Open Access

Veillonella, Firmicutes: Microbes disguised as Gram negatives

  • Tammi Vesth
  • , Aslı Ozen,
  • , Sandra C. Andersen
  • , Rolf Sommer Kaas
  • , Oksana Lukjancenko
  • , Jon Bohlin
  • , Intawat Nookaew
  • , Trudy M. Wassenaar
  • and David W. Ussery
Corresponding author

DOI: 10.4056/sigs.2981345

Received: 15 December 2013

Accepted: 15 December 2013

Published: 20 December 2013

Abstract

The Firmicutes represent a major component of the intestinal microflora. The intestinal Firmicutes are a large, diverse group of organisms, many of which are poorly characterized due to their anaerobic growth requirements. Although most Firmicutes are Gram positive, members of the class Negativicutes, including the genus Veillonella, stain Gram negative. Veillonella are among the most abundant organisms of the oral and intestinal microflora of animals and humans, in spite of being strict anaerobes. In this work, the genomes of 24 Negativicutes, including eight Veillonella spp., are compared to 20 other Firmicutes genomes; a further 101 prokaryotic genomes were included, covering 26 phyla. Thus a total of 145 prokaryotic genomes were analyzed by various methods to investigate the apparent conflict of the Veillonella Gram stain and their taxonomic position within the Firmicutes. Comparison of the genome sequences confirms that the Negativicutes are distantly related to Clostridium spp., based on 16S rRNA, complete genomic DNA sequences, and a consensus tree based on conserved proteins. The genus Veillonella is relatively homogeneous: inter-genus pair-wise comparison identifies at least 1,350 shared proteins, although less than half of these are found in any given Clostridium genome. Only 27 proteins are found conserved in all analyzed prokaryote genomes. Veillonella has distinct metabolic properties, and significant similarities to genomes of Proteobacteria are not detected, with the exception of a shared LPS biosynthesis pathway. The clade within the class Negativicutes to which the genus Veillonella belongs exhibits unique properties, most of which are in common with Gram-positives and some with Gram negatives. They are only distantly related to Clostridia, but are even less closely related to Gram-negative species. Though the Negativicutes stain Gram-negative and possess two membranes, the genome and proteome analysis presented here confirm their place within the (mainly) Gram positive phylum of the Firmicutes. Further studies are required to unveil the evolutionary history of the Veillonella and other Negativicutes.

Background

The genus Veillonella, belonging to Negativicutes, consists of anaerobic, non-fermentative, Gram-negative cocci, that are normally observed in pairs or short chains, and are non-sporulating and non-motile [1]. Veillonella spp. are abundant in the human microbiome and are found in the oral, respiratory, intestinal and genitourinary flora of humans and animals; they can make up as much as 10% of the bacterial community initially colonizing the enamel [2] and are found throughout the entire oral cavity [3], especially on the tongue dorsum and in saliva [4]. The importance of Veillonella spp. in human infections is uncertain, and they are generally considered to be of low virulence. Veillonella form biofilms, often with Streptococcus spp., and species of these genera have been found to be more abundant in the oral microflora of people with poor oral health [5]. Studies have shown that during formation of early dental plaque, the fraction of Veillonella spp. changes in mixed-microbial colonies with streptococci [6]. Thus, Veillonella spp. may play a role in caries formation as they utilize the lactic acid produced by the organisms conducive to caries [7]. Veillonella are also among the most common anaerobic species reported from pulmonary samples and are frequently recovered from cystic fibrosis cases [8]. The organisms are also abundant in the human gut flora, where their numbers were found to be higher in children with type I diabetes compared to healthy controls [9]. Currently, 12 species of Veillonella have been characterized [10,11] including V. parvula, V. atypica and V. dispar, which are found in the human oral cavity.

The Negativicutes are the only diderm (literally 'two skins') members of the phylum Firmicutes as they possess an inner and an outer membrane. Their placement within the Firmicutes has been widely accepted, and has been confirmed by 16S rRNA analysis [12]. However, their genomes have not been analyzed in detail to confirm their taxonomic position. This work presents a broad analysis of the Negativicutes with focus on the Veillonella spp. using comparative microbial genomics. A total of 24 genomes from the Negativicutes were compared to 121 genomes covering most of the taxonomic span of sequenced bacterial genomes. We investigated how the Negativicutes genomes compared to other bacterial genomes using three different and complementary approaches: 1) phylogenetic trees to visualize the relative distance of the Negativicutes genomes to other genomes; 2) amino acid composition, nucleotide tetramer frequency and metabolism analysis using 2-D clustering and heatmaps to compare genomes; and 3) proteomic comparison across the Negativicutes genomes.

Materials and Methods

Genome sequences used for analysis

The set of 145 genomes included in this study (24 Negativicutes genomes and 121 other prokaryotic genomes covering 26 phyla) are listed in Table 1.

Table 1

Genomes used in this study

Phylum

   Name of organism and strain

   Strain designation

  Type strain?

