Literature DB >> 22180814

Non-contiguous finished genome sequence and contextual data of the filamentous soil bacterium Ktedonobacter racemifer type strain (SOSP1-21).

Yun-Juan Chang, Miriam Land, Loren Hauser, Olga Chertkov, Tijana Glavina Del Rio, Matt Nolan, Alex Copeland, Hope Tice, Jan-Fang Cheng, Susan Lucas, Cliff Han, Lynne Goodwin, Sam Pitluck, Natalia Ivanova, Galina Ovchinikova, Amrita Pati, Amy Chen, Krishna Palaniappan, Konstantinos Mavromatis, Konstantinos Liolios, Thomas Brettin, Anne Fiebig, Manfred Rohde, Birte Abt, Markus Göker, John C Detter, Tanja Woyke, James Bristow, Jonathan A Eisen, Victor Markowitz, Philip Hugenholtz, Nikos C Kyrpides, Hans-Peter Klenk, Alla Lapidus.   

Abstract

Ktedonobacter racemifer corrig. Cavaletti et al. 2007 is the type species of the genus Ktedonobacter, which in turn is the type genus of the family Ktedonobacteraceae, the type family of the order Ktedonobacterales within the class Ktedonobacteria in the phylum 'Chloroflexi'. Although K. racemifer shares some morphological features with the actinobacteria, it is of special interest because it was the first cultivated representative of a deep branching unclassified lineage of otherwise uncultivated environmental phylotypes tentatively located within the phylum 'Chloroflexi'. The aerobic, filamentous, non-motile, spore-forming Gram-positive heterotroph was isolated from soil in Italy. The 13,661,586 bp long non-contiguous finished genome consists of ten contigs and is the first reported genome sequence from a member of the class Ktedonobacteria. With its 11,453 protein-coding and 87 RNA genes, it is the largest prokaryotic genome reported so far. It comprises a large number of over-represented COGs, particularly genes associated with transposons, causing the genetic redundancy within the genome being considerably larger than expected by chance. This work is a part of the Genomic Encyclopedia of Bacteria and Archaea project.

Entities:  

Keywords:  Chloroflexi; GEBA; Gram-positive; Ktedonobacteraceae; aerobic; broken-stick distribution; entropy; filamentous; heterotrophic; moderately acidophilic; non-motile; sporulating; transposon

Year:  2011        PMID: 22180814      PMCID: PMC3236041          DOI: 10.4056/sigs.2114901

Source DB:  PubMed          Journal:  Stand Genomic Sci        ISSN: 1944-3277


Introduction

Strain SOSP1-21T (= DSM 44963 = NRRL B-41538) is the type strain of the species Ktedonobacter racemifer, which is the type species of the monotypic genus Ktedonobacter, the type genus of the family Ktedonobacteraceae [1]. K. racemifer was first described in 2006 [1,2] as an aerobic, non-motile, filamentous, mesophilic, Gram-positive heterotroph also capable of growing under microaerophilic conditions [1]. The genus name was derived from the Greek word ktedon -onos, fiber, and the Neo-Latin bacter, a rod, meaning a filamentous rod [1]. The species epithet is derived from the Latin adjective racemifer, carrying clusters of grapes [1]. The original spelling, Ktedobacter racemifer was corrected in 2007 on validation according to Rule 61 and Recommendation 6(7) [2]. Strain SOSP1-21T was originally isolated from a soil sample of a black locust wood in Gerenzano, Northern Italy. Ten phylogenetically (class level) related strains were also isolated from soil samples collected at different locations in Northern Italy [1]. Only recently, a nearest cultivated neighbor, Thermosporothrix hazakensis, was isolated from hot compost in Japan [3]. Here we present a summary classification and a set of features for K. racemifer strain SOSP1-21T, together with the description of the complete genomic sequencing and annotation.

Classification and features

Using NCBI BLAST [4], a representative genomic 16S rRNA sequence of K. racemifer SOSP1-21T was compared under default settings (e.g., considering only the high-scoring segment pairs (HSPs) from the best 250 hits) with the most recent release of the Greengenes database [5] and the relative frequencies of taxa and keywords (reduced to their stem [6]) were determined, weighted by BLAST scores. The most frequently occurring genus was 'Ktedobacter' (100.0%) (1 hit in total; this represents the original, incorrect spelling of Ktedonobacter). No hits to sequences with (other) species names were found. (Note that the Greengenes database uses the INSDC (= EMBL/NCBI/DDBJ) annotation, which is not an authoritative source for nomenclature or classification.) The highest-scoring environmental sequence was AM180157 ('New lineage filamentous spore-forming soil isolate SOSP1-30SOSP1-30 str. SOSP1-30'), which showed an identity of 99.0% and an HSP coverage of 95.2%. The most frequently occurring keywords within the labels of environmental samples which yielded hits were 'soil' (11.2%), 'prari, tallgrass' (4.9%), 'miner, weather' (1.9%), 'new' (1.8%) and 'filament, lineag, spore-form' (1.6%) (249 hits in total). These keywords reflect some of the ecological properties reported for strain SOSP1-21T in the original description [1]. Environmental samples which yielded hits of a higher score than the highest scoring species were not found. Figure 1 shows the phylogenetic neighborhood of K. racemifer in a 16S rRNA based tree. The sequences of the eight 16S rRNA genes copies in the genome differ by up to nine nucleotides from each other and by up to five nucleotides from the previously published 16S rRNA sequence (AM180156), which contains two ambiguous base calls.
Figure 1

