astaxanthin mechanisms in asthma. (Fig.9c). Network construction We constructed network interaction maps using these 20 genes and 8 metabolites. More connectivity was indicated, which exerted a more substantial influence on the regulatory network (Fig.10a-b). Overall, these molecules were closely related. Pathway analysis To investigate astaxanthin functions, we screened pathways associated with the 20 genes and 8 metabolites using joint-pathway analysis. 5 significant pathways were identified (Tab.4): Cytochrome P450 drug metabolism, Metabolism of xenobiotic by cytochrome P450, Glutathione metabolism, Glycolysis or Gluconeogenesis, and Retinol metabolism. Bubble plots, showing descending impact value orders, calculate the −log10(p) value to prove the path difference, which were generated using MetaboAnalyst 5.0 (Fig.11a). Finally, we established a network diagram including genes, metabolites, and pathways (Fig.11b), screening out the core pathways. Square boxes are genes and circles are metabolites. Table 4. Gene-metabolite interaction pathways PATHWAY NAME Expected p -log(p) FDR Impact Drug metabolism-cytochrome P450 0.18207 2.41*10 -5 4.6188 0.004506 0.12264 Metabolism of xenobiotics by cytochrome P450 0.20254 3.66*10 -5 4.4371 0.004506 0.35714 Glutathione metabolism 0.11096 0.000162 3.7896 0.010616 0.17391 Glycolysis or Gluconeogenesis 0.1045 0.004709 2.3271 0.13998 0.18462 Retinol metabolism 0.12497 0.006668 2.176 0.18171 0.22034 Discussion It is reported that astaxanthin alleviates airway inflammation and lung damage [8] . In this study, we investigated the therapeutic potential of astaxanthin in asthma using an ovalbumin (OVA)-induced murine model. Our findings demonstrate that astaxanthin ameliorated airway inflammation, attenuated oxidative stress, modulated immune responses, and reduced pulmonary pathology. During the experiment, by observing the general condition of the mice, we found that the body weight of the mice in the ASTA group tended to increase slowly after ovalbumin stimulation, and we guessed that this might be related to the astaxanthin increases the activity of intestinal digestive and absorptive enzymes (intestinal creatine kinase), which provides energy for the digestion and absorption of nutrients, leading to weight gain and growth [12] . Based on the transcriptomics results, we identified 20 genes associated with asthma. Among the up-regulated genes Clic3 and Skp1 are associated with immune disorders, which would explain why IgE levels were elevated in the ASTA group. Clic3 and Skp1 can inhibit the activation of NF-κB, and restrain the release of inflammatory cytokines including IL-4, IL-6, IL-13, thereby reducing the level of inflammation. [13,14] . Adh1 was further associated with reduced S-nitrosoglutathione (GSNO) activity, a compound whose accumulation exacerbates asthma. Thus, elevated Adh1 expression may confer protection against asthma development. These observations are consistent with our study data [15] . Hbb-bs can increase the body's antioxidant level, thus reducing the level of oxidative stress [16] . Among the down-regulated genes, In the immune environment, low expression of banf1can boost antitumor immune responses with CD8+ T-cell infiltration and activation [17] . The level of Acta2 expression recognizes the level of airway smooth muscle cells, and high expression of Acta2 causes the thickening of airway smooth muscle and exacerbates asthma [18] . In contrast, astaxanthin treatment downregulated gene expression and reduced asthma risks. Glutathione S-transferases (GSTs) are associated with cell integrity, defenses against oxidative stress and DNA damage, and detoxifying endogenous/exogenous compounds in cells. Of the transferases, GST-P was implicated in asthma pathogenesis where it regulated protein oxidative status via S-glutathione [19] . A population-based study reported that Gstt1 and Gstm1 were risk factors for asthma and that Gstm2, which is abundantly expressed in human bronchi, increased oxidative stress by affecting glutathione metabolism [20] . Although Cnn2 is not directly related to asthma treatment that reflects the severity of asthma [21] . Our experiments identified 8 metabolites that may be relevant to astaxanthin in the treatment of asthma. Adenosine monophosphate, which is implicated in airway dysfunction due to abnormal lung gas exchange in asthma [22] , was identified in OVA mice and was reduced by astaxanthin. Studies have reported that citrulline supplementation reversed hyperoxia-induced impaired tracheal smooth muscle relaxation in rats [23] . High Glycero phosphocholine (PC (36:1)) levels were putatively associated with asthma risk in children [24] , with similar PC (36:1) modulation trends observed in our study. Oleic acid has many biological effects, such as lowering low-density lipoprotein, increasing high-density lipoprotein, lowering blood pressure [25] , exerting anti-inflammatory effects [26] , and inducing anti-asthmatic effects [27] . The glycolytic pathway, in which glyceraldehyde 1,3-bisphosphate occurs, is also involved in airway hyperresponsiveness and remodeling in asthma [19] . Retinyl acetate is a long-chain fatty acid lipid of vitamin A, and studies have shown that vitamin A deficiency exacerbates allergic asthma [28] . Our analysis also showed similar results in OVA mice, which were significantly ameliorated by astaxanthin. Integrating transcriptomic and metabolomic analyses, we identified potential correlations between key pathways. Given the critical roles of inflammatory responses and oxidative stress in asthma pathogenesis, astaxanthin demonstrated therapeutic effects by suppressing Gstt1 and Gstm1 expression. We hypothesize that this downregulation may inhibit actin-alpha 2 (Acta2), thereby reducing collagen deposition, while simultaneously attenuating glycolysis and inflammatory responses. We also identified an upstream product of Adh1, glyceraldehyde 1,3-bisphosphate, which promotes its expression. Previously, we demonstrated that retinol expression was reduced in Adh1-deficient mice and that retinyl acetate was involved in retinol metabolism and exerted antioxidant effects [29] . Our findings indicate that Adh1 participates in retinol metabolism as an upstream regulator of retinyl acetate formation. Based on this observation, we propose that astaxanthin may enhance Adh1 activity through glycerol 1,3-bisphosphate-mediated activation, thereby promoting retinyl acetate production and potentially mitigating asthma pathogenesis. Conclusion Astaxanthin ameliorates pathological symptoms in asthmatic mice and modulates the differential expression of genes and metabolites associated with asthma pathogenesis. Abbreviations Abbreviation Name ASTA Astaxanthin BP Biological Process CC Cell Component DEGs Differentially expressed genes FPKM Fragments per kilobase per million IL-13 Interleukin-13 IL-4 Interleukin-4 IL-6 Interleukin-6 IgE Immunoglobulin E GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes MF Molecular Function PCA Principal Component Analysis OPLS-DA Orthogonal Partial Least Squares Discriminant Analysis ROC Receiver Operating Characteristic Curve SOD Superoxide dismutase Declarations Ethics approval and consent to participate All experiments complied with ARRIVE 2.0 guidelines and were approved by the Ethics Committee of Harbin Medical University (ID: HMUIRB2024019). Funding There is no funding. Human Ethics and Consent to Participate declarations Not applicable Author Contribution Zihui Ma conducts relevant experiments and writes papers, and Yuteng Ma, and Hongquan Xie carry out relevant responsible software. Yue Li Conducts supervision. Guoling Wang and Haoyu Wang accountable for verification. Yanan Jiang and Ying Liu are responsible for the overall review. Acknowledgement This research part of drawing used the Omicshare database (http://omicshare.com/) and the Metware Cloud (http://cloud metware.cn/). Competing Interests:There are no competing interests.Funding:There is no funding. References Dharmage S C, Perret J L, Custovic A. Epidemiology of Asthma in Children and Adults[J]. Front Pediatr, 2019, 7:246. Pedersen S. Do inhaled corticosteroids inhibit growth in children?