Pathogen invasion enhances
the abundance of predatory protists and their prey associations in the
plant microbiome
Min
Gao1,2,3,
Chao
Xiong3,
Clement
K.
M.
Tsui4,5,6,
Lei Cai1,2*
1 State Key Laboratory of Mycology,
Institute
of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
2College
of Life Sciences, University of Chinese Academy of Sciences, Beijing
100049, China.
3 Hawkesbury Institute for the Environment, Western
Sydney University, Penrith, NSW 2753, Australia.
4 Division of Infectious Diseases, Faculty of
Medicine, University of British Columbia, Vancouver, BC, V6T 1Z3,
Canada.
5 National Center for Infectious Diseases, Tan Tock
Seng Hospital, Singapore.
6 LKC School of Medicine, Nanyang Technological
University, Singapore.
* Author for correspondence: Lei Cai
Corresponding author e-mail:
cail@im.ac.cn.
Abstract
Untangling the responses of protistan communities associated with soil
and plant compartments and their associations with bacterial and fungal
communities to pathogen invasion are critical for understanding the
ecological processes governing plant microbiome assembly. Here we
examined the protistan communities across the soil–plant continuum of
healthy chili peppers and those with Fusarium wilt disease (FWD)
and integrated the bacterial and fungal microbiome data from our
previous investigation in China. We found that FWD was associated with a
significant enrichment of phagotrophic protists in roots and an increase
in the proportion and connectivity of these phagotrophic protists in
intra- and interkingdom networks. Specifically, FWD increased the
negative correlations between phagotrophic protists (especially Cercozoa
and Ciliophora) and several members of Actinobacteria,
Alphaproteobacteria, and Gammaproteobacteria in the interkingdom
networks. Furthermore, the microbiomes of diseased plants not only
exhibit a higher relative abundance of functional genes related to
bacterial anti-predator responses compared to healthy plants, but also
contained a greater abundance of metagenome-assembled genomes possessing
functional traits involved in this response. The increased microbial
interkingdom correlations among bacteria, fungi, and protists, coupled
with the enhanced effects of protists on bacteria and fungi, as well as
the notable bacterial anti-predator feedback in the diseased plant
microbiome, all suggest that FWD catalyzes the associations between
different groups of microbiomes. These findings highlight the role of
predatory protists in shaping microbial assembly and functionality
through top-down forces during pathogenic stress, potentially
contributing to co-evolution within these soil and plant microbiomes.
KEYWORDS: Fusarium wilt disease, protists, plant
microbiome, prey defense, metagenomic
1 INTRODUCTION
Soil and plant associated microbiomes are essential in plant growth and
health, including nutrient acquisition, abiotic stress tolerance, and
disease suppression (D’Hondt et al.,
2021; Trivedi et al., 2020;
van der Heijden et al., 2016). The
assembly of the bacterial and fungal microbiome in soils and plants is
determined by certain crucial bottom-up controls, mainly driven by
environmental conditions and resource availability, such as the
concentration and diversity of root-secreted exudates or volatile
organic compounds (Gao et al., 2019;
Liu et al., 2019;
Zhalnina et al., 2018). In addition to
bacteria and fungi, protists are another widely recognized soil and
plant microbial components (Bates et al.,
2013; Gao et al., 2019;
Xiong et al., 2018), and their
communities are important in microbiome assembly, as microorganisms
dwell with myriad other microbes and are co-embedded in complex food
webs (Petters et al., 2021). However,
studies on the protistan communities associated with plants and soils
are limited and far less than bacterial and fungal communities, despite
the fact that protists are main top-down force in shaping bacterial and
fungal communities. The top-down control of predator-prey interactions,
largely dominate by protists, is considered to play a critical role in
boosting microbial turnover in the soil and improving plant performance
(Asiloglu et al., 2021;
Gao et al., 2019;
Geisen et al., 2018;
Xiong et al., 2018).
Deciphering
the ecological processes underlying the protistan community assembly and
uncovering the dynamics of protistan–bacterial–fungal interactions is
critical to understand the ecological mechanism governing plant
microbiome assembly.
