TETRAHYMENA AND THE MOLECULAR BARCODE:
A NEW APPROACH TO SPECIES IDENTIFICATION
A Thesis
Presented to
The Faculty of Graduate Studies
of
The University of Guelph
by
CHANDNI P. KHER
In partial fulfillment of requirements
for the degree of
Master of Science
September, 2008
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While these forms may be included Bien que ces formulaires in the document page count, aient inclus dans la pagination, their removal does not represent il n'y aura aucun contenu manquant. any loss of content from the thesis. Canada ABSTRACT
TETRAHYMENA AND THE MOLECULAR BARCODE: A NEW APPROACH TO SPECIES IDENTIFICATIONS
Chandni Kher Co-Advisors: University of Guelph, 2008 Professors D. H. Lynn and T. J. Crease
A variety of approaches have been used over the years to aid in the field of ciliate systematics. Recently, DNA barcoding using the mitochondrial cytochrome c oxidase subunit I (cox-1) gene has gained popularity as a tool for species identification. The primary objective of this research was to assess the overall feasibility of cox-1 barcoding in elucidating species identifications for isolates from the genus Tetrahymena. Increased sampling efforts confirmed that intraspecific sequence divergence is typically less than
1%; however, in the process, several misidentified cultures were uncovered. Nuclear small subunit rRNA (SSUrRNA) gene sequences were produced to confirm new or questionable species identifications revealed by cox-1 barcoding. cox-1 barcoding is an invaluable tool for protistologists, especially when used in conjunction with morphological studies. However, a two-gene barcoding approach should be explored in the future, and it is suggested that ITS-2 be used as the alternate barcode. ACKNOWLEDGEMENTS
Although this thesis is the culmination of over 2 years of work on my part, I have several people to thank without whom this work would never have been completed.
First and foremost, I would like to thank Dr. Denis Lynn for being an exemplary advisor. He is a treasure trove of knowledge on any topic imaginable, but more importantly, he cares about his students and always has time for them. I was always able to count on him for advice and support, and for this I am eternally grateful. I must also thank Dr. Teresa Crease for stepping in as my co-advisor. Whether I showed up at her office at 9 AM or 9 PM, she was never too busy to answer questions, provide comments on the umpteenth draft, or sign endless amounts of forms. Her generosity will not be forgotten.
I would also like to thank my advisory committee. Dr. Paul Hebert could always be counted on for advice and encouragement, and his amazing skill at wrangling money for barcoding provided the funding for this project. Additionally, Dr. Robert Hanner was kind enough to join my advisory committee at the 11th hour so that I would be able to graduate in a timely fashion. His comments helped make this thesis much more readable.
Thanks to all of my labmates past and present for their advice, support, and most importantly, their friendship. They made this process much more fun than it should have been! The analyses conducted for this thesis would not have happened without hours upon hours of patient tutelage from Dr. Michaela Struder-Kypke. Similarly, Eleni Gentekaki was always available to teach and troubleshoot any lab technique I could think of. Chitchai Chantangsi spent extensive amounts of time training me in basic molecular biology techniques and cell culturing while he was busy writing his own thesis. Megan Noyes provided much-needed company in the lab and was always ready for spontaneous road trips. Additionally, Michael Underell served as my field assistant and provided endless amounts of support and encouragement, which in turn helped to preserve my sanity. Christina Carr and Taika von Konigslow shared an office with me and thus dealt with my complaints about failed PCRs, fed me candy, kept me on schedule, and listened
l to me practice my presentations one too many times.
I would like to thank Dr. F. Paul Doerder from Cleveland State University for generously providing me with environmental samples of Tetrahymena not once, but twice. The American Type Culture Collection generously provided in-kind donations of extracted Tetrahymena DNA from their collection, which was facilitated by Pranvera Ikonomi, Jason Cooper, and Jeff Cole. The Culture Collection of Algae and Protozoa also provided extracted Tetrahymena DNA from their collection, and Frithjof Kuepper and Undine Achilles-Day were also generous enough to share some of their sequences with me. Cultures and/or extracted DNA were also obtained from Dr. Raul Iglesias from the Universidad de Vigo, Dr. Dina Zilberg and Marcia Pimenta-Liebowitz from Ben Gurion University of the Negev, and Dr. Harry W. Dickerson from the University of Georgia.
It is necessary to thank several people from the Department of Integrative Biology. Angela Hollis, Jeff Gross, Elizabeth Holmes, and Jing Zhang produced too many sequences for me to count at the CBS-AAC Genomics Facility. Franlie Allen, Mary Anne Davis, Lori Ferguson, Susan Mannhardt, Ian Smith, and Jennifer Taillefer happily performed the vital background tasks that kept the entire department from imploding. Various faculty members, particularly Beren Robinson, were always available at presentations to provide helpful feedback and to poke holes in my logic. Most importantly, my fellow grad students were counted upon for fun, laughter, TV nights, potluck dinners, workouts, dancing, and dining. Without them I would have eaten lunch alone at my desk everyday.
Last but certainly not least; I must thank my family: Dr. Ramesh Kher, Kiran Kher, and Manish Kher. Their unconditional love, support, and encouragement will never be forgotten, and without them I would never have made it this far!
"Our court shall be a little Academe, Still and contemplative in living art." -Ferdinand, King of Navarre in Love's Labour's Lost
n TABLE OF CONTENTS
A. INTRODUCTION 1 A. 1 BIODIVERSITY AND TAXONOMY 1 A.2 MICROBIAL DIVERSITY AND PHYLUM CILIOPHORA 2 A.3 CILIATE SYSTEMATICS 3 A.4 AN INTRODUCTION TO THE GENUS TETRAHYMENA 5 A.5 COX-1 BARCODING 9 A.6 OVERALL RESEARCH GOALS 13 B. METHODOLOGY 14 B.l LIST OF SAMPLES 14 B.2 CELL CULTURING 14 B.3 NUCLEIC ACID EXTRACTION 19 B.3.1 Nucleic acid extraction via Chelex 19 B.3.2 Nucleic acid extraction via MasterPure™ Kit 19 B.4 POLYMERASE CHAIN REACTION (PCR) 20 B.4.1 Amplification of cox-1 20 B.4.2 Amplification of the SSUrRNA Gene Region (SGR) 21 B.5 GEL ELECTROPHORESIS 21 B.6 PURIFICATION OF PCR FRAGMENTS 22 B.7 DNA SEQUENCING 23 B.8 SEQUENCE DATASET CONSTRUCTION 24 B.8.1 cox-1 sequence dataset 24 B. 8.2 SGR sequence dataset 24 B.9 PHYLOGENETIC TREE CONSTRUCTION 25 B.9.1 Neighbour Joining (NJ) trees 25 B.9.2 Maximum Parsimony (MP) trees 25 B.9.3 Bayesian Inference (BI) trees 26 B.9.4 Maximum Likelihood (ML) trees 26 B.10 SPECIES IDENTIFICATIONS 27 C. RESULTS 28
iii C. 1INTRASPECIFIC SEQUENCE DIVERGENCE VALUES 28 C. 1.1 Delineating known species of Tetrahymena 28 C.2 PHYLOGENETIC ANALYSES 30 C.2.1 cox-1 trees 30 C.2.2 SGR trees 34 C.2.3 Identifying unknown isolates 38 D. DISCUSSION 41 D. 1 EFFICACY OF cox-1 BARCODING IN TETRAHYMENA 41 D. 1.1 Intraspecific sequence divergence values 41 D. 1.2 Using cox-1 to identify unknown isolates 44 D. 1.3 Using cox-1 to delineate known species 46 D. 1.4 General comments " 48 D.2 SSUrRNA SPECIES CONFIRMATIONS 49 D.3 ITS-2 AS AN ALTERNATE BARCODE 50 D.4 CONCLUDING REMARKS 52 E. REFERENCES 54 F. APPENDICES 70 APPENDIX I: LOWER TRIANGULAR MATRICES FOR cox-1 70 APPENDIX II: LOWER TRIANGULAR MATRICES FOR SSUrRNA 74
IV LIST OF TABLES
Table 1. List of isolates from the ATCC sequenced for this study. 15
Table 2. Mean percentage intraspecific cox-1 sequence divergence 29 values for 13 different Tetrahymena species based on K2P distance values.
Table 3. Mean percentage intraspecific SSUrRNA sequence divergence 29 values for five different Tetrahymena species based on K2P distance values.
LIST OF FIGURES
Figure 1. A stylized drawing of Tetrahymena showing general 7 morphological features of the genus.
Figure 2. A neighbour-joining tree inferred from 822 nt of the 184 31 cox-1 gene sequences from Tetrahymena isolates.
Figure 3. A Bayesian inference tree using the GTR nucleotide 32 substitution model inferred from 822 nt of 184 cox-1 gene sequences from Tetrahymena isolates.
Figure 4. A maximum likelihood tree using the GTR nucleotide 33 substitution model inferred from 822 nt of 184 cox-1 gene sequences from Tetrahymena isolates.
Figure 5. A neighbour-joining tree inferred from 1,743 nt of 88 35 SSUrRNA gene sequences from Tetrahymena isolates.
Figure 6. A Bayesian inference tree using the GTR nucleotide 36 substitution model inferred from 1,641 nt of 88 SSUrRNA gene sequences from Tetrahymena isolates.
Figure 7. A maximum likelihood tree using the GTR nucleotide 37 substitution model inferred from 1,641 nt of 88 SSUrRNA gene sequences from Tetrahymena isolates.
Figure 8. A neighbour-joining tree inferred from 228 nt of 42 ITS-2 39 gene sequences from Tetrahymena isolates.
v A. INTRODUCTION
A.1 BIODIVERSITY AND TAXONOMY
Biological diversity, or biodiversity, is defined as "the variety and variability among living organisms and the ecological complexes in which they occur" (Office of
Technology Assessment, 1987). In general, biodiversity encompasses multiple levels of biological organization, that is: ecosystem, species, and genetic diversity (Norse et ah,
1986; Noss, 1990). Biodiversity is important to humans in particular for a variety of ecological, economic, and cultural reasons. In terms of science, it is important to study biodiversity in order to gain a richer understanding of the way in which "the world works". Over the years, many attempts have been made to quantify the diversity of life on Earth. This has mainly been done through taxonomy, the practice of the identification and grouping of organisms into formal systems of classification. Over time, taxonomists have described approximately 1.5-1.8 million species (Wilson, 2000). However, recent estimates show that there could be as many as 100 million species on Earth (Wilson,
2003). For this reason, the study of taxonomy is crucial in achieving a better understanding of biodiversity. Despite this, there are only a small number of practicing taxonomists worldwide, and this number is decreasing with time, thus creating a
"taxonomy crisis" (Disney, 1998; Hopkins and Freckleton, 2002). Although it is important to train more taxonomists over time, it is also essential for scientists from different fields of study to come together now and pool their knowledge to provide a better understanding of biodiversity. One of the ways in which this is possible is via a concept known as integrative taxonomy, which is defined as "the science that aims to delimit the units of life's diversity from multiple and complementary perspectives
1 [including] phylogeography, comparative morphology, population genetics, ecology, development, [and] behaviour" (Dayrat 2005). Through integrative taxonomy, it is possible for a diverse group of researchers to come together and combine their knowledge of different tools and techniques in order to facilitate the fast and efficient identifications of species, thus bridging the large gap between the groups of organisms that are known and those that are yet to be described formally.
A.2 MICROBIAL DIVERSITY AND PHYLUM CILIOPHORA
In terms of global biodiversity, the majority of life on Earth is microbial as both prokaryotes and microscopic eukaryotes are found in each of the three domains of life
(Weisse 2006). Protists in particular are a diverse, highly abundant group of organisms that can be found in a variety of different habitats, although they are mostly aquatic.
Protists are a crucial component of all food webs, often as primary producers, and are important to nutrient cycling, as they are common detritivores.
Although a large quantity of the Earth's diversity remains undescribed, ciliated protozoa (or ciliates) in particular are underrepresented in our current estimates of biodiversity (Dorigo et al, 2005). This is in contrast to the fact that ciliates are one of the most diverse and speciose of all extant protist groups (Corliss, 2002). Ciliated protozoa, or species belonging to Phylum Ciliophora, can usually be distinguished from all other protists based on three characteristics: 1) nuclear dualism, or the possession of both a vegetative macronucleus and a gametic micronucleus and sometimes multiples of both, 2) the presence of a complex infraciliature system composed of cilia and an associated cilium-fiber system during at least part of their life cycle, and 3) the demonstration of a
2 sexual process known as conjugation, during which cells can fuse together to exchange gametic material (Lynn, 1996).
