Optimization and characterization of bioactive secondary metabolites from Streptomyces sp CMSTAAHL-4 isolated from mangrove sediment

Abstract

Background

Ten morphologically different actinomycetes were isolated from mangrove sediments of Manakudy, Kanyakumari District, India. The potent strain was selected based on their primary screening against Gram positive Staphylococcus aureus, Enterococcus faecalis and Gram negative Klebsiella pneumoniae, Escherichia coli, Pseudomonas aeruginosa, Salmonella typhi bacterial pathogens. The selected strain was identified as Streptomyces sp CMSTAAHL-4 by 16S rRNA sequencing. The media optimization for secondary metabolites production was performed by One-Variable at a Time and Response Surface Methodology-Central Composite Design. Minimum inhibitory concentration and minimum bacterial concentration for the extracted secondary metabolites were determined. The antioxidant potential of the secondary metabolites showed that the concentration of the metabolites increases, with the percentage of inhibition. The anti-inflammatory activity of the secondary metabolites found that maximum activity was observed at 500 µg/ml of the metabolites. Alcohols, alkenes, alkynes, alkyl halides, carboxylic acids, aliphatic esters functional groups were identified by fourier transform infrared spectroscopy, gas chromatography and mass spectrometer analysis of the secondary metabolites revealed five bioactive compounds. The X-ray diffraction analysis revealed that the secondary metabolites are amorphous. The thermogravimetric analysis showed the thermal stability of secondary metabolites. Atomic force microscopy analysis revealed specific structural characteristics of the secondary metabolites, which may be associated with their potential biological activities.

Conclusions

The results showed that the antibacterial, antioxidant, and anti-inflammatory chemicals present in the isolated secondary metabolites give them therapeutic properties.

Background

Mangroves are classified as harsh intertidal ecosystems because of their recurrent tidal flooding, powerful winds, and intense UV radiation. It is well known that the mangrove ecosystems regularly experience environmental changes, such as shifts in salinity and tidal gradient [1, 2]. In the intertidal zone, mangroves are tidal forests that are typically regarded as unique marine ecosystems. Mangrove organisms differ significantly from terrestrial organisms due to the environmental conditions, such as high salt and moisture levels, and they may synthesize specific metabolites [3]. Studies on actinomycetes generated from mangroves (mostly isolated from sediments and mangrove plants) and their secondary metabolites have gained popularity in recent years. The mangroves are “home” to a vast array of new actinomycetes, which hold tremendous potential bioactive compounds [4]. The mangrove environment is a vast, uncharted area that is capable of producing variety of novel natural products, including compounds that are physiologically active [5, 6]. Mangrove actinomycetes are diverse and distinctive due to the diverse mangrove habitat, which includes geographic location, pH, temperature, salinity, moisture, and nutrient levels [7]. Because of the diverse microbiological, enzymatic, and metabolic activities occurring within the mangrove ecosystem, its environment is salty and extremely rich in organic materials [8]. The primary source of antibiotics is still natural items particularly marine actinomycetes [9]. The mangrove sediments have been shown significant populations of micromonosporae [10] and novel actinomycetes [11]. Both terrestrial and marine environments are home to actinomycetes, which are the most common source of naturally occurring bioactive chemicals [12]. Marine actinomycetes are the best sources of secondary metabolites, and the vast majority of these compounds originates from the single genus Streptomyces, and is widely distributed in both marine and terrestrial environments [13]. Marine Streptomyces are a particularly attractive source for studying marine natural products because they produce a variety of secondary metabolites with potent biological activities [14, 15]. The genus Streptomyces has considerably helped humanity because of its capacity to produce a wide range of bioactive compounds that endow its distinct biological capabilities [16, 17]. Currently, more than 7,000 bioactive substances from Streptomyces have been shown to have important clinical applications, including metabolites with antibacterial, antioxidant, antitumor, anticancer, antifungal, and immunosuppressive characteristics [18,19,20].

Streptomyces produce variety of beneficial compounds, such as enzymes, pigments, and ssubstances with antibacterial, anticancer, antioxidant, immunosuppressive, and other significant bioactivities [1, 21]. Streptomyces bioprospecting has so far resulted in the discovery of antifungals like nystatin, anticancer medications like doxorubicin [22], antibiotics like streptomycin and erythromycin [23], tetracycline, chloramphenicol, neomycin, nystatin and amphoter [24]. According to studies [25] the marine Streptomyces species 2-allyoxyphenol and streptopyrolidine have antioxidant properties.

In consideration of this, the actimomycetes were isolated from Manakudy mangroves, the potent strain was selected based on the antibacterial activity. The selected strain was identified by 16S rRNA sequencing and the media for the secondary metabolites were optimized by one-variable at a time (OVAT) and Response Surface Methodology-Central Composite Design (RSM-CCD). The minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), antioxidant activities and anti-inflammatory activities of the extracted secondary metabolites were determined. Finally, the secondary metabolites were characterized by Fourier Transform Infrared Spectroscopy (FT-IR), Gas Chromatography and Mass Spectrometer (GC–MS), X-ray Diffraction (XRD), Thermo Gravimetric (TG) analysis and Atomic Force Microscopy (AFM) analysis.

Results

Isolation of mangrove actinomycetes

In the present study totally twenty five actinomycetes isolates were isolated from Manakudy mangrove sediment. Among the 25 strains, 10 different morphologically coloured actinomycetes strains were selected for further processing (Fig. 1).

Fig. 1

Morphologically different coloured actinomycetes (CMSTAAHL-1 to CMSTAAHL-10) isolated from Manakudy mangrove sediment

Primary screening of isolated active strains

The initial screening of actinomycetes showed a significant result against the tested pathogenic bacteria. The outcome made it clear that all the pathogens were vulnerable to the isolated actinomycetes strains. The strain CMSTAAHL-4 showed very good activity against K. pneumoniae S. typhi, E. coli and E. faecalis. It revealed maximum activity against S. aureus thus selected for further studies (Table 1).