Unicellular
eukaryotic
protists, including plant-, fungus- and animal-like microorganisms, are
highly diverse and ubiquitous members of the biosphere
(Milner et al., 2021;
Sibbald et al., 2017;
Whittaker et al., 1978). Based on their
life cycles and the mode of nutrient-uptake, protists display parasitic,
phagotrophic, phototrophic, plant pathogenic, and saprotrophic behaviors
and are thus grouped accordingly (Geisen
et al., 2018). Phagotrophic protists, also known as predatory protists,
are major predators of ambient microbes, especially bacteria and
arguably fungi. Protists predation could increase both species turnover
and nutrient release, which fuels microbial activity and shifts in
microbiome composition (Bahroun et al.,
2021; Friman et al., 2013;
Gao et al., 2019;
Rosenberg et al., 2009;
Weidner et al., 2016). Bacteria respond
to predation through the stimulation of prey defense traits that are
morphological or structural modifications
(Queck et al., 2006;
Wildschutte et al., 2004); some defense
are chemicals or secretions of defensive secondary metabolites
(Jousset, 2012;
Alexandre Jousset et al., 2010;
Jousset et al., 2009;
Mazzola et al., 2009;
Weidner et al., 2016). These prey defense
traits in response to predation could be pertinent for plant health; for
instance, several secondary metabolites conferring resistance to
predation are known to be involved in plant pathogen suppression or
plant immunity priming (Jousset et al.,
2006; A. Jousset et al., 2010). Given
the influence of protists on the structure and functioning of
microbiomes, and the defense traits from their prey items, it has been
postulated that the protist–prey interaction can steer beneficial
plant–microbe interactions, leading to better plant growth and health
(Bonkowski, 2004;
Guo et al., 2021;
Krome et al., 2009;
Xiong et al., 2020).
Protists are key bioindicators in agroecosystems given their ecological
importance (Foissner, 1997), and they can
be served as the main predictor/determinants of the performance and
health of some crops (Guo et al., 2021;
Xiong et al., 2020). Several
investigations have been performed to explore the influence of abiotic
factors, including aridity, nitrogen fertilization, and seasonal
factors, on the assembly of protistan communities
(Caracciolo et al., 2022;
Chen et al., 2022;
Nolte et al., 2010;
Oliverio et al., 2020;
Ren et al., 2023;
Zhao et al., 2019). However, existing
literature rarely focused on how pathogen invasion affects the assembly
of soil and plant associated protistan communities and the complex
interactions among protists, bacteria, and fungi.
In this study, firstly, we investigated how pathogen invasion affected
the assembly of soil and plant-associated protistan communities by
performing high-throughput amplicon sequencing for multiple compartments
(e.g., soils, roots, stems, and fruits) of healthy and Fusariumwilt diseased (FWD) chili peppers from two locations in China. Secondly,
in order to understand the interaction among different microbial
communities, we integrated the bacterial and fungal microbiome data from
our previous investigation on the same pepper–FWD system
(Gao et al., 2021) to decipher the
potential role of protists in shaping bacterial and fungal communities
under FWD. We hypothesized that FWD would influence the protistan
community structures especially for those groups that have predatory
functions and alter the protists-prey associations which would drive a
change in the plant microbiome assembly and functions. Previous studies
showed that pathogen invasion influences the soil and plant associated
bacterial and fungal communities (Carrión
et al., 2019; Gao et al., 2021;
Liu et al., 2020), and the functions of
the microbiome; however, the role of protists compared with other
microbial groups in driving the plant microbiome assembly under pathogen
invasion remains poorly understood. We expect our integration of
protists, bacteria, and fungi, efforts to provide a new insight of the
plant microbiome assembly and function from protists perspective under a
fungus pathogen invasion.
2 MATERIALS AND METHODS
2.1 Sampling
Mature pepper plants were collected from the major chili pepper
production fields of Huishui (25°48’41”N, 106°31’24”E) and Guiyang (26°
29′31″N, 106° 39′16″E), in Guizhou province, China in August 2018. The
sampling and handling of soils, roots, stems and fruits samples, as well
as the distinguishing symptoms of diseased versus healthy peppers, have
been described in details previously (Gao
et al., 2021). In brief, healthy and diseased pepper plants and their
corresponding bulk and rhizosphere soils were collected, and the root
and fruit samples were divided into the episphere and endosphere by the
methods of filtering wash liquid and surface sterilization,
respectively. Also, the stem samples were fractionated into upper stem,
middle stem, and bottom stem section, with each section further divided
into the xylem and epidermis, accordingly. Altogether, each plant sample
was divided into 12 compartments (144 samples = 2 sites × 12
compartments × 2 (healthy and diseased) × 3 replicates). Total DNA of
each compartment was extracted with the FastDNA SPIN Kit for soil (MP
Biomedicals, Solon, USA) following the manufacturer’s instructions.
