Philippa Kaur

Martins, L. C. et al. Poluição atmosférica e atendimentos por pneumonia e gripe em São Paulo, Brasil. Revista de Saúde Pública 36, 88–94 (2002).Article 
PubMed 

Google Scholar 
Goudarzi, G. et al. Health risk assessment on human exposed to heavy metals in the ambient air PM10 in Ahvaz, Southwest Iran. Int. J. Biometeorol. 62, 1075–1083 (2018).Article 
ADS 
PubMed 

Google Scholar 
Makri, A. & Stilianakis, N. I. Vulnerability to air pollution health effects. Int. J. Hygiene Environ. Health 211, 326–336 (2008).Article 

Google Scholar 
Idani, E. et al. Characteristics, sources, and health risks of atmospheric PM10-bound heavy metals in a populated Middle Eastern City. Toxin Rev. 39, 266–274 (2020).Article 

Google Scholar 
Wang, J., Hu, Z., Chen, Y., Chen, Z. & Xu, S. Contamination characteristics and possible sources of PM10 and PM2.5 in different functional areas of Shanghai, China. Atmos. Environ. 68, 221–229 (2013).Article 
ADS 
CAS 

Google Scholar 
Guarnieri, M. & Balmes, J. R. Outdoor air pollution and asthma. Lancet 383, 1581–1592 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, J. O., Thundiyil, J. G. & Stolbach, A. Clearing the air: A review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8, 166–175 (2012).Article 
CAS 
PubMed 

Google Scholar 
Roy, D., Seo, Y.-C., Kim, S. & Oh, J. Human health risks assessment for airborne PM10-bound metals in Seoul, Korea. Environ. Sci. Pollut. Res. 26, 24247–24261 (2019).Article 
CAS 

Google Scholar 
Maesano, C. et al. Impacts on human mortality due to reductions in PM10 concentrations through different traffic scenarios in Paris, France. Sci. The Total. Environ. 698, 134257 (2020).Article 
ADS 
CAS 

Google Scholar 
Maleki, H., Sorooshian, A., Goudarzi, G., Nikfal, A. & Baneshi, M. M. Temporal profile of PM10 and associated health effects in one of the most polluted cities of the world (Ahvaz, Iran) between 2009 and 2014. Aeolian Res. 22, 135–140 (2016).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Medina, S., Le Tertre, A. & Saklad, M. The Apheis project: Air pollution and health—A European information system. Air Qual. Atmos. Heal. 2, 185–198 (2009).Article 

Google Scholar 
Medina, S., Plasencia, A., Ballester, F., Mücke, H. & Schwartz, J. Apheis: Public health impact of PM10 in 19 European cities. J. Epidemiol. Community Heal. 58, 831–836 (2004).Article 
CAS 

Google Scholar 
Pérez-Martínez, P. J., de Fátima Andrade, M. & de Miranda, R. M. Traffic-related air quality trends in São Paulo, Brazil. J. Geophys. Res. Atmos. 120, 6290–6304 (2015).Article 
ADS 

Google Scholar 
Sánchez-Ccoyllo, O. R. et al. Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil. Environ. Monitoring Assess. 149, 241–249 (2009).Article 

Google Scholar 
Ribeiro, H. & de Assunção, J. V. Historical overview of air pollution in São Paulo Metropolitan Area, Brazil: Influence of mobile sources and related health effects. WIT Trans. Built Environ. 52,10 (2001).Bravo, M. A. & Bell, M. L. Spatial heterogeneity of PM10 and O3 in São Paulo, Brazil, and implications for human health studies. J. Air Waste Manag. Assoc. 61, 69–77 (2011).Article 
CAS 
PubMed 

Google Scholar 
De Freitas, E. D., Martins, L. D., da Silva Dias, P. L. & de Fátima Andrade, M. A simple photochemical module implemented in rams for tropospheric ozone concentration forecast in the metropolitan area of Sao Paulo, Brazil: Coupling and validation. Atmos. Environ. 39, 6352–6361 (2005).Article 
ADS 

Google Scholar 
Encalada-Malca, A. A., Cochachi-Bustamante, J. D., Rodrigues, P. C., Salas, R. & López-Gonzales, J. L. A spatio-temporal visualization approach of PM10 concentration data in Metropolitan Lima. Atmosphere 12, 609 (2021).Article 
ADS 

Google Scholar 
do Meio Ambiente, C. N. Institutes the national air quality control programee. Tech. Rep., Official Journal of the Federative Republic of Brazil (1989).do Meio Ambiente, C. N. Sets standards of primary and secondary air quality and even the criteria for acute episodes of air pollution. Tech. Rep., Official Journal of the Federative Republic of Brazil (1990).Artaxo, P. O estado da qualidade do ar no brasil. Work. Pap. WRI Brasil 32 (2021).Costa, A. F., Hoek, G., Brunekreef, B. & Ponce de Leon, A. C. Air pollution and deaths among elderly residents of Sao Paulo, Brazil: An analysis of mortality displacement. Environ. Health Perspectives 125, 349–354 (2017).Article 
CAS 

Google Scholar 
Bravo, M. A., Son, J., De Freitas, C. U., Gouveia, N. & Bell, M. L. Air pollution and mortality in São Paulo, Brazil: Effects of multiple pollutants and analysis of susceptible populations. J. Exposure Sci. Environ. Epidemiol. 26, 150–161 (2016).Article 
CAS 

Google Scholar 
Chiarelli, P. S. et al. The association between air pollution and blood pressure in traffic controllers in Santo André, São Paulo, Brazil. Environ. Res. 111, 650–655 (2011).Article 
CAS 

Google Scholar 
Ventura, L. M. B., de Oliveira Pinto, F., Soares, L. M., Luna, A. S. & Gioda, A. Forecast of daily PM2.5 concentrations applying artificial neural networks and holt-winters models. Air Qual. Atmos. Heal. 12, 317–325 (2019).Article 
CAS 

Google Scholar 
Leão, M. L. P., Zhang, L. & da Silva Júnior, F. M. R. Effect of particulate matter (PM2.5 and PM10) on health indicators: Climate change scenarios in a Brazilian Metropolis. Environ. Geochem. Heal. 44, 1–12 (2022).Habermann, M. & Gouveia, N. Application of land use regression to predict the concentration of inhalable particular matter in São Paulo City, Brazil. Engenharia Sanit. e Ambiental 17, 155–162 (2012).Article 

Google Scholar 
Braga, A. L. F., Pereira, L. A. A., Procópio, M., André, P. A. D. & Saldiva, P. H. D. N. Association between air pollution and respiratory and cardiovascular diseases in Itabira, Minas Gerais State. Brazil. Cadernos de Saúde Pública 23, S570–S578 (2007).Article 
PubMed 

Google Scholar 
Pinto, W. D. P., Reisen, V. A. & Monte, E. Z. Previsão da concentração de material particulado inalável, na região da grande vitória, ES, Brasil, utilizando o modelo sarimax. Engenharia Sanitária e Ambiental 23, 307–318 (2018).Article 

Google Scholar 
Schornobay-Lui, E. et al. Prediction of short and medium term PM10 concentration using artificial neural networks. Manag. Environ. Qual. An Int. J. 30, 414–436 (2018).Neto, P. S. D. M. et al. Neural-based ensembles for particulate matter forecasting. IEEE Access 9, 14470–14490 (2021).Article 

Google Scholar 
Albuquerque Filho, F. S. D., Madeiro, F., Fernandes, S. M., de Mattos Neto, P. S. & Ferreira, T. A. Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks. Química Nova 36, 783–789 (2013).Lei, T. M., Siu, S. W., Monjardino, J., Mendes, L. & Ferreira, F. Using machine learning methods to forecast air quality: A case study in Macao. Atmosphere 13, 1412 (2022).Article 
ADS 
CAS 

Google Scholar 
Yu, T. et al. Study on the regional prediction model of PM2.5 concentrations based on multi-source observations. Atmos. Pollut. Res. 13, 101363 (2022).Article 
CAS 

Google Scholar 
Li, J., Xu, G. & Cheng, X. Combining spatial pyramid pooling and long short-term memory network to predict PM2.5 concentration. Atmos. Pollut. Res. 13, 101309 (2022).Article 
CAS 

Google Scholar 
Cordova, C. H. et al. Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru. Sci. Rep. 11, 1–19 (2021).Article 

Google Scholar 
Plocoste, T., Calif, R. & Jacoby-Koaly, S. Temporal multiscaling characteristics of particulate matter PM10 and ground-level ozone O3 concentrations in caribbean region. Atmos. Environ. 169, 22–35 (2017).Article 
ADS 
CAS 

Google Scholar 
Calif, R. & Schmitt, F. G. Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm. Nonlinear Process. Geophys. 21, 379–392 (2014).Article 
ADS 

Google Scholar 
Hyndman, R. J. & Khandakar, Y. Automatic time series forecasting: The forecast package for r. J. Stat. Softw. 27, 1–22 (2008).Article 

Google Scholar 
Harvey, A. C. Forecasting, structural time series models and the Kalman filter (Cambridge University Press, 1990).Book 
MATH 

Google Scholar 
Zhang, G. P. Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50, 159–175 (2003).Article 
MATH 

Google Scholar 
Liao, T. W. Clustering of time series data—A survey. Pattern Recognit. 38, 1857–1874 (2005).Article 
ADS 
MATH 

Google Scholar 
Bell, M. L., Samet, J. M. & Dominici, F. Time-series studies of particulate matter. Annu. Rev. Public Heal. 25, 247–280 (2004).Article 

Google Scholar 
Hyndman, R. J. & Athanasopoulos, G. Forecasting: Principles and Practice (OTexts, 2018).
Google Scholar 
Box, G. E., Hillmer, S. C. & Tiao, G. C. Analysis and modeling of seasonal time series. in Seasonal analysis of economic time series, 309–344 (NBER, 1978).Sulandari, W., Suhartono, Subanar & Rodrigues, P. C. Exponential smoothing on modeling and forecasting multiple seasonal time series: An overview. Fluctuation Noise Lett. 20, 2130003 (2021).Article 
ADS 

Google Scholar 
Rodrigues, P. C., Awe, O. O., Pimentel, J. S. & Mahmoudvand, R. Modelling the behaviour of currency exchange rates with singular spectrum analysis and artificial neural networks. Stats 3, 137–157 (2020).Article 

Google Scholar 
Sako, K., Mpinda, B. N. & Rodrigues, P. C. Neural networks for financial time series forecasting. Entropy 24, 657 (2022).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Coelho, Leite et al. Statistical and artificial neural networks models for electricity consumption forecasting in the Brazilian industrial sector. Energies 15, 588 (2022).Article 

Google Scholar 
Sulandari, W., Subanar, S., Lee, M. H. & Rodrigues, P. C. Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks. MethodsX 7, 101015 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Sulandari, W. et al. Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks. Energy 190, 116408 (2020).Article 

Google Scholar 
Rodrigues, P. C. & Mahmoudvand, R. The benefits of multivariate singular spectrum analysis over the univariate version. J. Frankl. Inst. 355, 544–564 (2018).Article 
MathSciNet 
MATH 

