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Symbiotic interplay of fungi, algae, and bacteria within the lung lichen Lobaria pulmonaria L. Hoffm. as assessed by state-of-the-art metaproteomics Christine Eymann, Christian Lassek, Uwe Wegner, Jörg Bernhardt, Ole Arno Fritsch, Stephan Fuchs, Andreas Otto, Dirk Albrecht, Ulf Schiefelbein, Tomislav Cernava, Ines Aschenbrenner, Gabriele Berg, Martin Grube, and Katharina Riedel J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00974 • Publication Date (Web): 14 Mar 2017 Downloaded from http://pubs.acs.org on March 16, 2017

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Journal of Proteome Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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Symbiotic interplay of fungi, algae, and bacteria within the lung lichen Lobaria pulmonaria L. Hoffm. as assessed by state-of-the-art metaproteomics

Christine Eymann, #,1 Christian Lassek, #,1 Uwe Wegner,1 Jörg Bernhardt,1 Ole Arno Fritsch,1 Stephan Fuchs,1,2 Andreas Otto,1 Dirk Albrecht,1 Ulf Schiefelbein,3 Tomislav Cernava,4 Ines Aschenbrenner,4,5 Gabriele Berg,4 Martin Grube,5 and Katharina Riedel1* #

these authors have contributed equally

*corresponding author

1

Institute of Microbiology, Ernst-Moritz-Arndt-University Greifswald, DE-17487 Greifswald, Germany

2

present address: Robert Koch Institute, Division Nosocomial Pathogens and Antibiotic Resistances, Department of Infectious Diseases, DE-38855 Wernigerode, Germany

3

Blücherstraße 71, DE-18055 Rostock, Germany

4

Institute of Environmental Biotechnology, Graz University of Technology, A-8010 Graz, Austria

5

Institute of Plant Sciences, University of Graz, A-8010 Graz, Austria

Correspondence: Katharina Riedel, Institute of Microbiology, Ernst-Moritz-Arndt-University Greifswald, F.L.-Jahnstr. 15, 17487 Greifswald, Tel. +49 (0)3834 86 4200, E-mail: [email protected], Fax: +49 (0)3834 86 4202

Keywords: Lichens, metaproteomics, microbial ecology, Lobaria pulmonaria, bacterial microbiome Abbreviations: PTMs – posttranslational modifications; NSAF – Normalized spectral abundance factor; COG or KOG (clusters of orthologous groups of proteins/clusters of eukaryotic orthologous groups; RuBisCo - Ribulose bisphosphate carboxylase

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Abstract Lichens are recognized by macroscopic structures formed by a heterotrophic fungus, the mycobiont, which hosts internal autotrophic photosynthetic algal and/or cyanobacterial partners, referred to as the photobiont. We analyzed structure and functionality of the entire lung lichen Lobaria pulmonaria L. Hoffm. collected from two different sites by state-of-the-art metaproteomics. In addition to the green algae and the ascomycetous fungus, a lichenicolous fungus, as well as a complex prokaryotic community (different from the cyanobacteria) was found, the latter dominated by methanotrophic Rhizobiales. Various partner-specific proteins could be assigned to the different lichen symbionts, e.g. fungal proteins involved in vesicle transport, algal proteins functioning in photosynthesis, cyanobacterial nitrogenase and GOGAT involved in nitrogen-fixation, and bacterial enzymes responsible for methanol/C1compounds metabolism as well as CO-detoxification. Structural and functional information on proteins expressed by the lichen community complemented and extended our recent symbiosis model depicting the functional multi-player network of single holobiont partners1. Our new metaproteome analysis strongly supports the hypothesis (i) that interactions within the self-supporting association are multifaceted and (ii) that the strategy of functional diversification within the single lichen partners may support the longevity of L. pulmonaria under certain ecological conditions.

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Introduction Lichen symbioses have been commonly recognized as very old symbiotic relationships between algae and fungi that arose in the lower Devon2. At least two very different organisms, a heterotrophic fungus (mycobiont) and an autotrophic photosynthetic partner (photobiont), generally green algae and/or cyanobacteria, account for the structure of the lichen holobiont3,4 and enable the lichen to survive also extreme unfavorable conditions. Molecular and genetic studies of the last decade showed that lichens also harbor diverse bacterial communities, which can form biofilm-like structures on specific parts of the lichen thallus and which seem to be species-specific1,5-11. Lichens can also be co-colonized by lichenicolous fungi, which are host-specific commensals or parasites, or saprotrophs12. The lichen thallus often comprises a stratified fungal structure, with peripheric layers formed by a fungal mesh glued together in an extracellular matrix that shelters an internal layer dominated by the algal partner3. Under these conditions the photobiont provides photosynthetically produced sugars to the fungus13-17. The epiphytic lung lichen Lobaria pulmonaria predominantly grows on old deciduous trees in areas with low as well high rainfall preferably in coastal, but also alpine regions and has turned out to be a suitable indicator for ecosystem health, forest biodiversity and continuity in Europe18-20. While occupying a wide range in the holoarctic across temperate and boreal zones, L. pulmonaria is threatened in many European countries21. L. pulmonaria is a tripartite lichen symbiosis, which consists of three main partners: (1) the mycobiont, formed by ascomycetous fungi, (2) the photobiont, predominantly built by the green alga Dictyochloropsis reticulata and (3) the cyanobiont belonging to the order Nostocales located in closed structures so called cephalodia22. L. pulmonaria develops large leaf-like thalli under appropriate 3 ACS Paragon Plus Environment

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environmental conditions. First insights into the structure, function and interaction between the single partners of the L. pulmonaria holobiont were gained by an initial metaproteomics approach analyzing the whole protein composition of the lichen thallus10. This study, for which L. pulmonaria samples have been collected in the Swiss Alps, demonstrated the dominance of fungal proteins (75% of all assigned spectra) but also a significant proportion of prokaryotic (10% of all assigned spectra) and green algal (9% of all assigned spectra) proteins. Whilst the latter proteins were found to be mainly associated with energy and carbohydrate metabolism, a major proportion

of

posttranslational

fungal

and

bacterial

modifications

proteins

(PTMs),

protein

appeared to turnover

be

and

involved

other

in

diverse

functions10. More recently, functions of the Lobaria microbiome were explored by a combined meta-genomic/-proteomic approach demonstrating the interaction of associated bacteria with the fungus as well as the algal partner and emphasizing their role in fitness of the L. pulmonaria holobiont1. Since our first metaproteomics study10, technical advances in mass spectrometry led to an enormous increase of sensitivity and velocity. Moreover, the number of protein entries in publically available sequence databases increased substantially. Based on these achievements, we were encouraged to deepen our metaproteomics analysis, which was expected to result in a significantly higher number of reliably assigned proteins and thus, was believed to extend our knowledge on structure, function and interactions of the lung-lichen L. pulmonaria. Hence, the aim of the here presented study was to shed further light on the phylogenetic and functional composition of L. pulmonaria and to dissect the specific contributions of the symbiotic partners to the holobiont in better detail. Moreover, we intended to evaluate the potential influence of location-related parameters on the development of the lichen-associated bacterial 4 ACS Paragon Plus Environment

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microbiome. To this end, L. pulmonaria samples were obtained from Johnsbach (Styria, Austria) and from Born (peninsula Darß, Germany) (Figure 1A) and concentrated bacterial proteins were analyzed by a state-of-the-art GeLC-MS/MSbased metaproteomics approach.

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Experimental section Sampling, sample preparation, protein extraction of the entire lichen and concentration of bacterial proteins Fresh thalli of L. pulmonaria were collected from the bark of maple trees (Acer spp.) in the Alps (Johnsbach, Styria, Austria; N 47°32’35”, E 14°37’38”; 11.10.2012) in October 2012 (averaged air temperature per month: 9.1°C, year’s rainfall: 795 mm, frosty days: 94 at Kapfenberg) and from the bark of beech trees (Fagus spp.) on the peninsula Darß (Born, Germany; N 54°26’, E 12°29’ (lichen 1) and N 54°26’, E 12°29’ (lichen 2) in March 2015 (averaged air temperature per month: 10.7°C, year’s rainfall: 554 mm, frosty days: 31 at Rostock) after removal of visible external organisms (e.g. leaves, moss, bark) by sterile tweezers to avoid non-lichen contaminations. A total amount of 300 mg of lichen thalli, respectively, was shock frozen with liquid nitrogen and subsequently processed as shown in Figure 1B. The lichen collected in Johnsbach was analyzed in total; moreover, its bacterial microbiome was concentrated to specifically extract bacterial proteins. In contrast, from the lichens collected in Born (lichen 1 & 2) only the concentrated microbiome has been used for protein extraction. Protein extractions from the entire lichen samples and the concentrated microbiomes were performed as described earlier23,1. For the concentration of bacterial cells, lichen pieces were transferred into 15-ml Falcon tubes and re-suspended in 4°C-cooled 0.85% NaCl in a 1:7 (m/V) ratio. After shaking in a Ribolyser (MP Biomedicals, USA) without glass beads (1 min, 4.5 m/s) the detached bacteria were filtered using a set of three sieves (500 µm; 250 µm, 63 µm); larger lichen parts were retained and colonizing bacteria were concentrated in the filtrate (see also1).

