Exploring microbial natural products through NMR-based metabolomics
De-Gao
Wang†
,
Jia-Qi
Hu†
,
Chao-Yi
Wang†
,
Teng
Liu
,
Yue-Zhong
Li
and
Changsheng
Wu
*
State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, 266237 Qingdao, P. R. China. E-mail: wuchangsheng@sdu.edu.cn
First published on 12th June 2025
Abstract
Covering: 2000. 01 to 2025. 03
The soaring demand for novel drugs has led to an increase in the requirement for smart methods to aid in the exploration of microbial natural products (NPs). Cutting-edge metabolomics excels at prompt identification of compounds from complex mixtures and accordingly accelerates the targeted discovery process. Although MS-based metabolomics has become a staple in this field, the utilization of NMR-based metabolomics has severely trailed in comparison. Herein, we summarize the key methodological advancements in 1D and 2D NMR techniques in the past two decades, especially for the invention of computational technologies and/or introduction of artificial intelligence for automated data processing, which significantly strengthen the ability of NMR-based metabolomics to analyze crude microbial extracts. Preliminary fractionation is advocated to deconvolute samples and thus enhance detection sensitivity towards minor components overshadowed by a complex matrix. Particularly, the synergistic application of NMR-based metabolomics and genomics provides an expedient approach to correlate biosynthetic gene clusters with cognate metabolites, greatly improving the efficiency of dereplication and, thus, targeted discovery of novel compounds. A variety of microbial NPs involving distinct chemical skeletons and/or biosynthetic logics are enumerated to prove the genuine prowess of NMR-based metabolomics. Overall, this review aims to encourage the broader adoption of NMR-based metabolomics in the realm of microbial NP research.
 De-Gao Wang | De-Gao Wang started his doctoral study and scientific research under the supervision of Prof. Changsheng Wu in 2023. He holds degrees in Biological Medicine (M.S. 2023) from Shandong University. His doctoral research focuses on the discovery of myxobacterial natural products with interesting skeletons and significant biological activity. |
 Dr Jia-Qi Hu | Dr Jia-Qi Hu obtained her Bachelor's Degree in 2016 and Master's Degree in 2019 from Beijing Forestry University. She studied Microbiology at the Institute of Microbial Technology, Shandong University and received her PhD in 2023. Then, she continued with post-doctoral research. Currently, she is aiming to discover novel bioactive secondary metabolites from myxobacteria through the approach of integrating genome mining and NMR-based metabolomics. |
 Dr Chao-Yi Wang | Dr Chao-Yi Wang obtained her PhD in Medicinal Chemistry from Ocean University of China in 2018 under the supervision of Prof. Chang-Yun Wang. She started working as a Postdoctoral Fellow at Peking University with Prof. Wen-Han Lin from 2018 to 2021, and now at Shandong University with Prof. Changsheng Wu. Her current research interests focus on the discovery of novel bioactive natural products from myxobacteria. |
 Prof. Yue-Zhong Li | Prof. Yue-Zhong Li received his PhD at Shandong University in 1993. Since 2000, he was appointed a Professor by Shandong University, and won the Outstanding Youth Fund of the National Foundation of China in 2008. He is mainly engaged in research on the cultivation, ecology, molecular biology, and secondary metabolites of myxobacteria. In recent years, he has utilized the (meta)genome mining approach to study the distribution of cultivable and uncultivable microorganisms in various ecological environments. |
 Prof. Changsheng Wu | Prof. Changsheng Wu studied Pharmacy for his BSc Degree (2008) and Natural Products Chemistry for his MSc Degree (2011) at Shandong University, China. He obtained his PhD in 2016 at Leiden University, The Netherlands, where he discovered new antibiotics from actinomycetes. In 2017, he conducted postdoctoral research at The Rockefeller University, USA, using the metagenomics tool to find new antibiotics. In 2019, he joined Shandong University as a Full Professor. His current research interests focus on the discovery of novel bioactive natural products from myxobacteria. |
1. Introduction
Microbial natural products (NPs) have a proven track record of success in the history of drug discovery by serving as an invaluable source of therapeutic agents and potential drug leads.1 Nowadays, most research groups dedicated to the discovery of microbial NPs maintain in-house culture collections, encompassing diverse strains such as actinomycetes, fungi, cyanobacteria, and myxobacteria, all recognized as prolific sources of bioactive secondary metabolites.2 A central challenge lies in effectively leveraging these invaluable strain libraries. Although traditional activity-based high-throughput screening has historically proven successful, it often results in the re-discovery of known compounds.3 Thus, to enhance research competitiveness and fill the drying drug discovery pipeline, there is a pressing need to develop more innovative and efficient methodological strategies to swiftly uncover new lead structures from microbial resources.Metabolomics, an analytical tool proficient at chemically scrutinizing biological samples, can quickly discern variations in secondary metabolomes across different bacterial fermentations with the aid of chemometric methods, significantly steering efforts toward potential biomarkers for the discovery of novel compounds and avoiding chemical redundancy in an early stage.4 Mass spectrometry (MS) and nuclear magnetic resonance (NMR) are the two most commonly used technologies for metabolomics, primarily because of their powerful ability to detect individual metabolites in complex mixtures. Although MS-based metabolomics has been widely applied in microbial NP research,5,6 NMR-based metabolomics is much less frequently employed in this field but more in plant or clinical applications.7,8 Considering its popularity in the discovery of microbial NPs, existing reviews overwhelmingly focus on MS-based metabolomics,5,6,9,10 whilst a comprehensive review dedicated specifically to the role of NMR-based metabolomics in this respect remains conspicuously absent to date. Moreover, the integration of genomics and metabolomics for accelerating the discovery of microbial NPs has emerged as a dominant strategy in the post-genomic era. Although numerous reviews detail the synergy between MS-based metabolomics and genomics,11–16 a dedicated overview of the same for NMR-based metabolomics is lacking. Given that no perfect method exists to solve any problem and both NMR and MS have strengths and weaknesses, NMR-based metabolomics holds great promise to provide a useful compliment to MS-based metabolomics for the bioprospecting of taxonomically diverse microbes. In fact, NMR boasts several unique advantages, such as being nondestructive, nonbiased, requiring no sample treatment, no ionization, and free from ion annotation. Thus, there has been a significant increase in recent decades in the use of NMR-based metabolomics, either independently or in conjunction with other disciplines such as MS-based metabolomics, genomics, and artificial intelligence (AI), resulting in the accelerated discovery of novel microbial NPs. Accordingly, a timely consolidation of this progress is crucial to encourage wider adoption of these techniques by the broad range of scientific communities engaged in microbial research.
