mixed sample were used as quality control during measurements. The polar phases and the aliquots of the sample mix were dried in a vacuum centrifuge for 5 h at 30 °C and 0.1 mbar.

The dried extracts were derivatized as described earlier (Doerfler et al. 2013) by dissolving the metabolite pellet carefully in 20 µL of 40 mg of methoxyamine hydrochloride (Sigma Aldrich) in 1 mL pyridine (anhydrous > 99.8%, Sigma Aldrich). After incubation at 30 °C and 700 rpm for 1.5 h on a thermoshaker, 80 µL of N-methyl-N-trimethylsilyl-trifluoroacetamid (Macherey-Nagel) was added. The samples were incubated for 30 min at 37 °C and 750 rpm and consecutively centrifuged for 4 min at room temperature and 14,000×g.

Metabolite analysis was performed on Agilent 7890B gas chromatograph equipped with a LECO Pegasus BT-TOF mass spectrometer (LECO Corporation). Derivatized metabolites were injected through a Split/Splitless inlet equipped with an ultra-inert single tapered glass liner with deactivated glass wool (5910-2293, Agilent Technologies), a split ratio of 1:25 was used and the temperature was set to 230 °C. Components were separated with helium as carrier gas on a Restek Rxi-5Sil MS column (length: 30 m, diameter: 0.25 mm, thickness of film: 0.25 µm). The initial oven temperature was set to 70 °C held for 1 min and ramped with a rate of 9 °C per minute until reaching 340 °C held for 10 min. Collection of spectra started after an acquisition delay of 280 s with a detector voltage of 1692.5 V, a rate of 15 spectra per second, and a mass range of 50 to 500 m/z. Retention indices were calculated based on the retention times of the alkane mixture C₁₀-C₄₀ run within each of the 2 batches. Samples were measured in randomized order and randomly distributed across the batches. Within each batch, a mixture of standard metabolites was measured for MSI level I identification of metabolites. Deconvolution, annotation, and processing of chromatograms were performed according to Zhang et al. (2023) using ChromaTOF (Version 5.55.29.0.1187, LECO Cooperation) and MS-DIAL, version 4.7 (Tsugawa et al. 2015). Areas of derivatization products of single metabolites were summed and normalized by the main targeted ion content of each sample.

## RNA extraction and sequencing

Using the same sampled leaf material as for targeted metabolite analyses, total RNA was extracted for each sample and time point in randomized batches of 4 to 6 samples, using the QIAGEN RNeasy Mini Kit in an RNAse-free laboratory. Samples were digested using the kit's RLT buffer with 1 µL/mL beta-mercaptoethanol. Elution was done in 2 steps. The purity and concentration of the extractions were measured using Nanodrop, and RIN and fragmentation profiles were obtained with a Fragment Analyzer system. RNA libraries were prepared by the Vienna Biocenter Core Facilities (VBCF) using a NEBNext stranded mRNA kit before sequencing 150-bp paired-end reads on 1 lane of Illumina NovaSeq S4.

## RNA-seq data processing

The raw RNA-seq data were evaluated with FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and MultiQC (Ewels et al. 2016), then quality-trimmed using AdapterRemoval v.2.3.1 (Schubert et al. 2016) with settings --trimms --trimqualities --minquality 20 --trimwindows 12 --minlength 36. The trimmed data were then aligned to both the *T. fasciculata* and *T. leiboldiana* genomes using STAR v.2.7.9 (Dobin et al. 2013) using GFF files to specify exonic regions. Because mapping bias was lowest when mapping to *T. fasciculata* (see Supplementary SI Note S8, Supplementary Figs. S17 and S18), our main analyses have been performed on the reads mapped to this genome. However, the alignments to *T. leiboldiana*

were used for verification or expansion of the main analysis (Supplementary SI Note S9).

## Differential gene expression analysis

We quantified read counts per exon using FeatureCounts from the Subread package v.2.0.3. (Liao et al. 2014) for paired-end and reversely stranded reads (-p -s 2). The counts were then summed up across exons per gene to obtain gene-level counts. The composition of the count data was investigated with PCA in EdgeR (Robinson et al. 2009). Then, counts were normalized using the TMM method in EdgeR, and every gene with a mean cpm < 1 was removed. We ran a differential gene expression (DE) analysis in maSigPro (Conesa et al. 2006), which detects genes with differential diurnal expression profiles between species using a regression approach. *T. leiboldiana* was used as the baseline in this analysis. Significant DE genes were then clustered using the hclust algorithm into modules, with the number of modules being determined with the K-means algorithm. Expression curves were plotted by taking the average expression in TPM (transcripts per million) across all replicates per species at each time. We calculated TPM by dividing the raw read count by the exonic length of the gene (RPK), which we then divided by the total sum of RPK values. Expression curves for entire clusters (Supplementary Fig. S11) were plotted by median-centering the log(TPM) of each gene and time point against the median of all genes at each time point, while expression curves for individual genes or gene families (Fig. 5C, Supplementary Figs. S10 and S14) report average TPM with standard deviation.

GO-term enrichments were performed for each cluster using the R package TopGO (Alexa and Rahnenführer 2006). Separately, known candidate genes underlying CAM and starch metabolism (see Materials and methods, Family size correction and latter section) were searched among differentially expressed genes.

## Annotation and enrichment of circadian clock-related motifs in promoter sequences

We counted the occurrences of 4 known circadian clock-related motifs in the 2-kb upstream regions of DE genes: the Morning Element (MOE: CCACAC) (Michael et al. 2008), the Evening Element (EE: AAAATATC) (Hudson and Quail 2003), the CCA1-binding site (CBS: AAAAATCT) (Franco-Zorrilla et al. 2014), and the G-box element (G-box: CACGTG) (Michael and McClung 2002). The same was done for all other curated genes that were not DE, which we considered as background sequences. We calculated the per-kb frequency of each motif based on the counts and total promoter length (2,000 × number of genes) for both sets of genes. The percentage of change in frequency was calculated between both sets for each motif. Significance of frequency changes of circadian motifs in promoter regions of DE genes compared to non-DE genes was calculated with the Mann–Whitney U test. We randomly subsampled the list of non-DE genes to 5,000 observations to ensure accurate P-values.

For a small set of genes known to underlie key CAM enzymes, we counted the occurrence of each motif in both the 2-kb upstream region of every homolog of that gene (including non-DE paralogs) in both species, to annotate and describe circadian motifs in promoter sequences in detail. The detection of motifs was extended to 3-kb regions to allow for a more distant presence of motifs.

## Intersecting findings of gene family evolution, TE insertion, and differential gene expression

### Spatial distribution of DE genes

The previously calculated per 1-kb window counts of robust genes were used to obtain the per-window proportion of DE genes. This
mixed sample were used as quality control during measurements. The polar phases and the aliquots of the sample mix were dried in a vacuum centrifuge for 5 h at 30 °C and 0.1 mbar.

The dried extracts were derivatized as described earlier (Doerfler et al. 2013) by dissolving the metabolite pellet carefully in 20 µL of 40 mg of methoxyamine hydrochloride (Sigma Aldrich) in 1 mL pyridine (anhydrous > 99.8%, Sigma Aldrich). After incubation at 30 °C and 700 rpm for 1.5 h on a thermoshaker, 80 µL of N-methyl-N-trimethylsilyl-trifluoroacetamid (Macherey-Nagel) was added. The samples were incubated for 30 min at 37 °C and 750 rpm and consecutively centrifuged for 4 min at room temperature and 14,000×g.

Metabolite analysis was performed on Agilent 7890B gas chromatograph equipped with a LECO Pegasus BT-TOF mass spectrometer (LECO Corporation). Derivatized metabolites were injected through a Split/Splitless inlet equipped with an ultra-inert single tapered glass liner with deactivated glass wool (5910-2293, Agilent Technologies), a split ratio of 1:25 was used and the temperature was set to 230 °C. Components were separated with helium as carrier gas on a Restek Rxi-5Sil MS column (length: 30 m, diameter: 0.25 mm, thickness of film: 0.25 µm). The initial oven temperature was set to 70 °C held for 1 min and ramped with a rate of 9 °C per minute until reaching 340 °C held for 10 min. Collection of spectra started after an acquisition delay of 280 s with a detector voltage of 1692.5 V, a rate of 15 spectra per second, and a mass range of 50 to 500 m/z. Retention indices were calculated based on the retention times of the alkane mixture C₁₀-C₄₀ run within each of the 2 batches. Samples were measured in randomized order and randomly distributed across the batches. Within each batch, a mixture of standard metabolites was measured for MSI level I identification of metabolites. Deconvolution, annotation, and processing of chromatograms were performed according to Zhang et al. (2023) using ChromaTOF (Version 5.55.29.0.1187, LECO Cooperation) and MS-DIAL, version 4.7 (Tsugawa et al. 2015). Areas of derivatization products of single metabolites were summed and normalized by the main targeted ion content of each sample.

