Preliminary Screening of Enzymatic and Metabolic Potential of Symbiotic Bacteria Associated with Marine Gastropods: Insights from Protease Activity and GC–MS-Based Profiling

2.1 Sample collection

Marine gastropods were collected from the intertidal zone of Sopapei Beach, Ambon, Indonesia, in September 2024. One specimen was collected from each host species, namely Mauritia arabica (Linnaeus, 1758), Cypraea tigris (Linnaeus, 1758), and Cypraea vitellus (Linnaeus, 1758). Sampling procedures, location selection, and handling methods are based on the protocol described by the study [9]. The samples were placed in sterile containers and transported to the laboratory under cooled conditions, specifically at 4 ℃, until further processing. Identification of the gastropod samples was done to the species level [9]. The whole soft tissue of each gastropod specimen was used as the source material for bacterial isolation.

2.2 Isolation and purification of symbiotic bacteria

Symbiotic bacteria were isolated from the whole soft tissue of each gastropod using the spread plate method on nutrient agar (NA) prepared with 100% seawater. One gram of a gastropod tissue sample was suspended in 1 mL of sterile seawater and serially diluted up to 10⁻⁴ to obtain the appropriate microbial density [10]. The suspension from each dilution was inoculated onto the surface of the medium and spread using a sterile glass spreader. The inoculated Petri dishes were then incubated at 28 ℃ for 48 h under aerobic conditions [11]. A total of 21 isolates were obtained.

The colonies that grew were observed visually and purified using the 4-quadrant streak method to obtain a single culture [12]. Sterilisation of the loop was performed at each stage of the streak to ensure the purity of the isolate [13]. The pure isolate was then stored and used for further characterisation and testing.

2.3 Morphological characterisation and culture preparation

Initial characterisation was conducted based on colony morphology, which included shape, colour, elevation, margin, and surface texture [14]. To obtain active cultures, each isolate was inoculated into Nutrient Broth (NB) and incubated on a shaker for 24–48 hours to increase cell density and homogeneity [15].

2.4 Protease activity screening

The protease production capabilities of the bacterial symbionts were tested using the disk diffusion method on NA medium enriched with 1% skimmed milk [10]. Active culture suspensions were inoculated onto sterile paper discs, which were then placed at equal distances on the surface of the medium. Each isolate was tested in three replicates. The incubation process was carried out at 28 ℃ for 48 hours with periodic observations [16]. The formation of a hydrolysis zone around the disc indicates protein hydrolysis. The diameter of the zone was measured and expressed as a proteolytic index in millimetres (mm) [17]. The proteolytic activity values represented the average hydrolysis zone diameters obtained from three independent replicates of each isolate, expressed as mean ± standard deviation (SD).

2.5 Consortium interaction test

Interactions between selected isolates were evaluated using the streak method on NA medium [18]. The cultures were incubated for 24 hours at 28 ℃, and the colony growth patterns were then observed. Interactions were categorised as antagonistic, synergistic, or neutral based on the presence of inhibition zones or growth integration [19].

2.6 Molecular identification

Genomic DNA from selected isolates was extracted using the Quick-DNA Fungal/Bacterial Miniprep Kit (Zymo Research) [20]. The 16S rRNA gene was amplified using universal primers 27F and 1429R with MyTaq HS Red Mix 2×, following the protocol described by research [21]. The Polymerase Chain Reaction (PCR) programme included a pre-denaturation step, followed by 33 cycles of denaturation, annealing at 55 ℃, elongation at 72 ℃, and a final extension. The amplification products were visualised by agarose gel electrophoresis in 1% TBE buffer at 100 V for 40 minutes [22]. The resulting DNA fragments were then sequenced and analysed using MEGA software version 7. The obtained 16S rRNA gene sequences were compared against the GenBank database using Basic Local Alignment Search Tool (BLAST) from NCBI (http://www.ncbi.nlm.nih.gov) to determine their closest phylogenetic relatives [23]. Close phylogenetic relationships were inferred when query coverage exceeded 98%, and sequence similarity was ≥99%. The obtained 16S rRNA gene sequences were deposited in the GenBank database under accession numbers PZ368869 (MA147) and PZ368914 (MA1414).

2.7 Secondary metabolite profile analysis

Selected isolates were cultured in 250 mL of NB for five days with continuous agitation [7]. The cultures were then extracted using 96% ethanol at a 1:1 (v/v) ratio between the culture and solvent, followed by incubation at 4 ℃ for 72 hours [24]. The mixture was centrifuged, and the supernatant was collected as a crude extract. The solvent blank used in this study was 96% ethanol (v/v). The ingredients of the culture medium were evaluated and did not correspond to any of the compounds identified in the GC–MS analysis. The major constituents of the medium are generally non-volatile and therefore are not expected to be detected under the GC–MS operating conditions.

