Formation and diversification of a paradigm biosynthetic gene cluster in plants

Liu, Zhenhua; Cheema, Jitender; Vigouroux, Marielle; Hill, Lionel; Reed, James; Paajanen, Pirita; Yant, Levi; Osbourn, Anne

doi:10.1038/s41467-020-19153-6

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Published: 23 October 2020

Formation and diversification of a paradigm biosynthetic gene cluster in plants

Nature Communications volume 11, Article number: 5354 (2020) Cite this article

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Abstract

Numerous examples of biosynthetic gene clusters (BGCs), including for compounds of agricultural and medicinal importance, have now been discovered in plant genomes. However, little is known about how these complex traits are assembled and diversified. Here, we examine a large number of variants within and between species for a paradigm BGC (the thalianol cluster), which has evolved recently in a common ancestor of the Arabidopsis genus. Comparisons at the species level reveal differences in BGC organization and involvement of auxiliary genes, resulting in production of species-specific triterpenes. Within species, the thalianol cluster is primarily fixed, showing a low frequency of deleterious haplotypes. We further identify chromosomal inversion as a molecular mechanism that may shuffle more distant genes into the cluster, so enabling cluster compaction. Antagonistic natural selection pressures are likely involved in shaping the occurrence and maintenance of this BGC. Our work sheds light on the birth, life and death of complex genetic and metabolic traits in plants.

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Introduction

Nonrandom gene organization in eukaryotes plays a significant role in genome evolution and function. Although most eukaryotic genomes lack operons, they do contain groups of physically clustered genes that are related in function despite being unrelated in sequence. Striking examples of this are biosynthetic gene clusters (BGCs)^1,2. BGCs have been studied extensively in fungi^1,2. Over the last few years numerous BGCs have also been reported from plants, including for compounds that determine agricultural traits such as disease resistance and drought tolerance^3,4 or have important pharmaceutical applications^5,6. There is compelling evidence that these plant clusters have not arisen by horizontal gene transfer from microbes, but instead are likely to be generated through gene duplication, neofunctionalization, and genomic relocation^1,2. However, very little is known about how these gene clusters form. Several examples of syntenic BGCs that make variants of the same types of molecules have been reported when different species are compared, including for steroidal glycoalkaloids in the Solanaceae and triterpenes associated with bitterness in the Cucurbitaceae^7,8. However, large-scale analysis of the evolutionary dynamics of individual BGCs within and across species within appropriate taxonomic windows has been hindered by the lack of available sequence resources.

Understanding the clustering of functionally related nonhomologous genes is crucial to our understanding of the relationship between genome organization and the evolution of complex adaptive traits. Over 10 years ago we characterized an “operon-like” gene cluster from the model plant species Arabidopsis thaliana (the thalianol BGC)⁹. More recently we have shown that this cluster plays an important role in shaping the root microbiota¹⁰.

Here, we take advantage of the rich resource of recently generated sequence data for A. thaliana and its relatives to investigate the occurrence, nature and evolution of this paradigm BGC at both the vertical (between species) and horizontal (within species) levels. Our results reveal that the thalianol BGC has evolved specifically in the Arabidopsis genus, and is still dynamically evolving between sister species through involvement of distant auxiliary genes. Within the A. thaliana species, the thalianol BGC is primarily fixed, as demonstrated by its conserved biochemical function and evidence of pervasive purifying selection imposed on individual BGC genes. In contrast, signatures for localized positive selection are identified when considering the cluster region as a whole. Analysis of de novo genome sequence assemblies of A. thaliana accessions from around the world further reveals compaction of the thalianol BGC through chromosomal inversions. The divergent and polymorphic cluster variants that we identify provide new insights into the birth, life and death of BGCs in plants.

Results

The biochemical function and evolutionary origin of the thalianol cluster

In our original discovery of the thalianol BGC in A. thaliana accession Col-0 we reported four physically adjacent coexpressed genes (THAS, THAH, THAO, and THAA1) for the biosynthesis of thalianol-derived triterpenes⁹. Subsequent analysis revealed three additional genes (a linked gene THAA2, and two unlinked genes THAR1 and THAR2) that are required for synthesis of the final pathway end product, thalianin (T10)¹⁰ (Fig. 1a). The original four core cluster genes are conserved between A. thaliana Col-0 and A. lyrata, both gene sets producing the acetylated thalianol derivative (-)-3β,7β-dihydroxy-16-keto-thalian-15-yl acetate (T7) when transiently expressed in Nicotiana benthamiana^10,11 (Fig. 1a). While the order of the four core genes is the same in these sister species, the THAA2 ortholog AL8G20050 in A. lyrata has a different orientation (Fig. 1b). In addition, there are intervening genes between THAA1 and THAA2 in the two species, which are not implicated in plant metabolism and are not syntenic (Fig. 1b).

