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. 2025 Dec 23;16:3088. doi: 10.1038/s41598-025-33013-7

Potent surface antimicrobial activity of hydrolyzable tannins from Aleppo oak galls

Antonia Kaz 1,✉, Keerthana Thirunavukkarasu 1, Lylla Almosd 1, Eunjin Cho 1, Joshua Henkin 1, Kenneth Acosta 1, Rinat Abzalimov 2, Ilya Raskin 1
PMCID: PMC12830916  PMID: 41436781

Abstract

Surface antimicrobial agents provide a first line of defense against pathogens, especially for immunocompromised individuals. Insect-induced plant galls, tumor-like structures formed on plant surfaces by insect larvae, have long been used as sources of antimicrobial compounds. Building on existing knowledge, this study evaluated the surface antimicrobial activity of a standardized, ethanolic extract of Aleppo oak galls (AGE) on agar, abiotic, and biotic surfaces. Using a novel surface antimicrobial assay, we demonstrated that the anti-Escherichia coli, -Staphylococcus aureus, -Candida albicans, and -Aspergillus brasiliensis activity of AGE approached that of common antibiotics and econazole. However, AGE had a comparatively lower antimicrobial activity in liquid cultures. AGE maintained strong antibacterial activity on non-nutritive surfaces, including stainless steel, collagen membranes, and cadaver skin. Untargeted and targeted metabolomic analyses revealed that hydrolyzable tannins and their precursors are the predominant constituents in AGE, and that hydrolyzable tannins are largely responsible for its potent surface activity. Tannic acid, a hydrolyzable tannin present in AGE, showed surface antibacterial effects similar to AGE. These findings support the potential of AGE and its hydrolyzable tannins as natural surface sterilants for reducing microbial load on skin and materials used in healthcare and food industry settings.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-33013-7.

Subject terms: Biochemistry, Biological techniques, Biotechnology, Microbiology, Plant sciences

Introduction

Surface sterilization to mitigate microbial transmission, reduce infection risk, and maintain hygiene is common practice in health care facilities, food safety, and personal care. In hospitals, pathogen spread often stems from contact between contaminated surfaces and patients1. Opportunistic pathogens such as Staphylococcus aureus and Escherichia coli can survive on inanimate surfaces or skin for long periods and pose a particular threat to immunocompromised patients2,3. During food production, pathogenic microorganisms which contaminate leafy vegetables can be introduced during any stage of the process, from harvest to packaging. E. coli is one of three pathogens posing a major health concern to individuals consuming minimally processed produce4. Public awareness of proper hygiene and sanitization in personal care increased substantially during the COVID-19 pandemic, with the demand for hand sanitizers causing shortages in their supply5. Though surface sterilization is an essential preventive measure against microorganism outbreaks, many surface sanitizers and disinfectants contain harsh chemicals that cause deleterious effects for human health and the environment. The efficacy of the most commonly used surface sterilizers, such as ethanol and other alcohols, is limited by their rapid evaporation and short residence time on surfaces6. As consumer preference toward natural and plant-derived products grows in global markets, plant-derived, natural surface sterilants may satisfy public health concerns and appeal to consumers seeking longer-lasting and non-toxic antimicrobials.

Plant galls are abnormal, morphologically complex outgrowths of plant tissues that develop in response to the activity of various gall-inducing organisms, including insects, mites, fungi, bacteria, and viruses (for review see7. These structures are formed through the reprogramming of host plant cellular development and differentiation pathways, likely initiated by chemical or mechanical stimuli introduced by the gall inducer. Insect-induced galls, the most structurally diverse and taxonomically widespread, form a specialized microenvironment that provides both nutrition and protection for the developing larvae8.

Plant secondary metabolites, such as polyphenolic compounds, with known antimicrobial activities often accumulate in insect-induced plant galls9,10. Aleppo oak (Quercus infectoria Oliv.) galls found on leaves and twigs are induced by the parasitic wasp Cynips gallae-tinctoriae and their developing larvae within the galls11. Hydroalcoholic extracts of Aleppo oak galls have a long history of use as antimicrobial agents with immunomodulatory functions12,13. Their use in medicine dates back to the Greek and Roman empires and their low toxicity has been demonstrated in animals and humans14,15. For example, hydroalcoholic extracts of Aleppo oak may accelerate wound healing in diabetic patients with chronic ulcers16. Phytochemical analyses suggest hydrolyzable tannins, a class of polyphenolic compounds, are the predominant antimicrobial compounds in Aleppo oak galls17–19. In addition to medicinal uses, gall tannins have been used since antiquity in leather tanning due to their ability to crosslink proteins in hide20. Iron gall ink, a rich, black ink produced from the oxidation of iron by Aleppo oak gall tannins replaced carbon black inks by the 5th century21. The ability of hydrolyzable tannins to bind and crosslink charged molecules, such as proteins and iron, are likely to contribute to the antimicrobial properties of Aleppo oak gall extracts22.

Relatively modest antimicrobial activity of hydroalcoholic Aleppo oak gall extracts has been well known and reported23. However, its powerful surface antimicrobial properties remained uncharacterized, because previous studies used liquid cultures or gel diffusion assays to measure antimicrobial activity. This study investigated the surface antimicrobial efficacy of a standardized Aleppo oak gall extract (AGE) in comparison to conventional antibacterial and antifungal agents and extracts derived from other oak galls and tissues. We employed both standard liquid assays and surface activity assays on abiotic and biotic surfaces to evaluate AGE’s antimicrobial performance. Using untargeted and targeted metabolomic profiling combined with our novel surface antimicrobial assay, we identified hydrolyzable tannins, such as tannic acid, as major bioactive components in AGE. Our findings provide scientific support for the development of AGE as a natural, plant-derived surface sterilant and as an active ingredient, at least in alcohol-based surface sterilizers.

