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ORIGINAL RESEARCH article

Front. Microbiol., 27 April 2021 | https://doi.org/10.3389/fmicb.2021.644662

Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment

Megan S. Beaudry1*†, Jincheng Wang1,2†‡, Troy J. Kieran1, Jesse Thomas1,3‡, Natalia J. Bayona-Vásquez1,4‡, Bei Gao1‡, Alison Devault5, Brian Brunelle5, Kun Lu1‡, Jia-Sheng Wang1,2, Olin E. Rhodes Jr.3 and Travis C. Glenn1,2,4
  • 1Department of Environmental Health Science, University of Georgia, Athens, GA, United States
  • 2Interdisciplinary Toxicology Program, University of Georgia, Athens, GA, United States
  • 3Savannah River Ecology Laboratory, University of Georgia, Aiken, SC, United States
  • 4Institute of Bioinformatics, University of Georgia, Athens, GA, United States
  • 5Daicel Arbor Biosciences, Ann Arbor, MI, United States

Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases, but are much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having <78% sequence identity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average > 400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely due in part to the extent and curation of the reference databases considered. Thus, enriching existing aliquots of shotgun metagenomic libraries and obtaining modest numbers of reads from them offers an efficient orthogonal method for assessment of bacterial community composition.

Introduction

The study of microbes is critically important, as they have many essential roles in ecosystem function, disease pathology, host physiology, and possibly assessing infectious disease outbreaks (Dueker et al., 2018; Gallardo-Escárate et al., 2020). As microbial communities can often be highly diverse and complex, it can be challenging to identify rare taxa in complex environmental samples (e.g., soil, freshwater, etc.) with traditional and modern techniques (i.e., culturing, 16S amplicons, or metagenomic shotgun libraries). Advances in sequencing technologies have transformed traditional microbiology. Microbial communities that were previously considered indiscernible or unstudied, can now be investigated at greater depths than ever before from many different environmental systems (Gilmour et al., 2010; Kustin et al., 2019).

For decades, the 16S small subunit ribosomal RNA (rRNA) gene has been the gold standard marker for microbial molecular taxonomic research (Woese and Fox, 1977; Meola et al., 2015), as this highly conserved gene contains nine rapidly evolving hypervariable regions that aid in species identification (Yuan et al., 2015). Amplicon sequencing, targeting the 16S rRNA, is a cost-effective and high-throughput method used to study aquatic, terrestrial, food- and host-associated microbial communities (Logares et al., 2014; Polka et al., 2015; Jiang et al., 2016; Jousselin et al., 2016; Jouglin et al., 2019; Suenami et al., 2019; Ziegler et al., 2019). However, studies relying on 16S rRNA amplicon sequencing have limitations and biases. Relevant biases in 16S rRNA amplicon sequencing are associated with DNA extraction, amplification via PCR, sequencing, and sequence analysis (Kennedy et al., 2014; Knight et al., 2018). Specifically, PCR biases include primer bias (Klindworth et al., 2013; Kelly et al., 2019) and varying GC content (Aird et al., 2011). Additional limitations associated with amplicon sequencing include challenges in the taxonomic characterization of microbial communities, as well as accuracy and availability of reference databases (Kennedy et al., 2014; Poretsky et al., 2014; Ritari et al., 2015; Knight et al., 2018). Furthermore, the selection of the hypervariable region used for the amplicon analysis (i.e., V1–V3; V3–V4; V4; etc.) can lead to differences in bacterial identification (Vetrovsky and Baldrian, 2013; Martinez-Porchas et al., 2016).

In more recent years, metagenomic shotgun sequencing has aimed to characterize taxonomic profiles of unique clade-specific marker genes to provide a balanced view of community composition and function (Neelakanta and Sultana, 2013; Knight et al., 2018). However, metagenomic sequencing has its own limitations; genomic DNA may contain non-target DNA (e.g., human DNA), which can affect downstream analysis (e.g., mis-assemblies of sequence contigs, spurious reads) thus leading to inaccurate conclusions (Schmieder and Edwards, 2011; Gasc and Peyret, 2018). Also, metagenomic libraries are more expensive, take longer to prepare, and are much more complex than amplicon libraries, requiring more computational effort (Sekse et al., 2017). In particular, it is difficult to identify low abundance genetic traits and rare taxa using metagenomic libraries, and extensive deep sequencing is often required to do so (Lasa et al., 2019). In summary, shotgun sequencing is less biased and yields data on many genomic regions, but the main tradeoffs are higher costs of library preparation, sequencing, analysis, and potential for differences vs. 16S amplicons (see below).

Mock communities can be used to help establish ground truth in microbial diversity studies, in particular when comparing different library preparation methods (Costea et al., 2017; Rausch et al., 2019). Rausch et al., 2019 provided a comparison of 16S rRNA amplicon sequencing and metagenomic sequencing, and revealed similar community makeup (i.e., abundance and taxa diversity) of their shallow mock community regardless of library type. Conversely, other studies have found key differences in abundance and taxa of mock communites attributed to wet-laboratory methods (Costea et al., 2017; Rausch et al., 2019). However, some of these differences may be attributed to varying bioinformatic tactics.

In terms of bioinformatic analyses, advantages and limitation of methods, reference databases, and software have been vastly described for both 16S rRNA and metagenomic strategies (Truong et al., 2015; Callahan et al., 2016; Costea et al., 2017; Escobar-Zepeda et al., 2018; Rausch et al., 2019). The variation among these can lead to a lack of sensitivity and specificity that may contribute to wrong classifications and/or no classification at a specific taxonomic level, and erroneous abundance assignments (Escobar-Zepeda et al., 2018). In particular, it can be challenging to analyze environmental samples, as most reference databases are based on human commensals (Dueholm et al., 2020). Furthermore, the number of 16S rRNA gene copies can vary widely between bacterial species, and may contribute to biases in abundance estimates (Vetrovsky and Baldrian, 2013).

Both strategies (i.e., 16S rRNA amplicon and metagenomic shotgun libraries) present their own challenges and variations in analyses (Knight et al., 2018), but metagenomic shotgun libraries tend to perform at a higher sensitivity and specificity than 16S rRNA amplicon data (Escobar-Zepeda et al., 2018). For metagenomic data, programs like MetaPhlAn2 may be used to classify and estimate the relative abundance of microbial cells by mapping reads against marker sequences to classify the sequences at the sub-species to higher taxonomic levels (i.e., marker-gene approach) (Segata et al., 2012; Truong et al., 2015). Whereas 16S rRNA amplicon data is commonly analyzed by inferring representative sequences using a variety of methods, some of which are influenced by fragment size and 16S region (Edgar, 2013; Callahan et al., 2016). Furthermore, some methods used to assign operational taxonomic units may result in limited resolution at lower taxonomic levels (e.g., genus and species levels), as even organisms that share 98.75% sequences may be different species (Mysara et al., 2017). Reference databases for 16S rRNA are much more extensive than those for metagenomic analyses, which is key for superior analysis, particularly in samples that are not from human commensals (Escobar-Zepeda et al., 2018). However, variation in taxonomic classification and abundance has also been associated with the use of different reference databases (Jovel et al., 2016; Rausch et al., 2019).

Hybridization capture (also known as sequence capture, target capture, or targeted sequence capture) is an enrichment technique that uses a set of biotinylated DNA or RNA baits that are complementary to DNA sequences of interest to increase the proportion of DNA fragments of interest within DNA libraries, subsequently characterizing the DNA by massively parallel sequencing (Lasa et al., 2019). Hybridization capture assays have been designed previously for the 16S rRNA gene, using 15–1,402 baits (Gasc and Peyret, 2018; Barrett et al., 2020). Additional hybridization capture bait sets have been designed for a variety of microbial projects, such as sets of defined pathogens or particular genes, including virulence genes for Vibrio spp. that infect oysters (Lasa et al., 2019), bifidobacterial in the gut of mammals (Lugli et al., 2019), and antibiotic resistance genes (Guitor et al., 2019). Importantly, unlike other culture independent techniques, hybridization capture provides greater phylogenetic resolution and increased sensitivity, while requiring fewer sequencing reads (Lasa et al., 2019; Barrett et al., 2020). More specifically, 16S rRNA capture baits provide a cost-effective way to identify bacteria in diverse environmental samples and identify rare taxa.