2.2 Sequencing for protistan communities and
bioinformatics
analysis
The V9 region of the eukaryotic 18S rRNA gene was amplified using
primers: 1389F (TTGTACACACCGCCC) and 1510R (CCTTCYGCAGGTTCACCTAC)
(Amaral-Zettler et al., 2009;
Bahram et al., 2018;
Mahe et al., 2017). Amplicon libraries
were sequenced using an Illumina HiSeq2500 platform (Guangdong Magigene
Biotechnology Co., Ltd., Guangzhou, China) to generate 250 bp paired-end
reads.
The amplicon data were processed using USEARCH v10 and QIIME v1.91
(Caporaso et al., 2010;
Edgar, 2010). Raw sequences were
quality-trimmed, and the paired-end reads were merged into a single
sequence. Biological reads were identified at 100% sequence similarity
by using UNOISE3 (Edgar, 2016), set to
its default parameters in USEARCH v10. Then, representative sequences
for protistan zero-radius operational taxonomic units (ZOTUs) were
aligned against the Protist Ribosomal Reference database (PR2)
(Guillou et al., 2013), from which these
ZOTUs assigned to Rhodophyta, Metazoa, Fungi, and unassigned
Opisthokonta sequences were discarded (Guo
et al., 2022; Guo et al., 2021). The
ZOTU table was rarefied based on the lowest reads among all samples, to
estimate the alpha diversity (Shannon and Chao1 indices); for beta
diversity analyses, METAGENOMESEQ’s cumulative sum scaling (CSS) was
used as the normalization method. Core taxa of healthy and diseased
plants protistan microbiomes were defined as those ZOTUs occurring in at
least 80% samples of healthy and diseased plants, respectively. In
addition, we categorized the protistan OTUs into distinct functional
groups based on their mode of nutrient-uptake using relevant literature
as a reference (Nguyen et al., 2020;
Xiong et al., 2020). These functional
groups include parasites, phagotrophs, phototrophs, plant pathogens, and
saprotrophs.
2.3 Statistical analysis
The beta diversity of protistan communities was calculated using
Bray–Curtis distance matrices and visualized by non-metric
multi-dimensional scaling (NMDS) ordinations. The relative contribution
of FWD and sampling site to the protistan community composition was
tested with permutational multivariate analysis of variance (PERMANOVA),
using the “adonis” function in the ‘vegan’ package in R
(Oksanen et al., 2007). Differential
abundance analysis between protistan ZOTUs of healthy and diseased
plants was implemented using EdgeR’s generalized linear model (GLM)
approach (Robinson et al., 2010).
To assess the importance of deterministic and stochastic processes in
the microbiome assembly, beta Nearest Taxon Index (βNTI) and
Bray–Curtis-based Raup-Crick Index (RCI) were calculated by using a
null model with 999 randomizations (Stegen
et al., 2013). Deterministic dominant processes were quantified as
|βNTI|≥2, in which βNTI<−2 indicates
heterogeneous selection, and βNTI > +2 indicates
homogeneous selection. Stochastic dominant processes were quantified as
|βNTI| < 2. Pairwise comparisons with βNTI
and RCI values were used to recognize homogenizing dispersal
(|βNTI| < 2 and RCI < –0.95),
dispersal limitation (|βNTI| < 2 and RCI
> 0.95), and undominated (|βNTI|
< 2 and |RCI| < 0.95) processes.
2.4 Microbial co-occurrence network
analysis
Co-occurrence networks were constructed using the CoNet app in Cytoscape
v3.7.1 (Faust, 2016;
Shannon et al., 2003), and visualized in
the Gephi platform (Bastian et al., 2009).
All 1844 protistan ZOTUs were used in the intra-kingdom network
analysis, for which only those with correlations having a Spearman’s
coefficient (ρ) > 0.70 and P < 0.05 were
retained. The interkingdom network analysis integrated 1152 protistan
ZOTUs, 2549 bacterial ZOTUs and 1030 fungal ZOTUs, for which a relative
abundance > 0.1% was criterion for inclusion in each
group. The ZOTUs for bacteria and fungi had been generated from a
previous study (Gao et al., 2021).