Table 1 Primary screening of the isolated actinomycetes against bacterial pathogens

Molecular characterization of selected strain CMSTAAHL-4

The actinomycetes have been identified to the genus level using the 16S rRNA gene. Although, further research such as whole genome sequencing and multilocus sequence analysis (MLSA) is needed to distinguish between the different species. Based on its cultural and molecular traits, which are frequently utilized for the identification of actinomycetes, the strain CMSTAAHL-4 was identified as a Streptomyces species at the genus level (Supplementary Fig. 1).

Optimization of media components and culture conditions (OVAT) secondary metabolites production

The results indicated that Streptomyces sp CMSTAAHL-4 showed maximum secondary metabolite production at starch (1%)—carbon source, yeast extract (1%)—nitrogen source, NH₄Cl (0.1%)—mineral sources and NaCl 5% at pH 7 for 9 days of incubation at 28 °C (Supplementary Figs. 2a and b).

Optimization of production media for secondary metabolites production through RSM-CCD

RSM proved to be a potent technique for the optimization of important media components. A popular statistical design for optimizing media components through a limited number of experiments is the central composite design (CCD).

The response obtained from experiments of central composite design (Table 2) were calculated with second order polynomial multiple regression equation.

Table 2 Experiment design and results of optimization of nutrient media for the production of secondary metabolites by Response Surface Methodology-Central Composite Design (RSM-CCD)

$$\mathrm{Antibacterial}\;\mathrm{activity}\;(\mathrm Y)\;=\;0.054+-2.170\mathrm A+1.140\mathrm B+0.883\mathrm C+-15.68\mathrm D+0.883\mathrm A^2+-0.277\mathrm B^2+-0.2242\mathrm C^2+90.3\mathrm D^2+1.340\mathrm{AB}+0.3900\mathrm{AC}+16.20\mathrm{AD}+0.1000\mathrm{BC}+-6.80\mathrm{BD}+0.400\mathrm{CD}$$

Where Y is the response (secondary metabolites) and A, B, C and D were the coded values of the independent factors, such as starch, yeast extract, NaCl and NH₄Cl respectively. From the Table 2, it was found that no much difference was revealed between the observed and predicted values.

In this investigation, the coefficient determination R² = 97.83% showed about 2.17% of variables were not accounted by the model. The model was also highly significant, as seen by the adjusted determination coefficient R² = 95.94% (Table 3).

Table 3 Analysis of Variance (ANOVA) table for the optimization of nutrient media against S. aureus response surface quadratic model

Data from experiments, as indicated in Table 3 were subjected to an Analysis of Variance (ANOVA). The proposed model for this investigation has a Fischer test value of 51.62 and P value of 0.000, which was considered as significant. The P values signify the importance of the coefficients is crucial for comprehending the pattern of how the variables interact with one another.

According to the significance of corresponding p-values, all of the linear term regression coefficients had a significant impact on the generation of secondary metabolites (Starch = 0, yeast extract = 0.001, NaCl = 0, NH₄Cl = 0). In the square interactions, starch², NaCl², and NH₄Cl² showed their greater effect during the activity of the secondary metabolites production. In case of two-way interactions, starch*yeast, starch*NaCl, starch*NH₄Cl, yeast*NH₄Cl played an important role in secondary metabolites production. The reliability of the model in this investigation was demonstrated by the p-value for the lack of fit being greater than 0.05, which indicated a substantial lack of fit.

Supplementary Table 1 showed that the co-efficient, standard error co-efficient, t-value, and p-value of each component from the outcome of an antibiotic assay at a 95% confidence level based on their effects. From the Table 4, it was verified that the secondary metabolites production of Streptomyces sp CMSTAAHL 4 against S. aureus, the linear factors such as starch, yeast extract, NaCl and NH₄Cl showed greater effect. Quadric coefficients such as starch*starch, NaCl*NaCl, NH₄Cl*NH₄Cl, starch*yeast extract, starch*NaCl, starch*NH₄Cl and yeast extract*NH₄Cl had greater influence on secondary metabolites production. Their p values were less than 0.05 which indicated their significant contribution for secondary metabolites production than those of other components. Supplementary Fig. 3 which showed that stronger effects were shown in the upper portion of the graph and progressed down to the bottom section. This figure directly showed that the most important factors for secondary metabolites production against S. aureus were starch*NH₄Cl (A,D), starch*yeast extract (A,B), NaCl*NaCl (C,C), starch* starch (A,A), starch (A), starch*NaCl (A,C), NH₄Cl*NH₄Cl (D,D), NH₄Cl (D), NaCl (C), yeast extract*NH₄Cl (B,D), yeast extract (B) yeast extract*yeast extract (B,B).

Table 4 Minimum Inhibitory Concentration (MIC) of secondary metabolites of Streptomyces sp. CMSTAAHL-4 against bacterial pathogens (S. aureus, K. pneumoniae, P. aeruginosa, E. faecalis and S. typhi)

The combination of input variable settings that best optimize a single response for secondary metabolites production was expressed by a single desire index, D, was found using the response optimization approach. The function rose linearly toward the intended target values to optimize secondary metabolites production when D was close to 1, and that was showed in the Supplementary Fig. 4. With a predicted secondary metabolites production against S. aureus in Streptomyces sp. CMSTAAHL-4, the optimal values of starch, yeast extract, NaCl, and NH₄Cl were calculated to be 1.250%, 0.250%, 3.1717%, and 0.0750% respectively.

Antimicrobial activity of the crude secondary metabolites

The ethyl acetate was found to be the most efficient solvent for the extraction of antibacterial secondary metabolites. When Streptomyces sp CMSTAAHL-4 was evaporated with ethyl acetate, a semi-solid residue with a dark yellow colour was produced. A maximum activity with 2.5 cm zone of inhibition was achieved when the antagonistic activity of ethyl acetate extract was tested against the human pathogen, S. aureus. (Fig. 2).

