Article Cite This: J. Nat. Prod. 2018, 81, 957−965
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HSQC−TOCSY Fingerprinting for Prioritization of Polyketide- and Peptide-Producing Microbial Isolates Larissa Buedenbender,† Leesa J. Habener,† Tanja Grkovic,‡ D. Iṗ ek Kurtböke,§ Sandra Duffy,⊥ Vicky M. Avery,⊥ and Anthony R. Carroll*,†,⊥ †
Environmental Futures Research Institute, Griffith University, Gold Coast Campus, Southport, Queensland 4222, Australia Natural Products Support Group, Leidos Biomedical Research, Incorporated, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States § Genecology Research Centre, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Maroochydore, Queensland 4558, Australia ⊥ Griffith Institute for Drug Discovery, Griffith University, Nathan Campus, Brisbane, Queensland 4111, Australia ‡
S Supporting Information *
ABSTRACT: Microbial products are a promising source for drug leads as a result of their unique structural diversity. However, reisolation of already known natural products significantly hampers the discovery process, and it is therefore important to incorporate effective microbial isolate selection and dereplication protocols early in microbial natural product studies. We have developed a systematic approach for prioritization of microbial isolates for natural product discovery based on heteronuclear single-quantum correlation−total correlation spectroscopy (HSQC−TOCSY) nuclear magnetic resonance profiles in combination with antiplasmodial activity of extracts. The HSQC−TOCSY experiments allowed for unfractionated microbial extracts containing polyketide and peptidic natural products to be rapidly identified. Here, we highlight how this approach was used to prioritize extracts derived from a library of 119 ascidian-associated actinomycetes that possess a higher potential to produce bioactive polyketides and peptides.
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species to overcome timely and costly reisolation of known metabolites. The increased knowledge of biosynthetic pathways has opened the door for new development of sequence-based approaches to natural product drug discovery that allows for identification of the metabolic potential of microbial species.3 However, a large proportion of biosynthetic gene clusters remains silent in laboratory cultures, and therefore, potential natural products associated with these metabolic pathways remain undetectable. Chemometric−metabolomic fingerprinting analysis can provide an indication of the expressed metabolites present in extracts in a high-throughput manner and highlight metabolite redundancy. One commonly used metabolomic fingerprinting tool is mass spectrometry (MS), which provides high resolution and sensitivity as well as the possibility to couple the analysis with liquid chromatography and tandem mass spectrometry (MS/MS) fragmentation. However, the technique is dependent upon the ability of a compound to ionize, and it is often difficult
nique and structurally diverse natural products and their derivatives continue to play an important role in the discovery and development of new therapeutic agents.1 Microorganisms present one of the most important sources for new drug candidates. Moreover, recent evidence has shown that a significant number of invertebrate-derived natural product drug leads are actually produced by microbial symbionts of the host species from which the natural product was originally isolated.2 With this in mind, microbial libraries, particularly derived from the marine environment, are expanding rapidly. Microbial isolates for drug discovery were traditionally selected on the basis of morphology and/or biological activity of their extracts. This was then followed by bioassay-guided fractionation to isolate new bioactive molecules. The problem herein is that two microbial cultures could appear visually distinct but produce the same secondary metabolites. Alternatively, two cultures could appear morphologically identical yet produce different metabolites. These aspects as well as genuine duplication of cultures within a microbial library have to be considered. Thus, new and innovative approaches are evolving to dereplicate cultured © 2018 American Chemical Society and American Society of Pharmacognosy
Received: December 21, 2017 Published: March 2, 2018 957
DOI: 10.1021/acs.jnatprod.7b01063 J. Nat. Prod. 2018, 81, 957−965
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Figure 1. HSQC−TOCSY spectrum highlighting chemical shift (ppm) regions that correspond to specific structure classes (presented in cream). Specific structure classes include peptides (PEP), alkaloids, heteroaromatics (HA), aromatics (ARO), olefinic signals (DB), polyketides (PK), sugars, and fatty acids (FA). Carbon chemical shifts are represented on f1 (ppm), and proton chemical shifts are represented on f2 (ppm). Approximate proton (red) and carbon (blue) chemical shifts are provided.
