Protists articles

Protists articles DEFAULT

What Are Protists?

Protists are a diverse collection of organisms. While exceptions exist, they are primarily microscopic and unicellular, or made up of a single cell. The cells of protists are highly organized with a nucleus and specialized cellular machinery called organelles. 

At one time, simple organisms such as amoebas and single-celled algae were classified together in a single taxonomic category: the kingdom Protista. However, the emergence of better genetic information has since led to a clearer understanding of evolutionary relationships among different groups of protists, and this classification system was rendered defunct. Understanding protists and their evolutionary history continues to be a matter of scientific discovery and discussion.


All living organisms can be broadly divided into two groups — prokaryotes and eukaryotes — which are distinguished by the relative complexity of their cells. In contrast to prokaryotic cells, eukaryotic cells are highly organized. Bacteria and archaea are prokaryotes, while all other living organisms — protists, plants, animals and fungi — are eukaryotes.

Many diverse organisms including algae, amoebas, ciliates (such as paramecium) fit the general moniker of protist. "The simplest definition is that protists are all the eukaryotic organisms that are not animals, plants or fungi," said Alastair Simpson, a professor in the department of biology at Dalhousie University. The vast majority of protists are unicellular or form colonies consisting of one or a couple of distinct kinds of cells, according to Simpson. He further explained that there are examples of multicellular protists among brown algae and certain red algae.


Like all eukaryotic cells, those of protists have a characteristic central compartment called the nucleus, which houses their genetic material. They also have specialized cellular machinery called organelles that execute defined functions within the cell. Photosynthetic protists such as the various types of algae contain plastids. These organelles serve as the site of photosynthesis (the process of harvesting sunlight to produce nutrients in the form of carbohydrates). The plastids of some protists are similar to those of plants. According to Simpson, others protists have plastids that differ in the color, the repertoire of photosynthetic pigments and even the number of membranes that enclose the organelle, as in the case of diatoms and dinoflagellates, which constitute phytoplankton in the ocean. 

Most protists have mitochondria, the organelle which generates energy for cells to use. The exceptions are some protists that live in anoxic conditions, or environments lacking in oxygen, according to an online resource published by University of California, Los Angeles. They use an organelle called the hydrogenosome (which is a greatly modified version of mitochondria) for some of their energy production. For example, the sexually transmitted parasite Trichomonas vaginalis, which infects the human vagina and causes trichomoniasis, contains hydrogenosomes.


Protists gain nutrition in a number of ways. According to Simpson, protists can be photosynthetic or heterotrophs (organisms that seek outside sources of food in the form of organic material). In turn, heterotrophic protists fall into two categories: phagotrophs and osmotrophs. Phagotrophs use their cell body to surround and swallow up food, often other cells, while osmotrophs absorb nutrients from the surrounding environment. "Quite a few of the photosynthetic forms are also phagotrophic," Simpson told Live Science. "This is probably true of most 'algal' dinoflagellates for example. They have their own plastids, but will also happily eat other organisms." Such organisms are called mixotrophs, reflecting the mixed nature of their nutritional habits.


Most protists reproduce primarily through asexual mechanisms according to Simpson. This can include binary fission, where a parent cell splits into two identical cells or multiple fission, where the parent cell gives rise to multiple identical cells. Simpson added that most protists probably also have some kind of sexual cycle, however, this is only well documented in some groups.

Classification: from Protozoa to Protista and beyond

The classification history of protists traces our understanding of these diverse organisms. Often complex, the long history of protist classification introduced two terms, still used today, into the scientific lexicon: protozoa and protists. However, the meaning of these terms has also evolved over time.

The observable living world was once neatly divided between plants and animals. But the discovery of various microscopic organisms (including what we now know as protists and bacteria) brought forth the need to understand what they were, and where they fit taxonomically.

The first instinct of scientists was to relate these organisms to plants and animals by relying on morphological characteristics. The term protozoan (plural: protozoa or protozoans), meaning "early animals," was introduced in 1820 by naturalist Georg A. Goldfuss, according to a 1999 article published in the journal International Microbiology. This term was used to describe a collection of organisms including ciliates and corals. By 1845, Protozoa was established as a phylum or subset of the animal kingdom by German scientist Carl Theodor von Seibold. This phylum included certain ciliates and amoebas, which were described by von Seibold as single-celled animals. In 1860, the concept of protozoans was further refined and they were elevated to the level of a taxonomic kingdom by paleontologist Richard Owen. The members of this Kingdom Protozoa, in Owen's view, had characteristics common to both plants and animals. 

Though the scientific rationale behind each of these classifications implied that protozoans were rudimentary versions of plants and animals, there was no scientific evidence of the evolutionary relationships between these organisms (International Microbiology, 1999). According to Simpson, nowadays "protozoa" is a term of convenience used in reference to a subset of protists, and is not a taxonomic group. "In order to be called a protozoan, they [protists] have to be non-photosynthetic and not very fungus-like," Simpson told Live Science.

The term protista, meaning "the first of all or primordial" was introduced in 1866 by German scientist Ernst Haeckel. He suggested Protista as a third taxonomic kingdom, in addition to Plantae and Animalia, consisting of all "primitive forms" of organisms, including bacteria (International Microbiology, 1999).

Since then, the kingdom Protista has been refined and redefined many times. Different organisms moved in and out (notably, bacteria moved into a taxonomic kingdom of their own). American scientist John Corliss proposed one of the modern iterations of Protista in the 1980s. His version included the multicellular red and brown algae, which are considered to be protists even today.

Scientists, often concurrently, have debated kingdom names and which organisms were eligible (for example, versions of yet another kingdom, Protoctista had been proposed over the years). However, it is important to note the lack of correlation between taxonomy and evolutionary relationships in these groupings. According to Simpson, these groupings were not monophyletic, meaning that they did not represent a single, whole branch of the tree of life; that is, an ancestor and all of its descendants.

Today's classification has shifted away from a system built on morphology to one based on genetic similarities and differences. The result is a family tree of sorts, mapping out evolutionary relationships between various organisms. In this system there are three main branches or "domains" of life: Bacteria, Archaea (both prokaryotic) and Eukarya (the eukaryotes).

Within the eukaryotic domain, the protists are no longer a single group. They have been redistributed amongst different branches of the family tree. According to Simpson, we now know most of the evolutionary relationships amongst protists, and these are often counterintuitive. He cited the example of dinoflagellate algae, which are more closely related to the malaria parasite than they are to diatoms (another group of algae) or even to land plants.

Still, there are pressing questions that remain. "We simply don't know what the earliest split was among the lineages that led to living eukaryotes," Simpson told Live Science. This point is called the "root" of the eukaryotic tree of life. Pinpointing the root will cement the understanding of eukaryotic origins and their subsequent evolution. As author Tom Williams said in a 2014 article published in the journal Current Biology, "For the eukaryotic tree, the root position is critical for identifying the genes and traits that may have been present in the ancestral eukaryote, for tracing the evolution of these traits throughout the eukaryotic radiation, and for establishing the deep relationships among the major eukaryotic groups."


Protists are responsible for a variety of human diseases including malaria, sleeping sickness, amoebic dysentery and trichomoniasis. Malaria in humans is a devastating disease. It is caused by five species of the parasite Plasmodium, which are transmitted to humans by female Anopheles mosquitoes, according to the Centers for Disease Control and Prevention (CDC). The species Plasmodium falciparum infects red blood cells, multiplies rapidly and destroys them. Infection can also cause red blood cells to stick to the walls of small blood vessels. This creates a potentially fatal complication called cerebral malaria (according to the CDC). The World Health Organization (WHO) states that Plasmodium falciparum is the most prevalent and lethal to humans. According to their recent malaria fact sheet, in 2015 there were an estimated 438,000 deaths due to malaria in the world, the majority of which (90 percent) occurred in Africa. Certain strides have been made in reducing the rates of incidence (occurrence of new cases) and mortality rates in part by supplying insecticide treated mosquito nets, spraying for mosquitoes and improving diagnostics. Between 2000 and 2015 the rate of incidence fell by 37 percent globally and mortality rates fell by 60 percent globally. The WHO has a goal of eliminating malaria in at least 35 countries by 2030.

Protists also play an important role in the environment. According to a 2009 review article published on the Encyclopedia of Life Sciences (eLS) website, nearly 50 percent of photosynthesis on Earth is carried out by algae. Protists act as decomposers and help in recycling nutrients through ecosystems, according to a 2002 review article published in the journal ACTA Protozoologica. In addition, protists in various aquatic environments, including the open water, waterworks and sewage disposal systems feed upon, and control bacterial populations (ACTA Protozoologica, 2002). "If you took all the protists out of the world, the ecosystem would collapse really quickly," Simpson said. 