   NCBI Taxon ID

    NCBI Project ID

Acidobacteria

   Acidobacterium capsulatum

   ATCC 51196

  Yes

   240015

    28085

Acidobacteria

   “Korebacter versatiles”

   Ellin 345

   204669

    15771

Acidobacteria

   “Solibacter usitatus”

   Ellin6076

   234267

    12638

Actinobacteria

   Bifidobacterium bifidum

   317B

  No

   1681

    42863

Actinobacteria

   Catenulispora acidiphila

   ID139908, DSM 44928

  Yes

   479433

    21085

Actinobacteria

   Corynebacterium pseudotuberculosis

   C231

  No

   681645

    40875

Actinobacteria

   Segniliparus rugosus

   ATCC BAA-974

  Yes

   679197

    40685

Actinobacteria

   Streptomyces bingchenggensis

   BCW-1

  Name not validly published

   749414

    46847

Actinobacteria

   Tropheryma whipplei

   Twist

  Yes

   203267

    95

Aquificae

   Persephonella marina

   EX-H1

  Yes

   123214

    12526

Aquificae

   Sulfurihydrogenibium sp.

   YO3AOP1

  No type strain available

   436114

    18889

Aquificae

   Thermocrinis albus

   HI 11/12, DSM 14484

  Yes

   638303

    37275

Bacteroidetes

   Bacteroides thetaiotaomicron

   VPI-5482

  Yes

   226186

    399

Bacteroidetes

   Candidatus Sulcia muelleri

   DMIN

   641892

    37785

Bacteroidetes

   Chitinophaga pinensis

   UQM 2034, DSM 2588

  Yes

   485918

    27951

Bacteroidetes

   Paludibacter propionicigenes

   WB4, DSM 17365

  Yes

   694427

    42009

Chlamydiae

   Protochlamydia amoebophila

   UWE25

  Yes

   264201

    10700

Chlamydiae

   Chlamydia trachomatis

   E/Sweden2

  No

   634464

    43167

Chlamydiae

   Chlamydophila pneumoniae

   AR39

  No

   115711

    247

Chlamydiae

   Waddlia chondrophila

   WSU 86-1044

  Yes

   716544

    43761

Chlorobi

   Chlorobium chlorochromatii”

   CaD3

  Name not validly published

   340177

    13921

Chlorobi

   Chlorobium tepidum

   TLS

  Yes

   194439

    302

Chloroflexi

   Chloroflexus aggregans

   DSM 9485

  Yes

   326427

    16708

Chloroflexi

   Dehalococcoides sp

   BAV1

  No

   216389

    15770

Chloroflexi

   Herpetosiphon aurantiacus

   ATCC 23779

  Yes

   316274

    16523

Chloroflexi

   Roseiflexus sp.

   RS-1

  No type strain available

   357808

    16190

Cyanobacteria

   Anabaena variabilis 3

   ATCC 2941

  No

   240292

    10642

Cyanobacteria

   Cyanothece sp.

   PCC 7822

  No

   497965

    28535

Cyanobacteria

   Prochlorococcus marinus

   MIT9301

  No

   167546

    15746

Cyanobacteria

   Synechocystis sp.

   PCC6803

  No

   1148

    60

Deferribacteres

   Calditerrivibrio nitroreducens

   Yu37-1, DSM 19672

  Yes

   768670

    49523

Deferribacteres

   Deferribacter desulfuricans

   SSM1, DSM 14783

  Yes

   197162

    37285

Deferribacteres

   Denitrovibrio acetiphilus

   N2460, DSM 12809

  Yes

   522772

    29431

Deinococcus-Thermus

   Oceanithermus profundus

   506, DSM 14977

  Yes

   670487

    40223

Deinococcus-Thermus

   Thermus thermophilus

   HB8

  Yes

   300852

    13202

Deinococcus-Thermus

   Truepera radiovictrix

   RQ-24, DSM 17093

  Yes

   649638

    38371

Dictyoglomi

   Dictyoglomus turgidum

   DSM 6724

  Yes

   515635

    29175

Elusimicrobia

   Elusimicrobium minutum

   Pei 191

  Yes

   445932

    19701

Fibrobacteres

   Fibrobacter succinogenes

   S85

  Yes

   59374

    32617

Firmicutes

   Acetohalobium arabaticum

   Z-7288, DSM 5501

  Yes

   574087

    32769

Firmicutes

   Acidaminococcus fermentans

   VR4, DSM 20731

  Yes

   591001

    33685

Firmicutes

   Acidaminococcus sp.

   D21

  No type strain available

   563191

    34117

Firmicutes

   Alkaliphilus oremlandii

   OhILAs

  Yes

   350688

    16083

Firmicutes

   Bacillus subtilis subsp. subtilis

   168

  Yes

   224308

    76

Firmicutes

   Clostridium botulinum

   F Langeland

  No

   441772

    19519

Firmicutes

   Clostridium cellulolyticum

   H10

  Yes

   394503

    17419

Firmicutes

   Clostridium difficile

   630 (epidemic type X)

  No

   272563

    78

Firmicutes

   Desulfotomaculum reducens”