Corliss (2000) suggests that there are 8,000 described morphospecies of ciliates known currently, but this number is likely an underestimate (Foissner et ah, 2002). The difficulty in describing ciliates occurs mainly due to their small size and the difficulty in isolating and establishing them in culture. Once established in culture, a lack of distinguishing phenotypic characters can also make identifications difficult even if one is well trained in both taxonomy and microscopy (Dorigo et ah, 2005). Additionally, undersampling can contribute to the underestimation of species numbers as up to 70% of the species present in an area can be missed since a large proportion may be in a dormant stage (Foissner, 2008). For this reason, there could be as many as 27,000 to 40,000 extant ciliate species, suggesting that 83-89% of ciliate diversity is yet to be discovered
(Foissner et ah, 2008). The difficulty in differentiating between closely related species is a major reason why this debate has not yet been fully resolved. This is especially significant if genetic species diversity, not just morphospecies diversity, is recognized to be ecologically meaningful (Schlegel and Meisterfeld, 2003). For this reason, a future study involving the use of genetics-based species descriptions (eg. sequence data) to fully describe the amount of ciliate diversity in a particular area could shed light on whether or not ciliates exhibit endemism.
A.3 CILIATE SYSTEMATICS
The study of ciliate systematics is an excellent example of a field of study in which a variety of approaches have been used to integrate different types of data to
3 elucidate the adaptive divergence of these organisms. These approaches can be divided chronologically into four distinct periods of time (Corliss, 1974). The first of these periods is known as the Age of Discovery (1880-1930). One of the most significant events of this period was the publication of Otto Butschli's "Infusoria", which can be considered the first comprehensive and well-organized account of ciliate diversity
(Corliss, 1974). During this period, information on ciliates came from light microscope observations, and as a result, taxonomic differences were defined by differences in the location and the composition of the ciliature of the cells (Lynn, 1996). One of the major limiting factors of this period was an absence of effective staining techniques for viewing cells. This changed in the early 20th century when silver staining techniques were pioneered by Klein, Chatton and Lwoff, and Gelei and Horvath (Lynn, 1996). Detailed observations of precise morphological characters that stemmed from these discoveries fueled the advancement into the second period, the Age of Exploitation (1924-1950)
(Corliss, 1974). This period was marked by an explosion in the number of scientists that were interested in protistology. By the early 1930s, nearly 3,000 different ciliate species had been recognized and described in Alfred Kahl's authoritative works on ciliate diversity (Corliss, 1974). This number doubled to 6,000 during the third period, the Age of Infraciliature (1950-1970), during which Emmanuel Faure-Fremiet conceived a new view on evolution based on detailed studies of the infraciliature (Corliss, 1974). The fourth major period, the Age of Ultrastructure (1963-1985), was defined by the acceptance of electron microscopy as the wave of the future in terms of studying ciliate ultrastructure (Lynn, 1996). The impact of electron microscopy has been phenomenal.
Even though only a portion of the 8,000 described species have been studied in detail,
4 this has led to significant changes in ciliate classification (Corliss, 1979; Small and Lynn,
1981; Small and Lynn 1985). Based on these observations, several different systems of classifications have been proposed by different researchers who have interpreted the significance of various structures differently (Lynn, 1996). The current or fifth period of time (1985-present) may be thought of as the Age of Refinement (Greenwood et al.,
1991). This is due to the fact that much attention must now be placed on clarifying relationships between major and minor lineages of ciliates. This period involves the use of molecular techniques such as the polymerase chain reaction (PCR), DNA and RNA sequencing, and computer-driven phylogenetic analyses (Lynn, 1996; Lynn, 2008). In essence, it can be said that the history of ciliate systematics represent a history of integrated taxonomy in which several different scientific techniques and analyses have come together to shape our understanding of the diversity of Phylum Ciliophora.
A.4 AN INTRODUCTION TO THE GENUS TETRAHYMENA
Species belonging to the genus Tetrahymena are arguably the most well studied ciliates today. They are easily isolated from environmental samples, can be maintained in axenic (sterile) cultures in a laboratory environment, and can even be frozen for long- term storage (Orias et ah, 2000). Because of this, they have been very well studied over time, especially with respect to cell and molecular biology, and have been referred to as the "Escherichia coli of the non-photosynthetic eukaryotes" (Hutner et al., 1972).
The genus Tetrahymena belongs to the class Oligohymenophorea within the
Phylum Ciliophora (Lynn and Small, 2002). W. H. Furgason (1940) formally named the genus for its four (Latin tetra-) membrane-like (L. hymen) oral structures. There are
5 currently 42 recognized species within this genus (Fenchel and Finlay, 2004).
Tetrahymena species are generally small ciliates that range from 25-250 urn in length, with an ovoid shape featuring uniform ciliature and a narrow anterior end (Lynn and
Small, 2002; Figure 1). Tetrahymena typically exhibit a single undulating membrane on the right side and three oral polykinetids on the left side of the oral structure, a single contractile vacuole, and a central macronucleus accompanied by a micronucleus (Curds et al, 1983; Elliott and Kennedy, 1973). Some Tetrahymena species are known to obligately or facultatively parasitize hosts such as mosquitoes, slugs, snails, fish, amphibians, and even dogs (Corliss, 1973; Jerome et al., 1996; Lynn et ah, 2000).
Tetrahymena are often found near dead or dying aquatic organisms, as they are typically bacterivorous.
Tetrahymena species are found in a variety of habitats, particularly in freshwater
(Corliss, 1973). It is possible that Tetrahymena species may exhibit patterns of geographic restriction as certain species such as Tetrahymena americanis have very rarely been found outside of North America (Nanney, 2006). Early observations of the patterns of geographic distribution within this genus suggest that ecological specialization may occur, thus providing the foundation for the geographic restriction of certain genotypes (Nanney, 2006). However, further molecular research is required to accurately assess species relationships and thus determine whether Tetrahymena species exhibit endemism.
In general, the examination of morphological characters via silver staining remains the primary way of differentiating between species of Tetrahymena that are morphologically distinct (Corliss, 1973). However, Tetrahymena species are not easily
6 Figure 1: A stylized drawing of Tetrahymena showing general morphological features of the genus. These include A) its ovoid shape with a narrow anterior end, B) uniform ciliature, C) a central macronucleus, D) a single micronucleus, E) and the oral region (from Lynn, 2008).
7 identified via microscopy due to the fact that some of them exhibit polymorphic life cycles and others are cryptic or sibling species (Chantangsi et ah, 2007; Corliss, 1973).
For this reason, a genetic approach to species identification may be prudent. Although a large number of environmental isolates are often asexual or sexually immature, mating types can be used to identify some species of Tetrahymena (Elliott and Gruchy, 1952;
Nanney et al., 1998). For example, Tetrahymena thermophila is known to exhibit seven different mating types (Doerder et al, 1995). Cells reproducing asexually will have the same mating type as the parent cell, and cells with the same mating type will not conjugate. Mating types were used to identify the species Tetrahymena pyriformis
(Corliss and Daggett, 1983; Nanney and McCoy, 1976). Isozyme analyses have also been used to differentiate between some species, but current research suggests that this may not be the most accurate method for identification, as similar polymorphisms among species can be reflected through isozyme mobilities (Chantangsi et al., 2007; Nanney et al, 1980; Tait, 1978).
In recent years, DNA-based molecular approaches have been used to elucidate phylogenetic relationships in the genus Tetrahymena as well as other ciliates. Protein coding genes, such as elongation factor la(EF-la), actin, a-tubulin, and histone H3 and
H4 have been used to build ciliate phylogenies, but they are not good for inferring deep phylogenetic relationships due to their relatively high rate of evolution (Baroin-
Touranchou et al., 1998; Katz et al., 2004; Kim et al., 2004; Lynn, 2008; Moreira et al.,
2002). Initial studies using small subunit ribosomal RNA (SSUrRNA) and large subunit ribosomal RNA (LSUrRNA) gene sequences were successful in building ciliate phylogenies congruent with our understanding of evolutionary history, that have since
8 been supported through further sequence analyses (Baroin et al, 1988; Lynn and Sogin,
1988; Nanney et al, 1998). Jerome and Lynn (1996) used SSUrRNA and LSUrRNA gene sequences to identify cryptic species within the T. pyriformis species complex, but they also concluded that the interspecific variation is very low in these genes and that a more fast-evolving and thus variable marker would be a better tool for sequence-based species identifications (Jerome and Lynn, 1996). It is possible that a gene that evolves faster than
SSUrRNA can be used as a good marker for species identification in Tetrahymena and other ciliate species, and the mitochondrial genome is widely known to evolve faster than the nuclear genome (Brown et al, 1979; Mcintosh et al., 1998). Lynn and Striider-Kypke
(2006) showed that species with identical SSUrRNA gene sequences showed variation in the mitochondrial gene, cytochrome c oxidase subunit I (cox-1). Since the macronuclear and mitochondrial genomes have been sequenced in T. thermophila, it is now easier for scientists to design suitable primers to amplify both mitochondrial and nuclear genes of interest (Brunk et al., 2003; Eisen et al., 2006).
A.5 COX-1 BARCODING
It is widely recognized that recent technological advances can play a role in expediting the practice of taxonomy (Bisby et al, 2002; Godfray, 2002; Tautz et al.,
2003). One major scientific technique that will play a significant role in present and future taxonomic studies is that of DNA barcoding. The idea of using sequences as barcodes for species or populations was first proposed by Arnot et al. (1993). Richard
Dawkins (1998) then proposed to use DNA sequences as a way of cataloguing life on
Earth. In theory, we should be able to sequence a small segment of DNA from any
9 organism and compare it to a reference library containing sequences from all recognized species on Earth in order to identify a species, similar to the use of barcodes to identify merchandise. Hebert et al. (2003a) proposed using a 650 base pair region of the mitochondrial gene, cytochrome c oxidase subunit I (cox-1) as the universal barcode sequence in animals. This gene was selected for a variety of reasons, the most important of which is that cox-1 is found in almost all eukaryotic organisms as a crucial component of the electron transport chain during aerobic respiration. Additionally, this gene is easily amplified in animals and thus sequences can theoretically be obtained from any species regardless of whether it is dead or alive, thus facilitating the use of some fossils and museum specimens (Blaxter, 2004; Folmer et al, 1994). Species identifications using cox-1 have even been performed using degraded DNA (Hajibabaei et al., 2006). The information obtained from these sequences is generally reliable as mitochondrial DNA is less susceptible to mass nucleotide insertions or deletions because mitochondria reproduce via binary fission, and rarely, if ever, undergo genetic recombination.
Furthermore, mitochondrial DNA generally evolves faster than nuclear DNA, which means that mitochondrial sequences are in theory more useful in differentiating between closely related species (Hebert et al., 2003b).
Initial DNA barcoding analyses of several different animal phyla performed using
650 base pairs located near the 5'-end of the cox-1 gene have been remarkably successful, with 96% of all samples identified to the phylum level (Hebert et al., 2003a). Within groups of insects, particularly Lepidoptera, this success rate was 100% when classifying specimens down to the level of order and morphospecies, respectively (Hebert et al.,
2003a). A subsequent study of 2,238 animal species showed sequence divergence values
10 of greater than 2% between congeneric species, confirming that the cox-1 barcode is
indeed an excellent tool for species identification in closely related animal species
(Hebert et al, 2003b). Research on several widely studied animal groups, including fish
and birds, has generally confirmed these earlier results (Hebert et al, 2004; Frezal and
Leblois, 2008; Ward et al, 2005).
The overall feasibility of a cox-1 barcoding approach to species identification in protists has not yet been fully examined (Stoeckle, 2003). Barth et al (2006) looked at
intraspecific variation in Paramecium, a free-living ciliate, and determined that the high variation in mitochondrial DNA made it a suitable marker for intraspecific genetic diversity studies. There is also a ~300-base pair insert in the cox-1 barcoding region in ciliates, thus making it longer than the typical animal barcode (M. Striider-Kypke, pers. coram.). An initial study by Lynn and Striider-Kypke (2006) showed that the intraspecific divergence value for cox-1 sequences was less than 1% in 14 isolates of Tetrahymena thermophila. This 1% threshold was empirically derived, as the isolates of T. thermophila had been previously identified using mating tests, morphological studies, and sequence data from other genes. The 1% threshold was further tested by Chantangsi et al. (2007), as they extended upon these results by analyzing cox-1 sequences from 75 isolates representing 36 species from the genus Tetrahymena, and found that <1% intraspecific sequence divergence values characterized strains of Tetrahymena borealis, Tetrahymena
Iwofji, and Tetrahymena patula, and confirmed this threshold value for additional strains of T. thermophila. Thus, all studies exploring cox-1 sequence divergences in species of
Tetrahymena have shown that an empirically-derived threshold value of less than 1% can be used to identify species, or molecular operation taxonomic units (Blaxter, 2004).
11 However, these studies were only able to accomplish sufficient amounts of intraspecific sampling for 4 out of the 36 recognized species of Tetrahymena, so there is a need for expanded intraspecific sampling in additional species to further support the 1% threshold value.
Chantangsi et al. (2007) also uncovered strains that could have been mislabeled or contaminated, and also identified some isolates that suggested some species might be junior synonyms, or the same species with more than one name. These findings, however, were based only on the mitochondrial cox-1 sequence. Confidence in these misidentifications would be more secure if they were confirmed by nuclear gene sequences, which Chantangsi et al. (2007) did not obtain.
