Genetic basis of alkaloid divergence in the Solanaceae

doi:10.21203/rs.3.rs-4849145/v1

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Genetic basis of alkaloid divergence in the Solanaceae

https://doi.org/10.21203/rs.3.rs-4849145/v1

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Plant lineages produce distinctive alkaloids, suggesting the existence of evolutionary mechanisms preventing the simultaneous metabolism of multiple alkaloid types in the same species. Tribes in the Solanaceae family produce either tropane alkaloids (TAs) or steroidal glycoalkaloids (SGAs). We integrated genomic, transcriptomic, and metabolomic variation across tens of species representing most Solanaceae tribes to evaluate three possible genetic causes of this mutually exclusive distribution, namely (1) loss of biosynthetic genes, (2) genomic clustering, and (3) regulatory changes. We found that both pathways are produced by multiple phylogenetic clades, suggesting that the differential loss of the biosynthetic pathways across lineages could explain their patchy distributions in the phylogeny. However, TA and SGA genes show contrasting patterns of genomic presence and distribution: SGA genes are grouped in large and dynamic biosynthetic clusters but are present across most species whereas TA genes are sparsely distributed and almost exclusively present in TA-producing species genomes. Additionally, SGA and TA genes are preferentially expressed in species that produce SGAs and TAs respectively, suggesting that expression differences were crucial for the divergence of the two pathways. Our results highlight the asymmetric significance of gene loss, genomic clustering, and gene expression differences in the divergence of TAs and SGAs.

Solanaceae

Phytochemical diversity

tropane alkaloids (TAs)

steroidal glycoalkaloids (SGAs)

biosynthetic genes

genomic clustering.

Understanding the biosynthesis of alkaloids in plants is crucial for understanding plant evolution. Here we used biodiversity to understand how plants from the Solanaceae family produce different types of alkaloids. By correlating changes in gene expression and alkaloid production across tens of species we discovered genetic networks that determine which types of alkaloids are produced by plants.

Alkaloids are diverse specialized metabolites (SMs) with scattered phylogenetic distributions across plant species, where most plant clades produce only a single alkaloid type and there are frequent transitions between the types of alkaloids produced by related clades. For instance, different tribes within the Solanaceae family produce distinct alkaloid types: Tropane alkaloids (TAs) are amino acid-derived compounds that serve as the primary active ingredients in important medicinal plants belonging to the Anthocercideae tribe from Oceania, the Hyosciameae and Lyciae from Eurasia, and the Datureae and Solandreae tribes from America (Kim et al. 2016; Ullrich et al. 2017; Kohnen-Johannsen & Kayser, 2019). In contrast, SGAs are steroidal alkaloids found in species from the widespread and megadiverse tribe Solaneae (genus Solanum), which includes crop species such as tomato, potato, and eggplant (Zhao et al. 2021; Xiang et al. 2022). The mutual exclusivity of TAs and SGAs across lineages suggests the existence of evolutionary mechanisms that preclude the simultaneous production of these two alkaloid classes in Solanaceae species. A promising approach to trace the evolution of these two biosynthetic pathways is to analyze the expression, and genomic distribution of biosynthetic genes across TA-producing clades (TPCs) and SGA-producing clades (SPCs). This approach has been used to discover the genes involved in the biosynthesis of TAs (Zhang et al. 2023) and SGAs (Itkin et al. 2013) independently. Specifically, TAs production and gene expression have been found in roots and leaves of different species (Yang et al. 2023; Zhang et al. 2023; Zhou et al. 2024). Furthermore, SGAs compound presence and gene expression have been detected among different tissues in Solanum species (Cárdenas et al. 2016; Sonawane et al. 2022).

To understand the divergence of TAs and SGAs in the Solanaceae it is necessary to study systematically TPCs and SPCs across the family. However, some tribes have not been studied at the genetic level, including groups of ethnobotanical and ecological importance like members of the diverse Lycianthes and Cestrum genus, or the Solandreae tribe. In this study we analyzed the biochemical diversity of the family, including neglected clades and wild species, to gain a deeper understanding of how alkaloids have evolved. Specifically, we analyzed the transcriptomes and metabolomes from leaves of 129 species belonging to 12 of the 15 different tribes from the Solanaceae family (Fig. 1, Table S1). We included 4 tribes reported to produce TAs (namely Datureae, Solandreae, Lycieae, and Hyoscyameae) and the two tribes known for producing SGAs, namely Solaneae and Cestreae. We also sampled 3 tribes that are not known to produce SGAs or TAs but produce other alkaloids and defensive SMs (Fig. 1): Members of the Physalideae and Petunieae produce withanolides, the Nicotianeae produce nicotinoids and some members of Capsiceae produce capsaicinoids (Knapp, 1992).