**Fig. 1: The emergence of the thalianol cluster in the Arabidopsis genus.**

Thalianin belongs to the large and structurally diverse family of plant natural products known as triterpenes. In a previous study, we investigated the occurrence of triterpene BGCs in the sequenced genomes of 13 different Brassicaceae species¹¹. We identified a superficially similar triterpene BGC in the genome of the related species Capsella rubella, which diverged from A. thaliana around 8–9 million years ago¹² (Fig. 1c). This cluster transpired to be the result of an independent evolutionary event¹¹. Other than the C. rubella example, thalianol-like clusters were not detected in any of the other species included in this analysis¹¹. To further investigate the origins of the thalianol cluster, we next interrogated the newly generated genome sequence assemblies of two other Arabidopsis species, Arabidopsis halleri¹³ and Arabidopsis arenosa (this study; see Methods section). The first committed step in triterpene biosynthesis is the cyclization of 2,3-oxidosqualene to different triterpene scaffolds, a reaction carried out by enzymes known as oxidosqualene cyclases (OSCs). Systematic mining of the four Arabidopsis genomes identified a total of 55 predicted OSC genes (Supplementary Fig. 1). Phylogenetic analysis, in combination with investigation of the genomic locations of these OSC genes, revealed that the occurrence of bona fide syntenic thalianol synthase (THAS) genes was restricted to a monophyletic branch including only A. thaliana and A. lyrata OSC genes (marked in red in Supplementary Fig. 1). Consistent with this, analyses of the four Arabidopsis genomes using plantiSMASH analysis, a BGC mining algorithm designed for plants¹⁴, detected thalianol BGCs only in A. thaliana and A. lyrata (Supplementary Fig. 1). Thus, the thalianol cluster likely evolved in a common ancestor of the Arabidopsis genus around 5.8 MYA¹² (Fig. 1c).

Comparison of the thalianol cluster between species

Although the full thalianin pathway has been extensively studied in A. thaliana accession Col-0^9,10, the full pathway in A. lyrata has not been characterized¹¹. We therefore cloned the putative THAA2 ortholog from A. lyrata (AL8G20050; 90.4% predicted protein identity to AtTHAA2). Coexpression of this gene with the four other A. lyrata thalianol cluster genes in N. benthamiana yielded epi-thalianin (T17) (Fig. 2a), indicating that these five clustered genes are functionally conserved between A. thaliana and A. lyrata. In line with this, all five genes are coexpressed in the roots in both A. thaliana and A. lyrata (Fig. 2b). In A. thaliana, two auxiliary oxidoreductase genes THAR1 (AT3G29250) and THAR2 (AT1G66800) are required for epimerization of the C3 hydroxy moiety of T7 to give the final pathway end product, thalianin (T10)¹⁰ (Fig. 1a). Surprisingly, in A. lyrata, targeted metabolite analysis of root extracts did not identify T10 but T17 (Fig. 2a). The two isomers were not detectable in C. rubella and A. arenosa, both of which lack the thalianol BGC (Figs. 1c and 2a). Together our results indicate that the thalianol BGC has evolved specifically in the Arabidopsis genus, but is still dynamically evolving when A. thaliana and A. lyrata are compared. The presence of T17 in A. lyrata root extracts further implies that the thalianol BGC in A. lyrata per se encodes a full biosynthetic pathway without the involvement of two auxiliary genes as in A. thaliana. In agreement with this, reciprocal BLASTP and synteny analysis were unable to identify putative orthologs of THAR1 and THAR2 in A. lyrata. Transcriptome analysis revealed that the most closely related gene to A. thaliana THAR2 in A. lyrata (AL1G20010; 70.5% predicted protein identity) was preferentially expressed in the flowers rather than the roots, although the gene most closely related to A. thaliana THAR1 (AL4G46670; 81.5% predicted protein identity) had a similar expression pattern to the thalianol cluster genes (Fig. 2b). In addition to being part of the thalianin pathway in A. thaliana, the THAR2 gene is also required for the biosynthesis of arabidin, a triterpene derived from another A. thaliana Col-0 BGC^10,15 (Supplementary Fig. 2a). However, the arabidin gene cluster is not present in A. lyrata¹⁶ (Supplementary Fig. 2b). Altogether, these results suggest that although the thalianol cluster genes share conserved expression patterns and enzymatic functions in A. thaliana and A. lyrata, in A. lyrata the five clustered genes encode a complete biosynthetic pathway for epi-thalianin (T17), while in A. thaliana the two additional unlinked genes THAR1 and THAR2 have been recruited to convert the T7 pathway intermediate into thalianin (T10) (Fig. 2c).