   MI-1

  Name not validly published

   349161

    13424

Firmicutes

   Dialister invisus

   DSM 15470

  Yes

   592028

    33143

Firmicutes

   Dialister micraerophilus

   Oral Taxon 843 DSM 19965

  Yes

   888062

    53029

Firmicutes

   Dialister micraerophilus

   UPII-345-E

  No

   910314

    59521

Firmicutes

   Enterococcus faecalis

   V583

  No

   226185

    70

Firmicutes

   Eubacterium cylindroides

   T2-87

  No

   717960

    45917

Firmicutes

   Eubacterium rectale

   A1-86, DSM 17629

  No

   39491

    39159

Firmicutes

   Exiguobacterium sibiricum

   255-15

  Yes

   262543

    10649

Firmicutes

   Geobacillus kaustophilus

   HTA426

  Yes

   235909

    13233

Firmicutes

   Lactococcus lactis    cremoris

MG1363

  No

   416870

    18797

Firmicutes

   Lysinibacillus sphaericus

   C3-41

  No

   444177

    19619

Firmicutes

   Megamonas hypermegale

   ART12/1

  No

   158847

    39163

Firmicutes

   Megasphaera genomo sp.

   type 1 28L

  No type strain available

   699218

    42553

Firmicutes

   Megasphaera micronuciformis

   F0359

  No

   706434

    43125

Firmicutes

   Mitsuokella multacida

   A 405-1, DSM 20544

  Yes

   500635

    28653

Firmicutes

   Paenibacillus sp.

   JDR-2

  No

   324057

    20399

Firmicutes

   Phascolarctobacterium sp.

   YIT 12067

  No

   626939

    48505

Firmicutes

   Selenomonas artemidis

   F0399

  No

   749551

    47277

Firmicutes

   Selenomonas flueggei

   ATCC 43531

  Yes

   638302

    37273

Firmicutes

   Selenomonas noxia

   ATCC 43541

  Yes

   585503

    34641

Firmicutes

   Selenomonas sp.

   Oral Taxon 137 F0430

  No type strain available

   879310

    52055

Firmicutes

   Selenomonas sp.

   Oral Taxon 149 67H29BP

  No type strain available

   864563

    50535

Firmicutes

   Selenomonas sputigena

   DSM 20758

  Yes

   546271

    51247

Firmicutes

   Staphylococcus aureus aureus

   ED98

  No

   681288

    39547

Firmicutes

   Streptococcus pneumoniae

   TIGR4

  No

   170187

    277

Firmicutes

   Thermoanaerobacter sp.

   X514

  Name not validly published

   399726

    16394

Firmicutes

   Thermosinus carboxydivorans

   Nor1

  Yes

   401526

    17587

Firmicutes

   Turicibacter sp.

   PC909 702450 42765

  No

Firmicutes

   Veillonella atypica

   ACS-049-V-Sch6

  No

   866776

    51075

Firmicutes

   Veillonella atypica

   ACS-134-V-Col7a

  No

   866778

    51079

Firmicutes

   Veillonella dispar

   ATCC 17748

  Yes

   546273

    30491

Firmicutes

   Veillonella parvula

   ATCC 17745

  No

   686660

    41557

Firmicutes

   Veillonella parvula

   Te3, DSM 2008

  Yes

   479436

    21091

Firmicutes

   Veillonella sp.

   3 1 44

  Name not validly published

   457416

    41975

Firmicutes

   Veillonella sp.

   6 1 27

  Name not validly published

   450749

    41977

Firmicutes

   Veillonella sp.

   Oral Taxon 158 F0412

  Name not validly published

   879309

    52053

Fusobacteria

   Fusobacterium nucleatum nucleatum

   ATCC 25586

  Yes

   190304

    295

Fusobacteria

   Ilyobacter polytropus

   CuHBu1, DSM 2926

  Yes

   572544

    32577

Fusobacteria

   Leptotrichia buccalis

   C-1013-b, DSM 1135

  Yes

   523794

    29445

Fusobacteria

   Sebaldella termitidis

   NCTC 11300

  Yes

   526218

    29539

Fusobacteria

   Streptobacillus moniliformis

   9901, DSM 12112

  Yes

   519441

    29309

Planctomycetes

   Pirellula staleyi

   DSM 6068

  Yes

   530564

    29845

Planctomycetes

   Planctomyces limnophilus

   Mu 290, DSM 3776

  Yes

   521674

    29411

Proteobacteria

   Acinetobacter baumannii

   SDF

  No

   509170

    13001

Proteobacteria

   Alkalilimnicola ehrlichii

   MLHE-1

  Yes

   187272

    15763

Proteobacteria

   Arcobacter nitrofigilis

   DSM 7299

  Yes

   572480

    32593

Proteobacteria

   Burkholderia xenovorans

   (fungorum) LB400

  Yes

   266265

    254

Proteobacteria

   Campylobacter jejuni    doylei

269.97

  No

   360109

    17163

Proteobacteria

   Candidatus Pelagibacter ubique

   SAR11 HTCC1062

  Name not validly published

   335992

    13989

Proteobacteria

   Candidatus Zinderia insecticola

   CARI

  Name not validly published

   871271

    51243

Proteobacteria

   Cellvibrio japonicus

   Ueda107

  Yes

   498211

    28329

Proteobacteria

   Cupriavidus taiwanensis

   LMG19424

  Yes

   164546

    15733

Proteobacteria

   Escherichia coli

   K-12, MG1655

  No

   511145

    225

Proteobacteria

   Geobacter uraniireducens

   Rf4

  Yes

   351605

    15768

Proteobacteria

   Hahella chejuensis

   KCTC 2396

  Yes

   349521

    16064

Proteobacteria

   Haliangium ochraceum

   SMP-2, DSM 14365

  Yes

   502025

    28711

Proteobacteria

   Helicobacter pylori

   908

  No

   869727

    50869

Proteobacteria

   Lawsonia intracellularis

   PHE/MN1-00

  No

   363253

    183

Proteobacteria

   Magnetococcus sp.