bond correlation (HMBC), have previously been used for metabolite profiling of extracts.5−8 COSY and TOCSY experiments can be rapidly acquired and provide proton− proton correlations, while heteronuclear 1H−13C HSQC and HMBC experiments offer further separation into the carbon dimension. 1H−13C HSQC spectra display specific correlations from protons to their adjacent carbons and can be acquired in short periods of time. HMBC experiments show correlations to carbons, including non-protonated carbons, two or three bonds away from the proton; however, HMBC experiments require increased acquisition time, or alternatively, samples have to be highly concentrated. HSQC−TOCSY NMR experiments resolve TOCSY correlations into a carbon dimension through HSQCs from a proton to the carbon that it is directly attached to as well as to protonated carbons within the same proton spin system.9 This experiment is particularly useful for the structure elucidation of molecules that contain many overlapping 1H and/or 13C resonances, such as those observed in peptides and polyketides. NMR resonances associated with structural fragments of these natural product classes occupy very specific chemical shift regions in 1H−13C HSQC−TOCSY NMR spectra. Microorganisms are capable of producing secondary metabolites of immense structural complexity, the majority of which are polyketides, ribosomal and non-ribosomal peptides, or compounds derived from a mixed biosynthesis involving polyketide and non-ribosomal synthases.10 The most characteristic features of polyketides are carbon chains that contain oxygen atoms and either a methyl group or just protons on alternating carbons. The oxygenated carbons can either be
to quantify and replicate the obtained results using different instruments. Acquisition of evaporative light scattering detector (ELSD) or photodiode array (PDA) spectra in parallel to liquid chromatography−mass spectrometry (LC−MS) can help to overcome this limitation. Nuclear magnetic resonance (NMR) provides an alternative chemical fingerprinting tool, which is associated with lower resolution and sensitivity compared to MS, but it is non-selective, non-destructive, and quantitative and requires minimal sample handling. Undeniably, the greatest advantage of NMR approaches is that they provide structural information about the constituents of the extracts. In recent years, NMR instrumentation advancements have led to higher sensitivity and faster acquisition through optimized cryogenic probes, stronger magnets, precision-controlled electronics, and automation, thus making this technique more powerful and more applicable to metabolomic studies. Microbial extracts can potentially contain hundreds of metabolites, resulting in a severe signal overlap in 1H NMR spectra. The complexity of the extracts can be reduced through fractionation by liquid chromatography prior to spectra acquisition.4 This would require a considerable increase in sample preparation and spectra acquisition time and, therefore, would be disadvantageous for fingerprinting approaches if specifically applied to a large number of samples. Implementation of two-dimensional (2D) NMR techniques can increase chemical shift resolution by spreading the signals across two dimensions and, thereby, permit assessment of unfractionated extracts. The 2D NMR experiments, such as correlation spectroscopy (COSY), total correlation spectroscopy (TOCSY), heteronuclear singlequantum correlation (HSQC), and heteronuclear multiple958
DOI: 10.1021/acs.jnatprod.7b01063 J. Nat. Prod. 2018, 81, 957−965
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with unique chemical fingerprints from a library of 119 ascidian-associated actinomycete extracts.
ketones, alcohols, ethers, esters, or ketals, and these carbons are also often further dehydrated to yield trisubstituted double bonds.11 Oxygenated methine carbons display characteristic chemical shifts between δC 60 and 80.12 HSQC−TOCSY correlations are generally observed between the secondary methyl protons, resonating between δH 0.5 and 1.2, and oxygenated carbons between δC 60 and 80 (Figure 1). The methylene or methine protons (δH ∼ 1.7) α to oxygenated carbon display correlations to the same carbon between δC 60 and 80. However, their correlations are generally less intense compared to those of methyl protons. Sugars are common metabolites in microbial extracts, and their resonances occupy a region in 1H, COSY, and HSQC spectra that is prone to overcrowding of signals (Figure 1). HSQC−TOCSY correlations from protons resonating at δH 3.0−4.0 and directly attached to oxygenated carbons (δC 60−80), methine (δC ∼ 45), and methyl carbons (δC ∼ 12) are very useful to distinguish proton signals associated with polyketides from the hydroxymethine resonances associated with sugars. In contrast, sugar hydroxymethine protons, which resonate in the same region of the HSQC spectrum as hydroxymethines in polyketides, generally