Plant Physiology

Koudounas, Konstantinos

doi: 10.1093/plphys/kiab592pmid: 35245382

Sun, Tianhu

doi: 10.1093/plphys/kiab594pmid: 35245384

Nguyen, Trinh-Don

doi: 10.1093/plphys/kiab598pmid: 35245383

Dell’Aglio, Elisa

doi: 10.1093/plphys/kiab593pmid: 35245380

Foroozani, Maryam

doi: 10.1093/plphys/kiab599pmid: 35245378

Zebell, Sophia

doi: 10.1093/plphys/kiab596pmid: 35245381

Hellmann, Eva; Campos, Marcelo Lattarulo

doi: 10.1093/plphys/kiab597pmid: 35245379

Freh, Matthias; Gao, Jinlan; Petersen, Morten; Panstruga, Ralph

doi: 10.1093/plphys/kiab590pmid: 34958371

Abstract The plant immune system is well equipped to ward off the attacks of different types of phytopathogens. It primarily relies on two types of immune sensors—plasma membrane-resident receptor-like kinases and intracellular nucleotide-binding domain leucine-rich repeat (NLRs) receptors that engage preferentially in pattern- and effector-triggered immunity, respectively. Delicate fine-tuning, in particular of the NLR-governed branch of immunity, is key to prevent inappropriate and deleterious activation of plant immune responses. Inadequate NLR allele constellations, such as in the case of hybrid incompatibility, and the mis-activation of NLRs or the absence or modification of proteins guarded by these NLRs can result in the spontaneous initiation of plant defense responses and cell death—a phenomenon referred to as plant autoimmunity. Here, we review recent insights augmenting our mechanistic comprehension of plant autoimmunity. The recent findings broaden our understanding regarding hybrid incompatibility, unravel candidates for proteins likely guarded by NLRs and underline the necessity for the fine-tuning of NLR expression at various levels to avoid autoimmunity. We further present recently emerged tools to study plant autoimmunity and draw a cross-kingdom comparison to the role of NLRs in animal autoimmune conditions. Plant immunity at a glance The plant immune system comprises two major realms: constitutive defense components and induced immune responses. The former encompasses preformed mechanical barriers like the waxy cuticle as well as chemical components such as constitutively produced antimicrobials (Wittstock and Gershenzon, 2002; Yeats and Rose, 2013). The latter rests on multiple interconnected cellular reactions in response to the perception of specific pathogen-linked molecules, which ultimately slows down or fully impedes pathogen proliferation, sometimes in association with localized host cell death (Zhou and Zhang, 2020). The induced immune responses can be further subdivided into two major branches: those triggered by extracellularly occurring molecules, both from the pathogen (pathogen-associated molecular patterns [PAMPs], Box 1) and the plant itself (damage-associated molecular patterns [DAMPs], Box 1), and those triggered by intracellularly delivered pathogen effector proteins (Box 1). Accordingly, these branches are referred to as PAMP-triggered immunity (PTI) and effector-triggered immunity (ETI), respectively (Box 1; Jones and Dangl, 2006; Zhou and Zhang, 2020). PTI mainly relies on membrane-resident receptor-like kinases (RLKs) as immune sensors, whereas ETI is primarily based on nucleotide-binding domain leucine-rich repeat (NLR) receptors that according to their N-terminal domain can be further subdivided into Toll/interleukin receptor type NLRs (TNLs) and coiled-coil type NLRs (CNLs) (Box 1). Apart from “full-length” TNLs, C-terminally truncated versions without leucine-rich repeats exist. These TN proteins are thought to co-operate with TNLs as complexes (Zhang and Gassmann, 2003; Liang et al., 2019; Cai et al., 2021; Chen et al., 2021). While some NLRs perceive pathogen effectors by direct binding, others monitor (“guard”) the status of host proteins (the “guardees”) including their possible biochemical manipulation by pathogen effectors (Figure 1, A and B). In case of manipulation or even degradation of the guardee by pathogen effectors, the respective NLR gets activated and induces downstream signaling, which ultimately leads to ETI-associated plant defense responses (Khan et al., 2016; Ngou et al., 2021). NLR activation often leads to a hypersensitive response (HR), frequently culminating in cell death at the site of pathogen ingress, to inhibit the growth of (hemi-)biotrophic pathogens that rely on living host cells (Balint-Kurti, 2019). Figure 1 Open in new tabDownload slide Mechanisms of pathogen perception and involvement of NLRs in plant autoimmunity. A, Inactive immune state without immune reaction mediated by NLRs. B, Immune reaction induced by effectors delivered during pathogen attack. The effectors are either directly recognized by NLRs or interact with guardee proteins guarded by NLRs. C–F, Autoimmunity induced by NLRs: (C) hybrid necrosis, (D) mutations in NLR genes, (E) overexpression of NLR genes, or (F) mutation of guardees that are monitored by NLRs. Figure 1 Open in new tabDownload slide Mechanisms of pathogen perception and involvement of NLRs in plant autoimmunity. A, Inactive immune state without immune reaction mediated by NLRs. B, Immune reaction induced by effectors delivered during pathogen attack. The effectors are either directly recognized by NLRs or interact with guardee proteins guarded by NLRs. C–F, Autoimmunity induced by NLRs: (C) hybrid necrosis, (D) mutations in NLR genes, (E) overexpression of NLR genes, or (F) mutation of guardees that are monitored by NLRs. Although it was originally assumed that individual NLR proteins are sufficient for the induction of resistance via ETI (Flor, 1971; Hammond-Kosack and Jones, 1997; Jones and Dangl, 2006; Rafiqi et al., 2009), it was recently discovered that the situation can be more complex. While indeed some “singleton” NLRs operate autonomously, such as the CNLs Mildew locus a 10 (Mla10) from barley (Hordeum vulgare) and HOPZ-ACTIVATED RESISTANCE 1 (ZAR1) from Arabidopsis (Arabidopsis thaliana; Maekawa et al., 2011a; Seto et al., 2017), many NLRs seem to work either in pairs or as components of complex networks (Adachi et al., 2019). In the latter case, “sensor” NLRs either directly or indirectly recognize pathogen effectors, while their associated “helper” NLRs (such as members of the ACTIVATED DISEASE RESISTANCE 1 [ADR1] and N REQUIREMENT GENE 1 [NRG1] families in Arabidopsis) mediate signaling downstream of “sensor” NLRs, thereby inducing strong defense responses and HR, probably by forming Ca2+-permeable cation channels (Saile et al., 2020; Bi et al., 2021; Jacob et al., 2021). Autoimmunity/lesion mimic phenotypes Plant autoimmunity can generally be described as the genetically conditioned inappropriate (over-)activation of plant immune responses, leading to severe disadvantages of the affected plants (Bruggeman et al., 2015; van Wersch et al., 2016). Characteristic phenotypes associated with autoimmunity are (sometimes severely) stunted growth (dwarfism), leaf chlorosis and necrosis, runaway cell death, reduced reproductive fitness, and occasionally even plant lethality (Table 1). An overactivation of immune responses may be caused by mutations or downregulation of negative regulators of plant immunity or the misactivation or overactivation of immune sensors. The latter might be caused by the absence or mutational modification of sensor NLR-monitored guardees (Chakraborty et al., 2018). In crop plants, autoimmune phenotypes have been classically described as “lesion mimic” phenotypes as they often resemble the lesions that plants develop in interactions with biotrophic pathogens (Walbot et al., 1983; Table 2). Later the term was also applied to mutants in Arabidopsis, and several of these were shown to be affected in the control of phytohormone and cell death pathways (Lorrain et al., 2003; Moeder and Yoshioka, 2008; Bruggeman et al., 2015). Hallmarks of autoimmunity comprise the spontaneous yet sometimes developmentally regulated accumulation of defense-related phytohormones such as salicylic acid (SA) and the constitutive expression of defense marker genes (Disch et al., 2016; Radojičić et al., 2018; Castel et al., 2019). Accordingly, second-site mutations in key regulators of plant immunity often relieve the autoimmune phenotypes due to a block in downstream immune signaling (Wang et al., 2013; Zhao et al., 2015; Rodriguez et al., 2016). Typical examples of the latter are components that play a key role in SA-dependent amplification of immunity such as ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1) and PHYTOALEXIN DEFICIENT 4 (PAD4; Vogelmann et al., 2012; Disch et al., 2016; Jia et al., 2021). Table 1 Recent examples of autoimmune phenotypes triggered by mutations or hybrid necrosis in A. thaliana Responsible Gene . Phenotype . Protein Function/Annotation . Comment . References . acd6 (At4G14400) Dwarfism and leaf necrosis Resistance to Pseudomonas syringae, putative ankyrin and transmembrane domains Dependent on SNC1 and SA-dependent Rate et al. (1999); Zhu et al. (2018) bak1 bkk1 (At4G33430, At2G13790) Stunted growth and necrosis Receptor-like kinases that act as co-receptors in PTI Dependent on ADR1 family Wu et al. (2020a) cad1 (At1G29690) Dwarfism and leaf necrosis MACPF protein; putative negative regulator of SA-mediated programmed cell death No guard protein known so far Holmes et al. (2021) camta3/sr1 (At5G01820) Dwarfism and early leaf chlorosis Putative calmodulin-binding transcription factor, acts in cold response pathway Dependent on DSC1/2 Du et al. (2009); Lolle et al. (2017); Yuan et al. (2018) cbp60b (At5G57580) Stunted growth and delayed, bushy flowering Calmodulin binding protein; putative activator of immunity Dependent on SNC1 Li et al. (2021) cngc20 (At3G17700) Stunted growth Cyclic nucleotide-gated channel No guard protein known so far Zhao et al. (2021) DM2/RPP1-like (At3G44670) Dwarfism TNL, confers resistance to Hyaloperonospora arabidopsidis Dependent on SULKI1/2, hybrid incompatibility between Ler and Kas2, only expressed under low temperatures (14–16°C) Atanasov et al. (2018); Ordon et al. (2021) DM10 (At5G58120) Lethal hybrid necrosis (∼3 weeks) Truncated allele of a TIR-NLR from Arabidopsis accession Cdm-0 Interacts with specific alleles of DM11 Barragan et al. (2021) HR4 (At3G50480) Impaired growth/development Involved in pathogen resistance, interaction with RPP7 Hybrid necrosis in specific allele combinations with itself or RPP7 Xiao et al. (2005); Barragan et al. (2019); Li et al. (2020) ka120 (At3G08960) Severe dwarfism Nuclear import receptor protein Constrains the nuclear activity of SNC1 Jia et al. (2021) lsd1 (At4G20380) Runaway cell death Negative regulation of basal defense, contains zinc- finger motifs Induced under long-day conditions and low temperature Dietrich et al. (1994, 1997); Huang et al. (2010b); Lv et al. (2019) mekk1, mkk2, mpk4 (At4G08500, At4G29810, At4G01370) From stunted growth up to extreme dwarfism Components of a MAP kinase cascade involved in response to pathogens and abiotic stress Guarded by SUMM2 and RPS6 Zhang et al. (2017b); Takagi et al. (2019) saul1 (At1G20780) Seedling lethality E3 ubiquitin ligase, associated with senescence Dependent on SOC3 together with CHS1 or TN2, as well as SUSA2 Tong et al. (2017); Liang et al. (2019, 2020) srfr1 (At4G37460) Dwarfism Shows similarity to transcriptional repressors; putative negative regulator of defense responses Dependent on SNC1; suppressed under elevated temperature Kim et al. (2010); Garner et al. (2021) topp4 (At2G39840) Dwarfism and curly leaves Serine/threonine phosphatase Dependent on SUT1, HSP70, and RAR1 Yan et al. (2019) tpr2, tpr3 (At3G16830, At5G27030) Enhanced dwarfism in srfr1 and snc1 background TOPLESS family proteins; negative regulators of SNC1 dependent autoimmune phenotypes Work antagonistically to TPR1 in modulating SNC1 Garner et al. (2021) zed1-D (At3G57750) Dwarfism/compressed inflorescence growth Pseudokinase that might act as a decoy for the P. syringae effector HopZ1a Dependent on ZAR1, gain-of-function, dose-dependent effect Lewis et al. (2013); Wang et al. (2017, 2019b) Responsible Gene . Phenotype . Protein Function/Annotation . Comment . References . acd6 (At4G14400) Dwarfism and leaf necrosis Resistance to Pseudomonas syringae, putative ankyrin and transmembrane domains Dependent on SNC1 and SA-dependent Rate et al. (1999); Zhu et al. (2018) bak1 bkk1 (At4G33430, At2G13790) Stunted growth and necrosis Receptor-like kinases that act as co-receptors in PTI Dependent on ADR1 family Wu et al. (2020a) cad1 (At1G29690) Dwarfism and leaf necrosis MACPF protein; putative negative regulator of SA-mediated programmed cell death No guard protein known so far Holmes et al. (2021) camta3/sr1 (At5G01820) Dwarfism and early leaf chlorosis Putative calmodulin-binding transcription factor, acts in cold response pathway Dependent on DSC1/2 Du et al. (2009); Lolle et al. (2017); Yuan et al. (2018) cbp60b (At5G57580) Stunted growth and delayed, bushy flowering Calmodulin binding protein; putative activator of immunity Dependent on SNC1 Li et al. (2021) cngc20 (At3G17700) Stunted growth Cyclic nucleotide-gated channel No guard protein known so far Zhao et al. (2021) DM2/RPP1-like (At3G44670) Dwarfism TNL, confers resistance to Hyaloperonospora arabidopsidis Dependent on SULKI1/2, hybrid incompatibility between Ler and Kas2, only expressed under low temperatures (14–16°C) Atanasov et al. (2018); Ordon et al. (2021) DM10 (At5G58120) Lethal hybrid necrosis (∼3 weeks) Truncated allele of a TIR-NLR from Arabidopsis accession Cdm-0 Interacts with specific alleles of DM11 Barragan et al. (2021) HR4 (At3G50480) Impaired growth/development Involved in pathogen resistance, interaction with RPP7 Hybrid necrosis in specific allele combinations with itself or RPP7 Xiao et al. (2005); Barragan et al. (2019); Li et al. (2020) ka120 (At3G08960) Severe dwarfism Nuclear import receptor protein Constrains the nuclear activity of SNC1 Jia et al. (2021) lsd1 (At4G20380) Runaway cell death Negative regulation of basal defense, contains zinc- finger motifs Induced under long-day conditions and low temperature Dietrich et al. (1994, 1997); Huang et al. (2010b); Lv et al. (2019) mekk1, mkk2, mpk4 (At4G08500, At4G29810, At4G01370) From stunted growth up to extreme dwarfism Components of a MAP kinase cascade involved in response to pathogens and abiotic stress Guarded by SUMM2 and RPS6 Zhang et al. (2017b); Takagi et al. (2019) saul1 (At1G20780) Seedling lethality E3 ubiquitin ligase, associated with senescence Dependent on SOC3 together with CHS1 or TN2, as well as SUSA2 Tong et al. (2017); Liang et al. (2019, 2020) srfr1 (At4G37460) Dwarfism Shows similarity to transcriptional repressors; putative negative regulator of defense responses Dependent on SNC1; suppressed under elevated temperature Kim et al. (2010); Garner et al. (2021) topp4 (At2G39840) Dwarfism and curly leaves Serine/threonine phosphatase Dependent on SUT1, HSP70, and RAR1 Yan et al. (2019) tpr2, tpr3 (At3G16830, At5G27030) Enhanced dwarfism in srfr1 and snc1 background TOPLESS family proteins; negative regulators of SNC1 dependent autoimmune phenotypes Work antagonistically to TPR1 in modulating SNC1 Garner et al. (2021) zed1-D (At3G57750) Dwarfism/compressed inflorescence growth Pseudokinase that might act as a decoy for the P. syringae effector HopZ1a Dependent on ZAR1, gain-of-function, dose-dependent effect Lewis et al. (2013); Wang et al. (2017, 2019b) Open in new tab Table 1 Recent examples of autoimmune phenotypes triggered by mutations or hybrid necrosis in A. thaliana Responsible Gene . Phenotype . Protein Function/Annotation . Comment . References . acd6 (At4G14400) Dwarfism and leaf necrosis Resistance to Pseudomonas syringae, putative ankyrin and transmembrane domains Dependent on SNC1 and SA-dependent Rate et al. (1999); Zhu et al. (2018) bak1 bkk1 (At4G33430, At2G13790) Stunted growth and necrosis Receptor-like kinases that act as co-receptors in PTI Dependent on ADR1 family Wu et al. (2020a) cad1 (At1G29690) Dwarfism and leaf necrosis MACPF protein; putative negative regulator of SA-mediated programmed cell death No guard protein known so far Holmes et al. (2021) camta3/sr1 (At5G01820) Dwarfism and early leaf chlorosis Putative calmodulin-binding transcription factor, acts in cold response pathway Dependent on DSC1/2 Du et al. (2009); Lolle et al. (2017); Yuan et al. (2018) cbp60b (At5G57580) Stunted growth and delayed, bushy flowering Calmodulin binding protein; putative activator of immunity Dependent on SNC1 Li et al. (2021) cngc20 (At3G17700) Stunted growth Cyclic nucleotide-gated channel No guard protein known so far Zhao et al. (2021) DM2/RPP1-like (At3G44670) Dwarfism TNL, confers resistance to Hyaloperonospora arabidopsidis Dependent on SULKI1/2, hybrid incompatibility between Ler and Kas2, only expressed under low temperatures (14–16°C) Atanasov et al. (2018); Ordon et al. (2021) DM10 (At5G58120) Lethal hybrid necrosis (∼3 weeks) Truncated allele of a TIR-NLR from Arabidopsis accession Cdm-0 Interacts with specific alleles of DM11 Barragan et al. (2021) HR4 (At3G50480) Impaired growth/development Involved in pathogen resistance, interaction with RPP7 Hybrid necrosis in specific allele combinations with itself or RPP7 Xiao et al. (2005); Barragan et al. (2019); Li et al. (2020) ka120 (At3G08960) Severe dwarfism Nuclear import receptor protein Constrains the nuclear activity of SNC1 Jia et al. (2021) lsd1 (At4G20380) Runaway cell death Negative regulation of basal defense, contains zinc- finger motifs Induced under long-day conditions and low temperature Dietrich et al. (1994, 1997); Huang et al. (2010b); Lv et al. (2019) mekk1, mkk2, mpk4 (At4G08500, At4G29810, At4G01370) From stunted growth up to extreme dwarfism Components of a MAP kinase cascade involved in response to pathogens and abiotic stress Guarded by SUMM2 and RPS6 Zhang et al. (2017b); Takagi et al. (2019) saul1 (At1G20780) Seedling lethality E3 ubiquitin ligase, associated with senescence Dependent on SOC3 together with CHS1 or TN2, as well as SUSA2 Tong et al. (2017); Liang et al. (2019, 2020) srfr1 (At4G37460) Dwarfism Shows similarity to transcriptional repressors; putative negative regulator of defense responses Dependent on SNC1; suppressed under elevated temperature Kim et al. (2010); Garner et al. (2021) topp4 (At2G39840) Dwarfism and curly leaves Serine/threonine phosphatase Dependent on SUT1, HSP70, and RAR1 Yan et al. (2019) tpr2, tpr3 (At3G16830, At5G27030) Enhanced dwarfism in srfr1 and snc1 background TOPLESS family proteins; negative regulators of SNC1 dependent autoimmune phenotypes Work antagonistically to TPR1 in modulating SNC1 Garner et al. (2021) zed1-D (At3G57750) Dwarfism/compressed inflorescence growth Pseudokinase that might act as a decoy for the P. syringae effector HopZ1a Dependent on ZAR1, gain-of-function, dose-dependent effect Lewis et al. (2013); Wang et al. (2017, 2019b) Responsible Gene . Phenotype . Protein Function/Annotation . Comment . References . acd6 (At4G14400) Dwarfism and leaf necrosis Resistance to Pseudomonas syringae, putative ankyrin and transmembrane domains Dependent on SNC1 and SA-dependent Rate et al. (1999); Zhu et al. (2018) bak1 bkk1 (At4G33430, At2G13790) Stunted growth and necrosis Receptor-like kinases that act as co-receptors in PTI Dependent on ADR1 family Wu et al. (2020a) cad1 (At1G29690) Dwarfism and leaf necrosis MACPF protein; putative negative regulator of SA-mediated programmed cell death No guard protein known so far Holmes et al. (2021) camta3/sr1 (At5G01820) Dwarfism and early leaf chlorosis Putative calmodulin-binding transcription factor, acts in cold response pathway Dependent on DSC1/2 Du et al. (2009); Lolle et al. (2017); Yuan et al. (2018) cbp60b (At5G57580) Stunted growth and delayed, bushy flowering Calmodulin binding protein; putative activator of immunity Dependent on SNC1 Li et al. (2021) cngc20 (At3G17700) Stunted growth Cyclic nucleotide-gated channel No guard protein known so far Zhao et al. (2021) DM2/RPP1-like (At3G44670) Dwarfism TNL, confers resistance to Hyaloperonospora arabidopsidis Dependent on SULKI1/2, hybrid incompatibility between Ler and Kas2, only expressed under low temperatures (14–16°C) Atanasov et al. (2018); Ordon et al. (2021) DM10 (At5G58120) Lethal hybrid necrosis (∼3 weeks) Truncated allele of a TIR-NLR from Arabidopsis accession Cdm-0 Interacts with specific alleles of DM11 Barragan et al. (2021) HR4 (At3G50480) Impaired growth/development Involved in pathogen resistance, interaction with RPP7 Hybrid necrosis in specific allele combinations with itself or RPP7 Xiao et al. (2005); Barragan et al. (2019); Li et al. (2020) ka120 (At3G08960) Severe dwarfism Nuclear import receptor protein Constrains the nuclear activity of SNC1 Jia et al. (2021) lsd1 (At4G20380) Runaway cell death Negative regulation of basal defense, contains zinc- finger motifs Induced under long-day conditions and low temperature Dietrich et al. (1994, 1997); Huang et al. (2010b); Lv et al. (2019) mekk1, mkk2, mpk4 (At4G08500, At4G29810, At4G01370) From stunted growth up to extreme dwarfism Components of a MAP kinase cascade involved in response to pathogens and abiotic stress Guarded by SUMM2 and RPS6 Zhang et al. (2017b); Takagi et al. (2019) saul1 (At1G20780) Seedling lethality E3 ubiquitin ligase, associated with senescence Dependent on SOC3 together with CHS1 or TN2, as well as SUSA2 Tong et al. (2017); Liang et al. (2019, 2020) srfr1 (At4G37460) Dwarfism Shows similarity to transcriptional repressors; putative negative regulator of defense responses Dependent on SNC1; suppressed under elevated temperature Kim et al. (2010); Garner et al. (2021) topp4 (At2G39840) Dwarfism and curly leaves Serine/threonine phosphatase Dependent on SUT1, HSP70, and RAR1 Yan et al. (2019) tpr2, tpr3 (At3G16830, At5G27030) Enhanced dwarfism in srfr1 and snc1 background TOPLESS family proteins; negative regulators of SNC1 dependent autoimmune phenotypes Work antagonistically to TPR1 in modulating SNC1 Garner et al. (2021) zed1-D (At3G57750) Dwarfism/compressed inflorescence growth Pseudokinase that might act as a decoy for the P. syringae effector HopZ1a Dependent on ZAR1, gain-of-function, dose-dependent effect Lewis et al. (2013); Wang et al. (2017, 2019b) Open in new tab Table 2 Examples of autoimmune phenotypes triggered by mutations in various crop plants Organism . Responsible Gene . Phenotype . Comment . References . Soybean dlm Necrotic leaf speckles Developing with the age of the leaf, light-dependent Chung et al. (1998); Kim et al. (2005) spl-1/lm1 (Gm04g242300) Necrotic leaf speckles Copper ion binding protein, phenotype developing with the age of the leaf, light-dependent (enhanced under summer-planting conditions) Al Amin et al. (2019) lmm2-1 (Gm14g003200) Small chlorotic lesions, subsequently turning necrotic Light-dependent, gene encodes a coproporphyrinogen III oxidase that participates in tetrapyrrole biosynthesis Ma et al. (2020a) Rice lmi Necrotic leaf lesions Starting from the leaf-tip, lethal before the complete spike is ripened (few seeds), light-dependent Liu et al. (2003) lmm9150/aba2 (Os03g59610) Necrotic leaf lesions, preharvest sprouting, enhanced resistance to bacterial blight and rice blast, H2O2 accumulation Alcohol dehydrogenase, abscisic acid biosynthesis Liao et al. (2018) oscul3a Red/brown scattered leaf lesions, ROS burst, lipid accumulation, increased resistance against bacterial blight and rice blast, reduced growth and yield Light-sensitive (delayed development in the greenhouse versus the field) Liu et al. (2017); Gao et al. (2019) osvoz1/osvoz2 Stunted growth, necrotic leaf lesions, and seedling lethality, in RNAi knockdown lines accompanied by ROS overaccumulation and enhanced resistance to rice blast Transcription factors that modulate immunity against MoO Wang et al. (2021) spl2 Spontaneous lesions and lesions triggered by wounding Ozone hypersensitive, light-dependent Kojo et al. (2006) spl3-t (Os03g06630) Necrotic spots, connecting over the whole leaf during development, decreased plant height and seed setting rate, increased ear length Light-dependent/induced YuChun et al. (2018) spl11 Small spot-like necrotic lesions E3 ubiquitin ligase protein Zeng et al. (2004) spl36 Necrotic leaf lesions