Biosynthetic crosstalk in bacteria: routes to chimeric natural products

Contents

• 1. Introduction

• 2. Routes to chimeric natural products
  • 2.1 Enzymatic assembly of chimeras from multiple separate BGCs
  • 2.2 Recruitment of primary-metabolism intermediates into specialized natural-product scaffolds
  • 2.3 Chemistry-driven coupling beyond enzymes via spontaneous fusion of pathway products

• 3. A multi-class metabolomics framework for systematic chimeric natural product discovery
  • 3.1 Recognizing chimeric spectra through multi-class MS/MS annotation
  • 3.2 Prioritizing candidate chimeras using taxonomy-guided spectral searches
  • 3.3 Empirical validation via stable isotope feeding
  • 3.4 A proposed hybrid-prioritization pipeline

• 4. Conclusions

• 5 Author contributions

• 6 Conflicts of interest

• 7 Data availability

• Acknowledgements

• References

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Abstract

Covering: up to the end of April 2026

Chimeric natural products are formed when biosynthetic inputs from distinct pathways, gene clusters, or metabolic branches are integrated into a single scaffold. In bacteria, such crosstalk-driven assembly can generate structurally diverse metabolites with distinct biological activities. However, these metabolites remain underexplored because most discovery pipelines are optimized to connect one metabolite family to one co-localized biosynthetic gene cluster (BGC). As a result, metabolites produced through inter-pathway collaboration, recruitment of primary-metabolic intermediates, or non-enzymatic coupling of products from separate pathways are often deprioritized as genome-metabolome mismatches. Here, we describe chimeric natural products as an important yet overlooked class of bacterial metabolites and present a discovery framework that prioritizes candidate hybrid scaffolds from LC-MS-based metabolomics. We suggest that public-repository spectral searching, multi-class MS/MS annotation, retrospective genome-to-metabolite linkage, and isotope-guided validation can improve discovery of these overlooked scaffolds. Prioritizing such metabolites can expand natural product discovery beyond canonical frameworks, uncover new biosynthetic design principles, and inform future efforts to engineer hybrid molecules.

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Wen-Chao Yu

Dr. Yu obtained his Ph.D. in chemical biology from Zhejiang University of Technology. His research focuses on metabolomics, natural product discovery, and computational approaches for small-molecule annotation. He develops mass spectrometry-based strategies to characterize bioactive metabolites in complex biological systems.

Zhiyuan Peng

Dr. Peng obtained his Ph.D. from the University of Rhode Island. His research interests include chemical biology, metabolomics, and natural product discovery, with a focus on using mass spectrometry and data analysis to study small molecules from complex biological systems.

Yan-Song Ye

Dr. Ye received his Ph.D. from the Kunming Institute of Botany, Chinese Academy of Sciences, where he studied polycyclic polyprenylated acylphloroglucinols from Hypericum. He later conducted postdoctoral research at the Kunming Institute of Botany, the University of South Carolina, and the University of Texas at Austin. His current research integrates natural products chemistry, metabolomics, and microbiology to study gut microbial metabolites and host–microbe interactions.

Qihao Wu

Dr. Wu is an assistant professor at the University of Pittsburgh School of Pharmacy. He received his Ph.D. in medicinal chemistry from Zhejiang University of Technology and completed postdoctoral training at Yale University, Princeton University, and the University of Wisconsin–Madison. His laboratory studies microbial chemical biology, with an emphasis on gut microbiota-mediated small-molecule metabolism and its impact on host physiology.

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1. Introduction

Bacteria produce a vast repertoire of specialized metabolites that mediate nutrient acquisition, interspecies competition, community interactions, and host-associated processes.^1–3 Many of these molecules are assembled by enzymes encoded in biosynthetic gene clusters (BGCs), and the growing number of microbial genome sequences has greatly expanded the pool of candidate pathways available for natural product discovery.^4–7 This genomic expansion helped establish the modern era of genome mining, which has been highly successful in linking co-localized biosynthetic genes to canonical metabolite families.^5,6,8,9 At the same time, however, this success has reinforced a working assumption that one metabolite family can usually be explained by one contiguous biosynthetic locus.^10,11 As a result, standard bottom-up discovery workflows remain biased toward pathways that fit a one-cluster, one-compound model.^9,12 This bias becomes limiting for metabolites assembled through interactions between distinct pathways, separate loci, or primary- and specialized-metabolic branches, which are often difficult to assign using standard genome-to-metabolite mapping strategies.

