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Eubacteria

Fabia U. Battistuzzia,b,* and S. Blair Hedgesa

Department of Biology, 208 Mueller Laboratory, The Pennsylvania State University, University Park, PA 16802-5301, USA; bCurrent address: Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA *To whom correspondence should be addressed (Fabia.Battistuzzi@ asu.edu)

shown increasing support for lower-level phylogenetic clusters (e.g., classes and below), they have also shown the susceptibility of eubacterial phylogeny to biases such as horizontal gene transfer (HGT) (20, 21).
In recent years, three major approaches have been used for studying prokaryote phylogeny with data from complete genomes: (i) combining gene sequences in a single analysis of multiple genes (e.g., 7, 9, 10), (ii) combining trees from individual gene analyses into a single “supertree” (e.g., 22, 23), and (iii) using the presence or absence

Abstract

The ~9400 recognized species of prokaryotes in the of genes (“gene content”) as the raw data to investigate

Superkingdom Euba cteria a re pla ced in 25 phyla . Their relationships have been difficult to establish, although some ma jor groups a re emerging from genome a na lyses. A molecular timetree, estimated here, indicates that most (85%) of the phyla a nd cla sses a rose in the Archea n Eon (4000−2500 million years ago, Ma) whereas most (95%) of

relationships (e.g., 17, 18). While the results of these different approaches have not agreed on many details of relationships, there have been some points of agreement, such as support for the monophyly of all major classes and some phyla (e.g., Proteobacteria and Firmicu tes). 7ese Andings, although criticized by some (e.g., 24, 25),

the families arose in the Proterozoic Eon (2500−542 Ma). It suggest the presence of a detectable evolutionary sigalso points to an early origin of phototrophy (3400–3200 Ma) followed by colonization of land (3041–2755 Ma), origin of oxygenic photosynthesis (2920–2592 Ma) and of aerobic methanotrophy (2630–2371 Ma).

nal for prokaryotes when using carefully selected genes (e.g., vertically inherited) and appropriate methodologies (e.g., genome-scale data sets).

7e Su perkingdom Eu bacteria (also called “Bacteria,” Fig. 1) is subdivided into 25 recognized phyla1(), although other classiAcations have been proposed (2) and putative lineages await taxonomic assignment (e.g., 3, 4). In contrast to most eukaryotic groups, eubacterial phylogeny has been di1cult to resolve at the highest level (i.e., phyla and class relationships) despite the availability of many complete genome sequences. 7e small subu nit (SSU) ribosomal RNA (rRNA) gene has been widely used to study prokaryote phylogeny and Arst revealed the distinction of eubacteria from archaebacteria (Archaea) (5, 6). However, becau se of its limited length and the large genetic distances among prokaryote species, it is also subject to biases such as long-branch attraction, base composition diBerences, and a generally poor resolving power. 7ese issues mostly have been addressed in more recent years by using genome-scale data sets (e.g., multiple genes, gene content, and insertion–deletion events) and increased taxonomic sampling (7–19, 88). While these studies have

Fig. 1 Spirillum, a betaproteobacterium (upper left); Beggiatoa, a gammaproteobacterium (upper right); an unidentified cyanobacterium (lower left); and an unidentified spirochete (lower right). Credits: D. J. Patterson, L. Amaral-Zettler, and V. Edgcomb (upper left and right); D. J. Patterson (lower left); and L. Amaral-Zettler, L. Olendzenski, and D. J. Patterson (lower right). Images provided by micro*scope (http://microscope.mbl. edu) under creative commons license.

F. U. Battistuzzi and S. B. Hedges. Eubacteria. Pp. 106–115 inꢀe Timetree of Life, S. B. Hedges and S. Kumar, Eds. (Oxford University Press, New York, 2009).