Light-dependent Yuchun et al. (2021) wlml1 Yellow to white patches and lesions, plant height, and grain weight reduced Temperature-dependent (reduced under high temperatures) Chen et al. (2018) Barley bspl1 Necrotic lesions on the whole leaf, H2O2 accumulation Light-dependent/induced Zhang et al. (2019) mlo Necrotic leaf spots, early leaf senescence, spontaneous formation of callose- containing call wall appositions, durable resistance to powdery mildew Wolter et al. (1993); Peterhänsel et al. (1997) Cotton Le4 Necrotic lesion leading to leaf shedding, abnormal thylakoid-structure, H2O2 accumulation in early leaf stages, infertile flowers Hybrid necrosis, photoperiod-sensitive Deng et al. (2019) Organism . Responsible Gene . Phenotype . Comment . References . Soybean dlm Necrotic leaf speckles Developing with the age of the leaf, light-dependent Chung et al. (1998); Kim et al. (2005) spl-1/lm1 (Gm04g242300) Necrotic leaf speckles Copper ion binding protein, phenotype developing with the age of the leaf, light-dependent (enhanced under summer-planting conditions) Al Amin et al. (2019) lmm2-1 (Gm14g003200) Small chlorotic lesions, subsequently turning necrotic Light-dependent, gene encodes a coproporphyrinogen III oxidase that participates in tetrapyrrole biosynthesis Ma et al. (2020a) Rice lmi Necrotic leaf lesions Starting from the leaf-tip, lethal before the complete spike is ripened (few seeds), light-dependent Liu et al. (2003) lmm9150/aba2 (Os03g59610) Necrotic leaf lesions, preharvest sprouting, enhanced resistance to bacterial blight and rice blast, H2O2 accumulation Alcohol dehydrogenase, abscisic acid biosynthesis Liao et al. (2018) oscul3a Red/brown scattered leaf lesions, ROS burst, lipid accumulation, increased resistance against bacterial blight and rice blast, reduced growth and yield Light-sensitive (delayed development in the greenhouse versus the field) Liu et al. (2017); Gao et al. (2019) osvoz1/osvoz2 Stunted growth, necrotic leaf lesions, and seedling lethality, in RNAi knockdown lines accompanied by ROS overaccumulation and enhanced resistance to rice blast Transcription factors that modulate immunity against MoO Wang et al. (2021) spl2 Spontaneous lesions and lesions triggered by wounding Ozone hypersensitive, light-dependent Kojo et al. (2006) spl3-t (Os03g06630) Necrotic spots, connecting over the whole leaf during development, decreased plant height and seed setting rate, increased ear length Light-dependent/induced YuChun et al. (2018) spl11 Small spot-like necrotic lesions E3 ubiquitin ligase protein Zeng et al. (2004) spl36 Necrotic leaf lesions Light-dependent Yuchun et al. (2021) wlml1 Yellow to white patches and lesions, plant height, and grain weight reduced Temperature-dependent (reduced under high temperatures) Chen et al. (2018) Barley bspl1 Necrotic lesions on the whole leaf, H2O2 accumulation Light-dependent/induced Zhang et al. (2019) mlo Necrotic leaf spots, early leaf senescence, spontaneous formation of callose- containing call wall appositions, durable resistance to powdery mildew Wolter et al. (1993); Peterhänsel et al. (1997) Cotton Le4 Necrotic lesion leading to leaf shedding, abnormal thylakoid-structure, H2O2 accumulation in early leaf stages, infertile flowers Hybrid necrosis, photoperiod-sensitive Deng et al. (2019) Open in new tab Table 2 Examples of autoimmune phenotypes triggered by mutations in various crop plants Organism . Responsible Gene . Phenotype . Comment . References . Soybean dlm Necrotic leaf speckles Developing with the age of the leaf, light-dependent Chung et al. (1998); Kim et al. (2005) spl-1/lm1 (Gm04g242300) Necrotic leaf speckles Copper ion binding protein, phenotype developing with the age of the leaf, light-dependent (enhanced under summer-planting conditions) Al Amin et al. (2019) lmm2-1 (Gm14g003200) Small chlorotic lesions, subsequently turning necrotic Light-dependent, gene encodes a coproporphyrinogen III oxidase that participates in tetrapyrrole biosynthesis Ma et al. (2020a) Rice lmi Necrotic leaf lesions Starting from the leaf-tip, lethal before the complete spike is ripened (few seeds), light-dependent Liu et al. (2003) lmm9150/aba2 (Os03g59610) Necrotic leaf lesions, preharvest sprouting, enhanced resistance to bacterial blight and rice blast, H2O2 accumulation Alcohol dehydrogenase, abscisic acid biosynthesis Liao et al. (2018) oscul3a Red/brown scattered leaf lesions, ROS burst, lipid accumulation, increased resistance against bacterial blight and rice blast, reduced growth and yield Light-sensitive (delayed development in the greenhouse versus the field) Liu et al. (2017); Gao et al. (2019) osvoz1/osvoz2 Stunted growth, necrotic leaf lesions, and seedling lethality, in RNAi knockdown lines accompanied by ROS overaccumulation and enhanced resistance to rice blast Transcription factors that modulate immunity against MoO Wang et al. (2021) spl2 Spontaneous lesions and lesions triggered by wounding Ozone hypersensitive, light-dependent Kojo et al. (2006) spl3-t (Os03g06630) Necrotic spots, connecting over the whole leaf during development, decreased plant height and seed setting rate, increased ear length Light-dependent/induced YuChun et al. (2018) spl11 Small spot-like necrotic lesions E3 ubiquitin ligase protein Zeng et al. (2004) spl36 Necrotic leaf lesions Light-dependent Yuchun et al. (2021) wlml1 Yellow to white patches and lesions, plant height, and grain weight reduced Temperature-dependent (reduced under high temperatures) Chen et al. (2018) Barley bspl1 Necrotic lesions on the whole leaf, H2O2 accumulation Light-dependent/induced Zhang et al. (2019) mlo Necrotic leaf spots, early leaf senescence, spontaneous formation of callose- containing call wall appositions, durable resistance to powdery mildew Wolter et al. (1993); Peterhänsel et al. (1997) Cotton Le4 Necrotic lesion leading to leaf shedding, abnormal thylakoid-structure, H2O2 accumulation in early leaf stages, infertile flowers Hybrid necrosis, photoperiod-sensitive Deng et al. (2019) Organism . Responsible Gene . Phenotype . Comment . References . Soybean dlm Necrotic leaf speckles Developing with the age of the leaf, light-dependent Chung et al. (1998); Kim et al. (2005) spl-1/lm1 (Gm04g242300) Necrotic leaf speckles Copper ion binding protein, phenotype developing with the age of the leaf, light-dependent (enhanced under summer-planting conditions) Al Amin et al. (2019) lmm2-1 (Gm14g003200) Small chlorotic lesions, subsequently turning necrotic Light-dependent, gene encodes a coproporphyrinogen III oxidase that participates in tetrapyrrole biosynthesis Ma et al. (2020a) Rice lmi Necrotic leaf lesions Starting from the leaf-tip, lethal before the complete spike is ripened (few seeds), light-dependent Liu et al. (2003) lmm9150/aba2 (Os03g59610) Necrotic leaf lesions, preharvest sprouting, enhanced resistance to bacterial blight and rice blast, H2O2 accumulation Alcohol dehydrogenase, abscisic acid biosynthesis Liao et al. (2018) oscul3a Red/brown scattered leaf lesions, ROS burst, lipid accumulation, increased resistance against bacterial blight and rice blast, reduced growth and yield Light-sensitive (delayed development in the greenhouse versus the field) Liu et al. (2017); Gao et al. (2019) osvoz1/osvoz2 Stunted growth, necrotic leaf lesions, and seedling lethality, in RNAi knockdown lines accompanied by ROS overaccumulation and enhanced resistance to rice blast Transcription factors that modulate immunity against MoO Wang et al. (2021) spl2 Spontaneous lesions and lesions triggered by wounding Ozone hypersensitive, light-dependent Kojo et al. (2006) spl3-t (Os03g06630) Necrotic spots, connecting over the whole leaf during development, decreased plant height and seed setting rate, increased ear length Light-dependent/induced YuChun et al. (2018) spl11 Small spot-like necrotic lesions E3 ubiquitin ligase protein Zeng et al. (2004) spl36 Necrotic leaf lesions Light-dependent Yuchun et al. (2021) wlml1 Yellow to white patches and lesions, plant height, and grain weight reduced Temperature-dependent (reduced under high temperatures) Chen et al. (2018) Barley bspl1 Necrotic lesions on the whole leaf, H2O2 accumulation Light-dependent/induced Zhang et al. (2019) mlo Necrotic leaf spots, early leaf senescence, spontaneous formation of callose- containing call wall appositions, durable resistance to powdery mildew Wolter et al. (1993); Peterhänsel et al. (1997) Cotton Le4 Necrotic lesion leading to leaf shedding, abnormal thylakoid-structure, H2O2 accumulation in early leaf stages, infertile flowers Hybrid necrosis, photoperiod-sensitive Deng et al. (2019) Open in new tab Hybrid incompatibility/necrosis A classic form of autoimmunity is the so-called hybrid incompatibility or hybrid necrosis. This type of autoimmunity, which phenotypically was first reported 100 years ago (reviewed in Li and Weigel, 2021), can occur in heterozygous progeny of different genotypes of the same plant species that carry incompatible alleles of polymorphic immune signaling components (Bomblies and Weigel, 2007; Wan et al., 2020; Figure 1C). It is thought that within-species hybrid incompatibility represents an intermediate state along the trajectory from population isolation to speciation. Such intraspecific incompatibilities can involve unsuited interactions between NLR genes (Atanasov et al., 2018; Barragan et al., 2021), NLRs with RLKs (Alcázar et al., 2010), NLRs with other genes (Barragan et al., 2019), or even rely on non-NLR components of the immune system (Chen et al., 2014). An example of hybrid necrosis that has been intensively studied involves the DANGEROUS MIX 2 (DM2 also RECOGNITION OF PERONOSPORA PARASITICA 1 [RPP1]-like) locus in Arabidopsis (Bomblies et al., 2007; Alcázar et al., 2014) (Table 1). This locus harbors seven to eight NLR genes (depending on the ecotype) whereof only one seems to be required and sufficient for the occurrence of hybrid incompatibility in several ecotype combinations, according to recent findings (Ordon et al., 2021). An interesting case is the recently reported allele-specific incompatibility between the RPP7 NLR gene cluster and the non-NLR gene cluster encoding RESISTANCE TO POWDERY MILDEW 8/HOMOLOG OF RPW8 (RPW8/HR) proteins. For this protein combination, the activation of autoimmunity is dependent on the number of HET-S-like repeats and the identity of the C-terminal tails in the RPW8/HR proteins in combination with specific RPP7 partners, which triggers the formation of a resistosome-like RPP7 oligomeric complex (Barragan et al., 2019; Li et al., 2020). Although the occurrence of hybrid necrosis is often linked to the presence of NLR clusters, typically exhibiting copy number variation of highly polymorphic NLR genes between genotypes within a species, a recent example demonstrates that this is not necessarily the case: DM10 codes for a C-terminally truncated singleton NLR involved in hybrid incompatibility (Barragan et al., 2021). Given that hybrid necrosis and heterosis (enhanced trait performance of hybrids) could be considered as the extreme phenotypic outcomes regarding the evolutionary tradeoff between growth and immunity, breeding efforts should aim at balancing these two forces, for example, based on genomic data-driven reverse breeding (Calvo-Baltanás et al., 2021). Autoimmunity based on constitutive activation or overexpression of NLRs Apart from hybrid necrosis/hybrid incompatibility, autoimmunity can originate from activating mutations in NLR genes or overexpression of these (Figure 1D and E), often in the context of the above-mentioned sensor and helper NLR pairs or networks. Several of these mutants originally resulted from genetic screens in Arabidopsis. A prominent example of this is a gain of function mutation in SUPPRESSOR OF NPR1-1, CONSTITUTIVE 1 (SNC1), a gene encoding a TNL protein (Zhang et al., 2003). In addition, mutations in several other genes (e.g. BONZAI 1 [BON1], BON ASSOCIATION PROTEIN 1 [BAP1], BRI1-ASSOCIATED RECEPTOR KINASE 1 [BAK1]-INTERACTING RECEPTOR-LIKE KINASE 1 [BIR1], SUPPRESSOR OF RESISTANT TO PSEUDOMONAS SYRINGAE 4 [RPS4]-RLD 1 [SRFR1], CONSTITUTIVE EXPRESSER OF PR GENES 1 [CPR1], and MITOGEN-ACTIVATED PROTEIN [MAP] KINASE PHOSPHATASE 1 [MKP1]) can result in SNC1-dependent autoimmunity, possibly via an SA-dependent upregulation of SNC1 transcript levels in some of these mutants (Gou and Hua, 2012). Alternatively, some of the proteins encoded by these genes might be guarded by SNC1 (Rodriguez et al., 2016). Another recent addition to the list of putative SNC1 guardees is CALMODULIN-BINDING PROTEIN 60b (CBP60b), a calmodulin-binding protein that serves a role as a transcriptional activator of immunity. Unexpectedly, loss of function of CBP60b results in autoimmunity, and this EDS1-PAD4-dependent phenotype can be partially rescued by a lack of SNC1 (Li et al., 2021). The nucleocytoplasmically distributed SNC1 protein requires the nuclear import receptor IMPORTIN-α3/MODIFIER OF SNC1, 6 for nuclear translocation, and its nuclear localization is a prerequisite for autoimmunity (Lüdke et al., 2021). Yet another nuclear transport receptor, the karyopherin KA120, seems to modulate the nucleocytoplasmic homoeostasis of SNC1 or may serve as a further guardee of the NLR (Jia et al., 2021). SNC1 appears to exert its immunity-activating activity through a complex interplay with several regulators of plant immunity, including SRFR1, a negative regulator of effector-triggered immunity, and TOPLESS-RELATED 2 (TPR2) and TPR3, two antagonistically acting members of the TOPLESS family of transcriptional co-repressors (Garner et al., 2021). Another example of a gain-of-function mutant is provided by the TN gene CHILLING SENSITIVE 1 (CHS1), which induces temperature-dependent autoimmunity by enhanced protein interaction with the TIR domain of the TNL encoded by the neighboring SUPPRESSOR OF CHS1-2, 3 (SOC3) gene (Zhang et al., 2017a). Similarly, autoimmunity conferred by the chs3-2D mutant, which is based on a dominant mutation in the TNL-encoding CHS3 gene, relies on CONSTITUTIVE SHADE-AVOIDANCE 1 (CSA1), another TNL-encoding gene that resides in very close (˂4 kb) chromosomal proximity to CHS3 (Xu et al., 2015). Apart from activating gain-of-function mutations, autoimmunity can be triggered by overexpression of NLR genes or truncated versions thereof (Lai and Eulgem, 2018; Figure 1E). A typical example is the temperature-dependent autoimmunity conditioned by the overexpression of full-length RPS4 (Heidrich et al., 2013). However, also overexpression of truncated NLR variants can trigger cell death and autoimmunity. For example, expression of a version of the NLR RPS5 lacking the C-terminal leucine-rich repeats results in constitutively activated programmed cell death, possibly due to a lack of sufficient autoinhibition in its truncated form (Ade et al., 2007). Notably, not only the overexpression of NLRs can result in autoimmunity but also the combined overexpression of EDS1 and PAD4 or the overexpression of the common RLK co-receptor BAK1 or its membrane-associated ectodomain, each triggering similar downstream defense responses as overactivated or overexpressed NLRs (Domínguez-Ferreras et al., 2015; Cui et al., 2017). Since accumulating evidence points to a mutual interplay between ETI and PTI (Ngou et al., 2021; Yuan et al., 2021), the constitutive activation of PTI pathways may be similar to the inadequate activation of NLRs result in autoimmunity. Autoimmunity based on the lack or modification of an NLR-guarded protein Mutations that affect a guardee can also condition autoimmunity. Because of the absence or modification of a guardee protein, the respective guarding NLR might become activated, resulting in constitutive defense responses (Figure 1F). An example of this scenario is the RESISTANCE TO PSEUDOMONAS SYRINGAE PV MACULICOLA 1 (RPM1)-INTERACTING PROTEIN 4 (RIN4), which is guarded by two NLRs (RPS2 and RPM1). Lack of RIN4 results in seedling lethality that can be rescued by second-site mutations in RPS2 and other immune signaling components (Mackey et al., 2003). Another case is the monitoring of the E3 ubiquitin ligase SENESCENCE-ASSOCIATED E3 UBIQUITIN LIGASE 1 (SAUL1) by the TNL SOC3. After SOC3 was found earlier in a suppressor screen for autoimmunity caused by the saul1-1 mutation (Tong et al., 2017), it was recently shown that SOC3 pairs with the genetically linked TN-type proteins CHS1 and TIR-NBS2 (TN2) to monitor SAUL1 homeostasis (Liang et al., 2019). The SOC3-CHS1 and SOC3-TN2 complexes additionally require the F-Box protein SUPPRESSOR OF SAUL1, 2 (SUSA2) for downstream activation of immune responses, linking these responses to the degradation of cellular proteins via polyubiquitination by E3 ligases (Liang et al., 2020). TN2 appears to guard additionally the exocyst complex subunit EXOCYST SUBUNIT EXO70 FAMILY PROTEIN B1 (EXO70B1). Loss-of-function exo70B1 mutants exhibit hyperactivated defense responses, including spontaneous cell death. This phenotype is dependent on TN2, which also interacts with EXO70B1 in yeast and in planta (Zhao et al., 2015). A similar model was proposed for the negative regulator of immunity, CALMODULIN-BINDING TRANSCRIPTION ACTIVATOR 3 (CAMTA3), and its guard NLRs DOMINANT SUPRESSOR OF CAMTA3 NUMBER 1 (DSC1) and DSC2. The latter were identified in a screen based on the expression of dominant-negative (DN) P-loop mutants of these NLRs in the background of the autoimmune camta3 mutant (Lolle et al., 2017; Yuan et al., 2018). Yet another presumed guard–guardee pair are the TYPE ONE SERINE/THREONINE PROTEIN PHOSPHATASE 4 (TOPP4) phosphatase and the CNL SUPPRESSORS OF TOPP4-1 (SUT1). Autoimmunity conditioned by the topp4 mutant is dependent on the immune receptor chaperones HEAT SHOCK PROTEIN 70 (HSP70) and REQUIRED FOR MLA RESISTANCE 1 (RAR1), and it is suppressed by lack of SUT1. Furthermore, SUT1 physically interacts with TOPP4 (Yan et al., 2019), while the previously mentioned CNL ZAR1 guards HOPZ-ETI-DEFICIENT 1 (ZED1) and several other temperature-dependent (pseudo-)kinases, that in turn modulate the transcription of SNC1 (see also above—(Wang et al., 2017)). A complex situation is provided by the monitoring of CALMODULIN-BINDING RECEPTOR-LIKE CYTOPLASMIC KINASE 3 (CRCK3) via the NLRs SUPPRESSOR OF MKK1 MKK2 2 (SUMM2) and RPS6. CRCK3 is a constitutively phosphorylated substrate of the MAPK/ERK KINASE KINASE 1 (MEKK1)-MAP KINASE (MPK)/ERK KINASE 1/2 (MKK1/2)-MAP KINASE 4 (MPK4) MAP kinase cascade. Functional disruption of this cascade results in SUMM2-dependent autoimmunity (Zhang et al., 2017b). The activation of SUMM2 by yet another MPK kinase kinase, MEKK2, requires a trimeric complex of two RLKs and a glycosylphosphatidylinositol-anchored protein (Huang et al., 2020). In addition to SUMM2, RPS6 also monitors the same MPK cascade comprised of MEKK1, MKK1/2, and MPK4 (Takagi et al., 2019). Proper RPS6 transcript accumulation requires the RNA helicase HUA ENHANCER 2 (HEN2), and, accordingly, hen2 mutants partially rescue the MPK cascade-associated autoimmune phenotype (Takagi et al., 2020). Interestingly, mutations in the mRNA decay genes PAT1 (a gene with homology to yeast PAT1) and SMG7 (a gene with homology to human SMG7) lead to autoimmune phenotypes, which in smg7 depend on a specific allele of RPS6 while autoimmunity in pat1 is suppressed by mutations in SUMM2 (Gloggnitzer et al., 2014; Roux et al., 2015). Thus, RPS6 and SUMM2 apparently also monitor the integrity of mRNA decay pathways, suggesting that these pathways are likely targets of phytopathogen effectors. We note that mutations in other decay components also lead to autoimmunity (Rayson et al., 2012; Chantarachot et al., 2020), raising the possibility that they may also be guarded by NLRs. In addition to these well-studied cases, there are some recent reports of additional mutants that exhibit EDS1-dependent autoimmunity, suggesting that also in these instances a (guarding?) NLR protein might be involved. These comprise a recessive gain-of-function mutation in the CYCLIC NUCLEOTIDE-GATED CHANNEL 20 (CNGC20) gene (Zhao et al., 2021) and a missense mutation in the CONSTITUTIVE ACTIVE DEFENSE 1 (CAD1) gene (Holmes et al., 2021). Interestingly, double mutants of the genes coding for the common ETI-associated microbe-associated molecular pattern (MAMP) co-receptor BAK1 and its paralog BAK1-LIKE 1 (BKK1) exhibit spontaneous cell death reminiscent of autoimmunity. This phenotype is dependent on the ADR1 family of helper NLRs, suggesting that BAK1 and BKK1 might also be guarded by a yet unrecognized sensor NLR (Wu et al., 2020a). Another example is the double mutant of PENETRATION 1 (PEN1) and its paralog, SYNTAXIN OF PLANTS 122 (SYP122), encoding plasma membrane-resident syntaxins, which also exhibits a severe autoimmune phenotype (Zhang et al., 2008). This requires the deubiquitinase-ASSOCIATED MOLECULE WITH THE SH3 DOMAIN OF STAM 3 (AMSH3), but intriguingly, amsh3 knockout mutants are likewise lesion mimic mutants, and lesions in both PEN1/SYP122 and AMSH3 could at least be partially rescued by mutations in EDS1 and/or PAD4 (Schultz-Larsen et al., 2018). Mutations in several genes implicated in ceramide metabolism also result in autoimmunity. This includes mutations in ACCELERATED CELL DEATH 5 (ACD5), coding for a ceramide kinase (Liang, 2003; Zeng et al., 2021), and in NEUTRAL CERAMIDASE 2, a ceramidase-encoding gene (Zeng et al., 2021). Given that the disruption of multiple components involved in ceramide metabolism causes EDS1/PAD4/ SA INDUCTION DEFICIENT 2 (SID2)-dependent autoimmune phenotypes, several NLRs may monitor ceramide metabolism and/or membrane structure and integrity. In line with this idea, disruption of INOSITOLPHOSPHORYLCERAMIDE SYNTHASE 2 (IPCS2) displays RPW8-dependent spontaneous HR-like cell death (Wang et al., 2008) and knockout of the ceramide-1-phosphate transfer protein ACD11 results in autoimmunity that depends on the NLR LAZARUS 5 (LAZ5; Palma et al., 2010). In summary, there is a rapidly growing list of guard–guardee pairs that can be linked to autoimmunity in Arabidopsis. Fine-tuning of NLR expression is essential to prevent autoimmunity The NLR gene family is highly expanded in plants and the Arabidopsis genome encodes some 150 NLRs (Meyers eal., 2003) while around 500 are found in rice (Oryza sativa; Zhou et al., 2004b; Li et al., 2010). NLRs tend to pair or appear in genomic clusters, which can expand or shrink rapidly probably as a consequence of adaptation to evolving pathogens. While singleton NLRs usually form homo-oligomers, paired and clustered NLRs are likely to co-function by hetero-dimerization or oligomerization (van Wersch and Li, 2019). To keep pace with an almost unlimited number of pathogen effectors with a limited set of NLR genes while at the same time avoiding the inappropriate activation of immune responses, NLR transcript integrity and abundance must be tightly regulated. Accumulating evidence indicates that NLR homeostasis is not only regulated transcriptionally and posttranscriptionally (e.g. by [alternative] mRNA splicing) but also at the translational and posttranslational level (Li et al., 2015; Lai and Eulgem, 2018; Richard et al., 2018; Sun et al., 2020; Ma et al., 2020b; Wu et al., 2020b; Parker et al., 2021), and that malfunctions in any of these processes can give rise to autoimmunity. The analysis of the Arabidopsis acd11 lesion mimic mutant led to the identification of LAZ2, encoding a histone lysine methyltransferase likely implicated in the epigenetic regulation of the LAZ5 gene, which encodes an RPS4-like NLR protein that is responsible for triggering cell death in the absence of ACD11 (Palma et al. 2010). NLR genes are generally under tight transcriptional control (Borrelli et al., 2018). A loss of negative regulation can result in autoimmunity, as evidenced by loss of SRFR1-mediated fine-tuning of SNC1 expression (Garner et al., 2021). An interesting case is the histone-binding protein ENHANCED DOWNY MILDEW 2 (EDM2), which promotes expression of some NLR genes while suppressing the expression of others, thereby balancing NLR transcript levels (Lai et al., 2020). NLR genes are further preferentially subject to alternative poly(A) site selection and associated premature