© 2004 Society of Systematic Biologists
Cytogenetics and Cladistics
Edited by Jack Sites: Associate Editor
1 Muséum National d'Histoire Naturelle, Laboratoire Origine, Structure et Evolution de la Biodiversité 55, rue Buffon, F75005 Paris, France
2 University of Stellenbosch, Evolutionary Genomics Group, Department of Zoology Private Bag X1, Matieland 7602, South Africa; E-mail: dobigny{at}sun.ac.za
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Abstract |
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 TopNotes Abstract Assessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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Chromosomal data have been underutilized in phylogenetic investigations despite the obvious potential that cytogenetic studies have to reveal both structural and functional homologies among taxa. In large part this is associated with difficulties in scoring conventional and molecular cytogenetic information for phylogenetic analysis. The manner in which chromosomal data have been used by most authors in the past was often conceptionally flawed in terms of the methods and principles underpinning modern cladistics. We present herein a review of the different methods employed, examine their relative strengths, and then outline a simple approach that considers the chromosomal change as the character, and its presence or absence the character state. We test this using one simulated and several empirical data sets. Features that are unique to cytogenetic investigations, including B-chromosomes, heterochromatic additions/deletions, and the location and number of nucleolar organizer regions (NORs), as well as the weighting of chromosomal characters, are critically discussed with regard to their suitability for phylogenetic reconstruction. We conclude that each of these classes of data have inherent problems that limit their usefulness in phylogenetic analyses and in most of these instances, inclusion should be subject to rigorous appraisal that addresses the criterion of unequivocal homology.
Keywords: Chromosomal data; evolution; phylogenomics; phylogeny
Received July 11, 2003; Revised November 16, 2003; Accepted January 15, 2004
Phylogenetics has had a significant impact on the development of evolutionary biology and especially so following the widespread incorporation of cladistic concepts and methods (Hennig, 1966). However, cytogenetic data have rarely been included in cladistic studies designed to retrieve information on the phylogenetic relationships of taxa and this has contributed to the lack of debate on their appropriateness for these types of investigations. That cladistic concepts and their rigorous application to cytogenetic data have almost been completely ignored is surprising given that chromosomes are hereditary elements of the whole nuclear genome, are independent units of mutation, and consequently meet important conditions for inclusion as characters in phylogenetic investigations. Moreover, chromosomal changes are discrete events, thus offering "a large cadre of cladistic characteristics which combine the advantages of previous molecular and morphological evolutionary tracks" (O'Brien et al., 1999).
We present herein a critical review of the use of chromosomal information in phylogenetic studies and propose a simple method for coding chromosomal data in accordance with the principles of cladistics. This protocol is subsequently tested using different data sets and we compare the recovered relationships to those obtained from morphoanatomical- and molecular-based investigations of the species groups under consideration. Finally, unique cytogenetic features, such as B-chromosomes, heterochromatic additions/deletions, and nucleolar organizer region (NOR) number and location, are discussed with regard to the problems they present for phylogenetic analysis.
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Assessing Primary Homology |
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TopNotesAbstractAssessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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Mitotic chromosomes offer an unique opportunity to observe the nuclear genome by microscopic means, allowing scrutiny of its components individually, as well as globally (the karyotype). Given that karyotypes provide a phenotypic view of the genotype, it is not surprising that comparative chromosome analysis found early use for understanding the systematic and phylogenetic relationships of species, as well as for providing insights to the possible mechanisms underpinning speciation (Kolt'sov, 1922; Gates, 1925; Lewitsky, 1925, 1931). Due to their Mendelian patterns of inheritance, it is possible to detect synapomorphies (character states that are shared due to common ancestry) and identify sister-group relationships among taxa (Hennig, 1966). These characteristics contribute to make chromosomal structural mutations powerful markers in modern phylogenetic investigations (Rare Genomic Changes sensu Rokas and Holland, 2000).
Prior to the application of chromosome banding and the new molecular cytogenetics techniques, chromosomal information for the study of phylogenetic relationships was restricted to distance analysis (see examples in Seberg and Petersen, 1998). This was done using various numerical and metric values that describe the karyotype such as diploid number (number of chromosomes or 2n) and fundamental number (number of chromosomal arms or Nfa). However, it is obvious that these values can be identical simply by chance and, if interpreted in a phylogenetic context, may be spurious indicators of relatedness (Hood et al., 1984; Ortells, 1995; Graphodatski et al., 2001). This is further underscored by an extensive literature that shows that karyotypes that are morphologically similar after conventional staining may be very different when banding patterns are compared (Insectivora: Searle and Wocjik, 1998; Nachman and Searle, 1995, Dobigny et al., 2002; Megachiroptera: Hood and Baker, 1986; Marsupialia: Glas et al., 1999).
Bianchi and Merani (1984) proposed a cytogenetic distance concept based on diploid and autosomal numbers, total chromosome length, and some structural changes (for example, heterochromatin/euchromatin ratio). This approach suffers, however, from several weaknesses the most important being that it reduces complex information to a few features. Additionally, quantitative and qualitative karyotypic features cannot be combined to obtain a mathematically rigorous calculation of distance, leading Ivanitskaya (1991) to propose a strictly quantitative distance measure based on relative length of the chromosomes and the global descriptors 2n and Nfa when constructing a phenogram.