## RNA extraction and sequencing

Using the same sampled leaf material as for targeted metabolite analyses, total RNA was extracted for each sample and time point in randomized batches of 4 to 6 samples, using the QIAGEN RNeasy Mini Kit in an RNAse-free laboratory. Samples were digested using the kit's RLT buffer with 1 µL/mL beta-mercaptoethanol. Elution was done in 2 steps. The purity and concentration of the extractions were measured using Nanodrop, and RIN and fragmentation profiles were obtained with a Fragment Analyzer system. RNA libraries were prepared by the Vienna Biocenter Core Facilities (VBCF) using a NEBNext stranded mRNA kit before sequencing 150-bp paired-end reads on 1 lane of Illumina NovaSeq S4.

## RNA-seq data processing

The raw RNA-seq data were evaluated with FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and MultiQC (Ewels et al. 2016), then quality-trimmed using AdapterRemoval v.2.3.1 (Schubert et al. 2016) with settings --trimms --trimqualities --minquality 20 --trimwindows 12 --minlength 36. The trimmed data were then aligned to both the *T. fasciculata* and *T. leiboldiana* genomes using STAR v.2.7.9 (Dobin et al. 2013) using GFF files to specify exonic regions. Because mapping bias was lowest when mapping to *T. fasciculata* (see Supplementary SI Note S8, Supplementary Figs. S17 and S18), our main analyses have been performed on the reads mapped to this genome. However, the alignments to *T. leiboldiana*

were used for verification or expansion of the main analysis (Supplementary SI Note S9).

## Differential gene expression analysis

We quantified read counts per exon using FeatureCounts from the Subread package v.2.0.3. (Liao et al. 2014) for paired-end and reversely stranded reads (-p -s 2). The counts were then summed up across exons per gene to obtain gene-level counts. The composition of the count data was investigated with PCA in EdgeR (Robinson et al. 2009). Then, counts were normalized using the TMM method in EdgeR, and every gene with a mean cpm < 1 was removed. We ran a differential gene expression (DE) analysis in maSigPro (Conesa et al. 2006), which detects genes with differential diurnal expression profiles between species using a regression approach. *T. leiboldiana* was used as the baseline in this analysis. Significant DE genes were then clustered using the hclust algorithm into modules, with the number of modules being determined with the K-means algorithm. Expression curves were plotted by taking the average expression in TPM (transcripts per million) across all replicates per species at each time. We calculated TPM by dividing the raw read count by the exonic length of the gene (RPK), which we then divided by the total sum of RPK values. Expression curves for entire clusters (Supplementary Fig. S11) were plotted by median-centering the log(TPM) of each gene and time point against the median of all genes at each time point, while expression curves for individual genes or gene families (Fig. 5C, Supplementary Figs. S10 and S14) report average TPM with standard deviation.

GO-term enrichments were performed for each cluster using the R package TopGO (Alexa and Rahnenführer 2006). Separately, known candidate genes underlying CAM and starch metabolism (see Materials and methods, Family size correction and latter section) were searched among differentially expressed genes.

## Annotation and enrichment of circadian clock-related motifs in promoter sequences

We counted the occurrences of 4 known circadian clock-related motifs in the 2-kb upstream regions of DE genes: the Morning Element (MOE: CCACAC) (Michael et al. 2008), the Evening Element (EE: AAAATATC) (Hudson and Quail 2003), the CCA1-binding site (CBS: AAAAATCT) (Franco-Zorrilla et al. 2014), and the G-box element (G-box: CACGTG) (Michael and McClung 2002). The same was done for all other curated genes that were not DE, which we considered as background sequences. We calculated the per-kb frequency of each motif based on the counts and total promoter length (2,000 × number of genes) for both sets of genes. The percentage of change in frequency was calculated between both sets for each motif. Significance of frequency changes of circadian motifs in promoter regions of DE genes compared to non-DE genes was calculated with the Mann-Whitney U test. We randomly subsampled the list of non-DE genes to 5,000 observations to ensure accurate P-values.

For a small set of genes known to underlie key CAM enzymes, we counted the occurrence of each motif in both the 2-kb upstream region of every homolog of that gene (including non-DE paralogs) in both species, to annotate and describe circadian motifs in promoter sequences in detail. The detection of motifs was extended to 3-kb regions to allow for a more distant presence of motifs.

## Intersecting findings of gene family evolution, TE insertion, and differential gene expression

### Spatial distribution of DE genes

The previously calculated per 1-kb window counts of robust genes were used to obtain the per-window proportion of DE genes. This
was then visualized with circlize as described above. Correlations of total DE gene count per scaffold and scaffold size were calculated with Kendall's rank correlation test after testing for normality with the Shapiro-Wilk test.

## Gene family evolution and differential gene expression

Orthogroups were split based on relative family size in *T. fasciculata* (*F*) versus *T. leiboldiana* (*L*) in the following categories: single-copy orthogroups (*F* = 1:1:1), orthogroups with family size larger in *T. fasciculata* (*F* > *L*), orthogroups with family size smaller in *T. fasciculata* (*F* < *L*), and orthogroups with equal family sizes that are larger than 1 (*F* = *L*). Orthogroups unique to 1 species (*F*:0 or 0:*L*) were not considered in this analysis. We counted the number of orthogroups belonging to each category for the non-DE orthogroup set, for the subset of orthogroups containing DE genes (DE orthogroups), and for the subset of orthogroups containing DE genes that have been previously described as CAM-related (CAM-DE orthogroups). We then tested whether counts in each orthogroup category were enriched in DE orthogroups and CAM-DE orthogroups compared to non-DE orthogroups. For comparisons of non-DE orthogroups versus DE orthogroups, we used the chi-square test of independence in *R*. For the comparison of CAM-DE orthogroups versus non-CAM DE orthogroups, we used Fisher's exact test due to small sample sizes with 2×2 contingency tables of the count of orthogroups in each orthogroup category versus all other categories, in DE orthogroups versus non-DE genes. To study the effect of the reference genome used on our findings on gene family evolution in DE genes, we performed the same analysis on read counts obtained from mapping to *T. leiboldiana* (Supplementary SI Note S9).

## Transcription factor families and gene family evolution

We identified transcription factor (TF) families by searching for genes with InterPro domains characteristic of the largest transcription factor families annotated during functional annotation with Blast2Go, which performs an InterProScan step (Paysan-Lafosse et al. 2023). The specific domains and their corresponding TF families used to identify genes are listed in Supplementary Table S12. For verification, a more conservative set of TF families was identified by finding the homologs of known *A. comosus* TFs listed on the Plant TF Database (https://planttfdb.gao-lab.org/). The majority of TFs identified using InterPro domains overlapped with homologs of known *A. comosus* TFs (81%). We then applied the same methodology as for DE orthogroups (section 5.12.2) to assess if gene families that have undergone recent evolution in gene copy number were overrepresented in the set of TF gene families. This analysis was performed on the 2 lists separately for verification and on the merger of both lists.

Using the full list of identified TF genes, we identified DE genes in both species that belonged to a TF gene family. We then obtained gene family evolution statistics as described in section 5.12.2, performing Fischer's Exact Test to compare the proportion of multicopy gene families in DE TF gene orthogroups with non-DE gene families.

## TE insertions and differential gene expression

Intronic TE insertions were obtained using *bedtools intersect* on the GFF files of the TE and gene annotations of both species. We used the full transcript length of a gene (feature = "mRNA" in GFF file) for this analysis, and only applied "known" TE annotations and the set of curated genes. This resulted in a data set reporting the

number of TE insertions per gene. We also obtained TE counts for genic regions including the 3-kb upstream region, by using *bedtools slop* with options -l 3000 -r 0 -s. For analyses on specific TE classes, we calculated TE insertion counts for the following 4 TE categories: LTR-Copia, LTR-Gypsy, Helitron, and DNA transposon.