Topological characteristics of each network, including the numbers of
positive and negative correlations, average degree, clustering
coefficient, and closeness centrality were calculated in Cytoscape
v3.7.1 software.
2.5 Structural equation modeling (SEM)
analysis
To evaluate the effects of phagotrophic protists on the communities of
bacteria, fungi, and protists, we utilized SEM to determine the
direction and strength of relationships among different microbial groups
(Delgado-Baquerizo et al., 2013;
Grace, 2006), using IBM SPSS Amos 21
(Chicago, IL: Amos Development Corporation). SEM was performed
independently to healthy and diseased pepper plant data. Variable of
Phagotrophs was represented by the composition of phagotrophic protists,
and the communities of bacteria, fungi, and protists were represented by
NMDS2, respectively. A maximum likelihood evaluation method was used to
fit the covariance matrix into the model, and the model fitness was
evaluated according to several diagnostic parameters: a non-significant
chi-square test, goodness of fit index (GFI), comparative fit index
(CFI), root mean square residual (RMR), normed fit index (NFI), and the
root mean square error of approximation (RMSEA).
2.6 Metagenomic binning and functional
analyses
As phagotrophic protists were significantly enriched in the roots of the
diseased plants, healthy and diseased root endosphere samples were
selected for additional metagenomic sequencing and binning. Six samples
were sequenced on an Illumina NovaSeq 6000 instrument (Majorbio
Bio-Pharm Technology Co. Ltd., Shanghai, China), yielding ca. 20 GB of
clean data for each DNA sample. The reads were quality-filtered and
assembled, using Trimmomatic v0.39 and Megahit v1.2.9
(Bolger et al., 2014;
Li et al., 2015), respectively; the
assembled sequences were predicted and functional annotated using Prokka
v1.14.5 (Seemann, 2014), and
eggNOG-mapper v1.0.3 (Huerta-Cepas et al.,
2017; Huerta-Cepas et al., 2019),
respectively, as described in the previous study
(Gao et al., 2021).
Furthermore, the metagenome-assembled genomes (MAGs) were recovered from
the assembled contigs, using MetaBAT2
(Kang et al., 2019) and MaxBin2
(Wu et al., 2016) according to the
MetaWRAP pipeline (Uritskiy et al.,
2018). To refine the ensuing bins, the Bin_refinement module was
applied, and the consolidated bin sets were then quality assessed using
CheckM v1.1.3 (Parks et al., 2015).
Overall, 56 MAGs of sufficient quality (i.e., completeness
> 50% and contamination < 10%) were obtained,
all of which met currently accepted standards for medium- to
high-quality MAGs (Bowers et al., 2017).
These MAGs were then classified with GTDB-Tk v1.7.0, on the basis of 120
bacterial marker genes, to generate the taxonomic information
(Parks et al., 2018). A phylogenetic tree
of 56 MAGs was built using FastTree (Price
et al., 2010), and visualized in iTOL v6.4
(Letunic et al., 2019). Also, genes of
MAGs were predicted by Prokka v1.14.5
(Seemann, 2014), and the functional
annotations organized using DIAMOND
(Buchfink et al., 2015) in combination
with the KEGG database (Kanehisa et al.,
2016). KEGG Orthology (KO) profiles were generated by summing up the
abundance of genes affiliated to the same KO. The abundance of MAGs on
the microbiomes of healthy and diseased plants was calculated by coverM
v 0.6.1 (https://github.com/wwood/CoverM).
3 RESULTS
3.1 FWD affects the assembly of pepper-associated
protistan
communities
After the removal of amplicon sequences that were of low-quality and/or
chimeras, as well as those belonged to non-protists and singletons, a
total of 1,765,274 high-quality protistan reads were retrieved, and they
were sorted into 1,844 protistan ZOTUs. NMDS ordinations and PERMANOVA
showed that protistan communities were most affected by plant
compartment (R 2 = 0.48, P <
0.001), and significantly affected by sampling site
(R 2 = 0.029, P < 0.001) and
FWD (R 2 = 0.012, P = 0.003) (Figure 1a).
Protistan community structures in the bottom, middle, and upper stem
epidermis and fruit episphere were significantly affected by FWD, with
the strongest effect found in upper stem epidermis (Figure 1b and Figure
S1).
