Assessment of the impact of variation in chloroplast and mitochondrial DNA (collectively termed the plasmotype) on plant phenotypes is challenging due to the difficulty in separating their effect from nuclear-derived variation (the nucleotype). Haploid-inducer lines can be used as efficient plasmotype donors to generate new plasmotype–nucleotype combinations (cybrids)1. We generated a panel comprising all possible cybrids of seven Arabidopsis thaliana accessions and extensively phenotyped these lines for 1,859 phenotypes under both stable and fluctuating conditions. We show that natural variation in the plasmotype results in both additive and epistatic effects across all phenotypic categories. Plasmotypes that induce more additive phenotypic changes also cause more epistatic effects, suggesting a possible common basis for both additive and epistatic effects. On average, epistatic interactions explained twice as much of the variance in phenotypes as additive plasmotype effects. The impact of plasmotypic variation was also more pronounced under fluctuating and stressful environmental conditions. Thus, the phenotypic impact of variation in plasmotypes is the outcome of multi-level nucleotype–plasmotype–environment interactions and, as such, the plasmotype is likely to serve as a reservoir of variation that is predominantly exposed under certain conditions. The production of cybrids using haploid inducers is a rapid and precise method for assessment of the phenotypic effects of natural variation in organellar genomes. It will facilitate efficient screening of unique nucleotype–plasmotype combinations to both improve our understanding of natural variation in these combinations and identify favourable combinations to enhance plant performance.
Subscribe to Journal
Get full journal access for 1 year
only $5.42 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Sequencing and transcriptome data are available through the European Nucleotide Archive with the primary accession codes PRJEB29654 and PRJEB35324. The raw datasets are available through Dryad at https://doi.org/10.5061/dryad.cz8w9gj05. The analysed datasets that support our findings are available as Supplementary Data. The associated raw data for Figs. 3 and 4 are provided in Supplementary Data 1, and the raw data for Fig. 2 are provided in Supplementary Data 2. Source data for Figs. 3 and Fig. 4 and Extended Data Figs. 1, 3, 4 and 7–9 are provided with the paper. The germplasm generated in this project will be available via the European Arabidopsis Stock Centre (www.arabidopsis.info).
Ravi, M. et al. A haploid genetics toolbox for Arabidopsis thaliana. Nat. Commun. 5, 5334 (2014).
Chan, K. X., Phua, S. Y., Crisp, P., McQuinn, R. & Pogson, B. J. Learning the languages of the chloroplast: retrograde signaling and beyond. Annu. Rev. Plant Biol. 67, 25–53 (2016).
Petrillo, E. et al. A chloroplast retrograde signal regulates nuclear alternative splicing. Science 344, 427–430 (2014).
Kleine, T. & Leister, D. Retrograde signaling: organelles go networking. Biochim. Biophys. Acta Bioenerg. 1857, 1313–1325 (2016).
Flood, P. J. et al. Whole-genome hitchhiking on an organelle mutation. Curr. Biol. 26, 1306–1311 (2016).
Joseph, B., Corwin, J. A., Li, B., Atwell, S. & Kliebenstein, D. J. Cytoplasmic genetic variation and extensive cytonuclear interactions influence natural variation in the metabolome. eLife 2, e00776 (2013).
Zeyl, C., Andreson, B. & Weninck, E. Nuclear-mitochondrial epistasis for fitness in Saccharomyces cerevisiae. Evolution 59, 910–914 (2005).
Montooth, K. L., Meiklejohn, C. D., Abt, D. N. & Rand, D. M. Mitochondrial-nuclear epistasis affects fitness within species but does not contribute to fixed incompatibilities between species of Drosophila. Evolution 64, 3364–3379 (2010).
Joseph, B. et al. Hierarchical nuclear and cytoplasmic genetic architectures for plant growth and defense within Arabidopsis. Plant Cell 25, 1929–1945 (2013).
Tang, Z. et al. Potential involvement of maternal cytoplasm in the regulation of flowering time via interaction with nuclear genes in maize. Crop Science 54, 544–553 (2014).
Roux, F. et al. Cytonuclear interactions affect adaptive traits of the annual plant Arabidopsis thaliana in the field. Proc. Natl Acad. Sci. USA 113, 3687–3692 (2016).
Mossman, J. A., Ge, J. Y., Navarro, F. & Rand, D. M. Mitochondrial DNA fitness depends on nuclear genetic background in Drosophila. G3 (Bethesda) 9, 1175–1188 (2019).