Credit: Monkey Business Images | Shutterstock

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Kingdom Protista

Protists Within Corals: The Hidden Diversity


Scleractinian corals build reefs all around the world. The ecological success of corals in the oligotrophic seawater of coral reefs mostly relies on the symbiosis with dinoflagellates (genus Symbiodinium). In particular, the symbiosis between corals and Symbiodinium takes place within coral cells, where the algal symbionts provide organic compounds to corals through their photosynthetic activity, and in turn receive nutrients and metabolic compounds from their host. Symbiodinium is a diverse genus divided into nine clades (Coffroth and Santos, 2005; Pochon et al., 2006; Quigley et al., 2014; Thornhill et al., 2017). Among these clades, five have been identified in coral cells (clades A, B, C, D, and F).

Corals are also associated with a high diversity of microorganisms (bacteria, archaea, fungi, endolithic algae, protozoa, and viruses) (Rohwer et al., 2002; Wegley et al., 2004; Thurber et al., 2017), and the complex formed by coral and the associated microorganisms corresponds to a single entity called the holobiont (Rohwer et al., 2002; Theis et al., 2016). Among them, the bacterial genus Endozoicomonas was found in high abundances within many coral species (Bayer et al., 2013; Neave et al., 2016, 2017). This genus was thought to be a beneficial symbiont to corals. Moreover, rare bacterial taxa (genera Ralstonia and Propionibacterium) were ubiquitous as well (Ainsworth et al., 2015). In addition to Symbiodinium, other unicellular eukaryotes (protists) were found to live with corals, including many Stramenopiles (Kramarsky-Winter et al., 2006; Harel et al., 2008; Siboni et al., 2010), several apicomplexans such as Chromera and coccidians (Toller et al., 2002; Moore et al., 2008; Janouškovec et al., 2012; Kirk et al., 2013; Mohamed et al., 2018), different fungi (Amend et al., 2012), and different boring microflora (e.g., Ostreobium, Phaeophila, and Porphyra) (Tribollet, 2008b; Pica et al., 2016).

Unlike for Symbiodinium, the role of these microorganisms remains unknown within the holobiont. First, they might provide protection against pathogens through the secretion of antimicrobial compounds (Ritchie, 2006; Shnit-Orland and Kushmaro, 2009). Secondly, in addition to Symbiodinium, they might also provide metabolic compounds to corals (Kramarsky-Winter et al., 2006; Harel et al., 2008; Siboni et al., 2010). Thirdly, microbial communities might play an important role for coral heat tolerance (Ziegler et al., 2017), and for the ecological resilience of coral reefs (McDevitt-Irwin et al., 2017). As a consequence, these observations suggested that microbial communities contribute to coral health and homeostasis, through the presence of Beneficial Microorganisms for Corals (BMC) (Peixoto et al., 2017).

To date, however, most studies have focused on Symbiodinium and coral-associated bacteria. In particular, very little is known concerning the diversity and the role of other protists (Ainsworth et al., 2017), though several studies have shown that they play an important role in the structure and function of marine ecosystems (Thingstad et al., 2008; de Vargas et al., 2015). Previous analyses of protists mainly used non-destructive sampling techniques (microscope, culture) or low-throughput methods for environmental DNA (qPCR, cloning), though these methods were less effective at detecting diversity when compared to mass sequencing of the 18S rRNA gene for example. Because most DNA in coral samples was extracted from the host and the 18S rRNA gene is shared between corals and protists, to date high-throughput studies of protist diversity have been a challenge (Šlapeta and Linares, 2013).

To tackle this issue, we designed blocking primers for Scleractinia sequences in order to decrease their proportions relative to protist sequences. Such an approach was already effective in the study of fish and krill gut contents (Vestheim and Jarman, 2008; Leray et al., 2013), and in the removal of metazoa sequences from seawater community samples (Tan and Liu, 2018). To the best of our knowledge, this is the first time that this strategy has been used on coral samples. These blocking primers targeted regions similar to the reverse primer for each of the two primer sets used to amplify variable loops of the 18S rRNA gene (V1V2 and V4) (Wuyts et al., 2000, 2002; Stoeck et al., 2010).

Both blocking primers were used to explore protist diversity within colonies of P. damicornis sensu lato that were sampled from two geographic regions with contrasting thermal regimes: Djibouti and New Caledonia (Figure 1 and Supplementary Table 1). A previous study on these samples showed different Symbiodinium clades between these regions using ITS2 (internal transcribed spacer 2) (Brener-Raffalli et al., 2018). These authors also highlighted that colonies from Djibouti and New Caledonia corresponded to two different clades of P. damicornis. As a consequence, geography, genetics and environmental conditions divided the two P. damicornis populations, and allowed for the comparison of different holobionts.

FIGURE 1. Sampling sites. Djibouti and New Caledonia had different thermal regimes and clades of P. damicornis and Symbiodinium.

Materials and Methods

Sampling Sites

Colonies of P. damicornis sensu lato growing between one to five meters depth were sampled by snorkeling within two regions (Djibouti and New Caledonia) in six localities (Figure 1 and Supplementary Table 1). A total of 16 colonies were sampled during this survey. The tip (1–2 cm) from one healthy branch of each colony was cut and placed individually in a plastic bag. Each bag was filled with seawater surrounding the colony to hold samples during the sampling cruise. Samples were subsequently transferred into modified CHAOS buffer (4 M guanidium thiocyanate, 0.5% N-lauryl sarcosine sodium 25 mM Tris–HCl pH 8, 0.1 M β-Mercaptoethanol) as previously described (Adjeroud et al., 2014).

Design of Blocking Primers for Scleractinia

A preliminary sequencing test was performed to study eukaryote diversity within a sample of P. damicornis using two primer sets targeting two differents regions of the 18S rRNA gene, 18SV1V2 and 18SV4 (Table 1). While primers for 18SV4 were designed previously to amplify all eukaryotic-specific 18S rDNA (Stoeck et al., 2010), primers for 18SV1V2 were designed using the Protist Ribosomal Reference database (PR2) (Guillou et al., 2012) in order to prevent amplification of metazoan 18S rRNA genes especially from Crassostrea gigas oysters. Both sequencing tests showed an excess of amplicons from P. damicornis, since they represented ∼99% of sequences (for a total of 3383 and 2460 cleaned sequences using 18SV1V2 and 18SV4, respectively; data not shown).

TABLE 1. Primers and blocking primers used in this study.

Thus we designed blocking primers for both primer sets in order to reduce the proportion of P. damicornis amplicons. First, we downloaded the non-redundant (99%) Silva SSU database (release 128, September 2016) (Quast et al., 2013; Yilmaz et al., 2014). Then we only kept sequences that matched with either the primer set for 18SV1V2 or 18SV4. Based on annotations, metazoa were removed to produce a metazoa-free database and sequences of Scleractinia were used to create a host database. In order to design blocking primers that overlap the reverse primer and the 3′-region of Scleractinia amplicons, we aligned the last 40 nucleotides (corresponding to the 3′-region of amplicon and the reverse primer) of Scleractinia with metazoa-free database using MUSCLE v3.8.31 (Edgar, 2004). Then, we analyzed the nucleotide polymorphism at each position of the alignment for Scleractinia and metazoa-free sequences using entropy decomposition (R package {otu2ot}, CalcEntropy.seq) (Ramette and Buttigieg, 2014). According to previous studies and entropy values (Supplementary Figure 1), we designed blocking primers <30 bp with 10 bp overlapping the reverse primer, and having a Tm similar to the targeted primer set (Vestheim and Jarman, 2008; Leray et al., 2013). The best candidate for each primer set (targeting either 18SV1V2 or 18SV4 region) was identified using specificity tests against the Scleractinia and metazoa-free databases. Lastly, these primers were synthesized and modified at the 3′-end with a Spacer C3 CPG (3 hydrocarbons).

DNA Extraction, PCR, and Sequencing

DNA extractions were performed using CTAB (Cetyl TrimethylAmmonium Bromide)-based extraction method (Winnepenninckx et al., 1993). Briefly, coral tips were lysed 2 h in 600 μL CTAB buffer (2% CTAB, 0.2% β-Mercaptoethanol, 1.4 M NaCI, 20 mM EDTA pH 8, 100 mM Tris–HCl pH 8, 100 μg/mL proteinase K) at 60°C. Thus protists of the whole coral tissues were analyzed in this study. An equal volume of chloroform:isoamyl-alcohol (24:1) was then added. After centrifugation for 10 min at 14,000 g, the aqueous phase was transferred to a new tube and DNA was precipitated by adding 400 μL of ice-cold isopropanol and incubating 1 h at −20°C. After centrifugation for 15 min at 14,000 g, the supernatant was discarded and the pellet washed with 70% ethanol. The pellet was air-dried for 5 min and resuspended in water. DNA concentration and quality were checked with Epoch microplate spectrophotometer (BioTek Instruments, Inc.).

