Paired-omics-based exploration and characterisation of biosynthetic diversity in lichenized fungi

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Navarro-Muñoz, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6073935/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : The increasing demand for novel drug leads requires bioprospecting non-model taxa. Comparative genomics and correlative omics are a fast and efficient method for linking bioactive but genetically orphan natural products to their biosynthetic gene clusters (BGCs) and identifying potentially novel drug leads. Here we implement these approaches for the first systematic comparison of the BGC diversity in lichen-forming fungi (LFF, comprising 20% of known fungi), prolific but underutilized producers of bioactive natural products. We first identified BGCs from all publicly available LFF genomes (111), encompassing 71 fungal genera and 23 families, and generated BGC similarity networks of each class. Results : We recovered 5,541 BGCs grouped into 4,464 gene cluster families. We used mass spectrometry (MS) and correlative metabolomics to link five MS-identified metabolites - alectoronic acid, alpha-collatolic acid, evernic acid, stenosporic acid, and perlatolic acid - to their putative BGCs. We subsequently used MS on additional 93 species to explore the taxonomic breadth of common lichen compounds, uncovering a strong pattern between specific families and secondary metabolites. Conclusions : We found that 1) approximately 98% of the BGCs in LFF are putatively novel, 2) lichen metabolic profiles contain a plethora of unidentified metabolites and 3) ribosomal peptide-related BGCs constitute about 20% of the LFF BGC landscape. Our study provides comprehensive insights into the BGC landscape of LFFs, highlighting unique, widespread, and previously uncharacterized BGCs. We anticipate that the approach we describe will serve as a baseline for leveraging biosynthetic research in non-model organisms, inspiring further investigations into microbial dark matter. Natural products fungi biosynthetic genes depsides BiG-SCAPE antiSMASH secondary metabolites drug discovery RiPPs PKSs Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Natural products (NPs) are a crucial source of bioactive compounds. The majority of modern medicines and therapeutic agents, including most anticancer compounds and antibiotics, are derived from plant and microbial NPs [ 1 , 2 ]. However, the emergence of novel infections combined with the appearance of antibiotic-resistant pathogens is posing an increasing demand for novel drugs to combat the global health crises [ 3 , 4 ]. The emergence of viral epidemics and drug resistance also points toward the urgent need for novel antimicrobial therapy [ 3 – 6 ]. Researchers have begun to explore novel organisms as well as novel classes of biosynthetic genes for their pharmaceutically usable bioactive properties. This includes BGCs as ribosomally synthesized and post-translationally modified peptides (RiPPs), owing to their strong antimicrobial activities and high stability, specificity and affinity for targets, cellular penetrability and great engineering potential [ 1 , 7 – 10 ]. Advances in genome sequencing, biosynthetic gene discovery pipelines, and synthetic biology approaches, enable the utilization of accumulating genomic data for culture-free bioprospecting of non-model organisms, addressing key bottlenecks in natural product discovery [ 11 , 12 ]. For instance, while lichenized fungi – fungi living in obligate symbiotic association with one or more photobionts [ 13 , 14 ] – are a treasure trove of secondary metabolites [ 15 , 16 ], with around 1,000 described to date and many exhibiting strong bioactivity, the industrial applications of lichen-derived compounds remain underutilized. This is primarily attributed to the experimental challenges inherent in symbiotic systems, the slow growth rate of lichens, difficulties to generate axenic mycobiont culture biomass and the lack of known triggers to stimulate metabolite synthesis [ 17 ]. While lichen genomes contain genes for synthesizing a diverse array of metabolites – non-ribosomal peptide synthases (NRPSs), polyketide synthetases (PKSs), hybrid NRPS–PKSs, terpene synthetases, and RiPP synthases, the biosynthetic research on lichenized fungi has primarily focused on PKSs [ 11 , 18 – 21 ] and more recently on terpene-related biosynthetic gene clusters (BGCs) [ 22 ]. One reason for the focus on PKSs is because most of the well-known, commonly analyzed lichen compounds are PKS-derived (e.g., depsides, depsidones, xanthones, anthraquinones, dibenzofurans). Computational approaches can bypass culture dependency and expand NP exploration beyond PKSs to enable the identification, dereplication and prioritization of the entire biosynthetic diversity of organisms. The genes encoding NPs often lie adjacent to each other in a linear fashion, forming biosynthetic gene clusters (BGCs) [ 23 – 25 ]. A BGC typically has one or a few of the following core genes that define the chemical class of the encoded compound [ 25 , 26 ] : NRPSs, PKSs, terpenes or RiPPs. Recently, automated pipelines have been developed to facilitate the bioinformatic linking of genes to molecules through similarity-based clustering of homologous BGCs into gene cluster families (e.g., BiG-SCAPE [ 27 ] and BiG-SLiCE [ 28 ]). This approach not only facilitates the linking of BGCs to their putative metabolites but also enables the prediction of biosynthetic functions and novelty by comparing them with characterized BGCs in databases such as MIBiG [ 29 ]. Studies implementing BGC clustering approaches on bacteria reveal that we have explored only a tiny fraction of the available diversity for its biosynthetic potential [ 30 – 32 ]. In lichenized fungi, only a few BGCs have been linked to the metabolites (e.g. grayanic acid [ 21 ], atranorin [ 11 ], lecanoric acid [ 12 ], usnic acid [ 19 , 33 , 34 ], olivetoric acid [ 35 ]) and metabolic diversity is known only for the commonly analyzed compound classes, e.g., those listed in [ 36 , 37 ]. A comprehensive analysis of the total metabolic potential of LFF is still lacking. With the recent surge in genomic data and the development of advanced BGC prediction and clustering pipelines, there is now an opportunity to better understand the BGCs diversity and novelty in lichenized fungi, enabling informed predictions about the most industrially relevant taxa and metabolites. Here, we investigate the metabolic diversity in lichenized fungi using a comparative genomics and molecular networking approach to bioinformatically characterize the lichen “BGC landscape”. We further employ correlative omics to link five bioactive metabolites to their biosynthetic origins. In addition, we assessed the taxonomic distribution of the most prevalent lichen metabolites by performing MS on additional 93 LFF. Specifically, we aim to 1) unravel the extent of biosynthetic novelty encoded in lichen-forming fungal genomes, 2) deorphanize common and potent bioactive metabolites using comparative metabolomics and network and clustering approaches, and 3) estimate the taxonomic breadth of common lichen metabolites. We anticipate that the novel BGCs predicted here will facilitate the discovery of novel bioactive NPs and inspire investigations into gene functions and metabolic pathways of non-model taxa. Results High diversity of BGCs in lichen-forming fungal genomes AntiSMASH detected 5,542 BGCs in 111 LFF (Supplementary Table 1). The number of BGCs varied at the family and genus levels, with Sarrameanaceae being the most BGC-poor family (14 BGCs) and Pyrenulaceae, Physciaceae, and Trypetheliaceae being the most BGC-rich families (~70 BGCs per taxa) (Figure 1A, 1B, Supplementary Table 2). The greatest number of BGCs (95 BGCs) was detected in Canoparmelia texana (Parmeliaceae), and the lowest in Caeruleum heppii (Acarosporaceae) (14 BGCs) (Figure 1A). Lichenized fungi contain an average of 47±20 BGCs per genome, approximately 23±11 PKSs and 15±7 NRPSs/taxon (Fig. 1B), making the proportion of PKS to NRPS clusters 3:2 (2,622:1,726). The most dominant BGC class in lichenized fungi is PKSs, accounting for approximately 50% of the total BGCs, followed by NRPSs (approximately 23%), RiPPs (16%) and terpenes (approximately 12%) (Supplementary Table 2). We surveyed the complete set of BGCs in LFF genomes and found that the metabolic potential varied among families, with Physiaceae constituting the richest source of terpenes and NRPSs, whereas Pyrenulaceae and Trypetheliaceae were the richest sources of PKSs (Figure 1B, Supplementary Table 2). Potentially novel BGCs BiG-SCAPE generated the network for each BGC class - within each network similar BGCs were organized into gene cluster families (GCFs), while unique BGCs remained as singletons. LFF have a vast repertoire of GCFs comprising only a few BGCs (Figure 2A. 2B, Supplementary material 3A, 3B). Most of the lichen GCFs are unique and do not display similarity to previously known BGCs in standard curated databases (AntiSMASH [38] and MIBiG [29]) (Figure 2C, 2D, Supplementary material 3A). Among the BGC classes, only the PKS BGCs showed similarity to BGCs in MIBiG, while all other classes, including NRPS, did not. This could be because polyketides are among the most prevalent as well as the most studied molecular classes in LFF [12,21,39,40]. Of approximately 1,500 PKS GCFs, only three clustered with BGCs listed in MIBiG (at a BiG-SCAPE clustering threshold of 0.6, Figure 2B), namely 6-methysalicyclic acid, 6-hydroxymellein and usnic acid (Supplementary material S4). The network approach facilitated the identification of these pathways/BGCs in many LFFs for the first time. RiPPs We identified 1,186 RiPP BGCs in our dataset, clustering into 987 GCFs (Supplementary material S3A and S3B). These RiPP BGCs showed low similarity (8-30%) to those known from Lecanoromycetes or other fungi (asperipin, ustiloxin and phomopsin BGCs), suggesting that LFF RiPP derivatives might be structurally and functionally diverse than those of non-lichenized fungi. Like PKSs, the diversity of RiPP BGC varied within Lecanoromycetes, with RiPP being the predominant BGC class in Agyriaceae, Icmadophilaceae, Pertusariaceae and Stictidaceae, outnumbering PKSs and NRPSs. Deorphanizing BGCs and metabolites: Clustering-based linking of genes to molecules Most known bioactive compounds of LFF belong to the orcinol or orcinol derivatives class [15,41–43]. However, the corresponding BGCs are known only for only a few compounds [18]. Based on the GCF recovered in PKS network (Figure 3A), for the first time, we infer most-likely BGCs for the following five orcinol metabolites: perlatolic acid (compound 7, Figure 3B), evernic acid (compound 8, Figure 3B), stenosporic acid (compound 9, Figure 3B), alectoronic acid (compound 10, Figure 3B), collatolic acid (compound 11, Figure 3B) and their derivatives (Figure 3B). This inference is based on correlative omics, integrating species-specific metabolite production patterns with clustering of BGCs with pre-characterized orcinol derivative gene clusters, including 6-methylsalicylic acid (compound 1, Figure 3B), grayanic acid (compound 3, Figure 3B) and olivetoric acid (compound 5, Figure 3B). Notably, all these NPs have unique chemical properties despite sharing a similar core structure (Table 1) [42–44]. Of these, grayanic acid and 6-methylsalicylic acid clusters are present in MIBiG, whereas the olivetoric acid BGC was recently identified based on in silico predictions [35]. The clusters from different taxa grouped with grayanic acid/olivetoric acid BGC code for the different orcinol compounds (Figure 3C). Since we retrieved only one candidate BGC per taxon and only one main metabolic product within this compound class has been reported for these taxa, we are certain that the BGC codes for the depside/depsidone reported from the lichen. In addition, we identified putative BGCs for two PKS-derived bioactive NPs—usnic acid and atranorin—from several taxa. The BGCs provided here differ in sequence conservation as well as gene composition from the previously reported usnic acid and atranorin BGCs [11,19,33,35]. The taxa and BGCs reported in this study represent novel sources of these metabolites. Given the diversity in BGC and gene sequences, they may encode slightly different structural and functional variants of these compounds. The GenBank files of these clusters are available as Supplementary material S5. Deorphanizing BGCs and metabolites: Metabolite profiling and correlative metabolomics We performed integrative omics analysis to identify the putative BGCs responsible for the synthesis of the identified depsides or depsidones. Using PKS clustering and BGC-to-compound structure correlations, we linked MS spectra to putative BGCs for five known bioactive compounds. Specifically, we deorphanized the following NPs– alectoronic acid (compound 10, Figure 3B), collatolic acid (compound 11, Figure 3B), evernic acid (compound 8, Figure 3B), stenosporic acid (compound 9, Figure 3B), and perlatolic acid (compound 7, Figure 3B). This was done by employing a multifaceted approach based on the BGC class and compound structure correspondence, gene clustering, and gene cluster similarity. For each compound, we systematically narrowed down the candidates to a single, most likely BGC. This congruence between the molecule structure, gene cluster, phylogenetic clustering provides strong evidence of identified BGCs being the exclusive candidates responsible for the biosynthesis of the respective compounds. The molecular networking (Figure 4) was constructed from metabolomics data. Compounds sharing similar mass spectra are clustered, and major chemical groups are visualized. Taxonomic breadth of four common, bioactive LFF metabolites We profiled a total of 93 species (138 samples) for their metabolites to identify the potential depsides and depsidones secreted by them. In the studied species, we found that stictic acid, a ß-orcinol depsidone, is predominant compound secreted by the members of the family Peltigeraceae. Notably, it is not exclusive to this family but also produced by certain members of Parmeliaceae, e.g. Usnea spp. and Acarospora spp. [45,46]. In contrast, members of studied Parmeliaceae taxa secrete a diverse array of orcinol didepsides and didepsidones, as lecanoric acid (compound 4, Figure 3B), olivetoric acid (compound 5, Figure 3B) and physodic acid (compound 6, Figure 3B) etc. Umbilicariaceae lichens on the other hand primarily produce orcinol tridepsides as gyrophoric acid and derivatives. This suggests that distantly related families may harbor structurally and functionally diverse metabolites and that novel, distantly related genera represent untapped source of unique NPs. Similarly, closely related taxa and genera are potential sources of known compounds or novel variants of these compounds. The taxonomic distribution of these bioactive compounds reveals a strong pattern between specific taxonomic families and their associated metabolites. Discussion This study provides the first systematic comparison