**Fig. 2: Involvement of auxiliary genes drives diversification of thalianol BGC between species.**

Variation in the thalianol cluster within species

We next systematically examined polymorphisms, including SNPs, small indels and large gene deletion variations, in the thalianol cluster genes based on genome sequences generated with short reads from 1135 A. thaliana accessions¹⁷ (https://tools.1001genomes.org/polymorph/). Overall, SNPs in the thalianol cluster genes were the most common types of polymorphisms (97%), followed by small indels (55% and 36% for insertions and deletions, respectively) (Supplementary Table 1). These polymorphisms were mostly benign, with only two accessions having SNPs that are predicted to have a large effect (i.e., premature stop codons) on the THAS gene and most accessions having small indels in the UTR regions of the cluster genes (Supplementary Table 1). Only ~2% (21/1135) of accessions were found to have large gene deletions involving one or more cluster genes. Of those, 16 accessions lacked the THAS, THAO, and THAA1 genes and a large 3′-UTR region of the THAA2 gene (~6 kb) (Fig. 3a). Other types of deletions were also found at much lower frequencies (Fig. 3a). The two unlinked genes (THAR1 and THAR2) are present in nearly all of the A. thaliana accessions (Supplementary Fig. 3a). In accordance with this, analysis of transcriptome data for seven A. thaliana accessions revealed similar expression patterns for the thalianol cluster genes as in Col-0⁹ (Supplementary Fig. 3b). Metabolite analysis confirmed that T10 could be detected in root extracts from accessions that had the thalianol cluster genes but not in those that lacked them (e.g., Cerv-1, Dr-0, and Lillö-1) (Supplementary Fig. 4). Taken together, we conclude that this cluster is likely to be functional in the vast majority of A. thaliana accessions.

**Fig. 3: Variation in the thalianol cluster within *A. thaliana* species.**

To gain insights into polymorphisms in gene order for the thalianol BGC, we analyzed 22 A. thaliana genome sequence assemblies generated with PacBio long reads (see Methods section). These analyses confirmed the deletion profile observed for the Lillö-1 accession (Fig. 3a). They further showed that, while the order of the four core genes is well conserved, that of the intervening genes between THAA1 and THAA2 is not. Of the 21 assembled A. thaliana genomes (excluded Lillö-1, since it lacked most of the core genes), only three had the same gene order as the Col-0 thalianol cluster. Strikingly, in the remaining 17 accessions the THAS, THAH, THAO, THAA1, and THAA2 genes were contiguous, with no predicted genes between THAA1 and THAA2. The reversed orientation of the three rearranged genes and detailed alignment analysis suggests that these natural accessions have experienced chromosomal inversions to form compact thalianol clusters relatively recently (Fig. 3b, c and Supplementary Fig. 5). Although our sample size for de novo sequenced genomes is relatively small (21 out of 1135 accessions), the contiguous forms of the thalianol cluster constitute the majority (17 out of 22; 77%) of these accessions and are phylogenetically widely distributed (Fig. 3d). We hypothesize that chromosomal inversion may facilitate the compaction of the thalianol BGC in natural populations.

Analysis of selection on the thalianol pathway

To investigate what types of selection may shape the within and between species variations of the thalianol BGC, we simulated variant data at within-species and/or between-species levels. Pairwise between-species nonsynonymous/synonymous substitution (dN/dS) analysis identified relaxed purifying selection for the thalianol cluster genes when compared with phytosterol pathway genes (which are highly conserved across land plants), reflecting the Arabidopsis-genus specific distribution of this gene cluster (Fig. 4a). Notably, the later thalianol pathway genes were far less constrained than the early pathway genes (Supplementary Fig. 6). This is consistent with different roles for THAA2 in A. thaliana and A. lyrata as shown in Fig. 2c. Branch-site model analysis (see Methods section) did not detect gene-wide positive selection for any of the thalianol cluster genes (Supplementary Table 2), although a site model analysis (see Methods section) did detect episodic positive selection on several sites across the clustered genes (Supplementary Table 3). These analyses are in agreement with the conserved enzymatic functions of the thalianol pathway genes between species. We next performed several versions of the McDonald–Kreitman (MK) test¹⁸ by using both polymorphism and divergence to test for positive selection in more recent evolutionary time (see Methods section). These analyses found no signature of current positive selection imposed on the thalianol cluster genes (Supplementary Table 4). Altogether, our results suggest that individual genes for thalianol BGC are mostly under purifying selection, presumably due to their conserved enzymatic functions and expression patterns within and between species.

