phylogenetic rearrangement of streptomyces spp. on the...
TRANSCRIPT
Indian Journal of Biotechnology Vol 12, January 2013, pp 67-79
Phylogenetic rearrangement of Streptomyces spp. on the basis of internal
transcribed spacer (ITS) region using molecular morphometrics approach
Kaushik Bhattacharjee and S R Joshi*
Microbiology Laboratory, Department of Biotechnology and Bioinformatics North-Eastern Hill University, Shillong 793 022, India
Streptomycetes, the Gram positive bacteria commonly found in soil, are among the well known antibiotic producers of microbial world. Moreover, Streptomyces spp. produce about 75% of commercially and medically useful antibiotics. They
have provided more than half of the naturally occurring antibiotics discovered to date and continue to be screened for useful compounds. Most taxonomic and phylogenetic characterizations of Streptomyces have focused on primary DNA information targeting linear 16S rRNA and ITS sequences. However, RNA secondary structures are particularly not been used for such taxonomic studies, especially the systematics analysis based on “molecular morphometrics information” that are usually not found in the primary sequences. The molecular morphometrics approach has been employed in the present study for comparing the primary and secondary structure information of internal transcribed spacer (ITS) region of Streptomyces spp. using bioinformatics tools. It is an established fact that rRNA structure is highly conserved throughout evolution as most of the folding is functionally important despite primary sequence divergence. The analysis revealed considerable differences between
the conventional liner rRNA based phylogeny and the phylogenetic alignment using molecular morphometrics tools.
Keywords: Internal transcribed spacer (ITS), molecular morphometrics, phylogenetics, Streptomyces
Introduction Streptomycetes, the Gram positive aerobic bacteria,
ubiquitous in nature, produce a great many antibiotics
and other classes of biologically active secondary metabolites. Streptomyces spp. produce about 75% of
commercially and medically useful antibiotics1,2
.
They have provided more than half of the naturally
occurring antibiotics discovered to date and continue to be screened for useful compounds
1. For
the detection of previously unknown bioactive
microorganisms, microbial systematics can be an effective tool
3. The genus Streptomyces is
taxonomically located in the diverse bacterial order
Actinomycetales characterized by an astonishing diversity in terms of morphology, ecology,
pathogenicity, genome size, genomic G+C content,
and the number of coding sequences in the genome4-6
.
Since the International Streptomyces Project in 1964, an attempt was made to produce valid species
descriptions with at least a minimal number of
standard phenotypic criteria7. However, the criteria
turned out to be too minimal and the proliferation of
species continued without any real attempt to compare
species thoroughly with each other. Several studies
have attempted to use sequence data from variable regions of 16S rRNA and internal transcribed spacer
(ITS) region to establish taxonomic structure within
the genus, but the variation was regarded as too limited to help resolve problems of species
differentiation8,9
. Most taxonomic and phylogenetic
characterizations of Streptomyces using current
methods have focused on primary DNA information targeting linear 16S rRNA and ITS sequences
10. Some
studies have suggested that length and sequence
polymorphisms in the ITS region could be used to distinguish different species of prokaryotes to
enhance the differentiating capacity of 16S gene
analysis11
. ITS1 sequences, found in the tandem arrays of the nuclear ribosomal RNA between the
16S and 23S genes, have not been considered useful
for molecular systematics in Streptomyces genus12-14
.
But, ITS1 sequences gave reliable phylogenetic information especially if the predicted RNA
secondary structures were compared15,16
. However,
RNA secondary structures have not been particularly used for such taxonomic studies, although they can
provide meaningful information on systematics
because of their inherent prospects of providing
characteristics on “molecular morphometrics” usually not found in the primary sequences. The basic idea
behind molecular morphometrics is to use the
——————
*Author for correspondence:
Tel: +91-364-2722405; Fax: +91-364-2550076 E-mail: [email protected]
INDIAN J BIOTECHNOL, JANUARY 2013
68
molecular structures as a direct source of measurable
information. This method is based on the assumption
that secondary structure can be as significant as primary sequence in deriving phylogenetic
relationship. In other words, one can consider the
secondary-structure elements of RNA molecules, i.e.,
the helices, loops, bulges, and separating single-stranded portions, as phylogenetic characters
17 instead
of the conventional linear sequences.