Here, we present a hybridization capture method (i.e., 16S-cap) to enrich metagenomic shotgun libraries for DNA sequences of 16S rRNA genes. Our protocol improves on the existing methods by including many more baits that better cover known sequence variation in 16S databases, taking advantage of the extensive reference databases and ease of analyses of 16S rRNA sequences for taxonomic classification and decreasing bias introduced from primer affinity, while reducing sequencing costs per sample compared to unenriched metagenomic libraries. For microbes, targeted sequence capture techniques for 16S rRNA have shown more accurate representation of microbial communities compared to traditional methods (i.e., 16S rRNA amplicons, shotgun libraries) (Gasc and Peyret, 2018). We provide a comparison of traditional methods for assessing composition of microbial communities (i.e., 16S rRNA amplicons and metagenomic shotgun libraries) with our 16S-cap method to characterize in silico mock, in vitro mock, and real microbial communities from genomic data.

Materials and Methods

Samples and DNA Extraction

We used two commercial standard genomic DNA mock community collections to characterize simple communities (HM-276D, BEI Resources, Manassas, VA; D6306, Zymo Research, Irvine, CA). For complex communities, we used a subset of fecal samples from previous studies that examined the impacts of environmental xenobiotic agents on the gut microbial communities of rodent models (Gao et al., 2017; Wang et al., 2018). The first study examined carbamate insecticide in male C57BL/6 mice (i.e., Mus musculus) (Gao et al., 2017), and the second examined green tea polyphenols in female Sprague-Dawley rats (i.e., Rattus norvegicus) (Wang et al., 2018). DNA was extracted using Qiagen Fast DNA Stool Mini Kit (QIAGEN, Valencia, CA, United States) or PowerSoil DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, CA, United States). Details on experimental design and extractions are previously described (Gao et al., 2017; Wang et al., 2018).

16S rRNA Amplicon Metabarcoding

The primer pairs targeting the V3 and V4 16S regions (S-D- Bact-0341-b-S-17 and S-D-Bact-0785-a-A-21) (Klindworth et al., 2013) were used for amplification of the 16S rRNA gene in rat fecal samples and mock communities; and the primer pair targeting the V4 region (515-F and 806-R) (Caporaso et al., 2012) was used on the mouse fecal samples. We created indexed fusion primers with TruSeq compatible sequencing oligos as previously described using the Adapterama I and Adapterama II systems (Glenn et al., 2019a, b) to generate amplicon libraries using two rounds of PCR [Method 5 of Table 3 from Glenn et al. (2019b)]. For the first PCR, we prepared individual 25 μL PCR reactions for each sample using KAPA HiFi reagents (KAPA Biosystems, Wilmington, MA, United States). Each PCR reaction mix included 5 μL 5× KAPA HiFi buffer, 0.75 μL 10 mM dNTPs, 0.5 μL KAPA HiFi HotStart, 1.5 μL 5 μM forward indexed-fusion primer, 1.5 μL 5 μM reverse indexed-fusion primer, and 1 μL of 20 ng/μL DNA. PCR conditions were as follows: initial denaturation at 95°C for 3 min; 15–18 cycles of 95°C for 20 s, 60°C for 30 s, and 72°C for 30 s; final extension at 72°C for 5 min.

In preparation for the second PCR, we normalized individually indexed PCR products with a SequalPrep Normalization Plate Kit (Invitrogen, Carlsbad, CA, United States) according to manufacturer’s protocols or by pooling them together based on agarose gel band brightness. These pools served as the template for a second limited cycle PCR. Each 25 μL PCR reaction mix included: 5 μL 5× KAPA HiFi buffer, 0.75 μL 10 mM dNTPs, 0.5 μL KAPA HiFi HotStart, 2.5 μL of 5 μM forward iTru5 primer, 2.5 μL of 5 μM reverse iTru7 primer, and 5 μL of product from the first PCR. The following were used as PCR conditions: initial denaturation at 95°C for 2 min; 10 cycles of 95°C for 20 s, 60°C for 15 s, and 72°C for 30 s; final extension at 72°C for 5 min. These PCR products were purified with Sera-Mag magnetic beads (Thermo Fisher Scientific, Waltham, MA, United States). We quantified the final products with a Qubit 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, United States) and pooled them in equal molar ratios for sequencing. Samples were sequenced using an Illumina MiSeq v2 600 cycle kit (Illumina, San Diego, CA, United States) at the Georgia Genomics and Bioinformatics Core (Athens, GA, United States).

Metagenomic Libraries

Extracted DNA was sheared on a Bioruptor UCD-300 (Diagenode, Denville, NJ, United States) to an average size of about 500 bp. We input ∼100 ng of fragmented DNA into each reaction of a KAPA HyperPrep Kit (KAPA Biosystems, Wilmington, MA, United States) following manufacturer’s protocol at half volume reaction size with 14 PCR cycles using iTru adaptors and indexed primers (Glenn et al., 2019b). Samples were sequenced on an Illumina HiSeq 3000 with PE150 reads (Oklahoma Medical Research Foundation, Oklahoma City, OK, United States).

16S rRNA Bait Design

We used Prokka v1.11 with default settings, to annotate and extract all 16S rRNA sequences in GreenGenes v13.5 to ensure that only 16S rRNA regions were represented in the final bait set (Seemann, 2014). The GreenGenes database was chosen because it is freely available, widely used, and still reasonably comprehensive. Stretches of up to 25 Ns were replaced with T bases to facilitate probe design across short unknown regions. We then used USEARCH v8.1 (Edgar, 2010) to sort by length (large to short) and cluster (query coverage 90%, identity 90%) sequences, retaining one centroid from each cluster. We then designed 120-mer baits with flexible ∼50% overlap. These baits were then clustered using USEARCH (query coverage 75%, identity 78%), and one centroid per cluster retained. These clustering parameters were chosen because they allow for a comprehensive bait set, without an excessively large number of individual baits. Furthermore, hybridization baits can tolerate substantial sequence divergence, which we used to our advantage when collapsing at 78% identity (Li et al., 2013). This combination of bait design and bait length facilitates the bait set capturing 16S sequences not present in the GreenGenes database (both filling in gaps and reaching out to new, unknown, sequences).

16S rRNA Hybridization Capture Enrichments

Metagenomic libraries were combined into 500 ng pools of eight samples for rodents or two samples for mock communities. Target enrichments of each pool were performed using myBaits kit (Arbor Biosciences CAT # 308616, Ann Arbor, MI, United States) using the designed 16S rRNA Capture Baits following manufacturer’s protocol (v3.01) with a 24 h 65°C hybridization. Following hybridization, we used Dynabeads M-280 Streptavidin magnetic beads (Life Technologies, Carlsbad, CA, US) for capturing and washing each biotinyalted bait library. We then performed a post-enrichment amplification using Illumina P5/P7 primers (Illumina, San Diego, CA, United States) and KAPA HiFi HotStart reagents (KAPA Biosystems, Wilmington, MA, United States) using 98°C for 45 s, followed by 16–22 cycles of 98°C for 20 s, 60°C for 30 s, and 72°C for 60 s, ending with a final extension of 72°C for 5 min. PCR products were cleaned 1:1 with Sera-Mag beads (Glenn et al., 2019a), quantified on Qubit and pooled in equimolar ratios for sequencing paired-end 150 and 300 bp reads on Illumina HiSeq 3000 (Oklahoma Medical Research Foundation, Oklahoma City, OK, United States) and MiSeq (Georgia Genomics Bioinformatics Core, Athens, GA, United States), respectively.