Google Scholar  More

Genome reduction is associated with a termite comb-associated lifestyleFor our studies, we collected fungus comb samples originating from mounds of Macrotermes natalensis, Odontotermes spp., and Microtermes spp. termites and were able to obtain seven viable Pseudoxylaria cultures (X802 [Microtermes sp.], Mn132, Mn153, X187, X3-2 [Macrotermes natalensis], and X167, X170LB [Odontotermes spp.], Table S1-S3).To test if a fungus comb-associated lifestyle of Pseudoxylaria was reflected in differences at the genome level, we sequenced the genomes of all seven isolates using a combination of paired-end shotgun sequencing (BGISEQ-500, BGI) and long-read sequencing (PacBio sequel, BGI or Oxford Nanopore Technologies, Oxford, UK). In addition, we sequenced the transcriptomes (BGISEQ, BGI) of two isolates (X802, X170LB). Eleven publicly available genomes of free-living Xylaria (Fig. 2A, B) were used as reference genomes (Table S4). Hybrid draft genomes were comprised on average of 33–742 scaffolds with total haploid assembly lengths of 33.2–40.4 Mb, and a high BUSCO completeness of genomes ( > 95 %) with a total number of predicted proteins ranging from 8.8 to 12.1 × 103. The GC content was comparable to reference genomes with 49.7–51.6%. To verify the phylogenetic placement of the isolates, different genetic loci encoding conserved protein sequences (α-actin (ACT), second largest subunit of RNA polymerase (RPB2), β-tubulin (TUB) and the internal transcribed spacer (ITS) were used as genetic markers [7, 13].Fig. 2: Geographic and comparative phylogenomic analysis of termite-associated Pseudoxylaria isolates (strains 1-7) and free-living Xylaria (strains 8–18).A Geographic origins of genome-sequenced free-living Xylaria and termite-associated Pseudoxylaria isolates, B phylogenomic placement based on single-copy ortholog protein sequences, and C comparison of genome assembly length, and numbers of predicted proteins per genome.Full size imagePhylogenies were reconstructed from ITS sequences and three aligned sequence datasets (RPB2, TUB, ACT) using reference sequences of twelve different taxa (Table S4–S7). Consistent with previous findings, all isolates grouped within the monophyletic termite-associated Pseudoxylaria group [9,10,11,12,13], which diverged from the free-living members of the genus Xylaria (Fig. 2B, Figure S1–S4).As our seven isolates covered a larger portion of the previously reported phylogenetic diversity of the termite-associated subgenus, we elaborated on genomic characteristics of our isolates to uncover features of the termite-associated ecology of Pseudoxylaria. Indeed, comparative genome analysis of the South African Pseudoxylaria isolates with publicly available genomes of free-living Xylaria species of similar genome quality revealed significantly reduced genome assembly lengths in Pseudoxylaria with reduced numbers of predicted genes per genome (Table S4). Comparison of the annotated mitochondrial (mt) genomes (Figure S5, Table S8) also indicated that all seven mt genomes were shorter in length (assembly lengths: 18.5–63.8 kbp) compared to the, albeit few, publicly available mitochondrial genomes of free-living species (48.9–258.9 kbp). The reduction in mitochondrial genome size also corresponded to a significantly reduced mean number of annotated genes (7.6) and tRNAs (14.3) in Pseudoxylaria spp. compared to on average 30.0 (annotated genes) and 25.8 (tRNAs) found in free-living species.Analysis of the abundance and composition of transposable elements (TEs), which account for up to 30–35% of the genomes of (endo)parasitic fungi due to the expansion of certain gene families [20, 21], showed that the mean total numbers of TEs across Pseudoxylaria spp. genomes were comparable (1530), but the numbers were reduced compared to free-living Xylaria species (3690) (Table S9). We also identified high variation in the TE composition across genomes (1.5–9.9 %), comparable to what was observed in free-living Xylaria spp. (1.3–8.1 %), with reductions in long terminal repeat retrotransposons (LTRs: Copia and unknown LTRs) in two inverted tandem repeat DNA transposons (TIRs; CACTA, Mutator and hAT). As Pseudoxylaria spp. contained increased numbers of non-ITR transposons of the helitron class and LTRs of the Gypsy class compared to Xylaria strains, we concluded that Pseudoxylaria exhibits no typical features of an (endo)parasitic lifestyle, but that the overall composition and the reduced numbers of TEs could serve as a fingerprint to distinguish the genetically divergent Pseudoxylaria taxa.Repertoire of carbohydrate-active enzymes indicates specialized substrate useAs the fungus comb is mostly composed of partially-digested plant material interspersed with fungal mycelium of the termite mutualist [3], we anticipated that Pseudoxylaria should exhibit features of a substrate specialist similar to the fungal mutualist Termitomyces, which should be reflected in a Carbohydrate-Active enzyme (CAZyme) repertoire distinguishable from  free-living saprophytic Xylaria species [22,23,24]. In particular, numbers and composition of redox-active enzymes (e.g., benzoquinone reductase (EC 1.6.5.6/EC 1.6.5.7), catalase (EC 1.11.1.6), glutathione reductase (EC 1.11.1.9), hydroxy acid oxidase (EC 1.1.3.15), laccase (EC 1.10.3.2), manganese peroxidase (EC 1.11.1.13), peroxiredoxin (EC 1.11.1.15), superoxide dismutase (EC 1.15.1.1), dye-decolorization or unspecific peroxygenase (EC 1.11.2.1), Table S10), which catalyze the degradation of lignin-rich biomass, were expected to differ between free-living strains and substrate specialists [22].Identification of CAZymes using Peptide Pattern Recognition (PPR) revealed that Pseudoxylaria genomes encoded on average a reduced number of CAZymes (mean 264) compared to the free-living taxa in the family Xylaria (mean 367 CAZymes, pANOVA; F = 41.4, p = 3.5 × 10–8, pairwise p = 1.69 × 10–7) (Fig. 3A, B, Figure S6), but similar numbers to those identified in Termitomyces (mean 265, pairwise p = 0.949).Fig. 3: Comparison of carbohydrate-active enzymes (CAZymes) in Xylaria, Pseudoxylaria and the fungal mutualist Termitomyces.A Predicted CAZymes, B Principal Coordinates Analysis (PCoA) of predicted CAZyme families, and C heatmap of representatives CAZyme families in the predicted proteomes of free-living Xylaria, Termitomyces and Pseudoxylaria species.Full size imageOverall, significant differences in the composition of CAZymes were observed [8], most notably in the reduction of auxiliary activity enzymes (AA), carbohydrate esterases (CE), glycosyl hydrolases (GH), and polysaccharide lyases (PL). The most significant reduction was observed in the AA3 family (Fig. 3C), which typically displays a high multigenicity in wood-degrading fungi as many  enzymes of this family catalyze the oxidation of alcohols or carbohydrates with the concomitant formation of hydrogen peroxide or hydroquinones thereby supporting lignocellulose degradation by other AA-enzymes, such as peroxidases (AA2). Similarly, although to a lesser extent, reduced numbers within the related AA1 family were detected, which included oxidizing enzymes like laccases, ferroxidases, and laccase-like multicopper oxidases. Along these lines, glycosyl hydrolases of the GH3 and GH5 family, including enzymes responsible for degradation of cellulose-containing biomass and xylose, were less abundant. We also noted that all Pseudoxylaria lacked homologs of the unspecific peroxygenases (UPO; EC 1.11.2.1), while almost all free-living Xylaria spp. and the fungal symbiont Termitomyces harbored at least one or two copies of similar gene sequences.
Pseudoxylaria shows reduced biosynthetic capacity for secondary metabolite productionA healthy termite colony is engulfed in several layers of social immunity [5, 6], which pose a constant selection pressure on associated and potentially antagonistic microbes. As Pseudoxylaria evolved measures to remain inconspicuously present within the comb environment, we hypothesized that one of the possible adaptations to evade hygiene measures of termites could be reflected in a reduced biosynthetic capability to produce antibiotic or volatile natural products, which often serve as infochemicals triggering defense mechanisms [25,26,27], or as alarm pheromones [4, 28].The biosynthesis of secondary metabolites is encoded in so called Biosynthetic Gene Cluster (BGC) regions. We explored the abundance and diversity of encoded BGCs using FungiSMASH 6.0.0 and manually cross-checked the obtained data set by BLAST to account for possible biases due to varying genome qualities across strains of both groups [29]. Overall, the herein investigated Xylaria genomes harbored on average 90 BGCs per genome, while Pseudoxylaria encoded on average 45 BGCs (Fig. 4, Figure S7). Fig. 4: Similarity network analysis of biosynthetic gene clusters.Comparative analysis of termite associated-associated Pseudoxylaria isolates (strains 1–7, red circles) and free-living Xylaria (strains 8–18, green circles) with BiG-SCAPE 1.0 annotations (blue hexagon) ACR ACR toxin, Alt alternariol, Bio biotin, Chr chromene, Cyt cytochalasins, Cur curvupalide, Dep depiudecin, Fus fusarin, Gri griseofulvin, Mon monascorubin, MSA 6-methylsalicylic acid, Pho phomasetin, Sol solanapyrone, Swa swasionine, Xen xenolozoyenone, Xsp xylasporins, Xyl xylacremolide. Singletons are not shown.Full size imageThe nature and relatedness of the BGCs were analyzed by creating a curated similarity network analysis using BiG-SCAPE 1.0 [30]. Overall, 28 orthologous BGCs were shared across all genomes, including the biosynthesis of polyketides like 6-methylsalicylic acid (MSA), chromenes (Chr) and polyketide-non-ribosomal peptide (PKS-NRPS) hybrids like the cytochalasins (Cyt) [31]. Furthermore, five BGC networks, which were shared by Pseudoxylaria and Xylaria, contained genes encoding natural product modifying dimethylallyltryptophan synthases (DMATS). In contrast, and despite the significant reduction in the biosynthetic capacity within Pseudoxylaria genomes [29], about 29 BGC networks were unique to Pseudoxylaria and thus could possibly relate to the comb-associated lifestyle (Figure S8 and S9). Notably, Pseudoxylaria genomes lacked genes encoding ribosomally synthesized and posttranslationally modified peptides (RiPPs) or halogenases. In comparision, free-living Xylaria spp. harbored at least one sequence encoding a RiPP, and up to two orthologous sequences encoding putative halogenases. In contrast, a reduced average number of terpene synthases (TPS) in Pseudoxylaria (9 TPS) compared to free-living Xylaria (18 TPS) was detected, which included three BGCs encoding TPSs that were unique to Pseudoxylaria.  In comparison, genomes of the fungal mutualist Termitomyces were reported to encode for about 20-25 terpene cyclases, but haboured only about two loci containing genes for a PKS and NRPS each [24].Manual BLAST searches were conducted to identify BGCs that could be putatively assigned to previously isolated metabolites from Pseudoxylaria (vide infra Fig. 7, Figure S8) [32, 33]. Using e.g., the known NRPS-PKS-hybrid cluster sequence ccs (Aspergillus clavatus) of cytochalasins as query, an orthologous BGC, here named cytA, was identified in the cytochalasin-producing strain X802 [34]. Although the putative PKS-NRPS hybrid and CcsA shared 60 % identical amino acids (aa), the sequences of the accessory enzymes were less related to CcsC-G (45–47% identical aa) and the BGC in X802 lacked a gene of a homologue to ccsB. Similarly, five free-living Xylaria species carried orthologous gene loci (Xylaria sp. BCC 1067, Xylaria sp. MSU_SB201401, X. flabelliformis G536, X. grammica EL000614, and X. multiplex DSM 110363) supporting previous isolation reports of cytochalasins with varying structural features. Furthermore, three Pseudoxylaria strains (X187, and closely related Mn153, and Mn132) were found to share a highly similar PKS-NRPS hybrid BGC (99–100 % identical aa, named xya), which likely encodes for the enzymatic production of previously identified xylacremolides [32]. Four Pseudoxylaria strains (X802, Mn132, Mn153, and X187) also shared a BGC (50–98 % amino acid identity) resembling the fog BGC (Aspergillus ruber) [35, 36], which putatively encodes the biosynthetic machinery to produce xylasporin/cytosporin-like metabolites. In this homology search, we also uncovered that fog-like BGC arrangements are likely more common than previously anticipated, as clusters with similar arrangements and identity were also found in genomes of Rosellinia necatrix, Pseudomasariella vexata, Stachybotrys chartarum, and Hyaloscypha bicolor (Fig. 4, Figure S8).A detailed analysis of the fog-like cluster arrangements within Pseudoxylaria genomes revealed – similar to homologs of the ccs cluster – variation in the abundance and arrangement of several accessory genes coding for a cupin protein (pxF), a short chain oxidoreductase (pxB; SDR), and an additional SnoaL-like polyketide cyclase (pxP), which could account for the production of strain-specific structural congeners (vide infra, Fig. 7).Change of nutrient sources causes dedicated transcriptomic changes in Pseudoxylaria
To further solidify our in silico indications of substrate specialization with comb material as preferred substrate and fungus garden as environment, we analyzed Pseudoxylaria growth on different media (PDA, and reduced medium 1/3-PDA) including comb-like agar matrices (wood-rice medium (WRM), agar-agar or 1/3-PDA medium containing lyophilized (dead) Termitomyces sp. T112 biomass (T112, respectively T112-PDA), PDB covering glass-based surface-structuring elements (GB), Table S11–S14).Cultivation of Pseudoxylaria on agar-agar containing lyophilized biomass of Termitomyces (T112) as the sole nutrient source allowed Pseudoxylaria to sustain growth, although to a reduced extent compared to growth on nutrient-rich PDA medium (Table S3). Wood-rice medium (WRM) induced comparable growth rates as observed on PDA and also the appearance of phenotypic stromata.To investigate the influence of these growth conditions on the transcriptomic level, we harvested RNA from vegetative mycelium after growth on comb-like media (WRM, T112, T112-PDA, and GB), PDA, and reduced medium 1/3-PDA (Fig. 5A). The most significant transcript changes (normalized to data obtained from growth on PDA) were observed for genes coding for specific CAZymes including several redox active enzymes (Fig. 5B). The 30 most variable transcripts coded for specific glycoside hydrolases (GH), lytic polysaccharide monooxygenases (AA), ligninolytic enzymes, and a glycoside transferase (GT). Similarly, chitinases (CHT2; CHT4; CHI2; CHI4) were upregulated (up to 243-fold on T112) under almost all conditions compared to PDA, but some of these specific transcript changes were exclusive to growth on Termitomyces biomass or artificial comb material (WRM) suggesting the ability to regulate and increase chitin metabolism if necessary [37].Fig. 5: Transcriptomic analysis of Pseudoxylaria sp. X802 in dependence of growth conditions.A Representative pictures of Pseudoxylaria sp. X802 growing on PDA, PDB on glass beads (GB), wood-rice medium (WRM), and agar-agar medium containing lyophilized Termitomyces sp. T112 biomass (T112). B Heatmap of the most variable transcripts coding for CAZymes (red), redox enzymes (orange), secondary metabolite-related core genes (green), and more specifically on key genes within the boundaries of cytochalasin (turquoise) and xylasporin/cytosporin BGCs (blue). RNA was obtained from vegetative mycelium after growth on PDA, reduced medium (1/3-PDA), PDB on glass beads (GB), wood-rice medium (WRM), 1/3-PDA-medium enriched with Termitomyces sp. T112 biomass (T112-PDA) and agar-agar medium containing lyophilized Termitomyces biomass (T112). Transcript counts are shown as log10 transformed transcripts per million (top; TPM). Significance of the changes in transcript counts are compared to control (X802 grown on PDA) and depicted in log-10 transformed p values.Full size imageWhen X802 was grown on T112 (agar matrix containing lyophilized Termitomyces sp. T112 biomass), we observed a >400-fold increase in the expression of transcripts encoding glycoside hydrolases in the GH43 family, GH7 (~140-fold), GH3, and GH64 (5–12-fold). Similarly, transcripts for a putative mannosyl-oligosaccharide-α-1,2-mannosidase (MNS1B; 8.2-fold), chitinase CHT4 (2.9-fold), β-glucosidase BGL4 (5.7-fold), and copper-dependent lytic polysaccharide monooxygenase AA11 (1.6-fold) were significantly upregulated. Growth on WRM (wood-rice medium) or T112 (Termitomyces sp. T112 biomass) also caused a significant upregulation of genes coding for glycoside transferase GT2, glycoside hydrolases GH15, GH3, and aldehyde oxidase AOX1, which indicated the ability to expand the degradation portfolio if necessary. Along these lines, specific transcript levels were reduced when X802 was grown on T112, in particular class II lignin-modifying peroxidases (AA2), carbohydrate-binding module family 21 (CBM21), multicopper oxidases (AA1), secreted β-glucosidases (SUN4), and glycoside hydrolases GH16, and GH128.When the fungus was challenged with lignocellulose-rich WRM medium, higher transcript levels putatively assigned to glutathione peroxidase (GXP2), superoxide dismutase (SOD2), and laccases (LCC5) were observed, which indicated that despite the reduced wood-degrading capacity, Pseudoxylaria activates available enzymatic mechanisms to degrade the provided material and respond to the resulting oxidative stress. Cultivation on GB (glass-based surfaces covered in liquid PD broth) influenced the expression of certain genes coding for glycoside hydrolases (GH64, GH76, GH72, GH128, BGL4) and lytic polysaccharide monooxygenases (AA1, AA2, AA11), presumably enabling the fungus to utilize soluble carbohydrates.To test the hypothesis that the presence of Termitomyces biomass stimulates secondary metabolite production in Pseudoxylaria to eventually displace the mutualist, we also analyzed changes in the transcript levels of core BGC genes that encode the production of bioactive secondary metabolites. Overall, only slight transcript variations were detectable within the  most variable expressed genes. (Fig. 5B). Cultivation on GB, WRM, and T112 media caused lower transcript levels of genes coding for terpene synthase TC1, polyketide synthases (PKS7, PKS8), and the NRPS-like1, while an upregulation of NRPS-like2 on WRM (2.5-fold), and of PKS7 (1.7-fold) on reduced 1/3-PDA medium was observed.Transcript levels of core genes within BGCs assigned to cytochalasines (cyt) or xylasporins/cytosporins (px), e.g., remained nearly constant, while minor transcript level variations of neighboring genes and reduced transcript levels for pxI (flavin-dependent monooxygenase), pxH (ABBA-type prenyltransferase), pxF (cupin fold oxidoreductase), and pxJ (short-chain dehydrogenase) were detectable. Hence, it was concluded that the presence of Termitomyces biomass only weakly triggers secondary metabolite production in general, but varying transcript levels coding for decorating enzymes could cause substantial structural alterations within the produced natural product composition. It was also notable that transcript levels of the terpene synthase TC1 were downregulated, which could cause a reduced production level of specific volatiles.
Pseudoxylaria antagonizes Termitomyces growth and metabolizes fungal biomassThe growth behavior of Pseudoxylaria isolates was also analyzed in co-culture assays with Termitomyces. As expected from prior studies, both fungi showed reduced growth when co-cultured on agar plates, often causing the formation of zones of inhibition (ZOI) between the fungal colonies (Fig. 6A–D, Table S11–S14) [7]. When fungus-fungus co-cultures were maintained for longer than two weeks on agar plates, Pseudoxylaria started to overcome the ZOI and overgrew Termitomyces via the extension of aerial mycelium. The observation was even more pronounced when co-cultures were performed on wood-rice medium (WRM), where Pseudoxylaria remained the only visible fungus after two weeks.Fig. 6: Co-cultivation of Pseudoxylaria sp. X170LB and Termitomyces sp. T112 and results of isotope fractionation experiments.Representative pictures of fungal growth and co-cultivation of A Termitomyces sp. T112, B Pseudoxylaria sp. X170LB, C co-culture of Pseudoxylaria sp. X802 and Termitomyces sp. T153 exhibiting a ZOI, in which X802 overgrowths T153 in proximity to the interaction zone (red arrow), and D Pseudoxylaria sp. X802 growing on the surface of a living Termitomyces sp. T153 culture. E, F Shown is the relative change in the carbon isotope pattern (δ13C values, ± standard deviation, with n = 3) of lipid and carbohydrate fractions isolated from fungal biomass of Termitomyces sp. T112, Pseudoxylaria sp. X170LB, and Pseudoxylaria sp. X170LB cultivated on vegetative Termitomyces sp. T112 biomass (T112ǂ), or on lyophilized Termitomyces sp. T112 biomass (T112). Fungal strains were grown on E medium with natural 13C abundance and F medium artificially enriched in 13C content.Full size imageTo verify whether Pseudoxylaria consumes Termitomyces or even partially degrades specific metabolites present within the fungal biomass, we pursued stable isotope fingerprinting commonly used to analyse trophic relations [38, 39]. This diagnostic method relies on measurable changes in the bulk stable isotope composition, because biosynthetic enzymes preferentially convert lighter metabolites enriched in 12C compared to their heavier 13C-enriched congeners. This intrinsic kinetic isotope effect results in an overall change in the 13C/12C ratio of the respective educts and products, in particular in biomarkers such as phospholipid fatty acids, carbohydrates, and amino acids. Using this isotope enrichment effect, we determined the natural trophic isotope fractionation of 13C in lipids and carbohydrates produced by Termitomyces sp. T112 and Pseudoxylaria sp. X170LB. For clearer differentiation, both fungi were cultivated on PDA medium containing naturally abundant 13C/12C, Fig. 6E) and on PDA medium enriched with 13C-glucose (Fig. 6F). Lipids and carbohydrates were isolated from mycelium harvested after 21 days (Fig. 6E, Table S15).Analysis of fungal carbohydrate and lipid-rich metabolite fractions by Elemental Analysis-Isotope Ratio Mass Spectrometry (EA-IRMS) [40, 41] uncovered that under normal growth conditions (full medium), Termitomyces sp. T112 and Pseudoxylaria sp. X170LB showed only a slight negative trophic fractionation of stable carbon isotopes (δ13C/12C ratio (expressed as δ13C values [‰]), Fig. 6F) within the carbohydrate fractions (T112: −1.2 ‰; for X170LB: −1.3 ‰), and expectedly a stronger depletion in the lipid fraction (T112: −6.7 ‰, and less pronounced for X170LB: −3.1 ‰). To determine if Pseudoxylaria metabolizes Termitomyces biomass, the isotope pattern of metabolites derived from Pseudoxylaria thriving on living biomass of Termitomyces (T112ǂ) was analysed next. Here, an overall positive carbon isotope (13C/12C) fractionation by approximately +0.6 ‰ relative to the control medium was detectable, while the δ13C values of lipids remained largely unchanged (Fig. 6F, Table S15). These results suggested that Pseudoxylaria might pursue a preferential uptake of Termitomyces-derived carbohydrates.In a last experiment, Pseudoxylaria was grown on lyophilized (dead) Termitomyces biomass (T112) as sole food source. In this experiment, the isotope fingerprint showed converging δ13C values of −1.9 ‰ (relative to the media) for both carbohydrate and lipid fractions, which indicated that Pseudoxylaria is able to simultaneously metabolize and cycle carbohydrates as well as lipids resulting in the equilibration of isotopic levels between carbohydrates and lipids. Thus, it was concluded that in nature, Pseudoxylaria likely harvests nutrients firstly from vegetative Termitomyces, and then—if possible—subsequently degrades dying or dead mycelium.
Pseudoxylaria produces antimicrobial secondary metabolitesBased on the observation that Pseudoxylaria antagonizes growth of Termitomyces, we questioned if the formation of a ZOI might be caused by the secretion of Pseudoxylaria-derived antimicrobial metabolites [26, 42]. Thus, we performed an ESI(+)-HRMS/MS based metabolic survey using the web-based platform “Global Natural Product Social Molecular Networking” (GNPS) [43] to correlate the encoded biosynthetic repertoire of Pseudoxylaria with secreted metabolites.A partial similar metabolic repertoire across the six analyzed strains was detectable and allowed us to match some of the detectable chemical features and previously isolated metabolites to the predicted shared BGCs, such as antifungal and histone deacetylase inhibitory xylacremolides (Xyl; X187/Mn132) [32, 33], pseudoxylaramides (Psa; X187/Mn132) [32], antibacterial pseudoxylallemycins (Psm; X802/OD126) [18], xylasporin/cytosporins (Xsp; X802/OD126/X187/Mn132) [36], and cytotoxic cytochalasins (X802/OD126) (Fig. 7A and B) [18].Fig. 7: Comparative metabolomic analysis of six Pseudoxylaria strains (OD126 (red), Mn132 (orange), X170 (black), X187 (green), X3.2 (yellow) and X802 (blue)).A Overview of the GNPS network. Identified metabolite clusters xylacremolides (Xyl; X187/Mn132) [32, 33], pseudoxylaramides [32] (Psa; X187/Mn132), pseudoxylallemycins (Psm; X802/OD126) [18], xylasporin/cytosporins (Xsp; X802/OD126/X187/Mn132) and cytochalasins (X802/OD126) [18]. B xylasporin/cytosporin-related cluster formed by nodes from X802 (blue), OD126 (red), X187 (green) and Mn132 (orange). C Chemical structures of natural products isolated from Pseudoxylaria species and related compounds. Red box highlights proposed structures of isolated xylasporin G and I in this study.Full size imageA cluster that contained MS2 signals of molecular ions assigned to the cytosporin/xylasporin family, which was shared by at least four strains, caught our attention as a certain degree of structural diversity of xylasporin/cytosporin family was predicted from the comparison of their respective BGCs. The assigned nodes of this GNPS cluster split into two subclusters with only very little overlap between both regions. Analysis of the mass fragment shifts suggested that both subclusters belong to two different families of xylasporin/cytosporin congeners (Figure S9). To verify these deductions, we pursued an MS-guided purification of xylasporin/cytosporins from chemical extracts of Pseudoxylaria sp. X187, which yielded xylasporin G (3.23 mg, pale-yellow solid) and xylasporin I (1.75 mg, pale-yellow solid). The sum formulas of xylasporin G and xylasporin I were determined to be C17H22O5 (calcd. for [M + H]+ C17H23O5+ = 307.1540, found 307.15347, −1.726 ppm) and C17H24O5 (calcd. for [M + H]+ C17H25O5+ = 309.1697, found 309.1691, −1.68 ppm) by ESI-(+)-HRMS and were predicted to have six degrees of unsaturation (Fig. 7B, Figure S10, Table S16-S17). Planar structures were deduced by comparative 1D and 2D NMR analyses, which revealed the presence of an unsaturated polyketide chain that matched the unsaturation degree and the anticipated structural variation from cytosporins (Fig. 7C, Figure S11-S25).To evaluate if Pseudoxylaria-derived culture extracts and produced natural products (e.g., cytochalasins) are responsible for the observed antimicrobial activity, standardized antimicrobial activity assays were performed (Table S17, S18 and Figure S26). As neither culture extracts nor single compounds exhibited significant antimicrobial activity, they could not be held fully accountable for the antagonistic behavior in co-cultures. Thus, we hypothesized that the observed ZOI might be caused by yet unknown effects like nutrient depletion or bioactive enzymes.
Pseudoxylaria has a negative impact on the fitness of insect larvaeDue to the production of structurally diverse and weakly antimicrobial secondary metabolites, we questioned if mycelium of Pseudoxylaria exhibits intrinsic insecticidal or other insect-detrimental activities, which could discourage or ward off grooming behavior of termite workers. Due to the technical challenges associated with behavioral studies of termites, we evaluated instead the effect of Pseudoxylaria biomass on Spodoptera littoralis, a well-established insect model system and a destructive agricultural lepidopterous pest [44, 45]. When S. littoralis larvae were fed with mycelium-covered agar plugs of Pseudoxylaria sp. X802, a clear decrease of the relative growth rate (RGR) and decline in survival was observed (Fig. 8: treatment D (green), Table S19, S20) compared to feeding with untreated agar plugs (treatment A (black)). In comparison, when larvae were fed with agar plugs covered with the fungal mutualist Termitomyces sp. T153 (treatment B (blue)) an increased growth rate of larvae was observed.Fig. 8: Effect of Termitomyces sp. T153 and Pseudoxylaria sp. X802 mycelia on the relative growth rate and survival of S. littoralis larvae.Insects were fed with either A PDA, B PDA agar plug covered with vegetative Termitomyces sp. T153, C PDA agar plug from which vegetative Termitomyces sp. T153 was removed prior to feeding, D PDA agar plug covered with vegetative Pseudoxylaria sp. X802 mycelium, and E PDA agar plug from which vegetative Pseudoxylaria sp. X802 mycelium was removed prior to feeding. All experiments were performed with 25 replicates per treatment, a duration of 10 days, and larval weights and survival rates were recorded every day. Statistical significances were determined using ANOVA on ranks (p  More

