Abstract Dirinaria aegialita is a foliose lichen with recognized antimicrobial potential; however, its antibacterial activity against phytopathogenic bacteria and the associated metabolite profile remain poorly understood. This study evaluated the antibacterial activity of solvent extracts of D. aegialita against Ralstonia solanacearum and Pseudomonas syringae and characterized the predominant metabolites in the most active extract using an integrated in vitro–in silico approach. Antibacterial activity varied with solvent polarity, with the acetone extract producing the highest extraction yield (6.32%) and the largest inhibition zones against R. solanacearum (21.67 ± 0.58 mm) and P. syringae (26.33 ± 0.58 mm) during preliminary agar cup diffusion screening. Direct gas chromatography–mass spectrometry (GC–MS) analysis without chemical derivatization tentatively identified a volatile and semi-volatile metabolite profile dominated by methyl stearate (18.71%) and hexadecanoic acid methyl ester (15.05%). A chromatographic peak tentatively assigned as (Z)-13-docosenamide (8.21%) was detected but excluded from biological interpretation because of its potential contaminant origin. SwissADME-based physicochemical characterization indicated high lipophilicity (Consensus LogP > 5.5) and low topological polar surface area (TPSA < 50 Ų), suggesting limited systemic mobility but physicochemical properties consistent with interactions with hydrophobic biological membranes. Independent PASS Online analyses predicted comparatively high probabilities for membrane permeability inhibition (Pa = 0.806) and lipid metabolism regulation (Pa = 0.740). Collectively, these findings highlight putative identified lipophilic metabolites associated with the antibacterial activity of the acetone extract and provide a hypothesis-generating foundation, supported by computational predictions for future experimental validation of lichen-derived antibacterial agents for sustainable plant disease management.

Keywords: Dirinaria aegialita, GC–MS profiling, Lichen secondary metabolites, PASS Online, Plant disease management

Funding: The authors affiliated to the Department of Botany, Handique Girls’ College, Guwahati, Assam-781001, India acknowledge the financial support received from the Department of Science and Technology (DST), Government of India, under the DST-CURIE (WISE KIRAN Division) scheme for Women PG Colleges (Grant No. DST/CURIE-PG/2022/88(G)).

Citation:  Ahmed, S., Saikia, M., Ahmed, J., Bujarbarua, P., and Nath, S.K. 2027. In vitro antibacterial efficacy and in silico membrane disruption profiling of lipophilic metabolites from Dirinaria aegialita against phytopathogenic bacteria. Natural and Life Sciences Communications. 26(1): e2027001.

Graphical Abstract:

INTRODUCTION

Lichens represent a unique evolutionary adaptation, existing as symbiotic organisms between a fungal partner (the mycobiont) and a photosynthetic partner (the photobiont), usually a green alga or cyanobacterium (Honegger, 1991; Ahmadjian, 1993). This symbiotic relationship enables lichens to live in diverse harsh environments, including deserts and high alpine and polluted urban habitats (Hawksworth and Honegger, 1994; Nash, 2008). The presence of structurally heterogeneous secondary metabolites synthesized mainly via fungal polyketides pathways has contributed majorly to their ecological stability and capacity to survive in high UV and desiccation conditions (Huneck, 1999; Boustie and Grube, 2005; Calcott et al., 2018). More than a thousand compounds, including depsides, depsidones and dibenzofurans, are known to be synthesized by lichenized fungi (Molnár and Farkas, 2010; Shukla et al., 2010). Many of these compounds have antimicrobial, antioxidant, antiviral, anti-inflammatory and cytotoxic activities (Lawrey, 1986; Müller, 2001; Ranković and Kosanić, 2015; Ahmed et al., 2020). Despite such pharmaceutical potential, lichens remain substantially under-explored in the study of managing agricultural pathogens (Bérdy, 2012; Zambare and Christopher, 2012).

Plant pathogenic bacteria contribute significantly to global crop losses. Recent global analyses estimate that microbial plant diseases account for substantial yield reductions in major food crops (Savary et al., 2019). Ralstonia solanacearum is one of the most devastating bacterial pathogens that induces bacterial wilt in more than 250 plant species and survives in the soil and water reservoirs making it hard to control their prevalence (Elphinstone, 2005; Peeters et al., 2013). Likewise, Pseudomonas syringae contains many pathovars that cause leaf blights, spots and cankers in major crops of the economy across the globe (Mansfield et al., 2012; Xin et al., 2018).

The growing prevalence of antibiotic resistance and environmental issues related to synthetic bactericides have increased interest in natural product-based plant protection strategies (Sundin and Wang, 2018; Calefi et al., 2025). Natural metabolites are likely to have multi-target effects, which may decrease the rapid development of resistance (Wagner and Ulrich-Merzenich, 2009). Among the analytical techniques available for natural product investigation, gas chromatography–mass spectrometry (GC–MS) is particularly suitable for profiling volatile and semi-volatile metabolites. However, many characteristic lichen secondary metabolites, including depsides and depsidones, are relatively non-volatile and thermally unstable, making them less amenable to direct GC–MS analysis without derivatization or complementary liquid chromatography-based techniques (Huneck and Yoshimura, 1996; Kind and Fiehn, 2010). Consequently, GC–MS-based metabolite profiles should be interpreted as representing only a portion of the extract's chemical composition rather than its complete secondary metabolome.

