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    Community composition of aquatic fungi across the thawing Arctic

    Study sitesWe sampled ponds in the following five sites representing different regional-scale permafrost integrity: Toolik, Alaska, USA; Qeqertarsuaq, Disko Island, Greenland, Denmark; Whapmagoostui-Kuujjuarapik, Nunavik, Quebec, Canada; Abisko, Sweden and Khanymey, Western Siberia, Russia (Online-only Table 1). The aim was to include representatives of different stages of permafrost thaw in order to understand whether responses can be generalized across different geographic and environmental conditions.The sampling site in Alaska is located in a continuous permafrost area, mostly dominated by moss-tundra characterized by tussock-sedge Eriophorum vaginatum and Carex bigelowii, and dwarf-shrub Betula nana and Salix pulchra15. The average depth of the active layer in 2017 was ~50 cm16. Records of surface air temperature from 1989 to 2014 showed no significant warming trend, and there was no significant increase in the mean maximum thickness of the active layer or maximum thaw depth17.The sampling site in Greenland is located in the Blæsedalen Valley, south of Disko Island, and is characterized as a discontinuous permafrost area. From 1991 to 2011, Hollensen et al.18 observed an increase of the mean annual air temperatures of 0.2 °C per year in the area, while Hansen et al.19 highlighted that sea ice cover reduced 50% from 1991 to 2004. Soil temperatures recorded by the Arctic Station from the active layer of the coarse marine stratified sediments also showed an increase over the years18. The sampling site is comprised of wet sedge tundra, and the dominating species are Carex rariflora, Carex aquatilis, Eriophorum angustifolium, Equisetum arvense, Salix arctophila, Tomentypnum nitens and Aulacomnium turgidum20.The Canadian site is located within a sporadic permafrost zone, in a palsa bog, in the valley of Great Whale river, close to the river mouth to Hudson Bay. The vegetation consists of a coastal forest tundra, dominated by the species Carex sp. and Sphagnum sp.21 Since the mid-1990s, there has been a significant increase in the surface air temperature of the region for spring and fall, which has been correlated to a decline of sea ice coverage in Hudson Bay22. This area has experienced an accelerated thawing of the permafrost over the past decades, resulting in the collapse of palsas and the emergence of thermokarst ponds as well as significant peat accumulation21,23. In this specific site, thermokarst ponds at different development stage can be found, from recently emerging to older, mature thermokarstic waterbodies. The stage of the ponds was estimated based on the distance between the pond and the edge of the closest palsa, as well as based on satellite images14. The edges of the emerging ponds reached a maximum of 1 m from the closest palsa and were less than 0.5 m deep, whereas the edges of the developing ponds had a maximum distance of 2–3 m to the closest palsa and were ~1 m deep. Mature ponds were identified based on satellite images and were up to 60 years old.The Swedish site is located in a discontinuous permafrost zone at the Stordalen palsa mire, on an area of collapsed peatland affected by active thermokarst. The region has experienced an increase in mean annual air temperature and active layer thickness since the 1980s, which has been followed by a shift to wetter conditions24. The vegetation found on the surface of the palsa depressions of Stordalen mire is dominated by sedges (Eriophorum vaginatum, Carex sp.) and mosses (Sphagnum sp.)24,25.The Russian site is located in a discontinuous permafrost area in Western Siberia Lowland, near Khanymey village. The sampling site is a flat frozen palsa bog with a peat depth no more than 2 m, and is affected by active thermokarst, resulting in the emergence of thermokarst ponds26,27. The vegetation is dominated by lichens (Cladonia sp.), schrubs (Ledum palustre, Betula nana, Vaccinium vitis-idaea, Andromeda polifolia, Rubus chamaemorus) and mosses (Sphagnum sp.)28.Sample collectionAt all sites, water from the depth of 10 cm was collected from 12 ponds, totaling 60 ponds for the full dataset. Unfiltered water samples were collected for total P analysis. For analyzing Fe, various dissolved anions and cations, DOC concentrations, and perform optical and mass spectrometry analyses on DOM, water was filtered through GF/F glass fiber filters (0.7 μm, 47 mm, Whatman plc, Maidstone, United Kingdom). Moreover, water samples were collected in order to measure GHG (CO2 and CH4) concentrations. Water, detritus and sediment samples were also collected from ponds for fungal community analyses. Water samples were collected and filtered sequentially first through 5 µm Durapore membrane filter (Millipore, Burlington, Massachusetts, USA) and then through a 0.22 µm Sterivex filter (Millipore) to capture fungal cells of different