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Meiofaunal diversity in the Atlantic Forest soil: A quest for nematodes in a native reserve using eukaryotic metabarcoding analysis
Forest Ecology and Management 453 (2019) 117591
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Meiofaunal diversity in the Atlantic Forest soil: A quest for nematodes in a native reserve using eukaryotic metabarcoding analysis
T
Carla Aristonara Müllera, Leandro de Mattos Pereiraa,d, Carina Lopesb, Juvenil Caresb, Luiz Gustavo dos Anjos Borgesc, Adriana Giongoc, Carlos Graeff-Teixeiraa, ⁎ Alessandra Loureiro Morassuttia,c, a
Laboratório de Biologia Parasitária, Escola de Ciências, Laboratório de Parasitologia Molecular, Instituto de Pesquisas Biomédicas, Pontifícia Universidade do Rio Grande do Sul (PUCRS), Av. Ipiranga 6681, 90690-900 Porto Alegre, RS, Brazil b Departamento de Fitopatologia, Instituto de Ciências Biológicas, Universidade de Brasília. Campus Universitário Darcy Ribeiro, Asa Norte, 70910-900 Brasília, DF, Brazil c Laboratório de Geobiologia, Instituto do Petróleo e Recursos Naturais, Pontifícia Universidade do Rio Grande do Sul (PUCRS), Av. Ipiranga 6681, 90690-900 Porto Alegre, RS, Brazil d Faculdade de Teologia e Ciências (FATEC), Rua José Sanches Peres 3040, 5501-210 Votuporanga, SP, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Nematode Morphology identification Functional feeding groups SAR Opisthokonta
The assessment of environmental communities may lead to the identification of the novel or not-yet-classified organisms. Few studies have focused on the meiofauna of poorly known habitats such as native soils in subtropical forests. We surveyed eukaryotic communities inhabiting soil in an unexplored area of a native Atlantic Forest in southern Brazil using high-throughput sequencing. We analyzed a total of 281,400 sequences using V4 and V9 hypervariable regions of 18S rRNA gene. Opisthokonta was the most abundant supergroup, with Fungi and Metazoa representing an average of 40.6% and 15.8% of the sequences, respectively. Among the metazoan, Nematoda was the second most abundant phylum (4.8%). Isolation and morphological assessment 1745 specimens of nematodes, classified by functional feeding groups as plant parasites (35%), bacterial feeders (30%), omnivores (7%) and predators (5%). Specimens belonged to the class Chromadorea (89.6%) and Enoplea (10.4%).Approximately 65% of them could be morphologically classified only to order level. Results identified a considerable number of newly or still uncharacterized nematodes in soil biota in Atlantic Forest soils. Furthermore, this is the first study in this subtropical area to show that both high throughput sequencing and morphology can give complementary indications of the diversity of nematodes in soil samples.
1. Introduction The Atlantic Forest is a complex biome in South America, known as one of the world’s biological hotspots (Myers et al., 2000). It covers approximately 17% of the Brazilian territory, from northeastern to southeastern (Joly et al., 2014). Studies of eukaryotic microorganisms in Atlantic Forest of southeast Brazil have focused on ciliates (Simão et al., 2017), algae (Sophia et al., 2016); mites and Collembola (Rieff et al., 2016) and parasitic nematodes of animals (Püttker et al., 2008; Ruiz-Torres et al., 2017) and plants (Antes et al., 2012). However, studies of free-living nematodes in this environment are scarce, except for a study that explored nematode trophic structure in phytotelmata of two bromeliad species in the Atlantic Forest of Rio de Janeiro state (Robaina et al., 2015). The total number of worldwide species described of soil nematodes
⁎
is around 25,000 (Powers, 2004). Nematodes role in on the soil food web, covering several trophic levels, including bacterial and fungal feeders, parasites, predators and omnivores specie (Yeates et al., 1993). The occurrence of free-living nematodes is dependent on changes in each ecological niche, trophic groups, and particular soil characteristics. Nematodes considered as biomarkers of soil quality (Yang et al., 2017, Moura and Franzener, 2017) and have the potential to provide insights related to the structure and functioning of the biogeochemical process in soil, which directly affects the rate of decomposition and nutrients availability (Ritz and Trudgill, 1999). Traditionally, nematodes species identification have been based on microscopy and morphology (Porazinska et al., 2014). However, the analysis of amplified and sequenced small subunit (SSU) rRNA genes has become a standard reference sequence for identification and classification of organisms. Metabarcoding provides short sequences that have been applied to the
Corresponding author at: Pontifícia Universidade do Rio Grande do Sul, Avenida Ipiranga 6681, prédio 12 CEP, 90690-900 Porto Alegre, RS, Brazil. E-mail address: [email protected] (A. Loureiro Morassutti).
https://doi.org/10.1016/j.foreco.2019.117591 Received 28 June 2019; Received in revised form 30 August 2019; Accepted 2 September 2019 0378-1127/ © 2019 Elsevier B.V. All rights reserved.
Forest Ecology and Management 453 (2019) 117591
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protocols. The resulting sequences were submitted to quality control that retained sequences with a minimum length of 100 bp and were trimmed to remove low-quality bases (minimum Phred score of 30) using PRINSEQ (Schmieder and Edwards, 2011). The remaining sequences were replicated and sorted by decreasing read abundance using USEARCH v7.0.1090 (Edgar, 2010). The OTUS (Operational Taxonomic Units) were obtained at 99% of identity using algorithm UPARSE (Edgar, 2013). UCHIME (Edgar et al., 2011) was used as chimera removal using RDP gold database (Cole et al., 2014). The taxonomic assignment was obtained using QIIME v1.8 (Caporaso et al., 2010) through the algorithm RDP Naïve Bayesian Classifier (Wang et al., 2007) with 1.0 confidence score and using as reference database SILVA 111 (Quast et al., 2013). Sequencing results were deposited in the National Center for Biotechnology Information (NCBI) under BioProject ID PRJNA471746. In most cases, classification using QIIME only achieved order and family levels. To classify Nematoda sequences to genus level, we used a complementary approach. The nucleotide sequences classified at the order or family level were retrieved using the script filter_otus_from_otu_table.py (http://qiime.org/scripts/filter_otus_from_otu_table. html) and filter_fasta.py (http://qiime.org/scripts/filter_fasta.html). After, sequences were submitted to the BL BLASTN (Basic Local Alignment Search Tool that search nucleotide databases by using a nucleotide query) alignment tool to identify the most closely related species present in the NCBI database nucleotide collections (nr/nt). We infer the sequence classification at the genus level by considering an identity value ≥ 96% and coverage above 97% between the search sequence and the sequence retrieved from the database.
study of microbial eukaryotic communities in soil (Hadziavdic et al., 2014; Geisen, 2016) and identification of nematode community (Porazinska et al., 2014; Sapkota and Nicolaisen, 2015; Traunspurger et al., 2017). In this study, we investigated for the first time the eukaryotic soil fauna, focusing on the nematofauna, in a native reserve of Atlantic Forest, Center of Research and Environmental Conservation Pró-Mata (CPCN Pró-Mata), southern Brazil using a high-throughput metabarcoding sequencing methodology. 2. Material and methods 2.1. Site description, sampling collection, and characterization The Center for Environmental Research and Conservation (CPCN Pró-Mata) is a area of native forest (29°26′17″ to 29°34′42″ S, and 50°08′14″ to 50°14′18″ W), with undisturbed forest soil, located 900 m above sea level in São Francisco de Paula, Rio Grande do Sul, Brazil. The region’s climate is classified by Köppen climate classification systems as Temperate Ocean Climate (Cfb), moist marine coast climate, with a mean rainfall of about 2250 mm per year, and an annual average temperature of 14.5 °C. At the sampling time, air temperature fluctuated between 19 °C and 30 °C, with air humidity ranged between 71% and 88%. The composite samples were randomly collected in five areas with old mature thick forest with the presence of large old trees from 0 to 15 cm depth. The sides were chosen for cover all the study area. Soils were then pooled to represent the area of study and sieved through a 5 mm mesh to remove large debris and root fragments before homogenization. Same composite soil sample was used for physical-chemical characterization (Table 1) and then stored at −80 °C for molecular analyses.