Although there may be subtle distinctions among the conceptual terms “NMR-based metabolomics,” “NMR-based metabolic profiling,” and “NMR fingerprinting,” we chose to use “NMR-based metabolomics” in the title as a unifying descriptor. This term reflects the shared underlying principle of utilizing NMR technology to analyze the chemical composition of complex mixtures.17–20 Rather than reiterating the fundamental concept of NMR-based metabolomics, which has been extensively covered elsewhere,17,18,21 we structured the content from an application-oriented perspective, with the aim to practically leverage this tool for the discovery of microbial NPs. Therefore, this review is structured into three major sections, each interconnected but with a distinct focus. Section 2 focuses on the general steps and principles of NMR-based metabolomics in microbial NP research. It enumerates the common one-dimensional (1D) and two-dimensional (2D) NMR-based metabolomics approaches, highlighting key advancements and exploring the application of machine learning and/or AI for NMR data processing and analysis. Section 3 emphasizes and addresses the widely acknowledged challenges in current NMR-based metabolomics, including the inherent complexity of metabolic components and limitations in sensitivity. The integration of prefractionation with NMR-based metabolomics alleviates the problematic signal overlapping and the detection sensitivity for minor components can be significantly improved. Recognizing the increasing dominance of genomics-driven microbial NP exploration, Section 4 focuses on the synergistic combination of NMR-based metabolomics and genomics, demonstrating how this integrated approach boosts the efficiency and target-specificity. Each section showcases illustrative examples of novel microbial NPs with diverse chemical scaffolds, unique structural features, and varying biosynthetic pathways, demonstrating the effectiveness and robustness of different NMR methodologies. However, to ensure the adequate illustration of the concepts and/or principles within each section, some compromises were made in example selection, even when certain examples could have served across multiple sections. Section 5 concludes and offers a perspective on the future of NMR-based metabolomics in microbial NP discovery, emphasizing the potential of its integration with multiple disciplines such as MS-based metabolomics, genomics, and AI to further strengthen its capacity. Ultimately, this review aims to encourage the broader adoption of NMR-based metabolomics in the realm of microbial NP research. We hope to inspire researchers to leverage this powerful tool not only for the discovery of novel bioactive compounds, but also the elucidation of complex biosynthetic pathways, and the detailed investigation of microbial chemical ecological interactions.
2. Advancements in NMR-based metabolomics for microbial NP discovery
2.1 General workflow of NMR-based metabolomics for microbial NP discovery
The typical workflow initiates with the extraction of metabolites from microbial cultures, followed by the acquisition of high-resolution NMR spectra. The resulting complex data are then subjected to rigorous processing, involving phasing, baseline correction, referencing, deconvolution, and peak picking. Subsequently, statistical tools are applied to highlight the metabolic differences between samples, identify potential biomarkers associated with desirable traits (e.g., bioactivity), and dereplicate known compounds. These identified biomarkers guide targeted isolation efforts, facilitating the purification of novel compounds for subsequent structural elucidation.8,20,22 Finally, Computer-Assisted Structure Elucidation (CASE) software, often coupled with density functional theory (DFT)-NMR calculations, is increasingly used to automate spectral interpretation and structure elucidation, streamlining the identification of novel microbial NPs (Fig. 1). The great advancements in both hardware and software make NMR-based metabolomics increasingly accessible to a broader range of researchers, further accelerating the pace of microbial NP discovery. | ||
| Fig. 1 General workflow of NMR-based metabolomics for the discovery of microbial NPs. | ||
2D NMR experiments, such as HSQC, HMBC, and COSY, provide valuable structural information for the identification and dereplication of metabolites. However, one of the major drawbacks of 2D NMR experiments is their time-intensive process, typically in the order of 3 to 4 h per sample. Gratifyingly, there has been a surge in new methods for shortening the measurement time of multidimensional NMR spectra, while retaining high spectral resolution along the indirect dimension(s).32 For instance, a number of non-uniform sampling (NUS) approaches bypass the traditional Fourier transformation (FT) processing.33 Besides, the covariance NMR has demonstrated effectiveness by improving the spectral resolution, boosting the sensitivity, shortening the experimental time, and enabling the observation of correlations among less sensitive heteronuclei.34 Absolute minimal sampling (AMS) enables a drastic reduction in the measurement time required for high-throughput metabolomics applications, facilitating the rapid identification and quantification of components within complex biological mixtures.35 NOAH supersequences36 combine multiple conventional NMR pulse sequences, such as HSQC, HMQC, HMBC, COSY, NOESY, and TOCSY, into a single measurement. This approach offers significant time savings and increases the efficiency of NMR measurements, thus dramatically increasing the sample throughput. However, although NUS and AMS can enhance the resolution or processing speed, their advantages in heteronuclear experiments with naturally abundant 13C are somewhat constrained due to inherent sensitivity limitations. To address this problem, Hansen et al. demonstrated that the inherent sensitivity of the HSQC and HSQC-TOCSY experiments can be augmented by incorporating an isotope-selective composite pulse sequence at the onset of the standard sensitivity-boosting protocols. Further incorporation of sensitivity-improved versions of HSQCsi and HSQCsi–TOCSY in NOAH supersequences enabled the compact acquisition of multiple 2D NMR data sets with significant improvements in sensitivity, resolution, and/or time.37 All these (continued) advancements hold great promise to propel the utility of 2D NMR-based metabolomics studies with large cohorts of microbial samples. Collectively, the synergistic combination of advanced instrumentation and optimized acquisition parameters ensures the acquisition of high-quality NMR data.
NMR spectral deconvolution is a critical in metabolomics studies, particularly for complex mixtures, given that it resolves overlapping signals to enable accurate the identification and quantification of individual components.49,50 By disentangling composite signals into their constituent peaks, defining their position, intensity, and line shape, deconvolution enhances the resolution of complex spectra, revealing low-abundance metabolites otherwise obscured by signal overlap. This process not only improves the accuracy of metabolite quantification but also simplifies spectral interpretation, ultimately facilitating comprehensive metabolomic profiling and biomarker discovery. Gratifyingly, recent advancements in deep learning algorithms have further enhanced the capabilities of NMR spectral deconvolution,49–51 allowing for the resolution of overlapping signals and the extraction of more detailed information from complex spectra. For example, Schmid et al.52 introduced a novel deep learning-based algorithm for the accurate and automated deconvolution of 1D NMR spectra, including resolving overlapping peaks, handling high dynamic range signals, and maintaining sparsity in the resulting peak lists. Nicolas Schmid and colleagues successfully developed an intelligent deconvolution algorithm based on deep learning, achieving expert-level accuracy in bioinformatics processing.52
Peak picking is another critical process for identifying and selecting effective signals in the NMR spectrum, which directly impacts downstream analysis and metabolite identification.53 The automatic and fast identification of individual peaks in 1D NMR spectra and cross-peaks in 2D or higher dimensional NMR spectra is essential for the high-throughput analysis of complex NMR spectra. Algorithms have been developed to automate this process, and simultaneously minimize the erroneous identification of spurious peaks, ensuring the reliability of subsequent analysis. For instance, Li et al. presented DEEP Picker,53 a DNN-based system for automated peak detection and deconvolution of 2D NMR spectra. This tool, trained on a broad set of synthetic spectra, employs deep convolutional layers to pinpoint even subtle overlapping peaks. It is compatible with platforms such as COLMARq54 to facilitate an integrated metabolomic analysis workflow that simplifies data handling and interpretation. In addition, Li et al. also developed DEEP Picker1D and Voigt Fitter1D for the fully quantitative deconvolution of 1D NMR spectra, greatly streamlining peak picking, fitting, and reconstruction.55
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168 candidates) within 90 min.92 The Wang group developed GFN2NMR,93 a deep graph convolutional network (GCN), for calculating 13C NMR chemical shifts with near-DFT accuracy, while maintaining low computational cost. The model demonstrated high performance in both general 13C NMR prediction and challenging structure assignment tasks, including elucidating the J/K ring junction stereochemistry of maitotoxin, the largest known non-polymeric natural product. The continued development and refinement of CASE tools are poised to further promote NMR-based metabolomics, making it more accessible and widely applicable in the field of microbial NP discovery.