of BGC diversity within LFF fungi and explores their metabolic uniqueness. We analyzed LFF genomes to bioinformatically characterize their metabolic diversity, grouping them by the chemical families of encoded compounds and identifying novel metabolic pathways. Interestingly, we found RiPPs, previously known known from bacteria and Basidiomycete fungi, to constitute about 15-20% of lichen biosynthetic space. This is the first study reporting RiPP contribution to total biosynthetic gene space of lichens. Concurrently, we performed correlative metabolomics on five metabolites to establish gene-to-molecule links and inferred the taxonomic breadth of widespread bioactive lichen metabolites, such as stictic acid and lecanoric acid. High diversity of BGCs and GCFs in lichen genomes On average, LFF contain approximately 47±20 BGCs, but the number of identified compounds per species is usually less than 10, indicating a majority of clusters are either temporally silent or orphan, or go undetected by regular detection techniques [18,19]. Even though most of these BGCs are yet orphan, their presence in high number in LFF appears to be an evolutionary strategy rather than the accumulation of silent and non-functional BGCs. A recent study showed that specialization towards metabolism is the primary feature of the genetic turnover in the evolution of fungi [47]. Interestingly, while Metazoa on average accumulated genes from diverse functional categories, in fungi only a few categories showed net gains during evolution—one of which is secondary metabolism. Fungi, in fact, allocate a higher proportion of their gene content to metabolic processes compared to metazoans. Given that specialization toward metabolism has been a primary feature of genetic turnover during fungal evolution, the limited number of functionally characterized BGCs may result from the absence of relevant ecological or biological cues, or the temporal instability of the metabolites, which makes their detection more challenging. Recent efforts to activate and express several silent BGCs via promoter activation, repressor deactivation or a combination of both have expanded the possibilities for the functional characterization of compounds beyond those already expressed [48]. Furthermore, the bioinformatic identification of an organism's entire BGC landscape reduces the likelihood of rediscovering known natural products and provides opportunities to explore novel structural diversity for promising leads. While the majority of BGCs are predicted to encode PKSs, consistent with the known diversity of orcinol derivatives in LFF, many novel GCFs appear to encode a variety of diverse bioactive compounds, including RiPPs, NRPSs, and terpenes (Fig. 5C). For instance, although the majority of reported NPs from LFF are PKS-derived (melanins, usnic acid, grayanic acid, olivetoric acid, gyrophoric acid, umbilicaric acid, etc.) [49–51], a typical LFF BGC landscape is biosynthetically diverse, encompassing three to five classes of BGCs [33,39,52,53] (Fig. 1A, B). This further highlights that only a small fraction of the chemical diversity in LFF has been explored to date. Moreover, research on lichen biosynthesis has focused on PKSs, but the diversity of other classes, with the exception of terpenes [22], remains largely unexplored. The BGCs and the BGC families reported here are valuable resources for elucidating the biochemical pathways of other, thus far neglected, classes of biosynthetic compounds. RiPPs A notable outcome of our study is the significant representation of RiPP-BGCs in LFF genomes, comprising 16% of the identified BGCs. Although RiPP-BGCs account for a substantial portion of the bacterial biosynthetic landscape—approximately one-fourth of all BGCs and ranking as the second most predominant class after NRPSs—their representation in fungal genomes was estimated to be markedly lower, at approximately 1%, based on data from 2,000 species. This may be attributed to the limitations of detection algorithms, coupled with the limited understanding of fungal RiPP biosynthesis [54]. For example, while plant and bacterial RiPPs have been extensively studied, the first fungal RiPP was not discovered until 2007, and only a handful have been characterized since then [54]. Our study is the first to show that the RiPP BGCs contributes more to the total LFF BGCs than previously recognized. In some LFF, RiPPs constitute approximately 50% of the BGCs. Notably, while most described fungal RiPPs, such as dikaritins in Ascomycota, are typically characterized by repeated core sequences, AntiSMASH analysis did not identify any repeated core peptides in Lecanoromycetes. This observation suggests that non-repeated core peptides may be more prevalent in Ascomycota fungi than previously recognized. Alternatively, it is possible that the core peptides in Lecanoromycetes are highly divergent and do not resemble any known sequences, potentially representing entirely novel RiPP classes. RiPPs are thought to serve a defensive role against mycophagy, as demonstrated by certain well-known RiPPs, such as amatoxins, which primarily affect the digestive systems of organisms. Since RiPPs are believed to have evolved in response to the threat of consumption by eukaryotic organisms, their higher prevalence in certain taxa may be attributed to increased exposure to grazing pressures [55]. Notably, a high number of RiPP BGCs were identified in lichens with diverse thallus morphology ranging from crustose, fruticose to foliose. This aligns with the toxic effect of RiPPs which affects insects, nematodes, and mammals alike [54]. For example, while crustose and foliose lichens are primarily threatened by fungivory from insects and nematodes, foliose lichens may face a greater threat from mammals. Given to their high bioactivity, RiPPs are considered promising sources of novel antibiotics and have been proposed as potential solutions for targeting previously undruggable sites, owing to their small size of just a few amino acids. Furthermore, the RiPP BGCs detected in LFF genomes are highly distinct from those reported in bacteria, non-lichenized fungi or plants, making them attractive targets for the discovery of novel drug leads. The promiscuity of their modifying enzymes, coupled with their structural and functional diversity and the potential for post-translational modifications, significantly expands the RiPP biochemical space. Biosynthetic diversity of LFF compared to bacteria and non-lichenized fungi We found that LFF fungi have ~50 BGCs and about 40 GCFs per species. In contrast, non-lichenized fungi were shown to have around 35 BGCs and 11 GCFs [31]. As a single lichen BGC can encode more than one compound [18,56], the biosynthetic potential of lichens surpasses the number of biosynthetic genes. Furthermore, given the data it seems that lichenized fungi are approximately 10 times richer and more diverse in their biosynthetic capacity than bacteria, which on average contain 5.4 BGCs and 3.5 GCFs per taxon (1,185,995 BGCs present in 217,647 [30]). This is particularly interesting because bacteria currently constitute the most prominent source of drugs. Considering the pressing demand for novel drugs, lichenized fungi constitute a very promising reservoir to explore. Our study highlights the importance of comprehensively bioprospecting nonmodal taxa for their secondary metabolic pathways. Studies show that the most dominant BGC class in fungi is NRPS (~42% of BGCs are NRPS clusters) [31]. Interestingly, we found that in LFF, PKSs are the most dominant BGC type (~ 38%), followed by NRPS (23%) (Fig. 1E). This is particularly interesting given the broad-spectrum bioactive potential and therapeutic properties of polyketide metabolites of lichens. Several bioactive lichen metabolites are unique to lichens and have not been reported in other organisms, including nonlichenized fungi. The lichen biosynthetic space represents an enormous unexplored source of bioactive compounds complementary to nonlichenized fungi and bacteria. Recent progress in the heterologous expression of lichen biosynthetic genes represents a promising step forward in the optimization of lichen metabolites for drug discovery [11,12]. The chemical repertoire varied among families, with Physiaceae constituting the source of most novel terpenes and NRPSs and Pyrenulaceae and Trypetheliaceae being the richest sources of PKSs (Figure 1), highlighting the complementarity of the BGC catalog of lichenized fungi. Deorphanized natural products Only a few PKS GCFs recovered in our network analysis grouped with a characterized BGC, for instance, 6-hydroxymellein and usnic acid (Supplementary material S4). We present novel sources, including BGCs and taxa, for these compounds. 6-Hydromellein displays broad-spectrum antimicrobial activity against both bacteria and fungi {Citation}. The oral bioavailability and drug-like characteristics of melellins in the human body have recently been shown via in silico absorption, distribution, metabolism, and excretion studies, indicating that these compounds are promising drug leads. Usnic acid, on the other hand, is one of the most studied lichen metabolites and displays anti-inflammatory, analgesic, healing, antioxidant, antimicrobial, antiviral, and anti-UV properties. Traditionally, usnic acid producers have been used as crude drugs; however, previous reports on overdose-led liver toxicity and possible allergies constitute major challenges that need to be overcome for its broad-scale application. The 6-hydroxymellein and usnic acid clusters from different organisms presented here (Supplementary material S4) provide a premise for combinatorial mellein biosynthesis to adapt the product for medicinal use. We found that, apart from PKS, the most common genes present in the LFF BGCs are CYP450 and oxidase. These genes are potentially involved in the modification of the compound synthesized by PKS to produce the final compound [11,21], adding to the chemical diversity of the organism, e.g., chemosyndrome in Pseudevernia furfuracea ; PKS codes for olivetoric acid, which, when oxidized by CYP450, produces the corresponding depsidone physodic acid (compound 6, Figure 3B), [35]. In most cases, the compound structure aligns with the expected biosynthetic gene content (Figure 3B). When multiple compounds are produced, different genes contribute to the synthesis of each specific compound. For example, Evernia prunastri produces lecanoric acid (compound 4, Figure 3B), physodic acid (compound 6, Figure 3B), perlatolic acid (compound 7, Figure 3B), and evernic acid (compound 8, Figure 3B). The synthesis of lecanoric acid requires only PKS, while physodic acid synthesis involves both PKS and CYP450, and evernic acid synthesis depends on PKS and OMT. Interestingly, high-performance liquid chromatography (HPLC) and thin-layer chromatography (TLC) have traditionally been the most common methods for profiling lichen chemistry. Consequently, reports in the literature should be interpreted with caution, as they primarily detect major natural products, potentially overlooking minor compounds. This could result in inconsistencies between the cluster gene composition and the genes required for producing the NP. We also found that tailoring enzymes in the orcinol clusters are omnipresent, although the NP produced may not require them. Lichens with orcinol BGC likely produce additional NP variants beyond those that have been described based on TLC and HPLC. A recent study implementing mass spectrometry (MS) on lichens reported novel NP variants of the compounds in Hypogymnia subphysodes , Evernia prunastri and Ophioparma ventosa [37]. The gene cluster compositions provided here are essential for expanding our understanding of the biosynthetic pathways involved in compound synthesis and for enabling the tailoring of these compounds through combinatorial biology and biotechnology to achieve desired properties. Unique BGCs: potential sources of novel natural products Our BGC exploration and comparison suggested that 98% of the lichen GCFs are exclusive and potentially capable of synthesizing many structurally and functionally novel natural products (Supplementary material S3B). Additionally, each LFF contained several unique GCFs not found in other lichenized fungi (Supplementary material S3A, S3B). However, the most diverse groups of metabolites are predicted to be produced by the taxa belonging to Parmeliaceae. Once the clusters are grouped into known, and novel clusters the next step is to prioritize the clusters for deorphanization. The prioritization of clusters for deorphanization can be based on the novelty of the chemical structure of the putative product, which is usually reflected by the evolutionary relationships of the orphan cluster with known clusters. Some families are particularly rich in unique GCFs, and these families are the most promising sources of novel biosynthetic diversity. We propose these orphan clusters to be the most interesting targets for drug discovery efforts. Our analysis provides a global overview of diverse known and promising understudied NP-producing taxa in lichens. We expect our study to be a milestone for bioprospecting novel taxa and their chemical dark matter to find novel drug leads. Correlative metabolomics In this study we perform the metabolite survey of 93 taxa (137 samples) using mass spectrometry (Supplementary material S6). We found both known and previously unidentified metabolites in the lichen MS profiles and performed large-scale comparison of MS profiles in relation to the taxonomy. We find a correlation between taxonomic distance and the production of distinct secondary metabolite families. Specifically, in the studied species, out of four networks, stictic acid, a ß-orcinol depsidone, is predominantly produced by the members of the family Peltigeraceae instead members of Parmeliaceae secrete a great variety of orcinol didepsides and didepsidones, as lecanoric acid, physodic acid, olivetoric acid etc. Umbilicariaceae lichens on the other hand primarily produce orcinol tridepsides as gyrophoric acid & derivatives. This suggests distantly related families potentially harbor structurally and functionally diverse metabolites and that novel and distantly related genera may comprise the untested source of exceptional natural products. Similarly, closely related taxa and genera are a potential source of known compounds or the as novel variants of these compounds, but certain metabolites have a wider taxonomic distribution and are found is distantly related families, e.g., stictic acid, rhizocarpic acid etc. The taxonomic range of these bioactive compounds revealed a strong pattern between specific taxonomic families and their associated compounds. We advocate that chances of discovering novel metabolites are greater in distantly related taxa. Conclusions Our study reveals that lichen-forming fungi are a rich source of novel natural products, with approximately 98% of their biosynthetic gene clusters (BGCs) being potentially novel to science. By exploring the biosynthetic landscape of lichens, we uncovered that polyketide synthase (PKS) BGCs predominate in lichens, whereas non-lichenized fungi are dominated by non-ribosomal peptide synthetase (NRPS) BGCs. Furthermore, for the first time, we demonstrate that ribosomal peptide-related BGCs constitute about 20% of LFF BGCs. Our study categorizes lichen BGCs into known, unknown but widespread, and novel groups. We propose that the comparative omics and genome mining approach employed in our study provides a foundation for advancing biosynthetic research in non-model organisms, fostering further exploration of microbial dark matter. Materials and methods Dataset, genome assembly and annotation A total of 111 taxa were included in the study (Supplementary material S1), from 71 genera from 23 lichenized fungal families (Fig. 1 A), encompassing broad phylogeographic ranges and ecological niches. For the functional annotation of genomes, including genes and proteins, prediction was performed with scripts implemented in the funannotate pipeline [ 57 ]. The genomes were first masked for repetitive elements, followed by gene prediction using BUSCO2 to train Augustus and self-training GeneMark-ES [ 58 ]. The functional annotation of the predicted genes was performed with InterProScan (51), egg-NOG-mapper [ 59 ], and BUSCO [ 60 , 61 ] using Ascomycota_db models. Secreted proteins were predicted using (54) as implemented in the functional “annotate” command. Genome completeness assessment and phylogenomic analysis We used the BUSCO Ascomycota dataset to estimate genome completeness [ 60 , 61 ]. The single-copy BUSCOs from 111 taxa were quality-filtered and then compared to filter out those present in most taxa (a maximum of one sample missing). Busco genes that passed the above steps were then selected for generating the phylogenomic tree. For each taxon, the single-copy buscos were concatenated, and the concatenated sequences from all the taxa were then aligned using MAFFT L-INS-i. Evolutionary relationships were inferred from this multiple sequence alignment using maximum likelihood (ML) analysis implemented in IQTree v1.5.5 [ 62 , 63 ] with standard model selection and 1,000 bootstrap replicates. The resulting tree was visualized using FigTree 1.3.1 [ 64 ] and annotated in iTOL [ 65 ]. BGC prediction and clustering BGCs were predicted and annotated using antiSMASH (antibiotics & SM Analysis Shell, v7.0 [ 66 ]) (Supplementary material S2). To quantify BGC diversity, we used Biosynthetic Genes Similarity Clustering and Prospecting Engine (BiG-SCAPE [ 67 ]) ( https://git.wageningenur.nl/medema-group/BiG-SCAPE ), a platform for comparing and grouping similar BGCs into gene cluster families (GCFs) based on distance matrices. BGCs assigned to a GCF potentially encode structurally similar natural products. BGCs that do not group with a MIBiG reference BGC code for putatively novel natural products. AntiSMASH results were compared against the MIBiG database of characterized BGCs using BiG-SCAPE. We computed the BGC assignment into GCF using the raw distance cut-offs of 0.20, 0.4, 0.6 and 0.80. The lower the cutoff is, the stricter the similarity clustering, resulting in fewer connections. We used a conservative approach and a cutoff of 0.6 to avoid overestimating the number of potentially novel BGCs. All the analyses (i.e., with different thresholds) were performed using the default settings with the ‘auto’ mode, with singletons retained and with the PFAM database. Identification of BGCs with known and unknown compounds and potentially novel BGCs Clustering with the MIBiG reference BGC indicated that it potentially encoded a similar compound. We identified the usnic acid, 6-methylsalicylic acid, 6-hydroxymellein and orcinol GCFs based on clustering with MIBiG reference BGCs [ 68 ] (Fig. 3 A, 4 ). In addition, we identified atranorin, a tridepside GCF [ 11 ] and olivetoric/physodic acid [ 56 ] GCF (Supplementary material S3A and S3B). These BGCs have been characterized from lichens based on experimental and/or phylogenetic evidence. The phylogenetic grouping of Pseudevernia furfuracea and Umbilicaria spp. was used as a reference for this characterization. BGCs that do not cluster with a MIBiG reference BGC or a pre-characterized lichen BGC are potentially novel. Metabolite profiling and molecular networking To identify potential depsides and depsidones in the lichen extracts, we conducted mass spectrometry (MS) analysis in negative ion mode on 47 samples, representing 27 distinct species. We aim to perform integrative omics analysis to identify the putative biosynthetic gene cluster (BGC) responsible for the synthesis of the identified depsides or depsidones. In addition, to infer the taxonomic depth of the identified metabolites we ran MS (negative ion mode) on 90 lichen samples, belonging to 66 additional LFF species. Overall, MS was performed on 137 samples and 93 species of which genomic data was available for 27 LFF. The lichen extracts were systematically surveyed for metabolites to perform large-scale MS profile comparisons among Lecanoromycete families and infer the relation between the degree of taxonomic diversity and secondary metabolite diversity (Supplementary material S6). For each specimen, lichen thallus materials (ca. 15 mg) were grinded into powders under liquid nitrogen. Metabolites were extracted three times from grinded powers with acetone (800 µL each time), and then the extracts were combined and evaporated. Dried residues were re-constituted in 2 mL solvent mixture of methanol and acetonitrile (50:50, v/v), and a 50 µL aliquot was diluted 20 times with the same solvent mixture and filtered (0.2 µm, PTFE) before liquid chromatography-mass spectrometry (LC-MS) analyses. LC-MS measurements were carried out on a Waters Acquity ultrahigh performance liquid chromatography (UPLC) system coupled to a SYNAPT XS quadrupole time of flight (QTOF) high resolution mass spectrometer with an electrospray ionization (ESI) interface. Chromatographic separation of lichen specialized metabolites was performed on a Kinetex EVO C18 column (150×2.1mm, 1.7µm). The mobile phase consisted of 0.1% formic acid in water (solvent A) and 0.1% formic acid in acetonitrile (solvent B). A gradient elution was used as follows: 0-0.5 min, 10%B; 0.5–10 min, linear gradient from 10%B to 100%B; 10–11 min, 100%B; 11-11.1 min, linear gradient from 100–10%B; 11.1–13 min, 10%B. Flow rate was 0.45 mL/min, and 5 µL test solution was injected. MS data on lichen acids were acquired from the negative ion mode (mass range 100–1200 m/z). Raw MS data were acquired in continuum mode and converted to centroid data using the accurate mass measure function embedded in the software MassLynx v4.2. Lock masses for negative ion modes is 554.2615 m/z, respectively. Centroid data were further converted to the mzML format using MSconvert [ 69 ] The mzML files were exported and uploaded to GNPS[ 70 ] for classic molecular networking, with each group separated by family. The tolerance of both precursor ion mass and fragment ion mass were 0.02. The molecular networks were visualized using Cytoscape 3.9.1. The molecular network (Fig. 4 ) was constructed from metabolomics data generated in negative ion mode, covering a broad spectrum of LFF-unique substances, e.g. depsidones, depsides, dibenzofurans, pulvinic acid derivatives and aliphatic lactones, etc. Compounds sharing similar mass spectra are clustered, and major chemical groups are visualized. Each node represents a lichen metabolite, and colors in the node indicate its presence in lichen-forming fungal families. Lichen metabolites were initially annotated by comparing our in-house dataset with reference data deposited in GNPS, and further dereplicated by literature search. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials All data generated or analysed during this study are included in this published article [and its supplementary information files. Competing interests None declared. Funding GS and AP were supported by the Italian Ministry of University and Research (project funded by the European Union—Next Generation EU: “PNRR Missione 4 Componente 2, “Dalla ricerca all’impresa”, Investimento 1.4, Progetto CN00000033”). Authors' contributions GS and MHM design and conceptualization GS, AP, MX, XY, MZ, JCN-M, SE, DP, NBS, JRH, analyzed and interpreted the data. FDG, IS, CS, PKS, EOS, MHM were major contributors in writing the manuscript. GS, AP, MX, SE, XY generated the figures GS, AP, MX, SE, DP, NBS, JRH were involved in data acquisition and generation All authors read and approved the final manuscript References Newman DJ, Cragg GM. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. Journal of Natural Products. 2020;83:770–803. Cragg GM, Newman DJ. Natural products: A continuing source of novel drug leads. Biochimica et Biophysica Acta - General Subjects. 2013;1830:3670–95. Ho CS, Wong CTH, Aung TT, Lakshminarayanan R, Mehta JS, Rauz S, et al. Antimicrobial resistance: a concise update. The Lancet Microbe [Internet]. 2024 [cited 2024 Nov 21];0. Available from: https://www.thelancet.com/journals/lanmic/article/PIIS2666-5247(24)00200-3/fulltext Brodin T, Bertram MG, Arnold KE, Boxall ABA, Brooks BW, Cerveny D, et al. The urgent need for designing greener drugs. Nat Sustain. 2024;7:949–51. Miethke M, Pieroni M, Weber T, Brönstrup M, Hammann P, Halby L, et al. Towards the sustainable discovery and development of new antibiotics. Nat Rev Chem. 2021;5:726–49. GBD 2021 Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance 1990-2021: a systematic analysis with forecasts to 2050. Lancet. 2024;404:1199–226. Vogt E, Künzler M. Discovery of novel fungal RiPP biosynthetic pathways and their application for the development of peptide therapeutics. Appl Microbiol Biotechnol. 2019;103:5567–81. Del Prado-Audelo ML, Cortés H, Caballero-Florán IH, González-Torres M, Escutia-Guadarrama L, Bernal-Chávez SA, et al. Therapeutic Applications of Terpenes on Inflammatory Diseases. Frontiers in Pharmacology [Internet]. 2021 [cited 2023 Sep 8];12. Available from: https://www.frontiersin.org/articles/10.3389/fphar.2021.704197 Tsomaia N. Peptide therapeutics: Targeting the undruggable space. European Journal of Medicinal Chemistry. 2015;94:459–70. Demain AL, Sanchez S. Microbial drug discovery: 80 years of progress. J Antibiot. 2009;62:5–16. Kim W, Liu R, Woo S, Kang KB, Park H, Yu YH, et al. Linking a Gene Cluster to Atranorin, a Major Cortical Substance of Lichens, through Genetic Dereplication and Heterologous Expression. mBio. 2021;12:e0111121. Kealey JT, Craig JP, Barr PJ. Identification of a lichen depside polyketide synthase gene by heterologous expression in Saccharomyces cerevisiae. Metab Eng Commun. 2021;13:e00172. Allen JL, Lendemer JC. A call to reconceptualize lichen symbioses. Trends in Ecology & Evolution. 2022;37:582–9. Hawksworth DL, Grube M. Lichens redefined as complex ecosystems. New Phytologist. 2020;227:1281–3. Boustie J, Grube M. Lichens—a promising source of bioactive secondary metabolites. Plant Genetic Resources. 2005;3:273–87. Pasinato A, Singh G. Lichens are a treasure chest of bioactive compounds: Fact or fake? New Phytologist. 2025; Singh G, Dal Grande F, Martin FM, Medema MH. Breaking into nature’s secret medicine cabinet: lichens – a biochemical goldmine ready for discovery. New Phytologist. 2025; Singh G. Linking lichen metabolites to genes: Emerging concepts and lessons from molecular biology and metagenomics. Journal of Fungi. 2023;9:160. Egbert S, Hoffman JR, McMullin RT, Lendemer JC, Sorensen JL. Unraveling usnic acid: A comparison of biosynthetic gene clusters between two reindeer lichen (Cladonia rangiferina and C. uncialis). Fungal Biology [Internet]. 2022 [cited 2022 Nov 8]; Available from: https://www.sciencedirect.com/science/article/pii/S1878614622000964 Abdel-Hameed M, Bertrand RL, Piercey-Normore MD, Sorensen JL. Identification of 6-Hydroxymellein Synthase and Accessory Genes in the Lichen Cladonia uncialis . J Nat Prod. 2016;79:1645–50. Armaleo D, Sun X, Culberson C. Insights from the first putative biosynthetic gene cluster for a lichen depside and depsidone. Mycologia. 2011;103:741–54. Singh G, Pasinato A, Yriarte AL-C, Pizarro D, Divakar PK, Schmitt I, et al. Are there conserved biosynthetic genes in lichens? Genome-wide assessment of terpene biosynthetic genes suggests ubiquitous distribution of the squalene synthase cluster. BMC Genomics. 2024;25:936. Jensen PR. Natural products and the gene cluster revolution. Trends in Microbiology. 2016. p. 968–77. Calcott MJ, Ackerley DF, Knight A, Keyzers RA, Owen JG. Secondary metabolism in the lichen symbiosis. Chemical Society Reviews. 2018;47:1730–60. Keller NP. Fungal secondary metabolism: regulation, function and drug discovery. Nature Reviews Microbiology. 2019;17:167–80. Aigle B, Lautru S, Spiteller D, Dickschat JS, Challis GL, Leblond P, et al. Genome mining of Streptomyces ambofaciens . Journal of Industrial Microbiology and Biotechnology. 2014;41:251–63. Navarro-Muñoz JC, Selem-Mojica N, Mullowney MW, Kautsar SA, Tryon JH, Parkinson EI, et al. A computational framework to explore large-scale biosynthetic diversity. Nat Chem Biol. 2020;16:60–8. Kautsar SA, van der Hooft JJJ, de Ridder D, Medema MH. BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters. GigaScience. 2021;10:giaa154. Zdouc MM, Blin K, Louwen NLL, Navarro J, Loureiro C, Bader CD, et al. MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration. Nucleic Acids Res. 2024;gkae1115. Gavriilidou A, Kautsar SA, Zaburannyi N, Krug D, Müller R, Medema MH, et al. Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes. Nat Microbiol. 2022;7:726–35. Robey MT, Caesar LK, Drott MT, Keller NP, Kelleher NL. An interpreted atlas of biosynthetic gene clusters from 1,000 fungal genomes. Proceedings of the National Academy of Sciences of the United States of America. 2021;118. Paoli L, Ruscheweyh H-J, Forneris CC, Hubrich F, Kautsar S, Bhushan A, et al. Biosynthetic potential of the global ocean microbiome. Nature. 2022;607:111–8. Pizarro D, Divakar PK, Grewe F, Crespo A, Dal Grande F, Lumbsch HT. Genome-Wide Analysis of Biosynthetic Gene Cluster Reveals Correlated Gene Loss with Absence of Usnic Acid in Lichen-Forming Fungi. Genome Biol Evol. 2020;12:1858–68. Abdel-Hameed M, Bertrand RL, Piercey-Normore MD, Sorensen JL. Putative identification of the usnic acid biosynthetic gene cluster by de novo whole-genome sequencing of a lichen-forming fungus. Fungal Biol. 2016;120:306–16. Singh G, Armaleo D, Dal Grande F, Schmitt I. Depside and depsidone synthesis in lichenized fungi comes into focus through a genome-wide comparison of the olivetoric acid and physodic acid chemotypes of Pseudevernia furfuracea . Biomolecules. 2021;11:1445. Huneck S, Yoshimura I. Data of Lichen Substances. In: Huneck S, Yoshimura I, editors. Identification of Lichen Substances [Internet]. Berlin, Heidelberg: Springer; 1996 [cited 2025 Jan 6]. p. 125–446. Available from: https://doi.org/10.1007/978-3-642-85243-5_3 Olivier-Jimenez D, Chollet-Krugler M, Rondeau D, Beniddir MA, Ferron S, Delhaye T, et al. A database of high-resolution MS/MS spectra for lichen metabolites. Sci Data. 2019;6:294. Blin K, Shaw S, Augustijn HE, Reitz ZL, Biermann F, Alanjary M, et al. antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Res. 2023;51:W46–50. Gerasimova JV, Beck A, Werth S, Resl P. High Diversity of Type I Polyketide Genes in Bacidia rubella as Revealed by the Comparative Analysis of 23 Lichen Genomes. J Fungi (Basel). 