Ploidy levels are sometimes used to compare species and/or races (mainly in plants) in a phyletic framework (Saideswara et al., 1989). Polyploidization can result from genome duplication (autopolyploidization; Mahy et al., 2000) or by hybridization (allopolyploidization; White and Contreras, 1979; Nelson and Elisens, 1999). In spite of the evolutionary importance of some polyploidization events (King, 1993), the usefulness of ploidy levels for assessing cladistic relationships is limited. Indeed, even if these changes are considered as rare (Rokas and Holland, 2000), convergence in ploidy may not be that uncommon (Sharbel and Mitchell-Olds, 2001). Moreover, genomes that are autopolyploid tend to revert to the diploid level, thus complicating the understanding of polyploidization patterns (King, 1993). They suffer from an additional shortcoming because primary homologies are often difficult to infer. However, the recently developed genomic in situ hybridization (GISH) technique holds promise as a powerful tool for investigating the evolution of polyploid organisms (D'Hont et al., 2002) and future work in this direction will likely reflect this.
It has also been proposed that it is possible to assess homology among chromosomes or chromosome segments by studying their pairing at meiosis in hybrids (reviewed in Seberg and Petersen, 1998). However, although clearly limited to taxa that hybridize, heterosynapsis (heterologous adjustments at meiosis) has been documented in many groups (De Wet and Harlan, 1972; Ashley et al., 1981; Borodin et al., 1990; Hale et al., 1991), a factor that will certainly confound any assessment of true homology (De Wet and Harlan, 1972).
Whatever method is adopted in constructing the phylogeny, a phenetic approach does not allow a return to the actual character used (Seberg and Petersen, 1998). This precludes inferences on ancestral karyotypes, which is limiting in chromosomal speciation studies where the detection of rearrangement at the origin of a given dichotomy is often of primary interest (Volobouev et al., 2002). In contrast to phenetic methods, however, the application of the parsimony principle is more appropriate for chromosomal data because rearrangements are discrete entities that can be assessed as homologous in each taxon and are transmitted in a Mendelian fashion (Farris, 1978; Qumsiyeh and Baker, 1988).
Modern cytogeneticists have at their disposal several powerful and routinely used tools to investigate chromosomal homology. Foremost among these are differential staining techniques that reveal a succession of bands along the length of a chromosome that vary in width and staining intensity. These bands reflect intrinsic properties of the genome (reviewed in Sumner, 1990, and Sessions, 1996). For instance, GTG- and RHG-bands correspond to euchromatic regions that are composed of particular isochores and types of repeated sequences (Schmid and Guttenbach, 1988; Korenberg and Rykowski, 1988; Boyle et al., 1990; Bernardi, 1993). These banding patterns are usually invariant between homologous chromosomes of a specimen. In many assemblages they may be readily used for comparison among specimens of different populations or different species, and are consequently useful for assessing primary homology at the chromosomal level. In addition, banding techniques allow access to information involving both structural (GTG-, RHG-, and CBG-banding) and functional patterns (replication RBG-banding; Viegas-Péquignot and Dutrillaux, 1978) of chromosomes. Lastly, the development of in situ hybridization, and in particular fluorescent in situ hybridization (FISH) using chromosome painting probes (Ferguson-Smith, 1997; Ried et al., 1998), has provided evidence that homology in banding patterns is significantly related to homology in synteny conservation and gene content. This does not extend to gene order that lies beyond the level of resolution routinely provided by chromosome painting. As a consequence, banding techniques are often quite reliable for assessing primary homologies between relatively closely related species and, in these instances, can provide accurate data for studies of genome evolution.
Nevertheless, banding techniques are limited in their abilities to assess homology within and across species in instances where karyotypes are highly rearranged, making the determination of homology ambiguous at best (Robinson, 2001). Although this often holds for evolutionarily distantly related organisms, it is also true for groups undergoing extensive chromosomal evolution (Chiroptera, Emballuronidae: Hood and Baker, 1986; Carnivora, Ursidae: Nash et al., 1998; Primates, Hylobates spp.: Nie et al., 2001). Banding also has limited application in vertebrates such as fishes, some reptiles, amphibians, and plants (reviewed in Schmid and Guttenbach, 1988; Bernardi, 1993), which led to the adoption of an alternative strategy to define chromosomal characters in absence of bands, for example, in lizard species (Flores-Villela et al., 2000). The development of powerful molecular cytogenetic and genomic technologies such as FISH, flow-cytometry, and chromosome painting coupled with gene mapping can overcome the limitations of conventional banding analyses (Ferguson-Smith, 1997; Wienberg and Stanyon, 1997; Chowdhary et al., 1998; Ried et al., 1998; O'Brien et al., 1999; Yang et al., 1999, 2000b; Murphy et al., 2001; Robinson et al., 2002). Based on the hybridization between labelled DNA probes and genomic DNA, in situ hybridization techniques allow for the unequivocal confirmation of homology among chromosomes. Additionally, an even more precise assessment of structural rearrangements is provided by the use of reciprocal cross-species painting schemes (Yang et al., 2000; Wienberg and Stanyon, 1997). Moreover, the orientation of the hybridized fragments can be detected using CO-FISH (hybridization of single-stranded probes; e.g., Robinson et al., 1998; Garagna et al., 2001), thus providing an additional tool for detecting inverted chromosomal segments. As a consequence, molecular cytogenetics makes it possible to assess homologies between distantly related taxa and this creates new opportunities for determining chromosomal relationships at higher taxonomic levels (Frönicke et al., 2003; Yang et al., 2003).