It would also be valuable to determine whether another gene can be used in addition to cox-1 to provide a two-gene approach to protist barcoding. Although cox-1 barcoding has been quite successful for Tetrahymena, it has been much more difficult to design cox-1 specific primers for other ciliates (E. Gentekaki and M. Striider-Kypke, pers. comm.). Due to the high likelihood of successful PCR amplification of the
SSUrRNA gene region (SGR) consisting of SSUrRNA, internal transcribed spacer 1 (ITS-
1), the 5.8S rRNA, internal transcribed spacer 2 (ITS-2), and a small portion ofLSUrRNA, it may be useful to consider one of these genes as an alternate barcode. Coleman (2005) showed that ITS-2 can be used quite successfully for species identifications in ciliates, but a thorough study of interspecific and intraspecific sequence divergences has not yet been explored.
12 A.6 OVERALL RESEARCH GOALS
The primary objective of this research is to further explore the efficacy of using cox-1 barcoding for species identification within the genus Tetrahymena. By increasing intraspecific sampling, I will confirm that intraspecific sequence divergence values remain at <1%. The second objective is to produce SSUrRNA sequences in order to confirm new or questionable species identifications revealed by cox-1 barcoding.
Additionally, comparison between phylogenies based on cox-1 and ITS-2 may shed light on whether ITS-2 can be used as an alternate barcode for species identification in
Tetrahymena and other ciliate species.
13 B. METHODOLOGY
B.l LIST OF SAMPLES
A total of 119 samples were analyzed in this study (Table 1). The American Type
Culture Collection donated extracted DNA from 41 Tetrahymena cultures. The Culture
Collection of Algae and Protozoa similarly donated extracted DNA from 12 Tetrahymena
cultures. Dr. F. P. Doerder of Cleveland State University provided 52 isolates of
Tetrahymena that were collected throughout North-Eastern USA. Dr. H. Dickerson of the
University of Georgia provided extracted DNA from seven isolates of Ichthyophthirius
multifiliis. Dr. D. Zilberg from Ben-Gurion University of the Negev in Israel provided
three isolates of unknown Tetrahymena species known as Tet-NI, Tet-NI4, and Tet-NI5.
Dr. R. Iglesias from the Universidad de Vigo in Spain provided three isolates of
Tetrahymena rostrata and one isolate of Tetrahymenapyriformis that were isolated from
slug and snail populations in Spain.
B.2 CELL CULTURING
All isolates received from Dr. F. P. Doerder were initially cultured in Cerophyl
medium (Agri-Tech Inc., Kansas City, MO, USA) bacterized with Enterobacter
aerogenes, as described by Chantangsi et al. (2007). These cultures were later transferred
via single cell isolation into sterile proteose peptone yeast extract (PPYE) medium (0.5 g dextrose, 2.0 g proteose peptone [Fisher Scientific, Ottawa, ON, Canada], and 2.0 g yeast
extract [Fisher Scientific, Ottawa, ON, Canada] in 400 mL of distilled water) that contained 0.5 g/L each of penicillin G, streptomycin, and erythromycin (all available
14 Table 1: List of isolates sequenced for this study. Donors, strain IDs, and localities are listed. ATCC = American Type Culture Collection; CCAP = Culture Collection of Algae and Protozoa.
No. Species Name Donor Strain ID Locality 1 /. multifiliis H. Dickerson G2 tomont Unknown 2 I. multifiliis H. Dickerson G2 theront Unknown 3 I. multifiliis H. Dickerson G3 theront Unknown 4 I. multifiliis H. Dickerson G4 theront Unknown 5 I. multifiliis H. Dickerson G5 theront Unknown 6 I. multifiliis H. Dickerson G6 theront Unknown 7 I. multifiliis H. Dickerson G7 theront Unknown 8 T. americanis CCAP 1630/7A USA 9 T. borealis CCAP 1630/5A Unknown 10 T. borealis CCAP 1630/5B Unknown 11 T. canadensis ATCC 30325 Buck's Hill, Advance, NC 12 T. canadensis ATCC 50097 Arapahoe Park, CO 13 T. canadensis ATCC 50098 Serdang, Malaysia 14 T. canadensis ATCC 50099 Unknown 15 T. canadensis ATCC 205113 Webber Pond, Bremen, ME 16 T. canadensis ATCC 205143 Creek, Ute Pass, CO 17 T. hegewischi ATCC 30832 Wolfe Lake, Chicago, IL 18 T. hegewischi ATCC 30833 Perkins Pond Marsh, NH 19 T. hegewischi ATCC 205056 Pembroke Pines, FL 20 T. hegewischi ATCC 205057 Pembroke Pines, FL 21 T. hegewischi ATCC 205058 S. of Duck Puddle Pond, ME 22 T. hegewischi ATCC 205144 Wolfe Lake, Chicago, IL 23 T. limacis CCAP 1630/16 Unknown 24 T. mobilis CCAP 1630/22 Unknown 25 T. nanneyi ATCC 30840 Sault St. Marie, ON 26 T. nipissingi ATCC 30838 laboratory cross, Chicago, IL 27 T, patula CCAP 1630/2 Unknown 28 T. pyriformis ATCC 30039 New York 29 T. pyriformis ATCC 30331 Unknown 30 T. pyriformis ATCC 205061 Route 1-75, Bradenton, FL 31 T. pyriformis ATCC 205063 Route 1-75, Bradenton, FL
15 Table 1: continued.
No. Species Name Donor Strain ID Locality 32 T. pyriformis CCAP 1630/1W Unknown 33 T. pyriformis R. Iglesias TR04 from Helix aspersa aspersa 34 T. rostrata R. Iglesias TROl from Helix aspersa aspersa 35 T. rostrata R. Iglesias TR02 from Helix aspersa maxima 36 T. rostrata R. Iglesias TR03 from Deroceras reticulatum 37 T. sonneborni ATCC 30834 Potawatomi Pond, Wheeling, IL 38 T. sonneborni ATCC 30835 Minnow Lake, Sudbury, ON 39 T. sp. ATCC 50402 jungle stream, Malaysia 40 T. sp. ATCC 50403 west of Muskegon, MI 41 T. sp. ATCC 50413 South Haven, MI 42 T. sp. ATCC 205048 River Falls, Inthanout, Thailand 43 T. sp. ATCC 205049 Roman Fort, Chesters, England 44 T. sp. ATCC 205050 Lake Beatrice, Elgin, IL 45 T. sp. ATCC 205078 south of Godhaven, Greenland 46 T. sp. ATCC 205079 south of Godhaven, Greenland 47 T. sp. ATCC 205080 Fort Lauderdale, FL 48 T. sp. F. P. Doerder 19054-1 GRWMA3, OH 49 T. sp. F. P. Doerder 19059-1 Lake SG69, PA 50 T. sp. F. P. Doerder 19060-1 Lake SG69, PA 51 T. sp. F. P. Doerder 19061-1 Lake SG69, PA 52 T. sp. F. P. Doerder 19069-1 Lake SG69, PA 53 T. sp. F. P. Doerder 19070-1 Lake SG69, PA 54 T. sp. F. P. Doerder 19072-1 Lake SG69, PA 55 T. sp. F. P. Doerder 19139-1 Peck's Pond, PA 56 T. sp. F. P. Doerder 19158-1 Mudge Pond, PA 57 T. sp. F. P. Doerder 19166-3 West Side Pond, NE 58 T. sp. F. P. Doerder 19166-5 West Side Pond, NE 59 71 sp. F. P. Doerder 19169-1 West Side Pond, NE 60 T. sp. F. P. Doerder 19170-1 Lower Bolton Lake, NE 61 T. sp. F. P. Doerder 19175-1 Middle Bolton Lake, NE 62 T. sp. F. P. Doerder 19176-1 Middle Bolton Lake, NE
16 Table 1: continued.
No. Species Name Donor Strain ID Locality 63 T. sp. F. P. Doerder 19185-1 Little Pond, NE 64 T. sp. F. P. Doerder 19188-1 Hickory Hills Lake, NE 65 T. sp. F. P. Doerder 19189-1 Harbor Pond, NE 66 T. sp. F. P. Doerder 19192-1 Harbor Pond, NE 67 T. sp. F. P. Doerder 19193-1 Nashua River, NE 68 T. sp. F. P. Doerder 19194-1 Nashua River, NE 69 T. sp. F. P. Doerder 19196-1 Baddacook Pond, NE 70 T. sp. F. P. Doerder 19198-1 Baddacook Pond, NE 71 T. sp. F. P. Doerder 19200-1 Knops Pond, NE 72 T. sp. F. P. Doerder 19201-1 Knops Pond, NE 73 T. sp. F. P. Doerder 19204-1 Northwood Lake, NE 74 T. sp. F. P. Doerder 19206-1 Jenness Pond, NE 75 T. sp. F. P. Doerder 19206-4 Jenness Pond, NE 76 T. sp. F. P. Doerder 19208-1 Lovell Lake, NE 77 T. sp. F. P. Doerder 19221-3 Lees Pond, NE 78 T. sp. F. P. Doerder 19231-3 Elbow Pond, NE 79 T. sp. F. P. Doerder 19246-3 Reeds Marsh, NE 80 T. sp. F. P. Doerder 19250-1 Goose Pond, NE 81 T. sp. F. P. Doerder 19252-1 Kilton Pond, NE 82 T. sp. F. P. Doerder 19254-2 Eagle Pond, NE 83 T. sp. F. P. Doerder 19254-5 Eagle Pond, NE 84 T. sp. F. P. Doerder 19255-1 Pleasant Lake, NE 85 T. sp. F. P. Doerder 19256-1 Pleasant Lake, NE 86 T. sp. F. P. Doerder 19257-1 Chase Pond, NE 87 T. sp. F. P. Doerder 19257-2 Chase Pond, NE 88 T. sp. F. P. Doerder 19258-2 Chase Pond, NE 89 T. sp. F. P. Doerder 19259-1 Lake NH4A, NE 90 T. sp. F. P. Doerder 19260-1 Lake NH4A, NE 91 T. sp. F. P. Doerder 19261-1 LakeNH4A,NE 92 T. sp. F. P. Doerder 19262-1 McDaniels Marsh, NE 93 T. sp. F. P. Doerder 19263-1 Little Lake Sunapee, NE Table 1: continued.
No. Species Name Donor Strain ID Locality 94 T. sp. F. P. Doerder 19266-5 Todd Lake, NE 95 T. sp. F. P. Doerder 19267-1 Lake Massasecum, NE 96 T. sp. F. P. Doerder 19268-4 Lake Massasecum, NE 97 T. sp. F. P. Doerder 19269-1 Lake Massasecum, NE 98 T. sp. F. P. Doerder 19270-2 Gregg Lake, NE 99 T. sp. F. P. Doerder 19272-4 Peterson WMA, NE 100 T. sp. D. Zilberg Tet-NI (new) from guppy gill blood vessels 101 T. sp. D. Zilberg Tet-NI4 from guppy muscles 102 T. sp. D. Zilberg Tet-NI5 from guppy muscles 103 T. thermophila CCAP 1630/1M Lupton, AZ 104 T. thermophila CCAP 1630/1N Lupton, AZ 105 T. thermophila CCAP 1630/1P Lupton, AZ 106 T. thermophila CCAP 1630/1U Lupton, AZ 107 T. thermophila CCAP 1630/4A Unknown 108 T. tropicalis ATCC 30275 Rio Aquacate, Panama 109 T. tropicalis ATCC 30277 Rio Chagres, Canal, Panama 110 T. tropicalis ATCC 30352 ditch near E. Mante, Mexico 111 T. tropicalis ATCC 30353 Serro Compana, Panama 112 T. tropicalis ATCC 205044 Unknown 113 T. tropicalis , ATCC 205045 Rio Chav, Dominican Republic 114 T. tropicalis ATCC 205046 east 115 T. tropicalis ATCC 205047 Kaskasia River, Logan, IL 116 T. tropicalis ATCC 205084 strawberry farm, Davie, FL 117 T. tropicalis ATCC 205098 Kingston, RI 118 T. tropicalis ATCC 205101 Potawatomi Pond, Wheeling, IL 119 T. tropicalis ATCC 205103 Mayaquez, Puerto Rico
18 from Fisher Scientific, Ottawa, ON, Canada). All cultures were transferred to fresh media every two weeks using sterile techniques.
B.3 NUCLEIC ACID EXTRACTION
B.3.1 Nucleic acid extraction via Chelex®
The protocol of Walsh et al. (1991) was used on all isolates obtained from Dr. F.
P. Doerder. Approximately 1 mL of a Tetrahymena culture was placed in a sterile 1.5-mL microcentrifuge tube. The cells were pelleted by centrifugation for 5 min at 9,000 g. The supernatant was removed and the remaining cells were washed with .700 uL of distilled water (dH20). The cells were centrifuged again for 5 min at 9,000 g after which the supernatant was discarded. This step was repeated once more to ensure thorough washing of cells. Next, 100 uL of 5% (w/v) Chelex® (Sigma, Oakville, ON, Canada) were added to the microcentrifuge tube which was then shaken for 1 min on a vortex mixer and placed in a 56 °C water bath for 30 min. Following this, the sample was mixed on a vortex mixer for 30 sec and transferred to a 100 °C water bath for 8 min. The sample was then mixed using a vortex mixer for 30 sec and centrifuged for 3 min at 11,000 g. The samples were stored at -20 °C.
