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1 Title:
2 Metagenomic dynamics in Olea europaea after
3 root damage and Verticillium dahliae infection
4 Running title:
5 Verticillium wilt of olive as a polymicrobial infection
1,� 1,� 1,� 1,� 6 Jose Manuel Martí , Luis F. Arias , Wladimiro Díaz , Vicente Arnau , Antonio
2,� 1,3,*,� 7 Rodriguez-Franco & Carlos P. Garay
1 2 8 Institute for Integrative Systems Biology (I SysBio), Valencia, Spain.
2 9 Department of Biochemistry and Molecular Biology, University of Cordoba, Spain.
3 10 Canfranc Underground Laboratory (LSC), Huesca, Spain.
11 February, 2019
12 Abstract
13 The olive tree is of particular economic interest in the Mediterranean basin. Researchers have conducted
14 several studies on one of the most devastating disorders affecting this tree, the Verticillium wilt of olive,
15 which causes significant economic damage in numerous areas of this crop. We have analyzed the temporal
16 metagenomic samples of a transcriptomic study in Olea europaea roots and leaves after root-damage and
17 after a root Verticillium dahliae infection (Jimenez-Ruiz et al. 2017). Our results indicate that this infection,
18 although led by Verticillium, is driven not by a single species but by a polymicrobial community, including
19 their natural endophytes, which acts as a consortium in the attack to the host plant. This community
20 includes both biotrophic and necrotrophic organisms that alternate and live together during the infection.
21 Our results not only describe how the microbial community progresses along these processes, but also explain
22 the high complexity of these systems, that in turn, could justify at least in part the occasional changes and
23 disparity found at the time of classifying the kind of parasitism of a determined organism.
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24 Keywords— longitudinal metagenomics, Verticillium wilt of olive, polymicrobial infection, biotroph, necrotroph, hemibiotroph
25
26 Introduction
27 The olive tree could be the earliest cultivated temperate fruit since paleobotanists have traced back its do-
28 mestication to the early Neolithic age (Terral and Arnold-Simard 1996). At present, both the cultivation of
29 olive and the olive-oil related industry have grown to the point of having a profound worldwide socioeconomic
30 and environmental impact.
31 As the Verticillium wilt of olive is one of the most devastating disorders affecting this crop, a transcriptomic
32 RNA-seq analysis was recently conducted to study the interaction between Olea europaea and Verticillium
33 dahliae (Jimenez-Ruiz et al. 2017), which concluded that mainly a ROS response appeared first in the pathogen
34 and later in the plant.
35 In recent years, there has been a deviation from studying individual species to study the whole community
36 that is actually living in a determined niche. Shotgun metagenomic sequencing (SMS) helps this paradigm
37 shift by the use of massive nucleic acid sequencing of whole metagenomes obtained from samples with the
38 extra advantage of not requiring prior knowledge of the species that are present.
39 Soil microorganisms constitute one of the most complex systems in nature, where many different life forms
40 interact one to another. Fungi play a major role as they act as parasites, saprotrophs or mutualists in a
41 myriad of environments, including the rhizosphere of many different plants (Cuadros-Orellana et al. 2013).
42 The description of these interactions are, however, variable and somehow confusing in many cases. So, V.
43 dahliae has been described as an hemi-biotrophic pathogen because it seems to behave as biotrophic during
44 the initial stages of plant infection, but changes to a necrotrophic lifestyle during subsequent stages (Scholz
45 et al. 2018; Shaban et al. 2018; Häffner and Diederichsen 2016). The same goes for other fungus like those
46 pertaining to the Fusarium gender (Lyons et al. 2015; Yang et al. 2013). However, the molecular or biological
47 bases underlying these different parasitical alternations are still not fully understood.
48 We must emphasize that the use of metagenomic data dealing with the process of a fungal plant infection
49 is almost lacking. A search in the ENA metagenomic database (Mgnify database) for fungal infections in
50 plants shows only one hit (case MGYS00001376) that deals with the still unpublished study of the infection by
51 Erysiphe alphitoides, the causal agent of oak powdery mildew, and other foliar microorganisms of pedunculate
52 oak (Quercus robur L.). Some recent studies (Donovan et al. 2018) focus on the characterization of fungal
53 metagenomics in animal species, especially those from pig and mouse microbiomes. Human metagenomics,
54 particularly that related to gut microbiome, has experienced a recent burst, mainly by the unexpected conse- PREPRINT2 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
55 quences for health and disease (Gilbert et al. 2018), which are especially evident through time series analysis
56 (Martí et al. 2017). However, generally speaking, animal, plant and fungal metagenomes are not sufficiently
57 studied in comparison with bacterial microbiomes.
58 We have reanalyzed the samples obtained by Jimenez-Ruiz et al. (2017) with the new perspective of
59 longitudinal (temporal) metagenomics to unravel the dynamics of the infection. Our results indicate that
60 the Verticillium wilt of olive is a complex infection process involving more contenders than just Olea and V.
61 dahliae. We show that along the infection under natural conditions, there is a biological succession of different
62 kind of parasites (mainly biotrophic and necrotrophic) that could at least partially explain the observed
63 parasitic alternations described in many systems. A careful design of the experimental conditions, such as the
64 using of sterile seeds and substrates to raise the plants ensuring that only the parasite is growing, is needed
65 to be able to reach the correct conclusions.
66 Methods
67 The complete RNA-seq dataset of the study by Jimenez-Ruiz et al. (2017) consisted in a 2x100 paired-ends
68 and unstranded library that was downloaded from the NCBI SRA servers with accession numbers provided
69 in the article (Jimenez-Ruiz et al. 2017).
70 Pre-analysis RNA-seq SMS data was quality checked using FastQC v0.11.5 (Babraham Bioinformatics
71 2018) and MultiQC v1.3 (Ewels et al. 2016).
72 Mapping of reads The whole SMS library was mapped against the olive and the Verticillium genomes per
73 separate using both the pseudomapper Kallisto v0.44 (Bray et al. 2016) and the RNA-seq aligner STAR v2.7
74 (Dobin et al. 2013).
75 SMS database preparation The database used for the Centrifuge program was generated in-house from
76 the complete NCBI nt database (nucleotide sequence database, with entries from all traditional divisions
77 of GenBank, EMBL, and DDBJ) and index databases (Wheeler et al. 2007), downloaded in Dec 2017. This
78 database was supplemented with all the sequences in the NCBI whole genome shotgun WGS database (Wheeler
79 et al. 2007) belonging to the Olea genus and the fungi kingdom. Once generated, the Centrifuge indexed
80 compressed database weighted more than 135 GB. So far, this is the most massive Centrifuge database we
81 have prepared and used successfully.
82 Classification The metagenomic sequences were analyzed with the Centrifuge software package (Kim et al.
83 2016) version 1.0.3-beta (Dec 2017), run in parallel within a shared-memory fat node, using 8 threads and PREPRINT3 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
84 peaking half a tebibyte of DRAM.
85 Post-analysis The results generated with Centrifuge were post-processed, analyzed and visualized using
86 Recentrifuge (Marti 2018), release v0.22.1 (Oct 2018). This parallel software was run with the flags --
87 minscore 50 (mhl set to 50) and -x CMPLXCRUNCHER to prepare the Recentrifuge quantitative output to
88 be introduced in cmplxCruncher (in preparation) as input. A recent development version of cmplxCruncher
89 (Oct 2018) was used to perform the final temporal metagenomic analysis and produce the plots shown.