2.2 One-dimensional NMR-based metabolomics applied for microbial NP discovery
In our 1H NMR-based metabolomics workflow for the bioprospecting of actinomycetes, antibiotic production by microbial NP producers was first perturbed by different eliciting strategies, such as microbial cocultivation,95 streptomycin-resistance mutation,96 overexpression of pathway-specific regulator,97 and varying the harvest time98 or culture media.99 Subsequently, chemometric methods were used to deconvolute the complex 1H NMR metabolic profile and compare the spectra of different experimental groups, accordingly illustrating the unique biomarkers that correlate with bioactivities. The biomarker-oriented structural elucidation was conducted based on 2D NMR experiments including 1H–1H COSY, 1H–13C HSQC, HSQC–TOCSY, 1H–13C HMBC, and J-resolved NMR to identify associated structural fragments. 1H NMR-guided separation by tracking the biomarker signals resulted in the discovery of tri-methoxylated isocoumarins (1),98 7-prenylisatin (2),96 aromatic polyketide GTRI-02 (3),95,100 and naphthoquinone juglomycin C amide (4).99 In addition to actinomycetes, we also leveraged 1H NMR-based metabolomics for investigating myxobacteria. We constructed the first publicly accessible database, MyxoDB (https://www.myxonpdb.sdu.edu.cn), which is solely dedicated to myxobacterial NPs.69 It greatly simplified the dereplication of commonly known myxobacterial compounds (e.g. phenalamide, myxothiazol, and corallopyronin) by analyzing the 1H NMR spectra of 147 strains from our in-house myxobacterial collection. Further PCA analysis could distinguish the outlier strain, Archangium gephyra SDU49, and the signals responsible for its distinct separation were pinpointed. NMR-guided separation by tracking the biomarker signals enabled us to swiftly identify a class of linear di-lipopeptides named archangiumins (5–8).
Beyond our own investigations, Betancur et al. also demonstrated the utility of 1H NMR-based metabolomics in the discovery of novel microbial NPs. In their study, Streptomyces sp. PNM-9 was cultivated in four distinct liquid media for 15 days to examine the influence of the growth medium composition on the production of bioactive metabolites. Samples were collected at specified time points (days 1, 2, 3, 4, 5, 10, and 15) and subjected to 1H NMR-based fingerprinting to profile the resulting metabolic landscapes. Subsequent OPLS-DA analysis was employed to correlate the obtained 1H NMR spectral data with observed bioactivity. Targeted isolation guided by biomarker identification from the OPLS score plot led to the characterization of 2-methyl-N-(2-phenylethyl)-butanamide (9) and 3-methyl-N-(2-phenylethyl)-butanamide (10).
2.3 Two-dimensional NMR-based metabolomics in microbial NP discovery
The restricted spectral dispersion and complex signal patterns in the 1D NMR spectra of crude extracts result in substantial signal overlap, hindering their utility in dereplication. Conversely, 2D NMR techniques, providing expanded spectral dispersion and additional orthogonal dimensions, minimize signal overlap in complex mixtures and yield more comprehensive structural insights. In theory, all commonly used 2D NMR experiments, such as 1H–1H COSY, 1H–1H TOCSY, 1H–13C HSQC, and 1H–13C HMBC, can be employed to extract the necessary structural information for identifying chemical entities in mixtures.308 1H–13C HSQC correlations from 1207 spectra as typical molecular features. Euclidean distances were calculated between sample peaks and the database, with outliers indicating potential unknown compounds for chromatographic isolation. Consequently, this method was applied to a mere 52 μg of ethyl acetate extract from marine-derived Streptomyces sp. CNB-982. A clear spectroscopic beacon signal (δH −0.90 and δC 32.3, distance 13.74%) was highlighted, guiding the targeted chromatographic separation. This enabled the identification of the antibacterial cyclic heptapeptide cyclomarin A (11).111 Although cyclomarin A was originally discovered in 1999, it still remained a compelling example of the capacity of AFM to reveal rare structural features at the crude extract level to speed up the targeted discovery process. Indeed, the authors subsequently applied the AFM methodology to 32 μg of crude extract from the marine sponge Plakortis halichondrioides, which led to the discovery of a new compound, gracilioether L (12). SMART (Small Molecule Accurate Recognition Technology), initially presented in 2017, employs a deep convolutional neural network (CNN) to automate the identification and annotation of compounds from HSQC spectra.112 SMART outputs a curated list of probable compound structures, leveraging a CNN model trained on a vast array of HSQC spectra from characterized natural molecules, 2054, in the original version and an expanded 53076 in SMART 2.0.113 In 2020, SMART 2.0 was first applied to mixture analysis, successfully predicting the presence of a novel macrolide termed symplocolide A (13) in an active fraction of the crude extract originating from the filamentous marine cyanobacterium Symploca sp., as well as fast dereplication of known compounds such swinholide A, samholides A. The researchers further compared the capabilities of SMART 2.0 and AFM in identifying rare structural motifs. When SMART 2.0 was applied to the HSQC spectra of pure cyclomarin A and Streptomyces sp. CNB-982 crude extract, all the top hits (2 for cyclomarin A and top 20 for the extract) were closely related ilamycins that harbor the rare N-(1,1-dimethyl-2,3-epoxypropyl)-tryptophan moiety, as also found in cyclomarin A, highlighting its ability to detect structural similarity based on chemical shift patterns, even without directly measuring heteroatoms or quaternary carbons. In effect, the rapid search, taking less than 30 min (structure prediction at 8 s) from spectra to data, demonstrates the efficiency of SMART 2.0. In contrast, three out of the four “novel features” identified by AFM method in cyclomarin A were found to be false positives regarding structural novelty. DeepSAT114 is another recently developed cheminformatic tool that leverages a CNN to directly extract chemical features from HSQC spectra, enabling molecule identification even without authentic NMR spectra. It is trained on a large and diverse dataset of both literature and computationally derived HSQC spectra, and it predicts chemical fingerprints, molecular weights, and structure classes. Compared to SMART 2.0, DeepSAT demonstrated a significant improvement in performance. Specifically, DeepSAT achieved over a two-fold higher correct identification rate than SMART 2.0 (41.0% vs. 18.7% in the top 1 output). Furthermore, unlike SMART 2.0, which does not support multiplicity-edited HSQC data, DeepSAT can be trained with both data to increase the prediction accuracy, with the latter reaching 45.2% and 64.9% in the top-1 and top-5 identifications, respectively. Also, SMART 2.0 relies on a limited in-house database (Moliverse, ∼130000 compounds), whereas, in principle, DeepSAT can be extended to search vast public databases through its structure prediction and fingerprint generation capabilities.