Then, the 18S rRNA gene of eukaryotic communities was amplified and sequenced using the variable V1V2 and V4 loops (Table 1; Wuyts et al., 2002; Stoeck et al., 2010). PCR reactions were carried in a 25 μl volume with final concentrations of 0.4 μM of each PCR primers, 0.02 U of the Qiagen HotStar Taq DNA Polymerase, 0.2 mM of the dNTP mix and 1×Taq buffer. In order to reduce amplification of P. damicornis amplicons, blocking primers were added to the PCR mix at a final concentration of 1.2 μM (Table 1). PCR cycling included an initial incubation of 15 min at 96°C followed by 35 cycles of 96°C for 30 s, 52°C for 30 s, and 72°C for 1 min, with a final 10 min incubation at 72°C. Paired-end sequencing (250 bp read length) was performed at the McGill University (Génome Québec Innovation Centre, Montréal, QC, Canada) on the MiSeq system (Illumina) using the v2 chemistry according to the manufacturer’s protocol. Raw sequence data are available in the Sequence Read Archive repository under accession ID PRJNA393088 (to be released upon publication).

Sequence Analyses

The FROGS pipeline (Find Rapidly OTU with Galaxy Solution) implemented into a galaxy instance was used to define Operational Taxonomic Units (OTU), and compute taxonomic annotations (Escudié et al., 2017). Briefly, paired reads were merged using FLASH (Magoc and Salzberg, 2011). After denoising and primer/adapters removal with cutadapt (Martin, 2011), de novo clustering was done using SWARM that uses a local clustering threshold, with aggregation distance d = 3 (Mahé et al., 2015). Chimera was removed using VSEARCH (Rognes et al., 2016). We filtered the dataset for singletons and performed affiliation using Blast+ against the Protist Ribosomal Reference database (PR2) (Guillou et al., 2012) to produce an OTU and affiliation table in standard BIOM format. Because we were interested in studying low frequency OTUs, we used additional steps to remove most PCR and sequencing errors. First, we removed OTUs having an annotation with a blast coverage <90%. Secondly, we computed a phylogenetic tree of the whole OTUs using MAFFT (Katoh et al., 2002) and FastTree (GTR model) (Price et al., 2010). OTUs were removed from the dataset if they corresponded to very long branches on the phylogenetic tree (according to minimal branch length values). Lastly, OTUs were considered present in each sample if they had at least three sequences.

Rarefaction curves of protist species richness were produced using the {phyloseq} R package, and the rarefy_even_depth and ggrare functions (McMurdie and Holmes, 2013). We also used phyloseq to obtain abundances at the genus taxonomic rank (tax_glom function). Pielou’s measure of evenness was computed using affiliated genera and the {vegan} R package.

Annotation of Symbiodinium OTUs

In order to characterize the environmental diversity of Symbiodinium, reference sequences of Symbiodinium (clades A, B, C, D, and G) were obtained for each 18S rRNA regions from the National Center for Biotechnology Information (NCBI) (Supplementary Table 2). In addition, we selected two outgroups: Polarella glacialis and Pelagodinium beii. The reference were aligned with the environmental sequences of Symbiodinium using MUSCLE v3.8.31 (Edgar, 2004), alignments were trimmed at each extremity, and sequences were clustered at different nucleotide similarities (from 90 to 99%) using Mothur (Schloss et al., 2009). The environmental sequences were annotated for a similarity of 95 and 97% for 18SV1V2 and 18SV4, respectively, i.e., when all the reference sequences of Symbiodinium clade A, B, C, D, and G clustered into five different groups.

Alignment and Phylogenetic Analyses

In one hand, phylogenetic reconstructions of Symbiodinium reference isolates were carried out to compare phylogenetic signals of both 18SV1V2 and 18SV4 markers with ITS2. We selected two reference sequences of Symbiodinium clade A, three reference sequences of clade B, two reference sequences of clade C, one reference sequences of clade D and clade G (Supplementary Table 3). Except the strain of clade G, sequences of the three markers were available for each reference isolate of Symbiodinium. For each marker, sequences were aligned using MUSCLE v3.8.31 (Edgar, 2004).

On the other hand, phylogenetic reconstructions were computed to describe either environmental Symbiodinium diversity, the whole protist genera or coccidians associated with P. damicornis samples. First, Symbiodinium diversity was studied according to the annotation step of Symbiodinium OTUs. In particular, we used the output alignment of Mothur obtained at 95 and 97% similarity cutoffs for 18SV1V2 and 18SV4, respectively. These alignments contained representative OTUs of environmental Symbiodinium, the two outgroups (P. glacialis and P. beii), and the reference sequences of Symbiodinium clades. Secondly, sequences of protist genera, Symbiodinium clades, and P. damicornis (accession number: LT631138.1 for both 18SV1V2 and 18SV4) were aligned using MUSCLE v3.8.31 (Edgar, 2004). Thirdly, we described the diversity of coral-associated coccidians using reference sequences of Apicomplexa (Schrével et al., 2016), and best BLASTn hits of coccidian sequences of this study against the National Center for Biotechnology Information (NCBI).

Finally, all alignments were trimmed at each extremity and maximum likelihood (ML) trees were computed with IQ-TREE v1.3.8 using the best model (selected with the Bayesian information criterion) (Nguyen et al., 2015), and validated via a ultrafast bootstrap procedure with 1000 replicates (Minh et al., 2013).

Statistical Analyses

All statistical analyses were done using R v3.3.1 (R Core Team, 2008).

First, phylogenetic signals were compared between ITS2, 18SV1V2 and 18SV4 using Symbiodinium reference isolates. Patristic distances were obtained from phylogenetic trees (R package {stats}, cophenetic), and compared using Mantel test (R package {vegan}, mantel) (Mantel, 1967).

Secondly, Fisher’s exact tests (R package {stats}, fisher.test) were computed to estimate the association between protist genera and Symbiodinium clades with both geographic regions, i.e., Djibouti and New Caledonia.

P-values were adjusted for multiple comparisons using the Bonferroni correction ({stats}, p.adjust). The threshold of significance level was set at 0.05.


Specificity of Blocking Primers

In order to describe protist diversity associated with P. damicornis colonies from Djibouti and New Caledonia, we performed a preliminary sequencing test using one sample and two primer sets targeting different variable loops of the 18S rRNA gene (V1V2 and V4). Since most sequences corresponded to P. damicornis, we designed blocking primers using the Silva SSU database (see Methods for more details). These blocking primers targeted 10 bp of reverse primers and ∼15 bp of the amplicon 3′-end of Scleractinia (Table 1 and Supplementary Figure 1). Because their 3′-end had a spacer C3 CPG, elongation is expected to abort, whereas annealing properties are not modified (Vestheim and Jarman, 2008). To estimate the specificity of these blocking primers, we identified sequences of the Silva database that matched with primer sets and blocking primers. We found a very high in silico specificity, since 100 and 93.8% of Scleractinia were removed by the 18SV1V2 and 18SV4 blocking primers, respectively (Table 2). In addition, they removed very low fractions of protists found in the Silva database.

TABLE 2.In silico specificity of blocking primers.

As a consequence, we used both primer sets and their blocking primers to study protists associated with the colonies of P. damicornis from Djibouti and New Caledonia. Because DNA extraction was done directly on coral tips, we expected to be able to describe the entire protist community (i.e., microbes that were part of the holobiont, and possibly environmental microbes). On average, each sample had ∼29,566 Pocillopora sequences and ∼18,724 protist sequences representing ∼26 OTUs (Table 3 and Supplementary Table 4). While in silico analyses showed very high specificity of both blocking primers, PCR and MiSeq sequencing displayed higher host contamination (Figure 2A). Indeed, Pocillopora sequences still represented 71 and 48% of amplicons for 18SV1V2 and 18SV4, respectively. In particular, Pocillopora proportion was lower for 18SV4 than for 18SV1V2.

TABLE 3. Number of sequences and OTUs.

FIGURE 2. Sequences of Pocillopora, Symbiodinium and the other protists for both marker regions. (A) Fraction of Pocillopora compared to protists. (B) Fraction of Symbiodinium compared to other protists. (C) Fraction of the other protists.

Symbiodinium Diversity

Rarefaction curves for protists tended to level off for both marker regions, suggesting that most diversity was sequenced in each sample (Supplementary Figure 2).

