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Determining the mode of action of anti-mycobacterial C17 diyne natural products using expression profiling: evidence for fatty acid biosynthesis inhibition

BMC Genomics volume 17, Article number: 621 (2016) Cite this article

Abstract

Background

The treatment of microbial infections is becoming increasingly challenging because of limited therapeutic options and the growing number of pathogenic strains that are resistant to current antibiotics. There is an urgent need to identify molecules with novel modes of action to facilitate the development of new and more effective therapeutic agents. The anti-mycobacterial activity of the C17 diyne natural products falcarinol and panaxydol has been described previously; however, their mode of action remains largely undetermined in microbes. Gene expression profiling was therefore used to determine the transcriptomic response of Mycobacterium smegmatis upon treatment with falcarinol and panaxydol to better characterize the mode of action of these C17 diynes.

Results

Our analyses identified 704 and 907 transcripts that were differentially expressed in M. smegmatis after treatment with falcarinol and panaxydol respectively. Principal component analysis suggested that the C17 diynes exhibit a mode of action that is distinct to commonly used antimycobacterial drugs. Functional enrichment analysis and pathway enrichment analysis revealed that cell processes such as ectoine biosynthesis and cyclopropane-fatty-acyl-phospholipid synthesis were responsive to falcarinol and panaxydol treatment at the transcriptome level in M. smegmatis. The modes of action of the two C17 diynes were also predicted through Prediction of Activity Spectra of Substances (PASS). Based upon convergence of these three independent analyses, we hypothesize that the C17 diynes inhibit fatty acid biosynthesis, specifically phospholipid synthesis, in mycobacteria.

Conclusion

Based on transcriptomic responses, it is suggested that the C17 diynes act differently than other anti-mycobacterial compounds in M. smegmatis, and do so by inhibiting phospholipid biosynthesis.

Background

Despite significant progress by the World Health Organization, tuberculosis (TB) remains a global health emergency with millions of patients succumbing to the disease annually [1–3]. Although more than 15 TB drug candidates are currently in pre-clinical or clinical phases of drug development, new candidates are needed to supplement the drug development pipeline [3, 4]. Highly bioactive drug candidates can be selected through high-throughput screening from compound libraries, but it is difficult to select candidates for further pre-clinical trials without determining their specific modes of action (MOAs) [5–9]. It is therefore important to characterize the MOAs of bioactive molecules early in the drug discovery/development process in order to select candidates with high potential of leading to new and improved pharmaceuticals [4, 10].

Metabolic response to environmental stimuli, such as the introduction of an exogenous chemical, can trigger specific gene expression responses that are necessary for growth and survival [11–14]. Transcriptional responses can therefore provide a global view of an organism’s response to exogenous chemical stimuli [15–17]. Gene expression analysis has been used successfully to characterize the molecular targets of pharmaceuticals [14, 18, 19]. For example, microarray analysis of Mycobacterium tuberculosis treated with isoniazid and rifampin (Fig. 1) not only generated data that was in good agreement with the known MOAs of these TB drugs; these studies also improved the knowledge of indirect and secondary cellular responses of M. tuberculosis under the effects of those drugs [11, 20, 21]. Microarray data obtained from M. tuberculosis treated with BTZ043 (Fig. 1), a TB drug candidate in pre-clinical development, led to the discovery that BTZ043 inhibits a key enzyme in the synthesis of cell wall arabinans [22]. Thus, transcriptomic profiling can offer a great deal of insight for drug-organism interactions.

Fig. 1
figure1

The structures of compounds mentioned in this study

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The objective of this study was to determine the transcriptome profile of M. smegmatis following treatment with falcarinol (1) and panaxydol (2) (Fig. 1) in order to identify the anti-mycobacterial MOAs of these compounds. Falcarinol and panaxydol were identified as anti-mycobacterial natural products from studies of the plant Aralia nudicaulis [23]; however, the anti-mycobacterial MOA of these two compounds was not known. Cognisant of the challenges associated with studying M. tuberculosis in the laboratory, we studied the closely related, non-pathogenic mycobacteria M. smegmatis MC2 155 as a surrogate model organism [24–27]. In addition to fewer safety precautions, other benefits related to the use of M. smegmatis include its superior growth rate, its comparable ability to M. tuberculosis to produce mycolic acid, and the fact that its genome has been completely sequenced and well annotated, which facilitates transcriptomics analyses [28].

Results and discussion

In order to explore the transcriptomic profile of M. smegmatis MC2 155 treated with falcarinol and panaxydol, the minimum inhibitory concentrations (MICs) of the two target compounds and three positive controls, isoniazid, ethambutol and kanamycin, were measured using a modified microplate resazurin assay. The MICs of falcarinol and panaxydol against M. smegmatis MC2 155 were 12.5 and 25 μg/mL respectively (Table 1), which is higher than their MICs against M. tuberculosis H37Ra (6.25 and 9.38 μg/mL respectively) [23]. Two of the positive controls, isoniazid and ethambutol, were selected because information regarding their MOA against Mycobacterium species is available [29, 30]. Additionally, M. smegmatis is naturally resistant to isoniazid because it has different peroxide stress response systems to M. tuberculosis [31] and the multidrug efflux pump LfrA [32], although the effect of these resistance mechanisms has not been investigated at the level of the transcriptome. The third positive control, kanamycin, was selected due to its potent activity against M. smegmatis [32]. The MICs against M. smegmatis that we obtained for of all three controls were similar to those previously reported [33, 34].