Results

Antimicrobial activity of AGE relative to standard antibiotics and econazole

We used liquid cultures and a surface activity assay to evaluate and compare the antibacterial effects of AGE and four commonly prescribed antibiotics according to Center for Disease Control and Prevention (CDC)24. E. coli and S. aureus were used as model Gram-negative and Gram-positive bacteria. The surface antibacterial activity of AGE and antibiotics was compared with their liquid antibacterial activities using a standard broth macrodilution assay. The minimum inhibitory concentration for 50% inhibition of growth (MIC50) of AGE in liquid assays were 139 to 6,156 times higher than all tested antibiotics against E. coli (Fig. 1. b, h). The MIC50 of AGE in liquid assays was 13 to 342 times higher than cephalexin, azithromycin, and amoxicillin against S. aureus. Only the MIC50 for spectinomycin was higher than that of AGE against S. aureus in liquid assays (Fig. 1. a, h). However, the surface MIC50s of AGE were only 4 to 16 times higher than that for tested antibiotics for E. coli (Fig. 1. e, h). Interestingly, the surface anti E. coli MIC50 of AGE was lower than that of cephalexin despite being 139 times higher in liquid cultures. The surface MIC50 of AGE was 44 to 394 times higher than cephalexin, azithromycin, and amoxicillin against S. aureus (Fig. 1d, h). The MIC50s for AGE were consistently lower than spectinomycin in both liquid and surface antibacterial assays (Fig. 1. a, d, h). Thus, the surface antibacterial activity of AGE was more comparable to clinically used antibiotics in the surface activity assay, while AGE was much less effective than the antibiotics in liquid cultures, particularly against E. coli.

Fig. 1.

Fig. 1

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Surface antimicrobial activity of AGE is comparable to standard antibiotics and econazole. Dose responses from liquid assays based on bacterial CFU counts (n = 5) (mean ± SEM) or OD for C. albicans (n = 3) (mean ± SEM) (a, b,c) expressed in µg or mg/ml. Anti-bacterial surface activity assay based on colony areas after 18-h incubations (n = 3) (mean ± SEM) (d, e,f). A. brasiliensis surface activity assay (g) is based on fungal mass after 48-h incubations (n = 3) (mean ± SEM). Data are expressed as % reduction of CFU counts, colony area, or fungal mass compared to untreated controls. Antibiotics include spectinomycin (Spec), azithromycin (Azith), cephalexin (Ceph), and amoxicillin (Amox). Econozole was used as an anti-fungal. Labels for antibacterial treatments are shown in panel (e) and labels for antifungal treatment are shown in panel (f). MIC50s for AGE, Spec, Azith, Ceph, and Amox against S. aureus and E. coli (h). MIC50s for AGE and econazole against C. albicans, and A. brasiliensis (i). All MIC50s calculated from nonlinear regression lines of dose-response curves plotted in Graphpad Prism.

Candida albicans and Aspergillus brasiliensis were used as representative yeast and filamentous fungal strains to assess the antifungal activity of AGE relative to a broad-spectrum topical antifungal, econazole25,26. The liquid antifungal activities of AGE and econazole were assessed only against C. albicans, and not A. brasiliensis, due to the challenges of using optical density measurements with filamentous fungi. Liquid and surface anti-fungal MIC50s of AGE were comparable to econazole for C. albicans, 1.3 and 2.2 times higher, respectively (Fig. 1. c, f, i). The surface MIC50 for AGE was 2.3 times higher than econazole in A. brasiliensis (Fig. 1. g, i). However, a broader dose-response analysis of the surface antifungal activity of AGE against A. brasiliensis suggests that AGE is consistently more active than econazole at concentrations below the respective MIC50 values. Furthermore, AGE reached 100% inhibition against C. albicans while econazole did not surpass 70% inhibition at the highest dose tested. MIC50 values for both antibacterial and antifungal activities were calculated using non-linear regression curve fitting, which may underestimate the full extent of activity at lower concentration ranges, particularly in cases with flatter dose-response curves.

AGE’s surface antibacterial activity is superior to other Quercus spp galls and Q. infectoria leaves and bark

The surface antibacterial activity of AGE was compared to 70% ethanolic extracts from seven Quercus spp galls collected in the US. At lower doses (0.34 and 0.69 µg/cm2), Q. macrocarpa and Q. gambelii exhibited notable growth promoting effects of E. coli (Fig. 2. b). Similar effects were observed in S. aureus with Q. lobata promoting growth at the lowest dose (0.34 µg/cm2) (Fig. 2. a). Bacterial growth promotion is relatively common as many plants produce nontoxic secondary metabolites to attract beneficial microorganisms27. AGE was the only gall extract to reach 100% bacterial inhibition at 5.51 µg/cm2. It also had the highest antibacterial activity at 1.38 and 2.75 µg/cm2 against both E. coli and S. aureus when compared to other galls (Fig. 2. a, b).

Fig. 2.

Fig. 2

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Surface antibacterial activity of AGE compared to other Quercus spp galls and Q. infectoria leaves and bark. Surface antibacterial activity of Quercus spp galls collected in the US (n = 3) (mean ± SEM) (a, b). Antibacterial efficacy of AGE relative to healthy Q. infectoria leaf and bark tissue based on colony areas (n = 3) (mean ± SEM) (c, d). Data are expressed as % reduction of colony areas compared to untreated controls after 18-h incubations. Significance calculated with one-way ANOVA with Tukey’s multiple comparisons test (****p < 0.0001).

The surface antibacterial effects of 70% ethanolic Q. infectoria bark and leaf extracts were also tested to confirm that the surface antibacterial activity of AGE was unique to gall tissue. AGE had significantly greater anti-E. coli and -S. aureus activity than Q. infectoria bark and leaf extracts when applied to agar surfaces (Fig. 2. c, d). Like Q. macrocarpa, Q. gambelii, and Q. stellata galls, Q. infectoria bark and leaf tissue demonstrated growth promoting effects in E. coli at lower doses (0.69 and 1.38 µg/cm2). These results confirm that the potent surface antibacterial activity is restricted to galls and not present in healthy leaf and bark tissues from the same species.

