Ultrasequencing of the meiofaunal...
Ultrasequencing of the meiofaunal biosphere: practice, pitfalls and promises S. CREER,* V. G. FONSECA,* D. L. PORAZINSKA,��� R. M. GIBLIN-DAVIS,��� W. SUNG,��� D. M. POWER,�� M. PACKER,��� G. R. CARVALHO,* M. L. BLAXTER,** P. J. D. LAMBSHEAD������ and W. K. THOMAS��� *School of Biological Sciences, Environment Centre Wales, Deiniol Road, College of Natural Sciences, Bangor University, Gwynedd LL57 2UW, UK, ���Fort Lauderdale Research and Education Center, University of Florida, IFAS, 3205 College Avenue, Fort Lauderdale, FL 33314, USA, ���Hubbard Center for Genome Studies, University of New Hampshire, 35 Colovos Rd, Durham, NH 03824, USA, ��Centre of Marine Sciences, CCMAR���CIMAR Associate Laboratory, University of Algarve, Gambelas, 8005-139 Faro, Portugal, ���Department of Zoology, The Natural History Museum, Cromwell Road, London SW7 5BD, UK, **Institute of Evolutionary Biology, Ashworth Laboratories, University of Edinburgh, King���s Buildings, West Mains Road, Edinburgh EH9 3JT, UK, ������School of Ocean and Earth Science, University of Southampton, National Oceanography Centre, European Way, Southampton, SO14 3ZH, UK Abstract Biodiversity assessment is the key to understanding the relationship between biodiver- sity and ecosystem functioning, but there is a well-acknowledged biodiversity identi- fication gap related to eukaryotic meiofaunal organisms. Meiofaunal identification is confounded by the small size of taxa, morphological convergence and intraspecific variation. However, the most important restricting factor in meiofaunal ecological research is the mismatch between diversity and the number of taxonomists that are able to simultaneously identify and catalogue meiofaunal diversity. Accordingly, a molecular operational taxonomic unit (MOTU)-based approach has been advocated for en mass meiofaunal biodiversity assessment, but it has been restricted by the lack of throughput afforded by chain termination sequencing. Contemporary pyrosequencing offers a solution to this problem in the form of environmental metagenetic analyses, but this represents a novel field of biodiversity assessment. Here, we provide an overview of meiofaunal metagenetic analyses, ranging from sample preservation and DNA extraction to PCR, sequencing and the bioinformatic interrogation of multiple, independent samples using 454 Roche sequencing platforms. We report two examples of environ- mental metagenetic nuclear small subunit 18S (nSSU) analyses of marine and tropical rainforest habitats and provide critical appraisals of the level of putative recombinant DNA molecules (chimeras) in metagenetic data sets. Following stringent quality control measures, environmental metagenetic analyses achieve MOTU formation across the eukaryote domain of life at a fraction of the time and cost of traditional approaches. The effectiveness of Roche 454 sequencing brings substantial advantages to studies aiming to elucidate the molecular genetic richness of not only meiofaunal, but also all complex eukaryotic communities. Keywords: 454 environmental sequencing, meiofaunal and eukaryotic biodiversity, metagenet- ics, metagenomics Received 19 June 2009 revision received 19 August 2009 accepted 21 August 2009 Introduction Robust, quantified biodiversity assessment is the key to deep understanding of the relationship between biodi- Correspondence: Simon Creer, Fax: +44(0)1248 382569 E-mail: email@example.com S. Creer and V. G. Fonseca are joint first authors of this work. �� 2010 Blackwell Publishing Ltd Molecular Ecology (2010), 19 (Suppl. 1), 4���20 doi: 10.1111/j.1365-294X.2009.04473.x
versity and ecosystem functioning. The effects of major anthropogenic stressors on global ecosystems, including elevated CO2, pollution, habitat loss and fragmentation, add urgency to this field, demanding an increasing focus on mechanistic and predictive studies. However, investigating the role of biodiversity in maintaining eco- system function, resilience and recovery (Sutherland et al. 2006) can be meaningfully addressed only if biodi- versity can first be identified. The identity of macrofa- unal and floral communities can be ascertained by teams of trained taxonomists ��� ecologists with their skills being augmented by globally integrated molecular bar- coding approaches (Hebert et al. 2003a Hajibabaei et al. 2007). Similarly, recent advances in sequencing power and the molecular identification of microbes are facili- tating the more realistic characterization of the phyloge- netic affinities, identity (DNA sequences), composition (Sogin et al. 2006 Huber et al. 2007), dynamics and even functional capacity (Edwards et al. 2006 Mou et al. 2008) of prokaryotic communities. The application of second-generation sequencing has also been applied to the identification of protist communities in this edition (Medinger et al. 2010 Stoeck et al. 2010). There remains, however, a