Simulating 16S rRNA Target Enrichment Data

To test the efficiency of our bait set under ideal conditions, we did an in-silico analysis to determine how well our baits works during an error- and bias- free hybridization process. Three metagenomes (i.e., Lindgreen synthetic metagenome (Lindgreen et al., 2016); Zymo Mock Community DS6306 genomes; and BEI Mock Community HM-276D) were used to simulate 16S rRNA capture data. In summary, a fasta file containing our 120-mer bait set was mapped to each metagenome fasta file (Supplementary Datas 1–3) using Burrows-Wheeler aligner (bwa) v.0.7.17 (Li and Durbin, 2009). Samtools v1.9 (Li et al., 2009) was used to convert the obtained sam file into a bam file. Following this, we obtained the mapping coordinates of the baits on the reference metagenomes and extracted the sequences + 200 bp to the upstream and downstream of the first position, if possible. Here, we sought to simulate a hybridization of the bait to the core of an ∼500 bp fragment while obtaining the flanking regions typically captured from use of biotinylated baits.

The software ART 2016.06.05 (Huang et al., 2012) was then used to simulate > 200,000 paired-end 150 bp fastq reads from these extended reference sequences from each metagenome. These fastq files were mapped to Greengenes 97% similarity database v.13.8 using BBmap v. 38.50 (Bushnell, 2014). For each metagenome, we recorded the number of paired reads mapped to Greengenes, number of forward reads, number of reverse reads and percentage average total mapped, and compared these results with those from real samples also mapped to the Greengenes database (see below) (Altschul et al., 1990).

Sequencing Data Processing

After obtaining demultiplexed Illumina pair-end raw sequences, we used library specific pipelines to process the data (Figure 1). For 16S rRNA amplicon libraries, primers were removed using cutadapt v1.15 (Martin, 2011). Following this, DADA2 (v1.8) was used with customized parameters according to the quality profile of DNA sequences for quality trimming and filtering (truncLen was set to be 0.9 of sequencing length of forward reads and 0.8 for reverse reads; maxEE was set to be 6 for PE250 library, and 8 for PE300; maxN to be 0; default for the other parameters), de-replication and sequence-variant inference, merging paired-end reads, construction of feature tables, removal of chimeras, and taxonomy assignment (Callahan et al., 2016). The relative abundance was calculated by normalizing feature counts to the total counts of a sample. The taxonomy assignment was based on 97% clustered OTU of Greengenes v13.8 database using Naïve Bayesian Classifier as implemented in the DADA2 pipeline (Wang et al., 2007).

FIGURE 1
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Figure 1. Overview of data analysis methods on the three library types (i.e., 16s amplicon, 16s hybridization bait capture, and metagenomic libraries).

For 16S-cap libraries, the resulting quality filtered reads were mapped to the 97% clustered OTU based on Greengenes v13.8 database using BBmap v37.78 (Bushnell, 2014). The resulting mapping information was filtered, and a hit was recorded if both ends of paired read hit the same reference, or only one end of the paired read hit a reference. The relative abundance was calculated by normalizing feature counts to the total counts of a sample. Also, we assessed the presence of non-target reads in the quality-filtered dataset by (1) running MetaPhlAn2 v2.7.8 (Segata et al., 2012; Truong et al., 2015), and (2) mapping to the rat and mice genomes using Burrows-Wheeler aligner (bwa) v.0.7.17 (Li and Durbin, 2009).

For unenriched metagenomic libraries, Trimmomatic v0.36 (Bolger et al., 2014) was used for quality trimming using a sliding window of three nucleotides with an average Q > 20, and minimum length of 75 nucleotides. Reads that passed initial quality filtering (including both paired reads and orphan reads) were fed to MetaPhlAn2 v2.7.14 for taxonomy assignment (Segata et al., 2012; Truong et al., 2015). The relative abundance was estimated based on the database hit and marker gene length by MetaPhlAn2. To further compare to the results from 16S-cap analysis, we performed the same 16S mapping steps to the GreenGenes database as described for 16S-cap libraries for the unenriched libraries.

Statistical Analysis

After obtaining feature tables from the above libraries using different bioinformatic tools, statistical summary and tests were carried out in R (R Development Core Team, 2010). Fold changes of observed relative abundance to theoretical relative abundance for the mock communities are calculated and ANOVA with Duncan’s multiple range test was used to compare different library types and analytical methods. Bray-Curtis distance matrix were generated using the relative abundance estimates from different libraries and methods as mentioned above, which was then analyzed by prinicle coordinate analysis (PCoA) to reveal clustering pattern. Additionally pairwise between-library/methods Bray-Curtis distance were compared by ANOVA with Duncan’s multiple range test. A significance level of 0.05 is used for the Duncan’s test.

Results

16S rRNA Capture Bait Design

The 1,262,986 sequences comprising Greengenes v13.5 were annotated and 1,261,075 16S rRNA sequences were retained. A total of 117 sequences containing consecutive runs of 25 or more ambiguous bases (Ns) were removed. A total of 18,649 centroidal sequences were obtained from USEARCH clustering. From these sequences, 413,480 120-mer baits were designed. These baits were then clustered using USEARCH, retaining one centroid per cluster, for a total of 37,745 baits (i.e., unique probe sequences), indicating there are an average of ∼3,000 probes at each nucleotide position of the 16S rRNA.

Sequencing Summary Statistics

A summary of average sequence statistics for each sample and library preparation type is given in Table 1. For the 16S rRNA amplicon data, the number of total raw read pairs per sample ranged from 49,828 for the Zymo mock community to 136,184 for the BEI mock community, with rodent fecal samples having intermediate depth. More reads (∼77%) remained from the rodent fecal samples after the denoising steps through the rigorous DADA2 pipelines vs. the mock communities. Low percentages of high quality reads remained following filtering for both the BEI and Zymo mock communities (38.7 and 48.8%, respectively). For the BEI mock community, initial index matching in R2 reads caused ∼30% loss of data (vs. less than 5% typically observed in other samples) and DADA2 quality trimming lost another ∼30% of data. For the Zymo mock community, the loss of data was mainly due to chimeric filtering (∼30% of data loss).

TABLE 1
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Table 1. A brief overview of the average summary statistics (i.e., number of samples, total raw read-pairs, average filtered/bar, average mapped/filtered) for each sample type of each library type (i.e., 16S amplicon libraries, 16S-cap enriched, and unenriched).

For the unenriched libraries, the highest number of total raw read pairs ranged from 4,985,957 in the Zymo mock community to 28,219,552 in the insecticide-treated mouse feces. The percentage of reads retained after filtering was greater than 65% for all unenriched libraries. The average percentage of reads mapped to GreenGenes ranged from 0.1 to 0.2% in the BEI and Zymo mock communities.

For 16S-cap libraries, the PE150 reads had higher numbers of reads on average per sample type than PE300 reads. The highest number of raw reads (i.e., 11,474,476) was obtained for the insecticide-treated mouse feces with PE150 reads. The percentage of reads after filtering were greater than 70% for all 16S-cap libraries. The average percentage of mapped reads was greater than 50% for all 16S-cap libraries, with the highest percentage of mapping in the 16S-cap BEI mock community sequenced with PE300 at 75.7%. On average among all sample types, the proportion of on target reads was increased 435-fold when compared to unenriched libraries (range 283–499 fold increase, Suppplementary Table 2).

16S rRNA Target Enrichment Simulated Reads

Summary information for simulated reads is given in Table 2. We observed a higher percentage of total mapped reads in our simulated mock communities than for the real data from those communities (Table 2). For example, the real data from the Zymo mock community had an average total mapping of 78.15% to GreenGenes, compared to 91.43% from the simulated data. Similarly, the BEI mock community had an average total mapping of 78.62% for the real data, compared to 92.37% for the simulated data.

