Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition

Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition Plant litter decomposition is a process enabling biogeochemical cycles closing in ecosystems, and decomposition in forests constitutes the largest part of this process taking place in terrestrial biomes. Microbial communities during litter decomposition were studied mainly with low-throughput techniques not allowing detailed insight, particularly into coniferous litter, as it is more difficult to obtain high quality DNA required for analyses. Motivated by these problems, we analyzed archaeal, bacterial, and eukaryotic communities at three decomposition stages: fresh, 3- and 8-month-old litter by 16/18S rDNA pyrosequencing, aiming at detailed insight into early stages of pine litter decomposition. Archaea were absent from our libraries. Bacterial and eukaryotic diversity was greatest in 8-month-old litter and the same applied to bacterial and fungal rDNA content. Community structure was different at various stages of decomposition, and phyllospheric organisms (bacteria: Acetobacteraceae and Pseudomonadaceae members, fungi: Lophodermium, Phoma) were replaced by communities with metabolic capabilities adapted to the particular stage of decomposition. Sphingomonadaceae and Xanthomonadaceae and fungal genera Sistotrema, Ceuthospora,and Athelia were characteristic for 3-month-old samples, while 8-month-old ones were characterized by Bradyrhizobiaceae and nematodes (Plectus). We suggest that bacterial and eukaryotic decomposer communities change at different stages of pine litter decompo- sition in a way similar to that in broadleaf litter. Interactions between bacteria and eukaryotes appear to be one of the key drivers of microbial community structure. . . . . Keywords Pine litter decomposition Bacterial community Fungal community Metagenomics 16S rDNA pyrosequencing Introduction closing in terrestrial ecosystems. Decomposition in forests constitutes the largest part of this process taking place in ter- Decomposition of organic matter is a key step in nutrients restrial biomes, due to their immense area (~ 30% of land cycling in all ecosystems. Plant litter decomposition is an im- surface [1]) and large quantities of organic matter stored. portant ecological process enabling biogeochemical cycles Forest litter decomposition was extensively studied since the early 1970s (reviewed, e.g., in [2]). However, chemical changes in decomposing materials as well as element cycling Electronic supplementary material The online version of this article in relation to temperature and precipitation were assessed, (https://doi.org/10.1007/s00248-018-1209-x) contains supplementary while microbial and macroorganismal aspects of this process material, which is available to authorized users. were less intensively studied [3, 4]. Nevertheless, it is estimat- ed that microbes are responsible for up to 90% of organic * Marcin Gołębiewski mgoleb@biol.umk.pl matter decomposition [5], and the dominating primary decom- posers in boreal and temperate forest soil systems are micro- organisms, mainly fungi and bacteria. The structure and de- Nicolaus Copernicus University, Lwowska 1, 87-100 Toruń,Poland velopment of decomposer communities can influence the pat- Centre for Modern Interdisciplinary Technologies, Nicolaus tern of decay [3]. Copernicus University, Toruń,Poland 3 As the material decomposes, chemical composition of the Institute of Environmental Sciences, Jagiellonian University, litter changes, and there is a shift from carbohydrates and Kraków, Poland 4 aliphatic components constituting the largest pool in initial Department of Microbiology, Nicolaus Copernicus University, litter, to aromatic compounds at late stages of decomposition. Toruń, Poland Gołębiewski M. et al. Many components of fresh plant litter, like sugars and pep- DNA and spectrum of lysed organisms. We hypothesized that tides, decompose quickly, as they are energy-rich and can be the treatment would increase the yield and diversity of 16/18S easily assimilated by soil microorganisms [6–11]. The chem- rDNA amplicons. As there were no reports of Archaea being ical changes coupled with mixing the original plant substrate found in litter, we expected that they would be absent from with soil particles, due to the action of annelids and arthro- pine litter samples. Bacterial and eukaryotic communities pods, enable supporting different decomposer communities. would be different at different stages of decomposition, spe- Microbial communities at various stages of litter decompo- cifically (i) microbial diversity would follow diversity of sub- sition were studied mostly with traditional microbiological strates, i.e., would be lower at later stages of decomposition, methods [12–14] and more recently using molecular approach (ii) late communities would be more dominated by specialists [15–20]; however, the studies mainly concerned broad-leaf capable of lignocellulose and lignin utilization, (iii) forest litter [17, 21, 22]. Coniferous substrates rich in waxes, phyllosphere-related organisms would prevail initially and resins, and lignin are more resistant to decomposition [23, 24] then would be replaced by those coming from soil, and (iv) and more difficult to study; thus, only a few studies were a shift in metabolic capabilities of the community was also performed to date [25, 26]. Nevertheless, a general picture of expected wherein organisms utilizing soluble small molecules microbial succession on litter was obtained, in which initially present in the phyllosphere would be superseded by phyllospheric organisms, such as members of those in whose genomes reside genes enabling cellulose and, Acetobacteraceae among bacteria and Leotiomycetes among later, lignin utilization. To test these hypotheses, we prepared, fungi act as early decomposers [27] and are quickly replaced sequenced, and analyzed pyrosequencing (454) libraries of with distinct communities characteristic for particular stages archaeal and bacterial 16S, as well as eukaryotic 18S rRNA of decomposition [17, 19]. Fungi seem to be the key decom- gene fragments derived from litter samples at three stages of posers responsible for producing extracellular hydrolytic and decomposition. oxidating enzymes, and among them, Ascomycota prevail at early stages of decomposition [16] and are replaced by Basidiomycota later on [17, 28]. Bacterial communities tend Materials and Methods to be dominated by Proteobacteria, Bacteroidetes, and Actinobacteria [19, 20]; however, Acidobacteria were found Study Design and Samples to be frequent in spruce litter [26]. It was found that both bacterial and fungal diversity generally increased in the pro- The study site was located in Sierbowice, southern Poland cess of decomposition [17, 19]. However, as decomposition (GPS coordinates: N 50° 34′ 15.20″, E19° 39′ 55.40″). The spans many years, one has to bear in mind that seasonality is vegetation at the site is dominated by approx. 50 years old also an important factor shaping litter microbial community Scots pine (Pinus sylvestris L.) with a small admixture of birch structure [26]. (Betula pendula Roth) and artificially introduced red oak Until fairly recently, microbes were studied with the use of (Quercus rubra L.), with European blueberry (Vaccinium culture-based methods, which limited the scope of the studies myrtillus L.) and mosses in the groundcover. to culturable organisms. Usually less than 1% of microbes We were interested in investigating three time points of from a given environment can be cultured under laboratory initial stages of litter decomposition: t = no field incubation, conditions [29–31]. A set of methods was devised, collective- t = 92 days, and t = 242 days of field incubation, because we 1 2 ly termed Bmetagenomics^ [32], which allows overcoming of expected changes in community composition in line with rap- the culturability problem. It is based on isolation of genetic id initial mass loss. In order to do so, freshly fallen brown pine material directly from the environment, without prior cultur- litter from over a dozen of randomly picked trees in the area of ing of microorganisms [33]. A pre-requisite for all of them is about 3 ha was collected in October 2012. The needles were DNA isolation directly from environmental sample. There are obtained by shaking the selected trees and their branches. A many DNA isolation methods successfully applied to soil PVC foil was spread under the trees to facilitate litter collec- samples, but there is only a couple of examples of DNA iso- tion and to prevent contamination of fresh litter with microbial lation from forest litter [34]; moreover, they concern mostly communities from soil. broadleaf forest litter [19], which has different chemical prop- The collected litter was transported to the laboratory in erties, with less phenolics and other compounds that could plastic bags. The material was thoroughly mixed and split into possibly affect DNA extraction efficacy and hamper subse- two major parts. The first part consisted of three subsamples quent molecular analyses [35]. Standard methodology of en- dedicated for water content analysis and DNA extractions to vironmental DNA isolation now includes the use of bead determine the t microbial community. The second major part beating-based kits. We wanted to check if combining a com- of the collected material was air dried in room temperature for mercial kit with enzymatic digestion with lysozyme, a month. Three subsamples from the air-dried material were achromopeptidase, and chitinase would improve the yield of taken for chemical analyses, and the rest was used to make Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition litter bags. The litter bags were made out of a nylon mesh achromopeptidase and chitinase, and incubation as above (20 × 20 cm, mesh size 1 × 1 mm) and were packed with ~ (ALCh method). Isolated DNA was quantified with Qubit 20 g of air dry litter. On the 1 December 2012, they were HS DNA kit (Invitrogen, USA), and the quality was measured placed back in the field under the soil organic layer. Six bags spectrofotometrically on NanoDrop ND-1000 (Thermo Fisher were collected at each of the time points. Every time, three Scientific, USA). DNA content was expressed in ng per 1 g of litter bags were dedicated for chemical and the remaining ones fresh litter. for molecular analyses. Before the DNA extractions, the sam- ples were placed in a climatic chamber and acclimated for a Primers Design week at 22 °C to 70% of water holding capacity (WHC) and frozen at − 80 °C. The acclimation step was performed in PrimersweredesignedbasingonSILVAv.119alignment [36]. order to standardize the physiological state of microorganisms The alignment was split into kingdom-specific parts with in the litter, as the collection of litter took place during differ- Mothur’s get.lineage, and consensus sequences were generat- ent seasons. In the end, we obtained three biological replicates ed at the 97% identity level (consensus.seqs). Visual inspec- per time point, and three technical replicates (independent tion of resulting summary files allowed identification of high- isolations) were made for each of four isolation methods used. ly conserved regions. Candidate pairs were checked with the Just as in case of the t series before the chemical analyses, t 0 1 online TestPrime tool [37] and IDT Oligoanalyzer [38]. Pine- and t samples were air dried. specific primers were designed in the same way, but an align- ment of Pinus 18S rRNA sequences from SILVA was used. Physicochemical Analyses Water content was measured gravimetrically in fresh litter 16S and 18S rRNA Gene Fragments Amplification samples, immediately after their transfer to the laboratory. and Pyrosequencing pH was measured in a slurry of 0.5-g air dried litter in 15 ml of demineralized water. The concentrations of particular ele- Libraries of 16S/18S rRNA gene fragments were created with ments were measured in powdered and dried material (12 h, the use of two-step method, involving gene-specific primers 105 °C). C and N concentrations were determined with Vario tagged with M13/M13R overhangs in the first round of PCR EL Cube (Elementar, Germany). The remaining and M13 bearing 9-nt MID sequences [39] and A adapter macroelements and microelements were determined with a sequence (Roche, Switzerland) paired with M13R with B PinAAcle 900 Z atomic absorption spectrometer (Perkin adapter overhang in the second round. Primer sequences and Elmer, USA) after wet digestion with nitric acid in Titan PCR conditions are listed in Table 1. The final products were MPS microwave sample preparation system (Perkin Elmer, quantified with Qubit HS DNA kit (Invitrogen), and 36 of USA). them were pooled in equimolar amounts for each library. The library quality was assessed with HS DNA chip on Climatic Data Bioanalyzer (Agilent, USA). Libraries were emPCR amplified with the use of Titanium Lib-L kits (Roche, Switzerland) and Climatic data (daily averages) were downloaded from dane. sequenced on GS-Junior machine with Titanium chemistry imgw.pl. As there is no meteorological station in the (Roche, Switzerland) as per the manufacturer’sprotocols. immediate vicinity of the sampling area, values from six nearest stations (Silniczka, Lgota Górna, Katowice- Pyrzowice, Olewin, Miechów, Jędrzejów-Sudół)were qPCR averaged and plotted in R. Real-time PCR analyses were conducted using primers listed DNA Isolation in Table 1 and FastStart SYBRGreen kit (Roche, Switzerland) on LightCycler 480 machine (Roche, Switzerland). The PowerSoil DNA Isolation kit (MoBio, USA) was used accord- reaction mix included 3 pmol of forward and reverse ing to the producer’s protocol for PowerLyzer 24 bead beater primers, 2 ng of template DNA, 5 μl of 2 x concentrated involving one 45-s cycle of bead beating at 4000 rpm for kit, and water up to 10 μl. Standards were prepared from control isolations and with the following modifications: (i) pure amplicons generated with primer pairs used for qPCR addition of lysozyme (Sigma, USA) to the final concentration on DNA isolated from Escherichia coli, P. sylvestris,and of 2 mg/ml and achromopeptidase (500 U/ml, Sigma, USA) to Boletus badius. Standard curves were replicated five times, the C1 buffer and incubation at 37 °C for 1 h (AL method), (ii) and samples were assayed in triplicates. Each run included addition of chitinase (Sigma, 0.01 U/ml) and incubation at for negative control (water). Resulting numbers of copies were 1 h (Ch method) and (iii) addition of lisozyme, converted to copies/g of litter. Gołębiewski M. et al. Table 1 Primer sequences and PCR conditions a b Name Sequence 5′→ 3′ Paired with Target PCR conditions Use Source B357fU GTTTTCCCAGTCACGAC CCT B786rU Bacterial 16S 95 °C–5 min; 25 cycles of 95 °C–15 s, Library generation Neefs, 1993 [81] ACG GGA GGC AGC AG 53 °C–30 s, 72 °C–30 s B786rU CAGGAAACAGCTATGAC AC B357fU Deja-Sikora, 2012 [61] CAG GGT ATC TAA WCC A519fU GTTTTCCCAGTCACGAC CAG A1048rU Archaeal 16S 95 °C–5 min; 25 cycles of 95 °C–15 s, Library generation Klindworth, 2013 [37] CMG CCG CGG TAA 51 °C–30 s, 72 °C–30 s A1048rU CAGGAAACAGCTATGAC CGR A519fU CRG CCA TGY ACC WC E566fU GTTTTCCCAGTCACGAC CAG E1200rU Eukaryotic18s 95 °C–10 min; 25 cycles of 95 °C–15 s, Library generation Hadziavdic, 2014 [82] CAG CCG CGG TAA TTC C 53 °C–30 s, 72 °C–30 s E1200rU CAGGAAACAGCTATGAC CCC E566fU GTG TTG AGT CAA ATT AAG C c, d M13-x-A CCA TCT CAT CCC TGC GTG TCT M13R-B M13-tagged amplicons 95 °C–5 min; 10 cycles of 95 °C–15 s, Library generation This work CCG AC TCAG X GTT TTC CCA 52 °C–45 s, 72 °C–45f s GTC ACG AC M13R-B CCT ATC CCC TGT GTG CCT TGG M13-x-A CAG TC TCAG CAG GAA ACA GCT ATG AC B969f ACG CGA RGA ACC TTA C B1072r Bacterial 16S 95 °C–10 min; 40 cycles of 95 °C–15 s, qPCR B1072r CGA GCT GAC GAC ARC CAT GCA B969f 53 °C–30 s, 72 °C–30 s ITS1 TCC GTA GGT GAA CCT GCG G qITS2 Fungal ITS 95 °C–10 min; 40 cycles of 95 °C–15 s, White, 1990 [79] 55 °C–30 s, 72 °C–30 s qITS2 TTY GCT GYG TTC TTC ATC G ITS1 Wakelin, 2007 [80] Con1256f TTA TTC CTG GTT CGA GA Pin1360r Coniferae 18S 95 °C–10 min; 40 cycles of 95 °C–15 s, This work Pin1360r TAG TCA ACA CGA GTT GA Con1256f Pinus 18S 49 °C–30 s, 72 °C–30 s U denotes M13/M13R tagged sequences M13 and M13R sequences are given in boldface font Key sequence in italics X denotes 10-nt barcode (MID sequence), for all barcode pairs Levenstein distance was min. 4 Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition Bioinformatics Analyses distance matrices were calculated in Mothur (unifrac.unweighted and unifrac.weighted) with subsampling the RNJ tree to include Pyrosequencing reads were processed with Mothur v. 1.32 700 and 1000 reads per sample for bacteria and eukaryota, [40] and custom-tailored Perl scripts, with modifications in- respectively. Morisita-Horn [50] and Bray-Curtis [51]dis- creasing the aggressiveness of denoising, chimera removal, as tance matrices were calculated in R (vegdist). NMDS and well as producing ten subsamples of the whole data and aver- CCA analyses were performed in R with vegan’s[52] aging the shared OTU table over those subsamples, as de- metaMDS and cca functions, respectively. For NMDS, scribed earlier [41, 42]. Brief summary of the procedure is 1000 tries were used, and the same number of permutations given below. was adopted in CCA. CCA models were built by backward The flows were extracted from the .sff files, forward, and selection with ordistep. reverse reads separately (sffinfo), then they were assigned to Co-correspondence analysis [53] was performed with the samples basing on the MID sequences, trimmed to min. 500 use of the coca function from the cocorresp R package [54]. andmax.650flows(trim.flows), anddenoisedwith Significance of the extracted axes was tested with permutation AmpliconNoise algorithms (shhh.flows and shhh.seqs; [43]). test (permutest), while the percent of fit was checked by leave- Primers and MIDs were removed from the denoised one-out cross-validation (crossval). seuqences, and the read set was dereplicated (unique.seqs) and aligned to the SILVA v.119 template alignment PICRUSt Analysis (align.seqs). Reads covering the desired region of the align- ment (pos. 6500–22,500 for bacteria and 13,876–22,550 for For PICRUSt [55] analysis, bacterial sequences were classi- eukaryotes) were chosen (screen.seqs) and gap only, and ter- fied as described above, but using GreenGenes taxonomy files minal gap-containing columns were removed from the align- (v. 13_8; [56]). Taxonomic information along with OTU table ment (filter.seqs). The set was dereplicated again, and residual was converted to a .biom file using Mothur’s make.biom func- sequencing and PCR noise was removed with Single Linkage tion. The file was then converted to v.1.0.0 format using biom pre-clustering (pre.cluster; [44]). Chimera identification and convert, as per https://github.com/rprops/PICRUSt_from_ removal were performed in two rounds: (i) with UCHIME mothur (visited Feb. 15, 2017). Normalized OTU table was (chimera.uchime; [45]) and (ii) with PERSEUS generated with normalize_by_copy_number.py, and predicted (chimera.perseus; [43]). metagenomes as well as NSTI scores were calculated with Full-length sequences (list.seqs, get.seqs) were used for predict_metagenomes.py. Functions were collapsed to classification with naive Bayesian classifier (classify.seqs; pathways at level 2 using categorize_by_function.py. [46]) using SILVA 119 template and taxonomy files (http:// Pathways pertaining to organismal systems were considered www.mothur.org/w/images/2/27/Silva.nr_v119.tgz, accessed spurious and removed. Predicted metagenomes were analyzed on September 4, 2014) for classification of bacterial reads with STAMP [57]. and PR2 database [47] for eukaryotic ones at the bootstrap confidence level of 80%. Taxons Bunknown^ and, in case of Statistical Analyses bacterial data, Bchloroplast^ were removed from the final set. OTUs at the 0.03 dissimilarity level were constructed via R[58] was used for statistical computations, with Hmisc [59], average linkage (UPGMA), and singletons together with phyloseq [60] and vegan [52] packages. Significance level doubletons were removed from the data (remove.rare). used was 0.01. ANOVA with Tukey’s HSD test was used to For the initial analyses of enzyme influence, the final read test for significance of differences in means, PERMANOVA set was subsampled to 500 reads per sample ten times (sub.