1 F2G Ltd, Lankro Way, Eccles, Manchester M30 0BH, UK
2 Institute of Grassland and Environmental Research, Plas Gogerddan, Aberystwyth, Ceredigion, SY23 3EB, UK
3 School of Biological Sciences, University of Manchester, Oxford Road, Manchester M13 9PT, UK
4 CSIRO Livestock Industries, Queensland Bioscience Precinct, Carmody Road, St Lucia, Brisbane, Australia
Correspondence
Michael K. Theodorou
mike.theodorou{at}bbsrc.ac.uk
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ABSTRACT |
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A representative alignment of 12 ITS1 sequences plus flanking regions from anaerobic gut fungi showing Variable Regions IIV is available as Supplementary Fig. S1. An alignment of the five Caecomyces and nine Cyllamyces sequences for ITS1 Variable Regions IIV, together with the ITS1 Variable Regions IIIV sequences from the eleven of the forty-eight environmental samples that group together as Novel Group 1, is available as Supplementary Fig. S2 with the online version of this paper at http://mic.sgmjournals.org.
Present address: AgResearch Grasslands, Private Bag 11008, Palmerston North, New Zealand.
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INTRODUCTION |
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Historically, the anaerobic gut fungi have been assigned to genera and species based on physiological and developmental characteristics, such as thallus morphology, the location of nuclei in monocentric and polycentric forms and the number of flagella on each zoospore (Munn et al., 1988). These characteristics tend to be pleomorphic, varying with culture condition and particularly carbon source, and are only visible by microscopy, prompting the application of more-recent DNA-based technologies in phylogenetic analyses (Brookman et al., 2000a
; Mennim, 1997
). Six genera of fungi belonging to the order Neocallimastigales are now recognized, namely: Anaeromyces, Caecomyces, Cyllamyces, Neocallimastix, Orpinomyces and Piromyces (Theodorou et al., 1996
; Ozkose et al., 2001
).
DNA-based techniques have facilitated the understanding of the phylogenetic relationships and diversity of micro-organisms in natural ecosystems: they introduce considerably fewer biases in sampling than culture-based methodologies, can be generated directly from DNA and are considered more representative of the entire community than culture-derived data alone (Ward et al., 1990). Favoured indicators of genetic diversity are the rRNA encoding gene sequences, particularly the internal transcribed spacers ITS1 and ITS2 and the intervening 5·8S rDNA; these can be used both to identify micro-organisms and to determine phylogenetic relationships within communities (Hausner et al., 2000
; Vainio & Hantula, 2000
; Ranjard et al., 2001
), including the gut fungi (Brookman et al., 2000a
).
Although a variety of DNA-based separation and visualization techniques, including RFLP, temperature gradient gel electrophoresis (TGGE) and denaturing gradient gel electrophoresis (DGGE), have been used to examine phylogenetic relationships and to highlight diversity in microbial communities, the identification of population shifts in ecosystems with rapidly changing microbial communities is hampered by the requirement for alignment of large numbers of previously determined ribosomal sequences. This process is particularly laborious when populations are changing in time and space and where only one or a few new sequences are obtained at any given time or in any given space. This situation is prevalent in the rumen ecosystem, where freshly ingested plant biomass is rapidly colonized by a succession of microbial species, and is the reason why the primary colonization hypothesis for the gut fungi has been so difficult to substantiate.
Fingerprinting programs [e.g. Prosite (Hulo et al., 2004), Prints (Attwood et al., 2003
), PFAM (Bateman et al., 2002
), Smart (Schultz et al., 1998
) and Interpro (Mulder et al., 2003
)] that rely upon pattern matching as well as sequence are widely used for the identification of proteins. A variety of different methodologies underlie these programs; for example, Prosite uses regular expressions, whilst PFAM uses hidden Markov models (HMMs). In contrast, little use of these pattern-matching methodologies has been made in nucleic acid studies. There are exceptions, however, such as MatInspector, for identifying transcription factor-binding sites (Quandt et al., 1995
). In this paper, the development of a fingerprinting (pattern matching) approach is described for the rapid identification and classification of gut fungi from ribosomal ITS1 and ITS2 sequences. The methodology was developed using DNA obtained from axenic cultures and has been used to interrogate the diversity of fungal populations in in vivo samples. While the technique has potential generic application in studying diversity and population dynamics in microbial ecology, it was developed for use in future studies to elucidate the role played by anaerobic fungi in the primary colonization of plant biomass in the rumen ecosystem.