Phylogenetic tree highlighting the position of K. racemifer relative to the other type strains within the phylum ‘Chloroflexi’. The tree was inferred from 1,359 aligned characters [7,8] of the 16S rRNA gene sequence under the maximum likelihood (ML) criterion [9]. Rooting was done initially using the midpoint method [10] and then checked for its agreement with the current classification (Table 1). The branches are scaled in terms of the expected number of substitutions per site. Numbers above branches are support values from 750 ML bootstrap replicates [11] (left) and from 1,000 maximum parsimony bootstrap replicates [12] (right) if larger than 60%. Lineages with type strain genome sequencing projects registered in GOLD [13] are labeled with one asterisk, those also listed as 'Complete and Published' with two asterisks [14-17] as well as CP001337, CP000804, CP000909, CP002084, and AP012029.

Phylogenetic tree highlighting the position of K. racemifer relative to the other type strains within the phylum ‘Chloroflexi’. The tree was inferred from 1,359 aligned characters [7,8] of the 16S rRNA gene sequence under the maximum likelihood (ML) criterion [9]. Rooting was done initially using the midpoint method [10] and then checked for its agreement with the current classification (Table 1). The branches are scaled in terms of the expected number of substitutions per site. Numbers above branches are support values from 750 ML bootstrap replicates [11] (left) and from 1,000 maximum parsimony bootstrap replicates [12] (right) if larger than 60%. Lineages with type strain genome sequencing projects registered in GOLD [13] are labeled with one asterisk, those also listed as 'Complete and Published' with two asterisks [14-17] as well as CP001337, CP000804, CP000909, CP002084, and AP012029.
Table 1

Classification and general features of K. racemifer SOSP1-21T according to the MIGS recommendations [18] and the NamesforLife database [19]

MIGS ID   Property   Term   Evidence code
   Current classification   Domain Bacteria   TAS [20]
   Phylum Chloroflexi   TAS [21,22]
   Class Ktedonobacteria   TAS [1-3]
   Order Ktedonobacterales   TAS [1,2]
   Family Ktedonobacteraceae   TAS [1,2]
   Genus Ktedonobacter   TAS [1,2]
   Species Ktedonobacter racemifer   TAS [1]
   Type strain SOSP1-21   TAS [1]
   Gram stain   positive   TAS [1]
   Cell shape   filamentous   TAS [1]
   Motility   non-motile   TAS [1]
   Sporulation   spherical spore-forming   TAS [1]
   Temperature range   mesophile   TAS [1]
   Optimum temperature   28-33°C   TAS [1]
   Salinity   NaCl up to 10 g/l growth w/o problem, inhibited at 30 g/l   TAS [1]
MIGS-22   Oxygen requirement   aerobic and microaerophilic   TAS [1]
   Carbon source   sugars and peptides   TAS [1]
   Energy metabolism   heterotrophic   TAS [1]
MIGS-6   Habitat   soil   TAS [1]
MIGS-15   Biotic relationship   free-living   NAS
MIGS-14   Pathogenicity   none   NAS
   Biosafety level   1   TAS [23]
   Isolation   soil from a black locust wood   TAS [1]
MIGS-4   Geographic location   Gerenzano, Northern Italy   TAS [1]
MIGS-5   Sample collection time   November 2001   NAS
MIGS-4.1   Latitude   45.64   NAS
MIGS-4.2   Longitude   9.00   NAS
MIGS-4.3   Depth   not reported
MIGS-4.4   Altitude   about 210 m   NAS

Evidence codes - TAS: Traceable Author Statement (i.e., a direct report exists in the literature); NAS: Non-traceable Author Statement (i.e., not directly observed for the living, isolated sample, but based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from of the Gene Ontology project [24].