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Mass spectrometry, two-step-database search and generation of raw data Aliquots of the extracted proteins (10 µg each) were separated on a 12% SDS polyacrylamide gel and stained overnight with Colloidal Coomassie Brilliant Blue G250. Ten (analysis of the concentrated microbiome) or 20 (in depth analysis of the entire lichen) protein bands were excised from the gel and digested with trypsin as follows: the excised gel pieces were destained using 30% v/v acetonitrile (ACN) in 0.2M (NH4)HCO3 for 15 minutes at 37°C and 1500 r.p.m.. This step was repeated twice. Subsequently, gel pieces were dehydrated in a vacuum concentrator (SpeedVac, Thermofisher). 50 µl modified trypsin (sequencing grade, Promega) was added in a concentration of 2 µg/mL in 0.25M (NH4)HCO3 and incubated at 37°C overnight. Peptides were extracted from the gel using ddH2O for 15 minutes in an ultrasonic bath. Supernatants containing peptides were kept, pooled and dried using a Speedvac concentrator (Eppendorf AG). Samples were finally resolved in ultrapure water and desalted using ZipTips (C18, Millipore). The resulting peptide mixtures of three technical replicates of each sample were analyzed by GeLC-MS/MS using an Orbitrap Elite mass spectrometer (Thermo Scientific, Waltham, MA, USA) (see also Figure 1B). The raw-files were converted to mgf-files by Proteome Discoverer (version 2.4, Thermo Scientific, Waltham, MA, USA), and searched with the Mascot search engine (version 2.4.1, Matrix Science, London, UK) with the following parameters: parent mass tolerance 10 ppm, fragment mass tolerance 0.5 Da, maximum missed cleavages 2, charge state 1+, 2+, 3+, and oxidation of methionine as variable modification. Scaffold (version 4.4.1.1, Proteome Software Inc., Portland, OR, USA) was used to validate MS/MS based peptide and protein identifications. Scaffold analysis was performed as a ‘MudPit experiment’ to merge the individual mascot result files into a single data set. In order to improve the 7 ACS Paragon Plus Environment

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identification of high confidence peptide sequence matches a two-step database search was performed, similar to the approach described previously24. To this end, mass spectra of all samples were searched in the primary step against the NCBInr protein database (version 2015 Nov, about 77.7 million entries). Hits obtained from the primary search were loosely filtered (50% peptide probability, 1 peptide, 50% protein probability) and corresponding protein identifiers were extracted. Based on these identifiers a subset target-decoy database of about 200,000 entries for the entire lichen and of about 53,000 entries for the concentrated bacterial samples was constructed using a database creator which was written as an in-house script. The database creator extracts a GI-list from Scaffold, deletes doubled entries and defines protein families resulting in a FASTA-file containing one entry per protein family and based on the NCBInr database, which has been used for the first database search. Results from the second database search were filtered applying stringent protein identification thresholds (99% peptide probability, 1 peptide, 99% protein probability) and a replicate filter, i.e. every protein had to be identified at least in 2 out of 3 technical replicates, achieving a FDR lower than 5%. FDR of all datasets: LP_whole_50_50 = 0.00226; MB_Styria_50_50 = 0.01874; MB_Darß_50_50 = 0.00213. All mass spectral data are available via the Proteomics Identifications database (PRIDE) at http://www.ebi.ac.uk/pride/ (ProteomeXchange accession: PXD005316; reviewer account: username: [email protected], password:

yJqsLJG6).

2.3

Prophane analysis and data visualization by Paver

Functional classification and phylogenetic distribution of the assigned proteins was performed

by

the

metaproteome

analyses

pipeline

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Prophane

2.0

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(http://www.prophane.de)10,1 (see also Figure 1B). Proteins were clustered based on the shared peptide spectrum matches (PSMs). Each protein cluster (or protein group) is represented by one master protein, which has been selected based on PSM coverage and probability scores. The respective master proteins are listed in tables 1, S-1, S-2, S-3, and S-4 together with the unambiguously identified single proteins. Relative protein quantification was based on normalized spectral abundance factor (NSAF) values25, considering exclusive spectral counts only. The averaged protein specific NSAF values of at least two of three technical replicates were used as basis for area size encoded data visualization by Voronoi treemaps using the Paver software 2.0 (Decodon, Greifswald, Germany). In case of the lichen collected from the Darß, the mean NSAF values of two independent samples (Born, B and C) were used. Accordingly, the cell sizes of each phylogenetic or functional category correspond to the sum of respective NSAF values. An adapted color code was used to depict the different holobiont partners or different phylogenetic groups.

Results Phylogenetic classification of the L. pulmonaria holobiont In total, 6,590 proteins or Prophane protein groups could be unambiguously assigned to diverse phylogenetic and functional groups, this is 14-times more than in our initial metaproteomics analysis10. Detailed information on all assigned proteins is given in table 1 and two supporting tables. Table 1 summarizes functional relevant proteins, contributing to the lichen symbiosis and mentioned in the following paragraphs. Table S-1 comprises all proteins or protein groups (6,590) and highlights all lichen-relevant proteins (5,243) assigned to each partner in the holobiont. Table S-2 summarizes representative specific proteins of each lichen partner. 9 ACS Paragon Plus Environment

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As shown in Figure S-1, the majority of proteins could be assigned to fungal origin (48.9%) followed by bacterial (24.4%), animal (10.9%), green algal (6.1%), green plant (4.4%), unclassified eukaryotic (2.6%), generally unclassified (1.2%), archaeal (0.6%), and viral (0.4%) origin. Subsequently, 5,243 protein groups of potentially lichen-relevant phylogenetic groups (excluding proteins assigned to e.g. green plants, animals or unclassified Eukaryota) were visualized using Voronoi treemaps (Figure 2A). Within this reduced dataset proteins were assigned to the L. pulmonaria mycobiont (59% of total quantity based on NSAF values, corresponding to 2419 proteins), algal photobiont (7.6%, 261 proteins), cyanobiont (3.1%, 119 proteins), a lichenicolous fungus (1.7%, 196 proteins), and diverse bacterial species forming the lichen bacterial microbiome (27.3%, 2161 proteins). Moreover, a limited number of proteins have been assigned to the Archaea (0.7%, 61 proteins) and viruses (0.5%, 25 proteins) (Figure 2B). Various proteins assigned to Basidiomycota suggest the presence of a lichenicolous fungus. Further eukaryotic proteins were of green algal origin and primarily expressed by members of the Trebouxiophyceae, most probably by the photosynthetic L. pulmonaria-specific green algae Dictyochloropsis reticulata that could be classified as one of the most prominent species. Cyanobacteria (to which 3.1% of all lichen-relevant proteins were assigned) were separated from the residual bacteria and recognized as the cyanobiont of the holobiont (Figure 2 A/B). The majority of cyanobacterial proteins could be assigned to the order Nostocales and the genus Nostoc, which is found in internal organs of the lichen22 (Table S-1). Our results indicate that the bacterial microbiome (without Cyanobacteria) contributes 27.3% (2162 proteins) to the entire metaproteome of the holobiont. As reported before1,10,11 and confirmed by our analysis, the majority of bacteria belong to the Proteobacteria (47.3%), which are predominated by Alphaproteobacteria (56%) (Figure S-2). About 47.9% of the alphaproteobacterial proteins could be assigned to 10 ACS Paragon Plus Environment

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the order Rhizobiales, belonging primarily to the families Beijerinckiaceae (21.9%), Bradyrhizobiaceae (18.6%), Rhizobiaceae (14.6%), Phyllobacteriaceae (13.6%), groups of well-known nitrogen fixing bacteria, as well as Methylocystaceae (12.7%) and Methylobacteriaceae (10.3%), which are able to utilize methanol. One third of the remaining eubacterial proteins were assigned to the Actinobacteria, primarily Streptomycetaceae (28.7%) and Nocardiaceae (12.5%) (for detailed information and further figures related to the bacterial microbiome see chapter 3.4).