sizes. The samples were filtered until clogging or up to a maximum of 3.5 liters (filtered volume ranging from 0.1 l to 3.5 l). Surface sediments were sampled from each of the ponds, with the exception of the Canadian site, where only one emerging and three developing ponds were sampled for sediments. From the sites in Alaska, Greenland, and Sweden, also detritus samples (dead plant material) were collected. The detritus was washed in the lab using tap water, followed by overnight incubation in 50 ml tap water to induce sporulation. The use of tap water may have added fungal spores to the samples, which should be kept in mind when using the detritus data. After the incubation, the water was filtered through a 5 μm pore size filter and the filter was stored at −20 °C.All the samples for DNA extraction were transported to the laboratory frozen, with the exception of the Alaskan samples, which were freeze dried prior to transportation. The samples transported frozen were freeze dried prior to DNA extraction to ensure similar treatment of all samples. The samples for nutrient and carbon measurements were transported frozen with the exception of samples for DOC and fluorescence analyses, which were transported cooled.Chemical analysesAll chemical, optical and mass spectrometry results are provided in OSF29. DOC quantification was carried out using a carbon analyzer (TOC-L + TNM-L, Shimadzu, Kyoto, Japan). Accuracy was assessed using EDTA at 11.6 mg C/l as a quality control (results were within + − 5%) and the standard calibration range was of 2–50 mg C/l. Fe(II) and Fe(III) were determined by using the ferrozine method30, but instead of reducing Fe(III) with hydroxylamine hydrochloride, ascorbic acid was used31. Absorbance was measured at 562 nm on a spectrophotometer (UV/Vis Spectrometer Lambda 40, Perkin Elmer, Waltham, Massachusetts, USA). The samples were diluted with milli-Q water if needed. The concentration of total P was determined using persulfate digestion32. The anion NO3− was measured on a Metrohm IC system (883 Basic IC Plus and 919 Autosampler Plus; Riverview, Florida, USA). NO3− were separated with a Metrosep A Supp 5 analytical column (250 × 4.0 mm) which was fit with a Metrosep A Supp 4/5 guard column at a flow rate of 0.7 ml/min, using a carbonate eluent (3.2 mM Na2CO3 + 1.0 mM NaHCO3). SO4 was analyzed using Metrohm IC system (883 Basic IC Plus and 919 Autosampler Plus, Riverview), NH4+ spectrophotometrically as described by Solórzano33, and NO2− and DN as in Greenberg et al.34.For the gas analyses, samples from Alaska and Canada were taken as previously described in Kankaala et al.35, except that room air was used instead of N2 for extracting the gas from the water. Shortly, 30 ml of water was taken into 50 ml syringes, which were warmed to room temperature prior to extraction of the gas. To each syringes 0.5 ml of HNO3 and 10 ml of room air was added and the syringes were shaken for 1 min. Finally, the volumes of liquid and gas phases were recorded and the gas was transferred into glass vials that had been flushed with N2 and vacuumed. For Greenland, Sweden and Russia 5 ml of water was taken for the gas samples with a syringe and immediately transferred to 20 ml glass vials filled with N and with 150 µL H2PO4 to preserve the sample. All gas samples were measured using gas chromatography (Clarus 500, Perkin Elmer, Polyimide Uncoated capillary column 5 m x 0.32 mm, TCD and FID detector respectively).Optical analysesIn order to characterize DOM, we recorded the absorbance of DOM using a UV-visible Cary 100 (Agilent Technologies, Santa Clara, California, USA) or a LAMBDA 40 UV/VIS (PerkinElmer) spectrophotometer, depending on sample origin. SUVA254 is a proxy of aromaticity and the relative proportion of terrestrial versus algal carbon sources in DOM36 and was determined from DOC normalized absorbance at 254 nm after applying a corrective factor based on iron concentration37. S289 enlights the importance of fulvic and humic acids related to algal production38 and were determined for the intervals 279–299 nm by performing regression calculations using SciLab v 5.5.2.39We also recorded fluorescence intensity on a Cary Eclipse spectrofluorometer (Agilent Technologies), across the excitation waveband from 250–450 nm (10 nm increments) and emission waveband of 300–560 nm (2 nm increments), or on a SPEX FluoroMax-2 spectrofluorometer (HORIBA, Kyoto, Japan), across the excitation waveband from 250–445 nm (5 nm increments) and emission waveband of 300–600 nm (4 nm increments), depending on sample origin. Based on the fluorometric scans, we constructed excitation-emission matrices (EEMs) after correction for Raman and Raleigh scattering and inner filter effect40. We calculated the FI as the ratio of fluorescence emission intensities at 450 nm and 500 nm at the excitation wavelength of 370 nm to investigate the origin of fulvic acids41. Higher values (~1.8) indicate microbial derived