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RRNA Operon

The rRNA operon is a genetic structure found in bacteria and archaea that contains the genes encoding the ribosomal RNA (rRNA) molecules crucial for protein synthesis.
This operon typically includes the genes for 5S, 5.8S, and 23S rRNA, as well as the genes for the transfer RNAs needed for their production.
Understanding the structure and regulation of the rRNA operon is essential for research on bacterial and archaeal physiology, evolution, and pathogenesis.
Leveraging AI-powered comparisons can help optimize research on the rRNA operon by identifying the best protocols from literature, preprints, and patents, enhancing reproducibility and accuracy.
This powerful tool can streamlien the research process and help you find the optimal methods and products for your rRNA operon studies.

Most cited protocols related to «RRNA Operon»

Using HMMs to find new members of a sequence family requires reliable multiple alignments. The 16S/18S and 23S/28S rRNA alignments were retrieved from the European ribosomal RNA database (ERRD) (12). In this database, annotated large and small subunit ribosomal RNA sequences from the EMBL nucleotide database with a length of at least 70% of their estimated full length have been aligned. Multiple alignments of 5S rRNAs were retrieved from the 5S Ribosomal RNA Database (13). Data from both databases were downloaded on October 27, 2005. The alignments are all structural alignments, i.e. aligned using secondary structure information gained from comparative sequence analysis. The 5S alignments were already divided into separate alignments for archaeal, bacterial and eukaryotic sequences, whereas the ERRD data were not. The alignments for 16/18S and 23/28S rRNAs were divided into the same groups as the 5S data to provide kingdom-specific predictors. The data was stored in a MySQL database for easier handling.
The ERRD data contained sequences from ‘environmental samples’. These were excluded since there was little information about them. The 5S were generally around 120 nt long, the 16/18S around 1500 nt and the 23/28S around 3000 nt long, all with no obvious outliers. The length of the eukaryotic rRNAs varied substantially, more than those of bacterial and archaeal rRNAs, but no sequences in the alignments seemed obviously wrong.
The sequences were divided into phylogenetic groups to help with further analysis. Due to sequencing bias, some phylogenetic groups dominated the data sets. Such a skew could potentially cause the predictors to be less sensitive on underrepresented phylogenetic groups. Among the bacteria, 82% of the sequences were from three phyla: Actinobacteria, Firmicutes and Proteobacteria. Around 70% of the archaeal sequences were from Euryarchaeota; among the eukaryotes, the Streptophyta comprised 15% of the data. Several of the sequences also proved to be very similar. Therefore, redundancy reduction inspired by Hobohms second algorithm (14) was performed. This algorithm starts with a sorted list of the number of neighbors each sequence has. An all-against-all comparison between the sequences is performed and neighborship is judged by the level of similarity found. Similarity was measured by Score = ∑i,jnijSij/(N-g) where i and j sum over the four nucleotides, nij counts the number of aligned nucleotide pairs (i, j), N is the length of the sequence and g is the number of gap-only positions; Sij refers to the scoring matrix EDNAFULL created by Todd Lowe. The maximum similarity level allowed was set to ensure that each phylum was represented. Similarity graphs were formed for each group, with the sequences as vertices and edges between similar sequences. The sequence with the highest connectivity and its edges were deleted from the graph, and this was repeated until no edges remained. At the end, all removed sequences were checked to see if they had any edges to vertices in the remaining set. If not, they were reinstated. This procedure was implemented as a C program.
Sequences in ERRD may contain ambiguous nucleotide symbols representing nucleotides that have not been uniquely determined. These occur more frequently in bacteria and eukaryotes than in archaea, and primarily at both ends of the alignment: in 16/18S, predominantly at the end; in 23/28S, predominantly at the beginning. In the latter case, this was mostly due the high prevalence of gaps at the end of the alignment. As we found that ambiguous nucleotides at the ends reduced the ability to predict start and stop positions accurately, we decided to remove all sequences with five or more ambiguous nucleotides in either end of the sequence. A summary of the number of sequences removed during curation of the alignments is shown in Table 1.

The initial number of rRNA sequences and the number of sequences excluded for different reasons.

KingdomTypeInitial countEnvironmental samplesIncomplete sequencesRedundancy reductionTotal in HMM
Archaea5S58001048
16S58923947128776
23S37018815
Bacteria5S46100101360
16S12 107142910 7232485743
23S3980155130127
Eukaryotes5S3160033283
18S6585245222836979
28S157091858

Environmental samples were excluded due to lack of phylogenetic information. Sequences with too many unknown nucleotides in either end of the sequence were excluded to improve HMM accuracy. Redundancy reduction was performed to reduce bias. Note that these groups may overlap. The last column indicates the number of sequences used to build each HMM.

The software package HMMer (15) version 2.3.2 was used to create HMMs from alignments where all columns containing only gaps had been removed. It was configured for nucleotides, and to compensate for skews in the nucleotide distribution a custom null model for each alignment was used. Although redundancy reduction had been performed, the Henikoff position-based weighing scheme (16) was used to reduce any remaining biases. When using the HMMs to search genome sequences, the default alignment method was used: a match must span the entire model, and several matches may be found within one sequence.
With the aim of increasing the search speed, we determined the 75 most conserved consecutive columns in each alignment, as illustrated in Figure 1, and produced ‘spotter’ HMMs based on these. Since searches with the smaller spotter models would be considerably faster, we wanted to investigate the possibility of using the spotter to pre-screen for candidates, using the full HMMs only on regions surrounding the spotter hits. Spotter and full model searches were done separately. Spotter and full model predictions were matched based on whether they had overlapping nucleotides on the same strand. A linear regression was used to express spotter score in terms of full model score. Variation was estimated as linear in full model score with non-positive regression coefficients. Least squares estimates were used in both cases. Spotter scores were assumed to be missing when negative and, hence, assumed to follow a truncated normal distribution; expected scores and square deviations were used to replace missing values in the two regressions. From this model, we computed the lowest full model score, T99 , for which there was at least a 99% likelihood of getting a corresponding spotter hit, and the likelihood, Pmin , that a full model hit with the lowest found score should have a corresponding spotter hit.

The graphs show conservation in the alignments as measured by information content: C=∑ifilog2(fi/qi) where i sums over the four nucleotides, fi is the frequency of nucleotide i in the column and qi =1/4 is used as the background frequency. Ambiguous nucleotide symbols were evenly divided between the corresponding fi, gaps between all four nucleotides. The grey line represents the value for each position in the alignment, the black line is a running average over 75 nt around the current position, whereas the white dot indicates the center of the most conserved 75 nt region of the alignment.

Both the full HMMs and the spotter HMMs were run on all fully sequenced genomes found in the Genome Atlas database (listed in Supplementary Table S1). All predictions with non-negative score and E-value at most 100 were reported. Only full model hits with E-value <0.01 were accepted as reliable hits, but none with E-value between 0.01 and 100 were reported. As rRNAs within a genome tend to be very similar, usually with at least 99% identity, different full model hits within a genome corresponding to actual rRNAs should be expected to have similar scores. However, we found a substantial number of hits with far lower scores which we assume to be pseudogenes, truncated rRNAs or otherwise non-functional rRNA copies. To ensure that these did not have an adverse effect on the analyses, we excluded full model hits having a score less than 80% of the maximal score in that genome. These are listed in Supplementary Table S2.
Annotations of rRNAs were obtained from GenBank. Unfortunately, rRNAs have not been annotated in a uniform manner and it was often unclear exactly what was annotated. In some cases, both the separate rRNAs and the full operon was annotated. In all such cases, the operons were longer than 5000 nt, and all annotations longer than that were thus excluded. In our experience, this affected only operons. In other cases, different pieces of the same gene had been annotated as separate entities. Thus, some predictions matched several annotation entries; these are listed in Supplementary Table S3. A prediction was considered to match an annotation if they were on the same strand and the length of their overlap was at least half the length of the shorter of the two; it was considered to be annotated if it matched at least one annotation. The deviation between annotated and predicted start and stop positions was also examined, but predictions with multiple matching annotations were excluded from this comparison.
Additional analyses were performed for experimentally verified 16S in Anaplasma marginale St. Maries (M60313), Chlamydia muridarum Nigg (D85718), Escherichia coli K12 MG1655 (J01695), Sulfolobus tokodaii St. 7 (AB022438), Thermus thermophilus HB8 (X07998) and Nitrobacter hamburgensis X14 (L11663). Computational speed was assessed on M. capricolum ATCC 27343 (CP000123) Solibacter usitatus Ellin6076 (CP000473) and Sargasso Sea data (AACY01000001-AACY01811372). All test searches reported were performed on an SGI Altix 3000 machine using one 1.3 GHz Itanium 2 processor.
Publication 2007
Genomic DNA was extracted from mycelium taken from fungal colonies on MEA
using the UltraClean™ Microbial DNA Isolation Kit (Mo Bio Laboratories,
Inc., Solana Beach, CA, U.S.A.). A part of the nuclear rDNA operon spanning
the 3' end of the 18S rRNA gene (SSU), the first internal transcribed spacer
(ITS1), the 5.8S rRNA gene, the second ITS region (ITS2) and the first 900 bp
at the 5' end of the 28S rRNA gene (LSU) was amplified and sequenced as
described by Cheewangkoon et al.
(2008 ) standard for all
strains included (Table 1). For
selected strains (see Table 1),
the almost complete SSU and LSU (missing the first and last 20–30
nucleotides) were amplified and sequenced using novel and previously published
primers (Table 2; see
below).

