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Link activationContingency tables corresponding to the three cases described in the “Methods” section are shown in Table 1. This Table is quite revealing in several ways. The most interesting aspect is that the highest probability of link establishment occurs when an agreement is activated (Operational Activation in t).Table 1 Contingency tables.Full size tableIn this case, the probability of activation of a new link is 8.8%—namely, the ratio of new activation 7.3% to the total number of links that were not active at year t-1 (82.6%)—which is significantly higher than in the case of links not covered by a commercial agreement (No Trade Agreement), amounting to 1.4%.Therefore, the findings show that operational activation is associated with creating new trade relations between two particular countries. The third set, which considers links where a trade agreement exists in both years (t-1) and t (Trade Agreement in t-1 and t), also shows a consistent activation probability of 6%. This result confirms the assumption that the coverage of a commercial agreement, and not only its implementation, encourages the genesis of new links.Moreover, Table 1 suggests some interesting considerations on trade persistence. To establish these probabilities, we focus on the row totals in which a trade relationship is present at year (t-1), i.e., 28.8% in the case Trade Agreement in t-1 and t. The presence of an agreement influences in a positive way the probability of maintaining a trade relationship. In fact, when a trade agreement is present in both years, (t-1) and t, the probability of preserving the trade relationship is 87.1% ((frac{25.1}{28.8}times {100})), while when a trade agreement is activated at year t, the probability slightly decreases to 81.6%. In cases where trade agreements are missing (No Trade Agreement in t) we observe the probability of retaining a relationship decreases to 77.3%.Another interesting aspect concerns the probability of link deactivation. Once more, the coverage of a trade agreement favors a lower likelihood of deactivation of existing links. The ratio of the percentage of links that were active at year (t-1) and are no more active at year t to the total is 22.7% ((frac{1}{4.4}times {100})) in the case of a lack of agreement. This probability decreases to 18.4% ((frac{3.2}{17.4}times {100})) if we consider only the year of activation of the agreement (Operational Activation), and drops to 12.8% ((frac{3.7}{28.8}times {100})) when looking at agreements present in both years.Together, these results provide insights into the role of trade agreements in the network topology of cereal trade. While the establishment of a trade agreement promotes the potential for new trade links, the presence of the agreement in two consecutive years allows both to maintain an existing relationship and reduce the likelihood of link shutdowns.Flow variationsIn this second part, we study the impact of trade agreements on existing trade flows, analyzing the relationship between the flows at time t and the flows at time (t-1) in each of the three cases described in the “Methods” section—i.e., No trade agreements, Operational Activation in t, and Trade agreement in t-1 and t—measured in US$, Kcal and m(^3) of virtual water.Figure 3Kernel Density scatterplot between trade flows of cereals at time t (on the y-axis) and time (t-1) (on the x-axis) for the three different sets: No trade agreements (column a), Operational Activation in t (b), and Trade agreement in (t-1) and t (c). Panels in the first, second and third row refer to flows in US$, Kcal, and virtual water (m(^3)), respectively. Flow values are shown on a logarithmic scale. The color bar indicates probability densities, and the bisector is highlighted. Notice (i) the higher volumes in the case of flows covered by trade agreement and (ii) a a less relevant increase in volume when the flows are seen in the virtual water lens.Full size imageFigure 3 shows three different scatterplots for each unit of measure (US$ and Kcal and m(^3)). The scatterplots are colored by Kernel Density Estimation (KDE), a non-parametric technique for probability density functions. KDE aims to take a finite sample of data and infer the underlying probability density function. Figure 3 relates the flows at time (t-1) with the flows at time t, both reported on a logarithmic scale since the quantities span several orders of magnitude. Let’s start focusing on flows in terms of dollars and kilocalories. What stands out from the figure is the displacement of the flows toward higher values when they are covered by trade agreements (Trade Agreement in t-1 and t), compared to the case where flows have no trade agreement.We have quantitative evidence of this result by looking at Table 2 where the average flows in both years are shown. The average values of flows in both US$ and Kcal are much higher when there is a trade agreement over time (Trade agreement in t-1 and t). Flows have an average value of (6.13times 10^{7})$, larger than the mean of (3.05times 10^{7})$ achieved by flows not covered by a trade agreement. By comparing the distributions of the two distinct sets with different dimensions by applying the non-parametric Mann-Whitney test, we stand to evaluate this result as extremely significant (p-value approximately 0).Table 2 Average values of trade flows and flow variation index (rho _{ij}) for each of the three sets, in US$ (a), Kcal (b), and Virtual water (VW, m(^3)). The bar indicates the average operator.Full size tableAlso, while operational activation plays a crucial role in creating new links in the global cereal trade, it does not appear to hold central importance in driving flow increases. The average value of flows in both years (t-1) and t are, in fact, smaller than those not covered by trade agreements.The view appears slightly different when we look at the values in terms of virtual water (VW, m(^3)), i.e., the sum of the blue and green components. Flows with a commercial agreement show higher averages values than those not covered by agreements (see panel (c) of Table 2), but the increase is significantly lower than the one recorded in the other two units (US$ and Kcal). The increase recorded in dollars is about 100%, while in terms of virtual water this increase is less than 30%. In the next subsection, we will focus on this