Metabolite–Activity Relationships in Three Ecologically Distinct Lichen Species Based on LC–QTOF–MS Profiling

Metabolite–Activity Relationships in Three Ecologically Distinct Lichen Species Based on LC–QTOF–MS Profiling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metabolite–Activity Relationships in Three Ecologically Distinct Lichen Species Based on LC–QTOF–MS Profiling Burcu Sümer Tüzün, Tuğçe FAFAL, Bijen KIVÇAK This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9331869/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Lichens are metabolically versatile symbiotic organisms that produce diverse secondary metabolites with potential biological activities. However, several taxa, particularly cyanolichens and members of the Umbilicariaceae, remain insufficiently characterized in terms of metabolite composition and functional bioactivity. This study aimed to investigate metabolite–activity relationships in three ecologically distinct lichen species ( Leptogium lichenoides, Scytinium palmatum , and Umbilicaria deusta ) using LC–QTOF–MS profiling combined with antioxidant and tyrosinase inhibition assays. Methanol and acetone extracts were analyzed for 15 targeted metabolites and evaluated using DPPH, ABTS, superoxide scavenging, metal-chelating, and CUPRAC assays, together with total phenolic and flavonoid content determination. The results revealed species-specific metabolite distributions associated with distinct bioactivity profiles. Scytinium palmatum showed high vanillic acid content (35.55 µg/g) and strong radical-scavenging activity (ABTS IC₅₀ = 88 µg/mL; superoxide IC₅₀ = 74.21 µg/mL). Umbilicaria deusta exhibited the highest phenolic (1139 mg GAE/g extract) and flavonoid contents (876 mg QE/g extract) together with the strongest reducing capacity (CUPRAC = 0.0235 TEAC). Leptogium lichenoides demonstrated solvent-dependent activity and selective tyrosinase inhibition (12.5%). Correlation-based evaluation indicated that antioxidant activity was not directly related to total phenolic content but was primarily driven by metabolite composition. Principal component analysis (PCA) further supported the separation of species according to metabolite composition and activity profiles. These findings suggest that metabolite-specific interactions and ecological adaptation play a key role in determining the functional bioactivity of lichen species. Lichen metabolites LC–MS Antioxidant activity Tyrosinase inhibition Structure–activity relationship Ecological adaptation Leptogium Scytinium Umbilicaria Figures Figure 1 Figure 2 1. INTRODUCTION Lichens are metabolically versatile symbiotic organisms capable of colonizing a wide range of ecological niches, from nutrient-poor substrates to high-UV alpine habitats (Huneck and Yoshimura, 1996 ; Nash, 2008 ). Their metabolic plasticity enables the biosynthesis of a broad spectrum of primary metabolites, including carotenoids, as well as diverse secondary metabolites such as depsides, depsidones, pulvinic acid derivatives and usnic acid, many of which exhibit notable biological activities (Molnár and Farkas, 2010 ; Elix, 2014 ). Increasing pharmacological evidence highlights the antioxidant, antimicrobial and enzyme-modulatory properties of lichen-derived compounds, underscoring their relevance for oxidative stress-related conditions and natural product research (Ranković and Kosanić, 2015 ; Fernández-Moriano et al., 2016 ). The metabolic composition of lichens is strongly influenced by ecological factors such as light intensity, hydration cycles and substrate type, which shape both metabolite diversity and functional activity. Despite growing interest, several taxa remain insufficiently explored, particularly cyanobacterial gelatinous lichens such as Leptogium lichenoides and Scytinium palmatum , and foliose taxa such as Umbilicaria deusta (Otálora and Wedin, 2013; Emsen et al., 2019). Previous studies indicate that these genera may contain phenolic acids (e.g., vanillic, protocatechuic and p-hydroxybenzoic acids), nitrogen-containing metabolites and carotenoids including lutein, neoxanthin and zeaxanthin, which are likely associated with photoprotective capacity (Czeczuga et al., 2008 ). However, comprehensive LC–QTOF–MS-based metabolite profiling and quantitative comparisons across extracts of different polarity remain limited. Oxidative stress is implicated in the pathogenesis of various chronic conditions, and natural antioxidants have therefore attracted considerable attention (Lobo et al., 2010 ; Valko et al., 2007 ). Although antioxidant and enzyme inhibitory activities have been reported for several lichen genera, the combined evaluation of multiple antioxidant mechanisms and tyrosinase inhibition in Leptogium, Scytinium and Umbilicaria species remains scarce (Fernández-Moriano et al., 2016 ; Kosanić and Ranković, 2011 ). In particular, tyrosinase inhibition is relevant for hyperpigmentation-related applications, as phenolic and carotenoid-associated compounds may interact with the copper-containing catalytic site of the enzyme (Masuda et al., 2005 ; Seo et al., 2003 ). Despite these advances, studies integrating high-resolution metabolite profiling with multi-assay biological evaluation are still lacking for these taxa. To date, no study has simultaneously characterized LC–QTOF–MS metabolite profiles, total phenolic and flavonoid contents, antioxidant capacities and tyrosinase inhibitory activity in Leptogium lichenoides, Scytinium palmatum and Umbilicaria deusta. Therefore, the present study aimed to investigate metabolite–activity relationships in these three ecologically distinct lichen species by combining LC–QTOF–MS profiling with comprehensive antioxidant and enzyme inhibition assays. By correlating metabolite composition with functional activity, this study provides new insights into the ecological and biochemical determinants of lichen bioactivity. 2. Materials and Methods 2.1. Materials Analytical grade methanol, ethanol, formic acid and acetonitrile were purchased from Sigma (St. Louis, MO, USA). Folin–Ciocalteu reagent, sodium carbonate and aluminum chloride were obtained from Merck (Darmstadt, Germany). Standards used for chromatographic and bioactivity analyses including gallic acid, quercetin, DPPH, ABTS, neocuproine, CuCl₂, EDTA, NBT, riboflavin, phosphate buffer, ascorbic acid, ferrozine, L-DOPA, kojic acid and mushroom tyrosinase were also sourced from Sigma. 2.2. Collection, identification and pre-treatment of lichen samples The lichen species Leptogium lichenoides , Scytinium palmatum and Umbilicaria deusta were collected from shaded forest habitats and rocky substrates in the Bursa region of Türkiye. Species identification was performed using standard morphological and chemical keys and confirmed by Prof. Dr. Şule Öztürk. Collected thalli were carefully cleaned under a Leica MZ6 stereomicroscope to remove extraneous materials such as bark particles and moss fragments. Samples were air-dried at room temperature for four days, powdered, and stored at 4°C in paper bags until extraction to preserve metabolite stability. 2.3. Preparation of extracts For each lichen species, methanol and acetone extracts were prepared to enable evaluation of metabolites across different polarity ranges. Exactly 1 g of powdered thallus was extracted with methanol or acetone using ultrasound-assisted extraction at 25°C (three cycles of 30 min each), following previously described methods (Adams et al., 1993 ; Zagoskina et al., 2013 ). Filtrates were pooled and evaporated under reduced pressure at 35–40°C to obtain dried extracts. Extracts were stored at 4°C until analysis and coded as follows: LLM / LLA ( Leptogium lichenoides , methanol / acetone), SPM / SPA ( Scytinium palmatum , methanol / acetone), UDM / UDA ( Umbilicaria deusta , methanol / acetone). All extractions were performed using pooled biomass per species; therefore, replicates represent technical replicates rather than independent biological replicates. 