2.3. Nematode extraction and identification The composite soil sample were sieved through a 5 mm mesh to remove large debris and root fragments before gently homogenized. For the nematode extraction, we followed the protocol described by Hooper (1986). Briefly, after sieving, the remaining sediment was soaked in water and centrifuged at 1800 × g for 5 min to obtain a compact pellet, and eliminated the supernatant. The pellet was suspended in 20 mL of Percoll, specific gravity of 1.15 and centrifuged for 1 min at the same rotation. Nematodes were recollected at the interface between the top and the bottom of supernatant using a 500-µm sieve. Nematodes were transferred to a new tube for further morphological and taxonomic classification. Identification per type of mouthparts was used, for trophic groups and functional guilds (phytoparasites, bacterial feeders, omnivores, and predators) assignment to Yeates et al. (1993) and Ferris et al. (2001). Reproductive organs (Andrassy, 1983; Kiontke, 1997) and morphological characters based on taxonomic keys described by Sudhaus (2011) and Rockman et al. (2011) were used for species identification.
2.2. Eukaryotic community identification by high throughput sequencing The composite soil sample was used for nematode extraction and submitted to DNA isolation using a DNeasy PowerSoil kit (QIAGEN) following the manufacturer’s protocol. One common marker for diversity analysis in eukaryotes is the small subunit (SSU) 18S rRNA (SSU rRNA) gene, whose sequence and structure has been characterized and contains nine highly variable regions: V1 to V9 (Nelles et al., 1984). For studies of eukaryotic diversity, several variable regions have been suggested, with the V4 and V9 being the most prominent candidate (Dunthorn, et al., 2012). Amplification of the V4 region of the 18S rRNA genes was performed using the eukaryotic primers Ek-NSF573 ( CGCGGTAATTCCAGCTCCA) and Ek- NSR951 (TTGGYRAATGCTTTCGC) (Mangot et al., 2013). PCR reactions were carried out in triplicates using Platinum Taq (Invitrogen) following the cycling conditions: 95 °C for 5 min; 25 cycles of 95 °C for 45 s; 55 °C for 30 s and 72 °C for 45 s with a final extension of 72 °C for 2 min. The V9 region was amplified using the eukaryotic primers F-1183 (AATTTGACTCAACAC GGG) and R-1631 (TACAAAGGGCAGGGACG) (Starke et al., 2016). The PCR reactions were performed as described by Christoff et al. (2017). Briefly, thermal cycling conditions included 95 °C for 5 min, ten cycles of 95 °C for 45 s, 66 °C for 30 s and 72 °C for 45 s and a final extension of 72 °C for 2 min. Sequencing was performed on the Illumina MiSeq nextgeneration sequencing system (Illumina) following the manufacturer’s
3. Results Two hypervariable regions of 18S rRNA gene, V4 and V9 were amplified from the soil sample and sequenced. From V4 141,218 highquality sequences were recovered, while from V9 were 140,182 highquality sequences. Sequences grouped in OTUs were assigned
Table 1 Physicochemical analysis of the soil used for high throughout sequencing and nematode isolation. Physicochemical parameter
OM
Clay
pH
% Pro-Mata Soil
> 6.7
Al cmol. dm
21.6
4.2
3.2
Ca
Mg
3
P mg. dm
3.1
1.6
OM = organic matter. 2
22.8
K
S
B
Mn
Zn
Cu
182
18
0.3
42
3.06
0.72
3
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Fig. 1. Relative abundance of the most abundant groups identified in the soil sample using primers annealing the hypervariable regions V4 and V9 of the 18S rRNA gene. (A) The average abundance of each main supergroup; (B) the most abundant groups in each supergroup using different sets of 18S rRNA gene primers. AM = Amoebozoa; Arch = Archaeplastida; CH = Centrohelida; EX = Excavata; SAR = clade Stramenopiles, Alveolata, and Rhizaria. *phylum; **class; ***order; ****family; *****genus.
Chromadorea and Enoplea (average 3.75% and 1.1% of the total sequences, respectively), followed by the Phylum Gastrotricha (family Chaetonotidae, 3.1%). Sequences belonging to the phyla Annelida, Rotifera and Platyhelminthes were the other Metazoan groups found in the sample, but in abundance lower than 0.4% with all together. Taxa in the SAR supergroup were also too much abundant, with Rhizaria (clade Cercozoa) representing up to 32.1% of the total sequences, followed by Stramenopiles and Alveolata, representing up to 5.3% and 4%, respectively. With the regards to Rhizaria, all sequences were classified as belonging to the Clade Cercozoa. The supergroup SAR are represent by the clades Cercomonadidae, Endomixa, Glissomonadida, Gymnophrys, PP1-8, Silicofilosea, and Thecofilosea. By far, the most abundant Rhizaria clade belonged to the Family Cercomonadidae (up to 17.1% of the total sequences), followed by the Classes Thecofilosea and Silicofilosea, with abundances up to 7.1 and 4.2%, respectively. Stramenopiles were primarily represented by the class Chrysophyceae (up to 4.25%), but also by the classes Diatomea, Peronosporomycetes and the MAST-12 clade (< 1% each). Alveolata was dominated by Ciliophora and Apicomplexa clades (up to 3.7% and 1.6%, respectively), with other clades, such as Dinoflagellata and Protalveolata also observed at low rates (< 0.3%). Less representative supergroups were Centrohelida, in which up to 1.3% of the total sequences belonged to the family Heterophrydae, followed by Archaeplastida (phyla Chlorophyta and Charophyta, each up to 0.66% and 0.54%, respectively) Excavata and Amoebozoa, comprising up to 0.6% and 0.4%, respectively (Fig. 1).