One of the best-studied examples of mutually exclusive compound classes is the evolution of pigments in the Caryophyllales, where betalains replace anthocyanins in multiple clades. Studies in these plants have identified three genetic mechanisms that could underlie metabolic switching between pathways (1) deletion or loss of function of biosynthetic genes; (2) reduced expression of biosynthetic genes; or (3) degeneration or co-option of transcription factors that are responsible for the activation of the pathways (Timoneda et al. 2019; Pucker et al. 2024). Recent studies provide evidence of the importance of some of these mechanisms in the evolution of SGAs and TAs.

Gene deletion/loss of function was crucial in the emergence and disappearance of TAs and SGAs in Solanaceae. For example, the divergence of three steroidal SM pathways—brassinolides, withanolides, and SGAs—was driven by the duplication of the DWF1 gene and subsequent neofunctionalization of three paralogs (Knoch et al. 2018). Furthermore, genomic synteny analyses and gain/loss of function experiments proved the loss of critical TA genes was responsible for the repeated loss of the TA pathway in the Solanaceae (Wang et al. 2023; Yang et al. 2023; Zhang et al. 2023; Zhou et al. 2024).

On the other hand, SGA and TA evolution also involved the formation of genomic biosynthetic clusters and regulatory networks (Nützmann et al. 2016; Tohge & Fernie, 2020; Zhan et al. 2022). Biosynthetic clusters are genomic regions containing multiple biosynthetic genes that are subject to common regulatory control and are inherited as evolutionary units that are not easily broken by recombination and can be lost or gained through genomic rearrangements (Nützmann et al. 2016; Tohge & Fernie, 2020; Zhan et al. 2022). For instance, SGA BCs (SBCs) contain tens of co-expressed biosynthetic genes, and the content of these clusters varies substantially across species, with multiple gene deletions, insertions, and duplications (Akiyama et al. 2021). Importantly, these structural differences contribute to interspecific differences in the repertoire of SGAs produced (Barchi et al. 2019; Szymański et al. 2020). Furthermore, an enhancer regulatory region located in an SBC is responsible for recruiting transcription factors that drive the coordinated regulation of biosynthetic genes contained in the SBC (Bai et al. 2024). The role of biosynthetic clusters in TA evolution is less understood but the availability of new genomic assemblies from multiple Solanaceae species allows us to study how BCs have shaped metabolic transitions in SPCs and TPCs.

We used a systematic analysis of genomic, transcriptomic, and metabolomic variation in the Solanaceae family to evaluate three non-exclusive hypotheses about the possible causes of the divergent evolution between TAs and SGAs. Specifically, we explored the role of (1) gene duplication and loss, (2) genomic clustering, and (3) expression regulation, in the mutually exclusive distribution of SGAs and TAs. Our findings revealed that genomic clustering and co-expression shaped the divergence between these two alkaloid classes but played asymmetric roles, where gene loss was more important in TA evolution and genomic clustering was more important in SGA evolution.

Plant collection and propagation

Plants used in this study are part of a working collection of wild and medicinal Solanaceous species from the GEME lab at the Universidad Nacional de Colombia, in Bogotá Colombia. Briefly, seeds and cuttings were collected during multiple field trips across Colombia (Table S1). All samples were collected legally under the permit “Permiso Marco de Recolección de Especímenes de Especies Silvestres de la Diversidad Biológica con Fines de Investigación Científica No Comercial” assigned to the Universidad Nacional de Colombia by the Ministerio de Medio Ambiente (Resolución 0255 del 12 de marzo de 2014). Propagules were brought to the greenhouse, disinfected, and grown under common conditions until the development of multiple sets of fully developed leaves.

The objective of this study was to sample species representing the major clades in the family. We thus selected 129 species from 12 different tribes and Ipomoea purpurea from the Convolvulaceae family as an outgroup. (Fig. 1, Table S1). To be able to correlate the metabolome and transcriptome of different samples we used the same samples to extract RNA and metabolites. We collected three mature leaves from all selected plants. The material was snap-freezed in liquid nitrogen and stored at -80°C until extraction.

Metabolite extraction and characterization

We conducted extractions from leaves of 1–3 individuals per species for a subset of 50 different taxa (Table S1). Briefly, 10 mg of lyophilized leaf material was macerated in liquid nitrogen, suspended in 100% methanol, incubated at 70°C for 15 minutes, and centrifuged for 10 minutes at 12,000 rpm. We then added 1:3 chloroform-water, vortexed, and centrifuged again for 10 minutes. The supernatant was transferred to a new tube and dried in a speed-vac.

