Metabolomic and Proteomic Analysis of Bone Marrow Supernatant in Myelodysplastic Syndrome Using Astral-based DIA and LC-MS/MS

Metabolomic and Proteomic Analysis of Bone Marrow Supernatant in Myelodysplastic Syndrome Using Astral-based DIA and LC-MS/MS | 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 Article Metabolomic and Proteomic Analysis of Bone Marrow Supernatant in Myelodysplastic Syndrome Using Astral-based DIA and LC-MS/MS Peizhen Jiang, Jiaqi He, Yaoyin Zhang, Yan Gao, Qingguo Liu, Dexiu Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7572811/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 Myelodysplastic syndromes (MDS) represent a heterogeneous group of clonal hematopoietic stem cell disorders characterized by malignant potential and complex pathobiological mechanisms. While specific gene mutations (SF3B1, TET2, ASXL1, TP53) contribute significantly to MDS, disease progression depends equally on malignant clones and the bone marrow microenvironment. We employed integrated Astral-DIA technology with LC-MS/MS to characterize protein and metabolic alterations in this microenvironment, enabling dynamic pathological analysis at functional and phenotypic levels. Comparative analysis of bone marrow supernatant from 28 MDS patients and 10 healthy controls identified pronounced proteomic imbalances, disrupted amino acid and energy metabolism pathways, and diagnostic biomarkers (L-Aspartate, L-Arginine, L-Tryptophan, GSR, APOA1) with strong discriminatory power for early-stage disease (AUC>0.9). Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Cell biology Biological sciences/Molecular biology metabolomic proteomic myelodysplastic syndrome bone marrow supernatant astral diagnostic Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Myelodysplastic syndromes (MDS) represent a heterogeneous group of clonal hematopoietic stem cell disorders with an inherent propensity for progression to acute myeloid leukemia (AML). Research has increasingly focused on elucidating MDS pathogenesis, which appears fundamentally driven by somatic mutations in hematopoietic stem cells that confer clonal advantage 1 , 2 . Epigenetic dysregulation, particularly aberrant DNA methylation and histone modifications, disrupts normal gene expression programs and contributes to ineffective hematopoiesis 3 , 4 . Concurrently, innate immune dysregulation and chronic inflammatory signaling mediated by myeloid-derived suppressor cells (MDSCs) and cytokine imbalances facilitate disease progression and immune evasion 5 . The bone marrow microenvironment further participates in MDS pathogenesis through dysfunctional hematopoietic-stromal interactions that promote clonal expansion while suppressing normal hematopoiesis 6 . Diagnostically, next-generation sequencing and cytogenetic analysis now constitute essential tools for accurate diagnosis, risk stratification, and identification of genetic predispositions 7 – 9 . The 2022 WHO and ICC classification systems incorporate specific genetic alterations (e.g., SF3B1 mutations, del(5q)) alongside morphological features to define MDS subtypes 10 . Treatment approaches increasingly reflect genetic profiles and risk status: erythropoiesis-stimulating agents remain first-line for lower-risk disease, with luspatercept or lenalidomide reserved for specific subtypes, while hypomethylating agents form the backbone of higher-risk MDS therapy 11 – 14 . Allogeneic hematopoietic stem cell transplantation persists as the sole potentially curative intervention, though relapse and drug resistance remain significant challenges 12 , 15 . Advances in metabolomics and proteomics have provided new mechanistic insights into hematologic malignancies, including MDS. These approaches reveal how perturbations in glucose, amino acid, and lipid metabolism contribute to disease pathogenesis and therapeutic resistance 16 – 18 . NMR- and MS-based metabolomics identify distinct metabolic signatures that differentiate hematologic malignancies from healthy states, while high-throughput proteomics enables detection of diagnostic biomarkers like free light chains and β₂-microglobulin 19 – 21 . Proteomic analyses further uncover dysregulated signaling pathways and potential therapeutic targets through system-wide profiling of protein expression and post-translational modifications 22 . Recent metabolomic studies demonstrate altered inflammatory and metabolic pathways in MDS patient plasma, involving amino acid, fatty acid, and energy metabolism 23 . Emerging evidence also implicates gut microbiome-plasma metabolome interactions in MDS pathogenesis 24 . However, the heterogeneity of protein expression patterns across MDS subtypes complicates the translation of population-level findings to individual patients 25 . Advanced MS platforms now enable sensitive detection of low-abundance plasma proteins that may serve as phase-specific biomarkers 26 . We hypothesize that integrated metabolomic-proteomic analysis of bone marrow supernatant from treatment-naïve MDS patients could reveal microenvironmental alterations and metabolic perturbations underlying disease progression. Our dual-omics approach aims to characterize dynamic molecular changes throughout treatment courses, potentially identifying novel therapeutic targets and informing personalized treatment strategies. 2. Material and method 2.1 Ethical approval The study complies with the Declaration of Helsinki, the study protocol was approved by the local ethics committees (Approval No. 2023XLA056), and informed written consent was obtained from each participant. and it was performed per national and international guidelines. 2.2 Participants Eligible participants of MDS met the following criteria: (1) confirmed myelodysplastic syndrome diagnosis, (2) absence of severe cardiac, hepatic, or renal comorbidities, (3) no concurrent participation in other clinical trials, (4) age between 18 and 75 years, and (5) provision of written informed consent. Control selection required: normal bone marrow function (hemoglobin ≥13 g/dL, absolute neutrophil count ≥1,800/μL, platelet count ≥125,000/μL); normal liver function (total bilirubin, albumin, aspartate aminotransferase, alanine aminotransferase, and alkaline phosphatase within reference ranges); normal renal function (serum creatinine within reference range); unremarkable urinalysis (negative for hematuria, proteinuria, and bacteriuria); and normocellular or moderately hypercellular bone marrow without dysplastic features. 