- (vegan’s adonis), AMOVA (amova of ade4 package), as well sample and regular expressions in the sed editor), subsamples as ANOSIM (vegan’s anosim) for testing separation of clus- were combined (cat), the whole set was dereplicated and used ters. Significance level used was 0.01. When testing for sig- for distance matrix calculation (dist.seqs), and OTU construc- nificance of grouping by enzymatic treatments, permutations tion via average neighbor clustering at 97% similarity level were restricted to a given decomposition level (strata = de- (cluster). Shared OTU table was constructed (make.shared), composition). PERMDISP test (vegan’s betadisper) was used and averaged table was calculated with a Perl script to test the homogeneity of variance in community data. (average_shared.perl). Final analyses were performed on a Differences in KEGG pathway content between litter de- dataset in which all enzymatic treatments coming from one composition stages were tested in STAMP using Kruskal- sample were combined. This dataset was subsampled to 700 Wallis test with two-sided Welch’s test as a post hoc analysis (bacteria) and 1000 (eukaryota) reads per sample and proc- and Benjamini-Hochberg FDR correction, which was re- essed as described above. quired due to the non-normal distribution of P values. Q value Relaxed neighbor joining (RNJ) tree was constructed from threshold of 0.05 was assumed. Significance of sample clus- the final alignment with clearcut (clearcut; [48]). UniFrac [49] ters separation was tested with AMOVA on a Bray-Curtis Gołębiewski M. et al. distance matrix derived from the simulated metagenome count recoverable with the primer pair used, were present in samples table (vegan’s vegdist). Functional diversity was calculated as under study. the number of categories at level 0, which is the level of individual Kegg Orthologies (might be understood as Addition of Enzymes Does Not Improve DNA Isolation functions). Yield and Microbial Diversity Recovered Species diversity was measured as Shannon’sdiversity (H ′), species richness was measured as observed number of Pre-treatment of litter samples with achromopeptidase and OTUs, and evenness was estimated as Shannon’s evennes (J lysozyme, chitinase, and all three enzymes together did not ′). From now on, for the sake of brevity, we will use terms affect the copy number of pine 18S rRNA gene in litter Bdiversity^ and Bevenness^ in place of their indices names. metagenomic DNA (P > 0.05, Fig. 1a). The overall DNAyield was not changed by the addition of enzymes (P > 0.05, Fig. 1b). The same applies to the number of copies of bacterial Results 16S rRNA genes measured with qPCR (P >0.05, Fig. 1c) and the number of copies of fungal ITS sequences (P >0.05, Changes of Climatic and Litter Physicochemical Fig. 1d). Variables over Time Bacterial and eukaryotic diversity (measured as Shannon’s H′), species richness as well as evenness were not influenced Average daily temperature at the sampling area was below by modifications of the DNA isolation method, but they dif- 0 °C for the majority of the period preceding t ,rose to around fered at various stages of decomposition (Fig. 2). The same 12 °C 3 weeks later, and then after a month it fluctuated applies to community structure, as assessed with AMOVA and around 15 °C until the end of the experiment. Precipitation ANOSIM performed on Bray-Curtis and Morisita-Horn dis- was low (1.2 mm daily on average) until day 150; in this tance matrices (P > 0.05; data not shown). period, it was in the form of snow, but the snow cover lasted until day 140. Later, rainfalls were more intensive (3.8 mm DNA Content and Bacterial as well as Fungal SSU daily) until day 230, after which a 3-week period of drought Gene Copy Numbers Change During Early Stages occurred. Final sampling was performed 2 weeks after the of Decomposition onset of drought (Supplementary Fig. 1). C/N ratio as well as K level were the highest in t samples DNA yield significantly increased over time (P < 0.05, and stayed at lower level in older ones (Table 2). An increas- Fig. 3a). A rapid decrease of pine 18S rRNA gene copy num- ing trend was apparent for Mg, Fe, and Zn, while concentra- ber in t and t samples was observed (P <0.05), indicating 1 2 tion of Mn was the highest in t and Cu in t samples. pH was that most of pine DNA was degraded after 3 months (Fig. 3d). 1 2 similar in all samples; the average was 5.22 ± 0.37. Water Thus, it seemed that the increase in yield was due to the higher content of freshly fallen litter (t samples) was ~ 27%, while bacterial and fungal cell numbers. Indeed, the copy numbers directly after removal from the field, t was significantly more of both bacteria 16S rRNA genes and fungal ITS sequences, humid and contained 53% water and humidity in t was even being proxies for bacterial and fungal cell numbers, were lower than in t (23%). greatest in t samples. However, the number of bacterial 0 2 SSU was significantly higher in t than in t (Fig. 3b), while 1 0 Archea Are Absent from Litter Samples fungal ITS number was significantly higher in t samples than in t (Fig. 3c). No archaeal reads were recovered during the project. The sequences coming from the libraries prepared using the pre- Microbial Diversity Changes During Litter sumed archaeal primers [37] were classified either as Bacteria Decomposition (majority as Actinobacteria) or as Eukaryotes (Fungi). As the primers have perfect matches to almost 80% of archaeal se- Bacterial diversity (measured as Shannon’s H′) was similar in quences in Silva, we think that no Archaea, at least such all samples; it was highest in fresh litter, then dropped after Table 2 Mean values of C/N ratio a a a a a a Code time C/N Mn Mg K Cu Fe Zn WC (%) and concentrations of other elements as well as water content t0 0 64.7A 665.7A 434.0A 3061.6A 3.3A 288.1A 125.3A 27 in litter samples. Significant differences (ANOVA with t1 92 48.4B 721.9B 644.1B 2659.9B 3.0A 330.4B 136.7B 53 Tukey’sHSD, P < 0.01) are t2 242 47.6B 601.3C 820.3C 2388.2B 5.8B 530.8C 189.7C 23 denoted with different letters a −1 mg-kg dry weight Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition 7.5 decomposition decomposition t0 t0 t1 t1 7.0 t2 t2 6.5 6.0 5.5 5.0 control al ch alch control al ch alch Enzyme treatment Enzyme treatment C D 8.5 8.6 decomposition decomposition t0 t0 t1 t1 8.4 t2 t2 8.0 8.2 8.0 7.5 7.8 7.0 7.6 control al ch alch control al ch alch Enzyme treatment Enzyme treatment Fig. 1 Influence of DNA isolation method modifications on: the number standard error of the mean (SEM). Control—no enzymatic treatment, of pine 18S rRNA genes (a), overall DNA yield (b), the number of al—achromopeptidase + lysozyme, ch—chitinase, alch— bacterial 16S rRNA genes (c), the number of fungal ITS sequences (d). achromopeptidase + lysozyme + chitinase Note the logarithmic y axis in panels a, c, and d. Whiskers denote 3 months to reach higher values again after 8 months; howev- Bacterial and Fungal Community Structure Is Driven er, these changes were not significant (Fig. 4a). On the other by Decomposition Stage, Physicochemical Variables, hand, eukaryotic diversity decreased rapidly over time and Bacteria-Eukaryote Interactions (Fig. 4a), and the difference between fresh and 8 months old litter was significant. Both observed and estimated total Regardless of the dissimilarity measure used, bacterial com- (Chao1) species richness grew with time in the case of bacteria munities coming from individual biological replicates clus- and decreased in the case of eukaryotes (Fig. 4,b,c), tered tightly on nMDS plots, and samples at the same stage while evenness followed the pattern of diversity in both of decomposition were also located together (Bray-Curtis cases (Fig. 4d). showed on Fig. 5a). It appeared that the t samples were more log(pine 18S rRNA/g of litter) log(bacterial 16S rRNA genes/g of litter) log(fungal ITS copies/g of litter) ng DNA/g of litter Gołębiewski M. et al. decomposition decomposition t0 t0 t1 t1 t2 t2 control al ch alch control al ch alch Enzyme treatment Enzyme treatment C D decomposition decomposition 0.80 0.70 t0 t0 t1 t1 t2 t2 0.65 0.75 0.60 0.70 0.55 0.65 0.50 control al ch alch control al ch alch Enzyme treatment Enzyme treatment 4.0 decomposition decomposition t0 t0 2.8 3.8 t1 t1 t2 t2 2.6 3.6 2.4 3.4 2.2 3.2 2.0 3.0 1.8 control al ch alch control al ch alch Enzyme treatment Enzyme treatment Shannon’s H’ Shannon’s evenness observed OTUs number) observed number of OTUs Shannon’s H’ Shannon’s evennes Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition Fig. 2 Influence of DNA isolation method modifications on: bacterial (a) (P < 0.05). PERMDISP test demonstrated that variance and eukaryotic (b) species richness (observed number of OTUs), was not homogeneous in different sample groups for bacterial (c) and eukaryotic (d) Shannon’s evenness, and bacterial bacterial community, with highest dispersion found in t (e) and eukaryotic (f) Shannon’s diversity. Whiskers denote samples (P < 0.05), while it was similar in all groups for standard error of the mean (SEM). Control—no enzymatic treatment, al—achromopeptidase + lysozyme, ch—chitinase, alch— eukaryotic community (P =0.848). achromopeptidase + lysozyme + chitinase CCA analysis (Supplementary Fig. 2) identified Zn and Na as significant environmental parameters shaping the similartothefreshones(t )thantothe t samples. On 0 2 bacterial community structure (P < 0.01), while C/N ratio the other hand, eukaryotic communities in t samples as well as Zn and Mn were factors that significantly in- were closer to t ones (Bray-Curtis showed on Fig. 5b). 2 fluenced eukaryotic community. The measured variables ANOSIM as well as AMOVA and PERMANOVA anal- explained 20.1% of inertia (variance) in case of bacteria yses showed that separation of clusters was significant and 37.2% in case of eukaryotes. 7e+08 6e+08 5e+08 4e+08 3e+08 2e+08 1e+08 0e+00 t0 t1 t2 t0 t1 t2 Decomposition stage Decomposition stage 8e+08 8e+07 6e+08 6e+07 4e+08 4e+07 2e+08 2e+07 0e+00 0e+00 t0 t2 t0 t2 t1 t1 Decomposition stage Decomposition stage Fig. 3 Changes in DNA composition and yield during decomposition. DNA yield (a), number of bacterial 16S rRNA genes (b), number of fungal ITS sequences (c), number of pine 18S rRNA genes (d). Whiskers denote standard error of the mean (SEM) copies/g of litter ng/g of litter copies/g of litter copies/g of litter Gołębiewski M. et al. a bacteria bacteria 4.0 eukarya eukarya 3.5 3.0 ab 2.5 ab 2.0 t0 t1 t2 t0 t1 t2 Decomposition stage Decomposition stage C D bacteria bacteria 0.75 eukarya eukarya 0.70 400 a 0.65 0.60 a b 0.55 0.50 t0 t1 t2 t0 t1 t2 Decomposition stage Decomposition stage Fig. 4 Changes in species richness, evennes and diversity estimates. Shannon’s H′ for bacterial (a), observed number of OTUs (b), Chao1 estimated total number of OTUs (c), Shannon’s evenness (d) Possible influence of eukaryotes on bacterial community (0.646) ones, indicating that stronger influence was and vice versa was assessed with co-correspondence analysis exerted in the latter two sample groups. (Supplementary Fig. 3). Two first COCA axes turned out to be significant in a permutation test (P < 0.05), and they were Bacterial Community in Litter Samples Is Dominated sufficient (i.e. adding more axes would not significantly in- by Proteobacteria crease percent variance explained) according to leave-one-out cross-validation. They explained 22.4% of variance in Although bacterial community structure varied greatly in bio- the bacterial community and 44.15% of variance in the logical replicates, certain general trends were visible. At all eukaryotic community. Mean distance between bacterial levels an increase of rare taxa levels in time was found, which and eukaryotic site coordinates was significantly higher was in line with increasing bacterial diversity. At the level of for t samples (0.985, P < 0.05), than for t (0.673) and t phylum, the community was dominated by Proteobacteria 2 0 1 number OTUs Shannon’s H’ Shannon’s E number OTUs Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition AB Fig. 5 Non-metric multidimensional scaling (nMDS) analysis of Bray-Curtis distance matrices for bacterial (a) and eukaryotic (b) communities. Stress: bacteria—0.085, eukaryota—0.099 (68–88%), and an increase of Actinobacteria was visible in t characteristic for t ;and Plectus (nematode), Mycena,as well 2 1 samples (Fig. 6a). Only four phyla (Proteobacteria, as Mytilinidion being typical for t . However, due to relatively Acidobacteria, Bacteroidetes, and Actinobacteria) accounted short 18S rRNA gene fragments being sequenced, only ~ 35 for the vast majority of reads in all samples. Among classes to 80% of reads could be classified down to this level. Alphaproteobacteria dominated in all libraries, and slight de- crease of Beta- and Gammaproteobacteria in t and t samples Metabolic Capabilities of Bacterial Community Are 1 2 was observed with concomitant increase of Actinobacteria, Different at Different Stages of Decomposition Sphingobacteriia, and rare taxa levels (Fig. 6b). More changes were visible at the order level, e.g., Rhodospirillales reads PICRUSt analysis was performed to reveal potential functions were abundant in t reads and significantly less numerous in encoded in genomes of bacteria whose 16S rRNA gene frag- t and t , Burkholderiales and Xanthomonadales were most ments were sequenced in our study. Nearest Sequenced Taxon 1 2 frequent in t samples, while Rhizobiales and Sphingobacteriales Index (NSTI) analysis indicated that in most cases, a se- reads were most frequent in t . The same pattern was observed quenced genome from the same genus could be found for families within the abovementioned orders (Fig. 6c) and (NSTI < 0.05). among genera, where Sphingomonas was the most frequent Overall diversity of functions was lower in the t samples one, and Pseudomonas, Burkholderia, Granulicella,and than in t and t (P <0.01; Fig. 8a). PCA analysis showed that 1 0 Rhizobium were also found in large quantities (Fig. 6d). functional composition of metagenomes was different in t , t , 0 1 and t (Fig. 8b; P < 0.05, AMOVA); however, this effect was Eukaryotic Community in Litter Samples Is Dominated partially due to differences in variance (P < 0.01, by Fungi PERMDISP). The pathways whose shares differed signifi- cantly between various sample types belonged mainly to Eukaryotic libraries differed in biological replicates, similarly BMetabolism^ supercategory, but there were also BSignaling to bacterial ones. The libraries were dominated by fungal Molecules and Interaction^ as well as BTransport and reads (Ascomycota 37–85% and Basidiomycota 7.5–57.8%) Catabolism^ (Fig. 9). with minor quantities of Nematoda (Chromadorea, up to Carbohydrate, amino acid, energy, lipid, xenobiotic, and 4.4%) at later stages of decomposition (Fig. 7a). terpenoid metabolism were the most frequent categories dif- Ascomycotal reads prevailed in the libraries derived from fering significantly. Genes encoding proteins participating in fresh litter, then Basidiomycota were the most numerous in carbohydrates, terpenoids and xenobiotic metabolism were t samples and finally Ascomycota levels increased to ~ 65% predicted to be more frequent in t communities than in the 1 2 in t litter.Eachsampletypeharboreduniqueeukaryotic com- other two, while those engaged in lipid metabolism were es- munity at the genus level (Fig. 7b), in case of t ones timated to be more frequent in t than in t only. Only the 0 2 1 hallmarked by Lophodermium and Phoma; Sistotrema, genes involved in energy metabolism were more probable to Ceuthospora (Phacidium), Trichoderma, and Athelia being be encoded by organisms forming t community. 