   MC-1

  Name not validly published

   156889

    262

Proteobacteria

   Methylobacterium nodulans

   ORS2060

  Yes

   460265

    20477

Proteobacteria

   Neisseria meningitidis

   Z2491

  No

   122587

    252

Proteobacteria

   Neorickettsia sennetsu

   Miyayama

  Yes

   222891

    357

Proteobacteria

   Nitrosomonas eutropha

   C91 (C71)

  Yes

   335283

    13913

Proteobacteria

   Photorhabdus luminescens laumondii

   TT01

  Yes

   243265

    9605

Proteobacteria

   Polynucleobacter necessarius

   STIR1

  No

   452638

    19991

Proteobacteria

   Pseudomonas aeruginosa

   LESB58

  No

   557722

    31101

Proteobacteria

   Pseudomonas fluorescens

   SBW25

  No

   216595

    31229

Proteobacteria

   Pseudomonas stutzeri

   A1501

  No

   379731

    16817

Proteobacteria

   Salmonella enterica enterica

   PT4 P125109

  No

   550537

    30687

Proteobacteria

   Shewanella oneidensis

   MR-1

  Yes

   211586

    335

Proteobacteria

   Sorangium cellulosum

   So ce56

  No

   448385

    28111

Proteobacteria

   Stigmatella aurantiaca

   DW4 /3-1

  No

   378806

    52561

Proteobacteria

   Sulfurospirillum deleyianum

   5175, DSM 6946

  No

   525898

    29529

Proteobacteria

   Vibrio cholerae

   O395

  No

   345073

    32853

Spirochaetes

   Borrelia turicatae

   91E135

  Yes

   314724

    13597

Spirochaetes

   Brachyspira murdochii

   56-150, DSM 12563

  Yes

   526224

    29543

Spirochaetes

   Leptospira interrogans

   lai 56601

  No

   189518

    293

Synergistetes

   Thermanaerovibrio acidaminovorans

   Su883, DSM 6589

  Yes

   525903

    29531

Tenericutes

   Acholeplasma laidlawii

   PG-8A

  No

   441768

    19259

Tenericutes

   Candidatus Phytoplasma asteris

   yellows witches'-broom AY-WB 322098

  Name not validly published

   13478

Tenericutes

   Candidatus Phytoplasma mali

   AT

  Name not validly published

   37692

    25335

Tenericutes

   Mycoplasma genitalium

   G37

  Yes

   243273

    97

Tenericutes

   Mycoplasma pneumoniae

   FH

  No

   722438

    49525

Tenericutes

   Ureaplasma parvum

   sv 3, ATCC 27815

  No

   505682

    19087

Thermotogae

   Fervidobacterium nodosum

   Rt17-B1

  Yes

   381764

    16719

Thermotogae

   Kosmotoga olearia

   TBF 19.5.1

  Yes

   521045

    29419

Thermotogae

   Petrotoga mobilis

   SJ95

  Yes

   403833

    17679

Thermotogae

   Thermotoga naphthophila

   RKU-10

  Yes

   590168

    33663

Verrucomicrobia

   Akkermansia muciniphila

   ATCC BAA-835

  Yes

   349741

    20089

Verrucomicrobia

   Opitutus terrae

  Yes

   PB90-1

    452637

Crenarchaeota

   Sulfolobus solfataricus

   P2

   273057

    108

Crenarchaeota

   Thermosphaera aggregans

   M11TL, DSM 11486

  Yes

   633148

    36571

Euryarchaeota

   Halogeometricum borinquense

   PR3, DSM 11551

  Yes

   469382

    20743

Euryarchaeota

   Methanocella sp.

   RC-I

  Name not validly published

   351160

    19641

Euryarchaeota

   Methanothermus fervidus

   V24S, DSM 2088

  Yes

   523846

    33689

Korarchaeota

   Candidatus Korarchaeum cryptofilum

   OPF8

  Name not validly published

   374847

    16525

Nanoarchaeota

   “Nanoarchaeum equitans”

   Kin4-M

  Name not validly published

   228908

    9599

16S rRNA tree

For this analysis, 16S rRNA sequences were predicted from the whole genome sequences of the selected organisms, using the RNAmmer algorithm [13]. These sequences were aligned using the MAFFT program, with the iterative refinement algorithm using maximum iteration (1000) and default parameters for gap penalties [14]. A distance tree was constructed using MEGA5 [15] with the Neighbor-joining algorithm [16] and 1,000 bootstrap re-samplings. The taxa in the resulting tree were collapsed to phyla, except for the Negativicutes.

Composition Vector Tree (CV)

A Composition Vector Tree was constructed based on protein sequences of the 145 selected genomes using a webserver (available at Web Site) with the K parameter set at 6 [17]. The outcome from the program is a distance matrix based on amino acid sequence comparisons, which is then used to generate a phylogenetic tree with the neighbor-joining method. In the shown tree, the outgroup chosen was Methanothermus fervidus (an Archaea). After tree visualization with MEGA5, branches were collapsed wherever possible with the exception of the Negativicutes branch, which remained expanded.