Dirinaria aegialita (Ach.) Moore is a foliose lichen common in tropical and subtropical areas. While D. aegialita exhibits antimicrobial activities against clinical pathogens (Pradhan et al., 2022) and various purified lichen substances have shown efficacy against plant pathogens (Paguirigan et al., 2022), there is a paucity of studies integrating antibacterial evaluation with GC–MS-based metabolite profiling and computational characterization of D. aegialita against phytopathogenic bacteria. This study was undertaken to evaluate the antibacterial activity of solvent extracts of Dirinaria aegialita against Ralstonia solanacearum and Pseudomonas syringae, to tentatively characterize the volatile and semi-volatile metabolites present in the most active extract using direct GC–MS analysis, and to explore the physicochemical properties and predicted biological activities of selected metabolites through an integrated in silico chemoinformatic workflow. Because conventional GC–MS primarily detects volatile and thermally stable constituents, the resulting metabolite profile represents only the volatile and semi-volatile fraction of the extract rather than
the complete lichen metabolome. Consequently, all compound assignments are considered putative, and the computational analyses presented herein are intended to generate mechanistic hypotheses requiring future experimental validation. By integrating preliminary antibacterial screening with metabolite profiling, SwissADME-based physicochemical characterization (Daina et al., 2017), Tice Rules evaluation (Tice, 2001), and PASS Online bioactivity prediction (Lagunin et al., 2000; Filimonov et al., 2014), this study provides a systematic framework for prioritizing candidate lichen-derived metabolites for future development as sustainable plant-protection agents.

MATERIALS AND METHODS

Collection and authentication

Corticolous foliose lichen for this study was collected from Kahikuchi area near Guwahati, Assam, India (latitude 26.107655 to 26.107799 and longitude 91.610269 to 91.610281). Identification was conducted using macroscopic morphology, microscopic anatomical examination and standard chemical spot tests following established lichenological protocols (Huneck and Yoshimura, 1996; Awasthi, 2000; Orange et al., 2001).

Extraction procedure

Air-dried thalli of D. aegialita were pulverized into fine powder and subjected to Soxhlet extraction using methanol, acetone, diethyl ether and petroleum ether as solvents (Azwanida, 2015). 10 g of powdered material were extracted with 100 mL of solvent with extraction temperatures maintained at the respective boiling points of the solvents. The extracts were subsequently concentrated under reduced pressure using a rotary evaporator, and the complete removal of residual solvent was verified by gravimetric stability. The final extracts were stored at 4°C and were subsequently dissolved in 5% DMSO to achieve the 1 mg/mL working concentration (Azwanida, 2015). The percentage yield was calculated based on the dry weight of the initial sample using the formula:

Antibacterial assay

Ralstonia solanacearum (MTCC 13168) and Pseudomonas syringae (MTCC 673) were obtained from CSIR-Institute of Microbial Technology, Chandigarh, India. Bacterial cultures were maintained on Nutrient Agar media. Antibacterial activity was evaluated using the agar cup diffusion method (Balouiri et al., 2016). Standardised wells (6.0 mm diameter) were loaded with 100 µL of 1 mg/mL extract solution. Streptomycin (1 mg mL⁻¹) was included as a positive control at the same nominal concentration as the crude extracts to provide a mass-equivalent reference during this preliminary agar diffusion screening. Because agar diffusion assays are influenced by compound-specific diffusion characteristics, the resulting inhibition zones should not be interpreted as direct measures of intrinsic antibacterial potency. 5% DMSO (v/v) served as the negative control. Zones of inhibition were measured after 24ย้ฟะh incubation at 37°C (Ahmed et al., 2020).

Statistical analysis

All bioassays were performed in technical triplicate (n = 3) using a single standardized biological preparation, and the results are expressed as mean ± standard deviation (SD). Data were assessed for normality and homogeneity of variance prior to one-way analysis of variance (ANOVA). Statistical analyses were performed in RStudio (Version 2026.01.1+403) using R software (R Core Team, 2023). Differences among treatments were evaluated using Tukey's Honestly Significant Difference (HSD) post hoc test at a significance level of α = 0.05. Graphical visualization of antibacterial activity was generated using the ggplot2 package (Wickham, 2016), while colour palettes were implemented using the viridis package (Garnier et al., 2024).

