Abiotic Stress and Plant-Microbe Interactions in Norway Spruce

Abiotic stress and plant-microbe interactions in Norway spruce

Julia Christa Haas

Umeå Plant Science Centre Department of Plant Physiology Umeå University Umeå, Sweden 2018 This work is protected by the Swedish Copyright Legislation (Act 1960:729) © Julia Christa Haas ISBN: 978-91-7601-970-2 Cover photo: Julia Haas, Robert Haas Electronic version available at: http://umu.diva-portal.org/ Printed by: KBC Service Centre, Umeå University Umeå, Sweden 2018

“Before I came here I was confused about this subject. Having listened to your lecture I am still confused. But on a higher level.”

Enrico Fermi

Table of Contents

Abstract ...... ii Enkel sammanfattning på svenska ...... iv List of publications ...... vi Acknowledgements ...... vii Introduction ...... 1 Abiotic stress in plants ...... 3 Mechanisms of cold tolerance ...... 3 Drought stress response ...... 7 Plant microbe interactions ...... 11 Objectives ...... 15 Results and Discussion ...... 16 Cold acclimation mechanisms in Norway spruce ...... 16 Gene regulation during drought in Norway spruce ...... 21 Nutrient optimization and seasonal effects on plant-microbe interactions in a Norway spruce stand ...... 26 Assessing the active tree-associated microbial metacommunities in Norway spruce 30 Conclusion and future perspectives ...... 34 References ...... 37

Abstract

Norway spruce (Picea abies) is a dominant tree species in boreal forests with extensive ecological and economic value. Climate change is threatening these ecosystems, with rising temperatures impacting cold hardening and increasing drought stress in regions experiencing lower precipitation. Increasing atmospheric CO2 concentrations and nitrogen deposition can, in contrast, partially offset such negative effects by improving tree growth and carbon uptake. Similar to aboveground carbon fixation, carbon sequestration in boreal soils is important. Bacteria and fungi mineralize organic matter and, by making nutrients available for plants, are important for tree health. The ability of Norway spruce and the associated microbiota to adapt to climate change is of fundamental importance for ecosystem functioning and is the focus of this thesis.

Norway spruce seedlings were subjected to cold or drought stress and the transcriptional response compared to known mechanisms in the model plant Arabidopsis thaliana. Analyses revealed that while there was overlap in the stress responses between species, including increased osmotic and oxidative stress tolerance, the majority of differentially expressed genes were stress- responsive only in Norway spruce. Importantly, transcription factors of the abscisic acid dependent and independent pathways were not differentially expressed or were missing homolog sequences in Norway spruce, indicating that different regulatory pathways are active in Norway spruce and suggesting that stress response has evolved differently in the species. Furthermore, differential gene expression in roots differed extensively from that of needles in response to stress and highlighted the need for separate profiling in above- and belowground tissues.

In another study at the Flakaliden research site in northern Sweden, the effects of long-term nutrient addition on the microbiota associated with mature Norway spruce were tested. In agreement with earlier findings, nutrient addition improved tree growth and phylogenetic marker gene analysis on DNA of fungi and bacteria provided new insights into associated changes in plant- microbe interactions. Microbial diversity increased over time and compositional changes in nitrophilic community members indicated changes in carbon and nitrogen cycling at the plant-microbe interface, which has implications for carbon storage in boreal forest soils in the future. Follow-up RNA-based techniques largely confirmed community members from marker gene analysis.

In summary, understanding of both the Norway spruce-specific responses to abiotic stress and the ability of the associated microbiota to cope with the environmental changes are essential for future productivity, survival and distribution of Norway spruce forests. Sustainability will depend on tree vitality

and a more holistic understanding of tree-microbe interactions is required to model future sustainability.

iii

Enkel sammanfattning på svenska

Gran (Picea abies) är en dominant art i boreala skogar, och har stort ekologiskt och ekonomiskt värde. Klimatförändringar hotar dessa ekosystem med stigande temperaturer som orsakar torkstress i regioner med låg nederbörd. Förändringar i årstidernas längd påverkar också de processer som växter använder för att skydda sig mot frost. Ökade halter av atmosfärisk koldioxid och deposition av atmosfäriskt kväve kan å andra sidan medföra ökad tillväxt och ökat upptag av kol, vilket till viss del kan minska de negativa effekter som klimatförändringarna orsakar. Inbindning av kol, i både vegetation och mark, är en viktig mekanism. I marken är det bakterier och svampar som mineraliserar organiskt material och tillgängliggör näringsämnen för växter, och dessa mikoorganismer är därför viktiga för växters vitalitet och tillväxt. Den här avhandlingen fokuserar på hur granen, och de mikroorganismer som lever associerade med granen, anpassar sig till klimatförändringarna, vilket är nödvändigt för ett fungerande ekosystem.

Granplantor utsattes för kyla eller torka och granens transkriptom jämfördes med resultat från experiment utförda i modellväxten backtrav (Arabidopsis thaliana). Gran och backtrav uppvisade till viss del liknande stressresponser med ökad tolerans mot osmotisk och oxidativ stress men majoriteten av de gener som var differentiellt uttryckta i gran var inte differentiellt uttryckta i backtrav. Detta indikerar att stressresponserna har utvecklats annorlunda i gran jämfört med backtrav. Flera välkarakteriserade transkriptionsfaktorer som i backtrav reglerar uttrycket av gener som ingår i flera stressresponser antingen saknade motsvarigheter i gran, eller var inte differentiellt uttryckta. Detta pekar i sin tur på att de regulatoriska nätverk som var aktiva i gran skiljer sig från de som var aktiva i backtrav. I tillägg var genuttrycket i respons till stress i rötter och barr distinkt olika och tydliggör behovet av analys av vävnader båda ovan och under jord.

I en annan studie vid Flakalidens försökspark i norra Sverige undersöktes effekterna av långsiktig gödsling på de mikroorganismer som är associerade med granen. Tidigare studier har visat att tillväxten hos vuxna granar ökade vid gödsling, men studier av mikroorganimer saknas. Här analyserades fylogenetiska markörer hos DNA från svamp och bakterier insamlade från Flakaliden, vilket gav nya insikter i hur växter interagerar med mikrober. Ökad diversitet över tid och förändrad sammansättning av samhället av nitrofila arter indikerar förändringar i utbytet av kväve och kol mellan växt och mikroorganism, vilket tyder på att ökad gödsling kan påverka framtida inlagring av kol i marken i den boreala skogen. Uppföljande försök med RNA-baserade tekniker bekräftade till stor del mikrobiomets sammansättning.

Vår förståelse av hur granen svarar på abiotisk stress och mikrobernas förmåga att anpassa sig till miljöförändringarna är avgörande för granens framtid i ett hållbart skogsbruk. En mer holistisk syn på hur träd och mikroorganismer interagerar kommer att vara nödvändig för att åstadkomma detta.

List of publications

This thesis is based on the following studies, referred to in text by their respective roman numerals.

I. Vergara A.*, Haas J. C.*, Stachula P., Street N. R. and Hurry V. (2018). Norway spruce deploys canonical and delayed cold acclimation responses in a tissue-specific manner (manuscript).

II. Haas J. C., Vergara A., Hurry V. and Street N.R. (2018). Candidate regulators and target genes of drought stress in needles and roots of Norway spruce (manuscript).

III. Haas J. C., Street N. R., Sjödin A., Lee N. M., Högberg M. N., Näsholm T. and Hurry V. (2018). Microbial community response to growing season and plant nutrient optimisation in a boreal Norway spruce forest. Soil Biology and Biochemistry 125, 197-209.

IV. Sundström G., Schneider A. N., Richau K., Haas J. C., Delhomme N., Sjödin A., Hurry V., Grabherr M. and Street N. R. (2018). Reproducibility and biological information of tree-associated metacommunities using RNA Sequencing and DNA amplicon profiling (submitted).

*joint first authors

Paper III is reprinted by kind permission of the publisher.

Contributions to the papers was as follows:

I. Major participation in planning, sampling and experimentation of Norway spruce seedling samples, discussion of data analysis and writing.

II. Major participation in planning, sampling and experimentation, data analysis and writing.

III. Major participation in experimentation, data analysis and writing.

IV. Contribution to design and execution of the amplicon data and revision of the manuscript.

Acknowledgements

If it weren’t for my supervisors Vaughan Hurry, Nathaniel Street and Torgny Näsholm I wouldn’t have gotten this far, therefor I am very glad and grateful. Thanks for the excellent supervision, guidance and advice whenever needed. Thanks for critical comments aiding my writing, for the possibilities to take courses and go on conferences, but also for being friendly and open for “off work” chats. Special thanks goes also to Holmen Skog AB that has financed my PhD and research. I have always been met with open arms in Örnsköldsvik, Gideå and Friggesund and was never short of Norway spruce seedlings for all my experiments. Thank you: Jan Åhlund, Erik Normark, Daniel Hägglund, Roger Larm, Ellinor Edvardsson and Yvonne Hedman (Bergvikskog) and many more. In this context, I would also like to thank Harry Wu, Ove Nilsson and Rosario Garcia Gil for initiating and organizing the Industry Research School of Forest Genetics, Biotechnology and Tree Breeding and the fellow PhD students associated with it (Jenny Lundströmer, Ainhoa Calleja-Rodriguez, Johanna Carlsson, Christopher Johnson, Alexis Sullivan, Biyue Tan, Irena Fundova). Furthermore, I would like to thank Ulrika Ganeteg for being a supportive reference group member and for Swedish translations. I would also like to express my thanks to the technical staff in the lab and the greenhouse and the administrative staff at UPSC (Rebecca Gramner, Thomas Hiltonen, Susanne Larsson, Jan Karlsson, Simon Birve, Jenny Lönnebrink, Ioana Gaboreanu, Anne Honsel, and many more). Many thanks also to the UPSC bioinformatics facility for technical support with data analysis, especially Nicolas Delhomme, Bastian Schiffthaler and Chanaka Mannapperuma. I would also like to thank the members of the Stable Isotope Laboratory located at SLU Umeå for nutrient analysis of my samples. My thanks includes also all the former and current group members, Catherine Campbell, Paulina Stachula, Mathieu Castelain, Zsofia Reka Stangl, Alexander Vergara, Claudia Colesie, David Castro, Lauri Lindfors for introduction to methods and exchange of ideas. In addition, this journey would not have been possible without support from colleagues and friends at UPSC, people I have shared offices with Yohann Daguerre, Jose Alfredo Zambrano, Lill Eilertsen, Agnieska Ziolkowska, Bastian Brouwer, the innebandy team and many more (just to mention a few Silvio Collani, Domenique André, Bernadette Sztojka, Anirban Baral, Sonali Ranade, Sonja Viljamaa, Franziska Bandau, Niklas Mähler, Noemi Skorzinski, Andreas Schneider, Jean Claude Nzayisenga). Of course there is also a life outside work and my friends and family are not forgotten, I will always be deeply grateful for all your support during this journey.

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Introduction

The boreal forest biome covers large parts of Fennoscandia, Russia, Canada and Alaska and comprises almost a third of the world’s forests (Brandt et al., 2013). The conditions for plant growth are harsh in this environment, due to the short growing seasons followed by long, severe winters and freezing temperatures for several month (Burton et al., 2010). Importantly, boreal forests are also characterized by low nutrient availability (Tamm, 1991). Furthermore, disturbances caused by fire, wind or insect outbreaks are part of the natural dynamics of this ecosystem (Kuuluvainen and Aakala, 2011; Shorohova et al., 2011). These disturbances increase habitat diversity and promote biodiversity but they also show that forest tree species have evolved a high adaptive capacity for survival and recovery (Bolton et al., 2015).

The dominant tree species are gymnosperms, such as Abies, Larix, Pinus and Picea in addition to a number of angiosperm species including Populus, Salix, Betula and Alnus species (Burton et al., 2010; Kneeshaw et al., 2011; Shorohova et al., 2011). Slow growth in boreal forests, coupled with the high demand for industrial wood and other products has required an intensification of wood production and forest management in Sweden and Finland (Burton et al., 2003; Kuuluvainen and Siitonen, 2013). Modern management strategies, including post-harvest planting of seedlings and the use of genetically improved seedlings, have changed the forest structure to younger and more homogenous stands that are possibly more susceptible to disturbances and an associated loss of biodiversity (Kuuluvainen and Siitonen, 2013; Venier et al., 2014).

Boreal forests do not only have economic value but they play important roles in the global carbon and water cycles and help regulate the global climate (Pan et al., 2011; Bradshaw and Warkentin, 2015; Steffen et al., 2015). They serve as carbon sinks and sequester ~20 % of the carbon in forests worldwide (Pan et al., 2011). Estimations of global terrestrial carbon stored in boreal forests are as high as 32 % and include atmospheric CO2 fixed in plant tissue by photosynthesis as well as the carbon stock in soils, peatlands and permafrost deposits, with these stores thought to be even higher than those in tropical forests (Kasischke and Stocks, 2000; Pan et al., 2011; Bradshaw and Warkentin, 2015).

