Bioactive potentiality of secondary metabolites from endophytic bacteria against SARS-COV-2: An in-silico approach.

Five endophytic bacterial isolates were studied to identify morphologically and biochemically, according to established protocols and further confirmed by 16S rDNA Sanger sequencing, as Priestia megaterium, Staphylococcus caprae, Neobacillus drentensis, Micrococcus yunnanensis, and Sphingomonas paucimobiliz, which were then tested for phytohormone, ammonia, and hydrolytic enzyme production. Antioxidant compounds total phenolic content (TPC), and total flavonoid content (TFC) were assessed by using bacterial crude extracts obtained from 24-hour shake-flask culture. Phylogenetic tree analysis of those identified isolates shared sequence similarities with the members of Bacillus, Micrococcus, Staphylococcus, and Pseudomonas species, and after GenBank submission, accession numbers for the nucleotide sequences were found to be MW494406, MW494408, MW494401, MW494402, and MZ021340, respectively. In silico analysis was performed to identify their bioactive genes and compounds in the context of bioactive secondary metabolite production with medicinal value, where nine significant bioactive compounds according to six different types of bioactive secondary metabolites were identified, and their structures, gene associations, and protein-protein networks were analyzed by different computational tools and servers, which were reported earlier with their antimicrobial, anti-infective, antioxidant, and anti-cancer capabilities. These compounds were then docked to the 3-chymotrypsin-like protease (3CLpro) of the novel SARS-COV-2. Docking scores were then compared with 3CLpro reference inhibitor (lopinavir), and docked compounds were further subjected to ADMET and drug-likeness analyses. Ligand-protein interactions showed that two compounds (microansamycin and aureusimine) interacted favorably with coronavirus 3CLpro. Besides, in silico analysis, we also performed NMR for metabolite detection whereas three metabolites (microansamycin, aureusimine, and stenothricin) were confirmed from the 1H NMR profiles. As a consequence, the metabolites found from NMR data aligned with our in-silico analysis that carries a significant outcome of this research. Finally, Endophytic bacteria collected from medicinal plants can provide new leading bioactive compounds against target proteins of SARS-COV-2, which could be an effective approach to accelerate drug innovation and development.

Introduction

Endophytes are symbiotic microorganisms that thrive in seemingly healthy interior plant tissues without causing any harm to the host plants, and they are mostly bacterial and fungal species [1]. Mutualism has become synonymous with endophytes, as both hosts and microorganisms exhibit a synergistic relationship with each other. To date, the symbiotic interaction between plants and endophytes appears to be beneficial to both sides: it benefits the endophyte by increasing the availability of the plant’s nutrients [2], and it benefits the plant by providing pathogen protection, improving nutrient uptake, promoting plant growth, and stress tolerance [1-3]. Natural compounds from endophytes exhibit activity as antimicrobials, antifungals, anticarcinogens, immunosuppressants, or antioxidants [4]; thus, secondary metabolites produced by endophytes can be implemented as therapeutics in the pharmaceutical and agricultural industries [3]. Endophytes have been linked to more than 200 genera from 16 phyla of bacteria [5], with the majority of the species belonging to the phyla Proteobacteria (90%) and most regularly Firmicutes and Actinobacteria [6, 7]. The diversity of endophytic bacteria ranges from gram-positive to gram-negative bacteria throughout all ecosystems [8]. Bacterial endophytes occupy an ecological niche comparable to phytopathogens, making them good candidates for biocontrol agents. Plant growth-promoting (PGP) endophytes have favorable influences on plant growth because of their metabolic activity and functional variety [7, 9]. They promote plant growth by developing symbiotic relationships, nitrogen fixation, phosphate solubilization, and the generation of important phytohormones such as indole acetic acid (IAA), abscisic acid, cytokinin, and others [10, 11]. Secondary metabolites are organic chemicals produced by plants, bacteria, or fungi that are not directly involved in the organism’s regular growth, development, or reproduction, known as specialized metabolites, toxins, secondary products, or natural products [12]. It is not required for bacteria to flourish, but it does help them to interact more effectively with their environment. Bacterial endophytes produce terpenoids, alkaloids, polyketides, nonribosomal peptides (NRPs), phenols, enzymes, and phytohormones that aid plant-bacteria interactions and colonization.

Endophytic bacteria from medicinal plants offer the possibility of discovering a wide range of chemicals as well as a sustainable source of natural products. It has been reported that in medicinal plants, Bacillales, Enterobacterales, and Pseudomonadales were the most prevalent orders, accounting for 72.62% of the total, and Bacillus, Pantoea, and Pseudomonas were the most prevalent genera, accounting for 58.92%. Bacillus, Pseudomonas, and Paenibacillus can influence the growth, stress resistance, and metabolism of medicinal plants, and Streptomyces has been observed to promote plant growth and development [13]. Surfactin, Iturin, Fengycin, Munumbicins, and many more bioactive compounds with anti-infective, antimicrobial, anticancer, and anti-inflammatory bioactive compounds have been purified from various medicinal plants throughout the world [14].

Genome sequencing, comparative genomics, microarray, next-generation sequencing, metagenomics, and metatranscriptomics are some of the techniques utilized or that can be employed in modern endophytic research to unravel the plant–endophyte connection. Through the genome mining approach, the genetic characteristics that directly or indirectly govern diverse bioactivities, as well as putative bioactive secondary metabolites, have been discovered. It helps to identify specific genes involved in antibiotic resistance mechanisms, antibiotic synthesis, plant growth promotion, the endophytic secretory system, surface attachment and insertion elements, the transport system, and other metabolic systems [15]. On the other hand, comparative multigenome analysis is very useful in understanding the genetic and metabolic diversity of comparable or related microorganisms that interact with plants and animals in various ways [16]. To identify the gene clusters (smBGCs) for the secondary metabolite biosynthetic pathways in the genome, several bioinformatics tools have been established, including BAGEL [17], ClustScan, CLUSEAN [18], NP searcher, PRISM [15], and antiSMASH [19]. The smBGC database has been set up to aid the search for known and novel metabolites that can be linked to traditional ways of characterizing molecules through chromatography and spectroscopy. Currently, in silico analysis has created a junction to predict and validate laboratory and computerized analyses [20].

Microbial secondary metabolites (MSMs) have been utilized to synthesize novel medicines to treat a variety of pathogens, including viruses, bacteria, fungi, and parasites, since they are simple and dependable sources [21, 22]. These secondary metabolites can be detected by different techniques such as NMR, GC, LC, CE etc [23]. Nuclear magnetic resonance (NMR) spectroscopy has been recognized as a reliable method that frequently applied in metabolomics studies [24]. A variety of metabolites can be identified by using 1H NMR spectroscopy as well as different bioactive antiviral compounds [25].

The antiviral bioactive compounds isolated from different bacteria are listed in . The recent pandemic officially known as coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) that poses a threat to global public health.

It was initially detected in December 2019 in Wuhan, China [26], and on 11 March 2020, the outbreak was declared by the World Health Organization (WHO) as a global pandemic that has caused a total death of 4,644,740 worldwide as of September 15, 2021, with nearly 4 million new cases reported globally in the past week (6–12 September) according to the World Health Organization. Most in silico research is ongoing around the world to identify anti-SARS-COV-2 drugs of either microbial or plant origins. Diversified microbial metabolites have been depicted to represent promising antiviral activity against a variety of DNA and RNA viruses, which is turning to an emerging trend for therapeutic applications of microbial metabolites as antiviral agents [27]. Therefore, metabolites produced by endophytic microorganisms might act as an emerging source of antiviral bioactive compound [1, 28–30]. Molecular docking and other computing approaches have been useful in the first large-scale screening of numerous natural chemicals and small molecules that directly inhibit critical target proteins in this direction. Reports on virtual screening of current antiviral medicines based on known knowledge of SARS-COV-2 and closely similar coronaviruses, available databases and natural agents against emerging targets such as viral spike proteins, envelope protein, protease, nucleocapsid protein, and 3CL hydrolase are rapidly emerging [31-33]. The 3-chymotrypsin-like protease (3CLpro), which is well known as the main protease (Mpro), and the papain-like protease (PLpro) of the virus are two proteases vital to transcription and replication of the proteins encoded by the viral genome due to their peculiarity in splitting the two translated polyproteins (PP1a and PP1ab) into separate functional constituents [34]. A variety of flavonoids have been reported to have antiviral activity against SARS-COV by inhibiting 3C-like protease (3CLpro) [35], where the 3CLpro of COVID-19, which is known as the main protease, depicted 96% sequence similarity with that of SARS-COV [36]. As the autocleavage process is very important for viral propagation, 3CLpro acts as an excellent drug target for anti-coronaviral infection. Therefore, 3CLpro can be considered an excellent drug target, and many efforts have been assigned to its study, as it has a key role in the replication cycle [33].

Therefore, the aims of the present study are to isolate and characterize endophytic bacteria from different parts of medicinal plants, evaluate their plant growth-promoting and antioxidant properties, predict and detect their secondary metabolites and analyze bioactive metabolites as potential antiviral compounds against SARS-COV-2.

Materials and methods

Sample collection

A total of 16 fresh and healthy medicinal plants were collected from the University of Chittagong campus () into sterile zipper bags and then labeled and sealed. The samples were then immediately transferred to the lab maintaining a cold chain and stored in a 4°C refrigerator for further processing.

Isolation of endophytic bacteria

The total procedure was performed according to Ferreira et al., 2008, where 48 collected plant parts (2–3 cm length) were cleaned under running tap water to remove debris and then air dried [37]. Surface sterilization was carried out by rinsing them with Tween-20 for 10 minutes, followed by further washing with dH2O at least 7 times. After that, samples were dipped into 70% alcohol for 30 seconds and then washed with dH2O. Twenty (20.0) ml of 0.2% Hg2Cl2 solution was added to the samples in a beaker, which was placed on a shaker at 240 rpm for 5 minutes at 27°C. Then, the samples were washed again with dH2O at least 7 times. The final samples rinsed water was used as a control and spread onto nutrient agar plates [38], which contained (g/L)—peptone 5.00, beef extract 2.00, yeast extract 3.00, NaCl 5.00 and agar 18.00, where the pH was adjusted to 7.0. For the isolation of endophytic bacteria, samples were further treated in sterile phosphate-buffered saline (PBS) [39] containing (g/L) NaCl 8.00, KCl 0.20, Na2HPO4 1.44 and KH2PO4 0.24, where the pH was adjusted to 7.4 and maintained at 28°C under 50 rpm agitation. All plates, including the control, were incubated at 37°C for 5 days, and the number of CFUs was determined () to estimate bacterial population density according to Addisu and Kiros, 2016 [38]. Following purification, morphologically distinct colonies were identified by observing colony characteristics such as gram nature, color, and shape using a binocular biological microscope (XSZ-107BN), where colonies of similar morphological features were grouped into the same species [40, 41]. Thus, isolates were selected, cultured, purified and stored in the laboratory at -80°C in glycerol stock (50%) solution for further studies.

Phenotypic and biochemical characterization of endophytic bacterial isolates

Standard tests for morphological and biochemical analysis were performed for the identification of endophytic bacteria. They were characterized by Gram staining and biochemical tests as described in the Cowan and Steel’s Manual for the identification of medical bacteria [42]. For the activities of oxidase, catalase, citrate utilization, indole production, methyl-red (MR), Voges-Proskauer (VP), urease production, and mannitol salt fermentation, isolates were biochemically analyzed [42]. Then, the standard protocol of Bergey’s Manual of Systemic Bacteriology was followed for identification of isolates provisionally up to the species level [43].