peculiar behavior, which reveals a different water content of the goods traded along links covered or not by agreements.Another significant result that emerges from Fig. 3 is the smaller amplitude (around the bisector) of the cloud in the case of link covered by agreements in both years (t-1) and t. This is confirmed by comparing the weighted average of the absolute value of the inter-annual flow variation index (overline{rho _{ij}}_{w}) (weights are the flows traded in the year (t-1)). The index (rho _{ij}) is used to highlight cases where the activation or the presence of the agreement generates a significant flow increase.Larger (rho _{ij}) values correspond to larger average variations from year (t-1) to year t. Accordingly, we observe that in the presence of trade agreement at time (t-1) and t a smaller (rho _{ij}) value of 24.79 percentage points (p.p) is found (see panel (a) of Table 2).Considering all the units (US$, Kcal, and m(^3)), this value is about half of the average inter-annual variation that occurs when there is no trade agreement. Hence, the presence of a commercial agreement over time reduces large fluctuations, stabilizing the year-to-year variations.To shed light on the response of water flows to the occurrence of the agreement, we refer to water productivity (WP)34, both in economic and nutritional terms. Table 3 shows that the Nutritional WP for the total virtual water is, on average, 35% higher in the flows under a trade agreement than in flows that are not under any treaty, while the Economic WP is 62% higher. We also analyze the two virtual water components, blue and green, separately.Interestingly, for blue water in the presence of a trade agreement, the Nutritional WP and the Economic WP for the flows covered by trade agreement are, on average, 68% and 93% higher than for the flows not covered by agreements. In other words, for one cubic meter of water used for grain production, more kilo-calories and dollars are exchanged when an agreement is in place, and this difference is even more significant in terms of blue water.Table 3 Average of nutritional ((mathrm {kcal/m^3})) and economic ((mathrm {US$/m^3})) water productivity (WP) for the total, blue and green virtual water.Full size tableWe also investigate in detail which products contribute most to the imbalance between flows in terms of kcal or water. To this aim, Fig. 4 reports the nutritional WP for each grain item distinguishing whether or not there is a commercial agreement (similar results occur if the economic WP is considered).The figure highlights that the nutritional WP is generally higher in the case where flows are covered by trade agreements (green bars). The most noticeable cases are Maize and Wheat, which are also the most traded products: the value of nutritional WP increases from 1978 (mathrm {kcal/m^3}) (No trade agreement) to 2851 (mathrm {kcal/m^3}) in case of a trade agreement for Wheat, and from 4471 (mathrm {kcal/m^3}) to 5026 for Maize.Figure 4The bar chart shows the nutritional WP for each cereal product in the two sets of Trade agreement in t-1 and t (in green) and No trade agreement (in red). The number over the bars represents the percentage of kcal traded for each product compared to the total kcal of all cereals. Note that green bars are higher than the red ones in 80% of cases.Full size imageA few products have a higher nutritional WP value when the flows are not involved in any treaty, e.g., Rye. This behavior can be traced back to a few flows that dominate the market between countries not linked by trade agreements. For example, trade in Rye in 2014 is attributable to just two major flows in terms of caloric intake relative to water quantity (notably, one between Germany and Japan, the other between Russia and Turkey).Figure 4 clearly shows that grains characterized by greater water efficiency generally move along the links covered by agreements.Performance of trade agreements in increasing flowOur results show that links covered by agreements exhibit larger flows than links not covered by treaties. We also intend to obtain information about the possible flow increase under a specific agreement.As mentioned in the “Methods” section, we selected only those operating links when the agreement came into force to evaluate the variation index ((rho _a)) under a specific treaty. Consequently, since there are trade agreements that came into force before the time interval considered, these are excluded from this analysis. As a result, the total number of agreements selected for this analysis is 99, 61 of which show an increase (positive (rho _{a}) values), while the remaining 38 exhibits a decrease in the flux intensities compared to the overall global trend. We present in Table 5 the results for positive (rho _{a}) variations, while trade agreements with negative (rho _{a}) values are reported in Supplementary Material (5). We provide this analysis in terms of economic flows (US$), but very similar results are obtained if calories (kcal) or virtual water (m(^3)) are chosen as the unit of measure.Table 4 Flow values in millions of dollars in year t and percent changes (rho _{a}) from (t-1) to t for each trade agreement.Full size tableWhat stands out in Table 4 is that most of the positive percentage changes occur in Europe and Central Asia regions. This may be due to long-term commercial activities in Europe, which are supported by the geographical proximity of the countries, as well as the wide variety of political and economic treaties among them. Europe, in fact, is characterized by a fourfold increase in cereal production since the 1960s due to the adoption of the Common Agricultural Policy, which has intensified trade in Europe and towards external markets30.A closer inspection of Table 4 shows that among the agreements with the most significant flows that showed the greatest increases, we find EEA (European Economic Area) in Europe and Central Asia, Japan-ASEAN in East Asia and Pacific, and COMESA in Sub-Saharan Africa.With lower flow values but large increases ((rho _{a})) due to the entry into force of trade agreements, the India-Sri Lanka agreement in South Asia stands out above all others. Also, the treaty signed in 2013 between EU-Colombia and Peru shows significant variations in terms of the percentage of flow increase, but the volume of the corresponding flow is inferior when compared with other trade agreements. On the other hand, the North American Free Trade Agreement (NAFTA), which became effective in 1994, has a lower (rho _{a}) value, but the flows on which the variation is calculated are significantly higher. More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Krabbenhoft, C. A. et al. Assessing placement bias of the global river gauge network. Nat. Sustain. https://doi.org/10.1038/s41893-022-00873-0 (2022). More