Chimeric natural products exemplify this overlooked biosynthetic potential because they can be generated through crosstalk between distinct biosynthetic pathways, yielding hybrid metabolites assembled from substructures supplied by separate gene sets and, in some cases, separate genomic loci.^13–15 Unlike canonical single-cluster hybrids such as PKS-NRPS systems, many chimeras are built through biosynthetic architectures distributed across separate gene sets, including spatially separated BGCs. These hybrid scaffolds are often difficult to connect to specific metabolomic features using standard genome-to-metabolite mapping workflows and may remain hidden within biosynthetic “dark matter”.^7,16,17 Chimeric natural products represent an important area for closer examination, not only because they expand known natural product chemical space, but also because they reveal gaps in how current discovery pipelines prioritize metabolites and assign biosynthetic origin.

This highlight discusses cases in which relatively simple genetic architectures generate unexpected chimeric natural products. To better uncover this space, we propose discovery frameworks that intentionally identify overlooked chimeric pathways by integrating metabolomics with genomics and retrospectively linking chimeric features to their BGCs. By adopting a crosstalk-centered discovery logic, the field can move beyond single-cluster mining toward deciphering the broader genomic networks that shape natural product diversity.

2. Routes to chimeric natural products

Chimeric natural products are assembled when components from two or more distinct metabolic pathways are integrated into a single molecule, combining precursors encoded by spatially or functionally separate BGCs (Fig. 1). Such products can form via biosynthetic crosstalk between distinct BGCs, through intersections between specialized and primary metabolism, or by spontaneous chemical coupling of independently generated intermediates (Fig. 1). The examples below illustrate how distributed biosynthetic networks can generate unexpected structural complexity and distinct biological functions, particularly when examined through integrative omics approaches. Given the difficulty of identifying and validating these pathways, many additional examples likely remain undiscovered or unrecognized. We therefore focus on representative cases with the strongest genetic and metabolomic support to highlight core design principles and practical strategies for uncovering chimeric biosynthesis. Representative compounds were selected to illustrate each chimeric route type (Fig. 2), with additional members summarized to demonstrate the chemical and biological diversity of these metabolites (Fig. 3). Because several chimeric natural products comprise large molecular families, additional related members are provided in the SI.

Fig. 1 Conceptual framework for chimeric natural product integration. Chimeric natural products arise when biosynthetic inputs from distinct BGCs, primary metabolism, or both are combined into a single molecular scaffold. These integrations can occur through inter-BGC crosstalk, recruitment of primary-metabolic intermediates, spontaneous coupling of pathway products, or post-assembly modification.

Fig. 2 Representative routes to chimeric natural product biosynthesis. (A) Inter-BGC collaboration through substrate promiscuity generates the chimeric siderophore avaroferrin (1). (B) Cross-class “Trojan-horse” assembly produces albomycin δ₁ (4), in which a siderophore enables delivery of a nucleoside antibiotic warhead. (C) Distributed multi-locus biosynthesis, followed by metal-mediated dimerization, generates the tribrid scaffold cobaltribin (6). (D) Pathway hijacking at the interface of primary and specialized metabolism incorporates a vitamin B12-derived building block into myxadazole A (10) biosynthesis. (E) Spontaneous, non-enzymatic coupling between intermediates from separate pathways gives rise to pyonitrin A (12).

