Molecular-Based Identification of Actinomycetes Species That Synthesize Antibacterial Silver Nanoparticles

Bizuye, Abebe; Gedamu, Lashitew; Bii, Christine; Gatebe, Erastus; Maina, Naomi

doi:https://doi.org/10.1155/2020/8816111

International Journal of Microbiology

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Abstract Introduction Materials and Methods Results Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2020 | Article ID 8816111 | https://doi.org/10.1155/2020/8816111

Molecular-Based Identification of Actinomycetes Species That Synthesize Antibacterial Silver Nanoparticles

Abebe Bizuye,^1,2Lashitew Gedamu,³Christine Bii,⁴Erastus Gatebe,⁵and Naomi Maina^2,6

Academic Editor: Diriba Muleta

Received18 Aug 2020

Revised17 Oct 2020

Accepted16 Nov 2020

Published24 Nov 2020

Abstract

Infectious diseases caused by antibiotic-resistant bacteria lead to a considerable increase in human morbidity and mortality globally. This requires to search potential actinomycete isolates from undiscovered habitats as a source of effective bioactive metabolites and to synthesis metabolite-mediated antibacterial silver nanoparticles (AgNPs). The main purpose of the present study was to identify actinomycetes isolated from Thika waste dump soils that produce bioactive metabolites to synthesize antibacterial AgNPs. The synthesis of metabolite-mediated AgNP was confirmed with visual detection and a UV-vis spectrophotometer, whereas the functional groups involved in AgNP synthesis were identified using a FTIR spectrophotometer. The antibacterial activity of the metabolite-mediated AgNPs was tested by a well diffusion assay. Identification of actinomycete isolates involved in the synthesis of antibacterial AgNPs was done based on 16S rRNA gene sequence analysis. The visual detection showed that dark salmon and pale golden color change was observed due to the formation of AgNPs by KDT32 and KGT32 metabolites, respectively. The synthesis was confirmed by a characteristic UV spectra peak at 415.5 nm for KDT32-AgNP and 416 nm for KGT32-AgNP. The FTIR spectra revealed that OH, C=C, and S-S functional groups were involved in the synthesis of KDT32-AgNP, whereas OH, C=C, and C-H were involved in the formation of KGT32-AgNP. The inhibition zone results revealed that KDT32-AgNP showed 22.0 ± 1.4 mm and 19.0 ± 1.4 mm against Escherichia coli and Salmonella typhi, whereas KGT32-AgNP showed 21.5 ± 0.7 mm and 17.0 ± 0.0 mm, respectively. KDT32 and KGT32 isolates were identified as genus Streptomyces and their 16S rRNA gene sequences were deposited in the GenBank database with MH301089 and MH301090 accession numbers, respectively. Due to the bactericidal activity of synthesized AgNPs, KDT32 and KGT32 isolates can be used in biomedical applications.

1. Introduction

Bacteria are one of the common causative agents of infectious diseases that can be treated via antibiotics produced by secondary metabolite producing microorganisms. However, bacterial pathogens acquire resistance to antibacterial agents through misuse and abuse of these chemicals. Acquired resistance to antibacterial can occur either by chromosomal gene mutation or by horizontal gene transfer via transduction, conjugation, and transformation [1]. These decrease the entry of antibacterial and change the antibacterial targets and metabolic inactivation of antibacterial agents [1]. These increase human morbidity and mortality rates [2] with considerable impact on clinical and economic issues globally [3, 4].

Escherichia coli, Salmonella spp., Shigella spp., and Vibrio spp. are frequently occurring drug-resistant Gram-negative bacteria in the East Africa region [5]. Resistance to amoxicillin, trimethoprim-sulfamethoxazole, tetracycline, nalidixic acid, ceftriaxone, ciprofloxacin, and ofloxacin has been reported for E. coli isolates [6]. Salmonella spp. showed resistance against fluoroquinolones, ceftriaxone, ciprofloxacin, and azithromycin [7]. Moreover, most Shigella spp. isolated from children in the hospital showed resistance to tetracycline and trimethoprim-sulfamethoxazole [8], and this indicates the need for effective antibacterial agents.