2.4. LC–QTOF–MS analysis Sample preparation and quality assurance Extracts were filtered through 0.22 µm PTFE membranes prior to injection. Solvent blanks and pooled quality control (QC) samples were injected periodically to monitor system stability and analytical reproducibility. Mass accuracy was verified using reference ions (m/z 121.0509 and 922.0098). Chromatographic conditions Chemical profiling was performed using an Agilent 1260 Infinity HPLC system equipped with a Poroshell 120 EC-C18 column (3.0 × 100 mm, 2.7 µm). The mobile phase consisted of water with 0.1% formic acid (A) and acetonitrile (B). The gradient program was as follows: 5% B (0–0.5 min), 25% B (0.5–7 min), 50% B (7–16 min), 75% B (16–23 min), 95% B (23–30 min), followed by re-equilibration at 5% B (30–40 min). Flow rate was 0.4 mL/min, column temperature was maintained at 35°C, and injection volume was 10 µL. Mass spectrometry Mass spectrometric detection was carried out using an Agilent 6550 iFunnel QTOF-MS system with Dual AJS electrospray ionization operated in both positive and negative modes. Instrument conditions were set as follows: drying gas 14 L/min at 290°C, nebulizer pressure 35 psi, sheath gas 14 L/min at 400°C, and nozzle voltage 1000 V. MS/MS spectra were acquired over an m/z range of 50–1800 at 20 eV collision energy. Metabolite identification was performed using METLIN and Agilent PCDL libraries (Fedrigo et al., 2018). 2.5. Determination of total phenolic and flavonoid contents 2.5.1. Total phenolic content (TPC) TPC was determined using the Folin–Ciocalteu method. Extract aliquots (100 µL; 5 µg/mL–1 mg/mL) were mixed with distilled water, sodium carbonate and Folin–Ciocalteu reagent. After incubation in the dark for 30 min, absorbance was measured at 750 nm. Results were expressed as mg gallic acid equivalents (GAE)/g extract (Cheung et al., 2003 ). All measurements were performed in triplicate. 2.5.2. Total flavonoid content (TFC) TFC was determined using the aluminum chloride colorimetric method. Extracts were mixed with ethanol, AlCl₃ and water, incubated for 40 min, and absorbance was measured at 415 nm. Results were expressed as mg quercetin equivalents (QE)/g extract. Measurements were performed in triplicate. 2.6. In vitro antioxidant assays All assays were conducted using freshly prepared reagents, and appropriate blanks were included to correct for background absorbance due to extract coloration. DPPH radical scavenging assay Extracts (10 µg/mL–1 mg/mL) were incubated with 0.1 mM DPPH• for 30 min in darkness, and absorbance was measured at 517 nm. α-Tocopherol was used as a reference standard. IC₅₀ values were calculated from concentration–response curves (Esmaeili and Khadadadi, 2002). ABTS radical cation scavenging assay ABTS•⁺ was generated by reacting ABTS (7 mM) with potassium persulfate (2.45 mM) for 12–16 h and diluted to an absorbance of 0.750 ± 0.02 at 734 nm. Extracts (20 µg/mL–1 mg/mL) were incubated for 6 min, and results were expressed as Trolox equivalent antioxidant capacity (TEAC) (Re et al., 1999 ). CUPRAC assay Reducing capacity was determined using the Cu(II) neocuproine method. Absorbance was measured at 450 nm after incubation with CuCl₂, neocuproine and ammonium acetate buffer (pH 7) for 30 min. Results were expressed as TEAC values (Apak et al., 2004 ). Superoxide radical scavenging assay Superoxide radicals were generated using the riboflavin–NBT–EDTA system under light exposure. Absorbance was measured at 560 nm after 5 min illumination. Background correction was applied using blanks without NBT (Patel et al., 2010 ). Metal-chelating activity Fe²⁺ chelating activity was evaluated using the ferrozine method. Absorbance was measured at 562 nm after incubation with FeCl₂ and ferrozine. EDTA was used as a positive control (Decker, 1997 ). 2.7. Tyrosinase inhibitory activity Tyrosinase inhibition was evaluated using mushroom tyrosinase and L-DOPA as substrate. Extracts were incubated with enzyme and substrate in phosphate buffer (pH 6.8) at 37°C for 15 min. Dopachrome formation was monitored at 475 nm. Results were expressed as percentage inhibition relative to control, with kojic acid as a reference inhibitor (Masuda et al., 2005 ). 2.8. Statistical analysis All measurements were performed in triplicate (n = 3) and expressed as mean ± standard deviation (SD). As extracts were prepared from pooled biomass, replicates represent technical replicates rather than independent biological samples. Therefore, statistical evaluation was considered exploratory. Pearson correlation analysis was performed to investigate relationships between metabolite abundance and biological activity parameters. In addition, principal component analysis (PCA) was applied to visualize patterns and clustering among samples based on metabolite composition and activity profiles. Multivariate analysis was preferred to better capture complex relationships among metabolites and activity parameters. One-way ANOVA followed by Tukey’s post hoc test was used only for descriptive comparison of extract groups, and results were interpreted cautiously. A significance level of p < 0.05 was applied. All analyses were performed using Python (SciPy) and SPSS. 3. RESULTS AND DISCUSSION The chemical and biological characterization of Leptogium lichenoides , Scytinium palmatum and Umbilicaria deusta revealed pronounced interspecies variation, reflecting both taxonomic differences and ecological adaptation strategies (Elix, 2014 ; Molnár and Farkas, 2010 ). The quantitative distribution of phenolic acids, lichen-specific metabolites and carotenoids obtained by LC–QTOF–MS analysis is presented in Table 1 . Table 1 LC–MS Metabolite Quantification (µg/g) Metabolite LLM LLA SPM SPA UDM UDA 2,3-Dihydroxybenzoic acid 0.009 0.000 0.015 0.000 0.008 0.007 Vanillic acid 4.058 0.767 35.552 0.103 57.293 32.245 Gallic acid 0.004 0.006 0.008 0.000 0.009 0.005 Quinic acid 0.178 0.006 0.453 0.006 0.602 0.004 Epicatechin 0.002 0.003 0.003 0.000 0.001 0.001 Evernic acid 0.423 0.422 0.021 0.015 0.196 0.043 Usnic acid 0.015 0.023 0.034 0.013 0.035 0.049 Rosmarinic acid 0.001 0.000 0.000 0.000 0.003 0.000 Zeaxanthin 0.000 0.000 0.000 0.000 0.000 0.645 Neoxanthin 2.405 0.626 1.998 0.715 2.847 0.000 Rutin 0.005 0.000 0.000 0.000 0.005 0.004 p-Coumaric acid 0.000 0.021 0.022 0.000 0.110 0.033 o-Coumaric acid 0.406 0.412 0.091 0.030 0.000 0.000 Naringenin 0.068 1.066 0.131 0.753 0.069 0.195 β-Carotene 0.049 0.062 0.074 0.000 0.020 0.133 Leptogium lichenoides exhibited a moderate metabolite profile. The methanol extract (LLM) was characterized by vanillic acid (4.06 µg/g) and neoxanthin (2.40 µg/g), whereas the acetone extract (LLA) showed enrichment in naringenin (1.06 µg/g) and o-coumaric acid (0.41 µg/g). Evernic acid was detected at similar levels in both extracts (~ 0.42 µg/g). These findings are consistent with previous reports describing Leptogium species as cyanolichens with relatively moderate phenolic content and limited carotenoid accumulation (Czeczuga et al., 2008 ). The elevated naringenin level observed in LLA may indicate species-specific metabolic responses to environmental stress conditions (Fernández-Moriano et al., 2016 ). Scytinium palmatum displayed a more distinct metabolite profile, particularly in the methanol extract (SPM), which contained high levels of vanillic acid (35.55 µg/g), together with neoxanthin (1.99 µg/g) and quinic acid (0.45 µg/g). These values suggest enhanced phenolic biosynthesis, potentially associated with the hydration–dehydration cycles