**Fig. 4: Molecular evolution analysis within and between species.**

Having carried out evolutionary tests on single genes, we then expanded our tests for positive selection to consider the thalianol cluster as a whole by performing genome-wide linkage disequilibrium (LD) analysis using SNP data from the 1135 A. thaliana genomes. The genome of A. thaliana accession Col-0 contains 13 OSC genes, each with different functions⁹. These genes are located in a total of nine genomic neighborhoods (GNs), tandem duplicated OSC genes being treated as one GN (see Methods section). We systematically examined the LD extensions of these nine OSC GNs. To do this, we counted the number of squared allele frequency correlation (r²) values (r² > 0.1) for biallelic sites located within the OSC-centric GNs (spanning 50 Kb on each side of an OSC locus; see Fig. 4b). Our results demonstrate that GNs bearing OSC gene clusters have significantly more nonrandom associations of biallelic sites than GNs containing OSC genes that are not part of BGCs (Student t-test; Fig. 4b). In addition, nonrandom associations of biallelic sites in the THAS-centric GN are mostly located in the flanking regions of the thalianol cluster and decay with increasing genetic distance (Fig. 4c), only two being detected in the region encompassing the four cluster genes shown in Fig. 4c (indicated by the red circles). This pattern suggests that the thalianol cluster is currently undergoing or has recently undergone a selective sweep, which has reduced the diversity in the cluster region. The elevated LDs in the flanking regions are presumably due to the hitch-hiking effect¹⁹. This interpretation is further supported by the low levels of single feature polymorphism (SFPs) across the thalianol core cluster in comparison to the flanking regions (Supplementary Fig. 8), suggesting that the thalianol cluster as a whole is under directional positive selection.

Discussion

Here, we have carried out large-scale investigation of the evolutionary dynamics of a plant BGC, the thalianol cluster. Our study suggests that an ancestral thalianol cluster evolved relatively recently in evolutionary time, before the split of A. thaliana and A. lyrata. The cluster was not detected in the A. halleri and A. arenosa accessions included in our study. Analysis of additional accessions as more genome sequences become available will establish whether lack of the thalianol BGC is a consistent feature of these two species. Comparisons of the thalianol clusters in A. thaliana and A. lyrata reveal differences in cluster organization and function. In A. lyrata, five clustered genes are necessary and sufficient for biosynthesis of the pathway end-product epi-thalianin (T17), while in A. thaliana a further two unlinked genes (THAR1 and THAR2) are required to give the corresponding pathway end-product, thalianin (T10) (Fig. 5). Thus the interplay between the core BGC and unlinked auxiliary genes may provide a mechanism for diversification of BGCs between plant species.

**Fig. 5: Schematic illustrating the cluster variants and polymorphisms identified for the *Arabidopsis thalianol* cluster in this study.**

Future experiments will establish whether epi-thalianin contributes to the establishment of microbial communities in and around A. lyrata roots, and if so how distinct these communities are from those of A. thaliana¹⁰.

SNPs and genomic deletion/insertion events have been implicated in the maintenance and diversification of BGCs in fungi, along with horizontal gene transfer from taxonomically distant fungal species²⁰. Our investigations of the genomes of over a thousand A. thaliana accessions from around the world reveal that SNPs and small indels are the most common sequence polymorphisms in the thalianol cluster genes, followed by chromosomal inversions and gene loss (Fig. 5). Overall, the frequency of predicted deleterious mutations was low, suggesting that the thalianol cluster is fixed and functional in the vast majority of accessions.

Our results provide evidence to suggest that chromosomal inversions may lead to cluster compaction (i.e., by bringing the THAA2 gene into the core thalianol cluster) (Fig. 3b, c). Although the role of chromosomal inversions in evolution in animals has been extensively investigated^21,22, examples of such studies in plants are few²³. Chromosomal inversions suppress recombination within and around the inversion region, and so may insulate clustered coadapted alleles against dispersal²⁴ (Fig. 6). Indeed, our systematic LD analysis identified significantly more coinherited biallelic SNPs in the genomic neighborhoods (GNs) of OSC genes that form parts of BGCs compared to those of nonclustered OSCs (Fig. 4b). In line with this, a recent population genomic study also identified evolutionarily recent inversions associated with adapted quantitative trait loci (QTLs) in Drummond’s rockcress (Boechera stricta)²⁵. The high frequency of compact thalianol BGCs in natural A. thaliana populations (Fig. 3b–d) is thus in accordance with the signatures of positive selection detected on the thalianol BGC as a whole, suggesting that the chromosomal inversions that we have identified are more likely to have driven cluster assembly rather than cluster break-up. Closer physical colocalization of genes will reduce the likelihood of loss of single pathway genes during recombination^2,26. Altogether, our results suggest that chromosomal inversion may be a genetic mechanism enabling growth and compaction of BGCs in plants.

**Fig. 6: A proposed model for compaction/assembly of plant BGCs through chromosomal inversions.**

The reasons for the existence of BGCs in eukaryotic genomes remain hotly debated. Several scenarios, i.e., coinheritance, coregulation, and avoidance of toxic effects from pathway intermediates have been proposed². These various hypotheses are not mutually exclusive. The overriding point, however, is that the formation and maintenance of BGCs must inevitably be a consequence of natural selection. Analysis within and between species for the thalianol BGC suggest that both positive and purifying selections are likely involved in shaping the formation and maintenance of this cluster (Figs. 3c and 4). These antagonistic selection pressures may further reduce the local recombination between physically linked enzymatic genes²⁶. As we learn more about the dynamics of BGCs within and between species, we will be able to gain further insights into the forces and mechanisms shaping genome architecture and adaptive evolution.