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Rooting |
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TopNotesAbstractAssessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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A Priori Polarization
Most studies dealing with evolutionary cytogenetics are restricted to comparisons of a limited set of karyotypes. When assessed, the chromosomal correspondence between cytotypes is often subjective, resting on assumptions and methods that are not clearly defined by the authors and with no or little discussion on the polarity of the chromosomal rearrangements. For an example, centromeric fusions (also called Robertsonian translocations) are usually considered more likely than fissions but with little thought given to alternative possibilities. Yet, even if less frequent than fusions, the evidence in support of a role for fissions in karyotype evolution is accumulating (Plants: Hall and Parker, 1995; Arthropoda, Hymenoptera: Rousselet et al., 2000; Mammalia, Canidae: Graphodatski et al., 2001; Mammalia, Primates: Finelli et al., 1999) and it has been suggested they may even play a causative role in speciation (Imai et al., 2001). As a consequence, the a priori acceptance of a fusion (leading to a biarmed product and the concomitant reduction in 2n) or fission (leading to two independent acrocentrics and an increase in 2n) when comparing two or several taxa without the use of an appropriate outgroup is flawed.
The Ancestral Karyotype
One of the most common features of phylogenetic studies dealing with chromosomes is to posit an ancestral karyotype (e.g., Neusser et al., 2001) for the group concerned. Indeed, this is often essential for interpreting various aspects of chromosomal evolution. Several authors refer to ancestral states (and thus to an ancestral karyotype) even if they are not explicitly formulated, whereas others often base these on inappropriate methods, intuition or ad hoc assumptions. We discuss these in detail below.
Many authors have reconstructed ancestral karyotypes by assuming that the most frequently encountered chromosomal configurations among the karyotypes compared were, by default, also present in the karyotype of the group's last common ancestor (Dutrillaux et al., 1978; Dutrillaux and Couturier, 1981; Viegas-Péquignot et al., 1983; Rofe and Hayman, 1985; Dutrillaux, 1988; Nash et al., 1998; Richard et al., 2000; Yang et al., 2003). In addition to the obvious fact that commonality does not necessarily translate to ancestral, this method is irrelevant as it is fully dependent on taxon representation (Qumsiyeh and Baker, 1988). As an example, Dutrillaux and Couturier (1981) proposed an ancestral karyotype for the Platyrrhinii monkeys. However, the study of five additional genera resulted in the modification of their earlier conclusions (Dutrillaux, 1988; see Dutrillaux et al., 1978 and Benazzou et al., 1984, for similar examples). Moreover, this approach is totally unreliable from a systematic point of view as it implies an a priori knowledge of which species to include in the group for which the ancestral karyotype is to be reconstructed. By using this approach, monophyly is never tested but becomes an ad hoc hypothesis. An additional approach used by Dutrillaux (1986) is to identify chromosomal homologies which are then used to "climb" the tree from the supposed ancestral to the most derived karyotypes. This wholly intuitive approach suffers from obvious circularity as the specific karyotypes are "first [used] to reconstruct the ancestral karyotype and then [used to] propose the sequence of rearrangements which occurred between this ancestral karyotype and that of living species" (Viegas-Péquignot et al., 1983).
The Use of the Outgroup Criterion
With the approaches discussed above, no coding is used, and no real test is performed. A topology is simply proposed and no alternative scenario is considered. This may be due to the widely held view that convergence and reversals are rare in chromosomal evolution (Farris, 1978) and consequently that character states can easily be identified using an intuitive approach. Furthermore, when homoplasic events are encountered by the "climbers", these are explained by reverting to ad hoc assumptions about the process involved (Dutrillaux et al., 1978, 1980; Benazzou et al., 1982; Dutrillaux, 1986; Richard and Dutrillaux, 1995; Richard et al., 2000). On the contrary, there is clear evidence that chromosomal evolution can sometimes be homoplasic (inversions in Anopheles spp.: Green, 1982; Caccone et al., 1998; Robertsonian translocations in gerbilline rodents: Qumsiyeh et al., 1987; heterochromatic variation and X chromosome morphology in arvicoline rodents: Iwasa and Suzuki, 2002). Indeed, identical chromosomal rearrangements can arise and be fixed independently in isolated populations, for example the well-documented cases of Robertsonian translocations in European Mus m. domesticus (Corti et al., 1986; Nachman et al., 1994; Riginos and Nachman, 1999) and Sorex araneus populations (Searle, 1993; Searle and Wojcik, 1998). As with morphological and molecular data, homoplasic chromosomal rearrangements can be identified by phylogenetic approaches that do not make ad hoc assumptions on the polarization of the karyotypic changes and/or the processes involved.
Cladistics provide a rigorous methodological framework for inferring plesiomorphic/apomorphic conditions and there is no reason for not analyzing chromosomal data in accordance with these concepts (Hennig, 1966, pp. 115–116; Farris, 1978). As pointed by Qumsiyeh and Baker (1988) the polarization of each character must be inferred a posteriori, using the criterion of outgroup analysis; the ontogenetic and paleontologic criteria (Hennig, 1966) clearly do not apply to comparative cytogenetic data. In addition, the cladistic approach allows one to identify ancestral karyotypes for species groups by returning to the character states defining each node of the ingroup (see De Oliveira et al., 2002). The use of ancestral karyotypes to root phylogenetic trees (e.g., Nie et al., 2002), or to infer higher phylogenetic relationships, too may be misleading. Moreover, any false interpretation in the composition of the presumed ancestral karyotype cannot be tested independently when working at higher phylogenetic levels since the errors are additive. However, any potential bias may be easily avoided as computational algorithms developed for phylogenetic reconstruction allow for the input of the original data in the analysis. In other words, specific karyotypes of the component species can be used as input rather than the ancestral karyotypes of higher groups.
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Encoding of Chromosomal Data |
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TopNotesAbstractAssessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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Both the defining and coding of phylogenetic characters have been extensively debated in the last decades (reviewed in Hawkins, 2000). Yet theoretical discussions dealing with chromosomal data have been rare (for an exception see Borowik, 1995) and, as a result, there is a lack of consensus on how these data should be encoded.
