Extreme genetic diversity in the lizard Atlantolacerta andreanskyi (Werner, 1929): A montane cryptic species complex

Mafalda Barata
Salvador Carranza
James Harris
Extreme genetic diversity in the lizard Atlantolacerta andreanskyi (Werner, 1929): A montane cryptic species complex
R E S E A R C H A R T I C L E Open Access
Background: Atlantolacerta andreanskyi is an enigmatic lacertid lizard that, according to the most recent molecular analyses, belongs to the tribe Eremiadini, family Lacertidae. It is a mountain specialist, restricted to areas above 2400 m of the High Atlas Mountains of Morocco with apparently no connection between the different populations. In order to investigate its phylogeography, 92 specimens of A. andreanskyi were analyzed from eight different populations across the distribution range of the species for up to 1108 base pairs of mitochondrial DNA (12S, ND4 and flanking tRNA-His) and 2585 base pairs of nuclear DNA including five loci (PDC, ACM4, C-MOS, RAG1, MC1R). Results: The results obtained with both concatenated and coalescent approaches and clustering methods, clearly show that all the populations analyzed present a very high level of genetic differentiation for the mitochondrial markers used and are also generally differentiated at the nuclear level.Conclusions:These results indicate that A. andreanskyi is an additional example of a montane species complex.

Background

An emerging pattern among European biotas is that the accentuated environmental instability that occurred during the Pleistocene did not lead to increased speciation rates, with many species and populations originating during the Miocene and proceeding through the Quaternary [1,2]. In many species, population fragmentation was triggered by the beginning of the Messinian Salinity Crisis, a short (600 000 years) but crucial period that occurred between 5.9 and 5.3 Mya during which the Mediterranean Sea desiccated almost completely, producing a general and drastic increase in aridity around the Mediterranean Basin [3,4]. As a result of this increased aridity, forests continued to be replaced by more open and arid landscapes forcing the mesic species to retreat to the moister Atlantic-influenced areas and to the mountainous regions, leading to high speciation in some groups [5,6].

Various studies have attempted to unravel the different roles that the global aridification at the end of the Miocene and the Pleistocene glacial cycles have played in the diversity and distribution of European faunas [7]. However, little is known about the effects that these climatic changes had on species living further South, in the African continent. Recent assessments of central African chameleons have uncovered evidence of long-isolated evolutionary histories, with the survival of palaeoendemics leading to considerable diversity [8]. In general, reptiles are excellent model organisms to assess the relative role that the Pre-Quaternary and Quaternary major climatic events have played in the origin, evolution and distribution of species [9]. Available data from some herpetofauna indicate that a similar pattern to the neighboring Iberian Peninsula exists in North Africa, with deep lineages originating at the end of the Miocene (Chalcides [10], Acanthodactylus [11][12][13], Podarcis [2,14,15], Saurodactylus [16], Ptyodactylus [17], Salamandra [18], Pleurodeles [19]). However, the lack of informative nuclear markers in most of these studies may prevent the recovery of the true evolutionary history of the group [eg. 20,21], and makes it difficult to ascertain if these lineages correspond to species complexes or not. Since there is a strong likelihood of discordance between gene trees and species trees [22][23][24], information from different genetic markers (mitochondrial and nuclear) is thus necessary for delimiting evolutionary lineages, as well as for establishing phylogenetic relationships.

Despite being key concepts in the fields of systematic and evolutionary biology, recognizing and delimiting species are highly controversial issues ([e.g. 25,26]). Recognizing species is not only a taxonomic challenge, but is also essential for other biological disciplines such as biogeography, ecology and evolutionary biology [27], and has serious consequences for conservation biology and the design of effective conservation plans [28,29]. Delimiting species is also the first step towards discussing broader questions on evolution, biogeography, ecology or conservation. Recently, thanks to intellectual progress made in the field with the aim of identifying a common element among all the different species concepts, a single, more general, concept of species known as General Lineage Species Concept has been suggested [30]. This unified species concept emphasizes the common element found in many species concepts, which is that species are separately evolving lineages. Therefore, properties like reciprocal monophyly at one or multiple loci, phenotypic diagnosability, ecological distinctiveness, etc. are not part of the species concept but are used to assess the separation of lineages and to species delimitation [31]. This separation between species conceptualization and species delimitation and the proposal of a unified species concept has concentrated efforts in the development of new approaches for species delimitation, as for example with "integrative taxonomy" [32,33, among others]. Under this new approach, species delineation is regarded as an objective scientific process that results in a taxonomic hypothesis. Therefore, the level of confidence in the taxonomic hypothesis supported by several independent character sets is much higher than for species supported by only one character [34]. Such an integrative view is especially useful in the case of taxonomic groups that are morphologically conservative, where cryptic species have probably been overlooked [17,35,36].

Normally, high altitude species carry signatures of the expansion and contraction cycles occurred during glacial and interglacial periods [37][38][39]. Because of this, they are of particular interest to study historical responses to climate change, since they are adapted to a small window of environmental changes, and usually present low tolerance to high temperatures [40]. In Europe, high altitude species often seem to have persisted through glacial periods by short movements to lower altitudes rather than to the classic "southern refugia" of lowland species. In this way current ranges may primarily reflect postglacial expansions [41]. However, it is not clear if the same phenomenon occurs in African montane taxa.

Atlantolacerta andreanskyi (Werner, 1929) is a lacertid lizard endemic to the western and central parts of the High Atlas Mountains of Morocco. It is restricted to areas above 2400 m [42,43], where it is frequently found in the vicinity of small watercourses or plateaus in the top of the mountains that retain some water from rain or snowmelt. Habitat is normally screes and areas with boulders, meadows and, in particular, the base of cushion-like thorny plants in these places [42; personal observation]. Although A. andreanskyi had initially been placed in several different genera within the subtribe Lacertini [44][45][46][47][48], recent phylogenetic analyses based on mitochondrial DNA and a combination of mitochondrial and nuclear markers [49,50] suggest that A. andreanskyi is a member of the subtribe Eremiadini, and apparently sister to the remaining Eremiadini. This position would conform to this species lacking the synapomorphies that characterize most other Eremiadini, namely a derived condition of the ulnar nerve and the presence of a fully developed armature in the hemipenis, which has folded lobes when retracted. It is also distinctive within the Eremiadini regarding the presence of enlarged masseteric scale [49]. Because of its phylogenetic position, without close relationship to any other genus of Eremiadini and its distinctive morphology it was recently placed in a new monotypic genus, Atlantolacerta [49]. Atlantolacerta andreanskyi is distributed across 440 Km (straight line) of mountainous terrain, with the different populations presenting an apparently disjunct distribution ([42,43; see Figure 1]). As with many montane species, the situation observed in A. andreanskyi is similar to an archipelago, with the different "islands" being represented by mountaintops disconnected due to areas of unsuitable habitat below 2400 m. As a result of this scenario, minimal gene flow is currently expected between the different populations; however, it is not known how the different climatic events occurred during the Miocene and Pleistocene have affected this species. Even though some aspects of the biology of A. andreanskyi are already well known [e.g. 51,52], the genetic structure of the different populations, as well as the relationships between the different populations have never been assessed before.

