Mycologia
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DOI: 10.3852/mycologia.99.5.681
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Mycologia, 99(5), 2007, pp. 681-692.
© 2007 by The Mycological Society of America

Using ITS2 secondary structure to create species-specific oligonucleotide probes for fungi


Frank C. Landis 1

     Botany Department, University of Wisconsin-Madison, Madison, Wisconsin 53706

Andrea Gargas

     Symbiology, LLC, Middleton, Wisconsin 53562

   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 LITERATURE CITED
 

Oligonucleotide microarray based on ITS2 rDNA sequences would be extremely useful in identifying fungi within soil samples. However ITS2 contains phylogenetic information and duplication of sequences among taxa make false positive detections likely unless a way could be found to identify taxon-specific portions of the ITS2 sequence a priori. Examination of component ITS2 sequences suggested one method of identifying species-specific probes. Analysis of 168 fungal ITS2 sequences showed that all 168 ITS2 rRNA sequences could be folded to produce similar secondary structures of 3–4 loops. Unique probes occurred most often in the second loop. While the loop 2 sequence was unique in all taxa, there were partial congeneric and intergeneric duplicates. Evidence for a decrease in duplicates with increasing phylogenetic distance was mixed. From the evidence, 2 or 3 disjunct oligonucleotide probes from the loop 2 sequence might be sufficient to identify most fungal species. This combination appears minimally susceptible to false positives and conceivably could be extended to design probes to identify any eukaryotic species.

Key words: ITS2 secondary structure, microarray design, oligonucleotide probes, soil fungi


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 LITERATURE CITED
 
Environmental and clinical microbiologists have poured considerable work into developing micro-arrays for sampling prokaryotic communities in soil, water, medical and other samples (Zhou 2003Go, Bodrossy and Sessitsch 2004Go). Less research has focused on fungal microarrays despite a need for them to sample both true fungi and organisms such as oomycetes (all of which will be termed fungi here). In addition to medically and economically important fungal pathogens of humans, animals and plants, fungi have unique functions in ecosystems. As with prokaryotes (Schleifer 2004Go) fungi are well known for their essential role in nutrient cycling and other ecosystem processes, and fungi have renowned effects on plant community patterns. Members of the symbiotic Glomeromycota repeatedly have been shown to substantially affect plant community composition and diversity (Grime et al 1987Go, van der Heijden et al 1998, Hartnett and Wilson 2002Go, van der Heijden 2002Go, Landis et al 2004Go). Fungi can have even more dramatic community effects: Pathogenic ascomycete fungi ranging from chestnut blight (Cryphonectria parasitica) (Anagnostakis 1987Go) to Dutch elm disease (Ophiostoma ulmi and O. novo-ulmi) (Buisman 1932Go, Brasier 1991Go, Ingrouille 1995Go) destroyed billions of trees in North America and Europe, turning former forest dominants into rare species, while oomycetes such as Phytophthora cinnamomi have changed forests into savannas and grasslands in Australia (Wills 1993Go, Weste et al 2002Go). Sudden oak death caused by P. ramorum is a lurking threat for a broad range of plant species (Rizzo et al 2002Go, Rizzo and Garbelotto 2003Go). Given their profound ecological effects rapid fungal identification from environmental samples is needed crucially.

An obvious approach is to create an oligonucleotide microarray that contains taxon-specific probes. Such an array would be simple to use; DNA could be extracted from a soil sample, hybridized to the array and the fungi present could be read from the array (although admittedly only known fungi would be found). Because the sequences for the ITS2 region of many fungi and oomycetes are readily available and the sequences are easy to extract from samples, this region appears to be a good target for finding unique probes. However ITS2 sequences do carry some phylogenetic information (Coleman 2003Go, Schultz et al 2005Go) and fungi that share identical regions of their ITS2 sequences will share probes. The probes ideally should contain only autapomorphies for species or strains of interest, not synapomorphies at higher phylogenetic levels such as genera, families and phyla.

Finding appropriate autapomorphies within the ITS2 region is even more difficult than it first appears. Given that most species of soil fungi are unknown (Hawksworth et al 1995Go) the array designer has to create probes that not only will register the taxon of interest but will not respond to some unknown fungus, especially if the array is designed to sample wild soils. The only apparent solution is to find a region of ITS2 that contains many autapomorphies, probes based on that region would have a higher a priori probability of being taxon specific.

