Metabolite profiling of SAE and SABE via UPLC-T-TOF-MS/MS

After extraction, SAE yielded 12–18% w/w across 5 independent biological replicates. The variation observed among replicates is primarily attributable to minor experimental variability during the extraction process. However, after biotransformation, the recovered yield was 75–97% w/w (45–58 g per 60 g of SAE), depending on the biological replicate and fermentation batch. This recovered mass represents a complex metabolite matrix comprising untransformed residual phytoconstituents together with newly generated fungal biotransformation products formed during the incubation period.

UPLC-MS/MS was used to analyse the metabolite profiles of SAE and SABE. The detection was performed in negative ionization mode. Representative chromatograms of both samples are depicted in Fig. 1. UPLC-MS led to the identification of 80 unique metabolites in SAE, reflecting a rich and diverse chemical composition (Table 1). Among the identified metabolites, flavonoids constituted the most prominent class, followed by tannins, phenolic acids, triterpenoids, and coumarins. Subsequent UPLC-MS/MS analysis of SABE revealed the emergence of 14 modified metabolites (Table 1). These results highlight the enzymatic versatility of A. niger in catalysing substantial molecular modifications, thereby expanding the chemical landscape of the extract through targeted bioconversion.

Fig. 1Fig. 1The alternative text for this image may have been generated using AI.

Base peak chromatograms of the UPLC-tandem mass spectrometry results of the (a); S. australe leaves extract (SAE) and (b); S. australe biotransformed extract (SABE) in negative ionization mode.

Comprehensive annotation of SAE and SABE metabolites via GNPS molecular networking

A major benefit of networking is its ability to identify known compounds along with their possible analogues31. This method was applied herein for the identification of SAE metabolites and their biotransformed metabolites via A. niger culture. The MN constructed from the MS/MS data of the analysed samples incorporated 369 interconnected nodes organized into 30 distinct clusters (Fig. 2a). Among these, two clusters were particularly noteworthy: cluster A, representing quercetin derivatives, while cluster B, corresponding to syringetin. Interestingly, the biotransformed metabolites on SABE appeared predominantly as self-looped nodes, suggesting that these unique metabolites presented limited structural similarity to other detected compounds (Fig. 2b).

Fig. 2Fig. 2The alternative text for this image may have been generated using AI.

(a) Full molecular network created via UPLC-T-TOF-MS-MS data in negative ionization mode from SAE (yellow color) and SABE (blue color) and (b) expansion of major clusters showing microbial transformation effects on S. australe metabolites. Cluster A: quercetin derivatives; cluster B: syringetin and its microbial metabolite dihydroxy-trimethoxy flavonol; C: self-looped nodes of microbial metabolic products of S. australe; and cluster D: Gallic acid-O-(6 galloyl hexoside).

Table 1 Tentatively identified metabolites via UPLC-T-TOF–MS/MS in the S. australe leaves extract (SAE) and S. australe biotransformed extract (SABE) in negative ionization mode.Flavonoids

The MS/MS fragmentation patterns of the flavonoid derivatives revealed the characteristic loss of hexosyl, deoxyhexosyl, and hexuronyl residues, corresponding to neutral losses of 162, 146, and 176 Da, respectively34. These fragmentation patterns were observed for metabolites such as quercetin-O-hexoside (M35) (Fig. S2), kaempferol-O-deoxyhexoside (M39) and myricetin-O-hexuronide (M2) (Fig. S3). The fragmentation processes were followed by Retro-Diels-Alder (RDA) fragmentation, a well-established pathway that further aids in the structural elucidation of flavonoid aglycones62.

