**Fruit Architecture in Polyamine-Rich Tomato Germplasm Is Determined via a Medley of Cell Cycle, Cell Expansion, and Fruit Shape Genes**

### **Raheel Anwar 1,2, Shazia Fatima 1, Autar K. Mattoo <sup>3</sup> and Avtar K. Handa 1,\***


Received: 25 August 2019; Accepted: 24 September 2019; Published: 29 September 2019

**Abstract:** Shape and size are important features of fruits. Studies using tomatoes expressing *yeast Spermidine Synthase* under either a constitutive or a fruit-ripening promoter showed obovoid fruit phenotype compared to spherical fruit in controls, suggesting that polyamines (PAs) have a role in fruit shape. The obovoid fruit pericarp exhibited decreased cell layers and pericarp thickness compared to wild-type fruit. Transgenic floral buds and ovaries accumulated higher levels of free PAs, with the bound form of PAs being predominant. Transcripts of the fruit shape genes, *SUN1* and *OVATE*, and those of *CDKB2*, *CYCB2*, *KRP1* and *WEE1* genes increased significantly in the transgenic ovaries 2 and 5 days after pollination (DAP). The levels of cell expansion genes *CCS52A*/*B* increased at 10 and 20 DAP in the transgenic fruits and exhibited negative correlation with free or bound forms of PAs. In addition, the cell layers and pericarp thickness of the transgenic fruits were inversely associated with free or bound PAs in 10 and 20 DAP transgenic ovaries. Collectively, these results provide evidence for a linkage between PA homeostasis and expression patterns of fruit shape, cell division, and cell expansion genes during early fruit development, and suggest role(s) of PAs in tomato fruit architecture.

**Keywords:** putrescine; spermidine; spermine; tomato; spermidine synthase; fruit shape; cell division; cell expansion

### **1. Introduction**

Domestication of tomato has led to different phenotypes, including diversity in fruit shape, color, and size [1]. QTL mapping and genomic analyses have identified several loci underlying the observed diversity in shape and size of tomato fruit [1–5]. Genes that regulate fruit architecture include shape and size genes, namely, *CNR*/*FW2.2* [6], *OVATE* [2], *FAS* [7], *SUN1* [8], *fw11.3* [9], *LC* [10,11], *fs8.1* [12], and *KLUH*/*FW3.2* [13]. The *FW2.2* locus encodes a protein with homology to a cell-membrane-localized ras-like G-protein, which has been implicated in controlling ~47% of variation in fruit mass in *Solanum pimpinellifolium* and *S. pennellii* [6,14]. A mutation in the *FW2.2* promoter inhibits cell division during flower development and causes a larger fruit phenotype [15]. Another gene *SUN1* encodes a protein harboring an IQ67 domain and affects cell number along the entire proximal-distal axis, resulting in fruit elongation [16–18]. *OVATE* family proteins (OFPs) and TONNEAU1 Recruiting Motif proteins affect fruit shape by regulating cell division patterns during ovary development [19,20]. It is known that a mutation in the carboxyl-terminal domain of *OVATE* results in changing fruit shape from roundto pear-shaped [2]. *SUN* has a stronger effect on transcriptome than *OVATE* and *fs8.1*. Auxin has been

implicated in regulating the expression of some of the fruit shape genes [18], but little is known about other molecules that may affect fruit size and shape.

Polyamines (PAs)—putrescine (Put), spermidine (Spd), and spermine (Spm), are ubiquitous polycations in all organisms, including plants, implicated in a myriad of developmental and physiological processes, including cell proliferation [21–24]. In plants, PAs play roles in biotic and abiotic stresses [25]; fate of flower, fruit, and seed development [26]; leaf and flower senescence [27,28]; in vitro somatic embryogenesis and organogenesis [29,30]; and fruit ripening and shelf life [27,31]. PAs homeostasis is a genetically regulated process with tight PAs homeostasis in most organisms and their perturbation results in altered phenotypes [26]. Exogenous application of PAs was found to increase the expression of cell division genes *CYCA* and *CYCB* in tobacco BY-2 cell cultures [32]. Overexpression of *KRP1*, a cyclin-dependent kinase inhibitor 1, affected cell division and led to several altered phenotypes in plants, including flower morphology and plant size [33].

PAs are predominantly present during growth and development in plants, in particular during cell division and elongation in apical shoots and meristems before flowering [21,34,35]. A crosstalk among various PAs and plant hormones has been implicated in several growth and developmental processes [36]. Transgenic tomato fruits expressing *CDKA1* under the control of a fruit-specific promoter increased cell division, resulting in higher thickness of pericarp and placenta, and larger septa and columella [37]. We previously developed isogenic tomato lines homozygous for expression of yeast Spd synthase (*ySpdSyn*) with altered levels of Spd and Spm in developing and ripening tomato fruit [27]. These isogenic transgenic fruits with altered PA homeostasis resulted in architectural changes in fruit such as a more obovoid phenotype, compared to an otherwise spherical phenotype in wild-type (WT) fruit. The altered phenotype of transgenic fruit manifests during early fruit development and is associated with higher levels of Spd/Spm. Our results show strong positive correlations between transcript levels of fruit shape-regulating genes (*SUN1* and *OVATE*), cell cycle genes (*CDKB2*, *CYCB2*, *KRP1* and *CCS52B*), and endogenous levels of free and bound PAs. These findings provide first molecular evidence for the role of PAs as fruit architecture regulators responsible for the obovoid phenotype of tomato fruit.

### **2. Results**

### *2.1. Transgenic Expression of ySpdSyn Caused Architectural Changes in Tomato Fruit*

The isogenic tomato germplasm homozygous for transgene *ySpdSyn* under the control of either constitutive CaMV35S (lines C4 and C15) or fruit-specific SlE8 promoters (line E8-8) have been previously described [27]. Fruits from the *ySpdSyn* transgenic lines exhibited an obovoid shape, compared to the relatively spherical phenotype of parental Wild-type cv. Ohio 8245 fruits (Figure 1a,b). The altered phenotype transition—from more spherical to more obovoid—of the transgenic fruit occurs at early stages of fruit development and continues thereafter to full maturity (Figure 1a,b). The effect of transgene was more pronounced under the constitutive CaMV35S promoter than under the developmentally regulated fruit-ripening SlE8 promoter. Collectively, these results associate the expression of *ySpdSyn* transgene with the observed altered tomato fruit architecture from round to obovoid.

The cytological examination of pericarp cell layers, cell size, and cell thickness of developing ovaries of WT and the three transgenic lines was performed at 5 days before pollination (DBP) and 5, 10 and 20 days after pollination (DAP) to ascertain the nature of the structural alterations. The medial-lateral sections of the fruit pericarp tissue from WT and transgenic lines at 10 and 20 DAP were stained with 0.04% toluidine blue–O, and are shown in Figure 2a,b; quantified data for the pericarp thickness and cell layer number from −5 to 20 DAP are shown in Figure 2c,d, respectively. The thickness of pericarp in all the three independent transgenic lines, particularly C15 and E8-8 fruit, was significantly reduced compared to WT fruit (Figure 2c), which was associated with a decreased number of cell layers at 20 DAP (Figure 2d). On an average, fruit pericarp of WT fruit tissues at 20 DAP had 38 cell layers while the transgenic fruit pericarp from C4, C15, and E8-8 lines had 32, 28, and 28 cell layers, respectively (Figure 2d). The cell sizes in endocarp, mesocarp, and exocarp of the transgenic

fruit were reduced compared to WT fruits, with significant reduction in the cell size of the 10 DAP mesocarp of C4 and C15 lines, as compared to WT fruit mesocarp (Figure 2e). At 20 DAP, however, the mesocarp cell size was unchanged in fruit of C4 and C15 lines, but significantly decreased in E8-8 fruit compared to WT fruit (Figure 2f). The cell sizes in 20 DAP fruit increased by 2- to 11-fold in endocarp, mesocarp, and exocarp, compared to the 10 DAP fruit, implicating a shift from cell division to cell expansion mode (Figure 2g). This increase was much larger in endocarp and mesocarp of the transgenic C4 and C15 fruits compared to WT fruit at 20 DAP.

**Figure 1.** Morphometric changes in transgenic tomato fruit expressing *ySpdSyn*. Phenotype (**a**) and fruit shape index (**b**) of field grown wild-type (WT, wild-type cv. Ohio 8245) and transgenic fruits expressing *ySpdSyn* under CaMV35S (C4 and C15) and fruit specific E8 (E8-8) promoters. The fruits shown represent average growth and development stage of indicated genotype. The white line in the bottom left corner (Figure 1a) represents 10 mm on the original scale. Vertically cut tomato fruits were scanned and analyzed with tomato analyzer 3.0 software (Figure 1b). Fruit shape index is length-to-width ratio of the fruit. Error bars in Figure 1b represent standard error of means (n > 3 biological replicates, where each replicate had at least 50 tomato fruits). Abbreviations: MG or G—mature green stage; BR or B—breaker stage; Ri or R—red ripe stage of tomato fruit development.

**Figure 2.** Histological analysis of WT and transgenic fruitlets at 5 days before pollination and at 5, 10 and 20 days after pollination (DAP). Toluidine blue–O staining of WT and transgenic fruitlets at (**a**) 10 DAP and (**b**) 20 DAP. (**c**) Changes in pericarp thickness; (**d**) number of anticlinal cell layers in pericarp; (**e**) cell size at 10 DAP and (**f**) 20 DAP; (**g**) cell size ratio of 20 DAP/10 DAP in endocarp (single innermost cell layer), mesocarp (middle 50% of the pericarp), and exocarp (2 outer cell layers) of tomato ovaries; (h) number of cells in each category of cell area within each genotype. Flowers were tagged and ovaries from flowers at 5 days before pollination and at 5, 10, and 20 DAP were fixed in 100% methanol, vertically sectioned and stained with 0.04% toluidine blue–O. Digital images of pericarp sections were acquired using AperioScan and analyzed using ImageScope 11. Average cell size (**e**,**f**) was calculated by dividing the total number of cells with the area of endocarp, mesocarp, or exocarp. Shown are average ± standard error (n ≥ 3 biological replicates). Different letters above the standard error bars indicate significant difference (at 95% confidence interval) among genotypes within the pericarp section.

In order to determine the phenotypic basis of reduced pericarp thickness of the fruit from transgenic lines, we evaluated the distribution of smaller and larger cells in mediolateral pericarp slices. The pericarp of the transgenic fruits from C4, C15, and E8-8 lines had a reduced number of cells per unit area than the WT pericarp (Figure 2h). Reduction in cell size was seen in almost all cell types, small to large. However, the percent of total distribution of small and large cells ranging from ≤500 to ≥5000 remained similar in all the genotypes (data not shown).

### *2.2. Expression of ySpdSyn Transgene and SlSpdSyn in Floral Buds and Fertilized Ovaries*

The expression of *ySpdSyn* in floral buds and fertilized ovaries of the transgenic and WT tomato lines was quantified by qRT-PCR to evaluate the association between transgene expression and altered phenotype. The *CaMV35S:ySpdSyn* transgene was expressed in C4 tissues as early as 5 DBP and in the fertilized ovaries at 2 to 20 DAP. The transgene expression in C4 was much higher in 5 DAP ovaries than the other stages of floral buds and ovaries examined (Figure 3a). Transcripts of *E8:ySpdSyn* transgene were also detectable at 5 DBP and 5 DAP stages, but the levels were much lower than the *CaMV35S:ySpdSyn* transcripts at all the fruit developmental stages examined (Figure 3a inset). Expression of the endogenous *SlSpdSyn* gene was also regulated during the floral bud development, with high levels observed in 5 DBP and 10 DAP fruits (Figure 3b). Other stages had low levels of *SlSpdSyn* transcripts (Figure 3b). Transgene under the CaMV35 promoter increased *SlSpdSyn* transcripts at 5 DBP and that under SlE8 at 10 DAP stages of fruit development (Figure 3b). Constitutive expression of *ySpdSyn* in C15 line exhibited upregulated expression of endogenous *SlSpdSyn* gene to as much as 5.8-fold in 10 DAP ovaries when compared with WT fruit tissues (Table S1).