Table 1 Anti-mycobacterial activities of falcarinol, panaxydol, isoniazid, ethambutol and kanamycin against M. smegmatis MC2 155
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Microarray analysis revealed that 704 and 907 genes, or 10 and 13 % of the M. smegmatis transcriptome, were differentially expressed following treatments with falcarinol and panaxydol compared to the control. Using a fold change threshold of ± 2 and an α value threshold of p < 0.05, falcarinol and panaxydol treatments resulted in 585 and 788 differentially expressed genes (DEGs) respectively compared to the vehicle control. When comparing the two treatments, 99 % of the genes differentially expressed were in common in both treatments, evidence that the two target compounds act via the same MOA against M. smegmatis. All gene expression profiles from the microarray analysis are provided as supplementary data (Additional file 1).

Principle component analysis (PCA) of all treatments revealed that exposure of M. smegmatis to 1 and 2 resulted in transcriptomic responses that were more similar to each other than the controls. Transcript responses to isoniazid and ethambutol treatments coincided with the vehicle control while the response to the kanamycin treatment was the most distinct of all other treatments (Fig. 2). PCA confirmed that falcarinol and panaxydol treatments resulted in relatively similar gene responses suggesting they have a common MOA. Additionally, the relative distance between the gene responses to 1 and 2 and the three positive controls in PCA provided further evidence that 1 and 2 have a MOA distinct to those of the controls; isoniazid and ethambutol both affect cell wall synthesis and kanamycin primarily targets protein synthesis (Fig. 2) [35].

Fig. 2
figure2

Principle component analysis of gene expression data. Each point represents one microarray conducted with one biological replicate

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The PCA provided a global view of the differences in the transcriptomic profiles of treatments; however, it does not identify what caused these differences. Therefore, we performed functional enrichment analysis to examine what biological functions may have contributed to the differences in transcriptome profiles. All of the genes in the M. smegmatis MC2 155 genome were grouped based on their functions by assigning them to one or multiple gene ontology (GO) terms, which were grouped into three categories (molecular function, cellular component and biological processes). In order to identify the biological functions that were affected by each treatment, functional enrichment analysis was used to assess which GO terms in each treatment were significantly different to the vehicle control. The GO terms that were preferentially affected by falcarinol and panaxydol treatments were determined from P-values generated from parametric analysis of gene set enrichment (PAGE) [36] following a post-hoc analysis with a false discovery rate (FDR) of 0.01 (Table 2). Nine common GO terms were found to be significantly different to the vehicle controls in both treatments, and the processes that these represent were related to fatty acid biosynthesis, ectoine biosynthesis, and protein metabolism, transport and biosynthesis. Thus, the C17 diynes appear to affect multiple pathways within the cell and likely have different MOAs, however for our discussion we focus on the prevalent themes based on multiple lines of evidence from bioinformatics. All functional enrichment analysis data are included as supplementary data.

Table 2 Functional enrichment analysis of gene ontology (GO) terms regulated by falcarinol and panaxydol (P-value < 0.01 after post-hoc analysis)
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To further probe the pathways that were affected by 1 and 2, a pathway enrichment analysis was performed using the Biocyc online program (http://biocyc.org/) which uses a Fisher exact test with a post-hoc application of the Benjamini-Hochberg method [37]. Differentially affected pathways identified in the falcarinol treatment included amino acid biosynthesis and fatty acid biosynthesis, such as “methionine biosynthesis” and “cyclopropane fatty acids biosynthesis”, whereas fatty acid biosynthesis was the most prevalent differentially affected pathway identified in the panaxydol treatment, such as “pyruvate fermentation to acetate” and “unusual fatty acid biosynthesis” (Table 3).

Table 3 Pathway enrichment analysis for pathways regulated by falcarinol and panaxydol (P-value < 0.01) after post-hoc analysis and significantly different genes related to the pathways
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Taken together, the results of the functional enrichment and pathway enrichment analysis suggest that genes coding for ectoine biosynthesis and cyclopropane-fatty-acyl-phospholipid synthesis are preferentially regulated by the treatment of 1 and 2. Ectoine and its derivatives are compounds commonly found in bacteria that balance extracellular osmotic pressure, without altering the ionic strength of the cytoplasm [38, 39]. It has previously been demonstrated that elevated extracellular salt concentrations increase ectoine production and cause up-regulation of the ectoine biosynthetic genes [40]. Interestingly, falcarinol and panaxydol treatment of M. smegmatis resulted in the down-regulation of the ectoine biosynthetic genes ectA, ectB and ectC, suggesting that the C17 diynes do not induce osmotic stress.