Recently, climate change has resulted in an increased mean annual temperature of 1.5 °C in boreal forests and is predicted to increase temperatures further between 4 °C and 11 °C by the end of this century (IPCC, 2013; World Bank, 2014). With the boreal forests experiencing one of the largest and most rapid increase in temperature as a result of global warming, their continued

functioning in the future as carbon sinks to balance anthropogenic CO2 emissions is doubtful (FAO, 2012; Kurz et al., 2013). Elevated temperatures, increasing CO2 concentrations, longer growing seasons and higher nitrogen mineralization rates would all have a positive effect on the growth of boreal forests, which has been reported for parts of the boreal forest (Kellomäki et al., 2005; Lapenis et al., 2005; Hyvönen et al., 2007; Kellomäki et al., 2008; Zhang et al., 2008; Poudel et al., 2011; Kauppi et al., 2014). However, increasing reports of a precipitation decrease during summer, causing severe droughts might offset higher net primary production and carbon uptake by increasing respiration and tree mortality (Girardin et al., 2008; Kurz et al., 2008; Allen et al., 2010; Beck et al., 2011; Kurz et al., 2013).

Positive effects of warmer temperatures on the productivity of northern tree species will depend on water availability in a drier summer climate. Likewise, the effects of longer growing seasons and elevated temperatures during autumn can have both positive and negative effects, especially on coniferous evergreen trees. Warmer temperatures may not only delay growth cessation, and thereby stimulate late-season net photosynthesis despite the declining photoperiod in autumn (Piao et al., 2008; Barichivich et al., 2013; Stinziano et al., 2015; Fréchette et al., 2016), but would also impact the onset of cold hardening and the development of freezing tolerance (Chang et al., 2016). In addition, warmer fluctuating winter temperatures, as well as earlier springs, will impact maintenance of cold hardiness and increase the risk for spring frost damage (Schlyter et al., 2006).

Both climate change and forest management practices have an impact on forest health and can reduce the ability of boreal forest biomes to adapt to changing conditions. Tree species react differently to changes of their environment; for example Norway spruce (Picea abies (L.) H. Karst) is more sensitive to drought than Scots pine and may be more affected by climate change (Kellomäki et al., 2005; Kellomäki et al., 2008). Therefore, with Norway spruce currently covering 19 million hectares of forest land and a growing stock of 2800 m3 in northern Europe (Norway, Sweden, Finland, Denmark, Lithuania) and a production of more than 350 million seedlings every year (Rytter et al., 2016), the adaptability of existing populations needs to be better assessed. In addition, selection of more robust genotypes by identifying relevant genetic markers and through breeding for stress-tolerance and vitality, instead of only for traits of economic value, will be needed if the forests are to sustainably produce biomass in the future.

Understanding of the mechanistic and genetic basis of abiotic stress (e.g. drought and frost) tolerance in plants has grown over the last couple of decades thanks to extensive studies utilizing Arabidopsis thaliana (Arabidopsis)

(Chinnusamy et al., 2004; Beck et al., 2007). However, incorporating findings that plants also serve as hosts for complex microbial communities, which have significant flow-on effects for vitality, fitness and productivity, is still in its beginning. Recently it has been proposed for agricultural crops that the plant- associated microbiota can have beneficial effects on stress resistance and productivity similar to those gained by breeding programs (Coleman-Derr and Tringe, 2014). These findings also open new perspectives and opportunities for improving abiotic stress tolerance in forest trees in the future and highlights the need to identify the much-ignored metacommunity of forest trees and determine their functional activities using approaches such as metatranscriptomics (e.g. to assay the transcriptome of all members of a metacommunity).

Abiotic stress in plants There is a large degree of commonality to plant response mechanisms induced by environmental stressors, especially so for cold and drought stress (Chinnusamy et al., 2004; Beck et al., 2007). They both induce cellular dehydration, either at low temperatures <0°C or at normal and elevated temperatures. At low temperatures the loss of cellular water occurs when extracellular ice is formed, while during drought water is lost through stomata and the cuticle. Both lead to the shrinkage of cells and cause turgor loss and osmotic stress. Tolerance mechanisms to dehydration involve both universal and stress-specific changes and cross talk between the pathways may exist.

In general, stress perception is followed by cascades of signal transduction, by secondary messengers such as Ca2+ or reactive oxygen species (ROS) and phosphorylation relays via protein kinases, and changes in gene expression of stress-responsive genes or transcription factors (TFs). TFs regulate a multitude of downstream genes involved in the complex response mechanisms for abiotic stress tolerance and have therefore been promising targets for engineering improved cultivars of agricultural crops. For example, overexpression of a CBF/DREB1 (C-repeat-binding factor/DRE-binding protein 1) TF successfully activated expression of target genes and increased tolerance to freezing, drought, and salt stress in transgenic plants (Jaglo-Ottosen et al., 1998; Kasuga et al., 1999) and demonstrated the existence of a common regulatory network of abiotic stress response.

Mechanisms of cold tolerance Low temperatures are limiting for plant growth and productivity and survival of plants is greatly dependent on their ability for cold acclimation. Plants vary in the degree of cold tolerance, ranging from being sensitive to chilling

temperatures (>0 °C) or being able to acclimate to freezing temperatures (<0 °C), including survival in temperatures as low as -40 °C (Rihan et al., 2017). Very cold-hardy plants, including most temperate woody species, can also develop extreme low temperature tolerance by intracellular vitrification processes (Wolfe and Bryant, 1999; Strimbeck et al., 2008; Strimbeck et al., 2015). During vitrification, molecular movement in the cytoplasm is arrested after transition of freeze-concentrated solutes into a glass-like state at temperatures between -20 and -30 °C, which allows survival even after immersion into liquid nitrogen (-196 °C) (Sakai, 1960; Hirsh, 1987; Strimbeck et al., 2007).

Membranes are considered the primary sites for freeze damage (Levitt, 1980; Steponkus, 1984). In acclimated plants, ice formation starts in the intercellular spaces (between the cells) at temperatures below 0 °C, because extracellular fluids have a lower solute concentration and, therefore, a higher freezing point than inside the cell (Thomashow, 1999). Water from inside the cells then moves outside following a gradient of water potential, causing severe cellular dehydration (Wisniewski and Fuller, 1999), up to a point where membrane lesions can occur (Steponkus et al., 1993; Uemura and Steponkus, 1999). In addition to the detrimental effects of dehydration and osmotic stress, the decrease in temperature itself can have negative effects on the plant. Low temperatures limit metabolic processes and photosynthetic reactions and lead to increased ROS formation, causing additional oxidative stress (Asada and M., 1997; Vogg et al., 1998).

Cold acclimation is the process by which plants gain cold tolerance. Exposure of model plants, such as Arabidopsis, to low but non-freezing temperatures rapidly induces acclimation to cold periods or frost events during the growing season and ceases again after the stress is removed (Thomashow, 1999; Heino and Palva, 2003). Winter, on the other hand, marks the end of the growing season and lasts for several months. Seasonal changes in late summer and autumn, such as an increasing night length and chilling temperatures, signal the arrival of winter for perennials and trigger initiation of growth cessation and dormancy processes in parallel with acquisition of cold tolerance (Weiser, 1970; Christersson, 1978; Sakai and Larcher, 1987; Bigras et al., 2001; Li et al., 2004).

Biochemical and molecular mechanisms of cold tolerance are induced in response to cold stress. However, the initial sensor of the environmental signal has still not been identified in plants (Chinnusamy et al., 2006). The plasma membrane has received particular attention in this context. As an early response to cold it becomes more rigid (Orvar et al., 2000; Vaultier et al., 2006) and mechanosensitive Ca2+ channels have been suggested to sense the changing physical properties and increase cytosolic Ca2+ (Knight et al., 1991). Only

recently, COLD1 (chilling-tolerance divergence 1) was discovered in studies of rice as a potential cold sensor in the plasma membrane that mediates changes in cellular Ca2+ levels (Ma et al., 2015). In a next step, Ca2+-binding proteins of the type Ca2+-dependent protein kinases (CDPK), calmodulin (CaM) and calcineurin B-like (CBL) proteins mediate the signal, which leads to activation of MAPK (mitogen-activated protein kinase) phosphorylation cascades (Monroy and Dhindsa, 1995; Saijo et al., 2000; Townley and Knight, 2002; Teige et al., 2004). Alternatively, ROS have been reported to function as a second messenger (Scheller and Haldrup, 2005; Tyystjärvi, 2013). ROS are generated, for example, in chloroplasts, where continued excitation of photosystem II (PSII) at low temperatures causes an imbalance between harvested light energy and the ability to utilize it for photosynthesis.

At the end of the signalling pathway, transcriptional changes of several genes follow. Especially, TFs have received increasing attention as they can amplify the cold signal by regulating expression of other cold-regulated genes (COR). During cold stress, transcription of three CBF genes (CBF1/DREB1B, CBF2/DREB1C, CBF3/DREB1A) increases rapidly, after only 15 min, in Arabidopsis and has been shown to regulate about 12% of the COR gene expression (Gilmour et al., 1998; Zeller et al., 2009; Matsui et al., 2010). Upstream of the CBF genes, ICE1 (inducer of CBF expression 1), a MYC-type TF, controls their expression and its constitutive expression and nuclear localization during non-stress conditions allows their fast induction in response to cold (Gilmour et al., 1998; Thomashow, 2001; Chinnusamy et al., 2003; Chinnusamy et al., 2007)

CBF homologs have been found in both herbaceous and woody plant species (Mizoi et al., 2012; Pearce et al., 2013; Wisniewski et al., 2014). In wheat 15 CBF genes have been reported while in apple and peach trees at least five have been identified (Badawi et al., 2007; Wisniewski et al., 2014; Wisniewski et al., 2015). The function of the CBF genes also seems to be widely conserved in plants (Jaglo et al., 2001). Overexpression in rice or tobacco improved cold tolerance in a manner similar to Arabidopsis and even ectopic expression of Arabidopsis CBF genes in potato or poplar promoted low temperature tolerance (Benedict et al., 2006; Behnam et al., 2007), suggesting that the same regulatory network with similar ‘wiring’ exists in common in these species. However, functional variation exists, with expression of AtCBF1 in rice having no effect on cold tolerance, while overexpression of the barley HvCBF4 in rice did improve cold tolerance (Lee et al., 2004; Oh et al., 2007), possibly indicating functional divergence between monocot and dicot species. This also shows that cold tolerance in cold sensitive plants, such as rice or maize, can be manipulated and depends to a large extent on the regulation of the CBF pathway and downstream genes. Cold sensitive and tolerant cultivars possess the same set of genes, but

they are either not expressed (Sarhan and Danyluk, 1998) or have expression that declines earlier (Monroy et al., 2007). There are also sensitive plants that lack the CRT/DRE binding elements for CBF TFs in the promoter region of COR genes (Yamasaki and Randall, 2016).

In woody plants, regulation of the CBF pathway in response to cold might be more complex. Especially in overwintering perennials, deciduous or evergreen, the simultaneous development of winter dormancy requires specific control of CBF genes (Artlip et al., 1997; Benedict et al., 2006; Welling and Palva, 2006). In evergreen woody species lacking dormancy, such as eucalyptus, the existence of four CBF1 genes with largely different expression profiles allowed the trees to fine-tune their response to cold (El Kayal et al., 2006; Navarro et al., 2009). Evergreen conifers retain their photosynthetic active tissues for several years and experience some of the most extreme low temperatures on earth. However, studies of the cold response in evergreen conifer transcriptomes are currently missing, but promise new insights in the future.

Expression of COR genes involves also other TFs (Vogel et al., 2005; Park et al., 2015) and plant hormones. Abscisic acid (ABA) is recognized as an important chemical signal during abiotic stress and might also play a role in developing freezing tolerance, but to a lesser extent than the above described ABA- independent pathway involving CBF genes (Shinozaki and Yamaguchi- Shinozaki, 2000; Xiong et al., 2001). Many COR genes encode dehydrins (COR78/RD29A, COR47, COR15A, COR6.6) that belong to the late embryogenesis abundant (LEA) proteins and are involved in membrane stabilization and prevent protein aggregation during dehydration (Ingram and Bartels, 1996; Thomashow, 1998; Hundertmark and Hincha, 2008). Heat-shock proteins (HSP) (HSP70, HSP90) provide similar protective functions during cold stress (Renaut et al., 2006; Timperio et al., 2008). Membrane stability can also be improved through changes in lipid composition. Increasing the proportion of unsaturated fatty acids increases the fluidity of membranes (Uemura and Yoshida, 1984; Yoshida and Uemura, 1984; Lynch and Steponkus, 1987; Thomashow, 1998; Rajashekar, 2000). Plants also change their metabolism during cold acclimation, with the typical starch-dominated carbohydrate metabolism changing to synthesis of low molecular osmolytes and production of cryoprotective molecules; leading to accumulation of soluble sugars, sugar alcohols and nitrogenous compounds (amino acids and polyamines) (Hansen et al., 1996; Strand et al., 2003). These metabolic adjustments are part of the osmotic adjustments to maintain turgor, but are also necessary to scavenge ROS and re-establish the redox balance amongst other things (Hare et al., 1998; Janska et al., 2010; Krasensky and Jonak, 2012).