characteristic of gelatinous lichens (Olivier-Jimenez et al., 2019 ). In contrast, the acetone extract (SPA) showed markedly lower metabolite abundance, dominated by naringenin (0.75 µg/g), consistent with polarity-dependent metabolite partitioning (Ranković and Kosanić, 2015 ). The elevated phenolic content compared to similar cyanolichen taxa further supports this interpretation (Emsen et al., 2019). Among the investigated species, Umbilicaria deusta exhibited the highest metabolite accumulation. The methanol extract (UDM) contained elevated vanillic acid (57.29 µg/g) and neoxanthin (2.84 µg/g), while the acetone extract (UDA) uniquely accumulated zeaxanthin (0.65 µg/g), which was not detected in the other species. This pattern is consistent with the chemotaxonomic characteristics of Umbilicariaceae, where phenolic compounds and aromatic polyketides are often abundant (Behera et al., 2006 ; Fernández-Moriano et al., 2016 ). Moreover, the enriched carotenoid profile reflects adaptation to high-light and exposed habitats, where enhanced photoprotection is required (Czeczuga et al., 2008 ). Total phenolic and flavonoid contents, together with antioxidant activity results, are summarized in Table 2 . Umbilicaria deusta exhibited the highest phenolic (1139–905 mg GAE/g extract) and flavonoid contents (875–326 mg QE/g extract), whereas Leptogium lichenoides showed moderate levels (LLM: 340 mg GAE/g; LLA: 166 mg GAE/g), consistent with previous findings in cyanolichens (Emsen et al., 2019). In contrast, Scytinium palmatum exhibited comparatively low total phenolic content, suggesting that its functional activity may be driven by specific metabolites rather than bulk phenolic abundance. Table 2 Total Phenolics, Flavonoids and Antioxidant Activities Parameter LLM LLA SPM SPA UDM UDA Total phenolics (mg GAE/g) 340.52 166.67 60.08 35.20 1139.54 905.04 Total flavonoids (mg QE/g) 19.42 56.51 10.18 1.24 875.89 326.58 DPPH (IC₅₀ µg/mL) 550.6 97.73 225.0 ND ND 150.1 ABTS (IC₅₀ µg/mL) 263.8 458.7 88.29 ND 18.89% inh 305.32 Superoxide (IC₅₀ µg/mL) 240.15 85.24 74.21 ND ND 180.15 CUPRAC (TEAC) 0.0087 ND 0.0040 ND 0.0235 0.0235 Metal chelation (IC₅₀ µg/mL) ND ND ND ND 254.8 ND Antioxidant assays revealed clear functional differentiation among the species. In the DPPH assay, the acetone extract of L. lichenoides (LLA) showed the strongest radical-scavenging activity (IC₅₀ = 97.73 µg/mL), likely associated with flavonoids and coumaric acid derivatives known for hydrogen-donating capacity (Masuda et al., 2005 ). In contrast, ABTS and superoxide assays identified Scytinium palmatum (SPM) as the most effective radical scavenger (IC₅₀ = 88.29 µg/mL and 74.21 µg/mL, respectively), reflecting the contribution of phenolic acids such as vanillic acid to electron-transfer mechanisms (Kosanić and Ranković, 2011 ). These findings are consistent with previous reports on ROS-scavenging strategies in lichens (Ranković and Kosanić, 2015 ). Superoxide radical scavenging activity followed a similar trend, with SPM showing the lowest IC₅₀ value, supporting the role of metabolite-specific antioxidant defense mechanisms in cyanolichens under fluctuating hydration conditions (Ranković and Kosanić, 2015 ). In contrast, CUPRAC results demonstrated that reducing capacity was highest in UDM and UDA (0.0235 TEAC), indicating that bulk phenolic content contributes more significantly to reducing power than to radical scavenging activity. Tyrosinase inhibition (Table 3 ) was observed only in Leptogium lichenoides , with the methanol extract (LLM) showing 12.5% inhibition compared to 5.92% for LLA. No inhibition was detected in Scytinium palmatum or Umbilicaria deusta , consistent with their lower abundance of hydroxylated phenolics typically associated with tyrosinase inhibition (Seo et al., 2003 ; Masuda et al., 2005 ). This selective activity suggests the presence of structurally specific mid-polarity metabolites in L. lichenoides. Table 3 Tyrosinase Inhibition (%) Extract Tyrosinase Inhibition (%) LLM 12.50 LLA 5.92 SPM ND SPA ND UDM ND UDA ND Correlation analysis further supported these observations by revealing positive associations between vanillic acid content and ABTS and superoxide scavenging activities, particularly in Scytinium palmatum extracts. In contrast, total phenolic content showed a stronger relationship with reducing capacity (CUPRAC), especially in Umbilicaria deusta extracts, rather than with radical scavenging activity. These findings are consistent with previous studies emphasizing the importance of metabolite composition over total phenolic content in determining antioxidant function (Kosanić and Ranković, 2011 ). Principal component analysis (PCA) further clarified these relationships and is presented in Fig. 1 . The analysis clearly separated the three lichen species according to their metabolite composition and bioactivity profiles. Scytinium palmatum clustered with strong radical-scavenging activity, Umbilicaria deusta with high phenolic content and reducing capacity, and Leptogium lichenoides with solvent-dependent activity and tyrosinase inhibition (Fig. 1 ). A heatmap analysis (Fig. 2 ) further illustrated the relationships between metabolite abundance and biological activities, highlighting metabolite-specific contributions to antioxidant responses. Detailed correlation matrix and PCA loadings are provided as supplementary data (Tables S1–S2). Principal component analysis (PCA) provided an integrated visualization of the dataset and clearly separated the three species based on their metabolite profiles and biological activities. Scytinium palmatum clustered with strong radical-scavenging activity, Umbilicaria deusta with high phenolic content and reducing capacity, and Leptogium lichenoides with solvent-dependent activity and tyrosinase inhibition. Overall, these results demonstrate that metabolite-specific composition plays a central role in determining the biological activity of lichen species. The observed interspecies differences highlight the importance of ecological adaptation in shaping both chemical diversity and functional bioactivity, supporting the concept that lichen metabolites contribute to species-specific defense strategies against environmental stress (Elix, 2014 ; Fernández-Moriano et al., 2016 ). 4. CONCLUSION This study provides the first integrated chemical and functional characterization of Leptogium lichenoides, Scytinium palmatum and Umbilicaria deusta , demonstrating distinct metabolite profiles associated with species-specific antioxidant and enzyme-modulating activities. The results clearly show that bioactivity is not directly correlated with total phenolic content but is primarily governed by metabolite composition and structure. Umbilicaria deusta exhibited high phenolic content linked mainly to reducing capacity, whereas Scytinium palmatum showed pronounced radical-scavenging activity driven by specific metabolites. In contrast, Leptogium lichenoides uniquely demonstrated tyrosinase inhibition, indicating the presence of structurally selective compounds with potential functional relevance. These findings highlight that functional bioactivity in lichens is determined by metabolite-specific composition rather than bulk phenolic abundance, emphasizing the importance of metabolite–activity relationships within an ecological context. Although further studies are needed to elucidate underlying mechanisms and isolate active compounds, the present work expands current chemotaxonomic and pharmacological knowledge of these underexplored taxa and identifies them as promising sources for future antioxidant and enzyme-targeted applications. Declarations Conflict of Interest The authors declare that they have no conflict of interest. Funding This study was supported by TÜBİTAK (Project No: 122S103). Author Contributions BST conceptualized and designed the study, performed data analysis, interpreted the results, and wrote the manuscript. TF contributed to experimental validation, data organization, and manuscript editing. BK contributed to data interpretation and critically revised the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated during the current study are available from the corresponding author on reasonable request. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this manuscript, AI-assisted tools were used for language editing and structural refinement. The authors have carefully reviewed and edited all content and take full responsibility for the final version of the manuscript. References Adams WW, Demmig-Adams B, Lange OL (1993) Carotenoid composition and metabolism in green and blue-green algal lichens in the field. 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Pharmacogn Res 2:152–158. https://doi.org/10.4103/0974-8490.65509 Ranković B, Kosanić M (2015) Lichens as a potential source of bioactive secondary metabolites. In: Ranković B (ed) Lichen secondary metabolites. Springer, London Re R, Pellegrini N, Proteggente A, Pannala A, Yang M, Rice-Evans C (1999) Antioxidant activity applying an improved ABTS radical cation decolorization assay. Free Radic Biol Med 26:1231–1237. https://doi.org/10.1016/S0891-5849(98)00315-3 Seo SY, Sharma VK, Sharma N (2003) Mushroom tyrosinase: Recent prospects. J Agric Food Chem 51:2837–2853. https://doi.org/10.1021/jf020826f Valko M, Leibfritz D, Moncol J, Cronin MTD, Mazur M, Telser J (2007) Free radicals and antioxidants in normal physiological functions and human disease. Int J Biochem Cell Biol 39:44–84. https://doi.org/10.1016/j.biocel.2006.07.001 Zagoskina NV, Nikolaeva TN, Lapshin PV, Zavarzin AA, Zavarzina AG (2013) Water-soluble phenolic compounds in lichens. Microbiology 82:445–452. https://doi.org/10.1134/S0026261713040138 Additional Declarations No competing interests reported. Supplementary Files suppfile.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 06 Apr, 2026 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-9331869","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634578242,"identity":"243aed10-b6eb-42b1-85c8-06596cdb52b3","order_by":0,"name":"Burcu Sümer Tüzün","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACAziLvQHEtSBFC88BEFeCFC0SCWCSsBZzidyHn3n+bJMzn/n86oYfBRIM/O3dCXi1WM5IN5bmbbttLHM7p+xmD9BhEmfObsDvsBtpbMy8DbcTZ0jnpN3gAWoxkMglQgvPn9v1MyTPpN38Q7wWttsJEhLsx24TZYtlzzNmyblttw1n8OSw3ZYxkOAh6Bdz9jTGD2/+3JaXYD/+7OabPzZy/O29+LUgAR5wHPEQqxwE2B+QonoUjIJRMApGEAAA0gRC3JtBW6IAAAAASUVORK5CYII=","orcid":"","institution":"Ege University","correspondingAuthor":true,"prefix":"","firstName":"Burcu","middleName":"Sümer","lastName":"Tüzün","suffix":""},{"id":634578244,"identity":"18cf93b0-afbc-4595-96f1-4b9e61641a5e","order_by":1,"name":"Tuğçe FAFAL","email":"","orcid":"","institution":"Ege University","correspondingAuthor":false,"prefix":"","firstName":"Tuğçe","middleName":"","lastName":"FAFAL","suffix":""},{"id":634578246,"identity":"beba7063-af76-44e7-a7b6-54e20836c477","order_by":2,"name":"Bijen KIVÇAK","email":"","orcid":"","institution":"Ege University","correspondingAuthor":false,"prefix":"","firstName":"Bijen","middleName":"","lastName":"KIVÇAK","suffix":""}],"badges":[],"createdAt":"2026-04-06 08:56:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9331869/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9331869/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108805467,"identity":"ccedc77d-3913-441b-b74c-ecf69e63ddff","added_by":"auto","created_at":"2026-05-08 15:26:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19513,"visible":true,"origin":"","legend":"\u003cp\u003ePCA score plot showing the distribution of lichen extracts based on metabolite composition and biological activity parameters.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9331869/v1/dbb605dcabfc6a31d73bc9b9.png"},{"id":108636799,"identity":"acc815cf-e73b-4253-b951-7eb0918df8a7","added_by":"auto","created_at":"2026-05-06 18:12:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38596,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing correlations between metabolite composition and biological activity parameters. The analysis highlights metabolite-specific contributions to antioxidant activity and reducing capacity.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9331869/v1/99b2b0f238b70ac02cebd515.png"},{"id":108809878,"identity":"33efda63-eab7-471f-8048-e6dd93c1d3bb","added_by":"auto","created_at":"2026-05-08 15:56:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":375597,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9331869/v1/734a607a-96c5-49ef-93a2-e721c313d9cb.pdf"},{"id":108636797,"identity":"1c136abc-780a-4271-9667-2c586fae7fe3","added_by":"auto","created_at":"2026-05-06 18:12:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16547,"visible":true,"origin":"","legend":"","description":"","filename":"suppfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-9331869/v1/93d0ca79fa779af686ba4ef4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolite–Activity Relationships in Three Ecologically Distinct Lichen Species Based on LC–QTOF–MS Profiling","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eLichens are metabolically versatile symbiotic organisms capable of colonizing a wide range of ecological niches, from nutrient-poor substrates to high-UV alpine habitats (Huneck and Yoshimura, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Nash, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Their metabolic plasticity enables the biosynthesis of a broad spectrum of primary metabolites, including carotenoids, as well as diverse secondary metabolites such as depsides, depsidones, pulvinic acid derivatives and usnic acid, many of which exhibit notable biological activities (Moln\u0026aacute;r and Farkas, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Elix, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Increasing pharmacological evidence highlights the antioxidant, antimicrobial and enzyme-modulatory properties of lichen-derived compounds, underscoring their relevance for oxidative stress-related conditions and natural product research (Ranković and Kosanić, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fern\u0026aacute;ndez-Moriano et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe metabolic composition of lichens is strongly influenced by ecological factors such as light intensity, hydration cycles and substrate type, which shape both metabolite diversity and functional activity. Despite growing interest, several taxa remain insufficiently explored, particularly cyanobacterial gelatinous lichens such as \u003cem\u003eLeptogium lichenoides\u003c/em\u003e and \u003cem\u003eScytinium palmatum\u003c/em\u003e, and foliose taxa such as Umbilicaria deusta (Ot\u0026aacute;lora and Wedin, 2013; Emsen et al., 2019).\u003c/p\u003e \u003cp\u003ePrevious studies indicate that these genera may contain phenolic acids (e.g., vanillic, protocatechuic and p-hydroxybenzoic acids), nitrogen-containing metabolites and carotenoids including lutein, neoxanthin and zeaxanthin, which are likely associated with photoprotective capacity (Czeczuga et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, comprehensive LC\u0026ndash;QTOF\u0026ndash;MS-based metabolite profiling and quantitative comparisons across extracts of different polarity remain limited.