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METHODS |
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Forty-eight ITS1 sequences from environmental samples, namely faeces of wild and domesticated animals collected in Australia and Africa, were also analysed using the HMM-based program in this study. The ITS1 sequences from the environmental samples were amplified using primers specific for the gut fungi, forward primer MN100 (TCCTACCCTTTGTGAATTTG) and reverse primer MNGM2 (CTGCGTTCTTCATCGTTGCG), to give gut-fungal-specific amplification of a truncated ITS1 sequence without Variable Region I at the 5' end (see Supplementary Fig. S1, available as supplementary data with the online version of this paper at http://mic.sgmjournals.org, for region nomenclature). This primer set was necessary, despite the loss of some sequence information compared with Brookman et al. (2000a), to prevent contamination of the amplicon population with aerobic fungal sequences from the environmental samples. The samples and the sequences isolated from them form part of a wider study described elsewhere (M. J. Nicholson and others, unpublished data).
Sequence analysis.
Multiple alignments of sequences were generated using CLUSTAL (Thompson et al., 1997) and manually edited using Align (http://science.do-mix.de/). RNA secondary-structure predictions were carried out using MFOLD 3.1 (Zuker et al., 1999
; Mathews et al., 1999
) at http://www.bioinfo.rpi.edu/applications/mfold/old/rna/, with a preset folding temperature of 37 °C, and the following default options: ionic conditions, 1 M NaCl, no divalent ions; percentage suboptimality number, 5; upper bound on number of computed foldings, 50. The 18S and 5·8S sequences were removed manually before folding. Predictions were carried out for the following representative sequences: 3-1, 7-4, 28-3, 29-1, 56-5, NMW5-2, NCS1-2 (this study), AUC1 (Brookman et al., 2000b
), PCS1-1, PLA1-1, OUC1A (OUC1-1, AF170189), NMW4, Neocallimastix patriciarum, PAC1, PAK1, PCG1, Piromyces (Brookman et al., 2000a
). Energies of structures ranged from
35 kcal mol1 to
93 kcal mol1. RNA structures were drawn using RNAviz (http://rrna.uia.ac.be/rnaviz/).
The pattern matching programs were written in PERL 5.6.1 with BioPERL 1.0 modules and run in Cygwin on a PC. HMMER2.2 was downloaded from http://hmmer.wustl.edu/ (Eddy, 1998, 2001
). HMMs were built and run according to the accompanying documentation using the following clusters to generate the group HMMs (Table 1
): the Anaeromyces model incorporates individual HMMs for I3, II6, III4 and IV4; Neocallimastix for I2, II2, II3, II4, II5, III2, III3 and an HMM for IV2 and IV3 together; the Piromyces I model has individual HMMs for I1, II1, III1 and IV1; Orpinomyces/Piromyces II for I5, I5a, II8, III6, III7, III8 and IV5; and finally Piromyces III has individual HMMs for I4, I6, II7, III5, a joint HMM for III9 and III10 together, and IV6. A copy of the program is available from the authors on request.
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RESULTS |
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Secondary-structure predictions indicated that despite significant sequence variation between the different ITS1 sequences, the overall secondary structure for ITS1 was broadly conserved for all isolates. Variable Region I formed a stem and loop (Fig. 1), consistent with the complementary sequences in the stem in this region. Although stem length and terminal bulb sequence varied, this structure was the consensus prediction for 11 of the 17 sequences and was seen in 65 of 113 of the predicted structures. The consensus prediction was not seen for the ITS1 structure from Piromyces isolates PAC1 and PAK1, and this region of sequence (nucleotides 6197) does not align with the other gut fungal sequences. The sequence from Variable Region II to Variable Region IV formed a long stem and bulb. This was the consensus prediction for all sequences, including PAC1 and PAK1, and was seen in
80 % of structures (Fig. 2
). The conserved regions between Variable Regions II and III and between Variable Regions III and IV were found midway along the stem and were complementary to one another, as illustrated by the boxed region in Fig. 2
. The stem regions flanking this section varied in length for the different isolate types (Fig. 2
).