Determination of antibiotic sensitivity

The susceptibility of five finally identified isolates to different antibacterial agents was measured in vitro by employing the modified Kirby-Bauer method [44]. This method helps to determine the efficiency of a drug rapidly by measuring the diameter of the zone of inhibition that results from diffusion of the agent into the medium surrounding the disc [45]. Ten commercially available antibiotic discs (Himedia, India) were used for the tests ().

Statistical analysis

Triplicate data were taken in all the cases during isolation, biochemical analysis and antibiotic sensitivity tests of the selected isolates. The results were analyzed according to the mean value ± standard deviation (SD) in triplicate. Microsoft Excel Software, version 2010, was used to calculate means and standard deviations by capturing all relevant data.

Molecular identification of bacteria

Genomic DNA was extracted [46] and stored at -20°C. Following a standard protocol, the DNA concentration was measured by a Thermo Scientific NanoDrop 2000 spectrophotometer (Thermo Scientific, USA) following a standard protocol. Polymerase chain reaction (PCR) was carried out for the detection of bacteria using previously published primers and targeted genes [47, 48]. The Basic Local Alignment Search Tool (BLAST) was used to determine primer specificity by searching for similar sequences in the microbial genome. During all experiments, positive and negative controls were carried out. The total composition of the target gene, primer sequences, cycling parameters, PCR master mixture and amplicon size (bp) were determined for PCR amplifications in a thermal cycler (NyxTechnik) and are shown in .

Amplified PCR products were then analyzed by electrophoresis (Micro-Bio-Tech Brand) in 2% (w/v) agarose gel in 1×TAE buffer, stained with ethidium bromide (1%) and compared with marker DNA (GeneRuler 1 kb DNA Ladder), finally visualized under ultraviolet (UV) trans-illuminator (Benda company) and then photographed. Then, PCR products were purified by an ATPTM Gel/PCR Fragment DNA Extraction Kit (Catalog No. ADF100/ADF300).

Five biochemically identified bacterial isolates were then sent for sequencing (Macrogen, South Korea). After sequencing, the results were visualized in DNA Baser software (V 5.15) and analyzed by the BLAST program in NCBI [49]. Then, the sequences were submitted to the GenBank database. Phylogenetic tree construction and evolutionary analyses were performed for every five isolates between BLAST searches of identified bacteria using MEGA 11 software [50]. The maximum composite likelihood method was used for computing evolutionary distances [51].

Screening for growth-promoting parameters

Indole acetic acid (IAA) production

The IAA production potential was calculated as per Gordon and Weber [46]. The endophytic bacterial isolates were grown on ISP2 broth containing 0.2% L-tryptophan incubated at 37°C with shaking at 150 rpm for 5 days. Cultures were centrifuged at 12,000 rpm for 10 min. Development of a pink–red color confirms IAA production by the addition of 0.5% Salkowski reagent into 1 ml of cell free supernatant. Estimation of IAA was measured by taking the absorbance at 530 nm using a spectrophotometer, and the amount of IAA was calculated in μg/ml compared with the standard curve of IAA.

Ammonia production

Ammonia production was estimated using the modified qualitative method [6], where endophytic isolates were incubated in peptone water broth at 37°C at 150 rpm for 7 to 14 days. Then, 0.5 ml of Nesseler’s reagent was added to water broth, and the development of a brown to yellow color confirmed ammonia production. The absorbance was measured at 530 nm using a spectrophotometer, and ammonia production was expressed in mg/ml when compared with the standard curve of (NH4)2SO4.

Production of hydrolytic enzymes

The proteolytic activity of endophytic bacteria was determined by streaking the isolates on skim milk agar medium. The isolates were single-streaked in skim milk agar medium and incubated at 37°C for 24–48 hours. A clear hollow zone around the areas where the organism has grown indicates proteolytic activity.

Preparation of crude extract

Isolated endophytic bacterial crude extracts were prepared following the methods described by Deljou and Goudarzi, 2016 with some modifications [52]. Endophytic bacterial isolates were inoculated in a 125 mL Erlenmeyer flask containing 25 mL nutrient broth. A rotary incubator shaker was used for incubation at 150 rpm and 37°C for 24 hours and 48 hours. After incubation, centrifugation was performed at 12,000 rpm for 10 minutes, and the cell and supernatant were extracted with organic solvent (1:1 v/v) using ethyl acetate (EA). A rotary vacuum evaporator was used to retrieve crude extracts by evaporating the organic solvents. The dry weight of the crude extracts was measured using a digital weighing machine and dissolved in 1% dimethyl sulfoxide (DMSO).

Determination of antioxidant compounds

Total phenolic content (TPC)

The total phenolic content was measured by following Folin-Ciocalteu’s colorimetric method [53], where 0.1 mL of sample and 0.5 mL of Folin-Ciocalteu were mixed with 6.0 mL of double-distilled water. After 1 min, 1.5 mL of 20% Na2CO3 (Merck, Germany) was added, and the total volume was made up to 10.0 mL with double-distilled water. The mixture was incubated for 2 h at 25°C. The absorbance was measured at 760 nm using a spectrophotometer against the blank solution containing all the reagents and the appropriate volume of the same solvent used for the sample. Gallic acid was used as the control indicator containing all the reaction agents except the sample.

Total flavonoid content (TFC)

The total flavonoid content was measured by using the AlCl3 colorimetric method [54] with slight modifications. Quercetin was used to make the calibration curve. One milligram of quercetin was dissolved in methanol and then diluted to 20, 40, 60, 80, and 100 μg/mL. Then, 0.5 mL diluted standard solutions were separately mixed with 1.5 mL of methanol, 0.1 mL of 10% AlCl3, 0.1 mL of 1 M potassium acetate, and 2.8 mL of distilled water. After incubation at room temperature for 30 min, the absorbance of the reaction mixture was measured at 415 nm using a UV–Vis spectrophotometer. The amount of 10% AlCl3 was substituted by the same amount of distilled water in the blank. Similarly, 0.5 mL of sample solution was used with AlCl3 for determination of flavonoid content as described above.

In silico analysis of bioactive secondary metabolite genes and compounds

Detection of bioactive secondary metabolite regions

The antiSMASH (antibiotics and Secondary Metabolite Analysis Shell) webserver tool (bacterial version) was used to detect the secondary metabolite regions of the isolated endophytes. First, the whole genome sequences and accession numbers of the bacterial strains were obtained from NCBI, and then sequences were uploaded as data input. KnownClusterBlast, SubClusterBlast, and ActiveSiteFinder were marked as featured options. From the graphical output, secondary metabolite regions and their types were identified.

Prediction of bioactive secondary compounds

The antiSMASH (antismash.secondarymetabolites.org) and Minimal Information about a Biosynthetic Gene cluster-MIBiG (mibig.secondarymetabolites.org) databases were used to identify the proximate secondary metabolite regions. According to the cluster, different compounds were found as hits through Knowledge Blast.

Analysis of biosynthetic genes and interaction of proteins

Secondary metabolite gene clusters were searched for core and additional biosynthetic genes involved in metabolite production. Later, the STRING database (string-db.org) was used to identify and analyze the protein–protein interaction between the core genes involved in secondary metabolism.

Analysis of PKS/NRPS domains

Statistical preferences of putative polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) domains at the genus level were analyzed via the SBSPKS v2 web server tool (www.nii.ac.in/~pksdb/sbspks) [55]. Datasets were generated from the MIBiG repository.

Molecular detection of biosynthetic genes

Four sets of degenerate primers were designed according to the study Ayuso-Sacido and Genilloud, 2005 [56], targeting the universal bacterial PKS (polyketide synthase) and NRPS (nonribosomal peptide synthetase) genes along with chalcone synthase (CHS) and ACC deaminase (acdS) gene primers.

PCR conditions were as follows: initial denaturation at 95°C for 4 min, followed by 30 cycles of denaturation at 94°C for 1 min, annealing at 57°C for NRPS, 58°C for PKS, 52°C for CHS and 55°C for ACCD primers for 1 min and extension at 72°C for 1 min with a final extension step at 72°C for 4 min.

Molecular docking of bioactive compounds against the SARS-COV2 protein

The crystal structures of coronavirus 3CLpro used in the docking analysis were retrieved from the Protein Databank (http://www.rcsb.org) with its PDB identification code (6lu7) [57, 58]. The 3CLpro structures were processed by eliminating existing ligands and water molecules, while missing hydrogen atoms were added according to the amino acid protonation state at pH 7.0 employing the Autodock version 4.2 program (Scripps Research Institute, La Jolla, CA) [59].

The structure data format (SDF) structures of the 3CLpro reference inhibitor lopinavir and the identified endophytic bioactive compounds were retrieved from the PubChem database (www.pubchem.ncbi.nlm.nih.gov) [60].

Virtual screening of the coronavirus 3CLpro active regions and determination of the binding affinities of the compounds and reference inhibitor were carried out using AutoDockvina 4.2 with full ligand flexibility. The molecular interactions between proteins and respective ligands with higher binding affinity were viewed using Discovery Studio Visualizer version 16.

ADMET and drug-likeness prediction

After the molecular docking studies, the absorption, distribution, metabolism, elimination and toxicity (ADMET) of the docked metabolites were screened using the online tools admetSAR (lmmd.ecust.edu.cn) [61] and SwissADME (www.Swissadme.ch) to predict their important pharmacokinetic properties [62].

1H-NMR spectrophotometry

The bacterial crude extract was sent to INARS Laboratory, Bangladesh Council of Scientific and Industrial Research (BCSIR), DHAKA for 1HNMR analysis. At BCSIR, data were acquired on a 400 MHz spectrometer (Bruker Corporation).

Results

Forty-eight plant parts (3 parts from 16 plants each) were subjected to surface sterilization [63], triturated with autoclaved phosphate-buffered saline (PBS) [64, 65], and then used to isolate endophytic bacteria. After spreading the plant extracts derived from forty-eight (48) plant parts over solid nutrient agar (NA) media, several bacterial colonies were found, and sixteen pure colonies were selected () after incubation for 48 hours at 37°C. Then, several subcultures were performed to isolate pure cultures from different types of endophytic bacteria () and finally preserved in a 4°C refrigerator for further studies.

Identification of endophytic bacteria by biochemical characterization

Pure cultures of five selected types of isolates were then subjected to a series of biochemical tests () as described in Bergey’s Manual for Determinative Bacteriology [66]. After the Gram staining procedure, four isolates, LL1, LL2, LF, and GL, showed violet colors, whereas GR showed pink colors under a microscope. Thus, GR was considered gram-negative, and the others were considered gram-positive bacteria. LL1, LF, and GR were rod in shape under a microscope, while LL2 and GL were identified as coccus-shaped bacteria. In the catalase test, four samples, LL1, LL2, LF, and GL, produced bubbles after the addition of H2O2, which indicated the presence of the catalase enzyme. Sample GR failed to produce any bubbles and thus was catalase-negative. In the oxidase test, all five isolates, LL1, LL2, LF, GL, and GR, showed a deep blue color within 10 seconds, indicating the ability to produce oxidase enzymes as a positive result. In the indole production test, the appearance of a red ring indicates a positive result. All five isolates tested negative, as no red ring appeared, indicating the absence of tryptophanase enzyme. Four isolates, LL1, LL2, LF, and GL, exhibited red color formation, indicating the capacity to produce and maintain a stable acid end product. Sample GR, on the other hand, was unable to show red color formation and thus tested negative. In the Voges-Proskauer test, all five samples were unable to yield red color formation, which indicated that they were unable to produce butylene glycol. Thus, all the samples were tested negative. In the citrate utilization test, only isolate LL2 was found to produce a blue color as a positive result, which indicated that the other four isolates were unable to utilize citrate due to the absence of citrate-utilizing enzymes. Mannitol salt agar (MSA) media was used to identify the isolates growing on high salt concentrations, and a positive test consisted of a color change from red to yellow, indicating a pH change to acidic. Among the five isolates, only LL1 showed a positive result ().