Study populations and sequencing strategyDNA libraries were prepared for 1261 D. sylvestris individuals from 115 populations (5–20 individuals per population) under a modified protocol49 of the Illumina Nextera DNA library preparation kit (Supplementary Methods S1.1, Supplementary Data 1). Individuals were indexed with unique dual-indexes (IDT Illumina Nextera 10nt UDI – 384 set) from Integrated DNA Technologies Co, to avoid index-hopping50. Libraries were sequenced (150 bp paired-end sequencing) in four lanes of an Illumina NovaSeq 6000 machine at Novogene Co. This resulted in an average coverage of ca. 2x per individual. Sequenced individuals were trimmed for adapter sequences (Trimmomatic version 0.3551), mapped (BWA-MEM version 0.7.1752,53) against a reference assembly54 (ca. 440 Mb), had duplicates marked and removed (Picard Toolkit version 2.0.1; http://broadinstitute.github.io/picard), locally realigned around indels (GATK version 3.555), recalibrated for base quality scores (ATLAS version 0.956) and had overlapping read pairs clipped (bamUtil version 1.0.1457) (Supplementary Methods S1.1). Population genetic analyses were performed on the resultant BAM files via genotype likelihoods (ANGSD version 0.93358 and ATLAS versions 0.9–1.056), to accommodate the propagation of uncertainty from the raw sequence data to population genetic inference.Population genetic structure and biogeographic barriersTo investigate the genetic structure of our samples (Fig. 2A, Supplementary Fig. S2), we performed principal component analyses (PCA) on all 1261 samples (“full” dataset) via PCAngsd version 0.9859, following conversion of the mapped sequence data to ANGSD genotype likelihoods in Beagle format (Supplementary Methods S1.2). To visualise PCA results in space (Supplementary Fig. S4), individuals’ principal components were projected on a map, spatially interpolated (linear interpolation, akima R package version 0.6.260) and had the first two principal components represented as green and blue colour channels. Given that uneven sampling can bias the inference of structure in PCA, PCA was also performed on a balanced dataset comprising a common, down-sampled size of 125 individuals per geographic region (“balanced” dataset; Fig. 2B, Supplementary Fig. S3; Supplementary Methods S1.2; Supplementary Data 1). Individual admixture proportions and ancestral allele frequencies were estimated using PCAngsd (-admix model) for K = 2–6, using the balanced dataset to avoid potential biases related to imbalanced sampling22,23 and an automatic search for the optimal sparseness regularisation parameter (alpha) soft-capped to 10,000 (Supplementary Methods S1.2). To visualise ancestry proportions in space, population ancestry proportions were spatially interpolated (kriging) via code modified from Ref. 61 (Supplementary Fig. S5).To test if between-lineage admixture underlies admixture patterns inferred by PCAngsd or if the data is better explained by alternative scenarios such as recent bottlenecks, we used chromosome painting and patterns of allele sharing to construct painting palettes via the programmes MixPainter and badMIXTURE (unlinked model)28 and compared this to the PCAngsd-inferred palettes (Fig. 2B, C; Supplementary Methods S1.2). We referred to patterns of residuals between these palettes to inform of the most likely underlying demographic scenario. For assessing Alpine–Balkan palette residuals (and hence admixture), 65 individuals each from the French Alps (inferred as pure Alpine ancestry in PCAngsd), Monte Baldo (inferred with both Alpine and Balkan ancestries in PCAngsd) and Julian Alps (inferred as pure Balkan ancestry in PCAngsd) were analysed under K = 2 in PCAngsd and badMIXTURE (Fig. 2C). For assessing Apennine–Balkan admixture, 22 individuals each from the French pre-Alps (inferred as pure Apennine ancestry in PCAngsd), Tuscany (inferred with both Apennine and Balkan ancestries in PCAngsd) and Julian Alps (inferred as pure Balkan ancestry in PCAngsd) were analysed under K = 2 in PCAngsd and badMIXTURE.To construct a genetic distance tree (Supplementary Fig. S1), we first calculated pairwise genetic distances between 549 individuals (5 individuals per population for all populations) using ATLAS, employing a distance measure (weight) reflective of the number of alleles differing between the genotypes (Supplementary Methods S1.2; Supplementary Data 1). A tree was constructed from the resultant distance matrix via an initial topology defined by the BioNJ algorithm with subsequent topological moves performed via Subtree Pruning and Regrafting (SPR) in FastME version 2.1.6.162. This matrix of pairwise genetic distances was also used as input for analyses of effective migration and effective diversity surfaces in EEMS25. EEMS was run setting the number of modelled demes to 1000 (Fig. 2A, Supplementary Fig. S8). For each case, ten independent Markov chain Monte Carlo (MCMC) chains comprising 5 million iterations each were run, with a 1 million iteration burn-in, retaining every 10,000th iteration. Biogeographic barriers (Fig. 2A, Supplementary Fig. S7) were further identified via applying Monmonier’s algorithm24 on a valuated graph constructed via Delauney triangulation of population geographic coordinates, with edge values reflecting population pairwise FST; via the adegenet R package version 2.1.163. FST between all population pairs were calculated via ANGSD, employing a common sample size of 5 individuals per population (Supplementary Fig. S6; Supplementary Methods S1.2; Supplementary Data 1). 100 bootstrap runs were performed to generate a heatmap of genetic boundaries in space, from which a weighted mean line was drawn (Supplementary Fig. S7). All analyses in ANGSD were performed with the GATK (-GL 2) model, as we noticed irregularities in the site frequency spectra (SFS) with the SAMtools (-GL 1) model similar to that reported in Ref. 58 with particular BAM files. All analyses described above were performed on the full genome.Ancestral sequence reconstructionTo acquire ancestral states and polarise site-frequency spectra for use in the directionality index ψ and demographic inference, we reconstructed ancestral genome sequences at each node of the phylogenetic tree of 9 Dianthus species: D. carthusianorum, D. deltoides, D. glacialis, D. sylvestris (Apennine lineage), D. lusitanus, D. pungens, D. superbus alpestris, D. superbus superbus, and D. sylvestris (Alpine lineage). This tree topology was extracted from a detailed reconstruction of Dianthus phylogeny based on 30 taxa by Fior et al. (Fior, Luqman, Scharmann, Zemp, Zoller, Pålsson, Gargano, Wegmann & Widmer; paper in preparation) (Supplementary Methods S1.3). For ancestral sequence reconstruction, one individual per species was sequenced at medium coverage (ca. 10x), trimmed (Trimmomatic), mapped against the D. sylvestris reference assembly (BWA-MEM) and had overlapping read pairs clipped (bamUtil) (Supplementary Methods S1.3). For each species, we then generated a species-specific FASTA using GATK FastaAlternateReferenceMaker. This was achieved by replacing the reference bases at polymorphic sites with species-specific variants as identified by freebayes64 (version 1.3.1; default parameters), while masking (i.e., setting as “N”) sites (i) with zero depth and (ii) that didn’t pass the applied variant filtering criteria (i.e., that are not confidently called as polymorphic; Supplementary Methods S1.3). Species FASTA files were then combined into a multi-sample FASTA. Using this, we probabilistically reconstructed ancestral sequences at each node of the tree via PHAST (version 1.4) prequel65, using a tree model produced by PHAST phylofit under a REV substitution model and the specified tree topology (Supplementary Methods S1.3). Ancestral sequence FASTA files were then generated from the prequel results using a custom script.Expansion signalTo calculate the population pairwise directionality index ψ for the Alpine lineage, we utilised equation 1b from Peter and Slatkin (2013)31, which defines ψ in terms of the two-population site frequency spectrum (2D-SFS) (Supplementary Methods S1.4). 2D-SFS between all population pairs (10 individuals per population; Supplementary Data 1) were estimated via ANGSD and realSFS66 (Supplementary Methods S1.4), for unfolded spectra. Unfolding of spectra was achieved via polarisation with respect to the ancestral state of sites defined at the D. sylvestris (Apennine lineage) – D. sylvestris (Alpine lineage) ancestral node. Correlation of pairwise ψ and (great-circle) distance matrices was tested via a Mantel test (10,000 permutations). To infer the geographic origin of the expansion (Fig. 3), we employed a time difference of arrival (TDOA) algorithm following Peter and Slatkin (2013);31 performed via the rangeExpansion R package version 0.0.0.900031,67. We further estimated the strength of the founder of this expansion using the same package.Demographic inferenceTo evaluate the demographic history of D. sylvestris, a set of candidate demographic models was formulated. To constrain the topology of tested models, we first inferred the phylogenetic tree of the three identified evolutionary lineages of D. sylvestris (Alpine, Apennine and Balkan) as embedded within the larger phylogeny of the Eurasian Dianthus clade (note that the phylogeny from Fior et al. (Fior, Luqman, Scharmann, Zemp, Zoller, Pålsson, Gargano, Wegmann & Widmer; paper in preparation) excludes Balkan representatives of D. sylvestris). Trees were inferred based on low-coverage whole-genome sequence data of 1–2 representatives from each D. sylvestris lineage, together with whole-genome sequence data of 7 other Dianthus species, namely D. carthusianorum, D. deltoides, D. glacialis, D. lusitanus, D. pungens, D. superbus alpestris and D. superbus superbus, that were used to root the D. sylvestris clade (Supplementary Methods S1.5). We estimated distance-based phylogenies using ngsDist68 that accommodates genotype likelihoods in the estimation of genetic distances (Supplementary Methods S1.5). Genetic distances were calculated via two approaches: (i) genome-wide and (ii) along 10 kb windows. For the former, 110 bootstrap replicates were calculated by re-sampling over similar-sized genomic blocks. For the alternative strategy based on 10 kb windows, window trees were combined using ASTRAL-III version 5.6.369 to generate a genome-wide consensus tree accounting for potential gene tree discordance (Supplementary Methods S1.5). Trees were constructed from matrices of genetic distances from initial topologies defined by the BioNJ algorithm with subsequent topological moves performed via Subtree Pruning and Regrafting (SPR) in FastME version 2.1.6.162. We rooted all resultant phylogenetic trees with D. deltoides as the outgroup70. Both approaches recovered a topology with the Balkan lineage diverging prior to the Apennine and Alpine lineages (Supplementary Fig. S9). This taxon topology for D. sylvestris was supported by high ASTRAL-III posterior probabilities ( >99%), ASTRAL-III quartet scores ( >0.5) and bootstrap values ( >99%). Topologies deeper in the tree were less well-resolved (with quartet scores More

