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. 2025 Jan 17;27(5):903–912. doi: 10.1111/plb.13759

Influences of plant maternal effects, chemotype, and environment on the leaf bacterial community

A Malacrinò 1,2, R Jakobs 3, S Xu 4, C Müller 3,5,✉
PMCID: PMC12255285  PMID: 39825591

Abstract

  • Plant individuals within a species can differ markedly in their leaf chemical composition, forming so‐called chemotypes. Little is known about whether such differences impact the microbial communities associated with leaves and how different environmental conditions may shape these relationships.

  • We used Tanacetum vulgare as a model plant to study the impacts of maternal effects, leaf terpenoid chemotype, and the environment on the leaf bacterial community by growing plant clones in the field and a greenhouse. We hypothesized that all three factors affect the bacterial community of the leaves and that terpenoid and bacterial profiles as well as chemodiversity and microbial diversity are correlated.

  • The results revealed that the leaf microbial community was significantly influenced by plant maternal effects and environmental conditions (field vs. greenhouse), but not by the leaf terpenoid profile. There was also no evidence for a correlation between terpenoid profiles and bacterial community composition and diversity. Overall, a higher number of unique amplicon sequence variants were found in the leaves of clones grown under field conditions than in those grown in the greenhouse. We also identified interactions between individual terpenoids and specific members of the leaf bacterial community.

  • Our study suggests that terpenoid chemodiversity has, overall, little effect on the leaf bacterial community, but some terpenoids might affect specific beneficial species. While more studies are needed to investigate the relationship between plant chemodiversity and plant microbiomes, our results highlight the importance of integrating plant maternal effects, chemodiversity, and environment in understanding plant–microbiome interactions.

Keywords: Asteraceae, chemical diversity, essential oils, microbiome

Plant maternal effects and growth environment, rather than terpenoid chemotype, determine the leaf microbial community of Tanacetum vulgare, but specific terpenoids are correlated with the abundance of certain bacteria.

graphic file with name PLB-27-903-g003.jpg

INTRODUCTION

Plants are closely associated with microorganisms, including bacteria, archaea, fungi, and protists, which can live as epiphytes on the plant surface or within tissues as endophytes (Brown et al. 2020). Microbiomes associated with different plant parts have been shown to play important roles in host ecology, evolution, and fitness (Trivedi et al. 2020, 2022). For example, they can protect the host against abiotic and biotic stresses (Lau & Lennon 2012), such as pathogens, enhancing their disease resistance (Bulgarelli et al. 2013; Ritpitakphong et al. 2016). Plant‐associated microbial communities may be affected by different internal and external factors. As an internal factor, the genotype of a plant can shape the microbial community (Bálint et al. 2013; Xing et al. 2021; Malacrinò et al. 2023), although the strength of this effect varies among plant organs and species (Wagner et al. 2016; Liu et al. 2019). The influence of the plant genotype or maternal effects on the plant‐associated microbial communities might be exerted by impacts on the plant's morphology, thereby realizing ecological niches that select for specific microbes or, more directly, by shaping the release of metabolites on plant surfaces, which affect microbial colonization. While microorganisms should be particularly sensitive to variation in plant chemistry, surprisingly little is known about whether the metabolic composition of a plant influences the diversity and structure of plant microbial communities. Intraspecific variation in the occurrence and ratio of compounds belonging to one major metabolite class, such as terpenoids, allows discrimination of chemotypes (Holopainen, Hiltunen, & Vonschantz 1987; Padovan et al. 2017). Variation in the rhizosphere microbiota was detected among at least three Cannabis sativa chemotypes differing in relative levels of tetrahydrocannabinol and cannabidiol (Comeau et al. 2020) and among Populus trichocarpa chemotypes differing in salicylic acid‐related metabolites (Veach et al. 2019). However, we lack knowledge about the role of maternal effects versus the chemotype in shaping leaf microbial communities and how these communities may interact with environmental conditions. Predicting variation in plant microbiome composition and its potential function is fundamental to move towards the manipulation of microbiomes for sustainable development.