Fig. 2

Antimicrobial activity of ethyl acetate extract of secondary metabolites of selected Streptomyces sp. against S. aureus

Minimum inhibition concentration (MIC) and Minimum Bactericidal Concentration (MBC) of secondary metabolites

Determination of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) The ethyl acetate crude extract of Streptomyces sp CMSTAAHL-4showed maximum activity against S. aureus of 60 μg/ mL, when compared to ethyl acetate (25 mg/ml). It also showed the MIC and MBC values of 10 μg/ mL against K. pneumoniae, 50 μg/ mL against E. coli, 40 μg/ mL against P. aeruginosa, 50 μg/ mL against S. typhi and E. faecalis respectively (Table 4).

Column chromatography

The solvent ratio of dichloromethane: ethyl acetate (2:8) resulted in better separation of compounds through silica gel column chromatography. Totally, 23 fractions were collected, and all the fractions were screened for antimicrobial activity against selected bacterial pathogens. Only one fraction, which was yellow in colour and showed maximum antibacterial activity.

Thin layer chromatography

The fraction had an R_f value of 0.812, and when it was compared to standards, it was discovered that the standard R_f value for quercetin was 0.816. The result confirmed the flavonoid compound's identification from the fraction that was closest to the quercetin standard R_f value (Fig. 3).

Fig. 3

TLC chromatograms for secondary metabolites using mobile phase (dichloromethane: ethyl acetate (2:8)

In vitro antioxidant activities of the secondary metabolites

In the present study, the secondary metabolite showed maximum inhibition of 86.98% of DPPH, 79.05% of hydroxyl radical scavenging assay, 81.34% of ABTS radical scavenging assay, 84.04% of hydrogen peroxide radical scavenging assay, 87.08% nitric oxide scavenging assay and 49.86 µg of total antioxidant when compared to that of standard ascorbic acid. The lowest IC50 value denoted the greatest antioxidant activity (Fig. 4).

Fig. 4

Antioxidant activities of the secondary metabolites of Streptomyces sp. CMSTAAHL-4. DPPH radical scavenging activity (a); Hydroxyl radical scavenging activity (b); ABTS radical scavenging activity (c); Hydrogen peroxide scavenging activity (d); Nitric oxide scavenging activity (e) and total antioxidant capacity (TAC) (f)

In vitro anti-inflammatory activities of the secondary metabolites

The crude secondary metabolites exhibited concentration-dependent anti-inflammatory properties, and the protection percentage increased as the crude secondary metabolites concentration increased. At 500 µg/ml concentration, the RBC membrane stabilizing activity (82.67 ± 0.95%) and protein denaturation activity (85.23 ± 0.86) were maximum for the extract when compared with the standard drug, diclofenac (Fig. 5).

Fig. 5

Anti-inflammatory activities of the secondary metabolites of Streptomyces sp. CMSTAAHL-4. RBC membrane stabilizing activity (a) and % of Protein denaturation activity (b)

Characterization of the secondary metabolites FT-IR analysis

The infrared spectrum for organic compounds specific functional groups and bond types are predominantly responsible for absorptions the peak positioned at 3436.45 cm⁻¹ was attributed to the presence of O–H free hydroxyl proved the presence of alcohols and phenols. The peak at 2984.50 cm⁻¹ attributed to C-H stretch. The alkenes' -C = C stretch is indicated by the peak at 1637.16 cm⁻¹. The aromatics' C–C stretch (in-ring) stretching was indicated by the signal at 1415.69 cm⁻¹. The C-N stretch out of aromatic amines peak located at 1269.92 cm⁻¹. The plane aliphatic amines' C-N stretch is represented by the peaks at 1078.55 cm⁻¹. These functional groups indicated the presence of alcohols, carboxylic acids, esters, and ethers (Fig. 6; Table 5).

Fig. 6

Fourier transform infrared spectroscopy (FT-IR) spectrum of the secondary metabolites of Streptomyces sp. CMSTAAHL-4

Table 5 Functional groups detected in the secondary metabolites by Fourier transform infrared spectroscopy (FT-IR) analysis

GC–MS profile

The chemical profile of crude secondary metabolites chemical profile was revealed by the spectral analysis performed using a gas chromatography-mass spectrometer (Fig. 7; Table 6). According to the findings, the extract contains significant amounts of oleic acid, pentadecanic acid, eicosanoic acid and saturated fatty acids (n-hexadecanoic acid and octadecanoic acid).

Fig. 7

Gas Chromatograph and Mass Spectrometer (GC–MS) chromatogram of the crude secondary metabolites of Streptomyces sp. CMSTAAHL-4

Table 6 Biological activity compounds identified in the extracted secondary metabolites by GC–MS analysis

LC–MS spectral analysis of the extracted secondary metabolite

LC–MS was used to screen the isolated chemicals present in the isolated secondary metabolites. Four compounds, namely 4-Hydroxy-2',3,4',6'-tetramethoxychalcone Rt 3.5–3.9 (min), Chrysoeriol Rt 4.7–4.9 (min), 6-Ethoxy-3(4'-hydroxyphenyl)−4-methylcoumarin Rt 54.8–55.2 (min), and 2', 6-Dihydroxyflavone 58.8 (min), were identified (Table 7).

Table 7 LC–MS screening and identification of bioactive compounds in isolated secondary metabolites

XRD analysis

The discrete structures of the secondary metabolites are described using the XRD studies. Based on particular diffraction peaks, XRD examination at 2 h in the current investigation revealed different peaks (Fig. 8). The XRD profile of secondary metabolites showed two distinct, intense diffraction peaks: one at 31.524˚ with a d-spacing of 0.2835 nm and another at 45.246˚ with a d-spacing of 0.2002 nm. A d-spacing value of 0.1634 nm allowed for the observation of the distinctive diffraction peak at about 56.229˚.

Fig. 8

X-ray diffraction (XRD) analysis of the crude secondary metabolites of Streptomyces sp. CMSTAAHL-4

Thermogravimetric analysis

The presence of moisture caused a weight loss of the secondary metabolites of up to 20% between 0 ⁰C and 100 ⁰C. Subsequently, from 100 ⁰C to 200 ⁰C, the first stage of deterioration started slowly (37%). The greatest loss (12.22%) happens between 200⁰C and 400˚C. The final weight reduction between 400⁰C and 800˚C was 15.4%. An endothermic reaction was visible during the burning of the samples (Fig. 9). Energy requirements for the first two stages were 67.6 and 99.9 Vs/mg, respectively.