Fig. 3 Structural diversity of bacterial chimeric natural products assembled through distinct biosynthetic routes. Representative examples are grouped by major mode of integration: (A) same-class inter-BGC collaboration, (B) cross-class inter-BGC assembly, (C) multi-machinery distributed biosynthesis, (D) recruitment of primary-metabolism intermediates, and (E) spontaneous non-enzymatic coupling. Colored arrows indicate the predicted biosynthetic origin of each molecular component, and dashed red circles mark representative junctions or fusion sites between biosynthetic inputs.

2.1 Enzymatic assembly of chimeras from multiple separate BGCs

2.1.1 Same-class inter-BGC chimeras sharing chemistry and function. Although related examples of biosynthetic pathway crosstalk had been reported earlier, Clardy and colleagues helped formalize the concept of chimeric natural products through the discovery of avaroferrin (1, Fig. 2A),¹⁸ a functional mashup that combines building blocks from the putrebactin and bisucaberin pathways. This principle is further illustrated by serratiochelins (serratiochelins A and B, compounds 2 and 3, Fig. 3A; other serratiochelins, Fig. S1),¹⁹ for which bioinformatic analyses and targeted gene disruptions showed that enterobactin- and vibriobactin-like loci are recruited, with inputs from both required to assemble the final siderophores. These two examples illustrate crosstalk between siderophore pathways of the same functional class, generating chimeras that preserve related biosynthetic logic and converge on a common biological outcome: iron acquisition.

2.1.2 Cross-class inter-BGC chimeras with distinct chemistry and coupled function. Albomycins (albomycin δ₁, compound 4, Fig. 2B; other albomycins, Fig. S1) exemplify a cross-class chimera in which a ferrichrome-type siderophore is covalently linked through a serine handle to a thionucleoside antibiotic warhead^20,21 (Fig. 2B). The siderophore promotes uptake through ferric hydroxamate transport systems, after which intracellular peptidase cleavage releases the active thionucleoside, SB-217452, a potent inhibitor of seryl-tRNA synthetase. Microcin E492 (compound 5, Fig. 3B) provides a related example of inter-pathway assembly at the gene-cluster level: the microcin locus encodes the ribosomal peptide precursor and dedicated tailoring enzymes, but the mature product, microcin E492m, is formed only when the enterobactin pathway supplies the catecholate siderophore precursor.^22–26 In microcin E492m, a C-glucosylated, linearized enterobactin-derived appendage is installed at the C terminus by the Mce C/D/I/J maturation machinery, creating a ribosomal peptide-siderophore chimera whose maturation, receptor-mediated uptake, and antibacterial activity are linked to siderophore biosynthesis and iron availability.

2.1.3 Multi-machinery distributed assembly lines spanning three or more loci. More complex tribrid scaffolds, such as cobaltribin (compound 6, Fig. 2C; other cobaltribins, Fig. S1), further extend this concept by integrating terpene, polyketide, and nonribosomal peptide biosynthetic programs.²⁷ Bioinformatic, isotope-labeling, and gene-deletion analyses support a pathway involving a terpene synthase candidate (TerF) outside the cbt BGC, the NRPS CbtF, and the type I PKS machinery CbtH-J, with downstream tailoring steps furnishing the final scaffold (Fig. 2C). Tridecaptins (tridecaptin B₁, compound 7, Fig. 3C) and zeamines (zeamine I, compound 8, Fig. 3C; other zeamines, Fig. S1) extend this principle in different ways: certain tridecaptin variants are produced through collaborative action between a stand-alone tridecaptin-like NRPS and TriE from the canonical tridecaptin BGC,²⁸ whereas zeamine biosynthesis involves interplay between a polyunsaturated fatty acid synthase-like/FAS-PKS system and a hybrid NRPS/PKS assembly line, which generate distinct fragments that are coupled post-assembly.²⁹