taxonomic categories based on the current eukaryotic classification (Adl et al., 2012). Eukaryotic sequences were mainly distributed among six supergroups: Amoebozoa, Archaeplastida, Centrohelida, Excavata, Opisthokonta, and the SAR supergroup, which includes Stramenopila, Alveolates, and Rhizaria (Fig. 1). Some differences were observed among the eukaryotic communities depending on the pair of primes used for the 18S rRNA sequencing. Primers targeting the hypervariable region V4 and V9 regions identified 27 and 29 eukaryotic groups, respectively. The amplification of V4 region was unable to recovery sequences from Endomyxa (Rhizaria), Nucletmycea (Opisthokonta/Fungi) and Discoba (Excavata), while the V9 region cannot identify sequences from Protalveolata (Alveolata). Besides these substantial differences, the number of sequences retrieved from both pairs of primers was more evident in some clades than in others. For example, Chytridiomycota and Ciliophora were observed four times more using V4 primers than using V9 primers (Fig. 2). Apicomplexa, Gastrotricha, and Glomeromycota clades were retrieved 58, 42 and 37 times more, respectively, using V9 primers than using V4 primers. The most abundant supergroup in the examined soil was Opisthokonta, with Fungi and Metazoa represented by average of 40.6% and 15.8% of the total sequences, respectively. Holozoa and uncultured eukaryotes from the clade RT5iin14 were observed in low abundance (< 0.1%), this cladeis an emerging eukaryotic groups which have greater divergence from known kingdoms, did not cluster with any known kingdoms (Wang et al., 2014). Within the Fungi, Mucoromycotina was the dominant clade followed by Ascomycota, Chytridiomycota, Basidiomycota, Glomeromycota, and Nucletmycea. Fungi clades LKM11 and LKM15 were found in low abundance (< 0.2% of the total sequences). Arthropoda was the most abundant metazoan phylum in the sample, represented by the class Maxillopoda (average 8.5%), and Collembola (average < 0.06%). Nematoda was the second most abundant Metazoan phylum, dominated by the classes
3.1. Nematode identification using high throughput sequencing Nematoda was the second most abundant Metazoan phylum identified using high throughput sequencing. The Chromadorea was represented by seven families and genera: Cephalobidae (Acrobeloides), Criconematidae (Discocriconemella), Microlaimidae (Prodesmodora), 3
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Fig. 2. Proportion of nematofauna composition according to their feeding habits and some of the representative genera within each feeding group identified by morphological characters. Table 2 List of nematofauna from Center for Environmental Research and Conservation (Pró-Mata) identified by morphology and 18S rRNA high throughput sequencing. Class
Order
Family
Genus or Species
Accession number
E-value
Identity(%)
QC1(%)
V4
V9
Chromadorea
Araeolaimida
Plectidae Rhabdolaimidae* Microlaimidae Cephalobidae
Plectys minimus Plec1 Rhabdolaimus terrestris RhDoTer1 Prodesmodora circulata PrDeCir3 Acrobeles nanus* Acrobeloides cf. buetschlii 1 JH-2012 Choriorhabditis dudichi Rhabditis sp. Ditylenchus sp. * Aphelenchoides sp. * Discocriconemella sp. TSH-2009 DiscLC4-02 Discocriconemella sp. * Hemicycliophoras sp. * Mesocriconema sp. * Aorolaimus sp. * Helicotylenchus sp. * Rotylenchus sp. * Scutellonema sp. * Meloidogyne sp. * Gracilacus sp. Nanjing isolate PM1 Tylenchus sp. *
KC206040.1 KJ636366.1 AY284721.1
4.00E-153 4.00E-138 6.00E-151
100% 97% 99%
94% 96% 96%
0 0.1 0
0.1 0 0.1
JQ957905.1 AF083012.1
6.00E-146 8.00E-135
100% 96%
94% 94%
0 3.8
0 2.5
FJ489553.1
8.00E-135
96%
94%
0
0.6
KR232933
4.00E-148
99%
94%
0
0.3
Amblydorylaimus isokaryon Aporcelaimellus sp. MAS-2013 Dorylaimellus virginianus 9 Mile 9–22 LP2-26 Xiphinema brasiliense SZX1305 Xiphinema herakliense N27 Eudorylaimus sp. MRR-2011 MZ_E2 Ironus sp. 1992 Odonrilaimus sp. OdLaSp1
KM092519.1 JX674034.1 AY552969.1 KP793050.1 KM586356.1 HQ270135.1 FJ040496.1 FJ969131.1
1.00E-153 4.00E-153 8.00E-150 5.00E-152 1.00E-147 2.00E-146 8.00E-135 1.00E-153
100% 100% 99% 99% 99% 99% 96% 99%
95% 94% 94% 95% 96% 94% 94% 96%
0 0.2 0 0 0 0 0 0.2
0.2 0 0 0 0 0.1 0.1 0
Prismatolaimus cf. dolichurus JH-2004 Prismatolaimus cf. intermedius 2 JH-2004 Prismatolaimus dolichurus PriMDoI4 Tripyla sp. 2 ZQZ-2010a
AY284728.1 KJ636367.1 AY593957.1 GQ503070.1
4.00E-143 2.00E-141 5.00E-147 4.00E-147
98% 98% 98% 100%
94% 94% 96% 94%
0.2 0.3 0.4 0.1
0.2 0 0.1 0
Desmodorida Rhabditida
Rhabditidae Tylenchida
Anguinidae Aphelenchoidedae Criconematidae
Hoplolaimidae
Meloidogynidae Tylenchulidae Enoplea
Dorylaimida
Aporcelaimidae Belondiridae Lomgidoridae
Enoplida Mononchida* Triplonchida
Qudsianematidae Ironidae Oxysrominiadae Prismatolaimidae
Tripylidae 1
Query Cover. * Taxa identified only in morphology.
4
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individuals per cm3 as observed in a study of Caixeta et al. (2016). PróMata soil has high air humidity, with values ranging 88% this may affect nematode abundance since only 7.5 individuals were present per cm3, this value was given by the number of individuals divided by the cm3 researched. In addition, high abundance of a particular nematode, such as plant parasites, is an indicative of soil disturbance or ecological imbalance (Huang and Cares, 2006; Silva et al., 2008), suggesting that Pró-Mata may be at optimum ecological equilibrium. Also, because the plant parasitic were the most abundant, representing 35% of the whole sample analyzed. These values are similar with others studies in Amazonian and Savannah regions (Huang and Cares, 2006), where the dominant nematode were the plant parasites. Evidencing that the environments where occur a great variety of vegetation can support different species of nematodes to the same ecological niche (Cares and Huang, 1991). Also, the correlation between the numbers of plant parasites found here (35%) may suggest chemical attraction for low pH (4.3) (Schmitt and Norton, 1972). Results indicates that soil of native Atlantic Forest may be a potential reservoir for plant parasites. Fungi may also directly affect the nematode community since predatory features are observed in many groups. Trapping structures with extensive mycelia, knobs, adhesive and non-adhesive pegs are specialized n nematode capture, and some fungi may be endoparasites of nematode body promoting infection through zoospores or conidia leading to death of adults and immature nematodes (Boddy, 2015). The most sequences obtained from Pró-Mata soil were from Fungi dominated by Mucoromycotina and Ascomycota., which might be influencing the abundance of nematode found here. The presence of individuals of different genera from the suborder Tylenchida, orders Mononchida and Dorylaimida indicates soil preservation. These suborder and orders are extremely sensitive to soil and environmental disturbancesAlso, the genera Discocriconemella and Dorylaimellus found here are associated with areas of native forest (Goulart et al., 2003; Tomazini et al., 2008). Other genera found in this study, such as Gracilacus and Scutellonema, have been described only in Atlantic Forest (Caixeta et al., 2016) possibly indicating a particular speciation process occurring into native Atlantic Forests. Mononchus and Aporcelaimus, both present in our analysis, were classified with higher value of c-p, that are an ecological parameter, this means, they are persistent organisms, which could indicate stability of the system analyzed, being absent in areas of environmental disturbance. The use of V4 and V9 regions proved useful for nematode assessment and allowed a broad evaluation of the taxonomy found in ProMata soil since many taxa were identified only after sequencing analysis (Table 2). In addition, the composition of other cohabitant Eukaryotes could be assessed and increase the knowledge of the soil ecosystem occurring in this area in Brazil. Studies regarding meiofauna community, especially nematodes, are scarce in Atlantic forest. Our study may contribute to basic research in soil meiofauna, as well as on a better understanding of soil ecology. To the best of our knowledge, this is the first study that analyzes the diversity of microeukaryotic community of nematode species using morphology combined with high throughput sequencing in a subtropical forest.
