Exploring the Role of Root Barriers in the Metal Hyperaccumulator Noccaea Caerulescens

Exploring the role of root barriers in the metal hyperaccumulator Noccaea caerulescens

M.Sc. Thesis Dario Galanti

September 2016

Exploring the role of root barriers in the metal hyperaccumulator Noccaea caerulescens

Dario Galanti 920605249070

September 2016

M.Sc. Thesis Laboratory of Genetics GEN-80436

Supervisors: Tânia Serra and Mark Aarts

WAGENINGEN UNIVERSITY

Table of contents Abstract ...... iv 1 Introduction ...... 1 1.1 Noccaea caerulescens, a Zn/Cd/Ni hyperaccumulator ______1 1.2 Hyperaccumulation in N. caerulescens ______2 1.3 Copper homeostasis and transport in plants ______3 1.4 Root Barriers: a N. caerulescens special feature ______8 2 Aims ...... 10 3 Materials and methods ...... 11 3.1 Mutants pre-selection ______11 3.2 Optical microscopy ______12 3.2.1 Root clearing and lignin autofluorescence of entire roots ...... 12 3.2.2 Autofluorescence of cross sections ...... 12 3.3 Hydroponics ______13 3.4 Ionomics ______13 3.5 Gene expression analysis ______13 3.5.1 RNA extraction and quality control ...... 14 3.5.2 cDNA synthesis ...... 15 3.5.3 Gene identification and primer design ...... 15 3.5.4 Primer testing ...... 16 3.5.5 qPCR analysis ...... 18 4 Results ...... 19 4.1 Pre-selected mutants ______19 4.2 Optical screening of RB mutants ______22 4.2.1 Lignin autofluorescence of entire roots ...... 22 4.2.2 Autofluorescence of cross sections ...... 23 4.3 Ionomics ______25 4.4 Gene expression analysis ______28 4.4.1 COPT family in N. caerulescens ...... 28 4.4.2 Primer efficiency ...... 29 4.4.3 Gene expression ...... 30 5 Discussion ...... 31 5.1 Root barriers and hyperaccumulation ______31 5.2 Copper homeostasis in Noccaea caerulescens ______32 6 Conclusions ...... 34 7 References ...... 36 Appendix A – Ionomics analysis ...... 40 Part 1 – Control treatment ______40 Part 2 – Cu stress treatment ______42 Appendix B – Gene expression analysis ...... 43 Appendix C – Mutant seed availability ...... 44 Acknowledgments ...... 45

iii

Abstract Noccaea caerulescens is an emergent model species for the study of metal hyperaccumulation, reported to accumulate very high levels of nickel (Ni), zinc (Zn) and cadmium (Cd) in leaves. Several studies partially unravelled the mechanisms underlying metal homeostasis and transport in this plant but many pieces are still to be placed in order to compose the entire puzzle. For this purpose, the first aim of this study was to enlighten the role that specific structures called lignin thickenings (LT), present in N. caerulescens roots, may play in the hyperaccumulation mechanism of this plant. By several selection steps, I was able to select three N. caerulescens mutants probably defective in the production of LT. From preliminary data, these mutants seem to have a lower metal accumulation than the wild- type, suggesting an active role of LT in the accumulation mechanism. Nevertheless a more detailed phenotyping and ionomics characterization of the mutants is needed to confirm this hypothesis. The second approach followed to shed light on N. caerulescens metal homeostasis was to carry out a preliminary study about copper (Cu) homeostasis and transport in this plant. Cu availability can influence homeostasis of other metals in several ways like competing with divalent cations as Zn and Cd for transport and tuning of the amounts and kind of superoxide dismutases (Fe-SOD against Cu/Zn-SOD). Aligning A. thaliana members of the COPT gene family of Cu transporters to the N. caerulescens genome, I was able to identify five orthologs. The ortholog of AtCOPT6 was not found. Ionomics and gene expression analysis performed in N. caerulescens, showed differences in Cu homeostasis between this species and A. thaliana. NcCOPT2 expression between roots and shoots is different compared to A. thaliana, suggesting that this gene could recover some of the functions of the absent copy COPT6. Furthermore NcCOPT5, which in A. thaliana is responsible for Cu mobilization from mesophyll vacuoles, seems to be Cu-responsive. This observation, together with the higher Cu translocation observed under Cu-excess conditions, suggests that N. caerulescens could be special also in the administration of this metal. Regarding interactions with other metals I show here that, under Cu-excess conditions, N. caerulescens seems to be able to distinguish between Cd and Zn xylem loading, to exclusively transport Zn. How this distinction works is still unknown, since Zn and Cd xylem loading is thought to be driven by the same pomp HMA4. With this last observation, I want to highlight how the study of Cu response in N. caerulescens can be useful also to understand mechanisms underlying the transport and homeostasis of other metals.

1 Introduction

1.1 Noccaea caerulescens, a Zn/Cd/Ni hyperaccumulator Soil and water pollution on earth is becoming more and more a threat for environmental and health reasons. Therefore interest in studying heavy metal hyperaccumulation in plants has grown and is growing, aiming to provide alternative solutions to conventional methods for soil cleaning. These methods have indeed the drawback to be expensive and sometimes very invasive for the soil. The phytoremediation approach offers an economic, easy and non invasive solution. Furthermore it is particularly indicated for heavy metal contamination, since these elements cannot be degraded using microorganisms as for organic pollutants (Sas-Nowosielska et al. 2008). In this case the most effective approach is phytoextraction, which is based on the ability of some so called, hyperaccumulator plants, to uptake from the soil high levels of metals and translocate them to the shoots (Reeves 1992). These plants usually stock the metals in vacuoles of mesophyll cells to levels that would be toxic for most of other plants. The first step in the development of plants that can be used for phytoremediation is the comprehension of the mechanisms of hyperaccumulation and tolerance, which is different depending on the metal involved. A first distinction must be done between plant micronutrients, which are toxic at high concentrations but necessary at low concentrations (Zn, Fe, Ni, Mo, Mn and Cu) and elements that are not necessary for plant growth (As, Cd, Pb and Hg). The first ones enter the plant through specific transporters, while the second ones usually enter through transporters of chemically similar elements or by simple diffusion (Epstein and Bloom, 2005). Noccaea caerulescens (Thlaspi caerulescens until few years ago, Al-Shehbaz 2014), from the Brassicaceae family, is an emergent model species to study the hyperaccumulation trait. This plant is able to accumulate very high amounts of Zn, Cd and Ni. Levels of these metals in shoots can be up to 30 mg/g of Zn, 4000 g/g of Ni and 2700 g/g of Cd on the dry weight (DW) (Brown et al. 1995; Lombi et al. 2000; Schat et al. 2000). The advantages of N. caerulescens as a model species are many: a relatively short life cycle, a small number of chromosomes (2n=14 chromosomes), an 88.5% DNA identity with A. thaliana in coding regions (Rigola et al. 2006), self-compatibility and possibility of easy out-crossing and, maybe the most interesting one, the availability of many different accessions showing high variation in the amount and kind of metal uptake (Table 1). Furthermore N. caerulescens genome is sequenced and available for research (unpublished data, available in the lab).

Table 1: Noccaea caerulescens most common accessions, site of isolation and related literature.

Accession Origin Isolation soil Literature Reeves et al. 2001; Roosens Ganges South of France Zn/Cd rich et al. 2003 Prayon North-east of Belgium Zn/Cd/Pb rich Ramaut 1964. La Calamine North-east of Belgium Zn/Cd/Pb rich Schat et al. 2000. Ni-contamined Monte prinzera North of Italy Schat et al. 2000. serpentine soil Lellingen Luxembourg Nonmetalliferous site Meerts and Van Isacker 1997. St. Félix-de- Reeves et al. 2001; Roosens South of France Zn/Pb rich Pallières et al. 2003.

1.2 Hyperaccumulation in N. caerulescens The mechanics of hyperaccumulation in N. caerulescens and the genes involved in transport, metal homeostasis and detoxification have been partially unravelled in the past 15 years. Studies comparing gene expression and function in A. thaliana and N. caerulescens allowed to discover many transporters acting in different plant tissues and cell organelles (van de Mortel et al. 2006; Oomen et al. 2009). The entire process of root uptake, root to shoot translocation and detoxification in the leaves requires indeed a wide range of genes. A clear overview of the current knowledge on this topic is given by Hassan and Aarts 2011 (Figure 1).

Figure 1: Path of metal hyperaccumulation in leaves of N. caerulescens. Transporters are indicated with arrows and the colour indicates the substrates (see key on the bottom). Vacuoles are represented in green. Chelators are highlighted in light green: His->histidine; NA -> Nicotiamine. (from Hassan and Aarts 2011). 2

- Transport until endodermis: In the external layers of the roots, until the Casparian strip (CS), metal ions can move through the apoplast freely but their flux to the stele can be enhanced or reduced depending on the symplastic transport and the vacuolar retention. IRT1 is a Fe2+ transporter that drives also the absorption of Cd and Zn in the roots and strongly enhances Cd uptake in “Ganges”. This accession indeed is able of a much higher Cd hyperaccumulation compared to “Prayon” (Lombi et al. 2001), which expresses a truncated copy of IRT1 (Plaza et al. 2007). Vacuolar retention in the cortex is probably regulated by CAX4, an antiport driving the influx in the vacuole in A. thaliana (Mei et al. 2009) and genes of the NRAMP family, which are likely to drive the Zn/Cd/Fe efflux (Oomen et al. 2009). - Influx in the central stele: At the endodermis level the apoplastic transport is blocked by the CS and metals enter endodermal cells through transporters. The bigger player in this step is probably NcZNT1, ortholog of A. thaliana ZIP4 that is able to transport both Zn and Cd (Assunção et al. 2003). Other candidates for metal transport in the roots are NcZNT 2 and 5, respectively orthologs of A. thaliana IRT3 and ZIP5 (van de Mortel et al. 2006). - From pericycle to xylem: Zn and Cd move through pericycle and root parenchyma probably driven by ZNT genes while Ni probably uses YSL genes; transporters of NA-metal complexes (NA: Nicotiamine). Xylem loading of cations must be driven by active transporters to overcome the electrical gradient given by the high [H+] in this compartment. Main candidate for this passage is HMA4 (and maybe HMA2), an ATPase transporting Zn and Cd (Hussain et al. 2004). - Xylem transport: In the xylem metals are usually chelated to histidine or nicotianamine (Krämer et al. 1996) or organic acids like citrate, malate and oxaloacetate. - Xylem unloading: metal-NA complexes are probably unloaded from xylem by YSL genes (Klatte et al. 2009) and other unknown transporters. - Vacuolar storage in the mesophyll: After xylem unloading, both symplastic (Marschner, 1995) and apoplastic (Clemens et al. 2002) transports have been observed for distribution of the metals in the mesophyll. Apoplastic diffusing metals are probably translocated inside cells by ZNT1 (Milner and Kochian 2008) and then the vacuolar final storage is likely to be carried out by ZTP1, a member of the CDF (cation diffusion facilitator) family.

1.3 Copper homeostasis and transport in plants Even though copper (Cu) homeostasis and transport was never studied before in N. caerulescens, several studies were carried out in other plants such as A. thaliana and Oryza sativa. From these studies it emerges that the Cu pathway and usage in the plant has several relations with other metals, in particular with divalent ions such as Zn2+, Fe2+ and Cd2+. For this reason I found interesting to open a door on the study of this trait in a Zn, Ni and Cd hyperaccumulator as N. caerulescens. 3

As mentioned before, copper is a plant micronutrient. It is a fundamental element of metabolic pathways like photosynthesis, mitochondrial respiration, protection from Reactive Oxygen Species (ROS) and others. Being a transition metal (oxidation states: 1+ and 2+), Cu is a redox cofactor used by many proteins as plastocyanin, cytochrome c oxidase, Cu/Zn-superoxide dismutase (Cu/Zn-SOD), laccases, ascorbate and others. For the same redox properties why it is essential, Cu can also cause the production of ROS species, being toxic at high cellular concentrations (Drążkiewicz et al. 2004). Due to this dual role of copper, plants developed a full set of genes to tightly regulate Cu uptake and distribution. The most important families are described in Box 1. The copper deficiency response in A. thaliana is mainly regulated by the Squamosa Promoter Binding Protein Like7 (SPL7) transcription factor (Yamasaki et al. 2009) and it is based on two mechanisms: - Increase of Cu uptake from the soil: SPL7 upregulates the expression of Cu2+ reductases (FRO4 and FRO5) and high affinity Cu transporters COPT1, 2 and 6 (Yamasaki et al. 2009; Jung et al. 2012). In fact these three members of the COPT family are Cu+ transporters (Sancenón et al. 2003) and work coupled with Cu2+ reductases. Furthermore upregulation of ZIP2 (ZRT-IRT-like protein 2) upon Cu deficiency, was also observed (Yamasaki et al. 2009), meaning that this gene may also be involved in Cu uptake. - The second mechanism is a very smart redistribution of the available Cu: SPL7 is able to degrade Cu-utilizing proteins through miRNAs and overexpress substitutes. More specifically, Yamasaki et al. 2007 observed that, under Cu deficiency, SPL7 downregulates both cytosolic and chloroplastic Cu/Zn-SOD (CSD1 and CSD2) through the expression of miRNA398 and promotes the expression of Fe-SOD (FSD1). The Cu subtracted to SODs can then be delivered to plastocyanin, entering chloroplasts through HMA6 (Catty et al. 2011).

Box1 – COPTs and other Cu-driving genes

Figure 2: A) Representation of COPT trimers in the membrane and mechanism of Cu+ bonding. For simplicity, only 2 TMDs are represented (in blue). Extracellular methionine/histidine-rich motifs are represented in green with methionine residues in white (Puig 2014). B) Phylogenetic tree of A. thaliana COPTs (adapted from Jung et al. 2012).