Antibiotic sensitivity test

Ten types of antibiotic discs (Himedia, India) were used to test the sensitivity [44], where every isolate showed resistance toward penicillin G. In addition, isolate LL2 showed resistance (R) to tetracycline, erythromycin and ceftriaxone. LL1 expressed intermediate (I) sensitivity to ampicillin [67], and the rest of the isolates were found to be sensitive to all antibiotics ().

Identification of bacteria depends on morphological and biochemical analysis

The overall morphological and biochemical test results of five distinct isolates and their identifications [66] are listed in .

Identification of bacterial strains by molecular characterization

After extraction of genomic DNA from five isolates, DNA concentration and purity were measured by a Nanodrop 2000 (Thermo Scientific, USA). Extracted DNA of five bacterial isolates was then amplified by 16S rDNA primers. Gel electrophoresis was performed afterwards on a 2% agarose gel and stained with ethidium bromide. After that, the gel was visualized by a UV transilluminator (Benda Company) (). After PCR amplification of five bacterial isolates, they were sent for Sanger sequencing by Macrogen’s sequencing service (South Korea). Purified PCR products along with their respective primers were sequenced and finally confirmed up to their species. Isolates LL1, LL2, LF, GL and GR were thus identified as Priestia megaterium, Staphylococcus caprae, Neobacillus drentensis, Micrococcus yunnanensis, and Sphingomonas paucimobiliz, respectively, with 99% identity with an e value of 0 in NCBI BLAST (Basic Local Alignment Search Tool) analysis. After analysis by the BLAST program, isolate information was submitted to GenBank by using the GenBank submission portal. Then, the accession numbers for the nucleotide sequences were obtained as MW494406, MW494408, MW494401, MW494402, and MZ021340.

Electrophoretic separation (2% agarose) of the 16S rDNA gene of different isolates.

M: 50 bp DNA ladder; 1: LL1; 2: LL2; 3: LF; 4: GL; 5: GR.

The five isolates were then subjected to phylogenetic tree construction according to their 16S rRNA sequences with MEGA 11 Version 5.0 Software (). The neighbor-joining method was used to interpret evolutionary history. The optimal tree is demonstrated with the sum of branch length = 0.06596246. The scale is shown in the tree, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were enumerated by applying the maximum composite likelihood method [51] in units of the number of base substitutions per site. The analysis has 20 nucleotide sequences. Codon positions comprised with 1st+2nd+3rd+Noncoding. All cryptic positions were eliminated for each sequence pair. There were a total of 1349 positions in the final dataset. Evolutionary analyses were conducted in MEGA 11 [50].

Phylogenetic tree of isolates.

(a) Priestia megaterium, (b) Staphylococcus caprae, (c) Bacillus drentensis, (d) Micrococcus yunnanensis, (e) Sphingomonas paucimobiliz.

Screening for growth-promoting parameters of isolated bacteria

Quantitative estimation of all isolates was performed in ISP2 broth medium [68], and the indole acetic acid (IAA) production rate ranged from 14.26 to 25.56 μg/mL (). A maximum IAA yield of 25.561 ± 0.05 μg/mL was observed by isolate GR (Sphingomonas paucimobiliz). IAA solution was used as a standard, and the concentration of samples was estimated by the standard curve ().

Quantitative estimation of ammonia production by all isolates in peptone water broth ranging from 5.3 to 24.98 mg/mL. Isolate LL2 (Staphylococcus caprae) produced the maximum amount of ammonia (24.98±0.03 mg/mL) (Table 7). Ammonium sulfate was used as the standard, and the concentration of samples was estimated by the standard curve ().

Table 7

Estimation of IAA, ammonia, TPC and TFC by five isolates.

Name of isolates	IAA		Ammonia		TPC		TFC
	Absorbance (530 nm)	Concentration (μg/mL)	Absorbance (530 nm)	Concentration (μg/mL)	Absorbance (760 nm)	Concentration (μg/mL)	Absorbance (415 nm)	Concentration (μg/mL)
Priestia megaterium (LL1)	0.416	14.268 ± 0.05	1.074	20.23 ± 0.01	0.327	258.901 ± 0.02	0.2237	36.09 ± 0.02
Staphylococcus caprae (LL2)	0.727	*23.902 ± 0.06	1.211	**24.98 ± 0.03	0.467	**406.653 ± 0.01	0.2971	**45.18 ± 0.06
Neobacillus drentensis (LF)	0.646	*23.612 ± 0.01	0.662	5.98 ± 0.01	0.366	300.060 ± 0.01	0.2381	38.92 ± 0.05
Micrococcus yunnanensis (GL)	0.536	19.143 ± 0.20	0.615	4.35 ± 0.02	0.314	245.181 ± 0.05	0.1380	19.29 ± 0.01
Sphingomonas paucimobiliz (GR)	0.694	** 25.561 ± 0.05	0.644	5.32 ± 0.04	0.302	232.517 ± 0.05	0.1416	20.01 ± 0.02

Note: ** indicates maximum result

Out of five isolates, two isolates were found to be positive for protease enzyme activity. Priestia megaterium (LL1) and Staphylococcus caprae (LL2) isolates showed clear degradation of the protein () in skim milk agar [69].

Isolates in skim milk agar.

LL1 (Priestia megaterium) and LL2 (Staphylococcus caprae) represented protease enzyme activity with a clear hollow degradation of protein throughout the inoculation area.

Determination of antioxidant compounds from crude extracts

Crude extracts (extracellular secondary metabolites) of isolated endophytic bacteria were estimated by using ethyl acetate solvent, and each sample was incubated for 24 hours and 48 hours [52]. Staphylococcus caprae (LL2) showed the maximum result, while Sphingomonas paucimobiliz (GR) showed the minimum (). Ethyl acetate (EA) extracts were stored in 1% dimethyl sulfoxide (DMSO) for further antioxidant and metabolite analysis [70].

Ethyl acetate extracts of the isolated bacteria were processed to analyze total phenolic compounds [71]. Isolate LL2 exhibited the maximum result (406.653±0.01 μg/mL), and isolate GR exhibited the lowest (232.517±0.05 μg/mL) (). Gallic acid was used as the standard to estimate the TPC ().

Ethyl acetate extracts of the isolated bacteria were processed to analyze total flavonoid compounds. Isolate Staphylococcus caprae (LL2) exhibited the maximum result (45.18±0.06 μg/mL), and isolate Micrococcus yunnanensis (GL) exhibited the lowest result (19.29±0.01 μg/mL) (). Quercetin was used as the standard [70] to estimate the TPC ().

In silico analysis of bioactive secondary metabolite genes and compounds

Antibiotics and secondary metabolite analysis shell (antiSMASH) server was used to identify different secondary metabolite regions present in the genome of identified bacterial strains (). The whole-genome sequences were taken from NCBI and input to the antiSMASH server. A total of 13 different bioactive secondary metabolite regions were identified within five isolates. Terpene was the most common type of cluster found in each isolate, followed by Type III polyketide synthase (T3PKS) and the siderophore cluster. Priestia megaterium had the highest number (9) of bioactive regions among the isolates ().

After the identification of core biosynthetic gene clusters by antiSMASH, the MIBig (Minimal Information about a Biosynthetic Gene cluster) specification was used to predict the bioactive compounds according to the region and their type (). Nine (9) putative compounds were found from the whole genome sequences of identified endophytes. The most similar known cluster was searched for the identified query clusters, and based on a percentage similarity, their molecular compounds were predicted as hits (). MIBiG produced the output file with details of biosynthetic genes (core and additional), transport-related genes, regulatory genes and other genes. Compound structures were also obtained from the output (. Predicted similar compounds () were analyzed through their core and additional biosynthetic genes obtained from the antiSMASH and MIBiG datasets (). The interaction of those genes and their protein products was checked through the STRING database (version 11.0) [72]. All genes associated with those 9 gene clusters of identified compounds were programmed to be enlisted according to the query in the Uniprot database of protein. The highest 12 biosynthetic genes were identified in the gene cluster responsible for the fengycin compound and likely 9 genes for surfactin, 6 genes for bacitracin, 3 genes for zeaxanthin, and 4 genes for both the carotenoid and staphyloferrin A compounds (. All the genes were retrieved with enough protein data from the Uniprot server [73], but unfortunately, no genes could be retrieved for aureusmin, stenothrin, or microanamycin. As we have used the STRING server to expose the interaction between the genes and the proteins, these three compounds may be unable to produce any associated network. Interactions between genes involved in secondary metabolite gene clusters of the compounds are shown in nodes and edges (), where significant protein–protein interactions were observed according to the functional properties of the genes and their respective products. ClusterBlast results (from the MIBiG database) were further analyzed to measure the PKS (polyketide synthase) and NRPS (nonribosomal peptide synthetase) in a comparative source of endophytic bacteria at their sequenced species level [19]. SBSPKS v2 was used to carry out functional analyses of the PKS and NRPS domains (). Bacillus was found to be rich in these domains, having confirmed and some hypothetical regions. A moderate percentage was found for Staphylococcus spp. possessing similar amounts and thus exhibited diverse types of PKSs and NRPSs. These particular conserved clusters are spotted domains of Fimicutes, Proteobacteria and Actinobacteria phyla [15].

Graphical representations of most similar known clusters as an output of ClusterBlast specified by the MIBiG repository.

Color codes represent similar gene regions.

Predicted association of proteins of biosynthetic genes demonstrated by the STRING server.

(A) Surfactin, (B) bacitracin, (C) carotenoid, (D) staphyloferrin A, (E) zeaxanthin and (F) fengycin protein network. Line Indicator: Red Line–Presence of fusion evidence, Green Line- neighborhood evidence, Blue Line- cooccurrence evidence, Purple Line- experimental evidence, Yellow Line- textmining evidence, Light Blue Line- database evidence, Black Line- coexpression evidence.

The presence of biosynthetic genes was determined in the identified strains with four sets of gene-specific primers. PCR amplification of polyketide synthase (PKS), nonribosomal peptide synthetase (NRPS) [56], 1-aminocyclopropane-1-carboxylate deaminase (ACCD) [4], and chalcone synthase (CHS) [19] genes was performed for our identified strains. All strains showed a prominent band (amplicon size ~600–700 bp) for the NRPS gene [83] (). PKS candidate amplicons (~700–800 bp) were detected in Priestia megaterium (LL1), Staphylococcus caprae (LL2), Micrococcus yunnanensis (GL), and Sphingomonas paucimobiliz (GR) (). For the ACCD gene, only Sphingomonas paucimobiliz (GR) was positive ( and all isolates showed negative results for the CHS gene, as no DNA band was found in PCR amplification (

PCR amplifications of biosynthetic genes.

(A) PKS gene, (B) NRPS gene, (C) ACCD gene and (D) CHS gene. M: DNA Marker for each case; 1: LL1; 2: LL2; 3: LF; 4: GL; 5: GR.

The binding energy from the docking analysis of bioactive compounds (5) and reference compound to 3-chymotrypsin-like protease (3CLpro) of the novel SARS-COV-2 is represented in . The ligand–protein binding interactions according to the residues of our compound and macromolecule (3CLpro) are shown in Protein–ligand interactions were exhibited through residues and bonds via 2D representation in .