Sequence variations in RNA sequences maintain base-pairing patterns that give rise to double-stranded
regions (secondary structure) in the molecule. Thus,
alignments of two sequences that specify the same
RNA molecules will show covariation at interacting base-pair positions. In addition to these covariable
positions, sequences of RNA-specifying genes may
also have rows of similar sequence characters that reflect the common ancestry of the genes. It is an
established fact that rRNA structure is highly
conserved throughout evolution as most of the folding is functionally important despite primary sequence
divergence. The present analysis presents the
molecular morphometrics based bioinformatics
approach employed for comparing the primary and secondary structure information of ITS region of
Streptomyces spp. for delineating phylogenetic
relationship.
Materials and Methods
Retrieval of Sequence, Conversion of DNA Sequence to RNA
and Kinetics Analysis
The gene sequence for ITS region between 16S and 23S rRNA of Streptomyces spp. were retrieved from
National Centre for Biotechnology Information
(http://www.ncbi.nlm.nih.gov/gen-omes/lproks.cgi)
and used for the study. Open source sequence conversion tools were used to convert the DNA
sequences to RNA sequences in upper case and lower
case format and a single ‘.fas’ file was created containing all the ITS sequences of Streptomyces spp..
Different physical and kinetics properties for these
ITS1 sequences were calculated using in silico open
source tool OligoCalc18
(Table 1).
Sequence Alignment Based on Primary Sequences, Alignment
Comparison and Phylogenic Analysis
Retrieved converted sequences were aligned using six different most popular progressive multiple
sequence alignment (MSA) algorithms, viz., ClustalW
(inbuilt in MEGA 4.1)19
, ClustalX220
, MAFFT21
, MUSCLE
22,23, Kalign
24, T-Coffee
25 and ProbCons
26
with default settings. The interleaved alignment
files were converted to FASTA format and used
to draw a threshold dot-plot two alignments using dotmatcher from EMBOSS tools
27. Visual comparison
tools SuiteMSA 2.1.0328
and AltAVisT 1.0 from
Bielefeld University Bioinformatics server
(http://bibiserv.techfak.uni-bielefeld.de/) were also used to check the differences between the alignments
in question.
The Tajima’s D test is a widely used test of neutrality in population genetics
29. The interleaved
alignment files were used to calculate the
Tajima’s statistics using the MEGA 4.1 software19
.
On the basis of similarity matrix of Tajima’s statistics, the Agglomerative Hierarchical Clustering
(AHC) of the alignment methods was performed
using XLSTAT 7.5.2. ITS sequences already aligned with different progressive multiple
sequence alignment methods were converted to
MEGA format and used for construction of phylogenetic trees that were inferred using neighbor-
joining30
, maximum parsimony31
and maximum
likelihood32
methods using MEGA 4.1 software. The
stability of relationships was assessed by performing bootstrap analysis of 1,000 replicates.
Prediction of Secondary Structure of RNA and Analysis
For prediction of RNA’s secondary structure
from its sequence, free energy minimization with
energy parameters based on the nearest-neighbor model and comparative analysis are the primary
methods33
. Restrictions and constraints of the
RNA sequences of the selected Streptomyces spp. were checked and secondary folding were predicted
using MFOLD 3.034
at a default temperature of 37ºC,
[Na+=1 M] and [Mg
++=0 M]. The structure chosen
from different output files was the ones with the highest negative free energy value but seeking the
one essentially identical for all the species (ring
model structure). The ‘.ct’ files and Vienna files were downloaded and used for secondary structure
display, and loop free-energy decomposition (LFD)
and minimum free energy (MFE) determination purpose. The energy dot-plots
35 of the sequences
were also drawn using the MFOLD 3.034
. Structure
annotation followed in the study was according
to Zuker and Jacobson method36
. The maximum weight matching (MWM) analysis and circle plot of
individual RNA sequences were drawn using Circle
0.1.1 package37,38
, which gives the assumptions regarding the pseudoknots.