3.2

Functional distribution within the symbiotic consortium

The majority of lichen-relevant proteins could be assigned to functional categories according to COG or KOG (clusters of orthologous groups of proteins/clusters of eukaryotic orthologous groups, update 2014) or according to TIGRFAM (release 15.0, 2014) or PFAM (version 27.0, 2014) (Table S-1), however, a large number of proteins remained uncharacterized or hypothetical. The distribution of protein functions varied strongly for each phylogenetic group. According to their relative contribution to the holobiont metaproteome, the highest number of functional groups was found in the mycobiont (21), followed by the lichenicolous fungus (19), and the bacterial microbiome (19), the photobiont (18), Archaea (16) and cyanobiont (15) and viruses (6). Despite of the very low proportion of the lichenicolous fungus and the cyanobiont, their proteins covered most of the main functional groups (Figure 3). Proteins involved in “translation, ribosomal structure and biogenesis” and “posttranslational modification, protein turnover, chaperones”, were found to be among the highest abundant gene products of the mycobiont but also within the lichen holobiont (20.2% and 16.2% of all assigned proteins, respectively; Figure 4). Also, within the bacterial microbiome, proteins belonging to the functional class 11 ACS Paragon Plus Environment

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“translation, ribosomal structure and biogenesis” showed the highest abundance (12.3%), whereas proteins involved in “photosynthesis” predominated the photobiont (28.8%) and the cyanobiont (25.7%). In contrast, proteins involved in “energy production and conversion” (17.2%) were highly abundant in the lichenicolous fungus followed by the photobiont (16.3%) (Figure 4). Notably, the photobiont showed the highest proportion of proteins involved in “carbohydrate transport and metabolism” (7.8%), whereas the bacterial microbiome was characterized by the highest percentage of proteins involved in “amino acid transport and metabolism” (5.5%) (Figure 4). To visualize individual protein functions by Voronoi treemaps, unclassified proteins comprising 7.4% of total lichen relevant protein were removed from the data set and proteins were color-coded according to their phylogenetic/lichen partner origin (Figure S-3). In cases, in which more than one lichen partner expressed the same protein functions, the corresponding Voronoi cells were split into the respective colors. Notably, a significant number of protein functions could be exclusively or predominantly assigned to only one of the lichen inhabitants and the corresponding proteins were therefore designated as “lichen-partner-specific proteins”.

Lichen-partner-specific

proteins

and

corresponding

pathways:

who

is

doing what? The mycobiont was found to express high levels of ribosomal proteins, translation factors, enzymes involved in glycolysis (8 assigned proteins), tricarboxylic acid cycle (9 assigned proteins) and pentose phosphate pathway (5 assigned proteins) (Table S-2,

all

“lichen-partner-specific

proteins”).

A

significant

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of

these

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proteins/enzymes were exclusively found in the mycobiont, which can be probably attributed to the high proportion of shape-giving fungal matter resulting in high detection of fungal proteins. Examples for such proteins are 40S ribosomal protein S14, phosphoglycerate mutase, and isocitrate dehydrogenase (Table 1, all mentioned, functionally relevant proteins). Further fungal specific proteins that were only found in the mycobiont but not in the photobiont or lichenicolous fungus were for instance a septin family protein, Ras-related GTPase, and the eisosome component PIL1 (see also Figure S-3). Eisosomes are large cytoplasmic protein complexes that localize near the plasma membrane and mark the sides of endo- and exocytosis allowing the transport of different substances via vesicles26. Additional proteins indicating important functions in the mycobiont are an oxysterol-binding protein, a taurine

catabolism

dioxygenase,

which

corresponds

to

a

putative

alpha-

ketoglutarate-dependent sulfonate dioxygenase, a non-further specified class III alcohol dehydrogenase and a sorbitol dehydrogenase (Table 1, Table S-2). Oxysterol-binding related proteins (ORPs) most likely affect the organelle membrane lipid composition, which impact on signaling and vesicle transport, but also on cellular lipid metabolism27,28. The taurine catabolism dioxygenase might be an indicator for sulfonate utilization in the mycobiont. Sulfonate utilization requires sulfite reductase, consistent with the formation of sulfite prior to assimilation29. A respective sulfite reductase was also identified. The class III alcohol dehydrogenase might be involved in the metabolism of rhizon aldehyde and rhizonyl alcohol that are known metabolites of the lung lichen30. The occurrence of sorbitol dehydrogenase confirmed the hypothesis that the mycobiont uses sugar alcohols produced by the photobiont31. The assignment of two enzymes involved in the biosynthesis of uroporphyrinogen-III, an important intermediate of heme/siroheme synthesis, together with respective “downstream” enzymes (e.g. uroporphyrin III methyltransferase) indicated that the 13 ACS Paragon Plus Environment

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mycobiont is effectively synthesizing heme and siroheme. Siroheme is an important prosthetic group of NADPH-dependent sulfite reductase, which is required for sulfonate utilization. Although both, algae and cyanobacteria employ photosynthesis for energy production, they can clearly be discriminated based on the expression of specific proteins. Notably, ribulose bisphosphate carboxylase (RuBisCo), catalyzing the first step in the CO2-fixing Calvin cycle, was found highly expressed in the algal photobiont whereas only little RuBisCo could be assigned to the cyanobiont. Moreover, chlorophyll A and B-binding proteins were abundant in the algal partner, whereas a high portion of a phycobilisome protein was expressed by the cyanobiont. Further exclusively available or highly abundant proteins of the algal photobiont belong to photosystem I and II (PsaA, B, E) or were functionally described as oxygen evolving

enhancer

protein

(PsbQ)

(Figure

S-4)

or

phosphoenolpyruvate

carboxykinase (PEPCK) and other enzymes involved in carbon assimilation32 (Figure S-5). Furthermore, beta-catenin, a cytoplasmatic component of desmosomes and adherens junctions, which is responsible for the signaling during cell-cell adhesion of unicellular organisms33, and porphobilinogen deaminase contributing to the uroporphyrinogen-III biosynthesis34 were assigned to the algal photobiont (Table 1, Table S-2). As mentioned before, the cyanobiont is also characterized by the expression of proteins involved in photosynthesis but its protein profile differs significantly from the one of the photobiont (see Figure S-4). In contrast to the algal photobiont, in which RuBisCo appeared to be one of the highest abundant proteins, the most dominant photosynthesis protein of the cyanobiont appeared to be the phycobilisome protein. Further specific cyanobacterial proteins are carbohydrate-selective porins, sulfite 14 ACS Paragon Plus Environment

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reductase as well as glutamate synthase (GOGAT) and molybdenum nitrogenase, the latter two of which indicating nitrogen fixation. Proteins that were predominantly expressed by the bacterial microbiome were identified as: GroEL, different dehydrogenases, a few non-further specified outer membrane proteins (porins) and outer membrane receptor proteins (mostly iron transporters), ABC transporters for amino acids, sugars, and xylose (assigned to Rhizobiales), acetyl/propionyl-CoA carboxylase (Rhizobiales) involved in fatty acid and amino acid metabolism/synthesis, and proteins belonging to type II, III or IV secretory pathways. Even though the dominant phylogenetic group Rhizobiales comprises several nitrogen-fixing bacterial species, nitrogenase and further related enzymes indicating nitrogen fixation could not be assigned to this phylogenetic group. Interestingly, three key enzymes for the biosynthesis of uroporphyrinogen-III, the precursor for heme/siroheme and cobalamin biosynthesis35, were assigned to be of bacterial origin: glutamyl-tRNA reductase, porphobilinogen deaminase and uroporphyrinogen-III synthase. Moreover, protoporphyrinogen oxidase (PPX) and ferrochelatase (cytochrome production), uroporphyrin III methylase (involved in siroheme and cobalamin biosynthesis), both prosthetic groups of NADPH-dependent sulfite reductase (hemoprotein) or nitrite reductase and several enzymes like methionine synthase were found to be highly expressed within the bacterial microbiome. The identification of a glutamyl-tRNA reductase expressed by Firmicutes and Betaproteobacteria proved the existence of the C5- or Beale-pathway for the synthesis of δ-aminolevulinate. Notably, the identification of catalases, cold shock proteins and chaperones as well as DNA-repair proteins indicates that the microbiome contributes important functions for detoxification, oxidative stress

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response, adaptation to low temperatures and desiccation and DNA repair (for further detailed information see chapter 3.4) (Table 1). The lichenicolous fungus proteome is dominated by the expression of hydrolytic enzymes such as cellulase and 1,2-alpha-mannosidase as well as arylacetamide deacetylase, which in humans deacetylates a variety of arylacetamide substrates including xenobiotic compounds and procarcinogens36 and has been shown to be involved in sterol homeostasis37. The lichen’s archaea were found to specifically express proteins involved in pseudouridylate synthesis, photolyase-mediated DNA repair and cobalamin biosynthesis. Notably, cobalamin synthesis proteins from our datasets were found to be exclusively of eubacterial and archaeal origin. Lichenassociated viruses express typical phage or envelope proteins together with reverse transcriptase or RNA-dependent RNA-polymerase confirming the existence of RNA viruses in fungi.

Structure and functioning of the L. pulmonaria microbiome from different Lobaria locations: Darß (Born, Northern Germany) and Styria (Johnsbach, Austria)

Taxonomic composition of site-specific microbiomes As we were mainly interested in geographically associated differences in the lichen microbiome, bacteria of L. pulmonaria were collected from two different sampling sites, i.e. Born (Darß, Northern Germany) and Johnsbach (Styria, Austria). To this end, samples were concentrated as described in the experimental section. Supplementary information on the proteins assigned to the different samples is given 16 ACS Paragon Plus Environment

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in the Tables S-3 (Darß) and S-4 (Styria). As shown in Figure S-6A the total percentage of bacteria (Darß, 59%; Styria 45%) was significantly increased at the expense of the fungal proteins in the microbiome-concentrated samples compared to the un-processed lichen samples collected in Styria (24%). It is interesting to note, that the portion of other Eukaryota than fungi or green algae, which are probably contaminations, as well as of green algae was nearly the same, independent from the sample preparation protocol (Figure S-6A). On the level of Proteobacteria or Alphaproteobacteria the composition of microbiomes of both locations showed neither difference between the samples nor in comparison to the non-treated lichen sample (data not shown). However, a comparison of non-processed and microbiome-concentrated samples revealed significant differences in the distribution of families within the Alphaproteobacteria order

Rhizobiales.

