We showed that multiplexing of amplicon libraries for studying the diversity in Our study shows good agreement between the taxonomic composition of the foraminiferal community inferred from DNA and cDNA sequences. All major benthic foraminiferal orders were represented in both datasets, except for the Robertinida found only in the cDNA sequences. Moreover, similar proportions were observed for the same abun-dantly sequenced OTUs shared between the two datasets (Fig. 2.1B). This contrasts with previous analyses of DNA and cDNA data for planktonic eukaryotes, dominated either by Alveolates (43.5%) or Stramenopiles (64.8%), respectively (Not et al., 2009).
This might be due to the use of highly specific primers targeting the foraminifera and thus probably selecting a more homogeneous set of taxa in terms of ribosomal gene cluster copy number as well as population sizes. Indeed, these features have been shown to vary greatly among distant eukaryotic taxa such as those constituting the marine picoplankton Zhu et al. (2005).
Still, there are some clear discrepancies in foraminiferal assemblage inferred from DNA and cDNA sequences. At first, completely different communities assigned to
135° 136° 137° 138° 139°E
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Vladivostok
A1
A2
A3
B1 B3
B2 B4
monothalamiids Rotaliida Textulariida Robertinida Undetermined
Figure 2.2: Taxonomic composition of metagenetic samples along two bathymetric transects from the West margin to the bathyal and abyssal area of the Sea of Japan. For each sample, the outer ring correspond to the cDNA data and the inner ring to the DNA data. Ring sections represent OTUs delineated at 3% divergence, the proportion corresponding to the number of sequences in each OTU. Hatched OTUs were assigned to environmental sequences. The OTUs shared between stations and/or datasets are indicated using colored dots. Sampling locations are indicated by black triangles: A1: st. A2-6; A2: st. A3-5; A3: st. A7-6; B1: st.
B1-5; B2: st. B4-4; B3: st. B5-3; B4: st. B7-2.
single-chambered monothalamids were retrieved depending on the dataset studied. In the DNA dataset we found sequences belonging to undescribed clades of monothalamids exclusively constituted of environmental sequences Pawlowski et al. (2011b). These se-quences were recovered from one of our deepest samples, confirming the presence of these unknown monothalamous lineages in deep, remote areas. In the cDNA dataset, we found sequences assigned to generaAllogromia,Micrometula andBathysiphon, sug-gesting better representation of taxa usually encountered at the deep-sea floor Gooday
A B1
B2
C
Figure 2.3: Alcohol-fixed and Rose Bengal stained specimens of calcareous taxa identified using metatranscriptomic approach. (A) Bolivina pseudopunctata (st. A2-5). (B) Epis-tominella sp. (st. B7-2; B1: apical view; B2: umbilical view). (C) Elphidium batiale (st.
A2-5).
(2002).
Second, the majority of cDNA sequences were assigned to Rotaliida. It is known that the deep-sea rotaliids tend to respond more rapidly and consume fresh, incoming organic matter at a higher rate than do soft-walled, monothalamous foraminifera Enge et al. (2011); Gooday et al. (2008). This could have an impact on higher proportion of rotaliids since active cells produce more RNA molecules than resting cells and thus tend to saturate the resulting sequence data, especially after exponential PCR ampli-fication. Moreover, sampling the first two centimeters constitutes an enrichment in rotaliids that feed on phytodetritus accumulating at the surface of the sediment.
The dominance of rotaliids in the cDNA dataset contrasts with the previous obser-vations of deep-sea sediment being dominated by undetermined and monothalamous foraminifera Lecroq et al. (2011). In fact, the richness of monothalamids exceeded that of rotaliids in one deep station only (A7-6). In this station, we found that 5 OTUs (out of five DNA sequences) were assigned to monothalamous foraminifera while 2 OTUs
(out of 10 sequences) were assigned to Rotaliida. Only 8 OTUs shared between both datasets gather almost 70% of all sequences — most of them belonging to Rotaliida.
This unexpected patterns could be explained by PCR biases preferentially amplifying the species with large population size, as already discussed by Stoeck et al. (2007). The remaining sequences of these shared OTUs corresponded to unknown environmental lineages of textulariids, and like monothalamids were restricted to the deepest stations (A7-6 and B1-5).
Third, the planktonic OTU appears only in the DNA dataset, confirming the pres-ence of irrelevant DNA molecules that need to be excluded prior to deep-sea species richness and diversity estimations. This is critically important on the deep-sea floor, a well-known repository for extracellular DNA material Dell’Anno and Danovaro (2005).