 | ||
| Fig. 2 Schematic of the MADBbyTE workflow. HSQC and TOCSY experiments are performed to acquire 2D NMR data. Subsequently, the spin system is constructed by integrating HSQC-derived 1H–13C correlations with TOCSY-derived 1H–1H coupling networks. After extracting all resolvable spin systems from the samples, similarity calculations and correlation analysis on the spin systems are performed for each extract. By constructing a spin system similarity network, topologically connecting nodes representing spin systems with similar chemical shift patterns, a visualized network model with dual analytical values is generated. The clustering of repeated nodes in the network directly reveals structural homogeneity among extracts, providing structural evidence for the rapid elimination of redundant information. Isolated nodes or weakly connected sub-networks located at the topological periphery of the network often correspond to unique spin system arrangements in the extracts, indicating the presence of novel compound frameworks or rare substituent groups. | ||
2D J-resolved (J-Res) spectroscopy separates coupling and chemical shift data across two axes, simplifying signal identification and multiplet analysis. It complements 1D NMR in metabolomics by resolving overlapping peaks more efficiently than most other 2D NMR experiments. Zeng et al. developed the CASCADE experiment for facile multiplet analysis and selective coupling measurement,125 surpassing traditional 2D J-resolved experiments in complex samples with extensive homonuclear couplings. SpinCouple is an online platform utilizing 1H–1H J-resolved spectroscopy and a database of nearly 600 metabolites to identify and annotate metabolites with minimal HSQC peak counts.126J-RESRGAN is a GAN specifically trained to enhance the peak resolution in metabolomic J-Res spectra.127 In the early 2000s, Gerwick et al. introduced DECODES and HETDECODES NMR methods to differentiate species by their diffusion coefficient, enabling the cursory structure elucidation and/or dereplication of the known peptidic compound symplostatin (17) in a complex mixture.128 Schroeder et al. employed double quantum filtered correlation spectroscopy (DQF-COSY) to examine a library of fungal extracts derived from an insect-pathogenic Tolypocladium cylindrosporum strain, which quickly revealed two new terpenoid indole alkaloids, 18 and 19.129 Forseth et al. applied dqfCOSY spectra to compare human pathogen Aspergillus fumigatus with its ΔgliZ mutant, a knock-out strain devoid of the gene encoding the transcriptional regulator of the gliotoxin biosynthetic gene cluster. The metabolomes of ΔgliZ mutant and wild-type strains were individually separated into three fractions to reduce the complexity and polarity range. Differential analysis of the obtained metabolome pools using dqfCOSY profiling allowed a detailed inventory of nine gli-derived metabolites (20–28) featuring unexpected structural motifs.130 In this regard, combined genetic manipulation and differential 2D NMR analysis offer a powerful, unbiased approach for characterizing small-molecule mixtures.
2.4 Specialized heteronuclear NMR techniques
Although microbial NPs are predominantly composed of the chemical elements H, C, O, N, and S, the incorporation of other elements, such as B,131 P,132,133 As,134–137 and Se,138–141 into organic molecules via covalent or coordination bonds can confer unique physiological or pharmacological activities. This has fueled great interest in the targeted discovery of microbial NPs containing these less common elements. In addition to the widely used 1H NMR and 3C NMR, other heteronuclear atoms-based NMR techniques, such as 15N NMR, 19F NMR, 11B NMR, and 31P NMR, can be valuable tools for the straightforward identification of microbial NPs containing specialized functionalities in complex crude extracts, effectively expanding the scope of NMR-based metabolomics. | ||
| Fig. 3 11B NMR-guided discovery of boron-containing NPs. A new pulse sequence (zgbs) can effectively suppress noise and improve the resolution in the 11Bzgbsig NMR experiment. This technique was used to analyze microbial crude extracts for the detection of boron-containing NPs. For example, the clear 11B signal in S. radiopugnans SNE-036 extract-guided 11B NMR-directed separation yielded diadenosine borate (29). | ||
3. NMR-based metabolomics meets prefractionation
3.1 General principle for concatenated prefractionation and NMR-based metabolomics
Microbial crude extracts are intricate blends, where each constituent has the potential to interact with the biological target under examination. However, only a tiny fraction of components is discernible and measurable by NMR fingerprinting because some may be present at concentrations too minuscule. Additionally, the signal of the bioassay can be obscured by interference from irrelevant or disruptive compounds or by the combined influence of multiple compounds, whether they act additively or synergistically.158 The substantial overlap of signals represents another fundamental obstacle that restricts the broader utilization of NMR-based metabolomics. The chromatographic prefractionation can effectively tackle these problems and strengthen the utility of NMR-based metabolomics. Generally, the strategic workflow (Fig. 4) involves the construction of microbial fraction libraries,159–161 followed by high-throughput screening using NMR-based metabolomic profiling. The development of microbial fraction libraries necessitates meticulous attention to various factors to maximize the structural diversity, bioactivity representation, and overall screening efficiency by NMR. The process commences with the cultivation of phylogenetically diverse microbial strains, followed by the recovery of metabolites to afford crude extracts. A key consideration in terms of the fermentation scale of each strain and/or conditions is balancing the need for sufficient sample material to acquire high-quality 1D and/or 2D NMR spectra and comprehensive bioactivity assay for each fraction, while maintaining a reasonable workload for high-throughput NMR-based screening. In principle, any chromatographic method with sufficient resolving power can be employed for fractionation, including normal-phase silica, size exclusion gels such as Sephadex LH-20, ion exchange resin, and reversed-phase C18 chromatography.162 Given the demands for high-throughput characterization, it is crucial to rationally select chromatographic methods that emphasize ease of operation, high resolving power, and minimal irreversible adsorption. Typically, reversed-phase C18 chromatography utilizing a methanol/water system with a step-gradient elution in 20% increments is empolyed.69 The initial fractions (0% methanol eluent) rich in highly polar medium components are deliberately excluded from further consideration. Therefore, each crude extract is partitioned into five fractions according to polarity, which not only ensures experimental reproducibility but also facilitates the direct NMR comparison of fractions eluted under identical conditions across different strains. The fractionation process can be efficiently performed using automated flash chromatography systems. With the simplified fractions in hand, the NMR-based metabolomic profiling workflow outlined in Fig. 1 can be effectively employed to excavate bioactive microbial NPs. The reduced complexity of the individual fractions significantly enhances the speed and accuracy of both metabolite identification and dereplication. Importantly, given that NMR is an insensitive but non-destructive technique, it is advisable to collect NMR data of fractions prior to bioactivity assays (e.g. antimicrobial, cytotoxicity). Multivariate statistical analyses (PCA, PLS-DA, OPLS-DA, etc.) can be employed to identify fractions with unique or desirable spectral features, and more importantly correlate and these features with bioactivity data. The biomarkers indicative of high activity are used as probes for the NMR-guided separation of bioactive compounds harbored in the fractions of interest. Therefore, prefractionation can improve the targeted discovery efficiency of NMR-based metabolomics. | ||
| Fig. 4 Schematic representation of the concatenated prefractionation and NMR-based metabolomics for bioactive microbial NP discovery. A microbial crude extract library is initially subjected to reverse-phase C18 column fractionation using stepwise methanol gradients (20%, 40%, 60%, 80%, and 100% MeOH) in water. Compounds of disparate polarity are separated into different fractions. Although the NMR profiling of crude extracts exhibits high complexity with severe signal overlap, the chromatographic fractionation efficiently reduces the complexity, enhances the sensitivity, and resolves the minor metabolites in the NMR spectra. After NMR measurement, each fraction is subjected to bioactivity assays. Subsequently, multivariate data analysis is employed to discriminate samples and correlate NMR spectral data with bioactivity to identify biomarker signals. Finally, NMR-guided isolation targets these biomarkers, enabling the identification and validation of novel bioactive microbial NPs. | ||
The Quinn group assembled a collection of lead-like enhanced (LLE) fractions,163–166 which were selected due to their advantageous physicochemical characteristics and molecular weight.167 They illustrated the effectiveness of 1H NMR fingerprinting on fractions to discover new substances from a collection of 20 sponges belonging to the Poecilosclerida order. The identification of a distinctive 1H NMR spectral pattern in 5 out of the 220 fractions facilitated the isolation of the novel compound iotrochotazine A (45), which was later found to exhibit phenotypic effects on cells derived from individuals with Parkinson's disease.163 They further applied the same strategy to explore the actinomycetes associated with termite gut, resulting in the discovery of six new compounds, including actinoglycosidines A and B (46 and 47), actinopolymorphol D (48), and niveamycins A, B, and C (49–51).166 Recently, our research group similarly integrated prefractionation with 1H NMR fingerprinting for the discovery of new compounds from myxobacteria. The crude extract of Corallococcus sp. SDU284 was subjected to chromatography on a reverse-phase C18 column, and each resulting fraction indeed gave a simplified 1H NMR fingerprint in contrast to the crude extract. A group of olefinic signals that was not visible in the crude extract became evident in the 1H NMR spectrum of one faction, directing the targeted isolation of corasterol (52), a rare rearranged sterol with noncanonical angular ring fusion pattern.69 In the ELINA workflow, bioactive crude extracts are fractionated to vary chemical compositions and bioactivities. Then, the 1H NMR data of each fraction are correlated with bioactivity using statistical heterocovariance analysis (HetcA) to distinguish biomarkers from active components. When this method was applied to Fomitopsis pinicola, it led to the identification of lanostane triterpenes that inhibit steroid sulfatase.168 In addition to 1H NMR fingerprinting, prefractionation can also work together with 13C NMR or 2D NMR profiling for the targeted discovery of new compounds, as mentioned in Sections 2.1.2 and 2.2.5.102–104,130 Therefore, the synergy between fraction libraries and NMR-based metabolomics improves the targeted efficiency discovery of bioactive microbial NPs.