Our

results

indicate

that

bacteria

of

the

family

Methylobacteriaceae and Xanthobacteriaceae have been strongly concentrated by our protocol, as they were present in significantly higher amounts, whereas bacteria of the family Beijerinckiaceae and Methylocystaceae have not been accumulated as they showed higher portions in the non-treated lichen samples (Figure S-6B). Notably, whilst also Planctomycetes appeared to be concentrated by the described procedure, cyanobacteria living in internal cephalodia were rather depleted (Figure S6C). Notably, significant differences were found within other bacterial groups of the L. pulmonaria samples collected from the Darß and Styria. Whilst Actinobacteria (especially the order Pseudonocardiales) seemed to be more abundant in the Darßlichen microbiome, Acidobacteria appeared to be more dominant in the Styria-

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derived samples (Figure S-6C/D). Furthermore, Planctomycetes seem to be more abundant in the bacterial microbiome of the Styria-derived sample (Figure S-6C).

Protein functions expressed by the microbiome of the two sampling sites The 2,603 bacterial proteins/protein groups of the bacteria-concentrated Darßsample could be mainly assigned to the following functional groups: carbohydrate transport

and

metabolism;

posttranslational

modification,

protein

turnover,

chaperones; energy production and conversion and lipid, as well as amino acid transport

and

metabolism.

Highly

expressed

proteins

were

found

to

be

dehydrogenases, GroEL, ABC-type sugar-transport proteins, cold shock proteins, nitrogen regulatory protein PII, and F0F1-type ATP synthase (Figure S-7). A comparison with the Styria sample indicates that there might be also differences on the functional level (Figure 5). Notably, ABC-type sugar-transport proteins and cold shock proteins were clearly more abundant in the Johnsbach (Styria) sample, contrarily dehydrogenases were found to be higher abundant in the Darß-lichen microbiome (Figure 5). As mentioned above in both L. pulmonaria locations, Alphaproteobacteria including Rhizobiales and on the other hand Actinobacteria were the most dominant taxonomical groups within the microbiomes. Furthermore, proteins assigned to Methylobacteriaceae and Xanthobacteriaceae (belonging to Rhizobiales) were highly abundant in both concentrated bacterial microbiome samples. Therefore, we analyzed the functional distribution of Rhizobiales proteins exemplarily

for

the

Darß-derived

samples

(Figure

S-8)

in

more

detail.

Methylobacteriaceae are (facultative) methylotrophic bacteria and thus enzymes involved in the metabolism of methanol and other C1-compounds could mainly be assigned to this taxonomic group. Formaldehyde-activating enzyme (Fae) appeared 18 ACS Paragon Plus Environment

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to be one of the highest abundant proteins in all concentrated bacterial samples and within the Methylobacteriaceae. Fae is involved in oxidation of methanol to CO2, formaldehyde

detoxification,

and has

been demonstrated to catalyze

the

condensation of formaldehyde with tetrahydromethanopterin (H4MPT) to methyleneH4MPT in various methylotrophic bacteria38,39. Methenyltetrahydromethanopterin cyclohydrolase assigned to the obligate methylotrophic Methylocystaceae, its coding gene is located downstream of fae, catalyzes the formation of N5-formyl H4MPT from methylene-H4MPT40 and was exclusively found in the Darß-derived sample. Moreover, about 43% of the middle subunit of aerobic-type carbon monoxide dehydrogenase

was

assigned

to

the

Rhizobiales,

especially

to

the

Methylobacteriaceae, which might indicate the existence of carbooxidotrophic bacteria

and/or

the

detoxification

of

CO,

which

competes

with

O2

for

cytochromoxidase of the respiration chain (Figure S-8). Nitroreductase, a flavoenzyme that catalyzes the NAD(P)H-dependent reduction of nitro groups on nitroaromatic and nitroheterocyclic compounds41, was found to be expressed by Methylobacteriaceae as well as by Burkholderiaceae but also by Firmicutes (e.g. Halobacteroidaceae) to a similar extend (Figure S-8, Table S-3). Phasin, a granule-associated protein covering bacterial polyhydroxyalkanoate (PHA) storage granules appeared to be predominantly expressed by members of the Methylobacteriaceae (Figure S-8), but also by the families Sphingomonadaceae and Acetobacteraceae (see Table S-3). Polyhydroxyalkanoates are linear polyesters produced by bacterial fermentation of sugar or lipids to store carbon and energy. Various bacteria accumulate these compounds as intracellular granula under unfavorable conditions, thereby enhancing their fitness and stress resistance42.

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Nitrogen regulatory protein P-II, which is involved in the regulation of glutamine synthetase (GS) in response to the nitrogen availability, could be assigned to eight different orders of Proteobacteria, in which Burkholderiales and Rhizobiales were found to express the highest amount of this protein. Whilst in the Born (Darß) sample this protein was assigned to the N-fixing Xanthobacteriaceae (Figure S-8), in the Johnsbach (Styria) sample the nitrogen regulatory protein P-II was found in members of the Rhizobiaceae (Table S-8). The corresponding glutamine synthetase could be assigned to three orders of the Proteobacteria including Rhizobiales. In the Darßsamples also nitrogenase has been found in Rhizobiales pointing to nitrogen fixation by Rhizobiales bacteria within the Lobaria microbiome. Rhizobiales are further characterized by the expression of high amounts of ABC-type sugar transport systems (assigned to Phyllobacteriaceae and Rhizobiaceae) and the chaparonin GroEL (mainly assigned to Bradyrhizobiaceae) (Figure S-8). GroEL is also highly expressed by members of the Sphingomonadales. In the L. pulmonaria microbiome obtained from Johnsbach (Styria) high amounts of xylose transport systems were not only

assigned

to

Methylobacteriaceae

and

Rhizobiaceae

but

also

to

Phyllobacteriaceae (Table S-4). The Darß-lichen microbiome contains significantly higher amounts of actinobacterial proteins

(especially

assigned to Pseudonocardiales)

than the Styria-lichen

microbiome (Figure S-9 A/B). One enzyme could clearly be assigned to the Pseudonocardiales of the Darß-sample, but not to any bacteria of the Styria-sample: (1)

methane/phenol/toluene

hydroxylase

corresponding

to

a

methane

monooxygenase (Figure S-9 C). This enzyme catalyzes the initial oxygenation of methane to methanol in methanotrophic bacteria. Interestingly, the aerobic-type carbon monoxide dehydrogenase (Figure S-9 C) has been mainly assigned to the 20 ACS Paragon Plus Environment

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Frankiales within the Styria-sample (Table S-4) but to the Pseudonocardiales and Rhizobiales within the Darß-sample (Table S-3). Moreover, a streptomycin 6-kinase and type VII secretion protein EccE were assigned to Streptomycetales present in both lichen microbiomes concentrated from the Darß- and Styria-samples (see Figure S-9 C, Darß-sample).

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Discussion

Compared to our pioneering lichen proteomics analyses, in which in total 46310 or 4,4051 unique proteins or protein groups were assigned to different phylogenetic and functional groups, 6,590 proteins/protein groups could be assigned in the present study. These significant advances are most probably due to the better identification by improved mass spectrometry as well as better taxonomical and functional allocation by a more refined Prophane software tool and the enormous increase of public database entries.

Composition of the lichen holobiont. The comprehensive state-of-the-art metaproteomic analysis of L. pulmonaria consolidated our current knowledge but provided also substantial novel insights how the different partners contribute to the symbiotic community. L. pulmonaria has been described as tripartite lichen composed of a fungus (mycobiont), green photosynthetic algae (photobiont) and cyanobacteria living in internal cephalodia (cyanobiont)21. The presence of these symbiotic partners has clearly been confirmed by our metaproteome analysis. The proteomic data also clearly demonstrate the predominance of the mycobiont (59%) followed by the bacterial microbiome (27.3%), the primary photobiont (7.6%), the cyanobiont (3.1%) and a lichenicolous fungus (1.7%). Notably, albeit not very dominant also Archaea (0.7%) and viruses (0.5%) seem to be associated with Lobaria. The bacterial microbiome of L. pulmonaria, which comprises more than twice as much assigned proteins compared to the initial study by Schneider et al.10 showed a clear predominance of Alphaproteobacteria, which has been also shown 22 ACS Paragon Plus Environment

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for other lichens6,7,9. Whilst Rhizobiales dominated the Alphaproteobacteria, Actinobacteria were most predominant within the other eubacterial groups. Our results are in good agreement with two previously reported meta-omics analyses for L. pulmonaria1,11.