Fig. 9

Thermogravimetric (TG) analysis of the crude secondary metabolites of Streptomyces sp. CMSTAAHL-4

Atomic Force Microscopy (AFM) analysis

Atomic Force Microscopy (AFM) was utilized to examine the surface morphology and topographical features of the secondary metabolites produced by Streptomyces sp. CMSTAAHL-4. The AFM images (Fig. 10) present a detailed 3D topographical map, revealing structures with heights predominantly ranging from 50 to 200 nm, with some aggregates reaching up to 200 nm. This variation in height suggests a complex aggregation pattern of the metabolites. Such aggregations may play a role in enhancing surface interactions, improving stability and solubility, and potentially facilitating controlled release, which could be relevant to their biological activity. However, further studies are necessary to directly correlate these structural characteristics with specific bioactive properties.

Fig. 10

Atomic Force Microscopy (AFM) imaging of the crude secondary metabolites of Streptomyces sp. CMSTAAHL-4

Discussion

In the current work, Streptomyces sp. was grown on starch casein Agar (SCA) media. Similar to this, other researchers have employed starch casein agar as the growth media for the growth of different strains of Streptomyces sp. [26]. Based on the earlier report Streptomyces can produce secondary metabolites, particularly antibiotics that are significant from a pharmacological perspective. It was discovered that Streptomyces sp., obtained from mangroves, has produced novel compounds with antibacterial properties [27].

The cross streak method was used for primary screening of the antagonistic activity of actinomycetes, which was in agreement with the earlier reports of [28]. Additionally, it rarely assesses the impact of multiple factors and their interactions at once, which is a drawback once the interactions between parameters are large [29]. It was found that in Streptomyces lincolnensis NRRL ISP-5355 [30]; Streptomyces sp. 1–14 [31]; Streptomyces youssoufiensis SNSAA03 [32] maximum secondary metabolites production was obtained in fermentation media supplemented with starch. In Streptomyces labedae VSM-6 [33]; Streptomyces rochei NAM-19 [34] and Streptomyces sp.SD1 [35], the different concentration of yeast extract as nitrogen source was optimized for maximizing the production of secondary metabolites. In Streptomyces rimosus NRRL 2455 strain [36] and VITBKA3 strain [37], it was found that culture condition at 28 °C lead to maximum metabolite production. In the isolate Streptomyces A41 maximum secondary metabolites production was achieved at 5% NaCl concentration in the culture media [38]. At pH 7, the generation of secondary metabolites rose, while it considerably decreased at lower and higher pH levels [39].

Several Streptomyces species have improved secondary production using the RSM technique with CCD were Micromonospora Y15 [40]; Streptomyces sp. JRG-04 [41]; Streptomyces lincolnensis [30]; Streptomyce srimosus MTCC 10792 [39]; Streptomyces kanasenisi ZX01 [42]; Streptomyces diastatochromogenes KX852460 [43]; Streptomyces sp. SBRK1 [44]; Streptomyces sp. RDA1496 [45] and Streptomyces alfalfae XN-04 [46].

Typically, a regression model with an R² value of more than 0.9 and close to 1.00 was regarded as having a very high correlation [45] The result in the coefficient determination R² = 97.83% and adjusted determination coefficient R² = 95.94% similar result was observed when P is less than 0.05 the model terms are considered as significant [46, 47]. The model's insignificant lack of fit value of 0.473 revealed that the observed experimental data were well fit [41, 46].

There are several reports, in which ethyl acetate was mostly used as an extraction solvent to isolate the secondary metabolites from Streptomyces [48, 49]. Similar reports about the antagonistic activity of secondary metabolites production ethyl acetate was more effective against bacterial pathogens compare with other pathogens by [50, 51]. Similar to this, the MIC was established using several bacterial pathogens by [52].

Law et al. [53] described potential Streptomyces from mangrove soils produce biologically active metabolites with antioxidant activities. The ethyl acetate extract of Streptomyces sp NMF6 [54], Streptomyces sp. strain GLD25 [55], exhibited strong antioxidant activities and confirmed by DPPH and radical scavenging assay. The antioxidant activities of the ethyl acetate extract of Streptomyces sp.S2A was analyzed through DPPH radical scavenging activity, metal chelating activity and ABTS radical scavenging activity [56].

Baskaran et al. [57] performed in vitro anti-inflammatory screening methods, namely protein denaturation inhibition and membrane stabilization method in the actinobacterial extracts of secondary metabolites. In the present study, NH stretch functional group represents the carboxylic acids [58], C–C stretch(C–C in ring aromatic) represents the functional group for aromatics [59]. The peak positioned at 1470 −1450 cm⁻¹ was assigned to CH₃. The peaks at 975 −965 cm⁻¹ represented the = C-H out of plane (cis RCH = CHR Alkenes) and the presence of C = O stretching of acid anhydrides [60] in the presence of signal at 1750 −1740 cm⁻¹ confirmed the presence of carbonyl group (C = O) of both aldehyde and ketone, confirming the stretching of carbonyl groups, fatty acid and polyketide derivatives [54].The presence of signal at 3640 −3610 cm⁻¹ confirmed the presence of O–H free hydroxyl alcohols and carboxylic acids [32]. The signal at 680—500 cm⁻¹ indicated C–Br stretch stretching of Alkyl halides [48].

The presence of N-hexadecanoic acid (palmitic acid), octadecanoic acid (stearic acid), pentadecanoic acid, and oleic acid, which were determined by GC–MS analysis, have the usual properties of fatty acids with a carboxyl group (-COOH) and a methyl group (-CH3) in the two ends of an aliphatic hydrocarbon chain [61] In ethnic and herbal medicines, fatty acids have been found to be the primary components of the antimalarial, antimycobacterial, and antifungal activities [62, 63]. Similar peaks of oleic acid, pentadecanoic acid, and octadecanoic acid were seen in GC–MS analysis was reported by [64], in Streptomyces sp.

