Invertase

Functional validation of the predicted signal peptide of Ps87 was conducted with a yeast secretion system [29] (link). The yeast signal trap vector pSUC2T7M13ORI (pSUC2), which carries a truncated invertase, SUC2, lacking both its initiation methionine and signal peptide, was used. Yeast cells were transformed with 0.5 µg of the individual pSUC2-derived plasmids using the lithium acetate method. DNA encoding the predicted signal peptide plus the two following amino acids of Ps87 or Avr1b, or the first 25 amino acids of the M. oryzae Ps87 homolog (Mg87) were cloned as an EcoRI–XhoI fragments into pSUC2 [11] (link), [26] (link), then transformed into the yeast strain YTK12. All transformants were confirmed by PCR with vector-specific primers. Transformants were grown on yeast minimal medium with sucrose in place of glucose (CMD-W medium: 0.67% yeast N base without amino acids, 0.075% tryptophan dropout supplement, 2% sucrose, 0.1% glucose, and 2% agar). To assay for invertase secretion, colonies were replica plated onto YPRAA plates containing raffinose and lacking glucose (1% yeast extract, 2% peptone, 2% raffinose, and 2 µg/mL antimycin A). The YTK12 transformed with the pSUC2 vector encoding the truncated invertase and untransformed YTK12 strain were used as negative controls.

We predicted the sub-cellular location of the proteins encoded in the genomes included in the analysis (see Genomic data, taxonomic classification, and phylogenetic reconstruction) using PSORTB v 3.1^{29 (link)}. The PSORTB model was selected based on the species’ monoderm/diderm classification (taken from the literature)⁶¹ . Only proteins classified as “extracellular” by PSORTB and lacking transmembrane domains where considered in our study. Proteins not matching these criteria were discarded. When more than one genome was available per species, we computed the average number of proteins per genome for that location (Supplementary data 5). Extracellular proteins were functionally classified by searching for sequence similarity, using HMMsearch from HMMer v.3.1.2b^{65 (link)}, in the eggNOG v. 4.5 database^{66 (link)}. We only considered hits with an e-value ≤10⁻⁵ and more than 50% similarity. Since different HMMs may be associated to the same functional category in different taxa, we kept the functional annotation of the best hit when more than half of the hits were associated to that same category (otherwise it was marked unknown).
Three functional categories were explored more carefully. First, we characterized the repertoire of extracellular bacteriocins. To do so, we searched for similarities to the extracellular proteins in the two bacteriocin databases Bagel and Bactibase^{67 (link),68 (link)} using HMMer. We kept the hits with an e-value < 0.05 and more than 50% coverage of the query sequence (Supplementary Table 2). Second, we identified the extracellular proteins with a degradative activity. We selected enzymatic activities often associated to the extracellular environment: amidase, amylase, cellulase, chitinase, dipeptidase, glycosyl hydrolase, invertase, inulinase, keratinase, and pectinase^{69 (link)}. For each degradative enzyme, we collected all previously validated bacterial protein candidates by searching for specific keywords in Uniprot1^{70 (link)}. We clustered them using usearch with the “cluster_smallmem” algorithm at 70% identity. We aligned the sequences of each cluster using mafft v.7 with the local pairwise alignment option and a maximum 1000 iterations (“linsi” option)^{71 (link)}. The resulting multiple alignments were used to build protein HMM profiles using hmmbuild from HMMer. HMM profiles were queried against the extracellular proteins previously predicted. Hits with more than 40% identity and less than 20% difference in length for the smallest of either the protein or profile where kept, and the best hit was used to classify them (Supplementary Table 2).

Soil samples with roots and debris removed using a 2 mm sieve were air-dried and stored at 4 °C for use. Fourteen common environmental factors in soil were detected according to previous studies, including available phosphorus (AP), available potassium (AK), ammonium nitrogen (AN), soil organic matter (SOM), total organic carbon (TOC) (Bao, 2000 ), nitrate nitrogen (NN) (Sun et al., 2016 ), Saccharase (SC), Urease (UE), alkaline phosphatase (AKP) (Guan, 1986 ); total nitrogen (TN), total hydrogen (TH), total carbon (TC), and total sulfur (TS) were detected by Elemental Analyzer (Elementar vario EL cube Elemental Analyzer; Elementar, Langenselbold, Germany) and the pH of soil samples was determined in 1:2.5 soil-water suspension using pH meter (Sartorius PB-10). The saccharase activity was expressed as mg glucose·d⁻¹·g⁻¹ soil, the urease activity was expressed as mg NH₃-N·d⁻¹·g⁻¹ soil and the alkaline phosphatase activity was expressed as mg P₂O₅·2h⁻¹·g⁻¹ soil.