B.3.2 Nucleic acid extraction via MasterPure™ Kit
The MasterPure™ Total Nucleic Acid Purification kit (EPICENTRE, Madison,
WI, USA) was used on isolates of Tet-NI and Tetrahymena rostrata. Approximately 1 mL of Tetrahymena culture was placed in a sterile 1.5-mL microcentrifuge tube. The cells were pelleted by centrifugation for 5 min at 9,000 g and the supernatant was then
19 removed. DNA was then extracted from the pelleted cells using the MasterPure™ Total
Nucleic Acid Purification kit according to the manufacturer's specifications. The
extracted DNA was then stored at -20 °C.
B.4 POLYMERASE CHAIN REACTION (PCR)
The same conditions were used for all PCR reactions regardless of which primer pair was used: 2 uL of a DNA sample were added to tubes containing puReTaq Ready-
To-Go PCR beads (Amersham Biosciences, Baie d'Urfe, PQ, Canada) along with 0.5 uL
of 25 mM MgCl2, 2.5 uL each of 10 uM forward and reverse primers, and 17.5 \iL of
dH20 to bring the total reaction volume to 25 uL. The PCR reaction was performed in a
PerkinElmer GeneAmp 2400 thermal cycler (PE Applied Biosystems, Mississauga, ON,
Canada).
B.4.1 Amplification of cox-1
The -980 base pairs comprising the barcoding region of cox-1 in ciliates were amplified using either forward primer COI-FW (5'-
ATGTGAGTTGATTTTATAGAGCAGA-3') or forward primer 288 (5'-
TCAGGTGCTGCACTAGC-3') in combination with reverse primer Fol-B (5'-
TAAACTTCAGGGTGACCAAAAAATCA-3') or reverse primer COI-2003 (5'-
CCTGGAATACCATTCATTTTAGC-3') (C. Chantangsi, pers. comm.; Chantangsi et al.,
2007; Folmer et ah, 1994; Lynn and Striider-Kypke, 2006). The thermal cycler was programmed as follows: initial denaturation at 94 °C for 4 min; 5 cycles of denaturation at 94 °C for 30 sec, annealing at 45 °C for 1 min, and extension at 72 °C for 1 min and 45
20 sec; 35 cycles of denaturation at 94 °C for 30 sec, annealing at 55 °C for 1 min, and
extension at 72 °C for 1 min and 45 sec; final extension of all fragments at 72 °C for 10
min; maintain temperature at 4 °C until sample is removed.
B.4.2 Amplification of the SSUrRNA Gene Region (SGR)
The -2,800 base pairs comprising the SSUrRNA gene region (SGR), which
consists of SSUrRNA as well as ITS-1, 5.8S rRNA, ITS-2, and part ofLSUrRNA were
amplified using the forward primer A (5'-CAACCTGGTTGATCCTGCCAGT-3') and the
reverse primer C (5'-TTGGTCCGTGTTTCAAGACG-3') (Jerome and Lynn, 1996;
Medlin et ah, 1988). The thermal cycler was programmed as follows: initial denaturation
at 94 °C for 4 min; 40 cycles of denaturation at 94 °C for 45 sec, annealing at 55 °C for 1
min and 15 sec, and extension at 72 °C for 3 min; final extension of all fragments at 72 °C
for 10 min; maintain temperature at 4 °C until sample is removed.
B.5 GEL ELECTROPHORESIS
The PCR products were visualized by horizontal agarose gel electrophoresis to
assess the success of each PCR reaction. A 0.8% (w/v) agarose gel was fully submerged
in IX Tris-Acetate-EDTA (TAE) buffer in the horizontal gel apparatus (Sambrook et ah,
1989). Then, 2 uL of PCR product were combined with 2 uL of loading buffer and
loaded into the wells; 3 \\L of a 500-base pair molecular weight ladder (EPICENTRE,
Madison, WI, USA) were loaded into one well for fragment size reference. The gel was run for 25 min at 100 V. The gel was subsequently stained for 2 min in an ethidium bromide (EtBr) solution composed of 5 uL of 10 mg/mL EtBr and 100 mL of ddHaO, and
21 then destained in tap water for approximately 15 min. The gel was photographed using an
Alpha Innotech Alphalmager 3400 Gel Documentation & Analysis System UV transilluminator (Fisher Scientific, Ottawa, ON, Canada).
B.6 PURIFICATION OF PCR FRAGMENTS
Once a successful PCR reaction was confirmed via gel electrophoresis, 2 more replicates of this same PCR reaction were conducted and gel electrophoresis was used to confirm their success. Using 3 PCR replicates for purification via the QIAGEN MinElute
Gel Extraction kit (QIAGEN, Mississauga, ON, Canada) ensured that enough DNA was obtained from the purification procedure for successful sequencing. All three PCR products were combined with 15 uL of concentrated loading buffer in a sterile 1.5-mL microcentrifuge tube, after which they were loaded into 3 wells of a 0.8% (w/v) agarose gel and run at 100 V for 25 min. The gel was stained for 2 min in EtBr and destained in tap water for 15 min. The gel was observed under a 365 nm long-wave UV light and the
DNA band of the desired product was excised from the gel using a scalpel and placed in a sterile (pre-weighed) 1.5-mL microcentrifuge tube. DNA was then purified from the gel slice using the QIAGEN MinElute Gel Extraction kit according to the manufacturer's specifications. The only change made to the protocol was that the final elution step was repeated twice to increase the DNA yield within a 10 uL volume. The purified DNA was then stored at -20 °C.
22 B.7 DNA SEQUENCING
After DNA purification, the concentration and purity of the DNA sample was determined using an ND-1000 Spectrophotometer (NanoDrop, Wilmington, DE, USA).
All samples were submitted to the CBS-AAC Genomics Facility at the University of
Guelph, where they were sequenced in both the forward and reverse directions and resolved with an Applied Biosystems 3730 DNA Analyzer using the BigDye Terminator
Version 3.1 Cycle Sequencing kit (Applied Biosystems, Foster City, CA, USA). Ten picomoles of each primer were used in all reactions. The cox-1 gene was sequenced using
28 ng of template along with the PCR amplification primers. The SGR was sequenced using 84 ng of template along with the amplification primers A and C and the following internal primers to provide sufficient sequence overlap: forward primers 1055-F (5'-
GGTGGTGCATGGCCG-3') and 690F (5*-YAGAGGTGAAATTCT-3'), and reverse primers 690R (5'-AGAATTTCACCTCTG-3') and B (5'-
GATCCTTCTGCAGGTTCACCTAC-3') (Elwood et al., 1985; Medlin et al, 1988).
The resulting DNA sequences were first verified as ciliate sequences for the genes of interest using NCBI BLAST (http://blast.ncbi.nlm.nih.gov). The electropherograms were then trimmed, assembled, and edited using the program Sequencher™ version 3.1 (Gene
Codes Corporation, Ann Arbor, MI, USA). All sequences were aligned by eye using version 4.0 of the program Molecular Evolutionary Genetics Analysis (MEGA) (Kumar et al., 2004). All sequences were then uploaded to the Barcode of Life Datasystems
(BOLD) website to Project DLTH (Tetrahymenine Barcode Project) for future reference
(http://www.barcodinglife.org).
23 B.8 SEQUENCE DATASET CONSTRUCTION
B.8.1 cox-1 sequence dataset
The cox-1 gene sequence dataset consists of 184 sequences. Of these sequences,
62 were published by Chantangsi et al. (2007), and the remaining 122 were completed in the course of this study. These 184 sequences represent 36 nominal species of
Tetrahymena, undescribed "putative" new species of Tetrahymena, as well as isolates of
Glaucoma chattoni, Colpidium campylum, Colpidium colpoda, and Ichthyophthirius multifiliis. These sequences represent 144 different haplotypes. Most sequences were approximately 1,200 nucleotides (nt) in length. However, only 822 positions were present in all of the sequences, and so only these were considered in the analyses that follow.
B.8.2 SGR sequence dataset
The SGR gene sequence dataset consists of 88 sequences: 44 of these sequences were published by Chantangsi et al. (2007), 5 were sequenced by Undine Achilles-Day from the Culture Collection of Algae and Protozoa, and the remaining 39 were completed in the course of this study. These 88 sequences represent 36 nominal species of
Tetrahymena, undescribed "putative" new species of Tetrahymena, as well as isolates of
Glaucoma chattoni, Colpidium campylum, Colpidium colpoda, and Ichthyophthirius multifiliis. These sequences represent 56 different haplotypes. The previously published sequences are 1,639 nt in length as they do not contain ITS-1, 5.8S rRNA, and ITS-2. All new sequences are approximately 2,820 nt in length, thus representing the entire SGR.
24 B.9 PHYLOGENETIC TREE CONSTRUCTION
Four different methods were used to construct phylogenetic trees based on
sequence data: neighbour joining (NJ), maximum parsimony (MP), Bayesian inference
(BI), and maximum likelihood (ML). Ichthyophthirius multifiliis was used as an outgroup
in all tree construction methods as it is known to be distinct from the genus Tetrahymena,
but still close enough to effectively root trees as it is in the same subclass
(Hymenostomatia).
B.9.1 Neighbour Joining (NJ) trees
Genetic distances for both the cox-1 and SGR gene sequence datasets were
calculated using the Kimura 2-parameter (K2P) model, implemented in the DNADIST program in PHYLIP version 3.65 (Felsenstein, 2005; Kimura 1980). The NJ algorithm was then employed to construct a phylogenetic tree based on these distances with the
NEIGHBOR program in the PHYLIP package (Saitou and Nei, 1987). Additionally,
SEQBOOT was used to resample 1,000 datasets out of the original dataset by the bootstrapping method and CONSENSE in PHYLIP was used to create a consensus tree
(Felsenstein, 1985).
E.9.2 Maximum Parsimony (MP) trees
The construction of phylogenetic trees using the maximum parsimony algorithm was attempted using PAUP* version 4.0 for both the cox-1 and SGR sequence datasets
(Swofford, 2003). Autapomorphic characters were removed from the data and thus 378 and 121 parsimony-informative characters were used for the cox-1 and SGR datasets
25 respectively. The tree-bisection-reconnection (TBR) branch-swapping algorithm was used and species were added randomly (n =5). There were 568,002 and 33,816 most parsimonious trees for the cox-1 and SGR datasets respectively. Therefore, the analyses were not continued due to high computational time and because the consensus tree would very likely have extremely low resolution.
B.9.3 Bayesian Inference (BI) trees
For both the cox-1 and SGR datasets, a Bayesian inference of phylogenetic relationships using the Markov chain Monte Carlo (MCMC) method was created using
MrBayes version 3.1.2 (Huelsenbeck and Ronquist 2001; Ronquist and Huelsenbeck
2003). MODELTEST version 3.7, through the MTgui interface, was first used to select the most suitable nucleotide substitution models for the two datasets (Nuin, 2004; Posada and Crandall, 1998). For both datasets, the General-Time-Reversible model with invariable sites and gamma distribution (GTR+I+G) for nucleotide substitution was estimated to be the best model. The program performed two parallel runs and used the
Markov chain Monte Carlo (MCMC) method to compute the posterior probability out of
18,000,000 trees for the cox-1 dataset and 9,000,000 trees for the SGR dataset
(approximately 1,000,000 trees per 10 taxa used) while sampling every 500th generation.
The first 50% of trees were discarded as burn-in.
B.9.4 Maximum Likelihood (ML) trees
For both the cox-1 and SGR datasets, the PhyML version 3.0 online web server
(http://www.atgc-montpellier.fr/phyml/) was used to construct phylogenetic trees based
26 on maximum likelihood (Guindon and Gascuel, 2003; Guindon et ah, 2005). The nucleotide substitution models previously determined by MODELTEST version 3.7 were used again for this analysis. These models were GTR+I+G for both the cox-1 and the
SGR dataset. 500 bootstrap replicate resamplings were conducted. CONSENSE in
PHYLIP was used to create a consensus tree (Felsenstein, 1985).
B.10 SPECIES IDENTIFICATIONS
In general, three distinct criteria were used to distinguish between species. The first of these was morphological data.-Although silver staining or other morphological studies were not completed during the course of this study, a majority of the isolates were received from culture collections and thus had species identifications attached to them based on prior morphological work. These species identifications were used as the preliminary species identifier. After this, the next criterion used was that of average intraspecific sequence divergence for cox-1 sequences. If this value was less than 1% for a group of isolates, then these isolates were considered to be of the same species.