We then performed 2 tests on the resulting TE counts per gene: (i) whether the proportion of genes with 1 or more TE insertions is elevated in DE genes compared to the full gene set (chi-square test), and (ii) whether the rate of TE insertions per gene measured, as the total count of intersections for each gene annotation with a TE annotation, is elevated in DE genes compared to non-DE genes (Mann-Whitney U test).

The same test was also applied to a restricted set of DE genes previously described as CAM-related, or involved in starch metabolism and gluconeogenesis. Then, genes of interest with a TE insertion rate higher than twice the genome-wide average were selected and the difference in number of TE insertions between orthologs of *T. leiboldiana* and *T. fasciculata* was taken in case of a one-to-one relationship.

## Accession numbers

The genome assemblies and raw data used in this study are available at NCBI-SRA under BioProject PRJNA927306. Specifically, the *T. fasciculata* genome assembly TFas_v1 can be downloaded here: https://ncbi.nlm.nih.gov/datasets/genome/GCA_029168755.2/. The *T. leiboldiana* genome assembly TLei_v1 can be found at: https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_029204045.2/. The annotation of both genomes is available on the GitHub repository at: https://github.com/cgrootrego/Tillandsia_Genomes, together with the list of orthogroups, counts table used for RNA-seq analyses, full GO-term enrichment results, and all scripts written for this manuscript. The *A. comosus* sequences used in this study stem from BioProject PRJNA371634 (F153): https://www.ncbi.nlm.nih.gov/bioproject/PRJNA371634/ and BioProject PRJNA747096 (CB5): https://www.ncbi.nlm.nih.gov/bioproject/PRJNA747096. Information on all accessions used in this study can be found in Supplementary Table S1.

## Acknowledgments

In memory of Christian LEXER—we will treasure your enthusiasm, guidance, and memory always. We thank Joachim Hermisson, Magnus Nordborg, Andrew Clark, Nicholas Barton, Virginie Courtier-Orgogozo, John Parsch, Andreas Futschik, Rui Borges, Aglaia Szukala, Florian Schwarz, Marta Pelizzolla, and Ahmad Muhammad for insightful discussions, advice, and feedback. We thank Gert Bachmann and Eline de Vos for help with the setup of the RNA-seq experiment; Peter Bak, Andreas Franzke, Nils Köster, and Helmut and Lieselotte Hromadnik for their generous donations of Tillandsia accessions; and Thelma Barbarà for assistance during RNA extractions. We also thank Barbara Knickmann, Viktor Vagovics, and Manfred Speckmaier for the care of Tillandsia plants at the Botanical Garden of the University of Vienna. Lastly, we thank Alisa Tscherko for assistance with chromosome photography. Computational resources were provided by the Life Science Computer Cluster (LiSC) of the University of Vienna and the Vienna Scientific Cluster. Lastly, we would like to thank the anonymous reviewers for their constructive comments that thoroughly improved this manuscript.
was then visualized with circlize as described above. Correlations
of total DE gene count per scaffold and scaffold size were calcu-
lated with Kendall's rank correlation test after testing for normal-
ity with the Shapiro-Wilk test.

Gene family evolution and differential gene expression

Orthogroups were split based on relative family size in *T. fasciculata* (*F* versus *T. leiboldiana* (*L*)) in the following categories: single-copy orthogroups (*F* = 1:L = 1), orthogroups with family size larger in *T. fasciculata* (*F* > *L*), orthogroups with family size smaller in *T. fasciculata* (*F* < *L*), and orthogroups with equal family sizes that are larger than 1 (*F* = *L*). Orthogroups unique to 1 species (*F*:0 or 0:*L*) were not considered in this analysis. We counted the number of orthogroups belonging to each category for the non-DE orthogroup set, for the subset of orthogroups containing DE genes (DE orthogroups), and for the subset of orthogroups containing DE genes that have been previously described as CAM-related (CAM-DE orthogroups). We then tested whether counts in each orthogroup category were enriched in DE orthogroups and CAM-DE orthogroups compared to non-DE orthogroups. For comparisons of non-DE orthogroups versus DE orthogroups, we used the chi-square test of independence in *R*. For the comparison of CAM-DE orthogroups versus non-CAM DE orthogroups, we used Fisher's exact test due to small sample sizes with 2 × 2 contingency tables of the count of orthogroups in each orthogroup category versus all other categories, in DE orthogroups versus non-DE genes. To study the effect of the reference genome used on our findings on gene family evolution in DE genes, we performed the same analysis on read counts obtained from mapping to *T. leiboldiana* (*Supplementary SI Note S9*).

Transcription factor families and gene family evolution

We identified transcription factor (TF) families by searching for
genes with InterPro domains characteristic of the largest transcription
factor families annotated during functional annotation with
Blast2Go, which performs an InterProScan step (Paysan-Lafosse
et al. 2023). The specific domains and their corresponding TF families
used to identify genes are listed in Supplementary Table S12. For ver-
ification, a more conservative set of TF families was identified by find-
ing the homologs of known A. comosus TFs listed on the Plant TF
Database (https://planttfdb.gao-lab.org/). The majority of TFs identi-
fied using InterPro domains overlapped with homologs of known A.
comosus TFs (81%). We then applied the same methodology as for
DE orthogroups (section 5.12.2) to assess if gene families that have
undergone recent evolution in gene copy number were overrepre-
sented in the set of TF gene families. This analysis was performed
on the 2 lists separately for verification and on the merger of both lists.

Using the full list of identified TF genes, we identified DE genes
in both species that belonged to a TF gene family. We then ob-
tained gene family evolution statistics as described in section
5.12.2, performing Fischer's Exact Test to compare the proportion
of multicopy gene families in DE TF gene orthogroups with non-DE
gene families.

TE insertions and differential gene expression

Intronic TE insertions were obtained using *bedtools intersect* on the
GFF files of the TE and gene annotations of both species. We used
the full transcript length of a gene (feature = "mRNA" in GFF file)
for this analysis, and only applied "known" TE annotations and
the set of curated genes. This resulted in a data set reporting the

number of TE insertions per gene. We also obtained TE counts
for genic regions including the 3-kb upstream region, by using
bedtools slop with options -l 3000 -r 0 -s. For analyses on specific TE
classes, we calculated TE insertion counts for the following 4 TE
categories: LTR-Copia, LTR-Gypsy, Helitron, and DNA transposon.

We then performed 2 tests on the resulting TE counts per gene:
(i) whether the proportion of genes with 1 or more TE insertions is
elevated in DE genes compared to the full gene set (chi-square
test), and (ii) whether the rate of TE insertions per gene measured,
as the total count of intersections for each gene annotation with a
TE annotation, is elevated in DE genes compared to non-DE genes
(Mann-Whitney U test).

The same test was also applied to a restricted set of DE genes previously described as CAM-related, or involved in starch metabolism and gluconeogenesis. Then, genes of interest with a TE insertion rate higher than twice the genome-wide average were selected and the difference in number of TE insertions between orthologs of *T. leiboldiana* and *T. fasciculata* was taken in case of a one-to-one relationship.

Accession numbers

The genome assemblies and raw data used in this study are available
at NCBI-SRA under BioProject PRJNA927306. Specifically, the
*T. fasciculata* genome assembly TFas_v1 can be downloaded here:
https://ncbi.nlm.nih.gov/datasets/genome/GCA_029168755.2/. The
*T. leiboldiana* genome assembly TLei_v1 can be found at:
https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_029204045.2/. The
annotation of both genomes is available on the GitHub repository
at: https://github.com/cgrootrego/Tillandsia_Genomes, together
with the list of orthogroups, counts table used for RNA-seq analyses,
full GO-term enrichment results, and all scripts written for
this manuscript. The A. comosus sequences used in this study
stem from BioProject PRJNA371634 (F153): https://www.ncbi.nlm.
nih.gov/bioproject/PRJNA371634/ and BioProject PRJNA747096
(CB5): https://www.ncbi.nlm.nih.gov/bioproject/PRJNA747096.
Information on all accessions used in this study can be found in
Supplementary Table S1.