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Clustering of ITS1 sequences and generation of ITS1 fingerprints
By clustering similar sequences, several different motifs with differing levels of degeneracy were identified within each of the four variable regions of the ITS1 sequences (Table 1). The motifs were based upon primary sequence alignment and predicted structure, and were generated from isolates previously classified by phylogenetic methodologies as references.
Almost all of the sequences in each variable region contained one of the recurring motifs for that region; for example, of the 84 ITS1 sequences, 79 contained one of only six motifs described for Variable Region I (Table 2). Furthermore, the combination of these motifs across the four variable regions could be used to define a fingerprint for each ITS1 sequence. Groupings made in this way correspond well with the generic assignments of the originating isolates made according to morphological criteria and/or sequence-based phylogenetic analyses. Thus, each of the groups were defined by the ITS1 fingerprints within that group; for example, Anaeromyces have the following fingerprint for the four variable regions: Region I, Motif 3; Region II, Motif 6; Region III, Motif 4 and Region IV, Motif 4 (I3,II6,III4,IV4), whilst Neocallimastix have fingerprints made up of a combination of the following motifs: Region I Motif 2, Region II Motif 2, 3, 4 or 5, Region 3 Motif 2 or 3, and Region IV, Motif 2, 3 or 5 (I2,II2/3/4/5,III2/3, and IV2/3/5; Table 2
).
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A pattern-matching program for ITS1 fingerprints
A pattern-matching program was written incorporating the sequence motifs described in Table 1 as explicit, regular expressions. All 84 ITS1 sequences were analysed using the pattern-matching program, and most matched just one motif in each of the four variable regions, that is, no single ITS1 sequence gave multiple matches to fingerprints within the same region, suggesting an appropriate level of condensation of consensus sequences within the fingerprints.
The pattern-matching program was also used to analyse a set of 48 ITS1 sequences amplified directly from faeces rather than from isolated, axenic cultures. Due to the complex mixture of eukaryotic DNA in these environmental samples, PCR was performed using gut fungal-specific primers producing a slightly shorter amplicon spanning Variable Regions IIIV of the ITS1. The program initially provided annotation for just five of the 48 sequences (matches to motifs in all three variable regions), with probable annotation for four others (matches to motifs in two of the three variable regions). Examination of these sequences indicated that in many cases the lack of a match was due to a simple difference, such as single base change between the regular expression and the experimental sequence. To counter this problem, the pattern-matching program was redesigned to use HMMs via the HMMER suite of programs (Eddy, 1998, 2001
). HMMs permits scoring of matches to motif sequences and thus offer flexibility compared to regular expressions. HMMs were generated for each of the motif sequences detailed in Table 1
, and these HMMs were then combined to give one for each group.
The modified program was then used to annotate the ITS1 sequences from characterized fungal isolates, and in all cases the sequences were assigned to groups in accordance with the original annotations. Examples of program outputs for sequences from isolates TAX1, AN and OUC1 are shown in Fig. 3. Matches to multiple HMMs, where a sequence showed similarity to more than one motif for any given variable region, could be resolved on the basis of E-value scores, such that the lower E value was taken as the best match. For example, the sequence for Anaeromyces isolate AN showed a fingerprint of I3/II6/III4/IV4, but for Variable Region II, the sequence also matched Motif 7 (II7), but at a much lower stringency (1·7x105 for Motif 7 cf. 1·9x1012 for Motif 6).