Therefore, in order to shed some light on the previous questions and attempt to assess the evolutionary history of the species and identify the number of lineages, we sampled the distribution area of the species and performed several combined phylogenetic reconstructions and clustering analyses, using both mtDNA and nuclear markers.

Results

Mitochondrial genealogies

A total of 1108 base pairs (bp) of concatenated mtDNA (12S rRNA 330 bp, ND4 592 bp and tRNA-His 186 bp) were obtained for 89 A. andreanskyi. The concatenated alignment of the ingroup sequences revealed 30 haplotypes (3 from Tizin Tichka, 7 from J. Ayache, 5 from J. Sirwa, 2 from Oukaimeden, 7 from J. Azourki, 2 from Outabati, 2 from Toubkal and 2 from J. Awlime) and contained 241 variable sites, of which 232 were parsimony informative.

Analyses of the concatenated mtDNA data were mostly congruent (Figure 2A). Seven well-supported lineages were recovered from these analyses (pp > 0. 95 and BP > 70%), corresponding to the populations from J. Awlime, J. Sirwa, Tizin Tichka, J. Azourki, Outabati, J. Ayache, and Oukaimeden and nearby Toubkal that were grouped together. Regarding the relationships among these clades, we could distinguish three main groups, Oukaimeden and Toubkal with J. Sirwa from the southern end of the distribution range; J. Ayache with Outabati from the northern distribution, and Tizin Tichka with J. Azourki from the central distribution range. The population from J. Awlime, from the extreme South of the range, is a genetically distinct lineage related to the northern group, although, both ML and BI analysis weakly support this topology (see Figure 2A).

All the populations present a low level of diversity in the mitochondrial DNA (uncorrected genetic distances 0-0.5% for the ND4 + tRNA-His and 0 -0.2% for the 12S; see Table 1), and a very high level of genetic divergence between populations (5.5 -16.5% in the ND4 + tRNA-His and 2.5 -6.6% in the 12S).

Nuclear genealogies

A total of 77 specimens of A. andreanskyi were sequenced for five nuclear genes. The ACM4 was 447 bp long, presenting 47 haplotypes and 34 polymorphic sites, 33 of them parsimony informative; C-MOS was 534 bp long, with 32 haplotypes and 21 polymorphic sites, all of them parsimony informative; MC1R was 635 bp long, with 57 haplotypes and 36 variable sites, 35 of them parsimony informative; PDC was 441 bp long, with 60 haplotypes and 29 variable sites, 26 of them parsimony informative; RAG1 was 528 bp long, with 38 haplotypes and 19 variable sites, 18 of them parsimony informative. The differences in the genetic distances between the lineages are congruent with the geographic distance between them, supporting the grouping of the lineages in three main groups as seen in the analysis of mitochondrial sequences.

The concatenated analyses of the 5 unphased nuclear markers are congruent with the results obtained in the mitochondrial DNA tree, although with some differences ( Figure 2B). Despite recovering the three main groups observed in the mtDNA analysis, according to the  nuclear markers the J. Awlime population is not sister to the northernmost populations but branches off inside a polytomy with the westernmost lineages at the base of the tree. It is possible to distinguish some of the lineages, although in some cases they are not monophyletic. The J. Ayache population is monophyletic but makes Outabati paraphyletic. The same happens with Tizin Tichka, which makes the population from J. Azourki paraphyletic. The population from Oukaimeden is polyphyletic.

Concatenated analysis (mtDNA and nDNA)

The results of the ML and BI analyses of the mtDNA and nDNA ( Figure 2C) support the same seven lineages as recovered in the mitochondrial analysis, although in this case J. Awlime is sister to the central and northern lineages (Tizin Tichka, J. Azourki, Outabati, and J. Ayache) instead of being sister to only the northernmost lineages ( Figure 2A). As in the mtDNA analysis (Figure 2A), the relationship of J. Awlime with the central and northern lineages is very poorly supported. This result was expected, given the higher resolving power of the mtDNA that contributed with 241 variable sites versus the 150 from the nDNA.

Nuclear networks

As show in Figure 3 and Table 2, there is a moderate degree of haplotype sharing between populations, with most of them lacking private alleles for the nuclear genes analyzed.

Clustering analysis and individual assignment

In our study, the obtained K differs with the combination between the ancestry model and the allele frequency model.

Species tree and divergence time estimates

The results of the clustering analysis with K = 6 were used to define the species for the species tree analysis in STARBEAST. The tree inferred with information from mitochondrial and nuclear markers (phased) (figure 2D) recovered the same topology as in Figure 2C, with all the relationships between the lineages supported by previous analyses. The divergence time estimates were calculated for the six populations (Table 1). High effective sample sizes were observed for all parameters in all BEAST analysis (posterior ESS values > 1000 for all four analyses) and assessment of convergence statistics in Tracer indicated that all analyses had converged. Maximum clade credibility tree for ND4 + tRNA-His was identical in topology to those produced by Bayesian and ML analyses. According to the inferred dates resulted from BEAST ( Figure 2D

Discussion

Several recently published analyses of North African herpetofauna have revealed high levels of endemism and cryptic species [12,14,15,17]. In this analysis, the surprising result was the extreme diversity of mitochondrial DNA found between almost all the populations analyzed The genetic differentiation observed between populations (2.8% -6.6% in 12S and 5.5% -16.5% in ND4 + tRNA-His) is similar and, in some cases, higher than the divergence found between Iberolacerta species (between 7.4% and 8.2% in the cytochrome b gene, [53]), a lacertid genus with most of its species occurring in the mountains of the Iberian Peninsula [41,54]. Initially considered one species, there are now seven recognized species of Iberolacerta in the Iberian Peninsula. Genetic differentiation between these species is lower than between the different populations of A. andreanskyi.

Although the mitochondrial phylogeny supports the existence of seven distinct groups, the clustering analysis only supports the existence of six lineages (J. Sirwa, Tizin Tichka, J. Azourki, Outabati, J. Ayache and a lineage formed by Oukaimeden, Toubkal and J. Awlime). Toubkal samples were always part of the same lineage as Oukaimeden, although, they show some divergence at least at the mitochondrial DNA level (1.7% in ND4 + tRNA-His and 0.3% in 12S). This is not unexpected, as these populations are geographically very close and are part of the High Atlas Mountains, where interconnectivity between populations could occur. The mitochondrial phylogenetic analyses supported the existence of a seventh isolated lineage, J. Awlime, however clustering analysis and the nuclear phylogeny did not support the distinctiveness of this population, possibly because of the small sampling size.