Conversely, cyclopropane-fatty-acyl-phospholipid synthesis is the process by which cyclopropane rings are introduced into unsaturated mycolic acids [41, 42]. Indeed, because this process plays a crucial role in M. tuberculosis pathogenesis [43], cyclopropane-fatty-acyl-phospholipid synthase was used as a target for screening potential anti-TB compounds [44]. Since the two target compounds are also fatty acid derivatives that share some structural similarities with precursors of cyclopropane phospholipid synthesis, we postulate that 1 and 2 act as competitive inhibitors of cyclopropane-fatty-acyl-phospholipid synthase that disrupt mycolic acid metabolism. Inhibition of mycolic and fatty acid biosynthesis is an acknowledged mechanism by which compounds can exhibit anti-mycobacterial activity [45] and inhibitors of these pathways have been shown to exert their effects on both M. tuberculosis and M. smegmatis [46]. Natural products that contain long aliphatic hydrocarbon chains have been demonstrated to be fatty acid synthesis inhibitors in Mycobacterium spp. in previous studies. A mixture of two fatty acids isolated from a Turkish sponge showed significant activity (IC50 = 0.35 μg/mL) against the enzyme Fab I, which is essential for type II fatty acid biosynthesis in M. tuberculosis [47]. An acetylenic thiolactomycin isolated from a soil bacterium, Nocardia spp., was found to inhibit the Fab H fatty acid condensing enzymes, mtFab H and Kas A, in M. tuberculosis [48]. These two examples support the hypothesis that the two C17 diynes are fatty acid synthesis inhibitors; however, additional experimental data are needed to support or refute this hypothesis.

To corroborate the results of our microarray analyses and confirm that the molecular targets of the two C17 diynes are involved in lipid biosynthesis, we used the Prediction of the Biological Activity Spectra of Organic Compounds (PASS) program to explore the prospective biological activities of 1 and 2 from their chemical structures. PASS has been used to predict successfully MOAs of anti-mycobacterial natural products from various sources [49]. All of the activities that related to lipid biosynthesis were contained in the PASS output and the majority of these were related to phospholipid biosynthesis (Table 4). The complete list of predicted biological activities of 1 and 2 are presented as supplementary information. There is therefore excellent congruence between the predicted activities of the diynes based on their structure and our transcriptomic data that identifies lipid biosynthesis as the likely pathway affected by these compounds.

Table 4 Predicted fatty acid synthesis related MOAs of falcarinol and panaxydol using PASS program
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The positive controls, isoniazid, ethambutol and kanamycin, with partially or fully known MOAs, were used as comparison compounds for the C17 diynes. In order to further validate the hypothesised MOA of 1 and 2 that was generated from microarray data, it is necessary to discuss the transcriptome profiles of the positive controls in relation to their partially or fully known MOAs. Isoniazid is thought to inhibit the NADH-dependent enoyl acyl carrier protein reductase (InhA) in the fatty acid synthase type II (FAS-II) pathway leading to depletion of mycolic acids in cell wall synthesis [50]. The kas operon, a set of five FAS-II genes, showed significant up-regulation in the isoniazid treatment of M. smegmatis (Additional file 2: Table S1), which is consistent with previous studies of M. tuberculosis treated with isoniazid [20, 51, 52]. In addition, similar to isoniazid treated M. tuberculosis [20], the main target of isoniazid, inhA, did not show induction in this microarray analysis and the reason for this observation, both by us and by others, remains unclear. Similar to isoniazid, ethambutol targets mycobacterial cell wall synthesis. However, instead of targeting the fatty acid component of cell wall synthesis, ethambutol inhibits arabinosyl transferases by disrupting mycobacterial cell wall synthesis [29]. From the pathway enrichment analysis, ethambutol treatment differentially affected mycolyl-arabinogalactan-peptidoglycan complex biosynthesis, heme biosynthesis, and cell structure biosynthesis (Additional file 3), whereas a previous proteomic analysis of ethambutol treated M. smegmatis also showed induction of transmembrane alanine and lysine rich protein in peptidoglycan biosynthesis and several proteins in the heme biosynthesis pathway [27]. In contrast to isoniazid and ethambutol, kanamycin targets the 30S subunit of prokaryotic ribosomes [53]. Interestingly, many of the genes coding for 30S or 50S ribosomal proteins showed significant down-regulation in this study (Additional file 2: Table S2). This observation may be explained by the high dosage of the treatment, which may have led to the complete inhibition of protein translation processes. Moreover, the PAGE analysis showed that GO terms, such as protein targeting, regulation of cell shape, protein transport by the sec complex, cell wall macromolecule biosynthetic processes and intracellular protein transmembrane transport (Additional file 4), were significantly affected by kanamycin treatment. It provided additional evidence for the cause of cell death by kanamycin, which is triggered by mis-translation of the membrane protein [54]. Therefore, there was congruence between the microarray data and the MOAs of these well-characterized antibiotics, increasing confidence that the microarray analyses of 1 and 2 provide insight into their MOA.

Microarray data were further verified by real-time polymerase chain reaction (PCR). Expression data were analyzed using the Kruskal-Wallis test with Dunn’s method for joint ranking. MSMEG 3359, MSMEG 3805 and 4-carboxymuconolactone decarboxylase (pcaC) showed expression patterns across each group that were consistent with the microarray data (Fig. 3). The real-time PCR and microarray data were significantly correlated in a linear regression model (Additional file 2: Figure S1). The gene pcaC is involved in the catabolism of protocatechuate to succinate and acetyl-CoA in the beta-ketoadipate pathway [55]. The increased expression of pcaC by the treatment of INH, which confers natural resistance of M. smegmatis against INH, may be a compensatory response to increase production of acetyl-CoA [56]. The mRNA levels of both MSMEG 3359 and MSMEG 3805 were higher in the panaxydol treatments compared to the negative control and isoniazid, suggesting that these genes are involved in the response to these compounds, however, functional data for MSMEG 3359 and MSMEG 3805 are lacking in the literature. The amplicons of the genes used in real-time PCR are provided in the supplementary data (Additional file 2: Table S3).

