Influence of DNA extraction and PCR amplification on studies of soil fungal communities based on amplicon sequencing

Most studies involving next-generation amplicon sequencing of microbial communities from environmental studies lack replicates. DNA extraction and PCR effects on the variation of read abundances of operational taxonomic units generated from deep amplicon 454 pyrosequencing was investigated using soil samples from an agricultural field with diseased pea. One sample was extracted four times, and one of these samples was PCR amplified four times to obtain eight replicates in total. Results showed that species richness was consistent among replicates. Variation among dominant taxa was low across replicates, whereas rare operational taxonomic units showed higher variation among replicates. The results indicate that pooling of several extractions and PCR amplicons will decrease variation among samples.     

Improved Inference of Taxonomic Richness from Environmental DNA

Accurate estimation of biological diversity in environmental DNA samples using high-throughput amplicon pyrosequencing must account for errors generated by PCR and sequencing. We describe a novel approach to distinguish the underlying sequence diversity in environmental DNA samples from errors that uses information on the abundance distribution of similar sequences across independent samples, as well as the frequency and diversity of sequences within individual samples. We have further refined this approach into a bioinformatics pipeline, Amplicon Pyrosequence Denoising Program (APDP) that is able to process raw sequence datasets into a set of validated sequences in formats compatible with commonly used downstream analyses packages. We demonstrate, by sequencing complex environmental samples and mock communities, that APDP is effective for removing errors from deeply sequenced datasets comprising biological and technical replicates, and can efficiently denoise single-sample datasets. APDP provides more conservative diversity estimates for complex datasets than other approaches; however, for some applications this may provide a more accurate and appropriate level of resolution, and result in greater confidence that returned sequences reflect the diversity of the underlying sample.     

Detection limits of quantitative and digital PCR assays and their influence in presence–absence surveys of environmental DNA

A set of universal guidelines is needed to determine the limit of detection (LOD) in PCR‐based analyses of low‐concentration DNA. In particular, environmental DNA (eDNA) studies require sensitive and reliable methods to detect rare and cryptic species through shed genetic material in environmental samples. Current strategies for assessing detection limits of eDNA are either too stringent or subjective, possibly resulting in biased estimates of species’ presence. Here, a conservative LOD analysis grounded in analytical chemistry is proposed to correct for overestimated DNA concentrations predominantly caused by the concentration plateau, a nonlinear relationship between expected and measured DNA concentrations. We have used statistical criteria to establish formal mathematical models for both quantitative and droplet digital PCR. To assess the method, a new Grass Carp (Ctenopharyngodon idella) TaqMan assay was developed and tested on both PCR platforms using eDNA in water samples. The LOD adjustment reduced Grass Carp occupancy and detection estimates while increasing uncertainty—indicating that caution needs to be applied to eDNA data without LOD correction. Compared to quantitative PCR, digital PCR had higher occurrence estimates due to increased sensitivity and dilution of inhibitors at low concentrations. Without accurate LOD correction, species occurrence and detection probabilities based on eDNA estimates are prone to a source of bias that cannot be reduced by an increase in sample size or PCR replicates. Other applications also could benefit from a standardized LOD such as GMO food analysis and forensic and clinical diagnostics.     

A fast,highly sensitive double‐nested PCR‐based method to screen fish immunobiomes

Efficient methods for constructing 16S tag amplicon libraries for pyrosequencing are needed for the rapid and thorough screening of infectious bacterial diversity from host tissue samples. Here we have developed a double‐nested PCR methodology that generates 16S tag amplicon libraries from very small amounts of bacteria/host samples. This methodology was tested for 133 kidney samples from the lake whitefish Coregonus clupeaformis (Salmonidae) sampled in five different lake populations. The double‐nested PCR efficiency was compared with two other PCR strategies: single primer pair amplification and simple nested PCR. The double‐nested PCR was the only amplification strategy to provide highly specific amplification of bacterial DNA. The resulting 16S amplicon libraries were synthesized and pyrosequenced using 454 FLX technology to analyse the variation of pathogenic bacteria abundance. The proportion of the community sequenced was very high (Good’s coverage estimator; mean = 95.4%). Furthermore, there were no significant differences of sequence coverage among samples. Finally, the occurrence of chimeric amplicons was very low. Therefore, the double‐nested PCR approach provides a rapid, informative and cost‐effective method for screening fish immunobiomes and most likely applicable to other low‐density microbiomes as well.     