Evidence codes - TAS: Traceable Author Statement (i.e., a direct report exists in the literature); NAS: Non-traceable Author Statement (i.e., not directly observed for the living, isolated sample, but based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from of the Gene Ontology project [24]. K. racemifer strain SOSP1-21T cells are rod-shaped, filamentous and grow both vegetative and aerial mycelia on solid medium (Figure 2a). The large aerial hyphae produce spherical spores that cluster together with a grape-like appearance (Figure 2b). All other K. racemifer strains produced rounded spores, although they were arranged differently on the aerial hyphae [1]. Filamentous growth of strain SOSP1-21T also occurred in submerged cultures, which contained the branched mycelia known from actinomycetes [1]. SOSP1-21T stains Gram-positive and is not acid fast [1]. It produces pigments ranging from cream to pinkish orange on all media [1]. Although essentially aerobic, SOSP1-21T is capable of growing under microaerophilic conditions [1]. The optimal growth temperature is 28-33°C [1]. It grows well at pH values between 4.8 and 6.8 with an optimum at pH 6 [1]. Salinity up to 10 g per liter does not inhibit the growth of the strain [1].
Figure 2a

Scanning electron micrographs of K. racemifer SOSP1-21T mycelium.

Figure 2b

Scanning electron micrographs of K. racemifer SOSP1-21T spores.

Scanning electron micrographs of K. racemifer SOSP1-21T mycelium. Scanning electron micrographs of K. racemifer SOSP1-21T spores. Strain SOSP1-21T was capable of hydrolyzing starch, casein, gelatin, and (to a lesser extent) keratin but not cellulose, xylan, or chitin [1]. Strain SOSP1-21T was catalase positive and produced H2S but could not reduce nitrates [1]. It is sensitive to 5 ug/ml novobiocin or ramoplanin and to 20 mg/ml apramycin and the glycopeptide A40926.

Chemotaxonomy

The peptidoglycan of strain SOSP1-21T contains ornithine, alanine, glutamic acid, serine, and glycine at a molar ratio of approximately 0.7:1.8:1.0:0.8:1.9 [1]. Serine was identified at the N-terminus of the interpeptide bridge [1]. When originally described, a detailed peptidoglycan structure had not been determined but A-type cross-linkage was suggested [1]. The cellular fatty acid pattern of strain SOSP1-21T was reported to be characterized by an unusual high abundance of C16:1 2-OH (30%) with other dominant lipids being branched-chain saturated fatty acids iso-C17:0 (25%), iso-C16:0 (11.5%) and anteiso-C17:0 (9.6%), as well as C16:0 10-Me (7.8%) and C16:0 (6.7%) [1]. Our own data (DSMZ) did not confirm this fatty acid spectrum, but revealed iso-C16:0 (20.1%) as the most frequent fatty acid, followed by anteiso-C17:0 (18.5%), iso-C17:0 (15.0%), only 13.1% C16:1 2-OH and 11.6% C16:0 10-Me. Polar lipids consisted of phosphatidylinisitol, phosphatitylglycerol, diphosphatidylglycerol and an unknown glycolipid [1]. MK-9(H) was the only menaquinone reported for strain SOSP1-21T [1].

Genome sequencing and annotation

Genome project history

This organism was selected for sequencing on the basis of its phylogenetic position [25], and is part of the enomic ncyclopedia of acteria and rchaea project [26]. The genome project is deposited in the Genomes OnLine Database [13] and the complete genome sequence is deposited in GenBank. Sequencing, finishing and annotation were performed by the DOE Joint Genome Institute (JGI). A summary of the project information is shown in Table 2.
Table 2

Genome sequencing project information

MIGS ID   Property   Term
MIGS-31   Finishing quality   Non-contiguous finished
MIGS-28   Libraries used   Two Sanger 8 kb pMCL200 and fosmid libraries; one 454 pyrosequence standard library
MIGS-29   Sequencing platforms   ABI3730, 454 GS FLX
MIGS-31.2   Sequencing coverage   10.1 × Sanger; 24.6 × pyrosequence
MIGS-30   Assemblers   Newbler version 1.1.02.15, phrap
MIGS-32   Gene calling method   Prodigal 1.4, Genemark 4.6b, tRNAScan-SE-1.23, infernal 0.81
   INSDC ID   ADVG00000000
   Genbank Date of Release   June 14, 2010
   GOLD ID   Gi02261
   NCBI project ID   27943
   Database: IMG-GEBA   648276680
MIGS-13   Source material identifier   DSM 44963
   Project relevance   Tree of Life, GEBA

Growth conditions and DNA isolation

K. racemifer SOSP1-21T, DSM 44963, was grown in DSMZ medium 65 (GYM Streptomyces medium) [27] adjusted to pH 6.0, at 28°C. DNA was isolated from 0.5-1 g of cell paste using Qiagen Genomic 500 DNA Kit (Qiagen 10262) following the manufacturer’s protocol, with cell lysis protocol st/LALMP as described in Wu et al. [26]. DNA is available through the DNA Bank Network [28].

