- Methodology article
- Open Access
Bacterial flora-typing with targeted, chip-based Pyrosequencing
© Sundquist et al; licensee BioMed Central Ltd. 2007
Received: 06 July 2007
Accepted: 30 November 2007
Published: 30 November 2007
The metagenomic analysis of microbial communities holds the potential to improve our understanding of the role of microbes in clinical conditions. Recent, dramatic improvements in DNA sequencing throughput and cost will enable such analyses on individuals. However, such advances in throughput generally come at the cost of shorter read-lengths, limiting the discriminatory power of each read. In particular, classifying the microbial content of samples by sequencing the < 1,600 bp 16S rRNA gene will be affected by such limitations.
We describe a method for identifying the phylogenetic content of bacterial samples using high-throughput Pyrosequencing targeted at the 16S rRNA gene. Our analysis is adapted to the shorter read-lengths of such technology and uses a database of 16S rDNA to determine the most specific phylogenetic classification for reads, resulting in a weighted phylogenetic tree characterizing the content of the sample. We present results for six samples obtained from the human vagina during pregnancy that corroborates previous studies using conventional techniques.
Next, we analyze the power of our method to classify reads at each level of the phylogeny using simulation experiments. We assess the impacts of read-length and database completeness on our method, and predict how we do as technology improves and more bacteria are sequenced. Finally, we study the utility of targeting specific 16S variable regions and show that such an approach considerably improves results for certain types of microbial samples. Using simulation, our method can be used to determine the most informative variable region.
This study provides positive validation of the effectiveness of targeting 16S metagenomes using short-read sequencing technology. Our methodology allows us to infer the most specific assignment of the sequence reads within the phylogeny, and to identify the most discriminative variable region to target. The analysis of high-throughput Pyrosequencing on human flora samples will accelerate the study of the relationship between the microbial world and ourselves.
Metagenomics enables the genomic study of microbial communities that are sampled directly from their environment, eliminating the need for isolating and cultivating specific microbes [1–3]. Metagenomic analyses of human flora samples  are a new type of assay with intriguing potential applications for the diagnosis and prediction of clinical outcomes . Studies of human vaginal bacterium during pregnancy so far include the use of direct culture methods and conventional PCR studies of clinically suspected infectious microorganisms. Although infection and inflammation likely play a major role in the pathogenesis of preterm labor and delivery [6, 7], these studies reveal only a fraction of the potential microorganic inhabitants. A comprehensive identification and catalog of these organisms will enable future investigators to target a defined population of species that may be correlated with preterm labor, premature rupture of amniotic membranes, chorioamnionitis, and other complications of pregnancy [8–12].
Metagenomics analyses will become increasingly practical as DNA sequencing costs fall dramatically with the advent of new technologies [13, 14] including Pyrosequencing™ . One challenge common to these revolutionary sequencing technologies is the short length of reads, which limits the amount of unique, discriminating sequence available within each read. Sequencing the 16S rRNA gene (16S rDNA) using conventional Sanger sequencing produces reads of at least 500 bp in length, which is sufficient to identify the precise source species for each gene . In fact, though there is a danger of producing chimeras, the reads are often long enough that they can be assembled into near-complete 16S rDNA sequences . Despite the promise of high-throughput technologies like Pyrosequencing, current versions produce short reads, making the accurate identification of the source of these reads a daunting task. One solution used chip-based Pyrosequencing targeted at a small variable region within the 16S rDNA to show that there exists a much greater variety of rare microorganisms than previously thought .
We describe a methodology for phylogenetic classification based on short, 16S rRNA gene sequence reads and apply the technique to reads obtained via high-throughput, chip-based Pyrosequencing of human vaginal flora samples during pregnancy. The resulting phylogenetic trees reveal the vast diversity of bacterial inhabitants seen in other studies, and will assist in future investigations of the link between microorganisms and pregnancy complications. Next, we examine the ability of our methodology to classify reads at different levels in the phylogeny and discuss limitations of our technique. Using simulations, we study the effect of read-length on our methodology to understand the consequence of using high-throughput Pyrosequencing instead of conventional technologies. Finally, we explore the effectiveness of isolating specific 16S variable regions using validated universal primers. Our methodology for analyzing short 16S rDNA sequence reads will enable the accurate and informative study of human flora samples using new, high-throughput sequencing technologies.