In most studies that use cytogenetic markers for the constructing phylogenies, authors present phylogenetic relationships without any explicit phylogenetic treatment. In few cases, a detailed list of characters and the character matrix are presented (see below) and from these two approaches can be distinguished. First, the chromosomes, or chromosomal segments are treated as the characters, and their presence/absence or the changes they have undergone represent the character states. Secondly, the chromosomal changes themselves are considered to represent the characters. We discuss these two philosophical approaches and attempt to illustrate why the latter is the only justifiable methodology applicable to both classic and molecular cytogenetic data sets.
When Chromosomes and/or Chromosomal Segments are the Characters
Although some studies have attempted a rigorous coding of chromosomal data, in most instances the definition used to describe the characters and character states is flawed. For example, entire chromosomes (Modi and O'Brien, 1987) or chromosomal segments (Ortells, 1995) have been used as characters and their presence/absence taken as the character states. In addition to the poor resolution inherent in cytogenetic studies, especially when entire chromosomes are considered, both cases lead to their overweighting because the characters are not independent. By way of example, where two chromosomes A and B are fused, a single evolutionary event (the fusion) may be coded three times for the presence/absence of A, B, and (A + B), respectively. In order to avoid this, some authors (Modi, 1987; Ortells, 1995) have attempted computational optimisation using parsimony. Although this reduces the effect of overweighting, it may be problematic with complex data sets in which many small chromosomal fragments are involved in several different, and sometimes successive, rearrangements. Of course, the same kind of bias is theoretically introduced when entire chromosomes or chromosomal fragments are used as the characters, and the mutations (fusions, fissions, and inversions) as the character states (Baker et al., 1983; Smith, 1990; Britton-Davidian et al., 1995; Ortells, 1995; Volleth and Heller, 1994, for inversions only).
When Rearrangements are the Characters
The only way to avoid redundancy in the scoring of characters is to consider the structural changes themselves as characters and the pattern observed before and after their occurrence, i.e., their presence or absence, as the character states. This has been implied and in some instances partially performed in studies that focused on inversions in Anopheles spp. (Green, 1982), Robertsonian fusions in Mus musculus (Corti et al., 1986), and Robertsonian translocations in Chiroptera (Volleth and Heller, 1994) and in Artiodactyla (Claro, 1994). A more detailed approach has recently been adopted by Borowik (1995), Nagamachi et al. (1999), and De Oliveira et al. (2002). This encoding strategy is quite similar to that used for morphological data but in cytogenetics one can retrieve information on the mutational event itself, something that is clearly not available to morphologists. As such, chromosomal mutations that accumulate along the tree are comparable to transitions, transversions, and insertions/deletions in molecular phylogenies.
From a practical point of view, most of chromosomal changes (paracentric and pericentric inversions, tandem and Robertsonian translocations) can be defined as binary characters. These are easy to code and do not pose theoretical problems (see below and Borowik, 1995). Nevertheless, like any class of characters, especially morphological ones, it is also possible to create multiple state characters when a sequence of chromosomal changes is likely to have occurred. This is particularly true for overlapping inversions or successive translocations. The two examples provided in Figure 1 can be coded as one character each with three ordered character states. In the case of successive nonreciprocal translocations (Fig. 1a), the number of steps implied by a multistate character is equal to that implied by two independent binary characters. In the case of overlapping inversions, it is more parsimonious to accept two inversions rather than five translocations when describing the patterns observed in Figure 1b. As is the case for successive translocations, the two inversions can be coded as a single but multistate character. Once again, the number of steps remains the same as the coding of two independent characters, but the possible sequence of evolutionary events is more restricted. In both cases it is not cladistically incorrect to encode multistate characters but ad hoc hypotheses are minimized when data are fully coded as binary characters.
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In contrast, however, monobrachial homologies represent a particularly ambiguous class because they can be described by one or several chromosomal characters. Indeed, in some cases where whole arm reciprocal translocations (WARTs) have been proposed (Capanna and Redi, 1995; Nanda et al., 1995; Catalan et al., 2000; Graphodatsky et al., 2001, among others), it is at least as parsimonious to code translocations and WARTs together rather than as independent fusion events. In fact, if no additional data are available, it is not possible to choose between them. Sometimes direct comparison with the outgroup for the particular chromosomes involved and the strict application of the parsimony principle can be helpful. For example, if the karyotypes of sp1 and sp2 shown in Figure 2 are considered independently, one can choose to encode four Robertsonian translocations (a/b, c/d, a/d, c/b) or two Robertsonian translocations (a/b, c/d) and one WART (b/d) to account for the observed patterns. If outgroup 1 is considered, it is more parsimonious to encode only a single WART (b/d) that will be an autapomorphy for sp2. But if outgroup 2 is considered, it is more parsimonious to encode two WARTs (b/c and c/d). However, in this latter case, the three patterns are suggestive of Robertsonian evolution and it is probably better to consider only the Robertsonian translocations that describe the origins of all the observed karyotypes (a/c, b/d, a/b, c/d, a/d, b/c). The number of steps inferred will be high using this approach (i.e., maximum), thus leading to the probable overweighting of these states. In general, however, monobrachial homologies appear difficult to encode and will be phylogenetically misleading if both Robertsonian translocations and WARTs occurred during chromosomal evolution.
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Contrary to the still limited knowledge on the way morphological and some molecular changes occur, chromosomal mutations and their mechanisms have been studied in detail (Capanna and Redi, 1995; Hall and Parker, 1995; Fagundes et al., 1998; Rousselet et al., 2000; Garagna et al., 2001). Consequently cladists using chromosomal data (or cytogenetists using cladistics) can benefit from the vast amount of functional and structural information that can be harnessed by correctly defining and describing chromosomal characters. For example, in situ hybridization can accurately identify recent Robertsonian and tandem fusions through the presence of cytogenetic signatures such as "fossil" centromeres and intercalary telomeric signals (Marsupialia: Svartman and Vianna-Morgante, 1999). In the same manner WARTs have sometimes been resolved by detailed molecular analyses (Catalan et al., 2000).