COPTs: The CTR/COPT family is well conserved among eukaryotes and it is composed of 6 members in A. thaliana. COPTs are high affinity (Km = 1-5M) Cu+ transporters (Sancenón et al. 2003; Jung et al. 2012). The protein structure (Fig. 2A) includes three trans-membrane domains (TMDs), and extracellular methionine/histidine-rich motifs important for the Cu+ transport. COPT1, 2 and 6 follow an SPL7dependent Cu-deficiency response, they are localized on the plasma membrane and drive Cu influx in the cytoplasm (Sancenón et al. 2003; Jung et al. 2012). COPT3, 4 and 5 are not Cu- responsive. COPT5 is localized on the tonoplast and drives Cu-efflux from the vacuoles (Klaumann et al. 2011). COPT4 lacks the methionine residue fundamental for Cu transport and its function, if any, is unknown. Relative levels of expression of COPTs in shoots and roots are shown in Figure 3.

Figure 3: Relative expression levels of A. thaliana COPTs in shoots and roots (Sancenón et al. 2003; Jung et al. 2012). FROs: Ferric Reductase Oxidases reduce some Cu2+ to Cu+, making it available for Cu+ transporters like COPT. FRO4 and 5 were shown to be necessary for root Cu acquisition and are upregulated under Cu-deficiency via SPL7 (Bernal et al. 2012). ZIPs: ZIP2 and 4, members of the Zinc-Regulated Transporter and Iron-Regulated

Transporter (ZRT-IRT)-like proteins family can transport Cu2+ (Wintz et al. 2003). The function of ZIP4 in Cu transport is still unknown. ZIP2 is most likely involved in Cu- uptake from the soil and it follows an SPL7 dependent Cu-deficiency response(del Pozo et al. 2010). HMAs: Heavy Metal P-type ATPases have an ATP binding site on the cytosolic side and transport metals out of this compartment (Rosenzweig and Argüello 2012). Metal substrates are delivered to HMAs by specific chaperones. In A. thaliana at least 8 members were identified. AtHMA1 to 4 transport divalent cations, AtHMA5 to 8 transport Cu+ (Zimmermann et al. 2009; Zorrig et al. 2011). YSLs: Yellow stripe-like proteins are transporters of metal-chelators complexes (Araki et al. 2011). Chelators are usually phytosiderophores (PS) or their direct precursor nicotiamine NA. Several members of this family are also involved in moving Cu- phytosiderophores in the plant.

Before describing step by step the mechanism that plants use to uptake Cu, it is important to have a gaze on the gradient moving Cu in plant cells. Cytoplasm of plant cells usually has a high Cu-chelating capacity, due to the abundance of Cu+/2+ binding ligands (Waldron et al. 2009). Furthermore plant cells display a high cytosolic pH, compared to other compartments. The resulting electro-chemical gradient, leads Cu+/2+ to diffuse freely when entering the cytoplasm from apoplast or vacuole (by transporters like COPTs), and to be actively transported (by ATPases like HMAs) when entering the vacuole or the xylem (Aguirre and Pilon 2016). Cu export from the chloroplast may be an exception, but so far there are still doubts on the role of the only candidate HMA1 (Seigneurin-Berny et al. 2006; Boutigny et al. 2014). A clear overview on the current knowledge on Cu transport and homeostasis is given by Pinto et al. 2014 (Figure 4).

Figure 4: Model of copper transport in plants coming from the combination of several studies. Cu uptake in roots is mainly driven by members of the COPT family, ZIP2 and YS1. Additional influx can be provided at the endodermis level by YSL2. HMA4 drives Cu sequestration in root vacuoles of some accessions of Oryza sativa. Cu xylem loading is mainly carried out by HMA5 but some other members of the HMA family may be involved. Members of the YSL family are responsible for xylem unloading and COPT6 for Cu distribution in the mesophyll. In the cytoplasm Cu is delivered to proteins by Cu-chaperones and several HMAs are involved in Cu delivery to chloroplasts. COPT5 localizes on the tonoplast of mesophyll cells and drives Cu mobilization from chloroplasts. Adapted from Pinto et al. 2014.

Root uptake: Cu uptake in roots can pass both by symplastic and apoplastic transport. The main path, at least in low Cu conditions, seems to be symplastic. COPT1 was shown to drive Cu+ uptake and to be upregulated under Cu-deficiency. COPT2 and 3 may also play a role (Sancenón et al. 2003; Puig 2014). Additional Cu acquisition capacity is likely to be provided by ZIP2, also upregulated under Cu-deficiency conditions (Yamasaki et al. 2009). ZIP4 is also likely to have a role but gene expression data from different authors are divergent (Wintz et al. 2003; del Pozo, Cambiazo, and González 2010). YS1 is a proton-coupled symporter transporting Fe-phytosiderophore (PS) complexes and also Cu-PS complexes (Schaaf et al. 2004; Murata et al. 2009). Ratio of apoplastic Cu transport may increase in Cu-excess conditions, when most of Cu transporters are downregulated. Cu diffusing until the endodermis is complexed in Cu-nicotiamine (NA)

7 complexes and transported by the metal-NA transporter YSL2 (DiDonato et al. 2004; Araki et al. 2011). This gene indeed does not respond to [Cu] in roots (DiDonato et al. 2004) even if it does in shoots (DiDonato et al. 2004; Aguirre and Pilon 2016). With the exception of COPTs, several genes involved in this step (ZIPs and YSLs) are not specific for Cu. Hence interactions with the absorption of other metals, especially Fe, Zn and Cd, are likely hypothesis. Xylem loading: Xylem loading is a crucial passage for Cu detoxification and distribution in the plant. In A. thaliana, Cu-chaperones (CCH) deliver Cu to the ATPase HMA5, which upload Cu in the xylem (Andrés-Colás et al. 2006). HMA5 expression analysis showed a higher expression in roots and upregulation under Cu-excess (Andrés-Colás et al. 2006). Furthermore HMA4, which in N. caerulescens drives Zinc and Cadmium xylem loading (Hussain et al. 2004), was recently shown to be responsible for sequestration in root- vacuoles of Oryza sativa accessions (Huang et al. 2016). We could then expect a role for NcHMA4 in Cu xylem loading. Shoot Cu distribution: In the xylem, Cu is likely to form complexes with chelators like NA or histidine. Members of the YSL family are thought to unload these complexes from the xylem (Irtelli, Petrucci, and Navari-Izzo 2009). Cu distribution in the mesophyll was proposed to be driven by COPT6, upregulated by SPL7 in Cu-deficiency conditions (Puig 2014). Cellular Cu delivery and homeostasis: Inside mesophyll cells, vacuoles have a buffer function to maintain Cu homeostasis. Vacuolar Cu influx is likely to be driven by some HMA pump, while COPT5, localized on the tonoplast, is responsible for Cu mobilization from the vacuole under Cu deficiency (Klaumann et al. 2011). While vacuoles have a storage and buffer function, the most important Cu target in plant cells are chloroplasts, especially in Cu-deficiency conditions when cytoplasmic Cu/Zn-SOD are downregulated. Cu must be delivered to plastocyanin on the thylakoid membrane. Two HMA transporters function in tandem to carry out this function. PPA1/HMA6, localized on the vacuolar membrane, drives Cu influx in chloroplasts and PPA2/HMA8 delivers Cu to thylakoids (Seigneurin-Berny et al. 2006; Catty et al. 2011; Boutigny et al. 2014). HMA1 is also localized on the chloroplast membrane and it is involved in Cu homeostasis, but substrate and direction of the transport are still unknown (Aguirre and Pilon 2016).

1.4 Root Barriers: a N. caerulescens special feature Many of the genes involved in Zn, Ni and Cd uptake, mentioned above, where found to be highly expressed in N. caerulescens compared to A. thaliana by van de Mortel et al. 2006, partially explaining the hyperaccumulation tendency of N. caerulescens. According to the same study there is another set of genes that are more expressed in N. caerulescens. These are genes related to the production of root barriers (RB); they are involved in lignin biosynthesis and some in suberin biosynthesis (van de Mortel et al. 2006). The higher expression of these genes is associated with the presence of an extra lignin layer in N. caerulescens roots (Fig 5a). We call it an “extra” lignin layer, since it coexists with

8 the Casparian Strip (CS); the belt-like structure, wrapping around endodermal cells, that plants use to interrupt apoplastic transport. N. caerulescens extra lignin layer can be easily observed my optical microscopy and it is composed of lignin thickenings (LT) located in the inner cell walls of peri-endodermal cells (Zelko et al. 2008). By peri- endodermal cells we refer to a layer of cortical cells, each one facing one single endodermal cell; typical pattern of N. caerulescens and few other species. These LT are absent in the closely related non-hyperaccumulator Thlaspi arvense (Zelko et al. 2008), as well as in A. thaliana. The presence of these thickenings derive from lignin deposition in the secondary walls of peri-endodermal cells and it has an irregular surface on the side facing the plasma membrane that may increase the exchange surface (Fig 5b).

Figure 5: Transversal sections of N. caerulescens roots. PE: peri-endodermis; E: endodermis; LT: lignin thickenings; Int: intercellular space. A) Stained semi-thin section observed by optical microscopy. B) TEM observation. (adapted from Zelko et al. 2008). The presence of this layer in N. caerulescens and not in T. arvense nor A. thaliana gave a hint for a possible involvement of this structure in hyperaccumulation. There are different hypothesis on the function of this layer: 1. Physical barrier to control metal influx: A. thaliana upregulates lignin biosynthesis genes in Cd stress conditions (van de Mortel et al. 2006), meaning the plant uses lignin depositions to control metal influx. A similar observation was done on willow; clones that showed the formation of a physical barrier closer to the root tip also showed a lower Cd accumulation in leaves (Lux et al. 2004). 2. Prevent excess metal efflux from the roots: This hypothesis is mentioned by van de Mortel et al. 2006 and is based on the observation that the lignin extra layer in N. caerulescens is present on older parts of the roots, which do not have a strong absorption function anymore. An option may be that by preventing metal efflux from the central stele, the plant can keep a lower metal concentration in the cortex, busting the uptake from the soil. 3. Increase metal uptake by increasing plasma membrane surface: Zelko et al. 2008 speculate that the increased exchange surface resulting from the irregular lignin thickening in peri-endodermal cells may result in a higher metal uptake in roots. 9

Some studies about RB were already carried out on A. thaliana. Several groups working on this topic developed a set of A. thaliana mutants, defective in the production of RB, that were used in this study. The mutations involved, relate to the production of the CS and the production and localization of suberin lamellae (Table 2). The esb1 mutants are described by Hosmani et al. 2013, the fact3 mutant is described by Kosma et al. 2012 and myb36 mutants are described by Kamiya et al. 2015. Information on other mutants are still unpublished.

Table 2: List of A. thaliana mutants that will be used to select N. caerulescens mutants. All the mutants were developed using Columbia-8 WT as starting accession. CS: Casparian strip. Mutant Mutated Summary Phenotype name gene sgn3_HUB At4g20140 Defective CS - severe Holes in CS. DIR18_SC At4g13580 Defective CS Few holes in CS. esb1-1_HUB AT2g28670 Defective CS and ectopic suberin Holes in CS. Ectopic lignin in corners of endodermal cells, ectopic esb1-2_HUB AT2g28670 Defective CS and ectopic suberin suberin in state I endodermis myb36-1_SC AT5G57620.1 Defective CS and ectopic suberin CS is not in the middle of the cells, lignin is only present in cell corners. Ectopic suberin is present myb36-2_SC AT5G57620.1 Defective CS and ectopic suberin in state I endodermis. prx11-1_SC At1g68850 Reduced suberin Delayed suberin deposition in state LTPA_SC At4g33550 Reduced suberin II endodermis. ANAC038_SC At2g24430 Increased suberin Almost all aliphatic suberin monomers are increased, total fact-3_SC At5g63560 Increased suberin suberin amount is increased.

2 Aims

The first aim of this study is to start unravelling the role that the lignin thickenings (LT) may play in the hyperaccumulation mechanism of N. caerulescens. Our approach is to try to find mutants defective in the production of the LT and observe the phenotypic consequence on hyperaccumulation. The following step will be to find the mutations responsible for the observed phenotypes. The final aim is to have the entire picture and be able to link the mutations of some genes to different defects in the production of LT and to specific accumulation patterns. As I will show in the following chapters, in this work I was able to identify N. caerulescens mutants carrying root barriers defects. These mutants will be useful for future research. The second aim of this work is to start investigating copper transport and homeostasis in N. caerulescens. The interactions between Cu and the metals hyperaccumulated by this plant (especially Zn and Cd) make this topic very interesting. I observed the ionome profile of N. caerulescens grown under different Cu concentrations and I performed gene expression analysis on some Cu transporters to observe responses to Cu deficiency and excess.