Not surprisingly, Symbiodinium accounted for most protist diversity within P. damicornis tissues for both primer sets (Figure 2B). In order to describe Symbiodinium diversity, we first estimated phylogenetic signals of both 18SV1V2 and 18SV4 compared to ITS2. ITS2 is the most common marker used to study Symbiodinium diversity (LaJeunesse et al., 2010; Wicks et al., 2010; Silverstein et al., 2011; Putnam et al., 2012; Tonk et al., 2013), since it has high polymorphism. The strength of correlation between the three markers was estimated using Mantel tests (Table 4). The three phylogenetic trees were significantly congruent (Supplementary Figure 3) (p = 0.003), but congruency with ITS2 was higher for 18SV1V2 (r = 0.88) than for 18SV4 (r = 0.72) (Table 4). We then annotated environmental Symbiodinium OTUs using a clustering approach and reference sequences of Symbiodinium clade A, B, C, D, and G (Supplementary Table 4). We also computed phylogenetic reconstruction with P. glacialis and P. beii as outgroups (Figure 3). While Symbiodinium clades A, C and D were common for both marker genes, strains from clade G were not identified with 18SV1V2 nor with 18SV4. Moreover, Symbiodinium clade B was found with 18SV4 but not with 18SV1V2, and we were not able to assign all environmental Symbiodinium to a known clade for 18SV4 in comparison to 18SV1V2 (Supplementary Table 4).

TABLE 4. Phylogenetic congruences between ITS2, 18SV1V2, and 18SV4 markers for Symbiodinium.

FIGURE 3. Phylogenetic analyses of environmental and reference sequences of Symbiodinium. (A) 18SV1V2 sequences. (B) 18SV4 sequences. Only representative sequences of environmental Symbiodinium were used for these trees. Representative sequences were identified using a clustering method, and a nucleotide identity of 95 and 97% for 18SV1V2 and 18SV4, respectively. The trees were rooted using two outgroups, Polarella glacialis and Pelagodinium beii. Numbers are bootstraps (%) reflecting clade support.

Diversity of the Other Dominant Genera

Although protist proportion was lower for 18SV1V2 (Figure 2A), the proportion of protists other than Symbiodinium was lower for 18SV4 (0.9% compared to 2.9% for 18SV1V2) (Figure 2B). The Symbiodinium genus was removed from the dataset to study the other protist genera of P. damicornis (Figure 2C and Supplementary Table 5). Licnophora, unidentified coccidians and Dino-Group I-Clade 1 (Syndiniales) were the main taxa found in P. damicornis samples among the 17 genera found with both primer sets. Among them, Licnophora represented a high fraction for 18SV1V2 and 18SV4, whereas coccidians showed different proportions between these markers. In particular, 18SV1V2 showed a more even protist diversity at the genus level than 18SV4 (0.05 > 0.03, Pielou’s measure of evenness). A BLASTn search against NCBI nucleotide collection suggested that for both markers, Licnophora sequences were related to Licnophora strains, and that Dino-Group I-Clade 1 (Syndiniales) were similar to uncultured eukaryotes (Table 5). Interestingly, coccidian sequences were similar to protists already described in healthy coral colonies of Agaricia agaricita, A. tenuifolia, Favia fragum, Montastraea annularis, M. faveolata, Mycetophyllia ferox, Porites astreoides, and Siderastrea siderea (Toller et al., 2002; Kirk et al., 2013; Šlapeta and Linares, 2013). As a consequence, we computed a phylogenetic reconstruction of coral-associated coccidians with other Apicomplexa genera to describe their diversity. We found that all coral-associated coccidians formed a robust monophyletic clade (Figure 4). In addition, a phylogenetic tree using the longest available sequences of these symbionts highlighted their relationships with other marine Apicomplexa (Supplementary Figure 4), and confirmed that they corresponded to coccidians (Schrével et al., 2016).

TABLE 5. BLASTn search of coccidians, Licnophora, and Dino-Group I-Clade 1 (Syndiniales) against NCBI.

FIGURE 4. Phylogenetic analysis of coral symbionts related to the coccidian sequences of this study. Because we found more coral symbionts with BLASTn using 18SV4 than 18SV1V2, we only included coccidian OTUs of 18SV4 from this study (Cluster_21, Cluster_76, and Cluster_77) within the multiple alignment. Reference sequences of marine Apicomplexa and outgroups were selected according to a previous study (Schrével et al., 2016). The tree was rooted using Phytophthora strains as outgroups. Accession numbers and genus are indicated for each sequence (except for Symbiodinium, see Figure 3 and Supplementary Table 2). Numbers are bootstraps (%) of major nodes reflecting clade support. The dashed box indicates coccidian OTUs and known sequences of coccidian symbionts associated to corals and their corresponding references (Toller et al., 2002; Kirk et al., 2013; Šlapeta and Linares, 2013).

Distribution of P. damicornis-Associated Protists

Because 18SV4 had (i) a low number of sequences related to protists other than Symbiodinium, (ii) low evenness for protist genera, and (iii) low phylogenetic signals (low congruency with ITS2 tree and low efficiency of annotations with reference Symbiodinium clades), we used 18SV1V2 amplicons to study protist distribution within the samples from Djibouti and New Caledonia.

A phylogenetic reconstruction of identified Symbiodinium clades and protist genera was carried out using maximum likelihood, and corresponding frequencies in samples were plotted in front of taxa (Figure 5). Two colors were used for frequencies to discriminate the high proportions of Symbiodinium, and lower values of other protists. Most protist genera were found in only one sample (e.g., Codonellopsis, Zoothamnopsis, Acineta, etc.). However, the different Symbiodinium clades, Licnophora and coccidians were present in several P. damicornis colonies. In particular, we observed different distribution between Djibouti and New Caledonia.

FIGURE 5. Phylogenetic diversity and distribution of P. damicornis-associated protists using 18SV1V2 marker region. The tree was rooted using P. damicornis as outgroup. Numbers are bootstraps (%) reflecting clade support. White circles indicate absence of taxa in samples. Brown circles indicate taxa frequency above 0.5. Blue circles indicate taxa frequency below 0.5. The gradient from light to dark colors indicates low to high frequencies of protists in each sample. indicates significant taxa associated to Djibouti (DJ) or New Caledonia (NC) based on Fisher’s exact test.

In order to statistically test differences between both geographic regions, we computed Fisher’s exact test for each protist genus and Symbiodinium clade (Figure 5). We found that coccidians and Symbiodinium clade D and A were linked to Djibouti, whereas Licnophora and Symbiodinium clade C were mostly associated with New Caledonia.


Efficiency of Blocking Primers

First, a very high specificity was obtained in silico for both blocking primers. In accordance with sequence entropy values (Supplementary Figure 1), all Scleractinia from the Silva database were expected to be blocked for 18SV1V2, and we found that ∼94% of them were targeted by the blocking primers of 18SV4. Although both blocking primers matched with Pocillopora sequences, we observed various efficiencies for the different samples from Djibouti and New Caledonia. On average Pocillopora still represented 70% (from 30 to 92%) and 39% (from 7 to 66%) of sequences for 18SV1V2 and 18SV4, respectively. Such variations were also described for artificial rDNA mixtures of algae and krill (between 26 and 42% of krill sequences were not blocked by blocking primers) (Vestheim and Jarman, 2008), and for gut content of fish (between 14 and 45% of sequences were not blocked by blocking primers) (Leray et al., 2013). These variations might be related to (i) the ratio between host and total sequences (Vestheim and Jarman, 2008), (ii) the ratio between blocking primers and targeted primer set concentrations (Vestheim and Jarman, 2008), and (iii) the complexity of samples for sequence composition, i.e., taxa diversity.

For environmental samples, the description of protist diversity might be improved by increasing the sequencing depth. In this exploratory study, we limited the number of sequences to an average of 60,000 per sample before the cleaning steps. One might also design blocking primers for Symbiodinium to increase the proportion of others protists. However, such an approach might fail if blocking primers for Symbiodinium and Scleractinia form primer dimers, and further if blocking primers for Symbiodinium target other closely related Suessiales or even alveolates. Indeed, other alveolates were previously identified in coral tissues (Moore et al., 2008).

18SV1V2 Is a More Suitable Marker Than 18SV4 to Explore Protist Diversity Within Corals

Protist diversity was mostly described using 18S rRNA and ITS2 markers in marine environments. While the 18S rRNA gene was effective to study the diversity of a wide phylogenetic range of taxa within a sample (Viprey et al., 2008; Bik et al., 2012; de Vargas et al., 2015; Tragin et al., 2016), ITS were more appropriate for closely related taxa (Arif et al., 2014; Su et al., 2017). Because ITS polymorphism is high, it offers a higher resolution than the 18S rRNA gene.