Acknowledgments

In memory of Christian LEXER—we will treasure your enthusiasm,
guidance, and memory always. We thank Joachim Hermisson,
Magnus Nordborg, Andrew Clark, Nicholas Barton, Virginie
Courtier-Orgogozo, John Parsch, Andreas Futschik, Rui Borges,
Aglaja Szukala, Florian Schwarz, Marta Pelizzolla, and Ahmad
Muhammad for insightful discussions, advice, and feedback. We
thank Gert Bachmann and Eline de Vos for help with the setup
of the RNA-seq experiment; Peter Bak, Andreas Franzke, Nils
Köster, and Helmut and Lieselotte Hromadnik for their generous
donations of Tillandsia accessions; and Thelma Barbarà for assis-
tance during RNA extractions. We also thank Barbara
Knickmann, Viktor Vagovics, and Manfred Speckmaier for the
care of Tillandsia plants at the Botanical Garden of the University
of Vienna. Lastly, we thank Alisa Tscherko for assistance with tri-
chome photography. Computational resources were provided by
the Life Science Computer Cluster (LiSC) of the University of
Vienna and the Vienna Scientific Cluster. Lastly, we would like
to thank the anonymous reviewers for their constructive com-
ments that thoroughly improved this manuscript.
## Author contributions

This study was conceived by C.L., J.H., O.P., T.L., and C.G.C. Sampling was conducted by M.H.J.B., W.T., G.Y., and M.d.L.H. Laboratory work was conducted by M.H.J.B., S.S., L.A.C.-S., and C.G.C. Cytogenetic work was performed by H.W.-S. and E.M.T. Extractions, GC-MS, and initial analysis of metabolomic data were performed by C.P. and W.W. The RNA-seq experiment and DE analysis were conducted by C.G.C. under the guidance of K.H. and O.P. Analyses were performed by C.G.C., J.H., C.P., G.Y., T.L., and F.B. The manuscript was primarily written by C.G.C. and amended following the dedicated reading and feedback of all coauthors, especially K.H., T.L., and O.P.

## Supplementary data

The following materials are available in the online version of this article.

**Supplementary Figure S1.** Loadings of 45 individual metabolites on PC1 and PC2.

**Supplementary Figure S2.** Genome size measurement histograms.

**Supplementary Figure S3.** Heterozygosity and genome size estimation with a k-mer based approach implemented in findGSE for *Tillandsia fasciculata* and *T. leiboldiana*.

**Supplementary Figure S4.** Scaffolds sizes and per-scaffold count of orthologous genes in the de novo assemblies of *T. fasciculata* and *T. leiboldiana*.

**Supplementary Figure S5.** Mitotic metaphase chromosomes and karyotypes of *Tillandsia fasciculata* and *Tillandsia leiboldiana*.

**Supplementary Figure S6.** TE, GC, and gene contents at 3-examples of syntenic chromosome triplets.

**Supplementary Figure S7.** In-depth visualization of large-scale rearrangements between *T. fasciculata* and *T. leiboldiana* based on local alignments with less than a 90% overlap with any other alignment.

**Supplementary Figure S8.** Genome-wide distribution of *d*<sub>N</sub>/d<sub>S</sub> values between single-copy orthologous genes.

**Supplementary Figure S9.** Heatmap of z-score normalized expression values of CAM-related DE genes featured in Fig. 6.

**Supplementary Figure S10.** Average expression curve of PEPC kinase (PPCK) in *T. fasciculata* and *T. leiboldiana* with standard deviation.

**Supplementary Figure S11.** Per-gene expression curves of all differentially expressed genes, spread over 7 co-expression clusters inferred with MaSigPro.

**Supplementary Figure S12.** Relationship between differentially expressed (DE) gene count per scaffold and scaffold size.

**Supplementary Figure S13.** Distribution of DE genes across the genome.

**Supplementary Figure S14.** Average expression curve of Aquaporin 2 to 6 in *T. fasciculata* and *T. leiboldiana*.

**Supplementary Figure S15.** Distribution of heterozygous sites per 1,000 mappable variants on a logarithmic scale.

**Supplementary Figure S16.** Distribution of mean per-gene coverage across different gene family categories.

**Supplementary Figure S17.** Percentage of uniquely mapping RNA-seq reads for 24 samples to the genome assemblies of *T. fasciculata*, *T. leiboldiana*, and *A. comosus*.

**Supplementary Figure S18.** Proportion of RNA-seq reads mapping in multiple genomic locations for 24 samples mapped to the genome assemblies of *T. fasciculata*, *T. leiboldiana*, and *A. comosus*.

**Supplementary Table S1.** List of accessions used in this study.

**Supplementary Table S2.** Assembly statistics.

**Supplementary Table S3.** TE abundances.

**Supplementary Table S4.** Orthology statistics.

**Supplementary Table S5.** List of CAM-related expanded orthogroups.

**Supplementary Table S6.** Full list of candidate genes under positive selection.

**Supplementary Table S7.** Full list of co-expression modules.

**Supplementary Table S8.** Per-kb frequencies of 4 promoter motifs associated with circadian clock transcription factors in DE and non-DE genes.

**Supplementary Table S9.** Annotation of circadian promoter motifs in the upstream regions of CAM genes.

**Supplementary Table S10.** Overview of gene family evolution in transcription factor families.

**Supplementary Table S11.** List of DE genes related to CAM, starch metabolism, and gluconeogenesis with a high TE insertion rate.

**Supplementary Table S12.** List of InterPro codes used to identify transcription factor families in the *T. leiboldiana* and *T. fasciculata* gene annotations.

**Supplementary Notes.**

**Supplementary Data Set 1.** Metabolomic Compound Abundances reported as raw area read-outs from MS-DIAL, including the level of identification according to the Metabolomics Standards Initiative.

**Supplementary Data Set 2.** Description and detailed results of statistical tests.

## Funding

This research was funded in part by the Austrian Science Fund (FWF) [grants https://doi.org/10.55776/W1225 to a faculty team including C.L. and O.P., and https://doi.org/10.55776/P35275 to O.P.], and by the professorship startup grant awarded to Christian Leker by the University of Vienna BE772002. For open access purposes, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission.

*Conflict of interest statement.* None declared.

## References

Alexa A, Rahnenführer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. *Bioinformatics*. 2006(22):1600–1607. https://doi.org/10.1093/bioinformatics/btl140

Ando E, Ohnishi M, Wang Y, Matsushita T, Watanabe A, Hayashi Y, Fujii M, Ma JF, Inoue S-I, Kinoshita T. TWIN SISTER OF FT, GIGANTEA, and CONSTANS have a positive but indirect effect on blue light-induced stomatal opening in Arabidopsis. *Plant Physiol.* 2013;162(3):1529–1538. https://doi.org/10.1104/pp.113.217984

Araújo WL, Nunes-Nesi A, Osorio S, Usadel B, Fuentes D, Nagy R, Balbo I, Lehmann M, Studart-Witkowski C, Tohge T, et al. Antisense inhibition of the iron–sulphur subunit of succinate dehydrogenase enhances photosynthesis and growth in tomato via an organic acid-mediated effect on stomatal aperture. *Plant Cell.* 2011;23(2):600–627. https://doi.org/10.1105/tpc.110.081224

Arnegard ME, Zwickl DJ, Lu Y, Zakon HH. Old gene duplication facilitates origin and diversification of an innovative communication system—twice. *Proc Natl Acad Sci U S A.* 2010;107(51):22172–22177. https://doi.org/10.1073/pnas.1011803107
## Author contributions

This study was conceived by C.L., J.H., O.P., T.L., and C.G.C. Sampling was conducted by M.H.J.B., W.T., G.Y., and M.d.L.H. Laboratory work was conducted by M.H.J.B., S.S., L.A.C.-S., and C.G.C. Cytogenetic work was performed by H.W.-S. and E.M.T. Extractions, GC-MS, and initial analysis of metabolomic data were performed by C.P. and W.W. The RNA-seq experiment and DE analysis were conducted by C.G.C. under the guidance of K.H. and O.P. Analyses were performed by C.G.C., J.H., C.P., G.Y., T.L., and F.B. The manuscript was primarily written by C.G.C. and amended following the dedicated reading and feedback of all coauthors, especially K.H., T.L., and O.P.

## Supplementary data

The following materials are available in the online version of this article.

**Supplementary Figure S1.** Loadings of 45 individual metabolites on PC1 and PC2.

**Supplementary Figure S2.** Genome size measurement histograms.

**Supplementary Figure S3.** Heterozygosity and genome size estimation with a k-mer based approach implemented in findGSE for *Tillandsia fasciculata* and *T. leiboldiana*.