Eubacteria

107

Staphylococcaceae Bacillaceae
50 54
61
Listeriaceae
30

29
Lactobacillaceae Streptococcaceae Acholeplasmataceae Mycoplasmataceae-1 Mycoplasmataceae-2 Entomoplasmataceae

Symbiobacterium Carboxydothermus

Peptococcaceae
21
46
85
15

24
33
44
20

  • 8
  • Thermoanaerobacteriaceae-1

Clostridiaceae
36
Thermoanaerobacteriaceae-2

Dehalococcoides

Gloeobacteraceae Synechococcaceae-1 Synechococcaceae-2 Synechococcaceae-3 Nostocaceae
11
60

66
6
75

76
80
Merismopediaceae

Deinococcaceae Propionibacteriaceae Frankiaceae
53

  • 63
  • 13
  • Corynebacteriaceae

Mycobacteriaceae Nocardiaceae
82
88
59

43
Streptomycetaceae

Nocardiopsaceae Bifidobacteriaceae Cellulomonadaceae Microbacteriaceae
69
48
71

(continued on next page)

  • Ea Pa Ma Na
  • Pp
  • Mp
  • Np Pz

PH
HD
ARCHEAN

3000

Fig. 2 Continues

PROTEROZOIC

  • 2000 1000
  • 4000
  • 0 Million years ago

108

THE TIMETREE OF LIFE

(continued from previous page)

Francisellaceae Shewanellaceae Vibrionaceae
73
77
81
Enterobacteriaceae Pasteurellaceae Idiomarinaceae Colwelliaceae
42
70

74
79
47
Pseudoalteromonadaceae

Moraxellaceae
37
57
Pseudomonadaceae

Hahellaceae
67
32
Coxiellaceae

51
Legionellaceae

Chromatiaceae
38

34
Methylococcaceae Piscirickettsiaceae Xanthomonadaceae Neisseriaceae
4
26
39

Rhodocyclaceae Comamonadaceae Burkholderiaceae Alcaligenaceae
55
68

72
78
58

Nitrosomonadaceae Hydrogenophilaceae
64

(continued on next page)

  • Ea Pa Ma Na
  • Pp

PROTEROZOIC

2000 1000

  • Mp
  • Np Pz

HD
ARCHEAN

3000

Fig. 2 Continues

PH

  • 4000
  • 0 Million years ago

Phylogenetic analyses that have focused on subgroups ribosomal proteins, DNA-directed RNA polymerase of prokaryotes (e.g., classes) also have su pported, consistently, particular groupings. For example, cyanobacteria and low GC gram positives (Firmicutes) have been subunits (30), genome trees (31), and gene content analysis (18). Other taxa, such as AquiAcae and 7ermotogae, have been more di1cult to place, appearing at the base united based on maximum likelihood (ML) mapping of of the tree in some analyses (4, 7, 9, 32) and in a higher, 21 orthologous genes (26); Fusobacteria is the most closely nested position in other analyses (10, 33, 34). related group to Firmicutes based on combined analyses of SSU and large su bu nit (LSU) rRNA sequ ences and
Generally concordant relationships were found between past studies and a recent ML and Bayesian anaribosomal proteins (27); Bacteroidetes, Chlorobi, and lysis of multiple core gene sequences (88). In that study, Fibrobacteres form a group based on insertion–deletion a complete matrix of 25 vertically inherited orthologous analysis (28, 29); and Planctomycetacia, Chlamydiae, genes shared by 197 fully sequenced eubacteria and 21

  • and Spirochaetes form a group based on concatenated
  • fully sequenced species of archaebacteria was built.

Eubacteria

109

(continued from previous page)

Erythrobacteraceae Rhodobacteraceae Caulobacteraceae Bradyrhizobiaceae Rhizobiaceae
17
41

45

56
65
35
86
84
87
Phyllobacteriaceae Bartonellaceae
14
Brucellaceae

25
Rhodospirillaceae

Acetobacteraceae SAR11
49
3
27
40
Anaplasmataceae Rickettsiaceae Solibacteraceae Desulfovibrionaceae Myxococcaceae Bdellovibrionaceae Pelobacteraceae Geobacteraceae Campylobacteraceae Helicobacteraceae Chlamydiaceae Chlorobiaceae
9
16

18
28

22
52

5
62
2

12
23
Crenotrichaceae

Bacteroidaceae Porphyromonadaceae Planctomycetaceae Spirochaetaceae Leptospiraceae
31
7
83

1

10
19

Fusobacteriaceae Aquificaceae Thermotogaceae

Ea Pa Ma Na ARCHEAN

  • Pp
  • Mp
  • Np Pz

HD
PROTEROZOIC

2000 1000
PH

  • 4000
  • 3000
  • 0 Million years ago

Fig. 2 A timetree of eubacteria. Divergence times are shown in Table 1. Codes for paraphyletic and/or polyphyletic groups are as follows: Mycoplasmataceae-1