[J]. Am J Respir Crit Care Med, 2001, 164(4):521–35. Jha S S, Kumar M, Agrawal P K, et al. Osteoporosis in Asthma and COPD[J]. Indian J Orthop, 2023, 57(Suppl 1):200–208. Crossingham I, Turner S, Ramakrishnan S, et al. Combination fixed-dose β agonist and steroid inhaler as required for adults or children with mild asthma: a Cochrane systematic review[J]. BMJ Evid Based Med, 2022, 27(3):178–184. Bi J, Cui R, Li Z, et al. Astaxanthin alleviated acute lung injury by inhibiting oxidative/nitrative stress and the inflammatory response in mice[J]. Biomed Pharmacother, 2017, 95:974–982. Yoshihisa Y, Andoh T, Matsunaga K, et al. Efficacy of Astaxanthin for the Treatment of Atopic Dermatitis in a Murine Model[J]. PLoS One, 2016, 11(3):e0152288. Chalyk N E, Klochkov V A, Bandaletova T Y, et al. Continuous astaxanthin intake reduces oxidative stress and reverses age-related morphological changes of residual skin surface components in middle-aged volunteers[J]. Nutr Res, 2017, 48:40–48. Hwang Y H, Hong S G, Mun S K, et al. The Protective Effects of Astaxanthin on the OVA-Induced Asthma Mice Model[J]. Molecules, 2017, 22(11). Underwood D C, Osborn R R, Novak L B, et al. Inhibition of antigen-induced bronchoconstriction and eosinophil infiltration in the guinea pig by the cyclic AMP-specific phosphodiesterase inhibitor, rolipram[J]. J Pharmacol Exp Ther, 1993, 266(1):306–13. Subali D, Kurniawan R, Surya R, et al. Revealing the mechanism and efficacy of natural products on treating the asthma: Current insights from traditional medicine to modern drug discovery[J]. Heliyon, 2024, 10(11):e32008. Pecak M, Korošec P, Kunej T. Multiomics Data Triangulation for Asthma Candidate Biomarkers and Precision Medicine[J]. Omics, 2018, 22(6):392–409. Lee J, Kim M H, Kim H. Anti-Oxidant and Anti-Inflammatory Effects of Astaxanthin on Gastrointestinal Diseases[J]. Int J Mol Sci, 2022, 23(24). Liang J, Long Z, Zhang Y, et al. Chloride intercellular channel 3 suppression-mediated macrophage polarization: a potential indicator of poor prognosis of hepatitis B virus-related acute-on-chronic liver failure[J]. Immunol Cell Biol, 2022, 100(5):323–337. Li R, Sano T, Mizokami A, et al. miR-582-5p targets Skp1 and regulates NF-κB signaling-mediated inflammation[J]. Arch Biochem Biophys, 2023, 734:109501. Que L G, Liu L, Yan Y, et al. Protection from experimental asthma by an endogenous bronchodilator[J]. Science, 2005, 308(5728):1618–21. Zhang Y, Wu M X, Li H M, et al. Potential benefits of Rehmanniae Radix after ancient rice-steaming process in promotion of antioxidant activity in rats' health[J]. Food Sci Nutr, 2023, 11(9):5532–5542. Wang M, Huang Y, Chen M, et al. Inhibition of tumor intrinsic BANF1 activates antitumor immune responses via cGAS-STING and enhances the efficacy of PD-1 blockade[J]. J Immunother Cancer, 2023, 11(8). Facciolongo N, Bonacini M, Galeone C, et al. Bronchial thermoplasty in severe asthma: a real-world study on efficacy and gene profiling[J]. Allergy Asthma Clin Immunol, 2022, 18(1):39. Van De Wetering C, Manuel A M, Sharafi M, et al. Glutathione-S-transferase P promotes glycolysis in asthma in association with oxidation of pyruvate kinase M2[J]. Redox Biol, 2021, 47:102160. Anttila S, Hirvonen A, Vainio H, et al. Immunohistochemical localization of glutathione S-transferases in human lung[J]. Cancer Res, 1993, 53(23):5643–8. Ong M S, Sordillo J E, Dahlin A, et al. Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma[J]. J Pers Med, 2024, 14(3). Manrique H A, Gómez F P, Muñoz P A, et al. Adenosine 5'-monophosphate in asthma: gas exchange and sputum cellular responses[J]. Eur Respir J, 2008, 31(6):1205–12. Sopi R B, Zaidi S I, Mladenov M, et al. L-citrulline supplementation reverses the impaired airway relaxation in neonatal rats exposed to hyperoxia[J]. Respir Res, 2012, 13(1):68. Zhu Z, Camargo C A, Jr., Raita Y, et al. Metabolome subtyping of severe bronchiolitis in infancy and risk of childhood asthma[J]. J Allergy Clin Immunol, 2022, 149(1):102–112. Asikin Y, Takahashi M, Mishima T, et al. Antioxidant activity of sugarcane molasses against 2,2'-azobis(2-amidinopropane) dihydrochloride-induced peroxyl radicals[J]. Food Chem, 2013, 141(1):466–72. Erdinest N, Shohat N, Moallem E, et al. Nitric oxide secretion in human conjunctival fibroblasts is inhibited by alpha linolenic acid[J]. J Inflamm (Lond), 2015, 12:59. Lee S Y, Bae C S, Seo N S, et al. Camellia japonica oil suppressed asthma occurrence via GATA-3 & IL-4 pathway and its effective and major component is oleic acid[J]. Phytomedicine, 2019, 57:84–94. Defnet A E, Shah S D, Huang W, et al. Dysregulated retinoic acid signaling in airway smooth muscle cells in asthma[J]. Faseb j, 2021, 35(12):e22016. Molotkov A, Duester G. Genetic evidence that retinaldehyde dehydrogenase Raldh1 (Aldh1a1) functions downstream of alcohol dehydrogenase Adh1 in metabolism of retinol to retinoic acid[J]. J Biol Chem, 2003, 278(38):36085–90. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6366020","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451717973,"identity":"34af9d20-db4a-483c-a1a0-7e2963c84177","order_by":0,"name":"Zi Hui Ma","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zi","middleName":"Hui","lastName":"Ma","suffix":""},{"id":451717974,"identity":"b35e8be5-bc63-4e6f-9cd3-89c1d3680d20","order_by":1,"name":"Yuteng Ma","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuteng","middleName":"","lastName":"Ma","suffix":""},{"id":451717975,"identity":"38dc3340-9876-4cac-b0de-e69834abd587","order_by":2,"name":"Hongquan Xie","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongquan","middleName":"","lastName":"Xie","suffix":""},{"id":451717976,"identity":"db56370d-e4b6-4a1c-a014-5a7ebd056615","order_by":3,"name":"Yue Li","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Li","suffix":""},{"id":451717982,"identity":"23a8acda-ff75-41df-9987-9f0b4adbd290","order_by":4,"name":"Guoliang Wang","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guoliang","middleName":"","lastName":"Wang","suffix":""},{"id":451717984,"identity":"17890e0e-dd5c-4db9-9e97-cc74fcab1054","order_by":5,"name":"Haoyu Wang","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haoyu","middleName":"","lastName":"Wang","suffix":""},{"id":451717985,"identity":"8aceec54-f224-4b42-a3cb-6d4f433e909a","order_by":6,"name":"Yanan Jang","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Jang","suffix":""},{"id":451717987,"identity":"76faea60-c2ce-4063-b40e-fc9286ec4a6d","order_by":7,"name":"Ying Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACPmYgkcDAwMPP3n7wAQPDAcJa2KBa5CR7ziQbEKcFShsb3EgwkyBOCzuPmcTDHbWJDQcS0ip/VNxJbGA/fHQDfocBtSSeOZ7Y2HDw2G2eM88SG3jS0m4Q1tJ2LLGZsSHtNmPb4cQGCR4z4rS0MTOYFf4kQUuNMQ8bgxkDL3Fa2IotEtsOyEnw8CRL85w5bNxGyC/8/Ic33vzZVsdjf//5wY8/Kg7L9rMfPoZXCxCwAKPjMJK9BJSDAPMHBoY6ItSNglEwCkbBiAUAv2pLVeL9EgIAAAAASUVORK5CYII=","orcid":"","institution":"Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-04-03 05:24:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6366020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6366020/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82300641,"identity":"6c02dffc-245d-4760-9b62-1c49519f6632","added_by":"auto","created_at":"2025-05-08 20:49:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModeling of asthmatic mice\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/333b89d36811df31362e5d4c.png"},{"id":82300643,"identity":"1acae0f2-5147-4f60-a33c-7080a5513be0","added_by":"auto","created_at":"2025-05-08 20:49:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87271,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe changes in body weight and food intake in each group during the experiment.\u003c/strong\u003e (a) weight. (b) food ration.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/d9072cbe42bf279a50e8b0cd.png"},{"id":82301396,"identity":"5587902f-cb0a-4e0f-805c-0a119c342fc9","added_by":"auto","created_at":"2025-05-08 20:57:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1359119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHE, MASSON, PAS staining of lung tissue of mice in five groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) HE staining. (b) MASSON staining. (c) PAS staining. (d) collagen deposition.\u003c/p\u003e\n\u003cp\u003e(e) glycogen deposition.* Compared with the NC group, \u003cem\u003ep\u0026lt;0.05, \u003c/em\u003e## Compared with the OVA group, \u003cem\u003ep\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/dc27d458535f66c44770ae67.png"},{"id":82300642,"identity":"a9306e8d-b8dc-4f0e-ad42-6534127840f4","added_by":"auto","created_at":"2025-05-08 20:49:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55656,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe changes in cell count and D/W ratio in each group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) cell count. (b) D/W ratio. * Compared with the NC group, \u003cem\u003eP\u0026lt;0.05, \u003c/em\u003e# Compared with the OVA group, \u003cem\u003ep\u0026lt;0.05, \u003c/em\u003e##\u003cem\u003ep\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/dec76b7534f7176a29171ff2.png"},{"id":82300657,"identity":"cbb2a2be-3d0d-42a2-8cb5-1dfdce41f907","added_by":"auto","created_at":"2025-05-08 20:49:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":107113,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum\u003c/strong\u003e \u003cstrong\u003eoxidative stress, inflammation, immunity of mice in each group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) IL-4 concentrations. (b) IL-6 concentrations. (c) IL-13 concentrations. (d) IgE level. (e) SOD activity. *Compared with the NC group, \u003cem\u003ep\u0026lt;0.05, \u003c/em\u003e#Compared with the OVA group, \u003cem\u003ep\u0026lt;0.05, \u003c/em\u003e##\u003cem\u003e p\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/a40d4bd8d9034ffc26abc2aa.png"},{"id":82301526,"identity":"54ba79d4-c161-4871-9b0a-544a81330656","added_by":"auto","created_at":"2025-05-08 21:05:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":55644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBubble map of differential gene KEGG enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Up-regulation of gene Enrichment Score (b) down-regulation of gene Enrichment Score (c) down-regulated Gene Ratio (d) Downregulating Gene Ratio\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/464fd25c8fc92926c9de0293.png"},{"id":82301398,"identity":"d6691c9c-9564-4b98-9d7b-89a6aeac783f","added_by":"auto","created_at":"2025-05-08 20:57:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":283964,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomics altered with ASTA treatment in asthmatic mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) PCA analysis of DEGs in the NC, OVA, and ASTA-H groups. (b) Venn diagram of DEGs. (c) Heatmap of expression of 198 DEGs.