**Figure 3.** Changes in steady state levels of (**a**) *ySpdSyn* and (**b**) *SlSpdSyn* transcripts during early development of WT and *ySpdSyn* expressing tomato fruits. Total RNA from three biological replicates of floral buds at 5 days before pollination, and flower ovaries at 2, 5, 10, and 20 days after pollination were independently extracted and reverse transcribed. The levels of *ySpdSyn* and *SlSpdSyn* transcripts were determined using qRT-PCR with gene-specific primers (Table S3). The inset in upper panel shows the transcript levels of *E8:ySpdSyn* transgene in E8-8 tissues at a higher magnification. The relative expression levels were calculated by the 2−ΔΔC<sup>т</sup> method using *SlACTIN* (Solyc04g011500.2.1) as housekeeping gene and plotted as fold-respective to WT tissues. Shown are average ± standard error. Statistical analyses were performed using the XLSTAT ANOVA method using −5 DAP WT as reference.

### *2.3. Expression Patterns of Fruit Size and Shape Genes in Transgenic Fruits*

The expression patterns of *SUN1*, *OVATE*, and *FW2.2* in isogenic transgenic fruit are shown as fold change to corresponding WT fruit at 5 DBP and 2, 5, 10, and 20 DAP, respectively, in Figure 4. Insets in Figure 4 show the expression patterns for *SUN1*, *OVATE*, and *FW2.2* in WT floral buds and developing ovaries as fold-*ACTIN* (a house keeping gene) transcript levels. In WT floral tissues, the levels of *SUN1* and *OVATE* transcripts were upregulated at 5/20 DAP and 2 DAP, respectively, whereas *FW2.2* transcripts significantly increased at 10 and 20 DAP during floral and ovary development (Figure 4 inset). The patterns of *OVATE* and *SUN1* transcript levels in Ohio 8245 (WT) fruit is consistent with the WT fruit associated with the non-mutated *OVATE* gene [2]. Similar results were obtained in C15 fruits (Table S1).

**Figure 4.** Changes in transcript levels of fruit shape-related genes *SUN1*, *OVATE*, and *FW2.2* in WT and *ySpdSyn*-expressing transgenic tomato floral buds and flower ovaries. Transcripts were quantified using qRT-PCR and relative expression levels were calculated by the 2−ΔΔC<sup>т</sup> method using *SlACTIN* (Solyc04g011500.2.1) as housekeeping gene and plotted as fold-respective to WT tissues. Other details are the same as in the legend in Figure 3 legend.

A single base pair mutation from GAA to TAA in *OVATE* gene has been linked with functionality of this gene [2]. We checked the nucleotide sequence of *OVATE* in tomato cultivar Ohio 8245 and its isogenic transgenic lines C4, C15, and E8-8 to determine if WT or a mutated gene was residing in under-study genotypes. *OVATE* transcripts cloned from WT and three transgenic lines fruits showed absence of any mutation and thus would have more spherical shape phenotypes as observed in this investigation (Figure S1). Taken together, these results suggested that the observed obovoid fruit phenotype of transgenic line was associated with *ySpdSyn* transgene and not with the *OVATE* gene.

Patterns of transcript levels of the three fruit shape genes in transgenic lines (C4, C15 and E8-8) varied significantly compared to WT fruit (Figure 4; Table S1). *SUN1* transcripts were at higher levels in E8-8 than C4 or WT fruits at most stages of ovary development (Figure 4). The levels of *SUN1* transcripts in C4 were higher than WT at 2 DAP and 5 DAP stages but declined in 20 DAP ovaries in all the transgenic lines compared to WT fruit ovaries (Figure 4; Table S1). The *OVATE* transcript levels were significantly higher (>4-fold) in 5 DAP ovaries from C4 and E8-8 lines compared to WT ovaries. At other stages, the patterns of *OVATE* transcripts in transgenic ovaries were variable compared to the WT ovaries, with significantly higher levels at −5 and 10 DAP in C4 ovaries and at −5 and 20 DAP in E8-8 ovaries (Figure 4; Table S1). Consistent significant patterns for *FW2.2* transcripts levels in the fruits from C4 and E8-8 were not obtained, but generally, they were lower at all stages of ovary development (Figure 4, Table S1).

### *2.4. Expression Patterns of Selected Cell Division and Cell Expansion Genes in ySpdSyn Transgenic and Wild-Type Tomato*

The relative steady state transcript levels of several genes implicated in regulating cell division and cell expansion were examined in floral buds and ovaries from WT, C4, and E8-8 lines. These included cyclin (*CYC*), cyclin-dependent kinases (*CDKs*), *FSM1* (inhibitor of cell expansion), and *CCS52A*/*CCS52B* (promoters of cell expansion) and their interacting partners *KPR1* and CDK inhibitor *WEE1*. In WT and transgenic flower and ovary tissues, the *CDKA1* transcript levels did not change significantly from 5 DBP to 20 DAP (Figure 5, Figure S2). In WT tissues, the *CDKB2* transcripts significantly decreased from 5 DBP to 2 DAP, followed by several-fold increase until 10 DAP before a precipitous decline at 20 DAP during WT fruit development (Figure S2). The two transgenic lines showed different pattern compared to WT tissues with the *CDKB2* transcripts being about 6-fold higher in C4 and E8-8 ovaries at 2 DAP compared to WT. Thereafter, a noticeable decrease in these genes occurred in all genotypes (Figure 5). In C15 tissues, the *CDKB2* transcripts significantly increased several-fold from 5 DBP to 2 DAP before precipitously declining at 20 DAP (Table S1).

Expression patterns for the three cyclins, *CYCA*2, *CYCB*2, *CYCD*3 (one from each of the three cyclin genes families), were different. Overall, their transcript levels were in the increasing order of *CYCB*2 > *CYCD*3 > *CYCA*2 (Figure 5, Figure S2). Levels of the *CYCA2* transcripts in WT ovaries were variable but not significantly different at any stage of floral and ovary development (Figure S2). In contrast, the transcript levels of *CYCB2* and *CYCD3* in WT tissues were about 4- and 12-fold higher, respectively, at 10 DAP before perceptible decline by 20 DAP (Figure S2). These transcript patterns in WT fruit are similar to that reported previously. The patterns of three cyclin gene transcripts in all transgenic ovaries were different. In the E8-8 tissues, several-fold increase in the *CYCA2* transcripts at 2 and 10 DAP stages was apparent, but this was not seen in C4 tissues (Figure 5). The steady state level of *CYCB2* was about 11-fold higher in both the transgenic lines compared to WT tissues at 2 DAP before declining notably in the transgenic tissues (Figure 5). The *CYCD3* transcripts levels had a bimodal pattern in E8-8 tissues showing about a 6-fold increase at 2 DAP and 5 DAP, while a 6-fold increase was obtained in C4 tissue only at 2 DAP (Figure 5). The steady state levels of all cyclin genes examined decreased considerably in 20 DAP tissues in all genotypes.

The pattern of *KRP1* (a CDK inhibitor) transcript accumulation did not change significantly during the floral and ovary development of WT (Figure S2). The C4 ovaries had 7-fold higher accumulation at 2 DAP, but in E8-8 ovaries, there was no change in *KRP1* transcripts at 2 DAP (Figure 5). The levels of CDK inhibitor gene, *WEE1*, increased until 10 DAP in WT (Figure S2), whereas in C4 and E8-8 tissues 6- to 7-fold increase in *WEE1* transcript levels was apparent at 2 DAP (Figure 5).

The transcript levels of *FSM1*, a cell expansion gene, increased several-fold in WT ovaries from 2 to 10 DAP and declined thereafter (Figure S2), a pattern similar to that reported in the facultative parthenocarpic line L-179 (*pat-2*/*pat-2*) [38]. The *FSM1* transcript levels in the C4 line were highest at 5 DBP (floral buds) and in E8-8 at 20 DAP compared to the other stages of development (Figure 5). The cell expansion promoting genes, *CCS52A* and *CCS52B*, had unique and differing expression patterns among the three genotypes analyzed (Figure 5). In WT tissues, transcript level of *CCS52A* was highest at 10 and 20 DAP and that of *CCS52B* was highest at 5 DBP and 10 DAP (Figure S2). The *CCS52A* transcript levels were significantly high at 5 DBP in C4 and E8-8, and at 5 DAP only in E8-8 ovaries (Figure 5). Highest levels of *CCS52B* transcripts during flower and ovary development was observed in E8-8 at 20 DAP (Figure 5).

**Figure 5.** Changes in steady state transcript levels of cyclin-dependent kinases (*CDKA1* and *CDKB2*), cyclins (*CYCA2*, *CYCB2* and *CYCD3*), CDK1-inhibitors (*KRP1* and *WEE1*), and cell expansion-regulating genes (*FSM1*, *CCS52A* and *CCS52B*) in WT and *ySpdSyn*-expressing transgenic tomato floral buds and flower ovaries. Transcripts were quantified using qRT-PCR and relative expression levels were calculated by the 2−ΔΔC<sup>т</sup> method using *SlACTIN* (Solyc04g011500.2.1) as housekeeping gene and plotted as fold-respective WT tissues. Other details were the same as described in the Figure 3 legend.

### *2.5. Levels of Free and Bound PAs in Floral Buds and Developing Ovaries*

The free and bound levels of Put, Spd, and Spm were quantified in floral tissues of WT and the transgenic lines (C4, C15, E8-8) at −10, −5, 2, 5, 10, and 20 DAP (Figure 6, Table S2). Put, Spd, and Spm levels varied in all the three genotypes examined, with transgenic fruit having greater increase in bound forms of these PAs (Figure 6; Table S2). In the WT, the highest levels of free PAs were found at −5 and 5 DAP for Put, 5 and 10 DAP for Spd, and 5 DAP for Spm. The levels of free PAs declined dramatically at 20 DAP stage of WT fruit. In the transgenic lines, free Put levels were lower than the WT at all the developmental stages examined, except for the E8-8 fruit, which had significantly higher free Put level at 5 DBP than the other stages of development (Figure 6; Table S2). In contrast, levels of bound Put in transgenic lines increased several-fold from 10 DBP to 5 DAP and then decreased to levels comparable with WT at 20 DAP (Figure 6).

Free Spd levels in the WT line slighty increased from 10 DBP to 10 DAP stages and then declined at 20 DAP. For WT tissues, the content of bound Spd was several-fold lower than free Spd at all the fruit developmental stages (Figure 6). Compared to the WT, the free Spd levels had similar pattern of accumulation in developing floral buds and ovaries from the transgenic lines. The C4 and E8-8 fruit had the highest levels of free Spd at 5 DAP and 5 DBP, respectively (Figure 6). Remarkably, the bound Spd levels were high in all the transgenic flowers and developing ovaries at most stages of development from 10 DBP to 5 DAP, as compared to the WT line, but declined after 10 DAP in all the transgenic lines. The bound Spd levels peaked at 5 DBP in all three transgenic lines i.e., C4, C15, and E8-8 (Figure 6; Table S2).

**Figure 6.** Changes in the levels of free and bound forms of putrescine, spermidine, and spermine (ïmol/g FW) in floral buds and ovaries of WT and *ySpdSyn*-expressing transgenic tomato plants. Free and bound PAs were extracted and quantified by HPLC, as described in the Material and Methods section. Shown are average ± standard error (n ≥ 3 biological replicates). Similar letters above standard error bars indicate non-significant difference (at 95% confidence interval) among genotypes.

