Ignoring heterozygous sites biases phylogenomic estimates of divergence times: implications for the evolutionary history of Microtus voles

Phylogenetic reconstruction of the evolutionary history of closely related organisms may be difficult because of the presence of unsorted lineages and of a relatively high proportion of heterozygous sites that are usually not handled well by phylogenetic programs. Genomic data may provide enough fixed polymorphisms to resolve phylogenetic trees, but the diploid nature of sequence data remains analytically challenging. Here, we performed a phylogenomic reconstruction of the evolutionary history of the common vole (Microtus arvalis) with a focus on the influence of heterozygosity on the estimation of intraspecific divergence times. We used genome-wide sequence information from 15 voles distributed across the European range. We provide a novel approach to integrate heterozygous information in existing phylogenetic programs by repeated random haplotype sampling from sequences with multiple unphased heterozygous sites. We evaluated the impact of the use of full, partial, or no heterozygous information for tree reconstructions on divergence time estimates. All results consistently showed four deep and strongly supported evolutionary lineages in the vole data. These lineages undergoing divergence processes split only at the end or after the last glacial maximum based on calibration with radiocarbon-dated paleontological material. However, the incorporation of information from heterozygous sites had a significant impact on absolute and relative branch length estimations. Ignoring heterozygous information led to an overestimation of divergence times between the evolutionary lineages of M. arvalis. We conclude that the exclusion of heterozygous sites from evolutionary analyses may cause biased and misleading divergence time estimates in closely related taxa.

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Creation and Validation of the Spanish Durum Wheat Core Collection.

Spanish wheat (Triticum spp.) landraces have a considerable polymorphism, containing many unique alleles, relative to other collections. The existence of a core collection is a favored approach for breeders to efficiently explore novel variation and enhance the use of germplasm. In this study, the Spanish durum wheat (Triticum turgidum L.) core collection (CC) was created using a population structure–based method, grouping accessions by subspecies and allocating the number of genotypes among populations according to the diversity of simple sequence repeat (SSR) markers. The CC of 94 genotypes was established, which accounted for 17% of the accessions in the entire collection. An alternative core collection (CH), with the same number of genotypes per subspecies and maximizing the coverage of SSR alleles, was assembled with the Core Hunter software. The quality of both core collections was compared with a random core collection and evaluated using geographic, agromorphological, and molecular marker data not previously used in the selection of genotypes. Both core collections had a high genetic representativeness, which validated their sampling strategies. Geographic and agromorphological variation, phenotypic correlations, and gliadin alleles of the original collection were more accurately depicted by the CC. Diversity arrays technology (DArT) markers revealed that the CC included genotypes less similar than the CH. Although more SSR alleles were retained by the CH (94%) than by the CC (91%), the results showed that the CC was better than CH for breeding purposes.

Origin and evolution of the slime molds (Mycetozoa)

The Mycetozoa include the cellular (dictyostelid), acellular (myxogastrid), and protostelid slime molds. However, available molecular data are in disagreement on both the monophyly and phylogenetic position of the group. Ribosomal RNA trees show the myxogastrid and dictyostelid slime molds as unrelated early branching lineages, but actin and β-tubulin trees place them together as a single coherent (monophyletic) group, closely related to the animal–fungal clade. We have sequenced the elongation factor-1α genes from one member of each division of the Mycetozoa, including Dictyostelium discoideum, for which cDNA sequences were previously available. Phylogenetic analyses of these sequences strongly support a monophyletic Mycetozoa, with the myxogastrid and dictyostelid slime molds most closely related to each other. All phylogenetic methods used also place this coherent Mycetozoan assemblage as emerging among the multicellular eukaryotes, tentatively supported as more closely related to animals + fungi than are green plants. With our data there are now three proteins that consistently support a monophyletic Mycetozoa and at least four that place these taxa within the “crown” of the eukaryote tree. We suggest that ribosomal RNA data should be more closely examined with regard to these questions, and we emphasize the importance of developing multiple sequence data sets.

Bacterial diversity within the human subgingival crevice

Molecular, sequence-based environmental surveys of microorganisms have revealed a large degree of previously uncharacterized diversity. However, nearly all studies of the human endogenous bacterial flora have relied on cultivation and biochemical characterization of the resident organisms. We used molecular methods to characterize the breadth of bacterial diversity within the human subgingival crevice by comparing 264 small subunit rDNA sequences from 21 clone libraries created with products amplified directly from subgingival plaque, with sequences obtained from bacteria that were cultivated from the same specimen, as well as with sequences available in public databases. The majority (52.5%) of the directly amplified 16S rRNA sequences were <99% identical to sequences within public databases. In contrast, only 21.4% of the sequences recovered from cultivated bacteria showed this degree of variability. The 16S rDNA sequences recovered by direct amplification were also more deeply divergent; 13.5% of the amplified sequences were more than 5% nonidentical to any known sequence, a level of dissimilarity that is often found between members of different genera. None of the cultivated sequences exhibited this degree of sequence dissimilarity. Finally, direct amplification of 16S rDNA yielded a more diverse view of the subgingival bacterial flora than did cultivation. Our data suggest that a significant proportion of the resident human bacterial flora remain poorly characterized, even within this well studied and familiar microbial environment.