Dobler, R., Rogell, B., Budar, F. & Dowling, D. K. A meta-analysis of the strength and nature of cytoplasmic genetic effects. J. Evol. Biol. 27, 2021–2034 (2014).
Bock, D. G., Andrew, R. L. & Rieseberg, L. H. On the adaptive value of cytoplasmic genomes in plants. Mol. Ecol. 23, 4899–4911 (2014).
Levings, C. S. The Texas cytoplasm of maize: cytoplasmic male sterility and disease susceptibility. Science 250, 942–947 (1990).
Miclaus, M. et al. Maize cytolines unmask key nuclear genes that are under the control of retrograde signaling pathways in plants. Genome Biol. Evol. 8, 3256–3270 (2016).
Sambatti, J. B., Ortiz‐Barrientos, D., Baack, E. J. & Rieseberg, L. H. Ecological selection maintains cytonuclear incompatibilities in hybridizing sunflowers. Ecol. Lett. 11, 1082–1091 (2008).
Dowling, D. K., Abiega, K. C. & Arnqvist, G. Temperature‐specific outcomes of cytoplasmic‐nuclear interactions on egg‐to‐adult development time in seed beetles. Evolution 61, 194–201 (2007).
Ravi, M. & Chan, S. W. L. Haploid plants produced by centromere-mediated genome elimination. Nature 464, 615–618 (2010).
El-Lithy, M. E. et al. Altered photosynthetic performance of a natural Arabidopsis accession is associated with atrazine resistance. J. Exp. Bot. 56, 1625–1634 (2005).
Flood, P. J. et al. Natural variation in phosphorylation of photosystem II proteins in Arabidopsis thaliana: is it caused by genetic variation in the STN kinases? Philos. Trans. R. Soc. B 369, 20130499 (2014).
Falconer, D. & Mackay, T. J. H. Introduction to Quantitative Genetics (Longmans Green, 1996).
Somerville, C. R. & Ogren, W. L. Photorespiration mutants of Arabidopsis thaliana deficient in serine-glyoxylate aminotransferase activity. Proc. Natl Acad. Sci. USA 77, 2684–2687 (1980).
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2012).
Strand, D. D., Nicholas, F. & Kramer, D. M. The higher plant plastid NAD(P)H dehydrogenase-like complex (NDH) is a high efficiency proton pump that increases ATP production by cyclic electron flow. Journal of Biological Chemistry 292, 11850–11860 (2017).
Laughlin, T. G. et al. Structure of the complex I-like molecule NDH of oxygenic photosynthesis. Nature 566, 411–414 (2019).
Kermicle, J. L. Androgenesis conditioned by a mutation in maize. Science 166, 1422–1424 (1969).
Schneerman, M., Charbonneau, M. & Weber, D. A survey of ig-containing materials. Maize Genet. Coop. Newsl. 74, 92–93 (2000).
Houben, A., Sanei, M. & Pickering, R. Barley doubled-haploid production by uniparental chromosome elimination. Plant Cell Tissue Organ Cult. 104, 321–327 (2011).
Karimi-Ashtiyani, R. et al. Point mutation impairs centromeric CENH3 loading and induces haploid plants. Proc. Natl Acad. Sci. USA 112, 11211–11216 (2015).
Kromdijk, J. et al. Improving photosynthesis and crop productivity by accelerating recovery from photoprotection. Science 354, 857–861 (2016).
Flood, P. J., Harbinson, J. & Aarts, M. G. M. Natural genetic variation in plant photosynthesis. Trends Plant Sci. 16, 327–335 (2011).
Murchie, E. H. et al. Measuring the dynamic photosynthome. Ann. Bot-London 122, 207–220 (2018).
Ruf, S. et al. High-efficiency generation of fertile transplastomic Arabidopsis plants. Nat. Plants 5, 282–289 (2019).
Kwak, S.-Y. et al. Chloroplast-selective gene delivery and expression in planta using chitosan-complexed single-walled carbon nanotube carriers. Nat. Nanotechnol. 14, 447–455 (2019).
Zhang, J. et al. Full crop protection from an insect pest by expression of long double-stranded RNAs in plastids. Science 347, 991–994 (2015).
Jin, S. & Daniell, H. The engineered chloroplast genome just got smarter. Trends Plant Sci. 20, 622–640 (2015).
