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Improved taxonomic assignment of rumen bacterial 16S rRNA sequences using a revised SILVA taxonomic framework

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Working #taxonomy of #rumen #bacteria https://t.co/8IHaWAyBFj via @thePeerJ
RT @RobinRohwer: Better Databases make better classifications- everybody's doin' it! Here it looks like they used 16S amplicons to identif…
480 days ago
RT @RobinRohwer: Better Databases make better classifications- everybody's doin' it! Here it looks like they used 16S amplicons to identif…
RT @RobinRohwer: Better Databases make better classifications- everybody's doin' it! Here it looks like they used 16S amplicons to identif…
RT @RobinRohwer: Better Databases make better classifications- everybody's doin' it! Here it looks like they used 16S amplicons to identif…
RT @RobinRohwer: Better Databases make better classifications- everybody's doin' it! Here it looks like they used 16S amplicons to identif…
481 days ago
RT @RobinRohwer: Better Databases make better classifications- everybody's doin' it! Here it looks like they used 16S amplicons to identif…
RT @RobinRohwer: Better Databases make better classifications- everybody's doin' it! Here it looks like they used 16S amplicons to identif…
481 days ago
RT @RobinRohwer: Better Databases make better classifications- everybody's doin' it! Here it looks like they used 16S amplicons to identif…
RT @RobinRohwer: Better Databases make better classifications- everybody's doin' it! Here it looks like they used 16S amplicons to identif…
Better Databases make better classifications- everybody's doin' it! Here it looks like they used 16S amplicons to identify existing SILVA ref's that needed a better name (for rumen env). @quendi @aqcrobial https://t.co/nJt3P8WTu6
RT @thePeerJ: Improved taxonomic assignment of rumen bacterial 16S rRNA sequences using a revised SILVA taxonomic framework https://t.co/GF…
RT @thePeerJ: Improved taxonomic assignment of rumen bacterial 16S rRNA sequences using a revised SILVA taxonomic framework https://t.co/GF…
Improved taxonomic assignment of rumen bacterial 16S rRNA sequences using a revised SILVA taxonomic framework https://t.co/GFaxvBELbh https://t.co/LxIslZhty7
RT @DrMicrobeson: “Improved taxonomic assignment of rumen bacterial 16S rRNA sequences using a revised SILVA taxonomic framework” https://t…
“Improved taxonomic assignment of rumen bacterial 16S rRNA sequences using a revised SILVA taxonomic framework” https://t.co/R8zWk7VHsn
RT @Leahy_SC: If you are working on the #rumen #microbiome #16S then you should consider SILVA 123. Still allot of work to be done on the…
RT @Leahy_SC: If you are working on the #rumen #microbiome #16S then you should consider SILVA 123. Still allot of work to be done on the…
RT @Leahy_SC: If you are working on the #rumen #microbiome #16S then you should consider SILVA 123. Still allot of work to be done on the…
487 days ago
RT @Leahy_SC: If you are working on the #rumen #microbiome #16S then you should consider SILVA 123. Still allot of work to be done on the…
487 days ago
RT @Leahy_SC: If you are working on the #rumen #microbiome #16S then you should consider SILVA 123. Still allot of work to be done on the…
RT @Leahy_SC: If you are working on the #rumen #microbiome #16S then you should consider SILVA 123. Still allot of work to be done on the…
RT @Leahy_SC: If you are working on the #rumen #microbiome #16S then you should consider SILVA 123. Still allot of work to be done on the…
487 days ago
RT @Leahy_SC: If you are working on the #rumen #microbiome #16S then you should consider SILVA 123. Still allot of work to be done on the…
RT @Leahy_SC: If you are working on the #rumen #microbiome #16S then you should consider SILVA 123. Still allot of work to be done on the…
Microbiology

Introduction

Ruminants have a complex digestive system, and digestion of feed takes place initially in the rumen. Rumen microorganisms break down feed components such as cellulose- and hemicellulose-rich plant fiber, other carbohydrates, and proteins, producing short chain fatty acids that provide energy for the host. These microorganisms are thus essential for the host, and play a key role in the nutrition and productivity of ruminants. Understanding the function and composition of rumen microbial communities is useful for efforts that aim to understand or improve animal productivity and to reduce energy loss to methane, as well as other animal production characteristics (Weimer, 2015).