BHATTACHARJEE & JOSHI: MOLECULAR MORPHOMETRICS OF STREPTOMYCES SPP.
69
INDIAN J BIOTECHNOL, JANUARY 2013
70
Sequence Alignment Based on Secondary Structure and Phylogenetic Analysis
The RNA sequences were folded and aligned, taking structural similarity in account and RNA free-energy model in parallel using the LocARNA 1.5.2
39,
which was achieved with Sankoff-style algorithms. This analysis was based on RIBOSUM85_60 model and other default scoring parameters. The structure annotated alignment, RNAalifold consensus structure and consensus minimum free energy (MFE) was also interpreted by LocARNA 1.5.2
39. The structure
annotated phylogenetic tree file in ‘tree’ format generated by the LocARNA 1.5.2 was used for construction of phylogenetic tree using TreeDyn 198.3
40. Branches with bootstrap value lower than
90 were collapsed and in each case the branch length were also displayed on each branch.
Results and Discussion
Sequence Retrieval, Conversion and Kinetics Analysis
After conversion of the retrieved DNA sequences of ITS1 between 16S and 23S rRNA of Streptomyces spp. to RNA, different physical and kinetics properties were calculated at standard condition (Table 1). Pearson's correlation test between GC% and melting temperature (Tm) of the RNA sequences shows a significant correlation (P<0.05) (Fig. 1) between them and they are linearly correlated (F=259.84, P< 0.0001) (Fig. 2). This kinetics analysis revealed the consistency of the selected RNA sequences of the ITS1.
Sequence Alignment Based on Primary Sequences, Alignment
Comparison and Phylogenetic Analysis
The sequence alignments based on primary sequence or linear sequence of the ITS1 using different multiple sequence alignment (MSA) algorithms showed a considerable amount of variability. The differences between the alignments were visible in the generated dot-plots (Fig. 3), which was also supported by the visual comparisons.
Earlier, studies have shown that bacterial
communities are neutrally evolved, especially in case
of members of the same genus41-44
. In the present
study, the neutrality test of molecular evolution of the population was interpreted with the Tajima’s D
(Table 2). But only ClustalX2, Kalign and ProbCons
Fig. 1—Scatter diagram of GC% and melting temperature (Tm) of the RNA sequences (P<0.05) contributed by 28 ITS1 sequences of Streptomyces spp.
Fig. 2—Regression line of GC% and melting temperature (Tm) of the RNA sequences (P<0.05) ITS1 sequences of Streptomyces spp.
Table 2—Tajima's D statistics for 28 ITS1 sequences of Streptomyces spp.
ClustalW-MEGA ClustalX2 Kalign MAFFT MUSCLE T-Coffee ProbCons
m* 28 28 28 28 28 28 28
S 96 89 109 119 98 99 104
Ps 0.53631 0.48901 0.56477 0.58333 0.51309 0.52381 0.530612
Θ 0.13782 0.12566 0.14513 0.1499 0.13185 0.1359 0.136353
π 0.21619 0.17299 0.21821 0.23941 0.20361 0.20813 0.191327
D 2.19493 1.45073 1.949399 2.31606 2.10166 2.06551 1.559185
*(m = No. of sites, S = No. of segregation sites, Ps = S/m, Θ = Ps/a1, π = Nucleotide diversity, D = Thajima's test statistics)
BHATTACHARJEE & JOSHI: MOLECULAR MORPHOMETRICS OF STREPTOMYCES SPP.