LC-MS analyses were conducted in Dr. Alisdair Fernie’s lab at the Max Planck Institute for Molecular Plant Physiology. Vacuum-dried polar phases were resuspended in 300 µL of 50% water methanol. Samples were sonicated for 10 min in an ice-cooled sonicator bath and then centrifuged for 15 min at full speed (> 12,000 rpm). The supernatant was transferred to LC tubes for analysis. The samples were run on a UPLC-LC-MS machine. In brief, the UPLC system was equipped with an HSS T3 C18 reverse-phase column (100 × 2.1 mm internal diameter, 1.8 µm particle size; Waters) that was operated at a temperature of 40°C. The mobile phases consisted of 0.1% formic acid in water (solvent A) and 0.1% formic acid in acetonitrile (solvent B). The flow rate of the mobile phase was 400 µL/min, and 2 µL of the sample was loaded per injection. The UPLC instrument was connected to an Exactive Orbitrap focus (Thermo Fisher Scientific) via a heated electrospray source (Thermo Fisher Scientific). The spectra were recorded using full-scan positive and negative ion-detection mode with all ion fragmentations (MS2), covering a mass range from m/z 100 to 1,500. The resolution was set to 70,000, and the maximum scan time was set to 250 ms. The sheath gas was set to a value of 60 while the auxiliary gas was set to 35. The transfer capillary temperature was set to 150°C while the heater temperature was adjusted to 300°C. The spray voltage was fixed at 3 kV, with a capillary voltage and a skimmer voltage of 25 V and 15 V, respectively. MS spectra were recorded from minutes 0 to 19 of the UPLC gradient. Processing of chromatograms, peak detection, and integration was performed using Refiner MS (version 5.3; GeneData) to generate a table of intensity variation across metabolomic features and samples (Table S3).

Identification and analysis of TAs and SGAs

To identify TAs and SGAs using untargeted LC-MS we assembled a database of ratios of mass-charge (m/z), fragmentation patterns, and retention times (RTs) of known TAs and SGAs. We created a tool in R programming language using the Shiny library that allows us to search for metabolites using this database and metabolomic data in Refiner MS format (available at https://github.com/EstevanGN/Metabolomic-search). Interesting clusters were then confirmed by manual inspection of the chromatograms using Xcalibur Data Acquisition and Interpretation Software (Thermo Fisher Scientific). We thus identified 24 metabolic features corresponding to TAs and 36 corresponding to SGAs (Table S2).

The MetaboAnalyst platform (http://www.metaboanalyst.ca) was used to normalyze metabolomic data using raw peak intensities and the log-transformation. For downstream analyses (WGCNA and PCCA) we averaged the transformed peak intensities across replicates from the same species (Table S2). To reduce dimensionality in metabolic data we conducted a PCA using JMP with the variation in TAs and SGAs separately and extracted the species loadings in the first component.

RNA extraction and analysis

For RNA extractions we pooled replicates to obtain a single pool per species. RNA was extracted with 5:1 trizol-chloroform and precipitated in isopropanol and 75% ethanol. Samples were sent to NovoGene for RNAseq genotyping using paired-end 150 bp libraries and an Illumina NovaSeq™ 6000 S4 Sequencing System. We obtained 6 GB of clean sequence data per sample, which provides sufficient sequencing depth for the assembly of de-novo transcriptomes, and the analysis of gene expression. Raw sequences were uploaded in the SRA archive (bioproject PRJNA1070281).

Transcriptome assembly

We assembled transcriptomes for all 129 species using a de-novo pipeline. Firstly, we removed adapters and low-quality sequences using Trimmomatic v0.39 (Bolger et al. 2014) with the following parameters: ILLUMINACLIP:adapters.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:140. We used paired clean reads to create de novo transcriptome assemblies for each species using Trinity v2.12.2 (Haas et al. 2013) with default program parameters. We assembled leaf transcriptomes for all species, and, for a subset of species, we combined root and leaf reads to create combined leaf-root transcriptomes (Table S1).

To reduce sequence redundancy and complexity and to remove non-coding transcripts we first obtained the longest representative sequence for each trinity “gene” using Trinity's get_longest_isoform script (Haas et al. 2013). Then TransDecoder v5.5.0 (https://github.com/TransDecoder/TransDecoder) was used to identify open reading frames and predict regions' CDS sequences. We finally used CDHIT (Li & Godzik, 2006; Fu et al. 2012) to remove short redundant sequences with the parameters -c 0.99 -n 10 -r 0 -T 6. The resulting fasta files of non-redundant CDS sequences were used to detect orthologs and quantify gene expression.

Orthology analysis

The non-redundant CDS sequences obtained from the previous step were used as input for the OrthoFinder tool (Emms & Kelly, 2019). We employed the multiple sequence alignment option, using DIAMOND (Buchfink et al. 2015) for initial sequence comparison, MUSCLE (Edgar, 2004) for the alignments and Fastree (Price et al. 2010) for tree rendering. We used the Orthofinder-generated species tree for the last phase of OG identification.

We used two datasets as input for Orthofinder. (1) Leaf transcriptomes from Solanaceae species, including 129 de novo transcriptomes assembled by us as well as the CDSs extracted from the genomes of 4 species with sequenced and annotated genomes (Atropa belladonna, Datura stramonium, Solanum lycopersicum, and Capsicum annuum. See table S1). This dataset was used for analyses that focus on gene expression variation across species. (2) CDSs extracted from the assembled genomes of 31 Solanaceae species (Table S2) downloaded from the National Genomics Data Center (https://ngdc.cncb.ac.cn/) and Solgenomics (https://solgenomics.net/). This dataset was used to analyze the genomic presence and distribution of SGA and TA OGs across species.