2022;8:449. Stocker-Wörgötter E. Metabolic diversity of lichen-forming ascomycetous fungi: culturing, polyketide and shikimatemetabolite production, and PKS genes. Nat Prod Rep. 2008;25:188–200. Kosanić M, Ranković B. Lichen Secondary Metabolites as Potential Antibiotic Agents. In: Ranković B, editor. Lichen Secondary Metabolites: Bioactive Properties and Pharmaceutical Potential [Internet]. Cham: Springer International Publishing; 2015 [cited 2022 Nov 15]. p. 81–104. Available from: https://doi.org/10.1007/978-3-319-13374-4_3 Ingelfinger R, Henke M, Roser L, Ulshöfer T, Calchera A, Singh G, et al. Unraveling the pharmacological potential of lichen extracts in the context of cancer and inflammation with a broad screening approach. Frontiers in Pharmacology. 2020;11:1322. Stanojković T. Investigations of Lichen Secondary Metabolites with Potential Anticancer Activity. In: Ranković B, editor. Lichen Secondary Metabolites: Bioactive Properties and Pharmaceutical Potential [Internet]. Cham: Springer International Publishing; 2015 [cited 2022 Nov 15]. p. 127–46. Available from: https://doi.org/10.1007/978-3-319-13374-4_5 Türk H, Yilmaz M, Tay T, Türk AO, Kivanç M. Antimicrobial activity of extracts of chemical races of the lichen Pseudevernia furfuracea and their physodic acid, chloroatranorin, atranorin, and olivetoric acid constituents. Z Naturforsch C J Biosci. 2006;61:499–507. Prokopiev I, Chesnokov S, Serebryakov E, Konoreva L. Chemical variation in the Arctoparmelia separata (Parmeliaceae, Lichenized Ascomycota). Biochemical Systematics and Ecology. 2022;102:104418. Oran S, Sahin S, Sahinturk P, Ozturk S, Demir C. Antioxidant and Antimicrobial Potential, and HPLC Analysis of Stictic and Usnic Acids of Three Usnea Species from Uludag Mountain (Bursa, Turkey). Iran J Pharm Res. 2016;15:527–35. Ocaña-Pallarès E, Williams TA, López-Escardó D, Arroyo AS, Pathmanathan JS, Bapteste E, et al. Divergent genomic trajectories predate the origin of animals and fungi. Nature. 2022;609:747–53. Chen R, Zhang Q, Tan B, Zheng L, Li H, Zhu Y, et al. Genome mining and activation of a silent PKS/NRPS gene cluster direct the production of totopotensamides. Org Lett. 2017;19:5697–700. Davydov EA, Peršoh D, Rambold G. Umbilicariaceae (lichenized Ascomycota) – Trait evolution and a new generic concept. TAXON. 2017;66:1282–303. Posner B, Feige GB, Huneck S. Studies on the chemistry of the lichen genus Umbilicaria hoffm. Zeitschrift fur Naturforschung - Section C Journal of Biosciences. 1992;47:1–9. Singh G, Calchera A, Schulz M, Drechsler M, Bode HB, Schmitt I, et al. Climate‐specific biosynthetic gene clusters in populations of a lichen‐forming fungus. Environmental Microbiology. 2021;00:1462-2920.15605. Singh G, Dal Grande F, Schmitt I. Genome mining as a biotechnological tool for the discovery of novel biosynthetic genes in lichens. Frontiers in Fungal Biology [Internet]. 2022 [cited 2022 Nov 22];3. Available from: https://www.frontiersin.org/articles/10.3389/ffunb.2022.993171 Calchera A, Dal Grande F, Bode HB, Schmitt I. Biosynthetic Gene Content of the “Perfume Lichens” Evernia prunastri and Pseudevernia furfuracea. Molecules. 2019;24:E203. Kessler SC, Chooi Y-H. Out for a RiPP: challenges and advances in genome mining of ribosomal peptides from fungi. Nat Prod Rep. 2022;39:222–30. Ford RE, Foster GD, Bailey AM. Exploring fungal RiPPs from the perspective of chemical ecology. Fungal Biol Biotechnol. 2022;9:12. Singh G, Calchera A, Merges D, Valim H, Otte J, Schmitt I, et al. A candidate gene cluster for the bioactive natural product gyrophoric acid in lichen-forming fungi. Microbiol Spectr. 2022;10:e0010922. Palmer J. Funannotate Documentation. 2020; Borodovsky M, Lomsadze A. Eukaryotic Gene Prediction Using GeneMark.hmm-E and GeneMark-ES. Curr Protoc Bioinformatics. 2011;CHAPTER:Unit-4.610. Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309–14. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–2. Manni M, Berkeley MR, Seppey M, Simão FA, Zdobnov EM. BUSCO update: Novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Molecular Biology and Evolution. 2021;38:4647–54. Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Molecular Biology and Evolution. 2015;32:268–74. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9. Rambaut A. FigTree [Internet]. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh.; 2010. Available from: http://tree.bio.ed.ac.uk/software/figtree/ Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Research. 2021;49:W293–6. Blin K, Shaw S, Kloosterman AM, Charlop-Powers Z, van Wezel GP, Medema MH, et al. antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic Acids Research. 2021;49:W29–35. Navarro-Muñoz JC, Selem-Mojica N, Mullowney MW, Kautsar SA, Tryon JH, Parkinson EI, et al. A computational framework to explore large-scale biosynthetic diversity. Nature Chemical Biology. 2020;16:60–8. Terlouw BR, Blin K, Navarro-Muñoz JC, Avalon NE, Chevrette MG, Egbert S, et al. MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters. Nucleic Acids Res. 2023;51:D603–10. Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918–20. Aron AT, Gentry EC, McPhail KL, Nothias L-F, Nothias-Esposito M, Bouslimani A, et al. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat Protoc. 2020;15:1954–91. Additional Declarations Competing interest reported. M.H.M. is a member of the scientific advisory board of Hexagon Bio. Supplementary Files S1Supplementarymaterial.xlsx S1 Sample voucher information along with the taxonomic information and total number of BGCs and the numbers of PKSs, NRPSs, RiPPs, and terpenes. S2SupplementaryTable2BGCoverviewlichens.xlsx S2 Overview of biosynthetic genes by class and taxa S3SupplementaryMaterial.pdf S3 A) Number of Gene cluster families for all the BGC classes per taxa identified by the BiG-SCAPE; B) Number of Gene cluster families per taxa identified by the BiG-SCAPE to demonstrate the sources of most unique BGCs in lichens S4SupplementaryMaterial.png S4 BGCs identified based on clustering with atranorin, usnic acid and gyrophoric acid gene clusters. S5SupplementaryMaterial.zip S5 GenBank files of the clusters deorphanized in this study S6SupplementaryMaterialcopy.xlsx S6 Voucher information of the taxa used for the MS Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6073935","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427408250,"identity":"8fd374cd-f5b2-4f66-89e6-42edf26e815a","order_by":0,"name":"Garima Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYJCCAyCCDYQ+MDDwMDAkQESI0sI4A6aFoB4oYGPmAdMJDHitkXc/+/DQjRoGez7p9muPbdvuyJizJzAe/oBHi+GZdIPDOccYEttkzpQb57Y947HseYDfYYYNaQyHc9gYEtgkctKkc9sO8xjcIOAXw/5nQC3/GOzBWiyJ0SIvAbQlt42BsU0i/Zg0IzFaDCSAtuT2SSS2SeSwSfacOwz0y8OGA2fw2dKfxvw555uNvfyM9GcSP8oO25uzJx/+UIHPFogTJICYxwAiwsDYgEcD0BaENPsDqJZRMApGwSgYBagAAN0dURmF03M/AAAAAElFTkSuQmCC","orcid":"","institution":"University of Padua","correspondingAuthor":true,"prefix":"","firstName":"Garima","middleName":"","lastName":"Singh","suffix":""},{"id":427408251,"identity":"3bed107c-ce56-4dcc-a571-866fe6619a93","order_by":1,"name":"Maonian Xu","email":"","orcid":"","institution":"University of Iceland","correspondingAuthor":false,"prefix":"","firstName":"Maonian","middleName":"","lastName":"Xu","suffix":""},{"id":427408252,"identity":"93299fe8-4975-4853-960c-d49514ce2061","order_by":2,"name":"Mitja Zdouc","email":"","orcid":"","institution":"Wageningen University \u0026 Research","correspondingAuthor":false,"prefix":"","firstName":"Mitja","middleName":"","lastName":"Zdouc","suffix":""},{"id":427408253,"identity":"24b168e5-7531-4c1a-965f-5103c20ab4e2","order_by":3,"name":"Anna Pasinato","email":"","orcid":"","institution":"University of Padua","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Pasinato","suffix":""},{"id":427408254,"identity":"86ac61fb-ebef-4823-89fb-b6af817c3b8c","order_by":4,"name":"Jorge C. 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Medema","email":"","orcid":"","institution":"Wageningen University \u0026 Research","correspondingAuthor":false,"prefix":"","firstName":"Marnix","middleName":"H.","lastName":"Medema","suffix":""}],"badges":[],"createdAt":"2025-02-20 17:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6073935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6073935/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78494233,"identity":"6b43eeae-d871-43cb-89a2-65131ff54642","added_by":"auto","created_at":"2025-03-14 03:39:58","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1224203,"visible":true,"origin":"","legend":"\u003cp\u003eGenome quality metrics and diversity of biosynthetic genes in lichenized fungi. BUSCO tree displaying the genome quality and number of BGCs (A). A heatmap showing the number of BGCs in each class and the total number of BGCs in the Lecanoromycete families and the number in brackets behind each family name is the number of genomes analysed. (B). The biosynthetic diversity of lichenized fungi, non-lichenized fungi and bacteria differed dramatically in their BGC landscape, most remarkably in RiPP and NRPSs (between lichenized fungi and non-lichenized fungi) and PKSs (between bacteria and lichenized fungi) (C).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6073935/v1/4235d3ca122f3246a3a99ca5.jpeg"},{"id":78494236,"identity":"9e8fe9ba-d1ac-47ee-a341-5cc694972fae","added_by":"auto","created_at":"2025-03-14 03:39:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":413460,"visible":true,"origin":"","legend":"\u003cp\u003eNovel biosynthetic genes encoded in genomes of selected lichen-forming fungi (LFF)\u003cstrong\u003e. \u003c/strong\u003e(A) ML phylogenomic tree highlighting the number of unique gene cluster families identified for each biosynthetic gene cluster (BGC) class in LFF. The inner ring corresponds to the taxonomic family within Lecanoromycetes. The colored bars represent the number of BGCs (total, PKS, NRPS, terpene, RiPP, and other BGCs including indoles, PKS-NRPS hybrids and isocyanide synthase, from inside to outward). B) Bar plots showing the total number of GCFs retrieved and those with similar clusters found in the MIBiG database (C), using varying thresholds in the BiG-SCAPE analysis, categorized by BGC classes. D) Gap between the total LFF GCFs and those associated with characterized BGCs in the MIBiG database.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6073935/v1/8755e5da1f513a44c33a2d93.png"},{"id":78494470,"identity":"828e6f8e-01c4-4dd3-84c1-e9b3ed88256f","added_by":"auto","created_at":"2025-03-14 03:47:58","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1133860,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Clinker plot showing the symmetry and synteny between three orcinol GCFs (6-methylsalicyclic acid (1), grayanic acid (3) and olivetoric acid (5) GCF and \u003cstrong\u003eB)\u003c/strong\u003e the compounds produced by these taxa. Taxa highlighted with gray boxes represent those from which the natural products were deorphanized, while taxa marked with stars indicate those analyzed using MS. The orcinol NPs are among the most structurally and functionally versatile compounds of LFF. Here, we identified the putative BGCs for five compounds. Notably, each taxon contains only one putative BGC, although the organism may produce one or more structurally related compounds. \u003cstrong\u003eC)\u003c/strong\u003e The compounds produced by the some of the common LFF and the genes required to produce the compound.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6073935/v1/51af20d8b7f8f18a6ab9524b.jpeg"},{"id":78494469,"identity":"2c09bfdc-7c69-4e1f-aee2-27b53969991f","added_by":"auto","created_at":"2025-03-14 03:47:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":862393,"visible":true,"origin":"","legend":"\u003cp\u003eClassical molecular networking based on LFF extracts. Different colors represent taxonomic families within the Lecanoromycetes. Network 1 consists of depsides from the Parmeliaceae, Umbilicariaceae, and Peltigeraceae families. Network 2 contains depsidones. Network 3 comprises depsidones from the families Parmeliaceae and Umbilicariaceae. Network 4 includes molecules with structures similar to pulvinic acid, found in the Peltigeraceae, and rhizocarpic acid, found in the Parmeliaceae.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6073935/v1/22e3007b68774eb3ce85c9ed.png"},{"id":78495999,"identity":"99e68b4e-3c68-4a8c-94b7-8d324866ade4","added_by":"auto","created_at":"2025-03-14 04:12:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4339236,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6073935/v1/8a83ce26-962c-4521-b07a-8330ad998906.pdf"},{"id":78494240,"identity":"85cd81db-854e-4ab0-97f8-abddd56b262a","added_by":"auto","created_at":"2025-03-14 03:39:58","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS1\u003c/strong\u003e Sample voucher information along with the taxonomic information and total number of BGCs and the numbers of PKSs, NRPSs, RiPPs, and terpenes.\u003c/p\u003e","description":"","filename":"S1Supplementarymaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6073935/v1/ef81fe7d54149dd7a382480f.xlsx"},{"id":78495476,"identity":"d8a3d011-8129-4dbf-8bd6-33ffb6662705","added_by":"auto","created_at":"2025-03-14 04:03:58","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17624,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS2 \u003c/strong\u003eOverview of biosynthetic genes by class and taxa\u003c/p\u003e","description":"","filename":"S2SupplementaryTable2BGCoverviewlichens.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6073935/v1/8e53e5f934aa01ade4ad91dc.xlsx"},{"id":78494249,"identity":"7605f25f-608e-4bc6-8f64-c73c8fc7bb32","added_by":"auto","created_at":"2025-03-14 03:39:58","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3979405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS3\u003c/strong\u003e A) Number of Gene cluster families for all the BGC classes per taxa identified by the BiG-SCAPE; B) Number of Gene cluster families per taxa identified by the BiG-SCAPE to demonstrate the sources of most unique BGCs in lichens\u003c/p\u003e","description":"","filename":"S3SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6073935/v1/ba8696de7530148b64d414ca.pdf"},{"id":78494265,"identity":"947ce1b2-6827-4845-a8cf-ac4b408a1742","added_by":"auto","created_at":"2025-03-14 03:39:58","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2278197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS4\u003c/strong\u003e BGCs identified based on clustering with atranorin, usnic acid and gyrophoric acid gene clusters.