Identification

of

partner-specific proteins. The functional assignment of

ascomycotal proteins suggested that the mycobiont consumes carbohydrates produced by the photobiont. Moreover, the fungal partner seems to generate energy mainly by glycolysis (eight assigned enzymes) and the tricarboxylic acid cycle (nine assigned enzymes), but also by the pentose-phosphate pathway (five assigned enzymes). Notably, the presence of a fungal eisosome protein suggests also the putative transfer of substrates or products by endo- and exocytosis via vesicles25. The identification of a taurine catabolism dioxygenase in the mycobiont might be an indicator for sulfonate utilization and for the synthesis and metabolism of taurine, 2aminoethanesulfonic acid, which has been demonstrated to stimulate the photosynthate release in algae43. The assignment of D. reticulata proteins confirms this species as photobiont, which produces and releases energy-rich carbohydrates such as sorbitol and glucan via photosynthesis (twelve photosynthetic enzymes). This release might be caused by autolysis and decay in a pH-dependent manner44. Alternative, osmotically active carbohydrates (i.e. polyols) might be passively transported to the fungus, e.g. it was shown that ribitol, produced by the algal partner Trebouxiophyceae, reaches the lichen fungus via diffusion17.

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The presence of phosphoenolpyruvat-carboxykinase (PEPCK) and further related enzymes of a C4-like mechanism of CO2 assimilation32 (see Figure S-5) in our metaproteomic dataset suggests that the green algae D. reticulata exhibits a specific CO2-concentrating mechanism (CCM), which has already been described for freeliving cyanobacteria45,46 and is characterized by its relatively low affinity for CO2. The CO2-fixing activity of RuBisCo is further limited by its dual role as an oxygenase and the consequently produced phosphoglycolate further inhibits RuBisCo carboxylase activity. Therefore, the phosphoglycolate has to be dephosphorylated by a phosphatase and the resulting glycolate can be metabolized via photorespiration or is lost due to excretion47. Consequently, glycolate could be (besides glucans or sorbitol/ribitol) an additional carbohydrate, which might be consumed by the fungus. Our data confirms that green alga can be considered as the primary photosynthetic and carbon-assimilating partner in the lichen symbiosis as only little RuBisCo was assigned to the cyanobiont. The functional assignment of proteins as ß-catenin, a cytoplasmic component of desmosomes, and adherens junctions33 indicates that within the lichen the typically unicellular algae seem to form dense aggregates via cell-cell adhesion allowing a rapid exchange of signals and substrates. As suggested before1 also bacterial proteins were determined in this study indicating that the bacterial microbiome may provide the mycobiont with cofactors and vitamins (e.g. cobalamin, biotin, folate, menachinone) and thereby contributes to growth of the total lichen. Notably, the microbial community expresses high amounts of stressadaptive proteins, which may account for the protection of the lichen against stress, i.e. oxidative or heat stress (e.g. catalase, GroEL). Furthermore, different proteins involved in diverse nutrient uptake and/or assimilation systems, i.e. for iron, sugar and xylose, were assigned to the bacterial microbiome demonstrating its enormous 24 ACS Paragon Plus Environment

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uptake capacity of important nutrients. Energy production within the lichen microbial community is mostly operated by glycolysis (five enzymes). Moreover, the expression of various secretion proteins points to the release of protective substances such as antimicrobial compounds. The fact that different hydrolytic enzymes appeared to be produced by the lichen microbiome suggests an important role of the bacteria in the degradation and recycling of senescent lichen thalli. Although a significant portion of the microbiome was assigned to the order Rhizobiales containing at least five classes of nitrogen-fixing bacteria, no proteins were found to contribute to nitrogen fixation. Nevertheless, signatures of nitrogen fixation have been found in the metagenome of L. pulmonaria11. We suggest that a consistent expression is not required, as nitrogen is not a limiting factor, which is complemented by the cyanobacterial partners. The detection of two phycobilisome proteins and one photosystem II protein suggest that the cyanobiont also contributes to energy production via photosynthesis. Phycobilins are able to absorb the green and yellow spectrum of light in contrast to the chlorophyll pigments, which absorb the red and blue light48. Thus, cyanobacteria might contribute to a better exploitation of the light at their position within the thallus, where they act as complementary photosynthetic partners, beside their primary role as nitrogen fixers. Notably, our metaproteomic data confirms that the cyanobiont22, and not the potentially nitrogenfixing bacteria of the microbiome, is responsible for nitrogen fixation. Cephalodia are analogous organs to nodules of leguminoses49, and were shown to contain a higher ratio of heterocysts than are found in photobiont layers of those lichens where cyanobacteria are the primary photobiont50. The analogy of these related structures suggests structural and functional similarities of the corresponding nitrogen fixing pathways and their importance for symbiosis. 25 ACS Paragon Plus Environment

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The lichenicolous Basidiomycete, proteins of which could predominantly be assigned to the class of Agaricomycetes, expressed various hydrolases (e.g. cellulase) and thus probably lives as saprotroph on the lichen. Basidiomycota only account for less than 5% of all known lichenicolous fungi, the majority of which belongs to Ascomycota12,51. The only species of Basidiomycetes, which has been reported as lichenicolous species of L. pulmonaria is the widespread parasite Tremella lobariacearum52, however the lichenicolous fungal proteins could not be assigned to this species. Arylacetamide deacetylase was assigned to the lichenicolous fungus and demonstrated to deacetylate a variety of arylacetamide substrates including xenobiotic compounds and procarcinogens in humans36. This enzyme might be involved in a potential detoxification pathway besides chitin degradation53. In order to investigate the effect of the location on the L. pulmonaria-associated bacterial microbiome as well as to enlarge the bacterial functions we investigated the metaproteome of two concentrated bacterial microbiomes derived from lichen samples collected either in Johnsbach (Styria, Austria) or in Born (Darß, Germany). In both samples, Rhizobiales were found to represent one of the most dominant phylogenetic groups of the Lobaria microbiome. These results are in good agreement with our previous integrated “meta-omics” analyses of L. pulmonaria1 and a recent metagenome analysis11. Moreover, this data support the findings of Cardinale et al.54,55, who suggested a relatively stable core microbiome of Lobaria independent from the region in which the lichen has been growing. The concentration protocol led to an increase of the Methylobacteriaceae and Xanthobacteriaceae, indicating that these bacteria might possibly be loosely associated with the lichen thalli. In contrast, Beijerinckaceae and Methylocystaceae were found to be depleted by the concentration protocol and are thus probably tightly 26 ACS Paragon Plus Environment

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connected with the lichen structure. Analogous to cyanobacteria of internal cephalodia such tighter integration in the host’s structure could apply to the potentially nitrogen fixing Xanthobacteria. Moreover, the concentration led to a significantly higher assignment of bacterial proteins and thus provides much more information on the functions specifically expressed by the microbiome, e.g. after the concentration a nitrogenase could be assigned to the Rhizobiaceae in the Darß-sample, whereas no proteins involved in nitrogen fixation were found in the non-concentrated lichen samples (see above). Nevertheless, we hypothesize that nitrogen fixation mediated by Rhizobiales plays only a minor role in the Lobaria symbiosis due to the low amount of nitrogenase and the lack of a specific GOGAT. Thus, we propose that the major nitrogen-fixing partner in the lichen is indeed the cyanobiont, whereas Rhizobiales are rather responsible for the metabolism of methanol and other C1-compounds as indicated by the

assignment

of

respective

enzymes

to

potential

methylotrophic

Methylobacteriaceae and Methylocystaceae in both, the Darß- and Styria-samples. Moreover, Pseudonocardiales might be involved in the oxidation and metabolism of methane/methanol. We suggest that methanol or other C1-compounds might result from the metabolism/catabolism of secondary phenolic products that are synthesized by the mycobiont. Lichens and seeds often produce secondary phenolic compounds with antioxidative properties56 including lignin phenol derivatives with antioxidant activities57. It was shown recently that methyl- or methanotrophic bacteria are associated with plants because of their emission of methanol or methane58. Furthermore, in case of the Darß-sample, wet moors, which surround the sampling site and emit methanol/methane might explain the presence of such methyl/methanotrophic bacteria. In poikilohydric lichens, the desiccation (and ROS) tolerance and prolonged longevity in the desiccated state depend on their ability to 27 ACS Paragon Plus Environment

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scavenge free radicals. The shape-giving fungus doesn’t only use ‘classic’ antioxidants such as glutathione, ascorbate, tocopherols and free radical-processing enzymes59 but rather synthesizes lignin monophenols such as syringyl phenols57 or lecanoric acid60. Our data indicate that a large group of bacteria of the microbial consortium living on the lichen is capable of metabolizing methanol/phenol and other C1-compounds and is thus probably involved in detoxifying formaldehyde and/or carbon monoxide. Other assigned proteins indicate that the microbiome also contributing to (i) stress protection (catalase, cold shock proteins, chaperones, DNA repair proteins), which has also demonstrated for bacteria associated to mosses or plants61,62, (ii) sulfur assimilation (sulfite-reductase), phosphate, and iron assimilation, (iii) degradation of biopolymers, as well as (iv) pathogen defense (e.g. by the synthesis of streptomycin 6-kinase). Interestingly, our analyses did reveal distinct differences in the functional and taxonomical distribution of microbial proteins between the two different Lobaria locations (Darß and Styria), albeit more samples from different sites and more biological replicates need to be analyzed in the future to verify this finding. Our data indicate differences in the abundance of Actinobacteria, Pseudonocardiales and Planctomycetes between the two sampling sites. Additionally, ABC-type sugartransport proteins and cold shock proteins were clearly more abundant in the Johnsbach (Styria) sample, contrarily dehydrogenases were found to be at higher abundance in the Darß-lichen microbiome. This might be due to diverse environmental factors such as season, humidity, temperature or insolation, which differed between the two sites, i.e. the averaged air temperature per month was significantly lower in the Alps of Austria than on the peninsula Darß.