Protistan Shannon index was significantly reduced in the rhizosphere
soil, bottom stem epidermis, and upper stem epidermis by FWD, but
increased that in the root endosphere (P < 0.05, Figure
S2). Differential abundance analysis indicated that several members
within the protistan groups Cercozoa, Ciliophora, Lobosa, Conosa, and
Pseudofungi were significantly enriched in the root of the diseased
plants (Figure 1c and Figure S4). Most of these enriched protistan taxa
belonged to the phagotrophic functional group, except for Pseudofungi.
(Figure S3). Although 97.2% (1793 of 1844) protistan ZOTUs were shared
between the healthy and diseased plants (Figure S5a), only 15 and 9
ZOTUs were recognized as core ZOTUs (i.e., in at least 80% of samples)
in each group, respectively (Figure S5b and Table S1).
3.2 FWD affects protistan intra-kingdom
network
Intra-kingdom co-occurrence
network analysis was conducted to explore how FWD impacted the
intra-kingdom relationships within protistan ZOTUs. The number of nodes
and edges, clustering coefficient, average degree, and network density,
which collectively convey a network’s complexity, were all higher in the
diseased network than in the healthy one (Figure 2a and Table S2). A
higher proportion of nodes belonging to Cercozoa were recorded in the
diseased (31%) than the healthy (26%) network (Figure 2a). Also, the
closeness centrality of nodes and the number of hub nodes (degree
> 50) in the diseased network were greater than those in
the healthy network (1 hub node in healthy network vs. 24 hub nodes in
diseased network) (Figure 2e). Importantly, 57% of the hub nodes in the
diseased network were Cercozoa ( Figure 2e), and the degree of
nodes belonging to Cercozoa (P = 7.7e−05) and Ciliophora
(P = 0.045) were higher in the diseased network than in the
healthy network (Figure 2f, g). In addition, healthy and diseased
networks present 33 overlapped nodes of Cercozoa and 24 overlapped nodes
of Ciliophora; all the former and most of the latter nodes featured an
average degree that was higher in the diseased than healthy network
(Figure S6a, b). Compared with the healthy network, a higher proportion
of FWD-enriched ZOTUs belonging to phagotrophic protists
(healthy/diseased: 1.9%/7.7%) were recorded in the diseased network
(Figure 2b). Furthermore, 5.06% and 3.16% of nodes in the healthy
network belonged to core ZOTUs and FWD-depleted ZOTUs (i.e., healthy
enriched), while 2.98% and 11.31% nodes in the diseased network
belonged to core ZOTUs and FWD-enriched ZOTUs, respectively (Figure 2c,
d).
3.3 Potential correlations
between bacteria, fungi, and phagotrophic protists under
FWD
After studying the patterns and processes that influenced the diversity
and composition of protist microbiomes in plants, we studied the
correlations among protists, bacteria and fungi in the healthy and FWD
impacted pepper systems; interkingdom network analysis has been
performed to integrate the current protist microbiome data, together
with the bacteria and fungi microbiome data published in 2021
(Gao et al., 2021).
The diseased network contained a higher number of fungal and protistan
nodes but a lower number of bacterial nodes, compared with healthy
network (Figure 3a). 43.3% of hub nodes (degree > 50) in
the diseased network were fungal taxa while all hub nodes in the healthy
network belonged to bacterial taxa, no fungal and protistan taxa were
inferred (Figure 3b). Also, the proportion of nodes belonging to
FWD-enriched ZOTUs was higher in the diseased network (9.5%) than
FWD-depleted ZOTUs in the healthy network (4.7%) (Figure S7). On the
other hand, more negative edges representing bacterial–fungal (BF) and
bacterial–protistan (BP) interkingdom correlations occurred in the
diseased network than in the healthy one; the diseased network had more
positive edges denoting fungal–fungal (FF), fungal–protistan (FP), and
protistan–protistan (PP) correlations (Figure 3c). When comparing the
interkingdom networks in the organs, higher number of protistan ZOTUs
and BP correlations were observed in roots, bottom stems, and fruit
networks in the diseased networks, compared to the healthy networks
(Figure S8 and Figure S9). Moreover, the number of nodes belonging to
Cercozoa and Ciliophora was higher in the diseased than the healthy
network (Figure 3d and Figure S6c, d); the degree of nodes belonging to
Cercozoa was also significantly higher in the diseased than the healthy
network (P = 0.042, Figure S6c). For those Cercozoa and
Ciliophora nodes shared by healthy and diseased networks, most of their
connectivity was also higher in diseased network than in healthy
networks (Figure S6e, f).