Visualization of SARS-CoV-2 3CLpro amino acid interactions with the ligands (A) lopinavir, (B) aureusimine, (C) bacitracin, (D) carotenoid, (E) microanasamycin, and (F) staphyloferrin.

2D representation of protein–ligand interactions.

Binding mode of 3CLpro with A) lopinavir, B) microanasamycin, and C) aureusimine.

The results generated from the Lipinski and ADME/tox filtering analyses are presented in Two compounds were found to be required for Lipinski analysis of the rule of five with corresponding favorable predicted ADME/tox parameters. The predicted physiochemical properties for the bioavailability of the lead compounds are further represented in

Summary of the pharmacokinetic properties of the compounds.

(a) Lopinavir, (b) aureusimine, (c) bacitracin, (d) carotenoid, (e) microanamycin, (f) staphyloferrin The color space is a suitable physiochemical space for oral bioavailability. LIPO Lipophility: -0.7 < XLOGP3 < +5.0. SIZE: 150 g/mol: < MW < 500 g/mol. POLAR (Polarity): 20Å2 < TPSA < 130 Å 2. INSOLU (insolubility): 0 < Log S (ESOL) < 6. INSATU (insaturation): 0.25 < Fraction Csp3 < 1. FLEX (Flexibity): 0 < Num. rotatable bonds < 9.

1H-NMR metabolomics analysis

With respect to the 1H-NMR metabolite data, the proton number was calculated for each metabolite and we have found three metabolites: microansamycin ( aureusimine ( and surfactin ( in our bacterial sample.

NMR 1H spectroscopy result: In the left the metabolite structure and on the right 1H NMR spectrum.

Every peak is responsible for a respective different proton type. (A) Microansamycin, (B) Aureusimine, and (C) Surfactin.

Discussion

Plants possess thousands of microbes residing inside their various tissues and forming a mutual relationship. Every living plant on earth is host to one or more endophytes: bacteria or fungi that colonize living plant tissues by producing a wide variety of specialized metabolites without causing any harm or disease to the host plants [29]. However, in the case of medicinal plants, relatively few endophytes have been studied thus far, and of course, for the endemic medicinal plants of Bangladesh as well. Therefore, one of the major objectives of this study is to isolate and characterize endophytic bacteria from our local medicinal plants to study their secondary metabolites, which have a wide range of applications in the global health sector.

Studies have revealed the capacity of endophytes to produce a diverse range of secondary metabolites [13]. Different types of important natural products, including antibiotics, antifungal, insecticidal, anticancer, immunosuppressant, antiviral, and volatile organic compounds, have been derived or synthesized from various endophytic bacteria. There has been growing interest in endophytic bacteria until last several years due to their efficacy in mimicking and producing similar bioactive compounds of their respective host plants as well as new bioactive compounds that are not present in host plants [9]. As medicinal plants harbor natural phytochemicals and bioactive compounds, endophytic bacteria within them can be a potential source of interest to investigate natural products [84]. Our study also aims to find a picture of bioactive potentiality inside endophytes by investigating biosynthetic gene clusters and secondary metabolites with molecular studies and in silico approaches.

In the subcontinent, approximately 2000 plants with medicinal properties have been reported, and among them, approximately 500 of such medicinal plants have thus far been enlisted as growing or available in Bangladesh [85]. In our study, we collected our plant samples around the campus of Chittagong University (), as it is a good reservoir of indigenous plants of Bangladesh [85]. As we are interested in identifying culturable endophytic bacteria from various parts of the plants, we collected root, stem, and leaf parts of each plant sample. We did not consider the flower and seed parts, as we selected different herbs, shrubs, and trees to avoid flowering and nonflowering properties [71]. In this study sixteen medicinal plant samples were collected from eight (8) different locations to obtain diversity.

The surface sterilization process was performed carefully to eliminate the epiphytes from the plant parts. Phosphate buffer solution (PBS) was used to triturate the internal plant extracts obtained from the leaves, roots, and stems [65]. The extracts were collected after trituration and then spread on the culture plates of nutrient agar (NA) media through the pour-plate technique [86]. We used nutrient agar (NA) and Luria-Bertani (LB) agar media to study further growth of isolated endophytic bacteria. Thus, 16 bacterial isolates (endophytes) were selected from forty eight medicinal plant parts which confirms the culture-dependent nature of those endophytic bacteria [5, 64]. Colonies were then separated by identifying colony color, shape and orientation for further pure culture () by the streaking method [29, 69]. Standard morphological and biochemical tests were performed to characterize and identify five bacterial isolates, LL1, LL2, LF, GL, and GR [66, 87]. In the case of gram staining study (), four isolates LL1, LL2, LF, and GL were considered gram-positive because of having cell walls of a thick peptidoglycan layer (50–90%) which stained violate under the microscope; on contrast, a thinner layer of peptidoglycan (less than 10%) was observed which stained pink was found for isolate GR and considered as gram-negative [88]. From the morphological study, LL2 and GL were found to be cocci, and LL1, LF, and GR were found to be rod-shaped bacteria.

LL2 and GL (gram-positive cocci) showed positive results for catalase, oxidase, and methyl red tests ( which implied that the isolates were either Staphylococcus spp. or Micrococcus spp. [66, 89]. Following negative results for the indole test and VP test (), LL2 showed positive results for mannitol salt fermentation () whereas GL showed a negative result. Thus, LL2 is strongly assumed to be Staphylococcus aureus and GL as Micrococcus luteus [65, 90]. Gram-positive rods LL1 and LF showed positive results for catalase, oxidase, and methyl red tests, as well as negative results for indole tests, VP tests, and mannitol salt fermentation (). Therefore, these two isolates were considered similar to Bacillus spp. [66, 91]. Positive test results in the citrate utilization test () conferred isolate LL1 to be similar to Bacillus subtilis. On the other hand, the gram-negative rod-shaped isolate GR showed negative results for all biochemical tests except the oxidase test. Thus, it is assumed that the isolate was similar to Pseudomonas spp. or Aeromonas spp. [66].

Antibiotics are natural or semisynthetic drugs used against bacterial infections. A number of antibiotics are in use for medications for a long time; hence, rapid and uncontrolled uses of antibiotics lead to antibiotic resistance. New pathogens have evolved to be resistant to antibiotic action, which creates the need to discover new novel antibiotics [92, 93]. An antibiotic sensitivity test was performed to identify suitable antibiotics that will be most effective against the specific type of bacteria. Here, we performed this test to evaluate the most commonly used antibiotics against bacterial isolates to determine the harmful bacterial strains. Ten frequently used antibiotics containing discs (Himedia, India), penicillin G (P), chloramphenicol (C), ampicillin (AMP), streptomycin (S), vancomycin (VA), norfloxacin (NX), tetracycline (TE), nalidixic acid (NA), erythromycin (E), and ceftriaxone (CTR), were used to identify their sensitivity against our five isolated endophytic bacteria (). Each isolate showed resistance (R) toward penicillin G. Isolate LL2 also had the highest resistance percentage by showing resistance to tetracycline, erythromycin, and ceftriaxone. LL1 expressed intermediate sensitivity (I) to ampicillin. The rest of the antibiotics were found to be sensitive (S) to other isolated endophytic bacteria. One of the major causes of sensitivity to antibiotics can be the reduced exposure of endophytic bacteria in the human system rather than the inner plant [39, 94, 95].

Antibiotic sensitivity test of isolates showing inhibition zones (mm).

Control (CON) indicates discs without antibiotics in each case. Disc codes are used to show the antibiotic discs for every isolate.

16S rDNA amplification was conducted through PCR for further confirmatory identification of isolated bacterial species. Genomic DNA was extracted by the conventional method [96], and DNA concentrations were measured by Nanodrop spectroscopy (Thermo Scientific, USA). Then the extracted DNA of five bacterial isolates was amplified by universal 16S rDNA primer pairs [5, 68]. All five isolates showed prominent bands (~800 bp) compared to the 50 bp DNA marker on a 2% agarose gel (). In general 16S rDNA and Sanger sequencing (Macrogen’s sequencing service, South Korea) were performed to identify the strains of the respective bacterial isolates [97]. Homology analysis inferred from 16S rDNA sequence comparison clearly verified that the five isolates clustered with Priestia megaterium (LL1), Staphylococcus caprae (LL2), Neobacillus drentensis (LF), Micrococcus yunnanensis (GL), and Sphingomonas paucimobiliz (GR) () having 99% identity in BLAST analysis [18]. All the results were submitted to GenBank where the accession numbers MW494406 (LL1), MW494408 (LL2), MW494401 (LF), MW494402 (GL), and MZ021340 (GR) were assigned.

Phylogenetic trees were constructed () to determine the similarity with other members of the strains found in homology analysis [71]. Evolutionary analyses were conducted in MEGA 11 [50] using the neighbor-joining method. Only sequences from the type of mostly identical strains were taken into account. More than 200 bacterial genera from 16 phyla have been reported to be associated with endophytes, with the majority of the species belonging to the phyla Actinobacteria, Proteobacteria, and Firmicutes [98]. Our study identified three Firmicutes (Priestia megaterium, Staphylococcus caprae, and Neobacillus drentensis), one actinobacterial (Micrococcus yunnanensis), and one proteobacterial (Sphingomonas paucimobiliz) endophyte from different medicinal host plants around the Chittagong University campus.

Endophytic bacteria have been reported to play a vital role in growth promotion, nutrient management, disease control, and biotic and abiotic stress tolerance in food and crop plants [9, 99]. Phytohormones play a significant role in plant growth promotion and it was suggested that IAA production helps the plants grow root parts, which increases the plants’ nutrient uptake [99]. Five identified isolates were screened for growth-promoting parameters and found positive for indole acetic acid (IAA) production. Quantitative estimation of all isolates was performed in ISP2 broth medium and production ranged from 14.26 μg/mL to 25.561 μg/mL (. The maximum IAA yield was observed in Sphingomonas paucimobiliz (25.561 μg/mL). This finding is similar to Khamna et al. (2009) who stated that IAA production levels ranged from 13.73 μg/ml to 142 μg/ml [71, 100] in bacterial endophytes. Ammonia production is an indirect mechanism of plant growth promotion and can play a significant role in suppressing phytopathogens [101]. In the present study, all isolates showed ammonia production in peptone water broth ranging from 5.3 mg/mL to 24.98 mg/mL (). Qualitative estimation showed that isolating Staphylococcus caprae resulted in the maximum yield of ammonia (24.98 mg/mL). Studies have suggested similar results of ammonia production range by endophytic bacteria and inferred an association in plant growth promotion [6, 71]. The isolated bacterial strains Priestia megaterium (LL1) and Staphylococcus caprae (LL2) showed protease positive results by degrading casein in skim milk agar (). Previous studies suggested that Bacillus, Paenibacillus, and Pseudomonas are highly recognized as protease producers [8, 63].

Phenolics and flavonoids are responsible for exhibiting the antioxidant properties of chemical compounds. The presence of antioxidant compounds such as total phenolic content (TPC) and total flavonoid content (TFC) in endophytic bacteria and solvent extracts was assessed [89] in the present study. As an extraction solvent, ethyl acetate extract (EA) was used because it is selective in extracting low molecular weight phenols and flavonoids [70]. The highest TPC value was found in Staphylococcus caprae (406.653±0.01 μg/mL) ( and it also exhibited the highest TFC value (45.18±0.5 μg/mL) (). The TPC and TFC values found in the present study were higher than those reported in previous studies [70, 102, 103]. Therefore, endophytic Staphylococcus caprae could be a source of natural phenolics and flavonoids to act as antioxidant compounds. These compounds also shows antiviral activity against various microorganisms [104, 105]. Moreover, some recent studies presented the efficacy of these compounds against SARS-COV-2 [106].