With increasing drug-resistant pathogens against bulk antimicrobial agents, nanomedicine has attracted attention [9]. The biosorption, bioaccumulation, biomineralization, and biodetoxification natures of microorganisms make them potential nanofactories [10]. Bacteria, the main group of microorganisms, are potential sources of bioactive metabolites that synthesize antibacterial AgNPs [11]. Previous reports showed that actinomycetes can produce bioactive compounds that are capable of reducing silver salts to AgNPs [12]. They are sources of diverse groups of bioactive compounds capable of synthesizing antibacterial AgNPs [13–16]. In particular, the synthesis of AgNPs by bioactive metabolites from Streptomyces spp. showed antibacterial activity against E. coli and S. typhi [17–20].

Actinomycetes isolated from Ethiopian and Kenyan soils produced bioactive metabolites that showed antibacterial activity against E. coli, S. typhi, and S. boydii [21–25]. However, the status of the synthesis of antibacterial AgNP using bioactive metabolites produced by actinomycetes isolated from the East African region is limited. The soil of waste dump sites in this region is one of the unexplored areas. Such sites may contain diverse types of nutrients (carbon source, nitrogen source, minerals, and metals such as silver) so that various bioactive metabolite producing potential actinomycetes can be found. In addition, the probability of getting antibacterial metabolites producing actinomycetes that involve the bioreduction of metals such as silver is high. The main purpose of this study was to identify actinomycete isolates isolated from Thika waste dump soil (central part of Kenya) that produce bioactive metabolites for the synthesis of antibacterial AgNPs.

2. Materials and Methods

2.1. Test Bacterial Pathogens

Escherichia coli, Salmonella typhi, and Shigella boydii are human bacteria pathogens used to perform in vitro antibacterial susceptibility tests. They are clinical isolates obtained from KEMRI (Kenya Medical Research Institute, Mycology Laboratory).

2.2. Bioactive Metabolites Preparation

KDT32 and KGT32 isolates that were isolated from composite soil samples collected from Kiganjo waste dump site in Thika, Central parts of Kenya, were used for bioactive metabolites preparation for this study. Selective isolation and bioassay-guided screening of KDT32 and KGT32 were previously described [22].

Metabolite preparation from isolates was done according to Składanowski et al. [26]. Cell-free supernatants (secondary bioactive metabolites) were prepared by centrifugation at 5000 rpm for 25 minutes and filtration by 0.22 µm pore size filter paper from 7-day-old cultures.

2.3. Synthesis of Silver Nanoparticles, Visual Detection, and UV-Visible Spectra Analysis

The synthesis of AgNPs was done according to Składanowski et al. [26]. A mixture (1 : 1) of metabolite solution and 10 mM AgNO3 solution was prepared from silver nitrate solution (10 mM AgNO3 solution or 1.6987 g AgNO3/1 L sterile distilled water) and incubated at 28°C for seven days.

The color change and absorption intensity measurement in the reaction solution at each reaction day interval was assessed through visual observation and using a 6800 double beam UV-visible spectrophotometer (Jenway, UK), respectively. UV-vis spectrophotometer was used to confirm the presence of a characteristic UV spectra peak between 400 and 500 nm. The types of AgNPs synthesized by metabolites from KDT32 and KGT32 isolates were represented as KDT32-AgNP and KGT32-AgNP, respectively.

2.4. FTIR Spectra Analysis of Biomolecules Used for AgNPs Synthesis

The sample preparation for FTIR spectra analysis of metabolite-mediated AgNPs was done according to Abd-Elnaby et al. (2016) [17]. The synthesized AgNPs were filtered using 0.22 µm pore size filter paper followed by centrifugation at 5000 rpm for 25 minutes. The pellet was washed with sterile distilled water followed by centrifugation and the supernatant was discarded. This step was repeated three times to remove unbound metabolites from metabolite-mediated nanoparticles.