Gallotannins

Gallotannins are characterized by their distinctive fragmentation behavior, enabling precise structural characterization. Their spectra are marked by the predominant formation of fragment ions [M-H-170]- and [M-H-152]-, which correspond to the neutral loss of gallic acid and galloyl residues, respectively. For example, gallic acid-O-(6-galloyl hexoside) (M63) (Fig. S4) was identified on the basis of its deprotonated molecular ion at m/z 483.0777, and its MS2 spectrum was attributed to the loss of a galloyl moiety, resulting in a key fragment ion at m/z 331.0669 and a fragment ion at m/z 313.0584, corresponding to the neutral loss of gallic acid. Similarly, tetra-O-galloyl-hexose (M65) (Fig. S5) exhibited a deprotonated ion at m/z 787.0918 and subsequent loss of 2 galloyl moieties, as confirmed by the 635.0852 and 483.0757 fragments.

Ellagitannins

Ellagitannins are characterized mainly by galloyl and HHDP moieties, which results in the detection of characteristic neutral losses, including galloyl, gallic acid and HHDP. Additionally, a prominent fragment ion at m/z 301 was observed, originating from the lactonization of the HHDP ester group into the more stable ellagic acid structure, and was the most detected among these metabolites. Casuarictin (M68) (Fig. S6) was detected with a deprotonated molecular ion at m/z 935.0728. Fragmentation of M68 produced an ion at m/z 783.0683, indicative of the neutral loss of a galloyl moiety, whereas the subsequent formation of a key ion at m/z 301.0073 reflected the conversion of the HHDP group into a stable ellagic acid structure. Similarly, di-galloyl-HHDP-hexoside (M69, Fig. S7) was identified with a deprotonated molecular ion at m/z 785.1982. Its MS2 fragmentation profile revealed an ion at m/z 633.0651, indicating the loss of a galloyl group, followed by the generation of a fragment ion at m/z 301.0071 due to HHDP lactonization.

Phenolic acids

The MS2 fragmentation of phenolic acids revealed typical losses of 18 Da (H₂O), 44 Da (CO₂), and 62 Da (H₂O and CO₂). For instance. sinapic acid (M82) (Fig. S8) was identified by its deprotonated ion at m/z 223.0600, with fragment ions at m/z 208.0378 [M-H–CH3]- and m/z 179.0547 [M-H-CO2]-. Vanillic acid (M86) (Fig. S9) was identified by its deprotonated ion at m/z 167.0359, and further deprotonation in MS2 resulted in a fragment ion at m/z 122.9670, indicating the loss of CO2.

Triterpenoids

In pentacyclic triterpenoid acids, the predominant fragmentation pathways typically begin with the loss of neutral molecules such as H₂O or CO₂, followed by characteristic cleavages within the ring system. Notably, RDA cleavage of ring C serves as a key diagnostic feature in the mass spectra of pentacyclic triterpenoid acid derivatives, particularly those bearing carboxyl functional groups in rings D or E63. For example, oleanolic acid (M93) (Fig. S10) exhibited a deprotonated molecular ion at m/z 455.3546. The [M − H]- ion underwent subsequent fragmentation in MS2, yielding fragment ions at m/z 437.1867 and m/z 411.0034, corresponding to the successive losses of H₂O and CO₂, respectively. The RDA cleavage of ring C generates a fragment ion at m/z 248.9605, representing a moiety comprising rings D and E along with a portion of ring C. Similarly, maslinic acid (M94) (Fig. S11) showed a deprotonated molecular ion at m/z 471.3481, with a fragment ion at m/z 453.1831 arising from the loss of H₂O. Further fragmentation results in the formation of an ion at m/z 409.3057 due to an additional loss of CO2. The characteristic RDA cleavage of ring C was observed at m/z 248.9613, confirming the structural integrity of the D and E rings.

GNPS-assisted prediction of the metabolites of S. australe biotransformed by A. niger

Clear distinctions between the SAE and SABE samples were apparent upon visual observation of the MNs. Initial analysis of the network revealed both qualitative and quantitative differences in the metabolite profiles of SAE and SABE.