TABLE 2
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Table 2. Summary statistics for simulated data and real data from mock communities, libraries were enriched for 16S using the 16S-cap enrichment and sequenced on an Illumina MiSeq PE150 reads.

Validation on Mock Community Samples

We initially prepared amplicon libraries, unenriched metagenomic libraries, and performed target enrichment for 16S rRNA (i.e., 16S-cap) on metagenomic libraries using two mock communities (Table 1). At the phylum level both samples appear to provide accurate identification of the microbes with good estimates of abundance, regardless of library type or data analysis method used (Figure 2). Additionally, in both the unenriched and 16S-cap libraries analyzed with a 16S mapping approach, Cyanobacteria was found in low abundance even though it was not expected to be present in the mock community. However, when analyzing the unenriched library using marker gene approach, Cyanobacteria was not found and instead Ascomycota was identified.

FIGURE 2
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Figure 2. Relative abundance of bacterial phyla in mock community controls sequenced and analyzed using different methods. Phyla listed as components of the mock communities are shown. Black vertical bar in each row represents the nominal abundance of respective phylum. Row panel strips labels identify the mock communities; colors identify library type (i.e., amplicon, enriched 16S-cap, unenriched metagenomic library) and analyzing strategy (i.e., denoising, 16Smapping, and marker gene).

At the genus level, 16S-cap and unenriched libraries reflect more accurate microbial community composition and abundance for most taxa (Figure 3). The 16S-cap and unenriched libraries with 16S mapping missed three genera: Escherichia, Listeria, and Bacillus for both mock community samples. However, three families with no genus identification, Enterobacteriaceae, Listeriaceae, Bacillaceae, were found, suggesting these are likely the missing genera, and are represented at a family level. In comparison, 16S rRNA amplicon-based analysis identified nearly all genera in mock samples, however, its estimates of abundance for Actinomyces, Propionibacterium, Pseudomonas, and Rhodobacter all greatly deviate from the nominal compositions. The unenriched metagenomic libraries analyzed with a marker-gene approach were able to identify all 18 genera in the mock communities, however, its estimate of Bacillus abundance in both mock communities deviate from the nominal composition (Figure 3).

FIGURE 3
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Figure 3. Relative abundance of bacterial genera in mock community controls sequenced and analyzed using different methods. Genera listed as components of the mock communities are shown. Three families with no genus identification, Enterobacteriaceae, Listeriaceae, Bacillaceae, are plotted below the probable genus (Escherichia, Listeria, and Bacillus), respectively. Black vertical bar in each row represents the nominal abundance of respective genus. Row panel strips labels identify the mock communities; color identify library type (i.e., amplicon, enriched 16S-cap, unriched metagenomic library) and analyzing strategy (i.e., denoising, 16Smapping, and marker gene).

In the BEI mock community libraries, relative abundance estimates in the 16S-cap libraries were more accurate than the amplicon and unenriched libraries as measured by fold change being very close to 1 (Figure 4). In the amplicon library, several genera (i.e., Pseudomonas, Actinomyces, Propionilbacterium, and Rhodobacter) are beyond the twofold change of their nominal compositions. In particular one genus, Rhodobacter, proved to be challenging for all three library preparation methods for accurate estimation of relative abundance. Duncan’s multiple range test revealed that there were significant differences (p 0.05) between the BEI mock community amplicon and 16S-cap libraries, whereas the unenriched libraries were not found to be significantly different than the amplicon or 16S-cap libraries. For the Zymo mock community libraries, relative abundance estimates in the 16S-cap libraries are more accurate than relative abundance estimates for the amplicon library. However, Duncan’s multiple range test did not detect a significant difference between the three library types (i.e., amplicon, unenriched, and enriched) (Figure 4).

FIGURE 4
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Figure 4. Fold change (i.e., upper or under) comparing the relative abundances of respective genera in each library to its nominal abundance. Duncan’s multiple range test was performed to compare each library type for each mock community. Letters indicate whether significant differences were detected.

Validation on Fecal Samples

Principle coordinate analysis was performed on mock community samples and additional samples from laboratory mice and rats to further validate the 16S-cap method. When Bray-Curtis was used to construct the dissimilarity matrix, which considers abundance estimates, we found that regardless of analyses at the level of family (Figure 5A, left) or genus (Figure 5B, right) similar themes emerged. We observed that the mock community samples were similar to each other regardless of library type. Conversely, in the mouse and rat samples, we found that the unenriched libraries analyzed with a marker-gene approach grouped together separately from amplicon, unenriched, and 16S-cap libraries, all of which were analyzed with the 16S mapping approach.

FIGURE 5
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Figure 5. PCoA plots were constructed using Bray-Curtis dissimilarity matrix at a family level (A) and genus level (B). Each project is represented by a colored dot (i.e., orange = BEI mock community, green = mouse samples, blue = rat samples, and purple = Zymo mock community). Each library type, sequencing read length and data analysis method is represented by a different shape (i.e., circle = amplicon library, square = 16S-cap enriched PE150 reads, diamond = unenriched PE150 analyzed with 16S mapping and triangle = unenriched PE150 analyzed with metagenome mapping). Numbers represent sample number.

A comparison of Bray-Curtis distance was performed for rodent fecal samples at the level of family and genus (Figure 6). This analysis revealed similar trends regardless of sample type or taxonomic rank. The 16S-cap and unenriched libraries analyzed with 16S mapping approach showed to be the most similar to each other, with a dissimilarity rate below 0.25. Bray-Curtis dissimilarity was higher when comparing the amplicon libraries to both 16S-cap and unenriched libraries. When comparing the unenriched libraries analyzed with two different analysis strategies (i.e., mapping reads to GreenGenes vs. gene-marker approach), we observed the highest degree of dissimilarity at both the family and genus levels with dissimilarity rates at approximately 0.75. Post-hoc analysis revealed that there were significant differences when comparing the unenriched and 16S-cap libraries to all other library types, regardless of sample type or taxonomic rank (Figure 6).

FIGURE 6
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Figure 6. A comparison of the Bray-Curtis distance metric was performed for each library type at a genus level using box plots. Bray-Curtis distance is indicated on the y-axis. Library type is indicated on the x-axis. Duncan’s multiple range test was performed to compare each library type for each mock community. Letters indicate whether significant differences were detected.

Discussion

Given the limitations of 16S rRNA amplicon and shotgun metagenomic libraries outlined in the introduction, we sought to provide an alternative method to identify microbial community composition by creating a 16S rRNA hybridization capture assay (i.e., 16S-cap). Our study revealed two important things: (1) our 16S-cap method is an efficient way to obtain sequences from the complete 16S rRNA gene to accurately reflect microbial community composition and abundance and (2) bioinformatic analysis methods greatly influence community composition in host-related samples, regardless of library type. In our study we observed that sequences from 16S-cap were not significantly different than sequences from unenriched shotgun libraries when analyzed using similar bioinformatic methods and databases. However, we did find that the 16S-cap assay requires far fewer reads, thus allowing enriched libraries to be characterized on benchtop sequencers, including Illumina MiSeq instruments, at reasonable cost while overcoming the previously mentioned limitations with direct 16S rRNA approaches and metagenomic approaches. These limitations include selection and drift bias in PCR during amplicon library preparation and the potential for non-target DNA (e.g., human DNA) in metagenomic libraries, which can lead to errors in downstream analyses.

Enrichment for genes of interest is an important technique in characterizing complex environmental and host-related samples. Previous studies have found other capture enrichment methods to increase the proportion of on target reads from ∼0.1% in unenriched shotgun libraries to ∼60% in enriched libraries (Gasc and Peyret, 2018). Similarly, we found 0.1–0.2% of unenriched libraries to map to the16S rRNA, whereas 58–76% of the enriched reads mapped to the 16S rRNA (Table 1). On average we achieved a 435-fold increase in reads mapped to the 16S rRNA in our 16S-cap libraries compared to the unenriched libraries (Supplementary Table 2). In silico simulations of 16S-cap revealed that under ideal conditions, 88–92% mapping to the 16S rRNA from mock communities could be achieved. Therefore, our 16S-cap enrichment process helps to achieve a very high percentage of on-target reads, but not quite as high as theoretically possible.