Accurate multiplexing and filtering for high-throughput amplicon-sequencing

Tagging amplicons with tag sequences appended to PCR primers allow the multiplexing of numerous samples for high-throughput sequencing (HTS). This approach is routinely used in HTS-based diversity analyses, especially in microbial ecology and biomedical diagnostics. However, amplicon library preparation is subject to pervasive sample sequence cross-contaminations as a result of tag switching events referred to as mistagging. Here, we sequenced seven amplicon libraries prepared using various multiplexing designs in order to measure the magnitude of this phenomenon and its impact on diversity analyses. Up to 28.2% of the unique sequences correspond to undetectable (critical) mistags in single- or saturated double-tagging libraries. We show the advantage of multiplexing samples following Latin Square Designs in order to optimize the detection of mistags and maximize the information on their distribution across samples. We use this information in designs incorporating PCR replicates to filter the critical mistags and to recover the exact composition of mock community samples. Being parameter-free and data-driven, our approach can provide more accurate and reproducible HTS data sets, improving the reliability of their interpretations.     

Inferring macro‐ecological patterns from local presence/absence data

Biodiversity provides support for life, vital provisions, regulating services and has positive cultural impacts. It is therefore important to have accurate methods to measure biodiversity, in order to safeguard it when we discover it to be threatened. For practical reasons, biodiversity is usually measured at fine scales whereas diversity issues (e.g. conservation) interest regional or global scales. Moreover, biodiversity may change across spatial scales. It is therefore a key challenge to be able to translate local information on biodiversity into global patterns. Many databases give no information about the abundances of a species within an area, but only its occurrence in each of the surveyed plots. In this paper, we introduce an analytical framework (implemented in a ready‐to‐use R code) to infer species richness and abundances at large spatial scales in biodiversity‐rich ecosystems when species presence/absence information is available on various scattered samples (i.e. upscaling). This framework is based on the scale‐invariance property of the negative binomial. Our approach allows to infer and link within a unique framework important and well‐known biodiversity patterns of ecological theory, such as the species accumulation curve (SAC) and the relative species abundance (RSA) as well as a new emergent pattern, which is the relative species occupancy (RSO). Our estimates are robust and accurate, as confirmed by tests performed on both in silico‐generated and real forests. We demonstrate the accuracy of our predictions using data from two well‐studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence to absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.     

A fungal mock community control for amplicon sequencing experiments

Microbial ecology has been profoundly advanced by the ability to profile complex microbial communities by sequencing of marker genes amplified from environmental samples. However, inclusion of appropriate controls is vital to revealing the limitations and biases of this technique. “Mock community” samples, in which the composition and relative abundances of community members are known, are particularly valuable for guiding library preparation and data processing decisions. I generated a set of three mock communities using 19 different fungal taxa and demonstrate their utility by contrasting amplicon sequencing data obtained for the same communities under modifications to PCR conditions during library preparation. Increasing the number of PCR cycles elevated rates of chimera formation, and of errors in the final data set. Extension time during PCR had little impact on chimera formation, error rate or observed community structure. Polymerase fidelity impacted error rates significantly. Despite a high error rate, a master mix optimized to minimize amplification bias yielded profiles that were most similar to the true community structure. Bias against particular taxa differed among ITS1 vs. ITS2 loci. Preclustering nearly identical reads substantially reduced error rates, but did not improve similarity to the expected community structure. Inaccuracies in amplicon sequence‐based estimates of fungal community structure were associated with amplification bias and size selection processes, as well as variable culling rates among reads from different taxa. In some cases, the numerically dominant taxon was completely absent from final data sets, highlighting the need for further methodological improvements to avoid biased observations of community profiles.     

Revisiting the effect of PCR replication and sequencing depth on biodiversity metrics in environmental DNA metabarcoding

Environmental DNA (eDNA) metabarcoding is an increasingly popular tool for measuring and cataloguing biodiversity. Because the environments and substrates in which DNA is preserved differ considerably, eDNA research often requires bespoke approaches to generating eDNA data. Here, we explore how two experimental choices in eDNA study design—the number of PCR replicates and the depth of sequencing of PCR replicates—influence the composition and consistency of taxa recovered from eDNA extracts. We perform 24 PCR replicates from each of six soil samples using two of the most common metabarcodes for Fungi and Viridiplantae (ITS1 and ITS2), and sequence each replicate to an average depth of ~84,000 reads. We find that PCR replicates are broadly consistent in composition and relative abundance of dominant taxa, but that low abundance taxa are often unique to one or a few PCR replicates. Taxa observed in one out of 24 PCR replicates make up 21–29% of the total taxa detected. We also observe that sequencing depth or rarefaction influences alpha diversity and beta diversity estimates. Read sampling depth influences local contribution to beta diversity, placement in ordinations, and beta dispersion in ordinations. Our results suggest that, because common taxa drive some alpha diversity estimates, few PCR replicates and low read sampling depths may be sufficient for many biological applications of eDNA metabarcoding. However, because rare taxa are recovered stochastically, eDNA metabarcoding may never fully recover the true amplifiable alpha diversity in an eDNA extract. Rare taxa drive PCR replicate outliers of alpha and beta diversity and lead to dispersion differences at different read sampling depths. We conclude that researchers should consider the complexity and unevenness of a community when choosing analytical approaches, read sampling depths, and filtering thresholds to arrive at stable estimates.     