Plectidae (Plectus), Rhabditidae (Choriorhabditis), Rhabdolaimidae (Rhabdolaimus), and Tylenchulidae (Gracilacus) (Table 2). The Enoplea was represented by eight families and nine genera: Aporcelaimidae (Amblydorylaimus and Aporcelaimellus), Belondiridae (Dorylaimellus), Ironidae (Ironus), Longidoridae (Xiphinema), Oxystominidae (Odontolaimus), Prismatolaimidae (Prismatolaimus), Qudsianematidae (Eudorylaimus), and Tripylidae (Tripyla) (Table 2). Choriorhabditis was the dominant nematode genus found, with abundance range from 2.5% to 3.1% of the total sequences when using V4 and V9 18S rRNA primers, respectively. 3.2. Nematode morphological identification The composite soil sample soil were examined and 1745 nematodes counted. Representatives of all trophic groups were found in the examined soil (Fig. 2). Plant parasites were represented by 522 individuals (35%), with the genus Discrocriconemella as the most representative. Bacterial feeders were represented by 426 individuals (28%), and mostly represented by the genus Rhabditis. Omnivores included 108 individuals (7%) and predators were represented by 74 specimens (5%) belonging to the order Mononchida. Twenty-five percent of the isolated nematodes could not be used for classification due to the damage or poor preservation of their morphological characters. The majority of specimens found belonged to the class Chromadorea (89.6%), whereas the class Enoplea was represented by 10.4% of the total specimens identified. Within the class Chromadorea, 1044 specimens belonged to the order Tylenchida, with 522 specimens identified only until the order level. The other specimens could be classified to genus level, represented by eleven genera, in order of abundance: Discocriconemella (101), Helicotylenchus (75), Mesocriconema (69), Meloidogyne (54), Scutellonema (52), Tylenchus (48), Aphelenchoides (34), Hemicycliophora (33), Aoralaimus (24), Rotylenchus (17), and Ditylenchus (15). Rhabditida was the second most abundant order, represented by 426 specimens classified only to the order level, and two genera, Rhabditis (71) and Acrobeles (22). The class Enoplea was represented by 182 specimens, all classified into subclass Dorylaimia and belonging to orders, Dorylaimida and Mononchida, with 108 and 74 specimens observed, respectively. It was not possible to assess family or genus to the specimens from this class through morphological analysis due to the damage or poor preservation of their morphological characters (Table 2). 4. Discussion We have a metabarcoding approach to characterize the biodiversity of meiofaunal eukaryotes from soil of a native portion of the Atlantic Forest in southern Brazil. High throughput sequencing using primers amplifying different regions from the 18S rRNA gene revealed the complex eukaryotic community from the Pró-Mata native forest soil. Primers targeting the 18S rRNA V4 region may present the best discrimination with short high throughput sequences (Hugerth et al., 2014). Here this approach helped document a wide occurrence of microorganisms, and also arthropods and annelids, that have been challenging to culture or isolate. Our study, include individuals from 8 orders in 21 families belonging to 30 genera (Table 2) present in the Pro-Mata soil. Pró-Mata is a complex mosaic of vegetation in different stages of ecological succession, mostly in native grasslands and mountain forests of Araucaria specie suggesting that plant diversity may be implicated in nematode abundance and biodiversity. A high diversity of nematodes species was found in Pró-Mata soil. According to Liang and Steinberger (2001), moisture difference in soil can affect abundance of the nematode communities. In dry areas, as in the Brazilian Caatinga where the average air humidity is 50% the abundance of nematode per cm3 is about of 500 individuals (Caixeta et al., 2016). While in humid forest the abundance of nematode may dramatically decay being around 3
5. Conclusions The soils of Atlantic Forests in southeast Brazil are a rich region of biodiversity of eukaryotes, but little known about the diversity and distribution of this fauna in this habitat. Here we show that high throughput sequencing and morphological characterization of eukaryotes are complementary approachs to biodiversity studies and may resolve taxonomic classification when only one of the methods fails. High throughput sequencing and morphological characterization are powerful tools to study the biological diversity and conservation management of Atlantic Forest soils species in Brazil. 5
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Sci. 110 (4), 713–719. Robaina, R.R., de Souza, R.M., Gomes, V.M., Cardoso, D.O., Almeida, A.M., Gonçalves, L.S.A., 2015. Nematode trophic structure in phytotelmata of Canistropsis billbergioides and Nidularium procerum (Bromeliaceae) in the Atlantic Forest - variability in relation to climate variables and plant architecture. Nematoda 2. https://doi. org/10.4322/nematoda.01615. Rockman, M.V., Ailion, M., Braendle, C., Fitch, D.H., Pénigault, J.-B., Félix, M.-A., Kiontke, K.C., 2011. A phylogeny and molecular barcodes for Caenorhabditis, with numerous new species from rotting fruits. BMC Evol. Biol. 11, 339. https://doi.org/ 10.1186/1471-2148-11-339. Ruiz-Torres, N., García-Prieto, L., Lagunas-Calvo, O., Violante-González, J., OsorioSarabia, D., 2017. Helminth infracommunity of the cane toad Rhinella marina (Anura: Bufonidae) within its native distribution range. Parasitol. Int. 66, 567–572. https://doi.org/10.1016/j.parint.2017.05.002. Sapkota, R., Nicolaisen, M., 2015. High-throughput sequencing of nematode communities from total soil DNA extractions. BMC Ecol. 15, 1–8. https://doi.org/10.1186/s12898014-0034-4. Schmieder, R., Edwards, R., 2011. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864. https://doi.org/10.1093/bioinformatics/ btr026. Schmitt, D.P., Norton, D.C., 1972. Relationships of plant parasitic nematodes to sites in native Iowa prairies. J. Nematol. 4, 200–205. Simão, T.L.L., Borges, A.G., Gano, K.A., Davis-Richardson, A.G., Brown, C.T., Fagen, J.R., Triplett, E.W., Dias, R., Mondin, C.A., da Silva, R.M., Eizirik, E., Utz, L.R.P., 2017. Characterization of ciliate diversity in bromeliad tank waters from the Brazilian Atlantic Forest. Eur. J. Protistol. 61, 359–365. https://doi.org/10.1016/j.ejop.2017. 05.005. Silva, R.A., 2008. Estudo da fauna fitonematológica na Mata Atlântica do estado de São Paulo e na Floresta Amazônica do estado do Mato Grosso. Tese (Doutorado em Agronomia). Universidade de São Paulo, Piracicaba, 93 pp. Sophia, M.G., Huszar, V.L.M., Silva, L.H.S., Domingues, C.D., Santos, J.B.O., Bicudo, C.E.M., 2016. Subaerial eukaryotic algae and cyanobacteria on dripping rocks in the Atlantic Forest of southeast Brazil: composition and abundance. Rev. Bras. Bot. 39, 741–749. https://doi.org/10.1007/s40415-016-0261-3. Starke, R., Kermer, R., Ullmann-Zeunert, L., Baldwin, I.T., Seifert, J., Bastida, F., von Bergen, M., Jehmlich, N., 2016. Bacteria dominate the short-term assimilation of plant-derived N in soil. Soil Biol. Biochem. 96, 30–38. https://doi.org/10.1016/j. soilbio.2016.01.009. Sudhaus, W., 2011. Phylogenetic systematisation and catalogue of paraphyletic “Rhabditidae” (Secernentea, Nematoda). J. nematode Morphol. Syst. 14, 113–178. Tomazini, M.D., Ferraz, L.C.C.B., Monteiro, A.R., 2008. Abundância e diversidade de nematóides em área contíguas de vegetação natural e submetidas a diferentes tipos de uso agrícola. Nematol. Bras. 32, 185–193. 