Thelevels of free Spm inWT floral tissues werelower thanits bound form throughout the development of floral and ovary tissues (Figure 6). Levels of free Spm increased from 5 DBP to 5 DAP before declining in both C4 and E8-8 genotypes (Figure 6). Like Spd, bound form of Spm in C4 and E8-8 was several-fold higher than the free form throughout the development of floral and ovary tissues. The pattern of bound form was similar in the three transgenic lines (Figure 6; Table S2). The highest bound Spm levels were found in E8-8 ovaries at 2 DAP (Figure 6). The C15 and C4 tissues had similar patterns (Table S2).

The levels of bound forms of Put, Spd, and Spm in WT tissues ranged from 2% to 52%, 4% to 88%, and 47% to 61% of that of total Put, Spd, and Spm, respectively. The percentage of bound-to-free PAs in

the transgenic tissue had a wide range, varying from 41% to 293%, 5% to 233%, and 56% to 1630% for Put, Spd, and Spm, respectively (Figure 6). The 5 DBP and 2 DAP transgenic tissues contained up to 16-fold higher bound Spm compared to free Spm (Figure 6). These data indicate that expression of *ySpdSyn* under a fruit-specific promoter SlE8 (E8-8 line) or under a constitutive CaMV35S promoter (lines C4, C15) results in large accumulation of bound forms of Put, Spd, and Spm, as compared to the WT.

### *2.6. Transgene Increased Expression of PA Biosynthesis and Catabolic Pathway Genes*

We also quantified the levels of the biosynthesis and catabolic pathway genes of PAs in WT, C4 and E8-8 ovaries at 2 DAP. The steady state transcript levels of *ADC* and *ODC* genes increased significantly in 2 DAP ovaries in both C4 and E8-8 lines (Figure 7a). In 2 DAP ovaries, the transcripts of three tomato *SAMdc* genes were found to be differentially affected, with the steady state levels of *SAMDC2* and *SAMDC3* being higher in E8-8 line and slightly downregulated in the C4 line (Figure 7b). In contrast, the steady state levels of *SAMDC1* transcripts remained unchanged in response to transgene expression. Among the PA catabolic genes, significantly higher levels of *CuAO* and *CuAO-like* gene transcripts accumulated in the C4 line compared to the WT, whereas significantly higher levels of *CuAO-like* and *PAO4-like* gene transcripts were observed in 2 DAP ovaries from E8-8 lines (Figure 7a).

**Figure 7.** Steady state transcript levels of *ODC*, *ADC*, *Polyamine oxidases* (*CuAo*, *CuAo-like* and *PAO4-like*) and *SAMDC* genes (*SAMDC1*, *SMADC2*, *SAMDC3*) in WT and transgenic tomato ovaries 2 days after pollination. Transcripts were quantified using qRT-PCR and relative expression levels were calculated by the 2−ΔΔC<sup>т</sup> method using *SlACTIN* (Solyc04g011500.2.1) as housekeeping gene and plotted as fold-respective to WT tissues. Other details were the same as in the Figure 3 legend.

### *2.7. Statistical Analyses Accentuate Positive Correlations between Specific PAs levels, Gene Transcripts, and Fruit Architectural Parameters During Early Fruit Development*

Principal Component Analyses (PCA) of free and bound Put, Spd, and Spm content, together with expression levels of various cell division and cell expansion genes were measured at developmental stages from 2 DAP to 20 DAP (Figure 8). The free and bound forms of all three PAs clustered in the positive half of F1 along with *CYCB2*, *CDKA1*, *CDKB2* (two gene associated with cell division) and their inhibitors *KRP1* and *WEE1* clustered primarily with 2 and 5 DAP, the stages that regulate cell number in developing ovaries (Figure 8a). The observed clustering of cell expansion genes *CCS52A*, *CCS52B*, and *FSM1* predominately with 10 and 20 DAP stages and away from the free and bound forms of three PAs suggests that PA levels are inversely associated with fruit expansion phases (Figure 8a). Among the fruit shape genes, *SUN1* and *OVATE* expression were positively associated with free forms of all the three PAs quantified, whereas *FW2.2* was found to have a negative association (Figure 8a). The number of cell layers and pericarp thickness were associated with 10 and 20 DAP stages of the three transgenic lines, but not with WT fruit (Figure 8b). However, with further progression of ovaries from 10 DAP stage to 20 DAP stage, the number of cell layers and pericarp thickness were associated with both transgenic lines and WT (Figure 8b), suggesting that PAs cause early changes in these parameters.

**Figure 8.** Principal component analyses (PCA) for polyamines (PAs), gene transcripts (cell division, cell expansion, fruit shape-realated and polyamine pathways) and cytological attributes of developing ovaries from WT and transgenic tomato lines. (**a**) PCA between free and bound forms of PAs and transcript levels of genes involved in cell division, cell expansion, and fruit shape development in tomato ovaries at 2, 5, 10 and 20 days after pollination (DAP); (**b**) PCA between free and bound forms of PAs and cell layers, and pericarp thickness of developing tomato ovaries at 5, 10, and 20 DAP; (**c**) PCA between free and bound forms of PAs and transcript levels of genes involved in PA biosynthesis (*ADC*, ODC, *SAMDCs* and *SlSpdSyn*/*ySpdSyn*) and catabolism (*CuAO*, *CuAO-like*, *PAO4-like*) in tomato ovaries at 2 DAP. Color codes: green—free and bound forms of PAs; blue—genotype and its ovary development stage (within parentheses); red—gene trasncripts or cells cytological attributes. Abbreviations: Put\_F/B—free or bound form of Put; Spd\_F/B—free or bound form of Spd; Spm\_F/B—free or bound Spm.

During the cell division phase (2 DAP), free Spd and bound forms of the three PAs were found positively associated with *ySpdSyn* and *SAMDC2* transcripts and negatively with all other PA biosynthetic as well as catabolic gene transcripts examined (Figure 8c). The Pearson correlations coefficient analysis showed that free Spd levels were positively correlated (α ≥ 0.05) with transcript levels of *SUN1* and *OVATE*, while bound forms of Put, Spd and Spm were positively correlated with *SUN1*, *OVATE*, *CDKA1*, *CDKB2*, *CYCB2*, *KRP1*, and *WEE1* (Figure S3). Notably, transcripts of fruit shape gene *FW2.2* and cell cycle genes *CCS52A*, *CCS52B*, *FSM1*, *CYCA2*, and *CYCD3* did not have significant correlation with free or bound forms of PAs, suggesting a limited role of these cell cycle genes in affecting phenotype of fruit carrying FW2.2 mutation (Figure S4).

### **3. Discussion**

We show here that PAs are cellular modifiers of fruit shape in tomato. This conclusion is based on the characterization of independent transgenic lines of tomato transformed with a heterologous *ySpdSyn* gene under two different promoters. The shape modification results in a more obovoid fruit shape, as assessed using three independent isogenic transgenic lines in comparison to the relatively spherical round shape of WT fruit (Figure 1a). The structural basis of altered morphology in the transgenic fruit is likely due to the reduction of cellular layers in pericarp, as the thickness of pericarp decreased by 50%, 54%, and 80% of WT in C15, E8-8, and C4 fruits, respectively (Figure 2c,d). Cytological examination of slices from ovaries at different stages of development revealed that PAs are associated with the periclinal cell division, leading to reduced number of cell layers in pericarp and the cell number in medial-lateral direction of pericarp, but not cell size per se. However, we could not conclude that reduced cellular layers and cell size are responsible for the obovoid shape and size of tomato fruit.

More extensive ultrastructure work should enable further understanding of the structural basis of diversity observed in tomato fruit. Altered fruit phenotypes have also been observed in mutants compromised in various growth and development processes, including flowering, fruiting, and photosynthesis, suggesting that many plant genes/proteins/processes influence fruit development [1,39]. Also, breeding for increased fruit size had previously led to large variations in fruit shape of cultivated tomato, from round to oblate, pear-shaped, torpedo-shaped, and bell pepper–shaped [1].

Several genes that affect tomato fruit shape have been identified and characterized [40]. Our results demonstrate that transgenic tomato with enhanced levels of PAs are altered in the expression of *SUN1* and *OVATE* during the early stages of development. Expression of *SUN1* leads to elongated fruit in tomato [17]. Expression of *SUN1* increased in the transgenic fruit, suggesting that it is associated with the elongation of fruit likely causing a more obovoid phenotype (Figure 4). In the mutated form, *OVATE* gene enhances obovoid over the round phenotype in tomato fruit [2]. By cloning *OVATE* mRNA from WT and three transgenic lines and determining DNA sequence of these clones, we have shown that the *OVATE* gene in Ohio 8245 tomato cultivar and its isogenic transgenic lines is a non-mutated type and would, therefore, favor a more spherical round fruit in Ohio 8245 cultivar (Figure S1). Thus, higher expression of this gene cannot be the cause of obovoid phenotype observed in the three transgenic lines. In addition to its phenotype, the *OVATE* gene is known to affect multiple factors in fruit, including metabolome, physiology, and fruit quality [41,42]. *Fw2.2* primarily affects fruit size as studies with transgenic fruit expressing WT *FW2.2* gene showed reduction in the size of fruit cv. Mogeor from a large one to a smaller one [6]. *FW2.2* has been also reported to have pleiotropic effects, especially on the distribution of photosynthate among all fruits on the plant [15]. The levels of *FW2.2* decreased significantly in transgenic fruit from 2 DAP to 20 DAP (Figure 4), which may result in the increase of fruit size and contribute to fruit shape. Expression of *SlSpdSyn* was upregulated during both the cell division and the cell expansion phases of fruit development, suggesting a dual role of Spd in fruit development (Figure 3).

It is generally accepted that the levels of free cellular PAs are under a tight genetic regulation to maintain PAs homeostasis during the growth and development of organisms [26,36,43]. Our data support this hypothesis. However, the role(s) of bound PAs in maintaining PAs homeostasis remains unknown. A nexus between PA levels, ratios of different PAs, and regulation of flower development is known [26]. Several mechanisms have been proposed to maintain the PAs homeostasis in organisms. These include biosynthesis, catabolism, conjugation, and transport of various PAs. Additionally, feedback inhibition and activation of PAs biosynthetic enzymes have been reported and suggested to play a role in establishing PA homeostasis. Due to the cationic nature of various PAs, their binding to various macromolecules in cellular milieu would also affect their cellular homeostasis [24]. Catabolism of PAs not only changes the levels of Put by back conversion of Spd and Spm, but also generates bioactive H2O2 [24]. Significant increases in the gene transcripts catalyzing PA catabolism, viz., *CuAO*, *CuAO-like*, and *PAO4-like* amine oxidases, suggest inter-conversion of Spm to Spd and Spd to Put (Figure 6), as reported by others [44,45]. PA depletion has also been implicated in a large number of plant responses and cytotoxic effects of excessive PAs [46,47]. Genetic regulation of PA homeostasis has been suggested as a means to maintain the cellular PAs concentration below toxic levels [43].

Expression of *SlSpdSyn* was upregulated both during the cell division and the cell expansion phase of fruit development, suggesting a dual role of PAs in fruit development (Figure 3). A large increase in bound forms of Put, Spd, and Spm in transgenic ovaries was observed in this study. However, the chemical nature of the bound PAs still needs to be investigated (Figure 6). The bound forms of various PAs could represent formation of conjugates or increased ionic binding to protein, RNA, DNA, and other chemical moieties. The molecular basis of enhanced Put, Spm, and Spm in such bound forms is not known, but would likely result from enhanced biosynthesis of PAs, especially when PA biosynthesis genes are upregulated, as observed in this investigation (Figure 7). However, the role of catabolism in this scenario remains to be determined. Elucidation of the chemical basis of massive accumulation of bound PAs in transgenic fruits should help understand their role in PAs homeostasis. In this context, it is known that PAs induce Spd/Spm *N1*-acetyltransferase and efflux transporters and likely self-regulate their levels [48–51].