Because of their broad pharmacological effects and extraordinarily low toxicity, flavonoids and isoflavonoids are well-known natural chemicals that have drawn attention from researchers trying to develop novel medications [65]. Hexadecanoic acid synthesized by Streptomyces cavouresis KU-V39 had antioxidant and anticancer properties [66]. The antioxidant properties of hexadecanoic acid and hexadecanoic acid methyl ester from Nocardiopsis dassonvillei MAD08 and actinomycetes SDJ10 were discovered by GC–MS analysis [67]. The ethyl acetate extract of Streptomyces sp. 1S1 and Actinomycetes SDJ10 displayed antibacterial activity in the octadecenoic acid (oleic acid) and hexadecanoic acid (palmitic acid) forms [68]. Actinomycetes AC-7's octadeconic acid possessed antifungal properties [34]. It is therefore expected that the fatty acids discovered in this investigation may also be accountable for the antibacterial and antioxidant properties. The secondary metabolites were high amorphous in nature, with 64.5% amorphousness. Because of the abundance of long side chains in the polymer, the presence of various units, and their uneven arrangement, the microbial EPSs frequently have low levels of crystallinity [69] But according to [70], the FP from the Actinomycetes species that were isolated from the soil of Rijal Almaa, Saudi Arabia, is crystalline in nature. There aren't many studies on XRD analysis of Actinomycetes' secondary metabolites, although there are more publications on polymers [71, 72].

As result shown Thermogravimetric Differential Thermal analysis analysis the initial weight loss in FP was noticed at 277.29 ⁰C, and the analysis demonstrated the heat stability of FP of actinomycetes isolated from the soil of Rijal Almaa, Saudi Arabia [70]. TG analyses of secondary metabolites have received very few investigations, although actinomycete-derived biodegradable polymers have received more reports of TGA [73]. The AFM analysis of secondary metabolites revealed surface features similar to those observed in other studies involving bioactive compounds. The protrusions seen in the samples might indicate molecular interactions that could be linked for their biological activity. [74, 75]. Despite having different origins, the extract was similar in that they contain macromolecular structures that interact with their surroundings and might cause the development of particular surface features like protrusions or height variations. Stability, solubility, and biocompatibility are important properties for biotechnological and pharmaceutical applications, and the surface topography and features might affect these properties [76].

Conclusion

Streptomyces sp.CMSTHHAL-4 isolated from the Manakudy, Kanyakumari District mangrove sediments was focused for the current study, which also emphasized the extraction of secondary metabolites with bioactive potential. Both Gram-positive and Gram-negative bacteria were resistant to the antibacterial actions of the selected strain. The extracted secondary metabolites showed excellent anti-inflammatory (RBC membrane stabilizing activity and protein denaturation activity) and antioxidant (DPPH, hydroxyradical scavenging assay, ABTS radical scavenging assay, hydrogen peroxide scavenging assay, and total antioxidant) properties. The secondary metabolites were examined using the FT-IR and GC–MS analysis. Moreover, the extracted compounds exhibited significant antibacterial and antioxidant properties. It is imperative to conduct further research on the separation, purification, and structure elucidation to understand the bioactive compounds from the secondary metabolites.

Materials and methods

Isolation of mangrove actinomycetes

Mangrove sediments were collected from Manakudy estuary (Latitude: 8°088'N, Longitude: 77°486'E), Kanyakumari District, Tamil Nadu. The samples were collected in a ziplock bag, kept briefly on ice, and then transported straightaway to the lab for temporary storage at 4 ⁰C until the further analysis [77]. The mangrove actinomycetes were isolated by serially diluting the sediment samples and plated on starch casein agar and incubated at 30 ⁰C for 7 days. The morphologically distinct colonies were selected; the pure culture was streaked on the starch casein agar for further analysis [28].

Primary screening of isolated actinomycetes

The preliminary assessment of the inhibitory activity of the isolated strains against clinical bacterial pathogens were carried out by duplicate perpendicular streak (or cross streak) method. The actinomycetes strains were allowed to grow in a straight line at the centre of Muller-Hinton agar medium plates and incubated to grow for three days at 30 ⁰C. The clinical bacterial pathogens such as Staphylococcus aureus, Klebsiella pneumoniae, Escherichia coli, Pseudomonas aeruginosa, Salmonella typhi and Enterococcus faecalis were streaked perpendicular to the already grown actinomycetes strains. The antibacterial potential of actinomycetes was determined following 24-h incubation at 37 ⁰C [41].

Molecular identification of the selected actinomycetes

By employing a non-enzymatic technique, genomic DNA was isolated [78]. Polymerase Chain Reaction (PCR) was then used to amplify the 16S rDNA gene of the isolated active strain using universal primers p27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-TACGGCTACC TTGTTACGACTT-3′). Purification and sequencing of the PCR product were carried out, and the similarity was assessed using the BLAST tool (www.ncbi.nlm.nih.gov/blst). Utilizing Mega-X software, 10.1.7, multiple sequence alignment and phylogenetic tree construction was carried out [79, 80].

Secondary metabolites production preparation of inoculum and culture condition

A seed culture (50 ml) was prepared using starch casein media (SCM). The components of the media (g / 100 ml) were: Starch—0.5, casein—0.5, peptone—0.5, NaCl—0.5, (NH₄)₂SO₄—0.2, MgSO₄ × 7H₂O—0.1, K₂HPO₄—0.1 and CaCO₃—0.2. Then, the seed culture was inoculated in 1lL of SCM, supplemented with 0.1 ml of trace element salt solution (stock solution (g/L water)—0.1 ZnSO₄, 0.3 H₃BO₃, 0.2 CoCl₂, 0.03 MnCl₂, 0.03 Na₂MO₄, 0.02 NiCl₂, 0.01 CuCl₂, pH 7) and agitated at 180 rpm for 7 days at 30 °C [41].