2.2 Recruitment of primary-metabolism intermediates into specialized natural-product scaffolds

A second major route to chimeric biosynthesis involves the interface between specialized and primary metabolism, where intermediates from central or cofactor biosynthetic pathways are recruited into natural-product assembly.²⁹ The ecteinamines (ecteinamine A, compound 9, Fig. 3D; other ecteinamines, Fig. S2) exemplify this logic by incorporating a menaquinone pathway-derived 2-naphthoate moiety into a nonribosomal peptidic metallophore scaffold.³⁰ A related hijacking strategy is seen in the myxadazoles (myxadazole A₁, compound 10, Fig. 2D; other myxadazoles, Fig. S2), which join an N-ribityl-5,6-dimethylbenzimidazole unit derived from vitamin B12 metabolism to a non-canonical PKS/NRPS-derived fatty-acid chain³¹ (Fig. 2D). Microcin C (compound 11, Fig. 3D) illustrates a related interface between RiPP biosynthesis and nucleotide metabolism: a ribosomally encoded peptide is adenylated by MccB to install a phosphoramidate (N–P) linkage to AMP, generating a Trojan-horse peptide-nucleotide antibiotic.^32–34 Collectively, these cases underscore how integrated metabolomics and genomics are often required to connect primary-metabolic branch points to structurally unusual natural products.¹⁶

2.3 Chemistry-driven coupling beyond enzymes via spontaneous fusion of pathway products

Spontaneous, non-enzymatic coupling presents a particular challenge for genome-mining workflows because no dedicated fusion enzyme may be encoded. Pyonitrins (pyonitrin A, compound 12, Fig. 2E; other pyonitrins, Fig. S2) exemplify this limitation, as they are formed through a spontaneous Pictet–Spengler condensation between intermediates from the pyochelin and pyrrolnitrin pathways, revealing a chimeric biosynthetic route that would have been difficult to predict from bioinformatic analysis alone.^35,36 A related process occurs in tasikamides (tasikamide A, compound 13, Fig. 3E; other tasikamides, Fig. S2), which are assembled through an in vivo, non-enzymatic Japp–Klingemann coupling between an NRPS-derived cyclic peptide precursor and an aryl diazonium species generated by a second pathway.³⁷ Chimedermycins (chimedermycin A, compound 14, Fig. 3E; other chimedermycins, Fig. S2) further exemplify spontaneous, non-enzymatic coupling, in which polycyclic scaffolds arise via Michael-type additions or condensations between intermediates from distinct biosynthetic pathways. As no dedicated fusion enzyme is encoded, these transformations are effectively “genomically invisible”, highlighting the prevalence of chemically driven assembly that operates beyond canonical BGC boundaries.³⁸ In such cases, the chimeric product emerges only after chemically reactive pathway products meet outside the canonical enzymatic assembly line. These systems therefore show the need for approaches that combine genomics, metabolomics, and chemical logic to capture cryptic pathway convergence.

3. A multi-class metabolomics framework for systematic chimeric natural product discovery

Identifying chimeric natural products remains challenging because widely used genome-mining platforms such as antiSMASH and PRISM are designed primarily to detect and annotate co-localized BGCs.^11,39 As discussed above, chimeric assembly can depend on spatially separated loci or inter-pathway coupling, features that are not readily captured by standard BGC-centric prediction workflows. To move from opportunistic discovery toward more systematic mining, these considerations point to an integrative hybrid-prioritization framework that combines genomics, metabolomics, and retrospective genome-to-metabolite linking to address current limitations in BGC-centered bioinformatic pipelines (Fig. 4).

Fig. 4 Hybrid-prioritization framework for chimeric natural product discovery. (A) Multi-class MS/MS annotation prioritizes candidate chimeric features by identifying co-occurring spectral hallmarks from distinct biosynthetic lineages, such as polyketide-, lipid-, and glycosyl-derived substructures. (B) Taxonomy-informed spectral searching, retrospective genome-to-metabolite linkage, and isotope-guided validation then connect prioritized features to candidate producers and their biosynthetic pathways.