**Supplementary Figure S4.** Scaffolds sizes and per-scaffold count of orthologous genes in the de novo assemblies of *T. fasciculata* and *T. leiboldiana*.

**Supplementary Figure S5.** Mitotic metaphase chromosomes and karyotypes of *Tillandsia fasciculata* and *Tillandsia leiboldiana*.

**Supplementary Figure S6.** TE, GC, and gene contents at 3-examples of syntenic chromosome triplets.

**Supplementary Figure S7.** In-depth visualization of large-scale rearrangements between *T. fasciculata* and *T. leiboldiana* based on local alignments with less than a 90% overlap with any other alignment.

**Supplementary Figure S8.** Genome-wide distribution of *d*<sub>N</sub>/d<sub>S</sub> values between single-copy orthologous genes.

**Supplementary Figure S9.** Heatmap of z-score normalized expression values of CAM-related DE genes featured in Fig. 6.

**Supplementary Figure S10.** Average expression curve of PEPC kinase (PPCK) in *T. fasciculata* and *T. leiboldiana* with standard deviation.

**Supplementary Figure S11.** Per-gene expression curves of all differentially expressed genes, spread over 7 co-expression clusters inferred with MaSigPro.

**Supplementary Figure S12.** Relationship between differentially expressed (DE) gene count per scaffold and scaffold size.

**Supplementary Figure S13.** Distribution of DE genes across the genome.

**Supplementary Figure S14.** Average expression curve of Aquaporin 2 to 6 in *T. fasciculata* and *T. leiboldiana*.

**Supplementary Figure S15.** Distribution of heterozygous sites per 1,000 mappable variants on a logarithmic scale.

**Supplementary Figure S16.** Distribution of mean per-gene coverage across different gene family categories.

**Supplementary Figure S17.** Percentage of uniquely mapping RNA-seq reads for 24 samples to the genome assemblies of *T. fasciculata*, *T. leiboldiana*, and *A. comosus*.

**Supplementary Figure S18.** Proportion of RNA-seq reads mapping in multiple genomic locations for 24 samples mapped to the genome assemblies of *T. fasciculata*, *T. leiboldiana*, and *A. comosus*.

**Supplementary Table S1.** List of accessions used in this study.

**Supplementary Table S2.** Assembly statistics.

**Supplementary Table S3.** TE abundances.

**Supplementary Table S4.** Orthology statistics.

**Supplementary Table S5.** List of CAM-related expanded orthogroups.

**Supplementary Table S6.** Full list of candidate genes under positive selection.

**Supplementary Table S7.** Full list of co-expression modules.

**Supplementary Table S8.** Per-kb frequencies of 4 promoter motifs associated with circadian clock transcription factors in DE and non-DE genes.

**Supplementary Table S9.** Annotation of circadian promoter motifs in the upstream regions of CAM genes.

**Supplementary Table S10.** Overview of gene family evolution in transcription factor families.

**Supplementary Table S11.** List of DE genes related to CAM, starch metabolism, and gluconeogenesis with a high TE insertion rate.

**Supplementary Table S12.** List of InterPro codes used to identify transcription factor families in the *T. leiboldiana* and *T. fasciculata* gene annotations.

**Supplementary Notes.**

**Supplementary Data Set 1.** Metabolomic Compound Abundances reported as raw area read-outs from MS-DIAL, including the level of identification according to the Metabolomics Standards Initiative.

**Supplementary Data Set 2.** Description and detailed results of statistical tests.

## Funding

This research was funded in part by the Austrian Science Fund (FWF) [grants https://doi.org/10.55776/W1225 to a faculty team including C.L. and O.P., and https://doi.org/10.55776/P35275 to O.P.], and by the professorship startup grant awarded to Christian Leker by the University of Vienna BE772002. For open access purposes, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission.

*Conflict of interest statement.* None declared.

## References

Alexa A, Rahnenführer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. *Bioinformatics*. 2006(22):1600–1607. https://doi.org/10.1093/bioinformatics/btl140

Ando E, Ohnishi M, Wang Y, Matsushita T, Watanabe A, Hayashi Y, Fujii M, Ma JF, Inoue S-I, Kinoshita T. TWIN SISTER OF FT, GIGANTEA, and CONSTANS have a positive but indirect effect on blue light-induced stomatal opening in Arabidopsis. *Plant Physiol.* 2013;162(3):1529–1538. https://doi.org/10.1104/pp.113.217984

Araújo WL, Nunes-Nesi A, Osorio S, Usadel B, Fuentes D, Nagy R, Balbo I, Lehmann M, Studart-Witkowski C, Tohge T, et al. Antisense inhibition of the iron–sulphur subunit of succinate dehydrogenase enhances photosynthesis and growth in tomato via an organic acid-mediated effect on stomatal aperture. *Plant Cell.* 2011;23(2):600–627. https://doi.org/10.1105/tpc.110.081224

Arnegard ME, Zwickl DJ, Lu Y, Zakon HH. Old gene duplication facilitates origin and diversification of an innovative communication system—twice. *Proc Natl Acad Sci U S A.* 2010;107(51):22172–22177. https://doi.org/10.1073/pnas.1011803107
Baduel P, Quadrana L, Hunter B, Bomblies K, Colot V. Relaxed purifying selection in autopolyploids drives transposable element over-accumulation which provides variants for local adaptation. *Nat Commun.* 2019;10(1):5818. https://doi.org/10.1038/s41467-019-13730-0

Barfuss MHJ, Till W, Leme EMC, Pinzón JP, Manzanares JM, Halbritter H, Samuel R, Brown GK. Taxonomic revision of Bromeliaceae sub-fam. Tillandsioideae based on a multi-locus DNA sequence phylogeny and morphology. *Phytotaxa.* 2016;279(1):1–97. https://doi.org/10.11646/phytotaxa.279.1.1

Benzing DH, Bennett B. *Bromeliaceae: profile of an adaptive radiation*. Cambridge, UK: Cambridge University Press; 2000.

Borland AM, Hartwell J, Weston DJ, Schlauch KA, Tschaplinski TJ, Tuskan GA, Yang X, Cushman JC. Engineering crassulacean acid metabolism to improve water-use efficiency. *Trends Plant Sci.* 2014;19(5):327–338. https://doi.org/10.1016/j.tplants.2014.01.006

Bräutigam A, Schlüter U, Eisenhut M, Gowik U. On the evolutionary origin of CAM photosynthesis. *Plant Physiol.* 2017;174(2):473–477. https://doi.org/10.1104/pp.17.00195

Brawand D, Wagner CE, Li YI, Malinsky M, Keller I, Fan S, Simakov O, Ng AY, Lim ZW, Bezault E, et al. The genomic substrate for adaptive radiation in African cichlid fish. *Nature*. 2015;513(7518): 375–381. https://doi.org/10.1038/nature13726

Brown GK, Gilmartin AJ. Chromosome numbers in Bromeliaceae. *Am J Bot.* 1989;76(5):657–665. https://doi.org/10.1002/j.1537-2197.1989.tb11361.x

Cai J, Liu X, Vanneste K, Proost S, Tsai W-C, Liu K-W, Chen L-J, He Y, Xu Q, Bian C, et al. The genome sequence of the orchid *Phalaenopsis equestris*. *Nat Genet.* 2015;47(1):65–72. https://doi.org/10.1038/ng.3149

Campbell MS, Holt C, Moore B, Yandell M. Genome annotation and curation using MAKER and MAKER-P. *Curr Protoc Bioinformatics.* 2014:2014.4.11.1–4.11.39. https://doi.org/10.1002/0471250953.bi0411s48

Carnal NW, Black CC. Soluble sugars as the carbohydrate reserve for CAM in pineapple leaves: implications for the role of pyrophosphate:6-phosphofructokinase in glycolysis. *Plant Physiol.* 1989;90(1):91–100. https://doi.org/10.1104/pp.90.1.91

Chen L-Y, VanBuren R, Paris M, Zhou H, Zhang X, Wai CM, Yan H, Chen S, Alonge M, Ramakrishnan S, et al. The bracteatus pineapple genome and domestication of clonally propagated crops. *Nat Genet.* 2019;51(10):1549–1558. https://doi.org/10.1038/s41588-019-0506-8