3.2 The hyphenated LC-NMR technique for metabolomics profiling
Online hyphenation of LC-NMR has emerged as a powerful technique to provide crucial advantages over standalone NMR-based approaches, particularly when analyzing complex microbial extracts containing numerous metabolites exhibiting significant signal overlap.169,170 By efficiently separating compounds based on their physicochemical properties prior to NMR analysis, LC techniques significantly reduce the spectral complexity, enabling the identification and characterization of even low-abundance metabolites. Techniques such as WATERGATE171 and WET172 have been adapted for on-flow LC-NMR to minimize solvent interference, which otherwise interfere with the detection of low-abundance microbial NPs. In addition, LC-NMR can be further coupled with MS spectrometry and/or solid phase extraction (SPE) in a more sophisticated and automated approach (e.g. LC-SPE-NMR, LC-NMR-MS, and LC-SPE-NMR-MS),173 offering complementary information for enhanced metabolite identification and structure characterization. Online LC-SPE-NMR systems automate the preconcentration and cleanup steps, minimizing sample handling and loss. The target microbial NPs are selectively retained on an SPE cartridge, while interfering matrix components are washed away, resulting in a more concentrated and purified sample for NMR analysis. Different SPE sorbents allow the selective enrichment of compounds based on polarity or other characteristics.24 Furthermore, the coupling of LC-NMR with MS provides complementary information for comprehensive metabolite profiling. High-resolution MS offers accurate mass measurements and characteristic fragmentation patterns, aiding compound identification and dereplication.174 For example, Lin and colleagues developed a microscale LC-MS-NMR platform that combines nanoSplitter LC-MS and microdroplet NMR for identifying unknown compounds present at low concentrations within complex mixtures. The platform design allows highly sensitive MS analysis, while directing the majority of the LC effluent to NMR for structural characterization. By using a segmented flow microdroplet system coupled with high automation, interpretable 1H NMR spectra are obtained from analytes at the 200 ng level within 1 h acquisition. The utility of this platform is demonstrated through the identification of known and unknown metabolites from a bioactive cyanobacterial extract, showcasing its potential for streamlining the discovery of microbial NPs.175 In another separate report, the initial LC-NMR analysis of EtOAc extracts of several actinomycetes revealed an LC peak exhibiting multiple methyl doublet signals (δH 0.4–1.6) and α-methine signals (δH 4–5), suggesting a peptidic structure. The subsequent large-scale purification, monitored by LC-NMR, enabled the targeted discovery of tumescenamides A (53) and B (54) from Streptomyces tumescens YM23-260. In addition, Trimble et al. exemplified the utility of LC-NMR in resolving diastereomeric microbial NPs, namely (16R)-and (16S)-hydroxyroquefortine C (55 and 56), from Penicillium crustosum DAOM 215343.176
4. NMR-based metabolomics meets genomics
In the post-genomic era, genomic mining has become a routine approach for exploring microbial NPs. Notwithstanding, an effective platform is still indispensable to correlate the chemotype and genotype in the crude metabolomic extracts to efficiently avoid chemical redundancy and accelerate the targeted discovery process. The synergy between genomic and metabolomic approaches marks a cutting-edge development in the field of NP chemistry and sets the stage for a new phase of discovery.11,13,15,16 MS-based metabolomics has been well integrated with genomics, including the metabologenomics,177,178 peptidogenomics,179 glycogenomics,180 NPRminer,181 MetaMina,182 and NPLinker platforms.183 Also, a synergistic link exists between NMR-based metabolomics and genomics, and this interface can be analogously harnessed for bioprospecting.4.1 General principle for paired genomics and NMR-based metabolomics
The underlying basic principle and/or workflow of paired NMR-based metabolomics and genomics for microbial NP discovery is illustrated in Fig. 5. In brief, advances in genomics, coupled with sophisticated bioinformatics tools, enable the rapid analysis of biosynthetic gene clusters (BGCs) within microbial genomes to discern their novelties through homology comparison.184–188 The accumulative biosynthetic knowledge allows the in silico structural prediction of chemical outputs of BGCs, including cursory chemical scaffolds and/or key functional groups. NMR spectroscopic data can be predicted with reasonable accuracy for bioinformatics-deduced theoretical chemical (sub)structures, including the chemical shifts and/or splitting patterns of each proton or carbon in 1H and 13C NMR spectra.189 Scrutiny of the corresponding NMR regions that potentially contain the predicted 1H and 13C resonances ensures the tracking of “biomarkers” arising from the BGC(s) of interest, achieving a direct correlation between the genotype and the chemotype of given BGCs. Despite the challenges in identifying all signals of a single molecule in a complex NMR fingerprint due to signal congestion, any distinguishable signals from (sub)structures predicted by BGCs can act as driven-peaks for the NMR-based matching of genotype and chemotype. | ||
| Fig. 5 Synergistic workflow of paired NMR-based metabolomics and genomics for the discovery of microbial NPs. The process begins with genomic analysis of microbial strains to identify BGCs. Bioinformatics analysis of the BGC of interest enable in silico structural prediction of cognate products. Then, the theoretically predicted NMR spectral data of metabolic products guides the targeted identification of characteristic “biomarkers” in the NMR profiling of crude extracts, establishing a genotype–chemotype correlation. Alternatively, the interpretation of experimental NMR profiling data allows the characterization of key structural fragments, which guide the assignment of responsible BGCs via retro-biosynthetic analysis. Then, sequence homology analysis of BGC and bioinformatics-aided structure prediction aid in the dereplication and/or structural novelty judgement of the final products represented by the characterized structural fragments. | ||
Alternatively, the integration of these two disciplines can alternatively commence with 1D and 2D NMR analyses of microbial crude extracts to dereplicate known compounds or identify structural fragments of unknown compounds. The obtained structural signatures can be used as probes for the assignment of responsible BGCs on bacterial (meta)genomes, a process termed retro-biosynthetic analysis.190,191 Although NMR-based metabolomics alone may not be sufficient to dereplicate or assess the novelty of metabolites based on structural fragments, the suggested potential BGCs in microbial genomes reverts to the above-mentioned “gene-to-chemistry” strategy by directly comparing the BGC sequence homology to determine the structural novelty of the final products. Specifically, this synergistic approach facilitates more efficient dereplication and targeted discovery by enabling clearer spectroscopic signals and a more focused search for predicted metabolites.