90 Results and Discussion
91 Quality check and mapping of the SMS reads
92 The analysis with FastQC and MultiQC showed a notorious change in the per base GC content in the
93 infected root RNA samples that increased with time after the inoculation (Figure 1a). The GC content
94 in the sequenced reads evolved from a unimodal distribution peaking at 43%, coincident with that of the
95 olive genome, to a pseudogaussian distribution reflecting a higher GC content ratio (53%) 15-day after the
96 inoculation. Figures 1b and 1c show the percentage of reads coming from infected roots mapped either with
97 Kallisto or STAR to the olive genome drastically decreasing along the infection. The number of Verticillium
98 reads was very low in the infected samples. As expected, Verticillium reads were negligible or missing in the
99 controls and leaves. However, the per base GC content and the proportion of mapped reads in the leaf samples
100 to the olive genome remained practically constant. All these data taken together confirmed that the infection
101 progressed through the roots and that there was a progressive emergence of other biological organisms during
102 infection that were displacing the olive tree in terms of mRNA abundance. The species origin of the unmapped
103 reads was unknown, and thus, a metagenomic analysis was required.
104 Temporal metagenomic analysis of the root infection process
105 Overall dynamics of the infection
106 Figure 2 shows how the infection with V. dahliae caused a profound impact in the rhizosphere of the olive
107 roots. This is shown by comparing the rank dynamics of mapped reads and the stability plot for species of
108 the roots control sample (first column) to the roots 48 hours after the infection (second column). Theboost
109 in the relative frequency of V. dahliae reads is the most obvious, but it is not the only change. The same
110 figure shows that other species are taking advantage ofthe Verticillium rise, and some others are suffering an
111 apparent displacement. The low values in the rank stability index —RSI— (Martí et al. 2017) column and
112 the extreme fluctuations in the RSI plot indicate that the rhizosphere experienced an intense perturbation PREPRINT4 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
6
4
2 Relative frequency
0 0 10 20 30 40 50 60 70 80 90 100 % GC
Control roots Roots infected 7d Control leaves Samples Roots infected 48h Roots infected 15d Leaves infected 15d
(a) FastQC: Per Sequence GC Content
100 100
75 75
50 50
25 25
0 0
Control Roots Roots infected 48h Roots Infected 7d Roots Infected 15d Control Leaves Leaves infected 15d Control Roots Roots infected 48h Roots Infected 7d Roots Infected 15d Control Leaves Leaves infected 15d
Organisms Unknown V.dahliae O.europaea Organisms Unknown V.dahliae O.europaea
(b) STAR aligner (c) Kallisto pseudomapper
Figure 1. (a): The average per base GC content of the reads. The GC content of the olive genome is roughly 43%. (b-c): Percentage of reads mapped to the olive (green) and V.dahliae (red) genomes, obtained through STAR and Kallisto, respectively. The cyan color corresponds to the proportion of unmapped reads of initially unknown origin.
PREPRINT5 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
113 with the inoculation of V. dahliae, also corroborated by peak values both in rank and differences variability
114 plots. Thereafter, the rhizosphere was undergoing a transient state as a complex system. The instability is
115 reflected in the lower taxa present in all samples compared to the non-perdurable taxa along theinfection
116 (see Supplemental Figure 10).
Olea europaea 1 1 1 1 100 Verticillium dahliae 957 2 2 2 43.8 Dactylonectria macrodidyma 8 3 7 3 99.0 [Nectria] haematococca 830 4 3 14 48.9 Fusarium sp. FSSC_6 839 7 4 17 48.4 Tulasnella calospora 13 17 6 4 98.7 Fusarium solani 831 8 5 20 48.7 Fusarium euwallaceae 838 9 8 21 48.7 Arabidopsis thaliana 4 6 9 9 99.6 Verticillium longisporum 959 5 11 12 43.5 Rhizoctonia solani 1124 255 36 5 37.2 Hirudo medicinalis 2 10 53 72 94.6 Phoma herbarum 711 270 10 286 42.8 Fusarium oxysporum 14 12 15 6 98.9 Saccamoeba lacustris 1680 275 13 7 20.5 Sterkiella histriomuscorum 1528 229 14 11 24.5 Cryptodifflugia operculata 1689 72 17 8 20.3 Arthrobotrys oligospora 522 796 12 182 33.3 Paraphoma sp. B47-9 688 942 18 10 34.8 Rank Stability Index (in %) uncultured bacterium 7 19 19 16 98.8 uncultured eukaryote 9 21 29 13 97.2 Alternaria alternata 51 20 25 15 96.5 Hesperelaea palmeri 5 11 33 41 97.2 Verticillium alfalfae 960 15 23 34 43.4 Penicillium janthinellum 3 854 1113 1152 36.1 Oryza sativa 27 36 35 19 98.0 Fusarium sp. JS1030 858 13 41 29 46.8 Stagonosporopsis tanaceti 720 384 16 521 34.0 Acanthamoeba castellanii 53 22 27 73 93.7 Naegleria gruberi 367 407 20 75 67.3 Fusarium virguliforme 834 32 21 176 43.2 Podospora anserina 68 29 28 49 95.3 Stachybotrys chartarum 901 50 22 207 39.3 Bodo saltans 1584 42 30 33 23.4 Fusarium azukicola 119 31 24 194 80.8 Ilyonectria destructans 137 16 58 30 85.9 Pseudopyrenochaeta lycopersici 723 970 26 82 32.7 uncultured fungus 48 33 39 27 97.4 Nectria sp. B-13 816 28 31 44 50.5 Hordeum vulgare 54 37 42 26 97.1 Bradysia odoriphaga 1394 1424 1625 18 16.8 Clonostachys rosea 77 14 68 96 89.1 Acremonium furcatum 955 80 55 23 44.7 Flavobacterium johnsoniae 271 132 38 47 82.4 Cadophora malorum 127 58 34 223 79.7 Epicoccum sorghinum 716 964 32 672 17.2 Ceratobasidium sp. AG-A 1117 1183 1497 22 16.5 Rhizoctonia sp. AG-Bo 1126 1191 1503 24 16.5 Bradysia impatiens 1392 1422 1623 25 17.0 Cryptococcus neoformans 32 35 98 31 90.0
16% 100%
1.2 8.0 Differences Variability
7.0 1.0 6.0
0.8 5.0
Rank Variability 4.0
0.6 3.0
RV (50 most abundant) DV (50 most abundant)
ControlRoots RootInfect07d RootInfect15d RootInfect048h Time samples
Figure 2. Rank dynamics and stability plot for mapped reads classified at species level during the process of infection with V. dahliae. The dynamics of the rank during the process shows the profound impact in the rhizosphere of the olive tree caused by the inoculation with V. dahliae. Numbers and colours (using perceptually uniform colormap for easier visualization) show the ranking by the accumulated species abundance in each column. Different rank variability and stability measurements (Martí etal. 2017) are given. The right panel shows the rank stability throughout species ordered by their overall abundance. The lower panel contains plots of the rank variability along time.
117 Figure 3, the clustered correlation and dendrogram plot for mapped reads at species level during the infec-
118 tion, shows the evident antagonism between the cluster formed by Olea europaea and Penicillium janthinellum,
119 and the cluster formed by Verticillium spp., located in opposite extremes of the clustered correlation matrix.
