Metabolomic Profiling, In Vitro Antimalarial Investigation and In Silico Modeling of the Marine Actinobacterium Strain Rhodococcus sp. UR111 Associated with the Soft Coral Nephthea sp.

2.2. Strain Isolation

Three different media [M1, ISP medium 2, and Oligotrophic medium (OLIGO) were used for the isolation of the actinobacteria. All of the media were supplemented with 0.2 µm pore size filtered cycloheximide (100 µg/mL), nystatin (25 µg/mL), and nalidixic acid (25 µg/mL). Cycloheximide and nystatin inhibit fungal growth, while nalidixic acid inhibits many fast-growing Gram-negative bacteria [10]. All of the media contained Difco Bacto agar (18 g/L) and were prepared in 1 L of artificial seawater (NaCl 234.7 g, MgCl₂·6 H₂O 106.4 g, Na₂SO₄ 39.2 g, CaCl₂ 11.0 g, NaHCO₃ 1.92 g, KCl 6.64 g, KBr 0.96 g, H₃BO₃ 0.26 g, SrCl₂ 0,24 g, NaF 0.03 g and ddH₂O to 10.0 L). The inoculated plates were incubated at 30 °C for 6–8 weeks. Distinct colony morphotypes were picked and re-streaked until visually free of contaminants. Thirty isolates were picked upon morphological differences. The isolates were maintained on plates for short-term storage, and long-term strain collections were set up in a medium supplemented with 30% glycerol at −80 °C.

2.3. Molecular Identification and Phylogenetic Analysis

The 16S rRNA gene amplification, cloning, and sequencing were performed according to Hentschel et al., 2015, using the universal primers 27F and 1492R. Chimeric sequences were identified by using the Pintail program. The genus-level affiliation of the sequence was validated using the Ribosomal Database Project Classifier. The genus-level identification of all the sequences was conducted with RDP Classifier (-g 16srrna, -f allrank) and validated with the SILVA Incremental Aligner (SINA) (search and classify option). An alignment was calculated again using the SINA web aligner (variability profile: bacteria). The gap-only positions were removed with trimAL (-noallgaps). For phylogenetic tree construction, the best fitting model was estimated initially with Model Generator. RAxML (-f a m GTRGAMMA –x 12345 –p 12345 -# 1000) and the estimated model was used with 1000 bootstrap resamples to generate the maximum-likelihood tree. The sequences of 16S rRNA from the type strains of Rhodococcus species were identified using the NCBI BLAST server implementing the megablast search program with the RhodUA111 16S rRNA sequence as a query. Preliminary phylogenetic analyses were used to identify the Rhodococcus species most closely to RhodUA111. The sequences were obtained from the NCBI database. The sequence alignment and phylogenetic analysis were performed with MEGA11. A multispecies sequence alignment was obtained with Muscle, and the phylogenetic relationships were revealed using Maximum Likelihood with the TN93+G+I substitution model.

2.4. Extract Preparation

The strain (low sequence similarity) was cultivated using 3 different production media (M1, ISP2, and OLIGO) liquid approaches to produce 3 organic extracts. The liquid cultures were grown for 10 days at 30 °C while shaking at 150 rpm. The culture was then filtered, and the supernatant was extracted with ethyl acetate while the cells and mycelia were extracted by shaking with methanol for 4 h. The ethyl acetate extracts were stored at 4 °C.

2.5. Antimalarial Assay

To determine the antiplasmodial effect of the fungal extracts on P. falciparum erythrocytic replication in vitro, the Malstat assay was used as described. To synchronize the NF54 culture, parasites with many ring stages were centrifuged, and the pellet was resuspended in five times pellet volume of 5% w/v sorbitol /ddH20 and incubated for 10 min at room temperature. The cells were washed once with RPMI to remove sorbitol and further cultivated. Synchronized ring-stage parasites with 1% parasitaemia of P. falciparum NF54 strains were plated in triplicate in 96-well plates (200 µL/well) in the presence of a serial dilution of the extracts dissolved in 0.5% v/v dimethyl sulfoxide (DMSO). The parasites were incubated with the extracts for 72 h at 37 °C in the presence of nitrogen-containing 5% O₂ and 5% CO₂. The incubation of parasites with DMSO at a concentration of 0.5% alone was used as negative control, and 20% was used as positive control. Afterwards, 20 µL was removed and added to 100 µL of the Malstat reagent (1% Triton X-100, 10 mg of l-lactate, 3.3 mg Tris and 0.33 mg of APAD (3-Acetylpyridine adenine dinucleotide) dissolved in 1 mL of distilled water, pH 9.0) in a new 96-well microtiter plate. The plasmodial lactate dehydrogenase activity was then assessed by adding a 20 μL mixture of NBT (Nitro Blue Tetrazolium)/Diaphorase (1:1; 1 mg/mL stock each) to the Malstat reaction. The optical densities were measured at 630 nm, and the IC₅₀ values were calculated from variable-slope sigmoidal dose–response curves using the GraphPad Prism program version 5.