2.3 Sample collection and processing All participants underwent sterile bone marrow aspiration from the posterior superior iliac spine. We collected 4 mL of bone marrow blood in EDTA vacuum tubes, centrifuged samples at 150 ×g for 8 minutes, aliquoted the supernatant into labeled cryovials, and stored them at -80°C. 2.4 Proteomic Profiles Analysis High-abundance proteins were removed from bone marrow plasma supernatant using Thermo-TOP14 depletion. Protein concentrations were quantified and normalized using the Bradford assay. For proteomic analysis, 200 ng of the depleted supernatant was loaded onto a Vanquish Neo nano-UHPLC system equipped with two Thermo Scientific columns: a 17450 C18 (0.5 mm × 300 μm, 5 μm) and a PepMap ES906 C18. Mass spectrometry was performed on a Thermo Orbitrap Astral instrument with an Easy-Spray ion source operating at 2.0 kV and 290°C in data-independent acquisition mode. The mobile phase consisted of 0.1% formic acid in water (phase A) and 0.1% formic acid in 80% acetonitrile (phase B), delivered at 2.5 μL/min. A step gradient elution was employed, increasing phase B from 4% to 55% over 12.6 minutes, followed by a 1-minute wash at 99% phase B. 2.5 Metabolomic Profiles Analysis After thawing, samples were mixed with 400 µL methanol, vortexed for 1 min, and centrifuged at 12,000 rpm (4°C) for 10 min. The supernatant was evaporated to dryness and reconstituted in 150 µL of 80% methanol-water solution containing 2-chloro-L-phenylalanine (4 ppm) before filtration and analysis. Chromatographic separation was achieved using a Thermo Vanquish UHPLC system equipped with an ACQUITY UPLC® HSS T3 column (2.1×100 mm, 1.8 µm) at 0.3 mL/min. Positive ion mode employed mobile phases A2 (0.1% formic acid in water) and B2 (0.1% formic acid in acetonitrile), while negative ion mode used A3 (5 mM ammonium formate in water) and B3 (acetonitrile), both with gradient elution. Mass spectrometric detection was performed on a Thermo Orbitrap Exploris 120 instrument with an ESI source, collecting data in both ionization modes. 2.6 Statistical and Bioinformatics Analysis Proteins were considered differentially expressed when meeting the thresholds of FDR 1.5. Gene Set Enrichment Analysis (GSEA) was conducted via the Metscape platform, incorporating Gene Ontology-Biological Process terms, Kyoto Encyclopedia of Genes and Genomes pathways, Small Molecule Pathway Database entries, and Reactome gene sets. GSEA revealed biological pathways linked to specific proteins and metabolites. Protein-protein interaction (PPI) network analysis examined functional relationships among differentially expressed biomolecules to identify key regulatory networks and modules. The Wilcoxon signed-rank test determined the statistical significance of intergroup expression differences. Metabolomics data were processed using the same analytical pipeline as the proteomics data, detailed elsewhere in this study. Metabolites with FDR 1.5 were classified as differentially expressed. Metabolite Set Enrichment Analysis was performed in MetaboAnalyst, encompassing Over Representation Analysis, Pathway Analysis, and Joint Pathway Analysis. 3. Results 3.1 Demographic feature 28 MDS patients and 10 healthy controls were enrolled in this study. There was no significant difference in age between the oligospermia group and the healthy control group. Demographic information and clinical parameters are given in Table 1. 3.2 Proteomic Profile of bone marrow supernatants in MDS patients OPLS-DA analysis revealed distinct proteomic separation between the MDS and healthy groups (Figure 1a), with model permutation tests demonstrating strong validity (R²=0.934, Q²=0.451; Figure 1b). The MDS group exhibited 315 differentially expressed proteins (Fc≥1.5 or ≤0.666, FDR<0.05) relative to controls, comprising 247 upregulated and 68 downregulated proteins (Figure 1c). GO enrichment analysis indicated that most differential proteins localized to macromolecular complexes and intracellular components, primarily involved in metabolic processes including cellular, protein, carbohydrate, and macromolecule metabolism (Figure 1d). KEGG pathway analysis identified the top 10 enriched pathways (Figure 1e), spanning Metabolism, Genetic Information Processing, Membrane Transport, Environmental Information Processing, Cellular Processes, and Organismal Systems. Among 87 pathway-related differential proteins, 12 were downregulated, suggesting impaired lipid metabolism and lysosomal function in MDS. The 75 upregulated proteins included glutathione system components (GSR: FC=1.88, P<0.001; GPX1: FC=1.58, P<0.05; GCLC: FC=1.55, P<0.01) and amino acid metabolism regulators (ARG1: FC=1.58, P<0.05; GLUL: FC=2.63, P<0.05; GOT1: FC=1.62, P<0.001), indicating redox imbalance and metabolic dysregulation in MDS marrow (Figure 1f). Protein-protein interaction network analysis of the DEPs yielded 393 nodes and 8,206 edges. Cytoscape's MCODE plugin identified the five highest-density functional modules based on network connectivity (Figure 1g). 3.3 Metabolic Profile of bone marrow supernatants in MDS patients The OPLS-DA analysis of MDS bone marrow supernatant metabolites (Figure 2a) revealed clear separation between the MDS and HC groups, demonstrating significant metabolic alterations. The permutation test yielded R²=0.9443 and Q²=-0.197 (Figure 2b), while MSI-based identification detected 1417 metabolites (Figure 2c). Applying strict screening criteria (VIP≥1, Fc≥1.5 or ≤0.666, FDR≤0.05), we identified 77 differential metabolites as potential MDS biomarkers, comprising 51 upregulated and 26 downregulated compounds classified into seven categories: amino acids, lipids, carbonyl compounds, carboxylic acids and derivatives, fatty acyls, hydroxy acids and derivatives, and others (Figure 2d). KEGG enrichment analysis highlighted key pathways including arginine biosynthesis (Impact>1), citrate cycle, glycolysis/gluconeogenesis (Impact 0.5-1), and unsaturated fatty acid synthesis, indicating disrupted energy and nitrogen metabolism in MDS patients. The top 15 metabolites contributing to major pathways (Figure 1f) included mukonine, 2-phosphoglycerate, L-arginine, and malic acid. HCA separated differential metabolites into two clusters: Cluster 1 (L-arginine, 2-phosphoglyceric acid) showed reduced expression in MDS, while Cluster 2 (3-dehydrosphinganine, malic acid, cysteinylglycine) exhibited elevated expression. Metabolite expression was more uniform in HC than in MDS patients, potentially reflecting disease heterogeneity that warrants subtype-specific analysis. 3.4 Integrated Analysis of Proteomics and Metabolomics Correlation analysis identified significant associations between differential proteins and metabolites (Figure 3a). In MDS patients, reduced L-arginine and elevated L-aspartate likely impair ammonia detoxification. Malate acid increases as compensatory mechanism through the aspartate-malate shuttle, ultimately disrupting the urea cycle. Cysteinylglycine directly depletes GSH, while 3-dehydrosphinganine exacerbates oxidative damage via sphingolipid toxicity, establishing a self-perpetuating cycle of oxidative stress. Concurrently, 2-PG inhibits glycolysis, and elevated tryptophan activates proteasomes while further compromising cellular energy supplies. KEGG enrichment analysis (Figure 3b) suggests that arginine metabolism defects elevate blood ammonia, potentially inducing DNA damage in hematopoietic stem cells. Glutathione depletion increases ceramide levels, which may promote p53-mediated stem cell apoptosis. Accelerated glycolysis/gluconeogenesis flux leads to lactate accumulation, suppressing normal hematopoietic progenitor function. PPI network analysis of key differential proteins and metabolites (Figure 3c) highlighted three primary interactors: 2-amino-4,6-dinitrotoluene glucoside, L-aspartic acid, and blumealactone C. These findings implicate environmental pollutants, dietary protein metabolism, and systemic metabolic dysregulation in MDS pathology. 