Externally, the environment in which a plant grows is highly decisive for the microbiota that colonize the plant (Wagner et al. 2016; Xing et al. 2021). Abiotic factors, such as solar radiation, temperature, nutrient conditions, and farming practices, as well as biotic factors, such as herbivory, have been shown to impact bacterial phyllosphere communities (Grube et al. 2011; Vorholt 2012; Humphrey et al. 2014; Legein et al. 2020). Studies in controlled conditions may lack important natural factors that can determine the microbiome, while overestimating the influence of the genotype on the microbiome (Wagner et al. 2016). In contrast, in field studies the microbiome may vary more due to seasonal effects or age and year of harvest (Wagner et al. 2016). At least in Boechera stricta (Brassicaceae), leaf microbiomes remain more stable with plant aging than root microbiomes (Wagner et al. 2016). Influenced by distinct environmental conditions, differences were found in leaf microbial communities between plants grown in greenhouses or in the field (Heuer & Smalla 1999; Wei et al. 2016). However, for proper comparisons, clones of the same plant individual should be grown under these two conditions to distinguish genotypic and environmental effects.

Both the bacterial composition and the overall diversity of the leaf bacterial community can be impacted by growth conditions. For example, the abundance of different classes of Proteobacteria can be either higher or lower under laboratory compared to field conditions (Maignien et al. 2014; Williams & Marco 2014; Wei et al. 2016). The microbiome diversity may also be driven by the phytochemical diversity, called chemodiversity, of the plants, as plant metabolites may be relevant in shaping the niche for microbes (Aleklett et al. 2014; Berlanga‐Clavero et al. 2020). This raises the interesting question of whether there is a relationship between the diversity of the bacterial community and the chemodiversity of a given plant part. Such relationships have not yet been well investigated. However, a recent study revealed relationships between intraspecific phytochemical composition and flower and leaf microbiota in two Fabaceae species (Gaube et al. 2023).

The aromatic plant Tanacetum vulgare L. (syn. Chrysanthemum vulgare (L.) Bernh., Asteraceae) offers a highly suitable study system for investigating maternal effects versus chemotype effects on the leaf microbiome. This species is outcrossing (Lokki et al. 1973); thus, flowers within one flower head can be pollinated by pollen from different plant individuals, leading to offspring from the same mother plant potentially consisting of different chemotypes. The chemotypes are characterized by one to three dominant terpenoids, which make up a large portion (>40%) of the leaf terpenoid profile (Holopainen, Hiltunen, & Vonschantz 1987). The metabolic fingerprints of the leaves, which include various classes of specialized metabolites, capture the chemotypes but can specifically predict the maternal origin of the T. vulgare plant (Dussarrat et al. 2023). Moreover, this species reproduces clonally as it readily forms ramets (Kleine et al. 2017). Thus, clones can be produced and exposed to different conditions to study the impact of the environment on the plant‐associated microbiome.

The aim of this study was to determine the impact of maternal effects, chemotype, and environment (field vs. greenhouse) on the composition of the leaf bacterial community in T. vulgare. Therefore, we selected offspring plants derived from three distinct mother plants. The offspring belonged to three different chemotypes, with each chemotype derived from at least two distinct mother plants. Two clones of each target plant were used, one placed in the greenhouse and the other in a field site, where they had grown for 2 years. We hypothesized that differences in the plant origin and chemistry, and in the abiotic environment, will influence dynamics of bacterial dispersal, within‐microbiota interactions, and taxon‐specific bacterial growth. Therefore, we predicted that the leaf bacterial composition will differ in dependence of maternal effects, leaf chemotype, as well as the growth condition (field vs. greenhouse). Moreover, we hypothesized that terpenoids will influence the leaf bacterial community, and that a wider range of plant metabolites might help to accommodate a wider range of microorganisms. Therefore, we predicted that the leaf terpenoid and bacterial community profiles will correlate and that a higher chemodiversity will lead to higher bacterial diversity. Since the environmental microbial diversity is assumed to be higher outdoors than indoors, we predicted that the bacterial richness of the leaves will be higher in plants grown in the field.