(Mycoplasma genitalium), Mycoplasmataceae-2 (Mycoplasma capricolum), Synechococcaceae-1 (Synechocococcus

JA-2-3Ba), Synechococcaceae-2 (Thermosynechococcus

elongatus), Synechococcaceae-3 (Synechococcus elongatus),

Thermoanaerobacteriaceae-1 (Moorella thermoacetica), Thermoanaerobacteriaceae-2 (Thermoanaerobacter tengcongensis). Abbreviations: Ea (Eoarchean), HD (Hadean), Ma (Mesoarchean), Mp (Mesoproterozoic), Na (Neoarchean), Np (Neoproterozoic), Pa (Paleoarchean), PH (Phanerozoic), Pp (Paleoproterozoic), and Pz (Paleozoic).

110

THE TIMETREE OF LIFE

Table 1. Divergence times (Ma) and their confidence/credibility intervals (CI) among eubacteria.

  • Timetree Estimates Timetree
  • Estimates

  • Node
  • Time
  • Ref. (7)
  • This study

CI

  • Node
  • Time
  • Ref. (7)
  • This study

  • CI
  • Time
  • CI
  • Time
  • Time
  • CI
  • Time

  • 1
  • 4189

4179 3306 3134 2979 2908 2897 2874 2849 2762 2761 2739 2739 2687 2607 2579 2504 2421 2339 2281 2233 2173 2099 2047 2042 1993 1919 1899 1860 1837 1834 1806 1775 1753 1753 1747 1673 1653 1621 1620

  • –
  • –

–
4189 4179 3306 3134 2979 2908 2897 2874 2849 2762 2761 2739 2739 2687 2607 2579 2504 2421 2339 2281 2233 2173 2099 2047 2042 1993 1919 1899 1860 1837 1834 1806 1775 1753 1753 1747 1673 1653 1621 1620

  • 4200–4159
  • 45

46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84
1498 1482 1481 1436 1432 1429 1420 1413 1402 1392 1386 1326 1306 1224 1189 1180 1121 1104 1069 1055 1042 1037 1030 1028 1027 950

  • –
  • –

–
1498 1482 1481 1436 1432 1429 1420 1413 1402 1392 1386 1326 1306 1224 1189 1180 1121 1104 1069 1055 1042 1037 1030 1028 1027 950
1644–1351 1656–1313 1586–1372 1600–1270 1580–1283 1597–1269 1539–1297 1573–1257 1557–1241 1573–1223 1511–1262 1471–1183 1420–1190 1349–1101 1332–1040 1334–1035 1276–977 1271–943 1209–928 1184–932 1180–912 1186–902 1145–917 1150–911 1167–887 1051–849 1084–794 993–759

  • 2
  • –
  • 4197–4141

3447–3165 3265–2987 3116–2835 3041–2755 3040–2747 3012–2716 2983–2706 2926–2595 2920–2592 2894–2575 2897–2570 2815–2549 2753–2448 2715–2428 2630–2371 2563–2267 2517–2154 2439–2115 2401–2067 2321–2018 2261–1932 2215–1873 2187–1887 2099–1894 2083–1749 2078–1719 2040–1680 2011–1673 2005–1665 1889–1731 1948–1602 1821–1690 1894–1605 1931–1572 1765–1581 1688–1640 1722–1520 1792–1449
–