Consensus tree of conserved genes

Using the list of universally conserved core genes, previously identified by Ciccarelli et al. [18], and an implementation of BLAST, a set of genes that was shared among all 145 genomes was identified. Proteins that had no match in at least one genome or showed poor E-value were eliminated. The 27 conserved core genes were extracted (Table 1) and a multiple alignment was produced using MUSCLE software [19]. A set of phylogenetic trees was constructed by PAUP [20] and a best-fit consensus tree was generated using Phylogeny Inference package (PHYLIP) as described elsewhere [21]. Bootstrap values were found after 27 re-samplings, which is equal to the number of gene families conserved in all the analyzed genomes.

DNA tetramer analysis and amino acid usage

A tetramer frequency heatmap was constructed from the observed ratios of tetra-nucleotide frequencies divided by estimated tetra-nucleotide frequencies for each genome [22]. The estimated tetra-nucleotides were computed from the genomes' base composition. The ratio of observed over expected frequency was used for hierarchical clustering using complete linkage and Euclidean distance, which was subsequently performed with respect to both strain and tetramer frequencies.

The amino acid heatmap is based on frequencies of deduced proteomic amino acids from each genome normalized with respect to the total number of amino acids in each genome. The amino acid frequencies for each genome were clustered using complete linkage and Euclidean distance with respect to both genomes and amino acids. The heatmap was made using the R package ggplot2 [23].

Comparison of metabolism potential

The protein sequences of Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology categories [24] were downloaded and only the Bacterial sequences were considered. The Hidden Markov model (HMM) of each ortholog was generated using HMMER version 3 [25] based on the multiple alignment of each orthologous set of KEGG proteins, using MUSCLE software [19]. The 145 proteomes were queried against the HMMs to infer their ontology. A cutoff of 1×10−30 was used for statistical significance. A heatmap of each pathway and process derived from the database KEGG was illustrated based on normalized abundance of the enzymes present in each pathway. The heatmap and hierarchical clustering were performed in the software R [23].

Construction of BLAST matrix and proteome comparison

Reciprocal BLAST was performed between each genome pair. The program blastall version 2.2.25 was used for BLAST implementation using default settings (BLASTp, E-value set to 1×10−5 for non-homologs and 1×10−8 for homologs, without filtering). A hit was considered significant at a BLAST cutoff of 95% identity and 95% coverage (of the longest gene in comparison). The number of hits was then given as a percentage of the genes in the column representing the corresponding genome. The diagonal designates internal homologs, computed by blasting each genome with itself. To avoid including identical genes, the second highest scoring hits were used. Furthermore, we also performed homology reduction of the diagonal hits, using an implementation of the Hobohm algorithm [26].

Results

Twenty-four Negativicutes genomes were compared to 121 other prokaryotic genomes covering 22 Bacterial and 4 Archaeal phyla. When available, at least two genomes were included for every phylum. The first analysis presented here is based on 16S rRNA alignments. A single 16S rRNA gene was extracted from each of the genomes and an alignment was produced spanning the maximum length of the gene. A phylogenetic tree was constructed based on this alignment, as shown in Figure 1. With the exception of the Negativicutes, branches of the tree were collapsed in those cases where the analyzed species within a phylum clustered together. With the exception of some Firmicutes, the analyzed genomes cluster according to their phylum, although the Deferribacteres phylum is mixed with the Proteobacteria phyla, and two members of Proteobacteria are not positioned with other members of their phylum (Lawsonia intracellularis and Magnetococcus). That most phyla could be collapsed is consistent with the weight of 16S rRNA similarities in currently accepted taxonomic descriptions of prokaryotes. The Firmicutes, however, show less consistency. Although most of the analyzed Firmicutes cluster together, two species are separated from the Firmicutes branch (Eubacterium cylindroides and Thermoanaerobacter sp., both members of Clostridia). The Negativicutes are positioned within the Firmicutes cluster, and this part of the tree is expanded in the figure for clarity. As can be seen, phylogeny of the 16S rRNA gene provides good resolution between the different genera of the analyzed Negativicutes. All Veillonella spp. are clustered within one branch of the Negativicutes. The Acidaminococcaceae (to which Phascolarctobacterium spp. also belong) are placed within the cluster of the Veillonellaceae, in accordance with their current classification [27]. The Acidaminococcaceae used to be recognized as a separate family within the Negativicutes, just like the Veillonellaceae, and during preparation of this contribution these two families were presented as such in the Taxonomy database at NCBI. Of note is the relatively close relationship between Negativicutes and two Clostridium species (C. botulinum and C. cellulolyticum), which does not cluster with other members of the Clostridium genus (Figure 1). That genus displays a high degree of variation and re-classification of some of the members of this genus is in progress (see for example [27]). That two members of the Clostridia are even placed outside the Firmicutes phylum is an indication of 16S rRNA gene sequence heterogeneity within this class.

Figure 1

Phylogenetic neighbor-joining tree based on 16S rRNA genes extracted from 145 genomes (24 Negativicutes and 121 prokaryotic genomes representing 26 phyla). Bootstrap values of 50 and higher are indicated. With the exception of the Negativicutes, branches where all organisms belong to the same phyla are collapsed and named by the phyla they represent. The green shading indicates the position of Firmicutes. The collapsed branch of the Bacilli, marked (1), contains Turicibacter sanguinis, a Firmicutes member of the Erysipelotrichales as well as Bacilli members. An uncollapsed tree is included in the supplementary material.