To date, ITS2 has been one of the most common markers used to describe Symbiodinium diversity (LaJeunesse et al., 2010; Wicks et al., 2010; Silverstein et al., 2011; Putnam et al., 2012; Tonk et al., 2013), because it provides enough resolution to describe Symbiodinium diversity within clades (LaJeunesse, 2002; Thornhill et al., 2017). In this study, we used two 18S rRNA markers to describe phylogenetically distant taxa, but the comparison of phylogenetic signals for Symbiodinium showed that 18SV1V2 was more congruent with ITS2 than 18SV4. This difference might explain why we easily annotated all environmental Symbiodinium for 18SV1V2 compared to 18SV4. In addition, the diversity of protist genera was more even for 18SV1V2 than for 18SV4, as Symbiodinium and Licnophora represented a lower proportion of protists using 18SV1V2.

Overall, in comparison to 18SV4, blocking primers and the primer set for 18SV1V2 showed a better phylogenetic signal for Symbiodinium, and a more even representation of protist diversity. Based on our findings, we recommend the use of 18SV1V2 to study protists associated with coral colonies.

Different Distributions Between Symbiodinium Clade C and A/D

Because of the advantages of 18SV1V2 and because we obtained sequences for protists other than Symbiodinium, we focused our analyses on this marker to study the distribution of protist genera and Symbiodinium clades within the different samples.

In order to describe Symbiodinium diversity, we looked for reference sequences of the different clades that matched with 18SV1V2 and 18SV4. However, since ITS2 was the most common marker used so far, only representative sequences of clades A, B, C, D, and G were found. Unfortunately, although clade F was sometimes identified in Scleractinia (LaJeunesse, 2001; Rodriguez-Lanetty et al., 2003; Pochon et al., 2006), we were not able to use this clade to annotate environmental sequences. Clade G was described in other Anthozoa (Van Oppen et al., 2005; Bo et al., 2011), but not in scleractinian corals so far, thus it was not surprising that sequences of this clade were absent from our dataset. In contrast, clades A, C, D were the most common. In particular, clade C was dominant in New Caledonia, whereas clades D and A were mainly found in Djibouti. This result was similar to the analysis of the same samples using ITS2 (Brener-Raffalli et al., 2018). Thus, 18SV1V2 not only had a similar phylogenetic signal to ITS2, but also offered similar community composition for Symbiodinium.

Coccidians and Licnophora Were the Two Other Main Taxa Within P. damicornis

Although eukaryotic microborers were common in coral colonies (Tribollet, 2008b; Pica et al., 2016), we did not find any of them in our samples. However, even though boring microflora inhabited live and dead corals, they were more abundant in the latter ones (Le Campion-Alsumard et al., 1995; Tribollet and Payri, 2001; Tribollet, 2008a). Moreover, in this study we did not crush coral skeleton (i.e., where microborers inhabited), but instead, we extracted DNA from coral tissue. Similar to previous studies, we identified many Stramenopiles (Kramarsky-Winter et al., 2006; Harel et al., 2008; Siboni et al., 2010), and in particular different Bacillariophyta. Among them, the genus Navicula was present in one sample and was already isolated from the soft coral Dendronephthya (Hutagalung et al., 2014). We also found many fungi from the family Agaricomycetes (class Basidiomycota). Despite being a terrestrial mushroom-forming fungi (Hibbett, 2007), this family was already identified in many marine samples, from deep-sea sediments to oxygen-deficient environments, as well as within Acropora hyacinthus coral colonies (Amend et al., 2012). Many studies highlighted the presence of fungi in coral tissues: they were very diverse, and might be parasites, commensalists, and possibly mutualists that participated in nitrogen recycling (Wegley et al., 2007; Amend et al., 2012).

Furthermore, our samples contained many alveolates from divisions Dinophyta, Apicomplexa and Ciliophora. In particular, Licnophora (ciliates) and unidentified coccidia genera were the most common genera after Symbiodinium in P. damicornis colonies. Both ciliates and coccidians were already observed in coral samples using low-throughput methods, such as microscopy and culture, and they were mainly associated with coral diseases (Upton and Peters, 1986; Sweet and Bythell, 2012; Sweet et al., 2013; Sweet and Séré, 2016). However, the presence of Licnophora together with disease were possibly indirect, i.e., resulting from a microbiota dysbiosis, since they are known to feed others protozoa (Sweet and Séré, 2016). Moreover, coccidians were also found within healthy coral colonies of A. agaricita, A. tenuifolia, F. fragum, M. annularis, M. faveolata, M. ferox, P. astreoides, and S. siderea (Toller et al., 2002; Kirk et al., 2013; Šlapeta and Linares, 2013). In this study, corals did not show any outward signs of pathology, suggesting that these genera might be commensalists or mutualists. Interestingly, coccidian sequences of this study were very similar to the other coral-associated coccidians, and these sequences formed a robust monophyletic clade within Apicomplexa. This observation suggested that a speciation event of coccidians was linked to interactions with corals. Future studies should test the role of coccidians in coral holobionts. For example, it would be interesting to know whether these coccidians have retained a relict or a functional plastid like the coral-associated chromerids (Janouškovec et al., 2012).

Finally, Licnophora and coccidians had different distributions within our samples from Djibouti and New Caledonia. Similarly to Symbiodinium clades, geographic locations, Pocillopora clades and thermal regimes might influence their distribution. However, because of our sampling strategy, it was not possible to identify the factors responsible for this pattern.

To conclude, we designed two blocking primers to characterize protist diversity using high-throughput amplicon sequencing for the first time within coral colonies. We were able to characterize the diversity of Symbiodinium and of other less known genera associated with P. damicornis sensu lato. Among them, Licnophora and unidentified coccidia genera were common in coral samples from Djibouti and New Caledonia. In particular, coccidian sequences were phylogenetically related to coccidians described in other scleractinian coral species. Furthermore, different distributions were highlighted between Licnophora and coccidians, and between Symbiodinium clades C and A/D. Because the dataset was limited to two geographic regions, we did not know the respective influence of geography, P. damicornis clades or thermal regimes on protist assemblages. Moreover, we could not confirm that Licnophora and coccidians were part of the coral holobiont, and not simply just a part of the larger environmental microbial community. Notably, future studies should decipher if they serve a specific function within the holobiont. However, we believe that these blocking primers are promising tools to bring new knowledge and understanding of the diversity and distribution of protists within P. damicornis colonies, as well as for other species of corals, as they were designed to target most Scleractinia.

Author Contributions

CC, J-ME, and ET conceived the project. SB and PL designed the experimental protocol to test blocking primers. JV-D and MA were involved in the collection of samples and data acquisition. CC, LG, and ET performed the analyses. CC drafted the manuscript. All authors contributed to critical revisions and approved the final manuscript.


CC benefited of post-doctoral fellowships from CNRS and IFREMER. This work was supported by the French National Research Agency ANR, project ANR-14-CE19-0023 DECIPHER (coordinator G. Mitta), Campus France PHC Hubert Curien program Mamonide-Israel, and by the DHOF program of the UMR5244/IHPE (

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We thank Lorenzo Bramanti for help in collecting corals from Djibouti, and IHPE members for stimulating discussions. We are grateful to the genotoul bioinformatics platform Toulouse Midi-Pyrenees and Sigenae group for providing help and computing resources thanks to Galaxy instance

Supplementary Material

The Supplementary Material for this article can be found online at:


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Keywords: holobiont, protists, symbiosis, metabarcoding, blocking primer, Scleractinia, Pocillopora damicornis

Citation: Clerissi C, Brunet S, Vidal-Dupiol J, Adjeroud M, Lepage P, Guillou L, Escoubas J-M and Toulza E (2018) Protists Within Corals: The Hidden Diversity. Front. Microbiol. 9:2043. doi: 10.3389/fmicb.2018.02043

Received: 12 June 2018; Accepted: 13 August 2018;
Published: 31 August 2018.

Copyright © 2018 Clerissi, Brunet, Vidal-Dupiol, Adjeroud, Lepage, Guillou, Escoubas and Toulza. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Camille Clerissi, [email protected] Eve Toulza, [email protected]


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Rhizosphere protists are key determinants of plant health

  • Wu Xiong1,2na1,
  • Yuqi Song1na1,
  • Keming Yang1,
  • Yian Gu1,
  • Zhong Wei1,
  • George A. Kowalchuk2,
  • Yangchun Xu1,
  • Alexandre Jousset1,2,
  • Qirong Shen1 &
  • Stefan GeisenORCID:,3,4

Microbiomevolume 8, Article number: 27 (2020) Cite this article

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Plant health is intimately influenced by the rhizosphere microbiome, a complex assembly of organisms that changes markedly across plant growth. However, most rhizosphere microbiome research has focused on fractions of this microbiome, particularly bacteria and fungi. It remains unknown how other microbial components, especially key microbiome predators—protists—are linked to plant health. Here, we investigated the holistic rhizosphere microbiome including bacteria, microbial eukaryotes (fungi and protists), as well as functional microbial metabolism genes. We investigated these communities and functional genes throughout the growth of tomato plants that either developed disease symptoms or remained healthy under field conditions.