MATERIAL AND METHODS

Plant material

Seed heads of three maternal T. vulgare plants (ID M09, M18, and M26) were collected in January 2019 at three different sites in Bielefeld (M09: 51°58.662 N, 8°27.273′ E; M18: 51°59.031 N, 8°28.302′ E; M26: 51°59.012 N, 8°28.304′ E). Since the leaves of these mother plants had already wilted, their chemotype could no longer be determined. Seeds were germinated and a leaf sample from each offspring plant was collected to determine the plant chemotype, as described below (terpenoid analysis). For this experiment, we selected plants of three chemotypes, one dominated by artemisia ketone (called “Keto” hereafter, n = 4 from M09, n = 3 from M18, n = 4 from M26), one dominated by α‐thujone and β‐thujone (“ABThu”, n = 5 from M09, n = 5 from M26), and one dominated by (Z)‐myroxide, santolina triene, and artemisyl acetate (“Myrox”, n = 5 from M18, n = 3 from M26). These target offspring plants were grown in a 1:1 mixture of sterilized potting soil and river sand and kept in the greenhouse at an average temperature of 21°C and a 16 h:8 h light:dark cycle. From each plant, clones were produced from rhizome cuttings and placed outside, next to the greenhouse, in winter 2019 for acclimatization. In May 2020, these clones were planted at a field site close to Bielefeld University (52°03′39.43′N, 8°49′46.66′E; 142 m a.s.l.; for details, see Ziaja & Müller 2023). Plants used for the present experiment were sampled from plots, in which plants of the same chemotype, but originating from different mother plants, grew in 1 × 1 m plots with five plants per plot. Samples were picked from various plots across the field (see Fig. S1 for exact outline of the field experiment and position of sampled plants). The environmental conditions between the greenhouse and the field differed strongly, with the plant cls growing in the greenhouse being exposed to standardized light and temperature, while plants outside had been exposed for more than 2 years to the outside conditions. We also expected the microbial communities in these two environments to differ.

Harvest of leaves

Leaf discs were collected from each target plant growing as clones in the greenhouse and in the field (n = 29 plants in total per site) in April (greenhouse) or May (field) of 2022. From each plant, five leaf discs were taken from the leaf tips of the upper, middle, and lower leaves using a sterilized cork borer (4 mm diameter). These disks were transferred to an Eppendorf tube and frozen in liquid nitrogen for later analysis of the bacterial community. Directly thereafter, additional disks were taken next to the sites where the first five disks had been cut, and also frozen for later analysis of the leaf terpenoid profiles.

Analysis of terpenoid profiles

Samples were lyophilized, homogenized, and 10 mg dry weight per sample was extracted by sonication for 5 min in 1 ml n‐heptane containing 1‐bromodecane (10 ng μL−1) as an internal standard. After centrifugation, the supernatants were analyzed using gas chromatography coupled with mass spectrometry (GC–MS; GC 2010plus – MS QP2020; Shimadzu, Kyoto, Japan) on a semi‐polar column (VF‐5 MS, 30 m length, 0.2 mm ID, 10 m guard column; Varian, Lake Forest, USA) in electron impact ionization mode at 70 eV, with helium as carrier gas. Samples were injected at 240°C with a 1:10 split. A starting temperature of 50°C was retained for 5 min, ramping up to 250°C at 10°C per min, then increasing at 30°C per min to a final temperature of 280°C, which was held for 3 min. Blanks of n‐heptane as the internal standard as well as a mix of alkanes (C7–C40; Sigma Aldrich, Taufkirchen, Germany) were measured regularly between sample batches. Terpenoids were identified based on their retention indices (RI) (van den Dool & Kratz 1963) and by comparing spectra to synthetic reference compounds, where available, or to entries of the libraries NIST (National Institute of Standards and Technology, Gaithersburg, USA, 2014), Pherobase (El‐Sayed 2012) and those reported in Adams (2007). Terpenoids were semi‐quantified based on the peak area of the extracted ion chromatogram, and the relative composition determined by dividing each peak area by the sum of the peak areas of all the peaks within each sample. For terpenoids with several commonly used synonyms, the first name listed in Adams (2007) was chosen for further reference. Unknown compounds, which contained a high relative abundance of the molecule ion 43, were named ‘unknown monoterpenes’, as an m/z of 43 is characteristic for terpenoids (Ryhage & von Sydow 1963). For quantification, confirmation was set to a default ion allowance of 30% in the absolute reference ion mode, and the similarity index was set to at least 75. Only peaks that had a signal‐to‐noise ratio >5 were picked for further analyses.

Amplicon metagenomics of the leaf bacterial community

The DNA was extracted from frozen samples (ca. 50–150 mg) with the Qiagen DNeasy Plant Pro Kit according to the manufacturer's protocol, with minor modifications. After the disruption step (one time 30 s at 25 Hz), the crude homogenized samples were transferred to bashing bead lysis tubes (0.5 mm and 0.1 mm diameter) with a mixed lysis matrix (Zymo Research S6012‐50), followed by a horizontal disruption step on a Vortex Genie II for 40 min at maximum speed. For each extraction batch, a negative control (only buffer) was included. To amplify the V5‐V7 region of 16S rRNA, we used the primer pair 799F + 1193R (5′‐AACMGGATTAGATACCCKG‐3′, 5′‐ACGTCATCCCCACCTTCC‐3′), which amplifies little host plant DNA (Haro et al. 2021). The amplicons were sequenced by Novogene Ltd. on an Illumina NovaSeq 6000 instrument (SP flow cell, 250PE) with an average depth of 144,556 reads per sample.