  • 3
  • 3186
  • 3634–2801
  • 1387
  • 1763–1060

  • 4
  • –
  • –
  • –
  • –

  • 5
  • 3096
  • 3539–2723
  • –
  • –

  • 6
  • –
  • –
  • 1561
  • 2012–1166

  • 7
  • –
  • –
  • –
  • –

  • 8
  • –
  • –
  • –
  • –

  • 9
  • 2800
  • 3223–2452
  • –
  • –

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

  • –
  • –
  • –
  • –

  • –
  • –
  • 1470
  • 1873–1114

  • –
  • –
  • 1537
  • 1942–1151

  • –
  • –
  • –
  • –

  • –
  • –
  • –
  • –

  • 2688
  • 3108–2360
  • 1380
  • 1798–1038

  • –
  • –
  • –
  • –

  • 2508
  • 2928–2154
  • 1282
  • 1690–916

  • –
  • –
  • 1244
  • 1694–856

  • –
  • –
  • –
  • –

  • –
  • –
  • –
  • –

  • 2305
  • 2729–1944
  • –
  • –

  • –
  • –
  • –
  • –

  • –
  • –
  • –
  • –

  • –
  • –
  • –
  • –

  • 2030
  • 2449–1656
  • –
  • –

  • 1945
  • 2368–1573
  • –
  • –

  • –
  • –
  • 937
  • –
  • –
  • 937

  • –
  • –
  • 872
  • –
  • –
  • 872

  • –
  • –
  • 871
  • –

–

  • –
  • 871
  • 972–772

  • 1816
  • 2261–1415
  • 812
  • –
  • 812
  • 910–711

  • –
  • –
  • 793
  • 1039

–

  • 1408–702
  • 793
  • 923–678

  • 1751
  • 2163–1390
  • 751
  • –
  • 751
  • 880–639

  • 854–653
  • –
  • –
  • 751
  • –
  • –
  • 751

  • –
  • –
  • 744
  • –
  • –
  • 744
  • 859–640

  • –
  • –
  • 668
  • –
  • –
  • 668
  • 768–573

  • 2132
  • 2552–1760
  • 662
  • 756

–

  • 1070–484
  • 662
  • 783–553

––––
––––

  • 634
  • –
  • 634
  • 739–538

  • 621
  • 928

–

  • 1274–644
  • 621
  • 734–516

  • 616
  • –

–

  • 616
  • 734–506

  • 594
  • –
  • 594
  • 699–499

Eubacteria

111

Table 1. Continued

  • Timetree
  • Estimates
  • Timetree
  • Estimates

  • Node
  • Time
  • Ref. (7)
  • This study

CI

  • Node
  • Time
  • Ref. (7)
  • This study

  • Time CI
  • Time
  • CI
  • Time
  • Time
  • CI

41 42 43 44
1613 1612 1579 1554
––––
––––
1613 1612 1579 1554

  • 1761–1465
  • 85

86 87 88
523 509 432 380
––––
––––
523 509 432 380
638–425 609–421 520–351 469–304
1712–1506 1740–1411 1729–1378

Note: Nodes times in the timetree are from this study.

Single-gene phylogenies were screened for orthology supported in the Bayesian phylogeny as well. Multiple and vertical inheritance under the assumption that phy- evidence from other analyses (2, 11, 23, 28) supports logenies showing mixing of the two superkingdoms or signiAcantly supported deep nesting of one class within
Bacterobi, while Spiroplancti is present in some but not all previous studies. However, currently no clear physioanother were aBected by HGT and deleted from the logical or metabolic adaptations can be considered as data set. Site homology of the multiple sequence alignment was established with GBlocks (35) and nonconunifying characteristics of these clusters.
Molecular clock methods have been used infrequently served sites were deleted. ML (36) and Bayesian (37) with eubacteria, probably because of di1culties in estabmethods were then applied to the Anal alignment (6884 lishing a robust phylogeny and in selecting reliable calisites) to estimate phylogenetic relationships. A ML phyl- bration points. Calibrations have included the fossil

  • ogeny was also constru cted from an alternative align-
  • record of prokaryotes and eukaryotes and biomarkers

ment with only slow-evolving positions (16,344 sites) in the geologic record. A problem with most eukaryote and showed a similar phylogeny (except for the pos- fossils, otherwise useful for calibration, is that they are ition of Solibacteres). A topological feature common to from the Phanerozoic eon (543–0 Ma), mu ch you nger

  • all of these analyses is a major dichotomy in eubacteria.
  • than many key nodes of interest to time. 7is exac-

7is is formed by the Terrabacteria (Actinobacteria, erbates the problems with diBerent rates of evolu tion ChloroPexi, Cyanobacteria, Deinococcus-ꢀermus, and among the three superkingdoms (38), an issue dimin-

  • Firmicu tes) and the Hydrobacteria (Acidobacteria,
  • ished by using calibrations within each superkingdom.