\u003c/p\u003e \u003cp\u003eOxidative stress is implicated in the pathogenesis of various chronic conditions, and natural antioxidants have therefore attracted considerable attention (Lobo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Valko et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Although antioxidant and enzyme inhibitory activities have been reported for several lichen genera, the combined evaluation of multiple antioxidant mechanisms and tyrosinase inhibition in \u003cem\u003eLeptogium, Scytinium\u003c/em\u003e and \u003cem\u003eUmbilicaria\u003c/em\u003e species remains scarce (Fern\u0026aacute;ndez-Moriano et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kosanić and Ranković, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In particular, tyrosinase inhibition is relevant for hyperpigmentation-related applications, as phenolic and carotenoid-associated compounds may interact with the copper-containing catalytic site of the enzyme (Masuda et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Seo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these advances, studies integrating high-resolution metabolite profiling with multi-assay biological evaluation are still lacking for these taxa. To date, no study has simultaneously characterized LC\u0026ndash;QTOF\u0026ndash;MS metabolite profiles, total phenolic and flavonoid contents, antioxidant capacities and tyrosinase inhibitory activity in \u003cem\u003eLeptogium lichenoides, Scytinium palmatum\u003c/em\u003e and \u003cem\u003eUmbilicaria deusta.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTherefore, the present study aimed to investigate metabolite\u0026ndash;activity relationships in these three ecologically distinct lichen species by combining LC\u0026ndash;QTOF\u0026ndash;MS profiling with comprehensive antioxidant and enzyme inhibition assays. By correlating metabolite composition with functional activity, this study provides new insights into the ecological and biochemical determinants of lichen bioactivity.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Materials\u003c/h2\u003e \u003cp\u003eAnalytical grade methanol, ethanol, formic acid and acetonitrile were purchased from Sigma (St. Louis, MO, USA). Folin\u0026ndash;Ciocalteu reagent, sodium carbonate and aluminum chloride were obtained from Merck (Darmstadt, Germany). Standards used for chromatographic and bioactivity analyses including gallic acid, quercetin, DPPH, ABTS, neocuproine, CuCl₂, EDTA, NBT, riboflavin, phosphate buffer, ascorbic acid, ferrozine, L-DOPA, kojic acid and mushroom tyrosinase were also sourced from Sigma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Collection, identification and pre-treatment of lichen samples\u003c/h2\u003e \u003cp\u003eThe lichen species \u003cem\u003eLeptogium lichenoides\u003c/em\u003e, \u003cem\u003eScytinium palmatum\u003c/em\u003e and \u003cem\u003eUmbilicaria deusta\u003c/em\u003e were collected from shaded forest habitats and rocky substrates in the Bursa region of T\u0026uuml;rkiye. Species identification was performed using standard morphological and chemical keys and confirmed by Prof. Dr. Şule \u0026Ouml;zt\u0026uuml;rk.\u003c/p\u003e \u003cp\u003eCollected thalli were carefully cleaned under a Leica MZ6 stereomicroscope to remove extraneous materials such as bark particles and moss fragments. Samples were air-dried at room temperature for four days, powdered, and stored at 4\u0026deg;C in paper bags until extraction to preserve metabolite stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Preparation of extracts\u003c/h2\u003e \u003cp\u003eFor each lichen species, methanol and acetone extracts were prepared to enable evaluation of metabolites across different polarity ranges.\u003c/p\u003e \u003cp\u003eExactly 1 g of powdered thallus was extracted with methanol or acetone using ultrasound-assisted extraction at 25\u0026deg;C (three cycles of 30 min each), following previously described methods (Adams et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Zagoskina et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Filtrates were pooled and evaporated under reduced pressure at 35\u0026ndash;40\u0026deg;C to obtain dried extracts.\u003c/p\u003e \u003cp\u003eExtracts were stored at 4\u0026deg;C until analysis and coded as follows:\u003c/p\u003e \u003cp\u003eLLM / LLA (\u003cem\u003eLeptogium lichenoides\u003c/em\u003e, methanol / acetone),\u003c/p\u003e \u003cp\u003eSPM / SPA (\u003cem\u003eScytinium palmatum\u003c/em\u003e, methanol / acetone),\u003c/p\u003e \u003cp\u003eUDM / UDA (\u003cem\u003eUmbilicaria deusta\u003c/em\u003e, methanol / acetone).\u003c/p\u003e \u003cp\u003eAll extractions were performed using pooled biomass per species; therefore, replicates represent technical replicates rather than independent biological replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. LC\u0026ndash;QTOF\u0026ndash;MS analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eSample preparation and quality assurance\u003c/b\u003e \u003c/p\u003e \u003cp\u003eExtracts were filtered through 0.22 \u0026micro;m PTFE membranes prior to injection. Solvent blanks and pooled quality control (QC) samples were injected periodically to monitor system stability and analytical reproducibility. Mass accuracy was verified using reference ions (m/z 121.0509 and 922.0098).\u003c/p\u003e \u003cp\u003e \u003cb\u003eChromatographic conditions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eChemical profiling was performed using an Agilent 1260 Infinity HPLC system equipped with a Poroshell 120 EC-C18 column (3.0 \u0026times; 100 mm, 2.7 \u0026micro;m). The mobile phase consisted of water with 0.1% formic acid (A) and acetonitrile (B). The gradient program was as follows: 5% B (0\u0026ndash;0.5 min), 25% B (0.5\u0026ndash;7 min), 50% B (7\u0026ndash;16 min), 75% B (16\u0026ndash;23 min), 95% B (23\u0026ndash;30 min), followed by re-equilibration at 5% B (30\u0026ndash;40 min).\u003c/p\u003e \u003cp\u003eFlow rate was 0.4 mL/min, column temperature was maintained at 35\u0026deg;C, and injection volume was 10 \u0026micro;L.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMass spectrometry\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMass spectrometric detection was carried out using an Agilent 6550 iFunnel QTOF-MS system with Dual AJS electrospray ionization operated in both positive and negative modes. Instrument conditions were set as follows: drying gas 14 L/min at 290\u0026deg;C, nebulizer pressure 35 psi, sheath gas 14 L/min at 400\u0026deg;C, and nozzle voltage 1000 V. MS/MS spectra were acquired over an m/z range of 50\u0026ndash;1800 at 20 eV collision energy. Metabolite identification was performed using METLIN and Agilent PCDL libraries (Fedrigo et al., 2018).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Determination of total phenolic and flavonoid contents\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1. Total phenolic content (TPC)\u003c/h2\u003e \u003cp\u003eTPC was determined using the Folin\u0026ndash;Ciocalteu method. Extract aliquots (100 \u0026micro;L; 5 \u0026micro;g/mL\u0026ndash;1 mg/mL) were mixed with distilled water, sodium carbonate and Folin\u0026ndash;Ciocalteu reagent. After incubation in the dark for 30 min, absorbance was measured at 750 nm. Results were expressed as mg gallic acid equivalents (GAE)/g extract (Cheung et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). All measurements were performed in triplicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Total flavonoid content (TFC)\u003c/h2\u003e \u003cp\u003eTFC was determined using the aluminum chloride colorimetric method. Extracts were mixed with ethanol, AlCl₃ and water, incubated for 40 min, and absorbance was measured at 415 nm. Results were expressed as mg quercetin equivalents (QE)/g extract. Measurements were performed in triplicate.