\u003c/p\u003e","description":"","filename":"S4SupplementaryMaterial.png","url":"https://assets-eu.researchsquare.com/files/rs-6073935/v1/bbca3a047bfc41f3df824ec8.png"},{"id":78494474,"identity":"cb52933b-6b62-4c05-b8bc-2b9eb3f3c30e","added_by":"auto","created_at":"2025-03-14 03:47:58","extension":"zip","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":835898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS5\u003c/strong\u003e GenBank files of the clusters deorphanized in this study\u003c/p\u003e","description":"","filename":"S5SupplementaryMaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-6073935/v1/e2e436a1f3ceec36f3b4caf8.zip"},{"id":78494246,"identity":"8250dcb1-d6e6-461f-87ae-c879e9845f01","added_by":"auto","created_at":"2025-03-14 03:39:58","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS6\u003c/strong\u003e Voucher information of the taxa used for the MS\u003c/p\u003e","description":"","filename":"S6SupplementaryMaterialcopy.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6073935/v1/e88d10c67677a013e79afeb0.xlsx"}],"financialInterests":"Competing interest reported. M.H.M. is a member of the scientific advisory board of Hexagon Bio.","formattedTitle":"Paired-omics-based exploration and characterisation of biosynthetic diversity in lichenized fungi","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNatural products (NPs) are a crucial source of bioactive compounds. The majority of modern medicines and therapeutic agents, including most anticancer compounds and antibiotics, are derived from plant and microbial NPs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the emergence of novel infections combined with the appearance of antibiotic-resistant pathogens is posing an increasing demand for novel drugs to combat the global health crises [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The emergence of viral epidemics and drug resistance also points toward the urgent need for novel antimicrobial therapy [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Researchers have begun to explore novel organisms as well as novel classes of biosynthetic genes for their pharmaceutically usable bioactive properties. This includes BGCs as ribosomally synthesized and post-translationally modified peptides (RiPPs), owing to their strong antimicrobial activities and high stability, specificity and affinity for targets, cellular penetrability and great engineering potential [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdvances in genome sequencing, biosynthetic gene discovery pipelines, and synthetic biology approaches, enable the utilization of accumulating genomic data for culture-free bioprospecting of non-model organisms, addressing key bottlenecks in natural product discovery [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For instance, while lichenized fungi \u0026ndash; fungi living in obligate symbiotic association with one or more photobionts [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] \u0026ndash; are a treasure trove of secondary metabolites [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], with around 1,000 described to date and many exhibiting strong bioactivity, the industrial applications of lichen-derived compounds remain underutilized. This is primarily attributed to the experimental challenges inherent in symbiotic systems, the slow growth rate of lichens, difficulties to generate axenic mycobiont culture biomass and the lack of known triggers to stimulate metabolite synthesis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. While lichen genomes contain genes for synthesizing a diverse array of metabolites \u0026ndash; non-ribosomal peptide synthases (NRPSs), polyketide synthetases (PKSs), hybrid NRPS\u0026ndash;PKSs, terpene synthetases, and RiPP synthases, the biosynthetic research on lichenized fungi has primarily focused on PKSs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and more recently on terpene-related biosynthetic gene clusters (BGCs) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. One reason for the focus on PKSs is because most of the well-known, commonly analyzed lichen compounds are PKS-derived (e.g., depsides, depsidones, xanthones, anthraquinones, dibenzofurans). Computational approaches can bypass culture dependency and expand NP exploration beyond PKSs to enable the identification, dereplication and prioritization of the entire biosynthetic diversity of organisms.\u003c/p\u003e \u003cp\u003eThe genes encoding NPs often lie adjacent to each other in a linear fashion, forming biosynthetic gene clusters (BGCs) [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A BGC typically has one or a few of the following core genes that define the chemical class of the encoded compound [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] : NRPSs, PKSs, terpenes or RiPPs. Recently, automated pipelines have been developed to facilitate the bioinformatic linking of genes to molecules through similarity-based clustering of homologous BGCs into gene cluster families (e.g., BiG-SCAPE [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and BiG-SLiCE [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]). This approach not only facilitates the linking of BGCs to their putative metabolites but also enables the prediction of biosynthetic functions and novelty by comparing them with characterized BGCs in databases such as MIBiG [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Studies implementing BGC clustering approaches on bacteria reveal that we have explored only a tiny fraction of the available diversity for its biosynthetic potential [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In lichenized fungi, only a few BGCs have been linked to the metabolites (e.g. grayanic acid [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], atranorin [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], lecanoric acid [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], usnic acid [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], olivetoric acid [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]) and metabolic diversity is known only for the commonly analyzed compound classes, e.g., those listed in [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A comprehensive analysis of the total metabolic potential of LFF is still lacking. With the recent surge in genomic data and the development of advanced BGC prediction and clustering pipelines, there is now an opportunity to better understand the BGCs diversity and novelty in lichenized fungi, enabling informed predictions about the most industrially relevant taxa and metabolites.\u003c/p\u003e \u003cp\u003eHere, we investigate the metabolic diversity in lichenized fungi using a comparative genomics and molecular networking approach to bioinformatically characterize the lichen \u0026ldquo;BGC landscape\u0026rdquo;. We further employ correlative omics to link five bioactive metabolites to their biosynthetic origins. In addition, we assessed the taxonomic distribution of the most prevalent lichen metabolites by performing MS on additional 93 LFF. Specifically, we aim to 1) unravel the extent of biosynthetic novelty encoded in lichen-forming fungal genomes, 2) deorphanize common and potent bioactive metabolites using comparative metabolomics and network and clustering approaches, and 3) estimate the taxonomic breadth of common lichen metabolites. We anticipate that the novel BGCs predicted here will facilitate the discovery of novel bioactive NPs and inspire investigations into gene functions and metabolic pathways of non-model taxa.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eHigh diversity of BGCs in lichen-forming fungal genomes\u003c/h3\u003e\n\u003cp\u003eAntiSMASH detected 5,542 BGCs in 111 LFF (Supplementary Table 1). The number of BGCs varied at the family and genus levels, with Sarrameanaceae being the most BGC-poor family (14 BGCs) and Pyrenulaceae, Physciaceae, and Trypetheliaceae being the most BGC-rich families (~70 BGCs per taxa) (Figure 1A, 1B, Supplementary Table 2). The greatest number of BGCs (95 BGCs) was detected in \u003cem\u003eCanoparmelia texana\u003c/em\u003e (Parmeliaceae), and the lowest in \u003cem\u003eCaeruleum heppii\u003c/em\u003e (Acarosporaceae) (14 BGCs)\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Figure 1A).\u003c/p\u003e\n\u003cp\u003eLichenized fungi contain an average of 47\u0026plusmn;20 BGCs per genome, approximately 23\u0026plusmn;11 PKSs and 15\u0026plusmn;7 NRPSs/taxon (Fig. 1B), making the proportion of PKS to NRPS clusters 3:2 (2,622:1,726). The most dominant BGC class in lichenized fungi is PKSs, accounting for approximately 50% of the total BGCs, followed by NRPSs (approximately 23%), RiPPs (16%) and terpenes (approximately 12%) (Supplementary Table 2). We surveyed the complete set of BGCs in LFF genomes and found that the metabolic potential varied among families, with Physiaceae constituting the richest source of terpenes and NRPSs, whereas Pyrenulaceae and Trypetheliaceae were the richest sources of PKSs (Figure 1B, Supplementary Table 2). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003ch3\u003ePotentially novel BGCs\u003c/h3\u003e\n\u003cp\u003eBiG-SCAPE generated the network for each BGC class - within each network similar BGCs were organized into gene cluster families (GCFs), while unique BGCs remained as singletons. LFF have a vast repertoire of GCFs comprising only a few BGCs (Figure 2A. 2B, Supplementary material 3A, 3B). Most of the lichen GCFs are unique and do not display similarity to previously known BGCs in standard curated databases (AntiSMASH [38] and MIBiG [29]) (Figure 2C, 2D, Supplementary material 3A).\u003c/p\u003e\n\u003cp\u003eAmong the BGC classes, only the PKS BGCs showed similarity to BGCs in MIBiG, while all other classes, including NRPS, did not. This could be because polyketides are among the most prevalent as well as the most studied molecular classes in LFF [12,21,39,40]. Of approximately 1,500 PKS GCFs, only three clustered with BGCs listed in MIBiG (at a BiG-SCAPE clustering threshold of 0.6, Figure 2B), namely 6-methysalicyclic acid, 6-hydroxymellein and usnic acid (Supplementary material S4). The network approach facilitated the identification of these pathways/BGCs in many LFFs for the first time.\u003c/p\u003e\n\u003ch3\u003eRiPPs\u003c/h3\u003e\n\u003cp\u003eWe identified 1,186 RiPP BGCs in our dataset, clustering into 987 GCFs (Supplementary material S3A and S3B). These RiPP BGCs showed low similarity (8-30%) to those known from Lecanoromycetes or other fungi (asperipin, ustiloxin and phomopsin BGCs), suggesting that LFF RiPP derivatives might be structurally and functionally diverse than those of non-lichenized fungi. Like PKSs, the diversity of RiPP BGC varied within Lecanoromycetes, with RiPP being the predominant BGC class in Agyriaceae, Icmadophilaceae, Pertusariaceae and Stictidaceae, outnumbering PKSs and NRPSs.\u003c/p\u003e\n\u003ch3\u003eDeorphanizing BGCs and metabolites: Clustering-based linking of genes to molecules\u003c/h3\u003e\n\u003cp\u003eMost known bioactive compounds of LFF belong to the orcinol or orcinol derivatives class [15,41\u0026ndash;43]. However, the corresponding BGCs are known only for only a few compounds [18]. Based on the GCF recovered in PKS network (Figure 3A), for the first time, we infer most-likely BGCs for the following five orcinol metabolites: perlatolic acid (compound 7, Figure 3B), evernic acid (compound 8, Figure 3B), stenosporic acid (compound 9, Figure 3B), alectoronic acid (compound 10, Figure 3B), collatolic acid (compound 11, Figure 3B) and their derivatives (Figure 3B). This inference is based on correlative omics, integrating species-specific metabolite production patterns with clustering of BGCs with pre-characterized orcinol derivative gene clusters, including 6-methylsalicylic acid (compound 1, Figure 3B), grayanic acid (compound 3, Figure 3B) and olivetoric acid (compound 5, Figure 3B). Notably, all these NPs have unique chemical properties despite sharing a similar core structure (Table 1) [42\u0026ndash;44]. Of these, grayanic acid and 6-methylsalicylic acid clusters are present in MIBiG, whereas the olivetoric acid BGC was recently identified based on in silico predictions [35]. The clusters from different taxa grouped with grayanic acid/olivetoric acid BGC code for the different orcinol compounds (Figure 3C). Since we retrieved only one candidate BGC per taxon and only one main metabolic product within this compound class has been reported for these taxa, we are certain that the BGC codes for the depside/depsidone reported from the lichen.\u003c/p\u003e\n\u003cp\u003eIn addition, we identified putative BGCs for two PKS-derived bioactive NPs\u0026mdash;usnic acid and atranorin\u0026mdash;from several taxa. The BGCs provided here differ in sequence conservation as well as gene composition from the previously reported usnic acid and atranorin BGCs [11,19,33,35]. The taxa and BGCs reported in this study represent novel sources of these metabolites. Given the diversity in BGC and gene sequences, they may encode slightly different structural and functional variants of these compounds. The GenBank files of these clusters are available as Supplementary material S5.\u003c/p\u003e\n\u003ch3\u003eDeorphanizing BGCs and metabolites: Metabolite profiling and correlative metabolomics\u003c/h3\u003e\n\u003cp\u003eWe performed integrative omics analysis to identify the putative BGCs responsible for the synthesis of the identified depsides or depsidones. Using PKS clustering and BGC-to-compound structure correlations, we linked MS spectra to putative BGCs for five known bioactive compounds. Specifically, we deorphanized the following NPs\u0026ndash; alectoronic acid (compound 10, Figure 3B), collatolic acid (compound 11, Figure 3B), evernic acid (compound 8, Figure 3B), stenosporic acid (compound 9, Figure 3B), and perlatolic acid (compound 7, Figure 3B). This was done by employing a multifaceted approach based on the BGC class and compound structure correspondence, gene clustering, and gene cluster similarity. For each compound, we systematically narrowed down the candidates to a single, most likely BGC. This congruence between the molecule structure, gene cluster, phylogenetic clustering provides strong evidence of identified BGCs being the exclusive candidates responsible for the biosynthesis of the respective compounds. The molecular networking (Figure 4) was constructed from metabolomics data. Compounds sharing similar mass spectra are clustered, and major chemical groups are visualized.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTaxonomic breadth of four common, bioactive LFF metabolites\u003c/h3\u003e\n\u003cp\u003eWe profiled a total of 93 species (138 samples) for their metabolites to identify the potential depsides and depsidones secreted by them. In the studied species, we found that stictic acid, a \u0026szlig;-orcinol depsidone, is predominant compound secreted by the members of the family Peltigeraceae. Notably, it is not exclusive to this family but also produced by certain members of Parmeliaceae, e.g. \u003cem\u003eUsnea\u003c/em\u003e spp. and \u003cem\u003eAcarospora\u003c/em\u003e spp. \u0026nbsp;[45,46]. In contrast, members of studied Parmeliaceae taxa secrete a diverse array of orcinol didepsides and didepsidones, as lecanoric acid (compound 4, Figure 3B), olivetoric acid (compound 5, Figure 3B) and physodic acid (compound 6, Figure 3B) etc. Umbilicariaceae lichens on the other hand primarily produce orcinol tridepsides as gyrophoric acid and derivatives. This suggests that distantly related families may harbor structurally and functionally diverse metabolites and that novel, distantly related genera represent untapped source of unique NPs. Similarly, closely related taxa and genera are potential sources of known compounds or novel variants of these compounds. The taxonomic distribution of these bioactive compounds reveals a strong pattern between specific taxonomic families and their associated metabolites.