The pellets were put on potassium bromide (KBr) discs and analyzed using FTIR spectrophotometer (Bruker alpha model, Germany) in transmittance mode in the range of 4000-400 cm⁻¹ wavenumbers at 4 cm⁻¹ resolution [27]. The results of FTIR spectra from both metabolites only and metabolite-mediated AgNPs were recorded for comparison of the band shape, band position, and band intensity change.

2.5. Testing of Antibacterial Activity of AgNPs

Both synthesized AgNPs (pellets) and the metabolites were freeze-dried to make a powder using a freeze dryer. The antibacterial activity of the metabolite-mediated AgNPs was evaluated by a well diffusion assay [10]. The pathogens (E. coli, S. typhi, and S. boydii) were grown overnight on Muller Hinton agar plates. The colonies were taken and suspended in 5 ml sterile water in test tubes and adjusted to 0.5 McFarland turbidity standards [28]. The suspensions were swabbed on Muller Hinton agar plates. Stock solution (2 mg/ml) was prepared from AgNO₃, KDT32-AgNPs, KGT32-AgNPs, and metabolites of KDT32 and KGT32. Streptomycin (0.1 mg/ml) and water were used as a positive and negative control, respectively. From each solution, 80 µl was added to each well, and the plates were incubated at 37°C for 18 hrs to determine the inhibition zone.

2.6. Characterization of Isolates for Identification

Colony and cell morphology, catalase test, pH, and salt concentration tolerance test were done according to previous reports [29]. The genomic DNA of KDT32 and KGT32 was extracted and purified by the QIAamp Mini kit according to the manufacturer’s instruction [30]. Amplification of 16S rRNA gene from genomic DNA of KDT32 and KGT32 isolates was done using 243F (5′-GGATGAGCCCGCGGCCTA-3′) and A3R (5′-CCAGCCCCACCTTCGAC-3′) primers as described previously [31]. The quality and integrity of the amplified gene product were confirmed by gel electrophoresis according to Chen et al. [32]. The gene product was sent to Macrogen (1105AZ Amsterdam, Netherlands) and sequenced according to the manufacturer’s instructions.

The quality of the obtained sequence was checked, trimmed, and prepared using BioEdit software and MEGA 7 software to get a consensus sequence. The consensus 16S rRNA gene sequence from both local isolates was submitted to GenBank to get GenBank accession number (http://www.ncbi.nlm.nih.gov/projects/Sequin/) using Sequin tool/program from NCBI web server [33].

Similarity search for consensus sequences was done using NCBI (https://blast.ncbi.nlm.nih.gov/) [32] and EzTaxon-e (http://eztaxon-e.ezbiocloud.net/) [34] database to get the closest reference sequences. The closest reference sequences obtained from the EzTaxon-e database were used for phylogenetic analysis using the MEGA 7 software package. Multiple sequence analysis for both reference sequences and sequences from local isolates was done using CLUSTAL W [32]. From these, character matrices and distance matrices were generated for maximum likelihood (ML) and neighbor-joining (NJ) tree construction, respectively. Neighbor joining and ML algorithm from the MEGA 7 software package were used to construct the phylogenetic tree. The tree topology (branching pattern) was validated by bootstrap values calculated by 1000 resampling replicates [32].

2.7. Data Analysis

The mean ± standard deviation values of inhibition zone comparisons were performed using one-way ANOVA ranked with Tukey’s multiple range test with descriptive analysis by SPSS version 20. The differences were tested on < 0.05 (95% probability level), and all statistical values at < 0.05 are statistically significantly different. The sequence similarity of KDT32 and KGT32 sequences with reference sequences was described using percentage. The taxonomic position of the isolates compared to the closest reference sequences was inferred by the construction of both character and distance matrices based on the phylogenetic tree and validated by bootstrap value.

2.8. Ethical Consideration

The antibacterial activity test was done in vitro against bacterial human pathogens available in KEMRI. These did not require any specific and strict permission.