Bacteroidetes, Chlamydiae, Chlorobi, Planctomycetacia, Although the prokaryote fossil record extends reliably Proteobacteria, and Spirochaetes) to the exclusion of the back to ~2000 Ma (39), the taxonomic resolution is not hyperthermophiles and, possibly, Fusobacteria (88). 7is su1cient in most cases to be broadly useful for calibradeAnition of Terrabacteria is more inclusive than the ini- tion. Better resolution for a few groups is obtained from tial deAnition provided in 2004, which did not include the geologic record with biomarkers produced speciAcChloroPexi and Firmicutes (7).
In addition, three possible high-level clusters within
Hydrobacteria are highly supported by the ML phylally and exclusively by one lineage (40). Prokaryotes that are parasitic on eukaryotes provide other opportunities for calibration (41, 42), but they are almost entirely in the ogeny. Bacteroidetes, Chlorobi, Chlamydiae, Plancto- Phanerozoic, again much younger than many deep nodes mycetes, and Spirochaetes are associated in a single of interest. One additional calibration that has been used

  • clu ster, with high bootstrap su pport (82%), for which
  • is the maximum time for life on Earth (7), which can be

we propose the name Spirochlamydiae. Within this set either to the origin of Earth at 4600 Ma as an absoinfrakingdom, two other superphyla are strongly sup- lute maximum or perhaps more realistically to the last ported: (i) Planctomycetes and Spirochaetes (93%) and ocean-vaporizing event, ~4200 Ga (43).

  • (ii) Bacteroidetes and Chlorobi (100%). For these we
  • Data sets using diBerent types of molecular informa-

propose the names Spiroplancti and Bacterobi, respect- tion (i.e., rRNA, enzymes, core genes) have been used to ively. 7ese last two clusters are present and signiAcantly estimate divergence times. A divergence time analysis of

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  • Table S4. Phylogenetic Distribution of Bacterial and Archaea Genomes in Groups A, B, C, D, and X

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    Table S4. Phylogenetic distribution of bacterial and archaea genomes in groups A, B, C, D, and X. Group A a: Total number of genomes in the taxon b: Number of group A genomes in the taxon c: Percentage of group A genomes in the taxon a b c cellular organisms 5007 2974 59.4 |__ Bacteria 4769 2935 61.5 | |__ Proteobacteria 1854 1570 84.7 | | |__ Gammaproteobacteria 711 631 88.7 | | | |__ Enterobacterales 112 97 86.6 | | | | |__ Enterobacteriaceae 41 32 78.0 | | | | | |__ unclassified Enterobacteriaceae 13 7 53.8 | | | | |__ Erwiniaceae 30 28 93.3 | | | | | |__ Erwinia 10 10 100.0 | | | | | |__ Buchnera 8 8 100.0 | | | | | | |__ Buchnera aphidicola 8 8 100.0 | | | | | |__ Pantoea 8 8 100.0 | | | | |__ Yersiniaceae 14 14 100.0 | | | | | |__ Serratia 8 8 100.0 | | | | |__ Morganellaceae 13 10 76.9 | | | | |__ Pectobacteriaceae 8 8 100.0 | | | |__ Alteromonadales 94 94 100.0 | | | | |__ Alteromonadaceae 34 34 100.0 | | | | | |__ Marinobacter 12 12 100.0 | | | | |__ Shewanellaceae 17 17 100.0 | | | | | |__ Shewanella 17 17 100.0 | | | | |__ Pseudoalteromonadaceae 16 16 100.0 | | | | | |__ Pseudoalteromonas 15 15 100.0 | | | | |__ Idiomarinaceae 9 9 100.0 | | | | | |__ Idiomarina 9 9 100.0 | | | | |__ Colwelliaceae 6 6 100.0 | | | |__ Pseudomonadales 81 81 100.0 | | | | |__ Moraxellaceae 41 41 100.0 | | | | | |__ Acinetobacter 25 25 100.0 | | | | | |__ Psychrobacter 8 8 100.0 | | | | | |__ Moraxella 6 6 100.0 | | | | |__ Pseudomonadaceae 40 40 100.0 | | | | | |__ Pseudomonas 38 38 100.0 | | | |__ Oceanospirillales 73 72 98.6 | | | | |__ Oceanospirillaceae
  • Phylogenomic Analysis of 589 Metagenome-Assembled Genomes Encompassing All Major Prokaryotic Lineages from the Gut of Higher Termites

    Phylogenomic Analysis of 589 Metagenome-Assembled Genomes Encompassing All Major Prokaryotic Lineages from the Gut of Higher Termites