(Exceptions are discussed in greater detail in sections C.l and D.l.) After this, the third criterion employed was a confirmation of species identification based on the reciprocal monophyly ofSSUrRNA trees in comparison to cox-1 trees. If all three of these criteria were met, then a species identification was given to an isolate. Not all of these steps were taken for all of the isolates examined during this study, and so suggestions for future work have been made in section D.l.
27 C. RESULTS
C.l INTRASPECIFIC SEQUENCE DIVERGENCE VALUES
C.l.l Delineating known species of Tetrahymena
Pairwise sequence divergence estimates among seven isolates oil. multifiliis, two
isolates of Tetrahymena americanis, seven isolates of Tetrahymena borealis, seven
isolates of Tetrahymena canadensis, seven isolates of Tetrahymena hegewischi, two isolates of Tetrahymena limacis, two isolates of Tetrahymena mobilis, three isolates of
Tetrahymena patula, 11 isolates of J! pyriformis, four isolates of T. rostrata, three isolates of Tetrahymena sonneborni, nine isolates of T. thermophila, and 17 isolates of
Tetrahymena tropicalis were calculated using the K2P model for cox-1. The average values of sequence divergence including standard error are listed in Table 2 and the lower triangular matrices from which these calculations were obtained are shown in Appendix
1. The values range from 0% to 7.1% without any correlation with the geographic range of the samples. Only/, multifiliis, T. borealis, T. limacis, T. mobilis, T. patula and T thermophila have average intraspecific divergence values of <1%.
Sequence divergences among seven isolates of Tetrahymena canadensis, seven isolates of Tetrahymena hegewischi, four isolates of T. rostrata, nine isolates of T. thermophila, and nine isolates of Tetrahymena tropicalis were calculated using the K2P model for SGR. The average values of sequence divergence including standard error are listed in Table 3 and the lower triangular matrices from which these calculations were obtained are shown in Appendix 2. The values range from 0% to 1.6%. Species with low average intraspecific divergence for cox-1 (T. canadensis and T. thermophila) exhibit an
28 Table 2: Mean percentage intraspecific cox-1 sequence divergence values for 13 different Tetrahymena species based on K2P distance values. Standard error values are also listed. Asterisks indicate species in which an isolate was mislabeled but was grouped with the correct species for these calculations.
Number of Mean Intraspecific Species Standard Error Isolates Sequence Divergences Ichthyophthirius multifiliis 7 0.3% ±0.04% Tetrahymena americanis 2 5.3% n/a Tetrahymena borealis 7 0.5% ±0.13% Tetrahymena canadensis 7 2.0% ±0.40% Tetrahymena hegewischi 7 3.6% ±0.71% Tetrahymena limacis * 2 0% n/a Tetrahymena mobilis 2 0% n/a Tetrahymena patula * 3 0.2% ±0.10% Tetrahymena pyriformis 11 5.1% ±0.59% Tetrahymena rostrata 4 7.1% ±3.04% Tetrahymena sonneborni 3 3.1% ±0.74% Tetrahymena thermophila 9 0% n/a Tetrahymena tropicalis 17 6.2% ±0.25%
Table 3: Mean percentage intraspecific SSUrRNA sequence divergence values for five different Tetrahymena species based on K2P distance values. Standard error values are also listed.
Number of Mean Intraspecific Species Standard Error Isolates Sequence Divergences Tetrahymena canadensis 7 0% n/a Tetrahymena hegewischi 7 0.5% ±0.16% Tetrahymena rostrata 4 1.6% ±0.51% Tetrahymena thermophila 9 0% n/a Tetrahymena tropicalis 9 0.1% ±0.02%
29 average intraspecific divergence value of 0% for SGR. Interestingly, T. tropicalis has a low SGR average sequence divergence although its cox-1 average sequence divergence value is relatively high at 12.2%, although this may be because the outliers did not have
SGR sequenced.
C.2 PHYLOGENETIC ANALYSES
C.2.1 cox-1 trees
The relationships within the genus Tetrahymena as inferred from cox-1 sequences using NJ, BI, and ML approaches were all fairly well resolved (Figures 2, 3, and 4). The trees based on each of these methods are similar with respect to major groupings. There are several distinct, monophyletic species clusters on each of these trees: an/, multifiliis group, a Glaucoma group, a T. borealis group, a T. canadensis group, a T. hegewischi group, a T. rostrata group, and a T. thermophila group. It should be noted that the Tet-NI isolates, which were presumed to be species of Tetrahymena, group closely with I. multifiliis, which was not an expected result. Certain species consistently cluster together to form distinct groups. There is a T pyriformis-T. tropicalis-Tetrahymena setosa group, a T. tropicalis-Tetrahymena sp.-Tetrahymena Iwoffi-Tetrahymena furgasoni, and a T. tropicalis-T. mobilis group. There is also a cluster of environmental Tetrahymena: sp. samples (19072-1, 19166-3,19166-5, 19200-1, 19206-1, 19252-1, 19257-2, 19258-2,
19267-1, and 19269-1) that is distinct from all other groups and may thus represent a new species of Tetrahymena.
Ichihyophthirius multifiliis, which was used as the outgroup, as well as G. chattoni and C. campylum all clustered outside of a monophyletic Tetrahymena as sister
30 • L muHJIilib G6 theront > I. multiltliis G2 Iheront I. muSilKs G7 tharont
OSihoront multililiis G3 tharont T. ap. Tat-NW 1 T.sp.Tat-NW2 T. sp. Trt-N11 ,T.sp.T«t-NI2 muKifitiis G4 iheront
.... JX 205177 mmt T, empfriohytM 50595
T. canadensis 30325
// //
T. caudala 50087
Figure 2: A neighbour-joining tree inferred from 822 nt of 184 cox-1 gene sequences from Tetrahymena isolates. Bootstrap percentage values are listed at the nodes. Genetic distances calculated using the K2P model are indicated by branch length (scale bar = 0.1 nucleotide substitutions per site).
31 'l.muMiaG5 ttomnt
sp. Tet-NI4 2
p. Tsl-NI5 2
-#-
T. caudate 50087
. sp. 205079
Figure 3: A Bayesian inference tree using the GTR nucleotide substitution model inferred from 822 nt of 184 cox-1 gene sequences from Tetrahymena isolates. Posterior probability values are listed at the nodes. Branch lengths correspond to genetic distances (scale bar = 0.1 expected changes per site).
32 T.sp. Tet-NI2 T. sp. Tei-NI1 I. muKMIiis G3 theront I. muMmila OS thacont I. muItiHis G4 lharont
mimbres 30330 50402 , m 205065 T.sp.20504B - - T. patula 1630/2 T.llmacla 30771 T. patula 205177 _ T. empidokyre« 5£ .. jopicalis 205156 T. sp. 50403 jp. fet-SI T- amaricanis 205052 18272-4 T. heoewischl 30S33 19175-1
19246-3 19139-1 _ T. hegewlschi 205144 I T. hagawischi 30832 ri T. amorleanis 1630/7 A . T. hagawischi 205057 1 T. hagawischi 205056 * 19060-1 • T. pigrnantosa 3027S I -19175-1 1 I 19192-1 • T- . hyperangulari• • sa 330270 3 T.nTplsslngj 30637 T. nann«yi«X)71 T.sonnebornl 30S34 T. nanayi 30840 .. sonnaborni 205040 J. eosmopditanis 30324 T. sonnaborni 30835 T. nipissingi30B38
.. jalula S0064 T. limacis 1630/15 . asiatica 205167 T. Capricorn is 30290 - T. shanghaiansis 205C - T. auslralis 30271 tTsp. 205079 • f. caudata 500S7 ~. rostrata TR03 T. tostrala TR02 T. rostrata TROI
1630/1K T. two! 1630/1G . T. furgasoni 30006 .. tropicalis 205097 T.sp. T«t-CO(new] T -n. Tot-CO (original) r. tropicalis 30353 T.tropiealis 30352 .. troplcalis 30276 T.tropiealis 30277 T. tropicalis 30275 T. tropicalis 205084 T. tropicalis 205045 , T.tropiealis205101 . T. heqewwchi 20505a 19070-1 T.mobilis 1630/22 T.mobilisPRA-174 19261-1 - • ' " 205096 p. 205050
-&- T. canadensis 31
T. boraalls 30322 " 205054. 205131 1630/5B T.boraalls1630/5A T.borealis 30317 19221-3 19198-1 * T. borealis 30203 I 19194-1 1 19054-1 T. pyriformis 205038 • T. pyriformis 205061 pyrjformi 205063 mformis 205062 „...jrmis 30202 .. Jopicalis 205060 T. pyriformis 30327 T. pyriformis TR04 T. pyriformis 30039 19254-2 T. latosa 307S2 T. pyriformis 1630/1W T. pyriformis 30331 T. pyriformis 30005 19270-2 19266-5 19254-5 1-1 19193-1
30007 1640/4A 1630/1U 1630/1P 61975
19072-1 cwlissi 50086 T. bargerl 50985
muttifiliis G7 tharotit muSfiliis G2 tomont musiftliis G6 theront muMfillis G2 (heront sp. T«t-N(5 2 sp. Tet>NI5 1 sp. Tat-NW 1 sp. Tet-NW 2
Figure 4: A maximum likelihood tree using the GTR nucleotide substitution model inferred from 822 nt of 184 cox-1 gene sequences from Tetrahymena isolates. Bootstrap percentage values are listed at the nodes. Branch lengths correspond to genetic distances (scale bar = 0.1 expected changes per site).
33 taxa to the genus in all trees. Colpidium colpoda grouped as a sister taxon to the genus
Tetrahymena in all of the trees except for NJ, where this node was not well supported based on bootstrap values. The unknown species labeled ATCC #205080 grouped as the sister taxon to Glaucoma in all of the trees, thus suggesting that it may not belong to the genus Tetrahymena.
C.2.2 SGR trees
The phylogenetic relationships within the genus Tetrahymena as inferred from
SSUrRNA sequences using NJ, BI, and ML approaches were not as well resolved as indicated by the prevalence of low support values throughout the trees (Figures 5, 6, and
7). These three trees show some similarity to each other with respect to certain species clusters, but there are also several inconsistencies. The major species clusters present on all trees are as follows: an I. multifiliis group, a Colpidium group, a T. canadensis group, a T hegewischi group, a T rostrata group, a T. thermophila group, and a T. tropicalis group. However, these groups all have samples from other species mixed in with them, thus suggesting that some of these isolates could have been misidentified, or that too little genetic information was used.
Glaucoma chattoni, C. campylum, and C. colpoda all group outside of the
Tetrahymena clade as sister taxa in all trees. The Tet-NI isolates still group closely with I. multifiliis on these trees, thus suggesting that these isolates do not belong to the genus
Tetrahymena. ATCC #205080 also groups outside of the genus Tetrahymena in all of these trees, thus further suggesting that it does not belong to this genus. Notably,
Tetrahymena paravorax also groups outside of the genus although this was not apparent
34 Ichthyophthirius multifiliis • T. sp. Tet-NI5 99 • T. sp. Tet-NI4 • T. paravorax 205177 • Colpidium colpoda
• T. sp. 205080 • Glaucoma chattoni 100* — T. rostrataTROI 1001| ~ T. rostrata TR02 100 IT. rostrata TR03 »T. leucophrys 50069 T. silvana 50084 — T. vorax 30421 •• T. sp. Foissner T. setosa 30782 93 90 T. pyriformisGl-c T. bergeri 50985 T. corlissi 50086 »T. limacis 30771 T. canadensis 205143 T. canadensis 30368 T. canadensis 50098 T. sp. 205050 T. rostrata 30770 98 T. borealis 205012 T. canadensis 50097 201 T. canadensis 205113 T. canadensis 30325 T. canadensis 50099 T. mimbres 30330 T.sp. 50413 T. elliotti 205065 T. sp. 50402 T. mataccensis 50065 . thermophila 1630/1M 1001l T.thermophilI a 1630/1U 951T. thermophila 1630/1P T. thermophila 30009 T. thermophila 30377 40 T. thermophila 30007 T. thermophila 1630/4A T. thermophila 1630/1Q 39 T. thermophila B1975 j^ T. farieyi 50748 J^— T. trapicalis 205103 tropicalis 205045 hegewischi 205058 tropicalis 205101 T. tropicalis 205098 T. tropicalis 30352 T. tropicalis 30275 T. tropicalis 30353 T. mobilisPRA-174 T. furgasoni 30006 T. tropicalis 205044 61 T.sp.Tet-CO T. sp. Tet-NI (original) T. tropicalis 205047 T.lwoffM 630/1K » T. sp. Tet-RA9 T. sp. Brandl T. caudata 50087 T. sp.205048 T. Sp. Tet-SIN T. sp. 50403 T. empidokyrea 50595 T. sp. 205078 T. sp. 205079 T. sp.205049 p T. shanghaiensis 205039 T* T. australis 30831 T. patula 50064 T. sonneborni 205040 T. hyperangularis 30273 T. cosmopolitans 30324 60 T. nanneyi 30840 T. nipissingi 30837 T. pigmentosa 30278 T. capricomis 30291 33 IT. asiatica 205167 T. hegewischi 205144 T. hegewischi 205056 98 T. hegewischi 205057 T. hegewischi 30823 0.01 T. hegewischi 30833 T. hegewischi 30832 T. americanis 205052
Figure 5: A neighbour-joining tree inferred from 1,743 nt of 88 SSUrRNA gene sequences from Tetrahymena isolates. Bootstrap percentage values are listed at the nodes. Genetic distances calculated using the K2P model are indicated by branch length (scale bar = 0.01 nucleotide substitutions per site.