Phylogenetic analyses

We created a phylogeny of Solanaceae species using transcriptomic data. We selected 243 OGs present in all species (Table S10). MAFFT alignments from each OG were used to create individual phylogenetic trees using Raxml (Revell & Harrison, 2008) with GTRGAMMA substitution model, rapid Bootstrap analysis (200 bootstraps), and searching for the best-scoring ML tree in one program run. Phylogenetic trees were then analyzed with ASTRAL pro2 for multi-copy gene tree inference (Zhang & Mirarab, 2022). We used default ASTRAL parameters and selected the best tree.

Gene Expression quantification

OGs were used to compare expression across species. Because we are working with de-novo transcriptomes, the differences in gene expression across OGs and species may reflect either a gene expression variation (expressed/not expressed) or the number of transcripts included in an OG. We used a cumulative expression methodology that takes into consideration differences in the number and length of sequences included in each transcriptome and OG. We first used the Salmon program (Patro et al. 2017) to map the clean reads of each sample to their respective CDSs using the parameters “-l A -p 8 --validateMappings”. We then calculated normalized estimates of cumulative expression for each OG, A_OG and TPM10K_OG, which were adapted from Munro and collaborators (Munro et al. 2022) using the formulas:

A_OG = (Sum of reads per OG * 10³)/(total effective length of CDSs in OG)

TPM_OG = (A * 10⁶) / (Sum of A across all OGs)

TPM10K_OG = (TPM * number of expressed OGs)/10⁴

Ortholog Annotation

We annotated OGs using the CDSs of species with sequenced and annotated genomes included in Orthofinder analyses. We assigned to the OGs the corresponding functional annotations of genes from reference genomes. Specifically, we used the Refseq-RNA codes of genes from reference genomes to retrieve functional annotations in the DAVID server (https://david.ncifcrf.gov/summary.jsp) and assign these annotations to OGs.

To identify SGA and TA genes we created a database of characterized SM genes from reference species. This database includes all biochemically validated enzymes, transporters, and regulatory genes involved in the biosynthesis of TAs and SGAs. We identified OGs containing these genes and assigned their respective functions. OG annotations are provided in Table S4.

We searched for functional enrichment in DEGs and coexpression modules (next sections) by using the “Functional Annotation Chart” tool from the DAVID server (https://david.ncifcrf.gov/home.jsp) using RefSeq codes from genes included in the different sets (Table S7).

Differential expression analyses

We used the EdgeR package (Robinson et al. 2010) to conduct differential expression analyses to identify OGs whose expression differs between the TPCs and SPCs identified through our metabolomic analyses and a literature review. We used as input the matrix of expression counts (A_OG) across 129 species. OGs with low expression were filtered and remaining data was normalized using the trimmed means of m values method (TMM). 11,740 OGs remained after filtering. We conducted two comparisons: (1) TA-producing species vs other species and (2) SGA-producing species vs other species. Those OGs showing an FDR-corrected p-value lower than 0.05 were considered significantly differentially expressed in each comparison. Full results are provided in Table S5.

Network analyses

We used the WGCNA package from the R software (Langfelder & Horvath, 2008) to identify co-expression modules related to alkaloid metabolism using expression data (TPM10K) from leaf transcriptomes of 129 species (Tables S5 and S6). WGCNA was run with the following parameters: power = 6, TOMType = "signed", minModuleSize = 20, maxBlockSize = 30000, deepSplit = 2.5, reassignThreshold = 0, mergeCutHeight = 0.25, corType = "pearson". To identify alkaloid-associated co-expression modules we searched for modules containing key TA genes (e.g., TRs, LS, H6H, HDH) and SGA genes (e.g., GAME genes). We used metabolomic data (first two loadings of metabolic PCAs for SGA and TA variation) to calculate Pearson correlations between gene expression and metabolic variation.

Analysis of genomic distribution

To analyze the distribution of SGA and TA across Solanaceae lineages we first identified SGA and TA OGs in the orthology analysis conducted with Orthofinder using CDSs of 32 Solanaceae assembled genomes (Table S1). We then used genomic annotations (gff files) to localize the genes belonging to these OGs and calculated the number of SGA and TA genes across all non-overlapping 1MB genomic windows. Regions with the highest numbers of OGs were investigated as potential BCs (Table S9). Finally, we analyzed the number of copies of known TA and SGA genes and genes preferentially expressed in TPCs or SPCs within genomic intervals containing putative BCs.

Identification of TPCs and SPCs

In this study, we sampled most major clades in the Solanaceae family, including 128 species from 12 tribes and 25 genera as well as the outgroup Ipomoea purpurea (Fig. 1, Table S1). We used Astral-Pro (Zhang & Mirarab, 2022) to reconstruct phylogenetic relationships between these species using transcriptomic sequences (Fig. 1). The relationships between species and major clades are consistent with previous phylogenetic analyses based on barcode markers (Särkinen et al. 2013) and genomic sequencing (Dodsworth et al. 2016; Huang et al. 2023). This phylogenetic backbone allows us to explore the genetic basis for the gain and loss of alkaloids in Solanaceous lineages.