DOM (autochthonous), whereas lower values (~1.2) indicate terrestrial derived DOM (allochthonous), from plant or soil42. HIX is a proxy of the humic content of DOM and was calculated as the sum of intensity under the emission spectra 435–480 nm divided by the peak intensity under the emission spectra 300–445 nm, at an excitation of 250 nm. Higher values of HIX indicate more complex, higher molecular weight, condensed aromatic compounds43,44. BIX emphasizes the relative freshness of the bulk DOM and was calculated as the ratio of emission at 380 nm divided by the emission intensity maximum observed between 420 and 436 nm at an excitation wavelength of 310 nm45. High values ( >1) are related to higher proportion of more recently derived DOM, predominantly originated from autochthonous production, while lower values (0.6–0.7) indicate lower production and older DOM42,44.High resolution mass spectrometry50 ml water samples were collected from each of the ponds and were filtered with a Whatman GF/F filter for mass spectrometry analyses. For each sample, 1.5 ml of water was dried completely with a vacuum drier, and was then re-dissolved in 100 µL 20% acetonitrile, 80% water with three added compounds as internal standards (Hippuric acid, glycyrrhizic acid and capsaicin, all at 400 ppb v/v). Samples were filtered to an autosampler vials and injected at 50 µL onto the column. In order not to overload the detectors, some of the higher concentration samples were injected at a lower volume, to give a maximum of 20 µg carbon loaded.High-performance liquid chromatography – high resolution mass spectrometry (ESI-HRMS) was conducted as described in Patriarca et al.46 using a C18-Evo column (100 × 2.1 mm, 2.6 µm; Phenomenex, Torrance, California, USA). The ESI-HRMS data was averaged from 2–17 min to allow formula assignment to a single mass list. Formulas considered had masses 150–800 m/z, 4–50 carbon (C) atoms, 4–100 hydrogen (H) atoms, 1–40 oxygen (O) atoms, 0–1 nitrogen (N) atoms and 0–1 13 C atoms. Formulas were only considered if they had an even number of electrons, H/C 0.3–2.2 and O/C ≤ 1. The data are presented as a number of assigned formulas and weighted average O/C ratio, H/C ratio and m/z.The analysis was run in two batches (36 and 24 samples per run, respectively) and to the latter run, three samples of Suwannee River fulvic acid (SRFA, reference material) were added. At the moment of the run, the DOC concentration of these samples was unknown, so 50 µL was injected. From high resolution mass spectrometry, average H/C and a number of assigned formulas were obtained. The H/C can be used as a proxy of DOM aliphatic content; higher H/C values (  > 1) indicate more saturated (aliphatic) compounds, whereas values lower than 1 indicate more unsaturated, aromatic molecules47.DNA extraction, ITS2 amplification and sequencingAll samples for molecular analyses (water and detritus filters and sediments) were extracted using DNeasy PowerSoil® kit (Qiagen, Hilden, Germany), following the manufacturer’s recommendations for low input DNA. Extracts were eluted in 100 µl of Milli-Q water and DNA concentrations were measured with Qubit dsDNA HS kit. The fungal ribosomal internal transcribed spacer 2 (ITS2) sequences were amplified using a modified ITS3 Mix2 forward primer from Tedersoo48, named ITS3-mkmix2 CAWCGATGAAGAACGCAG, and a reverse primer ITS4 (equimolar mix of cwmix1 TCCTCCGCTTAyTgATAtGc and cwmix2 TCCTCCGCTTAtTrATAtGc)14. Each sample received a unique combination of primers containing identification tags generated by Barcrawl49. All tags had a minimum base difference of 3 and a length of 8 nucleotides. Both forward and reverse primer tags were extended by two terminal bases (CA) at the ligation site to avoid bias during ligation of sequencing adaptors, and the forward primer tag also had a linker base (T) added to it50. The list of primers and tags is found in Supplementary Table S1. PCR reactions were performed on a final volume of 50 µl, with an input amount of DNA ranging from 0.07 ng to 10 ng, 0.25 µM of each primer, 200 µM of dNTPs, 1U of Phusion™ High-Fidelity DNA Polymerase (Thermo Fisher Scientific, Waltham, Massachusetts, USA), 1X PhusionTM HF Buffer (1X buffer provides 1.5 mM MgCl2, Thermo Fisher Scientifics) and 0.015 mg of BSA. PCR conditions consisted of an initial denaturation cycle at 95 °C for 3 min, followed by 21–35 cycles for amplification (95 °C for 30 sec, 57 °C for 30 sec and 72 °C for 30 sec), and final extension at 72 °C for 10 min. In order to reduce PCR bias, all samples (in duplicates) were first submitted to 21 amplification cycles. In case of insufficient yield, the number of cycles was increased up to 35 cycles (see the records on the number of cycles for each of the samples in Supplementary Table S2).The PCR products were purified with Sera-MagTM beads (GE Healthcare Life Sciences, Marlborough, Massachusetts, USA), visualized on a 1.5% agarose gel and quantified using Qubit dsDNA HS kit. The purified PCR products were randomly allocated into three DNA pools (20 ng of each sample), which were purified with E.Z.N.A.