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6. \u003cem\u003eIn vitro\u003c/em\u003e antioxidant assays\u003c/h2\u003e \u003cp\u003eAll assays were conducted using freshly prepared reagents, and appropriate blanks were included to correct for background absorbance due to extract coloration.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDPPH radical scavenging assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eExtracts (10 \u0026micro;g/mL\u0026ndash;1 mg/mL) were incubated with 0.1 mM DPPH\u0026bull; for 30 min in darkness, and absorbance was measured at 517 nm. α-Tocopherol was used as a reference standard. IC₅₀ values were calculated from concentration\u0026ndash;response curves (Esmaeili and Khadadadi, 2002).\u003c/p\u003e \u003cp\u003e \u003cb\u003eABTS radical cation scavenging assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eABTS\u0026bull;⁺ was generated by reacting ABTS (7 mM) with potassium persulfate (2.45 mM) for 12\u0026ndash;16 h and diluted to an absorbance of 0.750\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 at 734 nm. Extracts (20 \u0026micro;g/mL\u0026ndash;1 mg/mL) were incubated for 6 min, and results were expressed as Trolox equivalent antioxidant capacity (TEAC) (Re et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCUPRAC assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eReducing capacity was determined using the Cu(II) neocuproine method. Absorbance was measured at 450 nm after incubation with CuCl₂, neocuproine and ammonium acetate buffer (pH 7) for 30 min. Results were expressed as TEAC values (Apak et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSuperoxide radical scavenging assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSuperoxide radicals were generated using the riboflavin\u0026ndash;NBT\u0026ndash;EDTA system under light exposure. Absorbance was measured at 560 nm after 5 min illumination. Background correction was applied using blanks without NBT (Patel et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMetal-chelating activity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFe\u0026sup2;⁺ chelating activity was evaluated using the ferrozine method. Absorbance was measured at 562 nm after incubation with FeCl₂ and ferrozine. EDTA was used as a positive control (Decker, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Tyrosinase inhibitory activity\u003c/h2\u003e \u003cp\u003eTyrosinase inhibition was evaluated using mushroom tyrosinase and L-DOPA as substrate. Extracts were incubated with enzyme and substrate in phosphate buffer (pH 6.8) at 37\u0026deg;C for 15 min. Dopachrome formation was monitored at 475 nm. Results were expressed as percentage inhibition relative to control, with kojic acid as a reference inhibitor (Masuda et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Statistical analysis\u003c/h2\u003e \u003cp\u003eAll measurements were performed in triplicate (n\u0026thinsp;=\u0026thinsp;3) and expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). As extracts were prepared from pooled biomass, replicates represent technical replicates rather than independent biological samples. Therefore, statistical evaluation was considered exploratory.\u003c/p\u003e \u003cp\u003ePearson correlation analysis was performed to investigate relationships between metabolite abundance and biological activity parameters. In addition, principal component analysis (PCA) was applied to visualize patterns and clustering among samples based on metabolite composition and activity profiles. Multivariate analysis was preferred to better capture complex relationships among metabolites and activity parameters.\u003c/p\u003e \u003cp\u003eOne-way ANOVA followed by Tukey\u0026rsquo;s post hoc test was used only for descriptive comparison of extract groups, and results were interpreted cautiously. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied.\u003c/p\u003e \u003cp\u003eAll analyses were performed using Python (SciPy) and SPSS.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cp\u003eThe chemical and biological characterization of \u003cem\u003eLeptogium lichenoides\u003c/em\u003e, \u003cem\u003eScytinium palmatum\u003c/em\u003e and \u003cem\u003eUmbilicaria deusta\u003c/em\u003e revealed pronounced interspecies variation, reflecting both taxonomic differences and ecological adaptation strategies (Elix, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Moln\u0026aacute;r and Farkas, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The quantitative distribution of phenolic acids, lichen-specific metabolites and carotenoids obtained by LC\u0026ndash;QTOF\u0026ndash;MS analysis is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLC\u0026ndash;MS Metabolite Quantification (\u0026micro;g/g)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLLM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSPM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUDA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2,3-Dihydroxybenzoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVanillic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGallic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuinic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpicatechin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvernic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsnic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRosmarinic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZeaxanthin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeoxanthin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRutin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-Coumaric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo-Coumaric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaringenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Carotene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eLeptogium lichenoides\u003c/em\u003e exhibited a moderate metabolite profile. The methanol extract (LLM) was characterized by vanillic acid (4.06 \u0026micro;g/g) and neoxanthin (2.40 \u0026micro;g/g), whereas the acetone extract (LLA) showed enrichment in naringenin (1.06 \u0026micro;g/g) and o-coumaric acid (0.41 \u0026micro;g/g). Evernic acid was detected at similar levels in both extracts (~\u0026thinsp;0.42 \u0026micro;g/g). These findings are consistent with previous reports describing \u003cem\u003eLeptogium\u003c/em\u003e species as cyanolichens with relatively moderate phenolic content and limited carotenoid accumulation (Czeczuga et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The elevated naringenin level observed in LLA may indicate species-specific metabolic responses to environmental stress conditions (Fern\u0026aacute;ndez-Moriano et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eScytinium palmatum\u003c/em\u003e displayed a more distinct metabolite profile, particularly in the methanol extract (SPM), which contained high levels of vanillic acid (35.55 \u0026micro;g/g), together with neoxanthin (1.99 \u0026micro;g/g) and quinic acid (0.45 \u0026micro;g/g). These values suggest enhanced phenolic biosynthesis, potentially associated with the hydration\u0026ndash;dehydration cycles characteristic of gelatinous lichens (Olivier-Jimenez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, the acetone extract (SPA) showed markedly lower metabolite abundance, dominated by naringenin (0.75 \u0026micro;g/g), consistent with polarity-dependent metabolite partitioning (Ranković and Kosanić, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The elevated phenolic content compared to similar cyanolichen taxa further supports this interpretation (Emsen et al., 2019).