To identify TCPs and SPCs we searched for TAs and SGAs in the leaf metabolomes of 50 species, employing Liquid chromatography-tandem mass spectrometry (LC-MS/MS) with data-independent acquisition (DIA). We created an open-source tool software application named "Metabolomic Search" (MetS) to facilitate metabolite identification using correlation analysis and similarity scoring algorithms to match our DIA data to a database of retention times and fragmentation patterns of TAs and SGAs. After manual confirmation of the chromatograms, we identified 24 TAs and 36 SGAs (Fig. 3, Table S2).

We used a literature review and the presence of TAs and SGAs to define TA-producing clades (TPCs) and SGA-producing clades (SPCs) respectively (Figs. 1 and 2). TAs biosynthesis seems to have evolved in the early Solanaceae ancestor, but this pathway was subsequently changed and lost in some of the descendant lineages (Zhang et al. 2023). As expected, TAs were detected in the Datureae and Hyosciameae tribes (A. belladonna). Although we did not conduct metabolomic analyses in members of the Solandreae and Lycieae tribes, TAs have been reported in species of these clades (Evans et al. 1972; Eich, 2008). Based on this, we defined four TPCs: the Datureae, Hyosciameae, Lycieae, and Solandreae tribes (Figs. 1 and 2). Conversely, we found SGAs in the Solaneae and Cestreae tribes, where they have been previously reported. However, SGAs are also present in species from the Capsiceae tribe (genus Lycianthes; Fig. 2). These three tribes were designated as SPCs. The outgroup Ipomoea purpurea produces Calystegines which are biochemically related to TAs, but is not known to produce SGAs.

Identification of Alkaloid genes

Analysis of gene expression across species is challenging due to gene duplications and losses, which are particularly common for SM genes (Moghe & Last, 2015). To compare the transcriptomes of different species, we identified orthogroups (OGs; Table S4). These OGs are sets of homologous sequences with a recent shared ancestry containing ∫orthologs and close paralogs from the different species (Emms & Kelly, 2019). We used OGs as the basis for functional annotation and gene expression comparisons across taxa. To achieve this, we devised a pipeline that assembles transcriptomes de novo, removes isoforms and noncoding sequences, searches for OGs between transcriptomes, and determines read counts across species (see methods).

We identified OGs across all leaf transcriptomes and assigned functional annotations (Table S4) using “reference” Solanaceae species with assembled and annotated genomes (see methods). To identify OGs related to SGA and TA metabolism, we employed a personal database of sequences derived from characterized genes documented in the literature. This database encompasses all validated enzymes, transporters, and transcription factors involved in the biosynthesis of TAs and SGAs. As a result, we identified 19 OGs containing genes known to be involved in TA production and 99 OGs associated with SGA biosynthesis (Fig. 3, Table S4).

Gene expression differences between TPCs and SPCs

We evaluated if SGA and TA OGS are differentially expressed between TPCs and SPCs using EdgeR. Specifically, we evaluated differential expression in two comparisons between leaf transcriptomes of (1) TPCs vs other species and (2) SPCs vs other species. Interestingly, the set of OGs overexpressed in SPCs is highly enriched in the biosynthesis of steroids (Table S7). Furthermore, TA OGs tend to be more expressed in TPCs while SGA OGs are more expressed in SPCs. Specifically, 12 TA OGs are significantly more expressed in leaves from TPCs. In comparison, 46 SGA OGs are more expressed in SPCs (Fig. 3, Table S5). These biosynthetic genes encode enzymes that catalyze different stages of TA and SGA biosynthesis (Fig. 3, Table S5).

To evaluate if there is an expression tradeoff between genes from the TA and SGA pathways -where TA OGs are not expressed in SPCs and SGA OGs are not expressed in TPCs - we calculated correlations between gene expression and metabolic variation. Our findings revealed that the expression of genes involved in the biosynthesis of TAs and SGAs exhibits phylogeny-wide correlations to TA and SGA content. Specifically, TA OGs display positive expression-by-metabolism correlations for TA variation. Conversely, most of these OGs displayed negative correlations to SGA variation (Fig. 3, Table S5). On the other hand, SGA OGs exhibited significantly positive correlations to SGA variation while displaying negative correlations to TA variation (Fig. 3, Table S5).

Identification of gene networks involved in alkaloid production.

To identify regulatory networks involved in the evolution of alkaloid biosynthesis, we employed weighted correlation network analysis (Langfelder & Horvath, 2008). Our underlying assumption is that enzymes involved in the diversification of a pathway exhibit correlated expression patterns across species. We identified one module containing the majority of TA genes (9/19; Table S5) and two modules containing most SGA biosynthetic genes (46/99; Table S5). Importantly, these modules are enriched in functions related to specialized metabolism (e.g. Biosynthesis of secondary metabolites, Steroid biosynthesis, Fatty acid biosynthesis, Table S7).