3.1 Recognizing chimeric spectra through multi-class MS/MS annotation

A major limitation of conventional discovery pipelines is their reliance on annotation platforms optimized for a single metabolite class, such as peptides or polyketides. By contrast, MS/MS spectra from chimeric metabolites can contain diagnostic fragment ions or neutral-loss patterns associated with more than one structural class within a single precursor.^40,41 This suggests a prioritization strategy centered on spectra that display overlapping predictive features from multiple chemical classes (Fig. 4A). For example, polyketide-associated features can be recognized using mass-defect-filtering workflows such as NegMDF together with product ions consistent with PKS-derived backbones (Fig. 4A).⁴² In parallel, glycosylated substructures can be identified using glycogenomics-guided logic, which connects sugar-diagnostic tandem-MS features and characteristic fragmentation behavior to glycosylation chemistry and, ultimately, to candidate biosynthetic genes (Fig. 4A).⁴¹ Likewise, lipid-associated features can be captured by combining lipid-like fragment ions and informative adduct patterns with principles established in lipidomics and native-MS-guided workflows for identifying tightly associated lipids (Fig. 4A).⁴³ Integrating these orthogonal spectral signatures into a ranking scheme may improve prioritization of chimeric natural products that are easily missed by single-class annotation.¹⁶ Importantly, co-occurring spectral hallmarks should be treated as a prioritization cue rather than definitive evidence of chimerism, because unrelated scaffolds can generate convergent fragments, neutral losses, or adduct behavior. Experimental validation therefore remains essential.

3.2 Prioritizing candidate chimeras using taxonomy-guided spectral searches

The initial phase of this framework leverages public LC-MS/MS repositories and paired-omics resources (Fig. 4B) that contain a large reservoir of chemically unassigned microbial features.^7,16 Tools such as microbeMASST enable users to query MS/MS spectra against a curated reference database built from more than 60 000 microbial monoculture LC-MS/MS files, providing a practical top-down route to nominate candidate producers from fragmentation patterns before the responsible biosynthetic genes are assigned.⁴⁴ In this setting, related chimeric signatures can be tracked across phylogenetically diverse producers without requiring the biosynthetic genes responsible for their production to be identified at the outset.^16,44 By prioritizing strains that share convergent spectral hallmarks, this strategy helps focus downstream genomic analysis on organisms likely to harbor cryptic, dispersed, or otherwise hard-to-assign biosynthetic elements that may be missed by standard BGC-centric workflows.^10,16,44 However, spectral recurrence alone does not establish shared biosynthetic origin, and downstream genomic and experimental validation remain essential.

3.3 Empirical validation via stable isotope feeding

While top-down mining and multi-class annotation can prioritize candidate chimeric metabolites, assigning specific biosynthetic modules to a complex scaffold still requires experimental validation.^45,46 Stable isotope feeding is especially valuable because labeled precursors can be traced into diagnostic substructures (Fig. 4B), directly testing whether a candidate scaffold integrates inputs from distinct biosynthetic routes.^45,46 For example, cobaltribin provides a recent example in which bioinformatic, isotope-labeling, and gene-deletion analyses collectively supported a terpene-polyketide-nonribosomal peptide tribrid pathway rather than a single canonical BGC.²⁷ In that study, precursor-feeding experiments helped assign key substructures and, when interpreted alongside targeted deletions across the implicated loci, converted a plausible multi-pathway model into a biosynthetic assignment.²⁷ More broadly, isotope-guided validation can help distinguish true chimeric assembly from superficial structural resemblance and direct focused genetic and biochemical follow-up in BGC-rich genomes.^27,45,46