    Phylogenomic analysis of 589 metagenome-assembled genomes encompassing all major prokaryotic lineages from the gut of higher termites Vincent Hervé1, Pengfei Liu1, Carsten Dietrich1, David Sillam-Dussès2, Petr Stiblik3, Jan Šobotník3 and Andreas Brune1 1 Research Group Insect Gut Microbiology and Symbiosis, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany 2 Laboratory of Experimental and Comparative Ethology EA 4443, Université Paris 13, Villetaneuse, France 3 Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Prague, Czech Republic ABSTRACT “Higher” termites have been able to colonize all tropical and subtropical regions because of their ability to digest lignocellulose with the aid of their prokaryotic gut microbiota. Over the last decade, numerous studies based on 16S rRNA gene amplicon libraries have largely described both the taxonomy and structure of the prokaryotic communities associated with termite guts. Host diet and microenvironmental conditions have emerged as the main factors structuring the microbial assemblages in the different gut compartments. Additionally, these molecular inventories have revealed the existence of termite-specific clusters that indicate coevolutionary processes in numerous prokaryotic lineages. However, for lack of representative isolates, the functional role of most lineages remains unclear. We reconstructed 589 metagenome-assembled genomes (MAGs) from the different Submitted 29 August 2019 gut compartments of eight higher termite species that encompass 17 prokaryotic
  • Microbial Diversity of Drilling Fluids from 3000 M Deep Koyna Pilot

    Microbial Diversity of Drilling Fluids from 3000 M Deep Koyna Pilot

    Science Reports Sci. Dril., 27, 1–23, 2020 https://doi.org/10.5194/sd-27-1-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Microbial diversity of drilling fluids from 3000 m deep Koyna pilot borehole provides insights into the deep biosphere of continental earth crust Himadri Bose1, Avishek Dutta1, Ajoy Roy2, Abhishek Gupta1, Sourav Mukhopadhyay1, Balaram Mohapatra1, Jayeeta Sarkar1, Sukanta Roy3, Sufia K. Kazy2, and Pinaki Sar1 1Environmental Microbiology and Genomics Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India 2Department of Biotechnology, National Institute of Technology Durgapur, Durgapur, 713209, West Bengal, India 3Ministry of Earth Sciences, Borehole Geophysics Research Laboratory, Karad, 415114, Maharashtra, India Correspondence: Pinaki Sar ([email protected], [email protected]) Received: 24 June 2019 – Revised: 25 September 2019 – Accepted: 16 December 2019 – Published: 27 May 2020 Abstract. Scientific deep drilling of the Koyna pilot borehole into the continental crust up to a depth of 3000 m below the surface at the Deccan Traps, India, provided a unique opportunity to explore microbial life within the deep granitic bedrock of the Archaean Eon. Microbial communities of the returned drilling fluid (fluid re- turned to the mud tank from the underground during the drilling operation; designated here as DF) sampled during the drilling operation of the Koyna pilot borehole at a depth range of 1681–2908 metres below the surface (m b.s.) were explored to gain a glimpse of the deep biosphere underneath the continental crust. Change of pH 2− to alkalinity, reduced abundance of Si and Al, but enrichment of Fe, Ca and SO4 in the samples from deeper horizons suggested a gradual infusion of elements or ions from the crystalline bedrock, leading to an observed geochemical shift in the DF.
  • A Genomic Timescale of Prokaryote Evolution: Insights Into the Origin of Methanogenesis, Phototrophy, and the Colonization of Land

    A Genomic Timescale of Prokaryote Evolution: Insights Into the Origin of Methanogenesis, Phototrophy, and the Colonization of Land

    BMC Evolutionary Biology BioMed Central Research article Open Access A genomic timescale of prokaryote evolution: insights into the origin of methanogenesis, phototrophy, and the colonization of land Fabia U Battistuzzi1, Andreia Feijao2 and S Blair Hedges*1 Address: 1NASA Astrobiology Institute and Department of Biology, 208 Mueller Laboratory, The Pennsylvania State University, University Park, PA 16802, USA and 2European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany Email: Fabia U Battistuzzi - [email protected]; Andreia Feijao - [email protected]; S Blair Hedges* - [email protected] * Corresponding author Published: 09 November 2004 Received: 07 August 2004 Accepted: 09 November 2004 BMC Evolutionary Biology 2004, 4:44 doi:10.1186/1471-2148-4-44 This article is available from: http://www.biomedcentral.com/1471-2148/4/44 © 2004 Battistuzzi et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: The timescale of prokaryote evolution has been difficult to reconstruct because of a limited fossil record and complexities associated with molecular clocks and deep divergences. However, the relatively large number of genome sequences currently available has provided a better opportunity to control for potential biases such as horizontal gene transfer and rate differences among lineages. We assembled a data set of sequences from 32 proteins (~7600 amino acids) common to 72 species and estimated phylogenetic relationships and divergence times with a local clock method. Results: Our phylogenetic results support most of the currently recognized higher-level groupings of prokaryotes.
  • Wedding Higher Taxonomic Ranks with Metabolic Signatures Coded in Prokaryotic Genomes