71
Fig. 3—Threshold dot-plot between seven different multiple sequence alignments of the 16S-23S ITS1 region of Streptomyces spp.
showed a significant value for this ITS1 based analysis. ProCons, an algorithm primarily written for
protein sequences, was found to give a significant
Tajima’s D value (Table 2). The best Tajima’s D value was presented by ClustalX2 (D=1.450725).
Agglomerative hierarchical clustering (AHC) based on
similarity matrix of the Tajima’s statistics interpreted
a close relationship between Kalign and ProbCons, and between MUSCLE and T-Coffee (Fig. 4). The
progressive multiple sequence alignment algorithm
ClustalX2 showed the best possible result supporting neutral theory of molecular evolution
45 based on ITS1
sequences of the Streptomyces spp. from among the
seven approaches adopted in the present study. The phylogenetic trees constructed using seven
different algorithms by the neighbor-joining method
and comparing the ITS1 sequences retrieved showed
a variation in the clustering of selected isolates because of their differences in alignment algorithm
(Fig. 5). Moreover, the current phylogenetic tree
reconstructions can not infer a single underlying tree topology for each informative site along a
sequence. This ultimately leads to overestimation
of phylogenetic distances giving misleading results.
Fig. 4—Dendogram showing the relationship between the different sequence alignments algorithms based on agglomerative
hierarchical clustering (AHC) of similarity matrix of the Tajima’s statistics.
INDIAN J BIOTECHNOL, JANUARY 2013
72
Fig. 5—Unrooted phylogenetic tree constructed from seven different multiple sequence alignments of the 16S-23S ITS1 region of Streptomyces spp. Sequence distances were established by using the neighbour-joining method. Bootstrapping values (from 1000 bootstrap trials) are given at respective nodes. GenBank accession numbers for the sequences used to construct this tree are given in Table 1. (Bar=0.05 nucleotide substitutions per position)
BHATTACHARJEE & JOSHI: MOLECULAR MORPHOMETRICS OF STREPTOMYCES SPP.
73
Fig. 6 Contd.
INDIAN J BIOTECHNOL, JANUARY 2013
74
Contd. Fig. 6
Fig. 6—Predicted ITS1 RNA secondary structures for 28 species of the genus Streptomyces spp. and their structure formation enthalpies according to MFOLD.
Therefore, we propose that the use of ITS1 liner
sequences in case of Streptomyces for phylogenetic
tree inference is questionable, at least in the case of linear DNA or RNA sequences as supported by
some studies14,46,47
.
Prediction of Secondary Structure of RNA and Analysis
Initially, twenty eight predicted RNA secondary
structures were reconstructed to provide basic
information (Fig. 6). The secondary structural features of ITS1 regions as shown in the figures were analysed
based on the conserved stems and loops in all the
isolates. The ITS1 sequences exhibited the highly
conserved six-helicoidal ring-model structure.
Sequence Alignment Based on Secondary Structure and
Phylogenic Analysis
The structure annotated alignment of the ITS1 sequences of the Streptomyces spp. and RNAalifold
consensus structure showed structurally conserved
regions (Figs 7 & 8). The interpreted MFE was
-169.13 kcal/mol, which is much lower than all the calculated dG of the individual ITS1 sequences
that indicated more stable and accepted secondary
BHATTACHARJEE & JOSHI: MOLECULAR MORPHOMETRICS OF STREPTOMYCES SPP.
75
INDIAN J BIOTECHNOL, JANUARY 2013
76
Fig. 7—The structure annotated alignment of 28 of ITS1 sequences of Streptomyces spp. aligned by taking structural similarity in account and RNA free-energy model in parallel using the LocARNA 1.5.2.
BHATTACHARJEE & JOSHI: MOLECULAR MORPHOMETRICS OF STREPTOMYCES SPP.