® Cycle-Pure kit (Omega Bio-Tek, Norcross, Georgia, USA). Nine of the samples (4 water, 1 sediment and 4 detritus) were left out of the pools because of too little PCR product, giving a total of 203 samples for sequencing (Online-only Table 1). Negative PCR controls were added to each pool, as well as a mock community sample containing 10 different fragment sizes from the ITS2 region of a chimera of Heterobasidium irregular and Lophium mytilinum, ranging from 142 to 591 bases, as described by Castaño et al.51. The size distribution and quality of all the pools were verified with BioAnalyzer DNA 7500 (Agilent Technologies), and purity was assessed by spectrophotometry (OD 260:280 and 260:230 ratios) using NanoDrop (Thermo Fisher Scientific). The libraries were sequenced at Science for Life Laboratory (Uppsala University, Sweden), on a Pacific Biosciences Sequel instrument II, using 1 SMRT cell per pool. This PacBio technology allows the generation of highly accurate reads ( >99% accuracy) which are produced based on a consensus sequence after a circularization step.Quality filtering of reads, clustering and taxonomy identification of clustersThe sequencing resulted in a total of 1071489 sequences, ranging from 397 to 9184 sequences per sample (average on 2551 sequences per sample). The raw sequences were filtered for quality and clustered using the SCATA pipeline (https://scata.mykopat.slu.se/, accessed on May 19th, 2020). For quality filtering, sequences from each pool were screened for the primers and tags, requiring a minimum of 90% match for the primers and a 100% match for the tags. Reads shorter than 100 bp were removed, as well as reads with a mean quality lower than 20, or containing any bases with a quality lower than 7. After this filtering, 582234 sequences were retained in the data. The sequences were clustered at the species level by single-linkage clustering at a clustering distance of 1.5%, with penalties of 1 for mismatch, 0 for gap open, 1 for gap extension, and 0 for end gaps. Homopolymers were collapsed to 3 and unique genotypes across all pools were removed. For a preliminary taxonomy affiliation of the clusters, hereafter called OTUs (Operational Taxonomic Units), sequences from the UNITE + INSD dataset for Fungi52 database were included in the clustering process. After the clustering, the data included 518128 sequences, divided among 8218 OTUs. For taxonomical annotation, all OTUs with a minimum of ten total reads in the full dataset were included, retaining 3108 OTUs and 498414 sequences in the taxonomical analysis. More

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    Environmental stress leads to genome streamlining in a widely distributed species of soil bacteria

    A. Strain sampling and isolationBradyrhizobium is a commonly occurring genus in soil [21]. Closely related Bradyrhizobium diazoefficiens (previously Bradyrhizobium japonicum) strains were isolated from soil, as previously described [20, 22]. In brief, Bradyrhizobium isolates that formed symbiotic associations with a foundational legume species in the censused region, Acacia acuminata, were isolated from soil sampled along a large region spanning ~300,000 km2 in South West Australia, a globally significant biodiversity hotspot [23]. In total 60 soil samples were collected from twenty sites (3 soil samples per site; Supplementary Fig. S1) and 380 isolates were sequenced (19 isolates per site, 5 or 6 isolates per soil sample, each isolate re-plated from a single colony at least 2 times). Host A. acuminata legume plants were inoculated with field soil in controlled chamber conditions and isolates were cultured on Mannitol Yeast agar plates from root nodules (see [20, 22] for details). A total of 374 strains were included in this study after removing 5 contaminated samples and one sample that was a different Bradyrhizobium species; non- Bradyrhizobium diazoefficiens sample removal was determined from 16S rRNA sequences extracted from draft genome assemblies (Method C) using RNAmmer [24].B. Environmental variation among sampled sitesIn this study, I focus on environmental factors (temperature, rainfall, soil pH and salinity) previously identified to impact either rhizobia growth performance, functional fitness or persistence in soil [25,26,27,28] and where a directionality of rhizobial stress response could be attributed with respect to environmental variation present in the sampled region (i.e. stress occurs at high temperatures, low rainfall, high acidity and high salinity). Each environmental factor was standardised to a mean of 0 and a standard deviation of 1, and pH and rainfall scales were reversed to standardise stress responses directions so that low stress is at low values and high stress is at high values for all factors (Supplementary