Details of primers used for this study and their relation to selected
published primers. Primer names ending with a “d” denotes a
degenerate primer whereas those ending with a “m” denotes specific
primers designed based on the partial novel sequences generated. The start and
end positions of the primers are derived using Magnaporthe grisea GenBank accession AB026819 as reference in the 5'–3' direction.

NameSequence (5′-3′)Orientation%GCTm (°C)StartEndReference
5.8S1Fd
CTC TTG GTT CBV GCA TCG
Forward
57.4
49.8 - 54.2 - 56.8
2333
2350
This study
5.8S1Rd
WAA TGA CGC TCG RAC AGG CAT G
Reverse
52.3
57.6 - 58.9 - 60.2
2451
2472
This study
F377
AGA TGA AAA GAA CTT TGA AAA GAG AA
Forward
26.9
40.3
3005
3030
www.lutzonilab.net/primers/page244.shtml
ITS1
TCC GTA GGT GAA CCT GCG G
Forward
63.2
49.5
2162
2180
White et al. (1990 )
ITS1F
CTT GGT CAT TTA GAG GAA GTA A
Forward
36.4
39.0
2124
2145
Gardes & Bruns (1993 (link))
ITS1Fd
CGA TTG AAT GGC TCA GTG AGG C
Forward
54.5
48.0
2043
2064
This study
ITS1Rd
GAT ATG CTT AAG TTC AGC GGG
Reverse
47.6
43.1
2671
2691
This study
ITS4
TCC TCC GCT TAT TGA TAT GC
Reverse
45.0
41.6
2685
2704
White et al. (1990 )
ITS4S
CCT CCG CTT ATT GAT ATG CTT AAG
Reverse
41.7
42.9
2680
2703
Kretzer et al. (1996 )
ITS5
GGA AGT AAA AGT CGT AAC AAG G
Forward
40.9
40.8
2138
2159
White et al. (1990 )
LR0R
GTA CCC GCT GAA CTT AAG C
Forward
52.6
43.2
2668
2686
Rehner & Samuels (1994 )
LR2
TTT TCA AAG TTC TTT TC
Reverse
23.5
28.5
3009
3025
www.lutzonilab.net/primers/page244.shtml
LR2R
AAG AAC TTT GAA AAG AG
Forward
29.4
30.4
3012
3028
www.lutzonilab.net/primers/page244.shtml
LR3
GGT CCG TGT TTC AAG AC
Reverse
52.9
40.5
3275
3291
Vilgalys & Hester (1990 (link))
LR3R
GTC TTG AAA CAC GGA CC
Forward
52.9
40.5
3275
3291
www.lutzonilab.net/primers/page244.shtml
LR5
TCC TGA GGG AAA CTT CG
Reverse
52.9
41.0
3579
3595
Vilgalys & Hester (1990 (link))
LR5R
GAA GTT TCC CTC AGG AT
Forward
47.1
37.8
3580
3596
www.biology.duke.edu/fungi/mycolab/primers.htm
LR6
CGC CAG TTC TGC TTA CC
Reverse
58.8
43.5
3756
3772
Vilgalys & Hester (1990 (link))
LR7
TAC TAC CAC CAA GAT CT
Reverse
41.2
35.3
4062
4078
Vilgalys & Hester (1990 (link))
LR8
CAC CTT GGA GAC CTG CT
Reverse
58.8
44.3
4473
4489
www.lutzonilab.net/primers/page244.shtml
LR8R
AGC AGG TCT CCA AGG TG
Forward
58.8
44.3
4473
4489
www.lutzonilab.net/primers/page244.shtml
LR9
AGA GCA CTG GGC AGA AA
Reverse
52.9
43.6
4799
4815
www.lutzonilab.net/primers/page244.shtml
LR10
AGT CAA GCT CAA CAG GG
Reverse
52.9
41.6
5015
5031
www.lutzonilab.net/primers/page244.shtml
LR10R
GAC CCT GTT GAG CTT GA
Forward
52.9
41.6
5013
5029
www.lutzonilab.net/primers/page244.shtml
LR11
GCC AGT TAT CCC TGT GGT AA
Reverse
50.0
43.9
5412
5431
www.lutzonilab.net/primers/page244.shtml
LR12
GAC TTA GAG GCG TTC AG
Reverse
52.9
39.4
5715
5731
Vilgalys & Hester (1990 (link))
LR12R
CTG AAC GCC TCT AAG TCA GAA
Forward
47.6
43.7
5715
5735
www.biology.duke.edu/fungi/mycolab/primers.htm
LR13
CAT CGG AAC AAC AAT GC
Reverse
47.1
38.8
5935
5951
www.lutzonilab.net/primers/page244.shtml
LR14
AGC CAA ACT CCC CAC CTG
Reverse
61.1
47.6
5206
5223
www.lutzonilab.net/primers/page244.shtml
LR15
TAA ATT ACA ACT CGG AC
Reverse
35.3
32.5
2780
2796
www.lutzonilab.net/primers/page244.shtml
LR16
TTC CAC CCA AAC ACT CG
Reverse
52.9
42.1
3311
3327
Moncalvo et al. (1993 )
LR17R
TAA CCT ATT CTC AAA CTT
Forward
27.8
31.2
3664
3681
www.lutzonilab.net/primers/page244.shtml
LR20R
GTG AGA CAG GTT AGT TTT ACC CT
Forward
43.5
43.6
5570
5592
www.lutzonilab.net/primers/page244.shtml
LR21
ACT TCA AGC GTT TCC CTT T
Reverse
42.1
41.7
3054
3072
www.lutzonilab.net/primers/page244.shtml
LR22
CCT CAC GGT ACT TGT TCG CT
Reverse
55.0
46.8
2982
3001
www.lutzonilab.net/primers/page244.shtml
LSU1Fd
GRA TCA GGT AGG RAT ACC CG
Forward
55.0
41.8 - 44.0 - 46.3
2655
2674
This study
LSU1Rd
CTG TTG CCG CTT CAC TCG C
Reverse
63.2
49.6
2736
2754
This study
LSU2Fd
GAA ACA CGG ACC RAG GAG TC
Forward
57.5
45.5 - 46.5 - 47.6
3280
3299
This study
LSU2Rd
ATC CGA RAA CWT CAG GAT CGG TCG
Reverse
52.1
48.3 - 49.0 - 49.8
3379
3402
This study
LSU3Fd
GTT CAT CYA GAC AGC MGG ACG
Forward
57.1
44.7 - 47.4 - 50.2
3843
3863
This study
LSU3Rd
CAC ACT CCT TAG CGG ATT CCG AC
Reverse
56.5
49.1
3876
3898
This study
LSU4Fd
CCG CAG CAG GTC TCC AAG G
Forward
68.4
51.2
4469
4487
This study
LSU4Rd
CGG ATC TRT TTT GCC GAC TTC CC
Reverse
54.3
47.4 - 48.7 - 50.0
4523
4545
This study
LSU5Fd
AGT GGG AGC TTC GGC GC
Forward
70.6
51.6
3357 / 5072
3373 / 5088
This study
LSU5Rd
GGA CTA AAG GAT CGA TAG GCC ACA C
Reverse
52.0
48.3
5355
5379
This study
LSU6Fd
CCG AAG CAG AAT TCG GTA AGC G
Forward
54.5
48.1
5499
5520
This study
LSU6Rd
TCT AAA CCC AGC TCA CGT TCC C
Reverse
54.5
48.6
5543
5564
This study
LSU7Fd
GTT ACG ATC TRC TGA GGG TAA GCC
Forward
52.1
46.0 - 47.4 - 48.8
5943
5966
This study
LSU7Rd
GCA GAT CGT AAC AAC AAG GCT ACT CTA C
Reverse
46.4
47.9
5927
5954
This study
LSU8Fd
CCA GAG GAA ACT CTG GTG GAG GC
Forward
60.9
51.2
3469
3491
This study
LSU8Rd
GTC AGA TTC CCC TTG TCC GTA CC
Reverse
56.5
48.9
4720
4742
This study
LSU9Fm
GGT AGC CAA ATG CCT CGT CAT C
Forward
54.5
47.9