3 Materials and methods

3.1 Mutants pre-selection The starting material for the N. caerulescens mutant selection was a population developed during a previous study (Yanli Wang, Genetics, Wageningen University). Plants of the accession St. Félix-de-Paillères (SF) were treated with 0.4% EMS (Ethyl methanesulfonate) and selfed one time, generating an M2 population of about 7000 mutants. One leaf of each mutant was screened by ICP-MS for metal content (ionome profile) and the resulting dataset was used to carry out the pre-selection. Nineteen ICP- MS runs were performed to analyse the entire population. The ionome data were normalized per run on Microsoft Excel, using the Z-score on the WT (ICP-MS run mean): 푋 − 휇 푍 = 휎 X: [metal] of one plant μ: mean of the ICP-MS run σ: Standard deviation of the ICP-MS run and, in order to remove WT lines present in the dataset, lines with all Z-scores between -3 and +3 were excluded. The pre-selection was performed using the Z-scores of the metal concentrations (Li, B, Na, Mg, P, K, Ca, Mn, Co, Ni, Cu, Zn, As) as variables for Principal Component Analysis (PCA). N. caerulescens mutants from each ICP-MS run were plotted together with a group of A. thaliana mutants defective in RB (Table 2), for a total of 19 PCAs. N. caerulescens mutants that were grouping with specific A. thaliana mutants were selected to be phenotyped for RB morphology. The data from A. thaliana mutants was also calculated as Z-scores on the WT (Col-8). The choice of using Z-scores on the WT instead of raw ppm data is due to the diversity in ionome profile of N. caerulescens and A. thaliana. Our aim was not to select N. caerulescens mutants with an ionome profile similar to A. thaliana RB mutants. Instead by using the Z-scores I was able to select N. caerulescens lines that had a variation on the WT similar to the variation between the A. thaliana mutant and Col-8. PCA analysis were performed on the R platform using the “prcomp” function and outputs were obtained with the “ggbiplot” and “pca3d” packages. In addition to this strategy, the “conditional formatting” option of Microsoft Excel was used to highlight interesting accumulation patterns and select a few mutants with peculiar phenotypes.

3.2 Optical microscopy Seedlings grown in agar plates were used for optical screening of the mutants. Seeds were sterilized in a desiccator jar with chlorine fumes, obtained by adding 3 ml HCl in 100 ml of bleach. The desiccator jar was sealed and seeds were taken out after 7h. Agar plates were prepared using ½ MS (Murashige and Skoog 1962) medium and WT plants were included in every plate as control. Seeds were placed on the agar and one drop of 0.1mM GA (Gibberellic Acid) solution was pipetted directly on each seed to enhance germination. The starting 10g/L GA solution (0.03012 M) was diluted to 0.1mM using Milli-Q water and then filter sterilized to avoid contamination.

3.2.1 Root clearing and lignin autofluorescence of entire roots In order to specifically visualize lignin structures and remove all other cell components that may give a fluorescence signal, entire roots of 25-30 days old seedlings, grown in plates, were cleared by washing them with different solutions (protocol adapted from Malamy and Benfey 1997): 1. Shoots were cut out of the seedlings and roots were incubated in 0.24N HCl prepared in 20% methanol, at 57°C for 15 minutes. 2. The solution was replaced with 7%NaOH in 60% ethanol (EtOH), and incubated for 15 min at room temperature. 3. Roots were rehydrated in subsequent baths of 5 min in 40%, 20% and 10% EtOH. 4. Roots were transferred to a 5% EtOH, 25% glycerol solution for 15 min. 5. Roots were mounted on microscope slides with 50% glycerol. Samples were observed under a Zeiss optical microscope using 10x and 20x objectives with a standard GFP filter. Pictures were taken starting from the root tip using a Leica camera and stitched one to each other with the Fiji package of ImageJ (Preibisch et al. 2009).

3.2.2 Autofluorescence of cross sections Cross sections were cut from 20 to even 50 days old seedlings. Each mutant plant was analysed together with a SF plant grown on the same plate in order to obtain an unbiased comparison. Seedlings were taken from agar plates and submerged in warm 4% agarose, inside a Petri dish. Once the agarose was solidified, a small rectangle containing the root was cut out using a blade and very thin sections were cut and transferred to water in a small Petri dish. Under a dissection microscope, sections were transferred to a slide using forceps or a 20l pipet, and correct orientation of the sections was checked. Slides were finally observed under a Zeiss optical microscope with a standard GFP filter and pictures were taken with a Leica camera using a 20x objective.

3.3 Hydroponics Plant material for ionome and gene expression analysis was obtained with a hydroponics assay. Mutant lines 14D6, 47D6, 56H4, 73G12, 64H12 and wild-type SF were grown in a tray under control conditions (½ x Hoagland). Ten seeds were sown for each line. Lines 37G11 and SF were grown in pots under different copper concentrations: No Cu (Cu-), ½ x Hoagland (Control) and 1M Cu (Cu+). The Cu- treatment was applied from the beginning whereas the Cu+ treatment was applied in the last 16 days before harvesting. Six replicates were used for each line and treatment. All containers used for the experiment were washed for 3 days in 0.1M HCl and finally rinsed with demi water. Solid ½ x Hoagland was prepared by joining 1.1% Daishin agar and 1 x Hoagland in a 1:1 ratio. Cut tubes were filled with solid ½ x Hoagland and transferred to ~700ml blue boxes, filled with ½ Hoagland solution. HCl vapor-phase sterilized seeds (see section 3.2) were sown on cut tubes and a drop of 0.1mM GA solution was pipetted on each seed. After ~2 weeks, when roots crossed the agar, plants were transferred to the tray and the pots filled with ½ Hoagland. After 4 weeks from the transferring day, plants were harvested. Roots and shoots were separated, cleaned in demi-water and split: half was frozen in liquid nitrogen for gene expression analysis and half was dried in a stove for ionomics analysis.

3.4 Ionomics Plant material from the hydroponics assay (sec. 3.3) and from few mutants that were grown in soil, was dried in a stove for 2-3 days and sent for ionomics analysis (David Salt, Aberdeen University). Ionomics characterization of mutants was performed by Inductively Coupled Plasma– Mass Spectrometry (ICP-MS). In a nutshell this technique is based on the selection of very specific mass-to-charge ratios (m/z) and on the detection of the counts per second given by each m/z. This last parameter is used to rebuild the metal concentrations in the initial solution. Concentrations are referred to the DW. Once data were available, for each metal, the mean of the ICP-MS blanks was calculated and subtracted to the row data. Statistical analysis were then performed on Microsoft Excel. Means and standard deviations per treatment and line were calculated. Furthermore some tests including Wilcoxon Rank-sum test, T-tests and One way ANOVA (coupled with Fisher’s Least Square Difference) were performed.

3.5 Gene expression analysis Gene expression analysis were performed on all shoot and root samples, obtained from SF and 37G11 plants grown in pots (see section 3.3). Six weeks old plants were used. The Cu- and Control treatments were applied from the beginning, while the Cu+ treatment was applied only in the last two weeks.

Expression levels of four members of the COPT family, the SPL7 transcription factor and a reference gene Ref 3 (Contig11.284, UBX in A. thaliana), were checked by Reverse Transcriptase – qPCR (RT-qPCR).

3.5.1 RNA extraction and quality control Frozen plant material was grinded and around 0.1g were transferred to new Eppendorf tubes. RNA extraction was carried out using the “Direct-zol™ RNA MiniPrep Plus” kit produced by Zimo Research: 1. 600l of TRI Reagent® were added to the frozen samples and tubes were mixed to dissolve the plant material. 2. After centrifuging at 15000 x g for 1 min the supernatant was transferred to a new tube and an equal volume of 100% EtOH was added. Samples were mixed. 3. The mixture was transferred to a Zymo-Spin IIICG Column in a collection tube and after centrifugation the flow-through was discarded. For this and all next steps centrifugation was performed at 15000 x g for 30sec. 4. 400l of Direct-zol RNA PreWash were added twice to the column and discarded after centrifugation. 5. 700l of RNA Wash Buffer were added to the column and discarded after 2min centrifugation. 6. RNA was eluted in a new Eppendorf tube adding 50l of DNase/RNase-Free Water to the column and centrifuging. A DNase treatment immediately followed the extraction to ensure degradation of genomic DNA. The reaction was set up adding 6l of RNase-Free DNase 10x Reaction Buffer and 4l of RNase-Free DNase 1u/g to the RNA in water. Samples were incubated at 37°C for 1h. RNA was precipitated with salt and ethanol and resuspended in clean water as follows: 1. 140l of Diethylpyrocarbonate-treated milli-Q (mQ-DEPC) water, 100l of 7.5M Ammonium acetate and 800l of 100% EtOH were added to the reaction mix. 2. Samples were mixed well and incubated overnight at -20°C. 3. RNA was separated by centrifuging 20 min at 4°C, at 16000rpm and discarding the supernatant. 4. Pellet was washed with 300l of 70% EtOH by centrifuging 10 min at 12000 rpm. 5. The supernatant was carefully removed and the left over EtOH was evaporated in a fume hood. 6. Air-dried RNA was resuspended in 20l of mQ-DEPC water and samples were kept in ice. RNA concentration and purity were checked on the Nanodrop: - Samples were diluted, when necessary, to have a final RNA concentration between 200 and 350 ng/l. - The A260/280 and the A260/230 were checked to be respectively ~2 and 2-2.2 as for clean RNA samples.

In order to check for good quality of the RNA, 500ng of RNA were run in a 15l solution on a 1% agarose gel: 1. Volumes of samples to be used in order to load 500ng of RNA on the gel, were calculated starting from the RNA concentration checked on the Nanodrop. Volumes were then brought to 15l by adding mQ-DEPC water. 2. A 1% agarose TBE (Tris/Borate/EDTA) gel was prepared. 3. 5l of GoTaq Buffer (Promega) was added to each sample and 15l were loaded on the gel. 4. The gel was run at ~80V for ~45 min. 5. UV-light pictures were taken and printed. The presence of the two ribosomal RNA bands was used as indication of well conserved RNA. Good quality samples were frozen at -80°C to be used for gene expression analysis. For samples that did not show the ribosomal bands, the RNA extraction was repeated.

3.5.2 cDNA synthesis cDNA was synthetized with the iScript cDNA Synthesis Kit from BIO-RAD. This kit uses a modified Moloney murine leukemia virus reverse transcriptase. Each reaction mix was prepared by joining the followings: - 4l of 5x iScript Reaction Mix - 1l of iScript Reverse Transcriptase - 500ng of RNA resuspended in 15l of mQ-DEPC water. - Samples were loaded in a BIO-RAD T100 Thermal Cycler and the RT-PCR was carried out with the following protocol: - Priming: 5 min at 25°C - Reverse transcription: 20 min at 46°C - RT inactivation: 1 min at 95°C After the reaction was completed, samples were diluted five times with mQ-DEPC water and frozen at -20°C for further use.

3.5.3 Gene identification and primer design In order to perform gene expression analysis, orthologs of some A. thaliana genes were identified in N. caerulescens. Aiming to study Cu homeostasis, the members of the COPT family and the SPL7 transcription factor were identified. Furthermore two root barriers related genes (esb1 and ANAC038) were also identified. For each one of the genes above, the cDNA sequence of A. thaliana was downloaded from the TAIR website (https://www.arabidopsis.org) and aligned to the N. caerulescens genome (available in the lab). Alignments were found and, in most of the cases, it was directly possible to identify the orthologs. Where the alignment was not sure (as for AtCOPT2 and 6, that were aligning to the same N. caerulescens sequence), flanking genes were checked to be the same between the two species. Moreover, a phylogenetic tree of

N. caerulescens COPTs was produced using the Multiple Sequence Alignment tool of Crustal Omega (www.ebi.ac.uk/ Tools/msa/crustalo). Once the N. caerulescens genes were identified, the CDSs were used on the Primer3 open source platform (http://bioinfo.ut.ee/primer3-0.4.0/) and primers were picked keeping the default settings. The amplicon size was set between 70 and 150 bp and, when possible, forward and reverse primers were chosen on different exons (SPL7). Finally the amplicon was blasted against the N. caerulescens genome to check if it was unique. In addition, four reference genes were chosen for their stable expression among different accessions and conditions. Primers were picked as for the genes mentioned above and their efficiency was tested. The four reference genes chosen are: - Ref 1 (Contig2.1169) corresponding to Arabidopsis At3G11400 (EIF), which is one of the two genes that code for the G subunit of eukaryotic initiation factor 3. - Ref 2 (Contig14.218) corresponding to Arabidopsis At2G32760 (Unknown). - Ref 3 (Contig11.284) corresponding to Arabidopsis At1G14570 (UBX), which encodes a nuclear UBX-containing protein that can bridge ubiquitin. - Ref 4 (Contig122.33) corresponding to Arabidopsis At2G40730 (CTEXP), which is a cytoplasmic tRNA export protein. The primers tested, and the position in N. caerulescens genome are listed on Table 3.

Table 3: Primers designed for q-PCR and corresponding genes. The position of the CDS in the N. caerulescens genome is also indicated. Gene Gene position/code Forward primer Reverse primer SPL7 Contig31:2137143..2141175 CTCTTTGGCCCTGCATTTGT GGGTAGCGTTGAACTTCTGC COPT1 Contig320:238711..239202 AGGTTCTCTTCTCCGGTTGG GACGGAGAGGAAGAAGACGA COPT2/6 Contig116:518053..518493 TATGCTCGCCGTTATGTCCT AGCAAGAAACCCACTCCGTA COPT3 Contig320:241103..241558 GTTCTTCCATCATCGCCGTC GCCAGCCATCGAATAGAACC COPT4 Contig245:149436..149885 TGTATGCCGTCAAGTCTGGT ACAGCAAACCCAAGAGCATG COPT5 Contig31:2882234..2882665 TTCGTCTTCTCCGCCTTCTT ACGGGTGGAGGAAAGTGATT ESB1 Contig12:135119..136381 CTAGGGAAAGCGCAGGGATA TACCCACCGCTCTCAAACAT ANAC038 Contig12:2593213..2596091 GGTTAACAAGATCGCCGACC TCCTCCCATTTTCGCCTTCT Ref 1 (EIF) Contig2.1169 GCCATCTCACCATGGTCTCT ACACCATTAGCATTGCACCA Ref 2 (Unkn) Contig14.218 CTGGGATTATTGGGGAAGGT CGTCTGAAGGGAATGCAAAT Ref 3 (UBX) Contig11.284 ATTTCCTCAAAACCGACACG CCAACGTCTTCGATTCACCT Ref 4 (CTEXP) Contig122.33 AGGGCAAATTGTCGATGAAC AAGAGGGATCCGGAAAGTGT

3.5.4 Primer testing Since primers had never been used before, primer efficiency had to be tested. Primer pairs for a total of 12 genes were tested: five COPT genes, SPL7, esb1, ANAC038 and four Reference genes (Table 3). Two q-PCR plates were necessary to test all the 12 primers and they were designed as illustrated in Figure 6.