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General experimental proceduresAll chemicals were purchased from Sigma-Aldrich, Acros Organics or Iris BIOTECH. Isotope-labelled chemicals were purchased from Cambridge Isotope Laboratories. Genomic DNA of selected Xenorhabdus and Photorhabdus strains was isolated using the Qiagen Gentra Puregene Yeast/Bact Kit. DNA polymerases (Taq, Phusion and Q5) and restriction enzymes were purchased from New England Biolabs or Thermo Fisher Scientific. DNA primers were purchased from Eurofins MWG Operon. PCR amplifications were carried out on thermocyclers (SensoQuest). Polymerases were used according to the manufacturers’ instructions. DNA purification was performed from 1% Tris-acetate-EDTA (TAE) agarose gel using an Invisorb Spin DNA Extraction Kit (STRATEC Biomedical AG). Plasmids in E. coli were isolated by alkaline lysis. HPLC-UV-MS analysis was conducted on an UltiMate 3000 system (Thermo Fisher) coupled to an AmaZonX mass spectrometer (Bruker) with an ACQUITY UPLC BEH C18 column (130 Å, 2.1 mm × 100 mm, 1.7-μm particle size, Waters) at a flow rate of 0.6 ml min−1 (5–95% acetonitrile/water with 0.1% formic acid, vol/vol, 16 min, UV detection wavelength 190–800 nm). HPLC-UV-HRMS analysis was conducted on an UltiMate 3000 system (Thermo Fisher) coupled to an Impact II qTof mass spectrometer (Bruker) with an ACQUITY UPLC BEH C18 column (130 Å, 2.1 mm × 100 mm, 1.7-μm particle size, Waters) at a flow rate of 0.4 ml min−1 (5–95% acetonitrile/water with 0.1% formic acid, vol/vol, 16 min, UV detection wavelength 190–800 nm). Flash purification was performed on a Biotage SP1 flash purification system (Biotage) by a C18 main column (Interchim, PF50C18HP-F0080, 120 g) with a self-packed pre-column (Interchim, PF-DLE-F0012, Puriflash dry-load empty F0012 Flash column) coupled with a UV detector. HPLC purification was performed on preparative and semipreparative Agilent 1260 systems coupled to a diode array detector (DAD) and a single quadrupole detector with a C18 ZORBAX Eclipse XDB column (9.4 mm × 250 mm, 5 μm, 3 ml min−1; 21.2 mm × 250 mm, 5 μm, 20 ml min−1; 50 mm × 250 mm, 10 μm, 40 ml min−1). Freeze drying was performed using a BUCHI Lyovapor L-300 Continuous system. NMR experiments were carried out on a Bruker AVANCE 500-, 600- or 700-MHz spectrometer equipped with a 5-mm cryoprobe. 2R,3S-IOC (1) and GameXPeptide A (16) were synthesized by WuXi App Tec following the literature (ref. 73 for 2R,3S-1 and ref. 74 for 16).Genome sequencing, assembly and annotationIsolated DNA was sequenced on the Illumina NextSeq 500 platform. DNA libraries were constructed using the Nextera XT DNA preparation kit (Illumina) and whole-genome sequencing was performed using 2× 150-bp paired-end chemistry. A sequencing depth of >50× was targeted for each sample. Adapters and low-quality ends were trimmed with Trimmomatic 0.39 (ref. 75) and the parameters [2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:10 MINLEN:12] using a database of adapter sequences as provided by Illumina. All genomes were assembled using SPAdes v. 3.10.1 (ref. 76) executed with the following parameters: –cov-cutoff auto –careful in paired-end mode plus mate pairs (in cases where accompanying mate-pair libraries were available). Genome annotation was performed using Prokka v. 1.12 (ref. 77) with the following parameters: –usegenus–genus GENUS–addgenes–evalue 0.0001–rfam–kingdom Bacteria–gcode 11–gram –mincontiglen 200. Geneious Prime 2021 was used in genome visualization and analysis.antiSMASH annotations and BiG-FAM preliminary classificationThe antiSMASH 5.0 (ref. 23) web server was employed to mine all the genome sequences for the presence of putative natural-product BGCs. The annotations were conducted using default settings with the extended parameters of ClusterBlast, Cluster Pfam analysis and Pfam-based GO term annotation. The annotated BGCs were summarized for each strain (Supplementary Fig. 1) and visualized in the anvi’o 6.1 (refs. 28,78) layers (Fig. 1 and Supplementary Figs. 3 and 5). We then submitted the antiSMASH job IDs to the biosynthetic gene cluster families database (BiG-FAM 1.0.0)25 for preliminary GCF explorations and classifications of annotated BGCs (Supplementary Table 3), followed by BiG-SCAPE 1.0.0 (ref. 42) refinement with a cutoff of 0.65 (Source Data Fig. 3). The GCFs were double-checked manually via the interactive network (Fig. 3), and corrections were made if necessary. A putative thiopeptide BGC (Xszus_1.region006, Xsze_2.region003, Xsto_4.region001, Xpb_30.3_21.region001, Xmir_10.region001, Xmau_6.region001, Xkoz_3.region001, Xjap_NZ_FOVO01000011.region001, Xish_1.region003, Xhom_ANU1.region005, Xhom_2.region003, Xets_11.region001, XenKK7.region002, XenDL20_c00108_NODE_12.region001, Xekj_19.region001, Xehl_28.region001, Xe30TX1_c0031_NODE_38.region001, Xdo_HBLC131_1.region001, Xdo_FRM16.1.region005, Xbov_NC_013892.1.region004, Ptem_HBLC135_17.region001, Ppb6_4.region001, Plum_TT01_1.region008, Pthr_PT1.1_23.region001, Plau_IT4.1_12.region001, Plum_IL9_35_scf0001.region001, Pbod_HU2.3_20.region001, Plau_HB1.3_105.region001, Plum_EN01_24_scf0009.region001, Pbod_DE6.1_24.region001, Plau_DE2.2_108.region001, Phpb_1.region001, Pbod_LJ_007.region001, Pbod_CN4_25_scf0020.region001, Paeg_BKT4.5_19.region001, P_tem_1.region017 and so on) that exists throughout 45 XP genomes was excluded in the analysis, because it turned out that its annotation by antiSMASH 5.0 is a false positive and early reports suggest that this cluster is responsible for ribosomal methylthiolation79,80. Two BGCs, Xdo_HBLC131_4.region001 encoding the biosynthesis of glidobactins in X. doucetiae HBLC131 and Ptem_HBLC135_2.region002 encoding the biosynthesis of ririwpeptides in P. temperata HBLC135, were artificially integrated into their respective genome by CRAGE39 previously, and thus the two BGCs were also excluded in our analysis.Pangenome analysisBiosynthetic gene cluster boundary definitionThe