Breaking into nature's secret medicine cabinet: lichens - a biochemical goldmine ready for discovery

Abstract

Secondary metabolites are a crucial source of bioactive compounds playing a key role in the development of new pharmaceuticals. Recently, biosynthetic research has benefited significantly from progress on various fronts, including reduced sequencing costs, improved genome/metabolome mining strategies, and expanding tools/databases to compare and characterize chemical diversity. Steady advances in these fields are crucial for research on non-modal organisms such as lichen-forming fungi (LFF). Although most fungi produce bioactive metabolites, biosynthetic research on LFF (c. 21% of known fungi) lags behind, primarily due to experimental challenges. However, in recent years, several such challenges have been tackled, and, in parallel, a critical foundation of genomic data and pipelines has been established to accomplish the valorization of this potential. Integrating these concurrent advances to accelerate biochemical research in LFF provides a promising opportunity for new discoveries. This review summarizes the following: recent advances in fungal and LFF omics, and chemoinformatics research; studies on LFF biosynthesis, including chemical diversity and evolutionary/phylogenetic aspects; and experimental milestones in LFF biosynthetic gene functions. At the end, we outline a vision and strategy to combine the progress in these research areas to harness the biochemical potential of LFF for pharmaceutical development.

Keywords: bioactive metabolites; biosynthetic genes; drug discovery; lichenized fungi; natural products; omics; secondary metabolism; symbiotic fungi.

Fig. 1

Global fungal genome sequencing efforts at specific time intervals (a) and cumulatively (b). Genome sequencing efforts have been ongoing for c. 20 yr, with a significant focus on fungal classes, particularly model fungi, such as Saccharomyces (yeast), Sordariomycetes (Colletotrichum spp., Fusarium spp.), and Eurotiomycetes (Penicillium spp. and Aspergillus spp.). Black stars indicate the bar for Lecanoromycetes in the plot, a group that primarily includes lichen‐forming fungi (LFF). Although LFF comprise c. 20% of all fungi and produce a wide array of bioactive metabolites, genome sequencing efforts for LFF have only gained momentum after the 2020s, resulting in the accumulation of c. 400 publicly available genomes to date. Even so, sequencing efforts per year for Lecanoromycetes remain low compared to other fungal classes. BGC, biosynthetic gene cluster. The number of Lecanoromycete genomes is indicated by stars.

Fig. 2

Typical organization of a fungal biosynthetic gene cluster (BGC), predominant metabolite types in lichen‐forming fungi (LFF), and the bioactive properties of some common polyketide derivatives. (a) An example of a BGC, as shown in the presented figure, as predicted by AntiSMASH. A cluster is composed of one or more core genes, along with genes coding for tailoring enzymes, transport‐related proteins, and regulatory elements. Core genes encode the backbone of the compound, while tailoring genes modify this basic structure to produce the final molecule. (b) Common BGCs in LFF and some of the compounds they encode. Polyketides, including their derivatives, are synthesized by polyketide synthases (PKSs) and are the most predominant BGC class in LFF (c. 50%) (Kim et al., ; Singh et al., 2022). PKS derivatives are among the most extensively studied compounds from non‐LFF and LFF with respect to their molecular structures, synthesis pathways, regulatory mechanisms, and bioactivity. (c) Bioactive properties of some well‐studied PKS derivatives. The bioactive properties of PKS derivatives have been widely demonstrated, highlighting their potential in pharmaceutical and biotechnological applications. This figure was partially created in BioRender (https://BioRender.com/p88a746).

Fig. 3

Mass spectra of two common lichen‐forming fungi demonstrating a plethora of unidentified compounds. (a, b) showcase the mass spectra of Umbilicaria pustulata and Pseudevernia furfuracea, respectively (please see Supporting Information Notes S1 for details). Both species produce a range of bioactive metabolites (Türk et al., ; Kello et al., 2021), along with numerous yet‐uncharacterized compounds, as indicated by the MS peaks in positive ion mode. This figure highlights the enormous unexplored metabolic potential of lichens. Metabolites marked with stars are reported from a different chemotype of this species but not in this MS run.

Fig. 4

Box showing the key challenges in lichen experimental biochemistry and recent technological advances that help overcome or mitigate these challenges. This figure was created in BioRender (https://BioRender.com/l78b041).

Fig. 5

Phylogenetic position of some of the most biochemically studied lichens. (a) Usnea longissima, (b) Usnea sp., (c) Pseudevernia furfuracea, (d) Cladonia sp., (e) Cladonia rangiferina, (f) Xanthoria parietina, and (g) Umbilicaria spp. The phylogenomic tree was constructed using the genomic data from 102 lichen‐forming fungi (LFF) and 47 non‐lichenized fungi from the closely related classes Eurotiomycetes, Dothideomycetes, and Lichinomycetes (Supporting Information Table S1; Notes S1, S2). The colored horizontal bars indicate the total number of biosynthetic gene clusters (BGCs) per taxon. The blue vertical line indicates the highest number of BGC in Penicillium and Aspergillus spp. (indicated by the blue box). Lichen‐forming fungi with more BGCs than the modal taxa are marked by grey stars. This figure illustrates that many LFF exhibit greater biosynthetic potential than model fungi commonly used in drug discovery. Furthermore the most BGC‐rich LFF still remain underexplored in terms of their metabolic diversity. Part of this figure was created in BioRender (https://BioRender.com/n44w847).

Fig. 6

A framework for schematic study, comparison, and exploitation of lichen‐forming fungi (LFF) chemodiversity. Arrows indicate the direction of workflow steps, with bifurcated arrows representing potential or alternative possibilities. The pipelines, indicated by orange and green boxes, delineate the genotype‐up and phenotype‐down approaches, respectively. Fresh environmental samples can be collected and sent for genome sequencing (genotype‐up approach) and parallel metabolomic analysis (phenotype‐down approach), without the need for pure cultures. The resulting reads are obtained from the lichen metagenome, predominantly containing fungal reads but also from algae, bacteria, and viruses (genotype‐up approach). These reads are binned, either before or after assembly, to isolate the fungal genome. After several rounds of contaminant removal and purity assessments, the resulting fungal genomes are subjected to downstream analysis, including identification of biosynthetic genes, phylogeny construction, and similarity network generation to identify known, novel, and unique biosynthetic gene clusters (BGCs). These BGCs can then undergo targeted downstream analyses such as cloning and heterologous expression to validate the compounds they encode. Additionally, BGC data can be used to correlate genes with metabolites to estimate the most likely BGC for a natural product. The resulting candidate BGCs can then be characterized by heterologous expression to establish definitive connections. In the phenotype‐down approach the samples are subjected to metabolite detection following by bioactivity estimation, BGC prediction and heterologous expression of genes of interest. The last step, cloning and heterologous expression, is indicated by a green box with an orange border and is common to both approaches. PKS, polyketide synthases; NRPS, nonribosmal peptide synthetases; GCF: Gene Cluster family. This figure was created in BioRender (https://BioRender.com/m76z830).