Can Abundance of Protists Be Inferred from Sequence Data: A Case Study of Foraminifera

<div><p>Protists are key players in microbial communities, yet our understanding of their role in ecosystem functioning is seriously impeded by difficulties in identification of protistan species and their quantification. Current microscopy-based methods used for determining the abundance of protists are tedious and often show a low taxonomic resolution. Recent development of next-generation sequencing technologies offered a very powerful tool for studying the richness of protistan communities. Still, the relationship between abundance of species and number of sequences remains subjected to various technical and biological biases. Here, we test the impact of some of these biological biases on sequence abundance of SSU rRNA gene in foraminifera. First, we quantified the rDNA copy number and rRNA expression level of three species of foraminifera by qPCR. Then, we prepared five mock communities with these species, two in equal proportions and three with one species ten times more abundant. The libraries of rDNA and cDNA of the mock communities were constructed, Sanger sequenced and the sequence abundance was calculated. The initial species proportions were compared to the raw sequence proportions as well as to the sequence abundance normalized by rDNA copy number and rRNA expression level per species. Our results showed that without normalization, all sequence data differed significantly from the initial proportions. After normalization, the congruence between the number of sequences and number of specimens was much better. We conclude that without normalization, species abundance determination based on sequence data was not possible because of the effect of biological biases. Nevertheless, by taking into account the variation of rDNA copy number and rRNA expression level we were able to infer species abundance, suggesting that our approach can be successful in controlled conditions.</p> </div

Proportions of cDNA sequences of <i>Allogromia</i>, <i>Rosalina</i>, and <i>Bolivina</i> found in the five mock communities.

<p>(A) Observed proportions of cDNA sequences without normalization; (B) Observed proportions of cDNA sequences after normalization with the ELF (Expression Level Factor, see text for details). Raw and normalized proportions were used for Chi² goodness of fit test (df = 2; α = 0.05). ***: significant at 0.001; **: significant at 0.01; *: significant at 0.05; arrow: not significant, meaning that observed proportions did not differ significantly from expected proportions. Mix 3 - with three cells of each species; mix 10 - with ten cells of each species; mix Allogromia – with thirty cells of <i>Allogromia</i> and three cells of <i>Rosalina</i> and <i>Bolivina</i>; mix Rosalina – with thirty cells of <i>Rosalina</i> and three cells of <i>Allogromia</i> and <i>Bolivina</i>; mix Bolivina – with thirty cells of <i>Bolivina</i> and three cells of <i>Allogromia</i> and <i>Rosalina</i>.</p

Results of qPCR assays.

<p>(A) SSU rDNA copy number estimation per cell per species inferred from qPCR data, based on two replicate measurements. (B) SSU rRNA expression level per cell per species inferred from qPCR data based on one replicate measurement. Error bars are standard deviation.</p

sample sheet - individuals and position on the plate

this excel file shows the pooling of PCR amplicons (38 markers used) for 288 individuals in a 96-well plate. In each well, the amplicons of three individuals were pooled. Each well was sequenced using a Miseq sequencer

raw_miseq_data_2

this folder contains the raw Miseq data for the wells B11 to D9 (two fastq files per well, read 1 and read 2). each well corresponds to 3 individuals, for which 38 markers were amplifie

fasta alignments: 11 genetic markers

This folder contains the fasta alignments of the 11 genetic markers used in this study, for the Ophioderma longicauda clusters C3, C5 and C6. The sequences were obtained using Miseq sequencing