The nodes belonging to the phagotrophic protists Cercozoa and
Ciliophora, as well as related nodes that interacted with these two
protistan taxa were selected for networks’ visualization. Focusing on
Cercozoa in the networks, the correlations of Cercozoa–Actinobacteria,
Cercozoa–Alphaproteobacteria, and Cercozoa–Gammaproteobacteria were
primarily negative, whereas those of Cercozoa–Sordariomycetes,
Cercozoa–Cercozoa, Cercozoa–Ciliophora, and Cercozoa–Discoba were
predominantly positive, in both healthy and diseased networks (Figure
3d, e). Importantly, numbers of these correlations were increased in the
diseased network when compared with the healthy. Similar patterns were
also observed in the networks focused upon Ciliophora (Figure 3d,
e). Among the correlations of
Cercozoa/Ciliophora–Alphaproteobacteria, the genera Sphingomonasand Methylobacterium were the main two linking members of the
Alphaproteobacteria (Figure S10). Although the correlations between
Cercozoa/Ciliophora and
Actinobacteria/Alphaproteobacteria/Gammaproteobacteria were generally
negative, we did detect several positive correlations among them (Figure
3f). Specifically, some of these implied a linkage between
Cercozoa/Ciliophora and potential beneficial bacteria, such as that of
Cercozoa–Bacillus in the healthy network, as well as that for
Cercozoa/Ciliophora–Lysobacter , Cercozoa–Streptomyces ,
Cercozoa–Bacillus , and Cercozoa-Flavisolibacter in the
diseased network (Figure 3f; Figure S10; Table S3). Several positive
correlations were found linking Cercozoa/Ciliophora to potential
pathogenic fungi in the diseased network, such as cases of
Cercozoa/Ciliophora–Diaporthe , and
Cercozoa–Gibellulopsis (Figure S10 b, d).
Since the proportion and the links associated with phagotrophic protists
ZOTUs had increased from healthy to FWD networks, structural equation
modeling (SEM) was utilized to gain deeper insights on how phagotrophic
protists influenced the communities of bacteria, fungi, and protists
under FWD (Figure S11 and Table S4). The modeling results revealed that
phagotrophic protists had a significant, direct negative effect on the
bacterial community, but a significant, direct positive effect on the
fungal community, in both healthy and diseased SEM (Figure S11a, b). FWD
increased strongly the path coefficients of phagotrophic
protist–bacteria (healthy/diseased: 0.15/0.36) and phagotrophic
protist–fungi (0.18/0.38) relationships (Figure S11a, b; Table S5),
which indicated that the effects of phagotrophic protists on bacterial
and fungal communities strongly enhanced by FWD.
As FWD strongly enhanced the microbial correlations between bacteria,
fungi, and protists, we further hypothesized that the increased
microbial interaction would increase the relative contribution of the
deterministic processes in microbiome assembly of the diseased plants.
Consistent with this hypothesis, null model analysis demonstrated a
higher relative contribution of deterministic processes
(|βNTI|≥2), in protistan (healthy/diseased:
41.1%/44.6%), bacterial (determinism, healthy/diseased:
66.9%/69.5%), and fungal communities (healthy/diseased: 62.2%/80.2%)
of the diseased plant, compared with the healthy plants (Figure 4a).
Remarkably, FWD increased heterogeneous selection in the assembly of the
three microbial communities and increased homogeneous selection in
fungal community assembly (Figure 4b).
3.4 FWD enhanced functional traits related to prey
defense in root endosphere
microbiomes
We further performed metagenomic analysis to characterize the functional
genes in the pepper root-associated microbiomes. The data revealed that
the relative abundance of several functional genes associated with
bacterial prey defense, such as those associated with hydrogen cyanide
(increased by 106%–1842%), cyclic lipopeptides (increased by 295%),
and type III secretion systems (increased by 13.6%–138%) were higher
in the microbiome of diseased vis-à-vis healthy pepper plants (Figure
4c).