120 As indicated in the figure, the clusterization algorithm formed five superclusters with a total of 20clusters. PREPRINT6 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
20 cmplxCruncher clustered correlation 15 plot of the 50 top elements from 4 samples 10 5 superclusters 20 clusters 5
0 Verticillium longisporum Verticillium dahliae Verticillium alfalfae Arabidopsis thaliana Fusarium sp. FSSC_6 [Nectria] haematococca Phoma herbarum 1.00 Stagonosporopsis tanaceti Fusarium azukicola Arthrobotrys oligospora Epicoccum sorghinum Stachybotrys chartarum 0.75 Fusarium solani Cadophora malorum Acanthamoeba castellanii Fusarium virguliforme 0.50 Fusarium euwallaceae Naegleria gruberi Pseudopyrenochaeta lycopersici Podospora anserina Nectria sp. B-13 0.25 > Bodo saltans Flavobacterium johnsoniae Saccamoeba lacustris Tulasnella calospora 0.00 Sterkiella histriomuscorum uncultured bacterium uncultured fungus Alternaria alternata Hordeum vulgare 0.25 Paraphoma sp. B47-9 Cryptodifflugia operculata Dactylonectria macrodidyma Oryza sativa 0.50 uncultured eukaryote Fusarium oxysporum Acremonium furcatum Rhizoctonia solani Bradysia odoriphaga 0.75 Ceratobasidium sp. AG-A Bradysia impatiens Rhizoctonia sp. AG-Bo > Cryptococcus neoformans Ilyonectria destructans 1.00 > Fusarium sp. JS1030 Hesperelaea palmeri Clonostachys rosea Hirudo medicinalis > Penicillium janthinellum > Olea europaea Oryza sativa > Bodo saltans Fusarium solani Nectria sp. B-13 > Olea europaea Phoma herbarum Hordeum vulgare Naegleria gruberi Rhizoctonia solani uncultured fungus Hirudo medicinalis Bradysia impatiens Verticillium dahliae Fusarium azukicola Verticillium alfalfae Clonostachys rosea Alternaria alternata Podospora anserina Arabidopsis thaliana Tulasnella calospora Cadophora malorum Bradysia odoriphaga Hesperelaea palmeri Fusarium sp. FSSC_6 Fusarium oxysporum Paraphoma sp. B47-9 uncultured eukaryote uncultured bacterium Saccamoeba lacustris Fusarium virguliforme Rhizoctonia sp. AG-Bo Epicoccum sorghinum Fusarium euwallaceae Acremonium furcatum > Fusarium sp. JS1030 Ilyonectria destructans Arthrobotrys oligospora [Nectria] haematococca Stachybotrys chartarum Verticillium longisporum Ceratobasidium sp. AG-A Acanthamoeba castellanii Cryptodifflugia operculata > Penicillium janthinellum Stagonosporopsis tanaceti Sterkiella histriomuscorum Flavobacterium johnsoniae > Cryptococcus neoformans Dactylonectria macrodidyma Pseudopyrenochaeta lycopersici
Figure 3. Clustered correlation and dendrogram plot for species during the process of infection with V. dahliae. We show the 50 most abundant species ordered by clusterization based on the Pearson time correlation matrix. Several clusters and superclusters can be identified with this analysis by cmplxCruncher.
121 Based on our methods for the analysis of microbiota variability and stability (Martí et al. 2017), we have fit
122 a power-law to std σi vs. mean µi for the relative abundance of genera during the root infectious process (see
123 Figure 4). The scaling index β „ 1 of this Taylor’s law indicates that the biological system follows the model
124 of an exponential or a geometric distribution, which are characterized by β = 1. In addition, Supplemental
125 Figure 14 plots Taylor’s law parameter space with data from the fits for different taxonomic ranks performed
126 for the dataset of roots olive infection with V. dahliae. We can see that there is a correlation between β and V
127 depending on the taxonomic level. We can see how the no_rank subsample, with no separation by taxonomic
128 level, is located in an intermediate position. PREPRINT7 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
0
β 0.997±0.009 V x = (1.159± 0.129 )x Olea R 2 = 0.95 Verticillium -2 Fusarium
Dactylonectria
Tulasnella
Rhizoctonia
-4 Saccamoeba
Phoma
SD of relative abundances (log scale) Sterkiella
Cryptodifugia
-6
-6 -4 -2 0 mean of relative abundances (log scale)
Figure 4. Taylor’s law of the biological system consisting in the metagenome at genus level along the root infectious process. We see that Taylor’s power law spans six orders of magnitude, therefore, it is ubiquitous.
129 The GeneFilt10k subsample is an experimental dataset containing the 10,000 most representative expressed-
130 genes in each sampling time. Supplemental Figure 15 depicts the x-Weighted fit in logarithmic scale (see Martí ¯2 131 et al. (2017) for details on the fit), which shows a modest generalized coefficient of determination R because
132 this dataset has an important component of variability unaccountable by the power-law model. This disper-
133 sion of the residuals is evident in Supplemental Figure 16, the residual analysis plot of the correspondent
134 LLR-model fit, which in addition shows that the residuals are normally distributed but present somedegree
135 of deviation from homoscedasticity. Finally, Supplemental Figure 12 shows the Recentrifuge plots of fungal
136 SMS classified reads at species level for leaves of two specimens, the control sample and the sample 15days
137 after the Verticilium inoculation.
138 In the following subsections, we show the evolution during the infection for some significative clades:
139 Amoebae and Cilliates, Fungi, Bacteria, and Nematoda.
140 Amoebae and Ciliates
141 The Verticillium infection likely broke trophic network equilibria because direct or indirectly could cause
142 breakage, destructuring of tissues and lysis of cells, thus promoting the grown of opportunistic organisms. In PREPRINT8 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
143 this respect, in Figure 2 we can see how three protist species (Saccamoeba lacustris, Sterkiella histriomuscorum,
144 and Cryptodifflugia operculata), which were not among the 1500 most frequent species in the roots control
145 sample, evolve during the infection to be among the 15 most frequent species found by number of SMS reads
146 assigned. In the rhizosphere, the ubiquitous free-living Saccamoebae are living in biofilms and at the interfaces
147 between roots and water (Kebbi-Beghdadi and Greub 2014). The ciliate Sterkiella histriomuscorum (before
148 known as Oxytricha trifallax) is a cosmopolitan species in soil, but it is also habitual in limnetic habitats
149 (Foissner and Berger 1999; Kumar et al. 2017). The amoeba Cryptodifflugia operculata is a bacterivore which
150 is also able to prey on larger nematodes thanks to efficient and specialized cooperative hunting (Geisen etal.
151 2015).
152 Certain amoeboid protists are pathogenic for the olive. Concretely, some slime molds of the genus
153 Didymium are associated with a severe disease of the olive flower-buds, causing extensive destruction and
154 blockage of flower development (Medeira et al. 2006). During the infection process, Didymium spp. (particu-
155 larly D. squamulosum and D. iridis) appeared on day 7, and remained on day 15. In fact, reads belonging to
156 the Myxogastria class, which contains the genus Didymium, increased 3.8 times from the control sample to 48h
157 after infection, but they rocketed 10.8 times from 48 h to 7 days after the infection. Taking into account the
158 variation in the absolute number of reads assigned for each sample (see Supplemental Figure 11), Myxogastria
´6 ´4 159 relative frequency is 2 ˆ 10 in the control and about 1 ˆ 10 seven days after inoculation. Such a growth
160 is probably due to the increased availability of decaying plant material as a consequence of the appearance of
161 destructive necrotrophic species that took advantage of the V. dahliae isolate V937I inoculation, which is an
162 archetype of the highly virulent D pathotype (Jimenez-Ruiz et al. 2017).