only show HSQC−TOCSY correlations to oxygenated methine and acetal/hemiacetal carbons. HSQC−TOCSY fingerprinting can also be used to identify peptides. Exchangeable protons are generally visible when NMR solvents, such as deuterated dimethyl sulfoxide (DMSOd6), are used and characteristic HSQC−TOCSY correlations of amide protons to α and β carbons can be observed. Amide protons typically resonate between δH 6.0 and 10.0 and lack 1 H−13C HSQCs.12 In HSQC−TOCSY spectra, correlations through the proton spin system to carbons with shifts ranging from δC 10 to 80 can be observed. Examples of peptide partial structures that depict such correlations are shown in Figure 1. The indole N−H proton (δH ∼ 10) provides another specific example where HSQC−TOSCY correlations are diagnostic because aromatic carbons resonating at approximately δC 125 correlate to this proton.12 Other heteroaromatic resonances are even further deshielded; imidazoles show characteristic N−H resonances at δH 13.5, and these show correlations to aromatic carbons resonating at δC 135.12 Malaria, caused by parasites from the genus Plasmodium, presents a health threat in many tropical countries. As a result of the increased spread of drug resistance against primary malaria treatments, such as chloroquine and artemisinin, there is an urgent need for new antiplasmodial drugs with different modes of actions. Microbial-derived macrolide antibiotics have been considered as possible candidates as a result of their good safety for treatment of children and pregnant women.13 The antiplasmodial activity of macrolides, such as azithromycin, affects the parasite growth by targeting the apicoplast 50S ribosomal subunit and inhibiting protein synthesis in this organelle.14 For geldanamycin, antiplasmodial activity was reported by inhibition of heat-shock proteins, which are highly expressed in the early parasite blood stage.15 In our search for new antiplasmodial compounds sourced from marine actinomycetes, we explored a systematic approach that integrates antiplasmodial activity data and 2D HSQC− TOCSY NMR fingerprints of unfractionated microbial extracts to characterize the polyketide and peptide chemical diversity of the microorganisms. Here, the utility of this approach was demonstrated to select extracts with greater potential for natural product discovery by identification of microbial isolates
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RESULTS AND DISCUSSION Validation of the HSQC−TOCSY NMR Fingerprinting Approach. One agar plate (9 cm diameter) per actinomycete isolate was used to achieve the highest throughput at the lowest resource and time expense. A total of 119 diverse actinomycete cultures, previously isolated from three Australian ascidian species and one marine sediment sample, were grown at 28 °C for 2 weeks on glucose yeast extract media−artificial seawater (GYM−ASW, DSMZ 871, www.dsmz.de) agar plates.16 Subsequently, the culture plates were extracted with EtOAc to establish an actinomycete extract library for natural product discovery. For an isolate prioritization approach to be practically useful, it needs to be quick. Originally, we thought HMBC spectra would be most appropriate to identify polyketides and peptides in the extracts through two and three bond correlations from protons to non-protonated carbons. However, HMBC experiments were found to be significantly less sensitive in comparison to HSQC−TOCSY experiments. For instance, a HMBC spectrum of the extract of Streptomyces sp. USC-16022, acquired using 16 scans and 128 increments taking 32 min for its acquisition on an 800 MHz spectrometer equipped with a cryogenic probe, showed eight correlations from protons to quaternary amide carbons (δC 172 and 174) and ketone carbons (δC 196 and 208); these correlations are not present in HSQC−TOCSY spectra (Figure 2). However, no HMBCs to aromatic resonances could be observed. In contrast, when the same acquisition parameters were used to acquire a HSQC−TOCSY spectrum, 28 correlations associated with aromatic and olefinic structure fragments and 23 correlations associated with oxygenated methine fragments could be detected (Figure 2). The number of scans for a HMBC experiment would have to be increased, resulting in a drastic increase of the acquisition time to detect resonances associated with secondary metabolites. Peak intensity increases proportionally to the square root of the number of scans;17 for instance, when 16 scans were originally acquired, this number had to be increased 4-fold to 64 scans to double the intensity of peaks. According to these observations, the HSQC−TOCSY experiment (16 scans and 128 increments) with a 33 min acquisition time was chosen to be most practical for chemical fingerprinting. Using these parameters, a reasonable throughput of samples can be achieved in an overnight analysis on a NMR spectrometer equipped with an autosampler while still providing adequate sensitivity. Because