Methods

Plant materials and growth conditions

Seeds of A. thaliana accessions were obtained from Professor Jian Hua (Cornell University) and NASC (http://arabidopsis.info/). Seeds of A. arenosa were obtained from Prof. Kirsten Bomblies (ETH, Zurich). All seeds were surface sterilized in 5% sodium hypochlorite for 7 min, followed by incubation in 70% ethanol for 5 min, and then washed by ddH₂O for five times. The seeds were placed on ¼ MS medium, subjected to cold treatment (4 °C, 3 days) before moving to long-day conditions (22 °C; 16 h light/8 h dark). For A. lyrata, 3 weeks of cold treatment were applied. N. benthamiana plants were grown in a greenhouse under long-day conditions (22 °C; 16 h light/8 h dark).

Genome sequencing and assembly of A. arenosa

We used 0.4 g of A. arenosa leaf material from the accession SNO (Strecno, Slovakia, 49.17417N, 18.86167E). The frozen leaf material was ground in liquid nitrogen, and then 10 mL of CTAB DNA extraction buffer was added (Tris-HCl 100 mM (Fisher Scientific), CTAB 2% (w/v) (Sigma), NaCl 1.4 M (Sigma), EDTA 20 mM (Sigma-Aldrich)) and 20 μL of Proteinase K at 20 mg/mL (Qiagen). The mixture was incubated at 55 °C for 1 h then cooled on ice. Chloroform (0.5× volume; Fisher Scientific) was added and inverted to mix. Material was centrifuged at 1008×g for 30 min, and the upper phase recovered. Then 1× volume of phenol:chloroform:isoamyl alcohol (25:24:1; Sigma) was added and the mixture centrifuged for 30 min at 1008×g. The upper phase was recovered and 10% volume NaOAc at 3 M (Sigma) was added along with 2.5× volume of ice cold 100% ethanol (Sigma-Aldrich). The tubes were inverted to mix and incubated on ice for 30 min before centrifugation for 30 min at 1008×g (4 °C). The pellets were washed in 4 mL of ice cold 70% ethanol (Sigma-Aldrich). Tubes were spun for 10 min at 1008×g at 4 °C and the 70% ethanol wash repeated twice more. The pellets were air dried and resuspended in 300 μL nuclease-free water (Qiagen) with 3 μL RNase A at 4 mg/mL (Qiagen). DNA concentrations were determined on a Qubit Fluorometer 2.0 (Invitrogen) using the Qubit dsDNA HS Assay kit. Fragment sizes were assessed using a Q-card (OpGen Argus) and the Genomic DNA TapeStation assay (Agilent).

The DNA was diluted to 0.5 ng/μL with EB (Qiagen) and checked with a Qubit Fluorometer 2.0 (Invitrogen) using the Qubit dsDNA HS Assay kit. The Chromium User Guide was followed as per the manufacturer’s instructions (10× Genomics, CG00043, Rev A). The final library was quantified using qPCR (KAPA Library Quant kit, Illumina; ABI Prism qPCR Mix, Kapa Biosystems). The sizes of the library fragments were monitored using a Bioanalyzer (High Sensitivity DNA Reagents, Agilent). Samples were pooled based on the molarities calculated using the two QC measurements.

Sample SNO was sequenced on an Illumina HiSeq2500, using Rapid Run V2 mode150 PE, generating 82.10 Mb reads. These were assembled using Supernova 2.0.0²⁷, giving a raw coverage of 57.91× and effective coverage of 45.30×. The molecule length was 26.58 Kb.

The assembly scaffold N50 was 2.19 Mb and overall size (counting only scaffolds longer than 10 Kb) was 127.02 Mb. Gene content was estimated using BUSCO v2²⁸ by searching database embryophyte_odb9. This showed that the gene space of the A. arenosa assembly was nearly complete, with 97.5% of the plant-specific BUSCOs present and 1.4% missing. Of those present, 4.7% were duplicate copies (Supplementary Table 5).

