Exploring the use of metabarcoding to reveal eukaryotic associations with mononchids nematodes
Article Category: Research Paper
Publié en ligne: 10 mai 2024
Pages: -
Reçu: 18 nov. 2023
DOI: https://doi.org/10.2478/jofnem-2024-0016
Mots clés
© 2024 Joseph O. Maosa et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Mononchid nematodes (Mononchida Jairajpuri, 1969) are a common order of predatory animals in soil. They have enveloping and swallowing feeding organs that allow them to feed on invertebrates such as rotifers, enchytraeids, protozoa, and other nematodes (Cobb, 1917). Most studies on mononchids mainly focus on species identification (Tahseen et al., 2013), occurrence in ecological studies (Yeates et al., 1993), and their role as biocontrol organisms to plant-parasitic nematodes (Bilgrami, 2008). However, despite their importance, there are very few studies on the feeding habits of mononchids. Understanding their feeding habits not only aids comprehension of their position and role in the soil ecological environment (Ferris et al., 2001) but also helps in improving artificial rearing for biological control research (Salinas & Kotcon, 2005).
Early studies on mononchid feeding behavior and their food intake relied on 1) observing gut contents (Bilgrami et al., 1986); 2) observations from in vitro experiments (Yeates, 1969; Small & Grootaert, 1983); and 3) observations from pot experiments using defined specimens of preys and predators (Small, 1979; Khan & Kim, 2005). However, these traditional short-term experimental observations had limitations in accurately determining the composition of ingested food due to the long life-cycle of mononchids, which led to prey degradation. Similarly, use of species-specific primers to target DNA of plant-parasitic nematodes in the gut of excised predatory nematodes (Cabos et al., 2013) was also suboptimal as only the presence or absence of the plant-parasitic nematode prey DNA could be reported.
However, with advances in sequencing techniques, novel metabarcoding tools have become valuable for studying the food spectrum of various organisms. Metabarcoding tools can identify highly degraded food fragments, thereby revealing a more comprehensive food diversity in organisms (Pompanon et al., 2012). Currently, this technique has been applied to various diet assessments, e.g., in insects (Pons, 2006), herbivorous birds (Valentini et al., 2009), and mammals (Soininen et al., 2013; Deagle et al., 2009).
While metabarcoding has been used extensively in other biological groups, its application in terrestrial nematodes has been relatively limited and mainly used for nematode community profiling with a focus on primer efficiency comparison, species identification, and community characteristic analysis (Porazinska et al., 2009; Waeyenberge et al., 2019). However, some recent studies have examined nematode-associated organisms using metabarcoding. For instance, Schuelke et al. (2018) analyzed the microbial communities associated with marine nematodes and their relationship to host phylogeny, geographic region, or feeding morphology. Additionally, McQueen et al. (2022, 2023) compared the internal and external microbiomes of omnivorous
Mononchid populations (juvenile stages) were obtained from soil samples collected at six geographical locations (Table 1) in China. Nematodes were extracted by placing 200g of soil on a modified Baermann tray for 24 h at 25°C and recovered using a 50 μm sieve (Whitehead & Hemming, 1965). Thereafter, nematodes were individually picked under a dissecting microscope (Motic SMZ160) and underwent three sequential washes with sterile water to eliminate cuticular organisms, as outlined and validated in Derycke et al. (2016).
Sampling information of Mononchid populations used in this study.
| Grass land soil | Linzhi, Tibetan | |
| Moss soil | Zhouzhi, Shaanxi | |
| Moss soil | Nanjing, Jiangsu | |
| Qiqihar, Heilongjiang | ||
| Moss soil | Zhouzhi, Shaanxi | |
| Moss soil | Nanjing, Jiangsu | |
| Nanyang, Henan | ||
| Moss soil | Hulun Buir, Inner Mongolia | |
| Qiqihar, Heilongjiang | ||
| Moss soil | Zhouzhi, Shaanxi |
First, nematode identification was done morphologically, whereby 1–2 of the washed nematodes were mounted on temporary slides and examined microscopically using the Olympus BX optical microscope. Thereafter, DNA was extracted using freeze-thaw extraction method (Mikaeili et al., 2013) from the 10 mononchid species. Briefly, 8 μL of distilled water with individual nematodes in a 0.2 mL PCR tube was placed in liquid nitrogen and frozen for 2 min, quickly taken out, and placed in a 65°C water bath to melt for 1 min. These steps were alternately performed three times to make sure that the nematodes were fully broken. 50 μl 10× PCR buffer and 1 μL of 100 mg/μL proteinase K solution were added to the PCR tube, shook, mixed well, and subsequently incubated in a thermal cycler for 90 min at 65°C and 10 min at 95°C to obtain a crude DNA extract.