3.4 Correlation of Differential Metabolites and Proteins with Clinical Characteristics Correlation analyses of GEMs and GEPs in MDS versus HC and peripheral blood cells identified potential biomarkers. Sankey diagrams of major pathway metabolites (Figures 4a, 4e) demonstrate that hemoglobin (Hb)-associated metabolites predominate, followed by platelet (BPC)-related compounds, with white blood cell (WBC) and neutrophil (NEUT)-linked metabolites being less abundant. Several metabolites—L-Arginine, 2-Phosphoglyceric acid, 3-Dehydrosphinganine, Malic acid, and L-Aspartic acid—showed strong correlations with three blood cell types (Figure 4b). The differential proteins GSR, FAU, APOA1, ANTXR1, ARSB, and APOC1 also exhibited significant blood cell correlations (Figure 4b). ROC analysis revealed high diagnostic potential for L-Arginine, L-Aspartic acid, and L-Tryptophan (AUC≥0.92; Figures 4c, 4d), as well as for the proteins GSR and APOA1 (AUC≥0.92), supporting their utility as early MDS biomarkers. No significant associations emerged between differential metabolites and clinical features (WHO 2022 subtype, IPSS-R, or blast percentage ≥5% vs <5%). 4. DISCUSSION Here is the polished paragraph with the requested improvements while maintaining the original sentence count and technical accuracy: We collected bone marrow samples from 28 treatment-naive MDS patients and 10 healthy controls, comparing their proteomic and metabolic profiles. The analysis revealed significant depletion of lysosomal and lipid proteins alongside hyperactivation of amino acid and redox pathways, consistent with metabolomic enrichment patterns. Unlike previous reports of lipid metabolism alterations in CD34⁺ cells and plasma inflammation/fatty acid dysregulation²⁷, our supernatant data demonstrated predominant disruptions in amino acid and energy metabolism. MDS CD34⁺ cells display mitochondrial-coordinated metabolism dependent on glutamate catabolism and glucose-driven biosynthesis²⁸,²⁹, mirroring the glutamine dependence observed in AML³⁰. Increased expression of GSR, GPX1, and GCLC indicates adaptive responses to glucose-associated oxidative stress. The integrated analysis identified arginine and aspartate imbalances with compensatory malate accumulation, triggering oxidative stress cascades and energy dysregulation—clear markers of mitochondrial dysfunction. Accumulated oncometabolites (L-2HG, succinate), mitochondrial impairment, and redox imbalance establish conditions favoring ineffective hematopoiesis, clonal expansion, and leukemic transformation³¹⁻³³. Whether mitochondrial biomarkers directly initiate ineffective hematopoiesis remains to be determined. Key amino acids involved in hematopoiesis showed negative correlations with erythrocyte and platelet counts. Differential protein expression strongly correlated with erythrocyte parameters. While lacking direct erythroid connections like ATP, erythrocyte maturation depends on coordinated metabolite networks involving amino acids, sugars, and nucleotides³⁴. The hematologic specificity of these relationships requires further investigation. Compared to established plasma biomarkers (LRG, fetuin, VCAM, ICAM, clusterin, S100A8)³⁵, ROC analysis identified L-aspartate, L-arginine, L-tryptophan (metabolites) and GSR/APOA1 (proteins) as potential diagnostic markers (AUC≥0.92). Metabolites provide real-time pathophysiological insights unattainable through genomic or proteomic analysis alone. Capturing molecular-metabolic dynamics during MDS onset proves essential for understanding disease progression. Mass spectrometry integration significantly improves diagnostic precision. While metabolites are traditionally viewed as end products sustaining vital activities during disease, monitoring their dynamic changes offers real-time physiological snapshots. Given the inherently dynamic nature of MDS pathogenesis, investigating molecular functional shifts and metabolic features at disease onset is critical for deciphering MDS pathology. Simultaneous measurement of functional proteins and real-time metabolites through mass spectrometry enhances both diagnostic accuracy and early detection capabilities. In conclusion, significant protein imbalances, amino acid/energy dysregulation, and mitochondrial disturbances in MDS marrow represent valuable targets for early diagnosis. Declarations Ethical approval The study complies with the Declaration of Helsinki, the study protocol was approved by the local ethics committees (Approval No. 2023XLA056), and informed written consent was obtained from each participant. and it was performed per national and international guidelines. Funding Information: Funding was provided by National Natural Science Foundation of China, Grant/Award Number: 82074258, 82274502, 82104676. Author Contribution Peizhen Jiang was responsible for data collection and creating the manuscript.Jiaqi He was responsible for data collection.Yaoyin Zhang was responsible for data collection.Yan Gao was responsible for data collection.Qingguo Liu was responsible for data collection.Dexiu Wang was responsible for writing review, and provide funding acquisiton.Xudong Tang was responsible for writing review and editing, provide funding acquisiton. Data Availability The authors declare the availability of data upon request. 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Sci Rep 2019; 9 (1) : 12647. e-pub ahead of print 20190902; doi: 10.1038/s41598-019-49084-2 Table Table 1 Clinical Characteristics of MDS Patients MDS(n=28) Patients, n 28 Age, years Mean (range) 59 (26-80) Sex, n (%) Male 14 (50) Female 14 (50) Blood Parameters Hemoglobin, g/L (mean, range) 76 (36-132) Platelet count, ×10⁹/L (mean, range) 94 (8-326) WBC count, ×10⁹/L (mean, range) 4.18 (0.55-13.98) Neutrophil count, ×10⁹/L (mean, range) 2.77 (0.16-11.78) Bone Marrow Cellularity, n (%) Hypercellular 23 (82) Hypocellular 5 (18) Lineage Dysplasia, n (%) Unilineage 6 (21) Multilineage 22 (79) Blast Cells Mean % (range) 3 (0-17) ≥5%, n (%) 5 (18) Cytogenetics, n (%) Abnormal karyotype 11 (39) Complex karyotype* 3 (11) Mutation Profile, n (%) Any mutation 14 (50) Single mutation 2 (7) 2-3 mutations 5 (18) ≥4 mutations 7 (25) IPSS-R Risk Category, n (%) Low 10 (36) Intermediate 10 (36) High 8 (28) 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-7572811","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":556285043,"identity":"74fd5427-b0d2-4655-8004-f75242c9f6f9","order_by":0,"name":"Peizhen 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12:49:23","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98573,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7572811/v1/7ebee6d3d0904775e2691226.html"},{"id":97895061,"identity":"1d8e155e-782e-467e-9b38-58b3daeded4f","added_by":"auto","created_at":"2025-12-10 