Rumen microbial communities contain >1010 microorganisms per gram of rumen contents (Russell & Rychlik, 2001). These belong to many different species of bacteria, archaea, ciliate protozoa, fungi, and viruses, the majority of which are yet to be cultured or characterized (Berg Miller et al., 2012; Creevey et al., 2014; Henderson et al., 2015). Analysis of taxonomically-informative marker genes allows detection and quantification of uncultivated microorganisms and is widely used to identify microorganisms associated with differences such as response to diet (Henderson et al., 2015), methane yield (Kittelmann et al., 2014), feed conversion efficiency (Carberry et al., 2012), and milk composition (Jami, White & Mizrahi, 2014). Commonly used marker genes are those for the 16S and 18S rRNAs, which may be amplified by PCR or extracted bioinformatically from metagenomic datasets (Ellison et al., 2014; Kittelmann et al., 2013; Li et al., 2016). Taxonomic identities from large sequence datasets can be efficiently inferred using streamlined analysis pipelines such as mothur (Schloss et al., 2009), QIIME (Caporaso et al., 2010), RDPipeline (Cole et al., 2014), SILVAngs (Quast et al., 2013; Klindworth et al., 2013), and STAP (Wu et al., 2008), which match new sequences generated from samples to reference sequences in taxonomic reference frameworks such as Greengenes (McDonald et al., 2012), RDP (Cole et al., 2014), and SILVA (Quast et al., 2013). Each reference sequence has a taxonomic identity associated with it, allowing the abundance of sequences affiliated with different taxa to be inferred. The criteria for inclusion and the taxonomic designations (e.g., naming conventions, taxonomic resolution) applied to reference sequences differ between taxonomic frameworks. Efforts are being made to unify naming conventions (Konstantinidis & Tiedje, 2005; Rosselló-Móra, 2012; Yilmaz et al., 2014; Yarza et al., 2014; Hinchliff et al., 2015; Parks et al., 2018), but the choice of taxonomic reference framework used to analyze a dataset has a direct impact on the results and therefore also on the ability to directly compare studies analyzed using different frameworks (Liu et al., 2008; Newton & Roeselers, 2012; Balvočiūtė & Huson, 2017; Ritari et al., 2015).

The lack of taxonomic resolution of sequence data that results from using “general purpose” frameworks has led researchers to develop “environment-specific taxonomies” to improve the taxonomic assignment at lower classification levels. This is particularly necessary for microbial systems where only a comparatively small number of microorganisms have been validly characterized and described. Examples of such custom databases include DictDb for the analysis of termite and cockroach gut microbiota (Mikaelyan et al., 2015), TaxAss for freshwater bacteria (Rohwer et al., 2018), or RIM-DB for rumen and intestinal methanogenic archaea (Seedorf et al., 2014). Overall, use of environment-specific taxonomic frameworks and reference databases significantly improves taxonomic assignment of sequence data (Ritari et al., 2015; Mikaelyan et al., 2015; Seedorf et al., 2014).

Though attempts have been made to bring more rumen bacteria into cultivation in recent years (Kenters et al., 2011; Nyonyo et al., 2013; Seshadri et al., 2018), comparatively few novel isolates have been validly described or named. This means they are unlikely to be included as named species in general purpose taxonomic frameworks, and sequences that match these organisms will be classified as “other” or “uncultured” sequences in a given phylum, class, order, or family. Furthermore, many sequences classified together at the genus level in existing frameworks display low sequence similarities, meaning their taxonomic resolution needs to be improved. Separating these out into multiple genera has been progressing since 16S rRNA gene sequence data became routinely available (e.g., Collins et al., 1994; Yutin & Galperin, 2013). Pioneering work by Kim, Morrison & Yu (2011) initiated efforts to group high-quality 16S rRNA gene sequences from rumen bacteria into genus-level clusters. However, many rumen bacterial sequences still cannot be reliably classified to the genus level, and sometimes not even at the family level, meaning there is a need to further improve the classification and resolution of rumen bacterial sequencing data. The genus level is often used for taxonomic assignment of next generation sequencing data from rumen samples, mainly because the read lengths generated are short (200 to 400 bp), making resolution to the species level unreliable depending on which region of the 16S rRNA gene is used (Kim, Morrison & Yu, 2011).

We used the bacterial 16S rRNA gene sequence dataset generated during the global rumen census (GRC) project (Henderson et al., 2015) to identify rumen bacterial groups in need of taxonomic refinement, and then refined the taxonomic designations of the most abundant taxonomic groups found in rumen samples. Here, we report our process and the incorporation of our refined nomenclature into the ARB SILVA taxonomic framework and database for further improvement in the future.