Data was processed using nf‐core/ampliseq v2.7.1 (Straub et al. 2020). Primers were trimmed using Cutadapt (Martin 2011), and sequences were processed sample‐wise with DADA2 (Callahan et al. 2016) to eliminate PhiX contamination, discard reads with >2 expected errors, correct errors, merge read pairs, remove chimeras, and identify amplicon sequencing variants (ASVs). After filtering, an average of 110,804 reads per sample were retained. Taxonomic classification was performed by DADA2 using the SILVA database v 138 (Quast et al. 2013). Representative sequences of each ASV were aligned using MAFFT v 7.505 (Katoh & Standley 2013), and a phylogenetic tree was built using FastTree v 2.1.10 (Price et al. 2010). In the R v 4.3.2 (R Core Team 2023) environment, we grouped the ASV table, the taxonomic information for each ASV, the sample metadata, and the ASV phylogenetic tree using phyloseq v 1.46 (McMurdie & Holmes 2013). Singletons and sequences identified as “chloroplast” or “mitochondria” were discarded before downstream analyses (73.89 ± 3.21%; mean ± SE). Contaminants were removed using the data from the non‐template control samples and decontam v 1.22 (Davis et al. 2018). After clean‐up, our dataset included an average of 22,105 ± 2874 reads per sample. Microbiome data were normalized using Wrench v 1.2 (Kumar et al. 2018) before calculating relative abundances.

Data analysis

All data analysis were performed using R v 4.3.2 (R Core Team 2023). We first tested the influence of plant maternal effects, plant chemotype, and environment on the leaf terpenoid profile. Using the vegan package v 2.6 (Oksanen et al. 2024), a Bray–Curtis distance matrix was calculated between samples based on the terpenoid profile, and we used this matrix to perform PERMANOVA (999 permutations) to test the influence of the factors listed above on the leaf terpenoid profile, and nonmetric multidimensional scaling (NMDS) to visualize the results. Similarly, we constructed a weighted UniFrac distance matrix between samples based on the bacterial community composition. We used PERMANOVA (999 permutations) to test the influences of plant maternal effects, plant chemotype and environment on the structure of the bacterial community. Pairwise contrasts were performed using the package RVAideMemoire v 0.9. Amplicon sequence variants (ASVs) unique or shared between groups based on plant maternal effects, plant chemotype, and environment were identified by subsetting the ASV table to each group, removing singletons, and comparing the vectors with ASV identity using ggvenn v 0.1. ASVs with differential abundance between pairs of groups were identified using the package DESeq2 (Love et al. 2014) using the FDR‐adjusted P < 0.05 as threshold to identify significantly enriched taxa. We also tested plant maternal effects, and effects of plant chemotype and environment on leaf microbial richness by fitting a linear model using the packages lme4 (Bates et al. 2015) and car (Fox & Weisberg 2019). In addition, we tested for a correlation between the terpenoid distance matrix and the leaf bacterial distance matrix of samples using the Mantel test, as well as for a correlation between the Shannon diversity and richness of the terpenoids and Shannon diversity and richness of the leaf bacterial community, in both cases using Pearson's method. Finally, the terpenoid profile dataset and the leaf bacterial dataset were jointly used to build a multiomics factor analysis model using the package MOFA2 v 1.12 (Argelaguet et al. 2018). From this model, we selected the top 10% of ASVs contributing to the variation in leaf bacteria across groups, and we correlated their relative abundance with the relative abundance of each terpenoid using Pearson's method.

RESULTS

Maternal effects, chemotype, and environment affect leaf terpenoid profiles

Overall, 29 monoterpenoids were detected in the leaf samples. The terpenoid profiles differed significantly among the three chemotypes (R 2 = 0.87, Table 1, Fig. 1B) and mainly matched the original chemotype association, with the Keto chemotype being dominated by artemisia ketone, the ABThu chemotype dominated by α‐thujone and β‐thujone, and the Myrox chemotype dominated by (Z)‐myroxide, santolina triene, and artemisyl acetate (Fig. 1D). The terpenoid profile was also significantly influenced by maternal effects (Table 1, Fig. 1A) and environment (Table 1, Fig. 1C), although the amount of variance explained was very small (R 2 = 0.02 for maternal effects and R 2 < 0.01 for environment).

Table 1.