In recent years, bioactive compounds have been in high demand due to their benefits to humans and plants in various sectors of application. Thus, endophytic bacteria act as a promising resource of biotechnologically valuable bioactive compounds as well as secondary metabolites [107, 108].

Most bacterial secondary metabolites are produced by biosynthetic gene clusters consisting of key enzymes, such as polyketide synthases (PKSs) or non-ribosomal peptide synthetases (NRPSs), and contiguous genes encoding tailoring enzymes and transporters [6]. To explore the biosynthetic gene clusters and their associated bioactive secondary metabolites from our isolated bacterial strains, we used the webserver tool antiSMASH 5.0 bacterial version [109]. With default parameters of input data, we found thirteen different secondary metabolite regions from five isolates (). The most abundant regions found were Terpene, Siderophore, and Type III polyketide synthase regions followed by NRPS, Lanthipeptide, and Betalactone regions (). Among the five isolates, the Priestia megaterium strain showed the maximum number of metabolite regions, conferring itself as a potent source of bioactive compounds [19]. The antiSMASH server linked the data with the MIBig server, where we found the information of the most similar known cluster related to the database and represented the features of predicted bioactive compounds [110]. Nine compounds were found to be matched with the database, and structure, features and the list of core biosynthetic genes along with additional and other regulatory genes were retrieved from the results (). Four nonribosomal peptide (NRP)-type compounds–Surfactin (lipopeptide), Bacitracin, Microansamycin (cyclic despeptide), and Aureusimine–Three terpene type compounds–Carotenoid, Stenothricin, and Zeaxanthin–were identified. The other two compounds were staphyloferrin A (siderophore) and fengycin (betalactone).

Compound results are represented as the most similar known compounds from the BLAST result of the database, in which aureusimine, staphyloferrin A, and zeaxanthin were matched 100% with our query sequences (). Thus, S. caprae and S. paucimobiliz can be a great source of antioxidant metabolite extraction for further detailed study of their bioactivity.

Compound structure, features, and information on core and biosynthetic genes involved in the secondary metabolite clusters were also retrieved from the MIBiG server [26]. Later, the genes and protein products were evaluated through the UniProt server to obtain information on proteins, their biological processes, and their biosynthesis pathways (). Amino acid sequences from the clusters were run through the peptide search of UniProt and the protein products of the genes were extracted. After that, the STRING server (version 11.0) [72] was used to represent the networks, association, and correlation between the genes and proteins involved in biosynthetic gene clusters [17, 111] (). Unfortunately, enough protein data could not be retrieved for aureusimine, microansamycin, and stenothricin from the database to show the interactions.

Surfactin is widely known as a biosurfactant along with its antimicrobial properties and has already shown a handful of bioactive potentialities [74]. Defining the genes involved in the surfactin cluster, we found that srfAA, srfAB, srfAC, and srfAD were the core genes involved in surfactin biosynthesis (. srfAD is the core gene associated with synthetase genes and the transporter gene tcyC to modulate the surfactin biosynthesis pathway. It was shown to be a clear protein–protein interaction of these genes. On the other hand, the additional biosynthetic genes ycxB and ycxCadycxD encode regulation- and transport-related proteins involved in the processing of this secondary metabolite. Surfactin has shown experimental success due to its antibacterial capacity [112] and inhibitory mechanism against pathogenic bacteria [113]. It has been shown to enhance surfactin production from B. licheniformis and Bacillus amyloliquefaciens [114], and thus, it is promising to use Priestia megaterium as a potential source to synthesize this biosurfactant.

Bacitracin is a cyclic polypeptide antimicrobial derived from the bacterium Bacillus subtilis that has wide use for the treatment of superficial bacterial skin infections [75]. The bacitracin gene cluster comprises three core biosynthetic genes (bacA, bacB, and bacC) and three additional genes (bacR, bacS, and bacT) (). From the protein database, it was found that the core bac genes are directly involved in the antibiotic (bacilysin) biosynthesis pathway, conferring that P. megaterium possesses such a synthesis pathway. This isolate can be used for metabolic engineering to enhance bacitracin action, as it is now graded as less functional against multidrug-resistant bacteria. No data involved in using endophytic bacteria using bacitracin production have been found previously, and thus, the metabolic engineering strategy to enhance bacitracin production is attractive.

Carotenoids, a subfamily of isoprenoids, are among the most widespread, diverse classes of all-natural products and biomolecules. Bacteria synthesize isoprenoids from isopentenyldiphosphate (IPP) and its isomer dimethylallyldiphosphate (DMAPP) through the methylerythritol 4-phosphate (MEP) pathway [115]. The crtM gene is the core gene that initiates the biosynthesis pathway [116], which clarifies the central action of the protein network (). Carotenoids are well-known pigments in plants, algae and photosynthetic bacteria. It is also an antioxidant for humans consumed by foods and fruits.

Zeaxanthin is also a carotenoid essential for our eyes against photooxidative damage from UV light. Animals and humans cannot synthesize zeaxanthin, and thus, it must be obtained through diet [117]. Zeaxanthin is supplemented through food, and now, it has increased the commercial demand for production [118]. Endophytic bacteria can be a reliable source for commercially synthesized carotenoids such as zeaxanthin and from this research, the endophytic isolate S. paucimobiliz can be used as a novel resource for metabolite production [119].

Fengycin is a novel antimicrobial agent effective against the fungal community isolated from B. subtillis [93]. Twelve different genes distributed in 4 synthetic clusters are responsible for the synthesis of this metabolite. A glycoside hydrolase (yngK) plays a connector role in the regulation of neighborhood nonribosomal synthetase activity (fenA, fenB, fenC, fend) and regulation activity (yngE, yngF, yngG, yngH, yngI, yngJ) of the genes involved in the fengycin synthase pathway (). Fengycin exhibited strong antifungal activity and inhibited the growth of several plant pathogens, particularly many filamentous fungi [120]; thus, the isolate Neobacillus drentennsis can be utilized as a harbor of novel antifungal secondary metabolites. Last, the siderophore compound staphyloferrin A comprises 4 core biosynthetic genes related to nonribosomal peptide synthetase in the action of siderophore biosynthesis, an important mechanism to overcome iron limitation [121]. It has been used as a detector of multidrug-resistant Staphylococcus aureus (MRSA) [6] and can open a new window of research on endophytic bacteria against antibiotic resistance mechanisms.

Most biologically active polyketide and peptide compounds are synthesized by polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs), which have been widely utilized to assess the biosynthetic capacity of culturable and nonculturable bacteria [19, 114, 122]. PKS and NRPS domain occurrence was analyzed for our isolated bacterial strains from the SBSPKS V2 database. The identified strains showed significant PKS and NRPS domain data for evaluating endophytes of biosynthetic potential. Therefore, Bacillus spp. represented the highest percentage (PKS 36%, NRPS 72%), whereas Staphylococcus spp. (PKS 22%, NRPS 24%), Micrococcus spp. (PKS 13%, NRPS 17%) and Sphingomonas spp. (PKS 21%, NRPS 11%) having a moderate percentage (). This result demonstrated the universal distribution of these domains in terms of the identification of biosynthetic genes for secondary metabolite production [15, 56, 123].

The presence of biosynthetic genes encoding polyketide synthases (PKS), nonribosomal peptide synthetases (NRPS), 1-aminocyclopropane-1-carboxylate deaminase (ACCD), and chalcone synthase (CHS) was analyzed by identifying five strains using four sets of degenerate primers (). PCR amplification of all five isolates showed a band of the expected size (~700–800 bp) for the NRPS gene (. PKS candidate amplicons (~700–800 bp) were detected in Priestia megaterium (LL1), Staphylococcus caprae (LL2), Micrococcus yunnanensis (GL), and Sphingomonas paucimobiliz (GR) ( whereas Sphingomona paucimobiliz (GR) showed only positive amplification of the ACCD gene (. For the CHS gene, no amplicons were found for any endophytic strain (). A putative prospect for bioactive secondary metabolites indicates that the positive strains can be a good candidate to research for natural bioactive compounds [12, 124]. Sphingomonas paucimobiliz showed positive results for three of the genes except for ACCD, which indicates its involvement in the pathways of plant-endophytic interaction and may be a reservoir of secondary metabolites [4, 125]. Further research and investigation of these gene and gene clusters may explore the novel bioactive natural products defining endophytic bacteria and their host interaction in medicinal plants [107, 126].

Microbial metabolites have some major advantages over synthetic drugs. Following the failure of numerous conventional medicines to treat viral infections and the emergence of particular viral resistances, interest in microbial metabolites as potential antiviral agents has grown. The recent COVID pandemic also had an immense impact on sorting out the best possible compounds against the deadly coronavirus. To exploit the probable candidate against SARS-COV2, predicted bioactive secondary metabolites were subjected to molecular docking against 3-chymotrypsin-like protease (3CLpro) of SARS-COV2. Out of nine metabolites, five metabolites were used to dock against the target protein, and four of them (surfactin, stenothricin, fengycin, and zeaxanthin) were excluded due to the incomplete conformation of their structure. The results from this study revealed that lopinavir, the reference inhibitor, had a binding affinity of −7.2 Kcal/mol for 3CLpro of SARS-COV-2 (). The two top docked compounds to SARS-COV-2 3CLpro are Microansamycin (-7.7 kcal mol−1) and Aureusimine (−6.0 kcal.mol−1). The results obtained from the ligand-protein binding interaction showed that lopinavir docked into the receptor-binding site and interacted via a conventional hydrogen bond and Pi-alkyl bond to LYS137. It further interacted with GLU290 via a Pi-Anion bond. This was attributed to multiple noncovalent interactions, such as hydrogen bonds and van der Waals (VDW) interactions, with other amino acid residues (GLU288, THR169, GLY170, VAL171, HIS172, SER139, GLY138, TYR126, GLN127, CYS128, LYS5) at the active site of 3CLpro (.

Microansamycin, the topmost docked compound to 3CLpro of SARS-COV-2, interacted via three conventional hydrogen bonds to the GLN107, THR111, and SER158 residues, along with VDW noncovalent interactions with ILE106, GLN110, PHE294, ILE152, PHE, ASN151, ASP153, and VAL104 amino acid residues (

Aureusimine interacted with PHE294 via a Pi-Pi stacked interaction along with four conventional hydrogen bonds, THR111, ASP153, LYS102, and SER158, of 3CLpro. The other VDW bonds were ASP295, THR292, GLN110, ASN151, and VAL104 (

These with a possible inhibitory propensity against the SARS coronavirus were identified based on the findings of these docking results. The stronger interactions of these two with 3CLpro compared to the reference compound imply that they may alter the viral protease function, which is required for the processing of viral replicase polyproteins.

To validate our prediction of being a potential drug, we analyzed the pharmacokinetic properties of the docked compounds (. According to Lipinski’s rule, an orally active medication cannot contain more than one violation of the following conditions: Not >5 hydrogen bond donors (oxygen or nitrogen atoms with one or more hydrogen atoms); Not >10 hydrogen bond acceptors (nitrogen or oxygen atoms); A molecular mass <500 Daltons; and an octanol-water partition coefficient (logP) not greater than 5. Microansamycin and aureusimine fulfilled the requirements with corresponding favorable predicted ADMET parameters (). The five compounds’ anticipated filtering analyses revealed characteristics that point to favorable ADME/tox and pharmacokinetic capabilities. This also suggests that the best-docked compounds (microansamycin and aureusimine) have drug potentiality. However, we have found microansamycin, aureusimine, and stenothricin through NMR results () that were present in the bacterial crude extract. Both microansamycin and aureusimine show various bioactivities including antibacterial, antiviral, antifungal, antitumor, and immunosuppressive [127, 128]. Hence, our study also revealed the antiviral potentiality of those metabolites by using different computational tools. This might be a harbinger of a structure-based medication design for COVID-19 that targets the 3CLpro of SARS-COV-2. Therefore, antiviral microbial metabolites may present a significant opportunity in the field of pharmaceutical research and development in the near future, and endophytic bacterial bioactive compounds can be a rich source of natural products.