Methods

Rumen samples and microbial community data used in analyses

Sequencing data generated using 454 GS FLX Titanium chemistry from the 684 samples in the GRC study that generated usable sequence data for bacteria (Henderson et al., 2015), which covered a wide range of ruminant species on different diets, were used in this study. The samples were all processed for sequencing of bacterial 16S rRNA sequences as previously described (Henderson et al., 2015; Kittelmann et al., 2013). Raw sequencing reads (BioProjects PRJNA272135, PRJNA272136, and PRJNA273417 in NCBI’s Sequence Read Archive; Leinonen, Sugawara & Shumway (2011)) were processed in QIIME (Caporaso et al., 2010) using standard parameters unless indicated otherwise. The primers used generated amplicons containing the V1-V3 regions of the 16S rRNA gene (Henderson et al., 2015). During the split library process, sequences that were at least 400 bp long were retained (input arguments -l 400, -L 1000 -r–z truncate_remove), covering at least the V1 and V2 regions of the 16S rRNA gene. The resulting 4,557,252 sequencing reads were concatenated and grouped into 774,769 operational taxonomic units (OTUs) using UCLUST with a 97% similarity definition criterion. Taxonomic identities were assigned to the repset sequences in QIIME using a BLAST (Altschul et al., 1990) search against either the SILVA database version 119 (Quast et al., 2013), or version 119Rum (this study, see below), Greengenes (version 13_8; McDonald et al., 2012) or RDP training set versions 14 and 16 (obtained from http://www.mothur.org/wiki/RDP_reference_files). Data were summarized at the genus level. Additionally, taxonomic identities were assigned against RDP’s bacterial 16S rRNA gene dataset using Classifier release 11.4 (Cole et al., 2014; Wang et al., 2007) and a confidence cut-off of 80% to summarize data. The number of sequence reads assigned to each OTU was used to assign abundance to each repset sequence.

Improving the resolution of the SILVA taxonomic framework

Operational taxonomic units that contained at least 50 sequences at the 97% similarity cut-off were selected for further analysis (i.e., 8,985 or 1.16% of all OTUs, representing 2,833,335 or 62.2% of all GRC sequences). These representative OTU sequences were aligned with SINA version 1.2.11 (Pruesse, Peplies & Glöckner, 2012) using the SILVA SSURef database version 119 as a reference alignment. This contained 16S rRNA (gene) sequences ≥1,200 nt. The aligned representative OTU sequences were imported into ARB version 6.0.2 (Ludwig et al., 2004), together with information on their abundance and prevalence in rumen samples, and they were added to the guide tree using the inbuilt Escherichia coli filter for positions 1,044–11,892 (corresponding to E. coli 16S rRNA gene positions 28–514). The SILVA databases and associated guide trees are rigorously curated, and sequence quality inclusion criteria, guide tree construction, and maintenance are described in detail elsewhere (Pruesse et al., 2007; Glöckner et al., 2017). The 16S rRNA gene sequences of isolates included for genome sequencing in the Hungate1000 project (Seshadri et al., 2018) were also aligned and added to the guide tree as described above. We assigned taxonomic designations down to genus-equivalent level, where possible, to all predefined monophyletic bacterial groups in the guide tree that were found to contain rumen bacterial sequences. We used the following similarities between sequences in a monophyletic radiation to define a genus-level (>94.5%) or family-level (>86.5%) group (Yarza et al., 2014). Clusters that did not also contain sequences of described species were named to the lowest taxonomic level that could be assigned and a strain, clone, or uncultured genus-level group (UCG) identifier included in the name as appropriate (e.g., Bacteria; Bacteroidetes; Bacteroidia; Bacteroidales; Prevotellaceae; Prevotellaceae NK3B31 group or Bacteria; Firmicutes; Clostridia; Clostridiales; Ruminococcaceae; Ruminococcaceae UCG_001). Taxonomic designations were independently reviewed by three of the authors, and once agreed upon by all were incorporated into the SILVA 119Rum database used for testing (see below), and then incorporated into SILVA SSURef database release 123 (http://www.arb-silva.de/documentation/release-123/).

Assessing the taxonomic assignments generated by different taxonomic frameworks

The 50 most abundant and 50 most prevalent bacterial OTUs were used as a test subset to compare the differences in taxonomic assignments made using different frameworks. Abundance was defined as the average proportion of an OTU in all 684 samples. Prevalence was defined as the proportion of the 684 samples that the OTU occurred in. This subset contained 77 unique OTUs because some were both abundant and prevalent. Sequence alignments of these OTUs and the closest type strain and other cultured relatives based on sequence similarity were manually curated and used to generate similarity matrices. Sequence similarity cut-offs recommended by Yarza et al. (2014) were used to identify likely identities of OTU sequences at genus (94.5%), family (86.5%), order (82.0%), class (78.5%), and phylum (75.0%) levels, as these provide a unified basis for the classification and nomenclature of uncultured bacteria that is compatible with the taxonomy of cultured bacteria (Yarza et al., 2014).

Results

Revising the nomenclature of genus-level clusters for rumen bacteria

We first identified the radiations of abundant rumen bacteria using the SILVA 119 framework. To do this, we placed the repset sequences from the 8,985 largest OTUs, that is, those with the greatest number of sequence reads assigned to them, from the GRC dataset into the SILVA 119 tree. These fell into 241 pre-defined clusters in the guide tree provided with the SILVA 119 database. Later (see below), when the entire GRC dataset was reanalyzed after these clusters had been given unique taxonomic identifiers (again, see below), 97.8% of all sequences in the GRC dataset were assigned to these 241 clusters, showing that these potentially covered a large part of rumen bacterial diversity (Table S1).