### **4. Materials and Methods**

### *4.1. Plant Material and Growth Conditions*

Wild type and transgenic tomato lines homozygous for *ySpdSyn* gene fused to either constitutive CaMV35S promoter (lines C4 and C15) or fruit/ethylene-specific SlE8 promoter (line E8-8) have been described previously [27]. These plants were grown in high porosity potting mix (52Mix, Conrad Fafard, Inc., Agawam, MA, USA) in a greenhouse with 16 h day/8 h night photoperiod and 23 ◦C day/18 ◦C night temperature. Tomato flowers and fruit developmental stages were tagged. Samples were collected on day 10 and day 5 before pollination (DBP) and 2, 5, 10, and 20 days after pollination (DAP) (Figure S4), immediately frozen in liquid N2 and stored at −80 ◦C until further use. To determine fruit shape index, fruits at various stages of development were sliced from proximal-distal axis, scanned, and analyzed using 'Tomato Analyzer' software [52].

### *4.2. Cytological Analysis*

Axis slices of fresh tissues were fixed in 10% formalin (pH 6.8–7.2) and processed in Tissue-Tek VIP® (Sakura Finetek USA Inc., Torrance, CA, USA) using following sequential treatments: 70% ethanol for 50 s, 95% ethanol for 50 s (2 cycles), 100% ethanol for 33 s (3 cycles), toluene for 60 s (2 cycles), and paraffin for 45 s at 63 ◦C (4 cycles). For deparaffinization, tissues were dipped in xylene for 5 min (2 cycles), 100% ethanol for 2 min, 95% ethanol for 2 min, and 70% ethanol for 2 min, followed by rehydration in deionized water. Tissues were embedded in paraffin using Cryo-therm (Lipshaw, Tucker, GA, USA) and 5–7 μm thick sections were made using microtome (Finesse ME, Thermo Electron, Waltham, MA, USA). These were then stained with 1% toluidine blue–O, sectioned, dehydrated by serial quick dips in 70%, 95%, and 100% ethanol, and xylene [53]. Slides were scanned with ScanScope CS (Aperio Technologies Inc., Buffalo Grove, IL, USA) at different magnifications, 40x being the maximum. The digital images were analyzed for cell number and cell layers using

ImageScope 11 (Aperio Technologies Inc., Buffalo Grove, IL, USA), and for cell size by ImageJ [54]. Three independent biological replicates were analyzed at each stage from each genotype.

### *4.3. Transcript Analysis by Quantitative Real-Time PCR*

For qRT-PCR, the frozen tissues were ground to powder using liquid N2, total RNA was extracted from 100 mg tissue powder using QIAzol® Lysis reagent (Qiagen Sciences, Germantown, MD, USA) and purified using RNeasy® Mini Kit (Qiagen Sciences, Germantown, MD, USA). The RNA samples were treated with one unit RQ1 RNase-free DNase (Promega Corporation, Madison, WI, USA) and first-strand cDNA was synthesized using SuperScript II Reverse Transcriptase (Invitrogen, Waltham, MA, USA). GoTaq® qPCR Master Mix (Promega Corporation, Madison, WI, USA) was used in qRT-PCR reaction mixture. All primers and cDNA templates were optimized for gene expression analysis according to the 2−ΔΔCT method [55]. StepOnePlusTM Real-Time PCR System (Applied Biosystems, Waltham, MA, USA) was used with program sequence as follows: 95◦C for 10 min; 95 ◦C for 15 s and 60 ◦C for 60 sec (40 cycles); 95 ◦C for 15 s; and 60 ◦C for 60 s. Comparative CT values of gene expression were quantified using StepOneTM 2.0 software (Applied Biosystems, Waltham, MA, USA). *ACTIN* was used as standard housekeeping gene to normalize the expression of target genes. Accession numbers of all genes with their primer sequences used in this study are listed in Table S3. All data presented here represent average ± standard error of a minimum of three independent biological replicates.

### *4.4. Quantification of Polyamines by High Pressure Liquid Chromatography*

Polyamines were extracted from floral buds and fruit tissues of tomato plants and dansylated as described previously [56] with some modifications. Briefly, 200 mg of finely ground sample was homogenized in 800 μL of 5% ice-cold perchloric acid (PCA) using a hand-held homogenizer precooled at 4 ◦C for 1 h. The homogenate was centrifuged at 20,000 *g* for 30 min at 4 ◦C and both the supernatant and residue were collected separately. PAs present in the supernatant were labeled as free PAs. To quantify PCA-insoluble bound PAs, the 20,000 *g* pellet was washed twice with 5% PCA, re-suspended in 800 μL of 5% cold PCA, and then hydrolyzed in an equal volume of 6N HCl for 18 h at 110 ◦C. Saturated sodium carbonate (200 μL) and 1,7-heptanediamine (400 μL, as an internal standard) were added to the 100 μL supernatant or the hydrolyzates, and then dansylated with dansyl chloride for 60 min at 60 ◦C in the dark. Dansylation was terminated by adding 100 μL proline and incubating the reaction mixture for 30 min at 60 ◦C. Dansylated PAs were extracted in 500 μL toluene, air dried, and dissolved in 250 μL acetonitrile. The samples were then diluted four times with acetonitrile, filtered through a 0.45 μm syringe filter (National Scientific, Claremont, CA, USA), and fractionated on a reversed-phase Nova-Pak C18 column (3.9 × 150 mm, 4.0 μm pore size) using Waters 2695 Separation Module equipped with Waters 2475 Multi λ fluorescence detector (excitation 340 nm, emission 510 nm) with a binary gradient composed of solvent A (100% Water) and solvent B (100% acetonitrile) at 1 mL/min. Initial conditions were set at 60:40 (A:B) and then a linear gradient was processed with conditions set at 30:70 (A:B) at 3 min; 0:100 (A:B) at 10 min, and 60:40 (A:B) at 12 min. The column was flushed with 60:40 (A:B) for 3 min before the next sample injection. To determine PAs recovery and generate calibration curves, authentic PA standards (Sigma-Aldrich, St. Louis, MO, USA) were used as control. PAs were integrated and quantified using Millennium<sup>32</sup> 4.0 from Waters Corporation. After hydrolysis, PCA-soluble and PCA-insoluble samples were quantified and are designated as free and bound forms of each PA, respectively, throughout the manuscript.

### **5. Conclusions**

Our findings on gene transcripts regulating cell division and cell enlargement in response to the expression of *ySpdSyn* transgene and subsequent alteration in PAs levels are summarized in a model (Figure 9). The cellular levels of bound Put, Spd, and Spm correlate with *CDKA1*, *CDKB2*, *CYCB2*, *KRP1*, and *WEE1* transcripts, but not *CCS52A*, *CCS52B*, *CYCA2*, and *CYCD3* genes in transgene-associated change in fruit shape. The patterns of gene expression during early fruit development indicate that

the upregulated *CDKB2* may in turn activate *CYCB2* expression during ovary development at 2 DAP and 5 DAP stages, the phase of active cell division of pollinated ovaries. The precipitous decrease in the expression of all cyclins in 20 DAP tomato fruit is consistent with the end of cell division phase at this stage of fruit development [37]. It is noted here that fruit shape changes occur at an early stage of fruit development (before 5 DAP), during enhanced accumulation of PAs, and continue to persist during fruit development and ripening, as was also found to be the case for *SUN1* and *OVATE* transgenic fruits. Our results indicate that the expression of the transgene is associated with reduced cell division in the medial-lateral direction of fruit, which can cause more elongated fruit shape, an effect also seen in tomato fruit over-expresser for *SUN1* gene [17,57]. In our studies, the parental Ohio 8245 tomato cultivar expressed a functional *OVATE* gene, which would reduce cell division at the distal end and induce more round fruit shape. Thus, increase in obovoid shape of the transgenic fruit seems to be linked to both *SUN1* and *OVATE* genes, with *SUN1* likely having a stronger effect on the fruit phenotype.

**Figure 9.** A model showing the polyamine-mediated changes in transcript levels of genes involved in cell cycle progression and endoreduplication during tomato fruit development. In eukaryotes, cell cycle is mainly comprised of interphase and mitosis (M phase). Interphase is further divided into three phases: i) G1 (Gap 1) during which cell increases in size and becomes ready for DNA synthesis; ii) S (Synthesis) where DNA replication occurs; and iii) G2 (Gap 2) during which cell either continues to grow until ready for mitosis or enter into DNA amplification phase, called endoreduplication [58,59]. Expression levels of cyclins or CDKs during different phases of cell cycle progress are indicated in colors. CDK-activating kinases are highlighted in gray box. Transcript levels of genes enhanced by higher polyamines (Spd/Spm) are indicated with upward-facing thick red arrows. Abbreviations: G—Gap phase; M—Mitosis phase; S—Synthesis phase; Cyc—Cyclins; CDKs—Cyclin dependent kinases; KRPs—Kip-related proteins.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2223-7747/8/10/387/s1, Figure S1: Sequence alignment of *OVATE* gene in WT and transgenic fruits with its mutated version (*ovate*) containing stop codon (TAA); Figure S2: Expression patterns of various cell division and cell expansion genes

in WT developing floral and ovary tomato tissues; Figure S3: Correlation coefficient analysis of PA levels and transcripts of genes involved in fruit shape, cell cycle progression, cell expansion and polyamine biosynthesis and catabolism; Figure S4: Representative flower and fruit developmental stages registered in *Solanum lycopersicum* cv. Ohio8245; Table S1: Steady state transcript levels of cell division, cell expansion, fruit shape and polyamine pathway genes at various developmental stages of floral and ovaries tissues in transgenic tomato line C15; Table S2: Free and bound forms of putrescine, spermidine and spermine in floral buds and ovaries tissues at various developmental from wild-type (WT) and *35S:ySpdSyn* expressing transgenic tomato line C15; Table S3: List of genes and their primer sequences used for quantitative real-time PCR analyses. References [60] are cited in the Supplementary Materials.

**Author Contributions:** Conceptualization, A.K.H. and R.A.; Data curation, R.A. and S.F.; Formal analysis, A.K.M. and A.K.H.; Funding acquisition, A.K.M. and A.K.H.; Investigation, R.A.; Methodology, S.F.; Project administration, A.K.H.; Supervision, A.K.H.; Writing—original draft, R.A. and A.K.H.; Writing—review & editing, R.A., A.K.M. and A.K.H.

**Funding:** The studies were partly supported by a US–Israel Binational Agricultural Research and Development Fund to AKH and AKM (Grant No. IS-3441-03), and USDA-IFAFS program grant (Award No. 741740) to AKH. RA was partially supported by the Higher Education Commission, Islamabad (Pakistan). AKM is supported through USDA-ARS intramural Project No: 8042-21000-143-00D. The mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## **Variations of Secondary Metabolites among Natural Populations of Sub-Antarctic** *Ranunculus* **Species Suggest Functional Redundancy and Versatility**

**Bastien Labarrere 1, Andreas Prinzing 1, Thomas Dorey 2, Emeline Chesneau <sup>1</sup> and Françoise Hennion 1,\***


Received: 24 May 2019; Accepted: 16 July 2019; Published: 19 July 2019

**Abstract:** Plants produce a high diversity of metabolites which help them sustain environmental stresses and are involved in local adaptation. However, shaped by both the genome and the environment, the patterns of variation of the metabolome in nature are difficult to decipher. Few studies have explored the relative parts of geographical region versus environment or phenotype in metabolomic variability within species and none have discussed a possible effect of the region on the correlations between metabolites and environments or phenotypes. In three sub-Antarctic *Ranunculus* species, we examined the role of region in metabolite differences and in the relationship between individual compounds and environmental conditions or phenotypic traits. Populations of three *Ranunculus* species were sampled across similar environmental gradients in two distinct geographical regions in îles Kerguelen. Two metabolite classes were studied, amines (quantified by high-performance liquid chromatography and fluorescence spectrophotometry) and flavonols (quantified by ultra-high-performance liquid chromatography with triple quadrupole mass spectrometry). Depending on regions, the same environment or the same trait may be related to different metabolites, suggesting metabolite redundancy within species. In several cases, a given metabolite showed different or even opposite relations with the same environmental condition or the same trait across the two regions, suggesting metabolite versatility within species. Our results suggest that metabolites may be functionally redundant and versatile within species, both in their response to environments and in their relation with the phenotype. These findings open new perspectives for understanding evolutionary responses of plants to environmental changes.

