Assessment of REPLI-g Multiple Displacement Whole Genome Amplification (WGA) Techniques for Metagenomic Applications

Abstract

Amplification of minute quantities of DNA is a fundamental challenge in low-biomass metagenomic and microbiome studies because of potential biases in coverage, guanine-cytosine (GC) content, and altered species abundances. Whole genome amplification (WGA), although widely used, is notorious for introducing artifact sequences, either by amplifying laboratory contaminants or by nonrandom amplification of a sample's DNA. In this study, we investigate the effect of REPLI-g multiple displacement amplification (MDA; Qiagen, Valencia, CA, USA) on sequencing data quality and species abundance detection in 8 paired metagenomic samples and 1 titrated, mixed control sample. We extracted and sequenced genomic DNA (gDNA) from 8 environmental samples and compared the quality of the sequencing data for the MDA and their corresponding non-MDA samples. The degree of REPLI-g MDA bias was evaluated by sequence metrics, species composition, and cross-validating observed species abundance and species diversity estimates using the One Codex and MetaPhlAn taxonomic classification tools. Here, we provide evidence of the overall efficacy of REPLI-g MDA on retaining sequencing data quality and species abundance measurements while providing increased yields of high-fidelity DNA. We find that species abundance estimates are largely consistent across samples, even with REPLI-g amplification, as demonstrated by the Spearman's rank order coefficient (R² > 0.8). However, REPLI-g MDA often produced fewer classified reads at the species, genera, and family level, resulting in decreased species diversity. We also observed some areas with the PCR "jackpot effect," with varying input DNA values for the Metagenomics Research Group (MGRG) controls at specific genomic loci. We visualize this effect in whole genome coverage plots and with sequence composition analyses and note these caveats of the MDA method. Despite overall concordance of species abundance between the amplified and unamplified samples, these results demonstrate that amplification of DNA using the REPLI-g method has some limitations. These concerns could be addressed by future improvements in the enzymes or methods for REPLI-g to be considered a >99% robust method for increasing the amount of high-fidelity DNA from low-biomass samples or at the very least, accounted for during computational analysis of MDA samples.

Keywords: REPLI-g; jackpot effect; metagenomics; next-generation sequencing; whole genome amplification.

FIGURE 1.

FastQC quality plots and nucleotide composition with REPLI-g for Thunderbolt rollercoaster samples. (Top) Sequence quality scores (x-axis) were plotted as a function of the number of reads with each bin (y-axis). (Bottom) The percentage of bases (A, C, G, T) for each bin of sequence length (y-axis) was shown before (left) and after (right) REPLI-g reactions, with the composition only showing slight changes.

FIGURE 2.

Heatmaps of MetaPhlAn analysis. (A) Top-species hits across the Coney Island dataset. Note the differences and similarities between the REPLI-g-amplified and unamplified data. (B) Top-species hits across the MGRG standard at the various DNA input levels.

FIGURE 3.

MetaPhlAn and One Codex species abundance overlap. (A–D) The MetaPhlAn results of the MDA and non-MDA carousel floor, Ferris wheel door, railing, and Thunderbolt rollercoaster samples. Orange represents the REPLI-g-amplified sample, whereas blue represents the unamplified sample. Note that in most cases, the REPLI-g hits fall in the unamplified hits, with 6 and 1 species being found only in the REPLI-g sample and not in the unamplified results (A and D, respectively). (E) The high concordance of the presence of species before and after amplification is shown, as well as some loss of species diversity after amplification.

FIGURE 4.

Spearman rank R². The Spearman rank was consistently above 0.94 (R² range was 0.94–0.99), which indicates high concordance of species abundance between the amplified and unamplified pairwise samples. Note how the samples cluster by type.

FIGURE 5.

One Codex whole genome coverage plots for MGRG DNA. These coverage plots illustrate the alignment of the samples’ reads against the M. luteus genome. (A) The unamplified MGRG sample has the most even coverage, which is indicated by its classification as an isolate/low-complexity sample comprised of 59.22% of reads (n = 1,745,213) specific to M. luteus. The depth is 210.5 ± 81.6× with 99.5% coverage and 97.5% of reads aligned to reference genomes. (B) The MGRG 10 ng sample has a depth is 24.6 ± 243.6× with 38.3% of the genome covered and 98.1% of identity. (C) The MGRG 5 ng sample had 75.99% of 2,478,166 as classified reads. The depth is 18.8 ± 188.9× with 30.5% coverage and 98.1% identity. (D) The MGRG 1 ng sample was classified as a mixed/metagenomic sample, where 75.53% of 2,811,864 reads are classified. The depth is 23.5 ± 1267.2× with 24.1% coverage and 98.1% identity. (E) The MGRG 0.5 ng sample had 68.9% of 2,814,482 reads classified and 7.8 ± 1106.8× depth, 13.3% coverage, and 97.9% identity.