\u003c/p\u003e \u003cp\u003eAmong the investigated species, \u003cem\u003eUmbilicaria deusta\u003c/em\u003e exhibited the highest metabolite accumulation. The methanol extract (UDM) contained elevated vanillic acid (57.29 \u0026micro;g/g) and neoxanthin (2.84 \u0026micro;g/g), while the acetone extract (UDA) uniquely accumulated zeaxanthin (0.65 \u0026micro;g/g), which was not detected in the other species. This pattern is consistent with the chemotaxonomic characteristics of Umbilicariaceae, where phenolic compounds and aromatic polyketides are often abundant (Behera et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fern\u0026aacute;ndez-Moriano et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, the enriched carotenoid profile reflects adaptation to high-light and exposed habitats, where enhanced photoprotection is required (Czeczuga et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTotal phenolic and flavonoid contents, together with antioxidant activity results, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Umbilicaria deusta exhibited the highest phenolic (1139\u0026ndash;905 mg GAE/g extract) and flavonoid contents (875\u0026ndash;326 mg QE/g extract), whereas \u003cem\u003eLeptogium lichenoides\u003c/em\u003e showed moderate levels (LLM: 340 mg GAE/g; LLA: 166 mg GAE/g), consistent with previous findings in cyanolichens (Emsen et al., 2019). In contrast, \u003cem\u003eScytinium palmatum\u003c/em\u003e exhibited comparatively low total phenolic content, suggesting that its functional activity may be driven by specific metabolites rather than bulk phenolic abundance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal Phenolics, Flavonoids and Antioxidant Activities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLLM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSPM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUDA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal phenolics (mg GAE/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e340.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1139.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e905.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal flavonoids (mg QE/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e875.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e326.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPPH (IC₅₀ \u0026micro;g/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e550.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e150.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABTS (IC₅₀ \u0026micro;g/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e263.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.89% inh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e305.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperoxide (IC₅₀ \u0026micro;g/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e180.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCUPRAC (TEAC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetal chelation (IC₅₀ \u0026micro;g/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e254.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAntioxidant assays revealed clear functional differentiation among the species. In the DPPH assay, the acetone extract of L. lichenoides (LLA) showed the strongest radical-scavenging activity (IC₅₀ = 97.73 \u0026micro;g/mL), likely associated with flavonoids and coumaric acid derivatives known for hydrogen-donating capacity (Masuda et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In contrast, ABTS and superoxide assays identified \u003cem\u003eScytinium palmatum\u003c/em\u003e (SPM) as the most effective radical scavenger (IC₅₀ = 88.29 \u0026micro;g/mL and 74.21 \u0026micro;g/mL, respectively), reflecting the contribution of phenolic acids such as vanillic acid to electron-transfer mechanisms (Kosanić and Ranković, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These findings are consistent with previous reports on ROS-scavenging strategies in lichens (Ranković and Kosanić, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSuperoxide radical scavenging activity followed a similar trend, with SPM showing the lowest IC₅₀ value, supporting the role of metabolite-specific antioxidant defense mechanisms in cyanolichens under fluctuating hydration conditions (Ranković and Kosanić, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In contrast, CUPRAC results demonstrated that reducing capacity was highest in UDM and UDA (0.0235 TEAC), indicating that bulk phenolic content contributes more significantly to reducing power than to radical scavenging activity.\u003c/p\u003e \u003cp\u003eTyrosinase inhibition (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was observed only in \u003cem\u003eLeptogium lichenoides\u003c/em\u003e, with the methanol extract (LLM) showing 12.5% inhibition compared to 5.92% for LLA. No inhibition was detected in Scytinium palmatum or \u003cem\u003eUmbilicaria deusta\u003c/em\u003e, consistent with their lower abundance of hydroxylated phenolics typically associated with tyrosinase inhibition (Seo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Masuda et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This selective activity suggests the presence of structurally specific mid-polarity metabolites in L. lichenoides.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTyrosinase Inhibition (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtract\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyrosinase Inhibition (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCorrelation analysis further supported these observations by revealing positive associations between vanillic acid content and ABTS and superoxide scavenging activities, particularly in \u003cem\u003eScytinium palmatum\u003c/em\u003e extracts. In contrast, total phenolic content showed a stronger relationship with reducing capacity (CUPRAC), especially in Umbilicaria deusta extracts, rather than with radical scavenging activity. These findings are consistent with previous studies emphasizing the importance of metabolite composition over total phenolic content in determining antioxidant function (Kosanić and Ranković, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Principal component analysis (PCA) further clarified these relationships and is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The analysis clearly separated the three lichen species according to their metabolite composition and bioactivity profiles. \u003cem\u003eScytinium palmatum\u003c/em\u003e clustered with strong radical-scavenging activity, Umbilicaria deusta with high phenolic content and reducing capacity, and Leptogium lichenoides with solvent-dependent activity and tyrosinase inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A heatmap analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) further illustrated the relationships between metabolite abundance and biological activities, highlighting metabolite-specific contributions to antioxidant responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDetailed correlation matrix and PCA loadings are provided as supplementary data (Tables S1\u0026ndash;S2).\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) provided an integrated visualization of the dataset and clearly separated the three species based on their metabolite profiles and biological activities. Scytinium palmatum clustered with strong radical-scavenging activity, Umbilicaria deusta with high phenolic content and reducing capacity, and Leptogium lichenoides with solvent-dependent activity and tyrosinase inhibition.