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Acknowledgements The authors thank the National Council for Scientific and Technological Development (CNPq) of Brazil. Grant number: 406149/ 2016-0. Also, to the High Performance Computing Lab - LAD/PUCRS, Brazil, for allowing access to run the high-throughput computational analyses. Luiz Gustavo dos A. Borges thanks PEGA/PUCRS. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foreco.2019.117591. References Adl, S.M., Simpson, A.G.B., Lane, C.E., Lukeš, J., Bass, D., Bowser, S.S., Brown, M.W., Burki, F., Dunthorn, M., Hampl, V., Heiss, A., Hoppenrath, M., Lara, E., Gall, L. Le, Lynn, D.H., McManus, H., Mitchell, E.A.D., Mozley-Stanridge, S.E., Parfrey, L.W., Pawlowski, J., Rueckert, S., Shadwick, L., Schoch, C.L., Smirnov, A., Spiegel, F.W., 2012. The revised classification of eukaryotes. J. Eukaryot. Microbiol. 59, 429–493. https://doi.org/10.1111/j.1550-7408.2012.00644.x. Andrassy, I., 1983. A Taxonomic Review of the Suborder Rhabditina (Nematoda: Secernentia), Office de la Recherche Scientifique et Technique. Outre-Mer (ORSTOM). Antes, V.A., Comerlato, A.P., Schuelter, A.R., Carneiro, R.M.D.G., Furlanetto, C., 2012. Native-plant hosts of Meloidogyne spp. from Western Paraná, Brazil. Brazilian Arch. Biol. Technol. 55, 213–219. https://doi.org/10.1590/S1516-89132012000200005. Boddy, L., 2015. Interactions with Humans and Other Animals, The Fungi: Third Edition. https://doi.org/10.1016/B978-0-12-382034-1.00009-8. Caixeta, L.B., Pereira, T.J., Castañeda, N.E.N., Cares, J.E., 2016. Nematode communities as indicators of the status of a soil ecosystem influenced by mining practices in Brazil. Nematology 18, 265–276. https://doi.org/10.1163/15685411-00002958. Cares, J.E., Huang, S.P., 1991. Nematode fauna in natural and cultivated cerrados of 336 Central Brazil. Fitopatol. Bras. 16, 199–209. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Peña, A.G., Goodrich, K., Gordon, J.I., Huttley, G.a., Kelley, S.T., Knights, D., Jeremy, E., Ley, R.E., Lozupone, C.A., Mcdonald, D., Muegge, B.D., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., 2010. NIH public access. Nat Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303.QIIME. Christoff, A.P., Fernanda, A., Sereia, R., Rodrigues, D., Lucio, R., Moraes, V. De, Felipe, L., Oliveira, V. De, Bacterial identification through accurate library preparation and high-throughput sequencing. 2017. Cole, J.R., Wang, Q., Fish, J.A., Chai, B., McGarrell, D.M., Sun, Y., Brown, C.T., PorrasAlfaro, A., Kuske, C.R., Tiedje, J.M., 2014. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, 633–642. https://doi.org/ 10.1093/nar/gkt1244. Dunthorn, M., Klier, J., Bunge, J., Stoeck, T., 2012. Comparing the hyper-variable V4 and V9 regions of the small subunit rDNA for assessment of ciliate environmental diversity. J. Eukaryot. Microbiol. 59, 185–187. https://doi.org/10.1111/j.1550-7408. 2011.00602.x. Edgar, R.C., 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998. https://doi.org/10.1038/nmeth.2604. Edgar, R.C., 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461. Edgar, R.C., Haas, B.J., Clemente, J.C., Quince, C., Knight, R., 2011. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200. https:// doi.org/10.1093/bioinformatics/btr381. Ferris, H., Bongers, T., De Goede, R.G.M., 2001. A framework for soil food web diagnostics: extension of the nematode faunal analysis concept. Appl. Soil Ecol. 18, 13–29. https://doi.org/10.1016/S0929-1393(01)00152-4. Geisen, S., 2016. Thorough high-throughput sequencing analyses unravels huge diversities of soil parasitic protists. Environ. Microbiol. 18, 1669–1672. https://doi.org/ 10.1111/1462-2920.13309. Goulart, A.M.C., Monteiro, A.R., Ferraz, L.C.B., 2003. Comunidade de nematoides 362 em cerrado com vegetação original preservada ou substituída por culturas. Nematol 363 Bras 27, 129–137. Hadziavdic, K., Lekang, K., Lanzen, A., Jonassen, I., Thompson, E.M., Troedsson, C., 2014. Characterization of the 18s rRNA gene for designing universal eukaryote specific primers. PLoS ONE 9. https://doi.org/10.1371/journal.pone.0087624. Hugerth, L.W., Muller, E.E.L., Hu, Y.O.O., Lebrun, L.A.M., Roume, H., Lundin, D., Wilmes, P., Andersson, A.F., 2014. Systematic design of 18S rRNA gene primers for determining eukaryotic diversity in microbial consortia. PLoS ONE 9. https://doi.org/ 10.1371/journal.pone.0095567. Huang, S.P., Cares, J.E., 2006. Nematode communities in soils under different land-use systems in Brazilian Amazon and savanna vegetation. In: Moreira, F.M., Siqueira, J. O., Brussaard, L. (Eds.), Soil biodiversity in Amazonian and other Brazilian ecosystems. London, pp. 25–47. Hooper, D.J., 1986. Extraction of free-living stages from soil. In: Southey, J.F. (Ed.), Laboratory Methods for Work with Plant and Soil Nematodes. Wiley, London, pp. 5–30. Joly, Carlos A., Metzger, Jean Paul, Tabarelli, Marcelo, 2014. Experiences from the
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Meiofaunal diversity in the Atlantic Forest soil: A quest for nematodes in a native reserve using eukaryotic metabarcoding analysis
T
Carla Aristonara Müllera, Leandro de Mattos Pereiraa,d, Carina Lopesb, Juvenil Caresb, Luiz Gustavo dos Anjos Borgesc, Adriana Giongoc, Carlos Graeff-Teixeiraa, ⁎ Alessandra Loureiro Morassuttia,c, a
Laboratório de Biologia Parasitária, Escola de Ciências, Laboratório de Parasitologia Molecular, Instituto de Pesquisas Biomédicas, Pontifícia Universidade do Rio Grande do Sul (PUCRS), Av. Ipiranga 6681, 90690-900 Porto Alegre, RS, Brazil b Departamento de Fitopatologia, Instituto de Ciências Biológicas, Universidade de Brasília. Campus Universitário Darcy Ribeiro, Asa Norte, 70910-900 Brasília, DF, Brazil c Laboratório de Geobiologia, Instituto do Petróleo e Recursos Naturais, Pontifícia Universidade do Rio Grande do Sul (PUCRS), Av. Ipiranga 6681, 90690-900 Porto Alegre, RS, Brazil d Faculdade de Teologia e Ciências (FATEC), Rua José Sanches Peres 3040, 5501-210 Votuporanga, SP, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Nematode Morphology identification Functional feeding groups SAR Opisthokonta
The assessment of environmental communities may lead to the identification of the novel or not-yet-classified organisms. Few studies have focused on the meiofauna of poorly known habitats such as native soils in subtropical forests. We surveyed eukaryotic communities inhabiting soil in an unexplored area of a native Atlantic Forest in southern Brazil using high-throughput sequencing. We analyzed a total of 281,400 sequences using V4 and V9 hypervariable regions of 18S rRNA gene. Opisthokonta was the most abundant supergroup, with Fungi and Metazoa representing an average of 40.6% and 15.8% of the sequences, respectively. Among the metazoan, Nematoda was the second most abundant phylum (4.8%). Isolation and morphological assessment 1745 specimens of nematodes, classified by functional feeding groups as plant parasites (35%), bacterial feeders (30%), omnivores (7%) and predators (5%). Specimens belonged to the class Chromadorea (89.6%) and Enoplea (10.4%).Approximately 65% of them could be morphologically classified only to order level. Results identified a considerable number of newly or still uncharacterized nematodes in soil biota in Atlantic Forest soils. Furthermore, this is the first study in this subtropical area to show that both high throughput sequencing and morphology can give complementary indications of the diversity of nematodes in soil samples.