Next, we examined these clusters based on only the sequences in the SILVA 119 database, that is, excluding the short reads from the GRC dataset. These clusters therefore contained 16S rRNA (gene) sequences ≥1,200 nt. Of these 241 clusters, 99 had no unique or had inadequate taxonomic identifiers in the SILVA 119 framework. For example, there were 12 distinct radiations named “Lachnospiraceae incertae sedis,” 16 named “Ruminococcaceae uncultured,” and three named “Coprococcus” (Table S1). This means that sequences matched to them would be assigned to an undefined or unclassified group at higher than the genus level, or they would be given a genus assignment that would be incorrect because the named reference sequence was >5.5% different to that of the type species of the genus. Here, we applied the genus-level sequence to sequence similarity cut-off of 94.5% identity proposed by Yarza et al. (2014). Each of these 99 clusters was given a unique nomenclature based on named species in the cluster, current trivial names used in the literature, or the names of isolates or other sequences in the cluster. Based on the level of sequence similarity and the level of separation from other named groups, these were provisionally given genus- or family-level status. Examples include the definition of one of many radiations designated as “Lachnospiraceae uncultured” as Lachnospiraceae group NK4B4, named after an isolate that falls into this genus-level group, and the distinction of multiple radiations previously all designated as “Erysipelotrichaceae uncultured” as Erysipelotrichaceae UCG001 to Erysipelotrichaceae UCG010, where UCG indicates a genus-level cluster without recognized cultured isolates (Table S1).

The names of an additional 11 clusters (containing 13.9% of GRC sequences) were modified to clarify their taxonomic positions, but the original names were unique and so these changes had no material impacts on the classification of sequences assigned to them. No changes were made to the nomenclature of the remaining 131 clusters, containing 20.1% of GRC sequences. Where a group contained named isolates that were clearly not in the same genus-level group as the type species of the genus, the genus name was retained but written in square brackets to indicate this. An example is the [Eubacterium] ruminantium group (Table S1), which is in the family Lachnospiraceae, and not close to the type species of the genus Eubacterium, E. limosum, which is in the family Eubacteriaceae (Ludwig, Schleifer & Whitman, 2009).

All of these new taxon designations were linked to sequences available in the SILVA SSURef database release 123 (SILVA 123 framework, http://www.arb-silva.de/documentation/release-123/). A summary of these designations is given in Table S1.

Comparison of taxonomic assignments generated using different taxonomic frameworks

To allow us to assess the impact of the improved taxonomic resolution, we produced a temporary version of SILVA119 that included these new designations, which we called SILVA 119Rum. We compared the taxonomic assignments made using this new framework with those made using four other frameworks, namely Greengenes 13_8, RDP release 11.4 (using RDP Classifier), RDP training sets 14 and 16, and the parent, SILVA 119. For our comparisons, we used all 774,769 OTUs generated from the 4,557,252 partial bacterial 16S rRNA gene sequences that had been generated from 684 rumen samples in the GRC dataset, rather than just the 8,985 largest OTUs that we used to identify the bacterial groups that needed refinement. The repset sequences from this larger set of OTUs were assigned to between 723 and 1,441 uniquely taxonomic strings using the different frameworks (Fig. 1A), although not all were resolved to the genus level (Fig. 1B). The proportion of sequences in the GRC dataset that were assigned to a taxon at any rank that was uniquely labelled within the preceding rank was also calculated (Fig. 1B). For example, genus-level groups labelled uncultured or incertae sedis without further qualifiers were not considered unique, since it is not possible to tell how many genus-level groups with the same identifier there might be in a family. There were between 557 and 1,355 identifiable genus-level taxa (Fig. 1A) using the different frameworks.

Assignment to different taxonomic ranks with different frameworks.

Figure 1: Assignment to different taxonomic ranks with different frameworks.

(A) Number of taxa identified at different taxonomic ranks in the GRC dataset using different taxonomic frameworks. Also shown are the numbers of unique taxonomic strings returned. (B) Assignment (%) of GRC sequences to defined taxa at different taxonomic ranks using different frameworks.

Overall, assignments made using RDP release 11.4 were the most conservative, returning the smallest number of taxa (Fig. 1A) and assigning the lowest proportion of sequences to a taxon at any taxonomic rank (Fig. 1B). The Greengenes taxonomic framework resulted in the second most conservative assignment of taxonomic identities to sequences, followed by the SILVA 119 and SILVA 119Rum taxonomic frameworks. Greengenes and RDP release 11.4 resulted in fewer <50% and <30% of sequences being assigned to a named genus, respectively. The SILVA 119 taxonomic frameworks allowed 55.4% of sequence data to be assigned to 751 uniquely-named genera. The improved SILVA 119Rum framework resulted in 87.1% of all sequences in the GRC dataset being assigned to one of 871 uniquely identifiable genus-level groups.