Semin, G. R., Scandurra, A., Baragli, P., Lanatà, A. & D’Aniello, B. Inter- and intra-species communication of emotion: Chemosignals as the neglected medium. Animals 9, 887 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Désiré, L., Boissy, A. & Veissier, I. Emotions in farm animals: A new approach to animal welfare in applied ethology. Behav. Processes 60, 165–180 (2002).Article 
PubMed 

Google Scholar 
Briefer, E. F. & Le Comber, S. Vocal expression of emotions in mammals: mechanisms of production and evidence. J. Zool. 288, 1–20 (2012).Article 

Google Scholar 
Jardat, P. & Lansade, L. Cognition and the human–animal relationship: a review of the sociocognitive skills of domestic mammals toward humans. Anim. Cogn. 25, 369–384 (2022).Article 
PubMed 

Google Scholar 
Galvan, M. & Vonk, J. Man’s other best friend: domestic cats (F. silvestris catus) and their discrimination of human emotion cues. Anim. Cogn. 19, 193–205 (2016).Nawroth, C., Albuquerque, N., Savalli, C., Single, M. S. & McElligott, A. G. Goats prefer positive human emotional facial expressions. R. Soc. Open Sci. 5, 180491 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Proops, L., Grounds, K., Smith, A. V. & McComb, K. Animals remember previous facial expressions that specific humans have exhibited. Curr. Biol. 28, 1428-1432.e4 (2018).Article 
CAS 
PubMed 

Google Scholar 
Smith, A. V., Proops, L., Grounds, K., Wathan, J. & McComb, K. Functionally relevant responses to human facial expressions of emotion in the domestic horse (Equus caballus). Biol. Lett. 12, 20150907 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., D’Ingeo, S., Fornelli, S. & Quaranta, A. Lateralized behavior and cardiac activity of dogs in response to human emotional vocalizations. Sci. Rep. 8, 77 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., D’Ingeo, S. & Quaranta, A. Orienting asymmetries and physiological reactivity in dogs’ response to human emotional faces. Learn. Behav. 46, 574–585 (2018).Article 
PubMed 

Google Scholar 
Smith, A. V. et al. Domestic horses (Equus caballus) discriminate between negative and positive human nonverbal vocalisations. Sci. Rep. 8, 13052 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Trösch, M. et al. Horses categorize human emotions cross-modally based on facial expression and non-verbal vocalizations. Animals 9, 862 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Quaranta, A., D’ingeo, S., Amoruso, R. & Siniscalchi, M. Emotion recognition in cats. Animals 10, 1107 (2020).Nakamura, K., Takimoto-Inose, A. & Hasegawa, T. Cross-modal perception of human emotion in domestic horses (Equus caballus). Sci. Rep. 8, 8660 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Albuquerque, N. et al. Dogs recognize dog and human emotions. Biol. Lett. 12, 20150883 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Briefer, E. F. Vocal contagion of emotions in non-human animals. Proc. R. Soc. B Biol. Sci. 285 (2018).Sabiniewicz, A., Tarnowska, K., Świątek, R., Sorokowski, P. & Laska, M. Olfactory-based interspecific recognition of human emotions: Horses (Equus ferus caballus) can recognize fear and happiness body odour from humans (Homo sapiens). Appl. Anim. Behav. Sci. 230, 105072 (2020).Article 

Google Scholar 
Baba, C., Kawai, M. & Takimoto-Inose, A. Are horses (Equus caballus) sensitive to human emotional cues?. Animals 9, 630 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Brennan, P. A. & Kendrick, K. M. Mammalian social odours: attraction and individual recognition. Philos. Trans. R. Soc. B Biol. Sci. 361, 2061–2078 (2006).Saslow, C. A. Understanding the perceptual world of horses. Appl. Anim. Behav. Sci. 78, 209–224 (2002).Article 

Google Scholar 
Péron, F., Ward, R. & Burman, O. Horses (Equus caballus) discriminate body odour cues from conspecifics. Anim. Cogn. 17, 1007–1011 (2014).Article 
PubMed 

Google Scholar 
Krueger, K. & Flauger, B. Olfactory recognition of individual competitors by means of faeces in horse (Equus caballus). Anim. Cogn. 14, 245–257 (2011).Article 
PubMed 

Google Scholar 
Boissy, A., Terlouw, C. & Le Neindre, P. Presence of cues from stressed conspecifics increases reactivity to aversive events in cattle: evidence for the existence of alarm substances in urine. Physiol. Behav. 63, 489–495 (1998).Article 
CAS 
PubMed 

Google Scholar 
Vieuille-Thomas, C. & Signoret, J. P. Pheromonal transmission of an aversive experience in domestic pig. J. Chem. Ecol. 18, 1551–1557 (1992).Article 
CAS 
PubMed 

Google Scholar 
Siniscalchi, M., D’Ingeo, S. & Quaranta, A. The dog nose ‘KNOWS’ fear: Asymmetric nostril use during sniffing at canine and human emotional stimuli. Behav. Brain Res. 304, 34–41 (2016).Article 
PubMed 

Google Scholar 
Calvi, E. et al. The scent of emotions: A systematic review of human intra- and interspecific chemical communication of emotions. Brain Behav. 10 (2020).de Groot, J. H. B., Semin, G. R. & Smeets, M. A. M. On the communicative function of body odors: A theoretical integration and review. Perspect. Psychol. Sci. 12, 306–324 (2017).Article 
PubMed 

Google Scholar 
de Groot, J. H. B. et al. A sniff of happiness. Psychol. Sci. 26, 684–700 (2015).Article 
PubMed 

Google Scholar 
Destrez, A. et al. Male mice and cows perceive human emotional chemosignals: A preliminary study. Anim. Cogn. 24, 1205–1214 (2021).Article 
PubMed 

Google Scholar 
Wilson, Id. Dogs can discriminate between human baseline and psychological stress condition odours. PLoS ONE 17, e0274143 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
D’Aniello, B., Semin, G. R., Alterisio, A., Aria, M. & Scandurra, A. Interspecies transmission of emotional information via chemosignals: from humans to dogs (Canis lupus familiaris). Anim. Cogn. 21, 67–78 (2018).Article 
PubMed 

Google Scholar 
D’Aniello, B. et al. Sex differences in the behavioral responses of dogs exposed to human chemosignals of fear and happiness. Anim. Cogn. 24, 299–309 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., d’Ingeo, S., Quaranta, A., D’Ingeo, S. & Quaranta, A. Lateralized emotional functioning in domestic animals. Appl. Anim. Behav. Sci. 237, 105282 (2021).Article 

Google Scholar 
Rogers, L. & Vallortigara, G. Lateralized Brain Functions: Methods in Human and Non-Human Species. vol. 122 (2017).D’Ingeo, S. et al. Horses associate individual human voices with the valence of past interactions: A behavioural and electrophysiological study. Sci. Rep. 9, 11568 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., Padalino, B., Aubé, L. & Quaranta, A. Right-nostril use during sniffing at arousing stimuli produces higher cardiac activity in jumper horses. Laterality 20, (2015).De Boyer Des Roches, A., Richard-Yris, M.-A. A., Henry, S., Ezzaouïa, M. & Hausberger, M. Laterality and emotions: Visual laterality in the domestic horse (Equus caballus) differs with objects’ emotional value. Physiol. Behav. 94, 487–490 (2008).Albrecht, J. et al. Smelling chemosensory signals of males in anxious versus nonanxious condition increases state anxiety of female subjects. Chem. Senses 36, 19–27 (2011).Article 
CAS 
PubMed 

Google Scholar 
Derrickson, S. Sinister (VF)—YouTube. (Wild Bunch SA, 2001).Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing (2021).Wickham, H. Ggplot2: Elegant graphics for data analysis. (2016).Hothorn, T., Winell, H., Hornik, K., van de Wiel, M. A. & Zeileis, A. coin: Conditional inference procedures in a permutation test framework (2021).Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models (2021).Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. (2022).Hothersall, B., Harris, P., Sörtoft, L. & Nicol, C. J. Discrimination between conspecific odour samples in the horse (Equus caballus). Appl. Anim. Behav. Sci. 126, 37–44 (2010).Article 

Google Scholar 
Smeets, M. A. M. et al. Chemical fingerprints of emotional body odor. Metabolites 10, (2020).Sabiniewicz, A. et al. A preliminary investigation of interspecific chemosensory communication of emotions: Can Humans (Homo sapiens) recognise fear- and non-fear body odour from horses (Equus ferus caballus). Animal 11, 3499 (2021).Article 

Google Scholar 
Zhou, W. & Chen, D. Entangled chemosensory emotion and identity: Familiarity enhances detection of chemosensorily encoded emotion. Soc. Neurosci. 6, 270–276 (2011).Article 
PubMed 

Google Scholar 
Starling, M., McLean, A. & McGreevy, P. The contribution of equitation science to minimising horse-related risks to humans. Animal 6, 15 (2016).Article 

Google Scholar 
Basile, M. et al. Socially dependent auditory laterality in domestic horses (Equus caballus). Anim. Cogn. 12, 611–619 (2009).Article 
PubMed 

Google Scholar 
Hatfield, E., Cacioppo, J. T. & Rapson, R. L. Emotional contagion. Curr. Dir. Psychol. Sci. 2, 96–100 (1993).Article 

Google Scholar 
Austin, N. P. & Rogers, L. J. Limb preferences and lateralization of aggression, reactivity and vigilance in feral horses Equus caballus. Anim. Behav. 83, 239–247 (2012).Article 

Google Scholar 
Larose, C., Richard-Yris, M.-A., Hausberger, M. & Rogers, L. J. Laterality of horses associated with emotionality in novel situations Laterality Asymmetries Body. Brain Cogn. 11, 355–367 (2006).
Google Scholar 
Farmer, K., Krüger, K., Byrne, R. W. & Marr, I. Sensory laterality in affiliative interactions in domestic horses and ponies (Equus caballus). Anim. Cogn. 21, 631–637 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, D. & Haviland-Jones, J. Human olfactory communication of emotion. Percept. Mot. Skills 91, 771–781 (2000).Article 
CAS 
PubMed 

