Network Medicine Framework Links Chemical Classes to Physiological System-specific Therapeutic Benefits

Network Medicine Framework Links Chemical Classes to Physiological System-specific Therapeutic Benefits | 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 Network Medicine Framework Links Chemical Classes to Physiological System-specific Therapeutic Benefits Lufei Wang, Wei Jiang, Xiaoyi Lai, Nan Zhang, Hui Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6261027/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 The health benefits of herbal plants are fundamentally determined by their bioactive compounds, which exhibit substantial variations across species and processing techniques. The fresh tea leaves ( Camellia sinensis ) transform into six distinct tea types through specific processing methods, making them a potential model herb for investigating relationship between chemical classes and diseases. Here, we employed ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) based metabolomics to systematically characterize chemical classes across tea types. By establishing molecular fingerprint-based chemical hierarchies between tea compounds and approved small molecule drugs, we speculate on therapeutic potentials within specific chemical compound. Given that a single compound is insufficient to infer the role of an entire chemical class, we developed a network medicine framework that maps chemical class targets on the human protein-protein interactome. Targets of individual chemical class formed densely connected clusters in function-specific network neighborhoods. The network proximity between these chemical class neighborhoods and disease modules quantitatively predicted their therapeutic efficacy associated with physiological system. This multi-scale approach not only elucidates the relationship between chemical classes and physiological system-specific diseases but also provides a computational framework for natural product discovery and polypharmacology research. Health sciences/Medical research Biological sciences/Systems biology Figures Figure 1 Figure 2 Figure 3 Introduction Herbal remedies have served as cornerstone therapies for global healthcare throughout history. While randomized clinical trials 1-3 and mechanism of action studies 4,5 have validated specific therapeutic benefits, the systematic understanding of herb-disease relationships remains fragmented. Recent advances using protein-protein interactome analysis have enabled prediction of herbal effects through topological relationships between herb targets and disease proteins 6 . Despite these developments, two fundamental questions persist: (i) does isolation of single active compounds adequately represent holistic herb efficacy and (ii) can specific chemical classes (e.g., polyphenols in green tea 7,8 ) reliably predict therapeutic effects? In addition, the structural classification of herbal compounds, such as flavonoids, organic acids, shows correlation with biological activity patterns 9,10 . This raises the critical question of whether such chemical classification exhibits specific therapeutic effects. The chemical diversity for herbal compound is affected by interspecies variations and processing method, which complicates relationship between chemical classes and therapeutic effects 11-14 . Tea ( Camellia sinensis ) exhibits chemical diversity due to various processing methods. Fresh tea leaves can be processed into green tea, blue tea (also known as oolong tea), red tea (also known as black tea), white tea, black tea (also known as dark tea), yellow tea through different processing methods (Table S1) 15 . "Positive teas (Yang tea)" (green, blue, and red tea) undergo enzymatic conversion before heat inactivation, whereas "Negative teas (Yim tea)" (white, black, and yellow tea) experience reverse sequence 16 . This systematic processing diversity, coupled with well-documented health benefits against various diseases 7,17-21 , enables potetional investigation of chemical class-therapeutic effect relationships. Current research limitations fall into three areas: firstly, experimental methods using concentrated organic extracts 22 poorly reflect actual consumption patterns; Secondly, comparative metabolomics across tea types is lacking; Thirdly, no unified framework exists to map tea-chemical class-disease associations. The emerging network medicine offers a promising solution through human protein-protein interactome 23 , which has successfully applied in predicting drug efficacy and exploring disease-disease relationship 24-26 . In this study, we employed untargeted metabolomics to profile six tea types (Fig. 1A), identifying tea type-specific chemical classes. We constructed a molecular fingerprint-based chemical hierarchy to match tea compounds with clinically approved drugs, enabling therapeutic effect prediction. Finally, we developed a network medicine framework to map chemical class-disease associations, establishing a systematic understanding of chemical classes and therapeutic effects. Results Specific chemical classes among six types of tea Untargeted metabolomics analysis using UPLC-MS/MS identified 8,055 chemical features across six tea types after quality control and normalization (Fig. S1 A-B). The global metabolomics data revealed a clear separation between Positive and Negative tea (Fig. 1 B, Fig. S1 B-C), underscoring the impact of the endogenous enzymes involved in the conversion process on compound variations. Positive tea subdivided into green, blue, and red tea (Fig. 1 C and Fig. S1 D), while Negative tea subdivided into white, black, and yellow tea (Fig. 1 D and Fig. S1 E). This result is consistent with pattern of tea processing methods. Pairwise comparison across tea types identified 246 annotated compounds spanning eight classes (Fig. S2A), including flavonoids (flavan-3-ols, flavanones, flavones, flavonols, isoflavones), organic acids, nitrogen-containing compounds, phenols, esters, glycosides, alkaloids, and amino acids (Fig. 1 E, Fig. S3). Non-converted green tea exhibited fewer significantly altered compounds than other tea types (Fig. S2B), indicating the critical role of conversion process in chemical diversity. Notably, green tea retained high levels of flavan-3-ols, resembling fresh tea leaves 27 , 28 , whereas yellow tea showed flavone enrichment through C2-/C3-position dehydrogenation during yellowing fermentation. Overall, green, blue, red, white, black, and yellow tea significantly accumulated phenols, organic acids, nitrogen-containing compounds, esters, glycosides, and flavones, respectively (Fig. 1 E). These specific chemical profiles imply distinct synthetic pathways associated with specific processing techniques. Chemical hierarchy-based therapeutic effect prediction To elucidate the therapeutic potential of individual compounds within tea-specific chemical classes, we established a chemical hierarchy based on molecular fingerprint similarity. We excluded investigational, withdrawn, illicit, and nutraceutical agents to generate a refined dataset of 1,810 clinically approved small-molecule drugs. We constructed a chemical hierarchy that maps structural analogies between tea compounds and clinically approved molecular entities (Fig. 2 ). This approach enables functional inference by identifying structural analogies with the approved drugs 10 . For example, oxyresveratrol showed similarity to curcumin (regulative gastrointestinal tract), isoeugenol acetate to drotaverine (antispasmodic), kaempferol-3-rhamnoside to diosmin (liver protection), and isovetixin to flavoxate (hyperglycemic effect) (Table S2). These parallels suggest shared therapeutic effects between tea-derived compounds and clinically approved drugs. Function-specific localization of chemical class targets Given that a single compound is insufficient to infer the role of