2.6. LC-MS Profiling

Three ethyl acetate extracts from the samples were prepared at 1 mg/mL for mass spectrometry analysis. The recovered ethyl acetate extract was subjected to metabolic analysis using LC-HR-ESI-MS, according to Abdelmohsen et al., 2014. An Acquity Ultra Performance Liquid Chromatography system connected to a Synapt G2 HDMS quadrupole time-of-flight hybrid mass spectrometer (Waters, Milford, CT, USA) was used. The positive and negative ESI ionization modes were utilized to carry out the high-resolution mass spectrometry coupled with a spray voltage at 4.5 kV; the capillary temperature was 320 °C, and the mass ranged from m/z 150 to 1500. The MS dataset was processed, and the data were extracted using MZmine 2.20 based on the established parameters. Mass ion peaks were detected and accompanied by a chromatogram builder and a chromatogram deconvolution. The local minimum search algorithm was addressed, and isotopes were also distinguished via the isotopic peaks of the grouper. The missing peaks were displayed using the gap-filling peak finder. An adduct search, along with a complex search, was carried out. The processed data set was next subjected to molecular formula prediction and peak identification. The positive and negative ionization mode data sets from the respective extract were dereplicated against the DNP (Dictionary of Natural Products) databases. ChemBioDraw Ultra 14.0 software was used to create the chemical structure drawings.

2.7. In silico Molecular Docking Simulations

Molecular Operating Environment (MOE^® 2014.0901) software was used to evaluate and examine the possible interactions of the dereplicated molecules (3, 4, 5, 6, 11, and 12) within the crystal structure of 3 target proteins: P. falciparum kinase (PfPK5; PDB ID: 1V0P), P. falciparum cytochrome bc1 complex (Cyt bc1; PDB ID: 4PD4), and finally, P. falciparum lysyl-tRNA synthetase (PfKRS1; PDB ID: 6AGT). The docking results in the form of a docking score (S; kcal/mol), docking accuracy (root-mean-square deviation; RMSD in Å), and binding interactions with various amino acid residues lining the active site were listed (as shown in Table 1 and Table 2). The preparation of the structural formulas of the test molecules and the structure of the target proteins were performed as reported by [11]. The validation of the prepared protein structures was conducted via the re-docking of the co-crystallized ligands with their protein crystal structure obtained from the RSCB protein data bank (https://www.rcsb.org/ accessed on 8 August 2022), and their docking score and RMSD values were within an acceptable range for running docking simulations within the target proteins (as listed in Table 1). The docking simulations were performed according to a docking protocol reported elsewhere [11], and the results are reported in Table 1 and Table 2. The visual inspections of the produced docking poses (10 poses/molecule) for the binding interactions with various amino acid residues lining the active site of 3 target proteins crystal structures are listed in Table 2 and are represented as 2D and 3D diagrams.

Table 1

Docking simulations of dereplicated molecules and co-crystallized ligands within three of P. falciparum proteins.

#	Cpd ID	Isolated Molecules	1V0P a		4PD4 b		6AGT c
			S g	RMSD h	S	RMSD	S	RMSD
1	3	Indene propanoic acid derivative	−5.74	1.67	−5.55	1.97	−5.88	0.92
2	4	Indole-3-acetic acid	−4.45	1.40	−5.16	1.77	−5.15	1.10
3	5	Rhodinodohyde	−5.01	0.96	−4.85	0.81	−5.37	1.53
4	6	Mitomycin-K	−5.39	1.68	−5.37	0.73	−6.09	1.16
5	11	Perlolyrine	−5.98	1.04	−5.84	1.69	−6.91	1.12
6	12	3097-B2	−5.89	1.99	−6.50	1.56	−7.11	1.97
Co-crystallized Ligand			−7.32 d	1.21	−6.45 e	1.64	−7.98 f	0.63

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^a 1V0P: P. falciparum kinase; ^b 4PD4: cytochrome bc1; and ^c 6AGT: lysyl-tRNA synthetase (PfKRS1) active sites; ^d Purvalanol B (PVB); ^e 2-[trans-4-(4-chlorophenyl)cyclohexyl]-3-hydroxynaphthalene-1,4-dione (AOQ); ^f N-(cyclohexylmethyl)-4-oxo-4H-1-benzopyran-2-carboxamide (9X0); ^g Docking Score (S; Kcal/mol); ^h Root-Mean-Square Deviation (Å).