Google Scholar 
de Groot, J. H. B., Semin, G. R. & Smeets, M. A. M. Chemical communication of fear: A case of male-female asymmetry. J. Exp. Psychol. Gen. 143, 1515–1525 (2014).Article 
PubMed 

Google Scholar 
Marinier, S. L., Alexander, A. J. & Waring, G. H. Flehmen behaviour in the domestic horse: Discrimination of conspecific odours. Appl. Anim. Behav. Sci. 19, 227–237 (1988).Article 

Google Scholar 
Lansade, L., Pichard, G. & Leconte, M. Sensory sensitivities: Components of a horse’s temperament dimension. Appl. Anim. Behav. Sci. 114, 534–553 (2008).Article 

Google Scholar 
Lansade, L., Bouissou, M. F. & Erhard, H. W. Fearfulness in horses: A temperament trait stable across time and situations. Appl. Anim. Behav. Sci. 115, 182–200 (2008).Article 

Google Scholar 
Hoenen, M., Wolf, O. T. & Pause, B. M. The impact of stress on odor perception. https://doi.org/10.1177/030100661668870746,366-376 (2017).Article 
PubMed 

Google Scholar 
Rørvang, M. V., Nicova, K. & Yngvesson, J. Horse odor exploration behavior is influenced by pregnancy and age. Front. Behav. Neurosci. 16, 295 (2022).Article 

Google Scholar 
Doty, R. L. & Cameron, E. L. Sex differences and reproductive hormone influences on human odor perception. Physiol. Behav. 97, 213–228 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar  More

Raubenheimer, D., Simpson, S. J. & Mayntz, D. Nutrition, ecology and nutritional ecology: Toward an integrated framework. Funct. Ecol. 23, 4–16 (2009).Article 

Google Scholar 
Behmer, S. T. & Joern, A. Coexisting generalist herbivores occupy unique nutritional feeding niches. Proc. Natl. Acad. Sci. U. S. A. 105, 1977–1982. https://doi.org/10.1073/pnas.0711870105 (2008).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Lihoreau, M. et al. Nutritional ecology beyond the individual: A conceptual framework for integrating nutrition and social interactions. Ecol. Lett. 18, 273–286 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Raubenheimer, D., Simpson, S. J. & Tait, A. H. Match and mismatch: conservation physiology, nutritional ecology and the timescales of biological adaptation. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 367, 1628–1646. https://doi.org/10.1098/rstb.2012.0007 (2012).Article 
CAS 

Google Scholar 
von Liebig, J. Die organische Chemie in ihrer Anwendung auf Agricultur und Physiologie. (Vieweg, 1841).Simpson, C., Simpson, S. & Abisgold, J. In Symposium Biologica Hungarica. 39–46.Boersma, M. & Elser, J. Too much of a good thing: On stoichiometrically balanced diets and maximal growth. Ecology 87, 1325–1330 (2006).Article 
PubMed 

Google Scholar 
Simpson, S. J. & Raubenheimer, D. A multi-level analysis of feeding behaviour: The geometry of nutritional decisions. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 342, 381–402. https://doi.org/10.1098/rstb.1993.0166 (1993).Article 
ADS 

Google Scholar 
Zanotto, F. P., Raubenheimer, D. & Simpson, S. J. Haemolymph amino acid and sugar levels in locust fed nutritionally unbalanced diets. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 166, 223–229 (1996).Article 
CAS 

Google Scholar 
Kohl, K. D., Coogan, S. C. & Raubenheimer, D. Do wild carnivores forage for prey or for nutrients? Evidence for nutrient-specific foraging in vertebrate predators. BioEssays 37, 701–709. https://doi.org/10.1002/bies.201400171 (2015).Article 
PubMed 

Google Scholar 
Remonti, L., Balestrieri, A., Raubenheimer, D. & Saino, N. Functional implications of omnivory for dietary nutrient balance. Oikos 125, 1233–1240 (2016).Article 
CAS 

Google Scholar 
McIntyre, P. B. & Flecker, A. S. In Community Ecology of Stream Fishes: Concepts, Approaches, and Techniques. American Fisheries Society, Symposium. 539–558 (Citeseer).DeGabriel, J. L. et al. Translating nutritional ecology from the laboratory to the field: Milestones in linking plant chemistry to population regulation in mammalian browsers. Oikos 123, 298–308 (2014).Article 

Google Scholar 
Nielsen, S. E., Larsen, T. A., Stenhouse, G. B. & Coogan, S. C. P. Complementary food resources of carnivory and frugivory affect local abundance of an omnivorous carnivore. Oikos 126, 369–380. https://doi.org/10.1111/oik.03144 (2017).Article 
CAS 

Google Scholar 
Mayntz, D., Raubenheimer, D., Salomon, M., Toft, S. & Simpson, S. J. Nutrient-specific foraging in invertebrate predators. Science 307, 111–113 (2005).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Anderson, T. R., Boersma, M. & Raubenheimer, D. Stoichiometry: Linking elements to biochemicals. Ecology 85, 1193–1202 (2004).Article 

Google Scholar 
McManamay, R. A., Webster, J. R., Valett, H. M. & Dolloff, C. A. Does diet influence consumer nutrient cycling? Macroinvertebrate and fish excretion in streams. J. N. Am. Benthol. Soc. 30, 84–102. https://doi.org/10.1899/09-152.1 (2011).Article 

Google Scholar 
Vivas, M., Sánchez-Vázquez, F., García García, B. & Madrid, J. Macronutrient self-selection in European sea bass in response to dietary protein or fat restriction. Aquac. Res. 34, 271–280 (2003).Article 

Google Scholar 
Rubio, V., Navarro, D. B., Madrid, J. & Sánchez-Vázquez, F. Macronutrient self-selection in Solea senegalensis fed macronutrient diets and challenged with dietary protein dilutions. Aquaculture 291, 95–100 (2009).Article 
CAS 

Google Scholar 
Mayntz, D. et al. Balancing of protein and lipid intake by a mammalian carnivore, the mink, Mustela vison. Anim. Behav. 77, 349–355 (2009).Article 

Google Scholar 
Al Shareefi, E. & Cotter, S. C. The nutritional ecology of maturation in a carnivorous insect. Behav. Ecol. 30, 256–266 (2019).Article 

Google Scholar 
Jensen, K. et al. Nutrient-specific compensatory feeding in a mammalian carnivore, the mink, Neovison vison. Br. J. Nutr. 112, 1226–1233. https://doi.org/10.1017/S0007114514001664 (2014).Article 
CAS 
PubMed 

Google Scholar 
Hayward, M., Jędrzejewski, W. & Jedrzejewska, B. Prey preferences of the tiger Panthera tigris. J. Zool. 286, 221–231 (2012).Article 

Google Scholar 
Whitney, T. D., Sitvarin, M. I., Roualdes, E. A., Bonner, S. J. & Harwood, J. D. Selectivity underlies the dissociation between seasonal prey availability and prey consumption in a generalist predator. Mol. Ecol. 27, 1739–1748 (2018).Article 
PubMed 

Google Scholar 
Potter, T. I., Stannard, H. J., Greenville, A. C. & Dickman, C. R. Understanding selective predation: Are energy and nutrients important?. PLoS One 13, e0201300 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Machovsky-Capuska, G. E. et al. Sex-specific macronutrient foraging strategies in a highly successful marine predator: The Australasian gannet. Mar. Biol. 163, 75 (2016).Article 

Google Scholar 
Remonti, L., Balestrieri, A. & Prigioni, C. Percentage of protein, lipids, and carbohydrates in the diet of badger (Meles meles) populations across Europe. Ecol. Res. 26, 487–495 (2011).Article 
CAS 

Google Scholar 
Wilder, S. M. et al. Three-dimensional diet regulation and the consequences of choice for weight and activity level of a marsupial carnivore. J. Mammal. 97, 1645–1651 (2016).Article 

Google Scholar 
Yu, D.-H. et al. Effect of partial replacement of fish meal with soybean meal and feeding frequency on growth, feed utilization and body composition of juvenile Chinese sucker, Myxocyprinus asiaticus (Bleeker). Aquac. Res. 44, 388–394. https://doi.org/10.1111/j.1365-2109.2011.03043.x (2013).Article 
CAS 

Google Scholar 
Kaushik, S. J. & Seiliez, I. Protein and amino acid nutrition and metabolism in fish: current knowledge and future needs. Aquac. Res. 41, 322–332. https://doi.org/10.1111/j.1365-2109.2009.02174.x (2010).Article 
CAS 

Google Scholar 
Gaye-Siessegger, J., McCullagh, J. S. & Focken, U. The effect of dietary amino acid abundance and isotopic composition on the growth rate, metabolism and tissue delta13C of rainbow trout. Br. J. Nutr. 105, 1764–1771. https://doi.org/10.1017/S0007114510005696 (2011).Article 
CAS 
PubMed 

Google Scholar 
McCullagh, J., Gaye-Siessegger, J. & Focken, U. Determination of underivatized amino acid delta(13)C by liquid chromatography/isotope ratio mass spectrometry for nutritional studies: The effect of dietary non-essential amino acid profile on the isotopic signature of individual amino acids in fish. Rapid Commun. Mass Spectrom. RCM 22, 1817–1822. https://doi.org/10.1002/rcm.3554 (2008).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Gao, K. et al. Dietary L-arginine supplementation enhances placental growth and reproductive performance in sows. Amino Acids 42, 2207–2214 (2012).Article 
CAS 
PubMed 

Google Scholar 
Wu, G. et al. Amino acid nutrition in animals: Protein synthesis and beyond. Annu. Rev. Anim. Biosci. 2, 387–417. https://doi.org/10.1146/annurev-animal-022513-114113 (2014).Article 
CAS 
PubMed 

Google Scholar 
Dwyer, G. K., Stoffels, R. J., Silvester, E. & Rees, G. N. Prey amino acid composition affects rates of protein synthesis and N wastage of a freshwater carnivore. Mar. Freshw. Res. 71, 229–237. https://doi.org/10.1071/MF18410 (2020).Article 
CAS 

Google Scholar 
Kremen, N. et al. Body composition and amino acid concentrations of select birds and mammals consumed by cats in northern and central California. J. Anim. Sci. 91, 1270–1276 (2013).Article 
CAS 
PubMed 

Google Scholar 
Goodman-Lowe, G., Carpenter, J., Atkinson, S. & Ako, H. Nutrient, fatty acid, amino acid and mineral analysis of natural prey of the Hawaiian monk seal, Monachus schauinslandi. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 123, 137–146 (1999).Article 
CAS 

Google Scholar 
Dwyer, G. K., Stoffels, R. J., Rees, G. N., Shackleton, M. & Silvester, E. A predicted change in the amino acid landscapes available to freshwater carnivores. Freshw. Sci. 37, 000–000 (2018).Article 

Google Scholar 
Kolmakova, A. A. et al. Amino acid composition of epilithic biofilm and benthic animals in a large Siberian river. Freshw. Biol. 58, 2180–2195. https://doi.org/10.1111/fwb.12200 (2013).Article 
CAS 

Google Scholar 
Thera, J. C., Kidd, K. A. & Bertolo, R. F. Amino acids in freshwater food webs: Assessing their variability among taxa, trophic levels, and systems. Freshw. Biol. 65, 1101–1113 (2020).Article 
CAS 

Google Scholar 
Fargallo, J. A., Navarro-López, J., Palma-Granados, P. & Nieto, R. M. Foraging strategy of a carnivorous-insectivorous raptor species based on prey size, capturability and nutritional components. Sci. Rep. 10, 1–12 (2020).Article 

Google Scholar 
Shakya, M., Silvester, E., Holland, A. & Rees, G. Taxonomic, seasonal and spatial variation in the amino acid profile of freshwater macroinvertebrates. Aquat. Sci. 83, 1–15 (2021).Article 

Google Scholar 
Martinez, J. B., Chatzifotis, S., Divanach, P. & Takeuchi, T. Effect of dietary taurine supplementation on growth performance and feed selection of sea bass Dicentrarchus labrax fry fed with demand-feeders. Fish. Sci. 70, 74–79 (2004).Article 

Google Scholar 
Yamamoto, T. et al. Self-selection and feed consumption of diets with a complete amino acid composition and a composition deficient in either methionine or lysine by rainbow trout, Oncorhynchus mykiss (Walbaum). Aquac. Res. 32, 83–91 (2001).Article 
CAS 

Google Scholar 
Dabrowski, K., Arslan, M., Terjesen, B. F. & Zhang, Y. The effect of dietary indispensable amino acid imbalances on feed intake: Is there a sensing of deficiency and neural signaling present in fish?. Aquaculture 268, 136–142. https://doi.org/10.1016/j.aquaculture.2007.04.065 (2007).Article 
CAS 

Google Scholar 
Caprio, J. Olfaction and taste in the channel catfish: An electrophysiological study of the responses to amino acids and derivatives. J. Comp. Physiol. 123, 357–371 (1978).Article 

Google Scholar 
Hazlett, B. A. Crayfish feeding responses to zebra mussels depend on microorganisms and learning. J. Chem. Ecol. 20, 2623–2630. https://doi.org/10.1007/bf02036196 (1994).Article 
CAS 
PubMed 

Google Scholar 
Gietzen, D. W. & Aja, S. M. The brain’s response to an essential amino acid-deficient diet and the circuitous route to a better meal. Mol. Neurobiol. 46, 332–348. https://doi.org/10.1007/s12035-012-8283-8 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rees, G. N., Shackleton, M. E., Watson, G. O., Dwyer, G. K. & Stoffels, R. J. Metabarcoding demonstrates dietary niche partitioning in two coexisting blackfish species. Mar. Freshw. Res. 71, 512–517 (2020).Article 
CAS 

Google Scholar 
Antoine, F., Wei, C., Littell, R. & Marshall, M. HPLC method for analysis of free amino acids in fish using o-phthaldialdehyde precolumn derivatization. J. Agric. Food Chem. 47, 5100–5107 (1999).Article 
CAS 
PubMed 

Google Scholar 
Anderson, M. J. & Santana-Garcon, J. Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecol. Lett. 18, 66–73 (2015).Article 
PubMed 

Google Scholar 
Fountoulakis, M. & Lahm, H.-W. Hydrolysis and amino acid composition analysis of proteins. J. Chromatogr. A 826, 109–134 (1998).Article 
CAS 
PubMed 

Google Scholar 
McArdle, B. H. When are rare species not there?. Oikos 57, 276–277 (1990).Article 

Google Scholar 
Machovsky-Capuska, G. E., Coogan, S. C., Simpson, S. J. & Raubenheimer, D. Motive for killing: What drives prey choice in wild predators?. Ethology 122, 703–711 (2016).Article 

Google Scholar 
Tait, A. H., Raubenheimer, D., Stockin, K. A., Merriman, M. & Machovsky-Capuska, G. E. Nutritional geometry and macronutrient variation in the diets of gannets: The challenges in marine field studies. Mar. Biol. 161, 2791–2801 (2014).Article 
CAS 