Optimization of media components and culture conditions for secondary metabolites production- one-variable at a time OVAT

The sources of chemical factors such as carbon sources (1%)—starch, glucose, maltose, fructose, and lactose; nitrogen sources (1%)—yeast extract, peptone, tryptone, beef extract and urea; minerals source (0.1%)—Na₂HPO₄, KH₂PO₄, NH₄Cl, MgSO₄, FeSO₄, CaCO_3; various concentrations of NaCl (1, 2, 3, 4 and 5%) in the basal media were optimized for secondary metabolites production. The effect of cultural conditions like incubation temperatures (15, 20, 30, 35, 40, and 45 °C), pH (3, 5, 7, 9 and 11) and incubation period (3, 5, 7, 9, 11 days) in the basal media [81,82,83] for the secondary metabolites production was optimized. The secondary metabolites were extracted from the culture supernatant using ethyl acetate in the ratio 1:1 [31]. The antibacterial activity of the crude secondary metabolites against human pathogens such as S. aureus, K. pneumoniae, E. coli, P. aeruginosa, S. typhi and E. faecalis were studied by the agar well-diffusion method [84].

Media optimization of secondary metabolites production by Response Surface Methodology-Central Composite Design (RSM-CCD)

The Response Surface Methodology (RSM) was employed in the current study to identify the ideal media components for higher production of secondary metabolites. Using MINITAB 19 software with four factors and five levels, the combinations of 31 experiments were generated. The four components were starch (carbon source), yeast extract (nitrogen source), NaCl, and NH₄Cl (mineral source). A second order polynomial regression model equation was created using the specific coded values for the generation of secondary metabolites in accordance with the Response Surface Methodology-Central Composite Design (RSM-CCD) design [85].

$$\mathrm Y\;=\;\upbeta_0\;+\;\upbeta_1\mathrm A\;+\;\upbeta_2\mathrm B\;+\;\upbeta_3\mathrm C+\upbeta_4\mathrm D\;+\;\upbeta_{11}{\mathrm A}_2\;+\;\upbeta_{22}{\mathrm B}_2\;+\;\upbeta_{33}{\mathrm C}_2\;+\;\upbeta_{44}{\mathrm D}_2\;+\;\upbeta_{12}\mathrm{AB}\;+\;\upbeta_{13}\mathrm{AC}\;+\;\upbeta_{14}\mathrm{AD}+\;\upbeta_{23}\mathrm{BC}+\;\upbeta_{24}\mathrm{BD}\;+\;\upbeta_{34}\mathrm{CD}$$

where, Y is the predicted response, β₀ is the intercept, A- starch, B- yeast extract, C- NaCl and D- NH₄Cl represents the specific coded values. β₁, β₂, β₃, and β₄ represents the linear effect; β₁₁, β₂₂,β₃₃, and β₄₄ represents the squared effect, A₂, B₂, C₂, D₂, AB, AC, AD, BC, BD and CD represents the interaction effect. To assess the possible impact of multiple independent factors on the synthesis of secondary metabolites, the experimental run matrix was expanded to include five levels of factors.

Extraction of secondary metabolites selected Streptomyces sp

To extract of secondary metabolites, the selected strain was grown in 1000 mL Erlenmeyer flask containing 600 mL of starch casein medium. The medium was incubated 28 ^◦C for seven days at 150 rpm. The culture supernatant was collected by centrifugation for 15 min at 4 ^◦C. The crude culture extract: ethyl acetate (1:1 ratio) were vigorously shaken for 20 min and kept stationary from 15 to 30 min until separation of aqueous and organic phases. Organic phases were collected and concentrated in a rotary evaporator at 40 °C and 80 rpm [86]. Determination of Minimal Inhibitory Concentration (MIC).

Minimum Inhibitory Concentration (MIC) of the microbial strains in a flat bottom 96-well transparent microtiter plate. (S. aureus, K. pneumoniae, E. coli, P. aeruginosa, S. typhi and E. faecalis) [87, 88] 2 ml of sterile Mueller Hinton Agar broth with different concentrations of the extract (10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 μg/ mL) were taken in test tubes and to this 0.1 ml each of test pathogens were added. Similar test tube sets containing ethyl acetate at the concentration of 25 μg/ml were used as control. Then the test tubes were incubated at 37 °C for 24 h. For the detection of MIC, the plates were read with UV spectrophotometer at 570 nm and percentage of inhibition was calculated by using the following formula [89].

$$\left(\mathrm{Control}\;{\mathrm{OD}}_{570\;\mathrm{nm}}\;-\;\mathrm{Test}\;{\mathrm{OD}}_{570\;\mathrm{nm}}\right)\;/\;\mathrm{Control}\;{\mathrm{OD}}_{570\;\mathrm{nm}}\;\times\;100$$

Determination of MBC

The bactericidal potency of the crude secondary metabolites was evaluated using the Minimal Bactericidal Concentration (MBC) test. In order to conduct the MIC experiment, a loopful of culture was removed from the 96-well microtiter plate, streaked onto Petri dishes, and cultured in the static incubator. After the Petri dishes had been cultured for 24 h, the concentrations and their bactericidal efficacies were evaluated. [90].

Column chromatography

Purification of ethyl acetate extract was performed by column chromatography and the size of the column was 25 × 2.5 cm. The column was equilibrated with chloroform. 5 ml of the sample was passed through the column keeping the flow rate at 0.2 ml/min with gradient solvent system consisting of suitable solvent (dichloromethane: ethyl acetate 2:8). Finally, the column was washed with methanol. Each fraction was collected for further analysis [91].

Thin Layer Chromatography

The TLC plates were dipped into a suitable solvent system (mobile phases: dichloromethane: ethyl acetate 2:8) and the plates were then placed in a container with enough solvent in a well-covered tank. The presences of secondary metabolites in the extracts were detected by TLC. After drying, the R_f value of the spot separated on the TLC plate was determined. Then the plates were viewed under UV light.