Cheng Y, Zhou W, El Sheery NI, Peters C, Li M, Wang X, Huang J. Characterization of the Arabidopsis glycerophosphodiester phosphodiesterase (GDPD) family reveals a role of the plastid-localized AtGDPD1 in maintaining cellular phosphate homeostasis under phosphate starvation. *Plant J.* 2011;66(5):781–795. https://doi.org/10.1111/j.1365-313X.2011.04538.x

Christin P-A, Arakaki M, Osborne CP, Bräutigam A, Sage RF, Hibberd JM, Kelly S, Covshoff S, Wong GK-S, Hancock L, et al. Shared origins of a key enzyme during the evolution of C4 and CAM metabolism. *J Exp Bot.* 2014;65(13):3609–3621. https://doi.org/10.1093/jxb/eru087

Christopher JT, Holtum JAM. Carbohydrate partitioning in the leaves of Bromeliaceae performing C3 photosynthesis or crassulacean acid metabolism. * Funct Plant Biol.* 1998;25(3):371–376. https://doi.org/10.1071/PP98005

Cicconardi F, Lewis JJ, Martin SH, Reed RD, Danko CG, Montgomery SH. Chromosome fusion affects genetic diversity and evolutionary turnover of functional loci but consistently depends on chromosome size. *Mol Biol Evol.* 2021;38(10):4449–4462. https://doi.org/10.1093/molbev/msab185

Conesa A, Nueda MJ, Ferrer A, Talón M. maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. *Bioinformatics.* 2006;22(9):1096–1102. https://doi.org/10.1093/bioinformatics/btl056

Cosentino C, Di Silvestre D, Fischer-Schliebs E, Homann U, De Palma A, Comunian C, Mauri PL, Thiel G. Proteomic analysis of *Mesembryanthemum crystallinum* leaf microsomal fractions finds an imbalance in V-ATPase stoichiometry during the salt-induced transition from C3 to CAM. *Biochem J.* 2013;450(2):407–415. https://doi.org/10.1042/BJ20121087

Crayn DM, Winter K, Schulte K, Smith JAC. Photosynthetic pathways in Bromeliaceae: phylogenetic and ecological significance of CAM and C3 based on carbon isotope ratios for 1893 species. *Bot J Linn Soc.* 2015;178(2):169–221. https://doi.org/10.1111/boj.12275

Crayn DM, Winter K, Smith JAC. Multiple origins of crassulacean acid metabolism and the epiphytic habit in the Neotropical family Bromeliaceae. *Proc Natl Acad Sci U S A.* 2004;101(10):3703–3708. https://doi.org/10.1073/pnas.0400366101

Cushman JC. Crassulacean acid metabolism. A plastic photosynthetic adaptation to arid environments. *Plant Physiol.* 2001;127(4):1439–1448. https://doi.org/10.1104/pp.010818

Cushman JC, Tillett RL, Wood JA, Branco JM, Schlauch KA. Large-scale mRNA expression profiling in the common ice plant, *Mesembryanthemum crystallinum*, performing C3 photosynthesis and crassulacean acid metabolism (CAM). *J Exp Bot.* 2008;59(7):1875–1894. https://doi.org/10.1093/jxb/ern008

Dalchau N, Baek SJ, Briggs HM, Robertson FC, Dodd AN, Gardner MJ, Stancombe MA, Haydon MJ, Stan G-B, Gonçalves JM, et al. The circadian oscillator gene GIGANTEA mediates a long-term response of the *Arabidopsis thaliana* circadian clock to sucrose. *Proc Natl Acad Sci U S A.* 2011;108(12):5104–5109. https://doi.org/10.1073/pnas.1015452108

Davey JW, Chouteau M, Barker SL, Maroja L, Baxter SW, Simpson F, Merrill RM, Joron M, Mallet J, Dasmahapatra KK, et al. Major improvements to the *Heliconius melpomene* genome assembly used to confirm 10 chromosome fusion events in 6 million years of butterfly evolution. *G3 (Bethesda)*. 2016;3(6):695–708. https://doi.org/10.1534/g3.115.023655

De La Harpe M, Paris M, Hess J, Barfuss MHJ, Serrano-Serrano ML, Ghatak A, Chaturvedi P, Weckwerth W, Till W, Salamin N, et al. Genomic footprints of repeated evolution of CAM photosynthesis in a Neotropical species radiation. *Plant Cell Environ.* 2020;43(12):2987–3001. https://doi.org/10.1111/pce.13847

Deng H, Zhang L-S, Zhang G-Q, Zheng B-Q, Liu Z-J, Wang Y. Evolutionary history of PEPC genes in green plants: implications for the evolution of CAM in orchids. *Mol Phylogenet Evol.* 2016;94:559–564. https://doi.org/10.1016/j.ympev.2015.10.007

de Vos JM, Augustijnen H, Bätscher L, Lucek K. Speciation through chromosomal fusion and fission in Lepidoptera. *Philos Trans R Soc Lond B Biol Sci.* 2020;375(1806):20190539. https://doi.org/10.1098/rstb.2019.0539

Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. *Bioinformatics.* 2013;29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635

Doerfler H, Lyon D, Nägele T, Sun X, Fragner L, Hadacek F, Egelhofer V, Weckwerth W. Granger causality in integrated GC-MS and LC-MS metabolomics data reveals the interface of primary and secondary metabolism. *Metabolomics.* 2013;9(3):564–574. https://doi.org/10.1007/s11306-012-0470-0

Edwards EJ. Reconciling continuous and discrete models of C4 and CAM evolution. *Ann Bot.* 2023;132(4):717–725. https://doi.org/10.1093/aob/mcad125
Baduel P, Quadrana L, Hunter B, Bomblies K, Colot V. Relaxed purifying selection in autopolyploids drives transposable element over-accumulation which provides variants for local adaptation. *Nat Commun.* 2019;10(1):5818. https://doi.org/10.1038/s41467-019-13730-0

Barfuss MHJ, Till W, Leme EMC, Pinzón JP, Manzanares JM, Halbritter H, Samuel R, Brown GK. Taxonomic revision of Bromeliaceae sub-fam. Tillandsioideae based on a multi-locus DNA sequence phylogeny and morphology. *Phytotaxa.* 2016;279(1):1–97. https://doi.org/10.11646/phytotaxa.279.1.1

Benzing DH, Bennett B. *Bromeliaceae: profile of an adaptive radiation*. Cambridge, UK: Cambridge University Press; 2000.

Borland AM, Hartwell J, Weston DJ, Schlauch KA, Tschaplinski TJ, Tuskan GA, Yang X, Cushman JC. Engineering crassulacean acid metabolism to improve water-use efficiency. *Trends Plant Sci.* 2014;19(5):327–338. https://doi.org/10.1016/j.tplants.2014.01.006

Bräutigam A, Schlüter U, Eisenhut M, Gowik U. On the evolutionary origin of CAM photosynthesis. *Plant Physiol.* 2017;174(2):473–477. https://doi.org/10.1104/pp.17.00195

Brawand D, Wagner CE, Li YI, Malinsky M, Keller I, Fan S, Simakov O, Ng AY, Lim ZW, Bezault E, et al. The genomic substrate for adaptive radiation in African cichlid fish. *Nature*. 2015;513(7518): 375–381. https://doi.org/10.1038/nature13726

Brown GK, Gilmartin AJ. Chromosome numbers in Bromeliaceae. *Am J Bot.* 1989;76(5):657–665. https://doi.org/10.1002/j.1537-2197.1989.tb11361.x

Cai J, Liu X, Vanneste K, Proost S, Tsai W-C, Liu K-W, Chen L-J, He Y, Xu Q, Bian C, et al. The genome sequence of the orchid *Phalaenopsis equestris*. *Nat Genet.* 2015;47(1):65–72. https://doi.org/10.1038/ng.3149

Campbell MS, Holt C, Moore B, Yandell M. Genome annotation and curation using MAKER and MAKER-P. *Curr Protoc Bioinformatics.* 2014:2014.4.11.1–4.11.39. https://doi.org/10.1002/0471250953.bi0411s48

Carnal NW, Black CC. Soluble sugars as the carbohydrate reserve for CAM in pineapple leaves: implications for the role of pyrophosphate:6-phosphofructokinase in glycolysis. *Plant Physiol.* 1989;90(1):91–100. https://doi.org/10.1104/pp.90.1.91