GC-MS analysis and in silico profiling

The acetone extract, which exhibited the highest antibacterial activity during the preliminary screening assay, was selected for GC–MS analysis. The extract was analysed by direct injection without any prior chemical derivatization or methylation step in order to preserve its native volatile and semi-volatile chemical constituents. Approximately 2 μL of the filtered extract was injected in splitless mode into a Shimadzu GC–MS QP2010 system equipped with an SH-I-5Sil MS capillary column (30 m × 0.25 mm i.d., 0.25 μm film thickness). Helium was used as the carrier gas at a constant flow rate of 1.0 mL min⁻¹. The oven temperature programme was as follows: 45°C for 2 min, increased to 140°C at 5°C min⁻¹, then increased to 280°C and held isothermally for 10 min, giving a total chromatographic run time of approximately 52 min. Electron impact ionization (EI) was performed at 70 eV. The resulting chromatograms and mass spectra were processed using the instrument software, and compounds were tentatively identified by comparison of their mass spectra and experimental retention indices with entries in the NIST14.L Mass Spectral Library. Only putative metabolite annotations supported by acceptable spectral similarity (Similarity Index ≥ 90%), agreement between experimental and literature retention indices, and chromatographic consistency were retained for biological interpretation (Stein, 2012).

To minimise false-positive annotations, obvious analytical artefacts, including siloxane column bleed, silicone-derived contaminants, laboratory plasticizers and other ubiquitous background contaminants, were systematically excluded prior to biological interpretation. The remaining compounds were classified as putatively identified biogenic volatile and semi-volatile metabolites. Only the major curated metabolites selected for subsequent chemoinformatic analysis are presented in the main manuscript, whereas the complete chromatographic peak list together with tentative compound assignments is provided in Supplementary Table S1.

The major putatively identified metabolites were subsequently subjected to an integrated in silico chemoinformatic workflow. Canonical SMILES structures of the major putatively identified metabolites were retrieved from the PubChem database (Kim et al., 2023) and used as input for subsequent chemoinformatic analyses using the SwissADME web server (Daina et al., 2017). Agrochemical suitability was assessed according to Tice Rules (Tice, 2001), while Topological Polar Surface Area (TPSA), consensus LogP and other molecular descriptors were used to estimate physicochemical characteristics relevant to plant protection applications. Prediction of probable biological activities was performed using the PASS Online server (Lagunin et al., 2000; Filimonov et al., 2014). The Canonical SMILES used for each query are provided in Supplementary Table S2.

RESULTS

Extraction yield and antibacterial activity

The gravimetric yield of D. aegialita extracts varied considerably based on solvent polarity. Acetone provided the highest extraction yield at 6.32%, followed by methanol (5.49%), diethyl ether (2.18%) and petroleum ether (1.89%).

Quantitative antibacterial assessment demonstrated that the acetone extract produced the largest inhibition zones against both target phytopathogens during the preliminary agar diffusion assay. Against R. solanacearum, the acetone extract produced the largest inhibition zone (21.67 ± 0.58 mm), significantly exceeding that of the methanol extract (18.00 ± 1.00 mm). Under the conditions of the agar diffusion assay, the inhibition zone produced by the acetone extract was also larger than that observed for the streptomycin reference (7.33 ± 0.58 mm); however, because diffusion characteristics differ among compounds, these results should not be interpreted as direct evidence of superior intrinsic antibacterial potency. A similar trend was observed against P. syringae, where the acetone extract yielded a 26.33 ± 0.58 mm inhibition zone. In contrast, non-polar and weakly polar solvents exhibited limited efficacy; petroleum ether showed no detectable inhibition against P. syringae, and diethyl ether failed to inhibit R. solanacearum.

Table 1. Gravimetric yield analysis and quantitative assessment of antibacterial efficacy of D. aegialita extracts against R. solanacearum and P. syringae.

Note: Values are Mean ± SD (n = 3). Superscripts ^(a–d) indicate significant differences within columns (Tukey’s HSD,  P ≤ 0.05). ND: No detectable inhibition beyond the physical well diameter (6.0 mm).

Figure 1. In vitro antibacterial activity (zone of inhibition, mm) of Dirinaria aegialita extracts against Ralstonia solanacearum and Pseudomonas syringae. Values represent mean ± SD (n = 3). Different superscript letters indicate statistically significant differences (Tukey's HSD, P ≤ 0.05).

GC–MS profiling of the volatile and semi-volatile metabolites in the bioactive acetone extract

To investigate the volatile and semi-volatile metabolites associated with the bioactive acetone extract, GC–MS profiling was performed. The Total Ion Chromatogram (TIC) captured a distinct profile of biogenic secondary metabolites, primarily localized in the later retention times (32.187 to 45.310 min).

Spectral matching against the NIST Mass Spectral Library together with comparison of experimental retention indices enabled tentative annotation of the predominant volatile constituents detected in the acetone extract. Relative peak-area normalization indicated that methyl stearate (18.71%), hexadecanoic acid methyl ester (15.05%) and a chromatographic peak tentatively assigned as (Z)-13-docosenamide (8.21%) represented the most abundant chromatographic peaks (Table 2). Minor constituents included unsaturated fatty acid methyl esters, long-chain alkenes and alkanes. Because compound assignments were based solely on GC–MS library matching, all metabolite identifications should be regarded as putative pending confirmation by authentic reference standards or complementary analytical techniques such as LC–MS/MS or NMR spectroscopy.