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Conclusions

Our integrated metaproteomics analyses demonstrate that the mapping of the comprehensive lichen protein inventory can strongly contribute to a better understanding of the functional contributions of all participants in the lichen symbioses (summarized in Figure 6), which is not restricted to the classic view of lichens as dual and merely fungal-algal partnerships. Notably, our metaproteomics results indicate various functions specifically expressed by all the different partners of the lichen holobiont. Moreover, the comparison of two microbiome metaproteomes derived from lichens collected at two geographically and climatical distinct sites suggests site-/climate-specific adaption of the bacterial community. Thus, the stateof-the-art metaproteome analyses not only led to a significant increase in metaproteome coverage of the lung lichen L. pulmonaria, but added also novel information to common knowledge on the lichen holobiont and especially on the diverse and important functions contributed by the lichen bacterial microbiome.

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Supporting information The following files are available free of charge at ACS website http://pubs.acs.org:

FIGURE S-1

FIGURE S-2

FIGURE S-3

FIGURE S-4

FIGURE S-5

FIGURE S-6

FIGURE S-7

FIGURE S-8

FIGURE S-9

TABLE S-1

DESCRIPTION Assignment of all proteins/spectra obtained by GeLC-MS/MS to higher-level taxonomic groups associated to the lichen thalli or potential contaminations Assignment of all bacterial proteins/spectra obtained by GeLC-MS/MS to the taxonomic groups of Proteobacteria (A), Alphaproteobacteria (B), Rhizobiales (C) and other Eubacteria (D) Voronoi treemaps visualizing the L. pulmonaria lichen-associated metaproteome on the functional level Voronoi treemaps visualizing the functional distribution of proteins of the L. pulmonaria within the lichen partners (A) photobiont and (B) cyanobiont Pathway scheme of the suggested CO2 concentrating mechanism (CCM) probably functioning in the green algae as revealed by the assignment of respective relevant enzymes Comparison of samples derived after concentrating lichen-associated bacteria of two different L. pulmonaria sampling sites (Born, Darß, Germany and Johnsbach, Styria, Austria (SM)) with the un-processed lichen sample from Johnsbach (without concentration of bacterial microbiome (whole)) Voronoi treemaps visualizing the functional distribution of all assigned bacterial proteins/spectra (without cyanobacteria) of the concentrated bacterial microbiome of the Born, Darß sample Voronoi treemaps visualizing the functional distribution of all assigned bacterial proteins expressed by the order Rhizobiales of the Born, Darß sample Voronoi treemaps visualizing the taxonomical classification of proteins to the respective orders of Actino- and Acidobacteria from the concentrated microbiomes of L. pulmonaria collected f rom Johnsbach, Styria (A) and Born, Darß (B) Complete list of all assigned proteins from the un-processed L. pulmonaria sample (Austria, 30 ACS Paragon Plus Environment

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TABLE S-2

TABLE S-3

TABLE S-4

Johnsbach, Styria) Relevant partner specific proteins assigned to the respective symbiotic partner of the holobiont as revealed from the analysis of unprocessed L. pulmonaria sample (Austria, Johnsbach, Styria) All ass igned proteins from the concentrated bacterial microbiome of L. pulmonaria (Born, Germany, Darß All ass igned proteins from the concentrated bacterial microbiome of L. pulmonaria (Johnsbach, Austria, Steiermark [SM]

Author information Corresponding author: Email: [email protected] Notes: The authors declare no competing financial interests

Acknowledgements We thank DFG and FWF for funding of the bilateral DACH project (RI 969/5-1 and FWF I 882).

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References (1) Grube, M.; Cernava, T.; Soh, J.; Fuchs, S.; Aschenbrenner, I.; Lassek, C.; Wegner, U.; Becher, D.; Riedel, K.; Sensen, C. W.; Berg, G. Exploring functional contexts of symbiotic sustain within lichen-associated bacteria by comparative omics. ISME J. 2015, 9, 412– 424. (2) Honegger, R.; Edwards, D.; Axe, L. The earliest records of internally stratified cyanobacterial and algal lichens from the Lower Devonian of the Welsh Borderland. New Phytol. 2013, 197, 264–275. (3) Ahmadjian, V. 1993. The Lichen Symbiosis. Nord. J. Bot. 1994, 14, 588. (4) Honegger, R. Functional Aspects of the Lichen Symbiosis. Annu. Rev. Plant. Physiol. Plant. Mol. Biol. 1991, 42, 553–578. (5) Cardinale, M.; Puglia, A. M.; Grube, M. Molecular analysis of lichen-associated bacterial communities. FEMS Microbiol. Ecol. 2006, 57, 484–495. (6) Cardinale, M.; Vieira de Castro, João; Müller, H.; Berg, G.; Grube, M. In situ analysis of the bacterial community associated with the reindeer lichen Cladonia arbuscula reveals predominance of Alphaproteobacteria. FEMS Microbiol. Ecol. 2008, 66, 63– 71. (7) Grube, M.; Cardinale, M.; de Castro, João Vieira; Müller, H.; Berg, G. Species-specific structural and functional diversity of bacterial communities in lichen symbioses. ISME J. 2009, 3, 1105–1115. (8) Grube, M.; Berg, G. Microbial consortia of bacteria and fungi with focus on the lichen symbiosis. Fungal Biol. Rev. 2009, 23, 72–85. (9) Bates, S. T.; Cropsey, Garrett W G; Caporaso, J. G.; Knight, R.; Fierer, N. Bacterial communities associated with the lichen symbiosis. Appl. Environ. Microbiol. 2011, 77, 1309–1314.

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of

interaction

of

Stress

Protecting

Agent

Stenotrophomonas rhizophila DSM14405(T.). Front. Plant. Sci. 2013, 4, 141.

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(SPA)

description COG/KOG/Pfam Isocitrate dehydrogenase, alpha subunit Taurine catabolism dioxygenase TauD, TfdA family Phosphoglycerate mutase Septin family protein (P-loop GTPase) Uroporphyrin III methyltransferase Uroporphyrinogen III synthase UROS/HEM4 Ras-related GTPase Sulfite reductase (ferredoxin) Eisosome component PIL1 Oxysterol-binding protein Alcohol dehydrogenase, class III Sorbitol dehydrogenase 40S ribosomal protein S14 Aspartate aminotransferase/Glutamic oxaloacetic transaminase AAT1/GOT2 Kynurenine aminotransferase, glutamine transaminase K Phosphoenolpyruvate carboxykinase (PEPCK) Pyruvate kinase Porphobilinogen deaminase NAD-dependent malate dehydrogenase Chlorophyll A-B binding protein Oxygen evolving enhancer protein 3 (PsbQ) Photosynthetic reaction centre protein Photosystem I psaA/psaB protein Photosystem I reaction centre subunit III Photosystem I reaction centre subunit IV / PsaE Photosystem II protein Ribulose bisphosphate carboxylase large chain, catalytic domain Ribulose bisphosphate carboxylase large chain, N-terminal domain Ribulose bisphosphate carboxylase, small chain Armadillo/beta-Catenin/plakoglobin Glutamate synthase domain 2 Carbohydrate-selective porin, OprB family Carbohydrate-selective porin Nitrogenase molybdenum-iron protein, alpha and beta chains Nitrogenase subunit NifH (ATPase) Sulfite reductase, alpha subunit (flavoprotein) Phycobilisome Linker polypeptide Phycobilisome protein ABC-type amino acid transport system, permease component ABC-type amino acid transport/signal transduction systems, periplasmic component

COG/KOG/Pfam KOG0785 PF02668.12 KOG0235 KOG2655 KOG1527 KOG4132 KOG0395 KOG0560 PF13805.2 KOG1737 KOG0022 KOG0024 KOG0407 KOG1411 KOG0257 PF01293.16 KOG2323 KOG2892 KOG1494 PF00504.17 PF05757.7 PF00124.15 PF00223.15 PF02507.11 PF02427.13 PF00421.15 PF00016.16 PF02788.12 PF00101.16 KOG4203 COG0069 PF04966.8 COG3659 COG2710 COG1348 COG0369 PF00427.17 PF00502.15 COG0765 COG0834