77
Fig. 8—RNAalifold consensus structure of 28 of ITS1 sequences of Streptomyces spp.
structure. The structure annotated phylogenetic tree (Fig. 9) when rearranged provided a better evolutionary distance of the Streptomyces spp. in question. In this case, phylogenetic tree branches with bootstrap value lower than 50 were collapsed and not displayed for further phylogenetic affiliation. Molecular morphometrics and sequence comparison differ mainly on a methodological point in a nucleotide sequence, where the structural polymorphism appears as size variations, mostly in regions where insertion/deletion events take place, although point mutations can also lead to structural polymorphism. In other words, these regions are those in which the multiple sequence comparison programs lead to poorly reliable alignments, as the signal/noise ratio they provide is too low
48,49. The secondary
structures, which although depend on the primary nucleotide sequence, can in fact be considered as a distinct set of informative characters providing their own phylogenetic signals. In particular, the ITS1 sequences and predicted RNA secondary structures have afforded a new view of the phylogeny of the genus Streptomyces.
Fig. 9—The structure annotated phylogenetic tree of the Streptomyces spp. on the basis of molecular morphometrics data. The bootstrap value lower than 90 were collapsed and not displayed
in the tree. (Bar=0.5 nucleotide substitutions per position)
Conclusion
It is an established fact that rRNA structure
is highly conserved throughout evolution as most of
the folding is functionally important despite primary sequence divergence in case of ITS1
sequences of Streptomyces spp.. The present analysis,
however, reveales considerable variation between the conventionally used linear DNA based phylogeny
and the phylogenetic alignment derived using
molecular morphometrics tools. The superiority of RNA secondary based phylogeny over DNA or
RNA linear sequence phylogeny is also evident from
the present analysis.
Acknowledgement
Financial support received from the Department of
Information Technology (Ministry of Communication
and Information Technology), Government of India, New Delhi is gratefully acknowledged. Authors are
thankful to the Mr Shakti Kumar and Mr Graciously
Kharumniud, Bioinformatics Centre (BIC), NEHU, Shillong, for their help extended during the study.
References 1 Miyadoh S, Research on antibiotic screening in Japan over
the last decade: A producing microorganisms approach, Actinomycelogica, 7 (1993) 100-106.
INDIAN J BIOTECHNOL, JANUARY 2013
78
2 Baltz R H, Marcel Faber Roundtable: Is our antibioticpipeline unproductive because of starvation,
constipation or lack of inspiration?, J Ind Microbiol
Biotechnol, 33 (2006) 507-513. 3 Ward A & Goodfellow M, Taxonomy as a road map for
search and biodiscovery, Microbiol Aust, 25 (2004) 13-15. 4 Embley T M & Stackebrandt E, The molecular phylogeny
and systematics of the actinomycetes, Annu Rev Microbiol, 48 (1994) 257-289.
5 Hopwood D A, Streptomyces in nature and medicine: The
antibiotic makers (Oxford University Press, Inc., Oxford, UK) 2007.
6 Ventura M, Canchaya C, Tauch A, Chandra G, Fitzgerald G F et al, Genomics of Actinobacteria: Tracing the evolutionary history of an ancient phylum, Microbiol Mol
Biol Rev, 71 (2007) 495-548. 7 Shirling E B & Gottlieb D, Methods for characterization
of Streptomyces species, Int J Syst Bacteriol, 16 (1966)
313-340. 8 Witt D & Stackebrandt E, Unification of the genera
Streptoverticillium and Streptomyces, and amendation of Streptomyces Waksman and Henrici 1943, 339AL, Syst Appl
Microbiol, 13 (1990) 361-371. 9 Stackebrandt E, Witt D, Kemmerling C, Kroppenstsdt R &
Liesack W, Designation of Streptomycete 16S and 23S rRNA-based target regions for oligonucleotide probes, Appl
Environ Microbiol, 57 (1991) 1468-1477. 10 Park D H, Kim J S, Kwon S W, Wilson C, Yu Y M et al,
Streptomyces luridiscabiei sp. nov., Streptomyces
puniciscabiei sp. nov. and Streptomyces niveiscabiei sp. nov., which cause potato common scab disease in Korea, Int J Syst
Evol Microbiol, 53 (2003) 2049-2054. 11 Janssen P H, Yates P S, Grinton B E, Taylor P M & Sait M,
Improved culturability of soil bacteria and isolation in pure culture of novel members of the divisions Acidobacteria,
Actinobacteria, Proteobacteria and Verrucomicrobia, Appl
Environ Microbiol, 68 (2002) 2391-2396. 12 Gurtler V & Stanisich V A, New approaches to typing and
identification of bacteria using the 16S-23S rDNA spacer region, Microbiology, 142 (1996) 3-16.