In short, chromosomal markers should be treated as any other class of data in cladistic investigations as their analysis "rests on the same logical foundations as does analysis of other types of characters" (Farris, 1978). Homologies assessed with the help of banding and/or ZOO-FISH techniques allow for the accurate identification of truly homologous characters as well as providing a glimpse of the patterns "before and after" their occurrence. In most cases, polarization can be established a posteriori using the outgroup criterion. Phylogenetic reconstruction and statistical tests of robustness can and should be done using the parsimony principle and the available methods and software that are widely accepted for the analysis of molecular and morphological data.
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Features Unique to Chromosomal Data and their Implications for Cladistic Analysis |
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TopNotesAbstractAssessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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B-Chromosomes
Chromosomal evolution is characterized by some peculiarities that have no equivalency in other classes of DNA evolution and thus require specific treatment. B-chromosomes provide one of the clearest examples of this. They are elements additional to the normal karyotype and are not homologous, or only partly homologous, to members of the regular set (White, 1973). Although not especially rare in plant and animal genomes, relatively few detailed studies of B-chromosomes have been published (Page et al., 2001; Trifonov et al., 2002; Karamysheva et al., 2002; Volobouev, 1981; Green, 1990; Camacho et al., 2000). What is known, however, is that their numbers can vary among species and populations as well as among cells of the same specimen. As a consequence they should be excluded from phylogenetic analyses. Importantly though, this does not imply that the presence of B-chromosomes is of no phylogenetic interest. Indeed, their occurrence and persistence in a lineage probably has a genomic explanation and is thus of evolutionary significance (Camacho et al., 2000). But in the absence of a clearer understanding of their origin, dynamics and transmission it seems prudent not to include them in a cladistic analysis of chromosomal data at this stage.
Heterochromatin
Heterochromatin is an important feature of the genomes (reviewed in Sumner, 1990; Zuckerkandl and Hennig, 1995). It is composed of highly repeated DNA sequences and its location and amount may vary at the species, population, and/or individual level. However, it is clearly of biological and evolutionary importance (Thelma et al., 1988; Hale et al., 1991; Dernburg et al., 1996; Tolchkov et al., 2000; Schueler et al., 2001; Garagna et al., 2001; Zuckerkandl and Hennig, 1995) and can be of phylogenetic value. Unfortunately, shared C-banding patterns can be misleading in that they can differ in composition and level of repetition (Viegas-Péquignot et al., 1984; Modi, 1993; Rossi et al., 1995; Volobouev et al., 1995; Garagna et al., 1997; Bowers et al., 1998; Glas et al., 1999; Robinson et al., 2000; Slamovits et al., 2001). Therefore, to be of value, C-positive material should be defined by its presence or absence and, if present, by its location and its nature (type of sequences) and, more problematically, by a quantifiable measure of the abundance of each family of repeats. Put succinctly, the use of heterochromatin in cladistic investigation is confounded by problems of homology. Its inclusion should involve assessing the homology of heterochromatic blocks by molecular means and evaluating, at least approximately, the degree of repetition.
The elimination of pericentromeric heterochromatin following a Robertsonian translocation is a frequently observed and well-documented finding (Volobouev et al., 1988; Nanda et al., 1995; Garagna et al., 2001) that has implications for the encoding of heterochromatin in cladistic analyses. For example, Figure 3 clearly shows that the pericentromeric heterochromatin shared by species B and C is phylogenetically misleading. The loss of heterchromatin in A represents a reversal that falsely suggests a closer relationship between B and C, whereas in this particular case, heterochromatic amplification is a synapomorphy for species A, B, and C. Of course, the use of independent data sets, for example those based on morphology or DNA sequences, may greatly help to resolve chromosomal ambiguities such as these. Congruence among independent data sets always strengthens phylogenetic conclusion based on single markers (e.g., Rambau et al., 2003).
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Numbers and Locations of NORs
A cautionary warning on the use of NORs (the loci of ribosomal 28S genes) in determining phylogenetic relationships is important because the assumption that these are strictly homologous is rarely questioned. Although the conventional silver-staining method of Bloom and Goodpasture (1976) is purported to reveal only active major ribosomal genes, it not infrequently detects other genomic features (Sumner, 1990; Sanchez et al., 1995; Dobigny et al., 2003). This lack of specificity can be circumvented by the fluorescent in situ hybridization of ribosomal gene probes, but even so, the number of repeats may be variable and this is not usually evident even when using hybridization methods. In addition, DNA sequences similar to ribosomal intergenic spacers, but not linked with rDNA loci, have been detected in some genomes (Vicia faba: Maggini et al., 1991), suggesting that molecular investigations too may sometimes be misleading in terms of the location of rDNA clusters. Finally, recent studies have shown that among a single rDNA family several "lineages" may coexist as has been demonstrated for 5S genes in molluscs (Mytilus spp.; Insua et al., 2001), insects (Diptera; Kress et al., 2001), and fishes (interspersed sequences of Oreochromis niloticus; Martins et al., 2000).
These results underscore the importance of determining true homology because two positive signals for a particular rDNA gene array may, in fact, correspond to two paralogues. In conclusion, rDNA hybridization patterns may contribute valuable additional information on homologies between chromosomal segments, mainly between closely related species (Britton-Davidian et al., 1995). However, these must be carefully assessed for usefulness as cladistic characters given the difficulty of assessing homology between positive signals, particularly when these are derived from silver staining.