Our 16S-cap method identified several species that were not expected in the theoretical targets of the mock communities, which may be attributed to several factors. First, the lack of genus identification may be due to the mapping methods or clustering level used in data analysis rather than the library preparation method. Both the 16S-cap and unenriched libraries analyzed with a 16S mapping method failed to identify three genera Escherichia, Listeria, and some Bacillus in the mock communities. However, there are three familes, Enterobacteriaceae, Listeriaceae, and Bacillaceae, are associated with our missing genera. Thus, it appears that reads for these three genera appear to be present, but are not being assigned appropriately by the bioinformatic program at the genus level. By designating these unidentified genera as Escherichia, Listeria, and Bacillus respectively, the 16S-cap library is highly accurate in terms of taxonomic classification and abundance. Taxonomic misassignment is a known problem with 16S mapping methods (Park and Won, 2018; Abellan-Schneyder et al., 2021), and new software is in development (Schloss and Westcott, 2011; Pollock et al., 2018; Zinger et al., 2019; Djemiel et al., 2020). Moreover, several other studies have found bioinformatic databases have difficulty assigning Escherichia, Listeria, and Bacillus at a genus level (Park and Won, 2018; Abellan-Schneyder et al., 2021). Additional work on the mapping and assignment processes used here, as well as comparisons of newly developed and commonly used bioinformatic software is beyond the scope of this paper, but warranted in future work.

We compared theoretical target values of the BEI resources and Zymo mock communities to all three library types (i.e., amplicon, unenriched, and 16S-cap) (Figures 3, 4). We find that the 16S-cap libraries are representative of the target abundance values of the mock communities (Figure 3). Post-hoc analysis revealed that the 16S rRNA amplicon library and 16S-cap library made from the BEI mock community were significantly different from each other (p ≤ 0.05) based on relative abundance. A PCoA revealed that in the mouse and rat samples the unenriched libraries analyzed with a marker-gene approach grouped together separately from 16S rRNA amplicon libraries and 16S-cap and unenriched libraries analyzed with taxonomic binning approach (Figure 5). Thus, enrichment and amplicon sequencing result in similar library composition, as do 16S-cap and unenriched libraries analyzed with a 16S taxonomic binning approach. This indicates that our 16S-cap method may be less biased than 16S amplification, but that analysis methods or the reference database may greatly influence community composition results. Walsh et al. (2018) analyzed different species classifiers using marker gene approaches and taxonomic binning, and found that the results of the marker gene approach (i.e., MetaPhlAn2) were different from taxonomic binning methods. Taxonomic binning methods are influenced by the size of the reference genome, whereas marker gene approaches are not (Droge and McHardy, 2012; Balvociute and Huson, 2017; Walsh et al., 2018). The use of hybridization capture baits may help alleviate some of these issues.

Other groups have designed a more limited bait set to hybridize all known 16S rRNA gene sequences by focusing on highly conserved regions and incorporating ambiguities (Gasc and Peyret, 2018). When validating their bait set on a mock community, they found that they detected 24 of 26 genera tested, and that two less abundant species (i.e., Methanobrevibacter smithii and Methanococcus aelocius at 0.00006%) were missed. In addition, Cariou et al. (2018) tested hybridization capture probes designed by Gasc and Peyret (2018) on a previously characterized pea aphid and found their enriched libraries to be representative of the bacterial population. There are some key differences between the design of our baits set and Gasc and Peyret (2018). Foremost, is the number of baits included in the bait set. Our bait set included 37,745 120-mer baits and was designed from all 16S rRNA gene sequences in GreenGenes, whereas Gasc and Peyret bait set include 15 baits that are 28—50-mer and was designed by focusing on highly conserved regions of the 16S rRNA and incorporation of degenerate sites. We used 120-mer baits because 120 nt is the maximum practical size for the Arbor Biosciences platform and it maximizes the tolerance of non-complementary bases with reasonable hybridization times. Additionally, using more baits with more sequence variation among the baits helps to capture a greater range of diverse targets and thus generates more accurate abundance estimates of the full range of community members. Having a more extensive bait set, such as ours, may reduce some of the previous challenges, demonstrated by the ability to detect all genera in the mock communities. These aspects are critical when studying environmental and host-related samples and searching for rare taxa. In addition, the use of longer hybridization times or “double capture” (i.e., when captured product is captured again) can improve the percentage of on target reads and help capture rare sequences. Future work to identify the optimal bait set(s) for various microbial communities and research objectives should include a direct comparison of the Gasc and Peyret (2018) bait set verses our bait set.

Preparing 16S-cap libraries can most readily be accomplished by using an existing enrichment kit, which ranges in cost from $1,500 to $5,200 depending on the number of reactions purchased. To reduce reagent costs and hands-on time, we have successfully pooled multiple samples (see section “16S rRNA Hybridization Capture Enrichments”), which is commonly done (Glenn and Faircloth, 2016). For example, pooling samples in groups of eight reduces capture costs from $93.75 per sample to $11.72 per sample (Supplementary Table 3). Larger numbers of samples can be pooled to further reduce costs, but there are tradeoffs (see Glenn and Faircloth, 2016). Our baitset is commercially available from Arbor Biosciences in ready-to-use kit format, and the bait sequences are freely available to the scientific community (Supplementary Data 4). Thus, our baits can be modified and/or synthesized by any strategy any researcher desires.

Sequencing 16S-cap libraries require less extensive sequencing than unenriched shotgun metagenomic libraries, which reduces costs (Supplementary Tables 4, 5). It is important to note that the number of reads obtained for 16S-cap libraries here (Table 1) is far more than are necessary or would be reasonable when implementing this strategy. For example, a 100-fold 16S-cap enrichment sequenced on an Illumina MiSeq Nano PE150 provides a cost-savings of approximately $315 compared to an unenriched metagenomic shotgun library requiring 1 million reads (Supplementary Table 4). Indeed, 16S-cap makes it economically and logistically reasonable to routinely screen for 16S segments from enriched shotgun metagenomic libraries on Illumina MiSeqs. 16S-cap decreases costs when using a production scale Illumina sequencer (e.g., Illumina NovaSeq) to less than $0.10 per sample when achieving a 100-fold enrichment (Supplementary Table 5). However, because production scale sequencers produce 400–2,500 million read pairs, to achieve low cost for samples needing relatively few reads, each run requires huge numbers of samples or a mixture of some samples needing large numbers of reads (i.e., a mixture of projects; see Glenn et al., 2019a). Due to the limited savings possible on production sequencing costs (Supplementary Table 4), the savings in data transfer, storage, and compute time may be more significant than savings in sequencing costs.

In summary, our data demonstrates that the 16S-cap assay and unenriched shotgun metagenomic libraries produce very similar community profiles. Importantly, our 16S-cap library is produced from a metagenomic library, which eliminates primer (though not all PCR) biases. Additionally, our 16S-cap assay provides a deeper community profile (i.e., more 16S reads that can be queried to a database) with far fewer reads than the unenriched shotgun metagenomic libraries. In host-related samples, we routinely achieved > 400-fold enrichment. Thus, expensive deep sequencing is unnecessary for 16S-cap libraries because a few thousand reads provide the same number of 16S rRNA sequences as millions of shotgun reads. By trading modest additional library preparation costs for reduced sequencing costs (Supplementary Tables 3–5), 16S cap is economical and opens up the possibility of adding deep taxonomic sampling to studies that are capturing other genes of interests e.g., antibiotic resistance genes (Guitor et al., 2019; Oladeinde et al., 2019; Thomas et al., 2020). In comparison to amplicon libraries, the 16S-cap assay will be more expensive, however, it provides superior microbial community resolution, increased accuracy of relative abundance, an orthoganol approach to marker genes, and greater flexibility in terms of sequencer and kit choice. We believe that our bait set is a valuable tool to efficiently and accurately identify microbial community composition and would be well-suited to be used in combination with other bait sets targeting different genes of interest (e.g., antimicrobial resistance baits).