Results from PERMANOVA testing the influence of maternal effects, plant chemotype, and environment as well as their interactions on the leaf bacterial communities.

factor df terpenoid profile bacterial community
R 2 F P R 2 F P
Maternal effects (M) 2 0.02 5.19 <0.01 0.07 2.25 <0.01
Chemotype (C) 2 0.87 229.07 <0.01 0.02 0.79 0.73
Environment (E) 1 <0.01 3.84 0.02 0.08 5.19 <0.01
M × C 2 <0.01 1.84 0.10 0.04 1.46 0.09
M × E 2 <0.01 0.34 0.89 0.04 1.26 0.21
C × E 2 <0.01 2.48 0.05 0.03 0.99 0.47
M × C × E 2 <0.01 0.28 0.93 0.04 1.28 0.23
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P‐values <0.05 are highlighted in bold.

Fig. 1.

Fig. 1

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Leaf terpenoid profiles of Tanacetum vulgare. NMDS (nonmetric multidimensional scaling, stress 0.11) of monoterpenoids with points coloured by: (A) mother plant origin, (B) plant chemotype, and (C) environment. (D) Mean (n = 3–5) terpenoid composition of offspring originating from different mother plants (M09, M18, M26) across chemotypes and environments.

Maternal effects and environment, but not chemotype, shape the leaf bacterial community

The results from the multivariate analysis (Table 1, Fig. 2) revealed that both maternal effects and environment (field vs. greenhouse) had a significant effect (P < 0.01; Table 1, Fig. 2A,C) on the structure of the leaf bacterial community, each explaining ~7% of the variation. In pairwise contrasts, we found only marginal differences between the leaf microbiota in dependence of maternal effects (FDR‐corrected P > 0.06 for all pairwise comparisons). In contrast, we did not detect any effect driven by chemotype (P = 0.75; Table 1, Fig. 2B) or the interaction between chemotype and the other factors on the structure of the leaf bacterial communities.

Fig. 2.

Fig. 2

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Microbiota structure in leaves of Tanacetum vulgare. NMDS (stress = 0.176) of leaf bacterial communities with points coloured by: (A) plant maternal effects, (B) chemotype, and (C) environment. (D) Composition of reads of major bacterial genera (relative abundance >1%) across maternal effects and environments. Unique and shared ASVs between offspring plants from different (E) mother plants, (F) chemotypes, and (G) environments.

We also found that the taxonomic profile of leaf bacterial communities varied across offspring from different mother plants, and differed between plants grown under field of greenhouse conditions (Fig. 2D). Among the 2010 ASVs identified across all samples, 435 ASVs were commonly found across offspring of all mother plants, while lower portions of ASVs were unique to offspring from each mother plant (Fig. 2E). The leaf microbial richness varied between the maternal genotypes (F = 5.72, df = 1, 52, P = 0.005), with higher values for genotype M09 (290 ± 22.6; mean ± SE) compared to M18 (193 ± 28.2) and M26 (174 ± 24.6). We also found 463 ASVs common to all three chemotypes, while 206, 335, and 93 ASVs were uniquely associated to the Keto, ABThu, and Myrox chemotype, respectively (Fig. 2F), although we did not detect differences in microbial richness between chemotypes (F = 0.31, df = 1, 52, P = 0.73). Across environments, 742 ASVs were shared between samples from the field and the greenhouse, while 592 were unique to the field and 463 were unique to the greenhouse (Fig. 2G). In the field, we observed a higher number of unique ASVs than in the greenhouse, but on average, the observed richness did not differ (F = 0.24, df = 1, 52, P = 0.62) between the field (222.62 ± 25.37 ASVs, mean ± SE) and greenhouse samples (207.86 ± 19.40 ASVs).

We then identified the ASVs differentially abundant between samples from different maternal lines and chemotypes and between samples collected in the field and those collected in the greenhouse (Fig. 3). When contrasting offspring of the mother plants M09 versus M18, we found two ASVs enriched in offspring of M09 identified as Flavobacterium, while none were enriched in offspring of mother plant M18 (Fig. 3A). We also found two Flavobacterium ASVs enriched in offspring of mother plant M26 compared to M18, in which one Buchnera was enriched (Fig. 3B). We identified one Buchnera ASV enriched in offspring of mother plant M09 compared to those of M26, while one Staphylococcus ASV was enriched in offspring of M26 compared to M09 (Fig. 3C). When comparing chemotypes, we found that two ASVs (identified as Anoxybacillus and Dermabacter) were significantly more enriched in Keto than in ABThu, and a single Spiroplasma ASV was more enriched in Keto than in Myrox. Contrasting ABThu to Myrox, we identified a Spiroplasma ASV enriched in the ABThu chemotype and an Anoxybacillus ASV enriched in the Myrox chemotype. When comparing the ASVs with regard to environment, we found that three ASVs were enriched in samples from the field, identified as Buchnera, Candidatus Regiella, and Ralstonia, while 16 ASVs were enriched in samples from the greenhouse, identified as Corynebacterium (2 ASVs), Kocuria, Candidatus Portiera, Staphylococcus (2 ASVs), Brachybacterium, Neomicrococcus (3 ASVs), Nocardiopsis, and five unidentified ASVs (Fig. 3D).