Sample richness and genetic diversity as drivers of chimera formation in nSSU metagenetic analyses

Eukaryotic diversity in environmental samples is often assessed via PCR-based amplification of nSSU genes. However, estimates of diversity derived from pyrosequencing environmental data sets are often inflated, mainly because of the formation of chimeric sequences during PCR amplification. Chimeras are hybrid products composed of distinct parental sequences that can lead to the misinterpretation of diversity estimates. We have analyzed the effect of sample richness, evenness and phylogenetic diversity on the formation of chimeras using a nSSU data set derived from 454 Roche pyrosequencing of replicated, large control pools of closely and distantly related nematode mock communities, of known intragenomic identity and richness. To further investigate how chimeric molecules are formed, the nSSU gene secondary structure was analyzed in several individuals. For the first time in eukaryotes, chimera formation proved to be higher in both richer and more genetically diverse samples, thus providing a novel perspective of chimera formation in pyrosequenced environmental data sets. Findings contribute to a better understanding of the nature and mechanisms involved in chimera formation during PCR amplification of environmentally derived DNA. Moreover, given the similarities between biodiversity analyses using amplicon sequencing and those used to assess genomic variation, our findings have potential broad application for identifying genetic variation in homologous loci or multigene families in general.     

Groundtruthing next-gen sequencing for microbial ecology-biases and errors in community structure estimates from PCR amplicon pyrosequencing

Analysis of microbial communities by high-throughput pyrosequencing of SSU rRNA gene PCR amplicons has transformed microbial ecology research and led to the observation that many communities contain a diverse assortment of rare taxa-a phenomenon termed the Rare Biosphere. Multiple studies have investigated the effect of pyrosequencing read quality on operational taxonomic unit (OTU) richness for contrived communities, yet there is limited information on the fidelity of community structure estimates obtained through this approach. Given that PCR biases are widely recognized, and further unknown biases may arise from the sequencing process itself, a priori assumptions about the neutrality of the data generation process are at best unvalidated. Furthermore, post-sequencing quality control algorithms have not been explicitly evaluated for the accuracy of recovered representative sequences and its impact on downstream analyses, reducing useful discussion on pyrosequencing reads to their diversity and abundances. Here we report on community structures and sequences recovered for in vitro-simulated communities consisting of twenty 16S rRNA gene clones tiered at known proportions. PCR amplicon libraries of the V3-V4 and V6 hypervariable regions from the in vitro-simulated communities were sequenced using the Roche 454 GS FLX Titanium platform. Commonly used quality control protocols resulted in the formation of OTUs with >1% abundance composed entirely of erroneous sequences, while over-aggressive clustering approaches obfuscated real, expected OTUs. The pyrosequencing process itself did not appear to impose significant biases on overall community structure estimates, although the detection limit for rare taxa may be affected by PCR amplicon size and quality control approach employed. Meanwhile, PCR biases associated with the initial amplicon generation may impose greater distortions in the observed community structure.     

A parametric diversity measure combining the relative abundances and taxonomic distinctiveness of species

Most ecological diversity indices summarize the information about the relative abundances of species without reflecting taxonomic differences between species. Nevertheless, in environmental conservation practice, data on species abundances are mostly irrelevant and generally unknown. In such cases, to summarize the conservation value of a given site, so‐called ‘taxonomic diversity’ measures can be used. Such measures are based on taxonomic relations among species and ignore species relative abundances. In this paper, bridging the gap between traditional biodiversity measures and taxonomic diversity measures, I introduce a parametric diversity index that combines species relative abundances with their taxonomic distinctiveness. Due to the parametric nature of the proposed index, the contribution of rare and abundant species to each diversity measure is explicit.     