Hoekstra, L. A., Siddiq, M. A. & Montooth, K. L. Pleiotropic effects of a mitochondrial–nuclear incompatibility depend upon the accelerating effect of temperature in Drosophila. Genetics 195, 1129–1139 (2013).
Mossman, J. A., Biancani, L. M., Zhu, C.-T. & Rand, D. M. Mitonuclear epistasis for development time and its modification by diet in Drosophila. Genetics 203, 463–484 (2016).
Hill, G. E. et al. Assessing the fitness consequences of mitonuclear interactions in natural populations. Biol. Rev. 94, 1089–1104 (2019).
Yin, L. et al. Photosystem II function and dynamics in three widely used Arabidopsis thaliana accessions. PLoS ONE 7, e46206 (2012).
Gobron, N. et al. A cryptic cytoplasmic male sterility unveils a possible gynodioecious past for Arabidopsis thaliana. PLoS ONE 8, e62450 (2013).
Wijnker, E. et al. Hybrid recreation by reverse breeding in Arabidopsis thaliana. Nat. Protoc. 9, 761–772 (2014).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBO J. 17, 10–12 (2011).
Sloan, D. B., Wu, Z. & Sharbrough, J. Correction of persistent errors in Arabidopsis reference mitochondrial genomes. Plant Cell 30, 525–527 (2018).
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).
Li, H. et al. The sequence alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
The 1001 Genomes Consortium. 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell. 166, 481–491 (2016).
Flood, P. J. et al. Phenomics for photosynthesis, growth and reflectance in Arabidopsis thaliana reveals circadian and long-term fluctuations in heritability. Plant Methods 12, 1–14 (2016).
Kokorian, J., Polder, G., Keurentjes, J., Vreugdenhil, D. & Guzman, M. O. in Proc. ImageJ User and Developer Conference, Luxembourg, 27–29 October 2010 (eds Jahnen, A. & Moll, C.) 178–182 (Centre de Recherche Public Henri Tudor, 2010).
Cruz, J. A. et al. Dynamic environmental photosynthetic imaging reveals emergent phenotypes. Cell Systems 2, 365–377 (2016).
Joosen, R. V. L. et al. Germinator: a software package for high-throughput scoring and curve fitting of Arabidopsis seed germination. Plant J. 62, 148–159 (2010).
Peterson, R., Slovin, J. P. & Chen, C. A simplified method for differential staining of aborted and non-aborted pollen grains. Int. J. Plant Biol. 1, 66–69 (2010).
Lisec, J., Schauer, N., Kopka, J., Willmitzer, L. & Fernie, A. R. Gas chromatography mass spectrometry-based metabolite profiling in plants. Nat. Prot. 1, 387–396 (2006).
Carreno-Quintero, N. et al. Untargeted metabolic quantitative trait loci analyses reveal a relationship between primary metabolism and potato tuber quality. Plant Physiol. 158, 1306–1318 (2012).
Wehrens, R. et al. Improved batch correction in untargeted MS-based metabolomics. Metabolomics 12, 88 (2016).
Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).
Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Reimand, J. et al. g:Profiler—a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 44, W83–W89 (2016).
Wood, S. N., Pya, N. & Säfken, B. Smoothing parameter and model selection for general smooth models. J. Am. Stat. Assoc. 111, 1548–1563 (2016).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 48 (2015).
H. Blankestijn, J. van de Belt, D. Oberste-Lehn, E. Schijlen, C. Hanhart, J. ter Riele and S. Schop (Wageningen University & Research) are acknowledged for help with experiments; J. Klasen (Max Planck Institute for Plant Breeding Research), A. Languillaume and R. van Bezouw (Wageningen University & Research) for statistical advice; and D. Aanen (Wageningen University & Research) for helpful discussions. This work was, in part, supported by the Netherlands Organization for Scientific Research through ALW-TTI Green Genetics (P.J.F.) and ALWGS.2016.012 (T.P.J.M.T). The European Molecular Biology Organization supported this work through grant no. ALTF 679-2013 (E.W.), and the European Community through the Marie-Curie Initial Training Network ‘COMREC’ project no. 606956 funded under FP7-PEOPLE (V.C.-B.). ZonMw Enabling Technology Hotels and the Consortium for Improving Plant Yield Enabling Technology Hotels provided funds for the metabolomics, RNA-seq and seed phenotyping. Work at Michigan State University for DEPI phenotyping was supported by the US Department of Energy, Chemical Sciences, Geosciences, and Biosciences Division, Basic Energy Sciences, Office of Science at the US Department of Energy (through grant no. DE-FG02–91ER20021).