cluster boundary was defined by antiSMASH with the start nucleotide of the first biosynthetic gene (5′ end) and the stop nucleotide of the last biosynthetic gene (3′ end), and was manually corrected if necessary. Non-structural genes (such as transporters, regulators, transposases and so on) on the outer periphery of an operon were excluded. We compiled a table with contigs of all BGCs encoded by a given genome, BGC start and stop nucleotide positions, BGC classifications by antiSMASH and BiG-SCAPE (see the BiG-SCAPE analysis section), and possible biosynthetic pathways that the BGCs encode (Source Data Fig. 1). These tables would be integrated into the contigs databases of the pangenome for filtering the biosynthetic genes and monitoring distributions of biosynthetic gene homology groups.Interface generationAll genomes were obtained from the National Center for Biotechnology Information (NCBI). Supplementary Table 1 reports their accession numbers. The pangenome analysis herein mainly followed the anvi’o 6.1 pangenomic workflow28,78. After simplifying the header lines of 45 FASTA files for genomes using ‘anvi-script-reformat-fasta’, we converted FASTA files into anvi’o contigs databases by the ‘anvi-gen-contigs-database’ and then decorated the contigs database with hits from HMM models by ‘anvi-run-hmms’. The program ‘anvi-run-ncbi-cogs’ was run to annotate genes in the contigs databases with functions from the NCBI’s Clusters of Orthologous Groups (COGs). Tables of gene caller IDs with start and stop nucleotide positions were exported by ‘anvi-export-table’. By linking the gene caller IDs with BGCs via the start and stop nucleotide positions, genes that fell within a given BGC boundary were considered to be natural product biosynthetic genes (Source Data Fig. 1). Thereafter, the biosynthetic genes were furnished with a classification and a possible compound name, both of which were derived from the BGC that the biosynthetic genes made up. The obtained tables were imported back to contigs databases by ‘anvi-import-functions’. External genome storage was created by ‘anvi-gen-genomes-storage’ to store DNA and amino-acid sequences, as well as functional annotations of each gene. With the genome storage in hand, we used the program ‘anvi-pan-genome’ with the genomes storage database, the flag ‘–use-ncbi-blast’ and the parameter ‘–mcl-inflation 8’. The results were displayed in an interface by ‘anvi-display-pan’. The organization of the pangenome interface as shown in the dendrogram in the centre was represented by ‘presence/absence’ patterns. The core gene bin was characterized by searching the gene homology group (gene homology group represents amino-acid sequences from one or more genomes aligned by muscle81) using filters with ‘Min number of genomes gene homology group occurs, value = 45’. The singleton bin was identified by ‘Max number of genomes gene homology group occurs, value = 1’. The rest of the gene clusters that were neither sorted into the core gene bin nor the singleton bin were appended to the accessory bin. The single-copy-core-gene (scg) bin was found by ‘Min number of genomes gene homology group occurs, value = 45’ and ‘Max number of genes from each genome, value = 1’. The scg bin was refined by ‘Max functional homogeneity index 0.9’ and ‘Min geometric homogeneity index 1’. The resulting protein sequences were exported by ‘anvi-get-sequences-for-gene-clusters’ and aligned using ClustalW 1.2.2, which is incorporated in Geneious Prime 2021. Phylogenetic trees were generated using the Geneious tree builder utilizing the Jukes–Cantor distance model and the unweighted pair group method with arithmetic mean (UPGMA), and subsequently imported back to anvi’o by ‘anvi-import-misc-data’ and visualized by the interface. The statistical data of BGCs obtained from antiSMASH 5.0 (ref. 23) and BiG-SCAPE42 were imported to the layers of the interface by ‘anvi-import-misc-data’ for visualization.Biosynthetic gene and biosynthetic gene cluster filteringThe bin summary (scg, core, accessory and singleton) with BGC classifications was exported by ‘anvi-summarize’ to monitor the distributions of the biosynthetic gene homology group in the pangenomes (Source Data Fig. 1 and Supplementary Data). In the Excel sheets, ‘core’ and ‘scg’ filters were selected from the ‘bin_name’ column, and the ‘(Blank)’ filter from the ‘BGC_classification’ column was unselected. The table was then sorted by ‘genome_name’ and ‘gene_callers_id’ columns in ascending order. This then displayed consecutive core biosynthetic genes that could possibly make up a BGC. The same procedure was used to filter BGCs in the accessory or singleton region.BiG-SCAPE analysisBGCs in all genome sequences obtained from antiSMASH 5.0 (ref. 23) analyses were compared to reference BGCs from MIBiG repository 2.0 (refs. 41,82) using BiG-SCAPE 1.0.0 (ref. 42) with the PFAM database 32.0 (ref. 83). The analysis was conducted using default settings with the mode ‘auto’, mixing all classes and retaining singletons. Networks were computed for raw distance cutoffs of 0.30–0.95 in increments of 0.05. Results were visualized as a network using Cytoscape 3.7.2 (ref. 84) for a cutoff of 0.65 (Fig. 3 and Source Data Fig. 3). Statistical data for the BGCs were analysed and evaluated using Origin 2020b and Excel from Microsoft Office 365.Strain and culture conditionsWild-type strains and the mutants thereof and E. coli (Supplementary Table 14) were cultivated on lysogeny broth (LB) agar plates at 30 °C overnight, and subsequently inoculated into liquid LB culture at 30 °C with shaking at 200 r.p.m. For compound production, the overnight LB culture was transferred into 5 ml of LB, XPP19 or Sf-900 II SFM medium (1:100, vol/vol) with 2% (vol/vol) Amberlite XAD-16 resins, 0.1% l-arabinose as the inducer for mutants with a PBAD promoter, and selective antibiotics such as ampicillin (Am, 100 µg ml−1), kanamycin (Km, 50 µg ml−1) or chloramphenicol (Cm, 34 µg ml−1) at 30 °C, with shaking at 200 r.p.m.Culture extraction and HPLC-UV-MS analysisThe XAD-16 resins were collected after 72 h and extracted with 5 ml of methanol or ethyl acetate. The solvent was dried under rotary evaporators, and the dried extract was resuspended in 500 μl of methanol or acetonitrile/water (1:1 vol/vol for photoxenobactins), of which 5 μl was injected and analysed by HPLC-UV-MS or HPLC-UV-HRMS. Unless