Metagenomic binning successfully generated 15 and 41 bacterial MAGs of
sufficient quality from the healthy and diseased pepper samples,
respectively. These MAGs could be assigned to four phyla: Proteobacteria
(31 MAGs), Bacteroidota (11), Actinobacteriota (10), and Patescibacteria
(4) (Figure 5a). Among these MAGs, 8 MAGs harbored genes associated with
bacterial prey defense (1 MAG from healthy samples, 7 MAGs from diseased
samples) (Figure 5b). Intriguingly, MAGs that contained bacterial prey
defense genes all belonged to Actinobacteriota and Gammaproteobacteria
(Figure 5a), which was negatively correlated with phagotrophic protists
Cercozoa and Ciliophora in the interkingdom networks (Figure 3d, e).
Furthermore, 7 of these 8 MAGs were more abundant on the microbiome of
the diseased plants compared to the healthy plants (Figure 5c).
4 DISCUSSION
While considering the assembly and the interkingdom interactions of soil
and plant associated microbiomes under pathogen invasion, previous
studies have predominantly focused on the bacterial or fungal
communities. The responses of these communities to pathogen invasion
have been extensively studied in various agricultural crops
(Berendsen et al., 2018;
Carrión et al., 2019;
Gao et al., 2021;
Kwak et al., 2018;
Liu et al., 2020). The purpose of our
research was to broaden this perspective by delving into the diversity
and structure of protistan communities and exploring their responses to
pathogen invasion. Additionally, our work sought to unravel the
intricate interactions among protists, bacteria, and fungi, thereby
providing a more comprehensive understanding of soil and plant
microbiome assembly.
Our data showed that the protistan communities were influenced by
sampling locations and plant compartments. A significant portion of
protists display predatory behavior and selectivity in their feeding
preferences, targeting specific bacterial and fungal groups. The
distinct taxonomic and functional diversity harbored within plant
compartments can influence the community structure and functions of
bacteria and fungi, leading to variation in the protist communities
(Nguyen et al., 2023). Also, FWD
increased the relative abundance of phagotrophic protists in pepper
root. Previous studies showed that protists constitute an important
microbial component that is highly sensitive to fertilization
(Guo et al., 2021;
Zhao et al., 2019), aridity
(Chen et al., 2022), and seasonal
variation (Walden et al., 2021).
Expanding upon these observations, our results present compelling
evidence that soil and plant associated protistan communities could be
drastically altered by disease incurred by pathogen invasion. Members of
Cercozoa and Ciliophora are widely acknowledged as dominant and integral
component of the protistan communities in soils and plants
(Bates et al., 2013;
Oliverio et al., 2020;
Sapp et al., 2017). Both groups also
prominently featured in the intra-kingdom networks of protists in our
study. FWD-mediated enrichment of Cercozoa and Ciliophora seemed to
exert a significant influence on the diseased intra- and interkingdom
networks, implying that these two groups are crucial in microbe–microbe
interactions.
By unraveling the intricate ecological relationships among different
microbial groups, including predation, competition, and cooperative
correlations, we can gain a more comprehensive understanding of
microbe-microbe interactions within soil and plant ecosystems. The
complexity of these ecological relationships can be represented through
microbial networks, which is fundamental for characterizing the
correlations of microbial taxa and could reflect dynamics and functions
of ecosystems (Delgado-Baquerizo et al.,
2020; Wagg et al., 2019;
Yuan et al., 2021). Our network analysis
revealed that the intra-kingdom correlations between protists and fungi
were predominantly positive, while both groups demonstrated negative
correlations with bacteria. This preponderance of negative correlations
between protists and bacteria suggests a mutual exclusivity in species
abundance between the two. Specifically, members of Cercozoa and
Ciliophora were largely negatively associated with members of the
Actinobacteria, Alphaproteobacteria, and Gammaproteobacteria. According
to a microbiota reconstitution experiment, members of
Alphaproteobacteria and Gammaproteobacteria demonstrated lower
resistance to grazing by the leaf-associated protist Cercomonads
(Rhizaria: Cercozoa) (Flues et al.,
2017). This concurrence suggests that the observed negative
correlations between protists and bacteria in our networks may reflect
potential predatory relationships. Such predation could augment
microbial loops, as the consumption of bacteria or fungi by protists
releases nutrients that can then be utilized by other microbes or
assimilated by host plants (Bonkowski,
2004; Clarholm, 1985;
Gao et al., 2019;
Geisen et al., 2018). Our analysis also
identified several positive correlations between Cercozoa/Ciliophora and
potentially beneficial bacteria, as well as certain Sordariomycetes
fungi. Collectively, these findings imply that protistan predation may
play a key role in modulating microbial community structures,
significantly impacting the distribution and abundance of specific taxa.