One possible solution is to use ITS2 loop structure to find regions containing many autapomorphies. The ITS2 region appears to fold in comparable ways across a number of species, including Saccharomyces cerevisiae (Joseph et al 1999Go), plants (Mai and Coleman 1997Go, Coleman 2003Go), green algae (Coleman and Mai 1997Go), Drosophila (Young and Coleman 2004Go) and recently in a broad survey of 5000 sequences across the eukaryotes (Schultz et al 2005Go). Numbers of autapomorphies appear to vary by position within the secondary structure (Coleman and Mai 1997Go, Mai and Coleman 1997Go, Coleman 2003Go, Young and Coleman 2004Go) and secondary structure has been used as a species-level character within the fungal genus Polyporus (Krüger and Gargas 2004Go).

Following their lead we tested whether folded structures could be used to find regions with high numbers of autapomorphies likely to generate unique probes among fungal taxa. Our analysis of the ITS2 secondary structures, sequences and probes focused on three questions: (i) Do fungal ITS2 sequences have a common folding pattern? (ii) Does the number of duplicates per microarray probe depend on its position in its parent ITS2 sequence and structure? and (iii) Is there a correlation between number of duplicates and phylogenetic distance? In other words, if two fungi share a probe, is it more likely that they are near relatives? This last feature might be useful in designing an array because it would indicate that any fungus generating a false positive would be more likely to have a near relative on the array, rather than being a random organism that happened to match a particular probe.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 LITERATURE CITED
 
Sequences.— – One hundred sixty-eight sequences from Gen-Bank were used in the analysis (TABLE IGo). These sequences were used to generate probes of 20 nucleotide oligonucleotides. Each probe’s position within each ITS2 sequence was numbered by its position of its first nucleotide (5') in the ITS2. For example probe 1 contained sequence nucleotides 1–20, probe 2 contained nucleotides 2–21, and so on. In a 200-nucleotide sequence there were 181 numbered probes. By convention a probe was in a secondary structure if its first nucleotide was within that structure. For instance if a hairpin loop covers nucleotides 20–40 then probes 20–40 are considered within that loop, even though probes 30–40 had most of their nucleotides outside that structure. This naming convention proved useful for locating ITS2 probes and mapping them onto secondary structures.


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  		TABLE I. The 169 taxa used in our analysis. GenBank accession numbers are given in parentheses. Multiple sequences were used for a number of taxa to provide samples among conspecifics and near relatives

 
ITS2 foldings.— – To determine ITS2 secondary structures, each sequence was submitted to Mfold version 3.1 (http://www.bioinfo.rpi.edu/applications/mfold/old/rna/) (Mathews et al 1999Go, Zuker 2003Go) to determine likely RNA folding structures. The sequence was submitted both in one piece and also as 2–4 overlapping pieces (depending on length) to find folding patterns (long sequences tended to generate several equally probable folding patterns, while shorter sequences tended to generate one). The Mfold output was aligned portions of the ITS2 sequence within each folding region using Sequencher 4.2.2 (2003, Gene Codes Corp., Ann Arbor, Michigan). It is important to note that every part of each sequence was assigned to a particular folding region.

Probes.— – Numbers of probe duplicates within the dataset were calculated with Microsoft Access, both across the entire ITS2 sequence and by secondary structure. The number of duplicates was combined with probe numbering and mapping to determine the number of duplicates per nucleotide position along each ITS2 sequence. Mean number and standard deviation of duplicates per probe position were calculated for all sequences with Microsoft Excel. Because these ITS2 sequences were 130–334 nucleotides long their secondary structures also varied in length, and comparing duplicate numbers base pair by base pair was not practical. Thus we also compared the number of duplicates within each secondary structural feature with ANOVA with Type III sums of squares (to compensate for unequal sequence lengths within each structure) with differences compared by Tukey’s HSD test. These tests were run with S-plus version 6.0 (2001, Insightful Corp., Seattle, Washington).

Probes and phylogenetic distance.— – The ITS2 dataset could not be used to generate a phylogeny to test the correlation between the number of duplicates and phylogenetic distance because such a test would be circular. Phylogenetic distance therefore was determined in two ways.

The first approach was to use another gene to create the phylogeny and measure distances. Thirty-five representative species for the distance measurements and 35 proxy species (near relatives) were used to calculate phylogenetic distances (TABLE IIGo). A distance matrix was obtained from an alignment of these 35 species with heuristic search of PAUP* 4.0b 10 (Swofford 2002) using default settings with the distance optimality criterion set to minimum evolution. The number of duplicates was regressed against phylogenetic distance with two methods, a linear regression and a correlation whose probabilities were calculated from 999 permutations of the two datasets. This second method was implemented by Dr Bret Larget (University of Wisconsin at Madison).