35 jlchthyophthirius multifiliis |L|T. sp Tet-NI5 pHT. sp. Tet-NI4
»T. paravorax 205177 ^ T. malaccensis 50065 ^^L T. thermophila 30007 ^7^B| T. thermophila B1975 T. thermophila 1630/1U sol T. thermophila 30377 T. thermophila 1630/1M T. thermophila 30009 T. thermophila 1630/1P T. thermophila 163071Q T. thermophila 1630/4A i_ f T. bergeri 50985 ^»" T. corlissi 50086 T.tropicalis 205103 T. farleyi 50748 T. hegewischi 205058 T. tropicalis 205045 T. tropicalis 205101 T. mobilis PRA-174 T. tropicalis 30352 T. tropicalis 205098 T. tropicalis 30275 i: T. tropicalis 30353 T. furgasoni 30006 T. rwoffl 1630/1K T. sp. Tet-RA T. sp. Tet-NI (original) 100 T. tropicalis 205044 T. tropicalis 205047 T. sp. Tet-CO (new) T. sp. Brandl T. borealis 205012 T. canadensis 205113 T. canadensis 50099 T. canadensis 205143 T. canadensis 30325 T. rostrata 30770 T. canadensis 30368 T. canadensis 50098 T. canadensis 50097 T. sp. 205050 T. limacis 30771 elliotti 205065 sp. 50402 .sp. 50413 T. mimbres 30330 T. leucophrys 50069 'T. setosa 30782 T. pyriformis GL-c T. sp. Foissner T. silvana 50084 T. vorax 30421 T. rostrata TR01 T. rostrata TR02 T. rostrata TR03 T. sp. 205048 T. empidokyrea 50595 T. sp. 50403 T. sp. 205078 T.sp. 205079 27 |- T. sp. Tet-SIN f- T. sp. 205049 T. hegewischi 205056 25 T. americanis 205052 T. hegewischi 30823 T. hegwischi 30832 T. hegwischi 30833 T. hegewischi 205057 T. hegewischi 205144 T. australis 30831 T. shanghaiensis 205039 T. asiatica 205167 T. capricomis 30291 i|»T| T. patula 50064 ^^9 L T. nanneyi 30840 46 "- T. hyperangularis 30273 T. cosmopolitanis 30324 T. sonnebomi 205040 T. nipissingi 30837 T. pigmentosa 30278 T. caudata 50087 T. sp. 205080 0.1 100 Glaucoma chattoni Colpidium campylum Colpidium colpoda
Figure 6: A Bayesian inference tree using the GTR nucleotide substitution model inferred from 1,641 nt of 88 SSUrRNA gene sequences from Tetrahymena isolates. Posterior probability values are listed at the nodes. Branch lengths correspond to genetic distances (scale bar = 0.1 expected changes per site).
36 T. caudata 50087 — T.sp. 205048 r T. leucophrys 50069 r T. silvana 50084 r T. tropicalis 205103 t T. farleyi 50748 J T. sp. Tet-NI (Original) 1T. sp. Brandl • T. sp. Tet-RA9 1 T. sp. Tet-CO 1 T. tropicalis 205047 1 T. tropicalis 205044 1 T. Iwoffi 1630/1K 1 T. furgasoni 30006 T. tropicalis 205098 T. tropicalis 30353 T. tropicalis 30352 T. tropicalis 30275 T. mobilis PRA-174 T. tropicalis 205101 T. tropicalis 205045 T. hegewischi 205058 J" T. corlissi 50086 ~ T. bergeri 50985 T. limacls 30771 rT.sp. 50413 (• T. mimbres 30330 ioo II IT. sp. 50402 •T. elliotti 205065 T. sp. 205050 T. rostrata 30770 T. canadensis 205143 T. canadensis 205113 T. canadensis 50099 T. canadensis 50098 T. canadensis 50097 T. canadensis 30368 T. canadensis 30325 T. borealis 205012 r T. malaccensis 50065 — T. thermophila 1630/4A 9t T. thermophila 1630/1U T. thermophila 1630/1Q T. thermophila 1630/1P T. thermophila 1630/1M T. thermophila 30377 T. thermophila 30009 T. thermophila 30007 T. thermophila B1975 • T. sp. Foissner • T. vorax 30421 T. setosa 30782 T. pyriformis GL-c rT. rostrata TRQ1 SCTT. rostrata TR03 t l"T. rostrata TR02 ' T. sp. Tet-SIN . •T. sp. 205049 T. patula 50064 T. sonnebomi 205040 T. pigmentosa 30278 T. nipissingi 30837 T. nanneyi 30840 T. hyperangularis 30273 T. cosmopolitanis 30324
• »T. shanghaiensis 205039 T. australls 30831 T. hegewischi 205144 T. hegewischi 205057 T. hegewischi 205056 T. hegewischi 30833 T. hegewischi 30832 T. hegewischi 30823 T. americanis 205052 T. capricomis 30291 T. asiatica 205167 T. sp. 205079 LT . sp. 205078 1T . sp. 50403 1 - T. empidokyrea 50595 ^_X"~ Glaucoma chattoni l_ J^^T.sp. 205080 jT^ Colpidium campylum Ichthyophthirius multifiliis ' **•* T. sp. Tet-NI4 T. sp. Tet-NI5 2J
Figure 7: A maximum likelihood tree using the GTR nucleotide substitution model inferred from 1,641 nt of 88 SSUrRNA gene sequences from Tetrahymena isolates. Bootstrap percentage values are listed at the nodes. Branch lengths correspond to genetic distances (scale bar = 0.1 expected changes per site).
37 on any of the cox-1 trees, and is thus likely an artifact of long-branch attraction. Except for this outlier, the rest of the genus is recovered as monophyletic.
A neighbour-joining tree based on ITS-2 sequences was poorly resolved in terms of deeper phylogenetic relationships, as indicated by poor bootstrap support of the major groupings (Figure 8). However, the several species that had good intraspecific sampling did form well-supported groups, with some support/bootstrap values nearing 100%.
These species are T. hegewischi, T. thermophila, T. tropicalis, and T. canadensis.
Although this tree contains a small number of isolates, the general species clusters are the same as the ones present in both cox-1 and SGR trees. Again, it is noteworthy that many of the isolates of T. tropicalis group together in a fairly well supported clade (Figure 8).
C.2.3 Identifying unknown isolates
Almost all of the environmental samples from Dr. F. P. Doerder as well as some of the unknown Tetrahymena species from the ATCC can be assigned to a species or a species cluster based on cox-1 sequences (Figures 2-4). Samples 19170-1, 19189-1,
19250-1, and 19256-1 group with G chattoni. Samples 19054-1, 19194-1, 19198-1 and
19221-3 group with T. borealis. Samples 19206-4, 19263-1, 19259-1, and ATCC
#205050 group with T. canadensis. ATCC #50402 groups strongly with Tetrahymena elliotti. Samples 19139-1, 19176-1, 19185-1, 19204-1, 19208-1, 19257-1, 19262-1,
19266-3,19268-4, and 19272-4 group with T. hegewischi. Samples 19175-1 and 19192-1 group with Tetrahymena hyperangularis. Samples 19158-1 and 19231-3 group with T sonneborni. Samples 19059-1, 19061-1, 19069-1, 19188-1, and 19196-1 group with T. thermophila. Samples 19255-1 and 19260-1 group with Tetrahymena vorax. Samples
38 Ichthyophthirius multifiliis T. sp. Tet-NI4 T. sp. Tet-NI5 • T.sp. 205080 T. sp. 50403 86| T. sp. 205079 711 T. sp. 205078 T. hegewischi 30833 100 T. hegewischi 30832 100 44 T. hegewischi 205144 T. hegewischi 205056 T. hegewischi 205057 T. rostrata TR01 T. sp. 205049 42 35l T. sp. 205048 T. thermophila 30009 T. thermophila B1975 100 T. thermophila 1630/1M T. thermophila 1630/1U T. thermophila 1630/4A T. thermophila 1630/1Q T. thermophila 30007 T. thermophila 1630/1P 70 T. tropicalis 205098 T. tropicalis 205044 T. tropicalis 205101 AT . hegewischi 205058 84 fee T. tropicalis 205045 T. tropicalis 30275 39 T. tropicalis 30353 T. tropicalis 205047 T. tropicalis 205103 I T.. spS| . 50413 27 83LT. sp. 50402 T. canadensis 205143 72 T. canadensis 50097
97 T. rostrata 30770 T. canadensis 205113 T. canadensis 50098 T. canadensis 50099 T. canadensis 30325
T. sp. 205050
Figure 8: A neighbour-joining tree inferred from 228 nt of 42ITS-2 gene sequences from Tetrahymena isolates. Bootstrap percentage values are listed at the nodes. Genetic distances calculated using the K2P model are indicated by branch length (scale bar = 0.1 nucleotide substitutions per site.
39 19193-1, 19201-1, 19234-5,19254-2, 19266-5, and 19270-2 group within the T. pyriformis-T. tropicalis-T. setosa species cluster. Samples 19070-1, 19169-1, and 19261-
1 group within the T. tropicalis-T. mobilis species cluster. ATCC #50403 groups with T.
tropicalis ATCC #205156, although this isolate does not group with the rest of T.
tropicalis and may thus be misidentified.
The SSUrRNA trees were also used to confirm these species identifications based
on the topologies of all three trees (Figures 5-8). As in the cox-1 trees, ATCC #205050
can now be identified as T. canadensis, and ATCC #50402 can now be identified as T.
elliotti. The ITS-2 tree was also able to further confirm the assignment of ATCC #205050 to T. canadensis and again assigns T. rostrata ATCC #30770 to this species.
40 D. DISCUSSION
D.l EFFICACY OF cox-1 BARCODING IN TETRAHYMENA
D.l.l Intraspecific sequence divergence values
In general, although there were some exceptions, most species examined in this
study had mean intraspecific divergence values of greater than 1% (Table 2). However,
this does not immediately suggest that cox-1 barcoding is not an effective tool for species
identification, especially when one takes a closer look at the trees. In actuality, the
majority of the species that exhibit sequence divergence values of greater than 1% are not
monophyletic, and it is the isolates that are outliers that have inflated the divergence
values. For example, one isolate of T. hegewischi, ATCC #205058, groups within the T.
tropicalis species cluster in all trees (Figures 2-8). If this isolate is removed from the T.
hegewischi dataset, the mean intraspecific divergence value of the species drops from an
average of 3.6% to 1.6%. Upon looking at the trees further, the large genetic distances between the isolates suggest that all samples may not be "true" representatives of T.
hegewischi. A similar case occurs in T. rostrata, the presence of ATCC #30770 increases the mean intraspecific divergence value for the species from 0.4% to 7.6%. Outliers such
as these cause the large mean intraspecific sequence divergence values, and have likely been misidentified, as will be discussed later on. It should be noted that an exception to this pattern is evident in T. canadensis. The large genetic distance between ATCC
#30325 and the rest of the isolates increases the mean intraspecific divergence value from
0.9% to 2.0%. However, this isolate is still clusters together with the other T. canadensis isolates on all of the trees (Figures 2-8), although the branch for this isolate is quite long on the cox-1 trees (Figures 2-4). This suggests that isolate ATCC #30325 may just be
41 highly divergent from the rest of the species and not just a misidentification. For this reason, it is possible that a mean intraspecific sequence divergence value of greater than
1% can exist within species. However, this cannot be confirmed until further work is done on ATCC #30325 to determine whether it is reproductively isolated from other T. canadensis isolates, or possesses morphological characteristics that would separate it from the rest of the species. Ideally, a greater amount of sampling should occur in the region of North Carolina where this isolate was first found (Table 1) in order to increase intraspecific sampling within the group represented by ATCC #30325.
Chantangsi et al. (2007) previously found T. pyriformis and T. tropicalis to exhibit high mean intraspecific divergence values of 5.01% and 9.07%, respectively, due to several outliers (Table 2). When I sequenced additional representatives of these species for this study, these values changed to 5.1% and 6.2% due to the "dilution" of the means with lower divergence values. However, the position of the outliers remained unchanged, thus confirming the results of Chantangsi et al. (2007) and further suggesting that these outliers are likely misidentified, possibly because they were previously classified using isozyme data. This points to the need to go back and re-confirm these species identifications based on sequencing, which can provide more accurate data about species relationships than previous isozyme studies. In turn, this will lead to better quality assurance and quality control when dealing with specimens obtained from culture collections, which has been a notable problem in the past (Cooper et al., 2007; Lorenz et al, 2005).