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1: Coverage plots reveal a duplication on chromosome 2 in two cybrid lines. This coverage plot shows the normalized read coverage at the lower end of the long arm of chromosome 2 for wild-type Bur (Bur-WT) and six cybrids with the Bur nucleotype (genotypes indicated as NucleotypePlasmotype). The coverage plot reveals the presence of a spontaneous nuclear DNA duplication in two cybrid lines (BurBur and BurC24), presumably derived from their wild-type Bur progenitor. These lines were excluded from all further analyses. Source data
Neighbor joining (NJ) trees based on SNPs and INDELs for nucleus (a), chloroplast (b) and mitochondria (c) for the seven Arabidopsis thaliana accessions.
Extended Data Fig. 4 Scatterplots showing the correlation between the number of plasmotype additive and plasmotype epistatic effects.
a, Shows the correlation for additive and epistatic effects for all plasmotypes (including the Ely plasmotype; n = 7 plasmotypes) in every comparison, averaged over the nucleotypes, and counted over the 92 phenotypes. b, Shows the same correlation when excluding the Ely plasmotype (n = 6 plasmotypes). R is the Pearson correlation coefficient, the P value is based on a two-sided t-test and the shaded area shows the 95% confidence interval around the regression line. Source data
a, An ElySha plant was polinated on three open flowers using Ely wild-type pollen, which produced elongated siliques (indicated with red arrows), scale bar is 1 cm. b, Shows an anther and pollen of ElySha, stained using Alexander stain. Pollen viability was assessed in 250 pollen per flower (n = 3 flowers). Note the presence of a high percentage (45%) of greenish, almost colourless aborted pollen. Pollen with a red colour in this line are not able to fertilize ovules, as deduced from the male sterile phenotype of ElySha (as shown in panel a), scale bar is 500 μm. c, Anther and pollen of Ely wildtype. Note that all pollen have a dark red colour, suggesting high viability. Ely wildtype is able to fertilize ElySha (as shown in panel a), scale bar is 500 μm.
Extended Data Fig. 6 Changes in gene expression between cybrids with a Ler nucleus (panel a), an Ely nucleus (panel b) and changes they have in common (panel c).
Cybrid genotypes are indicated as NucleotypePlasmotype. The triangle in panel a shows cybrid comparisons with a Ler nuclear background (for plasmotypes Ely, Ler and Bur) and panel b shows cybrid comparisons with an Ely nuclear background for the same plasmotypes. Significantly differentially expressed (DE) genes between cybrid comparisons are indicated with black numbers. These DE genes are subdivided in upregulated genes (green numbers in superscript) and downregulated genes (red numbers in subscript), following the direction of the arrows between cybrids (that is the change from an Ely to a Ler plasmotype in a Ler nuclear background resulted in 726 DE genes, of which 426 were upregulated and 300 were downregulated). The green triangle in panel c shows what differentially expressed genes the comparisons in panels a and b have in common. For example, the Ler and Ely nuclear backgrounds show a common response of 78 DE genes when the Ely plasmotype is changed for a Ler plasmotype. The absence of one of the comparisons in this triangle is due to the absence of shared DE genes. The common effect of changing an Ely plasmotype for either Bur of Ler was derived by assessing what DE genes are similar along the axes in the green triangle c. These 78 and 150 genes have 40 shared DE genes (see Supplementary Table 6). For the raw data see Supplementary Data 3.
Extended Data Fig. 7 The fraction of explained genetic variation (H2) for changes in photosynthesis phenotypes (ΦPSII, ΦNPQ, ΦNO, NPQ, qE, qI) in response to light conditions.
a, Shows the fraction of H2 for epistatic interactions (nucleotype x plasmotype). b, Shows H2 for plasmotype additive effects. c, Shows the light intensity for five consecutive days with growth under: steady light (day 1); in- and decreasing light intensity (day 2); fluctuating in- and decreasing light intensity (day 3); steady light (day 4) and fluctuating in- and decreasing light intensity (day 5). Days are separated by nights (shaded areas). The first three days of this experiment are identical to the light conditions of the experiment shown in Fig. 3. Source data
Extended Data Fig. 8 KCN sensitive O2 consumption of mitochondria in seedlings of wild-type accessions.