Figure 6: Design of the two q-PCR plates used for primer efficiency testing. Each primer pair was tested on one line and it is indicated with one colour. For each primer pair, six subsequent dilutions (listed in the top row) were tested in duplicate and are indicated by the decreasing intensity of the colour. The last row of each plate was used to test the primer pairs in absence of the template. Six subsequent dilutions of cDNA were prepared with mQ-DEPC water, starting from a root sample of SF in Cu- conditions: 1/10, 1/50, 1/250, 1/1250, 1/6250, 1/31250. Each one of the six dilutions was tested in duplicate in order to estimate the variation of each dilution and calculate the R2 value for each primer pair. Primers were resuspended in mQ-DEPC water, in order to obtain 100M stock solutions. Stock solutions were diluted again ten times to obtain 10M working solutions. Primer mixes were then prepared joining Forward primer (FP), Reverse primer (RP) and the SYBR Green mix (containing the DNA intercalator SYBR Green, enzymes, nucleotides and other items needed for the reaction). The volumes of each mix were calculated multiplying the unitary volumes for the number of reactions and adding a 15% margin (Table 4).

Table 4: Calculations performed to prepare primer mixes for primer testing on q-PCR. Unitary # Dilutions #Replicates Control +15% volumes FP (10M dilution) 1l *6 *2 +1 15l RP (10M dilution) 1l *6 *2 +1 15l SYBR Green mix 5l *6 *2 +1 75l

When dilutions and primer mixes were ready, plates were prepared adding 7l of primer mix and 3l of cDNA dilution to each well. In order to check the tendency of each primer pair to form primer dimers and to check whether mixes were contaminated, each primer pair was also tested in absence of cDNA (mQ-DEPC water). A BIO-RAD CFX96 machine was used to perform the q-PCR and the thermal cycling was adapted from the “iQ SYBR Green Supermix” protocol (Table 5).

Table 5: Thermal cycling protocol used for q-PCR analysis adapted from the BIO-RAD “iQ SYBR Green Supermix” protocol.

Polymerase Amplification Melt Curve Activation and Analysis DNA Denaturation Denaturation at Annealing/Extension Cycles at 95°C 95°C + Plate Read at 60°C

3 min 10 sec 30 sec 39 60 -> 95°C

0.5°C increment each 5 sec

Data were extrapolated and analysed using the Bio-Rad CFX Manager 3.1 software. The amplification of each gene was considered reliable when it was possible to obtain a primer efficiency between 80 and 115%, and an R2 of at least 0.98.

3.5.5 qPCR analysis Preparation of the qPCR plates was carried out following the protocol previously described (3.5.4). Given a total of 32 samples, three genes were analyses in each qPCR plate (96 wells). When the qPCR run was finished data were checked with the Bio-Rad CFX Manager 3.1 software. Specificity of amplification was checked by looking at melting curves (Figure 7). Amplification curves were also checked and samples whose Cq values were not in exponential phase were erased and analysis were repeated. Finally, for each primer pair, I checked that Cq values did not overcome the maximum reliable Cq (list on Table 9).

Figure 7: Melting curves of a qPCR plate produced by the Bio-Rad CFX Manager 3.1 software. RFU: Relative Fluorescence Units. T: Time. The three picks, from lower to higher temperature, correspond to the melting temperatures of the amplicones of the 3 genes included in the plate (Ref3, COPT2 and COPT4). In this assay all samples were amplified properly. Cq values were downloaded and analysis were carried out on Microsoft Excel. Cq values of each gene were normalized to the reference gene “Ref 3”:

Cq(gene) – Cq(Ref3) = Cq

The 2^(-Ct) value, treatment means and Standard deviations were then calculated. Hypothesis testing was used to check whether there were differences between treatments. Differences between root samples were tested by One-way ANOVA and Fisher’s LSD. Since the Cu-, Control and Cu+ treatments are respectively composed of three, three and two replicates, two different Fisher’s LSD had to be calculated to compare the three treatments. Differences between shoot samples were calculated by t- tests, since only one replicate was available for the Cu+ treatment.

4 Results

4.1 Pre-selected mutants Up to four N. caerulescens mutants, grouping with A. thaliana RB mutants, were selected from each one of the 19 PCA analysis. A PCA plot, produced with the “pca3d” package, is shown as example in Figure 8.

Figure 8: PCA plot from the 4th ICP-MS run. N. caerulescens mutants are depicted in orange. A. thaliana mutants are depicted with different colours depending on the phenotype given by the mutation: Defective CS and ectopic suberin mutants (esb1 and myb36) are yellow. Reduced suberin mutants (prx11 and LTPA) are purple. Increased suberin mutants (ANAC038 and fact3) are blue. Defective CS mutant (DIR18) is light blue. Severe defective CS mutant (sgn3) is green. A cluster of selected N. caerulescens mutants is highlighted in red as example of the selection method.

Since for several mutant lines there were no seeds available, only 12 lines were selected from the PCAs. A list of all lines selected from the PCA analysis and informations about seed availability are shown in Appendix C. The grouping between each selected mutant and the corresponding A. thaliana mutant is shown in Table 6. In addition to these 12 lines, four more were selected for interesting accumulation patterns: - 37G11 showed high Cu and normal levels of other metals - 56H4 showed very high levels of Fe and As - 64H12 showed very high levels of several metals - 73G12 showed very high levels of several metals Furthermore, due to the chemical similarity between phosphate and arsenate, I found interesting to compare 56H4, accumulating high As but low P, and 64H12 with high As and high P. Ionome profiles of all the 16 selected lines are shown in Table 6.

73G12 64H12 61A11 56H4 53D2 51W11 47D6 41B11 41A6 37G11 22F9 16H10 15A1 14D6 11W11 11B6

Mutant

pattern are highlightedare inpattern colourswereyellow. formatting conditional Data withoptiona ofMicrosoft Green Excel:indicates Table

ID 6

High High Na, Ca, As... As... High S, P, Fe, ESB1-2; LTPA High As Fe; LTPA; fact3 ANAC038; LTPA ANAC038; DIR18 ANAC038; DIR18 Esb1; fact3 High Cu sgn3 Esb1-2 ANAC038; DIR18 Esb1-1 ESB1-1; MYB36-1 DIR18; LTPA : : Grouping with A.

thaliana mutant Metal concentrationZ Metal 10.594 -0.165 -0.397 -0.255 -0.597 -0.047 -1.132 -0.980 0.216 0.673 8.173 0.078 1.054 0.355 0.859 1.191

Li7

- scores onofICPWTthe(mean scores 19.418 -2.288 -0.793 -0.369 -0.576 -1.821 0.401 3.371 1.015 3.129 1.894 1.102 0.669 3.001 0.324 0.360 B11 13.175 -1.429 -0.866 -0.381 -0.482 -0.331 -0.833 -0.402 -0.199 0.356 5.915 0.015 0.744 1.347 5.485 3.633 Na23 -1.148 -0.921 -0.224 -1.369 -0.962 -0.620 -0.900 -0.638 -1.100 -0.452 -0.311 Mg25 3.074 0.216 0.073 2.537 0.757 19.543 -2.059 -0.050 -1.126 -0.544 -0.608 -1.251 -1.541 0.767 0.021 6.708 0.408 7.215 0.724 0.942 0.224

P31

- MS run). The reason oftheThereasonrun).MS 19.533 -0.155 -0.563 -1.257 -0.237 -0.614 -0.812 -0.295 -0.334 5.090 0.112 0.301 0.354 0.169 0.980 0.005 S34 -0.163 -2.360 -1.860 -3.252 -0.302 -0.612 -1.625 -0.575 -0.164 -0.211 0.898 0.480 3.332 0.306 4.695 0.403 K39 -0.483 -0.093 -0.683 -1.038 -0.387 -0.453 -1.735 -0.375 -0.780 -1.824 -0.621 -0.681 7.024 5.407 0.847 3.572 Ca43 -0.186 -2.367 -1.970 -0.672 -1.287 -1.759 -0.215 -1.094 -1.277 Mn55

2.512 8.427 3.320 0.552 3.385 0.160 1.646 selection is indicated in the second column. Lines selected forinterestingcolumn.in selectionsecondtheindicated is selected accumulation Lines Z-scores on the WT the on Z-scores 19.458 18.743 -0.456 -0.771 -0.024 -0.873 -0.024 -0.343 -1.125 -0.236 6.428 0.399 0.179 0.463 0.306 0.123 Fe57 -0.809 -1.020 -1.594 -0.545 -0.416 -0.988 -1.368 -0.829 -0.529 -1.065 -1.427 1.585 0.694 0.814 0.724 0.300 Co59 -0.875 -0.639 -0.970 -1.045 -0.597 -0.392 -0.794 -0.047 -1.781 -1.166 0.311 4.007 0.359 0.057 0.023 0.281 Ni60 13.310 17.191 -0.397 -1.092 -1.024 -1.053 -1.141 0.283 0.400 3.403 0.778 0.117 1.230 1.209 0.993 1.963

Cu65

highZ -0.884 -1.603 -1.841 -0.973 -0.859 -1.283 -1.331 -0.918 -0.111 -1.576 -1.912 -1.710 -0.390 0.284 0.600 2.438

Zn66

- scores whereas red indicates low Z redlow indicates whereas scores 19.391 16.839 -0.331 -0.236 -0.867 -0.665 9.149 0.222 6.143 0.415 0.111 0.637 0.355 0.985 0.705 0.348 As75 19.531 16.821 -0.115 -0.505 -0.157 -0.217 1.185 1.623 0.655 0.149 0.547 0.719 1.606 0.418 0.788 2.419 Se82 -0.955 -0.852 -0.773 -0.481 -0.577 -0.981 -0.370 -0.160 -0.457 -0.128 0.534 1.246 0.090 1.676 0.334 0.332 Rb85 -0.940 -0.697 -0.762 -0.746 -0.108 -1.214 -0.147 -0.856 -1.360 -0.784 6.215 1.311 0.712 0.272 3.279 1.606 Sr88 -2.050 -1.874 -0.369 -2.416 -2.082 -0.925 -0.126 -1.045 -1.644 -1.065 -0.741 -0.254 -1.161 -1.600 Mo98

0.533 0.120

- scores. -0.363 -0.895 -0.473 -0.229 -0.386 -0.270 -0.871 -0.784 Cd114

2.666 0.431 0.131 2.260 1.960 0.152 1.386 1.869

4.2 Optical screening of RB mutants

4.2.1 Lignin autofluorescence of entire roots Due to fungi contamination, lines 11W11 and 41A6 did not produce viable seedlings and therefore were not analysed. For the remaining 14 lines, between one and three seedlings were analysed, depending on the material available. Eight wild-type (Saint Felix) seedlings of different lengths were also analysed. The root clearing method (sec. 3.2.1) allowed us to observe the lignin distribution in roots and I was therefore able to find four mutants probably carrying RB defects (Table 7). Pictures of the four selected mutants are shown in Figure 9.

Table 7: Results acquired from optical microscopy observation of cleared roots. Mutants showing RB defects are highlighted in yellow. CS: Casparian strip; LT: Lignin thickenings. Mutant ID Replicates Observation 11B6 2 Presence of CS and LT 11W11 0 14D6 1 Few LT at half way of the root 15A1 2 Presence of CS and LT 16H10 2 LT signal very low 22F9 3 Presence of CS and LT 37G11 1 Presence of CS and LT 41A6 0 41B11 1 Absence of LT and low CS signal 47D6 2 Presence of CS and LT 51W11 3 LT are not always present 53D2 3 Presence of CS and LT 56H4 2 Presence of CS and LT 61A11 3 Presence of CS and LT 64H12 2 Presence of CS and LT 73G12 2 Presence of CS and LT

Figure 9: Lignin autofluorescence of entire roots obtained by root clearing. A) Saint Felix; B)14D6; C)16H10; D)51W11; E)41B11

4.2.2 Autofluorescence of cross sections The four lines selected from root clearing were included in this analysis. Two replicates were observed for each line except 41B11. This line showed a very delayed root development and only one replicate was available for analysis. All plants, including SF, did not show very well developed lignin thickenings. This problem was probably due to the growing conditions: by growing plants in plates roots remain thin and fragile, and seem to develop less LT. Instead old plants grown in hydroponics usually develop thicker and stronger roots with well developed LT (results not shown). Nevertheless, it is still possible to observe some differences between SF and the mutants. Lines 14D6 and 16H10 showed a reduced presence of LT (Figure 10B, C). Furthermore line 16H10 also showed a defective CS. Line 41B11 showed the almost complete absence of LT (Figure 10D). Line 51W11, in contrast with previous analysis, showed a conserved root structure with the presence of LT and CS (Figure 10E). It is possible that this line is not defective or that the root barrier development is only delayed.

Figure 10: Cross section of RB mutants. A)SF; B)14D6; C)16H10; D)41B11; E)51W11. None of the plants show very clear LT, but SF still have a clearer signal than the mutants. The mutant that seems less defective is 51W11.

4.3 Ionomics Due to germination issues and fungi contamination, the number of replicates per line is very variable, going down to zero for line 14D6. Furthermore some data is not yet available. Although in some cases the obtained data confirm the preliminary data that were used for the selection, this is not always the case. Data from lines 47D6, 56H4, 73G12, 64H12 and SF, grown in control conditions, are shown both as treatment mean concentrations

(ppm) and as Z-scores on SF (Table 8). The entire dataset can be found on Appendix A.