termination of transcription, which is mediated by the RNA-binding protein FLOWERING PROTEIN A (FPA; Borrelli et al., 2018; Parker et al., 2021) and can give rise to nonsense-mediated RNA decay. In some cases, NLR transcript accumulation is further adjusted by small RNAs (miRNA-derived siRNAs; Zhai et al., 2011). NLR protein homeostasis is controlled via ubiquitination-based and proteasome-dependent protein turnover, as suggested by the recent identification of two master E3 ubiquitin ligases (SNIPER1 and SNIPER2) in Arabidopsis that can ubiquitinate the nucleotide-binding domain of sensor NLRs, thereby ensuring their adequate levels (Wu et al., 2020b). A contribution of the proteasome in adjusting NLR abundance was revealed by the role of the proteasome regulator PROTEASOME REGULATOR 1 (PTRE1) in controlling SNC1 accumulation (Thulasi Devendrakumar et al., 2019). At the posttranslational level, NLR proteins are autoinhibited in the resting state in nonchallenged plant cells and only become active upon suitable triggers (reviewed in Chiang and Coaker, 2015). Modulation of autoimmunity by environmental conditions Independent of the species, many plant autoimmune phenotypes seem to be strongly modulated by external abiotic factors. These include temperature, light irradiance, photoperiod, and humidity. Most frequently described have been influences by light and temperature, where dependence on light intensity has been observed in many crop autoimmune mutants (Table 2), while many Arabidopsis autoimmune mutants show sensitivity toward elevated or reduced temperature. Autoimmunity in the Arabidopsis saul1-1 mutant conferred by the TNL SOC3, for example, is suppressed under high temperature (>24°C; Disch et al., 2016). Additionally, several of the “chilling-sensitive” (chs) mutants identified in genetic screenings for sensitivity at low temperature turned out to be dependent on TNL or TN-type family members, and expression of the autoimmune phenotype often involves additional neighboring TNLs (Schneider et al., 1995; Yang et al., 2010; Huang et al., 2010a; Zbierzak et al., 2013; Xu et al., 2015; Liang et al., 2019). Conversely, autoimmunity conferred by the zed1-D mutation in a gene encoding a heat-induced pseudokinase, is only expressed under higher temperatures (Wang et al., 2017). Similar to heat, high humidity may also suppress autoimmunity. Stunted growth and constitutive expression of RPW8 and EDS1 in suppressor of SA insensitive 4 (ssi4) mutant plants are, for example, suppressed by high humidity (Zhou et al., 2004a). In addition, spontaneous HR-like lesions in RPW8 overexpression lines could also be delayed or suppressed by high humidity, low light, and high temperature, probably by disrupting an EDS1/SA-dependent amplification circuit (Xiao et al., 2003). Although environmental conditions influence autoimmunity in many instances, the underlying mechanisms remain poorly understood. There is a general tendency that PTI is elevated at higher temperatures, while ETI, which typically relies on NLR activity, on the contrary is reduced (Cheng et al., 2013). Accordingly, HR-like cell death, which is an important aspect of many autoimmune phenotypes, is typically abolished under high temperatures and on top of that is also often light-dependent (Balint-Kurti, 2019). Only few exceptions such as the zed1-D mutant behave differently, possibly due to specific characteristics of the involved guardees. A recent meta-analysis of whole-genome shotgun (RNA-sequencing) data sets revealed that generally the expression of NLR genes is increased upon biotic stress (or treatment with SA) while their expression is mostly reduced under abiotic stress such as heat, but also drought or treatment with the phytohormone abscisic acid (Yang et al., 2021). This could explain why NLR-dependent autoimmune phenotypes are generally reduced at higher temperatures and often depend on the availability of light. One may also speculate that temperature-dependent alterations in NLR conformation could be a reason for the temperature sensitivity of some autoimmune phenotypes. Several NLR proteins need the help of (co-)chaperones such as HSP90, RAR1, and SUPPRESSOR OF THE G2 ALLELES OF SKP1 (SGT1) for stability, function, and possibly oligomerization, which may fail at elevated temperatures (Seo et al., 2008; Kadota et al., 2010). Alternatively, or in addition, the temperature sensitivity of autoimmunity may relate to cell biological abnormalities in the respective mutants. A decrease in temperature leads to chloroplast and cell wall changes in the saul1-1 mutant and enhances its autoimmune phenotype. This was also seen in the chs1 mutant, pointing at low temperature-induced alterations in chloroplast homeostasis and cell wall integrity, which may either directly or indirectly impact the activation of defense responses (Zbierzak et al., 2013; Disch et al., 2016). The erratic formation of (often callose- and lignin-containing) cell wall deposits is a common characteristic of lesion mimic mutants. Data of a recent study now suggest that such cell wall deposition is a consequence and not the cause of temperature-induced autoimmunity, since reduction of cell wall deposition in saul1-1 pmr4-1 double mutants, which lack the callose synthase POWDERY MILDEW RESISTANCE 4 (PMR4), did not affect autoimmunity triggered upon exposure of these plants to low temperature (Hessler et al., 2021). Autoimmunity in crop plants While autoimmune mutants and their mechanistic link to plant immunity have been well studied in the past in Arabidopsis (van Wersch et al., 2016), crop plants have been investigated less thoroughly in this respect. Next to the original discovery of so-called “lesion mimic mutants” in maize (Zea mays; Walbot et al., 1983; Johal et al., 1995), over the years many examples for this type of autoimmune mutants have been discovered in crop plants like soybean (Glycine max), rice, barley, and cotton (Gossypium hirsutum; Table 2; Walbot et al., 1983; Wolter et al., 1993; Johal et al., 1995; Chung et al., 1998; Rostoks et al., 2003; McGrann et al., 2015; Chen et al., 2018; Liao et al., 2018; YuChun et al., 2018; Al Amin et al., 2019). In crop plants, extreme phenotypes such as dwarfism and stunted development have been rarely reported, probably because these phenotypes are already excluded during the initial steps of breeding. The same applies to hybrid incompatibility in crops. While this phenomenon is probably of high interest as it can severely hinder breeding between specific crop lines and natural accessions, such interactions would be excluded in early stages of the breeding process. Although there is very little known about the biochemical function of the proteins encoded by the genes that underlie these autoimmune phenotypes, there still are several traits that many of these mutants share. This includes the spontaneous formation of necrotic leaf spots and the accumulation of reactive oxygen species (ROS) (Table 2). On top of that, lesion mimic mutants mostly show increased resistance to obligate biotrophic pathogens, while the interaction with facultative or hemi-biotrophic pathogens is more differentiated. For those, the interactions with lesion mimic mutants range from enhanced resistance to hyper-susceptibility (McGrann et al., 2015; Liao et al., 2018; Gruner et al., 2020). A prominent example of a lesion mimic phenotype in crop plants is the mutation of the so-called Mlo gene in barley. While the mlo mutant was originally discovered in the context of broad-spectrum powdery mildew resistance (Wiberg, 1973), it was later shown to also cause a lesion mimic phenotype whose intensity seems to depend on the genetic background (Bjørnstad and Aastveit, 1990; Wolter et al., 1993). As for many of the crop lesion mimic mutants, neither the biochemical function of the Mlo protein nor the mechanism underlying the mlo lesion mimic phenotype has been unraveled, yet (Kusch and Panstruga, 2017). It might be speculated that at least some of the “lesion mimic genes” encode guardees that, when absent or mutated, result in autoimmune responses due to the inadequate activation of their guarding NLRs. As in Arabidopsis, the lesion mimic autoimmune mutants in crop plants could be used to examine the presumptive defense pathways that get (over-)activated in these mutants. The availability of high-quality genome sequences and considerable improvements in gene cloning technologies have enabled the identification of the underlying genes in several instances (Lolle et al., 2017; Liang et al., 2019; Yan et al., 2019). Rice spotted leaf 11 (spl11) mutants, for example, display spontaneous cell death and increased resistance to both fungal and bacterial pathogens. SPL11 encodes an E3 ubiquitin ligase that can be phosphorylated by a monocot-specific RLK (SPL11 CELL DEATH SUPPRESSOR 2 (SDS2). Phosphorylated SPL11 acts in two branches to regulate cell death and immunity via the ROS-producing NADPH oxidase RESPIRATORY BURST OXIDASE HOMOLOG B (OsRBOHB): either by mediating ubiquitination and degradation of SDS2, which interacts with and phosphorylates the receptor-like cytoplasmic kinases OsRLCK118/176, or by promoting degradation of SPL11-INTERACTING PROTEIN 6 (SPIN6), which is a Rho GTPase-activating protein also involved in OsRBOHB-dependent immunity (Zeng et al., 2004; Liu et al., 2015; Fan et al., 2018). Another E3-related autoimmune phenotype in rice is conferred by the vascular plant one zinc-finger 1/2 (osvoz1/osvoz2) double mutant. The two transcription factors OsVOZ1 and OsVOZ2 co-function as positive regulators of the NLR gene PYRICULARIA ORYZAE RESISTANCE Z-T (PIZ-T) and are subject to ubiquitination by the E3 ligase AVRPIZ-T INTERACTING PROTEIN 10 (APIP10; Wang et al., 2021). SPL11 and APIP10 thus function differently than the Arabidopsis SNIPER E3 ligases, which regulate the turnover of NLRs by direct binding and ubiquitination (Wu et al., 2020b). It remains an open question if SNIPER-like E3 ligases exist in rice (and monocots in general) and whether SPL11 and APIP10 represent guardees of yet unrecognized NLRs. A very recent addition to the suite of crop lesion mimic mutants is the rice spl36 mutant. The wild-type gene encodes a receptor-like protein kinase that regulates defense responses in rice (Yuchun et al., 2021). Despite these advancements, the longer generation times and more challenging transformation and regeneration protocols in crop plants often still represent severe bottlenecks for detailed functional analyses compared to the model plant Arabidopsis (Altpeter et al., 2016). Thus, it remains that the extensive research that is done in Arabidopsis continues to keep informing the understanding of autoimmune mutants in crop plants. Tools to analyze NLR-mediated autoimmunity Although NLRs often contribute in one or the other way to plant autoimmunity (Figures 1 and 2, A), their participation is not always easy to demonstrate. Experimental approaches to interrogate a putative contribution of NLRs to an autoimmune phenotype are, thus, highly valuable and are best developed in Arabidopsis. Introgression of second-site mutations in EDS1, PAD4, and NONRACE-SPECIFIC DISEASE RESISTANCE 1 (NDR1) into the genetic background of lesion mimic mutants is widely used to gain a glimpse of whether TNLs or CNLs could be involved. A recently described tool to inquire the involvement of NLRs in autoimmunity is based on the so-called SNIPER E3 ubiquitin ligases. These Arabidopsis proteins specifically interact with the nucleotide-binding domains of sensor NLRs such as the CNL SUMM2, the TNL RPP4, and the TN protein CHS1, but not helper NLRs, tagging them with ubiquitin for proteasomal degradation (Figure 2B). SNIPER overexpression has been shown to suppress NLR-mediated autoimmunity in many mutants including snc1, chs1-2, chs2-1, and chs3-2D (Tong et al., 2017; Huang et al., 2018; Wu et al., 2020b). Accordingly, transformation of SNIPER overexpression constructs into autoimmune mutants could be used to probe the contribution of any sensor NLRs in the process. Another approach to explore the putative role of NLRs in plant autoimmunity is to take advantage of the recently discovered helper NLR networks. Mutants that are deficient in one or several helper NLRs can compromise the activity of entire groups of sensor NLRs (Feehan et al., 2020; Saile et al., 2020). Such helper NLR mutants could suppress sensor NLR-dependent autoimmune phenotypes, thereby narrowing down the involved type of sensor NLR (Figure 2C). Figure 2 Open in new tabDownload slide Mechanistic principles of three methods to investigate the involvement of NLRs in plant autoimmune mutants. A, (Auto-)immunity mediated by NLRs with or without the contribution of helper NLRs. Upon pathogen recognition or autoimmune induction by sensor NLRs, the sensor NLR itself or associated helper NLRs can form cation-permeable channels in the cell membrane. Influx of extracellular Ca2+ leads to cell death. B, SNIPER E3 ubiquitin ligases specifically target NLRs for proteasome-dependent degradation by poly-ubiquitination. Overexpression of SNIPER genes in autoimmune mutants is predicted to suppress the phenotype when NLRs are involved. C, Many sensor NLRs rely on the downstream signaling of helper NLRs (see (A)). Loss-of-function mutations in one or both families of helper NLRs can be a way to demonstrate the likely involvement of sensor NLRs relying on these families in autoimmune mutants. D, P-loop mutants are DN versions of specific NLRs with mutations in the P-loop motif of the nucleotide-binding domain. Expression of the respective DN allele in autoimmune mutants that depend on a specific NLR was shown to suppress the autoimmunity phenotype. Figure 2 Open in new tabDownload slide Mechanistic principles of three methods to investigate the involvement of NLRs in plant autoimmune mutants. A, (Auto-)immunity mediated by NLRs with or without the contribution of helper NLRs. Upon pathogen recognition or autoimmune induction by sensor NLRs, the sensor NLR itself or associated helper NLRs can form cation-permeable channels in the cell membrane. Influx of extracellular Ca2+ leads to cell death. B, SNIPER E3 ubiquitin ligases specifically target NLRs for proteasome-dependent degradation by poly-ubiquitination. Overexpression of SNIPER genes in autoimmune mutants is predicted to suppress the phenotype when NLRs are involved. C, Many sensor NLRs rely on the downstream signaling of helper NLRs (see (A)). Loss-of-function mutations in one or both families of helper NLRs can be a way to demonstrate the likely involvement of sensor NLRs relying on these families in autoimmune mutants. D, P-loop mutants are DN versions of specific NLRs with mutations in the P-loop motif of the nucleotide-binding domain. Expression of the respective DN allele in autoimmune mutants that depend on a specific NLR was shown to suppress the autoimmunity phenotype. Forward genetic screens represent a classic route to identify suppressors of any mutant phenotype, and summ2 and soc3 mutants were found to suppress autoimmunity in mkk1/2 and saul-1, respectively, via such an approach (Zhang et al., 2012; Tong et al., 2017). However, this kind of screens can lead to the identification of any type of suppressor genes (not only NLRs) because they are unbiased. To search for NLRs involved in autoimmunity in a targeted manner, Jia and colleagues performed a genetic screen using an artificial microRNA library targeting 108 NLRs for gene silencing and identified SNC1 to be responsible for the autoimmunity seen in the ka120 mutant (Jia et al., 2021). However, in some instances, more than one NLR may contribute to autoimmunity. DN NLRs that carry an inhibitory mutation in the P-loop, the phosphate-binding motif in the NLR ATPase domain, are another way of identifying NLRs involved in autoimmunity that may surpass the issue of functional redundancy. Lolle and co-workers introduced P-loop mutations into 108 Arabidopsis NLRs and tested whether their (over-)expression in the genetic background of an autoimmune mutant (camta3) can result in a wild type-like phenotype. This approach led to the identification of two unrelated dominant suppressors of camta3, DSC1 and DSC2, that could not be identified using single NLR knockout or knockdown approaches (Lolle et al., 2017; Figure 2D). Importantly, DN-DSC1 or DN-DSC2 expression is sufficient to bypass camta3-associated autoimmunity while dsc1/camta3 and dsc2/camta3 double mutants exhibit autoimmunity and both NLRs need to be knocked out to suppress the camta3 phenotype. The mechanistic basis of the P-loop-associated DN effect is, however, still poorly understood but may relate to the known homo- and hetero-oligomerization of activated NLR proteins (Adachi et al., 2019). Finally, chemical genetics can be used to discover new players related to plant autoimmunity. Testing of 13600 low-molecular-weight compounds led to the identification of a substance (Ro 8-4304) that suppresses both the stunted growth and constitutive immune responses of the chs3-2D mutant. A subsequent genetic screen for chs3-2D-derived mutants that are insensitive to Ro 8-4304 uncovered several methylosome components involved in mRNA splicing as negative regulators of plant immunity (Huang et al., 2016). NLR-mediated autoimmunity in animals Just as in plants, animals deploy NLR proteins for the intracellular perception of non-self and modified-self molecules. In contrast to plants, where NLRs typically mediate the direct or indirect detection of pathogen effector proteins, at least in vertebrates they are primarily implicated in MAMP and toxin recognition. Mammalian NLRs function in cell-autonomous immunity, mediating proinflammatory cytokine responses, and triggering cell death (Motta et al., 2015). While the overall domain architecture of plant and animal NLRs is very similar, especially the N-terminal head- and central nucleotide-binding domains have kingdom-specific characteristics (Maekawa et al., 2011b). For mammalian NLRs, it has been known for a while that they can assemble into large apoptosome or inflammasome complexes via their head domains when activated (Cain et al., 2002; Zhao et al., 2011), and only recently, similar homo-oligomerization was also demonstrated for activated plant NLRs (Burdett et al., 2019; Martin et al., 2020). While these mammalian complexes activate downstream signaling by activating caspases, a group of proteases implicated in initiating apoptotic cell death (Mermigka et al., 2020), the recently discovered ZAR1 and RECOGNITION OF XopQ 1 (ROQ1) plant “resistosomes” are hypothesized to mediate membrane pore formation and cleavage of nicotinamide-adenine dinucleotide (NAD+) for downstream signaling and cell death execution, respectively (Wang et al., 2019a; Martin et al., 2020). Similar to plants, mutations in animal NLRs are associated with a broad range of autoimmune-type disorders, which are best studied in humans and mice. Mutations in the genes encoding the human Nucleotide-binding oligomerization domain-containing protein 1 (NOD1) and NOD2 receptors, for example, are linked to chronic autoinflammatory and autoimmune diseases. Notably, there seems to be no domain specificity regarding the mutations and the associated conditions caused, and the sites of mutations linked to autoimmunity seem to be different in plant and animal NLRs. While mutations in the leucine-rich repeat regions of NOD1 and NOD2 are related to asthma and inflammatory bowel disease or Crohn’s disease, respectively, a mutation in the NOD2 nucleotide-binding domain could be connected to Blau syndrome (an inflammatory disorder that primarily affects the skin, joints, and eyes), for example, Motta et al. (2015). Concluding remarks The investigation of the molecular principles underlying plant autoimmunity and lesion mimic phenotypes continues to provide knowledge that is key for a full comprehension of the plant immune system. The studies provide important details regarding NLR expression, intracellular trafficking, and activity, and reveal candidate proteins likely safeguarded by NLRs. Several components that were previously thought to represent “negative regulators” of plant immunity upon closer inspection turned out to be in fact proteins surveilled by NLRs, which explains the autoimmunity phenotypes associated with their absence or mutation. Hence, advanced genetic and molecular tools are emerging to dissect the NLR network functions related to autoimmunity, which can also be useful for the broader community of plant scientists. However, a number of pertinent questions still need to be clarified in this field of research (see “Outstanding Questions” box). It remains a fruitful exercise to broaden the view to NLR-associated science conducted in the animal field, as NLR proteins seem to function according to a common biochemical principle, and, as in the case of other biological processes, much can be possibly learnt by a cross-kingdom perspective. Advances Lesion mimic and autoimmune mutants exhibit activation of immunity in the absence of infection. Leading causes of autoimmunity include hybrid necrosis seen in intraspecies crosses involving incompatible alleles of immune genes, mutations in NLR genes leading to increased expression or gain-of-function that trigger ETI, mutations in effector targets sensed by specific NLRs that trigger ETI, and mutations in negative regulators of immunity (e.g. transcriptional repressors and proteasomal components). Fine-tuning of NLR expression at all levels is crucial to avoid autoimmunity. Advanced tools to study NLR-mediated autoimmunity have recently emerged. OUTSTANDING QUESTIONS What can we learn from autoimmune and lesion mimic mutants regarding plant immunity? Which aspects need to be considered when reporting autoimmune and lesion mimic mutants? Are the events downstream of NLR activation identical in authentic plant defense and autoimmunity? Can the constitutive activation of PTI also result in autoimmunity? Are there truly cases where autoimmunity is not linked to NLR activity? What are the best experimental approaches to identify (NLR) genes underlying an autoimmune response? What is the molecular basis for the frequent modulation of autoimmunity by environmental conditions? Will climate change (elevated temperatures) result in more cases of autoimmunity in plants? Can autoimmunity be exploited to increase disease resistance in crops? Are there means to mitigate hybrid necrosis yet retain pathogen responsiveness? Box 1 Important acronyms in plant immunity CNL: NLR with a coiled-coiled type N-terminal (head-) domain DAMP: molecule derived from plant—endogenous structures (e.g. the cell wall) that is released upon attack by pathogens or herbivores; can be recognized by specific plant cell surface receptors Effector protein: protein delivered by a pathogen; often interferes with the host defense response or metabolism ETI: plant immune response triggered upon recognition of pathogen effectors by intracellular NLR receptors NLR: Member of a plant protein family of intracellular immune receptors that either directly or indirectly recognizes pathogen effectors and subsequently induces immune reactions PAMP: pathogen-derived molecule, often from the cell wall or outer structures of pathogens, such as bacterial flagellin or fungal chitin. Can be recognized by specific plant cell surface receptors. Sometimes also referred to as microbe-associated molecular pattern (MAMP) PTI: plant immune response triggered upon recognition of extracellular PAMPs or DAMPs by cell surface receptors TNL: plant NLR with an N-terminal (head-) domain that resembles the intracellular domain of animal Toll and interleukin receptors Funding This work was supported by the Novo Nordisk Foundation grant NNF19OC0056457 (PlantsGoImmune) to R.P. and M.P. Conflict of interest statement. None declared. R.P. designed the research. M.F., J.G., M.P., and R.P. wrote the paper. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plphys/pages/general-instructions) is Ralph Panstruga ([email protected]). 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This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact [email protected] © The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists.

