Anti-cancer and antimicrobial potential of five soil Streptomycetes: a metabolomics-based study

Abstract

Lack of new anti-cancer and anti-infective agents directed the pharmaceutical research to natural products' discovery especially from actinomycetes as one of the major sources of bioactive compounds. Metabolomics- and dereplication-guided approach has been used successfully in chemical profiling of bioactive actinomycetes. We aimed to study the metabolomic profile of five bioactive actinomycetes to investigate the interesting metabolites responsible for their antimicrobial and anti-cancer activities. Three actinomycetes, namely, Streptomyces sp. SH8, SH10 and SH13, were found to exhibit broad spectrum of antimicrobial activities, whereas isolate SH4 showed the broadest antimicrobial activity against all tested strains. In addition, isolates SH8, SH10 and SH12 displayed potent cytotoxicity against the breast cancer cell line Michigan Cancer Foundation-7 (MCF-7), whereas isolates SH4 and SH12 exhibited potent anti-cancer activity against the hepatoma cell line hepatoma G2 (HepG2) compared with their weak inhibitory properties on the normal breast cells MCF-10A and normal liver cells transformed human liver epithelial-2 (THLE2), respectively. All bioactive isolates were molecularly identified as Streptomyces sp. via 16S rRNA gene sequencing. Our actinobacterial dereplication analysis revealed putative identification of several bioactive metabolites including tetracycline, oxytetracycline and a macrolide antibiotic, novamethymycin. Together, chemical profiling of bioactive Streptomycetes via dereplication and metabolomics helped in assigning their unique metabolites and predicting the bioactive compounds instigating their diverse bioactivities.

Keywords: actinomycetes; anti-cancer; antimicrobial; dereplication; metabolomics; natural products.

Figure 1.

Phylogenetic study of the bioactive Streptomycetes' 16S rRNA sequences. The neighbour-joining method was used to create the phylogenetic tree [22]. The evolutionary history of the taxa studied is represented by a bootstrap consensus tree estimated from 1000 replicates [34]. Next to the branches is the percentage of replicate trees in which the associated taxa clustered together in the bootstrap test [34]. The Kimura 2-parameter method was employed to calculate evolutionary distances [35]. A total of 25 nucleotide sequences were examined. 1st + 2nd + 3rd + noncoding codon locations were included. Gaps and missing data were removed from all positions. MEGA-X was used to conduct the tree's evolutionary studies [36].

Figure 2.

Chemical structure of the closest matches to the metabolites identified from the actinomycetes by molecular weight from databases of natural products (table 2). (a) Chemical structure of compounds 1–20. (b) Chemical structure of compounds 22–40. The structure of the antibiotic S-583-B (21) is unavailable.

Figure 3.

Anti-cancer activity of the bioactive Streptomycetes against the cancer cell lines MCF-7 and HepG2 as well as their normal cell lines MCF-10A and THLE2, respectively. 5FU = 5-fluorouracil. The cytotoxicity of the crude extracts and 5-fluorouracil drug was evaluated using MTT assay in MCF-7, MCF-10A, HepG2 and THLE2 cell lines upon 48 h treatment with different concentrations of bacterial extracts and standard drug. Values of IC₅₀ were presented as mean ± s.d., while statistical significance of anti-cancer activity of different extracts and 5-fluorouracil against tested cancer cell lines comparing with normal cell lines was calculated using unpaired student's t-test where *p < 0.05,**p < 0.01 and ***p < 0.001. The experiment was performed in triplicates.

Figure 4.

The contribution of different principal components in determining the variation of the PCA model in the current study. (a) Pairwise PCA score plot of different principal components in the unsupervised model. (b) PCA scree plot of different principal components in the unsupervised model.

Figure 5.

Principal component analysis of bioactive actinobacterial extracts. (a) Two-dimensional PCA score plot. (b) Three-dimensional PCA score plot.

Figure 6.

Principal component analysis loadings plot and hierarchical cluster analysis of bioactive actinobacterial extracts. (a) Three-dimensional PCA loadings plot with discriminatory compounds to SH4, SH8 and SH10. (b) HCA plot.

Figure 7.

Supervised multivariate analysis of bioactive actinobacterial extracts. (a) Three-dimensional sPLS-DA score plot. (b) VIP score plot.

Figure 8.

Clustering of crude extracts from bioactive Streptomycetes according to the intensity of mass ion peaks of main metabolites as shown in the heat map. Red indicator is for high intensity of mass ion peaks, while blue is indicating lower intensity of mass ion peaks in the clustered crude extracts.