The extract was analysed using GC–MS in splitless injection mode. Operating conditions included an inlet temperature of 230 ℃, a carrier gas flow rate of 1 mL/min, and an HP-5MS UI column (30 m × 0.25 mm × 0.25 µm). The temperature programme started at 60 ℃ and increased to 280 ℃, with a total analysis time of 32 minutes. Detection was performed using electron ionisation (EI) with a scan range of m/z 40–500. Compound identification was based on matching mass spectra with the National Institute of Standards and Technology (NIST) library according to fragmentation patterns and retention time (RT) matching [25]. Relative abundance was determined using peak-area normalisation and expressed as percentage peak area relative to the total chromatographic area. The similarity index (SI) was used as a measure of agreement between the experimental electron ionisation mass spectrum and the corresponding reference spectrum in the database, with higher values indicating stronger spectral correspondence and greater confidence in compound assignment [26]. Only compounds with SI ≥ 800 were retained for interpretation and are presented only in the supplementary dataset.

Bacterial metabolites were extracted using 96% ethanol, and no derivatisation, transesterification, or methylation procedures were applied during sample preparation. Therefore, compounds detected as fatty acid methyl ester (FAME) compounds in the chromatograms were assigned based on NIST library spectral matching and retention characteristics during sample processing [27]. Consequently, because no derivatisation was performed, these compound identifications should be considered tentative and interpreted with appropriate caution. Compounds commonly reported as analytical contaminants, particularly siloxane derivatives, were interpreted cautiously and excluded from biological interpretation.

The morphological data presented in Table 1 indicate a high level of phenotypic diversity among gastropod-associated symbiotic bacteria, which may reflect differences in metabolic and physiological capacities. Such variability provides a critical foundation for downstream selection, as phenotypic traits are frequently associated with functional capabilities, including the production of extracellular enzymes and secondary metabolites in marine bacteria [10]. Thus, the observed morphological diversity is not merely descriptive but serves as an early indicator of the potential biotechnological value of individual isolates.

This functional relevance is further supported by the proteolytic activity screening results in Table 2, which reveal clear differences in enzymatic performance among isolates. The data show that not all isolates exhibited detectable activity, indicating heterogeneity in metabolic expression within the symbiotic bacterial community. The absence of hydrolysis zones in certain isolates suggests either limited protease production under the tested conditions or the requirement for specific environmental cues to induce enzyme expression [29].

In contrast, isolates such as MA147 and MA1414 demonstrated a significant increase in hydrolysis zone diameter over the incubation period, reflecting high and sustained proteolytic activity. The progressive enlargement of these zones indicates continuous enzyme secretion and efficient extracellular degradation of protein substrates. This pattern suggests stable extracellular enzyme production and efficient protein substrate degradation [7]. Furthermore, the marked increase in activity after 18 hours of incubation may indicate a transition from the adaptation phase to the exponential phase of enzyme production, which is typically associated with enhanced metabolic activity under favourable nutrient conditions in marine bacteria [10].

The swarming behaviour observed in certain isolates, such as MA164, also suggests high motility, which is often associated with bacteria from the genus Vibrio. This motility can influence the spatial distribution of colonies and the diffusion dynamics of extracellular enzymes within the medium, potentially affecting the visibility and uniformity of hydrolysis zones [30]. Therefore, the interpretation of enzymatic activity should consider not only zone diameter but also biological factors such as motility and growth patterns.

Taken together, the results presented in Table 2 demonstrate that only a subset of isolates exhibits strong enzymatic potential, emphasising the importance of selective screening in identifying candidates for further analysis, including GC–MS-based metabolite profiling. The formation of hydrolysis zones on the assay medium indicates that both isolates are capable of secreting extracellular protease enzymes that actively degrade protein substrates. As observed after 36 hours of incubation, the presence of clear and well-defined zones surrounding the colonies reflects effective enzyme diffusion into the medium and efficient extracellular protein degradation.

In marine bacteria, the production of extracellular enzymes represents an adaptive strategy to acquire nitrogen and carbon sources from environments that are relatively low in dissolved nutrients [18]. Such nutrient-limited conditions are characteristic of tropical marine ecosystems, where fluctuating resource availability necessitates the utilisation of hydrolytic enzymes to access complex organic substrates [28]. The enzymatic activity detected from the mid to late incubation phase suggests that enzyme production likely occurs during the late exponential or early stationary phase, when nutrient competition becomes more pronounced [10]. This temporal pattern reflects typical metabolic regulation in marine bacteria, where extracellular enzyme expression is triggered by nutrient limitation and coordinated through quorum-sensing mechanisms [31].