Three independent PCRs were conducted, and the resulting products from all three were combined together as one sample for the subsequent sequencing step. We used two sets of PCR primers that specifically target the eukaryote V4 and V1-3 regions of the 18s rRNA. For the V4 region, the primers were TAReuk454FWD1 (5′-CCA GCA SCY GCG GTA ATT CC-3′) and TAReukREV3 (5′-ACT TTC GTT CTT GAT YRA-3′) (Stoeck et al., 2010). For the V1-3 region, the primers were EUK20f (5′-TGC CAG TAG TCA TAT GCT TGT-3′) and EUK302r+3 (5′-ACC AGA CTT GYC CTC CAA T-3′) (Euringer & Lueders, 2008). We used a two-step PCR method to add sample-specific indices. The reaction mixture of the first step PCR consisted of 1 μL of nematode DNA template, 2 μL of forward and reverse primers with a concentration of 10 μM, 2 μL of 100 μM dNTPs, 2.4 μL of 10×ExTaq PCR buffer, 2 μL of 25 mM MgCl2, 0.1 μL of 5 U/μL ExTaq enzyme, and ddH2O added to make up to 25 μL. The reaction conditions were 95°C for 4 min; 30 cycles of 95°C for 30 s, 54°C for 30 s, and 72°C for 1 min; and total extension at 72°C for 10 min. In the second step of PCR, the first step PCR amplification product was diluted 10 times in sterile water and used as the template, and distinct samples were amplified using primers tagged with different barcodes, permitting sample discrimination after MiSeq sequencing. The PCR mixture was the same as above, and the reaction conditions were 95°C for 4 min; 95°C for 30 s, 54°C for 30 s, 72°C for 1 min, cycle 25 times; and 10 min extension at 72°C.
The Cycle-Pure Kit purified the ten 18S fragment products, and DNA was quantified using the Qubit® 1× dsDNA HS Detect Kit (Yeasen Biotech, Shanghai, China) on the Qubit® 3.0 Fluorometer (Thermo Fisher Scientific, USA), then mixed in equal amounts (100 ng per sample) to obtain a mixed pool. The genomic library preparation was performed using the Illumina TruSeq DNA Sample Preparation Kit (Illumina, Inc, USA). The paired-end sequencing was performed on Illumina MiSeq 2 × 300-bp platform, and sequencing work was completed by Shanghai Biozeron Co., Ltd.
Quality control was performed on the obtained raw data using fastp (Chen et al., 2018), and the reads with Q<20 were removed. The adapters were identified and removed using cutadapt (Martin, 2011). The processed sequence data were imported into the QIIME2 software (Hall & Beiko, 2018). The DADA2 (Divisive Amplicon Denoising Algorithm) (Callahan et al., 2016) algorithm was applied with default parameters to conduct quality control, noise reduction, and chimera removal on the sequences, to obtain amplicon sequence variants (ASVs) and optimized sequences. The obtained ASVs were assigned taxonomy using the Bayesian algorithm of the RDP classifier (
Fragment sizes of about 450 and 500 bp were obtained after successful amplification when using primer TAReuk454FWD1/TAReukREV3 and EUK20f/EUK302r+3 (Figs. 1A and 1B). A total of 304,751 raw reads were generated from the pooled samples by Illumina sequencing. After quality control from the cutadapt package, we obtained 184,429 sequences. Following quality control, denoising, and chimera removal, 157,297 reads remained, which were then demultiplexed into 10 samples using DADA2 (Table 2). The average amplification efficiency of EUK20f/EUK302r+3 primer was much higher than that of the TAReuk454FWD1/TAReukREV3 primer.