Genome sequencing and assembly

The genome was sequenced using a combination of Sanger and 454 sequencing platforms. All general aspects of library construction and sequencing can be found at the JGI website [29]. Pyrosequencing reads were assembled using the Newbler assembler (Roche). The initial Newbler contigs were broken into 14,080 overlapping fragments of 1,000 bp and entered as pseudo-reads into the subsequence assembly. The sequences were assigned quality scores based on Newbler consensus q-scores with modifications to account for overlap redundancy and to adjust inflated q-scores. A hybrid 454/Sanger assembly was produced using parallel phrap (High Performance Software, LLC). Possible mis-assemblies were corrected with Dupfinisher [30], or transposon bombing of bridging clones (Epicentre Biotechnologies, Madison, WI) [31]. Some gaps between contigs were closed by editing in Consed [32], custom primer walking or PCR amplification. A total of 3,354 Sanger finishing reads and five shatter libraries were produced to close gaps, to resolve some repetitive regions, and to raise the quality of the finished sequence. Illumina reads were also used to correct potential base errors and increase consensus quality using a software Polisher developed at JGI [33]. The error rate of the completed genome sequence is less than 1 in 100,000. Together, the combination of the Sanger and 454 sequencing platforms provided 34.7 × coverage of the genome. The final assembly contained 165,050 pyrosequence and 2,305,667 Illumina reads.

Genome annotation

Genes were identified using Prodigal [34] as part of the Oak Ridge National Laboratory genome annotation pipeline, followed by a round of manual curation using the JGI GenePRIMP pipeline [35]. The predicted CDSs were translated and used to search the National Center for Biotechnology Information (NCBI) non-redundant database, UniProt, TIGR-Fam, Pfam, PRIAM, KEGG, COG, and InterPro databases. Additional gene prediction analysis and functional annotation were performed within the Integrated Microbial Genomes - Expert Review (IMG-ER) platform [36].

Genome properties

The non-contiguous finished genome consists of ten contigs ranging in size from 1,579 bp to almost four Mbp, with five contigs being longer than one Mb (1,302,518 bp, 2,713,222 bp, 2,766,182 bp, 2,916,502 bp, and 3,837,106 bp) and a G+C content of 53.8% (Table 3 and Figure 3). Of the 11,540 genes predicted, 11,453 were protein-coding genes, and 87 RNAs; No pseudogenes were identified. The majority of the protein-coding genes (61.2%) were assigned a putative function while the remaining ones were annotated as hypothetical proteins. The distribution of genes into COGs functional categories is presented in Table 4.
Table 3

Genome Statistics

Attribute    Value    % of Total
Genome size (bp)    13,661,586    100.00%
DNA coding region (bp)    10,422,932    76.29%
DNA G+C content (bp)    7,348,426    53.79%
Number of contigs    10
Extrachromosomal elements    unknown
Total genes    11,540    100.00%
RNA genes    87    0.75%
rRNA operons    8
Protein-coding genes    11,453    99.25%
Pseudo genes    0
Genes with function prediction    7,065    61.22%
Genes in paralog clusters    4,919    42.63%
Genes assigned to COGs    6,654    57.66%
Genes assigned Pfam domains    7,250    62.82%
Genes with signal peptides    2,660    23.05%
Genes with transmembrane helices    2,581    22.27%
CRISPR repeats    7
Figure 3

Graphical linear map of the largest, 3,837,106 bp long contig. From bottom to the top: Genes on forward strand (color by COG categories), Genes on reverse strand (color by COG categories), RNA genes (tRNAs green, rRNAs red, other RNAs black), GC content, GC skew.

Table 4

Number of genes associated with the general COG functional categories

Code   value    %age    Description
J   224    2.9    Translation, ribosomal structure and biogenesis
A   0    0.0    RNA processing and modification
K   893    11.6    Transcription
L   975    12.6    Replication, recombination and repair
B   3    0.0    Chromatin structure and dynamics
D   34    0.4    Cell cycle control, cell division, chromosome partitioning
Y   0    0.0    Nuclear structure
V   215    2.8    Defense mechanisms
T   617    8.0    Signal transduction mechanisms
M   257    3.3    Cell wall/membrane/envelope biogenesis
N   20    0.3    Cell motility
Z   0    0.0    Cytoskeleton
W   0    0.0    Extracellular structures
U   54    0.7    Intracellular trafficking, secretion, and vesicular transport
O   195    2.5    Posttranslational modification, protein turnover, chaperones
C   416    5.4    Energy production and conversion
G   612    7.9    Carbohydrate transport and metabolism
E   474    6.2    Amino acid transport and metabolism
tF   135    1.8    Nucleotide transport and metabolism
H   264    3.4    Coenzyme transport and metabolism
I   236    3.1    Lipid transport and metabolism
P   255    3.3    Inorganic ion transport and metabolism
Q   217    2.8    Secondary metabolites biosynthesis, transport and catabolism
R   1,098    14.4    General function prediction only
S   519    6.7    Function unknown
-   4,886    42.3    Not in COGs
Graphical linear map of the largest, 3,837,106 bp long contig. From bottom to the top: Genes on forward strand (color by COG categories), Genes on reverse strand (color by COG categories), RNA genes (tRNAs green, rRNAs red, other RNAs black), GC content, GC skew.