Unfortunately, despite multiple attempts to sample in this remote region, only three individuals were captured. The analyses also could not recover the genetic relationship between J. Awlime and the other populations, because its position in the trees fluctuated between the two main groups (North and South), without support in any of the trees.

Non-reciprocal monophyly in nuclear markers and species delimitation

In the phylogenetic analyses of the concatenated nuclear loci, some of the lineages supported by mtDNA data were not monophyletic. This was observed only between the geographically closest lineages, as in the case of Oukaimeden and J. Sirwa; Tizin Tichka and J. Azourki; and Outabati and J. Ayache, that presumably were in contact more recently than the others. This may be due to the larger effective population size of the nuclear DNA compared to the mitochondrial DNA and the consequent stronger effect of the incomplete lineage sorting at each single nuclear loci [55]. Additionally, the slow evolutionary rate of some of these markers may be a factor. The conjugation of these two effects probably explains the absence of concordance in the single nuclear gene trees (not show), although the same general topology was recovered in the concatenated nuclear phylogeny. Reciprocal monophyly is one of the primary criteria to delimit species [31,56]. Although it is possible to delimit species without observing monophyly in gene trees, since a considerable amount of time must pass after the beginning of divergence of species until they show reciprocal monophyly at a sample of multiple loci [57,58]. Pinho et al. [59] have shown that Podarcis from the Iberian Peninsula and North Africa have a similar pattern (between mtDNA and nuclear) but in a smaller time window and using faster evolving nuclear loci and, in contrast to our case, some populations are in contact. Although we are aware that the determination of K, in STRUCTURE, is only an ad hoc guide to describe consistence between models and the data [60], the program has been commonly used for this end [61]. Several methods based on Bayesian clustering have been developed [62][63][64], however, STRUCTURE is the most widely used, and various studies show its efficiency in assigning individuals to their population of origin [65][66][67][68] and its ability to construct an appropriate clustering hypothesis [61]. However, in the present example the analysis was limited because it was based only in haplotype information. The obtained K differ with the combination model used, but in most of the combinations the analysis supports a K = 6 corresponding to the geographical populations and to the results recovered by the other analyses. This analysis also placed the samples from the J. Awlime population together with the Oukaimeden lineage, possibly due to the limited haplotype sampling. Similarly, the concatenated phylogenetic tree, based on all the genes, supports the existence of 7 lineages giving once more a low support to the relationship between J. Awlime and the other lineages.

The networks of the individual nuclear loci show high percentage of private alleles in some of the lineages, which fluctuate depending on the gene.

Dating the trees

All the lineages are grouped in two main clusters, the northern group composed by J. Ayache, Outabati, J. Azourki and Tizin Tichka; and the southern group that includes Oukaimeden and J. Sirwa. The divergence obtained for these two lineages was around 7.6 Mya, (4.3-11.9), which coincides approximately with the time of the final closing of the Rifian Strait (7.2 Mya; [3]). During the Miocene, tectonic activity in the region was intense and included the uplift of the Atlas Mountains that occurred around 9.0 Mya [69,70]. It was more or less at the same time that Podarcis invaded North Africa (7.5 ± 1.2 Mya, [2]) and the Iberian clade of Iberolacerta started to fragment (6.1 Mya, [1]). The split of the six lineages must have occurred later, probably during the Quaternary Glaciations (4.3 ± 3; 2.4 ± 2; 2.9 ± 2 Mya). However, the confidence intervals obtained were very large, increasing the time window for the events and the associated error. Determination of the time of the speciation events is important to understand the evolutionary biogeography of species [71]. However, it is difficult to estimate ages in phylogenies without several sources of error. Clearly the lineages of A. andreanskyi are pre-Pleistocenic and, as found in Central African chameleons [8] can be considered paleoendemics. However, without better calibration points it is difficult to date the split of the lineages more precisely than this.

Conclusions

Phylogeographic assessments of several taxa in northwest Africa have indicated the presence of cryptic diversity in organisms ranging from scorpions [72] to mammals [73], and reptiles are not an exception [e.g. 11,17,74]. What is exceptional in the case of A. andreanskyi are the high levels of mitochondrial divergence between almost every sampled populations, ranging from 5.5 up to 16.5% (ND4 + tRNA-His) between populations separated by low geographic distances (for example just 60 Km between Oukaimeden and J. Sirwa and 45 Km between Oukaimeden and Tizin Tichka). Six of the eight analyzed populations are highly distinct based on both mtDNA and multiple nuclear markers. This raises the issue not of whether A. andreanskyi is a species complex, but just how many species may occur within the group. Presumably, far more than the six possible species identified in this study, since, probably, many populations remain unsampled. However, preliminary morphological analyses suggest that all the different populations included in the present study are very homogeneous (unpublished data). This may imply the presence of cryptic diversity, but definitive conclusions should wait until a complete morphological study is carried out (work in progress).

Current models of reptiles species accessed for the region indicate low levels of diversity across much of the High Atlas Mountains [75]. Indeed only a few species are recorded at altitudes above 2000 m; typically A. andreanskyi, Quedenfeldtia species (Q. trachyblepharus and Q. moerens), Chalcides montanus and Vipera monticola [e.g. 42,76]. However, the finding of high genetic diversity in A. andreanskyi indicates that unidentified lineages occur, and that the other high mountain species should also be assessed as possible cryptic species candidates. Our results are also essential from a conservation point of view, as many forms may actually have smaller ranges than currently thought, and small isolated populations on high mountains have been identified as those of high concern under typical global warming scenarios [77]. Given these results it is necessary to increase the sampling in order to understand the relationship of J. Awlime with the other populations and try to find new populations. Furthermore it is very important to conduct a through morphological study to determine if there is phenotypic variation, and then to revise the taxonomy of the genus Atlantolacerta.

Methods

Species concept and integrative approach

Although the present study does not include a taxonomic revision of the genus Atlantolacerta, like many other works in which some of the authors of the present manuscript have participated [35,78,79], we advocate for the use of the General Lineage Species Concept proposed by de Queiroz [30]. Two lines of evidence have been defined on the basis of alleged independence of their respective datasets: mitochondrial DNA and nuclear DNA. In the present study, we have decided to retain as "putative species" only these lineages that were recovered as monophyletic in the phylogenetic analysis of the mtDNA data and that were supported by the analysis of the nDNA using STRUCTURE v.2.3.2 [60]. Within the framework of an integrative approach, and pending the inclusion of morphological data, this would correspond to Integration by total congruence (ITC). However, it is important to take into account that in the absence of a thorough morphological analysis we do not consider the molecular data presented here enough to revise the taxonomy of the genus Atlantolacerta.

DNA extraction, amplification, and sequencing

A total of 92 individuals from eight different populations distributed across the entire range of Atlantolacerta andreanskyi were sampled for this study: 14 from Oukaimeden, 15 from Tizin Tichka, 14 from Jebel Ayache, 15 from Jebel Azourki, 14 from Outabati, 15 from Jebel Sirwa and 2 from Toubkal and 3 from J. Awlime ( Figure 1 and Table 3). Specimens were caught by hand, identified on the basis of external features, measured and photographed for later morphological studies. Tail tips where collected and stored in 96% ethanol, after which individuals were released in the same place where they were caught.