Fig. 3
figure3

Real time PCR comparison to expression data from the microarray (log2) for (A) pcaC (B) MSMEG 3359 and (C) MSMEG 3805. The horizontal line in the box plots represents the median of the group, the boundaries of the box represent the 10th and 90th percentiles and the minimum and maximum data points are represented by the whiskers (Prism v5.0). Differences among groups were tested using a Kruskal Wallis test with Dunn method for joint ranking. The small case letters “a”, “b” and “c” were used to represent different groups. NT = negative treatment, INH = isoniazid, Pa = panaxydol, Fa = falcarinol, Ka = kanamycin, EMB = ethambutol

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Conclusion

Using microarray analysis and PASS, we hypothesize that both falcarinol and panaxydol inhibit phospholipid biosynthesis in mycobacteria and suggest that further studies should be directed at supporting or refuting this hypothesized MOA. To test this hypothesis, the enzyme(s) that 1 and 2 act upon should be isolated and an enzyme inhibition assay performed to measure the binding affinity of falcarinol and panaxydol to the enzyme. Cognisant that there are likely additional mechanisms of action for the C17 diynes within the cell that may not be reflected in a transcriptomic response, further studies should also include additional methods to elucidate the full effect of these compounds.

Methods

Mycobacterium smegmatis growth condition and treatments

Mycobacterium smegmatis strain MC2 155 (American Type Culture Collection [ATCC] 700084) was grown in Middlebrook 7H9 (Becton Dickinson, Mississauga, Ontario [ON], Canada) broth supplemented with 10 % albumin dextrose catalase (ADC) enrichment (Becton Dickinson, Mississauga, ON, Canada) and 0.2 % glycerol at 37 °C for 72 h in a humid environment before being diluted to a turbidity equivalent to a 1.0 McFarland standard (107 colony forming units [CFU]) using the same modified 7H9 broth and cryogenically preserved (−80 °C; 1.5 mL aliquots). Cryopreserved M. smegmatis was thawed and diluted with modified Middlebrook 7H9 broth (1:5) resulting in the mycobacterial suspension (2 × 106 CFU) used for the bioassays.

The MIC of target treatments (1 and 2) and positive controls (isoniazid, ethambutol and kanamycin) were measured using a M. smegmatis bioassay, which was modified from a microplate resazurin assay previously developed in our laboratory [57] by changing the resazurin addition time and total incubation time from 72 and 96 h to 24 and 48 h, respectively.

To prepare cultures of M. smegmatis for gene expression analysis, cyropreserved M. smegmatis suspensions were diluted as described above and transferred to 96 well plates. The plates were first incubated for 48 h to reach log phase growth and then each treatment, including one vehicle control, three positive controls and two target compounds, was added to corresponding rows of a 96 well plate except the peripheral wells resulting in 10 wells per treatment. The vehicle control consisted of modified 7H9 broth with 2 % dimethyl sulfoxide (DMSO), whereas the concentrations of positive controls and target compounds were 10 × MIC for each treatment against M. smegmatis. The plates were incubated at 37 °C for 6 h in a humid environment. The bioassay time point and doses were chosen based on previously published studies [11, 20, 21]. Following 6 h incubation, wells containing the same treatment in each plate were pooled into a 1.5 mL centrifuge tube and bacteria were harvested by centrifugation. The vehicle control and two positive controls (isoniazid and ethambutol) each had six biological replicates; the two target treatments (falcarinol and panaxydol) and one additional positive control (kanamycin) each had seven biological replicates. All biological replicates were used for microarray analysis and real-time PCR.

RNA extraction

Total ribonucleic acid (RNA) was extracted using TRIzol® (Invitrogen, Burlington, ON, Canada) and purified using an RNeasy® kit (Qiagen, Toronto, ON, Canada) according to the manufacture’s protocol. RNA quantity was measured on a NanoDrop ND 2000 spectrometer (Thermo Scientific, Wilmington, DE, USA) before and after purification. RNA quality was evaluated using an Agilent 2100 Bioanalyzer (Agilent, Mississauga, ON, Canada). All samples used in the microarray and real-time PCR experiments had RNA integrity numbers (RINs) > 7.5.

Microarray analysis and bioinformatics

Mycobacterium smegmatis 8 × 15 K Agilent microarrays (designed by Genotypic Technology, Bengaluru, India and manufactured by Agilent, Santa Clara, CA, USA) was used to investigate global mRNA profiles of M. smegmatis treated with 1 and 2. RNA labeling, microarray hybridization, and microarray scanning were conducted as directed in the Agilent one-colour microarray-based gene expression analysis protocol. Raw expression data along with tif images were extracted using Agilent Feature Extraction Software (v10.7.3.1). All microarray data reported in this study follow established guidelines (i.e. Minimum Information about a Microarray Experiment http://www.ncbi.nlm.nih.gov/geo/info/MIAME.html) and have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (GSE64323, GPL19567).

The raw expression data were normalized using Loess normalization (smoothing factor of 0.2). The limit of detection of the microarray normalized signals was determined to be 3.5 based on the lower limit of the standard curve for Agilent quality controls and negative controls (dark corners) so all intensity values that were lower than 3.5 were assigned a value of 3.5. DEGs were determined using one-way analysis of variance (ANOVA) followed by a post-hoc test using Benjamini and Hochberg method with a FDR at 0.01. PCA was performed on all transcripts using normalized expression values. Functional enrichment of GO terms was performed using PAGE analysis [36] followed by a post-hoc test using Benjamini and Hochberg method with FDR set at 0.01. The above data processing and bioinformatics analysis were performed in JMP® Genomics (v5.1).