Sugimoto, Koichi; Zager, Jordan J; Aubin, Brian St; Lange, Bernd Markus; Howe, Gregg A

doi: 10.1093/plphys/kiab488pmid: 34668550

Abstract Glandular trichomes (GTs) are epidermal structures that provide the first line of chemical defense against arthropod herbivores and other biotic threats. The most conspicuous structure on leaves of cultivated tomato (Solanum lycopersicum) is the type-VI GT (tVI-GT), which accumulates both flavonoids and volatile terpenoids. Although these classes of specialized metabolites are derived from distinct metabolic pathways, previous studies with a chalcone isomerase 1 (CHI1)-deficient mutant called anthocyanin free (af) showed that flavonoids are required for terpenoid accumulation in tVI-GTs. Here, we combined global transcriptomic and proteomic analyses of isolated trichomes as a starting point to show that the lack of CHI1 is associated with reduced levels of terpenoid biosynthetic transcripts and enzymes. The flavonoid deficiency in af trichomes also resulted in the upregulation of abiotic stress-responsive genes associated with DNA damage and repair. Several lines of biochemical and genetic evidence indicate that the terpenoid defect in af mutants is specific for the tVI-GT and is associated with the absence of bulk flavonoids rather than loss of CHI1 per se. A newly developed genome-scale model of metabolism in tomato tVI-GTs helped identify metabolic imbalances caused by the loss of flavonoid production. We provide evidence that flavonoid deficiency in this cell type leads to increased production of reactive oxygen species (ROS), which may impair terpenoid biosynthesis. Collectively, our findings support a role for flavonoids as ROS-scavenging antioxidants in GTs. Introduction Many plants have anatomical structures and specialized cell types that produce defense-related compounds. Prominent examples include resin ducts, laticifers, and glandular trichomes (GTs). Confining the high-level production and storage of specialized metabolites to these structures is thought to minimize adverse effects of autotoxicity or interference with primary metabolism needed for normal plant growth and development (Kliebenstein, 2013; Lange and Turner, 2013; Lange, 2015; Guo et al., 2018). Owing to their abundance and ease of isolation, GTs have received attention as a source of plant compounds that have practical uses as medicines, flavors, and food additives (Duke et al., 2000; Gibon et al., 2009; Schilmiller et al., 2010; Tissier, 2018). A better understanding of factors affecting GT metabolic pathways is therefore of both fundamental and applied interest. In providing a physicochemical interface between the plant and its environment, GTs constitute an important line of chemical defense against various plant consumers. Studies of mutants that are impaired in the development or biosynthetic capacity of GTs have helped to discern the role of these structures in plant anti-insect defense. Defects in GT development in the odorless-2 (od-2) mutant of cultivated tomato (Solanum lycopersicum), for example, are associated with increased susceptibility to attack by larvae of several beetle species (Kang et al., 2010a). Loss of resistance to herbivores was also observed in the hairless (hl) and anthocyanin-free (af) mutants of tomato, which are defective in the production of specialized metabolites in the abundant type VI GTs (tVI-GTs) (Kang et al., 2010b, 2014, 2016). Transgenic tobacco (Nicotiana tabacum) plants that overexpress a B-type cyclin gene were shown to accumulate reduced levels of terpenoids in GTs, which corresponded with a reduced resistance to insect feeding (Gao et al., 2017). Conversely, metabolic engineering of S. lycopersicum GTs to produce defensive metabolites found in wild tomato species resulted in enhanced herbivore resistance (Bleeker et al., 2012). The metabolic activity of tVI-GTs in tomato is dominated by the synthesis and accumulation of flavonoids and terpenoids (Kang et al., 2010b; McDowell et al., 2011), which constitute two diverse classes of specialized metabolites in plants. Available evidence indicates that trichome-borne terpenoids serve an important function in resistance of tomato to insect herbivores (Kennedy, 2003; Li et al., 2004; Bleeker et al., 2009, 2012). Although the role of GT-localized flavonoids in protection against biotic stress is less clear, the flavonol quercetin and its glycosides (e.g. rutin), which are highly abundant in tVI-GTs, exert growth-retarding effects on insects when added to artificial diets (Isman and Duffey, 1982, 1983). An increasing body of literature further indicates that flavonols are critical for detoxification of reactive oxygen species (ROS) in various tissues and cell types of tomato, including root hairs, guard cells, and pollen (Maloney et al., 2014; Watkins et al., 2017; Muhlemann et al., 2018). In addition to providing a chemical screen for ultraviolet (UV) radiation, it has been proposed that the antioxidant properties of some GT-borne flavonoids may reduce oxidative damage caused by short-wavelength solar radiation (Tattini et al., 2000; Agati and Tattini, 2010). Whether flavonoid production in GTs has a role in mitigating the effects of stress-associated ROS production has yet to be addressed through the use of flavonoid-deficient mutants. The flavonoid and terpenoid biosynthetic pathways are metabolically distinct and generally assumed to operate independently. We recently observed, however, that the anthocyanin free (af) mutant of tomato, which fails to produce a specific isoform, chalcone isomerase 1 (CHI1), of the flavonoid biosynthetic enzyme CHI, not only lacks anthocyanins and flavonoids in all tissues but is also severely impaired in volatile terpenoid production in tVI-GTs (Kang et al., 2014). The underlying mechanism by which CHI1 promotes trichome-borne production of volatile terpenoids remained to be determined. This study was designed to address this question, using tVI-GT-specific transcriptomic and proteomic datasets as a starting point. We show that the loss of CHI1 results in the downregulation of terpenoid biosynthetic genes and enzymes in tVI-GTs of the af mutant, concomitant with upregulation of genes involved in DNA repair and response to radiation. A series of follow-up experiments provided evidence that these phenotypes are linked to elevated ROS accumulation under flavonoid-deficient conditions in af trichomes. These findings are consistent with a role for flavonoids as ROS-scavenging antioxidants in a specialized cell type that is adapted to function under high levels of solar radiation. Results Loss of CHI1 results in global changes in metabolic and stress response pathways in tVI-GTs To assess the global contribution of CHI1 to metabolic and cellular processes in tVI-GTs, we obtained transcriptome datasets with tVI gland cells isolated from the af mutant and appropriate isogenic control lines. Because the af mutant was generated by X-ray mutagenesis (Von Wettstein-Knowles, 1968), this line could potentially harbor other mutations that confound the interpretation of gene expression profiles. Therefore, as an isogenic “wild-type” control, we used two independent transgenic lines (designated CHI4 and CHI9) in which the wild-type CHI1 gene was expressed in the af mutant background under control of the native CHI1 promoter, fully complementing the af phenotypes (Kang et al., 2014). Transcriptome data sets were generated by mRNA sequencing (RNA-seq) of RNA isolated from af and the two complemented lines (CHI4 and CHI9), using four biological replicates per genotype (12 independent samples) and two technical replicates per sample (Supplemental Figure S1; Supplemental Dataset S1). The following work flow was used to identify a robust set of genes that are differentially expressed in tVI-GTs as a result of CHI1 deficiency (Supplemental Table S1). First, the RNA-seq data were processed with three independent analysis programs (EdgeR;Robinson et al. (2010), TCC; Sun et al. (2013), and DESeq2; Love et al. (2014)) to identify differentially expressed genes (DEGs) in the af versus CHI4 and af versus CHI9 comparisons (threshold of log2 > 1.0 or less than −1.0 and adjusted P-value of permutation test <0.05). Second, we compared the DEGs obtained for each of the three data analysis tools to arrive at a list of common DEGs within each of the two genotypic comparisons. Finally, we compared the list of common DEGs between the two genotypic comparisons to arrive at a set of consensus DEGs. Using this filtering approach, the transcript abundance of 516 and 642 genes was determined to be lower in the af mutant compared to CHI4 and CHI9, respectively. Among these genes, all three analysis tools identified an overlapping, consensus set of 384 transcripts whose abundance was lower in af than in both CHI4 and CHI9 (Supplemental Table S1). Likewise, the transcript abundance of 1,602 and 1,533 genes was determined to be higher in the af mutant compared to CHI4 and CHI9, respectively, with a consensus set of 930 genes expressed to higher levels in af relative to both CHI4 and CHI9 (Supplemental Table S1). We used the consensus list of 384 downregulated and 930 upregulated genes in af for gene ontology (GO) enrichment analysis to gain insights into processes within tVI-GT cells that are affected by the loss of CHI1. Genes that were downregulated in the af mutant showed strong enrichment of GO categories related to metabolic pathways operating mainly in the chloroplast (Table 1;Supplemental Table S2). Most prominent among these pathways were isoprenoid/terpenoid metabolism, lipid metabolism, amino acid metabolism, and photosynthesis-related processes. GO analysis of the 930 genes that are upregulated in tVI gland cells of the af mutant revealed enrichment of functional categories related to abiotic stress responses. Categories related to DNA damage and repair were particularly overrepresented among upregulated genes in af trichomes, as were responses to heat, high light, and ROS (Table 1;Supplemental Table S2). These data indicate that the loss of CHI1 in af GTs both impairs the expression of genes involved in chloroplast metabolism and also enhances transcriptional responses related to high light and temperature stress. Table 1 Gene ontologies of differentially regulated transcripts in tVI-GTs of the tomato af mutant GO ID . GO description of genes downregulated in af mutanta . FDR . P-value . 0055114 Oxidation–reduction process 1.14E-11 2.77E-15 0044711 Single-organism biosynthetic process 4.87E-10 6.70E-13 1901565 Organonitrogen compound catabolic process 4.87E-10 7.05E-13 0006629 Lipid metabolic process 1.47E-09 2.48E-12 0008299 Isoprenoid biosynthetic process 1.64E-09 2.96E-12 0015979 Photosynthesis 1.99E-08 4.79E-11 1901606 Amino acid catabolic process 6.82E-07 1.97E-09 0016114 Terpenoid biosynthetic process 1.80E-06 6.30E-09 0042440 Pigment metabolic process 2.65E-06 9.91E-09 GO ID GO description of genes upregulated in af mutanta FDR P-value 0006310 DNA recombination 3.97E-03 2.37E-06 0009408 Response to heat 4.08E-03 3.59E-06 0006298 Mismatch repair 4.91E-03 5.33E-06 0051716 Cellular response to stimulus 8.00E-03 1.54E-05 0009314 Response to radiation 1.04E-02 2.22E-05 0007165 Signal transduction 1.19E-02 2.88E-05 0009628 Response to abiotic stimulus 1.38E-02 3.48E-05 1901700 Response to oxygen-containing compound 1.71E-02 5.19E-05 GO ID . GO description of genes downregulated in af mutanta . FDR . P-value . 0055114 Oxidation–reduction process 1.14E-11 2.77E-15 0044711 Single-organism biosynthetic process 4.87E-10 6.70E-13 1901565 Organonitrogen compound catabolic process 4.87E-10 7.05E-13 0006629 Lipid metabolic process 1.47E-09 2.48E-12 0008299 Isoprenoid biosynthetic process 1.64E-09 2.96E-12 0015979 Photosynthesis 1.99E-08 4.79E-11 1901606 Amino acid catabolic process 6.82E-07 1.97E-09 0016114 Terpenoid biosynthetic process 1.80E-06 6.30E-09 0042440 Pigment metabolic process 2.65E-06 9.91E-09 GO ID GO description of genes upregulated in af mutanta FDR P-value 0006310 DNA recombination 3.97E-03 2.37E-06 0009408 Response to heat 4.08E-03 3.59E-06 0006298 Mismatch repair 4.91E-03 5.33E-06 0051716 Cellular response to stimulus 8.00E-03 1.54E-05 0009314 Response to radiation 1.04E-02 2.22E-05 0007165 Signal transduction 1.19E-02 2.88E-05 0009628 Response to abiotic stimulus 1.38E-02 3.48E-05 1901700 Response to oxygen-containing compound 1.71E-02 5.19E-05 a Differential gene expression was determined by an RNA-seq experiment in which transcript abundance in isolated tVI trichomes from the CHI1-deficienct af mutant was compared to transcript levels in two CHI1-complemented control lines. Open in new tab Table 1 Gene ontologies of differentially regulated transcripts in tVI-GTs of the tomato af mutant GO ID . GO description of genes downregulated in af mutanta . FDR . P-value . 0055114 Oxidation–reduction process 1.14E-11 2.77E-15 0044711 Single-organism biosynthetic process 4.87E-10 6.70E-13 1901565 Organonitrogen compound catabolic process 4.87E-10 7.05E-13 0006629 Lipid metabolic process 1.47E-09 2.48E-12 0008299 Isoprenoid biosynthetic process 1.64E-09 2.96E-12 0015979 Photosynthesis 1.99E-08 4.79E-11 1901606 Amino acid catabolic process 6.82E-07 1.97E-09 0016114 Terpenoid biosynthetic process 1.80E-06 6.30E-09 0042440 Pigment metabolic process 2.65E-06 9.91E-09 GO ID GO description of genes upregulated in af mutanta FDR P-value 0006310 DNA recombination 3.97E-03 2.37E-06 0009408 Response to heat 4.08E-03 3.59E-06 0006298 Mismatch repair 4.91E-03 5.33E-06 0051716 Cellular response to stimulus 8.00E-03 1.54E-05 0009314 Response to radiation 1.04E-02 2.22E-05 0007165 Signal transduction 1.19E-02 2.88E-05 0009628 Response to abiotic stimulus 1.38E-02 3.48E-05 1901700 Response to oxygen-containing compound 1.71E-02 5.19E-05 GO ID . GO description of genes downregulated in af mutanta . FDR . P-value . 0055114 Oxidation–reduction process 1.14E-11 2.77E-15 0044711 Single-organism biosynthetic process 4.87E-10 6.70E-13 1901565 Organonitrogen compound catabolic process 4.87E-10 7.05E-13 0006629 Lipid metabolic process 1.47E-09 2.48E-12 0008299 Isoprenoid biosynthetic process 1.64E-09 2.96E-12 0015979 Photosynthesis 1.99E-08 4.79E-11 1901606 Amino acid catabolic process 6.82E-07 1.97E-09 0016114 Terpenoid biosynthetic process 1.80E-06 6.30E-09 0042440 Pigment metabolic process 2.65E-06 9.91E-09 GO ID GO description of genes upregulated in af mutanta FDR P-value 0006310 DNA recombination 3.97E-03 2.37E-06 0009408 Response to heat 4.08E-03 3.59E-06 0006298 Mismatch repair 4.91E-03 5.33E-06 0051716 Cellular response to stimulus 8.00E-03 1.54E-05 0009314 Response to radiation 1.04E-02 2.22E-05 0007165 Signal transduction 1.19E-02 2.88E-05 0009628 Response to abiotic stimulus 1.38E-02 3.48E-05 1901700 Response to oxygen-containing compound 1.71E-02 5.19E-05 a Differential gene expression was determined by an RNA-seq experiment in which transcript abundance in isolated tVI trichomes from the CHI1-deficienct af mutant was compared to transcript levels in two CHI1-complemented control lines. Open in new tab To further investigate the effects of CHI1-deficiency on cellular processes in tVI gland cells, we performed a quantitative proteomics analysis of extracts obtained from isolated tVI-GTs. Because the CHI4 and CHI9 lines showed very similar patterns of global gene expression (Supplemental Figure S1), we used only the CHI9 isogenic line as a control for proteomic analysis of three independent trichome gland preparations for each genotype (af and CHI9). Differential protein abundance in tVI-GTs of CHI9 versus af was assessed by isobaric mass-tag labeling (see “Materials and methods”) and was calculated on the basis of adjusted spectral counts (filter of log2 > 0.3 or less than −0.3 and adjusted P-value of permutation test <0.05). Comparison of the aggregate spectral-count data in all six samples showed that the protein content of GTs from the CHI9 and af lines was distinct (Supplemental Figure S1). Of 4,992 unique proteins represented by more than one peptide in our dataset, the abundance of 133 proteins was determined to be higher in tVI-GTs of the af mutant compared to CHI9, whereas the abundance of 52 proteins was lower in the same comparison (Supplemental Dataset S2). These results indicate that the overall effects of CHI1 deficiency were much less pronounced at the protein abundance level than they were at the transcript level. We found that proteins with functions in isoprenoid biosynthesis, volatile oxylipin biosynthesis, and photosynthesis were less abundant in GTs of the af mutant relative to the CHI9 control (Supplemental Dataset S2). In agreement with the terpenoid deficiency of af plants (Kang et al., 2014), mutant GTs contained reduced levels of several terpenoid biosynthetic enzymes, including 1-deoxy-D-xylulose 5-phosphate synthase (Solyc11g010850), 1-deoxy-D-xylulose-5-phosphate reductoisomerase (Solyc03g114340), undecaprenyl pyrophosphate synthase (Solyc08g005680), and various terpene synthases (Solyc08g005640, Solyc08g005670, and Solyc06g059930). Two additional chloroplast-localized proteins, lipoxygenase C and fatty acid hydroperoxide lyase (encoded by Solyc01g006540 and Solyc07g049690, respectively) involved in the formation of green leaf volatiles were also downregulated in the af trichomes. Further validation of the proteomics approach came from the finding that CHI1 (encoded by Solyc05g010320) protein levels were lower in af compared to CHI9, which accords with the fact that CHI1 does not accumulate in the af mutant (Kang et al., 2014). Among the 133 proteins that overaccumulated in af GTs were numerous stress-associated enzymes, including glutathione S-transferases, heat shock proteins, and molecular chaperones (Supplemental Dataset S2). We also found that af trichomes had increased levels of the UV-B receptor (UVR8; encoded by Solyc5g018620) and two UVR8-like proteins as well (encoded by Solyc02g085780 and Solyc04g081200). UVR8 mediates responses of tomato to UV-B stress by activating the expression of CHALCONE SYNTHASE (CHS) and other flavonoid biosynthetic genes (Li et al., 2018). Consistent with this, our RNA-seq data showed that CHS and most other flavonoid biosynthetic genes were upregulated in the af mutant (Figure 1; Supplemental Dataset S1). Two exceptions to this trend were the gene encoding flavonol synthase (Solyc10g083440), which had low expression levels across all samples, and the CHI1 gene that is mutated in af (Figure 1). Figure 1 Open in new tabDownload slide Loss of CHI1 in the af mutant results in higher expression of tVI-GT-expressed genes involved in flavonoid biosynthesis and lower expression of genes involved in terpenoid biosynthesis. A, Metabolic scheme of the flavonoid pathway (orange), MEP pathway (blue), mevalonate pathway (gray), and terpenoid-specific reactions (green). B, Heatmap representation of the relative transcript abundance of genes involved in flavonoid, MEP, mevalonate, and terpenoid-specific pathways. The color code depicts the relative expression level of a given gene in af, CHI4, and CHI9 GTs normalized to the highest expression value among genotypes, with the cut-off for expression level set at 50 transcripts per kilobase million. The complete list of gene identifiers and expression levels is provided in Supplemental Dataset S1. PAL, phenylalanine ammonia lyase; C4H, cinnamate 4-hydroxylase; 4CL, 4-hydroxy-cinnamoyl CoA ligase; F3H, flavanone-3-hydroxylase; F3′H, flavonoid-3′-hydroxylase; F3′5′H, flavonoid-3′5′-hydroxylase; FLS, flavonol synthase; DXS, 1-deoxy-D-xylulose 5-phosphate synthase; DXR, 1-deoxy-D-xylulose 5-phosphate reductoisomerase; MCT, 2C-methyl-D-erythritol 4-phosphate cytidyltransferase; CMK, 4-(cytidine 5'-diphospho)-2C-methyl-D-erythritol kinase; MCS, 2C-methyl-D-erythritol 2,4-cyclodiphosphate synthase; HDS, 1-hydroxy-2-methyl-2-butenyl 4-phosphate synthase; HDR, 1-hydroxy-2-methyl-2-butenyl 4-phosphate reductase; AACT, acetoacetyl-CoA thiolase; HMGS, 3-hydroxy-3-methylglutaryl-CoA synthase; HMGR, 3-hydroxy-3-methylglutaryl-CoA reductase; MK, mevalonate kinase; PMK, phosphomevalonate kinase; MDD, mevalonate 5-diphosphate decarboxylase; IPI, isopentenyl diphosphate isomerase; GPPS, geranyl diphosphate synthase; FPPS, farnesyl diphosphate synthase; NPPS, neryl diphosphate synthase; TPS, terpene synthase. The flavonoid biosynthetic reaction that is impaired in the af and are mutants are indicated in red. Figure 1 Open in new tabDownload slide Loss of CHI1 in the af mutant results in higher expression of tVI-GT-expressed genes involved in flavonoid biosynthesis and lower expression of genes involved in terpenoid biosynthesis. A, Metabolic scheme of the flavonoid pathway (orange), MEP pathway (blue), mevalonate pathway (gray), and terpenoid-specific reactions (green). B, Heatmap representation of the relative transcript abundance of genes involved in flavonoid, MEP, mevalonate, and terpenoid-specific pathways. The color code depicts the relative expression level of a given gene in af, CHI4, and CHI9 GTs normalized to the highest expression value among genotypes, with the cut-off for expression level set at 50 transcripts per kilobase million. The complete list of gene identifiers and expression levels is provided in Supplemental Dataset S1. PAL, phenylalanine ammonia lyase; C4H, cinnamate 4-hydroxylase; 4CL, 4-hydroxy-cinnamoyl CoA ligase; F3H, flavanone-3-hydroxylase; F3′H, flavonoid-3′-hydroxylase; F3′5′H, flavonoid-3′5′-hydroxylase; FLS, flavonol synthase; DXS, 1-deoxy-D-xylulose 5-phosphate synthase; DXR, 1-deoxy-D-xylulose 5-phosphate reductoisomerase; MCT, 2C-methyl-D-erythritol 4-phosphate cytidyltransferase; CMK, 4-(cytidine 5'-diphospho)-2C-methyl-D-erythritol kinase; MCS, 2C-methyl-D-erythritol 2,4-cyclodiphosphate synthase; HDS, 1-hydroxy-2-methyl-2-butenyl 4-phosphate synthase; HDR, 1-hydroxy-2-methyl-2-butenyl 4-phosphate reductase; AACT, acetoacetyl-CoA thiolase; HMGS, 3-hydroxy-3-methylglutaryl-CoA synthase; HMGR, 3-hydroxy-3-methylglutaryl-CoA reductase; MK, mevalonate kinase; PMK, phosphomevalonate kinase; MDD, mevalonate 5-diphosphate decarboxylase; IPI, isopentenyl diphosphate isomerase; GPPS, geranyl diphosphate synthase; FPPS, farnesyl diphosphate synthase; NPPS, neryl diphosphate synthase; TPS, terpene synthase. The flavonoid biosynthetic reaction that is impaired in the af and are mutants are indicated in red. Terpenoid deficiency in the af mutant is specific to glandular trichomes We next investigated the extent to which the flavonoid deficiency in the af mutant disrupts various isoprenoid-related processes in tVI-GTs and whole leaves. In-depth analysis of the RNA-seq data revealed that the af mutation broadly downregulates the expression of genes involved in the biosynthesis of monoterpenes and sesquiterpenes in tVI-GTs (Figure 1). Among these genes were those encoding enzymes in the methylerythritol 4-phosphate (MEP) pathway, which provides precursors for monoterpene biosynthesis, as well as the mevalonic acid (MVA) pathway, which is largely responsible for precursor supply into the sesquiterpene biosynthetic pathway (Hemmerlin et al., 2012). Reverse transcription-quantitative PCR (RT-qPCR) assays confirmed the attenuated expression in af trichomes of 11 terpenoid biosynthetic genes encoding enzymes that span the MEP and MVA pathways (Supplemental Figure S2). Analysis of volatile metabolites by gas chromatography–mass spectroscopy (GC–MS) confirmed that af trichomes accumulate very low levels of 10 individual terpenoids, including monoterpenes (e.g. β-phellandrene) and sesquiterpenes (e.g. β-caryophyllene) that are abundant in wild-type tVI-GTs (Supplemental Figure S3). We found that the total level of terpene volatiles (mono and sesquiterpenes) in tVI-GTs of the af mutant was <10% of that in GTs from the isogenic control lines (Figure 2A). These data demonstrate that the absence of CHI1 in tVI-GTs abrogates the expression of terpenoid biosynthetic genes and enzymes, as well as the production of volatile terpenoids in this cell type. Figure 2 Open in new tabDownload slide Downregulation of terpenoid biosynthesis in the af mutant is restricted to tVI-GTs. A, tVI-GTs of the af mutant accumulate low levels of volatile terpenoids (combined mono- and sesquiterpenes) relative to two CHI1-complemented lines (CHI4 and CHI9) (n = 6 independent collections of hand-picked trichomes per genotype). Quantitative data for individual terpenoids are shown in Supplemental Figure S3. B and C, Total chlorophyll (chl a and chl b) levels in isolated tVI-GTs (B, n = 5 independent collections of hand-picked trichomes per genotype) and in whole leaves (C, n = 12 plants per genotype from four independent experiments). D, Bulk carotenoid content in leaves of the indicated genotypes (n = 12 plants per genotype from four independent experiments). Center lines, box range, whiskers, and points indicate the median, lower to upper quartile, 1.5× interquartile, and outliers, respectively. Statistical significance was analyzed by Dunnett’s multiple comparison method and is indicated with an asterisk for P ≤ 0.01. NS, no statistical difference in comparison of the af mutant to isogenic control lines. Figure 2 Open in new tabDownload slide Downregulation of terpenoid biosynthesis in the af mutant is restricted to tVI-GTs. A, tVI-GTs of the af mutant accumulate low levels of volatile terpenoids (combined mono- and sesquiterpenes) relative to two CHI1-complemented lines (CHI4 and CHI9) (n = 6 independent collections of hand-picked trichomes per genotype). Quantitative data for individual terpenoids are shown in Supplemental Figure S3. B and C, Total chlorophyll (chl a and chl b) levels in isolated tVI-GTs (B, n = 5 independent collections of hand-picked trichomes per genotype) and in whole leaves (C, n = 12 plants per genotype from four independent experiments). D, Bulk carotenoid content in leaves of the indicated genotypes (n = 12 plants per genotype from four independent experiments). Center lines, box range, whiskers, and points indicate the median, lower to upper quartile, 1.5× interquartile, and outliers, respectively. Statistical significance was analyzed by Dunnett’s multiple comparison method and is indicated with an asterisk for P ≤ 0.01. NS, no statistical difference in comparison of the af mutant to isogenic control lines. Compared to the large negative effect of CHI1 deficiency on terpenoid accumulation in tVI-GTs, the levels of chlorophylls, which contain a C20 phytol side chain derived from the general terpenoid pathway, were not significantly different in tVI-GTs of the af mutant compared to the two control lines (Figure 2B). Consistent with this finding, RNA-seq data showed that the expression level of chlorophyll metabolic genes in af GTs was comparable to that in trichomes of the isogenic control lines (Supplemental Dataset S1). Analysis of whole leaf samples showed that the levels of bulk chlorophylls and carotenoids (C40 tetraterpenes), derived mainly from mesophyll cells, were also similar between af and the two complemented lines (Figure 2, C and D). These data indicate that the loss of CHI1 has a strong negative effect on the accumulation of terpene volatiles in tVI-GTs but most likely does not impact other terpenoid pathway-derived products (e.g. chlorophylls) in tVI-GTs and other cell types of the leaf. Nuclear localization of CHI1 is not required for terpenoid accumulation in tVI-GTs Previous studies have shown that canonical CHI enzymes and their corresponding green fluorescent protein (GFP) fusion proteins accumulate not only in the cytosol, which is the site of flavonoid biosynthesis, but also in the nucleus (Saslowsky et al., 2005; Dastmalchi and Dhaubhadel, 2015). This observation led to the suggestion that CHI may perform a “moonlighting” function in regulating gene expression (Saslowsky et al., 2005). To investigate the subcellular localization of CHI1 in gland cells of the tVI-GTs, we generated a transgenic line (pCHI1::CHI1–GFP) of tomato that stably expresses CHI1–GFP in the af mutant background and under the control of the native CHI1 promoter. Confocal microscopy of intact tVI-GTs from pCHI1::CHI1–GFP plants showed that the CHI1–GFP fusion protein was located in both the cytosol and the nucleus of gland cells (Figure 3A), thus confirming the dual localization of CHI1 in this cell type. The reticulate pattern of CHI1–GFP signal within the cytosol is consistent with the action of CHI on the cytosolic face of the ER (Saslowsky et al., 2005). Figure 3 Open in new tabDownload slide Exclusion of CHI1 from nuclei does not impair terpenoid accumulation in tVI-GTs. A and B, CHI1–GFP (A) or CHI1–GFP–GUS line (B) fusion proteins were expressed in the af mutant background under the control of the native CHI1 promoter. Additional complementation phenotypes in the line expressing CHI1–GFP–GUS (line #7) are provided in Supplemental Figure S4. Confocal microscopy was used to localize the GFP fusion proteins within the four-celled glandular head of tVI-GTs in the corresponding transgenic line. Left, GFP fluorescence; middle, propidium iodide (PI) fluorescence (nuclear stain); right, merged GFP and PI images. Asterisks denote nuclei and scale bars correspond to 20 µM. C and D, Quantification of rutin (C) and β-phellandrene (D) levels in tVI-GTs of the indicated genotypes (n = 8–15 collections of hand-picked trichomes per genotype for (C), and 13 collections of hand-picked trichomes per genotype for (D)). Center lines, box range, whiskers, and points indicate the median, lower to upper quartile, 1.5× interquartile, and outliers, respectively. Different lowercase letters indicate a statistical difference among genotypes by Tukey’s multiple comparisons (P < 0.05). Figure 3 Open in new tabDownload slide Exclusion of CHI1 from nuclei does not impair terpenoid accumulation in tVI-GTs. A and B, CHI1–GFP (A) or CHI1–GFP–GUS line (B) fusion proteins were expressed in the af mutant background under the control of the native CHI1 promoter. Additional complementation phenotypes in the line expressing CHI1–GFP–GUS (line #7) are provided in Supplemental Figure S4. Confocal microscopy was used to localize the GFP fusion proteins within the four-celled glandular head of tVI-GTs in the corresponding transgenic line. Left, GFP fluorescence; middle, propidium iodide (PI) fluorescence (nuclear stain); right, merged GFP and PI images. Asterisks denote nuclei and scale bars correspond to 20 µM. C and D, Quantification of rutin (C) and β-phellandrene (D) levels in tVI-GTs of the indicated genotypes (n = 8–15 collections of hand-picked trichomes per genotype