Figure 2. Total ion chromatogram (TIC) obtained by GC–MS analysis of the acetone extract of Dirinaria aegialita (m/z 44.95–500.30).

GC–MS analysis of the acetone extract detected a total of 67 chromatographic peaks. Following systematic curation, chromatographic peaks corresponding to solvent-derived compounds, siloxane artefacts, probable laboratory contaminants, and duplicate automated library assignments were excluded, resulting in a final dataset of 37 putatively biogenic metabolites (Supplementary Table S1). The volatile fraction of the extract was dominated by long-chain lipophilic compounds, with methyl stearate (18.71%) and hexadecanoic acid methyl ester (15.05%) representing the most abundant chromatographic peaks. A chromatographic peak tentatively assigned as (Z)-13-docosenamide accounted for 8.21% of the total chromatographic area; however, because this compound is recognised as a common laboratory contaminant associated with polymeric materials, it was excluded from subsequent mechanistic interpretation. Compound assignments were based on comparison with the NIST14.L Mass Spectral Library and corresponding retention indices and should therefore be regarded as putative identifications pending confirmation by complementary analytical techniques. The complete chromatographic peak list together with chromatographic parameters and tentative compound assignments is presented in Supplementary Table S1, whereas only the curated metabolites selected for subsequent chemoinformatic analyses are discussed in the main text.

The experimental retention index (RI) obtained for methyl stearate was slightly lower than some literature values reported for comparable stationary phases. Such minor deviations are commonly attributed to differences in chromatographic conditions, including oven temperature programming, stationary-phase characteristics, n-alkane calibration range, carrier-gas flow, and instrumental operating parameters, and therefore do not preclude tentative compound identification when supported by high mass spectral similarity together with acceptable retention index agreement (Rostad and Pereira, 1986; Bizzo et al., 2023).

Table 2. Detailed GC–MS profiling of the curated putatively identified metabolites of D. aegialita acetone extract.

Note: SI = Similarity Index obtained from the NIST14.L Mass Spectral Library; Exp. RI = Experimental Retention Index calculated relative to C8–C40 n-alkanes. Compound assignments are tentative and based on comparison of mass spectra and retention indices. Confirmation by authentic reference standards or complementary analytical techniques (e.g., LC–MS/MS, HRMS or NMR spectroscopy) was not performed. ¹Detected chromatographically but excluded from mechanistic interpretation because erucamide [(Z)-13-docosenamide] is widely recognised as a common laboratory contaminant originating from polymeric materials.

Figure 3. Relative abundance (Area %) of the putatively identified metabolites grouped according to chemical class in the acetone extract of Dirinaria aegialita. (Z)-13-docosenamide is displayed for chromatographic completeness but was excluded from mechanistic interpretation because of its potential contaminant origin.

Figure 4: Representative GC–MS mass spectra and tentative NIST library assignments of the major chromatographic peaks detected in the acetone extract of D. aegialita. Methyl stearate (A) and Hexadecanoic acid methyl ester (B).

Although Dirinaria species are well documented producers of characteristic lichen secondary metabolites, including depsides, depsidones and related aromatic polyketides (Huneck and Yoshimura, 1996; Molnár and Farkas, 2010), these compounds were not detected in the present chromatographic dataset. This apparent absence should not be interpreted as evidence that such metabolites were absent from the acetone extract. Rather, direct GC–MS preferentially detects volatile and semi-volatile constituents, whereas many characteristic lichen phenolics possess relatively high molecular weights, poor volatility and thermal instability, rendering them unsuitable for direct GC–MS analysis without derivatization or complementary LC–MS-based approaches. Consequently, the present chromatographic profile should be interpreted as representing primarily the volatile and semi-volatile lipophilic fraction of the extract rather than the complete secondary metabolome of D. aegialita.

Furthermore, although the extract was analysed by direct injection without chemical derivatization, the biological significance of the detected fatty acid methyl esters should be interpreted cautiously. Direct GC–MS analysis substantially reduces the likelihood of artefactual methyl ester formation during sample preparation; nevertheless, naturally occurring methyl esters, extraction-related transformations or other analytical factors cannot be completely excluded. Accordingly, these metabolites are interpreted here as putative constituents associated with the bioactive extract rather than experimentally confirmed antibacterial principles.

In silico physicochemical characterization and computational prediction of membrane-associated activity

To evaluate the translational potential of the major metabolites as agricultural protectants, in silico physicochemical profiling was conducted against Tice Rules for systemicity (Table 3). The dominant compounds namely hexadecanoic acid methyl ester and methyl stearate exhibited extreme lipophilicity (Consensus LogP > 5.5) and low polarity (TPSA < 50 Ų). As visualized in the systemicity model (Figure 5), this specific physicochemical profile falls well outside the optimal zone for systemic vascular translocation through the xylem and phloem. However, this physicochemical profile is consistent with molecular properties expected to favour membrane partitioning. The combination of high LogP and low TPSA is predicted to favour partitioning into the hydrophobic core of bacterial lipid bilayers. Similar membrane interactions have been reported for several highly lipophilic antimicrobial compounds, which disrupt bacterial membrane integrity by partitioning into membrane lipids and increasing membrane permeability (Helander et al., 1998). Accordingly, the present chemoinformatic predictions provide computational support for a membrane-associated hypothesis that requires future experimental validation (Figure 6).