DEGs (adjusted P-value < 0.01) obtained from ANOVA was uploaded to Biocyc online program (http://biocyc.org/) [58], and pathway enrichment analysis was performed using Fisher’s extract test with Benjamini-Hochberg [59] method as a post-hoc analysis.

cDNA synthesis, primer design and real-time PCR

The complementary deoxyribose nucleic acid (cDNA) synthesis, primer design and real-time PCR were performed as previously reported [60]. Primers used in this study are listed in Table 5. Mean expression levels of MSMEG 3584, MSMEG 5570 and gidB were determined to be the most stable combination of control genes to normalize all target expression data, with a mean M-value of 0.67 (mean coefficient variance = 0.25) as determined by the target stability function in CFX96 software. Normalized gene expression values were extracted using a relative ∆∆Cq method in CFX Manager™ 3.0 software (BioRad, Mississauga, ON, Canada). Normalized expression data were not normally distributed and a Kruskal–Wallis non-parametric test was used to determine if gene expression levels varied across treatments and a non-parametric Dunn method for joint ranking was used as a post-hoc test to determine which treatments were different from each other. All statistical analyses were conducted in GraphPad Prism 5.0.

Table 5 Primer sequences used for real-time PCR with amplicon size and annealing temperature. All primer sets had R2 > 0.98 and efficiency percentage between 90 % and 110 %
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Prediction of biological activities

The structures of 1 and 2 were converted to simplified molecular-input line-entry system (SMILES) format and uploaded to Prediction of the biological activity spectra of organic compounds (PASS) online program (http://www.way2drug.com/PASSOnline) for biological activity prediction [61]. A master list, which entails all potential bioactivities of the two target compounds and probabilities of each bioactivity being active or inactive, was generated through the PASS online program (Additional file 5) [61].

Abbreviations

ADC, albumin, dextrose, catalase; ANOVA, analysis of variance; ATCC, American Type Culture Collection; cDNA, complementary deoxyribose nucleic acid; CFU, colony forming units; DEG, differentially expressed gene; DMSO, dimethyl sulfoxide; FDR, false discovery rate; GEO, Gene Expression Omnibus; GO, gene ontology; MIC, minimum inhibitory concentration; MOA, mode of action; NCBI, National Center for Biotechnology Information; ON, Ontario; PAGE, parametric analysis of gene set enrichment; PASS, prediction of the biological activity spectra of organic compounds; PCA, principle component analysis; PCR, polymerase chain reaction; RIN, RNA integrity number; RNA, ribonucleic acid; SMILES, simplified molecular-input line-entry system; TB, tuberculosis

References

  1. 1.

    Lienhardt C, Raviglione M, Spigelman M, Hafner R, Jaramillo E, Hoelscher M, Zumla A, Gheuens J. New Drugs for the Treatment of Tuberculosis: Needs, Challenges, Promise, and Prospects for the Future. J Infect Dis. 2012;205:S241–9.

    Article  PubMed  Google Scholar 

  2. 2.

    Zumla A, Nahid P, Cole ST. Advances in the development of new tuberculosis drugs and treatment regimens. Nat Rev Drug Discov. 2013;12(5):388–404.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    WHO. Global tuberculosis report 2014. Geneva: World Health Organization; 2014.

    Google Scholar 

  4. 4.

    Zumla AI, Gillespie SH, Hoelscher M, Philips PPJ, Cole ST, Abubakar I, McHugh TD, Schito M, Maeurer M, Nunn AJ. New antituberculosis drugs, regimens, and adjunct therapies: needs, advances, and future prospects. Lancet Infect Dis. 2014;14(4):327–40.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Chan PF, Macarron R, Payne DJ, Zalacain M, Holmes DJ. Novel antibacterials: a genomics approach to drug discovery. Current Drug Targets - Infectious Disorders. 2002;2(4):291–308.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Ziegler S, Pries V, Hedberg C, Waldmann H. Target identification for small bioactive molecules: finding the needle in the haystack. Angewandte Chemie-International Edition. 2013;52(10):2744–92.

    CAS  Article  Google Scholar 

  7. 7.

    Cong F, Cheung AK, Huang SMA. Chemical Genetics-Based Target Identification in Drug Discovery. Annu Rev Pharmacol Toxicol. 2012;52:57–78.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Tashiro E, Imoto M. Target identification of bioactive compounds. Bioorg Med Chem. 2012;20(6):1910–21.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Sundberg SA. High-throughput and ultra-high-throughput screening: solution- and cell-based approaches. Curr Opin Biotechnol. 2000;11(1):47–53.

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Koul A, Arnoult E, Lounis N, Guillemont J, Andries K. The challenge of new drug discovery for tuberculosis. Nature. 2011;469(7331):483–90.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Boshoff HIM, Myers TG, Copp BR, McNeil MR, Wilson MA, Barry CE. The transcriptional responses of Mycobacterium tuberculosis to inhibitors of metabolism - Novel insights into drug mechanisms of action. J Biol Chem. 2004;279(38):40174–84.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Schena M, Heller RA, Theriault TP, Konrad K, Lachenmeier E, Davis RW. Microarrays: biotechnology’s discovery platform for functional genomics. Trends Biotechnol. 1998;16(7):301–6.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Debouck C, Goodfellow PN. DNA microarrays in drug discovery and development. Nat Genet. 1999;21:48–50.