Genomic DNA was extracted from ethanol-preserved tissue samples using standard high-salt protocols [80]. A total of 89 specimens of Atlantolacerta andreanskyi plus three outgroups (Podarcis hispanica, Podarcis carbonelli and Podarcis bocagei) were sequenced for two mitochondrial regions: partial 12S rRNA (12S) and partial NADH dehydrogenase 4 (ND4) and flanking tRNA (tRNA-His) and 77 specimens for five nuclear gene fragments, recombination-activating gene 1 (RAG1), acetylcholinergic receptor M4 (ACM4), melanocortin receptor 1 (MC1R), oocyte maturation factor Mos (C-MOS) and phosducin (PDC). Primers used for both amplification and sequencing were: 12Sa and 12Sb [81] for the 12S following the PCR conditions described in Harris and Arnold [82], ND4 and Leu for ND4 + tRNA-His, PCR conditions described in Arévalo et al. [83]; L2408 and H2920 for RAG1 following the PCR conditions from Vidal and Hedges [84]; tg-F and tg-R [85] for ACM4 with PCR conditions following Gamble et al. [86]; MC1RF and MC1RR for MC1R following PCR conditions described in Pinho et al. [87]; Lsc1 and Lsc2 for C-MOS following the PCR conditions from Godinho et al. [88]; and PHOF2 and PHOF1 for PDC, following PCR conditions described in Bauer et al. [89]. PCRs were carried out in 25 μl volumes, containing 5.0 μl of 10 reaction Buffer, 2.0 mM of MgCl2, 0.5 mM each dNTP, 0.2 μM each primer, 1 U of Taq DNA polymerase (Invitrogen), and approximately 100 ng of template DNA. Finally, PCR products were purified using exosap IT and the resulting amplified fragments were sequenced on an Applied Biosystem DNA Sequencing Apparatus. Chromatographs were checked manually, assembled and edited using Bioedit 7.0.1 [90]. Sequences were aligned for each gene independently using the online version of MAFFT v.6 [91] with default parameters (gap opening penalty = 1.53, gap extension = 0.0) and FFT-NS-1 algorithm. Coding gene fragments (ND4, C-MOS, ACM4, RAG1, PDC and MC1R) were translated into amino acids and no stop codons were observed, suggesting that the sequences were all functional. Heterozygous individuals were identified based on the presence of two peaks of approximately equal height at a single nucleotide site. SEQPHASE [92] was used to convert the input files, and the software PHASE v2.1.1 to resolve phased haplotypes [93]. Default settings of PHASE were used except for phase probabilities that were set as ≥ 0.7 [94]. All polymorphic sites with a probability of < 0.7 were coded in both alleles with the appropriate IUPAC ambiguity code. Phased nuclear sequences were used for the structure analysis; networks and species tree analysis, and the unphased sequences for the phylogenetic analyses (see below). DnaSP [95] was used to calculate the number of haplotypes (h) and mutations (η). Mega v.3.0 [96] was used to estimate uncorrected p-distances and to obtain the number of variable and parsimony informative sites.

Phylogenetic analyses

Phylogenetic analyses were performed using maximum likelihood (ML) and Bayesian (BI) methods. JModelTest [97] was used to select the most appropriate model of sequence evolution under the Akaike Information Criterion [98]. ML analyses were performed with RAxML v.7.0.4 [99] with 100 random addition replicates. A GTR + I + G model was used and parameters were estimated independently for each partition (by gene). Reliability of the ML tree was assessed by bootstrap analysis [100] including 1000 replications. Bayesian analyses were performed with MrBayes v.3.1.2 [101] with best fitting models applied to each partition by gene and all parameters unlinked across partitions. The models selected for the different partitions were: 12S, GTR + I + G; ND4, GTR + G; tRNA-His, GTR + I + G; ACM4, HKY + I; C-MOS, GTR + I + G; MC1R, HKY + I + G; PDC, GTR + I + G; and RAG1, GTR + I. Two independent runs of 5x10 6 generations were carried out, sampling at intervals of 1000 generations producing 5000 trees. Convergence and appropriate sampling were confirmed examining the    standard deviation of the split frequencies between the two simultaneous runs and the Potential Scale Reduction Factor (PSRF) diagnostic. Burn-in was performed discarding the first 1250 trees of each run (25%) and a majority-rule consensus tree was generated from the remaining trees. In both ML and BI alignment gaps were treated as missing data and the nuclear gene sequences were not phased.

Nuclear Networks

The genealogical relationships between the populations were assessed with haplotype networks for all the individual nuclear genes, constructed using statistical parsimony [102] implemented in the program TCS v 1.21 [103] with a connection limit of 95%. This analysis was made with the phased sequences. Haplotypes were colored taking into account the population of origin.

Population structure -Clustering analyses

A model-based Bayesian clustering method was applied to all haplotypes using STRUCTURE v.2.3.2 [60,104,105].

In this analysis, individuals are probabilistically assigned to either a single cluster (the population of origin), or more than one cluster (if there is admixture). STRUC-TURE was run with haplotype information from the nuclear fragments independently. We ran our data with the all parameters combinations between the Ancestry Model and the Allele Frequency Model to compare the results. The genetic structure was forced to vary from K = 2 to K = 10 clusters, the latter corresponding to the number of geographic populations sampled plus two. STRUCTURE ran for 550 000 steps, of which the first 50 000 were discarded as burn-in. For each value of K ten independent replicates of the Markov Chain Monte Carlo (MCMC) were conducted. To detect the true number of clusters (K) we followed the graphical methods and algorithms outlined in Evanno et al. [61], with the comparison of the average posterior probability values for K (log likelihood; ln L) using the online version, STRUCTURE HARVESTER v0.6.5 (available at: http:// taylor0.biology.ucla.edu/struct_ harvest/, April 2011).

Species tree, and divergence time estimates

Here we applied the coalescent-based species-tree approach implemented in STARBEAST [106] an extension of BEAST v1.6.1 [107] to test the origin and diversification patterns in Atlantolacerta, and to compare these results to those obtained from the ML and BI analyses of the concatenated dataset. This analysis needs a priori information regarding the species/populations delimitation and the species/populations assignation of the individuals in order to reconstruct the topology of the species tree. For this approach, we used the results obtained from previous clustering analyses to define the groups of individuals to be used as "species" (populations) in STARBEAST [106]. The clustering analysis supported the existence of six lineages, as Oukaimeden, Toubkal and J. Awlime were included in the same lineage. All five nuclear gene fragments, 12S and the fragment consistent of the ND4 and flanking tRNA-His were included in the analyses as 7 independent partitions. The phased dataset was used for the nuclear loci.