The retardation factor, R_f value, is used to characterize and compared the components of various samples.

$${\mathrm R}_{\mathrm f}\;\mathrm{value}\;=\;\frac{\mathrm{Distance}\;\mathrm{from}\;\mathrm{origin}\;\mathrm{to}\;\mathrm{component}\;\mathrm{spot}}{\mathrm{Distance}\;\mathrm{from}\;\mathrm{origin}\;\mathrm{to}\;\mathrm{solvent}\;\mathrm{front}}$$

In vitro antioxidant activities of the secondary metabolites

DPPH radical scavenging assay

DPPH radical-scavenging activity was determined by the method of Kasangana et al. (2015) [92]. A 2 ml aliquot of DPPH methanol solution (25 µg/ml) was added to 0.5 ml of secondary metabolites at 20, 40, 60, 80, and 100 (µg/mL). The mixture was shaken vigorously and allowed to stand at room temperature in the dark for30 min. Then the absorbance was measured at 517 nm in a spectrophotometer. Lower absorbance of the reaction mixture indicated higher free-radical scavenging activity.

$$\mathrm{Radical}\;\mathrm{scavenging}\;\mathrm{activity}\;\left(\%\right)\;=\;\;\;100\;-\;\left[{\mathrm A}_{\mathrm c}-\;{\mathrm A}_{\mathrm s}/\;{\mathrm A}_{\mathrm c}\right]\;\times100$$

where A_C= control is the absorbance and A_S= sample is the absorbance of reaction mixture (in the presence of secondary metabolites).

$$\mathrm{Percentage}\;\mathrm{of}\;\mathrm{Antioxidant}\;\mathrm{activity}\;=\;\left\{\left(\mathrm{absorbance}\;\mathrm{of}\;\mathrm{blank}\right)\;-\;\left(\mathrm{absorbance}\;\mathrm{of}\;\mathrm{test}\right)\;/\;\left(\mathrm{absorbance}\;\mathrm{of}\;\mathrm{blank}\right)\right\}\;\times\;100$$

Hydroxyl radical scavenging activity assay

The scavenging activity for hydroxyl radicals was measured with the Fenton reaction by the method of Wenli et al. (2004) [93]. The hydrogen peroxide was added to the reaction mixture containing 60 μl of 1.0 mM FeCl₃, 90 μl of 1 mM 1,10-phenanthroline, phosphate buffer (0.2 M; pH 7.8), 2.4 ml, 150 µl of 0.17 mM H₂O₂, and 1.5 ml of secondary metabolites at various concentrations 20, 40, 60, 80, and 100 (µg/mL). The absorbance was read at 560 nm after 5 min using a spectrophotometer.

The % inhibition hydroxyl radical scavenging activity was determined by the following equation:

$$\mathrm{Inhibition}\;\left(\%\right)\;=\;\frac{\left({\mathrm A}_0\;-\;{\mathrm A}_1\right)}{{\mathrm A}_0}\times100$$

where A₀ = absorbance of control and A₁ = absorbance of the secondary metabolites.

ABTS scavenging assay

The antioxidant effect of the secondary metabolites was studied using the ABTS (2,2'-azino-bis-3-ethyl benzthiazoline-6-sulphonic acid) radical cation decolourization assay according to the method of Re et al. (1999) [94]. ABTS radical cations (ABTS⁺) were produced by reacting ABTS solution (7 mM) with 2.45 mM potassium persulphate. The mixture was incubated at room temperature in the dark for 12 to 16 h to yield a dark-coloured solution containing ABTS^•+ radicals and diluted for an initial absorbance of about 0.700 (± 0.02) at 734 nm. Aliquots (10 μl) of the different concentrations 20, 40, 60, 80, and 100 (µg/mL) of extract were added to 1 ml of ABTS solution. The absorbance was read at 734 nm after 6 min in a spectrophotometer. L-Ascorbic acid was used as the standard. Appropriate solvent blanks were run in each assay. All determinations were carried out in triplicate, and the percent inhibition was calculated using the formula.

$$\mathrm{Inhibition}\;(\%)\;=\;\;\;\frac{\left(\mathrm{Control}\;-\;\mathrm{Test}\right)}{\mathrm{Control}}\times100$$

Total antioxidant capacity

The antioxidant activity of secondary metabolites was evaluated by the phosphomolybdenum method according to the procedure of Prieto et al. (1999) [95]. The assay is based on the fact that the sample changes Mo (VI) to Mo (V) and then forms a green phosphate/Mo (V) complex when the pH is low. Then, 0.3 ml of the metabolites was combined with 3 ml of reagent solution (0.6 M sulfuric acid, 28 mM sodium phosphate, and 4 mM ammonium molybdate). The tubes containing the reaction solution were incubated at 95 °C for 90 min. Then the absorbance of the solution was measured at 695 nm using a spectrophotometer against a blank after cooling to room temperature. A typical blank solution contained 1 ml of reagent solution and the appropriate volume of the same solvent used for the sample. The antioxidant activity is expressed as the number of equivalents of ascorbic acid.

Hydrogen peroxide scavenging activity

The hydrogen peroxide scavenging activity of the secondary metabolites was estimated by Zhao et al. (2016) [96]. 1.0 ml of 0.1 mM H₂O₂ and 1.0 ml of 20, 40, 60, 80, and 100 (µg/mL). of secondary metabolites were mixed, followed by adding 2 drops of 3% ammonium molybdate, 10 ml of 2 M H₂SO₄, and 7.0 ml of 1.8 M KI. The mixed solution was titrated with 5.09 mM NaS₂O₃ until the yellow colour was disappeared. The percentage of scavenging hydrogen peroxide was calculated as:

$$\%\;\mathrm{inhibition}\;=\;\left\{\left({\mathrm V}_0\;-\;{\mathrm V}_{\mathrm i}\right)\;/\;{\mathrm V}_0\right\}\;\times\;100$$

where V₀—volume of NaS₂O₃ solution used to titrate the control sample in the presence of hydrogen peroxide (without extract), V₁—volume of NaS₂O₃ solution used in the presence of the secondary metabolites.