Finally, it appears that the undetermined OTUs were not only very diverse but also mainly represented by cDNA sequences. This suggests that the large, unexplored diver-sity of deep-sea foraminifera revealed at larger sequencing scale by Lecroq et al. (2011) comprise many active rare species. Their proportion may be particularly high since this area was sampled for the first time for molecular study and many foraminiferal species might not be referenced in our current foraminiferal SSU rDNA database. However, we cannot exclude that these divergent sequences could represent natural polymorphisms, PCR artifacts generated by complex chimera, polymerase errors or even extracellular DNA sequences accumulating post-mortem mutations Gilbert et al. (2003a).
It is unlikely that the differences between the DNA and the cDNA datasets are due to the errors of assignation methods. As in a previous study Lecroq et al. (2011), the taxonomic assignment using microbarcode signatures was very successful and only few cases of conflicts and incongruences with global alignments were reported. These signa-tures correspond to the hypervariable region of foraminiferal specific helix 37f located in the 3’ part of the SSU rRNA gene Pawlowski and Lecroq (2010). Given the high divergence of this region both within and among distinct clades, we applied a conser-vative clustering allowing 3% divergence in order to balance possible over-estimation of the diversity due to intra-specific polymorphism Pillet et al. (2012) as well as sequenc-ing errors. Most of the similar sequences already grouped below 3% divergence. For instance, if we further increased the threshold value up to 7% divergence, only one more OTU was found shared between the DNA and cDNA datasets. This was also noticed
by Not et al. (2009), confirming that the OTUs detected in DNA and cDNA datasets are efficiently detected owing to very similar sequences (here with >97% identity), and most probably represent the same active and abundant species. Finally, the identifica-tion method based on foraminiferal taxonomic signatures is not sensitive to biases due to the persistence of undetectable chimeras. Indeed, individual hypervariable regions are not subject to PCR-driven recombination since these events preferentially occur in highly conserved regions Wintzingerode et al. (1997). Thus, a global alignment of a chimera to one of its parents sequence will have a poor score while the signature is conserved and could still be used for taxonomic assignment.
The RNA-derived proxy seem more accurately reveal patterns of distribution of selected taxa. For instance, the genus Robertina observed microscopically in shallow samples was found in the cDNA dataset only. However, the different ability of DNA and cDNA approaches to reveal distribution patterns might be an under-sampling artifact of the cloning approach used in our study. The absence of Robertina DNA sequence in samples A2-6 and B7-2 can be explained by a preferential amplification of some other sequences. On the other hand, the observed distribution patterns based on DNA dataset only, can be biased by the extracellular DNA transportation or preserva-tion of subfossil DNA. A recent study based on next-generapreserva-tion sequencing shows that at nearly exhaustive sampling coverage, using RNA-derived data provides resolution power similar to that of DNA for detecting occurrences, but seems more sensitive at indicating activity level variations of given taxa Logares et al. (2012).
The metatranscriptomic approach provides access to ecosystem activity, and ide-ally to relationships between the living species and their functional roles Bailly et al.
(2007). Furthermore, it is possible to use the number of occurrence of ribosomal RNA transcripts to link species abundance to the magnitude of some key processes. However, this approach requires a serious calibration and more knowledge on the abundance of the selected molecular markers in the genomes of the targeted species Prévost-Bouré et al. (2011). Finally, a proper description of metabolically active microbial eukaryotes should also account for sampling scale and micro-habitat heterogeneity Levin et al.
(2010) and extractions biases Koid et al. (2012).
As evidenced by the reliability of taxonomic assignments, the congruence of abun-dantly sequenced OTUs between the DNA and cDNA datasets and the fact that the
putatively alive, Rose Bengal stained species were exclusively detected using the RNA proxy, it seems that the sequences obtained by the metatranscriptomic approach better reflects the organisms actively contributing to the ecosystem functioning. Taxonomic composition biases that could arise from over-representation of metabolically very ac-tive foraminifera relaac-tively to species responding more slowly to environmental changes could be identified by increasing the sequence coverage. Thus, we believe that RNA-based approaches coupled with high throughput sequencing will shed more light on the actual protistan diversity of deep-sea sediment environments and pave the way toward comprehensive studies of the deep sea bottom ecosystem.