an entire chemical class, we developed a network medicine framework to analyze target clusters of chemical classes. We mapped targets of six chemical classes to the human interactome (19,060 proteins with 451,602 interactions) (Fig. 3 A). Non-metric multidimensional scaling (NMDS) based on molecular fingerprints revealed low similarity between chemical class pairs (average distance = 0.66) (Fig. S4A-B). Despite comparable numbers of targets across chemical classes, Jaccard index analysis demonstrated limited target overlap between pairs (average similarity = 0.29) (Fig. S4C-D). Gene ontology enrichment analysis identified distinct biological processes associated with different chemical classes, including cellular response to chemical stress for glycosides and negative regulation of apoptotic signaling pathway for flavones (Fig. S5). We subsequently quantified the largest connected component (LCC) formed by each chemical class's targets. All classes exhibited substantial target clusters (LCC ≥ 40; Fig. S6). Notably, organic acids, nitrogen-containing compounds, esters, glycosides, and flavones showed significantly larger LCCs than random expectation (Z-score > 1.75) (Fig. 3 B). These findings indicate that chemical class targets occupy specific, well-localized neighborhoods within the human interactome (Fig. 3 A), prompting investigation into their disease-related subnetworks. Network proximity reveals chemical class-specific associations with physiological system To explore the distinct effects of chemical classes on diseases, we computed the network proximity between chemical class targets and disease proteins. More negative network proximity indicates stronger network associations between chemical classes and diseases (Fig. 3 C). We ranked the chemical classes based on their network proximity to the 30 tea-related diseases (Fig. 3 C-D). Phenols may exhibit more effectively therapeutic effects on urological disorders; organic acids from blue tea on stomach carcinoma; nitrogen-containing compounds from red tea on cholestasis and endocrine system diseases; esters from white tea may on immunoproliferative and lung diseases; glycosides from black tea on glucose metabolism disorders and prostatic disease. Flavones from yellow tea may affect more diseases, which exhibit therapeutic effects on cardiovascular and neurodegenerative diseases (Fig. 3 D). Notably, we observed a framework where specific chemical classes predominantly affect physiological system-specific diseases. Phenols, organic acids, nitrogen-containing compounds, esters, glycosides, and flavones might effectively affect urological, digestive, endocrinological, immunological, metabolic, and cardio-kidney diseases, respectively. GO enrichment analysis supported these associations through pathway-specific mechanisms (Fig. S5). For example, nitrogen-containing compounds demonstrated endocrine-related activity via thyroid gland development pathways. Esters modulated immune function through immune response-activating receptor signaling pathway and regulation of myeloid cell differentiation. These findings establish connections between tea-specific chemical classes and physiological system-related diseases. Discussion Here, we utilized tea as a model herb to investigate the relationship between processing-induced chemical classes and physiological system-specific therapeutic effects. The metabolomic profiling revealed that tea’s chemical compounds closely correspond to its processing methods, enabling identification of tea type-specific chemical class. Through constructing a molecular fingerprint-based chemical hierarchy, we found that a single compound is insufficient to represent therapeutic effects of the entire chemical class. We developed a network medicine framework to uncover that chemical class targets occupy functionally specific neighborhoods on the interactome. Importantly, these neighborhoods exhibit differential proximity to disease modules associated with particular physiological systems, thereby establishing a systematic framework for understanding herb-disease relationships at the chemical class level. Our study addresses several persistent challenges in research of herbal remedy and traditional Chinese medicine (TCM) theory. First, whereas previous studies predominantly relied on isolating single bioactive compounds to explain herb therapeutic effects 29 , 30 , our approach infers therapeutic potential through chemical class identification. These chemical classes clustered within functionally specific neighborhoods on the interactome. Second, the selection of tea as a model herb helps overcome confounding factors due to interspecies variations that complicate chemical class identification 31 , 32 . Third, the network proximity metric quantitatively links chemical classes to physiological system-specific therapeutic effects. This explains why conventional " one disease-one target-one drug " drug discovery paradigms fail to capture herbal polypharmacology 33 . This limitation is further highlighted by the repurposing potential of drugs like metformin 34 , suggesting our framework offers a novel strategy for systematizing herbal functional analysis through molecular class characterization. The predictive power of network proximity is validated by accumulative evidence. Compared with other tea types, flavone-enriched yellow tea shows stronger therapeutic associations with cardiovascular and kidney diseases, consistent with its confirmed hypouricemic and hypoglycemic effects 35 – 39 . Furthermore, a prospective cohort study has shown an inverse relationship between flavone intake and cardiovascular diseases-related mortality 8 . Similarly, predicted metabolic regulation of glycosides aligns with black tea's documented anti-hypercholesterolemic effects and nutrition disorders mediated through the gut-liver-brain axis 40 , 41 . Besides, nitrogen-containing compounds' prioritized association with endocrine system diseases has received experimental validation 42 . These cross-validation results suggest that network proximity effectively captures more specifically therapeutic effects of the chemical classes. Inspired by the tissue-specific expression patterns of disease genes 43 , we proposed a “chemical class-organ axis-physiological system” hypothesis that specific chemical classes preferentially interact with the disease modules underlying inter-organ communication. This hypothesis proposes that phenols exert cardio-renal therapeutic effects potentially through heart-brain-kidney axis 44 , 45 , while glycosides regulate metabolic functions via gut-liver-brain axis 46 , 47 . Simultaneously, esters demonstrate immunomodulatory activity through spleen-lung-brain axis 48 , and phenols additionally mediate urinary system effects by modulating bladder-brain axis 49 . Organic acids support digestive functions through stomach-gut-brain axis 50 , 51 , whereas nitrogen-containing compounds coordinate secretory regulation via integrated HPT (Hypothalamic-Pituitary-Thyroid), HPA (Hypothalamic-Pituitary-Adrenal), HPG (Hypothalamic-Pituitary-Gonad) axes 52 – 54 and gallbladder-related networks 55 . This hypothesis may explain the therapeutic effects of certain herbs for organ-specific disorders in TCM, providing multiple further research direction for investigating molecular mechanism of the chemical class-physiological system associations. In summary, we established a quantitative relationship between chemical classes and physiological system-specific therapeutic effects. This work provides a systematic pharmacology framework for herbal efficacy. Our findings challenge the reductionist approach in phytochemical analysis. We proposed a “chemical class-organ axis-physiological system” hypothesis to explain organ-specific disorders of herbs. This paradigm shift opens new avenues for developing multi-target herbal