0 Gołębiewski M. et al. 100 100 unclassified unclassified rare rare Actinobacteria Actinobacteria Bacteroidetes Sphingobacteriia 80 Acidobacteria 80 Acidobacteria Proteobacteria Gammaproteobacteria Betaproteobacteria Alphaproteobacteria 60 60 40 40 20 20 0 0 t0 t1 t2 t0 t1 t2 C D 100 100 unclassified unclassified rare rare Chitinophagaceae Afipia X1174.901.12 Pedobacter Rhodospirillaceae Luteibacter 80 80 Microbacteriaceae Mucilaginibacter Caulobacteraceae Rhizobium Oxalobacteraceae Granulicella Beijerinckiaceae Burkholderia 60 Rhizobiaceae 60 Pseudomonas Bradyrhizobiaceae Sphingomonas Sphingobacteriaceae Pseudomonadaceae Xanthomonadaceae 40 Burkholderiaceae 40 Acidobacteriaceae_.Subgroup_1. Acetobacteraceae Sphingomonadaceae 20 20 0 0 t0 t1 t2 t0 t1 t2 Fig. 6 Classification of bacterial reads. Percentages of all reads are shown at the following levels: phyla (a), classes (b), families (c), and genera (d) 3+ At the level of individual genes, these involved in various as Fe transporting ATPases were predicted to be most fre- sugar transport (PTS system) and certain engaged in metabo- quent in genomes of organisms dwelling in t samples. lism (e.g., sugar kinases, epimerases) as well as regulation (sugar utilization operon regulatory proteins) were predicted to be most abundantly represented in genomes of organisms from t samples, virtually absent from t samples with t in the Discussion 0 2 1 middle. The same applies to other genes involved in import and utilization of soluble substrates such as amino acids, The short time span of our experiment together with the small lipids, or amines, while β-glucosidases, potentially engaged number of sampling time points causes our results to be rather in cellulose degradation, were predicted to be most frequent in a short sequence of snapshots than a time course of microbial t samples. In contrast, genes involved in biosynthesis of vi- succession on litter. Therefore, we avoid interpreting the re- tamins and cofactors, degradation of aromatic compounds sults as Btrends^ and concentrate on differences between pairs (oxygenases and oxidases responsible for degradation of ste- of time points. Nevertheless, we think that the results provide rols and phenolic compounds such as catechol, 2,4- an interesting insight into early stages of pine litter decompo- dichlorophenoxyacetate or 4-hydroxyacetophenone), as well sition at the molecular level. Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition 100 100 unclassified unclassified rare rare Nematoda Pucciniomycotina Basidiomycota Chromadorea Ascomycota Agaricomycotina 80 80 Pezizomycotina 60 60 40 40 20 20 0 0 t0 t1 t2 t0 t1 t2 100 80 unclassified rare rare Naemacyclus Tremellomycetes Marchandiomyces Microbotryomycetes Melanomma Chromadorea_X Coniophora Leotiomycetes Aphelenchoides Eurotiomycetes Slimacomyces Sordariomycetes Mytilinidion Pezizomycotina_X Chaetothyriales Dothideomycetes Rhodotorula Agaricomycetes Mycena Plectus 40 Phoma Athelia Trichoderma Lophodermium Ceuthospora Sistotrema 0 0 t0 t1 t2 t0 t1 t2 Fig. 7 Classification of eukaryotic reads. Percentages of classified reads are shown at the following levels: subkingdoms (a), phyla (b), subclasses (c), and genera (d) We observed no influence of enzymatic treatment neither conditions that were suboptimal for the chitinase used (ac- on the amount of DNA isolated nor on microbial diversity. cording to the producer the optimal pH is 4, while the buffer This might have been caused by the effectiveness of bead used for digestion had pH 8). It seems that there is no reason beating, making the action of enzymes pointless. In our pre- for enzymatic pre-treatment of litter samples prior to bead- vious study, we observed the influence of enzymes, as the beating-based DNA isolation. Other studies report significant method of isolation was based on less effective thermal/ influence of lysozyme digestion on soil DNA yield, even in chemical lysis [61]. However, an alternative explanation is combination with bead-beating [62]; however, the bead- possible, assuming that cell envelopes of microbes in our beating method was not as effective as in contemporary kits samples were not digested by the enzymes used. Albeit pos- using dedicated bead-beaters. sible, it is unlikely at least in the case of lysozyme, taking into DNA content of litter samples significantly increased dur- consideration high numbers of Proteobacterial reads generated ing the studied decomposition period. This increase was from the samples. We found that the enzyme worked well on caused by greater numbers of bacterial and fungal cells, as E. coli DH10B in the buffer used for digestion. However, no we found that pine DNA was almost completely degraded in increase in fungal DNA yield could be an effect of digestion t samples. This was confirmed by results of qPCR analysis of 1 Gołębiewski M. et al. 4500 0.04 t0 t1 t2 0.02 4300 0.00 -0.02 4100 0.04 0.06 t0 t1 t2 -0.075 0.00 0.075 Decomposition stage PCA1 (53.4%) Fig. 8 Diversity of functions encoded by bacterial genomes present in litter samples (whiskers denote standard error of the mean (SEM), a), nMDS analysis of Bray-Curtis distance matrix obtained from functional categories matrix (b). Stress: 0.187 bacterial 16S rRNA and fungal ITS copy numbers. The num- Although precipitation was lower in the t –t period than in 0 1 ber of bacterial sequences increased first (in the t samples), t –t one, water content was the highest in t samples, which 1 1 2 1 supporting our hypothesis that fresh litter is first colonized by was probably caused by low temperature. During the initial bacteria, albeit, alternatively, this increase might have been period of the experiment (t –t ), temperature was ~ 0 °C with 0 1 caused by growth of phyllospheric organisms. It seems plau- occasional snow, and diversity did not change significantly, sible that bacteria are responsible for pine DNA degradation. neither in the case of fungi, nor bacteria, which suggests weak We have found no archaeal reads in our libraries, which selection. Later, warm period was hallmarked by bacterial might mean that there were no Archaea in our samples. This species richness increase, showing possible colonization of is supported by results of a metaproteomic study concerning litter by bacteria from the surrounding environment. beech litter decomposition [16]. Nevertheless, it is possible Evenness was lower in t samples causing diversity to stay that Archaea in pine litter might belong to a group whose at the t level. In the case of eukaryotic community diversity, 16S rRNA sequences differ from those amplifiable with the species richness and evenness decreased significantly in the t primer system we used, e.g., Nanoarchaeota, Altiarchaeota, samples, suggesting strong selection. or Diapherotrites or, as in all phyla the coverage was well t samples harbored a bacterial community similar to pine below 90%, they could be members of generally amplifiable phyllosphere community [65], with high shares of groups having non-amplifiable variants. There are reports of Proteobacterial sequences, particularly members of the Archaea being cultured from Scots pine ectomycorrhizas Acetobacteraceae and Pseudomonadaceae families being the (Methanolobus, Halobacterium, and unknown member of hallmarks of phyllosphere. Differences might have resulted 11.c Crenarchaeota) [63], and ~ 2% of sequenced transcripts from tree species being different (Scots pine vs. limber pine), from spruce litter were coming from Archaea [26] and both geographical distance (Central Europe vs. California), and facts support the latter possibility. physiological state of needles (dry, fallen vs. fresh ones). Shannon’s diversity, evenness, and species richness were The eukaryotic t community consisted of Fungi, out of which consistently higher for bacterial communities than for fungal Ascomycotal class Pezizomycotina prevailed, which was sim- ones, which might be caused by the number of bacterial taxa ilar to fresh pine needles community studied by Millberg and being around one order of magnitude greater than the number colleagues [66] and unlike in senescent oak leaves, where of fungal ones in soils [64]. Moreover, bacterial diversity grew Dothideomycetes prevailed [17]. Again, the differences might over time, while fungal communities became less diverse. have resulted from geographical distance (Sweden vs. Poland) This effect might have been caused by three factors: litter and physiological state of the needles. Eukaryotic sequences humidity, temperature, and time (decomposition stage). that could be identified down to the genus level belonged Functional categories PCA2 (21.9%) Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition mainly to known phyllospheric fungi, such as Phoma, counts, as the most abundant phylotype of this group (Plectus) Trichoderma,or Lophodermium, and the latter might act as is a bacterivore. This is supported by the results of co- an early decomposer in litter [67, 68]. Low number of se- correspondence analysis, as the influence of eukaryotic com- quences was assigned to Naemacyclus (Helotiales, synonym munity on the bacterial one is significant and explains ~ 10% of Cyclaneusma), a genus comprising fungi causing needle cast variation. On the other hand, interactions among Eukaryota [69]; thus, it is plausible that certain amount of collected cannot be excluded, e.g., certain fungivorous nematodes most needles were shed prematurely due to its action. abundant in the t samples (e.g., members of the Differences between t and t samples were profound, in Aphelenchoides genus [75]) might be involved in much lower 1 0 spite of temperature being below 0 °C most of the time. After Sistotrema abundance in the t samples. The influence of bac- 3 months of field incubation, the samples harbored communi- teria on the eukaryotic community was even stronger (~ 22%). ties that were drastically different from t ones; this fact dem- It might be explained, e.g., by providing nitrogen (by N fixa- 0 2 onstrates that colonization of fresh litter by soil and older tion) that seems to be a limiting factor, particularly in t sam- litter-inhabiting organisms is rapid, regardless of harsh envi- ples. This is confirmed by the results of CCA, wherein C/N was ronmental conditions. This is in line with results of earlier identified as a significant environmental variable. It is also pos- studies on mass loss, e.g., [70] and with reports concerning sible that bacteria provided fungi with phosphorus, another lim- decomposition of broadleaf litter [17, 19, 20]. Reads coming iting nutrient, by solubilizing its soil resources [76]. As the from phyllospheric organisms were less abundant than in the measured environmental variables were responsible for t samples (e.g., members of Acetobacteraceae, as well as explaining of over 37% of variance, it seems plausible that they fungi Lophodermium and Phoma), showing that, in spite of were the key drivers of microbial community structure changes. the lack of diversity decrease, selection operates at this stage Bacterial community structure at the level of genus was sur- of litter decomposition, but organisms unable to survive are prisingly similar to this found at the corresponding stages of replaced by colonizers. Obvious fungal colonizers were mem- decomposition in oak leaf litter [77], where Pseudomonas, bers of Sistotrema, ectomycorrhizal/saprophytic fungi of the Sphingomonas,and Burkholderia were among the dominating Cantarellales order (Basidiomycota, Agaricomycetes), and genera. Differences were mainly quantitative and were more Ceuthospora (synonym Phacidium, of Helotiales pronounced in the phyllospheric communities. The most remark- (Ascomycota, Dothideomycetes), [71], a genus comprising able difference was the lack of Duganella (Oxalobacteriaceae, plant pathogenic fungi causing various diseases from needles Betaproteobacteria) and Frigoribacterium (Micrococcaceae, rot in conifers to fruit rot in cranberries, apples, or pears [72, Actinobacteria) in pine needles; these genera were replaced by 73]. Reads derived from these organisms were virtually absent unclassified members of the Acetobacteraceae family. from t samples and constituted over 50% of all eukaryotic Subsequent stages of pine litter decomposition harbored less reads in t ones. Small numbers of reads obtained from t Pedobacter. This fact suggests that, while plants assemble unique 1 0 samples might be explained by spores sedimenting from air phyllospheric communities, decomposers’ assemblages, at least on the collected needles. The eukaryotic community at this bacterial, may be more generic and similar regardless of the stage of decomposition was more similar to the initial one than quality of litter. the bacterial one, suggesting that bacteria were able to grow Fungal communities decomposing pine litter are less more actively under winter conditions. C:N ratio was highest similar to those degrading oak leaf litter described by in t samples, indicating nitrogen depletion, probably due to Voriskova and colleagues [17]; however, general picture organic nitrogen being consumed by microbes. looks similar: phyllospheric communities are rapidly re- To no surprise, at the end of the experiment, the samples placed by distinct communities characteristic for particu- harbored communities different from both t and t ones. As lar stages of decomposition. Certain fungi are abundant 0 1 seasonal differences in microbial community composition in both in oak and pine decomposing litter, e.g., Athelia, spruce litter were not found to be large [26], we suggest that Rhodotorula,and Sistotrema. Interestingly, Sistotrema, the differences visible in our data should be attributed to de- most abundant in 12-month-old oak litter [17], displays composition stage. Phyllospheric organisms were even less abundance peak in t samples. Thus, it is possible that abundant in t than in t , typical soil organisms could be found, fungal succession on decomposing pine litter Bovertakes^ 2 1 such as nematodes of the Chromadorea class or bacteria of the the one on oak leaves. Rhizobium genus, and the litter was colonized by organisms A model of litter decomposition assumes that soluble com- belonging to Dothideomycetes (Fungi, Ascomycota) and pounds are degraded first, then degradation of hemicelluloses Sphingomonadaceae as well Bradyrhizobiaceae (Bacteria, follows and the last stages comprise degradation of cellulose- Alphaproteobacteria), probably capable of degradation of recal- lignin complex leading to increasing lignin/cellulose ratio citrant compounds (lignocellulose, lignin) prevailing at this [78]. We used PICRUSt to model possible metagenomes of stage of decomposition [74]. The presence of nematodes might decomposing litter to learn if the probable gene content sup- be one of the causes of slower increase of bacterial 16S rDNA ports this model. Gołębiewski M. et al. Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition Fig. 9 Selected functional categories of significantly different decomposition. Microbial succession on decomposing pine abundance. Dot and whiskers plots to the right of the barplots show litter is rapid and initial phyllospheric communities are re- 95% confidence intervals on the difference between respective samples, placed with distinct assemblage characteristic for particular dot location indicates the mean, and the color of a given dot shows in stages of decomposition. Changes in communities seem to which sample the proportion was higher. Bonferroni-corrected P value is given to the right of dot and whiskers plots be driven not only by physicochemical variables of litter but also by interactions among bacteria, fungi, and nematodes. However, one should bear in mind that the results obtained Author Contributions Conceived the study—MG, MN, designed the ex- with PICRUSt are only an approximation of the Btrue^ periments—MG, contributed financial support and reagents—MN, MG, metagenome, and many factors, such as all biases influencing ATa, ATr, executed the experiments—MS, ATa, ED-S, MG, performed the underlying 16S rRNA gene fragment sequencing or differ- bioinformatic and statistical analyses—MG, interpreted data—MG, ATa, ences in gene content of closely related organisms due to lateral MN, wrote the draft of the manuscript—MG. All of the authors revised the manuscript and approved its content. gene transfer, may skew the results. Moreover, the set of func- tions derived from PICRUSt is only potential one, i.e., we do Funding Information This study was performed within an OPUS grant of not know if the functions are actually expressed. Another lim- the Polish National Science Center (No. UMO-2012/05/NZ8/001362) itation is that, due to the lack of a eukaryotic database, it was not and supported by DS758, DS759, and DSC funds of the Institute of possible to perform such an analysis for eukaryotic sequences. Environmental Sciences, Jagiellonian University and by statutory funding from Nicolaus Copernicus University (to MG and ATr). Therefore, the results should be treated with caution. We expected a shift in community composition from or- Open Access This article is distributed under the terms of the Creative ganisms capable of various carbohydrates utilization to Commons Attribution 4.0 International License (http:// cellulose-degrading specialists, to lignin degraders, although creativecommons.org/licenses/by/4.0/), which permits unrestricted use, the latter only to some extent, as our experiment concentrated distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link on the first 8 months of litter decomposition. Our results sup- to the Creative Commons license, and indicate if changes were made. port this view, as cellulolytic genes (β-glucosidases) were most abundant in t samples, and certain genes that might be involved in lignin degradation (oxygenases and oxidases) References were also found. The genes engaged in active transport of cellobiose (cellulose degradation product) into the cell (PTS 1. FAO (2010) Global forest resources assessment 2010. http://www. system) were most frequent in t samples and permeases fao.org/forest-resources-assessment/en/ allowing for passive import of this sugar were most numerous 2. Bradford MA, Berg B, Maynard DS, Wieder WR, Wood SA (2016) in t samples. This picture might mean that during the initial Understanding the dominant controls on litter decomposition. J. stage of litter decomposition (t ) after depletion of other easily Ecol. 104:229–238. https://doi.org/10.1111/1365-2745.12507 3. Berg B, Laskowski R (2006) Litter decomposition: a guide to car- accessible sugars, active transfer of cellobiose is required, most bon and nutrient turnover. Elsevier probably due to the low concentration of this compound; then, 4. David JF (2014) The role of litter-feeding macroarthropods in decom- fungi start producing cellulases causing increase of the cellobi- position processes: a reappraisal of common views. Soil Biol. ose concentration, which allows passive transfer; and finally, Biochem. 