163 Fungi
164 Figure 5 is a collection of four Recentrifuge plots (Marti 2018) showing the evolution of fungal SMS reads
165 during the V. dahliae infection of the olive roots. Penicillium janthinellum dominates the roots control sample
166 before the infection. P. janthinellum is an endophytic fungus which seems to be remedial to plants in the
167 alleviation of heavy metal stress by enhancing the host physiological status. Therefore, it is not by chance
168 that Olea europaea and Penicillium janthinellum appear clustered together in Figure 3.
169 As expected, the frequency of mapped reads indicated that V. dahliae became the dominant fungus in the
170 roots soon after the inoculation. Its number of reads were the second most frequent just after those pertaining
171 to the olive host (see Figure 2 and Figure 5). Nevertheless, in the following samples, without losing the second
172 position, its relative frequency started to decrease in favor of other fungi (see Figure 5) that we review below.
173 Dactylonectria macrodidyma is a fungus that was already present in the control sample, but which benefited
174 from the V. dahliae infection since it becomes the third most frequent species in the last temporal point (see PREPRINT9 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission. Roots control Penicillium sample janthinellum 48%
2% P. brasilianum 1% P. subrubescens
0.9% Capnodiales
Pezizomycotina 2% Pseudogymnoascus saccharomyceta 1% Trichoderma2 more Verticillium dahliae 64% Ascomycota 2% Fusarium oxysporum Dikarya
4% Dactylonectria macrodidyma Roots Fungi infection 0.8% Ophiocordycipitaceae aer 48 h 1% Colletotrichum
1 more Mucoromycota 1% Chaetomiaceae1 more Pezizomycotina Mucor...otina saccharomyceta Basidiomycota MucoralesAgaricomycotina Ascomycota S...s M...e Agari...cetes
1% Tremellomycetes Dikarya 2% Tulasnella calospora 0.9% Agaricales
Fungi 1% V. longisporum
Aspergillus 1%
Saccharomyces 0.8%
Talaromyces islandicus 0.8%
Talaromyces purpureogenus 1% Roots
infection 44% Verticillium dahliae aer 7 d
Pezizomycotina 5 more saccharomyceta
Dactylonectria Ascomycota
macrodidyma 3% Dactylonectria macrodidyma 2% Dikarya Fusarium sp. FSSC_6 1%
[Nectria] haematococca 1%
Fungi
3 more 40% Verticillium dahliae
3 more
Fusarium sp. FSSC_6 6% Pezizomycotina saccharomyceta Basidiomycota Ascomycota Agaricomycotina Dikarya Agaricomycetes
Fusarium euwallaceae 3% 1% Sordariomycetidae
3% Tulasnella calospora Fungi
Fusarium solani 4%
0.9% Sordariomycetidae
Didymellaceae 1% 134 [Nectria] haematococca 6% Basidiomycota Agaricomycotina 2 more Agaricomycetes 117 7% Tulasnella calospora 101 Dactylonectria macrodidyma 13% 3% Rhizoctonia solani 84 1% Agaricales
FSSC 2% 67 3 more Roots 1 more 5 more 50 infection Score (avg) aer 15 d Fusarium oxysporum 1%
Phaeosphaeriaceae 0.8% Eurotiomycetidae 0.8%
Figure 5. Recentrifuge plots of the evolution of fungal SMS classified reads at species level during the V. dahliae infection. The top pie corresponds to the root control sample, the others to the infected roots after 48 h, 7 days, and 15 days, respectively, of the Verticilium inoculation. An interactive and dynamic collection of Recentrifuge plots (Marti 2018) can be accessed via the official project’s webpage at https://www.uv.es/martijm/olea. PREPRINT10 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
175 Figure 5) just behind the host and the inoculated fungi (see Figure 2). D. macrodidyma is itself another
176 pathogenic and necrotrophic fungus in crops as it is the causative agent of root rot disease of many herbaceous
177 and woody plants such as grapevine, avocado, cherimola and olive (Malapi-Wight et al. 2015), some of them
178 fruit trees whose top exploiters are Spain and Chile (Auger et al. 2015). Tulasnella calospora is another
179 similar case to D. macrodidyma since it was present in the control sample but ended up as the fourth most
180 frequent species in the time series of the infection. T. calospora has been recently studied as a mycorrhizal
181 fungal symbiont of orchids (Fochi et al. 2017), but in our case, it seemed somehow to take advantage of the V.
182 dahliae infection probably due to the destructuration, destruction or lysis of tissues and cells. In fact, species
183 of Tulasnellaceae have been described to be both symbionts and saprotrophs simultaneously (Sarmiento 2016).
184 One week after the inoculation, reads assigned to the so named Fusarium solani species complex (FSSC)
185 represent a fifth of all the fungal reads (see Figure 5). Nectria haematococca and its asexual counterpart,
186 Fusarium solani, are the most relevant species in this complex. While researchers in Spain have reported that
187 F. solani is only weakly pathogenic on olive (Hernández et al. 1998), this fungus has caused fatal wilt of Olea
188 europaea in Nepal (Vettraino et al. 2009).
189 Additionally, the infected samples also contain Fusarium euwallaceae, a genealogically exclusive lineage
190 of fungi within Clade 3 of the FSSC discovered as a fungal symbiont of Euwallacea sp., an invasive ambrosia
191 beetle that causes serious damage to more than 20 species of olive tree (Freeman et al. 2013). This taxon,
192 with average score below the paired-ended read half value (100) may represent another close species in the
193 FSSC.
194 Another frequent Fusarium fungi in the studied samples, Fusarium oxysporum, is the causal agent of the
195 Fusarium wilt in many different plants, including tomato, chickpea, and others (García-Pedrajas etal. 1999),
196 but it is considered only slightly pathogenic for olive in Spain (Hernández et al. 1998). In fact, it is present
197 in the control root samples and keeps a similar rank throughout infection process, except for the last to 15
198 days, where it has advanced to the sixth position of all species (see Figure 2). Generally speaking, Fusarium
199 oxysporum is one of those cases in which a debate is open as to whether this fungus is considered a biotroph,
200 a hemibiotroph, or a necrotroph, able to kill plant tissue quickly and thereafter feeding saprotrophically on
201 the dead remains (Edgar et al. 2006; Thaler et al. 2004; Ma et al. 2013; Moore et al. 2011).
202 The fungi Rhizoctonia solani, R. sp. AG-Bo, and Ceratobasidium sp. AG-A belongs to the same cluster
203 (see Figure 3). As we can see in Figure 2, these fungi presented a very low frequency of mapped reads during
204 the time series except in the last sample corresponding to 15 days after the inoculation with V. dahliae.
205 Rhizoctonia solani is a soil-borne plant pathogen that has been related to rotten roots in olives (Hernández et
206 al. 1998). Both Rhizoctonia and Ceratobasidium genera belong to the family Ceratobasidiaceae of saprotrophic
207 and cosmopolitan fungi which could be facultative plant pathogens with a wide host range (Moore et al. 2011). PREPRINT11 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
208 Bacteria
209 The bacterial content of the samples was severely shrunk because the mRNA was isolated using poly(A)
210 columns (Jimenez-Ruiz et al. 2017), and it is hence, biased. Despite that limitation, the overall dynamics
211 of the bacterial community may still be outlined for the infection. Figure 6 shows the rank dynamics and
212 stability plot for bacterial species. The main difference with Figure 2, the overall rank dynamics and stability
213 plot for species, dominated by fungi, is the position of the differences variability —DV— (Martí et al. 2017)
214 peak. In the latter case, it is on the second sample (48 hours after the infection), while in the former case it
215 appears over the third sample (one week after the inoculation). That means that the effects of the infection
216 reached the bacterial community with some delay compared to the whole species population. The fact that
217 the minimum in DV is on the third sampling time for the whole population but on the fourth sampling time
218 for bacteria supports the existence of such delay.