Drought stress response Plants take up water from the soil and transport it to the leaves. The driving force for this transport is stomatal transpiration, which creates a lower water potential in the leaves than in the soil and water can move along this gradient. During drought stress, however, soil water levels are not sufficient to recover the water lost by transpiration. The water potential becomes more negative throughout the plant, which increases the tension on the water column within the vascular system. Greater tension is necessary under water stress to pull the water through the plant and maintain the soil-plant hydraulic continuum. This increased tension also comes with the risk that if the tension surpasses a critical value, xylem cavitation occurs and air enters into conduits leading to hydraulic and symplastic failure, desiccation of the plant and ultimately to its death (Tyree and Sperry, 1988; Tyree and Zimmerman, 2002).

Structural factors, such as rooting volume and leaf area (Ewers et al., 2000; Hacke et al., 2000), the ability to shed leaves (Tyree et al., 1993), the water storage capacitance (Goldstein et al., 1998) and height (McDowell et al., 2005) have an impact on the water status of plants (Martinez-Vilalta and Garcia- Forner, 2017). Especially in trees, the length of the water flow increases with height and high negative water potentials can develop during drought and lead to cavitation in costly perennial organs, such as the stem. The vulnerability of xylem conductance to low water potential differs between plants (Pockman and Sperry, 2000) and plants that tolerate drought try to keep the water potential above a critical value by closing their stomata.

Differences in the timing of stomatal closure observed in plants in response to water stress might represent different survival strategies and could have an evolutionary background (Brodribb and McAdam, 2013; Brodribb et al., 2014). Closing of stomata limits water loss by transpiration but also restricts gas exchange and uptake of CO2 for photosynthetic reactions (Cowan and Farquhar, 1977). A reduced carbon assimilation limits plant growth and biomass production but also increases dependency on stored carbon and the risk for carbon starvation in plants under prolonged periods of drought (McDowell et al., 2008). Early closure of stomata, as in isohydric species, maintains water potential at a stable level but restricts photosynthesis (McDowell et al., 2008). In contrast, anisohydric species allow a large decline in water potential before stomatal closure occurs and more easily risk hydraulic failure (Choat, 2013). The present distribution of plants may reflect the advantage that anisohydric species possess to occupy drought-prone habitats because of their greater plasticity in stomatal response and higher xylem resistance. In contrast to the more ancient conifers of the Pinaceae, including Norway spruce, that often show an isohydric behaviour, the more recently derived conifers of the

Cupressaceae and Taxaceae and angiosperms are considered anisohydric (Brodribb et al., 2014).

ABA functions as a chemical signal to control the hydroactive stomatal movements. ABA accumulates in guard cells during drought stress and changes the activity of several ion channels. Finally, the efflux of anions and K+ is followed by turgor loss in guard cells, which triggers stomatal closure (Schroeder and Hagiwara, 1989; Pei et al., 1997; Kwak et al., 2003; Negi et al., 2008; Vahisalu et al., 2008). Sensing of a soil water deficit in the roots rapidly leads to the production of ABA in dehydrated roots, both in herbaceous plants and trees. However, transport of ABA to the leaves via the vascular tissue to induce stomatal closure (Wilkinson and Davies, 2002) is questionable in trees. The time necessary for the long-distance transport (Zimmermann and Brown, 1971) would exceed the observed time needed to close stomata (Schulze and Hall, 1982). It might rather be that hydraulic signals function in the root-to- shoot communication during water stress and initiate biosynthesis and signalling of ABA in the shoot (Christmann et al., 2007). The exact way of sensing such a mechanical signal to activate ABA remains unknown. Turgor loss and osmotic changes might be perceived in plants by homologs of the plasma membrane osmosensor HK1 (histidine kinase 1), as in Arabidopsis (Urao et al., 1999; Tran et al., 2007), and induce downstream ABA signalling cascades.

ABA functions not only in stomatal control but also mediates transcription of many drought responsive genes (Yamaguchi-Shinozaki and Shinozaki, 2006). ABA is recognized by the PYR/PYL/RCAR (pyrabactin resistance/ pyrabactin resistance-like/ regulatory component of ABA receptor) receptors. Downstream, group A 2C-type protein phosphatases (PP2Cs) are inhibited, that otherwise function as negative regulators of ABA signalling and subclass III SNF1-related protein kinases 2 (SnRK2s) are activated. Phosphorylation activates ABA-responsive element binding proteins AREB/ABF (ABRE binding factor), a type of basic leucine zipper (bZip) TF that are major regulators of the ABA-dependent gene expression during drought (Nakashima et al., 2014; Singh and Laxmi, 2015).

Expression of ABA-inducible genes is regulated by an active cis-acting ABRE (ABA-responsive element) but requires additional ABRE copies or other cis- acting elements in the promoter region. Interestingly, CRT/DRE cis elements have been found to function as coupling elements (Narusaka et al., 2003), indicating that an interaction between ABA-dependent and ABA-independent pathways is required during transcriptional regulation of the drought response. However, CBF/DREB1 and DREB2 regulons were also shown to regulate gene- expression under drought independently and especially DREB2A and DREB2B were involved in response to osmotic stress conditions as it occurs during water

deficit (Nakashima et al., 2009), providing an alternative stress regulatory pathway in plants.

Other TFs are also induced by osmotic stress such as dehydration stress. For example members of the NAC (NAM ATAF CUC) family, such as NAC019, NAC055, NAC072/RD26 in Arabidopsis control drought gene regulons and were suggested to act in an ABA-independent manner (Tran et al., 2004). Additional TFs function in the ABA-dependent regulatory pathway, such as MYB/MYCs, WRKYs, NF-Ys (nuclear factor Ys) and other NACs (Singh and Laxmi, 2015) regulating drought responsive genes with similar functions to cold induced cellular dehydration (for an overview of cold and drought mechanisms see Fig. 1).

Ultimately, severity and duration of a drought stress determine survival of plants. In forest trees variation in the drought response is found among plant species but there is also extensive intraspecific genetic variation that contributes to variable responses to drought (Street et al., 2006; Wilkins et al., 2009). However, knowledge of the molecular basis and identification of the genes that account for contrasting adaptive mechanisms is still largely missing for forest trees, including for Norway spruce.

Cold Drought

Primary stress Low temperature, Dehydration Freeze dehydration

Secondary stress Osmotic and Oxidative

Plasma membrane

Stress perception Osmosensors, second messengers (e.g., Ca2+, ROS), and signalling Ca2+ sensors, MAP kinases, calcium-dependent protein kinases (e.g., CDPKs), ABA receptors (e.g., PYR/PYL/RCAR)

Transcription ABA-independent ABA-dependent factors ICE1 Nucleus

CBF/DREB1 DREB2 AREB/ABF MYB/MYC

NAC WRKY

Molecular Molecular chaperones (LEA, HSP), response Osmoprotection (sugars, proline) Detoxification Water and ion movement (aquaporins, ion transporter)

Physiological Stress tolerance, growth arrest, stomatal closure, cell death response Paper I Paper II Figure 1 Summary of cold and drought stress mechanisms in plants. The primary and secondary stress (e.g. changes in temperature and membrane fluidity) initiate a signalling cascade that activates transcription factors (TFs) of the abscisic acid (ABA)-dependent and ABA-independent pathways (the coloring of TFs emphasizes an effect in cold or drought stress and the position indicates the responsiveness to ABA) and downstream gene expression, triggering stress response mechanisms. Plants may therefore tolerate abiotic stress or cells are irreversibly damaged due to inadequate responses. AREB/ABF, ABA-responsive element binding protein/ABRE binding factor; CBF/DREB1, C-repeat-binding factor/DRE-binding protein1; CDPKs, Ca2+-dependent protein kinases; HSP, heat-shock protein; ICE1, inducer of CBF expression 1; LEA, late embryogenesis abundant; MAP kinases, mitogen-activated protein kinases; PYL, pyrabactin resistance-like; PYR, pyrabactin resistance; RCAR, regulatory component of ABA receptor; ROS, reactive oxygen species. Image sources: (N.D., 2011; Starbuck, 2011; Amorimbiotech, 2016).

Plant microbe interactions Even more than plants, microorganisms are ubiquitous on earth and interactions between plants and microbes date back as early as land colonization by plants occurred (Martin et al., 2017). Beneficial symbiosis between early plants and mycorrhizal-like fungi might have evolved to overcome limitations that a terrestrial ecosystem held for plants in the absence of soil, restricting water and nutrient availability (Pirozynski and Malloch, 1975; Simon et al., 1993; Kenrick and Crane, 1997; Kenrick and Strullu-Derrien, 2014). Plants have since then coevolved with diverse microorganisms (Pirozynski and Malloch, 1975; Selosse and Le Tacon, 1998), especially fungi and bacteria, surrounding the plants in their environment and living in or on plant tissues.

Belowground, soil is the primary niche of microorganisms associating with plant roots. Both the rhizosphere, which is the thin soil layer immediately surrounding roots, and the rhizoplane, defined as the surface of the root, are colonized by epiphytic microorganisms, often similar in composition to bulk soil communities, but the number of species (richness) may be lower, as shown in studies of bacteria associated with Arabidopsis roots (Bulgarelli et al., 2012; Lundberg et al., 2012; Schlaeppi et al., 2014). The factors that shape the root- associated microbial communities are not only local environmental parameters, such as soil properties (Shakya et al., 2013; Schreiter et al., 2014), but also the plant species and genotype (Manter et al., 2010; Bouffaud et al., 2014; Ofek et al., 2014) and the rhizodeposits released by plant roots (Jones et al. 2004).

In contrast to the rhizosphere and rhizoplane, the endosphere, which is the internal root compartment, has a boundary that needs to be overcome by microbiota for colonization. Distinct endophytic communities establish and diversity decreases from the bulk soil and rhizosphere to the endosphere (Bulgarelli et al., 2012; Lundberg et al., 2012; Schlaeppi et al., 2014). The plant host can actively drive recruitment of endophytes and plants have been found to select for certain bacteria, for example Streptomycetaceae in Arabidopsis (Bulgarelli et al., 2012), which can be producers of antimicrobial compounds (Lazzarini et al., 2000). In addition to symbiotic interactions with the plant host, many endophytes seem to be of a facultative or transient character or have a yet unknown function.

Symbiotic interactions between plants and their associated microbiota range from mutual, beneficial symbioses to plant pathogens. Benefits for plants result in improved growth and health, given for example by an increased abiotic and biotic stress resistance or through improved nutrient availability stimulated by the microbial partner (Bacon and White, 2016). The well-known

ectomycorrhizal fungi (EMF) can establish mutualistic symbiosis with a wide range of forest trees and are dominant in boreal coniferous forests, in which nutrients are low and limiting (Read et al., 2004; Smith and Read, 2008). In contact with plant roots, EMF sheathe the root by laying a mantle of hyphae around its surface and enter the intercellular apoplastic space of the outer root tissues (epidermis and cortex), enabling water and nutrient transport to the host (Kottke and Oberwinkler, 1987; Peterson and Massicotte, 2004). Due to their extensive extramatrical hyphal network in the soil, EMF enhance the absorbing surface of the plant host for mineral nutrients. In exchange, the plant host provides carbon to the mycorrhizal fungi (Smith and Read, 2008). Up to 34% of tree net primary production (NPP) can be allocated to symbionts (Hobbie and Hobbie, 2006; Ekblad et al., 2013; Allen and Kitajima, 2014).

In boreal forests plants are nitrogen limited, in part because decomposition of organic matter, mostly plant litter, is slow (Vitousek et al., 2002). The soil microbial community is the main driver of carbon and nutrient cycling in these ecosystems. Saprotrophic fungi degrade lignocellulose and mobilize carbon (Hori et al., 2013; Ruiz-Duenas et al., 2013; Lundell et al., 2014) and nitrogen is made available for plants from plant litter or other organic nitrogen sources, such as proteins, through the extracellular protease activity of EMF (Bending and Read, 1996; Nygren et al., 2007). Soils with low nitrogen availability are favored by fungi instead of bacteria (Wardle et al., 2004). Nevertheless, interactions with bacteria have been reported in the mycorrhizosphere and its close vicinity, not only competing for substrate, but also functionally complementing each other to degrade organic compounds (Bontemps et al., 2013; Uroz et al., 2013).

Anthropogenic activities have increased nitrogen deposition worldwide, including in boreal forests over the last century (Galloway et al., 2008), with positive effects on tree productivity. The consequences of this ongoing nitrogen deposition on the diversity, composition and function of soil and plant- associated microbial communities remain unclear. An increase in plant- available nitrogen might reduce allocation of carbon to mycorrhizae belowground (Wallenda and Kottke, 1998; Högberg et al., 2010) and have an indirect effect on the symbionts. In addition, nitrogen preferences differ for EMF species and copiotrophic taxa with higher nitrogen needs and fast growth might gain in advantage (Lilleskov et al., 2001), similar to bacteria that prefer more nutrient rich environments. Fungi are not only important for turnover of organic compounds as their biomass itself represents an important soil carbon storage (Clemmensen et al., 2013). To determine if soil carbon pools are decreasing or increasing in future and to determine the sensitivity of the boreal ecosystem to increasing nitrogen input, detailed studies of the microbiota driving these changes are needed. High-throughput marker gene sequencing

techniques (e.g. amplicon sequencing of the 16S or internal transcribed spacer (ITS) sequence of the rRNA gene) now provide the means to do this and will provide insights into the taxonomy and composition of microbial communities.