Table 3: In silico physicochemical profiling, Tice rules evaluation, and membrane permeability potential of major D. aegialita metabolites.

Note: Systemic mobility requires LogP 0–3. High LogP (>5) and low TPSA (<50 Ų) indicate localized membrane-associated activity rather than vascular translocation.

Figure 5. In silico evaluation of plant systemicity and vascular mobility potential for the dominant biogenic metabolites of D. aegialita.

Figure 6. Bivariate physicochemical mapping of the dominant fatty acid methyl esters putatively identified in the D. aegialita acetone extract. The scatter plot correlates lipophilicity (Consensus LogP) with Topological Polar Surface Area (TPSA), with marker size proportional to molecular weight. The spatial distribution illustrates that both metabolites exceed the lipophilicity threshold for optimal vascular systemicity (LogP > 3), exhibiting molecular descriptor profiles consistent with localized partitioning into hydrophobic biological membranes.

PASS online prediction of biological activities

PASS Online predictions were independently performed for methyl stearate and hexadecanoic acid methyl ester using their respective Canonical SMILES retrieved from the PubChem database. The analyses were carried out separately for each metabolite. Both compounds exhibited comparable prediction profiles, with moderate antibacterial probabilities (Pa = 0.263) and comparatively higher probabilities for membrane permeability inhibition (Pa = 0.806) and lipid metabolism regulation (Pa = 0.740) (Table 4). These predictions provide computational support for a membrane-associated hypothesis but should not be interpreted as experimental confirmation of antibacterial mechanisms (Figure 7).

Table 4: PASS online chemoinformatic prediction of membrane-associated biological activities for the dominant D. aegialita metabolites.

Note: Pa = probability to be active; Pi = probability to be inactive. PASS Online predictions were generated by independently submitting the canonical SMILES of each metabolite retrieved from PubChem. Although the two fatty acid methyl esters produced identical probability values for several predicted activities, these values originated from separate PASS Online analyses. The reported probabilities represent computational predictions rather than experimentally validated biological activities.

Figure 7. PASS Online chemoinformatic prediction heatmap for the dominant D. aegialita metabolites, illustrating the predicted probabilities (Pa) for relevant antibacterial, membrane permeability, and lipid metabolism regulation activities.

DISCUSSION

The present investigation demonstrates that the acetone extract of D. aegialita produced pronounced antibacterial activity against R. solanacearum and P. syringae during preliminary agar diffusion screening. Among the solvent extracts evaluated, the acetone extract consistently produced the largest inhibition zones against both phytopathogens, indicating the presence of biologically active constituents associated with the extract. However, because agar diffusion assays are influenced by compound-specific diffusion characteristics, the observed inhibition zones should be interpreted as evidence of preliminary antibacterial activity rather than direct measures of intrinsic antibacterial potency (Wiegand et al., 2008; Balouiri et al., 2016). Direct GC–MS analysis of the bioactive acetone extract revealed a volatile and semi-volatile metabolite profile dominated by long-chain lipophilic constituents, particularly fatty acid methyl esters (FAMEs). Nevertheless, the present study does not establish these metabolites as the definitive antibacterial principles of D. aegialita. Rather, GC–MS library matching provides tentative metabolite annotations, while the combined in vitro and in silico analyses identify these metabolites as candidate constituents associated with the bioactive extract that warrant future purification, structural confirmation and biological validation (Kind and Fiehn, 2010; Stein, 2012). By integrating preliminary antibacterial screening with metabolite profiling, physicochemical characterization and computational bioactivity prediction, the present study provides a computationally supported framework for prioritizing lichen-derived metabolites for future mechanistic investigation and sustainable botanical bactericide development. Accordingly, the proposed membrane-associated mechanism should be regarded as a hypothesis generated from complementary computational evidence rather than as experimentally confirmed biological activity (Filimonov et al., 2014; Daina et al., 2017).