219 In Figure 6, the RSI shows low values compatible with the perturbation introduced in the bacterial
220 community with the inoculation of V. dahliae. Intriguingly, a couple of Devosia species (sp. A16 and sp.
221 H5989) are exceptions to this behavior since present high RSI of 90% and 82%, respectively.
222 Other entirely different cases are Chitinophaga pinensis and Flavobacterium johnsoniae, which were not
223 very common in the first two samples but then moved forward more than 100 rank positions to reachthe
224 top 4 and top 5 in the last two sampling times, respectively. Both are soil-borne bacteria that belong to the
225 widespread and diverse Bacteroidetes phylum and are recognized for its capacity to degrade chitin, the main
226 component in the exoskeleton of arthropods and the cell walls of fungi, so that they could be endohyphal
227 bacteria (a kind of endosymbiont) of fungi belonging to the F. solani species complex (Shaffer et al. 2017).
228 Another relevant possibility is that those bacteria could have been recruited by the olive tree through root
229 exudates as an indirect plant defense mechanism against the fungal attack (Baetz 2016; Häffner and Diederich-
230 sen 2016). Indeed, chitinolytic bacteria are well-known antagonists of plant pathogenic fungi (Frändberg and
231 Schnürer 1998). In fact, the rhizosphere of wild olive is a reservoir of bacterial antagonists of V. dahliae
232 showing chitinolytic activity (Aranda et al. 2011). The dynamics of C. pinensis and F. johnsoniae shown in
233 Figure 6 and the dynamics of species belonging to the FSSC demonstrated in Figure 2 seem compatible with
234 such hypothesis.
235 Nematoda
236 About Nematoda, it is remarkable the detection of Oscheius tipulae both with a high score and relatively high
237 abundance on the sample of infected root after seven days. It also appears in the specimen of 8 h after root
238 damage and, with lower abundance, 15 days after the infection. O. tipulae is one of the most common and
239 cosmopolitan nematode species in soil (Félix 2006). While there is no clear relationship between this nematode PREPRINT12 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
uncultured bacterium 1 1 1 1 100 Flavobacterium johnsoniae 92 7 2 5 37.6 Actinoplanes missouriensis 7 15 42 2 46.4 Chitinophaga pinensis 109 107 3 4 31.7 Actinoplanes derwentensis 10 24 45 3 45.3 Dyadobacter fermentans 106 16 4 7 32.5 Streptomyces scabiei 128 4 13 6 20.6 Cellvibrio sp. PSBB023 30 14 5 11 74.1 Rhodospirillum sp. SL38 54 55 6 12 57.1 Methylophilus sp. TWE2 57 19 7 15 55.9 cyanobacterium TDX16 142 143 10 8 21.8 Flavobacterium gilvum 97 96 9 10 39.5 Bacillus licheniformis 2 12 115 41 10.1 Paucibacter sp. KCTC 42545 74 17 11 18 49.0 Prochlorothrix hollandica 144 144 14 16 23.0 Devosia sp. A16 34 23 18 13 81.8 Massilia putida 13 63 8 114 6.7 Hassallia byssoidea 140 141 27 9 22.7 Niastella koreensis 111 109 15 17 35.4 Rank Stability Index (in %) Verrucomicrobia bacterium IMCC26134 84 84 16 19 48.5 Streptomyces capitiformicae 135 3 38 20 10.5 Streptomyces cattleya 3 122 127 132 23.9 Actinoplanes friuliensis 123 117 17 23 29.8 Verrucomicrobia bacterium 85 85 28 14 48.5 Escherichia coli 18 5 31 42 60.9 Actinoplanes sp. N902-109 124 118 22 25 32.5 Herpetosiphon aurantiacus 116 112 12 127 5.7 Streptomyces sp. CdTB01 16 131 51 24 5.4 Mitsuaria sp. 7 70 71 23 33 55.3 Neorhizobium galegae 43 44 34 21 79.4 Devosia sp. H5989 33 35 33 26 90.1 Acidovorax sp. KKS102 66 67 19 57 40.4 Streptomyces albus 4 134 141 142 21.2 Methylotenera mobilis 58 58 20 61 44.3 Streptomyces sp. 3214.6 12 135 142 22 3.0 Tolypothrix campylonemoides 139 140 35 29 29.8 Streptomyces sp. RTd22 5 133 139 140 22.1 Shinella sp. HZN7 48 49 24 51 59.0 Gemmata sp. SH-PL17 90 90 36 35 57.8 Bosea sp. AS-1 32 34 26 47 74.1 Filimonas lacunae 112 110 21 77 18.7 Flavobacterium commune 96 95 40 37 55.3 Flavobacterium branchiophilum 93 92 32 46 46.4 Flavobacterium psychrophilum 94 93 54 32 53.6 Fluviicola taffensis 100 99 39 40 53.6 Cytophaga hutchinsonii 105 104 58 34 48.5 Chitinophaga sp. MD30 110 108 61 31 44.3 Rhizobium leguminosarum 37 38 29 68 61.6 Flavobacterium crassostreae 98 97 43 49 54.1 Azospirillum thiophilum 52 53 50 43 90.1
3% 100%
0.70 0.9 Differences Variability
0.65 0.8
0.60 0.7
0.55 0.6 Rank Variability 0.50 0.5
0.45 0.4 RV (50 most abundant) DV (50 most abundant)
ControlRoots RootInfect07d RootInfect15d RootInfect048h Time samples
Figure 6. Rank dynamics and stability plot for bacterial species during the process of infection with V. dahliae. Numbers and colours (using perceptually uniform colormap for easier visualization) show the ranking by the accumulated species abundance in each column. Different rank variability and stability measurements (Martí etal. 2017) are given. The right panel shows the rank stability throughout species ordered by their overall abundance. The lower panel contains plots of the rank variability along time.
240 and the infection dynamics in this study, it is also true that plants under attack are favoured by soilborne
241 mobile predators such as nematodes, which are efficiently attracted by root-emitted compounds (Baetz 2016)
242 With low frequency and modest score, but with more biological significance, it was the finding of Het-
243 erodera exclusively in samples corresponding to 7 and 15 days after the inoculation into roots with Verticillium
244 (see Supplemental Figure 13). Heretodera spp. are well-known plant-parasitic nematodes (PPN) associated
245 with the olive tree, especially in nurseries, with reported cases in Spain (Ali et al. 2014). Other PPN such
246 as Meloidogynidae incognita and Pratylenchidae vulnus (not found in the samples of this study) have been
247 associated with V. dahliae synergistic coinfections to olives since it seems that, with the indirect root damages
248 that they inflict on the trees, these nematodes act as the spearhead of other pathogenic soil-borne microor- PREPRINT13 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
249 ganisms like Verticillium. Interestingly, Castillo et al. (1999) suggested that Heretodera and Verticillium may
250 cooperate synergistically in the Verticillium wilt infection to yield both more widespread and more serious
251 harm to the crop (Castillo et al. 1999). Our results point precisely in that direction. Finally, with low score,
252 Bursaphelenchus also appears in Supplemental Figure 13. Bursaphelenchus spp. feeds on cells of trees by
253 using stylets that pierce cell walls thanks to industrially useful β-glucosidases degrading enzymes, causing
254 pests in palms and trees (Zhang et al. 2017).