The RDP training sets (RDP versions 14 and 16) resulted in >95% of sequences being assigned at the genus level when they were used as BLAST databases (Table S2). The RDP training sets predominantly contain sequences of characterized organisms whose taxonomic strings are nearly all resolved to the genus level. It contains few reference sequences from unnamed or taxonomically poorly-resolved groups. For this reason, these frameworks are not suitable for use with the QIIME “parallel_assign_taxonomy_blast.py” script. This script always assigns a match to a sequence, and by default that match will almost always return a genus name because the RDP training sets contain few sequences without a valid genus name. Sparse databases like the RDP training sets will result in an assignment to a match that may not be a close one. This results in an over-assignment of sequences to named genera to which the sequences do not belong; however, if these databases were used with Classifier as intended, it is likely these assignments would have been given low confidence scores (Wang et al., 2007) that would not result in a genus level assignment, as we observed (Fig. 1A).

Comparison of apparent microbial community composition using different frameworks

Apparent rumen microbial community compositions were broadly comparable at phylum level, regardless of the taxonomic framework used (Fig. 2A). However, at the genus level the apparent make-up within the dominant phyla Bacteroidetes (Fig. 2B) and Firmicutes (Fig. 2C) differed considerably. This was due to the different naming conventions, different nomenclature, and finer taxonomic resolution of groups in some of the databases. This means that data analyzed using different frameworks cannot be directly compared, as can be seen in Figs. 2B and 2C, where the same dataset gave very different apparent community structures using the different taxonomic frameworks. The impact of these differences is illustrated by the taxonomic identities assigned to individual OTUs using the different taxonomic frameworks. As examples, we show the taxonomic assignments of the 77 most abundant and prevalent OTUs in the GRC dataset made using the different frameworks (selected data are shown in Table 1; full data in Table S2). In some cases, the differences were in nomenclature, but in others it was due to the lack of resolution near the genus-level. Other examples of the taxonomic equivalence of groups with different names in the different frameworks are shown in Fig. 2.

Schematic showing relative abundances of taxa after assignment of sequences using different taxonomic frameworks.

Figure 2: Schematic showing relative abundances of taxa after assignment of sequences using different taxonomic frameworks.

(A) shows assignments at the phylum level. (B) and (C) show taxa affiliated with the phyla (B) Bacteroidetes and (C) Firmicutes that occur at an abundance of at least 0.05% in any one sample. Any genus level taxa with a relative abundance below 0.05% are grouped together as “Other groups.” Blocks with the same colors represent the same taxonomic designations in the different frameworks. More detailed microbial community compositions are provided in Table S3.
Table 1:
Comparison of the nomenclature of taxonomic assignments made using different databases.
OTU ID Average abundance (%) RDP release 11.4 Greengenes 13_8 SILVA 119 SILVA 119Rum
365725 0.240 g_Succinivibrio f_Succinivibrionaceae g_Succinivibrio g_Succinivibrio
722152 0.558 c_Gammaproteobacteria f_Succinivibrionaceae f_Succinivibrionaceae g_Succinivibrionaceae UCG_001
15480 0.265 c_Gammaproteobacteria f_Succinivibrionaceae f_Succinivibrionaceae g_Succinivibrionaceae UCG_002
9138 0.231 g_Ruminobacter g_Ruminobacter g_Ruminobacter g_Ruminobacter
142948 0.556 f_Prevotellaceae g_Prevotella g_Prevotella g_Prevotella 1
664059 0.243 g_Prevotella g_Prevotella g_Prevotella g_Prevotella 7
480108 0.475 g_Fibrobacter g_Fibrobacter g_Fibrobacter g_Fibrobacter
90393 0.504 o_Clostridiales o_Clostridiales f_Christensenellaceae g_Christensenellaceae R-7 group
284365 0.269 g_Succiniclasticum g_Succiniclasticum g_Succiniclasticum g_Succiniclasticum
237285 0.407 g_Pseudobutyrivibrio g_Pseudobutyrivibrio g_Pseudobutyrivibrio g_Pseudobutyrivibrio
301314 0.051 g_Butyrivibrio g_Butyrivibrio g_Butyrivibrio g_Butyrivibrio 2
493059 0.348 f_Lachnospiraceae g_Butyrivibrio g_Butyrivibrio g_Lachnospiraceae NK3A20 group
698124 0.265 f_Lachnospiraceae f_Lachnospiraceae g_Acetitomaculum g_Acetitomaculum
732718 0.145 f_Lachnospiraceae g_Blautia g_Blautia g_Blautia
109054 0.049 f_Lachnospiraceae g_Moryella g_Moryella g_Moryella
234051 0.067 f_Lachnospiraceae g_Coprococcus f_Lachnospiraceae g_[Eubacterium] ruminantium group
311462 0.060 f_Lachnospiraceae o_Clostridiales f_Lachnospiraceae g_[Ruminococcus] gauvreauii group
205298 0.077 f_Lachnospiraceae o_Clostridiales f_Lachnospiraceae g_Lachnospiraceae FCS020 group
605934 0.068 f_Lachnospiraceae f_Lachnospiraceae f_Lachnospiraceae g_Lachnospiraceae AC2044 group
295461 0.148 f_Ruminococcaceae f_Ruminococcaceae f_Ruminococcaceae g_[Eubacterium] coprostanoligenes group
401207 0.124 f_Ruminococcaceae f_Ruminococcaceae f_Ruminococcaceae g_Ruminococcaceae NK4A214 group
237588 0.070 o_Clostridiales f_Ruminococcaceae g_Ruminococcus g_Ruminococcus 1
580981 0.140 f_Ruminococcaceae g_Ruminococcus g_Ruminococcus g_Ruminococcus 2
139212 0.180 f_Ruminococcaceae f_Ruminococcaceae g_Saccharofermentans g_Saccharofermentans
DOI: 10.7717/peerj.6496/table-1