In contrast to belowground habitats, leaves are exposed to harsh climatic fluctuations on a daily and seasonal basis. A variety of adapted microbiota can be found in the tree phyllosphere, which is the surface (phylloplane) and internal tissue of the living leaf (Osono, 2006). The leaf surface is colonized by methylotrophic bacteria, which live off of metabolic by-products emitted from the plants (Vorholt, 2012). The internal, extracellular space of leaves offers protection for endophytic fungal species, which are found in high abundance and diversity in conifer needles (Carroll and Carroll, 1978; Müller et al., 2001; Arnold et al., 2007). Interactions beneficial for the plant have also been proposed. For example diazotrophic bacteria, found in poplar and conifer leaves (Khan et al., 2012; Carrell and Frank, 2014; Knoth et al., 2014; Carrell and Frank, 2015) could supply the plant hosts with atmospheric nitrogen.

The understanding that plant fitness is not only regulated by the plant but also by its associated microbiota led to the definition of the plant holobiont (Margulis, 1993; Knowlton and Rohwer, 2003; Zilber-Rosenberg and Rosenberg, 2008; Youle et al., 2013; Vandenkoornhuyse et al., 2015). As described, plants are linked to a complex network of symbiotic interactions, whereby the microorganisms provide additional genes and functions to their host and allow adaptation to local environmental conditions (for a model of the interactions see Fig. 2). From this, it is assumed that a core microbiome exists at all levels of the ecological hierarchy (from the individual plant to the ecosystem) that is functionally significant for the plant holobiont (Vandenkoornhuyse et al., 2015). Taxonomic identification of microorganisms using high-throughput DNA-based marker gene sequencing has made great advances, providing insights into diversity and composition at ever-increasing resolution. To an even greater extent than DNA-based assays, the gain of combined taxonomic and functional information available from metatranscriptomics analyses, which profile active gene expression within the microbial metacommunity, could revolutionize the understanding of the functional role of the microbial portion of the holobiont and its impact on the plant host fitness, productivity and health. Application of beneficial plant microbiota in tree production could make even faster growth possible and make trees more resistant to challenges associated with climate change or pathogens.

Phyllosphere Tree C N

(Mycor)rhizosphere

Soil

EMF C N

DNA RNA Marker gene Metatranscriptomics Bacteria (16S rRNA, ITS)

Diversity, Taxonomy Taxonomy, FuncAon

Paper III Paper IV

Figure 2 Overview of plant-microbe interactions in boreal Norway spruce forests. Complex interactions of plant, fungi (e.g. EMF, ectomycorrhizal fungi) and bacteria establish in the phyllosphere, mycorrhizosphere and the soil; driven for example by nutrient exchange (e.g. C, carbon for N, nitrogen). DNA (e.g. marker gene sequencing of the 16S or internal transcribed spacer (ITS) sequence of the rRNA gene) and RNA analysis strategies vary in their potential to describe communities. Image sources: (Brundrett et al., 1996; Ermintrudel, 2013; Hazard and Johnson, 2018).

Objectives

My doctoral thesis has the overall aim to gain a holistic view on abiotic and biotic factors, at the level of gene expression and tree-microbe interactions, that will contribute to determining the future of Norway spruce in boreal forests in light of the ongoing climate change. Norway spruce was the first gymnosperm tree with a sequenced genome and is of high economic and ecological value, making it a suitable model to study. In detail, the following topics are the subject of this thesis:

1. Understand the conservation and divergence of differential gene expression between Arabidopsis and Norway spruce in response to low temperatures and the different roles roots and needles have in the cold response of this coniferous tree. 2. Elucidate the transcriptional response in Norway spruce seedlings to progressing drought and reversibility after re-irrigation. Further, to establish existing differences in gene regulation and functions of needles and roots and compare to the known drought response exemplified in Arabidopsis. 3. Evaluate the effects of long-term low nutrient addition on the Norway spruce holobiont: Dissecting the effects of plant organ, tree nutrient optimization and season on the fungal and bacterial microbiota in association with this tree host. As such, changes in diversity and composition and the significance of certain community members driving these are characterized in detail. 4. Compare the use of modern DNA and RNA based sequencing techniques to study the metacommunities of tree-associated fungi and bacteria. Information on taxonomy and community structure by one can be complemented by functional information of the second.

The studies performed provide novel knowledge about the molecular mechanisms in response to cold and drought stress in the gymnosperm Norway spruce and insights into microbiota associated with Norway spruce at the whole tree level and under the influence of increasing nutrient availability. To understand and estimate interactions and feedback of changing environmental and climatic conditions on the tolerance and productivity of trees and the function of microbial communities, it is necessary to first gain insight into the individual responses, which was the aim of this thesis. These insights will now hopefully contribute to the design and implementation of follow-on studies that will help ensure future sustainability in boreal forests.

Results and Discussion

Differential gene expression has often been reported to overlap for abiotic stresses. For example, in Arabidopsis 11% of the genes responsive to cold, drought and salinity were common stress-responsive (Seki et al., 2002). The general stress response includes osmotic and oxidative stress response mechanisms. In addition to these unspecific secondary stress responses that indicate an existing signalling cross-talk, the primary stress initiates specific stress signals (Xiong et al., 2002; Beck et al., 2007). Little is known about the specific gene regulation of cold and drought in Norway spruce and work in this thesis provides insight into the molecular mechanisms.

Cold acclimation mechanisms in Norway spruce Plants differ in their tolerance of low temperature and the molecular mechanisms involved have not yet been fully discovered. Especially evergreen conifers are known for being able to maintain photosynthetically active foliage that remains undamaged and functional for several years, despite exposure to extremely low winter temperatures, which can be as low as -40 °C for several months every year (Sakai, 1966; Sakai and Weiser, 1973; Strimbeck et al., 2007, 2008). In contrast, more extensively studied plant species, such as the model plants Arabidopsis and poplar, are annual or shed their leaves during the establishment of dormancy at the end of the season and thereby avoid the loss of costly plant organs by frost damage. Therefore, one of the obvious questions is: what is the role of the transcriptome in gymnosperm trees, such as Norway spruce, during cold acclimation and how does it differ from that of angiosperms? Due to the evolutionary separation and substantial lifestyle differences it may not be unexpected to find that regulatory networks have a different wiring in Norway spruce and require determining per species. Secondly, while needles are developmentally mature, roots actively grow year- round and we do not know what effect cold has on roots. The transcriptional response to low temperatures, even though protected by soil and snow cover, might vary extensively from that of aboveground tissues.

In order to study transcriptional changes at the whole plant level, needle and root samples of non-dormant Norway spruce seedlings, undergoing stepwise cold acclimation to low temperatures, were collected for RNA sequencing (RNA- Seq) studies. Plants were shifted from 18 °C (control condition) to 5 °C for ten days and thereafter to -5 °C for an additional ten days at 16 h light (Paper I). Similarly, leaves of Arabidopsis plants were treated with low temperatures (5 °C) and sampled for transcriptomic analysis. Differentially expressed (DE) genes were then identified using a combined set of criteria of log2 fold change >

2 and an adjusted p value < 0.01, between control and treated samples, resulting in a total of 3335 DE genes in Arabidopsis and 6251 DE genes in Norway spruce needles in response to cold (5 °C), representing a similar proportion (~15 %) of genes of the respective cold transcriptomes at the maximum of expression after ten days (Paper I, Figure 1). Distribution of up- and down-regulated DE genes was similar between the species, but Arabidopsis exhibited an early up-regulation of a substantial number of genes after 6 hours, with further changes after three and ten days. In Norway spruce, on the other hand, the number of DE genes increased gradually until ten days, by which time there were four times the number of genes DE compared to at three days. A fast response in herbaceous plants, including Arabidopsis, to low temperatures has often been reported and was found to correspond to the early up-regulation of transcriptional regulators of the CBF-type belonging to the AP2 (APETALA 2) ERF (ethylene-responsive element-binding factor) subfamily of TFs (Chinnusamy et al., 2003; Vogel et al., 2005; Kim et al., 2013; Shen et al., 2015; Kim et al., 2017) (Paper I, Figure 3A). Identification of potential CBF- encoding genes in a woody perennial, Populus spp., also showed early induction between 3 to 6 hours of PtCBF1 and PtCBF3 after cold exposure in both leaves and perennial stems (Benedict et al., 2006), suggesting a potential function in both short term and seasonal cold acclimation. In addition, evergreen eucalyptus trees showed significant expression of CBF1s in leaves in response to cold (Navarro et al., 2009). The CBF TFs are the best characterized plant TFs regulating expression of functional cold genes and have been found to be widely conserved in plants (Jaglo et al., 2001; Hsieh et al., 2002; Hsieh et al., 2002; Lee et al., 2004; Puhakainen et al., 2004; Benedict et al., 2006). In Norway spruce, however, no orthologs for the CBF TFs have been identified by means of OrthoMCL, and best homologous matches were not among the up-regulated DE genes responding to cold (Paper I). Similarly, orthologs of full-length protein sequences for CBF TFs have also not been found in earlier plant divisions such as the moss Physcomitrella patens (Beike et al., 2015). Together, these findings indicate that development of these key regulators are evolutionary linked to vascular plants and that at least differential expression in response to cold stress differs in gymnosperms and angiosperms. However, also other ERF TFs are cold responsive (Bending and Read, 1996) and ERF TFs, such as for example ERF53, were found to be over-represented among the DE genes in Norway spruce (Fig. 3a; Paper I, Figure 3B). The closest representative to the CBFs (DREB1s) in Norway spruce was an ortholog of the DREB2B gene, but exhibited increased expression only after 10 days in the needles, further emphasizing the comparably late activation of TFs involved in cold acclimation in Norway spruce needles. Many other TFs of the NAC, MYB and WRKY families were up-regulated after 24 hours and 3 days and even more after 10 days only (Fig. 3a; Paper I, Figure 3B), coinciding with strong up-regulation of other DE genes after 10 days in needles (Paper I, Figure 6B). A core set of

475 up-regulated genes in Norway spruce had a conserved function in cold response in Arabidopsis. Associated with these were up-regulation of Gene Ontology (GO) terms of “response to stress”, “plasma membrane”, “carbohydrate metabolic process” and also down-regulation of processes in the “plastid” and “photosynthesis”, confirming a biologically adequate response in cold stressed needles (Paper I, Figure 2 & 4C). However, the greater number of up-regulated genes (2146) was specific to cold response in Norway spruce and involved in biological processes such as “transport”, “signal transduction”, “ion binding”, “DNA binding” and “cellular protein modification process”. The ability to develop deep frost tolerance in winter might explain some of the specific responses in Norway spruce needles. The massive reprogramming of the transcriptome and metabolome often observed during cold acclimation in plants are energy intensive (Huner et al., 1998; Dobrota, 2007; Fristedt et al., 2015; Wu et al., 2016) and Norway spruce may initiate large transcriptional changes in needles only after a persisting cold stress for several days, finally leading to the acclimation to both cold and freezing temperatures.

To better understand mechanisms specific to Norway spruce, above- as well as belowground, the response of roots was considered in further analyses. A striking response to cold was seen in both tissue types at 10 days 5 °C, and continued at the same level in needles, but further intensified in roots at freezing temperatures (-5 °C) (Fig. 3b; Paper I, Figure 4A). Roots were especially sensitive to freezing and induced genes related to “oxidoreductase activity” involved in protection from ROS and that might have a crucial role preventing cell death (Paper I, Figure 4C). Previous comparison of roots and leaves in Arabidopsis reported that less than 14% of the cold-induced transcriptional changes were shared between tissues (Kreps et al., 2002); similar to a study in Thellungiella salsuginea, closely related to Arabidopsis (Wang et al., 2017). Here, about 54% and 45% of the up-regulated genes were shared in needles and roots, respectively, considering both cold and freezing temperatures (Paper I, Figure 5 & 6). Co-expression network analysis could further identify central nodes of the cold-response transcriptome in Norway spruce (Fig. 3c; Paper I, Figure 7). Candidate regulatory genes that were the most highly connected hubs included an ICE1-like TF in both tissues, which is of great interest because of its known function in Arabidopsis for positive regulation of CBF TFs during cold (Chinnusamy et al., 2003; Ding et al., 2015). The use of co-expression network analysis can overcome limitations of sequence similarity comparisons and identify conservation and divergence of gene regulation between species, by combining expression similarity analysis with analysis of co-expressed links and neighborhood genes (Netotea et al., 2014). This could facilitate identification of CBF candidates in Norway spruce in the future that have so far not been predicted. This study is, however, a first hint that differences in the regulatory networks of cold stress exist between

Arabidopsis and Norway spruce and that knowledge transfer across species is not possible but instead determining per species is required. On the other hand, the presence of a root specific module, with a bHLH101-like TF in the center, emphasized the presence of tissue-specific responses to cold. Future molecular investigations of cold stress responses have to focus more on the differences between species and tissues of this complex multigenic trait to further understanding of acclimation strategies that will also provide useful information on the sensitivity to climate change.