3. Results

3.1. Actinomycete Metabolite-Mediated Synthesis and Characterization of Antibacterial Silver Nanoparticles

3.1.1. Visual Detection and UV-Visible Spectra Analysis of Metabolite-Mediated AgNPs

The visual and spectrophotometer detection confirmed that both KDT32 and KGT32 metabolites were successfully synthesized AgNPs (Figures 1–3). The formation of KDT32-AgNPs and KGT32-AgNPs was observed by a color change from straw to dark salmon (Figure 1(a)) and straw to a pale golden rod (Figure 1(b)), respectively.

(a)

(b)

The KDT32-AgNP and KGT32-AgNP represent a silver nanoparticle formed by KDT32 metabolite (produced by KDT32 isolate) and KGT32 metabolite (produced by KGT32 isolate), respectively.

As observed from Figures 2 and 3, there was no evidence of characteristic absorption peak scanned in the range of 400–500 nm by the reaction solution at the time of mixing and the controls (metabolite solution and AgNO₃ solution) (Figures 2 and 3). However, the reaction solution of KDT32-AgNO₃ and KGT32-AgNO₃ exhibited a clear excitation or characteristic peak after a 5-day reaction (Figures 2 and 3).

The formation of KDT32-AgNP progressively increased the absorption intensity from 1.3872 to 1.6925 as the reaction time increases from 5 to 7 days, respectively (Figure 2). A clear characteristic peak formation was observed and centered at 411.5 and 415.5 nm, respectively (Figure 2).

The KGT32-AgNPs formation also showed an increase in absorption intensity from 1.1331 to 1.3665 and decreased to 1.0114 as the reaction time increased from 5, 6 to 7 days (Figure 3). The position of the maximum spectra peaks was also shown with a minor shift center at 413.5 nm, 416 nm to 416.5 nm, respectively (Figure 3).

3.1.2. Identification of Biomolecules Used for AgNPs Synthesis Using FTIR

FTIR spectra results showed that KDT32 metabolites exhibited strong and medium absorption bands at different wavenumbers (3774.79, 3386.91, 2090.27, 1638.98, 1396.67, and 461.42 cm⁻¹) (Figure 4). The medium and sharp band at 3774.79 cm⁻¹ and the strong and broad band at 3386.91 cm⁻¹ corresponded to the O-H stretch. The medium band at 2090.27 cm⁻¹ and the strong band at 1638.98 cm⁻¹ represented –NCS (isothiocyanate) stretch and C=C (alkenes) stretch, respectively. The medium band at 1396.67 cm⁻¹ and the strong band at 461.42 cm⁻¹ represent organic sulfate stretches and stretch of S-S, respectively.

The FTIR spectra of KDT32-AgNPs results showed a change in band position, intensity, and shape compared to the spectra of KDT32 metabolite (Figure 4). This suggests the involvement of functional groups in the formation of KDT32-AgNP. FTIR spectra of KDT32-AgNPs showed a strong and broad band at 3391.31 cm⁻¹ assigned to the stretching vibration of OH. Moreover, the strong and sharp band was also observed at 1640.85 (stretching of-C=C-) and 445.93 cm⁻¹ (stretching of S–S). The shifting of the band position from 3386.91 to 3391.31 cm⁻¹, 1638.98 to 1640.85 cm⁻¹, and 461.42 to 445.93 cm⁻¹ may indicate the binding of the biomolecules to the AgNPs (Figure 4). These band shifts indicated that -OH, C=C, and S-S functional groups from KDT32 metabolite were involved in the synthesis of KDT32-AgNPs.