Fig. S2). Additionally, salinity was transformed using a log transformation (log(x + 0.01) to account for some zeroes) prior to standardisation.C. Isolate sequencing and pangenome annotationIllumina short reads (150 bp paired-end) were obtained and draft genome assemblies were generated using Unicycler from a previous study [29]. Resulting assemblies were of good assembly quality (99.2% of all strains had >95.0% genome completeness score according to BUSCO [30]; Table S1; assembled using reads that contained nominal 0.016 ± 0.00524% non-prokaryotic DNA content across all 374 isolates, according to Kraken classification [31]). Protein coding regions (CDS regions) were identified using Prokka [32] and assembled into a draft pangenome using ROARY [33], which produced a matrix of counts of orthologous gene clusters (i.e. here cluster refers to a set of protein-coding sequences containing all orthologous variants from all the different strains, grouped together and designated as a single putative gene). Gene clusters that occurred in 99% of strains were designated as ‘core genes’ and used to calculate the ‘efficiency of selection’ [34, 35] (measured as dN/dS, Method G.2) and population divergence measured as Fixation Index ‘Fst’, Method H) across each environmental stress factor. The identified gene clusters were then annotated using eggNOG-mapper V2 [36] and the strain by gene cluster matrix was reaggregated using the Seed ortholog ID returned by eggNOG-mapper as the protein identity. Out of the total 2,744,533 CDS regions identified in the full sample of 374 strains, eggNOG-mapper was able to assign 2,612,345 of them to 91,230 unique Seed orthologs. These 91,230 protein coding genes constituted the final protein dataset for subsequent analyses.D. Calculation and statistical analysis of gene richness and pangenome diversity along the stress gradientGene richness was calculated as the total number of unique seed orthologues for each strain (i.e. genome). Any singleton genes that occurred in only a single strain, as well as ‘core’ genes that occurred in every strain (for symmetry, and because these are equally uninformative with respect to variation between strains) were removed, leaving 74,089 genes in this analysis. Gene richness (being count data) was modelled on a negative binomial distribution (glmer.nb function) as a function of each of the four environmental stressors as predictors using the lme4 package in R [37], also accounting for hierarchical structure in the data by including site and soil sample as random effects.To rule out potentially spurious effects of assembly quality (i.e. missed gene annotations due to incomplete draft genomes) on key findings, I confirmed no significant association between gene richness and genome completeness (r = 0.042, p = 0.4224, Fig. S3).Finally, pangenome diversity was calculated as the total number of unique genes that occurred in each soil sample (since multiple strains were isolated from a single soil sample). Pangenome diversity was modelled the same as gene richness, except here soil sample was not included as a random effect.E. Calculation of network and duplication traits for each geneI used the seed orthologue identifier from eggNOG-mapper annotations to query matching genes within StringDB ([38]; https://string-db.org/), which collects information on protein-protein interactions. Out of 91,230 query seed orthologues, 73,126 (~80%) returned a match in STRING. For matching seed orthologue hits, a network was created by connecting any proteins that were annotated as having pairwise interactions in the STRING database using the igraph package in R [39]. Two vertex-based network metrics were calculated for each gene: betweenness centrality, which measures a genes tendency to connect other genes in the gene network, and mean cosine similarity, which is a measure of how much a gene’s links to other genes are similar to other genes.Betweenness centrality was calculated using igraph (functional betweenness). For mean cosine similarity, a pairwise cosine similarity was first calculated between all genes. To do this, the igraph network object was converted into a (naturally sparse yet large) adjacency matrix and the cosSparse function in qlcMatrix in R [40] was used to calculate cosine similarity between all pairs of genes. To obtain an overall cosine similarity trait value for each gene, the average pairwise cosine similarity to all other genes in the network was calculated.Finally, gene duplication level was calculated for each gene as one additional measure of ‘redundancy’, by calculating the average number of gene duplicates found within the same strain. Duplicates were identified as CDS regions with the same Seed orthologue ID.F. Gene distribution modelsTo determine how gene traits predict accessory genome distributions patterns along the stress gradients, I first calculated a model-based metric (hereafter and more specifically a standardised