The input file was formatted with the BEAUti utility included in the software package. We performed two independent runs of 1.5 x 10 8 generations, sampling every 15 000 generations, from which 10% were discarded as burn-in. Models and prior specifications applied were as follows (otherwise by default): 12S -GTR + G; ND4 and tRNA-His -HKY + G; MC1R -HKY + I; ACM4 -HKY + I; C-MOS -GTR + I + G; RAG1 -HKY + I; PDC -GTR + I; Relaxed Uncorrelated Lognormal Clock (estimate); Yule process of speciation; random starting tree; alpha Uniform (0, 10).

For all analyses implemented in BEAST, convergence for all model parameters was assessed by examining trace plots and histograms in Tracer v1.5 [108] after obtaining an effective sample size (ESS) > 200. The initial 10% of samples were discarded as burn-in. Runs were combined using LogCombiner, and maximum credibility trees with divergence time means and 95% highest probability densities (HPDs) were produced using Tree Annotator (both part of the BEAST package). Trees were visualized using the software FigTree v1.3.1 [109].

Several studies have already calculated divergence rates for reptiles, and particularly for lacertids [2,15,49]. Pinho et al. [15] used well-known and dated independent geological events in the Aegean [110] to estimate a maximum and minimum mutation rate for the ND4 mitochondrial fragment (and flanking tRNA-His) for the lacertid lizards of the genus Podarcis (0.0278 and 0.0174 mutation/site/million years, respectively). However, this was the only information available for our data, since we did not have any fossils or calibrations for nuclear markers. It is important to bear in mind that, in the absence of accurate calibration points in the phylogeny from external and independent data (fossil records, known biogeographic events, or paleoclimatic reconstructions) or as a result of the heterogeneity in the evolutionary rate between the calibrated and uncalibrated taxa, temporal estimates by means of molecular data could be a potential source of inference error, and, therefore, they should be treated with caution [111]. Despite the limitations of molecular clocks [111,112], divergence time estimates can still provide a proxy for the temporal window of evolutionary diversification in species groups of interest. Therefore and taking into account our data limitations and availability, we used BEAST v.1.6.1 [107] to estimate dates of the cladogenetic events using only ND4 and flanking tRNA-His. We used a phylogeny pruned arbitrarily to include one representative from each of the major lineages uncovered with the concatenated analysis (6 specimens in total, we excluded J. Awlime population, because of the lack of support of the branch in previous analyses). This method excludes closely related terminal taxa because the Yule tree prior (see below) does not include a model of coalescence, which can complicate rate estimation for closely related sequences [113]. Analyses were run four times for 5x10 7 generations with a sampling frequency of 10 000. Models and prior specifications applied were as follows (otherwise by default): GTR + G for 12S; HKY + G for ND4 and tRNA-His; HKY + I for MC1R; HKY + I for ACM4; GTR + G + I for C-MOS; HKY + I for RAG1; GTR + I for PDC; Relaxed Uncorrelated Lognormal Clock (estimate); Yule process of speciation; random starting tree; alpha Uniform (0, 10); ucld.mean of ND4 Normal (initial value: 0.0226, mean: 0.0226, Stdev: 0.0031).

Authors' contributions MB carried out the molecular laboratory work, analyzed the data and drafted a preliminary version of the manuscript. All authors participated in the conception and design of the study, collection of samples, writing and approval of the final manuscript.

Figure 1
1Atlantolacerta andreanskyi distribution map. The color dots represent the localities of the populations sampled for this work, J. Awlime (yellow), J. Sirwa (pink), Oukaimeden (red), Toubkal (orange), Tizin Tichka (dark blue), J. Azourki (light blue), Outabati (light green), and J. Ayache (dark green). The white dots represent the distributions of the species by Bons and Geniez[42].

Figure 2
2Trees resulting from partitioned Bayesian analysis. (A) mitochondrial DNA tree (12S, ND4 and flanking tRNA-His), (B) nuclear concatenated tree (RAG1, ACM4, MC1R, PDC and C-MOS), (C) Concatenated tree from the combined mitochondrial and nuclear DNA data. The partitions used the models described in the text. Bayesian posterior probabilities (0-1) and bootstrap values (> 50%) for ML (1-100) are indicated near the branches, (D) Species tree from mitochondrial and nuclear DNA data from the Bayesian Inference of Species Trees (STARBEAST). Clade posterior probabilities are shown to the left of the nodes, and divergence times and 95% intervals (calculated in BEAST using only ND4 + tRNA-His), to the right of the nodes. The trees were rooted using Podarcis bocagei, P. hispanica and P. carbonelli. The colors represent the different populations.

When combined the No Admixture Model (ancestry model) with the Allele Frequencies Independent Model (allele frequency model) the best resulting K values where for K = 3: South (Oukaimeden, J. Sirwa, Toubkal and J. Awlime), center (Tizin Tichka and J. Azourki) and North (Outabati and J. Ayache) groups. With the other three combinations between the models the best result were for K = 6: J. Sirwa, Tizin Tichka, J. Azourki, Outabati, J. Ayache, and a group formed by Oukaimeden, Toubkal and J. Awlime (Figure 4).

), the two main mitochondrial lineages of A. andreanskyi (South versus central and North) split approximately 7.6 Ma (95% high posterior density (HPD) interval 4.3-11.9 Ma). The populations that are grouped in the three main clades (South, central and North) split approximately at the same time, being Tizin Tichka and J. Azourki the first to split at about 4.3 Ma (1.4-7.8), followed by Oukaimeden and J. Sirwa 2.9 Ma (1-5.6), and

Figure 3
3Parsimony networks corresponding to MC1R (A), RAG1 (B), C-MOS (C), ACM4 (D) and PDC (E) nDNA sequence variation from all the populations. The colors used were the same as the used in the map (Figure 1) and trees (Figure 2), J. Awlime (yellow), Toubkal (orange), Oukaimeden (red), J. Sirwa (pink), Tizin Tichka (dark blue), J. Azourki (light blue), Outabati (light green), and J. Ayache (dark green). Lines represent a mutation step, circles represent haplotypes and dots missing haplotypes. The size of the circles is proportional to the number of alleles.

Figure 4
4Population structure estimation. Each individual is represented by a thin vertical line, which is partitioned into K colored segments that represent the individual's estimated membership fractions in K clusters. The bigger vertical divisions separate individuals from different populations. Populations are labeled below the figure. The colors used are the same used in Figure 1 and Figure 2.