255 Temporal metagenomic analysis of the root damage process
256 Figure 7, the rank dynamics and stability plot for species, shows that the damage of the roots had a substantial
257 effect on the rhizospheric microbiota but not as important as in the case of the infection with V. dahliae above.
258 Comparing with Figure 2, we can see that the rank variability and especially the differences variability had
259 lower values with the root damage than with the root infection.
260 However, there were also similarities in the evolution of both datasets despite their different timing. The
261 dynamics of fungi belonging to the FSSC and the drop of Penicillium janthinellum abundance after damage
262 are good examples. P. janthinellum abundance fell but the slump, being significant, was not as severe as in
263 the infection. Fusarium spp. also benefit from the perturbation to the roots, growing as in the infectious case.
264 Verticillium dahliae follows this same behavior even when it is in a different correlation cluster than FSSC
265 species, as shows Supplemental Figure 17, the clustered correlation and dendrogram plot for species during
266 the process after the roots damage.
267 Nevertheless, other taxa, at the end of the process (7 days), recovered a rank similar to the initial one.
268 That is the case of the plant pathogen Phytophthora sojae, which causes root rot of soybean. P. sojae had
269 a rank beyond 900 in the control samples, but it reached the second most frequent rank 48 hours after
270 damage and returned to positions close to 1,000 in the last sample. Rhizopus microsporus, Clitopilus hobsonii,
271 Hanseniaspora guilliermondii, and Arthrobotrys oligospora behaved similarly, having a final rank close to the
272 initial one after a transient period. In particular, H. guilliermondii recovered precisely the same rank at the
273 end (315).
274 Figure 8 shows the Taylor’s law fit for the relative abundance of genera throughout the root damage
275 process. Comparing with Figure 4, we see a lower scaling index β and, interestingly, a much lower variability
276 V . From a system dynamics perspective (Martí et al. 2017), these values indicate that the system is more
277 stable after the root damage than after the inoculation with Verticilium, thus corroborating the above rank
278 stability results. PREPRINT14 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
Olea europaea 1 1 1 1 1 100 Fusarium oxysporum 14 2 6 22 2 94.9 Phytophthora sojae 914 360 291 2 988 7.6 [Nectria] haematococca 491 17 4 11 3 58.4 Fusarium sp. FSSC_6 498 20 5 13 4 57.9 Dactylonectria macrodidyma 8 3 16 5 20 95.7 Arabidopsis thaliana 4 6 3 3 8 99.0 Fusarium solani 492 9 7 15 5 58.4 Cadophora malorum 127 4 212 24 10 56.4 Hirudo medicinalis 2 13 2 7 14 96.6 Fusarium euwallaceae 497 25 8 17 6 57.9 Alternaria alternata 51 11 18 34 7 91.3 Tulasnella calospora 13 8 13 4 67 92.0 Paraphoma sp. B47-9 386 5 48 157 442 40.0 Pyrenochaeta sp. DS3sAY3a 111 7 75 18 563 42.2 Hesperelaea palmeri 5 10 10 8 11 99.0 Rhizoctonia solani 691 126 77 12 9 47.3 Penicillium janthinellum 3 694 489 78 771 6.2 Verticillium dahliae 590 71 9 9 50 50.8 Rank Stability Index (in %) uncultured eukaryote 9 14 17 16 15 99.0 uncultured bacterium 7 21 14 19 16 97.1 Osmanthus fragrans 6 19 15 14 24 97.2 Candida metapsilosis 318 12 462 355 752 21.9 Saccamoeba lacustris 982 400 114 54 12 32.8 Bodo saltans 934 414 24 30 13 34.5 Rhizopus microsporus 816 58 307 6 961 3.5 Fusarium verticillioides 483 15 182 523 31 16.0 Sesamum indicum 12 26 20 21 29 97.1 Sesbania drummondii 19 23 22 20 30 98.3 uncultured fungus 48 27 31 46 19 93.5 Hanseniaspora guilliermondii 315 374 11 110 315 44.8 Helotiales sp. F229 430 16 558 469 57 16.3 Oryza sativa 27 30 32 35 21 97.8 Clitopilus hobsonii 738 803 12 762 942 9.3 Fusarium proliferatum 486 22 604 526 27 12.4 Hordeum vulgare 54 60 88 43 17 89.9 Solenostemon scutellarioides 18 32 26 33 39 96.7 Fusarium sp. JS1030 510 29 44 148 28 45.1 Triticum aestivum 42 79 69 31 22 90.9 Phoma herbarum 402 46 47 77 23 62.6 Acanthamoeba castellanii 53 37 89 47 26 87.5 Acremonium furcatum 589 18 111 65 97 43.9 Cryptococcus neoformans 32 34 27 36 41 97.7 Solicoccozyma terricola 62 40 25 28 42 94.7 Vigna angularis 30 39 30 38 45 96.7 Tolypocladium sp. Sup5 PDA-1 525 296 621 559 18 25.4 Fusarium fujikuroi 478 28 597 23 148 10.5 Ilyonectria destructans 137 35 58 69 34 83.9 Fraxinus mandshurica 21 41 35 37 59 95.1 Arthrobotrys oligospora 319 24 180 356 462 44.4
3% 100% 1.0 4.5 Differences Variability
0.9 4.0
0.8 3.5
0.7 3.0
0.6 2.5 Rank Variability 2.0 0.5
RV (50 most abundant) DV (50 most abundant)
ControlRoots
RootDamage7d RootDamage08h RootDamage24h RootDamage48h Time samples
Figure 7. Rank dynamics and stability plot for species during the process after roots damage. The dynamics of the rank during the process shows a significative impact in the rhizosphere of the olive. Numbers and colours (using perceptually uniform colormap for easier visualization) show the ranking by the accumulated species abundance in each column. Different rank variability and stability measurements (Martí etal. 2017) are given. The right panel shows the rank stability throughout species ordered by their overall abundance. The lower panel contains plots of the rank variability along time.
279 Finally, Supplemental Figure 18 shows the Recentrifuge plot of SMS classified reads for Dykaria fungi
280 for the sample of leaves 15 days after the roots damage. Candida albicans, a known human pathogen that has
281 been recently associated with ancient oaks too (Bensasson et al. 2019), appears with low frequency but good
282 average confidence.
PREPRINT15 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
V x β = (0.204± 0.083 )x0.895±0.027 2 -2 R = 0.81 Olea
Fusarium
Phytophthora Dactylonectria -4 Cadophora
Paraphoma
Tulasnella
Penicillium
SD of relative abundances (log scale) -6 Pyrenochaeta
Verticillium
-6 -4 -2 0 mean of relative abundances (log scale)
Figure 8. Taylor’s law of the biological system consisting in the metagenome at genus level throughout the root damage process. We see that Taylor’s power law seems to be ubiquitous, spanning in this case more than six orders of magnitude.
283 Conclusions
284 Our results suggest that this disease, although led by Verticillium, is driven not by a single species but by a
285 polymicrobial community which acts as a consortium in the attack of another community formed by the host
286 plant and its endophytes, as Figure 9 shows. Thus, an infectious process can be generalized with a systems
287 biology approach as an attack of one system, i.e. a polymicrobial community, to another, the host and its
288 symbionts. Indeed, our systems approach to the Verticillium wilt of olive has revealed a complex interaction
289 between complex systems.