Nitric oxide scavenging activity

The determination of Nitric oxide radical scavenging was performed by Saravana Kumar et al. (2014) [97]. Sodium nitroprusside generates nitric oxide in aqueous solution and interacts with O₂ to form nitrite ions, which can be measured by the Griess-Illosvoy reaction. About 2 ml of 10 mM sodium nitroprusside in 0.5 ml of phosphate buffered saline (pH 7.4) was mixed with 0.5 ml of secondary metabolites at 20, 40, 60, 80, and 100 (µg/mL)., and the mixture was incubated at 25°C for 150 min. From the incubated mixture, 0.5 ml of the sample was taken out and added to 1.0 ml of sulfanilic acid reagent (33% in 20% glacial acetic acid) and incubated at room temperature for 5 min. Finally, 1.0 ml of naphthylethylenediamine dihydrochloride (0.1% w/v) was mixed and incubated at room temperature for 30 min. The absorbance was read at 540 nm using a spectrophotometer. The percentage of nitric oxide inhibition was determined by the following equation:

$$\mathrm{Inhibition}\;(\%)\;=\;\frac{\left({\mathrm A}_0-{\mathrm A}_1\right)}{{\mathrm A}_0}\times100$$

where A₀ = absorbance of control and A₁ = absorbance of the secondary metabolites.

In vitro anti-inflammatory activities of the secondary metabolites

Protein denaturation assay using egg albumin

Chandra et al. (2012) described a method for testing the in vitro anti-inflammatory activity of secondary metabolites [98].The reaction mixture (5 ml) consisted of 0.2 ml of egg albumin (from a fresh hen’s egg), 2.8 ml of phosphate buffered saline (PBS, pH 6.4), and 2 ml of varying concentrations of secondary metabolites (100, 200, 300, 400, and 500 µg/ml respectively). A similar volume of double-distilled water served as a control. Then the mixtures were incubated at (37 ± 2 °C) in an incubator for 15 min and then heated at 70 °C for 5 min. After cooling, their absorbance was measured at 660 nm by using distilled water as a blank. Diclofenac sodium at the final concentrations (100–500 µg/ml) was used as a reference drug and treated similarly for the determination of absorbance. The percentage inhibition of protein denaturation was calculated by using the following formula:

$$\%\;\mathrm{inhibition}\;=\;100\;\times\;\left({\mathrm V}_{\mathrm t}\;/\;{\mathrm V}_{\mathrm c}\;-\;1\right)$$

where, V_t = absorbance of test sample, V_c = absorbance of control.

The secondary metabolites concentration for 50% inhibition (IC₅₀) was determined by plotting percentage inhibition with respect to control against treatment concentration.

Assay of membrane stabilizing activity

The anti-inflammatory activity of the secondary metabolites was evaluated by the membrane stabilizing activity described by Singh et al. (2013) [99]. 1 ml of phosphate buffer, 2 ml of hypotonic saline, 0.5 ml of secondary metabolites at various concentrations (100, 200, 300, 400, and 500 µg/ml) and 0.5 ml of 10% w/v human red blood cells were added in tube tubes, and all the assay mixtures were incubated at 37 °C for 30 min and centrifuged at 3000 rpm. The supernatant liquid was separated, and the hemoglobin content was estimated by a spectrophotometer at 560 nm. Diclofenac sodium (100 to 500 µg/ml) was used as a reference drug.

The percentage hemolysis was estimated by assuming the hemolysis produced in the control as 100%.

$$\mathrm{Percentage}\;\mathrm{of}\;\mathrm{stabilization}\;=\;\left(\mathrm{Control}\;\mathrm{hemolysis}\;-\;\mathrm{Test}\;\mathrm{hemolysis}\;/\;\mathrm{Control}\;\mathrm{hemolysis}\right)\;\times\;100$$

Characterization of the secondary metabolites by Fourier transform infrared spectroscopy (FT-IR)

The secondary metabolites weighed between 0.1—2.0 g were used for the analysis. The vibration spectrum was recorded as a graphical chart, and the frequency of the spectra was subjected to examination ranged from 400 to 5000 cm.⁻¹ wave number [98]

Gas Chromatograph and Mass Spectrometer (GC–MS) analysis

The Clarus 680 GC was used for the analysis of the secondary metabolites. The 1 μl of secondary metabolites were injected into the instrument and the fragments were detected from 40 to 600 Da. The spectrums of the components were compared with the database of spectrum of known components stored in the GC–MS NIST (2008) library [100].

Liquid Chromatography-Mass Spectrometry (LC–MS) Analysis

The column temperature was set at 40 °C and the injection volume of the samples was 10 μl. The volume of the sample for injection was 1.0 μl. The time range was set to 0.00 min until 70 min and in a mass ranges from m/z of 50 to 1500 [101].

X-ray diffraction (XRD)

Using CuK irradiation, the XRD was carried out using a D2 Phaser (BRUKER). At room temperature, the secondary metabolites were placed on a sample holder, and the diffractometer was run at 40 kV and 10 mA. A diffractogram was taken at 2θ angles ranging from 10˚ to 80˚. Bragg's law was used to determine the d-spacing at the value of θ, and the area beneath the crystalline peaks, normalized in relation to the overall scattering area, served as the basis for calculating the crystallinity index [102].

Thermogravimetric analysis

The secondary metabolites were subjected to TG–DTA analysis using the thermal system (EXSTAR: SIINT 6300). Dried secondary metabolites (around 20 mg) were utilized for the TG–DTA experiment, and thermograms were produced under air flow at a flow rate of 50 mL/min and a heating rate of 10 °C/min, yielding temperatures between 30˚C and 800 °C. Heat flow and weight loss (in mg) were graphed versus temperature [102].

Atomic Force Microscopic analysis

The AFM was carried out in the tapping mode using the AFM-Agilent 7500 AFM/SPM 3D. A charge-coupled device monitor was used to find the cantilever sample. The scanning area in the image was 10 μm × 10 μm, and the scan speed was 0.6 Hz per second. At 10 μm, 5 μm, and 2.5 μm scan widths, sample imaging related to size was carried out [103].

Data analysis

Two way Analysis of Variance (ANOVA) was carried out using KyPlot and the mean compared at 0.001% level. Correlation analysis was also performed to compare relationship between total antioxidant capacity of the secondary metabolites and standard ascorbic acid.