**Keywords:** *Ranunculus biternatus*; *Ranunculus pseudotrullifolius*; *Ranunculus moseleyi*; secondary metabolite variation; amines; quercetins; natural populations; environment; redundancy; sub-Antarctic plants

### **1. Introduction**

Plants are sessile organisms that have to face changes in their environment. The metabolome stands at an interface between perception of environmental signals and their translation in life history traits, therefore playing a major role in allowing the organism to sustain environmental constraints. Plant metabolites were selected through long-term adaptation and diversification [1]. While primary metabolites (e.g., protein amino acids, carbohydrates), defined as essential to plant physiology are relatively few, the secondary metabolites are especially diverse [2]. Secondary metabolites play major roles in the interactions between the plant and its environment [3], being involved in protection against environmental stresses, competition, or pollinator attraction [2,4,5] and some in vegetative or floral development [6,7]. While the macroevolution of secondary metabolites is becoming more and more

precisely deciphered [1,8–12], in contrast relatively little is known about their microevolution [13,14]. In particular, the patterns of variation of the metabolome in nature need to be further investigated.

In nature, plants form populations and in general, plants within populations are more closely related while plants among populations may show stronger evolutionary differences. Equally, populations within regions often tend to be more closely related to each other than populations among regions. Variability in the metabolite composition among populations within species is known for many plant species and metabolites [15–22]. Larger-scale variation such as among regions or latitudes was also investigated, sometimes with the aim to enrich biochemical resources. Thus, studies also show variability in plant metabolite composition across latitudinal gradients or regions [23,24]. Yet, environments may be more different among populations or regions as within, and differences in metabolite composition among populations or regions might hence be dependent or independent of environments. However, relatively few studies have examined differences in metabolite composition among populations or regions, trying to dissociate metabolite variations that are dependent on environments and such that are independent [23–27]. This latter part of variation in metabolite composition that would be independent of environmental factors would likely reflect either selection by unknown past environments or neutral microevolutionary differentiation among populations or regions [13,14] and remains largely to be described [9,28].

High diversity of secondary metabolites has already been reported to correlate with functional diversity [2,5,29]. First, functional convergence is observed through metabolite macroevolution [1,7]. Depending on lineage or species, different metabolites may respond to the same environment or be related to the same trait, called "functional redundancy". Such functional redundancy is observed across all metabolites and plant lineages [1]. Yet, functional redundancy of secondary metabolites might also be observed among plants within species, but this remains to be investigated [30]. Secondly, metabolite versatility has also been observed: a given metabolite may have different roles within a given plant species in different organs or in different environments [1,31,32]. However, metabolite versatility has had little attention. Notably, whether redundancy or versatility might also emerge among populations that occupy similar environments but distant regions remains unknown. We hypothesize that, within species, neutral microevolutionary differentiation among distant but environmentally similar regions exists, and it includes the function of metabolites. Specifically, we hypothesize that functional redundancy or versatility exist within species among plant populations occupying such distant regions. A first step to test these hypotheses would be to search for patterns of correlations in nature that are consistent with functional redundancy or versatility of metabolites, paving the ground for later investigation of their physiological functions. For such a correlational approach, we predict that depending on the region, different metabolites might be correlated to the same environmental factor or the same trait in plants, suggesting functional redundancy. Moreover, depending on the region, the same metabolite might be correlated to different environmental factors or different traits in plants, suggesting functional versatility.

We aim to determine differences in secondary metabolite composition or function in natural populations of plants located in distant but environmentally similar regions. This aim requires several populations of given species distributed across wide environmental gradients within each of multiple regions. These conditions are present in sub-Antarctic Iles Kerguelen [26]. Located in the southern Indian Ocean the Iles Kerguelen harbor a wide range of abiotic environmental conditions [33] and distinct regions across which plants are distributed. This also requires metabolites that are known to vary and play roles in the response to environment or in traits. Among other metabolites, such is the case with amines and flavonoids [34,35]. Previous work comparing the amine metabolomes of nine species showed that amine composition of populations of plants in Iles Kerguelen varied in relation to both species and the environment [26]. Furthermore, previous work on flavonoids in the three *Ranunculus* species growing in Iles Kerguelen showed that quercetins were the only flavonols in these species and that composition of populations varied in relation to both species and the environment [36]. Amines include aliphatic polyamines, acetyl conjugates, and aromatic amines. Polyamines are low

molecular weight polycationic molecules with amino groups [7,37] and are described as "growth regulators", being involved in various internal processes such as growth control, DNA replication and cell differentiation, and organ development [3,34]. Polyamines, and aromatic amines by now, are also known to respond to external environment and be involved in the protection of plants against abiotic stresses [7,37,38]. Quercetins are compounds with variable phenolic structures; they belong to flavonols among flavonoids [35]. Quercetins are mainly antioxidants and are involved in the protection of plants against abiotic stresses [35]. A trade-off between flavonoid (including quercetin) concentration and growth is commonly suggested [39]. Therefore, to address the complex variation of plant metabolite composition across populations, environments, traits, and regions in nature, we performed targeted analyses of amines and quercetins. Using two independent metabolite families, we aimed at reinforcing our conclusions.

We sampled populations of the three *Ranunculus* species native to Iles Kerguelen across environmental gradients in two different geographical regions. We focused on environmental factors known to have major effects on these plants, i.e., soil hydric conditions [36,40]. We measured plant metabolite composition (amines and quercetins) and morphological phenotype, and characterized the environment (soil hydric conditions) of populations. We asked whether (i) environment alone explains differences in metabolite composition among populations within species or whether environment-independent microevolutionary differentiation among regions also shapes metabolite composition, (ii) metabolites show patterns consistent with redundancy within species, different metabolites being related to the same environments or morphological traits in the two different regions, (iii) metabolites show patterns consistent with versatility within species, the same metabolites being related to different environments or morphological traits in the two different regions.

### **2. Results**

### *2.1. Populations Di*ff*er in Total Metabolite Contents and Metabolite Composition Partly Independently of the Environment*

To identify an effect of population on total metabolite contents that was not due to environmental differences among populations, we conducted an ANCOVA with environmental conditions as co-variables. We found that total contents of amines or quercetins differed significantly among populations across regions and within regions in most cases, i.e., statistically independent of the environmental variables considered (Table 1 and Figure 1).


**Table 1.** Differences in the total contents of amines or quercetins among populations \*.



\* For each species, ANCOVAs were conducted either across or within regions with "population" as factor and environmental conditions as co-variables. Each line represents a separate ANCOVA and only the result for the factor "population" is shown. *p*-values within regions are sequential Bonferroni's corrected and bold when <0.05.

**Figure 1.** Variance of total contents of amines or quercetins among populations. Means and SD (standard deviation) are given. See Table 1 for test statistics.

To identify an effect of population on compositions (rather than totals) of metabolites, we conducted multivariate ANCOVAS with environmental conditions as co-variables. We found that metabolite (amine or quercetin) composition differed significantly among populations across regions within species, i.e., statistically independent of the environmental variables considered (Table 2).


**Table 2.** Differences among populations in the composition of amines or quercetins \*.

\* For each species, multivariate ANCOVAS with "population" as factor, environmental variables as covariables, and the levels of all amines (top) or all quercetins (bottom) as dependent variables, done either across regions or within the Australia region (analysis was not possible in Isthme Bas due to collinearity of environmental conditions and populations in this region).

### *2.2. Environments Partly Explain Variation of Total Metabolite Contents and Metabolite Composition across Populations*

For total amine content, relationships with environmental conditions (soil water saturation, pH or conductivity) were significant for five out of nine comparisons in *R. biternatus* (across or within regions) and three in both *R. pseudotrullifolius* and *R. moseleyi* (Table S1).

Amine composition was significantly related to overall environment and individual environmental conditions, within or across regions in the three species (Table 3 and Figure 2 for an example of the full multivariate relationships between all environmental variables and all compounds). Testing the relationships between amine composition and individual environmental conditions, we found that individual amine levels were correlated to soil water saturation in 17 out of 48 comparisons (across or within regions) in *R. biternatus*, 16 in *R. pseudotrullifolius*, and six in *R. moseleyi* (Figure S1). Individual amine levels were correlated to soil pH in 11 out of 48 comparisons in *R. biternatus* and *R. pseudotrullifolius* and 15 in *R. moseleyi*. Individual amine levels were correlated to conductivity in 20 out of 48 comparisons in *R. biternatus*, 21 in *R. pseudotrullifolius*, and 17 in *R. moseleyi* (Figure S1).

**Table 3.** Multivariate relationship between a given environmental variable and compositions of either all amines or all quercetins. Multivariate multiple regression analysis relating a given environmental variable to levels of all compounds \*.



**Table 3.** *Cont*.

\* "Overall environment" represents scores along the first axis of an environmental PCA (Principal Component Analysis, Methods). *p*-values are sequential Bonferroni's corrected and bold when < 0.05. Sample size was insufficient for *R moseleyi* in Isthme Bas.

**Figure 2.** Illustration of region-dependent relationships of total amines to soil water saturation for *R. pseudotrullifolius*.

For total quercetin contents, relationships with environmental conditions were significant for five out of nine comparisons (across or within regions) in either *R. biternatus* or *R. pseudotrullifolius* and 2 in *R. moseleyi* (Table S1).

Quercetin composition was significantly related to overall environment in the three species (Table 3). Individual quercetin levels were significantly correlated to water saturation level in nine out of 27 comparisons (across or within regions) in *R. biternatus*, 11 in *R. pseudotrullifolius*, and 16 in *R. moseleyi* (Figure S2). Individual quercetin levels were significantly correlated to soil pH in 10 out of 27 comparisons in *R. biternatus*, 12 in *R. pseudotrullifolius*, and seven in *R. moseleyi*. Finally, they were significantly correlated to soil conductivity in 13 out of 27 comparisons in *R. biternatus*, eight in *R. pseudotrullifolius*, and six in *R. moseleyi* (Figure S2).

### *2.3. Morphological Phenotypes Partly Explain Variation of Total Metabolite Contents and Metabolite Composition across Populations*

For total amine content, relations with morphological traits were significant in one out of 15 comparisons (across or within regions) in *R. biternatus*, three in *R. pseudotrullifolius*, and four in *R. moseleyi* (Table S2).

Amine composition was significantly related to the morphological phenotype across or within regions in the three species (Table 4). Traits correlated to amine composition were mainly plant height and the number of leaves (Figure S3). Individual amine levels were correlated to plant height in nine out of 48 comparisons (across or within regions) in *R. biternatus*, 8 in *R. pseudotrullifolius*, and 11 in *R. moseleyi*. Individual amine levels were correlated to the number of leaves in four out of 48 comparisons in *R. biternatus*, six in *R. pseudotrullifolius* and four in *R. moseleyi* (Figure S3).

For total quercetin content, relations with phenotypic traits were significant in three out of 15 comparisons (across or within regions) in *R. biternatus*, three in *R. pseudotrullifolius*, and five in *R. moseleyi* (Table S2).

Quercetin composition was significantly related to the morphological phenotype in *R. biternatus*, *R. pseudotrullifolius* and *R. moseleyi* (Table 4). Traits influenced by quercetin composition were mainly plant height and the number of leaves (Figure S4). Individual quercetin levels were significantly correlated to plant height in three out of 27 comparisons in *R. biternatus*, eight in *R. pseudotrullifolius*, and nine in *R. moseleyi*. Individual quercetin levels were significantly correlated to the number of leaves in 13 out of 27 comparisons in *R. biternatus*, nine in *R. pseudotrullifolius*, and one in *R. moseleyi* (Figure S4).

