title: The bias associated with amplicon sequencing does not affect the quantitative assessment of bacterial community dynamics.
abstract: The performance of two sets of primers targeting variable regions of the 16S rRNA gene V1-V3 and V4 was compared in their ability to describe changes of bacterial diversity and temporal turnover in full-scale activated sludge. Duplicate sets of high-throughput amplicon sequencing data of the two 16S rRNA regions shared a collection of core taxa that were observed across a series of twelve monthly samples, although the relative abundance of each taxon was substantially different between regions. A case in point was the changes in the relative abundance of filamentous bacteria Thiothrix, which caused a large effect on diversity indices, but only in the V1-V3 data set. Yet the relative abundance of Thiothrix in the amplicon sequencing data from both regions correlated with the estimation of its abundance determined using fluorescence in situ hybridization. In nonmetric multidimensional analysis samples were distributed along the first ordination axis according to the sequenced region rather than according to sample identities. The dynamics of microbial communities indicated that V1-V3 and the V4 regions of the 16S rRNA gene yielded comparable patterns of: 1) the changes occurring within the communities along fixed time intervals, 2) the slow turnover of activated sludge communities and 3) the rate of species replacement calculated from the taxa-time relationships. The temperature was the only operational variable that showed significant correlation with the composition of bacterial communities over time for the sets of data obtained with both pairs of primers. In conclusion, we show that despite the bias introduced by amplicon sequencing, the variable regions V1-V3 and V4 can be confidently used for the quantitative assessment of bacterial community dynamics, and provide a proper qualitative account of general taxa in the community, especially when the data are obtained over a convenient time window rather than at a single time point.
authors.affiliation: Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET), Buenos Aires, Argentina.
authors.forename: Federico M
authors.initials: FM
authors.lastname: Ibarbalz
authors.affiliation: Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET), Buenos Aires, Argentina; Agua y Saneamientos Argentinos S.A. (AySA), Buenos Aires, Argentina.
authors.forename: María Victoria
authors.initials: MV
authors.lastname: Pérez
authors.affiliation: Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET), Buenos Aires, Argentina.
authors.forename: Eva L M
authors.initials: EL
authors.lastname: Figuerola
authors.affiliation: Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET), Buenos Aires, Argentina; Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.
authors.forename: Leonardo
authors.initials: L
authors.lastname: Erijman
mesh_terms: D001419:Bacteria / Q000145:classification* / Q000235:genetics* / Q000254:growth & development*; D015982:Bias; D044822:Biodiversity; D005784:Gene Amplification / Q000235:genetics*; D005798:Genes, Bacterial; D020131:Genes, Duplicate; D020459:Genes, rRNA; D059014:High-Throughput Nucleotide Sequencing; D059013:Microbial Consortia / Q000235:genetics*; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods / Q000706:statistics & numerical data*; D012722:Sewage / Q000032:analysis / Q000382:microbiology
chemical_list: D012336:RNA, Ribosomal, 16S; D012722:Sewage