Table 1
1Genetic distances and divergence time estimate between populations ND4 and tRNA-His. The the diversity of each population is below the population's names.A

Pop p-distance
(%) 12S, ND4

Tizin Tichka
Oukaimeden
J. Sirwa
J. Ayache
Outabati
J. Azourki
Toubkal
J. Awlime

0.1
0.4
0.3
0.5
0.2
0
0.4
0.1

Tizin Tichka
13.1
12.7
14.5
10.5
15.3
12.9
13.6

0

Oukaimeden
4
7.7
15
13.2
16.1
1.7
13.2

0

J. Sirwa
4.2
2.8
16.1
12.7
16.5
7.5
11.6

0.2

J. Ayache
5.4
5.7
4.8
12.7
5.5
14.4
14.1

0.1

Outabati
4.3
4.3
3.8
6.6
14.2
13.2
13.1

0.1

J. Azourki
5.4
5.7
4.2
1.6
6
16
14

0

Toubkal
3.7
0.3
2.5
5.4
4
5.4
12.6

0

J. Awlime
4
4.7
4.5
5.1
6.4
5
4.3

0

B
C

Pop p-distance
(%) 12S and ND4

J. Awlime

JAy + Out
Tiz + JAz
Ouk + JSi + Tou
Beast Ma (95% HPD)
ND4

0.9
2.3
0
1.5

JAy + JAz
13.7
13.4
14.6
North -South
7.6 (4.3-11.9)

2.9
Jaw -Ouk
5.6 (2.5-9.7)

Tiz + Ou
5.9
12.9
12.4
JAz + JAy -Out + Tiz
6.4 (3.1-10.2)

5.2
Ouk -JSi
2.9 (1.0-5.6)

J. Awlime
5
5.2
12.2
Out -Tiz
4.3 (1.4-7.8)

0.1
JAz -JAy
2.4 (0.8-4.4)

Ouk + JSi + Tou
5.1
4.1
4.6
Ouk -Tou
0.5 (0.1-1.2)

0.4

(A) Genetic distance (12S and ND4 + tRNA-His) between all the populations and (B) between main groups; and (C) divergence time estimates, calculated using
BEAST with

Table 2
2Percentage of private alleles in all the populations and for each nuclear locusPrivate Alleles (%)
MCIR
RAGI
C-MOS
ACM4
PDC

J. Awlime
33
50
0
67
0

J. Sirwa
96
12
0
42
92

Toubkal
50
0
50
100
50

Oukaimeden
41
33
70
29
57

Tizin Tichka
75
59
23
71
100

J. Azourki
60
100
60
84
90

Outabati
100
54
43
9
83

J. Ayache
92
85
57
80
20

Table 3
3Samples used in the work with localities (GPS coordinates; WGS84 coordinate system) and GenBank accession numbers for all the sequenced genesGenBank Acession codes

Table 3
3Samples used in the work with localities (GPS coordinates; WGS84 coordinate system) and GenBank accession numbers for all the sequenced genes
(Continued)

1394
1394a
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462085
JX462147
JX461559
JX461913
JX485244
JX461726
JX461384

1394b
JX461560
JX461914
JX485245
JX461727
JX461385

1489
1489a
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462086
JX462152
JX461561
JX461915
JX485246
JX461729
JX461387

1489b
JX461562
JX461916
JX485247
JX461730
JX461388

1498
1498a
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462087
JX462151
JX461563
JX461917
JX485248
JX461732
JX461389

1498b
JX461564
JX461918
JX485249
JX461733
JX461390

1598
1598a
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462158
JX462158
JX461573
JX461927
JX485256
JX461741
JX461399

1598b
JX461574
JX461928
JX485257
JX461742
JX461340

1633
1633a
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462096
JX462148
JX461583
JX461937
JX485264
JX461751
JX461409

1633b
JX461584
JX461938
JX485265
JX461752
JX461410

1638
1638
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462097
JX462159
. . .
. . .
. . .
. . .
. . .

1638
1588a
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
. . .
. . .
JX461567
JX461921
JX485250
JX461735
JX461393

1588b
JX461568
JX461922
JX485251
JX461736
JX461394

1626
1626a
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462160
JX462160
JX461579
JX461933
JX485260
JX461747
JX461405

1626b
JX461580
JX461934
JX485261
JX461748
JX461406

1616
1616a
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462154
JX462154
JX461577
JX461931
JX485258
JX461745
JX461403

1616b
JX461578
JX461932
JX485259
JX461746
JX461404

1609
1609
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462155
JX462155
. . .
. . .
. . .
. . .
. . .

1589
1589a
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462090
JX462156
JX461569
JX461923
JX485252
JX461737
JX461395

1589b
JX461570
JX461924
JX485253
JX461738
JX461396

1630
1630a
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462088
JX462149
JX461581
JX461935
JX485262
JX461749
JX461407

1630b
JX461582
JX461936
JX485263
JX461750
JX461408

1591
1591a
Jebel Sirwa (JSi)
30.77671
−7.65299
2561
JX462157
JX462157
JX461571
JX461925
JX485254
JX461739
JX461397

1591b
JX461572
JX461926
JX485255
JX461740
JX461398

1158
1158a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462069
JX462162
JX461531
JX461883
JX485211
JX461697
JX461355

1158b
JX461532
JX461884
JX485212
JX461698
JX461356

1154
1154a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462068
JX462161
JX461529
JX461881
JX485214
JX461695
JX461353

1154b
JX461530
JX461882
JX485215
JX461696
JX461354

2534
2534a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462070
JX462163
JX461587
JX461941
JX485220
JX461755
JX461411

2534b
JX461588
JX461942
JX485221
JX461756
JX461412

2553
2553a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462071
JX462164
JX461591
JX461945
JX485218
JX461759
JX461415

2553b
JX461592
JX461946
JX485219
JX461760
JX461416

Barata et al. BMC Evolutionary Biology 2012, 12:167

Page 11 of 19

http://www.biomedcentral.com/1471-2148/12/167

Table 3
3Samples used in the work with localities (GPS coordinates; WGS84 coordinate system) and GenBank accession numbers for all the sequenced genes
(Continued)

2619
2619a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
. . .
. . .
JX461621
JX461975
JX485238
JX461789
JX461443

2619b
JX461622
JX461976
JX485239
JX461790
JX461444

2620
2620a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
. . .
. . .
JX461623
JX461977
JX485236
JX461791
JX461445

2620b
JX461624
JX461978
JX485237
JX461792
JX461446

2577
2577a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462074
JX462167
JX461603
JX461957
JX485216
JX461771
JX461427

2577b
JX461604
JX461958
JX485217
JX461772
JX461428

2567
2567a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462072
JX462165
JX461599
JX461953
JX485222
JX461767
JX461423

2567b
JX461600
JX461954
JX485223
JX461768
JX461424

2569
2569a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462073
JX462166
JX461601
JX461955
JX485234
JX461769
JX461425

2569b
JX461602
JX461956
JX485235
JX461770
JX461426

2602
2602a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462075
JX462168
JX461607
JX461961
JX485232
JX461775
JX461429

2602b
JX461608
JX461962
JX485233
JX461776
JX461430

2604
2604a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462076
JX462169
JX461609
JX461963
JX485230
JX461777
JX461430