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  		TABLE II. Accessions used to calculate number of duplicate probes and phylogenetic distance. Where the accession number is listed, that accession is for the sequence from GenBank. Where another species is listed, that species’ accession was used as a proxy for the probe sequence. In all cases the proxy species is a known near relative and, where they are of different genera, typically one is an anamorphic species and one is a teleomorph

 
Second, because many taxa used for ITS2 sequences were not associated with sequences for other DNA regions, we simply counted numbers of conspecific, congeneric (same genus, different species) and intergeneric (between genera) probe duplications within the dataset because there were many accessions at each of these levels (TABLE IGo). However this accounting was complicated by the fact that many of the accessions were incompletely named, meaning that each unidentified taxon had to be designated as a separate species (e.g. the six Rhizoctonia sp. in TABLE IGo). In addition the dataset of fungal species included both teleomorphic (sexual) and anamorphic (asexual) genera. Although some, such as Thanetophorus cucumeris (teleomorph) and Rhizoctonia solani (anamorph), are known to be different names for the same taxon, the linking of anamorphic and teleomorphic genera is incomplete because there is no one-to-one concordance between sexual and asexual species names. For many species only one of the two forms is known (or even exists). Because of these discrepancies anamorphic and teleomorphic genera were treated as separate taxa, although this artificially inflated the number of intergeneric duplication events. This inflation more severely tests the utility of the analytic method because the method is designed to minimize the number of intergeneric matches.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 LITERATURE CITED
 
All ITS2 sequences shared a secondary structure: two well defined hairpin loops (coded as "loops") with well defined sequences joining them (coded as "joins"), and a complex structure that in different species was modeled by Mfold as a single loop, as two loops or as a complex forking structure (FIG. 1Go). Although many ITS2 secondary structures have been reported to have four well defined loops, some of our sequences lacked the fourth loop or it was included only in one of many equally probable structures. To describe the secondary structures, the regions were coded as either joins (J) or loops (L) with a number corresponding to their relative placement: J581 is the short sequence between the 5.8s ribosomal subunit and loop 1, L1 is loop 1, and so on. The structures in order were J581, L1, J12, L2, J23 and L3on. The last structure, L3on, abstracts the complexity of the 3' end without further subdividing it into loops and joining regions. The loop 2 sequences used in this analysis are provided (APPENDIX 1).


Figure 1
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  		FIG. 1. Labeled ITS2 folding structure. The diagram shows the folding structures and labels we found in ITS2. Loops 1 and 2 are labeled, joined to the 5.8s region by the J581 structure and joined to each other by J12. In this example loop 3on shows two loops, this fourth loop was missing, while in others loop 3 had several subloops. Due to this complexity, the end simply was labeled L3on, as described in the text.

 
Mean numbers of probe duplicates between fungal taxa per ITS2 sequence position showed a pronounced dip in the region corresponding to L2 in most sequences (FIG. 2aGo, at tip of arrow). In this region each probe had roughly 2.0 ± 2 duplicates and 80.8% of probes were unique. The similar low number of duplicates at the 3' end of the L3on structure likely reflects the low number of sequences in this analysis which included this region (FIG. 2bGo); only Armillaria had ITS2 sequences that long. L2 was a well defined secondary structure present in all samples, although it was somewhat variable in length (mean 36.04 ± 8.81 nt). ANOVA unambiguously showed that J12 and L2 both have significantly fewer probe duplicates (df = 7, P < 0.0001) than other regions (FIG. 3Go). Therefore subsequent analyses focused on the L2 region.


Figure 2
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  		FIG. 2. ITS2 probe duplicates and sequence structures. A. Mean number of probes per ITS2 sequence position, based on a survey of 147 sequences. Black diamonds are the means, whereas the lines indicate ±1 s.d. The arrow indicates a region starting at ITS position 38, where 66% of probes are unique. B. Secondary structures mapped onto ITS2 sequence position. Hairpin loops (L1, L2, L3on) are white, while joining regions (J581, J12, J23) are light gray. Sequences were 130–334 nucleotides long. Top to bottom this graph shows the proportion of accessions that contain a particular secondary structure at that sequence location. Note that the region marked by the arrow in A. falls within L2 (loop 2).

 

Figure 3
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  		FIG. 3. Mean number of duplicates per ITS2 secondary structure. Structure codes follow FIG. 2Go and are explained in the results. Letters indicate values that are significantly different, following Tukey’s HSD test with 95% confidence intervals.

 
Do close relatives share more probes? For the 35 species pairs used for the phylogenetic analysis there were five pairs of interspecific duplicates. With 630 possible pairs (not counting species paired to themselves) this means that less than 1% of the species shared probes. Regressions based on the 35 taxa datasets were both not significant, no matter what method was used. An examination of the pattern of duplications across phylogenetic distance provides an explanation (FIG. 4Go). Five pairs of species (out of a possible 630 pairs) were not unique, and there is no pattern to their distribution.