We found that pathogen dynamics across plant growth is best predicted by protists. More specifically, communities of microbial-feeding phagotrophic protists differed between later healthy and diseased plants at plant establishment. The relative abundance of these phagotrophs negatively correlated with pathogen abundance across plant growth, suggesting that predator-prey interactions influence pathogen performance. Furthermore, phagotrophic protists likely shifted bacterial functioning by enhancing pathogen-suppressing secondary metabolite genes involved in mitigating pathogen success.


We illustrate the importance of protists as top-down controllers of microbiome functioning linked to plant health. We propose that a holistic microbiome perspective, including bacteria and protists, provides the optimal next step in predicting plant performance.


Plant pathogens can colonize the rhizosphere and have a severe influence on plant health [1, 2]. However, pathogen success and plant health are ultimately controlled by other biota, particularly the rhizosphere microbiome [3, 4]. The plant rhizosphere microbiome is a complex assembly of diverse microorganisms, including bacteria, fungi, and protists that together influence plant health [5,6,7,8]. Despite the fact that the microbiome consists of diverse groups, most research aiming to understand the role of the microbiome in plant health or disease suppression has focused on bacteria [9,10,11] and fungi [12, 13]. A whole-microbiome view to decipher the main microbial determinants and their potential interactions that determine plant performance is currently missing [14]. As such, a more complete microbiome analysis is needed to identify the microbial groups and potential interactions that help predicting plant health.

In particular, protists that steer the taxonomic and functional composition of the rhizosphere microbiome through trophic predator-prey interactions have so far rarely been included in microbiome analyses linked to plant performance [8]. Protists, especially microbial-feeding phagotrophs [15, 16], have various functions within the rhizosphere [6, 17, 18]. For instance, some of these phagotrophs can directly prey on plant pathogens [19]. Studies using model protists have shown that protists control microbiome diversity and structure leading to plant growth promotion [17, 18, 20]. These changes are at least partly explained by the fact that protists feed selectively on microbial prey taxa, which differs between protistan species [21, 22]. Through this selective predation, protists can, for instance, increase those bacteria that produce pathogen-suppressive secondary metabolites [23, 24]. Yet, all these studies investigating potential links of protists with plant performance were carried out under artificial laboratory or greenhouse conditions focusing on one or few protistan species. As such, we have yet to identify the links between a complex diversity of protists, the microbiome and plant performance, especially in agricultural systems under field conditions.

Protists and their interactions with other microorganisms are also subject to change throughout plant growth [14, 25]. Yet, the composition of the microbiome is often investigated only once during plant growth, usually at the time of plant maturity or after disease has already developed. Such approaches make it difficult to disentangle causality between plant health and potentially underlying characteristics in microbial communities, especially for diseased plants that host high amounts of pathogens. Recently, it was shown that bacterial communities at plant establishment can predict plant health at maturity [26]. Yet, other microbial groups might be even better indicators to predict plant health, as for instance, protist communities were shown to respond more strongly to environmental inputs and vary more in their composition between seasons than bacteria and fungi [27].

To investigate potential key microbiome groups that might predict plant health, we here used a rhizobox system in an agricultural system under field conditions, in which we grew tomato plants. Soils were naturally infested with pathogenic Ralstonia solanacearum bacteria, one of the most devastating and globally distributed soil-borne plant pathogens that can infect a range of important crops [28, 29]. In the rhizosphere of plants that either later developed disease symptoms or remained healthy, we temporally investigated the microbiome composition, including bacteria, fungi, and protists, as well as potential microbial functions using metagenomics. We tested the hypothesis that protists rather than other microbial communities in the rhizosphere microbiome best predict pathogen dynamics and plant health.

Results and discussion

Here, we show that the community structure of protists could best predict the density of the R. solanacearum pathogen across plant growth in healthy and diseased datasets (Fig. 1a). In healthy plants, the diversity and community structure of bacteria could significantly predict pathogen density (Fig. 1b), which is in line with previous findings that soil bacterial composition can predetermine future plant health [26]. In diseased plants, the community structure of protists was the best predictor for pathogen density (Fig. 1c). At plant establishment, the community structure of bacteria differed (ANOSIM, P < 0.001; Table S2) between healthy and diseased plants as shown before [26] but not that of fungi and protists (ANOSIM, P > 0.05; Table S2). However, we found that the community structure of phagotrophic protists at plant establishment was indicative for later plant health, as indicated by the differences (ANOSIM, P = 0.013) observed between plants developing disease and those remaining healthy (Fig. 2a, b). The community composition of other protistan functional and taxonomic groups did not differ between later healthy or diseased plants at plant establishment (Fig. 2a). Indicator analysis revealed 13 protistan OTUs in healthy plants at plant establishment (with only 3 in diseased plants) that indicate later plant health (Fig. 2c and Table S3). Seven protistan OTUs indicative for healthy plants were identified as phagotrophs, including one amoebozoan and six cercozoan taxa, that likely prey entirely or as part of their diet on bacteria [30]. Of these, the protistan Pro_OTU8 (Cercozoa, Trinematidae) was the most abundant at plant establishment (Table S3) and across plant growth accounting for around 11% of all protistan reads (Table S5). This taxon likely represents an omnivorous protist that mostly feeds on bacteria [30]. Co-occurrence network analysis revealed more negative links between R. solanacearum and protistan OTUs in healthy than in diseased plants at plant establishment (Fig. 2d and Table S4). Particularly phagotrophs (a taxon within Trinematidae, Flectomonas ekelundi, Proleptomonas faecicola, and two Eocercomonas spp., all mostly bacterivorous Cercozoa) but also a phototrophic Chloroidium saccharophila were negatively linked with the pathogen at plant establishment (Fig. 2d). Although those protistan OTUs were also present in diseased plants, they did not correlate with the pathogen in the network analysis (Table S4). Thus, we conclude that phagotrophic protists in general as well as specific taxa at plant establishment can predict pathogen density and plant health at plant maturation, as supported by the community structure of phagotrophs, phagotrophic indicator taxa, and negative links between phagotrophic protistan OTUs and the pathogen in co-occurrence networks. This supports the perspective that functional units rather than taxonomic units underlie microbial functioning and as such should be considered as better indicators [31,32,33], even across different trophic levels in the microbiome. In addition, we found that the relative abundance of total phagotrophs correlated negatively (regression analysis, P < 0.05) with the abundance of R. solanacearum in diseased plants or in healthy and diseased combined datasets across plant growth (Fig. 2e). Interestingly, the relative abundance of total phagotrophs significantly decreased (regression analysis, P < 0.05) with plant growth time in diseased plants (Fig. S4). Phagotrophic protists may control pathogen development throughout plant growth, as a decreased relative abundance of phagotrophs in diseased plants coincided with pathogen outbreak. Although the pathogen was present in healthy plants, a stable relative abundance of phagotrophic protists throughout plant development might have helped to keep the pathogen in check. Together, these findings suggest that direct trophic interactions between phagotrophic protists and the pathogen at plant establishment and through plant growth steer later plant performance. In contrast, R. solanacearum in diseased plants at plant establishment was positively linked with two oomycete species (OTU), including one likely plant-pathogenic Pythium species (Fig. 2d). This suggests that a pathobiome forms in diseased plants [34, 35], here consisting of a simultaneous infection with different pathogens. While, a dominance of predator-prey interactions might mitigate negative pathogen effects and thereby stimulating plant health.