The surface antibacterial activity of AGE persists on abiotic and biotic surfaces

Stainless steel, a collagen membrane, and cadaver skin were used to determine the antibacterial activity of AGE on common biotic and abiotic surfaces found in healthcare settings and during food processing. AGE exhibited comparable antibacterial activity against S. aureus and E. coli on all three surfaces tested, with the greatest inhibition occurring at 1.03 to 2.07 µg/cm2 (Fig. 3. a, b). The lowest MIC50 doses were observed on stainless steel surfaces at 4.2 and 12.6 µg/cm2 against S. aureus and E. coli, respectively (Fig. 3. c). The MIC50s for the environmental surfaces are higher than the MIC50s reported for the surface activity assay on agar surfaces due to higher concentrations of bacteria added, as nutritive agar encourages rapid bacterial proliferation. The dose of bacteria added reflects environmental CFU/cm2 doses for abiotic surfaces and skin (104 CFU/ cm2)28,29. Thus, the antibacterial activity of AGE persists on abiotic and biotic surfaces at environmentally relevant bacteria concentrations.

Fig. 3.

Fig. 3

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Antibacterial activity of AGE on abiotic and biotic surfaces. Antibacterial activity of AGE applied to stainless steel, a collagen membrane, and cadaver skin for 30-min at room temperature (n = 3) (mean ± SD) (a, b). MIC50 values of AGE calculated from nonlinear regression lines of dose-response curves plotted in Graphpad Prism (c).

Identification of small metabolites in Aleppo oak extracts

To perform comparative metabolomic pathway analysis and mapping, we putatively identified small metabolites (< 1,300 m/z) in Q. infectoria leaf and gall tissues by LC-MS/MS using a Bruker maXis-II UHR-ESI-QqTOF mass spectrometer coupled with a Thermo Scientific Ultimate 3000 UHPLC system. An initial mass feature table was generated with Bruker MetaboScape software, and after data harmonization, subsequent processing and curation, a final table of 346 mass features was generated (Supplementary Table S1, Data S1). Putative metabolites (Level 3 confidence) were identified by matching mass feature spectra to known compounds using both experimental and in-silico spectral libraries30. A total of 330 distinct metabolites were putatively identified in the extracts. The leaf extract contained 299 metabolites, while the gall extract contained 246 metabolites. Venn diagram analysis revealed that 215 metabolites were shared between leaf and gall extracts, with 84 metabolites specific to the leaf extract and only 31 were specific to the gall extract (Fig. 4. a). Structural classification of metabolite diversity in the extracts revealed that leaf and gall extracts contained many metabolites from the shikimate and phenylpropanoid pathway (Supplementary Data S2, Table S2). Within this pathway, both extracts were rich in flavonoids and phenolic acids (Supplementary Table S3). Metabolite profiles were generated to assess the abundance of these metabolites in the extracts. The leaf extract consisted of metabolites from the shikimate and phenylpropanoid pathway (57.9%) and fatty acids (26.8%) (Fig. 4. b). In contrast, the gall extract was predominantly composed of shikimates and phenylpropanoids (80.9%), followed by polyketides (10.2%) (Fig. 4. b). Further analysis of the shikimate and phenylpropanoid profiles revealed that the leaf extract consisted of flavonoids (46.5%) and phenolic acids (35.7%), whereas the gall extract was almost entirely composed of phenolic acids (96.5%) (Fig. 4. c). These findings demonstrated that AGE has a lower diversity of small metabolites (< 1,300 m/z) than the leaf extract, with phenolic acids being the dominant small metabolites.

Fig. 4.

Fig. 4

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Untargeted metabolomic analysis comparing AGE with healthy leaf tissue (< 1,300 m/z). Venn diagram comparing metabolites between Q. infectoria leaf and gall tissue (a). Relative abundance of natural product biosynthetic pathways (b). Relative abundance of natural product superclasses within the shikimates and phenylpropanoids pathway (c). Dendrogram and intensity heatmap of gall-enriched metabolites. Dendrogram was constructed using Tanimoto dissimilarity based on PubChem fingerprints. Leaf labels indicate the PubChem CID of each metabolite if available. Red text indicates metabolites with gallic acid moieties. Black heatmap tiles indicate metabolites were not detected in the respective sample. A bar graph of log2-fold change (gall / leaf) is displayed to the right of the heatmap, with colors representing the natural product biosynthetic pathway of metabolites (M = million) (d). Chemical structures of gall-enriched metabolites containing gallic acid with a relative abundance greater than 1% and log2-fold change in galls greater than 2. Compounds identities listed in supplementary data (Supplemental Table S4) (e).

Small metabolites enriched in AGE compared to Aleppo oak leaves were further identified by differential abundance analysis. Almost all shared metabolites were differentially abundant (|log2 fold change| > 1, FDR < 0.05) between extracts, with 150 metabolites enriched in the leaf extract and only 35 metabolites enriched in the gall extract (Supplementary Fig. S1, Data S3). Shikimates and phenylpropanoids (n = 28) were the main classes of metabolites highly enriched in gall tissue. Most of these metabolites were gallic acid derivatives (n = 24) (Fig. 4. d-e, Supplementary Table S4). Structural classification of metabolites unique to AGE showed that most of these metabolites were from the shikimate and phenylpropanoid pathway (n = 20), followed by polyketides (n = 8) (Fig. 5. a). Most of the shikimate and phenylpropanoid metabolites contained gallic acid moieties. While most gall extract-specific metabolites were of low abundance, a group of gallic acid derivatives, including di-gallic acid (CID: 341, Compound 10), methyl gallate (CID: 7428, Compound 11), and gallic acid (CID: 370, Compound 12), were detected at higher intensities (> 5 million) (Fig. 5. b). These findings suggest that phenolic acids compounds containing gallic acid moieties are the predominant small metabolites (< 1,300 m/z) in AGE.

Fig. 5.

Fig. 5

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Untargeted metabolomic profile of most prominent small metabolites specific to AGE. Structural classification and abundance of metabolites specific to the gall extract (< 1,300 m/z). Dendrogram was constructed using Tanimoto dissimilarity based on PubChem fingerprints. Leaf labels indicate the PubChem CID of each metabolite if available. Red text indicates metabolites with gallic acid moieties. Points are colored by natural product biosynthetic pathway (a). Chemical structures of the most abundant compounds (intensity > 5 million) within the phenolic acids (C6-C1) superclass (< 1,300 m/z) (Compound 10, di-gallic acid) (Compound 11, methyl gallate) (Compound 12, gallic acid) (b).

