Supporting Information for: Total foliar microbiome transplants confer disease resistance to a critically-endangered Hawaiian endemic
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1 Supporting Information for: Total foliar microbiome transplants confer disease resistance to a critically-endangered Hawaiian endemic SI Figure 1) Community similarity vs. infection load Bray-Curtis community similarity as a function of infection load similarity, showing that a major driver in community identity was N. galeopsidis infection rate. At very low and very high infection rates, endophytic communities were more similar. This reflects lower diversity in greenhouse-reared plants prior to treatment and the dominance of the pathogen, respectively. At intermediate infection rates, communities were more dissimilar, reflecting the establishment of inoculated communities prior to pathogen proliferation.
2 SI Figure 2) NMDS of donor and recipient communities NMDS ordination of Bray-Curtis community similarity. Points are colored by treatment, with inoculum donors in red (P. hirsuta slurry) and green (fungal isolate slurry), and shapes represent sampling timepoints: Initial samples (circles), Mid-Growth samples (triangles), and Final samples (squares). Statistical analyses of Species and Timepoint are below.
3 SI Figure 3) Barcharts showing taxonomic profiles (at Phylum and Order levels) of endophytic communities grouped by treatment. Measurements are the sums across all sampling periods.
4 Code used to create figures: # Code used to generate figures - R version # # Associated data files are included in the.zip archive # library(ggplot2) pd = position_dodge(3) # Figure 1 disease_data = read.csv(file = "Plant_Disease_Progress.csv", stringsasfactors = FALSE) disease_data$date = as.date(disease_data$date) Fig_1 = ggplot(disease_data, aes(x = Date, y = Percent.Infected, col = Group, width = 5)) + geom_line(position=pd, size = 1.1) + geom_errorbar(aes(ymin = (Percent.Infected - ci), ymax = (Percent.Infected + ci), width = c(disease_data$wd)), position = pd, size = 1.1) + labs(title = "Phyllostegia Disease Progression", x = "Date", y = "Disease Severity", col = "Treatment") + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.background = element_rect(colour = "black"))+ scale_y_continuous(breaks = c(0,.25,.5,.75,1)) + coord_cartesian(ylim = c(-.1, 1.15)) + facet_wrap(~ Facet, scales = "free_x") + scale_color_manual(values = c("#cdcdcd","#858585","#4d4d4d")) + theme(axis.text = element_text(size=16), axis.title = element_text(size = 18), legend.text = element_text(size = 14), legend.title = element_text(size = 16), plot.title = element_text(size = 20), strip.text.x = element_text(size = 12, face = 'bold'), legend.position = c(.615,.89)) # Figure 2 Slurry_Data <- read.csv(file = "Slurry_Taxa_data_Frame.csv") Fig_2 = ggplot(slurry_data, mapping = aes(x = reorder(species, 1/Rel_Abund), y = Rel_Abund, fill = Round)) + geom_bar(stat = "identity") + theme_bw() + labs(x = "UNITE Taxonomy", y = "Relative abundance", title = "Composition of Slurry Treatments", fill = "Experimental\nRound") + scale_fill_manual(values = gray.colors(3)) + facet_wrap(~ Slurry_Source, scales = "free_x") + theme(axis.text.x = element_text(angle=90, face = 'bold.italic', size = 12), axis.title = element_text(size = 18), strip.text.x = element_text(size = 12, face = 'bold'), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.background = element_rect(colour = "black"), legend.text = element_text(size = 14), legend.title = element_text(size = 16), plot.title = element_text(size = 20), legend.position = c(.9,.8)) # Figure 3 P.aphidis_v_Infection = read.csv(file = "P.aphidis_v_Infection.csv") Fig_3 = ggplot(p.aphidis_v_infection, mapping = aes(x = sqrt(p.aphidis_v_infection$p.aphidis), y = P.aphidis_v_Infection$Infection_Load)) + geom_point() +