The relative importance of the main microbial parameters in predicting pathogenic Ralstonia solanacearum density across plant growth with the combined datasets including healthy and diseased plants (a), the healthy plant dataset (b), and the diseased plant dataset (c). Diversity (Shannon index) and structure (PCoA2) of bacterial, fungal, and protistan communities were selected as the six main microbial predictors (Fig. S2). Asterisk means P < 0.05, two asterisks mean P < 0.01, and three asterisks mean P < 0.001 (statistical significance was calculated by multiple regression using linear models between the microbial predictors and R. solanacearum)

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Community structure of protistan taxonomic and functional groups explaining differences between diseased and healthy plants at plant establishment (week 0) (a). Community structure of phagotrophic protists (b) and indicator protistan OTUs (c) in diseased and healthy plants at plant establishment, and networks of the functional groups of protistan OTUs directly associated with the R. solanacearum pathogen in healthy and diseased plants at plant establishment (d). Correlations between the relative abundance of phagotrophic protists and R. solanacearum in diseased and healthy plants across plant growth (e). In panel a, only abundant taxonomic and functional groups of protists were selected (average relative abundance over 1%). In panel a and b, asterisk means P < 0.05. In panel c, protistan OTUs with LDA score > 2.0 are indicators for healthy plants, while protistan OTUs with LDA score < − 2.0 are belonging to diseased plants. In panel d, blue lines indicate positive, and red lines indicate negative correlations. In panel e, the solid line shows a significant (P < 0.05) correlation, and the dashed line shows a non-significant (P > 0.05) correlation

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We also found that protists might determine pathogen development and plant health through functional changes in the bacterial microbiome. Healthy plants showed significantly (student’s t test, P < 0.05) higher relative abundances of metabolism genes related with carbohydrate and coenzyme functions at plant establishment (Fig. S5). Strikingly, most metabolism genes had significantly (student’s t test, P < 0.05) higher relative abundances in healthy than in diseased plants at week 5 (Fig. S5). Among the eight metabolism gene categories, secondary metabolite biosynthesis [Q] genes were most strongly linked (lineal model, P < 0.001) with R. solanacearum density (Fig. 3a). Furthermore, the relative abundance of metabolism [Q] genes increased over time in healthy plants, showing significantly (student’s t test, P < 0.05) higher relative abundance in healthy than in diseased plants at week 5 (Fig. 3b). Metabolism [Q] genes did not differ between healthy and diseased plants at weeks 0, 3, and 4 (Fig. S5). Heathy plants with a higher (student’s t test, P < 0.05) relative abundance of phagotrophic protists (Fig. 3c) had a higher (student’s t test, P < 0.05) relative abundance of metabolism [Q] genes (Fig. 3b), a higher (student’s t test, P < 0.05) relative abundance of Bacillus OTUs (Fig. 3d), and a lower (student’s t test, P < 0.01) level of pathogen density than diseased plants at week 5 (Fig. 3e and Fig. S1). In addition, co-occurrence networks encompassing phagotrophic protistan OTUs, bacterial OTUs, and metabolism [Q] genes across plant growth showed that phagotrophs had more correlations with bacteria (9 links with 7 negative) and functional genes (2 links) in healthy plants than in diseased plants (0 links) (Fig. 3f). Especially, Pro_OTU105 (Cercozoa; Eocercomonas sp.), which also negatively correlated with the pathogen at plant establishment, showed negative (Spearman’s correlation coefficient (ρ) < − 0.8 with P < 0.01) correlations with six bacterial OTUs across plant growth. Among those was one bacterial OTU (Bac_OTU17: Bacteroidetes; Terrimonas) that positively linked with non-ribosomal peptide synthetase gene (COG1020), one of the key genes involved in the suppression of R. solanacearum pathogen [26, 36] (Fig. 3f and Table S5). Future targeted experiments using isolated phagotrophic protists and bacterial strains are needed to evaluate such a role. Healthy plants showed higher numbers of phagotrophic protistan OTUs, bacterial OTUs, and metabolism [Q] genes, resulting in a more complex network (55 nodes with 90 links) than diseased plants (41 nodes with 59 links) (Fig. 3f and Table S6). Specific linkages within co-occurrence networks only provide information about potential interactions, but further mechanistic proof for the interaction needs specific co-culture experiments. In addition to individual links, network structure and composition can provide insights about system’s stability and increased potential for providing ecosystem services [37,38,39,40], suggesting that healthy plants benefit from the presence of a more complex network, among them higher numbers of phagotrophs (higher-trophic level organisms in general).

Relative importance of the eight metabolism gene categories in predicting R. solanacearum density across plant growth in the combined datasets including healthy and diseased plants (a). Changes in relative abundance of metabolism Q genes (secondary metabolite biosynthesis, transport, and catabolism genes) in diseased and healthy plants at week 0 and week 5 (b). Relative abundance of phagotrophic protists in diseased and healthy plants at week 0 and week 5 (c). Relative abundance of Bacillus OTUs in diseased and healthy plants at week 0 and week 5 (d). Abundance of R. solanacearum in diseased and healthy plants at week 0 and week 5 (e). Co-occurrence networks between abundant phagotrophic protistan OTUs, bacterial OTUs, and metabolism Q genes for healthy and diseased plants across plant growth (f). In panel a, asterisk means P < 0.05 and three asterisks mean P < 0.001 (statistical significance was calculated by multiple regression using linear models between metabolism genes and R. solanacearum pathogen). In panel b, c, d, and e, “ns” means non-significant, asterisk means P < 0.05 and two asterisks mean P < 0.01 under student’s t test (n = 4 for metabolism Q genes, n = 8 for phagotrophic protists, Bacillus and R. solanacearum). In panel d, relative abundance of Bacillus OTUs combines the two Bacillus OTUs from the bacterial OTU table. In panel f, blue lines indicate positive, and red lines indicate negative correlations; detailed annotation of bacterial OTUs and metabolism Q genes are provided in Table S5

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Our findings bridge evidence from laboratory or greenhouse studies focusing on single protist model species [18, 20, 41, 42] to the community level in agricultural systems under field conditions, showing that protists affect bacterial communities and their functioning through predation, leading to changes in plant performance. Compared with diseased plants, healthy plants hosted higher relative abundances of phagotrophic protists, potentially plant-beneficial bacteria, and secondary metabolite genes likely implicated in pathogen suppression 5 weeks after plant establishment—the time point when pathogen symptoms first developed in diseased plants (Fig. S1). Moreover, phagotrophic protists negatively correlated with bacteria that positively linked with a pathogen-suppressing gene coding for non-ribosomal peptides across plant growth. This finding might also contribute to pathogen suppression. However, the interaction between plants and the rhizosphere microbiome is a complex and dynamic process [43]. Future experiments are needed to further examine how plants affect bacterial, fungal, and protistan communities and their interactions and how those changes in the soil microbiome in turn affects plant performance. Together, we propose that predation-induced shifts in microbiome composition and functioning are likely involved in controlling pathogen development and therefore plant health.


Using a holistic microbiome investigation of bacteria, fungi, and protists in the rhizosphere across plant growth, we show that in addition to bacteria, protists serve as key indicators that predict plant health. Particularly, the community composition of phagotrophic protists during plant establishment can predict later plant performance in the presence of pathogens. These protists might indeed protect plants by directly feeding on the pathogen and through predation-induced shifts in the taxonomic and functional composition of bacteria. These results hold promise in creating tailor-made systems to predict plant performance based on protistan communities before a crop plant is grown. Furthermore, our findings suggest a potential for targeted microbiome engineering to promote plant performance through the application of key microbiome predators: protists. This would bring us closer to the holy grail in reaching a more sustainable, pesticide-reduced agriculture.


Experiment description and soil samples collection

We used a semi-open mesocosm system (rhizobox) as described previously [26], which allowed repeated collection of rhizosphere soil from each individual plant without damaging the root system under field condition. Briefly, each individual tomato plant was grown in a rhizobox filled with the original local soil. Triplicate soil samples were collected from the inner and outer sides of the middle sampling layer, which were thoroughly homogenized and pooled. These soil samples were regarded as the initial bulk soil samples (week 0). In order to track the imprint of the tomato rhizosphere, three nylon bags from each rhizobox were collected 3, 4, 5, and 6 weeks after transplantation. We focused our analyses on the two categories that clearly differed between plants, i.e., plants with wilt symptoms and detectable pathogen (R. solanacearum) levels and no wilt symptoms with no detectable pathogen levels. Ten other plants that did not show wilt symptoms, but did have detectable pathogen levels at a later stage (latently infected plants), were not included in further analyses [26]. Soils from the three nylon bags (4 g soil per bag) for each tomato plant at each time point were separately homogenized with sterilized forceps and stored at – 80 °C for further use. Soil DNA was extracted from 0.5 g soil using the MoBioPowerSoil™ DNA Isolation Kit (Mo Bio Laboratories Inc., Carlsbad, CA, USA) according to the manufacturer’s instructions. We used the DNA samples to determine rhizosphere bacterial and eukaryotic communities as well as functional genes in both healthy and diseased tomato plants across plant growth.