The extraction efficiency and subsequent biological activity of natural products are strongly influenced by solvent polarity because extraction solvents differ considerably in their ability to dissolve chemically diverse classes of secondary metabolites (Azwanida, 2015). In the present study, acetone produced both the highest extraction yield (6.32%) and the greatest antibacterial activity among the solvents investigated, suggesting that a substantial proportion of the bioactive constituents of D. aegialita are moderately polar to lipophilic compounds. Lichens represent highly specialized symbiotic associations between a mycobiont and one or more photosynthetic partners, producing an extensive diversity of structurally unique secondary metabolites primarily through polyketide biosynthetic pathways (Ahmadjian, 1993; Hawksworth and Honegger, 1994; Huneck, 1999; Calcott et al., 2018). These metabolites contribute to ecological adaptation by protecting lichens against environmental stress, ultraviolet radiation, microbial colonization and herbivory (Honegger, 1991; Nash, 2008). Numerous investigations have also demonstrated their broad spectrum of biological activities, including antibacterial, antifungal, antioxidant, antiviral and anticancer properties, highlighting their considerable pharmaceutical and agricultural potential (Lawrey, 1986; Müller, 2001; Boustie and Grube, 2005; Molnár and Farkas, 2010; Shukla et al., 2010; Zambare and Christopher, 2012; Ranković and Kosanić, 2015). Previous studies have documented antibacterial activities of foliose lichens against clinically important human pathogens (Ahmed et al., 2020; Pradhan et al., 2022), whereas comparatively few investigations have explored their activity against phytopathogenic bacteria (Paguirigan et al., 2022). Therefore, the present study expands current knowledge by integrating antibacterial evaluation with GC–MS-based metabolite profiling and computational characterization of D. aegialita against two economically important bacterial plant pathogens.

The comparatively large inhibition zones produced by the acetone extract against R. solanacearum and P. syringae are particularly relevant because these pathogens are responsible for substantial agricultural losses worldwide. R. solanacearum causes destructive bacterial wilt in more than 250 plant species and is recognized as one of the most economically important soil-borne phytopathogens because of its extensive host range, persistence in soil and irrigation water, and difficulty of eradication (Elphinstone, 2005; Peeters et al., 2013). Likewise, P. syringae comprises numerous pathovars responsible for bacterial speck, blight, canker and leaf spot diseases affecting a wide variety of economically important crops (Mansfield et al., 2012; Xin et al., 2018). Under the conditions employed in the present agar diffusion assay, the acetone extract produced larger inhibition zones than the streptomycin reference. However, this observation should not be interpreted as evidence of superior intrinsic antibacterial potency because diffusion through agar depends on several physicochemical properties, including molecular size, polarity and diffusion coefficient, which differ substantially between crude extracts and purified antibiotics (Wiegand et al., 2008; Balouiri et al., 2016). Nevertheless, the observed antibacterial activity indicates that D. aegialita contains metabolites worthy of further investigation through bioassay-guided fractionation, minimum inhibitory concentration (MIC) determination and purification of individual constituents. Considering the increasing emergence of resistance among phytopathogenic bacteria following prolonged application of synthetic bactericides and antibiotics (Bérdy, 2012; Sundin and Wang, 2018), natural products possessing multiple complementary mechanisms of action remain attractive candidates for the development of environmentally sustainable plant disease management strategies (Cowan, 1999; Wagner and Ulrich-Merzenich, 2009).

To investigate the chemical constituents associated with the antibacterial activity of the bioactive acetone extract, comprehensive GC–MS profiling was performed using direct injection without chemical derivatization or methylation. Compound annotation was based on spectral matching against the NIST mass spectral library together with comparison of experimentally determined retention indices, following standard approaches for tentative metabolite identification in GC–MS-based metabolomics (Kind and Fiehn, 2010; Stein, 2012). The analytical workflow was further supported by established microchemical and taxonomic references commonly employed in lichen chemistry (Huneck and Yoshimura, 1996; Awasthi, 2000; Orange et al., 2001). The chromatographic profile of the acetone extract was dominated by long-chain lipophilic metabolites, particularly methyl stearate (18.71%) and hexadecanoic acid methyl ester (15.05%), which represented the most abundant chromatographic peaks detected in the volatile and semi-volatile fraction. Because the present analysis relied on GC–MS library matching rather than structural confirmation using authentic standards, these metabolites should be regarded as putatively identified constituents of the acetone extract rather than definitively confirmed compounds (Kind and Fiehn, 2010; Stein, 2012).

An important analytical consideration is that direct GC–MS preferentially detects volatile and semi-volatile metabolites that are sufficiently thermostable for gas chromatographic separation. Many characteristic lichen secondary metabolites, including depsides, depsidones, dibenzofurans and several phenolic compounds, possess relatively low volatility and may undergo thermal degradation or exhibit poor chromatographic behaviour without prior derivatization. Consequently, the metabolite profile obtained in the present study represents only the volatile and semi-volatile fraction of the acetone extract rather than the complete metabolome of D. aegialita. Therefore, the absence of characteristic lichen metabolites from the chromatogram should not be interpreted as evidence that these compounds are absent from the lichen itself, but rather as a limitation inherent to direct GC–MS analysis (Kind and Fiehn, 2010; Molnár and Farkas, 2010; Stein, 2012).

