The Foraminifera model

The Foraminifera is a hyper-diverse protistan phylum belonging to the Rhizaria, and thus to the supergroup SAR (Stramenopiles, Alveolates, Rhizaria) (Fig. 1.1). Foraminifer-ans are single-celled eukaryotes that have been characterized by morphological features such as the granuloreticulopods as well as the external calcareous or agglutinated multi-chambered tests Sen Gupta (1999). Yet, most of the foraminiferal diversity corresponds to species that do not synthesize a calcium carbonate test or that do not form multi-chambered tests, but instead construct organic-walled or agglutinated single-multi-chambered tests, or no test at all (i.e. they are naked cells). The huge diversity of these intriguing monothalamous (single-chambered) foraminiferans is due to their very ancient evolu-tionary origins, dated back to the Precambrian period Pawlowski et al. (2003). Al-though ’soft-walled’ monothalamids are common in marine benthic environments and occur in freshwater Holzmann et al. (2003) and even in soils Lejzerowicz et al. (2010);

Meisterfeld et al. (2001), their diversity and ecological role remains poorly known. In-deed, few species are formally described and only one foraminiferal genome is currently available, that of the giant, nakedReticulomyxa filosa Glöckner et al. (2014). We were able to maintain R. filosa in culture in order to use it as internal control in our first HTS survey (Chapter 3).

Although the ecology of benthic foraminferans remains largely unknown, they have been shown to play a key role in the cycling of nitrate Bernhard et al. (2012) and carbon Gooday et al. (1992). Foraminiferans may constitute interesting proxies for the exploration of various environmental gradients, given their ability to respire nitrate Piña-Ochoa et al. (2010); Risgaard-Petersen et al. (2006), to resist to anoxic stress Langlet et al. (2014), or to the toxicity of heavy metals Frontalini et al. (2009); Le Cadre and Debenay (2006). The sensitivity and omnipresence of foraminifera justify current research efforts towards their development as novel bioindicators based on benthic foraminferans Barras et al. (2014). In the framework of this thesis, we tested the HTS metabarcoding tool for assessing the response of benthic foraminifera to organic pollution exposure, in order to develop the monitoring of marine fish farming activities (Chapter 9).

The molecular phylogeny greatly contributed to the revision of foraminiferal classi-fication. Even though rDNA data undeniably contributed to establish the monophyly of some foraminiferal classes, such as the Globothalamea and Tubothalamea Pawlowski et al. (2013), the monothalamids still form a messy paraphyletic group that remains

Figure 1.4: Foraminiferal SSU rDNA hypervariables regions. The first 30 positions of the first five hypervariable regions examined in Pawlowski and Lecroq (2010) are represented for each polythalamous and monothalamous clades identified in Pawlowski et al. (2013) and Pawlowski et al. (2011b), respectively. Each region is represented by a concentric ring, from the 5’-end (inside) to the 30^thposition in 3’ (outside). The 37f region corresponds to the inner-most ring. Each clade corresponds to a separate blocks, colored according to a taxon (red:

monothalamids, bleu: Rotaliida, green: Textulariida, black: Miliolida, yellow: Lagenida, grey: Robertinida). Unpublished results.

to be resolved Pawlowski et al. (2003). Hence, the definition of the taxonomic ranks to which environmental HTS sequences could be assigned relies on the definition of clades resulting from phylogenetic analyses of Sanger-sequencing data obtained either from specimens collected in the field or from bulk sediments using cloning-based metabar-coding (Habura et al., 2004, 2008). For the work presented in this thesis, I mostly relied on the clades defined by Pawlowski et al. (2011b), and thus on a custom, cu-rated reference database composed of published and unpublished SSU rDNA sequences.

0 500 1000 1500 2000 2500

Entropy signal of SSU rDNA foraminiferal multiple sequence aligment

position (5'−>3') 0

0.2 0.4 0.6 0.81 1.2 1.4

entropy

Figure 1.5: Entropy profile of foraminiferal SSU rDNA sequence alignment. The higher the entropy score, the more variable the alignment position. The 37f hypervariable region is highlighted by a red rectangle Unpublished results.

The taxonomic signal contained in the various hypervariable regions of the foraminiferal SSU rDNA has been investigated previously Pawlowski and Lecroq (2010), and a graph-ical representation of the first positions of each of these regions suffice to demonstrate that some regions are more resolutive than others within or across taxa (Fig. 1.4). In practical terms for HTS metabarcoding, it is noteworthy that Foraminifera possess a synapomorphic rRNA hypervariable region referred to as the 37f region. This region provides the best resolution for species-level identifications. Indeed, it represents a hot-spot of sequence variation, as illustrated by the entropy profile computed from a multiple sequence alignment (Fig. 1.5). Moreover, it is short and bounded by two highly conserved regions, which allowed the design of highly specific PCR primers for the amplification of foraminiferal sequences exclusively (forward primer: Pawlowski et al. (2002a) and reverse primer: Chapter 6).

In our first HTS study, we used the sequence signatures represented by the first 30 nt of the 37f region to perform species-level assignments without conflict, based on in-formation derived from our reference database (Chapter 3). Later, we only used these

signatures to pre-cluster environmental sequences into higher-level foraminiferal taxon bins. Indeed, as explained above and illustrated for ciliates (see Fig. 1.2), using the distances of global alignments could be misleading, especially for foraminifera because

0 5.73

Cassidulinidae UvigerinidaeClade3 BuliminidaeCalcarinidaeNummulitidaeRosalinidaeRotaliida-X SphaeroidinidaeRotaliidae

Elphidiidae Bolivinidae

Glabratellidae Rotaliellidae

Figure 1.6: Foraminiferal reference sequence variability and HTS sequence assignment depth.

For each foraminiferal family (or clade) in the cladogram are indicated the assignment depth of environmental OTUs (inner heatmap) and reads (outer heatmap), as well as the number (barplots) and variability (dots) of reference sequences. The assignment depths are indicated at the family (inner blocks), genus (middle blocks), and species (outer blocks) levels. The intra-clade (Kmax) and the inter-clade (Kmin) variability is represented along with the num-ber of unique reference sequences ranging from 77 (Clade3) to 1 (Rotaliellidae). The orders and classes are color-coded: monothalamiids (red), Spirillinida (violet), Miliolida (yellow), Textulariida (green), and Rotaliida (blue). From Pawlowski et al. (2014a)

the 37f sequences are short (Fig. 1.6). Hence, our analysis pipeline is based on the definition of pre-cluster specific thresholds for OTU clustering and taxonomic assign-ment as explained in Pawlowski et al. (2014a) and Lejzerowicz et al. (2014) (Chapter 5).

Foraminiferans are highly diverse in deep-sea sediment settings, and include numer-ous inconspicunumer-ous monothalamnumer-ous species Gooday et al. (2008); Lecroq et al. (2011);

Pawlowski et al. (2011b). This thesis reports our molecular contribution to the grow-ing body of evidence that monothalamids dominate deep-sea sediments foraminiferal assemblages (Chapter 3) and that their distribution is affected by the heterogeneity of sediment microhabitats (Chapter 5).