Hydrolyzable tannins account for the majority of surface antibacterial activity of AGE

Gallotannins, a diverse class of large molecular weight (> 1,300 m/z) hydrolyzable tannins composed of gallic acid residues esterified to a central sugar molecule, are among the most abundant compounds in Q. infectoria gall extracts (up to 70%). They are thought be responsible for its antimicrobial activity31. LC-MS metabolomic analysis of these large and very diverse compounds is complicated and their exact structural identification complex. Therefore, a ferric chloride assay was used to better quantify the total tannin content in Q. infectoria leaf and gall extracts. In agreement with the earlier data, it showed that AGE contains 73.6% tannins (w/w) while Q. infectoria leaves contain only 11.8% (w/w) (Fig. 6. a).

Fig. 6.

Fig. 6

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Quantification and antibacterial activities of tannins, tannic acid, and methyl gallate in AGE. Tannin content (ferric chloride assay) in AGE and Q. infectoria leaves determined from a linear regression of a tannic acid standard curve using Graphpad Prism. Data represented as the mean of two biological replicates (a). Representative chromatogram generated from the parallel reaction monitoring target for tannic acid in negative ionization mode (1699.166 m/z). Retention time (RT) reflects the average of the max peak across replicates (n = 3 with 2 biological replicates) (b). Representative mirror plot of AGE and tannic acid MS2 spectra normalized to the max peak and filtered for low-abundance peaks (n = 3) (c). Surface antibacterial efficacy of AGE relative to tannic acid and methyl gallate based on colony areas (n = 3) (mean ± SD). Significance calculated using one-way ANOVA with Tukey’s multiple comparisons test (****p < 0.0001) (d, e).

Only hydrolyzable tannins dominated by gallotannins have been reported in oak gall extracts31,32. Tannic acid(s) are the main family of hydrolyzable gallotannins in Q. infectoria and other oak galls, comprising a mixture of polygalloyl glucose esters containing 8–10 gallic acid (3,4,5-trihydroxybenzoic acid) units. The precise structures of tannic acid(s) vary depending on the botanical source and degree of galloylation. We used LC-MS/MS to quantify a commercially available tannic acid analog (empirical formula C76H52O46, CAS # 14-01-55-4), in AGE (Fig. 6. b, c). We found that this tannic acid analog makes up 3.4% of AGE (w/w).

To confirm that hydrolyzable tannins, such as tannic acid(s) contribute to the surface antibacterial activity of AGE, tannic acid (C76H52O4) and a low molecular weight phenolic found in AGE, methyl gallate (Compound 11, Fig. 5), were applied to agar surfaces using our surface activity assay. Tannic acid exhibited significantly greater anti-S. aureus activity than AGE at 1.38 µg/cm2, however, both AGE and tannic acid were 100% effective against S. aureus at 2.76 µg/cm2 (Fig. 6. d). Conversely, AGE exhibited significantly greater anti-E. coli activity than tannic acid at 1.38 µg/cm2, while tannic acid exhibited significantly greater anti-E. coli activity at 2.76 µg/cm2 (Fig. 6. e). Both AGE and tannic acid provided much greater antibacterial activity than methyl gallate (Fig. 6. d, e). These data support the hypothesis that hydrolyzable tannins, specifically gallotannins such as tannic acid(s), abundant in AGE are its main surface acting antimicrobials.

Discussion

Aleppo oak gall extracts are known to have moderate, broad-spectrum antimicrobial activity in standard in vitro assays31. Ethanolic Aleppo oak extracts inhibited the growth and biofilm formation of the primary etiological agent of dental caries, Streptococcus mutans, in ex vivo or clinical models and caused morphological and structural changes, such as rupturing the cell membranes of Shiga toxin-producing E. coli33,34. Aleppo oak gall extracts also caused lysis of several bacteria species that contaminate chicken eggs35. Textiles containing Aleppo oak gall extracts inhibited the growth of both wild type and antibiotic-resistant C. albicans and S. aureus in vitro36. Furthermore, an Aleppo oak gall-containing hand sanitizer exhibited antimicrobial activities against pathogenic organisms in liquid cultures37. While these studies support the general antimicrobial activity of Aleppo oak gall preparations, they do not report on their powerful surface antimicrobial effects.

To our knowledge, this study is the first to compare the antimicrobial effects of compounds from Aleppo oak galls on surfaces to standard liquid assays. As expected, and previously reported38,39, our data show that AGE was only moderately active in liquid antibacterial assays. However, AGE exhibited surface antimicrobial effects approaching that of several classes of antibiotics and the antifungal econazole (Fig. 1. d-i). Our results suggest a marked increase in AGE’s antibacterial activity, particularly against E. coli, when applied to agar surfaces (Fig. 1. a, b, h). These surface antibacterial effects also persist on abiotic and biotic surfaces, with AGE demonstrating the greatest antibacterial capacity on stainless steel (Fig. 3. c).

Hydrolyzable tannins are the most abundant antimicrobial compounds in hydroalcoholic Aleppo oak gall extracts and are likely responsible for their antimicrobial activity18,19. The ability to complex with metal ions and macromolecules, including various bacterial cell wall components like peptidoglycan, is a distinctive feature of these compounds22,40,41. These properties may contribute to the gross morphological changes in the cell walls of methicillin-resistant S. aureus and P. aeruginosa caused by plant tannins41,42. Tannic acid, one of the most widely distributed hydrolyzable tannins, disrupted the cell envelope and decreased protein and gene expression of enzymes involved in peptidoglycan and fatty acid synthesis in S. aureus43. We suggest that this phenomenon may be more pronounced when hydrolyzable tannins interact with bacteria on surfaces, as tannins easily bind to abiotic and biotic surfaces by chelating metals and forming non-covalent complexes with macromolecules44,45. Therefore tannin-induced crosslinking of bacterial cell components may effectively prevent microbial cell attachment to the surfaces and biofilm formation. In addition, this binding may promote cell wall and membrane disruption and cell death.