\u003c/p\u003e \u003cp\u003eOverall, these results demonstrate that metabolite-specific composition plays a central role in determining the biological activity of lichen species. The observed interspecies differences highlight the importance of ecological adaptation in shaping both chemical diversity and functional bioactivity, supporting the concept that lichen metabolites contribute to species-specific defense strategies against environmental stress (Elix, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fern\u0026aacute;ndez-Moriano et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e"},{"header":"4. CONCLUSION","content":"\u003cp\u003eThis study provides the first integrated chemical and functional characterization of \u003cem\u003eLeptogium lichenoides, Scytinium palmatum\u003c/em\u003e and \u003cem\u003eUmbilicaria deusta\u003c/em\u003e, demonstrating distinct metabolite profiles associated with species-specific antioxidant and enzyme-modulating activities. The results clearly show that bioactivity is not directly correlated with total phenolic content but is primarily governed by metabolite composition and structure.\u003c/p\u003e \u003cp\u003e \u003cem\u003eUmbilicaria deusta\u003c/em\u003e exhibited high phenolic content linked mainly to reducing capacity, whereas \u003cem\u003eScytinium palmatum\u003c/em\u003e showed pronounced radical-scavenging activity driven by specific metabolites. In contrast, \u003cem\u003eLeptogium lichenoides\u003c/em\u003e uniquely demonstrated tyrosinase inhibition, indicating the presence of structurally selective compounds with potential functional relevance.\u003c/p\u003e \u003cp\u003eThese findings highlight that functional bioactivity in lichens is determined by metabolite-specific composition rather than bulk phenolic abundance, emphasizing the importance of metabolite\u0026ndash;activity relationships within an ecological context.\u003c/p\u003e \u003cp\u003eAlthough further studies are needed to elucidate underlying mechanisms and isolate active compounds, the present work expands current chemotaxonomic and pharmacological knowledge of these underexplored taxa and identifies them as promising sources for future antioxidant and enzyme-targeted applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003cbr\u003e\u003c/strong\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e\u003c/strong\u003eThis study was supported by TÜBİTAK (Project No: 122S103).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003cbr\u003e\u003c/strong\u003eBST conceptualized and designed the study, performed data analysis, interpreted the results, and wrote the manuscript. TF contributed to experimental validation, data organization, and manuscript editing. BK contributed to data interpretation and critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003cbr\u003e\u003c/strong\u003eThe datasets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003cbr\u003e\u003c/strong\u003eDuring the preparation of this manuscript, AI-assisted tools were used for language editing and structural refinement. The authors have carefully reviewed and edited all content and take full responsibility for the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams WW, Demmig-Adams B, Lange OL (1993) Carotenoid composition and metabolism in green and blue-green algal lichens in the field. Oecologia 94:576\u0026ndash;584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF00317118\u003c/span\u003e\u003cspan address=\"10.1007/BF00317118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApak R, G\u0026uuml;\u0026ccedil;l\u0026uuml; K, \u0026Ouml;zy\u0026uuml;rek M, Karademir SE (2004) A novel total antioxidant capacity index for dietary polyphenols, vitamin C and E using their cupric ion reducing capability in the presence of neocuproine: The CUPRAC method. 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Microbiology 82:445\u0026ndash;452. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1134/S0026261713040138\u003c/span\u003e\u003cspan address=\"10.1134/S0026261713040138\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"biologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biol","sideBox":"Learn more about [Biologia](http://link.springer.com/journal/11756)","snPcode":"11756","submissionUrl":"https://www.editorialmanager.com/biol/default2.aspx","title":"Biologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lichen metabolites, LC–MS, Antioxidant activity, Tyrosinase inhibition, Structure–activity relationship, Ecological adaptation, Leptogium, Scytinium, Umbilicaria","lastPublishedDoi":"10.21203/rs.3.rs-9331869/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9331869/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLichens are metabolically versatile symbiotic organisms that produce diverse secondary metabolites with potential biological activities. However, several taxa, particularly cyanolichens and members of the Umbilicariaceae, remain insufficiently characterized in terms of metabolite composition and functional bioactivity.\u003c/p\u003e \u003cp\u003eThis study aimed to investigate metabolite\u0026ndash;activity relationships in three ecologically distinct lichen species (\u003cem\u003eLeptogium lichenoides, Scytinium palmatum\u003c/em\u003e, and \u003cem\u003eUmbilicaria deusta\u003c/em\u003e) using LC\u0026ndash;QTOF\u0026ndash;MS profiling combined with antioxidant and tyrosinase inhibition assays. Methanol and acetone extracts were analyzed for 15 targeted metabolites and evaluated using DPPH, ABTS, superoxide scavenging, metal-chelating, and CUPRAC assays, together with total phenolic and flavonoid content determination.\u003c/p\u003e \u003cp\u003eThe results revealed species-specific metabolite distributions associated with distinct bioactivity profiles. \u003cem\u003eScytinium palmatum\u003c/em\u003e showed high vanillic acid content (35.55 \u0026micro;g/g) and strong radical-scavenging activity (ABTS IC₅₀ = 88 \u0026micro;g/mL; superoxide IC₅₀ = 74.21 \u0026micro;g/mL). \u003cem\u003eUmbilicaria deusta\u003c/em\u003e exhibited the highest phenolic (1139 mg GAE/g extract) and flavonoid contents (876 mg QE/g extract) together with the strongest reducing capacity (CUPRAC\u0026thinsp;=\u0026thinsp;0.0235 TEAC). \u003cem\u003eLeptogium lichenoides\u003c/em\u003e demonstrated solvent-dependent activity and selective tyrosinase inhibition (12.5%).\u003c/p\u003e \u003cp\u003eCorrelation-based evaluation indicated that antioxidant activity was not directly related to total phenolic content but was primarily driven by metabolite composition. Principal component analysis (PCA) further supported the separation of species according to metabolite composition and activity profiles. These findings suggest that metabolite-specific interactions and ecological adaptation play a key role in determining the functional bioactivity of lichen species.\u003c/p\u003e","manuscriptTitle":"Metabolite–Activity Relationships in Three Ecologically Distinct Lichen Species Based on LC–QTOF–MS Profiling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 18:12:27","doi":"10.21203/rs.3.rs-9331869/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-27T11:18:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T07:10:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T07:09:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biologia","date":"2026-04-06T08:48:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biol","sideBox":"Learn more about [Biologia](http://link.springer.com/journal/11756)","snPcode":"11756","submissionUrl":"https://www.editorialmanager.com/biol/default2.aspx","title":"Biologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6e77e312-85a3-4c7b-b63c-22014f0392b0","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T18:12:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 18:12:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9331869","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9331869","identity":"rs-9331869","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}