Fig. 3.

Fig. 3

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Volcano plots showing the ASVs enriched in different sampling groups of bacterial communities in leaves of Tanacetum vulgare. (A) ASVs enriched in offspring collected from mother plant M09 (log2FC > 0) compared to those from plant M18 (log2FC < 0). (B) ASVs enriched in offspring of mother plant M18 (log2FC > 0) and M26 (log2FC < 0). (C) ASVs enriched in offspring of mother plant M09 (log2FC > 0) compared to those of mother plant M26 (log2FC < 0). (D) ASVs enriched in samples collected in the field (log2FC > 0) and greenhouse (log2FC < 0).

Relationship between leaf chemotype and leaf bacterial community

While the above results show little contribution of the plant chemotype, as determined by the leaf terpenoids, to the structure of the leaf bacterial community, we further tested the influence of the terpenoid profile on the structure and diversity of the leaf bacterial community. We found no correlation between the Shannon's diversity of terpenoids and that of bacteria (r = −0.08, P = 0.5), or microbial richness and terpenoid richness (r = 0.11, P = 0.41). There was also no correlation between the distance matrices of the terpenoid and the leaf bacterial profiles (Mantel test, r = 0.01, P = 0.29).

Using MOFA2 factor analysis, we integrated the terpenoid and bacterial datasets and then correlated the abundance of the 10% of the top ASVs that contributed the most to explaining the variation in the combined dataset to the abundance of each terpenoid. We found that the abundance of several bacterial ASVs correlated with the abundance of α‐tujone (n = 20), artemisia alcohol (n = 15), and artemisia ketone (n = 23). Among these, the abundance of 11 ASVs correlated with all three of these monoterpenoids (Fig. 4) and were identified as Thermicanus, Micrococcus, Brevibacterium, Corynebacterium, Anoxybacillus, Streptococcus, Tepidimonas, Tepidiphilus, Sodalis, Flavobacterium, and one unidentified Melioribacteraceae. In addition, the abundance of trans‐sabinol and umbellulone correlated with a group of ASVs identified as Arthrobacter, Mycobacterium, Pseudonocardia, Tabrizicola, and three other unidentified taxa. The abundance of several other ASVs correlated with the abundance of β‐tujone, yomogi alcohol, and other terpenoids (Table S2).

Fig. 4.

Fig. 4

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Correlogram of the terpenoid profile of leaves of Tanacetum vulgare and the top 10% of bacterial ASVs (ticks on the x‐axis) that contributed to explain the most variability in the combined dataset. Only significant (P < 0.05) correlations are shown; the colour indicates the Pearson's correlation coefficient.

DISCUSSION

Using T. vulgare as a model system, which shows pronounced differences in leaf chemical profiles and can be clonally propagated, we tested the effects of maternal effects, chemotype, and environmental conditions (field vs. greenhouse) on the leaf bacterial community. Our study revealed that the leaf microbial community is pronouncedly influenced by maternal effects and the environment in which the plants are growing, but, in contrast to our hypothesis, not by the terpenoid profile of the leaves. However, we identified few terpenoids that influence the relative abundance of specific bacterial taxa. We also found that terpenoid chemodiversity and bacterial diversity were not correlated. Furthermore, we found a higher number of unique ASVs in plants growing in the field than in those growing in the greenhouse, as we had predicted. Admittedly, the sample size of this study is a bit low, but the effects were very clear. More analyses in this direction are needed to test for the relationships between these different factors in T. vulgare, but also in other chemodiverse plant species.