15:33:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1884722,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the distinct bone marrow supernatant proteomic signature in MDS patients. (a) OPLS-DA analysis demonstrates significant protein-level discrimination between MDS patients and healthy controls, with cross-validation and permutation test results presented in panels (b) and (c), respectively. (d) Gene Ontology analysis reveals the key biological functions associated with differentially expressed proteins (DEPs). (e) Pathway analysis identifies metabolic alterations linked to the protein expression differences between MDS and healthy samples. (f) The heatmap highlights representative proteins showing differential expression patterns. (g) The protein-protein interaction network, analyzed using Cytoscape's MCODE plugin, identifies the top five densely connected modules based on network scores.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7572811/v1/e5513d56f5ab427d723752b5.png"},{"id":97893436,"identity":"0f333eb0-be53-48af-b515-91fe991ecaf6","added_by":"auto","created_at":"2025-12-10 15:30:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1228059,"visible":true,"origin":"","legend":"\u003cp\u003ereveals distinct metabolomic signatures in the bone marrow supernatant of MDS patients. The OPLS-DA analysis demonstrates clear metabolic discrimination between MDS patients and healthy controls (a), with validation through cross-validation and permutation tests (b) and volcano plot visualization (c). Major differential metabolites are classified in panel d, while panel e presents their associated metabolic pathways. Panel f displays VIP scores for the differentially expressed metabolites. Hierarchical clustering analysis (g) using the seven most statistically significant metabolites (Wilcoxon test) shows effective group separation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7572811/v1/999b535e3f8ad97b7a0a2ad3.png"},{"id":97704655,"identity":"99cf627f-c8da-49f5-811a-541b8dbb5b4a","added_by":"auto","created_at":"2025-12-08 12:49:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":823687,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated analysis revealed distinct metabolite and protein profiles between MDS patients and healthy controls. \u003cstrong\u003ea\u003c/strong\u003e displays the correlation between DEPs and DEMsin a heatmap, while Figures \u003cstrong\u003eb\u003c/strong\u003e and \u003cstrong\u003ec\u003c/strong\u003e present KEGG pathway enrichment and protein-protein interaction (PPI) networks, respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7572811/v1/4b05c1573756304021bab698.png"},{"id":97894372,"identity":"0675ab0b-74dd-4154-906d-4fde48b1c576","added_by":"auto","created_at":"2025-12-10 15:32:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1241667,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between DEPs and DEMs with clinical characteristics. Significant correlations between metabolites \u003cstrong\u003ea\u003c/strong\u003e and proteins \u003cstrong\u003eb\u003c/strong\u003e with cell counts. Correlation coefficients of The representative DEPs \u003cstrong\u003eb\u003c/strong\u003e and DEMs \u003cstrong\u003ef\u003c/strong\u003e . Multivariate ROC curves of biologically relevant DEPs and DEMs and Differences in expression between groups.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7572811/v1/cc07bf382f47ecbd6f13ab94.png"},{"id":108480795,"identity":"262b1fc9-6ffd-497b-9ea5-dacc9c85b2e6","added_by":"auto","created_at":"2026-05-05 07:56:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5084965,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7572811/v1/dc310516-dff2-4287-8fe4-55d9b128034a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolomic and Proteomic Analysis of Bone Marrow Supernatant in Myelodysplastic Syndrome Using Astral-based DIA and LC-MS/MS","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMyelodysplastic syndromes (MDS) represent a heterogeneous group of clonal hematopoietic stem cell disorders with an inherent propensity for progression to acute myeloid leukemia (AML). Research has increasingly focused on elucidating MDS pathogenesis, which appears fundamentally driven by somatic mutations in hematopoietic stem cells that confer clonal advantage\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Epigenetic dysregulation, particularly aberrant DNA methylation and histone modifications, disrupts normal gene expression programs and contributes to ineffective hematopoiesis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Concurrently, innate immune dysregulation and chronic inflammatory signaling mediated by myeloid-derived suppressor cells (MDSCs) and cytokine imbalances facilitate disease progression and immune evasion\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The bone marrow microenvironment further participates in MDS pathogenesis through dysfunctional hematopoietic-stromal interactions that promote clonal expansion while suppressing normal hematopoiesis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDiagnostically, next-generation sequencing and cytogenetic analysis now constitute essential tools for accurate diagnosis, risk stratification, and identification of genetic predispositions\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The 2022 WHO and ICC classification systems incorporate specific genetic alterations (e.g., SF3B1 mutations, del(5q)) alongside morphological features to define MDS subtypes\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Treatment approaches increasingly reflect genetic profiles and risk status: erythropoiesis-stimulating agents remain first-line for lower-risk disease, with luspatercept or lenalidomide reserved for specific subtypes, while hypomethylating agents form the backbone of higher-risk MDS therapy\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Allogeneic hematopoietic stem cell transplantation persists as the sole potentially curative intervention, though relapse and drug resistance remain significant challenges\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e Advances in metabolomics and proteomics have provided new mechanistic insights into hematologic malignancies, including MDS. These approaches reveal how perturbations in glucose, amino acid, and lipid metabolism contribute to disease pathogenesis and therapeutic resistance\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. NMR- and MS-based metabolomics identify distinct metabolic signatures that differentiate hematologic malignancies from healthy states, while high-throughput proteomics enables detection of diagnostic biomarkers like free light chains and β₂-microglobulin\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Proteomic analyses further uncover dysregulated signaling pathways and potential therapeutic targets through system-wide profiling