### *2.4. Regions and Metabolite-Environment Relationships*

For total metabolite contents, we found 3/12 (amines) and 4/12 (quercetins) significant effects of regions on the relationships between total metabolite contents and environmental conditions (overall environment, soil water saturation, pH, conductivity) (Table 5, examples in Figure 2). These significant effects concerned *R. pseudotrullifolius* and *R. moseleyi* but not *R. biternatus*.

For amine composition, in multivariate analyses the relationship between environmental conditions and levels of individual amines was significant in one region but not in another in five out of eight comparisons within *R. biternatus* and *R. pseudotrullifolius* (Table 3). Overall, environment explained variance in amine composition better within than across regions in nine out of 12 comparisons (Table 3). In univariate analyses, the relationship between environmental conditions and levels of individual amines was significant in one region but not the other in 57 out of 144 comparisons, was significant in both but changed sign in one comparison, and was significant in both with the same sign in 11 out of 144 comparisons (Figure S1). Changes among regions were two times more frequent for soil water saturation or conductivity than for pH (24/1/1 and 21/0/7 vs. 12/0/3; respectively changes from significant to not significant/positive significance to negative/significant with same sign). Also, changes among regions were two times more frequent in *R. pseudotrullifolius* and *R. biternatus* than in *R. moseleyi* (23/1/3 and 24/0/1 vs. 10/0/7) (Figure S1). For example, in *R. biternatus*, a redundancy analysis accounting simultaneously for all multiple amines showed major shifts of relationships among regions: see, for instance, the changes in relative positions of Agm, Put, Spd and Dop (Figure 3).


represents Bonferroni'scorrectedandboldwhen<0.05.Samplesizewasinsufficientfor*R.moseleyi* in IsthmeBas.

#### *Plants* **2019** , *8*, 234


46/26.

**Figure 3.** Full relationship between all environmental variables and all amine compounds, within and across regions, exemplified for *R. biternatus*. Environmental variables are soil water saturation, pH and conductivity. In redundancy analyses, axes are constrained to show, at best, variance explained by environmental variables. Individual, amines (in bold) and environmental variables (with arrows) are indicated. N = are 28 in Isthme Bas; 30 in Australia; 58 across regions. *p* values (*p*) are indicated. R2 are 0.49, 0.73, ad 0.54 for Isthme Bas, Australia, and across-region, respectively. Abbreviation: Agm: Agmatine; Put: Putrescine; Spm: Spermine; Spd: Spermidine; Cad: Cadaverine; DAP: 1,3-diaminopropane; Dop: Dopamine; Ser: Serotonin; Tyr: Tyramine; Oct: Octopamine; N1Ac-Spm: N1-acetylspermine; N8Ac-Spd: N8-acetylspermidine; Try: Tryptamine; 3M4OHPhe: 3-methoxy-4-hydroxy phenylethylamine (see Table 3, Figures S1 and S2 for more detailed analysis, for the full set of species, and for both amines and quercetins).

For quercetin composition, in multivariate analyses the relationship between environmental conditions and levels of individual quercetins was significant in one region but not in the other in one out of 12 comparisons (Table 3). Overall, environment explained variance in quercetin composition better within than across regions in nine out of 12 comparisons (Table 3). In univariate analyses, the relationship between environmental conditions and levels of individual quercetins was significant in one region but not the other in 36 out of 81 comparisons, was significant in both but changed sign in two comparisons, and was significant in both with the same sign in eight out of 81 comparisons (Figure S2). Changes among regions were somewhat more frequent with respect to pH than with respect to soil water or conductivity (15/1/1 vs. 10/0/4 and 11/1/3) and had roughly similar relative frequency in all three species (13/0/4; 12/2/1; 11/0/3) (Figure S2).

### *2.5. Regions and Metabolite-Phenotype Relationships*

For total metabolite contents, we found some 3/9 (amines) and 1/9 (quercetins) significant effects of regions on the relationships between total metabolite contents and morphological variables (Table 5). These significant effects concerned *R. pseudotrullifolius* and *R. moseleyi* but not *R. biternatus*.

For amine composition, in multivariate analyses the relationship between morphological variables and levels of individual amines was significant in one region but not in the other in three out of 12 comparisons (Table 4). Overall, morphology explained variance in amine composition better within than across regions in four out of 18 comparisons (Table 4). In univariate analyses, the relationship between morphological variables and levels of individual amines was significant in one region, but not the other in 27 comparisons, and once significant in both regions, but with opposite signs (out of 144 comparisons) (Figure S3). Changes among regions were two times more frequent for plant height than for leaf number (19/1/0 vs. 8/0/0). Changes among regions were somewhat rarer for *R. biternatus* than for either *R. pseudotrullifolius* or *R. moseleyi* (8/0/0, vs. 10/0/0 and 9/1/0) (Figure S3). Even for *R. biternatus,* a redundancy analysis accounting simultaneously for all multiple amines showed that amine/phenotype relationships are significant within one region (Isthme Bas) and non-significant across regions, with major shifts of the relationships among regions (Figure 4).

For quercetin composition, in multivariate analyses the relationship between morphological variables and levels of individual quercetins was significant in one region, but not in the other in two out of 18 comparisons (Table 4). Overall, morphology explained variance in quercetin composition better within than across regions in three out of 18 comparisons (Table 4). In univariate analyses, the relationship between morphological variables and levels of individual quercetins was significant in one region but not the other in 23 comparisons and was never significant in both regions (out of 81 comparisons) (Figure S4). Changes among regions were roughly equally frequent for plant height and for leaf number (11/0/0 vs. 12/0/0). Changes among regions were somewhat at least two times more abundant in *R. biternatus* and *R. pseudotrullifolius* than in *R. moseleyi* (9/0/0, vs. 10/0/0 and 4/1/0) (Figure S4).

**Figure 4.** Full relationship between all amine compounds and all traits in *R. biternatus* within and across regions, from redundancy analysis. Traits are Plant height, Number of leaves, Flower size, Leaf length, Leaf width, LDMC (Leaf Dry Matter Content). In redundancy analyses, axes are constrained to show, at best, variance explained by amines. Individual traits (in bold) and amines (with arrows) are indicated. N = are 28 in Isthme Bas; 30 in Australia; 58 across regions. *p* values (*p*) are indicated. Abbreviation: Agm: Agmatine; Put: Putrescine; Spm: Spermine; Spd: Spermidine; Cad: Cadaverine; DAP: 1,3-diaminopropane; Dop: Dopamine; Ser: Serotonin; Tyr: Tyramine; Oct: Octopamine; N1Ac.Spm: N1-acetylspermine; N8Ac.Spd: N8-acetylspermidine; Try: Tryptamine; 3M4OHPhe: 3-methoxy-4-hydroxy phenylethylamine (see Table 4, Figures S3 and S4 for more detailed analysis, for the full set of species, and for both amines and quercetins).

#### **3. Discussion**

While the macroevolution of secondary metabolites is becoming more and more deciphered, their microevolution at the intraspecific level remains far more obscure [14,25]. We investigated how secondary metabolites vary at the intraspecific level, where plants are subject to both environmental constraints and neutral microevolution. We hypothesized that microevolution within species could affect metabolite composition but also metabolite functions—i.e., the way metabolites respond to the environment or interact with the phenotype. In a correlational multi-species field study, we investigated two secondary metabolite families (amines and quercetins) among natural populations in sub-Antarctic Iles Kerguelen. Populations were distributed across two distinct regions, each region having similar environmental gradients. This pattern allows determining differences between regions in environment-metabolite or trait-metabolite relationships. Amine and quercetin compositions found in the three species were consistent with previous work [26,36]. We know from respectively Hennion et al. [26] and Hennion et al. [36] that amine or quercetin compositions in natural populations of *Ranunculus* species from Iles Kerguelen differ among environments and species. We found that variation of amine or quercetin composition among populations or regions within species is not only shaped by the environment, suggesting that either neutral microevolution or evolution under past environmental selection pressures may also shape metabolite composition within species. Moreover, we found that depending on regions, different metabolites may be correlated to the same environmental condition or the same trait, which is consistent with the hypothesis of metabolite redundancy within species. Also, depending on regions, a given metabolite may show different or even opposite correlations, which supports the hypothesis of metabolite versatility within species. Our results suggest that microevolution within species shapes secondary metabolite composition but also influences metabolite functions through metabolite redundancy and versatility.

### *3.1. Environments and Morphological Traits Partly Explained Variation of Total Metabolite Contents and Metabolite Composition across Populations*

For all three species, the highest number of correlations of individual amine levels was with soil or water conductivity (Table 3 and Figure S1). In *R. biternatus* and *R. pseudotrullifolius* many correlations of individual amine levels were found with soil water saturation, more than with pH, with the reverse case in *R. moseleyi*. These findings are consistent with the known involvement of amines in plant response to water and salinity, some having demonstrated protective roles against these stresses [37]. In contrast, relations with soil pH are less known.

Regarding quercetins, there were about as many correlations of individual quercetin levels with environmental conditions as with amines, invoking equally all three environmental conditions (Table 3 and Figure S2). Quercetins are involved in plant response to stress [35], however, to our knowledge, our study is the first that raised correlations between individual environmental conditions and quercetin composition of plants in nature. Our results suggest a possible involvement of quercetins in plant response to soil water saturation, conductivity and pH.

There were relatively few significant correlations of individual compounds (amines or quercetins) with morphological traits (except for quercetins with the number of leaves) (Table 4, Figures S3 and S4). Whatsoever, our study demonstrated that amine or quercetin compositions are related to both environmental conditions and traits in nature within species, a topic rarely addressed for amines [41] and never yet for quercetins [39].

### *3.2. Environment Did Not Explain All Di*ff*erences in Total Metabolite Contents and Metabolite Composition among Populations*

Only a few authors differentiate metabolite variation that is environment-dependent and metabolite variation that is environment-independent [13,25,26]. Here, we showed that the different populations within species share the same pool of compounds but differ quantitatively in the total contents and in the composition in amines or quercetins. Moreover, we showed that such differences in amine or quercetin totals and compositions among populations were only partly explained by the environment we had measured (Tables 1 and 2, Figure 1). Our results might possibly suggest that we have left out important environmental variables. However, we consider this unlikely given that our previous work supports the soil hydric variables we used as discriminant for the compositions in respectively amines [26] and quercetins [26,36] of our study species. More likely, our results suggest that, to some degree, present differentiation of amines and quercetins within plant species is shaped either by adaptation to unknown past environments (not correlated to present environments), or by neutral microevolutionary differentiation among populations.

### *3.3. Di*ff*erences in Relationship of Metabolites with Environment or Traits between Regions; Patterns Consistent with Metabolite Redundancy*

Often, in the two different regions, the same environmental condition correlated to different compounds among amines or among quercetins. However, this did not apply to the amine composition in *R. moseleyi*: in this species, the amines that correlated to pH or conductivity were the same (four compounds) across the two regions (Figure S1).

The change in correlation significance from one region to the other was also observed for morphological traits, however here, the numbers of correlations were far less than with individual environmental conditions and mainly concerned one region (Isthme Bas) (Figures S3 and S4). As a result, there were fewer examples of "alternative" compounds correlated to the same trait across the two regions than there were when considering environmental conditions.

Whereas all three *Ranunculus* species studied displayed changes of correlations between the two regions, some differences were observed among the species. *R. pseudotrullifolius* and *R. moseleyi* showed effects of regions on the relations between the total contents of amines or quercetins and environments or traits, but not *R. biternatus* (Table 5). In contrast, at least for environmental conditions, changes in correlations of individual amine or quercetin levels with individual conditions were more frequent in *R. biternatus* and *R. pseudotrullifolius* than in *R. moseleyi* (Table 3, Figure 3, Figure S1 and S2) (whereas correlations with traits were few—Table 4, Figure 4, Figure S3 and S4). The latter result may link to the fact that the changes in correlations among regions most frequently concerned soil water saturation and conductivity, two factors less variable in the aquatic biotopes of *R. moseleyi*. Clearly, the species-specific variation patterns deserve further investigation.