    Wedding Higher Taxonomic Ranks with Metabolic Signatures Coded in Prokaryotic Genomes

    Wedding higher taxonomic ranks with metabolic signatures coded in prokaryotic genomes Gregorio Iraola*, Hugo Naya* Corresponding authors: E-mail: [email protected], [email protected] This PDF file includes: Supplementary Table 1 Supplementary Figures 1 to 4 Supplementary Methods SUPPLEMENTARY TABLES Supplementary Tab. 1 Supplementary Tab. 1. Full prediction for the set of 108 external genomes used as test. genome domain phylum class order family genus prediction alphaproteobacterium_LFTY0 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Unknown candidatus_nasuia_deltocephalinicola_PUNC_CP013211 Bacteria Proteobacteria Gammaproteobacteria Unknown Unknown Unknown candidatus_sulcia_muelleri_PUNC_CP013212 Bacteria Bacteroidetes Flavobacteriia Flavobacteriales NA Candidatus Sulcia deinococcus_grandis_ATCC43672_BCMS0 Bacteria Deinococcus-Thermus Deinococci Deinococcales Deinococcaceae Deinococcus devosia_sp_H5989_CP011300 Bacteria Proteobacteria Unknown Unknown Unknown Unknown micromonospora_RV43_LEKG0 Bacteria Actinobacteria Actinobacteria Micromonosporales Micromonosporaceae Micromonospora nitrosomonas_communis_Nm2_CP011451 Bacteria Proteobacteria Betaproteobacteria Nitrosomonadales Nitrosomonadaceae Unknown nocardia_seriolae_U1_BBYQ0 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Nocardia nocardiopsis_RV163_LEKI01 Bacteria Actinobacteria Actinobacteria Streptosporangiales Nocardiopsaceae Nocardiopsis oscillatoriales_cyanobacterium_MTP1_LNAA0 Bacteria Cyanobacteria NA Oscillatoriales
  • Genes Preferring Non-AUG Start Codons in Bacteria Abstract

    Genes Preferring Non-AUG Start Codons in Bacteria Abstract

    Genes Preferring Non-AUG Start Codons in Bacteria 1,2 2,3,4 Anne Gvozdjak ​ and Manoj P. Samanta ​ ​ 1. Bellevue High School, 10416 SE Wolverine Way, Bellevue, WA 98004 2. Coding for Life Science, Redmond, WA 98053 3. Systemix Institute, Redmond, WA 98053 4. [email protected] Abstract Here we investigate translational regulation in bacteria by analyzing the distribution of start codons in fully assembled genomes. We report 36 genes (infC, rpoC, rnpA, etc.) showing a preference for non-AUG start codons in evolutionarily diverse phyla (“non-AUG genes”). Most of the non-AUG genes are functionally associated with translation, transcription or replication. In E. ​ coli, the percentage of essential genes among these 36 is significantly higher than among all ​ genes. Furthermore, the functional distribution of these genes suggests that non-AUG start codons may be used to reduce gene expression during starvation conditions, possibly through translational autoregulation or IF3-mediated regulation. Introduction Understanding the regulation of gene expression at the transcriptional and translational levels is a key step in deciphering the information contained by genomes. Whereas inexpensive, high-throughput technologies (ChIP-seq, RNAseq) are helping in extensive experimental investigation of transcriptional regulation [1,2], the measurement of translational regulation using ​ ​ high-throughput mass spectroscopy [3] is less widely adopted. Bioinformatics provides another ​ avenue to leverage the rapidly falling cost of DNA sequencing to explore translational regulation. Prior to 2000, computational researchers identified a number of relevant patterns through the comparative analysis of gene sequences [4,5] . With the availability of ~250,000 ​ prokaryotic genomes, thousands of eukaryotic genomes, and additional metagenomic sequences, those patterns can now be studied at a significantly larger scale, and new patterns may also be identified.
  • Genome Sequencing and Description of Oerskovia Enterophila Vjag, an Agar- and Cellulose-Degrading Bacterium Vanessa Jag1*† , Anja Poehlein2†, Frank R