otherwise specified, HPLC-UV-MS and HPLC-UV-HRMS chromatograms in the figures are shown on the same scale. Bruker Compass DataAnalysis 4.3 was used for data collection and analysis of chromatography and MS. MetabolicDetec 2.1 was utilized to differentiate MS profiles between induced and non-induced promoter insertion mutants for identifying possible metabolites produced by targeted BGCs.Construction of PBAD promoter insertion mutantsA 500–800-bp section upstream of the target gene (lpcS, pxbF, rdb1A and xvbA) was amplified with a corresponding primer pair as listed in Supplementary Table 15. The resulting fragments were cloned using Hot Fusion85 into a pCEP_kan or pCEP_cm backbone that was amplified by pCEP_Fw and pCEP_Rv. After transformation of a constructed plasmid into E. coli S17-1 λ pir, clones were verified by PCR with primers pCEP-Ve-Fw and pDS132-Ve-Rv. A wild-type strain (X. bovienii SS-2004, X. szentirmaii DSM 16338, X. budapestensis DSM 16342 or X. vietnamensis DSM 22392) or a deletion mutant (X. szentirmaii ∆hfq, X. budapestensis ∆rdb1P or X. budapestensis ∆rdb1P ∆hfq) was used as a recipient strain. The recipient strain was mated with E. coli S17-1 λ pir (donor) carrying a constructed plasmid (Supplementary Table 16). Both strains were grown in the LB medium to an optical density at 600 nm (OD600) of 0.6 to 0.7, and the cells were washed once with fresh LB medium. Subsequently, the donor and recipient strains were mixed on an LB agar plate in ratios of 1:3 and 3:1, and incubated at 37 °C for 3 h followed by incubation at 30 °C for 21 h. After that, the bacterial cell layer was collected with an inoculating loop and resuspended in 2 ml of fresh LB medium. A 200-μl sample of the resuspended culture was spread out on an LB agar plate with Am/Km or Am/Cm and incubated at 30 °C for two days. Individual insertion clones were cultivated and analysed by HPLC-UV-HRMS, and the genotype of all mutants was verified by plasmid- and genome-specific primers.Construction of deletion mutantsA ~1,000-bp upstream and a ~1,000-bp downstream fragment of hfq in X. budapestensis DSM 16342 were amplified using the primer pairs listed in Supplementary Table 15. The amplified fragments were fused using the complementary overhangs introduced by primers and cloned into the pEB17 vector that was linearized with PstI and BglII by Hot Fusion85. Transformation of E. coli S17-1 λ pir with the resulting plasmid (Supplementary Table 16) and conjugation with X. budapestensis DSM 16342, as well as the generation of double crossover mutants via counterselection on LB plates containing 6% sucrose, were carried out as previously described86. The deletion mutant was verified via PCR using the primer pairs listed in Supplementary Table 15, which yielded a ~2,000-bp fragment for mutants genetically equal to the WT strain and a ~1,000-bp fragment for the desired deletion mutant. The same procedure was used to generate Δrdb1P mutants, during which E. coli S17-1 λ pir carrying pEB17 rdb1P was mated with the X. budapestensis DSM 16342 wild-type and X. budapestensis ∆hfq mutant.Labelling experiments for structural elucidation of photoxenobactins C and D by MSThe cultivation of strains for labelling experiments was carried out as described above. For photoxenobactin C (6) labelling experiments, the overnight culture was transferred into LB medium additionally fed with 4-fluorosalicylate-SNAC, l-methionine-(methyl-d3), l-[U-13C,15N]cysteine and l-[U-34S]cysteine at a final concentration of 1 mM. In terms of inverse feeding experiments, cell pellets of the 100-μl overnight culture were washed once with ISOGRO 13C or 13C,15N medium (100 μl) and resuspended in the corresponding isotope labelling medium (100 μl). The feeding culture in the isotope labelling medium (5 ml) was inoculated with a washed overnight culture (50 μl) and additional l-cysteine was added at a final concentration of 1 mM.For photoxenobactin D (7) labelling experiments, the cell pellets of the 100-μl overnight culture were washed once with ISOGRO 13C or 15N medium (100 μl) and then resuspended in the corresponding isotope labelling medium (100 μl). A 5-ml isotope labelling medium was inoculated with a washed overnight culture (50 μl).Isolation and purificationFor photoxenobactin isolation, 10 ml of LB medium was inoculated with a colony of the X. szentirmaii PBAD pxbF ∆hfq mutant from an LB agar plate and cultivated overnight. A 10-ml culture was taken to inoculate 2 × 100 ml of LB medium (OD600 ≈ 0.1). The 2 × 100-ml cultures were incubated overnight and the whole culture volume (200 ml) was used to inoculate a 20-l LB fermenter (Braun) supplemented with 2% XAD-16 and 0.2% arabinose (antifoam was added when required). Fermenter settings were as follows: 30 °C without pH control, three six-blade impellers 150 r.p.m. After 24 h, 10 l of the culture was collected from the fermenter, and the XAD resins were separated from the cells by filtration. (1) The XAD resins were extracted with 2 × 1 l of ethyl acetate with 1% formic acid, and the combined organic phase was dried under reduced pressure. (2) The culture without XAD was centrifuged and the supernatant was extracted with 3 × 5 l of ethyl acetate with 1% formic acid, and the combined organic layers were dried under reduced pressure. (3) The cell pellet was extracted with 2 × 1 l of ethyl acetate with 1% formic acid, and the organic supernatant was dried under reduced pressure. After 48 h, the remaining 10 l of bacterial culture were extracted as described in steps (1) to (3). The combined extracts from 20 l of culture were fractionated by a flash purification system with a C18 column with a gradient elution of acetonitrile/water 20–100% at 20 ml min−1 (every 10% gradient step was performed with five column volumes, except the 60–70% step, which was performed with ten column volumes). Fractions containing photoxenobactins were combined and dried under reduced pressure. Final purification was achieved via preparative and semipreparative HPLCs with a gradient of 30% acetonitrile/water (0–30 min) and 30–100% acetonitrile/water (30–40 min). The fractions were combined in brown flasks and were immediately freeze-dried to afford photoxenobactin A (4, 0.8 mg), photoxenobactin B (5, 0.6 mg), photoxenobactin C (6, 1.2 mg) and photoxenobactin E (8, 2.2 mg).For the isolation and purification of lipocitides A and B, 2% of XAD-16 resins from a 6-l