2604b
JX461610
JX461964
JX485231
JX461778
JX461431

2612
2612a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462077
JX462170
JX461613
JX461967
JX485224
JX461781
JX461435

2612b
JX461614
JX461968
JX485225
JX461782
JX461436

2615
2615a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462078
JX462171
JX461615
JX461969
JX485228
JX461783
JX461437

2615b
JX461616
JX461970
JX485229
JX461784
JX461438

2616
2615a
Oukaimeden (Ouk)
31.20426
−7.86705
2600
JX462079
JX462172
JX461617
JX461971
JX485226
JX461785
JX461439

2615b
JX461618
JX461972
JX485227
JX461786
JX461440

1579
1579a
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462098
JX462178
JX461565
JX461919
JX485276
JX461733
JX461391

1579b
JX461566
JX461920
JX485277
JX461734
JX461392

2552
2552a
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462099
JX462177
JX461589
JX461943
JX485278
JX461757
JX461413

2552b
JX461590
JX461944
JX485279
JX461758
JX461414

2564
2694a
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462101
JX462176
JX461597
JX461951
JX485272
JX461765
JX461421

2694b
JX461598
JX461952
JX485273
JX461766
JX461422

2608
2608a
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462102
JX462175
JX461611
JX461965
JX485270
JX461779
JX461433

2608b
JX461612
JX461966
JX485271
JX461780
JX461434

2618
2618a
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462103
JX462174
JX461619
JX461973
JX485268
JX461787
JX461441

2618b
JX461620
JX461974
JX485269
JX461788
JX461442

9189
9189a
Jebel Ayache (Jay)
32.53671
−4.79110
3043
. . .
. . .
JX461675
JX462043
JX485282
JX461859
JX461509

9189b
JX461676
JX462044
JX485283
JX461860
JX461510

9199
9199
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462108
JX462183
. . .
. . .
. . .
. . .
. . .

Table 3
3Samples used in the work with localities (GPS coordinates; WGS84 coordinate system) and GenBank accession numbers for all the sequenced genes (Continued)9255
9255a
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462110
JX462179
JX461681
JX462051
JX485290
JX461867
JX461515

9255b
JX461682
JX462052
JX485291
JX461868
JX461516

9209
9209
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462109
JX462184
. . .
. . .
. . .
. . .
. . .

9191
9191a
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462106
JX462181
JX461677
JX462045
JX485292
JX461861
JX461511

9191b
JX461678
JX462046
JX485293
JX461862
JX461512

9336
9336
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462111
JX462182
. . .
. . .
. . .
. . .
. . .

9193
9193a
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462107
JX462188
JX461679
JX462047
JX485288
JX461863
JX461513

9193b
JX461680
JX462048
JX485289
JX461864
JX461514

2557
2557a
Jebel Ayache (Jay)
32.53671
−4.79110
3043
. . .
. . .
JX461595
JX461949
JX485274
JX461763
JX461419

2557b
JX461596
JX461950
JX485275
JX461764
JX461420

9145
9145a
Jebel Ayache (Jay)
32.53671
−4.79110
3043
JX462104
JX462185
JX461673
JX462041
JX485286
JX461857
JX461507

9145b
JX461674
JX462042
JX485287
JX461858
JX461508

5076
5076a
Jebel Azourki (Jaz)
31.75847
−6.28826
2789
JX462120
JX462206
JX461647
JX462005
JX485296
JX461821
JX461475

5076b
JX461648
JX462006
JX485297
JX461822
JX461476

5128
5128a
Jebel Azourki (Jaz)
31.75847
−6.28826
2789
JX462126
JX462207
JX461665
JX462029
JX485298
JX461845
JX461495

5128b
JX461667
JX462030
JX485299
JX461846
JX461496

5091
5091
Jebel Azourki (Jaz)
31.75847
−6.28826
2789
JX462122
JX462208
. . .
. . .
. . .
. . .
. . .

http://www.biomedcentral.com/1471-2148/12/167
Thanks to all colleagues from CIBIO who assisted during the hard fieldwork, especially to Anna Perera, Daniele Salvi, Fatima Jorge and Fernando Martinez-Freiria. We also want to thank to the anonymous reviewers that helped to improve this manuscript.
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Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution. Barata, Extreme genetic diversity in the lizard Atlantolacerta andreanskyi. Werner12167A montane cryptic species complex. Submit your manuscript at www.biomedcentral.com/submitCite this article as: Barata et al.: Extreme genetic diversity in the lizard Atlantolacerta andreanskyi (Werner, 1929): A montane cryptic species complex. BMC Evolutionary Biology 2012 12:167. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit


Cholesteatoma Introduction

Cholesteatoma Introduction
View this article online at: patient.info/doctor/cholesteatoma-pro
This lesion is inappropriately named, as it is not exactly a tumour, nor is it made of cholesterol. It is a three-dimensional collection of epidermal and connective tissues within the middle ear. Its significance lies in the fact that it grows independently and can be locally invasive and destructive, particularly affecting the bones of the middle ear. Bone erosion occurs mainly by pressure but release of osteolytic enzymes has been detected at the peripheral margins of the lesion. Osteolytic activity appears to be enhanced by the presence of infection. The (usually) unilateral lesion can give rise to a spectrum of problems, ranging from painless otorrhoea (discharge from the ear) through to serious central nervous system complications.ClassificationCongenital cholesteatomaThis occurs when squamous epithelium becomes trapped within the temporal bone during embryogenesis. It expands, resulting in conductive hearing loss either through obstruction of the Eustachian tube or by surrounding the ossicular chain. They account for only 2% of all cases of cholesteatoma[1].Primary acquired cholesteatomaThese account for around 80% of all cases[1]. The precise pathophysiology of these lesions is not absolutely certain but it is thought that chronic negative middle-ear pressure due to a dysfunction of the Eustachian tube causes the tympanic membrane to be 'sucked back' and retract[2]. As this process continues, there is erosion of the lateral wall of the epitympanum (the upper portion of the tympanic cavity which contains the head of the malleus and the body of the incus), producing a slowly expanding defect. A 'retraction pocket' lined by squamous, non-keratinising epithelium is thus formed. The erosion often goes on, as the ball of epithelium grows, to surround the ossicles and may extend into the mastoid bone, the lateral semicircular canal, the middle cranial fossa and the posterior cranial fossa.

Secondary acquired cholesteatoma

This arises as a result of insult to the tympanic membrane, such as perforation secondary to acute otitis media (AOM) or trauma, or due to surgical manipulation of the drum. Squamous epithelium may be inadvertently implanted by the insult so triggering the process of cellular growth resulting in cholesteatoma formation.

Spectrum of otitis media

Otitis media (OM) is an umbrella term for a group of complex infective and inflammatory conditions affecting the middle ear. All OM involves pathology of the middle ear and middle ear mucosa. OM is a leading cause of healthcare visits worldwide and its complications are important causes of preventable hearing loss, particularly in the developing world.