formula and repurposing existing pharmacopeia through network medicine. Methods Samples preparation Six types of tea were processed using the typical and traditional processing methods (Table S1 ) 15 , 16 , which were purchased from Guizhou Bud-Chemist Tea Co. (Tongren city, Guizhou province, China). The protocol of tea extraction has been described previously 16 , 35 . Briefly, green tea, cyan tea, and red tea were brewed with 80℃, 95℃, and 99℃ for 2–5 min, respectively. Yellow tea, black tea, and white tea were boiled for 15–20 min. A set of quality control (QC) samples was produced by pooling an equal volume aliquots of each tea infusions (20µL). All tea infusions were injected into ultra high-performance liquid chromatography (UPLC) after extraction, immediately. Untargeted metabolomics analysis All samples were analyzed using ultra high-performance liquid chromatography (Waters 2D UPLC, Waters, USA) coupled in tandem to a Q exactive mass spectrometer (Thermo Fisher Scientific, USA). Chromatographic separation was performed using Hypersil GOLDaQ column (100 × 2.1mm, 19µm, Thermo Fisher Scientific, USA). The Q exactive mass spectrometer (Thermo Fisher Scientific, USA) was fitted with an electrospray source operating in positive ionization mode. The relevant parameters were following: spray voltage, + 3800V; auxiliary gas heater temperature, 350 ℃; capillary temperature, 320 ℃; sheath gas flow rate, 40 arbitrary units (a.u.) and auxiliary gas flow rate, 10 a.u. The following parameters were used for MS scan: resolution (70,000), automatic gain control (AGC) target (1.0 × 10 6 ), maximum injection time (100 ms) and scan range (150–1,500 m/z). For the data-dependent in MS2, the following parameters were used: resolution (35,000), AGC target (2.0 × 10 5 ), maximum injection time (50 ms). The mobile phase consisted of 0.1% formic acid in 100% water (A) and 0.1% formic acid in 100% acetonitrile (B) at a flow rate of 300 µl min − 1 , and the column maintained at 40 ℃. The gradient elution was set as follows: 0-2min, 5% B; 2-22min, increase to 5–95%; 22-27min, hold at 95%; 27-30min, decrease to 5%. Each sample was performed in six repetitions. QC sample was used to optimize the experiment parameters and injected them periodically throughout the sequence. The raw data acquired from LC-MS analysis was initially imported into the Compound Discoverer 3.1 (Thermo Fisher Scientific, USA) to generate a peak table including peak extraction, alignment, retention time, and missing value imputation. Acquired data was preprocessed in R environment ( https://www.R-project.org ). Sample data were normalized using probabilistic quotient normalization (PQN) to generate relative peak area 56 . Then, QC-based robust LOESS signal correction (QC-RLSC) was calculated for analytical batch effect correction to adjust signal drift 57 . To ensure stability of metabolomics data, we analyzed the CV values of the relative abundance of the chemical composition of QC samples. The features with over 30% of CV in the QC were removed. Compounds were identified by comparison to in-house library entries of purified standards, mzCloud, Tea Metabolome Database (TMDB, http://pcsb.ahau.edu.cn:8080/TCDB/f ) 58 . The metabolic features and MS/MS spectra were matched according to accurate masses, retention time, and the MS/MS spectra similarity score, which was used to distinguish the different levels of confidence 59 . Molecular fingerprints-based chemical hierarchy Molecular fingerprints are binary vectors where each position encodes a substructure property of the molecule, including PubChem fingerprints, Molecular ACCess System (MACCS), Substructure, Extended fingerprints 60 . 2714 approved small molecule drugs were retrieved from Drugbank database ( https://go.drugbank.com/ ). 1810 small molecule drugs of which were retained after removing experimental, withdrawn, illicit, and nutraceutical drugs. First, the SMILES representations of each compound were manually retrieved using the PubMed database ( https://pubchem.ncbi.nlm.nih.gov/ ). Second, the SMILES can be parsed to binary molecular fingerprints using the R package “rcdk”. Extended fingerprints with a length (the number of bits) of 1024 of all compounds were calculated using R package RCDK. This fingerprint was preferred for aromatic compounds 61 . If two drug molecules have a and b bits set in their standard fragment bit-strings, with c of these bits being set in the fingerprints of both drugs, the Tanimoto coefficient ( Tc ) of a drug-drug pair is defined as: $$\:Tc=\:\frac{c}{a+b-c}$$ Tc is widely used in drug discovery and development, which provide an index in the range of zero (no bits in common) to one (all bits are the same). We can use molecular fingerprints to calculate pairwise distances between chemical features and hierarchically cluster the fingerprint vectors to generate a phylogenetic tree representing their chemical structural relationships 62 . The phylogenetic tree was visualized by R package “ggtree”. Constructing the interactome The human interactome was assembled from 22 public available databases that compile experimentally validated protein-protein interactions (PPIs) data 26 , 63 : 1) binary PPIs tested by the high-throughout yeast two-hybrid (Y2H) experiments (HI-Union 64 ); 2) high-quality PPIs that derived from three-dimensional protein structures (Interactome3D 65 , Instruct 66 , Insider 67 ); (3) kinase substrate interactions from KinomeNetworkX 68 , PhosphoSitePlus 69 , Human Protein Reference Database (HPRD) 70 ; (4) PPIs identified by affinity purification followed by mass spectrometry or literature curation (BioPlex 71 , QUBIC 72 , CoFrac 73 , PINA 74 , MINT 75 , LitBM17 64 , Interactome3D, Instruct, Insider, BioGrid 76 , HINT 77 , HIPPIE 78 , APID 79 , InWeb 80 , IntAct 81 ); (5) signaling network PPIs from SignaLink 82 and InnateDB 83 ; (6) regulatory PPIs from ENCODE consortium. All genes were transformed to their corresponding Entrez ID using the ClusterProfiler R package. The resulting human interactome in this study contains 19098 proteins and 451639 interactions (Dataset 1). The largest connected component (LCC) includes 19060 proteins with 451602 interactions. Compound targets and disease-related proteins The compound from each chemical class was mapped to their PubChem CIDs. We obtained compound targets retrieved from superPRED, which is a drug classification and target prediction-a machine learning approach 84 . Thirty clinically well-documented therapeutic effects of tea were selected to retrieve their corresponding disease-associated proteins from previous literature and database 11 , 24 . Network localization and proximity We used the z score of the LCC to measure the localization of a node set in the interactome network 24 . We compared the observed values of LCC with a mean value of LCC by randomized simulations preserving the degree of nodes. The z score of LCC (Z LCC ) was defined as: $$\:{z}_{LCC}=\:\frac{observed\:LCC-\mu\:\left(random\:LCC\right)}{\sigma\:\left(random\:LCC\right)}$$ Network proximity was used to describe a distance metric between compounds targets and disease proteins. The distance metric considered the shortest path lengths between two node sets. Given T, the set of disease proteins, V, the set of compound targets and d(t, v), the shortest path length between nodes t and v in the network, the proximity was defined as: $$\:d\left(T,V\right)=\frac{1}{\left|\right|V\left|\right|}{\sum\:}_{{v}_{0}\in\:V}\underset{{t}_{0}\in\:T}{\text{min}}dist({t}_{0},{v}_{0})$$ To evaluate the significance of the distance between two node sets (V, T), we produced a mean of randomized distance between two randomly generated node sets of proteins matching the size and the degrees of the original compound targets and disease proteins in the network 25 . We used degree-preserving approach to avoid the