Metabolite biotransformation

Flavonols, a prominent class of metabolites in S. australe, appeared to undergo significant sulfonation when exposed to A. niger culture. Among these, quercetin stood out in SABE, alongside its sulfonated metabolite, quercetin-O-sulfate. As revealed through the MN (Fig. 2b). Quercetin-O-sulfate (M11) (Fig. S12) exhibited a molecular ion at m/z 380.9909, and fragmentation led to a major ion at m/z 301.0329, indicating the neutral loss of the sulfate group (80 Da)64. Furthermore, two distinct aglycone fragments characteristic of the RDA fragmentation pattern were detected. The suggested mechanism for this sulfonation process is likely catalyzed by sulfotransferase enzymes produced by A. niger65.

Moreover, isorhamnetin undergoes selective methylation at the 4’ position on the ring B when incubated with A. niger culture. This transformation results in the formation of trihydroxy-dimethoxy flavonol (M46) detected at m/z 329.0688 in SABE. The fragmentation pattern revealed key ions at m/z 298 and 283. The suggested mechanism behind these methylation reactions likely involves methyltransferase enzymes produced by Aspergillus species10.

Metabolite 4 (Pentahydroxy-3-hydroxymethoxy-flavanone) was identified by its deprotonated ion at m/z 349.0553 (Fig. S13). Fragmentation revealed a key ion at m/z 331 due to the loss of an H2O molecule and a fragment at m/z 301 from subsequent methoxy cleavage. The biosynthesis of this compound begins with the enzymatic hydroxylation of the methoxy group at position 3 on ring C of 3-O-methylmyrecitin, which is mediated by the hydroxylase enzyme. This is followed by the reduction of ring C via reductase enzymes of Aspergillus, resulting in hydrogenation and stabilization of the chroman structure66.

Following incubation with A. niger culture, most glycosides in the SAE were no longer detectable, suggesting that extensive hydrolysis of their aglycon was mediated by microbial glycosidase enzymes67. This hydrolysing activity was confirmed through MN analysis, where glycosides such as quercetin-O-dihexoside (M1) and hyperoside (M33) were identified in SAE but were absent in SABE.

A. niger is an effective source for producing tannase enzymes68. Tannin acyl hydrolase (Tannase) is a vital enzyme that can hydrolyse ester bonds (galloyl esters of alcohols) and depside bonds (galloyl esters of gallic acid). It typically acts on the C–O and ester linkages found in hydrolysable tannins, resulting in biotransformed products such as gallic acid and glucose. For ellagitannins, tannase selectively targets galloyl groups, resulting in the degalloylation of ellagitannins and the formation of biotransformed products such as ellagic acid69. In the present study, the biotransformation of gallotannins was monitored through the detection of cluster D (gallic acid-O-galloyl hexoside), a precursor compound present before biotransformation. This compound was converted into gallic acid following enzymatic hydrolysis, which aligns with previous research on tannase-catalyzed reactions. Furthermore, hydrolysis of ellagitannins was observed, leading to the production of ellagic acid, which further corroborates the specificity of the enzyme for galloyl units within ellagitannin structures.

The production of citric acid by A. niger is attributed to its highly efficient central carbon metabolism, which primarily involves glycolysis and the tricarboxylic acid (TCA) cycle. During the biotransformation process, sugars such as glucose are metabolized through glycolysis, yielding pyruvate as a key intermediate. Pyruvate is subsequently converted into acetyl-CoA and enters the TCA cycle. This metabolic bottleneck resulted in the intracellular accumulation of citric acid. Citric acid is subsequently transported out of the mitochondria and secreted into the extracellular environment through specialized transport mechanisms70.

Multivariate analysis

Unsupervised PCA was initially employed to explore the intrinsic variation within the LC–MS dataset and to assess the impact of biotransformation on the metabolic profile of S. australe extract. As illustrated in Fig. 3a, a clear and well-defined separation between the SAE and SABE samples was achieved via the first principal component (PC1), which accounted for 89.7% of the total variance, whereas PC2 explained an additional minor proportion (~ 3.5%). The high cumulative explained variance (R²X = 0.932) reflects the robustness of the model. Notably, the tight clustering of replicates within each group indicates excellent analytical reproducibility and minimal intragroup variability, confirming the reliability of the dataset. The observed separation highlights the substantial metabolic reprogramming induced by A. niger biotransformation. This clustering pattern was further supported by HCA (Fig. 3b), which distinctly segregated the samples into two major clusters corresponding to the SAE and SABE groups. The absence of overlap between clusters, combined with strong intragroup similarity, reinforces the presence of significant biochemical divergence between the two conditions.