Variations in hydrolysis zone diameter among isolates indicate differences in secretion capacity and catalytic efficiency. The visual differences, which are consistent with the measured enzyme index values, indicate that the size of the clear zone can serve as a relative quantitative indicator of protease activity. Such variability may arise from differences in genetic regulation, growth kinetics, and protein secretion systems across bacterial taxa [19]. In the context of marine symbiotic bacteria, the ability to produce proteolytic enzymes is often associated with ecological roles in degrading complex organic matter surrounding the host, thereby contributing to localized nutrient cycling on host surfaces [10]. Additionally, enhanced proteolytic capability may confer a competitive advantage by enabling more efficient nutrient acquisition compared to coexisting microorganisms within the same microhabitat [5].

The consortium interaction assay revealed no formation of inhibition zones or clear growth boundaries, indicating the absence of strong antagonistic interactions between the two isolates. The closely associated colony growth pattern, without the appearance of an inhibition line, further suggests their compatibility under the consortium conditions. This pattern suggests a neutral or compatible relationship in artificial culture conditions. In marine microbial systems, non-antagonistic interactions are commonly observed among communities originating from the same microhabitat, reflecting long-term co-existence and ecological adaptation [18]. Such interactions are consistent with the concept of stable microbial assemblages, in which interspecies relationships tend to be cooperative or non-inhibitory [32].

This observation suggests that the consortium conditions do not suppress enzymatic expression and may instead contribute to maintaining metabolic stability. In some studies, bacterial consortia have been shown to enhance substrate degradation efficiency through metabolic complementation or cross-regulation mechanisms among species [18]. Furthermore, synergistic interactions within microbial communities may stimulate enzyme-related gene expression through cell-to-cell communication processes such as quorum sensing [16].

These findings demonstrate that both isolates possess not only strong individual proteolytic capabilities but also physiological compatibility in co-culture systems. The combination of high enzymatic activity and non-antagonistic interaction represents a key characteristic for the development of microbial consortia in biotechnological applications. Such stability is particularly advantageous, as microbial consortia often outperform single cultures in biomolecule degradation and in the production of industrial enzymes and bioactive compounds [4].

Molecular identification based on the 16S rRNA gene confirmed that the two isolates with the highest proteolytic activity belong to the genus Vibrio. Electrophoretic analysis revealed a single DNA band of approximately 1500 bp, indicating successful and specific amplification of the target gene, consistent with the typical length of bacterial 16S rRNA sequences. With a query cover of 100% and sequence identity above 99%, the taxonomic assignment can be considered highly reliable [23]. Although 16S rRNA gene sequence similarity above 98.7–99% is commonly used as an operational criterion for species‑level assignment, ecological and genomic studies have revealed cryptic species complexes that remain unresolved at this threshold, thereby providing a robust molecular foundation for downstream biological interpretation [33].

The isolates were identified as closely related to Vibrio sp. MA147 (V. tubiashii) and Vibrio sp. MA1414 (V. owensii) indicates that the selected gastropod-associated symbiotic bacteria belong to a group known for high metabolic plasticity. The genus Vibrio comprises facultative marine bacteria capable of adapting to a wide range of salinity and nutrient conditions and is characterised by efficient protein secretion systems for extracellular enzyme production [19]. In addition, members of this genus possess dynamic and flexible genomes, enabling rapid responses to environmental fluctuations and facilitating the expression of genes involved in metabolism and ecological adaptation [34]. These findings reinforce the correlation between genetic identity and enzymatic performance observed during the screening stage.

Such a relationship is particularly relevant in the context of biotechnological exploration, where enzymatic traits often reflect broader metabolic capabilities. Protease production by Vibrio spp. not only serves as a mechanism for nutrient acquisition but also reflects broader metabolic capabilities. In marine environments, hydrolytic enzymes play a key role in the decomposition of complex organic matter and contribute to nitrogen and carbon biogeochemical cycles [10]. Accordingly, the high proteolytic activity observed in the selected isolates in Table 2 can be interpreted as an indicator of integrated metabolic potential rather than solely an artefact of in vitro conditions. This suggests that the enzymatic activity is closely linked to ecological adaptation and may be associated with the biosynthesis of secondary metabolites.

Furthermore, several studies have reported that Vibrio species are capable of producing a wide range of bioactive compounds, including fatty acid derivatives, cyclic peptides, and small molecules with antibacterial and antifouling activities [13]. Recent genomic analyses have also identified diverse biosynthetic gene clusters (BGCs) within Vibrio, supporting their capacity to synthesise structurally diverse secondary metabolites with potential pharmacological relevance [16]. The identification of the most enzymatically active isolates within this genus provides a strong biological rationale for proceeding with GC–MS-based metabolite profiling. In this context, molecular identification extends beyond taxonomic classification and functions as a predictive framework for assessing biosynthetic potential.