Figure 1:
Detection of amplification fragments of rDNA 18S from 10 Mononchid species. A: Amplification with primer pair TAReuk454FWD1/TAReukREV3, lines 1–4 are
Primer pair used, generated reads, and ASVs per mononchid sample.
| 43 | 43 | 43 | 2 | TAR | |
| 42 | 42 | 36 | 3 | TAR | |
| 2317 | 2315 | 2315 | 6 | TAR | |
| 214 | 214 | 211 | 3 | TAR | |
| 5983 | 5983 | 3618 | 7 | EUK | |
| 43008 | 42672 | 40617 | 34 | EUK | |
| 31737 | 30422 | 28781 | 89 | EUK | |
| 3944 | 3944 | 3929 | 13 | EUK | |
| 34869 | 34863 | 34864 | 15 | EUK | |
| 35140 | 35140 | 34959 | 30 | EUK | |
ASVs (Amplicon sequence variants). TAR (TAReuk454FWD1/TAReukREV3). EUK (EUK20f/EUK302r+3).
To identify the eukaryotic species associated with the 10 mononchid species, a total of 202 ASVs were annotated using the SILVA taxonomic framework (Yilmaz et al., 2014). Taxonomy assignments were made at three levels, including kingdom, phylum, and order. At the kingdom level, a total of 157,297 reads were assigned to eight groups. The most dominant kingdoms were Animalia, mostly because of targeting the mononchid host, Fungi, and Plantae. At the phylum level, a total of 155,638 reads were assigned to 15 phyla (Fig. 2), with Nematoda being the most dominant group in all samples (86–100%). At the order level, a total of 149,373 reads were assigned to 18 groups (Fig. 3). As expected, the most dominant group in all samples was mononchid reads from the host. Remarkably, there was a consistent absence of DNA from non-mononchid nematodes across all samples (Figs. 2,3). Several species were assigned a remarkably low number of taxa at each of the three hierarchical levels: kingdom, phyla, or order. These species include
Figure 2:
Diversity of eukaryotic organisms presented in 10 mononchid species at phylum level. The classification system is based on the eukaryote classification system of Adl et al., (2012).
Figure 3:
Diversity of eukaryotic organisms presented in 10 mononchid species at order level. The classification system is based on the eukaryote classification system of Adl et al., (2012).
The five remaining mononchid species (
In addition, ciliates (Ciliophora: 0.2–14%) were also a relatively important part of the associated taxa, albeit obtained in only two of the 10 mononchid taxa studied (Fig. 2). Other taxa associated with the mononchid nematodes included Rosales (Plantae; 1.7%) (Fig. 3) and Tetrapoda (Animalia; 0.06%) (Fig. 2), found only in
To date, mononchids have been classified as carnivorous based on their buccal morphology (Cobb, 1917; Yeates et al., 1993). However, it has been well established that they also utilize other food sources (Yeates, 1987; Salinas & Kotcon, 2005). In fact, the determination of nematode feeding habits has mostly been “inferred rather than confirmed” through long-term observation under specific biological conditions (Yeates et al., 1993). Observation of bacteria in the intestines of
Previous studies have established that subjecting nematodes to a series of washing steps reduces organisms that might adhere to the cuticle (Berg et al., 2016; Derycke et al., 2016; Schuelke et al., 2018), and therefore, we speculate that the recovered eukaryotes are ”gut-associated”. These “gut-associated” eukaryotes may have been acquired in one of the following ways: (1) directly derived from the organisms ingested by the mononchid nematodes, (2) indirectly derived from the food sources of the ingested organisms, or (3) indirectly obtained from water and soil containing plant DNA that was passively ingested. Although the limited resolution of taxonomic categories was attained, we obtained a significant overlap in associated taxa across the different mononchid species at the order level, which represents our lowest achievable taxonomic resolution. Consequently, we posit that it is plausible to assume that the observed taxa might reasonably reflect the actual dietary items rather than just mere contaminants. For example, Fungi, particularly Ascomycota, were found to be associated with four mononchid nematode species, and Deuteromycotina and Basidiomycotina were also observed in most mononchid taxa. However, more precise data on fungi as a food source for nematodes are predominantly limited to fungal-feeding nematodes. For example, Ruess et al. (2002) demonstrated that
However, other DNA sequences obtained, such as those from flowering plants (Rosales), could not be readily explained and may have been indirectly obtained from food sources of the ingested organisms (2) or water and soil containing plant DNA that was passively ingested (3). It is unlikely that mononchids would consume prey without simultaneously ingesting plant material, as evidenced by the green pigmentation observed in the intestines of many mononchids and dorylaims (Cobb, 1917), suggesting a possible plant or algal origin. Similarly, the presence of tetrapod (limbed vertebrates) DNA in
Surprisingly, we failed to recover nematode species other than mononchids. This is consistent with our observation that no body remains of nematodes were found in our analyzed mononchid nematodes. However, this contradicts previous records that suggest mononchids prey on a wide variety of nematodes including
Nevertheless, despite its limitations, the metabarcoding approach utilized in this study represents an initial effort to unravel associated taxa that could serve as food sources for nematodes, followed by employing alternative methods to validate the genuine diversity of the recovered taxa. This is particularly valuable for nematodes that cannot be cultured or are fluid feeders, where direct observation of food intake or analysis of gut content is difficult or even impossible (Small, 1987). The feasibility of detecting and analyzing remnants of fluid-based or partially degraded food materials in these nematode species could only be achieved through the implementation of such an approach as outlined in this study. Additionally, for nematodes that can potentially be cultured, our understanding of the nutritional needs of these (aquatic) nematodes remains limited (Moens & Vincx, 1998). Feeding experiments based on the revealed diet from the metabarcoding approach, while taking into account the necessary experimental improvements, could provide further insights into this matter.