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/cc0b01e5ad828125e3bb4131.png"},{"id":82301527,"identity":"b526dfd8-6ae5-4628-8e6e-c6bb0e714486","added_by":"auto","created_at":"2025-05-08 21:05:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":195179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolomic altered with ASTA treatment in asthmatic mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a-b) PCA score plots involved ESI\u003csup\u003e+\u003c/sup\u003e and ESI\u003csup\u003e-\u003c/sup\u003e of the samples (n=6) in NC, OVA, and ASTA-H groups. The OPLS-DA score plots involved ESI\u003csup\u003e+\u003c/sup\u003e(c) and ESI\u003csup\u003e-\u003c/sup\u003e(d) of the OVA and the ASTA-H groups (n=6). The permutation plots involved ESI\u003csup\u003e+\u003c/sup\u003e(e) and ESI\u003csup\u003e-\u003c/sup\u003e(f) of asthmatic mice samples (n=6) of OVA and ASTA-H groups. Predictive ROC curves of the first 4(g) and last 4(h)identified metabolites in asthmatic mice treated with ASTA.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/976ee05dd78d026b85a5a742.png"},{"id":82300651,"identity":"b8f29068-297c-43ce-9b2c-5287c960981a","added_by":"auto","created_at":"2025-05-08 20:49:07","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":77453,"visible":true,"origin":"","legend":"\u003cp\u003e(a) CCA of top 20 DEGs and 8 different metabolites in asthmatic mice treated with ASTA. (b) A pathway in which 20 DEGs are involved. (c) Pearson correlation coefficient of top 20 DEGs and 8 different metabolites.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/591d64622bd92dc83155e1fb.png"},{"id":82300655,"identity":"eb03cf7e-b06d-4f6d-861f-95328013b11c","added_by":"auto","created_at":"2025-05-08 20:49:07","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":440111,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Gene-metabolite chord diagram. (b) Gene-metabolite network of hub DEGs and metabolite.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/0024e4677c2f665ba8c25628.png"},{"id":82300672,"identity":"9d5526b0-d486-4bd2-ba71-8b091b872bf8","added_by":"auto","created_at":"2025-05-08 20:49:07","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":97120,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The bubble chart of pathways. (b) Transcriptome and metabolomics interaction maps.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/1c764734dd11b07678a28b61.png"},{"id":82861266,"identity":"0f3c99ec-5f4d-4193-9021-8c378138d3b1","added_by":"auto","created_at":"2025-05-16 06:54:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3474150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6366020/v1/18ad9244-561f-45e8-8b56-fad4e7a2c7fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mechanistic Insights into the Effects of Astaxanthin on Asthma in Mice: A Combined Transcriptomic and Metabolomic Analysis ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAsthma a major common chronic non-communicable diseases in children and adults. Epidemiological studies indicate\u0026nbsp;its global prevalence in this cohort is increasing by 50% per decade, especially in developing countries\u003csup\u003e[1]\u003c/sup\u003e. Asthma is a complex multifactorial disease with heterogeneous phenotypes of varying etiology and prognosis outcomes, with etiology mostly attributed to interactions between genetic susceptibility, host factors, and environmental exposures. Current asthma treatments mainly include inhaled corticosteroids. In children, oral or systemic corticosteroid use increases fracture risk in a dose-dependent manner. The medication can also impact on height in adulthood, as well as poorly controlling asthma\u003csup\u003e[2]\u003c/sup\u003e. Prolonged exposure to systemic corticosteroid use also increases osteoporosis and cardiovascular disease risks\u003csup\u003e[3]\u003c/sup\u003e. Importantly, disease symptoms in patients with mild to moderate allergic asthma can be controlled using combined inhaled corticosteroids and long-term acting β-agonists\u003csup\u003e[4]\u003c/sup\u003e. However, these medications only treat the symptoms, while underlying disease characteristics remain unaltered. Asthma involves many inflammatory mediators, and targeting a single mediator or receptor is not effective, so identifying new therapeutic agents is necessary.\u003c/p\u003e\n\u003cp\u003eAstaxanthin is a lutein carotenoid found in various microorganisms and marine animals. Its potent antioxidant activity, which reduces oxidative/nitrative stress and protects cells/tissues from free radical damage, has excellent potential for clinical applications\u003csup\u003e[5]\u003c/sup\u003e. In terms of anti-inflammation processes, astaxanthin inhibits atopic dermatitis in mice\u003csup\u003e[6]\u003c/sup\u003e. Continuous astaxanthin supplementation (4 mg/day) for 4 weeks also significantly reduces plasma malondialdehyde (oxidative stress marker) concentrations, suggesting an ability to reduce systemic oxidative stress\u003csup\u003e[7]\u003c/sup\u003e. A recent study reported a role for astaxanthin in treating respiratory diseases, suggesting that it potentially treats asthma by inhibiting Th2-mediated cytokines and enhancing Th1-mediated cytokines \u003csup\u003e[8]\u003c/sup\u003e. Lung cyclic nucleotide levels are also significantly lowered in astaxanthin-fed animals compared to controls, reducing contractility and relaxing airways, thereby reducing asthma severity \u003csup\u003e[9]\u003c/sup\u003e. However, there no research examined astaxanthin mechanisms during asthma treatment.\u003c/p\u003e\n\u003cp\u003eCombined multi-omics analyses have been used to study respiratory diseases such as asthma\u003csup\u003e[10]\u003c/sup\u003e, and provide insights on the asthma pathogenesis and astaxanthin’s therapeutic mechanisms. For example, genomics, metabolomics, and epigenomics have been used to identify candidate biomarkers for asthma\u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis study aimed to evaluate the therapeutic effects of oral astaxanthin on asthma symptoms and elucidate its underlying mechanisms. Using an ovalbumin (OVA)-induced murine asthma model, we investigated the impact of astaxanthin administration through transcriptomic and non-targeted metabolomic analyses. These approaches enabled identification of differentially expressed genes (DEGs) and differential metabolites (DEMs), revealing potential asthma-related biomarkers and therapeutic targets. Our integrated multi-omics analysis provides insights into astaxanthin's mechanism of action, suggesting its potential as a natural therapeutic intervention for asthma management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eMaterials and reagents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAstaxanthin (Aladdin, A114383-250 mg, 99%, China), dexamethasone (DEX) (MCE, HY-14648 500mg, China). Albumin from chicken egg white grades V (Sigma-Aldrich, A5503-10g, USA) and II (Sigma-Aldrich, A5253-500g, USA), Aluminum oxide hexahydrate (Aladdin, A112509-100 g, China), mouse immunoglobulin E (IgE), interleukin IL-4, IL-6, IL-13, and superoxide dismutase (SOD) enzyme-linked immunosorbent assay (ELISA) kits (Elabscience Biotechnology, China), UPLC-Q/TOF-MS, Sartorius provided a 1 in 10,000 balance (BSA224S-CW, Germany), ultrasonic cleaner (KQ-500E, Kunshan Ultrasonic Instrument Co, China), Watson water purifier, MS acetonitrile, and MS formic acid (Fisher, USA).\u003c/p\u003e\n\u003cp\u003eEthical statement\u003c/p\u003e\n\u003cp\u003eAll experiments complied with (ARRIVE) 2.0 guidelines and were approved by the Ethics Committee of Harbin Medical University (ID: HMUIRB2024019).\u003c/p\u003e\n\u003cp\u003eAnimal management\u003c/p\u003e\n\u003cp\u003eFemale-specific pathogen-free (SPF) BALB/C mice (Beijing Viton Lever, China) were housed in the Laboratory Animal Center of Harbin Medical University under controlled conditions (24 \u0026plusmn; 2\u0026deg;C, 45%\u0026ndash;60% relative humidity, 12 h light/dark cycle) with ad libitum access to water.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter a 7-day acclimatization period, the mice were randomly assigned to five groups: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. Normal control (NC) \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. OVA-induced (OVA) \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Dexamethasone-treated (DEX, 0.01 mg/kg) \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4. Low-dose astaxanthin (ASTA-L, 25 mg/kg/day) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5. High-dose astaxanthin (ASTA-H, 50 mg/kg/day) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll groups except NC received intraperitoneal injections of 0.2 mL sensitization solution (0.01% OVA, 500 \u0026mu;g OVA + 25 mg Al(OH)₃ in PBS) on days 0, 7, and 14. From day 21 onward, they were subjected to alternate-day nebulization with 2.5% OVA (2.5 g OVA in 100 mL PBS) for 4 weeks. NC mice received equivalent PBS treatments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePharmacological interventions (DEX or astaxanthin) were administered daily via oral gavage starting on day 21, with treatments given 3 h prior to nebulization. The OVA and NC groups received PBS as a vehicle control. This protocol successfully established the mouse model (Fig. 