repeated selection of the same high degree nodes with setting 100 nodes bins in 10000 realizations 26 . Similarly, the z score of proximity was defined as: $$\:{z}_{d\left(T,V\right)}=\:\frac{d\left(T,V\right)-{\mu\:}_{random\:d(T,\:V)}}{{\sigma\:}_{random\:d(T,\:V)}}$$ The proximity between nodes sets T and V is asymmetric because average shortest path length from T to V is not equal to that from V to T. The lower of the d and z score of proximity, the closer node sets are in the network. All network localization metrics were implemented by Python 3.11 25,63 . Statistical analysis Principal component analysis (PCA) was employed to assess the overall distribution of samples using the R package factoextra, incorporating all 10,354 features. The two leading principal components (PC1 and PC2), capturing maximal variance in the dataset, were selected to visualize sample differentiation. Subsequently, partial least squares-discriminant analysis (PLS-DA) implemented through the ropls package was performed for cluster discrimination, with results visualized using ggplot2. Variable importance in projection (VIP) scores were derived from PLS-DA models to identify discriminative features. Non-metric multidimensional scaling (NMDS) was conducted via the metaMDS function from vegan package. Significant compound variations were determined through integrated criteria: 1) Student's t-test with Benjamini-Hochberg FDR correction (adj. p 1), and 3) VIP score filtering (> 1.0). All statistical computations were executed in R 4.2.2 with critical α set at 0.05. Declarations Acknowledgements This work was supported by National Key R&D Program of China (2020YFE0201600). Author contributions H.L. conceived the concept, acquired funding, and edited the manuscript. L.F.W. drafted the original manuscript. L.F.W., H.L. performed the experiments. 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Nucleic Acids Res. 50 , W726–w731. 10.1093/nar/gkac297 (2022). Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6261027","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443258107,"identity":"2b814dc3-52f4-46ae-ba66-375f550cb9eb","order_by":0,"name":"Lufei Wang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Lufei","middleName":"","lastName":"Wang","suffix":""},{"id":443258108,"identity":"266ded09-f352-4e2b-9649-e015d6c3b6f1","order_by":1,"name":"Wei Jiang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Jiang","suffix":""},{"id":443258109,"identity":"69f45c23-557d-46f8-bc05-c0b9637fc174","order_by":2,"name":"Xiaoyi Lai","email":"","orcid":"","institution":"Anhui Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyi","middleName":"","lastName":"Lai","suffix":""},{"id":443258110,"identity":"23a5dda4-9aba-4916-a944-f22938f90e52","order_by":3,"name":"Nan Zhang","email":"","orcid":"","institution":"Nanjing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Zhang","suffix":""},{"id":443258111,"identity":"84e984b0-9add-4508-9503-a8c90a9ab152","order_by":4,"name":"Hui Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACA4nkBwc+2DAwNoB4PMRpSTN8OCONNC05wsY8pGphk7ZJOCzbP7uB8cHbNgZ5c4Ja5N+wSeckHDaececAs+HcNgbDnQ3E2JL743Biw40ENmneNoYEgwOEtbD/tkg4nDj/RgL7b2K1iEkzALVsANrCTKSWNDPJnoR04403Epsl55yTMNxASIv9jORnEj8SrGXn3Ug++OFNmY08QVuQADhqJIhXPwpGwSgYBaMANwAAT1pAanhDdywAAAAASUVORK5CYII=","orcid":"","institution":"Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-03-19 11:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6261027/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6261027/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81297836,"identity":"7758fd53-17b6-4221-8a87-e13879be727a","added_by":"auto","created_at":"2025-04-24 13:17:17","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":624480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChemical profiles of six types of tea using untargeted metabolomics.\u003c/strong\u003e (A) Study design illustrating the tea metabolomics analysis and network medicine-based functional prediction. (B) Partial least squares discriminant analysis (PLS-DA) distributed individual samples of Positive and Negative tea. (C, D) Principal component analyses (PCA) demonstrated three subtypes of Positive and Negative tea. (E) Heatmap displaying the chemical diversity among six tea types and abundance patterns of annotated compounds with significant changes in abundance of at least one comparison (see also Fig. S3).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6261027/v1/e95b774444922cb67727d968.jpeg"},{"id":81298905,"identity":"adbf6b6e-9271-443c-b4c3-db3c51304e7b","added_by":"auto","created_at":"2025-04-24 13:33:17","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1785972,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural similarity based on molecular fingerprint-based chemical hierarchy. \u003c/strong\u003eThe relative abundance, chemical structure and drug analogues of phenols (A), organic acids (B), nitrogen-containing compounds (C), esters (D), glycosides (E), flavones (F).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6261027/v1/d82f66d0037436eb10da6948.jpeg"},{"id":81298550,"identity":"f978fbe9-5170-4a9b-b60f-ec77e77ec35d","added_by":"auto","created_at":"2025-04-24 13:25:17","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2309794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork proximity between the chemical class targets and disease proteins.\u003c/strong\u003e (A) Schematic illustrating the network localization of chemical class targets and disease proteins on the human interactome. (B) The z score of the largest connected component (LCC) formed by chemical class targets. Thered dotted line represents z = 1.6. (C) Histogram showing z score of the LCC among 30 diseases. Thered dotted line represents z = 1.6. (D) Heatmap showing ranking chemical class based on their network proximity to each disease proteins. White box represents the lowest proximity.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6261027/v1/4caeffd6ee58e1acea7241da.jpeg"},{"id":82606666,"identity":"5b87c695-d708-4df6-94cc-c8ab764abadf","added_by":"auto","created_at":"2025-05-13 10:08:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5687134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6261027/v1/845ea3f4-82cf-4cdb-80ba-1947d84adc55.pdf"},{"id":81297843,"identity":"b361bd0b-9025-415b-bf4a-709a5c0efae3","added_by":"auto","created_at":"2025-04-24 13:17:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28691151,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6261027/v1/e2dd8194924647682f1b0c0c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network Medicine Framework Links Chemical Classes to Physiological System-specific Therapeutic Benefits","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHerbal remedies have served as cornerstone therapies for global healthcare throughout history. While randomized clinical trials\u003csup\u003e1-3\u003c/sup\u003e and mechanism of action studies\u003csup\u003e4,5\u003c/sup\u003e have validated specific therapeutic benefits, the systematic understanding of herb-disease relationships remains fragmented. Recent advances using protein-protein interactome analysis have enabled prediction of herbal effects through topological relationships between herb targets and disease proteins\u003csup\u003e6\u003c/sup\u003e. Despite these developments, two fundamental questions persist: (i) does isolation of single active compounds adequately represent holistic herb efficacy and (ii) can specific chemical classes (e.g., polyphenols in green tea\u003csup\u003e7,8\u003c/sup\u003e) reliably predict therapeutic effects? In addition, the structural classification of herbal compounds, such as flavonoids, organic acids, shows correlation with biological activity patterns\u003csup\u003e9,10\u003c/sup\u003e. This raises the critical question of whether such chemical classification exhibits specific therapeutic effects.