19 A

A A CAMTA4 ICE1 CBF2 CBF3 VST CBF1 ZAT12

CAMTA4 CAMTA4 ICE1 ICE1 CBF2 CBF2 CBF3 CBF3 VST VST CBF1 CBF1 ZAT12 ZAT12

B ATH TF-DEC Spruce TF-DEC ORA47 ORA47 ERF6 AP2 MYB33 ERF53 MYB7 anac025 MA_493100g0010 ORA47 TINY2 B B ORA47 ATH TF-DEC ATH TF-DEC Spruce TF-DECORA47Spruce TF-DEC (a) Arabidopsis TFs Spruce TFs ORA47 ORA47 ORA47 ERF6 ORA47 ORA47 ERF6 AP2 AP2 MYB33 MYB33 ERF53 ERF53 MYB7 MYB7 anac025 anac025 MA_493100g0010MA_493100g0010 ORA47 ORA47 TINY2 TINY2 ORA47 ORA47 ORA47 ORA47 ORA47 ORA47 TINY2

DEAR4

DEAR4 Scaled VST

TINY2 TINY2 TINY2 ERF1 DEAR4 DEAR4

DEAR4 DEAR4 Scaled VST Scaled VST MYB123 TINY2 TINY2

ERF1 ERF1

TOE2 TOE3 MYB123 MYB123

ERF9 MYB101 ERF3 TOE2 TOE2 TOE3 TOE3 A anac078B AP2 AP2/ERF Superfamily ERF9 ERF9 bHLH TEM1 MYB101 MYB101 ESE2 ERF3 ERF3 ERF2 bZIP LBDB MYB anac078 anac078 NAC WRKY20 WRKY AP2 AP2 Other HY5 ZFP4 TEM1 TEM1 Target COR needles ESE2 ESE2 anac028 ERF2 ERF2 Target COREGL3 roots Target COR common Family 6h 5°C 3d 5°C

Control WRKY20 WRKY20 24h 5°C 10d 5°C HYH HY5 HY5 ZFP4 ZFP4 anac028 anac028 EGL3 EGL3 Fraction ofFraction nodes Family 6h 5°C 3d 5°C Control 24h 5°C 10d 5°C Family Family 6h 5°C 3d 5°C 6h 5°C 3d 5°C Control Control 24h 5°C 10d 5°C 24h 5°C Figure 3. Transcription factors analysis. A) Expression profiles of previously characterized Transcription factors10d 5°C (TF) involved in cold HYH HYH response were represented in our Arabidopsis thaliana data. CAMTA4 (AT1G67310), ICE1 (AT3G26744), CBF1 (AT4G25490), CBF2 (AT4G25470), CBF3 (AT4G25480) and ZAT12 (AT5G59820) profiles are represented using variance stabilizing transformation (VST) gene Family Family 6h 5°C 3d 5°C 6h 5°C 3d 5°C Control expression values using means of threeControl replicates. Errors bars represent SD. B) TF differentially expressed by cold (TF-DEC) were analyzed 24h 5°C 10d 5°C 24h 5°C in both analyzed species. TF with positive changes between10d 5°C regarding control were represented (corrected P-value ≤0.01 and Fold Change Figure 3. TranscriptionFigure(b) 3. ATranscription factors analysis.Spruce Needles factorsNeedles A) Expressionanalysis. A) profiles Expression of previously profiles ofcharacterizedSpruce Roots previouslyRoots characterizedTranscription TranscriptionfactorsB (TF) involved factors (TF) in cold involved in cold ≥2). VST data responsewere scaled were byresponse representedrow means. were in For represented our each Arabidopsis heatmap in our zoom thalianaArabidopsis versions data. thalianaincludingCAMTA4 data. all(AT1G67310), theCAMTA4 identifiers (AT1G67310), ICE1 are available (AT3G26744), ICE1 in Supplemental (AT3G26744), DegreeCBF1 (AT4G25490), Fig. CBF1 (AT4G25490), CBF2 CBF2 S6 and S7. (AT4G25470),(AT4G25470), CBF3 (AT4G25480) CBF3 (AT4G25480) and ZAT12 (AT5G59820) and ZAT12 (AT5G59820) profiles are represented profiles are using represented variance using stabilizing variance transformation stabilizing transformation (VST) gene (VST) gene 0.035

expression valuesexpression usingC means values0.035 of using three means replicates. of three Errors replicates. bars represent Errors bars SD. representB) TF differentially SD. B) TF expresseddifferentially by coldexpressed (TF-DEC) by cold were (TF-DEC) analyzed were analyzed AP2/ERF Superfamily in both analyzedin both species. analyzedbHLH TF with species. positive TF changeswith positive between changes regarding between control regarding were representedcontrol were (corrected represented P-value (corrected ≤0.01 P-value and Fold ≤0.01 Change and Fold Change

≥2). VST data≥2). were VST scaledbZIP data by were row scaled means. by For row each means. heatmap For eachzoom heatmap versions0.025 zoom including versions all theincluding identifiers all the are identifiers available inare Supplemental available in SupplementalFig. Fig. LBDBHeight S6 and S7. S6 and S7.MYB 0.020 Height NAC WRKY 10d 5°C 0.015 Other A B Target 0.005 COR needles Target COR roots A B 3d 5°C 3d 5°C AP2/ERF Superfamily 10d -5°C 6h 5°C 0.005

Target COR common Control 24h 5°C 24h 5°C 6h -5°C

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Control bZIP 10d -5°C ERF3LBDBbHLH MYBbZIP (c) LBDB NAC WRKYMYB OtherNAC WRKY TargetOther COR needles Target COR roots TargetTarget COR COR common needles Target COR roots anac47 Target COR common

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Figure 7. Regulatory network analysis. A) Network representation of regulatory interactions between Transcription Factors (TF) and cold responsive (COR) genes. TF are represented by diamonds and their family membership by colours. COR genes are represented by circles and colored according the tissue where they are differentially 20regulated. Topologyanac47 information and gene aliases of hubs is available in Supplemental Table S10 and Gene Ontology (GO) enrichments in their neighbourhoods in anac47Supplemental Table S11. B) Network Degree distribution in Log10/Log10 scale C) Sub-network representing the merge of first neighbourhood of 10 most connected nodes. BHLH101 BHLH101

anac28 DREB2 anac28 DREB2 anac28 anac28

anac78 AKS3 anac78 AKS3 anac47 ICE1 anac47 ICE1

Figure 7. Regulatory network analysis. A) Network representation of regulatory interactions between Transcription Factors (TF) and coldFigure responsive 7. Regulatory (COR) genes. network TF are analysis. represented A) Network by diamonds representation and their familyof regulatory membership interactions by colours. between COR Transcription genes are represented Factors (TF) by and circlescold and responsive colored according (COR) genes. the tissue TF are where represented they are bydifferentially diamonds regulated.and their family Topology membership information by andcolours. gene CORaliases genes of hubs are isrepresented available by in Supplementalcircles and colored Table S10 according and Gene the Ontologytissue where (GO) they enrichments are differentially in their regulated. neighbourhoods Topology in Supplementalinformation and Table gene S11. aliases B) Network of hubs Degreeis available distributionin Supplemental in Log10/Log10 Table S10 scale and C) Gene Sub-network Ontology representing(GO) enrichments the merge in their of firstneighbourhoods neighbourhood in Supplemental of 10 most connected Table S11. nodes. B) Network Degree distribution in Log10/Log10 scale C) Sub-network representing the merge of first neighbourhood of 10 most connected nodes.

Figure 3 Cold response of Arabidopsis and Norway spruce seedlings. Heatmap (a) representing the up-regulated transcription factors (TFs) differentially expressed during cold in leaves and needles of both species. Expression data was normalized using variance stabilizing transformation and a log2 fold change > 2 and adjusted p value of < 0.01 were set as significance cut-offs for differential expression. Family membership of TFs is indicated by colors. Hierarchical clustering (b) of expression data from needle and root samples of Norway spruce. Red numbers correspond to Approximately Unbiased (AU) values and green ones to Bootstrap Probability (BP) values; see methods part Paper I. Network analysis (c) of regulatory interaction between TFs and cold responsive (COR) genes. Displayed are the ten most connected nodes. TFs are represented by diamonds and their family membership by color. COR genes are represented by circles and colored according to the tissue they are differentially expressed in. For detailed gene identifications see Paper I.

Gene regulation during drought in Norway spruce Plants need to maintain their water status above a critical level in the presence of a drought stress and species have evolved different strategies to do so. Leaf water potential is an indicator of the water status and the threshold for stomatal regulation preventing further water loss by transpiration varies among plant species. In a broader sense, plants cover a spectrum of isohydric and anisohydric behaviors that describes the sensitivity of stomatal control (Klein, 2014). Isohydric plants maintain a relatively high leaf water status by early closure of stomata over a narrow range of water potential, whereas anisohydric plants enable leaf water potentials to decrease substantially during drought. These two strategies have concomitant impacts on the maintenance of photosynthesis and growth. Gymnosperms were found to have an intermediate response between a passive hydraulic stomatal control, as seen in ferns, and the elaborate ABA-dependent stomatal regulation of anisohydric angiosperms (Brodribb et al., 2014). There has been little exploration of the molecular mechanisms controlling the water status in gymnosperms and this was therefore the focus of this study (Paper II).

Norway spruce has been reported to have isohydric-like stomatal behavior (Brodribb et al., 2014). Studies of drought stress within this species will provide new insights into the molecular mechanism and regulatory control underlying isohydrism and provide insight into the extent of conservation between lineages. Furthermore, the availability of needle and root data from the same study will advance understanding of the contrasting mechanisms that may be induced in response to drought in source and sink tissues. Within this context, potted seedlings were subjected to increasing levels of soil water deficit and samples were collected from these to perform RNA-Seq analysis. To induce a mild drought stress water was withheld from previously well-watered plants at saturated field capacity (FC) (80%) until FC reached 30%, then maintaining FC at this level for one week (Paper II, Figure 1A). Subsequently, a severe drought stress was initiated until signs of stomatal dysfunction and of

assimilation were observed at day 18, after which seedlings were re-irrigated from day 21. Roots and needles of seedlings were sampled at control (80% FC), mild (day 2,4,5) and severe (day 18, 21) drought conditions, as well as several days after re-irrigation (day 25) and expression levels of genes compared to the control. Water potential measurements of lateral shoots at midday were taken to estimate the water status of the treated seedlings. In addition, non- destructive physiological measurements of stomatal conductance (gs) and photosynthetic assimilation (Amax) were performed on shoots of independent seedlings that were either well-watered or exposed to the same progressive drought regime. Water potential decreased after five days, indicating that a mild drought stress was already sensed in aboveground tissues (Fig. 4a; Paper II,

Figure 1D). However, gs and Amax only significantly decreased with severe drought, after 16 and 17 days, respectively and reached a minimum at day 18, but were recovered after re-irrigation to 60% and 80% (Paper II, Figure 1B & C). Stomatal closure occurred over a narrow range of water potential and allowed maintenance of a relatively high water potential (-2.1 MPa) for an extended period thereafter, which is similar to the response observed in another more isohydric conifer Pinus radiata (Brodribb and McAdam, 2013). For a subset of seedlings, initiation of re-irrigation was delayed until 23 instead of 21 days. Seedlings experiencing two additional days of severe drought stress died, clearly highlighting how severe the induced drought stress had been for the seedlings. This also indicates the large impact that extended drought periods may have as a result of future climate conditions if seedlings in the field respond in the same manner. Evergreen gymnosperms, such as Norway spruce, can not avoid drought by leaf shedding and entering dormancy as observed in deciduous trees (Arndt et al., 2001) and have been observed to die as a consequence of hydraulic failure and dessication (Ditmarová et al., 2010). The limited physiological changes observed in needles until the severe drought were reflected at the transcriptome level. In needles 1403 genes were DE, in contrast roots induced a far more extensive transcriptional response with 6194 genes DE in roots (Paper II, Figure 2B). A PCA plot on the transcriptomic data indicated separation of samples of the mild drought from the control in roots (Fig. 4b; Paper II, Figure 2A) but the greatest increase in the number of DE genes was seen in response to severe drought in both tissues (Fig. 4c & 4d; Paper II, Figure 2C). In roots there was also a substantial number of DE genes after re-irrigation. Functional annotation by Gene Ontology (GO) revealed that “response to stress” was common to both tissues under severe drought (Paper II, Figure 3). A more active transcriptional response in roots during severe drought was associated with biological processes of growth (e.g. “anatomical structure development”, “growth”, “cell wall organisation or biogenesis”), metabolism (e.g. “carbohydrate metabolic process”) and “transport”; with extensive down-regulation of genes within these categories (Paper II, Figure 4). The smaller number of genes DE in needles did not

result in significant enrichment of GO categories but there were a number of DE genes present in categories including “anatomical structure development”, “biosynthetic process”, “reproduction”, “small molecule metabolic process”, “cellular amino acid metabolic process”, “signal transduction” and “transport” that included more up-regulated genes, indicating that needles maintained metabolism at a business as usual state until severe damage occurred and may be of limited use for early recognition of drought stress (Paper II, Figure 3 & 4). In contrast, identification of TFs specific to roots that are induced early in response to abiotic stress, such as of MYB2, are especially interesting as drought marker genes and targets for molecular breeding of higher drought tolerance in Norway spruce seedlings. The limited transcriptional changes observed here in needles could further be explained by the fact that physiological adjustments in needles may not require modulation of the transcriptome and because they are developmentally mature, in contrast to roots that are actively growing. Root development and growth were under active transcriptional control during drought and after re-irrigation. In agreement with the transcriptional changes observed here that may restrict lateral and fine root growth, field and laboratory studies of Norway spruce and beech reported decreased fine root biomass, tip length and frequency (Eldhuset et al., 2013; Zang et al., 2014) and also lateral root growth was reportedly reduced in plants through meristem suppression in response to drought (Deak and Malamy, 2005). The TF MYB96 has a function in down-regulating lateral root growth in cross talk with ABA (Seo and Park, 2009) and was also found to be highly expressed in Norway spruce roots under severe drought (Paper II). However, in both trees and agricultural crops, primary root growth is often observed to be maintained longer than shoot growth during drought and, together with reduced shoot growth, this results in an increased root-to-shoot ratio, which in combination optimizes the balance of water loss from the shoot and water uptake from roots (Wu and Cosgrove, 2000; Sharp et al., 2004; Bogeat-Triboulot et al., 2007). A recent study of global gene expression of roots and shoots in Arabidopsis also found a high amount of root specific genes in response to drought in addition to genes that were up-regulated earlier in roots (Rasheed et al., 2016). Among these they identified DREB2A and DREB2B TFs in roots to be DE in advance of shoots (Rasheed et al., 2016). Similarly, DREB2B was DE only in roots of Norway spruce during severe drought and was induced more rapidly in roots than needles during cold (Paper I and II), highlighting the need to dissect the transcriptomes of needles and roots in response to different abiotic stresses and further emphasizing not only differences in the intensity of the response between tissues but also differences in the timing. Organ specificity of drought response has only been reported in a relatively small number of previous studies of Arabidopsis, rice and poplar (Kreps et al., 2002; Bogeat-Triboulot et al., 2007; Zhou et al., 2007).