we were specifically interested in extracts that contained polyketides and peptides, we established a list of HSQC−TOCSY coordinate regions that could be used to generate peaks of interest associated with these two compound classes (Table 1). This included oxygenated methines that are commonly associated with polyketides (PK), which could be identified through shielded methyl proton signals (δH −0.5− 2.0) correlating to the oxygenated carbon resonances (δC 60− 85). Alternatively, for the same structure fragment, correlations from the protons adjacent to oxygens (δH 3.0−5.0) to methyl carbons (δC 5−25) could be used. Furthermore, olefinic structure fragments could be determined as well as peptidic, aromatic, and heteroaromatic fragments. To further validate the use of HSQC−TOCSY spectra for chemical fingerprinting, the detection limit of the HSQC− TOCSY experiments was determined on the basis of a 959
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be linear (R2 = 0.95). At 0.5 mg/mL (0.1 mg in 200 μL of NMR solvent), only one-bond HSQCs of shielded protons were detected. HSQC−TOCSY correlations from the methyls to oxygenated carbons (PK indicative) could be detected at concentrations as low as 1.0 mg/mL (0.2 mg), and the peak intesities of these polyketide fragments relative to the DMSOd6 solvent peak were used to establish a concentration curve (Figure 3 and Figures S1−S3 of the Supporting Information). The masses of extracts obtained for actinomycetes grown on one agar culture plate ranged from 2 to 71 mg, with an average of 14 mg/plate. If 0.2 mg of polyketide natural product was present in an extract obtained from an actinomycete isolate grown on one agar plate, this would equate to a 1.4% yield on average, and this is a realistic yield for actinomycete secondary metabolites in crude extracts.18 This indicated that the HSQC− TOCSY fingerprinting approach could be applied to an actinomycete extract library to identify polyketide- and peptide-producing species. Systematic HSQC−TOCSY NMR Fingerprinting To Prioritize Unique Polyketide- and Peptide-Producing Microbial Isolates. HSQC−TOCSY fingerprinting was used to analyze extracts from a marine ascidian-associated actinomycete collection of 119 diverse isolates. Initially, antiplasmodial activity was tested. Of the 119 actinomycete extracts screened for antiplasmodial activity, 18 demonstrated above 80% inhibition against Plasmodium falciparum (3D7) at 0.2 μg/ mL. The biological activity was used as a first filter, and HSQC−TOCSY fingerprints were analyzed only for the active extracts (Figures S4−S21 of the Supporting Information). HSQC−TOCSY peaks of interest were picked (as detailed in Table 1), and the data was exported to Microsoft Excel; peaks were aligned by implementing an in-house macro allowing for a bin threshold of 0.04 ppm in the proton dimension and 0.4 ppm in the carbon dimension. Examination of respective HSQC−TOCSY spectra allowed for detailed insight about the structural constituents of the extracts. First, all peaks of interest were tallied; the highest number of peaks of interest (94) was detected in the extract of Streptomyces sp. USC-16041 (Figure 4). It was evident that metabolites present in Streptomyces sp. USC-16007 and USC16025 were present at concentrations below the NMR detection limit, with only seven and three peaks of interest observed in their HSQC−TOCSY spectra, respectively. Such information is equally valuable because it reflects the limited ability of these species to produce high amounts of secondary metabolites under the given culture conditions. Even though bioactivity was significant, further culture optimizations would be required for drug discovery efforts; therefore, these isolates were not prioritized in this study. Our prioritization of microbial species for further investigation was based on the biological activity of the samples and the presence of high secondary metabolite signal diversity detected under the given culturing conditions, and subsequently, specific structural classes, such as polyketides and peptides, were targeted. On the basis of these criteria, a prioritization list of 18 extracts was generated (Table 2). The 18 bioactive extracts were analyzed by HSQC−TOSCY in less than 10 h. Streptomyces sp. USC-16018 was the first chosen to target isolation of bioactive polyketides. The isolate, when grown on GYM−ASW solid media, had a total of 63 peaks of interest associated with secondary metabolites, of which 20 diverse peaks were associated with polyketide-type structure fragments. The isolates USC-16041 and USC-16022 also
Figure 2. Comparison of HMBC and HSQC−TOCSY spectra of actinomycete isolate USC-16022 (65 mg/mL) both acquired at 16 scans and 128 increments (800 MHz, 200 μL of DMSO-d6). (a) Unique HMBCs are highlighted in green (acquisition time of 32 min). (b) Correlations unique to the HSQC−TOCSY spectrum are highlighted in yellow (acquisition time of 33 min).