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides the first systematic comparison of BGC diversity within LFF fungi and explores their metabolic uniqueness. We analyzed LFF genomes to bioinformatically characterize their metabolic diversity, grouping them by the chemical families of encoded compounds and identifying novel metabolic pathways. Interestingly, we found RiPPs, previously known known from bacteria and Basidiomycete fungi, to constitute about 15-20% of lichen biosynthetic space. This is the first study reporting RiPP contribution to total biosynthetic gene space of lichens. Concurrently, we performed correlative metabolomics on five metabolites to establish gene-to-molecule links and inferred the taxonomic breadth of widespread bioactive lichen metabolites, such as stictic acid and lecanoric acid.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eHigh diversity of BGCs and GCFs in lichen genomes\u003c/h3\u003e\n\u003cp\u003eOn average, LFF contain approximately 47\u0026plusmn;20 BGCs, but the number of identified compounds per species is usually less than 10, indicating a majority of clusters are either temporally silent or orphan, or go undetected by regular detection techniques [18,19]. Even though most of these BGCs are yet orphan, their presence in high number in LFF appears to be an evolutionary strategy rather than the accumulation of silent and non-functional BGCs. A recent study showed that specialization towards metabolism is the primary feature of the genetic turnover in the evolution of fungi [47]. Interestingly, while Metazoa on average accumulated genes from diverse functional categories, in fungi only a few categories showed net gains during evolution\u0026mdash;one of which is secondary metabolism. Fungi, in fact, allocate a higher proportion of their gene content to metabolic processes compared to metazoans. Given that specialization toward metabolism has been a primary feature of genetic turnover during fungal evolution, the limited number of functionally characterized BGCs may result from the absence of relevant ecological or biological cues, or the temporal instability of the metabolites, which makes their detection more challenging. Recent efforts to activate and express several silent BGCs via promoter activation, repressor deactivation or a combination of both have expanded the possibilities for the functional characterization of compounds beyond those already expressed [48]. Furthermore, the bioinformatic identification of an organism\u0026apos;s entire BGC landscape reduces the likelihood of rediscovering known natural products and provides opportunities to explore novel structural diversity for promising leads.\u003c/p\u003e\n\u003cp\u003eWhile the majority of BGCs are predicted to encode PKSs, consistent with the known diversity of orcinol derivatives in LFF, many novel GCFs appear to encode a variety of diverse bioactive compounds, including RiPPs, NRPSs, and terpenes (Fig. 5C). For instance, although the majority of reported NPs from LFF are PKS-derived (melanins, usnic acid, grayanic acid, olivetoric acid, gyrophoric acid, umbilicaric acid, etc.) [49\u0026ndash;51], a typical\u003cem\u003e\u0026nbsp;\u003c/em\u003eLFF BGC landscape is biosynthetically diverse, \u0026nbsp;encompassing three to five classes of BGCs [33,39,52,53] (Fig. 1A, B). This further highlights that only a small fraction of the chemical diversity in LFF has been explored to date. Moreover, research on lichen biosynthesis has focused on PKSs, but the diversity of other classes, with the exception of terpenes [22], remains largely unexplored. The BGCs and the BGC families reported here are valuable resources for elucidating the biochemical pathways of other, thus far neglected, classes of biosynthetic compounds.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eRiPPs\u003c/h3\u003e\n\u003cp\u003eA notable outcome of our study is the significant representation of RiPP-BGCs in LFF genomes, comprising 16% of the identified BGCs. Although RiPP-BGCs account for a substantial portion of the bacterial biosynthetic landscape\u0026mdash;approximately one-fourth of all BGCs and ranking as the second most predominant class after NRPSs\u0026mdash;their representation in fungal genomes was estimated to be markedly lower, at approximately 1%, based on data from 2,000 species. This may be attributed to the limitations of detection algorithms, coupled with the limited understanding of fungal RiPP biosynthesis [54]. For example, while plant and bacterial RiPPs have been extensively studied, the first fungal RiPP was not discovered until 2007, and only a handful have been characterized since then [54]. Our study is the first to show that the RiPP BGCs contributes more to the total LFF BGCs than previously recognized. In some LFF, RiPPs constitute approximately 50% of the BGCs.\u003c/p\u003e\n\u003cp\u003eNotably, while most described fungal RiPPs, such as dikaritins in Ascomycota, are typically characterized by repeated core sequences, AntiSMASH analysis did not identify any repeated core peptides in Lecanoromycetes. This observation suggests that non-repeated core peptides may be more prevalent in Ascomycota fungi than previously recognized. Alternatively, it is possible that the core peptides in Lecanoromycetes are highly divergent and do not resemble any known sequences, potentially representing entirely novel RiPP classes.\u003c/p\u003e\n\u003cp\u003eRiPPs are thought to serve a defensive role against mycophagy, as demonstrated by certain well-known RiPPs, such as amatoxins, which primarily affect the digestive systems of organisms. Since RiPPs are believed to have evolved in response to the threat of consumption by eukaryotic organisms, their higher prevalence in certain taxa may be attributed to increased exposure to grazing pressures [55]. Notably, a high number of RiPP BGCs were identified in lichens with diverse thallus morphology ranging from crustose, fruticose to foliose. This aligns with the toxic effect of RiPPs which affects insects, nematodes, and mammals alike [54]. For example, while crustose and foliose lichens are primarily threatened by fungivory from insects and nematodes, foliose lichens may face a greater threat from mammals.\u003c/p\u003e\n\u003cp\u003eGiven to their high bioactivity, RiPPs are considered promising sources of novel antibiotics and have been proposed as potential solutions for targeting previously undruggable sites, owing to their small size of just a few amino acids. Furthermore, the RiPP BGCs detected in LFF genomes are highly distinct from those reported in bacteria, non-lichenized fungi or plants, making them attractive targets for the discovery of novel drug leads. The promiscuity of their modifying enzymes, coupled with their structural and functional diversity and the potential for post-translational modifications, significantly expands the RiPP biochemical space.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eBiosynthetic diversity of LFF compared to bacteria and non-lichenized fungi\u003c/h3\u003e\n\u003cp\u003eWe found that LFF fungi have ~50 BGCs and about 40 GCFs per species. In contrast, non-lichenized fungi were shown to have around 35 BGCs and 11 GCFs [31]. As a single lichen BGC can encode more than one compound [18,56], the biosynthetic potential of lichens surpasses the number of biosynthetic genes. Furthermore, given the data it seems that lichenized fungi are approximately 10 times richer and more diverse in their biosynthetic capacity than bacteria, which on average contain 5.4 BGCs and 3.5 GCFs per taxon (1,185,995 BGCs present in 217,647 [30]). This is particularly interesting because bacteria currently constitute the most prominent source of drugs. Considering the pressing demand for novel drugs, lichenized fungi constitute a very promising reservoir to explore. Our study highlights the importance of comprehensively bioprospecting nonmodal taxa for their secondary metabolic pathways.\u003c/p\u003e\n\u003cp\u003eStudies show that the most dominant BGC class in fungi is NRPS (~42% of BGCs are NRPS clusters) [31]. Interestingly, we found that in LFF, PKSs are the most dominant BGC type (~ 38%), followed by NRPS (23%) (Fig. 1E). This is particularly interesting given the broad-spectrum bioactive potential and therapeutic properties of polyketide metabolites of lichens. Several bioactive lichen metabolites are unique to lichens and have not been reported in other organisms, including nonlichenized fungi. The lichen biosynthetic space represents an enormous unexplored source of bioactive compounds complementary to nonlichenized fungi and bacteria. Recent progress in the heterologous expression of lichen biosynthetic genes represents a promising step forward in the optimization of lichen metabolites for drug discovery [11,12]. The chemical repertoire varied among families, with Physiaceae constituting the source of most novel terpenes and NRPSs and Pyrenulaceae and Trypetheliaceae being the richest sources of PKSs (Figure 1), highlighting the complementarity of the BGC catalog of lichenized fungi.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDeorphanized natural products\u003c/h3\u003e\n\u003cp\u003eOnly a few PKS GCFs recovered in our network analysis grouped with a characterized BGC, for instance,\u0026nbsp;6-hydroxymellein and usnic acid (Supplementary material S4). We present novel sources, including BGCs and taxa, for these compounds.\u003c/p\u003e\n\u003cp\u003e6-Hydromellein displays broad-spectrum antimicrobial activity against both bacteria and fungi {Citation}. The oral bioavailability and drug-like characteristics of melellins in the human body have recently been shown via in silico absorption, distribution, metabolism, and excretion studies, indicating that these compounds are promising drug leads. Usnic acid, on the other hand, is one of the most studied lichen metabolites and displays anti-inflammatory, analgesic, healing, antioxidant, antimicrobial, antiviral, and anti-UV properties. Traditionally, usnic acid producers have been used as crude drugs; however, previous reports on overdose-led liver toxicity and possible allergies constitute major challenges that need to be overcome for its broad-scale application. The 6-hydroxymellein and usnic acid clusters from different organisms presented here (Supplementary material S4) provide a premise for combinatorial mellein biosynthesis to adapt the product for medicinal use.\u003c/p\u003e\n\u003cp\u003eWe found that, apart from PKS, the most common genes present in the LFF BGCs are CYP450 and oxidase. These genes are potentially involved in the modification of the compound synthesized by PKS to produce the final compound [11,21], adding to the chemical diversity of the organism, e.g., chemosyndrome in \u003cem\u003ePseudevernia furfuracea\u003c/em\u003e; PKS codes for olivetoric acid, which, when oxidized by CYP450, produces the corresponding depsidone physodic acid (compound 6, Figure 3B), \u0026nbsp;[35].\u003c/p\u003e\n\u003cp\u003eIn most cases, the compound structure aligns with the expected biosynthetic gene content (Figure 3B). When multiple compounds are produced, different genes contribute to the synthesis of each specific compound. For example, \u003cem\u003eEvernia prunastri\u003c/em\u003e produces lecanoric acid (compound 4, Figure 3B), physodic acid (compound 6, Figure 3B), perlatolic acid (compound 7, Figure 3B), and evernic acid (compound 8, Figure 3B). The synthesis of lecanoric acid requires only PKS, while physodic acid synthesis involves both PKS and CYP450, and evernic acid synthesis depends on PKS and OMT. Interestingly, high-performance liquid chromatography (HPLC) and thin-layer chromatography (TLC) have traditionally been the most common methods for profiling lichen chemistry. Consequently, reports in the literature should be interpreted with caution, as they primarily detect major natural products, potentially overlooking minor compounds. This could result in inconsistencies between the cluster gene composition and the genes required for producing the NP.\u003c/p\u003e\n\u003cp\u003eWe also found that tailoring enzymes in the orcinol clusters are omnipresent, although the NP produced may not require them. Lichens with orcinol BGC likely produce additional NP variants beyond those that have been described based on TLC and HPLC. A recent study implementing mass spectrometry (MS) on lichens reported novel NP variants of the compounds in \u003cem\u003eHypogymnia subphysodes\u003c/em\u003e, \u003cem\u003eEvernia prunastri\u003c/em\u003e and \u003cem\u003eOphioparma ventosa\u0026nbsp;\u003c/em\u003e[37]. The gene cluster compositions provided here are essential for expanding our understanding of the biosynthetic pathways involved in compound synthesis and for enabling the tailoring of these compounds through combinatorial biology and biotechnology to achieve desired properties.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eUnique BGCs: potential sources of novel natural products\u003c/h3\u003e\n\u003cp\u003eOur BGC exploration and comparison suggested that 98% of the lichen GCFs are exclusive and potentially capable of synthesizing many structurally and functionally novel natural products (Supplementary material S3B). Additionally, each LFF contained several unique GCFs not found in other lichenized fungi (Supplementary material S3A, S3B). However, the most diverse groups of metabolites are predicted to be produced by the taxa belonging to Parmeliaceae. Once the clusters are grouped into known, and novel clusters the next step is to prioritize the clusters for deorphanization. The prioritization of clusters for deorphanization can be based on the novelty of the chemical structure of the putative product, which is usually reflected by the evolutionary relationships of the orphan cluster with known clusters. Some families are particularly rich in unique GCFs, and these families are the most promising sources of novel biosynthetic diversity. We propose these orphan clusters to be the most interesting targets for drug discovery efforts. Our analysis provides a global overview of diverse known and promising understudied NP-producing taxa in lichens. We expect our study to be a milestone for bioprospecting novel taxa and their chemical dark matter to find novel drug leads.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCorrelative metabolomics\u003c/h3\u003e\n\u003cp\u003eIn this study we perform the metabolite survey of 93 taxa (137 samples) using mass spectrometry (Supplementary material S6). We found both known and previously unidentified metabolites in the lichen MS profiles and performed large-scale comparison of MS profiles in relation to the taxonomy.