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The development of this fingerprinting methodology was designed around the four anaerobic gut fungal genera for which reliable data were available (Anaeromyces, Neocallimastix, Piromyces and Orpinomyces). Although one deposited ITS1 sequence from a fungal isolate designated Caecomyces (accession no. AF492020) was available, it showed over 99 % sequence identity to the Neocallimastix group of isolates and thus, as it was probably wrongly assigned, it was removed from the database. To extend the fingerprinting technique to cover all of the known anaerobic gut fungal genera, sequences representative of the missing genera have since been obtained (Ozkose, 2001), representing four undeposited ITS1 sequences from a single Caecomyces isolate and nine sequences from two Cyllamyces isolates. These, together with a single sequence from a Caecomyces isolate, NZB7 (M. J. Nicholson, unpublished data), were used to produce alignments and sequence fingerprints for the ITS1 Variable Regions IIV for Caecomyces and Cyllamyces (Table 3
). The sequences from the four novel groups identified from the faecal samples were then compared with these additional fingerprints. One of these novel groups, which consisted of 11 sequences, was identified as representing the genus Cyllamyces (Supplementary Fig. S2, available as supplementary data with the online version of this paper at http://mic.sgmjournals.org). The consensus from the 11 environmental sample sequences did not match the fingerprints exactly, often showing single base pair differences or differences in the numbers of repeated bases, as reported earlier for the other genera. Alignment of sequences in Variable Region III showed a larger difference in a subgroup of the faecal samples; nine of the 11 clustered together in this region and required a separate sequence motif (Variable Region III, Motif 13; Table 3
) to describe them. The two remaining sequences, J162 and J163, clustered with the sequences from the isolated Cyllamyces strains in this region with the Variable Region III Motif 12 fingerprint (see Supplementary Fig. S2, available as supplementary data with the online version of this paper at http://mic.sgmjournals.org). Motif sequences for Variable Regions IIIV for the Cyllamyces genus fingerprint have been modified to incorporate the additional sequence data (Table 3
). None of the novel groups identified in the faecal sequences represented the genus Caecomyces.
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ITS2 sequence fingerprints
The ITS2 sequences of the Australian anaerobic fungal isolates were aligned together with sequences from five isolates previously characterized phylogenetically from their ITS1 sequence (Brookman et al., 2000a) plus the sequence from the Anaeromyces isolate AUC4 and a Neocallimastix isolate, NMZ4, from the IGER culture collection (data not shown). The alignment was examined to identify regions of sequence that might be phylogenetically useful. As for the ITS1 sequences, regions of variation were found, flanked by regions of highly conserved sequence. Three variable regions were identified (Variable Regions V, VI and VII). The first of these (Variable Region V) was not suitable for generic delineation; however, the two downstream variable regions (VI and VII) were good discriminators, and the ITS2 sequences could be clustered on the basis of sequence similarity within these regions (Table 4
).
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Subsequent to this analysis, several ITS2 sequences were deposited into the public database by Fliegerova et al. (2004) and were added to the alignment, including three Orpinomyces (AY429671, AY429672, AY429673) and four Anaeromyces (AY429666, AY429667, AY429669, AY429670) sequences. The additional Orpinomyces sequences clustered with those from this study designated as belonging to the Orpinomyces group (Table 4
). The Anaeromyces sequences clustered with the single Anaeromyces sequence, AUC4, giving rise to the definition of motifs Variable Region VI Motif 4 and Variable Region VII Motif 7 (Table 4
).
The Variable Region VI motifs were less variable than those from Variable Region VII, with an almost unique sequence for each genus, whereas the Variable Region VII identified subgroups within the Piromyces III and Neocallimastix clusters (Table 4).
The subgroups identified using ITS2 sequences were broadly the same groups as seen with ITS1 fingerprints in Table 2. There were instances in which sequences that were identical across ITS1 were non-identical in their ITS2 sequences: two of the sequences from isolates TBT3, namely 62-1 and 62-4, were identical in their ITS1 sequences (ITS1 fingerprint I2,II2,III2,IV2), but sequence 62-1 had an ITS2 fingerprint of V4,VI3a,VII5, whilst sequence 62-4 had a fingerprint of V4,VI3b,VII6 (Table 4
). Similarly, the sequences from the Piromyces III group isolate AE1 (74-2 and 74-3/74-6) have different ITS2 fingerprints but identical ITS1 sequence and fingerprints (Table 4
). Conversely, sequences 28-3 and 28-4 from isolate TNL2 were identical in their ITS2 sequence but not in their ITS1 sequences. This variability within single isolates warns against over-interpretation of ITS1/ITS2 sequence differences between different fungi.