Genomics-aided structure prediction has also found increasing applications in determining the absolute configurations of complex microbial NPs. By identifying stereospecific enzymatic domains within BGCs, such as ketoreductase (KR) domains in modular PKSs184,201,202 and epimerization (E) domains in NRPSs,203,204 researchers can infer the stereochemical outcome of key steps in the biosynthetic pathway. The knowledge of KR domains can lead to the understanding of the 3D chiral centers on PKS products or the specific stereochemistry of amino acids. This information, in conjunction with NMR spectroscopic data enables the more accurate and confident assignment of the absolute configuration of microbial NPs. For instance, Kim et al. showcased the power of integrating bioinformatic approaches and NMR analysis (coupling constants analysis and ROESY correlations) for the rapid and accurate stereochemical determination of neaumycin B, a structurally complex macrolide possessing 19 asymmetric centers.205
4.2 Genomic and NMR spectroscopic signature-based approach
A unique hybrid genomics and NMR-based metabolomics strategy termed “genomic and spectroscopic signature-based approach” has gained attention for streamlining the isolation and characterization of microbial NPs containing unique signature structural motifs. The general pipeline is summarized in Fig. 6. The creation of unique signature compounds often relies on conserved biosynthetic genes amenable to degenerating the primer design. High-throughput PCR screening based on the degenerate primers swiftly identifies bacterial strains harboring BGCs responsible for microbial NPs with unique structural motifs. The resulting PCR amplicons are sequenced and yield natural product sequence tags (NPSTs),209 which are analyzed phylogenetically to assess the novelty of BGCs. NPSTs situated in distinct clades from known references pinpoint strains possessing novel biosynthetic pathways.210,211 Genome sequencing and bioinformatics analysis of the prioritized strains confirm the novelty of targeted BGCs containing unique genetic probes. Next, the combined NMR-based metabolomics and genomics approach, as illustrated in Fig. 5, can be applied for targeted discovery. In brief, based on the chemical properties and NMR predictions of the signature motifs, appropriate NMR methods (such as 1H NMR, 31P-NMR, 1H–13C HSQC, 1H–15N HMBC, and 1H–15N HSQC-TOCSY) are selected for chemispecific detection from microbial extracts, followed by NMR-guided chromatographic separation. Identifying the spectroscopic signatures in microbial culture extracts purely using NMR experiments may be not feasible owing to the invariable complexity and signals congestion, especially for targeted BGCs expressed at a low level. This limitation can be overcome by isotope labelling of the precursors to enhance the detection sensitivity of signature structural motifs by NMR profiling, given that the key biosynthetic precursors for the unique signature structural motifs are already known. Overall, the combination of degenerate primer PCR screening, isotope labelling, and NMR metabolic profiling offers a general strategy for the identification of a diverse array of microbial NPs featuring unique signature structural motifs. | ||
| Fig. 6 Genomic and spectroscopic signature-based approach for the discovery of microbial NPs. Degenerate primer PCR screening, targeting conserved biosynthetic genes, identifies strains harboring BGCs for microbial NPs with signature structural unit. The sequencing of PCR amplicons and phylogenetic analysis assess the BGC novelty. The NMR-based metabolomic profiling tool is used for the straightforward identification of the microbial NPs derived from the BGCs of interest in the prioritized strains. Isotope-labelled precursor feeding can pinpoint the NMR signal(s) in highly complex matrices that are originated from the investigated BGCs, which are then used as probes for the NMR-guided separation of microbial NPs containing the signature structural unit. | ||
4.3 Illustrative microbial NPs discovered by the joint strength of genomics and NMR-based metabolomics
In this section, we exemplify the proof of concept that the paired NMR-based metabolomics and genomics approaches, as discussed in Sections 4.2 and 4.3, can effectively accelerate the discovery rate of microbial NPs. The diversity in chemical structures and biosynthetic pathways elegantly supports the synergistic power of these two disciplines. | ||
| Fig. 7 Integrated genomics and NMR-based metabolomics workflow for the discovery of allenic macrolide. Bioinformatic analysis of a trans-AT PKS gene cluster arc in the A. violaceum SDU8 genome predicted its metabolic product as an allenic macrolide. Moreover, 2D NMR profiling of the SDU8 crude extract allowed the identification of the typical structural fragments of macrolides. This allowed the correlation of the genotype and chemotype of arc because it is the only BGC capable of biosynthesizing macrolide-class molecules in SDU8. Large-scale fermentation in tandem with NMR-guided separation streamlined the discovery of archangiumide (57). | ||
The OH group employed the genomic and spectroscopic signature-based approach to discover macrolactams originating from the type I modular PKS machinery (Fig. 8).214 The biosynthesis of macrolactams is characterized by a unique protection and deprotection mechanism catalyzed by an adenylation enzyme (AD) and a proline aminopeptidase (PAP), respectively.215 Taking advantage of knowledge, the investigators performed PCR screening of the genomic DNA library using the degenerate primers specific to the AD and PAP genes, which allowed them to identify 43 macrolactam producers from a collection of 1188 bacterial strains. Phylogenetic analysis of the PAP amplicons enabled the reasonable prediction of the subtype and novelty of macrolactams produced by the 43 selected strains. To achieve the early identification of macrolactams in the microbial crude extracts without purification, 1H–15N HSQC-TOCSY NMR spectroscopic analysis was applied to display the expected 1H spin system on the amide nitrogen (δN 110−130 ppm), given that all three subtypes (α-methyl, α-alkyl, or β-methyl) of macrolactams commonly possess proton-proficient carbons (CH, CH2, CH3) connected to the amide NH. Considering the low natural abundance of 15N (0.36%), 15NH4Cl was fed to the cultures of the hit strains to strengthen the 15N detection, and thus facilitate the direct detection of the spectroscopic signatures by 1H–15N HSQC-TOCSY NMR experiments. Finally, this combined genomic and spectroscopic signature-based method resulted in the structural revision of an α-methyl macrolactam salinilactam (58), and the discovery of a new α-alkyl-type macrolactam muanlactam (59) and a new β-methyl macrolactam concolactam (60) with a [16,6,6]-tricyclic skeleton.
 | ||
| Fig. 8 Genomic and spectroscopic signature-based approach for the discovery of macrolactams. Degenerate primers targeting the conserved adenylation (AD) and proline aminopeptidase (PAP) genes were utilized to screen a bacterial strain library via PCR, identifying strains that harbor the biosynthetic pathway for macrolactams. The isotope labelling with 15NH4Cl followed by 1H–15N HSQC-TOCSY NMR profiling displayed the diagnostic 1H spin systems surrounding the amide nitrogen in three subtypes of macrolactams. This integrated approach led to the discovery of 58–60. | ||
Likewise, the Oh group employed the same strategy to selectively discover oxazole-bearing natural products.216 A library of 1000 strains was screened via PCR using the degenerate primer specific to the key biosynthetic gene encoding oxazole cyclase. The unique one-bond coupling constants (1JCH values) and chemical shifts for the 1H and 13C atoms in oxazole provide diagnostic spectroscopic signatures in the 1H–13C HSQC NMR spectra. These signatures enable the selective and unequivocal detection of the terminal oxazole motif without the need for isotopic labeling or chromatographic purification of the strains selected by genomic screening. The application of this targeted method led to the discovery of five new compounds, including lenzioxazole (61), possessing an unprecedented cyclopentane, permafroxazole (62), bearing a tetraene conjugated with carboxylic acid, tenebriazine (63), incorporating two modified amino acids, and methyl-oxazolomycins A and B (64 and 65, respectively), expanding the chemical space of the terminal oxazole-bearing NP family.