Therefore, while metabarcoding is certainly a promising method for determining the “gut-associated” taxa of mononchid nematodes, several caveats should be considered. First, the universal eukaryote primers used in this study were designed to target a highly conserved region of the SSU DNA (V4 and V1–3), resulting in a wide range of taxa but with limited resolution. Consequently, the inability to identify taxa beyond the order level posed a challenge that made it impossible to fully answer specific dietary questions. For example, investigations into the possible consumption of other nematode taxa by the mononchids remained unresolved in our study. Thus, although an optimal primer set should reliably amplify DNA from a wide array of ingested food sources and provide an optimal resolution, all barcode primer sets developed so far entail certain trade-offs in addressing this scenario, as exemplified in this study and elsewhere (Pompanon et al., 2012). Another challenge was the primers’ inability to distinctly amplify preys’ DNA, resulting in a very high proportion of reads (86% to 100%) originating from mononchid nematodes themselves. To address this, future studies should attempt to limit the amplification of predator DNA by incorporating a blocking oligonucleotide in the same PCR (Vestheim & Jarman, 2008; Deagle et al., 2009). Alternatively, species-specific or group-specific primers designed to amplify target prey while discriminating predator’s DNA can be used (Vestheim & Jarman, 2008; King et al., 2010). However, the latter approach is problematic because designing non-universal primers while targeting a diverse community can be challenging (Vestheim & Jarman, 2008). Furthermore, it is currently impossible to detect DNA resulting from cannibalism as prey and predator DNA from the same species cannot be distinguished using current techniques (Shehzad et al., 2012).
Secondly, quantifying taxa based on the proportion of obtained sequences remains problematic. Metabarcoding-based approaches only indicate the presence and frequency of target DNA sequences in the sample, which does not correspond to the actual proportional biomass of ingested items (Harper et al., 2005; Casper et al., 2007). Additionally, DNA sequence quality can be affected by biological processes such as digestion while inside the host (Deagle et al., 2009) and PCR bias during amplification (Engelbrektson et al., 2010). Therefore, estimating the extent to which obtained DNA sequences reflect the actual relative food biomass requires feeding nematodes with a mock diet (diet of known proportions), which was beyond the scope of this study.
In summary, the results of the described metabarcoding approach underscore crucial methodological challenges that need to be addressed in future studies. A key concern is the need to differentiate between surface attached microbes that are possibly not effectively removed during the washing step, tissue symbionts, and actual gut contents. A possible solution would be to extract total genomic DNA content from the dissected nematode intestines rather than inferring diversity from entire organisms. Ideally, such an experiment could focus on individual predatory nematodes with observed prey remains in their digestive system. Alternatively, species of mononchs that can be grow on agar plates with different prey species can be used. In this way, the sensitivity and reliability of the metabarcoding approach used for identifying observed prey can be effectively tested. We, therefore, recommend more empirical efforts to improve metabarcoding tools for accessing the food diversity of nematodes before making any definitive assumptions about their dietary composition. Nevertheless, this study introduces for the first time a simple and rapid approach for evaluating the food intake diversity of mononchid nematodes despite several limitations.