The FTIR spectra of KGT32 metabolites showed that there were four strong bands (at 3117.76, 2074.04, 1603.98, and 743.82 cm⁻¹) and one medium band (at 1392.90 cm⁻¹) formed (Figure 5). The bands of these wavenumbers are assigned as OH-NCS or (isothiocyanate), C=C, C-H, out plan bending, and S-S, respectively. Moreover, FTIR spectra of KGT32-AgNPs indicated that there were three strong bands observed at 3408.99, 1639.15, and 451.60 cm⁻¹ that showed a shift in band position, shape, and intensity (Figure 5). The shifting band positions from 3117.76 to 3408.99 cm⁻¹, 1603.98 to 1639.15 cm⁻¹, and 743.82 to 451.6 cm⁻¹ indicate that OH, C=C, and C-H functional groups were involved in the reduction of Ag⁺ and capping of KGT32-AgNPs.

3.1.3. Antibacterial Activity Evaluation of Metabolite-Mediated AgNPs

The inhibition zone of KDT32-AgNP and KGT32-AgNPs as well as their corresponding crude extracts showed antibacterial activity against E. coli and S. typhi (Figure 6).

The inhibition zone of KDT32-AgNPs (19.0 ± 1.4 mm) against S. typhi was higher compared to the antibacterial activity of KDT32 crude extracts (15.0 ± 0.0 mm) (Table 1). Similarly, KGT32-AgNP (17.0 ± 0.0 mm) showed slightly better antibacterial activity against S. typhi when compared to the crude extracts of KGT32 (15.5 ± 0.7 mm) and AgNO₃ (15.5 ± 0.7 nm) (Table 1).

3.2. Identification of Isolates KDT32 and KGT32

3.2.1. Phenotypic Characterization of Isolates KDT32 and KGT32

The KDT32 and KGT32 colony color was light yellow and white, respectively (Figure 7 and Table 2). The colony shape and texture of both isolates were circular and hard, respectively. Both local isolates were Gram-positive bacteria with filamentous cellular shape and produced catalase enzyme (Figure 7 and Table 2).

The KDT32 and KGT32 isolates grew at a pH range of 6–12 and 5–12, respectively, and they also grew at 0–7% and 0–9% NaCl concentrations, respectively (Table 2).

3.2.2. Analysis of Amplified 16S rRNA Gene Product

The gel result confirmed that the band of amplified product of KDT32 and KGT32 was between 1000 and 2000 bp size (Figure 8).

3.2.3. Sequence Analysis for Identification of KDT32 and KGT32 Isolates

The GC (Guanine-Cytosine) content for the KDT32 and KGT32 sequence was 58.32% and 59.6%, respectively (Table 3). NCBI-BLASTn similarity search showed that there were multiple hits to members of Streptomyces species for both isolates. The first top hit that showed similarity to the KDT32 sequence was the sequence from Streptomyces sp. MBE174 (AB873097.1) (Figure 9). KDT32 sequence similarity and coverage with Streptomyces sp. MBE174 showed 99% and 100%, respectively (Table 3). The pairwise alignment result showed that the KDT32 sequence was almost aligned base to base with Streptomyces sp. MBE174 sequence between the subject nucleotide position (coordinate) 417 and 1401 (Figure 10). However, there was a gap observed at 1378 nucleotide position of Streptomyces sp. MBE174 sequence and T nucleotide were seen at nucleotide position 961 of the KDT32 sequence (Figure 10).

Streptomyces sp. strain SP4-AB2 (MH013316.1) was the first top closest species for KGT32 isolates (Figure 11). The KGT32 sequence was aligned with 28–975 nucleotides of Streptomyces sp. strain SP4-AB2 sequence (Figure 12). KGT32 sequence similarity and coverage with Streptomyces sp. strain SP4-AB2 sequence were 99% (946/948) and 100%, respectively. The statistics of this alignment indicated that there were 946 nucleotides aligned out of 948 (Figure 12). Thus, there were two nucleotide mismatches (2/948) between the two sequences (Table 3 and Figure 12). The first mismatch observed was a G at position 159 of the KGT32 sequence and at position 186 of Streptomyces sp. strain SP4-AB2 sequence, while the second mismatch was at position 866 of the KGT32 sequence and a G at position 893 of Streptomyces sp. strain SP4-AB2 sequence (Figure 12).