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Ravindranath AC, Perualila-Tan N, Kasim A, Drakakis G, Liggi S, Brewerton SC, Mason D, Bodkin MJ, Evans DA, Bhagwat A, et al. Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis. Mol BioSyst. 2015;11(1):86–96.

    CAS  PubMed  Google Scholar 

  15. 15.

    Brazas MD, Hancock REW. Using microarray gene signatures to elucidate mechanisms of antibiotic action and resistance. Drug Discov Today. 2005;10(18):1245–52.

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Marton MJ, DeRisi JL, Bennett HA, Iyer VR, Meyer MR, Roberts CJ, Stoughton R, Burchard J, Slade D, Dai HY, et al. Drug target validation and identification of secondary drug target effects using DNA microarrays. Nat Med. 1998;4(11):1293–301.

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Sassetti CM, Boyd DH, Rubin EJ. Comprehensive identification of conditionally essential genes in mycobacteria. Proc Natl Acad Sci U S A. 2001;98(22):12712–7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Shaw KJ, Morrow BJ. Transcriptional profiling and drug discovery. Current Opinion in Pharmacoogyl. 2003;3(5):508–12.

    CAS  Article  Google Scholar 

  19. 19.

    Zarate-Blades CR, Silva CL, Passos GA. The impact of transcriptomics on the fight against tuberculosis: focus on biomarkers, BCG vaccination, and immunotherapy. Clin Dev Immunol. 2011;2011:192630.

    Article  PubMed  Google Scholar 

  20. 20.

    Betts JC, McLaren A, Lennon MG, Kelly FM, Lukey PT, Blakemore SJ, Duncan K. Signature gene expression profiles discriminate between isoniazid-, thiolactomycin-, and triclosan-treated Mycobacterium tuberculosis. Antimicrob Agents Chemother. 2003;47(9):2903–13.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Wilson W, DeRisi J, Kristensen HH, Imboden P, Rane S, Brown PO, Schoolnik GK. Exploring drug-induced alterations in gene expression in Mycobacterium tuberculosis by microarray hybridization. Proc Natl Acad Sci U S A. 1999;96(22):12833–8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Makarov V, Manina G, Mikusova K, Mollmann U, Ryabova O, Saint-Joanis B, Dhar N, Pasca MR, Buroni S, Lucarelli AP, et al. Benzothiazinones Kill Mycobacterium tuberculosis by Blocking Arabinan Synthesis. Science. 2009;324(5928):801–4.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Li H, O’Neill T, Webster D, Johnson JA, Gray CA. Anti-mycobacterial diynes from the Canadian medicinal plant Aralia nudicaulis. J Ethnopharmacol. 2012;140(1):141–4.

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Tyagi JS, Sharma D. Mycobacterium smegmatis and tuberculosis. Trends Microbiol. 2002;10(2):68–9.

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Chaturvedi V, Dwivedi N, Tripathi RP, Sinha S. Evaluation of Mycobacterium smegmatis as a possible surrogate screen for selecting molecules active against multi-drug resistant Mycobacterium tuberculosis. J Gen Appl Microbiol. 2007;53(6):333–7.

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Wang J, Li QZ, Ivanochko G, Huang YG. Anticancer effect of extracts from a North American medicinal plant wild sarsaparilla. Anticancer Res. 2006;26(3A):2157–64.

    PubMed  Google Scholar 

  27. 27.

    Wang R, Marcotte EM. The proteomic response of Mycobacterium smegmatis to anti-tuberculosis drugs suggests targeted pathways. J Proteome Res. 2008;7(3):855–65.

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Waagmeester A, Thompson J, Reyrat JM. Identifying sigma factors in Mycobacterium smegmatis by comparative genomic analysis. Trends Microbiol. 2005;13(11):505–9.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Belanger AE, Besra GS, Ford ME, Mikusova K, Belisle JT, Brennan PJ, Inamine JM. The embAB genes of Mycobacterium avium encode an arabinosyl transferase involved in cell wall arabinan biosynthesis that is the target for the antimycobacterial drug ethambutol. Proc Natl Acad Sci U S A. 1996;93(21):11919–24.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Bernardes-Genisson V, Deraeve C, Chollet A, Bernadou J, Pratviel G. Isoniazid: An Update on the Multiple Mechanisms for a Singular Action. Curr Med Chem. 2013;20(35):4370–85.

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Dhandayuthapani S, Zhang Y, Mudd MH, Deretic V. Oxidative stress response and its role in sensitivity to isoniazid in mycobacteria: characterization and inducibility of ahpC by peroxides in Mycobacterium smegmatis and lack of expression in M. aurum and M. tuberculosis. J Bacteriol. 1996;178(12):3641–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Li X-Z, Zhang L, Nikaido H. Efflux pump-mediated intrinsic drug resistance in Mycobacterium smegmatis. Antimicrob Agents Chemother. 2004;48(7):2415–23.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Wallace RJ, Nash DR, Tsukamura M, Blacklock ZM, Silcox VA. Human disease disease due to Mycobacterium smegmatis. J Infect Dis. 1988;158(1):52–9.