RNA and cDNA preparation

A. lyrata plants postflowering were used for total RNA isolation. Roots were recovered from soil, washed thoroughly with sterile water and dried with a clean tissue. Around 10 mg of ground roots were extracted with 600 μL of TRIzol reagent (ThermoFisher, Carlsbad, CA, USA). Chloroform (120 μL) was added to remove proteins and other impurities. After centrifugation (13,000×g; 10 min), an aliquot (400 µL) of supernatant was recovered and an equal volume of isopropanol added. Following mixing the pellet was recovered by centrifugation (10,000×g; 10 min) and washed with 70% ethanol. After centrifugation (10,000×g; 5 min) the pellet was dried at room temperature for 10 min. Sterilized water (30 μL) was then added to dissolve the total RNA. Reverse transcription reactions were performed using ≤2 μg of total RNA, random primers and a reverse transcription kit (Agilent, Santa Clara, CA, USA). The cDNA library was then used for amplification of the coding sequence of AlTHAA2 (AL8G20050). A pair of gene-specific primers attached with GATEWAY att sites were used (Fwd: GGGGACAAGTTTGTACAAAAAAGCAGGCTTCATGGACACCATGAAGGTTGAAATC; Rev: GGGGACCACTTTGTACAAGAAAGCTGGGTTTTAGATCAATACACTTGGATTTG). cDNAs were cloned into the GATEWAY entry vector pDONR207 (Invitrogen).

Transient plant expression

The pEAQ-Dest-1 expression vector was used for transient plant expressing experiments²⁹. Constructs for transient expression of other A. lyrata cluster genes and A. thaliana thalianol cluster genes were adopted from previous work^10,11. All constructs were verified by sequencing and introduced into A. tumefaciens strain LBA4404. Agro-infiltration of N. benthamiana was carried out as described previously³⁰. N. benthamiana leaves were harvested 6 days after infiltration.

LC-MS-IT-TOF analysis

Ten mg of freeze-dried N. benthamiana leaves were homogenized with a tungsten bead (3 mm, Qiagen) and extracted with 200 µL of pure methanol (sonication 30 min). The extracts were centrifuged at 16,000×g for 1 min. Aliquot of (170 µL) supernatant was recovered and filtered through 0.45 µm PTFE spin columns before injection onto LC. For root metabolites profiling, ten-days roots grown on vertical ¼ MS plates were harvested and flash frozen with liquid nitrogen. Samples were then freeze-dried. For comparison, 10 mg of sample were weighed out and extracted as described for tobacco samples. LC-MS analysis was carried out on a Prominence/Nexera UPLC system attached to an Ion-trap Tof mass spectrometer (Shimadzu). Separation was performed on a 100 × 2.1 mm Kinetex reverse column (1.7 µm C18 100 Å), using the following gradient of acetonitrile (solvent B) versus 0.1% formic acid in miliQ water (solvent A): 0.5 min, 2% B; 5 min, 10% B; 17 min, 30% B; 33 min, 90% B; 35.8 min, 100% B; 43 min, 100% B; 45 min 2% B. The flow rate was 0.5 mL/min and the run temperature was set at 40 °C. Injection volume was 1 µL. The MS data were acquired in both positive and negative modes. Spectra were collected from m/z 200–2000 with automatic sensitivity control set to a target of 70% optimal base peak intensity. The instrument was calibrated immediately before analysis using sodium trifluoroacetate cluster ions, according to the manufacturer’s instructions. The standards (T10 and T17) were generated in previous work¹⁰.

In silico analysis of publicly available transcriptome data

RNA-seq data (SRA files) for five tissues (root, leaf, stem, flower, and silique) from A. thaliana (Col-0) and A. lyrata were downloaded from the National Center for Biotechnology Information (NCBI) BioProject PRJNA336053³¹. RNA sequence data for roots and shoots for A. thaliana accessions Col-0, Bay-0, Cvi-0, Ita-0, Kas-1, LP2-6, and Ws-2 were downloaded from NCBI BioProject PRJEB14092³². The sequencing quality was checked by FastQC v0.10.1³³. Trimmomatic v0.39³⁴ was used to trim adapters and overrepresented sequences. Sequence data were then aligned to reference genomes (PHYTOZOME v.12.0; https://phytozome.jgi.doe.gov/pz/portal.html) by HISAT2 v2.1.0³⁵. Abundance was called using StringTie v1.3.5³⁶. The transcripts per million (TPM) values were then extracted as expression levels for genes of interest.

Genome mining and plantiSMASH analysis

The Arabidopsis genome sequences (A. thaliana accession Col-0, A. lyrata, A. halleri) were retrieved from PHYTOZOME v.12.0. The genome of A. arenosa was generated de novo as part of this work. OSC genes were identified using HMMER3³⁷ with HMMER profiles (pHMMs) PF13243 and PF13249 downloaded from the PFAM library³⁸. The cut_tc (trusted cut-off) option was used. Pseudogenes for OSCs (predicted protein sequence < 600 amino acids) were discarded from the analysis. The HMMER output was aligned using MUSCLE³⁹. The alignment was used to infer the maximum likelihood tree by RaxML⁴⁰. The OSC tree topology was consistent with a previous study that included OSCs from the wider Brassicaceae¹¹. For plantiSMASH analysis¹⁴, the genome files (FASTA format) and annotation files (GFF3 format) were converted to GenBank format as input files.