76:109–118. https://doi.org/10.1016/j.soilbio.2014.05.009 5. Persson T, Bååth E, Clarholm M et al (1980) Trophic structure, bacterial cellulolytic activity may supplement fungal activity biomass dynamics and carbon metabolism of soil organisms in a (t ). The latter claim was partially supported by results of Scots pine forest. Oikos 32:419–459. https://doi.org/10.2307/ Zifcakova and colleagues, who found that Fungi were respon- sible for only half of carbohydrates metabolism-involved tran- 6. Berg B, Hannus K, Popoff T, Theander O (1982) Changes in or- scripts production in spruce litter [26]. Given the importance of ganic chemical components of needle litter during decomposition. Long-term decomposition in a Scots pine forest. I. Can. J. Bot. 60: coniferous litter decomposition and taking into account all the 1310–1319 limitations of the methodology as well as the general scarcity of 7. Staaf H, Berg B (1982) Accumulation and release of plant nutrients molecular studies devoted to coniferous litter decomposition, in decomposing Scots pine litter. Long-term decomposition in a more work is needed to understand which organisms are re- Scots pine forest. II. Can. J. Bot. 60:1561–1568. https://doi.org/ 10.1139/b82-199 sponsible for particular processes during decomposition. 8. Berg B (1986) Nutrient release from litter and humus in coniferous forest soils—a mini review. Scand. J. For. Res. 1:359–369 9. Berg B (1988) Dynamics of nitrogen (15N) in decomposing Scots pine (Pinus sylvestris) needle litter. Long-term decomposition in a Scots pine forest. VI. Can. J. Bot. 66:1539–1546 Conclusions 10. Bardgett RD (2005) The biology of soil: a community and ecosys- tem approach. Oxford University Press There is no need to supplement contemporary bead beating- 11. Cepáková Š, Frouz J (2015) Changes in chemical composition of based kits with cell wall-degrading enzymes. Bacterial and litter during decomposition: a review of published C NMR spec- fungal diversity changes differently during initial stages of tra. J. Soil Sci. Plant Nutr. 15:805–815 Gołębiewski M. et al. 12. Ponge J-FF (1991) Succession of fungi and fauna during decompo- decomposition processes of leaf litter. Can. J. Microbiol. 52:701– 716. https://doi.org/10.1139/w06-023 sition of needles in a small area of Scots pine litter. Plant Soil 138: 99–113. https://doi.org/10.1007/BF00011812 28. Voříšková J, Brabcová V, Cajthaml T, Baldrian P (2014) Seasonal 13. Tokumasu S, Aoki T, Oberwinkler F (1994) Fungal succession on dynamics of fungal communities in a temperate oak forest soil. New pine needles in Germany. Mycoscience 35:29–37. https://doi.org/ Phytol. 201:269–278. https://doi.org/10.1111/nph.12481 10.1007/BF02268525 29. Ward DM, Weller R, Bateson MM (1990) 16S rRNA sequences 14. Berg MP, Kniese J, Bedaux J, Verhoef H (1998) Dynamics and reveal numerous uncultured microorganisms in a natural communi- stratification of bacteria and fungi in the organic layers of a scots ty. Nature 345:63–65 pine forest soil. Biol. Fertil. Soils 26:268–284. https://doi.org/10. 30. Hugenholtz P, Goebel BM, Pace NR (1998) Impact of culture in- 1007/s003740050378 dependent studies on the emerging phylogenetic view of bacterial diversity. J. Bacteriol. 180: 4765–4774. doi: 0021–9193/98/ 15. Aneja MK, Sharma S, Fleischmann F, Stich S, Heller W, Bahnweg $04.00+0 G, Munch JC, Schloter M (2006) Microbial colonization of beech and spruce litter—influence of decomposition site and plant litter 31. Steele HL, Streit WR (2005) Metagenomics: advances in ecology species on the diversity of microbial community. Microb. Ecol. 52: and biotechnology. FEMS Microbiol. Lett. 247:105–111. https:// 127–135. https://doi.org/10.1007/s00248-006-9006-3 doi.org/10.1016/j.femsle.2005.05.011 16. Schneider T, Keiblinger KM, Schmid E, Sterflinger-Gleixner K, 32. Handelsman J, Rondon MR, Brady SF, Clardy J, Goodman RM Ellersdorfer G, Roschitzki B, Richter A, Eberl L, Zechmeister- (1998) Molecular biological access to the chemistry of unknown Boltenstern S, Riedel K (2012) Who is who in litter decomposition? soil microbes: a new frontier for natural products. Chem. Biol. 5: Metaproteomics reveals major microbial players and their biogeo- R245–R249 chemical functions. ISME J. 6:1749–1762. https://doi.org/10.1038/ 33. Rondon MR, Bettermann AD, Brady SF et al (2000) Cloning the ismej.2012.11 soil metagenome: a strategy for accessing the genetic and functional 17. Voříšková J, Baldrian P (2013) Fungal community on decomposing diversity of uncultured microorganisms. Appl. Environ. Microbiol. leaf litter undergoes rapid successional changes. ISME J. 7:477– 66:2541–2547 486. https://doi.org/10.1038/ismej.2012.116 34. Štursová M, Žifčáková L, Leigh MB, Burgess R, Baldrian P (2012) 18. Urbanová M, Šnajdr J, Baldrian P (2015) Composition of fungal Cellulose utilization in forest litter and soil: identification of bacte- and bacterial communities in forest litter and soil is largely deter- rial and fungal decomposers. FEMS Microbiol. Ecol. 80:735–746. mined by dominant trees. Soil Biol. Biochem. 84:53–64. https://doi. https://doi.org/10.1111/j.1574-6941.2012.01343.x org/10.1016/j.soilbio.2015.02.011 35. Tsai Y-L, Olson BH (1992) Detection of low numbers of bacterial 19. Tláskal V, Voříšková J, Baldrian P et al (2016) Bacterial succession cells in soils and sediments by polymerase chain reaction. Apllied on decomposing leaf litter exhibits a specific occurrence pattern of Environ. Microbiol. 58:754–757 cellulolytic taxa and potential decomposers of fungal mycelia. 36. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies FEMS Microbiol. Ecol. 92:fiw177. https://doi.org/10.1093/ J, Glöckner FO (2013) The SILVA ribosomal RNA gene database femsec/iw177 project: improved data processing and web-based tools. Nucleic 20. Purahong W, Wubet T, Lentendu G, Schloter M, Pecyna MJ, Acids Res. 41:590–596. https://doi.org/10.1093/nar/gks1219 Kapturska D, Hofrichter M, Krüger D, Buscot F (2016) Life in leaf 37. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, litter: novel insights into community dynamics of bacteria and fungi Glöckner FO (2013) Evaluation of general 16S ribosomal RNA during litter decomposition. Mol. Ecol. 25:4059–4074. https://doi. gene PCR primers for classical and next-generation sequencing- org/10.1111/mec.13739 based diversity studies. Nucleic Acids Res. 41:1–11. https://doi. 21. Dilly O, Munch J-C (1996) Microbial biomass content, basal res- org/10.1093/nar/gks808 piration and enzyme activities during the course of decomposition 38. IDT (2014) IDT Oligo Analyzer. http://eu.idtdna.com/calc/analyzer of leaf litter in a black alder (Alnus glutinosa (L.) Gaertn.) forest. 39. Faircloth BC, Glenn TC (2012) Not all sequence tags are created Soil Biol. Biochem. 28:1073–1081. https://doi.org/10.1016/0038- equal: designing and validating sequence identification tags robust 0717(96)00075-2 to indels. PLoS One 7:e42543. https://doi.org/10.1371/journal. 22. Baldrian P (2017) Forest microbiome: diversity, complexity, and pone.0042543 dynamics. FEMS Microbiol. Rev. 41:109–130. https://doi.org/10. 40. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister 1093/femsre/fuw040 EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, 23. Swift MJ, Heal OW, Anderson JM (1979) Decomposition in terres- Stres B, Thallinger GG, van Horn DJ, Weber CF (2009) Introducing trial ecosystems. University of California Press mothur: open-source, platform-independent, community-supported 24. Melillo JM, Aber JD, Linkins AE, Ricca A, Fry B, Nadelhoffer KJ software for describing and comparing microbial communities. (1989) Carbon and nitrogen dynamics along the decay continuum: Appl. Environ. Microbiol. 75:7537–7541. https://doi.org/10.1128/ plant litter to soil organic matter. Plant Soil 115:189–198. https:// AEM.01541-09 doi.org/10.1007/BF02202587 41. Gołębiewski M, Deja-Sikora E, Cichosz M, Tretyn A, Wróbel B 25. Baldrian P, Kolařík M, Štursová M, Kopecký J, Valášková V, (2014) 16S rDNA pyrosequencing analysis of bacterial community Větrovský T, Žifčáková L, ŠnajdrJ,RídlJ,Vlček Č,Voříšková J in heavy metals polluted soils. Microb. Ecol. 67:635–647. https:// (2012) Active and total microbial communities in forest soil are doi.org/10.1007/s00248-013-0344-7 largelydifferent andhighlystratified during decomposition. 42. Gołębiewski M, Całkiewicz J, Creer S, Piwosz K (2017) Tideless ISME J 6:248–258. https://doi.org/10.1038/ismej.2011.95 estuaries in brackish seas as possible freshwater-marine transition 26. Žifčáková L, Větrovský T, Howe A, Baldrian P (2016) Microbial zones for bacteria: the case study of the Vistula river estuary. activity in forest soil reflects the changes in ecosystem properties Environ. Microbiol. Rep. 9:129–143. https://doi.org/10.1111/ between summer and winter. Environ. Microbiol. 18:288–301. 1758-2229.12509 https://doi.org/10.1111/1462-2920.13026 43. Quince C, Lanzen A, Davenport RJ, Turnbaugh PJ (2011) 27. Osono T (2006) Role of phyllosphere fungi of forest trees in the Removing noise from pyrosequenced amplicons. BMC development of decomposer fungal communities and Bioinformatics 12:38. https://doi.org/10.1186/1471-2105-12-38 Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition 44. Huse SM, Welch DM, Morrison HG, Sogin ML (2010) Ironing out data. PLoS One 8:e61217. https://doi.org/10.1371/journal.pone. the wrinkles in the rare biosphere through improved OTU cluster- 61217 ing. Environ. Microbiol. 12:1889–1898. https://doi.org/10.1111/j. 61. Deja-Sikora E (2012) Search for bacterial cadmium, zinc, lead, 1462-2920.2010.02193.x copper and chromium resistance genes in metagenome of soils 45. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) polluted with heavy metals. Nicolaus Copernicus University UCHIME improves sensitivity and speed of chimera detection. 62. Krsek M, Wellington EMH (1999) Comparison of different Bioinformatics 27:2194–2200. https://doi.org/10.1093/ methods for the isolation and purification of total community bioinformatics/btr381 DNA from soil. J. Microbiol. Methods 39:1–16. https://doi.org/ 10.1016/S0167-7012(99)00093-7 46. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian 63. Bomberg M, Montonen L, Timonen S (2010) Anaerobic Eury- and classifier for rapid assignment of rRNA sequences into the new Crenarchaeota inhabit ectomycorrhizas of boreal forest Scots pine. bacterial taxonomy. Appl. Environ. Microbiol. 73:5261–5267. Eur. J. Soil Biol. 46:356–364. https://doi.org/10.1016/J.EJSOBI. https://doi.org/10.1128/AEM.00062-07 2010.09.002 47. Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, Boutte 64. Larsen BB, Miller EC, Rhodes MK, Wiens JJ (2017) Inordinate C, Burgaud G, de Vargas C, Decelle J, del Campo J, Dolan JR, fondness multiplied and redistributed: the number of species on Dunthorn M, Edvardsen B, Holzmann M, Kooistra WHCF, Lara earth and the new pie of life. Q. Rev. Biol. 92:229–265. https:// E, le Bescot N, Logares R, Mahé F, Massana R, Montresor M, doi.org/10.1086/693564 Morard R, Not F, Pawlowski J, Probert I, Sauvadet AL, Siano R, 65. Carrell AA, Frank AC (2014) Pinus flexilis and Picea engelmannii Stoeck T, Vaulot D, Zimmermann P, Christen R (2013) The Protist share a simple and consistent needle endophyte microbiota with a Ribosomal Reference database (PR2): a catalog of unicellular eu- potential role in nitrogen fixation. Front. Microbiol. 5: . doi: https:// karyote small sub-unit rRNA sequences with curated taxonomy. doi.org/10.3389/fmicb.2014.00333 Nucleic Acids Res. 41:597–604. https://doi.org/10.1093/nar/ 66. Millberg H, Boberg J, Stenlid J (2015) Changes in fungal commu- gks1160 nity of Scots pine (Pinus sylvestris) needles along a latitudinal gra- 48. Sheneman L, Evans J, Foster JA (2006) Clearcut: a fast implemen- dient in Sweden. Fungal Ecol. 17:126–139. https://doi.org/10.1016/ tation of relaxed neighbor joining. Bioinformatics 22:2823–2824. j.funeco.2015.05.012 https://doi.org/10.1093/bioinformatics/btl478 67. Boberg JB, Ihrmark K, Lindahl BD (2011) Decomposing capacity 49. Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method of fungi commonly detected in Pinus sylvestris needle litter. Fungal for comparing microbial communities. Appl. Environ. Microbiol. 71: Ecol. 4:110–114. https://doi.org/10.1016/J.FUNECO.2010.09.002 8228–8235. https://doi.org/10.1128/AEM.71.12.8228-8235.2005 68. Osono T, Hirose D (2011) Colonization and lignin decomposition 50. Horn HS (1966) Measurement of Boverlap^ in comparative ecolog- of pine needle litter by Lophodermium pinastri.For.Pathol.41: ical studies. Am. Nat. 100:419–424. https://doi.org/10.1086/ 156–162. https://doi.org/10.1111/j.1439-0329.2010.00648.x 69. van der PAS JB, Slater-hayes JD, Gadgil PD, Bulman L (1984) 51. Bray RJ, Curtis JT (1957) An ordination of the upland forest com- Cyclaneusma (Naemacyclus) needle-cast of Pinus radiata in New munities of southern Winsconin. Ecol. Monogr. 27:325–349. Zealand. 2: reduction in growth of the host, and its economic im- https://doi.org/10.2307/1942268 plication. New Zeal J For Sci 14:197–209 52. Oksanen J, Blanchet FG, Kindt R, et al (2013) Vegan: community 70. Vestgarden L (2001) Carbon and nitrogen turnover in the early ecology package. R package, https://cran.r-project.org/web/ stage of Scots pine (Pinus sylvestris L.) needle litter decomposition: packages/vegan/index.html effects of internal and external nitrogen. Soil Biol. Biochem. 33: 53. ter Braak CJF, Schaffers AP (2004) Co-correspondence analysis: a 465–474. https://doi.org/10.1016/S0038-0717(00)00187-5 new ordination method to relate two community compositions. 71. Crous PW, Quaedvlieg W, Hansen K, Hawksworth DL, Ecology 85:834–846. https://doi.org/10.1890/03-0021 Groenewald JZ (2014) Phacidium and Ceuthospora 54. Simpson GL (2009) Cocorresp: co-correspondence analysis ordi- (Phacidiaceae) are congeneric: taxonomic and nomenclatural im- nation methods. R package, https://github.com/gavinsimpson/ plications. IMA Fungus 5:173–193. https://doi.org/10.5598/ cocorresp imafungus.2014.05.02.02 55. Langille M, Zaneveld J, Caporaso JG et al (2013) Predictive func- 72. Farr DF, Rossman AY (2018) Fungal databases, U.S. National tional profiling of microbial communities using 16S rRNA marker Fungus Collecsions, ARS, USDA. https://nt.ars-grin.gov/ gene sequences. Nat. Biotechnol. 31:814–821. https://doi.org/10. fungaldatabases. Accessed 1 Feb 2018 1038/nbt.2676 73. Wiseman MS, Kim YK, Dugan FM, Rogers JD, Xiao CL (2016) A 56. DeSantis TZ, Hugenholtz P, Larsen N et al (2006) Greengenes, a new postharvest fruit rot in apple and pear caused by Phacidium chimera-checked 16S rRNA gene database and workbench com- lacerum. Plant Dis. 100:32–39. https://doi.org/10.1094/PDIS-02- patible with ARB. Appl. Environ. Microbiol. 72:5069–5072. 15-0158-RE https://doi.org/10.1128/AEM.03006-05 74. Berg B, McClaugherty C (2003) Plant litter. Decomposition, humus 57. Parks DH, Tyson GW, Hugenholtz P, Beiko RG (2014) STAMP: formation, carbon sequestration. Springer-Verlag Berlin statistical analysis of taxonomic and functional profiles. Heidelberg, Berlin Heidelberg Bioinformatics 30:3123–3124. https://doi.org/10.1093/ 75. Bae Y-S, Knudsen GR (2001) Influence of a fungus-feeding nem- bioinformatics/btu494 atode on growth and biocontrol efficacy of Trichoderma 58. R Core Team (2016) R: a language and environment for statistical harzianum. Phytopathology 91:301–306. https://doi.org/10.1094/ computing. https://www.r-project.org/ PHYTO.2001.91.3.301 59. Harrell Jr FE, with contributions from Charles Dupont, many 76. Taktek S, Trépanier M, Servin PM, St-Arnaud M, Piché Y, Fortin others. (2014) Hmisc: Harrell miscellaneous. R package, https:// JA, Antoun H (2015) Trapping of phosphate solubilizing bacteria cran.r-project.org/web/packages/Hmisc/index.html on hyphae of the arbuscular mycorrhizal fungus Rhizophagus 60. McMurdie PJ, Holmes S (2013) Phyloseq: an R package for repro- irregularis DAOM 197198. Soil Biol. Biochem. 90:1–9. https:// ducible interactive analysis and graphics of microbiome census doi.org/10.1016/j.soilbio.2015.07.016 Gołębiewski M. et al. 77. Baldrian P, Zrůstová P, Tláskal V, Davidová A, Merhautová V, application on soil microbial community structure and functional gene abundance under irrigated maize. FEMS Microbiol. Ecol. 59: Vrška T (2016) Fungi associated with decomposing deadwood in a natural beech-dominated forest. Fungal Ecol. 23:109–122. https:// 661–670. https://doi.org/10.1111/j.1574-6941.2006.00235.x doi.org/10.1016/j.funeco.2016.07.001 81. Neefs J-M, Van de Peer Y, De Rijk P et al (1993) Compilation of 78. Berg B, Matzner E (1997) Effect of N deposition on decomposition small ribosomal subunit RNA structures. Nucleic Acids Res. 21: of plant litter and soil organic matter in forest systems. Environ. 3025–3049 Rev. 5:1–25 82. Hadziavdic K, Lekang K, Lanzen A, Jonassen I, Thompson EM, 79. White TJ, Bruns T, Lee S, Taylor J (1990) Amplification and direct Troedsson C (2014) Characterization of the 18S rRNA gene for sequencing of fungal ribosomal RNA genes for phylogenetics. In: designing universal eukaryote specific primers. PLoS One 9: PCR protocols: a guide to methods and applications. pp 315–322 e87624. https://doi.org/10.1371/journal.pone.0087624 80. Wakelin SA, Colloff MJ, Harvey PR, Marschner P, Gregg AL, Rogers SL (2007) The effects of stubble retention and nitrogen http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Microbial Ecology Springer Journals

Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition

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Life Sciences; Microbiology; Ecology; Microbial Ecology; Geoecology/Natural Processes; Nature Conservation; Water Quality/Water Pollution
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Abstract

Plant litter decomposition is a process enabling biogeochemical cycles closing in ecosystems, and decomposition in forests constitutes the largest part of this process taking place in terrestrial biomes. Microbial communities during litter decomposition were studied mainly with low-throughput techniques not allowing detailed insight, particularly into coniferous litter, as it is more difficult to obtain high quality DNA required for analyses. Motivated by these problems, we analyzed archaeal, bacterial, and eukaryotic communities at three decomposition stages: fresh, 3- and 8-month-old litter by 16/18S rDNA pyrosequencing, aiming at detailed insight into early stages of pine litter decomposition. Archaea were absent from our libraries. Bacterial and eukaryotic diversity was greatest in 8-month-old litter and the same applied to bacterial and fungal rDNA content. Community structure was different at various stages of decomposition, and phyllospheric organisms (bacteria: Acetobacteraceae and Pseudomonadaceae members, fungi: Lophodermium, Phoma) were replaced by communities with metabolic capabilities adapted to the particular stage of decomposition. Sphingomonadaceae and Xanthomonadaceae and fungal genera Sistotrema, Ceuthospora,and Athelia were characteristic for 3-month-old samples, while 8-month-old ones were characterized by Bradyrhizobiaceae and nematodes (Plectus). We suggest that bacterial and eukaryotic decomposer communities change at different stages of pine litter decompo- sition in a way similar to that in broadleaf litter. Interactions between bacteria and eukaryotes appear to be one of the key drivers of microbial community structure. . . . . Keywords Pine litter decomposition Bacterial community Fungal community Metagenomics 16S rDNA pyrosequencing Introduction closing in terrestrial ecosystems. Decomposition in forests constitutes the largest part of this process taking place in ter- Decomposition of organic matter is a key step in nutrients restrial biomes, due to their immense area (~ 30% of land cycling in all ecosystems. Plant litter decomposition is an im- surface [1]) and large quantities of organic matter stored. portant ecological process enabling biogeochemical cycles Forest litter decomposition was extensively studied since the early 1970s (reviewed, e.g., in [2]). However, chemical changes in decomposing materials as well as element cycling Electronic supplementary material The online version of this article in relation to temperature and precipitation were assessed, (https://doi.org/10.1007/s00248-018-1209-x) contains supplementary while microbial and macroorganismal aspects of this process material, which is available to authorized users. were less intensively studied [3, 4]. Nevertheless, it is estimat- ed that microbes are responsible for up to 90% of organic * Marcin Gołębiewski mgoleb@biol.umk.pl matter decomposition [5], and the dominating primary decom- posers in boreal and temperate forest soil systems are micro- organisms, mainly fungi and bacteria. The structure and de- Nicolaus Copernicus University, Lwowska 1, 87-100 Toruń,Poland velopment of decomposer communities can influence the pat- Centre for Modern Interdisciplinary Technologies, Nicolaus tern of decay [3]. Copernicus University, Toruń,Poland 3 As the material decomposes, chemical composition of the Institute of Environmental Sciences, Jagiellonian University, litter changes, and there is a shift from carbohydrates and Kraków, Poland 4 aliphatic components constituting the largest pool in initial Department of Microbiology, Nicolaus Copernicus University, litter, to aromatic compounds at late stages of decomposition. Toruń, Poland Gołębiewski M. et al. Many components of fresh plant litter, like sugars and pep- DNA and spectrum of lysed organisms. We hypothesized that tides, decompose quickly, as they are energy-rich and can be the treatment would increase the yield and diversity of 16/18S easily assimilated by soil microorganisms [6–11]. The chem- rDNA amplicons. As there were no reports of Archaea being ical changes coupled with mixing the original plant substrate found in litter, we expected that they would be absent from with soil particles, due to the action of annelids and arthro- pine litter samples. Bacterial and eukaryotic communities pods, enable supporting different decomposer communities. would be different at different stages of decomposition, spe- Microbial communities at various stages of litter decompo- cifically (i) microbial diversity would follow diversity of sub- sition were studied mostly with traditional microbiological strates, i.e., would be lower at later stages of decomposition, methods [12–14] and more recently using molecular approach (ii) late communities would be more dominated by specialists [15–20]; however, the studies mainly concerned broad-leaf capable of lignocellulose and lignin utilization, (iii) forest litter [17, 21, 22]. Coniferous substrates rich in waxes, phyllosphere-related organisms would prevail initially and resins, and lignin are more resistant to decomposition [23, 24] then would be replaced by those coming from soil, and (iv) and more difficult to study; thus, only a few studies were a shift in metabolic capabilities of the community was also performed to date [25, 26]. Nevertheless, a general picture of expected wherein organisms utilizing soluble small molecules microbial succession on litter was obtained, in which initially present in the phyllosphere would be superseded by phyllospheric organisms, such as members of those in whose genomes reside genes enabling cellulose and, Acetobacteraceae among bacteria and Leotiomycetes among later, lignin utilization. To test these hypotheses, we prepared, fungi act as early decomposers [27] and are quickly replaced sequenced, and analyzed pyrosequencing (454) libraries of with distinct communities characteristic for particular stages archaeal and bacterial 16S, as well as eukaryotic 18S rRNA of decomposition [17, 19]. Fungi seem to be the key decom- gene fragments derived from litter samples at three stages of posers responsible for producing extracellular hydrolytic and decomposition. oxidating enzymes, and among them, Ascomycota prevail at early stages of decomposition [16] and are replaced by Basidiomycota later on [17, 28]. Bacterial communities tend Materials and Methods to be dominated by Proteobacteria, Bacteroidetes, and Actinobacteria [19, 20]; however, Acidobacteria were found Study Design and Samples to be frequent in spruce litter [26]. It was found that both bacterial and fungal diversity generally increased in the pro- The study site was located in Sierbowice, southern Poland cess of decomposition [17, 19]. However, as decomposition (GPS coordinates: N 50° 34′ 15.20″, E19° 39′ 55.40″). The spans many years, one has to bear in mind that seasonality is vegetation at the site is dominated by approx. 50 years old also an important factor shaping litter microbial community Scots pine (Pinus sylvestris L.) with a small admixture of birch structure [26]. (Betula pendula Roth) and artificially introduced red oak Until fairly recently, microbes were studied with the use of (Quercus rubra L.), with European blueberry (Vaccinium culture-based methods, which limited the scope of the studies myrtillus L.) and mosses in the groundcover. to culturable organisms. Usually less than 1% of microbes We were interested in investigating three time points of from a given environment can be cultured under laboratory initial stages of litter decomposition: t = no field incubation, conditions [29–31]. A set of methods was devised, collective- t = 92 days, and t = 242 days of field incubation, because we 1 2 ly termed Bmetagenomics^ [32], which allows overcoming of expected changes in community composition in line with rap- the culturability problem. It is based on isolation of genetic id initial mass loss. In order to do so, freshly fallen brown pine material directly from the environment, without prior cultur- litter from over a dozen of randomly picked trees in the area of ing of microorganisms [33]. A pre-requisite for all of them is about 3 ha was collected in October 2012. The needles were DNA isolation directly from environmental sample. There are obtained by shaking the selected trees and their branches. A many DNA isolation methods successfully applied to soil PVC foil was spread under the trees to facilitate litter collec- samples, but there is only a couple of examples of DNA iso- tion and to prevent contamination of fresh litter with microbial lation from forest litter [34]; moreover, they concern mostly communities from soil. broadleaf forest litter [19], which has different chemical prop- The collected litter was transported to the laboratory in erties, with less phenolics and other compounds that could plastic bags. The material was thoroughly mixed and split into possibly affect DNA extraction efficacy and hamper subse- two major parts. The first part consisted of three subsamples quent molecular analyses [35]. Standard methodology of en- dedicated for water content analysis and DNA extractions to vironmental DNA isolation now includes the use of bead determine the t microbial community. The second major part beating-based kits. We wanted to check if combining a com- of the collected material was air dried in room temperature for mercial kit with enzymatic digestion with lysozyme, a month. Three subsamples from the air-dried material were achromopeptidase, and chitinase would improve the yield of taken for chemical analyses, and the rest was used to make Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition litter bags. The litter bags were made out of a nylon mesh achromopeptidase and chitinase, and incubation as above (20 × 20 cm, mesh size 1 × 1 mm) and were packed with ~ (ALCh method). Isolated DNA was quantified with Qubit 20 g of air dry litter. On the 1 December 2012, they were HS DNA kit (Invitrogen, USA), and the quality was measured placed back in the field under the soil organic layer. Six bags spectrofotometrically on NanoDrop ND-1000 (Thermo Fisher were collected at each of the time points. Every time, three Scientific, USA). DNA content was expressed in ng per 1 g of litter bags were dedicated for chemical and the remaining ones fresh litter. for molecular analyses. Before the DNA extractions, the sam- ples were placed in a climatic chamber and acclimated for a Primers Design week at 22 °C to 70% of water holding capacity (WHC) and frozen at − 80 °C. The acclimation step was performed in PrimersweredesignedbasingonSILVAv.119alignment [36]. order to standardize the physiological state of microorganisms The alignment was split into kingdom-specific parts with in the litter, as the collection of litter took place during differ- Mothur’s get.lineage, and consensus sequences were generat- ent seasons. In the end, we obtained three biological replicates ed at the 97% identity level (consensus.seqs). Visual inspec- per time point, and three technical replicates (independent tion of resulting summary files allowed identification of high- isolations) were made for each of four isolation methods used. ly conserved regions. Candidate pairs were checked with the Just as in case of the t series before the chemical analyses, t 0 1 online TestPrime tool [37] and IDT Oligoanalyzer [38]. Pine- and t samples were air dried. specific primers were designed in the same way, but an align- ment of Pinus 18S rRNA sequences from SILVA was used. Physicochemical Analyses Water content was measured gravimetrically in fresh litter 16S and 18S rRNA Gene Fragments Amplification samples, immediately after their transfer to the laboratory. and Pyrosequencing pH was measured in a slurry of 0.5-g air dried litter in 15 ml of demineralized water. The concentrations of particular ele- Libraries of 16S/18S rRNA gene fragments were created with ments were measured in powdered and dried material (12 h, the use of two-step method, involving gene-specific primers 105 °C). C and N concentrations were determined with Vario tagged with M13/M13R overhangs in the first round of PCR EL Cube (Elementar, Germany). The remaining and M13 bearing 9-nt MID sequences [39] and A adapter macroelements and microelements were determined with a sequence (Roche, Switzerland) paired with M13R with B PinAAcle 900 Z atomic absorption spectrometer (Perkin adapter overhang in the second round. Primer sequences and Elmer, USA) after wet digestion with nitric acid in Titan PCR conditions are listed in Table 1. The final products were MPS microwave sample preparation system (Perkin Elmer, quantified with Qubit HS DNA kit (Invitrogen), and 36 of USA). them were pooled in equimolar amounts for each library. The library quality was assessed with HS DNA chip on Climatic Data Bioanalyzer (Agilent, USA). Libraries were emPCR amplified with the use of Titanium Lib-L kits (Roche, Switzerland) and Climatic data (daily averages) were downloaded from dane. sequenced on GS-Junior machine with Titanium chemistry imgw.pl. As there is no meteorological station in the (Roche, Switzerland) as per the manufacturer’sprotocols. immediate vicinity of the sampling area, values from six nearest stations (Silniczka, Lgota Górna, Katowice- Pyrzowice, Olewin, Miechów, Jędrzejów-Sudół)were qPCR averaged and plotted in R. Real-time PCR analyses were conducted using primers listed DNA Isolation in Table 1 and FastStart SYBRGreen kit (Roche, Switzerland) on LightCycler 480 machine (Roche, Switzerland). The PowerSoil DNA Isolation kit (MoBio, USA) was used accord- reaction mix included 3 pmol of forward and reverse ing to the producer’s protocol for PowerLyzer 24 bead beater primers, 2 ng of template DNA, 5 μl of 2 x concentrated involving one 45-s cycle of bead beating at 4000 rpm for kit, and water up to 10 μl. Standards were prepared from control isolations and with the following modifications: (i) pure amplicons generated with primer pairs used for qPCR addition of lysozyme (Sigma, USA) to the final concentration on DNA isolated from Escherichia coli, P. sylvestris,and of 2 mg/ml and achromopeptidase (500 U/ml, Sigma, USA) to Boletus badius. Standard curves were replicated five times, the C1 buffer and incubation at 37 °C for 1 h (AL method), (ii) and samples were assayed in triplicates. Each run included addition of chitinase (Sigma, 0.01 U/ml) and incubation at for negative control (water). Resulting numbers of copies were 1 h (Ch method) and (iii) addition of lisozyme, converted to copies/g of litter. Gołębiewski M. et al. Table 1 Primer sequences and PCR conditions a b Name Sequence 5′→ 3′ Paired with Target PCR conditions Use Source B357fU GTTTTCCCAGTCACGAC CCT B786rU Bacterial 16S 95 °C–5 min; 25 cycles of 95 °C–15 s, Library generation Neefs, 1993 [81] ACG GGA GGC AGC AG 53 °C–30 s, 72 °C–30 s B786rU CAGGAAACAGCTATGAC AC B357fU Deja-Sikora, 2012 [61] CAG GGT ATC TAA WCC A519fU GTTTTCCCAGTCACGAC CAG A1048rU Archaeal 16S 95 °C–5 min; 25 cycles of 95 °C–15 s, Library generation Klindworth, 2013 [37] CMG CCG CGG TAA 51 °C–30 s, 72 °C–30 s A1048rU CAGGAAACAGCTATGAC CGR A519fU CRG CCA TGY ACC WC E566fU GTTTTCCCAGTCACGAC CAG E1200rU Eukaryotic18s 95 °C–10 min; 25 cycles of 95 °C–15 s, Library generation Hadziavdic, 2014 [82] CAG CCG CGG TAA TTC C 53 °C–30 s, 72 °C–30 s E1200rU CAGGAAACAGCTATGAC CCC E566fU GTG TTG AGT CAA ATT AAG C c, d M13-x-A CCA TCT CAT CCC TGC GTG TCT M13R-B M13-tagged amplicons 95 °C–5 min; 10 cycles of 95 °C–15 s, Library generation This work CCG AC TCAG X GTT TTC CCA 52 °C–45 s, 72 °C–45f s GTC ACG AC M13R-B CCT ATC CCC TGT GTG CCT TGG M13-x-A CAG TC TCAG CAG GAA ACA GCT ATG AC B969f ACG CGA RGA ACC TTA C B1072r Bacterial 16S 95 °C–10 min; 40 cycles of 95 °C–15 s, qPCR B1072r CGA GCT GAC GAC ARC CAT GCA B969f 53 °C–30 s, 72 °C–30 s ITS1 TCC GTA GGT GAA CCT GCG G qITS2 Fungal ITS 95 °C–10 min; 40 cycles of 95 °C–15 s, White, 1990 [79] 55 °C–30 s, 72 °C–30 s qITS2 TTY GCT GYG TTC TTC ATC G ITS1 Wakelin, 2007 [80] Con1256f TTA TTC CTG GTT CGA GA Pin1360r Coniferae 18S 95 °C–10 min; 40 cycles of 95 °C–15 s, This work Pin1360r TAG TCA ACA CGA GTT GA Con1256f Pinus 18S 49 °C–30 s, 72 °C–30 s U denotes M13/M13R tagged sequences M13 and M13R sequences are given in boldface font Key sequence in italics X denotes 10-nt barcode (MID sequence), for all barcode pairs Levenstein distance was min. 4 Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition Bioinformatics Analyses distance matrices were calculated in Mothur (unifrac.unweighted and unifrac.weighted) with subsampling the RNJ tree to include Pyrosequencing reads were processed with Mothur v. 1.32 700 and 1000 reads per sample for bacteria and eukaryota, [40] and custom-tailored Perl scripts, with modifications in- respectively. Morisita-Horn [50] and Bray-Curtis [51]dis- creasing the aggressiveness of denoising, chimera removal, as tance matrices were calculated in R (vegdist). NMDS and well as producing ten subsamples of the whole data and aver- CCA analyses were performed in R with vegan’s[52] aging the shared OTU table over those subsamples, as de- metaMDS and cca functions, respectively. For NMDS, scribed earlier [41, 42]. Brief summary of the procedure is 1000 tries were used, and the same number of permutations given below. was adopted in CCA. CCA models were built by backward The flows were extracted from the .sff files, forward, and selection with ordistep. reverse reads separately (sffinfo), then they were assigned to Co-correspondence analysis [53] was performed with the samples basing on the MID sequences, trimmed to min. 500 use of the coca function from the cocorresp R package [54]. andmax.650flows(trim.flows), anddenoisedwith Significance of the extracted axes was tested with permutation AmpliconNoise algorithms (shhh.flows and shhh.seqs; [43]). test (permutest), while the percent of fit was checked by leave- Primers and MIDs were removed from the denoised one-out cross-validation (crossval). seuqences, and the read set was dereplicated (unique.seqs) and aligned to the SILVA v.119 template alignment PICRUSt Analysis (align.seqs). Reads covering the desired region of the align- ment (pos. 6500–22,500 for bacteria and 13,876–22,550 for For PICRUSt [55] analysis, bacterial sequences were classi- eukaryotes) were chosen (screen.seqs) and gap only, and ter- fied as described above, but using GreenGenes taxonomy files minal gap-containing columns were removed from the align- (v. 13_8; [56]). Taxonomic information along with OTU table ment (filter.seqs). The set was dereplicated again, and residual was converted to a .biom file using Mothur’s make.biom func- sequencing and PCR noise was removed with Single Linkage tion. The file was then converted to v.1.0.0 format using biom pre-clustering (pre.cluster; [44]). Chimera identification and convert, as per https://github.com/rprops/PICRUSt_from_ removal were performed in two rounds: (i) with UCHIME mothur (visited Feb. 15, 2017). Normalized OTU table was (chimera.uchime; [45]) and (ii) with PERSEUS generated with normalize_by_copy_number.py, and predicted (chimera.perseus; [43]). metagenomes as well as NSTI scores were calculated with Full-length sequences (list.seqs, get.seqs) were used for predict_metagenomes.py. Functions were collapsed to classification with naive Bayesian classifier (classify.seqs; pathways at level 2 using categorize_by_function.py. [46]) using SILVA 119 template and taxonomy files (http:// Pathways pertaining to organismal systems were considered www.mothur.org/w/images/2/27/Silva.nr_v119.tgz, accessed spurious and removed. Predicted metagenomes were analyzed on September 4, 2014) for classification of bacterial reads with STAMP [57]. and PR2 database [47] for eukaryotic ones at the bootstrap confidence level of 80%. Taxons Bunknown^ and, in case of Statistical Analyses bacterial data, Bchloroplast^ were removed from the final set. OTUs at the 0.03 dissimilarity level were constructed via R[58] was used for statistical computations, with Hmisc [59], average linkage (UPGMA), and singletons together with phyloseq [60] and vegan [52] packages. Significance level doubletons were removed from the data (remove.rare). used was 0.01. ANOVA with Tukey’s HSD test was used to For the initial analyses of enzyme influence, the final read test for significance of differences in means, PERMANOVA set was subsampled to 500 reads per sample ten times (sub.