for (C), and 13 collections of hand-picked trichomes per genotype for (D)). Center lines, box range, whiskers, and points indicate the median, lower to upper quartile, 1.5× interquartile, and outliers, respectively. Different lowercase letters indicate a statistical difference among genotypes by Tukey’s multiple comparisons (P < 0.05). The presence of CHI1–GFP in nuclei of tVI gland cells raised the possibility that CHI1 may act in the nucleus to positively regulate terpenoid production through the control of terpene biosynthetic gene expression. To test this hypothesis, we transformed the af mutant with a transgene (pCHI1::CHI1–GFP–GUS) that expresses a CHI1–GFP–GUS fusion protein under the control of the native CHI1 promoter. We reasoned that the high molecular mass (predicted 115 kDa) of CHI1–GFP–GUS should exclude its diffusion through the nuclear pore complex (Grossman et al., 2012), thereby attenuating any function of CHI1 in the nucleus. Confocal microscopy of tVI glands from pCHI1::CHI1–GFP–GUS plants showed that CHI1–GFP–GUS maintained the cytosolic, reticulated pattern of CHI1–GFP but, unlike CHI1–GFP, was not detectable in the nucleus (Figure 3B). We next assessed pCHI1::CHI1–GFP–GUS plants and appropriate control lines (af and CHI4) for the accumulation of the dominant flavonoid (rutin) and terpenoid (β-phellandrene) components of tVI-GTs. Consistent with the ability of pCHI1::CHI1–GFP–GUS to complement the anthocyanin deficiency in the af mutant (Supplemental Figure S4), CHI1–GFP–GUS fully restored the accumulation of rutin in tVI-GTs relative to the af mutant and CHI4 controls (Figure 3C). Importantly, we also found that CHI1–GFP–GUS fully complemented the β-phellandrene deficiency of af tVI-GTs (Figure 3D). These data indicate that the role of CHI1 in promoting terpenoid accumulation in tVI-GTs does not likely require a nuclear pool of CHI1 but rather depends on the canonical function of the enzyme in flavonoid biosynthesis. Genome-scale modeling predicts metabolic perturbations in glandular trichomes of the af mutant We have previously employed mathematical modeling as a tool for formulating hypotheses concerning GT metabolism in silico, thereby reducing the number of follow-up experiments required for testing the strongest hypotheses (Rios-Estepa et al., 2008, 2010; Lange and Rios-Estepa, 2014; Johnson et al., 2017; Zager and Lange, 2018; Turner et al., 2019). To apply this methodology to this study, we developed a genome-scale model (termed Solyc_tVI-GT_2021v1) of metabolism in tVI-GTs of tomato, which incorporates publicly available information and several datasets acquired as part of this study (details describing the model development process are provided in Supplemental Methods and Data File S1). The first-generation model featured 782 unique, stoichiometrically balanced reactions (Figure 4A;Supplemental Table S3). Using a custom script, each reaction in the model was associated with the appropriate tomato gene(s) and enzyme(s), which allowed us to integrate our transcriptomic and proteomic datasets (Supplemental Table S3). Due to the low abundance of many peptides in the proteomics dataset, simulations focused on constraining fluxes with transcriptome data. Figure 4 Open in new tabDownload slide Experimental testing of predictions of a mathematical model of metabolism in tomato tVI-GTs. A, Overview of workflow for generating the Solyc_tVI-GT_2021 model. B, Quantitative fatty acid analysis of isolated tVI-GTs from the af mutant (gray bars) and CHI9 isogenic control (open bars) to test the Solyc_tVI-GT_2021 prediction that flux through fatty acid biosynthesis is higher in the af mutant compared to CHI9. Asterisks denote P < 0.01 in comparisons between af and CHI9 (n = 5–6). C, Comparison of the amount of naringenin chalcone in tVI-GTs of the indicated genotypes (n =  8–15 collections of hand-picked trichomes per genotype) to test the Solyc_tVI-GT_2021 prediction that only the flavonoid pathway reactions upstream of CHI1, but not those downstream, carry significant flux in the af mutant. Bars represent mean ± standard error. Statistical difference across genotypes, based on Tukey’s multiple comparison test (P < 0.05), is indicated by lowercase letters. Figure 4 Open in new tabDownload slide Experimental testing of predictions of a mathematical model of metabolism in tomato tVI-GTs. A, Overview of workflow for generating the Solyc_tVI-GT_2021 model. B, Quantitative fatty acid analysis of isolated tVI-GTs from the af mutant (gray bars) and CHI9 isogenic control (open bars) to test the Solyc_tVI-GT_2021 prediction that flux through fatty acid biosynthesis is higher in the af mutant compared to CHI9. Asterisks denote P < 0.01 in comparisons between af and CHI9 (n = 5–6). C, Comparison of the amount of naringenin chalcone in tVI-GTs of the indicated genotypes (n =  8–15 collections of hand-picked trichomes per genotype) to test the Solyc_tVI-GT_2021 prediction that only the flavonoid pathway reactions upstream of CHI1, but not those downstream, carry significant flux in the af mutant. Bars represent mean ± standard error. Statistical difference across genotypes, based on Tukey’s multiple comparison test (P < 0.05), is indicated by lowercase letters. Transcriptome data obtained with isolated tVI-GTs were incorporated using a modified SPOT algorithm (Kim et al., 2016). As an initial test for the validity of the reaction network architecture, we asked whether Solyc_tVI-GT_2021v1 could be employed to predict biomass outputs in the form of metabolites accumulated in tVI-GTs. Consistent with recent metabolic studies of tomato tVI-GTs (Balcke et al., 2017), inputs of the model included sucrose as carbon source, ammonia, oxygen, hydrogen sulfide, inorganic phosphate, water, and carbon dioxide. Metabolic outputs of the model included flavonoids, monoterpenes, sesquiterpenes, acyl sugars, sterols, cell wall carbohydrates, and amino acid pools for protein synthesis. Notably, Solyc_tVI-GT_2021v1 accurately predicted the lack of flavonoid and monoterpene end products in the af mutant, whereas these classes of metabolites were projected to accumulate to appreciable levels in trichomes of the CHI9 isogenic control line (Supplemental Table S3). As the next step in our in silico analysis, experimentally determined concentrations of more than 50 metabolic end products, including flavonoids, monoterpenes, sesquiterpenes, acyl sugars, sterols, and cell wall carbohydrates, were integrated into Solyc_tVI-GT_2021v1 as part of a biomass equation. We also included the quantity of polyphenol oxidase, which accounts for ∼70% of the total protein in tVI-GTs (Yu et al., 1992) and therefore represents a major sink of amino acids in this cell type (Supplemental Methods and Data File S1). The E-Fmin algorithm (Song et al., 2014) was then employed to obtain a prediction for flux minimization, as a function of gene expression, through the metabolic network by employing the biomass equation as the constraint. Expectedly, flux was predicted to be particularly low toward flavonoid and monoterpene end products in the af mutant, whereas trichomes of the CHI1-complemented line (CHI9) were predicted to have a higher flux through the reactions that generate these compounds. Flux through many other pathways was also predicted to be lower in the af mutant relative to CHI9, which is likely explained by the fact that there is a lower demand for carbon flux through the entire network when flavonoid and terpenoid end product concentrations are substantially reduced or entirely eliminated from consideration. A notable exception to the general pattern of decreased flux through metabolic pathways in af GTs were the reactions of fatty acid biosynthesis, which were predicted by Solyc_tVI-GT_2021v1 to carry more flux (increased by 15%) in the af mutant relative to CHI9 (Supplemental Table S3). These results could not have been expected because fatty acid quantities had not been determined and integrated into the biomass equation. To further test the utility of our model, we quantified total fatty acids in isolated tVI-GTs. Indeed, the total fatty acid content in tVI-GTs was determined to be 1.8-fold higher in the af mutant compared to CHI9 (Figure 4B). This difference was primarily the result of increased accumulation of polyunsaturated fatty acids. For example, the levels of linoleic acid (18:2) and linolenic acid (18:3) were both increased approximately two-fold in af compared to CHI9 tVI-GTs (Figure 4B). Our RNA-seq data set showed that seven tomato homologs of functionally characterized fatty acid desaturases (FADs) in Arabidopsis thaliana (Li-Beisson et al., 2013) were expressed to varying levels in tVI-GTs. Among these expressed SlFAD genes, the abundance of transcripts derived from Solyc01g006430 was ∼10-fold greater than transcripts from all other SlFAD genes combined (Supplemental Dataset S1). The protein encoded by Solyc01g006430 (also known as SlFAD2-1) is an ortholog of the endoplasmic reticulum-localized FAD2 enzyme in Arabidopsis, which catalyzes the conversion of 18:1 to 18:2 (Okuley et al., 1994; Lee et al., 2020). Consistent with the higher level of 18:2 in trichomes of the af mutant, SlFAD2-1 transcripts accumulated to modestly but significantly higher levels in the mutant compared to CHI4 and CHI9 control lines (Supplemental Figure S5; Supplemental Dataset S1). This insight into the effect of CHI1 deficiency on fatty acid content led us to update Solyc_tVI-GT_2021 (second-generation model, version 2) to incorporate the reactions relevant for synthesizing unsaturated fatty acids (the first-generation model contained only reactions to produce a generic saturated fatty acid), which enabled flux predictions at higher resolution and accuracy for this pathway (Supplemental Table S3). Following these successful tests, we asked whether Solyc_tVI-GT_2021v2 could provide further insights into how the flavonoid and terpenoid pathways are coordinated in tVI-GTs. As mentioned above, the SPOT algorithm predicted an overall decreased flux through the flavonoid pathway in the af mutant relative to isogenic controls. We then asked how this might affect metabolite profiles. Our simulations indicated that all reactions of the flavonoid pathway in tVI-GTs of control lines carry flux (Supplemental Table S4) and, as previously established experimentally (Kang et al., 2014), rutin, kaempferol rhamnoside, quercetin trisaccharide, and 3-O-methylmyricetin would thus be expected to accumulate. The model also predicted that only the flavonoid pathway reactions upstream of CHI1, but not those downstream (see Figure 1), should carry flux in the af mutant (Supplemental Table S4). The loss of CHI1 activity in af therefore suggested that naringenin chalcone (an CHI1 substrate) would accumulate in tVI-GTs of the mutant. To test these predictions of the model, flavonoids were extracted from af mutant and CHI9 control plants and quantified by high performance liquid chromatography (HPLC)-MS. Notably, decreases of rutin (signature flavonoid of CHI9) and β-phellandrene (signature terpenoid of CHI9) in the af mutant (Figure 3, C and D) were accompanied by a large increase in the concentration of naringenin chalcone (Figure 4C), which agrees with model predictions. Correlation of flavonoid and terpenoid accumulation across genotypes To further investigate the relationship between flavonoid and terpenoid production, we employed anthocyanin-deficient mutants to test the hypothesis that bulk flavonoid deficiency correlates with low levels of terpenoids. Comparison of the af mutant and its isogenic control line (CHI9) revealed a strong positive correlation (Pearson correlation coefficient R = 0.992) between total flavonoid and terpenoid content based on leaf dip measurements (Figure 5A). Similar results (R = 0.994) were obtained for the anthocyanin reduced (are) mutant, which is impaired in the gene encoding flavonoid 3-hydroxylase (Figure 1; Maloney et al., 2014), and its wild-type parent VF36 (Figure 5B). Unlike the reduced density of tVI-GTs on af leaves (Rick et al., 1976; Kang et al., 2014), the flavonoid deficiency in leaves of the are mutant was not associated with altered trichome density (Supplemental Figure S6). Thus, the reduced terpenoid content in leaves of flavonoid-deficient mutants is likely attributed to factors other than trichome abundance. Figure 5 Open in new tabDownload slide Loss of flavonoids in tomato af and are mutants correlates with decreased terpenoid accumulation. A, Flavonoid-to-terpenoid correlation in the af mutant (gray circle) and CHI9 control (open circle). B, Flavonoid-to-terpenoid association in the are mutant (gray triangle) and VF36 parent (open triangle). C, Flavonoid-to-terpenoid correlation under various light conditions. Data were obtained for rutin (most abundant flavonoid in tVI-GTs) and β-phellandrene (most abundant terpenoid in tVI-GTs) from CHI4 (circle), CHI9 (triangle), and af (square) under three light conditions (100 μE m−2 s−1, green symbols; 200 μE m−2 s−1, blue symbols; 500 μE m−2 s−1, orange symbols). For all graphs, data points represent the average of concentration measurements from leaf dips, with bars corresponding to standard error (if invisible, error bars are smaller than symbol) (n = 5). Where appropriate, statistical significance of pairwise comparisons based on an unpaired Student’s t test is indicated by asterisks (*P ≤ 0.01; **P ≤ 0.001; ***P ≤ 0.001). Figure 5 Open in new tabDownload slide Loss of flavonoids in tomato af and are mutants correlates with decreased terpenoid accumulation. A, Flavonoid-to-terpenoid correlation in the af mutant (gray circle) and CHI9 control (open circle). B, Flavonoid-to-terpenoid association in the are mutant (gray triangle) and VF36 parent (open triangle). C, Flavonoid-to-terpenoid correlation under various light conditions. Data were obtained for rutin (most abundant flavonoid in tVI-GTs) and β-phellandrene (most abundant terpenoid in tVI-GTs) from CHI4 (circle), CHI9 (triangle), and af (square) under three light conditions (100 μE m−2 s−1, green symbols; 200 μE m−2 s−1, blue symbols; 500 μE m−2 s−1, orange symbols). For all graphs, data points represent the average of concentration measurements from leaf dips, with bars corresponding to standard error (if invisible, error bars are smaller than symbol) (n = 5). Where appropriate, statistical significance of pairwise comparisons based on an unpaired Student’s t test is indicated by asterisks (*P ≤ 0.01; **P ≤ 0.001; ***P ≤ 0.001). The reactivity and broad biological activities of chalcones (Sahu et al., 2012) raised the possibility that elevated levels of naringenin chalcone in the af mutant (Figure 4C) is a causal factor in the downregulation of the terpenoid pathway. To test this hypothesis, we analyzed the flavonoid composition in the af and are mutants. The af mutant had 12-fold lower total flavonoid levels in tVI-GTs compared to the CHI9 controls, with a concomitant relative increase in naringenin chalcone (70% of flavonoids in af, 0.7% in the CHI9 control) (Figure 5A;Supplemental Figure S7). The flavonoid content in tVI-GTs of the are mutant was also about 12-fold lower than in the wild-type control (VF36), but none of the early pathway intermediates accumulated to high levels (Figure 5B;Supplemental Figure S7). These results suggest that the correlation between total flavonoid and total terpenoid amounts across genotypes is not dependent on the accumulation of a particular intermediate (e.g. naringenin chalcone) in flavonoid biosynthetic mutants but rather results from a deficiency in bulk flavonoid levels. We further tested this hypothesis by investigating the flavonoid-to-terpenoid correlation in leaves from af, CHI4, and CHI9 plants grown at various light intensities, which is known to affect the levels of these two classes of compounds (Jaakola and Hohtola, 2010; Banerjee and Sharkey, 2014). We observed that the content of rutin and β-phellandrene, which are the most abundant flavonoid and terpenoid derivatives, respectively, in tVI-GTs, correlated (R = 0.93) under various light intensities and across genotypes (Figure 5C). Decreased flavonoid levels in mutants correlate with increased superoxide levels in tVI-GTs across genotypes Based on the observation that loss of CHI1 has pleiotropic effects on metabolism in tVI-GTs, we further explored the potential global effects of flavonoid deficiency. Plant flavonoids are often implicated in protection against UV-B light and ROS, as well as sunlight irradiance outside the UV spectrum (Pietta, 2000; Jenkins, 2009; Agati and Tattini, 2010). Consistent with a role for flavonoids in protection against photooxidative stress in tVI-GTs, GO categories related to DNA repair and response to radiation were among the biological processes most significantly overrepresented in the set of genes expressed to higher levels in the af mutant relative to CHI1-complemented controls (Table 1;Supplemental Table S2). We therefore evaluated the hypothesis that the observed loss of flavonoids in the af mutant might compromise processes involved in cellular protection of tVI-GTs. First, we compiled a list of previously reported UV-B-responsive genes (Brown et al., 2005; Oravecz et al., 2006; Brown and Jenkins, 2008) and identified the putative tomato orthologs using the BLASTX search algorithm. Of 132 genes in this list, 36 (27%) were more than two-fold upregulated in tVI-GTs of the af mutant compared to CHI9 (Supplemental Table S5). The same approach was employed to generate a consensus list of ROS-responsive genes in tomato (Lai et al., 2012). Of 134 genes in this list, 47 transcripts (37%) were more than two-fold upregulated in tVI-GTs of the af mutant (Supplemental Table S6). We next employed a fluorescence assay using the 2',7'-dichlorodihydrofluorescein diacetate indicator (Egan et al., 2007) to directly test the hypothesis that flavonoid deficiency in GTs is associated with increased production of ROS. The fluorescence signal from this reporter of active-oxygen species was 6.5-fold higher in isolated tVI-GTs of the af mutant compared to that observed in the CHI9 controls (P-value of 0.005; Supplemental Figure S8). ROS accumulation was 26% higher in tVI-GTs of the are mutant when compared to VF36 controls (P-value of 0.02). We also observed a negative correlation between flavonoid concentrations and ROS accumulation for both the af/CHI9 (Pearson correlation coefficient R = −0.66) and the are/VF36 comparisons (R = −0.49) (Figure 6, A and B). As an independent method for ROS evaluation, we used a staining assay that is based on the reaction of the superoxide anion radical (⁠ O2−• ⁠) with nitroblue tetrazolium (NBT; Liu et al., 2009). The results of these experiments showed that tVI-GTs of the af mutant, when compared to those of CHI9 controls, contained 6.2-fold higher concentrations of O2−• as an indicator for ROS-related stress. We also found that O2−• concentrations in tVI-GTs of the are mutant were 4.8-fold higher compared to those in VF36 controls (Supplemental Figure S8). A strong negative correlation was observed for flavonoid concentrations and NBT-detectable ROS for both the af/CHI9 (R = −0.97) and the are/VF36 comparisons (R = −0.77) (Figure 6, C and D). Importantly, there was no significant difference in the NBT staining of whole leaves of these mutants and the corresponding wild-type controls (Supplemental Figure S8), suggesting that increased ROS stress is specific for tVI-GTs of tomato mutants impaired in flavonoid biosynthesis. We also considered the possibility that increased O2−• accumulation in flavonoid-deficient tVI-GTs reflects decreased expression of ROS-quenching enzymes such as superoxide dismutase (SOD) and catalase (Sies, 2017). Although genes encoding multiple SOD and catalase isoforms were expressed in tVI-GTs, we found no evidence for differential expression of these genes in comparisons between af and CHI-complemented controls (Supplemental Table S7). These data indicate that the flavonoid deficiency in tVI-GTs strongly correlates both with elevated abundance of a cytotoxic ROS species (i.e. O2−• ⁠) and increased expression of stress-responsive genes. Figure 6 Open in new tabDownload slide Loss of flavonoids in tomato af and are mutants correlates with increased levels of ROS. A, Flavonoid-to-ROS correlation (measured as fluorescent signal generated by oxidation of the H2DCFDA reagent) in the af mutant (gray circle) and CHI9 control (open circle). B, Flavonoid-to-ROS correlation in the are mutant (gray triangle) and VF36 parent (open triangle). C, Flavonoid-to- O2−• correlation (measured as oxidized NBT) in the af mutant (gray circle) and CHI9 control (open circle). D, Flavonoid-to-ROS correlation in the are mutant (gray triangle) and VF36 parent (open triangle). Data points represent the average, with bars corresponding to standard error (if invisible, error bars are smaller than symbol) (n = 5). Statistical significance of pairwise comparisons based on a Student’s t tests is indicated by asterisks (*P ≤ 0.01; **P ≤ 0.001; ***P ≤ 0.001). Figure 6 Open in new tabDownload slide Loss of flavonoids in tomato af and are mutants correlates with increased levels of ROS. A, Flavonoid-to-ROS correlation (measured as fluorescent signal generated by oxidation of the H2DCFDA reagent) in the af mutant (gray circle) and CHI9 control (open circle). B, Flavonoid-to-ROS correlation in the are mutant (gray triangle) and VF36 parent (open triangle). C, Flavonoid-to- O2−• correlation (measured as oxidized NBT) in the af mutant (gray circle) and CHI9 control (open circle). D, Flavonoid-to-ROS correlation in the are mutant (gray triangle) and VF36 parent (open triangle). Data points represent the average, with bars corresponding to standard error (if invisible, error bars are smaller than symbol) (n = 5). Statistical significance of pairwise comparisons based on a Student’s t tests is indicated by asterisks (*P ≤ 0.01; **P ≤ 0.001; ***P ≤ 0.001). Discussion CHI1 is required for robust expression of terpenoid biosynthetic genes in tVI-GTs In addition to lacking anthocyanin pigments, the af mutant of tomato has a lower density of tVI-GTs, reduced emission of leaf volatiles, and increased susceptibility to coleopteran herbivores (Rick et al., 1976). The pleiotropic nature of this mutant led to the suggestion that af may define a regulatory factor rather than a flavonoid biosynthetic component (De Jong et al., 2004). Subsequent map-based cloning studies, however, demonstrated that Af encodes the flavonoid pathway enzyme CHI1, and confirmed that loss of CHI1 is responsible not only for the anthocyanin defect but also the deficiency in GT-borne terpenoid volatiles (Kang et al., 2014). In this study, we tested various hypotheses to explain the unexpected dependence of terpenoid production in GTs on CHI1. Transcriptomic and proteomic datasets acquired with isolated tVI-GTs of the af mutant and appropriate isogenic control lines showed that genes involved in terpenoid biosynthesis were generally downregulated in the af mutant, as was the abundance of several of the corresponding pathway enzymes. This finding demonstrates that the absence of CHI1 in tVI-GTs has a major negative effect on the expression of terpenoid biosynthetic components and thus provides an explanation for the low levels of volatile terpenoids observed in the mutant (Rick et al., 1976; Kang et al., 2014). Our data further suggest that the terpenoid deficiency in af plants may be specific for tVI-GTs and does not extend to other isoprenoid derivatives such as chlorophyll. Our conclusion that CHI1 is required for full expression of terpenoid-related genes in tVI-GTs is consistent with the emerging view that plant flavonoids modulate a wide variety of cellular processes, including gene expression (Grotewold, 2006; Pourcel et al., 2013). There is an increasing number of examples in which a protein serving a canonical function also performs a second, so-called moonlighting role (Jeffery, 2009). The finding that CHI1 is located both in the cytosol and in the nucleus of GT cells raised the possibility that the protein may have a moonlighting function related to the expression of flavonoid-related genes. However, our analysis of stable transgenic lines that express a functional CHI1 fusion protein (CHI1–GFP–GUS) that is excluded from the nucleus ruled out the hypothesis that a nuclear role of CHI1 is required for the accumulation of terpenoid volatiles in tVI-GTs. This finding supports the notion that terpenoid production depends on the canonical function of CHI1 as a cytosolic enzyme in the flavonoid pathway. Although nuclear localization of CHI has been reported for several plant species (Saslowsky et al., 2005; Dastmalchi and Dhaubhadel, 2015; this study), a potential role for CHI in this subcellular compartment, if any, remains unknown. We found that the total number of genes upregulated in af GTs was more than twice that of downregulated genes. A similar proportion of differentially upregulated versus downregulated genes was reported in CHI-deficient seedlings of Arabidopsis (Pourcel et al., 2013). Among the transcripts that overaccumulated in tVI-GTs of the af mutant were those encoding the flavonoid biosynthetic enzymes (Figure 1). This finding suggests that compensatory processes are activated in af plants in response to the lack of flavonoids in tVI-GTs. Similar observations were reported for mutants of Arabidopsis, where the impaired expression of a flavonoid biosynthetic gene resulted in a higher abundance of other transcripts or proteins related to flavonoid biosynthesis (Pelletier et al., 1999; Pourcel et al., 2013). The coordinated upregulation of flavonoid-associated genes in af GTs may reflect the increased activity of stress-responsive factors that modulate transcription. An attractive candidate for such a factor is the UV-B receptor UVR8, which exhibited increased mRNA and protein levels in af GTs. Model-based identification of altered flux distribution in the af mutant To inform strategies for experimental testing of hypotheses, we developed a genome-scale metabolic reconstruction of tomato tVI-GTs (Solyc_tVI-GT_2021) based on published bioanalytical information about this cell type. Incorporation of the gene expression and protein abundance data sets generated is this study allowed us to assess flux distribution through pathways of primary and specialized metabolism in tVI-GTs. Using the SPOT algorithm (Kim et al., 2016), our model expectedly predicted, for both transcriptomics and proteomics data, particularly high fluxes through the TCA cycle, sugar breakdown, and processing, the Calvin–Benson cycle, amino acid metabolism, and the pentose phosphate pathway. However, while simulations incorporating transcriptome data predicted high fluxes through flavonoid and terpenoid biosynthesis, these fluxes were returned as zero when incorporating proteomic (rather than transcriptomic) datasets. This discrepancy appears to reflect the generally low abundance of proteins involved in specialized metabolism. Thus, successful integration of proteomics data into modeling efforts will be aided by deep protein coverage across all relevant pathways. The development of testable hypotheses is an important benefit of using models for simulating flux distribution. In this study, we focused mainly on evaluating differences in flux distribution between tVI-GTs of the af mutant and those of isogenic controls. Our model correctly predicted reduced concentrations of total flavonoids and terpenoids in the af mutant, and also predicted increased accumulation of naringenin chalcone. Following up on this prediction, we expanded our analytical platform beyond the major flavonoid pathway end products and demonstrated that the naringenin chalcone intermediate indeed accumulated to high levels in tVI-GTs of the af mutant. This observation raised the possibility that naringenin chalcone might constitute a flavonoid signal with potential roles in crosstalk with the terpenoid pathway. However, a deficiency in volatile terpenoids was also observed in tVI-GTs of the are mutant that does not overaccumulate naringenin chalcone, which argues against this hypothesis (see below). Another example of an unexpected prediction of our model was that flux through fatty acid biosynthesis is elevated in the af mutant; subsequent bioanalytical assays confirmed that tVI-GTs of the af mutant contained higher concentrations of polyunsaturated fatty acids in comparison to trichomes isolated from control lines. The deep coverage of lipid-associated genes and proteins represented in our omics datasets provides an attractive starting point for further analysis of biochemical mechanisms that contribute to altered lipid homeostasis in tVI-GTs of flavonoid-deficient mutants. A high content in unsaturated fatty acids has been discussed as potentially playing a role in quenching ROS in GTs (Balcke et al., 2017). In accordance with this notion, we found that the elevated content of unsaturated fatty acids correlated with increased ROS stress in the af mutant. These examples from our study illustrate the utility of mathematical modeling for hypothesis generation. Once such hypotheses have been tested experimentally, the model can be updated to more accurately reflect measured values, which in turn allows new predictions. This iterative process has the potential to generate nontrivial predictions that provide novel insights into how metabolism is regulated in GTs. In the future, it will be important to expand the scope of our GT-specific models by assessing the tissue-level context of specialized metabolism, including processes and cell types involved in CO2 fixation and the transport of nitrogen and carbon (e.g. sucrose) sources into tVI-GTs. Recent studies demonstrating the prevalence of sugar import over photosynthetic CO2 fixation in tomato tVI-GTs provide an important advance toward addressing this challenge (Balcke et al., 2017). On the computational side, approaches for multi-scale modeling of plant metabolism have been described (de Oliveira Dal’Molin et al., 2010; Grafahrend-Belau et al., 2013; Bogart and Myers, 2016). It will also be desirable to further study temporal changes during the development of tVI-GTs or under different growing conditions. The first kinetic metabolic models for GTs were developed to quantitatively capture the dynamics of terpenoid biosynthesis in mint (Mentha) GTs (Rios-Estepa et al., 2008, 2010). A model of flavonoid biosynthesis has also been generated for flavonoid biosynthesis in tomato tissues (Groenenboom et al., 2013), which could