Minimizing polymerase biases in metabarcoding

DNA metabarcoding is an increasingly popular method to characterize and quantify biodiversity in environmental samples. Metabarcoding approaches simultaneously amplify a short, variable genomic region, or “barcode,” from a broad taxonomic group via the polymerase chain reaction (PCR), using universal primers that anneal to flanking conserved regions. Results of these experiments are reported as occurrence data, which provide a list of taxa amplified from the sample, or relative abundance data, which measure the relative contribution of each taxon to the overall composition of amplified product. The accuracy of both occurrence and relative abundance estimates can be affected by a variety of biological and technical biases. For example, taxa with larger biomass may be better represented in environmental samples than those with smaller biomass. Here, we explore how polymerase choice, a potential source of technical bias, might influence results in metabarcoding experiments. We compared potential biases of six commercially available polymerases using a combination of mixtures of amplifiable synthetic sequences and real sedimentary DNA extracts. We find that polymerase choice can affect both occurrence and relative abundance estimates and that the main source of this bias appears to be polymerase preference for sequences with specific GC contents. We further recommend an experimental approach for metabarcoding based on results of our synthetic experiments.     

Testing the limits of 454 pyrotag sequencing: reproducibility, quantitative assessment and comparison to T-RFLP fingerprinting of aquifer microbes

The characterization of microbial community structure via 16S rRNA gene profiling has been greatly advanced in recent years by the introduction of amplicon pyrosequencing. The possibility of barcoding gives the opportunity to massively screen multiple samples from environmental or clinical sources for community details. However, an on-going debate questions the reproducibility and semi-quantitative rigour of pyrotag sequencing, similar to the early days of community fingerprinting. In this study we demonstrate the reproducibility of bacterial 454 pyrotag sequencing over biological and technical replicates of aquifer sediment bacterial communities. Moreover, we explore the potential of recovering specific template ratios via quantitatively defined template spiking to environmental DNA. We sequenced pyrotag libraries of triplicate sediment samples taken in annual sampling campaigns at a tar oil contaminated aquifer in Düsseldorf, Germany. The abundance of dominating lineages was highly reproducible with a maximal standard deviation of ~4% read abundance across biological, and ~2% across technical replicates. Our workflow also allows for the linking of read abundances within defined assembled pyrotag contigs to that of specific 'in vivo' fingerprinting signatures. Thus we demonstrate that both terminal restriction fragment length polymorphism (T-RFLP) analysis and pyrotag sequencing are capable of recovering highly comparable community structure. Overall diversity was roughly double in amplicon sequencing. Pyrotag libraries were also capable of linearly recovering increasing ratios (up to 20%) of 16S rRNA gene amendments from a pure culture of Aliivibrio fisheri spiked to sediment DNA. Our study demonstrates that 454 pyrotag sequencing is a robust and reproducible method, capable of reliably recovering template abundances and overall community structure within natural microbial communities.     

SPIKEPIPE: A metagenomic pipeline for the accurate quantification of eukaryotic species occurrences and intraspecific abundance change using DNA barcodes or mitogenomes

The accurate quantification of eukaryotic species abundances from bulk samples remains a key challenge for community ecology and environmental biomonitoring. We resolve this challenge by combining shotgun sequencing, mapping to reference DNA barcodes or to mitogenomes, and three correction factors: (a) a percent‐coverage threshold to filter out false positives, (b) an internal‐standard DNA spike‐in to correct for stochasticity during sequencing, and (c) technical replicates to correct for stochasticity across sequencing runs. The SPIKEPIPE pipeline achieves a strikingly high accuracy of intraspecific abundance estimates (in terms of DNA mass) from samples of known composition (mapping to barcodes R² = .93, mitogenomes R² = .95) and a high repeatability across environmental‐sample replicates (barcodes R² = .94, mitogenomes R² = .93). As proof of concept, we sequence arthropod samples from the High Arctic, systematically collected over 17 years, detecting changes in species richness, species‐specific abundances, and phenology. SPIKEPIPE provides cost‐efficient and reliable quantification of eukaryotic communities.     

Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance

The abundance of different SSU rRNA (“16S”) gene sequences in environmental samples is widely used in studies of microbial ecology as a measure of microbial community structure and diversity. However, the genomic copy number of the 16S gene varies greatly – from one in many species to up to 15 in some bacteria and to hundreds in some microbial eukaryotes. As a result of this variation the relative abundance of 16S genes in environmental samples can be attributed both to variation in the relative abundance of different organisms, and to variation in genomic 16S copy number among those organisms. Despite this fact, many studies assume that the abundance of 16S gene sequences is a surrogate measure of the relative abundance of the organisms containing those sequences. Here we present a method that uses data on sequences and genomic copy number of 16S genes along with phylogenetic placement and ancestral state estimation to estimate organismal abundances from environmental DNA sequence data. We use theory and simulations to demonstrate that 16S genomic copy number can be accurately estimated from the short reads typically obtained from high-throughput environmental sequencing of the 16S gene, and that organismal abundances in microbial communities are more strongly correlated with estimated abundances obtained from our method than with gene abundances. We re-analyze several published empirical data sets and demonstrate that the use of gene abundance versus estimated organismal abundance can lead to different inferences about community diversity and structure and the identity of the dominant taxa in microbial communities. Our approach will allow microbial ecologists to make more accurate inferences about microbial diversity and abundance based on 16S sequence data.     

Ironing out the wrinkles in the rare biosphere through improved OTU clustering

Deep sequencing of PCR amplicon libraries facilitates the detection of low‐abundance populations in environmental DNA surveys of complex microbial communities. At the same time, deep sequencing can lead to overestimates of microbial diversity through the generation of low‐frequency, error‐prone reads. Even with sequencing error rates below 0.005 per nucleotide position, the common method of generating operational taxonomic units (OTUs) by multiple sequence alignment and complete‐linkage clustering significantly increases the number of predicted OTUs and inflates richness estimates. We show that a 2% single‐linkage preclustering methodology followed by an average‐linkage clustering based on pairwise alignments more accurately predicts expected OTUs in both single and pooled template preparations of known taxonomic composition. This new clustering method can reduce the OTU richness in environmental samples by as much as 30–60% but does not reduce the fraction of OTUs in long‐tailed rank abundance curves that defines the rare biosphere.     

SELECTIVE RECOVERY OF MICROALGAE FROM DIVERSE HABITATS USING “PHYTO‐SPECIFIC” 16S rDNA PRIMERS1

Environmental PCR is a common tool for surveying aquatic microalgae; however, universal primers generally employed are not specific to phytoplankton and typically recover nonphotosynthetic bacteria at high frequencies. Using a 16S rDNA “phyto‐specific” primer, we were able to selectively amplify sequences of photosynthetic species from several mixed aquatic samples, even when large numbers of nonphotosynthetic microorganisms were present. We identified 21 microalgal sequences from three different habitats: salt marshes in Virginia, river basins in North Carolina, and sea ice in Alaska. In contrast, universal 16S primers recovered a majority of nonphotosynthetic organisms from some of the same samples. Our results indicate that phytoplankton‐specific primers are efficient in selectively amplifying a broad diversity of microalgae in mixed environmental samples and, therefore, can reduce the noise from extraneous species that often dominates molecular surveys of aquatic samples.     

Amplification efficiency of thermostable DNA polymerases

The amplification efficiencies of several polymerase chain reaction (PCR) enzymes were compared using real-time quantitative PCR with SYBR Green I detection. Amplification data collected during the exponential phase of PCR are highly reproducible, and PCR enzyme performance comparisons based upon efficiency measurements are considerably more accurate than those based on endpoint analysis. DNA polymerase efficiencies were determined under identical conditions using five different amplicon templates that varied in length or percentage GC content. Pfu- and Taq-based formulations showed similar efficiencies when amplifying shorter targets (<900 bp) with 45 to 56% GC content. However, when amplicon length or GC content was increased, Pfu formulations with dUTPase exhibited significantly higher efficiencies than Taq, Pfu, and other archaeal DNA polymerases. We discuss the implications of these results.     

Removing Noise From Pyrosequenced Amplicons

Background  

In many environmental genomics applications a homologous region of DNA from a diverse sample is first amplified by PCR and then sequenced. The next generation sequencing technology, 454 pyrosequencing, has allowed much larger read numbers from PCR amplicons than ever before. This has revolutionised the study of microbial diversity as it is now possible to sequence a substantial fraction of the 16S rRNA genes in a community. However, there is a growing realisation that because of the large read numbers and the lack of consensus sequences it is vital to distinguish noise from true sequence diversity in this data. Otherwise this leads to inflated estimates of the number of types or operational taxonomic units (OTUs) present. Three sources of error are important: sequencing error, PCR single base substitutions and PCR chimeras. We present AmpliconNoise, a development of the PyroNoise algorithm that is capable of separately removing 454 sequencing errors and PCR single base errors. We also introduce a novel chimera removal program, Perseus, that exploits the sequence abundances associated with pyrosequencing data. We use data sets where samples of known diversity have been amplified and sequenced to quantify the effect of each of the sources of error on OTU inflation and to validate these algorithms.