Chen L-Y, VanBuren R, Paris M, Zhou H, Zhang X, Wai CM, Yan H, Chen S, Alonge M, Ramakrishnan S, et al. The bracteatus pineapple genome and domestication of clonally propagated crops. *Nat Genet.* 2019;51(10):1549–1558. https://doi.org/10.1038/s41588-019-0506-8

Cheng Y, Zhou W, El Sheery NI, Peters C, Li M, Wang X, Huang J. Characterization of the Arabidopsis glycerophosphodiester phosphodiesterase (GDPD) family reveals a role of the plastid-localized AtGDPD1 in maintaining cellular phosphate homeostasis under phosphate starvation. *Plant J.* 2011;66(5):781–795. https://doi.org/10.1111/j.1365-313X.2011.04538.x

Christin P-A, Arakaki M, Osborne CP, Bräutigam A, Sage RF, Hibberd JM, Kelly S, Covshoff S, Wong GK-S, Hancock L, et al. Shared origins of a key enzyme during the evolution of C4 and CAM metabolism. *J Exp Bot.* 2014;65(13):3609–3621. https://doi.org/10.1093/jxb/eru087

Christopher JT, Holtum JAM. Carbohydrate partitioning in the leaves of Bromeliaceae performing C3 photosynthesis or crassulacean acid metabolism. *Funct Plant Biol.* 1998;25(3):371–376. https://doi.org/10.1071/PP98005

Cicconardi F, Lewis JJ, Martin SH, Reed RD, Danko CG, Montgomery SH. Chromosome fusion affects genetic diversity and evolutionary turnover of functional loci but consistently depends on chromosome size. *Mol Biol Evol.* 2021;38(10):4449–4462. https://doi.org/10.1093/molbev/msab185

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Crayn DM, Winter K, Schulte K, Smith JAC. Photosynthetic pathways in Bromeliaceae: phylogenetic and ecological significance of CAM and C3 based on carbon isotope ratios for 1893 species. *Bot J Linn Soc.* 2015;178(2):169–221. https://doi.org/10.1111/boj.12275

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Heyduk K, McAssey EV, Leebens-Mack J. Differential timing of gene expression and recruitment in independent origins of CAM in the Agavoideae (Asparagaceae). *New Phytol.* 2022;235(5):2111–2126. https://doi.org/10.1111/nph.18267

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Heyduk K, Ray JN, Ayyampalayam S, Moledina N, Borland A, Harding SA, Tsai C-J, Leebens-Mack J. Shared expression of crassulacean acid metabolism (CAM) genes pre-dates the origin of CAM in the genus Yucca. *J Exp Bot.* 2019b:70(22):6597–6609. https://doi.org/10.1093/jxb/erz105

Hoang NV, Sogbohossou EOD, Xiong W, Simpson CJC, Singh P, Walden N, van den Bergh E, Becker FFM, Li Z, Zhu X-G, et al. The *Gynandropsis gynandra* genome provides insights into whole-genome duplications and the evolution of C4 photosynthesis in

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Hudson ME, Quail PH. Identification of promoter motifs involved in the network of phytochrome A-regulated gene expression by combined analysis of genomic sequence and microarray data. *Plant Physiol.* 2003;133(4):1605–1616. https://doi.org/10.1104/pp.103.030437

Jang T-S, Weiss-Schneeweiss H. Formamide-free genomic in situ hybridization allows unambiguous discrimination of highly similar parental genomes in diploid hybrids and allopolyploids. *Cytogenet Genome Res.* 2015;146(4):325–331. https://doi.org/10.1159/000441210

Kajala K, Brown NJ, Williams BP, Borrill P, Taylor LE, Hibberd JM. Multiple Arabidopsis genes primed for recruitment into C4 photosynthesis. *Plant J.* 2012:69(1):47–56. https://doi.org/10.1111/j.1365-313X.2011.04769.x

Katju V, Bergthorsson U. Copy-number changes in evolution: rates, fitness effects and adaptive significance. *Front Genet.* 2013:4:273. https://doi.org/10.3389/fgenet.2013.00273

Kong D, Hu H-C, Okuma E, Lee Y, Lee HS, Munemasa S, Cho D, Ju C, Pedoeim L, Rodriguez B, et al. L-Met activates arabidopsis GLR Ca²⁺ channels upstream of ROS production and regulates stomatal movement. *Cell Rep.* 2016;17(10):2553–2561. https://doi.org/10.1016/j.celrep.2016.11.015

Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. *Genome Res.* 2017;27(5): 722–736. https://doi.org/10.1101/gr.215087.116

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Heyduk K, Moreno-Villena JJ, Gilman IS, Christin P-A, Edwards EJ. The genetics of convergent evolution: insights from plant photosynthesis. *Nat Rev Genet.* 2019a;20(8):485–493. https://doi.org/10.1038/s41576-019-0107-5

Heyduk K, Ray JN, Ayyampalayam S, Moledina N, Borland A, Harding SA, Tsai C-J, Leebens-Mack J. Shared expression of crassulacean acid metabolism (CAM) genes pre-dates the origin of CAM in the genus Yucca. *J Exp Bot.* 2019b:70(22):6597–6609. https://doi.org/10.1093/jxb/erz105

Hoang NV, Sogbohossou EOD, Xiong W, Simpson CJC, Singh P, Walden N, van den Bergh E, Becker FFM, Li Z, Zhu X-G, et al. The *Gynandropsis gynandra* genome provides insights into whole-genome duplications and the evolution of C4 photosynthesis in

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Hoff KJ, Lomsadze A, Borodovsky M, Stanke M. Whole-genome annotation with BRAKER. *Gene prediction*. New York: Humana; 2019. p. 65–95.

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Hudson ME, Quail PH. Identification of promoter motifs involved in the network of phytochrome A-regulated gene expression by combined analysis of genomic sequence and microarray data. *Plant Physiol.* 2003;133(4):1605–1616. https://doi.org/10.1104/pp.103.030437

Jang T-S, Weiss-Schneeweiss H. Formamide-free genomic in situ hybridization allows unambiguous discrimination of highly similar parental genomes in diploid hybrids and allopolyploids. *Cytogenet Genome Res.* 2015;146(4):325–331. https://doi.org/10.1159/000441210

Kajala K, Brown NJ, Williams BP, Borrill P, Taylor LE, Hibberd JM. Multiple Arabidopsis genes primed for recruitment into C4 photosynthesis. *Plant J.* 2012:69(1):47–56. https://doi.org/10.1111/j.1365-313X.2011.04769.x

Katju V, Bergthorsson U. Copy-number changes in evolution: rates, fitness effects and adaptive significance. *Front Genet.* 2013:4:273. https://doi.org/10.3389/fgenet.2013.00273

Kong D, Hu H-C, Okuma E, Lee Y, Lee HS, Munemasa S, Cho D, Ju C, Pedoeim L, Rodriguez B, et al. L-Met activates arabidopsis GLR Ca²⁺ channels upstream of ROS production and regulates stomatal movement. *Cell Rep.* 2016;17(10):2553–2561. https://doi.org/10.1016/j.celrep.2016.11.015

Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. *Genome Res.* 2017;27(5): 722–736. https://doi.org/10.1101/gr.215087.116

Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. *Nat Methods.* 2012:9(4):357–359. https://doi.org/10.1038/nmeth.1923

Leroy T, Anselmetti Y, Tilak M-K, Bérard S, Csukonyi L, Gabrielli M, Scornavacca C, Milá B, Thébaud C, Nabholz B. A bird's white-eye view on avian sex chromosome evolution. *Peer Community J.* 2021:1:1–40. https://doi.org/10.24072/pcjournal.70

Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. *arXiv:1303.3997 [q-bio.GN].* https://doi.org/10.48550/arXiv.1303.3997, 26 May 2013, preprint: not peer reviewed.