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvaluating astaxanthin effects on asthma\u003c/p\u003e\n\u003cp\u003eBody weight and food intake were measured weekly. Upon completion of the intervention, mice were anesthetized via intraperitoneal injection of 1% pentobarbital sodium (50 mg/kg). Blood samples were collected from the orbital sinus of 10 mice per group and centrifuged at 4000 rpm for 10 min to obtain serum. Levels of IgE, SOD, IL-4, IL-6, and IL-13 were quantified using commercial ELISA kits (vendor, city, country). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBronchoalveolar lavage fluid (BALF) was collected, and total cell counts were determined using a hemocytometer. Lung tissues were harvested post-lavage for wet/dry weight ratio analysis. The remaining tissues were snap-frozen and stored at \u0026minus;80\u0026deg;C for subsequent transcriptomic and metabolomic studies. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTranscriptomics analyses\u003c/p\u003e\n\u003cp\u003eWe used lung tissues from NC, OVA, and ASTA-H groups (n=5) for transcriptomics analyses.\u003c/p\u003e\n\u003cp\u003eRNA was extracted according to the manufacturer\u0026rsquo;s instructions, with mRNA\u0026rsquo;s enriched with oligo (dT) magnetic beads. Fragmentation of mRNA enrichment processed RNA products, reverse transcription to generate the first-strand cRNA, and dUTP were used to synthesize double-stranded cDNA. The role of each polymerase on the double-stranded cDNA for end repair and ligase ligation of the Ilumina-specific junction, the final principal component analysis (PCA) amplification purification to get the final RNA sequencing library.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA analysis was performed by analyzing genes with significantly different mean values (one-way analysis of variance (ANOVA)) (\u003cem\u003ep-\u003c/em\u003evalue \u0026le; 0.05) in three sample sets. Correlation coefficients were calculated using FPKM expression data at gene levels, and correlation analyses were performed by squaring Pearson\u0026rsquo;s coefficients from biological replicate samples \u0026gt; 0.92 according to ENCODE_RNAseq_Standard metrics.\u003c/p\u003e\n\u003cp\u003eA threshold of 1 was set, with a 5-fold difference, \u003cem\u003ep\u003c/em\u003e-value \u0026le; 0.05, and mean FPKM value \u0026ge; 0.5 to screen for DEGs.\u003c/p\u003e\n\u003cp\u003eMetabolomics analysis\u003c/p\u003e\n\u003cp\u003eWe used NC, OVA, and ASTA-H lung tissues (n=6) for metabolomics analysis.\u003c/p\u003e\n\u003cp\u003eLung tissue samples were divided into six groups. Then, 400 \u0026mu;L of pre-cooled methanol/acetonitrile/water solution [4:4:2 (v/v)] was added, samples were vortexed by mixing, then incubated at -20℃ for 60 min, centrifuged at 14000 g at 4℃ for 20 min, and supernatants taken for drying under nitrogen. Then, mass spectrometry (MS) was performed as follows: 100 \u0026mu;L of aqueous acetonitrile (acetonitrile: water = 1:1 v/v) was added to re-dissolve samples, then samples were vortexed and sonicated in an ice-water bath for 1 min. Samples were centrifuged at 14000 g for 15 min at 4℃, from which a 2 \u0026mu;L aliquot underwent UPLC-MS analysis.\u003c/p\u003e\n\u003cp\u003eChromatographic conditions were as follows: an UPLC\u003csup\u003e\u0026reg;\u003c/sup\u003e BEH C18 column (1.7 \u0026mu;m, 2.1 \u0026times; 100 mm) with mobile phase consisting of acetonitrile (A)-0.1% formic acid water (B) was used. Gradient elution conditions are shown (Tab.1) The flow rate = 0.2 mL/min, cuvette temperature = 4℃, column temperature = 45℃, and injection volume = 1 \u0026mu;L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Gradient elution conditions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"572\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003eAcetonitrile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.965%;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.965%;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.965%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.965%;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.965%;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.965%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.965%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.965%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5175%;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.965%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSamples were measured using an electrospray ionization source. All data were collected in MSE mode and mass spectral data collected in positive and negative ion and resolution modes. The instrument was calibrated in real time for mass numbers using LE standards with positive ion mode m/z = 556.2771 and negative ion mode m/z = 554.2615 at 10 \u0026mu;L/min. MS data acquisition parameters are shown (Tab.2)\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Mass spectrum parameters\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eConditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eNegative mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003ePositive mode\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eCapillary voltage (kV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSample cone voltage (V)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSource offset voltage (V)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSource temperature (\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eDesolvation temperature (\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eCone gas flow rate (L/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eDesolvation gas flow rate (L/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eCollision energy (V)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eRamp collision energy (V)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0 - 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0 - 40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eAcquisition mass range (Da)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e100 - 1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e100 - 1500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eMS survey scan time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used MassLynx V4.1 software to collect MSE data, construct a compound database based on the literature and a DPN 24.2 natural product dictionary, and created a table to import all information into UNIFI software for peak identification and fingerprinting. Multivariate statistical analysis was performed on data using PCA, orthogonal projection by potential structure discriminant analysis (OPLS-DA), and permutation tests. Metabolites with VIP \u0026gt; 1.0 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 values were considered potential differential metabolites. Receiver operating characteristic curves were generated to assess data.Data processing\u003c/p\u003e\n\u003cp\u003eMassLynx V4.1 software was used to collect MSE data, compound database was constructed according to literature and DPN24.2 dictionary of natural products, and a table was established to import it into UNIFI software for peak identification and peak identification.\u003c/p\u003e\n\u003cp\u003eData analysis \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMetabolomics data were processed using Simca14.1. Principal Component Analysis (PCA) and Orthogonal Partial Least Squares-Discriminant Analysis were used. OPLS-DA) two multivariate statistical analysis methods. PCA analysis can directly reflect the overall distribution characteristics of sample data and effectively distinguish the metabolic differences among different groups. By constructing OPLS-DA model, the explanatory ability and predictive performance of the model were further evaluated. R2 and Q2 were used to measure the interpretability and predictability of the model, respectively. When R2 and Q2 values are both greater than 0.05, it indicates that the established model has good robustness and reliability, in which Q2\u0026gt;0.4 is considered to be an effective model, and Q2\u0026gt;0.9 is considered to be an excellent model. The Variable Importance in Projection (VIP) value calculated based on OPLS-DA model, Combined with the criteria of statistical significance (P\u0026lt;0.05), VIP threshold (VIP\u0026gt;1) and the Change factor of metabolite concentration (|Fold Change|\u0026gt;1.0), Differentially Expressed Metabolites (DEMs) with significant differences were screened. The selected DEMs data were imported into the Metabioalyst 5.0 pathway analysis module for metabolic pathway enrichment analysis. Paths with P \u0026lt; 0.05 were considered statistically significant.\u003c/p\u003e\n\u003cp\u003eJoint analysis of transcriptomics and metabolomics\u003c/p\u003e\n\u003cp\u003eCore regulatory networks based on gene and metabolite correlation analyses of transcriptomics and metabolomics data were generated.\u003c/p\u003e\n\u003cp\u003eNetwork construction\u003c/p\u003e\n\u003cp\u003eScreened genes and metabolites were imported into Cytoscape software to create gene-metabolite networks.