Figure 3. Rifampicin (1) concentration curve based on HSQC− TOCSY peak intensities of PK peaks of interest (δH −0.26/δC 73.4 and δH 1.03/δC 73.4) relative to DMSO-d6.
concentration curve of the commercially available macrolide polyketide rifampicin (1). Peak area intensities of two prominent PK peaks of interest (δH −0.26/δC 73.4 and δH 1.03/δC 73.4) relative to the DMSO-d6 solvent peak were established for 5.0, 2.5, 2.0, 1.5, and 1.0 mg/mL rifampicin. The relationship of the concentration to intensity was observed to 960
DOI: 10.1021/acs.jnatprod.7b01063 J. Nat. Prod. 2018, 81, 957−965
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Table 1. HSQC−TOCSY Peaks of Interest Associated with Polyketides and Peptides
identity of their 16S rDNA sequences. Also, the Micromonospora isolate USC-16035 (1315 bp) had 100% consensus with the 16S rDNA sequence of USC-16048 (1345 bp); however, the two isolates displayed different biological activities and chemical profiles. This shows that the phylogeny based on the 16S gene marker and expressed metabolism do not always correlate and further underlines the value of chemical fingerprinting over molecular techniques. Targeted Isolation of Bioactive Natural Products from Prioritized Species. As a proof of concept, the microbial species that exhibited the highest polyketide peak diversity was selected to investigate whether polyketide compounds could, in fact, be isolated from this species. Fermentation of Streptomyces sp. USC-16018 was scaled up to 55 agar plates, and extraction with EtOAc yielded 1.6 g of crude material. This extract was separated by reversed-phase high-performance liquid chromatography (HPLC), and natural product isolations were initially guided by 1H NMR analysis of HPLC fractions. Polyketide isolation was directed by first identifying fractions containing 1 H resonances between δH 4.00 and 6.00, indicative of oxygenated and olefinic protons potentially associated with polyketides. Next, short HSQC−TOCSY experiments (8 scans = 15 min) of the selected fractions were acquired to confirm the presence of polyketide-associated peaks in these fractions. Fraction 46 was identified to contain a polyketide, and two structural fragments (2a and 2b) could be quickly derived from analysis of the HSQC−TOCSY spectrum (Figure 5a). Furthermore, HSQC−TOCSY spectra can be processed using 2D-specific indirect covariance to generate high-resolution 13 C−13C TOCSY spectra, even though the original spectra was acquired with a proton detection pulse sequence.19 These indirect covariance spectra provide spin-connectivity information that could also be used as a second means to confirm the presence of polyketide-type compounds. In a carbon spin system of a polyketide-type molecule, correlations from
Figure 4. Account of HSQC−TOCSY peaks of interest for each bioactive isolate (PK, polyketide; DB, olefinic; PEP, peptide; HA, heteroaromatic; and ARO, aromatic).
scored high in the prioritization approach (Table 2). Both isolates actually had higher total peak diversity compared to USC-16018; however, their scores for polyketide- and peptideassociated peaks of interest were lower. The species USC-636 and USC-16008 were better candidates for the isolation of peptides. The genus Streptomyces is a prolific producer of bioactive natural products, and this was clearly reflected in the prioritization table, where among the 16 highest scoring isolates, all except for two isolates were from this genus. A comparison of nearly full-length 16S rDNA sequences obtained from the species USC-16003 (1348 bp) and USC-16018 (1385 bp) shared 100% pairwise identity when sequence ends were trimmed to the same length. In the same manner, USC-16017 (1352 bp) and USC-16022 (1358 bp) shared 100% pairwise 961
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Table 2. Order of Prioritization Based on Bioactivity, High PK and PEP Diversity, and Uniqueness of HSQC−TOCSY Fingerprints isolatea USC-16018 USC-16041 USC-16008 USC-16022 USC-636 USC-16016 USC-16003 USC-16013 USC-16101 USC-16017 USC-16005 USC-16115 USC-16004 USC-16107 USC-16048 USC-16000 USC-16007 USC-16025
a
b
a
b
c
genus
extract (mg)
percent inhibition (%)b
total peaks of interest
PK peaks
PEP peaks
unique peaks
order of prioritization
Streptomyces Streptomyces Streptomyces Streptomyces Streptomyces Streptomyces Streptomyces Streptomyces Streptomyces Streptomyces Streptomyces Streptomyces Streptomyces Micromonospora Micromonospora Streptomyces Streptomyces Streptomyces
15 7.7 8.3 13 7.9 39 16 13 4.8 8.1 5.5 16 4.0 24 2.2 14 15 4.0
94 93 96 71 84 99 96 83 94 99 89 84 92 99 97 83 94 84
69 94 59 74 37 54 27 50 39 38 67 57 43 57 22 16 7 3
17 4 11 12 4 9 4 3 5 3 1 5 0 5 0 2 1 1
10 7 7 8 15 4 0 7 2 1 4 2 7 2 4 0 1 0
48 53 32 30 24 9 5 16 7 8 26 21 12 14 5 6 5 0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
a
Letters a−c indicate whether isolates share high 16S rDNA sequence identity (USC-16035c and USC-16048 shared 100% identity but showed different biological activity and chemical fingerprints). bPercent inhibition against P. falciparum at 2.0 μg/mL.