MW_NSAF 0.00091 0.00024 0.00120 0.00514 0.00018 0.00010 0.00290 0.00002 0.00269 0.00067 0.00056 0.00061 0.00349 0.00009 0.00006 0.00008 0.00011 0.00008 0.00019 0.00180 0.00017 0.00096 0.00029 0.00029 0.00025 0.00285 0.00583 0.00148 0.00041 0.00030 0.00002 0.00047 0.00005 0.00035 0.00073 0.00034 0.00075 0.00473 0.00007 0.00044

hit list gi|493942583 gi|504328596 gi|750490878 gi|505416200 gi|648522855 gi|702866379 gi|515810120 gi|490320185 gi|501290791 gi|664258411 gi|651251513 gi|519020209 gi|748192987 gi|489547786 gi|503175176 gi|503208567 gi|502757154 gi|497863116 gi|703022872 gi|657923117 gi|504598965 gi|494607935 gi|676255972 gi|528843839 gi|515734748 gi|659059747 gi|648538151 gi|499850516 gi|737636102 gi|401888100 gi|443894194 gi|323507766 gi|500194951 gi|494347896 gi|494643322 gi|428782753 gi|383397586 gi|124299367 gi|429249596 gi|700587564

description COG/KOG/Pfam COG/KOG/Pfam Methionine synthase II (cobalamin-independent) COG0620 ABC-type polysaccharide/polyol phosphate transport system, ATPase component COG1134 ABC-type sugar transport system components COG1129 ABC-type xylose transport system, periplasmic component COG4213 Glucose/sorbosone dehydrogenases COG2133 Outer membrane protein and related peptidoglycan-associated (lipo)proteins COG2885 Glutamyl-tRNA reductase COG0373 Porphobilinogen deaminase COG0181 Protoheme ferro-lyase (ferrochelatase) COG0276 Protoporphyrinogen oxidase COG1232 Uroporphyrinogen-III methylase COG0007 Uroporphyrinogen-III synthase COG1587 ABC-type antimicrobial peptide transport system, permease component COG0577 ABC-type bacteriocin/lantibiotic exporters, contain an N-terminal double-glycine peptidase domain COG2274 Aerobic-type carbon monoxide dehydrogenase, large subunit CoxL/CutL homologs COG1529 Aerobic-type carbon monoxide dehydrogenase, middle subunit CoxM/CutM homologs COG1319 NAD-dependent aldehyde dehydrogenases COG1012 Catalase COG0753 Catalase (peroxidase I) COG0376 Outer membrane receptor proteins, mostly Fe transport COG1629 Type II secretion system (T2SS), protein F PF00482.19 Type III secretory pathway, component EscV COG4789 Type IV secretory pathway, VirB9 components COG3504 Acetyl/propionyl-CoA carboxylase, alpha subunit COG4770 Chaperonin GroEL (HSP60 family) COG0459 Molecular chaperone COG0443 DNA repair exonuclease COG0420 DNA repair proteins COG2003 Cold shock proteins COG1278 1, 2-alpha-mannosidase KOG2431 Arylacetamide deacetylase KOG1515 Cellulase (glycosyl hydrolase family 5) PF00150.14 Cobalamin biosynthesis protein CbiD COG1903 DNA repair photolyase COG1533 Predicted pseudouridylate synthase COG1258 Phage late control gene D protein (GPD) PF05954.7 Phage portal protein PF04860.8 phage tail tape measure protein, TP901 family, core region PF00077.16 Reverse transcriptase connection domain PF06815.9 RNA dependent RNA polymerase PF00680.16

MW_NSAF 0.00015 0.00010 0.00206 0.00115 0.00003 0.00216 0.00013 0.00008 0.00010 0.00006 0.00006 0.00009 0.00027 0.00004 0.00023 0.00012 0.00064 0.00007 0.00008 0.00068 0.00009 0.00006 0.00009 0.00002 0.00480 0.00093 0.00007 0.00012 0.00023 0.00006 0.00002 0.00006 0.00007 0.00011 0.00013 0.00009 0.00004 0.00017 0.00003 0.00001

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

hit list gi|629675912 gi|325088293 gi|656908088 gi|70995790 gi|169598848 gi|682392353 gi|448524215 gi|671149944 gi|682401302 gi|682284347 gi|366989497 gi|296425708 gi|295668645 gi|675354371 gi|545354089 gi|545366075 gi|545364434 gi|545367453 gi|545371723 gi|633910957 gi|745998861 gi|745998856 gi|302841912 gi|675351822 gi|745998863 gi|545368690 gi|545367478 gi|557673827 gi|554521759 gi|193230752 gi|748139830 gi|515340651 gi|505038549 gi|145558944 gi|501376218 gi|501379998 gi|501379996 gi|197267616 gi|654534724 gi|655498731

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Table 1

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Legends to tables and figures

Table 1 Complete list of all assigned proteins from the un-processed L. pulmonaria sample (Austria, Johnsbach, Styria) that are most relevant and also mentioned and/or discussed in the text. The affiliation to their respective lichen partner is indicated by the corresponding color in the first column: mycobiont (colored in bright brown), photobiont (colored in bright green), cyanobiont (colored in cyan blue), microbiome (colored in beige) and lichenicolous fungus (colored in dark brown). Proteins assigned to viruses or Archaea were colored in gray or red, respectively. Column 2 represents the hit list including the GI-number of each assigned protein, column 3 contains the description of the assigned proteins according to COG/KOG or Pfam (Protein families), column 4 depicts the entry number according to COG/KOG or Pfam is given in and column 5 shows the sum of all corresponding NSAF values.

Figure 1 (A) European map of eco-zones (www.eea.eu.int) including Lobaria pulmonaria sampling sites used for analysis. (B) Workflow of sample preparation of the unprocessed L. pulmonaria sample (Johnsbach, Austria, Styria (1)) and concentration of bacterial microbiome of the two different sampling sites (Johnsbach, Austria, Styria (1) and Born, Germany, Darß (2 and 3)).

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Figure 2 Voronoi treemaps visualizing the L. pulmonaria lichen-associated metaproteome on different taxonomic levels (holobiont partners, phylum and class). (A) Assignment of all lichen relevant proteins/spectra (without potential contaminations) obtained by GeLC-MS/MS to the specific holobiont-partners: mycobiont (colored in bright brown), microbiome (colored in beige), photobiont (colored in bright green), cyanobiont (colored in cyan blue) and lichenicolous fungus (colored in dark brown). Furthermore proteins allocated to viruses or archaea were colored in gray or red respectively. Furthermore the assignments of proteins/spectra to the respective phylum or class are indicated. (B) Assignment of all lichen-relevant proteins/spectra (without potential contaminations) obtained by GeLC-MS/MS to the specific holobiont-partners (for legend see A). The sums of respective NSAF values were used for calculation of Voronoi cell sizes (A) and percentages (B). Note that more than half of all protein amount (NSAF values) accounts for proteins of the mycobiont (phylum: Ascomycota).

Figure 3 (A) Voronoi treemap visualizing the L. pulmonaria lichen-associated metaproteome on the functional level. According to Figure 2A, all lichen relevant proteins/spectra (without potential contaminations) obtained by GeLC-MS/MS were assigned to the specific holobiont-partners as indicated by the same color code: mycobiont (colored in bright brown), microbiome (colored in beige), photobiont (colored in bright green), cyanobiont (colored in cyan blue) and lichenicolous fungus (colored in dark brown). Proteins assigned to viruses or Archaea were colored in gray or red, respectively. 41 ACS Paragon Plus Environment

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The subsequent functional classification of the respective proteins is based on subroles according to COG (clusters of orthologous groups) or KOG (Eukaryotic orthologous groups). The sums of the corresponding NSAF values were used for calculation of Voronoi cell sizes. (B) Numbers of known functional groups (without unclassified and unknown functions) of each holobiont-partner.

Figure 4 Distribution percentages of all known functional groups or unclassified proteins based on sub-roles according to COG (clusters of orthologous groups) or KOG (Eukaryotic orthologous groups) within the five main partners of the holobiont (without viruses and archaea). The different holobiont partners are distinguished by the same color code as in Fig. 2 and 3.

Figure 5 Percentages of the most abundant proteins of the concentrated bacterial microbiome obtained from L. pulmonaria (Born, Darß) compared with those obtained from L. pulmonaria (Johnsbach, Styria). Percentages were calculated based on the sum of the corresponding NSAF values.