In our study, AGE demonstrated superior antibacterial activity to eight Quercus spp gall extracts and Q. infectoria bark and leaf tissue, suggesting AGE accumulates more antimicrobial compounds than other Quercus galls and ungalled leaves and bark (Fig. 2. a-d). Using untargeted metabolomics, we confirmed that AGE is less chemically diverse than leaf tissue and contains more phenolic acids from the shikimate and phenylpropanoid pathway (Fig. 4. b, c). Gallic acid, a precursor of hydrolyzable tannins, is a phenolic compound produced through the shikimate pathway. It was found to be among the top three gall-enriched small metabolites in AGE (Fig. 5. b)22. Gallic acid is also a precursor for another gall-enriched metabolite that serves as a skeleton for more complex hydrolyzable tannins, pentagalloylglucose (Compound 2, Fig. 4. d, e)46. Transcriptomic analyses suggest unigenes involved in gallic acid biosynthesis, including the enzyme responsible for gallic acid biosynthesis from 3-dehydroshikimate, shikimate dehydrogenase, are highly expressed in Q. infectoria leaves47,48. Furthermore, tandem repeat clusters of a critical enzyme in the shikimate pathway, dehydroquinate dehydratase/shikimate dehydrogenase, were identified in 13 hydrolyzable tannin-rich species, including common galled species like Rhus chinensis and Q. robur49. Though no transcriptomic analysis has identified expression levels of these enzymes involved in hydrolyzable tannin metabolism in Aleppo oak galls, it is well-established that phenolic acids are synthesized inside galls rather than being transported from adjacent tissues50. This is supported by our finding that the tannin content of ungalled Aleppo oak leaves was dramatically lower than the tannin content of AGE (Fig. 6. a).

Hydrolyzable tannin complexity increases as more gallic acid residues or depsidically linked galloyl groups (Compound 10) (Fig. 5. b) attach to the galloyl groups on β-1,3,4,6-tetra-O-galloylglucose (Compound 4) in Q. infectoria or, more generally, pentagalloylglucose (Compound 2) (Fig. 4. d, e)22. Using targeted metabolomics, we found that a decagalloyl glucose, a common analogue of tannic acid, makes up 3.4% of AGE. Furthermore, this commercially available analogue exhibited superior or comparable surface antibacterial activity to AGE. However, methyl gallate (Compound 11), one of three most prominent small metabolites (< 1,300 m/z) in AGE, exhibited significantly lower surface antibacterial activity than AGE or tannic acid (Fig. 5. b) (Fig. 6. d, e). Thus, hydrolyzable tannins that comprise up to 70% of AGE (w/w) are likely to be its main surface antimicrobial compounds.

It was suggested that tannin accumulation in the galls benefits the cynipine wasp larvae, as tannins serve as a protective barrier and deterrent against other herbivores and microorganisms51. Our data give additional support to the antimicrobial protection function offered by tannins to the developing larvae. Bacterial and fungal colonization and infection of the insect larvae inside the gall may not be possible without the initial surface attachment and biofilm formation. Surface-active antimicrobial compounds may provide superior disease protection to the developing larvae than compounds that are active in liquid environments as the larval chamber inside galls has no liquid. We also hypothesize that the ability of hydrolyzable tannins to bind to surface charges may promote their retention on treated surfaces, whereas antibiotics may diffuse or disperse away, thus reducing their effective surface concentration.

Throughout history plant extracts were used as sources of antimicrobial compounds and surface sterilizing agents52. This study offers evidence supporting the use of AGE and its main bioactives, hydrolyzable tannins, in sanitizers and surface sterilants. Our findings are further supported by the development and deployment of a novel surface antimicrobial activity assay, as this assay directly measures antibiotic effects on surfaces rather than relying on the gel-diffusion and liquid-culture assays used in many earlier studies. Since AGE is prepared through 70% ethanol extraction, it may be a particularly efficacious active ingredient in alcohol-based sanitizers. The ability of AGE’s antimicrobial actives to persist on surfaces should prolong the effectiveness of alcohol- and, possibly, non-alcohol-based sanitizers used in medicine and agriculture53,54. Additional research into the formulation, toxicology and efficacy of AGE in surface sanitizers may further justify their product applications.

Materials and methods

Materials

Aleppo oak galls were sourced from Ecowise, India. Aleppo oak leaves were sourced from the Los Angeles County Herbarium & Botanic Garden. Quercus spp galls were provided by Dr. Andrew Forbes at the University of Iowa, Adam Kranz, and Dr. Heather Kirk-Ballard at Louisiana State University. The following antibiotics were purchased from Cayman chemicals: amoxicillin (hydrate) (CAS no 61336-70-7), azithromycin (CAS no 83905-01-5), and cephalexin (CAS no 15686-71-2). Spectinomycin dihydrochloride pentahydrate was purchased from Sigma Aldrich (CAS no 22189-32-8). Econazole was purchased from Sigma Aldrich (CAS no 24169-02-6). Tannic acid (CAS no 1401-55-4) and methyl gallate (CAS no 99-24-1) were purchased from Cayman chemicals.

Mueller Hinton broth (Cat no CM0405B) was purchased from ThermoFisher Scientific and agar (CAS 9002-18-0) was purchased from Sigma Aldrich. Potato Dextrose Agar (Product # P6685) and streptomycin (CAS no 3810-74-0) were purchased from Sigma Aldritch. Roswell Park Memorial Institute (RPMI) 1640 media powder (Cat no 31800105) from Sigma Aldritch. A 0.5 MacFarland Standard (Cat no 89426-218) was purchased from VWR Life Sciences. A 10X sterile phosphate buffer solution (PBS) (Cat no 97063-660) was purchased from VWR Life Sciences and diluted to 1X in sterile DI water for all experiments. Tween 20 (CAS no 9005-64-5) was purchased from Sigma Aldrich. Iron(III) chloride hexahydrate (CAS 10025-77-1) was purchased from ACROS Organics and dissolved in 0.1 M HCl to 0.1 M solution. Potassium ferricyanide (CAS no 13746-66-2) was purchased from Sigma Aldrich and dissolved in DI water to 0.008 M. Full thickness cadaver skin (approx. 2–3 mm thick) from Science Care was provided by Dr. Bozena Michniak’s laboratory at Rutgers University. A synthetic, non-biological, collagen membrane skin model, VITRO-SKIN, was purchased by Medelink Skin Research Solutions. Stainless steel sheets (0.1 mm thick) were purchased from RAMBEX. Glacial acetic acid (CAS no 64-19-7) and LC-MS grade water (CAS no 7732-18-5) and acetonitrile (CAS no 75-05-8) were purchased from Avantor. LC-MS grade methanol (CAS no 67-56-1) and formic acid (CAS 64-18-6) were purchased from Sigma Aldrich.