13 Hain T, Ward-Rainey N, Kroppenstedt R M, Stackebrandt E & Rainey F A, Discrimination of Streptomyces albidoflavus strains based on the size and number of 16S-23S ribosomal DNA
intergenic spacers, Int J Syst Bacteriol, 47 (1997) 202-206. 14 Song J, Lee S C, Kang J W, Baek H J & Suh J W,
Phylogenetic analysis of Streptomyces spp. isolated from potato scab lesions in Korea on the basis of 16S rRNA gene and 16S-23S rDNA intergenic transcribed spacer sequences, Int J Syst Evol Microbiol, 54 (2004) 203-209.
15 Seemann S E, Gorodkin J & Backofen R, Unifying evolutionary and thermodynamic information for RNA
folding of multiple alignments, Nucleic Acids Res, 36 (2008) 6355-6362.
16 Keller A, Förster F, Müller M, Dandekar T, Schultz J et al, Including RNA secondary structures improves accuracy and robustness in reconstruction of phylogenetic trees, Biol
Direct, 5 (2010) 4. 17 Billoud B, Guerrucci M A, Masselot M & Deutsch J S,
Cirripede phylogeny using a novel approach: Molecular
morphometrics, Mol Biol Evol, 17 (2000)1435-1445. 18 Kibbe W A, OligoCalc: An online oligonucleotide properties
calculator, Nucleic Acids Res, 35 (2007) W43-W46.
19 Tamura K, Dudley J, Nei M & Kumar S, MEGA4: Molecular evolutionary genetics analysis (MEGA) software
version 4.0, Mol Biol Evol, 24 (2007) 1596-1599. 20 Larkin M A, Blackshields G, Brown N P, Chenna R,
McGettigan PA et al, ClustalW and ClustalX version 2.0, Bioinformatics, 23 (2007) 2947-2948.
21 Katoh K, Misawa K, Kuma K & Miyata T, MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform, Nucleic Acids Res, 30 (2002) 3059-3066.
22 Edgar R C, MUSCLE: Multiple sequence alignment with high accuracy and high throughput, Nucleic Acids Res, 32 (2004) 1792-1797.
23 Edgar R C, MUSCLE: A multiple sequence alignment method with reduced time and space complexity, BMC
Bioinformatics, 5 (2004) 113. 24 Lassmann T, Frings O & Sonnhammer E L, Kalign2: High-
performance multiple alignment of protein and nucleotide sequences allowing external features, Nucleic Acids Res, 37 (2009) 858-65.
25 Notredame C, Higgins D G & Heringa J, T-Coffee: A novel method for fast and accurate multiple sequence alignment, J
Mol Biol, 302 (2000) 205-17. 26 Do C B, Mahabhashyam M S, Brudno M & Batzoglou S,
ProbCons: Probabilistic consistency-based multiple sequence
alignment, Genome Res, 15 (2005) 330-40. 27 Rice P, Longden I & Bleasby A, EMBOSS: The European
molecular biology open software suite, Trends Genet, 16 (2000) 276-277.
28 Anderson C L, Strope C L & Moriyama E N, SuiteMSA: Visual tools for multiple sequence alignment comparison and molecular sequence simulation, BMC Bioinformatics, 12 (2011) 184.
29 Tajima F, Statistical method for testing the neutral mutation hypothesis by DNA polymorphism, Genetics, 123 (1989) 585-595.