Conclusion

This study revealed that endophytic bacterial secondary metabolites could be used as valuable bioactive compounds against disease-causing pathogens. Our study also suggests that the endophytic bacterial bioactive compounds microansamycin and aureusimine expose pharmaceutical efficacy targeting 3CLpro of SARS-COV-2 which represents a potential ingredient for pharmaceutical applications with antiviral properties and are worthy of future study for medication against SARS-COV-2.

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(C) ACCD gene.

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(D) CHS gene (No Result).

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(B) NRPS gene.

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(A) PKS gene.

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All the compounds’ structures in 3D.

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In silico potentiality of secondary metabolites from endophytic bacteria against SARS-COV-2

Major Concerns-

1. Although authors of manuscript are claiming that their study is related with potentiality of secondary metabolites from endophytic bacteria against SARS-COV-2, but this claim seems far from reality after observing following facts-

(a) Authors have been started collecting plant samples since 15/07/2019 and 9 sample out of 16 were collected before Dec. 2019 which they referred as the date of first case of covid-19. So, it is quite unreliable that the study was started with the aim to produce potential candidates against SARS-COV-2.

(b) Surprisingly, after doing rigorous experimentation on 16 plants and 48 plant parts they got positive results only for five plants which they started since Feb. 2020 and so on. It is giving an indication that 9 plants which have been reported in manuscript before Feb. 2020 are just to enhance data in the manuscript, although this indication can be completely false but impression of the results is like this only.

(c) Authors have been claimed activity against SARS-COV-2, the ligand they used for docking is based on their prediction of secondary metabolites from different servers (antiSMASH etc…). But the major concern in this study is that, they have been characterized the bacterial species by experimentation but after that everything is just based on prediction.

(d) On first stage they predicted the secondary metabolites present in their characterized bacterial species. Predictions may be far from reality.

(e) But still if we assume that these predicted secondary metabolites are very near to the reality (which is generally not true), further they predicted their activity against SARS-COV-2 through molecular docking and drug likeness properties. The major concern in this docking protocol is that the research and available database in the servers is far too limited to claim promising results as most of the researches are in primitive level through out the world. If one wants to predict for the disease which is existed for the last many years then due to sound database available online; these database servers and software’s mostly show promising results which is rarely possible in the case of SARS-COV-2. So, if authors want to claim it, they should first find out true secondary metabolites rather than predicted and may enhance the work further. Otherwise, they may change claim of their work via rewriting the manuscript accordingly.

(f) Lines 138-143- “It was initially detected in December 2019 in Wuhan, China [31], and on 11 March 2020, the outbreak was declared by the World Health Organization (WHO) as a global pandemic that has caused a total death of 4,644,740 worldwide as of September 15, 2021, with nearly 4 million new cases reported globally in the past week (6-12 September) according to the World Health Organization. Unfortunately, there is no particular medication or therapy for COVID-19 at this time.” We received your manuscript in Jan. 2022 and these lines are extremely confusing and again indicating that the study may not aimed towards SARS-COV-2.

2. Authors are informing that “Five biochemically identified bacterial isolates were then sent for sequencing (Macrogen, South Korea).” This is the most important part of their study which they outsourced from South Korea. This is a major concern of the study.

3. As per table 6 they provisionally identified bacterial strains but actual findings are far from their assumptions, so how they will counter this gap in the study?

4. Introduction and discussion part is far too long concerning research article and must be rewritten thoroughly and concisely.

5. Authors are claiming “Determination of antioxidant compounds from crude extracts”, but in the discussion part they did not mention the importance and future prospects of this study.

6. Figure 15 is not self-explanatory, they referred it in Drug likeness and surely it is but it is not understandable without any explanation. Authors may omit this figure or should explain it thoroughly.

Minor concerns-

1. Lines 68-71 “Natural compounds from endophytes exhibit activity as antimicrobials, antifungals, anticarcinogens, immunosuppressants, or antioxidants; thus, secondary metabolites produced by endophytes can be implemented as therapeutics in the pharmaceutical and agricultural industries [3,4]”

Separate references are needed to claim every other activity (primary reference must be used). The same comment applied for lines 100-102, 119-120, 126-127, 541-543.

2. In Table-2 Location (around CU campus) is given as separate entry which I feel is not much important for international research community, it may be useful for local people available there in author’s country.

3. At many places English grammar is not correct and should be checked again thoroughly.

4. Line 395- heading must be in uniform font and style.

5. Lines 537-539 “Therefore, one of the major objectives of this study is to isolate and characterize endophytic bacteria from our local medicinal plants to study their secondary metabolites, which have a wide range of applications in our health sector”, rather than only concentrate on their health sector, Authors should show their concern globally as they are claiming study related with a global pandemic.

Reviewer #2: The authors presented interesting research area. The in silico techniques the authors employed are to be appreciated. However, the manuscript needs more modification to meet PLOS ONE’s requirements.

1. It lacks consistency of ideas.

2. Scientific ideas are appeared without citation or the authors used a reference for three to six lines of scientific ideas. It should be re written

3. The ideas between line numbers 89 and 90, that of 91 has no relationship. It is not clear how the authors write the first two lines.

4. Surprisingly the authors used wrong references. Example the reference used to cite SwissADME in the method section of the manuscript is totally wrong.

5. The method section of the presented manuscript suffers from appropriate method writing and citations.

6. The authors reported that Microansamycin (-7.7 kcal mol-1) and Aureusimine (-6.0 kcal.mol-1) are good against the studied target. However, they are not found to show interaction with Cys amino acid residue. How could they suggest the compounds are good against the studied target without interaction with Cys amino acid?

7. Abstract and conclusions are not presented in an appropriate fashion with data support.

I suggest this paper has to be resubmitted.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Dr. Ankita Garg

Reviewer #2: No

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Submitted filename: Plos one review.pdf

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2 Apr 2022

Response to reviewer #1

We thank the honorable reviewer #1 for valuable feedback. Please find our response below-

Comments on Manuscript Number PONE-D-22-01192: ‘In silico potentiality of secondary metabolites from endophytic bacteria against SARS-COV-2’

Major Concerns-

1. Although authors of manuscript are claiming that their study is related with potentiality of secondary metabolites from endophytic bacteria against SARS-COV-2, but this claim seems far from reality after observing following facts-

(a) Authors have been started collecting plant samples since 15/07/2019 and 9 sample out of 16 were collected before Dec. 2019 which they referred as the date of first case of covid-19. So, it is quite unreliable that the study was started with the aim to produce potential candidates against SARS-COV-2.

Response: The authors would like to thank the reviewer for pointing out the issues and helping us to improve its quality.

Our initial objective was to characterize and identify endophytic bacteria which can secrete secondary metabolites including medicinal value against microbial diseases. Subsequently, we found the significant medicinal value of those metabolites after diverse analysis. During our study Covid-19 pandemic starts, thus our interest and focus have been amended to find if those metabolites had any medicinal value against SARS-COV-2 including other pathogenic viruses.

(b) Surprisingly, after doing rigorous experimentation on 16 plants and 48 plant parts they got positive results only for five plants which they started since Feb. 2020 and so on. It is giving an indication that 9 plants which have been reported in manuscript before Feb. 2020 are just to enhance data in the manuscript, although this indication can be completely false but impression of the results is like this only.

Response: We would like to thank the reviewer for these valuable comments. 48 plant parts have been selected to identify and isolate endophytic bacteria during experiments. Then, the plant extracts derived from 48 plant parts were spread over the solid Nutrient Agar (NA) media. Subsequently, several bacterial colonies were found and finally, sixteen pure colonies were selected (Table 3) as well as isolated after doing rigorous experiments. So, all (48) collected plant parts were used throughout the experiment.

Consequently, we have collected samples during different seasons (15/07/2019 and Feb. 2020) to compare the prevalence of antimicrobial effects. Besides, an epidemiological study among large samples might give significant results, that’s why we increased our samples.

(c) Authors have been claimed activity against SARS-COV-2, the ligand they used for docking is based on their prediction of secondary metabolites from different servers (antiSMASH etc.). But the major concern in this study is that, they have been characterized the bacterial species by experimentation but after that everything is just based on prediction.

Response: Thanks for your valuable comments. After characterization of specific bacteria, we have identified different metabolites such as Total phenolic content (TPC), Total flavonoid content (TFC) by the colorimetric method mentioned in the result section (Table 7). To confirm specific metabolite detection we have studied different in silico metabolite predicting tools by which we predicted nine compounds. Then all bacterial crude extract was studied for NMR analysis for validation of those metabolites. By proton NMR spectroscopy, we found three metabolites (Microansamycin, Aureusimine, and, Surfactin) out of the nine predicted metabolites. The NMR data has been included in the manuscript (Fig 16). However, we could not find the other six metabolites, it may be because of the low expression of the relevant genes that are responsible for those metabolite productions.

(d) On first stage they predicted the secondary metabolites present in their characterized bacterial species. Predictions may be far from reality.

Response: Thank you for your valuable suggestion. In the first stage, our results predicted the presence of secondary metabolites in their characterized bacterial species. According to the reviewer’s suggestions, we did NMR analysis (Fig 16), where we found our targeted metabolites (Microansamycin, Aureusimine, and, Surfactin.) which are perfectly complemented with our predicted data.

(e) But still if we assume that these predicted secondary metabolites are very near to the reality (which is generally not true), further they predicted their activity against SARS-COV-2 through molecular docking and drug likeness properties. The major concern in this docking protocol is that the research and available database in the servers is far too limited to claim promising results as most of the researches are in primitive level throughout the world. If one wants to predict for the disease which is existed for the last many years then due to sound database available online; these database servers and software’s mostly show promising results which is rarely possible in the case of SARS-COV-2. So, if authors want to claim it, they should first find out true secondary metabolites rather than predicted and may enhance the work further. Otherwise, they may change claim of their work via rewriting the manuscript accordingly.

Response: Thanks for your valuable suggestion. According to the reviewer’s suggestion, we have enhanced our work further to discover true secondary metabolites determined by NMR spectroscopy. And we found secondary metabolites from the NMR data which matched with our predicted secondary metabolites, and then we have already included them in our manuscript accordingly (Fig 16).

(f) Lines 138-143- “It was initially detected in December 2019 in Wuhan, China [31], and on 11 March 2020, the outbreak was declared by the World Health Organization (WHO) as a global pandemic that has caused a total death of 4,644,740 worldwide as of September 15, 2021, with nearly 4 million new cases reported globally in the past week (6-12 September) according to the World Health Organization. Unfortunately, there is no particular medication or therapy for COVID-19 at this time.” We received your manuscript in Jan. 2022 and these lines are extremely confusing and again indicating that the study may not aimed towards SARS-COV-2.

Response: We would like to thank the reviewer for raising the issue. During our experiment, by literature review, we came to know about the antiviral potentiality of different bacterial metabolites (Table 1). When the COVID-19 pandemic situation started throughout the world, at that time, we tried to identify whether our extracted metabolites have bio-potential activity against the virus SARS-COV-2. Still, we know that there is no particular medication or therapy for COVID-19 at this time, for that purpose we tried to carry out some prediction-based as well as true experiments on the targeted drug.