3.4 A proposed hybrid-prioritization pipeline

Taken together, these considerations suggest a hybrid-prioritization workflow that integrates top-down spectral prioritization, taxonomy-guided searching, retrospective genome-to-metabolite linking, and stable-isotope validation (Fig. 4).^7,16,26,44 Rather than forcing each MS/MS feature into a single biosynthetic category at the outset, this workflow prioritizes spectra that contain diagnostic fragment ions, neutral losses, or mass-pattern signatures associated with more than one chemical class. Comparison against curated, class-specific fragmentation references can then reveal combined signatures consistent with polyketide, glycosylated, and lipid-associated substructures. This strategy is expandable and can incorporate additional spectral hallmarks, such as terpene-derived fragmentation patterns or markers of primary-metabolic branch capture, as new chimeric families are identified and validated. High-priority features showing multiple hallmarks can then be traced across microbial LC-MS/MS datasets using microbeMASST⁴⁴ to identify candidate producers independent of genomic co-localization, followed by retrospective genetic linkage through paired-omics resources and empirical validation by stable-isotope feeding. By tracing labeled atoms into diagnostic structural motifs, isotope-guided validation can convert bioinformatic predictions into direct biosynthetic evidence and help reveal cryptic chimeras that are easily missed by standard bottom-up genome-mining workflows. At the same time, this workflow remains subject to important limitations, including incomplete spectral reference coverage, ambiguity in assigning substructures from tandem MS alone, false positives from overlapping fragmentation signatures, and the difficulty of linking prioritized features to dispersed loci in gene-rich genomes.

4. Conclusions

The discovery of chimeric natural products broadens our view of microbial metabolism by showing that natural-product biosynthesis is more interconnected than implied by the classical “one BGC, one compound” paradigm. From an evolutionary perspective, such crosstalk offers an efficient route to chemical innovation, allowing microbes to repurpose shared intermediates, recruit inputs from neighboring pathways, and expand structural diversity without encoding entirely new assembly lines. We have presented clear examples of this principle, illustrating how genetic and metabolic convergence can generate functional chemical space beyond conventional genomic boundaries.

An additional distinction is whether the contributing pathways produce independent bioactive metabolites alongside the hybrid products. In several systems discussed above, each parental pathway generates its own active end product, and the chimeric scaffold arises in parallel rather than as the only functional output. This distinction is important for discovery because obligate hybrids may be readily detected in bioactivity-guided screens when the chimera is the principal bioactive product. By contrast, facultative hybrids may be overlooked when stronger parental signals dominate, unless metabolomics-based prioritization is incorporated into the screening workflow. This issue directly motivates the multi-class metabolomics framework described above, which is designed to detect chimeric features even when their parental pathways produce dominant bioactive products.

To uncover these hidden chimeras more systematically, the field will benefit from adopting hybrid-prioritization strategies built on top-down metabolomics. Platforms such as microbeMASST enable taxonomy-informed tracking of chimeric scaffolds across phylogenetically diverse producers, while multi-class spectral annotation shifts MS/MS analysis beyond single-class assignment toward recognition of overlapping biosynthetic signatures. In this framework, co-occurring hallmarks within a single spectrum may serve as indicators of structural convergence and potential novelty, helping transform mass spectrometry from a tool for compound identification into a guide for navigating biosynthetic “dark matter”. Establishing the genetic basis of these scaffolds will still require validation, but recent cases, including ecteinamines and cobaltribin, show how integrated metabolomics, genomics, and isotope-guided experiments can resolve complex biosynthetic origins and reveal chemically distinctive molecules with therapeutic promise.

More broadly, deciphering naturally occurring chimeras can also inform the design of synthetic hybrid molecules. Continued advances in metabolomics, structural analysis, and bioinformatics should expand access to new pharmacophores and mechanisms of action that would remain inaccessible under conventional single-pathway discovery frameworks. Ultimately, the study of chimeric natural products suggests a broader analytical lens: one that looks beyond physical gene co-localization and recognizes that nature often builds its most compelling molecules through distributed and collaborative biosynthetic logic.

5 Author contributions

Wen-Chao Yu – conceptualization, data curation, writing – original draft, and writing – review and editing. Zhiyuan Peng and Yan-Song Ye – writing – original draft, and writing – review and editing. Qihao Wu – conceptualization, supervision, funding acquisition, and writing – review and editing.

6 Conflicts of interest

There are no conflicts to declare.

7 Data availability

No primary research results, software, or code are included, and no new data were generated or analysed as part of this Highlight.

Supplementary information (SI) is available. See DOI: https://doi.org/10.1039/d6np00048g.

8. Acknowledgements

This work was supported by start-up funds provided to Qihao Wu by the Department of Pharmaceutical Sciences at the University of Pittsburgh.

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