Next, all protein-coding genes of the analyzed genomes were compared and a composition vector tree (CVtree) was produced, based on amino acid sequences (Figure 2). The topology of the resulting tree is generally in accordance with the 16S rRNA tree shown in the previous figure. As indicated by the collapsed branches, the CVtree grouped most genomes according to their known taxonomic phyla, although not all Spirochaetes cluster together. In contrast to the 16S rRNA tree, in this protein tree all the Firmicutes cluster together, and are distinct from other phyla. The Negativicutes genomes, nested within the Firmicutes, again have the Acidaminococcaceae placed within the Veillonellaceae, while all Veillonella spp. are found in one cluster. All Clostridia, this time divided into two collapsed branches, are positioned as the closest relatives to Negativicutes. It is of interest that among the closest relatives to Firmicutes, based on this analysis, are the Fusobacteria and the Elusimicrobia; these are atypical diderm bacteria that produce lipopolysaccharides [28]. However, the spirochete, Brachyspira murdochii, does not possess two membranes, but is nevertheless grouped with atypical diderms. On the other hand while the Synergistetes are atypical diderm bacteria, they are placed elsewhere in the tree (Figure 2).

Figure 2

Phylogenetic tree based on composition vector analysis (CVtree) of all protein coding genes (amino acid sequences) derived from the analyzed genomes. Note that the branch lengths in this plot are artificial. The coloring is the same as in Figure 1 and branches have been collapsed. The Firmicutes branch Bacilli, marked (1), contains Turicibacter sanguinis. An uncollapsed tree is included in the supplementary material.

A third analysis was based on a subset of proteins found conserved amongst all analyzed genomes. These conserved proteins were selected based on a protein BLAST (a cutoff of 50% identity and 50% coverage of the query length was used) and single linkage clustering. The analysis identified 29 genes that are shared among all 145 genomes [Table 2]. A consensus tree was constructed based on these 29 conserved proteins (Figure 3). The results confirm the global observations of the other two phylogenetic analyses: the Negativicutes cluster together and are most closely related to Clostridia (in this case the most closely related species are Desulfotomaculum reducens and Acetohalobium arabaticum). As before, the Acidaminococcaceae cluster together but within the Veillonellaceae. The position of Turicibacter sanguinis within the Bacilli group of Firmicutes is consistent with the other two trees but contrasts with its taxonomic description at NCBI as a member of the Erysipelotrichia.

Table 2

Universally conserved COGs

Group

    Average length (aa)

    Annotation

COG0012

    380

    Predicted GTPase, probable translation factor

COG0016

    423

    Phenylalanine-tRNA synthethase alpha subunit

COG0048

    137

    Ribosomal protein S12

COG0049

    182

    Ribosomal protein S7

COG0052

    240

    Ribosomal protein S2

COG0080

    154

    Ribosomal protein L11

COG0081

    230

    Ribosomal protein L1

COG0087

    288

    Ribosomal protein L3

COG0091

    157

    Ribosomal protein L22

COG0092

    240

    Ribosomal protein S3

COG0093

    130

    Ribosomal protein L14

COG0094

    182

    Ribosomal protein L5

COG0096

    131

    Ribosomal protein S8

COG0097

    177

    Ribosomal protein L6P/L9E

COG0098

    220

    Ribosomal protein S5

COG0100

    145

    Ribosomal protein S11

COG0102

    167

    Ribosomal protein L13

COG0103

    172

    Ribosomal protein S9

COG0172

    442

    Seryl-tRNA synthetase

COG0184

    154

    Ribosomal protein S15P/S13E

COG0186

    122

    Ribosomal protein S17

COG0197

    175

    Ribosomal protein L16/L10E

COG0200

    166

    Ribosomal protein L15

COG0201

    445

    Preprotein translocase subunit SecY

COG0202

    323

    DNA-directed RNA polymerase, alpha subunit

COG0256

    178

    Ribosomal protein L18

COG0495

    854

    Leucyl-tRNA synthetase

COG0522

    199

    Ribosomal protein S4 and related proteins

COG0533

    375

    Metal-dependent proteases with chaperone activity

Figure 3

Consensus tree based on the phylogenetic trees of 27 genes conserved in all 145 genomes. The collapsed branch of the Bacilli, marked (1), contains Turicibacter sanguinis. An uncollapsed tree is available as a supplemental figure.

In conclusion, based on three independent phylogenetic analyses, the closest relatives to the Negativicutes seem to be the Clostridiaceae. The observed clustering of species within the Negativicutes is consistent with their assigned taxonomy. Furthermore, these analyses show that Veillonella spp. form a distinct branch, most different from the other Negativicutes, while the recent change of status of the Acidaminococcaceae (they are no longer a separate family) is confirmed by these analyses.