coefficient, ‘z-score’) of the relative tendency of each gene to be found in different soil samples across the four stress gradients (heat, salinity, acidity, and aridity). This was achieved by modelling each gene’s presence or absence in a strain as a function of the four stress gradients, with site and soil sample as a random effect, using a binomial model in lme4 (the structure of the model being the same as the gene richness model, only the response is different). To reduce computational overhead, these models were only run for the set of genes that were used in the gene richness analysis (e.g. after removing singletons and core genes), and which had matching network traits calculated (e.g. they occurred in the STRING database; n = 64,867 genes). Distribution models were run in tandem for each gene using the manyany function in the R package mvabund [41]. Standardised coefficients, or z-scores (coefficient/standard error) indicating the change in the probability of occurrence for each gene across each of the stress gradients were extracted. More negative coefficients correspond to genes that are more likely to be absent in high stress (and vice versa for positive coefficients).To determine how network and duplication traits influence the distribution of genes across the stress gradient, I performed a subsequent linear regression model where the gene’s z-scores was the response and gene traits as predictors. The environmental stress type (i.e. acidity, aridity, heat and salinity) was included as a categorical predictor, and the interaction between stress category and gene function traits were used to infer the influence of gene function traits on gene distributions in a given stress type (see Supplementary Methods S1 for z-score transformation).G. Quantifying molecular signals of natural selection on accessory and core genesTo examine molecular signatures of selection in accessory and core genes, I calculated dN/dS for a subsample of the total pool (n=74,089 genes), which estimates the efficiency of selection [34, 35]. Two major questions relevant to dN/dS that are addressed here require a different gene subsampling approach:(1) Do variable environmental stress responses lead to different dN/dS patterns among accessory genes?Here, I subsampled accessory genes (total accessory gene pool across 374 strains, 74,089) to generate and compare dN/dS among 3 categorical groups, each representing a different level of stress response based on their z-scores (accessory genes that either strongly increase, decrease or have no change in occurrence as stress increases; n = 1000 genes/category; see Supplementary Methods S2 for subsample stratification details).For each gene, sequences were aligned using codon-aware alignment tool, MACSE v2 [42]. dN/dS was estimated by codon within each gene using Genomegamap’s Bayesian model-based approach [43], which is a phylogeny-free method optimised for within bacterial species dN/dS calculation (see Supplementary Methods S3 for dN/dS calculation/summarisation; S9 for xml configuration). The proportion of codons with dN/dS that were credibly less than 1 (purifying selection) and those credibly greater than 1 (positive selection) were analysed, with respect to the genes’ occurrence response to stress. Specifically, I modelled the proportion of codons with dN/dS  1 was overall too low to analyse in this way, so the binary outcome (a gene with any codons with dN/dS  > 1 or not) was modelled using a binomial response model with the response categories as predictors (see Supplementary Methods S4 for details of both models).(2) Does dN/dS among microbial populations vary across environmental stress?Here, I compared the average change in dN/dS in core genes present across all environments at the population level (i.e. all isolates from one soil sample), which is used here to measure the change in the efficiency of selection across each stress gradient. Core genes were used since they occur in all soil samples, allowing a consistent set and sample size of genes to be used in the population-level dN/dS calculation. This contrasts with the previous section, which quantifies gene-level dN/dS on extant accessory genes that intrinsically have variable presence or absence across soil samples. For computational feasibility, 500 core genes were subsampled (total core 1015 genes) and, for each gene, individual strain variants were collated into a single fasta file based on soil sample membership, such that dN/dS could be calculated separately for each gene within each soil sample (i.e. 60 soil samples × 500 genes = 30,000 fasta files). Each fasta file was then aligned in MACSE and dN/dS calculated using the same methodology for accessory genes (Supplementary Method S3). This enabled the average dN/dS in a sample to be associated with soil-sample level environmental stress variables. Specifically, I modelled the mean proportion of codons with dN/dS  1 due to overall rarity of positive