However, we have deleted the confusing line (142-143)-‘Unfortunately, there is no particular medication or therapy for COVID-19 at this time.’

2. Authors are informing that “Five biochemically identified bacterial isolates were then sent for sequencing (Macrogen, South Korea).” This is the most important part of their study which they outsourced from South Korea. This is a major concern of the study.

Response: Thanks for your valuable comments. All the biochemical, molecular experiments were done in our own lab. We carried out PCR by16s rDNA primer, and then the PCR product was purified in our laboratory. After that, we just sent our sample (PCR product) for DNA sequencing. Due to the unavailability of a DNA sequencing machine, we have to carry out the sequencing in South Korea.

3. As per table 6 they provisionally identified bacterial strains but actual findings are far from their assumptions, so how they will counter this gap in the study?

Response: Thanks for your suggestion. We know that sometimes the biochemical test cannot show 100% accurate results as a confirmatory test, therefore for further validation we sent our samples for DNA sequencing which is more reliable for the identification of bacterial species. Thus we claim the DNA sequencing result in our study is reliable.

4. Introduction and discussion part is far too long concerning research article and must be rewritten thoroughly and concisely.

Response: We would like to thank the reviewer for raising this issue to improve its quality. Introduction and discussion parts are rewritten thoroughly and concisely.

5. Authors are claiming “Determination of antioxidant compounds from crude extracts”, but in the discussion part, they did not mention the importance and future prospects of this study.

Response: Thanks for your comments. In the discussion part, we included the importance and future prospects of this study.

6. Figure 15 is not self-explanatory, they referred it in Drug likeness and surely it is but it is not understandable without any explanation. Authors may omit this figure or should explain it thoroughly.

Response: We have modified it as a self-explanatory figure.

Minor concerns-

1. Lines 68-71 “Natural compounds from endophytes exhibit activity as antimicrobials, antifungals, anticarcinogens, immunosuppressants, or antioxidants; thus, secondary metabolites produced by endophytes can be implemented as therapeutics in the pharmaceutical and agricultural industries [3,4]”

Separate references are needed to claim every other activity (primary reference must be used). The same comment applied for lines 100-102, 119-120, 126-127, 541-543.

Response: Thanks for your suggestion. We have modified it.

2. In Table-2 Location (around CU campus) is given as separate entry which I feel is not much important for international research community, it may be useful for local people available there in author’s country.

Response: The research work will not only be useful for local people available in our country but also will open a significant chance for carrying out future research by using this type of methodology in any international research community. Although our targeted location for the sample (medicinal plants) was around the CU (University of Chittagong) campus, the isolates collected from those medicinal plants can be found throughout the world.

3. At many places English grammar is not correct and should be checked again thoroughly.

Response: Thanks for your suggestions. Checked and modified it accordingly.

4. Line 395- heading must be in uniform font and style.

Response: Modified it accordingly.

5. Lines 537-539 “Therefore, one of the major objectives of this study is to isolate and characterize endophytic bacteria from our local medicinal plants to study their secondary metabolites, which have a wide range of applications in our health sector”, rather than only concentrate on their health sector, Authors should show their concern globally as they are claiming study related with a global pandemic.

Response: Modified it accordingly.

……….

Response to reviewer #2

We would like to thank reviewer#2 for raising the following issues.

Please find our response below on manuscript number PONE-D-22-01192: ‘In silico potentiality of secondary metabolites from endophytic bacteria against SARS-COV-2’

The authors presented interesting research area. The in silico techniques the authors employed are to be appreciated. However, the manuscript needs more modification to meet PLOS ONE’s requirements.

1. It lacks consistency of ideas.

Response: Thanks for your suggestions. We have improved and modified our manuscript as per the reviewer’s instructions.

2. Scientific ideas are appeared without citation or the authors used a reference for three to six lines of scientific ideas. It should be re written.

Response: We would like to thank the reviewer for this suggestion. We have rechecked the manuscript and corrected the error accordingly.

3. The ideas between line numbers 89 and 90, that of 91 has no relationship. It is not clear how the authors write the first two lines.

Response: We thank the honorable reviewer for valuable feedback. We have rechecked the manuscript and deleted these two lines accordingly.

4. Surprisingly the authors used wrong references. Example the reference used to cite SwissADME in the method section of the manuscript is totally wrong.

Response: We would like to thank the reviewer for this suggestion. We have modified our manuscript as per the reviewer’s instruction. Sorry for the inconvenience.

5. The method section of the presented manuscript suffers from appropriate method writing and citations.

Response: We have rechecked the manuscript and corrected the error accordingly.

6. The authors reported that Microansamycin (-7.7 kcal mol-1) and Aureusimine (-6.0 kcal.mol-1) are good against the studied target. However, they are not found to show interaction with Cys amino acid residue. How could they suggest the compounds are good against the studied target without interaction with Cys amino acid?

Response: Although Cysteine residue is not found in our targeted docking sites, other residues like asparagine, lysine, threonine, serine, etc. all are having multiple H-bond with the metabolites. That’s why it gives a strong interaction.

7. Abstract and conclusions are not presented in an appropriate fashion with data support.

Response: We would like to thank the reviewer for this suggestion. We have rechecked the manuscript and modified it accordingly.

I suggest this paper has to be resubmitted.

Response: Thanks for your suggestion. After all modifications, we have resubmitted our manuscript again.

……

Submitted filename: Response to Reviewer #2 .docx

Click here for additional data file.

14 Apr 2022

18 May 2022

Response to reviewers

We thank the honorable reviewer #1 for valuable feedback. Please find our response below-

Comments on Manuscript Number PONE-D-22-01192R1: ‘Bioactive potentiality of secondary metabolites from endophytic bacteria against SARS-COV-2: an in-silico approach’

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Response: The authors would like to thank the reviewer for pointing out all the issues in the previous round of review and helping us to improve its quality.

________________________________________

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Response: We would like to thank the reviewer for this positive comment.

________________________________________

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Response: We would like to thank the reviewer for this positive comment.

________________________________________

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Response: We would like to thank the reviewer for this positive comment to improve its quality.

________________________________________

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Response: We would like to thank the reviewer for this positive comment to improve its quality.

________________________________________

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

________________________________________

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Dr. Ankita Garg

……….

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response: Thanks for your suggestions. Checked and modified it accordingly.

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Response: Thanks for your suggestions. Checked and modified it accordingly.

Submitted filename: Response to reviewers.docx

Click here for additional data file.

1 Jun 2022

Bioactive potentiality of secondary metabolites from endophytic bacteria against SARS-COV-2: an in-silico approach

PONE-D-22-01192R2

Dear Dr. Lolo Wal Marzan,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Milkyas Endale, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

13 Jul 2022

PONE-D-22-01192R2

Bioactive potentiality of secondary metabolites from endophytic bacteria against SARS-COV-2: an in-silico approach

Dear Dr. Marzan:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Dr. Milkyas Endale

Academic Editor

PLOS ONE

Table 1

Antiviral bioactive compounds isolated from bacteria.

Microorganisms	Antiviral compounds	Group	Virus/Disease
Streptomyces avermitilis	Avermectin B1a	Lactone	NR
E. coli	Baicalein	Lactone	DENV-2, SARS-COV2
E. coli	Scutellarein	Lactone	SARS-COV
E. coli	Rosmarinic acid	Lactone	HCV, HIV
Lactobacillus sp	Exopolysaccharide	Polysaccharide	Adenovirus
Streptomyces koyangensis	Neoabyssomicin D	Polyketone	HSV
Streptomyces sp	Antimycin A	NR	Western equine encephalitis virus
Streptomyces roseus	Leupeptin	Peptide	Marburg virus
Bacillus licheniformis	Exopolysaccharide	Polysaccharide	HSV1
Myxococcus stipitatus	Phenalamide	Peptide	HIV-1
Bacillus pumilus	Pumilacidins A-G	Peptide	HSV1
Labilithrix luteola	Labindoles A; Labindoles B	NR	HCV

Note: HIV: Human immunodeficiency virus; HSV: Herpes simplex virus, HCV: Hepatitis C virus, DENV: Dengue virus, SARS-COV: Severe acute respiratory syndrome coronavirus

Table 2

List of collected medicinal plant samples and their collection areas.

Serial No.	Date of Collection	Season	Name of Plants	Location (Around CU campus)	Types of Plants	Plant Parts Used	Isolate code
1	15/07/2019	Rainy	Ocimum tenuiflorum (তুলসি)	1 no. Gate	Herb	L, R, S	TL, TR, TS
2	18/07/2019	Rainy	Eclipta prostrata (কালসোনা)	Science Faculty	Herb	L, R, S	KL, KR, KS
3	28/07/2019	Rainy	Justicia adhatoda (বাসক)	Central Mosque	Shrub	L, R, S	BL, BR, BS
4	03/08/2019	Rainy	Clerodendrum viscosum (ভাট)	Biol. Sci. Faculty	Shrub	L, R, S	VL, VR, VS
5	03/08/2019	Rainy	Leonurus sibiricus (দ্রোণপুষ্পি)	Biol. Sci. Faculty	Herb	L, R, S	DL, DR, DS
6	03/08/2019	Rainy	Melastoma malabathricum (দাঁতরাঙা)	Biol. Sci. Faculty	Shrub	L, R, S	DaL, DaR, DaS
7	14/10/2019	Autumn	Centella asiatica (থানকুনি)	Near Rail Gate	Herb	L, R, S	ThL, ThR, ThS
8	14/10/2019	Autumn	Catharanthus roseus (নয়নতারা)	Central Mosque	Herb	L, R, S	NL, NR, NS
9	14/10/2019	Autumn	Terminalia arjuna (অর্জুন)	Alaol Hall	Tree	L, R, S	ArL, ArR, ArS
10	05/02/2020	Winter	Mimosa pudica (লজ্জাবতী)	Biol. Sci. Faculty	Herb	L, R, S	LL, LR, LF
11	05/02/2020	Winter	Mikania micrantha (জার্মানি লতা)	Biol. Sci. Faculty	Herb	L, R, S	GL, GR, GS
12	07/04/2021	Summer	Terminalia chebula (হরিতকি)	Biol. Sci. Faculty	Tree	L, R, S	HL, HR, HS
13	10/04/2021	Summer	Phyllanthus emblica (আমলকী)	Biol. Sci. Faculty	Tree	L, R, S	AL, AR, AS
14	18/04/2021	Summer	Mentha spicata (পুদিনা)	Zero Point	Shrub	L, R, S	PL, PR, PS
15	23/04/2021	Summer	Terminalia bellirica (বহেড়া)	2 No. Gate	Tree	L, R, S	BoL, BoR, BoS
16	23/04/2021	Summer	Azadirachta indica (নিম)	2 No. Gate	Tree	L, R, S	NeL, NeR, NeS

Note: Leaf: L; Root: R; Stem: S. Biological Sciences Faculty: Biol. Sci. Faculty.

Table 3

List of isolated bacterial isolates and host medicinal plants.

Serial No.	Sample Code	Host Medicinal Plant	Plant parts used
1	GL	Mikania micrantha	Leaf
2	GR		Root
3	LL1	Mimosa pudica	Leaf
4	LL2		Leaf
5	LF		Stem
6	HL1	Terminalia chebula	Leaf
7	HL2		Leaf
8	HS1		Stem
9	HS2		Stem
10	AL1	Phyllanthus emblica	Leaf
11	AL2		Leaf
12	AS1		Stem
13	AS2		Stem
14	PL1	Mentha spicata	Leaf
15	PL2		Leaf
16	PS1		Stem

Table 4

Antibiotic sensitivity test result.