Insights from the genome sequence

Genome structure

With a length of 13,661,586 bp for the ten contigs (Table 3) K. racemifer SOSP1-21T has the largest of all completely sequenced 1,760 archaeal and bacterial genomes [37] thus far, followed by Sorangium cellulosum, 13.0 Mbp [38], Steptomyces bingchenggensis, 11.9 Mbp [39], Catenulispora acidiphila, 10.5 Mbp [40], and Streptosporangium roseum, 10.4 Mbp [41]. However, this genome was also one of the most difficult to assemble. Figure 4 shows the unusually high number of identical sequence fragments across the genome, which caused the termination of the project as non-contiguous finished genome without closure of the last ten sequence gaps.
Figure 4

Screen shot from CROSSMATCH [32] indicating the matches between sequences within and across the contigs. CROSSMATCH options were – minmatch 30 – minscore 60.

Screen shot from CROSSMATCH [32] indicating the matches between sequences within and across the contigs. CROSSMATCH options were – minmatch 30 – minscore 60.

Comparative genomics

Lacking an available genome sequence of the closest relative of K. racemifer, Thermosporothrix hazakensis [3] (Figure 1), the following comparative analyses were done with Sphaerobacter thermophilus [42] and Thermomicrobium roseum [43], the closest organisms phylogenetically for which there are publically available genome sequences [15,16]. K. racemifer stands out because of its enormous genome size of more than 13 Mbp. The genomes of S. thermophilus and T. roseum are significantly smaller, 3.9 Mbp and 2.9 Mbp, respectively. Whereas S. thermophilus and T. roseum have similar G+C-contents of 68% and 64%, respectively, the G+C-content of the K. racemifer genome is significantly lower (54%). The fraction of shared genes in the three genomes is shown in a Venn diagram (Figure 5). The numbers of pairwise shared genes were calculated with the phylogenetic profiler function of the IMG-ER platform [36]. Homologous genes within the genomes were detected with a maximum E-value of 10-5 and a minimum identity of 30%.
Figure 5

Venn diagram depicting the intersections of protein sets (total number of derived protein sequences in parentheses) of K. racemifer, S. thermophilus and T. roseum.

Venn diagram depicting the intersections of protein sets (total number of derived protein sequences in parentheses) of K. racemifer, S. thermophilus and T. roseum. A total of 1,393 genes are shared by the three genomes, referring to the whole genome sizes 39% and 48% of the genes in S. thermophilus and T. roseum have homologs in the three genomes, in the case of K. racemifer only 12% of the genes are shared by the other two genomes. The pairwise comparison of S. thermophilus and T. roseum revealed 2,249 genes which are shared by these two organisms, referring to the whole genomes 64% of the S. thermophilus genes and 79% of the T. roseum have homologous genes in the respective other genome. The genome of K. racemifer encodes an enormously high number of transposon-associated genes; its annotation revealed 601 genes encoding transposases, 151 genes encoding integrases and 107 genes encoding resolvases. The genes coding these enzymes are spread over the whole genome with some regions having a higher density than others. The extremely high number of transposases is due to several gene copies that are to a greater or lesser extent similar in their sequences. The presence of that many mobile elements may explain the unusually high number of identical sequence fragments across the genome and the resulting difficulties occurring during the genome assembly. Within the 9,539 unique genes of K. racemifer that have no detectable homologs in the genomes of S. thermophilus and T. roseum (under the sequence similarity thresholds used for the comparison) the 29 genes encoding xylose isomerases appear to be especially noteworthy; for 27 of these isomerase genes no homologous genes were detected in the other two genomes; only one gene was identified in T. roseum, and two in S. thermophilus. The high number of xylose isomerase genes suggests a strong utilization of pentoses by K. racemifer. To date K. racemifer was not tested regarding xylose utilization, but the close relative T. hazakensis is able to use xylose as the only carbon source [3]. Furthermore, a high number of genes encoding proteins responsible for resistance against several antibiotics were predicted: 61 bleomycin resistance proteins and 41 aminoglycoside phosphotransferases. An estimate of the overall similarity between K. racemifer, S. thermophilus and T. roseum, was generated with the GGDC Genome-to-Genome Distance Calculator [44,45]. This system calculates the distances by comparing the genomes to obtain HSPs (high-scoring segment pairs) and interfering distances from a set of formulas (1, HSP length / total length; 2, identities / HSP length; 3, identities / total length). Table 5 shows the results of the pairwise comparison between the three genomes.
Table 5

Pairwise comparison of K. racemifer, S. thermophilus and T. roseum using the GGDC-Calculator.