LB culture of the X. bovienii PBAD lpcS mutant induced by l-arabinose were collected after 72 h of incubation at 30 °C with shaking at 120 r.p.m., and were washed with water and extracted with methanol (3 × 1 l) to yield a crude extract (5.3 g after evaporation). The extract was dissolved in methanol and was subjected to preparative HPLC with a C18 column using an acetonitrile/water gradient (0.1% formic acid) for 0–32 min, 55–80%, 40 ml min−1 to afford lipocitides A (17, 4.8 mg) and B (18, 9.0 mg).Two percent of XAD-16 resins from a 12-l LB culture of the X. budapestensis PBAD rdb1A ∆rdb1P ∆hfq mutant induced by l-arabinose were collected after 72 h of incubation at 30 °C with shaking at 120 r.p.m. and washed with water and extracted with methanol (3 × 2 l) to yield a crude extract (15.3 g after evaporation). The extract was subject to a Sephadex LH-20 column eluted with methanol. The fraction (2.8 g) containing pre-rhabdobranins was subjected to preparative HPLC with a C18 column using an acetonitrile/water gradient (0.1% formic acid) for 0–20 min, 15–35%, 40 ml min−1 to afford a fraction (206 mg) mainly containing pre-rhabdobranin D, which was further purified by semipreparative HPLC with a C18 column using an acetonitrile/water gradient (0.1% formic acid) for 0–24 min, 5–53%, 3 ml min−1 to afford pre-rhabdobranin D (27, 59.1 mg).Benzobactin A (28) and its methyl ester (29), which were detected in X. vietnamensis PBAD xvbA, were also produced by Pseudomonas chlororaphis subsp. piscium DSM 21509 (unpublished). Owing to the high production level in Pseudomonas chlororaphis subsp. piscium DSM 21509, 28 and 29 were isolated from the Pseudomonas strain. Four percent of XAD-16 resins from a 12-l XPP culture of Pseudomonas chlororaphis subsp. piscium DSM 21509 PBAD pbzA mutant induced by l-arabinose were collected after 72 h of incubation at 30 °C with shaking at 120 r.p.m., and washed with water and extracted with methanol (3 × 2 l) to yield a crude extract (95.4 g after evaporation). The extract was dissolved in methanol and subjected to preparative HPLC with a C18 column using an acetonitrile/water gradient (0.1% formic acid) for 0–18 min, 5–59%, 20 ml min−1 to afford ten fractions. Fractions 2 (95.6 mg) and 3 (50.7 mg) were further purified by semipreparative HPLC with a C18 column using an acetonitrile/water gradient (0.1% formic acid) for 0–35 min, 5–95%, 3 ml min−1 to afford benzobactin A (28, 3.2 mg) and its methyl ester (29, 0.9 mg), respectively.NMR spectroscopyMeasurements were carried out using 1H and 13C NMR, 1H-13C heteronuclear single quantum coherence (HSQC), 1H-13C heteronuclear multiple bond correlation (HMBC), 1H-1H correlation spectroscopy (COSY), 1H-13C heteronuclear multiple quantum correlation/1H-1H correlation spectroscopy (HMQC-COSY) and 1H-13C heteronuclear single quantum coherence/1H-1H total correlation spectroscopy (HSQC-TOCSY). Chemical shifts (δ) were reported in parts per million (ppm) and referenced to the solvent signals. Data are reported as follows: chemical shift, multiplicity (br = broad, s = singlet, d = doublet, t = triplet, dd = doublet of doublet, m = multiplet and ov = overlapped) and coupling constants (in hertz). Bruker TopSpin 4.0 was used for NMR data collection and spectral interpretation.General synthetic proceduresThe Fmoc protecting group was removed with 2 ml of 40% piperidine/dimethylformamide (DMF; 5 min) followed by 2 ml of 20% piperidine/DMF (10 min). Washings between coupling and deprotection steps were performed with DMF (five syringe volumes) and dichloromethane (DCM) (five syringe volumes). Resin loadings were determined by Fmoc cleavage from a weighted resin sample87. The combined filtrates containing Fmoc cleavage products were quantified spectrophotometrically at 301 nm using a UV–vis spectrophotometer with Hellma absorption cuvettes with a path length of 1 cm. Loadings were calculated (in mmol resin) using Lambert–Beer’s law with ɛ = 7,800 M−1 cm−1: loading (mmol) = ({frac{{{rm{Abs}},{({rm{sample}})}}}{{varepsilon l}}} times V), where ɛ is the molar extinction coefficient, V is the sample volume in liter and l is the optical path length in cm. Final cleavage was achieved by shaking the resin in 2 ml of a mixture of TFA/TIPS/H2O (95:2.5:2.5) for 1 h. The filtrate was then collected and the resin washed three times (2 ml each) with DCM, and the combined filtrates were dried under reduced pressure.Syntheses of lipocitide AFmoc-protected Rink Amide resin (192 mg, 0.52 mmol g−1, 0.1 mmol) was placed in a polypropylene 6-ml syringe vessel fitted with polyethylene porous filter disks and swollen in 3 ml of DMF for 10 min. Subsequently, the Fmoc-protected resin was deprotected and then washed as described in the general synthetic procedures. Fmoc-d-Cit-OH (198.0 mg, 0.5 mmol, 5 equiv.), 1-hydroxy-7-azabenzotriazole (HOAT, 0.83 ml, 0.5 mmol, 5 equiv.), hexafluorophosphate azabenzotriazole tetramethyl uronium (HATU, 190.5 mg, 0.5 mmol, 5 equiv.) and N,N-diisopropylethylamine (DIPEA, 170 μl, 1.0 mmol, 10 equiv.) were dissolved in 1.5 ml of dry DMF. After 5 min, the clear solution was added to the resin and shaken at room temperature overnight. The resin was washed and loading was calculated (79.2%) as described in the general synthetic procedures. Acylation of Fmoc-l-Ala-OH (74.1 mg, 0.24 mmol, 3 equiv.), Fmoc-d-Leu-OH (84.8 mg, 0.24 mmol, 3 equiv.) and myristic acid (54.8 mg, 0.24 mmol, 3 equiv.) were carried out using the abovementioned procedure. Final cleavage was performed as described in the general synthetic procedures, and the crude product (70.8 mg) was purified by HPLC to obtain lipocitide A (17, Supplementary Fig. 100; 24.3 mg, 54.0%) as a white solid.Syntheses of lipocitide B2-CTC resin (63 mg, 1.6 mmol g−1, 0.1 mmol) was placed in a polypropylene 6-ml syringe vessel fitted with polyethylene porous filter disks. The resin was incubated with Fmoc-d-Cit-OH (119.0 mg, 0.3 mmol, 3 equiv.) and DIPEA (153 μl, 0.9 mmol, 9 equiv.) in 1.5 ml of dry DCM at room temperature overnight. The resin was washed and loading was calculated (56.7%) as described in the general synthetic procedures. Acylations of Fmoc-l-Ala-OH (52.9 mg, 0.17 mmol, 3 equiv.), Fmoc-d-Leu-OH (60.1 mg, 0.17 mmol, 3 equiv.) and myristic acid (38.9 mg, 0.24 mmol, 3 equiv.) were performed with additional HOAT (0.47 ml, 0.28 mmol, 5 equiv.), HATU (108 mg, 0.28 mmol, 5 equiv.) and DIPEA (96 μl, 0.56 