be adjusted to the specifics of tVI-GT metabolism. More broadly, these concepts may guide future efforts to assess how the interaction of different cell types and tissues affects the production of GT-borne compounds for anti-herbivore defense and other roles in plant resilience to environmental stress. Flavonoid deficiency in tVI-GTs correlates with increased ROS levels Our finding that attenuated production of volatile terpenoids in tVI-GTs of the af mutant extends to the tomato are mutant provides insight into how the flavonoid and terpenoid pathways may be coordinated in this unique cell type. First, unlike the accumulation of naringenin chalcone in the af mutant, the exceedingly low concentrations of this intermediate in are tVI-GTs argue against a role for this compound as a terpenoid-modulating signal. Second, the coordinate reduction in flavonoids and terpenoids in the are mutant appears to exclude a direct role for CHI1 in promoting terpenoid production. Rather, these findings support an alternative hypothesis in which terpenoid production in tVI-GTs depends on accumulation of flavonoid pathway end products. How might the flavonoid content of GTs promote terpenoid production in these cells? It is well established that various flavonoids, including flavonols such as quercetin and kaempferol that are deficient in the af and are mutants, have potent antioxidant and ROS-scavenging properties (Heim et al., 2002). Of particular relevance to our findings, flavonols are known to modulate cellular redox pathways in various tissues of tomato, including root hairs, stomata, and pollen tubes (Muhlemann et al., 2018; Gayomba et al., 2017). We suggest that increased ROS production arising from a genetic defect in flavonoid biosynthesis indirectly impairs the biosynthesis of terpenoids. The downregulation of multiple terpenoid biosynthetic genes in af trichomes suggests the involvement of stress-responsive changes in transcription factor activity, but additional studies are needed to test this hypothesis. In addition to transcriptional control, it is also possible that the iron–sulfur clusters of the MEP pathway enzymes 1-hydroxy-2-methyl-2-butenyl 4-phosphate synthase (HDS) and 1-hydroxy-2-methyl-2-butenyl 4-phosphate reductase (HDR; see Figure 1) predispose these enzymes to inactivation by oxidative stress (Banerjee and Sharkey, 2014). Given the many ways in which ROS modulate cellular processes in plant cells (Mittler, 2017), additional studies are needed to test the hypothesis that elevated ROS production is linked to reduced expression of terpenoid biosynthetic genes. Our analysis of the af and are mutants showed that decreased terpenoid production is a major metabolic consequence of flavonoid deficiency in tVI-GTs. However, the unbiased transcriptome and proteome analysis performed with af GTs suggest that the metabolic and cellular effects of flavonoid deficiency are more pleiotropic. A particularly striking effect was the upregulation in af tVI-GTs of genes involved in DNA damage repair and responses to UV stress. This observation is consistent with a role for flavonoids in ROS detoxification and protection against photooxidative damage in a cell type that is exposed to high levels of solar irradiance. The detrimental effects of short-wavelength light on plant genome integrity (Hu et al., 2016) suggests that disruption of ROS homeostasis in af GTs results in DNA damage. A previous transcriptomic survey provided evidence that genes involved in several ROS-response pathways are expressed at high levels in tomato tVI-GTs (Balcke et al., 2017). Our own cell type-specific transcriptome data established that ROS-responsive genes were expressed at higher levels in tVI-GTs of the af mutant compared to control lines, which is consistent with the increased occurrence of superoxide anions in tVI-GTs of the af and are mutants. A negative correlation between flavonoid content and ROS production has also been described for tomato guard cells (Watkins et al., 2014, 2017). Although our data support a model in which flavonoid deficiency in tVI-GTs attenuates terpenoid biosynthesis by a mechanism involving increased ROS accumulation, we cannot exclude the possibility that other cellular processes in tVI-GTs depend on flavonoid production. For example, a recent study showed that kaempferol, which is produced in tVI-GTs (Kang et al., 2010b), contributes to the biosynthesis of the essential respiratory cofactor ubiquinone (Soubeyrand et al., 2018). Evidence for the importance of mitochondrial respiration as an energy source to drive metabolism in tVI-GTs (Balcke et al., 2017; Johnson et al., 2017; this study) suggests that reduced pools of kaempferol may compromise specialized metabolism, which should be addressed in future studies. This and other examples of the diverse functions for flavonoids (Taylor and Grotewold, 2005; Gayomba et al., 2017) highlight the general importance of this class of specialized metabolites in governing cellular functions under changing environmental conditions. The experimentally amenable tVI-GT of tomato provides an attractive single cell-type system in which to study the role of flavonoids in photoprotection and ROS scavenging, and how these fundamental cellular processes govern specialized metabolism and plant resilience to environmental stress. Materials and methods Plant material and growth conditions Seed for the tomato (S. lycopersicum) af mutant (LA1049) was obtained from the C.M. Rick Tomato Genetics Resource Center. Seed for the are mutant (LA1526) and its wild-type control (VF36) was generously provided by Dr. Gloria Muday. For comparisons to af, the previously described CHI4 and CHI9 complemented lines, which express the wild-type CHI1 gene under control of its native promoter (Kang et al., 2014), were used as isogenic wild-type controls. Plants were grown in Jiffy-7 peat pots (Hummert International, Earth City, MO, USA) and maintained in growth chambers under a 16-h light/8-h dark photoperiod, at 28°C, 60% humidity, and with a light intensity of 250 μE m−2 s−1. To analyze the effect of variable light intensity on metabolism in tVI-GTs, plants grown in the same growth chamber were placed at three discrete distances from the light source located at the top of the chamber: proximal to the light source, 500 μE m−2 s−1; distal to the light source, 100 μE m−2 s−1; and an intermediate distance to the light source, 200 μE m−2 s−1. Stable expression of CHI1 fusion proteins The effect of CHI1 localization on flavonoid and terpenoid biosynthesis in tVI-GTs was analyzed in stable transgenic lines of tomato expressing a CHI1–GFP or CHI1–GFP–GUS fusion protein. A PCR product containing the CHI1 cDNA under the control of its native promoter (pCHI1::CHI1) was amplified (Supplemental Table S8) from a complementation vector (Kang et al., 2014) and transferred to pK7FWG2 and pKGWFS7 binary vectors (Karimi et al., 2002). The resulting pCHI1::CHI1–GFP and pCHI1::CHI1–GFP–GUS constructs were introduced into Agrobacterium tumefaciens strain AGL-0 and used to transform af cotyledon explants as described previously (Li and Howe, 2001; Li et al., 2004; Gonzales-Vigil et al., 2011). Leaves from stably transformed plants grown in soil were harvested for mRNA, protein, and metabolite profiling (see below). The localization of fusion proteins was assessed using an Olympus FluoView FV1000 confocal laser scanning microscope configured on an Olympus IX81 inverted microscope and a 20x UPLSAPO objective (NA 0.75) (Olympus Corporation, Tokyo, Japan). Fluorescence for eGPF was excited using a 488 nm Argon gas laser (10% of 10 mW laser) and detected through a 500–545 nm band pass emission filter using a photomultiplier tube for detection (high voltage = 820). Fluorescence for propidium iodide (PI) was excited using a 559 nm solid state laser (10% of 10 mW) and detected through a 570–620 nm band pass filter using a photomultiplier tube for detection (high voltage = 572). Fluorescence emissions for the eGFP and PI signals were collected sequentially to prevent detection of any emission crossover between the two detection channels. Quantitation of anthocyanins, flavonoids, and terpenoid volatiles Leaf anthocyanin levels were determined as previously described (Campos et al., 2016). Flavonoid quantitation and profiling were performed using the leaf-dip method (Kang et al., 2010b). Extracts were analyzed on a Xevo G2-XS QTOF-MS system (Waters, Broadway, NY, USA) following separation on an Ascentis Express C18 HPLC column (10 cm × 2.1 mm, 2.0 μm; Sigma-Aldrich, St Louis, MO, USA) as described by Kang et al. (2010b). Flavonoids were monitored at m/z 271.069 (naringenin chalcone), 609.153 (rutin), 463.153 (quercetin hexoside), 447.101 (quercetin rhamnoside), and 741.196 (quercetin dihexosyl rhamnoside). Peak areas were normalized to either the number of collected tVI-GTs or to tissue weight, and the intensity of the signal for the internal standard (propyl-4-hydroxybenzoate, monitored at m/z = 179.079). Analytes were quantified based on calibration curves obtained with authentic standards, with the exception of quercetin dihexosyl rhamnoside, for which a normalized peak area was calculated. Terpenoids were extracted from the cell surface using a leaf-dip method (Kang et al., 2010b) and the resulting extracts were analyzed on a 5975B GC–MS system (Agilent Technologies, Santa Clara, CA, USA). Alternatively, metabolites were extracted from tVI-GTs that were manually hand-picked using a glass micropipette (Kang et al., 2010b). Separation of analytes was achieved by injection of the extract onto a VF-5MS column (50 m × 0.25 mm × 0.25 μm; Agilent Technologies, Santa Clara, CA, USA) with the following GC–MS settings: injector at 280°C, transfer line at 280°C, and helium carrier gas at 1 mL/min. The mass spectrometer operated in scan mode with 70 eV electron ionization. The initial oven temperature of 40°C was held for 2 min and then ramped in four steps: 40–90°C at 40°C/min, 90–110°C at 15°C/min, 110–250°C at 25°C/min, and 250–320°C at 40°C/min. The final temperature of 320°C was held for 2 min before returning to starting conditions. Peak areas were normalized to an internal standard (tetradecane) and either the number of collected tVI-GTs or the weight of the leaf disk. Analytes were quantified based on calibration curves obtained with authentic monoterpene and sesquiterpene standards. For commercially unavailable β-phellandrene and δ-elemene, α-phellandrene, and β-caryophyllene were used as surrogate standards, respectively. Quantitation of chlorophylls and carotenoids Two different extractions methods were performed using leaves of 4-week-old plants: (1) 10,000 tVI-GTs, hand-picked from leaves using a glass micropipette (Kang et al., 2010b) under a dissection microscope, were extracted with 200 μL MeOH and (2) leaf disks (5 mm diameter) obtained with a hole puncher were immediately submerged in 1 mL MeOH. Extracts were analyzed on a Xevo TQD QQQ-MS (Waters, Broadway, NY, USA) following separation on an Ascentis Express C18 HPLC column (5 cm × 2.1 mm, 2.7 μm; Sigma-Aldrich, St Louis, MO, USA). Analytes were separated at a flow rate of 0.3 mL min−1 using a linear gradient from 65% solvent A (acetonitrile:water = 3:2, v:v) and 35% solvent B (acetonitrile:isopropanol = 1:9, v:v) to 100% solvent B at 4 min, followed by 100% B for another 1 min. Chlorophyll a and b were quantified by monitoring the m/z 893→ 555 and m/z 907 → 569 transitions, respectively. Bulk chlorophyll and carotenoid levels in leaf samples were determined spectrophotometrically as previously described (Lichtenthaler and Wellburn, 1983). Quantitation of sterols and fatty acids Isolated tVI-GTs, obtained as described by McCaskill et al. (1992), were flash frozen in liquid nitrogen and then homogenized with mortar and pestle. The homogenate was transferred to a screw-cap glass vial containing 4 mL CHCl3/MeOH (2:1; v:v), with 100 mg/mL epi-cholesterol as internal standard, and analyte extraction was allowed to proceed at 75°C for 1 h. The supernatant was removed and evaporated to dryness (Genevac EZ-Bio, Ipswich, UK). The residue was resuspended in 2-mL potassium hydroxide in MeOH (6%; w:v) and sterol esters were saponified at 90°C for 1 h. After the solvent had cooled to 25°C, 2 mL hexane and 2 mL H2O were added and the mixture vortexed at the highest speed setting for 30 s (Vortex-Genie 2, Scientific Instruments, West Palm Beach, FL, USA). Extracts were centrifuged at 1,800g for 2 min to allow for phase separation. The upper organic phase was transferred to a 2 mL glass vial and evaporated to dryness. Immediately before analysis by GC–MS, N-methyl-N-(trimethylsilyl) trifluoroacetamide (50 mL) was added, and the tube vortexed as above for 20 s. The solution was transferred to a 100 mL glass insert and the mixture was incubated for 5 min at 25°C. The GC–MS analysis was performed according to Lange et al. (2015). Analyte quantitation was achieved based on calibration curves obtained with authentic, derivatized sterol standards. Fatty acids were analyzed as described previously (Browse et al., 1986). Briefly, tVI-GT homogenate prepared as described above was suspended in 1.5 mL of 2.5% (v:v) H2SO4 in MeOH, containing 200 mg/mL heptadecanoic acid as internal standard. Following incubation at 80°C for 1 h, 1.5 mL water and 0.4 mL n-hexanes were added, the mixtures was vortexed at the highest speed setting for 30 s (Vortex-Genie 2, Scientific Instruments, West Palm Beach, FL, USA), and then centrifuged (2,500g, 5 min, 25°C). The upper organic phase was transferred to a 2 mL glass vial and a 1-µL aliquot analyzed by GC–MS. Calibration curves obtained with authentic methyl ester standards were used to quantify derivatized fatty acids. Determination of nonextractable residues tVI-GT homogenate prepared as described above (McCaskill et al., 1992) was lyophilized for 12 h (Lyp-Lock 12, Labconco). Following the addition of 90% aqueous MeOH (1.5 mL), the mixture was sonicated in a water bath at 25°C for 30 min and then centrifuged for 2 min at 2,500g. The solvent was decanted and soluble constituents were removed from the residue by successive extractions with 1.5 mL MeOH (twice), 1 M sodium chloride, 1% (w/v) SDS, water (twice), EtOH, CHCl3:MeOH (1:1; v/v), and t-butyl methyl ether. The remaining pellet containing nonextractable residue was allowed to dry in a fume hood for 2 h, before recording the weight. This value was used to calculate the molar concentration of cellulose as dominant carbohydrate sink. Quantitation of polyphenol oxidase as amino acid sink Homogenates from isolated tVI-GTs (McCaskill et al., 1992) were prepared as described above with the exception that a proteinase inhibitor cocktail (Sigma-Aldrich, St Louis, MO, USA) was included in the isolation buffer. The homogenate was extracted with a buffer containing 100 mM Tris–HCl (pH 6.8), 150 mM NaCl, 10% (v:v) glycerol, 4% (v:v) sodium dodecyl sulphate (SDS), 200-mM dithiothreitol (DTT), mixed briefly by vortexing, and sonicated in a water bath for three 30-s intervals (with 30 s cooling on ice between intervals). Following centrifugation at 14,000g for 20 min at 4°C, the supernatant was transferred to 2-mL Eppendorf tubes containing 300-mL water, 400-mL MeOH, and 100-mL CHCl3, and the mixture was vortexed for 5 min at high speed on a multi-tube vortexer (VWR Scientific, Radnor, PA, USA). Samples were then centrifuged at 14,000g for 5 min at 4°C. The lower organic phase was removed and discarded, and 400-mL MeOH was added to the remaining protein extract. Following mixing as above, samples were centrifuged at 14,000g for 5 min at 4°C. The resulting pellet was dried and protein content determined using the Bradford assay. Equal quantities of protein were then processed by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS–PAGE). The band for polyphenol oxidase at ∼70 kDa constituted, based on the relative intensity (MultiImage II, AlphaInnotech, San Leandro, CA, USA), 60% of the total protein in tVI-GTs, which allowed us to calculate its concentration in the extract. The known amino acid composition of polyphenol oxidase (Thipyapong and Steffens, 1997) was then used to calculate the quantity of each amino acid. Global transcript profiling of type VI trichomes tVI-GTs were isolated from leaves of 5-week-old plants as described previously (Schilmiller et al., 2010), using four independent sets of plants per genotype as experimental replicates. Total RNA was extracted from frozen trichomes using RNeasy Plant and RNase-free DNase kits (Qiagen, Germantown, MD, USA) according to the manufacturer’s instructions. RNA quality was monitored with a BioAnalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). Sequencing libraries were prepared from total RNA with the TruSeq RNA Library Prep Kit (Illumina, San Diego, CA, USA) and RNA-seq was performed using a HiSeq 2500 instrument (Illumina, San Diego, CA, USA) for paired-end 100-bp reads. Sequence reads were trimmed using the Trimmomatic software (Bolger et al., 2014) to eliminate adaptor sequences and low-quality reads. The remaining reads were mapped to the S. lycopersicum L. reference genome sequence (ITAG 2.5 genome) using Tophat2 software (Kim et al., 2013). This pipeline applied gene annotations based on ITAG 2.4 gene models provided from Sol Genomics Network (https://solgenomics.net). The mapped reads were then aligned using SAMtools (Li et al., 2009) and further analyzed by HTSeq (Anders et al., 2015). Genes expressed differentially between the af mutant and isogenic control lines were identified using three independent programs (EdgeR, DESeq2, and TCC). The threshold for differential expression was defined as log2FC > 1 or less than −1, with an adjusted P  < 0.05. GO categories were assessed as described by Martel et al. (2015). Enrichment analysis of GO terms employed Fisher’s exact test (adjusted for false discovery rate) and was implemented using the Blast2GO program (Conesa and Götz, 2008). RT-qPCR Total RNA (100 ng; obtained from isolated tVI-GTs as by Schilmiller et al., 2010) was converted to first-strand cDNA using the High-Capacity cDNA Reverse Transcription Kit (ThermoFisher Scientific, Foster City, CA, USA) and diluted to 0.1 ng RNA equivalents per microliter. Amplification was monitored using a 7500 Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) with the Power SYBR Green PCR master mix and gene-specific primers (listed in Supplemental Table S8). Quantitation was based on calibration curves constructed with a nucleic acid sample of known concentration. Expression level of the specific genes was calculated relative to an Actin gene (Solyc10g080500). Standard curves were constructed with serial dilutions of RNA from a CHI4 sample. Quantitative proteomic analysis tVI-GTs were isolated from leaves of 5-week-old af and CHI9 plants as described previously (Schilmiller et al., 2010). The trichome isolation was repeated three times using independent sets of plants as a source of biological replicates. Soluble proteins were extracted from frozen GTs using Buffer 1, which contained 100-mM Tris–HCl (pH 6.8), 150-mM NaCl, 10% (v:v) glycerol, 4% (v:v) SDS, 200-mM DTT, and the Complete Mini EDTA-free proteinase inhibitor cocktail (Sigma-Aldrich, St Louis, MO, USA). Proteins were then precipitated by adding four volumes of MeOH and CHCl3 as previously described (Schilmiller et al., 2010). Precipitated protein was resuspended in Buffer 2, which was identical to Buffer 1 with the exception that it contained 1% (w:v) SDS. Total protein was quantified using the Protein Assay kit (Bio-Rad, Hercules, CA, USA) based on calibration curves obtained with bovine serum albumin. Protein samples were digested with trypsin using the Filter-Aided Sample Preparation protocol (Wiśniewski et al., 2009) and spin ultrafiltration units with a nominal molecular weight cutoff of 30,000 Da. The resulting peptides were de-salted using custom-made reversed-phase tips (Stop and Go Extraction) and dried by vacuum centrifugation. Samples were then processed with the TMTsixplex isobaric label reagent set (ThermoFisher Scientific, Rockford, IL, USA) according to manufacturer’s instructions. After labeling, all six samples were combined and dried by vacuum centrifugation. The sample was re-suspended in 0.3% formic acid, de-salted as above and dried again by vacuum centrifugation. The combined peptide samples were separated over a pH gradient (pH 3–10) into 12 fractions using an Agilent OffGel 3100 fractionator (Agilent Technologies, St Clara, CA, USA) according to manufacturer’s instructions. Each fraction was de-salted as above, dried by vacuum centrifugation, and frozen at −20°C until use. Dried fractions were reconstituted in 50-µL 2% acetonitrile/0.1% trifluoroacetic acid, injected onto an Acclaim PepMap 100 C18 trapping column (0.1 mm × 20 mm, 5 µm, 100 Å; ThermoFisher Scientific), which was washed with Buffer A (0.1% formic acid) for 5 min on an EASY-nLC 1000 HPLC system (ThermoFisher Scientific). Bound peptides were then eluted onto an Acclaim PepMap RSLC C18 resolving column (0.075 mm × 150 mm, 2 µm, 100 Å; ThermoFisher Scientific) and separated with a gradient from 95% buffer A/5% buffer B (99.9% acetonitrile, 0.1% formic acid) to 70% Buffer A/30% Buffer B in 84 min, a short ramp to 100% B at 85 min, and a final hold at 100% B for 10 min. Eluted peptides were sprayed into the ion source of a Q-Exactive Hybrid Quadrupole-Orbitrap MS (ThermoFisher Scientific, Waltham, MA, USA) and spectra acquired with 70,000 resolution at m/z 200. The top ten ions in each survey scan were subjected to automatic higher energy collision-induced dissociation with fragment spectra acquired at 35,000 resolution. Conversion of MS/MS (tandem MS) spectra to peak lists and quantitation of tandem mass tag (TMT) reporter ions was done using the Proteome Discoverer software (version 1.4.1.14). Peptide-to-spectrum matching was performed using the Sequest HT, Mascot and X!Tandem search algorithms against the ITAG 2.4 tomato protein sequence database appended with common laboratory contaminants (downloaded from www.solgenomics.org and www.thegpm.org, respectively). The output from all three search algorithms was then combined and analyzed using the Scaffold Q + S software (version 4.4.5) (Proteome Software, Portland, OR, USA) to probabilistically validate protein identifications and quantification. Assignments validated using the Scaffold 1% false discovery rate confidence filter were considered true. Quantitation of ROS Quantitation of total ROS levels in tomato tVI-GTs was performed according to Egan et al. (2007). Briefly, 100 mg isolated tVI-GTs, obtained as described by McCaskill et al. (1992), were homogenized in 1.0 mL of a 10 mM Tris–HCl buffer (adjusted to pH 7.2). The homogenate was then centrifuged at 12,000g for 20 min at 4°C. A 100-µL aliquot of the clear supernatant was incubated with 10 µL of 1 mM H2DCFDA reagent (Life Technologies, Oregon, USA) in the dark for 10 min. Fluorescence was then measured (excitation at 492, emission detected at 525 nm) in a spectrophotometer (BioTek Instruments Synergy H1, Vermont, USA). Protein concentration of the cleared supernatant was determined using a Bradford assay. For the quantitation of ROS caused by accumulation of O2−• ⁠, leaf tissue (20 g fresh weight per plant) was removed and vacuum-infiltrated with 100 mM potassium phosphate buffer (pH 7.3) containing 0.3 mM NBT at 1.1 bar for 20 min. Leaflets were then imbibed in this buffer for an additional four h in the dark at 25°C. Control plants received the same treatment but with buffer lacking NBT. tVI-GTs were then isolated and homogenized as described by McCaskill et al. (1992). With minor modifications, a previously described protocol (Liu et al., 2009) was used to quantify NBT as an indicator of superoxide. GT homogenate was suspended in 0.5 mL 2 M KOH and sonicated in a water bath for 1 h at 25°C. CHCl3 (0.5 mL) was added, the mixture shaken at 4°C for 10 min, and phases were separated by centrifugation at 2,500g for 5 min. The organic layer was transferred to a new glass vial and extracted twice with CHCl3. The solvent was removed in vacuum (Genevac EZ-Bio, Ipswich, UK) from the combined organic phases. The dried residue was suspended in 0.8-mL dimethylsulfoxide and the absorbance measured at 680 nm and 730 nm for quantification of monoformazan and diformazan, respectively. Metabolic reconstruction and flux balance analysis The development of a genome-scale model of metabolism in tVI-GTs, and the subsequent modeling efforts to better understand flux distribution in these specialized anatomical structures, are described in detail in Supplemental Methods and Data File S1. Statistical analysis Transcriptomic and proteomic data sets were analyzed as described above. Metabolite correlation analyses were performed using the Dunnett’s and Tukey–Kramer methods with R software (version 3.3.0). Principal component and Pearson’s correlation coefficient analyses for all datasets were performed with the “prcomp” and “cor” programs within the R software environment. Phylogenetic analysis Amino acid sequences of Arabidopsis and tomato FADs were obtained from The Arabidopsis Information Resource (TAIR, https://www.arabidopsis.org/) and the Sol Genomics Network (SGN, https://solgenomics.net/) genomic databases. Phylogenetic trees were constructed using the Neighbor-Joining method with 1,000 replicates for bootstrap evaluation. Accession numbers RNA-seq data for analysis of gene expression in tVI-GTs of the af mutant and CHI1-complemented CHI4 and CHI19 lines were deposited in the NCBI Gene Expression Omnibus (GEO) as accession GSE118065. Supplemental data The following materials are available in the online version of this article. Supplemental Figure S1. Hierarchical clustering of samples from independent transcriptomic and proteomic experiments. Supplemental Figure S2. RT-q PCR analysis of expression levels for selected terpenoid biosynthetic genes in the MEP and MVA pathways. Supplemental Figure S3. GC–MS measurement of specific terpenoids in extracts obtained from hand-collected tVI-GTs of the af mutant and two isogenic control lines. Supplemental Figure S4. Phenotypic characterization of transgenic af plants expressing a CHI1–GFP–GUS fusion protein under the control of the native CHI1 promoter. Supplemental Figure S5. Transcript abundance of FAD genes in tVI-GTs. Supplemental Figure S6. Type VI trichome density on leaves of the tomato are mutant. Supplemental Figure S7. Flavonoid composition in tVI-GTs of various tomato genotypes. Supplemental Figure S8. Accumulation of ROS in various tomato genotypes. Supplemental Table S1. Number of DEGs in tVI-GTs of the af mutant. Supplemental Table S2. GO enrichment analysis of genes that are differentially regulated in tVI-GTs of the af mutant. Supplemental Table S3. Stoichiometric matrix for modeling metabolism in tVI-GTs and flux predictions using the EF-min and SPOT algorithms. Supplemental Table S4. SPOT flux predictions through reactions of flavonoid biosynthesis in the tomato af mutant and CHI9 control plants. Supplemental Table S5. Genes reported in the literature to respond to UV-B treatment and expression levels of putative tomato orthologs in tVI-GTs. Supplemental Table S6. Genes reported in the literature to respond to ROS and expression levels of putative tomato orthologs in tVI-GTs. Supplemental Table S7. Expression of SOD and catalase genes in tVI-GTs of the af mutant and control lines. Supplemental Table S8. Oligonucleotide primers used in this study. Supplemental Methods and Data File S1. Detailed description of development of models used for metabolic reconstruction and flux balance analysis. Supplemental Dataset S1. Transcript abundance based on RNA-seq analysis of tVI-GTs of af mutant and CHI1-complemented control lines. Supplemental Dataset S2. Proteomics data acquired from tVI-GTs of af mutant and CHI9 control line. B.M.L. and G.A.H. conceived the project and supervised the research; K.S., J.J.Z., B.M.L., B.S.A., and G.A.H. designed the experiments; K.S. J.J.Z., and B.S.A. performed experiments; K.S., J.J.Z., B.S.A., B.M.L., and G.A.H. analyzed the data. K.S., J.J.Z., B.M.L., and G.A.H. wrote the paper. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plphys/pages/general-instructions) is Gregg A. Howe ([email protected]). Acknowledgments We acknowledge Katerina Lay, Michael Das, Onyinye Nnamdi-Nwosu, and George Kapali for technical assistance during the course of this study. We thank Dr Gloria Muday (Wake Forest University) for providing seeds of the tomato are mutant, and the C.M. Rick Tomato Genetics Resource Center at the University of California, Davis, for providing other tomato seed stocks. We thank Jim Klug and Cody Keilen (MSU) as well as Julie Thayer, Sue Vogtman, and Devon Thrasher (WSU) for operating and maintaining plant growth facilities. We thank Curtis Wilkerson and Douglas Whitten in the MSU Research Technology Support Facility (RTSF) for valuable advice with proteomic analyses, as well as Dan Jones and Tony Schilmiller in the RTSF Mass Spectrometry and Metabolomics Core Facilities for their support with metabolite analyses. We are grateful to Melinda Frame for assistance with confocal microscopy and Guo-Qing Song in the MSU Plant Biotechnology Resource and Outreach Center for performing tomato transformations. We are grateful for access to equipment at the M.J. Murdock Metabolomics Laboratory and the Kamiak High-Performance Computing Cluster at Washington State University (WSU). Funding This study was supported by the National Science Foundation grants to G.A.H. [award 1456864] and B.M.L. [award 1457127], and a Japan Society for the Promotion of Science Fellowship for Young Scientists to K.S. [award 24·841]. G.A.H. also acknowledges support from the Michigan AgBioResearch Project MICL02278 from Michigan State University. This publication was made possible in part by a predoctoral training award to B.S.A. from Grant T32-GM110523 from the National Institute of General Medical Sciences of the National Institutes of Health. Conflict of interest statement. J.J.Z. and B.M.L. have financial interests in Dewey Scientific LLC. This company did not sponsor the current study and was not involved in the generation or interpretation of the data presented here. References Agati G , Tattini M ( 2010 ) Multiple functional roles of flavonoids in photoprotection . 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This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact [email protected] © The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists.