Amines are known to respond to environmental stresses (reviewed in [37]). Such is also true of quercetins [35]. So, raising correlations of these compounds to environmental conditions is not surprising. In contrast, the finding that the same environmental condition correlates to different compounds across different regions is, to our knowledge, reported for the first time. If we consider the studied compounds as functional, it means that the same function in plants growing in different regions may be ensured by different metabolites. This is the definition of functional redundancy. We found this pattern in two metabolite families (amines and quercetins), and we found it among metabolites within species. Such possible metabolite redundancy among plants within species has been studied very little so far [28]. This concept has been initially defined at the interspecific level. Groppa and Benavides [3] reported that across studies different secondary metabolites responded to the same environments among different species—even if many authors emphasized that different studies are often hardly comparable.

Within each of the amine and quercetin families, the compounds share metabolic pathways and may derive one from each other [7,39]. Moreover, metabolites belonging to the same family often show similar, or at least overlapping, functions in plants [7,37,39]. The proximity in synthesis pathway might trigger redundancy in function in respective amines and quercetins.

Our results strongly suggest the occurrence of functional redundancy of metabolites within species. We see an interesting issue that deserves further investigation. All the metabolites are found in all the populations studied across regions: hence, differences in metabolite-environment or metabolite-trait relationships between regions are not due to the presence or absence of different metabolites between regions. In contrast, each population potentially synthesizes several compounds that may not be used to respond to environment or interact with traits. Producing unused compounds may have a cost to plants and costs and benefits of metabolite redundancy remains to be understood.

### *3.4. Di*ff*erences in Relationship of Metabolites with Environment or Traits between Regions; Patterns Consistent with Metabolite Versatility*

In some cases, the same compound related to different environmental variables or morphological traits in different regions, or a given relationship changed sign (Figures S1–S4). Secondary metabolites may be involved in different functions in different organs or in different environments within the same plant species, i.e., metabolite versatility [5,31,32]. Here we expand the concept of metabolite versatility by suggesting that the same metabolites may have different functions, and even have opposite functions facing the same environment, in different regions. Due to different or opposite effects of the same metabolite in different regions, an analysis across regions comes to the false conclusion that this metabolite likely has no effect at all. Overall, we suggest to take into account versatility in analyzing metabolite functions. The capacity of a metabolite to have different roles increases the capacity of a species to respond to the environment.

### *3.5. Metabolite Redundancy and Versatility as a Result of Microevolution Driven by Distance rather than Environment*

In both regions we sampled roughly the same environmental gradients. Hence, we may probably exclude environmental differences as a cause of pattern of metabolite redundancy and versatility. Alternatively, such patterns might reflect either different metabolite plasticity or different heritable adaptations among populations within the different regions. We do have indication from previous work that some part of the amine composition of plants in natural populations is heritable or persistent over a few years [26,37].

If partly heritable metabolites relate differently to the same environments or traits in two spatially distant but environmentally similar regions, this suggests neutral microevolution between regions. In theory, such differences might also reflect adaptive microevolution under selection pressures relating to past environmental conditions, although general environmental conditions have persisted in Kerguelen since the last glacial maximum [42]. Future research should investigate whether differences of metabolite/environment/phenotype relationships between regions are heritable. Note that the three studied *Ranunculus* species exhibit frequent vegetative reproduction [43] and self-pollination, with even cleistogamy in submerged flowers in *R. pseudotrullifolius* and *R. moseleyi* [44]. Self-pollination and vegetative reproduction within populations may reduce gene flows across populations and may therefore increase microevolutionary differentiation among populations, possibly facilitating the origin of redundancy and versatility.

### **4. Materials and Methods**

### *4.1. Plant Collection*

The Iles Kerguelen (49◦20 00" S, 69◦20 00" E) are located in the Southern Indian Ocean within the sub-Antarctic region [45] (Figure 5). These islands are characterized by permanently low temperature (4.6 ◦C annual mean), strong and permanent winds (10 m·s−<sup>1</sup> annual mean), and high precipitation (60 years ago but drastically reduced in recent years) (annual mean of 760 mm in the studied regions) [46].

We studied three *Ranunculus* species (i.e., *R. biternatus, R. pseudotrullifolius* and *R. moseleyi*), of which *R. biternatus* is austral circumpolar, *R. pseudotrullifolius* magellanic and on Kerguelen, and *R. moseleyi* is a strict Kerguelen endemic [47]. All are perennial plants. On the Iles Kerguelen, these species have different ecological amplitudes but occupy partially overlapping habitats [43]. *Ranunculus biternatus* is widespread on the island occurring in habitats up to 500 m above sea level. *R. pseudotrullifolius* and *R. moseleyi* have more restricted distributions. The first one, being halophilous, occurs within a short distance of the coast, occupying peaty or sandy shorelines and ponds [43]. *Ranunculus moseleyi* is strictly aquatic, growing only in freshwater lakes and ponds [43].

**Figure 5.** Sampled regions in Iles Kerguelen (modified from IGN [Institut Géographique National, France] map).

Plants were sampled in 18 populations (six populations per species) equally distributed across two regions in Iles Kerguelen: Isthme Bas, a large flat isthmus (about 30 km2) and Ile Australia, a large island (about 20 km2) (Figure 3). Plants across the two regions were sampled at similar altitudes and subject to similar temperatures and across the same vegetation types (herbfields, shoreline and pond vegetation) [43]. Each population was subdivided into two sites of contrasting humidity conditions and in each a continuous group of plants living in a same site was sampled. Sampling was performed across a small area (between 0.5 and 5 square meters) so as to ensure that plants assigned to a same population were subject to roughly similar environmental conditions.

The entire sampling was performed in summer during a short period, 6 weeks from mid-December 2011 to mid-March 2012. Plants were sampled between 11 h and 17 h to avoid bias from daily variation of metabolism [48,49]. An average of five individual plants per population, of the most frequent size in the local population were sampled. As the three *Ranunculus* species propagate vegetatively via runners, to avoid pseudoreplication we sampled plants at a distance above average runner length one from each other [50]. In each individual we performed morphological measurements and collected two to four leaves to quantify amines and quercetins. As metabolite composition may vary within a given plant at a given moment between leaves of different developmental stages [49,51], we sampled an appropriate and constant leaf developmental stage, i.e., fully developed photosynthetic leaves as in previous work [52]. We moreover verified whether inclusion of sampling month into an ANOVA relating region to the different compounds reduces the number of significant relationships between

region and compounds and found that this is not the case. For amines, even the identity of the affected compounds remained the same in 15 out of 20 cases. The samples collected were frozen in liquid nitrogen and stored at −80 ◦C then lyophilized and ground to powder using a mixer mill MM 400 Retsch (Haan, Germany).

### *4.2. Plant Measurements*

In each individual we measured plant height, the length and width of the largest leaf, the numbers of leaves and flowers, the flowering stage and the size of the largest flower (largest diameter). Flowering stage of the individuals was estimated following Hennion et al. [26]. To determine leaf dry matter content (LDMC), in each population a total of 20 leaves were sampled from a minimum of 15 individuals and processed following Cornelissen et al. [53]. Leaves were collected then directly put in distilled water in airtight bags for rehydration. They were then weighed before and after 48 h drying at 80 ◦C. Some traits (flowering stage, number of flowers and the size of largest flower) were highly redundant. We thus only kept the continuously varying trait "size of largest flower" as floral trait. Likewise, we found low variability of LDMC among populations; hence we did not use this trait in our analysis.

### *4.3. Determination and Quantitation of Free Amines and Acetylated Polyamines*

This determination followed Hennion et al. [26], with modified quantities as follows. Samples from individual plants were analyzed individually. Several samples from the same population and environmental conditions were pooled in case of insufficient material (see Table S3). Five to ten milligrams of powdered samples were thoroughly mixed with 100 to 200 <sup>μ</sup>L of 1 mmol·L−<sup>1</sup> HCl supplemented with 10 <sup>μ</sup>mol·L−<sup>1</sup> diaminoheptane (Sigma, St. Louis, MO, USA), as an internal standard, on a magnetic stirring plate (2000 rpm) for 1 h at 4 ◦C. The homogenates were then centrifuged for 15 min at 10,000× *g* at 4 ◦C, and the supernatant of each sample collected. The pellet of each sample was further extracted twice with 100 to 200 <sup>μ</sup>L of 1 mmol·L−<sup>1</sup> HCL and 10 <sup>μ</sup>mol·L−<sup>1</sup> diaminoheptane. After a short stirring period, the homogenates were centrifuged for 15 min at 10,000× *g* at 4 ◦C. For each sample, the three supernatants were combined and used as the crude extracts for characterization and determination of free and acetylated amines and polyamines and stored frozen at −20 ◦C before chromatographic analyses. High Performance Liquid Chromatography (HPLC) and fluorescence spectrophotometry were used to separate and quantify amines prepared as their dansyl derivatives according to Smith and Davies [54] with some modifications as follows. Aliquots (200 μL) of the supernatant were added to 200 <sup>μ</sup>L of saturated sodium carbonate and 600 <sup>μ</sup>L of dansyl chloride in acetone (7.5 mg·mL−1) in a 5 mL tapered reaction vial. After a brief vortexing, the mixture was incubated in darkness at room temperature for 16 h. Excess dansyl chloride was converted to dansylproline by 30 min incubation after adding 300 <sup>μ</sup>L (150 mg·mL<sup>−</sup>1) of proline. Dansylated amines were extracted in 1 mL ethylacetate. The organic phase was collected then evaporated to dryness, and the residue was dissolved in methanol and stored in glass vials at −20 ◦C. External standards were made for agmatine (Agm), diaminopropane (DAP), putrescine (Put), cadaverine (Cad), spermidine (Spd), spermine (Spm), *N*-acetylputrescine (NAc-Put), *N*8-acetylspermidine (N8Ac-Spd), and *N*1-acetylspermine (N1Ac-Spm) for aliphatic amines and their acetylated conjugates; phenylethylamine (Phe), octopamine (Oct), 3-methoxy-4-hydroxy phenylethylamine (3M4OHPhe), tyramine (Tyr), and dopamine (Dop) for phenylalkylamines; tryptamine (Try) and serotonin (Ser) for indolalkylamines (all authentic products from Sigma, St. Louis, MO, USA). These standards were processed in the same way as samples, and 2 to 50 nmol (per assay) were dansylated for each standard alone or in combination. One standard combined these 15 amines plus diaminoheptane. The HPLC column was packed with reverse-phase SpherisorbODS2 C18 (particle size 5 μm; 4.6 × 250 mm, Waters, Milford, CT, USA). The mobile phase consisted of a solution of 17.5 mmol·L−<sup>1</sup> potassium acetate (pH 7.17) as eluent A and acetonitrile as eluent B. The solvent gradient, modified according to Hayman et al. [55] was as described by Jubault et al. [56]. The flow rate of the mobile phase was 1.5 mL·min<sup>−</sup>1. For fluorescence detection of dansyl amines, an excitation wavelength

of 366 nm was used with an emission wavelength of 490 nm. The external standards were injected in the HPLC system first to determine retention times of the various amines on the column and secondly to make calibration curves for quantitation. Peaks of amines in the samples were determined by their retention times on the column, and stability was checked by injection of the combined 16-amine standard in the system every 15 samples. In case of doubt, identities of peaks of amines were confirmed by spiking the sample with known amounts of the authentic standards. Amines in the samples were quantified after yield correction with the internal standard and calibration with the external standards. The stability of quantitative calibration was checked by injection of a Put standard every 10 samples. The HPLC design consisted of a thermoelectron pump (SpectraSystem P1000 XR, Thermo Fisher, San Jose, CA, USA) and (Spectra-Series AS100) autosampler with a 20 μL injection loop, and detection through an FP-2020 Plus fluorometer (Jasco, Inc., Easton, MD, USA). Signals were computed and analyzed using Azur software (Datalys, St Martin d'Hères, France).