\u003c/p\u003e\n\u003cp\u003eJoint-pathway analysis\u003c/p\u003e\n\u003cp\u003eThe Kyoto Encyclopedia of Genes and Genomes-based joint-pathway analysis was performed to analyze central genes and metabolites from the network, and major astaxanthin pathways were mined based on pathway impact values enriched by joint-pathway analysis.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eThe experimental data were analyzed by SPSS 26.0. t test was used for comparison between the two groups, and one-way analysis of variance was used for multiple groups. The results were represented by mean \u0026plusmn; standard deviation and plotted by origin. P\u0026lt;0.05 and P\u0026lt;0.001 were considered statistically significant. * means P\u0026lt;0.05 compared with the blank control group; # means P\u0026lt;0.05 compared with the model group; ## means P\u0026lt;0.001 compared with the model group.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eGeneral conditions\u003c/p\u003e\n\u003cp\u003eWhen compared with NC mice, OVA mice showed obvious sneezing, nose rubbing, and back arching, indicating successful OVA-induced asthma mouse model establishment. Compared with OVA mice, DEX, ASTA-H, and ASTA-L mice showed different level of improvement in the above phenomena, but the improvement of ASTA-L group was worse than the other two groups. (Fig.2a-b).\u003c/p\u003e\n\u003cp\u003eThe morphological effects of astaxanthin on lung tissue\u003c/p\u003e\n\u003cp\u003eMice were humanely euthanized after 4 weeks of continuous intervention, with lung tissues taken for Hematoxylin-eosin staining (HE), Masson\u0026rsquo;s trichrome staining (MASSON), and Periodic Acid-Schiff stain (PAS) staining. When compared with NC mice, OVA mice showed increased inflammatory cell infiltration and components, including eosinophils (Fig.3a), increased collagen fiber deposition (Fig.3b), and increased mucus secretion with lung septa thickening (Fig.3c). The area of blue collagen deposition indicated by the arrow was calculated using image j image processing software (Fig.3d), the collagen deposition in the OVA group of mice was significantly higher than that in the other groups (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), and it was significantly reduced after astaxanthin treatment, and increased mucus secretion, glycogen deposition, and thickening of the pulmonary septum. The area of glycogen deposition was calculated as indicated by the arrow (Fig.3c), in accordance with the results of collagen deposition area (Fig.3e) (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eInflammatory cell counts\u003c/p\u003e\n\u003cp\u003eTotal cell numbers in bronchial cell lavage fluid are shown (Fig.4a). Total inflammatory cell numbers in lavage fluid in OVA mice were significantly higher than the other groups (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05), which indicated successful modeling. The number of inflammatory cells in the ASTA-H (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) was less than ASTA-L (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), suggesting that astaxanthin inhibited inflammatory response.\u003c/p\u003e\n\u003cp\u003eLung dry-wet ratios\u003c/p\u003e\n\u003cp\u003eAfter bronchial lavage, the lungs were analyzed to measure wet/dry ratios. As shown (Fig.4b), compared with mice in the NC group, the OVA group had a 7% increase in the lung wet-to-dry ratio (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) and severe injury from elevated lung water content. In contrast, compared with the OVA group, the ASTA-H, ASTA-L, and DEX groups decreased by 6.8%, 5.2%, and 6.9% (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), respectively. These results indicated that astaxanthin reduced the risk of lung injury.\u003c/p\u003e\n\u003cp\u003eOxidative stress, inflammatory factors, and IgE analyses\u003c/p\u003e\n\u003cp\u003eSerum IL-4, IL-6, IL-13, and Ig-E levels were significantly higher in OVA mice (Fig.5a-d) when compared with NC mice, while levels were substantially lower in ASTA-H mice when compared with OVA mice (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). SOD activity levels were significantly lower in OVA mice (Fig.5e) (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). when compared with NC mice, whereas levels were significantly higher in ASTA-H and DEX mice (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), suggesting that astaxanthin attenuated oxidative damage associated with asthma.\u003c/p\u003e\n\u003cp\u003eAstaxanthin alters the transcriptome in asthmatic mice.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 6a-b, down-regulated Gene Enrichment Score in OVA and ASTA-H groups was obtained according to KEGG analysis to reflect the overall enrichment degree and Gene Ratio of gene set under specific conditions: The proportion of significant genes in a specific function or pathway is analyzed by combining the two values to analyze the pathway where the genes are mainly enriched. High Enrichment Score + high Gene Ratio indicates that this function and pathway are not only significantly enriched on the whole, but also the proportion of significant genes is relatively large.\u003c/p\u003e\n\u003cp\u003eThe up-regulated genes are mainly concentrated in Glutathione metabolism, Ribosome, Chemical carcinogens-reactive oxygen species and Hepatocellular carcinoma, Fluid shear stress and atherosclerosis, Drug metabolism other enzymes, Drug metabolism \u0026minus; cytochrome p450, Metabolism of xenobiotics by cytochrome. Down-regulated genes are mainly enriched in Cytosolic DNA-sensing pathway, Parkinson disease, Salivary secretion, Neurotrophin signaling pathway and Estrogen signaling pathway, Vascular smooth muscle contraction and other pathways.\u003c/p\u003e\n\u003cp\u003eTranscriptomics altered with ASTA treatment in asthmatic mice.\u003c/p\u003e\n\u003cp\u003eUsing PCA, NC, OVA, and ASTA-H lung tissue genes were grouped into different regions (Fig.7a). To identify DEGs in groups, we used the DEseq2 algorithm to process transcriptome data. Venn diagrams were used to screen for DEGs similar to NC and ASTA-H groups. We identified 198 DEGs (Fig.7b), which were visualized in a heatmap (Fig.7c).\u003c/p\u003e\n\u003cp\u003eAstaxanthin treatment alters metabolism in asthmatic mice.\u003c/p\u003e\n\u003cp\u003eTo perform metabolomics analysis on NC, OVA, and ASTA-H samples, we first validated UPLC-Q/TOF-MS stability, precision, reproducibility, and sample stability procedures, which indicated good strength, accuracy, and reproducibility. As shown Figure.8a, from PCA, NC, OVA, and ASTA-H samples were assigned to different areas. ASTA-H samples were assigned to the middle of the two groups, suggesting that astaxanthin altered the metabolic disorder in asthma model mice.\u003c/p\u003e\n\u003cp\u003eOPLS-DA obtained maximum separation using OVA and ASTA-H group samples. As can be seen from FIG. 15, samples in the OVA group and ASTA-H group were well separated from each other, indicating significant metabolomic differences between the two groups. The OPLS-DA model was then subjected to 200 permutation tests to ensure that there was no risk of fit. As shown in Figure 8a-b OPLS-DA, Q2 \u0026gt;0.4 in the original model is considered to be an effective model, and Q2 \u0026gt;0.9 is considered to be an excellent model. The cationic original model R2=0.99, Q2 \u0026gt;0.9, and the anionic R2=0.99, Q2 \u0026gt;0.8 did not appear overfitting. Therefore, the original model had strong interpretation and prediction ability of data, and could well capture the differences and patterns between groups and make reliable prediction of unknown data. OPLS-DA arrangement diagram (Fig. 8c-d) R2 (0.0, 0.986), Q2 (0.0, -0.518) in cations, R2 (0.0, 0.65), Q2 (0.0, -0.951) in anions, but most of the stochastic models have low R\u0026sup2; values, indicating that the stochastic models have poor ability to interpret and predict data. Therefore, R2 and Q2 in the original model were significantly higher than those in the random model, indicating that the model was statistically significant and could further effectively distinguish metabolic differences between different OVA and ASTA-H groups and screen out differential metabolites. In order to evaluate the diagnostic efficacy of 8 metabolites for target diseases, the receiver operating characteristic curve (ROC curve) of these 8 metabolites was predicted, and the area under the curve (AUC) was calculated, as shown in Figure 18g-h. The AUC area of metabolites glycerophospholipid, adenosine monophosphate, oleic acid and 2S-2-amino-5-amino-valeric acid were 0.9444, 0.8426, 0.8333, 0.8056, respectively, showing high diagnostic accuracy. Among them, the AUC value of glycerophospholipids was significantly higher than that of other metabolites (P\u0026lt;0.05), suggesting that glycerophospholipids could be used as potential biomarkers. And 8 differential metabolites were identified between groups (Tab.3) about 2S-2-amino-5-carbamoylaminopentanoicacid, Adenosine monophosphate, Glyceric acid 1,3-biphosphate, Colfosceril palmitate, Glycero phosphocholine, Retinyl ester, Chloro phenylalanine, Oleic acid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. 8 different metabolites identified in asthmatic mouse samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNO.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBiomarkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFormula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMass error (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFormula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVIP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2S-2-amino-5-\u003c/p\u003e\n \u003cp\u003eCarbamoylaminopentanoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eN\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.61*10\u003csup\u003e-7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.626974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdenosine monophosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003csub\u003e10\u003c/sub\u003eH\u003csub\u003e14\u003c/sub\u003eN\u003csub\u003e5\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.906834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.46173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlyceric acid 1,3-biphosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.706136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.46161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eColfosceril palmitate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003csub\u003e40\u003c/sub\u003eH\u003csub\u003e80\u003c/sub\u003eNO\u003csub\u003e8\u003c/sub\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0036298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.800583138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.32609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlycero phosphocholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e20\u003c/sub\u003eNO\u003csub\u003e6\u003c/sub\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0706894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.7718527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+H, +Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.24251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRetinyl ester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003csub\u003e20\u003c/sub\u003eH\u003csub\u003e30\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0170683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.587158582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.22148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChloro phenylalanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003csub\u003e9\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eCINO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0413623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.89928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eContinuation of table 3\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNO.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBiomarkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFormula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMass error (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFormula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVIP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOleic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003csub\u003e18\u003c/sub\u003eH\u003csub\u003e34\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.48*10\u003csup\u003e-11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.25492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+HCOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.53518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTypicality correlation analysis\u003c/p\u003e\n\u003cp\u003eTypicality correlation analysis is used to show correlations between two data groups. For this, we analyzed 198 DEGs and eight differential metabolites. The total explained variance of the explanatory variable matrix (metabolites) to the response variable matrix (genes) was 90.48%, indicating the reliability of this analysis (Fig.9a). We screened 20 genes (Rabac1, Hbb\u0026minus;bs, Acta2, Dynlt1b, Clic3, Tuba1a, Banf1, Skp1a, Gstm2, Rnf187, Arl3, Clec3b, Gstt1, Gstm1, Tuba1b, Cnn2, Mgp, H2afz, Adh1, C130074G19Rik) and 8 metabolites (2S-2-amino-5-carbamoylaminopentanoicacid, Adenosine monophosphate, Glyceric acid 1,3-biphosphate, Colfosceril palmitate, Glycero phosphocholine, Retinyl ester, Chloro phenylalanine, Oleic acid).based on gene-metabolite interaction heatmap data. These identified molecules may be implicated in astaxanthin mechanisms in asthma. (Fig.9c).\u003c/p\u003e\n\u003cp\u003eNetwork construction\u003c/p\u003e\n\u003cp\u003eWe constructed network interaction maps using these 20 genes and 8 metabolites. More connectivity was indicated, which exerted a more substantial influence on the regulatory network (Fig.10a-b). Overall, these molecules were closely related.\u003c/p\u003e\n\u003cp\u003ePathway analysis\u003c/p\u003e\n\u003cp\u003eTo investigate astaxanthin functions, we screened pathways associated with the 20 genes and 8 metabolites using joint-pathway analysis. 5 significant pathways were identified (Tab.4): Cytochrome P450 drug metabolism, Metabolism of xenobiotic by cytochrome P450, Glutathione metabolism, Glycolysis or Gluconeogenesis, and Retinol metabolism. Bubble plots, showing descending impact value orders, calculate the \u0026minus;log10(p) value to prove the path difference, which were generated using MetaboAnalyst 5.0 (Fig.11a).\u003c/p\u003e\n\u003cp\u003eFinally, we established a network diagram including genes, metabolites, and pathways (Fig.11b), screening out the core pathways. Square boxes are genes and circles are metabolites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Gene-metabolite interaction pathways\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003ePATHWAY NAME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eExpected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-log(p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eImpact\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eDrug metabolism-cytochrome P450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.18207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.41*10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.6188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.004506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.12264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eMetabolism of xenobiotics by cytochrome P450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.20254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.66*10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.4371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.004506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.35714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eGlutathione metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.11096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.000162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.7896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.010616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.17391\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eGlycolysis or Gluconeogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.1045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.004709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.3271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.13998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.18462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eRetinol metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.12497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.006668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.18171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.22034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt is reported that astaxanthin alleviates airway inflammation and lung damage\u003csup\u003e[8]\u003c/sup\u003e. In this study, we investigated the therapeutic potential of astaxanthin in asthma using an ovalbumin (OVA)-induced murine model. Our findings demonstrate that astaxanthin ameliorated airway inflammation, attenuated oxidative stress, modulated immune responses, and reduced pulmonary pathology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the experiment, by observing the general condition of the mice, we found that the body weight of the mice in the ASTA group tended to increase slowly after ovalbumin stimulation, and we guessed that this might be related to the astaxanthin increases the activity of intestinal digestive and absorptive enzymes (intestinal creatine kinase), which provides energy for the digestion and absorption of nutrients, leading to weight gain and growth\u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBased on the transcriptomics results, we identified 20 genes associated with asthma. Among the up-regulated genes Clic3 and Skp1 are associated with immune disorders, which would explain why IgE levels were elevated in the ASTA group. Clic3 and Skp1 can inhibit the activation of NF-κB, and restrain the release of inflammatory cytokines including IL-4, IL-6, IL-13, thereby reducing the level of inflammation. \u003csup\u003e[13,14]\u003c/sup\u003e. Adh1 was further associated with reduced S-nitrosoglutathione (GSNO) activity, a compound whose accumulation exacerbates asthma. Thus, elevated Adh1 expression may confer protection against asthma development.