290 Longitudinal (temporal) metagenomic analysis of the shotgun metagenomic sequencing data has allowed
291 us to study the overall dynamics of the system as well as to obtain results split into amoebae and ciliates,
292 fungi, bacteria, and nematodes. Besides, the longitudinal analysis of a root damage process has served as a
293 real “dynamic control dataset” of the infection process afflicting the olive rhizosphere.
294 Our results also have very important implications in relation to the assignation of a determined parasitic
295 species as biotroph, necrotroph or hemibiotroph. Verticillium, for example, has been sometimes defined as a
296 biotrophic fungus (Boogert and Deacon 1994), whereas some other studies define it as hemibiotrophic (Zhou PREPRINT16 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
Figure 9. Systems approach to the Verticillium wilt of olive: a complex interaction between complex systems. Polymicrobial community attacks a host community (a host and its symbionts). Our results suggest the relevance of a systems perspective as a generalization of the approach to an infectious process.
297 et al. 2012). The same happened with other fungi such as those of the Fusarium genus. However, our work
298 clearly demonstrates that this kind of assignation cannot be easily done in an open, natural, and non sterile
299 system, given the enormous complexity of an infection like the one shown in this work were both biotrophic and
300 necrotrophic species were simultaneously intervening throughout the process. Fusarium is clearly recognized
301 as hemi-biotroph simply because experiments have been conducted with plants supposedly raised from sterile
302 seeds and plant substrates where it was assumed that only this fungus grew. In our case, we cannot get clear
303 conclusions about it, since these plants were four month-old potted olives purchased from a commercial and
304 non-controlled nursery (Jimenez-Ruiz et al. 2017).
305 Finally, this study is another example of the usefulness of the draft genomes included in the NCBI WGS
306 database, since we have used draft genomes sequences of olive and fungi to enrich the NCBI nt database.
307 Using the enlarged database, taxonomic classification methods correctly include the information on individual
308 species gathered by alignment methods.
309 Acknowledgments
310 This research received no specific grant from any funding agency in the public, commercial, or not-for-profit
311 sectors.
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397 Shaban M, Miao Y, Khan AQ, Menghwar H, Khan AH, Ahmed MM, Tabassum MA, Zhu L, et al. 2018.
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399 and biochemistry.
400 Shaffer JP, U’Ren JM, Gallery RE, Baltrus DA, and Arnold AE. 2017. An Endohyphal Bacterium (Chitinophaga,
401 Bacteroidetes) Alters Carbon Source Use by Fusarium keratoplasticum (F. solani Species Complex, Nec-
402 triaceae). Frontiers in Microbiology. 8: 350.
403 Terral JF and Arnold-Simard G. 1996. Beginnings of Olive Cultivation in Eastern Spain in Relation to
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406 pathogens with a range of lifestyles. Plant Physiology. 135: 530–538.
407 Vettraino AM, Shrestha GP, and Vannini A. 2009. First Report of Fusarium solani Causing Wilt of Olea
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409 Wheeler DL et al. 2007. Database resources of the National Center for Biotechnology Information. Nucleic
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411 Yang F, Jacobsen S, Jørgensen HJ, Collinge DB, Svensson B, and Finnie C. 2013. Fusarium graminearum
412 and its interactions with cereal heads: studies in the proteomics era. Frontiers in plant science. 4: 37.
413 Zhang L, Fu Q, Li W, Wang B, Yin X, Liu S, Xu Z, and Niu Q. 2017. Identification and characterization
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PREPRINT21 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
419 Supplemental Material of
420 Verticillium wilt of olive as a polymicrobial infection
1600 core region
1400
1200
1000
800
600 Number of elements
400
200
0 0.0 0.2 0.4 0.6 0.8 1.0 ZRF (Zero Relative Frequency)
Figure 10. Zero relative frequency (ZRF) histogram for all the taxa of samples during the process of infection with V. dahliae. The ‘core’ region includes the taxa that are present in all the samples of the study, therefore, the perdurable taxa along the infection process.
1e7
4
3
2
1 Absolute frequency of elements
0 ControlRoots RootInfect07d RootInfect15d RootInfect048h Longitudinal sample
Figure 11. Absolute frequency plot for reads of samples during the process of infection with V. dahliae. PREPRINT22 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
Leaves control
sample Alternaria alternata 20%
1% Fusarium oxysporum
1% Helotiales 2% Fusarium azukicola
3% Verticillium dahliae
0.9% Magnaporthe oryzae
Pezizomycotina
5% Cryptococcus neoformans uncultured Mycosphaerellaceae 1% saccharomyceta Dikarya Tr...es Ascomycota Aga...ina
Basidiomycota
0.9% Agaricales
Fungi ...
... 1% Puccinia graminis
Puc...ina
Micro...ridia environm... samples Spraguea Muco...cota
Mucor...otina Penicillium expansum 12% Glomeromycetes 10% Spraguea lophii G...s Saccharomycetales M...s G...e M...e 2% uncultured fungus
1% Rhizophagus irregularis
1% Rhizopus oryzae
Fusarium solani species complex 1% Aspergillus fumigatus 2%
Fusarium azukicola 2%
Saccharomyces sp. 'boulardii' 8%
Saccharomyces cerevisiae 2%
Fusarium fujikuroi 14%
1% Dactylonectria macrodidyma
8% Verticillium dahliae
Pezizomycotina
Dikarya
Alternaria consortialis 1% saccharomyceta Tremel...ycetes Ascomycota
Agaricomycotina 11% Cryptococcus neoformans
Fungi
Basidiomycota Alternaria alternata 9% 1% Cantharellales Ag...es
environm...Mic...dia samples Mu...ta Pu...es Spraguea Pucciniomycotina Muco...tina 3% Puccinia graminis Glomeromycetes G...s M...s Sacchar...cetales G...e M...e 2% uncultured2% fungus Spraguea lophii 134
1% uncultured Glomus 117 0.9% Rhizophagus irregularis
1% Rhizopus oryzae 101 Cladosporium sphaerospermum 5%
84 1 more Aspergillus 1% Leaves
67 Aureobasidium pullulans 1% uncultured Mycosphaerellaceae 2% 15 days aer 50 root inoculation Score (avg) Saccharomyces cerevisiae 3% Saccharomyces sp. 'boulardii' 2% with V. dahliae
Figure 12. Recentrifuge plots (Marti 2018) of fungal SMS classified reads at species level for leaves during V. dahliae infection. The top pie corresponds to the leaves control sample while the bottom chart refers to the sample of leaves 15 days after the Verticilium inoculation. PREPRINT23 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
Aphelenchoididae
Aphelenchoidea
55% Bursaphelenchus
Tylenchoidea Heteroderidae
Heteroderinae Tylenchomorpha
Heterodera 44%
134 117 101 84 67 50 Score (avg)
Figure 13. Recentrifuge plot summarizing the results for suborder Tylenchomorpha 15 days after the inoculation with V. dahliae. Heretodera and Bursaphelenchus genera appear with relatively low scores, but over the cutoff limit of 50.