Botanical validation

Aleppo oak galls have a distinct and easily recognizable morphology that clearly differentiates them from other plant tissues. The galls used in this study were purchased in unprocessed form from a reputable botanical supplier (Ecowise, India, 188 KK Nagar, Sector 3, RannaPark, Ghatlodia P.O., Ahmedabad 380061, Gujarat, India) and originated from the Gujarat region of India. The purchased galls were indistinguishable from standard Aleppo oak galls. Furthermore, detailed metabolomic analysis conducted for this study fully corroborated earlier biochemical reports on the composition of Aleppo oak galls (Figs. 4, 5 and 6; Supplementary Tables S1–S3; Fig. S1).

A properly labeled representative sample of the gall material used in this research has been retained in Dr. Raskin’s laboratory, which is affiliated with the Rutgers-run Chrysler Herbarium (Temporary Deposition Number: QI-IN-01-060626). Botanical identification was confirmed by two experts: Mr. Mehul Rathod (India) and Dr. Ilya Raskin (USA).

The seven Quercus spp galls also have distinct and easily recognizable morphologies that differentiate them from each other and other plant tissue. Collection locations and, when available, coordinates were provided by gall collectors along with the dates and gall identification method. Dr. Andrew Forbes (University of Iowa) provided Quercus spp galls. Adam Kranz, the founder of the Gallformers website (https://www.gallformers.org/) collected Quercus spp galls from Quercus spp in Austin, Texas. Dr. Heather Kirk-Ballard (Louisiana State University) provided galls from Louisiana. All collection coordinates, dates of collections, and gall identification methods are available upon request.

Methods

Microorganism strains and growth conditions

E. coli (ATCC 25922) was purchased from ATCC, and S. aureus (RN4220) was provided by Dr. Jeffrey Boyd’s laboratory at Rutgers University. Strains were grown in LB medium at 37 °C. C. albicans (ATCC 10231) and A. brasiliensis (ATCC 16404) were purchased from ATCC. Fresh plates of each fungi were streaked and incubated at 37 °C for 24 to 48 h every week. These plates were used for liquid inoculums prepared before each experiment. C. albicans inoculum was prepared by vortexing 1 to 2 colonies in 3 ml of sterile DI water and brought to a concentration equivalent to 0.5 MacFarland Standard, or 1 to 5 × 106 CFU/ml, then diluted to a working concentration of 1 to 5 × 105 CFU/ml. A. brasiliensis inoculum was prepared using a sterile cotton tipped applicator. The applicator was soaked in sterile DI water and dabbed on sporulating A. brasiliensis hyphae to collect spores. The spore-covered applicator was rinsed in 5 ml of a 0.1% Tween 20 solution and vortexed for 5 to 10 min, or until no clumps of spores remained. The spore suspension was diluted to 1 to 5 × 105 CFU/ml by counting spores under a hemocytometer.

Extraction

Dry plant tissue was ground into a fine powder with mortar and pestle in liquid nitrogen and extracted in 70% ethanol at room temperature for 15 min (1:25 w/v). Whatman No.1 filter paper was used to filter extracts, and the filtrate was dried in a Savant Automatic Environmental SpeedVac System (AES2010). All assays were performed with fresh extracts dissolved in 70% ethanol.

Liquid antimicrobial testing

AGE and antibiotics were tested using a standard broth macrodilution assay as described in Nature Protocols55. Briefly, overnight inoculums of E. coli and S. aureus were grown to log phase and diluted to 10 × 105 CFU/ml. Each treatment incubated for 18 h at 37 °C on a shaker. Serial dilutions of each treatment were plated for CFU counting. CFU counts were normalized to growth controls to calculate percent inhibition.

Liquid antifungal testing was done using a broth microdilution method described by the European Committee of Antimicrobial Susceptibility (EUCAST) with modifications56. In summary, 100 µL of serially diluted extract were applied in triplicate to a 96-well plate and dried overnight to evaporate ethanol, leaving only botanical material. Dried extracts were reconstituted with 100 µL of RPMI-1640 media and 100 µL of C. albicans inoculum. Controls used include antibiotic control with no extract and 10 µL of 30 µg/ml econazole, growth control with inoculum, RPMI-1640 and no treatment, solvent control with a 70% ethanol treatment and a sterility control with only 200 µL of RPMI-1640 media. Optical density (OD) absorbances were read at 530 nm before and after a 24 h incubations at 37 °C. OD readings were normalized to the vehicle control to calculate percent inhibition. All MIC50 concentrations were calculated from nonlinear regressions of dose response curves in Graphpad Prism v 10.5.0. All OD readings were recorded on an Agilent BioTek Synergy HT microplate reader.

Surface activity assay

The antibacterial properties of AGE, antibiotics, Quercus spp galls, Aleppo oak bark, Aleppo oak leaves, tannic acid, and methyl gallate were determined using a surface activity assay we developed. All treatments were applied to Mueller-Hinton agar (1.7% w/v) surfaces using a bacterial spreader to achieve uniform plating. Once the ethanol solvent evaporated, E. coli and S. aureus were drop plated (10 µl) onto the surfaces. The bacteria grew on the surface of the agar for 18 h at 37 °C and colony areas were measured using a ThermoFisher EVOS M5000 Microscope.

The antifungal properties of AGE and econazole were determined after surface application to potato dextrose agar surfaces using a bacterial spreader to achieve uniform plating. Once the solvent evaporated, 10 µL of inoculum were plated in triplicate. C. albicans grew for 24 h at 37 °C and colony areas were measured using a ThermoFisher EVOS M5000 Microscope. A. brasiliensis grew for 48 h at 37 °C and hyphal mass diameter was measured with a ruler.

The surface concentrations of AGE, antibiotics, and econazole were calculated by multiplying the stock solution concentration by the volume added to agar surface (50 µL) and dividing by the surface area it was applied to (28.3 cm2). All colony area measurements or hyphal masses were normalized to the vehicle control, 70% ethanol, DMSO, or PBS, to calculate percent inhibition. Tukey’s multiple comparisons from ordinary one-way ANOVAs were produced using Graphpad Prism v 10.5.0. MIC50s were calculated from nonlinear regressions of dose response curves in Graphpad Prism v 10.5.0.