47D6

56H4

64H12

73G12

47D6

56H4

64H12

73G12

Line

47D6

56H4

64H12

73G12

47D6

56H4

64H12

73G12

Line

high values are green and lowandgreen arehigh values values a Z as and (ppm) concentration absolute as both shown is Table

# Repl

n.d.

: Ionome profiles of of profiles Ionome :

0.000

0.631

0.282

0.122

1.216

21.26

0.008

0.041

0.051

0.067

0.293

2.4247

3.1691

4.3883

Li7

207.26

463.11

104.54

265.40

3.3728

0.9717

0.0955

8.2371

0.7662

1.1233

6.2666

29.86

80.97

34.89

69.41

93.37

49.23

-0.645

B11

-0.19823

148.67

125.73

118.92

115.59

7.19946

5.63361

0.40049

5.16871

1.02552

1.35346

27.7235

43.22

49.08

Na23

17.30

20.93

16.59

22.10

Na23

1.79618

1.437752

1473.93

1808.93

1100.95

1207.59

3092.42

3559.59

2698.08

2730.27

2387.05

-1.99978

-1.42806

-2.83519

-1.21364

-1.11455

-2.17084

945.15

Mg25

roots and shoots of mutants 47D6, 56H4, 73G12, 64H12 and the wild type SF. Each mutant profile profile mutant Each SF. type wild the and 64H12 73G12, 56H4, 47D6, mutants of shoots and roots

-0.91701

10078.78

14277.55

0.240306

0.527464

20.66962

2.698109

1.541659

0.403318

9126.08

7469.77

9560.12

6650.21

4318.18

5618.23

5061.01

4512.52

-1.37076

P31

re red. “n.d.”indicates rered. nondetermined values.

0.3945217

-3.814649

-4.408778

-1.117089

-8.256293

11338.21

10863.99

-1.136964

-1.220196

9718.86

9466.65

7833.34

8787.42

9104.64

7873.22

7806.30

7795.73

-1.23334

S34

-0.4208122

-1.0672958

-1.0375281

0.6435776

0.1183687

1.2640407

21111.65

21395.64

24144.30

18551.03

18622.45

-0.034655

5246.46

4752.45

5232.47

5506.23

5076.60

-1.22391

Ca43

Mean of shoot [metal] (ppm)

Mean of root [metal] (ppm)

Shoot Z-scores on SF

Root Z-scores on SF

-2.1697

-9.5108

-2.9155

-10.201

401.78

340.81

134.52

319.85

115.12

-1.6626

-0.0722

-1.3754

-1.8076

172.62

118.69

170.28

128.01

113.99

Mn55

score on SF. A colour scale was used to highlight extreme Z extreme highlight to used was scale colour A SF. on score

-0.5483

-0.6296

-0.4884

-0.6133

349.91

448.92

454.13

561.74

551.81

158.72

124.06

118.92

127.84

119.95

3.4654

3.6477

7.4143

7.0666

Fe57

0.59604

0.43577

-0.2625

-1.0397

-1.2492

-1.8416

-0.8178

0.039

0.036

0.027

0.025

0.046

0.108

0.113

0.137

0.087

0.098

Co59

2.5648

Co59

0.4178

2.3696

0.364

0.429

0.348

0.260

0.730

1.5991

3.7047

3.3053

0.936

1.144

1.419

0.918

1.367

-0.102

-0.675

Ni60

-0.134

Ni60

-0.70828

-0.07983

-2.13168

0.67581

0.18683

2.31954

1.26594

0.03281

4.288

4.662

3.896

4.244

3.108

2.285

2.368

3.311

2.845

2.300

Cu65

1158.10

1675.71

1011.47

13.9294

3.52291

1.52397

337.23

786.42

441.73

450.84

312.97

916.14

921.75

-0.7524

-0.7124

-0.6959

-0.4317

Zn66

3.2404

2.0425

1.2727

0.0559

6.0213

0.6472

4.1192

4.2988

0.059

0.075

0.069

0.059

0.105

0.078

0.081

0.073

0.102

0.103

As75

-0.75

3.90204

2.10898

9.10116

-0.3275

-1.9421

-0.3943

1.991

3.060

3.061

1.873

3.395

10.26

2.9717

2.9749

Se82

5.70

4.73

5.50

6.76

-0.6715

-1.8735

-1.5955

-1.1168

-1.4818

3.011

1.480

3.457

2.634

1.960

3.503

2.592

2.865

4.307

2.657

0.7957

Rb85

1.4076

Rb85

-2.73

6.2895

6.3559

19.389

2.547

3.545

3.348

3.556

5.625

1.3437

1.2391

1.3055

4.323

5.380

5.298

4.316

5.350

Sr88

5.047

-0.01

scores: scores:

Mo98

50.66

41.44

44.86

49.17

32.00

0.1745

28.54

18.34

24.72

29.50

13.90

-0.869

-0.546

-1.757

-1.853

-0.693

-0.14

-2.66

0.459455

0.856485

7.493259

1.631778

5.062048

-0.17522

-0.31664

Cd114

1.97863

0.140

0.152

0.135

0.132

0.163

0.089

0.112

0.177

0.108 0.149

Even though results from these new data are different from data used for the pre- selection, some interesting phenotypes are present. In some cases just some of the replicates show the high/low accumulation phenotype, indicating that the trait is segregating (see Appendix A). In other cases the mutation seems to be fixed in the line. The fixed phenotypes that we can observe in each line are: - 47D6: very high phosphorus in shoots. - 56H4: high cadmium in shoots, high sodium and low manganese in roots - 73G12: low manganese and high strontium in roots, Data from lines 37G11 and SF grown under Cu-, Control and Cu+ did not meet the expectations. The mutant 37G11, which in the preliminary data was accumulating up to 17ppm of Cu, is accumulating a maximum of 5.9 ppm in Cu+ conditions. Furthermore the accumulation pattern is very similar to SF; only in Cu- conditions 37G11 accumulates 0.3ppm more than SF (p value = 0.0179). In addition, one 37G11 plant grown in soil (like plants used for preliminary data) also did not show a high Cu accumulation (4.7 ppm). Therefore I decided to keep working on SF. I report here the [Cu], [Fe], [Zn] and [Cd] in shoots and roots of SF (Figure 11). For the entire dataset see Appendix A.

Figure 11: Cu, Fe, Zn and Cd concentrations in SF shoots (green) and roots (brown). Error bars indicate the St. deviation. “ * “ indicates a significant difference between treatments. The low number of replicates does not always allow to perform statistical tests, but it is still possible to observe some trends.

The [Cu] is higher in roots compared to shoots and, as expected, it increases with increasing Cu in the solution. The [Fe] is also higher in roots and remains quite stable among the different treatments. The [Cd] seems to decrease, both in shoots and roots, from Cu- to Cu+. Root [Zn] is higher under Cu- conditions compared to Control and Cu+, while the shoot [Zn] seems to increase under Cu+ conditions. In addition, shoot to root ratios were calculated as indication of the xylem loading and translocation of the metals (Figure 12).

Figure 12: Shoot to root ratios of Cu, Fe, Cd and Zn in SF. Error bars indicate the Standard deviation.

Cu seems to be more translocated under Cu deficiency and excess, than under control conditions. A possible explanation could be that, under Cu- conditions the plant needs to have the more Cu possible available in leaves, and under Cu+ conditions translocation is a way to detoxify roots. Iron translocation does not seem to be affected by [Cu] in the solution, except for a possible slight decrease under Cu+. Different is the pattern for Cd and Zn, which show clear trends. Cd translocation decreases from Cu- to Cu+, indicating a possible competition between the two metals. Zn translocation increases from Cu- to Cu+, possibly indicating a positive relation between Zn usage in leaves and Cu availability.

4.4 Gene expression analysis

4.4.1 COPT family in N. caerulescens Five COPT genes were identified in N. caerulescens genome. The orthologs of A. thaliana COPT1, 3, 4 and 5 were located simply aligning A. thaliana sequences to the N. caerulescens genome. NcCOPT2, initially aligning with both AtCOPT2 and AtCOPT6, was identified comparing the flanking genes of the sequences (Figure 13).

Figure 13: Comparison of the genomic region of COPT2 in A. thaliana (A) and N. caerulescens (B). The sequences are in opposite directions. Aligning sequences are indicated by rectangles of the same colour. NcCOPT2 region is highlighted in yellow.

I checked weather the absence of COPT6 in N. caerulescens was due to a mistake in the genome assembly and I found that it is not the case: the genome region of AtCOPT6 is present in the N. caerulescens genome browser, but COPT6 is lacking (Figure 14).

Figure 14: Genomic region of AtCOPT6 in A. thaliana (A) and correspondent region in N. caerulescens (B). The sequences are in opposite directions. Aligning sequences are indicated by rectangles of the same colour.

We are therefore facing an adaptive difference between the two species. Since AtCOPT2 and AtCOPT6 are on the same branch of the AtCOPT phylogenetic tree (Figure 2B), it is possible that the speciation between N. caerulescens and A. thaliana occurred when just five COPTs were present, and afterwards a duplication of AtCOPT2 produced AtCOPT6. For all NcCOPTs, in order to confirm the correspondence with A. thaliana orthologs (Figure 2B), I produced a phylogenetic tree of N. caerulescens COPTs (Figure 15).

Figure 15: Phylogenetic tree of the COPT family in N. caerulescens. The brunching is the same depicted in A. thaliana (Figure 2B).

4.4.2 Primer efficiency Out of the 12 primer pairs that were tested, only seven were specific enough to be used (Table 9). Unfortunately primers for COPT3, ANAC038, Ref1, Ref2 and Ref4 did not meet the requirements (sec 3.5.4) and could not be used.

Table 9: Primer efficiency results extrapolated testing primer pairs with subsequent dilutions of cDNA.

Gene primers Efficiency R^2 Max reliable Ct

SPL7 82.3 0.991 ~33

COPT1 104.3 0.98 ~33

COPT2 111.3 0.984 ~34

COPT4 110.3 0.984 ~36

COPT5 98.9 0.990 ~35

ESB1 91.3 0.988 ~31

Ref3 (UBX) 100.7 0.990 ~35

4.4.3 Gene expression Expression levels of four members of the NcCOPT family and the NcSPL7 gene were checked in the 37G11 mutant and in SF, under Cu-, control and Cu+ conditions. Unfortunately primers designed for NcCOPT3 were not specific enough and this gene was not analysed. Since ionomics data of the 37G11 mutant were not interestingly different than SF, and the same was observed for gene expression analysis, I will show here only the expression levels in SF. The entire dataset can be found in Appendix B. The intrinsic high variation of gene expression analysis and the low number of replicates did not allow to reach any significant result but one. In any case it is still possible to observe some trends. Expression levels of all genes analysed are shown in Figure 16.

Figure 16: Relative expression of SPL7 and four COPT genes in shoots and roots of SF under No Cu (Cu-), Control and 1M Cu (Cu+). Error bars indicate St. errors. “ * “ indicates a significant difference.

As expected, NcSPL7 expression negatively relates to the [Cu] in roots, confirming the role of this transcription factor in signalling and driving Cu-deficiency in roots. The trend is not clear in shoots, where NcSPL7 expression does not seem to change strongly depending on [Cu]. NcSPL7 mRNA level is higher in roots like in A. thaliana (Yamasaki et al. 2009). For the four COPTs analysed, Cu deficiency responsiveness and shoots/roots expression were compared to A. thaliana. Overall NcCOPT1 and 4 behave similarly to A. thaliana orthologs, while NcCOPT2 and 5 show differences. NcCOPT1 expression is higher in shoots and it seems to be Cu responsive both in shoots and roots. NcCOPT4 expression is higher in roots and it does not seem to be Cu responsive, except for a slightly lower expression in shoots under control conditions. NcCOPT2 has a gradual response to [Cu] in shoots, while in roots there is a very strong downregulation under Cu+ but no difference between Cu- and Control. This trend is quite in line with the expectations since AtCOPT2 is a target of SPL7 and responds to Cu-deficiency. What was not expected is to find similar expression levels in shoots and roots (except for Cu+ conditions), since AtCOPT2 is much more expressed in shoots. Furthermore NcCOPT5 also showed differences with the A. thaliana ortholog: we can observe a [Cu] responsiveness in shoots and the expression level in shoots and roots is the same or even higher in roots.

5 Discussion

5.1 Root barriers and hyperaccumulation In this study I initiated a path to study the role of root barriers in the hyperaccumulation mechanism of the Brassicaceae Noccaea caerulescens. Exploiting similar studies performed on the closely related A. thaliana and carrying out a consistent phenotyping I was able to identify 3-4 mutants deficient in the production of root barriers. Mutants 14D6, 16H10 and 41B11 show root defects in both screening methods: the lignin autofluorescence of entire roots and the autofluorescence of cross sections. Line 51E11 seemed to be defective in the first analysis by lignin autofluorescence of entire roots but, when observed by autofluorescence of cross sections, 51E11 shows the presence of lignin thickenings. The three defective lines 14D6, 16H10 and 41B11 show a reduced lignification of peri-endodermal cells, suggesting the presence of mutations influencing this trait. Furthermore 16H10 seems to show also a defect in the production of the CS. This is a likely hypothesis if we consider that this mutant was selected due to its grouping with the A. thaliana esb1 mutant, carrying holes in the CS and ectopic lignin and suberin (Table 2). The identification of these mutants is a first step in building a relation between root barriers and hyperaccumulation. The lines probably carry different defects and more phenotyping is still needed to better describe each line. For this purpose the usage of the

Propidium iodine staining is a good option. This apoplastic tracer penetrates the central stele only where root barriers are deficient or have holes, allowing to check the integrity and performance of these structures. Once the mutant defects are well described it will be necessary to produce ionome data to observe the relation of each defect with the accumulation pattern. Unfortunately our attempt to grow the mutant 14D6 to check the ionome profile did not succeed. Even though I could use A. thaliana RB mutants to predict the accumulation tendencies of the three LT mutants, I expect defects in the CS and in LT production to cause different variations in the ionome. Furthermore A. thaliana RB mutants show very different phenotypes, making the interpretation quite difficult. Nevertheless, a first clue on the accumulation tendency of the three LT mutants 14D6, 16H10 and 41B11 can be obtained from the preliminary data (Table 6). Even though the preliminary data was shown not to be very reliable, the overall tendency of the three LT mutants seems to be to accumulate low levels of all metals. Some exceptions are present and it is necessary to produce reliable data, but, if this observation will prove correct, I will be prone to exclude the first hypothesis of section 1.4, indicating LT as a barrier to control metal influx. Since the lines selected are M3, one or two more steps of selfing would be necessary to clean up mutations that are not relevant for the study. If the entire process will be successful, the last step would be to come out with candidate genes and screen them for mutations.