\u003c/p\u003e\n\u003cp\u003eThe chemical diversity for herbal compound is affected by interspecies variations and processing method, which complicates relationship between chemical classes and therapeutic effects\u003csup\u003e11-14\u003c/sup\u003e. Tea (\u003cem\u003eCamellia sinensis\u003c/em\u003e) exhibits chemical diversity due to various processing methods. Fresh tea leaves can be processed into green tea, blue tea (also known as oolong tea), red tea (also known as black tea), white tea, black tea (also known as dark tea), yellow tea through different processing methods (Table S1)\u003csup\u003e15\u003c/sup\u003e. \u0026quot;Positive teas (Yang tea)\u0026quot; (green, blue, and red tea) undergo enzymatic conversion before heat inactivation, whereas \u0026quot;Negative teas (Yim tea)\u0026quot; (white, black, and yellow tea) experience reverse sequence\u003csup\u003e16\u003c/sup\u003e. This systematic processing diversity, coupled with well-documented health benefits against various diseases \u003csup\u003e7,17-21\u003c/sup\u003e, enables potetional investigation of chemical class-therapeutic effect relationships.\u003c/p\u003e\n\u003cp\u003eCurrent research limitations fall into three areas: firstly, experimental methods using concentrated organic extracts\u003csup\u003e22\u003c/sup\u003e poorly reflect actual consumption patterns; Secondly, comparative metabolomics across tea types is lacking; Thirdly, no unified framework exists to map tea-chemical class-disease associations. The emerging network medicine offers a promising solution through human protein-protein interactome\u003csup\u003e23\u003c/sup\u003e, which has successfully applied in predicting drug efficacy and exploring disease-disease relationship\u003csup\u003e24-26\u003c/sup\u003e. In this study, we employed untargeted metabolomics to profile six tea types (Fig. 1A), identifying tea type-specific chemical classes. We constructed a molecular fingerprint-based chemical hierarchy to match tea compounds with clinically approved drugs, enabling therapeutic effect prediction. Finally, we developed a network medicine framework to map chemical class-disease associations, establishing a systematic understanding of chemical classes and therapeutic effects.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eSpecific chemical classes among six types of tea\u003c/h2\u003e \u003cp\u003eUntargeted metabolomics analysis using UPLC-MS/MS identified 8,055 chemical features across six tea types after quality control and normalization (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B). The global metabolomics data revealed a clear separation between Positive and Negative tea (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB-C), underscoring the impact of the endogenous enzymes involved in the conversion process on compound variations. Positive tea subdivided into green, blue, and red tea (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD), while Negative tea subdivided into white, black, and yellow tea (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE). This result is consistent with pattern of tea processing methods. Pairwise comparison across tea types identified 246 annotated compounds spanning eight classes (Fig. S2A), including flavonoids (flavan-3-ols, flavanones, flavones, flavonols, isoflavones), organic acids, nitrogen-containing compounds, phenols, esters, glycosides, alkaloids, and amino acids (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, Fig. S3). Non-converted green tea exhibited fewer significantly altered compounds than other tea types (Fig. S2B), indicating the critical role of conversion process in chemical diversity. Notably, green tea retained high levels of flavan-3-ols, resembling fresh tea leaves\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, whereas yellow tea showed flavone enrichment through C2-/C3-position dehydrogenation during yellowing fermentation. Overall, green, blue, red, white, black, and yellow tea significantly accumulated phenols, organic acids, nitrogen-containing compounds, esters, glycosides, and flavones, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). These specific chemical profiles imply distinct synthetic pathways associated with specific processing techniques.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eChemical hierarchy-based therapeutic effect prediction\u003c/h2\u003e \u003cp\u003eTo elucidate the therapeutic potential of individual compounds within tea-specific chemical classes, we established a chemical hierarchy based on molecular fingerprint similarity. We excluded investigational, withdrawn, illicit, and nutraceutical agents to generate a refined dataset of 1,810 clinically approved small-molecule drugs. We constructed a chemical hierarchy that maps structural analogies between tea compounds and clinically approved molecular entities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This approach enables functional inference by identifying structural analogies with the approved drugs\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. For example, oxyresveratrol showed similarity to curcumin (regulative gastrointestinal tract), isoeugenol acetate to drotaverine (antispasmodic), kaempferol-3-rhamnoside to diosmin (liver protection), and isovetixin to flavoxate (hyperglycemic effect) (Table S2). These parallels suggest shared therapeutic effects between tea-derived compounds and clinically approved drugs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunction-specific localization of chemical class targets\u003c/h3\u003e\n\u003cp\u003eGiven that a single compound is insufficient to infer the role of an entire chemical class, we developed a network medicine framework to analyze target clusters of chemical classes. We mapped targets of six chemical classes to the human interactome (19,060 proteins with 451,602 interactions) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Non-metric multidimensional scaling (NMDS) based on molecular fingerprints revealed low similarity between chemical class pairs (average distance\u0026thinsp;=\u0026thinsp;0.66) (Fig. S4A-B). Despite comparable numbers of targets across chemical classes, Jaccard index analysis demonstrated limited target overlap between pairs (average similarity\u0026thinsp;=\u0026thinsp;0.29) (Fig. S4C-D). Gene ontology enrichment analysis identified distinct biological processes associated with different chemical classes, including cellular response to chemical stress for glycosides and negative regulation of apoptotic signaling pathway for flavones (Fig. S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe subsequently quantified the largest connected component (LCC) formed by each chemical class's targets. All classes exhibited substantial target clusters (LCC\u0026thinsp;\u0026ge;\u0026thinsp;40; Fig. S6). Notably, organic acids, nitrogen-containing compounds, esters, glycosides, and flavones showed significantly larger LCCs than random expectation (Z-score\u0026thinsp;\u0026gt;\u0026thinsp;1.75) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These findings indicate that chemical class targets occupy specific, well-localized neighborhoods within the human interactome (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), prompting investigation into their disease-related subnetworks.