There are various subtypes of OM. These include AOM, otitis media with effusion (OME), chronic suppurative otitis media (CSOM), mastoiditis and cholesteatoma. They are generally described as discrete diseases but in reality there is a great degree of overlap between the different types. OM can be seen as a continuum/spectrum of diseases:

AOM is acute inflammation of the middle ear and may be caused by bacteria or viruses [3] . A subtype of AOM is acute suppurative OM, characterised by the presence of pus in the middle ear. The eardrum may perforate. OME is a chronic inflammatory condition without acute inflammation, which often follows a slowly resolving AOM [4] . There is an effusion of glue-like fluid behind an intact tympanic membrane in the absence of signs and symptoms of acute inflammation. CSOM is long-standing suppurative middle ear inflammation, usually with a persistently perforated tympanic membrane [5] . Mastoiditis is acute inflammation of the mastoid periosteum and air cells occurring when AOM infection spreads out from the middle ear. Cholesteatoma occurs when keratinising squamous epithelium (skin) is present in the middle ear as a result of tympanic membrane retraction.

Epidemiology

The epidemiological data regarding this condition are somewhat scant, although it is one of the most common indications for otological surgery. In the USA, one study reported six cholesteatomas per 100,000 people but in Europe it is thought to be closer to 9 per 100,000 [6,7] . Acquired cholesteatomas are about 1.4 times more likely to occur in men compared to women and their incidence is higher among low-income patients.

Presentation

Symptoms and signs vary according to the size of the cholesteatoma. Small lesions are associated with a progressive conductive hearing loss but, as the lesion grows and erodes into adjacent structures, there may be additional features such as vertigo, headache and facial nerve palsy [2] . Associated infection can (rarely) manifest itself within the central nervous system (sigmoid sinus thrombosis, epidural abscess, meningitis) or as an abscess in the neck. Case reports indicate cholesteatoma growing in the external and internal auditory canal. The former most commonly presents with otorrhoea and hearing impairment. The latter may result in total deafness of the affected ear and impaired facial movement.

Congenital cholesteatoma

This usually presents in childhood (6 months to 5 years) but may occasionally present much later, in adulthood. There is often no history of recurrent suppurative ear disease, previous ear surgery or tympanic membrane perforation. It may be an incidental finding on routine otoscopy of an asymptomatic child -a pearly white mass is seen behind an intact tympanic membrane. Larger lesions lead to conductive hearing loss.

Acquired cholesteatoma

Frequent or unremitting painless otorrhoea which may be foul-smelling. Recurrent otitis, poorly responsive to antibiotic treatment. Progressive, unilateral conductive hearing loss. Tympanic membrane perforation (approximately 90% of cases) or retracted tympanum. The only finding may be a pus-filled canal with granulation tissue.

Risk factors

Congenital -cleft palate. Acquired -ear trauma (accidental or surgical), including insertion of grommets.

Differential diagnosis

Otitis media with effusion. Otitis externa. Tympanosclerosis -the white appearance of fibrotic scarring of the tympanic membrane, commonly seen after grommet insertion, can give the impression of a cholesteatoma deeper within the middle ear. Osteonecrosis of the external auditory canal.

Investigations

Following physical examination, these patients will undergo a spectrum of audiological tests:

CT imaging is the investigation of choice if there is a need for assessment of the extent of the lesion and to assess subtle bony defects [6] . This will guide the surgeon as to how to go ahead with surgery and to help in establishing how successful surgery is likely to be. However, patients should be advised that CT cannot always accurately predict what the operative findings will be and intraoperative 'surprises' are relatively common with this condition. Where there is concern about soft tissue involvement in addition to the bony erosion (eg, herniated brain, epidural abscess and sigmoid sinus thrombosis), an MRI scan may also be performed.

Management

Medical treatment

Medical therapy is reserved for those patients who refuse surgery or for whom a general anaesthetic would be too dangerous owing to comorbidity. In these patients, regular ear cleaning with treatment of infections (topical ± systemic antibiotics) is advised but, ultimately, this will not prevent cholesteatoma growth with its consequences.

Surgical treatment Overview

Surgery is the mainstay of treatment and the aim is to remove the cholesteatoma and leave a safe, dry ear. Surgery involves a general anaesthetic and an incision behind the ear and in the auditory meatus. There will be a dressing applied which, depending on local practice, may be removed by the patient or the team -patients will be advised. Follow-up is any time between a day and three weeks after surgery depending on each individual surgeon's techniques and preferences. There will be a need for topical antibiotics in the first instance and possibly topical steroids later on if excessive granulation tissue forms.

Techniques

There are two methods used: the open and the closed techniques; there is ongoing debate over which is ideal. Ultimately, it depends on the type and location of the cholesteatoma and, sometimes, a decision cannot made until the operation has started [6] .

Open technique (tympanomastoidectomy): this is a longer and more involved procedure that entails removal of a number of structures postoperatively. This is the more successful of the two types of procedure in terms of cholesteatoma removal and therefore a single procedure is usually sufficient. Its drawbacks are that there is a resultant enlarged auditory meatus (at least to twice it normal size) making hearing aids difficult to fit and occasionally resulting in dizziness on exposure to cold water or air. There is a need for annual or biannual auditory canal cleaning too. Closed technique (tympanoplasty): this method is associated with a better end result with regards to cosmetic appearance but, as it is associated with a higher risk of persistent or recurrent cholesteatomas, a second-look operation 6-12 months down the line is mandatory. MRI imaging may replace the traditional second-look surgery. The use of otoendoscopy is being investigated.

Outcomes

The aims of surgery are:

To eliminate the cholesteatoma and its associated complications. To enable all the usual activities of daily living, including swimming. To conserve residual hearing ± improve hearing if possible. To reconstruct the ear in a manner that reduces the chances of recurrence.

Complications

If managed conservatively a cholesteatoma left in situ will inevitably grow. The complications depend on which structures it grows and erodes into. Thus, the patient develops a progressive conductive deafness and then may go on to have problems with dizziness, facial nerve palsy, ot central nervous system problems (through a mass effect or as a consequence of abscess formation) [7] .

When a cholesteatoma is managed surgically, all but the first of the complications listed below are rare.

Temporary (few months) alteration of taste. Perichondritis. Chondritis. Ear canal stenosis. Facial nerve injury. Formation of a fistula into one of the semicircular canals. Labyrinthine or middle-ear injury leading to long-term dizziness. Acquired cholesteatoma following surgery for congenital cholesteatoma has been reported [8] .

Prognosis

Surgical removal of a cholesteatoma is usually complete, but the risk of residual disease after corrective surgery varies from 5% to 30% [7] . Between 7% and 10% of people diagnosed with cholesteatoma will develop a cholesteatoma in the other ear.

Further reading & references

Hearing loss in adults: assessment and management; NICE Guideline (July 2018) Discuss Cholesteatoma and find more trusted resources at Patient.
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