- (vegan’s adonis), AMOVA (amova of ade4 package), as well sample and regular expressions in the sed editor), subsamples as ANOSIM (vegan’s anosim) for testing separation of clus- were combined (cat), the whole set was dereplicated and used ters. Significance level used was 0.01. When testing for sig- for distance matrix calculation (dist.seqs), and OTU construc- nificance of grouping by enzymatic treatments, permutations tion via average neighbor clustering at 97% similarity level were restricted to a given decomposition level (strata = de- (cluster). Shared OTU table was constructed (make.shared), composition). PERMDISP test (vegan’s betadisper) was used and averaged table was calculated with a Perl script to test the homogeneity of variance in community data. (average_shared.perl). Final analyses were performed on a Differences in KEGG pathway content between litter de- dataset in which all enzymatic treatments coming from one composition stages were tested in STAMP using Kruskal- sample were combined. This dataset was subsampled to 700 Wallis test with two-sided Welch’s test as a post hoc analysis (bacteria) and 1000 (eukaryota) reads per sample and proc- and Benjamini-Hochberg FDR correction, which was re- essed as described above. quired due to the non-normal distribution of P values. Q value Relaxed neighbor joining (RNJ) tree was constructed from threshold of 0.05 was assumed. Significance of sample clus- the final alignment with clearcut (clearcut; [48]). UniFrac [49] ters separation was tested with AMOVA on a Bray-Curtis Gołębiewski M. et al. distance matrix derived from the simulated metagenome count recoverable with the primer pair used, were present in samples table (vegan’s vegdist). Functional diversity was calculated as under study. the number of categories at level 0, which is the level of individual Kegg Orthologies (might be understood as Addition of Enzymes Does Not Improve DNA Isolation functions). Yield and Microbial Diversity Recovered Species diversity was measured as Shannon’sdiversity (H ′), species richness was measured as observed number of Pre-treatment of litter samples with achromopeptidase and OTUs, and evenness was estimated as Shannon’s evennes (J lysozyme, chitinase, and all three enzymes together did not ′). From now on, for the sake of brevity, we will use terms affect the copy number of pine 18S rRNA gene in litter Bdiversity^ and Bevenness^ in place of their indices names. metagenomic DNA (P > 0.05, Fig. 1a). The overall DNAyield was not changed by the addition of enzymes (P > 0.05, Fig. 1b). The same applies to the number of copies of bacterial Results 16S rRNA genes measured with qPCR (P >0.05, Fig. 1c) and the number of copies of fungal ITS sequences (P >0.05, Changes of Climatic and Litter Physicochemical Fig. 1d). Variables over Time Bacterial and eukaryotic diversity (measured as Shannon’s H′), species richness as well as evenness were not influenced Average daily temperature at the sampling area was below by modifications of the DNA isolation method, but they dif- 0 °C for the majority of the period preceding t ,rose to around fered at various stages of decomposition (Fig. 2). The same 12 °C 3 weeks later, and then after a month it fluctuated applies to community structure, as assessed with AMOVA and around 15 °C until the end of the experiment. Precipitation ANOSIM performed on Bray-Curtis and Morisita-Horn dis- was low (1.2 mm daily on average) until day 150; in this tance matrices (P > 0.05; data not shown). period, it was in the form of snow, but the snow cover lasted until day 140. Later, rainfalls were more intensive (3.8 mm DNA Content and Bacterial as well as Fungal SSU daily) until day 230, after which a 3-week period of drought Gene Copy Numbers Change During Early Stages occurred. Final sampling was performed 2 weeks after the of Decomposition onset of drought (Supplementary Fig. 1). C/N ratio as well as K level were the highest in t samples DNA yield significantly increased over time (P < 0.05, and stayed at lower level in older ones (Table 2). An increas- Fig. 3a). A rapid decrease of pine 18S rRNA gene copy num- ing trend was apparent for Mg, Fe, and Zn, while concentra- ber in t and t samples was observed (P <0.05), indicating 1 2 tion of Mn was the highest in t and Cu in t samples. pH was that most of pine DNA was degraded after 3 months (Fig. 3d). 1 2 similar in all samples; the average was 5.22 ± 0.37. Water Thus, it seemed that the increase in yield was due to the higher content of freshly fallen litter (t samples) was ~ 27%, while bacterial and fungal cell numbers. Indeed, the copy numbers directly after removal from the field, t was significantly more of both bacteria 16S rRNA genes and fungal ITS sequences, humid and contained 53% water and humidity in t was even being proxies for bacterial and fungal cell numbers, were lower than in t (23%). greatest in t samples. However, the number of bacterial 0 2 SSU was significantly higher in t than in t (Fig. 3b), while 1 0 Archea Are Absent from Litter Samples fungal ITS number was significantly higher in t samples than in t (Fig. 3c). No archaeal reads were recovered during the project. The sequences coming from the libraries prepared using the pre- Microbial Diversity Changes During Litter sumed archaeal primers [37] were classified either as Bacteria Decomposition (majority as Actinobacteria) or as Eukaryotes (Fungi). As the primers have perfect matches to almost 80% of archaeal se- Bacterial diversity (measured as Shannon’s H′) was similar in quences in Silva, we think that no Archaea, at least such all samples; it was highest in fresh litter, then dropped after Table 2 Mean values of C/N ratio a a a a a a Code time C/N Mn Mg K Cu Fe Zn WC (%) and concentrations of other elements as well as water content t0 0 64.7A 665.7A 434.0A 3061.6A 3.3A 288.1A 125.3A 27 in litter samples. Significant differences (ANOVA with t1 92 48.4B 721.9B 644.1B 2659.9B 3.0A 330.4B 136.7B 53 Tukey’sHSD, P < 0.01) are t2 242 47.6B 601.3C 820.3C 2388.2B 5.8B 530.8C 189.7C 23 denoted with different letters a −1 mg-kg dry weight Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition 7.5 decomposition decomposition t0 t0 t1 t1 7.0 t2 t2 6.5 6.0 5.5 5.0 control al ch alch control al ch alch Enzyme treatment Enzyme treatment C D 8.5 8.6 decomposition decomposition t0 t0 t1 t1 8.4 t2 t2 8.0 8.2 8.0 7.5 7.8 7.0 7.6 control al ch alch control al ch alch Enzyme treatment Enzyme treatment Fig. 1 Influence of DNA isolation method modifications on: the number standard error of the mean (SEM). Control—no enzymatic treatment, of pine 18S rRNA genes (a), overall DNA yield (b), the number of al—achromopeptidase + lysozyme, ch—chitinase, alch— bacterial 16S rRNA genes (c), the number of fungal ITS sequences (d). achromopeptidase + lysozyme + chitinase Note the logarithmic y axis in panels a, c, and d. Whiskers denote 3 months to reach higher values again after 8 months; howev- Bacterial and Fungal Community Structure Is Driven er, these changes were not significant (Fig. 4a). On the other by Decomposition Stage, Physicochemical Variables, hand, eukaryotic diversity decreased rapidly over time and Bacteria-Eukaryote Interactions (Fig. 4a), and the difference between fresh and 8 months old litter was significant. Both observed and estimated total Regardless of the dissimilarity measure used, bacterial com- (Chao1) species richness grew with time in the case of bacteria munities coming from individual biological replicates clus- and decreased in the case of eukaryotes (Fig. 4,b,c), tered tightly on nMDS plots, and samples at the same stage while evenness followed the pattern of diversity in both of decomposition were also located together (Bray-Curtis cases (Fig. 4d). showed on Fig. 5a). It appeared that the t samples were more log(pine 18S rRNA/g of litter) log(bacterial 16S rRNA genes/g of litter) log(fungal ITS copies/g of litter) ng DNA/g of litter Gołębiewski M. et al. decomposition decomposition t0 t0 t1 t1 t2 t2 control al ch alch control al ch alch Enzyme treatment Enzyme treatment C D decomposition decomposition 0.80 0.70 t0 t0 t1 t1 t2 t2 0.65 0.75 0.60 0.70 0.55 0.65 0.50 control al ch alch control al ch alch Enzyme treatment Enzyme treatment 4.0 decomposition decomposition t0 t0 2.8 3.8 t1 t1 t2 t2 2.6 3.6 2.4 3.4 2.2 3.2 2.0 3.0 1.8 control al ch alch control al ch alch Enzyme treatment Enzyme treatment Shannon’s H’ Shannon’s evenness observed OTUs number) observed number of OTUs Shannon’s H’ Shannon’s evennes Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition Fig. 2 Influence of DNA isolation method modifications on: bacterial (a) (P < 0.05). PERMDISP test demonstrated that variance and eukaryotic (b) species richness (observed number of OTUs), was not homogeneous in different sample groups for bacterial (c) and eukaryotic (d) Shannon’s evenness, and bacterial bacterial community, with highest dispersion found in t (e) and eukaryotic (f) Shannon’s diversity. Whiskers denote samples (P < 0.05), while it was similar in all groups for standard error of the mean (SEM). Control—no enzymatic treatment, al—achromopeptidase + lysozyme, ch—chitinase, alch— eukaryotic community (P =0.848). achromopeptidase + lysozyme + chitinase CCA analysis (Supplementary Fig. 2) identified Zn and Na as significant environmental parameters shaping the similartothefreshones(t )thantothe t samples. On 0 2 bacterial community structure (P < 0.01), while C/N ratio the other hand, eukaryotic communities in t samples as well as Zn and Mn were factors that significantly in- were closer to t ones (Bray-Curtis showed on Fig. 5b). 2 fluenced eukaryotic community. The measured variables ANOSIM as well as AMOVA and PERMANOVA anal- explained 20.1% of inertia (variance) in case of bacteria yses showed that separation of clusters was significant and 37.2% in case of eukaryotes. 7e+08 6e+08 5e+08 4e+08 3e+08 2e+08 1e+08 0e+00 t0 t1 t2 t0 t1 t2 Decomposition stage Decomposition stage 8e+08 8e+07 6e+08 6e+07 4e+08 4e+07 2e+08 2e+07 0e+00 0e+00 t0 t2 t0 t2 t1 t1 Decomposition stage Decomposition stage Fig. 3 Changes in DNA composition and yield during decomposition. DNA yield (a), number of bacterial 16S rRNA genes (b), number of fungal ITS sequences (c), number of pine 18S rRNA genes (d). Whiskers denote standard error of the mean (SEM) copies/g of litter ng/g of litter copies/g of litter copies/g of litter Gołębiewski M. et al. a bacteria bacteria 4.0 eukarya eukarya 3.5 3.0 ab 2.5 ab 2.0 t0 t1 t2 t0 t1 t2 Decomposition stage Decomposition stage C D bacteria bacteria 0.75 eukarya eukarya 0.70 400 a 0.65 0.60 a b 0.55 0.50 t0 t1 t2 t0 t1 t2 Decomposition stage Decomposition stage Fig. 4 Changes in species richness, evennes and diversity estimates. Shannon’s H′ for bacterial (a), observed number of OTUs (b), Chao1 estimated total number of OTUs (c), Shannon’s evenness (d) Possible influence of eukaryotes on bacterial community (0.646) ones, indicating that stronger influence was and vice versa was assessed with co-correspondence analysis exerted in the latter two sample groups. (Supplementary Fig. 3). Two first COCA axes turned out to be significant in a permutation test (P < 0.05), and they were Bacterial Community in Litter Samples Is Dominated sufficient (i.e. adding more axes would not significantly in- by Proteobacteria crease percent variance explained) according to leave-one-out cross-validation. They explained 22.4% of variance in Although bacterial community structure varied greatly in bio- the bacterial community and 44.15% of variance in the logical replicates, certain general trends were visible. At all eukaryotic community. Mean distance between bacterial levels an increase of rare taxa levels in time was found, which and eukaryotic site coordinates was significantly higher was in line with increasing bacterial diversity. At the level of for t samples (0.985, P < 0.05), than for t (0.673) and t phylum, the community was dominated by Proteobacteria 2 0 1 number OTUs Shannon’s H’ Shannon’s E number OTUs Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition AB Fig. 5 Non-metric multidimensional scaling (nMDS) analysis of Bray-Curtis distance matrices for bacterial (a) and eukaryotic (b) communities. Stress: bacteria—0.085, eukaryota—0.099 (68–88%), and an increase of Actinobacteria was visible in t characteristic for t ;and Plectus (nematode), Mycena,as well 2 1 samples (Fig. 6a). Only four phyla (Proteobacteria, as Mytilinidion being typical for t . However, due to relatively Acidobacteria, Bacteroidetes, and Actinobacteria) accounted short 18S rRNA gene fragments being sequenced, only ~ 35 for the vast majority of reads in all samples. Among classes to 80% of reads could be classified down to this level. Alphaproteobacteria dominated in all libraries, and slight de- crease of Beta- and Gammaproteobacteria in t and t samples Metabolic Capabilities of Bacterial Community Are 1 2 was observed with concomitant increase of Actinobacteria, Different at Different Stages of Decomposition Sphingobacteriia, and rare taxa levels (Fig. 6b). More changes were visible at the order level, e.g., Rhodospirillales reads PICRUSt analysis was performed to reveal potential functions were abundant in t reads and significantly less numerous in encoded in genomes of bacteria whose 16S rRNA gene frag- t and t , Burkholderiales and Xanthomonadales were most ments were sequenced in our study. Nearest Sequenced Taxon 1 2 frequent in t samples, while Rhizobiales and Sphingobacteriales Index (NSTI) analysis indicated that in most cases, a se- reads were most frequent in t . The same pattern was observed quenced genome from the same genus could be found for families within the abovementioned orders (Fig. 6c) and (NSTI < 0.05). among genera, where Sphingomonas was the most frequent Overall diversity of functions was lower in the t samples one, and Pseudomonas, Burkholderia, Granulicella,and than in t and t (P <0.01; Fig. 8a). PCA analysis showed that 1 0 Rhizobium were also found in large quantities (Fig. 6d). functional composition of metagenomes was different in t , t , 0 1 and t (Fig. 8b; P < 0.05, AMOVA); however, this effect was Eukaryotic Community in Litter Samples Is Dominated partially due to differences in variance (P < 0.01, by Fungi PERMDISP). The pathways whose shares differed signifi- cantly between various sample types belonged mainly to Eukaryotic libraries differed in biological replicates, similarly BMetabolism^ supercategory, but there were also BSignaling to bacterial ones. The libraries were dominated by fungal Molecules and Interaction^ as well as BTransport and reads (Ascomycota 37–85% and Basidiomycota 7.5–57.8%) Catabolism^ (Fig. 9). with minor quantities of Nematoda (Chromadorea, up to Carbohydrate, amino acid, energy, lipid, xenobiotic, and 4.4%) at later stages of decomposition (Fig. 7a). terpenoid metabolism were the most frequent categories dif- Ascomycotal reads prevailed in the libraries derived from fering significantly. Genes encoding proteins participating in fresh litter, then Basidiomycota were the most numerous in carbohydrates, terpenoids and xenobiotic metabolism were t samples and finally Ascomycota levels increased to ~ 65% predicted to be more frequent in t communities than in the 1 2 in t litter.Eachsampletypeharboreduniqueeukaryotic com- other two, while those engaged in lipid metabolism were es- munity at the genus level (Fig. 7b), in case of t ones timated to be more frequent in t than in t only. Only the 0 2 1 hallmarked by Lophodermium and Phoma; Sistotrema, genes involved in energy metabolism were more probable to Ceuthospora (Phacidium), Trichoderma, and Athelia being be encoded by organisms forming t community. 