\u0026nbsp;\u0026nbsp;These observations are consistent with our study data\u003csup\u003e[15]\u003c/sup\u003e. Hbb-bs can increase the body's antioxidant level, thus reducing the level of oxidative stress\u003csup\u003e[16]\u003c/sup\u003e. Among the down-regulated genes, In the immune environment, low expression of banf1can boost\u0026nbsp;antitumor immune responses with\u0026nbsp;CD8+ T-cell infiltration and activation\u003csup\u003e[17]\u003c/sup\u003e. The level of Acta2 expression recognizes the level of airway smooth muscle cells, and high expression of Acta2 causes the thickening of airway smooth muscle and exacerbates asthma\u003csup\u003e[18]\u003c/sup\u003e. In contrast, astaxanthin treatment downregulated gene expression and reduced asthma risks. Glutathione S-transferases (GSTs) are associated with cell integrity, defenses against oxidative stress and DNA damage, and detoxifying endogenous/exogenous compounds in cells. Of the transferases, GST-P was implicated in asthma pathogenesis where it regulated protein oxidative status via S-glutathione\u003csup\u003e[19]\u003c/sup\u003e. A population-based study reported that Gstt1 and Gstm1 were risk factors for asthma and that Gstm2, which is abundantly expressed in human bronchi, increased oxidative stress by affecting glutathione metabolism\u003csup\u003e[20]\u003c/sup\u003e. Although Cnn2 is not directly related to asthma treatment that reflects the severity of asthma\u003csup\u003e[21]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur experiments identified 8 metabolites that may be relevant to astaxanthin in the treatment of asthma. Adenosine monophosphate, which is implicated in airway dysfunction due to abnormal lung gas exchange in asthma\u003csup\u003e[22]\u003c/sup\u003e, was identified in OVA mice and was reduced by astaxanthin. Studies have reported that citrulline supplementation reversed hyperoxia-induced impaired tracheal smooth muscle relaxation in rats\u003csup\u003e[23]\u003c/sup\u003e. High Glycero phosphocholine (PC (36:1)) levels were putatively associated with asthma risk in children\u003csup\u003e[24]\u003c/sup\u003e, with similar PC (36:1) modulation trends observed in our study. Oleic acid has many biological effects, such as lowering low-density lipoprotein, increasing high-density lipoprotein, lowering blood pressure\u003csup\u003e[25]\u003c/sup\u003e, exerting anti-inflammatory effects\u003csup\u003e[26]\u003c/sup\u003e, and inducing anti-asthmatic effects\u003csup\u003e[27]\u003c/sup\u003e. The glycolytic pathway, in which glyceraldehyde 1,3-bisphosphate occurs, is also involved in airway hyperresponsiveness and remodeling in asthma\u003csup\u003e[19]\u003c/sup\u003e. Retinyl acetate is a long-chain fatty acid lipid of vitamin A, and studies have shown that vitamin A deficiency exacerbates allergic asthma\u003csup\u003e[28]\u003c/sup\u003e. Our analysis also showed similar results in OVA mice, which were significantly ameliorated by astaxanthin.\u003c/p\u003e\n\u003cp\u003eIntegrating transcriptomic and metabolomic analyses, we identified potential correlations between key pathways. Given the critical roles of inflammatory responses and oxidative stress in asthma pathogenesis, astaxanthin demonstrated therapeutic effects by suppressing Gstt1 and Gstm1 expression. We hypothesize that this downregulation may inhibit actin-alpha 2 (Acta2), thereby reducing collagen deposition, while simultaneously attenuating glycolysis and inflammatory responses. \u0026nbsp;We also identified an upstream product of Adh1, glyceraldehyde 1,3-bisphosphate, which promotes its expression. Previously, we demonstrated that retinol expression was reduced in Adh1-deficient mice and that retinyl acetate was involved in retinol metabolism and exerted antioxidant effects\u003csup\u003e[29]\u003c/sup\u003e. Our findings indicate that Adh1 participates in retinol metabolism as an upstream regulator of retinyl acetate formation. Based on this observation, we propose that astaxanthin may enhance Adh1 activity through glycerol 1,3-bisphosphate-mediated activation, thereby promoting retinyl acetate production and potentially mitigating asthma pathogenesis. \u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAstaxanthin ameliorates pathological symptoms in asthmatic mice and modulates the differential expression of genes and metabolites associated with asthma pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"385\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7895%;\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 51.4551%;\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7895%;\"\u003e\n \u003cp\u003eASTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 51.4551%;\"\u003e\n \u003cp\u003eAstaxanthin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7895%;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 51.4551%;\"\u003e\n \u003cp\u003eBiological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7895%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 51.4551%;\"\u003e\n \u003cp\u003eCell Component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eDifferentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eFPKM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eFragments per kilobase per million\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eIL-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eInterleukin-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eIL-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eInterleukin-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eInterleukin-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eIgE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eImmunoglobulin E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eMolecular Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eOPLS-DA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eOrthogonal Partial Least Squares Discriminant Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eReceiver Operating Characteristic Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.4489%;\"\u003e\n \u003cp\u003eSOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.8824%;\"\u003e\n \u003cp\u003eSuperoxide dismutase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments complied with ARRIVE 2.0 guidelines and were approved by the Ethics Committee of Harbin Medical University (ID: HMUIRB2024019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZihui Ma conducts relevant experiments and writes papers, and Yuteng Ma, and Hongquan Xie carry out relevant responsible software. Yue Li Conducts supervision. Guoling Wang and Haoyu Wang accountable for verification. Yanan Jiang and Ying Liu are responsible for the overall review.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research part of drawing used the Omicshare database (http://omicshare.com/) and the Metware Cloud (http://cloud metware.cn/). Competing Interests:There are no competing interests.Funding:There is no funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDharmage S C, Perret J L, Custovic A. Epidemiology of Asthma in Children and Adults[J]. Front Pediatr, 2019, 7:246.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedersen S. Do inhaled corticosteroids inhibit growth in children?[J]. Am J Respir Crit Care Med, 2001, 164(4):521\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJha S S, Kumar M, Agrawal P K, et al. Osteoporosis in Asthma and COPD[J]. Indian J Orthop, 2023, 57(Suppl 1):200\u0026ndash;208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrossingham I, Turner S, Ramakrishnan S, et al. Combination fixed-dose β agonist and steroid inhaler as required for adults or children with mild asthma: a Cochrane systematic review[J]. BMJ Evid Based Med, 2022, 27(3):178\u0026ndash;184.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBi J, Cui R, Li Z, et al. Astaxanthin alleviated acute lung injury by inhibiting oxidative/nitrative stress and the inflammatory response in mice[J]. Biomed Pharmacother, 2017, 95:974\u0026ndash;982.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshihisa Y, Andoh T, Matsunaga K, et al. Efficacy of Astaxanthin for the Treatment of Atopic Dermatitis in a Murine Model[J]. PLoS One, 2016, 11(3):e0152288.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChalyk N E, Klochkov V A, Bandaletova T Y, et al. Continuous astaxanthin intake reduces oxidative stress and reverses age-related morphological changes of residual skin surface components in middle-aged volunteers[J]. Nutr Res, 2017, 48:40\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang Y H, Hong S G, Mun S K, et al. The Protective Effects of Astaxanthin on the OVA-Induced Asthma Mice Model[J]. Molecules, 2017, 22(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnderwood D C, Osborn R R, Novak L B, et al. Inhibition of antigen-induced bronchoconstriction and eosinophil infiltration in the guinea pig by the cyclic AMP-specific phosphodiesterase inhibitor, rolipram[J]. J Pharmacol Exp Ther, 1993, 266(1):306\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubali D, Kurniawan R, Surya R, et al. Revealing the mechanism and efficacy of natural products on treating the asthma: Current insights from traditional medicine to modern drug discovery[J]. Heliyon, 2024, 10(11):e32008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePecak M, Korošec P, Kunej T. Multiomics Data Triangulation for Asthma Candidate Biomarkers and Precision Medicine[J]. Omics, 2018, 22(6):392\u0026ndash;409.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J, Kim M H, Kim H. Anti-Oxidant and Anti-Inflammatory Effects of Astaxanthin on Gastrointestinal Diseases[J]. Int J Mol Sci, 2022, 23(24).