mmol, 10 equiv.). Final cleavage was carried out as described in the general synthetic procedures, and the crude (54.2 mg) was purified by HPLC to obtain lipocitide B (18, Supplementary Fig. 101; 18.6 mg, 57.6%) as a white solid.Synthesis of S-(2-acetamidoethyl)4-fluoro-2-hydroxybenzothioate (4-fluorosalicylate SNAC)To a solution of 4-fluorosalicylic acid (156 mg, 1.0 mmol, 1.0 equiv.) and hydroxybenzotriazole (HOBt, 162 mg, 1.2 mmol, 1.2 equiv.) in 45 ml of THF, N,N′-dicyclohexylcarbodiimide (DCC, 248 mg, 1.2 mmol, 1.2 equiv.) was added, followed by N-acetylcysteamine (112 µl, 1.0 mmol, 1.0 equiv.). After 1 h at room temperature, K2CO3 (138 mg, 1.0 mmol, 1.2 equiv.) was added and the reaction was stirred for an additional 2 h. The reaction mixture was then filtered and concentrated by rotary evaporation. The solid residue was dissolved in ethyl acetate and washed with sat. NaHCO3 (50 ml) and water (50 ml). The organic layer was dried over MgSO4, concentrated, and purified by flash chromatography (1–10% MeOH in CHCl3) to give 26 mg (10%) S-(2-acetamidoethyl)4-fluoro-2-hydroxybenzothioate (Supplementary Fig. 102).IC50 value determination with the purified yeast 20S proteasome core particleYeast 20S proteasome core particle (yCP) from Saccharomyces cerevisiae was purified according to previously described methods88,89. The concentration of purified yCP was determined spectrophotometrically at 280 nm. yCP (final concentration: 0.05 mg ml−1 in 100 mM Tris-HCl, pH 7.5) was mixed with dimethyl sulfoxide (DMSO) as a control or serial dilutions of IOC (1) in DMSO, thereby not surpassing a final concentration of 10% (vol/vol) DMSO. After an incubation time of 45 min at room temperature, fluorogenic substrates Boc-Leu-Arg-Arg-AMC (AMC, 7-amino-4-methylcoumarin), Z-Leu-Leu-Glu-AMC and Suc-Leu-Leu-Val-Tyr-AMC (final concentration of 200 µM) were added to measure the residual activity of caspase-like (C-L, β1 subunit), trypsin-like (T-L, β2 subunit) and chymotrypsin-like (ChT-L, β5 subunit), respectively. The assay mixture was incubated for another 60 min at room temperature, then diluted 1:10 in 20 mM Tris-HCl, pH 7.5. The AMC molecules released by hydrolysis were measured in triplicate with a Varian Cary Eclipse fluorescence spectrophotometer (Agilent Technologies) at λexc = 360 nm and λem = 460 nm. Relative fluorescence units were normalized to the DMSO-treated control. The calculated residual activities were plotted against the logarithm of the applied inhibitor concentration and fitted with GraphPad Prism 9.0.2. IC50 values were deduced from the fitted data. These depend on enzyme concentration and are comparable within the same experimental settings.Crystallization and structure determination of the yCP in complex with IOC (1)Crystals of the yCP were grown in hanging drops at 20 °C, as previously described88,89. The protein concentration used for crystallization was 40 mg ml−1 in Tris/HCl (20 mM, pH 7.5) and EDTA (1 mM). The drops contained 1 μl of protein and 1 μl of the reservoir solution (30 mM magnesium acetate, 100 mM 2-(N-morpholino)ethanesulfonic acid (pH 6.7) and 10% (wt/vol) 2-methyl-2,4-pentanediol). Crystals appeared after two days and were incubated with 1 at a final concentration of 10 mM for at least 24 h. Droplets were then complemented with a cryoprotecting buffer (30% (wt/vol) 2-methyl-2,4-pentanediol, 15 mM magnesium acetate, 100 mM 2-(N-morpholino)ethanesulfonic acid, pH 6.9) and vitrified in liquid nitrogen. The dataset from the yCP:IOC complex was collected using synchrotron radiation (λ = 1.0 Å) at the X06SA-beamline (Swiss Light Source). X-ray intensities and data reduction were evaluated using the XDS program package version 5 February 2021 (Supplementary Table 17)90. Conventional crystallographic rigid body, positional and temperature factor refinements were carried out with REFMAC5 5.0.32 (ref. 91) and the CCP4 Program Suite 7.1.016 (ref. 92) using coordinates of the yCP structure as the starting model (PDB 5CZ4)50. Model building was performed by the programs SYBYL-X and COOT 0.8.7 (ref. 93). The final coordinates yielded excellent residual factors, as well as geometric bond and angle values. Coordinates were confirmed to fulfil the Ramachandran plot and have been deposited in the RCSB (PDB 7O2L).Haemocyte-spreading assaysSpodoptera exigua larvae were collected from Welsh onion (Allium fistulsum L.) fields in Andong, Korea. Insects were reared in the laboratory under the following conditions: 25 ± 2 °C constant temperature, 16:8 h (light/dark) photoperiod and 60 ± 5% relative humidity. Larvae were reared on an artificial diet94 and 10% sucrose solutions were fed to adult insects. Fifth instar larvae were used in all experiments. For analysing haemocyte behaviours in vivo, fifth instar larvae of S. exigua were co-injected with 1 µl of heat-killed (95 °C for 10 min) E. coli TOP10 (2.4 × 104 cells per larva) with the test compound (0–1,000 ng per larva) by using a Hamilton microsyringe (Reno). At 1 h post-injection, 10 µl of haemolymph from each larva was collected on the glass slide and incubated for 5 min inside a dark wet chamber at room temperature. The medium was replaced with 3.7% of formaldehyde dissolved in phosphate buffered saline (PBS) and incubated for 10 min. After washing three times with PBS, cells were permeabilized with 0.2% Triton X-100 in PBS for 2 min at room temperature. After incubation, the slides were washed with PBS three times. Blocking was performed using 5% skimmed milk (Invitrogen) dissolved in PBS, followed by incubation for 10 min. After washing once with PBS, the cells were incubated with fluorescein isothiocyanate (FITC)-tagged phalloidin in PBS for 1 h at room temperature. After washing three times, the cells were incubated with 4′,6-diamidino-2-phenylindole (DAPI, 1 mg ml−1, Thermo Scientific) in PBS for nucleus staining. Finally, after washing twice in PBS, cells were observed under a fluorescence microscope (DM2500, Leica) at ×400 magnification. Haemocyte spreading was determined by the extension of F-actin out of the original cell boundary. For the in vitro assay, ~100 μl of haemolymph was collected into 400 μl of anticoagulation buffer (ACB; 186 mM NaCl, 17 mM Na2EDTA, 41 mM citric acid, pH 4.5). After adding ACB, the medium was incubated for 30 min on ice. After centrifugation at 300g for 5 min, 400 μl of supernatant was discarded. The rest of the suspension