of protein expression and post-translational modifications\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRecent metabolomic studies demonstrate altered inflammatory and metabolic pathways in MDS patient plasma, involving amino acid, fatty acid, and energy metabolism\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Emerging evidence also implicates gut microbiome-plasma metabolome interactions in MDS pathogenesis\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, the heterogeneity of protein expression patterns across MDS subtypes complicates the translation of population-level findings to individual patients\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Advanced MS platforms now enable sensitive detection of low-abundance plasma proteins that may serve as phase-specific biomarkers\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe hypothesize that integrated metabolomic-proteomic analysis of bone marrow supernatant from treatment-na\u0026iuml;ve MDS patients could reveal microenvironmental alterations and metabolic perturbations underlying disease progression. Our dual-omics approach aims to characterize dynamic molecular changes throughout treatment courses, potentially identifying novel therapeutic targets and informing personalized treatment strategies.\u003c/p\u003e"},{"header":"2. Material and method","content":"\u003cp\u003e\u003cstrong\u003e2.1 Ethical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study complies with the Declaration of Helsinki, the study protocol was approved by the local ethics committees (Approval No. 2023XLA056), and informed written consent was obtained from each participant. and it was performed per national and international guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEligible participants of MDS met the following criteria: (1) confirmed myelodysplastic syndrome diagnosis, (2) absence of severe cardiac, hepatic, or renal comorbidities, (3) no concurrent participation in other clinical trials, (4) age between 18 and 75 years, and (5) provision of written informed consent.\u003c/p\u003e\n\u003cp\u003eControl selection required: normal bone marrow function (hemoglobin \u0026ge;13 g/dL, absolute neutrophil count \u0026ge;1,800/\u0026mu;L, platelet count \u0026ge;125,000/\u0026mu;L); normal liver function (total bilirubin, albumin, aspartate aminotransferase, alanine aminotransferase, and alkaline phosphatase within reference ranges); normal renal function (serum creatinine within reference range); unremarkable urinalysis (negative for hematuria, proteinuria, and bacteriuria); and normocellular or moderately hypercellular bone marrow without dysplastic features. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Sample collection and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants underwent sterile bone marrow aspiration from the posterior superior iliac spine. We collected 4 mL of bone marrow blood in EDTA vacuum tubes, centrifuged samples at 150\u0026nbsp;\u0026times;g for 8 minutes, aliquoted the supernatant into labeled cryovials, and stored them at -80\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Proteomic Profiles Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-abundance proteins were removed from bone marrow plasma supernatant using Thermo-TOP14 depletion. Protein concentrations were quantified and normalized using the Bradford assay. For proteomic analysis, 200 ng of the depleted supernatant was loaded onto a Vanquish Neo nano-UHPLC system equipped with two Thermo Scientific columns: a 17450 C18 (0.5 mm \u0026times; 300 \u0026mu;m, 5 \u0026mu;m) and a PepMap ES906 C18. Mass spectrometry was performed on a Thermo Orbitrap Astral instrument with an Easy-Spray ion source operating at 2.0 kV and 290\u0026deg;C in data-independent acquisition mode. The mobile phase consisted of 0.1% formic acid in water (phase A) and 0.1% formic acid in 80% acetonitrile (phase B), delivered at 2.5 \u0026mu;L/min. A step gradient elution was employed, increasing phase B from 4% to 55% over 12.6 minutes, followed by a 1-minute wash at 99% phase B.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Metabolomic Profiles Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter thawing, samples were mixed with 400 \u0026micro;L methanol, vortexed for 1 min, and centrifuged at 12,000 rpm (4\u0026deg;C) for 10 min. The supernatant was evaporated to dryness and reconstituted in 150 \u0026micro;L of 80% methanol-water solution containing 2-chloro-L-phenylalanine (4 ppm) before filtration and analysis. Chromatographic separation was achieved using a Thermo Vanquish UHPLC system equipped with an ACQUITY UPLC\u0026reg; HSS T3 column (2.1\u0026times;100 mm, 1.8 \u0026micro;m) at 0.3 mL/min. Positive ion mode employed mobile phases A2 (0.1% formic acid in water) and B2 (0.1% formic acid in acetonitrile), while negative ion mode used A3 (5 mM ammonium formate in water) and B3 (acetonitrile), both with gradient elution. Mass spectrometric detection was performed on a Thermo Orbitrap Exploris 120 instrument with an ESI source, collecting data in both ionization modes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical and Bioinformatics Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProteins were considered differentially expressed when meeting the thresholds of FDR \u0026lt; 0.05 and FC \u0026gt; 1.5. Gene Set Enrichment Analysis (GSEA) was conducted via the Metscape platform, incorporating Gene Ontology-Biological Process terms, Kyoto Encyclopedia of Genes and Genomes pathways, Small Molecule Pathway Database entries, and Reactome gene sets. GSEA revealed biological pathways linked to specific proteins and metabolites. Protein-protein interaction (PPI) network analysis examined functional relationships among differentially expressed biomolecules to identify key regulatory networks and modules. The Wilcoxon signed-rank test determined the statistical significance of intergroup expression differences. Metabolomics data were processed using the same analytical pipeline as the proteomics data, detailed elsewhere in this study. Metabolites with FDR \u0026lt; 0.05 and FC \u0026gt; 1.5 were classified as differentially expressed. Metabolite Set Enrichment Analysis was performed in MetaboAnalyst, encompassing Over Representation Analysis, Pathway Analysis, and Joint Pathway Analysis.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Demographic feature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e28 MDS patients and 10 healthy controls were enrolled in this study. There was no significant difference in age between the oligospermia group and the healthy control group. Demographic information and clinical parameters are given in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Proteomic Profile of bone marrow supernatants in MDS patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOPLS-DA analysis revealed distinct proteomic separation between the MDS and healthy groups (Figure 1a), with model permutation tests demonstrating strong validity (R\u0026sup2;=0.934, Q\u0026sup2;=0.451; Figure 1b). The MDS group exhibited 315 differentially expressed proteins (Fc\u0026ge;1.5 or \u0026le;0.666, FDR\u0026lt;0.05) relative to controls, comprising 247 upregulated and 68 downregulated proteins (Figure 1c). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO enrichment analysis indicated that most differential proteins localized to macromolecular complexes and intracellular components, primarily involved in metabolic processes including cellular, protein, carbohydrate, and macromolecule metabolism (Figure 1d). KEGG pathway analysis identified the top 10 enriched pathways (Figure 1e), spanning Metabolism, Genetic Information Processing, Membrane Transport, Environmental Information Processing, Cellular Processes, and Organismal Systems. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong 87 pathway-related differential proteins, 12 were downregulated, suggesting impaired lipid metabolism and lysosomal function in MDS. The 75 upregulated proteins included glutathione system components (GSR: FC=1.88, P\u0026lt;0.001; GPX1: FC=1.58, P\u0026lt;0.05; GCLC: FC=1.55, P\u0026lt;0.01) and amino acid metabolism regulators (ARG1: FC=1.58, P\u0026lt;0.05; GLUL: FC=2.63, P\u0026lt;0.05; GOT1: FC=1.62, P\u0026lt;0.001), indicating redox imbalance and metabolic dysregulation in MDS marrow (Figure 1f). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProtein-protein interaction network analysis of the DEPs yielded 393 nodes and 8,206 edges. Cytoscape\u0026apos;s MCODE plugin identified the five highest-density functional modules based on network connectivity (Figure 1g).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Metabolic Profile of bone marrow supernatants in MDS patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe OPLS-DA analysis of MDS bone marrow supernatant metabolites (Figure 2a) revealed clear separation between the MDS and HC groups, demonstrating significant metabolic alterations. The permutation test yielded R\u0026sup2;=0.9443 and Q\u0026sup2;=-0.197 (Figure 2b), while MSI-based identification detected 1417 metabolites (Figure 2c). Applying strict screening criteria (VIP\u0026ge;1, Fc\u0026ge;1.5 or \u0026le;0.666, FDR\u0026le;0.05), we identified 77 differential metabolites as potential MDS biomarkers, comprising 51 upregulated and 26 downregulated compounds classified into seven categories: amino acids, lipids, carbonyl compounds, carboxylic acids and derivatives, fatty acyls, hydroxy acids and derivatives, and others (Figure 2d). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG enrichment analysis highlighted key pathways including arginine biosynthesis (Impact\u0026gt;1), citrate cycle, glycolysis/gluconeogenesis (Impact 0.5-1), and unsaturated fatty acid synthesis, indicating disrupted energy and nitrogen metabolism in MDS patients. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe top 15 metabolites contributing to major pathways (Figure 1f) included mukonine, 2-phosphoglycerate, L-arginine, and malic acid. HCA separated differential metabolites into two clusters: Cluster 1 (L-arginine, 2-phosphoglyceric acid) showed reduced expression in MDS, while Cluster 2 (3-dehydrosphinganine, malic acid, cysteinylglycine) exhibited elevated expression. Metabolite expression was more uniform in HC than in MDS patients, potentially reflecting disease heterogeneity that warrants subtype-specific analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Integrated Analysis of Proteomics and Metabolomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis identified significant associations between differential proteins and metabolites (Figure 3a). In MDS patients, reduced L-arginine and elevated L-aspartate likely impair ammonia detoxification. Malate acid increases as compensatory mechanism through the aspartate-malate shuttle, ultimately disrupting the urea cycle. Cysteinylglycine directly depletes GSH, while 3-dehydrosphinganine exacerbates oxidative damage via sphingolipid toxicity, establishing a self-perpetuating cycle of oxidative stress. Concurrently, 2-PG inhibits glycolysis, and elevated tryptophan activates proteasomes while further compromising cellular energy supplies. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG enrichment analysis (Figure 3b) suggests that arginine metabolism defects elevate blood ammonia, potentially inducing DNA damage in hematopoietic stem cells. Glutathione depletion increases ceramide levels, which may promote p53-mediated stem cell apoptosis. Accelerated glycolysis/gluconeogenesis flux leads to lactate accumulation, suppressing normal hematopoietic progenitor function. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPI network analysis of key differential proteins and metabolites (Figure 3c) highlighted three primary interactors: 2-amino-4,6-dinitrotoluene glucoside, L-aspartic acid, and blumealactone C. These findings implicate environmental pollutants, dietary protein metabolism, and systemic metabolic dysregulation in MDS pathology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Correlation of Differential Metabolites and Proteins with Clinical Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analyses of GEMs and GEPs in MDS versus HC and peripheral blood cells identified potential biomarkers. Sankey diagrams of major pathway metabolites (Figures 4a, 4e) demonstrate that hemoglobin (Hb)-associated metabolites predominate, followed by platelet (BPC)-related compounds, with white blood cell (WBC) and neutrophil (NEUT)-linked metabolites being less abundant. Several metabolites\u0026mdash;L-Arginine, 2-Phosphoglyceric acid, 3-Dehydrosphinganine, Malic acid, and L-Aspartic acid\u0026mdash;showed strong correlations with three blood cell types (Figure 4b). The differential proteins GSR, FAU, APOA1, ANTXR1, ARSB, and APOC1 also exhibited significant blood cell correlations (Figure 4b). ROC analysis revealed high diagnostic potential for L-Arginine, L-Aspartic acid, and L-Tryptophan (AUC\u0026ge;0.92; Figures 4c, 4d), as well as for the proteins GSR and APOA1 (AUC\u0026ge;0.92), supporting their utility as early MDS biomarkers. No significant associations emerged between differential metabolites and clinical features (WHO 2022 subtype, IPSS-R, or blast percentage \u0026ge;5% vs \u0026lt;5%).\u003c/p\u003e"},{"header":"4.\tDISCUSSION","content":"\u003cp\u003eHere is the polished paragraph with the requested improvements while maintaining the original sentence count and technical accuracy:\u003c/p\u003e\n\u003cp\u003eWe collected bone marrow samples from 28 treatment-naive MDS patients and 10 healthy controls, comparing their proteomic and metabolic profiles. The analysis revealed significant depletion of lysosomal and lipid proteins alongside hyperactivation of amino acid and redox pathways, consistent with metabolomic enrichment patterns. Unlike previous reports of lipid metabolism alterations in CD34⁺ cells and plasma inflammation/fatty acid dysregulation\u0026sup2;⁷, our supernatant data demonstrated predominant disruptions in amino acid and energy metabolism. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMDS CD34⁺ cells display mitochondrial-coordinated metabolism dependent on glutamate catabolism and glucose-driven biosynthesis\u0026sup2;⁸,\u0026sup2;⁹, mirroring the glutamine dependence observed in AML\u0026sup3;⁰. Increased expression of GSR, GPX1, and GCLC indicates adaptive responses to glucose-associated oxidative stress. The integrated analysis identified arginine and aspartate imbalances with compensatory malate accumulation, triggering oxidative stress cascades and energy dysregulation\u0026mdash;clear markers of mitochondrial dysfunction. Accumulated oncometabolites (L-2HG, succinate), mitochondrial impairment, and redox imbalance establish conditions favoring ineffective hematopoiesis, clonal expansion, and leukemic transformation\u0026sup3;\u0026sup1;⁻\u0026sup3;\u0026sup3;. Whether mitochondrial biomarkers directly initiate ineffective hematopoiesis remains to be determined. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKey amino acids involved in hematopoiesis showed negative correlations with erythrocyte and platelet counts. Differential protein expression strongly correlated with erythrocyte parameters. While lacking direct erythroid connections like ATP, erythrocyte maturation depends on coordinated metabolite networks involving amino acids, sugars, and nucleotides\u0026sup3;⁴. The hematologic specificity of these relationships requires further investigation. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to established plasma biomarkers (LRG, fetuin, VCAM, ICAM, clusterin, S100A8)\u0026sup3;⁵, ROC analysis identified L-aspartate, L-arginine, L-tryptophan (metabolites) and GSR/APOA1 (proteins) as potential diagnostic markers (AUC\u0026ge;0.92). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMetabolites provide real-time pathophysiological insights unattainable through genomic or proteomic analysis alone. Capturing molecular-metabolic dynamics during MDS onset proves essential for understanding disease progression. Mass spectrometry integration significantly improves diagnostic precision. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile metabolites are traditionally viewed as end products sustaining vital activities during disease, monitoring their dynamic changes offers real-time physiological snapshots. Given the inherently dynamic nature of MDS pathogenesis, investigating molecular functional shifts and metabolic features at disease onset is critical for deciphering MDS pathology. Simultaneous measurement of functional proteins and real-time metabolites through mass spectrometry enhances both diagnostic accuracy and early detection capabilities. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, significant protein imbalances, amino acid/energy dysregulation, and mitochondrial disturbances in MDS marrow represent valuable targets for early diagnosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical approval\u003c/h2\u003e\u003cp\u003eThe study complies with the Declaration of Helsinki, the study protocol was approved by the local ethics committees (Approval No. 2023XLA056), and informed written consent was obtained from each participant. and it was performed per national and international guidelines.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Information:\u003c/h2\u003e\u003cp\u003e Funding was provided by National Natural Science Foundation of China, Grant/Award Number: 82074258, 82274502, 82104676.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePeizhen Jiang was responsible for data collection and creating the manuscript.Jiaqi He was responsible for data collection.Yaoyin Zhang was responsible for data collection.Yan Gao was responsible for data collection.Qingguo Liu was responsible for data collection.Dexiu Wang was responsible for writing review, and provide funding acquisiton.Xudong Tang was responsible for writing review and editing, provide funding acquisiton.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe authors declare the availability of data upon request. Someone who would like to request data form this study, please contact with the Primary corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOgawa S. Genetics of MDS. \u003cem\u003eBlood \u003c/em\u003e2019; \u003cstrong\u003e133\u003c/strong\u003e(10)\u003cstrong\u003e: \u003c/strong\u003e1049-1059. doi: 10.1182/blood-2018-10-844621\u003c/li\u003e\n\u003cli\u003eAwada H, Thapa B, Visconte V. The Genomics of Myelodysplastic Syndromes: Origins of Disease Evolutio n, Biological Pathways, and Prognostic Implications. \u003cem\u003eCells\u003c/em\u003e; \u003cstrong\u003e9\u003c/strong\u003e(11)\u003cstrong\u003e: \u003c/strong\u003e2512. doi: 10.3390/cells9112512\u003c/li\u003e\n\u003cli\u003eGanguly B, Kadam N. Mutations of myelodysplastic syndromes (MDS): An update. \u003cem\u003eMutation research. 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Discovery and Validation of Clinical Biomarkers of Cancer: A Review Combining Metabolomics and Proteomics. \u003cem\u003ePROTEOMICS \u003c/em\u003e2018; \u003cstrong\u003e19\u003c/strong\u003e. doi: 10.1002/pmic.201700448\u003c/li\u003e\n\u003cli\u003eKwon YW, Jo H-S, Bae S, Seo Y, Song P, Song M\u003cem\u003e et al.\u003c/em\u003e Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery. \u003cem\u003eFrontiers in Medicine \u003c/em\u003e2021; \u003cstrong\u003e8\u003c/strong\u003e. doi: 10.3389/fmed.2021.747333\u003c/li\u003e\n\u003cli\u003eYuan Y, Zhao J, Li T, Ji Z, Xin Y, Zhang S\u003cem\u003e et al.\u003c/em\u003e Integrative metabolic profile of myelodysplastic syndrome based on UHPLC-MS. \u003cem\u003eBiomedical chromatography : BMC \u003c/em\u003e2021. doi: 10.1002/bmc.5136\u003c/li\u003e\n\u003cli\u003eJiang H, Zhao X, Zang M, Fu R, Shao Z, Liu C. Gut Microbiome and Plasma Metabolomic Analysis in Patients with Myelodysplastic Syndrome. \u003cem\u003eOxidative Medicine and Cellular Longevity \u003c/em\u003e2022; \u003cstrong\u003e2022\u003c/strong\u003e. doi: 10.1155/2022/1482811\u003c/li\u003e\n\u003cli\u003ePecankova K, Čerm\u0026aacute;k J, M\u0026aacute;jek P. Proteomic Case Studies of MDS in Progression: Heterogeneity and More Heterogeneity. \u003cem\u003eTurkish Journal of Hematology \u003c/em\u003e2022; \u003cstrong\u003e39: \u003c/strong\u003e272-274. doi: 10.4274/tjh.galenos.2022.2022.0290\u003c/li\u003e\n\u003cli\u003eGeyer P, Holdt L, Teupser D, Mann M. Revisiting biomarker discovery by plasma proteomics. \u003cem\u003eMolecular Systems Biology \u003c/em\u003e2017; \u003cstrong\u003e13\u003c/strong\u003e. doi: 10.15252/msb.20156297\u003c/li\u003e\n\u003cli\u003ePoulaki A, Katsila T, Stergiou I, Giannouli S, Gόmez-Tamayo JC, Piperaki E\u003cem\u003e et al.\u003c/em\u003e Bioenergetic Profiling of the Differentiating Human MDS Myeloid Lineage with Low and High Bone Marrow Blast Counts. \u003cem\u003eCancers \u003c/em\u003e2020; \u003cstrong\u003e12\u003c/strong\u003e. doi: 10.3390/cancers12123520\u003c/li\u003e\n\u003cli\u003ePoulaki A, Katsila T, Hatziyannis E, Stergiou I, Kapsogeorgou E, Hatzis S\u003cem\u003e et al.