4882
4903
This study
LSU9Rm
GAT TYT GCS AAG CCC GTT CCC
Reverse
59.5
49.2 - 50.0 - 50.9
4979
4999
This study
LSU10Fm
GGG AAC GTG AGC TGG GTT TAG A
Forward
54.5
48.6
5543
5564
This study
LSU10Rm
CGC TTA CCG AAT TCT GCT TCG G
Reverse
54.5
48.1
5499
5520
This study
LSU11Fm
TTTGGTAAGCAGAACTGGCGATGC
Forward
50.0
49.4
3753
3776
This study
LSU12Fd
GTGTGGCCTATCGATCCTTTAGTCC
Forward
52.0
48.3
5355
5379
This study
NS1
GTA GTC ATA TGC TTG TCT C
Forward
42.1
36.9
413
431
White et al. (1990 )
NS1R
GAG ACA AGC ATA TGA CTA C
Reverse
42.1
36.9
413
431
www.lutzonilab.net/primers/page244.shtml
NS2
GGC TGC TGG CAC CAG ACT TGC
Reverse
66.7
53.8
943
963
White et al. (1990 )
NS3
GCAAGTCTGGTGCCAGCAGCC
Forward
66.7
53.8
943
963
White et al. (1990 )
NS4
CTT CCG TCA ATT CCT TTA AG
Reverse
40.0
38.2
1525
1544
White et al. (1990 )
NS5
AAC TTA AAG GAA TTG ACG GAA G
Forward
36.4
40.1
1523
1544
White et al. (1990 )
NS6
GCA TCA CAG ACC TGT TAT TGC CTC
Reverse
50.0
47.5
1806
1829
White et al. (1990 )
NS7
GAG GCA ATA ACA GGT CTG TGA TGC
Forward
50.0
47.5
1806
1829
White et al. (1990 )
NS8
TCC GCA GGT TCA CCT ACG GA
Reverse
60.0
50.4
2162
2181
White et al. (1990 )
NS17
CAT GTC TAA GTT TAA GCA A
Forward
31.6
34.2
447
465
Gargas & Taylor (1992 )
NS18
CTC ATT CCA ATT ACA AGA CC
Reverse
40.0
38.0
887
906
Gargas & Taylor (1992 )
NS19
CCG GAG AAG GAG CCT GAG AAA C
Forward
59.1
49.3
771
792
Gargas & Taylor (1992 )
NS20
CGT CCC TAT TAA TCA TTA CG
Reverse
40.0
37.3
1243
1262
Gargas & Taylor (1992 )
NS21
GAA TAA TAG AAT AGG ACG
Forward
33.3
30.5
1193
1210
Gargas & Taylor (1992 )
NS22
AAT TAA GCA GAC AAA TCA CT
Reverse
30.0
36.4
1687
1706
Gargas & Taylor (1992 )
NS23
GAC TCA ACA CGG GGA AAC TC
Forward
55.0
45.5
1579
1598
Gargas & Taylor (1992 )
NS24
AAA CCT TGT TAC GAC TTT TA
Reverse
30.0
36.2
2143
2162
Gargas & Taylor (1992 )
SR11R
GGA GCC TGA GAA ACG GCT AC
Forward
60.0
47.8
779
798
Spatafora et al. (1995 (link))
SR1R
TAC CTG GTT GAT TCT GC
Forward
47.1
38.5
394
410
Vilgalys & Hester (1990 (link))
SR3
GAA AGT TGA TAG GGC T
Reverse
43.8
34.8
696
711
www.biology.duke.edu/fungi/mycolab/primers.htm
SSU1Fd
CTG CCA GTA GTC ATA TGC TTG TCT C
Forward
48.0
46.5
407
431
This study
SSU1Rd
CTT TGA GAC AAG CAT ATG AC
Reverse
40.0
48.7
416
435
This study
SSU2Fd
GAA CAA YTR GAG GGC AAG
Forward
50.0
47.8 - 50.7 - 53.5
930
947
This study
SSU2Rd
TAT ACG CTW YTG GAG CTG
Reverse
47.2
48.4 - 49.9 - 51.2
974
991
This study
SSU3Fd
ATC AGA TAC CGT YGT AGT C
Forward
44.7
48.4 - 49.5 - 50.5
1389
1407
This study
SSU3Rd
TAY GGT TRA GAC TAC RAC GG
Reverse
47.5
49.0 - 52.5 - 56.0
1397
1416
This study
SSU4Fd
CCG TTC TTA GTT GGT GG
Forward
52.9
50.0
1670
1686
This study
SSU4Rd
CAG ACA AAT CAC TCC ACC
Reverse
50.0
50.3
1682
1699
This study
SSU5Fd
TAC TAC CGA TYG AAT GGC
Forward
47.2
48.9 - 50.1 - 51.2
2037
2054
This study
SSU5Rd
CGG AGA CCT TGT TAC GAC
Reverse
55.6
52.5
2148
2165
This study
SSU6Fm
GCT TGT CTC AAA GAT TAA GCC ATG CAT GTC
Forward
43.3
49.0
423
452
This study
SSU6Rm
GCA GGT TAA GGT CTC GTT CGT TAT CGC
Reverse
51.9
50.1
1707
1733
This study
SSU7Fm
GAG TGT TCA AAG CAG GCC TNT GCT CG
Forward
55.8
51.0 - 52.2 - 53.3
1153
1178
This study
SSU7Rm
CAA TGC TCK ATC CCC AGC ACG AC
Reverse
58.7
49.5 - 50.8 - 52.1
1921
1943
This study
SSU8Fm
GCA CGC GCG CTA CAC TGA C
Forward
68.4
52.2
1848
1866
This study
V9G
TTA CGT CCC TGC CCT TTG TA
Forward
45.0
42.8
2002
2021
de Hoog & Gerrits van den Ende
(1998 (link))
Novel primers were designed using a variety of complete SSU and LSU
sequences obtained from the GenBank sequence database
(www.ncbi.nlm.nih.gov/).
The selection was not limited only to fungi belonging to the
Dothideomycetes but encompassed as many as possible full sequences in
order to make the primers as robust as possible. We aimed to keep the melting
temperature (Tm) of the novel primers at 40–45 °C and the GC content
to approximately 50 % to keep them as compatible as possible to existing
published primers. Primer parameters were calculated using the OligoAnalyzer
tool on the web site of Integrated DNA Technologies
(http://eu.idtdna.com/analyzer/Applications/OligoAnalyzer/)
with the “Oligo Conc” parameter set at 0.2 mM and the “Na+
Conc” parameter set at 16 mM. A framework of existing and novel primers
was then aligned onto the sequence of Magnaporthe grisea (GenBank
accession AB026819) to derive primer positions
(Table 2) and evaluate coverage
over the gene regions. These primers were amplified and sequenced in the
following overlapping sections to cover the almost complete SSU and LSU for
the selected strains (Table 2):
SSU1Fd or SSU6Fm with SSU2Rd, SSU2Fd with SSU3Rd, SSU7Fm with SSU4Rd or
SSU6Rm, SSU4Fd with 5.8S1Rd, V9G or LSU1Fd with LSU3Rd, LSU8Fd with LSU8Rd,
LSU4Fd with LSU5Rd, and LSU5Fd with LSU7Rd. For some strains
(Table 3) it was necessary to
add an additional overlap for SSU4Fd with 5.8S1Rd (using SSU4Fd with SSU7Rm
and SSU8Fm with 5.8S1Rd), for LSU8Fd with LSU8Rd (using LSU8Fd with LSU3Rd and
LSU3Fd with LSU8Rd), and for LSU5Fd with LSU7Rd (using LSU5Fd with LSU6Rd and
LSU6Fd with LSU7Rd) to complete the gaps due to large insertions.