\u003c/p\u003e\n\u003ch3\u003eNetwork proximity reveals chemical class-specific associations with physiological system\u003c/h3\u003e\n\u003cp\u003eTo explore the distinct effects of chemical classes on diseases, we computed the network proximity between chemical class targets and disease proteins. More negative network proximity indicates stronger network associations between chemical classes and diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). We ranked the chemical classes based on their network proximity to the 30 tea-related diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D). Phenols may exhibit more effectively therapeutic effects on urological disorders; organic acids from blue tea on stomach carcinoma; nitrogen-containing compounds from red tea on cholestasis and endocrine system diseases; esters from white tea may on immunoproliferative and lung diseases; glycosides from black tea on glucose metabolism disorders and prostatic disease. Flavones from yellow tea may affect more diseases, which exhibit therapeutic effects on cardiovascular and neurodegenerative diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Notably, we observed a framework where specific chemical classes predominantly affect physiological system-specific diseases. Phenols, organic acids, nitrogen-containing compounds, esters, glycosides, and flavones might effectively affect urological, digestive, endocrinological, immunological, metabolic, and cardio-kidney diseases, respectively. GO enrichment analysis supported these associations through pathway-specific mechanisms (Fig. S5). For example, nitrogen-containing compounds demonstrated endocrine-related activity via thyroid gland development pathways. Esters modulated immune function through immune response-activating receptor signaling pathway and regulation of myeloid cell differentiation. These findings establish connections between tea-specific chemical classes and physiological system-related diseases.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere, we utilized tea as a model herb to investigate the relationship between processing-induced chemical classes and physiological system-specific therapeutic effects. The metabolomic profiling revealed that tea\u0026rsquo;s chemical compounds closely correspond to its processing methods, enabling identification of tea type-specific chemical class. Through constructing a molecular fingerprint-based chemical hierarchy, we found that a single compound is insufficient to represent therapeutic effects of the entire chemical class. We developed a network medicine framework to uncover that chemical class targets occupy functionally specific neighborhoods on the interactome. Importantly, these neighborhoods exhibit differential proximity to disease modules associated with particular physiological systems, thereby establishing a systematic framework for understanding herb-disease relationships at the chemical class level.\u003c/p\u003e \u003cp\u003eOur study addresses several persistent challenges in research of herbal remedy and traditional Chinese medicine (TCM) theory. First, whereas previous studies predominantly relied on isolating single bioactive compounds to explain herb therapeutic effects\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, our approach infers therapeutic potential through chemical class identification. These chemical classes clustered within functionally specific neighborhoods on the interactome. Second, the selection of tea as a model herb helps overcome confounding factors due to interspecies variations that complicate chemical class identification\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Third, the network proximity metric quantitatively links chemical classes to physiological system-specific therapeutic effects. This explains why conventional \" one disease-one target-one drug \" drug discovery paradigms fail to capture herbal polypharmacology\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This limitation is further highlighted by the repurposing potential of drugs like metformin\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, suggesting our framework offers a novel strategy for systematizing herbal functional analysis through molecular class characterization.\u003c/p\u003e \u003cp\u003eThe predictive power of network proximity is validated by accumulative evidence. Compared with other tea types, flavone-enriched yellow tea shows stronger therapeutic associations with cardiovascular and kidney diseases, consistent with its confirmed hypouricemic and hypoglycemic effects\u003csup\u003e\u003cspan additionalcitationids=\"CR36 CR37 CR38\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Furthermore, a prospective cohort study has shown an inverse relationship between flavone intake and cardiovascular diseases-related mortality\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Similarly, predicted metabolic regulation of glycosides aligns with black tea's documented anti-hypercholesterolemic effects and nutrition disorders mediated through the gut-liver-brain axis\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Besides, nitrogen-containing compounds' prioritized association with endocrine system diseases has received experimental validation\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. These cross-validation results suggest that network proximity effectively captures more specifically therapeutic effects of the chemical classes.\u003c/p\u003e \u003cp\u003eInspired by the tissue-specific expression patterns of disease genes\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, we proposed a \u0026ldquo;chemical class-organ axis-physiological system\u0026rdquo; hypothesis that specific chemical classes preferentially interact with the disease modules underlying inter-organ communication. This hypothesis proposes that phenols exert cardio-renal therapeutic effects potentially through heart-brain-kidney axis\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, while glycosides regulate metabolic functions via gut-liver-brain axis\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Simultaneously, esters demonstrate immunomodulatory activity through spleen-lung-brain axis\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, and phenols additionally mediate urinary system effects by modulating bladder-brain axis\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Organic acids support digestive functions through stomach-gut-brain axis\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, whereas nitrogen-containing compounds coordinate secretory regulation via integrated HPT (Hypothalamic-Pituitary-Thyroid), HPA (Hypothalamic-Pituitary-Adrenal), HPG (Hypothalamic-Pituitary-Gonad) axes\u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and gallbladder-related networks\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. This hypothesis may explain the therapeutic effects of certain herbs for organ-specific disorders in TCM, providing multiple further research direction for investigating molecular mechanism of the chemical class-physiological system associations.\u003c/p\u003e \u003cp\u003eIn summary, we established a quantitative relationship between chemical classes and physiological system-specific therapeutic effects. This work provides a systematic pharmacology framework for herbal efficacy. Our findings challenge the reductionist approach in phytochemical analysis. We proposed a \u0026ldquo;chemical class-organ axis-physiological system\u0026rdquo; hypothesis to explain organ-specific disorders of herbs. This paradigm shift opens new avenues for developing multi-target herbal formula and repurposing existing pharmacopeia through network medicine.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSamples preparation\u003c/h2\u003e \u003cp\u003eSix types of tea were processed using the typical and traditional processing methods (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, which were purchased from Guizhou Bud-Chemist Tea Co. (Tongren city, Guizhou province, China). The protocol of tea extraction has been described previously\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Briefly, green tea, cyan tea, and red tea were brewed with 80℃, 95℃, and 99℃ for 2\u0026ndash;5 min, respectively. Yellow tea, black tea, and white tea were boiled for 15\u0026ndash;20 min. A set of quality control (QC) samples was produced by pooling an equal volume aliquots of each tea infusions (20\u0026micro;L). All tea infusions were injected into ultra high-performance liquid chromatography (UPLC) after extraction, immediately.