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DISCUSSION |
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Secondary-structure prediction is a tool used in protein studies, and more recently for ribosomal ITS data in plants, to improve multiple sequence alignments (Bateman et al., 2002; Coleman, 2003
). By using secondary-structure prediction to aid alignment of sequences and then highlight areas of sequence useful for constructing fingerprints for phylogenetic assignment of new sequences, this study has extended the approach into a new area for molecular ecology.
The inclusion of HMMs for comparison of sequence similarities gives flexibility when matching new sequences to fingerprints and allows for the observed level of variability in the ribosomal sequences. Use of HMMs with sequence and secondary-structure predictions has been pioneered in similarity scoring for protein comparisons and provides a method for rapid comparison of protein structures (Bateman et al., 2002; Pearl et al., 2003
). Similarly, combining the sequence alignment, secondary-structure prediction and pattern-matching approaches for the ITS DNA sequences gives a route for rapid clustering of new sequences with predetermined fingerprints from characterized genera of gut fungi.
The analysis of rDNA data described here has generated clusters of related sequences outside of the fungal genera characterized to date. This has also been the case for protein studies, where databases containing outputs from the large genome sequencing datasets have identified families of related proteins with unknown functions (Bateman et al., 2004). In both cases, new information will be generated that is likely to alter the description of the clustered protein or DNA sequences. For example, novel groups identified in the fungal ITS data may be assigned to newly defined genera as characterization of isolated strains progresses.
The utility of the approach described here in general terms is that it provides for environmental studies a rapid means of assigning new sequences to one of the previously characterized fungal genera or to the novel groups of related sequences. The fingerprinting approach provides a predetermined, discrete set of parameters for assignment of the sequences with the flexibility to update the information as new fingerprints are found.
On a more specific level, this study has shown a variety of polymorphic patterns of ITS1 and ITS2 sequences from the same isolate, for example, isolate TBT2 (sequences 56-1, 56-3, 56-4, 56-5, 56-6 and 56-8). As rRNA-encoding sequences are present as long tandem arrays in the genome of most eukaryotes, and it is thought that sequence conservation between individual repeats is preserved by concerted evolution (Liao, 1999; Wendel et al., 1995
), this polymorphism may be considered unusual. However, two ITS2 lineages have been demonstrated in a single Fusarium species (O'Donnel & Cigelnik, 1997
) and ITS polymorphisms have been reported elsewhere (Hausner et al., 2000
; Vogler & DeSalle, 1994
).
These sequence polymorphisms illustrate the limitations of this and any other approach for clustering of these fungal strains using ITS sequences at a level below that of genus. For example, the Neocallimastix sequences were tightly grouped in our previous analysis (Brookman et al., 2000a), although three broad clusters were observed, and the present fingerprint analysis supports at least one of these clusters. However, in this study, a sequence from isolate NMW5 which grouped away from N. frontalis in Brookman et al. (2000a)
is sorted into the Neocallimastix frontalis subgroup, suggesting that at least some of the intra-genus groupings are due to polymorphisms and do not represent different species. This uncertainty is compounded by the lack of agreement between subgroupings defined on the basis of ITS1 and ITS2, as also seen for the Piromyces III group in Table 4
, and indicates that the ITS sequences are unlikely to be of use in understanding the relationships at the species level.
In conclusion, we have sequenced ITS1 and ITS2 DNA from a number of anaerobic fungi, determined the relationship between these sequences and ITS1 secondary structure, and used this information to derive a fingerprinting program by which the genus of a given isolate can be determined. This will facilitate high-throughput approaches for the determination of gut fungal flora and its relationship to microbial succession in the colonization of feed boli. Furthermore, training the program with sequences from other fungal genera would broaden the utility of the approach. We suggest that a similar pattern-matching approach with other fungal ITS sequences would provide a useful tool for rapid identification of new sequences from environmental studies.
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ACKNOWLEDGEMENTS |
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Received 8 October 2004;
revised 24 January 2005;
accepted 26 January 2005.
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