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/sra/PRJNA689808.

Author Contributions

TG conceived of the project. JW, JT, TK, BG, KL, and TG designed the experiments. JW, TK, and BG performed the experiments. AD and BB designed the baits. JW and NB-V analyzed the data. AD, BB, KL, OR, J-SW, and TG provided funding and resources. MB wrote the manuscript. JW, TK, and NB-V wrote sections of the manuscript. MB and JW produced figures and tables. All authors critically reviewed, edited, and approve of this work.

Disclaimer

This report was prepared as an account of work sponsored by agencies of the United States Government. Neither the United States Government, nor any agency thereof, nor any of their employees makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Funding

Funding for this work was provided by the Center for Disease Control contract 200-2018-02889 (75D30118C02889), US Department of Energy Cooperative Agreement number DE-FC09-07SR22506, National Institute of Health (R01ES024950, P30ES010126, and P42ES031007), and United States Agency for International Development via Peanut and Mycotoxin Innovation Laboratory (ECG-A00-13-00001-00). Daicel Arbor Biosciences provided the customized in-solution capture reagents used in this work.

Conflict of Interest

The EHS DNA lab provides oligonucleotide aliquots and library preparation services at cost, including some oligonucleotides and services used in this manuscript (baddna.uga.edu). BB and AD were employed by, and thereby have financial interest in, Daicel Arbor Biosciences, who provided the in-solution capture reagents used in this work.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We acknowledge the contributions of Marissa Howard, Amanda Sullivan, Allison Perry, and Laura Rose.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2021.644662/full#supplementary-material

References

Abellan-Schneyder, I., Matchado, M. S., Reitmeier, S., Sommer, A., Sewald, Z., Baumbach, J., et al. (2021). Primer, pipelines, parameters: issues in 16S rRNA gene sequencing. mSphere 6:e001202-20. doi: 10.1128/mSphere.01202-20

PubMed Abstract | CrossRef Full Text | Google Scholar

Aird, D., Ross, M. G., Wei-Sheng, C., Danielsson, M., Fennell, T., Russ, C., et al. (2011). Analzying and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol. 12:R18.

Google Scholar

Altschul, S. F., Gish, W., Miller, W., Myers, E., and Lipman, D. J. (1990). Basic local alignment search tool. J. Mol. Biol. 215, 403–410.

Google Scholar

Balvociute, M., and Huson, D. H. (2017). SILVA, RDP, Greengenes, NCBI and OTT – how do these taxonomies compare? BMC Genomics 18(Suppl. 2):114. doi: 10.1186/s12864-017-3501-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Barrett, S. R., Hoffman, N. G., Rosenthal, C., Bryan, A., Marshall, D. A., Lieberman, J., et al. (2020). Sensitive identification of bacterial DNA in clinical specimens by broad range 16S rRNA enrichment. J. Clin. Microbiol. 58:e01605-20. doi: 10.1128/JCM.01605-20

PubMed Abstract | CrossRef Full Text | Google Scholar

Bolger, A. M., Lohse, M., and Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. doi: 10.1093/bioinformatics/btu170

PubMed Abstract | CrossRef Full Text | Google Scholar

Bushnell, B. (2014). “BBMAP: a fast, accurate, splice-aware aligner,” in Proceedings of the 9th Annual Genomics of Energy & Environment Meeting March 17–20, Walnut Creek, CA.

Google Scholar

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J., and Holmes, S. P. (2016). DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. doi: 10.1038/nmeth.3869

PubMed Abstract | CrossRef Full Text | Google Scholar

Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Huntley, J., Fierer, N., et al. (2012). Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624. doi: 10.1038/ismej.2012.8

PubMed Abstract | CrossRef Full Text | Google Scholar

Cariou, M., Ribière, C., Morlière, S., Gauthier, J. P., Simon, J. C., Peyret, P., et al. (2018). Comparing 16S rDNA amplicon sequencing and hybridization capture for pea aphid microbiota diversity analysis. BMC Res. Notes 11:461. doi: 10.1186/s13104-018-3559-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Costea, P. I., Zeller, G., Sunagawa, S., Pelletier, E., Alberti, A., Levenez, F., et al. (2017). Towards standards for human fecal sample processing in metagenomic studies. Nat. Biotechnol. 35, 1069–1076. doi: 10.1038/nbt.3960

PubMed Abstract | CrossRef Full Text | Google Scholar

Djemiel, C., Dequiedt, S., Karimi, B., Cottin, A., Girier, T., El Djoudi, Y., et al. (2020). BIOCOM-PIPE: a new user-friendly metabarcoding pipeline for the characterization of microbial diversity from 16S, 18S and 23S rRNA gene amplicons. BMC Bioinformatics 21:492. doi: 10.1186/s12859-020-03829-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Droge, J., and McHardy, A. C. (2012). Taxonomic binning of metagenome samples generated by next-generation sequencing technologies. Brief Bioinform. 13, 646–655. doi: 10.1093/bib/bbs031

PubMed Abstract | CrossRef Full Text | Google Scholar

Dueholm, M. S., Andersen, K. S., McIlroy, S. J., Kristensen, J. M., Yashiro, E., Karst, S. M., et al. (2020). Generation of comprehensive ecosystem-specific reference databases with species-level resolution by high-throughput full-length 16S rRNA gene sequencing and automated taxonomy assignment (AutoTax). mBio 11:e01557-20. doi: 10.1128/mBio.01557-20

PubMed Abstract | CrossRef Full Text | Google Scholar

Dueker, M. E., French, S., and O’Mullan, G. D. (2018). Comparison of bacterial diversity in air and water of a major urban center. Front. Microbiol. 9:2868. doi: 10.3389/fmicb.2018.02868

PubMed Abstract | CrossRef Full Text | Google Scholar

Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. doi: 10.1093/bioinformatics/btq461

PubMed Abstract | CrossRef Full Text | Google Scholar

Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998. doi: 10.1038/nmeth.2604

PubMed Abstract | CrossRef Full Text | Google Scholar

Escobar-Zepeda, A., Godoy-Lozano, E. E., Raggi, L., Segovia, L., Merino, E., Gutierrez-Rios, R. M., et al. (2018). Analysis of sequencing strategies and tools for taxonomic annotation: defining standards for progressive metagenomics. Sci. Rep. 8:12034. doi: 10.1038/s41598-018-30515-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Gallardo-Escárate, C., Valenzuela-Muñoz, V., Núñez-Acuña, G., Valenzuela-Miranda, D., Castellón, F., Benavente-Cartes, B., et al. (2020). The wastewater microbiome: a novel insight for COVID-19 surveillance. Res. Sq. 764:142867. doi: 10.21203/rs.3.rs-62651/v1

CrossRef Full Text | Google Scholar

Gao, B., Bian, X., Mahbub, R., and Lu, K. (2017). Sex-specific effects of organophosphate diazinon on the gut microbiome and its metabolic functions. Environ. Health Perspect. 125, 198–206. doi: 10.1289/EHP202

PubMed Abstract | CrossRef Full Text | Google Scholar

Gasc, C., and Peyret, P. (2018). Hybridization capture reveals microbial diversity missed using current profiling methods. Microbiome 6:61. doi: 10.1186/s40168-018-0442-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Gilmour, M. W., Graham, M., Van Domselaar, G., Tyler, S., Kent, H., Trout-Yakel, K. M., et al. (2010). High-throughput genome sequencing of two Listeria monocytogenes clinical isolates during a large foodborne outbreak. BMC Genomics 11:120. doi: 10.1186/1471-2164-11-120