Li H. Minimap2: pairwise alignment for nucleotide sequences. *Bioinformatics.* 2018;34(18):3094–3100. https://doi.org/10.1093/bioinformatics/bty191

Liao Y, Smyth GK, Shi W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. *Bioinformatics.* 2014;30(7):923–930. https://doi.org/10.1093/bioinformatics/btt656

Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I, Lajoie BR, Sabo PJ, Dorschner MO, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. *Science.* 2009;326(5950): 289–293. https://doi.org/10.1126/science.1181369

Liu L, Li X, Yuan L, Zhang G, Gao H, Xu X, Zhao H. XAP5 CIRCADIAN TIMEKEEPER specifically modulates 3' splice site recognition and is important for circadian clock regulation partly by alternative splicing of LHY and TIC. *Plant Physiol Biochem.* 2022:172:151–157. https://doi.org/10.1016/j.plaphy.2022.01.013

Liu L, Tumi L, Suni ML, Arakaki M, Wang Z-F, Ge X-J. Draft genome of *Puya raimondii* (Bromeliaceae), the queen of the Andes. *Genomics.* 2021;113(4):2537–2546. https://doi.org/10.1016/j.ygeno.2021.05.042
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Lowry DB, Willis JH. A widespread chromosomal inversion polymorphism contributes to a major life-history transition, local adaptation, and reproductive isolation. *PLoS Biol*. 2010;8(9):e1000500. https://doi.org/10.1371/journal.pbio.1000500

Luo J, Sun X, Cormack BP, Boeke JD. Karyotype engineering by chromosome fusion leads to reproductive isolation in yeast. *Nature*. 2018;560(7718):392–396. https://doi.org/10.1038/s41586-018-0374-x

Males J. Concerted anatomical change associated with crassulacean acid metabolism in the Bromeliaceae. *Funct Plant Biol*. 2018;45(7):681–695. https://doi.org/10.1071/FP17071

McClung CR. Plant circadian rhythms. *Plant Cell*. 2006;18(4):792–803. https://doi.org/10.1105/tpc.106.040980

McGee MD, Borstein SR, Meier JI, Marques DA, Mwaiko S, Taabu A, Kishe MA, O'Meara B, Bruggmann R, Excoffier L, et al. The ecological and genomic basis of explosive adaptive radiation. *Nature*. 2020;586(7827):75–79. https://doi.org/10.1038/s41586-020-2652-7

McRae SR, Christopher JT, Smith JAC, Holtum JAM. Sucrose transport across the vacuolar membrane of *Ananas comosus*. *Funct Plant Biol*. 2002;29(6):717–724. https://doi.org/10.1071/PP01227

Messerschmid TFE, Wehling J, Bobon N, Kahmen A, Klak C, Los JA, Nelson DB, dos Santos P, de Vos JM, Kadereit G. Carbon isotope composition of plant photosynthetic tissues reflects a crassulacean acid metabolism (CAM) continuum in the majority of CAM lineages. *Perspect Plant Ecol Evol Syst*. 2021;51:125619. https://doi.org/10.1016/j.ppees.2021.125619

Michael TP, McClung CR. Phase-specific circadian clock regulatory elements in Arabidopsis. *Plant Physiol*. 2002;130(2):627–638. https://doi.org/10.1104/pp.004929

Michael TP, Mockler TC, Breton G, McEntee C, Byer A, Trout JD, Hazen SP, Shen R, Priest HD, Sullivan CM, et al. Network discovery pipeline elucidates conserved time-of-day-specific cis-regulatory modules. *PLoS Genet*. 2008;4(2):e14. https://doi.org/10.1371/journal.pgen.0040014

Ming R, VanBuren R, Wai CM, Tang H, Schatz MC, Bowers JE, Lyons E, Wang M-L, Chen J, Biggers E, et al. The pineapple genome and the evolution of CAM photosynthesis. *Nat Genet*. 2015;47(12):1435–1442. https://doi.org/10.1038/ng.3435

Mondragón-Palomino M, Theissen G. Why are orchid flowers so diverse? Reduction of evolutionary constraints by paralogues of class B floral homeotic genes. *Ann Bot*. 2009;104(3):583–594. https://doi.org/10.1093/aob/mcn258

Moriyama Y, Ito F, Takeda H, Yano T, Okabe M, Kuraku S, Keeley FW, Koshiba-Takeuchi K. Evolution of the fish heart by sub/neofunctionalization of an elastin gene. *Nat Commun*. 2016;7(1):10397. https://doi.org/10.1038/ncomms10397

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Males J. Concerted anatomical change associated with crassulacean acid metabolism in the Bromeliaceae. *Funct Plant Biol*. 2018;45(7):681–695. https://doi.org/10.1071/FP17071

McClung CR. Plant circadian rhythms. *Plant Cell*. 2006;18(4):792–803. https://doi.org/10.1105/tpc.106.040980

McGee MD, Borstein SR, Meier JI, Marques DA, Mwaiko S, Taabu A, Kishe MA, O'Meara B, Bruggmann R, Excoffier L, et al. The ecological and genomic basis of explosive adaptive radiation. *Nature*. 2020;586(7827):75–79. https://doi.org/10.1038/s41586-020-2652-7

McRae SR, Christopher JT, Smith JAC, Holtum JAM. Sucrose transport across the vacuolar membrane of *Ananas comosus*. *Funct Plant Biol*. 2002;29(6):717–724. https://doi.org/10.1071/PP01227

Messerschmid TFE, Wehling J, Bobon N, Kahmen A, Klak C, Los JA, Nelson DB, dos Santos P, de Vos JM, Kadereit G. Carbon isotope composition of plant photosynthetic tissues reflects a crassulacean acid metabolism (CAM) continuum in the majority of CAM lineages. *Perspect Plant Ecol Evol Syst*. 2021;51:125619. https://doi.org/10.1016/j.ppees.2021.125619

Michael TP, McClung CR. Phase-specific circadian clock regulatory elements in Arabidopsis. *Plant Physiol*. 2002;130(2):627–638. https://doi.org/10.1104/pp.004929

Michael TP, Mockler TC, Breton G, McEntee C, Byer A, Trout JD, Hazen SP, Shen R, Priest HD, Sullivan CM, et al. Network discovery pipeline elucidates conserved time-of-day-specific cis-regulatory modules. *PLoS Genet*. 2008;4(2):e14. https://doi.org/10.1371/journal.pgen.0040014

Ming R, VanBuren R, Wai CM, Tang H, Schatz MC, Bowers JE, Lyons E, Wang M-L, Chen J, Biggers E, et al. The pineapple genome and the evolution of CAM photosynthesis. *Nat Genet*. 2015;47(12):1435–1442. https://doi.org/10.1038/ng.3435

Mondragón-Palomino M, Theissen G. Why are orchid flowers so diverse? Reduction of evolutionary constraints by paralogues of class B floral homeotic genes. *Ann Bot*. 2009;104(3):583–594. https://doi.org/10.1093/aob/mcn258

Moriyama Y, Ito F, Takeda H, Yano T, Okabe M, Kuraku S, Keeley FW, Koshiba-Takeuchi K. Evolution of the fish heart by sub/neofunctionalization of an elastin gene. *Nat Commun*. 2016;7(1):10397. https://doi.org/10.1038/ncomms10397

Ogburn RM, Edwards EJ. Anatomical variation in Cactaceae and relatives: trait lability and evolutionary innovation. *Am J Bot*. 2009;96(2):391–408. https://doi.org/10.3732/ajb.0800142

Ohno S. *Evolution by gene duplication*. Heidelberg: Springer Berlin; 1970.

Osmond CB. Crassulacean acid metabolism: a curiosity in context. *Annu Rev Plant Physiol*. 1978;29(1):379–414. https://doi.org/10.1146/annurev.pp.29.060178.002115

Otto FJ, Oldiges H, Göhde W, Jain VK. Flow cytometric measurement of nuclear DNA content variations as a potential in vivo mutagenicity test. *Cytometry*. 1981;2(3):189–191. https://doi.org/10.1002/cyto.990020311

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Yang X, Hu R, Yin H, Jenkins J, Shu S, Tang H, Liu D, Weighill DA, Cheol Yim W, Ha J, et al. The Kalanchoë genome provides insights into convergent evolution and building blocks of crassulacean acid metabolism. *Nat Commun.* 2017;8(1):1899. https://doi.org/10.1038/s41467-017-01491-7

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Zhang S, Ghatak A, Bazargani M, Kramml H, Zang F, Gao S, Ramšak Ž, Gruden K, Varshney RK, Jiang D, et al. Cell-type proteomic and metabolomic resolution of early and late grain filling stages of wheat endosperm. *Plant Biotechnol J.* 2024;22(3):555-571. https://doi.org/10.1111/pbi.14203

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