5.2 Copper homeostasis in Noccaea caerulescens The initial hypothesis to have a mutant able to stock high levels of copper in leaves did not result consistent. In contrast with preliminary data, our results did not show any relevant Cu accumulation difference with the wild type SF. If I initially thought that this discrepancy could be partially due to the different growing conditions (preliminary data came from plants grown in soil while our assay was performed in hydroponics), results from one 37G11 mutant grown in soil (Appendix A) showed that in this case the preliminary data on which I based the selection were not reliable. 37G11 is not accumulating high amounts of Cu. However, this study moved a first step in a new area of plant-metals interactions. The study of Cu homeostasis in N. caerulescens can be interesting, due to the many interactions between Cu and the metals hyperaccumulated by this plant. By combining ionomics and gene expression analysis, it was already possible to distinguish some of these interactions and come out with interesting hypothesis. Our study on the COPT gene family suggests that, despite the close relation between N. caerulescens and A. thaliana, some differences in Cu homeostasis are present between the two species. In the first place COPT6 is not present in N. caerulescens. AtCOPT2 and AtCOPT6 align to the same CDS in the N. caerulescens genome. By comparing the flanking genes of the three sequences I found that the N. caerulescens CDS is the ortholog of AtCOPT2 and was therefore called NcCOPT2. NcCOPT2 shows a different shoots/roots expression than the A. thaliana ortholog, suggesting that it could have a different

32 function that AtCOPT2, maybe covering also the functions of the missing COPT6. Furthermore, within the flowering plants, it would not be the first case the members of the COPT family are present in a different number: six members were described in A. thaliana while seven were described in O. sativa (Jung et al. 2012; Yuan et al. 2011). Another difference between the COPT families of A. thaliana and N. caerulescens is the behaviour of NcCOPT5. In contrast with AtCOPT5 (Sancenón et al. 2003), the N. caerulescens ortholog seems to be Cu-responsive in shoots. This gene is responsible for Cu mobilization from vacuoles of mesophyll cells (Klaumann et al. 2011), hence, the downregulation of this gene under Cu+ is a mechanism suggesting the capability to stock Cu in leaves. This observation does not mean that N. caerulescens is a Cu hyperaccumulator, because the absolute Cu concentrations are normal (max 6 ppm). Nevertheless our data show that, for increasing [Cu] in the growing medium, the concentration of this metal increases significantly both in shoots and roots. Furthermore Cu translocation is higher under Cu+ compared to Control, indicating that the plant translocates Cu to the leaves in order to keep root concentration below the toxicity level. As described in A. thaliana (Yamasaki et al. 2009), N. caerulescens controls Cu influx in roots through an SPL7 dependent regulation of specific copper transporters (COPT1 and 2). Other root transporters like ZIP2, which in A. thaliana is a target of SPL7 (Yamasaki et al. 2009), are also likely to be downregulated under Cu+. The consequent questions to these observations are: 1) How can Cu be high under Cu+ both in shoots and roots if COPTs, and probably ZIP2, are downregulated? 2) Which genes are responsible for root Cu detoxification? One possibility to address the first question is that, under Cu+, most Cu is transported as Cu-chelator complexes through YS1 and especially YSL2; this last gene indeed is not downregulated under Cu+ in A. thaliana roots (DiDonato et al. 2004). This explanation implies a higher apoplastic transport and Cu uptake at the endodermis level under Cu+ conditions. Such an assumption seems to be against the first hypothesis on the role of LT (sec 1.4), because if Cu absorption at the endodermis level increases under Cu+ conditions, the LT are not controlling metal influx. The second question is very likely to be explained by the role of HMA5. In A. thaliana this gene is upregulated when [Cu] in cytoplasm of root cells becomes too high, carrying out root Cu detoxification (Andrés-Colás et al. 2006). The same mechanism may be present in N. caerulescens. To confirm this hypothesis gene expression analysis on NcHMA5 should be carried out under Control and Cu+ conditions. Except for COPTs, which specifically transport Cu+, many other Cu transporters are not specific for this metal but transport also other metals, leading to competition between substrates. This effect is visible, in different ways, for Cd and Zn. Concentrations of both metals in roots negatively correlate to [Cu] in the solution, possibly due to the activity of ZIP2 that can transport all the three metals as divalent cations (Wintz et al. 2003). In contrast, when looking at the translocation to the shoots, Cd and Zn show opposite patterns. For an increasing [Cu], Cd translocation decreases while Zn translocation increases. This observation implies that, under Cu excess, the plant is able to distinguish between the two metals and specifically transport Zn. This mechanism sounds logic if we consider that under Cu-excess, in mesophyll cells Cu/Zn-SOD are upregulated and 33 they substitute Fe-SOD (Yamasaki et al. 2007). Consequently, Zn is needed in shoots and it is highly translocated, while Fe is less needed. In line with this reasoning, [Fe] in the only shoot sample under Cu+, is lower than in all plants under Cu- and Control. Even though the mechanism is very smart and logical, it is hard to explain how the plant can selectively transport Zn, because Cd and Zn are uploaded in the xylem by the same transporter NcHMA4 (Hussain et al. 2004). Furthermore it was recently shown that OsHMA4 is also able to use Cu as substrate (Huang et al. 2016), making the picture even more complicated. A possible explanation to unravel this knot is that Cu is mainly uploaded by NcHMA5 and NcHMA4 is more prone to transport Zn (and maybe Cu) that Cd, because Zn is quickly used in shoots. The reasoning above gives an example of how the study of Cu response in N. caerulescens can give hints to understand mechanisms of metal transport in this special plant. Overall, gene expression analysis on several other copper transporters than COPTs would help to better understand how Cu availability influences Zn and Cu uptake in N. caerulescens.

6 Conclusions

Even though I was not able to address the initial question and discover which role lignin thickenings (LT) play in hyperaccumulation, the identification of mutants defective in the production of these structures is a first step. Further phenotyping to better characterize the defects of the three mutants selected are required. The low accumulation tendency of the three LT mutants that I selected suggest that the LT are not controlling metal influx, but are instead actively participating to the hyperaccumulation mechanism. Proper ionomics analysis should be carried out on these mutants to confirm this hypothesis. Therefore further studies on the three mutants, including phenotyping and ionomics, could allow to understand the function of the LT in the hyperaccumulation ability of N. caerulescens. Unravelling this question will add a building block to understand the mechanisms driving hyperaccumulation of heavy metals in plants. When each aspect of this very interesting trait is discovered, it will possibly become feasible to transfer the mechanism to fast biomass accumulating plants; a fundamental step in order to use phytoremediation as an efficient method to clean up polluted soils. The study of copper homeostasis in N. caerulescens follows a different approach, but the final aim is always to understand something more of the mechanisms that drive metal transport and hyperaccumulation in this plant. With very few data I was able to show how Cu-response in N. caerulescens can influence absorption and accumulation of other metals. I am therefore confident that, carrying out a Cu-response experiment on a bigger scale and performing gene expression analysis on several other transporters than

COPTs, would help to better understand how Cu availability influences Zn and Cu uptake in N. caerulescens. Furthermore the understanding of Cu movement itself can also be interesting in N. caerulescens, since this plant seems to be special also in the management of this metal. In particular, the downregulation of COPT5 under Cu-excess, if confirmed by further analysis, could allow this plant to stock Cu in vacuoles of mesophyll cells better that many other plants.

7 References

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Appendix A – Ionomics analysis

Part 1 – Control treatment

Root samples Treatment: 0.5 Hoagland in a tray Line Replic Li7 B11 Na23 Mg25 P31 S34 K39 Ca43 Mn55 Fe57 Co59 Ni60 Cu65 Zn66 As75 Se82 Rb85 Sr88 Mo98 Cd114 SF 1 n.d. n.d. 26.458 1458.683 10240.704 11496.048 n.d. 5608.363 429.087 360.567 0.0320 0.2294 4.783 349.863 0.0577 1.809 3.802 2.667 48.131 0.1771 SF 2 n.d. n.d. 60.523 1511.805 10978.817 10772.028 n.d. 5486.024 385.454 380.088 0.0414 0.6126 4.289 309.412 0.0517 1.643 3.073 2.603 57.455 0.1316 SF 3 n.d. 26.727 31.294 1470.731 8624.496 11233.517 39102.91 4569.322 423.198 367.149 0.0242 0.2656 3.728 387.122 0.0578 2.079 2.769 2.319 58.392 0.1512 SF 4 n.d. 0.902 54.859 1726.205 9487.357 11935.892 n.d. 5253.162 409.701 309.539 0.0452 0.3035 4.896 311.667 0.0563 1.851 3.142 2.449 56.199 0.1360 SF 5 n.d. 121.696 42.949 1202.229 6299.018 11253.547 n.d. 5315.415 361.469 332.191 0.0533 0.4091 3.744 328.099 0.0719 2.571 2.267 2.694 33.119 0.1042 73G12 1 0.282 386.582 47.300 1105.442 7974.277 10711.509 n.d. 4270.263 106.795 576.862 0.0499 0.8068 3.244 375.775 0.1011 4.446 1.950 4.292 35.403 0.1524 73G12 2 0.114 294.566 82.506 977.343 6837.403 9690.362 n.d. 4344.410 120.686 580.143 0.0421 0.4496 3.261 324.781 0.0888 3.339 2.024 3.797 30.870 0.1409 73G12 3 1.803 572.216 168.258 872.018 8484.458 10767.066 n.d. 5063.753 141.135 396.341 0.0531 0.7833 3.208 121.017 0.1227 5.620 2.528 5.158 30.690 0.1311 73G12 4 4.046 1187.841 191.721 1536.454 5800.763 9592.424 n.d. 6765.365 195.585 969.407 0.0928 1.9513 3.998 225.713 0.1625 4.201 2.072 11.869 29.455 0.2721 73G12 5 0.979 458.608 133.153 712.426 5875.466 6504.383 n.d. 3939.005 61.130 462.701 0.0239 0.4615 3.182 362.586 0.1095 2.554 2.074 5.947 36.643 0.2235 73G12 6 0.335 164.340 28.023 719.638 6406.623 3656.580 n.d. 4171.992 67.670 493.020 0.0254 0.3092 2.694 528.627 0.0838 2.148 1.812 4.127 32.824 0.1401 73G12 7 0.953 177.586 181.492 692.697 5172.494 3911.087 n.d. 6981.435 112.854 384.186 0.0348 0.3466 2.172 252.301 0.0637 1.457 1.261 4.185 28.146 0.0808 64H12 1 n.d. 50.842 31.098 1143.850 8126.602 10547.670 n.d. 5359.753 328.452 447.274 0.0238 0.2214 3.583 427.003 0.0720 1.740 2.409 3.154 50.800 0.1142 64H12 2 n.d. n.d. 83.604 1548.690 14836.001 12066.610 n.d. 6127.674 344.353 843.283 0.0378 0.2762 5.930 368.115 0.0467 1.385 2.711 4.115 51.151 0.1602 64H12 3 n.d. 97.787 44.091 942.174 8335.094 12003.715 n.d. 5218.242 273.943 703.990 0.0227 0.3312 3.782 411.062 0.0604 1.951 2.276 3.604 53.515 0.1375 64H12 4 n.d. n.d. 41.511 1479.544 10006.222 11404.648 n.d. 5180.235 488.579 295.290 0.0248 0.2261 4.284 524.116 0.0548 1.942 2.947 3.146 49.183 0.1402 64H12 5 0.611 25.812 45.108 923.670 9089.996 8297.324 n.d. 5645.227 163.948 518.868 0.0161 0.2445 3.639 523.887 0.0636 2.347 2.828 3.760 41.225 0.1058 56H4 1 n.d. n.d. 169.423 1235.881 11588.121 8961.230 n.d. 6120.499 71.870 596.889 0.0201 0.2125 4.601 539.895 0.0639 3.164 4.053 3.468 44.508 0.1040 56H4 2 0.847 192.854 103.787 956.450 7456.872 7600.650 n.d. 4503.669 105.962 417.870 0.0254 0.3516 3.280 317.299 0.0818 3.268 2.740 3.938 34.680 0.1437 56H4 3 n.d. 50.066 103.983 1110.533 9635.364 11838.056 n.d. 5073.239 225.730 347.619 0.0368 0.4806 3.807 467.984 0.0604 2.752 3.577 2.638 55.401 0.1584 47D6 1 n.d. 148.687 84.735 2898.819 7643.168 9456.778 n.d. 5643.892 461.100 421.895 0.0289 0.4192 6.431 379.510 0.0484 1.761 0.939 2.971 33.724 0.1240 47D6 2 1.775 292.078 304.127 1464.905 7245.864 8783.324 n.d. 4503.364 307.028 427.542 0.0440 0.5627 3.707 1042.323 0.0927 4.066 1.916 4.485 43.410 0.1751 47D6 3 0.117 181.022 57.136 1063.076 7520.283 10916.472 31053.73 4110.082 254.309 497.313 0.0359 0.3037 3.848 937.414 0.0825 3.354 1.584 3.179 47.176 0.1580 Root metal content of plants grown in hydroponics under control conditions. Data is in g/g of DW (ppm). “n.d.” stands for non determinate data. Light green indicate interesting high values. Light purple indicate interesting low values.