HSP length /total length [%]    identities /HSP length [%]   identities /total length [%]
K. racemifer   S. thermophilus0.57    86.4   0.50
K. racemifer   T. roseum0.48    87.2   0.42
T. roseum   S. thermophilus9.41    83.1   7.82
The pairwise comparison (Table 5) of the genomes of K. racemifer with S. thermophilus and T. roseum revealed that only 0.57% and 0.48% of the average of the genome lengths are covered with HSPs. The identity within these HSPs was 86.4% and 87.2%, whereas the identity over the whole genome was only 0.50% and 0.42%, respectively. The comparison of T. roseum with S. thermophilus revealed that 9.41% of the average of both genome lengths are covered with HSPs, with an identity within these HSPs of 83.1%. The identity over the whole genome is 7.82%. These results show how distant the relationship between K. racemifer and S. thermophilus and T. roseum, respectively, is, if genome sizes are taken into consideration. In order to quantify the differences in gene redundancy between the three genomes, as well as to determine over-represented genes, we used approaches based on entropy and the broken-stick distribution, respectively, applied to the set of genes from either genome assigned to COGs. Shannon's entropy (see, e.g., pp. 214, 243 in [46]) H can be used as a measure of disorder for discrete distributions; it is maximum (H) if all categories (COGs in our case) are represented by exactly one item (gene) and then equal to the logarithm of the number of items (genes). Thus, one can measure the evenness (non-redundancy) within such a distribution as H/H and the corresponding redundancy as 1.0 – H/H. The broken-stick distribution reflects the relative abundance of a given number of categories within a random population of items (see, e.g., p. 244 and 410 in [46]). Over-represented items (here: COGs) are those whose real relative frequencies (here: number of genes assigned to this COG relative to the total number of genes assigned to COGs) are larger than the broken-stick value of the corresponding rank within the list of frequencies sorted in decreasing order. Moreover, the entropy H of the broken-stick distribution can be used as an estimate for the expected entropy, yielding 1.0 – H/H as an alternative measure of redundancy (which becomes negative when the evenness is larger than expected by chance). The 2,022 genes assigned to 1,300 distinct COGs in the genome of T. roseum corresponded to an entropy of 6.912, an expected entropy of 6.748 and, hence, a redundancy of 9.20% if measured using H and of -2.42% using H, whereas S. thermophilus (2,619 genes assigned to 1,383 COGs) yielded an entropy of 6.837 (expected: 6.810) and a redundancy of 13.14% with H and -0.39% with H. In contrast, the 6,654 genes assigned to 1,731 distinct COGs in the genome of K. racemifer yielded an entropy of only 6.455 (expected: 7.034) and a redundancy of 26.67% (using H) and 8.24% (using H). That is, in contrast to the other two genomes the genes within the genome of K. racemifer are distributed less even than expected by chance. Figure 6 compares the relative frequencies of the COGs in the genome of K. racemifer compared to their expected frequency. More than 80 COGs were judged as over-represented by this comparison, considerably more than in the genomes of S. thermophilus [33; Figure 7] and T. roseum ([15]; Figure 8). A closer look onto the 20 most over-represented COGs in K. racemifer, S. thermophilus and T. roseum revealed differences between the three organisms. Not surprisingly the genes coding transposases (COG0675; by far the most frequent one), integrases (COG3316) and resolvases (COG2452) can be found among the over-represented COGs in K. racemifer (Figure 6).
Figure 6

Relative frequencies of the 100 most frequent COGs in the genome of K. racemifer (blue line) compared to their expected frequency as estimated using the broken-stick distribution (red line). Over-represented COGs are labeled.

Figure 7

Relative frequencies of the 100 most frequent COGs in the genome of S. thermophilus (blue line) compared to their expected frequency as estimated using the broken-stick distribution (red line). Over-represented COGs are labeled.

Figure 8

Relative frequencies of the 100 most frequent COGs in the genome of T. roseum (blue line) compared to their expected frequency as estimated using the broken-stick distribution (red line). Over-represented COGs are labeled.

Relative frequencies of the 100 most frequent COGs in the genome of K. racemifer (blue line) compared to their expected frequency as estimated using the broken-stick distribution (red line). Over-represented COGs are labeled. Relative frequencies of the 100 most frequent COGs in the genome of S. thermophilus (blue line) compared to their expected frequency as estimated using the broken-stick distribution (red line). Over-represented COGs are labeled. Relative frequencies of the 100 most frequent COGs in the genome of T. roseum (blue line) compared to their expected frequency as estimated using the broken-stick distribution (red line). Over-represented COGs are labeled. Our analyses also showed that genes belonging to the category COG3344 are over-represented in the genome of K. racemifer. COG3344 represents retron type reverse transcriptases, which are found in group II introns. Group II introns are large catalytic RNA molecules that act as mobile genetic elements [47]. They were first identified in mitochondria and chloroplast genomes, but with the increasing number of bacterial genome sequencing projects, the number of group II intron sequences in the databases also increased. Dai and Zimmerly reported in 2003 that a quarter of the sequenced bacterial genomes contain group II introns [48,49]. By using the IMG-ER platform [36] we calculated that approximately one third of the 2,727 sequenced bacterial genomes contain group II introns. In the genome of K. racemifer, 34 genes coding reverse transcriptases could be identified, all of them having the same domain structure with the reverse transcriptase domain followed by a maturase-specific domain and the C-terminal HNH-endonuclease domain.
  32 in total

1.  Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis.