### *4.4. Determination and Quantitation of Quercetins*

Quercetins were the sole flavonols detected in *Ranunculus* species from Iles Kerguelen [36]. Samples from individual plants were analyzed individually. Several samples from the same population and environmental conditions were pooled in case of insufficient material (see Table S4). We weighed about 10 mg of plant powder in an Eppendorf tube, and added 1 mL of methanol acidified with 1% formic acid. The tube was vortex-agitated first and put in ultra-sonic bath for 5 min. The tube was then centrifuged briefly and 900 μL of the supernatant was removed using a 1 mL plastic syringe, and filtered using a PTFE 13 mm 0.45 μm syringe filter. The methanol extract was then poured in an injection vial for Ultra Performance Liquid Chromatography (UPLC) analysis; 2 μL of the extract were injected in the Waters UPLC\_PDA\_ESI\_TQD system for flavonol quantitation. The reversed phase column, an Acquity Waters C18 BEH (2.1 × 150 mm) 1.7 μm, was maintained at 30 ◦C. The solvents used for the binary gradient were A: ultra-pure water with 0.1% formic acid, B: acetonitrile with 0.1% formic, the flow was 0.4 mL/min. The gradient applied was 98% A from 0 to 0.2 min, 10% A from 0.2 to 14 min, 14 to 15 min isocratic 10% A, 15 min to 17 min 98% A, 17 to 20 min isocratic 98% A. The photo diode array detector scanned from 190 to 600 nm and flavonols were detected at 350 nm, external quantitation with some flavonol standards was applied. The UPLC-photodiode array-electrospray-triple quadrupole analytical system allows us to detect compounds for which the molecular ion produced in the electrospray source is in accordance with the molecular structure searched. The capillary voltage was 2.9 kV, the cone voltage was 37 V, the source temperature was maintained at 150 ◦C and the desolvatation temperature at 400 ◦C, the desolvatation gas flow was 800 L/h. On the basis of data from the literature on Antarctic *Ranunculus* flavonols, Gluchoff-Fiasson et al. [36], the mass spectrometer detector was programmed to focus on characteristic *m*/*z* of those flavonols yet identified. In negative mode, the ions, monitored by the Select Ion Recording method (SIR) for quantification were: 787 (Quercetin-tri-glucoside), 949 (Quercetin-caffeoyl-tri-glucoside), 933 (Quercetin-feruloyl-di-glucoside-pentoside), 919 (Quercetin-caffeoyl di-glucoside-pentoside), 757 (Quercetin-di-glucoside-pentoside), 595 (Quercetin-glucoside-pentoside), 625 (Quercetin-di-glucoside), 963 (Quercetin-feruloyl-tri-glucoside) and the standards 463 (isoquercitrin), 609 (rutin). Depending on compound structure, isoquercitrin or rutin were used as standards for the quantification on the mass spectrometer triple quadrupole detector, the external standard calibration was made daily and linear regression factors were at least 0.99.

#### *4.5. Amines Characterized*

We characterized 15 different amines which belonged to four biochemical categories: aliphatic amines and their acetylated conjugates, phenylalkylamines and indolalkylamines. The detected aliphatic amines were: agmatine (Agm), diaminopropane (DAP), putrescine (Put), cadaverine (Cad), spermidine (Spd), spermine (Spm), *N*8-acetylspermidine (N8Ac-Spd) and *N*1-acetylspermine (N1Ac-Spm). Phenylalkylamines were phenylethylamine (Phe), octopamine (Oct), 3-methoxy-4-hydroxy

phenylethylamine (3M4OHPhe), tyramine (Tyr), and dopamine (Dop). Indolalkylamines were tryptamine (Try) and serotonin (Ser). The raw data of individual compositions are shown in Table S3. All 15 compounds described were present in the three species, in the two regions. Thus, differences in amine composition between species or regions reflected shifts in levels and not qualitative differences.

### *4.6. Quercetins Characterized*

Following Hennion et al. [36], we performed an analysis of flavonols. Quercetins characterized were: quercetin 3-diglucoside-7-glucoside (Q-3GL), quercetin 3-(caffeyl-glucosyl)glucoside-7- glucoside (Q-3GL+Caf), quercetin 3-(ferulyl-glucosyl)glucoside-7-glucoside (Q-3GL+Fer), quercetin 3-(caffeylxylosyl)glucoside-7-glucoside (Q-2GL+Xyl+Caf), quercetin 3-(ferulyl- xylosyl)glucoside-7-glucoside (Q-2GL+Xyl+Fer), quercetin 3-xylosylglucoside-7-glucoside (Q-2GL+Xyl), quercetin 3-xylosylglucoside (Q-GL+Xyl), quercetin 3-diglucoside (Q-2GL) and isoquercitrin (IQC). The raw data of individual compositions are shown in Table S4. All nine compounds described were present in the three species, in the two regions. Thus, differences in quercetin composition between species or regions reflected shifts in levels and not qualitative differences.

### *4.7. Environmental Measurements*

In each population we measured soil water saturation, pH and conductivity. Three samples of soil, each of 20 mL, were collected at the rhizosphere level of the measured plants. To determine soil water saturation, half of each soil sample was dried at 105 ◦C during 48 h and weighed before and after drying [50]. Soil water saturation in each population was calculated as following: soil water saturation = (soil weight before drying – soil weight after drying) / sol weight after drying. Data were transformed following f(x)=log(x) to reduce positive skewness of the data distribution. The remaining soil was mixed with known volume of distilled water and was then left 18 to 24 h to permit sedimentation of soil particles. Immediately after, pH was determined using a pH meter (BASIC 20 PLUS CRISON, resolution 0.01 pH). After another 18 to 24 h sedimentation, conductivity was determined using a conductivity meter (CONSORT K810, resolution 0.1 μS cm<sup>−</sup>1) [50].

### *4.8. Statistical Analyses*

To determine differences of *total contents* of metabolites (amines or quercetins) among populations that are not due to differences in the environmental variables, we conducted ANCOVA analyses with the environment as a co-variable. We analyzed the difference between populations, accounting for environmental conditions: Total amine content in sample = pH (continuous) + Conductivity (continuous) + Water saturation (continuous) + population (six categories) + error. We consistently repeated this procedure across all populations and across only the populations within a given region to explore whether populations are more different in one region or another or whether populations vary only across regions, and not between. For this and all following analyses we used R 3.5.0 software [57]. To determine differences of total metabolite contents (amines or quercetins) among populations that may be due to differences in the environmental variables we measured, we regressed total metabolite levels against environmental predictors. We repeated this analysis separately within each of the two regions. Equally, to determine differences of total metabolite contents (amines or quercetins) among populations that relate to morphological phenotypes, we regressed total metabolite levels against morphological predictors. We repeated this analysis separately within each of the two regions.

To determine the relationships between *compositions* of metabolites (amines or flavonoids) and environment or phenotype we performed redundancy analyses, using cca function [58,59] in R 3.5.0 software [57]. We determined relationships between metabolite composition and individual environmental factors or individual traits using redundancy analyses with rda function. Also, we determined relationships between metabolite composition and the overall environment (i.e., taking into account all the environmental factors) or the overall phenotype (i.e., taking into account all the traits) using redundancy analyses. We repeated this analysis separately within each of the two regions.

To determine whether relationships between *compositions* of metabolites and the environment or metabolite-phenotype relationships differ between regions, we conducted multiple regression analyses. Dependent variables were metabolite compounds (either amines or quercetins) and independent variables were either of the environmental variables, region and the interaction term between both. We also used an integrative "overall environment" variable calculated as the scores along the first axis of a PCA across the individual environmental variables. This axis was most strongly correlated to water saturation in *R. biternatus*, and to pH in the two other species. To statistically test whether relationships between *compositions* of metabolites and the morphological phenotypes differ between regions, we took the same approach as for environment, replacing environmental by morphological variables. Again, we identified an integrative "overall phenotype" variable calculated as the scores along the first axis of a PCA across the individual morphological variables. This axis was most strongly correlated to plant height in all three species and to leaf number in *R. biternatus*, and to LDMC in the other two species.

For multiple testing on the same data set, *p*-values were corrected using sequential Bonferroni's correction [60]. In all regression analyses we verified the assumptions of the analyses using QQ plots and predicted-vs-residual plots.

### **5. Conclusions**

Recent authors [13,14] encouraged researchers to explore the relative contributions of genetic, environmental, microenvironmental and stochastic variation to secondary metabolite variation across plant taxa and environments. Our study provides several hints into secondary metabolite variation and microevolution within species. For two metabolite classes, we showed that variation of secondary metabolite composition among populations was only partly related to environment, suggesting that neutral microevolution also shapes metabolite composition within species. We showed differences in metabolite-environment and metabolite-trait relations among regions. The observed variation patterns may be interpreted as metabolite redundancy and versatility within species. Our results suggest that such possible metabolite redundancy and versatility may be shaped by neutral microevolution. Metabolite redundancy and versatility within species may contribute to the high functional diversity of individual secondary metabolites. We found patterns suggesting metabolite redundancy and versatility in three species and in two distinct families of secondary metabolites (i.e., amines and quercetins), relating to all environmental parameters and all morphological variables. Therefore, our observations likely do not result from a special case but may be extendable to other species, secondary metabolite families or locations. Future aims may be to assess the extent of metabolite redundancy and versatility, test the functions of metabolites in nature, and look at these processes in the light of costs and benefits for plant species.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2223-7747/8/7/234/s1, Figure S1: relationships between individual amines and individual environmental variables from simple regression analyses, within and across regions, Figure S2: relationships between individual quercetins and individual environmental variables from simple regression analyses, within and across regions, Figure S3: relationships between individual amines and individual traits from simple regression analyses, within and across regions, Figure S4: relationships between individual quercetins and individual traits from simple regression analyses, within and across regions, Table S1: simple regression analyses to describe the relationship between total levels of amines or quercetins and environmental conditions, Table S2: simple regression analyses to describe the relationship between total levels of amines or quercetins and traits. Table S3: sample distribution by population and region, collection dates, and amine composition. Table S4: sample distribution by population and region, collection dates, and quercetin composition.

**Author Contributions:** Conceptualization, F.H., A.P. and B.L.; methodology, F.H., A.P. and B.L.; validation, F.H., A.P. and B.L.; formal analysis, B.L., A.P. and T.D.; investigation, B.L., E.C. and F.H.; resources, F.H.; data curation, F.H., B.L., E.C. and T.D.; writing—original draft preparation, B.L., F.H. and A.P.; writing—review and editing, F.H. and A.P.; visualization, B.L., E.C. and T.D.; supervision, F.H. and A.P.; project administration, F.H.; funding acquisition, F.H.

**Funding:** This research was funded by INSTITUT POLAIRE FRANÇAIS IPEV, grants 1116 (PlantEvol) and 136 (Ecobio). B.L. was supported by a PhD grant from Ministry of Research and Education (France). The APC was funded by CNRS LIA "AntarctPlantAdapt".

**Acknowledgments:** We thank Marc Lebouvier (UMR ECOBIO, Rennes, France), volunteers Marine Pouvreau, Françoise Cardou, Julie Vingère and Marion Lombard (IPEV N◦136), Richard Winkworth (IFS, Massey University, New Zealand), Philippe Choler (LECA, Grenoble, France), the IPEV logistics team, and Réserve Naturelle TAAF for help during the 2011–2012 summer campaign in Iles Kerguelen, and Françoise Binet (UMR ECOBIO, Rennes, France) for support to the programme. We thank Nathalie Marnet who performed the flavonoid analyses at P2M2 facility (INRA, Le Rheu, France). We thank Jacqui Shykoff (CNRS, Université Paris-Saclay, Orsay) for helpful comments on an initial version of the manuscript. This research is linked to CNRS Zone-Atelier Antarctique, to CNRS LIA "AntarctPlantAdapt" (F.H.) with New Zealand, and the Scientific Committee on Antarctic Research programmes AntEco and AnT-ERA.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

### **References**


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