Figure 4
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  		FIG. 4. Mapping numbers of probe duplicates among accessions and phylogenetic distance for 35 species. Diamonds on the Y axis show numbers of duplicate probes within a single species (phylogenetic distance = 0), whereas diamonds on the X axis show where there are no duplicate probes between species. Only five pairs of species (out of a possible 630 pairs) have duplicate probes between species. No pattern was found for the duplication; numbers of duplicate probes did not increase or decrease with phylogenetic distance.

 
Looking at shared probes within the sample, within the L2 region, 7.4% of probes were unique to a single accession, 36.1% of the duplicates were found among conspecifics, 37.5% among congenerics and 19% apparently were shared among genera. The number of congenerics included fungi identified only to genus (such as Rhizoctonia sp.) so these congenerics probably included unrecognized conspecifics. Of the intergeneric duplicates, two-thirds occurred among five genera, the anamorphic Rhizoctonia, Fusarium and Epicoccum, and the teleomorphic Ceratobasidium and Gibberella. In this sample Rhizoctonia and Ceratobasidium were probably the same genus (and as noted above occasionally the same species), as were Fusarium and Gibberella. While there were certainly true intergeneric copying events, the anamorph/ teleomorph pairing holds for many of the other 53 genera on this list. Therefore 19% was undoubtedly a gross overestimate of probe duplication among genera, resulting from a suboptimal dataset. This number is higher than the result from the phylogenetic survey because that used no close relatives. Overall it appeared that close relatives (conspecifics and congenerics) shared more probes.

Overall in no case was the entire L2 region shared between truly different genera. Although some parts of the L2 region might be shared, duplication events inevitably were clustered at either the beginning or end of the ITS2 sequence. In a few cases up to half of the sequence was duplicated among species, but in those cases sequences at the other end of the loop were unique.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
LITERATURE CITED
 
The analysis demonstrated that fungal ITS2 rRNA have consistent secondary structures with a region in the second loop structure rich in autapomorphies suited for unique probe design. While we used a three-loop structure (rather than the four-loop secondary structure found for other taxa) our L2 corresponds roughly to the tip of the second loop demonstrated in plants (Coleman and Mai 1997Go, Coleman 2003Go) and green algae (Schultz et al 2005Go). The second loop also was identified as a region of high autapomorphies by other researchers (Coleman 2003Go).

Interspecific and intergeneric duplications did occur within L2, but this presents no major problems. A single probe cannot unambiguously identify a fungal species. Multiple, separated probes, for instance from the beginning, middle, and end of L2, likely will be sufficient to identify a species because we found no evidence of species that share entire loop 2 sequences. It is possible that groups of species share entire loop 2 sequences, but they did not appear in this sample. However any such groups identified in the future likely will appear in the literature and future researchers will be able to avoid problematic taxa. Searches of GenBank and other databases for related sequences should be a preliminary part of any probes designed with this technique.

The regression results do not unequivocally support the hypothesis that the number of duplicate probes decreases with increasing phylogenetic distance. The results of the duplicate counts in the entire dataset suggested otherwise, but that dataset could not be used to calculate phylogenetic distances in our research. More thorough sampling of sequences outside the ITS2 region likely will provide the accessions necessary for future re-examination of this hypothesis.

The fundamental conclusion is that ITS2 sequences can be used to design probes that are, if not species-specific, close to that level. The second loop of ITS2 has been shown to contain a high number of autapomorphies. By using a number of probes based on the loop 2 sequence, it should be possible to detect any known fungal species. Moreover a set of 2–3 loop 2 probes is resistant to false positives, a benefit given that unknown fungal species likely will be present in most environmental samples. While this analysis focused on probes for microarray design, these results are adaptable to any technology that uses oligonucleotide probes to sample communities. Finally, because eukaryotes in a number of phyla have been shown to have ITS2 sequences with similar secondary structures, ITS2 loop 2 sequences could be suitable for creating probes for any eukaryote.


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  		APPENDIX I. Loop 2 sequences

 

   ACKNOWLEDGMENTS
 
The authors thank the University of Wisconsin at Madison and UW Graduate School for financial support; Luigi Vitiritti, Steve Smith and Emile Nuwaysir of Nimblegen Systems Inc., Madison, Wisconsin, for microarray design and creation; and Sandra Splinter, David Frish and John Luecke of the Gene Expression Center at UW-Madison for microarray hybridization and analysis advice. Dirk Krüger and Paula T. DePriest provided helpful insight. We thank Bret Larget for patient guidance and designing a neat and useful permutation-based statistical test


   FOOTNOTES
 
Accepted for publication July 20, 2007.

1 Corresponding author. Tel: (310) 427-0624. E-mail: fclandis{at}wisc.edu


   LITERATURE CITED
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 LITERATURE CITED
 
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