Note:

Shown are the 24 unique examples among the 25 most abundant OTUs and 10 most abundant Lachnospiraceae and Ruminococcaceae, listing the lowest defined rank with a unique identifiable name. Each name is preceded by a letter giving the rank: c = class, o = order, f = family, g = genus. The average abundance in the GRC dataset is also given. The full dataset of 77 OTUs is given in Table S2.

Increased taxonomic resolution with SILVA 119Rum

More OTUs, and hence more sequences otherwise assigned only to the family level (such as Lachnospiraceae, Ruminococcaceae, and Succinivibrionaceae), were assigned to multiple new genus-level groups within these families using the SILVA 119Rum framework (Table 2). Other frameworks only assigned these OTUs to order or family levels, or to genera that are not monophyletic or contain sequences with sequences that have <5.5% identity. These finer scale subdivisions of families and genera are reflected in the greater number of genus-level taxa found in the GRC dataset classified using SILVA 119Rum (Figs. 1A, 2B and 2C).

Table 2:
Assignment of sequences in the GRC dataset to bacterial families and to named genera within those families.
Family Assigned RDP release 11.4 Greengenes 13_8 SILVA 119 SILVA 119Rum
Prevotellaceae To genera in family 13.76 (5) 22.20 (1) 20.61 (3) 24.80 (13)
To family 22.92 (6) 22.42 (2) 24.95 (5) 24.96 (15)
Christensenellaceae To genera in family 0 (0) 0 (0) 0.05 (1) 10.64 (1)
To family 0 (0) 0.93 (1) 10.70 (2) 10.69 (3)
Lachnospiraceae To genera in family 2.01 (25) 6.49 (20) 10.24 (27) 15.95 (69)
To family 15.56 (27) 12.59 (21) 16.90 (30) 16.98 (72)
Ruminococcaceae To genera in family 2.31 (21) 3.97 (7) 5.22 (18) 13.11 (43)
To family 10.59 (22) 11.64 (8) 13.25 (21) 13.17 (45)
Succinivibrionaceae To genera in family 0.76 (4) 0.63 (5) 0.75 (5) 1.78 (7)
To family 0.83 (5) 1.78 (6) 1.80 (6) 1.80 (8)
DOI: 10.7717/peerj.6496/table-2

Note:

The numbers are the average percentage that those sequences make up in samples in the GRC dataset. The numbers in parentheses are the number of genera to which the sequences are assigned or the number of groups within the family (these include subgroups designated as “unclassified” and “uncultured” that have no unique genus-level identifier).

The great diversity of rumen Lachnospiraceae and Ruminococcaceae that is not yet formally described and named has been reported previously (Creevey et al., 2014). Sequences from members of Lachnospiraceae made up >17% of the GRC dataset. Refinement of the taxonomy increased the genus level separation, with three quarters of those sequences assigned to more exact genus-level taxa (Table 3; Table S1). This included separation of undefined taxa named Lachnospiraceae incertae sedis and uncultured Lachnospiraceae. Named genera like Butyrivibrio, Blautia, and Coprococcus also appeared to contain sequences originating from multiple genus-level groups. Similarly, Ruminococcaceae contained >13% of all GRC sequences, and over half of these were classified in SILVA 119 as Ruminococcaceae incertae sedis or as uncultured Ruminococcaceae. These were separated into 20 new genus-level groups, one of which, Ruminococcaceae NK4A214 group, contained nearly 2.8% of all GRC sequences (Table S1). This group is named after isolate NK4A214, the first recognized isolate that belongs in this apparently new genus (Kenters et al., 2011).