PubMed Abstract | CrossRef Full Text | Google Scholar

Glenn, T. C., and Faircloth, B. C. (2016). Capturing Darwin’s dream. Mol. Ecol. Resour. 16, 1051–1058. doi: 10.1111/1755-0998.12574

PubMed Abstract | CrossRef Full Text | Google Scholar

Glenn, T. C., Nilsen, R. A., Kieran, T. J., Sanders, J. G., Bayona-Vasquez, N. J., Finger, J. W., et al. (2019a). Adapterama I: universal stubs and primers for 384 unique dual-indexed or 147,456 combinatorially-indexed Illumina libraries (iTru & iNext). PeerJ 7:e7755. doi: 10.7717/peerj.7755

PubMed Abstract | CrossRef Full Text | Google Scholar

Glenn, T. C., Pierson, T. W., Bayona-Vasquez, N. J., Kieran, T. J., Hoffberg, S. L., Thomas Iv, J. C., et al. (2019b). Adapterama II: universal amplicon sequencing on Illumina platforms (TaggiMatrix). PeerJ 7:e7786. doi: 10.7717/peerj.7786

PubMed Abstract | CrossRef Full Text | Google Scholar

Guitor, A. K., Raphenya, A. R., Klunk, J., Kuch, M., Alcock, B., Surette, M. G., et al. (2019). Capturing the resistome: a targeted capture method to reveal antibiotic resistance determinants in metagenomes. Antimicrob. Agents Chemother. 64, 1–37. doi: 10.1128/AAC.01324-19

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, W., Li, L., Myers, J. R., and Marth, G. T. (2012). ART: a next-generation sequencing read simulator. Bioinformatics 28, 593–594. doi: 10.1093/bioinformatics/btr708

PubMed Abstract | CrossRef Full Text | Google Scholar

Jiang, Y., Xiong, X., Danska, J., and Parkinson, J. (2016). Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome-specific functionality. Microbiome 4:2. doi: 10.1186/s40168-015-0146-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Jouglin, M., Blanc, B., de la Cotte, N., Bastian, S., Ortiz, K., and Malandrin, L. (2019). First detection and molecular identification of the zoonotic Anaplasma capra in deer in France. PLoS One 14:e0219184. doi: 10.1371/journal.pone.0219184

PubMed Abstract | CrossRef Full Text | Google Scholar

Jousselin, E., Clamens, A. L., Galan, M., Bernard, M., Maman, S., Gschloessl, B., et al. (2016). Assessment of a 16S rRNA amplicon Illumina sequencing procedure for studying the microbiome of a symbiont-rich aphid genus. Mol. Ecol. Resour. 16, 628–640. doi: 10.1111/1755-0998.12478

PubMed Abstract | CrossRef Full Text | Google Scholar

Jovel, J., Patterson, J., Wang, W., Hotte, N., O’Keefe, S., Mitchel, T., et al. (2016). Characterization of the Gut microbiome using 16S or shotgun metagenomics. Front. Microbiol. 7:459. doi: 10.3389/fmicb.2016.00459

PubMed Abstract | CrossRef Full Text | Google Scholar

Kelly, R. P., Shelton, A. O., and Gallego, R. (2019). Understanding PCR processes to draw meaningful conclusions from environmental DNA studies. Sci. Rep. 9:12133. doi: 10.1038/s41598-019-48546-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Kennedy, K., Hall, M. W., Lynch, M. D., Moreno-Hagelsieb, G., and Neufeld, J. D. (2014). Evaluating bias of illumina-based bacterial 16S rRNA gene profiles. Appl. Environ. Microbiol. 80, 5717–5722. doi: 10.1128/AEM.01451-14

PubMed Abstract | CrossRef Full Text | Google Scholar

Klindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C., Horn, M., et al. (2013). Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41:e1. doi: 10.1093/nar/gks808

PubMed Abstract | CrossRef Full Text | Google Scholar

Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., et al. (2018). Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16, 410–422. doi: 10.1038/s41579-018-0029-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Kustin, T., Ling, G., Sharabi, S., Ram, D., Friedman, N., Zuckerman, N., et al. (2019). A method to identify respiratory virus infections in clinical samples using next-generation sequencing. Sci. Rep. 9:2606. doi: 10.1038/s41598-018-37483-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Lasa, A., di Cesare, A., Tassistro, G., Borello, A., Gualdi, S., Furones, D., et al. (2019). Dynamics of the Pacific oyster pathobiota during mortality episodes in Europe assessed by 16S rRNA gene profiling and a new target enrichment next-generation sequencing strategy. Environ. Microbiol. 21, 4548–4562. doi: 10.1111/1462-2920.14750

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, C., Hofreiter, M., Staube, N., Corrigan, S., and Naylor, G. J. (2013). Capturing protein-coding genes across highly divergent species. Biotechniques 54, 321–326. doi: 10.2144/000114039

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, H., and Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760. doi: 10.1093/bioinformatics/btp324

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, R., Yu, C., Li, Y., Lam, T. W., Yiu, S. M., Kristiansen, K., et al. (2009). SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967. doi: 10.1093/bioinformatics/btp336

PubMed Abstract | CrossRef Full Text | Google Scholar

Lindgreen, S., Adair, K. L., and Gardner, P. P. (2016). An evaluation of the accuracy and speed of metagenome analysis tools. Sci. Rep. 6:19233. doi: 10.1038/srep19233

PubMed Abstract | CrossRef Full Text | Google Scholar

Logares, R., Sunagawa, S., Salazar, G., Cornejo-Castillo, F. M., Ferrera, I., Sarmento, H., et al. (2014). Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ. Microbiol. 16, 2659–2671. doi: 10.1111/1462-2920.12250

PubMed Abstract | CrossRef Full Text | Google Scholar

Lugli, G. A., Duranti, S., Milani, C., Mancabelli, L., Turroni, F., Sinderen, D. V., et al. (2019). Uncovering Bifidobacteria via targeted sequencing of the mammalian gut microbiota. Microorganisms 7, 1–11. doi: 10.3390/microorganisms7110535

PubMed Abstract | CrossRef Full Text | Google Scholar

Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet 17, 10–12.

Google Scholar

Martinez-Porchas, M., Villalpando-Canchola, E., and Vargas-Albores, F. (2016). Significant loss of sensitivity and specificity in the taxonomic classification occurs when short 16S rRNA gene sequences are used. Heliyon 2:e00170. doi: 10.1016/j.heliyon.2016.e00170

PubMed Abstract | CrossRef Full Text | Google Scholar

Meola, M., Lazzaro, A., and Zeyer, J. (2015). Bacterial composition and survival on sahara dust particles transported to the European Alps. Front. Microbiol. 6:1454. doi: 10.3389/fmicb.2015.01454

PubMed Abstract | CrossRef Full Text | Google Scholar

Mysara, M., Vandamme, P., Props, R., Kerckhof, F. M., Leys, N., Boon, N., et al. (2017). Reconciliation between operational taxonomic units and species boundaries. FEMS Microbiol. Ecol. 93, 1–12. doi: 10.1093/femsec/fix029

PubMed Abstract | CrossRef Full Text | Google Scholar

Neelakanta, G., and Sultana, H. (2013). The use of metagenomic approaches to analyze changes in microbial communities. Microbiol. Insights 6, 37–48. doi: 10.4137/MBI.S10819

PubMed Abstract | CrossRef Full Text | Google Scholar

Oladeinde, A., Cook, K., Lakin, S., Woyda, R., Abdo, Z., Looft, T., et al. (2019). horizontal gene transfer and acquired antibiotic resistance in Salmonella enterica serovar Heidelberg following in vitro incubation in broiler ceca. Appl. Environ. Microbiol. 85, e01903–e01919.