Fig. 3Fig. 3The alternative text for this image may have been generated using AI.

Multivariate analysis of metabolomic profiles illustrating sample clustering and variation: (a) unsupervised PCA score plot demonstrating the distribution and discrimination of samples based on their metabolic profiles.; (b) HCA dendrogram showing grouping patterns among samples.

To further enhance class discrimination and identify metabolites responsible for group separation, supervised PLS-DA was conducted. The PLS-DA score plot (Fig. 4a) demonstrated complete separation with no overlap between the SAE and SABE samples, with Component 1 and Component 2 explaining 89.6% and 2.8% of the variance, respectively. The model exhibited strong predictive ability, as indicated by a positive Q² value, confirming its robustness and reliability. Interpretation of the PLS-DA loading plot and biplot (Fig. 4b, c) respectively revealed key metabolites driving this separation. Specifically, the SABE samples were predominantly associated with citric acid, quercetin-O-sulfate, and kaempferol-O-sulfate, whereas the SAE samples were characterized by relatively high levels of hexahydroxydiphenoyl-hexoside, quercetin-O-dihexoside, and eriodictyol-O-hexoside. These metabolites represent major contributors to the biochemical distinction between SAE and SABE.

Fig. 4Fig. 4The alternative text for this image may have been generated using AI.

Supervised PLS-DA of metabolomic profiles: (a) PLS-DA score plot illustrating the discrimination between SAE and SABE samples; (b) loading plot showing metabolite contributions to model components; variables farther from the origin have the strongest influence on class discrimination; (c) PLS-DA biplot representing the relationship between samples and discriminant metabolites.

The supervised OPLS-DA model further refined class discrimination by removing orthogonal variation unrelated to class separation. The model demonstrated excellent statistical performance (R²X = 0.896, R²Y = 0.999, and Q² = 0.998), alongside a significant CV-ANOVA (p = 0.008), confirming model validity. Importantly, the minimal difference between R²Y and Q² indicates no evidence of model overfitting, which is further supported by permutation testing (Fig. S14).

The corresponding S-plot enabled visualization of variables with both high covariance and correlation. Metabolites such as hexahydroxydiphenoyl-hexoside, quercetin-O-dihexoside, and eriodictyol-O-hexoside were located in the upper-right quadrant, indicating a strong positive correlation with SAE samples. In contrast, citric acid, quercetin-O-sulfate, and kaempferol-O-sulfate were positioned in the lower-left quadrant, reflecting their association with SABE samples. These compounds can be considered robust discriminant biomarkers reflecting the biochemical consequences of fungal biotransformation. Further confirmation was obtained through VIP analysis (Fig. 5c), where all selected metabolites presented VIP scores > 1.0, indicating their significant contribution to class separation and model construction.

Finally, a volcano plot (Fig. 6) was generated to assess univariate statistical significance and fold-change magnitude. The analysis revealed a large-scale metabolic shift, with 977 features significantly upregulated and 2,358 features significantly downregulated in SAE relative to SABE (based on defined thresholds of log₂FC and p-value). Notably, metabolites such as hexahydroxydiphenoyl-hexoside and quercetin-O-dihexoside were markedly upregulated in SAE, whereas citric acid and flavonoid sulfates were enriched in SABE. Overall, the integration of unsupervised, supervised, and univariate analyses provides compelling evidence of profound metabolomic reprogramming induced by A. niger biotransformation, highlighting specific metabolite classes (phenolic glycosides vs. sulfated flavonoids) as key biochemical signatures.

Fig. 5Fig. 5The alternative text for this image may have been generated using AI.