 | ||
| Fig. 9 Paired genomics and NMR-based metabolomics strategy for the discovery of qinimycins.97 A cryptic type II PKS gene cluster (qin) contains genes predicted to encode molecules featuring deoxyamino-sugar forosamine (depicted in red), an aromatic polyketide pyranonaphthoquinone backbone (depicted in black), as well as a suite of genes associated with N-methylation. After the qin gene cluster was activated by the overexpression of its pathway-specific regulator, the comparative 1H NMR metabolic profiling enabled the detection of the characteristic proton signals assignable to the bioinformatics-predicted structural fragments in the mutant, as highlighted by the arrow. NMR-guided separation streamlined the characterization of qinimycins. The structural fragments of qinimycins are color-coded to match corresponding genes within qin. | ||
![[double bond, length as m-dash]](https://www.rsc.org/images/entities/char_e001.gif)
”.219 The combination of isotopic labelling with cost-effective 15NH4Cl and a suite of 2D 1H–15N NMR experiments including 1H–15N HMBC, 1H–15N HSQC-TOCSY and 1H–15N HSQC provided a diagnostic spectroscopic signature at δN 72−83 ppm for Piz and δN 310−320 ppm for Dpz (dehydropiperazic acid). These consolidated genomic and spectroscopic signature-based analyses facilitated the characterization of the dimeric cyclohexapeptides chloptosin (71) and depsidomycin D (72) with two units of Dpz, polyoxyperuin seco acid (73), and 31-membered cyclic decapeptides lenziamides A and B (74 and 75). Similarly, the Andersen group identified the strain Streptomyces incarnatus NRRL 8089 as a potential producer of Piz-containing peptides through genome mining using ktzT as a marker. By feeding the strain with 15N2-L-Orn, they enhanced the detection of the desired molecules in 1H–15N HSQC-TOCSY NMR profiling, and successfully isolated the low-abundance incarnatapeptins A and B (76 and 77, respectively),220 and dentigerumycins F (78) and G (79).221 Huang et al.222 performed 1H NMR metabolic profiling of Streptomyces sp. S063, whereby unusual negative chemical shifts (δH −0.34 ppm) were revealed. NMR-guided fractionation coupled with HSQC analysis revealed that the fraction containing the probe signal was dominated by peptides featuring multiple N-methyl groups (NMe), which strongly suggested an NRPS biosynthetic origin. Meanwhile, genome analysis of Streptomyces sp. S063 identified six NRPS gene clusters, and one of them (cluster 20) contained multiple N-methyltransferase (nMT) domains, which correlated well with the NMR-based metabolic profiling data. Further bioinformatic analysis of cluster 20 revealed that its chemical output should be Piz-containing peptides, considering the co-existence of the ktzT and ktzI genes. The limited similarity (<48%) of cluster 20 to known Piz-containing peptides spurred large-scale fermentation for the targeted isolation of lenziamides D1 (80) and B1 (81).
The Gerwick group described the “genomisotopic approach” for the discovery of a non-ribosomal peptide. Bioinformatics analysis of the Pseudomonas fluorescens Pf-5 genome led to the identification of an orphan NRPS gene cluster. Subsequent substrate specificity analysis of the central NRPS megasynthetases confirmed that 15N-labeled leucine (Leu) is an optimal choice for isotope labeling of the encoded lipopeptide because four A domains accept Leu as building blocks, allowing a more robust NMR resonance signal from the resulting labeled product. Further 1H–15N HMBC NMR-based metabolic profiling of the isotope-enriched crude extract was instrumental in the discovery of orfamide A (82),223 a novel lipopeptide with surfactant activity.224 Very recently, Xu et al. combined genome mining and 2D NMR-based metabolomics for the targeted isolation of nocobactin-type lipopeptides (83–92) from phychrophillic Nocardia sp. L-016. Initially, small-scale cultures were grown, and the resulting ethyl acetate extract was subjected to HSQC and HMBC analysis to identify key structural moieties such as oxazoles and phenol units directly at the crude extract level. This metabolomic profiling informed subsequent genome mining, enabling the identification of the responsible BGCs encoding the observed structural features, exemplified by the nocobactin-like BGC (noc) harboring an oxazole-coding homolog (nocF). Further LC-MS/MS-based metabolomics coupled with molecular networking was performed to uncover novel analogues within the identified compound class. This combined approach leveraged NMR-derived structural insights to focus genomic searches, leading to the efficient discovery of novel natural products.
In another separate report, three unknown plant P450 genes were expressed in yeast to metabolize the terpenoids thalianol, arabidiol, and marneral. HSQC-based metabolomic profiling proved to be effective for analyzing the crude extracts, distinguishing trace triterpenoids that were indistinguishable in GC–MS. The side-by-side comparison of HSQC was particularly instrumental in evaluating the crude extracts from the cultures with and without a P450 gene candidate, enabling the straightforward identification of metabolite peaks of interest. Known compounds were readily identified by matching pairs of 1H–13C correlation with literature data, and new derivatives (102–104) featuring differential oxidation patterns were unambiguously elucidated by intensive 2D NMR analysis.228 This study demonstrated that the combination of genome mining, heterologous expression, and HSQC-based metabolomic profiling is an effective methodology to elucidate unknown metabolic pathways and rapidly identify their novel products.
5. Conclusion and perspectives
Metabolomics offers a powerful strategy to accelerate the discovery of microbial NPs by providing a comprehensive snapshot of the small molecules present in complex biological samples. MS and NMR are the two most commonly used technologies for metabolomics, primarily because of their powerful ability to detect individual metabolites in complex mixtures, while requiring little or no physical separation. However, NMR and MS have both strengths and weaknesses for metabolomics study.230 MS is more sensitive than NMR and gives better discriminatory signals due to its more common hyphenation to liquid chromatography, but MS is not suitable for molecules resistant to ionization. NMR can universally detect all compound classes present in a mixture, regardless of their ionization properties or chemical derivatization requirements. Furthermore, the noninvasive nature of NMR preserves the integrity of the sample for subsequent analyses, such as bioactivity testing or further structural characterization (e.g. crystallization). In terms of structure elucidation, NMR possesses the distinctive capability to investigate structural details at the atomic scale for NMR-active nuclei, including 1H, 13C, 15N, 11B, and 31P, enabling the structural determination of unknown compounds by providing detailed atom–atom linkages (e.g. C–H, C–C, C–O, C–N, C–P, and C–B). Moreover, NMR spectroscopy demonstrates a distinct advantage over MS in determining absolute configurations and distinguishing diastereomers.74,75 However, the low sensitivity and signal overlapping potentially make it challenging to detect important compounds present at lower concentrations by NMR-based metabolic profiling22 because they are likely to be overshadowed by predominant peaks.18 This problem is being actively addressed through various strategies. Prefractionation techniques, as discussed in Section 3, offer a direct approach by reducing the complexity of the mixture prior to NMR analysis. Sophisticated algorithms for automated spectral deconvolution can disentangle overlapping signals, while machine learning approaches can identify patterns and biomarkers even in highly congested regions of the spectrum (see Section 2.1.3). Techniques that enhance spectral resolution, such as high-field NMR and cryogenic probes (see Section 2.1.1), indirectly alleviate signal overlap by spreading signals further apart. Additionally, multi-dimensional NMR methods and isotopic labeling can also highlight the NMR signals of interest in complex mixtures.The identification of unknown metabolites has been regarded as one of the major challenges in the metabolomics field, where NMR and MS alone are oftentimes not sufficient for the unambiguous identification of individual molecular species hidden in complex mixtures. Although NMR offers advantages for elucidating core structural frameworks of the predominant compounds, NMR metabolomics often struggles to resolve closely related analogues within complex mixtures. Conversely, MS, particularly when coupled with techniques such as GNPS molecular networking,231 excels at identifying and discriminating structural analogs through fragmentation analysis. Therefore, methods integrating both NMR and MS for the identification of metabolites in metabolomics have received particular attention, which heralds a promising future for navigating the complex chemical landscape of microbial metabolites with enhanced efficiency and precision.232 Brüschweiler and colleagues created tools such as SUMMIT233–235 and NMR/MS Translator,236 which integrate MS and NMR for the easy identification of unknowns in complex mixtures. The SUMMIT MS/NMR approach was used on an E. coli cell lysate, successfully identifying compounds such as N-acetylputrescine and spermidine.233 The NMR/MS Translator method begins with NMR spectra for structure identification, and then confirmed by MS analysis of potential ions, adducts, fragments, and isotope patterns.236 Ge et al. developed the Structure-Oriented Fractions Screening Platform (SFSP), a novel MS/NMR-hybrid metabolomics approach for accelerating natural product discovery.237 SFSP leverages machine learning algorithms to predict the 1H NMR chemical shift distributions from untargeted MS1 and 1H-PSYCHE NMR data, facilitating the functional-group-guided fractionation of complex mixtures. Unlike other metabolomics approaches, SFSP does not rely on pre-existing databases for compound identification, making it particularly useful for discovering novel compounds. This database-independent platform was successfully applied to the marine fungus Aspergillus sp. GE2-6, leading to the isolation and structural elucidation of 27 novel flavipidin and phenalenone derivatives. The integration of NMR and MS data within SFSP proved instrumental in the rapid dereplication of known compounds and the targeted identification of promising lead candidates based on characteristic NMR signals, thereby streamlining the natural product discovery workflow.