Serial No.	Name of Antibiotics	Disc Code	Disc Potency (μg)	Diameter of Inhibition (mm)
				LL1	LL2	LF	GL	GR
1	Penicillin G	P	10	0 (R)	20 (R)	21(R)	13 (R)	20 (R)
2	Chloramphenicol	C	30	29 (S)	24 (S)	30 (S)	27 (S)	30 (S)
3	Ampicillin	AMP	25	12 (I)	25 (S)	23 (S)	20 (S)	24 (S)
4	Streptomycin	S	10	23 (S)	33 (S)	24 (S)	24 (S)	25 (S)
5	Vancomycin	VA	30	23 (S)	30 (S)	25 (S)	25 (S)	25 (S)
6	Norfloxacin	NX	10	25 (S)	21 (S)	30 (S)	30 (S)	32 (S)
7	Tetracycline	TE	30	22 (S)	12 (R)	24 (S)	20 (S)	22 (S)
8	Nalidixic Acid	NA	30	26 (S)	26 (S)	29 (S)	27 (S)	30 (S)
9	Erythromycin	E	15	27 (S)	13 (R)	28 (S)	25 (S)	29 (S)
10	Ceftriaxone	CTR	30	26 (S)	15 (R)	31 (S)	22 (S)	32 (S)

Note: R = Resistant, S = Sensitive, I = Intermediate

Table 5

Target genes, primer sequences, cyclic conditions, PCR master mixture composition and amplicon size.

Target gene	Primer sequence (5ʹ-3ʹ)	Cycling parameters	Total Composition of PCR master mixture	Amplicon size (bp)	Reference (s)
Common Bacterial 16S rDNA	8F-AGAGTTTGATCCTGGCTCAG805R-GACTACCAGGGTATCTAAT	5 min at 95°C, 35 cycles of 95°C for 40 s, 57°C for 50 s and 72°C for 1 min	For 10 μl: 5 μl master mix, 2 μl template, 1 μla and 1 μlb, 1 μl water	800	[47, 48]

a = forward primer; b = reverse primer; s = seconds

Table 6

Summary of morphological and biochemical characterization.

Morphological and Biochemical Characteristics	ISOLATE CODE
	LL1	LL2	LF	GL	GR
Morphological Characteristics
Colony Color	Yellowish	Purple	White	Yellowish	Yellow
Gram Staining	+	+	+	+	-
Shape	Rod	Cocci	Rod	Cocci	Rod
Biochemical tests
Catalase	+	+	+	+	-
Oxidase	+	+	+	+	+
Indole	-	-	-	-	-
Methyl Red	+	+	+	+	-
VP	-	-	-	-	-
Mannitol Salt Fermentation	-	+	-	-	-
Citrate Utilization	+	-	-	-	-
Identified Strain (provisionally)	Bacillus spp.	Staphylococcus spp.	Bacillus spp.	Staphylococcus intermedius	Pseudomonas spp.

Note: (+) indicates a positive result and (-) indicates a negative result.

Table 8

Production of crude extracts from isolated endophytic bacteria.

Name of isolates	Incubation Period (hour)	Quantity of Crude Extract (gm)
Priestia megaterium (LL1)	24	0.34 ± 0.02
	48	0.36 ± 0.03
Staphylococcus caprae (LL2)	24	**0.45 ± 0.01
	48	**0.48 ± 0.01
Neobacillus drentensis (LF)	24	0.16 ± 0.02
	48	0.22 ± 0.03
Micrococcus yunnanensis (GL)	24	0.14 ± 0.04
	48	0.21 ± 0.01
Sphingomonas paucimobiliz (GR)	24	0.12 ± 0.05
	48	0.17 ± 0.04

Note: ** indicates maximum results

Table 9

List of predicted bioactive compounds from gene clusters.

** All the structures of the compound are given in the (

Identified isolates	Compound	Type	Bioactivity
Priestia megaterium (LL1)	Surfactin	Nonribosomal peptide (NRP)-lipopeptide	Surfactant, Antibacterial Antiviral [74]
	Bacitracin	Nonribosomal peptide (NRP)	Broad-spectrum antibiotic [75]
	Carotenoid	Terpene	Antioxidant, a precursor of vitamin [76]
Micrococcus yunnanensis (GL)	Microansamycin	NRP- Cyclic depsipeptide	Antioxidant [77]
	Stenothricin	Terpene (nonalpha poly-amino acids like e-Polylysin)	Antibiotic [78]
Staphylococcus caprae (LL2)	Aureusimine	Nonribosomal peptide (NRP)	Potent antibiotic [79]
	Staphyloferrin A	Siderophore	Potent antibiotic [80]
Neobacillus drentensis (LF)	Fengycin	Betalectone	Antifungal [81]
Sphingomonas paucimobiliz (GR)	Zeaxanthin	Terpene	Antioxidant [82]

Table 10

List of responsible genes and proteins from the secondary metabolite gene clusters of the identified compounds.

Compound	Gene	Protein	Biological Process	Pathway
Surfactin	srfAA	Surfactin synthase subunit 1	Antibiotic biosynthetic process	Surfactin biosynthesis
	srfAB	Surfactin synthase subunit 2
	srfAC	Surfactin synthase subunit 3
	srfAD	Surfactin synthase thioesterase subunit	Transport	Transmembrane transport
	ycxA	MFS type Transporter	Transport	Transmembrane transport
	ycxB	Uncategorized protein
	ycxC	Transporter Protein	Transport	Transmembrane transport
	ycxD	Transcriptional regulator protein	DNA binding cofactor	Alpha amino acid metabolic pathway
	tcyC	L-cystine import ATP-binding protein	ATPase-coupled amino acid transmembrane transporter activity	Amino acid transport pathway
Bacitracin	bacA	Prephenate decarboxylase	Antibiotic biosynthetic process	Bacilysin biosynthesis
	bacB	H2HPP isomerase
	bacC	bacilysin synthetase C
	bacT	Thioesterese family protein
	bacR	Transcriptional regulatory Protein	DNA binding
	bacS	Histidine kinase	Regulation process
Carotenoid	crtNa	CrtNa protein	Oxidoreductase activity	Carotenoid biosynthesis
	crtNc	CrtNc protein	oxidoreductase activity
	crtM	4,4’-diapophytoene synthase	Transferase activity
	crtNb	CrtNb protein	Oxidoreductase activity
Staphyloferrin A	sfaC (SAOUHSC_02433)	Staphyloferrin A synthetase	Nonribosomal peptide biosynthesis	Staphyloferrin biosynthesis
	sfaB (SAOUHSC_02434)	Staphyloferrin A synthetase	Nonribosomal peptide biosynthesis
	sfaA (SAOUHSC_02435)	Staphyloferrin A Transporter	Siderophore transporter
	iucC_3 (SAOUHSC_02436)	L-ornithine Racemase	Siderophore biosynthesis	Precursor biosynthesis
Zeaxanthine	crtY	Lycopene cyclase	lycopene beta cyclase activity	carotenoid biosynthetic process
	crtI	Phytoene dehydrogenase	oxidoreductase activity	carotenoid biosynthetic process
	crtB	Phytoene synthase	squalene synthase activity
Fengycin	yngE	Acyl-CoA carboxylase subunit beta	ligase activity
	yngF	Enoyl-CoA hydratase	catalytic activity
	yngG	Hydroxymethylglutaryl-CoA lyase	oxo-acid-lyase activity
	yngH	Acetyl-CoA carboxylase biotin carboxylase subunit	ATP binding
	yngI	AMP-binding protein	AMP-binding
	yngJ	Acyl-CoA dehydrogenase	Oxidoreductase
	yngK	Glycoside hydrolase family protein	hydrolase activity
	fenA	fengycin synthetase A	Nonribosomal peptide synthetase	Antibiotic biosynthetic process
	fenB	fengycin synthetase B
	fenC	fengycin synthetase C
	fend	fengycin synthetase D
	fenE	fengycin synthetase E

Table 11

Binding affinities of the reference compound (lopinavir) and other compounds to 3CLpro of coronaviruses.

Serial no.	Compound/Ligand	Binding Affinity (Kcal/mol)
1.	Lopinavir (ref. compound)	-7.2
2.	Aureusimine	-6.0
3.	Bacitracin	-3.8
4.	Carotenoid	-4.1
5.	Microansamycin	-7.7
6.	Staphyloferrin	-5.8

Note: Compounds having the highest binding affinity for the corresponding proteins are the ones indicated in bold values.

Table 12

Physicochemical properties of the binding compounds.

Lipinski filters analysis
Lipinski filters	Lopinavir	Aureusimine	Bacitracin	Carotenoid	Microansamycin	Staphyloferrin
Mol. weight (g/mol)	628.80	244.29	1422.69	568.87	319.35	480.38
Num. heavy atoms	46	18	100	42	23	33
Num. rotatable bonds	17	3	35	14	0	18
Num. H-bond acceptors	5	3	20	2	5	14
Num. H-bond donors	4	2	17	1	2	9
Molar Refractivity	187.92	70.91	399.90	188.21	84.24	101.10
Druglikeness
Lipinski Filter	Yes	Yes	No	No	Yes	No
Ghose	No	Yes	No	No	Yes	No
Veber	No	Yes	No	No	Yes	No
Egan	Yes	Yes	No	No	Yes	No
Muegge	No	Yes	No	No	Yes	No
ADMET analysis (Probability)
a) Absorption
Blood–Brain Barrier	+ (0.9104)	+0.9670	+0.9270	+0.9049	+0.9435	+0.9640
Human Intestinal Absorption	+ (0.9624)	+0.9925	+0.8746	+0.9879	+0.9063	-0.6694
Bioavailability Score	0.55	0.55	0.17	0.17	0.55	0.11
Caco-2	+ (0.9313)	+0.7591	-0.8675	-0.7841	+0.5576	-0.8885
P-glycoprotein Substrate	+ (0.7009)	-0.8841	+0.8733	-0.6950	-0.5051	-0.7300
P-glycoprotein Inhibitor	+ (0.9511)	-0.9197	+0.7422	+0.8359	-0.9012	-0.5469
b) Distribution
Subcellular localization	Mitochondria (0.7846)	Mitochondria (0.8990)	Mitochondria (0.4232)	Mitochondria 0.7289	Mitochondria 0.7657	Mitochondria 0.8125
c) Metabolism
CYP1A2 inhibition	-(0.8935)	+0.6112	-0.9046	-0.8585	-0.8444	-0.8870
CYP2C9 inhibition	-(0.7326)	-0.8667	-0.9071	-0.8478	-0.8855	-0.9408
CYP2C19 inhibition	-(0.7983)	-0.5533	-0.9025	-0.8443	-0.8605	-0.9215
CYP2D6 inhibition	-(0.9438)	-0.9369	-0.9231	-0.8980	-0.9441	-0.9250
d) Toxicity
AMES Toxicity	-(0.8300)	-0.8400	-0.6900	-0.8300	-0.7800	-0.7700
Carcinogens	-(0.6710)	-0.9286	-0.8714	-0.6888	-0.9286	-0.9571
Acute Oral Toxicity (kg/mol)	2.994	1.301	3.098	2.967	3.711	2.264
Hepatotoxicity	+(0.7000)	+0.6750	-0.6250	+0.5750	-0.7250	-0.6250
Aqueous solubility (LogS)	-3.414	-2.734	-3.079	-2.078	-3.091	-1.813
e) Pharmacokinetics
GI absorption	High	High	Low	Low	High	Low
Log Kp (skin permeation) cm/s	-5.93	-6.45	-17.88	-1.14	-8.52	-12.35