1. Introduction The Atlantic Forest is a complex biome in South America, known as one of the world’s biological hotspots (Myers et al., 2000). It covers approximately 17% of the Brazilian territory, from northeastern to southeastern (Joly et al., 2014). Studies of eukaryotic microorganisms in Atlantic Forest of southeast Brazil have focused on ciliates (Simão et al., 2017), algae (Sophia et al., 2016); mites and Collembola (Rieff et al., 2016) and parasitic nematodes of animals (Püttker et al., 2008; Ruiz-Torres et al., 2017) and plants (Antes et al., 2012). However, studies of free-living nematodes in this environment are scarce, except for a study that explored nematode trophic structure in phytotelmata of two bromeliad species in the Atlantic Forest of Rio de Janeiro state (Robaina et al., 2015). The total number of worldwide species described of soil nematodes
⁎
is around 25,000 (Powers, 2004). Nematodes role in on the soil food web, covering several trophic levels, including bacterial and fungal feeders, parasites, predators and omnivores specie (Yeates et al., 1993). The occurrence of free-living nematodes is dependent on changes in each ecological niche, trophic groups, and particular soil characteristics. Nematodes considered as biomarkers of soil quality (Yang et al., 2017, Moura and Franzener, 2017) and have the potential to provide insights related to the structure and functioning of the biogeochemical process in soil, which directly affects the rate of decomposition and nutrients availability (Ritz and Trudgill, 1999). Traditionally, nematodes species identification have been based on microscopy and morphology (Porazinska et al., 2014). However, the analysis of amplified and sequenced small subunit (SSU) rRNA genes has become a standard reference sequence for identification and classification of organisms. Metabarcoding provides short sequences that have been applied to the
Corresponding author at: Pontifícia Universidade do Rio Grande do Sul, Avenida Ipiranga 6681, prédio 12 CEP, 90690-900 Porto Alegre, RS, Brazil. E-mail address: [email protected] (A. Loureiro Morassutti).
https://doi.org/10.1016/j.foreco.2019.117591 Received 28 June 2019; Received in revised form 30 August 2019; Accepted 2 September 2019 0378-1127/ © 2019 Elsevier B.V. All rights reserved.
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protocols. The resulting sequences were submitted to quality control that retained sequences with a minimum length of 100 bp and were trimmed to remove low-quality bases (minimum Phred score of 30) using PRINSEQ (Schmieder and Edwards, 2011). The remaining sequences were replicated and sorted by decreasing read abundance using USEARCH v7.0.1090 (Edgar, 2010). The OTUS (Operational Taxonomic Units) were obtained at 99% of identity using algorithm UPARSE (Edgar, 2013). UCHIME (Edgar et al., 2011) was used as chimera removal using RDP gold database (Cole et al., 2014). The taxonomic assignment was obtained using QIIME v1.8 (Caporaso et al., 2010) through the algorithm RDP Naïve Bayesian Classifier (Wang et al., 2007) with 1.0 confidence score and using as reference database SILVA 111 (Quast et al., 2013). Sequencing results were deposited in the National Center for Biotechnology Information (NCBI) under BioProject ID PRJNA471746. In most cases, classification using QIIME only achieved order and family levels. To classify Nematoda sequences to genus level, we used a complementary approach. The nucleotide sequences classified at the order or family level were retrieved using the script filter_otus_from_otu_table.py (http://qiime.org/scripts/filter_otus_from_otu_table. html) and filter_fasta.py (http://qiime.org/scripts/filter_fasta.html). After, sequences were submitted to the BL BLASTN (Basic Local Alignment Search Tool that search nucleotide databases by using a nucleotide query) alignment tool to identify the most closely related species present in the NCBI database nucleotide collections (nr/nt). We infer the sequence classification at the genus level by considering an identity value ≥ 96% and coverage above 97% between the search sequence and the sequence retrieved from the database.
study of microbial eukaryotic communities in soil (Hadziavdic et al., 2014; Geisen, 2016) and identification of nematode community (Porazinska et al., 2014; Sapkota and Nicolaisen, 2015; Traunspurger et al., 2017). In this study, we investigated for the first time the eukaryotic soil fauna, focusing on the nematofauna, in a native reserve of Atlantic Forest, Center of Research and Environmental Conservation Pró-Mata (CPCN Pró-Mata), southern Brazil using a high-throughput metabarcoding sequencing methodology. 2. Material and methods 2.1. Site description, sampling collection, and characterization The Center for Environmental Research and Conservation (CPCN Pró-Mata) is a area of native forest (29°26′17″ to 29°34′42″ S, and 50°08′14″ to 50°14′18″ W), with undisturbed forest soil, located 900 m above sea level in São Francisco de Paula, Rio Grande do Sul, Brazil. The region’s climate is classified by Köppen climate classification systems as Temperate Ocean Climate (Cfb), moist marine coast climate, with a mean rainfall of about 2250 mm per year, and an annual average temperature of 14.5 °C. At the sampling time, air temperature fluctuated between 19 °C and 30 °C, with air humidity ranged between 71% and 88%. The composite samples were randomly collected in five areas with old mature thick forest with the presence of large old trees from 0 to 15 cm depth. The sides were chosen for cover all the study area. Soils were then pooled to represent the area of study and sieved through a 5 mm mesh to remove large debris and root fragments before homogenization. Same composite soil sample was used for physical-chemical characterization (Table 1) and then stored at −80 °C for molecular analyses.
2.3. Nematode extraction and identification The composite soil sample were sieved through a 5 mm mesh to remove large debris and root fragments before gently homogenized. For the nematode extraction, we followed the protocol described by Hooper (1986). Briefly, after sieving, the remaining sediment was soaked in water and centrifuged at 1800 × g for 5 min to obtain a compact pellet, and eliminated the supernatant. The pellet was suspended in 20 mL of Percoll, specific gravity of 1.15 and centrifuged for 1 min at the same rotation. Nematodes were recollected at the interface between the top and the bottom of supernatant using a 500-µm sieve. Nematodes were transferred to a new tube for further morphological and taxonomic classification. Identification per type of mouthparts was used, for trophic groups and functional guilds (phytoparasites, bacterial feeders, omnivores, and predators) assignment to Yeates et al. (1993) and Ferris et al. (2001). Reproductive organs (Andrassy, 1983; Kiontke, 1997) and morphological characters based on taxonomic keys described by Sudhaus (2011) and Rockman et al. (2011) were used for species identification.
2.2. Eukaryotic community identification by high throughput sequencing The composite soil sample was used for nematode extraction and submitted to DNA isolation using a DNeasy PowerSoil kit (QIAGEN) following the manufacturer’s protocol. One common marker for diversity analysis in eukaryotes is the small subunit (SSU) 18S rRNA (SSU rRNA) gene, whose sequence and structure has been characterized and contains nine highly variable regions: V1 to V9 (Nelles et al., 1984). For studies of eukaryotic diversity, several variable regions have been suggested, with the V4 and V9 being the most prominent candidate (Dunthorn, et al., 2012). Amplification of the V4 region of the 18S rRNA genes was performed using the eukaryotic primers Ek-NSF573 ( CGCGGTAATTCCAGCTCCA) and Ek- NSR951 (TTGGYRAATGCTTTCGC) (Mangot et al., 2013). PCR reactions were carried out in triplicates using Platinum Taq (Invitrogen) following the cycling conditions: 95 °C for 5 min; 25 cycles of 95 °C for 45 s; 55 °C for 30 s and 72 °C for 45 s with a final extension of 72 °C for 2 min. The V9 region was amplified using the eukaryotic primers F-1183 (AATTTGACTCAACAC GGG) and R-1631 (TACAAAGGGCAGGGACG) (Starke et al., 2016). The PCR reactions were performed as described by Christoff et al. (2017). Briefly, thermal cycling conditions included 95 °C for 5 min, ten cycles of 95 °C for 45 s, 66 °C for 30 s and 72 °C for 45 s and a final extension of 72 °C for 2 min. Sequencing was performed on the Illumina MiSeq nextgeneration sequencing system (Illumina) following the manufacturer’s
3. Results Two hypervariable regions of 18S rRNA gene, V4 and V9 were amplified from the soil sample and sequenced. From V4 141,218 highquality sequences were recovered, while from V9 were 140,182 highquality sequences. Sequences grouped in OTUs were assigned
Table 1 Physicochemical analysis of the soil used for high throughout sequencing and nematode isolation. Physicochemical parameter
OM
Clay
pH
% Pro-Mata Soil
> 6.7
Al cmol. dm
21.6
4.2
3.2
Ca
Mg
3
P mg. dm
3.1
1.6
OM = organic matter. 2
22.8
K
S
B
Mn
Zn
Cu
182
18
0.3
42
3.06
0.72
3
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Fig. 1. Relative abundance of the most abundant groups identified in the soil sample using primers annealing the hypervariable regions V4 and V9 of the 18S rRNA gene. (A) The average abundance of each main supergroup; (B) the most abundant groups in each supergroup using different sets of 18S rRNA gene primers. AM = Amoebozoa; Arch = Archaeplastida; CH = Centrohelida; EX = Excavata; SAR = clade Stramenopiles, Alveolata, and Rhizaria. *phylum; **class; ***order; ****family; *****genus.