Frusciante, Sarah; Demurtas, Olivia Costantina; Sulli, Maria; Mini, Paola; Aprea, Giuseppe; Diretto, Gianfranco; Karcher, Daniel; Bock, Ralph; Giuliano, Giovanni

doi: 10.1093/plphys/kiab583pmid: 34919714

Abstract Annatto (Bixa orellana) is a perennial shrub native to the Americas, and bixin, derived from its seeds, is a methoxylated apocarotenoid used as a food and cosmetic colorant. Two previous reports claimed to have isolated the carotenoid cleavage dioxygenase (CCD) responsible for the production of the putative precursor of bixin, the C24 apocarotenal bixin dialdehyde. We re-assessed the activity of six Bixa CCDs and found that none of them produced substantial amounts of bixin dialdehyde in Escherichia coli. Unexpectedly, BoCCD4-3 cleaved different carotenoids (lycopene, β-carotene, and zeaxanthin) to yield the C20 apocarotenal crocetin dialdehyde, the known precursor of crocins, which are glycosylated apocarotenoids accumulated in saffron stigmas. BoCCD4-3 lacks a recognizable transit peptide but localized to plastids, the main site of carotenoid accumulation in plant cells. Expression of BoCCD4-3 in Nicotiana benthamiana leaves (transient expression), tobacco (Nicotiana tabacum) leaves (chloroplast transformation, under the control of a synthetic riboswitch), and in conjunction with a saffron crocetin glycosyl transferase, in tomato (Solanum lycopersicum) fruits (nuclear transformation) led to high levels of crocin accumulation, reaching the highest levels (>100 µg/g dry weight) in tomato fruits, which also showed a crocin profile similar to that found in saffron, with highly glycosylated crocins as major compounds. Thus, while the bixin biosynthesis pathway remains unresolved, BoCCD4-3 can be used for the metabolic engineering of crocins in a wide range of different plant tissues. Introduction Carotenoids are C40 isoprenoid compounds acting as photoreceptors and photoprotectants in leaves, as precursors of signaling molecules such as ABA and strigolactones, and as pigments in fruits and flowers. Their cleavage products, apocarotenoids, are synthesized through the action of carotenoid cleavage dioxygenases (CCDs) endowed with different cleavage specificities and acting on different substrates (Giuliano et al., 2003a; Ahrazem et al., 2016). In plants, apocarotenoids have functions in both intra- and inter-plant signaling and as aromas and pigments (Giuliano et al., 2003a; Walter and Strack, 2011; Hou et al., 2016; D’Alessandro and Havaux, 2019; Moreno et al., 2020). Apocarotenoid pigments are often accumulated at very high levels in non-photosynthetic tissues of different groups of flowering plants. Examples include citraurin, a C30 apocarotenal from tangerine (Citrus clementina) peel (Rodrigo et al., 2013); crocins, a complex mixture of glucosyl esters of the C20 apocarotenoid crocetin, accumulated in saffron (Crocus sativus stigmas) (Frusciante et al., 2014), Buddleja (Buddleja davidii) flowers (Ahrazem et al., 2017), and gardenia (Gardenia jasminoides) fruits (Xu et al., 2020; Figure 1, A) and bixin, a methoxy ester of the C24 apocarotenoid norbixin produced by annatto (Bixa orellana) seeds (Giuliano et al., 2003b; Rivera-Madrid et al., 2016; Figure 1, B). Figure 1 Open in new tabDownload slide Apocarotenoid biosynthesis pathways in different plant species. A, Crocin biosynthesis pathways in C. sativus, B. davidii, and G. jasminoides. B, Proposed bixin biosynthesis pathway in B. orellana. C, Maximum-likelihood phylogenetic tree of CCDs from A. thaliana (At), B. orellana (Bo), B. davidii (Bd), C. clementina (Cc), C. sativus (Cs), C. ancyrensis (Ca), G. jasminoides (Gj), S. lycopersicum (Sl), O. sativa (Os), and Synechocystis sp. (Syn). The bootstrap consensus tree inferred from 500 replicates is shown. The percentage of replicate trees in which the associated CCDs clustered together in the bootstrap test is shown next to the branches and branches corresponding to partitions reproduced in less than 50% bootstrap replicates are collapsed. The carotenoid substrates, position of cleavage, and bootstrap values are indicated. Protein sequences are shown in Supplemental Figure S1. Bixin and crocins are highly prized as food colorants, and, in the case of crocins, also for their potential health applications (Alavizadeh and Hosseinzadeh, 2014; Rivera-Madrid et al., 2016). Crocins confer the characteristic red color to the saffron spice, while its flavor and aroma are mainly due to the apocarotenoids picrocrocin and safranal. Crocins combine high antioxidant activity, water solubility, and very low toxicity and have been claimed to possess antioxidant, antitumoral, antidepressant, and retina-protecting properties (Falsini et al., 2010; Alavizadeh and Hosseinzadeh, 2014). Due to the high number of chiral centers, chemical synthesis of crocins is very difficult to achieve and the natural sources of these compounds are the saffron spice (composed of the hand-picked stigmas of saffron and dried fruits of Gardenia). Both sources contain 8%–10% (w/w) crocins. Although Gardenia fruits are used in traditional Chinese medicine, they are not permitted for use as food additives in the USA or the EU, due to their high content of geniposide and their consequent hepatotoxicity (Yamano et al., 1988). The first step in crocin biosynthesis consists of the symmetric 7,8/7′,8′ cleavage of a carotenoid substrate by a CCD. The substrate and CCD are, respectively, zeaxanthin and CsCCD2 in C. sativus stigmas (Frusciante et al., 2014); zeaxanthin and BdCCD4.1/BdCCD4.3 in B. davidii (Ahrazem et al., 2017); lycopene, beta-carotene and zeaxanthin, and GjCCD4a in G. jasminoides (Xu et al., 2020; Figure 1). The cleavage produces crocetin dialdehyde, which is dehydrogenated and then glycosylated by dedicated aldehyde dehydrogenases and UDP-glucosyl transferases (Demurtas et al., 2018; Xu et al., 2020). In the case of saffron, the cleavage dehydrogenation, and glycosylation steps occur in the plastid, endoplasmic reticulum, and cytoplasm, respectively, while crocins are transported in the vacuole by ABC transporters (Demurtas et al., 2018). Bixin has been reported to be generated by the symmetric 5,6/5′,6′ cleavage of lycopene by Lycopene Cleavage Dioxygenase (BoLCD), a member of the CCD4 subfamily (Bouvier et al., 2003; Figure 1). However, BoLCD is truncated at the N-terminus, losing one of the seven beta-propeller blades shown to be essential for CCD activity, and is not found in a recent transcriptome survey of B. orellana seeds and leaves (Cardenas-Conejo et al., 2015). In the same survey, a series of transcripts encoding CCD1 and CCD4 enzymes were identified. Three of them (BoCCD4-1, BoCCD4-3, and BoCCD1-1) had expression patterns consistent with bixin accumulation during seed development. In a later study by the same group, BoCCD1-1 and BoCCD4-3 were reported to cleave symmetrically lycopene at the 5,6/5′,6′ positions, yielding bixin dialdehyde (Carballo-Uicab et al., 2019). To re-assess the role of different B. orellana CCDs in apocarotenoid biosynthesis, we expressed six BoCCD genes in Escherichia coli strains producing various carotenoids. To our surprise, none of the CCD enzymes was able to produce bixin dialdehyde in these conditions, while one of the proteins, BoCCD4-3, was able to cleave lycopene, β-carotene, and zeaxanthin at the 7,8/7′,8′ positions, producing crocetin dialdehyde. Heterologous expression of BoCCD4-3 in Nicotiana leaves and tomato (Solanum lycopersicum) fruits resulted in the production of high levels of crocins, confirming its suitability for the biotechnological production of these highly prized compounds. Results Identification and functional characterization of BoCCDs The B. orellana transcriptome (Cardenas-Conejo et al., 2015) was re-analyzed for the presence of members of the CCD1 and CCD4 sub-families, using sequence identity with known CCDs. Four CCD1s (BoCCD1-1, BoCCD1-2, BoCCD1-3, and BoCCD1-4) and four CCD4s (BoCCD4-1, BoCCD4-2, BoCCD4-3, and BoCCD4-4) were identified, following the nomenclature of (Cardenas-Conejo et al. 2015; Supplemental Figure S1). No homolog of Crocus CsCCD2 or Bixa BoLCD was found in this search. A phylogenetic tree of CCD protein sequences from several plant species and from Synechocystis spp. was inferred using the maximum-likelihood method (Figure 1, C). The BoCCD1 enzymes were closely related to each other. The closest enzymes to BoCCD1 enzymes were Arabidopsis and rice CCD1 which cleave carotenoids at the 9,10/9′,10′ positions, and at the 9,10/9′,10′, 9,10/7′,8′, and 9,10/5′,6′positions, respectively (Schwartz et al., 2001; Ilg et al., 2009). The BoCCD4 enzymes formed a cluster with Citrus CCD4b1, which cleaves zeaxanthin asymmetrically at the 7,8 position to yield citraurin, the main apocarotenoid in tangerine peel (Rodrigo et al., 2013). Six BoCCDs were cloned in the pTHIO-DAN1 expression vector, affording arabinose-inducible expression (Frusciante et al., 2014), and transformed into E. coli cells able to synthesize lycopene, β-carotene, or zeaxanthin, as described previously (Frusciante et al., 2014; Supplemental Figure S2). After induction of BoCCD expression with arabinose 0.2% (w/v) for 16 h at 20°C, carotenoids and apocarotenoid aldehydes were extracted and analyzed by Liquid Chromatography–Photodiode Array Spectroscopy–High Resolution Mass Spectrometry (LC–PDA–HRMS) (Frusciante et al., 2014). Four of the six BoCCDs tested did not show a major cleavage activity on any of the carotenoid substrates (Figure 2 and Supplemental Figures S3, S4). BoCCD4-3 was able to cleave all three substrates, producing a compound having a chromatographic mobility and an online spectrum reminiscent of crocetin dialdehyde, the C20 precursor of crocin biosynthesis in saffron stigmas (Frusciante et al., 2014). The second CCD, BoCCD1-3, was able to almost completely degrade all three carotenoid substrates without forming a major cleavage product (Figure 2 and Supplemental Figures S3, S4). Figure 2 Open in new tabDownload slide Lack of bixin dialdehyde production in bacterio by different BoCCDs. HPLC–PDA–HRMS profiles of E. coli cells producing lycopene (L) and expressing various Bixa carotenoid dioxygenases, induced for 16 h at 20°C with arabinose (0.2%, w/v). Cells expressing the pTHIO-DAN empty vector are indicated with C-. A, Maximum absorbance UV–Vis chromatogram in the 250–700 nm range. Online spectra of the main peaks (L, lycopene and CD?, putative crocetin dialdehyde) are shown in the insets. µAU, Micro-absorbance units. B, Ion chromatograms of lycopene (L, M+H+: 537.4442 ± 2 ppm) extracted with the Xcalibur Software (Thermo Fisher Scientific). C, Ion chromatograms of Bixin Dialdehyde (BD, M+H+: 349.2162 ± 2 ppm) were extracted with the Xcalibur Software (Thermo Fisher Scientific). We confirmed the identity of the putative crocetin dialdehyde peak produced by BoCCD4-3 by full MS, MS2 analysis, and comigration with an authentic CD standard (Figure 3). This result is unexpected, since crocetin dialdehyde is generated by cleavage at the 7,8/7′,8′ positions. To verify the presence of trace amounts of additional compounds, derived by single or double cleavage at the 5,6, 7,8, or 9,10 positions, the corresponding masses were extracted from chromatograms of E. coli cells and expressing either the empty pTHIO-DAN vector or different BoCCDs. The results (Supplemental Table S1) confirm that, in our experimental conditions, the only cleavage produced in high amounts (ion intensity 107) is crocetin dialdehyde in cells expressing BoCCD4-3. Its intermediate products (apo-8′-lycopenal, β-apo-8′-carotenal, and β-citraurin) also appear at much lower ion intensities (103–104) in the same cells. In cells expressing BoCCD1-1 or BoCCD1-3, we found ions corresponding to 5′/6′ and/or 9′/10′ cleavage activities, but at very low ion intensities (103–104). A compound with an experimental mass compatible with that of bixin dialdehyde (349.2161) appears at very low ion intensity (104) in all cells, including those expressing the empty pTHIO-DAN vector. We conclude that this compound is not a specific product of the BoCCD activity, but rather an endogenous molecule present in trace amounts in E. coli extracts. Given the unavailability of an authentic standard, we are not able to determine its chemical structure. Figure 3 Open in new tabDownload slide BoCCD4-3 cleaves completely lycopene, b-carotene, and zeaxanthin to produce crocetin dialdehyde in E. coli. A–C, LC–HRMS chromatograms of E. coli extracts accumulating lycopene (A), β-carotene (B), and zeaxanthin (C) after induction with 0.2% of arabinose (w/v) for 16 h at 20°C. The ion of crocetin dialdehyde CD (M+H)+: 297.1846 was extracted. Cells expressing BoCCD4-3 accumulate a compound that has both the accurate mass and the chromatographic mobility of the crocetin dialdehyde standard. D, MS spectrum and (inset) MS/MS spectrum of the crocetin dialdehyde peak produced by E. coli cells expressing BoCCD4-3. E, Cleavage reactions catalyzed by BoCCD4-3. It was reported previously (Carballo-Uicab et al., 2019) that BoCCD1-1 and BoCCD4-3 are able to cleave lycopene in E. coli, with the formation of very low levels of bixin dialdehyde. We find several inconsistencies in those data: (1) the mass spectrum of the compound shown in Figure 8 of that paper does not resemble bixin dialdehyde: the experimental mass of both the main peak (m/z 348.2507) and the +1 peak (m/z 349.2404) is too distant from that of the bixin dialdehyde M + H adduct (m/z 349.2162), given the fact that they were produced with a high-resolution instrument; in the absence of an authentic bixin dialdehyde standard or of MS/MS data, its identification as bixin dialdehyde is dubious; (2) the full MS scans shown in Figure 8 span from m/z 340 to 550, so the M + H adduct of crocetin dialdehyde (m/z 297.1846) produced by BoCCD4-3 would have been missed. Subcellular localization of BoCCD4-3 in N. benthamiana leaves Despite the fact that its carotenoid substrates are presumably localized in plastids, BoCCD4-3 does not carry a recognizable transit peptide at its N-terminus (Supplemental Figure S5). Since up to 12% of chloroplast-localized proteins lack a recognizable transit peptide (Armbruster et al., 2009), we decided to verify experimentally the subcellular localization of BoCCD4-3 through C-terminal fusion to an enhanced Green Fluorescent Protein (GFP) and transient expression in N. benthamiana leaves, as described previously (Demurtas et al., 2018). The results (Figure 4) clearly show that the BoCCD4-3:GFP localizes to plastids like CsCCD2:GFP, which carries a recognizable transit peptide. However, while the latter shows a speckle-like localization to specific areas close to the plastid envelope, the BoCCD4-3:GFP fluorescence localizes to the stroma. This is suggested both by the diffuse labeling that surrounds the thylakoids, and by the presence of green fluorescent tubular-like protrusions extending into the cytoplasm, which probably represent stromules (Delfosse et al., 2015). This difference between the localization of the two proteins is not due to the presence versus absence of a plastid transit peptide, since the N-terminal fusion of the CsCCD2 transit peptide to BoCCD4-3 does not substantially change its sub-plastidial localization. We therefore hypothesize that this difference is due to a structural difference in the mature CsCCD2 and BoCCD4-3 proteins. Indeed, while both proteins carry a central hydrophobic domain, only the CsCCD2 domain has a considerable transmembrane probability (Supplemental Figure S5). Figure 4 Open in new tabDownload slide Subcellular localization of BoCCD4-3 in N. benthamiana leaves. A, Confocal images of N. benthamiana leaves expressing (top to bottom): GFP, BoCCD4c:GFP, TP:BoCCD4c:GFP (BoCCD4c fused to the CsCCD2 transit peptide), and CsCCD2:GFP fusion proteins. Red (chlorophyll fluorescence), green (GFP fluorescence), and merged (overlap of chlorophyll and GFP fluorescence) are shown. The unfused GFP protein shows the typical cytoplasmic and nuclear localization. Both BoCCD4-3:GFP and CsCCD2:GFP localize to plastids. Scale bars: 7 μm. Arrows in the BoCCD4c:GFP composite image point at green fluorescent protrusions of the plastid stroma (stromules). B, 3D reconstruction of red and green fluorescence in plastids expressing BoCCD4-3:GFP, TP:BoCCD4-3:GFP, and CsCCD2:GFP. The latter localizes to plastid-associated speckles (Demurtas et al., 2018). Scale bars: 7 μm. Heterologous expression of CsCCD2 and BoCCD4-3 in Nicotiana leaves and tomato fruits results in the production of apocarotenoids found in saffron stigmas BoCCD4-3 shows a much broader substrate specificity than CsCCD2, cleaving lycopene, beta-carotene, and zeaxanthin. To compare the capacity of the two dioxygenases to cleave carotenoids in planta, we expressed them transiently in N. benthamiana leaves under the control of the CaMV 35S promoter. Four constructs were used, expressing different proteins: BoCCD4-3; TP:BoCCD4-3 (BoCCD4-3 fused to the CsCCD2 transit peptide); CsCCD2; and CsCCD2short (CsCCD2 lacking its transit peptide). The four constructs were agroinfiltrated in N. benthamiana leaves and leaf extracts were analyzed for the presence of compounds found in Bixa seeds or Crocus stigmas. No bixin-related compounds were detected in agroinfiltrated leaves, while several cis- and trans-isomers of crocetin and crocins were clearly detectable (Figure 5;Supplemental Figure S6, A and Supplemental Table S2). For both CsCCD2 and BoCCD2, the presence of the CCD2 transit peptide boosted apocarotenoid production, with the efficiency of the four constructs being TP:BoCCD4-3 > CsCCD2 > BoCCD4-3 > CsCCD2short. The higher efficiency of BoCCD4-3 is not surprising, given the fact that it is able to cleave β-carotene, which is abundant in leaves, while the CCD2 substrate, zeaxanthin, is present only in trace amounts. Interestingly, the CCD2 lacking the transit peptide gave some residual activity, in keeping with the crocetin accumulation detected in maize endosperm agroinfiltrated with the transit peptide-less CCD2 (Frusciante et al., 2014). The apocarotenoid profiles generated by the two CCDs differed also qualitatively: in leaves agroinfiltrated with CsCCD2 variants, crocetin and crocins with ≤2 glucose groups (Supplemental Figure S7) were, respectively, ≈14% and 70%–73% of total apocarotenoids, while crocins with ≥3 glucose groups were 12%–15%. The percentage of the latter increased up to 21% in leaves agroinfiltrated with BoCCD4-3, indicating that the latter CCD was able to induce synthesis of more highly glycosylated compounds (Supplemental Table S2). No crocins with ≥4 glucose groups were detected. Figure 5 Open in new tabDownload slide Crocetin/crocin production in N. benthamiana leaves transiently expressing CsCCD2 or BoCCD4-3. A, Representative LC–HRMS chromatograms of the extracted accurate mass of crocetin (M+H+ 329.1747), generated from crocin fragmentation, in leaves expressing BoCCD4-3, TP:BoCCD4-3, CsCCD2, and CsCCD2short (CsCCD2 lacking the TP) and C- (empty vector). B, Quantification of crocetin and crocins in agroinfiltrated leaves. Data are the avg ± sd of three independent pools of agroinfiltrated plants. See Supplemental Table S3 for quantitative data. HPLC–PDA chromatograms are shown in Supplemental Figure S6, A. Additionally, in leaves agroinfiltrated with CsCCD2, we observed the production of trace amounts of picrocrocin (Supplemental Table S2), an abundant apocarotenoid in saffron stigmas, which was not detected in BoCCD4-3-agroinfiltrated leaves. In saffron, picrocrocin is produced by the action of a specific glycosyltransferase named UGT709G1 (Diretto et al., 2019). However, its appearance in CsCCD2-transformed leaves is not surprising, since (1) picrocrocin is thought to originate from beta-hydroxy-cyclocitral (Figure 1), which is generated by the cleavage of zeaxanthin, the likely in planta substrate of CsCCD2, while the major substrate of BoCCD4-3 in leaves is beta-carotene, whose cleavage yields beta-cyclocitral, (2) the presence of endogenous glycosyltransferases in N. benthamiana leaves, able to synthesize picrocrocin, has been described previously (Demurtas et al., 2018; Marti et al., 2020). Next, we expressed the BoCCD4-3 protein in plastids by generating stable chloroplast-transformed (transplastomic) tobacco (Nicotiana tabacum) plants under the control of a theophylline-inducible, synthetic riboswitch. To this end, the coding sequence of BoCCD4 was codon optimized according to the preferred codon usage in the tobacco plastid genome, and the coding sequence was fused to the T7 RNA polymerase promoter and the 5′UTR from gene10 of bacteriophage T7. The expression cassette was integrated into a plastid transformation vector (Supplemental Figure S8, A) and used to transform, by biolistic transformation, a previously generated transplastomic tobacco line harboring a theophylline-inducible T7 RNA polymerase (T7RNAP; Emadpour et al., 2015; Supplemental Figure S8, A). Primary transplastomic clones were obtained by selection for spectinomycin resistance conferred by the chimeric aadA gene in the transformation vector (Svab and Maliga, 1993). Homoplasmic lines were selected by several additional rounds of regeneration on spectinomycin-containing regeneration medium (Bock, 2001; 2015) and presence of a uniform population of transformed plastid genomes was confirmed by Southern blot analysis (Supplemental Figure S8, B and C). Transplastomic BoCCD4-3 expressing plants showed a pale, slightly yellow/orange leaf phenotype when grown on synthetic medium, even in the absence of theophylline induction (Figure 6). When transferred to soil, the transplastomic plants were not able to survive, suggesting that they are unable to grow autotrophically. LC–PDA–HRMS analysis of the transplastomic plants confirmed the presence in leaves of crocetin and several crocins, including trans-crocin 4, which was the most highly glycosylated crocin and was not detected in N. benthamiana transient expression (Figure 6; Supplemental Figure S6, B and Supplemental Table S3). The apocarotenoid profile of tobacco transplastomic lines was more similar to that of saffron, with crocins with ≥3 glucose groups being ≈32% of the total apocarotenoids (Figure 6 and Supplemental Table S3). Figure 6 Open in new tabDownload slide Crocetin/crocin production in transplastomic tobacco plants expressing BoCCD4-3. A, Visual phenotype of transplastomic lines #2 and #11 cultured in vitro. B, Quantitation of crocetin and crocins in young leaves from lines #2 and #11. Data are the avg ± sd of three biological replicates. See Supplemental Table S4 for quantitative data. HPLC–PDA chromatograms are shown in Supplemental Figure S6, B. Finally, we performed combinatorial transformation of tomato (cv Microtom) plants with 35S:TP:BoCCD4-3 and 35S:CsUGT74AD1. The rationale behind this experiment is that tomato fruits accumulate very high levels of lycopene, a BoCCD4-3 substrate, while CsUGT74AD1 specifically catalyzes the glycosylation of crocetin (Demurtas et al., 2018). Two out of eight regenerated lines displayed a distinctive orange-colored fruit phenotype. These lines were characterized by PCR for the presence of the transgenes, and both were found to contain both the 35S:TP:BoCCD4-3 and 35S:CsUGT74AD1 transgenes (Supplemental Figure S9). Fruits from the T1 progeny displaying the orange phenotype were harvested and the crocin/crocetin content was determined. The crocin content of transgenic fruits was the highest of all the heterologous expression experiments (Figure 7; Supplemental Figure S6, C and Supplemental Table S4). Also, the qualitative profile of transgenic fruits was very different from that of agroinfected or transplastomic Nicotiana leaves, with crocetin representing ≤0.5% and crocins with ≥3 glucose groups representing ≈80% of total apocarotenoids, a percentage very similar to that of saffron extract (≈84%, Supplemental Table S4). Figure 7 Open in new tabDownload slide Crocetin/crocin production in transgenic Microtom fruits expressing TP:BoCCD4-3 and CsUGT74AD1. A, Phenotypes of Microtom T1 transgenic fruits expressing TP:BoCCD4-3 + CsUGT74AD1. B, Quantification of crocetin and crocins in ripe fruits. Data are the avg ± sd of three biological replicates (fruits) from each line. HPLC–PDA chromatograms are shown in Supplemental Figure S6, C. To verify if CCD4-3 was able to catalyze the synthesis of additional apocarotenoids in planta, we searched the chromatograms from transformed plant tissues for ions of known apocarotenoids. The only additional apocarotenoid was a compound with the brute formula C30H40O2, corresponding to citraurin, generated by a single 7,8 cleavage of lutein or zeaxanthin, respectively, and found in transformed tomato fruits (Supplemental Table S5). Discussion Convergent evolution of crocin biosynthesis in plants Crocins