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Chromosomal Phylogenies |
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TopNotesAbstractAssessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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Modeling Chromosomal Phylogenies
A simple model of chromosomal evolution in eight species (Fig. 4, karyotypes A to H) is considered. It is characterized by 10 chromosomal events, one of which is convergent because the Robertsonian fusion of chromosomes d1 and d2 arose independently in the lineages leading to species A and B (Fig. 5a). We applied different coding procedures in order to compare the results obtained with each method: (1) presence/absence of entire chromosomes, (2) chromosomes as characters and rearrangements as character states, and (3) rearrangements as characters. In each case the species H was used as a close outgroup. An exhaustive search in PAUP (Swofford, 1998) was performed with all three matrices (Appendix 1) and the strict consensus of each analysis compared (Fig. 5b, c, and d).
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First, it will be noticed that diploid and fundamental numbers are largely misleading in assessing relationships among the eight species (Figs. 4 and 5a
). In the same way, the reconstruction of an ancestral karyotype for species A to G following the approach that "most frequent is ancestral" results in erroneous conclusions on the ancestral nature of the acrocentric chromosomes a1 and a2 and the submetacentric b chromosomes (Fig. 4, Anc(A:G)). In contrast, the three other approaches to the coding of characters provide character states at the ancestral node of clades A to G that are in agreement with the "true" ancestral karyotype. This example clearly illustrates that considering chromosomes as the characters (methods 1 and 2) leads to inaccuracies. Moreover, the relationships within the (A, B, C) clade are incorrect in spite of high bootstrap support (93% and 97%). This is due to the convergent Robertsonian translocation being encoded twice, i.e., weighted twice, whereas the apomorphic inversion linking A and C is scored only once (Appendix 1, Fig. 5b and c). As outlined above, we have unequivocally demonstrated that overweighting can only be avoided if chromosomal rearrangements are considered as characters. Indeed, of the three approaches tested here, the only one to result in the expected unresolved trichotomy for the (A, B, C) clade is the one in which the rearrangements are used as characters. This is to be expected because it is not possible to choose between the synapomorphic Robertsonian fusion and the paracentric inversion. It should be noted that the overweighting strategies (methods 1 and 2) also led to higher bootstrap values. This is probably due to the fact that the only character in conflict (the paracentric inversion) is minimized in these data sets, because all the translocations are encoded twice, whereas the inversion is scored as a single character state.
Phylogenies Based on Empirical Chromosomal Data
Chromosomal phylogenies that use rearrangements as characters are relatively scarce and this, to some extent, probably precludes broad generalizations. For example, Corti et al. (1986) proposed a chromosomal phylogeny for Robertsonian populations of Mus musculus. It is, however, difficult to compare this phylogeny to those obtained using other genetic markers (Nachman et al., 1994; Riginos and Nachman, 1999), because European mice are unusual for their high karyotypic variation (reviews in Searle, 1993, and Searle and Wojcik, 1998). In addition, chromosomal evolution in M. musculus is influenced by the fact that Robertsonian translocations have been clearly demonstrated to be convergent in this species (Nachman et al., 1994; Riginos and Nachman, 1999). In constrast, investigations on the Anopheles Myzonya group (Arthropoda, Diptera) (Green, 1982) and on the verspertilionid bats (Mammalia, Chiroptera; Volleth and Heller, 1994) are well resolved. Nevertheless, to our knowledge, no other studies dealing with these groups and using independent data sets are available.
In sharp contrast to the limitations of the studies outlined above, Borowik's (1995) chromosomal phylogeny of great apes is, however, in perfect agreement with that obtained from morphological (Groves, 2001) and molecular (Page and Goodman, 2001) data. Similarly, in spite of a basal polytomy involving higher relationships, the chromosomal phylogeny of the Bovidae provided by Claro (1994) is in good agreement with recent molecular results (Hassanin and Douzery, 1999; Matthee and Robinson, 1999). For instance, most of the intergeneric relationships are congruent (Taurotragus and Tragelaphus, Alcephalus and Damaliscus, Gazella and Antilope, Bos, Bison, and Bubalus). The only difference concerns the placement of Tragelaphus and Taurotragus among Antilopinae on chromosomal data (Claro, 1994), and within Bovinae sensu Hassanin and Douzery (1999) in the two sequence based analyses (Hassanin and Douzery, 1999; Matthee and Robinson, 1999). In addition, with the exception of G. dorcas, the topology proposed by Claro (1994) for the genus Gazella (11 species and subspecies, plus Antilope cervicapra) is fully congruent with the parsimony cladogram obtained from two mitochondrial genes (Rebholz and Harley, 1999).
Other examples include the African rodent sibling species complexes, Arvicanthis (Muridae, Murinae), Mastomys (Muridae, Murinae), and Acomys (Muridae, Acomyinae), all of which have undergone extensive chromosomal evolution. The topologies obtained for eight species of Arvicanthis were fully congruent in respect of chromosomal (33 characters; Volobouev et al., 2002) and sequence data (complete cytochrome b; Ducroz et al., 1998). Although not fully resolved, the molecular (Barome et al., 2000) and chromosomal phylogenies obtained for several Acomys species (62 characters; Volobouev et al., 2002) were also in agreement. In contrast, however, the two data sets of Mastomys were only partially conguent, as two of the six species included were in conflict. This may be explained by persistent gene flow during progressive isolation caused by the accumulation of inversions (Volobouev et al., 2002). It is worth noting that in all three cytogenetic studies, the consistency and retention values were > 0.8.