selection (average proportion of genes where at least 1 codon with dN/dS  > 1 was ~0.006). This low level of positive selection is expected for core genes which tend to be under strong selective constraint.H. Calculation and analysis of Fixation index (Fst) along stress gradientsUsing the core genome alignment (all SNPs among 1015 core genes) generated previously with ROARY, I computed pairwise environmentally-stratified Fst. Each soil sample (n = 60) was first placed into one of 5 bins based on their distances in total environmental stress space (using all four stress gradients), with the overall goal of generating roughly evenly sized bins such that the environmental similarity of stress was greater within bins than between (see Supplementary Methods S6 and Fig. S4 for clustering algorithm details). Next, fasta alignments were converted to binary SNPs using the adegenet package. Pairwise Fst between all strains (originating from a particular soil sample) within a single bin was calculated using StAMPP in R [44]. For each strain pair, the average of the two stress gradient values was assigned.The relationship between pairwise Fst and the average stress value was evaluated using a linear regression model with each of the four stress values as predictors. Since the analysis uses pairwise data (thus violating standard independence assumptions), the significance of the relationship was determined using a permutation test (see Supplementary Methods S7 for details).I. Chromosomal structure analysis of gene loss patternsTo gain insight into structural variation and test for regional hotspots in gene loss along the chromosome, I mapped each gene’s stress response (i.e. probability of loss or gain indicated by each genes z-score) onto a completed Bradyrhizobium genome (strain ‘36_1’ from the same set of 374 strains (Genbank CP067102.1; [45]). Putative CDS positions on the complete genome were determined using Prokka and annotated with SEED orthologue ID’s using eggNOG-mapper. Matching accessory genes derived from the full set of 374 incomplete draft genomes (n = 74,089) were mapped to positions on the complete genome (6274 matches). The magnitude of gene loss or gain (as measured by their standardised z-scores for each environment from the gene distribution models; see Method F) was then modelled across the genome using a one-dimensional spatial smoothing model. This model was implemented in R INLA [46] (www.r-inla.org), and models a response in a one-dimensional space using a Matern covariance-based random effect. The method uses an integrated nested Laplace approximation to a Bayesian posterior distribution, with a cyclical coordinate system to accommodate circular genomes. The model accounts for spatial non-independence among sites and produces a continuous posterior distribution of average z-score predictions along the entire genome, which was then used to visualise potential ‘hotspots’ of gene loss or gain. The modelling procedure was repeated, instead with gene network traits, such that model predictions of similarity and betweenness could be visualised on the reference chromosome. More

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    Correction: Divergence of a genomic island leads to the evolution of melanization in a halophyte root fungus

    State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, ChinaZhilin Yuan, Huanshen Wei & Long PengResearch Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, ChinaZhilin Yuan, Xinyu Wang, Huanshen Wei & Long PengFungal Genomics Laboratory (FungiG), College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, ChinaIrina S. Druzhinina & Feng CaiDepartment of Food Science, University of Massachusetts, Amherst, MA, USAJohn G. GibbonsState Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops, College of Plant Protection, Fujian Agriculture and Forestry University, Fuzhou, ChinaZhenhui ZhongDepartment of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USAZhenhui ZhongDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, BelgiumYves Van de PeerVIB Center for Plant Systems Biology, Ghent, BelgiumYves Van de PeerCentre for Microbial Ecology and Genomics, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Hatfield, South AfricaYves Van de PeerAdaptive Symbiotic Technologies, University of Washington, Seattle, WA, USARussell J. RodriguezKey Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization at College of Landscape Architecture, Fujian Agriculture and Forestry University, Fuzhou, ChinaZhongjian LiuState Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, ChinaQi Wu & Guohui ShiKey Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, ChinaJieyu WangBeijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, ChinaFrancis M. MartinUniversité de Lorraine, INRAE, UMR Interactions Arbres/Micro-Organismes, Centre INRAE Grand Est Nancy, Champenoux, FranceFrancis M. Martin More

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