Results and Discussion
Twelve samples from vaginal epithelial tissue and discharge from pregnant women in all three trimesters were collected. DNA extraction was performed, followed by target-specific PCR amplification of approximately 1500 bp of the 16S rDNA using universal primers. The products were subjected to nebulization and clonal amplification, followed by Pyrosequencing of six samples with the Genome Sequencer 20 system (454 Life Sciences). As a result, 100,000 to 200,000 sequence reads of 100 bp average length were obtained for each of the six samples (details are provided in Additional file 1).
Further analysis of bacterial samples involving the translation of read counts to organism concentrations must be undertaken conservatively due to the following caveats. First, there may be an amplification bias of 16S rDNA sequence due to differences in primer annealing preference. Also, variation in 16S rDNA multiplicity in diverse bacterial genomes, among other complications, may result in the over- or under-representation of certain organisms' 16S sequences [18, 21].
Our ability to place reads in the phylogeny has two distinct limitations, namely short read-length and unrepresented organisms in the 16S rDNA sequence database. Short read-lengths often lead to high-fidelity matches to multiple 16S sequences in the database. This situation occurs whenever the region from which the read was sampled is highly similar across species of a given genus, family, or even phylum. In this case we are resolution-limited in placing a read below a certain depth in the phylogeny. On the other hand, because of the incomplete nature of the 16S rDNA database, a read may not match in its entire length to any known 16S sequence. However, since we believe a priori that all reads are derived from amplified 16S rDNA sequences, the closest partial matches of the read to known organisms still allow us to assign the read to the subtree that contains these organisms, although its placement below that level is labeled unknown.
Effect of read-length
For the sample profile of bacteria our ability to identify genera improves substantially when read-lengths are increased beyond 100 bp due to the high degree of similarity between bacteria in the actual samples we examined (Figure 6b). At 800 bp, we are able to accurately determine almost all of the reads at the genus level, and also correctly determine the species for over half of the reads. As demonstrated in Figure 6b, running the simulation for 100 bp read-lengths closely reproduced the read resolution graph obtained from our six samples, which lends confidence to the stability of the methodology.
For the more diverse, random profile of bacteria, the 16S rDNA sequences are sufficiently different that read-lengths greater than 100 bp do not provide much additional benefit (Figure 6a). A read-length of 100 bp, which corresponds to sample data presented here, appears to be competitive with even the longest read-length of 800 bp. Thus, with a very wide diversity of bacteria, it seems that our methodology does not require much greater read-lengths than 100 bp. In practice, however, the sample profile may be more relevant, and therefore longer reads are desirable to improve the resolution of read placement.
There is evidence to suggest that the classification of species within the RDP phylogeny has errors that limit the ability of our methodology to unambiguously classify reads down to the lowest levels of the phylogeny. As an example, suppose we have two species A and B that truly belong to the same genus X, but that species B was misclassified in genus Y. Then, a read that matches both species A and B will be assigned to the family of genera X and Y or an even broader classification. A more accurate database classification will improve the ability of our methodology to identify the genera of the reads.
Restriction to variable regions
16S variable region range definitions.
E. coli 16S rDNA range
V7 & VV8
For the random profile of bacteria we could slightly improve our resolving power by restricting reads to shorter variable regions, particularly with region V1 (Figure 7a). For the more realistic sample profile, by choosing the appropriate variable region we could improve results dramatically and achieve a resolution similar to 150 – 200 bp reads sampled from across the entire 16S gene. When reading from region V1 we were able to identify the genus of 79% of the reads instead of 57% when sampling across the entire 16S sequence and 44% of the species instead of only 14% (Figure 7). Region V2 was best able to determine the classification for the reads at the level of order, correctly identifying 84% of the reads compared to 70% when sampling from across the entire 16S gene. Thus, our study suggests that identifying the phylogenetic content of bacterial communities with short reads will be best achieved by targeting variable regions that are specifically chosen for each class of bacterial environment.
By combining high-throughput Pyrosequencing with a novel analysis methodology, we identified phylogenies of bacteria present in the human vagina during pregnancy. Previous studies of the correlation between identified bacteria and preterm labor, and attempts to treat such microorganisms have produced conflicting results [24–26]. Our methodology for studying in-depth the ecology of human pregnancy will assist in understanding the correlation between vaginal microorganisms and complications in pregnancy.
Our simulations indicate that the methodology is currently limited by two factors: short read-lengths of Pyrosequencing and the incomplete nature of 16S rDNA databases. As more bacteria are sequenced and added to the database, the effects of the second limitation will decrease. Improvements in sequencing technology will increase read-lengths and enhance our ability to distinguish between genera. In order to best identify particular species, using our methodology we can identify and isolate the most informative 16S variable region.