Importantly, divergence of transcriptional drought response between plant species exists and reliable drought genes in Arabidopsis (Bray, 2004) were not regulated in trees (Polle et al., 2006). In the study presented in this thesis, the drought response of Norway spruce was also compared to that of Arabidopsis by considering a curated set of validated and characterized drought-response genes. A manually curated set of 373 drought genes from Alter et al. (2015) and GO terms related to “response to water deprivation” was used to perform the comparison and showed that only 11% and 31% had DE orthologs or homologs in needles and roots, respectively and were also DE, supporting the idea of limited conservation of transcriptional response between species (Paper II, Table 2). Among those, conservation existed for induction of solute accumulation (sugars, amino acids), ROS scavenging, ABA biosynthesis and signalling, but also for genes that restrict proline catabolism, ROS production and auxin and cytokinin signalling. However, divergent transcriptional regulation of TFs of the ABA-dependent pathway in angiosperms, the AREB/ABFs, was observed. These were not DE in Norway spruce but instead, ABA-independent signalling via DREB2B and ERF53 was more pronounced (Paper II, Figure 5 & Supplementary file 3). This divergence might be specific to the more isohydric gymnosperms and suggests that contrasting adaptive strategies have evolved in the anisohydric angiosperms. This initial comparison requires further exploration by future studies as it represents only a single-point comparison between the two lineages and is complicated by the fragmented nature of the Norway spruce genome assembly, which makes ortholog inference challenging and noisy (Nystedt et al., 2013). Nevertheless, the fact that we identified similar expression changes in both cold (Paper I) and drought (Paper II) of genes involved in Ca2+ or ROS signalling and biosynthesis of solutes, which are typical components of abiotic stress responses, demonstrates that the current genome annotation does provide reliable functional information. In addition to those genes with conserved drought response between Norway spruce and Arabidopsis, the 12% of genes DE during drought that were Norway spruce specific (singletons), together with genes with a novel function in drought response of Norway spruce makes these interesting candidates to gain further insights into the basis of isohydrism. Both cold and drought experiments have shown that Norway spruce possesses some unique mechanisms separating it from angiosperm trees in its abilities to face abiotic stresses and these should be a focus of future investigation.

24 100

80 100 matrix_14 type

60 80 f ghij abcde 60 4 control 2 M60 SWC 40 40 gs_max 0 M40 20 -2 M30 20 0 -4 M307d matrix_23 S 0 S48h a R 0 5 10 15 20 25 0 5 10 15 20 25 b age days_of_treatment days_of_treatmentkl m n o PCA of drought stress c d Needle

e 80% type 60% 0.0 f 40% Control 30% (a) (b) control30%7d g Collapse M60 2 Days C2d h Rehydrate −0.5 4 Days 100 i M40 ] j M30 5 Days 80 −1.0 k M307d13 Days 60 MPa

[ S type 18 Days 40 l −1.5 m S48h21 Days PC2 [7%] 20 shoot R

Comp. 2 (7%) Comp. Recovery Ψ

Amax_max n C2d Root

0 80% Root 60% Root 40% Root 30% Root waterpotential o age

−2.0 C2d Needle 80% Needle 60% Needle 40% Needle 30% Needle 30%7d Root Needles Root Collapse Root 30%7d Needle

a ab a b a c c a Root Rehydrate Roots Needle Collapse Needle

−2.5 100 0 50 150 250 Root 10.0 7.5 −

Rehydrate Needle Rehydrate 6

7.5 5.0 0 5 10 15 20 25 0 5 10 15 20 25 VST 4 VST VST 5.0 −200 −100 0 1002.5 200 2 2.5 days_of_treatment days_of_treatmentDay of Experiment 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate PC1 [62%] 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 50 40

6 Comp. 1 (62%) (c) Needles Roots 25 Cluster 1 (56 genes) 20 0 VST 5 varianceVST stabilized counts VST 0matrix_14 type 10.0 7.5 -25 6 4 -20 bd f ghij abcde 7.5 10.0 5.07.5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate VST 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate VST VST 4 6 12 5.0 8 7.5 2.55.0

VST 7 VST VST 9 2 4 4 2.5control 6 5.0 6 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 2.5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 6 4

VST VST VST 52 50 2.5 40 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 2 6 3 4 Cluster 2 (400 genes) 25 2 M6050 2040 0 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 6 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 0 VST 5 VST VST 25 020 12 -25 7 0 4VST 5 VST -20VST 0 8 7.5 0 M40 6 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate -25 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 4 -20 12

VST VST VST 5.0 8 5 0 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate matrix_280% 60% 40% 30% 30%7d Collapse C2d Rehydrate 7 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 7.5 10.02.5912 6 6 -4 4 -2 M308 6 a 7 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 67.5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 5.04 VST Cluster 3 (451 genes) 9 VST VST VST VST VST 6 5 4 c 6 9 2 6 35.0 7 6 2.5 4 4 VST 8 matrix_2VST h VST 5 2 0 6 2.5 2 7 3 80% 60%-4 40% 30% 30%7d Collapse C2d Rehydrate M307d80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate a80%j 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 5 6 VST VST VST 1250 400 4 5 7 ck 2 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 6 8 7.5 3 42512 20 hl matrix_26 7 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate S 0 m VST VST VST VST VST VST 5.0 5 8 0 j 7.5 matrix_23 5 6 a 0 -25 k 4 2.5 4 VST Cluster 4 (29 genes) -20VST c VST 5.0 -4 4 5 10.00 matrix_27.5 l 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80%80% 60% 60% 40% 40% 30% 30% 30%7d Collapse C2d C2d Rehydrate Rehydrate 80%80% 60% 40% 30% 30% 30%7d 30%7d Collapse Collapse C2d C2d Rehydrate Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 6 S48h 2.5 7.5 h 10.012 m 6 87.5-4 5.0 4a 9 VST VST 6 VST 7 4 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 7 j 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 7.59 5.0 matrix_2 8

VST VST VST 65.0 a c 4 2.566 9 6 k 7 5.0 4 7 2 6 2.542.5 5 ah VST VST VST 6 8

VST VST R VST 7.5 5 10.0 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 2 6 2.5 6 l 7 2 4 4 5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 3 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 2 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 cj 7.5 50 40 5 6 5.0VST 3VST m 4VST VST VST VST 0 b 40 matrix_2 4 506 4hk 5 5.0 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 252 80%80% 60% 60% 40% 30% 30%7d Collapse Collapse C2d C2d Rehydrate Rehydrate 20 80%80% 60% 60% 40% 40% 30% 30% 30%7d 30%7d Collapse Collapse C2d C2d Rehydrate Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 6 2.5Cluster 5 (1236 genes) 3 4 2512 20 ajl 2 0 VST 52.5 VST 7 VST 0 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80%80% 60% 60% 40% 40% 30% 30% 30%7d 30%7d Collapse Collapse C2d C2d Rehydrateage Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 08 7.5 m VST 5 VST VST 0 ck kl m n o -25 c 450 640 -20 -254 type h VST VST VST 5.0 l 4 6 -20 25 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate matrix_280% 60% 40% 30% 30%7d Collapse C2d Rehydrate 0 20 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 12 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate j 8 2.5 m 0 12VST 5 VST -4 VST 40 7 a 8 d Needlek 9 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 7 6 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate -25 6 c

9 type 4 6 -20 9 l 66 6 4 VST VST 7 VST 5 h 6 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate Cluster 6 (192 genes) 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 8 m VST VST VST 5 2 12 3 6 47 j 2 84 e 3 4 075 6 VST VST VST 9 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80%k 60% 40% 30% 30%7d Collapse C2d Rehydrate 0 6 10 4 type 8 5 2 6 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 12 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 3 4 l 7 6 4 7 VST VST VST 6 12 10 5 8 8 5 7 2 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 7.5 m80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 6

f type 3 8 64 4 7 VST 7.5 VST VST 4 6 0 5 6VST 5 VST VST 5.0 0 2 6 4 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate control VST VST 0 VST 5.0 4 4 VST VST VST

12 5 type 2.5 0 0 5 7 -4 0 80%4 60% 40%g 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 2.5 2 8 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 7.5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 6 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate -4 4 Cluster 7 (278 genes) 4 5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 6 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 9 0 7.5 6 4 5 M60 VST VST VST 80%75.0 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 5 9 8 6 0 6 7 35 6 5.0 8 type 47

VST h VST VST 4 2.5 6 7.5 -4 4 2 7 5 6 4 VST 4 VST VST 5 3 2.5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 6 4 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 5 VST VST 2 1 VST M40 3 5.0 4 5 39 4 4 VST VST VST 2 6 type 7 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 3 2 4 8 i 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 6 3 2.5 6 7 6 4 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 1 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 9 5 6 (d) Needles VST Roots VST Cluster 8 (290 genes) 5 VST 5 M30 4 80%5 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 2 type 4 6 4 a b 3 VST 4 j VST VST matrix_14matrix_14 typetype3 6 6 3 3 9 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 2 bd f ghij abcde bd f ghij abcde 5 c 25 1 M307d 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 480% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80%6 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 VST VST VST k 3 6 d 3 44 controlcontrol 3 10 2 5 e 2 S 1 type 4 VST VST f 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 5 l 3 22 6 M60M60 0 2 Cluster 9 (130 genes) 10 5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate S48h80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 7 20 4 VST 5 m VST 6 00 M40M40 10 3 5 matrix_2 0 2

VST VST R 10 8 0 4 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate n 7 a -2-2 -10 M30M30 3 C2d Root 67 20

80% Root 60% Root 40% Root 30% Root 5 6 6 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80%c 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 10 VST VST VST

5 5 10 8 h 0 2 age VST o-4-4 VST M307dM307d 0 4 47 C2d Needle Cluster 10 (3531 genes) 6 0 j 80% Needle 60% Needle 40% Needle 30% Needle 30%7d Root 5 -10 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 3 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 6 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 64 VST VST VST k 5 80% 60% 40% 30% 30%7d Collapse C2d Rehydratematrix_3 5 S80%S 60% 40% 30% 30%7d Collapse C2d Rehydrate 10 8 0 matrix_12 type 7.5 Root 4 5 l Collapse Root matrix_23matrix_23 47 6 a 30%7d Needle 0 4 control 35 5.0 4 6 m

VST 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate VST 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate VST 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 S48hS48h 6 VST 2 VST 2 M60 VST Rehydrate Root Rehydrate e 5 matrix_3 3 52.5 10 2 0 M40 7.5 5 Collapse Needle 4 aa a g −2 M30 4 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 0 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 3 5.0 RR 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 80% 60% 40% 30% 30%7d Collapse C2de Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate VST VST −4 M307d h VST

Rehydrate Needle Rehydrate 26 6 5 g 9 3 S bb i 2.5 5 7.5 15 matrix_2 matrix_3 4 Cluster 11 (183 genes) 5 h 4 S48h 6 4 VST 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate VST a80% 60% 40% 30% 30%7d Collapse C2dk Rehydrate VST 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 3 5.0 a 4 i g 3 VST VST R ageageVST 3 b kl m n o kl m n o 26 c 3 6 c 2 k matrix_3e 3 age 2.59 2 c 15 15 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80%g 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 d Needle 6 a 4 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate VST VST VST h 36 e dd NeedleNeedle 3 6 6 type 3 e 10 2 5 f 9 i 2 5 5 1 control 4 g

VST 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate VST g80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80%k 60% 40% 30% 30%7d Collapse C2d Rehydrate 5 4 ee 6 matrix_3 4 M60 VST VST 3 h VST 63 typetypeh 3 M40

type 3 100 2 a 5 i 2 Cluster 12 (58 genes) M30 i 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 1 j 80% 60% 40% 30%f 30%7d Collapse C2d Rehydrate e 4 f 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate VST 7 VST 20 M307d 5 k k 3 g 6 6 S controlcontrol 10l 100 2 5 5 S48hgg h