Long read assemblies for the thalianol cluster genomic region

Long read genome sequences for the thalianol cluster region from 22 A. thaliana accessions were retrieved from the 1001 Genomes Plus project. Seven used in this study (Nd-1, An-1, Cvi-0, Eri-1, Kyo, Ler-0, and Sha) were previously published^41,42.

Pair-wise dN/dS analysis

The pairwise dN/dS (ω) analysis averages the dN/dS ratio across all sites. To determine the estimates of ω, pairwise CDS (coding sequence) alignments of A. thaliana accession Col-0 and A. lyrata for the thalianol cluster genes and phytosterol pathway genes⁴³ were used as an input for the codeml program from PAML 4.9⁴⁴. To determine if the dN/dS ratio was significantly different from 1, the program was executed twice with different control files. To obtain the maximum likelihood estimates of ω, the control file was set as: runmode = −2, model = 0, NSsites = 0, Fix_omega = 0. To obtain the likelihood with ω = 1, the control file was set as: runmode = −2, model = 0, NSsites = 0, Fix_omega = 1, Omega =1. The log likelihood values from the two executions were subtracted. The negative of twice of this value was used for likelihood ratio test (LR test). The p value was determined by comparison to χ² with one degree of freedom. The estimated ω values with p value < 0.001 were plotted.

Codon-based positive selection analysis

The protein-coding DNA sequences were aligned with TranslatorX⁴⁵. In order to minimize negative impacts due to sequence size, gene sequences less than (for THAS min = 2200 bp; for THAO min = 1450 bp; for THAH min = 1400 bp; for THAA min = 1200 bp) were not included for the analysis. The sequences were aligned by MUSCLE³⁹. Poorly aligned regions in the alignment were removed by Gblocks⁴⁶. The Gblocks filtered nucleotide sequence were further translated to protein sequence and used to infer phylogenetic tree. The final Gblocks nucleotide sequence alignment and the protein phylogenetic tree were used for various evolutionary tests using HyPhy⁴⁷. The input file for HyPhy analysis is shown in Supplementary Data 1. BUSTED analysis (similar to “branch-site” model in PAML) was used to infer whether a gene has experienced positive selection at at least one site on at least one branch⁴⁸. MEME analysis (similar to “site” model in PAML) was used to detect individual sites evolving under positive selection in a proportion of branches (labeled as {foreground} in the input file)⁴⁹. For both analyses, “universal genetic code” were selected and p < 0.05 was set for significance.

Tests for neutral evolution

McDonald and Kreitman analysis (MK test) is less sensitive to demographic factors such as migration, making it useful for detecting selection¹⁸. It captures infrequent adaptive mutations which fix fast relatively to regular neutral mutations, therefore elevating the divergence ratio (between species) over the polymorphism ratio (within species), a signature of positive selection. To examine departures from the neutral theory of molecular evolution, MK test was performed on a publicly available web interface (iMKT) (https://imkt.uab.cat/)⁵⁰. The input sequences for polymorphism were downloaded from the 1001 Arabidopsis genome browser (http://signal.salk.edu/atg1001/3.0/gebrowser.php). The corresponding outgroup gene sequences from A. lyrata were taken from either¹¹ for the thalianol core cluster genes or retrieved via reciprocal blast of the Col-0 reference gene on the Phytozome website (https://phytozome.jgi.doe.gov/pz/portal.html) for THAA2. Several versions of the MK test were performed to compute the polymorphism data from 1001 accession genomes and the outputs were compared with divergence data between A. thaliana and A. lyrata. The standard MK test can detect positive selection if adaptive mutations rapidly reach fixation and thus contribute relative more to divergence than to polymorphism when compared to neutral mutations. In this case, a positive alpha (α) indicates the proportion of nonsynonymous substitutions that have been fixed by positive selection. To avoid bias from the segregation of slightly deleterious nonsynonymous substitutions, we also applied Fay, Wycoff, and Wu (FWW) correction⁵¹ on the standard MKT by considering only those polymorphic sites with a frequency above the cutoff of 0.05. Sometimes when relaxed purifying selection reaches neutral, it can cause an increased level of polymorphism relative to divergence, therefore adaptive estimation (α) will be underestimated. By taking account of bias from both segregation of weakly deleterious variants and relaxed purifying selection, we further applied Extended MKT⁵² to the thalianol pathway genes.

Construction of a phylogenetic tree for A. thaliana accessions using SNP data

The 1001_SNP_MATRIX was downloaded from the 1001 genomes data center (https://1001genomes.org/data/GMI-MPI/releases/v3.1/SNP_matrix_imputed_hdf5/).