Authors:  J Castresana
Journal:  Mol Biol Evol       Date:  2000-04       Impact factor: 16.240

2.  GenePRIMP: a gene prediction improvement pipeline for prokaryotic genomes.

Authors:  Amrita Pati; Natalia N Ivanova; Natalia Mikhailova; Galina Ovchinnikova; Sean D Hooper; Athanasios Lykidis; Nikos C Kyrpides
Journal:  Nat Methods       Date:  2010-05-02       Impact factor: 28.547

3.  Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB.

Authors:  T Z DeSantis; P Hugenholtz; N Larsen; M Rojas; E L Brodie; K Keller; T Huber; D Dalevi; P Hu; G L Andersen
Journal:  Appl Environ Microbiol       Date:  2006-07       Impact factor: 4.792

4.  Genome sequence of the milbemycin-producing bacterium Streptomyces bingchenggensis.

Authors:  Xiang-Jing Wang; Yi-Jun Yan; Bo Zhang; Jing An; Ji-Jia Wang; Jun Tian; Ling Jiang; Yi-Hua Chen; Sheng-Xiong Huang; Min Yin; Ji Zhang; Ai-Li Gao; Chong-Xi Liu; Zhao-Xiang Zhu; Wen-Sheng Xiang
Journal:  J Bacteriol       Date:  2010-06-25       Impact factor: 3.490

5.  The Genomes On Line Database (GOLD) in 2009: status of genomic and metagenomic projects and their associated metadata.

Authors:  Konstantinos Liolios; I-Min A Chen; Konstantinos Mavromatis; Nektarios Tavernarakis; Philip Hugenholtz; Victor M Markowitz; Nikos C Kyrpides
Journal:  Nucleic Acids Res       Date:  2009-11-13       Impact factor: 16.971

6.  Reclassification of Sphaerobacter thermophilus from the subclass Sphaerobacteridae in the phylum Actinobacteria to the class Thermomicrobia (emended description) in the phylum Chloroflexi (emended description).

Authors:  Philip Hugenholtz; Erko Stackebrandt
Journal:  Int J Syst Evol Microbiol       Date:  2004-11       Impact factor: 2.747

7.  A phylogeny-driven genomic encyclopaedia of Bacteria and Archaea.

Authors:  Dongying Wu; Philip Hugenholtz; Konstantinos Mavromatis; Rüdiger Pukall; Eileen Dalin; Natalia N Ivanova; Victor Kunin; Lynne Goodwin; Martin Wu; Brian J Tindall; Sean D Hooper; Amrita Pati; Athanasios Lykidis; Stefan Spring; Iain J Anderson; Patrik D'haeseleer; Adam Zemla; Mitchell Singer; Alla Lapidus; Matt Nolan; Alex Copeland; Cliff Han; Feng Chen; Jan-Fang Cheng; Susan Lucas; Cheryl Kerfeld; Elke Lang; Sabine Gronow; Patrick Chain; David Bruce; Edward M Rubin; Nikos C Kyrpides; Hans-Peter Klenk; Jonathan A Eisen
Journal:  Nature       Date:  2009-12-24       Impact factor: 49.962

8.  The minimum information about a genome sequence (MIGS) specification.

Authors:  Dawn Field; George Garrity; Tanya Gray; Norman Morrison; Jeremy Selengut; Peter Sterk; Tatiana Tatusova; Nicholas Thomson; Michael J Allen; Samuel V Angiuoli; Michael Ashburner; Nelson Axelrod; Sandra Baldauf; Stuart Ballard; Jeffrey Boore; Guy Cochrane; James Cole; Peter Dawyndt; Paul De Vos; Claude DePamphilis; Robert Edwards; Nadeem Faruque; Robert Feldman; Jack Gilbert; Paul Gilna; Frank Oliver Glöckner; Philip Goldstein; Robert Guralnick; Dan Haft; David Hancock; Henning Hermjakob; Christiane Hertz-Fowler; Phil Hugenholtz; Ian Joint; Leonid Kagan; Matthew Kane; Jessie Kennedy; George Kowalchuk; Renzo Kottmann; Eugene Kolker; Saul Kravitz; Nikos Kyrpides; Jim Leebens-Mack; Suzanna E Lewis; Kelvin Li; Allyson L Lister; Phillip Lord; Natalia Maltsev; Victor Markowitz; Jennifer Martiny; Barbara Methe; Ilene Mizrachi; Richard Moxon; Karen Nelson; Julian Parkhill; Lita Proctor; Owen White; Susanna-Assunta Sansone; Andrew Spiers; Robert Stevens; Paul Swift; Chris Taylor; Yoshio Tateno; Adrian Tett; Sarah Turner; David Ussery; Bob Vaughan; Naomi Ward; Trish Whetzel; Ingio San Gil; Gareth Wilson; Anil Wipat
Journal:  Nat Biotechnol       Date:  2008-05       Impact factor: 54.908