VST 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate VST 0m80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 4 R VST 7 VST i 5 20 M60M60 3 -103 6 age k 10 hh type 0 2 5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40%Root 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate VST VST 0 type 4 M40M40 7 20

3 -10 6 ii Cluster 13 (55 genes) 10 5 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate M30

VST VST M30 0 10 84 jj type 7 3 -10 6 5 6 M307dM307d 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 4 h VST VST VST 5 kk 0 2 i j 4 S 0

type S type k l 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate m 6 ll 5 Day 0 2 4 5 13 18 21 25 0 2 4 5 13 18 21 25 type 7.5 4 5 S48hS48h type

3 5.0 4 mm type VST VST VST

2 3 2.5

C2d Root RR

80% Root 60% Root 1 40% Root 30% Root

C2d Needle nn 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% Needle 60% Needle 40% Needle 30% Needle C2d Root C2d Root 30%7d Root 80% Root 60% Root 40% Root 30% Root 80% Root 60% Root 40% Root 30% Root

6 Collapse Root 30%7d Needle 6 9 Rehydrate Root Rehydrate Collapse Needle 5 5 oo ageage Rehydrate Needle Rehydrate 4 6 C2d Needle C2d Needle 4 VST VST VST 80% Needle 60% Needle 40% Needle 30% Needle 30%7d Root 80% Needle 60% Needle 40% Needle 30% Needle 30%7d Root 3 3 25 3 2

2 1 RootRoot Collapse Root 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80%Collapse Root 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 30%7d Needle 30%7d Needle 6 Rehydrate Root Rehydrate 10 Root Rehydrate 5 Collapse Needle Collapse Needle 4 VST 5 VST 3 Rehydrate Needle Rehydrate Rehydrate Needle Rehydrate 0 2

80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate

7 20

6 10

VST VST 0 4

3 -10

80% 60% 40% 30% 30%7d Collapse C2d Rehydrate 80% 60% 40% 30% 30%7d Collapse C2d Rehydrate

Figure 4 Response of Norway spruce seedlings to drought. Midday water potential (ψshoot) (a) was measured on shoots of three to five independent plants per time point at control (day 0), mild (2, 4, 5 and 13 days) and severe (18 and 21 days) drought and after re-irrigation (25 days). Statistics were performed on the average values of three measurements per plant. Data are means and error bars represent the ± 95% confidence intervals. Letters below bars represent statistically significant differences (p < 0.05) between time points of the drought stress. Principal component analysis (PCA) plot of transcriptomic data of drought stressed needle and root (b) samples. Expression data of control, mild and severe drought stressed seedlings and after rehydration was normalized using variance stabilizing transformation before ordination analysis. Three to four plants were sampled at each time point and used for RNA sequencing. The first two components of the PCA are shown with samples colored by sampling time point. Heatmaps display differentially expressed genes with expression over the eight sampling time points (day 0, 2, 4, 5, 13, 18, 21 and 25), separated by tissue, needles (green) and roots (brown). Up-regulated genes (c) are sorted in needle (cluster 1 and 2) and root specific (cluster 4 to 7) clusters or in common between the tissues (cluster 3). Similar, down- regulated genes (d) are sorted in needle (cluster 8) and root specific clusters (10 to 12) or common between the tissues (cluster 9 and 13). The highly populated clusters are detailed on the side and the average trend of gene expression and a 95% confidence interval indicated for both tissues.

Nutrient optimization and seasonal effects on plant-microbe interactions in a Norway spruce stand Tree growth increases in boreal forests when nutrient availability is improved by fertilization (Iivonen et al., 2006) and changes in vegetation are observed adapting to higher N contents in the ecosystem (Bobbink et al., 2010). Since plants often establish intimate relationships with microbial partners, changes in the lifestyle of a plant, for example a higher carbon and nitrogen metabolism will also affect the associated symbiotic microbiota. Mutual plant-microbe interactions, such as for example observed for rhizobia and EMF and their plant partner, are usually described as win-win-situations. Positive effects at either side can be growth-promoting effects, nutrient exchange, improved biotic or abiotic resistance or availability of niche habitats. The extent to which the partners depend on each other and how sensitive the established links are to environmental changes needs to be investigated. In this study consequences of long-term tree nutrient optimization in a mature boreal Norway spruce forest identified effects on diversity and community composition of fungal and bacterial communities. In addition, seasonal changes in carbon and nitrogen cycling in trees can influence the associated microbial community and therefore provides information about adaptation of microbiota at a relatively short-term scale (Paper III).

A complete mineral nutrient mixture (plant macro- and micronutrients) was supplied by irrigation during the growing season for five and 25 years to a naturally low nutrient Norway spruce stand. Needles, fine roots and soil were collected throughout one growing season and high-throughput sequencing was performed using phylogenetic marker genes for bacteria (16S) and fungi (ITS). The fungal biome of soil and root samples in untreated plots was >60%

identical and showed a strong dominance of the genus Piloderma (Paper III, Figure 1). Tree nutrient optimization resulted in higher fungal diversity and especially higher diversity of EMF in the soil after five years and in association with roots after 25 years (Fig. 5a; Paper III, Figure 2 & 3). Changes in composition included increasing abundance of the genera Pseudotomentella and Tylospora and more so in roots and decreasing abundance of Cortinarius and Piloderma, especially in the soil samples (Fig. 5c; Paper III, Figure 4). Bacteria in the soil comprised about 29% more operational taxonomic units (OTUs; equal to species) than in root samples and were of all sample types the most diverse in untreated plots (Paper III, Figure 1). Taxa of the phyla Proteobacteria, Acidobacteria and Actinobacteria were highly abundant in both root and natural soil samples. Diversity of soil and root bacterial communities was higher after five and 25 years of nutrient optimization (Fig.5b; Paper III, Figure 2). In contact with roots, bacteria of the phylum Cyanobacteria decreased in abundance, whereas Actinobacteria increased, indicating a trend towards more copiotrophic types adapted to nutrient-rich conditions (Paper III, Figure 5). In contrast to the changes observed belowground, the improved tree nutrient status had no effect on diversity of fungi of the phyllosphere compartment from needle samples but was naturally the most diverse habitat of the three sample types and differed at sampling points during the growing season (Fig. 5a; Paper III, Figure 1 & 2, 6). Leaf fungal communities are in general described as highly diverse and conifers at high latitudes exceed deciduous trees in diversity (Bálint et al., 2013; Millberg et al., 2015). Diversity and composition of needle bacteria was highly influenced by changes in the season and only secondary by treatment effects (Fig. 5b; Paper III, Figure 1 & 2, 6). Seasonal changes in diversity and composition observed here in phyllosphere fungal and bacteria communities were most likely connected to varying sugar and starch contents in needles during the growing season (Linder, 1995).

EMF are adapted to environments with low nitrogen availability. They access nitrogen released from organic compounds and transfer it to the host tree (Smith and Read, 2008) in exchange for carbohydrates (Talbot et al., 2008). However, application of inorganic nutrients increases plant available nitrogen and reduces carbon allocation to small and fine roots (Iivonen et al., 2006), which might indirectly affect EMF. This fitted with the observation that taxa with mycelia of the short distance type and therefore reduced carbon needs for mycelial growth (Colpaert et al., 1992; Wallenda et al., 1996; Nilsson et al., 2005) were more represented in treated plots. In addition, EMF differ in their nitrogen preferences and are described as “nitrophilic” or “nitrophobic” regarding their nitrogen tolerance (Fransson et al., 2000; Lilleskov et al., 2001). Higher soil nitrogen contents thereby also directly affected EMF and could explain abundance changes. However, the positive effects on diversity showed

that the observed compositional changes are part of a dynamic response, where certain mycorrhizal fungi replace or compensate others. That the response of fungi varies under increasing levels of nitrogen has also been shown for root-tip colonization (Arnebrant and Söderström, 1992; Kårén and Nylund, 1997; Jonsson et al., 2000; Taylor et al., 2000), mycelial growth (Nilsson and Wallander, 2003; Nilsson et al., 2005) and species richness (Kranabetter et al., 2009; Högberg et al., 2014), but were not per se negative, as implied by early sporocarp observations (Gardes and Bruns, 1996; Wallenda and Kottke, 1998; Jonsson et al., 2000; Dahlberg, 2001; Lilleskov et al., 2001). Similarly, increasing nutrient availability influenced bacteria in soil and root samples, for which changes in diversity preceded those in fungal communities. Free living bacteria make use of litter as a carbon source and directly profit from higher litter inputs observed at the research site after nutrient optimization (Olsson et al., 2005). On the other hand, bacteria also interact with plant roots and mycorrhizal fungi and are sometimes specifically selected for certain functions, for example to improve phosphorus uptake in a Quercus petraea-Scleroderma citrinum mycorrizhosphere by Burkholderia bacteria (Uroz et al., 2013). Here, decreasing abundance of bacteria with nitrogen fixing abilities, such as Acetobacteraceae (order Rhizobiales) and Cyanobacteria, after long-term nutrient addition indicated adaptation of the host tree or mycorrhizal partners to lower nutrient demands. In combination, these responses suggest a flow-on effect of the treatment from the tree-level to the microbial communities, with likely changes in soil carbon and nitrogen cycling and requires further investigations to determine consequences for carbon accumulation in boreal forests and ecosystem stability. These results highlight the highly complex, and inter-dependent, nature of the holobiont and wider microbial communities of boreal forests and the clear need for future studies to more fully understand how anthropogenic influences will impact these communities and interactions.

(a) (b)

(c)

Figure 5 Response of fungi and bacteria in a boreal Norway spruce stand to long-term N addition. Boxplots show median fungal (a) and bacterial (b) alpha diversity, using the Shannon diversity index of operational taxonomic units (OTUs), colored by treatment: Control (blue), five years of nutrient optimization (yellow) and 25 years of nutrient optimization (red) and split by season within each treatment (from left to right: early June, late June, August and October). Shannon diversity index represents log-transformed values. Lowercase letters represent Tukey honestly significant differences (HSDs) at P < 0.05, testing the effect of treatment within each sample type. Whiskers represent interquartile range (IQR) x 1.5. Heatmap of fungal OTUs (c) differing in abundance in root samples across nutrient levels. OTUs were filtered to contain > 10 counts and a relative abundance of > 0.005 %. Log2 fold change > 2 and an adjusted p value of < 0.01 were set as significance cut-offs and 48 OTUs were found to differ significantly. For display, OTU abundances

are centered across all samples. Names of the most similar database sequences on species level are shown along with each OTU. OTUs with an abundance of > 1 % are indicated in bold letters. Ectomycorrhizal taxa are marked red. The colored bars at the bottom represent the treatments, control in blue, five-year nutrient optimization in yellow and 25 years of nutrient optimization in red.

Assessing the active tree-associated microbial metacommunities in Norway spruce Phylogenetic marker gene sequencing, also called amplicon sequencing, is usually performed on DNA extracted from environmental samples and provides information on taxonomic composition, providing an idea of the relative abundance of species within the microbiota (see Paper III). While use of modern molecular methods based on amplification by polymerase chain reaction (PCR) and high-throughput next-generation sequencing have replaced labor intense culture-dependent techniques and Sanger sequencing, which provided low resolution, there are still limitations. Amplicon metagenomics make use of universal primers to study often complex microbial communities, based on the idea that in all domains of life genomic regions exist, for example in the ribosomal operon (rDNA gene), which contains both highly conserved and variable regions (Fierer et al., 2007; Schoch et al., 2012; Lindahl et al., 2013; Tonge et al., 2014). These regions are variable enough to discriminate between species but provide no functional information as to the biological processes being expressed by those species. In addition, the use of DNA limits information on the activity of microbial members and total microbial communities are biased by persistence of free DNA (Urich et al., 2008). Analysis of total RNA can overcome some of the limitations mentioned above, but bioinformatic tools for sequence analysis of whole meta-transcriptomes are not yet readily available. The following study compares taxonomic composition of microbial communities of plant samples based on both DNA and RNA sequencing (Paper IV).