EzTaxon-e server search results confirmed that 30 Streptomyces species showed 99.19%–99.8% sequence similarity to KDT32 isolate (Figure 13). Streptomyces lavenduligriseus strain NRRL ISP-5487 is the first top closest hit that showed 99.8% sequence similarity to KDT32 sequence (Table 4). In this case, 985/986 bp of the sequence of KDT32 was aligned between 403 and 1388 sequence of S. lavenduligriseus strain NRRL ISP-5487. The number of mismatch nucleotides between the sequence of KDT32 and S. lavenduligriseus strain NRRL ISP-5487 was 2 bps (Table 4).

Figure 13

Taxonomic position and evolutionary relationship determination of KDT32 and KGT32 isolate sequences with reference sequences (strains of genus Streptomyces) from phylogenetic tree constructed by ML algorithm. The phylogenetic tree was validated by a bootstrap analysis (1000 replications) with values shown at branch nodes. Bar indicates substitutions per nucleotide position. Evolutionary analyses were conducted in MEGA7. Streptomyces violarus NBRC 13104 AB184316 99.68: Streptomyces violarus = taxon name; NBRC 13104 = strain type; AB184316 = GenBank accession number; 99.68 = percent of sequence similarity).

Similarly, 30 Streptomyces species obtained from the EzTaxon-e database that showed 99.26%–99.79% sequence similarities to KGT32 isolate were retrieved (Figure 9). Four strains showed 99.79% sequence similarity to the KGT32 sequence. The first most top closest species to the sequence of KGT32 was the sequence of Streptomyces albidoflavus strain DSM40455 (Table 4). From this, 947/948 bp of the sequence of KGT32 was aligned with 264–1211 bp range of sequence of S. albidoflavus strain DSM40455. The number of mismatched nucleotides between KGT32 and S. albidoflavus strain DSM40455 was 2/947 (Table 4). Therefore, the isolates KDT32 and KGT32 belonged to Streptomyces species and were deposited in the GenBank database with MH301089 and MH301090 GenBank accession numbers, respectively.

3.2.4. Molecular Phylogenetic Analysis of KDT32 and KGT32 Isolates

The sequence of KDT32 and KGT32 with sequences from 60 Streptomyces type strains retrieved from the EzTaxon-e database was analyzed using NJ and ML algorithms to see the taxonomic position of local isolates (Figure 13). The resulting trees showed that the local isolates were found at different taxonomic positions (Figure 13). It also confirmed that both local isolates showed a clade with different Streptomyces species retrieved from the EzTaxon-e database.

The result of both NJ and ML tree analysis confirmed that the KDT32 isolate formed a monophyletic clade with Streptomyces nodosus strain ATCC 14899 (CP009313; Figure 13). Moreover, the sequence of KDT32 and S. nodosus strain ATCC 14899 showed 99.79% sequence similarity (Table 4). On the other hand, KGT32 formed a distinct clade with Streptomyces fabae T66 (KM229360) and Streptomyces cinerochromogenes NBRC13822 (AB184507). The KGT32 sequence showed 99.68% and 99.58% sequence similarity with S. fabae T66 (KM229360) and S. cinerochromogenes NBRC13822 (AB184507), respectively (Figure 13). Thus, these confirmed that the local isolates that produced bioactive metabolites capable of synthesis of antibacterial silver nanoparticles were grouped under the genus Streptomyces species.

4. Discussion

The visual detection of the present study revealed that KDT32 metabolite-AgNO₃ and KGT32 metabolite-AgNO₃ reaction solution showed dark salmon and pale golden rod color change, respectively. These may indicate bioreduction of Ag⁺ and morphological indicators to detect the synthesis of AgNPs. The synthesis of AgNPs by cell-free filtrate from Streptomyces rochei MHM13 treated with AgNO₃ showed pale yellow color change [17]. In other studies, AgNPs synthesized by metabolite from Streptomyces sp. LK3 [35], S. parvulus SSNP11 [19], Streptomyces sp. NH21 [26], and Streptomyces sp. SS2 [36] showed a dark brown color change. These color changes occur due to the absorption of visible light and the effect of surface plasmon resonance on silver. The differences in color changes may be due to differences in the type of biomolecules involved in bioreduction of Ag⁺ and capping of AgNPs. These clearly indicate that KDT32 and KGT32 metabolites have the potential to reduce Ag⁺ to form AgNPs.