3.2. Antimalarial Assay

As shown in Table 4, the activity of the culture extracts was tested in vitro against Plasmodium falciparum. The results revealed that the extract ISP2 exhibited the highest inhibitory activity, with IC₅₀ values of 8.5 µg/mL, while Oligo was inactive when compared to the positive control drug chloroquine’s IC₅₀ value (0.022 µg/mL).

3.3. Metabolomic Analysis

Since marine actinomycetes are producing an increasing number of natural products, particularly species of Streptomyces, Nocardia, Salinispora, Micromonospora, and Rhodococcus, these microbes have also helped researchers find a number of clinically significant drugs. In addition, the obtained results of the antimalarial assay encouraged us to investigate the chemical profile of each of the tested extracts in order to identify the active antimalarial chemical components. Given this, metabolomic profiling of the actinomycete Rhodococcus sp. UA111 obtained from soft marine coral using LC-HR-MS for dereplication purposes has led to the identification of a number of metabolites, of which alkaloids were identified to predominate. LC-HRMS analysis was performed using both positive and negative ionization modes to detect the greatest number of metabolites possible. From the DNP database (Table 5 and Figure 2), the mass ion peak at m/z 135.0442 [M_H]_ (RT, 3.0118min) for the suggested molecular formulas C₈H₈O₂ was identified as 2-(2′-Hydroxyphenyl) ethen-1-ol, which was previously obtained from Rhodococcus benzothiophene [12]. The mass ion peak at m/z 293.1755 [M_H]_ (RT, 4.668 min), in accordance with the molecular formula C₁₇H₂₆O₄ was recognized as octahydro-7α-methyl-1-(1-methyl-2-oxo-propyl)-5-oxo-1H-indene-4-propanoic acid that was previously isolated from Rhodococcus corallines [13]. Whereas that at m/z 174.1022 [M_H]_ (RT, 2.6807min), corresponding to the molecular formula C₁₀H₉NO₂, was suggested to be indole-3-acetic acid, which was previously isolated from Rhodococcus sp. BFI 332 [14]. The mass ion peak at m/z 155.0237 [M_H]_ (RT, 2.58059 min), in accordance with the molecular formula C₇H₈O₄, was recognized as 2,5-Dihydro-3-methyl-5-oxo-2-furanacetic acid (S-form), which was previously isolated from Rhodococcus rhodochrous [10]. Additionally, another metabolite with the molecular formula C₁₀H₉NO₂, for the mass ion peak at m/z 176.0712 [M_H]+ (RT, 4.1051 min), was characterized as rhodinodohyde, which was previously described from Rhodococcus sp. UA13 [3]. The metabolite, namely Mitomycin-K, with the molecular formula C₁₆H₁₈N₂O₄, was also dereplicated from the mass ion peak at m/z 303.134 [M_H]+ (RT, 2.684719 min); this compound was previously reported from Rhodococcus sp. UR59 [15]. Furthermore, the mass ion peak at m/z 116.0497 [M_H]_ (RT, 3.077 min) for the predicted molecular formula C₈H₇N was distinguished as 2-Phenylacetonitrile, which was isolated before from marine Streptomyces sp. GWS-BW-H5 [16]. The mass ion peak at m/z 131.0707 [M_H]_ (RT, 4.6038 min), for the suggested molecular formulas C₆H₁₂O₃ was identified as 3,4-Dihydroxy-3-methylpentan-2-one that was previously obtained from Streptomyces griseorubens sp. ASMR4 [17]. Moreover, the mass ion peak at m/z 160.0395 [M_H]_ (RT, 3.08194 min), corresponding to the proposed molecular formula C₉H₇NO₂, was identified as Indole-3-carboxylic acid, which was earlier obtained from Streptomyces sp. TK-VL_333 [18]. The mass ion peak at m/z 166.0503 [M_H]- (RT, 2.3045 min), with the molecular formula C₈H₉NO₃, was recognized as 2-Hydroxy-5-methoxybenzamide that was previously isolated from marine Streptomyces sp. [19]. The mass ion peak at m/z 265.0973 [M_H]+ (RT, 3.67572 min), in accordance with the molecular formula C₁₆H₁₂N₂O₂, was recognized as Perlolyrin, which was previously isolated from a terrestrial Streptomyces sp. GW11/1695 [20] and a marine Streptomyces sp. [21] The metabolite, 3097-B2, with the molecular formula C₁₄H₁₉NO₄, was also dereplicated from the mass ion peak at m/z 264.1238 [M_H]_ (RT, 2.9919 min); this compound was previously reported from marine Streptomyces sp. [22].

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Figure 2

Metabolites dereplicated from Rhodococcus sp. crude extract.