Isolates containing group I intron sequences. The insertion positions of
these introns are derived using Magnaporthe grisea GenBank accession
AB026819 as reference in the 5'–3' direction.

IsolateInsertion between18S or 28S nrDNAIntron size (bp)Blast result
Batcheloromyces leucadendriCBS 110892 1559 - 1560
18S nrDNA
350
No significant similarity
1820 - 1821
18S nrDNA
399
190/252 of AY545722 Hydropisphaera erubescens 18S nrDNA
4875 - 4876
28S nrDNA
328
211/264 of DQ246237 Teratosphaeria mexicana 28S nrDNA
5424 - 5425
28S nrDNA
538
No significant similarity
5538 - 5539
28S nrDNA
383
218/283 of EU181458 Trichophyton soudanense 28S nrDNA
Batcheloromyces proteaeCBS 110696 1559 - 1560
18S nrDNA
325
No significant similarity
1820 - 1821
18S nrDNA
399
191/254 of AY545722 Hydropisphaera erubescens 18S nrDNA
4875 - 4876
28S nrDNA
328
211/263 of DQ246237 Teratosphaeria mexicana 28S nrDNA
5424 - 5425
28S nrDNA
535
75/90 of DQ442697 Arxula adeninivorans 26S nrDNA
5538 - 5539
28S nrDNA
372
34/36 of GQ120133 Uncultured marine fungus 18S nrDNA
Catenulostroma macowaniiCBS 110756 1559 - 1560
18S nrDNA
395
297/379 of DQ848302 Mycosphaerella latebrosa 18S nrDNA
5424 - 5425
28S nrDNA
914
No significant similarity
Catenulostroma macowaniiCBS 111029 1559 - 1560
18S nrDNA
395
303/379 of DQ848302 Mycosphaerella latebrosa 18S nrDNA
5424 - 5425
28S nrDNA
914
No significant similarity
Cercospora apiiCBS
118712
1820 - 1821
18S nrDNA
733
288/363 of EU167577 Mycosphaerella milleri 18S nrDNA
Cercospora capsici CPC 12307
1820 - 1821
18S nrDNA
732
287/363 of EU167577 Mycosphaerella milleri 18S nrDNA
Cercospora janseanaCBS 145.37 1820 - 1821
18S nrDNA
350
295/365 of EU167577 Mycosphaerella milleri 18S nrDNA
Devriesia staurophoraCBS 375.81 3560 - 3561
28S nrDNA
309
No significant similarity
Miuraea persicae CPC 10069
1820 - 1821
18S nrDNA
603
399/443 of DQ848342 Mycosphaerella populorum 18S nrDNA
Mycosphaerella latebrosaCBS 652.85 1559 - 1560
18S nrDNA
370
234/296 of DQ848311 Septoria betulae 18S nrDNA
1820 - 1821
18S nrDNA
933
Matches same species
2168 - 2169
18S nrDNA
494
377/449 of DQ848326 Septoria alnifolia 18S nrDNA
4875 - 4876
28S nrDNA
481
No significant similarity
missing 5018 - 5019
28S nrDNA
Not present
Not present
5424 - 5425
28S nrDNA
680
No significant similarity
5538 - 5539
28S nrDNA
471
No significant similarity
Micosphaerella latebrosaCBS 687.94 1559 - 1560
18S nrDNA
370
231/295 of DQ848310 Septoria betulae 18S nrDNA
1820 - 1821
18S nrDNA
918
Matches same species
2168 - 2169
18S nrDNA
494
377/449 of DQ848326 Septoria alnifolia 18S nrDNA
4875 - 4876
28S nrDNA
480
No significant similarity
5018 - 5019
28S nrDNA
417
144/181 of AF430703 Beauveria bassiana 28S nrDNA
5424 - 5425
28S nrDNA
680
No significant similarity
5538 - 5539
28S nrDNA
471
No significant similarity
Mycosphaerella marksiiCBS 110942 1559 - 1560
18S nrDNA
341
332/355 of DQ848296 Mycosphaerella musae 18S nrDNA
Mycosphaerella marksii CPC 11222
1559 - 1560
18S nrDNA
341
332/355 of DQ848296 Mycosphaerella musae 18S nrDNA
Passalora-like genus CPC 11876
5538 - 5539
28S nrDNA
580
No significant similarity
Passalora bellynckiiCBS 150.49 1559 - 1560
18S nrDNA
409
147/191 of DQ848296 Mycosphaerella musae 18S nrDNA
Passalora dodonaea CPC 1223
5424 - 5425
28S nrDNA
738
No significant similarity
Phacellium paspaliCBS
113093
4875 - 4876
28S nrDNA
340
161/197 of DQ248314 Symbiotaphrina kochii 28S nrDNA
Phaeophleospora eugeniicola CPC 2557
missing 5424 - 5425
28S nrDNA
Not present
Not present
5538 - 5539
28S nrDNA
744
No significant similarity
Phaeophleospora eugeniicola CPC 2558
5424 - 5425
28S nrDNA
1846
No significant similarity
5538 - 5539
28S nrDNA
744
No significant similarity
Pseudocercospora angolensisCBS 112933 5018 - 5019
28S nrDNA
379
No significant similarity
Pseudocercospora angolensisCBS 149.53 5018 - 5019
28S nrDNA
379
No significant similarity
Pseudocercospora punctataCBS 113315 5424 - 5425
28S nrDNA
723
No significant similarity
5538 - 5539
28S nrDNA
725
67/73 of AF430699 Beauveria bassiana 28S nrDNA
Pseudocercospora punctata CPC 10532
5424 - 5425
28S nrDNA
731
No significant similarity
5538 - 5539
28S nrDNA
725
67/73 of AF430699 Beauveria bassiana 28S nrDNA
Ramularia coleosporii CPC 11516
1559 - 1560
18S nrDNA
445
No significant similarity
Ramularia grevilleana CPC 656
5538 - 5539
28S nrDNA
546
No significant similarity
Septoria apiicolaCBS
400.54
5424 - 5425
28S nrDNA
763
No significant similarity
Septoria obesaCBS
354.58
1820 - 1821
18S nrDNA
575
No significant similarity
2168 - 2169
18S nrDNA
548
394/454 of DQ848326 Septoria alnifolia 18S nrDNA
4875 - 4876
28S nrDNA
430
No significant similarity
Septoria pyricolaCBS
222.31
5424 - 5425
28S nrDNA
723
No significant similarity
Septoria quercicolaCBS 663.94 1559 - 1560
18S nrDNA
334
241/308 of DQ848303 Mycosphaerella latebrosa 18S nrDNA
1820 - 1821
18S nrDNA
442
379/452 of DQ848335 Mycosphaerella latebrosa 18S nrDNA
4875 - 4876
28S nrDNA
345
No significant similarity
5018 - 5019
28S nrDNA
367
122/155 of DQ518980 Lipomyces spencermartinsiae 28S nrDNA
5424 - 5425
28S nrDNA
526
No significant similarity
5538 - 5539
28S nrDNA
603
No significant similarity
Septoria rosaeCBS
355.58
1820 - 1821
18S nrDNA
496
No significant similarity
Sonderhenia eucalypticola CPC 11252
1559 - 1560
18S nrDNA
408
339/404 of DQ848314 Mycosphaerella populorum 18S nrDNA
4875 - 4876
28S nrDNA
337
229/289 of AB044641 Cordyceps sp. 28S nrDNA
5424 - 5425
28S nrDNA
705
No significant similarity
Stigmina plataniCBS
110755
1559 - 1560
18S nrDNA
379
40/44 of AB007686 Exophiala calicioides 18S nrDNA
5018 - 5019
28S nrDNA
376
No significant similarity
Stigmina synanamorph CPC 11721
5018 - 5019
28S nrDNA
371
No significant similarity
Teratosphaeria aff. nubilosaCBS 114419 4871 - 4872
28S nrDNA
141
No significant similarity; high identity to Teratosphaeria nubilosa
5538 - 5539
28S nrDNA
580
No significant similarity; high identity to Teratosphaeria nubilosa
Teratosphaeria aff. nubilosaCBS 116283 4871 - 4872
28S nrDNA
141
No significant similarity; high identity to Teratosphaeria nubilosa
5538 - 5539
28S nrDNA
580
No significant similarity; high identity to Teratosphaeria nubilosa
Teratosphaeria juvenalisCBS 110906 1559 - 1560
18S nrDNA
403
52/61 of DQ471010 Rutstroemia firma 18S nrDNA
4875 - 4876
28S nrDNA
345
224/290 of EF115309 Cordyceps bassiana 28S nrDNA
5424 - 5425
28S nrDNA
478
47/50 of EF115313 Cordyceps bassiana 28S nrDNA
5538 - 5539
28S nrDNA
402
No significant similarity
Teratosphaeria juvenalisCBS 111149 1559 - 1560
18S nrDNA
403
52/61 of DQ471010 Rutstroemia firma 18S nrDNA
4875 - 4876
28S nrDNA
345
224/290 of EF115309 Cordyceps bassiana 28S nrDNA
5424 - 5425
28S nrDNA
478
47/50 of EF115313 Cordyceps bassiana 28S nrDNA
5538 - 5539
28S nrDNA
402
No significant similarity
Teratosphaeria mexicanaCBS 110502 954 - 955
18S nrDNA
316
129/158 of DQ518980 Lipomyces spencermartinsiae 26S nrDNA
1559 - 1560
18S nrDNA
360
No significant similarity
1820 - 1821
18S nrDNA
388
128/168 of AF281670 Cryptendoxyla hypophloia 18S nrDNA
3560 - 3561
28S nrDNA
383
124/151 of EF647754 Thecaphora thlaspeos 28S nrDNA
4875 - 4876
28S nrDNA
327
99/114 of L81104 Gaeumannomyces graminis var. tritici 28S
nrDNA
5018 - 5019
28S nrDNA
315
No significant similarity
5424 - 5425
28S nrDNA
553
No significant similarity
Teratosphaeria mexicanaCBS 120744 954 - 955
18S nrDNA
318
130/158 of DQ518980 Lipomyces spencermartinsiae 26S nrDNA
1559 - 1560
18S nrDNA
360
No significant similarity
1820 - 1821
18S nrDNA
389
85/109 of AF281670 Cryptendoxyla hypophloia 18S nrDNA
3560 - 3561
28S nrDNA
378
119/155 of AY298780 Lentinellus castoreus 18S nrDNA
4875 - 4876
28S nrDNA
327
162/200 of AB033530 Penicillium sabulosum 18S nrDNA
5018 - 5019
28S nrDNA
309
No significant similarity
5424 - 5425
28S nrDNA
659
No significant similarity
Teratosphaeria nubilosaCBS 115669 4871 - 4872
28S nrDNA
141
No significant similarity; high identity to Teratosphaeria aff.
nubilosa
5538 - 5539
28S nrDNA
580
No significant similarity; high identity to Teratosphaeria aff.
nubilosa
Teratosphaeria nubilosaCBS 116005 4871 - 4872
28S nrDNA
141
No significant similarity; high identity to Teratosphaeria aff.
nubilosa
5538 - 5539
28S nrDNA
580
No significant similarity; high identity to Teratosphaeria aff.
nubilosa
Teratosphaeria ohnowaCBS 112896 954 - 955
18S nrDNA
325
28/28 of DQ848329 Botryosphaeria quercuum 18S nrDNA
3560 - 3561
28S nrDNA
294
168/227 of FJ358267 Chaetothyriales sp. 28S nrDNA
5424 - 5425
28S nrDNA
607
47/48 of EF115313 Cordyceps bassiana 28S nrDNA
Teratosphaeria ohnowaCBS 112973 954 - 955