Nitrate reductase (NR), glutamine synthetase (GS), and glutamate synthase (GOGAT) contents were determined following the method of Hu et al. (2016) (link). Nitrite reductase (NiR) activity was assayed according to the method previously described (Seith et al., 1994 (link)).
The activity of C-metabolizing enzymes was measured as follows. For the preparation of enzyme solution, 1 g of the fruit sample was ground into an ice bath in a precooled mortar; 100 mol·l^-1 of 5 ml Tris–HCl (pH 7.0) buffer, containing 2% glycol, 2 mol·l^-1 EDTA, 5 mol·l^-1 MgCl₂, 2% PVPP, 2% bovine serum protein (BSA), and 5 mmol·L^-1 DTT, was added for fractional times. 3 ml of the supernatant was put into a dialysis bag after centrifugation at 4°C and 10,000 r·min^-1 for 20 min. The extraction buffer diluted five times (removing PVPP) was used for dialysis for 15 to 24 h at low temperature (2°C–4°C). The enzyme solution after dialysis was used for determination of various enzyme activities. Sorbitol dehydrogenase (SDH) activity was determined as described by Rufly and Huber (1983) (link). Sorbitol oxidase (SOX) activity was determined as described by Yamaki and Asakura (1991) (link). Sucrose synthase decomposition direction activity (SS-c) was determined, as described by Huber (1983) (link). Sucrose synthase (SS) and sucrose phosphate synthase (SPS) activities were determined as described by Xu et al. (2012) (link). The activities of acid invertase (AI) and neutral invertase (NI) were determined as described by Merlo and Passera (1991) (link).

Almost 10 hetares of harvested sites of eucalypt plantation were divided into four equal blocks, and three planting patterns were randomly arranged within each block in July 2016. The first planting pattern (E) was the continuous planting of pure Eucalyptus. urograndis (hybrid strain of Eucalyptus urophylla and Eucalyptus grandis) plantations of the third generation at a density of 1,667 plants/ha. The second planting pattern (EC) was the creation of mixed plantations of E. urograndis and Cinnamomum camphora (mixed pattern: inter-row, mixed density: 1667 plants/ha). The third planting pattern (EH) was the creation of mixed plantations of E. urograndis and Castanopsis hystrix (mixed pattern: inter-row, mixed density: 1667 plants/ha). Simultaneously, four unmanaged first-generation E. urophylla plantations in Luogangling Forest Park were selected as controls (CK). Information on forestland preparation, seedling specifications of eucalypts and native trees, and later plantation tending can be found in Xu et al. (2022) (link).
Sixteen mixed topsoil samples in the 10-cm layer were collected in December 2019 by removing the humus and litterfall from four different planting patterns. Soil samples for fungal community structure analysis were preserved with dry ice in centrifuge tubes and transferred to a-80°C freezer as soon as possible. Other soil samples for analyses of soil chemical properties and enzyme activities were stored in a portable refrigerator at 4°C.
The pH of each sample was determined with an electronic pH meter (soil: water, 1:2.5). Soil OM was determined by the potassium dichromate-sulfate colorimetric method (Sims and Haby, 1971 (link)). Total nitrogen (TN) and total phosphorus (TP) were measured with the Kjeldahl method (Tsiknia et al., 2014 (link)) and sodium hydroxide fusion-molybdenum antimony colorimetric method (Liu H. et al., 2017 (link)), respectively. Nitrate nitrogen (NO¯ 3_N) was determined by 2 mol·L⁻¹ KCl leaching-indophenol blue colorimetric method and ammonium nitrogen (NH+ 4_N) was determined by UV spectrophotometry (Lu, 1999 ). Available phosphorus (AP) was measured by the hydrochloric acid-ammonium fluoride extraction-molybdenum antimony colorimetric method (Lu, 1999 ). Soil available zinc (AZn) and available calcium (ACa) were measured by hydrochloric acid extract, atomic absorption spectrophotometry and ammonium acetate exchange, atomic absorption spectrophotometry, respectively (Liu J. et al., 2017 (link)). For soil enzyme activities, acid phosphatase (ACP) was determined by Phenylphosphonium-4-amino-antipyrine colorimetric method (Guan, 1986 ), urease (URE) by alkaline dish diffusion-HCL titration method (Guan, 1986 ), and invertase (INV) by 3,5-Dinitrosalicylic acid colorimetric method (Lu, 1999 ).