The absorption intensity result showed that the maximum spectra peak was centered at 415.5 nm and 416 nm for KDT32-AgNP and KGT32-AgNPs, respectively. Previous studies revealed that the typical AgNPs show maximum UV-vis spectra peaks between 400 and 500 nm, and this is a reliable criterion indicating the formation of AgNPs. Silver nanoparticles synthesized by metabolites from Streptomyces rochei MHM13 showed a characteristic peak at 410 nm [17]. Silver nanoparticles synthesized by metabolites from Streptomyces sp. SS2 [36] and Streptomyces sp. LK3 [35] showed a characteristic peak at 420 nm. Another study showed that UV-vis spectra of AgNPs synthesized by metabolites from Streptomyces sp. NH21 were observed at 402 and 424 nm [26].

The functional groups involved in the synthesis of nanoparticles were identified by observing the change in FTIR spectra, band position, shape, and intensity in AgNPs compared to the spectra band of the metabolite only. The band position shift from 3386.91 to 3391.31 cm⁻¹, 1638.98 to 1640.85 cm⁻¹, and 461.42 to 445.93 cm⁻¹ indicates that O-H stretch, C=C, and S-S from KDT32 metabolites were major functional groups involved in the synthesis of KDT32-AgNPs. Similarly, the shift in band position from 3117.76 to 3408.99 cm⁻¹, 1603.98 to 1639.15 cm⁻¹, and 743.82 to 451.6 cm⁻¹ reveals that O-H, C=C, and C-H biomolecules from KGT32 metabolites were involved in the synthesis of KGT32-AgNPs. This band peak shift alteration between the metabolite and the metabolite mediated by AgNPs shows the existence of biomolecules that were involved in the reduction of Ag⁺ and capping of AgNPs. As reported, antibacterial compounds have active functional groups such as amide and hydroxyl groups to reduce Ag⁺ and cap AgNPs [18]. Another study showed that the active functional group O-H (3417 cm⁻¹) [35] was involved in the reduction of Ag⁺ and capping of antibacterial AgNPs. Similarly, earlier studies have reported that -OH (3384 cm⁻¹) is one of the functional groups used to reduce Ag⁺ and capping of AgNPs [37]. These clearly indicate that KDT32 and KGT32 isolates are the source of metabolites having potential functional groups that reduce Ag⁺ and capping AgNPs.

The bioassay study of KDT32-AgNP and KGT32-AgNP showed antibacterial activity against E. coli and S. typhi. The inhibition zone of KDT32-AgNP against E. coli and S. typhi was 22.0 ± 1.4 mm and 19.0 ± 1.4 mm, respectively. Similarly, KGT32-AgNP revealed an inhibition zone of 21.5 ± 0.7 mm and 17.0 ± 0.0 mm against E. coli and S. typhi, respectively. However, the present study showed that no antibacterial activity was observed against S. boydii and a similar result was reported by Chauhan et al. [18]. Earlier reports showed that AgNP synthesized by Streptomyces rochei MHM13 metabolite showed 16 mm and 18 mm inhibition zone against E. coli and S. typhi, respectively [17]. In another study [36], AgNP synthesized by metabolites from Streptomyces sp. SS2 showed 18.25 ± 1.5 mm inhibition zone against E. coli. Silver nanoparticles synthesized by S. parvulus SSNP11 metabolites showed a 26 mm inhibition zone against S. typhi [19]. These clearly indicate that KDT32 and KGT32 metabolite mediated the synthesis of antibacterial AgNPs with potential bionanotechnology that can be used in biomedical applications.