5 stat_smooth(method = "loess", color = 'black') + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.background = element_rect(colour = "black")) + labs(x = expression(italic("pseudozyma aphidis")~"abundance (sqrt)"), y = "Pathogen Infection Load") + theme(axis.title = element_text(size = 14)) + ggtitle(expression("greater"~italic("p. aphidis")~"abundance results in lower disease severity")) + theme(axis.title = element_text(size = 18), plot.title = element_text(size = 20), axis.text = element_text(size = 12)) # Figure S1 PK.Infection.and.otus = read.csv(file = "Community_vs_Infection.csv") Fig_S1 = ggplot(pk.infection.and.otus, mapping = aes(x=jitter(pk.infection.and.otus[,2]), y = PK.Infection.and.otus[,1], col = PK.Infection.and.otus[,3])) + geom_point() + geom_smooth(se = FALSE) + theme_bw() + labs(x = "Infection Load", y = "Community Similarity", colour = "Treatment") + ggtitle(expression(italic("p. kaala")~"community similarity")) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.background = element_rect(colour = "Black"), axis.title = element_text(size = 18), axis.text = element_text(size = 12), plot.title = element_text(size = 20), legend.text = element_text(size = 14), legend.title = element_text(size = 16), legend.position = c(.2,.85)) # Figure S2 NMDS = read.csv(file = "NMDS_Data.csv") NMDS$Timepoint = factor(nmds$timepoint) Fig_S2 = ggplot(nmds, aes(x=mds1, y=mds2, col=species, shape = Timepoint)) + geom_point(size = 3) + theme_bw() + labs(title = "NMDS Plot", x= "MDS1", y= "MDS2") + guides(col=guide_legend(title="source")) + theme(legend.text = element_text(size = 14), legend.title = element_text(size = 16), legend.background = element_rect(colour = "black"), title = element_text(size = 20, hjust = 0.5), axis.title = element_text(size = 18), legend.position = c(.9,.23))
6 Clustal alignment of OTUs assigned to N. galeopsidis and voucher sequence from Hawaii found on P. kaalaensis (GenBank Accession: AB ) CLUSTAL O(1.2.3) multiple sequence alignment AB _Neoerysiphe_galeopsidis New.ReferenceOTU New.CleanUp.ReferenceOTU1041_singleton New.CleanUp.ReferenceOTU136_singleton New.CleanUp.ReferenceOTU154_singleton New.CleanUp.ReferenceOTU320_singleton CAGAGCGTGAGGCTCTGCCCGGCTTCCCGCCGCGCGCAGAGTCGACCCTCCACCCGTGTT CATAGCTTGAGGCTCTGCCCGGCTTCCCGCCTCTCTCAGAGTCGACCCTCCACCCGTGTT GGGAGCGTGAGGGGGGGGGGGGCTTCCCGCCGCGCGCAGAGTCGACCCTCCACCCGTGTT CATATCTTGAGGCTCTGCCCGGCTTCCCGCCGCGCGCAGAGTCGACCCTCCACCCGTGTT GGGAGCGTGAGGCTGTGCCCGGCTGCCGGCCGCGCGCAGAGGGGGGCCTCCACCCGTGTT CATAGCGTGAGGCTCTGCCCGGCTTCCCGCCGCTCTCAGAGTCGACCCTCCACCCGTGTT * * ***** * **** ** *** * * ***** * ************** AB _Neoerysiphe_galeopsidis New.ReferenceOTU New.CleanUp.ReferenceOTU1041_singleton New.CleanUp.ReferenceOTU136_singleton New.CleanUp.ReferenceOTU154_singleton New.CleanUp.ReferenceOTU320_singleton AACCTATATCATGTTGCTTTGGCGGATCGAGCCCTCGGCCCA CGGCTTTTGCTGGAGCG AACCTTTATCATGTTGCTTTGGCGGATCGAGCCCTCGTCCCACCGGCTTTTGCTGGAGCG AACCTTTATCATGTTGCTTTGGCGGATCGAGCCCTCGGCCCACCGGCTTTTGCTGGAGCG AACCTTTATCATGTTGATTTGGCGGATCGAGCCCTCGGCCCACCGGCTTTTGCTGGAGCG AACCTTTATCATGTTGCTTTGGCGGATCGAGCCCTCGGCCCACCGGCTTTTGCTGGAGCG AACCTTTATCATGTTGATTTGGCGGATCGAGCCCTCGGCCCACCGGCTTTTGCTGGAGCG ***** ********** ******************** **** ***************** 119 AB _Neoerysiphe_galeopsidis New.ReferenceOTU New.CleanUp.ReferenceOTU_singleton New.CleanUp.ReferenceOTU136_singleton New.CleanUp.ReferenceOTU154_singleton New.CleanUp.ReferenceOTU320_singleton TGTCCGCCAAAGACTCAACCTAACTCGTGTAAACATGCAGTCTAAGGAAAGATTTTGAAT TGTCCGCCAAAGACTCAACCTAACTCGTGTAAACATGCAGTCTAAGGAAAGATTTTGAAT TGTCCGCCAAAGACTCAACCTAACTCGTGTAAACATGCAGTCTAAGGAAAGATTTTGAAT TGTCCGCCAAAGACTCAAAATAACTCGTGTAAACATGAAGTCTAAGGAAAGATTTTGAAT TGTCCGCCAAAGACTCAACCTAACTCGTGTAAACATGCAGTCTAAGGAAAGATTTTGAAT TGTCCGCCAAAGACTCAACATAACTAGTGTAAAAATGCAGTCTAAGGAAAGATTTTGAAT ****************** ***** ******* *** ********************** 179 AB _Neoerysiphe_galeopsidis New.ReferenceOTU New.CleanUp.ReferenceOTU1041_singleton New.CleanUp.ReferenceOTU136_singleton New.CleanUp.ReferenceOTU154_singleton New.CleanUp.ReferenceOTU320_singleton CATTA AATTA CATTA AATTA CATTA CATTA **** 184
7 Permanova results for community distance matrix: PLANT SPECIES adonis(otus_dist ~ Species) Permutation: free Number of permutations: 999 Terms added sequentially (first to last) Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) Species Residuals Total TIMEPOINT adonis(otus_dist ~ Timepoint) Permutation: free Number of permutations: 999 Terms added sequentially (first to last) Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) Timepoint Residuals Total
8 SI Figure 4) Rarefaction curves of all samples
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