A chromatographic peak tentatively assigned as (Z)-13-docosenamide (8.21%) was also detected during GC–MS analysis. However, this compound was deliberately excluded from biological interpretation because numerous analytical studies have reported oleamide and related long-chain fatty acid amides as common laboratory contaminants originating from plasticware, polymeric materials, septa and other laboratory consumables. Furthermore, the simultaneous detection of trace amounts of bis(2-ethylhexyl) phthalate and other background contaminants within the chromatogram further supports the possibility that (Z)-13-docosenamide represents an analytical artefact rather than a genuine lichen metabolite. Consequently, no mechanistic conclusions regarding the antibacterial activity of (Z)-13-docosenamide are drawn in the present study, and all subsequent computational analyses were restricted to metabolites considered more likely to represent authentic constituents of the extract (Stein, 2012).

Because the acetone extract was analysed by direct GC–MS injection without chemical derivatization, the detected fatty acid methyl esters are unlikely to have arisen through analytical methylation during sample preparation. Nevertheless, direct GC–MS analysis alone cannot conclusively establish their endogenous origin because compound annotation was based primarily on spectral similarity and chromatographic behaviour. Confirmation of these metabolites will therefore require complementary analytical approaches, including LC–MS/MS, high-resolution mass spectrometry (HRMS), nuclear magnetic resonance (NMR) spectroscopy and comparison with authentic reference standards. Accordingly, the detected fatty acid methyl esters should be regarded as candidate metabolites associated with the antibacterial acetone extract rather than definitive bioactive principles of D. aegialita (Kind and Fiehn, 2010; Stein, 2012).

Long-chain fatty acid derivatives have previously been reported to exhibit antibacterial activity through interactions with microbial membranes, including disruption of membrane organization, increased membrane permeability and interference with membrane-associated physiological processes (Desbois and Smith, 2010). However, the present study did not experimentally investigate these mechanisms, nor does it demonstrate that methyl stearate or hexadecanoic acid methyl ester alone account for the antibacterial activity observed for the crude extract. Crude lichen extracts contain complex mixtures of chemically diverse metabolites that may interact synergistically, additively or antagonistically to produce the overall biological response. Consequently, the relative contribution of individual metabolites remains unresolved and should be investigated through activity-guided fractionation, purification of individual constituents and subsequent antibacterial evaluation. The current findings therefore identify these abundant lipophilic metabolites as promising candidates for future mechanistic investigation rather than as experimentally confirmed antibacterial agents.

To further assess the potential agricultural applicability of the predominant metabolites detected in the acetone extract, their physicochemical characteristics were evaluated using SwissADME and interpreted according to the agrochemical guidelines proposed by Tice (2001) (Daina et al., 2017). These guidelines suggest that compounds intended for systemic movement within plants generally possess molecular weights below 300 g/mol together with moderate lipophilicity (LogP between 0 and 3). Although methyl stearate and hexadecanoic acid methyl ester satisfy the molecular weight criterion, both compounds exhibit comparatively high lipophilicity (Consensus LogP > 5.5), indicating that they are unlikely to undergo efficient xylem or phloem translocation. Nevertheless, their low topological polar surface area (TPSA < 50 Ų) and pronounced hydrophobicity are physicochemical properties that may favour interactions with biological membranes. Accordingly, these compounds may be more suitable as contact-active antibacterial agents than as systemic agrochemicals.

The outer membrane of Gram-negative bacteria, including Ralstonia solanacearum and Pseudomonas syringae, functions as an effective permeability barrier that restricts the penetration of many antimicrobial compounds (Nikaido, 2003). However, highly lipophilic molecules have been reported to partition into the hydrophobic regions of biological membranes, where they may alter membrane organization, modify permeability and interfere with membrane-associated physiological processes (Helander et al., 1998; Ultee et al., 2002; Desbois and Smith, 2010). In the present study, the physicochemical characteristics of the dominant fatty acid methyl esters are consistent with molecular properties that could favour membrane association. Nevertheless, the present investigation did not directly evaluate membrane integrity or membrane permeability. Consequently, the proposed membrane-associated mode of antibacterial action should be regarded as a biologically plausible hypothesis generated from complementary computational analyses rather than as experimentally validated evidence.

To further explore the potential biological activities of the predominant metabolites, independent PASS Online analyses were performed using the canonical SMILES of methyl stearate and hexadecanoic acid methyl ester (Filimonov et al., 2014). Both metabolites produced comparatively high probabilities for membrane permeability inhibition (Pa = 0.806) together with favourable predictions for lipid metabolism regulation (Pa = 0.740). PASS Online employs quantitative structure–activity relationship (QSAR)-based algorithms that estimate the probability of biological activities by comparing molecular descriptors with compounds contained within its training dataset; therefore, its predictions represent computational probabilities rather than experimental confirmation of biological function (Filimonov et al., 2014). Consequently, the PASS Online predictions reported in the present study should be interpreted as hypothesis-generating computational evidence that complements the observed antibacterial activity but does not establish the underlying antibacterial mechanism.