Figure 5. (a) HSQC−TOCSY spectrum (8 scans, 800 MHz) in DMSO-d6 of the PK-containing HPLC fraction 46. 1H and 13C chemical shifts (ppm) associated with the structure fragments 2a and 2b are labeled. (b) Indirect covariance spectrum generated from the HSQC−TOCSY spectrum of fraction 46. (c) Elaiophylin (2) correlations (labeled with 1H and 13C chemical shifts in ppm) observed in the crude HSQC−TOCSY spectra of species USC-16018.
oxygenated carbons (δC 60−80) to methyl (δC 9−20) carbons should be observed. We generated an indirect covariance spectrum from the HSQC−TOCSY spectrum of fraction 46, and this enabled complete assignment of the two structure
fragments 2a and 2b (Figure 5b). Using these two structure fragments as the search criteria in the freely available DEREP_NP database yielded 12 hits, all of which were elaiophylin derivatives (Figure S24 of the Supporting 962
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Information).18 A full set of two-dimensional NMR experiments (1H−1H COSY, 1H−13C HSQC, and 1H−13C HMBC) was obtained to confirm the structure to be the known antibiotic dimer elaiophylin (2), which was previously reported to exhibit antibiotic and antiplasmodial (IC50 of 0.22 μg/mL) properties.20,21 It was of interest to confirm the presence of signals associated with this molecule in the original HSQC− TOCSY extract spectra of species USC-16018. A total of 19 elaiophylin-associated correlations were observed in the crude HSQC−TOCSY spectra (Figure 5c). Three indole-containing peptides were previously isolated from the Streptomyces sp. USC-636, of which one exhibited antiplasmodial activity (IC50 of 3.5 μM).22 In comparison to the other active isolates, USC-636 exhibited the most diverse peptide-associated HSQC−TOCSY peaks in the extract. Cross peaks at δH 10.78 and 10.82/δC 123.7 clearly identified the indole group found in naseseazines A and C (Figure S25 of the Supporting Information). Typical peptide HSQC−TOCSY correlations of the diketopiperazine fragments were observed in the extract, where the N−H (δH 7.45) correlated to α carbon (δC 54.8) adjacent to the amide carbonyl. Utility of HSQC−TOCSY Fingerprinting for Prioritization of Microbial Isolates and Compound Dereplication. The advantage of working with microorganisms is that their secondary metabolism can be manipulated through small modifications of the culture media.23 The one strain many compounds (OSMAC) principle is commonly used to diversify chemical extracts of microorganisms. HSQC−TOCSY fingerprinting is a useful way to determine which cultivation media and conditions are most successful at triggering secondary metabolite production. Figure 6 represents how the cultivation of Streptomyces sp. USC-16022 on GYM−ASW compared to OMA media produced a greater diversity of secondary metabolites. It appears that GYM−ASW media particularly triggered the synthesis of peptides, and many more aromatic peaks can be observed. For instance in the GYM−ASW spectrum, 25 unique aromatic and olefinic cross peaks were detected, while in the OMA spectrum, 11 unique peaks could be observed in this region and only 5 peaks overlapped between the two different spectra. Another solution to enhance peak detection by NMR would be media enrichment with 13Clabeled substrates.24 A limitation of this approach is the requirement for analysis to be carried out on a high-field NMR spectrometer equipped with a cryogenic probe. The experiments in this study were performed on a Bruker 800 MHz NMR spectrometer equipped with a cryoprobe, and on average, the HSQC−TOCSY approach proved to be useful. However, extracts with low yields (