9.  Standard operating procedure for calculating genome-to-genome distances based on high-scoring segment pairs.

Authors:  Alexander F Auch; Hans-Peter Klenk; Markus Göker
Journal:  Stand Genomic Sci       Date:  2010-01-28

10.  Complete genome sequence of Streptosporangium roseum type strain (NI 9100).

Authors:  Matt Nolan; Johannes Sikorski; Marlen Jando; Susan Lucas; Alla Lapidus; Tijana Glavina Del Rio; Feng Chen; Hope Tice; Sam Pitluck; Jan-Fang Cheng; Olga Chertkov; David Sims; Linda Meincke; Thomas Brettin; Cliff Han; John C Detter; David Bruce; Lynne Goodwin; Miriam Land; Loren Hauser; Yun-Juan Chang; Cynthia D Jeffries; Natalia Ivanova; Konstantinos Mavromatis; Natalia Mikhailova; Amy Chen; Krishna Palaniappan; Patrick Chain; Manfred Rohde; Markus Göker; Jim Bristow; Jonathan A Eisen; Victor Markowitz; Philip Hugenholtz; Nikos C Kyrpides; Hans-Peter Klenk
Journal:  Stand Genomic Sci       Date:  2010-01-28
View more
  24 in total

Review 1.  Combining CRISPR/Cas9 and rAAV Templates for Efficient Gene Editing.

Authors:  Manuel Kaulich; Steven F Dowdy
Journal:  Nucleic Acid Ther       Date:  2015-11-05       Impact factor: 5.486

2.  Mechanisms of Evolutionary Innovation Point to Genetic Control Logic as the Key Difference Between Prokaryotes and Eukaryotes.

Authors:  William Bains; Dirk Schulze-Makuch
Journal:  J Mol Evol       Date:  2015-07-25       Impact factor: 2.395

3.  1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life.

Authors:  Supratim Mukherjee; Rekha Seshadri; Neha J Varghese; Emiley A Eloe-Fadrosh; Jan P Meier-Kolthoff; Markus Göker; R Cameron Coates; Michalis Hadjithomas; Georgios A Pavlopoulos; David Paez-Espino; Yasuo Yoshikuni; Axel Visel; William B Whitman; George M Garrity; Jonathan A Eisen; Philip Hugenholtz; Amrita Pati; Natalia N Ivanova; Tanja Woyke; Hans-Peter Klenk; Nikos C Kyrpides
Journal:  Nat Biotechnol       Date:  2017-06-12       Impact factor: 54.908

4.  Analysis of microbial communities in heavy metals-contaminated soils using the metagenomic approach.

Authors:  M H Hemmat-Jou; A A Safari-Sinegani; A Mirzaie-Asl; A Tahmourespour
Journal:  Ecotoxicology       Date:  2018-09-21       Impact factor: 2.823

5.  Linking microbial communities to ecosystem functions: what we can learn from genotype-phenotype mapping in organisms.

Authors:  Andrew Morris; Kyle Meyer; Brendan Bohannan
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-03-23       Impact factor: 6.237

6.  Propionate metabolism and diversity of relevant functional genes by in silico analysis and detection in subsurface petroleum reservoirs.

Authors:  Tao Yang; Serge Maurice Mbadinga; Lei Zhou; Shi-Zhong Yang; Jing-Feng Liu; Ji-Dong Gu; Bo-Zhong Mu
Journal:  World J Microbiol Biotechnol       Date:  2017-09-23       Impact factor: 3.312

7.  Evolution of default genetic control mechanisms.

Authors:  William Bains; Enrico Borriello; Dirk Schulze-Makuch
Journal:  PLoS One       Date:  2021-05-13       Impact factor: 3.240

8.  Reconstructing rare soil microbial genomes using in situ enrichments and metagenomics.

Authors:  Tom O Delmont; A Murat Eren; Lorrie Maccario; Emmanuel Prestat; Özcan C Esen; Eric Pelletier; Denis Le Paslier; Pascal Simonet; Timothy M Vogel
Journal:  Front Microbiol       Date:  2015-04-30       Impact factor: 5.640

9.  Phylogeny-driven target selection for large-scale genome-sequencing (and other) projects.

Authors:  Markus Göker; Hans-Peter Klenk
Journal:  Stand Genomic Sci       Date:  2013-05-20

10.  Programmable repression and activation of bacterial gene expression using an engineered CRISPR-Cas system.

Authors:  David Bikard; Wenyan Jiang; Poulami Samai; Ann Hochschild; Feng Zhang; Luciano A Marraffini
Journal:  Nucleic Acids Res       Date:  2013-06-12       Impact factor: 16.971

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.