\u003c/p\u003e\n\u003cp\u003eWe find a correlation between taxonomic distance and the production of distinct secondary metabolite families. Specifically, in the studied species, out of four networks, stictic acid, a \u0026szlig;-orcinol depsidone, is predominantly produced by the members of the family Peltigeraceae instead members of Parmeliaceae secrete a great variety of orcinol didepsides and didepsidones, as lecanoric acid, physodic acid, olivetoric acid etc. Umbilicariaceae lichens on the other hand primarily produce orcinol tridepsides as gyrophoric acid \u0026amp; derivatives. This suggests distantly related families potentially harbor structurally and functionally diverse metabolites and that novel and distantly related genera may comprise the untested source of exceptional natural products. Similarly, closely related taxa and genera are a potential source of known compounds or the as novel variants of these compounds, but certain metabolites have a wider taxonomic distribution and are found is distantly related families, e.g., stictic acid, rhizocarpic acid etc. The taxonomic range of these bioactive compounds revealed a strong pattern between specific taxonomic families and their associated compounds. We advocate that chances of discovering novel metabolites are greater in distantly related taxa.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study reveals that lichen-forming fungi are a rich source of novel natural products, with approximately 98% of their biosynthetic gene clusters (BGCs) being potentially novel to science. By exploring the biosynthetic landscape of lichens, we uncovered that polyketide synthase (PKS) BGCs predominate in lichens, whereas non-lichenized fungi are dominated by non-ribosomal peptide synthetase (NRPS) BGCs. Furthermore, for the first time, we demonstrate that ribosomal peptide-related BGCs constitute about 20% of LFF BGCs. Our study categorizes lichen BGCs into known, unknown but widespread, and novel groups. We propose that the comparative omics and genome mining approach employed in our study provides a foundation for advancing biosynthetic research in non-model organisms, fostering further exploration of microbial dark matter.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDataset, genome assembly and annotation\u003c/h2\u003e \u003cp\u003eA total of 111 taxa were included in the study (Supplementary material S1), from 71 genera from 23 lichenized fungal families (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), encompassing broad phylogeographic ranges and ecological niches. For the functional annotation of genomes, including genes and proteins, prediction was performed with scripts implemented in the funannotate pipeline [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The genomes were first masked for repetitive elements, followed by gene prediction using BUSCO2 to train Augustus and self-training GeneMark-ES [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The functional annotation of the predicted genes was performed with InterProScan (51), egg-NOG-mapper [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], and BUSCO [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] using Ascomycota_db models. Secreted proteins were predicted using (54) as implemented in the functional \u0026ldquo;annotate\u0026rdquo; command.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGenome completeness assessment and phylogenomic analysis\u003c/h2\u003e \u003cp\u003eWe used the BUSCO Ascomycota dataset to estimate genome completeness [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The single-copy BUSCOs from 111 taxa were quality-filtered and then compared to filter out those present in most taxa (a maximum of one sample missing). Busco genes that passed the above steps were then selected for generating the phylogenomic tree. For each taxon, the single-copy buscos were concatenated, and the concatenated sequences from all the taxa were then aligned using MAFFT L-INS-i. Evolutionary relationships were inferred from this multiple sequence alignment using maximum likelihood (ML) analysis implemented in IQTree v1.5.5 [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] with standard model selection and 1,000 bootstrap replicates. The resulting tree was visualized using FigTree 1.3.1 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] and annotated in iTOL [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBGC prediction and clustering\u003c/h2\u003e \u003cp\u003eBGCs were predicted and annotated using antiSMASH (antibiotics \u0026amp; SM Analysis Shell, v7.0 [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]) (Supplementary material S2). To quantify BGC diversity, we used Biosynthetic Genes Similarity Clustering and Prospecting Engine (BiG-SCAPE [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://git.wageningenur.nl/medema-group/BiG-SCAPE\u003c/span\u003e\u003cspan address=\"https://git.wageningenur.nl/medema-group/BiG-SCAPE\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a platform for comparing and grouping similar BGCs into gene cluster families (GCFs) based on distance matrices. BGCs assigned to a GCF potentially encode structurally similar natural products. BGCs that do not group with a MIBiG reference BGC code for putatively novel natural products. AntiSMASH results were compared against the MIBiG database of characterized BGCs using BiG-SCAPE. We computed the BGC assignment into GCF using the raw distance cut-offs of 0.20, 0.4, 0.6 and 0.80. The lower the cutoff is, the stricter the similarity clustering, resulting in fewer connections. We used a conservative approach and a cutoff of 0.6 to avoid overestimating the number of potentially novel BGCs. All the analyses (i.e., with different thresholds) were performed using the default settings with the \u0026lsquo;auto\u0026rsquo; mode, with singletons retained and with the PFAM database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of BGCs with known and unknown compounds and potentially novel BGCs\u003c/h2\u003e \u003cp\u003eClustering with the MIBiG reference BGC indicated that it potentially encoded a similar compound. We identified the usnic acid, 6-methylsalicylic acid, 6-hydroxymellein and orcinol GCFs based on clustering with MIBiG reference BGCs [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In addition, we identified atranorin, a tridepside GCF [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and olivetoric/physodic acid [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] GCF (Supplementary material S3A and S3B). These BGCs have been characterized from lichens based on experimental and/or phylogenetic evidence. The phylogenetic grouping of \u003cem\u003ePseudevernia furfuracea\u003c/em\u003e and \u003cem\u003eUmbilicaria\u003c/em\u003e spp. was used as a reference for this characterization.\u003c/p\u003e \u003cp\u003eBGCs that do not cluster with a MIBiG reference BGC or a pre-characterized lichen BGC are potentially novel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMetabolite profiling and molecular networking\u003c/h2\u003e \u003cp\u003eTo identify potential depsides and depsidones in the lichen extracts, we conducted mass spectrometry (MS) analysis in negative ion mode on 47 samples, representing 27 distinct species. We aim to perform integrative omics analysis to identify the putative biosynthetic gene cluster (BGC) responsible for the synthesis of the identified depsides or depsidones. In addition, to infer the taxonomic depth of the identified metabolites we ran MS (negative ion mode) on 90 lichen samples, belonging to 66 additional LFF species. Overall, MS was performed on 137 samples and 93 species of which genomic data was available for 27 LFF.\u003c/p\u003e \u003cp\u003eThe lichen extracts were systematically surveyed for metabolites to perform large-scale MS profile comparisons among Lecanoromycete families and infer the relation between the degree of taxonomic diversity and secondary metabolite diversity (Supplementary material S6).\u003c/p\u003e \u003cp\u003eFor each specimen, lichen thallus materials (ca. 15 mg) were grinded into powders under liquid nitrogen. Metabolites were extracted three times from grinded powers with acetone (800 \u0026micro;L each time), and then the extracts were combined and evaporated. Dried residues were re-constituted in 2 mL solvent mixture of methanol and acetonitrile (50:50, v/v), and a 50 \u0026micro;L aliquot was diluted 20 times with the same solvent mixture and filtered (0.2 \u0026micro;m, PTFE) before liquid chromatography-mass spectrometry (LC-MS) analyses.\u003c/p\u003e \u003cp\u003eLC-MS measurements were carried out on a Waters Acquity ultrahigh performance liquid chromatography (UPLC) system coupled to a SYNAPT XS quadrupole time of flight (QTOF) high resolution mass spectrometer with an electrospray ionization (ESI) interface. Chromatographic separation of lichen specialized metabolites was performed on a Kinetex EVO C18 column (150\u0026times;2.1mm, 1.7\u0026micro;m). The mobile phase consisted of 0.1% formic acid in water (solvent A) and 0.1% formic acid in acetonitrile (solvent B). A gradient elution was used as follows: 0-0.5 min, 10%B; 0.5\u0026ndash;10 min, linear gradient from 10%B to 100%B; 10\u0026ndash;11 min, 100%B; 11-11.1 min, linear gradient from 100\u0026ndash;10%B; 11.1\u0026ndash;13 min, 10%B. Flow rate was 0.45 mL/min, and 5 \u0026micro;L test solution was injected. MS data on lichen acids were acquired from the negative ion mode (mass range 100\u0026ndash;1200 m/z). Raw MS data were acquired in continuum mode and converted to centroid data using the accurate mass measure function embedded in the software MassLynx v4.2. Lock masses for negative ion modes is 554.2615 m/z, respectively. Centroid data were further converted to the mzML format using MSconvert [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe mzML files were exported and uploaded to GNPS[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] for classic molecular networking, with each group separated by family. The tolerance of both precursor ion mass and fragment ion mass were 0.02. The molecular networks were visualized using Cytoscape 3.9.1. The molecular network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) was constructed from metabolomics data generated in negative ion mode, covering a broad spectrum of LFF-unique substances, e.g. depsidones, depsides, dibenzofurans, pulvinic acid derivatives and aliphatic lactones, etc. Compounds sharing similar mass spectra are clustered, and major chemical groups are visualized. Each node represents a lichen metabolite, and colors in the node indicate its presence in lichen-forming fungal families. Lichen metabolites were initially annotated by comparing our in-house dataset with reference data deposited in GNPS, and further dereplicated by literature search.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files.\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNone declared.\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eGS and AP were supported by the Italian Ministry of University and Research (project funded by the European Union\u0026mdash;Next Generation EU: \u0026ldquo;PNRR Missione 4 Componente 2, \u0026ldquo;Dalla ricerca all\u0026rsquo;impresa\u0026rdquo;, Investimento 1.4, Progetto CN00000033\u0026rdquo;).\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eGS and MHM design and conceptualization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGS, AP, MX, XY, MZ, JCN-M, SE, DP, NBS, JRH, analyzed and interpreted the data. FDG, IS, CS, PKS, EOS, MHM were major contributors in writing the manuscript.\u003c/p\u003e\n\u003cp\u003eGS, AP, MX, SE, XY generated the figures\u003c/p\u003e\n\u003cp\u003eGS, AP, MX, SE, DP, NBS, JRH were involved in data acquisition and generation\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNewman DJ, Cragg GM. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. Journal of Natural Products. 2020;83:770\u0026ndash;803. \u003c/li\u003e\n\u003cli\u003eCragg GM, Newman DJ. Natural products: A continuing source of novel drug leads. Biochimica et Biophysica Acta - General Subjects. 2013;1830:3670\u0026ndash;95. \u003c/li\u003e\n\u003cli\u003eHo CS, Wong CTH, Aung TT, Lakshminarayanan R, Mehta JS, Rauz S, et al. Antimicrobial resistance: a concise update. The Lancet Microbe [Internet]. 2024 [cited 2024 Nov 21];0. Available from: https://www.thelancet.com/journals/lanmic/article/PIIS2666-5247(24)00200-3/fulltext\u003c/li\u003e\n\u003cli\u003eBrodin T, Bertram MG, Arnold KE, Boxall ABA, Brooks BW, Cerveny D, et al. The urgent need for designing greener drugs. Nat Sustain. 2024;7:949\u0026ndash;51. \u003c/li\u003e\n\u003cli\u003eMiethke M, Pieroni M, Weber T, Br\u0026ouml;nstrup M, Hammann P, Halby L, et al. Towards the sustainable discovery and development of new antibiotics. Nat Rev Chem. 2021;5:726\u0026ndash;49. \u003c/li\u003e\n\u003cli\u003eGBD 2021 Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance 1990-2021: a systematic analysis with forecasts to 2050. Lancet. 2024;404:1199\u0026ndash;226. \u003c/li\u003e\n\u003cli\u003eVogt E, K\u0026uuml;nzler M. Discovery of novel fungal RiPP biosynthetic pathways and their application for the development of peptide therapeutics. Appl Microbiol Biotechnol. 2019;103:5567\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003eDel Prado-Audelo ML, Cort\u0026eacute;s H, Caballero-Flor\u0026aacute;n IH, Gonz\u0026aacute;lez-Torres M, Escutia-Guadarrama L, Bernal-Ch\u0026aacute;vez SA, et al. Therapeutic Applications of Terpenes on Inflammatory Diseases. Frontiers in Pharmacology [Internet]. 2021 [cited 2023 Sep 8];12. Available from: https://www.frontiersin.org/articles/10.3389/fphar.2021.704197\u003c/li\u003e\n\u003cli\u003eTsomaia N. Peptide therapeutics: Targeting the undruggable space. European Journal of Medicinal Chemistry. 2015;94:459\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eDemain AL, Sanchez S. Microbial drug discovery: 80 years of progress. J Antibiot. 2009;62:5\u0026ndash;16. \u003c/li\u003e\n\u003cli\u003eKim W, Liu R, Woo S, Kang KB, Park H, Yu YH, et al. Linking a Gene Cluster to Atranorin, a Major Cortical Substance of Lichens, through Genetic Dereplication and Heterologous Expression. mBio. 2021;12:e0111121. \u003c/li\u003e\n\u003cli\u003eKealey JT, Craig JP, Barr PJ. Identification of a lichen depside polyketide synthase gene by heterologous expression in Saccharomyces cerevisiae. Metab Eng Commun. 2021;13:e00172. \u003c/li\u003e\n\u003cli\u003eAllen JL, Lendemer JC. A call to reconceptualize lichen symbioses. Trends in Ecology \u0026amp; Evolution. 2022;37:582\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eHawksworth DL, Grube M. Lichens redefined as complex ecosystems. New Phytologist. 2020;227:1281\u0026ndash;3. \u003c/li\u003e\n\u003cli\u003eBoustie J, Grube M. Lichens\u0026mdash;a promising source of bioactive secondary metabolites. Plant Genetic Resources. 