This finding supports the hypothesis that gastropod-associated symbiotic bacteria represent a potential source of microbial candidates for biotechnological applications, particularly in enzyme production and bioactive compound discovery. By integrating molecular identification Table 3 with enzymatic activity and metabolic profiling, this approach demonstrates the effectiveness of function-driven selection in marine microbial exploration. Consequently, the molecular identification stage serves as a conceptual bridge between enzymatic screening and subsequent chemical analysis of bioactive compounds.

GC–MS analysis of the two selected isolates, V. tubiashii (MA147) and V. owensii (MA1414), revealed metabolite profiles dominated by composed identified as of long-chain FAMEs, as clearly illustrated in Figures 3 and 4. Only compounds with an SI ≥ 800 were retained. Based on this criterion, the consistent chromatographic patterns observed across both figures indicate a conserved metabolic orientation toward lipid biosynthesis.

The presence of octadecenoic acid and hexadecanoic acid methyl ester as major components suggests that fatty acid biosynthesis represents the dominant metabolic pathway in both isolates. As shown in Figure 3, isolate MA147 exhibited a pronounced dominance of 10-octadecenoic acid methyl ester, whereas MA1414 contained both saturated and unsaturated FAMEs, including hexadecanoic acid methyl ester and trans-13-octadecanoic acid methyl ester.

In marine bacteria, unsaturated fatty acids play a crucial role in maintaining membrane fluidity in response to osmotic pressure and temperature fluctuations in coastal environments. Such adaptive mechanisms are essential for preserving membrane integrity and cellular function under dynamic marine conditions [33, 34]. Consequently, the high proportion of oleic and palmitic acid derivatives likely reflects physiological adaptation strategies to tropical marine habitats.

From a biotechnological perspective, the predominance of compounds identified as FAMEs suggests active lipid-associated metabolic pathways in the selected isolates. Long-chain FAMEs are not only structural components of cellular membranes but have also been reported to exhibit biological activities, including antibacterial, anti-inflammatory, and surface-active properties. Compounds such as hexadecanoic acid methyl ester have been associated with antibacterial activity in various marine microbial extracts [10]. In addition, fatty acid derivatives are increasingly recognised for their ability to disrupt microbial membranes and modulate inflammatory responses, highlighting their potential in pharmaceutical and cosmeceutical applications [4].

Although both isolates exhibited lipid-dominated metabolite profiles, distinct differences in relative composition were observed. Both chromatograms were dominated by compounds identified as FAMEs. Among the retained high-confidence identifications (SI ≥ 800), isolate MA147 was characterised predominantly by 10-octadecenoic acid methyl esters. Meanwhile, isolate MA1414 contained 9-hexadecenoic acid methyl ester, hexadecanoic acid methyl ester, and trans-13-octadecenoic acid methyl ester.

This contrast suggests differences in metabolic regulation, enzyme expression, or adaptive strategies between species within the same genus [32]. Such variation may also indicate the potential for differential biological activity, which warrants further investigation in downstream bioassays.

Compounds identified with lower confidence and commonly reported analytical contaminants, particularly siloxane derivatives, were excluded from biological interpretation to ensure a conservative interpretation of the GC–MS data.

Taken together, the dominance of FAMEs in both isolates reinforces the central theme and literature background of this study: enzymatic activity-based selection effectively identifies bacteria with high metabolic potential, as reflected in their lipid-associated metabolite profiles. However, this study has several limitations that should be considered. Each gastropod host species was represented by a single specimen, which limits the generalizability of the observed bacterial diversity and enzymatic characteristics. The bacterial community associated with individual hosts may vary according to environmental conditions, host physiology, and geographic location. Therefore, the present findings should be regarded as preliminary and require further validation using larger sample sizes, multiple biological replicates, and broader geographic sampling.

Furthermore, GC–MS analysis was performed without derivatization and therefore provides only a partial metabolite profile that does not represent the full metabolic capacity of the isolates. Polar, thermally unstable, and high-molecular-weight metabolites are likely underrepresented under the analytical conditions employed. Accordingly, the detected metabolites should be interpreted as a preliminary characterization of the volatile and semi-volatile metabolite composition rather than a comprehensive metabolomic profile.

The integration of enzymatic screening (Table 2), molecular identification (Table 3), and metabolite profiling (Figures 3 and 4) highlights the effectiveness of a function-driven selection strategy. In the context of gastropod symbiosis, lipid-associated metabolites may contribute to chemical defence, microbial competition, and the stability of host–microbe interactions. These compounds may also play roles in chemical signalling and antimicrobial defence within the host microenvironment [2].