Mitochondrial ATP-synthesis proceeds mainly through the phosphoryl ating cytochrome (KCN sensitive) pathway. KCN sensitive O2 consumption by Bur does not differ significantly from C24, Col and Ws-4. Error bars represent the standard error of the mean (n = minimally 6 biologically independent samples). Letters indicate significant differences using the Tukey posthoc test, with an α = 0.05 threshold. Source data
Normalized read depth for the chloroplast (a) and mitochondrial (b) genome sequences were calculated in a sliding window of 1-kb. Because we observe no unique deletions or duplications in the Bur plasmotype that might be causal to the phenotypic effects observed in cybrids with the Bur plasmotype. Source data
Supplementary Figs. 1–3 and Supplementary Tables 1–8.
Supplementary Data 1. Summary and test statistics for all 1,859 phenotypes. Table 1 shows the least squared means for all phenotypes per cybrid; these are calculated via the models as specified in Supplementary Table 4. Table 2 shows the fraction of explained variance for nucleotype, plasmotype and interaction separately, including cybrids with the Ely plasmotype. We show the variance components for all terms in the model. Using the variance components of the genetic components, we calculated the broad-sense heritability (H2). We used a H2 threshold of 0.05 for phenotypes to be included in summary and test statistics. The fraction of H2 for all three genetic components is also provided. Table 3 shows the same data as given in Table 2, but excluding cybrids with the Ely plasmotype. Table 4 shows significant plasmotype additive effects, with Hochberg’s P value correction. Letters indicate significance or not. Table 5 shows significant plasmotype epistatic effects. This was performed for every plasmotype within each nucleotype (with Hochberg’s P value correction), as well as a comparison between self-cybrids and other cybrids within each nucleotype (with Dunnett’s P value correction).
Supplementary Data 2. Summary and test statistics of 92 phenotypes, with easy-to-use tables provided to search for significant differences among all separate phenotypes. Table 1 shows the significant additive effects for each pairwise comparison, with a text box to explain the interpretation of the data presented. Table 1 contains the underlying data used for generation of Table 1a. Table 2 shows the significant difference for every pairwise comparison within a nucleotype—that is, epistatic effects. A text box is provided to explain interpretation of the data. Table 2 contains the underlying data used for generation of Table 1b. The remaining tables show the summary and test statistics for the 92 phenotypes, as in Supplementary Data 1.
Supplementary Data 3. Differential expression overview from the RNA-seq experiment for the six cybrid comparisons. Tables 1–6 show pairwise comparisons of cybrid lines, indicated as ‘nucleotype–plasmotype versus nucleotype–plasmotype’. For every nuclear gene detected, the differential expression data are given with the adjusted P value cut-off set at α = 0.05 (in yellow). The summary statistics for this are given in Supplementary Table 5. Table 7 shows Gene Ontology enrichment for five cybrid comparisons. For one comparison (LerBur versus LerLer) we detected only three significantly expressed genes for which Gene Ontology enrichment yielded no results. This table also shows Gene Ontology enrichment for genes that differentially changed expression when the Ely plasmotype was replaced by Ler or Bur in a Ler or Ely nuclear background (indicated as ‘Ely main’).
Supplementary Data 4. Predicted impact of SNPs and INDELs on the chloroplastic and mitochondrial genomes of all seven accessions used. For every variant the reference allele, based on TAIR10.1, is given next to the alternative allele. We indicate, for all seven accessions, whether they share the reference (0/0) or alternative allele (1/1), and used SnpEff to predict the impact. The changes are ranked as ‘Low’, ‘Modifier’, ‘Moderate’ or ‘High’ based on the location in respect to a gene, and these predicted amino acid change. In our interpretation we used the ‘Moderate-’ and ‘High’-impact variants. The genes affected, as well as nucleotide and amino acid change, are provided.
Raw data for figure.
Raw data for figure.
Raw data for figure.
Raw data for figure.
Raw data for figure.
Raw data for figure.
Statistical Source data.
Raw data for figure.
About this article
Cite this article
Flood, P.J., Theeuwen, T.P.J.M., Schneeberger, K. et al. Reciprocal cybrids reveal how organellar genomes affect plant phenotypes. Nat. Plants 6, 13–21 (2020). https://doi.org/10.1038/s41477-019-0575-9