Ortho PLS-DA-based metabolomic differentiation of SAE and SABE: (a) Score plot showing robust separation between SAE and SABE samples; (b) S-plot highlighting metabolites with the strongest contributions to class discrimination; (c) VIP analysis identifying 15 key metabolites as putative biomarkers in SAE versus SABE, with red denoting high dominance and blue indicating low dominance.

Fig. 6Fig. 6The alternative text for this image may have been generated using AI.

The volcano plot shows the differentially abundant metabolite expression levels in SAE and SABE samples. Red, blue, and gray dots indicate upregulated, downregulated, and nonsignificantly differentially expressed metabolites, respectively.

ABTS and DPPH-based in vitro antioxidant screening

The antioxidant activities of SAE and SABE were comprehensively evaluated via two complementary assays; DPPH and ABTS. The dose-dependent scavenging activities of SAE and SABE are illustrated in (Fig. 7). SAE and SABE exhibited strong free radical neutralization, as indicated by a progressive increase in percentage inhibition with increasing concentration. This trend reflects a marked reduction in absorbance in the presence of the extracts, underscoring their significant antioxidant capacity mediated by electron or hydrogen atom donation.

Fig. 7Fig. 7The alternative text for this image may have been generated using AI.

Percent inhibition of (a) DPPH and (b) ABTS free radicals by SAE, SABE, and Trolox standard.

In the DPPH assay, SAE exhibited IC₅₀ value of 36.96 ± 1.20 µg/mL (n = 3), highlighting its potent radical-scavenging ability. In comparison, SABE demonstrated a higher IC₅₀ value of 48.50 ± 2.10 µg/mL, indicating slight reduction of antioxidant activity after biotransformation. The standard antioxidant Trolox showed a markedly lower IC₅₀ value (12.32 ± 1.01 µg/mL), confirming its strong antioxidant potential. Similarly, in the ABTS assay, SAE exhibited a lower IC₅₀ value (19.80 ± 0.85 µg/mL) than SABE (23.79 ± 1.10 µg/mL), indicating stronger antioxidant activity before biotransformation. Trolox showed the highest activity, with an IC₅₀ value of 5.23 ± 1.03 µg/mL, confirming its effectiveness as a reference antioxidant. The SAE contained a high level of tannins; however, during microbial biotransformation, these compounds underwent hydrolysis. This degradation likely explains the observed reduction in the antioxidant activity of the SABE.

In vitro evaluation of the inhibition of α-glucosidase, α-amylase, and pancreatic lipase enzymes

Table 2 reveals that SAE has a significant α-glucosidase inhibitory activity, surpassing the reference standard drug, along with moderate inhibitory effects on α-amylase and lipase. Upon microbial biotransformation with A. niger, the lipase inhibitory activity of SAE improved substantially, where the percent inhibition increased from 47.98 ± 1.73% to 74.49 ± 4.80% at 500 µg/mL. For α-amylase at 500 µg/mL, the percentages of inhibition showed minimal variation after biotransformation, ranging from 81.95 ± 0.59% to 79.93 ± 2.69%. However, a marked reduction in α-glucosidase inhibitory activity was observed, where the inhibition decreased from 58.84 ± 1.28% to 54.41 ± 1.64% at 1000 µg/mL following microbial transformation.

Table 2 Effects of S. australe extract (SAE) and its biotransformed extract (SABE) on lipase, α-amylase, and α-glucosidase enzymes.

The notable inhibitory activities of SAE and SABE against α-glucosidase and α-amylase are likely attributable to their high content of polyphenolic compounds, which are known for their enzyme-modulating properties. Previous studies have demonstrated that polyphenols can inhibit carbohydrate-digesting enzymes through specific interactions with catalytic or allosteric residues within their active sites, thereby interfering with substrate binding or catalysis71. Additionally, glycosylated phenolics and tannins may act as competitive inhibitors, mimic natural substrates of α-glucosidase, and thus reduce enzymatic activity72. Interestingly, a slight reduction in α-glucosidase inhibition following fungal biotransformation may be explained by the enzymatic hydrolysis of glycosides and tannins. The partial hydrolysis or degradation of tannins during biotransformation could therefore contribute to the observed decline in the inhibitory potency of the SABE.