Google Scholar

Park, S. C., and Won, S. (2018). Evaluation of 16S rRNA databases for taxonomic assignments using mock community. Genomics Inform. 16:e24. doi: 10.5808/GI.2018.16.4.e24

PubMed Abstract | CrossRef Full Text | Google Scholar

Polka, J., Rebecchi, A., Pisacane, V., Morelli, L., and Puglisi, E. (2015). Bacterial diversity in typical Italian salami at different ripening stages as revealed by high-throughput sequencing of 16S rRNA amplicons. Food Microbiol. 46, 342–356. doi: 10.1016/j.fm.2014.08.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Pollock, J., Glendinning, L., Wisedchanwet, T., and Watson, M. (2018). the madness of microbiome: attempting to find consensus “Best Practice” for 16S microbiome studies. Appl. Environ. Microbiol. 84:e02627-17. doi: 10.1128/AEM.02627-17

PubMed Abstract | CrossRef Full Text | Google Scholar

Poretsky, R., Rodriguez, R. L., Luo, C., Tsementzi, D., and Konstantinidis, K. T. (2014). Strengths and limitations of 16S rRNA gene amplicon sequencing in revealing temporal microbial community dynamics. PLoS One 9:e93827. doi: 10.1371/journal.pone.0093827

PubMed Abstract | CrossRef Full Text | Google Scholar

R Development Core Team, (2010). A language and environment for statisticalcomputing. R Foundation for Statistical Computing. Vienna: R Foundation for Statistical Computing. Available online at: https://www.R-project.org/

Google Scholar

Rausch, P., Ruhlemann, M., Hermes, B. M., Doms, S., Dagan, T., Dierking, K., et al. (2019). Comparative analysis of amplicon and metagenomic sequencing methods reveals key features in the evolution of animal metaorganisms. Microbiome 7:133. doi: 10.1186/s40168-019-0743-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Ritari, J., Salojarvi, J., Lahti, L., and de Vos, W. M. (2015). Improved taxonomic assignment of human intestinal 16S rRNA sequences by a dedicated reference database. BMC Genomics 16:1056. doi: 10.1186/s12864-015-2265-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Schloss, P. D., and Westcott, S. L. (2011). Assessing and improving methods used in operational taxonomic unit-based approaches for 16S rRNA gene sequence analysis. Appl. Environ. Microbiol. 77, 3219–3226. doi: 10.1128/AEM.02810-10

PubMed Abstract | CrossRef Full Text | Google Scholar

Schmieder, R., and Edwards, R. (2011). Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS One 6:e17288. doi: 10.1371/journal.pone.0017288

PubMed Abstract | CrossRef Full Text | Google Scholar

Seemann, T. (2014). Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069. doi: 10.1093/bioinformatics/btu153

PubMed Abstract | CrossRef Full Text | Google Scholar

Segata, N., Waldron, L., Ballarini, A., Narasimhan, V., Jousson, O., and Huttenhower, C. (2012). Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814. doi: 10.1038/nmeth.2066

PubMed Abstract | CrossRef Full Text | Google Scholar

Sekse, C., Holst-Jensen, A., Dobrindt, U., Johannessen, G. S., Li, W., Spilsberg, B., et al. (2017). High throughput sequencing for detection of foodborne pathogens. Front. Microbiol. 8:2029. doi: 10.3389/fmicb.2017.02029

PubMed Abstract | CrossRef Full Text | Google Scholar

Suenami, S., Konishi Nobu, M., and Miyazaki, R. (2019). Community analysis of gut microbiota in hornets, the largest eusocial wasps, Vespa mandarinia and V. simillima. Sci. Rep. 9:9830. doi: 10.1038/s41598-019-46388-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Thomas, J. C., Oladeinde, A., Kieran, T. J., Finger, J. W. Jr., Bayona-Vasquez, N. J., Cartee, J. C., et al. (2020). Co-occurrence of antibiotic, biocide, and heavy metal resistance genes in bacteria from metal and radionuclide contaminated soils at the Savannah River Site. Microb. Biotechnol. 13, 1179–1200. doi: 10.1111/1751-7915.13578

PubMed Abstract | CrossRef Full Text | Google Scholar

Truong, D. T., Franzosa, E. A., Tickle, T. L., Scholz, M., Weingart, G., Pasolli, E., et al. (2015). MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903. doi: 10.1038/nmeth.3589

PubMed Abstract | CrossRef Full Text | Google Scholar

Vetrovsky, T., and Baldrian, P. (2013). The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS One 8:e57923. doi: 10.1371/journal.pone.0057923

PubMed Abstract | CrossRef Full Text | Google Scholar

Walsh, A. M., Crispie, F., O’Sullivan, O., Finnegan, L., Claesson, M. J., and Cotter, P. D. (2018). Species classifier choice is a key consideration when analysing low-complexity food microbiome data. Microbiome 6, 50. doi: 10.1186/s40168-018-0437-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, J., Tang, L., Zhou, H., Zhou, J., Glenn, T. C., Shen, C. L., et al. (2018). Long-term treatment with green tea polyphenols modifies the gut microbiome of female sprague-dawley rats. J. Nutr. Biochem. 56, 55–64. doi: 10.1016/j.jnutbio.2018.01.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Q., Garrity, G. M., Tiedje, J. M., and Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. doi: 10.1128/AEM.00062-07

PubMed Abstract | CrossRef Full Text | Google Scholar

Woese, C. R., and Fox, G. E. (1977). Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proc. Natl. Acad. Sci. U.S.A. 74, 5088–5090. doi: 10.1073/pnas.74.11.5088

PubMed Abstract | CrossRef Full Text | Google Scholar

Yuan, C., Lei, J., Cole, J., and Sun, Y. (2015). Reconstructing 16S rRNA genes in metagenomic data. Bioinformatics 31, i35–i43. doi: 10.1093/bioinformatics/btv231

PubMed Abstract | CrossRef Full Text | Google Scholar

Ziegler, M., Grupstra, C. G. B., Barreto, M. M., Eaton, M., BaOmar, J., Zubier, K., et al. (2019). Coral bacterial community structure responds to environmental change in a host-specific manner. Nat. Commun. 10:3092. doi: 10.1038/s41467-019-10969-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Zinger, L., Bonin, A., Alsos, I. G., Balint, M., Bik, H., Boyer, F., et al. (2019). DNA metabarcoding-need for robust experimental designs to draw sound ecological conclusions. Mol. Ecol. 28, 1857–1862. doi: 10.1111/mec.15060

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: amplicon, microbial diversity, microbiome, mock communities, next generation sequencing, shotgun libraries, target enrichment

Citation: Beaudry MS, Wang J, Kieran TJ, Thomas J, Bayona-Vásquez NJ, Gao B, Devault A, Brunelle B, Lu K, Wang J-S, Rhodes OE Jr and Glenn TC (2021) Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment. Front. Microbiol. 12:644662. doi: 10.3389/fmicb.2021.644662

Received: 21 December 2020; Accepted: 22 March 2021;
Published: 27 April 2021.

Edited by:

Daniel Yero, Autonomous University of Barcelona, Spain

Reviewed by:

Bruno Fosso, National Research Council, Italy
Yanni Sun, City University of Hong Kong, Hong Kong
Ashish Pathak, Florida Agricultural and Mechanical University, United States

Copyright © 2021 Beaudry, Wang, Kieran, Thomas, Bayona-Vásquez, Gao, Devault, Brunelle, Lu, Wang, Rhodes and Glenn. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Megan S. Beaudry, Megan.Beaudry@uga.edu

These authors share first authorship

Present address: Jincheng Wang, Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, United States; Jesse Thomas, Center for Disease Control, Atlanta, GA, United States; Natalia J. Bayona-Vásquez, Department of Biology, Oxford College of Emory University, Oxford, GA, United States; Bei Gao, School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China; Kun Lu, Department of Environmental Sciences and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States