Google Scholar 
Bosch, G., Hagen-Plantinga, E. A. & Hendriks, W. H. Dietary nutrient profiles of wild wolves: Insights for optimal dog nutrition?. Br. J. Nutr. 113, S40–S54 (2015).Article 
CAS 
PubMed 

Google Scholar 
Machovsky-Capuska, G. E., Senior, A. M., Simpson, S. J. & Raubenheimer, D. The multidimensional nutritional niche. Trends Ecol. Evol. 31, 355–365 (2016).Article 
PubMed 

Google Scholar 
Jensen, K. et al. Optimal foraging for specific nutrients in predatory beetles. Proc. R. Soc. B 279, 2212–2218. https://doi.org/10.1098/rspb.2011.2410 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Machovsky-Capuska, G. E. & Raubenheimer, D. The nutritional ecology of marine apex predators. Ann. Rev. Mar. Sci. 12, 361–387 (2020).Article 
PubMed 

Google Scholar 
Schindler, D. E. & Eby, L. A. Stoichiometry of fishes and their prey: Implications for nutrient recycling. Ecology 78, 1816–1831 (1997).Article 

Google Scholar 
Morosinotto, C., Villers, A., Varjonen, R. & Korpimäki, E. Food supplementation and predation risk in harsh climate: Interactive effects on abundance and body condition of tit species. Oikos 126, 863–873. https://doi.org/10.1111/oik.03476 (2017).Article 

Google Scholar 
Österblom, H., Olsson, O., Blenckner, T. & Furness, R. W. Junk-food in marine ecosystems. Oikos 117, 967–977 (2008).Article 

Google Scholar 
Dwyer, G. K., Stoffels, R. J. & Pridmore, P. A. Morphology, metabolism and behaviour: responses of three fishes with different lifestyles to acute hypoxia. Freshw. Biol. 59, 819–831. https://doi.org/10.1111/fwb.12306 (2014).Article 
CAS 

Google Scholar 
Hubel, T. Y. et al. Energy cost and return for hunting in African wild dogs and cheetahs. Nat. Commun. 7, 11034 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ip, Y. K., Lim, C. K., Lee, S. L., Wong, W. P. & Chew, S. F. Postprandial increases in nitrogenous excretion and urea synthesis in the giant mudskipper Periophthalmodon schlosseri. J. Exp. Biol. 207, 3015–3023 (2004).Article 
CAS 
PubMed 

Google Scholar 
Wilkie, M. P. Mechanisms of ammonia excretion across fish gills. Comp. Biochem. Physiol. A Physiol. 118, 39–50 (1997).Article 

Google Scholar 
Yamamoto, T. et al. Self-selection of diets with different amino acid profiles by rainbow trout (Oncorhynchus mykiss). Aquaculture 187, 375–386 (2000).Article 
CAS 

Google Scholar 
Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M. & Altman, D. G. J. P. B. Improving bioscience research reporting: The ARRIVE guidelines for reporting animal research. J. Pharmacol. Pharmacother. 8, e1000412 (2010).
Google Scholar  More

Screening for viral AMGsViral protein annotation using VIBRANT and DRAM-v software combined with manual proofreading identified the viral AMGs in Napahai plateau wetland, including the viral AMGs phoH, phoU and pstS, which were associated with phosphorus metabolism.Phylogenetic analysis of AMGs associated with phosphorus metabolism in Napahai plateau wetlandThere were 24 amino acid sequences of phoH gene in Napahai plateau wetland (Fig. 1A). They were divided into 5 clusters, the largest of which had 10 sequences, while the smallest cluster had only 1 sequence. The remaining 3 clusters contained 6, 5 and 2 sequences, respectively. The phoH gene was genetically diverse in Napahai plateau wetland, which might be related to the different host origins. A total of 74 sequences of phoU gene could be found in seven clusters (Fig. 1B), with the largest cluster containing 27 sequences and the smallest cluster having two sequences. Similar to phoH, phoU was also genetically diverse, but richer than that of phoH. There were 71 pstS sequences forming 9 clusters, with the largest cluster of 23 sequences and the smallest cluster only 1 sequence (Fig. 1C). It could be seen that the genetic diversity of pstS was better than that of phoH and phoU, which might be related to the unique geographical location. Napahai plateau wetland is located in the Three Parallel Rivers of Yunnan protected areas, which forms a complex landscape, and then controls the evolution and characteristics of organisms, thus showing abundant biodiversity. Li et al. obtained 58 phoH gene sequences from Northeastern wetland sediments of China, which were 22%–99% consistent at the amino acid level, and found that the phoH gene could regulate phosphate uptake and metabolism under the low phosphate or phosphate limitation conditions16. However, the exact function remained unclear. The phoH gene clustered into five clusters in Napahai plateau wetland, indicating high genetic diversity. Additionally, water and soil samples were collected from eight separate sampling sites, and there were differences between samples environments, which might also have an impact on the genetic diversity of the three genes.Figure 1Phylogenetic analysis of phosphorus metabolism AMGs in Napahai plateau wetland, different colors represent different branches. (A) Phylogenetic analysis of phoH genes. (B) Phylogenetic analysis of phoU genes. (C) Phylogenetic analysis of pstS genes.Full size imagePhylogenetic analysis and PCoA analysis of AMGs associated with phosphorus metabolism from different habitats and host originsIn order to understand the genetic diversity of viral AMGs (phoH, phoU, pstS) associated with phosphorus metabolism in Napahai plateau wetland, a phylogenetic tree of phosphorus metabolism AMGs from different habitats was constructed, and PCoA analysis was performed (Fig. 2). The results showed that most sequences of phoH, phoU and pstS genes in Napahai plateau wetland clustered individually, especially phoU and pstS genes, and only a few sequences were closely related to those of other habitats. In Fig. 2A, 14 sequences clustered individually and were relatively far from sequences of other habitats, whereas 7 sequences were close to those from freshwater lakes, and other 3 sequences were close to those from rice fields, oceans and other wetlands, respectively. Therefore, the genetic diversity of phoH in Napahai plateau wetland was independent of the habitat. Moreover, some of the phoH sequences were clustered with those of other habitats and distributed in the fourth quadrants (Fig. 2D). From Fig. 2B, apart from 3 sequences which clustered with those from the marine habitats and freshwater lakes, the rest were clustered separately. Whereas in Fig. 2E, apart from only a few sequences, most sequences of phoU were far away from those of different habitats, which was consistent with Fig. 2B. Thus, the genetic diversity of phoU gene in Napahai wetland was also independent of habitat, where the separately clustered sequences may be unique. From Fig. 2C, we can seen that apart from 8 sequences which more closely related to those from the freshwater lake, ocean, rice field, and other wetlands, all the rest were individually clustered. The result was consistent with that of Fig. 2F. Therefore, the genetic diversity of the pstS gene was also habitat-independent.Figure 2Phylogenetic analysis and PCoA of phosphorus metabolism AMGs in different habitats, different colors represent different habitats. (A) Phylogenetic analysis of phoH genes in different habitats. (B) Phylogenetic analysis of phoU genes in different habitats. (C) Phylogenetic analysis of pstS genes in different habitats. (D) PCoA analysis of phoH genes in different habitats. (E) PCoA analysis of phoU genes in different habitats. (F) PCoA analysis of pstS genes in different habitats.Full size imageTo study whether the genetic diversity was related to host origins, three AMGs associated with phosphorus metabolism were selected for phylogenetic and PCoA analyses with AMGs sequences from different host origins (Fig. 3). It showed that some sequences of all three genes were similar to those from different host origins, while the remaining were separately clustered. In Fig. 3A, apart from 14 sequences which clustered with those from fungi, bacteria, non-culturable phages, phages and viruses, all the rest were clustered separately. Whereas, most sequences were clustered with those from different host origins together, and only six sequences were far from other sequences of different host origins based on PCoA analysis (Fig. 3D). Only three sequences were clustered with those of archaea and uncultured archaea, and the rest were clustered together to form independent clusters (Fig. 3B). A small amount of sequences were gathered with bacteria, uncultured bacteria, archaea and uncultured archaea, and the rest were clustered individually (Fig. 3E). As can be seen in Fig. 3C, six sequences were clustered with those of archaea, fungi, bacteria, while the rest were clustered separately. Some sequences were gathered with bacteria, uncultured bacteria, archaea and uncultured archaea, and others were clustered separately (Fig. 3F). PCoA analysis was largely consistent with phylogenetic analysis. So the genetic diversity of phoH, phoU and pstS genes in Napahai plateau wetland was independent of the host origins.Figure 3Phylogenetic analysis and PCoA of phosphorus metabolism AMGs from different host origins, different colors represent different host origins. (A) Phylogenetic analysis of phoH gene from different host origins. (B) Phylogenetic analysis of phoU gene from different host origins. (C) Phylogenetic analysis of pstS gene from different host origins. (D) PCoA analysis of phoH genes from different host origins. (E) PCoA analysis of phoU genes from different host origins. (F) PCoA analysis of pstS genes from different host origins.Full size imageOverall, the genetic diversity of phoH, phoU and pstS genes associated with phosphorus metabolism in Napahai plateau wetland was independent of both the habitats and host origins based on phylogenetic and PCoA analyses. It suggested that three genes showed relatively rich genetic diversity and were not genetically limited by differences in habitats or host origins. Han et al. showed that phoH sequences were widely distributed in soil, freshwater, and seawater environments in different locations around the world, indicating the genetic diversity independent of the environment17, which corroborated the conclusions in our study. Phylogenetic analysis of the 58 viral phoH gene sequences in Northeastern wetland of China revealed that some sequences were clustered with bacterial sequences and others clustered with phages sequences16. In Napahai plateau wetland, some phoH gene sequences were clustered with fungal, bacterial, phage, uncultured phage, and viruses. Hence, the genetic diversity of phoH gene was independent of the host origins in either Northeastern wetland or Napahai plateau wetland. Compared with Northeastern wetland, the phoH genes in Napahai plateau wetland showed more abundant genetic diversity, which may be related to geographical location and climate. Additionally, compared with sequences from different habitats and host sources, partial sequences from Napahai plateau wetland were clustered individually, thus they were unique, which might be related to the unique geography. Napahai plateau wetland is located in the Three Parallel Rivers with low latitude and high altitude, and shows specific characteristics which not found in other habitats, and then the species very different, thus providing the possibility for the emergence of unique genetic sequences. Of course, it would require further verification by subsequent study.As far as the current studies are concerned, most reports on phosphorus AMGs focused on the function. Wang et al. mentioned that the phoH gene regulated phosphate uptake or metabolism under the low phosphorus or phosphate limitation conditions18. Kelly et al. isolated several phages from oligotrophic water bodies with low phosphorus condition, found that they contained the phosphate binding transporter gene pstS by sequencing, which enhanced the host cell with increasing the infection cycle of phages by increasing phosphate utilization19. Gardner et al. studied the PhoR-PhoB two-component regulatory system in E. coli, which regulated the expression of relevant genes according to environmental phosphate concentration and enabled cells to adapt the phosphate starvation20. The phoU existed in many bacteria and was identified as an auxiliary protein of the phosphate-specific transporter system, regulating phosphate metabolism in the host cell acting as phosphate regulators21. Few studies had been conducted on its genetic diversity, therefore, the information on the genetic diversity was relatively scarce.α diversity analysis of phosphorus metabolism AMGs in different habitats and different host originsChao, Shannon and Simpson diversity indices are common mathematical measure of species alpha diversity in the community. Chao focuses on species richness. Shannon index and Simpson index measure species richness and evenness. Simpson reinforces evenness and Shannon reinforces richness22.Sequences from different habitats, such as Napahai plateau wetland, Pacific Ocean, Lake Baikal, Northeast rice fields, glaciers, and wetlands, were selected for α-diversity analysis (Fig. 4). The genetic diversity indices, such as Chao, Shannon and Simpson, calculated based on the OUT dataset, were used to characterize the alpha diversity. Among them, larger Chao values, smaller Simpson values or larger Shannon values indicate higher genetic diversity. Only at the level of Chao values (Fig. 4A,D,G) and Shannon values (Fig. 4B,E,H), the values of phoH, phoU, and pstS in Napahai plateau wetland were greater than those from other habitats, indicating better heritable, which might be related to the unique geographical location and abundant water resources. The geographical location made it unique and less influenced by external factors, and abundant water resources created a rich biodiversity, thus providing a good genetic environment. From the Simpson values (Fig. 4C,F,I), the values of phoU and pstS genes were smaller than those of other habitats, indicating better inherited. For the phoH gene, the Simpson value was closer in magnitude and lower than those in Antarctic Lake and wetlands, indicating better heritable.Figure 4Plots of genetic diversity indices analysis of phosphorus metabolism AMGs in different habitats, different colors represent different genetic diversity indices. (A, D, G) Represent respectively the Chao values of phoH, phoU, and pstS genes in different habitats. (B, E, H) Represent respectively the Shannon values of phoH, phoU, and pstS genes in different habitats. (C, F, I) Represent respectively the Simpson values of phoH, phoU, and pstS genes in different habitats.Full size imageThree AMGs associated with phosphorus metabolism in Napahai plateau wetland were selected for α-diversity analysis with AMGs sequences from different host origins (Fig. 5). In Fig. 5A, the Chao values of phoH gene from bacteria, phages, uncultured phages and uncultured viruses in Napahai plateau wetland were smaller than those of bacteria, phages, uncultured phages and uncultured viruses, indicating the poor genetic diversity. In addition, compared to the genetic diversity of sequences from other host sources, the genetic diversity of phoH gene from bacteria in Napahai plateau wetland was better. As can be seen in Fig. 5D, G, the Chao values of phoU and pstS genes from bacteria in Napahai plateau wetland were greater than those of other host origins, indicating better genetic diversity, while the Chao values of pstS genes from archaea in Napahai plateau wetland were smaller than those of other host origins, indicating poor genetic diversity.Figure 5Plots of genetic diversity indices analysis of phosphorus metabolism AMGs from different host origins, different colors represent different genetic diversity indices. (A, D, G) Represent respectively the Chao values of phoH, phoU, and pstS genes from different host origins. (B, E, H) Represent respectively the Shannon values of phoH, phoU, and pstS genes from different host origins. (C, F, I) Represent respectively the Simpson values of phoH, phoU, and pstS genes from different host origins.Full size imageThe Shannon value of phoH gene from bacteria in Napahai plateau wetland was smaller than that of bacteria and uncultured viruses, indicating poor diversity, but larger than other host sources, indicating better genetic diversity (Fig. 5B). The Shannon values of phoH gene from phages and uncultured phages in Napahai plateau wetland were lower than those of other host origins, indicating poor diversity. The Shannon value of phoH genes from uncultured viruses in Napahai plateau wetland was 0, probably due to sample size too small to calculate the Shannon value. In Fig. 5E, H, the Chao values of phoU and pstS genes from bacteria in Napahai plateaus wetland were greater than those from other host sources, indicating better diversity, while the Shannon value of pstS gene from archaea in the Napahai plateau wetland was 0, probably small sample size.The Simpson values of phoH genes from phage, uncultured phage and uncultured virus in Napahai plateau wetland were smaller than those of other host origins (except uncultured virus), indicating better diversity. The smaller Simpson values of phoH genes related to fungi, phages, uncultured phages, and viruses indicated better diversity, while the larger Simpson values compared to bacteria, phages, and uncultured viruses indicated poor diversity (Fig. 5C). As can be seen in Figs. 5F,I, the Simpson values of phoU genes from bacteria and pstS genes from bacteria and archaea in Napahai plateau wetland were smaller than those of other host origins, indicating better genetic diversity.Currently, most studies on phosphorus AMGs employed phylogenetic analysis16,23. In contrast, relatively few AMGs associated with phosphorus had been reported based on α-diversity analysis, so it was difficult to obtain specific values of α-diversity indices in other studies.Biogeochemical cycling of AMGs associated with phosphorus metabolism in Napahai plateau wetlandViruses are the gene carriers in susceptible hosts, and AMGs introduced by viruses into new hosts can enhance viral replication and/or influence key microbial metabolic pathways of the biogeochemical cycles24. It is well known that phosphorus is an essential nutrient and plays essential roles in cells25. Phosphorus deficiency leads to restricted cell division, down-regulation of photosynthesis, reduced protein and nitrogen content and chlorophyll synthesis26. To study the effect of AMGs associated with phosphorus metabolism, a phosphorus metabolic pathway containing phoH, phoU and pstS genes was constructed based on metagenomic data (Fig. 6). When phosphorus deficiency occurs in the host, it leads to the expression of phoH, phoU and pstS genes. phoH is a phosphate starvation inducible gene, while pstS acts as a phosphate transport gene and phoU belongs to a phosphate regulatory gene that produces dissolved inorganic phosphorus (DIPs), which then undergoes a series of reactions to produce ATP. The generated ATP becomes PolyP under the action of ppK which encoding polyphosphate kinase, or is used in Calvin cycle to provide energy for Ru5P to produce RuBP, or is used for DNA biosynthesis to provide energy. PolyP is regenerate into DIP with ppX which encoding exopolyphosphatase, and also involves in the biosynthesis process of DNA as Pi to provide phosphate for the nucleic acids synthesis. Thus, phosphorus metabolism of AMGs invoved plays a significant role in the life process of the virus and host. In addition, phoE and ugpQ genes also are identified in Napahai plateau wetland, but their roles in the phosphorus cycling are currently unknown and need further study.Figure 6Biogeochemical cycling of AMGs associated with phosphorus metabolism in Napahai plateau wetland. Red line indicates the process of phosphorus metabolism.Full size imageBased on the phylogenetic and PCoA analyses, we found that the phoH, phoU, and pstS genes all showed unique sequences, which might be drive the microorganisms to produce the phosphorus metabolic pathway in Napahai plateau wetland. Of course, in order to prove this pathway, further validation might be done by metabolomics or metabolic flow method. Furthermore, the phosphorus metabolic pathway was poorly reported, so we could not compare with the phosphorus pathway from other environment to find commonalities and differences. More