To identify potentially important regulatory genes, we searched for transcription factors (TFs) contained in alkaloid modules and displaying differential expression between TPCs and SPCs. In the SGA modules, we identified five transcription factors containing genes already associated with SGA biosynthesis (ARF, ERF, AP2-ERF, HY5, DELLA) as well as a WRKY transcription factor (Fig. 4). Inside the TA module, we identified four transcription factors, including the ortholog of an LBD1-ARF4 TF involved in TA production and root maturation (Zeng et al. 2020), an MYB TF, and a WRKY (Fig. 4).

Gene presence and genomic clustering of alkaloid genes

We studied the presence and organization of TA and SGA OGs across the genomes of 32 Solanaceae species. Firstly, we evaluated whether TA and SGA OGs are present in the genomes of TPCs or SPCs. We found that six TA OGs are present in the genomes from TPCs but absent from SPCs and the outgroup, I. purpurea (Fig. 5; Table S8), including AT4, CYP80F1, H6H, HDH, LS, and PPAR. Contrastingly, SGA OGs are present across most species, including TPCs and SPCs and I. purpurea (Fig. 5; Table S8).

We then located biosynthetic clusters (BSs) by searching for genomic intervals containing multiple TA or SGA genes. We thus identified six TA biosynthetic clusters (TBCs) in the genomes of A. belladonna and D. stramonium, including the cluster highlighted in a recent study (Zhang et al. 2023) (Fig. 6; Table S9). These TBCs were relatively small, containing between two and three non-homologous genes (Fig. 6). Importantly, three of these clusters were only detected in TPCs. Surprisingly, the orthologues of four SGA genes (GAME9, GAME17, PRT, and NPF) are located in the vicinity of these TBCs (Fig. 6).

In contrast to TA genes, SGA genes are highly clustered (Fig. 7). Two genomic regions, located on chromosomes 7 and 12 of tomato, contain SBCs identified previously (Itkin et al. 2013). We conducted an extensive analysis to examine the presence and composition of these two SGA biosynthetic clusters (SBCs) across taxa (Fig. 7). Importantly, we discovered that the SBCs contained late-acting GAME genes but also the orthologues of multiple genes involved in the early stages of SGA biosynthesis (i.e. ACAT, CAS, CV86, HMGR, OSC, STS) and the biosynthesis of other SMs (Fig. 7). Furthermore, while the two SBCs were found in all Solanum species, they were absent from the genomes of Capsicum, Petunia, and Nicotiana species. Additionally, the SBC at Chr07 was not detected in Physalideae species and Datura stramonium. Similarly, the SBC at Chr12 was absent from the genome of Atropa belladonna. The composition of these clusters varied significantly across taxa due to multiple gene duplications and insertions/deletions in genes like GAME17, GAME12, GAME 25, and GAME3 (Fig. 7). In general, SBCs exhibited a greater number of secondary metabolite-related OGs in Solanum species compared to other taxa, with 15 SM-related OGs exclusively found in Solanum species. Surprisingly, the Chr12 SBC was particularly gene-rich in members of the Physalideae tribe that do not produce SGAs but produce withanolides (Fig. 7).

Gene loss from ancestral SGAs and TA pathways

Alkaloids have been repeatedly gained and lost during the evolution of plants. We found support for the repeated evolution of TAs and SGAs since both alkaloids are produced by multiple independent clades within the Solanaceae family. Recent studies have used genomic analyses from a relatively small number of species to prove that TA genes were likely present in early Solanaceae species but have been lost in most of the extant lineages (Yang et al. 2023; Zhang et al. 2023). Here we analyzed the genomic presence SGA and TA OGs in a large set of Solanaceous genomes to determine plausible evolutionary scenarios for the repeated evolution of these alkaloids. We found that TA and SGA genes display contrasting patterns of genomic presence in Solanaceae lineages: While most TA genes are only present in TPCs, SGAs genes tend to be present in all clades, including some early divergent lineages like the Cestrum genus and the outgroup Ipomoea. This suggests that core SGA genes were present in the ancestor of Solanaceae species and have been largely retained in clades that do not produce SGAs (Fig. 5). Therefore, gene loss determined the patchy distribution of TAs in Solanaceae species but likely did not play an important role in defining the presence of SGAs across clades.

Genomic clustering

Genomic clustering of biosynthetic genes is a common feature in the evolution of specialized metabolism. The analysis of variation in the structure of BCs has been used to understand the appearance and diversification of multiple SM pathways, including SGAs (Akiyama et al. 2021) and TAs (Yang et al. 2023; Zhang et al. 2023). In this study, we analyzed SBCs and TBCs in Solanaceae genomes and found that their presence and composition across lineages are correlated with the production of TAs or SGAs. Specifically, we found that TBCs are absent from the genomes of some SPCs while SBCs are absent from the genomes of some TPCs. Additionally, the number of biosynthetic genes included in SBCs is greater in SPCs than in other taxa. Altogether these results indicate that the assembly of BCs was a complex and progressive process that played an important role in the appearance/disappearance of both types of alkaloids.