Figure 6 Schematic presentation and advanced model of potential functions and interactions within the L. pulmonaria lichen based on published knowledge and data of the here presented proteome analysis. 42 ACS Paragon Plus Environment

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TOC graphics (for TOC only)

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A

holobiont partners

phylum Nanohaloarchaeota

unclassified

Archaea

class Nephroselmidophyceae

Crenarchaeota

Journal of Proteome Research

Nanohaloarchaea

unclassified

unclassified

Thermoplasmata

Thermoprotei

Orbiliomycetes

Nitrososphaeria

Ulvophyceae

Euryarchaeota Thaumarchaeota

Methanomicrobia

Methanobacteria

B

heterogeneous

unclassified

unclassified

Mamiellophyceae

heterogeneous

Archaeoglobi

Methanococci

Pezizomycetes

unclassified

Halobacteria Pedinophyceae

Lecanoromycetes

Photobiont

Chlorophyta

0.5 %

0.7 %

3.1 % 1.7 %

7.6 %

Dothideomycetes

Schizosaccharomycetes

Trebouxiophyceae

Archaea

Leotiomycetes

Viruses

Mycobiont

Deferribacteres

Armatimonadetes

Tenericutes

unclassified

Fusobacteria

Ascomycota

Lichenicolous fungus

Chlorophyceae Deferribacteres

Fimbriimonadia

unclassified

Mollicutes

27.3 %

Fusobacteriia

Rubrobacteria

Eurotiomycetes

unclassified

Poribacteria Ktedonobacteria

Thermomicrobia

Chloroflexia Chloroflexi

Actinobacteria

unclassified Anaerolineae

Ignavibacteriae

Actinobacteria

Planctomycetes

unclassified

Ignavibacteria

Pneumocystidomycetes

Mycobiont

unclassified unclassified

Thermotogae

Thermotogae

unclassified

Coriobacteriia

Cytophagia

Nitrospirae Aquificae

Flavobacteriia

Chlorobi

Bacteroidetes

Nitrospira Aquificae

Chlorobia

Epsilonproteobacteria

59 %

Gammaproteobacteria

Sphingobacteriia

Zetaproteobacteria

Bacteroidia

Spirochaetes

Chrysiogenetes

DeinococcusThermus

Proteobacteria

Sordariomycetes

Spirochaetia

Chlamydiae

Chlamydiia

Chrysiogenetes

Deinococci

Acidobacteria

Betaproteobacteria

Synergistetes

Alphaproteobacteria

Acidobacteriia

Synergistia

Saccharomycetes

Holophagae

Erysipelotrichia

Negativicutes heterogeneous unclassified

Solibacteres

Cyanobiont

Microbiome

Phycisphaerae

Elusimicrobia

Parcubacteria Atribacteria

Microbiome

Taphrinomycetes

Planctomycetia

Thermoleophilia Elusimicrobia

Cyanobiont

Cyanobacteria candidate division Zixibacteria

Lichenicolous fungus

Lentisphaerae

Firmicutes

unclassified

Tremellomycetes

Mixiomycetes

Deltaproteobacteria

unclassified

Basidiomycota

Lentisphaeria

Clostridia

Chitinivibrionia Aminicenantes

Microbotryomycetes

Agaricomycetes

Pucciniomycetes

Malasseziomycetes

Ustilaginomycetes

Verrucomicrobia Gemmatimonadetes

Bacilli

unclassified Spartobacteria

Fibrobacteres

Tissierellia

Gloeobacteria

ACS Paragon Plus Environment

Gemmatimonadetes

Opitutae

Verrucomicrobiae unclassified

Wallemiomycetes Exobasidiomycetes

Photobiont

Viruses

Journal of Proteome Research

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

25 20 15

21

RNA processing and modification

Transcription

19 19 18

Function unknown

General function prediction only

Translation, ribosomal structure and biogenesis

16 15

General function prediction only

Function unknown Inorganic ion transport and metabolism

Nucleotide transport and metabolism

unclassified

Energy production and conversion

Amino acid transport and metabolism

Cell motility Cell wall/ membrane/ envelope biogenesis

5

Lipid transport and metabolism

Cell cycle control, cell division, chromosome partitioning

Energy Photosynthesis Carbohydrate transport and production metabolism and conversion

Cell cycle control, cell Cytoskeleton division, chromosome

Amino acid transport and metabolism

partitioning

Posttranslational modification, protein turnover, chaperones

Signal transduction mechanisms

0

Function Posttranslational unknown modification, protein turnover, Transcription chaperones

RNA processing and modification

Intracellular trafficking, secretion, and vesicular transport

Replication, recombination and repair

unclassified

Signal transduction mechanisms

recombination and repair

6

Cell cycle control, cell division, chromosome partitioning

Coenzyme transport and metabolism

Chromatin structure and dynamics Replication,

10

Lipid transport and metabolism

Defense mechanisms

Translation, ribosomal structure and biogenesis

Carbohydrate transport and metabolism

Defense mechanisms

Secondary metabolites biosynthesis, transport and catabolism

Transcription

Page 48 of 51

Posttranslational modification, protein turnover, chaperones

Intracellular trafficking, secretion, and vesicular transport

Posttranslational Signal modification, transduction protein mechanisms turnover, chaperones

unclassified

Coenzyme transport and metabolism

Inorganic ion transport and metabolism

General function prediction only

Coenzyme transport and metabolism

subrole [COG/ KOG]

Amino acid transport and metabolism

Nuclear structure

Inorganic ion transport and metabolism

Cell wall/ membrane/ envelope biogenesis

Carbohydrate transport and metabolism

Cytoskeleton

Prophage functions

unclassified viral envelopes

unclassified

functional groups #

Signal transduction mechanisms

Function unknown Cell cycle control, cell division, chromosome partitioning

Cell wall/ membrane/envelope biogenesis

Cell motility

Lipid transport and metabolism

RNA processing and modification

Inorganic ion transport and metabolism

Function unknown Cell cycle control, cell division, chromosome partitioning

Coenzyme transport and metabolism

Energy production and conversion

Secondary metabolites biosynthesis, transport and catabolism

Nucleotide transport and metabolism Coenzyme transport and metabolism

Amino acid transport and metabolism

Carbohydrate transport and metabolism Photosynthesis

Translation, ribosomal structure and biogenesis

Lipid transport and metabolism

Inorganic ion transport and metabolism

unclassified Replication, recombination and repair

Function unknown

Translation, ribosomal structure and biogenesis Transcription

RNA processing and modification

General function prediction only

Chromatin structure and dynamics

Replication, recombination and repair Amino acid transport and metabolism

Signal transduction mechanisms

Posttranslational modification, protein turnover, Cell wall/ chaperones membrane/ envelope biogenesis

Signal transduction mechanisms

Secondary metabolites biosynthesis, transport and catabolism

Defense mechanisms

Amino acid transport and metabolism

Posttranslational modification, protein turnover, chaperones

General function prediction only

Energy production and conversion

Energy production and conversion

Secondary metabolites biosynthesis, transport and catabolism

Intracellular trafficking, secretion, and vesicular transport

Intracellular trafficking, secretion, and vesicular transport

Secondary metabolites biosynthesis, transport and catabolism

Amino acid transport and metabolism

Nucleotide transport and metabolism

Carbohydrate transport and metabolism

Nucleotide transport and metabolism ACS Paragon Plus Environment

Cytoskeleton

Transcription

Replication, recombination and repair

Translation, ribosomal structure and biogenesis

Defense mechanisms

Posttranslational modification, protein turnover, chaperones

other Carbohydrate transport and metabolism

Energy production and conversion

Secondary metabolites biosynthesis, transport and catabolism

Intracellular trafficking, secretion, and vesicular transport

Function unknown

General function prediction only

Lipid transport and metabolism

Nucleotide Inorganic ion transport and transport and metabolism metabolism

Cell wall/ membrane/envelope biogenesis

Replication, recombination and repair

Cell cycle control, cell division, chromosome partitioning

Transcription

Translation, ribosomal structure and biogenesis

Chromatin structure and dynamics

unclassified

RNA processing and modification

Figure 4

Page 49 of 51

unclassified

Journal of Proteome Research

Photosynthesis 1 2 Translation, ribosomal structure and biogenesis 3 Transcription 4 5 Signal transduction mechanisms 6 7 Secondary metabolites biosynthesis, transport and catabolism 8 RNA processing and modification 9 10 Replication, recombination and repair 11 12 Posttranslational modification, protein turnover, chaperones 13 Nucleotide transport and metabolism 14 15 nuclear structure 16 Lipid transport and metabolism 17 18 Intracellular trafficking, secretion, and vesicular transport 19 20 Inorganic ion transport and metabolism 21 General function prediction only 22 23 Function unknown 24 25 Energy production and conversion 26 Defense mechanisms 27 28 Cytoskeleton 29 30 Coenzyme transport and metabolism 31 Chromatin structure and dynamics 32 33 Cell wall/membrane/envelope biogenesis 34 35 Cell motility 36 Cell cycle control, cell division, chromosome partitioning 37 38 Carbohydrate transport and metabolism 39 Amino acid transport and metabolism ACS Paragon Plus Environment 40 41 0 5 10 15 42

Photobiont Mycobiont Microbiome Lichenicolous fungus Cyanobiont

20

25

30

35

Figure 5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

Journal of Proteome Research

Page 50 of 51

8 7 6 5 4 3 2 1

Born (Darß) Johnsbach (Styria)

0

ACS Paragon Plus Environment

Page 51 of 51

Figure 6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

Journal of Proteome Research

ACS Paragon Plus Environment