0 Gołębiewski M. et al. 100 100 unclassified unclassified rare rare Actinobacteria Actinobacteria Bacteroidetes Sphingobacteriia 80 Acidobacteria 80 Acidobacteria Proteobacteria Gammaproteobacteria Betaproteobacteria Alphaproteobacteria 60 60 40 40 20 20 0 0 t0 t1 t2 t0 t1 t2 C D 100 100 unclassified unclassified rare rare Chitinophagaceae Afipia X1174.901.12 Pedobacter Rhodospirillaceae Luteibacter 80 80 Microbacteriaceae Mucilaginibacter Caulobacteraceae Rhizobium Oxalobacteraceae Granulicella Beijerinckiaceae Burkholderia 60 Rhizobiaceae 60 Pseudomonas Bradyrhizobiaceae Sphingomonas Sphingobacteriaceae Pseudomonadaceae Xanthomonadaceae 40 Burkholderiaceae 40 Acidobacteriaceae_.Subgroup_1. Acetobacteraceae Sphingomonadaceae 20 20 0 0 t0 t1 t2 t0 t1 t2 Fig. 6 Classification of bacterial reads. Percentages of all reads are shown at the following levels: phyla (a), classes (b), families (c), and genera (d) 3+ At the level of individual genes, these involved in various as Fe transporting ATPases were predicted to be most fre- sugar transport (PTS system) and certain engaged in metabo- quent in genomes of organisms dwelling in t samples. lism (e.g., sugar kinases, epimerases) as well as regulation (sugar utilization operon regulatory proteins) were predicted to be most abundantly represented in genomes of organisms from t samples, virtually absent from t samples with t in the Discussion 0 2 1 middle. The same applies to other genes involved in import and utilization of soluble substrates such as amino acids, The short time span of our experiment together with the small lipids, or amines, while β-glucosidases, potentially engaged number of sampling time points causes our results to be rather in cellulose degradation, were predicted to be most frequent in a short sequence of snapshots than a time course of microbial t samples. In contrast, genes involved in biosynthesis of vi- succession on litter. Therefore, we avoid interpreting the re- tamins and cofactors, degradation of aromatic compounds sults as Btrends^ and concentrate on differences between pairs (oxygenases and oxidases responsible for degradation of ste- of time points. Nevertheless, we think that the results provide rols and phenolic compounds such as catechol, 2,4- an interesting insight into early stages of pine litter decompo- dichlorophenoxyacetate or 4-hydroxyacetophenone), as well sition at the molecular level. Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition 100 100 unclassified unclassified rare rare Nematoda Pucciniomycotina Basidiomycota Chromadorea Ascomycota Agaricomycotina 80 80 Pezizomycotina 60 60 40 40 20 20 0 0 t0 t1 t2 t0 t1 t2 100 80 unclassified rare rare Naemacyclus Tremellomycetes Marchandiomyces Microbotryomycetes Melanomma Chromadorea_X Coniophora Leotiomycetes Aphelenchoides Eurotiomycetes Slimacomyces Sordariomycetes Mytilinidion Pezizomycotina_X Chaetothyriales Dothideomycetes Rhodotorula Agaricomycetes Mycena Plectus 40 Phoma Athelia Trichoderma Lophodermium Ceuthospora Sistotrema 0 0 t0 t1 t2 t0 t1 t2 Fig. 7 Classification of eukaryotic reads. Percentages of classified reads are shown at the following levels: subkingdoms (a), phyla (b), subclasses (c), and genera (d) We observed no influence of enzymatic treatment neither conditions that were suboptimal for the chitinase used (ac- on the amount of DNA isolated nor on microbial diversity. cording to the producer the optimal pH is 4, while the buffer This might have been caused by the effectiveness of bead used for digestion had pH 8). It seems that there is no reason beating, making the action of enzymes pointless. In our pre- for enzymatic pre-treatment of litter samples prior to bead- vious study, we observed the influence of enzymes, as the beating-based DNA isolation. Other studies report significant method of isolation was based on less effective thermal/ influence of lysozyme digestion on soil DNA yield, even in chemical lysis [61]. However, an alternative explanation is combination with bead-beating [62]; however, the bead- possible, assuming that cell envelopes of microbes in our beating method was not as effective as in contemporary kits samples were not digested by the enzymes used. Albeit pos- using dedicated bead-beaters. sible, it is unlikely at least in the case of lysozyme, taking into DNA content of litter samples significantly increased dur- consideration high numbers of Proteobacterial reads generated ing the studied decomposition period. This increase was from the samples. We found that the enzyme worked well on caused by greater numbers of bacterial and fungal cells, as E. coli DH10B in the buffer used for digestion. However, no we found that pine DNA was almost completely degraded in increase in fungal DNA yield could be an effect of digestion t samples. This was confirmed by results of qPCR analysis of 1 Gołębiewski M. et al. 4500 0.04 t0 t1 t2 0.02 4300 0.00 -0.02 4100 0.04 0.06 t0 t1 t2 -0.075 0.00 0.075 Decomposition stage PCA1 (53.4%) Fig. 8 Diversity of functions encoded by bacterial genomes present in litter samples (whiskers denote standard error of the mean (SEM), a), nMDS analysis of Bray-Curtis distance matrix obtained from functional categories matrix (b). Stress: 0.187 bacterial 16S rRNA and fungal ITS copy numbers. The num- Although precipitation was lower in the t –t period than in 0 1 ber of bacterial sequences increased first (in the t samples), t –t one, water content was the highest in t samples, which 1 1 2 1 supporting our hypothesis that fresh litter is first colonized by was probably caused by low temperature. During the initial bacteria, albeit, alternatively, this increase might have been period of the experiment (t –t ), temperature was ~ 0 °C with 0 1 caused by growth of phyllospheric organisms. It seems plau- occasional snow, and diversity did not change significantly, sible that bacteria are responsible for pine DNA degradation. neither in the case of fungi, nor bacteria, which suggests weak We have found no archaeal reads in our libraries, which selection. Later, warm period was hallmarked by bacterial might mean that there were no Archaea in our samples. This species richness increase, showing possible colonization of is supported by results of a metaproteomic study concerning litter by bacteria from the surrounding environment. beech litter decomposition [16]. Nevertheless, it is possible Evenness was lower in t samples causing diversity to stay that Archaea in pine litter might belong to a group whose at the t level. In the case of eukaryotic community diversity, 16S rRNA sequences differ from those amplifiable with the species richness and evenness decreased significantly in the t primer system we used, e.g., Nanoarchaeota, Altiarchaeota, samples, suggesting strong selection. or Diapherotrites or, as in all phyla the coverage was well t samples harbored a bacterial community similar to pine below 90%, they could be members of generally amplifiable phyllosphere community [65], with high shares of groups having non-amplifiable variants. There are reports of Proteobacterial sequences, particularly members of the Archaea being cultured from Scots pine ectomycorrhizas Acetobacteraceae and Pseudomonadaceae families being the (Methanolobus, Halobacterium, and unknown member of hallmarks of phyllosphere. Differences might have resulted 11.c Crenarchaeota) [63], and ~ 2% of sequenced transcripts from tree species being different (Scots pine vs. limber pine), from spruce litter were coming from Archaea [26] and both geographical distance (Central Europe vs. California), and facts support the latter possibility. physiological state of needles (dry, fallen vs. fresh ones). Shannon’s diversity, evenness, and species richness were The eukaryotic t community consisted of Fungi, out of which consistently higher for bacterial communities than for fungal Ascomycotal class Pezizomycotina prevailed, which was sim- ones, which might be caused by the number of bacterial taxa ilar to fresh pine needles community studied by Millberg and being around one order of magnitude greater than the number colleagues [66] and unlike in senescent oak leaves, where of fungal ones in soils [64]. Moreover, bacterial diversity grew Dothideomycetes prevailed [17]. Again, the differences might over time, while fungal communities became less diverse. have resulted from geographical distance (Sweden vs. Poland) This effect might have been caused by three factors: litter and physiological state of the needles. Eukaryotic sequences humidity, temperature, and time (decomposition stage). that could be identified down to the genus level belonged Functional categories PCA2 (21.9%) Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition mainly to known phyllospheric fungi, such as Phoma, counts, as the most abundant phylotype of this group (Plectus) Trichoderma,or Lophodermium, and the latter might act as is a bacterivore. This is supported by the results of co- an early decomposer in litter [67, 68]. Low number of se- correspondence analysis, as the influence of eukaryotic com- quences was assigned to Naemacyclus (Helotiales, synonym munity on the bacterial one is significant and explains ~ 10% of Cyclaneusma), a genus comprising fungi causing needle cast variation. On the other hand, interactions among Eukaryota [69]; thus, it is plausible that certain amount of collected cannot be excluded, e.g., certain fungivorous nematodes most needles were shed prematurely due to its action. abundant in the t samples (e.g., members of the Differences between t and t samples were profound, in Aphelenchoides genus [75]) might be involved in much lower 1 0 spite of temperature being below 0 °C most of the time. After Sistotrema abundance in the t samples. The influence of bac- 3 months of field incubation, the samples harbored communi- teria on the eukaryotic community was even stronger (~ 22%). ties that were drastically different from t ones; this fact dem- It might be explained, e.g., by providing nitrogen (by N fixa- 0 2 onstrates that colonization of fresh litter by soil and older tion) that seems to be a limiting factor, particularly in t sam- litter-inhabiting organisms is rapid, regardless of harsh envi- ples. This is confirmed by the results of CCA, wherein C/N was ronmental conditions. This is in line with results of earlier identified as a significant environmental variable. It is also pos- studies on mass loss, e.g., [70] and with reports concerning sible that bacteria provided fungi with phosphorus, another lim- decomposition of broadleaf litter [17, 19, 20]. Reads coming iting nutrient, by solubilizing its soil resources [76]. As the from phyllospheric organisms were less abundant than in the measured environmental variables were responsible for t samples (e.g., members of Acetobacteraceae, as well as explaining of over 37% of variance, it seems plausible that they fungi Lophodermium and Phoma), showing that, in spite of were the key drivers of microbial community structure changes. the lack of diversity decrease, selection operates at this stage Bacterial community structure at the level of genus was sur- of litter decomposition, but organisms unable to survive are prisingly similar to this found at the corresponding stages of replaced by colonizers. Obvious fungal colonizers were mem- decomposition in oak leaf litter [77], where Pseudomonas, bers of Sistotrema, ectomycorrhizal/saprophytic fungi of the Sphingomonas,and Burkholderia were among the dominating Cantarellales order (Basidiomycota, Agaricomycetes), and genera. Differences were mainly quantitative and were more Ceuthospora (synonym Phacidium, of Helotiales pronounced in the phyllospheric communities. The most remark- (Ascomycota, Dothideomycetes), [71], a genus comprising able difference was the lack of Duganella (Oxalobacteriaceae, plant pathogenic fungi causing various diseases from needles Betaproteobacteria) and Frigoribacterium (Micrococcaceae, rot in conifers to fruit rot in cranberries, apples, or pears [72, Actinobacteria) in pine needles; these genera were replaced by 73]. Reads derived from these organisms were virtually absent unclassified members of the Acetobacteraceae family. from t samples and constituted over 50% of all eukaryotic Subsequent stages of pine litter decomposition harbored less reads in t ones. Small numbers of reads obtained from t Pedobacter. This fact suggests that, while plants assemble unique 1 0 samples might be explained by spores sedimenting from air phyllospheric communities, decomposers’ assemblages, at least on the collected needles. The eukaryotic community at this bacterial, may be more generic and similar regardless of the stage of decomposition was more similar to the initial one than quality of litter. the bacterial one, suggesting that bacteria were able to grow Fungal communities decomposing pine litter are less more actively under winter conditions. C:N ratio was highest similar to those degrading oak leaf litter described by in t samples, indicating nitrogen depletion, probably due to Voriskova and colleagues [17]; however, general picture organic nitrogen being consumed by microbes. looks similar: phyllospheric communities are rapidly re- To no surprise, at the end of the experiment, the samples placed by distinct communities characteristic for particu- harbored communities different from both t and t ones. As lar stages of decomposition. Certain fungi are abundant 0 1 seasonal differences in microbial community composition in both in oak and pine decomposing litter, e.g., Athelia, spruce litter were not found to be large [26], we suggest that Rhodotorula,and Sistotrema. Interestingly, Sistotrema, the differences visible in our data should be attributed to de- most abundant in 12-month-old oak litter [17], displays composition stage. Phyllospheric organisms were even less abundance peak in t samples. Thus, it is possible that abundant in t than in t , typical soil organisms could be found, fungal succession on decomposing pine litter Bovertakes^ 2 1 such as nematodes of the Chromadorea class or bacteria of the the one on oak leaves. Rhizobium genus, and the litter was colonized by organisms A model of litter decomposition assumes that soluble com- belonging to Dothideomycetes (Fungi, Ascomycota) and pounds are degraded first, then degradation of hemicelluloses Sphingomonadaceae as well Bradyrhizobiaceae (Bacteria, follows and the last stages comprise degradation of cellulose- Alphaproteobacteria), probably capable of degradation of recal- lignin complex leading to increasing lignin/cellulose ratio citrant compounds (lignocellulose, lignin) prevailing at this [78]. We used PICRUSt to model possible metagenomes of stage of decomposition [74]. The presence of nematodes might decomposing litter to learn if the probable gene content sup- be one of the causes of slower increase of bacterial 16S rDNA ports this model. Gołębiewski M. et al. Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition Fig. 9 Selected functional categories of significantly different decomposition. Microbial succession on decomposing pine abundance. Dot and whiskers plots to the right of the barplots show litter is rapid and initial phyllospheric communities are re- 95% confidence intervals on the difference between respective samples, placed with distinct assemblage characteristic for particular dot location indicates the mean, and the color of a given dot shows in stages of decomposition. Changes in communities seem to which sample the proportion was higher. Bonferroni-corrected P value is given to the right of dot and whiskers plots be driven not only by physicochemical variables of litter but also by interactions among bacteria, fungi, and nematodes. However, one should bear in mind that the results obtained Author Contributions Conceived the study—MG, MN, designed the ex- with PICRUSt are only an approximation of the Btrue^ periments—MG, contributed financial support and reagents—MN, MG, metagenome, and many factors, such as all biases influencing ATa, ATr, executed the experiments—MS, ATa, ED-S, MG, performed the underlying 16S rRNA gene fragment sequencing or differ- bioinformatic and statistical analyses—MG, interpreted data—MG, ATa, ences in gene content of closely related organisms due to lateral MN, wrote the draft of the manuscript—MG. All of the authors revised the manuscript and approved its content. gene transfer, may skew the results. Moreover, the set of func- tions derived from PICRUSt is only potential one, i.e., we do Funding Information This study was performed within an OPUS grant of not know if the functions are actually expressed. Another lim- the Polish National Science Center (No. UMO-2012/05/NZ8/001362) itation is that, due to the lack of a eukaryotic database, it was not and supported by DS758, DS759, and DSC funds of the Institute of possible to perform such an analysis for eukaryotic sequences. Environmental Sciences, Jagiellonian University and by statutory funding from Nicolaus Copernicus University (to MG and ATr). Therefore, the results should be treated with caution. We expected a shift in community composition from or- Open Access This article is distributed under the terms of the Creative ganisms capable of various carbohydrates utilization to Commons Attribution 4.0 International License (http:// cellulose-degrading specialists, to lignin degraders, although creativecommons.org/licenses/by/4.0/), which permits unrestricted use, the latter only to some extent, as our experiment concentrated distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link on the first 8 months of litter decomposition. Our results sup- to the Creative Commons license, and indicate if changes were made. port this view, as cellulolytic genes (β-glucosidases) were most abundant in t samples, and certain genes that might be involved in lignin degradation (oxygenases and oxidases) References were also found. The genes engaged in active transport of cellobiose (cellulose degradation product) into the cell (PTS 1. FAO (2010) Global forest resources assessment 2010. http://www. system) were most frequent in t samples and permeases fao.org/forest-resources-assessment/en/ allowing for passive import of this sugar were most numerous 2. Bradford MA, Berg B, Maynard DS, Wieder WR, Wood SA (2016) in t samples. This picture might mean that during the initial Understanding the dominant controls on litter decomposition. J. stage of litter decomposition (t ) after depletion of other easily Ecol. 104:229–238. https://doi.org/10.1111/1365-2745.12507 3. Berg B, Laskowski R (2006) Litter decomposition: a guide to car- accessible sugars, active transfer of cellobiose is required, most bon and nutrient turnover. Elsevier probably due to the low concentration of this compound; then, 4. David JF (2014) The role of litter-feeding macroarthropods in decom- fungi start producing cellulases causing increase of the cellobi- position processes: a reappraisal of common views. Soil Biol. ose concentration, which allows passive transfer; and finally, Biochem. 