30 Saitou N & Nei M, The neighbor-joining method: A new method for reconstructing phylogenetic trees, Mol Biol Evol, 4 (1987) 406-425.
31 Sourdis J & Nei M, Relative efficiencies of the maximum parsimony and distance-matrix methods in obtaining the
correct phylogenetic tree, Mol Biol Evol, 5 (1988) 298-311. 32 Felsenstein J, Evolutionary trees from DNA sequences:
A maximum likelihood approach, J Mol Evol, 17 (1981) 368-376.
33 Doshi K J, Cannone J J, Cobaugh C W & Gutell R R, Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction, BMC Bioinformatics, 5 (2004) 105.
34 Zuker M, Mfold web server for nucleic acid folding and hybridization prediction, Nucleic Acids Res, 31 (2003) 3406-3415.
35 Jacobson A B & Zuker M, Structural analysis by energy dot plot of a large mRNA, J Mol Biol, 233 (1993) 261-269.
36 Zuker M & Jacobson A B, “Well-determined” regions in RNA secondary structure prediction: Analysis of small subunit ribosomal RNA, Nucleic Acids Res, 23 (1995) 2791-2798.
37 Tabaska J E, Cary R B, Gabow H N & Stormo G D, An RNA folding method capable of identifying pseudoknots and base triples, Bioinformatics, 14 (1998) 691-699.
BHATTACHARJEE & JOSHI: MOLECULAR MORPHOMETRICS OF STREPTOMYCES SPP.
79
38 Page R D M, Circles: Automating the comparative analysis of RNA secondary structure, Bioinformatics, 16 (2000)
1042-1043.
39 Smith C, Heyne S, Richter A S, Will S & Backofen R, Freiburg RNA Tools: A web server integrating IntaRNA, ExpaRNA and LocARNA, Nucleic Acids Res, 38 (2010) W373-W377.
40 Chevenet F, Brun C, Bañuls A L, Jacq B & Christen R, TreeDyn: Towards dynamic graphics and annotations for analyses of trees, BMC Bioinformatics, 7 (2006) 439.
41 Sloan W T, Lunn M, Woodcock S, Head IM, Nee S et al, Quantifying the roles of immigration and chance in shaping prokaryote community structure, Environ Microbiol, 8 (2006) 732-740.
42 Woodcock S, Gast C J V D, Bell T, Lunn M, Curtis T P et al, Neutral assembly of bacterial communities, FEMS Microbiol
Ecol, 62 (2007) 171-180.
43 Ofiţeru I D, Lunn M, Curtis T P, Wells G F, Criddle C S et al, Combined niche and neutral effects in a microbial
wastewater treatment community, Proc Natl Acad Sci USA, 107 (2010) 15345-15350.
44 Fierer N & Lennon J T, The generation and maintenance of diversity in microbial communities, Am J Bot, 98 (2011)
439-448. 45 Kimura M, Genetic variability maintained in a finite
population due to mutational production of neutral and nearly neutral isoalleles, Genet Res, 11 (1968) 247-269.
46 Wenner T, Roth V, Decaris B & Leblond P, Intragenomic and intraspecific polymorphism of the 16S-23S rDNA internally transcribed sequences of Streptomyces
ambofaciens, Microbiology, 148 (2002) 633-642.
47 Savic M, Bratic I & Vasiljevic B, Streptomyces
durmitorensis sp. nov., a producer of an FK506-like immunosuppressant, Int J Syst Evol Microbiol, 57 (2007) 2119-2124.
48 Wheeler W C, Sources of ambiguity in nucleic acid sequence alignment, in Molecular ecology and evolution:
Approaches and applications, edited by B Schierwater, B Streit, P G Wagner & R DeSalle (Birkhäuser Verlag, Basel,
Switzerland) 1994, 323-352. 49 Grundy W N & Naylor G J, Phylogenetic inference from
conserved sites alignments, J Exp Zool, 285 (1999) 128-139.