Table 3:
Genus-level taxa in the family Lachnospiraceae.
SILVA 119 SILVA 119Rum
Taxon Abundance (%) Abundance (%) Taxon
Acetitomaculum 1.194 1.194 Acetitomaculum
Blautia 1.574 0.558 Blautia
1.016 Lachnospiraceae NK4A136 group
Butyrivibrio 4.197 1.479 Butyrivibrio 2
2.718 Lachnospiraceae NK3A20 group
Coprococcus 0.331 0.161 Coprococcus 1
0.139 Coprococcus 2
0.030 Coprococcus 3
Dorea 0.098 0.098 Dorea
Lachnospiraceae incertae sedis 2.552 0.031 (Clostridium) aminophilum group
0.017 (Clostridium) herbivorans group
0.260 (Clostridium) phytofermentans group
0.013 (Clostridium) saccharolyticum group
0.065 (Eubacterium) cellulosolvens group
0.313 (Eubacterium) hallii group
0.267 (Eubacterium) oxidoreducens group
0.701 (Eubacterium) ruminantium group
0.173 (Eubacterium) ventriosum group
0.651 (Ruminococcus) gauvreauii group
0.015 Lachnospiraceae UCG 004
0.046 Lachnospiraceae UCG 005
Lachnospira 0.099 0.099 Lachnospira
Marvinbryantia 0.503 0.503 Marvinbryantia
Moryella 0.163 0.163 Moryella
Oribacterium 0.344 0.344 Oribacterium
Pseudobutyrivibrio 1.355 1.355 Pseudobutyrivibrio
Roseburia 0.610 0.610 Roseburia
Shuttleworthia 0.125 0.125 Shuttleworthia
Syntrophococcus 0.198 0.198 Syntrophococcus
Lachnospiraceae uncultured 3.692 0.965 Lachnospiraceae AC2044 group
0.281 Lachnospiraceae FCS020 group
0.016 Lachnospiraceae FE2018 group
0.049 Lachnospiraceae NC2004 group
0.389 Lachnospiraceae ND3007 group
0.291 Lachnospiraceae NK4B4 group
0.052 Lachnospiraceae UCG 001
0.102 Lachnospiraceae UCG 002
0.012 Lachnospiraceae UCG 003
0.138 Lachnospiraceae UCG 006
0.020 Lachnospiraceae UCG 007
0.311 Lachnospiraceae UCG 008
0.087 Lachnospiraceae UCG 009
0.979 Lachnospiraceae XPB1014 group
DOI: 10.7717/peerj.6496/table-3

Note:

The taxa are grouped so that the finer divisions using SILVA 119Rum are lined up alongside the original divisions made using SILVA 119. The abundances are the averages in the GRC dataset.

We divided some genera, such as Prevotella and Ruminococcus, into multiple genus-level groups based on their degree of sequence divergence. Sequences assigned to Ruminococcus were split between two different genus level groups, Ruminococcus 1, characterized by R. flavefaciens and R. albus and containing 67% of all Ruminococcus sequences in the GRC dataset, and Ruminococcus 2, which contains R. bromii and 33% of all Ruminococcus sequences in the GRC dataset (Fig. 2C; Table S1). The potential separation of R. bromii from the species that fall into Ruminococcus 1 was suggested by an early 16S rRNA gene sequence analysis of this genus (Rainey & Janssen, 1995).

The genus Prevotella contained 22.0% of all sequences in the GRC dataset when originally analyzed (Henderson et al., 2015). Finer scale subdivision of this taxon into genus-level groups still resulted in 18.3% of all sequences falling into a genus we designated Prevotella 1, which contained the type species of the genus, P. melaninogenica, and also P. ruminicola. Four additional genus-level groups of sequences that previously fell into Prevotella were identified and given unique designations: Prevotella 2, Prevotella 6, Prevotella 7, and Prevotella 9. Six further genus-level clusters, all previously named “uncultured,” were given unique designations, and so increased the resolution within the family Prevotellaceae (Fig. 2B; Table S1).

Other groupings defined in the SILVA 119Rum framework allowed assignment of sequences to taxa that were not included in the other databases. The RC9 gut group was absent from the Greengenes and RDP release 11.4 frameworks, meaning this group, making up on average 5.7% of sequences, was only identified when the SILVA 119 or SILVA 119Rum databases were used. Otherwise they were assigned to the order Bacteroidales. Members of the family Christensenellaceae were prominent in the GRC dataset, making up 10.7% of all sequences when reanalyzed using the SILVA 119Rum framework. This group was poorly resolved until a cluster containing isolate R-7 was defined and given a unique identifier (Christensenellaceae R-7 group) in SILVA 119Rum (Table 2). This genus-level group contained 10.6% of all GRC sequences and 99.5% of all of the sequences that were assigned to the family Christensenellaceae. In some cases, sequences that were previously assigned to poorly-defined groups were added to taxa that had names, because the reference sequences they matched to were given the same family-level designation. Examples include the family-level Bacteroidales BS11 gut group and S24-7 group.