RootsInfect_mhl50.rcf_raw_samples_no_rank 0.95 RootsInfect_mhl50.rcf_CTRL_species RootsInfect_mhl50.rcf_CTRL_genus RootsInfect_mhl50.rcf_CTRL_family RootsInfect_mhl50.rcf_CTRL_order 0.90 RootsInfect_mhl50.rcf_CTRL_class RootsInfect_mhl50.rcf_CTRL_phylum RootsInfect_mhl50.rcf_GeneFilt10k
0.85
0.80
0.75
0.70
0.65 0.0 0.1 0.2 0.3 0.4 0.5 V
Figure 14. Taylor’s law parameters summary plot for x-Weighted fit during the process of infection with V. dahliae for various Recentrifuge datasets. See Martí et al. (2017) for details on the calculations for fitting a x-Weighted model. PREPRINT24 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
10 1
Vx = (0.16 ± 0.02)x0.93 ± 0.01 10 2 R2 = 0.528405
10 3
10 4 std (log scale) 10 5
10 6
10 7
10 5 10 4 10 3 10 2 mean (log scale)
Figure 15. X-weighted fit for the 10,000 most representative expressed-genes during the process of infection with V. dahliae. See Martí et al. (2017) for details on the calculations for fitting a x-Weighted model.
Model residual plot Probability plot of (ordered) residuals
2 2
1 1
0 0
1 1
2 Residuals (model scale)
Residuals (model scale) 2
2 3 3 R = 0.9955
10 5 10 4 10 3 10 2 3 2 1 0 1 2 3 mean (log scale) Quantiles (normal distribution)
Accumulated ESS plot Normality plot of the residuals 0.00012
0.7 0.00010 0.6
0.00008 0.5
0.00006 0.4
0.3 0.00004 Probability density 0.2 Accumulated ESS (lin scale) 0.00002 0.1 0.00000 0.0 10 5 10 4 10 3 10 2 3 2 1 0 1 2 3 mean (log scale) Residuals
Figure 16. Residual analysis plot of LLR-model fit for the 10,000 most representative expressed-genes during the process of infection with V. dahliae. See Martí et al. (2017) for details on the calculations for fitting a x-Weighted model. PREPRINT25 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
20 cmplxCruncher clustered correlation 15 plot of the 50 top elements from 5 samples 10 5 superclusters 20 clusters 5
0 > Rhizopus microsporus Phytophthora sojae > Penicillium janthinellum Hirudo medicinalis Clitopilus hobsonii > Hanseniaspora guilliermondii Verticillium dahliae 1.00 Tolypocladium sp. Sup5 PDA-1 Saccamoeba lacustris Fusarium euwallaceae Fusarium sp. FSSC_6 Rhizoctonia solani 0.75 Bodo saltans [Nectria] haematococca Alternaria alternata Triticum aestivum 0.50 Hordeum vulgare Fusarium solani uncultured fungus Oryza sativa Acanthamoeba castellanii 0.25 Phoma herbarum uncultured bacterium Ilyonectria destructans > Fusarium sp. JS1030 0.00 Fusarium oxysporum Fusarium proliferatum Osmanthus fragrans Solicoccozyma terricola Cryptococcus neoformans 0.25 Solenostemon scutellarioides Vigna angularis uncultured eukaryote Hesperelaea palmeri 0.50 Sesamum indicum Sesbania drummondii Arabidopsis thaliana Olea europaea Fusarium verticillioides 0.75 Fraxinus mandshurica Cadophora malorum Acremonium furcatum Helotiales sp. F229 Pyrenochaeta sp. DS3sAY3a 1.00 Dactylonectria macrodidyma Paraphoma sp. B47-9 Candida metapsilosis Arthrobotrys oligospora Fusarium fujikuroi > Tulasnella calospora Oryza sativa Bodo saltans Olea europaea Vigna angularis Fusarium solani Phoma herbarum Hordeum vulgare Fusarium fujikuroi Triticum aestivum Rhizoctonia solani Clitopilus hobsonii uncultured fungus Sesamum indicum Hirudo medicinalis Helotiales sp. F229 Verticillium dahliae Phytophthora sojae Alternaria alternata Arabidopsis thaliana Osmanthus fragrans Cadophora malorum Hesperelaea palmeri Fusarium sp. FSSC_6 Fusarium oxysporum Candida metapsilosis Paraphoma sp. B47-9 uncultured eukaryote uncultured bacterium Saccamoeba lacustris Sesbania drummondii Fraxinus mandshurica Fusarium proliferatum Fusarium euwallaceae Acremonium furcatum > Fusarium sp. JS1030 > Tulasnella calospora Ilyonectria destructans Fusarium verticillioides Arthrobotrys oligospora Solicoccozyma terricola [Nectria] haematococca > Rhizopus microsporus Acanthamoeba castellanii Cryptococcus neoformans > Penicillium janthinellum Pyrenochaeta sp. DS3sAY3a Dactylonectria macrodidyma Solenostemon scutellarioides Tolypocladium sp. Sup5 PDA-1 > Hanseniaspora guilliermondii
Figure 17. Clustered correlation and dendrogram plot for species during the process after Olea europaea roots damage. We show the 50 most abundant species ordered by clusterization based on the Pearson time correlation matrix. Several clusters and superclusters can be identified with this analysis by cmplxCruncher.
PREPRINT26 bioRxiv preprint doi: https://doi.org/10.1101/555185; this version posted February 19, 2019. The copyright holder for this preprint (which was not certifiedbioRxiv by peer review) is the pre-print author/funder. All rights reserved. LA TNo reuseEX allowed v0.1 without permission.
Alternaria alternata 7%
2 more
4% Fusarium azukicola
0.5% Fusarium virguliforme
Cladosporium sphaerospermum UM 843 9% 1 more 0.5% Microascales1% Magnaporthe oryzae Hypocre...cetidae Leotiomycetes Sordariomycetes Sordari...cetidae Pleosporomycetidae sordariomyceta
0.8% Cryptococcus neoformans var. grubii D17-1
3% Cryptococcus neoformans var. grubii Br795
Cryptococcus neoformans species complex
leotiomyceta 0.9% Cryptococcus neoformans var. grubii CHC193 Dothideomycetes dothideomyceta Pezizomycotina
Cryptococcaceae
Cryptococcus
Tremellomycetes 17% Cryptococcus neoformans Tremellales uncultured Mycosphaerellaceae 4% Dothideomycetidae
Ascomycota saccharomyceta Dikarya Fungi
Agaricomycotina
Aureobasidium pullulans 4%
Basidiomycota Eurotiomycetes Eurotiomycetidae Onygenales 0.6%
Eurotiales 0.7% Polyporales Agaricom...ae sedis Agaricomycetes Ag...es 1 more Pucciniomycotina Tr...ae Agaric...etidae Saccharomycotina Bae...ora Microbot...omycetes A...s Ustilaginomycotina 0.7% Baeospora myosura ... A...e Saccharomycetes Pucci...cetes Microbotryales ... Ustilaginomycetes ... Pil...rma ... Malas...cetes Pucciniales Saccharomycetales 1 more Malasseziales 2% Piloderma croceum F 1598 Ustilaginales Pucci...aceae 0.6% Microbotryum violaceum Penicillium expansum 3% Sacchar...etaceae Puccinia ...... Saccharomyces ... Puccinia graminis 134 Debaryo...etaceae ... 1 more Candida/L...ces clade 5% Puccinia graminis f. sp. tritici 117
0.6% Ustilago bromivora 101 1 more Metschnikowia 1% 84
67
1 more 1 more 50 Saccharomyces sp. 'boulardii' 3% Score (avg) Saccharomyces cerevisiae 6%
Candida albicans 0.6%
Figure 18. Recentrifuge plot of SMS classified reads for Dykaria fungi for the sample of leaves 15days after the roots damage. In the bottom of the pie, Candida albicans appears with a good average score but low frequency.
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