Chromadorea and Enoplea (average 3.75% and 1.1% of the total sequences, respectively), followed by the Phylum Gastrotricha (family Chaetonotidae, 3.1%). Sequences belonging to the phyla Annelida, Rotifera and Platyhelminthes were the other Metazoan groups found in the sample, but in abundance lower than 0.4% with all together. Taxa in the SAR supergroup were also too much abundant, with Rhizaria (clade Cercozoa) representing up to 32.1% of the total sequences, followed by Stramenopiles and Alveolata, representing up to 5.3% and 4%, respectively. With the regards to Rhizaria, all sequences were classified as belonging to the Clade Cercozoa. The supergroup SAR are represent by the clades Cercomonadidae, Endomixa, Glissomonadida, Gymnophrys, PP1-8, Silicofilosea, and Thecofilosea. By far, the most abundant Rhizaria clade belonged to the Family Cercomonadidae (up to 17.1% of the total sequences), followed by the Classes Thecofilosea and Silicofilosea, with abundances up to 7.1 and 4.2%, respectively. Stramenopiles were primarily represented by the class Chrysophyceae (up to 4.25%), but also by the classes Diatomea, Peronosporomycetes and the MAST-12 clade (< 1% each). Alveolata was dominated by Ciliophora and Apicomplexa clades (up to 3.7% and 1.6%, respectively), with other clades, such as Dinoflagellata and Protalveolata also observed at low rates (< 0.3%). Less representative supergroups were Centrohelida, in which up to 1.3% of the total sequences belonged to the family Heterophrydae, followed by Archaeplastida (phyla Chlorophyta and Charophyta, each up to 0.66% and 0.54%, respectively) Excavata and Amoebozoa, comprising up to 0.6% and 0.4%, respectively (Fig. 1).
taxonomic categories based on the current eukaryotic classification (Adl et al., 2012). Eukaryotic sequences were mainly distributed among six supergroups: Amoebozoa, Archaeplastida, Centrohelida, Excavata, Opisthokonta, and the SAR supergroup, which includes Stramenopila, Alveolates, and Rhizaria (Fig. 1). Some differences were observed among the eukaryotic communities depending on the pair of primes used for the 18S rRNA sequencing. Primers targeting the hypervariable region V4 and V9 regions identified 27 and 29 eukaryotic groups, respectively. The amplification of V4 region was unable to recovery sequences from Endomyxa (Rhizaria), Nucletmycea (Opisthokonta/Fungi) and Discoba (Excavata), while the V9 region cannot identify sequences from Protalveolata (Alveolata). Besides these substantial differences, the number of sequences retrieved from both pairs of primers was more evident in some clades than in others. For example, Chytridiomycota and Ciliophora were observed four times more using V4 primers than using V9 primers (Fig. 2). Apicomplexa, Gastrotricha, and Glomeromycota clades were retrieved 58, 42 and 37 times more, respectively, using V9 primers than using V4 primers. The most abundant supergroup in the examined soil was Opisthokonta, with Fungi and Metazoa represented by average of 40.6% and 15.8% of the total sequences, respectively. Holozoa and uncultured eukaryotes from the clade RT5iin14 were observed in low abundance (< 0.1%), this cladeis an emerging eukaryotic groups which have greater divergence from known kingdoms, did not cluster with any known kingdoms (Wang et al., 2014). Within the Fungi, Mucoromycotina was the dominant clade followed by Ascomycota, Chytridiomycota, Basidiomycota, Glomeromycota, and Nucletmycea. Fungi clades LKM11 and LKM15 were found in low abundance (< 0.2% of the total sequences). Arthropoda was the most abundant metazoan phylum in the sample, represented by the class Maxillopoda (average 8.5%), and Collembola (average < 0.06%). Nematoda was the second most abundant Metazoan phylum, dominated by the classes
3.1. Nematode identification using high throughput sequencing Nematoda was the second most abundant Metazoan phylum identified using high throughput sequencing. The Chromadorea was represented by seven families and genera: Cephalobidae (Acrobeloides), Criconematidae (Discocriconemella), Microlaimidae (Prodesmodora), 3
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Fig. 2. Proportion of nematofauna composition according to their feeding habits and some of the representative genera within each feeding group identified by morphological characters. Table 2 List of nematofauna from Center for Environmental Research and Conservation (Pró-Mata) identified by morphology and 18S rRNA high throughput sequencing. Class
Order
Family
Genus or Species
Accession number
E-value
Identity(%)
QC1(%)
V4
V9
Chromadorea
Araeolaimida
Plectidae Rhabdolaimidae* Microlaimidae Cephalobidae
Plectys minimus Plec1 Rhabdolaimus terrestris RhDoTer1 Prodesmodora circulata PrDeCir3 Acrobeles nanus* Acrobeloides cf. buetschlii 1 JH-2012 Choriorhabditis dudichi Rhabditis sp. Ditylenchus sp. * Aphelenchoides sp. * Discocriconemella sp. TSH-2009 DiscLC4-02 Discocriconemella sp. * Hemicycliophoras sp. * Mesocriconema sp. * Aorolaimus sp. * Helicotylenchus sp. * Rotylenchus sp. * Scutellonema sp. * Meloidogyne sp. * Gracilacus sp. Nanjing isolate PM1 Tylenchus sp. *
KC206040.1 KJ636366.1 AY284721.1
4.00E-153 4.00E-138 6.00E-151
100% 97% 99%
94% 96% 96%
0 0.1 0
0.1 0 0.1
JQ957905.1 AF083012.1
6.00E-146 8.00E-135
100% 96%
94% 94%
0 3.8
0 2.5
FJ489553.1
8.00E-135
96%
94%
0
0.6
KR232933
4.00E-148
99%
94%
0
0.3
Amblydorylaimus isokaryon Aporcelaimellus sp. MAS-2013 Dorylaimellus virginianus 9 Mile 9–22 LP2-26 Xiphinema brasiliense SZX1305 Xiphinema herakliense N27 Eudorylaimus sp. MRR-2011 MZ_E2 Ironus sp. 1992 Odonrilaimus sp. OdLaSp1
KM092519.1 JX674034.1 AY552969.1 KP793050.1 KM586356.1 HQ270135.1 FJ040496.1 FJ969131.1
1.00E-153 4.00E-153 8.00E-150 5.00E-152 1.00E-147 2.00E-146 8.00E-135 1.00E-153
100% 100% 99% 99% 99% 99% 96% 99%
95% 94% 94% 95% 96% 94% 94% 96%
0 0.2 0 0 0 0 0 0.2
0.2 0 0 0 0 0.1 0.1 0
Prismatolaimus cf. dolichurus JH-2004 Prismatolaimus cf. intermedius 2 JH-2004 Prismatolaimus dolichurus PriMDoI4 Tripyla sp. 2 ZQZ-2010a
AY284728.1 KJ636367.1 AY593957.1 GQ503070.1
4.00E-143 2.00E-141 5.00E-147 4.00E-147
98% 98% 98% 100%
94% 94% 96% 94%
0.2 0.3 0.4 0.1
0.2 0 0.1 0
Desmodorida Rhabditida
Rhabditidae Tylenchida
Anguinidae Aphelenchoidedae Criconematidae
Hoplolaimidae
Meloidogynidae Tylenchulidae Enoplea
Dorylaimida
Aporcelaimidae Belondiridae Lomgidoridae
Enoplida Mononchida* Triplonchida
Qudsianematidae Ironidae Oxysrominiadae Prismatolaimidae
Tripylidae 1
Query Cover. * Taxa identified only in morphology.