We found contrasting patterns of clustering in the two alkaloid pathways, where SGA genes are grouped in large clusters that contain early and late-acting steroidal genes, while TA genes are spread throughout the genome forming relatively small clusters. These contrasting genomic architectures would likely impose different evolutionary dynamics for both pathways. For instance, a high level of clustering facilitates a coordinated regulation of the genes from the pathway and therefore clustered pathways can evolve through regulatory “switches” that enhance or reduce the expression of all the genes from a BC. For instance, a regulatory switch was involved in the downregulation of clustered SGA genes during the domestication of tomatoes (Bai et al. 2024). We propose that the high clustering of SGA genes could explain why these genes are present across all Solanaceae lineages, while TA genes tend to be only present in TPCs. Namely, the SGA pathway possibly evolved through gene duplications and changes in the genomic location and regulation of biosynthetic genes while gene loss played a central role in the evolution of TAs. Such differences in the dynamics of active and silenced BCs have been described in other plants (Nützmann et al. 2020). Interestingly, the SBC at chr12 contains tandem duplications of steroidal genes in species that do not produce SGAs but produce other types of steroids, namely withanolides. This suggests that this biosynthetic cluster was also instrumental for the divergence of these SGAs and withanolides, adding to evidence that SBCs are also involved in the evolution of other SM pathways, including acylsugars (Fan et al. 2020; Kerwin et al. 2024).

Regulatory changes involved in alkaloid divergence

Metabolic pathways can be switched on and off via genetic changes in TFs regulating the pathway. For instance, mutations in MYB TFs are responsible for rapid transitions in anthocyanin biosynthesis (Timoneda et al. 2019; Pucker et al. 2024). We found that SGA genes are preferentially expressed in SCPs despite being present in the genomes of most Solanaceae species. This suggests that regulatory switches could be important for the evolution of the pathway. We used co-expression analysis to identify TFs potentially involved in switching between TAs and SGAs. Co-expression analyses across organs or treatments have been used to uncover molecular networks that explain metabolomic differences between organs or environmental conditions. For example, the analysis of gene expression across organs of Solanaceae species such as S. lycopersicum and A. belladonna, led to the discovery of genes involved in the biosynthesis of specific SGAs (Itkin et al. 2013; Cárdenas et al. 2016) and TAs (Chavez et al. 2022; Zhang et al. 2023), respectively. Similarly, co-expression analyses across related taxa could shed light on the networks involved in the evolution and diversification of biochemical traits. In this study, we used trans-specific analyses to identify genetic modules that determine differences in alkaloid type and content across taxa.

We found that the expression of known alkaloid genes is correlated with alkaloid content (Fig. 3), suggesting that gene expression changes contribute to alkaloid diversification. As expected, there is a positive correlation between the expression of TA and SGA genes and the content of TAs and SGAs respectively. Additionally, most genes belonging to the same pathway are contained in the same SM-related co-expression modules. However, there is a negative correlation between the expression of genes and compounds from different pathways. This supports the existence of a regulatory switch that determines evolutionary transitions between SGAs and TAs, preventing the simultaneous production of both alkaloid types.

Alkaloid production is regulated across organs and environmental conditions by hormones like auxins and ethylene (Rieseberg et al. 2023). For example, Apetala2/ethylene response factor (AP2/ERF) TFs form gene clusters that play a fundamental role in coordinating the expression of different alkaloid pathways in plants, including SGAs and Nicotinoids (Shoji et al. 2010; De Boer et al. 2011; Paul et al. 2020). We found that alkaloid modules contained multiple ERFs and auxin-responsive factors (ARFs) that were preferentially expressed SCPs. These include known SGA regulators, GAME9 (Cárdenas et al. 2016), SlAP2-ERF (Guo et al. 2022), MYC1 (Swinnen et al. 2022), and HY5 (Zhang et al. 2022), which mediate SGA regulation under stress responses and through morphogenesis.

The regulatory mechanisms involved in TA metabolism are relatively less known but two important TFs have been previously identified, a bHLH (Gou et al. 2024) and an ARF named AbLBD1 (Zeng et al. 2020). We found support for the evolutionary importance of AbLBD1 since an OG containing AbLBD1 is exclusively expressed in TA-producing species and is contained in TA co-expression modules. Moreover, an inspection of co-expression modules and BCs allowed us to identify other TFs that could be coordinating the expression of SGA and TA genes. We identified four OGs containing WRKY TFs that have not been associated with TA or SGA production. More importantly, we found two interesting TFs located in SBCs, namely an ARF and a MYB TF. Surprisingly, the MYB TF is preferentially expressed in TPCs suggesting that it governs TA production. We also found the orthologues of SGA regulators like GAME9 nearby TBCs. These results suggest that the recruitment of TFs in BCs could constitute a mechanism to switch between alkaloid pathways.

Our results show that gene loss, genomic clustering, and expression regulation played an important role in this divergence of TAs and SGAs. However, the contributions of these three processes are asymmetrical, where expression regulation and clustering played a more important role in the evolution of SGAs and gene loss influenced more significantly the evolution of TAs.