well-acknowledged biodiversity identification gap related to eukaryotic meiofaunal organisms (Blaxter 2003 Blaxter & Floyd 2003 Tautz et al. 2003 Blaxter et al. 2005). Meiofaunal taxa are a paraphyletic assemblage, grouped on the basis of size (i.e. organisms that pass through a 0.5-mm sieve but are retained on 25���65 lm sieves). Approximately 60% of animal phyla have meio- faunal representatives and meiofaunal Platyhelminthes, Nemertea, Nematoda, Rotifera, Annelida, Arthropoda, Tardigrada, Mollusca and Chordata have taxa that occupy key roles in marine, freshwater and terrestrial habitats (Higgins & Thiel 1988 Giere 2009). Meiofaunal assemblages are dominated by nematodes and are char- acterized by high abundances (up to 108 individuals per 1 m2) and diversity (up to 60 morphological species per 75 cm3 of sediment) (Lambshead 2004). Thus, although meiofaunal organisms are conceptually and demonstra- bly ecologically important (Snelgrove et al. 1997 Danov- aro et al. 2008), current estimates of global species richness remain a matter of conjecture (Lambshead & Boucher 2003). For nematodes, global estimates of species richness range from 100 000 to 1 000 000, but only 27 000 species have been described (Platt & Warwick 1983 Coomans 2000 Hugot et al. 2001), and contempo- rary studies routinely recover between 30% and 40% of sampled taxa that are new to science (Lambshead & Bou- cher 2003). Meiofaunal taxon diversity and abundance is so great that effectively studying communities requires a huge investment in resources and labour. The effort expended in assigning only 10% of nematodes to known species was 120-fold that required to successfully assign all vertebrate morphospecies to known taxa (Lawton et al. 1998) in tropical forest habitats. The identification bottleneck associated with meiofa- unal taxonomy is confounded by a range of taxonomic hurdles: the small size and fragility of organisms, con- vergent evolution, morphological conservatism (Dery- cke et al. 2005, 2008 Bhadury et al. 2008 Fontaneto et al. 2009) and developmental and sexual variation in morphology (Tautz et al. 2003 Lambshead 2004 Blaxter et al. 2005). Perhaps the most restricting factor in meio- faunal research is the mismatch between the diversity and abundance of multiple phyla occupying a range of ecological niches and habitats and the number of taxon- omists that are able to simultaneously identify and cata- logue meiofaunal diversity. In order to address this impediment, it has been suggested that en mass molecu- lar identification of meiofaunal communities may signif- icantly advance knowledge and progress in meiofaunal research (Blaxter & Floyd 2003 Markmann & Tautz 2005). Although the molecular identification of meiofa- unal communities shares similarities with current molecular barcoding (Hebert et al. 2003a,b) and micro- bial phylotype approaches (Kemp & Aller 2004 Shaw et al. 2008), there remains a difference in methodology, taxonomic richness and diversity. Phylotypes, molecular operational taxonomic units and barcoding for the identification of biodiversity With a molecular barcoding approach, a standardized homologous region of the genome [e.g. the mitochon- drial cytochrome oxidase subunit I gene (COI) for ani- mals] is used for species identification, and is linked to a virtual or actual physical molecular voucher specimen (Hebert et al. 2003a Ratnasingham & Hebert 2007). However, when dealing with individuals or communi- ties of microscopic organisms, the whole voucher speci- mens are usually sacrificed in order to extract genomic DNA (Blaxter et al. 2005 De Ley et al. 2005). Advances in video capture technology of microscopic organisms (De Ley et al. 2005) and individual organismal PCRs (Floyd et al. 2002, 2005 De Ley et al. 2005 Bhadury et al. 2006 Meldal et al. 2007) can overcome this prob- lem and forge a link between taxon ecology ��� morphol- ogy and community-based DNA analyses. Such research provides potential for linking taxonomy, phylogeny (Forest et al. 2007 Warwick & Somerfield 2008), func- tional (Petchey & Gaston 2006) and molecular ecology. It also effectively engages and links morphological taxono- mists with molecular ecologists, a connection that will be vital for a holistic approach towards ecosystem-based research. However, standard barcoding approaches are ULTRASEQUENCING THE MEIOFAUNAL BIOSPHERE 5 �� 2010 Blackwell Publishing Ltd