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individuals per cm3 as observed in a study of Caixeta et al. (2016). PróMata soil has high air humidity, with values ranging 88% this may affect nematode abundance since only 7.5 individuals were present per cm3, this value was given by the number of individuals divided by the cm3 researched. In addition, high abundance of a particular nematode, such as plant parasites, is an indicative of soil disturbance or ecological imbalance (Huang and Cares, 2006; Silva et al., 2008), suggesting that Pró-Mata may be at optimum ecological equilibrium. Also, because the plant parasitic were the most abundant, representing 35% of the whole sample analyzed. These values are similar with others studies in Amazonian and Savannah regions (Huang and Cares, 2006), where the dominant nematode were the plant parasites. Evidencing that the environments where occur a great variety of vegetation can support different species of nematodes to the same ecological niche (Cares and Huang, 1991). Also, the correlation between the numbers of plant parasites found here (35%) may suggest chemical attraction for low pH (4.3) (Schmitt and Norton, 1972). Results indicates that soil of native Atlantic Forest may be a potential reservoir for plant parasites. Fungi may also directly affect the nematode community since predatory features are observed in many groups. Trapping structures with extensive mycelia, knobs, adhesive and non-adhesive pegs are specialized n nematode capture, and some fungi may be endoparasites of nematode body promoting infection through zoospores or conidia leading to death of adults and immature nematodes (Boddy, 2015). The most sequences obtained from Pró-Mata soil were from Fungi dominated by Mucoromycotina and Ascomycota., which might be influencing the abundance of nematode found here. The presence of individuals of different genera from the suborder Tylenchida, orders Mononchida and Dorylaimida indicates soil preservation. These suborder and orders are extremely sensitive to soil and environmental disturbancesAlso, the genera Discocriconemella and Dorylaimellus found here are associated with areas of native forest (Goulart et al., 2003; Tomazini et al., 2008). Other genera found in this study, such as Gracilacus and Scutellonema, have been described only in Atlantic Forest (Caixeta et al., 2016) possibly indicating a particular speciation process occurring into native Atlantic Forests. Mononchus and Aporcelaimus, both present in our analysis, were classified with higher value of c-p, that are an ecological parameter, this means, they are persistent organisms, which could indicate stability of the system analyzed, being absent in areas of environmental disturbance. The use of V4 and V9 regions proved useful for nematode assessment and allowed a broad evaluation of the taxonomy found in ProMata soil since many taxa were identified only after sequencing analysis (Table 2). In addition, the composition of other cohabitant Eukaryotes could be assessed and increase the knowledge of the soil ecosystem occurring in this area in Brazil. Studies regarding meiofauna community, especially nematodes, are scarce in Atlantic forest. Our study may contribute to basic research in soil meiofauna, as well as on a better understanding of soil ecology. To the best of our knowledge, this is the first study that analyzes the diversity of microeukaryotic community of nematode species using morphology combined with high throughput sequencing in a subtropical forest.
Plectidae (Plectus), Rhabditidae (Choriorhabditis), Rhabdolaimidae (Rhabdolaimus), and Tylenchulidae (Gracilacus) (Table 2). The Enoplea was represented by eight families and nine genera: Aporcelaimidae (Amblydorylaimus and Aporcelaimellus), Belondiridae (Dorylaimellus), Ironidae (Ironus), Longidoridae (Xiphinema), Oxystominidae (Odontolaimus), Prismatolaimidae (Prismatolaimus), Qudsianematidae (Eudorylaimus), and Tripylidae (Tripyla) (Table 2). Choriorhabditis was the dominant nematode genus found, with abundance range from 2.5% to 3.1% of the total sequences when using V4 and V9 18S rRNA primers, respectively. 3.2. Nematode morphological identification The composite soil sample soil were examined and 1745 nematodes counted. Representatives of all trophic groups were found in the examined soil (Fig. 2). Plant parasites were represented by 522 individuals (35%), with the genus Discrocriconemella as the most representative. Bacterial feeders were represented by 426 individuals (28%), and mostly represented by the genus Rhabditis. Omnivores included 108 individuals (7%) and predators were represented by 74 specimens (5%) belonging to the order Mononchida. Twenty-five percent of the isolated nematodes could not be used for classification due to the damage or poor preservation of their morphological characters. The majority of specimens found belonged to the class Chromadorea (89.6%), whereas the class Enoplea was represented by 10.4% of the total specimens identified. Within the class Chromadorea, 1044 specimens belonged to the order Tylenchida, with 522 specimens identified only until the order level. The other specimens could be classified to genus level, represented by eleven genera, in order of abundance: Discocriconemella (101), Helicotylenchus (75), Mesocriconema (69), Meloidogyne (54), Scutellonema (52), Tylenchus (48), Aphelenchoides (34), Hemicycliophora (33), Aoralaimus (24), Rotylenchus (17), and Ditylenchus (15). Rhabditida was the second most abundant order, represented by 426 specimens classified only to the order level, and two genera, Rhabditis (71) and Acrobeles (22). The class Enoplea was represented by 182 specimens, all classified into subclass Dorylaimia and belonging to orders, Dorylaimida and Mononchida, with 108 and 74 specimens observed, respectively. It was not possible to assess family or genus to the specimens from this class through morphological analysis due to the damage or poor preservation of their morphological characters (Table 2). 4. Discussion We have a metabarcoding approach to characterize the biodiversity of meiofaunal eukaryotes from soil of a native portion of the Atlantic Forest in southern Brazil. High throughput sequencing using primers amplifying different regions from the 18S rRNA gene revealed the complex eukaryotic community from the Pró-Mata native forest soil. Primers targeting the 18S rRNA V4 region may present the best discrimination with short high throughput sequences (Hugerth et al., 2014). Here this approach helped document a wide occurrence of microorganisms, and also arthropods and annelids, that have been challenging to culture or isolate. Our study, include individuals from 8 orders in 21 families belonging to 30 genera (Table 2) present in the Pro-Mata soil. Pró-Mata is a complex mosaic of vegetation in different stages of ecological succession, mostly in native grasslands and mountain forests of Araucaria specie suggesting that plant diversity may be implicated in nematode abundance and biodiversity. A high diversity of nematodes species was found in Pró-Mata soil. According to Liang and Steinberger (2001), moisture difference in soil can affect abundance of the nematode communities. In dry areas, as in the Brazilian Caatinga where the average air humidity is 50% the abundance of nematode per cm3 is about of 500 individuals (Caixeta et al., 2016). While in humid forest the abundance of nematode may dramatically decay being around 3
5. Conclusions The soils of Atlantic Forests in southeast Brazil are a rich region of biodiversity of eukaryotes, but little known about the diversity and distribution of this fauna in this habitat. Here we show that high throughput sequencing and morphological characterization of eukaryotes are complementary approachs to biodiversity studies and may resolve taxonomic classification when only one of the methods fails. High throughput sequencing and morphological characterization are powerful tools to study the biological diversity and conservation management of Atlantic Forest soils species in Brazil. 5
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