To investigate the genetics, evolution, and diversity of specialized metabolism, researchers often employ evolutionarily intriguing species as lineage-specific “anchors” (Mahood et al. 2020). In this study, we created novel “anchor” species to gain insights into the functional foundations of alkaloid diversification within the Solanaceae family. Our study included several clades that have not been explored at the genomic or metabolomic level, despite their ethnobotanical significance and diversity. For example, genomic information on members of the Brugmansia and Solandra genera is scarce, despite their extensive use by indigenous communities (Bristol, 1969; Knab, 1977; Lockwood, 1979). Furthermore, Cestrum and Lycianthes are the second and third most diverse genera in the family (after Solanum), with over 250 species per genus; however, their biochemical characteristics remain largely unexplored. Investigating these clades unveiled novel and unexpected patterns in the biosynthesis of TAs and SGAs.

The assembly and analysis of genomes from clades such as Cestreae and Anthocercideae are crucial for reconstructing how alkaloid biosynthetic clusters evolved in the family. For example, we currently lack information on whether SBCs are present in Cestrum species and have limited knowledge about the genes responsible for TA production in the genus Duboisia, which serves as a significant source of TAs for pharmacological purposes. Another clade that warrants further investigation at the genomic and metabolomic levels is the Solandreae tribe. This tribe is known to produce TAs while expressing both SGA and TA genes (Fig. 3). Consequently, members of the Solandreae tribe might possess the ability to simultaneously produce both types of alkaloids, contradicting the mutual exclusivity previously emphasized. Alternatively, certain species within the tribe may express TAs, while others produce SGAs. This provides an excellent model for studying rapid evolutionary transitions between these two pathways, provided that genomic analyses are accompanied by the development of platforms to validate and manipulate biosynthetic genes from these new anchor species. Overall, our study supports the study of neglected clades as a viable strategy for understanding and manipulating plant metabolism. This strategy can promote plant conservation and catalyze the creation of more diverse and resilient biotechnology systems.

ARF: Auxin responsive factor

BAHD: BAHD acyltransferases

BC: Biosynthetic cluster

CYP450: Cytochrome P450

ERF: Ethylene responsive factor

MYB: MYB transcription factor

ODD: Oxoglutarate-dependent oxygenase

OG: Orthogroup

SBC: Steroidal glycoalkaloid biosynthetic cluster

SCD: Short-chain dehydrogenase/reductase

SGA: Steroidal glycoalkaloid

SM: Specialized metabolism/metabolite

SPC: Steroidal glycoalkaloid-producing clade

TA: Tropane alkaloid

TBC: TA biosynthetic cluster

TF: Transcription factor

TPC: Tropane alkaloid-producing clade

UGT: UDP glucuronosyltransferase

WRKY: WRKY transcription factor

ZF: Zinc finger transcription factor

Acknowledgments

We thank Dr. Federico Scossa, Dr. Clara I Bermúdez, and Dr. Clara I. Orozco for their generous advice, which was instrumental in the writing of this manuscript.

We thank the “Centro de Excelencia en Computación Científica” at Unal, which is directed by Dr. Clara I. Bermúdez and supported by the German Academic Service (DAAD), for giving us access to the computing cluster from the Center and guiding our work there.

We are thankful to the Bioinformatics and Systems Biology group (GiBBS) at the Universidad Nacional de Colombia, directed by Dr. Andrés Pinzón for providing access to his computing cluster and for orienting our work in the Cluster.

We thank the Fundación Proaves for giving us access to the Reserva ProAves Las Tángaras and Reserva ProAves El Pangán for sample collection.

We thank Professor Maria C. Delgado, Javier Sandoval, Lady D. Gomez, and Juan P. Avellaneda for their support in lab work and plant growth.

We thank Emilio Constantino, Alistair Hay, Domingo Cuatindioy, Francisca Jacanamejoy, Reynerio Ruíz and Juan R. Sierra for providing some of the Brugmansia plants used in this study, which were part of their private collections.

We thank Dr. Teresa Mosquera for providing access to molecular biology labs.

Competing interests

None declared.

Author contributions

PPM, RAP, SA, AF, and FRF conceived this study and carried out manuscript writing; AO and GPS conducted plant collection in the field; RAP, SRC, PPM, and GPS conducted ARN and Metabolite extractions; RAP, SRC, SA, DEG, and LLK conducted metabolomic analyses; PPM, RAP, SRC, GPS, and FRF conducted transcriptomic analyses and data integration.

Data availability.

Supplementary data will be deposited in the Dryad repository. Raw reads from RNA-seq were deposited in the SRA database at NCBI under bioproject name PRJNA1070281. Metabolomic data was submitted to the GNPS database (MassIVE ID MSV000094778).

Funding

This research was funded by the Convenio 566 of 2014 between Universidad Nacional de Colombia (https://unal.edu.co/) and Colciencias (Now Minciencias https://minciencias.gov.co/).

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Genetic basis of alkaloid divergence in the Solanaceae

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