The present study demonstrated that both KDT32 and KGT32 isolates were Gram positive, had filamentous cell shape, and produced catalase enzyme that grew at 6–12 and 5–12 pH values and 0–7% and 0–9% NaCl concentrations, respectively. This is in agreement with studies that antibacterial metabolites producing Streptomyces species isolated from soil showed growth between 4 and 12 pH and 0–7% salt concentration [38, 39].

NCBI-BLASTn search results indicate that sequence of KDT32 isolate showed 99% sequence similarity with a sequence of Streptomyces sp. MBE174. The biomedical and pharmaceutical application of Streptomyces sp. MBE174 is unknown. Moreover, based on the EzTaxon-e database search result, the sequence of KDT32 isolate showed 99.8% sequence similarity with S. lavenduligriseus strain NRRL ISP-5487 that produced polyene macrolides (pentenomycine II, pentenomycine III, and narangomycine) [40].

Similarly, the sequences of KGT32 isolate showed 99% similarity with Streptomyces sp. strain SP4-AB2 (MH013316.1) with both medical and industrial roles but yet to be determined. EzTaxon-e similarity search indicated that sequence of KGT32 isolate showed 99.79 % similarity with S. albidoflavus strain DSM40455, S. koyangensis strain VK-A60, S. hydrogenans strain NBRC 13475, S. daghestanicus strain NRRL B-5418, and S. violascens strain ISP 5183. These microbes are known in the production of different antimicrobial compounds such as paulomycin A and paulomycin B [41], 4-phenyl-3-butenoic acid [42], actinomycin D [43], and valinomycin [44], respectively. Unlike local isolates KDT32 and KGT32, a literature search about the role of these reference Streptomyces species in nanodrug synthesis has not been reported.

The phylogenetic tree constructed from KDT32, KGT32, and reference sequences retrieved from the EzTaxon-e database revealed that the taxonomic position of both KDT32 and KGT32 isolates formed different clades with members of genus Streptomyces. Both NJ and ML trees inferred that the KDT32 isolate formed a monophyletic clade with S. nodosus strain ATCC 14899 that had 99.7% sequence similarity. Streptomyces nodosus is known in the production of antifungal antibiotics (polyene macrolide antibiotic, amphotericin B) [45] and other bioactive compounds (polyketides, peptides, siderophores, and terpenes) [46]. On the other hand, the KGT32 isolate formed a distinct clade with S. fabae strain T66 and S. cinerochromogenes strain NBRC13822. Streptomyces fabae is known for the production of antibacterial and antifungal antibiotics [47] and S. cinerochromogenes produced cineromycin B that has a role in antiadipocyte differentiation [48]. However, the role of these reference Streptomyces species in nanodrug synthesis is unknown. Thus, not only do KDT32 and KGT32 isolates produce antibacterial metabolites but also the metabolites are capable of synthesizing antibacterial AgNPs.

5. Conclusion

Bioactive metabolites from KDT32 and KGT32 isolates synthesize antibacterial AgNP (KDT32-AgNP and KGT32-AgNP) against E. coli and S. typhi but not against S. boydii. Due to its bactericidal activity of the synthesized AgNPs, hence, KDT32 and KGT32 metabolites can be used for antibacterial activity in different biomedical applications. The KDT32 and KGT32 isolates that produce bioactive metabolites to synthesize antibacterial AgNPs were identified as Streptomyces species.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

This research work was funded by the African Union Commission (ADF/BD/WP/2013/68 to CNM) and Japanese International Co-operation Agency (JICA) (00025 to CNM), and the authors also thank the institutes. The authors are also grateful to PAUISTI Molecular and Biotechnology lab, KIRDI, KEMRI, and JKUAT chemistry lab staff for allowing the lab facilities to be used for this research work. The first author also acknowledges Gondar University that gave him supportive material used for this study.

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Copyright © 2020 Abebe Bizuye et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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