Extraction of total active proteomes from sediment samplesWe sampled 14 sediments along the coastlines of the Irish Sea, the Mediterranean Sea, and the Red Sea (from 16°N to 53°N), applying uniform sampling and storage procedures. Location details and sediment temperature fluctuations are summarized in Supplementary Table S1. We collected sediments (5 Kg) in triplicate and extracted the total proteins using a well-established microbial detachment procedure67, with some modifications. We mixed 100 g of sediment with 300 ml of sterilized saline solution (5 mM sodium pyrophosphate and 35 g L−1 of NaCl) containing 150 mg L−1 of Tween 80 (from Merck Life Science S.L.U., Madrid, Spain) in an ice water bath. After re-suspension, samples were kept in a water bath ultra-sonicator (Bandelin SONOREX, Berlin, Germany) on ice and sonicated (60 W output) for 120 min. We repeated this procedure twice, with an ice water bath incubation of 60 min between each cycle. We then centrifuged the samples at 500 g for 15 min at 4 °C to remove the sediments in a centrifuge 5810 R (Eppendorf AG, Hamburg, Germany). Supernatants were carefully transferred to a new tube, minimizing disruption of the sediments, and the resulting supernatants were centrifuged at 13,000 g for 15 min at 4 °C to produce microbial cell pellets. We used the resulting cell mix to extract the total protein by mixing the cells with 1.2 ml BugBuster® Protein Extraction Reagent (Novagen, Darmstadt, Germany) for 30 min with shaking (250 rpm). Subsequently, samples were disrupted by sonication using a pin Sonicator® 3000 (Misonix, New Highway Farmingdale, NY, USA) for a total time of 2 min (10 watts) on ice (4 cycles × 0.5 min with 1 min ice-cooling between each cycle). Extracts were centrifuged for 10 min at 12,000 g at 4 °C to separate cellular debris and intact cells. Supernatants were carefully aspirated (to avoid disturbing the pellet), transferred to new tubes, and stored at –80 °C until use. The protein solution was filtered at 15 °C for 7 h using Vivaspin filters (Sartorius, Goettingen, Germany) with a molecular weight (MW) cut-off of 3,000 Da to concentrate the proteins up to a final concentration of 10 mg ml−1, according to the Bradford Protein Assay (Bio-Rad Laboratories, S.A., Madrid, Spain)68. The average total amount of proteins extracted per each 100 g of sediment was 612 µg (interquartile range, 31 µg, see details in Supplementary Fig. S2). In all cases, extensive dialysis of protein solutions against 40 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffer was performed using a Pur-A-LyzerTM Maxi 1200 dialysis kit (Merck Life Science S.L.U., Madrid, Spain)69, and active proteins stored at a concentration of 10 mg ml−1at –86 °C until use. As reported previously70, 2DE was performed using GE Healthcare reagents and equipment, 11 cm IPG strips in the pH range of 3–10 and molecular weight ranging from 10 to 250 kDa (Precision Plus Protein Dual Color Standards #1610374, Bio-Rad Laboratories, S.A., Madrid, Spain). The 2-DE was performed using a validated pooling strategy71, in which proteins extracted from three independent biological replicates (i.e., sediments) were mixed in equal amounts and a total of 150 µg of protein were further loaded per gel. Staining was performed with SYPRO Ruby Protein Gel Stain (Invitrogen, Waltham, MA, USA). The two-dimensional SDS-PAGE (12% acrylamide) gels of extracted proteins are reported in Supplementary Fig. S2 (original gels in Source Data). The same protocol was applied to extract and analyse by SDS-PAGE the total active proteins extracted from sediment samples with different temperature variability levels (HTV, ITV, and LTV) collected in the Red Sea (Supplementary Table S4). The total amount of protein extracted per each 100 g of sediment is given in Supplementary Table S8. Coomassie-stained one-dimension SDS-PAGE (1-DE) gels of extracted proteins are shown in Supplementary Fig. S9 (original gel in Source Data).Source, expression and purification of esterases and EXDOs from a wide geographical rangeWe recovered 83 enzymes (78 esterases and 5 EXDO) from microbial communities inhabiting marine sediments across ten distinct locations from the latitudinal transect described above: Ancona harbour (Anc), Priolo Gargallo (Pri), Gulf of Genoa, Messina harbour (Mes), Milazo harbour (Mil), Mar Chica lagoon (MCh), Bizerte lagoon (Biz), El-Max site (ElMax), Gulf of Aqaba (Aq), and Menai Strait (MS); further details are provided in Supplementary Data S3. Sources of the enzymes were the corresponding shotgun metagenomes (see Supplementary Table S3) and the metagenome clone libraries generated from the extracted DNA71. The sediment sample from the Gulf of Genoa was not used for activity tests and metaproteome analysis because no raw sample material was available; however, because of the possibility to access its shotgun metagenome (see Supplementary Table S3) and a metagenome clone library72, we used the sample for screening esterases to incorporate an additional latitude in our transect. In the case of Menai Strait (Irish Sea), five additional esterases were retrieved from a metagenome obtained from enriched cultures prepared with samples collected on 22nd June 2019 from Menai Strait (School of Ocean Sciences, Bangor University, St. George’s Pier, Menai Bridge, N53°13′31.3″; W4°09′33.3”). The water temperature was 14 °C and the salinity was 32 p.s.u. Two enrichment cultures were set up at 20 °C: (i) SW: seawater enrichment with 0.1% lignin; the enrichment was set up using 50 ml of the sample as inoculum with the addition of 0.1% lignin (Sigma-Aldrich, Gillingham, United Kingdom) (w/v); (ii) AW: algal surface wash-off in seawater, enriched with 0.1% lignin; the enrichment was set up using 50 ml of surface wash-off after mixing of ca. 10 g of Fucus (brown algae) in the seawater and removal of plant tissue, 0.1% lignin (w/v) was added. After 92 days of incubation, 5 ml of each enrichment cultures were transferred into the new flask containing 45 ml autoclaved and filtered seawater with 0.1% lignin. This procedure was repeated on days 185 and 260, and the incubation was stopped on day 365. The DNA was extracted using 12 months using MetaGnome extraction kit (EpiCentre, Biotechnologies, Madison, WI, USA), sequenced on Illumina MiSeq™ platform (Illumina Inc., San Diego, CA, USA) using paired-end 250 bp reads at the Centre for Environmental Biotechnology (Bangor, UK), and sequencing reads were processed and analysed as described previously73.The screening, cloning and activity of a subset of 35 identified esterases have been reported previously72. The remaining 48 enzymes are reported for the first time in this study and were identified using naive and in silico metagenomic approaches, as detailed below. The environmental site from which each enzyme originated and the method employed for its identification are detailed in Supplementary Data S3. For naive screens addressing the recovery of new sequences encoding esterases and EXDO, the large-insert pCCFOS1 fosmid libraries made using the corresponding DNA samples, the CopyControl Fosmid Library Kit (Epicentre Biotechnologies, Madison, WI, USA) and the Escherichia coli EPI300-T1R strain were used. The nucleic acid extraction, construction and the functional screens of such libraries have been previously described72. In brief, fosmid clones were plated onto large (22.5 × 22.5 cm) Petri plates with Luria Bertani (LB) agar containing chloramphenicol (12.5 µg ml−1) and induction solution (Epicentre Biotechnologies; WI, USA), at a quantity recommended by the supplier to induce a high fosmid copy number. Clones were scored by the ability to hydrolyze α-naphthyl acetate and tributyrin (for esterase activity), and catechol (for EXDO activity)72,74. Positive clones presumed to contain esterases and EXDOs were selected, and their DNA inserts were sequenced using a MiSeq Sequencing System (Illumina, San Diego, USA) with a 2 × 150-bp sequencing v2 kit at Lifesequencing S.L. (Valencia, Spain). After sequencing, the reads were quality-filtered and assembled to generate nonredundant meta-sequences, and genes were predicted and annotated via BLASTP and the PSI-BLAST tool72. For in silico screens, addressing the recovery of new sequences encoding esterases, the predicted protein-coding genes, obtained after the sequencing of DNA material from resident microbial communities in each of the samples, were used. The meta-sequences are available from the National Center for Biotechnology Information (NCBI) nonredundant public database (accession numbers reported in Supplementary Data S3). Protein-coding genes identified from the DNA inserts of positive clones (naive screen) or from the meta-sequences were screened for enzymes of interest using the Blastp algorithm via the DIAMOND v2.0.9 program with default parameters (percentage of identity ≥60%; alignment length ≥70; e-value ≤1e−5)29, against the Lipase Engineering sequence databases (to screen for esterases) and AromaDeg database (for EXDO)74. Since the collection of sediments across locations experiencing different MATs was limited by our sampling capacity, to expand our range of exploration at a global scale and to validate our dataset, we added our single enzyme analysis to the seawater metagenomes retrieved from the Tara Ocean Expedition database (accession number in Supplementary Data S4). Due to the volume of sequences generated, this database provides access to a large number of enzymes, including those studied here through homology search. Esterases were selected as target sequences, and the following pipeline was used. First, we selected a sequence encoding an esterase reported as one of the most substrate-ambiguous esterases out of 145 tested (EH1, Protein Data Bank acc. nr. 5JD4) and well-distributed in the marine environment72. Second, we performed a homology search of this sequence against the Tara Ocean metagenome21 to retrieve similar sequences, using the Blastp algorithm via the DIAMOND v2.0.9 program30 (e-value 98% using SDS-PAGE analysis in a Mini PROTEAN electrophoresis system (Bio-Rad Laboratories, S.A., Madrid, Spain). Purified protein was stored at –86 °C until use at a concentration of 10 mg ml−1 in 40 mM HEPES buffer (pH 7.0). A total of approximately 5–40 mg of total purified recombinant protein was obtained from 1 L of culture. Supplementary Fig. S1 illustrates a schematic representation of the pipeline implemented in this work to investigate enzyme activities in a large set of marine samples, starting from samples collected (sediments) and available metagenomes.Enzyme activity assessmentsAll substrates used for activity tests were of the highest purity and, if not indicated otherwise, were obtained from Merck Life Science S.L.U. (Madrid, Spain): 4-nitrophenyl-propionate (ref. MFCD00024664), 4-nitrophenyl phosphate (ref. 487663), 4-nitrophenyl β-D-galactose (ref. N1252), bis(p-nitrophenyl) phosphate (ref. 123943), benzaldehyde (ref. B1334), 2-(4-nitrophenyl)ethan-1-amine (ref. 184802-5G), pyridoxal phosphate (ref. P9255), acetophenone (ref. A10701), NADPH (ref. N5130) and catechol (ref. PHL82372). We directly tested total protein extracts for esterase, phosphatase, beta-galactosidase, and nuclease activity using 4-nitrophenyl-propionate, 4-nitrophenyl phosphate, 4-nitrophenyl β-D-galactose, and bis(p-nitrophenyl) phosphate, respectively, by following the production of 4-nitrophenol at 348 nm (extinction coefficient [ε], 4147 M−1 cm−1), as previously described69. For determination: [total protein]: 5 μg ml−1; [substrate]: 0.8 mM; reaction volume: 200 μl; T: 4–85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). The hydrolysis of 4-nitrophenyl-propionate was used to determine, under these standard conditions, the effects of temperature on the purified esterase. Transaminase activity was determined using benzaldehyde as amine acceptor, 2-(4-nitrophenyl)ethan-1-amine as amine donor, and pyridoxal phosphate as a cofactor, by following the production of a colour amine at 600 nm (extinction coefficient, 537 M−1 cm−1), as previously described75. For determination, [total protein]: 5 μg ml−1; [substrates]: 25 mM; [pyridoxal phosphate]: 1 mM; reaction volume: 200 μL; T: 4-85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). Aldo-keto reductase activity was determined using acetophenone as a substrate and NADPH as a cofactor, by following the consumption of NADPH at 340 nm (extinction coefficient, 6220 M−1 cm−1), as described76. For determination, [total protein]: 5 μg ml−1; [substrate]: 1 mM; [cofactor]: 1 mM; reaction volume: 200 μL; T: 4–85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). We determined EXDO activity using catechol as substrate, by following the increase of absorbance at 375 nm of the ring fission products (extinction coefficient, 36000 M−1 cm−1), as previously described74. For determination, [protein]: 5 μg ml−1; [catechol]: 0.5 mM; reaction volume: 200 μL; T: 4–85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). The hydrolysis of catechol was used to determine, under these standard conditions, the effects of temperature on the purified EXDOs. All measurements were performed in 96-well plates (ref. 655801, Greiner Bio-One GmbH, Kremsmünster, Austria), in biological triplicates over 180 min in a Synergy HT Multi-Mode Microplate Reader (Biotek Instruments, Winooski, VT, USA) in continuous mode (measurements every 30 s) and determining the absorbance per minute from the slopes generated and applying the formula (1). All values were corrected for nonenzymatic transformation.$${Rate}left(frac{mu {mol}}{{{min }}{mg},{protein}}right)= frac{frac{triangle {{{{{rm{Abs}}}}}}}{{{min }}}}{{{{{{rm{varepsilon }}}}}},{{{{{rm{M}}}}}}-1{{{{{rm{cm}}}}}}-1}*frac{1}{0.4,{cm}}*frac{{10}^{6},mu M}{1{{{{{rm{M}}}}}}}\ *0.0002,L*frac{1}{{mg},{protein}}$$
(1)
Shotgun proteomicsProteomics was performed by using total active proteins (extracted as above), which were then subjected to protein precipitation, protein digestion and Liquid Chromatography-Electrospray Ionization Tandem Mass Spectrometric (LC-ESI-MS/MS) analysis, as previously described77. High-quality reference metagenomes corresponding to each sample (BioProject number in Supplementary Table S3) were used for protein calling, with a threshold of only one identified peptide per protein identification because False Discovery Rates (FDR) controlled experiments counter-intuitively suffer from the two-peptide rule. The confidence interval for protein identification was set to ≥95% (p  50 °C for which the second phase transition was chosen to focus on the decomposition of the core. It is important to note that applying CNA to MD simulations at room temperature may lead to an evening out of Tp values for esterases that transition around this temperature, i.e., systems with a Tp at or below room temperature might all be influenced similarly by loosening their bonding network. By contrast, systems with a transition temperature at or above room temperature would still be discriminated against. The data generated in this study for analyzing Tp values have been deposited at researchdata.hhu.de under accession code DOI: 10.25838/d5p-42101 [https://doi.org/10.25838/d5p-42].Relationship of temperature-induced changes in enzymeRelationship between MAT and enzyme response to temperature (i.e., Topt, Td and Tp) were evaluated by performing linear regression in R. In the case of enzymes retrieved from the Tara ocean dataset we calculated first the break point (flexus) using the package segmented in R102 and then we computed separately the linear model describing the two linear regressions before and after the breakpoint. To evaluate the possible relation between enzyme thermal response and other environmental parameters, salinity and pH data were retrieved from Bio-ORACLE52 using GPS coordinates of each location.Environmental characterization and sediment collection from different temperature variability levels in the Red SeaWe recorded the temperatures of surface sediments from March 2015 to September 2016 along the coast of the Red Sea using HOBO data loggers (Onset, USA) in nine stations located at 3, 25, and 50 m depth. Details on the location, depth and temperature fluctuations of the studied sediments are reported in Supplementary Table S4 and Source Data. We first assess the differences in the homogeneity of the temperature variance in the three types of sediments to evaluate the magnitude of thermal variation and then we test the difference among their MATs using a non-parametric ANOVA (Dunnett’s multiple comparisons tests). We identified three different levels of temperature variability (Fig. 3a–c; Supplementary Table S5): high, intermediate, and low thermal variability (HTV, ITV, and LTV, respectively), where sediments experienced temperature variations of 12.8 °C, 8.8 °C, and 6.7 °C, respectively. From each station, we sampled 200 g of surface sediment (0–5 cm depth) in triplicate in August and December 2015 with a Van der Venn grab (1 dm3) equipped with a MicroCat 250 Seabird CTD (Conductivity, Temperature, Depth), which was assembled on board the research vessel R/V Explorer (KAUST). During sampling, we measured the temperature of the sediments and the water layer covering the sediments using a digital thermometer and the CTD, respectively. We conducted all sampling in compliance with the guidelines specified by KAUST and Saudi Arabian authorities.Sediment processing for analysis of bacterial communitiesFrom each sample (in triplicate), we immediately removed subsamples of sediment (n = 54, ~10 g) and stored them at –20 °C for molecular analysis. Separately, sediment 25 ± 1 g was transferred to 50 ml tubes and added 30 ml of filtered (0.2 µm) water from the Red Sea. The tubes were shaken at 500 rpm for one hour and then centrifuged them at 300 g for 15 min to detach the microbial cells in the sediments without affecting their vitality103,104. The supernatant containing the extracted cells was collected in sterile tubes and was immediately used to measure microbial growth rates.Evaluation of bacterial growth in sediments at different temperaturesWe evaluated the microbial growth rate of the heterotrophic community extracted from the sediments under HTV, ITV, and LTV at 10 °C, 20 °C, 30 °C, 40 °C and 50 °C, using Marine Broth as the cultivation medium (Zobell Marine Broth 2216) supplemented with 0.1 g/L cycloheximide; a rich-medium was selected to avoid the nutrient limitation effect that can affect bacterial physiology63,105. We inoculated 96-well plates with 200 µl of cultivation medium and 25 µl of the cell suspension extracted from the sediments. We inoculated the three biological replicates from each station and each level of temperature variability in eight wells, giving a total of 72 wells for each plate, with 24 wells used as a negative control inoculated with water. We assembled a total of three plates for each incubation temperature from August and December. Plates were spectrophotometrically measured at 3 h intervals using an optical density of 600 nm (Spectramax® M5) for 72 h. Wells with optical density 90%) for further analysis (Supplementary Tables S9 and S10). We calculated the compositional similarity matrix (Bray-Curtis of the log-transformed OTU table) with Primer 6109. Using the same software, canonical analysis of principal coordinates (CAP)110 was used to compare the temperature variability samples (temperature variability levels: HTV, ITV, and LTV; season levels: August and December) based on the compositional similarity matrix. We applied permutational multivariate analyses of variance to the matrix (PERMANOVA; main and multiple comparison tests). We tested the occurrence of thermal-decay patterns in sediments with different temperature variability levels using linear regression (Prism 9.2 software, La Jolla California USA, www.graphpad.com) between the bacterial community similarities (Bray-Curtis) and the temperature differences among sediments (∆T°C) at the time of sampling. We calculated alphadiversity indices (richness and evenness) using the paleontological statistics (PAST) software, and their correlation with temperature was modelled using linear regression in Prism 9.2. Spearman correlation among temperature and relative abundance of OTUs within each sediment sample was evaluated; OTUs were classified based on their positive (enriched) and negative (depleted) correlation with sediment temperature.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