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang J, Long Z, Zhang Y, et al. Chloride intercellular channel 3 suppression-mediated macrophage polarization: a potential indicator of poor prognosis of hepatitis B virus-related acute-on-chronic liver failure[J]. Immunol Cell Biol, 2022, 100(5):323\u0026ndash;337.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi R, Sano T, Mizokami A, et al. miR-582-5p targets Skp1 and regulates NF-κB signaling-mediated inflammation[J]. Arch Biochem Biophys, 2023, 734:109501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQue L G, Liu L, Yan Y, et al. Protection from experimental asthma by an endogenous bronchodilator[J]. Science, 2005, 308(5728):1618\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Wu M X, Li H M, et al. Potential benefits of Rehmanniae Radix after ancient rice-steaming process in promotion of antioxidant activity in rats' health[J]. Food Sci Nutr, 2023, 11(9):5532\u0026ndash;5542.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, Huang Y, Chen M, et al. Inhibition of tumor intrinsic BANF1 activates antitumor immune responses via cGAS-STING and enhances the efficacy of PD-1 blockade[J]. J Immunother Cancer, 2023, 11(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFacciolongo N, Bonacini M, Galeone C, et al. Bronchial thermoplasty in severe asthma: a real-world study on efficacy and gene profiling[J]. Allergy Asthma Clin Immunol, 2022, 18(1):39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan De Wetering C, Manuel A M, Sharafi M, et al. Glutathione-S-transferase P promotes glycolysis in asthma in association with oxidation of pyruvate kinase M2[J]. Redox Biol, 2021, 47:102160.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnttila S, Hirvonen A, Vainio H, et al. Immunohistochemical localization of glutathione S-transferases in human lung[J]. Cancer Res, 1993, 53(23):5643\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOng M S, Sordillo J E, Dahlin A, et al. Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma[J]. J Pers Med, 2024, 14(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManrique H A, G\u0026oacute;mez F P, Mu\u0026ntilde;oz P A, et al. Adenosine 5'-monophosphate in asthma: gas exchange and sputum cellular responses[J]. Eur Respir J, 2008, 31(6):1205\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSopi R B, Zaidi S I, Mladenov M, et al. L-citrulline supplementation reverses the impaired airway relaxation in neonatal rats exposed to hyperoxia[J]. Respir Res, 2012, 13(1):68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Z, Camargo C A, Jr., Raita Y, et al. Metabolome subtyping of severe bronchiolitis in infancy and risk of childhood asthma[J]. J Allergy Clin Immunol, 2022, 149(1):102\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsikin Y, Takahashi M, Mishima T, et al. Antioxidant activity of sugarcane molasses against 2,2'-azobis(2-amidinopropane) dihydrochloride-induced peroxyl radicals[J]. Food Chem, 2013, 141(1):466\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErdinest N, Shohat N, Moallem E, et al. Nitric oxide secretion in human conjunctival fibroblasts is inhibited by alpha linolenic acid[J]. J Inflamm (Lond), 2015, 12:59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee S Y, Bae C S, Seo N S, et al. Camellia japonica oil suppressed asthma occurrence via GATA-3 \u0026amp; IL-4 pathway and its effective and major component is oleic acid[J]. Phytomedicine, 2019, 57:84\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDefnet A E, Shah S D, Huang W, et al. Dysregulated retinoic acid signaling in airway smooth muscle cells in asthma[J]. Faseb j, 2021, 35(12):e22016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolotkov A, Duester G. Genetic evidence that retinaldehyde dehydrogenase Raldh1 (Aldh1a1) functions downstream of alcohol dehydrogenase Adh1 in metabolism of retinol to retinoic acid[J]. J Biol Chem, 2003, 278(38):36085\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"astaxanthin asthma transcriptomics metabonomics","lastPublishedDoi":"10.21203/rs.3.rs-6366020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6366020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eAsthma is a complex disease characterised by chronic airway inflammation and airway remodelling, and its pathogenesis involves a variety of factors such as inflammatory response, oxidative stress and immunomodulatory imbalance. Although existing treatments (e.g., glucocorticosteroids and β2 agonists) are effective in controlling symptoms, some patients still suffer from treatment resistance or drug side effects, so it is important to explore new therapeutic strategies and targets. In recent years, astaxanthin has received much attention for its potent antioxidant and anti-inflammatory activities. Astaxanthin is able to play a protective role in a variety of inflammatory, immune diseases by scavenging free radicals and inhibiting inflammatory pathways such as NF-κB. However, studies on astaxanthin in asthma are still relatively limited and mostly focus on a single mechanism using a single histological technique, which makes it difficult to comprehensively reveal the regulatory network of astaxanthin at the gene and metabolic levels. The aim of this study was to investigate the ameliorative effects of oral astaxanthin on asthma symptoms by integrating metabolomics and transcriptomics analyses, and to screen potential asthma-related biomarkers and therapeutic targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod \u003c/strong\u003eSeventy-five female BALB/C mice (SPF grade, 6-8 weeks old) were divided into five groups on the basis of randomisation: the blank control group (NC), the model group (OVA), the dexamethasone group (DEX), the astaxanthin low-dose group (ASTA-L), and the astaxanthin high-dose group (ASTA-H), and the mice in the remaining four groups were injected intraperitoneally with 0.01% ovoacidin (OVA) and 0.01% ovoacidin (OVA) on days 0, 7, and 14, respectively. The asthma model was constructed by intraperitoneal injection of 0.01% ovalbumin on days 0, 7 and 14, and nebulisation of 2.5% ovalbumin every other day from day 21 onwards. dexamethasone and astaxanthin were administered to the DEX group, the ASTA-L group (25 mg/kg), and the ASTA-H group (50 mg/kg) by gavage 3 hours before each nebulisation. The body weight and food intake of mice in each group were observed weekly. After 4 weeks of continuous nebulisation, 5 mice in each group were anaesthetised and bronchoalveolar lavage was performed to collect the lavage fluid to calculate the total cell count. The remaining 10 eyes were blood sampled and killed, and serum interleukin 4 (IL-4), interleukin 6 (IL-6), interleukin 13 (IL-13), immunoglobulin E (IgE), superoxide dismutase (SOD) levels were measured, and the lung wet/dry ratios were calculated, and the lung tissues were subjected to histological staining by HE, MASSON, and PAS, and analyses of transcriptomics and nontarget metabolomics. Screening of differentially expressed genes(DEGs) and metabolites(DEGs), joint study to construct gene metabolic network to analyse core pathways.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult \u003c/strong\u003e(1) Compared with the OVA group, the lung dry-to-wet ratio and the total number of cells in the bronchoalveolar lavage fluid were reduced in the ASTA-L and ASTA-H groups (P\u0026lt;0.001) ASTA-H group was more obvious. (2) HE, MASSON, and PAS staining analysis showed that compared with the OVA group, the airway wall inflammatory cell infiltration, pulmonary septal thickness, collagen deposition, and glycogen deposition area were improved in the ASTA-L and ASTA-H groups, but the improvement was more significant in the ASTA-H group compared with the ASTA-L group. (3) Serum levels of IL-4, IL-6, IL-13, and IgE were reduced and SOD activity was increased in the ASTA-H group compared with the OVA group (P\u0026lt;0.001). (4) Transcriptomics analysed 20 genes including Gstt1, Gstm1 and Adh1, and metabolomics analysed 8 metabolites including glyceraldehyde-1,3-bisphosphate. These genes and metabolites were mainly involved in key processes such as inflammatory response, oxidative stress and immune regulation. (5) Combined analyses further revealed that astaxanthin up-regulates Adh1 expression by activating the expression of glyceraldehyde-1,3-bisphosphate, and the up-regulation of Adh1 further contributes to the synthesis of retinyl acetate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eAstaxanthin attenuates airway inflammation, oxidative stress and immune stress in asthmatic mice. The mechanism may be related to the activation of glyceraldehyde-1,3-bisphosphate up-regulation of Adh1 expression contributing to the synthesis of retinyl acetate.\u003c/p\u003e","manuscriptTitle":"Mechanistic Insights into the Effects of Astaxanthin on Asthma in Mice: A Combined Transcriptomic and Metabolomic Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 20:49:02","doi":"10.21203/rs.3.rs-6366020/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"97b04458-bd66-4c21-b53d-4adc3589a108","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-24T06:08:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-08 20:49:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6366020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6366020","identity":"rs-6366020","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}