not appropriate for large-scale environmental analyses mainly because of extensive abundances and putative hyperdiversity of some taxa (e.g. nematodes, Lambshead 2004 Lambshead & Boucher 2003). Further to this, the extent of taxonomic coverage and lack of taxonomic expertise, manpower and resources makes the task of barcoding environmental samples inefficient. Instead, the proposed identification of operational taxonomic units (OTUs) in eukaryotic metagenetic anal- yses has more in common with prokaryotic phylotype (Kemp & Aller 2004) delineation than with species iden- tification using standardized barcoding approaches. The term metagenomics is sometimes used to consider the analysis of any environmentally derived genomic DNA (Hugenholtz & Tyson 2008). Here though, we distin- guish between metagenetics, the large-scale analysis of taxon richness via the analysis of homologous genes, and metagenomics, the functional analysis of environ- mentally derived DNA from unculturable organisms (Edwards et al. 2006 Rodriguez-Brito et al. 2006 Blow 2008 Hugenholtz & Tyson 2008 Mou et al. 2008). Bacterial phylotypes are groups of sequences that are created by subjecting a larger community of sample- derived shotgun sequences to a user-defined base pair cutoff algorithm. In most cases, phylotypes of a particu- lar grouping (e.g. 97% for bacteria, Venter et al. 2004 Shaw et al. 2008) are used as a proxy for ���species���. Although microbial communities can be orders of mag- nitude more diverse than micro-eukaryotic communi- ties, the similarities of their intractable community compositions have led to similar approaches in study- ing eukaryotic protists (Moon-van der Staay et al. 2001 Moreira & Lopez-Garcia 2002) and meiofaunal organ- isms (Floyd et al. 2002 Blaxter & Floyd 2003). For meio- faunal organisms, Floyd et al. (2002) formally defined the molecular operational taxonomic unit (MOTU) con- cept whereby sequences derived from individual speci- mens are defined as belonging to the same MOTU, based on a user-defined cutoff. The term was later extended to community DNA extractions in Blaxter et al. (2005). Normally, the MOTUs do not have any formal correlation with published species descriptions. However, correlations can be achieved by de novo eluci- dation of cryptic species (Abebe & Blaxter 2003), bioin- formatic sequence comparisons to existing databases (with both molecular and morphological data), further sequencing or future classifications, termed ���reverse tax- onomy��� (Markmann & Tautz 2005). Environmental metagenetics Until recently, most molecular identification was achieved using Sanger chain-termination sequencing (Kemp & Aller 2004 Venter et al. 2004). However, there has recently been a rise in the use of ultrasequencing platforms (Margulies et al. 2005) for metagenetic identi- fication of microbial phylotypes using homologous gene regions (Sogin et al. 2006 Hall 2007 Huber et al. 2007) derived from environmental DNA. The recent increases in sequencing throughput represent a significant shift in our ability to disentangle the biotic complexity of ecosys- tems. From sample collection to data analysis, there are numerous steps, questions and an exponentially large number of hypotheses that could be tested in order to optimally analyse environmental meiofaunal diversity. Here, we first provide an overview of the relevant focal areas in an attempt to highlight potential approaches and pitfalls in meiofaunal metagenetics. Sec- ond, we present independent data sets derived from ultrasequencing experiments of two different ecological communities the marine benthos and tropical rain forest habitats. By overviewing separate approaches to envi- ronmental metagenetics, we aim to illustrate a range of protocols that can be utilized to analyse contrasting, yet hitherto, inaccessible meiofaunal communities on a scale that has previously not been possible. We aim to illus- trate the advantages and limitations of ultrasequencing approaches in addressing large-scale identification of complex eukaryotic communities. Furthermore, we introduce a bioinformatic pipeline that can be used to analyse the data, derived from different but closely related genomic regions, in a computationally expedient fashion. The tropical rain forest case study predomi- nantly targeted nematodes, whereas the marine example targeted collective meiofauna (extended to include organisms ranging from 45 to 1000 lm in size). The approaches and data presented here do not test specific hypotheses regarding metagenetic analyses, but are intended to provide a resource that will be useful to researchers wishing to pursue similar research. Although meiofaunal organisms are the primary focus, the general principles are easily transferrable to other eukaryotic as well as prokaryotic taxa. Methodological overview and rationale Sample preservation and extraction Once an ecologically suitable sampling strategy has been designed, an appropriate decision needs to be made regarding sample processing. Given the diverse and dynamic nature of the micro- and meiofauna, it is predicted that after removing a small subsample of the community, a natural progression of ecological interac- tions will change the population composition. It is therefore important to either preserve or process sam- ples shortly after collection. Some experiments (e.g. those with small sample sizes or local collection 6 S. CREER ET AL. �� 2010 Blackwell Publishing Ltd