Analysis of expressed transcripts offers both taxonomic and functional information of fungi and bacteria simultaneously. There have, however, been few studies to determine how similar the information is in comparison to amplicon sequencing and whether it provides the same detailed insight into community diversity and species composition. RNA-Seq data of Norway spruce and Eurasian aspen tissues, as representative boreal forest tree species, were compared to amplicon sequencing of the fungal Internal Transcribed Spacer 1 (ITS1) region and the bacterial 16S rRNA gene (Paper IV). mRNA reads from needles/leaves, buds and bark were assembled into contigs, representing expressed transcripts, and aligned against protein sequences from NCBI for taxonomic and functional annotation. 35 % of the assembled contigs were annotated, but only 1-2% were of fungal and 2-3% of bacterial origin, with 90%

of annotated contigs assigned to the host plant (Fig. 6a; Paper IV, Figure 2). As such, the vast majority of assembled transcripts had no sequence homology based match in the NCBI database. These contigs are potentially informative but their utility is negated by the limitations of current database resources. The number of reads forming a contig was used as a direct estimate of transcript abundance for the fungi and bacteria identified and was used as the input for sample clustering analysis in Principal Coordinate Analysis (PCoA); similar to OTU count tables generated from amplicon data, containing information about species and their relative abundance. Interestingly, the clustering showed similar patterns in both the metatranscriptome RNA-Seq and ITS1 OTU DNA amplicon dataset, indicating that composition and structural diversity of fungi were similarly represented in the metagenomic and metatranscriptomic datasets (Fig. 6b & 6c; Paper IV, Figure 3). Samples from mature trees were most distinct from samples of seedlings and needle samples and buds from mature trees separated by age class along the second coordinate. Similarly, different plant tissues (leaves, bark and adventitious shoots) were all clearly separated from each other in the aspen datasets. Analysis of bacteria, on the other hand, did not result in a similar sample clustering on the basis of the 16S OTU DNA amplicon and RNA-Seq datasets and only the latter resembled clustering observed in the fungal data (Fig. 6d & 6e; Paper IV, Figure 3). Comparison of the fungal classes identified, showed that both methods identified Agaricomycetes, Dothideomycetes, Eurotiomycetes, Leotiomycetes, Sordariomycetes and Tremellomycetes in Norway spruce samples and Saccharomycetes in aspen samples, while other taxa were uniquely identified by one or the other method (Paper IV, Figure 4). Most of the taxonomic differences are likely due to database limitations rather than differential representation of the taxa between the two data types. Less consistency was observed between the two methods for taxonomic identification of common classes of bacteria, in agreement with the structural differences mentioned before in bacteria datasets. Nevertheless, Actinobacteria, Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Bacilli and Flavobacteria were found in all samples independent of the sequencing method. A clear advantage of metatranscriptomics is the availability of additional functional information. However, currently only 20% of the fungal transcripts were annotated to genes with known functions and included mostly genes with housekeeping functions, such as actin, ubiquitin, ADP, ADT carriers, cytochrome oxidases and elongation factor 1-alpha (Paper IV, Figure 5). Expression patterns in the foliar samples of the mature Norway spruce trees confirmed the clustering in PCoA plots (Paper IV, Figure 3), highlighting that the observed compositional differences are most likely linked to functional differences in the fungal community.

A key step in the analysis was to assign taxonomy to the sequences, a process that very much depends on the quality of the reference database. Taxonomic affiliation is assigned to OTUs and RNA contigs using BLAST-based similarity searches, but while for marker genes highly curated databases such as UNITE (Abarenkov et al., 2010; Tedersoo et al., 2011) or SILVA (Pruesse et al., 2007) exist, where thousands of ribosomal sequences are annotated, similar databases are missing for complete genomes of fungi or bacteria and functional annotation of genes is lagging behind. This difference becomes visible when comparing the datasets at lower taxonomic levels, such as at species level instead of class level, where the two datasets become less compatible. Nevertheless, the fact that fungal and bacterial classes identified were found exclusively in the RNA analysis shows that DNA approaches miss considerable portions of active microbial communities, as was seen also previously in comparison of RNA and DNA sequences after use of marker genes only (Baldrian et al., 2012). Another challenge, especially for bacterial datasets is the contamination with host/eukaryotic rRNA. In the case of 16S rRNA marker gene analysis in plant samples high amounts of organellar (chloroplast and mitochondria) sequences dilute the sequences of bacterial origin (Paper III and IV). Whereas in metatranscriptome experiments the problem that prokaryotic mRNA contributes with only 1-5% to the pool of total RNA requires enrichment of the mRNA or depletion of rRNA prior to the sequencing (Peano et al., 2013). These complications might explain why taxonomic distributions in analysis of bacteria from RNA and DNA were divergent (Paper IV, Figure 3) and less consistent than fungal taxonomic comparisons (Paper IV, Figure 3). The assembly of a contig can comprise a combination of reads derived from transcripts from multiple species in cases of highly sequence-conserved genes; in such it is not possible to ascertain the exact taxonomic origin of transcripts in metatranscriptomic analysis using the sequencing strategy deployed in our study. The approach does, however, provide a holistic view of the processes active within a microbial community, and integration of this data type will complement studies of microbial community composition (Paper III) and provide comprehensive understanding advancing microbiome research in the future.

(a) a RNAseq b Amplicon seq Raw reads Raw reads reads quality Low

Assembly Pre-processing - Ananas assembled - Demultiplexing andlow quality reads

Mix oflost potential - Quality filtering not assembled - Merging a b 0.2 0.2 Filtered 0.1 a Contigsb 0.2 reads 0.2 0.0 Annotation 0.0 OTU

35% 65% singletons - Blastx 0.1 clustering using - Global −0.2 0.0 diamond not annotated* −0.1 clustering Axis.2 Axis.2 [19.9%] on NCBI nr 0.0 Axis.2 [12.4%] - Mapping database per sample −0.2 −0.2 −0.4 −0.1 Axis.2 Axis.2 [19.9%] Annotated Axis.2 [12.4%] Raw OTUs −0.3

Contigs Other applications −0.4 0.2 −0.2−0.20.0 −0.4 0.4 0.00.2 Normalisation Axis.1 [26.9%] OTU filtering Axis.1 [22%] - Grouping by - ITS: hmmer Non annotation Fungi (1-2%)−0.3 alignment

c -

d target 0.2 - −0.20.0 Extract read−0.4 Bacteria (2-3%)0.4 -0.00.2 16S: filtering Axis.1 [26.9%]counts Plant (~ Axis.1 [22%] host 0.1 organelles c 0.25 d 90%)

Downstream0.1 0.0 Filtered 0.25 analyses 0.00 OTUs Axis.2 Axis.2 [16.7%] 0.0 Axis.2 − [10.3%] 0.1

0.00 (b) a (c) b 0.2 ● −0.25 ●

Axis.2 Axis.2 [16.7%] 0.2 Axis.2 Axis.2 [10.3%] −0.2 −0.1 ● 0.1

● −0.25 0.25 −0.250.00 −0.1 0.0 0.1 0.2 0.3 0.0 Axis.1 [23.2%] Axis.1 [13.5%] ● −0.2 ● 0.0 ●

● ● ●● Buds Jun H ● ● ● ● 0.25 −0.250.00 Buds−0.1 Jun S 0.0 0.1 0.2 0.3 ● ● −0.2 ● Buds Aug S Needles 1st Jul S Axis.1 [23.2%] Axis.1 [13.5%]−0.1 ● Buds Aug ster S Needles 2nd Jul S Axis.2 Axis.2 [19.9%] Axis.2 Axis.2 [12.4%] Needles 3rd Jul S Seedlings needles Aug U Seedlings needles Aug ster U ● ● −0.2 ● ●−0.4Buds Aug S Needles 1st Jul S Needles 2nd Jul S Needles 3rd Jul S ●● ● −0.3 ● 0.2 −0.20.0 −0.4 0.4 0.00.2 Axis.1 [26.9%] Axis.1 [22%] (d) c (e) d

● ● 0.1

0.25 ● ● ● ●

● ● ●

● ● 0.0 ●

0.00 ●

● ● Axis.2 Axis.2 [16.7%] Axis.2 Axis.2 [10.3%] ● −0.1 ● ● ● ● ● ● ● ● ● −0.25 ● ● ● −0.2

● ●

0.25 −0.250.00 −0.1 0.0 0.1 0.2 0.3 Axis.1 [23.2%] Axis.1 [13.5%]

● Buds Aug S Needles 1st Jul S Figure 6 Analysis of tree-associated microbiota by metagenomicNeedles 2nd or Jul metatranscriptomic S approaches. Needles 3rd Jul S Visual overview of workflows (a) used to analyze transcriptomic and amplicon seq data. Arrow thickness represents proportions of reads that were either used or discarded in the process. Principal Coordinates Analysis (PCoA) of Bray-Curtis dissimilarities between Norway spruce samples. (b) Based on ITS OTUs. (c) Based on fungal genes in the Norway spruce RNA-Seq data. (d)

Based on 16S OTUs. (e) Based on bacterial genes. RNA-Seq read counts were log10 transformed before plotting. X or y axes were reversed, wherever necessary, to emphasize visual congruity between the two methods.

Conclusion and future perspectives

Climate change is threatening productivity and, potentially, the existence of Norway spruce all over Europe. Tree populations are adapted to local environments but increasing temperatures and changing precipitation patterns are associated with drought stress in Norway spruce and are projected to reduce growth and cause tree dieback in large parts of Central Europe, southern boreal regions and alpine forests (Bergh et al., 2003; Solberg, 2004; Bergh et al., 2005; Büntgen et al., 2006; Lebourgeois et al., 2010; Ge et al., 2011; Schuster and Oberhuber, 2013; Hartl et al., 2014; Zang et al., 2014; Čermák et al., 2017; Frank et al., 2017). Consequently, the natural distribution will likely shift further north and into higher altitudes (Sykes et al., 1996; Bradshaw et al., 2000; Frank et al., 2017). However, Norway spruce is of high economic importance and by selecting seed sources better adapted to future climates it will be possible to continue growing Norway spruce outside its natural range. Selection of reproductive material has traditionally focused on enhanced growth and quality traits (Langlet, 1971), but less on tree vitality. In future, tree vitality will depend on tolerance to abiotic stresses, such as drought and cold in the form of for example more frequent summer droughts and late spring frost events, and decrease susceptibility to other disturbances caused by storm or pests. Work in this thesis has individually looked at cold (Paper I) and drought (Paper II) stress mechanisms in Norway spruce and identified genes with potential use as marker genes in breeding or genetic engineering of more tolerant plants in addition to tissue and species-specific genes, which will benefit from future annotation efforts. The work presented here has highlighted the divergence from angiosperm model systems, especially for regulatory genes, such as TFs, and underlined the need to increase our understanding in conifer trees. Several highly stress-responsive genes, such as TFs DREB2B and ERF53, were commonly found in cold and drought responses of Norway spruce seedlings (Paper I & II), but functional differences in tissues were recognized. While response to cold was similar in needles and roots, physiological and molecular adaptations to drought were limited in needles, contrasting a strong response in roots and emphasizing that future tree improvement programs for conifer trees aiming for drought tolerance need to start considering root specific mechanisms.

Human-assisted relocation of Norway spruce and natural range shifts in adaptation to climate change will influence the host-associated microbiota as well. Not only the changes in genetic composition of host populations, as indicated above, but also changes of environmental factors will restructure plant microbiomes (Bálint et al., 2015; Lladó et al., 2017). Boreal forests are important for the global carbon balance and future carbon sequestration in this

ecosystem will depend on both trees and microorganisms and their ability to adapt to changing environmental conditions. Increasing CO2 concentrations and rising temperatures in addition to higher nitrogen inputs in ecosystems are associated with anthropogenic activities that drive the climate change. In Paper III long-term nutrient addition was linked to changes in belowground fungal composition. Major differences were observed for the categories of short- and long-distance exploration types of ectomycorrhizal fungi in root and soil samples of control and nutrient enriched plots. Long-distance types, such as Cortinarius and Piloderma spp. (Agerer, 2006), contributed more to communities of the control. Interestingly, this group of ectomycorrhizal fungi has been associated with ecosystems where long-term carbon and nitrogen sequestration is reduced and greater biomass turnover and necromass degradation occurs (Clemmensen et al., 2015). On the other hand nitrogen addition has been reported to increase the soil carbon sequestration in boreal forests and impair decomposition (Ramirez et al., 2012; Maaroufi et al., 2015) and large accumulation of organic matter below ground can be attributed to necromass of roots and associated mycelium, in addition to litter (Clemmensen et al., 2013). Thereby observations of shifts in fungal community composition can indicate changes in carbon storage in boreal forests and may be incorporated in predictive models in the future. Bacterial communities also change, with oligotrophic bacteria replaced by copiotrophic Actinobacteria in nutrient enriched plots (Paper III). Shifts in C/N ratios in the soil or changes in the nutrients provided by the plant or fungi may explain some of the observed differences. In studies where oligotrophic bacteria decreased, enzymatic activity related to cellulose breakdown also decreased (Ramirez et al., 2012; Freedman et al., 2016), linking shifts in bacterial communities to changes in decomposition and carbon and nitrogen cycling in boreal forests.

In addition to functioning in decomposition and nutrient acquisition tree- associated microbiota increase abiotic and biotic stress tolerance in plants. Gehring et al. (2017) showed that distinct EMF communities established with drought tolerant pine trees, further demonstrating that the interaction of tree genotype and microbial community are of great importance for plant performance during climate stress. Climate change will also affect aboveground foliar fungal communities. Even though nitrogen addition had only small effects (Paper III), warming in high-latitudes decreased diversity and caused formation of distinct foliar fungal communities and increased the abundance of potential pathogens (Bálint et al., 2015). In combination, these observations are evidence that compositional changes will have functional consequences and highlight the need to perform RNA-based process analyses at the ecosystem level, including the transcriptomes of both the tree and the associated microbiota. Metatranscriptomic studies already have the advantage of separating the active microbial community from the pool of dead DNA sources

(Paper IV), however, analysis of biological processes in mixed species approaches is still hampered. There is great potential in the future for studies profiling the host transcriptome in parallel to the hosted microbiome to provide holistic insights for identification of genes of carbon and nutrient transport expressed in the mutualistic partners, improving estimations for changes in carbon and nitrogen cycling in ecosystems and for identifying molecular mechanisms underlying improved stress tolerance or enhanced growth.

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