\u003c/em\u003e Metabolic Reprogramming in Myelodysplastic Syndromes. \u003cem\u003eBlood \u003c/em\u003e2024. doi: 10.1182/blood-2024-211216\u003c/li\u003e\n\u003cli\u003eMcGraw K, Larson D. Implications for metabolic disturbances in myelodysplastic syndromes. \u003cem\u003eSeminars in hematology \u003c/em\u003e2024. doi: 10.1053/j.seminhematol.2024.11.004\u003c/li\u003e\n\u003cli\u003eShen YA, Chen CL, Huang YH, Evans EE, Cheng CC, Chuang YJ\u003cem\u003e et al.\u003c/em\u003e Inhibition of glutaminolysis in combination with other therapies to improve cancer treatment. \u003cem\u003eCurr Opin Chem Biol \u003c/em\u003e2021; \u003cstrong\u003e62: \u003c/strong\u003e64-81. e-pub ahead of print 20210312; doi: 10.1016/j.cbpa.2021.01.006\u003c/li\u003e\n\u003cli\u003eGon\u0026ccedil;alves A, Cortes\u0026atilde;o E, Oliveiros B, Alves V, Espadana A, Rito L\u003cem\u003e et al.\u003c/em\u003e Oxidative stress and mitochondrial dysfunction play a role in myelodysplastic syndrome development, diagnosis, and prognosis: A pilot study. \u003cem\u003eFree Radical Research \u003c/em\u003e2015; \u003cstrong\u003e49: \u003c/strong\u003e1081-1094. doi: 10.3109/10715762.2015.1035268\u003c/li\u003e\n\u003cli\u003eGon\u0026ccedil;alves A, Alves R, Baldeiras I, Marques B, Oliveiros B, Pereira A\u003cem\u003e et al.\u003c/em\u003e DNA Methylation Is Correlated with Oxidative Stress in Myelodysplastic Syndrome\u0026mdash;Relevance as Complementary Prognostic Biomarkers. \u003cem\u003eCancers \u003c/em\u003e2021; \u003cstrong\u003e13\u003c/strong\u003e. doi: 10.3390/cancers13133138\u003c/li\u003e\n\u003cli\u003eSezaki M, Hashimoto M, Yokota A, Salomonis N, Grimes H, Huang G. Downregulation of Mitochondrial Complex II (MC II) in Myelodysplastic Syndromes. \u003cem\u003eBlood \u003c/em\u003e2023. doi: 10.1182/blood-2023-186829\u003c/li\u003e\n\u003cli\u003eJoly A, Schott A, Phadke I, Gonz\u0026aacute;lez-Men\u0026eacute;ndez P, Kinet S, Taylor N. Beyond ATP: Metabolite networks as regulators of erythroid differentiation. \u003cem\u003ePhysiology \u003c/em\u003e2024. doi: 10.1152/physiol.00035.2024\u003c/li\u003e\n\u003cli\u003eChrastinov\u0026aacute; L, Pastva O, Bockov\u0026aacute; M, Lynn NS, \u0026Scaron;\u0026aacute;cha P, Hub\u0026aacute;lek M\u003cem\u003e et al.\u003c/em\u003e A New Approach for the Diagnosis of Myelodysplastic Syndrome Subtypes Based on Protein Interaction Analysis. \u003cem\u003eSci Rep \u003c/em\u003e2019; \u003cstrong\u003e9\u003c/strong\u003e(1)\u003cstrong\u003e: \u003c/strong\u003e12647. e-pub ahead of print 20190902; doi: 10.1038/s41598-019-49084-2\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 Clinical Characteristics of MDS Patients\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDS(n=28)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients, n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59 (26-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood Parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHemoglobin, g/L (mean, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76 (36-132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlatelet count, \u0026times;10⁹/L (mean, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94 (8-326)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWBC count, \u0026times;10⁹/L (mean, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.18 (0.55-13.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeutrophil count, \u0026times;10⁹/L (mean, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.77 (0.16-11.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBone Marrow Cellularity, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypercellular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypocellular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLineage Dysplasia, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnilineage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultilineage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlast Cells\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean % (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (0-17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;5%, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCytogenetics, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbnormal karyotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplex karyotype*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutation Profile, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAny mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSingle mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2-3 mutations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;4 mutations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIPSS-R Risk Category, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (28)\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"}],"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":"metabolomic, proteomic, myelodysplastic syndrome, bone marrow supernatant, astral, diagnostic","lastPublishedDoi":"10.21203/rs.3.rs-7572811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7572811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMyelodysplastic syndromes (MDS) represent a heterogeneous group of clonal hematopoietic stem cell disorders characterized by malignant potential and complex pathobiological mechanisms. While specific gene mutations (SF3B1, TET2, ASXL1, TP53) contribute significantly to MDS, disease progression depends equally on malignant clones and the bone marrow microenvironment. We employed integrated Astral-DIA technology with LC-MS/MS to characterize protein and metabolic alterations in this microenvironment, enabling dynamic pathological analysis at functional and phenotypic levels. Comparative analysis of bone marrow supernatant from 28 MDS patients and 10 healthy controls identified pronounced proteomic imbalances, disrupted amino acid and energy metabolism pathways, and diagnostic biomarkers (L-Aspartate, L-Arginine, L-Tryptophan, GSR, APOA1) with strong discriminatory power for early-stage disease (AUC\u0026gt;0.9).\u003c/p\u003e","manuscriptTitle":"Metabolomic and Proteomic Analysis of Bone Marrow Supernatant in Myelodysplastic Syndrome Using Astral-based DIA and LC-MS/MS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 12:49:17","doi":"10.21203/rs.3.rs-7572811/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":"1d48eb8a-5ce3-4fc7-9189-245aef40fb14","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59209962,"name":"Health sciences/Biomarkers"},{"id":59209963,"name":"Biological sciences/Cancer"},{"id":59209964,"name":"Biological sciences/Cell biology"},{"id":59209965,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2026-05-05T07:55:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 12:49:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7572811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7572811","identity":"rs-7572811","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}