SNPs with a minor allele frequency (MAF) of 0.05 or greater (28,775 variants) were then computed by SNPhylo⁵³ to generate a maximum likelihood tree.

Linkage disequilibrium analysis

The 1001_SNP_MATRIX was used to simulate the genome-wide linkage disequilibrium (squared allele frequency correlation, r²) using TASSEL 5⁵⁴. Only the top 50 r² values are reported. The p-values for biallelic sites were calculated using Fisher’s exact test in Tassel. r² = 0.1 was used as the threshold. r² values from biallelic sites that are both located within the defined OSC gene-linked genomic regions (50 kb before and after the coding region of an OSC gene) were extracted. There are 13 OSC loci in the A. thaliana Col-0 genome⁹. Five OSC genes were located in clustered OSC gene-centric genomic regions: THAS (At5g48010)⁹, MRN (At5g42600)⁵⁵, PEN3 (At5g36150)⁵⁶, and ABDS (At4g15340, At4g15370)¹⁵. The nonclustered OSC genes¹¹ were: LAS1 (At3g45130), PEN6 (At1g78500), At1g66960, At1g78950-970 (At1g78950, At1g78955, At1g78960, At1g78970), and CAS1 (At2g07050). The tandem duplicates in the At1g78950-970 GN were considered as a single locus, so the 50 kb on the left was taken before At1g78950 and the 50 kb on the right was taken after At1g78970 gene.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Data supporting the findings of this work are available within the paper and its Supplementary Information files. The datasets and plant materials generated and analyzed during the current study are available from the corresponding author upon request. The sequence data for A. arenosa have been deposited in the European Nucleotide Archive with the project ID PRJEB37828. The sequences for the 22 thalianol cluster de novo assemblies were deposited to NCBI. The accession numbers are listed in Supplementary Table 6. MK test was performed on a publicly available web interface (https://imkt.uab.cat/). The input sequences for polymorphism were downloaded from the 1001 Arabidopsis genome browser (http://signal.salk.edu/atg1001/3.0/gebrowser.php). Source data are provided with this paper.

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Acknowledgements

This work was supported by the joint Engineering and Physical Sciences Research Council/BBSRC-funded OpenPlant Synthetic Biology Research Centre grant BB/L014130/1 (Z.L. and A.O.) and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program [grant number ERC-StG 679056 HOTSPOT] (L.Y.). A.O.’s program is supported by the BBSRC Institute Strategic Programme Grant “Molecules from Nature-Products and Pathways” (BBS/E/J/000PR9790) and the John Innes Foundation. We thank the 1001 Genomes Plus project, especially Haim Ashkenazy, Almudena Mollá-Morales, Magnus Nordborg, Fernando Rabanal, and Detlef Weigel, for access to newly assembled A. thaliana accession genomes, and for discussion and critical reading of the manuscript. The 1001 Genomes Plus project is funded by ERA-CAPS through BBSRC, DFG, and FWF. We also thank P. Brett for assistance in metabolite analysis, A.C. Huang for providing the triterpene standards T10 and T17, K. Bomblies for providing Arabidopsis arenosa leaf material, B. Steuernagel for confirming the deletion of thalianol cluster in Lillö-1 using short reads, and B. Castel, C. Owen, P. Monnahan, and G. Polturak for their critical reading and useful comments. This research was supported in part by the NBI Computing infrastructure for Science (CiS) group.

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Department of Metabolic Biology, John Innes Centre, Norwich Research Park, Colney Lane, Norwich, NR4 7UH, UK
Zhenhua Liu, Lionel Hill, James Reed & Anne Osbourn
Computational and Systems Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK
Jitender Cheema, Marielle Vigouroux & Pirita Paajanen
School of Life Sciences and Future Food Beacon, University of Nottingham, Nottingham, NG7 2RD, UK
Levi Yant

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Zhenhua Liu
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Contributions

Z.L. and A.O. conceived and designed the project. A.O. supervised the project. Z.L. carried out genome mining, gene cluster polymorphism analysis, gene cloning, transcriptome analysis and metabolite analysis. J.C. constructed phylogenetic tree using SNP data. J.C. and Z.L. performed linkage disequilibrium analysis. Z.L. and M.V. performed phylogenetic and selection analysis. L.H. and J.R. contributed to metabolite analysis. L.Y. and P.P. performed genome sequencing and annotation of A. arenosa. Z.L. and A.O. wrote the manuscript with contributions from the other authors.

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Correspondence to Anne Osbourn.

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Liu, Z., Cheema, J., Vigouroux, M. et al. Formation and diversification of a paradigm biosynthetic gene cluster in plants. Nat Commun 11, 5354 (2020). https://doi.org/10.1038/s41467-020-19153-6

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Received: 25 June 2020
Accepted: 29 September 2020
Published: 23 October 2020
DOI: https://doi.org/10.1038/s41467-020-19153-6

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