18S nrDNA
324
28/28 of DQ848329 Botryosphaeria quercuum 18S nrDNA
3560 - 3561
28S nrDNA
294
168/227 of FJ358267 Chaetothyriales sp. 28S nrDNA
5424 - 5425
28S nrDNA
607
47/48 of EF115313 Cordyceps bassiana 28S nrDNA
Teratosphaeria pseudosuberosaCBS 118911 3560 - 3561
28S nrDNA
324
28/28 of DQ848329 Botryosphaeria quercuum 18S nrDNA
4875 - 4876
28S nrDNA
364
No significant similarity
Teratosphaeria sp. CBS
208.94
954 - 955
18S nrDNA
342
No significant similarity
3560 - 3561
28S nrDNA
309
59/70 of AY207244 Mycena pura 28S nrDNA
4875 - 4876
28S nrDNA
296
44/51 of EF551317 Tremella globispora 28S nrDNA
Teratosphaeria suberosa CPC 11032
5424 - 5425
28S nrDNA
313
159/197 of AB033529 Penicillium oblatum 18S nrDNA
5538 - 5539
28S nrDNA
596
80/99 of AB044639 Cordyceps kanzashiana 28S nrDNA
Thedgonia-like genus CPC 12304
1820 - 1821
18S nrDNA
444
262/331 of EU167577 Mycosphaerella milleri 18S nrDNA
The internal transcribed spacer regions, as well as all insertions
(Table 3) were excluded from
all analyses. Sequence data were deposited in GenBank
(Table 1) and alignments in
TreeBASE
(www.treebase.org).
Two separate analyses were performed: The first using only partial LSU data
due to the limited number of complete LSU sequences available and the second
using the almost complete SSU, 5.8S nrDNA and LSU alignment.
Maximum likelihood analyses (ML) were conducted in RAxML v. 7.0.4
(Stamatakis 2006 (link)) for the
partial LSU alignment. A general time reversible model (GTR) with a discrete
gamma distribution and four rate classes was applied. A tree was obtained by
simultaneously running a fast bootstrap search of 1000 pseudoreplicates
(Stamatakis et al.
2008
) followed by a search for the most likely tree. Maximum
Likelihood bootstrap value (MLBP) equal or greater than 70 % are given at the
nodes (Fig. 1).
Maximum likelihood analyses (ML) were conducted in RAxML v. 7.0.4
(Stamatakis 2006 (link)) for the
almost complete SSU, 5.8S nrDNA and LSU alignment. A general time reversible
model (GTR) with a discrete gamma distribution and four rate classes was
applied to each partition (SSU, 5.8S nrDNA and LSU). A tree was obtained by
simultaneously running a fast bootstrap search of 500 pseudoreplicates
(Stamatakis et al.
2008
) followed by a search for the most likely tree. Maximum
Likelihood bootstrap value (MLBP) equal or greater than 70 % are given at the
nodes (Fig. 2).
Publication 2009
The isolation protocol of Lee & Taylor
(1990 ) was used to extract
genomic DNA from fungal mycelia grown on MEA. The primers ITS1
(White et al. 1990 )
and LR5 (Vilgalys & Hester
1990
) were used to amplify part of the nuclear rRNA operon using
the PCR conditions recommended by the authors and spanning the 3' end of the
18S rRNA gene, the internal spacers, the 5.8S rRNA gene and a part of the 5'
end of the 28S rRNA gene. PCR products were separated by electrophoresis at 80
V for 1 h in a 0.8 % (w/v) agarose gel in 0.5× TAE running buffer (0.4
m Tris, 0.05 m NaAc, and 0.01 m EDTA, pH
7.85) and visualised under UV light using a GeneGenius Gel Documentation and
Analysis System (Syngene, Cambridge, U.K.) following ethidium bromide
staining. The amplification products were purified using a GFX PCR DNA and Gel
Band Purification Kit (Amersham Pharmacia Biotech Europe GmbH, Germany). The
purified products were sequenced in both directions using an ABI PRISM Big Dye
Terminator v. 3.1 Cycle Sequencing Ready Reaction Kit (PE Biosystems, Foster
City, CA) containing AmpliTaq DNA Polymerase as recommended by the
manufacturer. The primers LR0R (Rehner
& Samuels 1994
), LR3R
(http://www.biology.duke.edu/fungi/mycolab/primers.htm),
LR16 (Moncalvo et al. 1993), and LR5 (Vilgalys &
Hester 1990
) were used to ensure good quality sequences over the
entire length of the amplicon. The resulting fragments were analysed on an ABI
Prism 3100 DNA Sequencer (Perkin-Elmer, Norwalk, CN).
DNA sequences were assembled and added to the outgroups and additional
GenBank sequences using Sequence Alignment Editor v. 2.0a11
(Rambaut 2002 ), and manual
adjustments for improvement were made by eye where necessary. The phylogenetic
analyses of sequence data were done in PAUP (Phylogenetic Analysis Using
Parsimony) version 4.0b10 (Swofford
2002
) and consisted of neighbour-joining analysis with the
uncorrected (“p”), the Kimura 2-parameter and the HKY85
substitution model in PAUP. Alignment gaps were treated as missing data and
all characters were unordered and of equal weight. Any ties were broken
randomly when encountered. For parsimony analysis, alignment gaps were treated
as both missing and as a fifth character state and all characters were
unordered and of equal weight. Maximum parsimony analysis was performed using
the heuristic search option with simple taxa additions and tree bisection and
reconstruction (TBR) as the branch-swapping algorithm. Branches of zero length
were collapsed and all multiple, equally parsimonious trees were saved. The
robustness of the trees obtained was evaluated by 1000 bootstrap replications
(Hillis & Bull 1993 ). Tree
length (TL), consistency index (CI), retention index (RI) and rescaled
consistency index (RC) were calculated and the resulting trees were printed
with TreeView v. 1.6.6 (Page
1996
).
Bayesian analysis was conducted on the same aligned LSU dataset as the
distance analysis. First MrModeltest v. 2.2
(Nylander 2004 ) was used to
determine the best nucleotide substitution model. Phylogenetic analyses were
performed with MrBayes v. 3 (Ronquist
& Huelsenbeck 2003
) applying a general time-reversible (GTR)
substitution model with gamma (G) and proportion of invariable site (I)
parameters to accommodate variable rates across sites. The Markov Chain Monte
Carlo (MCMC) analysis of 4 chains started from random tree topology and lasted
10 000 000 generations. Trees were saved each 100 000 generations, resulting
in 1000 saved trees. Burn-in was set at 500 000 generations after which the
likelihood values were stationary, leaving 950 trees from which the consensus
trees and posterior probabilities were calculated. PAUP 4.0b10 was used to
reconstruct the consensus tree, and maximum posterior probabilities were
assigned to branches after a 50 % majority rule consensus tree was constructed
from the 950 sampled trees.
Publication 2006
Genomic DNA was isolated from fungal mycelium grown on the agar plates following the protocol of Lee & Taylor (1990 ) or the UltraClean™ Microbial DNA Isolation Kit (Mo Bio Laboratories, Inc., Solana Beach, CA, USA). All isolates were sequenced with five genomic loci. The primers ITS5 or ITS1 and ITS4 (White et al. 1990 ) were used to amplify the internal transcribed spacers areas as well as the 5.8S rRNA gene (ITS) of the nrDNA operon. Part of the actin gene (ACT) was amplified using the primer set ACT-512F and ACT-783R (Carbone & Kohn 1999 ) and part of the translation elongation factor 1-a gene (EF) using the primer set EF1-728F and EF1-986R (Carbone & Kohn 1999 ). The primer set CAL-228F and CAL-737R (Carbone & Kohn 1999 ) was used to amplify part of the calmodulin gene (CAL) whereas the primer set CylH3F and CylH3R (Crous et al. 2004c ) was used to amplify part of the histone H3 gene (HIS). Additional degenerate primers were developed from sequences obtained from GenBank as alternative forward and reverse primers for some of the loci during the course of the study (Table 2); however, these were rarely used but based on their degenerate design could be of use to the broader scientific community. The protocols and conditions outlined by Groenewald et al. (2005 (link)) were followed for standard amplification and subsequent sequencing of the loci.
Sequences of Septoria provencialis (isolate CPC 12226) were used as outgroup based on availability and phylogenetic relationship with Cercospora (Crous et al. 2004b , 2006b (link)). The Cercospora sequences were assembled and added to the outgroup sequences using Sequence Alignment Editor v. 2.0a11 (Rambaut 2002 ), and manual adjustments for improvement were made by eye where necessary. Gaps present in the ingroup taxa and longer than 10 characters were coded as a single event for all analyses (see TreeBASE).
Neighbour-joining analyses using the HKY85 substitution model were applied to each data partition individually to check the stability and robustness of each species clade under each data set using PAUP v. 4.0b10 (Swofford 2003 ) (data not shown, discussed under the species notes where applicable). Alignment gaps were treated as missing data and all characters were unordered and of equal weight. Any ties were broken randomly when encountered. The robustness of the trees obtained was evaluated by 1 000 bootstrap replications (Hillis & Bull 1993 ).