To further emphasize the usefulness of the approaches advocated herein, we examined one empirically derived data set (Borowik, 1995) that was correctly coded by the author but which we reanalyze using chromosomes as characters, and their presence/absence as character states (see our coding of these characters in Appendix 2). An exhaustive search of this matrix yielded a single tree (L = 30; CI = 0.9; RI = 0.889; Fig. 6) with an identical topology to that presented by Borowik (1995, see her Fig. 5). Importantly, however, and in contrast to her study, which correctly identified characters 4, 9 and 10 as shared derived characters, our reanalysis failed to recover synapomorphies for the grouping Gorilla, Pan spp., and Homo. Moreover, two characters are convergent in our reanalysis (9 and 25 in Appendix 2), whereas the data set selected by Borowik is free of homoplasy as is evidenced by the consistency index value (CI = 1) obtained by this author. Secondly, in order to illustrate the problems associated with scoring translocations as characters, and mutations as character states (which precludes using the Borowik study because this only documents inversions), we reanalyzed the Gazella data in Claro (1994). Our reanalysis (not shown) led to the expected overestimation of branch lengths (see above) and the aberrant occurrence of the same mutational event at two different locations in the tree. This simple survey clearly shows that using chromosomes as characters and their presence/absence as character states or, alternatively, the scoring of translocations as characters and mutations as character states leads in both instances to reconstruction abnormalities and erroneous results.
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In conclusion, the results obtained for one hypothetical and several empirical data sets clearly illustrate the usefulness of chromosomal characters for inferring phylogenies in a cladistical framework when rearrangements are used as characters. Importantly, chromosomes may sometimes be pivotal characters in cases when morphological and molecular data fail to resolve evolutionary relationships, and particularly so in recently differentiated sibling species (Volobouev et al., 2002).
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Weighting of Chromosomal Characters |
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TopNotesAbstractAssessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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A variety of weighting strategies have been developed for the analysis of molecular data sets. In general, two main categories can be distinguished. The first, and probably most widely used, is grounded on statistical arguments based on the frequency of the occurrence of an event (e.g., transitions and transversions; Brown et al., 1982). The second category groups strategies that rely on biologically supported elements such as codon position in coding genes (Yoder et al., 1996), the biochemichal properties of the different amino acids (Dayhoff, 1979), and the precise locations of the DNA bases within a gene (e.g., paired nucleotides in secondary structures of rDNA; Wheeler and Honeycutt, 1988).
It is now well documented that certain chromosomal changes may have a significant impact on reproductive isolation and may contribute to the process of speciation, with the effect greatly dependant on the type of mutation involved (King, 1993; Searle, 1993; Noor et al., 2001). As a consequence, it is tempting to adopt weighting strategies for chromosomal data that take these properties into account. Here we outline two approaches, the statistical and the biological, but conclude that each has limitations. First, a frequency index can be developed that combines cytogenetic data from many different groups, allowing for the calculation of the average frequency of occurrence for each type of rearrangement. This frequency index may reflect the impact that these specific rearrangements have on the normal progression of meiosis, which could subsequently be used for determining the weighting of each rearrangement. However, the approach is weakened by karyotypic orthoselection (White, 1973) in which a given lineage often displays a preference for a specific type of chromosomal change (e.g., Robertsonian fusions in the Bovidae: Gallagher and Womack, 1992; fissions in the Canidae: Graphodatski et al., 2001; inversions and heterochromatin variations in the rodent Peromyscus: Bowers et al., 1998). The frequencies used for weighting purpose would therefore have to be calculated within the context of the group studied. With this as background, it seems prudent to evaluate the frequencies in an initial analysis and thereafter conduct a reanalysis using a frequency-based weighting strategy. Nevertheless, and especially when dealing with higher taxonomic levels, different patterns of orthoselection encountered in different lineages could result in an average frequency value that does not reflect the true occurrence of each chromosomal changes in each lineage.
The second approach relies on the biological impact of each rearrangement on fertility as directly measured in heterozygotes and/or hybrids. Indeed, the greater the underdominance, the more likely a specific rearrangement will be to play a mechanistic role the speciation process either by initiating or reinforcing reproductive isolation (Sites and Moritz, 1987; King, 1993; Searle, 1993; Noor et al., 2001; Rieseberg, 2001; Delneri et al., 2003). The fixation of these rearrangements in the karyotype must be considered as significant evolutionary features that may justify their overweighting for phylogenetic inferences. Nevertheless, as with the frequency approach, the meiotic impact of the different chromosomal changes may differ in individual lineages. As a result, it may be preferable to use meiotic data appropriate to the group under study. However, it should be noted that at higher systematic levels, meiotic observations are not possible given that hybrids are generally sterile. As a consequence, in spite of being the most acceptable strategy, the weighting of a specific rearrangement's impact on meiosis is, in most instances, probably not feasible. Finally, maximum likelihood methods constitute another promising approach to the weighting of chromosomal characters because they combine both statistical approaches and biological arguments. It seems likely that these could be expanded to test models of chromosomal evolution that take parameters such as mutational probability into account. Although not currently available, these would be particularly useful especially if they accommodate intrinsic genomic properties that are known to favor specific rearrangements (transposons and inversions in Drosophila: Evgen'ev et al., 2000; specific satellite DNA and Robertsonian or tandem translocations in mammals: Garagna et al., 2001; Li et al., 2000).
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Conclusion and Perspectives |
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TopNotesAbstractAssessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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In spite of their rather infrequent use, chromosomes are undoubtedly powerful characters for inferring phylogenetic relationships. By their very nature (discrete hereditary units of mutation responsible for the transmission of the nuclear genome), they bear direct testimony of the evolutionary history of extant lineages. Like any other type of data, however, chromosomes have their limitations. First, they are incapable of providing resolution within groups that are characterized by extreme chromosomal conservatism (turtles: Bickham, 1981; marsupials: Rofe and Hayman, 1985; Svartman and Vianna-Morgante, 1999), just in the same way certain groups lack sufficient morphological or molecular variation for phylogenetic inference. Secondly, they may also prove misleading when many homoplasic events are involved. On the other hand, they may represent the most appropriate and informative approach for investigations of sibling species that are not accompanied by significant nucleotide divergence (Volobouev et al., 2002).