Identifying 16S sequence
The first stage of our analysis matches reads against known 16S rDNA sequences, or finds the closest matches to known organisms. We leverage the Ribosomal Database Project release 9 update 39 for its catalog of bacteria and their phylogenetic relationships  and the prokMSA database as a representative set of archaeal sequence . For each read, we used the tool BLAT (BLAST-Like Alignment Tool)  to quickly identify matches between reads and the combined bacterial/archaeal database, using a minimum identity of 90% and a minimum match/mismatch score of 15 bases.
where M i are indicator variables that are 1 if position i in the read matches with the database sequence in their alignment and 0 otherwise. The variables Q i are the probabilities that the read bases were called correctly, derived from the sequence quality scores.
Then, to judge whether or not we believe a read came from the organism's phylotype, we compare this probability against the probability for a hypothetical read that falls at the boundary of similarity
P (related limit) = p pL (1 - p)(1 - p) L
Reads that score above this probability limit are classified as known, while reads that score below the limit are classified as unknown.
Placing reads in the phylogeny
Each read that results in a known match will typically also match with many additional organisms. For example, a read may match with several species within the same genus, in which case we cannot identify the exact species of the read. However, if all the known hits at least fall within the same genus then we are confident the read was sampled from an organism belonging to that genus. In this way, for each read our goal is to determine the most specific classification within the phylogeny that likely contains the organism from which the read was obtained.
Next, we traverse down the tree starting at the root node. At each internal node, if the ratio of the two maximal child nodes j and k exceeds a threshold T (i.e. B(j)/B(k) > T), or if B(j) is the only non-zero child, then we descend to node j and repeat the procedure. Once the procedure terminates in an internal node or a leaf node i, we believe with a confidence level related to T that the read came from an organism belonging to the subtree rooted at i. An example of this is illustrated in Figure 8b. We experimented with the choice of T over several orders of magnitude and found that the resulting analysis varied only very slightly. For the analyses performed in this study, we used T = 0.01.
For our analysis of read-length and variable region resolving power, we simulated reads from two hypothetical collections of species. The random profile consists of 387 species of bacteria, selected by randomly traversing down the phylogenetic tree from the root to a leaf, picking each branch with uniform probability, resulting in very high diversity. The sample profile constitutes 330 species of bacteria, created by sampling species from a distribution of genera and species that was consistent with the analysis results from our six samples.
For each simulation, a read simulator generated reads sampled from 16S rDNA sequence selected randomly according to either the random or the sample profile. Reads were sampled with uniform probability from across the rDNA sequence, with a read-length drawn from a Gaussian distribution with average read-lengths of 30, 60, 100, 150, 200, 400, or 800 bp and standard deviations of 20%. Sequencing errors were introduced into the reads at a rate of 1% that consisted of mutations, insertions, deletions, and homopolymer run count errors characteristic of Pyrosequencing.
To understand the effect of read-length on the resolving power of our methodology, we simulated reads from both the random and sample profile with the seven read-lengths L of 30 – 800 bp. For each of the 2 × 7 = 14 parameter sets we produced 30 Mb of simulated read data (N reads @ L bp = 30 · 106 bp) and applied our analysis. By annotating the source species for each read we able to measure the accuracy of its placement in the phylogenetic tree, as in Figure 6.
To study the effectiveness of restricting the sequencing to known variable regions, we first selected a set of eight minimally-overlapping regions within the 16S rDNA sequence: seven regions each contained one of the known variable regions V1 – V6 and V9, and one region contained both V7 and V8 . These regions are listed in Table 1 with their E. coli 16S rDNA sequence coordinate ranges as well as 5' and 3' broad-range amplification primers, which we validated by PCR amplifying 16 rDNA from E. coli. For each primer pair we performed 15 cycles of touch-down PCR, going from 94°C for 30 s, to an annealing temperature ranging from 70°C to 50°C for 30 s, and finally extending at 72°C for 30 s. We then performed 30 additional cycles at 94°C for 45 s, 50°C for 45 s, and 72°C for 45 s, and verified the resulting products via gel electrophoresis.
Next, we again produced sets of simulated reads for both the random and the sample profile, restricted to each region, for a total of 2 × 8 = 16 sets. Each read data set consisted of 300,000 reads with an average read-length of 100 bp and a 1% error rate. We applied our analysis and measured the accuracy of read placement in the phylogeny in Figure 7.
The authors would like to thank the members of the Batzoglou lab for their support for this work. AS is partly supported by an SAP Labs Stanford Graduate Fellowship. This project is supported in part by NIH grant R01HG003571-02, the Stanford Pediatric Research Fund, and research funds from the Division of Maternal-Fetal Medicine at Stanford University.
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