PyroTRF-ID: a novel bioinformatics methodology for the affiliation of terminal-restriction fragments using 16S rRNA gene pyrosequencing data

Abstract

BackgroundIn molecular microbial ecology, massive sequencing is gradually replacing classical fingerprinting techniques such as terminal-restriction fragment length polymorphism (T-RFLP) combined with cloning-sequencing for the characterization of microbiomes. Here, a bioinformatics methodology for pyrosequencing-based T-RF identification (PyroTRF-ID) was developed to combine pyrosequencing and T-RFLP approaches for the description of microbial communities. The strength of this methodology relies on the identification of T-RFs by comparison of experimental and digital T-RFLP profiles obtained from the same samples. DNA extracts were subjected to amplification of the 16S rRNA gene pool, T-RFLP with the HaeIII restriction enzyme, 454 tag encoded FLX amplicon pyrosequencing, and PyroTRF-ID analysis. Digital T-RFLP profiles were generated from the denoised full pyrosequencing datasets, and the sequences contributing to each digital T-RF were classified to taxonomic bins using the Greengenes reference database. The method was tested both on bacterial communities found in chloroethene-contaminated groundwater samples and in aerobic granular sludge biofilms originating from wastewater treatment systems.ResultsPyroTRF-ID was efficient for high-throughput mapping and digital T-RFLP profiling of pyrosequencing datasets. After denoising, a dataset comprising ca. 10′000 reads of 300 to 500 bp was typically processed within ca. 20 minutes on a high-performance computing cluster, running on a Linux-related CentOS 5.5 operating system, enabling parallel processing of multiple samples. Both digital and experimental T-RFLP profiles were aligned with maximum cross-correlation coefficients of 0.71 and 0.92 for high- and low-complexity environments, respectively. On average, 63±18% of all experimental T-RFs (30 to 93 peaks per sample) were affiliated to phylotypes.ConclusionsPyroTRF-ID profits from complementary advantages of pyrosequencing and T-RFLP and is particularly adapted for optimizing laboratory and computational efforts to describe microbial communities and their dynamics in any biological system. The high resolution of the microbial community composition is provided by pyrosequencing, which can be performed on a restricted set of selected samples, whereas T-RFLP enables simultaneous fingerprinting of numerous samples at relatively low cost and is especially adapted for routine analysis and follow-up of microbial communities on the long run.

Technicalities and Glitches of Terminal Restriction Fragment Length Polymorphism (T-RFLP)

“…A number of tools have been developed for the handling of raw T-RFLP data [32,45,46]. Currently, a number of online tools like TAP-T-RFLP (http://rdp.cme.msu.edu), torast (http://www.torast.de), MiCA (http://mica.ibest.…”

- Taxonomic Precision of Different Hypervariable Regions of 16S rRNA Gene and Annotation Methods for Functional Bacterial Groups in Biological Wastewater Treatment

“…OTUs were affiliated to phylotypes with the pyrosequencingbased PyroTRF-ID bioinformatics methodology (Weissbrodt et al, 2012b) applied to biomass grab samples collected on days 2 and 59 (BC-SBR), 109 (PAO-SBR), and 398 (GAO-SBR). The DNA extracts of these samples were sent to Research and Testing Laboratory LLC (Lubbock, TX, USA) for pyrosequencing analysis according to the protocol developed by the company (Sun et al, 2011).…”

“…Phylotypes were identified with the PyroTRF-ID methodology (Weissbrodt et al, 2012b) and were compiled for each reactor in Supplementary material 5. In the BC-SBR, Dechloromonas was mainly contributing to OTU-214 during the first 60 days, whereas Accumulibacter was mainly contributing to this OTU in the mature granular sludge.…”

“…The pyrosequencing dataset of a mature AGS sample (BC-II) originating from a precedent reactor operated under similar conditions was used to affiliate OTUs at later stage in the BC-SBR. Technical and biological replicates previously measured in Weissbrodt et al (2012b) demonstrated that the pyrosequencing-based PyroTRF-ID method is reliable.In order to obtain estimates of the evolution of the mass of target OTUs, biomass equivalents were calculated by multiplying the relative abundances of these OTUs with the VSS concentration, according to Weissbrodt et al (2012a). It should nevertheless be pointed out that such calculations are only very rough estimations since the number of 16S rRNA copies per cell can range from 1 to 15 depending on the genome and that cell biomass vary between bacterial species (Kembel et al, 2012).…”

“…Richness and Shannon's H' diversity indices were computed in R in order to assess the impact of operation conditions on the overall bacterial community structures (R Development Core Team, 2008). Three biomass samples were analyzed in triplicates, leading to a relative standard deviation of 6% on the T-RFLP method.OTUs were affiliated to phylotypes with the pyrosequencingbased PyroTRF-ID bioinformatics methodology (Weissbrodt et al, 2012b) applied to biomass grab samples collected on days 2 and 59 (BC-SBR), 109 (PAO-SBR), and 398 (GAO-SBR). The DNA extracts of these samples were sent to Research and Testing Laboratory LLC (Lubbock, TX, USA) for pyrosequencing analysis according to the protocol developed by the company (Sun et al, 2011).…”