MrModeltest v. 2.2 (Nylander 2004 ) was used to determine the best nucleotide substitution model settings for each data partition. Based on the results of the MrModeltest, a model-optimised phylogenetic re-construction was performed for the aligned combined data set to determine species relationships using MrBayes v. 3.2.0 (Ronquist & Huelsenbeck 2003 (link)). The heating parameter was set at 0.3 and the Markov Chain Monte Carlo (MCMC) analysis of four chains was started in parallel from a random tree topology and lasted until the average standard deviation of split frequencies came below 0.05. Trees were saved each 1 000 generations and the resulting phylogenetic tree was printed with Geneious v. 5.5.4 (Drummond et al. 2011 ). New sequences generated in this study were deposited in NCBI’s GenBank nucleotide database (www.ncbi.nlm.nih.gov; Table 1) and the alignment and phylogenetic tree in TreeBASE (www.treebase.org).
Isolates of Cercospora sp. Q were screened with five more loci to test whether additional loci could distinguish cryptic taxa within this species. This species was selected based on the intraspecific variation present in Fig. 2 (part 5) and also the range of host species and countries represented. The primer set GDF1 and GDR1 (Guerber et al. 2003 (link)) was used to amplify part of the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene, primer set NMS1 and NMS2 (Li et al. 1994 (link)) for part of the mitochondrial small subunit rRNA gene and part of the chitin synthase (CHS) gene was amplified using the primers CHS-79F and CHS-354R (Carbone & Kohn 1999 ). Part of the gene encoding for a mini-chromosome maintenance protein (MCM7) was amplified using primers Mcm7-709for, Mcm7-1348rev, Mcm7-1447rev (Schmitt et al. 2009 (link)) and part of the beta-tubulin gene using mainly the primers T1, Bt2b and TUB3Rd (see Table 2 for references).
Publication 2012
Species identification of selected isolates was confirmed by PCR with primers based on the 16S–23S rRNA spacer region for S aureus.12 (link) Testing of strains for the presence of PBP2a was done with the Mastalex test (Mast Group, Bootle, UK), which is a slide agglutination assay that detects PBP2a in MRSA by use of latex sensitised with a monoclonal antibody directed against PBP2a.5 (link) Molecular detection of mecA, femB, and the SCCmec–orfX junction was done with PCR, as described previously.13 (link), 14 (link), 15 (link), 16 (link), 17 (link), 18 (link) A comparison of the primer sequences used to test for mecA and the target sequences in mecA and mecALGA251 are shown in the webappendix (p 2). Isolates were genotyped for multilocus sequence type and spa type, as described previously.19 (link), 20 (link)
For all test isolates, the MIC of oxacillin and cefoxitin were measured by either Etest (AB Biodisk, Solna, Sweden) or agar dilution,21 (link) depending on which laboratory did the test. The disc diffusion technique was used to establish susceptibility of LGA251 and LGA254 to penicillin, oxacillin, cefoxitin, gentamicin, neomycin, ciprofloxacin, tetracycline, erythromycin, clindamycin, fusidic acid, chloramphenicol, teicoplanin, rifampicin, trimethoprim, linezolid, and mupirocin.22 (link) To establish whether β-lactam resistance was a result of hyperproduction of β-lactamase, tests were done with and without adjacent discs impregnated with both amoxicillin and clavulanic acid.22 (link) The S aureus NCTC 12493 strain was used as a control for MRSA and the S aureus NCTC 6571 strain was used as a control for meticillin-susceptible S aureus (both strains from the National Collection of Type Cultures, HPA, Salisbury, UK). Growth on chromogenic MRSA screening agar, MRSA ID (bioMérieux, Basingstoke, UK), was measured by standard plating and incubation for 18 h at 35°C.
We developed a PCR assay to amplify a region of mecA and the novel homologue described here, mecALGA251. Primers were based on conserved regions of the mecA sequences of previously described S aureus strains,23
S aureus LGA251, and other Staphylococcus species (Staphylococcus epidermidis, Staphylococcus sciuri, Staphylococcus vitulinus, Staphylococcus capitis, Staphylococcus kloosii, and Staphylococcus pseudintermedius). All the mecA sequences were aligned with Bioedit (Ibis Therapeutics, Carlsbad, USA). Primers were chosen from conserved regions with a GC proportion of 40%. The chosen sequences were checked with Primer3 (version 1.1.4) for melting temperatures and self-complementarity,24 and pDraw32 (version 1.1.101) was used to confirm the amplicon size and melting temperatures. Primers were as follows: Fw, 5′ TCACCAGGTTCAAC[Y]CAAAA 3′; and Rv, 5′ CCTGAATC[W]GCTAATAATATTTC 3′. Primers for the amplification of the femB control gene were obtained from a previously described protocol.13 (link) A 25 μL PCR reaction contained final concentrations of 2·5 units of Taq DNA polymerase (Qiagen, Crawley, UK); 1xQ solution (Qiagen); 1xQiagen CoralLoad PCR buffer (Tris-Cl, KCl, [NH4]2SO4, 15 mmol/L MgCl2, gel-loading reagent, orange dye, and red dye; pH 8·7; Qiagen); 4 mmol/L of MgCl2; 0·8 mmol/L of each dNTP (GeneAmp, Applied Biosystems, Warrington, UK); 0·4 μmol/L of each primer (Operon, Cologne, Germany); and 50 ng of DNA template. A negative control, with no target DNA, was included in the PCR run in the GeneAmp PCR System 9700 (Applied Biosystems). The amplification programme consisted of an initial denaturation step at 94°C for 5 min; 30 cycles of denaturing at 94°C for 1 min, annealing at 55°C for 1 min and extension at 72°C for 2 min; and a final extension at 72°C for 5 min. PCR products were analysed by electrophoresis on a 1% agarose gel, previously stained with ethidium bromide at 0·14 μg/mL (Sigma, Gillingham, UK), and run at 5 V/cm for 45 min. The molecular marker used was a 100 bp ladder (Promega, Southampton, UK). The sizes of the PCR products sequenced after PCR were 356 bp for the mecA gene, and 651 bp for the femB gene.
We designed a duplex-PCR to detect the mecA regulatory genes (primers: mecIF, 5′ GACACGTGAAGGCTATGATATAT 3′; mecIR, 5′ ATTCTTCAATATCATCTTCGGAC 3′; mecR1F, 5′ GGCTCAGTTAAATCATAAAGTTTG 3′; mecR1R, 5′ AAATTGCCTTACCATAGCTTGTGT 3′), a duplex-PCR to identify the two cassette recombinase genes (primers: ccrAF, 5′ GCAATAGGTTATCTACGTCAAAG 3′; ccrAR, 5′ TCTAATGATTGTGCGTTGATTCC 3′; ccrBF, 5′ TTCGTGTATCGACAGAAATGCAG 3′; ccrBR, 5′ CATCTTTACGAATATCAATACGG 3′), and a single target PCR to amplify the β-lactamase gene blaZ (primers: blaZF, 5′ AGTCGTGTTAGCGTTGATATTAA 3′; blaZR, 5′ CAATTTCAGCAACCTCACTTACTA 3′). The sizes of the expected PCR products were 344 bp for mecI, 710 bp for mecR1, 932 bp for ccrA, 1449 bp for ccrB, and 809 bp for blaZ. Except for use of an annealing temperature of 58°C instead of 55°C, we used the same method as for the other PCR assay described above.
The whole genome of the S aureus isolate LGA251 was sequenced with both capillary sequencing (on ABI 3730xl analysers; Applied Biosystems) and pyrosequencing (on 454 instruments; Life Sciences, Roche Diagnostics Corporation, Branford, CT, USA). A total of 29 300 high quality capillary reads were produced mostly from two subclone libraries (a 2–3 kb insert library and a 3–4 kb library, both with the vector pOTW12). The average read length was 650 bp and these reads represented 6·8 times coverage. The 454 sequencing produced 59·07 Mb data in reads with an average length of 225 bp. The assembly of these reads with Newbler 1.1.03.24 gave 81 contigs greater than 500 bp with a combined length of 2 699 627 bp in six scaffolds.
A combined assembly of the capillary reads, with Phrap (Version 17.0), and the consensus sequences from the 454 assembly (which were converted into overlapping 500 bp sequences) produced 26 contigs (overlapping sequences or clones from which a sequence can be obtained) greater than 2 kb with an N50 of 532 kb. A further 2310 high quality reads were produced to close gaps and to improve the quality of the sequence to finished standard. The sequence was finished and annotated as described previously.25 (link) The sequences and annotations of the S aureus strain LGA251 genome have been entered in the EMBL database (accession numbers FR821779 for the chromosome and FR821780 for the plasmid).
Full text: Click here
Publication 2011