3.4. Multivariate Data Analysis

After being processed by the mzmine software, an MVA on the gathered LC-HRMS data was carried out in order to condense the enormous amount of data obtained and identify correlation and distinction among the tested samples. The LC-MS analysis dataset was statistically treated using MetaboAnalyst 5.0 [23,24].

3.4.1. Unsupervised Analysis

As an initial “blind” step, the unsupervised principal component analysis (PCA) method was implemented first. As shown in the PCA pairwise score plots (Figure 3A), there are five PCA components (PCs) that explained 100.1% of the total variation, in which the first and second PCs separately contributed to 97.9% of the total variation (PC1 and PC2 represent 55.8% and 42.1%, respectively) (Figure 3A). The samples were primarily allocated to three distinct areas between PC1 and PC2, which revealed statistically significant differences between the three extracts, indicating their chemical variation in the PCA 2D scores plot (Figure 3B). On the other hand, the heat map plot (Figure 3D) showed distinct patterns for the culture extracts ISP2 and M1. The metabolites (m/z) that contributed to the fluctuation of the anomalous samples were shown by the PCA loadings plot (Figure 3C); the unique clusters those metabolites (m/z) formed in the loading plot correspond to the locations of the anomalous samples in the scores plot. Such metabolites were dereplicated using the Dictionary of Natural Products (DNP); the annotated discriminatory compounds for ISP2 corresponding to m/z (retention time in min) 180.10233 [M_H]⁺ (2.4039) was identified as p-tolyl-3-aminopropanoate (C₁₀H₁₃NO₂) [25]. The mass ion peak at m/z 164.07093 [M_H] (RT, 1.95985 min), corresponding to the proposed molecular formula C₉H₁₁NO₂, was identified as Anthranilic acid ethyl ester [26]. The mass ion peak at m/z 182.08152 [M_H]⁺ (RT, 2.997325 min), in accordance with the molecular formula C₉H₁₁NO₃, was recognized as tyrosol carbamate [27]. In contrast, that at m/z 152.07104 [M_H]⁻ (RT, 2.9552 min) corresponding to the molecular formula C₈H₉NO₂, was suggested to be streptokordin [28]. The mass ion peak at m/z 211.08715 [M_H]⁺ (RT, 3.872317 min), in accordance with the molecular formula C₁₃H₁₀N₂O, was identified as penicinoline E [29]. On the other hand, the annotated discriminatory compounds for M1, corresponding to m/z (retention time in min) 308.13568 [M_H]⁺ (2.36095) and 210.0403 [M+H]⁻ (2.304102) were putatively identified as antibiotic A 53868A (C₁₁H₂₂N₃O₅P) and antibiotic T0007 B₂ (C₉H₉NO₅) [30], respectively.

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Figure 3

Metabolomics multivariate analysis. (A) PCA pairwise score plot of the unsupervised method; (B) 2D PCA scores plot of the unsupervised method; (C) 2D PCA loadings plot of the unsupervised method; (D) Heatmap plot.

3.4.2. Supervised Analysis

The supervised approach frequently employed in metabolomics studies for classification and supervised biomarker discovery, partial least squares discriminant analysis (PLS-DA), makes use of multivariate linear regression techniques [31]. Both the prediction and the performance of the developed model were good (predictive power of models, Q2 = 1.0; model goodness, R2 = 1.0); a high Q2 value indicates good predictions (Q2 values were those that were very near to 1.0) (Figure 4B). The differences between the samples were confirmed after PLS-DA supervised analysis; in the PLS-DA 2D paired score plot (Figure 4A), five components accounted for 100 percent of the total variation. Two PLS components (PCs) explained 97.8% of the overall variances in the PLS-DA 2D scores plot, with the first and second PCs contributing 46.2 and 51.6 percent, respectively (Figure 4D). One of the crucial measurements in PLS-DA is the variable Importance in Projection (VIP) (Figure 4C), which is shown in Figure 4C. The top 15 most significant traits with the highest value, according to PLS-DA, were demonstrated by VIP. The top 15 significant metabolites (m/z) were listed on the vertical axis in descending order according to the scores they received on the horizontal axis, and the relative concentrations of the important metabolites in the tested samples are shown in the attached colored box on the right (Figure 4B) [32].

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Figure 4

Metabolomics multivariate analysis. (A) PLS pairwise score plot; (B) 2D PCA scores plot of the unsupervised method; (C) 2D PCA loadings plot of the unsupervised method; (D) Heatmap plot.

The observed variations emphasize how crucial it is to use a diverse range of media to improve the isolation effectiveness of metabolites from actinomycetes associated with the marine environment. Based on prior experience and published reports, the actinomycete cultivation media M1, ISP2, and OLIGO were selected [2]. The results showed that ISP2 media was preferred for growing the genus Rhodococcus and the production of bioactive antimalarial compounds.

The authors acknowledge the support from Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R15), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.