2005;3:273\u0026ndash;87. \u003c/li\u003e\n\u003cli\u003ePasinato A, Singh G. Lichens are a treasure chest of bioactive compounds: Fact or fake? New Phytologist. 2025; \u003c/li\u003e\n\u003cli\u003eSingh G, Dal Grande F, Martin FM, Medema MH. Breaking into nature\u0026rsquo;s secret medicine cabinet: lichens \u0026ndash; a biochemical goldmine ready for discovery. New Phytologist. 2025; \u003c/li\u003e\n\u003cli\u003eSingh G. Linking lichen metabolites to genes: Emerging concepts and lessons from molecular biology and metagenomics. Journal of Fungi. 2023;9:160. \u003c/li\u003e\n\u003cli\u003eEgbert S, Hoffman JR, McMullin RT, Lendemer JC, Sorensen JL. Unraveling usnic acid: A comparison of biosynthetic gene clusters between two reindeer lichen (Cladonia rangiferina and C. uncialis). Fungal Biology [Internet]. 2022 [cited 2022 Nov 8]; Available from: https://www.sciencedirect.com/science/article/pii/S1878614622000964\u003c/li\u003e\n\u003cli\u003eAbdel-Hameed M, Bertrand RL, Piercey-Normore MD, Sorensen JL. Identification of 6-Hydroxymellein Synthase and Accessory Genes in the Lichen \u003cem\u003eCladonia uncialis\u003c/em\u003e. J Nat Prod. 2016;79:1645\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eArmaleo D, Sun X, Culberson C. Insights from the first putative biosynthetic gene cluster for a lichen depside and depsidone. Mycologia. 2011;103:741\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eSingh G, Pasinato A, Yriarte AL-C, Pizarro D, Divakar PK, Schmitt I, et al. Are there conserved biosynthetic genes in lichens? Genome-wide assessment of terpene biosynthetic genes suggests ubiquitous distribution of the squalene synthase cluster. BMC Genomics. 2024;25:936. \u003c/li\u003e\n\u003cli\u003eJensen PR. Natural products and the gene cluster revolution. Trends in Microbiology. 2016. p. 968\u0026ndash;77. \u003c/li\u003e\n\u003cli\u003eCalcott MJ, Ackerley DF, Knight A, Keyzers RA, Owen JG. Secondary metabolism in the lichen symbiosis. Chemical Society Reviews. 2018;47:1730\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eKeller NP. Fungal secondary metabolism: regulation, function and drug discovery. Nature Reviews Microbiology. 2019;17:167\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eAigle B, Lautru S, Spiteller D, Dickschat JS, Challis GL, Leblond P, et al. Genome mining of \u003cem\u003eStreptomyces ambofaciens\u003c/em\u003e. Journal of Industrial Microbiology and Biotechnology. 2014;41:251\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eNavarro-Mu\u0026ntilde;oz JC, Selem-Mojica N, Mullowney MW, Kautsar SA, Tryon JH, Parkinson EI, et al. A computational framework to explore large-scale biosynthetic diversity. Nat Chem Biol. 2020;16:60\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eKautsar SA, van der Hooft JJJ, de Ridder D, Medema MH. BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters. GigaScience. 2021;10:giaa154. \u003c/li\u003e\n\u003cli\u003eZdouc MM, Blin K, Louwen NLL, Navarro J, Loureiro C, Bader CD, et al. MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration. Nucleic Acids Res. 2024;gkae1115. \u003c/li\u003e\n\u003cli\u003eGavriilidou A, Kautsar SA, Zaburannyi N, Krug D, M\u0026uuml;ller R, Medema MH, et al. Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes. Nat Microbiol. 2022;7:726\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eRobey MT, Caesar LK, Drott MT, Keller NP, Kelleher NL. An interpreted atlas of biosynthetic gene clusters from 1,000 fungal genomes. Proceedings of the National Academy of Sciences of the United States of America. 2021;118. \u003c/li\u003e\n\u003cli\u003ePaoli L, Ruscheweyh H-J, Forneris CC, Hubrich F, Kautsar S, Bhushan A, et al. Biosynthetic potential of the global ocean microbiome. Nature. 2022;607:111\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003ePizarro D, Divakar PK, Grewe F, Crespo A, Dal Grande F, Lumbsch HT. Genome-Wide Analysis of Biosynthetic Gene Cluster Reveals Correlated Gene Loss with Absence of Usnic Acid in Lichen-Forming Fungi. Genome Biol Evol. 2020;12:1858\u0026ndash;68. \u003c/li\u003e\n\u003cli\u003eAbdel-Hameed M, Bertrand RL, Piercey-Normore MD, Sorensen JL. Putative identification of the usnic acid biosynthetic gene cluster by de novo whole-genome sequencing of a lichen-forming fungus. Fungal Biol. 2016;120:306\u0026ndash;16. \u003c/li\u003e\n\u003cli\u003eSingh G, Armaleo D, Dal Grande F, Schmitt I. Depside and depsidone synthesis in lichenized fungi comes into focus through a genome-wide comparison of the olivetoric acid and physodic acid chemotypes of \u003cem\u003ePseudevernia furfuracea\u003c/em\u003e. Biomolecules. 2021;11:1445. \u003c/li\u003e\n\u003cli\u003eHuneck S, Yoshimura I. Data of Lichen Substances. In: Huneck S, Yoshimura I, editors. Identification of Lichen Substances [Internet]. Berlin, Heidelberg: Springer; 1996 [cited 2025 Jan 6]. p. 125\u0026ndash;446. Available from: https://doi.org/10.1007/978-3-642-85243-5_3\u003c/li\u003e\n\u003cli\u003eOlivier-Jimenez D, Chollet-Krugler M, Rondeau D, Beniddir MA, Ferron S, Delhaye T, et al. A database of high-resolution MS/MS spectra for lichen metabolites. Sci Data. 2019;6:294. \u003c/li\u003e\n\u003cli\u003eBlin K, Shaw S, Augustijn HE, Reitz ZL, Biermann F, Alanjary M, et al. antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Res. 2023;51:W46\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eGerasimova JV, Beck A, Werth S, Resl P. High Diversity of Type I Polyketide Genes in Bacidia rubella as Revealed by the Comparative Analysis of 23 Lichen Genomes. J Fungi (Basel). 2022;8:449. \u003c/li\u003e\n\u003cli\u003eStocker-W\u0026ouml;rg\u0026ouml;tter E. Metabolic diversity of lichen-forming ascomycetous fungi: culturing, polyketide and shikimatemetabolite production, and PKS genes. Nat Prod Rep. 2008;25:188\u0026ndash;200. \u003c/li\u003e\n\u003cli\u003eKosanić M, Ranković B. Lichen Secondary Metabolites as Potential Antibiotic Agents. In: Ranković B, editor. Lichen Secondary Metabolites: Bioactive Properties and Pharmaceutical Potential [Internet]. Cham: Springer International Publishing; 2015 [cited 2022 Nov 15]. p. 81\u0026ndash;104. Available from: https://doi.org/10.1007/978-3-319-13374-4_3\u003c/li\u003e\n\u003cli\u003eIngelfinger R, Henke M, Roser L, Ulsh\u0026ouml;fer T, Calchera A, Singh G, et al. Unraveling the pharmacological potential of lichen extracts in the context of cancer and inflammation with a broad screening approach. Frontiers in Pharmacology. 2020;11:1322. \u003c/li\u003e\n\u003cli\u003eStanojković T. Investigations of Lichen Secondary Metabolites with Potential Anticancer Activity. In: Ranković B, editor. Lichen Secondary Metabolites: Bioactive Properties and Pharmaceutical Potential [Internet]. Cham: Springer International Publishing; 2015 [cited 2022 Nov 15]. p. 127\u0026ndash;46. Available from: https://doi.org/10.1007/978-3-319-13374-4_5\u003c/li\u003e\n\u003cli\u003eT\u0026uuml;rk H, Yilmaz M, Tay T, T\u0026uuml;rk AO, Kivan\u0026ccedil; M. Antimicrobial activity of extracts of chemical races of the lichen Pseudevernia furfuracea and their physodic acid, chloroatranorin, atranorin, and olivetoric acid constituents. Z Naturforsch C J Biosci. 2006;61:499\u0026ndash;507. \u003c/li\u003e\n\u003cli\u003eProkopiev I, Chesnokov S, Serebryakov E, Konoreva L. Chemical variation in the \u003cem\u003eArctoparmelia separata\u003c/em\u003e (Parmeliaceae, Lichenized Ascomycota). Biochemical Systematics and Ecology. 2022;102:104418. \u003c/li\u003e\n\u003cli\u003eOran S, Sahin S, Sahinturk P, Ozturk S, Demir C. Antioxidant and Antimicrobial Potential, and HPLC Analysis of Stictic and Usnic Acids of Three Usnea Species from Uludag Mountain (Bursa, Turkey). Iran J Pharm Res. 2016;15:527\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eOca\u0026ntilde;a-Pallar\u0026egrave;s E, Williams TA, L\u0026oacute;pez-Escard\u0026oacute; D, Arroyo AS, Pathmanathan JS, Bapteste E, et al. Divergent genomic trajectories predate the origin of animals and fungi. Nature. 2022;609:747\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eChen R, Zhang Q, Tan B, Zheng L, Li H, Zhu Y, et al. Genome mining and activation of a silent PKS/NRPS gene cluster direct the production of totopotensamides. Org Lett. 2017;19:5697\u0026ndash;700. \u003c/li\u003e\n\u003cli\u003eDavydov EA, Per\u0026scaron;oh D, Rambold G. Umbilicariaceae (lichenized Ascomycota) \u0026ndash; Trait evolution and a new generic concept. TAXON. 2017;66:1282\u0026ndash;303. \u003c/li\u003e\n\u003cli\u003ePosner B, Feige GB, Huneck S. Studies on the chemistry of the lichen genus \u003cem\u003eUmbilicaria\u003c/em\u003e hoffm. Zeitschrift fur Naturforschung - Section C Journal of Biosciences. 1992;47:1\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eSingh G, Calchera A, Schulz M, Drechsler M, Bode HB, Schmitt I, et al. Climate‐specific biosynthetic gene clusters in populations of a lichen‐forming fungus. Environmental Microbiology. 2021;00:1462-2920.15605. \u003c/li\u003e\n\u003cli\u003eSingh G, Dal Grande F, Schmitt I. Genome mining as a biotechnological tool for the discovery of novel biosynthetic genes in lichens. Frontiers in Fungal Biology [Internet]. 2022 [cited 2022 Nov 22];3. Available from: https://www.frontiersin.org/articles/10.3389/ffunb.2022.993171\u003c/li\u003e\n\u003cli\u003eCalchera A, Dal Grande F, Bode HB, Schmitt I. Biosynthetic Gene Content of the \u0026ldquo;Perfume Lichens\u0026rdquo; Evernia prunastri and Pseudevernia furfuracea. Molecules. 2019;24:E203. \u003c/li\u003e\n\u003cli\u003eKessler SC, Chooi Y-H. Out for a RiPP: challenges and advances in genome mining of ribosomal peptides from fungi. Nat Prod Rep. 2022;39:222\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eFord RE, Foster GD, Bailey AM. Exploring fungal RiPPs from the perspective of chemical ecology. Fungal Biol Biotechnol. 2022;9:12. \u003c/li\u003e\n\u003cli\u003eSingh G, Calchera A, Merges D, Valim H, Otte J, Schmitt I, et al. A candidate gene cluster for the bioactive natural product gyrophoric acid in lichen-forming fungi. Microbiol Spectr. 2022;10:e0010922. \u003c/li\u003e\n\u003cli\u003ePalmer J. Funannotate Documentation. 2020; \u003c/li\u003e\n\u003cli\u003eBorodovsky M, Lomsadze A. Eukaryotic Gene Prediction Using GeneMark.hmm-E and GeneMark-ES. Curr Protoc Bioinformatics. 2011;CHAPTER:Unit-4.610. \u003c/li\u003e\n\u003cli\u003eHuerta-Cepas J, Szklarczyk D, Heller D, Hern\u0026aacute;ndez-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309\u0026ndash;14. \u003c/li\u003e\n\u003cli\u003eSim\u0026atilde;o FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210\u0026ndash;2. \u003c/li\u003e\n\u003cli\u003eManni M, Berkeley MR, Seppey M, Sim\u0026atilde;o FA, Zdobnov EM. BUSCO update: Novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Molecular Biology and Evolution. 2021;38:4647\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eNguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Molecular Biology and Evolution. 2015;32:268\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eKalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eRambaut A. FigTree [Internet]. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh.; 2010. Available from: http://tree.bio.ed.ac.uk/software/figtree/\u003c/li\u003e\n\u003cli\u003eLetunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Research. 2021;49:W293\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eBlin K, Shaw S, Kloosterman AM, Charlop-Powers Z, van Wezel GP, Medema MH, et al. antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic Acids Research. 2021;49:W29\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eNavarro-Mu\u0026ntilde;oz JC, Selem-Mojica N, Mullowney MW, Kautsar SA, Tryon JH, Parkinson EI, et al. A computational framework to explore large-scale biosynthetic diversity. Nature Chemical Biology. 2020;16:60\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eTerlouw BR, Blin K, Navarro-Mu\u0026ntilde;oz JC, Avalon NE, Chevrette MG, Egbert S, et al. MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters. Nucleic Acids Res. 2023;51:D603\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eChambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eAron AT, Gentry EC, McPhail KL, Nothias L-F, Nothias-Esposito M, Bouslimani A, et al. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat Protoc. 2020;15:1954\u0026ndash;91. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Natural products, fungi, biosynthetic genes, depsides, BiG-SCAPE, antiSMASH, secondary metabolites, drug discovery, RiPPs, PKSs","lastPublishedDoi":"10.21203/rs.3.rs-6073935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6073935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The increasing demand for novel drug leads requires bioprospecting non-model taxa. Comparative genomics and correlative omics are a fast and efficient method for linking bioactive but genetically orphan natural products to their biosynthetic gene clusters (BGCs) and identifying potentially novel drug leads. Here we implement these approaches for the first systematic comparison of the BGC diversity in lichen-forming fungi (LFF, comprising 20% of known fungi), prolific but underutilized producers of bioactive natural products. We first identified BGCs from all publicly available LFF genomes (111), encompassing 71 fungal genera and 23 families, and generated BGC similarity networks of each class.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: We recovered 5,541 BGCs grouped into 4,464 gene cluster families. We used mass spectrometry (MS) and correlative metabolomics to link five MS-identified metabolites - alectoronic acid, alpha-collatolic acid, evernic acid, stenosporic acid, and perlatolic acid - to their putative BGCs. We subsequently used MS on additional 93 species to explore the taxonomic breadth of common lichen compounds, uncovering a strong pattern between specific families and secondary metabolites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: We found that 1) approximately 98% of the BGCs in LFF are putatively novel, 2) lichen metabolic profiles contain a plethora of unidentified metabolites and 3) ribosomal peptide-related BGCs constitute about 20% of the LFF BGC landscape. Our study provides comprehensive insights into the BGC landscape of LFFs, highlighting unique, widespread, and previously uncharacterized BGCs. We anticipate that the approach we describe will serve as a baseline for leveraging biosynthetic research in non-model organisms, inspiring further investigations into microbial dark matter.\u003c/p\u003e","manuscriptTitle":"Paired-omics-based exploration and characterisation of biosynthetic diversity in lichenized fungi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-14 03:39:53","doi":"10.21203/rs.3.rs-6073935/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99b5e889-5c45-49f7-bd02-b77a45bb4164","owner":[],"postedDate":"March 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-14T03:39:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-14 03:39:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6073935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6073935","identity":"rs-6073935","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}