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUntargeted metabolomics analysis\u003c/h3\u003e\n\u003cp\u003eAll samples were analyzed using ultra high-performance liquid chromatography (Waters 2D UPLC, Waters, USA) coupled in tandem to a Q exactive mass spectrometer (Thermo Fisher Scientific, USA). Chromatographic separation was performed using Hypersil GOLDaQ column (100 \u0026times; 2.1mm, 19\u0026micro;m, Thermo Fisher Scientific, USA). The Q exactive mass spectrometer (Thermo Fisher Scientific, USA) was fitted with an electrospray source operating in positive ionization mode. The relevant parameters were following: spray voltage, +\u0026thinsp;3800V; auxiliary gas heater temperature, 350 ℃; capillary temperature, 320 ℃; sheath gas flow rate, 40 arbitrary units (a.u.) and auxiliary gas flow rate, 10 a.u. The following parameters were used for MS scan: resolution (70,000), automatic gain control (AGC) target (1.0 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e), maximum injection time (100 ms) and scan range (150\u0026ndash;1,500 m/z). For the data-dependent in MS2, the following parameters were used: resolution (35,000), AGC target (2.0 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e), maximum injection time (50 ms).\u003c/p\u003e \u003cp\u003eThe mobile phase consisted of 0.1% formic acid in 100% water (A) and 0.1% formic acid in 100% acetonitrile (B) at a flow rate of 300 \u0026micro;l min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and the column maintained at 40 ℃. The gradient elution was set as follows: 0-2min, 5% B; 2-22min, increase to 5\u0026ndash;95%; 22-27min, hold at 95%; 27-30min, decrease to 5%. Each sample was performed in six repetitions. QC sample was used to optimize the experiment parameters and injected them periodically throughout the sequence.\u003c/p\u003e \u003cp\u003eThe raw data acquired from LC-MS analysis was initially imported into the Compound Discoverer 3.1 (Thermo Fisher Scientific, USA) to generate a peak table including peak extraction, alignment, retention time, and missing value imputation. Acquired data was preprocessed in R environment (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Sample data were normalized using probabilistic quotient normalization (PQN) to generate relative peak area\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Then, QC-based robust LOESS signal correction (QC-RLSC) was calculated for analytical batch effect correction to adjust signal drift\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. To ensure stability of metabolomics data, we analyzed the CV values of the relative abundance of the chemical composition of QC samples. The features with over 30% of CV in the QC were removed. Compounds were identified by comparison to in-house library entries of purified standards, mzCloud, Tea Metabolome Database (TMDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pcsb.ahau.edu.cn:8080/TCDB/f\u003c/span\u003e\u003cspan address=\"http://pcsb.ahau.edu.cn:8080/TCDB/f\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e58\u003c/sup\u003e. The metabolic features and MS/MS spectra were matched according to accurate masses, retention time, and the MS/MS spectra similarity score, which was used to distinguish the different levels of confidence \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eMolecular fingerprints-based chemical hierarchy\u003c/h3\u003e\n\u003cp\u003eMolecular fingerprints are binary vectors where each position encodes a substructure property of the molecule, including PubChem fingerprints, Molecular ACCess System (MACCS), Substructure, Extended fingerprints\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. 2714 approved small molecule drugs were retrieved from Drugbank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/\u003c/span\u003e\u003cspan address=\"https://go.drugbank.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). 1810 small molecule drugs of which were retained after removing experimental, withdrawn, illicit, and nutraceutical drugs.\u003c/p\u003e \u003cp\u003eFirst, the SMILES representations of each compound were manually retrieved using the PubMed database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Second, the SMILES can be parsed to binary molecular fingerprints using the R package \u0026ldquo;rcdk\u0026rdquo;. Extended fingerprints with a length (the number of bits) of 1024 of all compounds were calculated using R package RCDK. This fingerprint was preferred for aromatic compounds\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. If two drug molecules have \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u003c/em\u003e bits set in their standard fragment bit-strings, with \u003cem\u003ec\u003c/em\u003e of these bits being set in the fingerprints of both drugs, the Tanimoto coefficient (\u003cem\u003eTc\u003c/em\u003e) of a drug-drug pair is defined as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Tc=\\:\\frac{c}{a+b-c}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTc is widely used in drug discovery and development, which provide an index in the range of zero (no bits in common) to one (all bits are the same). We can use molecular fingerprints to calculate pairwise distances between chemical features and hierarchically cluster the fingerprint vectors to generate a phylogenetic tree representing their chemical structural relationships\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. The phylogenetic tree was visualized by R package \u0026ldquo;ggtree\u0026rdquo;.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstructing the interactome\u003c/h2\u003e \u003cp\u003eThe human interactome was assembled from 22 public available databases that compile experimentally validated protein-protein interactions (PPIs) data\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e: 1) binary PPIs tested by the high-throughout yeast two-hybrid (Y2H) experiments (HI-Union\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e); 2) high-quality PPIs that derived from three-dimensional protein structures (Interactome3D\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, Instruct\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, Insider\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e); (3) kinase substrate interactions from KinomeNetworkX\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, PhosphoSitePlus\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, Human Protein Reference Database (HPRD)\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e; (4) PPIs identified by affinity purification followed by mass spectrometry or literature curation (BioPlex\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, QUBIC\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, CoFrac\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, PINA\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e, MINT \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e, LitBM17\u003csup\u003e64\u003c/sup\u003e, Interactome3D, Instruct, Insider, BioGrid\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, HINT\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, HIPPIE\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, APID\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e, InWeb\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e, IntAct\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e); (5) signaling network PPIs from SignaLink\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e and InnateDB\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e; (6) regulatory PPIs from ENCODE consortium. All genes were transformed to their corresponding Entrez ID