Apart from comparing proteins and genes, genomes can also be compared based on nucleotide composition irrespective of their coding capacity. For instance, the frequency of nucleotide combinations can reveal similarities between genomes that are independent of protein-coding information. We compared the frequency of tetranucleotides for all 145 genomes. The observed frequency of all 64 tetranucleotide combinations was extracted for each genome and these frequencies were divided by the theoretically calculated, expected frequencies (corrected for differences in base composition). This ratio, which could be interpreted as a genomic signature, was expected to reflect taxonomic divisions [29]. However, although the analysis identified a high similarity in tetranucleotide frequency for all of the analyzed Veillonella genomes, most of the clustering observed was not in accordance with known taxonomic relationships. Not only were Negativicutes other than Veillonella separated from each other and strewn across the phyla, but also several other Firmicutes were distributed over various branches (data shown as supplementary material). In fact, for most of the analyzed genomes, members of identical phyla did not cluster together and even the Archaea were mixed with Bacteria, although some closely related species were indeed clustered. This may explain why all Veillonella genomes grouped together. Several organisms with similar tetranucleotide frequencies did not share a common ecological niche, in contrast to previously reported observations (reviewed in [30]). Neither was the obtained clustering dictated by GC-content. The conclusion from this analysis was that tetranucleotide analysis is only taxonomically informative for closely related genomes.

We also compared whole-genome amino acid frequencies in each of the deduced proteomes. Although the results are slightly more in agreement with known taxonomy as compared with the genomic signatures discussed above, this analysis does not cluster organisms according to their phyla, and again some Archaea are mixed with Bacteria. The relevant part of the heatmap based on amino acid frequency is shown in Figure 4. All Veillonella genomes cluster together within the Negativicutes, with the exception of two of the three Dialister genomes, which are found most closely related to Clostridium species (See supplemental information for a version of this figure showing all the genomes). The major Negativicutes cluster also contains a Geobacillus (which is a Gram-positive Firmicutes) and a methanogenic Archaean. Interestingly, the closest relatives to this cluster are not Clostridia, as the previous phylogenetic trees suggest, but a number of Proteobacteria. It is striking that the amino acid frequency analysis detects similarities to Proteobacteria, with which the Negativicutes have their two membranes in common.

Figure 4

A zoomed heatmap of the amino acid frequency found in the deduced proteomes of all 145 genomes. A fragment of the heatmap is shown, presenting the cluster in which all but two Negativicutes are found. The remaining two, both Dialister microaerophilus genomes, are positioned elsewhere in the tree, closest to Clostridium cellulolyticum (not shown in this zoom). The color scale indicates highly underrepresented (orange) to highly overrepresented amino acid frequency (magentum). The full figure is available as supplementary information.

The metabolic properties encoded by the genomes were analyzed next, based on KEGG comparisons [24]. The results are again visualized in a heatmap (Figure 5). We hypothesized that this analysis could identify similarities based on niche adaptation. For simplicity, only a selected number of phyla are shown: apart from the Firmicutes, genomes are included that represent Bacteroidetes and Proteobacteria (both of which contain members frequently found in the oral or gut microbiome), while Cyanobacteria are included as representatives of a phylum that occupy an environmental niche. Since the genomes are compared based on predicted proteomes, their annotation was standardized in order to reduce artificial variation caused by gene annotation differences. As can be seen in Figure 5, the Veillonella genomes all cluster together at the right-hand side of the plot, within a larger cluster containing most of the other Negativicutes and some Firmicutes. The three Dialister species are placed outside the Negativicutes cluster. The other Firmicutes that are found combined with the Negativicutes, based on their metabolic potential, are Clostridium cellulolyticum, Eubacterium rectale, Lactococcus lactis, Streptococcus pneumoniae and Turicibacter sanguinis. These are all common members of the oral or intestine microbiome. As expected, the metabolic pathway for lipopolysaccharide biosynthesis is shared between the Negativicutes and other Gram-negative species, as indicated by the arrows in Figure 5. Interestingly, the Cyanobacteria form a small cluster within, not outside the tree, together with a Haliangium and a Sorangium species as their closest neighbors (both are social Myxococcales belonging to the Deltaproteobacteria). The exclusive ability of carbon fixation by Cyanobacteria is apparent from the dark red square in the block 'energy'. The lanes of Veillonella in Figure 5 are dominated by light colors, indicative of medium metabolic potential; that is, in contrast to some genomes where most of the pathways are present (dark red for Proteobacteria for example) or missing (dark green for other Negativicutes), the Veillonella genomes have partial pathways (based on knowledge primarily from aerobic genomes). There is no reason to believe that the Veillonella genomes should have less metabolic potential than other Negativicutes. Indeed, it is likely that the differences in metabolic potential of Veillonella are truly reflective of alternative capabilities for these bacteria.

Figure 5

Heatmap of metabolism potential, based on Kyoto Encyclopedia of Genes and Genomes ontology (KEGG). The green color in the heatmap indicates weak metabolic potential, while red signals strong potential. The arrows to the right indicate the scores for lipopolysaccharide biosynthesis. A version summarizing the metabolism pathways and showing the species legend is available as supplementary material.