    Article  PubMed  Google Scholar 

  34. 34.

    Teng R, Dick T. Isoniazid resistance of exponentially growing Mycobacterium smegmatis biofilm culture. Fems Microbiology Letters. 2003;227(2):171–4.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Rastogi N, David HL. Mode of action of antituberculous drugs and mechanisms of drug-resistance in Mycobacterium tuberculosis. Res Microbiol. 1993;144(2):133–43.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Kim SY, Volsky DJ. PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics. 2005;6:144.

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Karp PD, Ouzounis CA, Moore-Kochlacs C, Goldovsky L, Kaipa P, Ahrén D, Tsoka S, Darzentas N, Kunin V, López-Bigas N. Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res. 2005;33(19):6083–9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Oren A, Heldal M, Norland S, Galinski EA. Intracellular ion and organic solute concentrations of the extremely halophilic bacterium Salinibacter ruber. Extremophiles. 2002;6(6):491–8.

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Roessler M, Muller V. Osmoadaptation in bacteria and archaea: common principles and differences. Environ Microbiol. 2001;3(12):743–54.

    CAS  Article  Google Scholar 

  40. 40.

    Ofer N, Wishkautzan M, Meijler MM, Wang Y, Speer A, Niederweis M, Gur E. Ectoine Biosynthesis in Mycobacterium smegmatis. Appl Environ Microbiol. 2012;78(20):7483–6.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Yuan Y, Barry CE. A common mechanism for the biosynthesis of methoxy and cyclopropyl mycolic acids in Mycobacterium tuberculosis. Proc Natl Acad Sci U S A. 1996;93(23):12828–33.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    George KM, Yuan Y, Sherman DR, Barry CE. The biosynthesis of cyclopropanated mycolic acids in Mycobacterium tuberculosis - identification and functional-analysis of Cmas-2. J Biol Chem. 1995;270(45):27292–8.

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Glickman MS, Cox JS, Jacobs Jr WR. A novel mycolic acid cyclopropane synthetase is required for cording, persistence, and virulence of Mycobacterium tuberculosis. Mol Cell. 2000;5(4):717–27.

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Guianvarc’h D, Guangqi E, Drujon T, Rey C, Wang Q, Ploux O. Identification of inhibitors of the E. coli cyclopropane fatty acid synthase from the screening of a chemical library: In vitro and in vivo studies. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 2008;1784(11):1652–8.

    Article  Google Scholar 

  45. 45.

    North EJ, Jackson M, Lee RE. New approaches to target the mycolic acid biosynthesis pathway for the development of tuberculosis therapeutics. Curr Pharm Des. 2014;20(27):4357–78.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Slayden RA, Lee RE, Armour JW, Cooper AM, Orme IM, Brennan PJ, Besra GS. Antimycobacterial action of thiolactomycin: an inhibitor of fatty acid and mycolic acid synthesis. Antimicrob Agents Chemother. 1996;40(12):2813–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Tasdemir D, Topaloglu B, Perozzo R, Brun R, O’Neill R, Carballeira NM, Zhang X, Tonge PJ, Linden A, Rüedi P. Marine natural products from the Turkish sponge Agelas oroides that inhibit the enoyl reductases from Plasmodium falciparum, Mycobacterium tuberculosis and Escherichia coli. Bioorg Med Chem. 2007;15(21):6834–45.

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Senior SJ, Illarionov PA, Gurcha SS, Campbell IB, Schaeffer ML, Minnikin DE, Besra GS. Acetylene-based analogues of thiolactomycin, active against Mycobacterium tuberculosis mtFabH fatty acid condensing enzyme. Bioorg Med Chem Lett. 2004;14(2):373–6.

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Salomon CE, Schmidt LE. Natural products as leads for tuberculosis drug development. Curr Top Med Chem. 2012;12(7):735–65.

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Takayama K, Wang L, David HL. Effect of isoniazid on the in vivo mycolic acid synthesis, cell growth, and viability of Mycobacterium tuberculosis. Antimicrob Agents Chemother. 1972;2(1):29–35.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Slayden RA, Barry CE. The role of KasA and KasB in the biosynthesis of meromycolic acids and isoniazid resistance in Mycobacterium tuberculosis. Tuberculosis. 2002;82(4-5):149–60.

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Slayden RA, Lee RE, Barry CE. Isoniazid affects multiple components of the type II fatty acid synthase system of Mycobacterium tuberculosis. Mol Microbiol. 2000;38(3):514–25.

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    Disney MD, Barrett OJ. An aminoglycoside microarray platform for directly monitoring and studying antibiotic resistance. Biochemistry. 2007;46(40):11223–30.

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Kohanski MA, Dwyer DJ, Wierzbowski J, Cottarel G, Collins JJ. Mistranslation of membrane proteins and two-component system activation trigger antibiotic-mediated cell death. Cell. 2008;135(4):679–90.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Deshayes C, Perrodou E, Gallien S, Euphrasei D, Scjaeffer C, Van-Dprsselaer A, Poch O, Lecompte O, Reyrat JM. Interrupted coding sequences in Mycobacterium smegmatis: authentic mutations or sequencing errors? Genome Biol. 2007;8(2):R20.

    Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Unissa AN, Sudha S, Selvakumar N, Hassan S. Binding of activated isoniazid with acetyl-CoA carboxylase from Mycobacterium tuberculosis. Bioinformation. 2011;7(3):107–11.

    Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    O’Neill TE, Li H, Colquhoun CD, Johnson JA, Webster D, Gray CA. Optimisation of the Microplate Resazurin Assay for Screening and Bioassay-guided Fractionation of Phytochemical Extracts against Mycobacterium tuberculosis. Phytochem Anal. 2014;25(5):461–7.

    Article  PubMed  Google Scholar 

  58. 58.

    Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA, Holland TA, Keseler IM, Kothari A, Kubo A, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2014;42(D1):D459–71.

    CAS  Article  PubMed  Google Scholar 

  59. 59.

    Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B Methodol. 1995;57(1):289–300.

    Google Scholar 

  60. 60.

    Chishti YZ, Feswick A, Munkittrick KR, Martyniuk CJ. Transcriptomic profiling of progesterone in the male fathead minnow (Pimephales promelas) testis. Gen Comp Endocrinol. 2013;192:115–25.

    CAS  Article  PubMed  Google Scholar 

  61. 61.

    Filimonov DA, Lagunin AA, Gloriozova TA, Rudik AV, Druzhilovskii DS, Pogodin PV, Poroikov VV. Prediction of the biological activity spectra of organic compounds Using the pass online web resource. Chem Heterocycl Compd. 2014;50(3):444–57.

    CAS  Article  Google Scholar 

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Acknowledgements

The authors would like to thank Jennifer Loughery and Kathleena Sarty for technical support relating to microarray and real-time PCR experiments. Financial assistance provided by the Horizon Health Network, the New Brunswick Innovation and the National Sciences and Engineering Research Council of Canada is gratefully acknowledged.

Funding

The research reported in this article was supported by funding from the Horizon Health Network (Chesley Research Fund 73160 to CAG, CJM, DW and JAJ), the New Brunswick Innovation Foundation (Research Assistanship Initiative Grants 2012-23 and 2013-66 to CAG) and the National Sciences and Engineering Research Council of Canada (Discovery Grant 350764-2009 to CAG).

Availability of data and materials

The data sets supporting the results of this article are available in the NCBI GEO database [GSE64323, GPL19567 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cg i?acc = GSE64323)].

Authors’ contributions

HL designed the experiments, performed the experiments, analyzed all data and wrote the manuscript. AMC assisted in microarray experiments, participated in drafting the manuscript and oversaw data analysis. CJM contributed to the design of the experiments and data analysis. DW, JAJ and CAG conceived and directed the study. All authors critically read, revised and approved the manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

As this study did not involve humans, human data or animals, ethics approval and consent to participate were not required. The research was conducted with the approval of the University of New Brunswick Biohazards Safety Committee (Biohazards Containment Certificates 2013-1 and 2015-1) in a containment level 2 certified laboratory (Public Health Agency of Canada, C-13-2155 and C-15-4222).

Author information

Affiliations

  1. Department of Biological Sciences, University of New Brunswick, PO Box 5050, 100 Tucker Park Road, E2L 4L5, Saint John, NB, Canada

    Haoxin Li, Andrew Cowie, John A. Johnson, Christopher J. Martyniuk & Christopher A. Gray

  2. Department of Medicine, Division of Infectious Diseases, Saint John Regional Hospital, 400 University Ave, E2L 4L4, Saint John, NB, Canada

    Duncan Webster

  3. Present address: Center for Environmental and Human Toxicology & Department of Physiological Sciences, UF Genetics Institute, College of Veterinary Medicine, University of Florida, 1333 Center Drive, 32610-0144, Gainesville, FL, USA

    Christopher J. Martyniuk

  4. Department of Chemistry, University of New Brunswick, PO Box 4400, 30 Dineen Drive, E3B 5A3, Fredericton, NB, Canada

    Christopher A. Gray

Authors
  1. Haoxin Li
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  2. Andrew Cowie
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  3. John A. Johnson
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  4. Duncan Webster
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  5. Christopher J. Martyniuk
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  6. Christopher A. Gray
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Corresponding author

Correspondence to Christopher A. Gray.

Additional files

Additional file 1:

ANOVA results for all transcripts in each treatment group compared to vehicle control. (XLSX 24787 kb)

Additional file 2:

Supplementary data; Figure S1. Correlation of fold change (log10) between real-time PCR and microarray data; Table S1. Sequenced genes and their amplicons in M. smegmatis; Table S2. Response of the kas operon in M. smegmatis to isoniazid treatment compared with vehicle control; Table S3. Fold changes of significantly different ribosomal protein genes from M. smegmatis treated with kanamycin. (DOCX 91 kb)

Additional file 3:

Enriched pathways for each treatment group. (XLSX 32 kb)

Additional file 4:

Enriched GO terms for each treatment group. (XLSX 231 kb)

Additional file 5:

Predicted bioactivities for falcarinol and panaxydol from PASS. (XLSX 81 kb)

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Li, H., Cowie, A., Johnson, J.A. et al. Determining the mode of action of anti-mycobacterial C17 diyne natural products using expression profiling: evidence for fatty acid biosynthesis inhibition. BMC Genomics 17, 621 (2016). https://doi.org/10.1186/s12864-016-2949-y

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Keywords

  • Gene network analysis
  • Natural products
  • Mode of action
  • Mycobacterium smegmatis
  • Falcarinol
  • Panaxydol

BMC Genomics

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