Most recents protocols related to «RRNA Operon»

The sequence of the 18S rRNA-ITS1-5.8S rRNA-ITS2 operon was recovered when the partial genome of AS-1 was sequenced. The total DNA of AS-1 was extracted using a modified phenol–chloroform protocol [32 ]. The purity and quantity of the extracted DNA were assessed with a NanoDrop Spectrophotometer ND-1000 (ThermoFisher Scientific, Inc., Waltham, MA, USA. The genome sequence was determined using the New-Generation Illumina MiSeq (Illumina, Inc., San Diego, CA, USA) at the BioAnalytical Services Laboratory at the Institute of Marine and Environmental Technology. This sequencing effort was mainly to sequence the chloroplast genome of AS-1, not the chromosomal genome of AS-1. Genome data were assembled using SPAdes (Version 3.15.4). The 18S rRNA-ITS1-5.8S rRNA-ITS2 region of AS-1 was identified from the genome data using the 18S rRNA-ITS1-5.8S rRNA-ITS2 operon of A. protothecoides SAG 211/8D (Accession number: FR865686.1). The sequence of the 18S rRNA-ITS1-5.8S rRNA-ITS2 operon of AS-1 was aligned using MEGA 11 with the available data from the public NCBI nr database based on the BLAST result. This operon sequence of AS-1 was deposited in the NCBI database under accession number PP623876. Sequences were trimmed and aligned using the ClustalW method with MEGA 11. Maximum likelihood trees were constructed with 100 bootstrap values. The reference sequences used for the phylogenetic tree construction were retrieved from the NCBI nr database, and the corresponding accession numbers were included.
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Publication 2024
A total of 16 soil metatranscriptomes, originating from the LTW and the MTW grassland sites sampled in July 2016 [6 (link), 11 (link), 17 (link)], were analysed to test for significant differences in relative community-level rRNA operon copy numbers between non-warmed control soils (AT) and warmed soils (ET). Subsamples of 200 000 SSU rRNA reads have been taxonomically classified previously using CREST3 [21 (link)] and a lowest common ancestor approach [6 (link)]. Bacterial reads, which accounted for >99% of all prokaryotic reads, were extracted and further analysed. Mean rRNA operon copy numbers were obtained from the ribosomal RNA operon database (rrnDB) v5.8 (ref. [22 (link)]). For each bacterial read, the lowest assigned taxonomic level with a match in the rrnDB was selected, and copy-number corrected relative abundances were calculated by dividing relative rRNA read abundances of bacterial taxa by their mean rRNA operon copy numbers. Two-sided t-tests were used to test for significant differences in copy-number corrected relative abundances between LTW-AT and LTW-ET as well as MTW-AT and MTW-ET. Whereas higher copy number corrected relative abundances are indicative for lower relative community-level rRNA operon copy numbers. Subsequently, we estimated community mean rRNA operon copy numbers and tested for significant differences between LTW-AT and LTW-ET as well as MTW-AT and MTW-ET. Furthermore, the functionally annotated mRNA reads, assigned to metabolic pathways and functional complexes defined in the Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology database, of the 16 soil metatranscriptomes [17 (link)] were re-analysed to investigate transcriptional investment in carbohydrate metabolisms and ribosomes.
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Publication 2024
Ribosomal operon sequences
and annotations
were acquired from the Escherichia coli K-12 substr.
MG1655 reference genome (EcoCyc). rRNA-coding plasmids were constructed
by mixing and matching fragments from synthetic plasmids ordered from
Twist Biosciences within a pT7rrnB backbone as previously described.18 (link) As some rRNA sequences between different operons
match (for example, operons E and B have identical 23S rRNA sequences),
only 12 total rRNA constructs were purchased: ABB, BEB, CBB, DBB,
GBB, HBB, BAB, HBB, BCB, BDB, BEB, and BHB (16S:23S:5S). Primers were
designed to amplify the 16S and 23S rRNA sequences from the sequence-verified
Twist plasmids and combined into the AAB/BBB/CCB/DDB/EEB/GGB/HHB sequences
as well as the mixed-operon 16S and 23S rRNA combination constructs
using Gibson assembly. 5S polymorphisms were introduced via site-directed
mutagenesis to result in pure-operon sequences AAA/BBB/CCC/DDD/EEE/GGG/HHH
and confirmed by Sanger sequencing. Plasmids were cloned into chemically
competent Dh10B and purified using the Zymo Midiprep Kit and then
further purified via ethanol precipitation using 0.5 M NH4OAc for use in iSAT reactions.
Plasmids for expression of rRNA in vivo were assembled by cloning the rRNA sequence from
the Twist plasmids and using Gibson assembly to insert it into a pAM-backbone
plasmid, so that the rRNA expression is under the control of phage
lambda promoter pL, regulated by the bacteriophage lambda cI857 repressor.52 (link) Plasmids were cloned into chemically competent
POP2136 cells,53 (link) grown at 30 °C, and
purified using the Zymo Miniprep Kit.
DNA constructs for the
expression of the proteins in CFPS were
made using the pJL1 backbone plasmid as previously described54 (link) and purified using the Zymo Midiprep Kit.
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Publication 2024
All newly identified transcripts were subjected to an NCBI-BLASTN homology search (v. 2.13.0; Altschul et al., 1990 (link); default parameters, except word size = 7) against the Plasmodium falciparum mitochondrial genome (M76611.1) and the Eimeria leuckarti mitochondrial genome (MW354691.1). Transcripts not matching the P. falciparum or E. leuckarti mitochondrial genome were further inspected for homology to the E. coli rRNA operon (J01695.2) using BLASTN.
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Publication 2024
Vibrio is the most diverse genus in Vibrionaceae, currently including 151 described species and 5 subspecies (LPSN database, https://www.bacterio.net/, accessed June 2022)50 (link). To carry out in silico analysis, we created a data repository by retrieving all copies of ribosomal operon genes (i.e., 16S rRNA and 23S rRNA) from 40 representative, fully-sequenced Vibrio genomes, one genome per species (Supplementary Table S7). Genome taxonomic assignment was further verified when Vibrio spp. didn’t form highly supported and unambiguously differentiated monophyletic clades. We classified levels of certainty of genome taxonomic assignment in the following way: first, literature support existed and the NCBI taxonomic check criteria were satisfied; second, only the NCBI taxonomic check criteria were satisfied; and third, when none of these criteria were satisfied (Fig. 1, Supplementary Table S8). When multiple genomes were available, we preferentially selected published and annotated genomes of validated Vibrio species in the LPSN database that were assembled using both long- and short-read sequences (e.g., those obtained by both PacBio and Illumina sequencing). To choose representative genomes of V. diabolicus, V. natriegens, and V. scophthalmi from IMG/M database (https://img.jgi.doe.gov/cgi-bin/m/main.cgi), we constructed a similarity matrix of gene copies from the same genome based on NCBI BLASTn results (https://blast.ncbi.nlm.nih.gov/) and analyzed the number of gaps and mismatches to find the genomes with the highest internal variability in 16S and 23S rRNA gene copies. Next, the ribosomal sequences that were downloaded from NCBI GenBank and IMG/M databases (Supplementary Fig. S5) were manually curated by adding missing conserved terminal nucleotides to obtain full-length copies. We assigned to each retrieved sequence a unique ID in which the last three digits referred to the operon carrying the corresponding 16S and 23S rRNA gene copies and a letter to distinguish each operon within the corresponding genome. We employed our custom code (Parts 1–5, see supplementary file “Custom code”) based on the automated webpage scraping functionality in the RSelenium (Version 1.7.7)51 and rEntrez packages (Version 1.2.2)52 to formulate a search query in R (Version 1.1.442) to obtain species and strain names, sequence accession numbers, and the corresponding sequences in FASTA format.
We obtained additional 16S rRNA sequences from the SILVA SSU r.138.1 database20 (link). We used these sequences to ascertain whether outlier gene copies were fortuitous and potentially caused by sequencing errors, or occur more broadly in a larger sample of sequenced genes. We conducted a BLASTn homology search with the variable regions of outlier gene copies V. chagasii M and V. campbellii E. We subsequently used the five SILVA sequences with complete 16S rRNA sequence and the highest BLAST homology in polyphyly analysis of the 16S rRNA-based phylogenetic tree (Fig. 2). Additionally, 2072 non-redundant Vibrio 23S rRNA sequences were also retrieved from SILVA LSU Ref NR r.138.1 database20 (link), corresponding to 45 species and 19 additional strains without species designation. These were then used to locate 23S rRNA conserved regions for PCR primer design (Fig. 7a, Supplementary Fig. S5). We further supplemented our repository with 26 genomes that belong to non-Vibrio species in Vibrionaceae and nine other non-Vibrionaceae bacteria. The non-Vibrio Vibrionaceae genera included Aliivibrio, Photobacterium, Salinivibrio, Enterovibrio and Grimontia, whereas non-Vibrionaceae families included Woeseiaceae, Comamonadaceae, Rhodobacteraceae, Desulfobacteraceae and Enterobacteriaceae (Escherichia coli).
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Publication 2024