Illumina MiSeq sequencing of the 16S rRNA gene and the 18S rRNA gene

The V4 region of the 16S rRNA gene was PCR-amplified to investigate bacterial communities using the primer set 563F and 802R [44] as described previously [26]. In addition, we selected 80 DNA samples (2 symptoms × 5 time points × 8 replicates) for eukaryotic community profiling. For that, the V4 region of the 18S rRNA gene was broadly targeted to investigate eukaryotes using the primer set V4_1f (CCAGCASCYGCGGTAATWCC) and TAReukREV3 (ACTTTCGTTCTTGATYRA) [45]. PCR was performed in a 20 μl volume consisting of 4 μl of 5× reaction buffer, 2 μl dNTPs (2.5 mM), 0.8 μl of each primer (10 uM), FastPfu Polymerase 0.4 μl, 10 ng of DNA template, and the rest being ddH2O. Amplification was performed with the following temperature regime: 5 min of initial denaturation at 95 °C, followed by 30 cycles of denaturation (95 °C for 30 s), annealing (55 °C for 30 s), extension (72 °C for 45 s), and a final extension at 72 °C for 10 min. PCR products were pooled in equimolar concentrations of 10 ng μl− 1. Paired-end sequencing was performed on an Illumina MiSeq sequencer at Shanghai Biozeron Biological Technology Co. Ltd (Shanghai, China).

Bioinformatic analyses of bacteria, fungi, and protist communities

16S rRNA gene sequence data was processed with the UPARSE pipeline as described previously [26]. After removing the reads assigned as chloroplast, mitochondria, and unknown taxa, we obtained 9108 prokaryotic OTUs (9051 bacteria OTUs and 57 archaea OTUs). We further removed archaeal OTUs (accounting for less than 0.05% of total prokaryotic reads) to generate a bacterial OTU table. We selected 8 replicates from the 12 replicates for bacterial community profiles which matched the 80 eukaryotic datasets (2 symptoms × 5 time points × 8 replicates). Each sample from the bacterial OTU table was rarefied to 26,014 reads resulting in 8656 bacterial OTUs. We extracted bacterial OTUs from Bacillus and Pseudomonas (Fig. 3d and Fig. S6), both well-known potentially biocontrol agents against various soil-borne pathogens including R. solanacearum [46,47,48].

Eukaryotic sequences were processed according to previously established protocols [49, 50] with some modifications. In short, sequences with expected errors > 1.0 or a length shorter than 350 bp were removed. After discarding singletons, the remaining reads were assigned to operational taxonomic units (OTUs) with a 97% similarity threshold, followed by a removal of chimeras using UCHIME [51]. Finally, eukaryotic OTUs were matched against the PR2 database [52]. In order to obtain the protistan OTU table, we removed sequences belonging to Rhodophyta, Streptophyta, Metazoa, and Fungi, resulting in 1,475,483 reads for the 80 samples (average 18,444 reads per sample). In order to obtain an equivalent sequencing depth for later analyses, all samples were rarefied to 4537 sequences in 1926 protistan OTUs. We further assigned the protistan OTUs into different functional groups according to their nutrient-uptake mode based on literature [49, 50], including parasites, phagotrophs, phototrophs, plant pathogens, and saprotrophs (Table S1). From the eukaryotic OTU table, we extracted OTUs assigned as fungi resulting in 525,927 reads for the 80 samples (average 6574 reads per sample). Each sample from the fungal OTU table was rarefied to 1085 reads in 234 fungal OTUs.

Functional genes from meta-genomic sequencing

We had 12 replicates (each time point) for both diseased and healthy plants. We selected 4 of those replicates (40 samples in total: 2 symptoms × 5 time points × 4 replicates) for metagenome analyses. Meta-genomic analysis and functional annotation were performed previously [26]. In short, all reads were trimmed by the Sickle software that removing reads quality below 20 and shorter than 50 bp. Filtered reads were assembled with SOAPdenovo. Assembled contigs were then predicted using MetaGene [53] and clustered with a 0.95 similarity threshold using CD-HIT to generate non-redundant gene catalog. The quality filtered reads from each sample were subsequently mapped to the represent genes using SOAPaligner. Functional gene annotation was carried out against eggNOG database [54]. In order to focus on potentially functional activities of the microbiome in the rhizosphere across plants growth, we extracted microbial metabolism genes (representing 44.85% of all functional genes, Fig. S5), including the following eight general categories: [C] energy production and conversion, [E] amino acid transport and metabolism, [F] nucleotide transport and metabolism, [G] carbohydrate transport and metabolism, [H] coenzyme transport and metabolism, [I] lipid transport and metabolism, [P] inorganic ion transport and metabolism, and [Q] secondary metabolite biosynthesis, transport, and catabolism.

Co-occurrence network and statistical analyses

First, we used co-occurrence networks to uncover the potential interactions between the functional groups of protists and the pathogen R. solanacearum for diseased and healthy plants at each time point. We selected abundant functional groups of protistan OTUs (with average relative abundance > 0.1% across all the samples) and the abundance of R. solanacearum for network constructions. Second, we used the co-occurrence networks to uncover potential interactions between phagotrophic protists, bacteria, and functional genes for diseased and healthy plants across plant growth (combined all time point samples in diseased or heathy plants). As we selected 8 replicates from the 12 replicates for eukaryotic community profiles with 3 metagenomic replicates matching both bacterial and eukaryotic datasets, we used the 30 samples in total (2 symptoms × 5 time points × 3 replicates) for the analyses. We further selected abundant phagotrophic protistan OTUs (top 30), bacterial OTUs (top 30), and metabolism Q genes (top 30 genes in metabolism Q category) for network constructions (detailed information provided in Table S5). A pairwise Spearman correlation matrix was calculated with the “corr.test” function in the package “psych” in R (version 3.4.4). The P values were adjusted with the false discovery rate method [55]. Spearman’s correlation coefficient (ρ) higher than 0.7 (or lower than − 0.7) with P values < 0.05 was selected for the networks of “functional groups of protistan OTUs with the R. solanacearum.” In order to select robust correlations between phagotrophic protistan OTUs, bacterial OTUs, and metabolism Q genes, Spearman’s correlation coefficients (ρ) higher than 0.8 (or lower than − 0.8) with P values < 0.01 were selected for the network of “phagotrophic protists, bacteria, and functional genes”. Network properties were characterized via the “igraph” package in R (version 3.4.4). Finally, networks of “functional groups of protists directly associated with the R. solanacearum” at plant establishment for healthy and diseased plants were visualized in Cytoscape (v3.5.1), and co-occurrence networks of “phagotrophic protistan OTUs, bacterial OTUs, and metabolism Q genes” for healthy and diseased plants were visualized via the “igraph” package in R (version 3.4.4).

The α-diversity of bacterial, fungal, and protistan communities across plant growth was estimated using the non-parametric Shannon index [56]. A principal coordinate analysis (PCoA) based on Bray–Curtis distance metrics was performed in R (version 3.4.4) to explore the differences in bacterial, fungal, and protist community structures (Hellinger transformed) across plant growth. ANOSIM was applied to investigate significant differences of microbial community structures between diseased and healthy plants at each time point. Abundant protistan OTUs (average relative abundance > 0.1%) were used to examine indicator species, which were assessed in LEfSe [57] through the “lefse command” in Mothur [58]. In addition, we used the “relaimpo” package [59] in R (version 3.4.4) to calculate the relative importance of main microbial parameters in predicting R. solanacearum density across plant growth in the combined dataset including healthy and diseased plants, healthy plant dataset, and diseased plant dataset. We selected the diversity (Shannon index) and structure (PCoA2) of bacteria, fungi, and protists as the six main microbial predictors (Fig. S2) and used multiple regression by lineal models in R (version 3.4.4) to calculate the significance of the correlation between microbial predictors and R. solanacearum (all data was standardized by “scale” function in R). We also used the “relaimpo” package to calculate the relative importance of the eight metabolism genes for R. solanacearum density across plant growth in the combined healthy and diseased plant samples. Other linear regression relationships were examined by the “lm” function in R (version 3.4.4). Student’s t test was used to compare the microbial taxon and functional gene differences between diseased and healthy plants at each time point. Normal distribution was tested by the Shapiro-Wilk test; non-normal data were log or log (x + 1) transformed [60].

Availability of data and materials

All raw 16S rRNA gene sequence data is available at the DDBJ Sequence Read Archive (DRA) under the accession number SRP090147. All raw 18S rRNA gene sequences are available at the NCBI Sequence Read Archive (SRA) under the accession number PRJNA525676. The raw data of metagenomics-derived gene catalogs are publicly available under the accession number PRJNA492172.


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Now discussing:

Now let's deal with the Olympiad. By the prom night, Diana not only became a class leader, she was irreplaceable. First place in the All-Russian Olympiad in Geography, first place among volleyball teams in city competitions, first place. In general, so that the new girl does not take on, there is only victory everywhere.

Cool, resigned to her confident second place among Summer rain pounded merrily on the roofs, on the leaves of the trees and on the asphalt, still.

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