Shoot samples Treatment: 0.5 Hoagland in a tray Line Replic Li7 B11 Na23 Mg25 P31 S34 K39 Ca43 Mn55 Fe57 Co59 Ni60 Cu65 Zn66 As75 Se82 Rb85 Sr88 Mo98 Cd114 SF 1 0.0000 40.895 23.405 3228.751 5030.597 8491.549 n.d. 20922.460 163.170 114.714 0.0993 0.7578 2.106 1169.534 0.0872 6.502 4.453 3.366 29.565 0.0884 SF 2 0.0000 38.771 15.109 3272.356 4037.450 7715.053 n.d. 24338.835 156.255 151.932 0.1130 0.9756 1.818 869.064 0.0740 5.260 3.200 4.626 26.819 0.0780 SF 3 0.0000 63.842 14.772 2994.751 3955.835 9858.143 n.d. 22455.029 190.952 125.079 0.1138 0.9389 2.333 1516.356 0.0735 5.676 3.524 4.434 37.586 0.1089 SF 4 0.0104 97.504 17.249 2572.080 4606.487 9217.131 n.d. 19686.051 134.613 132.719 0.0923 0.8909 2.165 766.945 0.0790 5.295 3.380 3.788 23.877 0.0864 SF 5 0.0308 106.025 15.950 3394.185 3960.544 8655.207 n.d. 18155.894 218.128 269.173 0.1201 1.1165 3.003 1468.608 0.0742 5.768 2.959 5.403 24.853 0.0838 73G12 1 0.0839 142.803 24.475 2487.872 4569.124 6440.588 n.d. 20287.440 157.157 114.601 0.1109 1.3551 2.366 1326.401 0.0948 6.892 2.515 4.730 13.572 0.1725 73G12 2 0.0821 91.449 14.431 2602.756 5248.635 5787.195 n.d. 23013.834 146.245 124.190 0.1038 1.1365 2.258 997.965 0.0934 6.473 2.221 5.416 13.379 0.1037 73G12 3 0.0764 101.196 63.629 2721.582 4966.695 5832.475 n.d. 22308.604 150.124 123.404 0.1086 1.1837 2.352 1221.379 0.0957 6.526 2.710 5.721 13.573 0.1440 73G12 4 1.5305 1130.087 446.059 2075.527 3828.768 11717.229 n.d. 12042.555 66.709 125.255 0.0861 2.4021 1.749 446.277 0.1497 28.624 3.730 6.581 6.700 0.2270 73G12 5 0.1433 146.565 210.721 1930.172 4744.885 9552.736 n.d. 13151.082 59.518 110.195 0.0716 0.8665 2.809 650.825 0.0917 8.328 2.387 4.289 15.935 0.1047 73G12 6 0.1127 150.864 30.038 1956.782 3936.540 8376.811 n.d. 18855.753 105.408 102.336 0.1040 1.4369 2.200 1425.218 0.0965 8.826 2.614 4.988 16.855 0.1978 73G12 7 0.0229 94.867 19.797 2934.676 4292.961 6863.073 n.d. 20697.886 112.761 139.654 0.1029 1.1863 2.363 1012.197 0.0960 6.175 2.420 5.725 17.284 0.0902 64H12 1 0.0246 90.956 24.487 2960.432 4695.595 8222.650 n.d. 17391.067 95.006 121.816 0.0613 0.8221 2.425 666.793 0.1042 6.803 4.191 4.018 29.689 0.0823 64H12 2 0.0000 42.989 10.462 2511.702 5656.823 8459.904 n.d. 18694.068 131.386 152.876 0.0852 0.8755 3.293 905.255 0.0786 5.679 5.197 4.236 37.035 0.1394 64H12 3 0.1746 216.800 37.798 2876.965 4307.897 7028.119 n.d. 19082.642 85.417 108.587 0.0847 0.8840 2.361 558.159 0.1378 8.798 3.957 4.327 26.955 0.0865 64H12 4 0.1361 117.314 23.578 2850.688 4908.202 6952.898 n.d. 19122.620 185.857 111.182 0.0985 0.9618 2.880 1157.759 0.1023 6.638 3.451 4.547 21.375 0.0986 64H12 5 0.0000 54.641 14.154 2451.577 5736.531 8367.920 n.d. 18464.768 142.383 144.755 0.1028 1.0488 3.266 1320.794 0.0847 5.869 4.742 4.450 32.448 0.1346 56H4 1 0.0000 28.915 17.256 2643.218 5915.748 7640.117 n.d. 24755.539 186.378 131.312 0.1113 1.1808 3.395 1978.848 0.0680 5.170 3.039 5.144 28.479 0.1804 56H4 2 0.1521 110.618 15.783 2472.039 4915.966 7031.875 n.d. 22218.103 149.172 103.466 0.1415 1.6972 2.868 1463.317 0.0941 7.162 2.646 4.955 22.319 0.2076 56H4 3 0.0000 8.163 16.743 2978.974 6022.973 8947.676 n.d. 25459.253 175.297 121.976 0.1588 1.3784 3.671 1584.965 0.0577 4.175 2.910 5.794 23.373 0.1433 47D6 1 0.0070 65.911 12.559 3502.186 14284.453 8760.861 n.d. 23687.096 124.233 134.367 0.1218 1.3845 2.224 823.149 0.0712 3.767 2.083 5.867 14.470 0.1016 47D6 2 0.1152 166.971 22.006 3656.207 13757.573 8004.444 n.d. 18958.974 118.663 102.292 0.1123 1.1365 2.302 1034.858 0.0988 6.636 2.826 5.010 18.051 0.1763 47D6 3 0.0000 47.229 28.234 3520.388 14790.637 10548.609 n.d. 21540.850 113.182 135.514 0.1041 0.9120 2.577 890.408 0.0740 3.776 2.867 5.263 22.509 0.0592 Treatment: soil 11B6 0.48622 41.66451 167.8547 3353.53421 5846.5017 11184.482 0 17759.0987 98.53833 95.97202 6.12513 14.4923 5.2882 375.9439 0.141629 4.758849 17.936 45.4842 42.13629 2.81551 11W11 1 0.5228 80.54846 251.7719 3114.38497 8012.9003 13336.621 0 14598.1428 88.31952 87.83688 4.26207 12.3707 6.2895 355.6968 0.21709 6.7773 20.2893 39.1997 26.63703 2.340912 11W11 2 2.36183 107.7651 2782.697 5471.20868 2671.6866 4963.6055 0 28382.4181 56.03717 118.7417 2.83262 6.74867 1.35588 95.38777 1.292375 8.635408 15.9283 88.4454 0.735378 0.648958 15A1 0.61827 38.55303 265.3745 3002.46168 5293.61 9235.3191 0 18972.7221 151.9892 119.3672 8.9861 11.7801 5.84677 287.0383 0.370657 6.567009 16.8591 56.7937 17.42875 2.116753 16H10 0.95977 30.77377 424.3088 3099.49993 5514.521 12631.676 0 16254.9112 95.45932 94.26627 7.93358 18.7513 4.5605 246.0111 0.16343 5.9962 21.41 45.0219 40.01059 3.055949 22F9 1.83926 143.5661 927.2301 2903.86789 2313.3041 6716.3229 0 20654.4857 77.26926 124.2137 1.99471 3.09475 2.51114 121.0022 0.432747 8.875219 15.515 68.9477 7.219002 0.609883 37G11 0.71279 85.98815 408.8049 3212.21199 4527.2504 10583.304 0 14929.0969 72.84139 93.57552 4.88204 12.4839 4.69155 287.8806 0.181066 7.38664 16.4108 43.7089 26.1523 1.726209 61A11 1 0.85137 61.23723 322.1166 3275.65176 4835.8323 11894.582 0 15476.0571 71.72472 91.54273 2.92588 6.96283 4.37538 284.7551 0.207526 6.531105 17.0718 41.7952 18.02701 2.092329 61A11 2 1.23355 17.7413 520.5167 3474.47577 3113.0672 9117.387 0 26513.3223 124.2136 148.3983 8.31293 8.38118 4.1319 486.5747 0.336389 5.5082 11.7749 74.425 18.56866 1.182101 61A11 3 1.30753 50.3489 156.205 3423.47233 4892.189 9057.6444 0 21112.3304 122.511 118.8329 5.41761 3.42131 3.10956 185.5962 0.308858 4.991246 17.801 59.0714 36.93969 1.228584 Shoot metal content of plants grown under control conditions in hydroponics (first table) and in soil (second table). Data is in g/g of DW (ppm). “n.d.” stands for non determinate data. Light green indicate interesting high values. Light purple indicate interesting low values. In these data is included the most interesting phenotype: the high P accumulation in shoots of the mutant 47D6.