was gently mixed with 200 μl of TC100 insect tissue culture medium (Welgene). From this suspension, 10 µl of haemolymph was collected on the glass slide. The slides were co-injected with 1 µl of E. coli TOP10 (2.4 × 104 cells per larva) with the test compound (0–1,000 ng per larva), followed by the procedure described above. Means were compared by a least squared difference (LSD) test of one-way analysis of variance (ANOVA) using POC GLM of the SAS program (SAS Institute, 1989) and discriminated at type I error = 0.05.Nodulation assaysE. coli TOP10 was heat-killed by incubating at 95 °C for 10 min. Fifth-instar larvae of S. exigua were injected with 1 µl of bacteria (2.4 × 104 cells per larva) using a Hamilton microsyringe along with 1 µl of different concentrations (10, 50, 100, 500 and 1,000 ppm) of inhibitors. Control larvae were injected with bacteria and DMSO. At 8 h after bacterial injection, nodules were counted by dissecting larvae under a stereomicroscope (Stemi SV 11, Zeiss) at ×50 magnification.Phenoloxidase activity assaysThe PO activity from plasma was estimated as previously described95. Briefly, DOPA (l-3,4-dihydroxyphenylalanine) was used as a substrate for determining PO activity from treated larvae plasma. For PO activation, each fifth-instar larva of S. exigua was challenged with 2.4 × 104 cells of heat-killed E. coli TOP10. Different inhibitors were co-injected (1 µg per larva) along with E. coli TOP10. After 8 h of bacterial challenge, haemolymph was collected from treated larvae in a 1.5-ml tube containing a few granules of phenylthiocarbamide (Sigma-Aldrich) to prevent melanization. Haemocytes were separated from plasma by centrifuging at 4 °C for 5 min at 300g. A reaction volume of 200 µl consisted of 180 µl of 10 mM DOPA in PBS (pH 7.4) and 20 µl of plasma. Absorbance was measured using a VICTOR multi-label plate reader (PerkinElmer) at 490 nm. PO activity was expressed as ΔABS per min per µl of plasma. Each treatment was replicated three times with independent samples.Measurement of nitric oxideThe NO was indirectly quantified by measuring its oxidized form, nitrate (NO3−), using the Griess reagent of a Nitrate/Nitrite Colorimetric Assay Kit (Cayman Chemical). Fifth-instar larvae were injected with 1 µl of heat-killed E. coli TOP10 (2.4 × 104 cells per larva) using a Hamilton microsyringe along with 1 µl of the test compound. Haemolymph was collected from each sample 1 h post infection. A 150-μl volume of haemolymph from three L5 larvae was collected and homogenized in 350 μl of 100 mM PBS pH 7.4 with a homogenizer (Ultra-Turrax T8, Ika Laboratory). After centrifugation at 14,000g for 20 min at 4 °C, the supernatant was used to measure the nitrate amounts, and the total protein was measured in each sample by a Bradford assay. The samples were analysed in a 200-μl final reaction volume. Briefly, 80 μl of samples were added to the wells, then 10 μl of enzyme cofactor mixture and 10 μl of nitrate reductase mixture were added. After incubation at room temperature for 1 h, 50 μl of Griess reagent R1 and immediately 50 μl of Griess reagent R2 were added to each well. The plate was left at room temperature for 10 min for colour development. For a standard curve to quantify the nitrate concentrations of the samples, nitrates with final concentrations of 0, 5, 10, 15, 20, 25, 30 and 35 μM in a 200-μl reaction volume were used. The absorbance was recorded at 540 nm on a VICTOR multi-label plate reader. Our measurements used three larvae per sample, and we repeated the treatment with three biological samples.Galleria injection assaysPrecultures of X. szentirmaii DSM wild-type strain and the mutants thereof were grown in LB medium and inoculated into fresh cultures at an OD600 of 0.1. Cells were grown to exponential phase (OD600 ≈ 1) and then diluted to an OD600 of 0.00025. A 5-µl volume of the diluted bacterial culture was injected into the last left pro-leg of the larvae (LB medium as a negative control). G. mellonella larvae were kept at 4 °C for 10 min before injection. After infection, the larvae were incubated at 25 °C. Dead Galleria larvae were frozen at −20 °C, then at −80 °C, and freeze-dried for one day. Freeze-dried larvae were ground. Every injection experiment was aliquoted into two portions, one of which was extracted with 25 ml of acetone/ethyl acetate (vol/vol, 1:1) while the other one was extracted with acetone/methanol. Extracts were dried and resuspended in 3 ml of acetonitrile/water (1:1, vol/vol) with a tenfold dilution for HPLC-MS-UV analysis. To compare the survival percentage of G. mellonella larvae infected with the WT strain and mutants and to determine median lethal time (LT50) values, Kaplan–Meier curves were generated by GraphPad PRISM 8.4.3.Cytotoxicity assaysHepG2 cells (hepatoblastoma cell line; ACC 180, DSMZ) were cultured under conditions recommended by the depositor, and cells were propagated in Dulbecco’s modified Eagle medium supplemented with 10% fetal bovine serum. To determine the cytotoxicity of test compounds, cells were seeded at 6 × 103 cells per well of 96-well plates in 120 μl of complete medium. After 2 h of equilibration, compounds were added in serial dilution in 60 µl of complete medium. Compounds as well as the solvent control and doxorubicin as an in-assay positive control (IC50 of 0.06 ± 0.01 µg ml−1) were tested as duplicates in two independent experiments. After 5 days of incubation, 20 μl of 5 mg ml−1 MTT (thiazolyl blue tetrazolium bromide) in PBS was added per well, and the cells were further incubated for 2 h at 37 °C. The medium was then discarded and cells were washed with 100 μl of PBS before adding 100 μl of 2-propanol/10 N HCl (250:1) to dissolve the formazan granules. The absorbance at 570 nm was measured using a microplate reader (Tecan Infinite M200Pro with Tecan iControl 2.0), and cell viability was expressed as a percentage relative to the respective solvent control. IC50 values were determined by sigmoidal curve fitting using GraphPad PRISM 8.4.3.Statistical analysisIn Fig. 5d,e,g,j,k, means were compared using an LSD test of one-way ANOVA using POC GLM of the SAS program (SAS Institute, 1989) for continuous variables and discriminated at type I error = 0.05. The results were plotted using Sigma Plot 12.0.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this Article. More

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