Statistical analysis of in-vitro bioassays

The antioxidant (DPPH and ABTS) and antihyperglycemic (α-amylase, α-glucosidase, and pancreatic lipase) assays results were reported as mean ± SD based on triplicate measurements (n = 3). Statistical significance of the scavenging and inhibitory activities was assessed using one-way analysis of variance (ANOVA) across all concentrations and treatment groups followed by Tukey’s Honest Significant Difference (HSD) post-hoc test for multiple comparisons using GraphPad Prism V.11.0.0.The ANOVA results revealed highly significant differences among all treatment groups (p < 0.0001), with F-values substantially exceeding the corresponding critical F-value, indicating strong evidence against the null hypothesis Table (S 1,2). When a significant F-test result was obtained and (p < 0.05), Tukey’s HSD post-hoc test was applied for multiple pairwise comparisons. This analysis enabled the detection of dose-dependent effects and facilitated comparison of SAE and SABE with reference standards (Trolox, Acarbose, and Orlistat). Differences were considered statistically significant at p < 0.05 and are denoted in the figures by letters (e.g., A, B, C); groups sharing the same letter are not significantly different at the 95% confidence level shown in (Fig. 8) and Table (S 3,4).

Fig. 8Fig. 8The alternative text for this image may have been generated using AI.

Tukey’s post HSD test for (a) pancreatic lipase, (b) α-amylase, (c) α-glucosidase, (d) DPPH, and (e) ABTS.

The statistical analysis, based on one-way ANOVA and Tukey’s HSD test (p < 0.05), showed that both SAE and SABE have a strong multitarget biological activity. For enzyme inhibition, SABE (500 µg/mL) showed the highest lipase inhibition (74.49 ± 4.80%, Group A), significantly higher than orlistat (Group B). SAE showed a strong α-glucosidase inhibition, matching acarbose (Group A) at both tested concentrations. In the α-amylase assay, acarbose was the most effective (Group A), while SAE & SABE at 500 µg/mL showed similar moderate activity (Group B). For antioxidant activity (DPPH and ABTS), Trolox (Group A) was the most effective, followed by SAE (Group B) and then SABE (Group C). SABE was more effective in lipase inhibition, while SAE demonstrated strong α-glucosidase inhibition along with superior antioxidant activity.

Molecular docking study

A blind molecular docking strategy was implemented to explore the binding interactions between PL and sulfated flavonoids uniquely enriched in SABE. This approach enabled an in-depth characterization of the ligand‒protein complexes, with key parameters such as binding affinity (ΔGa), RMSD, interactions, interaction types, and their associated distances, as detailed in Table 3.

Table 3 Docked conformations of orlistat and sulfated flavonoids from SABE on the pancreatic lipase-colipase complex (PDB ID: 1LPB).

Among the analysed compounds, isorhamnetin-3-O-sulfate (Fig. 9) exhibited the strongest binding affinity compared to orlistat (Fig. S15), with a ΔGa of − 12.47 kcal/mol (fig. Its hydrogen bond interaction with His 263 (3.02 Å, − 7.0 kcal/mol) highlights a stable and favourable binding pose. The low RMSD value (1.36 Å) further supports its precise and stable binding conformation. Moreover, kaempferol 3-O-sulfate (Fig. S16) closely follows, with a ΔGa of − 12.24 kcal/mol. It forms a strong hydrogen bond with His 263 (3.14 Å, − 7.8 kcal/mol) and achieves the lowest RMSD among all the compounds (0.95 Å), suggesting exceptional conformational stability.

Fig. 9Fig. 9The alternative text for this image may have been generated using AI.

The 2D interactions of isorhamnetin-O-sulfate in the binding pocket of the pancreatic lipase-colipase complex (PDB ID: 1LPB).