are synthesized by plants as distant as Crocus (Iridaceae), Buddleja (Scrophulariaceae), and Gardenia (Rubiaceae). The evolutionary distance of these genera and the fact that different subfamilies of CCDs are involved in crocin biosynthesis (CCD2 in Crocus and CCD4 in Buddleja) strongly suggest that the crocin pathway has appeared multiple times during plant evolution, through parallel or convergent evolution (Stern, 2013). Convergent evolution is not a rare phenomenon in plant specialized metabolism: gene duplication and neofunctionalization of different N-methyltransferases has given rise to caffeine biosynthesis independently in coffee (Rubiaceae) and tea (Theaceae) (Denoeud et al., 2014). The CCD1/CCD2 phylogeny shown in Figure 1, C is consistent with the accepted taxonomy: CCD1s from B. orellana, Arabidopsis thaliana, and B. davidii (dicots) are more similar to each other than to CCD1 of C. sativus (monocots). All enzymatically characterized CCDs within this clade possess 9,10/9′,10′ cleavage activity, with the exception of CCD2s, which instead perform 7,8/7′,8′ cleavage. The latter are well separated from CCD1s, confirming that they belong to a different subfamily of probable monophyletic origin (Frusciante et al., 2014). In contrast, the CCD4 clade shows multiple subclades, comprising enzymes from both monocots and dicots and with different cleavage specificities. The closest relative to BoCCD4s is CCD4b1 from C. clementina, which possesses 7,8 cleavage activity (Rodrigo et al., 2013). The CCD4s from B. davidii and from G. jasminoides, which possess 7,8/7′8′ cleavage activity, belong to subclades that are distant from that of BoCCD4.3. This suggests that the capacity to synthesize crocetin dialdehyde has evolved multiple times in the CCD4 subfamily, that is, is of polyphyletic origin. The recent sequencing of the G. jasminoides genome indicated that the crocin-synthesizing CCD in this species, GjCCD4a, evolved through recent tandem duplication of a CCD4 gene that is still in single copy in the closely related genus Coffea (Xu et al., 2020). None of the other CCD4s in Coffea or Gardenia exhibit 7,8/7′,8′ cleavage. Metabolic engineering of crocin biosynthesis In bacterio, BoCCD4-3 cleaves lycopene and, with lower efficiency β-carotene and zeaxanthin to yield crocetin dialdehyde. This broad substrate specificity is similar to the one exhibited by the Gardenia enzyme (GjCCD4a) (Xu et al., 2020) and contrasts with those of the Crocus and Buddleja enzymes (CsCCD2, BdCCD4.1, and BdCCD4.3) which cleave only zeaxanthin (Frusciante et al., 2014; Ahrazem et al., 2017; Marti et al., 2020). We took advantage of this broad substrate specificity to express BoCCD4-3 in Nicotiana leaves (containing mainly lutein and beta-carotene) and in tomato fruits that instead accumulate lycopene and beta-carotene. As observed before for CsCCD2, overexpression in Nicotiana leaves results in the accumulation of crocins (Demurtas et al., 2018; Marti et al., 2020). This result suggests that Nicotiana express endogenous ALDH and UGT enzymes catalyzing the dehydrogenation of crocetin dialdehyde and the glycosylation of crocetin. Since leaves are known to contain glycosylated apocarotenoids (Latari et al., 2015; Mi et al., 2019), it is possible that the enzymes mediating their synthesis are promiscuous and that they are recruited for crocetin/crocin biosynthesis in Nicotiana leaves expressing the appropriate CCD enzyme. A corollary of this hypothesis is that the synthesis of a given glycosylated apocarotenoid in leaves is largely controlled at the cleavage step. In agreement with this corollary, we observe the formation of picrocrocin in leaves overexpressing CsCCD2, which cleaves zeaxanthin to form the picrocrocin precursor 3-OH-β-cyclocitral. In contrast, BoCCD4-3 expression does not produce picrocrocin, despite the higher activity demonstrated by this enzyme in crocin formation. The likely BoCCD4-3 substrate in leaves is β-carotene, both because BoCCD4-3 cleaves β-carotene more efficiently than zeaxanthin in E. coli, and because β-carotene is much more abundant than zeaxanthin in N. benthamiana leaves. Cleavage of β-carotene would generate β-cyclocitral, which is not a picrocrocin precursor. Thus, the ratio of crocins (the main saffron color) to picrocrocin (the main flavor) can be manipulated through the expression of CCDs with different substrate specificities in tissues with different carotenoid composition. BoCCD4-3 is plastid-localized in N. benthamiana leaves, despite the lack of a recognizable transit peptide. While CcCCD2 localizes to speckle-like structures, probably associated with the plastid envelope, BoCCD4-3 seems to have a stromal localization. This type of localization is not changed by the N-terminal fusion of the CsCCD2 transit peptide to BoCCD4-3, suggesting that the different sub-organellar localization of the two enzymes is due to determinants found in the mature proteins. Indeed, while both proteins have a central hydrophobic domain, CsCCD2 has a much higher transmembrane probability than BoCCD4-3 (Supplemental Figure S5). A “short” CsCCD2 lacking the transit peptide, and therefore showing a cytoplasmic localization (Frusciante, 2014, p. 31), was able to produce low levels of crocins when expressed in N. benthamiana leaves. This result suggests that CsCCD2 is able to cleave carotenoids localized in the outer plastid envelope (Markwell et al., 1992), and may explain the mechanism through which CCD1 enzymes, that are closely related to CsCCD2 and lack an identifiable transit peptide, access their carotenoid substrates. Transplastomic expression of BoCCD4-3 results in the production of crocins, indicating that the enzyme expressed from the chloroplast genome is biologically active and able to access its carotenoid substrate. This is in agreement with previous work reporting the transplastomic expression of carotenoid biosynthesis genes in tomato and tobacco (Wurbs et al., 2007; Hasunuma et al., 2008). Given the broad substrate specificity of BoCCD4-3 and the fact that carotenoids are essential for photosynthetic activity, we expressed the gene under the control of the phage T7 promoter and transformed the expression cassette into a transplastomic recipient line harboring a T7 RNA polymerase gene controlled by a theophylline-inducible synthetic riboswitch (Verhounig et al., 2010; Emadpour et al., 2015). However, even in the absence of theophylline induction, transplastomic plants exhibited bleaching and were unable to grow on soil, indicating that some expression is occurring due to leakiness of the riboswitch. Leaky expression was confirmed by the accumulation of crocins in young leaves of these plants. Transgenic expression in tomato of BoCCD4-3 in combination with CsUGT74AD1, a C. sativus UGT mediating the glycosylation of crocetin (Demurtas et al., 2018), results in fruits containing high levels (>100 µg/g dry weight [DW]) of crocins. Although the quantity of these crocins is still 1,000-fold lower than in saffron, their qualitative profile resembles very much that of the spice, in which crocins with ≥3 glucose groups represent >80% of total apocarotenoids. Glycosylation has a profound influence on the water solubility and hence on the bioavailability of crocins, and therefore the ability to modulate its levels is an important prerequisite for their production through metabolic engineering. Tomatoes present many ideal characteristics for the production of carotenoid-derived nutritional supplements. They contain up to 1 mg/g DW of the BoCCD4-3 substrate, lycopene, and, contrary to tobacco leaves and Gardenia fruits, they are generally regarded as safe (GRAS). Tomato-derived products are an inexpensive source of nutraceuticals (mainly lycopene), are available both as spray-dried powder and as oleoresins, present very low toxicity even at very high doses, and a good stability and bioavailability profile, as exemplified by their use for the supplementation of aquaculture feed with transgenically produced ketocarotenoids (Nogueira et al., 2017). Worldwide tomato production stands at 180 M tons annually and thus, at the crocin levels reported here, converting 10% of this production to crocin-producing tomatoes would yield 180 tons of crocins, that is, 60-fold more than the total worldwide saffron production. In the transgenic lines reported here, only about 10% of fruit lycopene is converted into crocins, and some adverse leaf phenotypes are observed in the highest expressing lines. Thus, we believe that fruit crocin levels can be further improved through a combination of mutations increasing lycopene content, the use of different transgenes derived from Gardenia and saffron for boosting crocin biosynthesis and storage, and the use of fruit-specific promoters for avoiding adverse leaf phenotypes. Conclusions and outlook Our results open two major questions related to apocarotenoid biosynthesis in B. orellana: (1) what are the “in vivo” products, if any, of BoCCD4a and (2) what is the pathway that mediates bixin biosynthesis? Regarding the first question, apocarotenoids derived from crocetin dialdehyde have not been reported in B. orellana seeds (Mercadante et al., 1997, 1999; Chiste et al., 2011). However, LC–HRMS analysis of non-polar extracts from B. orellana leaves and seeds revealed several peaks with an accurate mass compatible with that of apocarotenoids generated from a single or double 7/8 cleavage, including α- or β-citraurin and dimethyl-8,8′-diapocarotene-8,8′-dioate (dimethylcrocetin) (Supplemental Table S6 and Supplemental Figure S10). The full characterization of the structure of these compounds is outside the scope of this paper. The second question relates to the bixin biosynthesis pathway in B. orellana, which remains unresolved. Two previous reports (Bouvier et al., 2003; Carballo-Uicab et al., 2019) claimed to have identified the Bixa CCD responsible for the formation of bixin dialdehyde. However, the BoLCD identified by Bouvier et al. (2003) is not present in Bixa transcriptome data (Cardenas-Conejo et al., 2015), and the data presented in this paper do not confirm the findings of Carballo-Uicab et al. (2019) that the main product of BoCCD1-3 and BoCCD4-3 is bixin dialdehyde. It is attractive to speculate that bixin biosynthesis is mediated by the action of a hitherto non-characterized CCD, or that, similar to what was discovered in strigolactone biosynthesis (Alder et al., 2012), an additional isomerase-like enzyme is needed for the synthesis of this apocarotenoid pigment. Materials and methods Bioinformatic analyses An annatto (B. orellana) transcriptome (Cardenas-Conejo et al., 2015) was searched for contigs bearing homology to CCD1 and CCD4 enzymes using BLAST (Altschul et al., 1990). The obtained sequences were very similar, or identical, to those deposited in GeneBank by Rivera-Madrid and collaborators (Supplemental Table S1). For expression analysis, we used NGS data from Cardenas-Conejo et al. (2015) (accession number SRX1117606) and from the 1,000 Plants Project (Matasci et al., 2014) (accession number ERX2099513). Raw reads were cleaned from adapter sequences, low-quality residues, and very short length reads using Cutadapt with 20 as quality cut-off (Martin, 2011). Reads were aligned to the B. orellana transcriptome (Cardenas-Conejo et al., 2015) with BWA (Li and Durbin, 2009) with default options and expression values were estimated with Cufflinks (Trapnell et al., 2012). Phylogenetic trees were generated using MEGA X version 10.2.2 with the maximum-likelihood method (Kumar et al., 2016). Predictions of subcellular localization and hydrophobic profiles were obtained using the TargetP1.1 server (Emanuelsson et al., 2000) and the TMHMM 2.0 software (www.cbs.dtu.dk/services/TMHMM), respectively. Gene synthesis and cloning Gene sequences were synthesized by the Invitrogen GeneArt Gene Synthesis service (www.thermofisher.com/us/en/home/life-science/cloning/gene-synthesis/geneart-gene-synthesis.html) and cloned into pTHIO-Dan1 vector using Gibson assembly (Gibson et al., 2009). The vector was digested with the XbaI and KpnI restriction enzymes and the CCD CDS were amplified with Q5 High Fidelity DNA polymerase (NEB, Cat No. M0491S). Purified PCR fragments were then assembled obtaining pTHIO-Dan1-CCD1-1, pTHIO-Dan1-CCD1-3, pTHIO-Dan1-CCD1-4, pTHIO-Dan1-CCD4-2, pTHIO-Dan1-CCD4-3, and pTHIO-Dan1-CCD4-4 (Supplemental Figure S2) and re-sequenced before transformation into E. coli cells able to synthesize lycopene, β-carotene, or zeaxanthin (Frusciante et al., 2014). Primers used are listed in Supplemental Table S7. For in planta expression, BoCCD4-3 was C-terminally fused to the enhanced GFP (eGFP) in the pBI-eGFP vector using Gibson assembly, generating the pBI-CCD4.3:eGFP construct. To obtain TP:BoCCD4-3, the CsCCD2 transit peptide was isolated by PCR and cloned by Gibson assembly into pBI:BoCCD4-3:eGFP, generating pBI:TP:CCD4.3:eGFP. pBI:BoCCD4-3 and pBI:TP:CCD4.3 were also generated through Gibson assembly. CsUGT4AD1 was amplified by PCR from pTHIO-UGT4AD1 (Demurtas et al., 2018) and cloned in pBI digested by XbaI through Gibson assembly. All primers used are listed in Supplemental Table S7. The maps of the plasmids are shown in Supplemental Figure S2. All plasmids were checked by resequencing before transformation in Agrobacterium tumefaciens strain C58C1 (for agroinfiltration of Nicotiana benthamiana leaves) or EHA105 (for tomato [S. lycopersicum] transformation). Expression in E. coli Escherichia coli strains engineering to accumulate lycopene, β-carotene, and zeaxanthin were transformed with pTHIO-Dan1 vectors carrying the various CCDs (Frusciante et al., 2014). Overnight cultures were inoculated in 25 mL of LB medium containing 50 µg/mL of ampicillin and 25 µg/mL of kanamycin, grown at 37°C to an OD600 of 0.7 and induced with arabinose 0.2% (w/v) for 16 h at 20°C. Cells were pelleted and extracted as described previously (Frusciante et al., 2014) with slight modifications: pellets were resuspended with 4 mL of acetone, lysed on ice by 5 sonication at 10 Hz output (10 s each), and centrifuged at 6,000g for 10 min. The supernatant was dried and dissolved in 200 μL of ethyl acetate 100%, then centrifuged at 18,000g for 20 min and subjected to HPLC–PDA–HRMS analysis. Expression in N. benthamiana Nicotiana benthamiana were agroinfiltrated as described previously (Demurtas et al., 2018), using the RK19 silencing suppressor (Francisco et al., 2013). After 4 d, leaves were analyzed by a confocal laser-scanning microscope (Olympus FV1000 and Zeiss LSM880) as reported before (Demurtas et al., 2018). Images were acquired using a 40× oil immersion objective (N.A. 1,30) with optical zooming 1× or 3× and analyzed with the ImageJ software. For each construct, at least three N. benthamiana plants were infiltrated and observed. Representative images, including 3D reconstructions are shown in Figure 4. For functional characterization studies, leaves were infiltrated with C58C1 cells containing the eGFP-less constructs and pBI121 as negative control (Frusciante, 2014, p. 31), or with pBI121:CsCCD2 (Demurtas et al., 2018) and the RK19 silencing suppressor. To minimize variability, plants of the same size and developmental stage (4 weeks old) were selected, infiltrating only apical leaves. For each construct, leaves of four different plants were infiltrated, collected as a pool 4 d post infiltration, and stored at −80°C. Each experiment was repeated with three different batches of plants. Construction of transgenic and transplastomic plants Transgenic tomato plants were obtained as described previously (Qiu et al., 2007), using a mixture of three Agrobacterium (EHA105) strains, transformed with plasmids pBI:TP:BoCCD4-3 and pBI:CsUGT74AD1, respectively (Supplemental Figure S2). Double transformants were selected through PCR with the following primers: CCD4-3_for_1515/UGT74AD1_for_1080 and NosT_rev161 (Supplemental Table S5). An expression cassette consisting of the T7 RNA polymerase promoter, the T7 gene10 leader sequence, the coding sequence of BoCCD4, and the terminator of the chloroplast atpA gene from Chlamydomonas reinhardtii was synthesized (GeneCust, Boynes, France). The coding sequence of CCD4.3 was codon optimized according to the codon usage of the tobacco (N. tabacum) chloroplast genome. The CCD4.3 expression cassette was integrated into a chloroplast transformation vector that contains a cloned fragment of the tobacco chloroplast genome from position 37,657 to 40,209 (Yukawa et al., 2005), to facilitate integration into the plastid genome by homologous recombination. The transformation vector contained a chimeric aadA gene, driven by the psaA promoter and the atpB 3′-UTR from Chlamydomonas, and was introduced into plastids of a previously produced recipient line that harbors a T7 RNA polymerase gene in the intergenic spacer between the ycf3 and psaA genes. Plastid transformation was performed by the biolistic protocol followed by selection for spectinomycin resistance (conferred by the aadA marker). Briefly, young leaves from aseptically grown tobacco (N. tabacum cv. Petit Havana) plants were bombarded with vector DNA-coated 0.6 μm gold particles with a helium-driven particle gun (PDS-1000/He, equipped with the Hepta adaptor; Bio-Rad). Primary spectinomycin-resistant lines were selected on regeneration medium containing 500 mg/L spectinomycin (Svab and Maliga, 1993). Several independent transplastomic lines were selected, and homoplasmic transplastomic lines were obtained by two to three additional cycles of plant regeneration in the presence of spectinomycin (500 mg/L). Plant DNA was isolated from frozen leaf tissue by a cetyltrimethylammoniumbromide (CTAB)-based method (Doyle and Doyle, 1990). Five micrograms of total DNA were digested with the restriction enzyme BamHI, separated by gel electrophoresis in 0.8% (w/v) agarose gels, and transferred onto Hybond nylon membranes (GE Healthcare) by capillary blotting. A 550-bp PCR product derived from the psaB coding region in the plastid DNA was amplified with the primer pair P7247 (5′-CCCAGAAAGAGGCTGGCCC-3′) and P7244 (5′-CCCAAGGGGCGGGAACTGC-3′), subsequently gel purified, labeled with [α-32P]dCTP by random priming, and used as an RFLP probe in Southern blot hybridizations to verify plastid transformation and to assess homoplasmy. LC–HRMS analysis HPLC separation, ionization, and MS parameters for the E. coli extracts were as described previously (Frusciante et al., 2014). Metabolites were identified on the basis of their absorbance spectra, accurate masses, and by comparison with authentic standards. For analysis of crocetin and crocins, Nicotiana leaves or tomato fruits were freeze-dried, and 10 mg of powder was ground with tungsten beads at 20 Hz for 2 min in a mixer mill (MM 400, Retsch). Samples were suspended in 750 µL of 75% (v/v) cold methanol spiked with 0.5 µg/mL formononetin (Sigma–Aldrich, Cat. No. 47752-25MG-F). Metabolites were extracted at room temperature by continuous agitation for 30 min in MM 400 at 20 Hz. Samples were centrifuged at 20,000g for 20 min, the supernatant was collected, filtered with HPLC filter tubes (0.45 µm pore size), and subjected to LC–PDA–HRMS analysis as described (Demurtas et al., 2018). Crocins were quantified by LC–MS, integrating the areas of M + H adduct plus those of fragmentation ions corresponding to deglucosylation products (due to in-source loss of glucose moieties). A calibration curve was established using a purified trans-crocin 3 standard obtained by preparative HPLC as described previously (Demurtas et al., 2019). The data were interpolated with crocin signal intensities in leaves and fruits, and metabolite quantification was performed using peak areas normalized to leaf DW. Non-polar analyses of B. orellana leaves were carried out as reported previously (Sulli et al., 2017). Non-polar metabolites were extracted from 2 mg of lyophilized, homogeneous leaf tissue using 100% (v/v) methanol, chloroform, 50 mM Tris–HCl (1:2:1), spiked with 10 μg/mL dl-α-tocopherol acetate as internal standard. The organic extracts were dried with a Speed Vac concentrator and the residue was resuspended in ethyl acetate (100 μL). LC parameters were as reported previously (Sulli et al., 2017). Atmospheric pressure chemical ionization (APCI) parameters were as follows: nitrogen was used as sheath and auxiliary gas, set to 20 and 10 units, respectively. The vaporizer temperature was 300°C, the capillary temperature was 250°C, the discharge current was set to 5.5 μA, and S-lens RF level was set at 50. The acquisition was carried out in the 110/1600 m/z scan range, with the following parameters: resolution 70,000, microscan 1, AGC target 1e6, and maximum injection time 50. Full scan MS with data-dependent MS/MS fragmentation in both positive and negative ionization mode was used for metabolite identification. Accession numbers Sequence data from this article can be found in the GenBank under accession numbers of the BoCCDs are BK011288 (BoCCD1-1), KT359019 (BoCCD1-2), BK011289 (BoCCD1-3), BK011290 (BoCCD1-4), BK011291 (BoCCD4-1), BK011292 (BoCCD4-2), BK011293 (BoCCD4-3), and BK011294 (BoCCD4-4). Supplemental data The following materials are available in the online version of this article. Supplemental Figure S1. Accession numbers of plant CCDs and protein sequences of BoCCDs used to construct the phylogenetic tree in Figure 1. Supplemental Figure S2. Constructs used for expression in bacteria or plants. Supplemental Figure S3. In bacterio assays of different BoCCDs expressed in a β-carotene-accumulating E. coli strain. Supplemental Figure S4. In bacterio assays of different BoCCDs expressed in a zeaxanthin-accumulating E. coli strain. Supplemental Figure S5. Prediction of subcellular localization and of hydrophobic profiles of BoCCD4-3 and CsCCD2 proteins. Supplemental Figure S6. HPLC–PDA analysis shows chromatographic profiles recorded at maximum absorbance of main apocarotenoids (440 nm) of plant tissues expressing BoCCD4-3. Supplemental Figure S7. Structures of different saffron crocins. Supplemental Figure S8. Generation of transplastomic plants expressing BoCCD4 from the plastid genome. Supplemental Figure S9. PCR screening of regenerated T1 plants. Supplemental Figure S10. Structures of non-polar apocarotenoids identified in B. orellana leaves and seeds. Supplemental Table S1. Extraction of diagnostic ion masses produced in E. coli expressing lycopene, b-carotene and zeaxanthin + various BoCCDs. Supplemental Table S2. Identification and quantification (μg/g DW) of apocarotenoids in N .benthamiana leaves transiently expressing various CCD constructs. Supplemental Table S3. Identification and quantification (μg/g DW) of apocarotenoids in N. tabacum transplastomic lines. Supplemental Table S4. Identification and quantification (μg/g DW) of crocins in T1 Microtom fruits expressing TP:BoCCD4.3 in combination with CsUGT74AD1 (#B18_2 and #B20_1,3,4). Supplemental Table S5. Tentative identification of additional apocarotenoids in BoCCD4-3 expressing plants. Supplemental Table S6. Apocarotenoids tentatively identified in B. orellana leaves (L) and seeds (S). Supplemental Table S7. List of primers. S.F., O.C.D., M.S., P.M., and D.K. produced the data. S.F., O.C.D., M.S., G.D., G.A., and D.K. analyzed the data. G.G. conceived the project, coordinated the research, and wrote the manuscript with contributions from S.F. G.G. and R.B. designed the research. All authors reviewed the results and approved the final version of the manuscript. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plphys/pages/General-Instructions) is: Sarah Frusciante ([email protected]) Acknowledgments We thank E. Romano at the Centre of Advanced Microscopy “Patrizia Albertano,” U of Rome Tor Vergata, for the confocal images, L. Frigerio (U of Warwick) and J. Mathur (U of Guelph) for helpful comments on the sub-organellar localization of CsCCD2 and BoCCD4-3, Paola Ferrante (ENEA) for preparation of plant expression constructs, and P. Fraser and J. Enfissi (Royal Holloway U of London) for supplying Bixa leaves. Funding This work was supported by the European Commission (Project NEWCOTIANA, grant agreement 760331) and by COST action CA15136 (EUROCAROTEN). Conflict of interest statement. The authors declare no conflict of interest. References Ahrazem O , Diretto G, Argandona J, Rubio-Moraga A, Julve JM, Orzaez D, Granell A, Gomez-Gomez L ( 2017 ) Evolutionarily distinct carotenoid cleavage dioxygenases are responsible for crocetin production in Buddleja davidii . J Exp Bot 68: 4663 – 4677 Google Scholar Crossref Search ADS PubMed WorldCat Ahrazem O , Gomez-Gomez L, Rodrigo MJ, Avalos J, Limon MC ( 2016 ) Carotenoid cleavage oxygenases from microbes and photosynthetic organisms: features and functions . 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