Antibacterial testing on abiotic and biotic surfaces

The antibacterial properties of AGE on abiotic surfaces were determined using an assay we developed. Stainless steel and VITRO-SKIN collagen membrane sheets were cut into squares (4.8 cm2) and sterilized in ethanol. Once the ethanol evaporated, AGE and 70% ethanol were evenly spread onto sterilized stainless steel or the VITRO-SKIN collagen membrane squares. The surface concentrations of AGE were calculated by multiplying the stock solution concentration (mg/mL) by the volume added to abiotic and biotic surfaces (5 µL) and dividing by the surface area it was applied to (4.8 cm2). Once the ethanol evaporated (70% ethanol or ethanol from AGE), 104 CFU/cm2 of E. coli or S. aureus in PBS and 0.1% Tween 20 were applied to each surface and incubated for 30 min at room temperature. Bacteria were removed by running a PBS wash over the surfaces and further diluted for plating on Mueller Hinton agar. CFU counts were obtained after an 18 h incubation at 37 °C.

Full thickness cadaver skin sheets were cut into circles (1 cm2) before sterilizing in ethanol. Once the ethanol evaporated, AGE and 70% ethanol were evenly spread onto sterilized cadaver skin circles. The surface concentrations of AGE were calculated by multiplying the stock solution concentration (mg/mL) by the volume added to cadaver skin (5 µL) and dividing by the cadaver skin area it was applied to (1 cm2). Once the ethanol evaporated (70% ethanol or ethanol from AGE), 104 CFU/cm2 of E. coli or S. aureus in PBS were applied to the cadaver skin and incubated for 30 min at room temperature. Bacteria were removed by shaking the circles in a 12-well plate with PBS and further diluted for plating on MH agar. CFU counts were obtained after an 18 h incubation at 37 °C. MIC50s were calculated from nonlinear regressions of dose response curves in Graphpad Prism v 10.5.0.

Untargeted metabolomic analyses

Analytical measurements were performed using a Bruker Daltonics maXis-II UHR-ESI-QqTOF mass spectrometer coupled to a Thermo Scientific Ultimate 3000 UHPLC system. All samples were run in duplicates, and blank runs were included between sample analyses to minimize cross-contamination. Up to 10 µL of sample (1 mg/ml) was injected onto an Agilent Acclaim 120 C18 column (2.1 mm × 150 mm, 2.2 μm) maintained at 30 °C with a flow rate of 150 µL/min. The gradient elution program consisted of an initial composition of 2% solvent B (acetonitrile with 0.15% formic acid) and 98% solvent A (water with 0.15% formic acid) for the first 2 min, followed by a linear gradient increase to 40% solvent B over 20 min, a further increase to 98% solvent B over the next 10 min, and a final hold at 98% solvent B for an additional 10 min. To reduce inclusion of in-source fragments, ion-source parameters were optimized for gentle electrospray ionization.

Mass spectrometry data were acquired over an m/z range of 50–1300 in negative-ion mode electrospray ionization. Raw data were analyzed using Bruker’s MetaboScape 2024b software, alongside several metabolomics databases, including the Bruker MetaboBASE Personal Library 3.0 (https://store.bruker.com/products/bruker-metabobase-personal-library-3-0), the MassBank of North America (MoNA) with LipidBlast 2022 (https://mona.fiehnlab.ucdavis.edu/), and the Human Metabolome Database (HMDB) (https://www.hmdb.ca/). In-silico fragmentation analysis was performed using integrated MetaboScape tools and SIRIUS software with CSI: FingerID scoring (reference: Dührkop, Fleischauer et al., 2019; Hoffmann, Nothias et al., 2022). The resulting mass-feature table was further filtered and manually curated. Features identified as non-natural compounds, or potential contaminants were removed or re-evaluated by inspection of extracted ion chromatograms. Only compounds with high-confidence MS/MS library matches, higher spectral fidelity mSigma < 30, and mass accuracy better than 5 ppm were retained.

First, manual data harmonization was performed by identifying mass features with a MetaboScape compound name but no International Chemical Identifier (InChI)57 and manually searching these names in PubChem (accessed December 6, 2024). The MetaboScape compound names were harmonized to match PubChem compound names where possible. PubChem was then searched with the harmonized names to retrieve missing InChIs, PubChem IUPAC names, and PubChem CIDs using PubChemPy (version 1.0.4)58. To avoid duplicate analysis of stereoisomers with the same parent compound, stereochemistry was removed from the InChIs, which were then used to obtain the corresponding PubChem IUPAC names and CIDs. The mass feature table was further processed as follows using custom programming scripts59. Filtering was performed to select high-quality mass features based on the following criteria: manually identified features, annotation source as “ASRC_metabolites,” annotation source as “HMDB_metabolites_2022” with an MS/MS score > 800, or an MS/MS score > 600. Imputation was applied on the high-quality mass feature table by replacing missing values with 0 if the feature was absent in all sample group replicates. If the feature was absent in some replicates, missing values were replaced with the minimum intensity minus 1 observed in each sample group. Noise removal (denoising) was then performed by excluding features that met any of the following criteria: present only in blank samples, exhibiting greater intensity in blank samples than in oak gall extract samples, or having a maximum intensity of less than 20,000. The median intensity of blank samples was subtracted from oak gall extract sample intensities. The resulting processed mass feature table was used for subsequent analysis.