Part 2 – Cu stress treatment

Shoot samples Treat Line Replic Li7 B11 Na23 Mg25 P31 S34 K39 Ca43 Mn55 Fe57 Co59 Ni60 Cu65 Zn66 As75 Se82 Rb85 Sr88 Mo98 Cd114 Cu- SF 1 n.d. 32.617876 29.35698 2709.172 5996.637 12537.87 n.d. 15895.2 84.20079 118.5777 0.054458 0.661035 0.439038 352.3753 0.078961 4.765739 4.340086 3.690186 33.69868 0.194999 Cu- SF 2 0.026239 81.480286 18.30916 2476.713 4925.037 11009.72 n.d. 17063.96 151.7973 120.3349 0.085061 0.818904 0.444305 483.0874 0.098063 5.953785 3.981587 3.276762 32.69998 0.296096 Cu- SF 3 0.104564 103.32419 22.28777 2288.835 4411.698 9800.479 n.d. 17872.87 160.9084 132.8587 0.103578 0.866344 0.395094 404.5094 0.101809 6.36689 3.182949 3.454086 33.22135 0.454728 Cu- 37G11 1 n.d. 106.05162 14.28539 2406.14 5185.08 6897.26 n.d. 23627.41 211.73 203.9556 0.096072 1.155179 1.156766 984.8882 0.088475 5.571456 4.29684 5.499897 36.04651 1.134793 Cu- 37G11 2 0.022292 105.68389 10.49626 2070.813 5056.589 10384.69 n.d. 16903.07 109.8675 111.2384 0.080698 0.989491 0.522085 576.8124 0.097366 6.927607 4.242809 3.416048 38.32466 0.727588 Cu- 37G11 3 0.063704 133.09197 22.75757 2071.997 4625.566 9628.469 n.d. 15898.98 137.6993 121.0734 0.084781 0.81912 0.560602 478.7017 0.106427 6.923384 4.305174 3.226484 30.37632 1.308137 Cu- 37G11 4 n.d. 79.796475 15.55248 2615.967 3491.152 8322.3 n.d. 23747.32 156.6869 161.4039 0.092446 0.925632 0.785037 546.1531 0.081753 5.353412 2.734681 4.295452 30.42711 0.900067 Cu- 37G11 5 0.031753 114.97895 22.87739 2119.501 4806.42 8641.045 n.d. 19557.31 132.4761 128.7697 0.066765 0.86386 0.736293 472.7869 0.099735 6.183837 3.236927 4.136377 31.73588 0.818853 Control SF 1 0.089796 144.53153 47.04473 2181.363 3886.037 10787.85 n.d. 16445.31 69.17782 109.8784 0.047234 1.088772 1.317947 346.8723 0.127351 6.988446 3.094068 3.716382 21.44848 0.12861 Control SF 2 n.d. 71.532378 18.4385 2886.236 4572.48 9886.511 n.d. 18225.35 121.6846 134.0893 0.05731 0.786571 1.370363 261.0463 0.10708 5.555791 2.706172 3.51286 23.3949 0.14155 Control SF 3 0.019454 97.927065 23.39377 2886.632 3827.509 7633.761 n.d. 19818.47 200.7933 141.7686 0.075531 1.009692 2.017197 479.6011 0.105414 6.575604 3.677951 4.324006 19.6208 0.245729 Control 37G11 1 n.d. 56.571004 17.82351 2453.071 4694.539 8742.565 n.d. 22124.05 160.015 169.295 0.068121 0.812364 1.754901 509.654 0.079174 4.381924 3.335686 4.53797 33.94598 0.055997 Control 37G11 2 n.d. 84.384583 19.57557 2288.237 5002.57 9674.905 n.d. 19195.93 149.9183 137.6537 0.065798 0.834259 1.873761 478.568 0.091759 5.402399 3.26911 3.811079 25.43211 0.048727 Cu+ SF 1 0.073921 91.934695 21.71877 3022.271 4399.448 8490.061 n.d. 19784.65 116.0947 109.5545 0.083927 1.234388 5.968007 530.4306 0.092797 6.751893 2.937791 4.174518 16.39963 0.111405 Cu+ SF miss n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. Cu+ 37G11 1 n.d. 130.70017 99.19477 2900.44 3552.883 12659.46 n.d. 15789.48 27.06649 103.5745 0.04888 0.601229 7.085722 172.3917 0.093047 5.311186 2.906416 4.594021 17.37816 0.064642 Cu+ 37G11 2 0.008792 115.50434 10.25872 2610.085 5827.113 10383.57 n.d. 14710.99 85.34694 86.91152 0.058096 0.527495 4.656124 737.6238 0.096479 6.021474 3.642056 2.978928 26.27764 0.169454 Root samples TreatmentLine Replic Li7 B11 Na23 Mg25 P31 S34 K39 Ca43 Mn55 Fe57 Co59 Ni60 Cu65 Zn66 As75 Se82 Rb85 Sr88 Mo98 Cd114 Cu- SF 1 n.d. 107.16424 54.45095 1953.608 9212.273 13883.17 n.d. 6184.656 343.5112 253.6679 0.01426 0.12793 0.890975 152.1063 0.066816 2.004769 2.776186 3.352972 59.79812 0.188054 Cu- SF 2 n.d. n.d. 39.53515 1658.881 10722.26 14044.85 n.d. 6400.722 517.275 260.0077 0.017933 0.124707 0.890074 169.0406 0.052581 1.688212 3.055942 3.109198 48.07624 0.210826 Cu- SF 3 n.d. n.d. 161.1997 1911.17 12627.89 14075.71 n.d. 6033.228 497.0301 215.915 0.018572 0.229324 1.172435 171.5363 0.03274 1.271365 3.3074 3.555033 54.27361 0.230146 Cu- 37G11 1 n.d. n.d. 99.55205 1788.691 14632.75 12274.37 n.d. 5290.458 476.9727 328.6025 0.019085 0.167576 1.526518 141.4272 0.025986 1.49366 3.853775 3.049566 55.87633 0.384819 Cu- 37G11 2 n.d. 130.75896 49.94853 1323.311 9202.178 12977.07 n.d. 4537.061 161.534 354.486 0.015121 0.131546 1.089364 116.3288 0.054549 2.420627 2.230562 2.644817 59.24938 0.235439 Cu- 37G11 3 n.d. 110.88272 212.6031 1645.848 8374.276 11247.45 n.d. 6037.773 251.8883 375.426 0.017028 0.155877 1.139296 151.3809 0.063686 2.127316 2.671166 3.207499 64.36784 0.321974 Cu- 37G11 4 n.d. n.d. 142.7694 1616.572 12935.46 12004.83 n.d. 5972.071 408.0699 292.1911 0.022693 0.257644 1.492114 119.3362 0.052083 1.583611 2.6084 2.910072 45.53725 0.349949 Cu- 37G11 5 n.d. n.d. 86.75245 1966.354 14656.65 12965.09 n.d. 6442.602 255.6639 368.7795 0.023851 0.215456 1.741208 141.017 0.036849 1.022607 3.033202 3.980738 51.01455 0.379351 Control SF 1 n.d. n.d. 119.1638 1944.764 11816.69 14229.43 n.d. 6757.512 281.2368 204.266 0.024409 0.232166 4.171382 80.5471 0.06855 1.638844 3.04521 3.398584 50.34631 0.187146 Control SF 2 n.d. n.d. 115.8469 2421.225 11506.23 16722.56 n.d. 6751.338 312.879 260.1575 0.025224 0.185844 4.303645 121.933 0.051548 1.346146 3.302322 3.718054 49.40289 0.182564 Control SF 3 n.d. n.d. 82.58862 2009.533 13351.31 15698.55 n.d. 7088.52 308.4204 251.9401 0.027598 0.255336 5.625668 134.0657 0.032124 1.103057 3.458469 3.874097 50.06048 0.195853 Control 37G11 1 n.d. n.d. 119.4878 2197.056 14139.58 14265.87 n.d. 6721.851 253.3002 361.8618 0.025712 0.209143 4.271418 126.6444 0.026109 1.092089 3.52984 4.019394 52.59142 0.123723 Control 37G11 2 n.d. n.d. 56.20052 2030.249 13302.81 13366.49 n.d. 6494.716 432.0411 310.943 0.021593 0.116746 4.194563 81.81417 0.034923 1.293584 3.135097 3.596017 54.9649 0.142427 Cu+ SF 1 n.d. 116.43876 56.62257 1381.812 8257.895 11139.38 53012 4637.561 572.2055 227.0004 0.019735 0.163665 8.404359 76.76412 0.07085 2.213312 2.924182 2.569827 62.17819 0.163011 Cu+ SF 2 n.d. n.d. 36.16102 1981.98 11550.7 12830.8 n.d. 5347.326 592.754 357.2092 0.018089 0.131984 11.89109 127.6068 0.035565 1.471092 4.034246 3.167462 47.66881 0.17854 Cu+ 37G11 1 n.d. 167.10763 60.72864 1875.029 8429.433 10543.5 22655 9852.186 66.5117 411.3919 0.026207 0.207049 9.254014 163.5901 0.058877 1.803152 1.782665 4.439924 34.23252 0.089856 Cu+ 37G11 2 n.d. 47.147672 34.54145 1828.097 10619.51 11520.77 39718 5117.654 574.7567 832.8906 0.029583 0.159376 11.44839 501.0397 0.058937 1.460661 2.898534 3.165676 77.84194 0.464181 Metal content of SF and 37G11 plants, grown under No Cu (Cu-), ½ Hoagland (Control) and 1M Cu (Cu+). Data is in g/g of DW (ppm). “n.d.” stands for non determinate data. The [Cu] column is highlighted in light blue.

Appendix B – Gene expression analysis

Shoot samples Ref1 SPL7 COPT1 COPT2/6 COPT4 COPT5 Means of 2^(-Ct) values Treat Line Replic Cq Cq 2^(-Ct) Cq 2^(-Ct) Cq 2^(-Ct) Cq 2^(-Ct) Cq 2^(-Ct) SPL7 COPT1 COPT2/6 COPT4 COPT5 Cu- SF 1 24.916 29.939 0.045508 25.587 0.628040849 25.138 0.857473 30.347 0.023181 24.295 1.537885587 Cu- SF 2 24.207 28.690 0.063422 24.117 1.063997525 24.875 0.629518 30.437 0.013321 24.151 1.039550848 Cu- SF 3 22.901 25.345 0.222323 22.211 1.613047865 24.019 0.460464 27.575 0.03915 23.129 0.853708 0.110418 1.101695 0.649152 0.025218 1.143715 Cu- 37G11 1 23.869 27.938 0.081831 25.615 0.298211203 24.019 0.901657 28.258 0.047759 25.153 0.410687806 Cu- 37G11 2 22.771 26.015 0.135947 22.759 1.008670022 23.336 0.675807 27.466 0.038609 23.872 0.46613336 Cu- 37G11 3 22.099 24.884 0.180327 20.186 3.765657339 22.636 0.689463 27.072 0.031845 22.213 0.924200591 Cu- 37G11 4 22.290 25.546 0.134936 21.880 1.329105891 22.479 0.877673 27.271 0.031671 22.624 0.793416752 Cu- 37G11 5 21.795 25.167 0.125636 22.137 0.788846657 22.391 0.661536 28.035 0.013229 22.558 0.589207532 0.131735 1.438098 0.761227 0.032622 0.636729 Control SF 1 23.193 26.779 0.110125 23.495 0.811163232 23.584 0.762588 29.364 0.013881 23.533 0.789809235 Control SF 2 22.916 26.238 0.129564 23.553 0.643028186 24.626 0.305502 28.680 0.018404 23.375 0.727553908 Control SF 3 22.978 25.689 0.188608 23.149 0.888455414 24.068 0.469878 28.973 0.015677 23.054 0.948935114 0.142766 0.780882 0.512656 0.015988 0.822099 Control 37G11 1 25.532 30.260 0.054559 27.817 0.205233591 27.050 0.349078 30.739 0.027069 26.547 0.494745176 Control 37G11 2 23.729 28.255 0.061779 23.308 1.339340573 23.701 1.019375 28.741 0.031 24.078 0.785013048 0.058169 0.772287 0.684226 0.029035 0.639879 Cu+ SF 2 22.152 25.488 0.128479 22.873 0.606616722 23.408 0.418594 27.275 0.028691 24.207 0.24069919 0.128479 0.606617 0.418594 0.028691 0.240699 Cu+ 37G11 2 22.693 26.325 0.107071 24.334 0.320686819 23.188 0.70975 30.549 0.004318 24.831 0.227199405 0.107071 0.320687 0.70975 0.004318 0.227199 Root samples Cu- SF 1 21.755 23.159 0.421465 25.934 0.05519453 22.875 0.459955 23.972 0.215007 20.983 1.707509517 Cu- SF 2 22.083 23.772 0.353841 26.008 0.065873057 22.399 0.803229 24.984 0.13393 22.002 1.057808762 Cu- SF 3 22.027 24.080 0.28277 26.382 0.048868344 22.498 0.721496 25.569 0.085819 22.036 0.993277637 0.352692 0.056645 0.66156 0.144918 1.252865 Cu- 37G11 1 21.990 23.350 0.433243 26.124 0.056983731 22.772 0.581547 25.651 0.07908 21.143 1.799230049 Cu- 37G11 2 24.750 27.515 0.182481 27.265 0.174934614 25.252 0.705942 25.493 0.597399 23.139 3.054029236 Cu- 37G11 3 23.224 26.564 0.128095 28.059 0.035026461 23.432 0.865264 27.970 0.037265 22.482 1.671632385 Cu- 37G11 4 22.024 23.944 0.306996 27.114 0.029371327 22.745 0.607059 25.260 0.106182 22.024 1.000107436 Cu- 37G11 5 22.710 25.451 0.185169 27.716 0.031123046 22.604 1.075835 26.379 0.07861 22.364 1.270992981 0.247197 0.065488 0.767129 0.179707 1.759198 Control SF 1 21.983 23.926 0.302566 25.754 0.073214366 23.002 0.493303 24.164 0.220536 21.019 1.950676407 Control SF 2 22.543 24.603 0.28152 27.650 0.029008361 23.872 0.39803 25.844 0.101402 23.292 0.594828073 Control SF 3 21.936 24.175 0.252234 25.969 0.061106707 21.702 1.176282 25.093 0.112134 22.329 0.761673696 0.278773 0.054443 0.689205 0.144691 1.102393 Control 37G11 1 21.472 23.664 0.259678 27.468 0.015669034 25.077 0.082204 25.675 0.054319 21.569 0.935325774 Control 37G11 2 21.151 23.788 0.19737 26.472 0.025016483 23.485 0.198246 24.642 0.088904 22.414 0.416444855 0.228524 0.020343 0.140225 0.071612 0.675885 Cu+ SF 1 21.255 23.588 0.238011 26.709 0.022812269 32.559 0.000395 24.657 0.094585 21.970 0.609020398 Cu+ SF 2 21.240 23.856 0.20001 26.294 0.030111743 31.772 0.000676 24.263 0.123015 20.771 1.38398182 0.219011 0.026462 0.000535 0.1088 0.996501 Cu+ 37G11 1 21.363 23.110 0.34137 26.153 0.036156606 30.336 0.00199 24.288 0.131693 21.648 0.821063847 Cu+ 37G11 2 20.922 22.285 0.432384 25.501 0.04184747 32.282 0.000381 24.500 0.083771 21.697 0.584349665 0.386877 0.039002 0.001185 0.107732 0.702707 Results of gene expression analysis performed on SF and the 37G11 mutant, grown under No Cu (Cu-), ½ Hoagland (Control) and 1M Cu (Cu+). Means of 2^(-Cq) values per treatment are listed in the last five columns.

Appendix C – Mutant seed availability

Grouping with A. Mutant ID Tray # Plant # Seeds availability thaliana/ Metal pattern 1E2 LTPA 41 101 Bag not found 1A3 DIR18 41 95 Bag not found 5E12 Low metals 9 76 No 8G6 High metals 12 115 No 11B6 DIR18 15 26 Yes 11E11 esb1 - myb1 15 79 Yes 14D6 esb1 19 66 Yes 15A1 ANAC038 20 17 Yes 16H10 Esb1 22 26 Yes 19F3 High metals 24 104 No 19D9 Esb1 25 43 Bag not found 22F9 sgn3 28 82 Yes 24G3 High Li and As 30 14 Bag not found 32G11 Low metals, high P 53 94 Bag not found 37G11 High Cu 49 37 Yes 40C8 DIR18 33 1 No 40H8 prx11 33 11 No 41A6 Esb1 51 59 Yes 41F8 High metals 51 112 No 41B11 ANAC038 47 35 Yes 47D6 DIR18 2 86 Yes 49F10 High Li, As, Na 13 3 Bag not found 50A9 High Li, As, B and Mn 18 54 No 51E11 ANAC038 23 19 Yes 53C2 LTPA 37 31 Not found 53D2 fact3 37 30 Yes 55H9 High metals 39 111 Bag not found 56H4 High As, Li, Na 32 87 Yes 61H7 myb36 36 82 No 61A11 esb1 42 5 Yes 62F8 prx11 6 5 No 64D7 esb1 52 3 No 64H12 High B, P, Cu, As 44 2 Yes 67B1 ANAC038 55 3 Bag not found 69G10 esb1 21 105 No 73G12 High Li, Na, As 43 13 Yes Mutants pre-selected from PCA analysis (grouping with A. thaliana) and conditional formatting (interesting metal accumulation pattern). Mutants with available seeds are highlighted in green.

Acknowledgments

In the first place, I want to express my true satisfaction for working with a very nice and motivating group of people. I would like to acknowledge and express my gratitude to my supervisors Tânia Serra and Mark Aarts, which taught me and inspired me during all the progression of my thesis. Tânia for teaching me a lot about working in the lab and for always helping me and giving me feedbacks and advices. Mark for helping me to access the Genetics thesis and for all the useful advices he gave me during these months. I want to thank Corrie Hanhart and Frank Becker for helping me in the lab, all the people of the Wageningen Genetics department for the useful advices and support and John M. C. Danku and David Salt, from the University of Aberdeen, for carrying out the ionomics analysis. Furthermore I would like to thank all the Genetics and Biosystematics students working in the lab for creating a nice and friendly atmosphere. Finally I want to thank my family and friends for helping and supporting me all along my master and studies.