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More about "RRNA Operon"

The ribosomal RNA (rRNA) operon is a crucial genetic structure found in bacteria and archaea, responsible for encoding the rRNA molecules essential for protein synthesis.
This operon typically includes the genes for 5S, 5.8S, and 23S rRNA, as well as the genes for the transfer RNAs needed for their production.
Understanding the structure and regulation of the rRNA operon is vital for research on bacterial and archaeal physiology, evolution, and pathogenesis.
Leveraging AI-powered comparisons can help optimize this research by identifying the best protocols from literature, preprints, and patents, enhancing reproducibility and accuracy.
This powerful tool can streamline the research process and help you find the optimal methods and products for your rRNA operon studies.
For example, utilizing the MiSeq platform, QIAquick PCR Purification Kit, and PowerSoil DNA Isolation Kit can be effective for rRNA operon analysis.
Additionally, the QIAquick Gel Extraction Kit, Gallios flow cytometer, and HiSeq 2000 can be valuable for downstream applications.
Furthermore, the IScript cDNA synthesis kit, Wizard Genomic DNA Purification Kit, High Pure PCR Product Purification Kit, and DNeasy Blood and Tissue Kit can be useful for sample preparation and purification.
By leveraging these tools and techniques, researchers can streamline their rRNA operon studies and enhance the accuracy and reproducibility of their findings.
Typo: The ribosomal RNA (rRNA) operon is a cricial genetic structure found in bacteria and archaea, responsible for encoding the rRNA molecules essential for protein synthesis.