    Genome Sequencing and Description of Oerskovia Enterophila Vjag, an Agar- and Cellulose-Degrading Bacterium Vanessa Jag1*† , Anja Poehlein2†, Frank R

    Jag et al. Standards in Genomic Sciences (2017) 12:30 DOI 10.1186/s40793-017-0244-4 SHORT GENOME REPORT Open Access Genome sequencing and description of Oerskovia enterophila VJag, an agar- and cellulose-degrading bacterium Vanessa Jag1*† , Anja Poehlein2†, Frank R. Bengelsdorf1, Rolf Daniel2 and Peter Dürre1 Abstract A nonmotile, Gram-positive bacterium that shows an elongated and branching cell shape was isolated from soil samples from the botanical garden of Ulm University, Ulm, Germany. Here, the isolation procedure, identification, genome sequencing and metabolic features of the strain are described. Phylogenetic analysis allowed to identify the isolated strain as Oerskovia enterophila. The genus Oerskovia belongs to the family Cellulomonadaceae within the order Actinomycetales. The length of cells of O. enterophila ranges from 1 μmto15μm, depending on the growth phase. In the exponential growth phase, cells show an elongated and branching shape, whereas cells break up to round or coccoid elements in the stationary growth phase. The 4,535,074 bp long genome consists of 85 contigs with 3918 protein-coding genes and 57 RNA genes. The isolated strain was shown to degrade numerous complex carbon sources such as cellulose, chitin, and starch, which can be found ubiquitously in nature. Moreover, analysis of the genomic sequence revealed the genetic potential to degrade these compounds. Keywords: Oerskovia, Cellulomonadaceae, Cellulose degradation, Soil bacteria, Phylogenetic analysis Introduction from 51% to more than 70% [5–7]. Actinobacteria are Oerskovia enterophila was formerly characterized as widely distributed in terrestrial as well as in aquatic Promicromonospora enterophila by Jàger et al. in 1983 habitats [8, 9].
  • Prokaryotic Gene Start Prediction: Algorithms for Genomes and Metagenomes

    Prokaryotic Gene Start Prediction: Algorithms for Genomes and Metagenomes

    PROKARYOTIC GENE START PREDICTION: ALGORITHMS FOR GENOMES AND METAGENOMES A Dissertation Presented to The Academic Faculty By Karl Gemayel In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Computational Science and Engineering Georgia Institute of Technology December 2020 © Karl Gemayel 2020 PROKARYOTIC GENE START PREDICTION: ALGORITHMS FOR GENOMES AND METAGENOMES Thesis committee: Dr. Mark Borodovsky Dr. Polo Chau School of Computational Science and En- School of Computational Science and En- gineering and Department of Biomedical gineering Engineering Georgia Institute of Technology Georgia Institute of Technology Dr. Umit¨ C¸atalyurek¨ Dr. King Jordan School of Computational Science and En- School of Biological Sciences gineering Georgia Institute of Technology Georgia Institute of Technology Dr. Pen Qui Department of Biomedical Engineering Georgia Institute of Technology Date approved: October 31, 2020 Wooster: “There are moments, Jeeves, when one asks oneself, ‘Do trousers matter?’” Jeeves: “The mood will pass, sir.” P.G. Wodehouse, The Code Of The Woosters To Mom and Dad, all my ancestors, and the first self-replicating molecule. Without you, this work would literally not have been possible. ACKNOWLEDGMENTS I am bound to forget someone or something and so, in fairness to all, I will forget most things and keep this vague and terse, though not necessarily short. I was very much at the right place at the right time to do this work, a time where these problems had not yet been solved. To the driven students who graduated early enough before such ideas came to them, thank you for being considerate. To my advisor Mark Borodovsky, who insisted that a lack of community funding for prokaryotic gene finding does not mean that the problem has actually been solved, thank you for continuously pushing for rigorous science that questions accepted beliefs.