Cheminformatics, data analysis, and statistics

Non-stereochemical line notations for corresponding mass features were used to represent metabolites and for all analyses throughout this study. Chemical line notation conversion, manipulation, substructure searching, and drawing were performed using RDkit (version 2023.9.5)60. Gallic acid and glucose moieties were searched using the SMARTS line notations “OC(= O)c1cc(O)c(O)c(O)c1” and “OCC1OC(O)C(O)C(O)C1O”, respectively. Metabolites were structurally classified by NPClassifier (https://ccms-ucsd.github.io/GNPSDocumentation/api/, accessed February 12, 2025)61 using SMILES notations and by ClassyFire (https://github.com/JamesJeffryes/pyclassyfire, accessed February 12, 2025)62using InChI keys. InChIs or SMILES were then used to retrieve chemical properties (CID, molecular formula, IUPAC name, monoisotopic mass, xlogP, TPSA, PubChem fingerprint) from PubChem (accessed February 12, 2025) using PubChemPy. The Biopython (version 1.82)63 KEGG Compound package (accessed February 12, 2025) was used to annotate InChIs or SMILES notations with KEGG compound IDs, KEGG map IDs, and KEGG map names. Finally, the scikit-fingerprints Python package (version 1.11.0)64 was used to generate PubChem fingerprints for each metabolite from SMILES notations and to calculate Tanimoto similarities.

Data wrangling, data visualization, and statistics were performed using R Statistical Software (version 4.3.2)65 and the “tidyverse” (version 2.0.0)66 suite of R packages. Venn diagrams were created using the “VennDiagram” R package (version 1.6.0)67. Dendrograms and heatmaps were created using “ggtree” (version 3.10.1)68 and “aplot” (version 0.2.4)69 R packages. Differential abundance testing was performed on the SMILES table after adding 1 to eliminate zeros and applying a log2 transformation. P-values were calculated using the “t.test()” function in R and adjusted using the false discovery rate (FDR). Differentially abundant mass features were identified using a log2 fold change greater than 1 and an FDR less than 0.05.

Quantification of tannins in AGE

AGE diluted in DI water (0.1 mg/ml) and tannic acid standard (empirical formula C₇₆H₅₂O₄₆, CAS # 14-01-55-4) dissolved in DI water (125 µg/ml to 2 µg/ml) were incubated with iron(III) chloride hexahydrate (0.1 M) and potassium ferricyanide (8mM) for 10 min at room temperature70,71. Absorbance (OD700) was measured on an Agilent BioTek Synergy HT microplate reader. The percentage of tannins in AGE was calculated from interpolated tannic acid equivalent concentrations from a standard curve using a simple linear regression in Graphpad Prism v 10.5.0. The average tannin percentage calculated from two biological replicates were reported.

Targeted metabolomics

UHPLC-MS/MS analysis was performed using the Dionex UltiMate 3000 RSLC ultra-high pressure liquid chromatography system, through a workstation equipped with ThermoFisher Scientific’s Xcalibur v. 4.0 software package combined with Dionex’s SII LC control software, solvent rack/degasser SRD-3400, pulseless chromatography pump HPG-3400RS, autosampler WPS-3000RS, column compartment TCC-3000RS, and photodiode array detector DAD-3000RS. After the photodiode array detector, the eluent flow was guided to a Q Exactive Plus Orbitrap high-resolution high-mass-accuracy mass spectrometer (MS) (Thermo Scientific, Waltham, MA). Mass detection employed a parallel reaction monitoring (PRM) method, with an inclusion list of 1699.16570 and 849.07921 m/z ([M-H]- and [M-2 H]- 2 adducts, respectively, for tannic acid) in negative ionization mode with an electrospray interface (ESI). Sheath gas flow rate was 10 arbitrary units, auxiliary gas flow rate was 0, and sweep gas flow rate was 0. The spray voltage was 3000 volts (− 3000 for negative ESI) with a capillary temperature of 320 °C and an S-lens RF level of 65. The MS2 resolution was 17,500. Compounds were separated on a Phenomenex Kinetex C8 reverse phase column, size 100 × 2.1 mm, particle size 2.6 μm, pore size 100 Å. The mobile phase consisted of 2 components: Solvent B (0.1% formic acid in LCMS grade water), and Solvent C (0.1% formic acid in LCMS grade acetonitrile). The mobile phase flow was 0.20 ml/min, and a gradient mode was used for all analyses. The initial conditions of the gradient were 95% C and 5% B; at 30 min the proportion reaches 5% B and 95% C, which was maintained for the next 8 min, after which, for the following 3 min, the ratio was brought back to initial conditions. A 9 min equilibration interval was included between subsequent injections. The average pump pressure using these parameters was typically around 2800 psi for the initial conditions. For quantification, a tannic acid calibration curve ranging from 5 to 0.31 mg/ml (R squared = 0.97) was generated to determine the concentration of tannic acid in two biological replicates of AGE. Peak tables for all sample injections were exported from the Thermo FreeStyle software and manually integrated from the first appearance after 9 min of base peak m/z 1091.12 until the last retention time where peak area was ≥ 1%.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (3MB, docx)
Supplementary Material 2 (195.7KB, xlsx)
Supplementary Material 3 (160.3KB, xlsx)
Supplementary Material 4 (45.7KB, xlsx)

Acknowledgements

The authors would like to thank Drs. Jeffrey Boyd and Bozena Michniak for their technical advice. The authors would also like to acknowledge the Information Technology Services group at the School of Environmental and Biological Sciences, Rutgers University – New Brunswick, for providing the computational resources used for data analysis in this study.

Author contributions

AK and IR contributed to the conceptualization and design of the study. AK, KT, LA, EC, JH, and RA collected the data. AK and KA conducted the analyses. AK led the writing of the original draft. KA wrote the untargeted metabolomics section of the original draft. All figures were produced by AK and KA. KA, IR, JH, and EC edited the manuscript and provided feedback regarding the manuscript. IR supervised the study, acquired funding, and managed the entire project. All authors had full access to the data and approved the manuscript for publication.

Funding

This work was partially supported by NIH / ODS / NCCIH Botanical Center Grant (P50 AT002776 to IR), NJ Agricultural Experiment Station of Rutgers, and The State University of NJ, and the NCCIH (5T32AT004094 to KA and JH).

Data availability

Raw data and computational workflow used for data analysis can be found on Figshare: https://doi.org/10.6084/m9.figshare.29615462.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (3MB, docx)
Supplementary Material 2 (195.7KB, xlsx)
Supplementary Material 3 (160.3KB, xlsx)
Supplementary Material 4 (45.7KB, xlsx)

Data Availability Statement

Raw data and computational workflow used for data analysis can be found on Figshare: https://doi.org/10.6084/m9.figshare.29615462.

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

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