Global genetic structure of the pathogen tracks the historical spread of wheatWe assessed the evolutionary trajectory of the pathogen in conjunction with the history of global wheat cultivation (Fig. 1a). For this, we assembled a worldwide collection of Z. tritici isolates from naturally infected fields (Fig. 1b). We selected isolates covering most wheat production areas, both in the center of origin of the crop (i.e., the Fertile Crescent in the Middle East), and in areas where wheat was introduced during the last millennia (i.e., Europe and North Africa), or last centuries (i.e., the Americas and Oceania; Fig. 1c). We called variants in a set of 1109 high-quality short-read resequencing datasets (Supplementary Data 1, 2) covering 42 countries and a broad range of climates. Using a joint genotyping approach, we produced raw variant calls mapped to the telomere-to-telomere assembled reference genome IPO323. To assess genotyping accuracy, we used eight isolates with replicate sequencing data to analyze discrepancies. We adjusted quality thresholds targeting specifically the type of genotyping errors observed in our data set (Fig. S1). The improved filtering yielded 8,406,818 high-confidence short variants (short indels and SNPs). The final variant set included 5,578,488 biallelic SNPs corresponding to 14.1% of the genome.Fig. 1: Global sampling of the wheat pathogen Zymoseptoria tritici retracing the historical spread of its host.a Schematic representation of the introduction of wheat across continents. b Septoria tritici blotch symptoms caused by Z. tritici on wheat leaves. Pictures taken by B. A. McDonald, ETH Zurich. c Map of the sampling scheme for the global collection of 1109 isolates for whole-genome sequencing.Full size imageWe tested whether global diversity patterns of pathogen populations are likely a consequence of the history of wheat cultivation. We first performed unsupervised clustering of genotypes and identified eleven well-supported clusters (Fig. 2a, Figs. S2,3). Over 90% of the genotypes were clearly assigned to a single cluster (Fig. 2a, Supplementary Data 3). Two clusters were identified among genotypes originating from the pathogen center of origin, distinguishing collections from Iran and Middle Eastern regions. Genotypes from Africa and Europe split into two distinct genetic clusters without any apparent secondary structure within clusters. This lack of any fine-scale structure is remarkable given the extensive geographic sampling of European genotypes and suggests extensive gene flow within the continent. Genotypes from Oceania grouped into three distinct clusters marked by collections from Tasmania, the Australian mainland, and New Zealand. Genotypes from North America formed two clusters along a North-South separation. Finally, South American genotypes formed two clusters split along the Andes (Chile versus Argentina and Uruguay). Some uncertainty exists in the assessment of regional population structure by low coverage of major wheat-producing countries such as Russia and Ukraine. Septoria tritici blotch is only sporadically reported in China. In complementary analyses, we found that a phylogenetic network accounting for the high frequency of recombination consistently reflected the global population structure (Fig. S4). A principal component analysis of all genotypes confirmed the nested genetic structure with differentiation at the continent level, subdivisions within some continents and the existence of admixed genotypes (Fig. 2b, Fig. S5).Fig. 2: Global genetic structure based on 1109 genomes.a Map of the genetic clustering based on a thinned genome-wide SNP dataset using sNMF. Each color represents a different genetic cluster, and the sizes of the slices represent the average attribution to the cluster across the isolates from each location. Fractions representing less than 10% of all genotypes of a location were colored in grey to improve clarity. The large pie chart outside of the map represents the proportion of isolates assigned clearly (≥75%) to a single genetic cluster (pure; in teal) and isolates identified as hybrids (admixed) between clusters (in yellow). Names of the clusters include an abbreviation of continents and a more precise geographical location (MEA: Middle East and Africa; NA: North America; SA: South America; OC: Oceania). b Principal component analysis, showing the first and second component (PCs) based on a subset of variants. Colors and shapes indicate the genomic clusters identified with the sNMF method (with hybrids in grey). The marginal distributions represent the distribution for each PC. PCs 1 to 8 are shown in Fig S4. c Population tree based on Treemix, rooted using two genomes from the sister species Z. passerinii and Z. ardabiliae. The colors are the same as in the previous panels and only samples which were fully assigned to a cluster were used. d Diversity estimated with using pi per genetic cluster. The boxplots are ordered according to the tree of panel. c. The lower and upper hinges correspond to the first and third quartiles, the whiskers to the largest value are within 1.5 times the inter-quartile range, and the central horizontal line defines the median. e Linkage disequilibrium (r2) between variants per genetic cluster. Colors are identical among panels.Full size imageWe analyzed the history of population splits and admixture using allele frequency information (Fig. 2c). The analyses largely supported a genetic structure shaped by the introduction of wheat across continents. The historical relationships between clusters show an early divergence of the Middle Eastern and North African clusters matching the early introduction of agriculture in these regions. Populations in Europe and the Americas share a similar time point of divergence consistent with extensive contributions of European genotypes to the Western hemisphere. Oceanian groups have diverged as a single branch from genotypes most closely related to extant European populations. Matching the introduction of wheat to Oceania from the European continent, the Australian and New Zealand pathogen populations share a common origin rooted in European genetic diversity. Populations from Australia show also a striking loss of diversity and higher linkage disequilibrium compared to European diversity consistent with a significant founder effect (Fig. 2d, e). Similarly, populations in South and North America have reduced genetic diversity compared to extant European populations as suggested previously based on Sanger sequencing16. The highest diversity was found in populations from Africa and the Middle East closest to the center of origin. Overall, the global genetic structure of the pathogen reveals multiple founder events associated with the introduction of wheat to new continents.Ongoing gene flow among regions should lead to admixed genotypes. We found that nearly 10% of all analyzed genotypes showed contributions from at least two clusters. The most significant recent gene flow was detected between Middle Eastern/North African clusters and European clusters in North Africa (i.e., Algeria and Tunisia) as well as Southern and Eastern Europe (i.e., France, Italy, Hungary, Ukraine, Portugal, and Spain; Supplementary Data 3). We found a particularly high incidence of recent immigration in a durum wheat population in the south of France. The population consisted only of hybrids or atypical genotypes suggesting either recent migration from North Africa or host specialization on durum wheat varieties. Additionally, we found hybrid genotypes with European ancestry in both North America and in Oceania. The relatively balanced ancestry proportions in these hybrids suggest very recent gene flow dating back to only a few generations. We further investigated past gene flow between clusters by allowing Treemix to infer migration events, thus creating a population network (Fig. S6a–d). Three distinct recent migration events were best explaining the data with specific migration routes from the Middle East/African clusters to North America, from an Australian cluster to South America and between two Oceanian clusters (Fig. S6d). However, the migration events did not affect the overall shape of the inferred population tree (Fig. 2c, Fig. S6b–d). To better understand effects of long-distance gene flow, we investigated the relationship between relatedness among genotypes (i.e., identity-by-state) and geographic distance. At the continent level, we observed a negative relationship between identity-by-state and geographic distance (Fig. S7). The wide distribution of identity-by-state values shows that although closely related isolates tend to be found at closer geographic distance, distantly related isolates can be found at both far and close geographic distances. Long-distance migration events are most likely caused by international trade similar as for other crop pathogens17,18,19. In combination, our findings show an important role of long-distance dispersal impacting the genetic make-up of populations from individual fields to continental scale genetic diversity.Relaxation of genomic defenses against transposable elements concurrent with global spreadTransposable elements (TEs) are drivers of genome evolution. In Z. tritici, TE activity created beneficial mutations for fungicide resistance and virulence on the wheat host20,21. Rapid recent adaptation of the pathogen has benefitted from the activity of TEs with consequences for genome size22. Unchecked transposition of TEs can be deleterious and an array of defenses mechanisms has evolved to counteract their activity both at the genomic and epigenetic level including targeted mutations and silencing23. To analyze the effectiveness of genomic defenses against active TEs, we screened all genomes for evidence of TE insertions. We mapped short-read sequencing data on the reference genome and a species-specific TE sequence library. We classified evidence for TEs in each of the analyzed isolates as reference TEs (i.e. also present in the reference genome) and non-reference TE (i.e. absent). Detected TEs among isolates were binned into loci (width 100 bp) to account for uncertainties about the precise mapping of the insertion point. We found that the frequency spectrum of TE insertions is heavily skewed towards low frequencies with 77% of TE insertions being found in single isolates (~0.1% frequency) and 96% of insertions were found in ten or fewer isolates ( More