Intraspecific divergence values were calculated for T. americanis and T. sonneborni using sequences from two and three isolates, respectively. For T. americanis,
42 the high divergence value of 5.3% is due to the fact that CCAP #1630/7A groups strongly within the T. hegewischi species cluster on all trees (Figures 2-8). This strain has likely been misidentified and/or mislabeled over time. This result can be confirmed in the future by sequencing the ITS regions for this isolate to confirm its placement within T. hegewischi. Similarly, ATCC #30834, identified as T. sonneborni, groups strongly with
T. nanneyi on all trees (Figures 2-8), and away from the two other representatives of T. sonneborni. It is possible that this isolate has also been mislabeled, misidentified, or contaminated. This result can be confirmed in the future by sequencing the ITS regions to confirm its placement within T nanneyi. Increased intraspecific sequencing should be undertaken for species such as these where a small number of isolates were used to calculate mean intraspecific divergence values.
High mean intraspecific divergence values were used to confirm the mislabeling of T limacis CCAP #1630/16 and T patula CCAP #1630/2. These isolates were likely switched prior to labeling and shipment, as they group strongly with the opposite species on all of the cox-1 trees (Figure 2-5). If these isolates are grouped based on their original labels, the mean intraspecific sequence divergence values are 12.2% for T. patula and
7.6% for T. limacis. However, once the labels are corrected, these values both drop below the 1% threshold, thus classifying them as "unproblematic" species (Table 2). These results were confirmed by sequencing each isolate twice. As they were both received in the same shipment from the CCAP, and it is possible that their labels could have been switched before shipment. Obtaining new DNA extractions from the CCAP and re- sequencing cox-1 is an easy way of confirming these results.
43 All of the large mean intraspecific sequence divergence values are easily
explained when the common problem of mislabeling or misidentification of laboratory
strains is considered (Nanney, 2006). Despite all of these "problematic" groupings
discussed above, none of the species with substantial intraspecific sampling exhibit large
mean intraspecific sequence divergence values: intraspecific divergence values of less
than 1% are observed for/, multifiliis, T. borealis,T. Hmacis, T. mobilis, T.patula, and T.
thermophila (Table 2). This result is consistent with the fact that the cox-1 gene is
generally quite conserved within well defined-species of a genus. Thus, I conclude that
when species sampling is substantial and original species identifications are
unproblematic, the mean intraspecific divergence from cox-1 is typically less than 1%.
D.1.2 Using cox-1 to identify unknown isolates
As stated earlier, several of the environmental samples from Dr. F. P. Doerder can be assigned to species based on the results from the analyses of the cox-1 sequences.
These species identifications were also confirmed by using the 1% threshold for
intraspecific sequence divergence values. Since these isolates were only identified based
on cox-1 sequences, it is suggested that morphological analyses, such as silver staining to
examine taxonomic characters of interest, and further sequencing of ribosomal DNA
(SSUrRNA and/or ITS-2) should occur to confirm these identifications.
A significant number of samples received from the ATCC were only classified as
Tetrahymena sp. Based on both cox-1 and SSUrRNA sequences, some of these isolates can be classified to a species or a species cluster. ATCC #50402 groups strongly with T. elliotti on all of the cox-1 and SSUrRNA trees (Figures 2-8). ATCC #205050 groups
44 strongly with T. canadensis on all of the cox-1 and SGR trees, and thus belongs to this species (Figures 2-8). ATCC #50413 groups closest to T. mimbres on all of the SSUrRNA trees (Figures 5-7), but the cox-1 trees reveal that it is actually a distinct branch and thus likely a new species (Figures 2-4). However, ATCC #50402 and #50413 group together on the ITS-2 tree (Figure 8), which confirms that ATCC #50413 is more closely related to
ATCC #50402 than to any other isolate. Further morphological work should be done on all of these isolates to confirm these species identifications.
This study was also able to highlight a number of putative new species. ATCC
#205048, #205049, and #205079 all group distinctly from all other isolates on all of the cox-1 and SGR trees, and so they cannot be assigned to any known Tetrahymena species
(Figures 2-8). It is likely that these isolates represent novel species. These results can be confirmed by collecting more of these isolates for further study. ATCC #205078 similarly could represent a new species of Tetrahymena. A cox-1 sequence could not be obtained for this isolate by using any combination of primers, which raised the suspicion that it may not be a true Tetrahymena. However, it's SGR sequence indicates that this isolate is indeed a Tetrahymena that groups closest to ATCC #205079, thus suggesting that these two isolates may together represent a novel species (Figures 5-7). ATCC
#50403 groups strongly with T. tropicalis ATCC #205156 on all of the cox-1 trees
(Figures 2-4). Chantangsi et al. (2007) did not recognize T. tropicalis ATCC #205156 as the type culture for T. tropicalis, and since this isolate does not group closely to any other species except for ATCC #50403, it is possible that these two isolates together represent a new species. The last unknown Tetrahymena sp. isolate sequenced was ATCC
#205080. This isolate groups strongly outside of the genus Tetrahymena on all cox-1 and
45 SGR trees (Figures 2-8). Since it groups closest to G. chattoni, it may actually belong to the genus Glaucoma, or a related genus within the order Tetrahymenida. A further study of this isolate's morphological characters is suggested to confirm this result.
It has been shown in this study that cox-1 sequences can be used to assign unknown isolates to the species level. Barcoding using cox-1 sequences has been used successfully to do this in groups such as insects, bats, and fish (Armstrong and Ball,
2005; E. Clare, pers. comm.; Wong and Hanner, 2008). Although this is the first study of its kind to perform this task for the genus Tetrahymena, cox-1 barcoding has been used to successfully identify other protist species, such as red algae and diatoms, and more recently, the ciliate Paramecium (Barth et ah, 2006; Evans et al., 2007; Robba et ah,
2006; Saunders, 2005). Based on this evidence from other groups and the results of this study, it can be concluded that cox-1 barcoding is a promising tool for identifying unknown isolates of Tetrahymena to the species level.
D.1.3 Using cox-1 to delineate known species
cox-1 sequences were also to use to delineate known species of Tetrahymena and to clarify earlier misidentifications or culture contaminations. For example, it was previously suspected by Chantangsi et al. (2007) that ATCC #30770 is not a true representative of T. rostrata and is rather an isolate of T. canadensis, as assessed by cox-
1, SSUrRNA, and ITS-2 sequences. Furthermore, it does not display the characteristics typical of T. rostrata, which include a "rostrate" anterior end, an ovoid micronucleus, a caudal cilium, the formation of resting cysts, and a unique method of autogamy
(Chantangsi et al., 2007; Corliss, 1973). This suspicion further arose from the fact that
46 the provenance of this ATCC strain is complicated as it was transferred through several
labs known to culture other Tetrahymena species prior to its being deposited in the
ATCC: it could have become mislabeled or contaminated during this time (D. Lynn, pers.
comm.). Chantangsi et al. (2007) were not able to confirm this hypothesis without
receiving any environmental isolates of T. rostrata, and they suggested that this would be
necessary in the future. My study includes three such environmental isolates (TROl,
TR02, and TR03) obtained from Dr. R. Iglesias at the Universidad de Vigo in Spain.
These environmental isolates are known to exhibit characteristics typical of T. rostrata
and were found to be parasitizing snails, which has been reported for other confirmed
isolates of T. rostrata (Corliss 1973; R. Iglesias, pers. comm.; Nanney, 2006). Moreover,
they grouped very strongly together on all cox-1 and SGR trees (Figures 2-8). These
results suggest that the environmental isolates are truly representatives of T. rostrata,
while ATCC #30770 is an isolate of T. canadensis, as hypothesized by Chantangsi et al.
(2007). These identifications are considered to be accurate as they take all 3 criteria for
species identifications into account: cox-1 sequencing, ribosomal DNA sequencing, and
morphological work.
Chantangsi et al. (2007) also sequenced some unknown Tetrahymena isolates that
were found to parasitize fish. I received new isolates that were presumably from the same
species two years later from Dr. D. Zilberg at the Ben Gurion University of the Negev in
Israel. However, these new Tet-NI isolates grouped strongly with I. multijiliis on all cox-
1 and SGR trees (Figures 2-8). This result was puzzling at first, and so a second set of these isolates was received from Dr. Zilberg and sequenced, but they also grouped with I. multifiliis. This second set of isolates never came into contact with any /. multifiliis DNA
47 in the lab as they were received in culture and sterile lab techniques were strictly followed during the extraction to ensure that no contamination took place. During this time, the I. multifiliis DNA had already been stored at -80 °C for several months and thus was unlikely to have contaminated these samples. These results are particularly interesting, as these isolates are known to morphologically resemble Tetrahymena species and not I. multifiliis (D. Zilberg and M. Pimenta Liebowitz, pers. comm.). However, they are also known to parasitize fish in a manner similar to Ichthyophthirius species. For this reason, the sequencing results suggest that perhaps the morphology of these isolates has evolved to become more Tetrahymena-like or that these are "life cycle or developmental mutants" of I. multifiliis that grow and divide immediately rather than growing to a large size and falling off the fish to subsequently divide as does I. multifiliis (Lynn, 2008;
Small and Lynn, 2002). This hypothesis can be tested in the future by sequencing additional nuclear genes in the Tet-NI isolates such as the protein coding genes histone
H3 and H4 and comparing these published gene sequences from I. multifiliis to see whether the similarity remains consistent.
D.1.4 General comments
Although cox-1 has been shown to be an effective gene to use for species identifications in the genus Tetrahymena, this is not necessarily the case with all other ciliates. The primers used in this study are very effective at amplifying cox-1 in
Tetrahymena, as well as other members of the subclass Hymenostomatia, such as
Colpidium and Glaucoma, but they are not able to amplify cox-1 in any other subclass. In general, it is necessary to develop group-specific cox-1 primers for ciliates (E. Gentekaki
48 and M. Striider-Kypke, pers. comm.). However, developing these primers in other groups has not proven to be an easy task, especially in species that do not already have any published mitochondrial gene sequences (E. Gentekaki and M. Struder-Kypke, pers. comm.). For this reason, it may be more efficient to use a two-gene approach towards species identifications (see section D.2 below), especially until more group-specific cox-1 primers are developed.
D.2 SSUrRNA SPECIES CONFIRMATIONS
In general, the phylogenetic trees based on SSUrRNA sequences (Figures 5-7) reflected the major patterns shown in the cox-1 trees (Figures 2-4). However, the cox-1 trees provided better resolution at the species level, especially for relationships within species clusters. In fact, many of the species clusters present on the cox-1 trees were absent in the SSUrRNA trees, as this gene is not able to provide as fine a level of resolution as cox-1. This is reflected by the fact that species such as T. canadensis, T. hegewischi, and T. tropicalis showed little or no variation in their SSUrRNA sequences with intraspecific divergence values ranging from 0% to 1.6% (Table 3). However, these same isolates showed variation in their cox-1 sequences with intraspecific divergence values ranging from 2.0% to 6.2%, and were well supported with ITS-2 sequences. This suggests that cox-1 evolves more rapidly and is perhaps a better gene in terms of elucidating close species relationships, as more resolution is provided. This makes sense as the mitochondrial genome evolves faster than the nuclear genome, particularly in protists (Mcintosh et ah, 1998).
49 Moreover, SSUrRNA sequences have been shown to be more useful at deducing ancient relationships in such different groups as insects, fish, tetrapods, and other metazoans (Abouheif et al., 1998; Rokas et al., 2002; Zardoya and Meyer, 1996).
Similarly, SSUrRNA is an excellent tool for elucidating ancient phylogenetic relationships among microbial eukaryotes, and is currently the best-sampled gene due to its ease of amplification in these organisms (Lynn, 2008; Richards and Bass, 2005; Van de Peer and De Wachter, 1997). In my study, this is shown by the higher bootstrap support on the deeper clades of the SSUrRNA trees (Figures 5-7) in comparison to the cox-1 trees (Figures 2-4). This suggests that the ancient relationships are better supported through SSUrRNA sequence studies. For this reason, it is possible to use SSUrRNA to elucidate deeper relationships in Tetrahymena and other ciliates, after which one can use cox-1 to perform more precise species identifications.
D.3 ITS-2 AS AN ALTERNATE BARCODE
Due to the difficulty in establishing universal cox-1 primers for ciliates and several other groups of protists, it has been suggested that a two-gene approach to barcoding should be used (Evans et al., 2007, E. Gentekaki and M. Striider-Kypke, pers. comm.). In general, it has been suggested that a hierarchical two-gene approach should be used in which ribosomal DNA (such as the SSUrRNA gene) is first used to "narrow the field" and identify specimens to order, family, or even genus, after which the cox-1 gene can be sequenced to obtain a precise species identification. Recently, however, studies have focused on using ITS-2 sequences to perform species identifications (Coleman,
2005). There is already a well-established set of working primers for ITS-2, which
50 practically guarantees successful PCR amplification (M. Striider-Kypke, pers. comm.).