The terpenoid profiles of our leaf samples matched the plant chemotypes, as expected. The offspring of the three mother plants used in this study belonged to different chemotypes. Plants of T. vulgare that had been assigned to certain chemotypes as seedlings could also be assigned to the same chemotypes after 2 years of growth in the field or the greenhouse, indicating that the chemotype is a stable and heritable trait. While chemotypes of T. vulgare are known to be largely genetically determined (Lokki et al. 1973; Holopainen, Hiltunen, Lokki, et al. 1987), we found only a low impact of plant maternal effects on the leaf terpenoid profile. Since T. vulgare is outcrossing, the chemotypes, as well as microbiomes, are most likely determined by the specific combination of both parental genotypes. Moreover, the fact that not all chemotypes can be produced from each maternal plant, known for this species from other work (e.g. Holopainen, Hiltunen, & Vonschantz 1987; Ziaja & Müller 2023), suggests that some combinations of maternal plant material and pollen may be incompatible. In this experiment, plants were clonally propagated, and from each plant, one individual clone was grown outside while the other was grown in the greenhouse for 2 years. Similar to the maternal effects, also the environmental conditions had only a low impact on the terpenoid profile, although various factors are known to change leaf terpenoid profiles in other plant species. This has been well studied with regard to the induction of volatile terpenoids in response to herbivory (Gershenzon & Croteau 1991; Arimura et al. 2009), but abiotic factors such as drought are also known to modulate terpenoids in herbaceous and woody plants (Turtola et al. 2003; Nowak et al. 2010). Previous laboratory experiments with T. vulgare did not reveal impacts of drought on leaf terpenoids (Kleine & Müller 2014), but terpenoid concentrations were lower when plants received more fertilizer (Kleine & Müller 2013). The relatively low influence of environmental conditions on the terpenoid profile in T. vulgare is particularly interesting considering that the leaf terpenoid profile can mediate numerous interactions between T. vulgare and other species. For example, different herbivores are known to have distinct preferences for different T. vulgare chemotypes in laboratory assays (Wolf et al. 2012; Jakobs & Müller 2018; Neuhaus‐Harr et al. 2023), and their abundances differ in the field (Kleine & Müller 2011; Clancy et al. 2016; Ziaja & Müller 2023). The plant chemotype of this species can even shape entire arthropod food webs (Bálint et al. 2016).

Both the host genotype and the environment in which a plant grows are widely acknowledged as major determinants of the structure of plant microbiomes, so our results largely agree with current knowledge. Previous studies revealed host genotype effects on leaf bacterial communities in tobacco (Xing et al. 2021), wild mustard (Wagner et al. 2016), and several other species. We also identified some bacterial ASVs and genera that were specifically found only in offspring of specific mother plants. A previous study on Brassica napus revealed that the paternal genotype has a greater effect than the maternal genotype on the seed microbiome (Wassermann et al. 2022). Understanding the relative contribution of the exact parental plant genotypes (and their microbiomes) to the assembly of offspring plant microbiomes is an intriguing topic that needs further exploration, and might help us to predict the assembly of the offspring microbiome in new varieties or hybridizing species. Impacts of environmental conditions on microbial communities, as we detected in T. vulgare, have also been found in other plant species (Heuer & Smalla 1999; Wei et al. 2016). Differences in the assembly of the leaf microbiome among individual plants growing under different environmental conditions can result from a high level of stochasticity (Chaudhry et al. 2021; Almario et al. 2022). Alternatively, such differences may be determined by variation in the soil microbiome across environments (Malacrinò et al. 2021).

Contrary to our prediction, we did not find any influence of plant chemotype on the leaf bacterial community, and we did not observe any relationship between terpenoid composition and leaf bacterial structure. An influence of specialized plant metabolites on the structure of plant‐associated microbial communities is common in roots, where root exudates can shape the composition of the root and rhizosphere microbial communities (Jacoby et al. 2021). In contrast, less is known about the effect of leaf chemistry on leaf microbiota. A previous study on several Oregano species (Lamiaceae) differing in terpenoid composition suggested that terpenoids may shape the bacterial endophytic microbiome (Semenzato et al. 2023). Thus, the impacts of host terpenoid profiles on bacteria may be plant species‐specific or terpenoid‐specific. While our analyses showed no impact of the terpenoid profile on the overall leaf microbial community, we identified a few terpenoids (α‐tujone, artemisia alcohol, artemisia ketone, trans‐sabinol, and umbellulone) that correlated with the abundance of certain bacterial strains. In particular, we observed a positive correlation between specific terpenoids and taxa with potential beneficial effects to plants, such as Arthrobacter, Brevibacterium, and Flavobacterium, which have been previously described for their ability to help plants to counteract biotic and abiotic stressors (Berg et al. 2021; Hone et al. 2021). However, the exact functions of these microbes towards the host plant remain largely unclear. Several terpenoids are also known for their antimicrobial activities (Gershenzon & Dudareva 2007; Zeljković & Maksimović 2015; Devrnja et al. 2017), which may suppress certain bacterial strains. Thus, plants with a higher production of specific terpenoids might recruit, or protect against, certain microbial taxa that can confer higher fitness or protection against threats. However, we cannot clearly disentangle who drives this process, that is, whether the terpenoids impact the bacteria or whether some bacteria also influence terpenoid production, as has been indicated previously (Castronovo et al. 2021). In T. vulgare, compounds other than terpenoids may also influence the bacterial community. Pronounced differences were found in numerous specialized metabolites among the same set of field plants between chemotypes but also among offspring of different mother plants of T. vulgare (Dussarrat et al. 2023), indicating the importance of plant genetic background impacting on the leaf microbiota. This also suggests that leaf microbiota might be heritable in T. vulgare. A higher terpenoid chemodiversity does not necessarily relate to a higher bacterial diversity, as evident from the present study. However, chemodiversity can be studied at various levels (Petrén et al. 2023), and the diversity of other chemical classes may be more relevant in driving plant–microbe associations.