It was further investigated how conserved the predicted proteomes are within the Negativicutes. As a quantitative measure for homology, shared protein-coding genes were identified by pairwise BLASTP comparison and expressed as a percentage of the combined proteomes. The results are shown in a matrix (Figure 6). In addition to the proteomes of the 24 Negativicutes, the comparison includes Clostridium botulinum, Cl. cellulolyticum and Desulfotomaculum reducens, as these Firmicutes were shown to share characteristics with Negativicutes in previous analyses (cf. Figures 1 and 3). The proteome of E. coli K12 is included as an example of a Gram-negative intestinal bacterium. The BLAST matrix was constructed using reciprocal best BLAST hits to determine the presence of shared protein family between two genomes. Inspection of Figure 6 shows that the genus Veillonella is relatively homogeneous; any two members of this genus share between 67% and 90% homology (1,357 to 1,682 protein families), irrespective of the species. The genus Selenomonas is more heterogeneous, with pairwise homology varying from 42% to 82% between any two species (980 to 1659 protein families). The three proteomes of Dialister spp., covering two species, share between 40% and 84% homology. The highest homologous fraction identified between two members of different genera within the Negativicutes is 43% (Mitsuokella multacida compared to Selenomonas sputigena, whereas the lowest homology is 15% (Dialister spp. compared to Thermosinus carboxydivorans). Negativicutes share between 9% and 33% homology with the analyzed Firmicutes, whereas slightly lower homology is detected with E. coli (between 7% and 24%).

Figure 6

Proteome comparison represented by a BLAST matrix, based on 24 Negativicutes genomes with reciprocal best hits. The genomes of Clostridium botulinum, Cl. cellulolyticum, Desulfotomaculum reducens and E. coli are added for comparison. Inter-genus comparisons are indicated by black squares. A version reporting the numerical values of homology percentages is available as supplementary information.

Finally, we assessed the gene pool conserved within all analyzed Negativicutes. Using the same cutoff for protein BLAST comparison as before, a core-genome is identified that contains about 300 conserved protein families (data not shown). This is a relatively low number of conserved proteins, reflective of the extensive genetic heterogeneity within this bacterial class.

Discussion

The availability of complete sequences for a large and diverse set of Bacterial genomes has helped in exploring the conundrum of the genus Veillonella, a genus within the Negativicutes class, all of which are Gram negative Firmicutes. The 16S rRNA tree shown as Figure 1 illustrates how “close” the Negativicutes are to other Firmicutes. The closest Gram positive Clostridium species are actually quite distant to Veillonella and other Negativicutes genomes, as can be seen in the low fraction of shared protein families in Figure 6. The Gram-negative Firmicutes are even more distant to other Gram negatives, such as Proteobacteria (e.g., E. coli). It should be noted that the family Clostridiaceae is a largely diverse group with many members being re-classified [27]. It is therefore possible that the taxonomic description of some Clostridium genomes may change in future. However, our analyses did not identify one single Gram-positive Firmicutes (Clostrida or others) that consistently was identified as most closely related to Veillonella. As seen from three types of phylogenetic analysis, the Negativicutes class genomes form a distinct cluster within the Firmicutes, and the Veillonella genus forms a relatively homogeneous group of species within the Negativicutes, with relatively conserved metabolic properties (Figure 5). In comparison, the Selenomonas genus is more heterogeneous, at least based on their total gene comparison, as illustrated in Figure 6.

In contrast to expectations, relatively little homology between Negativicutes and other Gram-negative genomes was detected in our analyses. Neither gene-dependent phylogenetic analysis, nor gene-independent DNA tetramer analysis identified a significant commonness between Negativicutes and, say, Proteobacteria. Only whole-genome frequency analysis of amino acid usage identified some similarity to a few Proteobacteria, and this might be more reflective of environment the organism is adapted to, and not phylogeny. Using KEGG pathways for metabolic comparison of the proteomes we found few pathways in common, with the exception of a shared lipopolysaccharide biosynthesis pathway. From all analyses combined, it is clear that the taxonomic placement of Negativicutes within the Firmicutes reflects their genetic and genomic characteristics, although the proteins encoded by the Negativicutes genomes are quite distinct from their Gram-positive cousins. It could be speculated that the double membrane of the Negativicutes evolved in a lineage that used to be a single-membrane (Gram-positive) Firmicute. Whether this event co-evolved independently of the formation of other Gram-negative phyla, or was the result of lateral gene transfer, cannot be stated for certain at present; estimations of horizontally transferred regions in Veillonella parvula DSM 2008, the only fully assembled Veillonella genome available, using the least conservative method on the Islandviewer web-site [31], revealed that only 2% of the genome is of foreign origin. In comparison, 9% of the E. coli K-12 subsp. MG1655 genome was predicted as horizontally transferred. Further analyses are therefore needed to assess this in more detail.

Author’s contributions

Tammi Vesth was a main contributor to the writing of the manuscript and to the organization of the work. Trudy Wassenaar helped considerably in editing and improving the manuscript. Individual contributions: Asli Ozen (16s rRNA and CV tree), Oksana Lukjancenko (consensus tree), Sandra Andersen (initial investigations and background research, early version of the manuscript), Rolf Sommer Kaas (BLAST matrix), Jon Bohlin (tetramer and amino acid usage heatmaps), Intawat Nookaew (metabolism heatmaps). David Ussery provided the original idea for this manuscript, suggested the figures, helped in early drafts of the manuscript, and supervised the project.

Declarations

Acknowledgements

This research was supported by grants from the Danish Research Council, and in part by a grant 09-067103/DSF from the Danish Council for Strategic Research.


This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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