76:109–118. https://doi.org/10.1016/j.soilbio.2014.05.009 5. Persson T, Bååth E, Clarholm M et al (1980) Trophic structure, bacterial cellulolytic activity may supplement fungal activity biomass dynamics and carbon metabolism of soil organisms in a (t ). The latter claim was partially supported by results of Scots pine forest. Oikos 32:419–459. https://doi.org/10.2307/ Zifcakova and colleagues, who found that Fungi were respon- sible for only half of carbohydrates metabolism-involved tran- 6. Berg B, Hannus K, Popoff T, Theander O (1982) Changes in or- scripts production in spruce litter [26]. Given the importance of ganic chemical components of needle litter during decomposition. Long-term decomposition in a Scots pine forest. I. Can. J. Bot. 60: coniferous litter decomposition and taking into account all the 1310–1319 limitations of the methodology as well as the general scarcity of 7. Staaf H, Berg B (1982) Accumulation and release of plant nutrients molecular studies devoted to coniferous litter decomposition, in decomposing Scots pine litter. Long-term decomposition in a more work is needed to understand which organisms are re- Scots pine forest. II. Can. J. Bot. 60:1561–1568. https://doi.org/ 10.1139/b82-199 sponsible for particular processes during decomposition. 8. Berg B (1986) Nutrient release from litter and humus in coniferous forest soils—a mini review. Scand. J. For. Res. 1:359–369 9. Berg B (1988) Dynamics of nitrogen (15N) in decomposing Scots pine (Pinus sylvestris) needle litter. Long-term decomposition in a Scots pine forest. VI. Can. J. Bot. 66:1539–1546 Conclusions 10. Bardgett RD (2005) The biology of soil: a community and ecosys- tem approach. Oxford University Press There is no need to supplement contemporary bead beating- 11. Cepáková Š, Frouz J (2015) Changes in chemical composition of based kits with cell wall-degrading enzymes. Bacterial and litter during decomposition: a review of published C NMR spec- fungal diversity changes differently during initial stages of tra. J. Soil Sci. Plant Nutr. 15:805–815 Gołębiewski M. et al. 12. Ponge J-FF (1991) Succession of fungi and fauna during decompo- decomposition processes of leaf litter. Can. J. Microbiol. 52:701– 716. https://doi.org/10.1139/w06-023 sition of needles in a small area of Scots pine litter. Plant Soil 138: 99–113. https://doi.org/10.1007/BF00011812 28. Voříšková J, Brabcová V, Cajthaml T, Baldrian P (2014) Seasonal 13. Tokumasu S, Aoki T, Oberwinkler F (1994) Fungal succession on dynamics of fungal communities in a temperate oak forest soil. New pine needles in Germany. Mycoscience 35:29–37. https://doi.org/ Phytol. 201:269–278. https://doi.org/10.1111/nph.12481 10.1007/BF02268525 29. Ward DM, Weller R, Bateson MM (1990) 16S rRNA sequences 14. Berg MP, Kniese J, Bedaux J, Verhoef H (1998) Dynamics and reveal numerous uncultured microorganisms in a natural communi- stratification of bacteria and fungi in the organic layers of a scots ty. Nature 345:63–65 pine forest soil. Biol. Fertil. Soils 26:268–284. https://doi.org/10. 30. Hugenholtz P, Goebel BM, Pace NR (1998) Impact of culture in- 1007/s003740050378 dependent studies on the emerging phylogenetic view of bacterial diversity. J. Bacteriol. 180: 4765–4774. doi: 0021–9193/98/ 15. Aneja MK, Sharma S, Fleischmann F, Stich S, Heller W, Bahnweg $04.00+0 G, Munch JC, Schloter M (2006) Microbial colonization of beech and spruce litter—influence of decomposition site and plant litter 31. Steele HL, Streit WR (2005) Metagenomics: advances in ecology species on the diversity of microbial community. Microb. Ecol. 52: and biotechnology. FEMS Microbiol. Lett. 247:105–111. https:// 127–135. https://doi.org/10.1007/s00248-006-9006-3 doi.org/10.1016/j.femsle.2005.05.011 16. Schneider T, Keiblinger KM, Schmid E, Sterflinger-Gleixner K, 32. Handelsman J, Rondon MR, Brady SF, Clardy J, Goodman RM Ellersdorfer G, Roschitzki B, Richter A, Eberl L, Zechmeister- (1998) Molecular biological access to the chemistry of unknown Boltenstern S, Riedel K (2012) Who is who in litter decomposition? soil microbes: a new frontier for natural products. Chem. Biol. 5: Metaproteomics reveals major microbial players and their biogeo- R245–R249 chemical functions. ISME J. 6:1749–1762. https://doi.org/10.1038/ 33. Rondon MR, Bettermann AD, Brady SF et al (2000) Cloning the ismej.2012.11 soil metagenome: a strategy for accessing the genetic and functional 17. Voříšková J, Baldrian P (2013) Fungal community on decomposing diversity of uncultured microorganisms. Appl. Environ. Microbiol. leaf litter undergoes rapid successional changes. ISME J. 7:477– 66:2541–2547 486. https://doi.org/10.1038/ismej.2012.116 34. Štursová M, Žifčáková L, Leigh MB, Burgess R, Baldrian P (2012) 18. Urbanová M, Šnajdr J, Baldrian P (2015) Composition of fungal Cellulose utilization in forest litter and soil: identification of bacte- and bacterial communities in forest litter and soil is largely deter- rial and fungal decomposers. FEMS Microbiol. Ecol. 80:735–746. mined by dominant trees. Soil Biol. Biochem. 84:53–64. https://doi. https://doi.org/10.1111/j.1574-6941.2012.01343.x org/10.1016/j.soilbio.2015.02.011 35. Tsai Y-L, Olson BH (1992) Detection of low numbers of bacterial 19. Tláskal V, Voříšková J, Baldrian P et al (2016) Bacterial succession cells in soils and sediments by polymerase chain reaction. Apllied on decomposing leaf litter exhibits a specific occurrence pattern of Environ. Microbiol. 58:754–757 cellulolytic taxa and potential decomposers of fungal mycelia. 36. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies FEMS Microbiol. Ecol. 92:fiw177. https://doi.org/10.1093/ J, Glöckner FO (2013) The SILVA ribosomal RNA gene database femsec/iw177 project: improved data processing and web-based tools. Nucleic 20. Purahong W, Wubet T, Lentendu G, Schloter M, Pecyna MJ, Acids Res. 41:590–596. https://doi.org/10.1093/nar/gks1219 Kapturska D, Hofrichter M, Krüger D, Buscot F (2016) Life in leaf 37. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, litter: novel insights into community dynamics of bacteria and fungi Glöckner FO (2013) Evaluation of general 16S ribosomal RNA during litter decomposition. Mol. Ecol. 25:4059–4074. https://doi. gene PCR primers for classical and next-generation sequencing- org/10.1111/mec.13739 based diversity studies. Nucleic Acids Res. 41:1–11. https://doi. 21. Dilly O, Munch J-C (1996) Microbial biomass content, basal res- org/10.1093/nar/gks808 piration and enzyme activities during the course of decomposition 38. IDT (2014) IDT Oligo Analyzer. http://eu.idtdna.com/calc/analyzer of leaf litter in a black alder (Alnus glutinosa (L.) Gaertn.) forest. 39. Faircloth BC, Glenn TC (2012) Not all sequence tags are created Soil Biol. Biochem. 28:1073–1081. https://doi.org/10.1016/0038- equal: designing and validating sequence identification tags robust 0717(96)00075-2 to indels. PLoS One 7:e42543. https://doi.org/10.1371/journal. 22. Baldrian P (2017) Forest microbiome: diversity, complexity, and pone.0042543 dynamics. FEMS Microbiol. Rev. 41:109–130. https://doi.org/10. 40. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister 1093/femsre/fuw040 EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, 23. Swift MJ, Heal OW, Anderson JM (1979) Decomposition in terres- Stres B, Thallinger GG, van Horn DJ, Weber CF (2009) Introducing trial ecosystems. University of California Press mothur: open-source, platform-independent, community-supported 24. Melillo JM, Aber JD, Linkins AE, Ricca A, Fry B, Nadelhoffer KJ software for describing and comparing microbial communities. (1989) Carbon and nitrogen dynamics along the decay continuum: Appl. Environ. Microbiol. 75:7537–7541. https://doi.org/10.1128/ plant litter to soil organic matter. Plant Soil 115:189–198. https:// AEM.01541-09 doi.org/10.1007/BF02202587 41. Gołębiewski M, Deja-Sikora E, Cichosz M, Tretyn A, Wróbel B 25. Baldrian P, Kolařík M, Štursová M, Kopecký J, Valášková V, (2014) 16S rDNA pyrosequencing analysis of bacterial community Větrovský T, Žifčáková L, ŠnajdrJ,RídlJ,Vlček Č,Voříšková J in heavy metals polluted soils. Microb. Ecol. 67:635–647. https:// (2012) Active and total microbial communities in forest soil are doi.org/10.1007/s00248-013-0344-7 largelydifferent andhighlystratified during decomposition. 42. Gołębiewski M, Całkiewicz J, Creer S, Piwosz K (2017) Tideless ISME J 6:248–258. https://doi.org/10.1038/ismej.2011.95 estuaries in brackish seas as possible freshwater-marine transition 26. Žifčáková L, Větrovský T, Howe A, Baldrian P (2016) Microbial zones for bacteria: the case study of the Vistula river estuary. activity in forest soil reflects the changes in ecosystem properties Environ. Microbiol. Rep. 9:129–143. https://doi.org/10.1111/ between summer and winter. Environ. Microbiol. 18:288–301. 1758-2229.12509 https://doi.org/10.1111/1462-2920.13026 43. Quince C, Lanzen A, Davenport RJ, Turnbaugh PJ (2011) 27. Osono T (2006) Role of phyllosphere fungi of forest trees in the Removing noise from pyrosequenced amplicons. BMC development of decomposer fungal communities and Bioinformatics 12:38. https://doi.org/10.1186/1471-2105-12-38 Rapid Microbial Community Changes During Initial Stages of Pine Litter Decomposition 44. Huse SM, Welch DM, Morrison HG, Sogin ML (2010) Ironing out data. PLoS One 8:e61217. https://doi.org/10.1371/journal.pone. the wrinkles in the rare biosphere through improved OTU cluster- 61217 ing. Environ. Microbiol. 12:1889–1898. https://doi.org/10.1111/j. 61. Deja-Sikora E (2012) Search for bacterial cadmium, zinc, lead, 1462-2920.2010.02193.x copper and chromium resistance genes in metagenome of soils 45. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) polluted with heavy metals. Nicolaus Copernicus University UCHIME improves sensitivity and speed of chimera detection. 62. Krsek M, Wellington EMH (1999) Comparison of different Bioinformatics 27:2194–2200. https://doi.org/10.1093/ methods for the isolation and purification of total community bioinformatics/btr381 DNA from soil. J. Microbiol. Methods 39:1–16. https://doi.org/ 10.1016/S0167-7012(99)00093-7 46. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian 63. Bomberg M, Montonen L, Timonen S (2010) Anaerobic Eury- and classifier for rapid assignment of rRNA sequences into the new Crenarchaeota inhabit ectomycorrhizas of boreal forest Scots pine. bacterial taxonomy. Appl. Environ. Microbiol. 73:5261–5267. Eur. J. Soil Biol. 46:356–364. https://doi.org/10.1016/J.EJSOBI. https://doi.org/10.1128/AEM.00062-07 2010.09.002 47. Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, Boutte 64. Larsen BB, Miller EC, Rhodes MK, Wiens JJ (2017) Inordinate C, Burgaud G, de Vargas C, Decelle J, del Campo J, Dolan JR, fondness multiplied and redistributed: the number of species on Dunthorn M, Edvardsen B, Holzmann M, Kooistra WHCF, Lara earth and the new pie of life. Q. Rev. Biol. 92:229–265. https:// E, le Bescot N, Logares R, Mahé F, Massana R, Montresor M, doi.org/10.1086/693564 Morard R, Not F, Pawlowski J, Probert I, Sauvadet AL, Siano R, 65. Carrell AA, Frank AC (2014) Pinus flexilis and Picea engelmannii Stoeck T, Vaulot D, Zimmermann P, Christen R (2013) The Protist share a simple and consistent needle endophyte microbiota with a Ribosomal Reference database (PR2): a catalog of unicellular eu- potential role in nitrogen fixation. Front. Microbiol. 5: . doi: https:// karyote small sub-unit rRNA sequences with curated taxonomy. doi.org/10.3389/fmicb.2014.00333 Nucleic Acids Res. 41:597–604. https://doi.org/10.1093/nar/ 66. Millberg H, Boberg J, Stenlid J (2015) Changes in fungal commu- gks1160 nity of Scots pine (Pinus sylvestris) needles along a latitudinal gra- 48. Sheneman L, Evans J, Foster JA (2006) Clearcut: a fast implemen- dient in Sweden. Fungal Ecol. 17:126–139. https://doi.org/10.1016/ tation of relaxed neighbor joining. Bioinformatics 22:2823–2824. j.funeco.2015.05.012 https://doi.org/10.1093/bioinformatics/btl478 67. Boberg JB, Ihrmark K, Lindahl BD (2011) Decomposing capacity 49. Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method of fungi commonly detected in Pinus sylvestris needle litter. Fungal for comparing microbial communities. Appl. Environ. Microbiol. 71: Ecol. 4:110–114. https://doi.org/10.1016/J.FUNECO.2010.09.002 8228–8235. https://doi.org/10.1128/AEM.71.12.8228-8235.2005 68. Osono T, Hirose D (2011) Colonization and lignin decomposition 50. Horn HS (1966) Measurement of Boverlap^ in comparative ecolog- of pine needle litter by Lophodermium pinastri.For.Pathol.41: ical studies. Am. Nat. 100:419–424. https://doi.org/10.1086/ 156–162. https://doi.org/10.1111/j.1439-0329.2010.00648.x 69. van der PAS JB, Slater-hayes JD, Gadgil PD, Bulman L (1984) 51. Bray RJ, Curtis JT (1957) An ordination of the upland forest com- Cyclaneusma (Naemacyclus) needle-cast of Pinus radiata in New munities of southern Winsconin. Ecol. Monogr. 27:325–349. Zealand. 2: reduction in growth of the host, and its economic im- https://doi.org/10.2307/1942268 plication. New Zeal J For Sci 14:197–209 52. Oksanen J, Blanchet FG, Kindt R, et al (2013) Vegan: community 70. Vestgarden L (2001) Carbon and nitrogen turnover in the early ecology package. R package, https://cran.r-project.org/web/ stage of Scots pine (Pinus sylvestris L.) needle litter decomposition: packages/vegan/index.html effects of internal and external nitrogen. Soil Biol. Biochem. 33: 53. ter Braak CJF, Schaffers AP (2004) Co-correspondence analysis: a 465–474. https://doi.org/10.1016/S0038-0717(00)00187-5 new ordination method to relate two community compositions. 71. Crous PW, Quaedvlieg W, Hansen K, Hawksworth DL, Ecology 85:834–846. https://doi.org/10.1890/03-0021 Groenewald JZ (2014) Phacidium and Ceuthospora 54. Simpson GL (2009) Cocorresp: co-correspondence analysis ordi- (Phacidiaceae) are congeneric: taxonomic and nomenclatural im- nation methods. R package, https://github.com/gavinsimpson/ plications. IMA Fungus 5:173–193. https://doi.org/10.5598/ cocorresp imafungus.2014.05.02.02 55. Langille M, Zaneveld J, Caporaso JG et al (2013) Predictive func- 72. Farr DF, Rossman AY (2018) Fungal databases, U.S. National tional profiling of microbial communities using 16S rRNA marker Fungus Collecsions, ARS, USDA. https://nt.ars-grin.gov/ gene sequences. Nat. Biotechnol. 31:814–821. https://doi.org/10. fungaldatabases. Accessed 1 Feb 2018 1038/nbt.2676 73. Wiseman MS, Kim YK, Dugan FM, Rogers JD, Xiao CL (2016) A 56. DeSantis TZ, Hugenholtz P, Larsen N et al (2006) Greengenes, a new postharvest fruit rot in apple and pear caused by Phacidium chimera-checked 16S rRNA gene database and workbench com- lacerum. Plant Dis. 100:32–39. https://doi.org/10.1094/PDIS-02- patible with ARB. Appl. Environ. Microbiol. 72:5069–5072. 15-0158-RE https://doi.org/10.1128/AEM.03006-05 74. Berg B, McClaugherty C (2003) Plant litter. Decomposition, humus 57. Parks DH, Tyson GW, Hugenholtz P, Beiko RG (2014) STAMP: formation, carbon sequestration. Springer-Verlag Berlin statistical analysis of taxonomic and functional profiles. Heidelberg, Berlin Heidelberg Bioinformatics 30:3123–3124. https://doi.org/10.1093/ 75. Bae Y-S, Knudsen GR (2001) Influence of a fungus-feeding nem- bioinformatics/btu494 atode on growth and biocontrol efficacy of Trichoderma 58. R Core Team (2016) R: a language and environment for statistical harzianum. Phytopathology 91:301–306. https://doi.org/10.1094/ computing. https://www.r-project.org/ PHYTO.2001.91.3.301 59. Harrell Jr FE, with contributions from Charles Dupont, many 76. Taktek S, Trépanier M, Servin PM, St-Arnaud M, Piché Y, Fortin others. (2014) Hmisc: Harrell miscellaneous. R package, https:// JA, Antoun H (2015) Trapping of phosphate solubilizing bacteria cran.r-project.org/web/packages/Hmisc/index.html on hyphae of the arbuscular mycorrhizal fungus Rhizophagus 60. McMurdie PJ, Holmes S (2013) Phyloseq: an R package for repro- irregularis DAOM 197198. Soil Biol. Biochem. 90:1–9. https:// ducible interactive analysis and graphics of microbiome census doi.org/10.1016/j.soilbio.2015.07.016 Gołębiewski M. et al. 77. Baldrian P, Zrůstová P, Tláskal V, Davidová A, Merhautová V, application on soil microbial community structure and functional gene abundance under irrigated maize. FEMS Microbiol. Ecol. 59: Vrška T (2016) Fungi associated with decomposing deadwood in a natural beech-dominated forest. Fungal Ecol. 23:109–122. https:// 661–670. https://doi.org/10.1111/j.1574-6941.2006.00235.x doi.org/10.1016/j.funeco.2016.07.001 81. Neefs J-M, Van de Peer Y, De Rijk P et al (1993) Compilation of 78. Berg B, Matzner E (1997) Effect of N deposition on decomposition small ribosomal subunit RNA structures. Nucleic Acids Res. 21: of plant litter and soil organic matter in forest systems. Environ. 3025–3049 Rev. 5:1–25 82. Hadziavdic K, Lekang K, Lanzen A, Jonassen I, Thompson EM, 79. White TJ, Bruns T, Lee S, Taylor J (1990) Amplification and direct Troedsson C (2014) Characterization of the 18S rRNA gene for sequencing of fungal ribosomal RNA genes for phylogenetics. In: designing universal eukaryote specific primers. PLoS One 9: PCR protocols: a guide to methods and applications. pp 315–322 e87624. https://doi.org/10.1371/journal.pone.0087624 80. Wakelin SA, Colloff MJ, Harvey PR, Marschner P, Gregg AL, Rogers SL (2007) The effects of stubble retention and nitrogen

Journal

Microbial EcologySpringer Journals

Published: May 30, 2018

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