Chemodiversity is an understudied driver of biodiversity that may be highly influential in shaping niche dimensions for organisms (Müller & Junker 2022). A recent study also indicated that microbiota may modulate the phytochemical composition of plants (Gaube et al. 2023). Our results show that in T. vulgare the leaf microbial community is influenced by maternal effects and the environment, and we identified terpenoids that influence the relative abundance of specific bacterial taxa. Specialized metabolites differing in their bioactivities are known to shape the relative composition of bacterial and fungal communities on leaves and other plant parts (Jacoby et al. 2021). This effect can be exploited by plants to alter the function of their microbiome and, often, to recruit microorganisms to counteract biotic and abiotic stressors (Hong et al. 2021; Trivedi et al. 2022). More studies providing a deeper understanding of the relationships between chemodiversity and the plant microbiome can help us to understand the plant holobiont and provide the tools to steer plant–microbiome interactions to improve agricultural sustainability and ecosystem resilience.

AUTHOR CONTRIBUTIONS

AM, SX, and CM designed the study. RJ harvested the leaf material and performed the terpenoid analyses. AM and SX performed analyses of the bacterial data. AM analyzed the data and generated the figures. CM wrote the first draft of the manuscript, and all authors contributed to the final version.

FUNDING INFORMATION

This research was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project MU1829/29–2, as part of the research unit FOR 3000. AM is supported by the Italian Ministry of University and Research (MUR) through the PRIN 2022 PNRR program (project no. P2022KY74N “Dissecting the genetic architecture of plant microbiome assembly and recruitment”, financed by the European Union—NextGenerationEU).

CONFLICT OF INTEREST STATEMENT

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supporting information

Figure S1. Experimental common garden design (figure adjusted from Ziaja & Müller 2023).

Table S1. Terpenoids detected in heptane extracts of leaves of Tanacetum vulgare, with retention indices, retention indices from Adams (2007) and way of identification.

PLB-27-903-s001.pdf (317.1KB, pdf)

Table S2. Abundance of ASVs and of terpenoids.

PLB-27-903-s002.csv (26KB, csv)

ACKNOWLEDGEMENTS

We thank Tanja Bloss, Lukas Brokate and Stephanie Champion from Bielefeld University, as well as Ursula Martine from University of Mainz, for practical help.

Editor: S. Whitehead

DATA AVAILABILITY STATEMENT

The raw data are available at NCBI SRA under the bioproject PRJNA1080585. Data and code are available on GitHub at: https://github.com/amalacrino/malacrino_et_al_chemodiversity.

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Associated Data

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Supplementary Materials

Figure S1. Experimental common garden design (figure adjusted from Ziaja & Müller 2023).

Table S1. Terpenoids detected in heptane extracts of leaves of Tanacetum vulgare, with retention indices, retention indices from Adams (2007) and way of identification.

PLB-27-903-s001.pdf (317.1KB, pdf)

Table S2. Abundance of ASVs and of terpenoids.

PLB-27-903-s002.csv (26KB, csv)

Data Availability Statement

The raw data are available at NCBI SRA under the bioproject PRJNA1080585. Data and code are available on GitHub at: https://github.com/amalacrino/malacrino_et_al_chemodiversity.

Articles from Plant Biology (Stuttgart, Germany) are provided here courtesy of Wiley

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