In conclusion, we have provided a critical review of the methods generally used for encoding chromosomal data and have formalized an approach for their analysis that is in accordance with the principles and concepts of cladistics. Primary homology can often be assessed by conventional cytogenetic means and unequivocally by chromosome painting or by a combination of both conventional and molecular cytogenetic approaches. The rearrangements (Green, 1982; Corti et al., 1986; Claro, 1994; Volleth and Heller, 1994; Borowik, 1995; Nagamachi et al., 1999; De Oliveira et al., 2002) are the characters and the polarization of character states must be done using appropriate outgroup criteria (Qumsiyeh and Baker, 1988). On a cautionary note, however, some features that are unique to chromosomes (number and location of NORs, variation in heterochromatin, B-chromosomes) should be treated with caution because determination of homology is often problematic.
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Appendix 1 |
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TopNotesAbstractAssessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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Definition of the characters and the associated matrices used in the phylogenetic reconstruction of species A to H described in Figure 4, based on the presence/absence of chromosomes (matrix 1), chromosomes as characters and rearrangements as character states (matrix 2), and rearrangements as the characters (matrix 3). The strict consensus obtained with each of these methods is given in Figure 5b, c, and d, respectively. The nomenclature used in the three character sets correspond to the karyotypes presented in Figure 4.
In matrix 1, presence and absence are encoded by "1" and "0," respectively. In matrix 3, the translocated and nontranslocated states of Robertsonian (rob) and tandem (tan) translocations are encoded by "1" and "0," respectively. The acrocentric and metacentric conditions of pericentrically inverted chromosomes (inv) are encoded by "0" and "1," respectively. The paracentric inversion observed for chromosome f is described by "1" for the pattern displayed by species A and C, and by "0" for the pattern displayed by the six other species.
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c1: presence/absence (p/a) of a1; c2: (p/a) of a2; c3: (p/a) of a1 + a2; c4: (p/a) of b; c5: (p/a) of inv(b); c6: (p/a) of c1; c7: (p/a) of c2; c8: (p/a) of c1 + c2; c9: (p/a) of d1; c10: (p/a) of d2; c11: (p/a) of d1 + d2; c12: (p/a) of d2 + e1; c13: (p/a) of e1; c14: (p/a) of e2; c15: (p/a) of e1 + e2; c16: (p/a) of f; c17: (p/a) of inv(f); c18: (p/a) of g; c19: (p/a) of inv(g); c20: (p/a) of h1; c21: (p/a) of h2; c22: (p/a) of h1 + h2.
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c1: a1 fused with a2 (1) or not (0); c2: a2 fused with a1 (1) or not (0); c3: b inverted (1) or not (0); c4: c1 fused with c2 (1) or not (0); c5: c2 fused with c1 (1) or not (0); c6: d1 fused with d2 (1) or not (0); c7: d2 fused with d1 (1), e1 (2) or not (0); c8: e1 fused with e2 (1), d2 (2) or not (0); c9: e2 fused with (e1) or not (0); c10: f inverted (1) or not (0); c11: h1 fused with h2 (1) or not; c12: h2 fused with h1 (1) or not (0); c13: g inverted (1) or not (0).
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c1: tan (h1/h2); c2: inv (g); c3: rob (a1/a2); c4: rob (e1/d2); c5: tan (c1/c2); c6: inv (b); c7: rob (d1/d2); c8: rob (e1/e2); c9: inv (f).
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Appendix 2 |
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TopNotesAbstractAssessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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Matrix obtained by using data recoded from Table 1 and Appendix in Borowik (1995). Here we use the chromosomes as characters, and their presence/absence as character states. The chromosomal nomenclature follows Borowik (1995).
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c1: presence/absence (p/a) of inv(2p); c2: p/a of 2p; c3: p/a of 4; c4: p/a of a linked inv(inv(4)); c5: p/a of inv(4); c6: p/a of inv2(4); c7: p/a of 3; c8: p/a of inv(3); c9: p/a of inv(inv(7)); c10: p/a of inv(7); c11: p/a of inv(inv(inv(7))); c12: p/a of 7; c13: p/a of inv(9); c14: p/a of inv(inv(9); c15: p/a of 9; c16: p/a of inv(10); c17: p/a of inv(inv(10)); c18: p/a of 10; c19: p/a of inv(12); c20: p/a of 12; c21: p/a of inv(15); c22: p/a of 15; c23: p/a of inv(3); c24: p/a of 3; c25: p/a of inv3(2p); c26: p/a of inv(inv3(2p)); c27: p/a of 2p.
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Acknowledgments |
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TopNotesAbstractAssessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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We are grateful to Janice Britton-Davidian, Alexandre Hassanin, Jean-Pierre Hugot, Julio C. Pieczarka, Willem Rens and Fengtang Yang for comments on earlier drafts of this manuscript. The manuscript greatly benefitted from suggestions by Jack Sites and two anonymous reviewers.
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Notes |
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TopNotesAbstractAssessing Primary HomologyRootingEncoding of Chromosomal DataFeatures Unique to Chromosomal...Chromosomal PhylogeniesWeighting of Chromosomal...Conclusion and PerspectivesAppendix 1Appendix 2AcknowledgmentsReferences |
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3 Present address: Centre for Environmental Research (UFZ), Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, Germany.
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References |
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