Discussion

In the initial study of the GRC, nearly half (45.8%) of all sequences in the dataset could not be assigned to a named genus (Henderson et al., 2015). The four most abundant of these groups in the GRC dataset were unclassified Clostridiales (15.3% of all sequences in the GRC dataset), unclassified Bacteroidales (8.4%), unclassified Ruminococcaceae (7.9%), and unclassified Lachnospiraceae (6.3%), which together accounted for 37.9% of the GRC dataset. The single largest named genus in the GRC dataset was the genus Prevotella, containing 22.0% of all sequences. The monophyletic radiation of Prevotella spp. that contains the type species P. melaninogenica has 16S rRNA sequences with average similarities of 90.6% (with similarities as low as 72.4%). This indicates that it may contain multiple genera, if a similarity threshold of 93% (Kenters et al., 2011) or 94.5% (Yarza et al., 2014) is applied. These similarity thresholds may not be applicable to this genus, and 16S rRNA gene sequence differences may not reflect phenotypic diversity (Achenbach & Coates, 2000). However, until the genus is thoroughly revised, some definition within it might uncover differences between samples that are masked when 22% of all sequences fall into one genus-level group. Similarly, the use of a 93% or 94.5% similarity threshold for other poorly-defined genera, or genus-level clusters without cultured representatives must be regarded as a temporary criterion until more is known about these radiations of bacteria. Overall, there is a need to further describe and classify novel and poorly characterized bacteria into appropriate taxa with validly published names to obtain a better understanding of the true diversity and nature of rumen microorganisms. Ideally, this should include isolation of representative strains in pure culture and their physiological characterization in combination with genome sequencing.

To improve the taxonomic assignment of sequences to identifiable groups at the genus level, we refined the nomenclature associated with bacterial 16S rRNA gene sequences from the rumen or from ruminal isolates in the SILVA 119 framework (Quast et al., 2013). We did this because the genus-level is a taxonomic rank that may be more likely to include organisms that share similar functional or structural features (Philippot et al., 2010) compared to higher taxonomic ranks, and so is a useful level to compare differences in community structure. The refinements that we developed were included in the release of SILVA that followed 119, namely SILVA 123, and are currently in the latest version, SILVA 132.

The parent SILVA 119 framework allowed 55.4% of the sequence data to be assigned to 751 uniquely identifiable genus-level groups, which was greater than when using RDP Classifier and Greengenes. The improved resolution of the taxonomy of rumen bacteria in the SILVA 119Rum framework increased this to 87.1% of all sequences being assigned to one of 871 uniquely identifiable genus-level groups. The new designations must be considered to be pragmatic decisions based on a desire to name these clusters for use in microbial community profiling. These designations have no formal standing in the taxonomic literature. Accurate taxonomic and nomenclatural decisions will rely on far more detailed polyphasic study of the organisms in these clusters. They have been defined purely to allow a better naming resolution of rumen bacterial community sequence data when they are grouped phylogenetically. Further refinements may also allow better taxonomic definition of genomes assembled from metagenomic data (if these have 16S rRNA genes associated with them), which in turn will allow better assessment of likely metabolic functions of uncultured taxa of rumen bacteria.

Conclusions

The refined framework of nomenclature for 16S rRNA gene sequences from rumen bacteria developed here should be useful for investigating rumen microbial community structure. It provides a better separation of some of the large undefined catch-all groups above genus level by applying unique names to radiations that were previously not individually identifiable. We expected that the interim designations developed here will gradually be replaced by valid Linnaean nomenclature as these bacteria are systematically described.

Supplemental Information

Taxonomic strings of rumen bacterial groups as they were in SILVA 119 (“tax_slv” string in SSURef_NR99_119), and what they were after refinement in SILVA 123 (“tax_slv” string in SSURef_NR99_123).

Also given are the average abundance, prevalence (presence in samples), and maximum abundance (in any single sample) of sequences in 684 samples in the GRC dataset that were assigned to each taxon using the SILVA 123 framework. Prevalence of an OTU is how many rumen samples it appears in (n = 684).

DOI: 10.7717/peerj.6496/supp-1

Extended data for Table 1.

Prevalence of an OTU is in how many rumen samples it appears (n = 684).

DOI: 10.7717/peerj.6496/supp-2

Taxonomic assignment of sequences in the GRC dataset to taxa using five different taxonomic frameworks.

DOI: 10.7717/peerj.6496/supp-3