using the ClusterProfiler R package. The resulting human interactome in this study contains 19098 proteins and 451639 interactions (Dataset 1). The largest connected component (LCC) includes 19060 proteins with 451602 interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCompound targets and disease-related proteins\u003c/h2\u003e \u003cp\u003eThe compound from each chemical class was mapped to their PubChem CIDs. We obtained compound targets retrieved from superPRED, which is a drug classification and target prediction-a machine learning approach\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. Thirty clinically well-documented therapeutic effects of tea were selected to retrieve their corresponding disease-associated proteins from previous literature and database\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNetwork localization and proximity\u003c/h2\u003e \u003cp\u003eWe used the z score of the LCC to measure the localization of a node set in the interactome network\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. We compared the observed values of LCC with a mean value of LCC by randomized simulations preserving the degree of nodes. The z score of LCC (Z\u003csub\u003eLCC\u003c/sub\u003e) was defined as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{z}_{LCC}=\\:\\frac{observed\\:LCC-\\mu\\:\\left(random\\:LCC\\right)}{\\sigma\\:\\left(random\\:LCC\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eNetwork proximity was used to describe a distance metric between compounds targets and disease proteins. The distance metric considered the shortest path lengths between two node sets. Given T, the set of disease proteins, V, the set of compound targets and d(t, v), the shortest path length between nodes t and v in the network, the proximity was defined as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:d\\left(T,V\\right)=\\frac{1}{\\left|\\right|V\\left|\\right|}{\\sum\\:}_{{v}_{0}\\in\\:V}\\underset{{t}_{0}\\in\\:T}{\\text{min}}dist({t}_{0},{v}_{0})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo evaluate the significance of the distance between two node sets (V, T), we produced a mean of randomized distance between two randomly generated node sets of proteins matching the size and the degrees of the original compound targets and disease proteins in the network\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. We used degree-preserving approach to avoid the repeated selection of the same high degree nodes with setting 100 nodes bins in 10000 realizations\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Similarly, the z score of proximity was defined as:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{z}_{d\\left(T,V\\right)}=\\:\\frac{d\\left(T,V\\right)-{\\mu\\:}_{random\\:d(T,\\:V)}}{{\\sigma\\:}_{random\\:d(T,\\:V)}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe proximity between nodes sets T and V is asymmetric because average shortest path length from T to V is not equal to that from V to T. The lower of the d and z score of proximity, the closer node sets are in the network. All network localization metrics were implemented by Python 3.11\u003csup\u003e25,63\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) was employed to assess the overall distribution of samples using the R package factoextra, incorporating all 10,354 features. The two leading principal components (PC1 and PC2), capturing maximal variance in the dataset, were selected to visualize sample differentiation. Subsequently, partial least squares-discriminant analysis (PLS-DA) implemented through the ropls package was performed for cluster discrimination, with results visualized using ggplot2. Variable importance in projection (VIP) scores were derived from PLS-DA models to identify discriminative features. Non-metric multidimensional scaling (NMDS) was conducted via the metaMDS function from vegan package. Significant compound variations were determined through integrated criteria: 1) Student's t-test with Benjamini-Hochberg FDR correction (adj. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 2) fold-change thresholds (|log2FC|\u0026gt;1), and 3) VIP score filtering (\u0026gt;\u0026thinsp;1.0). All statistical computations were executed in R 4.2.2 with critical α set at 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Key R\u0026amp;D Program of China (2020YFE0201600).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.L. conceived the concept, acquired funding, and edited the manuscript. L.F.W. drafted the original manuscript. L.F.W., H.L. performed the experiments. L.F.W., X.Y.L., W.J., N.Z. conducted the data preparation. L.F.W., W.J. conducted the bioinformatics analysis and generated the figures. X.Y.L. and N.Z. provided advice for data analysis. All authors reviewed and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary materials to this research article can be found online.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYang, Y. et al. 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SuperPred 3.0: drug classification and target prediction-a machine learning approach. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, W726\u0026ndash;w731. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkac297\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkac297\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\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":"","lastPublishedDoi":"10.21203/rs.3.rs-6261027/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6261027/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe health benefits of herbal plants are fundamentally determined by their bioactive compounds, which exhibit substantial variations across species and processing techniques. The fresh tea leaves (\u003cem\u003eCamellia sinensis\u003c/em\u003e) transform into six distinct tea types through specific processing methods, making them a potential model herb for investigating relationship between chemical classes and diseases. Here, we employed ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) based metabolomics to systematically characterize chemical classes across tea types. By establishing molecular fingerprint-based chemical hierarchies between tea compounds and approved small molecule drugs, we speculate on therapeutic potentials within specific chemical compound. Given that a single compound is insufficient to infer the role of an entire chemical class, we developed a network medicine framework that maps chemical class targets on the human protein-protein interactome. Targets of individual chemical class formed densely connected clusters in function-specific network neighborhoods. The network proximity between these chemical class neighborhoods and disease modules quantitatively predicted their therapeutic efficacy associated with physiological system. This multi-scale approach not only elucidates the relationship between chemical classes and physiological system-specific diseases but also provides a computational framework for natural product discovery and polypharmacology research.\u003c/p\u003e","manuscriptTitle":"Network Medicine Framework Links Chemical Classes to Physiological System-specific Therapeutic Benefits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 13:17:12","doi":"10.21203/rs.3.rs-6261027/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":"08492423-c0a6-49d0-ad56-858fef413493","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47192133,"name":"Health sciences/Medical research"},{"id":47192134,"name":"Biological sciences/Systems biology"}],"tags":[],"updatedAt":"2025-05-13T10:08:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 13:17:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6261027","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6261027","identity":"rs-6261027","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}