Thus, this fragment is easily amplified, and due to its small size of around 190 base pairs, it can be completely sequenced using only two primers. It is not as highly conserved as
ITS-1 and 5.8S, and thus more phylogenetic information can be extracted from these sequences. For these reasons, I suggest that future studies should emphasize the use of a complementary two-gene approach in which ITS-2 and cox-1 sequences are used together to provide more support for species identifications. ITS-2 is difficult to align among deeply divergent taxa, but the presence of universal primers makes it easier to produce these sequences.
With respect to this study, the phylogenetic relationships within the genus
Tetrahymena as inferred by ITS-2 sequences (Figure 8) were generally similar to those inferred by SSUrRNA sequences (Figures 5-7). This confirms that like SSUrRNA, ITS-2 can be used in order to elucidate phylogenetic relationships that go deeper than the species level. However, species with sufficient amounts of intraspecific sampling such as
T. hegewischi, T. thermophila, T. tropicalis, and T. canadensis were well-supported on this tree (Figure 8), thus suggesting that this gene is also good for species identification purposes, as previously maintained by Coleman (2005). Although ITS-2 is difficult to align among deeply divergent taxa, these sequences can be created easily due to the availability of universal primers, and the sequences can be used quite successfully to perform species identifications. Until cox-1 primers can be developed for other groups of ciliates besides Tetrahymena, it may be fruitful to explore using ITS-2 sequences for barcoding. In the future, ITS-2 barcodes can be used together with cox-1 barcodes to provide greater support for species identifications. In order to confirm this approach,
51 more intraspecific sampling should be undertaken in several different Tetrahymena species, such as T. borealis and T. pyriformis, as well as in other groups of ciliates and protists.
D.4 CONCLUDING REMARKS
The major purpose of this research project was to determine whether cox-J barcoding is an effective taxonomic tool for differentiating between closely related ciliate species. This technique has been shown to be effective, and thus DNA barcoding (using both cox-1 or ITS-2) can be used as an invaluable tool for protistologists in the future, and when used in conjunction with classical microscopy approaches for species identifications. Future work on barcoding in protists should place an emphasis on the further development of working cox-1 primers as well as an exploration of the efficiency and reliability of a two-gene barcoding approach. Additionally, more work should be done on developing single-cell PCR techniques in order to obtain sequence data from specimens that cannot be cultured in a laboratory environment.
The ability to perform fast and efficient species identifications is important to the field of ciliophorology, as there is still no consensus as to how many species of ciliates there are on Earth today, mainly due to the problem of recognizing species (Foissner et ah, 2008; Schlegel and Meisterfeld, 2003). It is important to be able to gain a better understanding of ciliate diversity, as ciliates are often significant components of all aquatic ecosystems (Weisse, 2006). Thus, by understanding ciliate diversity, we can better understand how aquatic food webs function, which is a large component of biodiversity as the majority of the Earth's surface is composed of water. A better
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69 F. APPENDICES
APPENDIX I: LOWER TRIANGULAR MATRICES FOR cox-1 All lower triangular matrices show percentages of pairwise intraspecific sequence divergence values based on Kimura-2 parameter model calculations.
1. Ichthyophthirius multifiliis [1] = I. multifiliis G2 theront [2] I. multifiliis G2 tomont [3] = J. multifiliis G3 theront [4] I. multifiliis G4 theront [5] = I. multifiliis G5 theront [6] J. multifiliis G6 theront [7] = J. multifiliis G7 theront
[1] [2] [3] [4] [5] [6] [7] [1] - [2] 0.1 - [3] 0.5 0.3 - [4] 0.6 0.5 0.3 - [5] 0.5 0.3 0 0.3 - [6] 0.2 0.1 0.5 0.6 0.5 [7] 0.1 0 0.3 0.5 0.3 0.1
2. Tetrahymena americanis [1] = T. americanis ATCC #205052 [2] T. americanis CCAP #1630/7A
[1] [2] [1] [2] 5.3 -
3. Tetrahymena borealis [1] = T. borealis ATCC #302023 [2] T. borealis ATCC #3 0317 [3] = T. borealis ATCC #30322 [4] T. borealis ATCC #205054 [5] = T. borealis ATCC #205131 [6] T. borealis CCAP #163 0/5A [7] = T. borealis CCAP #163 0/5B
[1] [2] [3] [4] [5] [6] [7] [1] [2] 1.4 - [3] 1.5 0.2 [4] 1.5 0.2 0.3 [5] 1.4 0 0.2 0.2 - [6] 1.4 0 0.2 0.2 0 [7] 1.4 0 0.2 0.2 0 0 -
4. Tetrahymena canadensis [1] = T. canadensis ATCC #30325 [2] T. canadensis ATCC #30368 [3] = T. canadensis ATCC #50097 [4] T. canadensis ATCC #50098 [5] = T. canadensis ATCC #50099 [6] T. canadensis ATCC #205113
70 [7] T. canadensis ATCC #205143
[1] C2] [3] [4] . [5] [6] [7] [l] [2] 4.5 [3] 5.2 1.2 [4] 4.5 0.5 0.7 [5] 4.2 0.5 1.2 0.5 [6] 5.4 1.4 2.2 1.4 1.2 [7] 4.5 0.5 0.7 0 0.5 1.4
5. Tetrahymena hegewischi [1] = T. hegewischi ATCC #30354 [2] T. hegewischi ATCC #3 0832 [3] = T. hegewischi ATCC #30833 [4] T. hegewischi ATCC #2 05 056 [5] = T. hegewischi ATCC #205057 [6] T. hegewischi ATCC #205058 [7] = T. hegewischi ATCC #205144
[1] [2] [3] [4] [5] [6] [7] [1] - [2] 0.7 -
[3] 0.6 0.6 - • [4] 3 .2 2 .4 3 .0 - [5] 3.2 2.4 3.0 0 - [6] 8.5 8.1 8.1 8.7 8.7 [7] 0.7 0 0.6 2.4 2 .4 8.1
6. Tetrahymena limacis [1] = T. limacis ATCC #30771 [2] 'T. patula" CCAP #1630/2
[11 [2] [1] - [2] 0
7. Tetrahymena mobilis [1] = T. mobilis ATCC #PRA-174 [2] T. mobilis CCAP #1630/22
[1] [2] [1] [2] 0
8. Tetrahymena patula [1] = "T. limacis" CCAP #1630/16 [2] T. patula ATCC #30769 [3] = T. patula ATCC #50064
[1] [2] [3] [1] [2] 0.3 [3] • 0 0.3 -
71 9. Tetrahymenapyriformis [I] = X. pyriformis ATCC #30005 [2] = X. pyriformis ATCC #30039 [3] = X. pyriformis ATCC #30202 [4] = X. pyriformis ATCC #30327 [5] = X. pyriformis ATCC #30331 [6] = X. pyriformis ATCC #205038 [7] = X. pyriformis ATCC #2 05061 [8] = T. pyriformis ATCC #205062 [9] = X. pyriformis ATCC #2 05063 [10] = X. pyriformis TR04 [II] = X. pyriformis CCAP #1630/lW
[1] [2] [3] [4]] [5]] [6] [7] [8] [9] [10] [11] [1] - [2] 0.2 - [3] 0 0.2 - [4] 0 0.2 0 - [5] 0 0.2 0 0 - [6] 8.1 8.2 8.1 8.1 8.1 - [7] 9.8 10.0 9.8 9.8 9.8 8.8 - [8] 8.2 8.4 8.2 8.2 8.2 7.4 5.7 - [9] 8.2 . 8.4 8.2 8.2 8.2 7.4 5.7 0 - [10] 0.6 0.8 0.6 0.6 0.6 8.4 9.8 8.6 8.6 [11] 0 0.2 0 0 0 8.1 9.8 8.2 8.2 0.6
10. Tetrahymena rostrata [1] = X, rostrata ta ATCC #30770 [2] = X. rostrata TROl [3] = X. rostrata ta ATCC TR02 [4] = X. rostrata TR03
[1] [2] [3] [4] [1] [2] 13.7 [3] 14.1 0.4 [4] 13.9 0.2 0.2
11. Tetrahymena sonneborni [1] = X. sonneborni ATCC #3 0834 [2] = X. sonneborni ATCC #30835 [3] = X. sonneborni ATCC #205040
[1] [2] [3] [1] [2] 3.6 ['3 ] 4.0 1.6
12. Tetrahymena thermophila [1] = X. thermophila B1975 [2] = X. thermophila ATCC #30007 [3] = X. thermophila ATCC #30009 [4] = X. thermophila ATCC #30377 [5] = X. thermophila CCAP #1630/11M [6] = X. thermophila CCAP #1630/1N [7] = X. thermophila CCAP #1630/1IP [8] = X. thermophila CCAP #1630/lU [9] = X. thermophila CCAP #1630/4A
72 [1] [2] [3] [4] [5] [6] [7] [8] [9] [1] - [2] 0 - [3] 0 0 - [4] 0 0 0 - [5] 0 0 0 0 - [6] 0 0 0 0 0 - [7] 0 0 0 0 0 0 - [8] 0 0 0 0 0 0 0 [9] 0 0 0 0 . 0 0 0
13. Tetrahymena tropicalis [I] = T. tropicalis ATCC #30005 [2] = T. tropicalis ATCC #30039 [3] = T. tropicalis ATCC #30202 [4] = T. tropicalis ATCC #30327 [5] = X. tropicalis ATCC #30331 [6] = T. tropicalis ATCC #205038 [7] = T. tropicalis ATCC #205061 [8].= T. tropicalis ATCC #205062 [9] = T. tropicalis ATCC #205063 [10] = T. tropicalis TR04 [II] = T. tropicalis ATCC #205063 [12] = T. tropicalis TR04 [13] = T. tropicalis ATCC #205063 [14] = T. tropicalis TR04 . [15] = T. tropicalis ATCC #205063 [16] = X. tropicalis TR04 [17] = T. tropicalis ATCC #205063
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [1] - • [2] 0 - [3] 0 0 - [4] 3.3 3.3 3.3 [5] 2.6 2.6 2.6 3.3 [6] 7.2 7.2 7.2 7.9 6.8 [7] 3.3 3.3 3.3 4.0 3.4 6.0 [8] 5.8 5.8 5.8 6.2 5.8 2.8 4.4 [9] 6.7 6.7 6.7 7.0 6.1 5.5 5.8 3.4 [10]8.4 8.4 8.4 8.1 8.3 7.9 8.3 7.4 8.1 [11]6.5 6.5 6.5 6.9 6.2 2.8 5.6 1.5. 3.9 7.7 [12]3.5 3.5 3.5 4.1 4.0 6.2 0.5 4.6 6.0 8.4 5.8 [1316.5 6.5 6.5 6.5 6.5 2.8 5.3 1.5 3.9 7.7 0.6 5.1 [14]5.1 5.1 5.1 5.5 5.3 6.0 4.6 4.8 5.3 8.3 5.8 4.4 5.5 [15]4.1 4.1 4.1 5.2 4.6 6.3 3.5 5.1 5.6 8.1 6.0 3.3 6.0 4.6 [1618.1 8.1 8.1 8.3 7.9 6.0 6.7 4.3 6.7 9.5 4.6 7.2 4.6 6.3 7.2 [17]12.7 12.712.7 12.3 12.9 13.0 12.1 11.5 11.0 12.5 12.7 12.3 12.3 11.2 12.5 11.4
73 APPENDIX II: LOWER TRIANGULAR MATRICES FOR SSUrRNA All lower triangular matrices show percentages of pairwise intraspecific sequence divergence values based on Kimura-2 parameter model calculations.
1. Tetrahymena canadensis [1] = • T. canadensis ATCC #30325 [2] T. canadensis ATCC #3 03 6 8 [3] = T. canadensis ATCC #50097 [4.] T. canadensis ATCC #50098 [5] = T. canadensis ATCC #50099 [6] T. canadensis ATCC #2 05113 [7] = T. canadensis ATCC #205143
[1] [2] [3] [4] [5] [6] [7] [1] - [2] 0 - [3] 0 0 - [4] 0 0 0 - [5] 0 0 0 0 -. [6] 0 0 0 0 0 [7] 0 0 0 0 0 0 -
2. Tetrahymena hegewischi [1] = T. hegewischi ATCC #30354 [2] T. hegewischi ATCC #30832 [3] = T. hegewischi ATCC #30833 [4] T. hegewischi ATCC #205056 [5] = T. hegewischi ATCC #205057 [6] T. hegewischi ATCC #205 058 [7] = T. hegewischi ATCC #205144
[1] [2] [3] [4] [5] [6] [7] [1] - [2] 0 - [3] 0 0 - [4] 0 0 0 - [5] 0 0 0 0 - [6] 1.7 1.6 1.6 1.6 1.6 [7] 0 0 0 0 0 1.6
3. Tetrahymena rostrata [1] = T. rostrata ATCC #30770 [2] = T. rostrata TROl [3] = T. rostrata. ATCC TRQ2 [4] = T. rostrata TR03
[1] [2] [3] [4] [1] [2] 2.7 [3] 2.8 0.7 [4] 2.6 0.6 0.1
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