The identical PASS Online probability values obtained independently for methyl stearate and hexadecanoic acid methyl ester should not be interpreted as evidence that the two metabolites are chemically identical or necessarily possess identical biological activities. Instead, these predictions most likely reflect the high degree of structural similarity shared by these homologous saturated fatty acid methyl esters, resulting in comparable molecular descriptors within the PASS Online prediction algorithm. To facilitate independent verification, the canonical SMILES used for each PASS Online analysis together with the corresponding prediction outputs have been provided in the Supplementary Information. Future studies should experimentally examine these computational predictions using membrane permeability assays, propidium iodide uptake, potassium ion leakage, membrane depolarization assays and antibacterial evaluation of purified metabolites to determine whether the predicted membrane-associated activities are reflected under biological conditions.

Although the present findings provide promising preliminary evidence supporting the antibacterial potential of D. aegialita, several limitations should be acknowledged. First, the antibacterial activity was evaluated using agar diffusion, which is appropriate as an initial screening method but cannot replace quantitative determination of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) by standardized broth microdilution assays (Wiegand et al., 2008; Balouiri et al., 2016). Second, metabolite identification was based on GC–MS library matching and retention index comparison without structural confirmation using authentic reference standards or complementary analytical techniques such as LC–MS/MS, high-resolution mass spectrometry (HRMS) or nuclear magnetic resonance (NMR) spectroscopy. Third, although analytical artefacts were carefully considered and obvious contaminants were excluded from biological interpretation, future investigations should incorporate procedural blanks and additional quality-control measures to further minimise the possibility of laboratory-derived contaminants. Finally, the antibacterial activity of the crude extract cannot presently be attributed to individual metabolites because synergistic or antagonistic interactions among multiple constituents remain unresolved. Bioassay-guided fractionation followed by purification and biological evaluation of individual compounds will therefore be essential for establishing definitive structure–activity relationships.

Collectively, the present study integrates antibacterial screening, GC–MS metabolite profiling, physicochemical characterization and computational prediction to identify promising lipophilic metabolites associated with the antibacterial activity of D. aegialita. Rather than providing definitive mechanistic evidence, these complementary approaches establish a rational framework for prioritizing candidate metabolites for future investigation. Subsequent studies should focus on purification of the major metabolites, experimental validation of the proposed membrane-associated mechanism, formulation development, greenhouse efficacy trials, environmental persistence and compatibility with integrated disease management strategies. Such investigations will ultimately determine whether lichen-derived metabolites can be developed as environmentally sustainable antibacterial agents for the management of economically important bacterial plant diseases.

CONCLUSION

The present study demonstrates that the acetone extract of Dirinaria aegialita exhibits promising antibacterial activity against Ralstonia solanacearum and Pseudomonas syringae during preliminary agar diffusion screening. Direct GC–MS analysis identified a volatile and semi-volatile metabolite profile dominated by lipophilic constituents, particularly fatty acid methyl esters, which were subsequently prioritized for computational characterization. SwissADME physicochemical analysis and PASS Online predictions suggest that these metabolites possess molecular properties consistent with membrane-associated antibacterial activity; however, these predictions remain computational hypotheses requiring experimental validation. Collectively, the findings provide a scientifically supported foundation for future purification, structural confirmation and mechanistic evaluation of lichen-derived metabolites as potential environmentally sustainable antibacterial agents for plant disease management.

DECLARATION OF GENERATIVE AI USE

During the preparation of this work, the author used Google Gemini to assist with content organization, language refinement, and readability. After using this tool, the author thoroughly reviewed, edited, and verified the accuracy and authenticity of the generated material. The author takes full responsibility and accountability for the final content, scientific integrity, and references within this publication.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the Department of Botany, Handique Girls’ College, Guwahati, and the Department of Botany, Rabindranath Thakur Vishwavidyalaya, Hojai, for providing the essential laboratory facilities and infrastructure required to conduct the experiments. We wish to thank the I.C.F.R.E. – Bamboo and Rattan Centre (BRC), Mizoram, for their expertise in the authentication of the lichen samples. Special thanks are extended to AGT Bioscience, Guwahati Biotech Park, for their technical assistance with the GC-MS analysis. We also gratefully acknowledge the CSIR-Institute of Microbial Technology (CSIR-IMTECH), Chandigarh, for supplying the Ralstonia solanacearum and Pseudomonas syringae type strains utilized in this study.

AUTHOR CONTRIBUTIONS

Sadiqul Ahmed: Conceptualization (Lead), Methodology (Lead), Formal Analysis (Lead), Investigation (Lead), Data Curation (Lead), Writing–Original Draft (Lead); Monjit Saikia: Supervision (Lead), Conceptualization (Supporting), Methodology (Supporting), Validation (Lead), Writing–Review & Editing (Lead); Jannat Ahmed: Investigation (Supporting), Data Curation (Supporting); Pranab Bujarbarua: Resources (Lead), Validation (Supporting), Writing–Review & Editing (Lead); Sanjeeb Kumar Nath: Investigation (Supporting), Validation (Supporting).

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest.

ETHICS STATEMENT

The data supporting the findings of this study are included within this article and its Supplementary Information. Additional data are available from the corresponding author upon reasonable request.

Not applicable, as this study did not involve human or animal subjects.

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