The effectiveness of genome mining is well-known,1,185,206 but it misses a considerable amount of undescribed chemistry due to the intricacy of NP biosynthesis, which happens in the constrained space of a cell, such as the involvement of non-enzymatic reactions238 and the crosstalk among multiple different BGCs.239,240 Besides, identifying phylogenetic uniqueness does not automatically ensure that a microorganism possesses the requisite biosynthetic pathways to produce new metabolites, or that these pathways are dormant. Thus, genomics needs to work in conjunction with metabolomics because the latter is directly geared towards the final stage of the metabolic process. In turn, metabolomics also reciprocally relies on genomics to guide targeted discovery and/or simplifies the structure elucidation in mixtures. Numerous studies have validated the working interface between genomics and MS-based metabolomics.15 Comparatively, the strategy of combining NMR-based metabolomics with genomics seriously lags behind. We reason that NMR-based metabolomics offers advantages over or at least complementary to MS-based metabolomics owing to the following: (1) NMR is more versatile in solving any characteristic structural fragments predicted from the biosynthetic cassettes of a given BGC, and thus more effectively bridges the gap between the chemical phenotypes and the genotypes. (2) More flexibly, by recognizing the signal distribution pattern in the NMR spectra, it can still match the fuzzy chemical categories predicted from novel BGCs, and thus the NMR-based metabolomics strategy is anticipated to find “unknown unknowns”.241,242 However, NMR-based metabolomics also has obvious limitations when combined with genomics, as follows: (1) the NMR-based metabolomics workflow relies on the expression level of BGCs of interest, otherwise the signals overlapping in the NMR spectra would be problematic, especially for minor components; (2) NMR-based metabolomics is less effective than MS-based approaches such as molecular networking in terms of uncovering structurally related congeners, especially for those produced in tiny amounts; and (3) NMR structural elucidation requires intensive expertise and experience, and thus is seemingly more difficult to be popularized than MS, especially for biological researchers, although the advancements in CASE84,85 for automated NMR interpretation can somehow mitigate this dilemma for non-spectroscopists. It is crucial to judiciously select between NMR and MS metabolic fingerprinting methods based on the specific types of BGCs. For example, MS-based metabolic profiling is more suitable for the RiPP class of compounds, whereas NMR-based metabolomics is more effective for detecting aromatic polyketides derived from type II PKS. Collectively, the pairwise complementarity of genomics, NMR-based metabolomics and MS-based metabolomics [“genomics-MS” and “genomics-NMR”, “NMR-MS”] implicates the feasibility of integrating three methodologies as a new platform to provide a new impetus for the discovery of novel microbial NPs, given that they offer different strengths but also display drawbacks.
Finally, AI is poised to revolutionize the discovery of microbial NPs by accelerating and refining various aspects of the process. From genome mining and BGCs prediction to structure elucidation and bioactivity prediction, AI algorithms offer the potential to overcome longstanding bottlenecks in this field.243–245 In the context of NMR-based metabolomics, AI can significantly enhance data processing, spectral analysis, and compound identification. For example, deep learning models can be trained to automate peak picking, chemical shift prediction, and spectral deconvolution, improving the speed and accuracy of NMR data interpretation, as discussed in Section 2. Furthermore, AI can facilitate the correlation of NMR spectral features with bioactivity data, enabling the rapid identification of lead compounds and the design of more targeted discovery campaigns. By integrating AI with NMR-based metabolomics workflows, researchers can unlock new avenues for microbial NP discovery and gain a deeper understanding of microbial chemistry.51,101,246,247 It is conceivable that the continued development of more AI technologies can revolutionize aspects of microbial NP discovery, especially in conjunction with orthogonal information derived from genomics and MS-based metabolomics. Furthermore, there is a growing emphasis from the NP community on the sharing and dissemination of NMR raw data.248 This trend is crucial for supplying ample datasets for the optimization of deep machine learning models, thereby reinforcing the application of AI in the paradigm of NMR-based metabolomics and enhancing its analytical capabilities. Also, it is urgently necessary to develop a reliable protocol with initiatives of integrating the existing algorithms for genomics188,192 and NMR-based compound annotation in mixtures. A quick way to boost the strength is probably to emulate the successful MS-mediated linking of genomics and metabolomics.177–183 For instance, the binary correlation between the biosynthetic gene cluster families (GCFs) and corresponding NPs relies on similarity scoring across data sets,177 which can be potentially referenced by NMR-based metabolomics in the differential analysis of a bunch of genetically-associated bacterial strains such as actinomycetes and myxobacteria. In the future, huge opportunities exist for the development of sophisticated computational tools to integrate genomics, MS, and NMR datasets as a whole and use them synergistically for the bioprospecting of new bioactive compounds as well as associated enzymology in an automated and high-throughput manner, which will require extensive interdisciplinary knowledge and cooperation of researchers from chemistry, biology, and cheminformatics.
6. Conflicts of interest
There are no conflicts to declare.7. Acknowledgements
This work was financially supported by the National Key Research and Development Programs of China (No. 2024YFA0917100), the National Natural Science Foundation of China (NSFC) (No. 32222003 and 82304353), the Fundamental Research Funds of Shandong University (No. 2023QNTD001, and ts20230201), Postdoctoral Fellowship Program of CPSF (No. GZC20231463), the Natural Science Foundation of Shandong Province (No. ZR2024QB114), and the Postdoctoral Innovation Project of Shandong Province (No. SDCX-ZG-202303002).8. References
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Footnote |
| † These authors contributed equally to this manuscript. |
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