Our metagenomic data revealed that that the relative abundance of
functional genes associated with bacterial prey defense mechanisms,
including hydrogen cyanide, cyclic lipopeptides, and type III secretion
systems, increased in the microbiome of diseased pepper plants when
compared with healthy ones. Bacteria have evolved a range of adaptive
strategies, such as morphological adaptation, secondary metabolites and
secretions, in response to the selective pressure exerted by predatory
protists (Gao et al., 2019;
Jousset, 2012;
Jousset et al., 2009). These adaptive
mechanisms can, in turn, impact the abundance of predatory protists
(Becks et al., 2012;
Hiltunen et al., 2014). Given that the
growth of predator population was affected more by prey evolution than
by prey abundance (Becks et al., 2012), we
propose that the elevated abundance of prey defense traits and
phagotrophic protists in the diseased plants may represent the main
components of an eco-evolutionary feedback loop in the predator–prey
system. This feedback loop, mediated by predator-prey interactions could
further drive the functions of the microbiome
(Becks et al., 2012); particularly, the
stimulation of the prey defense traits could influence the capacity of
the microbiome to suppress the disease invasion of host plant. For
example, the toxic secondary metabolite hydrogen cyanide produced by
bacteria can be induced by protists to reduce predation pressure
(A. Jousset et al., 2010), and this
metabolite has been a potent deterrent of various plant fungal pathogens
(Rijavec et al., 2016). Similarly,
antifungal metabolites cyclic lipopeptides are known to confer
resistance to predation by protists
(Mazzola et al., 2009;
Nielsen et al., 2003). The overlapping
suite of the traits linked to predation defense and pathogen suppression
could be considered an adaptation which beneficial functions are
promoted throughout the microbiome (Gao et
al., 2019). Another persuasive piece of evidence is that the presence
of protists can stimulate the antifungal activity of beneficial
bacteria; therefore, co-inoculating beneficial bacteria and protists
(Rosculus terrestris , Bodomorpha sp., and Cercomonas
lenta ) significantly enhanced faba bean to suppress against soil-borne
pathogen Fusarium solani (Bahroun et
al., 2021). Further, organic fertilization can stimulate predator-prey
interactions that catalyze keystone interactions between protistan
predators and plant-beneficial bacteria, thus decreasing bananaFusarium wilt disease incidence and ultimately enhancing plant
health and yield (Guo et al., 2022).
Drawing on these findings, our results propose that, similar to the
influence of organic fertilizer inputs, fungal pathogen invasions could
also stimulate predator-prey correlations.
Beyond catalyzing associations between protists and bacteria, FWD also
amplified the associations between various other microbes within the
networks. This augment may be related to the disruption of the stable
systems maintained by host plants. Lots of evidences support that the
composition of the microbial communities could be stabilized during
plant growth and development (Edwards et
al., 2018; Xiong et al., 2021;
Zhang et al., 2018). When mature plants
are infected by the pathogens, this would spur a series of changes in
the physiochemical conditions, such as water losses, withering, and
litter fall (Michielse et al., 2009),
potentially altering the nutrients and space available to microbes in
the soil and plants directly or indirectly. Moreover, the plant’s immune
system response can trigger alterations to root exudates or other
volatile substances to recruit protective microbial members
(Berendsen et al., 2018;
H. Liu et al., 2019;
Liu et al., 2020;
Liu et al., 2019), or provide legacy for
the next generation under pathogen stress
(Bakker et al., 2018). Changes of these
complex substances and resources may alter the selection pressure of
host plant to microbes, thereby inducing competition or cooperation
among them. The increased deterministic process, particularly
heterogeneous selection observed in the assembly process of the diseased
plant microbiome, lends further support to this perspective. Still,
However, selective predation by phagotrophic protists might play a
crucial role in maintaining microbial balance within such unstable
communities instigated by pathogen infection. Existing research has
demonstrated that phagotrophic protists are main determinants of crop
health and yield by interacting with plant-beneficial microorganisms
(Bahroun et al., 2021;
Guo et al., 2021), and influencing the
pathogenicity of pathogens (Chakraborty et
al., 1983; Xiong et al., 2020). Given
that plant health, particularly for future generations, requires a
favourable microenvironment, predation by phagotrophic protists may
assist in shaping a microbiome more conducive to the fitness of host
plants.